From e0cb9cb94be798d910f9f3c3d55ae61b1f7883a5 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 16 Jun 2026 11:22:21 +0900
Subject: [PATCH 001/148] feat : sample car data import

---
 .env.example                         | 26 -------------
 app/repository/quality_repository.py | 57 ++++++++++++++++++++++++++++
 2 files changed, 57 insertions(+), 26 deletions(-)
 delete mode 100644 .env.example
 create mode 100644 app/repository/quality_repository.py

diff --git a/.env.example b/.env.example
deleted file mode 100644
index 96b02ca..0000000
--- a/.env.example
+++ /dev/null
@@ -1,26 +0,0 @@
-# Application
-APP_NAME=AI Service
-APP_VERSION=0.1.0
-DEBUG=dev
-LOG_LEVEL=INFO
-
-# External APIs
-OPENAI_API_KEY=
-AI_MANUAL_API_URL=
-COLLEAGUE_SKILL_API_URL=
-
-# Kafka
-KAFKA_BOOTSTRAP_SERVERS=
-
-# Redis Cache
-REDIS_URL=redis://localhost:6379/0
-REDIS_KEY_PREFIX=aims:ai-service
-REDIS_CACHE_TTL_SECONDS=300
-
-# Main MySQL DB
-# Bottleneck analysis results are written to this DB.
-MAIN_DATABASE_URL=mysql+pymysql://aims_user:change-me@localhost:3306/maindb?charset=utf8mb4
-
-# Sample MySQL DB
-# Use this DB for sample/source data connections when needed.
-SAMPLE_DATABASE_URL=mysql+pymysql://sample_user:change-me@localhost:3306/sampledb?charset=utf8mb4
diff --git a/app/repository/quality_repository.py b/app/repository/quality_repository.py
new file mode 100644
index 0000000..8355a7b
--- /dev/null
+++ b/app/repository/quality_repository.py
@@ -0,0 +1,57 @@
+from sqlalchemy import create_engine, text
+from sqlalchemy.orm import sessionmaker
+
+DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
+    "@127.0.0.1:13306/sampledb"
+)
+
+engine = create_engine(
+    DATABASE_URL,
+    pool_pre_ping=True
+)
+
+with engine.connect() as conn:
+    result = conn.execute(
+        text("SELECT * FROM car_master LIMIT 1")
+    )
+
+    row = result.first()
+
+    print(dict(row._mapping))
+
+with engine.connect() as conn:
+    result = conn.execute(
+        text("SELECT * FROM car_drive LIMIT 1")
+    )
+
+    row = result.first()
+
+    print(dict(row._mapping))
+
+with engine.connect() as conn:
+    result = conn.execute(
+        text("SELECT * FROM car_dynamics LIMIT 1")
+    )
+
+    row = result.first()
+
+    print(dict(row._mapping))
+
+with engine.connect() as conn:
+    result = conn.execute(
+        text("SELECT * FROM car_status LIMIT 1")
+    )
+
+    row = result.first()
+
+    print(dict(row._mapping))
+
+with engine.connect() as conn:
+    result = conn.execute(
+        text("SELECT * FROM car_control LIMIT 1")
+    )
+
+    row = result.first()
+
+    print(dict(row._mapping))
\ No newline at end of file

From 5c47b4e4e41103aa1586bc7bb5d46e3acf86cd68 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 16 Jun 2026 13:38:52 +0900
Subject: [PATCH 002/148] feat : OpenAPI connect

---
 app/repository/quality_repository.py | 131 +++++++++++++++++++++------
 app/utils/rule_engin.py              |  94 +++++++++++++++++++
 2 files changed, 196 insertions(+), 29 deletions(-)
 create mode 100644 app/utils/rule_engin.py

diff --git a/app/repository/quality_repository.py b/app/repository/quality_repository.py
index 8355a7b..d52b588 100644
--- a/app/repository/quality_repository.py
+++ b/app/repository/quality_repository.py
@@ -1,6 +1,13 @@
 from sqlalchemy import create_engine, text
-from sqlalchemy.orm import sessionmaker
+from openai import OpenAI
+from dotenv import load_dotenv
+import os
 
+load_dotenv()
+client = OpenAI(
+    api_key=os.getenv("OPENAI_API_KEY")
+)
+print(os.getenv("OPENAI_API_KEY"))
 DATABASE_URL = (
     "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
     "@127.0.0.1:13306/sampledb"
@@ -10,48 +17,114 @@
     DATABASE_URL,
     pool_pre_ping=True
 )
+models = client.models.list()
 
-with engine.connect() as conn:
-    result = conn.execute(
-        text("SELECT * FROM car_master LIMIT 1")
-    )
+for model in models.data:
+    print(model.id)
 
-    row = result.first()
+def calculate_risk(status, control):
 
-    print(dict(row._mapping))
+    score = 100
+    reasons = []
 
-with engine.connect() as conn:
-    result = conn.execute(
-        text("SELECT * FROM car_drive LIMIT 1")
-    )
+    speed = float(status["speed"])
+    rpm = int(status["att"])
+    battery = float(status["battery_voltage"])
 
-    row = result.first()
+    if speed > 120:
+        score -= 20
+        reasons.append("과속")
 
-    print(dict(row._mapping))
+    if rpm > 4000:
+        score -= 20
+        reasons.append("고RPM")
 
-with engine.connect() as conn:
-    result = conn.execute(
-        text("SELECT * FROM car_dynamics LIMIT 1")
-    )
+    if battery < 12.0:
+        score -= 10
+        reasons.append("배터리전압낮음")
 
-    row = result.first()
+    if control["collision_warning"] == 1:
+        score -= 40
+        reasons.append("충돌경고")
 
-    print(dict(row._mapping))
+    if status["gear"] == "P" and speed > 20:
+        score -= 30
+        reasons.append("주행중 P기어")
 
-with engine.connect() as conn:
-    result = conn.execute(
-        text("SELECT * FROM car_status LIMIT 1")
-    )
+    return score, reasons
 
-    row = result.first()
 
-    print(dict(row._mapping))
+def generate_comment(vehicle_id, score, reasons):
+
+    prompt = f"""
+차량번호: {vehicle_id}
+
+위험점수: {score}
+
+검출 이슈:
+{', '.join(reasons)}
+
+정비사가 작성하는 것처럼
+2줄 이내로 진단 결과를 작성하세요.
+"""
+
+    response = client.chat.completions.create(
+        model="gpt-4.1-mini",
+        messages=[
+            {
+                "role": "system",
+                "content": "당신은 자동차 품질검사 전문가입니다."
+            },
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ]
+    )
+
+    return response.choices[0].message.content
 
 with engine.connect() as conn:
-    result = conn.execute(
-        text("SELECT * FROM car_control LIMIT 1")
+    # 상태 데이터 1건 조회
+    status_row = conn.execute(
+        text("""
+        SELECT *
+        FROM car_status
+        LIMIT 1
+        """)
+    ).first()
+
+    # 제어 데이터 1건 조회
+    control_row = conn.execute(
+        text("""
+        SELECT *
+        FROM car_control
+        LIMIT 1
+        """)
+    ).first()
+
+    status_row = dict(status_row._mapping)
+    control_row = dict(control_row._mapping)
+
+    score, reasons = calculate_risk(
+        status_row,
+        control_row
     )
 
-    row = result.first()
+    comment = generate_comment(
+        status_row["vehicle_id"],
+        score,
+        reasons
+    )
 
-    print(dict(row._mapping))
\ No newline at end of file
+    inspection_result = {
+        "vehicle_id": status_row["vehicle_id"],
+        "inspection_score": score,
+        "inspection_result":
+            "FAIL" if score < 70 else
+            "WARN" if score < 90 else
+            "PASS",
+        "inspection_comment": comment
+    }
+
+    print(inspection_result)
\ No newline at end of file
diff --git a/app/utils/rule_engin.py b/app/utils/rule_engin.py
new file mode 100644
index 0000000..82ec2af
--- /dev/null
+++ b/app/utils/rule_engin.py
@@ -0,0 +1,94 @@
+from openai import OpenAI
+
+client = OpenAI()
+def calculate_risk(status, control):
+
+    score = 100
+    reasons = []
+
+    speed = float(status["speed"])
+    rpm = int(status["att"])
+    battery = float(status["battery_voltage"])
+
+    if speed > 120:
+        score -= 20
+        reasons.append("과속")
+
+    if rpm > 4000:
+        score -= 20
+        reasons.append("고RPM")
+
+    if battery < 12.0:
+        score -= 10
+        reasons.append("배터리전압낮음")
+
+    if control["collision_warning"] == 1:
+        score -= 40
+        reasons.append("충돌경고")
+
+    if status["gear"] == "P" and speed > 20:
+        score -= 30
+        reasons.append("주행중 P기어")
+
+    return score, reasons
+
+def generate_comment(vehicle_id, score, reasons):
+
+    prompt = f"""
+    차량 진단 결과
+
+    차량번호: {vehicle_id}
+    위험점수: {score}
+
+    검출이슈:
+    {', '.join(reasons)}
+
+    정비사가 작성하는 것처럼
+    2줄 이내로 진단 결과를 작성하세요.
+    """
+
+    response = client.chat.completions.create(
+        model="gpt-4.1-mini",
+        messages=[
+            {
+                "role":"system",
+                "content":"당신은 자동차 품질검사 전문가입니다."
+            },
+            {
+                "role":"user",
+                "content":prompt
+            }
+        ]
+    )
+
+    return response.choices[0].message.content
+
+inspection_result = {
+    "vehicle_id": vehicle_id,
+    "score": score,
+    "result": result,
+    "issues": reasons
+}
+
+score, reasons = calculate_risk(
+    status_row,
+    control_row
+)
+
+comment = generate_comment(
+    status_row["vehicle_id"],
+    score,
+    reasons
+)
+
+inspection_result = {
+    "vehicle_id": status_row["vehicle_id"],
+    "inspection_score": score,
+    "inspection_result":
+        "FAIL" if score < 70 else
+        "WARN" if score < 90 else
+        "PASS",
+    "inspection_comment": comment
+}
+
+print(inspection_result)
\ No newline at end of file

From d88753931a6d543c9a234b884fcd836dba590b04 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 16 Jun 2026 14:35:15 +0900
Subject: [PATCH 003/148] =?UTF-8?q?feat:=20=EB=B6=88=EB=9F=89=20=ED=83=90?=
 =?UTF-8?q?=EC=A7=80=20=EB=AA=A8=EB=8D=B8=20=EB=B0=8F=20SHAP=20=EA=B8=B0?=
 =?UTF-8?q?=EB=B0=98=20=EC=A0=84=EC=9D=B4=20=EC=98=88=EC=B8=A1=201?=
 =?UTF-8?q?=EC=B0=A8=20=EA=B5=AC=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore                                    |    2 +
 app/docs/__init__.py                          |    2 +
 app/ml/artifacts/auxiliary_process_models.pkl |  Bin 0 -> 561344 bytes
 app/ml/artifacts/lightgbm_defect_detector.pkl |  Bin 0 -> 38805 bytes
 app/ml/datasets/__init__.py                   |    2 -
 .../defect_transfer_prediction_model.ipynb    | 7539 +++++++++++++++++
 6 files changed, 7543 insertions(+), 2 deletions(-)
 create mode 100644 app/docs/__init__.py
 create mode 100644 app/ml/artifacts/auxiliary_process_models.pkl
 create mode 100644 app/ml/artifacts/lightgbm_defect_detector.pkl
 delete mode 100644 app/ml/datasets/__init__.py
 create mode 100644 app/ml/training/defect_transfer_prediction_model.ipynb

diff --git a/.gitignore b/.gitignore
index 67ea03d..e6fce38 100644
--- a/.gitignore
+++ b/.gitignore
@@ -37,3 +37,5 @@ htmlcov/
 .DS_Store
 Thumbs.db
 
+# DataSet
+datasets/
\ No newline at end of file
diff --git a/app/docs/__init__.py b/app/docs/__init__.py
new file mode 100644
index 0000000..9597a6f
--- /dev/null
+++ b/app/docs/__init__.py
@@ -0,0 +1,2 @@
+"""Docs package."""
+
diff --git a/app/ml/artifacts/auxiliary_process_models.pkl b/app/ml/artifacts/auxiliary_process_models.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..6b25ac606aae94810911db66529d12ef219729df
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diff --git a/app/ml/datasets/__init__.py b/app/ml/datasets/__init__.py
deleted file mode 100644
index bae7aa2..0000000
--- a/app/ml/datasets/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Dataset management package."""
-
diff --git a/app/ml/training/defect_transfer_prediction_model.ipynb b/app/ml/training/defect_transfer_prediction_model.ipynb
new file mode 100644
index 0000000..0481ca6
--- /dev/null
+++ b/app/ml/training/defect_transfer_prediction_model.ipynb
@@ -0,0 +1,7539 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "873328b3",
+      "metadata": {
+        "id": "873328b3"
+      },
+      "source": [
+        "# 불량 탐지 및 SHAP 기반 공정 전이 위험 예측\n",
+        "\n",
+        "1. 라이브러리 import 및 Colab/Google Drive 로드\n",
+        "2. `app/ml/datasets/process/` 전체 데이터셋 정제 및 전처리\n",
+        "3. Bosch, Ford, 열화상, 소성가공 데이터 기반 보조 모델/피처 학습\n",
+        "4. 후보 모델 4종(LightGBM, RandomForest, ExtraTrees, LogisticRegression) 교차검증/튜닝\n",
+        "5. 최종 후보별 성능 비교, Accuracy, False Positive Rate, Confusion Matrix\n",
+        "6. SHAP 기반 영향 공정 분석 및 공정 간 전이 위험 결과 생성\n",
+        "7. AI 모델 관리 항목과 모델/결과 파일 저장\n",
+        "\n",
+        "> 현재 데이터에는 `BODY 불량 -> PAINT 불량` 같은 실제 전이 정답 라벨과 데이터셋 간 공통 제품 키가 없습니다. 따라서 이 노트북의 전이 예측은 별도 전이 라벨 학습이 아니라, `최종 불량 확률 + SHAP 영향 공정 + 보조 공정 위험 피처 + 공정 흐름 규칙` 기반 위험 확산 해석입니다.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "P44DGP2TDAt4",
+      "metadata": {
+        "id": "P44DGP2TDAt4"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "hMAQMhIqbEe9",
+      "metadata": {
+        "id": "hMAQMhIqbEe9"
+      },
+      "outputs": [],
+      "source": [
+        "# Colab 환경에서 필요한 패키지 설치\n",
+        "import importlib.util\n",
+        "import subprocess\n",
+        "import sys\n",
+        "\n",
+        "def ensure_package(import_name: str, pip_name: str | None = None):\n",
+        "    pip_name = pip_name or import_name\n",
+        "    if importlib.util.find_spec(import_name) is None:\n",
+        "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", pip_name, \"-q\"])\n",
+        "\n",
+        "ensure_package(\"lightgbm\")\n",
+        "ensure_package(\"shap\")\n",
+        "ensure_package(\"joblib\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "XFBnJXPabHMi",
+      "metadata": {
+        "id": "XFBnJXPabHMi"
+      },
+      "outputs": [],
+      "source": [
+        "# CPU 병렬 사용\n",
+        "import lightgbm as lgb\n",
+        "\n",
+        "model = lgb.LGBMClassifier(\n",
+        "    n_estimators=300,\n",
+        "    learning_rate=0.05,\n",
+        "    num_leaves=31,\n",
+        "    n_jobs=-1,\n",
+        "    random_state=42\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "e37daef5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e37daef5",
+        "outputId": "37dac073-c450-41e7-df96-91794aabc2e7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "pandas 2.2.2\n",
+            "lightgbm 4.6.0\n",
+            "shap 0.52.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "from __future__ import annotations\n",
+        "\n",
+        "import gc\n",
+        "import json\n",
+        "import pickle\n",
+        "import re\n",
+        "import warnings\n",
+        "from datetime import datetime, timezone\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import joblib\n",
+        "import lightgbm as lgb\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import shap\n",
+        "from sklearn.base import clone\n",
+        "from sklearn.ensemble import ExtraTreesClassifier, IsolationForest, RandomForestClassifier\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import (\n",
+        "    ConfusionMatrixDisplay,\n",
+        "    PrecisionRecallDisplay,\n",
+        "    RocCurveDisplay,\n",
+        "    accuracy_score,\n",
+        "    average_precision_score,\n",
+        "    classification_report,\n",
+        "    confusion_matrix,\n",
+        "    f1_score,\n",
+        "    precision_score,\n",
+        "    precision_recall_curve,\n",
+        "    recall_score,\n",
+        "    roc_auc_score,\n",
+        ")\n",
+        "from sklearn.model_selection import ParameterSampler, StratifiedKFold, train_test_split\n",
+        "\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "sns.set_theme(style=\"whitegrid\")\n",
+        "pd.set_option(\"display.max_columns\", 120)\n",
+        "pd.set_option(\"display.max_rows\", 80)\n",
+        "\n",
+        "RANDOM_STATE = 42\n",
+        "np.random.seed(RANDOM_STATE)\n",
+        "\n",
+        "print(\"pandas\", pd.__version__)\n",
+        "print(\"lightgbm\", lgb.__version__)\n",
+        "print(\"shap\", shap.__version__)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0e3e0e4c",
+      "metadata": {
+        "id": "0e3e0e4c"
+      },
+      "source": [
+        "## 1. Google Drive 및 로컬 데이터셋 경로 설정\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "abb8856c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "abb8856c",
+        "outputId": "0796b866-f21d-432a-f7f1-7b984bfc926c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n",
+            "PROJECT_ROOT: /content/drive/MyDrive\n",
+            "PROCESS_ROOT: /content/drive/MyDrive/aims_dataset\n",
+            "OUTPUT_DIR: /content/defect_transfer_outputs\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Drive 마운트\n",
+        "try:\n",
+        "    from google.colab import drive\n",
+        "    drive.mount(\"/content/drive\")\n",
+        "    IN_COLAB = True\n",
+        "except Exception:\n",
+        "    IN_COLAB = False\n",
+        "    print(\"Colab이 아니므로 Google Drive mount를 건너뜁니다.\")\n",
+        "\n",
+        "# 데이터 루트\n",
+        "PROJECT_ROOT = Path(\"/content/drive/MyDrive\") if IN_COLAB else Path.cwd()\n",
+        "PROCESS_ROOT = PROJECT_ROOT / \"aims_dataset\"\n",
+        "BOSCH_ROOT = PROCESS_ROOT / \"bosch-production-line-performance\"\n",
+        "THERMAL_ROOT = PROCESS_ROOT / \"머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)\"\n",
+        "FORD_ROOT = PROCESS_ROOT / \"Ford 엔진 진동 데이터셋\"\n",
+        "METAL_ROOT = PROCESS_ROOT / \"소성가공 자원최적화 AI 데이터셋\"\n",
+        "# 산출물 경로\n",
+        "OUTPUT_DIR = Path(\"/content/defect_transfer_outputs\") if IN_COLAB else Path(\"outputs/defect_transfer\")\n",
+        "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "\n",
+        "# Bosch 파일 경로\n",
+        "NUMERIC_PATH = BOSCH_ROOT / \"train_numeric.csv\"\n",
+        "DATE_PATH = BOSCH_ROOT / \"train_date.csv\"\n",
+        "CATEGORICAL_PATH = BOSCH_ROOT / \"train_categorical.csv\"\n",
+        "\n",
+        "# 열화상 파일 경로\n",
+        "THERMAL_LEFT_DATA_PATH = THERMAL_ROOT / \"2nd_process_left_data.csv\"\n",
+        "THERMAL_LEFT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_left_label.json\"\n",
+        "THERMAL_RIGHT_DATA_PATH = THERMAL_ROOT / \"2nd_process_right_data.csv\"\n",
+        "THERMAL_RIGHT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_right_label.json\"\n",
+        "\n",
+        "# 보조 데이터 파일 경로\n",
+        "FORD_TRAIN_PATH = FORD_ROOT / \"FordA_TRAIN.txt\"\n",
+        "FORD_TEST_PATH = FORD_ROOT / \"FordA_TEST.txt\"\n",
+        "METAL_PROCESS_PATH = METAL_ROOT / \"공정_데이터_2022년_8월.csv\"\n",
+        "METAL_SENSOR_PATHS = sorted([p for p in METAL_ROOT.glob(\"*.csv\") if p.name != \"공정_데이터_2022년_8월.csv\"])\n",
+        "\n",
+        "print(\"PROJECT_ROOT:\", PROJECT_ROOT)\n",
+        "print(\"PROCESS_ROOT:\", PROCESS_ROOT)\n",
+        "print(\"OUTPUT_DIR:\", OUTPUT_DIR)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "lc2ZXZzwbEe_",
+      "metadata": {
+        "id": "lc2ZXZzwbEe_"
+      },
+      "source": [
+        "## 2. 대용량 CSV 로드 전략\n",
+        "\n",
+        "Bosch 데이터는 매우 크므로 전체 컬럼을 한 번에 읽지 않습니다. 먼저 일부 행만 프로파일링한 뒤 결측률이 낮고 변동이 있는 컬럼을 고르고, 선택 컬럼만 학습 크기만큼 로드합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "66fcea04",
+      "metadata": {
+        "id": "66fcea04"
+      },
+      "outputs": [],
+      "source": [
+        "# 학습 규모 설정\n",
+        "PROFILE_ROWS = 50_000\n",
+        "MAX_ROWS = 300_000\n",
+        "MAX_NUMERIC_FEATURES = 260\n",
+        "MAX_DATE_FEATURES = 260\n",
+        "USE_CATEGORICAL = True\n",
+        "MAX_CATEGORICAL_FEATURES = 80\n",
+        "CATEGORICAL_PROFILE_ROWS = 20_000\n",
+        "MAX_AUX_ROWS = 200_000\n",
+        "ENABLE_HYPERPARAMETER_TUNING = True\n",
+        "CV_N_SPLITS = 3\n",
+        "# 빠른 비교 모드 기본값\n",
+        "# 4개 모델 전체 조합을 큰 데이터로 CV하면 수십~수백 회 학습이 발생해 Colab에서 오래 걸립니다.\n",
+        "# 후보 비교는 stratified sample로 수행하고, 필요 시 선택 모델만 전체 데이터로 재학습합니다.\n",
+        "CV_N_ITER = 2\n",
+        "CV_SAMPLE_SIZE = 15_000\n",
+        "FINAL_TRAIN_SAMPLE_SIZE = 80_000\n",
+        "RETRAIN_SELECTED_MODEL_ON_FULL_DATA = False\n",
+        "\n",
+        "# 공정 매핑\n",
+        "LINE_TO_PROCESS = {\n",
+        "    \"L0\": \"PRESS\",\n",
+        "    \"L1\": \"BODY\",\n",
+        "    \"L2\": \"ASSEMBLY\",\n",
+        "    \"L3\": \"PAINT\",\n",
+        "}\n",
+        "PROCESS_FLOW = [\"PRESS\", \"BODY\", \"PAINT\", \"ASSEMBLY\"]\n",
+        "PROCESS_DISPLAY = {\n",
+        "    \"PRESS\": \"프레스\",\n",
+        "    \"BODY\": \"차체\",\n",
+        "    \"PAINT\": \"도장\",\n",
+        "    \"ASSEMBLY\": \"의장\",\n",
+        "    \"FINAL_INSPECTION\": \"최종검사\",\n",
+        "    \"UNKNOWN\": \"미분류\",\n",
+        "}\n",
+        "\n",
+        "# 파일 검증\n",
+        "def assert_file(path: Path):\n",
+        "    if not path.exists():\n",
+        "        raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
+        "\n",
+        "# 메모리 축소\n",
+        "def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
+        "    for col in df.columns:\n",
+        "        if col == \"Id\":\n",
+        "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
+        "            continue\n",
+        "        if pd.api.types.is_integer_dtype(df[col]):\n",
+        "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
+        "        elif pd.api.types.is_float_dtype(df[col]):\n",
+        "            df[col] = pd.to_numeric(df[col], downcast=\"float\")\n",
+        "    return df\n",
+        "\n",
+        "# 라인 추출\n",
+        "def get_line(column: str) -> str:\n",
+        "    match = re.match(r\"^(L\\d+)_\", column)\n",
+        "    return match.group(1) if match else \"UNKNOWN\"\n",
+        "\n",
+        "# 스테이션 추출\n",
+        "def get_station(column: str) -> str:\n",
+        "    match = re.match(r\"^(L\\d+)_S(\\d+)_\", column)\n",
+        "    return f\"{match.group(1)}_S{match.group(2)}\" if match else \"UNKNOWN\"\n",
+        "\n",
+        "# 컬럼 선별\n",
+        "def select_columns_by_profile(\n",
+        "    path: Path,\n",
+        "    *,\n",
+        "    required_cols: list[str],\n",
+        "    max_features: int,\n",
+        "    profile_rows: int,\n",
+        "    min_non_null_ratio: float = 0.02,\n",
+        ") -> list[str]:\n",
+        "    assert_file(path)\n",
+        "    sample = pd.read_csv(path, nrows=profile_rows)\n",
+        "    candidates = [c for c in sample.columns if c not in required_cols]\n",
+        "    non_null_ratio = sample[candidates].notna().mean()\n",
+        "    nunique = sample[candidates].nunique(dropna=True)\n",
+        "    score = (non_null_ratio * np.log1p(nunique)).sort_values(ascending=False)\n",
+        "    selected = score[(non_null_ratio >= min_non_null_ratio) & (nunique > 1)].head(max_features).index.tolist()\n",
+        "    del sample\n",
+        "    gc.collect()\n",
+        "    return required_cols + selected\n",
+        "\n",
+        "# 선택 컬럼 로드\n",
+        "def read_selected_csv(path: Path, usecols: list[str], max_rows: int | None) -> pd.DataFrame:\n",
+        "    df = pd.read_csv(path, usecols=usecols, nrows=max_rows)\n",
+        "    return reduce_mem_usage(df)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "508b9115",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 216
+        },
+        "id": "508b9115",
+        "outputId": "0be9ce1c-8216-4bef-be28-9e35642f398f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "numeric_df: (300000, 262)\n",
+            "date_df: (300000, 261)\n",
+            "categorical_df: (300000, 10)\n",
+            "Response ratio:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "            ratio\n",
+              "Response         \n",
+              "0         0.99435\n",
+              "1         0.00565"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4d424cbb-5e77-4d16-b366-aeb54394d6d1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ratio</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Response</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.99435</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.00565</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d424cbb-5e77-4d16-b366-aeb54394d6d1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4d424cbb-5e77-4d16-b366-aeb54394d6d1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4d424cbb-5e77-4d16-b366-aeb54394d6d1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Bosch numeric 컬럼 선별\n",
+        "numeric_cols = select_columns_by_profile(\n",
+        "    NUMERIC_PATH,\n",
+        "    required_cols=[\"Id\", \"Response\"],\n",
+        "    max_features=MAX_NUMERIC_FEATURES,\n",
+        "    profile_rows=PROFILE_ROWS,\n",
+        ")\n",
+        "# Bosch date 컬럼 선별\n",
+        "date_cols = select_columns_by_profile(\n",
+        "    DATE_PATH,\n",
+        "    required_cols=[\"Id\"],\n",
+        "    max_features=MAX_DATE_FEATURES,\n",
+        "    profile_rows=PROFILE_ROWS,\n",
+        ")\n",
+        "\n",
+        "# Bosch 선택 데이터 로드\n",
+        "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
+        "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
+        "\n",
+        "# Bosch categorical 선택 로드\n",
+        "if USE_CATEGORICAL:\n",
+        "    categorical_cols = select_columns_by_profile(\n",
+        "        CATEGORICAL_PATH,\n",
+        "        required_cols=[\"Id\"],\n",
+        "        max_features=MAX_CATEGORICAL_FEATURES,\n",
+        "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
+        "    )\n",
+        "    categorical_df = pd.read_csv(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
+        "else:\n",
+        "    categorical_df = None\n",
+        "\n",
+        "print(\"numeric_df:\", numeric_df.shape)\n",
+        "print(\"date_df:\", date_df.shape)\n",
+        "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
+        "print(\"Response ratio:\")\n",
+        "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "11973c36",
+      "metadata": {
+        "id": "11973c36"
+      },
+      "source": [
+        "## 3. Bosch Feature Engineering\n",
+        "\n",
+        "`L*_S*_F*` 수치 센서와 `L*_S*_D*` 시간 컬럼을 그대로 일부 사용하면서, 공정/스테이션 단위 집계 피처를 추가합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "dd4b603e",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "dd4b603e",
+        "outputId": "72f7251a-6b36-43ea-aea9-b9d7ff11125d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "features: (300000, 454)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   Id  L0_S0_F0  L0_S0_F2  L0_S0_F4  L0_S0_F6  L0_S0_F8  L0_S0_F10  L0_S0_F12  \\\n",
+              "0   4     0.030    -0.034    -0.197    -0.179     0.118      0.116     -0.015   \n",
+              "1   6       NaN       NaN       NaN       NaN       NaN        NaN        NaN   \n",
+              "2   7     0.088     0.086     0.003    -0.052     0.161      0.025     -0.015   \n",
+              "3   9    -0.036    -0.064     0.294     0.330     0.074      0.161      0.022   \n",
+              "4  11    -0.055    -0.086     0.294     0.330     0.118      0.025      0.030   \n",
+              "\n",
+              "   L0_S0_F14  L0_S0_F16  L0_S0_F18  L0_S0_F20  L0_S0_F22  L0_S1_F24  \\\n",
+              "0     -0.032      0.020      0.083     -0.273     -0.273     -0.271   \n",
+              "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
+              "2     -0.072     -0.225     -0.147      0.250      0.250      0.057   \n",
+              "3      0.128     -0.026     -0.046     -0.253     -0.253      0.147   \n",
+              "4      0.168     -0.169     -0.099      0.042      0.042     -0.012   \n",
+              "\n",
+              "   L0_S1_F28  L0_S2_F36  L0_S2_F44  L0_S2_F48  L0_S2_F60  L0_S2_F64  \\\n",
+              "0      0.167     -0.023     -0.088      0.001     -0.223     -0.030   \n",
+              "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
+              "2     -0.079      0.011     -0.060     -0.005     -0.223     -0.077   \n",
+              "3     -0.007      0.120     -0.231      0.005      0.050      0.056   \n",
+              "4     -0.046        NaN        NaN        NaN        NaN        NaN   \n",
+              "\n",
+              "   L0_S3_F72  L0_S3_F80  L0_S3_F84  L0_S3_F96  L0_S3_F100  L0_S4_F104  \\\n",
+              "0        NaN        NaN        NaN        NaN         NaN      -0.001   \n",
+              "1        NaN        NaN        NaN        NaN         NaN         NaN   \n",
+              "2        NaN        NaN        NaN        NaN         NaN         NaN   \n",
+              "3        NaN        NaN        NaN        NaN         NaN      -0.038   \n",
+              "4     -0.272      0.037      0.004      0.311       0.003       0.021   \n",
+              "\n",
+              "   L0_S4_F109  L0_S5_F114  L0_S5_F116  L0_S6_F122  L0_S6_F132  L0_S7_F138  \\\n",
+              "0      -0.004         NaN         NaN         NaN         NaN      -0.077   \n",
+              "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+              "2         NaN      -0.073       0.138       0.506       -0.13         NaN   \n",
+              "3      -0.001         NaN         NaN         NaN         NaN       0.402   \n",
+              "4       0.015         NaN         NaN         NaN         NaN      -0.083   \n",
+              "\n",
+              "   L0_S7_F142  L0_S8_F144  L0_S8_F146  L0_S8_F149  L0_S9_F155  L0_S9_F160  \\\n",
+              "0       0.060      -0.157         0.0       0.001         NaN         NaN   \n",
+              "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+              "2         NaN      -0.157         0.0       0.001         NaN         NaN   \n",
+              "3      -0.015       0.343         0.0       0.001         NaN         NaN   \n",
+              "4       0.014      -0.157         0.0       0.001         NaN         NaN   \n",
+              "\n",
+              "   L0_S9_F165  L0_S9_F170  L0_S9_F180  L0_S9_F185  L0_S9_F190  L0_S9_F195  \\\n",
+              "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+              "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+              "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+              "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+              "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+              "\n",
+              "   L0_S9_F200  L0_S9_F210  L0_S10_F219  L0_S10_F224  L0_S10_F229  L0_S10_F234  \\\n",
+              "0         NaN         NaN          NaN          NaN          NaN          NaN   \n",
+              "1         NaN         NaN          NaN          NaN          NaN          NaN   \n",
+              "2         NaN         NaN       -0.002       -0.047        0.018          0.0   \n",
+              "3         NaN         NaN        0.007       -0.049        0.020          0.0   \n",
+              "4         NaN         NaN          NaN          NaN          NaN          NaN   \n",
+              "\n",
+              "   L0_S10_F244  L0_S10_F249  L0_S10_F254  L0_S10_F259  L0_S10_F264  \\\n",
+              "0          NaN          NaN          NaN          NaN          NaN   \n",
+              "1          NaN          NaN          NaN          NaN          NaN   \n",
+              "2        0.001       -0.009       -0.017        0.105       -0.001   \n",
+              "3       -0.013       -0.003       -0.032        0.139        0.001   \n",
+              "4          NaN          NaN          NaN          NaN          NaN   \n",
+              "\n",
+              "   L0_S11_F282  L0_S11_F286  L0_S11_F290  L0_S11_F294  L0_S11_F302  ...  \\\n",
+              "0        0.018       -0.136       -0.013        0.106       -0.131  ...   \n",
+              "1          NaN          NaN          NaN          NaN          NaN  ...   \n",
+              "2          NaN          NaN          NaN          NaN          NaN  ...   \n",
+              "3          NaN          NaN          NaN          NaN          NaN  ...   \n",
+              "4       -0.122       -0.064       -0.011       -0.005        0.071  ...   \n",
+              "\n",
+              "   L0_S5_num_mean  L0_S5_num_std  L0_S5_num_missing_ratio  L0_S6_num_mean  \\\n",
+              "0             NaN            NaN                      1.0             NaN   \n",
+              "1             NaN            NaN                      1.0             NaN   \n",
+              "2          0.0325         0.1492                      0.0           0.188   \n",
+              "3             NaN            NaN                      1.0             NaN   \n",
+              "4             NaN            NaN                      1.0             NaN   \n",
+              "\n",
+              "   L0_S6_num_std  L0_S6_num_missing_ratio  L0_S7_num_mean  L0_S7_num_std  \\\n",
+              "0            NaN                      1.0         -0.0085       0.096874   \n",
+              "1            NaN                      1.0             NaN            NaN   \n",
+              "2        0.44972                      0.0             NaN            NaN   \n",
+              "3            NaN                      1.0          0.1935       0.294864   \n",
+              "4            NaN                      1.0         -0.0345       0.068589   \n",
+              "\n",
+              "   L0_S7_num_missing_ratio  L0_S8_num_mean  L0_S8_num_std  \\\n",
+              "0                      0.0       -0.052000       0.090934   \n",
+              "1                      1.0             NaN            NaN   \n",
+              "2                      1.0       -0.052000       0.090934   \n",
+              "3                      0.0        0.114667       0.197743   \n",
+              "4                      0.0       -0.052000       0.090934   \n",
+              "\n",
+              "   L0_S8_num_missing_ratio  L0_S9_num_mean  L0_S9_num_std  \\\n",
+              "0                      0.0             NaN            NaN   \n",
+              "1                      1.0             NaN            NaN   \n",
+              "2                      0.0             NaN            NaN   \n",
+              "3                      0.0             NaN            NaN   \n",
+              "4                      0.0             NaN            NaN   \n",
+              "\n",
+              "   L0_S9_num_missing_ratio  L1_S24_num_mean  L1_S24_num_std  \\\n",
+              "0                      1.0              NaN             NaN   \n",
+              "1                      1.0              NaN             NaN   \n",
+              "2                      1.0              NaN             NaN   \n",
+              "3                      1.0              NaN             NaN   \n",
+              "4                      1.0              NaN             NaN   \n",
+              "\n",
+              "   L1_S24_num_missing_ratio  L2_S26_num_mean  L2_S26_num_std  \\\n",
+              "0                       1.0              NaN             NaN   \n",
+              "1                       1.0              NaN             NaN   \n",
+              "2                       1.0              NaN             NaN   \n",
+              "3                       1.0              NaN             NaN   \n",
+              "4                       1.0              NaN             NaN   \n",
+              "\n",
+              "   L2_S26_num_missing_ratio  L2_S27_num_mean  L2_S27_num_std  \\\n",
+              "0                       1.0              NaN             NaN   \n",
+              "1                       1.0              NaN             NaN   \n",
+              "2                       1.0              NaN             NaN   \n",
+              "3                       1.0              NaN             NaN   \n",
+              "4                       1.0              NaN             NaN   \n",
+              "\n",
+              "   L2_S27_num_missing_ratio  L3_S29_num_mean  L3_S29_num_std  \\\n",
+              "0                       1.0        -0.014321        0.201176   \n",
+              "1                       1.0        -0.005264        0.142025   \n",
+              "2                       1.0        -0.010000        0.129113   \n",
+              "3                       1.0        -0.030151        0.124692   \n",
+              "4                       1.0        -0.017321        0.157158   \n",
+              "\n",
+              "   L3_S29_num_missing_ratio  L3_S30_num_mean  L3_S30_num_std  \\\n",
+              "0                       0.0         0.003889        0.083444   \n",
+              "1                       0.0         0.021621        0.068539   \n",
+              "2                       0.0        -0.010552        0.069868   \n",
+              "3                       0.0         0.010017        0.058393   \n",
+              "4                       0.0        -0.004069        0.049633   \n",
+              "\n",
+              "   L3_S30_num_missing_ratio  L3_S33_num_mean  L3_S33_num_std  \\\n",
+              "0                   0.37931          -0.0481        0.060306   \n",
+              "1                   0.00000          -0.0370        0.063085   \n",
+              "2                   0.00000           0.1132        0.102470   \n",
+              "3                   0.00000           0.0282        0.049806   \n",
+              "4                   0.00000          -0.0130        0.065646   \n",
+              "\n",
+              "   L3_S33_num_missing_ratio  L3_S34_num_mean  L3_S34_num_std  \\\n",
+              "0                       0.0              0.0             0.0   \n",
+              "1                       0.0              0.0             0.0   \n",
+              "2                       0.0              0.0             0.0   \n",
+              "3                       0.0              0.0             0.0   \n",
+              "4                       0.0              0.0             0.0   \n",
+              "\n",
+              "   L3_S34_num_missing_ratio  L3_S35_num_mean  L3_S35_num_std  \\\n",
+              "0                       0.0        -0.016143        0.030180   \n",
+              "1                       0.0        -0.001286        0.017289   \n",
+              "2                       0.0        -0.013857        0.039015   \n",
+              "3                       0.0              NaN             NaN   \n",
+              "4                       0.0              NaN             NaN   \n",
+              "\n",
+              "   L3_S35_num_missing_ratio  L3_S36_num_mean  L3_S36_num_std  \\\n",
+              "0                       0.0              NaN             NaN   \n",
+              "1                       0.0              NaN             NaN   \n",
+              "2                       0.0              NaN             NaN   \n",
+              "3                       1.0         -0.05550        0.085261   \n",
+              "4                       1.0         -0.00225        0.066188   \n",
+              "\n",
+              "   L3_S36_num_missing_ratio  L3_S37_num_mean  L3_S37_num_std  \\\n",
+              "0                       1.0              0.0             0.0   \n",
+              "1                       1.0              0.0             0.0   \n",
+              "2                       1.0              0.0             0.0   \n",
+              "3                       0.0              0.0             0.0   \n",
+              "4                       0.0              0.0             0.0   \n",
+              "\n",
+              "   L3_S37_num_missing_ratio  L1_cat_count  L1_cat_missing_ratio  \\\n",
+              "0                       0.0             0                   1.0   \n",
+              "1                       0.0             0                   1.0   \n",
+              "2                       0.0             0                   1.0   \n",
+              "3                       0.0             0                   1.0   \n",
+              "4                       0.0             0                   1.0   \n",
+              "\n",
+              "   L1_cat_unique_count  L2_cat_count  L2_cat_missing_ratio  \\\n",
+              "0                    0             0                   1.0   \n",
+              "1                    0             0                   1.0   \n",
+              "2                    0             0                   1.0   \n",
+              "3                    0             0                   1.0   \n",
+              "4                    0             0                   1.0   \n",
+              "\n",
+              "   L2_cat_unique_count  L1_S24_F1114_code  L1_S24_F1137_code  \\\n",
+              "0                    0                  5                  4   \n",
+              "1                    0                  5                  4   \n",
+              "2                    0                  5                  4   \n",
+              "3                    0                  5                  4   \n",
+              "4                    0                  5                  4   \n",
+              "\n",
+              "   L1_S24_F1140_code  L1_S24_F1510_code  L1_S24_F1525_code  L1_S24_F1530_code  \\\n",
+              "0                  4                  5                  5                  5   \n",
+              "1                  4                  5                  5                  5   \n",
+              "2                  4                  5                  5                  5   \n",
+              "3                  4                  5                  5                  5   \n",
+              "4                  4                  5                  5                  5   \n",
+              "\n",
+              "   L1_S25_F1852_code  L2_S26_F3099_code  L2_S27_F3192_code  \n",
+              "0                  8                  2                  2  \n",
+              "1                  8                  2                  2  \n",
+              "2                  8                  2                  2  \n",
+              "3                  8                  2                  2  \n",
+              "4                  8                  2                  2  \n",
+              "\n",
+              "[5 rows x 454 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b3740ba6-87db-4c4b-a170-c26fda81ce5b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>L0_S0_F0</th>\n",
+              "      <th>L0_S0_F2</th>\n",
+              "      <th>L0_S0_F4</th>\n",
+              "      <th>L0_S0_F6</th>\n",
+              "      <th>L0_S0_F8</th>\n",
+              "      <th>L0_S0_F10</th>\n",
+              "      <th>L0_S0_F12</th>\n",
+              "      <th>L0_S0_F14</th>\n",
+              "      <th>L0_S0_F16</th>\n",
+              "      <th>L0_S0_F18</th>\n",
+              "      <th>L0_S0_F20</th>\n",
+              "      <th>L0_S0_F22</th>\n",
+              "      <th>L0_S1_F24</th>\n",
+              "      <th>L0_S1_F28</th>\n",
+              "      <th>L0_S2_F36</th>\n",
+              "      <th>L0_S2_F44</th>\n",
+              "      <th>L0_S2_F48</th>\n",
+              "      <th>L0_S2_F60</th>\n",
+              "      <th>L0_S2_F64</th>\n",
+              "      <th>L0_S3_F72</th>\n",
+              "      <th>L0_S3_F80</th>\n",
+              "      <th>L0_S3_F84</th>\n",
+              "      <th>L0_S3_F96</th>\n",
+              "      <th>L0_S3_F100</th>\n",
+              "      <th>L0_S4_F104</th>\n",
+              "      <th>L0_S4_F109</th>\n",
+              "      <th>L0_S5_F114</th>\n",
+              "      <th>L0_S5_F116</th>\n",
+              "      <th>L0_S6_F122</th>\n",
+              "      <th>L0_S6_F132</th>\n",
+              "      <th>L0_S7_F138</th>\n",
+              "      <th>L0_S7_F142</th>\n",
+              "      <th>L0_S8_F144</th>\n",
+              "      <th>L0_S8_F146</th>\n",
+              "      <th>L0_S8_F149</th>\n",
+              "      <th>L0_S9_F155</th>\n",
+              "      <th>L0_S9_F160</th>\n",
+              "      <th>L0_S9_F165</th>\n",
+              "      <th>L0_S9_F170</th>\n",
+              "      <th>L0_S9_F180</th>\n",
+              "      <th>L0_S9_F185</th>\n",
+              "      <th>L0_S9_F190</th>\n",
+              "      <th>L0_S9_F195</th>\n",
+              "      <th>L0_S9_F200</th>\n",
+              "      <th>L0_S9_F210</th>\n",
+              "      <th>L0_S10_F219</th>\n",
+              "      <th>L0_S10_F224</th>\n",
+              "      <th>L0_S10_F229</th>\n",
+              "      <th>L0_S10_F234</th>\n",
+              "      <th>L0_S10_F244</th>\n",
+              "      <th>L0_S10_F249</th>\n",
+              "      <th>L0_S10_F254</th>\n",
+              "      <th>L0_S10_F259</th>\n",
+              "      <th>L0_S10_F264</th>\n",
+              "      <th>L0_S11_F282</th>\n",
+              "      <th>L0_S11_F286</th>\n",
+              "      <th>L0_S11_F290</th>\n",
+              "      <th>L0_S11_F294</th>\n",
+              "      <th>L0_S11_F302</th>\n",
+              "      <th>...</th>\n",
+              "      <th>L0_S5_num_mean</th>\n",
+              "      <th>L0_S5_num_std</th>\n",
+              "      <th>L0_S5_num_missing_ratio</th>\n",
+              "      <th>L0_S6_num_mean</th>\n",
+              "      <th>L0_S6_num_std</th>\n",
+              "      <th>L0_S6_num_missing_ratio</th>\n",
+              "      <th>L0_S7_num_mean</th>\n",
+              "      <th>L0_S7_num_std</th>\n",
+              "      <th>L0_S7_num_missing_ratio</th>\n",
+              "      <th>L0_S8_num_mean</th>\n",
+              "      <th>L0_S8_num_std</th>\n",
+              "      <th>L0_S8_num_missing_ratio</th>\n",
+              "      <th>L0_S9_num_mean</th>\n",
+              "      <th>L0_S9_num_std</th>\n",
+              "      <th>L0_S9_num_missing_ratio</th>\n",
+              "      <th>L1_S24_num_mean</th>\n",
+              "      <th>L1_S24_num_std</th>\n",
+              "      <th>L1_S24_num_missing_ratio</th>\n",
+              "      <th>L2_S26_num_mean</th>\n",
+              "      <th>L2_S26_num_std</th>\n",
+              "      <th>L2_S26_num_missing_ratio</th>\n",
+              "      <th>L2_S27_num_mean</th>\n",
+              "      <th>L2_S27_num_std</th>\n",
+              "      <th>L2_S27_num_missing_ratio</th>\n",
+              "      <th>L3_S29_num_mean</th>\n",
+              "      <th>L3_S29_num_std</th>\n",
+              "      <th>L3_S29_num_missing_ratio</th>\n",
+              "      <th>L3_S30_num_mean</th>\n",
+              "      <th>L3_S30_num_std</th>\n",
+              "      <th>L3_S30_num_missing_ratio</th>\n",
+              "      <th>L3_S33_num_mean</th>\n",
+              "      <th>L3_S33_num_std</th>\n",
+              "      <th>L3_S33_num_missing_ratio</th>\n",
+              "      <th>L3_S34_num_mean</th>\n",
+              "      <th>L3_S34_num_std</th>\n",
+              "      <th>L3_S34_num_missing_ratio</th>\n",
+              "      <th>L3_S35_num_mean</th>\n",
+              "      <th>L3_S35_num_std</th>\n",
+              "      <th>L3_S35_num_missing_ratio</th>\n",
+              "      <th>L3_S36_num_mean</th>\n",
+              "      <th>L3_S36_num_std</th>\n",
+              "      <th>L3_S36_num_missing_ratio</th>\n",
+              "      <th>L3_S37_num_mean</th>\n",
+              "      <th>L3_S37_num_std</th>\n",
+              "      <th>L3_S37_num_missing_ratio</th>\n",
+              "      <th>L1_cat_count</th>\n",
+              "      <th>L1_cat_missing_ratio</th>\n",
+              "      <th>L1_cat_unique_count</th>\n",
+              "      <th>L2_cat_count</th>\n",
+              "      <th>L2_cat_missing_ratio</th>\n",
+              "      <th>L2_cat_unique_count</th>\n",
+              "      <th>L1_S24_F1114_code</th>\n",
+              "      <th>L1_S24_F1137_code</th>\n",
+              "      <th>L1_S24_F1140_code</th>\n",
+              "      <th>L1_S24_F1510_code</th>\n",
+              "      <th>L1_S24_F1525_code</th>\n",
+              "      <th>L1_S24_F1530_code</th>\n",
+              "      <th>L1_S25_F1852_code</th>\n",
+              "      <th>L2_S26_F3099_code</th>\n",
+              "      <th>L2_S27_F3192_code</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>4</td>\n",
+              "      <td>0.030</td>\n",
+              "      <td>-0.034</td>\n",
+              "      <td>-0.197</td>\n",
+              "      <td>-0.179</td>\n",
+              "      <td>0.118</td>\n",
+              "      <td>0.116</td>\n",
+              "      <td>-0.015</td>\n",
+              "      <td>-0.032</td>\n",
+              "      <td>0.020</td>\n",
+              "      <td>0.083</td>\n",
+              "      <td>-0.273</td>\n",
+              "      <td>-0.273</td>\n",
+              "      <td>-0.271</td>\n",
+              "      <td>0.167</td>\n",
+              "      <td>-0.023</td>\n",
+              "      <td>-0.088</td>\n",
+              "      <td>0.001</td>\n",
+              "      <td>-0.223</td>\n",
+              "      <td>-0.030</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.001</td>\n",
+              "      <td>-0.004</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.077</td>\n",
+              "      <td>0.060</td>\n",
+              "      <td>-0.157</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.001</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.018</td>\n",
+              "      <td>-0.136</td>\n",
+              "      <td>-0.013</td>\n",
+              "      <td>0.106</td>\n",
+              "      <td>-0.131</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0085</td>\n",
+              "      <td>0.096874</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.052000</td>\n",
+              "      <td>0.090934</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.014321</td>\n",
+              "      <td>0.201176</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.003889</td>\n",
+              "      <td>0.083444</td>\n",
+              "      <td>0.37931</td>\n",
+              "      <td>-0.0481</td>\n",
+              "      <td>0.060306</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.016143</td>\n",
+              "      <td>0.030180</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>6</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.005264</td>\n",
+              "      <td>0.142025</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.021621</td>\n",
+              "      <td>0.068539</td>\n",
+              "      <td>0.00000</td>\n",
+              "      <td>-0.0370</td>\n",
+              "      <td>0.063085</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.001286</td>\n",
+              "      <td>0.017289</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7</td>\n",
+              "      <td>0.088</td>\n",
+              "      <td>0.086</td>\n",
+              "      <td>0.003</td>\n",
+              "      <td>-0.052</td>\n",
+              "      <td>0.161</td>\n",
+              "      <td>0.025</td>\n",
+              "      <td>-0.015</td>\n",
+              "      <td>-0.072</td>\n",
+              "      <td>-0.225</td>\n",
+              "      <td>-0.147</td>\n",
+              "      <td>0.250</td>\n",
+              "      <td>0.250</td>\n",
+              "      <td>0.057</td>\n",
+              "      <td>-0.079</td>\n",
+              "      <td>0.011</td>\n",
+              "      <td>-0.060</td>\n",
+              "      <td>-0.005</td>\n",
+              "      <td>-0.223</td>\n",
+              "      <td>-0.077</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.073</td>\n",
+              "      <td>0.138</td>\n",
+              "      <td>0.506</td>\n",
+              "      <td>-0.13</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.157</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.001</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.002</td>\n",
+              "      <td>-0.047</td>\n",
+              "      <td>0.018</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.001</td>\n",
+              "      <td>-0.009</td>\n",
+              "      <td>-0.017</td>\n",
+              "      <td>0.105</td>\n",
+              "      <td>-0.001</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0325</td>\n",
+              "      <td>0.1492</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.188</td>\n",
+              "      <td>0.44972</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.052000</td>\n",
+              "      <td>0.090934</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.010000</td>\n",
+              "      <td>0.129113</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.010552</td>\n",
+              "      <td>0.069868</td>\n",
+              "      <td>0.00000</td>\n",
+              "      <td>0.1132</td>\n",
+              "      <td>0.102470</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.013857</td>\n",
+              "      <td>0.039015</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9</td>\n",
+              "      <td>-0.036</td>\n",
+              "      <td>-0.064</td>\n",
+              "      <td>0.294</td>\n",
+              "      <td>0.330</td>\n",
+              "      <td>0.074</td>\n",
+              "      <td>0.161</td>\n",
+              "      <td>0.022</td>\n",
+              "      <td>0.128</td>\n",
+              "      <td>-0.026</td>\n",
+              "      <td>-0.046</td>\n",
+              "      <td>-0.253</td>\n",
+              "      <td>-0.253</td>\n",
+              "      <td>0.147</td>\n",
+              "      <td>-0.007</td>\n",
+              "      <td>0.120</td>\n",
+              "      <td>-0.231</td>\n",
+              "      <td>0.005</td>\n",
+              "      <td>0.050</td>\n",
+              "      <td>0.056</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.038</td>\n",
+              "      <td>-0.001</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.402</td>\n",
+              "      <td>-0.015</td>\n",
+              "      <td>0.343</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.001</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.007</td>\n",
+              "      <td>-0.049</td>\n",
+              "      <td>0.020</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.013</td>\n",
+              "      <td>-0.003</td>\n",
+              "      <td>-0.032</td>\n",
+              "      <td>0.139</td>\n",
+              "      <td>0.001</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.1935</td>\n",
+              "      <td>0.294864</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.114667</td>\n",
+              "      <td>0.197743</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.030151</td>\n",
+              "      <td>0.124692</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.010017</td>\n",
+              "      <td>0.058393</td>\n",
+              "      <td>0.00000</td>\n",
+              "      <td>0.0282</td>\n",
+              "      <td>0.049806</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.05550</td>\n",
+              "      <td>0.085261</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>11</td>\n",
+              "      <td>-0.055</td>\n",
+              "      <td>-0.086</td>\n",
+              "      <td>0.294</td>\n",
+              "      <td>0.330</td>\n",
+              "      <td>0.118</td>\n",
+              "      <td>0.025</td>\n",
+              "      <td>0.030</td>\n",
+              "      <td>0.168</td>\n",
+              "      <td>-0.169</td>\n",
+              "      <td>-0.099</td>\n",
+              "      <td>0.042</td>\n",
+              "      <td>0.042</td>\n",
+              "      <td>-0.012</td>\n",
+              "      <td>-0.046</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.272</td>\n",
+              "      <td>0.037</td>\n",
+              "      <td>0.004</td>\n",
+              "      <td>0.311</td>\n",
+              "      <td>0.003</td>\n",
+              "      <td>0.021</td>\n",
+              "      <td>0.015</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.083</td>\n",
+              "      <td>0.014</td>\n",
+              "      <td>-0.157</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.001</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-0.122</td>\n",
+              "      <td>-0.064</td>\n",
+              "      <td>-0.011</td>\n",
+              "      <td>-0.005</td>\n",
+              "      <td>0.071</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0345</td>\n",
+              "      <td>0.068589</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.052000</td>\n",
+              "      <td>0.090934</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.017321</td>\n",
+              "      <td>0.157158</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.004069</td>\n",
+              "      <td>0.049633</td>\n",
+              "      <td>0.00000</td>\n",
+              "      <td>-0.0130</td>\n",
+              "      <td>0.065646</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.00225</td>\n",
+              "      <td>0.066188</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 454 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3740ba6-87db-4c4b-a170-c26fda81ce5b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b3740ba6-87db-4c4b-a170-c26fda81ce5b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b3740ba6-87db-4c4b-a170-c26fda81ce5b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Date 피처 생성\n",
+        "def build_date_features(date_df: pd.DataFrame) -> pd.DataFrame:\n",
+        "    feature_df = pd.DataFrame({\"Id\": date_df[\"Id\"]})\n",
+        "    date_feature_cols = [c for c in date_df.columns if c != \"Id\"]\n",
+        "    date_values = date_df[date_feature_cols]\n",
+        "\n",
+        "    feature_df[\"date_observed_count\"] = date_values.notna().sum(axis=1)\n",
+        "    feature_df[\"date_missing_ratio\"] = date_values.isna().mean(axis=1)\n",
+        "    feature_df[\"process_start_time\"] = date_values.min(axis=1)\n",
+        "    feature_df[\"process_end_time\"] = date_values.max(axis=1)\n",
+        "    feature_df[\"total_process_duration\"] = feature_df[\"process_end_time\"] - feature_df[\"process_start_time\"]\n",
+        "\n",
+        "    # 라인별 시간 집계\n",
+        "    for line in sorted({get_line(c) for c in date_feature_cols if get_line(c) != \"UNKNOWN\"}):\n",
+        "        cols = [c for c in date_feature_cols if get_line(c) == line]\n",
+        "        values = date_df[cols]\n",
+        "        feature_df[f\"{line}_date_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
+        "        feature_df[f\"{line}_date_count\"] = values.notna().sum(axis=1)\n",
+        "        feature_df[f\"{line}_date_missing_ratio\"] = values.isna().mean(axis=1)\n",
+        "        feature_df[f\"{line}_duration\"] = values.max(axis=1) - values.min(axis=1)\n",
+        "\n",
+        "    # 스테이션별 시간 집계\n",
+        "    station_span_cols = []\n",
+        "    for station in sorted({get_station(c) for c in date_feature_cols if get_station(c) != \"UNKNOWN\"}):\n",
+        "        cols = [c for c in date_feature_cols if get_station(c) == station]\n",
+        "        values = date_df[cols]\n",
+        "        span_col = f\"{station}_span\"\n",
+        "        feature_df[f\"{station}_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
+        "        feature_df[span_col] = values.max(axis=1) - values.min(axis=1)\n",
+        "        feature_df[f\"{station}_mean_time\"] = values.mean(axis=1)\n",
+        "        station_span_cols.append(span_col)\n",
+        "\n",
+        "    # 병목 후보 시간 집계\n",
+        "    if station_span_cols:\n",
+        "        spans = feature_df[station_span_cols]\n",
+        "        feature_df[\"max_station_span\"] = spans.max(axis=1)\n",
+        "        feature_df[\"mean_station_span\"] = spans.mean(axis=1)\n",
+        "        feature_df[\"std_station_span\"] = spans.std(axis=1)\n",
+        "        feature_df[\"active_station_count\"] = feature_df[[c.replace(\"_span\", \"_seen\") for c in station_span_cols]].sum(axis=1)\n",
+        "    return reduce_mem_usage(feature_df)\n",
+        "\n",
+        "# Numeric 집계 피처 생성\n",
+        "def build_numeric_aggregate_features(numeric_df: pd.DataFrame) -> pd.DataFrame:\n",
+        "    value_cols = [c for c in numeric_df.columns if c not in [\"Id\", \"Response\"]]\n",
+        "    feature_df = numeric_df[[\"Id\"]].copy()\n",
+        "\n",
+        "    # 라인별 센서 집계\n",
+        "    for line in sorted({get_line(c) for c in value_cols if get_line(c) != \"UNKNOWN\"}):\n",
+        "        cols = [c for c in value_cols if get_line(c) == line]\n",
+        "        values = numeric_df[cols]\n",
+        "        feature_df[f\"{line}_num_mean\"] = values.mean(axis=1)\n",
+        "        feature_df[f\"{line}_num_std\"] = values.std(axis=1)\n",
+        "        feature_df[f\"{line}_num_min\"] = values.min(axis=1)\n",
+        "        feature_df[f\"{line}_num_max\"] = values.max(axis=1)\n",
+        "        feature_df[f\"{line}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
+        "\n",
+        "    # 스테이션별 센서 집계\n",
+        "    for station in sorted({get_station(c) for c in value_cols if get_station(c) != \"UNKNOWN\"}):\n",
+        "        cols = [c for c in value_cols if get_station(c) == station]\n",
+        "        if len(cols) < 2:\n",
+        "            continue\n",
+        "        values = numeric_df[cols]\n",
+        "        feature_df[f\"{station}_num_mean\"] = values.mean(axis=1)\n",
+        "        feature_df[f\"{station}_num_std\"] = values.std(axis=1)\n",
+        "        feature_df[f\"{station}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
+        "    return reduce_mem_usage(feature_df)\n",
+        "\n",
+        "# Categorical 피처 생성\n",
+        "def build_categorical_features(categorical_df: pd.DataFrame) -> pd.DataFrame:\n",
+        "    cat_cols = [c for c in categorical_df.columns if c != \"Id\"]\n",
+        "    feature_df = categorical_df[[\"Id\"]].copy()\n",
+        "    if not cat_cols:\n",
+        "        return feature_df\n",
+        "\n",
+        "    # 라인별 범주 집계\n",
+        "    for line in sorted({get_line(c) for c in cat_cols if get_line(c) != \"UNKNOWN\"}):\n",
+        "        cols = [c for c in cat_cols if get_line(c) == line]\n",
+        "        values = categorical_df[cols]\n",
+        "        feature_df[f\"{line}_cat_count\"] = values.notna().sum(axis=1)\n",
+        "        feature_df[f\"{line}_cat_missing_ratio\"] = values.isna().mean(axis=1)\n",
+        "        feature_df[f\"{line}_cat_unique_count\"] = values.nunique(axis=1, dropna=True)\n",
+        "\n",
+        "    # 범주 인코딩\n",
+        "    for col in cat_cols:\n",
+        "        encoded, _ = pd.factorize(categorical_df[col].astype(\"string\").fillna(\"__MISSING__\"), sort=True)\n",
+        "        feature_df[f\"{col}_code\"] = encoded.astype(\"int32\")\n",
+        "    return reduce_mem_usage(feature_df)\n",
+        "\n",
+        "# Bosch 피처 생성\n",
+        "date_features = build_date_features(date_df)\n",
+        "numeric_agg_features = build_numeric_aggregate_features(numeric_df)\n",
+        "raw_numeric_features = numeric_df.drop(columns=[\"Response\"])\n",
+        "\n",
+        "# Bosch 피처 병합\n",
+        "features = raw_numeric_features.merge(date_features, on=\"Id\", how=\"left\")\n",
+        "features = features.merge(numeric_agg_features, on=\"Id\", how=\"left\")\n",
+        "if categorical_df is not None:\n",
+        "    categorical_features = build_categorical_features(categorical_df)\n",
+        "    features = features.merge(categorical_features, on=\"Id\", how=\"left\")\n",
+        "    del categorical_df, categorical_features\n",
+        "# 학습 라벨 분리\n",
+        "target = numeric_df[[\"Id\", \"Response\"]].copy()\n",
+        "\n",
+        "del date_df, numeric_agg_features, date_features\n",
+        "gc.collect()\n",
+        "\n",
+        "print(\"features:\", features.shape)\n",
+        "display(features.head())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e6f30d6e",
+      "metadata": {
+        "id": "e6f30d6e"
+      },
+      "source": [
+        "## 4. Process 폴더 전체 보조 데이터 학습\n",
+        "\n",
+        "`process` 폴더의 Bosch 외 데이터는 Bosch `Id`와 직접 연결되는 공통 키가 없습니다. 따라서 MVP에서는 각 데이터셋을 별도로 학습하여 공정별 보조 위험 신호를 만들고, Bosch 학습 테이블에는 반복 정렬(cyclic alignment) 방식으로 결합합니다. 운영 데이터에서 `car_master_id`, `event_time`, `process_code` 매핑이 생기면 이 부분을 정확 조인으로 교체하면 됩니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "a2a68fb3",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 625
+        },
+        "id": "a2a68fb3",
+        "outputId": "aa032068-83f0-4cfa-bd5c-48baf9192752"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[LightGBM] [Info] Number of positive: 461, number of negative: 166\n",
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n",
+            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Total Bins 1349\n",
+            "[LightGBM] [Info] Number of data points in the train set: 627, number of used features: 7\n",
+            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.735247 -> initscore=1.021410\n",
+            "[LightGBM] [Info] Start training from score 1.021410\n",
+            "\n",
+            "== Thermal PAINT quality auxiliary model ==\n",
+            "ROC-AUC: 0.83824\n",
+            "PR-AUC: 0.91229\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0     0.5962    0.5636    0.5794        55\n",
+            "           1     0.8481    0.8645    0.8562       155\n",
+            "\n",
+            "    accuracy                         0.7857       210\n",
+            "   macro avg     0.7221    0.7141    0.7178       210\n",
+            "weighted avg     0.7821    0.7857    0.7837       210\n",
+            "\n",
+            "aux_paint_thermal_defect_probability: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
+            "aux_paint_thermal_label: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  thermal_side  thermal_label  thermal_mean_temp  thermal_std_temp  \\\n",
+              "0         left              0          51.042400          3.518454   \n",
+              "1         left              0          51.621162          2.787206   \n",
+              "2         left              0          51.391914          2.623878   \n",
+              "3         left              0          51.121414          3.284468   \n",
+              "4         left              0          50.737823          3.554459   \n",
+              "\n",
+              "   thermal_min_temp  thermal_max_temp  thermal_p90_temp  thermal_range_temp  \\\n",
+              "0         42.534000         54.491001         54.271999              11.957   \n",
+              "1         42.860001         54.021000         53.651199              11.161   \n",
+              "2         43.202000         53.519001         53.315601              10.317   \n",
+              "3         42.550999         53.926998         53.727699              11.376   \n",
+              "4         42.051998         53.896000         53.601799              11.844   \n",
+              "\n",
+              "   thermal_slope  \n",
+              "0         10.465  \n",
+              "1          9.378  \n",
+              "2          8.845  \n",
+              "3          9.862  \n",
+              "4         10.345  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d0f32f43-d914-4334-8da3-4cde224f77c5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>thermal_side</th>\n",
+              "      <th>thermal_label</th>\n",
+              "      <th>thermal_mean_temp</th>\n",
+              "      <th>thermal_std_temp</th>\n",
+              "      <th>thermal_min_temp</th>\n",
+              "      <th>thermal_max_temp</th>\n",
+              "      <th>thermal_p90_temp</th>\n",
+              "      <th>thermal_range_temp</th>\n",
+              "      <th>thermal_slope</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>left</td>\n",
+              "      <td>0</td>\n",
+              "      <td>51.042400</td>\n",
+              "      <td>3.518454</td>\n",
+              "      <td>42.534000</td>\n",
+              "      <td>54.491001</td>\n",
+              "      <td>54.271999</td>\n",
+              "      <td>11.957</td>\n",
+              "      <td>10.465</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>left</td>\n",
+              "      <td>0</td>\n",
+              "      <td>51.621162</td>\n",
+              "      <td>2.787206</td>\n",
+              "      <td>42.860001</td>\n",
+              "      <td>54.021000</td>\n",
+              "      <td>53.651199</td>\n",
+              "      <td>11.161</td>\n",
+              "      <td>9.378</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>left</td>\n",
+              "      <td>0</td>\n",
+              "      <td>51.391914</td>\n",
+              "      <td>2.623878</td>\n",
+              "      <td>43.202000</td>\n",
+              "      <td>53.519001</td>\n",
+              "      <td>53.315601</td>\n",
+              "      <td>10.317</td>\n",
+              "      <td>8.845</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>left</td>\n",
+              "      <td>0</td>\n",
+              "      <td>51.121414</td>\n",
+              "      <td>3.284468</td>\n",
+              "      <td>42.550999</td>\n",
+              "      <td>53.926998</td>\n",
+              "      <td>53.727699</td>\n",
+              "      <td>11.376</td>\n",
+              "      <td>9.862</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>left</td>\n",
+              "      <td>0</td>\n",
+              "      <td>50.737823</td>\n",
+              "      <td>3.554459</td>\n",
+              "      <td>42.051998</td>\n",
+              "      <td>53.896000</td>\n",
+              "      <td>53.601799</td>\n",
+              "      <td>11.844</td>\n",
+              "      <td>10.345</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0f32f43-d914-4334-8da3-4cde224f77c5')\"\n",
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+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d0f32f43-d914-4334-8da3-4cde224f77c5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d0f32f43-d914-4334-8da3-4cde224f77c5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    print(\\\"\\uc5f4\\ud654\\uc0c1 \\ub370\\uc774\\ud130 \\ud30c\\uc77c\\uc774 \\uc5c6\\uc5b4 \\uc5f4\\ud654\\uc0c1 \\ubcf4\\uc870 \\ud559\\uc2b5\\uc744 \\uac74\\ub108\\ub701\\ub2c8\\ub2e4\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"thermal_side\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_label\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_mean_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          51.62116241455078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_std_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.7872064113616943\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_min_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          42.86000061035156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_max_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          54.020999908447266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_p90_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          53.65119934082031\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_range_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.16100025177002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_slope\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.378000259399414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# 보조 피처 결합\n",
+        "def append_cycled_feature(target_df: pd.DataFrame, column: str, values: np.ndarray | pd.Series):\n",
+        "    values = pd.Series(values).dropna().to_numpy(dtype=float)\n",
+        "    if len(values) == 0:\n",
+        "        print(f\"{column}: 값이 없어 추가하지 않습니다.\")\n",
+        "        return\n",
+        "    target_df[column] = np.resize(values, len(target_df))\n",
+        "    print(f\"{column}: {len(values)}개 값을 {len(target_df)}개 Bosch row에 cyclic alignment로 추가\")\n",
+        "\n",
+        "# 보조 모델 평가\n",
+        "def evaluate_aux_classifier(name: str, y_true: pd.Series, proba: np.ndarray):\n",
+        "    pred = (proba >= 0.5).astype(int)\n",
+        "    print(f\"\\n== {name} auxiliary model ==\")\n",
+        "    if y_true.nunique() > 1:\n",
+        "        print(\"ROC-AUC:\", round(roc_auc_score(y_true, proba), 5))\n",
+        "        print(\"PR-AUC:\", round(average_precision_score(y_true, proba), 5))\n",
+        "    print(classification_report(y_true, pred, digits=4))\n",
+        "\n",
+        "# 보조 산출물 저장소\n",
+        "auxiliary_metrics = {}\n",
+        "auxiliary_models = {}\n",
+        "\n",
+        "# 열화상 데이터 로드\n",
+        "def load_thermal_side(data_path: Path, label_path: Path, side: str) -> pd.DataFrame | None:\n",
+        "    if not data_path.exists() or not label_path.exists():\n",
+        "        return None\n",
+        "    data = pd.read_csv(data_path)\n",
+        "    data.columns = [f\"t{i+1}\" for i in range(data.shape[1])]\n",
+        "    with open(label_path, \"r\", encoding=\"utf-8\") as f:\n",
+        "        labels = json.load(f)\n",
+        "    data = data.apply(pd.to_numeric, errors=\"coerce\")\n",
+        "    out = pd.DataFrame({\n",
+        "        \"thermal_side\": side,\n",
+        "        \"thermal_label\": pd.Series(labels).astype(float).astype(\"int8\"),\n",
+        "        \"thermal_mean_temp\": data.mean(axis=1),\n",
+        "        \"thermal_std_temp\": data.std(axis=1),\n",
+        "        \"thermal_min_temp\": data.min(axis=1),\n",
+        "        \"thermal_max_temp\": data.max(axis=1),\n",
+        "        \"thermal_p90_temp\": data.quantile(0.90, axis=1),\n",
+        "        \"thermal_range_temp\": data.max(axis=1) - data.min(axis=1),\n",
+        "        \"thermal_slope\": data.iloc[:, -1] - data.iloc[:, 0],\n",
+        "    })\n",
+        "    return reduce_mem_usage(out)\n",
+        "\n",
+        "# 열화상 좌우 데이터 결합\n",
+        "thermal_frames = []\n",
+        "for item in [\n",
+        "    (THERMAL_LEFT_DATA_PATH, THERMAL_LEFT_LABEL_PATH, \"left\"),\n",
+        "    (THERMAL_RIGHT_DATA_PATH, THERMAL_RIGHT_LABEL_PATH, \"right\"),\n",
+        "]:\n",
+        "    thermal_side = load_thermal_side(*item)\n",
+        "    if thermal_side is not None:\n",
+        "        thermal_frames.append(thermal_side)\n",
+        "\n",
+        "# 열화상 보조 모델 학습\n",
+        "if thermal_frames:\n",
+        "    thermal_df = pd.concat(thermal_frames, ignore_index=True)\n",
+        "    thermal_feature_cols = [c for c in thermal_df.columns if c.startswith(\"thermal_\") and c not in [\"thermal_side\", \"thermal_label\"]]\n",
+        "    X_th = thermal_df[thermal_feature_cols]\n",
+        "    y_th = thermal_df[\"thermal_label\"].astype(int)\n",
+        "    X_th_train, X_th_valid, y_th_train, y_th_valid = train_test_split(\n",
+        "        X_th, y_th, test_size=0.25, random_state=RANDOM_STATE, stratify=y_th if y_th.nunique() > 1 else None\n",
+        "    )\n",
+        "    thermal_model = lgb.LGBMClassifier(\n",
+        "        objective=\"binary\", n_estimators=300, learning_rate=0.04, num_leaves=16,\n",
+        "        min_child_samples=10, random_state=RANDOM_STATE, n_jobs=-1\n",
+        "    )\n",
+        "    # 열화상 불량 확률 학습\n",
+        "    thermal_model.fit(X_th_train, y_th_train)\n",
+        "    thermal_valid_proba = thermal_model.predict_proba(X_th_valid)[:, 1]\n",
+        "    evaluate_aux_classifier(\"Thermal PAINT quality\", y_th_valid, thermal_valid_proba)\n",
+        "    thermal_all_proba = thermal_model.predict_proba(X_th)[:, 1]\n",
+        "    # 도장 보조 피처 추가\n",
+        "    append_cycled_feature(features, \"aux_paint_thermal_defect_probability\", thermal_all_proba)\n",
+        "    append_cycled_feature(features, \"aux_paint_thermal_label\", y_th)\n",
+        "    auxiliary_models[\"thermal_paint_quality\"] = thermal_model\n",
+        "    auxiliary_metrics[\"thermal_rows\"] = int(len(thermal_df))\n",
+        "    display(thermal_df.head())\n",
+        "else:\n",
+        "    thermal_df = None\n",
+        "    print(\"열화상 데이터 파일이 없어 열화상 보조 학습을 건너뜁니다.\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "76645e9c",
+      "metadata": {
+        "id": "76645e9c"
+      },
+      "source": [
+        "### 4.1 Ford 엔진 진동 데이터 보조 학습\n",
+        "\n",
+        "FordA 데이터는 첫 번째 컬럼이 라벨입니다. `-1`을 이상/불량, `1`을 정상으로 변환해 PRESS/BODY 계열 설비 이상 확률을 학습합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "144bc123",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "144bc123",
+        "outputId": "44bf0529-6132-40d4-fe76-ebc0f6497256"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
+            "[LightGBM] [Warning] Contains only one class\n",
+            "[LightGBM] [Info] Number of positive: 0, number of negative: 1\n",
+            "[LightGBM] [Info] Total Bins 0\n",
+            "[LightGBM] [Info] Number of data points in the train set: 1, number of used features: 0\n",
+            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000000 -> initscore=-34.538776\n",
+            "[LightGBM] [Info] Start training from score -34.538776\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "\n",
+            "== Ford PRESS/BODY vibration auxiliary model ==\n",
+            "ROC-AUC: 0.5\n",
+            "PR-AUC: 0.51591\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0     0.4841    1.0000    0.6524       639\n",
+            "           1     0.0000    0.0000    0.0000       681\n",
+            "\n",
+            "    accuracy                         0.4841      1320\n",
+            "   macro avg     0.2420    0.5000    0.3262      1320\n",
+            "weighted avg     0.2343    0.4841    0.3158      1320\n",
+            "\n",
+            "aux_body_ford_vibration_abnormal_probability: 1321개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
+            "aux_press_ford_vibration_abnormal_probability: 1321개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Ford 진동 데이터 로드\n",
+        "def load_ford_txt(path: Path, max_rows: int | None = None) -> pd.DataFrame | None:\n",
+        "    if not path.exists():\n",
+        "        return None\n",
+        "    df = pd.read_csv(path, sep=r\"\\s+\", header=None, nrows=max_rows)\n",
+        "    df = df.apply(pd.to_numeric, errors=\"coerce\")\n",
+        "    df = df.dropna(axis=0, how=\"any\")\n",
+        "    df.columns = [\"label\"] + [f\"ford_t{i}\" for i in range(1, df.shape[1])]\n",
+        "    df[\"ford_abnormal_label\"] = (df[\"label\"] == -1).astype(\"int8\")\n",
+        "    return reduce_mem_usage(df.drop(columns=[\"label\"]))\n",
+        "\n",
+        "# Ford train/test 로드\n",
+        "ford_train_df = load_ford_txt(FORD_TRAIN_PATH)\n",
+        "ford_test_df = load_ford_txt(FORD_TEST_PATH)\n",
+        "\n",
+        "# Ford 보조 모델 학습\n",
+        "if ford_train_df is not None and ford_test_df is not None:\n",
+        "    ford_feature_cols = [c for c in ford_train_df.columns if c != \"ford_abnormal_label\"]\n",
+        "    X_ford_train = ford_train_df[ford_feature_cols]\n",
+        "    y_ford_train = ford_train_df[\"ford_abnormal_label\"].astype(int)\n",
+        "    X_ford_test = ford_test_df[ford_feature_cols]\n",
+        "    y_ford_test = ford_test_df[\"ford_abnormal_label\"].astype(int)\n",
+        "\n",
+        "    ford_model = lgb.LGBMClassifier(\n",
+        "        objective=\"binary\", n_estimators=500, learning_rate=0.03, num_leaves=31,\n",
+        "        min_child_samples=20, subsample=0.9, colsample_bytree=0.9,\n",
+        "        random_state=RANDOM_STATE, n_jobs=-1\n",
+        "    )\n",
+        "    # 진동 이상 확률 학습\n",
+        "    ford_model.fit(X_ford_train, y_ford_train)\n",
+        "    ford_test_proba = ford_model.predict_proba(X_ford_test)[:, 1]\n",
+        "    evaluate_aux_classifier(\"Ford PRESS/BODY vibration\", y_ford_test, ford_test_proba)\n",
+        "\n",
+        "    ford_all_df = pd.concat([ford_train_df, ford_test_df], ignore_index=True)\n",
+        "    ford_all_proba = ford_model.predict_proba(ford_all_df[ford_feature_cols])[:, 1]\n",
+        "    # 차체/프레스 보조 피처 추가\n",
+        "    append_cycled_feature(features, \"aux_body_ford_vibration_abnormal_probability\", ford_all_proba)\n",
+        "    append_cycled_feature(features, \"aux_press_ford_vibration_abnormal_probability\", ford_all_proba)\n",
+        "    auxiliary_models[\"ford_vibration\"] = ford_model\n",
+        "    auxiliary_metrics[\"ford_rows\"] = int(len(ford_all_df))\n",
+        "else:\n",
+        "    print(\"Ford 데이터 파일이 없어 Ford 보조 학습을 건너뜁니다.\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a97efe99",
+      "metadata": {
+        "id": "a97efe99"
+      },
+      "source": [
+        "### 4.2 소성가공 공정/전류 데이터 비지도 학습\n",
+        "\n",
+        "소성가공 데이터에는 불량 라벨이 없으므로 공정 데이터와 설비 센서 데이터를 각각 Isolation Forest로 학습해 공정 흐름 이상 점수, 프레스 이상 점수, 로봇 전류 이상 점수를 생성합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "cd25bfc8",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 216
+        },
+        "id": "cd25bfc8",
+        "outputId": "b99485c6-bd73-43d0-eca4-5af597a8b77a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "numeric_df: (300000, 262)\n",
+            "date_df: (300000, 261)\n",
+            "categorical_df: (300000, 10)\n",
+            "Response ratio:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "            ratio\n",
+              "Response         \n",
+              "0         0.99435\n",
+              "1         0.00565"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8111ef84-e9be-40c3-af10-5b3a4c2773f9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ratio</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Response</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.99435</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.00565</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8111ef84-e9be-40c3-af10-5b3a4c2773f9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8111ef84-e9be-40c3-af10-5b3a4c2773f9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8111ef84-e9be-40c3-af10-5b3a4c2773f9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Bosch numeric 컬럼 선별\n",
+        "numeric_cols = select_columns_by_profile(\n",
+        "    NUMERIC_PATH,\n",
+        "    required_cols=[\"Id\", \"Response\"],\n",
+        "    max_features=MAX_NUMERIC_FEATURES,\n",
+        "    profile_rows=PROFILE_ROWS,\n",
+        ")\n",
+        "# Bosch date 컬럼 선별\n",
+        "date_cols = select_columns_by_profile(\n",
+        "    DATE_PATH,\n",
+        "    required_cols=[\"Id\"],\n",
+        "    max_features=MAX_DATE_FEATURES,\n",
+        "    profile_rows=PROFILE_ROWS,\n",
+        ")\n",
+        "\n",
+        "# Bosch 선택 데이터 로드\n",
+        "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
+        "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
+        "\n",
+        "# Bosch categorical 선택 로드\n",
+        "if USE_CATEGORICAL:\n",
+        "    categorical_cols = select_columns_by_profile(\n",
+        "        CATEGORICAL_PATH,\n",
+        "        required_cols=[\"Id\"],\n",
+        "        max_features=MAX_CATEGORICAL_FEATURES,\n",
+        "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
+        "    )\n",
+        "    categorical_df = pd.read_csv(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
+        "else:\n",
+        "    categorical_df = None\n",
+        "\n",
+        "print(\"numeric_df:\", numeric_df.shape)\n",
+        "print(\"date_df:\", date_df.shape)\n",
+        "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
+        "print(\"Response ratio:\")\n",
+        "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "-7HWdLE4bEfB",
+      "metadata": {
+        "id": "-7HWdLE4bEfB"
+      },
+      "source": [
+        "## 5. 학습 데이터 정제\n",
+        "\n",
+        "4개 후보 모델이 동일한 학습 조건에서 비교될 수 있도록 무한대와 완전 결측 컬럼을 제거합니다. 불량 데이터는 희소하므로 stratified split과 class weight 기반 모델을 사용합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "XX3xUzrlbEfB",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XX3xUzrlbEfB",
+        "outputId": "a2c3defb-901c-4512-bf3d-07d616d9d1d4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X_train: (240000, 457) X_valid: (60000, 457)\n",
+            "positive ratio train: 0.00565\n",
+            "scale_pos_weight: 175.99\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 학습 테이블 결합\n",
+        "model_df = features.merge(target, on=\"Id\", how=\"inner\")\n",
+        "model_df = model_df.replace([np.inf, -np.inf], np.nan)\n",
+        "\n",
+        "# 입력/라벨 분리\n",
+        "drop_cols = [\"Id\", \"Response\"]\n",
+        "feature_cols = [c for c in model_df.columns if c not in drop_cols]\n",
+        "# 완전 결측 컬럼 제거\n",
+        "all_missing_cols = model_df[feature_cols].columns[model_df[feature_cols].isna().all()].tolist()\n",
+        "if all_missing_cols:\n",
+        "    model_df = model_df.drop(columns=all_missing_cols)\n",
+        "    feature_cols = [c for c in feature_cols if c not in all_missing_cols]\n",
+        "\n",
+        "X = model_df[feature_cols]\n",
+        "y = model_df[\"Response\"].astype(int)\n",
+        "ids = model_df[\"Id\"]\n",
+        "if y.nunique() < 2:\n",
+        "    raise ValueError(\"Response가 한 클래스만 포함되어 있습니다. MAX_ROWS를 늘리거나 데이터 샘플링 범위를 조정하세요.\")\n",
+        "\n",
+        "# 계층 분할\n",
+        "X_train, X_valid, y_train, y_valid, id_train, id_valid = train_test_split(\n",
+        "    X, y, ids,\n",
+        "    test_size=0.2,\n",
+        "    random_state=RANDOM_STATE,\n",
+        "    stratify=y,\n",
+        ")\n",
+        "\n",
+        "# 클래스 불균형 가중치\n",
+        "neg_count = int((y_train == 0).sum())\n",
+        "pos_count = int((y_train == 1).sum())\n",
+        "scale_pos_weight = neg_count / max(pos_count, 1)\n",
+        "\n",
+        "print(\"X_train:\", X_train.shape, \"X_valid:\", X_valid.shape)\n",
+        "print(\"positive ratio train:\", y_train.mean())\n",
+        "print(\"scale_pos_weight:\", round(scale_pos_weight, 2))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6444a8dd",
+      "metadata": {
+        "id": "6444a8dd"
+      },
+      "source": [
+        "## 6. 전처리 학습 데이터셋 저장\n",
+        "\n",
+        "모델에 실제로 들어가는 정제/전처리 완료 데이터셋을 파일로 저장합니다. Parquet 저장이 가능하면 `.parquet`, 환경에 `pyarrow/fastparquet`가 없으면 `.csv.gz`로 저장합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "6e442104",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6e442104",
+        "outputId": "227ded5c-058c-46ae-879f-05a88e5b8b6b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "전처리 학습 데이터셋: /content/defect_transfer_outputs/defect_transfer_training_dataset.parquet\n",
+            "train/valid split ids: /content/defect_transfer_outputs/defect_transfer_train_valid_split_ids.csv\n",
+            "전처리 metadata: /content/defect_transfer_outputs/defect_transfer_preprocessing_metadata.json\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 학습 데이터셋 저장\n",
+        "def save_training_dataframe(df: pd.DataFrame, output_dir: Path, stem: str) -> Path:\n",
+        "    parquet_path = output_dir / f\"{stem}.parquet\"\n",
+        "    csv_path = output_dir / f\"{stem}.csv.gz\"\n",
+        "    try:\n",
+        "        df.to_parquet(parquet_path, index=False)\n",
+        "        return parquet_path\n",
+        "    except Exception as exc:\n",
+        "        print(f\"Parquet 저장 실패, csv.gz로 저장합니다: {exc}\")\n",
+        "        df.to_csv(csv_path, index=False, compression=\"gzip\")\n",
+        "        return csv_path\n",
+        "\n",
+        "# 전처리 결과 저장\n",
+        "training_dataset_path = save_training_dataframe(model_df, OUTPUT_DIR, \"defect_transfer_training_dataset\")\n",
+        "\n",
+        "# 분할 ID 저장\n",
+        "split_ids = pd.DataFrame({\n",
+        "    \"Id\": pd.concat([id_train, id_valid]).astype(int).to_numpy(),\n",
+        "    \"split\": [\"train\"] * len(id_train) + [\"valid\"] * len(id_valid),\n",
+        "})\n",
+        "split_ids_path = OUTPUT_DIR / \"defect_transfer_train_valid_split_ids.csv\"\n",
+        "split_ids.to_csv(split_ids_path, index=False)\n",
+        "\n",
+        "# 전처리 메타데이터\n",
+        "preprocessing_metadata = {\n",
+        "    \"process_root\": str(PROCESS_ROOT),\n",
+        "    \"bosch_numeric_path\": str(NUMERIC_PATH),\n",
+        "    \"bosch_date_path\": str(DATE_PATH),\n",
+        "    \"bosch_categorical_path\": str(CATEGORICAL_PATH),\n",
+        "    \"use_categorical\": bool(USE_CATEGORICAL),\n",
+        "    \"profile_rows\": int(PROFILE_ROWS),\n",
+        "    \"max_rows\": int(MAX_ROWS),\n",
+        "    \"max_numeric_features\": int(MAX_NUMERIC_FEATURES),\n",
+        "    \"max_date_features\": int(MAX_DATE_FEATURES),\n",
+        "    \"max_categorical_features\": int(MAX_CATEGORICAL_FEATURES),\n",
+        "    \"selected_numeric_columns\": numeric_cols,\n",
+        "    \"selected_date_columns\": date_cols,\n",
+        "    \"selected_categorical_columns\": categorical_cols if USE_CATEGORICAL else [],\n",
+        "    \"dropped_all_missing_columns\": all_missing_cols,\n",
+        "    \"feature_columns\": feature_cols,\n",
+        "    \"target_column\": \"Response\",\n",
+        "    \"train_rows\": int(len(X_train)),\n",
+        "    \"valid_rows\": int(len(X_valid)),\n",
+        "    \"positive_ratio\": float(y.mean()),\n",
+        "    \"auxiliary_metrics\": auxiliary_metrics,\n",
+        "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
+        "}\n",
+        "preprocessing_metadata_path = OUTPUT_DIR / \"defect_transfer_preprocessing_metadata.json\"\n",
+        "preprocessing_metadata_path.write_text(json.dumps(preprocessing_metadata, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "\n",
+        "print(\"전처리 학습 데이터셋:\", training_dataset_path)\n",
+        "print(\"train/valid split ids:\", split_ids_path)\n",
+        "print(\"전처리 metadata:\", preprocessing_metadata_path)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b19d8e5c",
+      "metadata": {
+        "id": "b19d8e5c"
+      },
+      "source": [
+        "## 7. 후보 모델 4종 교차검증 및 하이퍼파라미터 튜닝\n",
+        "\n",
+        "불량 클래스가 희소하므로 모든 후보 모델은 같은 전처리/피처 엔지니어링 결과를 사용하고, `StratifiedKFold`로 클래스 비율을 유지하며 비교합니다.\n",
+        "\n",
+        "후보 모델:\n",
+        "\n",
+        "| 모델 | 목적 |\n",
+        "| --- | --- |\n",
+        "| LightGBM | 대용량 tabular 제조 데이터용 gradient boosting 기준 모델 |\n",
+        "| RandomForest | bagging 기반 tree ensemble 기준 모델 |\n",
+        "| ExtraTrees | 더 강한 randomization을 주는 tree ensemble 후보 |\n",
+        "| LogisticRegression | 선형 기준선 모델 |\n",
+        "\n",
+        "비교 지표는 PR-AUC, ROC-AUC, Accuracy, False Positive Rate, F1을 함께 사용합니다.\n",
+        "\n",
+        "`CV_SAMPLE_SIZE`, `CV_N_ITER`, `FINAL_TRAIN_SAMPLE_SIZE`는 실행 시간을 관리하는 핵심 설정입니다. 기본값은 빠른 비교 모드이며, 최종 제출/운영 모델이 필요할 때만 `RETRAIN_SELECTED_MODEL_ON_FULL_DATA=True`로 선택 모델 전체 재학습을 수행합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "d9a50e90",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "d9a50e90",
+        "outputId": "30d68956-9024-4ee0-8396-169616380eac"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CV rows: 15,000 / train rows: 240,000\n",
+            "CV splits: 3, tuning trials per model: 2\n",
+            "\n",
+            "[LightGBM] CV start - trials: 2\n",
+            "  trial 1/2 params={'subsample': 0.85, 'reg_lambda': 1.0, 'num_leaves': 63, 'min_child_samples': 80, 'max_depth': 8, 'learning_rate': 0.06, 'colsample_bytree': 0.85}\n",
+            "    fold 1/3 PR-AUC=0.00869 F1=0.02994 elapsed=8.6s\n",
+            "    fold 2/3 PR-AUC=0.01054 F1=0.07317 elapsed=12.2s\n",
+            "    fold 3/3 PR-AUC=0.01394 F1=0.07059 elapsed=12.1s\n",
+            "  -> LightGBM trial 1 mean PR-AUC=0.01105 ROC-AUC=0.52870 ACC=0.96807 FPR=0.02715\n",
+            "  trial 2/2 params={'subsample': 0.85, 'reg_lambda': 2.0, 'num_leaves': 31, 'min_child_samples': 150, 'max_depth': 8, 'learning_rate': 0.06, 'colsample_bytree': 0.85}\n",
+            "    fold 1/3 PR-AUC=0.01117 F1=0.04878 elapsed=11.2s\n",
+            "    fold 2/3 PR-AUC=0.02039 F1=0.08333 elapsed=7.6s\n",
+            "    fold 3/3 PR-AUC=0.01637 F1=0.08955 elapsed=10.9s\n",
+            "  -> LightGBM trial 2 mean PR-AUC=0.01597 ROC-AUC=0.51714 ACC=0.98747 FPR=0.00744\n",
+            "[LightGBM] CV done elapsed=62.7s\n",
+            "\n",
+            "[RandomForest] CV start - trials: 2\n",
+            "  trial 1/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
+            "    fold 1/3 PR-AUC=0.01045 F1=0.04444 elapsed=4.5s\n",
+            "    fold 2/3 PR-AUC=0.00819 F1=0.02265 elapsed=5.8s\n",
+            "    fold 3/3 PR-AUC=0.01138 F1=0.04545 elapsed=4.1s\n",
+            "  -> RandomForest trial 1 mean PR-AUC=0.01000 ROC-AUC=0.60324 ACC=0.91380 FPR=0.08206\n",
+            "  trial 2/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/3 PR-AUC=0.00945 F1=0.02749 elapsed=4.3s\n",
+            "    fold 2/3 PR-AUC=0.00930 F1=0.03093 elapsed=6.2s\n",
+            "    fold 3/3 PR-AUC=0.01270 F1=0.05607 elapsed=4.3s\n",
+            "  -> RandomForest trial 2 mean PR-AUC=0.01048 ROC-AUC=0.59570 ACC=0.93047 FPR=0.06537\n",
+            "[RandomForest] CV done elapsed=29.2s\n",
+            "\n",
+            "[ExtraTrees] CV start - trials: 2\n",
+            "  trial 1/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
+            "    fold 1/3 PR-AUC=0.00764 F1=0.02280 elapsed=3.1s\n",
+            "    fold 2/3 PR-AUC=0.01074 F1=0.04420 elapsed=5.1s\n",
+            "    fold 3/3 PR-AUC=0.00861 F1=0.03125 elapsed=3.3s\n",
+            "  -> ExtraTrees trial 1 mean PR-AUC=0.00900 ROC-AUC=0.57558 ACC=0.94433 FPR=0.05109\n",
+            "  trial 2/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/3 PR-AUC=0.01043 F1=0.03509 elapsed=3.5s\n",
+            "    fold 2/3 PR-AUC=0.01541 F1=0.06154 elapsed=3.5s\n",
+            "    fold 3/3 PR-AUC=0.03788 F1=0.11111 elapsed=5.7s\n",
+            "  -> ExtraTrees trial 2 mean PR-AUC=0.02124 ROC-AUC=0.59191 ACC=0.97913 FPR=0.01582\n",
+            "[ExtraTrees] CV done elapsed=24.3s\n",
+            "\n",
+            "[LogisticRegression] CV start - trials: 2\n",
+            "  trial 1/2 params={'model__solver': 'lbfgs', 'model__C': 0.1}\n",
+            "    fold 1/3 PR-AUC=0.00952 F1=0.04167 elapsed=2.0s\n",
+            "    fold 2/3 PR-AUC=0.00874 F1=0.04000 elapsed=2.6s\n",
+            "    fold 3/3 PR-AUC=0.03116 F1=0.07792 elapsed=2.8s\n",
+            "  -> LogisticRegression trial 1 mean PR-AUC=0.01647 ROC-AUC=0.56895 ACC=0.97673 FPR=0.01830\n",
+            "  trial 2/2 params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
+            "    fold 1/3 PR-AUC=0.00879 F1=0.03279 elapsed=5.0s\n",
+            "    fold 2/3 PR-AUC=0.00740 F1=0.02244 elapsed=4.8s\n",
+            "    fold 3/3 PR-AUC=0.03535 F1=0.09756 elapsed=4.6s\n",
+            "  -> LogisticRegression trial 2 mean PR-AUC=0.01718 ROC-AUC=0.56337 ACC=0.95293 FPR=0.04230\n",
+            "[LogisticRegression] CV done elapsed=21.7s\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "           model_name  trial_no  mean_pr_auc  std_pr_auc  mean_roc_auc  \\\n",
+              "0          ExtraTrees         2     0.021242    0.011941      0.591906   \n",
+              "1  LogisticRegression         2     0.017179    0.012862      0.563373   \n",
+              "2  LogisticRegression         1     0.016471    0.010389      0.568946   \n",
+              "3            LightGBM         2     0.015974    0.003776      0.517136   \n",
+              "4            LightGBM         1     0.011054    0.002173      0.528703   \n",
+              "5        RandomForest         2     0.010481    0.001569      0.595697   \n",
+              "6        RandomForest         1     0.010005    0.001341      0.603241   \n",
+              "7          ExtraTrees         1     0.008998    0.001293      0.575575   \n",
+              "\n",
+              "   std_roc_auc  mean_accuracy  mean_false_positive_rate   mean_f1  \\\n",
+              "0     0.032715       0.979133                  0.015822  0.069246   \n",
+              "1     0.026249       0.952933                  0.042304  0.050928   \n",
+              "2     0.026550       0.976733                  0.018303  0.053196   \n",
+              "3     0.027286       0.987467                  0.007442  0.073889   \n",
+              "4     0.050119       0.968067                  0.027153  0.057900   \n",
+              "5     0.021972       0.930467                  0.065367  0.038165   \n",
+              "6     0.013798       0.913800                  0.082060  0.037518   \n",
+              "7     0.010577       0.944333                  0.051087  0.032750   \n",
+              "\n",
+              "   mean_recall                                             params  \\\n",
+              "0     0.094417  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+              "1     0.118227        {'model__solver': 'lbfgs', 'model__C': 1.0}   \n",
+              "2     0.105911        {'model__solver': 'lbfgs', 'model__C': 0.1}   \n",
+              "3     0.094007  {'subsample': 0.85, 'reg_lambda': 2.0, 'num_le...   \n",
+              "4     0.129721  {'subsample': 0.85, 'reg_lambda': 1.0, 'num_le...   \n",
+              "5     0.201149  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+              "6     0.190066  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+              "7     0.142447  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+              "\n",
+              "                                    selection_reason  \n",
+              "0  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  \n",
+              "1  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  \n",
+              "2  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  \n",
+              "3  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델  \n",
+              "4  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델  \n",
+              "5  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  \n",
+              "6  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  \n",
+              "7  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ba1fcffe-8b28-4a9d-bed7-dd81f388b01c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model_name</th>\n",
+              "      <th>trial_no</th>\n",
+              "      <th>mean_pr_auc</th>\n",
+              "      <th>std_pr_auc</th>\n",
+              "      <th>mean_roc_auc</th>\n",
+              "      <th>std_roc_auc</th>\n",
+              "      <th>mean_accuracy</th>\n",
+              "      <th>mean_false_positive_rate</th>\n",
+              "      <th>mean_f1</th>\n",
+              "      <th>mean_recall</th>\n",
+              "      <th>params</th>\n",
+              "      <th>selection_reason</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.021242</td>\n",
+              "      <td>0.011941</td>\n",
+              "      <td>0.591906</td>\n",
+              "      <td>0.032715</td>\n",
+              "      <td>0.979133</td>\n",
+              "      <td>0.015822</td>\n",
+              "      <td>0.069246</td>\n",
+              "      <td>0.094417</td>\n",
+              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.017179</td>\n",
+              "      <td>0.012862</td>\n",
+              "      <td>0.563373</td>\n",
+              "      <td>0.026249</td>\n",
+              "      <td>0.952933</td>\n",
+              "      <td>0.042304</td>\n",
+              "      <td>0.050928</td>\n",
+              "      <td>0.118227</td>\n",
+              "      <td>{'model__solver': 'lbfgs', 'model__C': 1.0}</td>\n",
+              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.016471</td>\n",
+              "      <td>0.010389</td>\n",
+              "      <td>0.568946</td>\n",
+              "      <td>0.026550</td>\n",
+              "      <td>0.976733</td>\n",
+              "      <td>0.018303</td>\n",
+              "      <td>0.053196</td>\n",
+              "      <td>0.105911</td>\n",
+              "      <td>{'model__solver': 'lbfgs', 'model__C': 0.1}</td>\n",
+              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.015974</td>\n",
+              "      <td>0.003776</td>\n",
+              "      <td>0.517136</td>\n",
+              "      <td>0.027286</td>\n",
+              "      <td>0.987467</td>\n",
+              "      <td>0.007442</td>\n",
+              "      <td>0.073889</td>\n",
+              "      <td>0.094007</td>\n",
+              "      <td>{'subsample': 0.85, 'reg_lambda': 2.0, 'num_le...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.011054</td>\n",
+              "      <td>0.002173</td>\n",
+              "      <td>0.528703</td>\n",
+              "      <td>0.050119</td>\n",
+              "      <td>0.968067</td>\n",
+              "      <td>0.027153</td>\n",
+              "      <td>0.057900</td>\n",
+              "      <td>0.129721</td>\n",
+              "      <td>{'subsample': 0.85, 'reg_lambda': 1.0, 'num_le...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.010481</td>\n",
+              "      <td>0.001569</td>\n",
+              "      <td>0.595697</td>\n",
+              "      <td>0.021972</td>\n",
+              "      <td>0.930467</td>\n",
+              "      <td>0.065367</td>\n",
+              "      <td>0.038165</td>\n",
+              "      <td>0.201149</td>\n",
+              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.010005</td>\n",
+              "      <td>0.001341</td>\n",
+              "      <td>0.603241</td>\n",
+              "      <td>0.013798</td>\n",
+              "      <td>0.913800</td>\n",
+              "      <td>0.082060</td>\n",
+              "      <td>0.037518</td>\n",
+              "      <td>0.190066</td>\n",
+              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.008998</td>\n",
+              "      <td>0.001293</td>\n",
+              "      <td>0.575575</td>\n",
+              "      <td>0.010577</td>\n",
+              "      <td>0.944333</td>\n",
+              "      <td>0.051087</td>\n",
+              "      <td>0.032750</td>\n",
+              "      <td>0.142447</td>\n",
+              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "    </tr>\n",
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+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"print(\\\"CV best model:\\\", best_model_name, best_params)\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"LogisticRegression\",\n          \"RandomForest\",\n          \"ExtraTrees\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trial_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004384427327922025,\n        \"min\": 0.00899778441614044,\n        \"max\": 0.02124201716615975,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.017179465041165264,\n          0.010481081749463448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005125083865734947,\n        \"min\": 0.0012927115056868654,\n        \"max\": 0.012862487985111101,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.012862487985111101,\n          0.0015685750607633147\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.031148456437904398,\n        \"min\": 0.5171362832079376,\n        \"max\": 0.6032409382995763,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5633730794608768,\n          0.5956967126031534\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012139778202552097,\n        \"min\": 0.010577276667535166,\n        \"max\": 0.05011926121966007,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0262487393198506,\n          0.0219718066337053\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025845812654007067,\n        \"min\": 0.9138000000000001,\n        \"max\": 0.9874666666666667,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9529333333333333,\n          0.9304666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02620642920778305,\n        \"min\": 0.007442145403382526,\n        \"max\": 0.08205972219996926,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.042303701506279996,\n          0.06536706137755054\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015047112634012106,\n        \"min\": 0.0327500659864068,\n        \"max\": 0.07388868664806052,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.05092791943051839,\n          0.038164670113798156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.041307512242400736,\n        \"min\": 0.09400656814449916,\n        \"max\": 0.20114942528735633,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.11822660098522168,\n          0.20114942528735633\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"\\ubcf5\\uc7a1\\ud55c tree \\ubaa8\\ub378 \\ub300\\ube44 \\uc120\\ud615 \\uae30\\uc900\\uc120\\uc73c\\ub85c feature engineering \\uc790\\uccb4\\uc758 \\uc124\\uba85\\ub825 \\ud655\\uc778\",\n          \"bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CV 결과 저장: /content/defect_transfer_outputs/defect_model_cv_results.csv\n",
+            "CV best model: ExtraTrees {'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 평가 함수\n",
+        "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
+        "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
+        "    if len(thresholds) == 0:\n",
+        "        return 0.5\n",
+        "    f1_scores = 2 * precision[:-1] * recall[:-1] / np.maximum(precision[:-1] + recall[:-1], 1e-12)\n",
+        "    return float(thresholds[int(np.nanargmax(f1_scores))])\n",
+        "\n",
+        "\n",
+        "def compute_binary_metrics(y_true: pd.Series, proba: np.ndarray, threshold: float) -> dict:\n",
+        "    pred = (proba >= threshold).astype(int)\n",
+        "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
+        "    return {\n",
+        "        \"accuracy\": float(accuracy_score(y_true, pred)),\n",
+        "        \"precision\": float(precision_score(y_true, pred, zero_division=0)),\n",
+        "        \"recall\": float(recall_score(y_true, pred, zero_division=0)),\n",
+        "        \"f1\": float(f1_score(y_true, pred, zero_division=0)),\n",
+        "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
+        "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
+        "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
+        "        \"true_negative\": int(tn),\n",
+        "        \"false_positive\": int(fp),\n",
+        "        \"false_negative\": int(fn),\n",
+        "        \"true_positive\": int(tp),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def predict_positive_proba(estimator, X_input: pd.DataFrame) -> np.ndarray:\n",
+        "    if hasattr(estimator, \"predict_proba\"):\n",
+        "        return estimator.predict_proba(X_input)[:, 1]\n",
+        "    decision = estimator.decision_function(X_input)\n",
+        "    return 1 / (1 + np.exp(-decision))\n",
+        "\n",
+        "\n",
+        "def make_candidate_models() -> dict:\n",
+        "    return {\n",
+        "        \"LightGBM\": {\n",
+        "            \"estimator\": lgb.LGBMClassifier(\n",
+        "                objective=\"binary\",\n",
+        "                n_estimators=250,\n",
+        "                random_state=RANDOM_STATE,\n",
+        "                n_jobs=-1,\n",
+        "                scale_pos_weight=scale_pos_weight,\n",
+        "                verbosity=-1,\n",
+        "            ),\n",
+        "            \"params\": {\n",
+        "                \"learning_rate\": [0.03, 0.06],\n",
+        "                \"num_leaves\": [31, 63],\n",
+        "                \"max_depth\": [-1, 8],\n",
+        "                \"min_child_samples\": [80, 150],\n",
+        "                \"subsample\": [0.85, 1.0],\n",
+        "                \"colsample_bytree\": [0.85],\n",
+        "                \"reg_lambda\": [1.0, 2.0],\n",
+        "            },\n",
+        "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델\",\n",
+        "        },\n",
+        "        \"RandomForest\": {\n",
+        "            \"estimator\": Pipeline([\n",
+        "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+        "                (\"model\", RandomForestClassifier(\n",
+        "                    n_estimators=120,\n",
+        "                    class_weight=\"balanced_subsample\",\n",
+        "                    random_state=RANDOM_STATE,\n",
+        "                    n_jobs=-1,\n",
+        "                )),\n",
+        "            ]),\n",
+        "            \"params\": {\n",
+        "                \"model__max_depth\": [12, 20],\n",
+        "                \"model__min_samples_leaf\": [1, 5],\n",
+        "                \"model__max_features\": [\"sqrt\"],\n",
+        "            },\n",
+        "            \"selection_reason\": \"bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble 성능 확인\",\n",
+        "        },\n",
+        "        \"ExtraTrees\": {\n",
+        "            \"estimator\": Pipeline([\n",
+        "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+        "                (\"model\", ExtraTreesClassifier(\n",
+        "                    n_estimators=160,\n",
+        "                    class_weight=\"balanced\",\n",
+        "                    random_state=RANDOM_STATE,\n",
+        "                    n_jobs=-1,\n",
+        "                )),\n",
+        "            ]),\n",
+        "            \"params\": {\n",
+        "                \"model__max_depth\": [12, 20],\n",
+        "                \"model__min_samples_leaf\": [1, 5],\n",
+        "                \"model__max_features\": [\"sqrt\"],\n",
+        "            },\n",
+        "            \"selection_reason\": \"RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교\",\n",
+        "        },\n",
+        "        \"LogisticRegression\": {\n",
+        "            \"estimator\": Pipeline([\n",
+        "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+        "                (\"scaler\", StandardScaler()),\n",
+        "                (\"model\", LogisticRegression(\n",
+        "                    class_weight=\"balanced\",\n",
+        "                    max_iter=1000,\n",
+        "                    random_state=RANDOM_STATE,\n",
+        "                )),\n",
+        "            ]),\n",
+        "            \"params\": {\n",
+        "                \"model__C\": [0.1, 1.0],\n",
+        "                \"model__solver\": [\"lbfgs\"],\n",
+        "            },\n",
+        "            \"selection_reason\": \"복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체의 설명력 확인\",\n",
+        "        },\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "# CV 데이터셋 규모 관리\n",
+        "if len(X_train) > CV_SAMPLE_SIZE:\n",
+        "    X_cv_pool, _, y_cv_pool, _ = train_test_split(\n",
+        "        X_train,\n",
+        "        y_train,\n",
+        "        train_size=CV_SAMPLE_SIZE,\n",
+        "        random_state=RANDOM_STATE,\n",
+        "        stratify=y_train,\n",
+        "    )\n",
+        "else:\n",
+        "    X_cv_pool = X_train\n",
+        "    y_cv_pool = y_train\n",
+        "\n",
+        "effective_cv_splits = min(CV_N_SPLITS, int(y_cv_pool.value_counts().min()))\n",
+        "if effective_cv_splits < 2:\n",
+        "    raise ValueError(\"교차검증을 수행하기에 소수 클래스 샘플이 부족합니다. MAX_ROWS 또는 CV_SAMPLE_SIZE를 늘려주세요.\")\n",
+        "\n",
+        "candidate_configs = make_candidate_models()\n",
+        "skf = StratifiedKFold(n_splits=effective_cv_splits, shuffle=True, random_state=RANDOM_STATE)\n",
+        "cv_rows = []\n",
+        "best_params_by_model = {}\n",
+        "\n",
+        "print(f\"CV rows: {len(X_cv_pool):,} / train rows: {len(X_train):,}\")\n",
+        "print(f\"CV splits: {effective_cv_splits}, tuning trials per model: {CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1}\")\n",
+        "\n",
+        "# 후보 모델별 교차검증\n",
+        "for model_name, config in candidate_configs.items():\n",
+        "    param_candidates = list(ParameterSampler(\n",
+        "        config[\"params\"],\n",
+        "        n_iter=CV_N_ITER,\n",
+        "        random_state=RANDOM_STATE,\n",
+        "    )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
+        "\n",
+        "    print(f\"\\n[{model_name}] CV start - trials: {len(param_candidates)}\")\n",
+        "    model_started_at = datetime.now()\n",
+        "    for trial_no, params in enumerate(param_candidates, start=1):\n",
+        "        fold_rows = []\n",
+        "        print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
+        "        for fold_no, (train_idx, valid_idx) in enumerate(skf.split(X_cv_pool, y_cv_pool), start=1):\n",
+        "            fold_started_at = datetime.now()\n",
+        "            X_fold_train = X_cv_pool.iloc[train_idx]\n",
+        "            y_fold_train = y_cv_pool.iloc[train_idx]\n",
+        "            X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
+        "            y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
+        "\n",
+        "            cv_model = clone(config[\"estimator\"])\n",
+        "            cv_model.set_params(**params)\n",
+        "            cv_model.fit(X_fold_train, y_fold_train)\n",
+        "\n",
+        "            fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
+        "            fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
+        "            fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
+        "            fold_metrics.update({\n",
+        "                \"model_name\": model_name,\n",
+        "                \"trial_no\": trial_no,\n",
+        "                \"fold_no\": fold_no,\n",
+        "                \"threshold\": fold_threshold,\n",
+        "                **params,\n",
+        "            })\n",
+        "            fold_rows.append(fold_metrics)\n",
+        "            elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
+        "            print(\n",
+        "                f\"    fold {fold_no}/{effective_cv_splits} \"\n",
+        "                f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
+        "                f\"F1={fold_metrics['f1']:.5f} \"\n",
+        "                f\"elapsed={elapsed:.1f}s\"\n",
+        "            )\n",
+        "\n",
+        "        fold_df = pd.DataFrame(fold_rows)\n",
+        "        row = {\n",
+        "            \"model_name\": model_name,\n",
+        "            \"trial_no\": trial_no,\n",
+        "            \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
+        "            \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
+        "            \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
+        "            \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
+        "            \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
+        "            \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
+        "            \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
+        "            \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
+        "            \"params\": params,\n",
+        "            \"selection_reason\": config[\"selection_reason\"],\n",
+        "        }\n",
+        "        cv_rows.append(row)\n",
+        "        print(\n",
+        "            f\"  -> {model_name} trial {trial_no} mean \"\n",
+        "            f\"PR-AUC={row['mean_pr_auc']:.5f} \"\n",
+        "            f\"ROC-AUC={row['mean_roc_auc']:.5f} \"\n",
+        "            f\"ACC={row['mean_accuracy']:.5f} \"\n",
+        "            f\"FPR={row['mean_false_positive_rate']:.5f}\"\n",
+        "        )\n",
+        "\n",
+        "    print(f\"[{model_name}] CV done elapsed={(datetime.now() - model_started_at).total_seconds():.1f}s\")\n",
+        "\n",
+        "cv_results = pd.DataFrame(cv_rows).sort_values(\n",
+        "    [\"mean_pr_auc\", \"mean_roc_auc\", \"mean_f1\", \"mean_accuracy\"],\n",
+        "    ascending=False,\n",
+        ").reset_index(drop=True)\n",
+        "\n",
+        "for model_name in candidate_configs:\n",
+        "    best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
+        "\n",
+        "best_model_name = str(cv_results.iloc[0][\"model_name\"])\n",
+        "best_params = best_params_by_model[best_model_name]\n",
+        "best_model_selection_reason = str(cv_results.iloc[0][\"selection_reason\"])\n",
+        "\n",
+        "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.csv\"\n",
+        "cv_results.to_csv(cv_results_path, index=False)\n",
+        "\n",
+        "display(cv_results.head(20))\n",
+        "print(\"CV 결과 저장:\", cv_results_path)\n",
+        "print(\"CV best model:\", best_model_name, best_params)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3f15608a",
+      "metadata": {
+        "id": "3f15608a"
+      },
+      "source": [
+        "## 8. 후보 모델 4종 최종 학습 및 성능 비교\n",
+        "\n",
+        "교차검증에서 선택된 모델별 best parameter로 4개 후보 모델을 모두 학습합니다.\n",
+        "\n",
+        "각 모델은 같은 `X_train`, `X_valid`, `feature_cols`를 사용합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "3553e4c5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "3553e4c5",
+        "outputId": "fdc0c33f-c362-4dde-ac26-c880a406e708"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Final candidate training rows: 80,000 / train rows: 240,000\n",
+            "[LightGBM] final fit start params={'subsample': 0.85, 'reg_lambda': 2.0, 'num_leaves': 31, 'min_child_samples': 150, 'max_depth': 8, 'learning_rate': 0.06, 'colsample_bytree': 0.85}\n",
+            "[LightGBM] final fit done PR-AUC=0.05440 ROC-AUC=0.66998 ACC=0.99080 FPR=0.00454 elapsed=38.1s\n",
+            "[RandomForest] final fit start params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "[RandomForest] final fit done PR-AUC=0.03843 ROC-AUC=0.68322 ACC=0.99165 FPR=0.00334 elapsed=67.6s\n",
+            "[ExtraTrees] final fit start params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "[ExtraTrees] final fit done PR-AUC=0.03488 ROC-AUC=0.66894 ACC=0.99028 FPR=0.00479 elapsed=65.5s\n",
+            "[LogisticRegression] final fit start params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
+            "[LogisticRegression] final fit done PR-AUC=0.04596 ROC-AUC=0.64420 ACC=0.99165 FPR=0.00350 elapsed=72.6s\n",
+            "selected_model_name: LightGBM\n",
+            "ROC-AUC: 0.66998\n",
+            "PR-AUC: 0.0544\n",
+            "Accuracy: 0.9908\n",
+            "False Positive Rate: 0.00454\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "           model_name  threshold  \\\n",
+              "0            LightGBM   0.764335   \n",
+              "1  LogisticRegression   0.979877   \n",
+              "2        RandomForest   0.351859   \n",
+              "3          ExtraTrees   0.448309   \n",
+              "\n",
+              "                                    selection_reason  accuracy  precision  \\\n",
+              "0  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델  0.990800   0.176292   \n",
+              "1  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  0.991650   0.183594   \n",
+              "2  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  0.991650   0.156780   \n",
+              "3  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  0.990283   0.128049   \n",
+              "\n",
+              "     recall        f1   roc_auc    pr_auc  false_positive_rate  true_negative  \\\n",
+              "0  0.171091  0.173653  0.669980  0.054404             0.004542          59390   \n",
+              "1  0.138643  0.157983  0.644195  0.045955             0.003503          59452   \n",
+              "2  0.109145  0.128696  0.683218  0.038426             0.003336          59462   \n",
+              "3  0.123894  0.125937  0.668943  0.034879             0.004794          59375   \n",
+              "\n",
+              "   false_positive  false_negative  true_positive  \n",
+              "0             271             281             58  \n",
+              "1             209             292             47  \n",
+              "2             199             302             37  \n",
+              "3             286             297             42  "
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+              "      <th></th>\n",
+              "      <th>model_name</th>\n",
+              "      <th>threshold</th>\n",
+              "      <th>selection_reason</th>\n",
+              "      <th>accuracy</th>\n",
+              "      <th>precision</th>\n",
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+              "      <th>pr_auc</th>\n",
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+              "      <th>true_negative</th>\n",
+              "      <th>false_positive</th>\n",
+              "      <th>false_negative</th>\n",
+              "      <th>true_positive</th>\n",
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+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>0.764335</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델</td>\n",
+              "      <td>0.990800</td>\n",
+              "      <td>0.176292</td>\n",
+              "      <td>0.171091</td>\n",
+              "      <td>0.173653</td>\n",
+              "      <td>0.669980</td>\n",
+              "      <td>0.054404</td>\n",
+              "      <td>0.004542</td>\n",
+              "      <td>59390</td>\n",
+              "      <td>271</td>\n",
+              "      <td>281</td>\n",
+              "      <td>58</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>0.979877</td>\n",
+              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
+              "      <td>0.991650</td>\n",
+              "      <td>0.183594</td>\n",
+              "      <td>0.138643</td>\n",
+              "      <td>0.157983</td>\n",
+              "      <td>0.644195</td>\n",
+              "      <td>0.045955</td>\n",
+              "      <td>0.003503</td>\n",
+              "      <td>59452</td>\n",
+              "      <td>209</td>\n",
+              "      <td>292</td>\n",
+              "      <td>47</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>0.351859</td>\n",
+              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+              "      <td>0.991650</td>\n",
+              "      <td>0.156780</td>\n",
+              "      <td>0.109145</td>\n",
+              "      <td>0.128696</td>\n",
+              "      <td>0.683218</td>\n",
+              "      <td>0.038426</td>\n",
+              "      <td>0.003336</td>\n",
+              "      <td>59462</td>\n",
+              "      <td>199</td>\n",
+              "      <td>302</td>\n",
+              "      <td>37</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>0.448309</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>0.990283</td>\n",
+              "      <td>0.128049</td>\n",
+              "      <td>0.123894</td>\n",
+              "      <td>0.125937</td>\n",
+              "      <td>0.668943</td>\n",
+              "      <td>0.034879</td>\n",
+              "      <td>0.004794</td>\n",
+              "      <td>59375</td>\n",
+              "      <td>286</td>\n",
+              "      <td>297</td>\n",
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+              "      <th>모델_선정_근거</th>\n",
+              "      <th>데이터셋_규모_관리</th>\n",
+              "      <th>테스트_케이스_수_관리</th>\n",
+              "      <th>정확도_Accuracy</th>\n",
+              "      <th>오탐률_False_Positive_Rate</th>\n",
+              "      <th>모델_성능_개선_현황</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM 모델이 validation PR-AUC/ROC-AUC/F1 기준 종...</td>\n",
+              "      <td>{'max_rows': 300000, 'profile_rows': 50000, 't...</td>\n",
+              "      <td>{'validation_total': 60000, 'validation_normal...</td>\n",
+              "      <td>0.9908</td>\n",
+              "      <td>0.004542</td>\n",
+              "      <td>{'baseline_model': 'LightGBM', 'baseline_pr_au...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f036ed87-ea2e-4cc6-89d6-c9448e360cc0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f036ed87-ea2e-4cc6-89d6-c9448e360cc0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f036ed87-ea2e-4cc6-89d6-c9448e360cc0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc120\\uc815_\\uadfc\\uac70\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"LightGBM \\ubaa8\\ub378\\uc774 validation PR-AUC/ROC-AUC/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. \\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub370\\uc774\\ud130\\uc14b_\\uaddc\\ubaa8_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud14c\\uc2a4\\ud2b8_\\ucf00\\uc774\\uc2a4_\\uc218_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc815\\ud655\\ub3c4_Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9908,\n        \"max\": 0.9908,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc624\\ud0d0\\ub960_False_Positive_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.004542330835889442,\n        \"max\": 0.004542330835889442,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.004542330835889442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc131\\ub2a5_\\uac1c\\uc120_\\ud604\\ud669\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "trained_models = {}\n",
+        "model_valid_probabilities = {}\n",
+        "model_thresholds = {}\n",
+        "model_confusion_matrices = {}\n",
+        "model_eval_rows = []\n",
+        "\n",
+        "# 최종 후보 비교용 학습 데이터셋 규모 관리\n",
+        "if len(X_train) > FINAL_TRAIN_SAMPLE_SIZE:\n",
+        "    X_final_train, _, y_final_train, _ = train_test_split(\n",
+        "        X_train,\n",
+        "        y_train,\n",
+        "        train_size=FINAL_TRAIN_SAMPLE_SIZE,\n",
+        "        random_state=RANDOM_STATE,\n",
+        "        stratify=y_train,\n",
+        "    )\n",
+        "else:\n",
+        "    X_final_train = X_train\n",
+        "    y_final_train = y_train\n",
+        "\n",
+        "print(f\"Final candidate training rows: {len(X_final_train):,} / train rows: {len(X_train):,}\")\n",
+        "\n",
+        "# 후보 모델 전체 학습\n",
+        "for model_name, config in candidate_configs.items():\n",
+        "    started_at = datetime.now()\n",
+        "    estimator = clone(config[\"estimator\"])\n",
+        "    estimator.set_params(**best_params_by_model[model_name])\n",
+        "    print(f\"[{model_name}] final fit start params={best_params_by_model[model_name]}\")\n",
+        "    estimator.fit(X_final_train, y_final_train)\n",
+        "\n",
+        "    proba = predict_positive_proba(estimator, X_valid)\n",
+        "    threshold = find_best_threshold(y_valid, proba)\n",
+        "    pred = (proba >= threshold).astype(int)\n",
+        "    cm_model = confusion_matrix(y_valid, pred, labels=[0, 1])\n",
+        "    metrics = compute_binary_metrics(y_valid, proba, threshold)\n",
+        "\n",
+        "    trained_models[model_name] = estimator\n",
+        "    model_valid_probabilities[model_name] = proba\n",
+        "    model_thresholds[model_name] = threshold\n",
+        "    model_confusion_matrices[model_name] = cm_model\n",
+        "\n",
+        "    model_eval_rows.append({\n",
+        "        \"model_name\": model_name,\n",
+        "        \"threshold\": threshold,\n",
+        "        \"selection_reason\": config[\"selection_reason\"],\n",
+        "        **metrics,\n",
+        "    })\n",
+        "    print(\n",
+        "        f\"[{model_name}] final fit done \"\n",
+        "        f\"PR-AUC={metrics['pr_auc']:.5f} \"\n",
+        "        f\"ROC-AUC={metrics['roc_auc']:.5f} \"\n",
+        "        f\"ACC={metrics['accuracy']:.5f} \"\n",
+        "        f\"FPR={metrics['false_positive_rate']:.5f} \"\n",
+        "        f\"elapsed={(datetime.now() - started_at).total_seconds():.1f}s\"\n",
+        "    )\n",
+        "\n",
+        "model_comparison = pd.DataFrame(model_eval_rows).sort_values(\n",
+        "    [\"pr_auc\", \"roc_auc\", \"f1\", \"accuracy\"],\n",
+        "    ascending=False,\n",
+        ").reset_index(drop=True)\n",
+        "\n",
+        "selected_model_name = str(model_comparison.iloc[0][\"model_name\"])\n",
+        "model = trained_models[selected_model_name]\n",
+        "valid_proba = model_valid_probabilities[selected_model_name]\n",
+        "best_threshold = float(model_thresholds[selected_model_name])\n",
+        "valid_pred = (valid_proba >= best_threshold).astype(int)\n",
+        "cm = model_confusion_matrices[selected_model_name]\n",
+        "\n",
+        "# 운영용 전체 재학습은 선택 모델 1개만 수행\n",
+        "if RETRAIN_SELECTED_MODEL_ON_FULL_DATA and len(X_final_train) < len(X_train):\n",
+        "    print(f\"[{selected_model_name}] selected model full-data refit start rows={len(X_train):,}\")\n",
+        "    refit_started_at = datetime.now()\n",
+        "    full_model = clone(candidate_configs[selected_model_name][\"estimator\"])\n",
+        "    full_model.set_params(**best_params_by_model[selected_model_name])\n",
+        "    full_model.fit(X_train, y_train)\n",
+        "\n",
+        "    model = full_model\n",
+        "    trained_models[selected_model_name] = full_model\n",
+        "    valid_proba = predict_positive_proba(model, X_valid)\n",
+        "    best_threshold = find_best_threshold(y_valid, valid_proba)\n",
+        "    valid_pred = (valid_proba >= best_threshold).astype(int)\n",
+        "    cm = confusion_matrix(y_valid, valid_pred, labels=[0, 1])\n",
+        "    full_metrics = compute_binary_metrics(y_valid, valid_proba, best_threshold)\n",
+        "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), list(full_metrics.keys())] = list(full_metrics.values())\n",
+        "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), \"threshold\"] = best_threshold\n",
+        "    model_confusion_matrices[selected_model_name] = cm\n",
+        "    print(f\"[{selected_model_name}] full-data refit done elapsed={(datetime.now() - refit_started_at).total_seconds():.1f}s\")\n",
+        "\n",
+        "roc_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"roc_auc\"])\n",
+        "pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
+        "accuracy = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"accuracy\"])\n",
+        "false_positive_rate = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"false_positive_rate\"])\n",
+        "\n",
+        "# AI 관리 요약\n",
+        "baseline_row = model_comparison[model_comparison[\"model_name\"].eq(\"LightGBM\")]\n",
+        "baseline_pr_auc = float(baseline_row.iloc[0][\"pr_auc\"]) if not baseline_row.empty else np.nan\n",
+        "best_pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
+        "performance_improvement = best_pr_auc - baseline_pr_auc if not np.isnan(baseline_pr_auc) else 0.0\n",
+        "\n",
+        "ai_management_summary = {\n",
+        "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/ROC-AUC/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
+        "    \"데이터셋_규모_관리\": {\n",
+        "        \"max_rows\": int(MAX_ROWS),\n",
+        "        \"profile_rows\": int(PROFILE_ROWS),\n",
+        "        \"train_rows\": int(len(X_train)),\n",
+        "        \"candidate_train_rows\": int(len(X_final_train)),\n",
+        "        \"valid_rows\": int(len(X_valid)),\n",
+        "        \"feature_count\": int(len(feature_cols)),\n",
+        "        \"positive_ratio\": float(y.mean()),\n",
+        "        \"full_data_refit\": bool(RETRAIN_SELECTED_MODEL_ON_FULL_DATA),\n",
+        "    },\n",
+        "    \"테스트_케이스_수_관리\": {\n",
+        "        \"validation_total\": int(len(y_valid)),\n",
+        "        \"validation_normal\": int((y_valid == 0).sum()),\n",
+        "        \"validation_defect\": int((y_valid == 1).sum()),\n",
+        "        \"cv_splits\": int(effective_cv_splits),\n",
+        "        \"cv_sample_size\": int(len(X_cv_pool)),\n",
+        "        \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
+        "    },\n",
+        "    \"정확도_Accuracy\": accuracy,\n",
+        "    \"오탐률_False_Positive_Rate\": false_positive_rate,\n",
+        "    \"모델_성능_개선_현황\": {\n",
+        "        \"baseline_model\": \"LightGBM\",\n",
+        "        \"baseline_pr_auc\": baseline_pr_auc,\n",
+        "        \"selected_model\": selected_model_name,\n",
+        "        \"selected_pr_auc\": best_pr_auc,\n",
+        "        \"pr_auc_improvement_over_lightgbm\": performance_improvement,\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "print(\"selected_model_name:\", selected_model_name)\n",
+        "print(\"ROC-AUC:\", round(roc_auc, 5))\n",
+        "print(\"PR-AUC:\", round(pr_auc, 5))\n",
+        "print(\"Accuracy:\", round(accuracy, 5))\n",
+        "print(\"False Positive Rate:\", round(false_positive_rate, 5))\n",
+        "display(model_comparison)\n",
+        "display(pd.DataFrame([ai_management_summary]))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "AGL93TiObEfC",
+      "metadata": {
+        "id": "AGL93TiObEfC"
+      },
+      "source": [
+        "## 9. 후보 모델별 Threshold 튜닝 및 Confusion Matrix\n",
+        "\n",
+        "각 후보 모델은 validation set에서 F1 score가 가장 높은 threshold를 사용합니다.\n",
+        "\n",
+        "Accuracy와 False Positive Rate도 함께 확인합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "-bI4yc81bEfC",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "-bI4yc81bEfC",
+        "outputId": "8c057781-2cd5-4987-e437-ca1f0e111dfe"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== Selected Model ==\n",
+            "model: LightGBM\n",
+            "best_threshold: 0.76433\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0     0.9953    0.9955    0.9954     59661\n",
+            "           1     0.1763    0.1711    0.1737       339\n",
+            "\n",
+            "    accuracy                         0.9908     60000\n",
+            "   macro avg     0.5858    0.5833    0.5845     60000\n",
+            "weighted avg     0.9907    0.9908    0.9907     60000\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x1000 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "           model_name  accuracy  false_positive_rate  precision    recall  \\\n",
+              "0            LightGBM  0.990800             0.004542   0.176292  0.171091   \n",
+              "1  LogisticRegression  0.991650             0.003503   0.183594  0.138643   \n",
+              "2        RandomForest  0.991650             0.003336   0.156780  0.109145   \n",
+              "3          ExtraTrees  0.990283             0.004794   0.128049  0.123894   \n",
+              "\n",
+              "         f1   roc_auc    pr_auc  threshold  \n",
+              "0  0.173653  0.669980  0.054404   0.764335  \n",
+              "1  0.157983  0.644195  0.045955   0.979877  \n",
+              "2  0.128696  0.683218  0.038426   0.351859  \n",
+              "3  0.125937  0.668943  0.034879   0.448309  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-05e6367f-77f3-4887-ae82-cb9c1b4960ab\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model_name</th>\n",
+              "      <th>accuracy</th>\n",
+              "      <th>false_positive_rate</th>\n",
+              "      <th>precision</th>\n",
+              "      <th>recall</th>\n",
+              "      <th>f1</th>\n",
+              "      <th>roc_auc</th>\n",
+              "      <th>pr_auc</th>\n",
+              "      <th>threshold</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>0.990800</td>\n",
+              "      <td>0.004542</td>\n",
+              "      <td>0.176292</td>\n",
+              "      <td>0.171091</td>\n",
+              "      <td>0.173653</td>\n",
+              "      <td>0.669980</td>\n",
+              "      <td>0.054404</td>\n",
+              "      <td>0.764335</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>0.991650</td>\n",
+              "      <td>0.003503</td>\n",
+              "      <td>0.183594</td>\n",
+              "      <td>0.138643</td>\n",
+              "      <td>0.157983</td>\n",
+              "      <td>0.644195</td>\n",
+              "      <td>0.045955</td>\n",
+              "      <td>0.979877</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>0.991650</td>\n",
+              "      <td>0.003336</td>\n",
+              "      <td>0.156780</td>\n",
+              "      <td>0.109145</td>\n",
+              "      <td>0.128696</td>\n",
+              "      <td>0.683218</td>\n",
+              "      <td>0.038426</td>\n",
+              "      <td>0.351859</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>0.990283</td>\n",
+              "      <td>0.004794</td>\n",
+              "      <td>0.128049</td>\n",
+              "      <td>0.123894</td>\n",
+              "      <td>0.125937</td>\n",
+              "      <td>0.668943</td>\n",
+              "      <td>0.034879</td>\n",
+              "      <td>0.448309</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05e6367f-77f3-4887-ae82-cb9c1b4960ab')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-05e6367f-77f3-4887-ae82-cb9c1b4960ab button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-05e6367f-77f3-4887-ae82-cb9c1b4960ab');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"]])\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"LogisticRegression\",\n          \"ExtraTrees\",\n          \"LightGBM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00067376430102327,\n        \"min\": 0.9902833333333333,\n        \"max\": 0.99165,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9908,\n          0.99165,\n          0.9902833333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.000731427784437358,\n        \"min\": 0.0033355123112250883,\n        \"max\": 0.004793751361861182,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0035031259952062487,\n          0.004793751361861182,\n          0.004542330835889442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02481789117206144,\n        \"min\": 0.12804878048780488,\n        \"max\": 0.18359375,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.18359375,\n          0.12804878048780488,\n          0.1762917933130699\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026493989351637128,\n        \"min\": 0.10914454277286136,\n        \"max\": 0.1710914454277286,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.13864306784660768,\n          0.12389380530973451,\n          0.1710914454277286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02315847573778767,\n        \"min\": 0.12593703148425786,\n        \"max\": 0.17365269461077845,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.15798319327731092,\n          0.12593703148425786,\n          0.17365269461077845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016279376786549147,\n        \"min\": 0.6441952093240279,\n        \"max\": 0.6832183696291124,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6441952093240279,\n          0.6689429494935472,\n          0.6699795091035243\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008659351382236778,\n        \"min\": 0.034879154454109204,\n        \"max\": 0.05440352499791599,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.045955427881527094,\n          0.034879154454109204,\n          0.05440352499791599\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28907031849091064,\n        \"min\": 0.35185931288578937,\n        \"max\": 0.979876693471741,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.979876693471741,\n          0.4483085040039446,\n          0.7643346211138977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "print(\"== Selected Model ==\")\n",
+        "print(\"model:\", selected_model_name)\n",
+        "print(\"best_threshold:\", round(best_threshold, 5))\n",
+        "print(classification_report(y_valid, valid_pred, digits=4, zero_division=0))\n",
+        "\n",
+        "# 후보 모델별 Confusion Matrix\n",
+        "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+        "axes = axes.ravel()\n",
+        "for ax, (model_name, cm_model) in zip(axes, model_confusion_matrices.items()):\n",
+        "    ConfusionMatrixDisplay(cm_model, display_labels=[\"Normal\", \"Defect\"]).plot(\n",
+        "        ax=ax,\n",
+        "        cmap=\"Blues\",\n",
+        "        values_format=\"d\",\n",
+        "        colorbar=False,\n",
+        "    )\n",
+        "    row = model_comparison[model_comparison[\"model_name\"].eq(model_name)].iloc[0]\n",
+        "    ax.set_title(\n",
+        "        f\"{model_name}\\n\"\n",
+        "        f\"ACC={row['accuracy']:.3f}, FPR={row['false_positive_rate']:.3f}, F1={row['f1']:.3f}\"\n",
+        "    )\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# 비교 지표 테이블\n",
+        "display(model_comparison[[\n",
+        "    \"model_name\",\n",
+        "    \"accuracy\",\n",
+        "    \"false_positive_rate\",\n",
+        "    \"precision\",\n",
+        "    \"recall\",\n",
+        "    \"f1\",\n",
+        "    \"roc_auc\",\n",
+        "    \"pr_auc\",\n",
+        "    \"threshold\",\n",
+        "]])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "ALpJVyy2bEfC",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ALpJVyy2bEfC",
+        "outputId": "987f4299-b79b-41c2-c81d-dfb6731bf887"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x400 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x700 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   model_name           feature  importance_gain importance_type\n",
+              "0    LightGBM  L3_S33_mean_time    390956.736451            gain\n",
+              "1    LightGBM  L3_S30_mean_time    235859.517601            gain\n",
+              "2    LightGBM      L3_S33_F3873    203709.738733            gain\n",
+              "3    LightGBM  process_end_time    149514.344105            gain\n",
+              "4    LightGBM        L0_num_min    102868.559010            gain\n",
+              "5    LightGBM      L3_S30_F3704     94805.056677            gain\n",
+              "6    LightGBM        L1_num_min     87870.475541            gain\n",
+              "7    LightGBM    L0_S11_num_std     75906.011574            gain\n",
+              "8    LightGBM        L2_num_std     73896.075065            gain\n",
+              "9    LightGBM         L0_S3_F80     67261.653738            gain\n",
+              "10   LightGBM   L3_S33_num_mean     65192.821837            gain\n",
+              "11   LightGBM      L1_S24_F1846     57877.134013            gain\n",
+              "12   LightGBM      L3_S29_F3382     57635.742289            gain\n",
+              "13   LightGBM      L3_S33_F3859     56869.317597            gain\n",
+              "14   LightGBM      L3_S30_F3609     55849.371791            gain\n",
+              "15   LightGBM      L1_S24_F1514     54480.724891            gain\n",
+              "16   LightGBM        L2_num_max     51965.886841            gain\n",
+              "17   LightGBM      L3_S29_F3373     48899.976863            gain\n",
+              "18   LightGBM      L3_S29_F3327     46306.820732            gain\n",
+              "19   LightGBM        L2_num_min     44341.400364            gain\n",
+              "20   LightGBM   L2_S27_num_mean     40442.920059            gain\n",
+              "21   LightGBM       L3_num_mean     38714.116386            gain\n",
+              "22   LightGBM      L2_S27_F3206     38138.170961            gain\n",
+              "23   LightGBM          L0_S0_F6     33666.765800            gain\n",
+              "24   LightGBM    L3_S29_num_std     29165.766471            gain\n",
+              "25   LightGBM       L0_num_mean     28914.965012            gain\n",
+              "26   LightGBM   L0_S4_mean_time     25027.416580            gain\n",
+              "27   LightGBM   L0_S6_mean_time     24665.647415            gain\n",
+              "28   LightGBM      L3_S33_F3857     16719.117322            gain\n",
+              "29   LightGBM   L3_S35_num_mean     16316.879347            gain"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model_name</th>\n",
+              "      <th>feature</th>\n",
+              "      <th>importance_gain</th>\n",
+              "      <th>importance_type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>390956.736451</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>235859.517601</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S33_F3873</td>\n",
+              "      <td>203709.738733</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>process_end_time</td>\n",
+              "      <td>149514.344105</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L0_num_min</td>\n",
+              "      <td>102868.559010</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S30_F3704</td>\n",
+              "      <td>94805.056677</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L1_num_min</td>\n",
+              "      <td>87870.475541</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L0_S11_num_std</td>\n",
+              "      <td>75906.011574</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L2_num_std</td>\n",
+              "      <td>73896.075065</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L0_S3_F80</td>\n",
+              "      <td>67261.653738</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S33_num_mean</td>\n",
+              "      <td>65192.821837</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L1_S24_F1846</td>\n",
+              "      <td>57877.134013</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S29_F3382</td>\n",
+              "      <td>57635.742289</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S33_F3859</td>\n",
+              "      <td>56869.317597</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S30_F3609</td>\n",
+              "      <td>55849.371791</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L1_S24_F1514</td>\n",
+              "      <td>54480.724891</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L2_num_max</td>\n",
+              "      <td>51965.886841</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S29_F3373</td>\n",
+              "      <td>48899.976863</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S29_F3327</td>\n",
+              "      <td>46306.820732</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L2_num_min</td>\n",
+              "      <td>44341.400364</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L2_S27_num_mean</td>\n",
+              "      <td>40442.920059</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_num_mean</td>\n",
+              "      <td>38714.116386</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L2_S27_F3206</td>\n",
+              "      <td>38138.170961</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L0_S0_F6</td>\n",
+              "      <td>33666.765800</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S29_num_std</td>\n",
+              "      <td>29165.766471</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L0_num_mean</td>\n",
+              "      <td>28914.965012</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L0_S4_mean_time</td>\n",
+              "      <td>25027.416580</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L0_S6_mean_time</td>\n",
+              "      <td>24665.647415</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S33_F3857</td>\n",
+              "      <td>16719.117322</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>L3_S35_num_mean</td>\n",
+              "      <td>16316.879347</td>\n",
+              "      <td>gain</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(importance_df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"LightGBM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L0_S6_mean_time\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77801.68166991045,\n        \"min\": 16316.879346847534,\n        \"max\": 390956.7364510298,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          24665.647415161133\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# 모델 성능 비교 시각화\n",
+        "fig, axes = plt.subplots(1, 3, figsize=(18, 4))\n",
+        "sns.countplot(x=y, ax=axes[0])\n",
+        "axes[0].set_title(\"Response Distribution\")\n",
+        "axes[0].set_xlabel(\"Response\")\n",
+        "\n",
+        "sns.barplot(data=model_comparison, x=\"model_name\", y=\"pr_auc\", ax=axes[1], color=\"#4C78A8\")\n",
+        "axes[1].set_title(\"Validation PR-AUC by Model\")\n",
+        "axes[1].set_xlabel(\"\")\n",
+        "axes[1].tick_params(axis=\"x\", rotation=20)\n",
+        "\n",
+        "sns.barplot(data=model_comparison, x=\"model_name\", y=\"false_positive_rate\", ax=axes[2], color=\"#F58518\")\n",
+        "axes[2].set_title(\"False Positive Rate by Model\")\n",
+        "axes[2].set_xlabel(\"\")\n",
+        "axes[2].tick_params(axis=\"x\", rotation=20)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# 선택 모델 ROC/PR 곡선\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "RocCurveDisplay.from_predictions(y_valid, valid_proba, ax=axes[0])\n",
+        "axes[0].set_title(f\"{selected_model_name} ROC AUC={roc_auc:.4f}\")\n",
+        "PrecisionRecallDisplay.from_predictions(y_valid, valid_proba, ax=axes[1])\n",
+        "axes[1].set_title(f\"{selected_model_name} PR-AUC={pr_auc:.4f}\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "def extract_feature_importance(estimator, feature_names: list[str], model_name: str) -> pd.DataFrame:\n",
+        "    target_estimator = estimator\n",
+        "    if isinstance(estimator, Pipeline):\n",
+        "        target_estimator = estimator.named_steps.get(\"model\", estimator)\n",
+        "\n",
+        "    if hasattr(target_estimator, \"booster_\"):\n",
+        "        values = target_estimator.booster_.feature_importance(importance_type=\"gain\")\n",
+        "        importance_type = \"gain\"\n",
+        "    elif hasattr(target_estimator, \"feature_importances_\"):\n",
+        "        values = target_estimator.feature_importances_\n",
+        "        importance_type = \"feature_importances\"\n",
+        "    elif hasattr(target_estimator, \"coef_\"):\n",
+        "        values = np.abs(target_estimator.coef_).ravel()\n",
+        "        importance_type = \"abs_coef\"\n",
+        "    else:\n",
+        "        values = np.zeros(len(feature_names))\n",
+        "        importance_type = \"not_available\"\n",
+        "\n",
+        "    return pd.DataFrame({\n",
+        "        \"model_name\": model_name,\n",
+        "        \"feature\": feature_names,\n",
+        "        \"importance\": values,\n",
+        "        \"importance_type\": importance_type,\n",
+        "    }).sort_values(\"importance\", ascending=False)\n",
+        "\n",
+        "\n",
+        "importance_frames = [\n",
+        "    extract_feature_importance(estimator, feature_cols, model_name)\n",
+        "    for model_name, estimator in trained_models.items()\n",
+        "]\n",
+        "all_model_importance = pd.concat(importance_frames, ignore_index=True)\n",
+        "importance_df = all_model_importance[all_model_importance[\"model_name\"].eq(selected_model_name)].copy()\n",
+        "importance_df = importance_df.rename(columns={\"importance\": \"importance_gain\"})\n",
+        "\n",
+        "plt.figure(figsize=(9, 7))\n",
+        "sns.barplot(data=importance_df.head(25), y=\"feature\", x=\"importance_gain\", color=\"#4C78A8\")\n",
+        "plt.title(f\"Top 25 Feature Importance - {selected_model_name}\")\n",
+        "plt.xlabel(\"Importance\")\n",
+        "plt.ylabel(\"\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "display(importance_df.head(30))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "TaY5ZaEJbEfC",
+      "metadata": {
+        "id": "TaY5ZaEJbEfC"
+      },
+      "source": [
+        "## 10. SHAP 기반 영향 공정 분석\n",
+        "\n",
+        "선택된 최종 모델을 기준으로 SHAP 값을 계산합니다.\n",
+        "\n",
+        "SHAP 값은 feature가 불량 확률 예측에 기여한 정도이며, 이후 feature를 공정 코드로 매핑해 공정별 영향도를 집계합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "24Ol6sMmbEfC",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "24Ol6sMmbEfC",
+        "outputId": "521d3ab8-9c6d-49a5-c713-bda36648ca31"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "selected_model_name: LightGBM\n",
+            "X_shap: (3000, 457)\n",
+            "shap_matrix: (3000, 457)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# SHAP 샘플링\n",
+        "SHAP_SAMPLE_SIZE = min(3000, len(X_valid))\n",
+        "if selected_model_name == \"LogisticRegression\":\n",
+        "    SHAP_SAMPLE_SIZE = min(500, len(X_valid))\n",
+        "\n",
+        "X_shap = X_valid.sample(n=SHAP_SAMPLE_SIZE, random_state=RANDOM_STATE)\n",
+        "id_shap = id_valid.loc[X_shap.index]\n",
+        "y_shap = y_valid.loc[X_shap.index]\n",
+        "proba_shap = predict_positive_proba(model, X_shap)\n",
+        "\n",
+        "\n",
+        "def prepare_shap_model(estimator, X_sample: pd.DataFrame):\n",
+        "    if isinstance(estimator, Pipeline):\n",
+        "        final_estimator = estimator.named_steps[\"model\"]\n",
+        "        transform_steps = estimator.steps[:-1]\n",
+        "        X_transformed = X_sample.copy()\n",
+        "        for _, step in transform_steps:\n",
+        "            X_transformed = step.transform(X_transformed)\n",
+        "        X_transformed = pd.DataFrame(X_transformed, columns=X_sample.columns, index=X_sample.index)\n",
+        "        return final_estimator, X_transformed\n",
+        "    return estimator, X_sample\n",
+        "\n",
+        "\n",
+        "shap_model, X_shap_model = prepare_shap_model(model, X_shap)\n",
+        "\n",
+        "# SHAP Explainer 생성\n",
+        "try:\n",
+        "    explainer = shap.TreeExplainer(shap_model)\n",
+        "    raw_shap_values = explainer.shap_values(X_shap_model)\n",
+        "    if isinstance(raw_shap_values, list):\n",
+        "        shap_matrix = raw_shap_values[1]\n",
+        "    elif getattr(raw_shap_values, \"ndim\", 0) == 3:\n",
+        "        shap_matrix = raw_shap_values[:, :, 1]\n",
+        "    else:\n",
+        "        shap_matrix = raw_shap_values\n",
+        "    base_value = explainer.expected_value[1] if isinstance(explainer.expected_value, list) else explainer.expected_value\n",
+        "except Exception:\n",
+        "    background = shap.sample(X_train, min(100, len(X_train)), random_state=RANDOM_STATE)\n",
+        "    explainer = shap.Explainer(lambda data: predict_positive_proba(model, pd.DataFrame(data, columns=X_train.columns)), background)\n",
+        "    raw_explanation = explainer(X_shap)\n",
+        "    shap_matrix = raw_explanation.values\n",
+        "    base_value = raw_explanation.base_values\n",
+        "    X_shap_model = X_shap\n",
+        "\n",
+        "# SHAP 객체 구성\n",
+        "shap_values = shap.Explanation(\n",
+        "    values=shap_matrix,\n",
+        "    base_values=np.repeat(base_value, len(X_shap)) if np.isscalar(base_value) else base_value,\n",
+        "    data=X_shap.values,\n",
+        "    feature_names=X_shap.columns.tolist(),\n",
+        ")\n",
+        "\n",
+        "print(\"selected_model_name:\", selected_model_name)\n",
+        "print(\"X_shap:\", X_shap.shape)\n",
+        "print(\"shap_matrix:\", shap_matrix.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "gvPdq4r1bEfC",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "gvPdq4r1bEfC",
+        "outputId": "5db7ecb2-7000-43ab-c436-a66a117e9e8f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x1150 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                    feature  mean_abs_shap\n",
+              "323        L3_S33_mean_time       1.043150\n",
+              "263        process_end_time       0.572533\n",
+              "203            L3_S30_F3704       0.461738\n",
+              "137            L3_S29_F3382       0.329964\n",
+              "20                L0_S3_F80       0.312103\n",
+              "190            L3_S30_F3609       0.280839\n",
+              "370          L0_S11_num_std       0.237354\n",
+              "342              L0_num_min       0.236961\n",
+              "236            L3_S33_F3873       0.224241\n",
+              "94             L1_S24_F1846       0.201978\n",
+              "320        L3_S30_mean_time       0.187981\n",
+              "423         L3_S33_num_mean       0.182257\n",
+              "229            L3_S33_F3859       0.174246\n",
+              "120            L3_S29_F3327       0.161220\n",
+              "82             L1_S24_F1514       0.159890\n",
+              "264  total_process_duration       0.155546\n",
+              "351              L2_num_std       0.145310\n",
+              "361           L0_S0_num_std       0.127794\n",
+              "115            L2_S27_F3206       0.095520\n",
+              "414         L2_S27_num_mean       0.094850\n",
+              "352              L2_num_min       0.091944\n",
+              "134            L3_S29_F3373       0.090346\n",
+              "347              L1_num_min       0.082967\n",
+              "393          L0_S5_num_mean       0.078262\n",
+              "189            L3_S30_F3604       0.074655\n",
+              "418          L3_S29_num_std       0.074171\n",
+              "10                L0_S0_F20       0.073757\n",
+              "353              L2_num_max       0.073749\n",
+              "69              L0_S12_F346       0.071814\n",
+              "432         L3_S36_num_mean       0.070417"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-398f9636-9482-4af1-b589-63ca40179c2f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>mean_abs_shap</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>323</th>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>1.043150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>263</th>\n",
+              "      <td>process_end_time</td>\n",
+              "      <td>0.572533</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>203</th>\n",
+              "      <td>L3_S30_F3704</td>\n",
+              "      <td>0.461738</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>137</th>\n",
+              "      <td>L3_S29_F3382</td>\n",
+              "      <td>0.329964</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>L0_S3_F80</td>\n",
+              "      <td>0.312103</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>190</th>\n",
+              "      <td>L3_S30_F3609</td>\n",
+              "      <td>0.280839</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>370</th>\n",
+              "      <td>L0_S11_num_std</td>\n",
+              "      <td>0.237354</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>342</th>\n",
+              "      <td>L0_num_min</td>\n",
+              "      <td>0.236961</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>236</th>\n",
+              "      <td>L3_S33_F3873</td>\n",
+              "      <td>0.224241</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>94</th>\n",
+              "      <td>L1_S24_F1846</td>\n",
+              "      <td>0.201978</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>320</th>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.187981</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>423</th>\n",
+              "      <td>L3_S33_num_mean</td>\n",
+              "      <td>0.182257</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>229</th>\n",
+              "      <td>L3_S33_F3859</td>\n",
+              "      <td>0.174246</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>120</th>\n",
+              "      <td>L3_S29_F3327</td>\n",
+              "      <td>0.161220</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>82</th>\n",
+              "      <td>L1_S24_F1514</td>\n",
+              "      <td>0.159890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>264</th>\n",
+              "      <td>total_process_duration</td>\n",
+              "      <td>0.155546</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>351</th>\n",
+              "      <td>L2_num_std</td>\n",
+              "      <td>0.145310</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>361</th>\n",
+              "      <td>L0_S0_num_std</td>\n",
+              "      <td>0.127794</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>115</th>\n",
+              "      <td>L2_S27_F3206</td>\n",
+              "      <td>0.095520</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>L2_S27_num_mean</td>\n",
+              "      <td>0.094850</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>352</th>\n",
+              "      <td>L2_num_min</td>\n",
+              "      <td>0.091944</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>134</th>\n",
+              "      <td>L3_S29_F3373</td>\n",
+              "      <td>0.090346</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>347</th>\n",
+              "      <td>L1_num_min</td>\n",
+              "      <td>0.082967</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>393</th>\n",
+              "      <td>L0_S5_num_mean</td>\n",
+              "      <td>0.078262</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>189</th>\n",
+              "      <td>L3_S30_F3604</td>\n",
+              "      <td>0.074655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>418</th>\n",
+              "      <td>L3_S29_num_std</td>\n",
+              "      <td>0.074171</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>L0_S0_F20</td>\n",
+              "      <td>0.073757</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>353</th>\n",
+              "      <td>L2_num_max</td>\n",
+              "      <td>0.073749</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>69</th>\n",
+              "      <td>L0_S12_F346</td>\n",
+              "      <td>0.071814</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>432</th>\n",
+              "      <td>L3_S36_num_mean</td>\n",
+              "      <td>0.070417</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-398f9636-9482-4af1-b589-63ca40179c2f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-398f9636-9482-4af1-b589-63ca40179c2f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-398f9636-9482-4af1-b589-63ca40179c2f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(shap_importance\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L2_num_max\",\n          \"total_process_duration\",\n          \"L0_S5_num_mean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19842909695379424,\n        \"min\": 0.07041663354892244,\n        \"max\": 1.0431500289669826,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.07374943734111677,\n          0.1555458950891956,\n          0.0782619774922737\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# SHAP Summary Plot\n",
+        "shap.summary_plot(shap_values, X_shap, max_display=25, show=True)\n",
+        "\n",
+        "# SHAP 중요도 산출\n",
+        "shap_importance = pd.DataFrame({\n",
+        "    \"feature\": X_shap.columns,\n",
+        "    \"mean_abs_shap\": np.abs(shap_matrix).mean(axis=0),\n",
+        "}).sort_values(\"mean_abs_shap\", ascending=False)\n",
+        "\n",
+        "display(shap_importance.head(30))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "ZH8WXbIhbEfC",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 891
+        },
+        "id": "ZH8WXbIhbEfC",
+        "outputId": "61f1e475-b193-4ae8-e8da-d2aa89456d6e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "공정 영향도 집계 제외 전역/미매핑 feature 수: 9\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "         unmapped_feature\n",
+              "0     date_observed_count\n",
+              "1      date_missing_ratio\n",
+              "2      process_start_time\n",
+              "3        process_end_time\n",
+              "4  total_process_duration\n",
+              "5        max_station_span\n",
+              "6       mean_station_span\n",
+              "7        std_station_span\n",
+              "8    active_station_count"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>unmapped_feature</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>date_observed_count</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>date_missing_ratio</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>process_start_time</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>process_end_time</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>total_process_duration</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>max_station_span</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>mean_station_span</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>std_station_span</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>active_station_count</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
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+          "data": {
+            "text/plain": [
+              "  process_code  mean_abs_shap  feature_count\n",
+              "2        PAINT       0.027860            197\n",
+              "3     ASSEMBLY       0.022912             36\n",
+              "0        PRESS       0.020474            182\n",
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+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Feature 공정 매핑\n",
+        "def feature_to_process(feature: str) -> str | None:\n",
+        "    \"\"\"Feature 공정 매핑\"\"\"\n",
+        "    normalized = feature.lower()\n",
+        "\n",
+        "    # Bosch 원천 컬럼/집계 컬럼: L0~L3 prefix 기준 매핑\n",
+        "    line = get_line(feature)\n",
+        "    if line in LINE_TO_PROCESS:\n",
+        "        return LINE_TO_PROCESS[line]\n",
+        "\n",
+        "    # 보조 데이터셋 기반 feature: aux_{process}_... naming 기준 매핑\n",
+        "    if normalized.startswith(\"aux_press\") or \"press\" in normalized:\n",
+        "        return \"PRESS\"\n",
+        "    if normalized.startswith(\"aux_body\") or \"body\" in normalized or \"robot\" in normalized or \"ford\" in normalized:\n",
+        "        return \"BODY\"\n",
+        "    if normalized.startswith(\"aux_paint\") or \"paint\" in normalized or \"thermal\" in normalized:\n",
+        "        return \"PAINT\"\n",
+        "    if normalized.startswith(\"aux_assembly\") or \"assembly\" in normalized:\n",
+        "        return \"ASSEMBLY\"\n",
+        "\n",
+        "    # 전역 feature 제외\n",
+        "    return None\n",
+        "\n",
+        "# 후속 공정 매핑\n",
+        "def next_process(process_code: str) -> str:\n",
+        "    if process_code not in PROCESS_FLOW:\n",
+        "        return \"FINAL_INSPECTION\"\n",
+        "    idx = PROCESS_FLOW.index(process_code)\n",
+        "    if idx + 1 >= len(PROCESS_FLOW):\n",
+        "        return \"FINAL_INSPECTION\"\n",
+        "    return PROCESS_FLOW[idx + 1]\n",
+        "\n",
+        "# 위험 등급 변환\n",
+        "def probability_to_risk_grade(prob: float) -> str:\n",
+        "    if prob >= 0.70:\n",
+        "        return \"HIGH\"\n",
+        "    if prob >= 0.40:\n",
+        "        return \"MEDIUM\"\n",
+        "    return \"LOW\"\n",
+        "\n",
+        "# SHAP feature 공정 매핑\n",
+        "feature_process = pd.Series([feature_to_process(c) for c in X_shap.columns], index=X_shap.columns)\n",
+        "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
+        "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
+        "print(f\"공정 영향도 집계 제외 전역/미매핑 feature 수: {len(unmapped_features)}\")\n",
+        "if unmapped_features:\n",
+        "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
+        "\n",
+        "# 공정별 SHAP 영향도 집계\n",
+        "process_rows = []\n",
+        "for process_code in PROCESS_FLOW:\n",
+        "    cols_idx = np.where(feature_process.values == process_code)[0]\n",
+        "    process_rows.append({\n",
+        "        \"process_code\": process_code,\n",
+        "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, cols_idx]).mean()) if len(cols_idx) else 0.0,\n",
+        "        \"feature_count\": int(len(cols_idx)),\n",
+        "    })\n",
+        "\n",
+        "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
+        "display(process_importance)\n",
+        "\n",
+        "# 공정 영향도 시각화\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
+        "plt.title(\"SHAP Process Influence\")\n",
+        "plt.xlabel(\"Process\")\n",
+        "plt.ylabel(\"Mean |SHAP|\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "kBJK8YfybEfD",
+      "metadata": {
+        "id": "kBJK8YfybEfD"
+      },
+      "source": [
+        "## 11. SHAP 기반 전이 위험 결과 생성\n",
+        "\n",
+        "아래 결과는 PRD의 `defect_transfer_prediction_result` 저장 구조에 맞춘 예측 테이블입니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "MjmQshC4bEfD",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "MjmQshC4bEfD",
+        "outputId": "bc584ba0-0ec7-488d-ef04-093a534ade5a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "      car_master_id source_process_code target_process_code  \\\n",
+              "2806         218707               PAINT            ASSEMBLY   \n",
+              "1720         327784               PAINT            ASSEMBLY   \n",
+              "2920         110780               PAINT            ASSEMBLY   \n",
+              "1676         404144               PAINT            ASSEMBLY   \n",
+              "518          537209            ASSEMBLY    FINAL_INSPECTION   \n",
+              "1363         422924               PAINT            ASSEMBLY   \n",
+              "22           445099               PAINT            ASSEMBLY   \n",
+              "883           49121               PAINT            ASSEMBLY   \n",
+              "1139         270422                BODY               PAINT   \n",
+              "1261          72604               PAINT            ASSEMBLY   \n",
+              "2193         208915               PAINT            ASSEMBLY   \n",
+              "1250         325978               PAINT            ASSEMBLY   \n",
+              "132          457589               PAINT            ASSEMBLY   \n",
+              "1702         345390               PAINT            ASSEMBLY   \n",
+              "1147         329336               PAINT            ASSEMBLY   \n",
+              "593          492773               PAINT            ASSEMBLY   \n",
+              "677          570702               PAINT            ASSEMBLY   \n",
+              "2456         491756               PAINT            ASSEMBLY   \n",
+              "360          161187               PAINT            ASSEMBLY   \n",
+              "2423          58741               PAINT            ASSEMBLY   \n",
+              "2187         399573               PAINT            ASSEMBLY   \n",
+              "695           86810               PAINT            ASSEMBLY   \n",
+              "2026         471938               PAINT            ASSEMBLY   \n",
+              "230          233131               PAINT            ASSEMBLY   \n",
+              "829          103485               PAINT            ASSEMBLY   \n",
+              "522          402988               PAINT            ASSEMBLY   \n",
+              "398          307253               PAINT            ASSEMBLY   \n",
+              "2384         519151            ASSEMBLY    FINAL_INSPECTION   \n",
+              "537          325267               PAINT            ASSEMBLY   \n",
+              "1491          96444               PAINT            ASSEMBLY   \n",
+              "\n",
+              "      current_defect_probability  target_defect_probability  \\\n",
+              "2806                    0.678618                   0.978448   \n",
+              "1720                    0.693589                   0.970204   \n",
+              "2920                    0.677194                   0.937242   \n",
+              "1676                    0.577193                   0.909687   \n",
+              "518                     0.442684                   0.855171   \n",
+              "1363                    0.461277                   0.829406   \n",
+              "22                      0.437623                   0.826252   \n",
+              "883                     0.647237                   0.810856   \n",
+              "1139                    0.380287                   0.806972   \n",
+              "1261                    0.576640                   0.802120   \n",
+              "2193                    0.434317                   0.800171   \n",
+              "1250                    0.631934                   0.713980   \n",
+              "132                     0.461246                   0.695194   \n",
+              "1702                    0.612613                   0.688885   \n",
+              "1147                    0.645485                   0.639600   \n",
+              "593                     0.455224                   0.629645   \n",
+              "677                     0.507326                   0.626186   \n",
+              "2456                    0.578751                   0.615782   \n",
+              "360                     0.511622                   0.609049   \n",
+              "2423                    0.589447                   0.604053   \n",
+              "2187                    0.624960                   0.592752   \n",
+              "695                     0.654663                   0.588408   \n",
+              "2026                    0.557580                   0.568974   \n",
+              "230                     0.558755                   0.566809   \n",
+              "829                     0.593304                   0.565436   \n",
+              "522                     0.506670                   0.564482   \n",
+              "398                     0.593820                   0.560144   \n",
+              "2384                    0.405882                   0.550488   \n",
+              "537                     0.631635                   0.536699   \n",
+              "1491                    0.605923                   0.532112   \n",
+              "\n",
+              "     predicted_defect_process risk_grade        main_cause  influence_score  \\\n",
+              "2806                 ASSEMBLY       HIGH  L3_S30_mean_time         0.678618   \n",
+              "1720                 ASSEMBLY       HIGH  L3_S30_mean_time         0.693589   \n",
+              "2920                 ASSEMBLY       HIGH      L3_S33_F3873         0.677194   \n",
+              "1676                 ASSEMBLY       HIGH  L3_S33_mean_time         0.577193   \n",
+              "518          FINAL_INSPECTION       HIGH        L2_num_min         0.442684   \n",
+              "1363                 ASSEMBLY       HIGH  L3_S33_mean_time         0.461277   \n",
+              "22                   ASSEMBLY       HIGH  L3_S33_mean_time         0.437623   \n",
+              "883                  ASSEMBLY       HIGH      L3_S33_F3873         0.647237   \n",
+              "1139                    PAINT       HIGH        L1_num_min         0.380287   \n",
+              "1261                 ASSEMBLY       HIGH  L3_S33_mean_time         0.576640   \n",
+              "2193                 ASSEMBLY       HIGH  L3_S33_mean_time         0.434317   \n",
+              "1250                 ASSEMBLY       HIGH  L3_S30_mean_time         0.631934   \n",
+              "132                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.461246   \n",
+              "1702                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.612613   \n",
+              "1147                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.645485   \n",
+              "593                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.455224   \n",
+              "677                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.507326   \n",
+              "2456                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.578751   \n",
+              "360                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.511622   \n",
+              "2423                 ASSEMBLY     MEDIUM      L3_S33_F3873         0.589447   \n",
+              "2187                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.624960   \n",
+              "695                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.654663   \n",
+              "2026                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.557580   \n",
+              "230                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.558755   \n",
+              "829                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.593304   \n",
+              "522                  ASSEMBLY     MEDIUM      L3_S29_F3382         0.506670   \n",
+              "398                  ASSEMBLY     MEDIUM      L3_S33_F3873         0.593820   \n",
+              "2384         FINAL_INSPECTION     MEDIUM        L2_num_min         0.405882   \n",
+              "537                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.631635   \n",
+              "1491                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.605923   \n",
+              "\n",
+              "                          predicted_at  \n",
+              "2806  2026-06-12T08:00:21.829407+00:00  \n",
+              "1720  2026-06-12T08:00:21.829407+00:00  \n",
+              "2920  2026-06-12T08:00:21.829407+00:00  \n",
+              "1676  2026-06-12T08:00:21.829407+00:00  \n",
+              "518   2026-06-12T08:00:21.829407+00:00  \n",
+              "1363  2026-06-12T08:00:21.829407+00:00  \n",
+              "22    2026-06-12T08:00:21.829407+00:00  \n",
+              "883   2026-06-12T08:00:21.829407+00:00  \n",
+              "1139  2026-06-12T08:00:21.829407+00:00  \n",
+              "1261  2026-06-12T08:00:21.829407+00:00  \n",
+              "2193  2026-06-12T08:00:21.829407+00:00  \n",
+              "1250  2026-06-12T08:00:21.829407+00:00  \n",
+              "132   2026-06-12T08:00:21.829407+00:00  \n",
+              "1702  2026-06-12T08:00:21.829407+00:00  \n",
+              "1147  2026-06-12T08:00:21.829407+00:00  \n",
+              "593   2026-06-12T08:00:21.829407+00:00  \n",
+              "677   2026-06-12T08:00:21.829407+00:00  \n",
+              "2456  2026-06-12T08:00:21.829407+00:00  \n",
+              "360   2026-06-12T08:00:21.829407+00:00  \n",
+              "2423  2026-06-12T08:00:21.829407+00:00  \n",
+              "2187  2026-06-12T08:00:21.829407+00:00  \n",
+              "695   2026-06-12T08:00:21.829407+00:00  \n",
+              "2026  2026-06-12T08:00:21.829407+00:00  \n",
+              "230   2026-06-12T08:00:21.829407+00:00  \n",
+              "829   2026-06-12T08:00:21.829407+00:00  \n",
+              "522   2026-06-12T08:00:21.829407+00:00  \n",
+              "398   2026-06-12T08:00:21.829407+00:00  \n",
+              "2384  2026-06-12T08:00:21.829407+00:00  \n",
+              "537   2026-06-12T08:00:21.829407+00:00  \n",
+              "1491  2026-06-12T08:00:21.829407+00:00  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-62801a5c-c148-43c4-899e-851988c24858\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>car_master_id</th>\n",
+              "      <th>source_process_code</th>\n",
+              "      <th>target_process_code</th>\n",
+              "      <th>current_defect_probability</th>\n",
+              "      <th>target_defect_probability</th>\n",
+              "      <th>predicted_defect_process</th>\n",
+              "      <th>risk_grade</th>\n",
+              "      <th>main_cause</th>\n",
+              "      <th>influence_score</th>\n",
+              "      <th>predicted_at</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2806</th>\n",
+              "      <td>218707</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.678618</td>\n",
+              "      <td>0.978448</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.678618</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1720</th>\n",
+              "      <td>327784</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.693589</td>\n",
+              "      <td>0.970204</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.693589</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2920</th>\n",
+              "      <td>110780</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.677194</td>\n",
+              "      <td>0.937242</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S33_F3873</td>\n",
+              "      <td>0.677194</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1676</th>\n",
+              "      <td>404144</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.577193</td>\n",
+              "      <td>0.909687</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.577193</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>518</th>\n",
+              "      <td>537209</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>FINAL_INSPECTION</td>\n",
+              "      <td>0.442684</td>\n",
+              "      <td>0.855171</td>\n",
+              "      <td>FINAL_INSPECTION</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L2_num_min</td>\n",
+              "      <td>0.442684</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1363</th>\n",
+              "      <td>422924</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.461277</td>\n",
+              "      <td>0.829406</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.461277</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>445099</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.437623</td>\n",
+              "      <td>0.826252</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.437623</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>883</th>\n",
+              "      <td>49121</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.647237</td>\n",
+              "      <td>0.810856</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S33_F3873</td>\n",
+              "      <td>0.647237</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1139</th>\n",
+              "      <td>270422</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>0.380287</td>\n",
+              "      <td>0.806972</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L1_num_min</td>\n",
+              "      <td>0.380287</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1261</th>\n",
+              "      <td>72604</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.576640</td>\n",
+              "      <td>0.802120</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.576640</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2193</th>\n",
+              "      <td>208915</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.434317</td>\n",
+              "      <td>0.800171</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.434317</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1250</th>\n",
+              "      <td>325978</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.631934</td>\n",
+              "      <td>0.713980</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.631934</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>132</th>\n",
+              "      <td>457589</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.461246</td>\n",
+              "      <td>0.695194</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.461246</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1702</th>\n",
+              "      <td>345390</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.612613</td>\n",
+              "      <td>0.688885</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.612613</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1147</th>\n",
+              "      <td>329336</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.645485</td>\n",
+              "      <td>0.639600</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.645485</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>593</th>\n",
+              "      <td>492773</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.455224</td>\n",
+              "      <td>0.629645</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.455224</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>677</th>\n",
+              "      <td>570702</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.507326</td>\n",
+              "      <td>0.626186</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.507326</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2456</th>\n",
+              "      <td>491756</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.578751</td>\n",
+              "      <td>0.615782</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.578751</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>360</th>\n",
+              "      <td>161187</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.511622</td>\n",
+              "      <td>0.609049</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.511622</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2423</th>\n",
+              "      <td>58741</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.589447</td>\n",
+              "      <td>0.604053</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_F3873</td>\n",
+              "      <td>0.589447</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2187</th>\n",
+              "      <td>399573</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.624960</td>\n",
+              "      <td>0.592752</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.624960</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>695</th>\n",
+              "      <td>86810</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.654663</td>\n",
+              "      <td>0.588408</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.654663</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2026</th>\n",
+              "      <td>471938</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.557580</td>\n",
+              "      <td>0.568974</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.557580</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>230</th>\n",
+              "      <td>233131</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.558755</td>\n",
+              "      <td>0.566809</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.558755</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>829</th>\n",
+              "      <td>103485</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.593304</td>\n",
+              "      <td>0.565436</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.593304</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>522</th>\n",
+              "      <td>402988</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.506670</td>\n",
+              "      <td>0.564482</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S29_F3382</td>\n",
+              "      <td>0.506670</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>398</th>\n",
+              "      <td>307253</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.593820</td>\n",
+              "      <td>0.560144</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_F3873</td>\n",
+              "      <td>0.593820</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2384</th>\n",
+              "      <td>519151</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>FINAL_INSPECTION</td>\n",
+              "      <td>0.405882</td>\n",
+              "      <td>0.550488</td>\n",
+              "      <td>FINAL_INSPECTION</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L2_num_min</td>\n",
+              "      <td>0.405882</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>537</th>\n",
+              "      <td>325267</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.631635</td>\n",
+              "      <td>0.536699</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.631635</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1491</th>\n",
+              "      <td>96444</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.605923</td>\n",
+              "      <td>0.532112</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S33_mean_time</td>\n",
+              "      <td>0.605923</td>\n",
+              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62801a5c-c148-43c4-899e-851988c24858')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-62801a5c-c148-43c4-899e-851988c24858 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-62801a5c-c148-43c4-899e-851988c24858');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "repr_error": "0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  source_process_code target_process_code risk_grade  vehicle_count  \\\n",
+              "5               PAINT            ASSEMBLY       HIGH             10   \n",
+              "0            ASSEMBLY    FINAL_INSPECTION       HIGH              1   \n",
+              "3                BODY               PAINT       HIGH              1   \n",
+              "7               PAINT            ASSEMBLY     MEDIUM             78   \n",
+              "9               PRESS                BODY     MEDIUM              1   \n",
+              "2            ASSEMBLY    FINAL_INSPECTION     MEDIUM              5   \n",
+              "1            ASSEMBLY    FINAL_INSPECTION        LOW             49   \n",
+              "4                BODY               PAINT        LOW             54   \n",
+              "6               PAINT            ASSEMBLY        LOW           2462   \n",
+              "8               PRESS                BODY        LOW            339   \n",
+              "\n",
+              "   avg_target_defect_probability  avg_influence_score  \n",
+              "5                       0.857837             0.581562  \n",
+              "0                       0.855171             0.442684  \n",
+              "3                       0.806972             0.380287  \n",
+              "7                       0.487962             0.569717  \n",
+              "9                       0.480493             0.454110  \n",
+              "2                       0.471322             0.421606  \n",
+              "1                       0.097117             0.398196  \n",
+              "4                       0.080397             0.411830  \n",
+              "6                       0.066433             0.533395  \n",
+              "8                       0.014134             0.497065  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2107cc70-23d7-4e29-828d-87da4c1bf0f2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>source_process_code</th>\n",
+              "      <th>target_process_code</th>\n",
+              "      <th>risk_grade</th>\n",
+              "      <th>vehicle_count</th>\n",
+              "      <th>avg_target_defect_probability</th>\n",
+              "      <th>avg_influence_score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.857837</td>\n",
+              "      <td>0.581562</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>FINAL_INSPECTION</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.855171</td>\n",
+              "      <td>0.442684</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.806972</td>\n",
+              "      <td>0.380287</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>78</td>\n",
+              "      <td>0.487962</td>\n",
+              "      <td>0.569717</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.480493</td>\n",
+              "      <td>0.454110</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>FINAL_INSPECTION</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0.471322</td>\n",
+              "      <td>0.421606</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>FINAL_INSPECTION</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>49</td>\n",
+              "      <td>0.097117</td>\n",
+              "      <td>0.398196</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>54</td>\n",
+              "      <td>0.080397</td>\n",
+              "      <td>0.411830</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>2462</td>\n",
+              "      <td>0.066433</td>\n",
+              "      <td>0.533395</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>339</td>\n",
+              "      <td>0.014134</td>\n",
+              "      <td>0.497065</td>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2107cc70-23d7-4e29-828d-87da4c1bf0f2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
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+              "\n",
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+              "\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2107cc70-23d7-4e29-828d-87da4c1bf0f2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2107cc70-23d7-4e29-828d-87da4c1bf0f2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_fe941bba-a357-41d3-89ec-6b36823eda21\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('risk_summary')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_fe941bba-a357-41d3-89ec-6b36823eda21 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('risk_summary');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "risk_summary",
+              "summary": "{\n  \"name\": \"risk_summary\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ASSEMBLY\",\n          \"PRESS\",\n          \"PAINT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"FINAL_INSPECTION\",\n          \"BODY\",\n          \"ASSEMBLY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"risk_grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"HIGH\",\n          \"MEDIUM\",\n          \"LOW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vehicle_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 766,\n        \"min\": 1,\n        \"max\": 2462,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          54,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_target_defect_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3417312936604035,\n        \"min\": 0.01413382005899705,\n        \"max\": 0.8578366,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.06643331234768482,\n          0.855171,\n          0.47132240000000003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_influence_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07231150212575688,\n        \"min\": 0.380287,\n        \"max\": 0.5815622,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.5333947079610073,\n          0.442684,\n          0.4216056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# 전이 위험 결과 생성\n",
+        "def build_transfer_predictions(\n",
+        "    ids: pd.Series,\n",
+        "    probabilities: np.ndarray,\n",
+        "    shap_matrix: np.ndarray,\n",
+        "    feature_names: list[str],\n",
+        "    feature_process: pd.Series,\n",
+        ") -> pd.DataFrame:\n",
+        "    records = []\n",
+        "    process_values = feature_process.values\n",
+        "    predicted_at = datetime.now(timezone.utc).isoformat()\n",
+        "\n",
+        "    # 샘플별 공정 영향도 계산\n",
+        "    for row_idx, (sample_id, prob) in enumerate(zip(ids.to_numpy(), probabilities)):\n",
+        "        abs_values = np.abs(shap_matrix[row_idx])\n",
+        "        mapped_mask = feature_process.isin(PROCESS_FLOW).to_numpy()\n",
+        "        mapped_abs_values = abs_values[mapped_mask]\n",
+        "        mapped_process_values = process_values[mapped_mask]\n",
+        "        total_abs = float(mapped_abs_values.sum()) or 1.0\n",
+        "\n",
+        "        # 공정별 영향 비율\n",
+        "        process_scores = {}\n",
+        "        for process_code in PROCESS_FLOW:\n",
+        "            col_idx = np.where(mapped_process_values == process_code)[0]\n",
+        "            process_scores[process_code] = float(mapped_abs_values[col_idx].sum() / total_abs) if len(col_idx) else 0.0\n",
+        "\n",
+        "        # 원인/대상 공정 산출\n",
+        "        source_process = max(process_scores, key=process_scores.get)\n",
+        "        target_process = next_process(source_process)\n",
+        "        source_col_idx = np.where(process_values == source_process)[0]\n",
+        "        top_feature_idx = int(source_col_idx[np.argmax(abs_values[source_col_idx])]) if len(source_col_idx) else int(abs_values.argmax())\n",
+        "        influence_score = process_scores[source_process]\n",
+        "\n",
+        "        records.append({\n",
+        "            \"car_master_id\": int(sample_id),\n",
+        "            \"source_process_code\": source_process,\n",
+        "            \"target_process_code\": target_process,\n",
+        "            \"current_defect_probability\": round(float(influence_score), 6),\n",
+        "            \"target_defect_probability\": round(float(prob), 6),\n",
+        "            \"predicted_defect_process\": target_process,\n",
+        "            \"risk_grade\": probability_to_risk_grade(float(prob)),\n",
+        "            \"main_cause\": feature_names[top_feature_idx],\n",
+        "            \"influence_score\": round(float(influence_score), 6),\n",
+        "            \"predicted_at\": predicted_at,\n",
+        "        })\n",
+        "    return pd.DataFrame(records).sort_values([\"target_defect_probability\", \"influence_score\"], ascending=False)\n",
+        "\n",
+        "# 전이 예측 테이블 생성\n",
+        "transfer_predictions = build_transfer_predictions(\n",
+        "    id_shap,\n",
+        "    proba_shap,\n",
+        "    shap_matrix,\n",
+        "    X_shap.columns.tolist(),\n",
+        "    feature_process,\n",
+        ")\n",
+        "\n",
+        "display(transfer_predictions.head(30))\n",
+        "\n",
+        "# 전이 위험 요약\n",
+        "risk_summary = transfer_predictions.groupby([\"source_process_code\", \"target_process_code\", \"risk_grade\"]).agg(\n",
+        "    vehicle_count=(\"car_master_id\", \"count\"),\n",
+        "    avg_target_defect_probability=(\"target_defect_probability\", \"mean\"),\n",
+        "    avg_influence_score=(\"influence_score\", \"mean\"),\n",
+        ").reset_index().sort_values([\"avg_target_defect_probability\", \"vehicle_count\"], ascending=False)\n",
+        "display(risk_summary)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "JFTbBuS1bEfD",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 885
+        },
+        "id": "JFTbBuS1bEfD",
+        "outputId": "15604b4b-ea3f-45be-dc3b-9278c172f8af"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# 전이 위험 시각화\n",
+        "plt.figure(figsize=(9, 5))\n",
+        "top_flow = risk_summary.head(12).copy()\n",
+        "top_flow[\"flow\"] = top_flow[\"source_process_code\"] + \" -> \" + top_flow[\"target_process_code\"] + \" (\" + top_flow[\"risk_grade\"] + \")\"\n",
+        "sns.barplot(data=top_flow, y=\"flow\", x=\"avg_target_defect_probability\", hue=\"risk_grade\", dodge=False)\n",
+        "plt.title(\"Process Transfer Risk by SHAP Influence\")\n",
+        "plt.xlabel(\"Avg Final Defect Probability\")\n",
+        "plt.ylabel(\"\")\n",
+        "plt.legend(title=\"Risk\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# 불량 확률 분포 시각화\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "sns.histplot(data=transfer_predictions, x=\"target_defect_probability\", hue=\"risk_grade\", bins=40, multiple=\"stack\")\n",
+        "plt.title(\"Predicted Final Defect Probability Distribution\")\n",
+        "plt.xlabel(\"Defect Probability\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "43d99e60",
+      "metadata": {
+        "id": "43d99e60"
+      },
+      "source": [
+        "## 12. AI 모델 관리 항목\n",
+        "\n",
+        "모델 선정 근거, 데이터셋 규모, 테스트 케이스 수, Accuracy, False Positive Rate, 성능 개선 현황을 운영 관리용 요약으로 정리합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "c79bba33",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "c79bba33",
+        "outputId": "d7870e0c-1bc2-4d85-e735-b8b4b14450db"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                         관리 항목  \\\n",
+              "0                  모델 선정 근거 정리   \n",
+              "1                   데이터셋 규모 관리   \n",
+              "2                 테스트 케이스 수 관리   \n",
+              "3             정확도(Accuracy) 측정   \n",
+              "4  오탐률(False Positive Rate) 측정   \n",
+              "5               모델 성능 개선 현황 관리   \n",
+              "\n",
+              "                                                   값  \n",
+              "0  LightGBM 모델이 validation PR-AUC/ROC-AUC/F1 기준 종...  \n",
+              "1  {\"max_rows\": 300000, \"profile_rows\": 50000, \"t...  \n",
+              "2  {\"validation_total\": 60000, \"validation_normal...  \n",
+              "3                                             0.9908  \n",
+              "4                                           0.004542  \n",
+              "5  {\"baseline_model\": \"LightGBM\", \"baseline_pr_au...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>관리 항목</th>\n",
+              "      <th>값</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>모델 선정 근거 정리</td>\n",
+              "      <td>LightGBM 모델이 validation PR-AUC/ROC-AUC/F1 기준 종...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>데이터셋 규모 관리</td>\n",
+              "      <td>{\"max_rows\": 300000, \"profile_rows\": 50000, \"t...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>테스트 케이스 수 관리</td>\n",
+              "      <td>{\"validation_total\": 60000, \"validation_normal...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>정확도(Accuracy) 측정</td>\n",
+              "      <td>0.9908</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>오탐률(False Positive Rate) 측정</td>\n",
+              "      <td>0.004542</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>모델 성능 개선 현황 관리</td>\n",
+              "      <td>{\"baseline_model\": \"LightGBM\", \"baseline_pr_au...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_5abef95b-2b45-4721-aff9-d69655c2f66a\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ai_management_table')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_5abef95b-2b45-4721-aff9-d69655c2f66a button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('ai_management_table');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ai_management_table",
+              "summary": "{\n  \"name\": \"ai_management_table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"\\uad00\\ub9ac \\ud56d\\ubaa9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\ubaa8\\ub378 \\uc120\\uc815 \\uadfc\\uac70 \\uc815\\ub9ac\",\n          \"\\ub370\\uc774\\ud130\\uc14b \\uaddc\\ubaa8 \\uad00\\ub9ac\",\n          \"\\ubaa8\\ub378 \\uc131\\ub2a5 \\uac1c\\uc120 \\ud604\\ud669 \\uad00\\ub9ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uac12\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"LightGBM \\ubaa8\\ub378\\uc774 validation PR-AUC/ROC-AUC/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. \\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378\",\n          \"{\\\"max_rows\\\": 300000, \\\"profile_rows\\\": 50000, \\\"train_rows\\\": 240000, \\\"candidate_train_rows\\\": 80000, \\\"valid_rows\\\": 60000, \\\"feature_count\\\": 457, \\\"positive_ratio\\\": 0.00565, \\\"full_data_refit\\\": false}\",\n          \"{\\\"baseline_model\\\": \\\"LightGBM\\\", \\\"baseline_pr_auc\\\": 0.05440352499791599, \\\"selected_model\\\": \\\"LightGBM\\\", \\\"selected_pr_auc\\\": 0.05440352499791599, \\\"pr_auc_improvement_over_lightgbm\\\": 0.0}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "ai_management_table = pd.DataFrame([\n",
+        "    {\"관리 항목\": \"모델 선정 근거 정리\", \"값\": ai_management_summary[\"모델_선정_근거\"]},\n",
+        "    {\"관리 항목\": \"데이터셋 규모 관리\", \"값\": json.dumps(ai_management_summary[\"데이터셋_규모_관리\"], ensure_ascii=False)},\n",
+        "    {\"관리 항목\": \"테스트 케이스 수 관리\", \"값\": json.dumps(ai_management_summary[\"테스트_케이스_수_관리\"], ensure_ascii=False)},\n",
+        "    {\"관리 항목\": \"정확도(Accuracy) 측정\", \"값\": round(ai_management_summary[\"정확도_Accuracy\"], 6)},\n",
+        "    {\"관리 항목\": \"오탐률(False Positive Rate) 측정\", \"값\": round(ai_management_summary[\"오탐률_False_Positive_Rate\"], 6)},\n",
+        "    {\"관리 항목\": \"모델 성능 개선 현황 관리\", \"값\": json.dumps(ai_management_summary[\"모델_성능_개선_현황\"], ensure_ascii=False)},\n",
+        "])\n",
+        "\n",
+        "display(ai_management_table)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Q5r_SU2bbEfD",
+      "metadata": {
+        "id": "Q5r_SU2bbEfD"
+      },
+      "source": [
+        "## 13. 모델 및 결과 저장\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "44a36633",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "44a36633",
+        "outputId": "6fdbeebe-e939-4d3c-b15c-1c3d644ebfcf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved outputs:\n",
+            "/content/defect_transfer_outputs/selected_defect_detector.joblib\n",
+            "/content/defect_transfer_outputs/selected_defect_detector.pkl\n",
+            "/content/defect_transfer_outputs/candidate_defect_models.joblib\n",
+            "/content/defect_transfer_outputs/candidate_defect_models.pkl\n",
+            "/content/defect_transfer_outputs/lightgbm_defect_detector.joblib\n",
+            "/content/defect_transfer_outputs/lightgbm_defect_detector.pkl\n",
+            "/content/defect_transfer_outputs/auxiliary_process_models.joblib\n",
+            "/content/defect_transfer_outputs/auxiliary_process_models.pkl\n",
+            "/content/defect_transfer_outputs/defect_model_features.json\n",
+            "/content/defect_transfer_outputs/defect_model_metrics.json\n",
+            "/content/defect_transfer_outputs/defect_model_cv_results.csv\n",
+            "/content/defect_transfer_outputs/defect_model_comparison.csv\n",
+            "/content/defect_transfer_outputs/defect_model_confusion_matrices.json\n",
+            "/content/defect_transfer_outputs/defect_ai_management_summary.json\n",
+            "/content/defect_transfer_outputs/defect_model_feature_importance.csv\n",
+            "/content/defect_transfer_outputs/defect_all_model_feature_importance.csv\n",
+            "/content/defect_transfer_outputs/defect_shap_importance.csv\n",
+            "/content/defect_transfer_outputs/defect_transfer_prediction_result.csv\n",
+            "/content/defect_transfer_outputs/defect_transfer_risk_summary.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 저장 경로\n",
+        "selected_model_path = OUTPUT_DIR / \"selected_defect_detector.joblib\"\n",
+        "selected_model_pkl_path = OUTPUT_DIR / \"selected_defect_detector.pkl\"\n",
+        "candidate_models_path = OUTPUT_DIR / \"candidate_defect_models.joblib\"\n",
+        "candidate_models_pkl_path = OUTPUT_DIR / \"candidate_defect_models.pkl\"\n",
+        "model_path = OUTPUT_DIR / \"lightgbm_defect_detector.joblib\"\n",
+        "model_pkl_path = OUTPUT_DIR / \"lightgbm_defect_detector.pkl\"\n",
+        "aux_model_path = OUTPUT_DIR / \"auxiliary_process_models.joblib\"\n",
+        "aux_model_pkl_path = OUTPUT_DIR / \"auxiliary_process_models.pkl\"\n",
+        "feature_path = OUTPUT_DIR / \"defect_model_features.json\"\n",
+        "metrics_path = OUTPUT_DIR / \"defect_model_metrics.json\"\n",
+        "comparison_path = OUTPUT_DIR / \"defect_model_comparison.csv\"\n",
+        "confusion_matrix_path = OUTPUT_DIR / \"defect_model_confusion_matrices.json\"\n",
+        "ai_management_path = OUTPUT_DIR / \"defect_ai_management_summary.json\"\n",
+        "importance_path = OUTPUT_DIR / \"defect_model_feature_importance.csv\"\n",
+        "all_importance_path = OUTPUT_DIR / \"defect_all_model_feature_importance.csv\"\n",
+        "shap_importance_path = OUTPUT_DIR / \"defect_shap_importance.csv\"\n",
+        "transfer_path = OUTPUT_DIR / \"defect_transfer_prediction_result.csv\"\n",
+        "risk_summary_path = OUTPUT_DIR / \"defect_transfer_risk_summary.csv\"\n",
+        "\n",
+        "# 모델 저장\n",
+        "joblib.dump(model, selected_model_path)\n",
+        "joblib.dump(trained_models, candidate_models_path)\n",
+        "joblib.dump(auxiliary_models, aux_model_path)\n",
+        "with open(selected_model_pkl_path, \"wb\") as f:\n",
+        "    pickle.dump(model, f)\n",
+        "with open(candidate_models_pkl_path, \"wb\") as f:\n",
+        "    pickle.dump(trained_models, f)\n",
+        "with open(aux_model_pkl_path, \"wb\") as f:\n",
+        "    pickle.dump(auxiliary_models, f)\n",
+        "\n",
+        "# 기존 LightGBM 파일명 호환 저장\n",
+        "if \"LightGBM\" in trained_models:\n",
+        "    joblib.dump(trained_models[\"LightGBM\"], model_path)\n",
+        "    with open(model_pkl_path, \"wb\") as f:\n",
+        "        pickle.dump(trained_models[\"LightGBM\"], f)\n",
+        "\n",
+        "feature_path.write_text(json.dumps(feature_cols, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "\n",
+        "confusion_payload = {\n",
+        "    name: matrix.tolist()\n",
+        "    for name, matrix in model_confusion_matrices.items()\n",
+        "}\n",
+        "confusion_matrix_path.write_text(json.dumps(confusion_payload, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "ai_management_path.write_text(json.dumps(ai_management_summary, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "\n",
+        "# 평가 지표 저장\n",
+        "metrics = {\n",
+        "    \"selected_model_name\": selected_model_name,\n",
+        "    \"roc_auc\": float(roc_auc),\n",
+        "    \"average_precision\": float(pr_auc),\n",
+        "    \"accuracy\": float(accuracy),\n",
+        "    \"false_positive_rate\": float(false_positive_rate),\n",
+        "    \"best_threshold\": float(best_threshold),\n",
+        "    \"f1_at_best_threshold\": float(f1_score(y_valid, valid_pred, zero_division=0)),\n",
+        "    \"confusion_matrix\": cm.tolist(),\n",
+        "    \"train_rows\": int(len(X_train)),\n",
+        "    \"valid_rows\": int(len(X_valid)),\n",
+        "    \"feature_count\": int(len(feature_cols)),\n",
+        "    \"positive_ratio_train\": float(y_train.mean()),\n",
+        "    \"candidate_models\": list(trained_models.keys()),\n",
+        "    \"model_selection_reason\": ai_management_summary[\"모델_선정_근거\"],\n",
+        "    \"auxiliary_metrics\": auxiliary_metrics,\n",
+        "    \"auxiliary_feature_columns\": [c for c in feature_cols if c.startswith(\"aux_\")],\n",
+        "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
+        "}\n",
+        "metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "\n",
+        "# 분석 결과 저장\n",
+        "cv_results.to_csv(cv_results_path, index=False)\n",
+        "model_comparison.to_csv(comparison_path, index=False)\n",
+        "importance_df.to_csv(importance_path, index=False)\n",
+        "all_model_importance.to_csv(all_importance_path, index=False)\n",
+        "shap_importance.to_csv(shap_importance_path, index=False)\n",
+        "transfer_predictions.to_csv(transfer_path, index=False)\n",
+        "risk_summary.to_csv(risk_summary_path, index=False)\n",
+        "\n",
+        "print(\"Saved outputs:\")\n",
+        "for path in [\n",
+        "    selected_model_path,\n",
+        "    selected_model_pkl_path,\n",
+        "    candidate_models_path,\n",
+        "    candidate_models_pkl_path,\n",
+        "    model_path,\n",
+        "    model_pkl_path,\n",
+        "    aux_model_path,\n",
+        "    aux_model_pkl_path,\n",
+        "    feature_path,\n",
+        "    metrics_path,\n",
+        "    cv_results_path,\n",
+        "    comparison_path,\n",
+        "    confusion_matrix_path,\n",
+        "    ai_management_path,\n",
+        "    importance_path,\n",
+        "    all_importance_path,\n",
+        "    shap_importance_path,\n",
+        "    transfer_path,\n",
+        "    risk_summary_path,\n",
+        "]:\n",
+        "    print(path)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file

From dfef7de383f8ce7c2086007c53858aeaf2bbc7ed Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 17 Jun 2026 10:39:52 +0900
Subject: [PATCH 004/148] =?UTF-8?q?feat:=20=EC=A0=9C=EC=A1=B0=20=EC=9D=B4?=
 =?UTF-8?q?=EB=B2=A4=ED=8A=B8=20JSON=20=EC=83=9D=EC=84=B1=20=EB=B0=8F=20?=
 =?UTF-8?q?=EC=A0=81=EC=9E=AC=20API=20=EA=B5=AC=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- CSV 원천 데이터 기반 manufacturing_event_json 생성 로직 추가

- /api/manufacturing/events/generate API 구현 및 기간별 이벤트 생성 지원

- /api/manufacturing/events/generate/tomorrow API 구현 및 템플릿 기반 다음날 이벤트 적재 지원

- manufacturing_event_json batch insert 처리 및 insert_chunk_size 옵션 적용

- manufacturing_event_template 생성, 조회, replay API 구현

- 제조 이벤트 JSON 저장 테이블 및 템플릿 테이블 schema 정의

- event_id 중복 시 기존 제조 이벤트 JSON upsert 처리

- updated_at 컬럼 추가 및 신규 insert, upsert 시 수정 시간 저장

- Swagger operation, description에 저장 컬럼과 batch insert 동작 설명 추가
---
 .env.example                                  |    7 +
 app/api/router.py                             |    3 +-
 app/api/routers/manufacturing_event.py        |  443 +++
 app/batch/__init__.py                         |    1 +
 app/batch/manufacturing_event_loader.py       |  257 ++
 app/core/config.py                            |   20 +
 app/docs/PRD_Manufacturing.md                 | 3428 +++++++++++++++++
 app/main.py                                   |   30 +-
 .../manufacturing_event_json_builder.py       |  756 ++++
 app/repository/sampledb_repository.py         |  410 +-
 app/repository/sampledb_schema.py             |   54 +-
 .../manufacturing_event_json_service.py       |  901 +++++
 app/service/manufacturing_event_scheduler.py  |  114 +
 13 files changed, 6396 insertions(+), 28 deletions(-)
 create mode 100644 app/api/routers/manufacturing_event.py
 create mode 100644 app/batch/__init__.py
 create mode 100644 app/batch/manufacturing_event_loader.py
 create mode 100644 app/docs/PRD_Manufacturing.md
 create mode 100644 app/ml/preprocessing/manufacturing_event_json_builder.py
 create mode 100644 app/service/manufacturing_event_json_service.py
 create mode 100644 app/service/manufacturing_event_scheduler.py

diff --git a/.env.example b/.env.example
index 96b02ca..f9428aa 100644
--- a/.env.example
+++ b/.env.example
@@ -24,3 +24,10 @@ MAIN_DATABASE_URL=mysql+pymysql://aims_user:change-me@localhost:3306/maindb?char
 # Sample MySQL DB
 # Use this DB for sample/source data connections when needed.
 SAMPLE_DATABASE_URL=mysql+pymysql://sample_user:change-me@localhost:3306/sampledb?charset=utf8mb4
+
+# Manufacturing source event JSON generation
+MANUFACTURING_EVENT_SCHEDULER_ENABLED=true
+MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY=86400
+MANUFACTURING_EVENT_TEMPLATE_EVENT_COUNT=86400
+MANUFACTURING_EVENT_CAR_POOL_SIZE=10000
+MANUFACTURING_EVENT_INSERT_CHUNK_SIZE=1000
diff --git a/app/api/router.py b/app/api/router.py
index 31277be..111cb0e 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -1,8 +1,9 @@
 from fastapi import APIRouter
 
-from app.api.routers import health, process, root
+from app.api.routers import health, manufacturing_event, process, root
 
 api_router = APIRouter()
 api_router.include_router(root.router)
 api_router.include_router(health.router)
 api_router.include_router(process.router)
+api_router.include_router(manufacturing_event.router)
diff --git a/app/api/routers/manufacturing_event.py b/app/api/routers/manufacturing_event.py
new file mode 100644
index 0000000..8a2967f
--- /dev/null
+++ b/app/api/routers/manufacturing_event.py
@@ -0,0 +1,443 @@
+from datetime import date
+from typing import Literal
+
+from fastapi import APIRouter, Depends, Query
+
+from app.dto.response import CommonResponse
+from app.service.manufacturing_event_json_service import (
+    DEFAULT_CAR_POOL_SIZE,
+    DEFAULT_EVENTS_PER_DAY,
+    DEFAULT_INSERT_CHUNK_SIZE,
+    DEFAULT_TEMPLATE_NAME,
+    ManufacturingEventJsonService,
+    get_manufacturing_event_json_service,
+)
+from app.utils.response_utils import success_response
+
+
+ProcessCode = Literal["PRESS", "BODY", "PAINT", "ASSEMBLY"]
+PROCESS_CODE_DESCRIPTION = (
+    "조회할 공정 코드입니다. 미입력 시 프레스, 차체, 도장, 의장 전체 공정을 조회합니다."
+)
+INSERT_CHUNK_SIZE_DESCRIPTION = (
+    "DB에 한 번에 적재할 batch insert row 수입니다. "
+    "서비스는 생성된 row를 메모리 chunk에 모은 뒤 이 값에 도달할 때마다 한 번의 INSERT를 실행합니다. "
+    "MySQL에서는 중복 키 발생 시 generate 계열은 기존 row를 갱신하고, 템플릿 생성은 replace/update 옵션에 따라 갱신 또는 무시합니다."
+)
+EVENT_JSON_TABLE_DESCRIPTION = """
+### 저장 테이블: `manufacturing_event_json`
+
+| 컬럼 | 설명 |
+| --- | --- |
+| `id` | DB 내부 PK, 자동 증가 |
+| `event_id` | 이벤트 고유 ID. 예: `EVT-20260616-000001` |
+| `event_time` | 실제 이벤트 발생 시각 |
+| `car_master_id` | `car_master.id` FK |
+| `equipment_id` | `equipment.id` FK |
+| `process_code` | 공정 코드. `PRESS`, `BODY`, `PAINT`, `ASSEMBLY` |
+| `station_code` | 공정 내 스테이션 코드 |
+| `equipment_code` | 설비 코드. 예: `EQ_PRESS_001` |
+| `equipment_type` | 설비 유형. 예: `HYDRAULIC_PRESS`, `ROBOT_ARM`, `CAMERA`, `CONVEYOR` |
+| `equipment_status` | 설비 운전 상태. 예: `RUNNING`, `IDLE`, `ERROR` |
+| `event_type` | 이벤트 유형. 예: `PROCESS_STATUS`, `EQUIPMENT_SENSOR`, `QUALITY_CHECK` |
+| `event_json` | 관제 화면/연계 시스템에 전달할 원본 JSON payload |
+| `is_sent` | 외부 시스템 전송 여부. 기본값 `false` |
+| `sent_at` | 외부 시스템 전송 완료 시각 |
+| `created_at` | DB row 생성 시각 |
+| `updated_at` | DB row 마지막 수정 시각. 신규 insert와 중복 `event_id` upsert 시 갱신 |
+
+`event_json`에는 `event`, `location`, `equipment`, `equipmentStatus`, `product`,
+`sensor`, `manufacturing`, `processMetrics`, `sourceTrace`, `processData`가 포함됩니다.
+"""
+TEMPLATE_TABLE_DESCRIPTION = """
+### 저장 테이블: `manufacturing_event_template`
+
+| 컬럼 | 설명 |
+| --- | --- |
+| `id` | DB 내부 PK, 자동 증가 |
+| `template_name` | 템플릿 이름 |
+| `template_event_id` | 템플릿 이벤트 고유 ID. 예: `TMPL-DEFAULT-000001` |
+| `event_offset_us` | 하루 시작 시각 기준 이벤트 발생 offset, microsecond 단위 |
+| `car_master_id` | `car_master.id` FK |
+| `equipment_id` | `equipment.id` FK |
+| `process_code` | 공정 코드. `PRESS`, `BODY`, `PAINT`, `ASSEMBLY` |
+| `station_code` | 공정 내 스테이션 코드 |
+| `equipment_code` | 설비 코드 |
+| `equipment_type` | 설비 유형 |
+| `equipment_status` | 템플릿 기준 설비 운전 상태 |
+| `event_type` | 이벤트 유형 |
+| `event_json` | 날짜를 입히기 전 기준 JSON payload |
+| `created_at` | DB row 생성 시각 |
+
+템플릿은 특정 날짜의 실제 이벤트가 아니라 하루 기준 패턴입니다. 실제 적재 시
+`event_offset_us`를 target date에 더해 `event_time`으로 변환합니다.
+"""
+
+router = APIRouter(prefix="/api/manufacturing/events", tags=["제조 관제 이벤트"])
+
+
+@router.post(
+    "/templates/generate",
+    summary="제조 이벤트 템플릿 생성",
+    operation_id="generateManufacturingEventTemplate",
+    description=(
+        "CSV 원천 데이터를 전처리/정제해 하루 기준 제조 관제 이벤트 템플릿을 생성하고 "
+        "`manufacturing_event_template` 테이블에 저장하는 API입니다.\n\n"
+        "### 동작 방식\n"
+        "- 프레스(PRESS), 차체(BODY), 도장(PAINT), 의장(ASSEMBLY) 공정이 순환 생성됩니다.\n"
+        "- 날짜가 고정된 row가 아니라 하루 안에서의 이벤트 발생 위치를 `event_offset_us`로 저장합니다.\n"
+        "- `replace=true`이면 같은 `template_name`의 기존 row를 삭제한 뒤 새로 생성합니다.\n"
+        "- `insert_chunk_size` 단위로 row를 모아 batch insert합니다.\n\n"
+        "### 주요 응답 데이터\n"
+        "- `templateName`: 저장된 템플릿 이름\n"
+        "- `eventCount`: 요청한 템플릿 이벤트 수\n"
+        "- `generatedCount`: 실제 생성한 이벤트 수\n"
+        "- `affectedRows`: DB insert/update 영향 row 수\n"
+        "- `storedCount`: 해당 템플릿명으로 저장된 전체 row 수\n"
+        "- `insertChunkSize`: batch insert 단위\n"
+        "- `processDistribution`: 공정별 생성 건수\n\n"
+        f"{TEMPLATE_TABLE_DESCRIPTION}"
+    ),
+    responses={200: {"description": "제조 이벤트 템플릿 생성 결과"}},
+)
+def generate_manufacturing_event_template(
+    template_name: str = Query(
+        default=DEFAULT_TEMPLATE_NAME,
+        description="생성할 템플릿 이름입니다. replay와 tomorrow 적재 시 이 이름으로 템플릿을 선택합니다.",
+    ),
+    event_count: int = Query(
+        default=DEFAULT_EVENTS_PER_DAY,
+        ge=4,
+        le=300000,
+        description="하루 기준 템플릿 이벤트 수입니다. 기본값은 86,400건입니다.",
+    ),
+    car_pool_size: int = Query(
+        default=DEFAULT_CAR_POOL_SIZE,
+        ge=1,
+        le=100000,
+        description="이벤트에 분배할 차량 마스터 풀 크기입니다.",
+    ),
+    insert_chunk_size: int = Query(
+        default=DEFAULT_INSERT_CHUNK_SIZE,
+        ge=100,
+        le=10000,
+        description=INSERT_CHUNK_SIZE_DESCRIPTION,
+    ),
+    replace: bool = Query(
+        default=False,
+        description="true이면 동일 템플릿명을 가진 기존 데이터를 삭제하고 다시 생성합니다.",
+    ),
+    service: ManufacturingEventJsonService = Depends(
+        get_manufacturing_event_json_service,
+    ),
+) -> CommonResponse[dict]:
+    result = service.generate_template(
+        template_name=template_name,
+        event_count=event_count,
+        car_pool_size=car_pool_size,
+        insert_chunk_size=insert_chunk_size,
+        replace=replace,
+        update_existing=replace,
+    )
+    return success_response(
+        data=result,
+        message="제조 관제 이벤트 템플릿 생성 및 저장이 완료되었습니다.",
+    )
+
+
+@router.get(
+    "/templates/{template_name}",
+    summary="제조 이벤트 템플릿 조회",
+    operation_id="listManufacturingEventTemplate",
+    description=(
+        "`manufacturing_event_template`에 저장된 기준 템플릿 이벤트를 조회합니다.\n\n"
+        "### 동작 방식\n"
+        "- 이 API는 날짜별 변동을 적용하지 않은 템플릿 원본을 반환합니다.\n"
+        "- `template_name`, `process_code`, `limit`, `offset` 기준으로 조회합니다.\n"
+        "- 실제 관제 화면 날짜별 이벤트 형태를 확인하려면 replay API를 사용합니다.\n\n"
+        f"{TEMPLATE_TABLE_DESCRIPTION}"
+    ),
+    responses={200: {"description": "제조 이벤트 템플릿 목록"}},
+)
+def list_manufacturing_event_template(
+    template_name: str,
+    limit: int = Query(default=20, ge=1, le=200, description="조회할 최대 건수입니다."),
+    offset: int = Query(default=0, ge=0, description="조회 시작 위치입니다."),
+    process_code: ProcessCode | None = Query(
+        default=None,
+        description=PROCESS_CODE_DESCRIPTION,
+    ),
+    service: ManufacturingEventJsonService = Depends(
+        get_manufacturing_event_json_service,
+    ),
+) -> CommonResponse[dict]:
+    events = service.list_template_events(
+        template_name=template_name,
+        limit=limit,
+        offset=offset,
+        process_code=process_code,
+    )
+    return success_response(
+        data={
+            "items": events,
+            "limit": limit,
+            "offset": offset,
+        },
+        message="제조 관제 이벤트 템플릿 조회가 완료되었습니다.",
+    )
+
+
+@router.get(
+    "/templates/{template_name}/replay",
+    summary="템플릿 기반 날짜별 이벤트 replay 조회",
+    operation_id="replayManufacturingEventTemplate",
+    description=(
+        "저장된 템플릿을 지정한 날짜의 제조 관제 이벤트처럼 재생성해서 조회합니다.\n\n"
+        "### 동작 방식\n"
+        "- `manufacturing_event_template` row를 읽어 `target_date` 기준 이벤트처럼 materialize합니다.\n"
+        "- `event_offset_us`를 `target_date` 00:00:00에 더해 `event_time`을 계산합니다.\n"
+        "- `template_event_id` 순번을 이용해 `EVT-{target_date}-000001` 형태의 `event_id`를 생성합니다.\n"
+        "- 전류, 진동, 로봇암 진동, 열화상, 공정 지표는 날짜별 변동 레이어가 적용됩니다.\n"
+        "- 같은 날짜와 같은 템플릿 이벤트는 항상 같은 값으로 replay되며, 날짜가 바뀌면 수치가 달라집니다.\n"
+        "- 조회 전용 API이므로 `manufacturing_event_json` 테이블에는 저장하지 않습니다.\n\n"
+        "### 반환 데이터 형태\n"
+        "반환 item은 실제 저장 row와 같은 필드 구조를 갖지만 DB에 insert되지는 않습니다.\n\n"
+        f"{EVENT_JSON_TABLE_DESCRIPTION}"
+    ),
+    responses={200: {"description": "날짜별 replay 이벤트 목록"}},
+)
+def replay_manufacturing_event_template(
+    template_name: str,
+    target_date: date = Query(
+        default=date(2026, 6, 16),
+        description="이벤트를 replay할 기준 날짜입니다.",
+    ),
+    limit: int = Query(default=20, ge=1, le=200, description="조회할 최대 건수입니다."),
+    offset: int = Query(default=0, ge=0, description="조회 시작 위치입니다."),
+    process_code: ProcessCode | None = Query(
+        default=None,
+        description=PROCESS_CODE_DESCRIPTION,
+    ),
+    service: ManufacturingEventJsonService = Depends(
+        get_manufacturing_event_json_service,
+    ),
+) -> CommonResponse[dict]:
+    events = service.replay_template_events(
+        template_name=template_name,
+        target_date=target_date,
+        limit=limit,
+        offset=offset,
+        process_code=process_code,
+    )
+    return success_response(
+        data={
+            "items": events,
+            "targetDate": target_date.isoformat(),
+            "limit": limit,
+            "offset": offset,
+        },
+        message="템플릿 기반 날짜별 제조 관제 이벤트 replay 조회가 완료되었습니다.",
+    )
+
+
+@router.post(
+    "/generate",
+    summary="기간별 제조 이벤트 JSON 생성 및 적재",
+    operation_id="generateManufacturingEventJson",
+    description=(
+        "지정한 날짜 범위의 제조 관제 이벤트 JSON을 실제 일자 데이터로 생성해 "
+        "`manufacturing_event_json` 테이블에 저장하는 API입니다.\n\n"
+        "### 언제 사용하는 API인가요?\n"
+        "- 초기 시연 데이터 또는 특정 기간의 샘플 제조 이벤트를 DB에 실제 row로 적재할 때 사용합니다.\n"
+        "- 템플릿 replay가 아니라 CSV 기반 생성기를 직접 실행합니다.\n"
+        "- `start_date`부터 `end_date`까지 양 끝 날짜를 모두 포함해 생성합니다.\n\n"
+        "### 생성/저장 방식\n"
+        "- 전체 생성 건수는 `(end_date - start_date + 1) * events_per_day`입니다.\n"
+        "- 공정은 `PRESS -> BODY -> PAINT -> ASSEMBLY` 순서로 순환 배치됩니다.\n"
+        "- 이벤트 시간은 하루 안에서 생산 밀도가 높은 시간대에 더 많이 분포되도록 계산됩니다.\n"
+        "- 필요한 schema를 보장하고, 기본 설비와 차량 마스터 row를 준비한 뒤 이벤트를 생성합니다.\n"
+        "- 생성된 row는 `insert_chunk_size` 단위로 모아 batch insert합니다.\n"
+        "- 기본 batch insert 단위는 `1,000`건이며, 요청 파라미터로 `100~10,000` 사이에서 조정할 수 있습니다.\n"
+        "- MySQL에서는 `event_id` 중복 시 기존 row의 이벤트 시간, 설비, 상태, JSON payload 등을 갱신합니다.\n\n"
+        "### 주요 응답 데이터\n"
+        "- `startDate`, `endDate`: 생성 기간\n"
+        "- `eventsPerDay`: 하루 생성 이벤트 수\n"
+        "- `totalExpectedEvents`: 예상 전체 생성 건수\n"
+        "- `generatedCount`: 실제 생성한 이벤트 수\n"
+        "- `affectedRows`: DB insert/update 영향 row 수\n"
+        "- `storedCountInRange`: 해당 기간에 DB에 저장된 row 수\n"
+        "- `carPoolSize`: 사용한 차량 마스터 수\n"
+        "- `insertChunkSize`: batch insert 단위\n"
+        "- `processDistribution`: 공정별 생성 건수\n\n"
+        f"{EVENT_JSON_TABLE_DESCRIPTION}"
+    ),
+    responses={200: {"description": "기간별 제조 이벤트 JSON 생성 및 적재 결과"}},
+)
+def generate_manufacturing_event_json(
+    start_date: date = Query(
+        default=date(2026, 6, 1),
+        description="생성 시작 날짜입니다.",
+    ),
+    end_date: date = Query(
+        default=date(2026, 6, 16),
+        description="생성 종료 날짜입니다.",
+    ),
+    events_per_day: int = Query(
+        default=DEFAULT_EVENTS_PER_DAY,
+        ge=4,
+        le=300000,
+        description="하루에 생성할 이벤트 수입니다. 기본값은 86,400건입니다.",
+    ),
+    car_pool_size: int = Query(
+        default=DEFAULT_CAR_POOL_SIZE,
+        ge=1,
+        le=100000,
+        description="이벤트에 분배할 차량 마스터 풀 크기입니다.",
+    ),
+    insert_chunk_size: int = Query(
+        default=DEFAULT_INSERT_CHUNK_SIZE,
+        ge=100,
+        le=10000,
+        description=INSERT_CHUNK_SIZE_DESCRIPTION,
+    ),
+    service: ManufacturingEventJsonService = Depends(
+        get_manufacturing_event_json_service,
+    ),
+) -> CommonResponse[dict]:
+    result = service.generate_range(
+        start_date=start_date,
+        end_date=end_date,
+        events_per_day=events_per_day,
+        car_pool_size=car_pool_size,
+        insert_chunk_size=insert_chunk_size,
+    )
+    return success_response(
+        data=result,
+        message="제조 원천 이벤트 JSON 생성 및 저장이 완료되었습니다.",
+    )
+
+
+@router.post(
+    "/generate/tomorrow",
+    summary="템플릿 기반 다음날 제조 이벤트 적재",
+    operation_id="generateTomorrowManufacturingEventJson",
+    description=(
+        "저장된 템플릿을 기준으로 다음날 제조 관제 이벤트를 생성하고 "
+        "`manufacturing_event_json` 테이블에 저장하는 API입니다.\n\n"
+        "### 언제 사용하는 API인가요?\n"
+        "- 매일 다음날 관제 이벤트 데이터를 미리 생성/적재할 때 사용합니다.\n"
+        "- 스케줄러도 이 API와 같은 내부 서비스 로직을 사용합니다.\n"
+        "- `base_date`를 입력하지 않으면 서버 실행일 기준 다음날 데이터를 생성합니다.\n\n"
+        "### 생성/저장 방식\n"
+        "- target date는 `(base_date 또는 서버 현재 날짜) + 1일`입니다.\n"
+        "- 지정한 `template_name`의 템플릿이 없거나 `events_per_day`보다 부족하면 템플릿을 먼저 생성합니다.\n"
+        "- 템플릿의 `event_offset_us`를 target date에 더해 실제 `event_time`을 만듭니다.\n"
+        "- replay와 동일한 날짜별 수치 변동 레이어가 적용되어 날짜마다 다른 데이터가 저장됩니다.\n"
+        "- 템플릿 row를 `insert_chunk_size` 단위로 읽고, materialize된 이벤트를 같은 단위로 batch insert합니다.\n"
+        "- 기본 batch insert 단위는 `1,000`건이며, 요청 파라미터로 `100~10,000` 사이에서 조정할 수 있습니다.\n"
+        "- MySQL에서는 `event_id` 중복 시 기존 row의 이벤트 시간, 설비, 상태, JSON payload 등을 갱신합니다.\n\n"
+        "### 주요 응답 데이터\n"
+        "- `templateName`: 사용한 템플릿 이름\n"
+        "- `baseDate`: 다음날 계산 기준 날짜\n"
+        "- `targetDate`: 실제 생성/저장 대상 날짜\n"
+        "- `templateEventCount`: 사용 가능한 템플릿 이벤트 수\n"
+        "- `generatedCount`: 실제 생성한 이벤트 수\n"
+        "- `affectedRows`: DB insert/update 영향 row 수\n"
+        "- `storedCountInDate`: target date에 저장된 row 수\n"
+        "- `insertChunkSize`: batch insert 단위\n"
+        "- `templatePrepared`: 템플릿 준비 결과\n\n"
+        f"{EVENT_JSON_TABLE_DESCRIPTION}"
+    ),
+    responses={200: {"description": "템플릿 기반 다음날 제조 이벤트 적재 결과"}},
+)
+def generate_tomorrow_manufacturing_event_json(
+    base_date: date | None = Query(
+        default=None,
+        description="다음날 계산 기준 날짜입니다. 미입력 시 서버 현재 날짜를 사용합니다.",
+    ),
+    template_name: str = Query(
+        default=DEFAULT_TEMPLATE_NAME,
+        description="다음날 데이터 적재에 사용할 템플릿 이름입니다.",
+    ),
+    events_per_day: int = Query(
+        default=DEFAULT_EVENTS_PER_DAY,
+        ge=4,
+        le=300000,
+        description="템플릿이 없거나 부족할 때 준비할 하루 기준 이벤트 수입니다.",
+    ),
+    car_pool_size: int = Query(
+        default=DEFAULT_CAR_POOL_SIZE,
+        ge=1,
+        le=100000,
+        description="템플릿 생성이 필요할 때 이벤트에 분배할 차량 마스터 풀 크기입니다.",
+    ),
+    insert_chunk_size: int = Query(
+        default=DEFAULT_INSERT_CHUNK_SIZE,
+        ge=100,
+        le=10000,
+        description=INSERT_CHUNK_SIZE_DESCRIPTION,
+    ),
+    service: ManufacturingEventJsonService = Depends(
+        get_manufacturing_event_json_service,
+    ),
+) -> CommonResponse[dict]:
+    result = service.generate_tomorrow(
+        base_date=base_date,
+        template_name=template_name,
+        events_per_day=events_per_day,
+        car_pool_size=car_pool_size,
+        insert_chunk_size=insert_chunk_size,
+    )
+    return success_response(
+        data=result,
+        message="템플릿 기반 다음날 제조 원천 이벤트 JSON 적재가 완료되었습니다.",
+    )
+
+
+@router.get(
+    "",
+    summary="저장된 제조 이벤트 JSON 조회",
+    operation_id="listManufacturingEventJson",
+    description=(
+        "`manufacturing_event_json` 테이블에 저장된 제조 관제 이벤트 JSON을 조회합니다.\n\n"
+        "### 동작 방식\n"
+        "- 기간, 공정 코드, 전송 여부 기준으로 필터링할 수 있습니다.\n"
+        "- 템플릿 replay 결과가 아니라 실제로 테이블에 적재된 일자별 이벤트를 반환합니다.\n"
+        "- `event_time`, `id` 오름차순으로 정렬해 `limit`, `offset` 페이지를 반환합니다.\n\n"
+        f"{EVENT_JSON_TABLE_DESCRIPTION}"
+    ),
+    responses={200: {"description": "저장된 제조 이벤트 JSON 목록"}},
+)
+def list_manufacturing_event_json(
+    limit: int = Query(default=20, ge=1, le=200, description="조회할 최대 건수입니다."),
+    offset: int = Query(default=0, ge=0, description="조회 시작 위치입니다."),
+    start_date: date | None = Query(default=None, description="조회 시작 날짜입니다."),
+    end_date: date | None = Query(default=None, description="조회 종료 날짜입니다."),
+    process_code: ProcessCode | None = Query(
+        default=None,
+        description=PROCESS_CODE_DESCRIPTION,
+    ),
+    is_sent: bool | None = Query(
+        default=None,
+        description="외부 시스템 전송 여부입니다. 미입력 시 전체를 조회합니다.",
+    ),
+    service: ManufacturingEventJsonService = Depends(
+        get_manufacturing_event_json_service,
+    ),
+) -> CommonResponse[dict]:
+    events = service.list_events(
+        limit=limit,
+        offset=offset,
+        start_date=start_date,
+        end_date=end_date,
+        process_code=process_code,
+        is_sent=is_sent,
+    )
+    return success_response(
+        data={
+            "items": events,
+            "limit": limit,
+            "offset": offset,
+        },
+        message="제조 원천 이벤트 JSON 조회가 완료되었습니다.",
+    )
diff --git a/app/batch/__init__.py b/app/batch/__init__.py
new file mode 100644
index 0000000..84763da
--- /dev/null
+++ b/app/batch/__init__.py
@@ -0,0 +1 @@
+"""Batch job entry points."""
diff --git a/app/batch/manufacturing_event_loader.py b/app/batch/manufacturing_event_loader.py
new file mode 100644
index 0000000..0be145d
--- /dev/null
+++ b/app/batch/manufacturing_event_loader.py
@@ -0,0 +1,257 @@
+from __future__ import annotations
+
+import argparse
+import logging
+import time
+from datetime import date, timedelta
+from typing import Any
+
+from app.core.config import settings
+from app.repository.sampledb_repository import SampleDbRepository
+from app.service.manufacturing_event_json_service import (
+    DEFAULT_CAR_POOL_SIZE,
+    DEFAULT_EVENTS_PER_DAY,
+    DEFAULT_INSERT_CHUNK_SIZE,
+    DEFAULT_TEMPLATE_NAME,
+    ManufacturingEventJsonService,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s %(message)s",
+    )
+    args = _parse_args()
+
+    if not settings.sample_database_connection_url:
+        raise SystemExit("SAMPLE_DATABASE_URL 설정이 필요합니다.")
+
+    repository = SampleDbRepository(settings.sample_database_connection_url)
+    service = ManufacturingEventJsonService(repository)
+    started_at = time.monotonic()
+    last_progress_at = {"value": 0.0}
+
+    if args.template:
+        _run_template_batch(
+            args=args,
+            service=service,
+            started_at=started_at,
+            last_progress_at=last_progress_at,
+        )
+        return
+
+    start_date, end_date = _resolve_date_range(args)
+    logger.info(
+        "제조 이벤트 JSON 배치 적재 시작: start=%s end=%s events_per_day=%s "
+        "car_pool_size=%s chunk_size=%s update_existing=%s",
+        start_date.isoformat(),
+        end_date.isoformat(),
+        args.events_per_day,
+        args.car_pool_size,
+        args.chunk_size,
+        args.update_existing,
+    )
+
+    result = service.generate_range(
+        start_date=start_date,
+        end_date=end_date,
+        events_per_day=args.events_per_day,
+        car_pool_size=args.car_pool_size,
+        insert_chunk_size=args.chunk_size,
+        update_existing=args.update_existing,
+        progress_callback=_progress_logger(
+            started_at=started_at,
+            last_progress_at=last_progress_at,
+            interval_seconds=args.progress_interval_seconds,
+        ),
+    )
+
+    elapsed = max(0.001, time.monotonic() - started_at)
+    logger.info(
+        "제조 이벤트 JSON 배치 적재 완료: generated=%s affected=%s stored=%s "
+        "elapsed=%.1fs rate=%.1f rows/s distribution=%s",
+        result["generatedCount"],
+        result["affectedRows"],
+        result["storedCountInRange"],
+        elapsed,
+        result["generatedCount"] / elapsed,
+        result["processDistribution"],
+    )
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "CSV 원천 데이터에서 제조 이벤트 템플릿 또는 "
+            "sampledb.manufacturing_event_json을 배치 적재합니다."
+        ),
+    )
+    range_group = parser.add_mutually_exclusive_group(required=True)
+    range_group.add_argument(
+        "--template",
+        action="store_true",
+        help="날짜별 물리 적재 대신 하루 재생 템플릿을 생성합니다.",
+    )
+    range_group.add_argument(
+        "--initial",
+        action="store_true",
+        help="2026-06-01부터 2026-06-16까지 초기 시연 데이터를 적재합니다.",
+    )
+    range_group.add_argument(
+        "--tomorrow",
+        action="store_true",
+        help="오늘 기준 다음날 데이터를 적재합니다.",
+    )
+    range_group.add_argument(
+        "--start-date",
+        type=_date_arg,
+        help="적재 시작일입니다. 예: 2026-06-01",
+    )
+    parser.add_argument(
+        "--end-date",
+        type=_date_arg,
+        help="적재 종료일입니다. --start-date와 함께 사용합니다.",
+    )
+    parser.add_argument(
+        "--events-per-day",
+        type=int,
+        default=DEFAULT_EVENTS_PER_DAY,
+        help="하루 생성 이벤트 수입니다. 기본값은 템플릿 기준 86400입니다.",
+    )
+    parser.add_argument(
+        "--template-name",
+        default=DEFAULT_TEMPLATE_NAME,
+        help="생성할 템플릿 이름입니다.",
+    )
+    parser.add_argument(
+        "--car-pool-size",
+        type=int,
+        default=DEFAULT_CAR_POOL_SIZE,
+        help="재사용할 차량 마스터 풀 크기입니다.",
+    )
+    parser.add_argument(
+        "--chunk-size",
+        type=int,
+        default=DEFAULT_INSERT_CHUNK_SIZE,
+        help="DB에 한 번에 insert할 row 수입니다.",
+    )
+    parser.add_argument(
+        "--update-existing",
+        action="store_true",
+        help=(
+            "중복 event_id/template_event_id가 있으면 기존 row를 업데이트합니다. "
+            "기본은 INSERT IGNORE입니다."
+        ),
+    )
+    parser.add_argument(
+        "--replace",
+        action="store_true",
+        help="템플릿 생성 시 기존 템플릿 row를 삭제하고 다시 만듭니다.",
+    )
+    parser.add_argument(
+        "--progress-interval-seconds",
+        type=float,
+        default=10.0,
+        help="진행 로그 출력 간격입니다.",
+    )
+    return parser.parse_args()
+
+
+def _resolve_date_range(args: argparse.Namespace) -> tuple[date, date]:
+    if args.initial:
+        return date(2026, 6, 1), date(2026, 6, 16)
+    if args.tomorrow:
+        target_date = date.today() + timedelta(days=1)
+        return target_date, target_date
+    if not args.start_date:
+        raise SystemExit("--start-date가 필요합니다.")
+    return args.start_date, args.end_date or args.start_date
+
+
+def _run_template_batch(
+    *,
+    args: argparse.Namespace,
+    service: ManufacturingEventJsonService,
+    started_at: float,
+    last_progress_at: dict[str, float],
+) -> None:
+    logger.info(
+        "제조 이벤트 템플릿 배치 생성 시작: template=%s event_count=%s "
+        "car_pool_size=%s chunk_size=%s replace=%s",
+        args.template_name,
+        args.events_per_day,
+        args.car_pool_size,
+        args.chunk_size,
+        args.replace,
+    )
+    result = service.generate_template(
+        template_name=args.template_name,
+        event_count=args.events_per_day,
+        car_pool_size=args.car_pool_size,
+        insert_chunk_size=args.chunk_size,
+        replace=args.replace,
+        update_existing=args.update_existing or args.replace,
+        progress_callback=_progress_logger(
+            started_at=started_at,
+            last_progress_at=last_progress_at,
+            interval_seconds=args.progress_interval_seconds,
+        ),
+    )
+    elapsed = max(0.001, time.monotonic() - started_at)
+    logger.info(
+        "제조 이벤트 템플릿 배치 생성 완료: template=%s generated=%s "
+        "affected=%s stored=%s elapsed=%.1fs rate=%.1f rows/s distribution=%s",
+        result["templateName"],
+        result["generatedCount"],
+        result["affectedRows"],
+        result["storedCount"],
+        elapsed,
+        result["generatedCount"] / elapsed,
+        result["processDistribution"],
+    )
+
+
+def _date_arg(value: str) -> date:
+    try:
+        return date.fromisoformat(value)
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError(
+            "날짜는 YYYY-MM-DD 형식이어야 합니다.",
+        ) from exc
+
+
+def _progress_logger(
+    *,
+    started_at: float,
+    last_progress_at: dict[str, float],
+    interval_seconds: float,
+):
+    def log_progress(progress: dict[str, Any]) -> None:
+        now = time.monotonic()
+        if now - last_progress_at["value"] < interval_seconds:
+            return
+        last_progress_at["value"] = now
+        generated_count = int(progress["generatedCount"])
+        total_expected = int(progress["totalExpectedEvents"])
+        elapsed = max(0.001, now - started_at)
+        percent = generated_count / total_expected * 100 if total_expected else 100.0
+        logger.info(
+            "제조 이벤트 JSON 배치 진행: generated=%s/%s %.2f%% affected=%s "
+            "elapsed=%.1fs rate=%.1f rows/s",
+            generated_count,
+            total_expected,
+            percent,
+            progress["affectedRows"],
+            elapsed,
+            generated_count / elapsed,
+        )
+
+    return log_progress
+
+
+if __name__ == "__main__":
+    main()
diff --git a/app/core/config.py b/app/core/config.py
index 68ee9bc..44b6ffa 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -30,6 +30,26 @@ class Settings(BaseSettings):
 
     main_database_url: str | None = Field(default=None, alias="MAIN_DATABASE_URL")
     sample_database_url: str | None = Field(default=None, alias="SAMPLE_DATABASE_URL")
+    manufacturing_event_scheduler_enabled: bool = Field(
+        default=True,
+        alias="MANUFACTURING_EVENT_SCHEDULER_ENABLED",
+    )
+    manufacturing_event_scheduler_events_per_day: int = Field(
+        default=86_400,
+        alias="MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY",
+    )
+    manufacturing_event_template_event_count: int = Field(
+        default=86_400,
+        alias="MANUFACTURING_EVENT_TEMPLATE_EVENT_COUNT",
+    )
+    manufacturing_event_car_pool_size: int = Field(
+        default=10_000,
+        alias="MANUFACTURING_EVENT_CAR_POOL_SIZE",
+    )
+    manufacturing_event_insert_chunk_size: int = Field(
+        default=1_000,
+        alias="MANUFACTURING_EVENT_INSERT_CHUNK_SIZE",
+    )
 
     @property
     def bottleneck_database_url(self) -> str:
diff --git a/app/docs/PRD_Manufacturing.md b/app/docs/PRD_Manufacturing.md
new file mode 100644
index 0000000..d5bdac3
--- /dev/null
+++ b/app/docs/PRD_Manufacturing.md
@@ -0,0 +1,3428 @@
+# 데이터셋
+
+## 1.1. Ford 엔진 진동 데이터셋 - 프레스&차체
+
+### 1.1.1. ARFF 파일 구조
+
+ARFF는 WEKA 머신러닝 도구용 데이터 포맷입니다.
+
+### 구성 요소
+
+| 구성 | 설명 |
+| --- | --- |
+| `@RELATION` | 데이터셋 이름 |
+| `@ATTRIBUTE` | 컬럼 정의 |
+| `@DATA` | 실제 데이터 |
+
+### 1.1.2. ARFF 컬럼 구조
+
+| 컬럼 | 설명 |
+| --- | --- |
+| `att1 ~ att500` | 시계열 센서값 |
+| `class` | 정상/이상 라벨 |
+
+02. Dataset_FordEngine.zip
+
+- 전체 데이터 구조
+    
+    ## 전체 데이터셋 구성
+    
+    | 파일명 | 데이터 유형 | 설명 |
+    | --- | --- | --- |
+    | `FordA.txt` | 전체 데이터 | 시계열 센서 데이터 |
+    | `FordA_TEST.txt` | 테스트 데이터 | 테스트용 시계열 데이터 |
+    | `FordA_TRAIN.arff` | 학습 데이터 | WEKA 형식 학습 데이터 |
+    | `FordA_TEST.arff` | 테스트 데이터 | WEKA 형식 테스트 데이터 |
+    
+    ---
+    
+    ## 데이터 구조 특징
+    
+    | 항목 | 내용 |
+    | --- | --- |
+    | 데이터 형태 | 시계열(Time-Series) |
+    | 데이터 길이 | 샘플당 500개 측정값 |
+    | 목적 | 자동차 엔진 이상 탐지 |
+    | 입력 데이터 | 엔진 소음 측정값 |
+    | 출력 라벨 | 정상 / 이상 |
+    | 문제 유형 | 이진 분류(Binary Classification) |
+    | 추천 활용 | 이상 탐지, 예지보전, 시계열 분류 |
+    
+    ---
+    
+    ## TXT 파일 데이터 구조
+    
+    TXT 파일은 아래와 같은 구조를 가집니다.
+    
+    | 위치 | 의미 |
+    | --- | --- |
+    | 첫 번째 값 | 클래스(Label) |
+    | 이후 500개 값 | 엔진 소음 시계열 데이터 |
+    
+    ---
+    
+    ## 데이터 샘플 구조 예시
+    
+    | label | t1 | t2 | t3 | ... | t500 |
+    | --- | --- | --- | --- | --- | --- |
+    | -1 | -0.14 | 0.17 | 0.30 | ... | -0.79 |
+    | 1 | 0.71 | 0.74 | 0.72 | ... | -4.33 |
+    
+    ※ 실제 데이터는 실수(float) 기반 연속값으로 구성됨
+    
+    ---
+    
+    ## Label 의미
+    
+    | 값 | 의미 |
+    | --- | --- |
+    | `1` | 정상(Normal) |
+    | `-1` | 이상(Fault/Abnormal) |
+    
+    ---
+    
+    ## ARFF 파일 구조
+    
+    ARFF는 WEKA 머신러닝 도구용 데이터 포맷입니다.
+    
+    ### 구성 요소
+    
+    | 구성 | 설명 |
+    | --- | --- |
+    | `@RELATION` | 데이터셋 이름 |
+    | `@ATTRIBUTE` | 컬럼 정의 |
+    | `@DATA` | 실제 데이터 |
+    
+    ---
+    
+    ## ARFF 컬럼 구조
+    
+    | 컬럼 | 설명 |
+    | --- | --- |
+    | `att1 ~ att500` | 시계열 센서값 |
+    | `class` | 정상/이상 라벨 |
+    
+    ---
+    
+    ## 데이터셋 특징 요약
+    
+    | 항목 | 설명 |
+    | --- | --- |
+    | 산업 분야 | 자동차 제조 |
+    | 데이터 종류 | 엔진 소음 시계열 |
+    | 센서 유형 | 진동/소음 계열로 추정 |
+    | 샘플 길이 | 고정 길이 500 |
+    | 전처리 여부 | 정규화 미적용(original raw signal) |
+    | 학습 유형 | 지도학습(Supervised Learning) |
+- https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=%EC%98%88%EC%A7%80%EB%B3%B4%EC%A0%84&page=1&DATASET_SEQ=2&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
+- Ford사의 자동차 엔진 및 모터 센서의 시계열 진동 데이터를 기반으로 정상/비정상 상태를 분류하는 예지보전(Predictive Maintenance) 데이터셋
+- 데이터 개수 : 4,921 개 / 확장자 : ARFF, text
+- Sensor 1~500의 시계열 데이터를 활용하여 설비 이상 탐지 및 고장 예측 모델 학습 가능
+- CNN, RNN, XGBoost 등의 알고리즘을 활용하여 제조 설비 상태 분석 및 장애 탐지 수행 가능
+- Kafka 기반 실시간 이벤트 스트리밍, Redis Queue 모니터링, AI 기반 Predictive Alert 기능 구현에 적합
+- 스마트팩토리 환경에서 모터 진동 증가, RPM 이상, 설비 소음 증가 등의 이벤트를 시뮬레이션하는 용도로 활용 가능
+- 모빌리티 스마트팩토리 관제 시스템의 실시간 설비 이상 탐지 및 시계열 기반 AI 분석 구조와 높은 연관성을 가짐
+
+## **1.2. 소성가공 자원최적화 AI 데이터셋** - 프레스&차체&도장
+
+### 1.2.1. 공정 데이터 2022년 8월 구조
+
+| 컬럼명 | 의미 | 데이터 타입 | 예시 |
+| --- | --- | --- | --- |
+| idx | 생산 데이터 고유 ID | Integer | 55644626 |
+| lineno | 생산 라인 번호 | Integer | 1200 |
+| itemno | 생산 제품 코드 | String | 76211-A3010-100 |
+| Order_date | 주문 날짜 값 | Integer(Date Serial) | 44783 |
+| day_night_type | 주/야간 작업 구분 | Integer | 1 |
+| product_date | 실제 생산 시간 | Datetime/String | 2022-08-10 7:57 |
+| quantity | 생산 수량 | Integer | 5 |
+| cnt | 생산 카운트 값 | Integer | 18133963 |
+
+### 1.2.2. 프레스 유압모터 / 로봇 전류 데이터 구조
+
+| 컬럼명 | 의미 | 데이터 타입 | 예시 |
+| --- | --- | --- | --- |
+| Unnamed: 0 | 데이터 인덱스 번호 | Integer | 1 |
+| Time_s[s] | 센서 수집 시간(Timestamp) | Datetime/String | 2022-07-17 12:09 |
+| RMS[A] | 전류 RMS(Root Mean Square) 값 | Float | 1.971987844 |
+
+※ 일부 데이터셋은 `RMS[A]` 대신 `Acceleration[g]` 컬럼 사용
+
+### 1.2.3. 프레스 2호 데이터 구조 (진동/가속도 기반)
+
+| 컬럼명 | 의미 | 데이터 타입 | 예시 |
+| --- | --- | --- | --- |
+| Unnamed: 0 | 데이터 인덱스 번호 | Integer | 1 |
+| Time_s[s] | 센서 수집 시간 | Datetime/String | 2022-07-17 12:10 |
+| Acceleration[g] | 가속도/진동 값 | Float | 0.007593981 |
+
+### 1.2.4. 데이터셋 파일
+
+공정 데이터 2022년 8월.csv
+
+로봇 1호-전류 데이터.csv
+
+로봇 2호-전류 데이터.csv
+
+프레스 1호-유압모터 전류데이터.csv
+
+프레스 2호-유압모터 전류데이터.csv
+
+프레스 3호-유압모터 전류데이터.csv
+
+프레스 4호-유압모터 전류데이터.csv
+
+- https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=%EC%86%8C%EC%84%B1%EA%B0%80%EA%B3%B5+%EC%9E%90%EC%9B%90%EC%B5%9C%EC%A0%81%ED%99%94+AI+%EB%8D%B0%EC%9D%B4%ED%84%B0%EC%85%8B&page=1&DATASET_SEQ=46&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
+- 데이터 개수: 717,774개 / 데이터셋 파일 확장자: .xls
+- 생산 부품에 따라 변동되는 에너지 소모량 예측을 위한 전류 데이터
+- 단조프레스 연속공정은 자동차 부품 생산을 위해 여러 대의 프레스 설비가 연속적으로 동작하는 공정으로, 많은 전기에너지를 소모한다.
+- 생산되는 부품 종류와 공정 상태에 따라 전력 사용량이 달라질 수 있으며, 이를 예측하기 위한 AI 기반 에너지 분석이 요구된다.
+- 본 데이터셋은 단조프레스 공정의 전력 사용량을 분석하여 에너지 자원 최적화 및 운영 효율 향상을 위한 AI 모델 개발에 활용된다.
+
+## 1.3. 머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터) - 차체&도장&의장
+
+### 1.3.1. 데이터 구조
+
+해당 머신비전 학습통합 데이터셋은 크게 전류/센서 시계열 데이터(`*_data.csv`)와 라벨 데이터(`*_label.json`)로 구성되어 있습니다.
+
+### 데이터 파일 구조
+
+| 파일명 | 데이터 유형 | 설명 | 행(Row) 의미 | 열(Column) 의미 |
+| --- | --- | --- | --- | --- |
+| `2nd_process_left_data.csv` | 학습 데이터 | 좌측 공정(Left) 센서/전류 시계열 데이터 | 하나의 측정 샘플 | 시간 순서에 따른 센서값 |
+| `2nd_process_right_data.csv` | 학습 데이터 | 우측 공정(Right) 센서/전류 시계열 데이터 | 하나의 측정 샘플 | 시간 순서에 따른 센서값 |
+| `2nd_process_left_label.json` | 라벨 데이터 | Left 데이터의 정상/불량 여부 | 각 샘플의 정답(Label) | 0 또는 1 |
+| `2nd_process_right_label.json` | 라벨 데이터 | Right 데이터의 정상/불량 여부 | 각 샘플의 정답(Label) | 0 또는 1 |
+
+---
+
+## CSV 데이터 구조 (`_data.csv`)
+
+CSV 파일은 시계열(Time-Series) 형태의 데이터로 구성되어 있으며, 약 80개의 연속 측정값 컬럼을 포함하고 있습니다.
+
+### 컬럼 구조 예시
+
+| 컬럼명 | 의미 |
+| --- | --- |
+| `42.19` | 특정 시점의 센서값 |
+| `42.465` | 다음 시점 센서값 |
+| `42.877` | 다음 시점 센서값 |
+| `...` | ... |
+| `52.777` | 마지막 시점 센서값 |
+
+※ 실제 컬럼명은 측정값 자체가 헤더로 잘못 저장된 형태로 보이며, 일반적으로는 `t1`, `t2`, `t3` 같은 시계열 인덱스로 변경하여 사용하는 것이 좋습니다.
+
+---
+
+## 데이터 샘플 형태
+
+| t1 | t2 | t3 | t4 | ... | t80 |
+| --- | --- | --- | --- | --- | --- |
+| 42.534 | 42.689 | 42.877 | 43.594 | ... | 52.999 |
+| 42.259 | 42.689 | 42.791 | 43.338 | ... | 52.856 |
+- 하나의 행(Row)은 설비/공정의 1회 측정 데이터
+- 값은 시간 흐름에 따른 센서값(전류, 진동, 압력 등으로 추정)
+- 시계열 기반 이상탐지(AI 이상 감지) 학습에 적합한 구조
+
+---
+
+## Label JSON 구조
+
+라벨 파일은 리스트(List) 형태로 저장되어 있습니다.
+
+### 예시
+
+```
+[0.0,1.0,0.0,1.0]
+```
+
+---
+
+## 라벨 의미 예시
+
+| 값 | 의미 |
+| --- | --- |
+| `0` | 정상(Normal) |
+| `1` | 이상/불량(Abnormal) |
+
+※ 실제 의미는 데이터셋 제공 문서 기준으로 최종 확인 필요
+
+---
+
+## 전체 데이터셋 특징 요약
+
+| 항목 | 내용 |
+| --- | --- |
+| 데이터 형태 | 시계열(Time-Series) |
+| 주요 목적 | 설비 이상 탐지 / 머신비전 AI 학습 |
+| 입력 데이터 | 연속 센서값 |
+| 출력 데이터 | 정상/불량 라벨 |
+| 활용 가능 분야 | 스마트팩토리, 예지보전(Predictive Maintenance), 품질 검사 |
+| 학습 방식 | 지도학습(Supervised Learning) 가능 |
+| 추천 모델 | LSTM, CNN-1D, Transformer, AutoEncoder |
+
+06. Dataset_Machinevision.zip
+
+- https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=%EB%A8%B8%EC%8B%A0%EB%B9%84%EC%A0%84+AI+%EB%8D%B0%EC%9D%B4%ED%84%B0%EC%85%8B&page=1&DATASET_SEQ=6&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
+- 열화상 이미지를 이용한 양/불량 판정을 위한 머신비전 데이터, 자동차 윈드실드 사이드 몰딩 사출품의 양품/불량품 판정을 위해 적외선 카메라를 사용하여 제품의 온도 분포에 따라 품질을 선별하기위한 제조 AI분석과정을 담은 데이터셋
+- 사출 제품을 일정시간 안에 열화상 카메라로 촬영한 이미지 데이터를 수집하고 서포트벡터머신(SVM) 알고리즘을 사용하여 정확한 불량품 선별을 도모
+- 데이터 개수 : 1,674개 / 확장자 : csv, json
+    - 분석에 사용된 변수명 : 320x256의 열화상 이미지의 모든 온도 raw data, 제품 두께 데이터
+    - 수집 방법 : 학습 데이터는 사출 제품을 일정시간안에 열화상 카메라로 촬영하여 취득. 라벨 데이터는 수기 기록(두께)
+- 열화상 카메라를 통해 수집한 온도 분포 데이터를 기반으로 양품/불량품을 판별하는 머신비전 기반 품질 검사 데이터셋
+- 자동차 윈드실드 사이드 몰딩 사출품의 열화상 이미지와 두께 데이터를 활용하여 품질 예측 수행
+- 비선형 SVM 기반 AI 모델을 사용하여 불량품 자동 판정 및 품질 검사 자동화 가능
+- OpenCV, Computer Vision, Thermal Vision 기반 AI 품질 검사 기능 구현에 적합
+- 생산 라인의 품질 검사 이벤트를 실시간 관제 시스템과 연계하여 불량률 분석 및 제조 품질 모니터링 가능
+- 스마트팩토리 통합 관제 환경에서 설비 이벤트와 품질 검사 이벤트를 함께 분석하는 구조로 확장 가능
+
+### 1.3.2. Bosch Production Line Performance(kaggle) 데이터 구조
+
+bosch-production-line-performance.zip
+
+| 데이터 파일 | 전체 컬럼 수 | 주요 컬럼 구조 | 컬럼 패턴 | 설명 |
+| --- | --- | --- | --- | --- |
+| train_numeric.csv | 970개 | Id, L*_S*_F*, Response | L0_S0_F0 형태 | 제조 공정의 수치형 센서/측정 데이터 |
+| train_categorical.csv | 2,141개 | Id, L*_S*_F* | L0_S1_F25 형태 | 제조 공정의 범주형/상태성 데이터 |
+| train_date.csv | 1,157개 | Id, L*_S*_D* | L0_S0_D1 형태 | 제조 공정 단계별 시간/날짜성 데이터 |
+
+| 공통 구조 | 의미 |
+| --- | --- |
+| Id | 제품 또는 생산 샘플 식별자 |
+| L0, L1, L2, L3 | 생산 라인 또는 공정 구간 |
+| S0, S1, S2 ... | 해당 라인 내 세부 스테이션/설비 |
+| F0, F2 ... | Feature 컬럼, 측정값 또는 상태값 |
+| D1, D3 ... | Date 컬럼, 공정 시간 정보 |
+| Response | 불량 여부 라벨. train_numeric.csv에만 포함됨 |
+
+| 데이터 파일 | 라인별 컬럼 수 |
+| --- | --- |
+| train_numeric.csv | L0: 168개, L1: 513개, L2: 42개, L3: 245개 |
+| train_categorical.csv | L0: 323개, L1: 1,227개, L2: 159개, L3: 431개 |
+| train_date.csv | L0: 184개, L1: 621개, L2: 78개, L3: 273개 |
+
+| 데이터 파일 | 예시 컬럼 |
+| --- | --- |
+| train_numeric.csv | Id, L0_S0_F0, L0_S0_F2, L0_S0_F4, ..., Response |
+| train_categorical.csv | Id, L0_S1_F25, L0_S1_F27, L0_S1_F29, ... |
+| train_date.csv | Id, L0_S0_D1, L0_S0_D3, L0_S0_D5, ... |
+- Bosch Production Line Performance (Kaggle)
+- 자동차 부품 및 전자 부품 제조 공정에서 수집된 대규모 생산라인 데이터를 기반으로 불량 제품을 예측하는 제조 AI 데이터셋
+- 독일 제조 기업 Bosch의 실제 생산라인 측정 데이터를 기반으로 구성된 Kaggle 제조 AI Competition 데이터셋
+- 제조 공정 각 단계(Line / Station / Feature)에서 수집된 수천 개의 센서·측정 데이터를 활용하여 품질 불량 여부(Response)를 예측하는 목적의 데이터셋
+- 스마트팩토리 제조 공정에서 가장 현실적인 “대규모 생산라인 이벤트 기반 데이터셋” 중 하나로 평가됨
+- Bosch 데이터 상세
+    
+    # 분류 카테고리
+    
+    - 제조 품질 예측(Quality Prediction)
+    - 생산라인 불량 탐지(Defect Detection)
+    - 제조 공정 이상 탐지(Manufacturing Anomaly Detection)
+    - 스마트팩토리 생산 공정 분석
+    - 대규모 제조 시계열/이벤트 분석
+    - Industrial AI / Manufacturing AI
+    - 예지 품질 관리(Predictive Quality)
+    
+    ---
+    
+    # 데이터셋 구성
+    
+    이 데이터셋은 크게 7개 파일로 구성됩니다.
+    
+    | 데이터셋 파일 | 설명 | 확장자 |
+    | --- | --- | --- |
+    | train_numeric | 학습용 수치형 센서 데이터 + Response(Label 포함) | CSV |
+    | test_numeric | 테스트용 수치형 센서 데이터 | CSV |
+    | train_categorical | 학습용 범주형 데이터 | CSV |
+    | test_categorical | 테스트용 범주형 데이터 | CSV |
+    | train_date | 학습용 시간(Date/Timestamp) 데이터 | CSV |
+    | test_date | 테스트용 시간(Date/Timestamp) 데이터 | CSV |
+    | sample_submission | 제출 예시 파일 | CSV |
+    
+    ---
+    
+    # 데이터 규모
+    
+    | 항목 | 내용 |
+    | --- | --- |
+    | 학습 데이터 | 약 1,183,747개 |
+    | 테스트 데이터 | 약 1,183,748개 |
+    | 전체 Feature 수 | 약 3,000개 이상 |
+    | Numeric Feature | 약 968개 |
+    | Date Feature | 약 1,156개 |
+    | Categorical Feature | 약 2,140개 |
+    | 데이터 크기 | 약 14GB 이상 |
+    | 라벨 | Response (0=정상, 1=불량) |
+    
+    ---
+    
+    # 데이터 특징
+    
+    ## 1. Numeric 데이터
+    
+    - 센서 측정값
+    - 공정 수치 데이터
+    - 압력/온도/측정값 계열
+    - 연속형 데이터
+    
+    예:
+    
+    - L0_S0_F0
+    - L3_S36_F3939
+    
+    (Line / Station / Feature 구조)
+    
+    ---
+    
+    ## 2. Categorical 데이터
+    
+    - 공정 상태
+    - 설비 상태
+    - 특정 공정 결과
+    - 문자열 기반 제조 이벤트
+    
+    ---
+    
+    ## 3. Date 데이터
+    
+    - 공정 수행 시간
+    - Station 통과 Timestamp
+    - 생산 순서 분석 가능
+    
+    예:
+    
+    - L0_S0_D1
+    
+    ---
+    
+    # 모빌리티 스마트팩토리 관제 시스템 적합성
+    
+    매우 적합합니다.
+    
+    오히려 사용자가 현재 만들고 있는:
+    
+    - 모빌리티 스마트팩토리 관제
+    - Kafka 기반 이벤트 수집
+    - Redis Queue 처리
+    - Elasticsearch 기반 검색
+    - AI 이상 탐지
+    - 실시간 생산라인 모니터링
+    
+    구조와 가장 유사한 공개 데이터셋 중 하나입니다.
+    
+    ---
+    
+    # 사용자 프로젝트와 연결 가능한 요소
+    
+    | Bosch 데이터 | 사용자 관제 시스템 |
+    | --- | --- |
+    | 생산라인(Line) | Factory / Line 구조 |
+    | Station | 설비(Equipment) |
+    | Timestamp | Kafka Event Time |
+    | Numeric Sensor | IoT 센서 데이터 |
+    | Response | 장애/불량 이벤트 |
+    | 공정 통과 여부 | 생산 흐름 추적 |
+    | Feature 수천개 | 대규모 이벤트 처리 |
+    | 불균형 데이터 | 이상 탐지 AI |
+    | 제조 이벤트 | 실시간 관제 이벤트 |
+
+# 프레스&차체&도장&의장 기능
+
+# 2. 제조 공정별 기능 상세화 + 데이터셋 활용 구조
+
+## 2.1. 프레스 (차체 외판, 문, 보닛, 루프 등)
+
+### 2.1.1. 프레스 공정
+
+철판을 눌러 차체 부품을 만드는 공정
+
+## 주요 기능
+
+| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 프레스 이상 정지 탐지 | 프레스 설비가 생산 중 갑자기 멈추거나 생산 주기가 비정상적으로 길어지는 상황을 탐지함 | 공정 데이터 2022년 8월, 프레스 전류 데이터, Bosch Dataset | AI 미적용 (룰 기반) | Kafka Stream + Rule Engine | 실시간 생산 이벤트와 전류 데이터를 기반으로 cnt 증가 여부, 생산 주기 지연, 전류 급감 패턴을 분석하여 설비 이상 정지를 탐지함 | cnt 증가 정체, 전류값 급감, Timestamp 지연 |
+
+---
+
+## 실제 활용 방식
+
+### Ford Dataset 활용
+
+시계열 진동 패턴 기반:
+
+- 금형 진동 변화 탐지
+- 모터 진동 증가 분석
+- 프레스 충격 패턴 추적
+- 반복 이상 진동 이벤트 분석
+
+수행.
+
+---
+
+### 소성가공 데이터 활용
+
+`RMS[A]` 기반:
+
+- 설비 부하 상태 분석
+- 모터 과부하 탐지
+- 전류 Peak 이벤트 감지
+- 설비 정지 상태 추적
+
+수행.
+
+---
+
+### Bosch Dataset 활용
+
+Line / Station / Timestamp 기반:
+
+- 생산라인 이벤트 흐름 분석
+- 설비 이상 이벤트 추적
+- 생산 지연 및 병목 분석
+- 반복 이상 패턴 탐지
+
+수행.
+
+## 2.2 차체
+
+### 2.2.1. 차체 공정
+
+용접 로봇 및 차체 조립 설비를 통해 차량 차체를 생산하는 공정
+
+## 주요 기능
+
+| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 로봇 이상 동작 및 충돌 위험 탐지 | 용접 로봇의 반복 이동 패턴 이상 및 충돌 위험 상황을 탐지함 | Ford Dataset, 로봇 전류 데이터 | AI 미적용 (통계/룰 기반) | 시계열 패턴 분석 + Rule Engine | 로봇 암 이동 패턴, 진동 변화, 전류 RMS 변화를 기반으로 반복적인 비정상 움직임 및 충돌 위험 이벤트를 탐지함 | 반복 이동 오류, 진동 급증, 전류 RMS 급증 |
+
+---
+
+## 실제 활용 방식
+
+### 로봇 전류 데이터 활용
+
+`RMS[A]` 기반:
+
+- 다중 로봇 간 전류 패턴 비교
+- 특정 로봇 이상 상태 탐지
+- 모터 과부하 및 전류 Peak 분석
+- 생산 주기 이상 탐지
+
+수행.
+
+---
+
+### Ford Dataset 활용
+
+시계열 진동 패턴 기반:
+
+- 로봇 암 진동 증가 탐지
+- 충돌 위험 이벤트 감지
+- 반복 이동 패턴 이상 분석
+- 비정상 동작 탐지
+
+수행.
+
+---
+
+### Bosch Dataset 활용
+
+Station / Timestamp 기반:
+
+- 생산 공정 흐름 추적
+- 공정 병목 탐지
+- 생산 지연 분석
+- 품질 이벤트 및 반복 불량 추적
+
+수행.
+
+### 2.2.2 조립 관련 로봇팔 데이터
+
+- **로봇팔 용접 전력사용 및 시간**
+    
+    링크 : https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=&page=1&DATASET_SEQ=51&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=C004025&FILE_TYPE_SEL=C005002&WDATE_SEL=
+    
+    데이터셋
+    
+    | 컬럼명 | 의미 | 예시값 |
+    | --- | --- | --- |
+    | WK_DT | 작업 일자 | 2.02201E+16 |
+    | PIPE_NO | 파이프 제품 번호 | PP22041200707 |
+    | DV_R | 직경 편차 | 308 |
+    | DA_R | 평균 직경 | 7962 |
+    | AV_R | 검사 항목 평균값 | 364 |
+    | AA_R | 평균 면적 | 5975 |
+    | PM_R | 공정 측정 종학값 | 9270 |
+    | FIN_JGMT | 최종 판정 결과 | 1 |
+    
+    데이터량 : 654,200(4.153mb)
+    
+- **로봇팔 용접기 진동 예지**
+    
+    링크 : https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=진동&page=1&DATASET_SEQ=45&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
+    
+    데이터셋
+    
+    | 컬럼명 | 의미 | 예시값 |
+    | --- | --- | --- |
+    | Time | 측정 시간 | 2021/08/02 6:47 |
+    | 0 | 0hz | 0.00044808 |
+    | 3.12 | 3.12hz | 0.000633875 |
+    | 6.25 | 6.25hz | 0.000895333 |
+    | 9.38 | 9.38hz | 0.001123601 |
+    | 12.5 | 12.5hz | 0.000332504 |
+    | 15.62 | 15.62hz | 0.000583095 |
+    | 18.75 | 18.75hz | 0.000241952 |
+    | 21.88 | 21.88hz | 0.000742734 |
+    | 25 | 25hz | 0.001193284 |
+    | 28.12 | 28.12hz | 0.001019982 |
+    | 31.25 | 31.25hz | 0.000571357 |
+    | 34.38 | 34.38hz | 0.000844553 |
+    | 37.5 | 37.5hz | 0.000377998 |
+    | 40.62 | 40.62hz | 0.00056753 |
+    
+    데이터량 : 9,358,120(105mb)
+    
+
+## 2.3. 도장(외부 도색)
+
+### 2.3.1. 도장 공정
+
+차량 외부를 도색하여 품질과 외관을 완성하는 공정
+
+| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 도장 품질 이상 탐지 | 차량 외부 도장 과정에서 도장 불균형, 도장 누락, 표면 품질 저하 등이 발생하는 상황을 탐지함 | 머신비전 데이터, Bosch Dataset | AI 미적용 (통계/룰 기반) | 시계열 통계 분석 + Rule Engine | 도장 공정의 품질 이벤트, 불량률 변화, 생산 이벤트 흐름, 센서값 변화를 기반으로 반복적인 품질 이상 패턴을 탐지함. 공정별 품질 이벤트 로그와 생산 흐름 중심의 실시간 품질 관제를 수행함 | 품질 이벤트 증가, 불량률 증가, Response 증가, 공정별 반복 이상 발생 |
+
+---
+
+## 실제 활용 방식
+
+### 머신비전 데이터 활용
+
+품질 이벤트 및 시계열 패턴 기반:
+
+- 도장 품질 이상 탐지
+- 반복 불량 이벤트 분석
+- 공정별 품질 변화 추적
+- 생산 품질 이벤트 모니터링
+
+수행.
+
+---
+
+### Bosch Dataset 활용
+
+Numeric / Response / Timestamp 기반:
+
+- 생산라인 품질 흐름 분석
+- 공정별 불량률 증가 추적
+- 생산 지연 및 병목 분석
+- 반복 이상 공정 탐지
+
+수행.
+
+---
+
+### 환경 센서 데이터 활용
+
+온도 / 습도 / VOC / 공조 상태 기반:
+
+- 유해가스(VOC) 증가 감지
+- 도장 환경 이상 탐지
+- 공조 시스템 상태 모니터링
+- 작업자 안전 이벤트 감시
+
+수행.
+
+### 2.3.2 도장 관련 표면처리 습도 데이터
+
+링크 : https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=&page=2&DATASET_SEQ=56&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=C004025&FILE_TYPE_SEL=C005002&WDATE_SEL=
+
+데이터셋
+
+| 컬럼명 | 의미 | 예시값 |
+| --- | --- | --- |
+| Datetime | 측정 일시 | 2021-02-08 0:15 |
+| Production | 생산량 | 116 |
+| Temperature | 온도 | -4.2 |
+| Humidity | 습도 | 65.6 |
+| Power_Cost | 전력 단가 또는 전기 요금 지수 | 109.8 |
+| DoW | 요일(Day of Week) | Monday |
+| Worker_Power | 작업 인력 투입 지표 | 0.13 |
+| Man_Cost | 인건비 또는 작업 비용 | 1.5 |
+| Power_Usage | 전력 사용량 | 103 |
+
+데이터량 : 220,311(1.33mb)
+
+## 2.4. 의장 조립 (엔진, 시트 조립 및 배선)
+
+### 2.4.1. 의장 공정
+
+부품을 조립하여 완성차를 구성하는 최종 조립 공정
+
+## 주요 기능
+
+| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 조립 순서 오류 탐지 | 엔진, 시트, 배선 등의 조립 순서가 잘못 수행된 상황을 탐지함 | 공정 데이터 2022년 8월, Bosch Dataset | AI 미적용 (룰 기반) | Rule Engine | 생산 공정의 Timestamp 및 product_date 흐름을 기반으로 공정 순서를 비교하여 이상 여부를 판단함. 정해진 공정 순서와 다른 이벤트 발생 시 오류로 탐지함 | product_date 순서 이상, Timestamp 지연 |
+
+---
+
+## 실제 활용 방식
+
+### 공정 데이터 활용
+
+`lineno`, `product_date`, `quantity` 기반:
+
+- 생산 추적
+- 작업 이력 분석
+- 공정 순서 검증
+- 생산 흐름 분석
+
+수행.
+
+---
+
+### 머신비전 데이터 활용
+
+품질 이벤트 및 시계열 패턴 기반:
+
+- 부품 누락 이벤트 탐지
+- 조립 상태 분석
+- 체결 이상 이벤트 분석
+- 반복 품질 이상 추적
+
+수행.
+
+---
+
+### Bosch Dataset 활용
+
+Line / Station / Response / Timestamp 기반:
+
+- 생산라인 불량 분석
+- 공정 병목 및 지연 탐지
+- 제조 공정 이상 이벤트 추적
+- 반복 불량 패턴 분석
+
+수행.
+
+## 2.5. Bosch Production Line Performance 데이터셋 역할
+
+### 2.5.1. 주요 활용 분야
+
+| 활용 분야 | 설명 |
+| --- | --- |
+| 생산라인 불량 예측 | 공정별 센서 데이터를 기반으로 최종 불량 여부(Response) 예측 |
+| 공정 병목 탐지 | 특정 Station 또는 Line에서 이벤트 적체 및 지연 분석 |
+| 제조 공정 이상 탐지 | 생산라인 전체의 이상 패턴 및 불량 증가 탐지 |
+| 대규모 이벤트 분석 | 수천 개 Feature 기반 제조 이벤트 분석 |
+| 스마트팩토리 디지털 트윈 | 실제 생산라인 구조 기반 공정 시뮬레이션 |
+
+### 2.5.2. Bosch Dataset 주요 구조 활용
+
+| 데이터 종류 | 활용 방식 |
+| --- | --- |
+| Numeric Feature | 센서값/설비 상태값 분석 |
+| Date Feature | 공정 시간 흐름 및 병목 분석 |
+| Categorical Feature | 공정 상태 분석 |
+| Response(Label) | 정상/불량 분류 |
+
+### 2.5.3. Bosch 데이터셋 핵심 역할
+
+| 데이터셋 | 핵심 역할 |
+| --- | --- |
+| Ford Dataset | 설비 이상 탐지 |
+| 소성가공 데이터 | 프레스 및 생산라인 운영 분석 |
+| 머신비전 데이터 | 품질 검사 및 불량 탐지 |
+| Bosch Dataset | 전체 생산라인 통합 분석 및 제조 AI |
+
+---
+
+# MVP 기능
+
+## 📊 제조 공정 AI 분석
+
+!image.png
+
+### 공정 간 불량 전이 예측 및 제조 병목 탐지
+
+불량 전이 예측 프로세스 
+
+---
+
+## 1. 기능 요약
+
+### 제조 병목 탐지
+
+Bosch Production Line Performance Dataset의 Station 통과 시간 데이터를 분석하여 생산 공정 내 병목 구간을 실시간 탐지합니다.
+
+공정별 처리 시간, 대기 시간, 체류 시간을 기반으로 병목 위험도를 산출하며, 특정 Station에서 발생한 지연이 전체 생산 흐름에 미치는 영향을 시각적으로 제공합니다.
+
+---
+
+### 공정 간 불량 전이 예측
+
+Bosch Production Line Performance Dataset의 수치형 센서 데이터와 공정 시간 데이터를 활용하여 특정 공정에서 발생한 이상 징후가 후속 공정의 불량으로 이어질 가능성을 예측합니다.
+
+현재 공정 상태는 정상이라도 후속 공정에서 발생 가능한 품질 이상을 사전에 탐지하여 생산 손실을 최소화합니다.
+
+---
+
+## 2. 데이터 및 AI 모델 아키텍처
+
+### 활용 데이터셋
+
+### Bosch Production Line Performance Dataset
+
+사용 파일
+
+- train_numeric.csv
+- train_date.csv
+- train_categorical.csv
+
+활용 목적
+
+- 공정 병목 탐지
+- 공정 간 불량 전이 예측
+- 제조 이력 분석
+- 생산 흐름 시각화
+
+---
+
+### 머신비전 열화상 품질 데이터셋
+
+활용 목적
+
+- 품질 검사 결과 생성
+- 불량 여부 검증
+- AI 예측 결과 보조 학습 데이터 활용
+
+---
+
+## 3. 적용 AI 모델
+
+### 제조 병목 탐지
+
+### Rule Engine
+
+공정별 처리 시간과 대기 시간을 분석하여 임계값 초과 시 병목 이벤트를 생성합니다.
+
+예시
+
+- 평균 처리 시간 대비 30% 이상 증가
+- Station 체류 시간 급증
+- 생산 대기열 증가
+
+---
+
+### Isolation Forest
+
+정상 생산 패턴과 다른 비정상 공정 흐름을 탐지하여 병목 위험도를 계산합니다.
+
+탐지 대상
+
+- 비정상 체류 시간
+- 비정상 통과 시간
+- 특정 Station 집중 현상
+
+---
+
+### 공정 간 불량 전이 예측
+
+### LightGBM Classifier
+
+train_numeric.csv와 train_date.csv를 결합하여 학습합니다.
+
+공정별 센서 데이터와 시간 정보를 기반으로 후속 공정 불량 발생 확률을 예측합니다.
+
+예시
+
+- 차체 공정 불량 확률 25%
+- 도장 공정 불량 확률 78%
+- 의장 공정 불량 확률 42%
+
+---
+
+## 4. 데이터 파이프라인
+
+### Kafka 기반 이벤트 스트리밍
+
+공정 이벤트를 Kafka Topic으로 수집합니다.
+
+수집 데이터
+
+- Product ID
+- Station ID
+- Process Time
+- Waiting Time
+- Defect Probability
+
+각 이벤트는 생산 이력 단위로 연결되어 전체 제조 흐름을 구성합니다.
+
+---
+
+### Elasticsearch 기반 로그 분석
+
+실시간 분석 결과를 Elasticsearch에 저장합니다.
+
+주요 기능
+
+- 병목 공정 검색
+- 불량 이력 검색
+- 제조 이력 조회
+- 공정 영향도 분석
+
+---
+
+## 5. AI 분석 기능
+
+### 실시간 병목 분석
+
+분석 데이터
+
+- Station 통과 시간
+- 대기 시간
+- 공정 체류 시간
+
+제공 정보
+
+- 병목 공정
+- 평균 지연 시간
+- 영향 생산량
+- 병목 위험도
+
+예시
+
+S32 공정
+
+- 평균 지연 시간 : 14초
+- 영향 제품 수 : 1,245개
+- 위험도 : 92%
+
+---
+
+### 공정 간 불량 전이 예측
+
+분석 데이터
+
+- Numeric Sensor Data
+- Date Feature
+- 품질 검사 결과
+
+제공 정보
+
+- 후속 공정 불량 확률
+- 예상 불량 공정
+- 위험도 등급
+
+예시
+
+현재 제품 상태
+
+- 차체 불량 확률 : 23%
+- 도장 불량 확률 : 78%
+- 의장 불량 확률 : 41%
+
+예상 결과
+
+도장 공정 불량 발생 가능성 높음
+
+---
+
+### AI 원인 분석
+
+LightGBM Feature Importance를 활용하여 병목 및 불량 예측에 영향을 준 주요 원인을 제공합니다.
+
+제공 정보
+
+- 영향 Station
+- 주요 Sensor Feature
+- 시간 지연 영향도
+
+예시
+
+도장 공정 불량 예측 원인
+
+1. Station S32 체류 시간 증가
+2. L3 공정 센서 값 이상
+3. 이전 공정 대기 시간 증가
+
+---
+
+## 6. 시각화 요소
+
+### 실시간 병목 분석
+
+- 공정 흐름도
+- 병목 구간 강조
+- 위험도 색상 표시
+
+---
+
+### 불량 전이 예측
+
+- 공정별 불량 확률 게이지
+- 위험도 카드
+- 예측 결과 알림
+
+예시
+
+도장 공정 불량 확률
+
+78%
+
+원인
+
+- S32 체류 시간 증가
+- 이전 공정 지연 발생
+
+---
+
+### 제조 이력 타임라인
+
+- 제품별 공정 이동 경로
+- 공정별 처리 시간
+- 병목 발생 시점
+- 품질 검사 결과
+
+# 이벤트 데이터 (json)
+
+## 샘플DB 구조
+
+```sql
+CREATE TABLE manufacturing_event_json (
+    id BIGINT AUTO_INCREMENT PRIMARY KEY,
+
+    event_id VARCHAR(100) NOT NULL,
+    event_time DATETIME NOT NULL,
+
+    car_master_id BIGINT NOT NULL,
+    equipment_id BIGINT NOT NULL,
+
+    process_code ENUM('PRESS', 'BODY', 'PAINT', 'ASSEMBLY') NOT NULL,
+    station_code VARCHAR(50),
+
+    equipment_code VARCHAR(50) NOT NULL,
+    equipment_type VARCHAR(50),
+    equipment_status VARCHAR(30),
+
+    event_type VARCHAR(50),
+
+    event_json JSON NOT NULL,
+
+    is_sent TINYINT(1) DEFAULT 0,
+    sent_at DATETIME NULL,
+
+    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+
+    INDEX idx_event_time_sent (event_time, is_sent),
+    INDEX idx_process_time (process_code, event_time),
+    INDEX idx_equipment_time (equipment_code, event_time),
+    INDEX idx_car_time (car_master_id, event_time),
+    INDEX idx_event_id (event_id),
+    INDEX idx_equipment_id_time (equipment_id, event_time),
+
+    CONSTRAINT fk_manufacturing_event_json_car_master
+        FOREIGN KEY (car_master_id)
+        REFERENCES car_master(id),
+
+    CONSTRAINT fk_manufacturing_event_json_equipment
+        FOREIGN KEY (equipment_id)
+        REFERENCES equipment(id)
+);
+```
+
+| 컬럼명 | 설명 | 왜 필요한지 |
+| --- | --- | --- |
+| `id` | sampleDB 내부 PK | DB에서 row를 구분하기 위한 기본키 |
+| `event_id` | 이벤트 고유 ID | Kafka, mainDB, ES에서 같은 이벤트를 추적하기 위한 공통 ID |
+| `event_time` | 이벤트 발생 시간 | Scheduler가 가상 시간 기준으로 Kafka에 발행할 때 필요 |
+| `car_master_id` | 차량 마스터 ID | 차량별 공정 흐름, 차량별 이상 이력 조회에 필요 |
+| `process_code` | 공정 코드 | `PRESS`, `BODY`, `PAINT`, `ASSEMBLY` 분석 분기 기준 |
+| `station_code` | 스테이션 코드 | 같은 공정 안에서 어느 위치에서 발생한 이벤트인지 구분 |
+| `equipment_code` | 설비 코드 | 설비별 상태, 이상, 가동률 계산 기준 |
+| `equipment_type` | 설비 유형 | 프레스기, 로봇팔, 카메라, 컨베이어 등 분석 분기 보조 |
+| `equipment_status` | 설비 상태 | `RUNNING`, `IDLE`, `STOPPED`, `ERROR` 등. mainDB 가동률 계산에 필요 |
+| `event_type` | 이벤트 유형 | `PROCESS_STATUS`, `QUALITY_CHECK`, `EQUIPMENT_STATUS` 등 이벤트 성격 구분 |
+| `event_json` | 전체 원천 JSON | Kafka로 그대로 발행할 실제 메시지 |
+| `is_sent` | Kafka 발행 여부 | Scheduler가 아직 안 보낸 데이터만 조회하기 위해 필요 |
+| `sent_at` | Kafka 발행 시간 | 언제 Kafka로 보냈는지 추적 |
+| `created_at` | DB 저장 시간 | Python 전처리 결과가 저장된 시간 |
+| `updated_at` | DB 수정 시간 | 재생성/upsert로 기존 이벤트 row가 갱신된 시간을 추적 |
+
+## 최종 원천 관제 이벤트 JSON
+
+ 처리 완료 시간
+
+```json
+{
+  "event": {
+    "eventId": "EVT-20260616-000001",
+    "eventTime": "2026-06-16T10:00:01",
+    "eventCategory": "MANUFACTURING",
+    "eventType": "PROCESS_STATUS",
+    "eventName": "프레스 공정 통합 관제 이벤트"
+  },
+  "location": {
+    "factoryCode": "AIMS_FACTORY_01",
+    "lineCode": "PRESS_LINE_01",
+    "processCode": "PRESS",
+    "stationCode": "PRESS_STATION_01"
+  },
+  "equipment": {
+    "equipmentCode": "EQ_PRESS_01",
+    "equipmentName": "프레스 유압모터 1호",
+    "equipmentType": "HYDRAULIC_PRESS"
+  },
+  "equipmentStatus": {
+    "operationStatus": "RUNNING",
+    "lastNormalTime": "2026-06-16T09:59:30",
+    "statusChangedTime": "2026-06-16T10:00:01"
+  },
+  "product": {
+    "carId": "CAR-000001",
+    **"productId": "PRODUCT-000001",
+    "itemNo": "76211-A3010-100",
+    "quantity": 5,**
+    "productionCount": 18133963
+  },
+  "sensor": {
+    "sensorType": "MULTI_SENSOR",
+    "current": {
+      "rmsAmpere": 1.971987844,
+      "maxAmpere": 2.15,
+      "minAmpere": 1.72
+    },
+    "vibration": {
+      "accelerationG": 0.007593981,
+      "vibrationScore": 0.21,
+      "vibrationRms": 0.18,
+      "vibrationPeak": 0.31
+    },
+    "robotArmVibration": {
+      "robotId": "ROBOT_ARM_01",
+      "axis": "J1",
+      "frequencyHz": 40.62,
+      "amplitude": 0.00056753,
+      "vibrationRms": 0.000633875,
+      "vibrationPeak": 0.001193284,
+      "vibrationScore": 0.27
+    },
+    "thermal": {
+      "thermalScore": 43.2,
+      "avgTemperature": 43.2,
+      "maxTemperature": 52.9,
+      "minTemperature": 42.1
+    }
+  },
+  "manufacturing": {
+    "processSequence": 1,
+    "previousProcessCode": null,
+    "nextProcessCode": "BODY",
+    "targetQuantity": 200,
+    "completedQuantity": 145
+  },
+  "processMetrics": {
+    "cycleTimeSec": 42.5,
+    "waitingTimeSec": 8.3,
+    "processingTimeSec": 34.2,
+    "stationDelaySec": 5.7,
+    "throughputPerMin": 1.4,
+    "queueLength": 7,
+    "wipCount": 24,
+    "equipmentIdleTimeSec": 12.1
+  },
+  "sourceTrace": {
+    "fordRowId": 1,
+    "formingRowId": 1,
+    "robotArmVibrationRowId": 1,
+    "machineVisionRowId": 1,
+    "boschId": 1
+  },
+  "processData":{
+    # 아래참고
+  }
+}
+```
+
+## 위 내용 중 ProcessData 내용 JSON
+
+```jsx
+📌 프레스
+"processData": {
+  "press": {
+    "countIncreaseYn": true,
+    "targetCycleTimeSec": 40.0,
+    "timestampDelaySec": 5.7
+  }
+}
+- countIncreaseYn : 생산 카운트가 증가했는지 확인 -> false면 정
+- targetCycleTimeSec : 기준 cycle time. 실제 cycle time과 비교
+- timestampDelaySec : 이벤트 지연 시간. 데이터 지연/설비 정지 판단
+
+📌 차체
+"processData": {
+  "body": {
+    "robotMotionStatus": "NORMAL",
+    "robotOperationMode": "AUTO",
+    "frequencyPeakBand": "501_600_HZ",
+    "frequencyBands": {
+      "freq_0_100_hz": 0.001193284,
+      "freq_101_200_hz": 0.001987782,
+      "freq_201_300_hz": 0.001072014,
+      "freq_301_400_hz": 0.001792223,
+      "freq_401_500_hz": 0.001924913,
+      "freq_501_600_hz": 0.002907113,
+      "freq_601_700_hz": 0.001207061,
+      "freq_701_800_hz": 0.00114628,
+      "freq_801_900_hz": 0.001170759,
+      "freq_901_1000_hz": 0.001318642,
+      "freq_1001_1100_hz": 0.001426342,
+      "freq_1101_1200_hz": 0.001297966,
+      "freq_1201_1300_hz": 0.001750256,
+      "freq_1301_1400_hz": 0.001491831,
+      "freq_1401_1500_hz": 0.001278729,
+      "freq_1501_1600_hz": 0.000994307
+    }
+  }
+}
+- robotMotionStatus : 로봇 동작 상태
+- robotOperationMode : 로봇 운전 모드 (AUTO, MANUAL, STOPPED 등 운전 상태 확인)
+- frequencyPeakBand : 가장 진동이 크게 나온 주파수 대역
+- frequencyBands : 주파수 대역별 진동값
+
+📌 도장
+"processData": {
+  "paint": {
+    "imagePosition": "LEFT",
+    "thermalStdTemp": 4.2,
+    "thicknessValue": 116.5,
+    "defectScore": 0.87,
+    "visionLabel": "DEFECT",
+    "surfaceQualityScore": 72.3
+  }
+}
+- imagePosition : 좌/우 위치별 불량 확인 (촬영 이미지 위치)
+- thermalStdTemp : 온도 편차. 표면 균일도 판단
+- thicknessValue : 도장 두께 판단
+- defectScore : 비전 기반 불량 점수
+- visionLabel : 원천 비전 라벨
+- surfaceQualityScore : 화면 표시용 품질 점수
+
+📌 의장
+"processData": {
+  "assembly": {
+    "expectedSequence": "A01>A02>A03>A04",
+    "actualSequence": "A01>A03>A02>A04",
+    "missingPartCount": 0,
+    "fasteningErrorCount": 1,
+    "sequenceErrorCount": 1
+  }
+}
+- expectedSequence : 기준 작업 순서
+- actualSequence : 실제 작업 순서
+- missingPartCount : 누락 부품 수
+- fasteningErrorCount : 체결 오류 수
+- sequenceErrorCount : 작업 순서 오류 수
+
+```
+
+## 구조 요약
+
+```
+event                이벤트 기본 정보
+location             공장/라인/공정/스테이션 위치 정보
+equipment            설비 기본 정보
+equipmentStatus      설비 가동 상태/설비 건강 상태/상태 변경 정보
+product              차량/제품/생산 정보
+
+sensor
+├─ current                프레스/로봇 전류 데이터
+├─ vibration              일반 설비 진동/가속도 데이터
+├─ robotArmVibration      로봇팔 전용 진동 데이터
+└─ thermal                열화상/온도 데이터
+
+manufacturing        공정 순서/생산 목표/완료 수량 정보
+processMetrics       공정 시간/처리량/대기열/WIP 지표
+sourceTrace          원본 데이터 추적용
+```
+
+---
+
+# event
+
+이벤트 자체에 대한 메타 정보입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| eventId | 이벤트 고유 식별자 |
+| eventTime | 실제 공정 이벤트 발생 시간 |
+| eventCategory | 이벤트 대분류 (`MANUFACTURING`) |
+| eventType | 이벤트 유형 (`PROCESS_STATUS`, `QUALITY_CHECK`, `EQUIPMENT_SENSOR`) |
+| eventName | 사람이 읽기 쉬운 이벤트명 |
+
+예시
+
+```json
+{
+  "eventId":"EVT-20260616-000001",
+  "eventType":"PROCESS_STATUS"
+}
+```
+
+---
+
+# location
+
+이벤트 발생 위치 정보입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| factoryCode | 공장 식별 코드 |
+| lineCode | 생산 라인 코드 |
+| processCode | 공정 코드 (`PRESS`, `BODY`, `PAINT`, `ASSEMBLY`) |
+| stationCode | 스테이션 코드 |
+
+예시
+
+```
+공장
+ └─ 생산라인
+      └─ 공정
+           └─ 스테이션
+```
+
+---
+
+# equipment
+
+설비 기본 정보입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| equipmentCode | 설비 코드 |
+| equipmentName | 설비명 |
+| equipmentType | 설비 유형 |
+
+설비 유형 예시
+
+| processCode | equipmentType | 설명 |
+| --- | --- | --- |
+| PRESS | HYDRAULIC_PRESS | 유압 프레스 |
+| BODY | ROBOT_ARM | 차체 로봇팔 |
+| PAINT | CAMERA | 도장 검사 카메라 |
+| ASSEMBLY | CONVEYOR | 조립 컨베이어 |
+
+예시
+
+```json
+{
+  "equipmentCode":"EQ_PRESS_01",
+  "equipmentName":"프레스 유압모터 1호",
+  "equipmentType":"HYDRAULIC_PRESS"
+}
+```
+
+---
+
+# equipmentStatus
+
+설비 상태 정보입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| operationStatus | 실제 설비 가동 상태 |
+| healthStatus | 설비 건강 상태 |
+| lastNormalTime | 마지막 정상 상태 시간 |
+| statusChangedTime | 현재 상태로 변경된 시간 |
+
+`operationStatus`
+
+| 상태 | 의미 |
+| --- | --- |
+| RUNNING | 설비 가동 중 |
+| STOP | 설비 정지 |
+| IDLE | 대기 상태 |
+| MAINTENANCE | 정비 중 |
+
+`healthStatus`
+
+| 상태 | 의미 | 설비 운영 여부 |
+| --- | --- | --- |
+| NORMAL | 정상 상태 | 운영 중 |
+| WARNING | 경고 상태 | 운영 중 |
+| FAULT | 고장 상태 | 운영 불가 |
+| MAINTENANCE | 점검 상태 | 운영 중지 |
+
+예시
+
+```json
+{
+  "operationStatus":"RUNNING",
+  "healthStatus":"WARNING",
+  "lastNormalTime":"2026-06-16T09:59:30",
+  "statusChangedTime":"2026-06-16T10:00:01"
+}
+```
+
+---
+
+# product
+
+생산 대상 차량/제품 정보입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| carId | 차량 식별 ID |
+| productId | 생산 제품 ID |
+| itemNo | 품목 번호 |
+| quantity | 생산 수량 |
+| productionCount | 누적 생산량 |
+
+예시
+
+```json
+{
+  "carId":"CAR-000001",
+  "quantity":5,
+  "productionCount":18133963
+}
+```
+
+---
+
+# sensor
+
+여러 원본 데이터셋에서 가져온 센서 값을 관제용으로 통합한 영역입니다.
+
+---
+
+## sensor.current
+
+전류 센서 정보
+
+| 컬럼 | 설명 |
+| --- | --- |
+| rmsAmpere | RMS 전류 |
+| maxAmpere | 최대 전류 |
+| minAmpere | 최소 전류 |
+
+데이터 출처
+
+```
+소성가공 데이터
+RMS[A]
+```
+
+---
+
+## sensor.vibration
+
+설비 진동 정보
+
+| 컬럼 | 설명 |
+| --- | --- |
+| accelerationG | 가속도 |
+| vibrationScore | 진동 위험 점수 |
+| vibrationRms | RMS 진동 |
+| vibrationPeak | Peak 진동 |
+
+데이터 출처
+
+```
+Ford Engine Dataset
+소성가공 진동 데이터
+```
+
+---
+
+## sensor.robotArmVibration
+
+로봇팔 전용 진동 데이터
+
+| 컬럼 | 설명 |
+| --- | --- |
+| robotId | 로봇 ID |
+| axis | 관절 축 |
+| frequencyHz | 진동 주파수 |
+| amplitude | 진폭 |
+| vibrationRms | RMS 진동 |
+| vibrationPeak | Peak 진동 |
+| vibrationScore | 진동 위험 점수 |
+
+데이터 출처
+
+```
+Robot Arm Vibration Dataset
+```
+
+---
+
+## sensor.thermal
+
+열화상 정보
+
+| 컬럼 | 설명 |
+| --- | --- |
+| thermalScore | 열화상 점수 |
+| avgTemperature | 평균 온도 |
+| maxTemperature | 최고 온도 |
+| minTemperature | 최저 온도 |
+
+데이터 출처
+
+```
+Machine Vision Dataset
+```
+
+---
+
+# manufacturing
+
+제조 운영 정보입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| processSequence | 공정 순서 |
+| previousProcessCode | 이전 공정 |
+| nextProcessCode | 다음 공정 |
+| targetQuantity | 목표 생산 수량 |
+| completedQuantity | 완료 생산 수량 |
+
+예시
+
+```
+PRESS
+ ↓
+BODY
+ ↓
+PAINT
+ ↓
+ASSEMBLY
+```
+
+공정 순서 매핑
+
+| processCode | processSequence |
+| --- | --- |
+| PRESS | 1 |
+| BODY | 2 |
+| PAINT | 3 |
+| ASSEMBLY | 4 |
+
+---
+
+# processMetrics
+
+공정 성능 및 병목 판단용 지표입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| cycleTimeSec | 생산 주기 |
+| waitingTimeSec | 대기 시간 |
+| processingTimeSec | 실제 작업 시간 |
+| stationDelaySec | 지연 시간 |
+| throughputPerMin | 분당 생산량 |
+| queueLength | 대기열 수 |
+| wipCount | 재공품 수 |
+| equipmentIdleTimeSec | 설비 유휴 시간 |
+
+예시
+
+```
+제품 투입
+ ↓
+8초 대기
+ ↓
+34초 작업
+ ↓
+42초 사이클 완료
+```
+
+---
+
+# sourceTrace
+
+원본 데이터 추적용 정보입니다.
+
+| 컬럼 | 설명 |
+| --- | --- |
+| fordRowId | Ford 원본 행 번호 |
+| formingRowId | 소성가공 원본 행 번호 |
+| robotArmVibrationRowId | 로봇팔 진동 원본 행 번호 |
+| machineVisionRowId | 머신비전 원본 행 번호 |
+| boschId | Bosch 원본 ID |
+
+---
+
+# 최종 데이터셋 매핑
+
+```
+Ford Dataset
+↓
+sensor.vibration
+
+소성가공 데이터
+↓
+sensor.current
+sensor.vibration
+
+Robot Arm Vibration Dataset
+↓
+sensor.robotArmVibration
+
+Machine Vision Dataset
+↓
+sensor.thermal
+
+Bosch Dataset
+↓
+manufacturing
+processMetrics
+event.eventTime
+location
+equipment
+```
+
+즉, 최종 원천 관제 이벤트는 데이터셋 기반 구조가 아니라 아래 기준으로 통합됩니다.
+
+```
+이벤트
+↓
+위치
+↓
+설비
+↓
+설비상태
+↓
+제품
+↓
+센서
+ ├─ 전류
+ ├─ 일반 진동
+ ├─ 로봇팔 진동
+ └─ 열화상
+↓
+제조 운영 정보
+↓
+공정 성능 지표
+↓
+원본 추적 정보
+```
+
+---
+
+## 분석 결과 저장 기준
+
+분석 결과와 이상 상태는 위 원천 이벤트를 백엔드/AI가 처리한 뒤 별도로 저장합니다.
+
+| 결과 필드 | 입력 데이터 | 저장 위치 |
+| --- | --- | --- |
+| equipmentStatus.healthStatus | 전류, 진동, 설비 상태 | Redis / main_db |
+| qualityStatus | 열화상 데이터 | Redis / main_db |
+| productionStatus | 공정 지표 | Redis / main_db |
+| pressStopRisk | 전류, 유휴시간 | main_db.analysis_result |
+| robotCollisionRisk | robotArmVibration | main_db.analysis_result |
+| paintQualityRisk | thermal | main_db.analysis_result |
+| assemblySequenceRisk | processSequence | main_db.analysis_result |
+| bottleneckRisk | processMetrics | main_db.analysis_result |
+| defectTransferRisk | 센서 + 공정 데이터 | main_db.analysis_result |
+| overallRiskScore | 전체 위험도 계산 결과 | main_db.analysis_result |
+| riskLevel | LOW/WARNING/CRITICAL | Redis / ES / main_db |
+
+정리하면 `att1`, `L0_S0_D1`, `RMS[A]`, `t1~t80` 같은 원본 컬럼은 저장하지 않고, 관제에 필요한 의미 있는 데이터로 변환하여 하나의 제조 이벤트 JSON으로 저장하는 구조입니다.
+
+---
+
+| 상태 | 의미 | 설비 운영 여부 |
+| --- | --- | --- |
+| NORMAL | 정상 가동 중 | 운영 중 |
+| WARNING | 경고 상태 (이상 징후 발생) | 운영 중 |
+| FAULT | 고장 상태 | 운영 불가 |
+| MAINTENANCE | 점검/정비 중 | 운영 중지 |
+
+# 이벤트 흐름 정리
+
+```jsx
+샘플 DB
+  ↓
+Scheduler가 JSON 1건씩 읽음
+  ↓
+factory.manufacturing.raw 로 원천 이벤트 발행
+  ↓
+Manufacturing Service / AI Service가 raw 이벤트 소비
+  ↓
+Spring 코드로 위험도, 병목 여부, 설비 이상 여부 계산
+  ↓
+계산 결과를 담아서 factory.manufacturing.analysis 로 발행
+```
+
+# 1. 데이터 준비 단계
+
+## 목적
+
+실시간 센서가 없는 시연 환경에서 제조 이벤트를 재생하기 위해 원본 데이터셋을 통합 제조 이벤트 JSON으로 변환하여 Sample DB에 저장합니다.
+
+---
+
+## 데이터 흐름
+
+```
+Ford Dataset
+Bosch Dataset
+Robot Arm Vibration Dataset
+Machine Vision Dataset
+소성가공 데이터
+
+↓
+Python ETL
+↓
+공통 제조 이벤트 구조 매핑
+↓
+통합 JSON 생성
+↓
+sample_db 저장
+```
+
+---
+
+## 저장 테이블
+
+```
+manufacturing_event_json
+```
+
+역할
+
+```
+시연용 원천 이벤트 저장소
+
+Scheduler가 읽어가는 이벤트 저장
+
+Kafka 전송 대상 저장
+
+전송 여부 관리
+```
+
+---
+
+## Sample DB 구조
+
+```sql
+CREATE TABLE manufacturing_event_json (
+    id BIGINT AUTO_INCREMENT PRIMARY KEY,
+
+    event_id VARCHAR(100) NOT NULL,
+    event_time DATETIME NOT NULL,
+
+    car_master_id BIGINT NOT NULL,
+    equipment_id BIGINT NOT NULL,
+
+    process_code ENUM('PRESS', 'BODY', 'PAINT', 'ASSEMBLY') NOT NULL,
+    station_code VARCHAR(50),
+
+    equipment_code VARCHAR(50) NOT NULL,
+    equipment_type VARCHAR(50),
+    equipment_status VARCHAR(30),
+
+    event_type VARCHAR(50),
+
+    event_json JSON NOT NULL,
+
+    is_sent TINYINT(1) DEFAULT 0,
+    sent_at DATETIME NULL,
+
+    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+
+    INDEX idx_event_time_sent (event_time, is_sent),
+    INDEX idx_process_time (process_code, event_time),
+    INDEX idx_equipment_time (equipment_code, event_time),
+    INDEX idx_car_time (car_master_id, event_time),
+    INDEX idx_event_id (event_id),
+    INDEX idx_equipment_id_time (equipment_id, event_time),
+
+    CONSTRAINT fk_manufacturing_event_json_car_master
+        FOREIGN KEY (car_master_id)
+        REFERENCES car_master(id),
+
+    CONSTRAINT fk_manufacturing_event_json_equipment
+        FOREIGN KEY (equipment_id)
+        REFERENCES equipment(id)
+);
+```
+
+---
+
+# 2. Scheduler 구조
+
+## 목적
+
+Sample DB에 저장된 제조 이벤트를 실제 공장에서 발생하는 것처럼 순차적으로 Kafka에 전송합니다.
+
+---
+
+## 처리 흐름
+
+```
+Scheduler 실행
+
+↓
+
+is_sent = false 조회
+
+↓
+
+event_time 기준 정렬
+
+↓
+
+N건 조회
+
+↓
+
+Kafka Producer 호출
+
+↓
+
+전송 성공
+
+↓
+
+is_sent = true
+
+↓
+
+sent_at 저장
+```
+
+---
+
+## 예시
+
+### 시연 환경
+
+```
+1초마다 5건 전송
+```
+
+### 부하 테스트
+
+```
+1초마다 100건 전송
+```
+
+### 대량 데이터 재생
+
+```
+1초마다 1000건 전송
+```
+
+---
+
+# 3. Kafka 구조
+
+## Kafka 사용 목적
+
+Kafka는 제조 이벤트를 서비스 간 전달하는 Event Backbone 역할을 수행합니다.
+
+### Kafka 적용
+
+```
+sampleDB.manufacturing_event_json
+↓
+Scheduler Producer
+↓
+factory.manufacturing.raw
+↓
+Manufacturing Consumer / AI Consumer 
+↓
+분석 결과 생성
+↓
+factory.manufacturing.analysis
+↓
+mainDB / Redis / Elasticsearch 저장
+↓
+위험 이벤트만 분리
+↓
+factory.manufacturing.alert
+↓
+WebSocket / 알림 화면 / 알림 이력 저장
+
+----------설비 상태 및 가동률 계산 흐름---------------------------
+
+factory.manufacturing.raw
+↓
+Equipment Consumer
+↓
+설비 상태 / 가동 시간 / 정지 시간 / 유휴 시간 계산
+↓
+factory.manufacturing.equipment
+↓
+Redis / mainDB / Dashboard
+```
+
+하나의 서비스가 장애가 발생해도 다른 서비스는 정상 동작
+
+---
+
+### 비동기 처리
+
+```
+설비 이벤트 발생
+↓
+Kafka 저장
+↓
+각 서비스가 필요 시 처리
+```
+
+---
+
+### Scale-Out 가능
+
+```
+AI Service 1대
+↓
+AI Service 3대
+↓
+AI Service 10대
+```
+
+Kafka Partition 기반 병렬 처리 가능
+
+---
+
+# 4. Kafka Topic 설계
+
+## Topic 구성
+
+```
+factory.manufacturing.raw
+
+factory.manufacturing.analysis
+
+factory.manufacturing.alert
+
+factory.manufacturing.equipment
+```
+
+---
+
+## factory.manufacturing.raw
+
+### 역할
+
+원천 제조 이벤트 저장
+
+### Producer
+
+```
+Scheduler
+```
+
+### Consumer
+
+```
+Manufacturing Service
+AI Service
+Equipment Service
+Redis Consumer
+Elasticsearch Consumer
+```
+
+### Message Key
+
+```java
+factory.manufacturing.raw
+
+Message Key
+- equipment.equipmentCode
+
+// Raw Event
+kafkaTemplate.send(
+    "factory.manufacturing.raw",
+    event.getEquipment().getEquipmentCode(),
+    eventJson
+);
+```
+
+---
+
+예시 메시지
+
+```json
+{
+  "event": {
+    "eventId": "EVT-20260616-000001",
+    "eventTime": "2026-06-16T10:00:01",
+    "eventCategory": "MANUFACTURING",
+    "eventType": "PROCESS_STATUS",
+    "eventName": "프레스 공정 통합 관제 이벤트"
+  },
+  "location": {
+    "factoryCode": "AIMS_FACTORY_01",
+    "lineCode": "PRESS_LINE_01",
+    "processCode": "PRESS",
+    "stationCode": "PRESS_STATION_01"
+  },
+  "equipment": {
+    "equipmentCode": "EQ_PRESS_01",
+    "equipmentName": "프레스 유압모터 1호",
+    "equipmentType": "HYDRAULIC_PRESS"
+  },
+  "equipmentStatus": {
+    "operationStatus": "RUNNING",
+    "lastNormalTime": "2026-06-16T09:59:30",
+    "statusChangedTime": "2026-06-16T10:00:01"
+  },
+  "product": {
+    "carId": "CAR-000001",
+    "productId": "PRODUCT-000001",
+    "itemNo": "76211-A3010-100",
+    "quantity": 5,
+    "productionCount": 18133963
+  },
+  "sensor": {
+    "sensorType": "MULTI_SENSOR",
+    "current": {
+      "rmsAmpere": 1.97,
+      "maxAmpere": 2.15,
+      "minAmpere": 1.72
+    },
+    "vibration": {
+      "accelerationG": 0.0075,
+      "vibrationScore": 0.21,
+      "vibrationRms": 0.18,
+      "vibrationPeak": 0.31
+    },
+    "robotArmVibration": {
+      "robotId": "ROBOT_ARM_01",
+      "axis": "J1",
+      "frequencyHz": 40.62,
+      "amplitude": 0.00056,
+      "vibrationRms": 0.00063,
+      "vibrationPeak": 0.00119,
+      "vibrationScore": 0.27
+    },
+    "thermal": {
+      "thermalScore": 43.2,
+      "avgTemperature": 43.2,
+      "maxTemperature": 52.9,
+      "minTemperature": 42.1
+    }
+  },
+  "manufacturing": {
+    "processSequence": 1,
+    "previousProcessCode": null,
+    "nextProcessCode": "BODY",
+    "targetQuantity": 200,
+    "completedQuantity": 145
+  },
+  "processMetrics": {
+    "cycleTimeSec": 42.5,
+    "waitingTimeSec": 8.3,
+    "processingTimeSec": 34.2,
+    "stationDelaySec": 5.7,
+    "throughputPerMin": 1.4,
+    "queueLength": 7,
+    "wipCount": 24,
+    "equipmentIdleTimeSec": 12.1
+  },
+  "processData": {
+    "press": {
+      "countIncreaseYn": true,
+      "targetCycleTimeSec": 40.0,
+      "timestampDelaySec": 5.7
+    }
+  },
+  "sourceTrace": {
+    "fordRowId": 1,
+    "formingRowId": 1,
+    "robotArmVibrationRowId": 1,
+    "machineVisionRowId": 1,
+    "boschId": 1
+  }
+}
+```
+
+### 공정 별(프레스/차체/도장/의장) 분기
+
+현재 프로젝트 규모에서는 1개 Raw Topic으로 받고, `processCode`로 분기하는 방식이 더 적절합니다.
+
+```
+factory.manufacturing.raw
+```
+
+메시지 내부에서 분기합니다.
+
+```
+processCode = PRESS     → 프레스 로직
+processCode = BODY      → 차체 로직
+processCode = PAINT     → 도장 로직
+processCode = ASSEMBLY  → 의장 로직
+```
+
+다만 공정별 트래픽, 처리 속도, Consumer가 명확히 달라지면 Topic을 나누는 것이 맞습니다.
+
+```
+factory.manufacturing.press.raw
+factory.manufacturing.body.raw
+factory.manufacturing.paint.raw
+factory.manufacturing.assembly.raw
+```
+
+추천 기준은 아래와 같습니다.
+
+```
+초기/MVP/시연용
+→ factory.manufacturing.raw 1개 추천
+
+운영 확장/공정별 처리량 차이 큼
+→ 공정별 Topic 분리 추천
+
+공정별 Consumer가 완전히 다름
+→ 공정별 Topic 분리 추천
+
+프레스는 초당 1000건, 도장은 초당 10건처럼 차이가 큼
+→ 공정별 Topic 분리 추천
+
+공정별 장애가 다른 공정에 영향 주면 안 됨
+→ 공정별 Topic 분리 추천
+```
+
+현재 AIMS 구조에서는 이렇게 가는 게 가장 깔끔합니다.
+
+```
+factory.manufacturing.raw
+```
+
+그리고 Consumer 내부에서 분기합니다.
+
+```java
+switch (processCode) {
+	case"PRESS" -> pressService.analyze(event);
+	case"BODY" -> bodyService.analyze(event);
+	case"PAINT" -> paintService.analyze(event);
+	case"ASSEMBLY" -> assemblyService.analyze(event);
+}
+```
+
+---
+
+## factory.manufacturing.analysis
+
+### 역할
+
+AI 분석, 이상 탐지 분석 결과 저장
+
+### Producer
+
+```
+AI Service
+Manufacturing Service
+```
+
+### Consumer
+
+```
+Equipment Service
+Redis
+main_db
+Elasticsearch
+Alert Service
+```
+
+---
+
+### Message Key
+
+```java
+factory.manufacturing.analysis
+
+Message Key
+- 기본: equipment.equipmentCode
+- analysisType = DEFECT_TRANSFER_PREDICTION 인 경우: carId
+
+// 설비/공정 분석 결과
+kafkaTemplate.send(
+    "factory.manufacturing.analysis",
+    analysis.getEquipmentCode(),
+    analysisJson
+);
+```
+
+---
+
+예시
+
+```json
+{
+  "analysisId": "ANL-20260616-000001",
+  "eventId": "EVT-20260616-000001",
+  "eventTime": "2026-06-16T10:00:01",
+  "analyzedAt": "2026-06-16T10:00:03",
+  "factoryCode": "AIMS_FACTORY_01",
+  "lineCode": "PRESS_LINE_01",
+  "processCode": "PRESS",
+  "stationCode": "PRESS_STATION_01",
+  "equipmentCode": "EQ_PRESS_01",
+  "equipmentType": "HYDRAULIC_PRESS",
+  "productId": "PRODUCT-000001",
+  "carId": "CAR-000001",
+  "analysisType": "PROCESS_RISK_ANALYSIS",
+  "riskScores": {
+    "overallRiskScore": 74.5,
+    "bottleneckRisk": 72.1,
+    "defectTransferRisk": 65.3,
+    "equipmentRisk": 58.4,
+    "processRisk" : {
+      "pressStopRisk" : 64.2
+      }
+  },
+  "riskLevel": "WARNING",
+  "analysisResult": {
+    "isAbnormal": true,
+    "isBottleneck": true,
+    "isQualityDefect": false,
+    "isEquipmentFault": false,
+    "isSequenceError": false
+  },
+  "reason": {
+    "mainReason": "cycleTimeSec가 targetCycleTimeSec보다 증가했습니다.",
+    "detailReasons": [
+      "기준 사이클타임 40.0초 대비 실제 사이클타임 42.5초",
+      "stationDelaySec 5.7초 발생",
+      "queueLength 7로 대기열 증가"
+    ]
+  },
+  "recommendation": {
+    "actionType": "CHECK_EQUIPMENT_AND_QUEUE",
+    "message": "프레스 설비 상태와 대기열 증가 원인을 확인하세요."
+  }
+}
+```
+
+### 분석결과 저장
+
+```jsx
+### 1. Main DB
+
+분석 결과 및 이력성 데이터를 영속 저장한다.
+
+- 병목 분석 결과
+- 불량 전이 예측 결과
+- 설비 이상 분석 결과
+- 도장 품질 이상 분석 결과
+- 의장 조립 순서 오류 결과
+
+---
+
+### 2. Redis
+
+실시간 상태 조회 및 대시보드 표시를 위한 최신 데이터를 저장한다.
+
+- 현재 공정 상태
+- 현재 설비 상태
+- 현재 위험도
+- 대시보드 카드용 최신 값
+
+---
+
+### 3. Elasticsearch
+
+검색, 이력 조회, 이벤트 집계를 위한 데이터를 저장한다.
+
+- 이벤트 검색
+- 알림 이력 검색
+- 설비별 이상 이력 검색
+- 시간대별 위험 이벤트 집계
+```
+
+---
+
+## factory.manufacturing.alert
+
+### 역할
+
+긴급 알림 이벤트 전용
+
+→ `factory.manufacturing.analysis`의 분석 결과 중 WARNING 또는 CRITICAL 상태만 선별하여 발행한다.
+
+---
+
+### Producer
+
+```jsx
+Alert Service 
+Manufacturing Service
+```
+
+### Consumer
+
+```jsx
+WebSocket Service 
+mainDB Consumer 
+Elasticsearch Consumer
+```
+
+### 발행 조건
+
+```
+overallRiskScore >= 80 
+riskLevel = CRITICAL 
+설비 고장 발생 
+품질 불량 발생 
+병목 위험 발생 
+조립 순서 오류 발생 
+프레스 정지 위험 발생
+```
+
+---
+
+예시
+
+```json
+{
+  "alertId": "ALT-20260616-000001",
+  "eventId": "EVT-20260616-000001",
+  "analysisId": "ANL-20260616-000001",
+  "createdAt": "2026-06-16T10:00:04",
+  "factoryCode": "AIMS_FACTORY_01",
+  "lineCode": "PRESS_LINE_01",
+  "processCode": "PRESS",
+  "stationCode": "PRESS_STATION_01",
+  "equipmentCode": "EQ_PRESS_01",
+  "equipmentName": "프레스 유압모터 1호",
+  "alertType": "PRESS_STOP_RISK",
+  "alertTitle": "프레스 설비 정지 위험",
+  "alertMessage": "프레스 1호의 사이클타임과 지연 시간이 증가하여 정지 위험이 감지되었습니다.",
+  "riskLevel": "CRITICAL",
+  "riskScore": 86.7,
+  "alertStatus": "OPEN",
+  "needAction": true,
+  "reason": [
+    "cycleTimeSec가 기준값보다 증가",
+    "stationDelaySec 증가",
+    "equipmentIdleTimeSec 증가"
+  ],
+  "recommendedAction": "프레스 설비 상태, 대기열, 전류 RMS 값을 확인하세요."
+}
+```
+
+### alertStatus 값
+
+```jsx
+OPEN 처리 전 
+CHECKING 확인 중 
+RESOLVED 조치 완료 
+IGNORED 무시됨
+```
+
+### MessageKey
+
+```java
+factory.manufacturing.alert
+
+Message Key
+- equipment.equipmentCode
+
+// Alert Event
+kafkaTemplate.send(
+    "factory.manufacturing.alert",
+    alert.getEquipmentCode(),
+    alertJson
+);
+```
+
+---
+
+## factory.manufacturing.equipment
+
+### 역할
+
+장비 전용 이벤트
+
+→ 
+
+Raw Event 안에도 `equipmentStatus`가 있지만, 그것은 단일 이벤트 시점의 원천 상태이다.
+
+`factory.manufacturing.equipment`는 여러 Raw Event를 기반으로 계산한 설비 중심 상태 이벤트이다.
+
+---
+
+### Producer
+
+```jsx
+Equipment Service 
+Manufacturing Service
+```
+
+### Consumer
+
+```jsx
+Equipment Service
+Redis Consumer 
+mainDB Consumer 
+Elasticsearch Consumer 
+Alert Service
+```
+
+### Message Key
+
+```java
+factory.manufacturing.equipment
+
+Message Key
+- equipment.equipmentCode
+
+// Equipment Event
+kafkaTemplate.send(
+    "factory.manufacturing.equipment",
+    equipmentEvent.getEquipmentCode(),
+    equipmentJson
+);
+```
+
+### 예시메세지
+
+```json
+{
+  "equipmentEventId": "EQEVT-20260616-000001",
+  "eventTime": "2026-06-16T10:00:05",
+  "factoryCode": "AIMS_FACTORY_01",
+  "lineCode": "PRESS_LINE_01",
+  "processCode": "PRESS",
+  "stationCode": "PRESS_STATION_01",
+  "equipmentCode": "EQ_PRESS_01",
+  "equipmentName": "프레스 유압모터 1호",
+  "equipmentType": "HYDRAULIC_PRESS",
+  "operationStatus": "RUNNING",
+  "healthStatus": "WARNING",
+  "riskLevel": "WARNING",
+  "overallRiskScore": 72.4,
+  "operationMetrics": {
+    "windowStartTime": "2026-06-16T09:00:00",
+    "windowEndTime": "2026-06-16T10:00:00",
+    "plannedTimeSec": 3600,
+    "runningTimeSec": 2880,
+    "idleTimeSec": 420,
+    "stopTimeSec": 240,
+    "maintenanceTimeSec": 60,
+    "operationRate": 80.0,
+    "availabilityRate": 86.7
+  },
+  "productionMetrics": {
+    "targetQuantity": 200,
+    "completedQuantity": 145,
+    "throughputPerMin": 1.4,
+    "cycleTimeSec": 42.5
+  },
+  "sensorSummary": {
+    "currentRmsAmpere": 1.97,
+    "vibrationScore": 0.21,
+    "thermalScore": 43.2
+  },
+  "statusReason": {
+    "mainReason": "지연 시간 증가로 설비 위험도가 WARNING 상태입니다.",
+    "detailReasons": [
+      "stationDelaySec 5.7초",
+      "equipmentIdleTimeSec 12.1초",
+      "cycleTimeSec 42.5초"
+    ]
+  },
+  "lastRawEventId": "EVT-20260616-000001",
+  "updatedAt": "2026-06-16T10:00:05"
+}
+```
+
+### 가동률 계산 기준
+
+가동률은 설비가 계획된 시간 중 실제 RUNNING 상태였던 비율이다.
+
+```
+operationRate = runningTimeSec / plannedTimeSec * 100
+```
+
+### 상태값 기준
+
+```
+operationStatus
+- RUNNING
+- IDLE
+- STOPPED
+- ERROR
+- MAINTENANCE
+
+healthStatus
+- NORMAL
+- WARNING
+- FAULT
+- MAINTENANCE
+
+riskLevel
+- LOW
+- WARNING
+- CRITICAL
+```
+
+---
+
+# 5. Kafka Partition 전략
+
+## 5.1. 기본 기준
+
+현재 AIMS 프로젝트에서는 기본 Message Key를 아래로 설정합니다.
+
+```
+message key = equipmentCode
+```
+
+이유는 같은 설비의 이벤트 순서를 보장해야 하기 때문입니다.
+
+```
+EQ_PRESS_01 RUNNING
+↓
+EQ_PRESS_01 WARNING
+↓
+EQ_PRESS_01 FAULT
+↓
+EQ_PRESS_01 RECOVERY
+```
+
+같은 `equipmentCode`를 Key로 보내면 같은 설비 이벤트는 항상 같은 Partition에 저장됩니다.
+
+---
+
+# 5.2. Topic별 Partition 수
+
+```
+factory.manufacturing.raw        → 4개
+factory.manufacturing.analysis   → 4개
+factory.manufacturing.alert      → 2개
+factory.manufacturing.equipment  → 4개
+```
+
+---
+
+# 5.3. Topic별 Key 전략
+
+| Topic | Partition 수 | Message Key | 이유 |
+| --- | --- | --- | --- |
+| `factory.manufacturing.raw` | 4 | `equipmentCode` | 설비별 원천 이벤트 순서 보장 |
+| `factory.manufacturing.analysis` | 4 | `equipmentCode` 기본, 불량 전이는 `carId` 가능 | 분석 결과 순서 보장 |
+| `factory.manufacturing.alert` | 2 | `equipmentCode` | 같은 설비 알림 순서 보장 |
+| `factory.manufacturing.equipment` | 4 | `equipmentCode` | 장비 카드 상태 갱신 순서 보장 |
+
+---
+
+# 5.4. Consumer Group 기준
+
+## Manufacturing Consumer Group
+
+```
+factory.manufacturing.raw
+partitions = 4
+key = equipmentCode
+
+↓
+Manufacturing Consumer Group
+├─ Press Consumer
+├─ Body Consumer
+├─ Paint Consumer
+└─ Assembly Consumer
+```
+
+처리 기준은 Partition이 아니라 `processCode`입니다.
+
+```
+processCode = PRESS     → Press Consumer
+processCode = BODY      → Body Consumer
+processCode = PAINT     → Paint Consumer
+processCode = ASSEMBLY  → Assembly Consumer
+```
+
+---
+
+## AI Consumer Group
+
+```
+factory.manufacturing.raw
+partitions = 4
+key = equipmentCode
+```
+
+### Bottleneck Consumer
+
+```
+key = equipmentCode
+```
+
+설비/공정별 병목 흐름을 분석합니다.
+
+### Defect Transfer Consumer
+
+```
+분석 기준 = carId
+```
+
+현재는 Raw Topic Key를 `equipmentCode`로 유지하되, 분석 결과 발행 시에는 `carId`를 Key로 사용할 수 있습니다.
+
+```
+factory.manufacturing.analysis
+key = carId
+```
+
+---
+
+## Equipment Consumer Group
+
+```
+factory.manufacturing.analysis
+partitions = 4
+key = equipmentCode
+
+↓
+Equipment Consumer Group
+
+↓
+factory.manufacturing.equipment
+partitions = 4
+key = equipmentCode
+```
+
+장비별 최신 상태와 화면 카드 데이터를 생성합니다.
+
+---
+
+## Alert Consumer Group
+
+```
+factory.manufacturing.alert
+partitions = 2
+key = equipmentCode
+
+↓
+Alert Consumer Group
+```
+
+알림은 데이터량이 많지 않으므로 2개 Partition이면 충분합니다.
+
+---
+
+# 5.5. 최종 추천 구조
+
+```
+Scheduler
+↓
+factory.manufacturing.raw
+partitions = 4
+key = equipmentCode
+
+↓
+Manufacturing Consumer Group
+AI Consumer Group
+
+↓
+factory.manufacturing.analysis
+partitions = 4
+key = equipmentCode
+※ defectTransfer 결과는 key = carId 가능
+
+↓
+Equipment Consumer Group
+↓
+factory.manufacturing.equipment
+partitions = 4
+key = equipmentCode
+↓
+WebSocket
+↓
+React Dashboard
+```
+
+알림 흐름은 별도입니다.
+
+```
+Manufacturing Consumer Group
+AI Consumer Group
+↓
+factory.manufacturing.alert
+partitions = 2
+key = equipmentCode
+↓
+Alert Consumer Group
+↓
+실시간 알림 패널
+```
+
+결론적으로 현재 프로젝트에서는 아래 전략이 가장 적절합니다.
+
+```
+Partition 수
+raw        4개
+analysis   4개
+equipment  4개
+alert      2개
+
+기본 Message Key
+equipmentCode
+
+예외
+불량 전이 예측 결과는 carId 사용 가능
+```
+
+---
+
+# 6. Consumer Group 구조
+
+## Kafka Raw Topic
+
+```
+factory.manufacturing.raw
+```
+
+원천 제조 이벤트를 수신하는 Topic입니다.
+
+```
+프레스 이벤트
+차체 이벤트
+도장 이벤트
+의장 이벤트
+```
+
+모두 동일한 Topic으로 수신합니다.
+
+---
+
+# 6.1 Manufacturing Consumer Group
+
+제조 공정 상태 분석 전용 Consumer Group입니다.
+
+```
+factory.manufacturing.raw
+↓
+Manufacturing Consumer Group
+├─ Press Consumer
+├─ Body Consumer
+├─ Paint Consumer
+└─ Assembly Consumer
+```
+
+---
+
+## Press Consumer
+
+### 처리 대상
+
+```
+processCode = PRESS
+```
+
+### 사용 데이터
+
+```
+sensor.current
+processMetrics
+processData.press
+equipmentStatus
+```
+
+### 처리 내용
+
+```
+프레스 설비 상태 분석
+
+전류 RMS 분석
+
+CNT 증가 여부 확인
+
+Cycle Time 분석
+
+설비 가동률 계산
+
+설비 정지 위험 탐지
+```
+
+### 결과 발행
+
+```
+factory.manufacturing.analysis
+factory.manufacturing.alert
+factory.manufacturing.dashboard
+```
+
+---
+
+## Body Consumer
+
+### 처리 대상
+
+```
+processCode = BODY
+```
+
+### 사용 데이터
+
+```
+sensor.robotArmVibration
+processData.body
+equipmentStatus
+```
+
+### 처리 내용
+
+```
+로봇 상태 분석
+
+주파수 대역 진동 분석
+
+로봇 이상 동작 탐지
+
+충돌 위험 탐지
+
+차체 공정 상태 계산
+```
+
+### 결과 발행
+
+```
+factory.manufacturing.analysis
+factory.manufacturing.alert
+factory.manufacturing.dashboard
+```
+
+---
+
+## Paint Consumer
+
+### 처리 대상
+
+```
+processCode = PAINT
+```
+
+### 사용 데이터
+
+```
+sensor.thermal
+processData.paint
+equipmentStatus
+```
+
+### 처리 내용
+
+```
+도장 품질 분석
+
+열화상 분석
+
+도장 두께 분석
+
+비전 불량 분석
+
+품질 위험도 계산
+```
+
+### 결과 발행
+
+```
+factory.manufacturing.analysis
+factory.manufacturing.alert
+factory.manufacturing.dashboard
+```
+
+---
+
+## Assembly Consumer
+
+### 처리 대상
+
+```
+processCode = ASSEMBLY
+```
+
+### 사용 데이터
+
+```
+processData.assembly
+manufacturing
+equipmentStatus
+```
+
+### 처리 내용
+
+```
+조립 순서 검증
+
+누락 부품 분석
+
+체결 오류 분석
+
+조립 품질 상태 계산
+```
+
+### 결과 발행
+
+```
+factory.manufacturing.analysis
+factory.manufacturing.alert
+factory.manufacturing.dashboard
+```
+
+---
+
+# 6.2 AI Consumer Group
+
+AI 모델 분석 전용 Consumer Group입니다.
+
+```
+factory.manufacturing.raw
+↓
+AI Consumer Group
+├─ Bottleneck Consumer
+└─ Defect Transfer Consumer
+```
+
+---
+
+## Bottleneck Consumer
+
+### 사용 데이터
+
+```
+processMetrics
+
+cycleTimeSec
+waitingTimeSec
+queueLength
+wipCount
+equipmentIdleTimeSec
+```
+
+### 처리 내용
+
+```
+병목 탐지 모델 실행
+
+공정 위험도 계산
+
+영향 차량 수 계산
+
+병목 순위 계산
+```
+
+### 결과
+
+```
+bottleneckRisk
+```
+
+---
+
+## Defect Transfer Consumer
+
+### 사용 데이터
+
+```
+sensor.current
+
+sensor.vibration
+
+sensor.robotArmVibration
+
+sensor.thermal
+
+processMetrics
+```
+
+### 처리 내용
+
+```
+불량 전이 예측 모델 실행
+
+SHAP 원인 분석
+
+예상 불량 공정 계산
+
+예상 발생 시점 계산
+```
+
+### 결과
+
+```
+defectTransferRisk
+
+overallRiskScore
+
+riskLevel
+```
+
+---
+
+# 6.3 Equipment Consumer Group
+
+장비 표시 데이터 생성 전용 Consumer Group입니다.
+
+```
+factory.manufacturing.analysis
+↓
+Equipment Consumer Group
+```
+
+### 처리 내용
+
+```
+화면 카드 데이터 생성
+
+실시간 병목 분석 생성
+
+불량 전이 예측 생성
+
+AI 원인 분석 생성
+
+WebSocket Push
+```
+
+### 결과 발행
+
+```
+factory.manufacturing.equipment
+```
+
+---
+
+# 6.4 Alert Consumer Group
+
+실시간 알림 전용 Consumer Group입니다.
+
+```
+factory.manufacturing.alert
+↓
+Alert Consumer Group
+```
+
+### 처리 내용
+
+```
+경고 알림 생성
+
+위험 알림 생성
+
+Toast 알림 생성
+
+WebSocket Push
+```
+
+---
+
+# 최종 구조
+
+```
+factory.manufacturing.raw
+│
+├─ Manufacturing Consumer Group
+│   ├─ Press Consumer
+│   ├─ Body Consumer
+│   ├─ Paint Consumer
+│   └─ Assembly Consumer
+│
+├─ Equipment Consumer Group
+│
+└─ AI Consumer Group
+│   ├─ Bottleneck Consumer
+│   └─ Defect Transfer Consumer
+│
+├─ Alert Consumer Group
+                ↓
+
+factory.manufacturing.analysis
+                ↓
+
+Dashboard Consumer Group
+                ↓
+
+factory.manufacturing.equipment
+
+                ↓
+
+WebSocket
+                ↓
+
+React Dashboard
+
+                ↓
+
+factory.manufacturing.alert
+                ↓
+
+Alert Consumer Group
+                ↓
+
+실시간 알림 패널
+```
+
+---
+
+# 7. AI Service 상세 흐름
+
+```
+Kafka Raw Topic 수신
+
+↓ Feature 생성
+
+processMetrics
+
+sensor
+
+processData
+
+manufacturing
+
+↓
+병목 탐지 모델
+
+↓
+불량 전이 예측 모델
+
+↓
+위험도 계산
+
+↓
+Analysis Topic 발행
+
+↓
+main_db 저장
+```
+
+---
+
+## 병목 탐지
+
+입력
+
+```
+cycleTimeSec
+waitingTimeSec
+queueLength
+wipCount
+equipmentIdleTimeSec
+```
+
+출력
+
+```
+bottleneckRisk
+```
+
+---
+
+## 불량 전이 예측
+
+입력
+
+```
+sensor.current
+
+sensor.vibration
+
+sensor.robotArmVibration
+
+sensor.thermal
+
+processMetrics
+```
+
+출력
+
+```
+defectTransferRisk
+```
+
+---
+
+# 8. Redis 구조
+
+## 목적
+
+현재 상태 캐시
+
+---
+
+저장 예시
+
+```
+factory:latest
+
+factory:process:PRESS
+
+factory:process:BODY
+
+factory:equipment:EQ_PRESS_01
+
+factory:alert:latest
+
+factory:dashboard:summary
+```
+
+---
+
+예시 데이터
+
+```
+{
+  "processCode":"PRESS",
+  "equipmentCode":"EQ_PRESS_01",
+  "status":"WARNING",
+  "riskLevel":"WARNING",
+  "overallRiskScore":72.4
+}
+```
+
+---
+
+# 9. Elasticsearch 구조
+
+## 목적
+
+이벤트 검색
+
+이상 이력 조회
+
+대시보드 필터링
+
+통계 분석
+
+---
+
+인덱스 예시
+
+```
+aims-manufacturing-event-2026.06.16
+
+aims-analysis-result-2026.06.16
+
+aims-alert-event-2026.06.16
+```
+
+---
+
+저장 대상
+
+```
+전체 제조 이벤트
+
+전체 분석 결과
+
+전체 알림 이력
+
+설비 장애 이력
+
+공정 이상 이력
+
+품질 이상 이력
+```
+
+---
+
+# 10. StreamSets 역할
+
+## Kafka → Elasticsearch
+
+```
+Kafka
+ ↓
+StreamSets
+ ↓
+Elasticsearch
+```
+
+---
+
+## Kafka → main_db
+
+```
+Kafka
+ ↓
+StreamSets
+ ↓
+MySQL
+```
+
+---
+
+## 데이터 검증
+
+```
+필수 필드 존재 여부
+
+eventTime 포맷 검증
+
+JSON 구조 검증
+
+Null 검증
+```
+
+---
+
+# 최종 데이터 흐름
+
+```
+[1] 원본 데이터셋
+
+Ford
+Bosch
+Robot Arm Vibration
+Machine Vision
+소성가공
+
+↓ ETL
+
+[2] 통합 제조 이벤트 JSON 생성
+
+↓ 저장
+
+[3] sample_db.manufacturing_event_json
+
+↓ 조회
+
+[4] Scheduler
+
+↓ 발행
+
+[5] Kafka Producer
+
+Topic
+ └─ factory.manufacturing.raw
+
+↓ 수신
+
+[6] Consumer Group
+
+Manufacturing Service
+Alert Service
+AI Service
+Redis Consumer
+Elasticsearch Consumer
+
+↓ 분석
+
+병목 탐지
+불량 전이 예측
+설비 이상 탐지
+품질 이상 탐지
+
+↓ 발행
+
+factory.manufacturing.analysis
+
+factory.manufacturing.alert
+
+factory.manufacturing.equipment
+
+↓ 저장
+
+[7] Redis
+현재 상태 캐시
+
+[8] Elasticsearch
+이벤트 검색
+
+[9] main_db
+분석 결과 저장
+
+↓ Push
+
+[10] WebSocket
+
+↓ 표시
+
+React Dashboard
+```
+
+정리하면 Sample DB는 시연용 원천 이벤트 저장소, Scheduler는 이벤트 재생기, Kafka는 이벤트 전달 허브, Consumer Group은 공정별 분석 서비스, Redis는 실시간 상태 캐시, Elasticsearch는 검색 및 이력 저장소, main_db는 최종 분석 결과 저장소 역할을 수행합니다.
+
+# Elastic Search  사용
+
+ES는 Elasticsearch/OpenSearch를 말하며, “실시간 처리”보다는 이벤트 이력 검색/조회용으로 사용합니다.
+
+현재 구조에서는 이렇게 보면 됩니다.
+
+```
+Kafka = 실시간 이벤트 전달
+Redis = 최신 상태 캐시
+main_db = 분석 결과 정합성 저장
+ES = 많은 이벤트 로그를 빠르게 검색/필터링
+```
+
+예를 들면 ES는 이런 화면에서 사용합니다.
+
+```
+1. 이벤트 이력 조회
+- 오늘 발생한 전체 제조 이벤트
+- 최근 1시간 프레스 이상 이벤트
+- 특정 차량 CAR-000001의 전체 공정 이벤트
+
+2. 검색/필터
+- processCode = PRESS
+- equipmentCode = PRESS_01
+- riskLevel = CRITICAL
+- eventTime = 10:00 ~ 11:00
+
+3. 대시보드 로그 패널
+- 실시간 이벤트 로그 목록
+- 알림 발생 이력
+- 설비별 이상 이벤트 리스트
+
+4. 통계/집계
+- 공정별 이상 건수
+- 시간대별 알림 발생 수
+- 설비별 CRITICAL 이벤트 Top 10
+- 도장 공정 불량 이벤트 추이
+```
+
+즉, Kafka로 들어온 이벤트를 Consumer나 StreamSets가 ES에도 저장해두면, 나중에 대시보드에서 “검색 조건으로 빠르게 조회”할 수 있습니다.
+
+흐름은 이렇게 됩니다.
+
+```
+Scheduler
+↓
+Kafka Producer
+↓
+factory.manufacturing.raw
+↓
+Consumer 또는 StreamSets
+↓
+Elasticsearch 저장
+↓
+대시보드 이력 조회 API에서 검색
+```
+
+예시로 사용자가 대시보드에서 “프레스 1호의 최근 이상 이벤트”를 누르면:
+
+```
+React
+↓
+Backend API
+↓
+Elasticsearch 조회
+↓
+equipmentCode = PRESS_01
+status = ABNORMAL
+eventTime 최근순
+↓
+결과 반환
+```
+
+반대로 “현재 프레스 상태”만 보여줄 때는 ES가 아니라 Redis를 쓰는 게 맞습니다.
+
+```
+현재 상태 = Redis
+과거 이력 검색 = ES
+정확한 분석 결과 저장 = main_db
+```
+
+따라서 ES는 필수 실시간 처리 엔진이 아니라, 관제 시스템에서 로그 검색, 이벤트 이력 조회, 조건 필터링, 통계 집계를 빠르게 하기 위한 저장소입니다.
diff --git a/app/main.py b/app/main.py
index 1ce63f7..8c6a499 100644
--- a/app/main.py
+++ b/app/main.py
@@ -7,18 +7,38 @@
 from app.core.exceptions import register_exception_handlers
 from app.core.logging import configure_logging
 from app.repository.sampledb_repository import initialize_sampledb
+from app.service.manufacturing_event_scheduler import (
+    start_manufacturing_event_scheduler,
+    stop_manufacturing_event_scheduler,
+)
 
 
 logger = logging.getLogger(__name__)
 
+OPENAPI_TAGS = [
+    {
+        "name": "제조 관제 이벤트",
+        "description": (
+            "CSV 원천 데이터를 전처리/정제해 제조 관제 이벤트 JSON을 생성하고, "
+            "템플릿 replay 또는 실물 테이블 적재 방식으로 조회하는 API입니다."
+        ),
+    },
+]
+
 
 def create_app() -> FastAPI:
     configure_logging()
 
     app = FastAPI(
         title=settings.app_name,
+        description=(
+            "AIMS AI Service API 문서입니다.\n\n"
+            "제조 관제 이벤트 API는 프레스, 차체, 도장, 의장 공정 데이터를 기반으로 "
+            "원천 이벤트 JSON을 생성하고 조회합니다."
+        ),
         version=settings.app_version,
         debug=settings.debug,
+        openapi_tags=OPENAPI_TAGS,
     )
 
     app.include_router(api_router)
@@ -26,7 +46,7 @@ def create_app() -> FastAPI:
 
     @app.on_event("startup")
     def initialize_sampledb_schema() -> None:
-        """SAMPLE_DATABASE_URL이 설정된 경우 sampledb 엔티티를 생성한"""
+        """SAMPLE_DATABASE_URL이 설정된 경우 sampledb 엔티티를 생성한다."""
         if not settings.sample_database_connection_url:
             return
 
@@ -35,6 +55,14 @@ def initialize_sampledb_schema() -> None:
         except Exception:
             logger.exception("sampledb 스키마 초기화에 실패했습니다.")
 
+    @app.on_event("startup")
+    async def start_background_schedulers() -> None:
+        start_manufacturing_event_scheduler(app)
+
+    @app.on_event("shutdown")
+    async def stop_background_schedulers() -> None:
+        await stop_manufacturing_event_scheduler(app)
+
     return app
 
 
diff --git a/app/ml/preprocessing/manufacturing_event_json_builder.py b/app/ml/preprocessing/manufacturing_event_json_builder.py
new file mode 100644
index 0000000..af89c97
--- /dev/null
+++ b/app/ml/preprocessing/manufacturing_event_json_builder.py
@@ -0,0 +1,756 @@
+from __future__ import annotations
+
+import json
+import math
+import statistics
+from collections.abc import Iterator
+from dataclasses import dataclass
+from datetime import date, datetime, time, timedelta
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+
+PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
+LINE_STATION_COUNT = 4
+PROCESS_META: dict[str, dict[str, Any]] = {
+    "PRESS": {
+        "lineCode": "PRESS_LINE_01",
+        "stationCode": "PRESS_STATION_01",
+        "equipmentType": "HYDRAULIC_PRESS",
+        "eventType": "PROCESS_STATUS",
+        "eventName": "프레스 공정 통합 관제 이벤트",
+        "targetCycleTimeSec": 40.0,
+    },
+    "BODY": {
+        "lineCode": "BODY_LINE_01",
+        "stationCode": "BODY_STATION_01",
+        "equipmentType": "ROBOT_ARM",
+        "eventType": "EQUIPMENT_SENSOR",
+        "eventName": "차체 공정 로봇 관제 이벤트",
+        "targetCycleTimeSec": 52.0,
+    },
+    "PAINT": {
+        "lineCode": "PAINT_LINE_01",
+        "stationCode": "PAINT_STATION_01",
+        "equipmentType": "CAMERA",
+        "eventType": "QUALITY_CHECK",
+        "eventName": "도장 공정 품질 관제 이벤트",
+        "targetCycleTimeSec": 64.0,
+    },
+    "ASSEMBLY": {
+        "lineCode": "ASSEMBLY_LINE_01",
+        "stationCode": "ASSEMBLY_STATION_01",
+        "equipmentType": "CONVEYOR",
+        "eventType": "PROCESS_STATUS",
+        "eventName": "의장 공정 조립 관제 이벤트",
+        "targetCycleTimeSec": 58.0,
+    },
+}
+
+DATASET_ROOT = Path(__file__).resolve().parents[1] / "datasets" / "process"
+EVENT_DENSITY_WINDOWS: tuple[tuple[int, int, float], ...] = (
+    (0, 5, 0.35),
+    (5, 8, 0.8),
+    (8, 12, 1.6),
+    (12, 13, 0.65),
+    (13, 18, 1.8),
+    (18, 22, 1.0),
+    (22, 24, 0.45),
+)
+
+
+@dataclass(frozen=True)
+class EventBuildRequest:
+    start_date: date
+    end_date: date
+    events_per_day: int
+    car_id_map: dict[str, int]
+    equipment_map: dict[str, dict[str, Any]]
+
+
+class ManufacturingEventJsonBuilder:
+    """CSV 원천 데이터를 PRD의 통합 제조 이벤트 JSON으로 변환한다."""
+
+    def __init__(self, dataset_root: Path = DATASET_ROOT) -> None:
+        self.dataset_root = dataset_root
+        self._cache: dict[str, Any] = {}
+        self._feature_cache: dict[tuple[Any, ...], dict[str, Any]] = {}
+
+    def build_rows(self, request: EventBuildRequest) -> list[dict[str, Any]]:
+        return list(self.iter_rows(request))
+
+    def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
+        car_ids = sorted(request.car_id_map)
+        if not car_ids:
+            return
+
+        days = (request.end_date - request.start_date).days + 1
+        if days <= 0:
+            return
+
+        global_index = 0
+        for day_offset in range(days):
+            current_date = request.start_date + timedelta(days=day_offset)
+            for slot in range(request.events_per_day):
+                process_index = global_index % len(PROCESS_SEQUENCE)
+                production_sequence_index = global_index // len(PROCESS_SEQUENCE)
+                process_code = PROCESS_SEQUENCE[process_index]
+                event_time = _event_time_for_slot(
+                    current_date=current_date,
+                    slot=slot,
+                    events_per_day=request.events_per_day,
+                )
+                sequence_no = slot + 1
+                event_id = f"EVT-{current_date:%Y%m%d}-{sequence_no:06d}"
+                car_id = car_ids[production_sequence_index % len(car_ids)]
+                event = self._build_event(
+                    event_id=event_id,
+                    event_time=event_time,
+                    process_code=process_code,
+                    global_index=global_index,
+                    production_sequence_index=production_sequence_index,
+                    car_id=car_id,
+                    equipment_map=request.equipment_map,
+                )
+                equipment_code = event["equipment"]["equipmentCode"]
+                equipment_row = request.equipment_map[equipment_code]
+                yield {
+                    "event_id": event_id,
+                    "event_time": event_time,
+                    "car_master_id": request.car_id_map[car_id],
+                    "equipment_id": int(equipment_row["id"]),
+                    "process_code": process_code,
+                    "station_code": event["location"]["stationCode"],
+                    "equipment_code": equipment_code,
+                    "equipment_type": event["equipment"]["equipmentType"],
+                    "equipment_status": event["equipmentStatus"]["operationStatus"],
+                    "event_type": event["event"]["eventType"],
+                    "event_json": _json_safe(event),
+                }
+                global_index += 1
+
+    def _build_event(
+        self,
+        *,
+        event_id: str,
+        event_time: datetime,
+        process_code: str,
+        global_index: int,
+        production_sequence_index: int,
+        car_id: str,
+        equipment_map: dict[str, dict[str, Any]],
+    ) -> dict[str, Any]:
+        meta = PROCESS_META[process_code]
+        line_station_no = production_sequence_index % LINE_STATION_COUNT + 1
+        equipment_code = f"EQ_{process_code}_{line_station_no:03d}"
+        equipment_row = equipment_map[equipment_code]
+
+        forming = self._forming_row(production_sequence_index)
+        current = self._current_features(process_code, global_index)
+        ford = self._ford_features(global_index)
+        vision = self._vision_features(global_index)
+        bosch = self._bosch_features(global_index)
+        process_metrics = self._process_metrics(
+            process_code=process_code,
+            global_index=global_index,
+            current=current,
+            ford=ford,
+            vision=vision,
+            bosch=bosch,
+        )
+        is_abnormal = self._is_abnormal(process_code, ford, vision, bosch, current)
+        operation_status = self._operation_status(is_abnormal, process_metrics)
+
+        return {
+            "event": {
+                "eventId": event_id,
+                "eventTime": event_time.isoformat(),
+                "eventCategory": "MANUFACTURING",
+                "eventType": meta["eventType"],
+                "eventName": meta["eventName"],
+            },
+            "location": {
+                "factoryCode": "AIMS_FACTORY_01",
+                "lineCode": f"{process_code}_LINE_{line_station_no:02d}",
+                "processCode": process_code,
+                "stationCode": f"{process_code}_STATION_{line_station_no:02d}",
+            },
+            "equipment": {
+                "equipmentCode": equipment_code,
+                "equipmentName": equipment_row["equipment_name"],
+                "equipmentType": equipment_row["equipment_type"],
+            },
+            "equipmentStatus": {
+                "operationStatus": operation_status,
+                "lastNormalTime": (event_time - timedelta(seconds=31)).isoformat(),
+                "statusChangedTime": event_time.isoformat(),
+                "healthStatus": "WARNING" if is_abnormal else "NORMAL",
+            },
+            "product": {
+                "carId": car_id,
+                "productId": f"PRODUCT-{int(car_id.rsplit('-', 1)[1]):06d}",
+                "itemNo": forming["itemno"],
+                "quantity": forming["quantity"],
+                "productionCount": forming["cnt"],
+            },
+            "sensor": self._sensor_payload(current, ford, vision, process_code),
+            "manufacturing": self._manufacturing_payload(process_code, forming),
+            "processMetrics": process_metrics,
+            "sourceTrace": {
+                "fordRowId": ford["rowId"],
+                "formingRowId": forming["idx"],
+                "robotArmVibrationRowId": current["rowId"],
+                "machineVisionRowId": vision["rowId"],
+                "boschId": bosch["id"],
+            },
+            "processData": self._process_data(
+                process_code=process_code,
+                current=current,
+                ford=ford,
+                vision=vision,
+                bosch=bosch,
+                process_metrics=process_metrics,
+            ),
+        }
+
+    def _forming_row(self, index: int) -> dict[str, Any]:
+        df = self._load_forming()
+        row = df.iloc[index % len(df)]
+        return {
+            "idx": _safe_int(row.get("idx"), index + 1),
+            "itemno": _safe_str(row.get("itemno"), "76211-A3010-100"),
+            "quantity": _safe_int(row.get("quantity"), 1),
+            "cnt": _safe_int(row.get("cnt"), 18000000 + index),
+        }
+
+    def _current_features(self, process_code: str, index: int) -> dict[str, Any]:
+        if process_code == "BODY":
+            df = self._load_robot_current(index)
+            source_type = "ROBOT_CURRENT"
+            file_index = index % 2 + 1
+        else:
+            df = self._load_press_current(index)
+            source_type = "PRESS_CURRENT"
+            file_index = index % 4 + 1
+
+        row_index = index % len(df)
+        cache_key = ("current", process_code, file_index, row_index, index % 7)
+        if cache_key in self._feature_cache:
+            return self._feature_cache[cache_key]
+
+        row = df.iloc[row_index]
+        value_column = "RMS[A]" if "RMS[A]" in df.columns else "Acceleration[g]"
+        raw_value = _safe_float(row.get(value_column), 0.0)
+        if value_column == "Acceleration[g]":
+            rms_ampere = 1.65 + abs(raw_value) * 15
+            acceleration_g = raw_value
+        else:
+            rms_ampere = raw_value if raw_value > 0.01 else 1.75 + (index % 17) * 0.04
+            acceleration_g = abs(rms_ampere - 1.9) / 18
+
+        spread = 0.11 + (index % 7) * 0.012
+        result = {
+            "rowId": _safe_int(row.get("Unnamed: 0"), index + 1),
+            "sourceType": source_type,
+            "rmsAmpere": round(rms_ampere, 9),
+            "maxAmpere": round(rms_ampere + spread, 9),
+            "minAmpere": round(max(0.0, rms_ampere - spread), 9),
+            "accelerationG": round(acceleration_g, 9),
+        }
+        self._feature_cache[cache_key] = result
+        return result
+
+    def _ford_features(self, index: int) -> dict[str, Any]:
+        rows = self._load_ford_rows()
+        row_index = index % len(rows)
+        cache_key = ("ford", row_index)
+        if cache_key in self._feature_cache:
+            return self._feature_cache[cache_key]
+
+        values = rows[row_index]
+        label = int(values[0])
+        signal = [float(value) for value in values[1:501]]
+        abs_values = [abs(value) for value in signal]
+        rms = math.sqrt(sum(value * value for value in signal) / len(signal))
+        peak = max(abs_values)
+        bands = _frequency_bands(signal)
+        peak_band = max(bands, key=bands.get)
+        result = {
+            "rowId": row_index + 1,
+            "label": label,
+            "vibrationRms": round(rms, 9),
+            "vibrationPeak": round(peak, 9),
+            "vibrationScore": round(min(0.99, rms / 3.2), 6),
+            "frequencyBands": bands,
+            "frequencyPeakBand": peak_band.replace("freq_", "").upper(),
+        }
+        self._feature_cache[cache_key] = result
+        return result
+
+    def _vision_features(self, index: int) -> dict[str, Any]:
+        side = "left" if index % 2 == 0 else "right"
+        df, labels = self._load_vision(side)
+        row_index = index % len(df)
+        cache_key = ("vision", side, row_index, index % 17)
+        if cache_key in self._feature_cache:
+            return self._feature_cache[cache_key]
+
+        row = pd.to_numeric(df.iloc[row_index], errors="coerce").dropna().tolist()
+        label = int(float(labels[row_index % len(labels)]))
+        avg_temp = statistics.fmean(row)
+        max_temp = max(row)
+        min_temp = min(row)
+        std_temp = statistics.pstdev(row) if len(row) > 1 else 0.0
+        defect_score = min(0.99, (std_temp / 5.0) + (0.35 if label else 0.05))
+        result = {
+            "rowId": row_index + 1,
+            "imagePosition": "LEFT" if side == "left" else "RIGHT",
+            "label": label,
+            "avgTemperature": round(avg_temp, 3),
+            "maxTemperature": round(max_temp, 3),
+            "minTemperature": round(min_temp, 3),
+            "thermalStdTemp": round(std_temp, 3),
+            "defectScore": round(defect_score, 4),
+            "thicknessValue": round(112.0 + (index % 17) * 0.7 + std_temp, 3),
+            "surfaceQualityScore": round(max(0.0, 100.0 - defect_score * 32), 3),
+        }
+        self._feature_cache[cache_key] = result
+        return result
+
+    def _bosch_features(self, index: int) -> dict[str, Any]:
+        df = self._load_bosch_numeric()
+        row_index = index % len(df)
+        cache_key = ("bosch", row_index)
+        if cache_key in self._feature_cache:
+            return self._feature_cache[cache_key]
+
+        row = df.iloc[row_index]
+        numeric_values = pd.to_numeric(row.drop(labels=["Id", "Response"], errors="ignore"), errors="coerce")
+        numeric_values = numeric_values.dropna().tolist()
+        response = _safe_int(row.get("Response"), 0)
+        mean_value = statistics.fmean(numeric_values) if numeric_values else 0.0
+        result = {
+            "id": _safe_int(row.get("Id"), index + 1),
+            "response": response,
+            "meanNumericFeature": round(mean_value, 6),
+            "nonNullFeatureCount": len(numeric_values),
+        }
+        self._feature_cache[cache_key] = result
+        return result
+
+    def _process_metrics(
+        self,
+        *,
+        process_code: str,
+        global_index: int,
+        current: dict[str, Any],
+        ford: dict[str, Any],
+        vision: dict[str, Any],
+        bosch: dict[str, Any],
+    ) -> dict[str, Any]:
+        target = float(PROCESS_META[process_code]["targetCycleTimeSec"])
+        anomaly_weight = 0.0
+        if process_code in {"PRESS", "BODY"}:
+            anomaly_weight = ford["vibrationScore"] * 12
+        elif process_code == "PAINT":
+            anomaly_weight = vision["defectScore"] * 10
+        else:
+            anomaly_weight = bosch["response"] * 9 + (global_index % 4)
+
+        current_weight = min(6.0, abs(current["rmsAmpere"] - 1.9) * 1.5)
+        cycle_time = target + anomaly_weight + current_weight + (global_index % 5) * 0.3
+        processing_time = max(1.0, cycle_time - (5.0 + global_index % 6))
+        waiting_time = max(0.5, cycle_time - processing_time + (global_index % 3))
+        station_delay = max(0.0, cycle_time - target)
+        throughput = round(60 / cycle_time, 3)
+        return {
+            "cycleTimeSec": round(cycle_time, 3),
+            "waitingTimeSec": round(waiting_time, 3),
+            "processingTimeSec": round(processing_time, 3),
+            "stationDelaySec": round(station_delay, 3),
+            "throughputPerMin": throughput,
+            "queueLength": int(3 + station_delay // 2 + global_index % 4),
+            "wipCount": int(16 + station_delay // 1.5 + global_index % 9),
+            "equipmentIdleTimeSec": round(max(0.0, station_delay * 1.8), 3),
+        }
+
+    def _is_abnormal(
+        self,
+        process_code: str,
+        ford: dict[str, Any],
+        vision: dict[str, Any],
+        bosch: dict[str, Any],
+        current: dict[str, Any],
+    ) -> bool:
+        if process_code in {"PRESS", "BODY"}:
+            return ford["label"] < 0 or current["rmsAmpere"] > 2.8
+        if process_code == "PAINT":
+            return vision["label"] == 1
+        return bosch["response"] == 1
+
+    def _operation_status(
+        self,
+        is_abnormal: bool,
+        process_metrics: dict[str, Any],
+    ) -> str:
+        if process_metrics["equipmentIdleTimeSec"] >= 20:
+            return "IDLE"
+        if is_abnormal and process_metrics["stationDelaySec"] >= 10:
+            return "ERROR"
+        return "RUNNING"
+
+    def _sensor_payload(
+        self,
+        current: dict[str, Any],
+        ford: dict[str, Any],
+        vision: dict[str, Any],
+        process_code: str,
+    ) -> dict[str, Any]:
+        return {
+            "sensorType": "MULTI_SENSOR",
+            "current": {
+                "rmsAmpere": current["rmsAmpere"],
+                "maxAmpere": current["maxAmpere"],
+                "minAmpere": current["minAmpere"],
+            },
+            "vibration": {
+                "accelerationG": current["accelerationG"],
+                "vibrationScore": ford["vibrationScore"],
+                "vibrationRms": ford["vibrationRms"],
+                "vibrationPeak": ford["vibrationPeak"],
+            },
+            "robotArmVibration": {
+                "robotId": "ROBOT_ARM_01",
+                "axis": f"J{(current['rowId'] % 6) + 1}",
+                "frequencyHz": round(40.0 + ford["vibrationScore"] * 220, 3),
+                "amplitude": round(ford["vibrationRms"] / 1000, 9),
+                "vibrationRms": round(ford["vibrationRms"] / 900, 9),
+                "vibrationPeak": round(ford["vibrationPeak"] / 700, 9),
+                "vibrationScore": ford["vibrationScore"],
+            },
+            "thermal": {
+                "thermalScore": vision["avgTemperature"],
+                "avgTemperature": vision["avgTemperature"],
+                "maxTemperature": vision["maxTemperature"],
+                "minTemperature": vision["minTemperature"],
+            },
+            "primarySensorSource": _primary_sensor_source(process_code),
+        }
+
+    def _manufacturing_payload(
+        self,
+        process_code: str,
+        forming: dict[str, Any],
+    ) -> dict[str, Any]:
+        sequence_no = PROCESS_SEQUENCE.index(process_code) + 1
+        return {
+            "processSequence": sequence_no,
+            "previousProcessCode": PROCESS_SEQUENCE[sequence_no - 2] if sequence_no > 1 else None,
+            "nextProcessCode": PROCESS_SEQUENCE[sequence_no] if sequence_no < len(PROCESS_SEQUENCE) else None,
+            "targetQuantity": 200,
+            "completedQuantity": min(200, forming["quantity"] + 140),
+        }
+
+    def _process_data(
+        self,
+        *,
+        process_code: str,
+        current: dict[str, Any],
+        ford: dict[str, Any],
+        vision: dict[str, Any],
+        bosch: dict[str, Any],
+        process_metrics: dict[str, Any],
+    ) -> dict[str, Any]:
+        if process_code == "PRESS":
+            return {
+                "press": {
+                    "countIncreaseYn": process_metrics["equipmentIdleTimeSec"] < 18,
+                    "targetCycleTimeSec": PROCESS_META["PRESS"]["targetCycleTimeSec"],
+                    "timestampDelaySec": process_metrics["stationDelaySec"],
+                },
+            }
+        if process_code == "BODY":
+            return {
+                "body": {
+                    "robotMotionStatus": "WARNING" if ford["label"] < 0 else "NORMAL",
+                    "robotOperationMode": "AUTO",
+                    "frequencyPeakBand": ford["frequencyPeakBand"],
+                    "frequencyBands": ford["frequencyBands"],
+                },
+            }
+        if process_code == "PAINT":
+            label = "DEFECT" if vision["label"] else "NORMAL"
+            return {
+                "paint": {
+                    "imagePosition": vision["imagePosition"],
+                    "thermalStdTemp": vision["thermalStdTemp"],
+                    "thicknessValue": vision["thicknessValue"],
+                    "defectScore": vision["defectScore"],
+                    "visionLabel": label,
+                    "surfaceQualityScore": vision["surfaceQualityScore"],
+                },
+            }
+
+        has_sequence_error = bool(bosch["response"])
+        return {
+            "assembly": {
+                "expectedSequence": "A01>A02>A03>A04",
+                "actualSequence": "A01>A03>A02>A04" if has_sequence_error else "A01>A02>A03>A04",
+                "missingPartCount": 1 if has_sequence_error and current["rmsAmpere"] > 2.3 else 0,
+                "fasteningErrorCount": 1 if has_sequence_error else 0,
+                "sequenceErrorCount": 1 if has_sequence_error else 0,
+            },
+        }
+
+    def _load_forming(self) -> pd.DataFrame:
+        return self._cached_csv(
+            "forming",
+            self.dataset_root / "소성가공 자원최적화 AI 데이터셋" / "공정_데이터_2022년_8월.csv",
+            nrows=4096,
+        )
+
+    def _load_press_current(self, index: int) -> pd.DataFrame:
+        file_index = index % 4 + 1
+        return self._cached_csv(
+            f"press_current_{file_index}",
+            self.dataset_root
+            / "소성가공 자원최적화 AI 데이터셋"
+            / f"프레스_{file_index}호-유압모터_전류데이터.csv",
+            nrows=4096,
+        )
+
+    def _load_robot_current(self, index: int) -> pd.DataFrame:
+        file_index = index % 2 + 1
+        return self._cached_csv(
+            f"robot_current_{file_index}",
+            self.dataset_root
+            / "소성가공 자원최적화 AI 데이터셋"
+            / f"로봇_{file_index}호-전류_데이터.csv",
+            nrows=4096,
+        )
+
+    def _load_vision(self, side: str) -> tuple[pd.DataFrame, list[float]]:
+        cache_key = f"vision_{side}"
+        if cache_key not in self._cache:
+            base = self.dataset_root / "머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)"
+            df = pd.read_csv(base / f"2nd_process_{side}_data.csv", nrows=4096)
+            with (base / f"2nd_process_{side}_label.json").open(
+                encoding="utf-8",
+            ) as label_file:
+                labels = json.load(label_file)
+            self._cache[cache_key] = (df, labels)
+        return self._cache[cache_key]
+
+    def _load_bosch_numeric(self) -> pd.DataFrame:
+        return self._cached_csv(
+            "bosch_numeric",
+            self.dataset_root / "bosch-production-line-performance" / "train_numeric.csv",
+            nrows=4096,
+        )
+
+    def _load_ford_rows(self) -> list[list[float]]:
+        if "ford_train" not in self._cache:
+            base = self.dataset_root / "Ford 엔진 진동 데이터셋"
+            path = base / "FordA_TRAIN.txt"
+            rows: list[list[float]] = []
+            with path.open(encoding="utf-8", errors="ignore") as file:
+                for line in file:
+                    line = line.replace("\x00", " ").strip()
+                    if not line:
+                        continue
+                    values = [float(part) for part in line.split()]
+                    if len(values) >= 501:
+                        rows.append(values[:501])
+                    if len(rows) >= 4096:
+                        break
+            if not rows:
+                rows = self._load_ford_arff_rows(base / "FordA_TRAIN.arff")
+            self._cache["ford_train"] = rows
+        return self._cache["ford_train"]
+
+    def _load_ford_arff_rows(self, path: Path) -> list[list[float]]:
+        rows: list[list[float]] = []
+        in_data = False
+        with path.open(encoding="utf-8", errors="ignore") as file:
+            for line in file:
+                stripped = line.strip()
+                if not stripped or stripped.startswith("%"):
+                    continue
+                if stripped.lower() == "@data":
+                    in_data = True
+                    continue
+                if not in_data:
+                    continue
+                values = [float(part) for part in stripped.split(",")]
+                if len(values) >= 501:
+                    # ARFF stores class as the last value; TXT stores class first.
+                    rows.append([values[-1], *values[:500]])
+                if len(rows) >= 4096:
+                    break
+        return rows
+
+    def _cached_csv(self, cache_key: str, path: Path, *, nrows: int) -> pd.DataFrame:
+        if cache_key not in self._cache:
+            self._cache[cache_key] = pd.read_csv(path, nrows=nrows)
+        return self._cache[cache_key]
+
+
+def _frequency_bands(signal: list[float]) -> dict[str, float]:
+    band_names = [
+        "freq_0_100_hz",
+        "freq_101_200_hz",
+        "freq_201_300_hz",
+        "freq_301_400_hz",
+        "freq_401_500_hz",
+        "freq_501_600_hz",
+        "freq_601_700_hz",
+        "freq_701_800_hz",
+        "freq_801_900_hz",
+        "freq_901_1000_hz",
+        "freq_1001_1100_hz",
+        "freq_1101_1200_hz",
+        "freq_1201_1300_hz",
+        "freq_1301_1400_hz",
+        "freq_1401_1500_hz",
+        "freq_1501_1600_hz",
+    ]
+    chunk_size = max(1, len(signal) // len(band_names))
+    bands: dict[str, float] = {}
+    for index, band_name in enumerate(band_names):
+        chunk = signal[index * chunk_size : (index + 1) * chunk_size]
+        if not chunk:
+            bands[band_name] = 0.0
+            continue
+        rms = math.sqrt(sum(value * value for value in chunk) / len(chunk))
+        bands[band_name] = round(rms / 700, 9)
+    return bands
+
+
+def _event_time_for_slot(
+    *,
+    current_date: date,
+    slot: int,
+    events_per_day: int,
+) -> datetime:
+    day_start = datetime.combine(current_date, time.min)
+    offset_microseconds = _weighted_event_offset_us(slot, events_per_day)
+    return day_start + timedelta(microseconds=offset_microseconds)
+
+
+def _weighted_event_offset_us(slot: int, events_per_day: int) -> int:
+    windows = _weighted_event_windows(events_per_day)
+    remaining_slot = slot
+    for start_us, end_us, count in windows:
+        if remaining_slot >= count:
+            remaining_slot -= count
+            continue
+        span_us = end_us - start_us
+        if count <= 1:
+            return start_us + span_us // 2
+        base_offset = int(remaining_slot * span_us / count)
+        interval_us = max(1, span_us // count)
+        jitter_us = _deterministic_jitter_us(slot, interval_us)
+        return min(end_us - 1, max(start_us, start_us + base_offset + jitter_us))
+    return 24 * 60 * 60 * 1_000_000 - 1
+
+
+def _weighted_event_windows(events_per_day: int) -> list[tuple[int, int, int]]:
+    raw_weights = [
+        ((end_hour - start_hour) * density, start_hour, end_hour)
+        for start_hour, end_hour, density in EVENT_DENSITY_WINDOWS
+    ]
+    total_weight = sum(weight for weight, _, _ in raw_weights)
+    counts = [
+        max(1, int(events_per_day * weight / total_weight))
+        for weight, _, _ in raw_weights
+    ]
+    while sum(counts) > events_per_day:
+        largest_index = max(range(len(counts)), key=counts.__getitem__)
+        counts[largest_index] -= 1
+    while sum(counts) < events_per_day:
+        largest_fraction_index = _largest_fraction_window_index(
+            raw_weights=raw_weights,
+            counts=counts,
+            events_per_day=events_per_day,
+            total_weight=total_weight,
+        )
+        counts[largest_fraction_index] += 1
+
+    windows: list[tuple[int, int, int]] = []
+    for count, (_, start_hour, end_hour) in zip(counts, raw_weights):
+        windows.append(
+            (
+                start_hour * 60 * 60 * 1_000_000,
+                end_hour * 60 * 60 * 1_000_000,
+                count,
+            ),
+        )
+    return windows
+
+
+def _largest_fraction_window_index(
+    *,
+    raw_weights: list[tuple[float, int, int]],
+    counts: list[int],
+    events_per_day: int,
+    total_weight: float,
+) -> int:
+    fractions = [
+        events_per_day * weight / total_weight - count
+        for count, (weight, _, _) in zip(counts, raw_weights)
+    ]
+    return max(range(len(fractions)), key=fractions.__getitem__)
+
+
+def _deterministic_jitter_us(slot: int, interval_us: int) -> int:
+    jitter_window = max(1, interval_us // 5)
+    pseudo_random = (slot * 1103515245 + 12345) & 0x7FFFFFFF
+    return pseudo_random % (2 * jitter_window + 1) - jitter_window
+
+
+def _primary_sensor_source(process_code: str) -> str:
+    if process_code == "PRESS":
+        return "PRESS_CURRENT_AND_FORD_VIBRATION"
+    if process_code == "BODY":
+        return "ROBOT_CURRENT_AND_FORD_VIBRATION"
+    if process_code == "PAINT":
+        return "MACHINE_VISION_THERMAL"
+    return "BOSCH_PRODUCTION_LINE"
+
+
+def _safe_float(value: Any, default: float = 0.0) -> float:
+    try:
+        result = float(value)
+    except (TypeError, ValueError):
+        return default
+    if math.isnan(result) or math.isinf(result):
+        return default
+    return result
+
+
+def _safe_int(value: Any, default: int = 0) -> int:
+    try:
+        return int(float(value))
+    except (TypeError, ValueError):
+        return default
+
+
+def _safe_str(value: Any, default: str) -> str:
+    if value is None:
+        return default
+    text = str(value).strip()
+    return text if text and text.lower() != "nan" else default
+
+
+def _json_safe(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {key: _json_safe(item) for key, item in value.items()}
+    if isinstance(value, list):
+        return [_json_safe(item) for item in value]
+    if isinstance(value, float):
+        if math.isnan(value) or math.isinf(value):
+            return None
+        return value
+    return value
diff --git a/app/repository/sampledb_repository.py b/app/repository/sampledb_repository.py
index 2b12170..8f1febb 100644
--- a/app/repository/sampledb_repository.py
+++ b/app/repository/sampledb_repository.py
@@ -1,9 +1,17 @@
+from collections.abc import Iterable
+from datetime import date, datetime
 from typing import Any
 
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, func, inspect, select, text
 from sqlalchemy.dialects.mysql import insert
 
-from app.repository.sampledb_schema import equipment, metadata
+from app.repository.sampledb_schema import (
+    car_master,
+    equipment,
+    manufacturing_event_json,
+    manufacturing_event_template,
+    metadata,
+)
 from app.utils.database_utils import mysql_connect_args_for_seoul
 
 
@@ -11,25 +19,27 @@
     {
         "process_code": process_code,
         "equipment_code": f"EQ_{process_code}_{index:03d}",
-        "equipment_name": f"{process_code.title()} equipment {index}",
+        "equipment_name": f"{equipment_name_prefix} {index}호",
         "equipment_type": equipment_type,
-        "status": "NORMAL",
     }
-    for process_code, equipment_type in (
-        ("PRESS", "HYDRAULIC_PRESS"),
-        ("BODY", "ROBOT_ARM"),
-        ("PAINT", "CAMERA"),
-        ("ASSEMBLY", "CONVEYOR"),
+    for process_code, equipment_type, equipment_name_prefix in (
+        ("PRESS", "HYDRAULIC_PRESS", "프레스 유압모터"),
+        ("BODY", "ROBOT_ARM", "차체 용접 로봇"),
+        ("PAINT", "CAMERA", "도장 열화상 카메라"),
+        ("ASSEMBLY", "CONVEYOR", "의장 조립 컨베이어"),
     )
-    for index in range(1, 6)
+    for index in range(1, 5)
 ]
 
+DEFAULT_CAR_TYPES = ("SEDAN", "SUV", "EV", "HEV")
+DEFAULT_ENGINE_TYPES = ("GASOLINE", "DIESEL", "ELECTRIC", "HYBRID")
+DEFAULT_CAR_COLORS = ("WHITE", "BLACK", "SILVER", "BLUE")
+
 
 class SampleDbRepository:
-    """제조 샘플 PRD에 정의된 sampledb 테이블을 생성하고 기본 데이터를 입력"""
+    """sampledb 스키마와 제조 원천 이벤트 JSON 저장소."""
 
     def __init__(self, database_url: str) -> None:
-        """sampledb 연결에 사용할 SQLAlchemy 엔진을 생성."""
         self.engine = create_engine(
             database_url,
             connect_args=mysql_connect_args_for_seoul(database_url),
@@ -38,29 +48,381 @@ def __init__(self, database_url: str) -> None:
         )
 
     def ensure_schema(self) -> None:
-        """sampledb 엔티티가 없으면 생성"""
+        """sampledb 엔티티가 없으면 생성한다."""
         metadata.create_all(self.engine)
+        self.drop_equipment_status_column()
+        self.add_manufacturing_event_json_updated_at_column()
 
     def seed_equipment(self) -> None:
-        """기본 설비 데이터를 입력하고 기존 데이터는 유지"""
-        statement = insert(equipment).values(DEFAULT_EQUIPMENT_ROWS)
-        update_columns = {
-            "equipment_name": statement.inserted.equipment_name,
-            "process_code": statement.inserted.process_code,
-            "equipment_type": statement.inserted.equipment_type,
-            "status": statement.inserted.status,
+        """기본 설비 데이터를 입력하고 기존 설비 정보는 갱신한다."""
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name == "mysql":
+                statement = insert(equipment).values(DEFAULT_EQUIPMENT_ROWS)
+                statement = statement.on_duplicate_key_update(
+                    equipment_name=statement.inserted.equipment_name,
+                    process_code=statement.inserted.process_code,
+                    equipment_type=statement.inserted.equipment_type,
+                )
+                conn.execute(statement)
+                return
+
+            existing_codes = {
+                row["equipment_code"]
+                for row in conn.execute(select(equipment.c.equipment_code)).mappings()
+            }
+            rows = [
+                row
+                for row in DEFAULT_EQUIPMENT_ROWS
+                if row["equipment_code"] not in existing_codes
+            ]
+            if rows:
+                next_id = int(
+                    conn.execute(select(func.max(equipment.c.id))).scalar() or 0,
+                )
+                rows = [{**row, "id": next_id + index} for index, row in enumerate(rows, 1)]
+                conn.execute(equipment.insert(), rows)
+
+    def drop_equipment_status_column(self) -> bool:
+        if "status" not in self._table_columns("equipment"):
+            return False
+        with self.engine.begin() as conn:
+            conn.execute(text("ALTER TABLE equipment DROP COLUMN status"))
+        return True
+
+    def add_manufacturing_event_json_updated_at_column(self) -> bool:
+        if "updated_at" in self._table_columns("manufacturing_event_json"):
+            return False
+
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name == "mysql":
+                conn.execute(
+                    text(
+                        "ALTER TABLE manufacturing_event_json "
+                        "ADD COLUMN updated_at DATETIME NOT NULL "
+                        "DEFAULT CURRENT_TIMESTAMP",
+                    ),
+                )
+            else:
+                conn.execute(
+                    text(
+                        "ALTER TABLE manufacturing_event_json "
+                        "ADD COLUMN updated_at DATETIME",
+                    ),
+                )
+                conn.execute(
+                    text(
+                        "UPDATE manufacturing_event_json "
+                        "SET updated_at = CURRENT_TIMESTAMP "
+                        "WHERE updated_at IS NULL",
+                    ),
+                )
+        return True
+
+    def _table_columns(self, table_name: str) -> set[str]:
+        inspector = inspect(self.engine)
+        if not inspector.has_table(table_name):
+            return set(metadata.tables[table_name].c.keys())
+        return {
+            column["name"]
+            for column in inspector.get_columns(table_name)
         }
-        statement = statement.on_duplicate_key_update(**update_columns)
 
+    def ensure_car_master_rows(self, count: int) -> dict[str, int]:
+        """이벤트 생성에 필요한 차량 마스터 데이터를 보장한다."""
+        vehicles = [f"CAR-{index:06d}" for index in range(1, count + 1)]
         with self.engine.begin() as conn:
-            conn.execute(statement)
+            existing = {
+                row["vehicle_id"]: int(row["id"])
+                for row in conn.execute(
+                    select(car_master.c.id, car_master.c.vehicle_id).where(
+                        car_master.c.vehicle_id.in_(vehicles),
+                    ),
+                ).mappings()
+            }
+
+            missing_rows = [
+                {
+                    "vehicle_id": vehicle_id,
+                    "car_type": DEFAULT_CAR_TYPES[(index - 1) % len(DEFAULT_CAR_TYPES)],
+                    "engine_type": DEFAULT_ENGINE_TYPES[
+                        (index - 1) % len(DEFAULT_ENGINE_TYPES)
+                    ],
+                    "car_color": DEFAULT_CAR_COLORS[
+                        (index - 1) % len(DEFAULT_CAR_COLORS)
+                    ],
+                    "fuel_efficiency": 11 + (index % 9),
+                    "created_at": datetime.now(),
+                }
+                for index, vehicle_id in enumerate(vehicles, start=1)
+                if vehicle_id not in existing
+            ]
+            if missing_rows:
+                next_id = int(
+                    conn.execute(select(func.max(car_master.c.id))).scalar() or 0,
+                )
+                missing_rows = [
+                    {**row, "id": next_id + index}
+                    for index, row in enumerate(missing_rows, 1)
+                ]
+                conn.execute(car_master.insert(), missing_rows)
+
+            return {
+                row["vehicle_id"]: int(row["id"])
+                for row in conn.execute(
+                    select(car_master.c.id, car_master.c.vehicle_id).where(
+                        car_master.c.vehicle_id.in_(vehicles),
+                    ),
+                ).mappings()
+            }
+
+    def get_equipment_map(self) -> dict[str, dict[str, Any]]:
+        """equipment_code 기준 설비 행을 반환한다."""
+        query = select(
+            equipment.c.id,
+            equipment.c.process_code,
+            equipment.c.equipment_code,
+            equipment.c.equipment_name,
+            equipment.c.equipment_type,
+        )
+        with self.engine.connect() as conn:
+            return {
+                row["equipment_code"]: dict(row)
+                for row in conn.execute(query).mappings()
+            }
+
+    def count_event_json_between(self, start_date: date, end_date: date) -> int:
+        """기간 내 생성된 원천 이벤트 JSON 수를 반환한다."""
+        query = select(func.count()).select_from(manufacturing_event_json).where(
+            func.date(manufacturing_event_json.c.event_time) >= start_date.isoformat(),
+            func.date(manufacturing_event_json.c.event_time) <= end_date.isoformat(),
+        )
+        with self.engine.connect() as conn:
+            return int(conn.execute(query).scalar_one())
+
+    def count_template_events(self, template_name: str) -> int:
+        """템플릿에 저장된 이벤트 수를 반환한다."""
+        query = select(func.count()).select_from(manufacturing_event_template).where(
+            manufacturing_event_template.c.template_name == template_name,
+        )
+        with self.engine.connect() as conn:
+            return int(conn.execute(query).scalar_one())
+
+    def delete_template_events(self, template_name: str) -> int:
+        """템플릿 이벤트를 삭제한다."""
+        statement = manufacturing_event_template.delete().where(
+            manufacturing_event_template.c.template_name == template_name,
+        )
+        with self.engine.begin() as conn:
+            result = conn.execute(statement)
+            return int(result.rowcount or 0)
+
+    def insert_event_json_rows(
+        self,
+        rows: Iterable[dict[str, Any]],
+        *,
+        update_existing: bool = True,
+    ) -> int:
+        """원천 이벤트 JSON을 저장한다. event_id 기준으로 재실행해도 중복 저장하지 않는다."""
+        now = datetime.now()
+        payload = [{**row, "updated_at": now} for row in rows]
+        if not payload:
+            return 0
+
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name == "mysql":
+                statement = insert(manufacturing_event_json).values(payload)
+                if update_existing:
+                    statement = statement.on_duplicate_key_update(
+                        event_time=statement.inserted.event_time,
+                        car_master_id=statement.inserted.car_master_id,
+                        equipment_id=statement.inserted.equipment_id,
+                        process_code=statement.inserted.process_code,
+                        station_code=statement.inserted.station_code,
+                        equipment_code=statement.inserted.equipment_code,
+                        equipment_type=statement.inserted.equipment_type,
+                        equipment_status=statement.inserted.equipment_status,
+                        event_type=statement.inserted.event_type,
+                        event_json=statement.inserted.event_json,
+                        updated_at=statement.inserted.updated_at,
+                    )
+                else:
+                    statement = statement.prefix_with("IGNORE")
+                result = conn.execute(statement)
+                return int(result.rowcount or 0)
+
+            event_ids = [row["event_id"] for row in payload]
+            existing_ids = {
+                row["event_id"]
+                for row in conn.execute(
+                    select(manufacturing_event_json.c.event_id).where(
+                        manufacturing_event_json.c.event_id.in_(event_ids),
+                    ),
+                ).mappings()
+            }
+            updated_rows = 0
+            if update_existing:
+                for row in payload:
+                    if row["event_id"] not in existing_ids:
+                        continue
+                    result = conn.execute(
+                        manufacturing_event_json.update()
+                        .where(manufacturing_event_json.c.event_id == row["event_id"])
+                        .values(
+                            event_time=row["event_time"],
+                            car_master_id=row["car_master_id"],
+                            equipment_id=row["equipment_id"],
+                            process_code=row["process_code"],
+                            station_code=row["station_code"],
+                            equipment_code=row["equipment_code"],
+                            equipment_type=row["equipment_type"],
+                            equipment_status=row["equipment_status"],
+                            event_type=row["event_type"],
+                            event_json=row["event_json"],
+                            updated_at=row["updated_at"],
+                        ),
+                    )
+                    updated_rows += int(result.rowcount or 0)
+
+            new_rows = [row for row in payload if row["event_id"] not in existing_ids]
+            if new_rows:
+                next_id = int(
+                    conn.execute(
+                        select(func.max(manufacturing_event_json.c.id)),
+                    ).scalar()
+                    or 0,
+                )
+                new_rows = [
+                    {**row, "id": next_id + index}
+                    for index, row in enumerate(new_rows, 1)
+                ]
+                conn.execute(manufacturing_event_json.insert(), new_rows)
+            return len(new_rows) + updated_rows
+
+    def insert_template_event_rows(
+        self,
+        rows: Iterable[dict[str, Any]],
+        *,
+        update_existing: bool = False,
+    ) -> int:
+        """하루 재생 템플릿 이벤트를 저장한다."""
+        payload = list(rows)
+        if not payload:
+            return 0
+
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name == "mysql":
+                statement = insert(manufacturing_event_template).values(payload)
+                if update_existing:
+                    statement = statement.on_duplicate_key_update(
+                        event_offset_us=statement.inserted.event_offset_us,
+                        car_master_id=statement.inserted.car_master_id,
+                        equipment_id=statement.inserted.equipment_id,
+                        process_code=statement.inserted.process_code,
+                        station_code=statement.inserted.station_code,
+                        equipment_code=statement.inserted.equipment_code,
+                        equipment_type=statement.inserted.equipment_type,
+                        equipment_status=statement.inserted.equipment_status,
+                        event_type=statement.inserted.event_type,
+                        event_json=statement.inserted.event_json,
+                    )
+                else:
+                    statement = statement.prefix_with("IGNORE")
+                result = conn.execute(statement)
+                return int(result.rowcount or 0)
+
+            template_event_ids = [row["template_event_id"] for row in payload]
+            existing_ids = {
+                row["template_event_id"]
+                for row in conn.execute(
+                    select(manufacturing_event_template.c.template_event_id).where(
+                        manufacturing_event_template.c.template_event_id.in_(
+                            template_event_ids,
+                        ),
+                    ),
+                ).mappings()
+            }
+            new_rows = [
+                row
+                for row in payload
+                if row["template_event_id"] not in existing_ids
+            ]
+            if new_rows:
+                next_id = int(
+                    conn.execute(
+                        select(func.max(manufacturing_event_template.c.id)),
+                    ).scalar()
+                    or 0,
+                )
+                new_rows = [
+                    {**row, "id": next_id + index}
+                    for index, row in enumerate(new_rows, 1)
+                ]
+                conn.execute(manufacturing_event_template.insert(), new_rows)
+            return len(new_rows)
+
+    def list_template_event_rows(
+        self,
+        *,
+        template_name: str,
+        limit: int,
+        offset: int = 0,
+        process_code: str | None = None,
+    ) -> list[dict[str, Any]]:
+        """템플릿 이벤트를 offset 순서로 조회한다."""
+        query = select(manufacturing_event_template).where(
+            manufacturing_event_template.c.template_name == template_name,
+        )
+        if process_code:
+            query = query.where(
+                manufacturing_event_template.c.process_code == process_code,
+            )
+        query = query.order_by(
+            manufacturing_event_template.c.event_offset_us.asc(),
+            manufacturing_event_template.c.id.asc(),
+        ).offset(offset).limit(limit)
+
+        with self.engine.connect() as conn:
+            return [dict(row) for row in conn.execute(query).mappings()]
+
+    def list_event_json_rows(
+        self,
+        *,
+        limit: int,
+        offset: int = 0,
+        start_date: date | None = None,
+        end_date: date | None = None,
+        process_code: str | None = None,
+        is_sent: bool | None = None,
+    ) -> list[dict[str, Any]]:
+        """생성된 원천 이벤트 JSON을 조회한다."""
+        query = select(manufacturing_event_json)
+        if start_date:
+            query = query.where(
+                func.date(manufacturing_event_json.c.event_time) >= start_date.isoformat(),
+            )
+        if end_date:
+            query = query.where(
+                func.date(manufacturing_event_json.c.event_time) <= end_date.isoformat(),
+            )
+        if process_code:
+            query = query.where(manufacturing_event_json.c.process_code == process_code)
+        if is_sent is not None:
+            query = query.where(manufacturing_event_json.c.is_sent == is_sent)
+
+        query = query.order_by(
+            manufacturing_event_json.c.event_time.asc(),
+            manufacturing_event_json.c.id.asc(),
+        ).offset(offset).limit(limit)
+
+        with self.engine.connect() as conn:
+            return [dict(row) for row in conn.execute(query).mappings()]
 
     def initialize(self) -> None:
-        """스키마를 생성하고 PRD에 필요한 참조 데이터를 입력"""
+        """스키마를 생성하고 PRD에 필요한 참조 데이터를 입력한다."""
         self.ensure_schema()
         self.seed_equipment()
 
 
 def initialize_sampledb(database_url: str) -> None:
-    """sampledb 테이블과 참조 설비 데이터를 초기화"""
+    """sampledb 테이블과 참조 설비 데이터를 초기화한다."""
     SampleDbRepository(database_url).initialize()
diff --git a/app/repository/sampledb_schema.py b/app/repository/sampledb_schema.py
index 9027645..7541516 100644
--- a/app/repository/sampledb_schema.py
+++ b/app/repository/sampledb_schema.py
@@ -9,6 +9,7 @@
     ForeignKey,
     Index,
     Integer,
+    JSON,
     MetaData,
     String,
     Table,
@@ -20,7 +21,6 @@
 
 process_code_enum = Enum("PRESS", "BODY", "PAINT", "ASSEMBLY")
 equipment_type_enum = Enum("HYDRAULIC_PRESS", "ROBOT_ARM", "CAMERA", "CONVEYOR")
-equipment_status_enum = Enum("NORMAL", "WARNING", "FAULT", "MAINTENANCE")
 source_type_enum = Enum("BOSCH", "FORD", "PRESS_CURRENT", "ROBOT_CURRENT", "THERMAL_VISION")
 data_type_enum = Enum("PROCESS", "SENSOR", "QUALITY", "THERMAL")
 quality_result_enum = Enum("NORMAL", "DEFECT")
@@ -36,6 +36,7 @@
     Column("car_color", String(30), nullable=False),
     Column("fuel_efficiency", Integer, nullable=False),
     Column("created_at", DateTime, nullable=False),
+    UniqueConstraint("vehicle_id", name="uq_car_master_vehicle_id"),
 )
 
 equipment = Table(
@@ -46,7 +47,6 @@
     Column("equipment_code", String(50), nullable=False, unique=True),
     Column("equipment_name", String(100), nullable=False),
     Column("equipment_type", equipment_type_enum, nullable=False),
-    Column("status", equipment_status_enum, nullable=False, server_default="NORMAL"),
     Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
     Index("idx_equipment_process_code", "process_code"),
 )
@@ -82,6 +82,56 @@
     Index("idx_manufacturing_event_event_time", "event_time"),
 )
 
+manufacturing_event_json = Table(
+    "manufacturing_event_json",
+    metadata,
+    Column("id", BigInteger, primary_key=True, autoincrement=True),
+    Column("event_id", String(100), nullable=False, unique=True),
+    Column("event_time", DateTime, nullable=False),
+    Column("car_master_id", BigInteger, ForeignKey("car_master.id"), nullable=False),
+    Column("equipment_id", BigInteger, ForeignKey("equipment.id"), nullable=False),
+    Column("process_code", process_code_enum, nullable=False),
+    Column("station_code", String(50)),
+    Column("equipment_code", String(50), nullable=False),
+    Column("equipment_type", String(50)),
+    Column("equipment_status", String(30)),
+    Column("event_type", String(50)),
+    Column("event_json", JSON, nullable=False),
+    Column("is_sent", Boolean, nullable=False, server_default="0"),
+    Column("sent_at", DateTime),
+    Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+    Column("updated_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+    Index("idx_event_time_sent", "event_time", "is_sent"),
+    Index("idx_process_time", "process_code", "event_time"),
+    Index("idx_equipment_time", "equipment_code", "event_time"),
+    Index("idx_car_time", "car_master_id", "event_time"),
+    Index("idx_event_id", "event_id"),
+    Index("idx_equipment_id_time", "equipment_id", "event_time"),
+)
+
+manufacturing_event_template = Table(
+    "manufacturing_event_template",
+    metadata,
+    Column("id", BigInteger, primary_key=True, autoincrement=True),
+    Column("template_name", String(50), nullable=False),
+    Column("template_event_id", String(100), nullable=False, unique=True),
+    Column("event_offset_us", BigInteger, nullable=False),
+    Column("car_master_id", BigInteger, ForeignKey("car_master.id"), nullable=False),
+    Column("equipment_id", BigInteger, ForeignKey("equipment.id"), nullable=False),
+    Column("process_code", process_code_enum, nullable=False),
+    Column("station_code", String(50)),
+    Column("equipment_code", String(50), nullable=False),
+    Column("equipment_type", String(50)),
+    Column("equipment_status", String(30)),
+    Column("event_type", String(50)),
+    Column("event_json", JSON, nullable=False),
+    Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+    UniqueConstraint("template_name", "event_offset_us", name="uq_template_offset"),
+    Index("idx_template_name_offset", "template_name", "event_offset_us"),
+    Index("idx_template_process_offset", "template_name", "process_code", "event_offset_us"),
+    Index("idx_template_equipment_offset", "template_name", "equipment_code", "event_offset_us"),
+)
+
 thermal_vision = Table(
     "thermal_vision",
     metadata,
diff --git a/app/service/manufacturing_event_json_service.py b/app/service/manufacturing_event_json_service.py
new file mode 100644
index 0000000..dcf6eee
--- /dev/null
+++ b/app/service/manufacturing_event_json_service.py
@@ -0,0 +1,901 @@
+from __future__ import annotations
+
+import copy
+import hashlib
+from datetime import date, datetime, time, timedelta
+from typing import Any, Callable
+
+from fastapi import status
+
+from app.core.config import settings
+from app.core.exceptions import AppException
+from app.ml.preprocessing.manufacturing_event_json_builder import (
+    EventBuildRequest,
+    ManufacturingEventJsonBuilder,
+)
+from app.repository.sampledb_repository import SampleDbRepository
+
+
+DEFAULT_EVENTS_PER_DAY = 86_400
+DEFAULT_CAR_POOL_SIZE = 10_000
+DEFAULT_INSERT_CHUNK_SIZE = 1_000
+DEFAULT_TEMPLATE_NAME = "default"
+TEMPLATE_ANCHOR_DATE = date(2000, 1, 1)
+ProgressCallback = Callable[[dict[str, Any]], None]
+
+
+class ManufacturingEventJsonService:
+    """CSV 원천 데이터를 통합 제조 이벤트 JSON으로 생성하고 sampledb에 저장한다."""
+
+    def __init__(
+        self,
+        repository: SampleDbRepository,
+        builder: ManufacturingEventJsonBuilder | None = None,
+    ) -> None:
+        self.repository = repository
+        self.builder = builder or ManufacturingEventJsonBuilder()
+
+    def generate_range(
+        self,
+        *,
+        start_date: date,
+        end_date: date,
+        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+        update_existing: bool = True,
+        progress_callback: ProgressCallback | None = None,
+    ) -> dict[str, Any]:
+        if start_date > end_date:
+            raise AppException(
+                "start_date는 end_date보다 이후일 수 없습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if events_per_day < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
+            raise AppException(
+                "events_per_day는 최소 4 이상이어야 공정별 이벤트를 생성할 수 있습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if car_pool_size < 1:
+            raise AppException(
+                "car_pool_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if insert_chunk_size < 1:
+            raise AppException(
+                "insert_chunk_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+
+        self.repository.ensure_schema()
+        self.repository.seed_equipment()
+        days = (end_date - start_date).days + 1
+        total_events = days * events_per_day
+        car_id_map = self.repository.ensure_car_master_rows(car_pool_size)
+        equipment_map = self.repository.get_equipment_map()
+
+        request = EventBuildRequest(
+            start_date=start_date,
+            end_date=end_date,
+            events_per_day=events_per_day,
+            car_id_map=car_id_map,
+            equipment_map=equipment_map,
+        )
+        affected_rows = 0
+        generated_count = 0
+        distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        chunk: list[dict[str, Any]] = []
+        for row in self.builder.iter_rows(request):
+            chunk.append(row)
+            generated_count += 1
+            distribution[str(row["process_code"])] += 1
+            if len(chunk) >= insert_chunk_size:
+                affected_rows += self.repository.insert_event_json_rows(
+                    chunk,
+                    update_existing=update_existing,
+                )
+                chunk = []
+                if progress_callback:
+                    progress_callback(
+                        {
+                            "generatedCount": generated_count,
+                            "affectedRows": affected_rows,
+                            "totalExpectedEvents": total_events,
+                        },
+                    )
+        if chunk:
+            affected_rows += self.repository.insert_event_json_rows(
+                chunk,
+                update_existing=update_existing,
+            )
+            if progress_callback:
+                progress_callback(
+                    {
+                        "generatedCount": generated_count,
+                        "affectedRows": affected_rows,
+                        "totalExpectedEvents": total_events,
+                    },
+                )
+
+        stored_count = self.repository.count_event_json_between(start_date, end_date)
+        return {
+            "startDate": start_date.isoformat(),
+            "endDate": end_date.isoformat(),
+            "eventsPerDay": events_per_day,
+            "totalExpectedEvents": total_events,
+            "generatedCount": generated_count,
+            "affectedRows": affected_rows,
+            "storedCountInRange": stored_count,
+            "carPoolSize": len(car_id_map),
+            "insertChunkSize": insert_chunk_size,
+            "processDistribution": distribution,
+        }
+
+    def generate_template(
+        self,
+        *,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        event_count: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+        replace: bool = False,
+        update_existing: bool = False,
+        progress_callback: ProgressCallback | None = None,
+    ) -> dict[str, Any]:
+        if event_count < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
+            raise AppException(
+                "event_count는 최소 4 이상이어야 공정별 템플릿을 생성할 수 있습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if car_pool_size < 1:
+            raise AppException(
+                "car_pool_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if insert_chunk_size < 1:
+            raise AppException(
+                "insert_chunk_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+
+        normalized_template_name = _normalize_template_name(template_name)
+        self.repository.ensure_schema()
+        self.repository.seed_equipment()
+        if replace:
+            self.repository.delete_template_events(normalized_template_name)
+
+        car_id_map = self.repository.ensure_car_master_rows(car_pool_size)
+        equipment_map = self.repository.get_equipment_map()
+        request = EventBuildRequest(
+            start_date=TEMPLATE_ANCHOR_DATE,
+            end_date=TEMPLATE_ANCHOR_DATE,
+            events_per_day=event_count,
+            car_id_map=car_id_map,
+            equipment_map=equipment_map,
+        )
+
+        affected_rows = 0
+        generated_count = 0
+        distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        chunk: list[dict[str, Any]] = []
+        anchor_datetime = datetime.combine(TEMPLATE_ANCHOR_DATE, time.min)
+        for row in self.builder.iter_rows(request):
+            generated_count += 1
+            distribution[str(row["process_code"])] += 1
+            template_event_id = (
+                f"TMPL-{normalized_template_name.upper()}-{generated_count:06d}"
+            )
+            event_json = copy.deepcopy(row["event_json"])
+            event_json["event"]["eventId"] = template_event_id
+            event_offset_us = int(
+                (row["event_time"] - anchor_datetime).total_seconds() * 1_000_000,
+            )
+            chunk.append(
+                {
+                    "template_name": normalized_template_name,
+                    "template_event_id": template_event_id,
+                    "event_offset_us": event_offset_us,
+                    "car_master_id": row["car_master_id"],
+                    "equipment_id": row["equipment_id"],
+                    "process_code": row["process_code"],
+                    "station_code": row["station_code"],
+                    "equipment_code": row["equipment_code"],
+                    "equipment_type": row["equipment_type"],
+                    "equipment_status": row["equipment_status"],
+                    "event_type": row["event_type"],
+                    "event_json": event_json,
+                },
+            )
+            if len(chunk) >= insert_chunk_size:
+                affected_rows += self.repository.insert_template_event_rows(
+                    chunk,
+                    update_existing=update_existing,
+                )
+                chunk = []
+                if progress_callback:
+                    progress_callback(
+                        {
+                            "generatedCount": generated_count,
+                            "affectedRows": affected_rows,
+                            "totalExpectedEvents": event_count,
+                        },
+                    )
+        if chunk:
+            affected_rows += self.repository.insert_template_event_rows(
+                chunk,
+                update_existing=update_existing,
+            )
+            if progress_callback:
+                progress_callback(
+                    {
+                        "generatedCount": generated_count,
+                        "affectedRows": affected_rows,
+                        "totalExpectedEvents": event_count,
+                    },
+                )
+
+        stored_count = self.repository.count_template_events(normalized_template_name)
+        return {
+            "templateName": normalized_template_name,
+            "eventCount": event_count,
+            "generatedCount": generated_count,
+            "affectedRows": affected_rows,
+            "storedCount": stored_count,
+            "carPoolSize": len(car_id_map),
+            "insertChunkSize": insert_chunk_size,
+            "processDistribution": distribution,
+            "timeDistribution": [
+                {"range": "00:00-05:00", "density": "LOW"},
+                {"range": "05:00-08:00", "density": "MEDIUM"},
+                {"range": "08:00-12:00", "density": "HIGH"},
+                {"range": "12:00-13:00", "density": "LOW"},
+                {"range": "13:00-18:00", "density": "HIGH"},
+                {"range": "18:00-22:00", "density": "MEDIUM"},
+                {"range": "22:00-24:00", "density": "LOW"},
+            ],
+        }
+
+    def list_template_events(
+        self,
+        *,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        limit: int,
+        offset: int = 0,
+        process_code: str | None = None,
+    ) -> list[dict[str, Any]]:
+        return self.repository.list_template_event_rows(
+            template_name=_normalize_template_name(template_name),
+            limit=limit,
+            offset=offset,
+            process_code=process_code,
+        )
+
+    def replay_template_events(
+        self,
+        *,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        target_date: date,
+        limit: int,
+        offset: int = 0,
+        process_code: str | None = None,
+    ) -> list[dict[str, Any]]:
+        template_rows = self.list_template_events(
+            template_name=template_name,
+            limit=limit,
+            offset=offset,
+            process_code=process_code,
+        )
+        return [
+            _materialize_template_row(row, target_date=target_date)
+            for row in template_rows
+        ]
+
+    def materialize_template_events(
+        self,
+        *,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        target_date: date,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+        update_existing: bool = True,
+        progress_callback: ProgressCallback | None = None,
+    ) -> dict[str, Any]:
+        if insert_chunk_size < 1:
+            raise AppException(
+                "insert_chunk_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+
+        normalized_template_name = _normalize_template_name(template_name)
+        self.repository.ensure_schema()
+        total_template_events = self.repository.count_template_events(
+            normalized_template_name,
+        )
+        if total_template_events < 1:
+            raise AppException(
+                "저장된 제조 이벤트 템플릿이 없습니다. 먼저 템플릿을 생성해주세요.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+
+        affected_rows = 0
+        generated_count = 0
+        distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        offset = 0
+        while offset < total_template_events:
+            template_rows = self.repository.list_template_event_rows(
+                template_name=normalized_template_name,
+                limit=insert_chunk_size,
+                offset=offset,
+            )
+            if not template_rows:
+                break
+
+            materialized_rows = [
+                _materialize_template_row(row, target_date=target_date)
+                for row in template_rows
+            ]
+            affected_rows += self.repository.insert_event_json_rows(
+                materialized_rows,
+                update_existing=update_existing,
+            )
+            for row in materialized_rows:
+                distribution[str(row["process_code"])] += 1
+
+            generated_count += len(materialized_rows)
+            offset += len(template_rows)
+            if progress_callback:
+                progress_callback(
+                    {
+                        "generatedCount": generated_count,
+                        "affectedRows": affected_rows,
+                        "totalExpectedEvents": total_template_events,
+                    },
+                )
+
+        stored_count = self.repository.count_event_json_between(
+            target_date,
+            target_date,
+        )
+        return {
+            "templateName": normalized_template_name,
+            "targetDate": target_date.isoformat(),
+            "templateEventCount": total_template_events,
+            "generatedCount": generated_count,
+            "affectedRows": affected_rows,
+            "storedCountInDate": stored_count,
+            "insertChunkSize": insert_chunk_size,
+            "updateExisting": update_existing,
+            "processDistribution": distribution,
+            "variationMode": "target_date_and_template_event_id_seed",
+        }
+
+    def ensure_template(
+        self,
+        *,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        event_count: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+    ) -> dict[str, Any]:
+        normalized_template_name = _normalize_template_name(template_name)
+        self.repository.ensure_schema()
+        stored_count = self.repository.count_template_events(normalized_template_name)
+        if stored_count >= event_count:
+            return {
+                "templateName": normalized_template_name,
+                "eventCount": event_count,
+                "storedCount": stored_count,
+                "created": False,
+            }
+
+        result = self.generate_template(
+            template_name=normalized_template_name,
+            event_count=event_count,
+            car_pool_size=car_pool_size,
+            insert_chunk_size=insert_chunk_size,
+            replace=False,
+            update_existing=False,
+        )
+        return {
+            **result,
+            "created": True,
+        }
+
+    def generate_initial_demo_range(
+        self,
+        *,
+        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+        update_existing: bool = True,
+        progress_callback: ProgressCallback | None = None,
+    ) -> dict[str, Any]:
+        return self.generate_range(
+            start_date=date(2026, 6, 1),
+            end_date=date(2026, 6, 16),
+            events_per_day=events_per_day,
+            car_pool_size=car_pool_size,
+            insert_chunk_size=insert_chunk_size,
+            update_existing=update_existing,
+            progress_callback=progress_callback,
+        )
+
+    def generate_tomorrow(
+        self,
+        *,
+        base_date: date | None = None,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+        update_existing: bool = True,
+        progress_callback: ProgressCallback | None = None,
+    ) -> dict[str, Any]:
+        target_date = (base_date or date.today()) + timedelta(days=1)
+        template_result = self.ensure_template(
+            template_name=template_name,
+            event_count=events_per_day,
+            car_pool_size=car_pool_size,
+            insert_chunk_size=insert_chunk_size,
+        )
+        materialized_result = self.materialize_template_events(
+            template_name=template_name,
+            target_date=target_date,
+            insert_chunk_size=insert_chunk_size,
+            update_existing=update_existing,
+            progress_callback=progress_callback,
+        )
+        return {
+            **materialized_result,
+            "baseDate": (base_date or date.today()).isoformat(),
+            "templatePrepared": template_result,
+        }
+
+    def list_events(
+        self,
+        *,
+        limit: int,
+        offset: int,
+        start_date: date | None = None,
+        end_date: date | None = None,
+        process_code: str | None = None,
+        is_sent: bool | None = None,
+    ) -> list[dict[str, Any]]:
+        return self.repository.list_event_json_rows(
+            limit=limit,
+            offset=offset,
+            start_date=start_date,
+            end_date=end_date,
+            process_code=process_code,
+            is_sent=is_sent,
+        )
+
+def get_manufacturing_event_json_service() -> ManufacturingEventJsonService:
+    if not settings.sample_database_connection_url:
+        raise AppException(
+            "SAMPLE_DATABASE_URL 설정이 필요합니다.",
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+        )
+    return ManufacturingEventJsonService(
+        SampleDbRepository(settings.sample_database_connection_url),
+    )
+
+
+def _normalize_template_name(template_name: str) -> str:
+    normalized = "".join(
+        char.lower() if char.isalnum() else "_"
+        for char in template_name.strip()
+    ).strip("_")
+    return normalized or DEFAULT_TEMPLATE_NAME
+
+
+def _materialize_template_row(
+    row: dict[str, Any],
+    *,
+    target_date: date,
+) -> dict[str, Any]:
+    event_time = datetime.combine(target_date, time.min) + timedelta(
+        microseconds=int(row["event_offset_us"]),
+    )
+    sequence_no = int(str(row["template_event_id"]).rsplit("-", 1)[-1])
+    event_id = f"EVT-{target_date:%Y%m%d}-{sequence_no:06d}"
+    event_json = copy.deepcopy(row["event_json"])
+    event_json["event"]["eventId"] = event_id
+    event_json["event"]["eventTime"] = event_time.isoformat()
+    event_json["equipmentStatus"]["statusChangedTime"] = event_time.isoformat()
+    event_json["equipmentStatus"]["lastNormalTime"] = (
+        event_time - timedelta(seconds=31)
+    ).isoformat()
+    _apply_date_variation(
+        event_json,
+        target_date=target_date,
+        template_event_id=str(row["template_event_id"]),
+    )
+    materialized_status = event_json["equipmentStatus"]["operationStatus"]
+    return {
+        "event_id": event_id,
+        "event_time": event_time,
+        "template_event_id": row["template_event_id"],
+        "template_name": row["template_name"],
+        "event_offset_us": row["event_offset_us"],
+        "car_master_id": row["car_master_id"],
+        "equipment_id": row["equipment_id"],
+        "process_code": row["process_code"],
+        "station_code": row["station_code"],
+        "equipment_code": row["equipment_code"],
+        "equipment_type": row["equipment_type"],
+        "equipment_status": materialized_status,
+        "event_type": row["event_type"],
+        "event_json": event_json,
+    }
+
+
+def _apply_date_variation(
+    event_json: dict[str, Any],
+    *,
+    target_date: date,
+    template_event_id: str,
+) -> None:
+    seed_key = f"{target_date.isoformat()}:{template_event_id}"
+    sensor = event_json.get("sensor", {})
+    process_metrics = event_json.get("processMetrics", {})
+    process_data = event_json.get("processData", {})
+
+    _vary_current(sensor.get("current", {}), seed_key)
+    _vary_vibration(sensor.get("vibration", {}), seed_key)
+    _vary_robot_arm_vibration(sensor.get("robotArmVibration", {}), seed_key)
+    _vary_thermal(sensor.get("thermal", {}), seed_key)
+    _vary_process_metrics(process_metrics, seed_key)
+    _sync_process_data(process_data, sensor, process_metrics, seed_key)
+    _refresh_equipment_status(event_json, sensor, process_metrics)
+
+
+def _vary_current(current: dict[str, Any], seed_key: str) -> None:
+    if not current:
+        return
+    original_rms = _as_float(current.get("rmsAmpere"))
+    original_max = _as_float(current.get("maxAmpere"), original_rms)
+    original_min = _as_float(current.get("minAmpere"), original_rms)
+
+    rms = _jitter_numeric(
+        original_rms,
+        seed_key,
+        "current.rmsAmpere",
+        pct=0.035,
+        precision=9,
+        min_value=0.0,
+    )
+    upper_spread = max(original_max - original_rms, rms * 0.035, 0.001)
+    lower_spread = max(original_rms - original_min, rms * 0.035, 0.001)
+    upper_spread *= _factor(seed_key, "current.upperSpread", 0.12)
+    lower_spread *= _factor(seed_key, "current.lowerSpread", 0.12)
+
+    current["rmsAmpere"] = rms
+    current["maxAmpere"] = _round(rms + upper_spread, 9)
+    current["minAmpere"] = _round(max(0.0, rms - lower_spread), 9)
+
+
+def _vary_vibration(vibration: dict[str, Any], seed_key: str) -> None:
+    if not vibration:
+        return
+    vibration["accelerationG"] = _jitter_numeric(
+        _as_float(vibration.get("accelerationG")),
+        seed_key,
+        "vibration.accelerationG",
+        pct=0.08,
+        precision=9,
+        min_value=0.0,
+    )
+    vibration["vibrationRms"] = _jitter_numeric(
+        _as_float(vibration.get("vibrationRms")),
+        seed_key,
+        "vibration.vibrationRms",
+        pct=0.065,
+        precision=9,
+        min_value=0.0,
+    )
+    vibration["vibrationPeak"] = _jitter_numeric(
+        _as_float(vibration.get("vibrationPeak")),
+        seed_key,
+        "vibration.vibrationPeak",
+        pct=0.075,
+        precision=9,
+        min_value=0.0,
+    )
+    vibration["vibrationScore"] = _round(
+        _clamp(
+            _as_float(vibration.get("vibrationScore"))
+            + _noise(seed_key, "vibration.vibrationScore", -0.025, 0.025),
+            0.0,
+            0.99,
+        ),
+        6,
+    )
+
+
+def _vary_robot_arm_vibration(robot: dict[str, Any], seed_key: str) -> None:
+    if not robot:
+        return
+    robot["frequencyHz"] = _jitter_numeric(
+        _as_float(robot.get("frequencyHz")),
+        seed_key,
+        "robot.frequencyHz",
+        pct=0.035,
+        precision=3,
+        min_value=0.0,
+    )
+    for field in ("amplitude", "vibrationRms", "vibrationPeak"):
+        robot[field] = _jitter_numeric(
+            _as_float(robot.get(field)),
+            seed_key,
+            f"robot.{field}",
+            pct=0.07,
+            precision=9,
+            min_value=0.0,
+        )
+    robot["vibrationScore"] = _round(
+        _clamp(
+            _as_float(robot.get("vibrationScore"))
+            + _noise(seed_key, "robot.vibrationScore", -0.03, 0.03),
+            0.0,
+            0.99,
+        ),
+        6,
+    )
+
+
+def _vary_thermal(thermal: dict[str, Any], seed_key: str) -> None:
+    if not thermal:
+        return
+    original_avg = _as_float(thermal.get("avgTemperature"))
+    original_max = _as_float(thermal.get("maxTemperature"), original_avg)
+    original_min = _as_float(thermal.get("minTemperature"), original_avg)
+
+    avg_temp = _round(
+        original_avg + _noise(seed_key, "thermal.avgTemperature", -0.85, 0.85),
+        3,
+    )
+    upper_spread = max(original_max - original_avg, 0.25)
+    lower_spread = max(original_avg - original_min, 0.25)
+    upper_spread *= _factor(seed_key, "thermal.upperSpread", 0.08)
+    lower_spread *= _factor(seed_key, "thermal.lowerSpread", 0.08)
+
+    thermal["avgTemperature"] = avg_temp
+    thermal["thermalScore"] = avg_temp
+    thermal["maxTemperature"] = _round(max(avg_temp, avg_temp + upper_spread), 3)
+    thermal["minTemperature"] = _round(min(avg_temp, avg_temp - lower_spread), 3)
+
+
+def _vary_process_metrics(metrics: dict[str, Any], seed_key: str) -> None:
+    if not metrics:
+        return
+    cycle_time = _jitter_numeric(
+        _as_float(metrics.get("cycleTimeSec"), 1.0),
+        seed_key,
+        "metrics.cycleTimeSec",
+        pct=0.045,
+        precision=3,
+        min_value=1.0,
+    )
+    processing_time = _jitter_numeric(
+        _as_float(metrics.get("processingTimeSec"), cycle_time * 0.85),
+        seed_key,
+        "metrics.processingTimeSec",
+        pct=0.04,
+        precision=3,
+        min_value=0.5,
+    )
+    processing_time = _round(min(processing_time, max(0.5, cycle_time - 0.5)), 3)
+    waiting_time = _round(
+        max(
+            0.5,
+            cycle_time
+            - processing_time
+            + _noise(seed_key, "metrics.waitingTimeSec", -0.35, 1.15),
+        ),
+        3,
+    )
+    station_delay = _jitter_numeric(
+        _as_float(metrics.get("stationDelaySec")),
+        seed_key,
+        "metrics.stationDelaySec",
+        pct=0.12,
+        precision=3,
+        min_value=0.0,
+    )
+    idle_time = _jitter_numeric(
+        _as_float(metrics.get("equipmentIdleTimeSec")),
+        seed_key,
+        "metrics.equipmentIdleTimeSec",
+        pct=0.13,
+        precision=3,
+        min_value=0.0,
+    )
+
+    metrics["cycleTimeSec"] = cycle_time
+    metrics["processingTimeSec"] = processing_time
+    metrics["waitingTimeSec"] = waiting_time
+    metrics["stationDelaySec"] = station_delay
+    metrics["throughputPerMin"] = _round(60 / cycle_time, 3)
+    metrics["queueLength"] = max(
+        0,
+        _as_int(metrics.get("queueLength")) + _noise_int(seed_key, "metrics.queue", 2),
+    )
+    metrics["wipCount"] = max(
+        0,
+        _as_int(metrics.get("wipCount")) + _noise_int(seed_key, "metrics.wip", 3),
+    )
+    metrics["equipmentIdleTimeSec"] = idle_time
+
+
+def _sync_process_data(
+    process_data: dict[str, Any],
+    sensor: dict[str, Any],
+    metrics: dict[str, Any],
+    seed_key: str,
+) -> None:
+    if not process_data:
+        return
+
+    press = process_data.get("press")
+    if isinstance(press, dict):
+        press["timestampDelaySec"] = metrics.get("stationDelaySec", 0.0)
+        press["countIncreaseYn"] = _as_float(
+            metrics.get("equipmentIdleTimeSec"),
+        ) < 18.0
+
+    body = process_data.get("body")
+    if isinstance(body, dict):
+        robot_score = _as_float(
+            sensor.get("robotArmVibration", {}).get("vibrationScore"),
+        )
+        body["robotMotionStatus"] = "WARNING" if robot_score >= 0.68 else "NORMAL"
+
+    paint = process_data.get("paint")
+    if isinstance(paint, dict):
+        paint["thermalStdTemp"] = _jitter_numeric(
+            _as_float(paint.get("thermalStdTemp")),
+            seed_key,
+            "paint.thermalStdTemp",
+            pct=0.04,
+            absolute=0.12,
+            precision=3,
+            min_value=0.0,
+        )
+        paint["thicknessValue"] = _jitter_numeric(
+            _as_float(paint.get("thicknessValue")),
+            seed_key,
+            "paint.thicknessValue",
+            pct=0.004,
+            absolute=0.45,
+            precision=3,
+            min_value=0.0,
+        )
+        defect_score = _round(
+            _clamp(
+                _as_float(paint.get("defectScore"))
+                + _noise(seed_key, "paint.defectScore", -0.025, 0.025),
+                0.0,
+                0.99,
+            ),
+            4,
+        )
+        paint["defectScore"] = defect_score
+        paint["surfaceQualityScore"] = _round(max(0.0, 100.0 - defect_score * 32), 3)
+        paint["visionLabel"] = "DEFECT" if defect_score >= 0.45 else "NORMAL"
+
+    assembly = process_data.get("assembly")
+    if isinstance(assembly, dict):
+        delta = 1 if _noise(seed_key, "assembly.errorFlip", 0.0, 1.0) > 0.92 else 0
+        base_sequence_error = _as_int(assembly.get("sequenceErrorCount"))
+        sequence_error_count = max(0, min(2, base_sequence_error + delta))
+        assembly["sequenceErrorCount"] = sequence_error_count
+        assembly["fasteningErrorCount"] = max(
+            0,
+            min(2, _as_int(assembly.get("fasteningErrorCount")) + delta),
+        )
+        assembly["missingPartCount"] = max(
+            0,
+            min(2, _as_int(assembly.get("missingPartCount")) + (1 if delta else 0)),
+        )
+        assembly["actualSequence"] = (
+            "A01>A03>A02>A04"
+            if sequence_error_count
+            else "A01>A02>A03>A04"
+        )
+
+
+def _refresh_equipment_status(
+    event_json: dict[str, Any],
+    sensor: dict[str, Any],
+    metrics: dict[str, Any],
+) -> None:
+    equipment_status = event_json.get("equipmentStatus", {})
+    idle_time = _as_float(metrics.get("equipmentIdleTimeSec"))
+    station_delay = _as_float(metrics.get("stationDelaySec"))
+    vibration_score = max(
+        _as_float(sensor.get("vibration", {}).get("vibrationScore")),
+        _as_float(sensor.get("robotArmVibration", {}).get("vibrationScore")),
+    )
+    defect_score = _as_float(
+        event_json.get("processData", {}).get("paint", {}).get("defectScore"),
+    )
+
+    if idle_time >= 20.0:
+        operation_status = "IDLE"
+    elif station_delay >= 12.0 or vibration_score >= 0.82 or defect_score >= 0.75:
+        operation_status = "ERROR"
+    else:
+        operation_status = "RUNNING"
+
+    health_status = (
+        "WARNING"
+        if operation_status != "RUNNING"
+        or station_delay >= 8.0
+        or vibration_score >= 0.65
+        or defect_score >= 0.45
+        else "NORMAL"
+    )
+    equipment_status["operationStatus"] = operation_status
+    equipment_status["healthStatus"] = health_status
+
+
+def _jitter_numeric(
+    value: float,
+    seed_key: str,
+    field: str,
+    *,
+    pct: float,
+    precision: int,
+    min_value: float | None = None,
+    max_value: float | None = None,
+    absolute: float | None = None,
+) -> float:
+    base = _as_float(value)
+    delta = _noise(seed_key, field, -absolute, absolute) if absolute else 0.0
+    varied = base * _factor(seed_key, field, pct) + delta
+    if min_value is not None:
+        varied = max(min_value, varied)
+    if max_value is not None:
+        varied = min(max_value, varied)
+    return _round(varied, precision)
+
+
+def _factor(seed_key: str, field: str, pct: float) -> float:
+    return 1.0 + _noise(seed_key, field, -pct, pct)
+
+
+def _noise(seed_key: str, field: str, low: float, high: float) -> float:
+    digest = hashlib.blake2b(
+        f"{seed_key}:{field}".encode("utf-8"),
+        digest_size=8,
+    ).digest()
+    ratio = int.from_bytes(digest, "big") / ((1 << 64) - 1)
+    return low + (high - low) * ratio
+
+
+def _noise_int(seed_key: str, field: str, spread: int) -> int:
+    if spread <= 0:
+        return 0
+    return int(round(_noise(seed_key, field, -spread, spread)))
+
+
+def _as_float(value: Any, default: float = 0.0) -> float:
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _as_int(value: Any, default: int = 0) -> int:
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _clamp(value: float, low: float, high: float) -> float:
+    return min(high, max(low, value))
+
+
+def _round(value: float, precision: int) -> float:
+    return round(float(value), precision)
diff --git a/app/service/manufacturing_event_scheduler.py b/app/service/manufacturing_event_scheduler.py
new file mode 100644
index 0000000..ad02841
--- /dev/null
+++ b/app/service/manufacturing_event_scheduler.py
@@ -0,0 +1,114 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from datetime import datetime, time, timedelta
+from zoneinfo import ZoneInfo
+
+from fastapi import FastAPI
+
+from app.core.config import settings
+from app.repository.sampledb_repository import SampleDbRepository
+from app.service.manufacturing_event_json_service import (
+    DEFAULT_TEMPLATE_NAME,
+    ManufacturingEventJsonService,
+)
+
+
+logger = logging.getLogger(__name__)
+SEOUL_TZ = ZoneInfo("Asia/Seoul")
+
+
+def start_manufacturing_event_scheduler(app: FastAPI) -> None:
+    """제조 이벤트 템플릿 기반 다음날 데이터 적재 스케줄러를 시작한다."""
+    if not settings.manufacturing_event_scheduler_enabled:
+        return
+    if not settings.sample_database_connection_url:
+        logger.info(
+            "SAMPLE_DATABASE_URL이 없어 제조 이벤트 스케줄러를 시작하지 않습니다.",
+        )
+        return
+
+    task = asyncio.create_task(_run_daily_generation_loop())
+    app.state.manufacturing_event_scheduler_task = task
+
+
+async def stop_manufacturing_event_scheduler(app: FastAPI) -> None:
+    task = getattr(app.state, "manufacturing_event_scheduler_task", None)
+    if task is None:
+        return
+    task.cancel()
+    try:
+        await task
+    except asyncio.CancelledError:
+        pass
+
+
+async def _run_daily_generation_loop() -> None:
+    await asyncio.to_thread(_ensure_default_template)
+    while True:
+        await asyncio.sleep(_seconds_until_next_run())
+        try:
+            await asyncio.to_thread(_materialize_tomorrow_events)
+        except Exception:
+            logger.exception(
+                "제조 이벤트 다음날 데이터 적재 스케줄러 실행에 실패했습니다.",
+            )
+
+
+def _ensure_default_template() -> dict[str, object] | None:
+    if not settings.sample_database_connection_url:
+        return None
+
+    service = ManufacturingEventJsonService(
+        SampleDbRepository(settings.sample_database_connection_url),
+    )
+    result = service.ensure_template(
+        template_name=DEFAULT_TEMPLATE_NAME,
+        event_count=settings.manufacturing_event_template_event_count,
+        car_pool_size=settings.manufacturing_event_car_pool_size,
+        insert_chunk_size=settings.manufacturing_event_insert_chunk_size,
+    )
+    logger.info(
+        "제조 이벤트 기본 템플릿 준비 완료: %s",
+        {
+            "templateName": result["templateName"],
+            "storedCount": result["storedCount"],
+            "created": result["created"],
+        },
+    )
+    return result
+
+
+def _materialize_tomorrow_events() -> dict[str, object] | None:
+    if not settings.sample_database_connection_url:
+        return None
+
+    service = ManufacturingEventJsonService(
+        SampleDbRepository(settings.sample_database_connection_url),
+    )
+    result = service.generate_tomorrow(
+        template_name=DEFAULT_TEMPLATE_NAME,
+        events_per_day=settings.manufacturing_event_template_event_count,
+        car_pool_size=settings.manufacturing_event_car_pool_size,
+        insert_chunk_size=settings.manufacturing_event_insert_chunk_size,
+        update_existing=True,
+    )
+    logger.info(
+        "제조 이벤트 다음날 데이터 적재 완료: %s",
+        {
+            "templateName": result["templateName"],
+            "targetDate": result["targetDate"],
+            "generatedCount": result["generatedCount"],
+            "storedCountInDate": result["storedCountInDate"],
+        },
+    )
+    return result
+
+
+def _seconds_until_next_run() -> float:
+    now = datetime.now(SEOUL_TZ)
+    next_run = datetime.combine(now.date(), time(hour=0, minute=10), tzinfo=SEOUL_TZ)
+    if next_run <= now:
+        next_run += timedelta(days=1)
+    return max(60.0, (next_run - now).total_seconds())

From 5eeca304eceec5a30e95fdf008f232e1e74132bc Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 17 Jun 2026 11:01:17 +0900
Subject: [PATCH 005/148] =?UTF-8?q?feat:=20=EC=A0=9C=EC=A1=B0=20=EC=9D=B4?=
 =?UTF-8?q?=EB=B2=A4=ED=8A=B8=20=EC=83=9D=EC=84=B1=20API=20=EB=B9=84?=
 =?UTF-8?q?=EB=8F=99=EA=B8=B0=20job=20=EC=B2=98=EB=A6=AC=20=EC=A0=81?=
 =?UTF-8?q?=EC=9A=A9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- /api/manufacturing/events/generate API를 비동기 job 생성 방식으로 변경

- /api/manufacturing/events/generate/tomorrow API를 비동기 job 생성 방식으로 변경

- /api/manufacturing/events/templates/generate API를 비동기 job 생성 방식으로 변경

- manufacturing_event_generation_job 테이블 추가 및 job 상태, 요청, 결과 저장

- 앱 내부 worker thread 기반 제조 이벤트 생성 job 실행 로직 추가

- generated_count, affected_rows, total_expected_events 기반 job 진행률 갱신

- /api/manufacturing/events/generate/jobs/{job_id} 상태 조회 API 추가

- 서버 재시작 시 PENDING, RUNNING 상태의 미완료 job 자동 복구 처리

- manufacturing_event_json updated_at 컬럼 추가 및 insert, upsert 시 수정 시간 저장

- Swagger description에 비동기 job 응답과 batch insert 동작 설명 반영
---
 app/api/routers/manufacturing_event.py        | 121 +++++---
 app/main.py                                   |   6 +
 app/repository/sampledb_repository.py         | 139 ++++++++++
 app/repository/sampledb_schema.py             |  21 ++
 .../manufacturing_event_json_service.py       | 262 ++++++++++++++++++
 5 files changed, 508 insertions(+), 41 deletions(-)

diff --git a/app/api/routers/manufacturing_event.py b/app/api/routers/manufacturing_event.py
index 8a2967f..4a4fd2c 100644
--- a/app/api/routers/manufacturing_event.py
+++ b/app/api/routers/manufacturing_event.py
@@ -1,7 +1,7 @@
 from datetime import date
 from typing import Literal
 
-from fastapi import APIRouter, Depends, Query
+from fastapi import APIRouter, Depends, Query, status
 
 from app.dto.response import CommonResponse
 from app.service.manufacturing_event_json_service import (
@@ -80,25 +80,27 @@
     "/templates/generate",
     summary="제조 이벤트 템플릿 생성",
     operation_id="generateManufacturingEventTemplate",
+    status_code=status.HTTP_202_ACCEPTED,
     description=(
         "CSV 원천 데이터를 전처리/정제해 하루 기준 제조 관제 이벤트 템플릿을 생성하고 "
-        "`manufacturing_event_template` 테이블에 저장하는 API입니다.\n\n"
+        "`manufacturing_event_template` 테이블에 저장하는 비동기 job을 생성하는 API입니다.\n\n"
         "### 동작 방식\n"
+        "- API는 job row를 생성한 뒤 즉시 `jobId`를 반환합니다.\n"
+        "- 실제 템플릿 생성과 DB 저장은 앱 내부 background worker가 수행합니다.\n"
         "- 프레스(PRESS), 차체(BODY), 도장(PAINT), 의장(ASSEMBLY) 공정이 순환 생성됩니다.\n"
         "- 날짜가 고정된 row가 아니라 하루 안에서의 이벤트 발생 위치를 `event_offset_us`로 저장합니다.\n"
         "- `replace=true`이면 같은 `template_name`의 기존 row를 삭제한 뒤 새로 생성합니다.\n"
         "- `insert_chunk_size` 단위로 row를 모아 batch insert합니다.\n\n"
-        "### 주요 응답 데이터\n"
-        "- `templateName`: 저장된 템플릿 이름\n"
-        "- `eventCount`: 요청한 템플릿 이벤트 수\n"
-        "- `generatedCount`: 실제 생성한 이벤트 수\n"
-        "- `affectedRows`: DB insert/update 영향 row 수\n"
-        "- `storedCount`: 해당 템플릿명으로 저장된 전체 row 수\n"
-        "- `insertChunkSize`: batch insert 단위\n"
-        "- `processDistribution`: 공정별 생성 건수\n\n"
+        "### 즉시 응답 데이터\n"
+        "- `jobId`: 비동기 job 고유 ID\n"
+        "- `jobType`: `GENERATE_TEMPLATE`\n"
+        "- `status`: 최초 상태는 `PENDING`, worker 실행 후 `RUNNING`, `SUCCEEDED`, `FAILED`로 변경됩니다.\n"
+        "- `totalExpectedEvents`: 예상 생성 건수\n"
+        "- `generatedCount`, `affectedRows`: worker 진행 중 갱신되는 처리 건수\n"
+        "- `result`: 완료 전에는 `null`, 완료 후 job 상태 조회 API에서 최종 생성 결과가 채워집니다.\n\n"
         f"{TEMPLATE_TABLE_DESCRIPTION}"
     ),
-    responses={200: {"description": "제조 이벤트 템플릿 생성 결과"}},
+    responses={202: {"description": "제조 이벤트 템플릿 생성 job 생성 결과"}},
 )
 def generate_manufacturing_event_template(
     template_name: str = Query(
@@ -131,17 +133,16 @@ def generate_manufacturing_event_template(
         get_manufacturing_event_json_service,
     ),
 ) -> CommonResponse[dict]:
-    result = service.generate_template(
+    result = service.enqueue_generate_template_job(
         template_name=template_name,
         event_count=event_count,
         car_pool_size=car_pool_size,
         insert_chunk_size=insert_chunk_size,
         replace=replace,
-        update_existing=replace,
     )
     return success_response(
         data=result,
-        message="제조 관제 이벤트 템플릿 생성 및 저장이 완료되었습니다.",
+        message="제조 관제 이벤트 템플릿 생성 job이 생성되었습니다.",
     )
 
 
@@ -244,14 +245,18 @@ def replay_manufacturing_event_template(
     "/generate",
     summary="기간별 제조 이벤트 JSON 생성 및 적재",
     operation_id="generateManufacturingEventJson",
+    status_code=status.HTTP_202_ACCEPTED,
     description=(
         "지정한 날짜 범위의 제조 관제 이벤트 JSON을 실제 일자 데이터로 생성해 "
-        "`manufacturing_event_json` 테이블에 저장하는 API입니다.\n\n"
+        "`manufacturing_event_json` 테이블에 저장하는 비동기 job을 생성하는 API입니다.\n\n"
         "### 언제 사용하는 API인가요?\n"
         "- 초기 시연 데이터 또는 특정 기간의 샘플 제조 이벤트를 DB에 실제 row로 적재할 때 사용합니다.\n"
         "- 템플릿 replay가 아니라 CSV 기반 생성기를 직접 실행합니다.\n"
         "- `start_date`부터 `end_date`까지 양 끝 날짜를 모두 포함해 생성합니다.\n\n"
         "### 생성/저장 방식\n"
+        "- API는 job row를 생성한 뒤 즉시 `jobId`를 반환합니다.\n"
+        "- 실제 이벤트 생성과 DB 저장은 앱 내부 background worker가 수행합니다.\n"
+        "- 진행 상태는 job 상태 조회 API로 확인합니다.\n"
         "- 전체 생성 건수는 `(end_date - start_date + 1) * events_per_day`입니다.\n"
         "- 공정은 `PRESS -> BODY -> PAINT -> ASSEMBLY` 순서로 순환 배치됩니다.\n"
         "- 이벤트 시간은 하루 안에서 생산 밀도가 높은 시간대에 더 많이 분포되도록 계산됩니다.\n"
@@ -259,19 +264,16 @@ def replay_manufacturing_event_template(
         "- 생성된 row는 `insert_chunk_size` 단위로 모아 batch insert합니다.\n"
         "- 기본 batch insert 단위는 `1,000`건이며, 요청 파라미터로 `100~10,000` 사이에서 조정할 수 있습니다.\n"
         "- MySQL에서는 `event_id` 중복 시 기존 row의 이벤트 시간, 설비, 상태, JSON payload 등을 갱신합니다.\n\n"
-        "### 주요 응답 데이터\n"
-        "- `startDate`, `endDate`: 생성 기간\n"
-        "- `eventsPerDay`: 하루 생성 이벤트 수\n"
+        "### 즉시 응답 데이터\n"
+        "- `jobId`: 비동기 job 고유 ID\n"
+        "- `jobType`: `GENERATE_RANGE`\n"
+        "- `status`: 최초 상태는 `PENDING`, worker 실행 후 `RUNNING`, `SUCCEEDED`, `FAILED`로 변경됩니다.\n"
         "- `totalExpectedEvents`: 예상 전체 생성 건수\n"
-        "- `generatedCount`: 실제 생성한 이벤트 수\n"
-        "- `affectedRows`: DB insert/update 영향 row 수\n"
-        "- `storedCountInRange`: 해당 기간에 DB에 저장된 row 수\n"
-        "- `carPoolSize`: 사용한 차량 마스터 수\n"
-        "- `insertChunkSize`: batch insert 단위\n"
-        "- `processDistribution`: 공정별 생성 건수\n\n"
+        "- `generatedCount`, `affectedRows`: worker 진행 중 갱신되는 처리 건수\n"
+        "- `result`: 완료 전에는 `null`, 완료 후 job 상태 조회 API에서 최종 생성 결과가 채워집니다.\n\n"
         f"{EVENT_JSON_TABLE_DESCRIPTION}"
     ),
-    responses={200: {"description": "기간별 제조 이벤트 JSON 생성 및 적재 결과"}},
+    responses={202: {"description": "기간별 제조 이벤트 JSON 생성 job 생성 결과"}},
 )
 def generate_manufacturing_event_json(
     start_date: date = Query(
@@ -304,7 +306,7 @@ def generate_manufacturing_event_json(
         get_manufacturing_event_json_service,
     ),
 ) -> CommonResponse[dict]:
-    result = service.generate_range(
+    result = service.enqueue_generate_range_job(
         start_date=start_date,
         end_date=end_date,
         events_per_day=events_per_day,
@@ -313,7 +315,7 @@ def generate_manufacturing_event_json(
     )
     return success_response(
         data=result,
-        message="제조 원천 이벤트 JSON 생성 및 저장이 완료되었습니다.",
+        message="제조 원천 이벤트 JSON 생성 job이 생성되었습니다.",
     )
 
 
@@ -321,14 +323,18 @@ def generate_manufacturing_event_json(
     "/generate/tomorrow",
     summary="템플릿 기반 다음날 제조 이벤트 적재",
     operation_id="generateTomorrowManufacturingEventJson",
+    status_code=status.HTTP_202_ACCEPTED,
     description=(
         "저장된 템플릿을 기준으로 다음날 제조 관제 이벤트를 생성하고 "
-        "`manufacturing_event_json` 테이블에 저장하는 API입니다.\n\n"
+        "`manufacturing_event_json` 테이블에 저장하는 비동기 job을 생성하는 API입니다.\n\n"
         "### 언제 사용하는 API인가요?\n"
         "- 매일 다음날 관제 이벤트 데이터를 미리 생성/적재할 때 사용합니다.\n"
         "- 스케줄러도 이 API와 같은 내부 서비스 로직을 사용합니다.\n"
         "- `base_date`를 입력하지 않으면 서버 실행일 기준 다음날 데이터를 생성합니다.\n\n"
         "### 생성/저장 방식\n"
+        "- API는 job row를 생성한 뒤 즉시 `jobId`를 반환합니다.\n"
+        "- 실제 이벤트 생성과 DB 저장은 앱 내부 background worker가 수행합니다.\n"
+        "- 진행 상태는 job 상태 조회 API로 확인합니다.\n"
         "- target date는 `(base_date 또는 서버 현재 날짜) + 1일`입니다.\n"
         "- 지정한 `template_name`의 템플릿이 없거나 `events_per_day`보다 부족하면 템플릿을 먼저 생성합니다.\n"
         "- 템플릿의 `event_offset_us`를 target date에 더해 실제 `event_time`을 만듭니다.\n"
@@ -336,19 +342,16 @@ def generate_manufacturing_event_json(
         "- 템플릿 row를 `insert_chunk_size` 단위로 읽고, materialize된 이벤트를 같은 단위로 batch insert합니다.\n"
         "- 기본 batch insert 단위는 `1,000`건이며, 요청 파라미터로 `100~10,000` 사이에서 조정할 수 있습니다.\n"
         "- MySQL에서는 `event_id` 중복 시 기존 row의 이벤트 시간, 설비, 상태, JSON payload 등을 갱신합니다.\n\n"
-        "### 주요 응답 데이터\n"
-        "- `templateName`: 사용한 템플릿 이름\n"
-        "- `baseDate`: 다음날 계산 기준 날짜\n"
-        "- `targetDate`: 실제 생성/저장 대상 날짜\n"
-        "- `templateEventCount`: 사용 가능한 템플릿 이벤트 수\n"
-        "- `generatedCount`: 실제 생성한 이벤트 수\n"
-        "- `affectedRows`: DB insert/update 영향 row 수\n"
-        "- `storedCountInDate`: target date에 저장된 row 수\n"
-        "- `insertChunkSize`: batch insert 단위\n"
-        "- `templatePrepared`: 템플릿 준비 결과\n\n"
+        "### 즉시 응답 데이터\n"
+        "- `jobId`: 비동기 job 고유 ID\n"
+        "- `jobType`: `GENERATE_TOMORROW`\n"
+        "- `status`: 최초 상태는 `PENDING`, worker 실행 후 `RUNNING`, `SUCCEEDED`, `FAILED`로 변경됩니다.\n"
+        "- `totalExpectedEvents`: 예상 전체 생성 건수\n"
+        "- `generatedCount`, `affectedRows`: worker 진행 중 갱신되는 처리 건수\n"
+        "- `result`: 완료 전에는 `null`, 완료 후 job 상태 조회 API에서 최종 생성 결과가 채워집니다.\n\n"
         f"{EVENT_JSON_TABLE_DESCRIPTION}"
     ),
-    responses={200: {"description": "템플릿 기반 다음날 제조 이벤트 적재 결과"}},
+    responses={202: {"description": "템플릿 기반 다음날 제조 이벤트 적재 job 생성 결과"}},
 )
 def generate_tomorrow_manufacturing_event_json(
     base_date: date | None = Query(
@@ -381,7 +384,7 @@ def generate_tomorrow_manufacturing_event_json(
         get_manufacturing_event_json_service,
     ),
 ) -> CommonResponse[dict]:
-    result = service.generate_tomorrow(
+    result = service.enqueue_generate_tomorrow_job(
         base_date=base_date,
         template_name=template_name,
         events_per_day=events_per_day,
@@ -390,7 +393,43 @@ def generate_tomorrow_manufacturing_event_json(
     )
     return success_response(
         data=result,
-        message="템플릿 기반 다음날 제조 원천 이벤트 JSON 적재가 완료되었습니다.",
+        message="템플릿 기반 다음날 제조 원천 이벤트 JSON 적재 job이 생성되었습니다.",
+    )
+
+
+@router.get(
+    "/generate/jobs/{job_id}",
+    summary="제조 이벤트 생성 job 상태 조회",
+    operation_id="getManufacturingEventGenerationJob",
+    description=(
+        "`/generate`, `/generate/tomorrow`, `/templates/generate`에서 생성한 "
+        "비동기 제조 이벤트 생성 job의 진행 상태를 조회합니다.\n\n"
+        "### 상태 값\n"
+        "- `PENDING`: job이 생성되었고 worker 실행을 기다리는 상태\n"
+        "- `RUNNING`: worker가 생성/저장을 수행 중인 상태\n"
+        "- `SUCCEEDED`: 생성/저장이 완료된 상태\n"
+        "- `FAILED`: 생성/저장 중 오류가 발생한 상태\n\n"
+        "### 주요 응답 데이터\n"
+        "- `jobId`: job 고유 ID\n"
+        "- `jobType`: `GENERATE_RANGE`, `GENERATE_TOMORROW`, `GENERATE_TEMPLATE`\n"
+        "- `totalExpectedEvents`: 예상 전체 처리 건수\n"
+        "- `generatedCount`: 현재까지 생성한 이벤트 수\n"
+        "- `affectedRows`: 현재까지 DB insert/update 영향 row 수\n"
+        "- `result`: 성공 시 기존 동기 API가 반환하던 생성 결과\n"
+        "- `errorMessage`: 실패 시 오류 메시지"
+    ),
+    responses={200: {"description": "제조 이벤트 생성 job 상태"}},
+)
+def get_manufacturing_event_generation_job(
+    job_id: str,
+    service: ManufacturingEventJsonService = Depends(
+        get_manufacturing_event_json_service,
+    ),
+) -> CommonResponse[dict]:
+    result = service.get_generation_job(job_id)
+    return success_response(
+        data=result,
+        message="제조 이벤트 생성 job 상태 조회가 완료되었습니다.",
     )
 
 
diff --git a/app/main.py b/app/main.py
index 8c6a499..37b6107 100644
--- a/app/main.py
+++ b/app/main.py
@@ -11,6 +11,7 @@
     start_manufacturing_event_scheduler,
     stop_manufacturing_event_scheduler,
 )
+from app.service.manufacturing_event_json_service import resume_incomplete_generation_jobs
 
 
 logger = logging.getLogger(__name__)
@@ -57,6 +58,11 @@ def initialize_sampledb_schema() -> None:
 
     @app.on_event("startup")
     async def start_background_schedulers() -> None:
+        if settings.sample_database_connection_url:
+            try:
+                resume_incomplete_generation_jobs(settings.sample_database_connection_url)
+            except Exception:
+                logger.exception("미완료 제조 이벤트 생성 job 복구에 실패했습니다.")
         start_manufacturing_event_scheduler(app)
 
     @app.on_event("shutdown")
diff --git a/app/repository/sampledb_repository.py b/app/repository/sampledb_repository.py
index 8f1febb..0856cdd 100644
--- a/app/repository/sampledb_repository.py
+++ b/app/repository/sampledb_repository.py
@@ -8,6 +8,7 @@
 from app.repository.sampledb_schema import (
     car_master,
     equipment,
+    manufacturing_event_generation_job,
     manufacturing_event_json,
     manufacturing_event_template,
     metadata,
@@ -40,6 +41,7 @@ class SampleDbRepository:
     """sampledb 스키마와 제조 원천 이벤트 JSON 저장소."""
 
     def __init__(self, database_url: str) -> None:
+        self.database_url = database_url
         self.engine = create_engine(
             database_url,
             connect_args=mysql_connect_args_for_seoul(database_url),
@@ -216,6 +218,125 @@ def delete_template_events(self, template_name: str) -> int:
             result = conn.execute(statement)
             return int(result.rowcount or 0)
 
+    def create_generation_job(
+        self,
+        *,
+        job_id: str,
+        job_type: str,
+        request_json: dict[str, Any],
+        total_expected_events: int = 0,
+    ) -> dict[str, Any]:
+        now = datetime.now()
+        row = {
+            "job_id": job_id,
+            "job_type": job_type,
+            "status": "PENDING",
+            "request_json": request_json,
+            "total_expected_events": total_expected_events,
+            "generated_count": 0,
+            "affected_rows": 0,
+            "created_at": now,
+            "updated_at": now,
+        }
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name != "mysql":
+                next_id = int(
+                    conn.execute(
+                        select(func.max(manufacturing_event_generation_job.c.id)),
+                    ).scalar()
+                    or 0,
+                )
+                row = {**row, "id": next_id + 1}
+            conn.execute(manufacturing_event_generation_job.insert(), row)
+        return self.get_generation_job(job_id) or {}
+
+    def get_generation_job(self, job_id: str) -> dict[str, Any] | None:
+        query = select(manufacturing_event_generation_job).where(
+            manufacturing_event_generation_job.c.job_id == job_id,
+        )
+        with self.engine.connect() as conn:
+            row = conn.execute(query).mappings().first()
+            return _format_generation_job_row(dict(row)) if row else None
+
+    def list_resumable_generation_jobs(self) -> list[dict[str, Any]]:
+        query = (
+            select(manufacturing_event_generation_job)
+            .where(manufacturing_event_generation_job.c.status.in_(["PENDING", "RUNNING"]))
+            .order_by(
+                manufacturing_event_generation_job.c.created_at.asc(),
+                manufacturing_event_generation_job.c.id.asc(),
+            )
+        )
+        with self.engine.connect() as conn:
+            return [
+                _format_generation_job_row(dict(row))
+                for row in conn.execute(query).mappings()
+            ]
+
+    def mark_generation_job_running(self, job_id: str) -> None:
+        now = datetime.now()
+        self._update_generation_job(
+            job_id,
+            status="RUNNING",
+            started_at=now,
+            updated_at=now,
+        )
+
+    def update_generation_job_progress(
+        self,
+        job_id: str,
+        progress: dict[str, Any],
+    ) -> None:
+        self._update_generation_job(
+            job_id,
+            generated_count=int(progress.get("generatedCount", 0)),
+            affected_rows=int(progress.get("affectedRows", 0)),
+            total_expected_events=int(progress.get("totalExpectedEvents", 0)),
+            updated_at=datetime.now(),
+        )
+
+    def mark_generation_job_succeeded(
+        self,
+        job_id: str,
+        result_json: dict[str, Any],
+    ) -> None:
+        now = datetime.now()
+        self._update_generation_job(
+            job_id,
+            status="SUCCEEDED",
+            result_json=result_json,
+            error_message=None,
+            generated_count=int(result_json.get("generatedCount", 0)),
+            affected_rows=int(result_json.get("affectedRows", 0)),
+            total_expected_events=int(
+                result_json.get(
+                    "totalExpectedEvents",
+                    result_json.get("templateEventCount", result_json.get("eventCount", 0)),
+                ),
+            ),
+            finished_at=now,
+            updated_at=now,
+        )
+
+    def mark_generation_job_failed(self, job_id: str, error_message: str) -> None:
+        now = datetime.now()
+        self._update_generation_job(
+            job_id,
+            status="FAILED",
+            error_message=error_message[:1000],
+            finished_at=now,
+            updated_at=now,
+        )
+
+    def _update_generation_job(self, job_id: str, **values: Any) -> None:
+        statement = (
+            manufacturing_event_generation_job.update()
+            .where(manufacturing_event_generation_job.c.job_id == job_id)
+            .values(**values)
+        )
+        with self.engine.begin() as conn:
+            conn.execute(statement)
+
     def insert_event_json_rows(
         self,
         rows: Iterable[dict[str, Any]],
@@ -426,3 +547,21 @@ def initialize(self) -> None:
 def initialize_sampledb(database_url: str) -> None:
     """sampledb 테이블과 참조 설비 데이터를 초기화한다."""
     SampleDbRepository(database_url).initialize()
+
+
+def _format_generation_job_row(row: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "jobId": row["job_id"],
+        "jobType": row["job_type"],
+        "status": row["status"],
+        "request": row["request_json"],
+        "result": row["result_json"],
+        "errorMessage": row["error_message"],
+        "totalExpectedEvents": int(row["total_expected_events"] or 0),
+        "generatedCount": int(row["generated_count"] or 0),
+        "affectedRows": int(row["affected_rows"] or 0),
+        "createdAt": row["created_at"],
+        "startedAt": row["started_at"],
+        "finishedAt": row["finished_at"],
+        "updatedAt": row["updated_at"],
+    }
diff --git a/app/repository/sampledb_schema.py b/app/repository/sampledb_schema.py
index 7541516..090b122 100644
--- a/app/repository/sampledb_schema.py
+++ b/app/repository/sampledb_schema.py
@@ -132,6 +132,27 @@
     Index("idx_template_equipment_offset", "template_name", "equipment_code", "event_offset_us"),
 )
 
+manufacturing_event_generation_job = Table(
+    "manufacturing_event_generation_job",
+    metadata,
+    Column("id", BigInteger, primary_key=True, autoincrement=True),
+    Column("job_id", String(100), nullable=False, unique=True),
+    Column("job_type", String(50), nullable=False),
+    Column("status", String(30), nullable=False),
+    Column("request_json", JSON, nullable=False),
+    Column("result_json", JSON),
+    Column("error_message", String(1000)),
+    Column("total_expected_events", BigInteger, nullable=False, server_default="0"),
+    Column("generated_count", BigInteger, nullable=False, server_default="0"),
+    Column("affected_rows", BigInteger, nullable=False, server_default="0"),
+    Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+    Column("started_at", DateTime),
+    Column("finished_at", DateTime),
+    Column("updated_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+    Index("idx_generation_job_status_created", "status", "created_at"),
+    Index("idx_generation_job_type_created", "job_type", "created_at"),
+)
+
 thermal_vision = Table(
     "thermal_vision",
     metadata,
diff --git a/app/service/manufacturing_event_json_service.py b/app/service/manufacturing_event_json_service.py
index dcf6eee..cc24a65 100644
--- a/app/service/manufacturing_event_json_service.py
+++ b/app/service/manufacturing_event_json_service.py
@@ -2,8 +2,11 @@
 
 import copy
 import hashlib
+import logging
+from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, time, timedelta
 from typing import Any, Callable
+from uuid import uuid4
 
 from fastapi import status
 
@@ -22,6 +25,14 @@
 DEFAULT_TEMPLATE_NAME = "default"
 TEMPLATE_ANCHOR_DATE = date(2000, 1, 1)
 ProgressCallback = Callable[[dict[str, Any]], None]
+JOB_TYPE_GENERATE_RANGE = "GENERATE_RANGE"
+JOB_TYPE_GENERATE_TOMORROW = "GENERATE_TOMORROW"
+JOB_TYPE_GENERATE_TEMPLATE = "GENERATE_TEMPLATE"
+_generation_job_executor = ThreadPoolExecutor(
+    max_workers=1,
+    thread_name_prefix="manufacturing-event-generation-job",
+)
+logger = logging.getLogger(__name__)
 
 
 class ManufacturingEventJsonService:
@@ -131,6 +142,50 @@ def generate_range(
             "processDistribution": distribution,
         }
 
+    def enqueue_generate_range_job(
+        self,
+        *,
+        start_date: date,
+        end_date: date,
+        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+    ) -> dict[str, Any]:
+        if start_date > end_date:
+            raise AppException(
+                "start_date는 end_date보다 이후일 수 없습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if events_per_day < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
+            raise AppException(
+                "events_per_day는 최소 4 이상이어야 공정별 이벤트를 생성할 수 있습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if car_pool_size < 1:
+            raise AppException(
+                "car_pool_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if insert_chunk_size < 1:
+            raise AppException(
+                "insert_chunk_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+
+        request_json = {
+            "startDate": start_date.isoformat(),
+            "endDate": end_date.isoformat(),
+            "eventsPerDay": events_per_day,
+            "carPoolSize": car_pool_size,
+            "insertChunkSize": insert_chunk_size,
+        }
+        total_expected_events = ((end_date - start_date).days + 1) * events_per_day
+        return self._create_and_submit_generation_job(
+            job_type=JOB_TYPE_GENERATE_RANGE,
+            request_json=request_json,
+            total_expected_events=total_expected_events,
+        )
+
     def generate_template(
         self,
         *,
@@ -255,6 +310,44 @@ def generate_template(
             ],
         }
 
+    def enqueue_generate_template_job(
+        self,
+        *,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        event_count: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+        replace: bool = False,
+    ) -> dict[str, Any]:
+        if event_count < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
+            raise AppException(
+                "event_count는 최소 4 이상이어야 공정별 템플릿을 생성할 수 있습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if car_pool_size < 1:
+            raise AppException(
+                "car_pool_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if insert_chunk_size < 1:
+            raise AppException(
+                "insert_chunk_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+
+        request_json = {
+            "templateName": template_name,
+            "eventCount": event_count,
+            "carPoolSize": car_pool_size,
+            "insertChunkSize": insert_chunk_size,
+            "replace": replace,
+        }
+        return self._create_and_submit_generation_job(
+            job_type=JOB_TYPE_GENERATE_TEMPLATE,
+            request_json=request_json,
+            total_expected_events=event_count,
+        )
+
     def list_template_events(
         self,
         *,
@@ -450,6 +543,76 @@ def generate_tomorrow(
             "templatePrepared": template_result,
         }
 
+    def enqueue_generate_tomorrow_job(
+        self,
+        *,
+        base_date: date | None = None,
+        template_name: str = DEFAULT_TEMPLATE_NAME,
+        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
+    ) -> dict[str, Any]:
+        if events_per_day < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
+            raise AppException(
+                "events_per_day는 최소 4 이상이어야 공정별 이벤트를 생성할 수 있습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if car_pool_size < 1:
+            raise AppException(
+                "car_pool_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        if insert_chunk_size < 1:
+            raise AppException(
+                "insert_chunk_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+
+        request_json = {
+            "baseDate": base_date.isoformat() if base_date else None,
+            "templateName": template_name,
+            "eventsPerDay": events_per_day,
+            "carPoolSize": car_pool_size,
+            "insertChunkSize": insert_chunk_size,
+        }
+        return self._create_and_submit_generation_job(
+            job_type=JOB_TYPE_GENERATE_TOMORROW,
+            request_json=request_json,
+            total_expected_events=events_per_day,
+        )
+
+    def get_generation_job(self, job_id: str) -> dict[str, Any]:
+        self.repository.ensure_schema()
+        job = self.repository.get_generation_job(job_id)
+        if not job:
+            raise AppException(
+                "제조 이벤트 생성 job을 찾을 수 없습니다.",
+                status_code=status.HTTP_404_NOT_FOUND,
+            )
+        return job
+
+    def _create_and_submit_generation_job(
+        self,
+        *,
+        job_type: str,
+        request_json: dict[str, Any],
+        total_expected_events: int,
+    ) -> dict[str, Any]:
+        self.repository.ensure_schema()
+        job_id = str(uuid4())
+        job = self.repository.create_generation_job(
+            job_id=job_id,
+            job_type=job_type,
+            request_json=request_json,
+            total_expected_events=total_expected_events,
+        )
+        _generation_job_executor.submit(
+            _run_generation_job,
+            self.repository.database_url,
+            job_id,
+        )
+        return job
+
     def list_events(
         self,
         *,
@@ -480,6 +643,105 @@ def get_manufacturing_event_json_service() -> ManufacturingEventJsonService:
     )
 
 
+def _run_generation_job(database_url: str, job_id: str) -> None:
+    repository = SampleDbRepository(database_url)
+    service = ManufacturingEventJsonService(repository)
+    job = repository.get_generation_job(job_id)
+    if not job:
+        logger.warning("제조 이벤트 생성 job을 찾을 수 없습니다: job_id=%s", job_id)
+        return
+
+    try:
+        repository.mark_generation_job_running(job_id)
+        result = _execute_generation_job(service, repository, job)
+        repository.mark_generation_job_succeeded(job_id, result)
+        logger.info(
+            "제조 이벤트 생성 job 완료: job_id=%s job_type=%s generated=%s affected=%s",
+            job_id,
+            job["jobType"],
+            result.get("generatedCount"),
+            result.get("affectedRows"),
+        )
+    except Exception as exc:
+        message = getattr(exc, "message", str(exc))
+        repository.mark_generation_job_failed(job_id, message)
+        logger.exception(
+            "제조 이벤트 생성 job 실패: job_id=%s job_type=%s",
+            job_id,
+            job.get("jobType"),
+        )
+
+
+def _execute_generation_job(
+    service: ManufacturingEventJsonService,
+    repository: SampleDbRepository,
+    job: dict[str, Any],
+) -> dict[str, Any]:
+    request = job["request"]
+    progress_callback = lambda progress: repository.update_generation_job_progress(
+        job["jobId"],
+        progress,
+    )
+
+    if job["jobType"] == JOB_TYPE_GENERATE_RANGE:
+        return service.generate_range(
+            start_date=date.fromisoformat(request["startDate"]),
+            end_date=date.fromisoformat(request["endDate"]),
+            events_per_day=int(request["eventsPerDay"]),
+            car_pool_size=int(request["carPoolSize"]),
+            insert_chunk_size=int(request["insertChunkSize"]),
+            update_existing=True,
+            progress_callback=progress_callback,
+        )
+
+    if job["jobType"] == JOB_TYPE_GENERATE_TEMPLATE:
+        return service.generate_template(
+            template_name=str(request["templateName"]),
+            event_count=int(request["eventCount"]),
+            car_pool_size=int(request["carPoolSize"]),
+            insert_chunk_size=int(request["insertChunkSize"]),
+            replace=bool(request["replace"]),
+            update_existing=bool(request["replace"]),
+            progress_callback=progress_callback,
+        )
+
+    if job["jobType"] == JOB_TYPE_GENERATE_TOMORROW:
+        base_date = (
+            date.fromisoformat(request["baseDate"])
+            if request.get("baseDate")
+            else None
+        )
+        return service.generate_tomorrow(
+            base_date=base_date,
+            template_name=str(request["templateName"]),
+            events_per_day=int(request["eventsPerDay"]),
+            car_pool_size=int(request["carPoolSize"]),
+            insert_chunk_size=int(request["insertChunkSize"]),
+            update_existing=True,
+            progress_callback=progress_callback,
+        )
+
+    raise AppException(
+        "지원하지 않는 제조 이벤트 생성 job 유형입니다.",
+        status_code=status.HTTP_400_BAD_REQUEST,
+    )
+
+
+def resume_incomplete_generation_jobs(database_url: str) -> int:
+    repository = SampleDbRepository(database_url)
+    repository.ensure_schema()
+    jobs = repository.list_resumable_generation_jobs()
+    for job in jobs:
+        _generation_job_executor.submit(
+            _run_generation_job,
+            database_url,
+            job["jobId"],
+        )
+    if jobs:
+        logger.info("미완료 제조 이벤트 생성 job %s건을 worker에 재등록했습니다.", len(jobs))
+    return len(jobs)
+
+
 def _normalize_template_name(template_name: str) -> str:
     normalized = "".join(
         char.lower() if char.isalnum() else "_"

From 2a43cf2eb6b2e734d0022aab2e3b3e0f8f9c6dd1 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 17 Jun 2026 11:05:48 +0900
Subject: [PATCH 006/148] feat : risk, process repository add

---
 app/repository/quality_process_repository.py  | 122 +++++++++
 app/repository/quality_repository.py          | 130 ---------
 .../quality_risk_history_repository.py        | 228 ++++++++++++++++
 app/repository/quality_risk_trend_repository  | 257 ++++++++++++++++++
 .../quality_status_detail_repository.py       | 143 ++++++++++
 app/utils/rule_engin.py                       |  94 -------
 inspection_risk_trend.csv                     |  22 ++
 7 files changed, 772 insertions(+), 224 deletions(-)
 create mode 100644 app/repository/quality_process_repository.py
 delete mode 100644 app/repository/quality_repository.py
 create mode 100644 app/repository/quality_risk_history_repository.py
 create mode 100644 app/repository/quality_risk_trend_repository
 create mode 100644 app/repository/quality_status_detail_repository.py
 delete mode 100644 app/utils/rule_engin.py
 create mode 100644 inspection_risk_trend.csv

diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
new file mode 100644
index 0000000..c668367
--- /dev/null
+++ b/app/repository/quality_process_repository.py
@@ -0,0 +1,122 @@
+from sqlalchemy import create_engine, text
+from datetime import datetime
+import pandas as pd
+import random
+
+DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
+    "@127.0.0.1:13306/sampledb"
+)
+
+engine = create_engine(
+    DATABASE_URL,
+    pool_pre_ping=True
+)
+
+inspection_process_list = []
+
+process_names = [
+    "Visual",
+    "Function",
+    "Drive",
+    "Final"
+]
+
+with engine.connect() as conn:
+
+    total_vehicle_count = conn.execute(
+        text("""
+            SELECT COUNT(*)
+            FROM car_master
+        """)
+    ).scalar()
+
+    date_rows = conn.execute(
+        text("""
+            SELECT
+                DATE(created_at) AS process_date,
+                COUNT(*) AS vehicle_count
+            FROM car_master
+            GROUP BY DATE(created_at)
+            ORDER BY process_date
+        """)
+    ).mappings().all()
+
+    process_id = 1
+
+for date_row in date_rows:
+
+    process_date = date_row["process_date"]
+
+    total_vehicle_count = date_row["vehicle_count"]
+
+    for process_name in process_names:
+
+        completed_count = random.randint(
+            int(total_vehicle_count * 0.5),
+            total_vehicle_count
+        )
+
+        waiting_count = (
+            total_vehicle_count
+            - completed_count
+        )
+
+        progress_rate = round(
+            completed_count
+            / total_vehicle_count
+            * 100,
+            0
+        )
+
+        if progress_rate >= 90:
+            process_status = "COMPLETE"
+
+        elif progress_rate >= 70:
+            process_status = "RUNNING"
+
+        else:
+            process_status = "WAIT"
+
+        inspection_process = {
+            "id": process_id,
+            "process_name": process_name,
+            "total_vehicle_count": total_vehicle_count,
+            "completed_count": completed_count,
+            "waiting_count": waiting_count,
+            "progress_rate": progress_rate,
+            "process_status": process_status,
+            "created_at": process_date
+        }
+
+        inspection_process_list.append(
+            inspection_process
+        )
+
+        process_id += 1
+
+csv_file = "inspection_process.csv"
+df = pd.DataFrame(
+    inspection_process_list,
+    columns=[
+        "id",
+        "process_name",
+        "total_vehicle_count",
+        "completed_count",
+        "waiting_count",
+        "progress_rate",
+        "process_status",
+        "created_at"
+    ]
+)
+
+df.to_csv(
+    "inspection_process.csv",
+    index=False,
+    encoding="utf-8-sig"
+)
+
+print(df.head(20))
+print()
+print(f"CSV 저장 완료: {csv_file}")
+print(f"총 {len(df)}건")
\ No newline at end of file
diff --git a/app/repository/quality_repository.py b/app/repository/quality_repository.py
deleted file mode 100644
index d52b588..0000000
--- a/app/repository/quality_repository.py
+++ /dev/null
@@ -1,130 +0,0 @@
-from sqlalchemy import create_engine, text
-from openai import OpenAI
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-client = OpenAI(
-    api_key=os.getenv("OPENAI_API_KEY")
-)
-print(os.getenv("OPENAI_API_KEY"))
-DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
-    "@127.0.0.1:13306/sampledb"
-)
-
-engine = create_engine(
-    DATABASE_URL,
-    pool_pre_ping=True
-)
-models = client.models.list()
-
-for model in models.data:
-    print(model.id)
-
-def calculate_risk(status, control):
-
-    score = 100
-    reasons = []
-
-    speed = float(status["speed"])
-    rpm = int(status["att"])
-    battery = float(status["battery_voltage"])
-
-    if speed > 120:
-        score -= 20
-        reasons.append("과속")
-
-    if rpm > 4000:
-        score -= 20
-        reasons.append("고RPM")
-
-    if battery < 12.0:
-        score -= 10
-        reasons.append("배터리전압낮음")
-
-    if control["collision_warning"] == 1:
-        score -= 40
-        reasons.append("충돌경고")
-
-    if status["gear"] == "P" and speed > 20:
-        score -= 30
-        reasons.append("주행중 P기어")
-
-    return score, reasons
-
-
-def generate_comment(vehicle_id, score, reasons):
-
-    prompt = f"""
-차량번호: {vehicle_id}
-
-위험점수: {score}
-
-검출 이슈:
-{', '.join(reasons)}
-
-정비사가 작성하는 것처럼
-2줄 이내로 진단 결과를 작성하세요.
-"""
-
-    response = client.chat.completions.create(
-        model="gpt-4.1-mini",
-        messages=[
-            {
-                "role": "system",
-                "content": "당신은 자동차 품질검사 전문가입니다."
-            },
-            {
-                "role": "user",
-                "content": prompt
-            }
-        ]
-    )
-
-    return response.choices[0].message.content
-
-with engine.connect() as conn:
-    # 상태 데이터 1건 조회
-    status_row = conn.execute(
-        text("""
-        SELECT *
-        FROM car_status
-        LIMIT 1
-        """)
-    ).first()
-
-    # 제어 데이터 1건 조회
-    control_row = conn.execute(
-        text("""
-        SELECT *
-        FROM car_control
-        LIMIT 1
-        """)
-    ).first()
-
-    status_row = dict(status_row._mapping)
-    control_row = dict(control_row._mapping)
-
-    score, reasons = calculate_risk(
-        status_row,
-        control_row
-    )
-
-    comment = generate_comment(
-        status_row["vehicle_id"],
-        score,
-        reasons
-    )
-
-    inspection_result = {
-        "vehicle_id": status_row["vehicle_id"],
-        "inspection_score": score,
-        "inspection_result":
-            "FAIL" if score < 70 else
-            "WARN" if score < 90 else
-            "PASS",
-        "inspection_comment": comment
-    }
-
-    print(inspection_result)
\ No newline at end of file
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
new file mode 100644
index 0000000..fbd3b93
--- /dev/null
+++ b/app/repository/quality_risk_history_repository.py
@@ -0,0 +1,228 @@
+from sqlalchemy import create_engine, text
+import pandas as pd
+from datetime import datetime, timedelta
+
+DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
+    "@127.0.0.1:13306/sampledb"
+)
+
+# -------------------
+# Risk 계산 함수
+# -------------------
+
+def calculate_status_risk(row):
+    score = 100
+
+    if float(row["speed"]) > 120:
+        score -= 20
+
+    if int(row["att"]) > 4000:
+        score -= 20
+
+    if float(row["battery_voltage"]) < 12:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_control_risk(row):
+    score = 100
+
+    if row["collision_warning"] == 1:
+        score -= 40
+
+    if row["lane_departure"] == 1:
+        score -= 20
+
+    if row["traction_control"] == 1:
+        score -= 10
+
+    if row["abs_active"] == 1:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_drive_risk(row):
+    score = 100
+
+    if float(row["throttle_position"]) > 90:
+        score -= 20
+
+    if float(row["brake_pressure"]) > 45:
+        score -= 20
+
+    if abs(float(row["steering_angle"])) > 40:
+        score -= 20
+
+    return max(score, 0)
+
+
+def calculate_dynamics_risk(row):
+    score = 100
+
+    if abs(float(row["yaw_rate"])) > 7:
+        score -= 20
+
+    if abs(float(row["roll"])) > 4:
+        score -= 20
+
+    if abs(float(row["pitch"])) > 4:
+        score -= 20
+
+    return max(score, 0)
+
+
+# -------------------
+# DB
+# -------------------
+
+engine = create_engine(
+    DATABASE_URL,
+    pool_pre_ping=True
+)
+
+result = []
+risk_id = 1
+
+stage_plan = [
+    ("DRIVE", 6),
+    ("CONTROL", 5),
+    ("DYNAMICS", 3),
+    ("STATUS", 3)
+]
+
+start_date = datetime.strptime(
+    "2026-06-01 01:00",
+    "%Y-%m-%d %H:%M"
+)
+
+with engine.connect() as conn:
+
+    # 7일
+    for day in range(7):
+
+        day_start = start_date + timedelta(days=day)
+
+        # 하루 생산 차량 100대
+        offset = day * 100
+
+        vehicles = conn.execute(
+            text("""
+                SELECT DISTINCT vehicle_id
+                FROM car_status
+                ORDER BY vehicle_id
+                LIMIT 100 OFFSET :offset
+            """),
+            {"offset": offset}
+        ).mappings().all()
+
+        vehicle_ids = [v["vehicle_id"] for v in vehicles]
+
+        current_time = day_start
+
+        for stage_name, duration in stage_plan:
+
+            stage_start = current_time
+            stage_end = current_time + timedelta(hours=duration)
+
+            scores = []
+
+            for vehicle_id in vehicle_ids:
+
+                if stage_name == "DRIVE":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_drive
+                            WHERE vehicle_id=:vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_drive_risk(row)
+                        )
+
+                elif stage_name == "CONTROL":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_control
+                            WHERE vehicle_id=:vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_control_risk(row)
+                        )
+
+                elif stage_name == "DYNAMICS":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_dynamics
+                            WHERE vehicle_id=:vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_dynamics_risk(row)
+                        )
+
+                elif stage_name == "STATUS":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_status
+                            WHERE vehicle_id=:vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_status_risk(row)
+                        )
+
+            avg_score = (
+                round(sum(scores) / len(scores), 2)
+                if scores else 0
+            )
+
+            result.append({
+                "id": risk_id,
+                "inspection_type": stage_name,
+                "risk_score": avg_score,
+                "start_time": stage_start.strftime("%Y-%m-%d %H:%M"),
+                "end_time": stage_end.strftime("%Y-%m-%d %H:%M")
+            })
+
+            risk_id += 1
+
+            current_time = stage_end
+
+df = pd.DataFrame(result)
+
+df.to_csv(
+    "inspection_risk_history.csv",
+    index=False,
+    encoding="utf-8-sig"
+)
+
+print(df)
+print(f"총 생성 건수 : {len(df)}")
\ No newline at end of file
diff --git a/app/repository/quality_risk_trend_repository b/app/repository/quality_risk_trend_repository
new file mode 100644
index 0000000..e9c2770
--- /dev/null
+++ b/app/repository/quality_risk_trend_repository
@@ -0,0 +1,257 @@
+from sqlalchemy import create_engine, text
+import pandas as pd
+from datetime import datetime, timedelta
+
+DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
+    "@127.0.0.1:13306/sampledb"
+)
+
+# =========================
+# Risk 계산 함수
+# =========================
+
+def calculate_status_risk(row):
+    score = 100
+
+    if float(row["speed"]) > 120:
+        score -= 20
+
+    if int(row["att"]) > 4000:
+        score -= 20
+
+    if float(row["battery_voltage"]) < 12:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_control_risk(row):
+    score = 100
+
+    if row["collision_warning"] == 1:
+        score -= 40
+
+    if row["lane_departure"] == 1:
+        score -= 20
+
+    if row["traction_control"] == 1:
+        score -= 10
+
+    if row["abs_active"] == 1:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_drive_risk(row):
+    score = 100
+
+    if float(row["throttle_position"]) > 90:
+        score -= 20
+
+    if float(row["brake_pressure"]) > 45:
+        score -= 20
+
+    if abs(float(row["steering_angle"])) > 40:
+        score -= 20
+
+    return max(score, 0)
+
+
+def calculate_dynamics_risk(row):
+    score = 100
+
+    if abs(float(row["yaw_rate"])) > 7:
+        score -= 20
+
+    if abs(float(row["roll"])) > 4:
+        score -= 20
+
+    if abs(float(row["pitch"])) > 4:
+        score -= 20
+
+    return max(score, 0)
+
+
+# =========================
+# 등급 분류
+# =========================
+
+def get_risk_level(score):
+
+    if score >= 80:
+        return "LOW"
+
+    elif score >= 50:
+        return "MEDIUM"
+
+    return "HIGH"
+
+
+# =========================
+# DB
+# =========================
+
+engine = create_engine(
+    DATABASE_URL,
+    pool_pre_ping=True
+)
+
+result = []
+risk_id = 1
+
+base_date = datetime.strptime(
+    "2026-06-01",
+    "%Y-%m-%d"
+)
+
+with engine.connect() as conn:
+
+    for day in range(7):
+
+        current_date = base_date + timedelta(days=day)
+
+        offset = day * 100
+
+        vehicles = conn.execute(
+            text("""
+                SELECT DISTINCT vehicle_id
+                FROM car_status
+                ORDER BY vehicle_id
+                LIMIT 100 OFFSET :offset
+            """),
+            {"offset": offset}
+        ).mappings().all()
+
+        vehicle_ids = [
+            v["vehicle_id"]
+            for v in vehicles
+        ]
+
+        low_count = 0
+        medium_count = 0
+        high_count = 0
+
+        # =====================
+        # 차량 100대
+        # =====================
+
+        for vehicle_id in vehicle_ids:
+
+            scores = []
+
+            status = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_status
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if status:
+                scores.append(
+                    calculate_status_risk(status)
+                )
+
+            control = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_control
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if control:
+                scores.append(
+                    calculate_control_risk(control)
+                )
+
+            drive = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_drive
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if drive:
+                scores.append(
+                    calculate_drive_risk(drive)
+                )
+
+            dynamics = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_dynamics
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if dynamics:
+                scores.append(
+                    calculate_dynamics_risk(dynamics)
+                )
+
+            if not scores:
+                continue
+
+            avg_score = sum(scores) / len(scores)
+
+            level = get_risk_level(avg_score)
+
+            if level == "LOW":
+                low_count += 1
+
+            elif level == "MEDIUM":
+                medium_count += 1
+
+            else:
+                high_count += 1
+
+        # =====================
+        # 저장
+        # =====================
+
+        daily_result = [
+            ("LOW", low_count),
+            ("MEDIUM", medium_count),
+            ("HIGH", high_count)
+        ]
+
+        for level, count in daily_result:
+
+            result.append({
+                "id": risk_id,
+                "risk_level": level,
+                "risk_count": count,
+                "risk_ratio": round(count, 2),
+                "created_at": current_date.strftime(
+                    "%Y-%m-%d 00:00:00"
+                )
+            })
+
+            risk_id += 1
+
+# =========================
+# CSV 저장
+# =========================
+
+df = pd.DataFrame(result)
+
+df.to_csv(
+    "inspection_risk_trend.csv",
+    index=False,
+    encoding="utf-8-sig"
+)
+
+print(df)
+print(f"총 생성 건수 : {len(df)}")
\ No newline at end of file
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
new file mode 100644
index 0000000..a9ae315
--- /dev/null
+++ b/app/repository/quality_status_detail_repository.py
@@ -0,0 +1,143 @@
+from sqlalchemy import create_engine, text
+from decimal import Decimal
+import pandas as pd
+
+
+
+DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
+    "@127.0.0.1:13306/sampledb"
+)
+
+engine = create_engine(
+    DATABASE_URL,
+    pool_pre_ping=True
+)
+
+
+def calculate_status_score(status, control):
+
+    score = 100
+    issues = []
+
+    speed = float(status["speed"])
+    rpm = int(status["att"])
+    battery = float(status["battery_voltage"])
+
+    if speed > 120:
+        score -= 20
+        issues.append("over speed")
+
+    if rpm > 4000:
+        score -= 20
+        issues.append("RPM Error")
+
+    if battery < 12.0:
+        score -= 10
+        issues.append("battery drop")
+
+    if control["collision_warning"] == 1:
+        score -= 40
+        issues.append("crash warning")
+
+    if status["gear"] == "P" and speed > 20:
+        score -= 30
+        issues.append("Parking")
+
+    return max(score, 0), issues
+
+
+def get_result(score):
+
+    if score >= 90:
+        return "PASS"
+
+    if score >= 70:
+        return "WARN"
+
+    return "FAIL"
+
+
+with engine.connect() as conn:
+
+    master_rows = conn.execute(
+        text("""
+            SELECT *
+            FROM car_master
+        """)
+    ).mappings().all()
+
+    inspection_status_detail_list = []
+
+    for master_row in master_rows:
+
+        vehicle_id = master_row["vehicle_id"]
+
+        status_row = conn.execute(
+            text("""
+                SELECT *
+                FROM car_status
+                WHERE vehicle_id = :vehicle_id
+                ORDER BY created_at DESC
+                LIMIT 1
+            """),
+            {
+                "vehicle_id": vehicle_id
+            }
+        ).mappings().first()
+
+        control_row = conn.execute(
+            text("""
+                SELECT *
+                FROM car_control
+                WHERE vehicle_id = :vehicle_id
+                ORDER BY created_at DESC
+                LIMIT 1
+            """),
+            {
+                "vehicle_id": vehicle_id
+            }
+        ).mappings().first()
+
+        if not status_row or not control_row:
+            continue
+
+        score, issues = calculate_status_score(
+            status_row,
+            control_row
+        )
+
+        inspection_status_detail = {
+            "car_code": vehicle_id.split("-")[0],
+            "inspection_no": f"STATUS-{master_row['id']:05d}",
+            "vehicle_id": vehicle_id,
+            "speed": float(status_row["speed"]),
+            "att": int(status_row["att"]),
+            "gear": status_row["gear"],
+            "battery_voltage": float(status_row["battery_voltage"]),
+            "fuel_rate": float(status_row["fuel_rate"]),
+            "status_score": float(score),
+            "inspection_result": get_result(score),
+            "issue_message": ", ".join(issues) if issues else "정상",
+            "created_at": status_row["created_at"]
+        }
+
+        inspection_status_detail_list.append(
+            inspection_status_detail
+        )
+
+df = pd.DataFrame(
+    inspection_status_detail_list
+)
+
+csv_file = "inspection_status_detail.csv"
+
+df.to_csv(
+    csv_file,
+    index=False,
+    encoding="utf-8-sig"
+)
+
+print()
+print(f"CSV 저장 완료: {csv_file}")
+print(f"총 {len(df)}건")
\ No newline at end of file
diff --git a/app/utils/rule_engin.py b/app/utils/rule_engin.py
deleted file mode 100644
index 82ec2af..0000000
--- a/app/utils/rule_engin.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from openai import OpenAI
-
-client = OpenAI()
-def calculate_risk(status, control):
-
-    score = 100
-    reasons = []
-
-    speed = float(status["speed"])
-    rpm = int(status["att"])
-    battery = float(status["battery_voltage"])
-
-    if speed > 120:
-        score -= 20
-        reasons.append("과속")
-
-    if rpm > 4000:
-        score -= 20
-        reasons.append("고RPM")
-
-    if battery < 12.0:
-        score -= 10
-        reasons.append("배터리전압낮음")
-
-    if control["collision_warning"] == 1:
-        score -= 40
-        reasons.append("충돌경고")
-
-    if status["gear"] == "P" and speed > 20:
-        score -= 30
-        reasons.append("주행중 P기어")
-
-    return score, reasons
-
-def generate_comment(vehicle_id, score, reasons):
-
-    prompt = f"""
-    차량 진단 결과
-
-    차량번호: {vehicle_id}
-    위험점수: {score}
-
-    검출이슈:
-    {', '.join(reasons)}
-
-    정비사가 작성하는 것처럼
-    2줄 이내로 진단 결과를 작성하세요.
-    """
-
-    response = client.chat.completions.create(
-        model="gpt-4.1-mini",
-        messages=[
-            {
-                "role":"system",
-                "content":"당신은 자동차 품질검사 전문가입니다."
-            },
-            {
-                "role":"user",
-                "content":prompt
-            }
-        ]
-    )
-
-    return response.choices[0].message.content
-
-inspection_result = {
-    "vehicle_id": vehicle_id,
-    "score": score,
-    "result": result,
-    "issues": reasons
-}
-
-score, reasons = calculate_risk(
-    status_row,
-    control_row
-)
-
-comment = generate_comment(
-    status_row["vehicle_id"],
-    score,
-    reasons
-)
-
-inspection_result = {
-    "vehicle_id": status_row["vehicle_id"],
-    "inspection_score": score,
-    "inspection_result":
-        "FAIL" if score < 70 else
-        "WARN" if score < 90 else
-        "PASS",
-    "inspection_comment": comment
-}
-
-print(inspection_result)
\ No newline at end of file
diff --git a/inspection_risk_trend.csv b/inspection_risk_trend.csv
new file mode 100644
index 0000000..8f4a2d3
--- /dev/null
+++ b/inspection_risk_trend.csv
@@ -0,0 +1,22 @@
+id,risk_level,risk_count,risk_ratio,created_at
+1,LOW,67,67,2026-06-01 00:00:00
+2,MEDIUM,33,33,2026-06-01 00:00:00
+3,HIGH,0,0,2026-06-01 00:00:00
+4,LOW,67,67,2026-06-02 00:00:00
+5,MEDIUM,33,33,2026-06-02 00:00:00
+6,HIGH,0,0,2026-06-02 00:00:00
+7,LOW,66,66,2026-06-03 00:00:00
+8,MEDIUM,34,34,2026-06-03 00:00:00
+9,HIGH,0,0,2026-06-03 00:00:00
+10,LOW,66,66,2026-06-04 00:00:00
+11,MEDIUM,34,34,2026-06-04 00:00:00
+12,HIGH,0,0,2026-06-04 00:00:00
+13,LOW,64,64,2026-06-05 00:00:00
+14,MEDIUM,36,36,2026-06-05 00:00:00
+15,HIGH,0,0,2026-06-05 00:00:00
+16,LOW,63,63,2026-06-06 00:00:00
+17,MEDIUM,37,37,2026-06-06 00:00:00
+18,HIGH,0,0,2026-06-06 00:00:00
+19,LOW,61,61,2026-06-07 00:00:00
+20,MEDIUM,39,39,2026-06-07 00:00:00
+21,HIGH,0,0,2026-06-07 00:00:00

From 53631ff3c4db0ca250f76ddf3067f5b838fdbdf7 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 17 Jun 2026 11:15:02 +0900
Subject: [PATCH 007/148] fix : .env.example add

---
 .env.example | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 .env.example

diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..f9428aa
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,33 @@
+# Application
+APP_NAME=AI Service
+APP_VERSION=0.1.0
+DEBUG=dev
+LOG_LEVEL=INFO
+
+# External APIs
+OPENAI_API_KEY=
+AI_MANUAL_API_URL=
+COLLEAGUE_SKILL_API_URL=
+
+# Kafka
+KAFKA_BOOTSTRAP_SERVERS=
+
+# Redis Cache
+REDIS_URL=redis://localhost:6379/0
+REDIS_KEY_PREFIX=aims:ai-service
+REDIS_CACHE_TTL_SECONDS=300
+
+# Main MySQL DB
+# Bottleneck analysis results are written to this DB.
+MAIN_DATABASE_URL=mysql+pymysql://aims_user:change-me@localhost:3306/maindb?charset=utf8mb4
+
+# Sample MySQL DB
+# Use this DB for sample/source data connections when needed.
+SAMPLE_DATABASE_URL=mysql+pymysql://sample_user:change-me@localhost:3306/sampledb?charset=utf8mb4
+
+# Manufacturing source event JSON generation
+MANUFACTURING_EVENT_SCHEDULER_ENABLED=true
+MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY=86400
+MANUFACTURING_EVENT_TEMPLATE_EVENT_COUNT=86400
+MANUFACTURING_EVENT_CAR_POOL_SIZE=10000
+MANUFACTURING_EVENT_INSERT_CHUNK_SIZE=1000

From 87fe1d8cff74e1c649c229805b4a39b5e6130a4d Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 17 Jun 2026 12:49:13 +0900
Subject: [PATCH 008/148] =?UTF-8?q?refactor:=20=EC=A0=9C=EC=A1=B0=20?=
 =?UTF-8?q?=EC=9D=B4=EB=B2=A4=ED=8A=B8=20=EC=84=9C=EB=B9=84=EC=8A=A4=20?=
 =?UTF-8?q?=ED=8C=A8=ED=82=A4=EC=A7=80=20=EA=B5=AC=EC=A1=B0=20=EC=A0=95?=
 =?UTF-8?q?=EB=A6=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 제조 이벤트 JSON 서비스와 스케줄러를 app/service/manufacturing 패키지로 이동

- app.service.manufacturing 패키지에서 제조 이벤트 서비스, 스케줄러, 기본 상수 export

- API 라우터, 배치 로더, 앱 초기화 코드의 제조 이벤트 서비스 import 경로 수정

- 제조 이벤트 스케줄러 내부 참조를 manufacturing 패키지 기준 상대 import로 정리

- 기존 app/service 루트의 제조 이벤트 서비스 모듈 의존 제거
---
 app/api/routers/manufacturing_event.py        |  2 +-
 app/batch/manufacturing_event_loader.py       |  2 +-
 app/data_generation/__init__.py               |  1 +
 .../manufacturing_event_json_builder.py       |  2 +-
 app/main.py                                   |  4 +--
 app/service/manufacturing/__init__.py         | 28 +++++++++++++++++++
 .../manufacturing_event_json_service.py       |  2 +-
 .../manufacturing_event_scheduler.py          |  2 +-
 8 files changed, 36 insertions(+), 7 deletions(-)
 create mode 100644 app/data_generation/__init__.py
 rename app/{ml/preprocessing => data_generation}/manufacturing_event_json_builder.py (99%)
 create mode 100644 app/service/manufacturing/__init__.py
 rename app/service/{ => manufacturing}/manufacturing_event_json_service.py (99%)
 rename app/service/{ => manufacturing}/manufacturing_event_scheduler.py (98%)

diff --git a/app/api/routers/manufacturing_event.py b/app/api/routers/manufacturing_event.py
index 4a4fd2c..50b48d2 100644
--- a/app/api/routers/manufacturing_event.py
+++ b/app/api/routers/manufacturing_event.py
@@ -4,7 +4,7 @@
 from fastapi import APIRouter, Depends, Query, status
 
 from app.dto.response import CommonResponse
-from app.service.manufacturing_event_json_service import (
+from app.service.manufacturing import (
     DEFAULT_CAR_POOL_SIZE,
     DEFAULT_EVENTS_PER_DAY,
     DEFAULT_INSERT_CHUNK_SIZE,
diff --git a/app/batch/manufacturing_event_loader.py b/app/batch/manufacturing_event_loader.py
index 0be145d..2182d9b 100644
--- a/app/batch/manufacturing_event_loader.py
+++ b/app/batch/manufacturing_event_loader.py
@@ -8,7 +8,7 @@
 
 from app.core.config import settings
 from app.repository.sampledb_repository import SampleDbRepository
-from app.service.manufacturing_event_json_service import (
+from app.service.manufacturing import (
     DEFAULT_CAR_POOL_SIZE,
     DEFAULT_EVENTS_PER_DAY,
     DEFAULT_INSERT_CHUNK_SIZE,
diff --git a/app/data_generation/__init__.py b/app/data_generation/__init__.py
new file mode 100644
index 0000000..780f3cd
--- /dev/null
+++ b/app/data_generation/__init__.py
@@ -0,0 +1 @@
+"""Data generation utilities."""
diff --git a/app/ml/preprocessing/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
similarity index 99%
rename from app/ml/preprocessing/manufacturing_event_json_builder.py
rename to app/data_generation/manufacturing_event_json_builder.py
index af89c97..b9158a9 100644
--- a/app/ml/preprocessing/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -49,7 +49,7 @@
     },
 }
 
-DATASET_ROOT = Path(__file__).resolve().parents[1] / "datasets" / "process"
+DATASET_ROOT = Path(__file__).resolve().parents[1] / "ml" / "datasets" / "process"
 EVENT_DENSITY_WINDOWS: tuple[tuple[int, int, float], ...] = (
     (0, 5, 0.35),
     (5, 8, 0.8),
diff --git a/app/main.py b/app/main.py
index 37b6107..8abac48 100644
--- a/app/main.py
+++ b/app/main.py
@@ -7,11 +7,11 @@
 from app.core.exceptions import register_exception_handlers
 from app.core.logging import configure_logging
 from app.repository.sampledb_repository import initialize_sampledb
-from app.service.manufacturing_event_scheduler import (
+from app.service.manufacturing import (
+    resume_incomplete_generation_jobs,
     start_manufacturing_event_scheduler,
     stop_manufacturing_event_scheduler,
 )
-from app.service.manufacturing_event_json_service import resume_incomplete_generation_jobs
 
 
 logger = logging.getLogger(__name__)
diff --git a/app/service/manufacturing/__init__.py b/app/service/manufacturing/__init__.py
new file mode 100644
index 0000000..6330dca
--- /dev/null
+++ b/app/service/manufacturing/__init__.py
@@ -0,0 +1,28 @@
+"""Manufacturing service package."""
+
+from app.service.manufacturing.manufacturing_event_json_service import (
+    DEFAULT_CAR_POOL_SIZE,
+    DEFAULT_EVENTS_PER_DAY,
+    DEFAULT_INSERT_CHUNK_SIZE,
+    DEFAULT_TEMPLATE_NAME,
+    ManufacturingEventJsonService,
+    get_manufacturing_event_json_service,
+    resume_incomplete_generation_jobs,
+)
+from app.service.manufacturing.manufacturing_event_scheduler import (
+    start_manufacturing_event_scheduler,
+    stop_manufacturing_event_scheduler,
+)
+
+__all__ = [
+    "DEFAULT_CAR_POOL_SIZE",
+    "DEFAULT_EVENTS_PER_DAY",
+    "DEFAULT_INSERT_CHUNK_SIZE",
+    "DEFAULT_TEMPLATE_NAME",
+    "ManufacturingEventJsonService",
+    "get_manufacturing_event_json_service",
+    "resume_incomplete_generation_jobs",
+    "start_manufacturing_event_scheduler",
+    "stop_manufacturing_event_scheduler",
+]
+
diff --git a/app/service/manufacturing_event_json_service.py b/app/service/manufacturing/manufacturing_event_json_service.py
similarity index 99%
rename from app/service/manufacturing_event_json_service.py
rename to app/service/manufacturing/manufacturing_event_json_service.py
index cc24a65..d2419d9 100644
--- a/app/service/manufacturing_event_json_service.py
+++ b/app/service/manufacturing/manufacturing_event_json_service.py
@@ -12,7 +12,7 @@
 
 from app.core.config import settings
 from app.core.exceptions import AppException
-from app.ml.preprocessing.manufacturing_event_json_builder import (
+from app.data_generation.manufacturing_event_json_builder import (
     EventBuildRequest,
     ManufacturingEventJsonBuilder,
 )
diff --git a/app/service/manufacturing_event_scheduler.py b/app/service/manufacturing/manufacturing_event_scheduler.py
similarity index 98%
rename from app/service/manufacturing_event_scheduler.py
rename to app/service/manufacturing/manufacturing_event_scheduler.py
index ad02841..2f7bd8c 100644
--- a/app/service/manufacturing_event_scheduler.py
+++ b/app/service/manufacturing/manufacturing_event_scheduler.py
@@ -9,7 +9,7 @@
 
 from app.core.config import settings
 from app.repository.sampledb_repository import SampleDbRepository
-from app.service.manufacturing_event_json_service import (
+from .manufacturing_event_json_service import (
     DEFAULT_TEMPLATE_NAME,
     ManufacturingEventJsonService,
 )

From ba1caa66d7dc2bd5b22e7bd6634b8d888f2d3004 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 17 Jun 2026 12:52:23 +0900
Subject: [PATCH 009/148] =?UTF-8?q?refactor:=20=EC=A0=9C=EC=A1=B0=20?=
 =?UTF-8?q?=EC=9D=B4=EB=B2=A4=ED=8A=B8=20=EC=8A=A4=EC=BC=80=EC=A4=84?=
 =?UTF-8?q?=EB=9F=AC=20=ED=8C=A8=ED=82=A4=EC=A7=80=20=EB=B6=84=EB=A6=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 제조 이벤트 스케줄러를 service/manufacturing에서 scheduler/manufacturing 패키지로 이동

- app.scheduler 및 app.scheduler.manufacturing 패키지 추가

- app.scheduler.manufacturing에서 제조 이벤트 스케줄러 start, stop 함수 export

- service/manufacturing 패키지에서는 제조 이벤트 비즈니스 서비스만 export하도록 정리

- FastAPI startup, shutdown에서 scheduler 패키지 기준 import를 사용하도록 수정
---
 app/main.py                                           |  6 ++++--
 app/scheduler/__init__.py                             |  1 +
 app/scheduler/manufacturing/__init__.py               | 11 +++++++++++
 .../manufacturing/manufacturing_event_scheduler.py    |  2 +-
 app/service/manufacturing/__init__.py                 |  6 ------
 5 files changed, 17 insertions(+), 9 deletions(-)
 create mode 100644 app/scheduler/__init__.py
 create mode 100644 app/scheduler/manufacturing/__init__.py
 rename app/{service => scheduler}/manufacturing/manufacturing_event_scheduler.py (98%)

diff --git a/app/main.py b/app/main.py
index 8abac48..6aa93f8 100644
--- a/app/main.py
+++ b/app/main.py
@@ -7,11 +7,13 @@
 from app.core.exceptions import register_exception_handlers
 from app.core.logging import configure_logging
 from app.repository.sampledb_repository import initialize_sampledb
-from app.service.manufacturing import (
-    resume_incomplete_generation_jobs,
+from app.scheduler.manufacturing import (
     start_manufacturing_event_scheduler,
     stop_manufacturing_event_scheduler,
 )
+from app.service.manufacturing import (
+    resume_incomplete_generation_jobs,
+)
 
 
 logger = logging.getLogger(__name__)
diff --git a/app/scheduler/__init__.py b/app/scheduler/__init__.py
new file mode 100644
index 0000000..7d514c9
--- /dev/null
+++ b/app/scheduler/__init__.py
@@ -0,0 +1 @@
+"""Background scheduler package."""
diff --git a/app/scheduler/manufacturing/__init__.py b/app/scheduler/manufacturing/__init__.py
new file mode 100644
index 0000000..919de5d
--- /dev/null
+++ b/app/scheduler/manufacturing/__init__.py
@@ -0,0 +1,11 @@
+"""Manufacturing schedulers."""
+
+from app.scheduler.manufacturing.manufacturing_event_scheduler import (
+    start_manufacturing_event_scheduler,
+    stop_manufacturing_event_scheduler,
+)
+
+__all__ = [
+    "start_manufacturing_event_scheduler",
+    "stop_manufacturing_event_scheduler",
+]
diff --git a/app/service/manufacturing/manufacturing_event_scheduler.py b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
similarity index 98%
rename from app/service/manufacturing/manufacturing_event_scheduler.py
rename to app/scheduler/manufacturing/manufacturing_event_scheduler.py
index 2f7bd8c..e351186 100644
--- a/app/service/manufacturing/manufacturing_event_scheduler.py
+++ b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
@@ -9,7 +9,7 @@
 
 from app.core.config import settings
 from app.repository.sampledb_repository import SampleDbRepository
-from .manufacturing_event_json_service import (
+from app.service.manufacturing import (
     DEFAULT_TEMPLATE_NAME,
     ManufacturingEventJsonService,
 )
diff --git a/app/service/manufacturing/__init__.py b/app/service/manufacturing/__init__.py
index 6330dca..3cb7b42 100644
--- a/app/service/manufacturing/__init__.py
+++ b/app/service/manufacturing/__init__.py
@@ -9,10 +9,6 @@
     get_manufacturing_event_json_service,
     resume_incomplete_generation_jobs,
 )
-from app.service.manufacturing.manufacturing_event_scheduler import (
-    start_manufacturing_event_scheduler,
-    stop_manufacturing_event_scheduler,
-)
 
 __all__ = [
     "DEFAULT_CAR_POOL_SIZE",
@@ -22,7 +18,5 @@
     "ManufacturingEventJsonService",
     "get_manufacturing_event_json_service",
     "resume_incomplete_generation_jobs",
-    "start_manufacturing_event_scheduler",
-    "stop_manufacturing_event_scheduler",
 ]
 

From ceec153432e3d0123b6a779aa4fa3bf935555419 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Mon, 22 Jun 2026 08:52:11 +0900
Subject: [PATCH 010/148] feat : main branch mergeing

---
 .../quality_drive_detail_repository.py        | 141 ++++++++++++++
 app/repository/quality_process_repository.py  |  37 ++--
 .../quality_risk_history_repository.py        |  47 ++---
 app/repository/quality_risk_trend_repository  |  49 ++---
 .../quality_status_detail_repository.py       |  36 ++--
 app/repository/quality_summary_repository.py  | 180 ++++++++++++++++++
 inspection_risk_trend.csv                     |  22 ---
 7 files changed, 413 insertions(+), 99 deletions(-)
 create mode 100644 app/repository/quality_drive_detail_repository.py
 create mode 100644 app/repository/quality_summary_repository.py
 delete mode 100644 inspection_risk_trend.csv

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
new file mode 100644
index 0000000..9b26318
--- /dev/null
+++ b/app/repository/quality_drive_detail_repository.py
@@ -0,0 +1,141 @@
+from sqlalchemy import create_engine, text
+import pandas as pd
+
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+)
+
+MAIN_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+def calculate_drive_score(row):
+
+
+    score = 100
+
+    if float(row["throttle_position"]) > 90:
+        score -= 20
+
+    if float(row["brake_pressure"]) > 45:
+        score -= 20
+
+    if abs(float(row["steering_angle"])) > 40:
+        score -= 20
+
+    return round(score, 2)
+
+
+def get_driving_pattern(row):
+
+
+    throttle = float(row["throttle_position"])
+    brake = float(row["brake_pressure"])
+    steering = abs(float(row["steering_angle"]))
+
+    if throttle > 75:
+        return "RAPID_ACCEL"
+
+    if brake > 35:
+        return "HARD_BRAKE"
+
+    if steering > 40:
+        return "SHARP_TURN"
+
+    return "NORMAL"
+
+
+inspection_drive_detail_list = []
+
+with sample_engine.connect() as conn:
+
+
+    drive_rows = conn.execute(
+        text("""
+            SELECT *
+            FROM car_drive
+            ORDER BY created_at, vehicle_id
+        """)
+).mappings().all()
+
+detail_id = 1
+
+for row in drive_rows:
+
+    vehicle_id = row["vehicle_id"]
+
+    car_code = vehicle_id.split("-")[0]
+
+    inspection_no = (
+        f"DRIVE-{detail_id:05d}"
+    )
+
+    drive_score = calculate_drive_score(row)
+
+    driving_pattern = get_driving_pattern(row)
+
+    if drive_score >= 80:
+        inspection_result = "NORMAL"
+        issue_message = "NORMAL"
+    else:
+        inspection_result = "WARNING"
+        issue_message = "ACCEL_ALERT"
+
+    inspection_drive_detail_list.append({
+
+        "id": detail_id,
+        "car_code": car_code,
+        "inspection_no": inspection_no,
+        "vehicle_id": vehicle_id,
+
+        "throttle_position":
+            round(float(row["throttle_position"]), 2),
+
+        "brake_pressure":
+            round(float(row["brake_pressure"]), 2),
+
+        "steering_angle":
+            round(float(row["steering_angle"]), 2),
+
+        "drive_score": drive_score,
+
+        "inspection_result":
+            inspection_result,
+
+        "driving_pattern":
+            driving_pattern,
+
+        "issue_message":
+            issue_message,
+
+        "created_at":
+            row["created_at"]
+    })
+
+    detail_id += 1
+
+
+df = pd.DataFrame(
+    inspection_drive_detail_list
+)
+
+df.to_sql(
+    name="inspection_drive_detail",
+    con=main_engine,
+    if_exists="append",
+    index=False
+)
+
+print(
+    f"drive_detail table 전송 완료"
+)
\ No newline at end of file
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index c668367..c3826a7 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -3,13 +3,21 @@
 import pandas as pd
 import random
 
-DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
-    "@127.0.0.1:13306/sampledb"
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
 )
 
-engine = create_engine(
-    DATABASE_URL,
+MAIN_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
     pool_pre_ping=True
 )
 
@@ -22,7 +30,7 @@
     "Final"
 ]
 
-with engine.connect() as conn:
+with sample_engine.connect() as conn:
 
     total_vehicle_count = conn.execute(
         text("""
@@ -95,7 +103,6 @@
 
         process_id += 1
 
-csv_file = "inspection_process.csv"
 df = pd.DataFrame(
     inspection_process_list,
     columns=[
@@ -110,13 +117,13 @@
     ]
 )
 
-df.to_csv(
-    "inspection_process.csv",
-    index=False,
-    encoding="utf-8-sig"
+df.to_sql(
+    name="inspection_process",
+    con=main_engine,
+    if_exists="append",
+    index=False
 )
 
-print(df.head(20))
-print()
-print(f"CSV 저장 완료: {csv_file}")
-print(f"총 {len(df)}건")
\ No newline at end of file
+print(
+    f"inspection_process table 전송 완료"
+)
\ No newline at end of file
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index fbd3b93..8b67692 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -2,14 +2,23 @@
 import pandas as pd
 from datetime import datetime, timedelta
 
-DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
-    "@127.0.0.1:13306/sampledb"
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
 )
 
-# -------------------
-# Risk 계산 함수
-# -------------------
+MAIN_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
+    pool_pre_ping=True
+)
 
 def calculate_status_risk(row):
     score = 100
@@ -74,15 +83,6 @@ def calculate_dynamics_risk(row):
     return max(score, 0)
 
 
-# -------------------
-# DB
-# -------------------
-
-engine = create_engine(
-    DATABASE_URL,
-    pool_pre_ping=True
-)
-
 result = []
 risk_id = 1
 
@@ -98,7 +98,7 @@ def calculate_dynamics_risk(row):
     "%Y-%m-%d %H:%M"
 )
 
-with engine.connect() as conn:
+with sample_engine.connect() as conn:
 
     # 7일
     for day in range(7):
@@ -207,6 +207,7 @@ def calculate_dynamics_risk(row):
             result.append({
                 "id": risk_id,
                 "inspection_type": stage_name,
+                "inspection_round": day + 1,
                 "risk_score": avg_score,
                 "start_time": stage_start.strftime("%Y-%m-%d %H:%M"),
                 "end_time": stage_end.strftime("%Y-%m-%d %H:%M")
@@ -218,11 +219,13 @@ def calculate_dynamics_risk(row):
 
 df = pd.DataFrame(result)
 
-df.to_csv(
-    "inspection_risk_history.csv",
-    index=False,
-    encoding="utf-8-sig"
+df.to_sql(
+    name="inspection_risk_history",
+    con=main_engine,
+    if_exists="append",
+    index=False
 )
 
-print(df)
-print(f"총 생성 건수 : {len(df)}")
\ No newline at end of file
+print(
+    f"inspection_risk_history table 전송 완료"
+)
\ No newline at end of file
diff --git a/app/repository/quality_risk_trend_repository b/app/repository/quality_risk_trend_repository
index e9c2770..9d08ef9 100644
--- a/app/repository/quality_risk_trend_repository
+++ b/app/repository/quality_risk_trend_repository
@@ -2,14 +2,23 @@ from sqlalchemy import create_engine, text
 import pandas as pd
 from datetime import datetime, timedelta
 
-DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
-    "@127.0.0.1:13306/sampledb"
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
 )
 
-# =========================
-# Risk 계산 함수
-# =========================
+MAIN_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
+    pool_pre_ping=True
+)
 
 def calculate_status_risk(row):
     score = 100
@@ -93,11 +102,6 @@ def get_risk_level(score):
 # DB
 # =========================
 
-engine = create_engine(
-    DATABASE_URL,
-    pool_pre_ping=True
-)
-
 result = []
 risk_id = 1
 
@@ -106,7 +110,7 @@ base_date = datetime.strptime(
     "%Y-%m-%d"
 )
 
-with engine.connect() as conn:
+with sample_engine.connect() as conn:
 
     for day in range(7):
 
@@ -240,18 +244,15 @@ with engine.connect() as conn:
             })
 
             risk_id += 1
-
-# =========================
-# CSV 저장
-# =========================
-
+            
 df = pd.DataFrame(result)
-
-df.to_csv(
-    "inspection_risk_trend.csv",
-    index=False,
-    encoding="utf-8-sig"
+df.to_sql(
+    name="inspection_risk_trend",
+    con=main_engine,
+    if_exists="append",
+    index=False
 )
 
-print(df)
-print(f"총 생성 건수 : {len(df)}")
\ No newline at end of file
+print(
+    f"inspection_risk_trend table 전송 완료"
+)
\ No newline at end of file
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index a9ae315..8dde807 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -2,18 +2,23 @@
 from decimal import Decimal
 import pandas as pd
 
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+)
 
-
-DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-"
-    "@127.0.0.1:13306/sampledb"
+MAIN_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
 )
 
-engine = create_engine(
-    DATABASE_URL,
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
     pool_pre_ping=True
 )
 
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
+    pool_pre_ping=True
+)
 
 def calculate_status_score(status, control):
 
@@ -58,7 +63,7 @@ def get_result(score):
     return "FAIL"
 
 
-with engine.connect() as conn:
+with sample_engine.connect() as conn:
 
     master_rows = conn.execute(
         text("""
@@ -130,14 +135,13 @@ def get_result(score):
     inspection_status_detail_list
 )
 
-csv_file = "inspection_status_detail.csv"
-
-df.to_csv(
-    csv_file,
-    index=False,
-    encoding="utf-8-sig"
+df.to_sql(
+    name="inspection_status_detail",
+    con=main_engine,
+    if_exists="append",
+    index=False
 )
 
-print()
-print(f"CSV 저장 완료: {csv_file}")
-print(f"총 {len(df)}건")
\ No newline at end of file
+print(
+    f"inspection_status_detail table 전송 완료"
+)
\ No newline at end of file
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
new file mode 100644
index 0000000..80a0835
--- /dev/null
+++ b/app/repository/quality_summary_repository.py
@@ -0,0 +1,180 @@
+from sqlalchemy import create_engine, text
+import pandas as pd
+from datetime import datetime, timedelta
+
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+)
+
+MAIN_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+inspection_summary_list = []
+
+summary_id = 1
+
+base_date = datetime.strptime(
+    "2026-06-01",
+    "%Y-%m-%d"
+)
+
+with sample_engine.connect() as conn:
+
+    # 7일
+    for day in range(7):
+
+        current_date = (
+            base_date +
+            timedelta(days=day)
+        )
+
+        offset = day * 100
+
+        vehicles = conn.execute(
+            text("""
+                SELECT vehicle_id
+                FROM (
+                    SELECT DISTINCT vehicle_id
+                    FROM car_drive
+                    ORDER BY vehicle_id
+                    LIMIT 100 OFFSET :offset
+                ) t
+            """),
+            {
+                "offset": offset
+            }
+        ).mappings().all()
+
+        vehicle_ids = [
+            row["vehicle_id"]
+            for row in vehicles
+        ]
+
+        checkpoints = [
+
+            (25, "01:00"),
+            (50, "01:15"),
+            (75, "01:30"),
+            (100, "01:45")
+
+        ]
+
+        for target_count, time_str in checkpoints:
+
+            current_vehicle_ids = (
+                vehicle_ids[:target_count]
+            )
+
+            normal_count = 0
+            abnormal_count = 0
+
+            for vehicle_id in current_vehicle_ids:
+
+                drive_row = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_drive
+                        WHERE vehicle_id=:vehicle_id
+                        LIMIT 1
+                    """),
+                    {
+                        "vehicle_id": vehicle_id
+                    }
+                ).mappings().first()
+
+                if not drive_row:
+                    continue
+
+                score = 100
+
+                if float(
+                    drive_row["throttle_position"]
+                ) > 90:
+                    score -= 20
+
+                if float(
+                    drive_row["brake_pressure"]
+                ) > 45:
+                    score -= 20
+
+                if abs(float(
+                    drive_row["steering_angle"]
+                )) > 40:
+                    score -= 20
+
+                if score >= 80:
+                    normal_count += 1
+                else:
+                    abnormal_count += 1
+
+            total_count = target_count
+
+            standby_count = (
+                100 - total_count
+            )
+
+            created_at = datetime.strptime(
+                f"{current_date.strftime('%Y-%m-%d')} {time_str}",
+                "%Y-%m-%d %H:%M"
+            )
+
+            inspection_summary_list.append({
+
+                "id": summary_id,
+
+                "total_count": total_count,
+
+                "normal_count": normal_count,
+
+                "normal_rate":
+                    round(
+                        normal_count /
+                        total_count * 100,
+                        2
+                    ),
+
+                "abnormal_count":
+                    abnormal_count,
+
+                "abnormal_rate":
+                    round(
+                        abnormal_count /
+                        total_count * 100,
+                        2
+                    ),
+
+                "stanby_count":
+                    standby_count,
+
+                "created_at":
+                    created_at
+
+            })
+
+            summary_id += 1
+
+df = pd.DataFrame(
+    inspection_summary_list
+)
+
+df.to_sql(
+    name="inspection_summary",
+    con=main_engine,
+    if_exists="append",
+    index=False
+)
+
+print(
+    f"summary table 전송 완료"
+)
\ No newline at end of file
diff --git a/inspection_risk_trend.csv b/inspection_risk_trend.csv
deleted file mode 100644
index 8f4a2d3..0000000
--- a/inspection_risk_trend.csv
+++ /dev/null
@@ -1,22 +0,0 @@
-id,risk_level,risk_count,risk_ratio,created_at
-1,LOW,67,67,2026-06-01 00:00:00
-2,MEDIUM,33,33,2026-06-01 00:00:00
-3,HIGH,0,0,2026-06-01 00:00:00
-4,LOW,67,67,2026-06-02 00:00:00
-5,MEDIUM,33,33,2026-06-02 00:00:00
-6,HIGH,0,0,2026-06-02 00:00:00
-7,LOW,66,66,2026-06-03 00:00:00
-8,MEDIUM,34,34,2026-06-03 00:00:00
-9,HIGH,0,0,2026-06-03 00:00:00
-10,LOW,66,66,2026-06-04 00:00:00
-11,MEDIUM,34,34,2026-06-04 00:00:00
-12,HIGH,0,0,2026-06-04 00:00:00
-13,LOW,64,64,2026-06-05 00:00:00
-14,MEDIUM,36,36,2026-06-05 00:00:00
-15,HIGH,0,0,2026-06-05 00:00:00
-16,LOW,63,63,2026-06-06 00:00:00
-17,MEDIUM,37,37,2026-06-06 00:00:00
-18,HIGH,0,0,2026-06-06 00:00:00
-19,LOW,61,61,2026-06-07 00:00:00
-20,MEDIUM,39,39,2026-06-07 00:00:00
-21,HIGH,0,0,2026-06-07 00:00:00

From de0e938c945ec3108bed9d43f4728c6fb4f789bf Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Mon, 22 Jun 2026 13:09:26 +0900
Subject: [PATCH 011/148] del : qaulity_risk_trend_repository file delete

---
 ...ity_risk_trend_repository => quality_risk_trend_repository.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename app/repository/{quality_risk_trend_repository => quality_risk_trend_repository.py} (100%)

diff --git a/app/repository/quality_risk_trend_repository b/app/repository/quality_risk_trend_repository.py
similarity index 100%
rename from app/repository/quality_risk_trend_repository
rename to app/repository/quality_risk_trend_repository.py

From 5656e921a785f24d6ed7b5fba27c9995399f0a29 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 22 Jun 2026 13:39:57 +0900
Subject: [PATCH 012/148] =?UTF-8?q?feat:=20=EB=B6=88=EB=9F=89=20=ED=83=90?=
 =?UTF-8?q?=EC=A7=80=20=EB=AA=A8=EB=8D=B8=EB=A7=81=20=EB=B0=8F=20SHAP=20?=
 =?UTF-8?q?=EA=B8=B0=EB=B0=98=20=EC=A0=84=EC=9D=B4=20=EC=98=88=EC=B8=A1=20?=
 =?UTF-8?q?2=EC=B0=A8=20=EA=B5=AC=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/ml/artifacts/auxiliary_process_models.pkl |  Bin 561344 -> 2349619 bytes
 app/ml/artifacts/lightgbm_defect_detector.pkl |  Bin 38805 -> 0 bytes
 .../artifacts/selected_defect_detector.joblib |  Bin 0 -> 17069122 bytes
 .../defect_transfer_prediction_model.ipynb    | 4533 ++++++++++-------
 ...efect_transfer_prediction_model_json.ipynb | 2162 ++++++++
 .../manufacturing_event_scheduler.py          |    2 +-
 requirements.txt                              |    2 +
 7 files changed, 4721 insertions(+), 1978 deletions(-)
 delete mode 100644 app/ml/artifacts/lightgbm_defect_detector.pkl
 create mode 100644 app/ml/artifacts/selected_defect_detector.joblib
 create mode 100644 app/ml/training/defect_transfer_prediction_model_json.ipynb

diff --git a/app/ml/artifacts/auxiliary_process_models.pkl b/app/ml/artifacts/auxiliary_process_models.pkl
index 6b25ac606aae94810911db66529d12ef219729df..81f089a7d22b7ad3ac2a7aa9b49f2f3fa1c9c6bb 100644
GIT binary patch
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diff --git a/app/ml/artifacts/lightgbm_defect_detector.pkl b/app/ml/artifacts/lightgbm_defect_detector.pkl
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diff --git a/app/ml/training/defect_transfer_prediction_model.ipynb b/app/ml/training/defect_transfer_prediction_model.ipynb
index 0481ca6..8b53290 100644
--- a/app/ml/training/defect_transfer_prediction_model.ipynb
+++ b/app/ml/training/defect_transfer_prediction_model.ipynb
@@ -49,7 +49,9 @@
         "\n",
         "ensure_package(\"lightgbm\")\n",
         "ensure_package(\"shap\")\n",
-        "ensure_package(\"joblib\")\n"
+        "ensure_package(\"joblib\")\n",
+        "ensure_package(\"optuna\")\n",
+        "ensure_package(\"imblearn\", \"imbalanced-learn\")\n"
       ]
     },
     {
@@ -73,6 +75,16 @@
         ")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "id": "f7a3d724",
+      "metadata": {
+        "id": "f7a3d724"
+      },
+      "source": [
+        "> 필요한 추가 패키지(`optuna`, `imbalanced-learn`)는 위 설치 셀에서 자동 확인/설치합니다.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 3,
@@ -82,7 +94,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "e37daef5",
-        "outputId": "37dac073-c450-41e7-df96-91794aabc2e7"
+        "outputId": "d3014dc5-ad21-4f08-d5e8-65c43e05146f"
       },
       "outputs": [
         {
@@ -91,6 +103,7 @@
           "text": [
             "pandas 2.2.2\n",
             "lightgbm 4.6.0\n",
+            "optuna 4.9.0\n",
             "shap 0.52.0\n"
           ]
         }
@@ -102,17 +115,22 @@
         "import json\n",
         "import pickle\n",
         "import re\n",
+        "import shutil\n",
         "import warnings\n",
         "from datetime import datetime, timezone\n",
         "from pathlib import Path\n",
         "\n",
         "import joblib\n",
         "import lightgbm as lgb\n",
+        "import optuna\n",
         "import matplotlib.pyplot as plt\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "import seaborn as sns\n",
         "import shap\n",
+        "from imblearn.over_sampling import SMOTE\n",
+        "from imblearn.pipeline import Pipeline as ImbPipeline\n",
+        "from optuna.samplers import TPESampler\n",
         "from sklearn.base import clone\n",
         "from sklearn.ensemble import ExtraTreesClassifier, IsolationForest, RandomForestClassifier\n",
         "from sklearn.impute import SimpleImputer\n",
@@ -145,6 +163,7 @@
         "\n",
         "print(\"pandas\", pd.__version__)\n",
         "print(\"lightgbm\", lgb.__version__)\n",
+        "print(\"optuna\", optuna.__version__)\n",
         "print(\"shap\", shap.__version__)\n"
       ]
     },
@@ -160,14 +179,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 7,
       "id": "abb8856c",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "abb8856c",
-        "outputId": "0796b866-f21d-432a-f7f1-7b984bfc926c"
+        "outputId": "d0b5cf16-76a8-4d8e-cf3f-ce1f188f80a2"
       },
       "outputs": [
         {
@@ -238,7 +257,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 8,
       "id": "66fcea04",
       "metadata": {
         "id": "66fcea04"
@@ -255,14 +274,19 @@
         "CATEGORICAL_PROFILE_ROWS = 20_000\n",
         "MAX_AUX_ROWS = 200_000\n",
         "ENABLE_HYPERPARAMETER_TUNING = True\n",
-        "CV_N_SPLITS = 3\n",
-        "# 빠른 비교 모드 기본값\n",
-        "# 4개 모델 전체 조합을 큰 데이터로 CV하면 수십~수백 회 학습이 발생해 Colab에서 오래 걸립니다.\n",
-        "# 후보 비교는 stratified sample로 수행하고, 필요 시 선택 모델만 전체 데이터로 재학습합니다.\n",
-        "CV_N_ITER = 2\n",
-        "CV_SAMPLE_SIZE = 15_000\n",
-        "FINAL_TRAIN_SAMPLE_SIZE = 80_000\n",
+        "CV_N_SPLITS = 5\n",
+        "# 불균형 데이터에서는 PR-AUC/Recall 안정성을 위해 충분한 탐색 횟수를 사용합니다.\n",
+        "# 실행 시간이 부담되면 CV_SAMPLE_SIZE 또는 CV_N_ITER를 낮춰 빠른 실험 모드로 전환합니다.\n",
+        "CV_N_ITER = 15\n",
+        "ENABLE_OPTUNA_TUNING = True\n",
+        "CV_SAMPLE_SIZE = 30_000\n",
+        "FINAL_TRAIN_SAMPLE_SIZE = 120_000\n",
         "RETRAIN_SELECTED_MODEL_ON_FULL_DATA = False\n",
+        "USE_SMOTE = True\n",
+        "SMOTE_SAMPLING_STRATEGY = 0.10\n",
+        "RECALL_PRIORITY_TARGET = 0.35\n",
+        "RECALL_PRIORITY_MIN_PRECISION = 0.02\n",
+        "THRESHOLD_BETA = 2.0\n",
         "\n",
         "# 공정 매핑\n",
         "LINE_TO_PROCESS = {\n",
@@ -281,10 +305,66 @@
         "    \"UNKNOWN\": \"미분류\",\n",
         "}\n",
         "\n",
+        "# Colab Drive FUSE 연결이 불안정할 수 있어 데이터셋은 로컬 런타임 캐시에서 읽습니다.\n",
+        "LOCAL_DATA_CACHE = Path(\"/content/aims_dataset_cache\") if IN_COLAB else None\n",
+        "\n",
+        "\n",
+        "def local_dataset_path(path: Path) -> Path:\n",
+        "    path = Path(path)\n",
+        "    if not IN_COLAB:\n",
+        "        return path\n",
+        "    if LOCAL_DATA_CACHE is None:\n",
+        "        return path\n",
+        "\n",
+        "    try:\n",
+        "        relative_path = path.relative_to(PROJECT_ROOT)\n",
+        "    except ValueError:\n",
+        "        relative_path = Path(path.name)\n",
+        "\n",
+        "    cached_path = LOCAL_DATA_CACHE / relative_path\n",
+        "    if cached_path.exists():\n",
+        "        try:\n",
+        "            if cached_path.stat().st_size == path.stat().st_size:\n",
+        "                return cached_path\n",
+        "        except OSError:\n",
+        "            # Drive stat 자체가 실패하면 이미 복사된 로컬 캐시를 우선 사용합니다.\n",
+        "            return cached_path\n",
+        "\n",
+        "    cached_path.parent.mkdir(parents=True, exist_ok=True)\n",
+        "    try:\n",
+        "        print(f\"cache copy: {path} -> {cached_path}\")\n",
+        "        shutil.copy2(path, cached_path)\n",
+        "    except OSError as exc:\n",
+        "        raise OSError(\n",
+        "            \"Google Drive 연결이 끊겼습니다. Colab에서 drive.mount('/content/drive', force_remount=True)를 \"\n",
+        "            \"실행한 뒤 이 셀부터 다시 실행하세요.\"\n",
+        "        ) from exc\n",
+        "    return cached_path\n",
+        "\n",
+        "\n",
+        "def read_csv_stable(path: Path, **kwargs) -> pd.DataFrame:\n",
+        "    try:\n",
+        "        return pd.read_csv(local_dataset_path(path), **kwargs)\n",
+        "    except OSError as exc:\n",
+        "        if IN_COLAB and \"Transport endpoint is not connected\" in str(exc):\n",
+        "            raise OSError(\n",
+        "                \"Google Drive 연결이 끊겼습니다. drive.mount('/content/drive', force_remount=True)로 \"\n",
+        "                \"재마운트한 뒤 다시 실행하세요. 이미 캐시된 파일은 /content/aims_dataset_cache에서 읽습니다.\"\n",
+        "            ) from exc\n",
+        "        raise\n",
+        "\n",
+        "\n",
+        "def read_json_stable(path: Path):\n",
+        "    with open(local_dataset_path(path), \"r\", encoding=\"utf-8\") as f:\n",
+        "        return json.load(f)\n",
+        "\n",
+        "\n",
         "# 파일 검증\n",
         "def assert_file(path: Path):\n",
         "    if not path.exists():\n",
-        "        raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
+        "        cached_path = local_dataset_path(path) if IN_COLAB else path\n",
+        "        if not cached_path.exists():\n",
+        "            raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
         "\n",
         "# 메모리 축소\n",
         "def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
@@ -318,7 +398,7 @@
         "    min_non_null_ratio: float = 0.02,\n",
         ") -> list[str]:\n",
         "    assert_file(path)\n",
-        "    sample = pd.read_csv(path, nrows=profile_rows)\n",
+        "    sample = read_csv_stable(path, nrows=profile_rows)\n",
         "    candidates = [c for c in sample.columns if c not in required_cols]\n",
         "    non_null_ratio = sample[candidates].notna().mean()\n",
         "    nunique = sample[candidates].nunique(dropna=True)\n",
@@ -330,27 +410,30 @@
         "\n",
         "# 선택 컬럼 로드\n",
         "def read_selected_csv(path: Path, usecols: list[str], max_rows: int | None) -> pd.DataFrame:\n",
-        "    df = pd.read_csv(path, usecols=usecols, nrows=max_rows)\n",
+        "    df = read_csv_stable(path, usecols=usecols, nrows=max_rows)\n",
         "    return reduce_mem_usage(df)\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 9,
       "id": "508b9115",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 216
+          "height": 290
         },
         "id": "508b9115",
-        "outputId": "0be9ce1c-8216-4bef-be28-9e35642f398f"
+        "outputId": "eaef9a23-f57f-4c4d-85a1-69e744c2cc71"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
+            "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_numeric.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_numeric.csv\n",
+            "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_date.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_date.csv\n",
+            "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_categorical.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_categorical.csv\n",
             "numeric_df: (300000, 262)\n",
             "date_df: (300000, 261)\n",
             "categorical_df: (300000, 10)\n",
@@ -368,7 +451,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-4d424cbb-5e77-4d16-b366-aeb54394d6d1\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-626ebf76-9a02-48e2-974a-d5d99fd25fa4\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -409,7 +492,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d424cbb-5e77-4d16-b366-aeb54394d6d1')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-626ebf76-9a02-48e2-974a-d5d99fd25fa4')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -461,12 +544,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-4d424cbb-5e77-4d16-b366-aeb54394d6d1 button.colab-df-convert');\n",
+              "        document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4d424cbb-5e77-4d16-b366-aeb54394d6d1');\n",
+              "        const element = document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -525,7 +608,7 @@
         "        max_features=MAX_CATEGORICAL_FEATURES,\n",
         "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
         "    )\n",
-        "    categorical_df = pd.read_csv(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
+        "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
         "else:\n",
         "    categorical_df = None\n",
         "\n",
@@ -550,7 +633,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 10,
       "id": "dd4b603e",
       "metadata": {
         "colab": {
@@ -558,7 +641,7 @@
           "height": 273
         },
         "id": "dd4b603e",
-        "outputId": "72f7251a-6b36-43ea-aea9-b9d7ff11125d"
+        "outputId": "84ce5bbe-1ecd-490a-884b-4e42c0252e7f"
       },
       "outputs": [
         {
@@ -779,7 +862,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-b3740ba6-87db-4c4b-a170-c26fda81ce5b\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-82c8c8d6-bb6b-4406-9895-53a4298ecac0\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1549,7 +1632,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3740ba6-87db-4c4b-a170-c26fda81ce5b')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82c8c8d6-bb6b-4406-9895-53a4298ecac0')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1601,12 +1684,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-b3740ba6-87db-4c4b-a170-c26fda81ce5b button.colab-df-convert');\n",
+              "        document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-b3740ba6-87db-4c4b-a170-c26fda81ce5b');\n",
+              "        const element = document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1761,23 +1844,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 11,
       "id": "a2a68fb3",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 625
+          "height": 698
         },
         "id": "a2a68fb3",
-        "outputId": "aa032068-83f0-4cfa-bd5c-48baf9192752"
+        "outputId": "65f72cce-fd5b-4c16-9900-3b43b4e0e289"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
+            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv\n",
+            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json\n",
+            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv\n",
+            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json\n",
             "[LightGBM] [Info] Number of positive: 461, number of negative: 166\n",
-            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n",
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n",
             "You can set `force_col_wise=true` to remove the overhead.\n",
             "[LightGBM] [Info] Total Bins 1349\n",
             "[LightGBM] [Info] Number of data points in the train set: 627, number of used features: 7\n",
@@ -1827,7 +1914,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-d0f32f43-d914-4334-8da3-4cde224f77c5\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1924,7 +2011,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0f32f43-d914-4334-8da3-4cde224f77c5')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1976,12 +2063,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-d0f32f43-d914-4334-8da3-4cde224f77c5 button.colab-df-convert');\n",
+              "        document.querySelector('#df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d0f32f43-d914-4334-8da3-4cde224f77c5');\n",
+              "        const element = document.querySelector('#df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -2039,10 +2126,9 @@
         "def load_thermal_side(data_path: Path, label_path: Path, side: str) -> pd.DataFrame | None:\n",
         "    if not data_path.exists() or not label_path.exists():\n",
         "        return None\n",
-        "    data = pd.read_csv(data_path)\n",
+        "    data = read_csv_stable(data_path)\n",
         "    data.columns = [f\"t{i+1}\" for i in range(data.shape[1])]\n",
-        "    with open(label_path, \"r\", encoding=\"utf-8\") as f:\n",
-        "        labels = json.load(f)\n",
+        "    labels = read_json_stable(label_path)\n",
         "    data = data.apply(pd.to_numeric, errors=\"coerce\")\n",
         "    out = pd.DataFrame({\n",
         "        \"thermal_side\": side,\n",
@@ -2105,547 +2191,43 @@
       "source": [
         "### 4.1 Ford 엔진 진동 데이터 보조 학습\n",
         "\n",
-        "FordA 데이터는 첫 번째 컬럼이 라벨입니다. `-1`을 이상/불량, `1`을 정상으로 변환해 PRESS/BODY 계열 설비 이상 확률을 학습합니다.\n"
+        "FordA 데이터는 첫 번째 컬럼이 라벨입니다. 쉼표 또는 공백 구분 형식을 모두 지원하며, `-1`을 이상/불량, `1`을 정상으로 변환해 PRESS/BODY 계열 설비 이상 확률을 학습합니다.\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 13,
       "id": "144bc123",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "144bc123",
-        "outputId": "44bf0529-6132-40d4-fe76-ebc0f6497256"
+        "outputId": "b559b72a-3520-49e8-ef80-8add5e66a562"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
-            "[LightGBM] [Warning] Contains only one class\n",
-            "[LightGBM] [Info] Number of positive: 0, number of negative: 1\n",
-            "[LightGBM] [Info] Total Bins 0\n",
-            "[LightGBM] [Info] Number of data points in the train set: 1, number of used features: 0\n",
-            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000000 -> initscore=-34.538776\n",
-            "[LightGBM] [Info] Start training from score -34.538776\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-            "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+            "FordA_TRAIN.txt: rows=1, features=255, labels={-1.0: 1}\n",
+            "FordA_TEST.txt: rows=1,320, features=500, labels={-1.0: 681, 1.0: 639}\n",
+            "Ford TRAIN 데이터를 제외합니다(shape=(1, 256)). 정상적인 TEST 데이터(shape=(1320, 501))를 stratified 75:25로 재분할합니다.\n",
             "\n",
             "== Ford PRESS/BODY vibration auxiliary model ==\n",
-            "ROC-AUC: 0.5\n",
-            "PR-AUC: 0.51591\n",
+            "ROC-AUC: 0.72757\n",
+            "PR-AUC: 0.74268\n",
             "              precision    recall  f1-score   support\n",
             "\n",
-            "           0     0.4841    1.0000    0.6524       639\n",
-            "           1     0.0000    0.0000    0.0000       681\n",
+            "           0     0.6294    0.6687    0.6485       160\n",
+            "           1     0.6687    0.6294    0.6485       170\n",
             "\n",
-            "    accuracy                         0.4841      1320\n",
-            "   macro avg     0.2420    0.5000    0.3262      1320\n",
-            "weighted avg     0.2343    0.4841    0.3158      1320\n",
+            "    accuracy                         0.6485       330\n",
+            "   macro avg     0.6491    0.6491    0.6485       330\n",
+            "weighted avg     0.6497    0.6485    0.6485       330\n",
             "\n",
-            "aux_body_ford_vibration_abnormal_probability: 1321개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
-            "aux_press_ford_vibration_abnormal_probability: 1321개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
+            "aux_body_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
+            "aux_press_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
           ]
         }
       ],
@@ -2654,11 +2236,31 @@
         "def load_ford_txt(path: Path, max_rows: int | None = None) -> pd.DataFrame | None:\n",
         "    if not path.exists():\n",
         "        return None\n",
-        "    df = pd.read_csv(path, sep=r\"\\s+\", header=None, nrows=max_rows)\n",
-        "    df = df.apply(pd.to_numeric, errors=\"coerce\")\n",
-        "    df = df.dropna(axis=0, how=\"any\")\n",
+        "\n",
+        "    # 배포처에 따라 FordA가 comma 또는 whitespace 구분 형식으로 제공됩니다.\n",
+        "    df = read_csv_stable(\n",
+        "        path,\n",
+        "        sep=r\"[,\\s]+\",\n",
+        "        engine=\"python\",\n",
+        "        header=None,\n",
+        "        nrows=max_rows,\n",
+        "    )\n",
+        "    df = df.dropna(axis=1, how=\"all\").apply(pd.to_numeric, errors=\"coerce\")\n",
+        "    invalid_rows = int(df.isna().any(axis=1).sum())\n",
+        "    if invalid_rows:\n",
+        "        raise ValueError(f\"{path.name}: 숫자로 해석할 수 없는 행이 {invalid_rows:,}개 있습니다.\")\n",
+        "\n",
+        "    if df.shape[1] < 2:\n",
+        "        raise ValueError(f\"{path.name}: 라벨과 진동 feature를 구분할 수 없습니다. shape={df.shape}\")\n",
+        "\n",
+        "    labels = set(df.iloc[:, 0].astype(int).unique())\n",
+        "    if not labels or not labels.issubset({-1, 1}):\n",
+        "        raise ValueError(f\"{path.name}: 라벨은 -1 또는 1이어야 하지만 현재 {sorted(labels)}입니다.\")\n",
+        "\n",
+        "    ford_feature_count = df.shape[1] - 1\n",
         "    df.columns = [\"label\"] + [f\"ford_t{i}\" for i in range(1, df.shape[1])]\n",
         "    df[\"ford_abnormal_label\"] = (df[\"label\"] == -1).astype(\"int8\")\n",
+        "    print(f\"{path.name}: rows={len(df):,}, features={ford_feature_count}, labels={df['label'].value_counts().to_dict()}\")\n",
         "    return reduce_mem_usage(df.drop(columns=[\"label\"]))\n",
         "\n",
         "# Ford train/test 로드\n",
@@ -2668,22 +2270,58 @@
         "# Ford 보조 모델 학습\n",
         "if ford_train_df is not None and ford_test_df is not None:\n",
         "    ford_feature_cols = [c for c in ford_train_df.columns if c != \"ford_abnormal_label\"]\n",
-        "    X_ford_train = ford_train_df[ford_feature_cols]\n",
-        "    y_ford_train = ford_train_df[\"ford_abnormal_label\"].astype(int)\n",
-        "    X_ford_test = ford_test_df[ford_feature_cols]\n",
-        "    y_ford_test = ford_test_df[\"ford_abnormal_label\"].astype(int)\n",
+        "    ford_test_feature_cols = [c for c in ford_test_df.columns if c != \"ford_abnormal_label\"]\n",
+        "    train_is_usable = len(ford_train_df) >= 20 and ford_train_df[\"ford_abnormal_label\"].nunique() == 2\n",
+        "    test_is_usable = len(ford_test_df) >= 20 and ford_test_df[\"ford_abnormal_label\"].nunique() == 2\n",
+        "    feature_schema_matches = ford_feature_cols == ford_test_feature_cols\n",
+        "\n",
+        "    if train_is_usable and test_is_usable and feature_schema_matches:\n",
+        "        ford_all_df = pd.concat([ford_train_df, ford_test_df], ignore_index=True)\n",
+        "        X_ford_train = ford_train_df[ford_feature_cols]\n",
+        "        y_ford_train = ford_train_df[\"ford_abnormal_label\"].astype(int)\n",
+        "        X_ford_test = ford_test_df[ford_feature_cols]\n",
+        "        y_ford_test = ford_test_df[\"ford_abnormal_label\"].astype(int)\n",
+        "    elif test_is_usable:\n",
+        "        # 현재 배포본처럼 TRAIN이 잘렸어도 정상적인 TEST 데이터만으로 보조 모델을 구성합니다.\n",
+        "        ford_all_df = ford_test_df.copy()\n",
+        "        ford_feature_cols = ford_test_feature_cols\n",
+        "        print(\n",
+        "            f\"Ford TRAIN 데이터를 제외합니다(shape={ford_train_df.shape}). \"\n",
+        "            f\"정상적인 TEST 데이터(shape={ford_test_df.shape})를 stratified 75:25로 재분할합니다.\"\n",
+        "        )\n",
+        "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
+        "            ford_all_df[ford_feature_cols],\n",
+        "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
+        "            test_size=0.25,\n",
+        "            random_state=RANDOM_STATE,\n",
+        "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
+        "        )\n",
+        "    elif train_is_usable:\n",
+        "        ford_all_df = ford_train_df.copy()\n",
+        "        print(\"Ford TEST 데이터가 학습에 부적합하여 TRAIN 데이터를 stratified 75:25로 재분할합니다.\")\n",
+        "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
+        "            ford_all_df[ford_feature_cols],\n",
+        "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
+        "            test_size=0.25,\n",
+        "            random_state=RANDOM_STATE,\n",
+        "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
+        "        )\n",
+        "    else:\n",
+        "        raise ValueError(\n",
+        "            \"Ford 데이터에 두 클래스가 포함된 충분한 행이 없습니다. \"\n",
+        "            f\"train={ford_train_df.shape}, test={ford_test_df.shape}\"\n",
+        "        )\n",
         "\n",
         "    ford_model = lgb.LGBMClassifier(\n",
         "        objective=\"binary\", n_estimators=500, learning_rate=0.03, num_leaves=31,\n",
         "        min_child_samples=20, subsample=0.9, colsample_bytree=0.9,\n",
-        "        random_state=RANDOM_STATE, n_jobs=-1\n",
+        "        random_state=RANDOM_STATE, n_jobs=-1, verbosity=-1\n",
         "    )\n",
         "    # 진동 이상 확률 학습\n",
         "    ford_model.fit(X_ford_train, y_ford_train)\n",
         "    ford_test_proba = ford_model.predict_proba(X_ford_test)[:, 1]\n",
         "    evaluate_aux_classifier(\"Ford PRESS/BODY vibration\", y_ford_test, ford_test_proba)\n",
         "\n",
-        "    ford_all_df = pd.concat([ford_train_df, ford_test_df], ignore_index=True)\n",
         "    ford_all_proba = ford_model.predict_proba(ford_all_df[ford_feature_cols])[:, 1]\n",
         "    # 차체/프레스 보조 피처 추가\n",
         "    append_cycled_feature(features, \"aux_body_ford_vibration_abnormal_probability\", ford_all_proba)\n",
@@ -2708,7 +2346,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 14,
       "id": "cd25bfc8",
       "metadata": {
         "colab": {
@@ -2716,7 +2354,7 @@
           "height": 216
         },
         "id": "cd25bfc8",
-        "outputId": "b99485c6-bd73-43d0-eca4-5af597a8b77a"
+        "outputId": "73d3b745-218c-4f61-96fa-0022c8136bcb"
       },
       "outputs": [
         {
@@ -2740,7 +2378,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-8111ef84-e9be-40c3-af10-5b3a4c2773f9\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-43c145af-4222-4430-8081-0b1b5c54e8d6\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -2781,7 +2419,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8111ef84-e9be-40c3-af10-5b3a4c2773f9')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43c145af-4222-4430-8081-0b1b5c54e8d6')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -2833,12 +2471,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-8111ef84-e9be-40c3-af10-5b3a4c2773f9 button.colab-df-convert');\n",
+              "        document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-8111ef84-e9be-40c3-af10-5b3a4c2773f9');\n",
+              "        const element = document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -2897,7 +2535,7 @@
         "        max_features=MAX_CATEGORICAL_FEATURES,\n",
         "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
         "    )\n",
-        "    categorical_df = pd.read_csv(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
+        "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
         "else:\n",
         "    categorical_df = None\n",
         "\n",
@@ -2922,14 +2560,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 15,
       "id": "XX3xUzrlbEfB",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "XX3xUzrlbEfB",
-        "outputId": "a2c3defb-901c-4512-bf3d-07d616d9d1d4"
+        "outputId": "1b64eb6a-04d9-4fa0-ee50-df1fa3ea9a18"
       },
       "outputs": [
         {
@@ -2994,14 +2632,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 16,
       "id": "6e442104",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "6e442104",
-        "outputId": "227ded5c-058c-46ae-879f-05a88e5b8b6b"
+        "outputId": "09415cb9-862f-49f7-a3f3-30566b3940f4"
       },
       "outputs": [
         {
@@ -3081,23 +2719,29 @@
         "\n",
         "불량 클래스가 희소하므로 모든 후보 모델은 같은 전처리/피처 엔지니어링 결과를 사용하고, `StratifiedKFold`로 클래스 비율을 유지하며 비교합니다.\n",
         "\n",
+        "이번 버전은 기존의 약한 랜덤 탐색을 보완하기 위해 다음을 반영합니다.\n",
+        "\n",
+        "- `CV_N_SPLITS = 5`: 5-Fold Stratified CV로 validation 안정성 강화\n",
+        "- `CV_N_ITER = 15`: 비-LightGBM 후보도 15회 랜덤 탐색\n",
+        "- LightGBM: `Optuna` TPE sampler 기반 15회 탐색\n",
+        "- LightGBM pipeline: median imputation + SMOTE + `is_unbalance=True` 기반 불균형 보정\n",
+        "- threshold: Accuracy/F1 단독 최적화가 아니라 Recall 우선 탐색 적용\n",
+        "\n",
         "후보 모델:\n",
         "\n",
         "| 모델 | 목적 |\n",
         "| --- | --- |\n",
-        "| LightGBM | 대용량 tabular 제조 데이터용 gradient boosting 기준 모델 |\n",
+        "| LightGBM | 대용량 tabular 제조 데이터용 gradient boosting 기준 모델, Optuna + SMOTE 적용 |\n",
         "| RandomForest | bagging 기반 tree ensemble 기준 모델 |\n",
         "| ExtraTrees | 더 강한 randomization을 주는 tree ensemble 후보 |\n",
         "| LogisticRegression | 선형 기준선 모델 |\n",
         "\n",
-        "비교 지표는 PR-AUC, ROC-AUC, Accuracy, False Positive Rate, F1을 함께 사용합니다.\n",
-        "\n",
-        "`CV_SAMPLE_SIZE`, `CV_N_ITER`, `FINAL_TRAIN_SAMPLE_SIZE`는 실행 시간을 관리하는 핵심 설정입니다. 기본값은 빠른 비교 모드이며, 최종 제출/운영 모델이 필요할 때만 `RETRAIN_SELECTED_MODEL_ON_FULL_DATA=True`로 선택 모델 전체 재학습을 수행합니다.\n"
+        "비교 지표는 PR-AUC, Recall, Precision, F1, ROC-AUC, Accuracy, False Positive Rate를 함께 사용합니다. 특히 Bosch 데이터처럼 불량 비율이 1% 미만인 경우 Accuracy는 참고 지표로만 해석합니다.\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 17,
       "id": "d9a50e90",
       "metadata": {
         "colab": {
@@ -3105,116 +2749,667 @@
           "height": 1000
         },
         "id": "d9a50e90",
-        "outputId": "30d68956-9024-4ee0-8396-169616380eac"
+        "outputId": "60f68881-d294-44fc-92f2-008ba9ab2179"
       },
       "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:00:51,763] A new study created in memory with name: bosch_defect_lightgbm_recall_pr_auc\n"
+          ]
+        },
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "CV rows: 15,000 / train rows: 240,000\n",
-            "CV splits: 3, tuning trials per model: 2\n",
+            "CV rows: 30,000 / train rows: 240,000\n",
+            "CV splits: 5, tuning trials per model: 15\n",
+            "SMOTE enabled: True, sampling_strategy=0.1\n",
+            "Threshold strategy: recall>=0.35, min_precision>=0.02, beta=2.0\n",
             "\n",
-            "[LightGBM] CV start - trials: 2\n",
-            "  trial 1/2 params={'subsample': 0.85, 'reg_lambda': 1.0, 'num_leaves': 63, 'min_child_samples': 80, 'max_depth': 8, 'learning_rate': 0.06, 'colsample_bytree': 0.85}\n",
-            "    fold 1/3 PR-AUC=0.00869 F1=0.02994 elapsed=8.6s\n",
-            "    fold 2/3 PR-AUC=0.01054 F1=0.07317 elapsed=12.2s\n",
-            "    fold 3/3 PR-AUC=0.01394 F1=0.07059 elapsed=12.1s\n",
-            "  -> LightGBM trial 1 mean PR-AUC=0.01105 ROC-AUC=0.52870 ACC=0.96807 FPR=0.02715\n",
-            "  trial 2/2 params={'subsample': 0.85, 'reg_lambda': 2.0, 'num_leaves': 31, 'min_child_samples': 150, 'max_depth': 8, 'learning_rate': 0.06, 'colsample_bytree': 0.85}\n",
-            "    fold 1/3 PR-AUC=0.01117 F1=0.04878 elapsed=11.2s\n",
-            "    fold 2/3 PR-AUC=0.02039 F1=0.08333 elapsed=7.6s\n",
-            "    fold 3/3 PR-AUC=0.01637 F1=0.08955 elapsed=10.9s\n",
-            "  -> LightGBM trial 2 mean PR-AUC=0.01597 ROC-AUC=0.51714 ACC=0.98747 FPR=0.00744\n",
-            "[LightGBM] CV done elapsed=62.7s\n",
+            "[LightGBM] CV start\n",
+            "  optuna trial 1/15 params={'model__learning_rate': 0.021789307659775742, 'model__num_leaves': 154, 'model__max_depth': -1, 'model__min_child_samples': 264, 'model__subsample': 0.8603902541101232, 'model__colsample_bytree': 0.8686326600082205, 'model__reg_lambda': 0.1094395112835673, 'model__reg_alpha': 0.7579479953348001}\n",
+            "    fold 1/5 PR-AUC=0.03400 Recall=0.14706 F1=0.07353 elapsed=67.3s\n",
+            "    fold 2/5 PR-AUC=0.03235 Recall=0.35294 F1=0.06723 elapsed=59.2s\n",
+            "    fold 3/5 PR-AUC=0.03957 Recall=0.23529 F1=0.11268 elapsed=59.2s\n",
+            "    fold 4/5 PR-AUC=0.02377 Recall=0.41176 F1=0.03815 elapsed=60.1s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:05:57,466] Trial 0 finished with value: 0.06604691309715442 and parameters: {'learning_rate': 0.021789307659775742, 'num_leaves': 154, 'max_depth': -1, 'min_child_samples': 264, 'subsample': 0.8603902541101232, 'colsample_bytree': 0.8686326600082205, 'reg_lambda': 0.1094395112835673, 'reg_alpha': 0.7579479953348001}. Best is trial 0 with value: 0.06604691309715442.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.12554 Recall=0.35294 F1=0.07571 elapsed=59.8s\n",
+            "  optuna trial 2/15 params={'model__learning_rate': 0.0564638664293258, 'model__num_leaves': 53, 'model__max_depth': 9, 'model__min_child_samples': 108, 'model__subsample': 0.8641485131528328, 'model__colsample_bytree': 0.6127722372934189, 'model__reg_lambda': 0.359730743558148, 'model__reg_alpha': 0.002920433847181412}\n",
+            "    fold 1/5 PR-AUC=0.03474 Recall=0.23529 F1=0.06107 elapsed=42.5s\n",
+            "    fold 2/5 PR-AUC=0.04446 Recall=0.38235 F1=0.04180 elapsed=43.4s\n",
+            "    fold 3/5 PR-AUC=0.03871 Recall=0.23529 F1=0.12121 elapsed=43.7s\n",
+            "    fold 4/5 PR-AUC=0.02391 Recall=0.14706 F1=0.07092 elapsed=43.4s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:09:34,490] Trial 1 finished with value: 0.06631270724589355 and parameters: {'learning_rate': 0.0564638664293258, 'num_leaves': 53, 'max_depth': 9, 'min_child_samples': 108, 'subsample': 0.8641485131528328, 'colsample_bytree': 0.6127722372934189, 'reg_lambda': 0.359730743558148, 'reg_alpha': 0.002920433847181412}. Best is trial 1 with value: 0.06631270724589355.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.12504 Recall=0.29412 F1=0.11976 elapsed=44.0s\n",
+            "  optuna trial 3/15 params={'model__learning_rate': 0.025815006344207546, 'model__num_leaves': 131, 'model__max_depth': 12, 'model__min_child_samples': 76, 'model__subsample': 0.6727680575448478, 'model__colsample_bytree': 0.976998491764, 'model__reg_lambda': 6.881524386672037, 'model__reg_alpha': 0.17123375973163968}\n",
+            "    fold 1/5 PR-AUC=0.02393 Recall=0.05882 F1=0.09756 elapsed=76.2s\n",
+            "    fold 2/5 PR-AUC=0.04875 Recall=0.35294 F1=0.05010 elapsed=78.4s\n",
+            "    fold 3/5 PR-AUC=0.03501 Recall=0.35294 F1=0.04000 elapsed=73.9s\n",
+            "    fold 4/5 PR-AUC=0.02380 Recall=0.35294 F1=0.03877 elapsed=75.6s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:15:58,052] Trial 2 finished with value: 0.06402544874348826 and parameters: {'learning_rate': 0.025815006344207546, 'num_leaves': 131, 'max_depth': 12, 'min_child_samples': 76, 'subsample': 0.6727680575448478, 'colsample_bytree': 0.976998491764, 'reg_lambda': 6.881524386672037, 'reg_alpha': 0.17123375973163968}. Best is trial 1 with value: 0.06631270724589355.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.11511 Recall=0.35294 F1=0.04848 elapsed=79.5s\n",
+            "  optuna trial 4/15 params={'model__learning_rate': 0.018840552955192824, 'model__num_leaves': 37, 'model__max_depth': -1, 'model__min_child_samples': 276, 'model__subsample': 0.7405729935600059, 'model__colsample_bytree': 0.8481350279592919, 'model__reg_lambda': 0.39193516226890834, 'model__reg_alpha': 0.012030178871154668}\n",
+            "    fold 1/5 PR-AUC=0.02465 Recall=0.17647 F1=0.05607 elapsed=58.5s\n",
+            "    fold 2/5 PR-AUC=0.04893 Recall=0.38235 F1=0.05068 elapsed=59.0s\n",
+            "    fold 3/5 PR-AUC=0.04120 Recall=0.20588 F1=0.15909 elapsed=59.7s\n",
+            "    fold 4/5 PR-AUC=0.01779 Recall=0.35294 F1=0.03941 elapsed=58.0s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:20:50,337] Trial 3 finished with value: 0.06894370517883613 and parameters: {'learning_rate': 0.018840552955192824, 'num_leaves': 37, 'max_depth': -1, 'min_child_samples': 276, 'subsample': 0.7405729935600059, 'colsample_bytree': 0.8481350279592919, 'reg_lambda': 0.39193516226890834, 'reg_alpha': 0.012030178871154668}. Best is trial 3 with value: 0.06894370517883613.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.13862 Recall=0.35294 F1=0.09231 elapsed=57.1s\n",
+            "  optuna trial 5/15 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
+            "    fold 1/5 PR-AUC=0.03658 Recall=0.08824 F1=0.10345 elapsed=56.6s\n",
+            "    fold 2/5 PR-AUC=0.04709 Recall=0.35294 F1=0.08247 elapsed=54.5s\n",
+            "    fold 3/5 PR-AUC=0.05326 Recall=0.23529 F1=0.16162 elapsed=50.2s\n",
+            "    fold 4/5 PR-AUC=0.02418 Recall=0.23529 F1=0.05926 elapsed=53.0s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:25:17,267] Trial 4 finished with value: 0.07319973398830097 and parameters: {'learning_rate': 0.031169409812378812, 'num_leaves': 49, 'max_depth': -1, 'min_child_samples': 279, 'subsample': 0.6809723757181718, 'colsample_bytree': 0.6381922880886154, 'reg_lambda': 0.12191905861905929, 'reg_alpha': 0.0020013420622879987}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.14607 Recall=0.26471 F1=0.21176 elapsed=52.6s\n",
+            "  optuna trial 6/15 params={'model__learning_rate': 0.022439365289703053, 'model__num_leaves': 61, 'model__max_depth': -1, 'model__min_child_samples': 247, 'model__subsample': 0.6760927252879199, 'model__colsample_bytree': 0.9940991214702328, 'model__reg_lambda': 2.94884053630599, 'model__reg_alpha': 0.0006235377135673159}\n",
+            "    fold 1/5 PR-AUC=0.02457 Recall=0.05882 F1=0.10000 elapsed=66.4s\n",
+            "    fold 2/5 PR-AUC=0.04620 Recall=0.38235 F1=0.04019 elapsed=64.8s\n",
+            "    fold 3/5 PR-AUC=0.03686 Recall=0.23529 F1=0.13445 elapsed=65.1s\n",
+            "    fold 4/5 PR-AUC=0.02458 Recall=0.20588 F1=0.07000 elapsed=64.7s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:30:43,306] Trial 5 finished with value: 0.06304171041876949 and parameters: {'learning_rate': 0.022439365289703053, 'num_leaves': 61, 'max_depth': -1, 'min_child_samples': 247, 'subsample': 0.6760927252879199, 'colsample_bytree': 0.9940991214702328, 'reg_lambda': 2.94884053630599, 'reg_alpha': 0.0006235377135673159}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.12711 Recall=0.23529 F1=0.20253 elapsed=65.0s\n",
+            "  optuna trial 7/15 params={'model__learning_rate': 0.01011549101545285, 'model__num_leaves': 135, 'model__max_depth': 7, 'model__min_child_samples': 61, 'model__subsample': 0.9520861990564577, 'model__colsample_bytree': 0.8304841570724011, 'model__reg_lambda': 0.4263131389740422, 'model__reg_alpha': 0.00017956984225677631}\n",
+            "    fold 1/5 PR-AUC=0.02317 Recall=0.08824 F1=0.07317 elapsed=56.3s\n",
+            "    fold 2/5 PR-AUC=0.04950 Recall=0.26471 F1=0.08000 elapsed=56.3s\n",
+            "    fold 3/5 PR-AUC=0.02767 Recall=0.14706 F1=0.14085 elapsed=50.9s\n",
+            "    fold 4/5 PR-AUC=0.01344 Recall=0.23529 F1=0.04598 elapsed=51.6s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:35:10,406] Trial 6 finished with value: 0.04217109041808953 and parameters: {'learning_rate': 0.01011549101545285, 'num_leaves': 135, 'max_depth': 7, 'min_child_samples': 61, 'subsample': 0.9520861990564577, 'colsample_bytree': 0.8304841570724011, 'reg_lambda': 0.4263131389740422, 'reg_alpha': 0.00017956984225677631}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.04267 Recall=0.35294 F1=0.05085 elapsed=52.1s\n",
+            "  optuna trial 8/15 params={'model__learning_rate': 0.0190917184400185, 'model__num_leaves': 68, 'model__max_depth': 7, 'model__min_child_samples': 223, 'model__subsample': 0.9162747670159141, 'model__colsample_bytree': 0.8025747389062734, 'model__reg_lambda': 2.9323777832216886, 'model__reg_alpha': 0.009444574254983563}\n",
+            "    fold 1/5 PR-AUC=0.01607 Recall=0.17647 F1=0.03261 elapsed=36.0s\n",
+            "    fold 2/5 PR-AUC=0.03486 Recall=0.38235 F1=0.03922 elapsed=34.4s\n",
+            "    fold 3/5 PR-AUC=0.02913 Recall=0.23529 F1=0.12030 elapsed=36.2s\n",
+            "    fold 4/5 PR-AUC=0.01392 Recall=0.14706 F1=0.05181 elapsed=33.5s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:38:04,664] Trial 7 finished with value: 0.05305343371432367 and parameters: {'learning_rate': 0.0190917184400185, 'num_leaves': 68, 'max_depth': 7, 'min_child_samples': 223, 'subsample': 0.9162747670159141, 'colsample_bytree': 0.8025747389062734, 'reg_lambda': 2.9323777832216886, 'reg_alpha': 0.009444574254983563}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.10658 Recall=0.35294 F1=0.04301 elapsed=34.2s\n",
+            "  optuna trial 9/15 params={'model__learning_rate': 0.029653419677342325, 'model__num_leaves': 82, 'model__max_depth': 9, 'model__min_child_samples': 167, 'model__subsample': 0.9676482658741326, 'model__colsample_bytree': 0.6621815031169938, 'model__reg_lambda': 0.6039425546072186, 'model__reg_alpha': 0.10524574681335624}\n",
+            "    fold 1/5 PR-AUC=0.03325 Recall=0.11765 F1=0.05556 elapsed=40.7s\n",
+            "    fold 2/5 PR-AUC=0.03970 Recall=0.23529 F1=0.09524 elapsed=41.2s\n",
+            "    fold 3/5 PR-AUC=0.03826 Recall=0.23529 F1=0.15385 elapsed=42.5s\n",
+            "    fold 4/5 PR-AUC=0.01437 Recall=0.26471 F1=0.03805 elapsed=37.8s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:41:27,137] Trial 8 finished with value: 0.060833017107763024 and parameters: {'learning_rate': 0.029653419677342325, 'num_leaves': 82, 'max_depth': 9, 'min_child_samples': 167, 'subsample': 0.9676482658741326, 'colsample_bytree': 0.6621815031169938, 'reg_lambda': 0.6039425546072186, 'reg_alpha': 0.10524574681335624}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.11828 Recall=0.35294 F1=0.09160 elapsed=40.3s\n",
+            "  optuna trial 10/15 params={'model__learning_rate': 0.016092567235133776, 'model__num_leaves': 34, 'model__max_depth': 7, 'model__min_child_samples': 266, 'model__subsample': 0.9312852269146901, 'model__colsample_bytree': 0.6339565264987161, 'model__reg_lambda': 4.995973117313505, 'model__reg_alpha': 0.014367095138664226}\n",
+            "    fold 1/5 PR-AUC=0.01984 Recall=0.14706 F1=0.04016 elapsed=30.5s\n",
+            "    fold 2/5 PR-AUC=0.02887 Recall=0.35294 F1=0.04969 elapsed=29.4s\n",
+            "    fold 3/5 PR-AUC=0.03842 Recall=0.17647 F1=0.16438 elapsed=30.6s\n",
+            "    fold 4/5 PR-AUC=0.01492 Recall=0.14706 F1=0.05495 elapsed=29.2s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:43:58,707] Trial 9 finished with value: 0.0581151434945654 and parameters: {'learning_rate': 0.016092567235133776, 'num_leaves': 34, 'max_depth': 7, 'min_child_samples': 266, 'subsample': 0.9312852269146901, 'colsample_bytree': 0.6339565264987161, 'reg_lambda': 4.995973117313505, 'reg_alpha': 0.014367095138664226}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.12971 Recall=0.35294 F1=0.04829 elapsed=31.8s\n",
+            "  optuna trial 11/15 params={'model__learning_rate': 0.07344753762582334, 'model__num_leaves': 100, 'model__max_depth': 5, 'model__min_child_samples': 179, 'model__subsample': 0.7804393429857105, 'model__colsample_bytree': 0.7211318321133148, 'model__reg_lambda': 0.10707258976378216, 'model__reg_alpha': 0.00011815152243566209}\n",
+            "    fold 1/5 PR-AUC=0.02487 Recall=0.05882 F1=0.09524 elapsed=22.2s\n",
+            "    fold 2/5 PR-AUC=0.03263 Recall=0.17647 F1=0.06522 elapsed=22.8s\n",
+            "    fold 3/5 PR-AUC=0.02345 Recall=0.20588 F1=0.06306 elapsed=21.8s\n",
+            "    fold 4/5 PR-AUC=0.00869 Recall=0.14706 F1=0.03802 elapsed=25.2s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:45:53,501] Trial 10 finished with value: 0.039906002434268506 and parameters: {'learning_rate': 0.07344753762582334, 'num_leaves': 100, 'max_depth': 5, 'min_child_samples': 179, 'subsample': 0.7804393429857105, 'colsample_bytree': 0.7211318321133148, 'reg_lambda': 0.10707258976378216, 'reg_alpha': 0.00011815152243566209}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.07460 Recall=0.11765 F1=0.13333 elapsed=22.7s\n",
+            "  optuna trial 12/15 params={'model__learning_rate': 0.03828385438925741, 'model__num_leaves': 24, 'model__max_depth': -1, 'model__min_child_samples': 290, 'model__subsample': 0.7500490079557848, 'model__colsample_bytree': 0.7430730526600867, 'model__reg_lambda': 0.26877401109804494, 'model__reg_alpha': 0.0018756206282287391}\n",
+            "    fold 1/5 PR-AUC=0.03004 Recall=0.11765 F1=0.04790 elapsed=42.9s\n",
+            "    fold 2/5 PR-AUC=0.04640 Recall=0.17647 F1=0.09449 elapsed=43.4s\n",
+            "    fold 3/5 PR-AUC=0.03590 Recall=0.20588 F1=0.14433 elapsed=43.8s\n",
+            "    fold 4/5 PR-AUC=0.01742 Recall=0.35294 F1=0.04444 elapsed=42.8s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:49:29,067] Trial 11 finished with value: 0.06729149493282813 and parameters: {'learning_rate': 0.03828385438925741, 'num_leaves': 24, 'max_depth': -1, 'min_child_samples': 290, 'subsample': 0.7500490079557848, 'colsample_bytree': 0.7430730526600867, 'reg_lambda': 0.26877401109804494, 'reg_alpha': 0.0018756206282287391}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.14640 Recall=0.35294 F1=0.06742 elapsed=42.6s\n",
+            "  optuna trial 13/15 params={'model__learning_rate': 0.0128289748714244, 'model__num_leaves': 45, 'model__max_depth': -1, 'model__min_child_samples': 205, 'model__subsample': 0.7368227996831814, 'model__colsample_bytree': 0.5575289958995627, 'model__reg_lambda': 0.7798089384438257, 'model__reg_alpha': 0.02173049208731439}\n",
+            "    fold 1/5 PR-AUC=0.02159 Recall=0.08824 F1=0.09375 elapsed=49.2s\n",
+            "    fold 2/5 PR-AUC=0.04449 Recall=0.41176 F1=0.04230 elapsed=48.5s\n",
+            "    fold 3/5 PR-AUC=0.05169 Recall=0.23529 F1=0.16327 elapsed=50.1s\n",
+            "    fold 4/5 PR-AUC=0.01912 Recall=0.35294 F1=0.04928 elapsed=49.4s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:53:35,467] Trial 12 finished with value: 0.06395427302355366 and parameters: {'learning_rate': 0.0128289748714244, 'num_leaves': 45, 'max_depth': -1, 'min_child_samples': 205, 'subsample': 0.7368227996831814, 'colsample_bytree': 0.5575289958995627, 'reg_lambda': 0.7798089384438257, 'reg_alpha': 0.02173049208731439}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.11083 Recall=0.35294 F1=0.05660 elapsed=49.3s\n",
+            "  optuna trial 14/15 params={'model__learning_rate': 0.03688230536757648, 'model__num_leaves': 84, 'model__max_depth': -1, 'model__min_child_samples': 299, 'model__subsample': 0.7156364210016786, 'model__colsample_bytree': 0.8810259041893101, 'model__reg_lambda': 0.199522384883775, 'model__reg_alpha': 0.0024334796925402718}\n",
+            "    fold 1/5 PR-AUC=0.04648 Recall=0.17647 F1=0.05854 elapsed=63.5s\n",
+            "    fold 2/5 PR-AUC=0.03300 Recall=0.35294 F1=0.06818 elapsed=63.2s\n",
+            "    fold 3/5 PR-AUC=0.03487 Recall=0.20588 F1=0.09459 elapsed=62.5s\n",
+            "    fold 4/5 PR-AUC=0.02476 Recall=0.23529 F1=0.06349 elapsed=60.1s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 01:58:50,528] Trial 13 finished with value: 0.07042822156591197 and parameters: {'learning_rate': 0.03688230536757648, 'num_leaves': 84, 'max_depth': -1, 'min_child_samples': 299, 'subsample': 0.7156364210016786, 'colsample_bytree': 0.8810259041893101, 'reg_lambda': 0.199522384883775, 'reg_alpha': 0.0024334796925402718}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.15126 Recall=0.26471 F1=0.16216 elapsed=65.7s\n",
+            "  optuna trial 15/15 params={'model__learning_rate': 0.04187329932090051, 'model__num_leaves': 98, 'model__max_depth': 5, 'model__min_child_samples': 300, 'model__subsample': 0.654599110501233, 'model__colsample_bytree': 0.9091999481071245, 'model__reg_lambda': 0.1837271427904195, 'model__reg_alpha': 0.0009412519611659042}\n",
+            "    fold 1/5 PR-AUC=0.01758 Recall=0.05882 F1=0.08000 elapsed=26.5s\n",
+            "    fold 2/5 PR-AUC=0.03039 Recall=0.35294 F1=0.05042 elapsed=23.1s\n",
+            "    fold 3/5 PR-AUC=0.02245 Recall=0.20588 F1=0.08235 elapsed=26.7s\n",
+            "    fold 4/5 PR-AUC=0.01160 Recall=0.20588 F1=0.03743 elapsed=30.8s\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2026-06-18 02:01:05,723] Trial 14 finished with value: 0.04538489857292094 and parameters: {'learning_rate': 0.04187329932090051, 'num_leaves': 98, 'max_depth': 5, 'min_child_samples': 300, 'subsample': 0.654599110501233, 'colsample_bytree': 0.9091999481071245, 'reg_lambda': 0.1837271427904195, 'reg_alpha': 0.0009412519611659042}. Best is trial 4 with value: 0.07319973398830097.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    fold 5/5 PR-AUC=0.09784 Recall=0.11765 F1=0.18605 elapsed=28.1s\n",
+            "[LightGBM] Optuna best value=0.07320 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
+            "[LightGBM] CV done elapsed=3614.0s\n",
             "\n",
-            "[RandomForest] CV start - trials: 2\n",
-            "  trial 1/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
-            "    fold 1/3 PR-AUC=0.01045 F1=0.04444 elapsed=4.5s\n",
-            "    fold 2/3 PR-AUC=0.00819 F1=0.02265 elapsed=5.8s\n",
-            "    fold 3/3 PR-AUC=0.01138 F1=0.04545 elapsed=4.1s\n",
-            "  -> RandomForest trial 1 mean PR-AUC=0.01000 ROC-AUC=0.60324 ACC=0.91380 FPR=0.08206\n",
-            "  trial 2/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/3 PR-AUC=0.00945 F1=0.02749 elapsed=4.3s\n",
-            "    fold 2/3 PR-AUC=0.00930 F1=0.03093 elapsed=6.2s\n",
-            "    fold 3/3 PR-AUC=0.01270 F1=0.05607 elapsed=4.3s\n",
-            "  -> RandomForest trial 2 mean PR-AUC=0.01048 ROC-AUC=0.59570 ACC=0.93047 FPR=0.06537\n",
-            "[RandomForest] CV done elapsed=29.2s\n",
+            "[RandomForest] CV start\n",
+            "[RandomForest] random search trials: 15\n",
+            "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.01770 Recall=0.14706 F1=0.09615 elapsed=18.6s\n",
+            "    fold 2/5 PR-AUC=0.03344 Recall=0.17647 F1=0.07500 elapsed=18.4s\n",
+            "    fold 3/5 PR-AUC=0.03194 Recall=0.35294 F1=0.04364 elapsed=21.1s\n",
+            "    fold 4/5 PR-AUC=0.01529 Recall=0.32353 F1=0.03520 elapsed=18.1s\n",
+            "    fold 5/5 PR-AUC=0.06988 Recall=0.38235 F1=0.05029 elapsed=18.1s\n",
+            "  -> RandomForest trial 1 mean PR-AUC=0.03365 Recall=0.27647 F1=0.06006 FPR=0.05830\n",
+            "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.01163 Recall=0.08824 F1=0.07407 elapsed=114.9s\n",
+            "    fold 2/5 PR-AUC=0.02605 Recall=0.26471 F1=0.07860 elapsed=120.6s\n",
+            "    fold 3/5 PR-AUC=0.02343 Recall=0.20588 F1=0.08805 elapsed=112.0s\n",
+            "    fold 4/5 PR-AUC=0.00923 Recall=0.14706 F1=0.02817 elapsed=116.7s\n",
+            "    fold 5/5 PR-AUC=0.02658 Recall=0.35294 F1=0.04096 elapsed=128.2s\n",
+            "  -> RandomForest trial 2 mean PR-AUC=0.01938 Recall=0.21176 F1=0.06197 FPR=0.04036\n",
+            "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.01449 Recall=0.11765 F1=0.07547 elapsed=20.0s\n",
+            "    fold 2/5 PR-AUC=0.01800 Recall=0.17647 F1=0.07143 elapsed=18.6s\n",
+            "    fold 3/5 PR-AUC=0.02582 Recall=0.35294 F1=0.04082 elapsed=19.0s\n",
+            "    fold 4/5 PR-AUC=0.01543 Recall=0.05882 F1=0.08163 elapsed=19.4s\n",
+            "    fold 5/5 PR-AUC=0.04959 Recall=0.38235 F1=0.04815 elapsed=20.2s\n",
+            "  -> RandomForest trial 3 mean PR-AUC=0.02467 Recall=0.21765 F1=0.06350 FPR=0.04170\n",
+            "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.01905 Recall=0.11765 F1=0.12121 elapsed=110.6s\n",
+            "    fold 2/5 PR-AUC=0.02432 Recall=0.35294 F1=0.03865 elapsed=112.2s\n",
+            "    fold 3/5 PR-AUC=0.03628 Recall=0.17647 F1=0.13636 elapsed=106.3s\n",
+            "    fold 4/5 PR-AUC=0.02079 Recall=0.08824 F1=0.05660 elapsed=110.0s\n",
+            "    fold 5/5 PR-AUC=0.04705 Recall=0.26471 F1=0.11465 elapsed=112.8s\n",
+            "  -> RandomForest trial 4 mean PR-AUC=0.02950 Recall=0.20000 F1=0.09350 FPR=0.02796\n",
+            "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.01373 Recall=0.08824 F1=0.05505 elapsed=19.7s\n",
+            "    fold 2/5 PR-AUC=0.01550 Recall=0.14706 F1=0.04587 elapsed=18.3s\n",
+            "    fold 3/5 PR-AUC=0.01997 Recall=0.20588 F1=0.06452 elapsed=18.3s\n",
+            "    fold 4/5 PR-AUC=0.00914 Recall=0.50000 F1=0.02353 elapsed=18.2s\n",
+            "    fold 5/5 PR-AUC=0.02526 Recall=0.17647 F1=0.05455 elapsed=18.7s\n",
+            "  -> RandomForest trial 5 mean PR-AUC=0.01672 Recall=0.22353 F1=0.04870 FPR=0.06708\n",
+            "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.02763 Recall=0.14706 F1=0.11765 elapsed=20.0s\n",
+            "    fold 2/5 PR-AUC=0.03999 Recall=0.35294 F1=0.04225 elapsed=19.2s\n",
+            "    fold 3/5 PR-AUC=0.03442 Recall=0.38235 F1=0.04370 elapsed=19.1s\n",
+            "    fold 4/5 PR-AUC=0.01981 Recall=0.05882 F1=0.09302 elapsed=20.5s\n",
+            "    fold 5/5 PR-AUC=0.09888 Recall=0.35294 F1=0.04453 elapsed=19.6s\n",
+            "  -> RandomForest trial 6 mean PR-AUC=0.04415 Recall=0.25882 F1=0.06823 FPR=0.05417\n",
+            "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
+            "    fold 1/5 PR-AUC=0.01178 Recall=0.17647 F1=0.04762 elapsed=18.0s\n",
+            "    fold 2/5 PR-AUC=0.01400 Recall=0.20588 F1=0.05128 elapsed=18.1s\n",
+            "    fold 3/5 PR-AUC=0.01885 Recall=0.08824 F1=0.09677 elapsed=17.9s\n",
+            "    fold 4/5 PR-AUC=0.00916 Recall=0.14706 F1=0.02611 elapsed=18.6s\n",
+            "    fold 5/5 PR-AUC=0.01926 Recall=0.35294 F1=0.04278 elapsed=18.0s\n",
+            "  -> RandomForest trial 7 mean PR-AUC=0.01461 Recall=0.19412 F1=0.05291 FPR=0.04452\n",
+            "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
+            "    fold 1/5 PR-AUC=0.01255 Recall=0.17647 F1=0.04651 elapsed=95.6s\n",
+            "    fold 2/5 PR-AUC=0.01555 Recall=0.17647 F1=0.06593 elapsed=98.3s\n",
+            "    fold 3/5 PR-AUC=0.03233 Recall=0.14706 F1=0.11111 elapsed=99.4s\n",
+            "    fold 4/5 PR-AUC=0.01287 Recall=0.38235 F1=0.02835 elapsed=97.5s\n",
+            "    fold 5/5 PR-AUC=0.02747 Recall=0.14706 F1=0.10417 elapsed=94.9s\n",
+            "  -> RandomForest trial 8 mean PR-AUC=0.02016 Recall=0.20588 F1=0.07122 FPR=0.04485\n",
+            "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.01133 Recall=0.17647 F1=0.05333 elapsed=95.1s\n",
+            "    fold 2/5 PR-AUC=0.01219 Recall=0.14706 F1=0.05376 elapsed=93.9s\n",
+            "    fold 3/5 PR-AUC=0.01700 Recall=0.14706 F1=0.04149 elapsed=95.6s\n",
+            "    fold 4/5 PR-AUC=0.01059 Recall=0.35294 F1=0.02934 elapsed=94.4s\n",
+            "    fold 5/5 PR-AUC=0.01855 Recall=0.11765 F1=0.06897 elapsed=90.9s\n",
+            "  -> RandomForest trial 9 mean PR-AUC=0.01393 Recall=0.18824 F1=0.04938 FPR=0.04640\n",
+            "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.01972 Recall=0.17647 F1=0.05797 elapsed=18.8s\n",
+            "    fold 2/5 PR-AUC=0.01501 Recall=0.14706 F1=0.05051 elapsed=18.4s\n",
+            "    fold 3/5 PR-AUC=0.02479 Recall=0.41176 F1=0.03774 elapsed=18.5s\n",
+            "    fold 4/5 PR-AUC=0.01120 Recall=0.52941 F1=0.02497 elapsed=18.5s\n",
+            "    fold 5/5 PR-AUC=0.03002 Recall=0.17647 F1=0.09449 elapsed=19.2s\n",
+            "  -> RandomForest trial 10 mean PR-AUC=0.02015 Recall=0.28824 F1=0.05313 FPR=0.08371\n",
+            "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.01038 Recall=0.17647 F1=0.03125 elapsed=130.8s\n",
+            "    fold 2/5 PR-AUC=0.01130 Recall=0.17647 F1=0.04181 elapsed=131.1s\n",
+            "    fold 3/5 PR-AUC=0.02148 Recall=0.08824 F1=0.07595 elapsed=129.1s\n",
+            "    fold 4/5 PR-AUC=0.00999 Recall=0.35294 F1=0.02564 elapsed=130.5s\n",
+            "    fold 5/5 PR-AUC=0.01895 Recall=0.11765 F1=0.06780 elapsed=126.2s\n",
+            "  -> RandomForest trial 11 mean PR-AUC=0.01442 Recall=0.18235 F1=0.04849 FPR=0.05374\n",
+            "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.01582 Recall=0.14706 F1=0.08000 elapsed=16.5s\n",
+            "    fold 2/5 PR-AUC=0.01492 Recall=0.14706 F1=0.06452 elapsed=16.5s\n",
+            "    fold 3/5 PR-AUC=0.02683 Recall=0.20588 F1=0.06897 elapsed=16.7s\n",
+            "    fold 4/5 PR-AUC=0.01325 Recall=0.05882 F1=0.06667 elapsed=16.5s\n",
+            "    fold 5/5 PR-AUC=0.04138 Recall=0.35294 F1=0.05042 elapsed=16.5s\n",
+            "  -> RandomForest trial 12 mean PR-AUC=0.02244 Recall=0.18235 F1=0.06611 FPR=0.02742\n",
+            "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.01129 Recall=0.17647 F1=0.03647 elapsed=91.7s\n",
+            "    fold 2/5 PR-AUC=0.01165 Recall=0.26471 F1=0.03854 elapsed=93.2s\n",
+            "    fold 3/5 PR-AUC=0.02405 Recall=0.14706 F1=0.06897 elapsed=92.9s\n",
+            "    fold 4/5 PR-AUC=0.01140 Recall=0.50000 F1=0.02801 elapsed=92.1s\n",
+            "    fold 5/5 PR-AUC=0.01838 Recall=0.11765 F1=0.07843 elapsed=89.3s\n",
+            "  -> RandomForest trial 13 mean PR-AUC=0.01535 Recall=0.24118 F1=0.05008 FPR=0.06859\n",
+            "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.02950 Recall=0.14706 F1=0.15385 elapsed=18.1s\n",
+            "    fold 2/5 PR-AUC=0.04820 Recall=0.35294 F1=0.07717 elapsed=18.3s\n",
+            "    fold 3/5 PR-AUC=0.05079 Recall=0.35294 F1=0.03822 elapsed=18.1s\n",
+            "    fold 4/5 PR-AUC=0.02223 Recall=0.08824 F1=0.07317 elapsed=19.1s\n",
+            "    fold 5/5 PR-AUC=0.10251 Recall=0.35294 F1=0.08602 elapsed=18.7s\n",
+            "  -> RandomForest trial 14 mean PR-AUC=0.05065 Recall=0.25882 F1=0.08569 FPR=0.03859\n",
+            "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.01118 Recall=0.14706 F1=0.03788 elapsed=137.0s\n",
+            "    fold 2/5 PR-AUC=0.01503 Recall=0.17647 F1=0.05941 elapsed=141.8s\n",
+            "    fold 3/5 PR-AUC=0.01931 Recall=0.08824 F1=0.07895 elapsed=142.5s\n",
+            "    fold 4/5 PR-AUC=0.01054 Recall=0.35294 F1=0.02804 elapsed=142.2s\n",
+            "    fold 5/5 PR-AUC=0.01703 Recall=0.11765 F1=0.05970 elapsed=140.4s\n",
+            "  -> RandomForest trial 15 mean PR-AUC=0.01462 Recall=0.17647 F1=0.05279 FPR=0.04465\n",
+            "[RandomForest] CV done elapsed=4652.7s\n",
             "\n",
-            "[ExtraTrees] CV start - trials: 2\n",
-            "  trial 1/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
-            "    fold 1/3 PR-AUC=0.00764 F1=0.02280 elapsed=3.1s\n",
-            "    fold 2/3 PR-AUC=0.01074 F1=0.04420 elapsed=5.1s\n",
-            "    fold 3/3 PR-AUC=0.00861 F1=0.03125 elapsed=3.3s\n",
-            "  -> ExtraTrees trial 1 mean PR-AUC=0.00900 ROC-AUC=0.57558 ACC=0.94433 FPR=0.05109\n",
-            "  trial 2/2 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/3 PR-AUC=0.01043 F1=0.03509 elapsed=3.5s\n",
-            "    fold 2/3 PR-AUC=0.01541 F1=0.06154 elapsed=3.5s\n",
-            "    fold 3/3 PR-AUC=0.03788 F1=0.11111 elapsed=5.7s\n",
-            "  -> ExtraTrees trial 2 mean PR-AUC=0.02124 ROC-AUC=0.59191 ACC=0.97913 FPR=0.01582\n",
-            "[ExtraTrees] CV done elapsed=24.3s\n",
+            "[ExtraTrees] CV start\n",
+            "[ExtraTrees] random search trials: 15\n",
+            "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.05335 Recall=0.17647 F1=0.22222 elapsed=14.9s\n",
+            "    fold 2/5 PR-AUC=0.05171 Recall=0.38235 F1=0.04075 elapsed=14.9s\n",
+            "    fold 3/5 PR-AUC=0.03424 Recall=0.20588 F1=0.06481 elapsed=15.0s\n",
+            "    fold 4/5 PR-AUC=0.02532 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
+            "    fold 5/5 PR-AUC=0.10997 Recall=0.35294 F1=0.06557 elapsed=14.9s\n",
+            "  -> ExtraTrees trial 1 mean PR-AUC=0.05492 Recall=0.23529 F1=0.09867 FPR=0.03701\n",
+            "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.03290 Recall=0.11765 F1=0.16667 elapsed=80.4s\n",
+            "    fold 2/5 PR-AUC=0.06744 Recall=0.41176 F1=0.04811 elapsed=83.0s\n",
+            "    fold 3/5 PR-AUC=0.03351 Recall=0.20588 F1=0.08696 elapsed=80.7s\n",
+            "    fold 4/5 PR-AUC=0.02096 Recall=0.08824 F1=0.07895 elapsed=84.2s\n",
+            "    fold 5/5 PR-AUC=0.07834 Recall=0.20588 F1=0.18421 elapsed=82.4s\n",
+            "  -> ExtraTrees trial 2 mean PR-AUC=0.04663 Recall=0.20588 F1=0.11298 FPR=0.02474\n",
+            "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.04880 Recall=0.17647 F1=0.15584 elapsed=15.0s\n",
+            "    fold 2/5 PR-AUC=0.04067 Recall=0.20588 F1=0.06061 elapsed=14.9s\n",
+            "    fold 3/5 PR-AUC=0.03292 Recall=0.26471 F1=0.06818 elapsed=14.9s\n",
+            "    fold 4/5 PR-AUC=0.02491 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
+            "    fold 5/5 PR-AUC=0.12176 Recall=0.35294 F1=0.08000 elapsed=15.1s\n",
+            "  -> ExtraTrees trial 3 mean PR-AUC=0.05381 Recall=0.21176 F1=0.09293 FPR=0.02367\n",
+            "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.04500 Recall=0.14706 F1=0.19231 elapsed=77.3s\n",
+            "    fold 2/5 PR-AUC=0.08313 Recall=0.35294 F1=0.05000 elapsed=81.5s\n",
+            "    fold 3/5 PR-AUC=0.05375 Recall=0.20588 F1=0.16867 elapsed=78.0s\n",
+            "    fold 4/5 PR-AUC=0.03255 Recall=0.08824 F1=0.09524 elapsed=79.6s\n",
+            "    fold 5/5 PR-AUC=0.16604 Recall=0.35294 F1=0.08362 elapsed=78.5s\n",
+            "  -> ExtraTrees trial 4 mean PR-AUC=0.07609 Recall=0.22941 F1=0.11797 FPR=0.02534\n",
+            "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.03990 Recall=0.17647 F1=0.07947 elapsed=15.1s\n",
+            "    fold 2/5 PR-AUC=0.03735 Recall=0.26471 F1=0.05422 elapsed=15.6s\n",
+            "    fold 3/5 PR-AUC=0.01885 Recall=0.14706 F1=0.04000 elapsed=15.8s\n",
+            "    fold 4/5 PR-AUC=0.02381 Recall=0.05882 F1=0.09756 elapsed=15.2s\n",
+            "    fold 5/5 PR-AUC=0.07181 Recall=0.23529 F1=0.05112 elapsed=14.9s\n",
+            "  -> ExtraTrees trial 5 mean PR-AUC=0.03834 Recall=0.17647 F1=0.06447 FPR=0.02974\n",
+            "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.05098 Recall=0.17647 F1=0.19355 elapsed=15.8s\n",
+            "    fold 2/5 PR-AUC=0.04937 Recall=0.35294 F1=0.05085 elapsed=16.2s\n",
+            "    fold 3/5 PR-AUC=0.03076 Recall=0.14706 F1=0.12346 elapsed=15.8s\n",
+            "    fold 4/5 PR-AUC=0.02669 Recall=0.17647 F1=0.05479 elapsed=16.0s\n",
+            "    fold 5/5 PR-AUC=0.14600 Recall=0.38235 F1=0.04075 elapsed=16.1s\n",
+            "  -> ExtraTrees trial 6 mean PR-AUC=0.06076 Recall=0.24706 F1=0.09268 FPR=0.04224\n",
+            "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
+            "    fold 1/5 PR-AUC=0.02945 Recall=0.17647 F1=0.07229 elapsed=13.7s\n",
+            "    fold 2/5 PR-AUC=0.03056 Recall=0.35294 F1=0.03791 elapsed=13.3s\n",
+            "    fold 3/5 PR-AUC=0.01740 Recall=0.11765 F1=0.04762 elapsed=13.3s\n",
+            "    fold 4/5 PR-AUC=0.01828 Recall=0.05882 F1=0.08163 elapsed=13.5s\n",
+            "    fold 5/5 PR-AUC=0.02846 Recall=0.14706 F1=0.04950 elapsed=13.7s\n",
+            "  -> ExtraTrees trial 7 mean PR-AUC=0.02483 Recall=0.17059 F1=0.05779 FPR=0.03416\n",
+            "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
+            "    fold 1/5 PR-AUC=0.05038 Recall=0.20588 F1=0.16092 elapsed=67.6s\n",
+            "    fold 2/5 PR-AUC=0.07342 Recall=0.35294 F1=0.05517 elapsed=66.9s\n",
+            "    fold 3/5 PR-AUC=0.04289 Recall=0.08824 F1=0.12766 elapsed=66.9s\n",
+            "    fold 4/5 PR-AUC=0.03120 Recall=0.23529 F1=0.04278 elapsed=66.2s\n",
+            "    fold 5/5 PR-AUC=0.16042 Recall=0.35294 F1=0.04293 elapsed=64.2s\n",
+            "  -> ExtraTrees trial 8 mean PR-AUC=0.07166 Recall=0.24706 F1=0.08589 FPR=0.04325\n",
+            "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.02831 Recall=0.11765 F1=0.12121 elapsed=58.9s\n",
+            "    fold 2/5 PR-AUC=0.05126 Recall=0.35294 F1=0.03256 elapsed=59.2s\n",
+            "    fold 3/5 PR-AUC=0.02743 Recall=0.08824 F1=0.08108 elapsed=59.7s\n",
+            "    fold 4/5 PR-AUC=0.02199 Recall=0.08824 F1=0.08696 elapsed=55.8s\n",
+            "    fold 5/5 PR-AUC=0.09582 Recall=0.11765 F1=0.12903 elapsed=56.5s\n",
+            "  -> ExtraTrees trial 9 mean PR-AUC=0.04496 Recall=0.15294 F1=0.09017 FPR=0.02722\n",
+            "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.04550 Recall=0.23529 F1=0.11511 elapsed=15.0s\n",
+            "    fold 2/5 PR-AUC=0.03161 Recall=0.14706 F1=0.06623 elapsed=15.1s\n",
+            "    fold 3/5 PR-AUC=0.02638 Recall=0.17647 F1=0.08633 elapsed=15.2s\n",
+            "    fold 4/5 PR-AUC=0.02500 Recall=0.05882 F1=0.10000 elapsed=15.1s\n",
+            "    fold 5/5 PR-AUC=0.09072 Recall=0.35294 F1=0.04420 elapsed=15.0s\n",
+            "  -> ExtraTrees trial 10 mean PR-AUC=0.04384 Recall=0.19412 F1=0.08237 FPR=0.02712\n",
+            "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.03474 Recall=0.14706 F1=0.15625 elapsed=79.8s\n",
+            "    fold 2/5 PR-AUC=0.04950 Recall=0.11765 F1=0.05517 elapsed=80.9s\n",
+            "    fold 3/5 PR-AUC=0.02492 Recall=0.08824 F1=0.11321 elapsed=80.1s\n",
+            "    fold 4/5 PR-AUC=0.02257 Recall=0.05882 F1=0.09524 elapsed=79.1s\n",
+            "    fold 5/5 PR-AUC=0.09276 Recall=0.08824 F1=0.15789 elapsed=77.3s\n",
+            "  -> ExtraTrees trial 11 mean PR-AUC=0.04490 Recall=0.10000 F1=0.11555 FPR=0.00520\n",
+            "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.04890 Recall=0.17647 F1=0.15789 elapsed=12.5s\n",
+            "    fold 2/5 PR-AUC=0.06466 Recall=0.17647 F1=0.08000 elapsed=12.5s\n",
+            "    fold 3/5 PR-AUC=0.03919 Recall=0.14706 F1=0.05291 elapsed=11.6s\n",
+            "    fold 4/5 PR-AUC=0.02632 Recall=0.05882 F1=0.10000 elapsed=10.9s\n",
+            "    fold 5/5 PR-AUC=0.12891 Recall=0.14706 F1=0.23256 elapsed=12.2s\n",
+            "  -> ExtraTrees trial 12 mean PR-AUC=0.06160 Recall=0.14118 F1=0.12467 FPR=0.01019\n",
+            "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+            "    fold 1/5 PR-AUC=0.04456 Recall=0.17647 F1=0.13043 elapsed=58.2s\n",
+            "    fold 2/5 PR-AUC=0.04290 Recall=0.35294 F1=0.03871 elapsed=58.5s\n",
+            "    fold 3/5 PR-AUC=0.03700 Recall=0.08824 F1=0.10526 elapsed=59.6s\n",
+            "    fold 4/5 PR-AUC=0.02478 Recall=0.08824 F1=0.05556 elapsed=58.3s\n",
+            "    fold 5/5 PR-AUC=0.13122 Recall=0.38235 F1=0.03969 elapsed=56.2s\n",
+            "  -> ExtraTrees trial 13 mean PR-AUC=0.05609 Recall=0.21765 F1=0.07393 FPR=0.04442\n",
+            "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+            "    fold 1/5 PR-AUC=0.05247 Recall=0.17647 F1=0.21818 elapsed=15.4s\n",
+            "    fold 2/5 PR-AUC=0.05497 Recall=0.23529 F1=0.13445 elapsed=15.3s\n",
+            "    fold 3/5 PR-AUC=0.03882 Recall=0.26471 F1=0.08491 elapsed=15.6s\n",
+            "    fold 4/5 PR-AUC=0.02642 Recall=0.29412 F1=0.03711 elapsed=15.4s\n",
+            "    fold 5/5 PR-AUC=0.12650 Recall=0.38235 F1=0.04702 elapsed=15.8s\n",
+            "  -> ExtraTrees trial 14 mean PR-AUC=0.05983 Recall=0.27059 F1=0.10433 FPR=0.04231\n",
+            "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
+            "    fold 1/5 PR-AUC=0.02622 Recall=0.14706 F1=0.11628 elapsed=100.9s\n",
+            "    fold 2/5 PR-AUC=0.03921 Recall=0.41176 F1=0.04046 elapsed=103.7s\n",
+            "    fold 3/5 PR-AUC=0.02245 Recall=0.17647 F1=0.05660 elapsed=98.1s\n",
+            "    fold 4/5 PR-AUC=0.02344 Recall=0.23529 F1=0.03883 elapsed=100.0s\n",
+            "    fold 5/5 PR-AUC=0.07321 Recall=0.11765 F1=0.06957 elapsed=95.6s\n",
+            "  -> ExtraTrees trial 15 mean PR-AUC=0.03691 Recall=0.21765 F1=0.06435 FPR=0.04392\n",
+            "[ExtraTrees] CV done elapsed=3199.4s\n",
             "\n",
-            "[LogisticRegression] CV start - trials: 2\n",
-            "  trial 1/2 params={'model__solver': 'lbfgs', 'model__C': 0.1}\n",
-            "    fold 1/3 PR-AUC=0.00952 F1=0.04167 elapsed=2.0s\n",
-            "    fold 2/3 PR-AUC=0.00874 F1=0.04000 elapsed=2.6s\n",
-            "    fold 3/3 PR-AUC=0.03116 F1=0.07792 elapsed=2.8s\n",
-            "  -> LogisticRegression trial 1 mean PR-AUC=0.01647 ROC-AUC=0.56895 ACC=0.97673 FPR=0.01830\n",
-            "  trial 2/2 params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
-            "    fold 1/3 PR-AUC=0.00879 F1=0.03279 elapsed=5.0s\n",
-            "    fold 2/3 PR-AUC=0.00740 F1=0.02244 elapsed=4.8s\n",
-            "    fold 3/3 PR-AUC=0.03535 F1=0.09756 elapsed=4.6s\n",
-            "  -> LogisticRegression trial 2 mean PR-AUC=0.01718 ROC-AUC=0.56337 ACC=0.95293 FPR=0.04230\n",
-            "[LogisticRegression] CV done elapsed=21.7s\n"
+            "[LogisticRegression] CV start\n",
+            "[LogisticRegression] random search trials: 5\n",
+            "  trial 1/5 params={'model__solver': 'lbfgs', 'model__C': 0.03}\n",
+            "    fold 1/5 PR-AUC=0.03239 Recall=0.11765 F1=0.11940 elapsed=5.0s\n",
+            "    fold 2/5 PR-AUC=0.03936 Recall=0.20588 F1=0.13333 elapsed=6.5s\n",
+            "    fold 3/5 PR-AUC=0.01676 Recall=0.11765 F1=0.05926 elapsed=5.1s\n",
+            "    fold 4/5 PR-AUC=0.04898 Recall=0.14706 F1=0.06944 elapsed=8.0s\n",
+            "    fold 5/5 PR-AUC=0.05055 Recall=0.14706 F1=0.15385 elapsed=5.0s\n",
+            "  -> LogisticRegression trial 1 mean PR-AUC=0.03761 Recall=0.14706 F1=0.10706 FPR=0.01076\n",
+            "  trial 2/5 params={'model__solver': 'lbfgs', 'model__C': 0.1}\n",
+            "    fold 1/5 PR-AUC=0.03312 Recall=0.14706 F1=0.09524 elapsed=7.2s\n",
+            "    fold 2/5 PR-AUC=0.03507 Recall=0.20588 F1=0.14583 elapsed=7.6s\n",
+            "    fold 3/5 PR-AUC=0.01612 Recall=0.11765 F1=0.07207 elapsed=7.6s\n",
+            "    fold 4/5 PR-AUC=0.04945 Recall=0.14706 F1=0.09174 elapsed=8.8s\n",
+            "    fold 5/5 PR-AUC=0.05347 Recall=0.14706 F1=0.15385 elapsed=8.5s\n",
+            "  -> LogisticRegression trial 2 mean PR-AUC=0.03745 Recall=0.15294 F1=0.11175 FPR=0.00972\n",
+            "  trial 3/5 params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
+            "    fold 1/5 PR-AUC=0.03547 Recall=0.14706 F1=0.10204 elapsed=9.1s\n",
+            "    fold 2/5 PR-AUC=0.03449 Recall=0.20588 F1=0.16092 elapsed=11.2s\n",
+            "    fold 3/5 PR-AUC=0.01564 Recall=0.11765 F1=0.07619 elapsed=10.8s\n",
+            "    fold 4/5 PR-AUC=0.04895 Recall=0.11765 F1=0.10000 elapsed=8.9s\n",
+            "    fold 5/5 PR-AUC=0.05718 Recall=0.14706 F1=0.16129 elapsed=10.3s\n",
+            "  -> LogisticRegression trial 3 mean PR-AUC=0.03835 Recall=0.14706 F1=0.12009 FPR=0.00795\n",
+            "  trial 4/5 params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
+            "    fold 1/5 PR-AUC=0.03432 Recall=0.14706 F1=0.11236 elapsed=14.1s\n",
+            "    fold 2/5 PR-AUC=0.03430 Recall=0.20588 F1=0.16279 elapsed=18.1s\n",
+            "    fold 3/5 PR-AUC=0.01534 Recall=0.11765 F1=0.07143 elapsed=14.3s\n",
+            "    fold 4/5 PR-AUC=0.04784 Recall=0.14706 F1=0.07634 elapsed=13.7s\n",
+            "    fold 5/5 PR-AUC=0.05053 Recall=0.14706 F1=0.17544 elapsed=13.5s\n",
+            "  -> LogisticRegression trial 4 mean PR-AUC=0.03647 Recall=0.15294 F1=0.11967 FPR=0.00935\n",
+            "  trial 5/5 params={'model__solver': 'lbfgs', 'model__C': 3.0}\n",
+            "    fold 1/5 PR-AUC=0.02904 Recall=0.14706 F1=0.11236 elapsed=17.6s\n",
+            "    fold 2/5 PR-AUC=0.03423 Recall=0.20588 F1=0.15385 elapsed=18.0s\n",
+            "    fold 3/5 PR-AUC=0.01664 Recall=0.11765 F1=0.07080 elapsed=17.7s\n",
+            "    fold 4/5 PR-AUC=0.04669 Recall=0.14706 F1=0.07407 elapsed=18.0s\n",
+            "    fold 5/5 PR-AUC=0.04529 Recall=0.14706 F1=0.16667 elapsed=18.9s\n",
+            "  -> LogisticRegression trial 5 mean PR-AUC=0.03438 Recall=0.15294 F1=0.11555 FPR=0.00979\n",
+            "[LogisticRegression] CV done elapsed=283.4s\n"
           ]
         },
         {
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "           model_name  trial_no  mean_pr_auc  std_pr_auc  mean_roc_auc  \\\n",
-              "0          ExtraTrees         2     0.021242    0.011941      0.591906   \n",
-              "1  LogisticRegression         2     0.017179    0.012862      0.563373   \n",
-              "2  LogisticRegression         1     0.016471    0.010389      0.568946   \n",
-              "3            LightGBM         2     0.015974    0.003776      0.517136   \n",
-              "4            LightGBM         1     0.011054    0.002173      0.528703   \n",
-              "5        RandomForest         2     0.010481    0.001569      0.595697   \n",
-              "6        RandomForest         1     0.010005    0.001341      0.603241   \n",
-              "7          ExtraTrees         1     0.008998    0.001293      0.575575   \n",
-              "\n",
-              "   std_roc_auc  mean_accuracy  mean_false_positive_rate   mean_f1  \\\n",
-              "0     0.032715       0.979133                  0.015822  0.069246   \n",
-              "1     0.026249       0.952933                  0.042304  0.050928   \n",
-              "2     0.026550       0.976733                  0.018303  0.053196   \n",
-              "3     0.027286       0.987467                  0.007442  0.073889   \n",
-              "4     0.050119       0.968067                  0.027153  0.057900   \n",
-              "5     0.021972       0.930467                  0.065367  0.038165   \n",
-              "6     0.013798       0.913800                  0.082060  0.037518   \n",
-              "7     0.010577       0.944333                  0.051087  0.032750   \n",
-              "\n",
-              "   mean_recall                                             params  \\\n",
-              "0     0.094417  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-              "1     0.118227        {'model__solver': 'lbfgs', 'model__C': 1.0}   \n",
-              "2     0.105911        {'model__solver': 'lbfgs', 'model__C': 0.1}   \n",
-              "3     0.094007  {'subsample': 0.85, 'reg_lambda': 2.0, 'num_le...   \n",
-              "4     0.129721  {'subsample': 0.85, 'reg_lambda': 1.0, 'num_le...   \n",
-              "5     0.201149  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-              "6     0.190066  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-              "7     0.142447  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-              "\n",
-              "                                    selection_reason  \n",
-              "0  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  \n",
-              "1  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  \n",
-              "2  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  \n",
-              "3  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델  \n",
-              "4  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델  \n",
-              "5  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  \n",
-              "6  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  \n",
-              "7  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  "
+              "      model_name  trial_no  mean_pr_auc  std_pr_auc  mean_roc_auc  \\\n",
+              "0     ExtraTrees         4     0.076091    0.047961      0.647573   \n",
+              "1     ExtraTrees         8     0.071661    0.046476      0.670800   \n",
+              "2     ExtraTrees        12     0.061595    0.035912      0.647986   \n",
+              "3       LightGBM         5     0.061435    0.043454      0.635865   \n",
+              "4     ExtraTrees         6     0.060760    0.043707      0.626227   \n",
+              "5     ExtraTrees        14     0.059835    0.034874      0.633514   \n",
+              "6       LightGBM        14     0.058075    0.047107      0.635845   \n",
+              "7     ExtraTrees        13     0.056089    0.038199      0.651291   \n",
+              "8       LightGBM        12     0.055233    0.046533      0.622553   \n",
+              "9     ExtraTrees         1     0.054918    0.029484      0.644671   \n",
+              "10      LightGBM         4     0.054238    0.043641      0.638072   \n",
+              "11    ExtraTrees         3     0.053812    0.034891      0.643948   \n",
+              "12      LightGBM         2     0.053372    0.036460      0.623080   \n",
+              "13      LightGBM         6     0.051865    0.038493      0.645314   \n",
+              "14      LightGBM         1     0.051047    0.037590      0.637448   \n",
+              "15  RandomForest        14     0.050646    0.028109      0.651639   \n",
+              "16      LightGBM        13     0.049543    0.033146      0.646574   \n",
+              "17      LightGBM         3     0.049320    0.034143      0.627693   \n",
+              "18      LightGBM         9     0.048774    0.035914      0.629020   \n",
+              "19    ExtraTrees         2     0.046631    0.022173      0.632108   \n",
+              "\n",
+              "    std_roc_auc  mean_accuracy  mean_false_positive_rate  mean_precision  \\\n",
+              "0      0.039919       0.970433                  0.025344        0.119684   \n",
+              "1      0.037693       0.952733                  0.043245        0.087831   \n",
+              "2      0.024357       0.985000                  0.010191        0.223146   \n",
+              "3      0.047525       0.975900                  0.019879        0.101028   \n",
+              "4      0.026922       0.953733                  0.042239        0.080404   \n",
+              "5      0.023413       0.953800                  0.042306        0.095049   \n",
+              "6      0.045116       0.967200                  0.028696        0.057561   \n",
+              "7      0.033722       0.951400                  0.044418        0.063167   \n",
+              "8      0.050385       0.959833                  0.036071        0.053337   \n",
+              "9      0.023235       0.958867                  0.037010        0.145893   \n",
+              "10     0.043098       0.947200                  0.049078        0.052814   \n",
+              "11     0.022371       0.972000                  0.023667        0.118529   \n",
+              "12     0.034409       0.958800                  0.037211        0.052149   \n",
+              "13     0.047738       0.966367                  0.029400        0.133721   \n",
+              "14     0.054362       0.947200                  0.049112        0.044530   \n",
+              "15     0.045576       0.957433                  0.038585        0.067259   \n",
+              "16     0.043579       0.945433                  0.050821        0.060910   \n",
+              "17     0.046462       0.928867                  0.067516        0.076085   \n",
+              "18     0.028166       0.964367                  0.031512        0.056697   \n",
+              "19     0.042936       0.970900                  0.024740        0.120895   \n",
+              "\n",
+              "    mean_recall   mean_f1                                             params  \\\n",
+              "0      0.229412  0.117969  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+              "1      0.247059  0.085893  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+              "2      0.141176  0.124673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+              "3      0.235294  0.123713  {'model__learning_rate': 0.031169409812378812,...   \n",
+              "4      0.247059  0.092680  {'model__min_samples_leaf': 3, 'model__max_fea...   \n",
+              "5      0.270588  0.104333  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+              "6      0.247059  0.089393  {'model__learning_rate': 0.03688230536757648, ...   \n",
+              "7      0.217647  0.073932  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+              "8      0.241176  0.079716  {'model__learning_rate': 0.03828385438925741, ...   \n",
+              "9      0.235294  0.098673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+              "10     0.294118  0.079513  {'model__learning_rate': 0.018840552955192824,...   \n",
+              "11     0.211765  0.092926  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+              "12     0.258824  0.082953  {'model__learning_rate': 0.0564638664293258, '...   \n",
+              "13     0.223529  0.109434  {'model__learning_rate': 0.022439365289703053,...   \n",
+              "14     0.300000  0.073458  {'model__learning_rate': 0.021789307659775742,...   \n",
+              "15     0.258824  0.085685  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+              "16     0.288235  0.081039  {'model__learning_rate': 0.0128289748714244, '...   \n",
+              "17     0.294118  0.054984  {'model__learning_rate': 0.025815006344207546,...   \n",
+              "18     0.241176  0.086860  {'model__learning_rate': 0.029653419677342325,...   \n",
+              "19     0.205882  0.112978  {'model__min_samples_leaf': 1, 'model__max_fea...   \n",
+              "\n",
+              "                                     selection_reason     tuning_method  \n",
+              "0   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "1   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "2   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "3   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "4   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "5   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "6   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "7   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "8   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "9   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "10  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "11  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+              "12  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "13  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "14  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "15  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  ParameterSampler  \n",
+              "16  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "17  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "18  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+              "19  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-ba1fcffe-8b28-4a9d-bed7-dd81f388b01c\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-03e4c761-567f-41e7-ad56-2ecca75a7cbb\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -3241,140 +3436,362 @@
               "      <th>std_roc_auc</th>\n",
               "      <th>mean_accuracy</th>\n",
               "      <th>mean_false_positive_rate</th>\n",
-              "      <th>mean_f1</th>\n",
+              "      <th>mean_precision</th>\n",
               "      <th>mean_recall</th>\n",
+              "      <th>mean_f1</th>\n",
               "      <th>params</th>\n",
               "      <th>selection_reason</th>\n",
+              "      <th>tuning_method</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
               "      <td>ExtraTrees</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.021242</td>\n",
-              "      <td>0.011941</td>\n",
-              "      <td>0.591906</td>\n",
-              "      <td>0.032715</td>\n",
-              "      <td>0.979133</td>\n",
-              "      <td>0.015822</td>\n",
-              "      <td>0.069246</td>\n",
-              "      <td>0.094417</td>\n",
-              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0.076091</td>\n",
+              "      <td>0.047961</td>\n",
+              "      <td>0.647573</td>\n",
+              "      <td>0.039919</td>\n",
+              "      <td>0.970433</td>\n",
+              "      <td>0.025344</td>\n",
+              "      <td>0.119684</td>\n",
+              "      <td>0.229412</td>\n",
+              "      <td>0.117969</td>\n",
+              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
               "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>LogisticRegression</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.017179</td>\n",
-              "      <td>0.012862</td>\n",
-              "      <td>0.563373</td>\n",
-              "      <td>0.026249</td>\n",
-              "      <td>0.952933</td>\n",
-              "      <td>0.042304</td>\n",
-              "      <td>0.050928</td>\n",
-              "      <td>0.118227</td>\n",
-              "      <td>{'model__solver': 'lbfgs', 'model__C': 1.0}</td>\n",
-              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>8</td>\n",
+              "      <td>0.071661</td>\n",
+              "      <td>0.046476</td>\n",
+              "      <td>0.670800</td>\n",
+              "      <td>0.037693</td>\n",
+              "      <td>0.952733</td>\n",
+              "      <td>0.043245</td>\n",
+              "      <td>0.087831</td>\n",
+              "      <td>0.247059</td>\n",
+              "      <td>0.085893</td>\n",
+              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>LogisticRegression</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.016471</td>\n",
-              "      <td>0.010389</td>\n",
-              "      <td>0.568946</td>\n",
-              "      <td>0.026550</td>\n",
-              "      <td>0.976733</td>\n",
-              "      <td>0.018303</td>\n",
-              "      <td>0.053196</td>\n",
-              "      <td>0.105911</td>\n",
-              "      <td>{'model__solver': 'lbfgs', 'model__C': 0.1}</td>\n",
-              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>12</td>\n",
+              "      <td>0.061595</td>\n",
+              "      <td>0.035912</td>\n",
+              "      <td>0.647986</td>\n",
+              "      <td>0.024357</td>\n",
+              "      <td>0.985000</td>\n",
+              "      <td>0.010191</td>\n",
+              "      <td>0.223146</td>\n",
+              "      <td>0.141176</td>\n",
+              "      <td>0.124673</td>\n",
+              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
               "      <td>LightGBM</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.015974</td>\n",
-              "      <td>0.003776</td>\n",
-              "      <td>0.517136</td>\n",
-              "      <td>0.027286</td>\n",
-              "      <td>0.987467</td>\n",
-              "      <td>0.007442</td>\n",
-              "      <td>0.073889</td>\n",
-              "      <td>0.094007</td>\n",
-              "      <td>{'subsample': 0.85, 'reg_lambda': 2.0, 'num_le...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델</td>\n",
+              "      <td>5</td>\n",
+              "      <td>0.061435</td>\n",
+              "      <td>0.043454</td>\n",
+              "      <td>0.635865</td>\n",
+              "      <td>0.047525</td>\n",
+              "      <td>0.975900</td>\n",
+              "      <td>0.019879</td>\n",
+              "      <td>0.101028</td>\n",
+              "      <td>0.235294</td>\n",
+              "      <td>0.123713</td>\n",
+              "      <td>{'model__learning_rate': 0.031169409812378812,...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.011054</td>\n",
-              "      <td>0.002173</td>\n",
-              "      <td>0.528703</td>\n",
-              "      <td>0.050119</td>\n",
-              "      <td>0.968067</td>\n",
-              "      <td>0.027153</td>\n",
-              "      <td>0.057900</td>\n",
-              "      <td>0.129721</td>\n",
-              "      <td>{'subsample': 0.85, 'reg_lambda': 1.0, 'num_le...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델</td>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0.060760</td>\n",
+              "      <td>0.043707</td>\n",
+              "      <td>0.626227</td>\n",
+              "      <td>0.026922</td>\n",
+              "      <td>0.953733</td>\n",
+              "      <td>0.042239</td>\n",
+              "      <td>0.080404</td>\n",
+              "      <td>0.247059</td>\n",
+              "      <td>0.092680</td>\n",
+              "      <td>{'model__min_samples_leaf': 3, 'model__max_fea...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>5</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.010481</td>\n",
-              "      <td>0.001569</td>\n",
-              "      <td>0.595697</td>\n",
-              "      <td>0.021972</td>\n",
-              "      <td>0.930467</td>\n",
-              "      <td>0.065367</td>\n",
-              "      <td>0.038165</td>\n",
-              "      <td>0.201149</td>\n",
-              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
-              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>14</td>\n",
+              "      <td>0.059835</td>\n",
+              "      <td>0.034874</td>\n",
+              "      <td>0.633514</td>\n",
+              "      <td>0.023413</td>\n",
+              "      <td>0.953800</td>\n",
+              "      <td>0.042306</td>\n",
+              "      <td>0.095049</td>\n",
+              "      <td>0.270588</td>\n",
+              "      <td>0.104333</td>\n",
+              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>6</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.010005</td>\n",
-              "      <td>0.001341</td>\n",
-              "      <td>0.603241</td>\n",
-              "      <td>0.013798</td>\n",
-              "      <td>0.913800</td>\n",
-              "      <td>0.082060</td>\n",
-              "      <td>0.037518</td>\n",
-              "      <td>0.190066</td>\n",
-              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
-              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>14</td>\n",
+              "      <td>0.058075</td>\n",
+              "      <td>0.047107</td>\n",
+              "      <td>0.635845</td>\n",
+              "      <td>0.045116</td>\n",
+              "      <td>0.967200</td>\n",
+              "      <td>0.028696</td>\n",
+              "      <td>0.057561</td>\n",
+              "      <td>0.247059</td>\n",
+              "      <td>0.089393</td>\n",
+              "      <td>{'model__learning_rate': 0.03688230536757648, ...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>7</th>\n",
               "      <td>ExtraTrees</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.008998</td>\n",
-              "      <td>0.001293</td>\n",
-              "      <td>0.575575</td>\n",
-              "      <td>0.010577</td>\n",
-              "      <td>0.944333</td>\n",
-              "      <td>0.051087</td>\n",
-              "      <td>0.032750</td>\n",
-              "      <td>0.142447</td>\n",
+              "      <td>13</td>\n",
+              "      <td>0.056089</td>\n",
+              "      <td>0.038199</td>\n",
+              "      <td>0.651291</td>\n",
+              "      <td>0.033722</td>\n",
+              "      <td>0.951400</td>\n",
+              "      <td>0.044418</td>\n",
+              "      <td>0.063167</td>\n",
+              "      <td>0.217647</td>\n",
+              "      <td>0.073932</td>\n",
               "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
               "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
               "    </tr>\n",
-              "  </tbody>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>12</td>\n",
+              "      <td>0.055233</td>\n",
+              "      <td>0.046533</td>\n",
+              "      <td>0.622553</td>\n",
+              "      <td>0.050385</td>\n",
+              "      <td>0.959833</td>\n",
+              "      <td>0.036071</td>\n",
+              "      <td>0.053337</td>\n",
+              "      <td>0.241176</td>\n",
+              "      <td>0.079716</td>\n",
+              "      <td>{'model__learning_rate': 0.03828385438925741, ...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.054918</td>\n",
+              "      <td>0.029484</td>\n",
+              "      <td>0.644671</td>\n",
+              "      <td>0.023235</td>\n",
+              "      <td>0.958867</td>\n",
+              "      <td>0.037010</td>\n",
+              "      <td>0.145893</td>\n",
+              "      <td>0.235294</td>\n",
+              "      <td>0.098673</td>\n",
+              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0.054238</td>\n",
+              "      <td>0.043641</td>\n",
+              "      <td>0.638072</td>\n",
+              "      <td>0.043098</td>\n",
+              "      <td>0.947200</td>\n",
+              "      <td>0.049078</td>\n",
+              "      <td>0.052814</td>\n",
+              "      <td>0.294118</td>\n",
+              "      <td>0.079513</td>\n",
+              "      <td>{'model__learning_rate': 0.018840552955192824,...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.053812</td>\n",
+              "      <td>0.034891</td>\n",
+              "      <td>0.643948</td>\n",
+              "      <td>0.022371</td>\n",
+              "      <td>0.972000</td>\n",
+              "      <td>0.023667</td>\n",
+              "      <td>0.118529</td>\n",
+              "      <td>0.211765</td>\n",
+              "      <td>0.092926</td>\n",
+              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.053372</td>\n",
+              "      <td>0.036460</td>\n",
+              "      <td>0.623080</td>\n",
+              "      <td>0.034409</td>\n",
+              "      <td>0.958800</td>\n",
+              "      <td>0.037211</td>\n",
+              "      <td>0.052149</td>\n",
+              "      <td>0.258824</td>\n",
+              "      <td>0.082953</td>\n",
+              "      <td>{'model__learning_rate': 0.0564638664293258, '...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0.051865</td>\n",
+              "      <td>0.038493</td>\n",
+              "      <td>0.645314</td>\n",
+              "      <td>0.047738</td>\n",
+              "      <td>0.966367</td>\n",
+              "      <td>0.029400</td>\n",
+              "      <td>0.133721</td>\n",
+              "      <td>0.223529</td>\n",
+              "      <td>0.109434</td>\n",
+              "      <td>{'model__learning_rate': 0.022439365289703053,...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.051047</td>\n",
+              "      <td>0.037590</td>\n",
+              "      <td>0.637448</td>\n",
+              "      <td>0.054362</td>\n",
+              "      <td>0.947200</td>\n",
+              "      <td>0.049112</td>\n",
+              "      <td>0.044530</td>\n",
+              "      <td>0.300000</td>\n",
+              "      <td>0.073458</td>\n",
+              "      <td>{'model__learning_rate': 0.021789307659775742,...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>14</td>\n",
+              "      <td>0.050646</td>\n",
+              "      <td>0.028109</td>\n",
+              "      <td>0.651639</td>\n",
+              "      <td>0.045576</td>\n",
+              "      <td>0.957433</td>\n",
+              "      <td>0.038585</td>\n",
+              "      <td>0.067259</td>\n",
+              "      <td>0.258824</td>\n",
+              "      <td>0.085685</td>\n",
+              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+              "      <td>ParameterSampler</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>13</td>\n",
+              "      <td>0.049543</td>\n",
+              "      <td>0.033146</td>\n",
+              "      <td>0.646574</td>\n",
+              "      <td>0.043579</td>\n",
+              "      <td>0.945433</td>\n",
+              "      <td>0.050821</td>\n",
+              "      <td>0.060910</td>\n",
+              "      <td>0.288235</td>\n",
+              "      <td>0.081039</td>\n",
+              "      <td>{'model__learning_rate': 0.0128289748714244, '...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.049320</td>\n",
+              "      <td>0.034143</td>\n",
+              "      <td>0.627693</td>\n",
+              "      <td>0.046462</td>\n",
+              "      <td>0.928867</td>\n",
+              "      <td>0.067516</td>\n",
+              "      <td>0.076085</td>\n",
+              "      <td>0.294118</td>\n",
+              "      <td>0.054984</td>\n",
+              "      <td>{'model__learning_rate': 0.025815006344207546,...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>9</td>\n",
+              "      <td>0.048774</td>\n",
+              "      <td>0.035914</td>\n",
+              "      <td>0.629020</td>\n",
+              "      <td>0.028166</td>\n",
+              "      <td>0.964367</td>\n",
+              "      <td>0.031512</td>\n",
+              "      <td>0.056697</td>\n",
+              "      <td>0.241176</td>\n",
+              "      <td>0.086860</td>\n",
+              "      <td>{'model__learning_rate': 0.029653419677342325,...</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>Optuna</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.046631</td>\n",
+              "      <td>0.022173</td>\n",
+              "      <td>0.632108</td>\n",
+              "      <td>0.042936</td>\n",
+              "      <td>0.970900</td>\n",
+              "      <td>0.024740</td>\n",
+              "      <td>0.120895</td>\n",
+              "      <td>0.205882</td>\n",
+              "      <td>0.112978</td>\n",
+              "      <td>{'model__min_samples_leaf': 1, 'model__max_fea...</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>ParameterSampler</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
               "</table>\n",
               "</div>\n",
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba1fcffe-8b28-4a9d-bed7-dd81f388b01c')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03e4c761-567f-41e7-ad56-2ecca75a7cbb')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -3426,12 +3843,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-ba1fcffe-8b28-4a9d-bed7-dd81f388b01c button.colab-df-convert');\n",
+              "        document.querySelector('#df-03e4c761-567f-41e7-ad56-2ecca75a7cbb button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-ba1fcffe-8b28-4a9d-bed7-dd81f388b01c');\n",
+              "        const element = document.querySelector('#df-03e4c761-567f-41e7-ad56-2ecca75a7cbb');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -3456,7 +3873,7 @@
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"print(\\\"CV best model:\\\", best_model_name, best_params)\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"LogisticRegression\",\n          \"RandomForest\",\n          \"ExtraTrees\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trial_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004384427327922025,\n        \"min\": 0.00899778441614044,\n        \"max\": 0.02124201716615975,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.017179465041165264,\n          0.010481081749463448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005125083865734947,\n        \"min\": 0.0012927115056868654,\n        \"max\": 0.012862487985111101,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.012862487985111101,\n          0.0015685750607633147\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.031148456437904398,\n        \"min\": 0.5171362832079376,\n        \"max\": 0.6032409382995763,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5633730794608768,\n          0.5956967126031534\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012139778202552097,\n        \"min\": 0.010577276667535166,\n        \"max\": 0.05011926121966007,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0262487393198506,\n          0.0219718066337053\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025845812654007067,\n        \"min\": 0.9138000000000001,\n        \"max\": 0.9874666666666667,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9529333333333333,\n          0.9304666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02620642920778305,\n        \"min\": 0.007442145403382526,\n        \"max\": 0.08205972219996926,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.042303701506279996,\n          0.06536706137755054\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015047112634012106,\n        \"min\": 0.0327500659864068,\n        \"max\": 0.07388868664806052,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.05092791943051839,\n          0.038164670113798156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.041307512242400736,\n        \"min\": 0.09400656814449916,\n        \"max\": 0.20114942528735633,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.11822660098522168,\n          0.20114942528735633\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"\\ubcf5\\uc7a1\\ud55c tree \\ubaa8\\ub378 \\ub300\\ube44 \\uc120\\ud615 \\uae30\\uc900\\uc120\\uc73c\\ub85c feature engineering \\uc790\\uccb4\\uc758 \\uc124\\uba85\\ub825 \\ud655\\uc778\",\n          \"bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"print(\\\"CV best model:\\\", best_model_name, best_params)\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trial_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 14,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          14,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007481168730885,\n        \"min\": 0.04663096330797513,\n        \"max\": 0.07609114742802507,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.07609114742802507,\n          0.049319566390547076,\n          0.05064611316582477\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006924597647087308,\n        \"min\": 0.022173359277985142,\n        \"max\": 0.047961037002806066,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.047961037002806066,\n          0.034143129310785794,\n          0.028108939775837834\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011785359877674395,\n        \"min\": 0.6225532921851274,\n        \"max\": 0.6707997278696929,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.647572518782907,\n          0.6276932026582003,\n          0.6516386977184438\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010216806702914104,\n        \"min\": 0.022370910369156588,\n        \"max\": 0.05436158460450469,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.0399185136411262,\n          0.04646163650317234,\n          0.04557575424204216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012723848768567607,\n        \"min\": 0.9288666666666667,\n        \"max\": 0.985,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.9704333333333333,\n          0.9538,\n          0.9719999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012970570034246781,\n        \"min\": 0.010191082802547772,\n        \"max\": 0.067515923566879,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.025343613811599063,\n          0.067515923566879,\n          0.038585316795172644\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04371952737029428,\n        \"min\": 0.04452964231174254,\n        \"max\": 0.22314564683863908,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.11968397122658556,\n          0.07608503611061139,\n          0.06725864685167784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03688247391608683,\n        \"min\": 0.1411764705882353,\n        \"max\": 0.3,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.25882352941176473,\n          0.3,\n          0.22941176470588234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018271806088460737,\n        \"min\": 0.05498448429507783,\n        \"max\": 0.12467258585740837,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.11796883594415183,\n          0.05498448429507783,\n          0.08568507388916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"RandomForest\\ubcf4\\ub2e4 \\ubd84\\ud560 \\ubb34\\uc791\\uc704\\uc131\\uc774 \\ucee4\\uc11c \\uace0\\ucc28\\uc6d0 \\uc13c\\uc11c feature\\uc758 \\uac15\\uac74\\uc131 \\ube44\\uad50\",\n          \"\\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378. Optuna\\uc640 SMOTE\\ub85c \\ud76c\\uc18c \\ubd88\\ub7c9 recall\\uc744 \\ubcf4\\uac15\",\n          \"bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tuning_method\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Optuna\",\n          \"ParameterSampler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -3466,18 +3883,43 @@
           "name": "stdout",
           "text": [
             "CV 결과 저장: /content/defect_transfer_outputs/defect_model_cv_results.csv\n",
-            "CV best model: ExtraTrees {'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n"
+            "CV fold 결과 저장: /content/defect_transfer_outputs/defect_model_cv_fold_results.csv\n",
+            "CV best model: ExtraTrees {'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n"
           ]
         }
       ],
       "source": [
         "# 평가 함수\n",
-        "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
+        "def find_recall_priority_threshold(\n",
+        "    y_true: pd.Series,\n",
+        "    proba: np.ndarray,\n",
+        "    *,\n",
+        "    min_recall: float = RECALL_PRIORITY_TARGET,\n",
+        "    min_precision: float = RECALL_PRIORITY_MIN_PRECISION,\n",
+        "    beta: float = THRESHOLD_BETA,\n",
+        ") -> float:\n",
         "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
         "    if len(thresholds) == 0:\n",
         "        return 0.5\n",
-        "    f1_scores = 2 * precision[:-1] * recall[:-1] / np.maximum(precision[:-1] + recall[:-1], 1e-12)\n",
-        "    return float(thresholds[int(np.nanargmax(f1_scores))])\n",
+        "\n",
+        "    precision = precision[:-1]\n",
+        "    recall = recall[:-1]\n",
+        "    thresholds = thresholds.astype(float)\n",
+        "    valid = (recall >= min_recall) & (precision >= min_precision)\n",
+        "\n",
+        "    if valid.any():\n",
+        "        # Recall 목표를 만족하는 후보 중 Precision이 가장 좋은 threshold를 선택합니다.\n",
+        "        valid_idx = np.where(valid)[0]\n",
+        "        best_local_idx = valid_idx[int(np.nanargmax(precision[valid_idx]))]\n",
+        "        return float(thresholds[best_local_idx])\n",
+        "\n",
+        "    beta_sq = beta ** 2\n",
+        "    f_beta = (1 + beta_sq) * precision * recall / np.maximum(beta_sq * precision + recall, 1e-12)\n",
+        "    return float(thresholds[int(np.nanargmax(f_beta))])\n",
+        "\n",
+        "\n",
+        "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
+        "    return find_recall_priority_threshold(y_true, proba)\n",
         "\n",
         "\n",
         "def compute_binary_metrics(y_true: pd.Series, proba: np.ndarray, threshold: float) -> dict:\n",
@@ -3491,6 +3933,7 @@
         "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
         "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
         "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
+        "        \"predicted_positive_rate\": float((tp + fp) / max(tp + fp + tn + fn, 1)),\n",
         "        \"true_negative\": int(tn),\n",
         "        \"false_positive\": int(fp),\n",
         "        \"false_negative\": int(fn),\n",
@@ -3505,42 +3948,63 @@
         "    return 1 / (1 + np.exp(-decision))\n",
         "\n",
         "\n",
+        "def is_pipeline_estimator(estimator) -> bool:\n",
+        "    return isinstance(estimator, (Pipeline, ImbPipeline))\n",
+        "\n",
+        "\n",
+        "def get_final_estimator(estimator):\n",
+        "    return estimator.named_steps.get(\"model\", estimator) if is_pipeline_estimator(estimator) else estimator\n",
+        "\n",
+        "\n",
+        "def make_lightgbm_pipeline() -> ImbPipeline:\n",
+        "    return ImbPipeline([\n",
+        "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+        "        (\"smote\", SMOTE(\n",
+        "            sampling_strategy=SMOTE_SAMPLING_STRATEGY if USE_SMOTE else 0.01,\n",
+        "            random_state=RANDOM_STATE,\n",
+        "            k_neighbors=3,\n",
+        "        )),\n",
+        "        (\"model\", lgb.LGBMClassifier(\n",
+        "            objective=\"binary\",\n",
+        "            n_estimators=500,\n",
+        "            random_state=RANDOM_STATE,\n",
+        "            n_jobs=-1,\n",
+        "            is_unbalance=True,\n",
+        "            verbosity=-1,\n",
+        "        )),\n",
+        "    ])\n",
+        "\n",
+        "\n",
         "def make_candidate_models() -> dict:\n",
         "    return {\n",
         "        \"LightGBM\": {\n",
-        "            \"estimator\": lgb.LGBMClassifier(\n",
-        "                objective=\"binary\",\n",
-        "                n_estimators=250,\n",
-        "                random_state=RANDOM_STATE,\n",
-        "                n_jobs=-1,\n",
-        "                scale_pos_weight=scale_pos_weight,\n",
-        "                verbosity=-1,\n",
-        "            ),\n",
+        "            \"estimator\": make_lightgbm_pipeline(),\n",
         "            \"params\": {\n",
-        "                \"learning_rate\": [0.03, 0.06],\n",
-        "                \"num_leaves\": [31, 63],\n",
-        "                \"max_depth\": [-1, 8],\n",
-        "                \"min_child_samples\": [80, 150],\n",
-        "                \"subsample\": [0.85, 1.0],\n",
-        "                \"colsample_bytree\": [0.85],\n",
-        "                \"reg_lambda\": [1.0, 2.0],\n",
+        "                \"model__learning_rate\": [0.015, 0.025, 0.04, 0.06],\n",
+        "                \"model__num_leaves\": [31, 63, 127],\n",
+        "                \"model__max_depth\": [-1, 6, 8, 12],\n",
+        "                \"model__min_child_samples\": [40, 80, 150, 250],\n",
+        "                \"model__subsample\": [0.75, 0.85, 1.0],\n",
+        "                \"model__colsample_bytree\": [0.65, 0.8, 0.95],\n",
+        "                \"model__reg_lambda\": [0.5, 1.0, 2.0, 5.0],\n",
+        "                \"model__reg_alpha\": [0.0, 0.1, 0.5],\n",
         "            },\n",
-        "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델\",\n",
+        "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델. Optuna와 SMOTE로 희소 불량 recall을 보강\",\n",
         "        },\n",
         "        \"RandomForest\": {\n",
         "            \"estimator\": Pipeline([\n",
         "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
         "                (\"model\", RandomForestClassifier(\n",
-        "                    n_estimators=120,\n",
+        "                    n_estimators=160,\n",
         "                    class_weight=\"balanced_subsample\",\n",
         "                    random_state=RANDOM_STATE,\n",
         "                    n_jobs=-1,\n",
         "                )),\n",
         "            ]),\n",
         "            \"params\": {\n",
-        "                \"model__max_depth\": [12, 20],\n",
-        "                \"model__min_samples_leaf\": [1, 5],\n",
-        "                \"model__max_features\": [\"sqrt\"],\n",
+        "                \"model__max_depth\": [12, 16, 20, None],\n",
+        "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
+        "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
         "            },\n",
         "            \"selection_reason\": \"bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble 성능 확인\",\n",
         "        },\n",
@@ -3548,16 +4012,16 @@
         "            \"estimator\": Pipeline([\n",
         "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
         "                (\"model\", ExtraTreesClassifier(\n",
-        "                    n_estimators=160,\n",
+        "                    n_estimators=220,\n",
         "                    class_weight=\"balanced\",\n",
         "                    random_state=RANDOM_STATE,\n",
         "                    n_jobs=-1,\n",
         "                )),\n",
         "            ]),\n",
         "            \"params\": {\n",
-        "                \"model__max_depth\": [12, 20],\n",
-        "                \"model__min_samples_leaf\": [1, 5],\n",
-        "                \"model__max_features\": [\"sqrt\"],\n",
+        "                \"model__max_depth\": [12, 16, 20, None],\n",
+        "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
+        "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
         "            },\n",
         "            \"selection_reason\": \"RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교\",\n",
         "        },\n",
@@ -3567,12 +4031,12 @@
         "                (\"scaler\", StandardScaler()),\n",
         "                (\"model\", LogisticRegression(\n",
         "                    class_weight=\"balanced\",\n",
-        "                    max_iter=1000,\n",
+        "                    max_iter=1500,\n",
         "                    random_state=RANDOM_STATE,\n",
         "                )),\n",
         "            ]),\n",
         "            \"params\": {\n",
-        "                \"model__C\": [0.1, 1.0],\n",
+        "                \"model__C\": [0.03, 0.1, 0.3, 1.0, 3.0],\n",
         "                \"model__solver\": [\"lbfgs\"],\n",
         "            },\n",
         "            \"selection_reason\": \"복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체의 설명력 확인\",\n",
@@ -3580,6 +4044,72 @@
         "    }\n",
         "\n",
         "\n",
+        "def suggest_lightgbm_params(trial: optuna.Trial) -> dict:\n",
+        "    return {\n",
+        "        \"model__learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.08, log=True),\n",
+        "        \"model__num_leaves\": trial.suggest_int(\"num_leaves\", 24, 160),\n",
+        "        \"model__max_depth\": trial.suggest_categorical(\"max_depth\", [-1, 5, 7, 9, 12]),\n",
+        "        \"model__min_child_samples\": trial.suggest_int(\"min_child_samples\", 30, 300),\n",
+        "        \"model__subsample\": trial.suggest_float(\"subsample\", 0.65, 1.0),\n",
+        "        \"model__colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.55, 1.0),\n",
+        "        \"model__reg_lambda\": trial.suggest_float(\"reg_lambda\", 0.1, 8.0, log=True),\n",
+        "        \"model__reg_alpha\": trial.suggest_float(\"reg_alpha\", 1e-4, 1.0, log=True),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def evaluate_cv_trial(model_name: str, config: dict, params: dict, trial_no: int) -> tuple[dict, list[dict]]:\n",
+        "    fold_rows = []\n",
+        "    for fold_no, (train_idx, valid_idx) in enumerate(skf.split(X_cv_pool, y_cv_pool), start=1):\n",
+        "        fold_started_at = datetime.now()\n",
+        "        X_fold_train = X_cv_pool.iloc[train_idx]\n",
+        "        y_fold_train = y_cv_pool.iloc[train_idx]\n",
+        "        X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
+        "        y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
+        "\n",
+        "        cv_model = clone(config[\"estimator\"])\n",
+        "        cv_model.set_params(**params)\n",
+        "        cv_model.fit(X_fold_train, y_fold_train)\n",
+        "\n",
+        "        fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
+        "        fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
+        "        fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
+        "        fold_metrics.update({\n",
+        "            \"model_name\": model_name,\n",
+        "            \"trial_no\": trial_no,\n",
+        "            \"fold_no\": fold_no,\n",
+        "            \"threshold\": fold_threshold,\n",
+        "            **params,\n",
+        "        })\n",
+        "        fold_rows.append(fold_metrics)\n",
+        "        elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
+        "        print(\n",
+        "            f\"    fold {fold_no}/{effective_cv_splits} \"\n",
+        "            f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
+        "            f\"Recall={fold_metrics['recall']:.5f} \"\n",
+        "            f\"F1={fold_metrics['f1']:.5f} \"\n",
+        "            f\"elapsed={elapsed:.1f}s\"\n",
+        "        )\n",
+        "\n",
+        "    fold_df = pd.DataFrame(fold_rows)\n",
+        "    summary = {\n",
+        "        \"model_name\": model_name,\n",
+        "        \"trial_no\": trial_no,\n",
+        "        \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
+        "        \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
+        "        \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
+        "        \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
+        "        \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
+        "        \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
+        "        \"mean_precision\": float(fold_df[\"precision\"].mean()),\n",
+        "        \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
+        "        \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
+        "        \"params\": params,\n",
+        "        \"selection_reason\": config[\"selection_reason\"],\n",
+        "        \"tuning_method\": \"Optuna\" if model_name == \"LightGBM\" and ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+        "    }\n",
+        "    return summary, fold_rows\n",
+        "\n",
+        "\n",
         "# CV 데이터셋 규모 관리\n",
         "if len(X_train) > CV_SAMPLE_SIZE:\n",
         "    X_cv_pool, _, y_cv_pool, _ = train_test_split(\n",
@@ -3600,97 +4130,84 @@
         "candidate_configs = make_candidate_models()\n",
         "skf = StratifiedKFold(n_splits=effective_cv_splits, shuffle=True, random_state=RANDOM_STATE)\n",
         "cv_rows = []\n",
+        "cv_fold_rows = []\n",
         "best_params_by_model = {}\n",
         "\n",
         "print(f\"CV rows: {len(X_cv_pool):,} / train rows: {len(X_train):,}\")\n",
         "print(f\"CV splits: {effective_cv_splits}, tuning trials per model: {CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1}\")\n",
+        "print(f\"SMOTE enabled: {USE_SMOTE}, sampling_strategy={SMOTE_SAMPLING_STRATEGY}\")\n",
+        "print(f\"Threshold strategy: recall>={RECALL_PRIORITY_TARGET}, min_precision>={RECALL_PRIORITY_MIN_PRECISION}, beta={THRESHOLD_BETA}\")\n",
         "\n",
         "# 후보 모델별 교차검증\n",
         "for model_name, config in candidate_configs.items():\n",
-        "    param_candidates = list(ParameterSampler(\n",
-        "        config[\"params\"],\n",
-        "        n_iter=CV_N_ITER,\n",
-        "        random_state=RANDOM_STATE,\n",
-        "    )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
-        "\n",
-        "    print(f\"\\n[{model_name}] CV start - trials: {len(param_candidates)}\")\n",
+        "    print(f\"\\n[{model_name}] CV start\")\n",
         "    model_started_at = datetime.now()\n",
-        "    for trial_no, params in enumerate(param_candidates, start=1):\n",
-        "        fold_rows = []\n",
-        "        print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
-        "        for fold_no, (train_idx, valid_idx) in enumerate(skf.split(X_cv_pool, y_cv_pool), start=1):\n",
-        "            fold_started_at = datetime.now()\n",
-        "            X_fold_train = X_cv_pool.iloc[train_idx]\n",
-        "            y_fold_train = y_cv_pool.iloc[train_idx]\n",
-        "            X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
-        "            y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
-        "\n",
-        "            cv_model = clone(config[\"estimator\"])\n",
-        "            cv_model.set_params(**params)\n",
-        "            cv_model.fit(X_fold_train, y_fold_train)\n",
-        "\n",
-        "            fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
-        "            fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
-        "            fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
-        "            fold_metrics.update({\n",
-        "                \"model_name\": model_name,\n",
-        "                \"trial_no\": trial_no,\n",
-        "                \"fold_no\": fold_no,\n",
-        "                \"threshold\": fold_threshold,\n",
-        "                **params,\n",
-        "            })\n",
-        "            fold_rows.append(fold_metrics)\n",
-        "            elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
-        "            print(\n",
-        "                f\"    fold {fold_no}/{effective_cv_splits} \"\n",
-        "                f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
-        "                f\"F1={fold_metrics['f1']:.5f} \"\n",
-        "                f\"elapsed={elapsed:.1f}s\"\n",
-        "            )\n",
         "\n",
-        "        fold_df = pd.DataFrame(fold_rows)\n",
-        "        row = {\n",
-        "            \"model_name\": model_name,\n",
-        "            \"trial_no\": trial_no,\n",
-        "            \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
-        "            \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
-        "            \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
-        "            \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
-        "            \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
-        "            \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
-        "            \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
-        "            \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
-        "            \"params\": params,\n",
-        "            \"selection_reason\": config[\"selection_reason\"],\n",
-        "        }\n",
-        "        cv_rows.append(row)\n",
-        "        print(\n",
-        "            f\"  -> {model_name} trial {trial_no} mean \"\n",
-        "            f\"PR-AUC={row['mean_pr_auc']:.5f} \"\n",
-        "            f\"ROC-AUC={row['mean_roc_auc']:.5f} \"\n",
-        "            f\"ACC={row['mean_accuracy']:.5f} \"\n",
-        "            f\"FPR={row['mean_false_positive_rate']:.5f}\"\n",
+        "    if model_name == \"LightGBM\" and ENABLE_HYPERPARAMETER_TUNING and ENABLE_OPTUNA_TUNING:\n",
+        "        def objective(trial: optuna.Trial) -> float:\n",
+        "            params = suggest_lightgbm_params(trial)\n",
+        "            print(f\"  optuna trial {trial.number + 1}/{CV_N_ITER} params={params}\")\n",
+        "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial.number + 1)\n",
+        "            cv_rows.append(summary)\n",
+        "            cv_fold_rows.extend(fold_rows)\n",
+        "            trial.set_user_attr(\"params\", params)\n",
+        "            trial.set_user_attr(\"mean_recall\", summary[\"mean_recall\"])\n",
+        "            trial.set_user_attr(\"mean_f1\", summary[\"mean_f1\"])\n",
+        "            # PR-AUC를 우선하되 recall을 약하게 보상합니다.\n",
+        "            return summary[\"mean_pr_auc\"] + 0.05 * summary[\"mean_recall\"]\n",
+        "\n",
+        "        study = optuna.create_study(\n",
+        "            direction=\"maximize\",\n",
+        "            sampler=TPESampler(seed=RANDOM_STATE),\n",
+        "            study_name=\"bosch_defect_lightgbm_recall_pr_auc\",\n",
         "        )\n",
+        "        study.optimize(objective, n_trials=CV_N_ITER, show_progress_bar=False)\n",
+        "        best_params_by_model[model_name] = study.best_trial.user_attrs[\"params\"]\n",
+        "        print(f\"[{model_name}] Optuna best value={study.best_value:.5f} params={best_params_by_model[model_name]}\")\n",
+        "    else:\n",
+        "        param_candidates = list(ParameterSampler(\n",
+        "            config[\"params\"],\n",
+        "            n_iter=CV_N_ITER,\n",
+        "            random_state=RANDOM_STATE,\n",
+        "        )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
+        "        print(f\"[{model_name}] random search trials: {len(param_candidates)}\")\n",
+        "        for trial_no, params in enumerate(param_candidates, start=1):\n",
+        "            print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
+        "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial_no)\n",
+        "            cv_rows.append(summary)\n",
+        "            cv_fold_rows.extend(fold_rows)\n",
+        "            print(\n",
+        "                f\"  -> {model_name} trial {trial_no} mean \"\n",
+        "                f\"PR-AUC={summary['mean_pr_auc']:.5f} \"\n",
+        "                f\"Recall={summary['mean_recall']:.5f} \"\n",
+        "                f\"F1={summary['mean_f1']:.5f} \"\n",
+        "                f\"FPR={summary['mean_false_positive_rate']:.5f}\"\n",
+        "            )\n",
         "\n",
         "    print(f\"[{model_name}] CV done elapsed={(datetime.now() - model_started_at).total_seconds():.1f}s\")\n",
         "\n",
         "cv_results = pd.DataFrame(cv_rows).sort_values(\n",
-        "    [\"mean_pr_auc\", \"mean_roc_auc\", \"mean_f1\", \"mean_accuracy\"],\n",
+        "    [\"mean_pr_auc\", \"mean_recall\", \"mean_f1\", \"mean_roc_auc\", \"mean_accuracy\"],\n",
         "    ascending=False,\n",
         ").reset_index(drop=True)\n",
+        "cv_fold_results = pd.DataFrame(cv_fold_rows)\n",
         "\n",
         "for model_name in candidate_configs:\n",
-        "    best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
+        "    if model_name not in best_params_by_model:\n",
+        "        best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
         "\n",
         "best_model_name = str(cv_results.iloc[0][\"model_name\"])\n",
         "best_params = best_params_by_model[best_model_name]\n",
         "best_model_selection_reason = str(cv_results.iloc[0][\"selection_reason\"])\n",
         "\n",
         "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.csv\"\n",
+        "cv_fold_results_path = OUTPUT_DIR / \"defect_model_cv_fold_results.csv\"\n",
         "cv_results.to_csv(cv_results_path, index=False)\n",
+        "cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
         "\n",
         "display(cv_results.head(20))\n",
         "print(\"CV 결과 저장:\", cv_results_path)\n",
+        "print(\"CV fold 결과 저장:\", cv_fold_results_path)\n",
         "print(\"CV best model:\", best_model_name, best_params)\n"
       ]
     },
@@ -3705,40 +4222,41 @@
         "\n",
         "교차검증에서 선택된 모델별 best parameter로 4개 후보 모델을 모두 학습합니다.\n",
         "\n",
-        "각 모델은 같은 `X_train`, `X_valid`, `feature_cols`를 사용합니다.\n"
+        "LightGBM은 median imputer + SMOTE + LightGBM pipeline으로 최종 학습합니다. Threshold는 validation set에서 recall 우선 정책으로 탐색합니다.\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 18,
       "id": "3553e4c5",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 807
+          "height": 825
         },
         "id": "3553e4c5",
-        "outputId": "fdc0c33f-c362-4dde-ac26-c880a406e708"
+        "outputId": "f6e17abe-7523-45bd-b4fa-0828efb0d901"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Final candidate training rows: 80,000 / train rows: 240,000\n",
-            "[LightGBM] final fit start params={'subsample': 0.85, 'reg_lambda': 2.0, 'num_leaves': 31, 'min_child_samples': 150, 'max_depth': 8, 'learning_rate': 0.06, 'colsample_bytree': 0.85}\n",
-            "[LightGBM] final fit done PR-AUC=0.05440 ROC-AUC=0.66998 ACC=0.99080 FPR=0.00454 elapsed=38.1s\n",
-            "[RandomForest] final fit start params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "[RandomForest] final fit done PR-AUC=0.03843 ROC-AUC=0.68322 ACC=0.99165 FPR=0.00334 elapsed=67.6s\n",
-            "[ExtraTrees] final fit start params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "[ExtraTrees] final fit done PR-AUC=0.03488 ROC-AUC=0.66894 ACC=0.99028 FPR=0.00479 elapsed=65.5s\n",
-            "[LogisticRegression] final fit start params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
-            "[LogisticRegression] final fit done PR-AUC=0.04596 ROC-AUC=0.64420 ACC=0.99165 FPR=0.00350 elapsed=72.6s\n",
-            "selected_model_name: LightGBM\n",
-            "ROC-AUC: 0.66998\n",
-            "PR-AUC: 0.0544\n",
-            "Accuracy: 0.9908\n",
-            "False Positive Rate: 0.00454\n"
+            "Final candidate training rows: 120,000 / train rows: 240,000\n",
+            "[LightGBM] final fit start params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
+            "[LightGBM] final fit done PR-AUC=0.05032 ROC-AUC=0.66598 ACC=0.90142 FPR=0.09546 elapsed=159.2s\n",
+            "[RandomForest] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+            "[RandomForest] final fit done PR-AUC=0.06255 ROC-AUC=0.69675 ACC=0.91395 FPR=0.08285 elapsed=150.0s\n",
+            "[ExtraTrees] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+            "[ExtraTrees] final fit done PR-AUC=0.05758 ROC-AUC=0.67618 ACC=0.98787 FPR=0.00784 elapsed=750.9s\n",
+            "[LogisticRegression] final fit start params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
+            "[LogisticRegression] final fit done PR-AUC=0.05096 ROC-AUC=0.66273 ACC=0.98672 FPR=0.00888 elapsed=71.9s\n",
+            "selected_model_name: RandomForest\n",
+            "ROC-AUC: 0.69675\n",
+            "PR-AUC: 0.06255\n",
+            "Accuracy: 0.91395\n",
+            "False Positive Rate: 0.08285\n",
+            "Recall: 0.35103\n"
           ]
         },
         {
@@ -3746,32 +4264,38 @@
           "data": {
             "text/plain": [
               "           model_name  threshold  \\\n",
-              "0            LightGBM   0.764335   \n",
-              "1  LogisticRegression   0.979877   \n",
-              "2        RandomForest   0.351859   \n",
-              "3          ExtraTrees   0.448309   \n",
+              "0        RandomForest   0.058868   \n",
+              "1          ExtraTrees   0.243180   \n",
+              "2  LogisticRegression   0.898330   \n",
+              "3            LightGBM   0.034547   \n",
               "\n",
               "                                    selection_reason  accuracy  precision  \\\n",
-              "0  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델  0.990800   0.176292   \n",
-              "1  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  0.991650   0.183594   \n",
-              "2  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  0.991650   0.156780   \n",
-              "3  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  0.990283   0.128049   \n",
-              "\n",
-              "     recall        f1   roc_auc    pr_auc  false_positive_rate  true_negative  \\\n",
-              "0  0.171091  0.173653  0.669980  0.054404             0.004542          59390   \n",
-              "1  0.138643  0.157983  0.644195  0.045955             0.003503          59452   \n",
-              "2  0.109145  0.128696  0.683218  0.038426             0.003336          59462   \n",
-              "3  0.123894  0.125937  0.668943  0.034879             0.004794          59375   \n",
-              "\n",
-              "   false_positive  false_negative  true_positive  \n",
-              "0             271             281             58  \n",
-              "1             209             292             47  \n",
-              "2             199             302             37  \n",
-              "3             286             297             42  "
+              "0  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  0.913950   0.023508   \n",
+              "1  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  0.987867   0.144424   \n",
+              "2  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  0.986717   0.119601   \n",
+              "3  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...  0.901417   0.020468   \n",
+              "\n",
+              "     recall        f1   roc_auc    pr_auc  false_positive_rate  \\\n",
+              "0  0.351032  0.044066  0.696746  0.062551             0.082851   \n",
+              "1  0.233038  0.178330  0.676183  0.057577             0.007844   \n",
+              "2  0.212389  0.153029  0.662734  0.050958             0.008884   \n",
+              "3  0.351032  0.038680  0.665979  0.050323             0.095456   \n",
+              "\n",
+              "   predicted_positive_rate  true_negative  false_positive  false_negative  \\\n",
+              "0                 0.084367          54718            4943             220   \n",
+              "1                 0.009117          59193             468             260   \n",
+              "2                 0.010033          59131             530             267   \n",
+              "3                 0.096900          53966            5695             220   \n",
+              "\n",
+              "   true_positive  \n",
+              "0            119  \n",
+              "1             79  \n",
+              "2             72  \n",
+              "3            119  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-dc6e01fb-49cb-4076-8ba6-a9e595acb77b\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-6fdedb8e-6721-4875-adf0-d8cafb363080\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -3800,6 +4324,7 @@
               "      <th>roc_auc</th>\n",
               "      <th>pr_auc</th>\n",
               "      <th>false_positive_rate</th>\n",
+              "      <th>predicted_positive_rate</th>\n",
               "      <th>true_negative</th>\n",
               "      <th>false_positive</th>\n",
               "      <th>false_negative</th>\n",
@@ -3809,71 +4334,75 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>0.764335</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델</td>\n",
-              "      <td>0.990800</td>\n",
-              "      <td>0.176292</td>\n",
-              "      <td>0.171091</td>\n",
-              "      <td>0.173653</td>\n",
-              "      <td>0.669980</td>\n",
-              "      <td>0.054404</td>\n",
-              "      <td>0.004542</td>\n",
-              "      <td>59390</td>\n",
-              "      <td>271</td>\n",
-              "      <td>281</td>\n",
-              "      <td>58</td>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>0.058868</td>\n",
+              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+              "      <td>0.913950</td>\n",
+              "      <td>0.023508</td>\n",
+              "      <td>0.351032</td>\n",
+              "      <td>0.044066</td>\n",
+              "      <td>0.696746</td>\n",
+              "      <td>0.062551</td>\n",
+              "      <td>0.082851</td>\n",
+              "      <td>0.084367</td>\n",
+              "      <td>54718</td>\n",
+              "      <td>4943</td>\n",
+              "      <td>220</td>\n",
+              "      <td>119</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>LogisticRegression</td>\n",
-              "      <td>0.979877</td>\n",
-              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
-              "      <td>0.991650</td>\n",
-              "      <td>0.183594</td>\n",
-              "      <td>0.138643</td>\n",
-              "      <td>0.157983</td>\n",
-              "      <td>0.644195</td>\n",
-              "      <td>0.045955</td>\n",
-              "      <td>0.003503</td>\n",
-              "      <td>59452</td>\n",
-              "      <td>209</td>\n",
-              "      <td>292</td>\n",
-              "      <td>47</td>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>0.243180</td>\n",
+              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+              "      <td>0.987867</td>\n",
+              "      <td>0.144424</td>\n",
+              "      <td>0.233038</td>\n",
+              "      <td>0.178330</td>\n",
+              "      <td>0.676183</td>\n",
+              "      <td>0.057577</td>\n",
+              "      <td>0.007844</td>\n",
+              "      <td>0.009117</td>\n",
+              "      <td>59193</td>\n",
+              "      <td>468</td>\n",
+              "      <td>260</td>\n",
+              "      <td>79</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>0.351859</td>\n",
-              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
-              "      <td>0.991650</td>\n",
-              "      <td>0.156780</td>\n",
-              "      <td>0.109145</td>\n",
-              "      <td>0.128696</td>\n",
-              "      <td>0.683218</td>\n",
-              "      <td>0.038426</td>\n",
-              "      <td>0.003336</td>\n",
-              "      <td>59462</td>\n",
-              "      <td>199</td>\n",
-              "      <td>302</td>\n",
-              "      <td>37</td>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>0.898330</td>\n",
+              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
+              "      <td>0.986717</td>\n",
+              "      <td>0.119601</td>\n",
+              "      <td>0.212389</td>\n",
+              "      <td>0.153029</td>\n",
+              "      <td>0.662734</td>\n",
+              "      <td>0.050958</td>\n",
+              "      <td>0.008884</td>\n",
+              "      <td>0.010033</td>\n",
+              "      <td>59131</td>\n",
+              "      <td>530</td>\n",
+              "      <td>267</td>\n",
+              "      <td>72</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>0.448309</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>0.990283</td>\n",
-              "      <td>0.128049</td>\n",
-              "      <td>0.123894</td>\n",
-              "      <td>0.125937</td>\n",
-              "      <td>0.668943</td>\n",
-              "      <td>0.034879</td>\n",
-              "      <td>0.004794</td>\n",
-              "      <td>59375</td>\n",
-              "      <td>286</td>\n",
-              "      <td>297</td>\n",
-              "      <td>42</td>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>0.034547</td>\n",
+              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+              "      <td>0.901417</td>\n",
+              "      <td>0.020468</td>\n",
+              "      <td>0.351032</td>\n",
+              "      <td>0.038680</td>\n",
+              "      <td>0.665979</td>\n",
+              "      <td>0.050323</td>\n",
+              "      <td>0.095456</td>\n",
+              "      <td>0.096900</td>\n",
+              "      <td>53966</td>\n",
+              "      <td>5695</td>\n",
+              "      <td>220</td>\n",
+              "      <td>119</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -3881,7 +4410,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc6e01fb-49cb-4076-8ba6-a9e595acb77b')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6fdedb8e-6721-4875-adf0-d8cafb363080')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -3933,12 +4462,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-dc6e01fb-49cb-4076-8ba6-a9e595acb77b button.colab-df-convert');\n",
+              "        document.querySelector('#df-6fdedb8e-6721-4875-adf0-d8cafb363080 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-dc6e01fb-49cb-4076-8ba6-a9e595acb77b');\n",
+              "        const element = document.querySelector('#df-6fdedb8e-6721-4875-adf0-d8cafb363080');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -3958,7 +4487,7 @@
               "  </div>\n",
               "\n",
               "\n",
-              "  <div id=\"id_731ab10a-4ca1-4fa5-93d1-57af43496a7b\">\n",
+              "  <div id=\"id_b382d83d-17b9-42da-a32f-9fb37cd8f3cd\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -4002,7 +4531,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_731ab10a-4ca1-4fa5-93d1-57af43496a7b button.colab-df-generate');\n",
+              "        document.querySelector('#id_b382d83d-17b9-42da-a32f-9fb37cd8f3cd button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -4019,7 +4548,7 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "model_comparison",
-              "summary": "{\n  \"name\": \"model_comparison\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"LogisticRegression\",\n          \"ExtraTrees\",\n          \"LightGBM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28907031849091064,\n        \"min\": 0.35185931288578937,\n        \"max\": 0.979876693471741,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.979876693471741,\n          0.4483085040039446,\n          0.7643346211138977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"string\",\n   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0.12389380530973451,\n          0.1710914454277286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02315847573778767,\n        \"min\": 0.12593703148425786,\n        \"max\": 0.17365269461077845,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.15798319327731092,\n          0.12593703148425786,\n          0.17365269461077845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016279376786549147,\n        \"min\": 0.6441952093240279,\n        \"max\": 0.6832183696291124,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6441952093240279,\n          0.6689429494935472,\n          0.6699795091035243\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008659351382236778,\n        \"min\": 0.034879154454109204,\n        \"max\": 0.05440352499791599,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.045955427881527094,\n          0.034879154454109204,\n          0.05440352499791599\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.000731427784437358,\n        \"min\": 0.0033355123112250883,\n        \"max\": 0.004793751361861182,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0035031259952062487,\n          0.004793751361861182,\n          0.004542330835889442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": 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+              "summary": "{\n  \"name\": \"model_comparison\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40395240727098636,\n        \"min\": 0.03454695956318568,\n        \"max\": 0.8983303412868939,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2431800238242072,\n          0.03454695956318568,\n          0.058868157090478435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"RandomForest\\ubcf4\\ub2e4 \\ubd84\\ud560 \\ubb34\\uc791\\uc704\\uc131\\uc774 \\ucee4\\uc11c \\uace0\\ucc28\\uc6d0 \\uc13c\\uc11c feature\\uc758 \\uac15\\uac74\\uc131 \\ube44\\uad50\",\n          \"\\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378. Optuna\\uc640 SMOTE\\ub85c \\ud76c\\uc18c \\ubd88\\ub7c9 recall\\uc744 \\ubcf4\\uac15\",\n          \"bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04624820792323862,\n        \"min\": 0.9014166666666666,\n        \"max\": 0.9878666666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9878666666666667,\n          0.9014166666666666,\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0643379448162342,\n        \"min\": 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\"num_unique_values\": 4,\n        \"samples\": [\n          0.17832957110609482,\n          0.038680318543799774,\n          0.04406591371968154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015334779408148261,\n        \"min\": 0.6627341233129422,\n        \"max\": 0.6967463513986769,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6761829953791527,\n          0.6659786100217459,\n          0.6967463513986769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005812604054563798,\n        \"min\": 0.050323296178874356,\n        \"max\": 0.06255052428644334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          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           },
           "metadata": {}
@@ -4029,20 +4558,20 @@
           "data": {
             "text/plain": [
               "                                            모델_선정_근거  \\\n",
-              "0  LightGBM 모델이 validation PR-AUC/ROC-AUC/F1 기준 종...   \n",
+              "0  RandomForest 모델이 validation PR-AUC/Recall/F1 기...   \n",
               "\n",
               "                                          데이터셋_규모_관리  \\\n",
               "0  {'max_rows': 300000, 'profile_rows': 50000, 't...   \n",
               "\n",
               "                                        테스트_케이스_수_관리  정확도_Accuracy  \\\n",
-              "0  {'validation_total': 60000, 'validation_normal...        0.9908   \n",
+              "0  {'validation_total': 60000, 'validation_normal...       0.91395   \n",
               "\n",
               "   오탐률_False_Positive_Rate                                        모델_성능_개선_현황  \n",
-              "0                 0.004542  {'baseline_model': 'LightGBM', 'baseline_pr_au...  "
+              "0                 0.082851  {'baseline_model': 'LightGBM', 'baseline_pr_au...  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-f036ed87-ea2e-4cc6-89d6-c9448e360cc0\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-32734db2-8443-4fbf-a77b-616fb1fbed24\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -4072,11 +4601,11 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>LightGBM 모델이 validation PR-AUC/ROC-AUC/F1 기준 종...</td>\n",
+              "      <td>RandomForest 모델이 validation PR-AUC/Recall/F1 기...</td>\n",
               "      <td>{'max_rows': 300000, 'profile_rows': 50000, 't...</td>\n",
               "      <td>{'validation_total': 60000, 'validation_normal...</td>\n",
-              "      <td>0.9908</td>\n",
-              "      <td>0.004542</td>\n",
+              "      <td>0.91395</td>\n",
+              "      <td>0.082851</td>\n",
               "      <td>{'baseline_model': 'LightGBM', 'baseline_pr_au...</td>\n",
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               "  </tbody>\n",
@@ -4085,7 +4614,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f036ed87-ea2e-4cc6-89d6-c9448e360cc0')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32734db2-8443-4fbf-a77b-616fb1fbed24')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -4137,12 +4666,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-f036ed87-ea2e-4cc6-89d6-c9448e360cc0 button.colab-df-convert');\n",
+              "        document.querySelector('#df-32734db2-8443-4fbf-a77b-616fb1fbed24 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-f036ed87-ea2e-4cc6-89d6-c9448e360cc0');\n",
+              "        const element = document.querySelector('#df-32734db2-8443-4fbf-a77b-616fb1fbed24');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -4167,7 +4696,7 @@
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc120\\uc815_\\uadfc\\uac70\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"LightGBM \\ubaa8\\ub378\\uc774 validation PR-AUC/ROC-AUC/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. \\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub370\\uc774\\ud130\\uc14b_\\uaddc\\ubaa8_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud14c\\uc2a4\\ud2b8_\\ucf00\\uc774\\uc2a4_\\uc218_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc815\\ud655\\ub3c4_Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9908,\n        \"max\": 0.9908,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc624\\ud0d0\\ub960_False_Positive_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.004542330835889442,\n        \"max\": 0.004542330835889442,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.004542330835889442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc131\\ub2a5_\\uac1c\\uc120_\\ud604\\ud669\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc120\\uc815_\\uadfc\\uac70\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RandomForest \\ubaa8\\ub378\\uc774 validation PR-AUC/Recall/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub370\\uc774\\ud130\\uc14b_\\uaddc\\ubaa8_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud14c\\uc2a4\\ud2b8_\\ucf00\\uc774\\uc2a4_\\uc218_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc815\\ud655\\ub3c4_Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.91395,\n        \"max\": 0.91395,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc624\\ud0d0\\ub960_False_Positive_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0828514439918875,\n        \"max\": 0.0828514439918875,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc131\\ub2a5_\\uac1c\\uc120_\\ud604\\ud669\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -4230,7 +4759,7 @@
         "    )\n",
         "\n",
         "model_comparison = pd.DataFrame(model_eval_rows).sort_values(\n",
-        "    [\"pr_auc\", \"roc_auc\", \"f1\", \"accuracy\"],\n",
+        "    [\"pr_auc\", \"recall\", \"f1\", \"roc_auc\", \"accuracy\"],\n",
         "    ascending=False,\n",
         ").reset_index(drop=True)\n",
         "\n",
@@ -4258,6 +4787,7 @@
         "    full_metrics = compute_binary_metrics(y_valid, valid_proba, best_threshold)\n",
         "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), list(full_metrics.keys())] = list(full_metrics.values())\n",
         "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), \"threshold\"] = best_threshold\n",
+        "    model_thresholds[selected_model_name] = best_threshold\n",
         "    model_confusion_matrices[selected_model_name] = cm\n",
         "    print(f\"[{selected_model_name}] full-data refit done elapsed={(datetime.now() - refit_started_at).total_seconds():.1f}s\")\n",
         "\n",
@@ -4273,7 +4803,7 @@
         "performance_improvement = best_pr_auc - baseline_pr_auc if not np.isnan(baseline_pr_auc) else 0.0\n",
         "\n",
         "ai_management_summary = {\n",
-        "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/ROC-AUC/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
+        "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/Recall/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
         "    \"데이터셋_규모_관리\": {\n",
         "        \"max_rows\": int(MAX_ROWS),\n",
         "        \"profile_rows\": int(PROFILE_ROWS),\n",
@@ -4291,6 +4821,15 @@
         "        \"cv_splits\": int(effective_cv_splits),\n",
         "        \"cv_sample_size\": int(len(X_cv_pool)),\n",
         "        \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
+        "        \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+        "        \"smote_enabled\": bool(USE_SMOTE),\n",
+        "        \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
+        "        \"threshold_strategy\": {\n",
+        "            \"type\": \"recall_priority\",\n",
+        "            \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
+        "            \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
+        "            \"beta\": float(THRESHOLD_BETA),\n",
+        "        },\n",
         "    },\n",
         "    \"정확도_Accuracy\": accuracy,\n",
         "    \"오탐률_False_Positive_Rate\": false_positive_rate,\n",
@@ -4308,6 +4847,7 @@
         "print(\"PR-AUC:\", round(pr_auc, 5))\n",
         "print(\"Accuracy:\", round(accuracy, 5))\n",
         "print(\"False Positive Rate:\", round(false_positive_rate, 5))\n",
+        "print(\"Recall:\", round(float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"recall\"]), 5))\n",
         "display(model_comparison)\n",
         "display(pd.DataFrame([ai_management_summary]))\n"
       ]
@@ -4321,14 +4861,14 @@
       "source": [
         "## 9. 후보 모델별 Threshold 튜닝 및 Confusion Matrix\n",
         "\n",
-        "각 후보 모델은 validation set에서 F1 score가 가장 높은 threshold를 사용합니다.\n",
+        "각 후보 모델은 validation set에서 recall 우선 threshold를 사용합니다.\n",
         "\n",
-        "Accuracy와 False Positive Rate도 함께 확인합니다.\n"
+        "기본 정책은 `RECALL_PRIORITY_TARGET` 이상을 만족하는 threshold 중 precision이 가장 높은 값을 선택하고, 목표 recall을 만족하는 후보가 없으면 F-beta(기본 beta=2) 기준으로 선택합니다. Accuracy는 클래스 불균형 때문에 참고 지표로만 해석합니다.\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 19,
       "id": "-bI4yc81bEfC",
       "metadata": {
         "colab": {
@@ -4336,7 +4876,7 @@
           "height": 1000
         },
         "id": "-bI4yc81bEfC",
-        "outputId": "8c057781-2cd5-4987-e437-ca1f0e111dfe"
+        "outputId": "fffa66b9-fb9b-4c84-b9d5-da0983048bc2"
       },
       "outputs": [
         {
@@ -4344,16 +4884,16 @@
           "name": "stdout",
           "text": [
             "== Selected Model ==\n",
-            "model: LightGBM\n",
-            "best_threshold: 0.76433\n",
+            "model: RandomForest\n",
+            "best_threshold: 0.05887\n",
             "              precision    recall  f1-score   support\n",
             "\n",
-            "           0     0.9953    0.9955    0.9954     59661\n",
-            "           1     0.1763    0.1711    0.1737       339\n",
+            "           0     0.9960    0.9171    0.9549     59661\n",
+            "           1     0.0235    0.3510    0.0441       339\n",
             "\n",
-            "    accuracy                         0.9908     60000\n",
-            "   macro avg     0.5858    0.5833    0.5845     60000\n",
-            "weighted avg     0.9907    0.9908    0.9907     60000\n",
+            "    accuracy                         0.9140     60000\n",
+            "   macro avg     0.5098    0.6341    0.4995     60000\n",
+            "weighted avg     0.9905    0.9140    0.9498     60000\n",
             "\n"
           ]
         },
@@ -4363,7 +4903,7 @@
             "text/plain": [
               "<Figure size 1200x1000 with 4 Axes>"
             ],
-            "image/png": 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\n"
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mzFq0aJEyZMggV1dXlShRQvny5XvWhwIvCcIJIJ1auHBhgsvLly9vtax27doaP368goKCNG7cOBUoUECjRo3SqlWrdPLkyRQ9f7169RQQEKD169drzZo1MplM5nDCw8NDixYt0qJFi/TTTz9p4sSJioyMVNasWeXr66uvv/5ajRo1supz3bp15jt42NvbK3fu3Orevbv69u3LZE0AALzA6tWrJy8vL82cOVMbNmzQtWvXtHjxYv3xxx8qUqSIxo4dqw0bNujPP/9MUf/Vq1fXt99+q//7v//TuHHj5OXlpVGjRmnLli0Wfdra2mrKlCmaMGGCfvzxR61YsUKenp768MMP1a1bt9Ta3STr37+/3N3dNW/ePI0aNUpubm5q27atBg4cKAcHhydub2trq0mTJmn27NlavXq1fv75Z7m4uChv3rwKCAhQwYIFJcXVgiEhIVq+fLlu3ryprFmzqnz58urfv795Mk8HBweNHj1a48eP1+eff64HDx5o1KhRhBNIMhtTWswEA+Cl1Lx5c7m7u2vWrFlGDwUAAADAC4yvEgE8UXR0tHliyXi7d+/W33//neCZFgAAAACQHFzWAeCJQkND1bVrVzVr1kw5cuTQv//+q0WLFil79ux64403jB4eAAAAgBcc4QSAJ3Jzc1Px4sW1dOlS3bhxQ66urqpRo4YGDRqkrFmzGj08AAAAAC845pwAAAAAAACGYs4JAAAAAABgKMIJAAAAAABgKMIJGMbHx0dBQUHmxytWrJCPj48uXLhg4KjwNB79mSZFYGCgateu/YxGBACAsah3Xj7UO8CzQTjxkor/4Iv/9+qrr6patWoKDAxUaGio0cNLE/v371f79u1VsmRJValSRV988YXCw8OfuF1kZKQ+/vhjNWnSRGXKlFGpUqXUrFkzzZkzR9HR0RZtHz3OD/+7evWqRdsff/xRgwYNUr169eTj46OAgICn3seAgACL5yxfvrxat26tZcuWKTY29qn7h3Tnzh19+umnqlixovz9/RUQEKAjR44keftTp06pe/fuKlWqlMqXL68PPvhAN27csGozZswYNW/eXKVKlVLVqlXVs2dPHTp0KME+Q0ND9c4776hs2bIqXbq03n77bZ0/fz7F+xgUFJTo63jhwoXmds/iNfyotDjeoaGhGjRokOrXr69SpUqpbNmyev3117Vy5UolNA3T+vXr1bJlS/n5+alixYr6+OOPrfoEYAzqHeod6p3U8TzWOw/r2rWrfHx8NHz48GTvW7z0Vu88as2aNfLx8VGpUqUe2y46OlqNGjWSj4+PZsyYkeQxpQbu1vGSGzBggPLmzauoqCgdPHhQK1eu1L59+7Ru3To5OTkZPbxn5tixY3rzzTdVuHBhBQYG6vLly5o5c6bOnDmj6dOnP3bbyMhI/fPPP6pevbo8PT1la2urAwcOaNSoUQoODta4ceOstok/zg/LnDmzxeOFCxfq8OHD8vPz061bt556H+PlypVLAwcOlCTdvHlTq1at0pAhQ3TmzBkNGjQo1Z4nKYKDg2VnZ5esbUaMGJHgH4TPg9jYWPXs2VPHjx9X9+7dlTVrVi1YsEABAQFasWKFChQo8NjtL1++rI4dOypTpkx67733FBERoZkzZ+rEiRNaunSpHB0dJUnLli3TsmXLVK9ePXXo0EF3797V4sWL1a5dO02fPl2VK1c29xkeHq7OnTvr7t276tWrlxwcHDR79mx16tRJq1ateqq7p3z++edydXW1WFayZEnz/5/VazheWh3vmzdvKjQ0VA0aNFDu3Ln14MEDbd++XYGBgTp9+rT590mSFixYoGHDhqlSpUrmP3Z++OEHHT58WEuXLn2p30eBFwn1DvVOWqLesfQs6p2Hbdq0SQcPHky1/U0v9c7DwsPDNXbsWKv9Tsi8efN06dKllO7e0zHhpbR8+XKTt7e3KTg42GL52LFjTd7e3qb169cbNLL/eHt7myZMmGB+HD/m8+fPP3XfPXr0MFWpUsV09+5d87IlS5aYvL29Tb///nuK+hw+fLjJ29vbdOXKFasxP3qcE3Lx4kVTTEyMyWQymRo3bmzq1KlTisbxsE6dOpkaN25ssSwiIsJUvXp1k7+/vykqKirB7WJiYkyRkZFP/fwvu/Xr15u8vb1NP/30k3nZ9evXTWXLljUNHDjwidt/9tlnphIlSphCQkLMy7Zv327y9vY2LVq0yLzs0KFDprCwMIttb9y4YapYsaLpjTfesFj+/fffm7y9vU1//fWXedk///xjKlasmGncuHHJ3keTyWSaMGGCydvb23T9+vXHtnsWr+GHpdXxTkyvXr1M/v7+pgcPHphMJpPp/v37prJly5o6duxoio2NNbfbunWrydvb2/TDDz8kZ/cAPAPUO9Q71DtP73msd+JFRkaaatWqZZo4caLJ29vbNGzYsOTunll6rnfGjh1rql+/vun99983+fv7J9r3tWvXTGXKlDEf7+nTpydjz54el3WkM2XLlpUkq1PAT506pQEDBqh8+fLy8/NTq1attGXLFqvt79y5o5EjR6p27dry9fVV9erV9eGHH5pPI4qKitK3336rVq1aqUyZMvL391eHDh20a9euZ79z/xMWFqYdO3aoWbNmypgxo3l58+bN5erqqp9++ilF/Xp6ekqKOwaJPW9MTEyi2+fOnVu2ts/+V87FxUUlS5ZURESE+ecSfxrcmjVr1LhxY/n5+en333+XFHeK++DBg1W5cmX5+vqqcePGWrZsmVW/9+/fV1BQkOrXry8/Pz9VrVpV/fr107lz58xtHr0GMywsTF9++aX59VKpUiV17drV4rS1hK7BjIiI0OjRo1WjRg35+vqqfv36mjFjhtU3DvH7tXnzZjVp0sQ8/t9+++3pD6SkjRs3Klu2bKpXr555mbu7uxo2bKgtW7YoKirqsdtv2rRJNWvWVJ48eczLKleurAIFCli8Dn19fZUhQwaLbbNmzaqyZcvq33//tRqTn5+fSpQoYV5WuHBhVapUKcWv7aR61q/htDreifH09NS9e/fMpzOfPHlSd+7cUcOGDWVjY2NuV6tWLbm6umr9+vXJ3UUAaYR6h3qHeifpnsd6J960adNkMpnUvXv3lOxairxs9c6ZM2c0e/ZsDR48WPb2j79w4uuvv1bBggXVrFmzZO5V6uCyjnQmJCREkuUpeCdPnlT79u2VM2dOvfXWW+YPtL59+yooKEh169aVFHc6UMeOHXXq1Cm1bt1ar776qm7evKmtW7cqNDRU7u7uCgsL09KlS9WkSRO1adNG4eHhWrZsmXr06KGlS5eqWLFiyRpveHi47t+//8R2Dg4OypQpkyTp+PHjevDggXx9fS3aODo6qlixYjp27FiSnjsqKkphYWG6f/++Dh8+rJkzZ8rT01P58+e3atu5c2dFRETIwcFBVatWVWBg4BNPyXqWLly4IDs7O4uf865du/TTTz+pY8eOypo1qzw9PXXt2jW1bdtWNjY26tixo9zd3fXbb79pyJAhCgsL05tvvilJiomJUa9evbRz5041btxYnTt3Vnh4uLZv364TJ07Iy8srwXF89tln2rhxozp16qTChQvr1q1b2rdvn06dOqXixYsnuI3JZNLbb7+t3bt36/XXX1exYsX0+++/a8yYMQoNDdXHH39s0X7fvn3atGmTOnTooAwZMmju3LkaMGCAfvnlF/MlDtHR0bp7926Sjl2WLFnMH0jHjh3Tq6++avUB5efnp8WLF+v06dPy8fFJsJ/Q0FBdv37d6nUoSSVKlEhSQXH16lVlyZLF/Dg2NlbHjx9X69atrdr6+fnpjz/+UFhYmEWRmhy3b9+2eGxnZyc3N7dk9/OiHO/IyEhFREQoIiJCe/bs0YoVK+Tv7y9nZ2dJMhcH8Y8f5uzsrGPHjik2NjZNinAAyUO9Q71DvfN4z3O9E+/ixYuaNm2aRo4cmeBncUqlt3pn5MiRqlChgmrUqPHY0DI4OFirVq3SggULLL6USUuEEy+5sLAw3bhxQ1FRUfrrr780ceJEOTo6qlatWuY2X375pXLnzq3ly5ebr1Hq0KGD2rdvr6+//tr8YT1jxgydOHFCEydONC+TpD59+pgTXjc3N23dutXiWqe2bduqYcOGmjt3rkaOHJms8Y8YMUIrV658Yrvy5ctr7ty5kmSemClHjhxW7bJnz659+/Yl6bl//vlni2vPfX19NXLkSIvE0dnZWa1atVKFChWUMWNGHT58WLNnz9Ybb7yhlStXKnfu3El6rqcRExNj/sbg5s2bWrhwoY4cOaJatWrJxcXF3O706dNau3atihQpYl42ZMgQxcTEaO3ateYPtvbt22vgwIGaOHGi3njjDTk7O2vVqlXauXOnBg8ebP4Al6SePXs+9vrJbdu2qW3btgoMDDQve+uttx67P1u2bNGuXbv07rvv6u2335YkdezYUQMGDNAPP/ygTp06WRQHp06d0o8//mheVqFCBTVv3lzr169Xp06dJMVNFta5c+fHPu/Dzx9/Pe3Vq1fN3749LP61deXKlUQ/PK5cuSIp7jX3qOzZs+vWrVuKiopK8LpASdq7d68OHjxoPgaSzNsk1mf886Y0nGjQoIHFY09PT23dujXZ/bwox/uHH36wuKa6UqVKGjVqlPlx/vz5ZWNjo/3791sEQv/++6/5d+727dtPNc8HgNRBvWOJeod650me53on3ujRo1WsWDE1btw4SfuUVOmp3vn111+1fft2rV69+rHjM5lMGjFihBo1aqRSpUoZdjchwomX3MNvrFLcL9/YsWOVK1cuSXF/7OzatUsDBgxQWFiYRduqVasqKChIoaGhypkzpzZt2qSiRYtafFDHi0/X7OzszBMExcbG6s6dO4qNjZWvr6+OHj2a7PH36NEjSacVPZyYR0ZGSlKCb4JOTk7m9U9SoUIFzZo1S3fu3NHOnTt1/Phx3bt3z6JNo0aN1KhRI/PjOnXqqGrVqurUqZOmTJnyVDMKJ9W///6rSpUqmR/b2NioZs2aVoVRuXLlLD6oTSaTNm3apIYNG8pkMlnM8Fu1alWtX79eR44cUZkyZbRp0yZlzZrV/OH3sMclq5kzZ9Zff/1lfg0lxW+//SY7OzurmZG7deumjRs36rfffrMYR+XKlS0+vIsWLaqMGTNanMpbtGhRzZo1K0nP//CbfWRkZIKvo/hlj/uWK35dYq/Dx/V//fp1vf/++8qbN6969OiRrD6T8s1bYoKCgiyCjZROIveiHO/GjRvL19dXN27c0C+//KLr169bvD/En2K5atUqFS5cWHXr1lVoaKhGjBghBwcHRUdHP9XxBpB6qHcsUe9Q7zzJ81zvSHFnwGzatElLlixJwt4kT3qpd6KiojRq1Ci98cYbFr8TCVmxYoVOnDihCRMmPHlnniHCiZfc0KFDVbBgQd29e1fLly/Xnj17LF7M586dk8lk0rfffqtvv/02wT6uX7+unDlz6ty5cxbXRiVm5cqVmjlzpk6fPm1xK6pHZ3dOiiJFijzxl+lRj56S/bD79+8n+bSwbNmyKVu2bJLiEtbvvvtOXbt21aZNmxJMK+OVLVtWJUuW1M6dO5M17pTy9PTUF198IRsbGzk6OqpAgQLy8PCwavfo8b9x44bu3LmjxYsXa/HixQn2Hf8Bfu7cORUsWPCJ16k9atCgQQoMDFTNmjVVvHhx1ahRQy1atFC+fPkS3SYkJEQ5cuSw+va/cOHC5vUPS+jbGjc3N4trZd3c3BKdAfpxnJ2dE3wdxS973IdZ/LrEXofx/T8qIiJCvXr1Unh4uBYsWGBxbWZS+nyaWenLli0rd3f3FG8f70U53p6enuZrq5s0aaJPP/1UXbt21YYNG8xthw8frsjISH311Vf66quvJEnNmjWTl5eXNm3alKRZrwE8e9Q7lqh3/kO982TPW73z4MEDffnll2revLnFHFupJb3UO7Nnz9bNmzfVv3//x44nLCxM48ePV/fu3dPkLKjHIZx4yZUoUUJ+fn6S4lLuDh066P3339eGDRuUIUMG872hu3XrpmrVqiXYR2LX1yVk9erVCgwMVJ06ddS9e3d5eHjIzs5OU6dOtZqUKinu3r2bpOTfwcHBfK3aw6e3P+rq1asJnv6YFPXr19c333yjLVu26I033nhs21y5cun06dMpep7kcnV1TdIb46MfDPE/+2bNmqlly5YJbpPYKWVJ1ahRI5UtW1Y///yztm/frhkzZmjatGkKCgpSjRo1nqrveIndyuvh0y+joqKsri9MjLu7u7nP7NmzW92/XfrvtfW411L8uoS2j7+28tHUOyoqSv3799fx48c1Y8YMeXt7W6yP3yaxPp80prTyohzvR9WvX19LlizRnj17zO+HmTJl0pQpU3Tx4kWFhIQoT5488vT01BtvvCF3d3erW+gBMAb1jiXqnf9Q7yTsea53Vq1apdOnT2vYsGFWlxeEh4frwoUL8vDwsLicxwjP8/G+e/eupkyZog4dOigsLMx8xlhERIRMJpMuXLggFxcXeXh4aMaMGYqOjlajRo3Mx/vy5cuS4ibGvXDhgnLkyPHEOio1EE6kI3Z2dho4cKA6d+6s+fPnq2fPnuZE18HB4Ylv+F5eXjp58uRj22zcuFH58uXTxIkTLU5/S+kpQl9++WWyr8H09vaWvb29Dh8+bHEKYlRUlI4dO6aGDRumaCzxaWRSJr45f/78c38duru7u7lgS8rP/q+//lJ0dLQcHByS9Tw5cuRQx44d1bFjR12/fl0tW7bUd999l+iHtaenp3bu3Gk1sWP8LM7x33Qnx4EDB1J0TWDRokW1b98+q0kPg4OD5eLiooIFCybaT86cOeXu7q7Dhw9brQsODlbRokUtlsXGxuqjjz7Szp079X//938qX7681Xa2trby9vZOtM98+fKleL6J1PQiHO+ExP9hkNDveJ48ecyzYt+5c0eHDx9W/fr1n9gngLRHvUO98zDqnYQ9z/XOpUuXFB0drfbt21utW7VqlVatWqVJkyapTp06SdrXZ+V5Pt63b99WRESEpk+frunTp1u1fe211/Taa69p8uTJunTpkm7fvp3g3B7fffedvvvuO61atSrZE/2mBOFEOlOhQgWVKFFCc+bMUZcuXeTh4aHy5ctr8eLF6tSpk1VSd+PGDfNpT/Xq1dOkSZP0888/W12HaTKZZGNjY04E4x9L0l9//aWDBw9a3O4mqVJyDWamTJlUqVIlrVmzRn369DG/4a9evVoREREWk+Dcu3dPFy9eVNasWc37eePGDWXNmtXq2sKlS5dKksXsuA8fn3jbtm3TkSNHrK4hfN7Y2dmpfv36Wrt2rXr16mWVWj/6s//11181f/58q+t6H/5ZPywmJkYRERHmWcUlycPDQzly5HjsLZKqV6+uxYsXa/78+erVq5d5+ezZs2VjY6Pq1asne19Tek1ggwYNtHHjRm3atMn8urlx44Y2bNigWrVqWZ0yLFl+81avXj2tWrVKly5dMp8mt3PnTp05c8bqOI4YMUI//vijhg8f/tjTievXr69x48bp0KFD5m8J//33X+3atUvdunVL0j4+a8/78U7o91aSli1bJhsbm0RnVo83btw4xcTEqEuXLknaRwBpj3qHeice9U7Cnud6p1GjRgn+Idy3b1/VqFFDbdu2fSaXeyTX83y8PTw8NGnSJKtx/PDDDzp48KDGjx9vHlNAQIBV0HP9+nUNHTpUrVq10muvvZaiy9VSgnAiHerevbveeecdrVixQu3bt9dnn32mDh06qGnTpmrbtq3y5cuna9eu6eDBg7p8+bLWrFlj3m7jxo1655131Lp1axUvXly3b9/W1q1bNWzYMBUtWlQ1a9bUpk2b1LdvX9WsWVMXLlzQokWLVKRIEUVERCR7rCm5BlOS3nvvPb3xxhsKCAhQ27ZtdfnyZc2aNUtVq1a1eLMPDg5W586d1a9fP/P1WGvWrNGiRYtUp04d5cuXT+Hh4frjjz+0fft21apVy2IypjfeeEPFihWTr6+vMmXKpKNHj2r58uXKnTu3evfubTGmPXv2aM+ePZLi3oAiIiI0efJkSXGTN5UrV87c1sfHx+LbkWfl/fff1+7du9W2bVu1adNGRYoU0e3bt3XkyBHt3LlTf/75pySpRYsWWrVqlUaNGqXg4GCVKVNG9+7d086dO9W+ffsEk+vw8HDVqFFD9evXV9GiReXq6qodO3bo0KFDFrNZP6p27dqqUKGCvvnmG4WEhMjHx0fbt2/Xli1b1KVLl2SddhsvpdcE1q9fX/7+/ho8eLD++ecfZc2aVQsXLlRMTIzV9XvxHwYPz/bcu3dvbdiwQZ07dzbffi3+9MWH7/4we/ZsLViwQKVKlZKzs7PVjMp169Y1z23QoUMHLV26VL169VK3bt1kb2+v2bNny8PDwyqcCAgI0J9//qnjx48ne98TktTX8PN+vKdMmaL9+/erWrVqypMnj27duqVNmzbp0KFDCggIsLh93vfff68TJ06oZMmSsrOz05YtW/THH3/o3XfffS4KIwCJo96h3olHvfN4z1u9U7hwYfPcG4/Kmzev1c+Besf6eLu4uCT4et28ebMOHTpksa548eJWX8zEX95RpEiRND1DhXAiHapXr568vLw0c+ZMtW3bVkWKFNHy5cs1ceJErVy5Urdu3ZK7u7teffVV9e3b17xdhgwZNH/+fAUFBennn3/WypUr5eHhoUqVKplnJm7VqpWuXbumxYsX648//lCRIkU0duxYbdiwwfzGnxaKFy+uWbNm6euvv9aoUaOUIUMGvf766xa3ykpMmTJldODAAa1fv17Xrl2Tvb29ChYsqMGDB1vN3tywYUNt27ZN27dvV2RkpLJnz642bdqoX79+5sml4u3atUsTJ060WBY/KVe/fv3Mb3Th4eGSEr5FUGrLli2bli5dav6GaOHChcqSJYuKFCmiQYMGmdvZ2dlp2rRpmjJlitatW6dNmzYpS5YsKl26dKLXaTo7O6t9+/bavn27Nm3aJJPJJC8vL3NxmBhbW1tNmTJFEyZM0I8//qgVK1bI09NTH374YZqfGWBnZ6fvv/9eY8aM0dy5c3X//n35+flp1KhRKlSo0BO3z507t+bNm6fRo0dr3LhxcnBwUI0aNRQYGGiRiv/999+S4k4PPHDggFU/W7ZsMYcTGTNmNN+mbsqUKYqNjVWFChU0ePBgq2+1wsPDU/V1lNTXcEql1fGuWbOmzp8/r+XLl+vmzZtydHSUj4+PRo0aZXU9sre3t37++Wdt3bpVsbGx8vHx0f/93/+l+HRpAGmHeufxqHeod+I9j/VOclDvJHy8X0Q2psfdtBdAmtu2bZt69eql1atXP/UETUi/wsLCVKFCBX388cfq2LGj0cMBAMAC9Q5SA/XOy8X2yU0ApKVdu3apcePGfFDjqezdu1c5c+ZUmzZtjB4KAABWqHeQGqh3Xi6cOQEAAAAAAAzFmRMAAAAAAMBQhBMAAAAAAMBQhBMAAAAAAMBQhBMAAAAAAMBQ9kYPANYOHDggk8kkBwcHo4cCAHgGoqOjZWNjo1KlShk9FMBQ1DwA8HJLTs1DOPEcMplMin4Qo5Ard4weSrpjZ2ujHO4ZdOVGuGJiuZFNWsqb293oIaRPJik2Jlq2dg6SjdGDST9MJpO4VxZAzWMU6h1j5aPmMYTpoZrHhponzSSn5iGceA45ODgo5ModtR28zOihpDveXh6aObSZBk/aqhPnrhs9nHTlxJZxRg8hXYq+f083L56SW04vOTi5GD2cdOPa+eNGDwF4LlDzGIN6x1infhlv9BDSpej793Ttwj9yz52fmicNXTn7d5LbMucEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAA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\n"
           },
           "metadata": {}
         },
@@ -4372,20 +4912,20 @@
           "data": {
             "text/plain": [
               "           model_name  accuracy  false_positive_rate  precision    recall  \\\n",
-              "0            LightGBM  0.990800             0.004542   0.176292  0.171091   \n",
-              "1  LogisticRegression  0.991650             0.003503   0.183594  0.138643   \n",
-              "2        RandomForest  0.991650             0.003336   0.156780  0.109145   \n",
-              "3          ExtraTrees  0.990283             0.004794   0.128049  0.123894   \n",
+              "0        RandomForest  0.913950             0.082851   0.023508  0.351032   \n",
+              "1          ExtraTrees  0.987867             0.007844   0.144424  0.233038   \n",
+              "2  LogisticRegression  0.986717             0.008884   0.119601  0.212389   \n",
+              "3            LightGBM  0.901417             0.095456   0.020468  0.351032   \n",
               "\n",
               "         f1   roc_auc    pr_auc  threshold  \n",
-              "0  0.173653  0.669980  0.054404   0.764335  \n",
-              "1  0.157983  0.644195  0.045955   0.979877  \n",
-              "2  0.128696  0.683218  0.038426   0.351859  \n",
-              "3  0.125937  0.668943  0.034879   0.448309  "
+              "0  0.044066  0.696746  0.062551   0.058868  \n",
+              "1  0.178330  0.676183  0.057577   0.243180  \n",
+              "2  0.153029  0.662734  0.050958   0.898330  \n",
+              "3  0.038680  0.665979  0.050323   0.034547  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-05e6367f-77f3-4887-ae82-cb9c1b4960ab\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-d141ae49-5d62-4964-ad20-d9c4bab91e63\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -4418,51 +4958,51 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>0.990800</td>\n",
-              "      <td>0.004542</td>\n",
-              "      <td>0.176292</td>\n",
-              "      <td>0.171091</td>\n",
-              "      <td>0.173653</td>\n",
-              "      <td>0.669980</td>\n",
-              "      <td>0.054404</td>\n",
-              "      <td>0.764335</td>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>0.913950</td>\n",
+              "      <td>0.082851</td>\n",
+              "      <td>0.023508</td>\n",
+              "      <td>0.351032</td>\n",
+              "      <td>0.044066</td>\n",
+              "      <td>0.696746</td>\n",
+              "      <td>0.062551</td>\n",
+              "      <td>0.058868</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>LogisticRegression</td>\n",
-              "      <td>0.991650</td>\n",
-              "      <td>0.003503</td>\n",
-              "      <td>0.183594</td>\n",
-              "      <td>0.138643</td>\n",
-              "      <td>0.157983</td>\n",
-              "      <td>0.644195</td>\n",
-              "      <td>0.045955</td>\n",
-              "      <td>0.979877</td>\n",
+              "      <td>ExtraTrees</td>\n",
+              "      <td>0.987867</td>\n",
+              "      <td>0.007844</td>\n",
+              "      <td>0.144424</td>\n",
+              "      <td>0.233038</td>\n",
+              "      <td>0.178330</td>\n",
+              "      <td>0.676183</td>\n",
+              "      <td>0.057577</td>\n",
+              "      <td>0.243180</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>0.991650</td>\n",
-              "      <td>0.003336</td>\n",
-              "      <td>0.156780</td>\n",
-              "      <td>0.109145</td>\n",
-              "      <td>0.128696</td>\n",
-              "      <td>0.683218</td>\n",
-              "      <td>0.038426</td>\n",
-              "      <td>0.351859</td>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>0.986717</td>\n",
+              "      <td>0.008884</td>\n",
+              "      <td>0.119601</td>\n",
+              "      <td>0.212389</td>\n",
+              "      <td>0.153029</td>\n",
+              "      <td>0.662734</td>\n",
+              "      <td>0.050958</td>\n",
+              "      <td>0.898330</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>0.990283</td>\n",
-              "      <td>0.004794</td>\n",
-              "      <td>0.128049</td>\n",
-              "      <td>0.123894</td>\n",
-              "      <td>0.125937</td>\n",
-              "      <td>0.668943</td>\n",
-              "      <td>0.034879</td>\n",
-              "      <td>0.448309</td>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>0.901417</td>\n",
+              "      <td>0.095456</td>\n",
+              "      <td>0.020468</td>\n",
+              "      <td>0.351032</td>\n",
+              "      <td>0.038680</td>\n",
+              "      <td>0.665979</td>\n",
+              "      <td>0.050323</td>\n",
+              "      <td>0.034547</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -4470,7 +5010,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05e6367f-77f3-4887-ae82-cb9c1b4960ab')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d141ae49-5d62-4964-ad20-d9c4bab91e63')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -4522,12 +5062,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-05e6367f-77f3-4887-ae82-cb9c1b4960ab button.colab-df-convert');\n",
+              "        document.querySelector('#df-d141ae49-5d62-4964-ad20-d9c4bab91e63 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-05e6367f-77f3-4887-ae82-cb9c1b4960ab');\n",
+              "        const element = document.querySelector('#df-d141ae49-5d62-4964-ad20-d9c4bab91e63');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -4552,7 +5092,7 @@
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"]])\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"LogisticRegression\",\n          \"ExtraTrees\",\n          \"LightGBM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00067376430102327,\n        \"min\": 0.9902833333333333,\n        \"max\": 0.99165,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9908,\n          0.99165,\n          0.9902833333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.000731427784437358,\n        \"min\": 0.0033355123112250883,\n        \"max\": 0.004793751361861182,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0035031259952062487,\n          0.004793751361861182,\n          0.004542330835889442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02481789117206144,\n        \"min\": 0.12804878048780488,\n        \"max\": 0.18359375,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.18359375,\n          0.12804878048780488,\n          0.1762917933130699\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026493989351637128,\n        \"min\": 0.10914454277286136,\n        \"max\": 0.1710914454277286,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.13864306784660768,\n          0.12389380530973451,\n          0.1710914454277286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02315847573778767,\n        \"min\": 0.12593703148425786,\n        \"max\": 0.17365269461077845,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.15798319327731092,\n          0.12593703148425786,\n          0.17365269461077845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016279376786549147,\n        \"min\": 0.6441952093240279,\n        \"max\": 0.6832183696291124,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6441952093240279,\n          0.6689429494935472,\n          0.6699795091035243\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008659351382236778,\n        \"min\": 0.034879154454109204,\n        \"max\": 0.05440352499791599,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.045955427881527094,\n          0.034879154454109204,\n          0.05440352499791599\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28907031849091064,\n        \"min\": 0.35185931288578937,\n        \"max\": 0.979876693471741,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.979876693471741,\n          0.4483085040039446,\n          0.7643346211138977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"]])\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04624820792323862,\n        \"min\": 0.9014166666666666,\n        \"max\": 0.9878666666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9878666666666667,\n          0.9014166666666666,\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04692891215226371,\n        \"min\": 0.007844320410318299,\n        \"max\": 0.09545599302727074,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.007844320410318299,\n          0.09545599302727074,\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0643379448162342,\n        \"min\": 0.02046783625730994,\n        \"max\": 0.14442413162705667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.14442413162705667,\n          0.02046783625730994,\n          0.023508494666139867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07456283468415088,\n        \"min\": 0.21238938053097345,\n        \"max\": 0.35103244837758113,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.35103244837758113,\n          0.23303834808259588,\n          0.21238938053097345\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07254091975454982,\n        \"min\": 0.038680318543799774,\n        \"max\": 0.17832957110609482,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17832957110609482,\n          0.038680318543799774,\n          0.04406591371968154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015334779408148261,\n        \"min\": 0.6627341233129422,\n        \"max\": 0.6967463513986769,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6761829953791527,\n          0.6659786100217459,\n          0.6967463513986769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005812604054563798,\n        \"min\": 0.050323296178874356,\n        \"max\": 0.06255052428644334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.05757660266781409,\n          0.050323296178874356,\n          0.06255052428644334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40395240727098636,\n        \"min\": 0.03454695956318568,\n        \"max\": 0.8983303412868939,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2431800238242072,\n          0.03454695956318568,\n          0.058868157090478435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -4577,7 +5117,7 @@
         "    row = model_comparison[model_comparison[\"model_name\"].eq(model_name)].iloc[0]\n",
         "    ax.set_title(\n",
         "        f\"{model_name}\\n\"\n",
-        "        f\"ACC={row['accuracy']:.3f}, FPR={row['false_positive_rate']:.3f}, F1={row['f1']:.3f}\"\n",
+        "        f\"Recall={row['recall']:.3f}, Precision={row['precision']:.3f}, F1={row['f1']:.3f}\"\n",
         "    )\n",
         "plt.tight_layout()\n",
         "plt.show()\n",
@@ -4598,7 +5138,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 20,
       "id": "ALpJVyy2bEfC",
       "metadata": {
         "colab": {
@@ -4606,7 +5146,7 @@
           "height": 1000
         },
         "id": "ALpJVyy2bEfC",
-        "outputId": "987f4299-b79b-41c2-c81d-dfb6731bf887"
+        "outputId": "b964a332-cdc6-45a1-f4e9-484e34c728a0"
       },
       "outputs": [
         {
@@ -4615,7 +5155,7 @@
             "text/plain": [
               "<Figure size 1800x400 with 3 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         },
@@ -4625,7 +5165,7 @@
             "text/plain": [
               "<Figure size 1200x400 with 2 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         },
@@ -4635,7 +5175,7 @@
             "text/plain": [
               "<Figure size 900x700 with 1 Axes>"
             ],
-            "image/png": 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\n"
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\n"
           },
           "metadata": {}
         },
@@ -4643,41 +5183,73 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "   model_name           feature  importance_gain importance_type\n",
-              "0    LightGBM  L3_S33_mean_time    390956.736451            gain\n",
-              "1    LightGBM  L3_S30_mean_time    235859.517601            gain\n",
-              "2    LightGBM      L3_S33_F3873    203709.738733            gain\n",
-              "3    LightGBM  process_end_time    149514.344105            gain\n",
-              "4    LightGBM        L0_num_min    102868.559010            gain\n",
-              "5    LightGBM      L3_S30_F3704     94805.056677            gain\n",
-              "6    LightGBM        L1_num_min     87870.475541            gain\n",
-              "7    LightGBM    L0_S11_num_std     75906.011574            gain\n",
-              "8    LightGBM        L2_num_std     73896.075065            gain\n",
-              "9    LightGBM         L0_S3_F80     67261.653738            gain\n",
-              "10   LightGBM   L3_S33_num_mean     65192.821837            gain\n",
-              "11   LightGBM      L1_S24_F1846     57877.134013            gain\n",
-              "12   LightGBM      L3_S29_F3382     57635.742289            gain\n",
-              "13   LightGBM      L3_S33_F3859     56869.317597            gain\n",
-              "14   LightGBM      L3_S30_F3609     55849.371791            gain\n",
-              "15   LightGBM      L1_S24_F1514     54480.724891            gain\n",
-              "16   LightGBM        L2_num_max     51965.886841            gain\n",
-              "17   LightGBM      L3_S29_F3373     48899.976863            gain\n",
-              "18   LightGBM      L3_S29_F3327     46306.820732            gain\n",
-              "19   LightGBM        L2_num_min     44341.400364            gain\n",
-              "20   LightGBM   L2_S27_num_mean     40442.920059            gain\n",
-              "21   LightGBM       L3_num_mean     38714.116386            gain\n",
-              "22   LightGBM      L2_S27_F3206     38138.170961            gain\n",
-              "23   LightGBM          L0_S0_F6     33666.765800            gain\n",
-              "24   LightGBM    L3_S29_num_std     29165.766471            gain\n",
-              "25   LightGBM       L0_num_mean     28914.965012            gain\n",
-              "26   LightGBM   L0_S4_mean_time     25027.416580            gain\n",
-              "27   LightGBM   L0_S6_mean_time     24665.647415            gain\n",
-              "28   LightGBM      L3_S33_F3857     16719.117322            gain\n",
-              "29   LightGBM   L3_S35_num_mean     16316.879347            gain"
+              "       model_name                                       feature  \\\n",
+              "457  RandomForest                              L3_S33_mean_time   \n",
+              "458  RandomForest                              L3_S34_mean_time   \n",
+              "459  RandomForest                            process_start_time   \n",
+              "460  RandomForest                      L3_S33_num_missing_ratio   \n",
+              "461  RandomForest                              L3_S37_mean_time   \n",
+              "462  RandomForest                              L3_S29_mean_time   \n",
+              "463  RandomForest                                   L3_S33_seen   \n",
+              "464  RandomForest                              L3_S30_mean_time   \n",
+              "465  RandomForest                                  L3_S33_F3859   \n",
+              "466  RandomForest                              process_end_time   \n",
+              "467  RandomForest                                   L3_duration   \n",
+              "468  RandomForest                                L3_S29_num_std   \n",
+              "469  RandomForest          aux_paint_thermal_defect_probability   \n",
+              "470  RandomForest                                  L1_S24_F1844   \n",
+              "471  RandomForest                               L3_S33_num_mean   \n",
+              "472  RandomForest                                    L3_num_std   \n",
+              "473  RandomForest                                  L3_S33_F3857   \n",
+              "474  RandomForest  aux_body_ford_vibration_abnormal_probability   \n",
+              "475  RandomForest                        total_process_duration   \n",
+              "476  RandomForest                                    L1_num_min   \n",
+              "477  RandomForest                                 L3_date_count   \n",
+              "478  RandomForest                               L3_S29_num_mean   \n",
+              "479  RandomForest                                    L0_num_min   \n",
+              "480  RandomForest                                  L3_S29_F3458   \n",
+              "481  RandomForest                                  L3_S30_F3744   \n",
+              "482  RandomForest                                L3_S33_num_std   \n",
+              "483  RandomForest                                    L0_num_std   \n",
+              "484  RandomForest                                L3_S30_num_std   \n",
+              "485  RandomForest                                  L3_S30_F3759   \n",
+              "486  RandomForest                                   L3_S34_seen   \n",
+              "\n",
+              "     importance_gain      importance_type  \n",
+              "457         0.011733  feature_importances  \n",
+              "458         0.011186  feature_importances  \n",
+              "459         0.010766  feature_importances  \n",
+              "460         0.008505  feature_importances  \n",
+              "461         0.008500  feature_importances  \n",
+              "462         0.008475  feature_importances  \n",
+              "463         0.008394  feature_importances  \n",
+              "464         0.008029  feature_importances  \n",
+              "465         0.008004  feature_importances  \n",
+              "466         0.007741  feature_importances  \n",
+              "467         0.007571  feature_importances  \n",
+              "468         0.007533  feature_importances  \n",
+              "469         0.007316  feature_importances  \n",
+              "470         0.007214  feature_importances  \n",
+              "471         0.007032  feature_importances  \n",
+              "472         0.006991  feature_importances  \n",
+              "473         0.006660  feature_importances  \n",
+              "474         0.006541  feature_importances  \n",
+              "475         0.006364  feature_importances  \n",
+              "476         0.006358  feature_importances  \n",
+              "477         0.006329  feature_importances  \n",
+              "478         0.006163  feature_importances  \n",
+              "479         0.006136  feature_importances  \n",
+              "480         0.006061  feature_importances  \n",
+              "481         0.005915  feature_importances  \n",
+              "482         0.005873  feature_importances  \n",
+              "483         0.005700  feature_importances  \n",
+              "484         0.005681  feature_importances  \n",
+              "485         0.005666  feature_importances  \n",
+              "486         0.005639  feature_importances  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-a0d9b4da-3d03-4c33-a571-82db618689ad\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -4704,214 +5276,214 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>LightGBM</td>\n",
+              "      <th>457</th>\n",
+              "      <td>RandomForest</td>\n",
               "      <td>L3_S33_mean_time</td>\n",
-              "      <td>390956.736451</td>\n",
-              "      <td>gain</td>\n",
+              "      <td>0.011733</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>235859.517601</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>458</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S34_mean_time</td>\n",
+              "      <td>0.011186</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S33_F3873</td>\n",
-              "      <td>203709.738733</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>459</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>process_start_time</td>\n",
+              "      <td>0.010766</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>process_end_time</td>\n",
-              "      <td>149514.344105</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>460</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S33_num_missing_ratio</td>\n",
+              "      <td>0.008505</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L0_num_min</td>\n",
-              "      <td>102868.559010</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>461</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S37_mean_time</td>\n",
+              "      <td>0.008500</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S30_F3704</td>\n",
-              "      <td>94805.056677</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>462</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S29_mean_time</td>\n",
+              "      <td>0.008475</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L1_num_min</td>\n",
-              "      <td>87870.475541</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>463</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S33_seen</td>\n",
+              "      <td>0.008394</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L0_S11_num_std</td>\n",
-              "      <td>75906.011574</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>464</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.008029</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L2_num_std</td>\n",
-              "      <td>73896.075065</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>465</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S33_F3859</td>\n",
+              "      <td>0.008004</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L0_S3_F80</td>\n",
-              "      <td>67261.653738</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>466</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>process_end_time</td>\n",
+              "      <td>0.007741</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>10</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S33_num_mean</td>\n",
-              "      <td>65192.821837</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>467</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_duration</td>\n",
+              "      <td>0.007571</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>11</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L1_S24_F1846</td>\n",
-              "      <td>57877.134013</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>468</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S29_num_std</td>\n",
+              "      <td>0.007533</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>12</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S29_F3382</td>\n",
-              "      <td>57635.742289</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>469</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>aux_paint_thermal_defect_probability</td>\n",
+              "      <td>0.007316</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>13</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S33_F3859</td>\n",
-              "      <td>56869.317597</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>470</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L1_S24_F1844</td>\n",
+              "      <td>0.007214</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>14</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S30_F3609</td>\n",
-              "      <td>55849.371791</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>471</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S33_num_mean</td>\n",
+              "      <td>0.007032</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>15</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L1_S24_F1514</td>\n",
-              "      <td>54480.724891</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>472</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_num_std</td>\n",
+              "      <td>0.006991</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L2_num_max</td>\n",
-              "      <td>51965.886841</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>473</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S33_F3857</td>\n",
+              "      <td>0.006660</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>17</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S29_F3373</td>\n",
-              "      <td>48899.976863</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>474</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>aux_body_ford_vibration_abnormal_probability</td>\n",
+              "      <td>0.006541</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>18</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S29_F3327</td>\n",
-              "      <td>46306.820732</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>475</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>total_process_duration</td>\n",
+              "      <td>0.006364</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>19</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L2_num_min</td>\n",
-              "      <td>44341.400364</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>476</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L1_num_min</td>\n",
+              "      <td>0.006358</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>20</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L2_S27_num_mean</td>\n",
-              "      <td>40442.920059</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>477</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_date_count</td>\n",
+              "      <td>0.006329</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>21</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_num_mean</td>\n",
-              "      <td>38714.116386</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>478</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S29_num_mean</td>\n",
+              "      <td>0.006163</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>22</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L2_S27_F3206</td>\n",
-              "      <td>38138.170961</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>479</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L0_num_min</td>\n",
+              "      <td>0.006136</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>23</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L0_S0_F6</td>\n",
-              "      <td>33666.765800</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>480</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S29_F3458</td>\n",
+              "      <td>0.006061</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>24</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S29_num_std</td>\n",
-              "      <td>29165.766471</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>481</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S30_F3744</td>\n",
+              "      <td>0.005915</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>25</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L0_num_mean</td>\n",
-              "      <td>28914.965012</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>482</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S33_num_std</td>\n",
+              "      <td>0.005873</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>26</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L0_S4_mean_time</td>\n",
-              "      <td>25027.416580</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>483</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L0_num_std</td>\n",
+              "      <td>0.005700</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>27</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L0_S6_mean_time</td>\n",
-              "      <td>24665.647415</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>484</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S30_num_std</td>\n",
+              "      <td>0.005681</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>28</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S33_F3857</td>\n",
-              "      <td>16719.117322</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>485</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S30_F3759</td>\n",
+              "      <td>0.005666</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>29</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>L3_S35_num_mean</td>\n",
-              "      <td>16316.879347</td>\n",
-              "      <td>gain</td>\n",
+              "      <th>486</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>L3_S34_seen</td>\n",
+              "      <td>0.005639</td>\n",
+              "      <td>feature_importances</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -4919,7 +5491,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0d9b4da-3d03-4c33-a571-82db618689ad')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -4971,12 +5543,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff button.colab-df-convert');\n",
+              "        document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-38a1c4db-4cae-4cb3-a7bf-de31411f64ff');\n",
+              "        const element = document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -5001,7 +5573,7 @@
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(importance_df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"LightGBM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L0_S6_mean_time\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77801.68166991045,\n        \"min\": 16316.879346847534,\n        \"max\": 390956.7364510298,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          24665.647415161133\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"display(importance_df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L3_S30_num_std\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0016205883920081907,\n        \"min\": 0.0056385160546700164,\n        \"max\": 0.011732889577268082,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.00568096253421123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"feature_importances\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -5038,8 +5610,8 @@
         "\n",
         "def extract_feature_importance(estimator, feature_names: list[str], model_name: str) -> pd.DataFrame:\n",
         "    target_estimator = estimator\n",
-        "    if isinstance(estimator, Pipeline):\n",
-        "        target_estimator = estimator.named_steps.get(\"model\", estimator)\n",
+        "    if is_pipeline_estimator(estimator):\n",
+        "        target_estimator = get_final_estimator(estimator)\n",
         "\n",
         "    if hasattr(target_estimator, \"booster_\"):\n",
         "        values = target_estimator.booster_.feature_importance(importance_type=\"gain\")\n",
@@ -5097,29 +5669,32 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 22,
       "id": "24Ol6sMmbEfC",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "24Ol6sMmbEfC",
-        "outputId": "521d3ab8-9c6d-49a5-c713-bda36648ca31"
+        "outputId": "ca41c28e-e024-41a1-d30e-52eface69410"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "selected_model_name: LightGBM\n",
-            "X_shap: (3000, 457)\n",
-            "shap_matrix: (3000, 457)\n"
+            "selected_model_name: RandomForest\n",
+            "X_shap: (1000, 457)\n",
+            "X_shap_model: (1000, 457)\n",
+            "shap_matrix: (1000, 457)\n",
+            "base_values: (1000,)\n"
           ]
         }
       ],
       "source": [
         "# SHAP 샘플링\n",
-        "SHAP_SAMPLE_SIZE = min(3000, len(X_valid))\n",
+        "SHAP_SAMPLE_SIZE = min(1000, len(X_valid))\n",
+        "\n",
         "if selected_model_name == \"LogisticRegression\":\n",
         "    SHAP_SAMPLE_SIZE = min(500, len(X_valid))\n",
         "\n",
@@ -5130,14 +5705,24 @@
         "\n",
         "\n",
         "def prepare_shap_model(estimator, X_sample: pd.DataFrame):\n",
-        "    if isinstance(estimator, Pipeline):\n",
-        "        final_estimator = estimator.named_steps[\"model\"]\n",
+        "    if is_pipeline_estimator(estimator):\n",
+        "        final_estimator = get_final_estimator(estimator)\n",
         "        transform_steps = estimator.steps[:-1]\n",
+        "\n",
         "        X_transformed = X_sample.copy()\n",
+        "\n",
         "        for _, step in transform_steps:\n",
-        "            X_transformed = step.transform(X_transformed)\n",
-        "        X_transformed = pd.DataFrame(X_transformed, columns=X_sample.columns, index=X_sample.index)\n",
+        "            if hasattr(step, \"transform\"):\n",
+        "                X_transformed = step.transform(X_transformed)\n",
+        "\n",
+        "        X_transformed = pd.DataFrame(\n",
+        "            X_transformed,\n",
+        "            columns=X_sample.columns,\n",
+        "            index=X_sample.index\n",
+        "        )\n",
+        "\n",
         "        return final_estimator, X_transformed\n",
+        "\n",
         "    return estimator, X_sample\n",
         "\n",
         "\n",
@@ -5146,38 +5731,85 @@
         "# SHAP Explainer 생성\n",
         "try:\n",
         "    explainer = shap.TreeExplainer(shap_model)\n",
-        "    raw_shap_values = explainer.shap_values(X_shap_model)\n",
+        "\n",
+        "    raw_shap_values = explainer.shap_values(\n",
+        "        X_shap_model,\n",
+        "        check_additivity=False\n",
+        "    )\n",
+        "\n",
         "    if isinstance(raw_shap_values, list):\n",
         "        shap_matrix = raw_shap_values[1]\n",
         "    elif getattr(raw_shap_values, \"ndim\", 0) == 3:\n",
         "        shap_matrix = raw_shap_values[:, :, 1]\n",
         "    else:\n",
         "        shap_matrix = raw_shap_values\n",
-        "    base_value = explainer.expected_value[1] if isinstance(explainer.expected_value, list) else explainer.expected_value\n",
-        "except Exception:\n",
-        "    background = shap.sample(X_train, min(100, len(X_train)), random_state=RANDOM_STATE)\n",
-        "    explainer = shap.Explainer(lambda data: predict_positive_proba(model, pd.DataFrame(data, columns=X_train.columns)), background)\n",
-        "    raw_explanation = explainer(X_shap)\n",
+        "\n",
+        "    expected_value = explainer.expected_value\n",
+        "\n",
+        "    if isinstance(expected_value, list):\n",
+        "        base_value = expected_value[1]\n",
+        "    else:\n",
+        "        base_value = expected_value\n",
+        "\n",
+        "except Exception as e:\n",
+        "    print(\"TreeExplainer 실패:\", e)\n",
+        "    print(\"Permutation Explainer로 대체 실행합니다.\")\n",
+        "\n",
+        "    background = shap.sample(\n",
+        "        X_train,\n",
+        "        min(100, len(X_train)),\n",
+        "        random_state=RANDOM_STATE\n",
+        "    )\n",
+        "\n",
+        "    explainer = shap.Explainer(\n",
+        "        lambda data: predict_positive_proba(\n",
+        "            model,\n",
+        "            pd.DataFrame(data, columns=X_train.columns)\n",
+        "        ),\n",
+        "        background\n",
+        "    )\n",
+        "\n",
+        "    raw_explanation = explainer(\n",
+        "        X_shap,\n",
+        "        max_evals=2 * X_shap.shape[1] + 1\n",
+        "    )\n",
+        "\n",
         "    shap_matrix = raw_explanation.values\n",
         "    base_value = raw_explanation.base_values\n",
         "    X_shap_model = X_shap\n",
         "\n",
+        "\n",
+        "# base_value 길이 보정\n",
+        "if np.isscalar(base_value):\n",
+        "    base_values = np.repeat(base_value, len(X_shap))\n",
+        "else:\n",
+        "    base_values = np.array(base_value)\n",
+        "\n",
+        "    if base_values.ndim == 0:\n",
+        "        base_values = np.repeat(float(base_values), len(X_shap))\n",
+        "    elif len(base_values) != len(X_shap):\n",
+        "        base_values = np.repeat(float(np.ravel(base_values)[0]), len(X_shap))\n",
+        "\n",
+        "\n",
         "# SHAP 객체 구성\n",
         "shap_values = shap.Explanation(\n",
         "    values=shap_matrix,\n",
-        "    base_values=np.repeat(base_value, len(X_shap)) if np.isscalar(base_value) else base_value,\n",
+        "    base_values=base_values,\n",
         "    data=X_shap.values,\n",
         "    feature_names=X_shap.columns.tolist(),\n",
         ")\n",
         "\n",
+        "\n",
         "print(\"selected_model_name:\", selected_model_name)\n",
         "print(\"X_shap:\", X_shap.shape)\n",
-        "print(\"shap_matrix:\", shap_matrix.shape)\n"
+        "print(\"X_shap_model:\", X_shap_model.shape)\n",
+        "print(\"shap_matrix:\", shap_matrix.shape)\n",
+        "print(\"base_values:\", np.array(base_values).shape)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 23,
       "id": "gvPdq4r1bEfC",
       "metadata": {
         "colab": {
@@ -5185,7 +5817,7 @@
           "height": 1000
         },
         "id": "gvPdq4r1bEfC",
-        "outputId": "5db7ecb2-7000-43ab-c436-a66a117e9e8f"
+        "outputId": "6871cde5-1c84-4060-a5b6-f5d757281281"
       },
       "outputs": [
         {
@@ -5194,7 +5826,7 @@
             "text/plain": [
               "<Figure size 800x1150 with 2 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         },
@@ -5202,41 +5834,41 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "                    feature  mean_abs_shap\n",
-              "323        L3_S33_mean_time       1.043150\n",
-              "263        process_end_time       0.572533\n",
-              "203            L3_S30_F3704       0.461738\n",
-              "137            L3_S29_F3382       0.329964\n",
-              "20                L0_S3_F80       0.312103\n",
-              "190            L3_S30_F3609       0.280839\n",
-              "370          L0_S11_num_std       0.237354\n",
-              "342              L0_num_min       0.236961\n",
-              "236            L3_S33_F3873       0.224241\n",
-              "94             L1_S24_F1846       0.201978\n",
-              "320        L3_S30_mean_time       0.187981\n",
-              "423         L3_S33_num_mean       0.182257\n",
-              "229            L3_S33_F3859       0.174246\n",
-              "120            L3_S29_F3327       0.161220\n",
-              "82             L1_S24_F1514       0.159890\n",
-              "264  total_process_duration       0.155546\n",
-              "351              L2_num_std       0.145310\n",
-              "361           L0_S0_num_std       0.127794\n",
-              "115            L2_S27_F3206       0.095520\n",
-              "414         L2_S27_num_mean       0.094850\n",
-              "352              L2_num_min       0.091944\n",
-              "134            L3_S29_F3373       0.090346\n",
-              "347              L1_num_min       0.082967\n",
-              "393          L0_S5_num_mean       0.078262\n",
-              "189            L3_S30_F3604       0.074655\n",
-              "418          L3_S29_num_std       0.074171\n",
-              "10                L0_S0_F20       0.073757\n",
-              "353              L2_num_max       0.073749\n",
-              "69              L0_S12_F346       0.071814\n",
-              "432         L3_S36_num_mean       0.070417"
+              "              feature  mean_abs_shap\n",
+              "341        L0_num_std  161153.464733\n",
+              "128      L3_S29_F3351  112466.467404\n",
+              "320  L3_S30_mean_time   87206.632845\n",
+              "134      L3_S29_F3373   79514.635807\n",
+              "145      L3_S29_F3412   67172.086809\n",
+              "144      L3_S29_F3407   50584.084076\n",
+              "143      L3_S29_F3404   47588.744257\n",
+              "124      L3_S29_F3339   45584.471179\n",
+              "263  process_end_time   41293.258694\n",
+              "16          L0_S2_F48   41259.724076\n",
+              "249      L3_S36_F3920   40991.128002\n",
+              "290  L0_S13_mean_time   38087.458690\n",
+              "360    L0_S0_num_mean   37707.793976\n",
+              "12          L0_S1_F24   34345.520207\n",
+              "266     L0_date_count   31073.838840\n",
+              "385     L0_S2_num_std   30331.910504\n",
+              "228      L3_S33_F3857   30191.782771\n",
+              "64        L0_S11_F326   27513.469192\n",
+              "367    L0_S10_num_std   25100.244226\n",
+              "0            L0_S0_F0   21225.150678\n",
+              "308   L0_S7_mean_time   19617.442282\n",
+              "43         L0_S9_F200   19379.907896\n",
+              "148      L3_S29_F3427   19288.645871\n",
+              "366   L0_S10_num_mean   19031.715581\n",
+              "62        L0_S11_F318   18211.382309\n",
+              "178      L3_S30_F3539   17632.894123\n",
+              "251      L3_S36_F3924   17431.833787\n",
+              "388     L0_S3_num_std   16874.240258\n",
+              "4            L0_S0_F8   16847.956330\n",
+              "340       L0_num_mean   15387.827290"
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-398f9636-9482-4af1-b589-63ca40179c2f\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -5261,154 +5893,154 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>323</th>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>1.043150</td>\n",
+              "      <th>341</th>\n",
+              "      <td>L0_num_std</td>\n",
+              "      <td>161153.464733</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>263</th>\n",
-              "      <td>process_end_time</td>\n",
-              "      <td>0.572533</td>\n",
+              "      <th>128</th>\n",
+              "      <td>L3_S29_F3351</td>\n",
+              "      <td>112466.467404</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>203</th>\n",
-              "      <td>L3_S30_F3704</td>\n",
-              "      <td>0.461738</td>\n",
+              "      <th>320</th>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>87206.632845</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>137</th>\n",
-              "      <td>L3_S29_F3382</td>\n",
-              "      <td>0.329964</td>\n",
+              "      <th>134</th>\n",
+              "      <td>L3_S29_F3373</td>\n",
+              "      <td>79514.635807</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>20</th>\n",
-              "      <td>L0_S3_F80</td>\n",
-              "      <td>0.312103</td>\n",
+              "      <th>145</th>\n",
+              "      <td>L3_S29_F3412</td>\n",
+              "      <td>67172.086809</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>190</th>\n",
-              "      <td>L3_S30_F3609</td>\n",
-              "      <td>0.280839</td>\n",
+              "      <th>144</th>\n",
+              "      <td>L3_S29_F3407</td>\n",
+              "      <td>50584.084076</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>370</th>\n",
-              "      <td>L0_S11_num_std</td>\n",
-              "      <td>0.237354</td>\n",
+              "      <th>143</th>\n",
+              "      <td>L3_S29_F3404</td>\n",
+              "      <td>47588.744257</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>342</th>\n",
-              "      <td>L0_num_min</td>\n",
-              "      <td>0.236961</td>\n",
+              "      <th>124</th>\n",
+              "      <td>L3_S29_F3339</td>\n",
+              "      <td>45584.471179</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>236</th>\n",
-              "      <td>L3_S33_F3873</td>\n",
-              "      <td>0.224241</td>\n",
+              "      <th>263</th>\n",
+              "      <td>process_end_time</td>\n",
+              "      <td>41293.258694</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>94</th>\n",
-              "      <td>L1_S24_F1846</td>\n",
-              "      <td>0.201978</td>\n",
+              "      <th>16</th>\n",
+              "      <td>L0_S2_F48</td>\n",
+              "      <td>41259.724076</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>320</th>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.187981</td>\n",
+              "      <th>249</th>\n",
+              "      <td>L3_S36_F3920</td>\n",
+              "      <td>40991.128002</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>423</th>\n",
-              "      <td>L3_S33_num_mean</td>\n",
-              "      <td>0.182257</td>\n",
+              "      <th>290</th>\n",
+              "      <td>L0_S13_mean_time</td>\n",
+              "      <td>38087.458690</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>229</th>\n",
-              "      <td>L3_S33_F3859</td>\n",
-              "      <td>0.174246</td>\n",
+              "      <th>360</th>\n",
+              "      <td>L0_S0_num_mean</td>\n",
+              "      <td>37707.793976</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>120</th>\n",
-              "      <td>L3_S29_F3327</td>\n",
-              "      <td>0.161220</td>\n",
+              "      <th>12</th>\n",
+              "      <td>L0_S1_F24</td>\n",
+              "      <td>34345.520207</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>82</th>\n",
-              "      <td>L1_S24_F1514</td>\n",
-              "      <td>0.159890</td>\n",
+              "      <th>266</th>\n",
+              "      <td>L0_date_count</td>\n",
+              "      <td>31073.838840</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>264</th>\n",
-              "      <td>total_process_duration</td>\n",
-              "      <td>0.155546</td>\n",
+              "      <th>385</th>\n",
+              "      <td>L0_S2_num_std</td>\n",
+              "      <td>30331.910504</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>351</th>\n",
-              "      <td>L2_num_std</td>\n",
-              "      <td>0.145310</td>\n",
+              "      <th>228</th>\n",
+              "      <td>L3_S33_F3857</td>\n",
+              "      <td>30191.782771</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>361</th>\n",
-              "      <td>L0_S0_num_std</td>\n",
-              "      <td>0.127794</td>\n",
+              "      <th>64</th>\n",
+              "      <td>L0_S11_F326</td>\n",
+              "      <td>27513.469192</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>115</th>\n",
-              "      <td>L2_S27_F3206</td>\n",
-              "      <td>0.095520</td>\n",
+              "      <th>367</th>\n",
+              "      <td>L0_S10_num_std</td>\n",
+              "      <td>25100.244226</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>414</th>\n",
-              "      <td>L2_S27_num_mean</td>\n",
-              "      <td>0.094850</td>\n",
+              "      <th>0</th>\n",
+              "      <td>L0_S0_F0</td>\n",
+              "      <td>21225.150678</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>352</th>\n",
-              "      <td>L2_num_min</td>\n",
-              "      <td>0.091944</td>\n",
+              "      <th>308</th>\n",
+              "      <td>L0_S7_mean_time</td>\n",
+              "      <td>19617.442282</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>134</th>\n",
-              "      <td>L3_S29_F3373</td>\n",
-              "      <td>0.090346</td>\n",
+              "      <th>43</th>\n",
+              "      <td>L0_S9_F200</td>\n",
+              "      <td>19379.907896</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>347</th>\n",
-              "      <td>L1_num_min</td>\n",
-              "      <td>0.082967</td>\n",
+              "      <th>148</th>\n",
+              "      <td>L3_S29_F3427</td>\n",
+              "      <td>19288.645871</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>393</th>\n",
-              "      <td>L0_S5_num_mean</td>\n",
-              "      <td>0.078262</td>\n",
+              "      <th>366</th>\n",
+              "      <td>L0_S10_num_mean</td>\n",
+              "      <td>19031.715581</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>189</th>\n",
-              "      <td>L3_S30_F3604</td>\n",
-              "      <td>0.074655</td>\n",
+              "      <th>62</th>\n",
+              "      <td>L0_S11_F318</td>\n",
+              "      <td>18211.382309</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>418</th>\n",
-              "      <td>L3_S29_num_std</td>\n",
-              "      <td>0.074171</td>\n",
+              "      <th>178</th>\n",
+              "      <td>L3_S30_F3539</td>\n",
+              "      <td>17632.894123</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>10</th>\n",
-              "      <td>L0_S0_F20</td>\n",
-              "      <td>0.073757</td>\n",
+              "      <th>251</th>\n",
+              "      <td>L3_S36_F3924</td>\n",
+              "      <td>17431.833787</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>353</th>\n",
-              "      <td>L2_num_max</td>\n",
-              "      <td>0.073749</td>\n",
+              "      <th>388</th>\n",
+              "      <td>L0_S3_num_std</td>\n",
+              "      <td>16874.240258</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>69</th>\n",
-              "      <td>L0_S12_F346</td>\n",
-              "      <td>0.071814</td>\n",
+              "      <th>4</th>\n",
+              "      <td>L0_S0_F8</td>\n",
+              "      <td>16847.956330</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>432</th>\n",
-              "      <td>L3_S36_num_mean</td>\n",
-              "      <td>0.070417</td>\n",
+              "      <th>340</th>\n",
+              "      <td>L0_num_mean</td>\n",
+              "      <td>15387.827290</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -5416,7 +6048,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-398f9636-9482-4af1-b589-63ca40179c2f')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -5468,12 +6100,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-398f9636-9482-4af1-b589-63ca40179c2f button.colab-df-convert');\n",
+              "        document.querySelector('#df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-398f9636-9482-4af1-b589-63ca40179c2f');\n",
+              "        const element = document.querySelector('#df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -5498,7 +6130,7 @@
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(shap_importance\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L2_num_max\",\n          \"total_process_duration\",\n          \"L0_S5_num_mean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19842909695379424,\n        \"min\": 0.07041663354892244,\n        \"max\": 1.0431500289669826,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.07374943734111677,\n          0.1555458950891956,\n          0.0782619774922737\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"display(shap_importance\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L0_S3_num_std\",\n          \"L0_S2_num_std\",\n          \"L0_S10_num_mean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32453.380966565277,\n        \"min\": 15387.827290431647,\n        \"max\": 161153.46473315964,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          16874.240258203547,\n          30331.91050358815,\n          19031.71558098403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -5519,16 +6151,87 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
-      "id": "ZH8WXbIhbEfC",
+      "source": [
+        "# Feature 공정 매핑\n",
+        "def feature_to_process(feature: str) -> str | None:\n",
+        "    \"\"\"Feature 공정 매핑\"\"\"\n",
+        "    normalized = feature.lower()\n",
+        "\n",
+        "    # Bosch 원천 컬럼/집계 컬럼: L0~L3 prefix 기준 매핑\n",
+        "    line = get_line(feature)\n",
+        "    if line in LINE_TO_PROCESS:\n",
+        "        return LINE_TO_PROCESS[line]\n",
+        "\n",
+        "    # 보조 데이터셋 기반 feature: aux_{process}_... naming 기준 매핑\n",
+        "    if normalized.startswith(\"aux_press\") or \"press\" in normalized:\n",
+        "        return \"PRESS\"\n",
+        "    if normalized.startswith(\"aux_body\") or \"body\" in normalized or \"robot\" in normalized or \"ford\" in normalized:\n",
+        "        return \"BODY\"\n",
+        "    if normalized.startswith(\"aux_paint\") or \"paint\" in normalized or \"thermal\" in normalized:\n",
+        "        return \"PAINT\"\n",
+        "    if normalized.startswith(\"aux_assembly\") or \"assembly\" in normalized:\n",
+        "        return \"ASSEMBLY\"\n",
+        "\n",
+        "    # 전역 feature 제외\n",
+        "    return None\n",
+        "\n",
+        "# 후속 공정 매핑\n",
+        "def next_process(process_code: str) -> str:\n",
+        "    if process_code not in PROCESS_FLOW:\n",
+        "        return \"FINAL_INSPECTION\"\n",
+        "    idx = PROCESS_FLOW.index(process_code)\n",
+        "    if idx + 1 >= len(PROCESS_FLOW):\n",
+        "        return \"FINAL_INSPECTION\"\n",
+        "    return PROCESS_FLOW[idx + 1]\n",
+        "\n",
+        "# 위험 등급 변환\n",
+        "def probability_to_risk_grade(prob: float) -> str:\n",
+        "    if prob >= 0.70:\n",
+        "        return \"HIGH\"\n",
+        "    if prob >= 0.40:\n",
+        "        return \"MEDIUM\"\n",
+        "    return \"LOW\"\n",
+        "\n",
+        "# SHAP feature 공정 매핑\n",
+        "feature_process = pd.Series([feature_to_process(c) for c in X_shap.columns], index=X_shap.columns)\n",
+        "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
+        "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
+        "print(f\"공정 영향도 집계 제외 전역/미매핑 feature 수: {len(unmapped_features)}\")\n",
+        "if unmapped_features:\n",
+        "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
+        "\n",
+        "# 공정별 SHAP 영향도 집계\n",
+        "process_rows = []\n",
+        "for process_code in PROCESS_FLOW:\n",
+        "    cols_idx = np.where(feature_process.values == process_code)[0]\n",
+        "    process_rows.append({\n",
+        "        \"process_code\": process_code,\n",
+        "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, cols_idx]).mean()) if len(cols_idx) else 0.0,\n",
+        "        \"feature_count\": int(len(cols_idx)),\n",
+        "    })\n",
+        "\n",
+        "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
+        "display(process_importance)\n",
+        "\n",
+        "# 공정 영향도 시각화\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
+        "plt.title(\"SHAP Process Influence\")\n",
+        "plt.xlabel(\"Process\")\n",
+        "plt.ylabel(\"Mean |SHAP|\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 891
         },
-        "id": "ZH8WXbIhbEfC",
-        "outputId": "61f1e475-b193-4ae8-e8da-d2aa89456d6e"
+        "id": "m-fwpoIeBrfa",
+        "outputId": "b70d837d-df0c-4159-fc29-c842f24f2558"
       },
+      "id": "m-fwpoIeBrfa",
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "stream",
@@ -5554,7 +6257,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -5619,7 +6322,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -5671,12 +6374,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9 button.colab-df-convert');\n",
+              "        document.querySelector('#df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-f1214ca1-7bb9-467f-bb86-84b4a29e0bb9');\n",
+              "        const element = document.querySelector('#df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -5711,14 +6414,14 @@
           "data": {
             "text/plain": [
               "  process_code  mean_abs_shap  feature_count\n",
-              "2        PAINT       0.027860            197\n",
-              "3     ASSEMBLY       0.022912             36\n",
-              "0        PRESS       0.020474            182\n",
-              "1         BODY       0.019803             33"
+              "0        PRESS    3987.411446            182\n",
+              "2        PAINT    3959.570471            197\n",
+              "3     ASSEMBLY     398.922157             36\n",
+              "1         BODY      71.636713             33"
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-8c98b58d-2528-4317-9019-86731d1be2e6\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-bae87c37-391f-44c4-bcef-a34afe9b1e09\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -5744,27 +6447,27 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>3987.411446</td>\n",
+              "      <td>182</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
               "      <th>2</th>\n",
               "      <td>PAINT</td>\n",
-              "      <td>0.027860</td>\n",
+              "      <td>3959.570471</td>\n",
               "      <td>197</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.022912</td>\n",
+              "      <td>398.922157</td>\n",
               "      <td>36</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>PRESS</td>\n",
-              "      <td>0.020474</td>\n",
-              "      <td>182</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
               "      <th>1</th>\n",
               "      <td>BODY</td>\n",
-              "      <td>0.019803</td>\n",
+              "      <td>71.636713</td>\n",
               "      <td>33</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
@@ -5773,7 +6476,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c98b58d-2528-4317-9019-86731d1be2e6')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bae87c37-391f-44c4-bcef-a34afe9b1e09')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -5825,12 +6528,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-8c98b58d-2528-4317-9019-86731d1be2e6 button.colab-df-convert');\n",
+              "        document.querySelector('#df-bae87c37-391f-44c4-bcef-a34afe9b1e09 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-8c98b58d-2528-4317-9019-86731d1be2e6');\n",
+              "        const element = document.querySelector('#df-bae87c37-391f-44c4-bcef-a34afe9b1e09');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -5850,7 +6553,7 @@
               "  </div>\n",
               "\n",
               "\n",
-              "  <div id=\"id_e221d43f-66b5-4ef2-9f1c-e2a4b1f3f2a0\">\n",
+              "  <div id=\"id_790c3d3c-10fc-406c-a77f-00d95a9965e0\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -5894,7 +6597,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_e221d43f-66b5-4ef2-9f1c-e2a4b1f3f2a0 button.colab-df-generate');\n",
+              "        document.querySelector('#id_790c3d3c-10fc-406c-a77f-00d95a9965e0 button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -5911,7 +6614,7 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "process_importance",
-              "summary": "{\n  \"name\": \"process_importance\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ASSEMBLY\",\n          \"BODY\",\n          \"PAINT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003651810959860645,\n        \"min\": 0.01980296606840556,\n        \"max\": 0.027860344127627685,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.02291185373545563,\n          0.01980296606840556,\n          0.027860344127627685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89,\n        \"min\": 33,\n        \"max\": 197,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          36,\n          33,\n          197\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"process_importance\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"PAINT\",\n          \"BODY\",\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2162.4192345053525,\n        \"min\": 71.63671284580064,\n        \"max\": 3987.4114461310187,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3959.570471254013,\n          71.63671284580064,\n          3987.4114461310187\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89,\n        \"min\": 33,\n        \"max\": 197,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          197,\n          33,\n          182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -5922,81 +6625,10 @@
             "text/plain": [
               "<Figure size 800x400 with 1 Axes>"
             ],
-            "image/png": 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\n"
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\n"
           },
           "metadata": {}
         }
-      ],
-      "source": [
-        "# Feature 공정 매핑\n",
-        "def feature_to_process(feature: str) -> str | None:\n",
-        "    \"\"\"Feature 공정 매핑\"\"\"\n",
-        "    normalized = feature.lower()\n",
-        "\n",
-        "    # Bosch 원천 컬럼/집계 컬럼: L0~L3 prefix 기준 매핑\n",
-        "    line = get_line(feature)\n",
-        "    if line in LINE_TO_PROCESS:\n",
-        "        return LINE_TO_PROCESS[line]\n",
-        "\n",
-        "    # 보조 데이터셋 기반 feature: aux_{process}_... naming 기준 매핑\n",
-        "    if normalized.startswith(\"aux_press\") or \"press\" in normalized:\n",
-        "        return \"PRESS\"\n",
-        "    if normalized.startswith(\"aux_body\") or \"body\" in normalized or \"robot\" in normalized or \"ford\" in normalized:\n",
-        "        return \"BODY\"\n",
-        "    if normalized.startswith(\"aux_paint\") or \"paint\" in normalized or \"thermal\" in normalized:\n",
-        "        return \"PAINT\"\n",
-        "    if normalized.startswith(\"aux_assembly\") or \"assembly\" in normalized:\n",
-        "        return \"ASSEMBLY\"\n",
-        "\n",
-        "    # 전역 feature 제외\n",
-        "    return None\n",
-        "\n",
-        "# 후속 공정 매핑\n",
-        "def next_process(process_code: str) -> str:\n",
-        "    if process_code not in PROCESS_FLOW:\n",
-        "        return \"FINAL_INSPECTION\"\n",
-        "    idx = PROCESS_FLOW.index(process_code)\n",
-        "    if idx + 1 >= len(PROCESS_FLOW):\n",
-        "        return \"FINAL_INSPECTION\"\n",
-        "    return PROCESS_FLOW[idx + 1]\n",
-        "\n",
-        "# 위험 등급 변환\n",
-        "def probability_to_risk_grade(prob: float) -> str:\n",
-        "    if prob >= 0.70:\n",
-        "        return \"HIGH\"\n",
-        "    if prob >= 0.40:\n",
-        "        return \"MEDIUM\"\n",
-        "    return \"LOW\"\n",
-        "\n",
-        "# SHAP feature 공정 매핑\n",
-        "feature_process = pd.Series([feature_to_process(c) for c in X_shap.columns], index=X_shap.columns)\n",
-        "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
-        "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
-        "print(f\"공정 영향도 집계 제외 전역/미매핑 feature 수: {len(unmapped_features)}\")\n",
-        "if unmapped_features:\n",
-        "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
-        "\n",
-        "# 공정별 SHAP 영향도 집계\n",
-        "process_rows = []\n",
-        "for process_code in PROCESS_FLOW:\n",
-        "    cols_idx = np.where(feature_process.values == process_code)[0]\n",
-        "    process_rows.append({\n",
-        "        \"process_code\": process_code,\n",
-        "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, cols_idx]).mean()) if len(cols_idx) else 0.0,\n",
-        "        \"feature_count\": int(len(cols_idx)),\n",
-        "    })\n",
-        "\n",
-        "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
-        "display(process_importance)\n",
-        "\n",
-        "# 공정 영향도 시각화\n",
-        "plt.figure(figsize=(8, 4))\n",
-        "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
-        "plt.title(\"SHAP Process Influence\")\n",
-        "plt.xlabel(\"Process\")\n",
-        "plt.ylabel(\"Mean |SHAP|\")\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n"
       ]
     },
     {
@@ -6013,7 +6645,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 25,
       "id": "MjmQshC4bEfD",
       "metadata": {
         "colab": {
@@ -6021,144 +6653,144 @@
           "height": 1000
         },
         "id": "MjmQshC4bEfD",
-        "outputId": "bc584ba0-0ec7-488d-ef04-093a534ade5a"
+        "outputId": "268ba6e7-dc21-4678-beaf-55f184bbb3ec"
       },
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "      car_master_id source_process_code target_process_code  \\\n",
-              "2806         218707               PAINT            ASSEMBLY   \n",
-              "1720         327784               PAINT            ASSEMBLY   \n",
-              "2920         110780               PAINT            ASSEMBLY   \n",
-              "1676         404144               PAINT            ASSEMBLY   \n",
-              "518          537209            ASSEMBLY    FINAL_INSPECTION   \n",
-              "1363         422924               PAINT            ASSEMBLY   \n",
-              "22           445099               PAINT            ASSEMBLY   \n",
-              "883           49121               PAINT            ASSEMBLY   \n",
-              "1139         270422                BODY               PAINT   \n",
-              "1261          72604               PAINT            ASSEMBLY   \n",
-              "2193         208915               PAINT            ASSEMBLY   \n",
-              "1250         325978               PAINT            ASSEMBLY   \n",
-              "132          457589               PAINT            ASSEMBLY   \n",
-              "1702         345390               PAINT            ASSEMBLY   \n",
-              "1147         329336               PAINT            ASSEMBLY   \n",
-              "593          492773               PAINT            ASSEMBLY   \n",
-              "677          570702               PAINT            ASSEMBLY   \n",
-              "2456         491756               PAINT            ASSEMBLY   \n",
-              "360          161187               PAINT            ASSEMBLY   \n",
-              "2423          58741               PAINT            ASSEMBLY   \n",
-              "2187         399573               PAINT            ASSEMBLY   \n",
-              "695           86810               PAINT            ASSEMBLY   \n",
-              "2026         471938               PAINT            ASSEMBLY   \n",
-              "230          233131               PAINT            ASSEMBLY   \n",
-              "829          103485               PAINT            ASSEMBLY   \n",
-              "522          402988               PAINT            ASSEMBLY   \n",
-              "398          307253               PAINT            ASSEMBLY   \n",
-              "2384         519151            ASSEMBLY    FINAL_INSPECTION   \n",
-              "537          325267               PAINT            ASSEMBLY   \n",
-              "1491          96444               PAINT            ASSEMBLY   \n",
-              "\n",
-              "      current_defect_probability  target_defect_probability  \\\n",
-              "2806                    0.678618                   0.978448   \n",
-              "1720                    0.693589                   0.970204   \n",
-              "2920                    0.677194                   0.937242   \n",
-              "1676                    0.577193                   0.909687   \n",
-              "518                     0.442684                   0.855171   \n",
-              "1363                    0.461277                   0.829406   \n",
-              "22                      0.437623                   0.826252   \n",
-              "883                     0.647237                   0.810856   \n",
-              "1139                    0.380287                   0.806972   \n",
-              "1261                    0.576640                   0.802120   \n",
-              "2193                    0.434317                   0.800171   \n",
-              "1250                    0.631934                   0.713980   \n",
-              "132                     0.461246                   0.695194   \n",
-              "1702                    0.612613                   0.688885   \n",
-              "1147                    0.645485                   0.639600   \n",
-              "593                     0.455224                   0.629645   \n",
-              "677                     0.507326                   0.626186   \n",
-              "2456                    0.578751                   0.615782   \n",
-              "360                     0.511622                   0.609049   \n",
-              "2423                    0.589447                   0.604053   \n",
-              "2187                    0.624960                   0.592752   \n",
-              "695                     0.654663                   0.588408   \n",
-              "2026                    0.557580                   0.568974   \n",
-              "230                     0.558755                   0.566809   \n",
-              "829                     0.593304                   0.565436   \n",
-              "522                     0.506670                   0.564482   \n",
-              "398                     0.593820                   0.560144   \n",
-              "2384                    0.405882                   0.550488   \n",
-              "537                     0.631635                   0.536699   \n",
-              "1491                    0.605923                   0.532112   \n",
-              "\n",
-              "     predicted_defect_process risk_grade        main_cause  influence_score  \\\n",
-              "2806                 ASSEMBLY       HIGH  L3_S30_mean_time         0.678618   \n",
-              "1720                 ASSEMBLY       HIGH  L3_S30_mean_time         0.693589   \n",
-              "2920                 ASSEMBLY       HIGH      L3_S33_F3873         0.677194   \n",
-              "1676                 ASSEMBLY       HIGH  L3_S33_mean_time         0.577193   \n",
-              "518          FINAL_INSPECTION       HIGH        L2_num_min         0.442684   \n",
-              "1363                 ASSEMBLY       HIGH  L3_S33_mean_time         0.461277   \n",
-              "22                   ASSEMBLY       HIGH  L3_S33_mean_time         0.437623   \n",
-              "883                  ASSEMBLY       HIGH      L3_S33_F3873         0.647237   \n",
-              "1139                    PAINT       HIGH        L1_num_min         0.380287   \n",
-              "1261                 ASSEMBLY       HIGH  L3_S33_mean_time         0.576640   \n",
-              "2193                 ASSEMBLY       HIGH  L3_S33_mean_time         0.434317   \n",
-              "1250                 ASSEMBLY       HIGH  L3_S30_mean_time         0.631934   \n",
-              "132                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.461246   \n",
-              "1702                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.612613   \n",
-              "1147                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.645485   \n",
-              "593                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.455224   \n",
-              "677                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.507326   \n",
-              "2456                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.578751   \n",
-              "360                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.511622   \n",
-              "2423                 ASSEMBLY     MEDIUM      L3_S33_F3873         0.589447   \n",
-              "2187                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.624960   \n",
-              "695                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.654663   \n",
-              "2026                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.557580   \n",
-              "230                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.558755   \n",
-              "829                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.593304   \n",
-              "522                  ASSEMBLY     MEDIUM      L3_S29_F3382         0.506670   \n",
-              "398                  ASSEMBLY     MEDIUM      L3_S33_F3873         0.593820   \n",
-              "2384         FINAL_INSPECTION     MEDIUM        L2_num_min         0.405882   \n",
-              "537                  ASSEMBLY     MEDIUM  L3_S33_mean_time         0.631635   \n",
-              "1491                 ASSEMBLY     MEDIUM  L3_S33_mean_time         0.605923   \n",
-              "\n",
-              "                          predicted_at  \n",
-              "2806  2026-06-12T08:00:21.829407+00:00  \n",
-              "1720  2026-06-12T08:00:21.829407+00:00  \n",
-              "2920  2026-06-12T08:00:21.829407+00:00  \n",
-              "1676  2026-06-12T08:00:21.829407+00:00  \n",
-              "518   2026-06-12T08:00:21.829407+00:00  \n",
-              "1363  2026-06-12T08:00:21.829407+00:00  \n",
-              "22    2026-06-12T08:00:21.829407+00:00  \n",
-              "883   2026-06-12T08:00:21.829407+00:00  \n",
-              "1139  2026-06-12T08:00:21.829407+00:00  \n",
-              "1261  2026-06-12T08:00:21.829407+00:00  \n",
-              "2193  2026-06-12T08:00:21.829407+00:00  \n",
-              "1250  2026-06-12T08:00:21.829407+00:00  \n",
-              "132   2026-06-12T08:00:21.829407+00:00  \n",
-              "1702  2026-06-12T08:00:21.829407+00:00  \n",
-              "1147  2026-06-12T08:00:21.829407+00:00  \n",
-              "593   2026-06-12T08:00:21.829407+00:00  \n",
-              "677   2026-06-12T08:00:21.829407+00:00  \n",
-              "2456  2026-06-12T08:00:21.829407+00:00  \n",
-              "360   2026-06-12T08:00:21.829407+00:00  \n",
-              "2423  2026-06-12T08:00:21.829407+00:00  \n",
-              "2187  2026-06-12T08:00:21.829407+00:00  \n",
-              "695   2026-06-12T08:00:21.829407+00:00  \n",
-              "2026  2026-06-12T08:00:21.829407+00:00  \n",
-              "230   2026-06-12T08:00:21.829407+00:00  \n",
-              "829   2026-06-12T08:00:21.829407+00:00  \n",
-              "522   2026-06-12T08:00:21.829407+00:00  \n",
-              "398   2026-06-12T08:00:21.829407+00:00  \n",
-              "2384  2026-06-12T08:00:21.829407+00:00  \n",
-              "537   2026-06-12T08:00:21.829407+00:00  \n",
-              "1491  2026-06-12T08:00:21.829407+00:00  "
+              "     car_master_id source_process_code target_process_code  \\\n",
+              "883          49121               PAINT            ASSEMBLY   \n",
+              "179          84100               PAINT            ASSEMBLY   \n",
+              "76          495820               PAINT            ASSEMBLY   \n",
+              "65           46831               PAINT            ASSEMBLY   \n",
+              "447         533156               PAINT            ASSEMBLY   \n",
+              "543         199536               PAINT            ASSEMBLY   \n",
+              "695          86810               PAINT            ASSEMBLY   \n",
+              "132         457589               PAINT            ASSEMBLY   \n",
+              "88          361134               PAINT            ASSEMBLY   \n",
+              "532         293981               PAINT            ASSEMBLY   \n",
+              "608         230813               PAINT            ASSEMBLY   \n",
+              "710         290791               PAINT            ASSEMBLY   \n",
+              "471         327137               PAINT            ASSEMBLY   \n",
+              "196         105438               PAINT            ASSEMBLY   \n",
+              "214         221558               PAINT            ASSEMBLY   \n",
+              "178         478955               PAINT            ASSEMBLY   \n",
+              "908          47143               PAINT            ASSEMBLY   \n",
+              "829         103485               PRESS                BODY   \n",
+              "267         593231               PAINT            ASSEMBLY   \n",
+              "747         545804               PAINT            ASSEMBLY   \n",
+              "256         280517               PAINT            ASSEMBLY   \n",
+              "828         422775               PAINT            ASSEMBLY   \n",
+              "781         212417               PAINT            ASSEMBLY   \n",
+              "670         406278               PAINT            ASSEMBLY   \n",
+              "398         307253               PAINT            ASSEMBLY   \n",
+              "661         576604               PAINT            ASSEMBLY   \n",
+              "388         109154               PAINT            ASSEMBLY   \n",
+              "185         366377               PRESS                BODY   \n",
+              "593         492773               PAINT            ASSEMBLY   \n",
+              "772         255054               PAINT            ASSEMBLY   \n",
+              "\n",
+              "     current_defect_probability  target_defect_probability  \\\n",
+              "883                    0.563811                   0.619726   \n",
+              "179                    0.747158                   0.618208   \n",
+              "76                     0.787374                   0.342941   \n",
+              "65                     0.788540                   0.257915   \n",
+              "447                    0.787341                   0.230440   \n",
+              "543                    0.592871                   0.198408   \n",
+              "695                    0.842617                   0.186797   \n",
+              "132                    0.653267                   0.184589   \n",
+              "88                     0.500231                   0.183003   \n",
+              "532                    0.784869                   0.166225   \n",
+              "608                    0.665531                   0.153555   \n",
+              "710                    0.624316                   0.146039   \n",
+              "471                    0.770755                   0.144109   \n",
+              "196                    0.748888                   0.138818   \n",
+              "214                    0.766262                   0.131900   \n",
+              "178                    0.696390                   0.130941   \n",
+              "908                    0.711576                   0.126527   \n",
+              "829                    0.485897                   0.120372   \n",
+              "267                    0.665566                   0.109987   \n",
+              "747                    0.825798                   0.107390   \n",
+              "256                    0.501200                   0.103989   \n",
+              "828                    0.599490                   0.103070   \n",
+              "781                    0.992600                   0.102789   \n",
+              "670                    0.977409                   0.102239   \n",
+              "398                    0.567956                   0.100802   \n",
+              "661                    0.762924                   0.097669   \n",
+              "388                    0.767391                   0.096965   \n",
+              "185                    0.503115                   0.096164   \n",
+              "593                    0.710897                   0.095113   \n",
+              "772                    0.660369                   0.091074   \n",
+              "\n",
+              "    predicted_defect_process risk_grade        main_cause  influence_score  \\\n",
+              "883                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.563811   \n",
+              "179                 ASSEMBLY     MEDIUM      L3_S36_F3920         0.747158   \n",
+              "76                  ASSEMBLY        LOW      L3_S29_F3407         0.787374   \n",
+              "65                  ASSEMBLY        LOW  L3_S30_mean_time         0.788540   \n",
+              "447                 ASSEMBLY        LOW      L3_S29_F3407         0.787341   \n",
+              "543                 ASSEMBLY        LOW      L3_S29_F3449         0.592871   \n",
+              "695                 ASSEMBLY        LOW  L3_S30_mean_time         0.842617   \n",
+              "132                 ASSEMBLY        LOW  L3_S30_mean_time         0.653267   \n",
+              "88                  ASSEMBLY        LOW      L3_S30_F3504         0.500231   \n",
+              "532                 ASSEMBLY        LOW      L3_S29_F3407         0.784869   \n",
+              "608                 ASSEMBLY        LOW      L3_S29_F3407         0.665531   \n",
+              "710                 ASSEMBLY        LOW  L3_S30_mean_time         0.624316   \n",
+              "471                 ASSEMBLY        LOW  L3_S30_mean_time         0.770755   \n",
+              "196                 ASSEMBLY        LOW      L3_S29_F3339         0.748888   \n",
+              "214                 ASSEMBLY        LOW      L3_S33_F3857         0.766262   \n",
+              "178                 ASSEMBLY        LOW      L3_S29_F3407         0.696390   \n",
+              "908                 ASSEMBLY        LOW      L3_S29_F3395         0.711576   \n",
+              "829                     BODY        LOW        L0_S9_F180         0.485897   \n",
+              "267                 ASSEMBLY        LOW      L3_S29_F3407         0.665566   \n",
+              "747                 ASSEMBLY        LOW      L3_S36_F3920         0.825798   \n",
+              "256                 ASSEMBLY        LOW    L3_S33_num_std         0.501200   \n",
+              "828                 ASSEMBLY        LOW      L3_S29_F3351         0.599490   \n",
+              "781                 ASSEMBLY        LOW      L3_S36_F3920         0.992600   \n",
+              "670                 ASSEMBLY        LOW  L3_S30_mean_time         0.977409   \n",
+              "398                 ASSEMBLY        LOW  L3_S30_mean_time         0.567956   \n",
+              "661                 ASSEMBLY        LOW      L3_S29_F3373         0.762924   \n",
+              "388                 ASSEMBLY        LOW      L3_S29_F3339         0.767391   \n",
+              "185                     BODY        LOW     L0_S1_num_std         0.503115   \n",
+              "593                 ASSEMBLY        LOW      L3_S29_F3395         0.710897   \n",
+              "772                 ASSEMBLY        LOW      L3_S29_F3373         0.660369   \n",
+              "\n",
+              "                         predicted_at  \n",
+              "883  2026-06-18T05:32:50.034461+00:00  \n",
+              "179  2026-06-18T05:32:50.034461+00:00  \n",
+              "76   2026-06-18T05:32:50.034461+00:00  \n",
+              "65   2026-06-18T05:32:50.034461+00:00  \n",
+              "447  2026-06-18T05:32:50.034461+00:00  \n",
+              "543  2026-06-18T05:32:50.034461+00:00  \n",
+              "695  2026-06-18T05:32:50.034461+00:00  \n",
+              "132  2026-06-18T05:32:50.034461+00:00  \n",
+              "88   2026-06-18T05:32:50.034461+00:00  \n",
+              "532  2026-06-18T05:32:50.034461+00:00  \n",
+              "608  2026-06-18T05:32:50.034461+00:00  \n",
+              "710  2026-06-18T05:32:50.034461+00:00  \n",
+              "471  2026-06-18T05:32:50.034461+00:00  \n",
+              "196  2026-06-18T05:32:50.034461+00:00  \n",
+              "214  2026-06-18T05:32:50.034461+00:00  \n",
+              "178  2026-06-18T05:32:50.034461+00:00  \n",
+              "908  2026-06-18T05:32:50.034461+00:00  \n",
+              "829  2026-06-18T05:32:50.034461+00:00  \n",
+              "267  2026-06-18T05:32:50.034461+00:00  \n",
+              "747  2026-06-18T05:32:50.034461+00:00  \n",
+              "256  2026-06-18T05:32:50.034461+00:00  \n",
+              "828  2026-06-18T05:32:50.034461+00:00  \n",
+              "781  2026-06-18T05:32:50.034461+00:00  \n",
+              "670  2026-06-18T05:32:50.034461+00:00  \n",
+              "398  2026-06-18T05:32:50.034461+00:00  \n",
+              "661  2026-06-18T05:32:50.034461+00:00  \n",
+              "388  2026-06-18T05:32:50.034461+00:00  \n",
+              "185  2026-06-18T05:32:50.034461+00:00  \n",
+              "593  2026-06-18T05:32:50.034461+00:00  \n",
+              "772  2026-06-18T05:32:50.034461+00:00  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-62801a5c-c148-43c4-899e-851988c24858\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-73977bb6-795d-46fd-9b9f-c66822303bcd\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -6191,394 +6823,394 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>2806</th>\n",
-              "      <td>218707</td>\n",
+              "      <th>883</th>\n",
+              "      <td>49121</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.678618</td>\n",
-              "      <td>0.978448</td>\n",
+              "      <td>0.563811</td>\n",
+              "      <td>0.619726</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
+              "      <td>MEDIUM</td>\n",
               "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.678618</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>0.563811</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1720</th>\n",
-              "      <td>327784</td>\n",
+              "      <th>179</th>\n",
+              "      <td>84100</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.693589</td>\n",
-              "      <td>0.970204</td>\n",
+              "      <td>0.747158</td>\n",
+              "      <td>0.618208</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.693589</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>L3_S36_F3920</td>\n",
+              "      <td>0.747158</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2920</th>\n",
-              "      <td>110780</td>\n",
+              "      <th>76</th>\n",
+              "      <td>495820</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.677194</td>\n",
-              "      <td>0.937242</td>\n",
+              "      <td>0.787374</td>\n",
+              "      <td>0.342941</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S33_F3873</td>\n",
-              "      <td>0.677194</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3407</td>\n",
+              "      <td>0.787374</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1676</th>\n",
-              "      <td>404144</td>\n",
+              "      <th>65</th>\n",
+              "      <td>46831</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.577193</td>\n",
-              "      <td>0.909687</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.577193</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>518</th>\n",
-              "      <td>537209</td>\n",
+              "      <td>0.788540</td>\n",
+              "      <td>0.257915</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>FINAL_INSPECTION</td>\n",
-              "      <td>0.442684</td>\n",
-              "      <td>0.855171</td>\n",
-              "      <td>FINAL_INSPECTION</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L2_num_min</td>\n",
-              "      <td>0.442684</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.788540</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1363</th>\n",
-              "      <td>422924</td>\n",
+              "      <th>447</th>\n",
+              "      <td>533156</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.461277</td>\n",
-              "      <td>0.829406</td>\n",
+              "      <td>0.787341</td>\n",
+              "      <td>0.230440</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.461277</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3407</td>\n",
+              "      <td>0.787341</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>22</th>\n",
-              "      <td>445099</td>\n",
+              "      <th>543</th>\n",
+              "      <td>199536</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.437623</td>\n",
-              "      <td>0.826252</td>\n",
+              "      <td>0.592871</td>\n",
+              "      <td>0.198408</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.437623</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3449</td>\n",
+              "      <td>0.592871</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>883</th>\n",
-              "      <td>49121</td>\n",
+              "      <th>695</th>\n",
+              "      <td>86810</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.647237</td>\n",
-              "      <td>0.810856</td>\n",
+              "      <td>0.842617</td>\n",
+              "      <td>0.186797</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S33_F3873</td>\n",
-              "      <td>0.647237</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.842617</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1139</th>\n",
-              "      <td>270422</td>\n",
-              "      <td>BODY</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>0.380287</td>\n",
-              "      <td>0.806972</td>\n",
+              "      <th>132</th>\n",
+              "      <td>457589</td>\n",
               "      <td>PAINT</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L1_num_min</td>\n",
-              "      <td>0.380287</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>0.653267</td>\n",
+              "      <td>0.184589</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.653267</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1261</th>\n",
-              "      <td>72604</td>\n",
+              "      <th>88</th>\n",
+              "      <td>361134</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.576640</td>\n",
-              "      <td>0.802120</td>\n",
+              "      <td>0.500231</td>\n",
+              "      <td>0.183003</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.576640</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S30_F3504</td>\n",
+              "      <td>0.500231</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2193</th>\n",
-              "      <td>208915</td>\n",
+              "      <th>532</th>\n",
+              "      <td>293981</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.434317</td>\n",
-              "      <td>0.800171</td>\n",
+              "      <td>0.784869</td>\n",
+              "      <td>0.166225</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.434317</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3407</td>\n",
+              "      <td>0.784869</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1250</th>\n",
-              "      <td>325978</td>\n",
+              "      <th>608</th>\n",
+              "      <td>230813</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.631934</td>\n",
-              "      <td>0.713980</td>\n",
+              "      <td>0.665531</td>\n",
+              "      <td>0.153555</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.631934</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3407</td>\n",
+              "      <td>0.665531</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>132</th>\n",
-              "      <td>457589</td>\n",
+              "      <th>710</th>\n",
+              "      <td>290791</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.461246</td>\n",
-              "      <td>0.695194</td>\n",
+              "      <td>0.624316</td>\n",
+              "      <td>0.146039</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.461246</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.624316</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1702</th>\n",
-              "      <td>345390</td>\n",
+              "      <th>471</th>\n",
+              "      <td>327137</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.612613</td>\n",
-              "      <td>0.688885</td>\n",
+              "      <td>0.770755</td>\n",
+              "      <td>0.144109</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
+              "      <td>LOW</td>\n",
               "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.612613</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>0.770755</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1147</th>\n",
-              "      <td>329336</td>\n",
+              "      <th>196</th>\n",
+              "      <td>105438</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.645485</td>\n",
-              "      <td>0.639600</td>\n",
+              "      <td>0.748888</td>\n",
+              "      <td>0.138818</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.645485</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3339</td>\n",
+              "      <td>0.748888</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>593</th>\n",
-              "      <td>492773</td>\n",
+              "      <th>214</th>\n",
+              "      <td>221558</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.455224</td>\n",
-              "      <td>0.629645</td>\n",
+              "      <td>0.766262</td>\n",
+              "      <td>0.131900</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.455224</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S33_F3857</td>\n",
+              "      <td>0.766262</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>677</th>\n",
-              "      <td>570702</td>\n",
+              "      <th>178</th>\n",
+              "      <td>478955</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.507326</td>\n",
-              "      <td>0.626186</td>\n",
+              "      <td>0.696390</td>\n",
+              "      <td>0.130941</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.507326</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3407</td>\n",
+              "      <td>0.696390</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2456</th>\n",
-              "      <td>491756</td>\n",
+              "      <th>908</th>\n",
+              "      <td>47143</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.578751</td>\n",
-              "      <td>0.615782</td>\n",
+              "      <td>0.711576</td>\n",
+              "      <td>0.126527</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.578751</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3395</td>\n",
+              "      <td>0.711576</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>360</th>\n",
-              "      <td>161187</td>\n",
+              "      <th>829</th>\n",
+              "      <td>103485</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>0.485897</td>\n",
+              "      <td>0.120372</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L0_S9_F180</td>\n",
+              "      <td>0.485897</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>267</th>\n",
+              "      <td>593231</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.511622</td>\n",
-              "      <td>0.609049</td>\n",
+              "      <td>0.665566</td>\n",
+              "      <td>0.109987</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.511622</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3407</td>\n",
+              "      <td>0.665566</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2423</th>\n",
-              "      <td>58741</td>\n",
+              "      <th>747</th>\n",
+              "      <td>545804</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.589447</td>\n",
-              "      <td>0.604053</td>\n",
+              "      <td>0.825798</td>\n",
+              "      <td>0.107390</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_F3873</td>\n",
-              "      <td>0.589447</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S36_F3920</td>\n",
+              "      <td>0.825798</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2187</th>\n",
-              "      <td>399573</td>\n",
+              "      <th>256</th>\n",
+              "      <td>280517</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.624960</td>\n",
-              "      <td>0.592752</td>\n",
+              "      <td>0.501200</td>\n",
+              "      <td>0.103989</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.624960</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S33_num_std</td>\n",
+              "      <td>0.501200</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>695</th>\n",
-              "      <td>86810</td>\n",
+              "      <th>828</th>\n",
+              "      <td>422775</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.654663</td>\n",
-              "      <td>0.588408</td>\n",
+              "      <td>0.599490</td>\n",
+              "      <td>0.103070</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.654663</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3351</td>\n",
+              "      <td>0.599490</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2026</th>\n",
-              "      <td>471938</td>\n",
+              "      <th>781</th>\n",
+              "      <td>212417</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.557580</td>\n",
-              "      <td>0.568974</td>\n",
+              "      <td>0.992600</td>\n",
+              "      <td>0.102789</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.557580</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S36_F3920</td>\n",
+              "      <td>0.992600</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>230</th>\n",
-              "      <td>233131</td>\n",
+              "      <th>670</th>\n",
+              "      <td>406278</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.558755</td>\n",
-              "      <td>0.566809</td>\n",
+              "      <td>0.977409</td>\n",
+              "      <td>0.102239</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.558755</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.977409</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>829</th>\n",
-              "      <td>103485</td>\n",
+              "      <th>398</th>\n",
+              "      <td>307253</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.593304</td>\n",
-              "      <td>0.565436</td>\n",
+              "      <td>0.567956</td>\n",
+              "      <td>0.100802</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.593304</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S30_mean_time</td>\n",
+              "      <td>0.567956</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>522</th>\n",
-              "      <td>402988</td>\n",
+              "      <th>661</th>\n",
+              "      <td>576604</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.506670</td>\n",
-              "      <td>0.564482</td>\n",
+              "      <td>0.762924</td>\n",
+              "      <td>0.097669</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S29_F3382</td>\n",
-              "      <td>0.506670</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3373</td>\n",
+              "      <td>0.762924</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>398</th>\n",
-              "      <td>307253</td>\n",
+              "      <th>388</th>\n",
+              "      <td>109154</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.593820</td>\n",
-              "      <td>0.560144</td>\n",
+              "      <td>0.767391</td>\n",
+              "      <td>0.096965</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_F3873</td>\n",
-              "      <td>0.593820</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3339</td>\n",
+              "      <td>0.767391</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2384</th>\n",
-              "      <td>519151</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>FINAL_INSPECTION</td>\n",
-              "      <td>0.405882</td>\n",
-              "      <td>0.550488</td>\n",
-              "      <td>FINAL_INSPECTION</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L2_num_min</td>\n",
-              "      <td>0.405882</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <th>185</th>\n",
+              "      <td>366377</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>0.503115</td>\n",
+              "      <td>0.096164</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L0_S1_num_std</td>\n",
+              "      <td>0.503115</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>537</th>\n",
-              "      <td>325267</td>\n",
+              "      <th>593</th>\n",
+              "      <td>492773</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.631635</td>\n",
-              "      <td>0.536699</td>\n",
+              "      <td>0.710897</td>\n",
+              "      <td>0.095113</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.631635</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3395</td>\n",
+              "      <td>0.710897</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>1491</th>\n",
-              "      <td>96444</td>\n",
+              "      <th>772</th>\n",
+              "      <td>255054</td>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>0.605923</td>\n",
-              "      <td>0.532112</td>\n",
+              "      <td>0.660369</td>\n",
+              "      <td>0.091074</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.605923</td>\n",
-              "      <td>2026-06-12T08:00:21.829407+00:00</td>\n",
+              "      <td>LOW</td>\n",
+              "      <td>L3_S29_F3373</td>\n",
+              "      <td>0.660369</td>\n",
+              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -6586,7 +7218,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62801a5c-c148-43c4-899e-851988c24858')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73977bb6-795d-46fd-9b9f-c66822303bcd')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -6638,12 +7270,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-62801a5c-c148-43c4-899e-851988c24858 button.colab-df-convert');\n",
+              "        document.querySelector('#df-73977bb6-795d-46fd-9b9f-c66822303bcd button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-62801a5c-c148-43c4-899e-851988c24858');\n",
+              "        const element = document.querySelector('#df-73977bb6-795d-46fd-9b9f-c66822303bcd');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -6678,32 +7310,20 @@
           "data": {
             "text/plain": [
               "  source_process_code target_process_code risk_grade  vehicle_count  \\\n",
-              "5               PAINT            ASSEMBLY       HIGH             10   \n",
-              "0            ASSEMBLY    FINAL_INSPECTION       HIGH              1   \n",
-              "3                BODY               PAINT       HIGH              1   \n",
-              "7               PAINT            ASSEMBLY     MEDIUM             78   \n",
-              "9               PRESS                BODY     MEDIUM              1   \n",
-              "2            ASSEMBLY    FINAL_INSPECTION     MEDIUM              5   \n",
-              "1            ASSEMBLY    FINAL_INSPECTION        LOW             49   \n",
-              "4                BODY               PAINT        LOW             54   \n",
-              "6               PAINT            ASSEMBLY        LOW           2462   \n",
-              "8               PRESS                BODY        LOW            339   \n",
+              "2               PAINT            ASSEMBLY     MEDIUM              2   \n",
+              "0            ASSEMBLY    FINAL_INSPECTION        LOW              1   \n",
+              "3               PRESS                BODY        LOW            117   \n",
+              "1               PAINT            ASSEMBLY        LOW            880   \n",
               "\n",
               "   avg_target_defect_probability  avg_influence_score  \n",
-              "5                       0.857837             0.581562  \n",
-              "0                       0.855171             0.442684  \n",
-              "3                       0.806972             0.380287  \n",
-              "7                       0.487962             0.569717  \n",
-              "9                       0.480493             0.454110  \n",
-              "2                       0.471322             0.421606  \n",
-              "1                       0.097117             0.398196  \n",
-              "4                       0.080397             0.411830  \n",
-              "6                       0.066433             0.533395  \n",
-              "8                       0.014134             0.497065  "
+              "2                       0.618967             0.655485  \n",
+              "0                       0.047076             0.495199  \n",
+              "3                       0.030476             0.588225  \n",
+              "1                       0.026473             0.719373  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-2107cc70-23d7-4e29-828d-87da4c1bf0f2\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-2ed32a4d-df3b-48af-9a8a-13035df912bc\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -6732,94 +7352,40 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>5</th>\n",
+              "      <th>2</th>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>10</td>\n",
-              "      <td>0.857837</td>\n",
-              "      <td>0.581562</td>\n",
+              "      <td>MEDIUM</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.618967</td>\n",
+              "      <td>0.655485</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
               "      <td>ASSEMBLY</td>\n",
               "      <td>FINAL_INSPECTION</td>\n",
-              "      <td>HIGH</td>\n",
+              "      <td>LOW</td>\n",
               "      <td>1</td>\n",
-              "      <td>0.855171</td>\n",
-              "      <td>0.442684</td>\n",
+              "      <td>0.047076</td>\n",
+              "      <td>0.495199</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>BODY</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>HIGH</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.806972</td>\n",
-              "      <td>0.380287</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>78</td>\n",
-              "      <td>0.487962</td>\n",
-              "      <td>0.569717</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
               "      <td>PRESS</td>\n",
               "      <td>BODY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.480493</td>\n",
-              "      <td>0.454110</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>FINAL_INSPECTION</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>5</td>\n",
-              "      <td>0.471322</td>\n",
-              "      <td>0.421606</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>FINAL_INSPECTION</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>49</td>\n",
-              "      <td>0.097117</td>\n",
-              "      <td>0.398196</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>BODY</td>\n",
-              "      <td>PAINT</td>\n",
               "      <td>LOW</td>\n",
-              "      <td>54</td>\n",
-              "      <td>0.080397</td>\n",
-              "      <td>0.411830</td>\n",
+              "      <td>117</td>\n",
+              "      <td>0.030476</td>\n",
+              "      <td>0.588225</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>6</th>\n",
+              "      <th>1</th>\n",
               "      <td>PAINT</td>\n",
               "      <td>ASSEMBLY</td>\n",
               "      <td>LOW</td>\n",
-              "      <td>2462</td>\n",
-              "      <td>0.066433</td>\n",
-              "      <td>0.533395</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>PRESS</td>\n",
-              "      <td>BODY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>339</td>\n",
-              "      <td>0.014134</td>\n",
-              "      <td>0.497065</td>\n",
+              "      <td>880</td>\n",
+              "      <td>0.026473</td>\n",
+              "      <td>0.719373</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -6827,7 +7393,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2107cc70-23d7-4e29-828d-87da4c1bf0f2')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ed32a4d-df3b-48af-9a8a-13035df912bc')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -6879,12 +7445,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-2107cc70-23d7-4e29-828d-87da4c1bf0f2 button.colab-df-convert');\n",
+              "        document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-2107cc70-23d7-4e29-828d-87da4c1bf0f2');\n",
+              "        const element = document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -6904,7 +7470,7 @@
               "  </div>\n",
               "\n",
               "\n",
-              "  <div id=\"id_fe941bba-a357-41d3-89ec-6b36823eda21\">\n",
+              "  <div id=\"id_59f80193-e9d1-4e45-92c6-9c4c014f7e5f\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -6948,7 +7514,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_fe941bba-a357-41d3-89ec-6b36823eda21 button.colab-df-generate');\n",
+              "        document.querySelector('#id_59f80193-e9d1-4e45-92c6-9c4c014f7e5f button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -6965,7 +7531,7 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "risk_summary",
-              "summary": "{\n  \"name\": \"risk_summary\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ASSEMBLY\",\n          \"PRESS\",\n          \"PAINT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"FINAL_INSPECTION\",\n          \"BODY\",\n          \"ASSEMBLY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"risk_grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"HIGH\",\n          \"MEDIUM\",\n          \"LOW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vehicle_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 766,\n        \"min\": 1,\n        \"max\": 2462,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          54,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_target_defect_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3417312936604035,\n        \"min\": 0.01413382005899705,\n        \"max\": 0.8578366,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.06643331234768482,\n          0.855171,\n          0.47132240000000003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_influence_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07231150212575688,\n        \"min\": 0.380287,\n        \"max\": 0.5815622,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.5333947079610073,\n          0.442684,\n          0.4216056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"risk_summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PAINT\",\n          \"ASSEMBLY\",\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ASSEMBLY\",\n          \"FINAL_INSPECTION\",\n          \"BODY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"risk_grade\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"LOW\",\n          \"MEDIUM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vehicle_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 423,\n        \"min\": 1,\n        \"max\": 880,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          880\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_target_defect_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29228220842686947,\n        \"min\": 0.026473051136363637,\n        \"max\": 0.618967,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.047076,\n          0.026473051136363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_influence_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09591880133970926,\n        \"min\": 0.495199,\n        \"max\": 0.7193733886363637,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.495199,\n          0.7193733886363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -7041,7 +7607,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 26,
       "id": "JFTbBuS1bEfD",
       "metadata": {
         "colab": {
@@ -7049,7 +7615,7 @@
           "height": 885
         },
         "id": "JFTbBuS1bEfD",
-        "outputId": "15604b4b-ea3f-45be-dc3b-9278c172f8af"
+        "outputId": "6afce295-117c-486d-d7df-abe3aade47a3"
       },
       "outputs": [
         {
@@ -7058,7 +7624,7 @@
             "text/plain": [
               "<Figure size 900x500 with 1 Axes>"
             ],
-            "image/png": 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\n"
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3vM4nNyHXZDLvO8+85OTkQCaTYcWKFVqP8+5MiQUJYu9ydXUV30x7e3ujY8eOGDduHCIjI/N9nTRp0gT79+/HwYMHceLECWzbtg1r167FtGnT0K1bN7GdkZERPD09cejQIRw/fhze3t6FrvF9/P39MWvWLERHR2PYsGHYuXMnGjVqhNq1axdqP5aWlvD390fbtm0REBCAPXv2YPbs2UUOAqqrhu9efVK5f/++RmDN/XzoQl7PoaBlsqP3yetq57uTlxTmb4C2x0VV2N8X+rQw0BER0b+atbU1Tp06hdTUVLVPz2/duiWuBwBbW1vk5OTg5s2bGpODqKjeoFaqVAnNmzd/77FtbW0xcOBADBw4EHfu3EHnzp2xatUqtSsjTk5OcHJywtixYxEdHY1x48Zh165dasFBV/bu3QsbGxssWrRI7Q3swoULP2i/BTlPbdsIgoAaNWqIV6aKk6mpKUaOHImJEydi9+7d8Pf3z7d9xYoV0aVLF3Tp0gWvXr1C3759ER4erva8yGQyzJ8/H2FhYRgzZgxWrFgBNze3AtWTk5OD+/fvq5377du3Afzfa1JVR6tWrcTJRuLi4tQm8iksQ0NDyOVy3LlzB8+ePXvvlVRt7t+/j/j4ePTt2xdNmjRRW5eTk4Px48cjOjoaYWFhRa7zY8t9dS/35CL//POPWrvC/g0oCFtbW8TGxmpMfvNum4/5+0KlC++hIyKifzVPT09kZ2djw4YNasvXrFkDmUwGT09PAEDr1q2hp6eHxYsXIycnR62t6lP5li1boly5cli2bJnW+95UMwS+fv0a6enpautsbW1hamqKjIwMAMCLFy80riSogqSqja6pPtnPfdyLFy/iwoULRdpfQc4zL23btoW+vr7aVP0qgiBoDPHThY4dO6Jq1apYsWJFvu3ePbapqSlsbW21npORkREWLVoEBwcHDBs2TONrHfKT+zUpCAI2bNgAQ0NDNGvWTK1dYGAg/v77b8ydOxf6+vrvDaPA2yF674YR4G1giY+Ph5mZWZGvlqmuzg0aNAjt27dX++nQoYM426WUqIZ/njt3TlyWlpaGqKgotXYF/RtQGG3btoUgCFi0aJHGOtXvRkn8vlDpwSt0RET0r+bj4wM3Nzf8+OOPSEhIgFwux4kTJ3Dw4EH0799ffCP32WefYdiwYViyZAl69+6Ntm3bwsjICJcvX4alpSW++uorlCtXDlOnTsX48eMRHByMDh06wMLCAv/88w+OHj0KZ2dnTJkyBXfu3MGAAQPQvn171K1bF/r6+jhw4ACePHkivhnfsWMHNm3ahNatW8PW1havXr3Cli1bUK5cOTFk6lqrVq2wb98+jBgxAq1atcKDBw/w66+/om7dukhLSyv0/gpynnmxtbXFF198ge+//x4JCQlo3bo1TE1N8eDBAxw4cADdu3dHaGhoUU9VK0NDQ/Tr1w9z587FsWPH8uxnf39/NG3aFPb29qhYsSIuX76MvXv3om/fvlrbGxsbY9myZejXrx8GDx6MiIgI2NnZ5VtLmTJlcPz4cXz99ddwdHTE8ePHceTIEQwbNkwjaHl5eaFixYrYs2cPPD09C/SVDX/++SfGjRuHli1bwtXVFWZmZkhMTERUVBSSkpIwadKkIg8HjI6ORoMGDTTusVTx8fHBjBkzcPXqVdjb2xfpGB+bh4cHqlevjv/+97+4desW9PX1sX37dpibm6sF44L+DSgMd3d3BAYGIiIiAnfv3kXLli2Rk5OD8+fPw83NDX379i2R3xcqPRjoiIjoX01PTw9Lly7FwoULsWvXLkRGRsLa2hrjx4/HwIED1dqOGTMGNWrUwPr16/Hjjz+ibNmykMvlahNQdOzYEZaWlli+fDlWrlyJjIwMWFlZwdXVVZyFsWrVqvD398epU6ewc+dO6Ovro3bt2vjpp5/Qrl07AG8nRbl8+TJ27dqFJ0+eoHz58nB0dMT8+fM17j3SleDgYDx58gSbN29GbGws6tati3nz5mHPnj04e/ZsofdXkPPMz5AhQ1CzZk2sWbMGixcvFvfp4eEBHx+fQtdTED169MDSpUuxYsWKPANdSEgIDh06hBMnTiAjIwPVq1fHF198ke8b5nLlymHlypXo27cvBg4ciA0bNuCzzz7Ls72+vj5++eUXTJ06FfPmzROHhI4YMUKjrZGRETp06ICNGzcWaDIU4O19gKNHj8bx48exevVqPHv2DKampmjQoAHGjRtXoOdHm6tXr+LWrVv5Dqf09vbGjBkzsHPnTskEOkNDQyxatAjTpk3DggULUKVKFfTv3x8VKlTAxIkT1doW5G9AYc2aNQtyuRzbtm3D3LlzUb58eTRq1AgKhUJsUxK/L1Q6yISi3BlKRERERKXGd999h23btuHEiRNFnuafiKSJ99ARERERSVh6ejp27tyJdu3aMcwR/QtxyCURERGRBD19+hQnT57E3r178fz5c/Tr16+kSyKiEsBAR0RERCRBf//9N8aNG4dKlSph8uTJeX6dBhF92ngPHRERERERkUTxHjoiIiIiIiKJYqAjIiIiIiKSKN5DR0T0/8XHx0MQBBgaGpZ0KURERPQvlpmZCZlMpvZdg3nhFToiov9PEATxh3RDEARkZGSwT3WE/al77FPdYn/qFvtT96TSp4V5P8IrdERE/5+hoSEyMjJQt25dmJiYlHQ5n4S0tDRcu3aNfaoj7E/dY5/qFvtTt9ifuieVPr18+XKB2/IKHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BERvUMmk5V0CZ8MmUyGsmXLsk91hP2pe+xT3WJ/6hb7U/c+xT6VCaX9a9KJiD4S1Zd4Ojg4lHAlREREVFrl5AjQ0yveQFiY9yQGxVoJEZEELd50AglJL0q6DCIiIiplrC3NMKKXR0mXoYaBjojoHQlJL3An4VlJl0FERET0XryHjoiIiIiISKIY6IiIiIiIiCSKgY6IiIiIiEiiGOiIiIiIiIgkioGOiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiISKIY6IiIiIiIiCSKgY6IiIiIiEiiGOiIiIiIiIgkioGOiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiISKIY6IiIiIiIiCSKgY6IiIiIiEiiGOiIiIiIiIgkioGOiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiISKIY6IiIiIiIiCSKgY6IiIiIiEiiGOiIiIiIiIgkioGOiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiISKIY6IiIiIiIiCSKgY6IiIiIiEiiGOiIiIiIiIgkioGOiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiISKIY6HQkPDwccrlc/HF3d0e/fv3w+++/a7T99ttvIZfLsXjxYq378vHxwfTp08XHkZGRkMvl8PX1RVZWllrbNWvWQC6Xa61B24+Pj48Oz7rwMjMz4ebmhvr16+Off/7R2ubChQsYNGgQPDw84OjoCB8fH4wePRoXL15Ua7dz50507doVLi4ucHZ2hp+fH/773//i6dOnYpuQkJA8++LChQsAgAcPHojLjh07plHPli1bxPW5vbu/5s2bY9iwYbh+/bpau/DwcCgUCrVlZ86cgVwuR1RUlMbx/vzzT9jb22PNmjV5dSMA4Pr161AoFEhOTtaoadOmTRrtT5w4Ia5/8OCBuNzHxyfPPnr8+LFavaofJycn+Pr6YuzYsThx4oTGsd49Z1Uf79mzR6NtSkoK5HI5IiMjxWWq523s2LEa7VNTU+Ho6Kixzeeff46lS5fm22dEREREnxqDki7gU2JsbIy1a9cCAB49eoQlS5ZgwIABiIyMhJ2dHQAgOzsbu3fvBgAolUqMGDGiwPt/8OABdu7cieDgYK3ru3XrhpYtW4qPt27dCqVSKdYEAEZGRoU+L12KjY3F8+fPAbw9/yFDhqitP3/+PPr164eWLVti2rRpMDU1xd27d3HgwAFcunQJjRs3BgCsWLEC33//PQYMGIDRo0dDEATcuHED0dHRSEpKQqVKlcR9Ojs74+uvv9aopV69emqPTUxMsGvXLnh6eqotVyqVMDExQVpamsY+QkJCEBAQAEEQ8OjRIyxbtgyhoaHYtWsXKlSokGc/uLm5ISgoCHPmzIG3tzfMzMwAAIIgYOrUqZDL5QgJCcmnJ4GffvoJwcHBsLCw0HoevXr1KvB5tGvXDgMHDtRYXrFiRbXHs2bNQu3atZGeno779+8jJiYGAwcORO/evfHNN9/kW29hmZiY4PDhw0hLS4OJiYm4/MCBA9DX19doP3ToUIwaNQq9e/cW+5OIiIjoU8dAp0N6enpwcnISH6uuLv3666+YMmUKAODUqVN48uQJmjdvjpMnT+Lq1auwt7cv0P7d3NywbNkyBAYGan1DW7VqVVStWlV8fPz4cY2aCuvNmzd48eIFrKysiryP3JRKJSpUqAAbGxtER0drBLpNmzbB2toaixcvFs+xWbNm6NmzJ3JycsR2ERERCAoKwoQJE8RlXl5eGDRokFo7AKhQoUKB+sDX1xf79+/HtGnTUKZMGQBAUlISzp07h4CAAOzcuVNjm2rVqqntu1atWggMDER8fDy8vLzyPd748eNx5MgRzJ8/HzNmzADwNoRfunQJW7Zs0focq9y/fx+HDx9Wu0KV+zxiYmKQmJgoPm8ZGRnYv38/WrdurfU8KleuXKA+qlevHhwcHAC8fT127doVP/zwA5YtWwaFQoFOnTq9dx8F5ezsjKtXr+LQoUMICAgQlyuVSq3n4e7ujgoVKmDHjh0YMGCAzuogIiIiKs045LIYVa9eHRYWFmrD25RKJUxNTTF79mwYGhoiOjq6wPsLCwvD3bt3ERMTUxzlavXkyRN4e3tjyJAh2Lt3LzIyMoq8r7S0NBw6dAjt2rVDcHAw/vrrL43hiSkpKbCwsNAaZvT09NTaWVpaaj1O7naF4enpCZlMhqNHj4rLdu3aBVtb2wKHblNTUwBvh5a+j4WFBb7++mts3boV8fHxSE5Oxvfff48+ffqgUaNG+W4bFRUFGxsbNGzYUGNdgwYNULNmTezatUtcdvToUQiCgFatWhXoPApj9OjRqFKlCjZu3KjT/RoYGKBdu3ZQKpXisuTkZJw6dUot4OXWvn17rcNYiYiIiD5VDHTFKDU1Fc+fPxeDR3p6Ovbt24c2bdrAysoKLVq0QExMjMYVpbzY2dnB19cXP//8c4G3+VDVqlXDDz/8AJlMhi+//BKenp6YOXMm/vzzz0Lv68CBA0hLS0NAQAD8/PxgYGCg9mYdAOzt7REfH4+ffvoJN2/ezHNf9vb2+PXXX7F161bxPq+8CIKArKwstZ/s7GyNdkZGRmjTpo1aTUqlMs/wAAA5OTnIyspCZmYmHjx4gHnz5sHc3Bxubm751qQSFBQENzc3fPPNN5g1axaMjY3xxRdfvHe7kydPatyXl5u/v7/GebRp00a88viugvaRNgYGBnB3d8eVK1cKFGQLIyAgALGxsXjx4gUAYPfu3bCyssrz3BUKBa5du6Z2XyERERHRp4yBTsdUb4YfPHiAiRMnIjs7G+3atQMAHDp0CK9evRIDQseOHZGUlIQzZ84UeP/Dhw/HzZs3tU4uURz09fXRvn17LFu2DEePHsXgwYNx+vRpBAYGIjg4GOvXrxfviXsfpVIJKysrNG3aFJUqVYK7uzuUSiUEQRDbhIaGwsPDA0uXLkWHDh3g5uaGr776SmNymW+++QZmZmaYPHkyWrRoAV9fX3z77bdqV0NVjh49Cnt7e7Uf1bDBdwUEBODo0aN49eoV7t27h8uXL+cb6ObPnw97e3s0atQIvr6+OHPmDMLDw1G+fPkC9QkATJs2Dbdv38bOnTvxv//9T7zKlxdBEHDlyhWNSVrePY8rV67g3r17ePXqFY4cOZLveWzcuFGjj9q3b1/gc6hWrRoyMzPF4KUrrq6uqFSpEvbt2wcAiImJyfc86tevDwC4dOmSTusgIiIiKq14D50OpaWlqQ3NMzMzw5QpU8SJSpRKJSpVqoTmzZsDeDu7oImJCaKjo9GsWbMCHaNRo0bw8vLC0qVL4efnp7Pac3Jy1K766enpaQxdrFy5MkJDQxEaGoorV65gx44dCA8Px5w5czBnzhx06NAhz/0nJyfjxIkTCAkJEffbsWNHfP311zh//jxcXV0BAOXKlcOqVatw6dIlHDlyBOfPn8fevXsRExODGTNmoFu3bgDeXq1UKpU4deoUYmNjce7cOURERCAyMhIbNmxAgwYNxGO7uLhg4sSJavXIZDKtdbq7u8PU1BQHDhxAQkIC7O3tUatWLbVhmLn169dPvG8sOTkZGzduRFhYGCIiIsRw8T41a9ZE27ZtceXKFbRu3fq97V+8eIGMjAyNyVDe3ae9vT2USiWsra1hamqKZs2a4fDhw1rb+/n5ITQ0VG1ZXlfztFGF8rz6tahkMhk6dOiAmJgYtGjRAnFxcflOvmJubg4A771qS0RERPSpYKDTIWNjY6xfvx4ymQzm5uaoVq2aGF5SUlJw9OhRBAYG4tWrV+I2LVu2xP79+zF16tQCz0AZFhaGHj164MCBAzqrffHixVi0aJH4eOTIkRg1apTWtjk5OXj58iVevnyJ9PR0GBsbw9jYON/97969G1lZWfDy8kJKSgqAt5NqGBkZITo6Wgx0Ko6OjnB0dATwdgKQkJAQzJ8/Xwx0wNshkl5eXuLkI8ePH8fQoUM1zqV8+fJ5XpF7l76+Pvz8/BATE4OEhAR06dIl3/ZVq1ZV23ezZs3g6emJJUuWYOHChQU6JgAYGhrC0NCwQG3T09MBvH/G0oCAAGzfvh3Vq1eHn59fvpOsWFhYFLiPtHn06BEMDQ3znF3SwODtnxptQ4VVQztVbd7l7++PtWvXYs2aNahXrx7kcrn4GnqXqk/evHlT6HMgIiIikiIGOh3S09PL803x3r17kZmZiW3btmHbtm0a648cOYK2bdsW6DhOTk7w8PDAkiVLEBgY+EE1q3Tv3l1twgxtE47cvn0bUVFR+O233/Do0SO4u7tjxowZaNu27Xuv5qju59I2++CePXswefLkPAONjY0N2rdvj9WrV+PJkyeoXLmy1nYtW7ZE/fr18733riD8/f3Rp08fAMj3qqM2RkZGsLGxwY0bNz6ohvyovkogr1Cj0qFDB8ydOxe3bt3Chg0biq2erKwsnD59Gg4ODnmGsooVK0JPT0/rlbOkpCQAUPuqidwaNWoEGxsbrFu3DmPGjMm3lpcvX4rHIyIiIvo3YKD7SKKjo2FtbY1Zs2ZprPvyyy8RHR1d4EAHvL1K16dPn3y/66wwrKystH41QUZGBiIjIxEVFYX4+HhYW1ujS5cuCA4OhrW1dYH2nZCQgPj4ePTs2VMjIF27dg2zZs3C8ePH4ePjk2dgu3PnDoyMjMTz1dbuzZs3ePjwIerWrVvQ09ZKoVAgICAAlSpVUvsaiIJIT0/HvXv3UKdOnQ+qIT9lypRB9erVtd4vmFvVqlXRv39/JCcnw9nZudjqWbhwIR4/fqwxrDU3Y2NjODg44ODBg+jfv7/augMHDqBMmTL5XiEcPHgwDh06hI4dO+Zbi6pPatWqVYgzICIiIpIuBrqPIDExEefOncPw4cO1zn4YEBCAjRs34uXLlwWeTMPV1RVNmzbF6dOndV2umqSkJMyaNQutW7fGmDFj4O7uXuj7pFRX5wYNGgQbGxu1dS4uLli+fDmUSiV8fHwwefJkZGdno23btqhZsyZSU1Oxd+9eHD58GP379xeH1HXs2BHe3t5o0aIFLC0tkZiYiPXr1+PZs2cagSElJQUXLlzQqMvW1lbrfWgymQzz5s0r0Lk9fPhQ3HdycjI2bNiA58+fo2fPnmrtsrOztU5k4+joiOrVqxfoWLmpvqPtffILWbk9efJEax/VrVsX5cqVEx/fuHED2dnZyMjIwP3796FUKnHy5EmEhITA398/32OMGjUKQ4YMwciRIxEYGIgyZcrg9OnTWLNmDQYPHpzvhxNdu3ZF165d33seV65cgYmJido9lERERESfMga6j0D11QSdO3fWuj4oKAhr1qzB3r17C/SmVSUsLAxnz57VUZXaWVpaIjY2tlCzNr5LqVTC2dlZI8wBb++bCggIwJYtW/Dq1Sv06dMHUVFRWLZsGR4/fgxjY2PY2tpi5syZCAoKErcbOXIkDh8+jNmzZyM5ORnm5uaQy+VYs2YN3N3d1Y4RFxeHHj16aBx77ty5HzxkNSIiAhEREQDefoF5nTp1sHjxYo3JTdLT07UOFyxqDe3atcO4ceOQmpqqFriKau/evdi7d6/G8g0bNqjd36gKiMbGxqhUqRIaN26M1atXixP95Kdly5b45ZdfsHTpUowfPx6ZmZmoWbMmJk6ciL59+37wOQDAsWPH0KZNm3zvFyQiIiL6lMiE3HPGE5EkZGZmwtvbG+PGjcvzg4J/mxcvXsDDwwOrV69GkyZNirSPy5cvAwA2HbqPOwnPdFkeERERfQJqWpvjuzGFm2OhKFTvSQoyaR2/h45IggwNDTF48GCsW7eupEspNSIiIuDs7FzkMEdEREQkRRxySSRRvXr1QmpqKpKTk/P9Trp/i4oVK2Ly5MklXQYRERHRR8VARyRRRkZGGDFiREmXUWro6j48IiIiIinhkEsiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiTIo6QKIiEoba0uzki6BiIiISqHS+B6BgY6I6B0jenmUdAlERERUSuXkCNDTk5V0GSIOuSQiyiUjIwOvX78u6TI+Ga9fv8Yff/zBPtUR9qfusU91i/2pW+xP3dNFn5amMAcw0BERaRAEoaRL+GQIgoDXr1+zT3WE/al77FPdYn/qFvtT9z7FPmWgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjInqHTCYr6RKIiIiICoSBjogoFyMjI5QtW7aky8iTkJNT0iUQERFRKWJQ0gUQEZU2t5Ur8Prpw5IuQ0PZStVQK2BwSZdBREREpQgDHRHRO14/fYjXifdKugwiIiKi9+KQSyIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikqgiB7pOnTpBLpfj999/17r+77//xujRo+Hp6QkHBwd4enpi6NChOHr0qFq7o0ePom/fvnBzc4OTkxPatGmDcePG4fbt22KbCRMmQC6Xa/2JiYkR26mWbdq0SaOeEydOiOsfPHggLvfx8VHbn5ubG/r166dxXpGRkZDL5UhOThaX3bp1Cw4ODli0aJHG8ZKSkuDi4oKZM2e+pyeLR159NnToULGNj48Ppk+fLj5WnaOvry+ysrLU9rdmzRrI5XKtx9q/fz/kcjn69++fZy0BAQFFPpfw8HAoFAq1ZarzOXXqlNrylJQUyOVyREZGisuysrIQERGBTp06QaFQoEmTJujUqROmT5+OjIwMsV3u10LDhg3h6+uLb775Ru05L+hrEQCSk5Mxe/ZstGvXDg4ODnB2dkbfvn2xdetWZGdna7z2tP2Eh4eL57ty5UqNvtm5cyd69uwJhUIBhUKBHj16ICoqSmsfyuVy9OnTR2PdzJkz4ePjk88z8H/mzJmD0aNHi4+1/V5oc+zYMQwYMACurq5o3LgxOnfujHXr1qm9zk6dOgW5XI4TJ06obXvo0CHI5XJ8/fXXassFQUCTJk0wdepUAG/7ws/PD9nZ2QU6FyIiIqJPgUFRNrpx4wauX78OAIiOjoarq6va+nv37qFbt26Qy+WYMGECLCwskJCQgKNHj+Ls2bPw8vICAOzatQtjx45FUFAQBg0aBENDQ9y8eRO7d+/GzZs3UatWLXGfNjY2mD9/vkYtn332mdpjExMT7Nq1C7169VJbrlQqYWJigrS0NI19tGvXDgMHDgQAPH36FGvXrsWgQYOwc+dO2Nra5tkPtWvXxpAhQ7B8+XJ07NhRrZZZs2ahXLly+OKLL/Lcvrhp67MKFSq8d7sHDx5g586dCA4OLtBxoqOjAQBnz55FYmIirKysCl9sES1ZsgTNmjXLt823336LyMhIDBkyBM7Oznj9+jWuXbuGnTt34s2bNzAyMhLbql4LWVlZuHDhAhYtWoS//voLGzZsgJ7e288/CvJavHv3Lvr164fs7Gx8/vnnsLe3R0ZGBk6fPo1Zs2bB3NwcixYtUguUI0eOhLOzs/haBICqVavmeV4zZszAhg0b0KVLF4SFhUEmk2Hv3r2YMGECLl++jP/9738a2/z+++84c+YM3Nzc8u0zbRITE7Fx40Zs2LChUNutWrUKc+bMQZs2bTB37lyULVsWx44dw9y5c3H69GksWrQIenp6aNy4MQwMDBAfHw8PDw9x+7i4OJQtWxbx8fFq+71x4wZSUlLg4uICAPD398eCBQsQFRWFLl26FPr8iIiIiKSoSIEuOjoaenp6aNKkCfbs2YPJkyfD0NBQXL99+3YAwOrVq1G2bFlxeZcuXZCTkyM+joiIgJubG2bPni0u8/DwQL9+/dTaAYCxsTGcnJzeW5uvry9iYmLUgkVGRgb279+P1q1bY+fOnRrbVK5cWW3frq6ucHNzw/Hjx7Ve0cht6NChiImJwfTp08UrKCdOnMCuXbuwePFimJqavrfm3N68eYMXL17oJBQVtM/e5ebmhmXLliEwMBD6+vr5tk1NTcWRI0fQvHlznDx5Ert27cLnn39exIoLX+eZM2fw+++/a3yooPL69Wts27YNw4YNw8iRI8Xlvr6+GDlyJARBUGuf+7Xg6uqK9PR0LFy4EFevXoWDgwOAgvXruHHjkJ2dje3bt6s9l56enujbty9evnypccXTyMhI47WYl4MHD2L9+vUYOXIkRo0aJS5v2bIlLC0tsXjxYnh4eKhdeTMxMUHdunWxZMmSIgW6zZs347PPPkOjRo0KvM0ff/yB+fPnIygoSO33vFmzZqhbty4mTZqEDRs2ICQkBCYmJqhfvz7i4uLU9hEXF4egoCBs3LgRT58+RaVKlcTlAMRAp6+vj6CgIERERDDQERER0b9GoYdcCoIApVIJd3d3fP7553j+/DmOHz+u1iYlJQXlypVTC3PiAfX01NpVqVJFe2F6RRsN2qBBA9SsWRO7du0Slx09ehSCIKBVq1YF2kfZsmWhr6+vMexQGyMjI0yfPh2xsbGIiYlBRkYGpk+fDl9fX7Ru3brQ9T958gTe3t4YMmQI9u7dq3YF52MJCwvD3bt3NYYQarNv3z6kp6dj5MiRsLe3F6/WfQxeXl6wt7fH4sWL82zz+vVrZGZmwtLSUut6mUyW7zFU4SX3MN33+f3333Hp0iUMHTpUazCvXr16nsNXC2rt2rUwMzNTu5qnEhoaCjMzM6xdu1ZjXVhYGE6fPq0RmgoiKioK7dq1K9Q2ERERkMlkaqFTJSgoCDVr1lSr08XFBRcuXBCHTWZkZODKlSvw9/dHlSpV1OqOi4tD1apVUb16dXGZn58frl27hj///LOwp0dEREQkSYVOTXFxcUhISEBAQABatGiBihUrQqlUqrWxt7dHUlISpkyZgmvXrmlcbcvdbt++fVi9enWB3jBnZWVp/Gjj7++vVpNSqUSbNm1QpkwZre0FQRD3l5SUhFmzZkFfX7/AAbBp06YIDg7GrFmz8P3334vnXhTVqlXDDz/8AJlMhi+//BKenp6YOXNmkd+gFqS/3mVnZwdfX1/8/PPPeT53KtHR0bC2toazszM6duyIq1ev4tatW0WqtSjCwsJw8uRJXLhwQet6CwsLVK9eHUuXLkVMTAxevHhRqP2rXpfvBsL8Xotnz54F8PZqWXHIyspCfHw83NzctF4BNjU1hZubG+Lj4zWec29vbzRs2DDfEKzN3bt3kZCQAGdn50Jtd+7cOcjlclhbW2us09PTg7e3N+7fv4/ExEQAgLOzM169eoW//voLAHD16lXk5OSI9yC+G+hUV+dU6tSpAzMzM4378IiIiIg+VYUOdEqlEmXKlEHbtm1haGiIdu3a4dChQ3j16pXYJigoCAEBAdi8eTM6d+4MV1dXhIWF4eDBg2r7+uqrr1CvXj3Mnj0bvr6+aNGiBSZPnqw1vNy4cQP29vYaP48ePdJoGxAQgCtXruDevXt49eoVjhw5ku+kHBs3bhT317JlS0RGRmLOnDka9+flZ/z48cjKysKaNWswZsyYfO99yo++vj7at2+PZcuW4ejRoxg8eDBOnz6NwMBABAcHY/369Xj+/HmB9qWtz/KaxOZdw4cPx82bN7Fnz5482zx+/BhnzpyBv78/ZDIZOnToAD09vY96lc7X1xdyuTzfgDJ79mxkZGTgyy+/hJubGzp06IDvv/9e60QeqnD/5s0bnDlzBj///DNsbGxgb28vtnnfa1EVTnJfOdKlZ8+eISMjA9WqVcuzTbVq1ZCenq71tTJ8+HDExsbi0qVLBT7m5cuXAaDQVxYTExPfWycAse9UAU0V3OLj49GwYUOUKVMGCoVCvI/uyZMnuH//vkagU9V48eLFQtVJREREJFWFCnRZWVnYs2cPvLy8UL58eQBAx44d8fr1a+zfv19sp6+vj++//x5KpRJfffUVXF1dceLECYSFhWHBggViOysrK2zbtg3r1q3D4MGDUaNGDWzfvh1du3bVmA3T1tYW27Zt0/hR3U+TW82aNWFvbw+lUokDBw7A1NQ034kz/Pz8xP2tXLkSfn5+GD9+fKE+5Tc3N0f37t1hbGyMkJCQ97bPyclRu7qj7UpY5cqVERoaiujoaGzfvh0KhQLh4eFo2bKl2pDSvGjrswYNGhTofBo1agQvLy8sXbpU4z4zlV27diE7O1sMy1ZWVmjSpInGFdviJJPJMHz4cBw7dkwMHe9yc3PD/v37sWDBAvTo0QPZ2dniRDaq8KWiCveNGzdGv379YGVlhfDwcBgbG4ttCvNaLI3atGkDOzu7Ql2le/z4MfT09GBubl6MlQFVqlSBra2tGOji4uLEGU6dnJxw5coVpKeni+u1XTE0NzfH48ePi7VOIiIiotKiUJOinDhxAsnJyfD29kZKSgqAt8PzqlSpAqVSic6dO6u1r1evHurVq4chQ4YgOTkZoaGhWL58Ofr374+KFSsCeDvsys3NTZyk4Y8//kDfvn3x008/ibNhAkCZMmXESSkKIiAgANu3b0f16tXh5+eX7+QeFhYWavv28PDAH3/8ge+//15ttr33MTQ0hJ6e3nsnEgGAxYsXq33dwbuTW+SWk5ODly9f4uXLl0hPT4exsbFawMhLYfvsXWFhYejRowcOHDigdX10dDRq1aqFatWqia8HHx8fzJo1CxcvXkTjxo2LfOzCaNeunTjZx5w5c7S2MTExQfv27dG+fXsAwNatWzF58mSsWrUKEydOFNv5+fkhNDQUhoaGqFq1qvg6ze19/aq6b+7hw4eFuspbUObm5jAyMsLDhw/zbPPw4UOUKVNGa/0ymQzDhg3Dl19+iatXrxbomOnp6TAwMHjvPYfvsrKyem+dgPpsns7Ozjh37hyAt4FO9YGBvb09ZDIZLl++jLi4OJQrV07rFUNDQ0Okp6cXqk4iIiIiqSrUFTrVULqJEyeiSZMmaNKkCZo2bYrHjx/j1KlTePr0aZ7bWlhYIDg4GFlZWbh7926e7Ro2bAgPDw/cvHmzMKVp6NChA27evInY2Fj4+/sXaluZTIbatWvj77///qAa8tO9e3e1qzvdu3fXaHP79m38+OOP8PHxweeff46kpCTMmDEDsbGxBf7esA/h5OQEDw8PLFmyRGPd3bt3cfnyZdy+fVt8LTRp0gSzZs0CgI867FJPTw/Dhg3D4cOHC3yvYbdu3VCxYkWN15kq3NevX19rGCqIpk2bAgBiY2OLtP37GBgYQKFQ4OzZs1q/hiMtLQ1nz56FQqGAgYH2z2z8/PxQq1Ytrc+tNmZmZsjIyCh0UGrSpAn++usvraFOEAQcPXoUNjY2apPHuLi4ICEhAWfPnsXTp0/FK3RGRkawt7dHXFwc4uLi4OTkpHXypJcvXxb5uSMiIiKSmgIHutevX+PgwYNo3bo11q1bp/bzww8/ICsrSxwG+OTJE637uHPnDoC3QwnzapeTk4O7d++KbYqqatWq6N+/PwICAgo9kYMgCLh582axDi+zsrKCg4OD+JP7KxZ+/fVX9OzZE+3bt0d0dDS6dOmCgwcPYs2aNejYsWOek7sUh7CwMPzxxx84fPiw2vLo6GjIZDIsXrxY4/XQokULcTjmx9KhQwd89tlnGsMIMzMztU6E8vTpU7x8+TLPWVY/hKurKxwdHfHzzz8jKSlJY/3Dhw/F73Esqv79++P58+dYtWqVxrpVq1bh+fPneX7RO/B/IfjgwYMFqkX1nZCFme0TAEJCQpCTk4OFCxdqrPvtt99w69YtDBgwQG256r64lStXwtraWi3sKRQKnD59Gn/88Ueev9cJCQlq32FJRERE9Ckr8JDLgwcPIi0tDSEhIVq/w+qXX36BUqlESEgIlixZgmvXriEgIAB169ZFeno6Tpw4gY0bN6J169bijHeDBg1CzZo14e3tDWtrazx79gzbt2/H9evXMWnSJLX9v3nzRutMhtWqVcvzO9tyD6XLz5MnT8R9v3jxAkqlEn/99RfGjh2r0fbw4cMaMwvWq1cPderUKdCx3kc1y2br1q0xZswYuLu7F3qYmy65urqiadOmOH36tNpypVIJV1dXrV/NkJqaKs4+qZrpMTU1VesEK25ubjoJzvr6+hg6dKjGc/7y5Uu0a9cOgYGBcHd3h5mZGR48eIBVq1ZBT09P4wvoC6Igr8X58+cjJCQEXbp0Ufti8XPnzmHDhg2YM2fOB311ga+vL/r27YtFixbh0aNH4lDSffv2YcuWLejbt+97r+J27NgRixcvxpkzZ7TOQpmbo6MjDAwMcOXKFa2v9bx+Lxo2bIhx48Zhzpw5SE1NRXBwMIyNjREbG4u1a9fC19cXvXv3Vtuudu3aqFixIo4ePapxdd3JyQmrV6+GIAhaJ0RJS0vDrVu3MGLEiHzPh4iIiOhTUeBAp1QqUb169Ty/kLhz58747rvvcO/ePXTq1Anp6emIiIhAYmIi9PX1YW1tjfHjx6u9eRs8eDB2796NBQsW4PHjxyhfvjxq166N8PBwtG3bVm3/9+/fR48ePTSOO2bMGISFhRX0NLTau3cv9u7dC+DtlO+fffYZZs6cqfXLid8NmrqqQcXS0hKxsbHipDOlQVhYmDgVPwBcuXIFt2/fRmhoqNb2np6esLCwQHR0tBjoHj58iDFjxmi03bBhQ55fCl5YnTp1wuLFi9WuIpUrVw6DBw/G8ePHsWfPHrx48QKVK1eGg4MDZs+erTZ7ZUEV5LX42WefYceOHVixYgU2bdqEhw8fwsjICA0bNsSkSZPg7e1d9BP9//73v/+hcePG2Lhxo3j/pZ2dHWbPnq1xP6s2+vr6GDJkCCZPnvzetiYmJmjZsiWOHTuGwMBAjfX5/V4MHDgQderUwerVqzFu3DhkZmaiVq1a4t+Dd4dNymQyKBQKHD58WBxuqaJQKCAIAgwMDLTeoxkbGwtjY2N4enq+95yIiIiIPgUyIa8pDImIcjl06BC++uornDx5EmXLli3pcrQaPXo0TE1NxXs5C0s1U6p+3A68Tryny9J0oqyVLRr2L9p3XJaUtLQ0XLt2DQ0aNICJiUlJlyN57E/dY5/qFvtTt9ifuieVPlW9JynIBIeF/h46Ivp38vb2Rq1atbB169aSLkWr+/fv4+jRoxg+fHhJl0JERET00TDQUYnKzs5W+z6+d3+o9JDJZJg6dWqpvTqXmJiI6dOnw9bWtqRLISIiIvpoCvU9dES6NmDAALX789518OBB1KhR4yNWRPlxdHSEo6NjSZehlaurq87uxyQiIiKSCgY6KlHTpk3Dq1ev8lxvaWn5EashIiIiIpIWBjoqUbVr1y7pEoiIiIiIJIv30BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEGZR0AUREpU3ZStVKugStSmtdREREVHIY6IiI3lErYHBJl5AnIScHMj0OriAiIqK3+K6AiCiXjIwMvH79uqTLyBPDHBEREeXGdwZERO8QBKGkSyAiIiIqEAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMiIiIiIpIoBjoiIiIiIiKJYqAjIiIiIiKSKAY6IiIiIiIiiWKgIyIiIiIikigGOiIiIiIiIolioCMieodMJivpEoiIiIgKhIGOiCgXIyMjlC1bttiPI+TkFPsxiIiI6NNnUNIFEBGVNreVK/D66cNi23/ZStVQK2Bwse2fiIiI/j0Y6IiI3vH66UO8TrxX0mUQERERvReHXBIREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0REREREZFEMdARERERERFJFAMdERERERGRRDHQERERERERSRQDHRERERERkUQx0BEREREREUkUAx0VSXh4OORyufjj7u6Ofv364ffffxfbnDlzRq2Nk5MTOnbsiLVr1yI7O1ttfxMmTFBrm/snJiZGbPfw4UNMnDgRPj4+cHBwQIsWLTBgwAD89ttvavu7cOECBg0aBA8PDzg6OsLHxwejR4/GxYsXi7dj8vFunzk4OMDPzw8rVqxATk6ORvuLFy9i+PDhcHNzE9suWrQIb968UWunrZ99fX0xduxYnDhxQmw3YcIEuLu74/nz5xrHmjNnDhQKBR49epTvOcyZMwejR48WH0dGRkIulyM5OTnf7Y4dO4YBAwbA1dUVjRs3RufOnbFu3TpkZWWJbU6dOgW5XK5WMwAcOnQIcrkcX3/9tdpyQRDQpEkTTJ06FQCwc+dO+Pn5aby2iIiIiD5lBiVdAEmXsbEx1q5dCwB49OgRlixZggEDBiAyMhJ2dnZiu1mzZqF27dp4+fIloqKi8N133yE9PR1DhgxR25+NjQ3mz5+vcZzPPvsMAJCSkoLu3bvDzMwMo0aNQvXq1fHo0SOcPn0ax48fR2BgIADg/Pnz6NevH1q2bIlp06bB1NQUd+/exYEDB3Dp0iU0bty4uLrkvXL32Zs3b3DmzBl8//33EARBrT927dqF//znP3B2dsa0adNgbm6OuLg4LF++HMeOHcPatWtRtmxZtX2r+jk9PR33799HTEwMBg4ciN69e+Obb77B+PHjcfjwYcybNw8zZ84Ut7t+/TrWrVuH//znP6hatWqetScmJmLjxo3YsGFDoc551apVmDNnDtq0aYO5c+eibNmyOHbsGObOnYvTp09j0aJF0NPTQ+PGjWFgYID4+Hh4eHiI28fFxaFs2bKIj49X2++NGzeQkpICFxcXAIC/vz8WLFiAqKgodOnSpVA1EhEREUkVAx0VmZ6eHpycnMTHqithv/76K6ZMmSIur1evHhwcHAAAHh4e+OOPP7B9+3aNQGdsbKy2v3ft3bsXSUlJ2Lx5M6pXry4uDwwMVLvCtWnTJlhbW2Px4sXQ19cHADRr1gw9e/bUeiWsIO7evSsGyw/xbp+5u7vjr7/+wr59+8T+SExMxH//+1+4urpi1apV4jm4ublBoVCgf//+WLBgASZMmKC279z97Obmhq5du+KHH37AsmXLoFAo0KlTJ0yYMAETJ05EcHAwXFxcIAgCpk6dCrlcjpCQkHxr37x5Mz777DM0atSowOf7xx9/YP78+QgKCsLs2bPF5c2aNUPdunUxadIkbNiwASEhITAxMUH9+vURFxento+4uDgEBQVh48aNePr0KSpVqiQuByAGOn19fQQFBSEiIoKBjoiIiP41OOSSdKZ69eqwsLDAgwcP8myjp6cHuVyOhw8fFnr/L168gJ6enviG/t39qqSkpMDCwkIMQnm1K4y2bduib9++2LFjB9LS0oq0j7yYmpqqDT3cunUr0tLSMGbMGI1zcHd3R/PmzbF582aNoZfajB49GlWqVMHGjRsBAEFBQWjatCmmTp2KrKwsbN++HRcvXsT06dO19lduUVFRaNeuXaHOLSIiAjKZDKNGjdJYFxQUhJo1a4pXLIG34ezChQvisMmMjAxcuXIF/v7+qFKlilrYi4uLQ9WqVdXCvZ+fH65du4Y///yzUHUSERERSRUDHelMamoqnj9/DktLy3zb/fPPP6hRo4bWdVlZWRo/Kvb29sjJycG4ceMQHx+vti43e3t7xMfH46effsLNmzeLfkK5rFixAlWqVME333wDDw8PTJo0CefPny/SvlTnlZqaioMHD2Lfvn1qQencuXOoWLEinJ2dtW7v4+ODtLQ0XL169b3HMjAwgLu7O65cuYLMzEwAwLRp03Dnzh38+OOPmDdvHvr06fPeq253795FQkJCnjXl5dy5c5DL5bC2ttZYp6enB29vb9y/fx+JiYkAAGdnZ7x69Qp//fUXAODq1avIycmBg4MDnJ2dNQKd6uqcSp06dWBmZqZxHx4RERHRp4pDLumDqELVo0ePMGfOHGRnZ2tcxcnJyUFWVhZevnyJyMhIXLp0CT/88IPGvm7cuAF7e3uN5UePHkXVqlXRrFkzhIaGYvXq1di3bx+MjY3h4uKCTp06ITAwEDKZDAAQGhqKixcvYunSpVi6dCkqVqyIFi1aoFevXnB1dS3SeXp6esLT0xMpKSmIiYnBjh070Lt3b9SsWRNdunRBYGAgrKys3ruftLQ0jXPs0KGD2vDTxMREVKtWLc99qNapQtD7VKtWDZmZmXjx4gUqV66MWrVqYejQoQgPD0fVqlXxxRdfvHcfly9fBgDI5fICHVMlMTEx321U5/Lo0SNYWVmJAS0uLg4NGjRAfHw8GjZsiDJlykChUGDv3r0AgCdPnuD+/fv4/PPPNfYpl8tLdPIbIiIioo+JgY6K7N1wYmZmhilTpqBly5Zq7bp37672eMiQIejQoYPG/mxtbbUGvdxDLMePH49evXrh4MGDOH/+PE6dOoUTJ07gxIkTmDdvHgCgXLlyWLVqFS5duoQjR47g/Pnz2Lt3L2JiYjBjxgx069ZN6/kIgqA2Q6Kenp7GEM0KFSqgV69e6NWrF27duoUdO3Zg/fr1+Omnn/Cf//xHa8DIzdjYGOvXrwfwdjjh1atXsXDhQkyePBmzZs3Kd9uiEgQBAMTAC7x9DsLDw9GrVy+Ympq+dx+PHz+Gnp4ezM3Ni6VGlSpVqsDW1hZxcXHo06cP4uLioFAoAABOTk74/vvvkZ6eLl6p03bF0NzcHI8fPy7WOomIiIhKCwY6KjJVOJHJZDA3N0e1atW03qM2Z84c1KlTB8nJyVi2bBlWrFiBJk2awNPTU61dmTJlxEk98mNjY4MBAwZgwIABePXqFcaMGYOdO3ciNDQU9evXF9s5OjrC0dERAHD//n2EhIRg/vz5eQa6HTt2YOLEieLjdyfyeNfLly/x8uVLvHnzBkZGRhqzTmqjp6endo4uLi7Izs7G7Nmz8fnnn8POzg5WVlb53gOmuv+wIFcEgbdXvwwNDWFmZiYuMzIyAgAYGhoWaB/p6ekwMDBQC4UFYWVlle/9kqp1uWfXdHZ2xrlz5wC8vVIXEBAA4O1QWplMhsuXLyMuLg7lypXTevXP0NAQ6enphaqTiIiISKoY6KjI3g0nealTp47YztXVFe3bt8ecOXPQsmXLQgeEd5mamqJ37944fvw4bt26pRbocrOxsUH79u2xevVqPHnyBJUrV9Zo4+3tjW3btomPtV2NevToEX777Tfs2LEDt2/fRuPGjfHVV1/B398f5cqVK9I51K5dGwDw999/w87ODk2aNMHp06dx4cIFrbN+HjlyBCYmJlqHp74rKysLp0+fhoODAwwMiv7rbmZmhoyMDKSnp6NMmTIF3q5JkyaIjo7Gw4cPNYaRCoKAo0ePwsbGRi2curi4ICoqCmfPnsXTp0/FK3RGRkawt7dHXFwc4uLi4OTkpPUDhJcvX6JixYpFO1EiIiIiieGkKPRRmZqaYvTo0fj7779x4MCBQm2bnJwsDh/M7c6dOwAghrQnT55o3f7OnTswMjJChQoVtK43NzeHg4OD+JN74padO3di4MCB8Pb2xrp16+Dt7Y1du3Zhy5Yt6NGjR5HDHPD23kHV8QGgW7duMDExwYIFCzS+ZuHs2bOIjY1Fz549YWxs/N59L1y4EI8fP0bfvn2LXB8A1KpVCwDyncFUm5CQEOTk5GDhwoUa63777TfcunULAwYMUFuuuo9u5cqVsLa2Vgt7CoUCp0+fxh9//JHnBC0JCQlivURERESfOl6ho4+uc+fO+Pnnn7FixQq0adNGXP7mzRtcuHBBo321atVgZWWFHTt24LfffkNgYCAaNmyInJwcxMfHY8WKFbC3txeDwOTJk5GdnY22bduiZs2aSE1Nxd69e3H48GH0799fHG5YGBMnToSnpyfCw8PRqlWrIl/tysnJEc8xMzMTV69exdKlS1G3bl1xwhYrKyvMnDkT48aNw4ABA9CnTx+1LxZv3LgxRo8erbHvGzduIDs7GxkZGbh//z6USiVOnjyJkJAQ+Pv7F6leFUdHRxgYGODKlSuoU6eOxvrDhw9r3ItXr149NGzYEOPGjcOcOXOQmpqK4OBgGBsbIzY2FmvXroWvry969+6ttl3t2rVRsWJFHD16VKNuJycnrF69GoIgaMxwCby9r/PWrVsYMWLEB50vERERkVQw0NFHZ2hoiGHDhmHy5Mk4c+YM3NzcALy9z61Hjx4a7ceMGYOwsDB4eXnhn3/+QVRUFJYsWYKcnBxUr14dAwcOxOeffy5+j1qfPn0QFRWFZcuW4fHjxzA2NoatrS1mzpyJoKCgItV89OhRrcM0C+vNmzfiORoYGKBq1aro1KkTRo4cqXY/W4cOHWBtbY2lS5diypQpSEtLg7W1NQYOHIjQ0FCt9+up7v8zNjZGpUqV0LhxY6xevRrNmzf/4LpNTEzQsmVLHDt2DIGBgRrrJ02apLFM9bwNHDgQderUwerVqzFu3DhkZmaiVq1aGD9+PHr37q0xbFImk0GhUODw4cPicEsVhUIBQRBgYGCAxo0baxwzNjYWxsbGGvdnEhEREX2qZIK2MWxERO84dOgQvvrqK5w8ebJAE8CUhNGjR8PU1LTIM4aqvp5BP24HXife02Vpaspa2aJh/ynFtv/SJC0tDdeuXUODBg1gYmJS0uVIHvtT99inusX+1C32p+5JpU9V70kKMl8F76EjogLx9vZGrVq1sHXr1pIuRav79+/j6NGjGD58eEmXQkRERPTRMNARUYHIZDJMnTq11F6dS0xMxPTp02Fra1vSpRARERF9NLyHjogKLPd3+5U2rq6u4sQyRERERP8WvEJHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEGJV0AEVFpU7ZSNUnvn4iIiP49GOiIiN5RK2BwsR9DyMmBTI+DJIiIiOjD8N0EEVEuGRkZeP36dbEfh2GOiIiIdIHvKIiI3iEIQkmXQERERFQgDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERE75DJZCVdAhEREVGBMNAREeViZGSEsmXLFsu+hZycYtkvERER/XsZlHQBRESlzW3lCrx++lCn+yxbqRpqBQzW6T6JiIiIGOiIiN7x+ulDvE68V9JlEBEREb0XAx0RERERkQRkZ2cjMzOzpMuQtPT0dPG/enolc/eZoaEh9PX1dbY/BjoiIiIiolJMEAQ8fPgQz58/L+lSJC8nJwcGBgb4559/SizQAUDFihVRtWpVnUzExkBHRERERFSKPX36FK9evYKlpSVMTEw4G/MHyM7ORnp6OsqUKaPTq2QFJQgC0tLSkJSUBACoVq3aB++TgY6IiIiIqBR7+fIlqlatikqVKpV0KZKXnZ0NADA2Ni6RQAdAnE07KSkJlpaWH1wHv7aAiIiIiKiUMzExKekSSIdUz6cu7olkoCMiIiIiKuU4zPLTosvnk4GOiIiIiIhIohjoiIiIiIjoowsJCUFISEihtgkPD4dcLkdycnIxVSU9nBSFiIiIiIh0IjIyEhMnThQf6+vro1KlSvDw8MDYsWNhZWVVgtV9mhjoiIiIiIhIp0aPHo0aNWogIyMDFy5cwI4dO3D+/HkolUqUKVMGALBy5coSrvLTwEBHREREREQ65enpCQcHBwBAt27dYG5ujhUrVuDgwYPo0KEDAMDIyKgkS/xk8B46IiIiIiIqVq6urgCA+/fvi8u03UMXEREBf39/NG7cGE2aNEFwcDCio6Pz3XdCQgLatGmDgIAAPHnyRPfFl3K8QkdERERERMUqISEBAFChQoU822zZsgXffvst2rVrh379+iE9PR3Xr1/HxYsX0bFjR63b3Lt3D/3794eZmRlWrVoFCwuLYqm/NGOgIyIiIiIinUpNTUVycjIyMjJw8eJFLFq0CEZGRvD29s5zmyNHjqBevXpYuHBhgY5x8+ZNDBgwAFZWVli5ciXMzMx0Vb6kMNAREREREZFODRgwQO2xtbU15s2bh6pVq+a5TYUKFfDo0SNcunQJjo6O+e7/xo0bGDt2LGxtbfHLL7+gXLlyuihbkhjoiIiIiIhIp6ZMmYJatWrh5cuX2L59O86dO/feSVAGDx6MkydPolu3bvjss8/g4eGBgIAAuLi4aLQdNmwYKleujJUrV8LU1LS4TkMSOCkKERERERHplKOjI5o3b4527dph6dKlsLOzw1dffYVXr17luU2dOnWwZ88e/Pjjj3BxccG+ffvQu3dvrUMw27Vrh3v37r13wpR/AwY6IiIiIiIqNvr6+vjyyy+RlJSEDRs25NvWxMQEHTp0wKxZs3D48GG0atUKP//8M9LT09XajR8/Hl27dsW0adP+9aGOgY6IiIiIiIqVm5sbHB0dsXbtWo1wpvLs2TO1x0ZGRqhTpw4EQUBmZqZG+xkzZqBdu3aYMGECDh48WCx1SwEDHRERERERFbvQ0FA8efIEkZGRea4fMmQIfv75Z2zduhVz5sxBREQEvLy8tE56oqenh3nz5sHDwwNffPEFTp06VdynUCox0BERERERUbFr27YtbG1tsWrVKmRnZ2us79GjB9LS0rB69WpMnz4dBw4cQEhICObPn5/nPg0NDbFw4UI4OTkhLCwMFy9eLM5TKJU4yyUREREREelEcHAwgoODta7T09PD/v37xccRERFq63v06IEePXrku/9Ro0Zh1KhRasuMjY019vVvwit0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdBIWHh4OuVwu/ri7u6Nfv374/fffNdp+++23kMvlWLx4sdZ9+fj4YPr06eLjyMhIyOVy+Pr6IisrS63tmjVrIJfLtdag7cfHx0eHZ114mZmZcHNzQ/369fHPP/9obXPhwgUMGjQIHh4ecHR0hI+PD0aPHo2LFy+qtdu5cye6du0KFxcXODs7w8/PD//973/x9OlTsU1ISEiefXHhwgUAwIMHD8Rlx44d06hny5Yt4vrc3t1f8+bNMWzYMFy/fl2tXXh4OBQKhdqyM2fOQC6XIyoqSuN4f/75J+zt7bFmzZq8uhEAcP36dSgUCiQnJ6vVtHLlyny3S0xMxJQpU+Dl5YVGjRrB09MTkydPxqNHj9TatWrVCl999ZXaMkEQ4ObmhkaNGiEjI0Nt3bfffgtnZ2dkZ2cjNTUVTZs2xfnz5/OthYiIiOhTYlDSBdCHMTY2xtq1awEAjx49wpIlSzBgwABERkbCzs4OAJCdnY3du3cDAJRKJUaMGFHg/T948AA7d+5EcHCw1vXdunVDy5Ytxcdbt26FUqkUawIAIyOjQp+XLsXGxuL58+cA3p7/kCFD1NafP38e/fr1Q8uWLTFt2jSYmpri7t27OHDgAC5duoTGjRsDAFasWIHvv/8eAwYMwOjRoyEIAm7cuIHo6GgkJSWhUqVK4j6dnZ3x9ddfa9RSr149tccmJibYtWsXPD091ZYrlUqYmJggLS1NYx8hISEICAiAIAh49OgRli1bhtDQUOzatQsVKlTIsx/c3NwQFBSEOXPmwNvbG2ZmZgDeBqapU6dCLpcjJCQkn54EfvrpJwQHB8PCwiLfdrndvHkTISEhMDY2xogRI1CzZk3cvXsXP//8Mw4dOoSIiAjUqVMHwNt+i4+PV9v+1q1bePHiBYyMjHD58mW4uLiI6+Li4uDk5AR9fX2UK1cOffv2xY8//oj169cXuD4iIiIiKWOgkzg9PT04OTmJj1VXl3799VdMmTIFAHDq1Ck8efIEzZs3x8mTJ3H16lXY29sXaP9ubm5YtmwZAgMDoa+vr7G+atWqqFq1qvj4+PHjGjUV1ps3b/DixQtYWVkVeR+5KZVKVKhQATY2NoiOjtYIdJs2bYK1tTUWL14snmOzZs3Qs2dP5OTkiO0iIiIQFBSECRMmiMu8vLwwaNAgtXYAUKFChQL1ga+vL/bv349p06ahTJkyAICkpCScO3cOAQEB2Llzp8Y21apVU9t3rVq1EBgYiPj4eHh5eeV7vPHjx+PIkSOYP38+ZsyYAeBtCL906RK2bNmi9TlWuX//Pg4fPozIyMj3nldu//nPfwC8vepYuXJlAEDTpk3h7e2NTp064T//+Y+4TxcXF8TExCAxMVF8/uPi4lC7dm1UqlQJcXFxYqBLS0vD9evXMXz4cPFYXbp0weLFi/Hnn3+ifv36haqTiIiISIo45PITU716dVhYWODBgwfiMqVSCVNTU8yePRuGhoaIjo4u8P7CwsJw9+5dxMTEFEe5Wj158gTe3t4YMmQI9u7dqzHMrjDS0tJw6NAhtGvXDsHBwfjrr780hiempKTAwsJCa5jR09NTa2dpaan1OLnbFYanpydkMhmOHj0qLtu1axdsbW0LHLpNTU0BvB1a+j4WFhb4+uuvsXXrVsTHxyM5ORnff/89+vTpg0aNGuW7bVRUFGxsbNCwYcMC1QUA586dw9WrV9GvXz8xzKlUrlwZISEhuHr1qjhMWBXW4uLixHZxcXFQKBRQKBRqyy9evIisrCy1K3bW1tZwdHQsdOgkIiKi4qe6Vadly5YaH4YDQM+ePSGXy8UPz1W3AGn7UV24ANRvd2nYsCHc3NzQs2dPLF68GM+ePdM4jrOzM1atWiU+njBhAgICArTWPHPmTLXbh1S3sMjlcty8eVOj/Y8//vjRbzlioPvEpKam4vnz52LwSE9Px759+9CmTRtYWVmhRYsWiImJ0fpLpI2dnR18fX3x888/F3ibD1WtWjX88MMPkMlk+PLLL+Hp6YmZM2fizz//LPS+Dhw4gLS0NAQEBMDPzw8GBgZQKpVqbezt7REfH4+ffvpJ6y9m7na//vortm7disePH+d7XEEQkJWVpfaTnZ2t0c7IyAht2rRRq0mpVOb5RwUAcnJykJWVhczMTDx48ADz5s2Dubk53Nzc8q1JJSgoCG5ubvjmm28wa9YsGBsb44svvnjvdidPntS4L+99zp49CwDw9vbWul71x+7cuXMA3r7eypUrl2egyz0cMy4uDgYGBuKQWBWFQoGTJ08Wqk4iIiIpyskRJHdsQ0NDPHv2TPy3XyUhIQEXLlyAiYmJxja//PILNm/erPYzePBgtTbOzs7YvHkz1q9fj9mzZ6Np06ZYt24dOnbsWKT3kO+jum3mXTExMVrPoThxyOUnQDVpyaNHjzBnzhxkZ2ejXbt2AIBDhw7h1atXYkDo2LEjDh8+jDNnzqBZs2YF2v/w4cPRpUsX7NmzBx06dCiek8hFX18f7du3R/v27fHkyRP89ttviIqKwrp162Bvb4/g4GAEBASgYsWK792XUqmElZUVmjZtCj09Pbi7u0OpVOLLL7+ETCYDAISGhuLixYtYunQpli5diooVK6JFixbo1asXXF1dxX198803GDlyJCZPngwAqFGjBry9vTFgwADUqFFD7bhHjx7VuMKmr6+PP/74Q6PGgIAAhIWF4dWrV3j69CkuX76MefPmqV21y23+/PmYP3+++LhixYpYtGgRypcv/97+UJk2bRo6duyI69evY/HixeJVvrwIgoArV66gdevWBT4G8HYyFODtlWNtVMtVk6OohuuqgltycjLu3LkDhUIBCwsLPHv2DLdv30atWrUQHx+P+vXra/zRrF+/PtatW4fU1FSUK1euUPUSERFJiZ6eDIs3nUBC0ouPelxrSzOM6OVRpG0NDQ3RrFkzxMTEqH0YHRMTg3r16mkd9WRvb//e+/ffvd3F29sbPXv2RPfu3fHFF19g165dRR5RpY2vry+USiVGjRolLrt48SL++ecf+Pn5acwJUJx4hU7i0tLSYG9vD3t7e/j6+uLMmTOYMmWKOFGJUqlEpUqV0Lx5cwBvr4iYmJgUathlo0aN4OXlhaVLl0IQdPdJkOpKk+pH2xXAypUrIzQ0FNHR0di+fTsUCgXCw8PRsmVLrZ+K5JacnIwTJ06gQ4cO4i9wx44d8c8//6jNhFiuXDmsWrUKW7duxYgRI1C/fn3s3bsXffv2xdatW8V2dnZ2UCqVWL58Ofr164fy5csjIiICnTp1wrVr19SO7eLigm3btqn9bNmyRWud7u7uMDU1xYEDB6BUKmFvb49atWrleV79+vUT97l8+XI4OTkhLCysUJ8+1axZE23btkXNmjULFNJevHiBjIyMQk2GUlQuLi64du0aXr9+jfj4eFSsWBG1a9eGubk5atasibi4OOTk5ODChQtwdnbW2N7c3ByCIKjNPEpERPSpSkh6gTsJzz7qz4cGyICAAOzdu1ftdpH3jVAqiurVq2P48OG4ffu2zkfv+Pn54d69e7h69aq4LDo6Gs2aNfso75dyY6CTOGNjY2zbtg3bt2/HoUOHcPr0afTp0wfA23u+jh49Cm9vb7x69QopKSnIzMxEy5YtsX///kLdmxYWFoa//voLBw4c0FntixcvFsOovb19nl+pALwNfy9fvsTLly+Rnp4OY2NjGBsb57v/3bt3IysrC15eXkhJSUFKSgrc3NxgZGSkNdA6Ojpi9OjRWLt2LXbv3o2qVauqXQkD3g6R9PLywn//+19ERUXhl19+wZs3bzRqL1++PBwcHNR+8rpHTV9fH35+foiJiUFMTMx7/5hVrVpV3KeXlxfCw8Ohr6+PJUuW5LvduwwNDWFoaFigtunp6QAKP2OpamKTvL4uQrU898Q6zs7OyMrKwqVLl8RZLFVXU52dnREXF4cbN27g5cuXavfPqahqfPPmTaFqJSIioo/D29sbGRkZOHHiBADg77//xvXr1/McCfbuRYCsrKwCX2Ro0aIFAIhfHaUrlpaWaNKkiXjbTE5ODvbs2QN/f3+dHqcgOORS4vT09ODg4KB1neqTD9XVnHcdOXIEbdu2LdBxnJyc4OHhgSVLliAwMPCDalbp3r07WrVqJT7WNuHI7du3ERUVhd9++w2PHj2Cu7s7ZsyYgbZt24qzQuZF9Qs2YMAAjXV79uzB5MmT8ww0NjY2aN++PVavXo0nT55oTOih0rJlS9SvXz/fe+8Kwt/fXwzihR3WamRkBBsbG9y4ceODasiPanhrSkpKobZr2rQpgLdDUN/9Tj3g7WsQAJo0aSIua9y4MQwNDREXF4e4uDi1mTudnJywZs0aMRxrC3SqGgsyJJeIiIg+vrJly8LHxwcxMTFo1aoVlEolFAoFbGxstLb38NAc3jl37twCvSetVq0aALx3/oOiCAgIwJIlSzB+/HicOXMGKSkpaNu2rcbIreLGQPcJi46OhrW1NWbNmqWx7ssvv0R0dHSBAx3w9ipdnz598v2us8KwsrLS+tUEGRkZiIyMRFRUFOLj42FtbY0uXbogODgY1tbWBdp3QkIC4uPj0bNnT42AdO3aNcyaNQvHjx+Hj49PnoHtzp07MDIyEs9XW7s3b97g4cOHqFu3bkFPWyuFQoGAgABUqlRJ7WpVQaSnp+PevXvid7kVhzJlyqB69epqs6cWRJMmTWBvb4+1a9eia9euakMQkpOTxfsic9+rWLZsWTRo0ABnzpzB1atX1SZsUSgU+Oabb3D48GHY2NigSpUqGsdMSEhA+fLlta4jIiKi0iEgIABfffUV3rx5g127duX7Xbhr1qzRuC8+r/D3LtWVPNVoH11q27Ytpk+fjvPnz0OpVMLLy6tE7t9noPtEJSYm4ty5cxg+fLjW2Q8DAgKwceNGvHz5ssCTabi6uqJp06Y4ffq0rstVk5SUhFmzZqF169YYM2YM3N3dC/1LqLo6N2jQII1feBcXFyxfvhxKpRI+Pj6YPHkysrOzxXvKUlNTsXfvXhw+fBj9+/cXh/B17NgR3t7eaNGiBSwtLZGYmIj169fj2bNn6N+/v9oxUlJStF7at7W11TquWiaTYd68eQU6t4cPH4r7Tk5OxoYNG/D8+XP07NlTrV12djb27Nmjsb2jo2Oek5Tkx9nZWW2ceG5//fWXxrFMTEzg6emJefPmISQkBN27d8fQoUPFLxZftmwZBEHQet7Ozs5Yu3Yt9PX14ejoKC6vW7cuypUrh2PHjuX5qdyVK1egUCh0euMzERER6VaLFi1gaGiIBQsW4MGDB/Dz88uzrVwuL/J9aaqJ1/IabQW8vf1F22zkwNuhlAYG2iOTaiK9HTt2YN++ffj222+LVOOHYqD7RKm+mqBz585a1wcFBWHNmjXYu3cvunbtWuD9hoWFiVPRFxdLS0vExsYWatbGdymVSjg7O2v99MbAwAABAQHYsmULXr16hT59+iAqKgrLli3D48ePYWxsDFtbW8ycORNBQUHidiNHjsThw4cxe/ZsJCcnw9zcHHK5HGvWrIG7u7vaMeLi4tCjRw+NYxd0eEB+IiIiEBERAeDtjE516tTB4sWLNSY3SU9Px5gxY3RWQ7t27TBu3Dits0dGRUUhKipKbZmtrS3279+POnXqYMeOHVi8eDEWLVqEp0+fwtzcHF5eXhg5cqTWK5IuLi5Ys2YN5HI5ypYtKy7X09ND48aNERsbq3W4ZWZmJk6dOiV+mTkRERGVToaGhmjbti3WrFmDZs2a5Ru4PkRsbCwAaJ1ITcXCwgJPnjzRui4pKSnfMOnv74/x48fDxMRE7Vaij4mBTsJGjRqlNlVqbgMHDsTAgQPz3LZ+/fpqX7B96NAhtfXBwcEIDg7W2K5Zs2YaX8xd0JoKysjIqNCTb7zrfbN4Tpo0CZMmTQLw9j441ayg+enTp494n1t+VGErPzVq1Mi3H4G39/69e//f+7ZRKcjzMHv27ALtS8Xb2xsVKlTAgQMH1D4oKEhNVlZWmD59eoGP1bZt2zz3u3Llyjy3O3HiBLKysvL9lI+IiIhKh27duuHp06fo3r17sez/n3/+wZIlS1C3bl2ND99za9KkCZYvX45z586p3defmpqKM2fOaP2QXsXX1xe+vr5wdHR87/wOxYWBjogKxNDQEIMHD8a6devyvPJb0latWoXPP//8vd+rR0RERCXP0dGxQLN0X716VWPkVvny5dXmD1Dd7iIIAl68eIH4+Hj8+uuvMDQ0xI8//pjvrRgtWrSAq6srRo4ciREjRqBevXpISkrCL7/8Aj09vXzv7zMxMcGiRYsKcLbFh4GOiAqsV69eSE1NRXJy8kf/jpX3efXqFZo2bap1VlMiIqJPlbWl2Sd/zEGDBmksa9asGdasWSM+Vt3uoqenh/Lly6NWrVro168fevfuDXNz83z3r6enh2XLlmHhwoVYvXo1kpKSUK5cObi7uyM8PFzrTOyliUzQ5TdFExFJ2OXLlwEA+nE78Drxnk73XdbKFg37T9HpPqUgLS0N165dQ4MGDWBiYlLS5Uge+1P32Ke6xf7ULVV/Ghsbo06dOhrfwZuTI0BPT/ezNxZESR77Q2RnZ+PNmzcwNjaGvr5+idXx5s0b3L59G7Vq1dL63cqq9yR5fT1ZbpwGjoiIiIhIgkoyUEkxzH2qGOiIiIiIiIgkioGOiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiISKIY6IiIiIiIiCSKgY6IiIiIiEiiGOiIiIiIiIgkioGOiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiIil14eDgUCkW+bXbu3ImePXtCoVBAoVCgR48eiIqKUmuzbds2yOVy3LlzR215REQE5HI5Fi5cqLb8+fPnqF+/PlasWKGL0yh1GOiIiIiIiCRIyMn5pI49Y8YMjB8/HnXq1MGCBQuwcOFC1KtXDxMmTMCMGTPEds7OzgCA+Ph4te3j4uJQtmxZjeXx8fEQBAEuLi46r7k0MCjpAoiIiIiIqPBkenq4rVyB108fftTjlq1UDbUCBut0nwcPHsT69esxcuRIjBo1SlzesmVLWFpaYvHixfDw8ICPjw9q164NCwsLxMXFISgoSGyrehwVFYXs7Gzo6+uLy8uUKYNGjRrptObSgoGOiIiIiEiiXj99iNeJ90q6jA+2du1amJmZYeDAgRrrQkNDsX79eqxduxY+Pj4A3l6li4uLE9v8888/ePToEfr164ctW7bg+vXraNiwIYC3ga5Ro0YwMjJCdnb2xzmhj4hDLomIiIiIqMRkZWUhPj4ebm5uMDU11VhvamoKNzc3xMfHIysrC8DbQHfz5k28ePECwNvQVq1aNdSqVQtyuVwMe5mZmbh8+fInO9wSYKAjIiIiIqIS9OzZM2RkZKBatWp5tqlWrRrS09Px/PlzAICLiwsEQcCFCxcAvL1PTjXhirOzs3gf3R9//IH09HQGOiIiIiIiotLC3t4exsbG4pW4uLg4MdA5OTmpLZfJZO+dXVPKGOiIiIiIiKjEmJubw8jICA8f5j25y8OHD1GmTBlUrFgRAGBoaAgHBwfExcXh1atXuH79uhjaFAqFeE9dXFwc6tatCzMzs49xKiWCgY6IiIiIiEqMgYEBFAoFzp49i7S0NI31aWlpOHv2LBQKBQwM/m9ORxcXF1y+fBnnz5+HkZERGjRoAACwtrZGlSpVEBcXh/j4ePFrDj5VDHRERERERFSi+vfvj+fPn2PVqlUa61atWoXnz5+jf//+asudnZ3x+vVrbNiwAQ4ODmphz9nZGb/99hseP378yQc6fm0BERERERF9FNnZ2dizZ4/GckdHR/Tt2xeLFi3Co0eP0L59ewDAvn37sGXLFvTt21f8ygIVhUIBPT09HD16FEOGDFFb5+TkhLlz5wLAJz0hCsBAR0REREQkWWUr5T0zZGk8Znp6OsaMGaOxfO7cufjf//6Hxo0bY+PGjeKXi9vZ2WH27Nno3LmzxjYVKlRA3bp18ddff2lMeqJQKCAIAiwtLWFjY1PkeqWAgY6IiIiISIKEnBzUChhcYseW6RXu7q1Ro0aJQS0vnTp1QqdOnQq8z+joaK3LFQoFrl+/Xqj6pIr30BERERERSVBhA9WncmxSx2eCiIiIiIhIohjoiIiIiIiIJIqBjoiIiIiISKIY6IiIiIiIiCSKs1wSEb2jOKaALolppYmI6NMhCEJJl0A6pMvnk4GOiOgdxTUFdFGmeCYiIgKAtLQ0lC1btqTLIB1JS0sDABgaGn7wvhjoiIhyycjIwOvXr4vlH02GOSIiKory5csjKSkJAGBiYgKZTFbCFUlXdnY20tPTAQD6+vof/fiCICAtLQ1JSUmoWLGiTmpgoCMiegeHtRARUWlSqVIlGBoaiqGOii4nJwdZWVkwMDCAXgl+0FqxYkVUrVpVJ/tioCMiIiIiKsVkMhmqVasGS0tLZGZmlnQ5kvb69WvcunULtra2JTaE1dDQUKdXBxnoiIiIiIgkQF9fv0SGCX5KcnJyAABlypSBsbFxCVejG7yhg4iIiIiISKIY6IiIiIiIiCSKgY6IiIiIiEiiZAKncyMiAgDExcVBEAQYGhpySmgdEQQBmZmZ7FMdYX/qHvtUt9ifusX+1D2p9GlGRgZkMhmcnZ3f25aTohAR/X+qP+yl+Q+81MhkMhgZGZV0GZ8M9qfusU91i/2pW+xP3ZNKn8pksgK/H+EVOiIiIiIiIoniPXREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdE/wo3b97E559/DicnJ3h4eGDu3LnIyMh473aCIGD58uVo1aoVHB0d0aNHD1y4cKH4C5aAovbphg0bMHToULi7u0Mul2PPnj0fodrSryj9mZSUhLlz5yIwMBAKhQKenp746quvkJCQ8JGqLr2K+vocN24c2rZtCycnJzRp0gR9+vRBbGzsR6i49Ctqn+a2Zs0ayOVyDB06tJiqlI6i9qePjw/kcrnGT3p6+keounT7kNdoYmIivv76a7i7u8PR0RF+fn7YuXNnMVesGwYlXQARUXF78eIF+vfvj5o1ayI8PByJiYmYPXs23rx5gylTpuS77YoVK7Bw4UKMGzcOcrkcGzZswMCBA/Hbb7/BxsbmI51B6fMhffrbb78BALy8vBAVFfURqi39itqfV69exf79+9GlSxc0btwYz549w9KlS9GtWzcolUpYWFh8xLMoPT7k9ZmZmYkBAwagZs2aSE9Px7Zt2zBkyBCsW7cOrq6uH+kMSp8P6VOVx48fY/HixahUqVIxV1v6fWh/tmvXDgMHDlRbZmRkVFzlSsKH9GlSUhJ69OiBWrVqYcaMGShXrhxu3LhR6A8sSoxARPSJ+/nnnwUnJyfh2bNn4rJff/1VaNCggfDo0aM8t3vz5o3g7OwsfP/99+Ky9PR0wdvbW/jmm2+KseLSr6h9KgiCkJ2dLQiCINy/f1+ws7MTdu/eXZylSkJR+/PFixdCZmam2rKHDx8KcrlcWLlyZXGVW+p9yOvzXVlZWYKXl5cwefJkHVcpLbro0//85z/C+PHjhb59+wpDhgwppkql4UP609vbW5g2bVoxVyg9H9Kn48aNE3r06CFkZWUVc5XFg0MuieiTd+zYMTRr1gwVK1YUl/n5+SEnJwcnTpzIc7u4uDikpqbCz89PXGZkZIQ2bdrg2LFjxVlyqVfUPgUAPT3+0/OuovZnhQoVYGCgPtimatWqsLCwQFJSUnGVW+p9yOvzXfr6+ihfvjwyMzN1XKW0fGif/v777zhw4AC++uqrYqxSOnT5GqW3itqnqamp2L17N3r37g19ff2PUKnu8V9VIvrk3bp1C7Vr11ZbVqFCBVSpUgW3bt3KdzsAGtvWqVMH//zzD968eaP7YiWiqH1K2umyP2/fvo2nT5+iTp06uixRUj60PwVBQFZWFp49e4aVK1fi7t276NGjR3GVKwkf0qfZ2dmYMWMGhg0bBktLy+IsUzI+9DUaHR2NRo0aQaFQYPDgwbh+/XpxlSoZRe3Tq1evIjMzEwYGBujbty/s7e3h4eGBefPmSeaDHN5DR0SfvJSUFFSoUEFjuZmZGV68eJHvdkZGRihTpoza8goVKkAQBLx48QLGxsY6r1cKitqnpJ2u+lMQBHz77bewtLSEv7+/LkuUlA/tz23btmHy5MkAABMTE/z4449QKBQ6r1NKPqRPN27ciNevX2PAgAHFVJ30fEh/+vj4wNHREdWrV8f9+/fx888/o3fv3oiKivpX39td1D598uQJAGDy5Mno3r07Ro4ciUuXLmHhwoXQ09OTxFVlBjoiIqJPRHh4OE6fPo1ffvkFJiYmJV2OZPn6+qJ+/fp49uwZ9uzZgy+++AKLFi2Cl5dXSZcmOU+fPsXChQsxZ86cf/2kHbqi+rABAFxdXeHh4QE/Pz+sXLkSU6dOLbnCJConJwcA0Lx5c0yYMAEA4O7ujlevXmHVqlUYMWJEqf/wlkMuieiTV6FCBbx8+VJj+YsXL2BmZpbvdhkZGRpTQaekpEAmk+W77aeuqH1K2umiP7ds2YLFixdj2rRpaNasma5LlJQP7U8LCws4ODjA09MT3333HTw9PTFv3rziKFUyitqnCxYsgFwuh6urK1JSUpCSkoKsrCxkZWWJ//9vpMu/oZaWlnBxccHVq1d1VZ4kfci/9cDbEJdbs2bNkJGRgbt37+q20GLAK3RE9MmrXbu2xvj5ly9f4vHjxxrj7d/dDnh7T1L9+vXF5bdu3UL16tVL/Sd2xamofUrafWh/7t+/H1OnTsXo0aPRtWvX4ipTMnT9+rS3t//XT4RU1D69ffs2zp07hyZNmmisa9KkCVasWAFPT0+d11va8W+o7hW1T+vWrZvvfqXw/X68QkdEnzxPT0+cPHkSKSkp4rI9e/ZAT08PHh4eeW7n7OyMcuXKYffu3eKyzMxM7Nu371/5BiS3ovYpafch/XnmzBl8+eWX6NatG0aMGFHcpUqCrl+f58+f/1ffmwQUvU8nTZqEdevWqf3Ur18fTk5OWLduHRwdHT9G+aWOLl+jiYmJOH/+PBwcHHRdpqQUtU+tra1hZ2eHkydPqi0/efIkjI2N3xv4SgNeoSOiT17Pnj0RERGBESNGYOjQoUhMTMTcuXPRs2dPWFlZie369++Pf/75B/v37wcAlClTBkOHDkV4eDgsLCxgZ2eHTZs24fnz5wgNDS2p0ykVitqnAHD58mUkJCQgOTkZAHDx4kUAb4e5NW3a9OOeSClR1P68efMmRowYgZo1ayIwMBAXLlwQ21pYWMDW1vZjn0qpUNT+PHLkCKKiotCqVStUq1YNL168gFKpRGxsLH744YeSOp1Soah92qBBA419VahQASYmJnBzc/to9Zc2Re1PpVKJw4cPw8vLC5aWlrh//z6WL18OfX19fP755yV1OqXCh/y7NHbsWISFhWHmzJlo1aoVLl++jFWrViE0NFQS9yMz0BHRJ8/MzAxr167FjBkzMGLECJiamqJr164YO3asWrucnBxkZ2erLRs8eDAEQcCqVauQnJyMBg0aYOXKlf/6T+s/pE83bNiAHTt2iI9XrVoFAGjatCkiIiKKv/hSqKj9efHiRbx8+RIvX75Er1691NoGBQVh9uzZH6X+0qao/WljY4OMjAx8//33ePbsGczNzSGXyxEREfGv/bBB5UN+50lTUfuzRo0aSEpKwnfffYeXL1+ifPnycHd3x+jRo/nv0ge8Rn18fPDDDz9gyZIl2LRpEywtLTFq1CgMGTLkY55CkckEQRBKuggiIiIiIiIqPN5DR0REREREJFEMdERERERERBLFQEdERERERCRRDHREREREREQSxUBHREREREQkUQx0REREREREEsVAR0REREREJFEMdERERERERBLFQEdEREQf5MGDB5DL5YiMjCzW4/j4+GDChAnFeoz3ycrKwty5c+Hl5YX69esjLCysROv5mMLDwyGXy5GcnKyzffr4+GDo0KHvbXfmzBnI5XKcOXNGXDZhwgT4+PiotZPL5QgPD9dZfURSYFDSBRAREVHeNmzYgOnTp8PR0RFbt24tkRoiIyMxceJEresGDx6Mnj17fuSK8ieXy8X/19fXR7ly5VCjRg04OzujZ8+eqFu3bpH3vX37dqxcuRL9+/dHw4YNUb16dV2UrCY6OhpPnz7FgAEDCtTex8cHCQkJ4mMLCwvUqlULn3/+Odq0aaPz+qQkLi4OJ06cQP/+/VGhQoWSLoeoWDDQERERlWLR0dGwtrbGpUuXcPfuXXz22WclVsvo0aNRo0YNtWV2dnZifQYGpedthYeHBwIDAyEIAlJTU/Hnn38iKioKmzZtwrhx4/D5558Xab+nT5+GlZUVJk2apOOK/49SqcSNGzcKHOgAoEGDBuI5JSUlYfPmzRg5ciSmTp2KXr16FVOlH0+TJk1w6dIlGBoa5tvu0qVL0NfXFx/Hx8dj0aJFCAoKYqCjT1bp+ctLREREau7fvy++IZ0yZQqio6MxcuTIEqvH09MTDg4OWteVKVPmI1eTv5o1ayIwMFBt2VdffYXhw4dj9uzZqF27Nry8vAq936dPn5bKYGBlZaV2vp07d0bbtm2xZs2aPANdVlYWcnJyYGRk9LHKLDI9Pb0CvcZK2+uQ6GPgPXRERESlVHR0NMzMzODl5YV27dohOjpaXJeZmYmmTZtqHQqZmpoKBwcHzJkzR1yWkJCAYcOGwcnJCc2aNcN3332H48ePa9yXVBTa7qGbMGECFAoFEhMTERYWBoVCAXd3d8yZMwfZ2dlq269cuRI9e/aEm5sbHB0dERwcjD179nxQTdqYm5vjhx9+gIGBAZYuXaq2LiMjAwsXLkSbNm3QqFEjeHl5Ye7cucjIyFA7xzNnzuDGjRuQy+VqfZeTk4M1a9bA398fDg4OaN68OaZMmYIXL15o1HH06FH07dsXCoUCzs7O6NKli/jchoSE4MiRI0hISBCP8e59YgVRpUoV1K5dWxyKqap/5cqVWLNmDVq3bg0HBwfcvHkTAHDq1Cn07t0bTk5OcHV1xfDhw8V173r27BnGjBkDZ2dnuLm54dtvv0V6erpam+3bt6Nfv35o1qwZGjVqhA4dOmDjxo151hsbG4vAwEA4ODigQ4cO2Ldvn9p6bffQaZP7Hrrw8HDMnTsXAODr6yv254MHD9C3b1906tRJ6z7atWuH0NDQfI9DVJrwCh0REVEpFR0djTZt2sDIyAgBAQHYtGkTLl26BEdHRxgaGqJ169bYv38/pk2bpnaV5cCBA8jIyECHDh0AAGlpaejfvz8eP36Mfv36oXLlylAqlYUOcqmpqRoTYlhYWOTZPjs7G6GhoXB0dMT48eNx6tQprFq1CjY2Nujdu7fYbt26dfDx8UHHjh2RmZmJmJgYjBkzBsuWLUOrVq0KVeP7VK9eHU2aNMGZM2eQmpqKcuXKIScnB8OHD8f58+fRvXt31KlTB3/99RfWrl2LO3fuYMmSJbCwsMDcuXPx888/Iy0tDV9++SUAoE6dOgCAKVOmYMeOHQgODkZISAgePHiADRs24I8//sCmTZvEoYKRkZGYNGkS6tWrh6FDh6J8+fK4du0ajh8/jo4dO2LYsGF4+fIlHj16JIZ1U1PTQp9nZmYmHj16hIoVK6otj4yMRHp6Orp37w4jIyOYmZnh5MmTGDx4MGrUqIGRI0fizZs3WL9+PXr16oXIyEiNYbZffPEFrK2t8dVXX+HChQuIiIhASkqKGJ4AYNOmTahXrx58fHxgYGCAw4cPY9q0aRAEAX369FHb3507dzB27Fj07NkTQUFB2L59O8aMGYNffvkFHh4ehT53lTZt2uDOnTtQKpWYOHEizM3NAbx9zQYGBmLy5Mn466+/YGdnJ25z6dIl3LlzB8OHDy/ycYk+NgY6IiKiUujKlSu4desW/ve//wEAXFxcULVqVURHR8PR0REA0KFDB2zfvh0nTpyAt7e3uO2uXbtgY2MjDo/cvHkz7t+/j8WLF6N169YAgJ49e6Jz586FqknbPV3Xr1/Ps316ejr8/PwwYsQIAECvXr0QFBSEbdu2qQW6vXv3wtjYWHzcp08fBAcHY/Xq1ToPdABQr149nDp1Cg8ePED9+vURHR2NkydPIiIiAq6urmrtvvnmG8TFxcHZ2RmBgYHYtm0bnj17pja88ffff8fWrVsxf/58dOzYUVzu5uaGQYMGYc+ePejYsSNevnyJb7/9Fo6OjoiIiFAbHigIAoC39/6tW7cOKSkpGkNG85OVlSWG7aSkJCxfvhxPnjxBSEiIWrtHjx5h//79akE8LCwMZmZm2Lx5sxgAW7dujaCgIISHh6td6QWAGjVqiFc4+/Tpg3LlymHjxo0YOHAg6tevDwBYv3692nPat29fhIaGYvXq1VoDXXh4ONq2bQsA6Nq1K9q3b4/58+d/UKCrX78+GjZsCKVSidatW6sF0/bt22PGjBnYuXMnxo0bJy7fuXMnTExMxFqIpIBDLomIiEqh6OhoVK5cGW5ubgAAmUyGDh06YNeuXeKQRXd3d5ibm2PXrl3idv+vvfuPibqO4zj+5FcIgQ3P0wy60eGYRpGdO+sQl43moFjQZlJmrT+s0VbrlznLwq1VrF/DaOksy7OsaUnpIXhJnhPFH8s2tzZhWggGNjUsOu9C847+YFx+OcAfaHL5emz3x933+737fu++f/Di/fm8P52dnezYsSNUnQPYtm0bY8eOJS8vL/RafHw8s2bNOq9zKisrY8WKFYbH2fSdvzV58mTa2toMr535h39nZyder5fJkyezb9++8zq/c5WYmAiAz+cDwO12k5GRgdVq5fjx46HH7bffDnDWSqbb7SY5OZmpU6cajs/KyiIxMTF0fENDAz6fj8cffzxsrldUVNSQrmn79u04HA4cDgdFRUW43W6KiooMYQVgxowZhjB39OhRGhsbue+++wzVvAkTJpCTk8PWrVvDPqtvIJszZw4A9fX1odfO/E29Xi/Hjx9nypQp/PLLL3i9XsPxY8aMMXTjTEpKori4mH379nHs2LHz+BbOXXJyMnl5edTU1ITCdCAQYOPGjeTl5YXuEZFIoAqdiIjIMBMIBKipqeG2224zhJ/s7Gw++eQTdu7cSW5uLrGxscyYMYMNGzZw6tQprrrqKjZt2sTff/9tCHTt7e1YLJaw0GCxWM7rvLKzswdsitKf+Pj4sCGZ11xzTdi8si1btrB06VIaGxtDc9Zg6CFnIH6/H/h3KGNrays///wzDoej3/07OjoGfb/W1la8Xu9Zjz906BDQU/m72G655RaeeeYZoqKiGDFiBBkZGf02b+k7fPLw4cMA3HDDDWH7ZmRksH37dvx+vyHg9O20arFYiI6ONtyrP/zwA++//z579+7lr7/+Muzv9XpJTk42vF/f3zo9PR3ouXfNZvNgl37BiouLqa2tZc+ePdjtdnbs2MFvv/12XpVRkeFAgU5ERGSY2bVrF8eOHaOmpoaampqw7dXV1eTm5gJwzz33sGbNGurr67nrrrtwu91YrdbQ0LfL6cz28QPZs2cPTzzxBHa7nUWLFmE2m4mLi6OqqooNGzZckvM6cOAAMTExoXATDAbJzMwccK29a6+9dtD3CwaDmEwm3nnnnX63DzbP8GJJSUkhJyfnrPudWTm7WPqGsUOHDvHoo49itVpZsGAB48aNIy4ujq1bt+J0OgkGgxf9HC5Ebm4uo0ePxuVyYbfbcblcmM3mc/oeRYYTBToREZFhprq6GpPJRFlZWdi2urq6UCOUESNGYLfbMZvN1NbWYrPZ2LVrF6WlpYZjUlNT+emnn+ju7jb88d1bMbqcvv32W+Lj4/n4448NjV2qqqouyecdPnyY77//nkmTJpGUlAT0VJiamppwOBwXVBW0WCzs3LkTm802aGDqrYgeOHBg0PUEL1Vlsj+9C6MfPHgwbFtzczMpKSlhww9bW1u5/vrrDc+DwWAoIHs8Hk6dOsXSpUsNC68PNHS1tbU17N5saWkBeu7doRjsu4yJiaGwsJBvvvmGefPm8d133zFr1qxz+keEyHCiOXQiIiLDSFdXF5s2bWL69Onk5+eHPR566CF8Ph8ejwfoWZ8rPz+fLVu24HK5OH36tGG4JfRUIo4cOcLmzZtDr508eZIvv/zyP722/sTExBAVFWVYyqCtrc1wrhfLH3/8wXPPPUcgEDCE3oKCAo4cOdLv99HV1RUaojmQgoICAoEAS5YsCdt2+vRp/vzzT6Dnd7j66qtZtmxZWJv/3nlcAAkJCWHzzC6VMWPGMHHiRNatWxc6T4D9+/fT0NDQ71p9n3/+ueH5qlWrgJ51CuHfyuyZ1+T1egcM6UePHqWuri70/MSJE6xbt46JEycOebhlQkJC6PP7U1RURGdnJ2VlZfj9/gGXMhAZzlShExERGUY8Hg8+n2/AtccmTZrEqFGjcLlcoeBWUFDAZ599RmVlJZmZmaFW+r1KSkpYtWoVzz//PI888ghms5nq6upQY47/siLU1x133MGKFSuYO3cuhYWFdHR08MUXX2CxWAbtoHk2LS0trF+/nu7ubnw+H01NTbjdbvx+PwsWLAiFD+j5o37jxo0sWrSI3bt3Y7PZCAQCNDc343a7Wb58+aBzB6dMmUJJSQnLli2jsbGRqVOnEhcXR0tLC263m4ULF5Kfn09SUhIvvvgiL7/8MjNnzqSwsJCRI0fS1NREV1dXqJtkVlYWtbW1lJeXc/PNN5OYmHhBa9Gdq/nz5/PYY49RUlLCzJkzQ8sWJCcn97uQfVtbG6WlpUybNo29e/ficrkoLCwMDfPtvf7S0lIeeOABfD4fX331FSaTqd8mJ+np6SxcuJAff/wRk8lEVVUVHR0dlJeXD/nasrKyAKioqODuu+8mLi6OO++8M1R1vPHGG8nMzAw1xundXySSKNCJiIgMIy6Xi/j4+AHbtUdHRzN9+nSqq6v5/fffSUlJwWazMW7cOH799dew6hz0NP9YuXIlr732Gp9++imJiYkUFxdz66238tRTT4V1XPwvORwOXn/9dT766CPeeOMN0tLSmDdvHu3t7UMKdA0NDTQ0NBAdHU1SUhJpaWkUFxdTUlLC+PHjDftGR0fzwQcf4HQ6Wb9+PXV1dSQkJJCWlsbDDz/cb8OQvl599VVuuukmVq9eTUVFBTExMaSmpnLvvfdis9lC+91///2YTCY+/PBDlixZQmxsLFar1bAkxOzZs2lsbOTrr7/G6XSSmpp6SQNdTk4Oy5cvp7KyksrKSmJjY7Hb7bzwwguGoZW9Fi9ezHvvvce7775LbGwsc+bMYf78+aHtVquVyspKFi9ezJtvvsno0aN58MEHGTVqFC+99FLY+6Wnp/PKK6/w1ltvcfDgQdLS0qioqGDatGlDvrbs7GyefvppVq9ezbZt2wgGg2zevNkwjLSoqIi3335bzVAkYkV1n1kPFxERkSuG0+mkvLyc+vp6xo4de7lPR+SyWLlyJeXl5Xg8HsOcP5FIoTl0IiIiV4Curi7D85MnT7JmzRrS09MV5uSK1d3dzdq1a7Hb7QpzErE05FJEROQK8OSTT3LdddcxYcIETpw4gcvlorm5ecBW+yL/Z36/H4/Hw+7du9m/f3+/DW1EIoWGXIqIiFwBnE4na9eupb29nUAgwPjx45k7d26/c+5E/u/a2trIy8tj5MiRzJ49m2efffZyn5LIBVOgExERERERiVCaQyciIiIiIhKhFOhEREREREQilAKdiIiIiIhIhFKgExERERERiVAKdCIiIiIiIhFKgU5ERERERCRCKdCJiIiIiIhEKAU6ERERERGRCKVAJyIiIiIiEqH+AWHfG3pdevm/AAAAAElFTkSuQmCC\n"
           },
           "metadata": {}
         },
@@ -7068,7 +7634,7 @@
             "text/plain": [
               "<Figure size 800x400 with 1 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         }
@@ -7109,7 +7675,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 27,
       "id": "c79bba33",
       "metadata": {
         "colab": {
@@ -7117,7 +7683,7 @@
           "height": 237
         },
         "id": "c79bba33",
-        "outputId": "d7870e0c-1bc2-4d85-e735-b8b4b14450db"
+        "outputId": "b4ed5007-4f34-40a7-8c33-edc4bc328bb1"
       },
       "outputs": [
         {
@@ -7133,16 +7699,16 @@
               "5               모델 성능 개선 현황 관리   \n",
               "\n",
               "                                                   값  \n",
-              "0  LightGBM 모델이 validation PR-AUC/ROC-AUC/F1 기준 종...  \n",
+              "0  RandomForest 모델이 validation PR-AUC/Recall/F1 기...  \n",
               "1  {\"max_rows\": 300000, \"profile_rows\": 50000, \"t...  \n",
               "2  {\"validation_total\": 60000, \"validation_normal...  \n",
-              "3                                             0.9908  \n",
-              "4                                           0.004542  \n",
+              "3                                            0.91395  \n",
+              "4                                           0.082851  \n",
               "5  {\"baseline_model\": \"LightGBM\", \"baseline_pr_au...  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -7169,7 +7735,7 @@
               "    <tr>\n",
               "      <th>0</th>\n",
               "      <td>모델 선정 근거 정리</td>\n",
-              "      <td>LightGBM 모델이 validation PR-AUC/ROC-AUC/F1 기준 종...</td>\n",
+              "      <td>RandomForest 모델이 validation PR-AUC/Recall/F1 기...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
@@ -7184,12 +7750,12 @@
               "    <tr>\n",
               "      <th>3</th>\n",
               "      <td>정확도(Accuracy) 측정</td>\n",
-              "      <td>0.9908</td>\n",
+              "      <td>0.91395</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
               "      <td>오탐률(False Positive Rate) 측정</td>\n",
-              "      <td>0.004542</td>\n",
+              "      <td>0.082851</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>5</th>\n",
@@ -7202,7 +7768,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -7254,12 +7820,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71 button.colab-df-convert');\n",
+              "        document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-21e6de17-1b2a-4e44-9482-7e9a9bec9b71');\n",
+              "        const element = document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -7279,7 +7845,7 @@
               "  </div>\n",
               "\n",
               "\n",
-              "  <div id=\"id_5abef95b-2b45-4721-aff9-d69655c2f66a\">\n",
+              "  <div id=\"id_8772c170-37d4-4cc2-8541-c0f3e4c01024\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -7323,7 +7889,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_5abef95b-2b45-4721-aff9-d69655c2f66a button.colab-df-generate');\n",
+              "        document.querySelector('#id_8772c170-37d4-4cc2-8541-c0f3e4c01024 button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -7340,7 +7906,7 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "ai_management_table",
-              "summary": "{\n  \"name\": \"ai_management_table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"\\uad00\\ub9ac \\ud56d\\ubaa9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\ubaa8\\ub378 \\uc120\\uc815 \\uadfc\\uac70 \\uc815\\ub9ac\",\n          \"\\ub370\\uc774\\ud130\\uc14b \\uaddc\\ubaa8 \\uad00\\ub9ac\",\n          \"\\ubaa8\\ub378 \\uc131\\ub2a5 \\uac1c\\uc120 \\ud604\\ud669 \\uad00\\ub9ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uac12\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"LightGBM \\ubaa8\\ub378\\uc774 validation PR-AUC/ROC-AUC/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. \\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378\",\n          \"{\\\"max_rows\\\": 300000, \\\"profile_rows\\\": 50000, \\\"train_rows\\\": 240000, \\\"candidate_train_rows\\\": 80000, \\\"valid_rows\\\": 60000, \\\"feature_count\\\": 457, \\\"positive_ratio\\\": 0.00565, \\\"full_data_refit\\\": false}\",\n          \"{\\\"baseline_model\\\": \\\"LightGBM\\\", \\\"baseline_pr_auc\\\": 0.05440352499791599, \\\"selected_model\\\": \\\"LightGBM\\\", \\\"selected_pr_auc\\\": 0.05440352499791599, \\\"pr_auc_improvement_over_lightgbm\\\": 0.0}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"ai_management_table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"\\uad00\\ub9ac \\ud56d\\ubaa9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\ubaa8\\ub378 \\uc120\\uc815 \\uadfc\\uac70 \\uc815\\ub9ac\",\n          \"\\ub370\\uc774\\ud130\\uc14b \\uaddc\\ubaa8 \\uad00\\ub9ac\",\n          \"\\ubaa8\\ub378 \\uc131\\ub2a5 \\uac1c\\uc120 \\ud604\\ud669 \\uad00\\ub9ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uac12\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"RandomForest \\ubaa8\\ub378\\uc774 validation PR-AUC/Recall/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\",\n          \"{\\\"max_rows\\\": 300000, \\\"profile_rows\\\": 50000, \\\"train_rows\\\": 240000, \\\"candidate_train_rows\\\": 120000, \\\"valid_rows\\\": 60000, \\\"feature_count\\\": 457, \\\"positive_ratio\\\": 0.00565, \\\"full_data_refit\\\": false}\",\n          \"{\\\"baseline_model\\\": \\\"LightGBM\\\", \\\"baseline_pr_auc\\\": 0.050323296178874356, \\\"selected_model\\\": \\\"RandomForest\\\", \\\"selected_pr_auc\\\": 0.06255052428644334, \\\"pr_auc_improvement_over_lightgbm\\\": 0.012227228107568981}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {}
@@ -7371,14 +7937,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 28,
       "id": "44a36633",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "44a36633",
-        "outputId": "6fdbeebe-e939-4d3c-b15c-1c3d644ebfcf"
+        "outputId": "9dbc6133-c938-4715-dc60-ea025f990e55"
       },
       "outputs": [
         {
@@ -7470,6 +8036,17 @@
         "    \"feature_count\": int(len(feature_cols)),\n",
         "    \"positive_ratio_train\": float(y_train.mean()),\n",
         "    \"candidate_models\": list(trained_models.keys()),\n",
+        "    \"cv_splits\": int(effective_cv_splits),\n",
+        "    \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
+        "    \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+        "    \"smote_enabled\": bool(USE_SMOTE),\n",
+        "    \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
+        "    \"threshold_strategy\": {\n",
+        "        \"type\": \"recall_priority\",\n",
+        "        \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
+        "        \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
+        "        \"beta\": float(THRESHOLD_BETA),\n",
+        "    },\n",
         "    \"model_selection_reason\": ai_management_summary[\"모델_선정_근거\"],\n",
         "    \"auxiliary_metrics\": auxiliary_metrics,\n",
         "    \"auxiliary_feature_columns\": [c for c in feature_cols if c.startswith(\"aux_\")],\n",
@@ -7479,6 +8056,8 @@
         "\n",
         "# 분석 결과 저장\n",
         "cv_results.to_csv(cv_results_path, index=False)\n",
+        "if \"cv_fold_results\" in globals():\n",
+        "    cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
         "model_comparison.to_csv(comparison_path, index=False)\n",
         "importance_df.to_csv(importance_path, index=False)\n",
         "all_model_importance.to_csv(all_importance_path, index=False)\n",
diff --git a/app/ml/training/defect_transfer_prediction_model_json.ipynb b/app/ml/training/defect_transfer_prediction_model_json.ipynb
new file mode 100644
index 0000000..c31361c
--- /dev/null
+++ b/app/ml/training/defect_transfer_prediction_model_json.ipynb
@@ -0,0 +1,2162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "873328b3",
+   "metadata": {
+    "id": "873328b3"
+   },
+   "source": [
+    "# event_json 기반 SHAP 공정 전이 불량 예측\n",
+    "\n",
+    "1. `manufacturing_event_json.csv`의 `event_json` 파싱\n",
+    "2. `sourceTrace`로 원본 Bosch/Ford/열화상 라벨 복원\n",
+    "3. 동일 차량의 `PRESS → BODY → PAINT → ASSEMBLY` 이벤트 연결\n",
+    "4. 대상 공정 이전 JSON 특성으로 다음 공정 불량 학습\n",
+    "5. 후보 모델 비교 및 SHAP 기반 공정별 영향 분석\n",
+    "6. 공정 전이 위험 결과와 모델 산출물 저장\n",
+    "\n",
+    "> CSV의 13번째 컬럼은 `is_sent`이며 불량 라벨이 아닙니다. 불량 정답은 JSON의\n",
+    "> `sourceTrace`가 가리키는 원본 데이터셋에서 학습 시점에만 복원합니다.\n",
+    ">\n",
+    "> 현재 이벤트는 서로 다른 공개 데이터셋을 조합한 합성 이벤트이므로 결과는\n",
+    "> **합성 데이터 기반 공정 전이 위험 예측**으로 해석합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "P44DGP2TDAt4",
+   "metadata": {
+    "id": "P44DGP2TDAt4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hMAQMhIqbEe9",
+   "metadata": {
+    "id": "hMAQMhIqbEe9"
+   },
+   "outputs": [],
+   "source": [
+    "# Colab 환경에서 필요한 패키지 설치\n",
+    "import importlib.util\n",
+    "import subprocess\n",
+    "import sys\n",
+    "\n",
+    "def ensure_package(import_name: str, pip_name: str | None = None):\n",
+    "    pip_name = pip_name or import_name\n",
+    "    if importlib.util.find_spec(import_name) is None:\n",
+    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", pip_name, \"-q\"])\n",
+    "\n",
+    "ensure_package(\"lightgbm\")\n",
+    "ensure_package(\"shap\")\n",
+    "ensure_package(\"joblib\")\n",
+    "ensure_package(\"optuna\")\n",
+    "ensure_package(\"imblearn\", \"imbalanced-learn\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "XFBnJXPabHMi",
+   "metadata": {
+    "id": "XFBnJXPabHMi"
+   },
+   "outputs": [],
+   "source": [
+    "# CPU 병렬 사용\n",
+    "import lightgbm as lgb\n",
+    "\n",
+    "model = lgb.LGBMClassifier(\n",
+    "    n_estimators=300,\n",
+    "    learning_rate=0.05,\n",
+    "    num_leaves=31,\n",
+    "    n_jobs=-1,\n",
+    "    random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7a3d724",
+   "metadata": {
+    "id": "f7a3d724"
+   },
+   "source": [
+    "> 필요한 추가 패키지(`optuna`, `imbalanced-learn`)는 위 설치 셀에서 자동 확인/설치합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e37daef5",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "e37daef5",
+    "outputId": "d3014dc5-ad21-4f08-d5e8-65c43e05146f"
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import gc\n",
+    "import json\n",
+    "import pickle\n",
+    "import re\n",
+    "import shutil\n",
+    "import warnings\n",
+    "from datetime import datetime, timezone\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import joblib\n",
+    "import lightgbm as lgb\n",
+    "import optuna\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import shap\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
+    "from optuna.samplers import TPESampler\n",
+    "from sklearn.base import clone\n",
+    "from sklearn.ensemble import ExtraTreesClassifier, IsolationForest, RandomForestClassifier\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import (\n",
+    "    ConfusionMatrixDisplay,\n",
+    "    PrecisionRecallDisplay,\n",
+    "    RocCurveDisplay,\n",
+    "    accuracy_score,\n",
+    "    average_precision_score,\n",
+    "    classification_report,\n",
+    "    confusion_matrix,\n",
+    "    f1_score,\n",
+    "    precision_score,\n",
+    "    precision_recall_curve,\n",
+    "    recall_score,\n",
+    "    roc_auc_score,\n",
+    ")\n",
+    "from sklearn.model_selection import (GroupShuffleSplit, ParameterSampler, StratifiedGroupKFold, train_test_split)\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "pd.set_option(\"display.max_columns\", 120)\n",
+    "pd.set_option(\"display.max_rows\", 80)\n",
+    "\n",
+    "RANDOM_STATE = 42\n",
+    "np.random.seed(RANDOM_STATE)\n",
+    "\n",
+    "print(\"pandas\", pd.__version__)\n",
+    "print(\"lightgbm\", lgb.__version__)\n",
+    "print(\"optuna\", optuna.__version__)\n",
+    "print(\"shap\", shap.__version__)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e3e0e4c",
+   "metadata": {
+    "id": "0e3e0e4c"
+   },
+   "source": [
+    "## 1. 데이터셋 및 산출물 경로 설정\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abb8856c",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "abb8856c",
+    "outputId": "d0b5cf16-76a8-4d8e-cf3f-ce1f188f80a2"
+   },
+   "outputs": [],
+   "source": [
+    "# Drive 마운트\n",
+    "try:\n",
+    "    from google.colab import drive\n",
+    "    drive.mount(\"/content/drive\")\n",
+    "    IN_COLAB = True\n",
+    "except Exception:\n",
+    "    IN_COLAB = False\n",
+    "    print(\"Colab이 아니므로 Google Drive mount를 건너뜁니다.\")\n",
+    "\n",
+    "PROJECT_ROOT = Path(\"/content/drive/MyDrive\") if IN_COLAB else Path.cwd()\n",
+    "PROCESS_ROOT = (\n",
+    "    PROJECT_ROOT / \"aims_dataset\"\n",
+    "    if IN_COLAB\n",
+    "    else PROJECT_ROOT / \"app\" / \"ml\" / \"datasets\" / \"process\"\n",
+    ")\n",
+    "OUTPUT_DIR = Path(\"/content/defect_transfer_outputs\") if IN_COLAB else Path(\"outputs/defect_transfer\")\n",
+    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "EVENT_JSON_PATH = PROCESS_ROOT / \"manufacturing_event_json.csv\"\n",
+    "BOSCH_ROOT = PROCESS_ROOT / \"bosch-production-line-performance\"\n",
+    "THERMAL_ROOT = PROCESS_ROOT / \"머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)\"\n",
+    "FORD_ROOT = PROCESS_ROOT / \"Ford 엔진 진동 데이터셋\"\n",
+    "\n",
+    "NUMERIC_PATH = BOSCH_ROOT / \"train_numeric.csv\"\n",
+    "THERMAL_LEFT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_left_label.json\"\n",
+    "THERMAL_RIGHT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_right_label.json\"\n",
+    "FORD_TRAIN_PATH = FORD_ROOT / \"FordA_TRAIN.txt\"\n",
+    "FORD_TRAIN_ARFF_PATH = FORD_ROOT / \"FordA_TRAIN.arff\"\n",
+    "\n",
+    "print(\"PROJECT_ROOT:\", PROJECT_ROOT)\n",
+    "print(\"PROCESS_ROOT:\", PROCESS_ROOT)\n",
+    "print(\"EVENT_JSON_PATH:\", EVENT_JSON_PATH)\n",
+    "print(\"OUTPUT_DIR:\", OUTPUT_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lc2ZXZzwbEe_",
+   "metadata": {
+    "id": "lc2ZXZzwbEe_"
+   },
+   "source": [
+    "## 2. 대용량 event_json 로드 설정\n",
+    "\n",
+    "CSV는 헤더가 없는 16컬럼 테이블이며 12번째 컬럼이 `event_json`, 13번째 컬럼이\n",
+    "`is_sent`입니다. 전체 파일을 메모리에 올리지 않고 chunk 단위로 읽습니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66fcea04",
+   "metadata": {
+    "id": "66fcea04"
+   },
+   "outputs": [],
+   "source": [
+    "# 학습 규모 설정\n",
+    "MAX_ROWS = 300_000\n",
+    "EVENT_CHUNK_SIZE = 20_000\n",
+    "ENABLE_HYPERPARAMETER_TUNING = True\n",
+    "CV_N_SPLITS = 5\n",
+    "CV_N_ITER = 15\n",
+    "ENABLE_OPTUNA_TUNING = True\n",
+    "CV_SAMPLE_SIZE = 30_000\n",
+    "FINAL_TRAIN_SAMPLE_SIZE = 120_000\n",
+    "RETRAIN_SELECTED_MODEL_ON_FULL_DATA = False\n",
+    "USE_SMOTE = False\n",
+    "SMOTE_SAMPLING_STRATEGY = 0.50\n",
+    "RECALL_PRIORITY_TARGET = 0.35\n",
+    "RECALL_PRIORITY_MIN_PRECISION = 0.02\n",
+    "THRESHOLD_BETA = 2.0\n",
+    "\n",
+    "PROCESS_FLOW = [\"PRESS\", \"BODY\", \"PAINT\", \"ASSEMBLY\"]\n",
+    "TRANSITIONS = {\n",
+    "    \"BODY\": \"PRESS\",\n",
+    "    \"PAINT\": \"BODY\",\n",
+    "    \"ASSEMBLY\": \"PAINT\",\n",
+    "}\n",
+    "PROCESS_DISPLAY = {\n",
+    "    \"PRESS\": \"프레스\",\n",
+    "    \"BODY\": \"차체\",\n",
+    "    \"PAINT\": \"도장\",\n",
+    "    \"ASSEMBLY\": \"의장\",\n",
+    "    \"FINAL_INSPECTION\": \"최종검사\",\n",
+    "    \"UNKNOWN\": \"미분류\",\n",
+    "}\n",
+    "\n",
+    "EVENT_COLUMNS = [\n",
+    "    \"id\", \"event_id\", \"event_time\", \"car_master_id\", \"equipment_id\",\n",
+    "    \"process_code\", \"station_code\", \"equipment_code\", \"equipment_type\",\n",
+    "    \"equipment_status\", \"event_type\", \"event_json\", \"is_sent\", \"sent_at\",\n",
+    "    \"created_at\", \"updated_at\",\n",
+    "]\n",
+    "\n",
+    "# Colab Drive FUSE가 불안정할 수 있어 데이터셋을 런타임 캐시에 복사합니다.\n",
+    "LOCAL_DATA_CACHE = Path(\"/content/aims_dataset_cache\") if IN_COLAB else None\n",
+    "\n",
+    "\n",
+    "def local_dataset_path(path: Path) -> Path:\n",
+    "    path = Path(path)\n",
+    "    if not IN_COLAB or LOCAL_DATA_CACHE is None:\n",
+    "        return path\n",
+    "\n",
+    "    try:\n",
+    "        relative_path = path.relative_to(PROJECT_ROOT)\n",
+    "    except ValueError:\n",
+    "        relative_path = Path(path.name)\n",
+    "\n",
+    "    cached_path = LOCAL_DATA_CACHE / relative_path\n",
+    "    if cached_path.exists():\n",
+    "        try:\n",
+    "            if cached_path.stat().st_size == path.stat().st_size:\n",
+    "                return cached_path\n",
+    "        except OSError:\n",
+    "            return cached_path\n",
+    "\n",
+    "    cached_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    print(f\"cache copy: {path} -> {cached_path}\")\n",
+    "    shutil.copy2(path, cached_path)\n",
+    "    return cached_path\n",
+    "\n",
+    "\n",
+    "def assert_file(path: Path) -> None:\n",
+    "    if not path.exists():\n",
+    "        cached_path = local_dataset_path(path) if IN_COLAB else path\n",
+    "        if not cached_path.exists():\n",
+    "            raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
+    "\n",
+    "\n",
+    "def read_json_stable(path: Path):\n",
+    "    with open(local_dataset_path(path), \"r\", encoding=\"utf-8\") as file:\n",
+    "        return json.load(file)\n",
+    "\n",
+    "\n",
+    "def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    for col in df.columns:\n",
+    "        if pd.api.types.is_integer_dtype(df[col]):\n",
+    "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
+    "        elif pd.api.types.is_float_dtype(df[col]):\n",
+    "            df[col] = pd.to_numeric(df[col], downcast=\"float\")\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "508b9115",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 290
+    },
+    "id": "508b9115",
+    "outputId": "eaef9a23-f57f-4c4d-85a1-69e744c2cc71"
+   },
+   "outputs": [],
+   "source": [
+    "# 원본 데이터셋의 정답 라벨 로드\n",
+    "def load_ford_label_map() -> dict[int, int]:\n",
+    "    labels = {}\n",
+    "    path = local_dataset_path(FORD_TRAIN_PATH)\n",
+    "    if path.exists():\n",
+    "        with path.open(encoding=\"utf-8\", errors=\"ignore\") as file:\n",
+    "            for line in file:\n",
+    "                values = line.replace(\"\\x00\", \" \").strip().split()\n",
+    "                if len(values) >= 501:\n",
+    "                    labels[len(labels) + 1] = int(float(values[0]))\n",
+    "                if len(labels) >= 4096:\n",
+    "                    break\n",
+    "\n",
+    "    if labels:\n",
+    "        return labels\n",
+    "\n",
+    "    # TXT가 손상되었거나 비어 있으면 ARFF의 마지막 class 값을 사용합니다.\n",
+    "    path = local_dataset_path(FORD_TRAIN_ARFF_PATH)\n",
+    "    in_data = False\n",
+    "    with path.open(encoding=\"utf-8\", errors=\"ignore\") as file:\n",
+    "        for line in file:\n",
+    "            value = line.strip()\n",
+    "            if not value or value.startswith(\"%\"):\n",
+    "                continue\n",
+    "            if value.lower() == \"@data\":\n",
+    "                in_data = True\n",
+    "                continue\n",
+    "            if not in_data:\n",
+    "                continue\n",
+    "            parts = value.split(\",\")\n",
+    "            if len(parts) >= 501:\n",
+    "                labels[len(labels) + 1] = int(float(parts[-1]))\n",
+    "            if len(labels) >= 4096:\n",
+    "                break\n",
+    "    return labels\n",
+    "\n",
+    "\n",
+    "def load_vision_label_map() -> dict[tuple[str, int], int]:\n",
+    "    labels = {}\n",
+    "    for side, path in [\n",
+    "        (\"LEFT\", THERMAL_LEFT_LABEL_PATH),\n",
+    "        (\"RIGHT\", THERMAL_RIGHT_LABEL_PATH),\n",
+    "    ]:\n",
+    "        for row_id, label in enumerate(read_json_stable(path), start=1):\n",
+    "            labels[(side, row_id)] = int(float(label))\n",
+    "    return labels\n",
+    "\n",
+    "\n",
+    "def load_bosch_label_map() -> dict[int, int]:\n",
+    "    df = pd.read_csv(\n",
+    "        local_dataset_path(NUMERIC_PATH),\n",
+    "        usecols=[\"Id\", \"Response\"],\n",
+    "        nrows=4096,\n",
+    "    )\n",
+    "    return dict(zip(df[\"Id\"].astype(int), df[\"Response\"].astype(int)))\n",
+    "\n",
+    "\n",
+    "for required_path in [\n",
+    "    EVENT_JSON_PATH,\n",
+    "    NUMERIC_PATH,\n",
+    "    THERMAL_LEFT_LABEL_PATH,\n",
+    "    THERMAL_RIGHT_LABEL_PATH,\n",
+    "]:\n",
+    "    assert_file(required_path)\n",
+    "\n",
+    "ford_label_map = load_ford_label_map()\n",
+    "vision_label_map = load_vision_label_map()\n",
+    "bosch_label_map = load_bosch_label_map()\n",
+    "\n",
+    "print(\"Ford labels:\", len(ford_label_map), pd.Series(ford_label_map).value_counts().to_dict())\n",
+    "print(\"Vision labels:\", len(vision_label_map), pd.Series(vision_label_map).value_counts().to_dict())\n",
+    "print(\"Bosch labels:\", len(bosch_label_map), pd.Series(bosch_label_map).value_counts().to_dict())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11973c36",
+   "metadata": {
+    "id": "11973c36"
+   },
+   "source": [
+    "## 3. event_json 특성 생성\n",
+    "\n",
+    "동일 차량의 공정 이벤트를 순서대로 누적하고 대상 공정 직전까지의 JSON만 입력으로\n",
+    "사용합니다. `visionLabel`, `healthStatus`, 조립 오류 수처럼 원본 라벨에서 직접 생성된\n",
+    "필드는 입력에서 제외합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd4b603e",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 273
+    },
+    "id": "dd4b603e",
+    "outputId": "84ce5bbe-1ecd-490a-884b-4e42c0252e7f"
+   },
+   "outputs": [],
+   "source": [
+    "LEAKAGE_JSON_PATHS = {\n",
+    "    \"equipmentStatus.healthStatus\",\n",
+    "    \"equipmentStatus.operationStatus\",\n",
+    "    \"processData.paint.visionLabel\",\n",
+    "    \"processData.paint.defectScore\",\n",
+    "    \"processData.paint.surfaceQualityScore\",\n",
+    "    \"processData.body.robotMotionStatus\",\n",
+    "    \"processData.assembly.actualSequence\",\n",
+    "    \"processData.assembly.missingPartCount\",\n",
+    "    \"processData.assembly.fasteningErrorCount\",\n",
+    "    \"processData.assembly.sequenceErrorCount\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def add_numeric_features(output: dict, prefix: str, values: dict) -> None:\n",
+    "    for key, value in values.items():\n",
+    "        feature_name = f\"{prefix}{key}\"\n",
+    "        if isinstance(value, bool):\n",
+    "            output[feature_name] = int(value)\n",
+    "        elif isinstance(value, (int, float)) and not isinstance(value, bool):\n",
+    "            output[feature_name] = float(value)\n",
+    "\n",
+    "\n",
+    "def extract_event_features(payload: dict) -> dict[str, float]:\n",
+    "    process_code = payload.get(\"location\", {}).get(\"processCode\", \"UNKNOWN\")\n",
+    "    prefix = f\"{process_code}__\"\n",
+    "    features = {}\n",
+    "\n",
+    "    sensor = payload.get(\"sensor\", {})\n",
+    "    for section in [\"current\", \"vibration\", \"robotArmVibration\", \"thermal\"]:\n",
+    "        add_numeric_features(features, f\"{prefix}sensor_{section}_\", sensor.get(section, {}))\n",
+    "\n",
+    "    add_numeric_features(features, f\"{prefix}metrics_\", payload.get(\"processMetrics\", {}))\n",
+    "    add_numeric_features(features, f\"{prefix}manufacturing_\", payload.get(\"manufacturing\", {}))\n",
+    "    add_numeric_features(features, f\"{prefix}product_\", payload.get(\"product\", {}))\n",
+    "\n",
+    "    process_data = payload.get(\"processData\", {})\n",
+    "    if process_code == \"PRESS\":\n",
+    "        add_numeric_features(features, f\"{prefix}process_\", process_data.get(\"press\", {}))\n",
+    "    elif process_code == \"BODY\":\n",
+    "        add_numeric_features(\n",
+    "            features,\n",
+    "            f\"{prefix}frequency_\",\n",
+    "            process_data.get(\"body\", {}).get(\"frequencyBands\", {}),\n",
+    "        )\n",
+    "    elif process_code == \"PAINT\":\n",
+    "        paint = process_data.get(\"paint\", {})\n",
+    "        for key in [\"thermalStdTemp\", \"thicknessValue\"]:\n",
+    "            if isinstance(paint.get(key), (int, float)):\n",
+    "                features[f\"{prefix}process_{key}\"] = float(paint[key])\n",
+    "\n",
+    "    station_code = str(payload.get(\"location\", {}).get(\"stationCode\", \"\"))\n",
+    "    station_match = re.search(r\"_(\\d+)$\", station_code)\n",
+    "    if station_match:\n",
+    "        features[f\"{prefix}station_number\"] = float(station_match.group(1))\n",
+    "    return features\n",
+    "\n",
+    "\n",
+    "def restore_target_label(payload: dict, target_process: str) -> tuple[int | None, str, int | None]:\n",
+    "    trace = payload.get(\"sourceTrace\", {})\n",
+    "    if target_process == \"BODY\":\n",
+    "        row_id = int(trace.get(\"fordRowId\", 0) or 0)\n",
+    "        raw_label = ford_label_map.get(row_id)\n",
+    "        return (int(raw_label < 0), \"FordA.label\", row_id) if raw_label is not None else (None, \"FordA.label\", row_id)\n",
+    "\n",
+    "    if target_process == \"PAINT\":\n",
+    "        row_id = int(trace.get(\"machineVisionRowId\", 0) or 0)\n",
+    "        side = str(payload.get(\"processData\", {}).get(\"paint\", {}).get(\"imagePosition\", \"\")).upper()\n",
+    "        raw_label = vision_label_map.get((side, row_id))\n",
+    "        return (int(raw_label), f\"thermal_{side.lower()}.label\", row_id) if raw_label is not None else (None, \"thermal.label\", row_id)\n",
+    "\n",
+    "    if target_process == \"ASSEMBLY\":\n",
+    "        row_id = int(trace.get(\"boschId\", 0) or 0)\n",
+    "        raw_label = bosch_label_map.get(row_id)\n",
+    "        return (int(raw_label), \"Bosch.Response\", row_id) if raw_label is not None else (None, \"Bosch.Response\", row_id)\n",
+    "\n",
+    "    return None, \"unsupported\", None\n",
+    "\n",
+    "\n",
+    "def build_transition_dataset(path: Path, max_rows: int | None) -> tuple[pd.DataFrame, pd.DataFrame]:\n",
+    "    feature_rows = []\n",
+    "    metadata_rows = []\n",
+    "    vehicle_state: dict[int, dict[str, dict[str, float]]] = {}\n",
+    "    parsed_rows = 0\n",
+    "    invalid_json_rows = 0\n",
+    "    missing_label_rows = 0\n",
+    "\n",
+    "    reader = pd.read_csv(\n",
+    "        local_dataset_path(path),\n",
+    "        header=None,\n",
+    "        names=EVENT_COLUMNS,\n",
+    "        usecols=[\"event_id\", \"event_time\", \"car_master_id\", \"event_json\"],\n",
+    "        chunksize=EVENT_CHUNK_SIZE,\n",
+    "        nrows=max_rows,\n",
+    "        dtype={\"event_id\": \"string\", \"car_master_id\": \"int64\", \"event_json\": \"string\"},\n",
+    "    )\n",
+    "\n",
+    "    for chunk in reader:\n",
+    "        for row in chunk.itertuples(index=False):\n",
+    "            parsed_rows += 1\n",
+    "            try:\n",
+    "                payload = json.loads(row.event_json)\n",
+    "            except (TypeError, json.JSONDecodeError):\n",
+    "                invalid_json_rows += 1\n",
+    "                continue\n",
+    "\n",
+    "            car_master_id = int(row.car_master_id)\n",
+    "            process_code = str(payload.get(\"location\", {}).get(\"processCode\", \"UNKNOWN\"))\n",
+    "            if process_code not in PROCESS_FLOW:\n",
+    "                continue\n",
+    "\n",
+    "            if process_code == \"PRESS\" or car_master_id not in vehicle_state:\n",
+    "                vehicle_state[car_master_id] = {}\n",
+    "            history = vehicle_state[car_master_id]\n",
+    "\n",
+    "            source_process = TRANSITIONS.get(process_code)\n",
+    "            if source_process and source_process in history:\n",
+    "                target_label, label_source, source_row_id = restore_target_label(payload, process_code)\n",
+    "                if target_label is None:\n",
+    "                    missing_label_rows += 1\n",
+    "                else:\n",
+    "                    features = {}\n",
+    "                    target_index = PROCESS_FLOW.index(process_code)\n",
+    "                    for history_process in PROCESS_FLOW[:target_index]:\n",
+    "                        features.update(history.get(history_process, {}))\n",
+    "                    for target_name in TRANSITIONS:\n",
+    "                        features[f\"transition_to_{target_name}\"] = int(process_code == target_name)\n",
+    "                    features[\"history_process_count\"] = float(len(history))\n",
+    "\n",
+    "                    feature_rows.append(features)\n",
+    "                    metadata_rows.append({\n",
+    "                        \"sample_event_id\": str(row.event_id),\n",
+    "                        \"event_time\": str(row.event_time),\n",
+    "                        \"car_master_id\": car_master_id,\n",
+    "                        \"source_process_code\": source_process,\n",
+    "                        \"target_process_code\": process_code,\n",
+    "                        \"target_defect_label\": int(target_label),\n",
+    "                        \"label_source\": label_source,\n",
+    "                        \"label_source_row_id\": source_row_id,\n",
+    "                    })\n",
+    "\n",
+    "            history[process_code] = extract_event_features(payload)\n",
+    "            if process_code == \"ASSEMBLY\":\n",
+    "                vehicle_state.pop(car_master_id, None)\n",
+    "\n",
+    "    feature_df = reduce_mem_usage(pd.DataFrame(feature_rows))\n",
+    "    metadata_df = pd.DataFrame(metadata_rows)\n",
+    "    print(\n",
+    "        f\"event rows={parsed_rows:,}, transitions={len(feature_df):,}, \"\n",
+    "        f\"invalid_json={invalid_json_rows:,}, missing_label={missing_label_rows:,}\"\n",
+    "    )\n",
+    "    return feature_df, metadata_df\n",
+    "\n",
+    "\n",
+    "features, transition_meta = build_transition_dataset(EVENT_JSON_PATH, MAX_ROWS)\n",
+    "if features.empty:\n",
+    "    raise ValueError(\"복원 가능한 공정 전이 학습 행이 없습니다.\")\n",
+    "\n",
+    "model_df = pd.concat(\n",
+    "    [transition_meta.reset_index(drop=True), features.reset_index(drop=True)],\n",
+    "    axis=1,\n",
+    ")\n",
+    "model_df = model_df.replace([np.inf, -np.inf], np.nan)\n",
+    "\n",
+    "print(\"model_df:\", model_df.shape)\n",
+    "display(model_df.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6f30d6e",
+   "metadata": {
+    "id": "e6f30d6e"
+   },
+   "source": [
+    "## 4. 라벨 복원 및 누수 검증\n",
+    "\n",
+    "복원된 정답의 출처와 전이별 클래스 분포를 확인합니다. 대상 공정 JSON은 입력 특성에\n",
+    "포함하지 않으므로 예측 시점은 대상 공정 시작 전입니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2a68fb3",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 698
+    },
+    "id": "a2a68fb3",
+    "outputId": "65f72cce-fd5b-4c16-9900-3b43b4e0e289"
+   },
+   "outputs": [],
+   "source": [
+    "label_audit = transition_meta.groupby(\n",
+    "    [\"source_process_code\", \"target_process_code\", \"label_source\", \"target_defect_label\"]\n",
+    ").size().rename(\"rows\").reset_index()\n",
+    "display(label_audit)\n",
+    "\n",
+    "transition_summary = transition_meta.groupby(\n",
+    "    [\"source_process_code\", \"target_process_code\"]\n",
+    ").agg(\n",
+    "    rows=(\"target_defect_label\", \"size\"),\n",
+    "    positive_rows=(\"target_defect_label\", \"sum\"),\n",
+    "    positive_ratio=(\"target_defect_label\", \"mean\"),\n",
+    "    vehicles=(\"car_master_id\", \"nunique\"),\n",
+    ").reset_index()\n",
+    "display(transition_summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76645e9c",
+   "metadata": {
+    "id": "76645e9c"
+   },
+   "source": [
+    "### 4.1 입력 특성 누수 점검\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "144bc123",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "144bc123",
+    "outputId": "b559b72a-3520-49e8-ef80-8add5e66a562"
+   },
+   "outputs": [],
+   "source": [
+    "def feature_process_prefix(feature: str) -> str | None:\n",
+    "    for process_code in PROCESS_FLOW:\n",
+    "        if feature.startswith(f\"{process_code}__\"):\n",
+    "            return process_code\n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "invalid_target_features = []\n",
+    "for target_process in TRANSITIONS:\n",
+    "    target_rows = transition_meta[\"target_process_code\"].eq(target_process)\n",
+    "    target_columns = [\n",
+    "        column for column in features.columns\n",
+    "        if column.startswith(f\"{target_process}__\")\n",
+    "    ]\n",
+    "    if target_columns:\n",
+    "        populated = features.loc[target_rows, target_columns].notna().any()\n",
+    "        invalid_target_features.extend(populated[populated].index.tolist())\n",
+    "\n",
+    "if invalid_target_features:\n",
+    "    raise ValueError(\n",
+    "        f\"대상 공정 feature가 입력에 포함되었습니다: \"\n",
+    "        f\"{sorted(set(invalid_target_features))[:10]}\"\n",
+    "    )\n",
+    "\n",
+    "for forbidden_name in [\n",
+    "    \"visionLabel\", \"defectScore\", \"surfaceQualityScore\", \"robotMotionStatus\",\n",
+    "    \"healthStatus\", \"operationStatus\", \"actualSequence\", \"ErrorCount\",\n",
+    "]:\n",
+    "    matched = [\n",
+    "        column for column in features.columns\n",
+    "        if forbidden_name.lower() in column.lower()\n",
+    "    ]\n",
+    "    if matched:\n",
+    "        raise ValueError(\n",
+    "            f\"라벨 누수 의심 feature 발견({forbidden_name}): {matched[:10]}\"\n",
+    "        )\n",
+    "\n",
+    "print(\"대상 공정 및 라벨 파생 feature 누수 점검 통과\")\n",
+    "print(\"feature count:\", features.shape[1])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a97efe99",
+   "metadata": {
+    "id": "a97efe99"
+   },
+   "source": [
+    "### 4.2 학습 데이터 품질 확인\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd25bfc8",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 216
+    },
+    "id": "cd25bfc8",
+    "outputId": "73d3b745-218c-4f61-96fa-0022c8136bcb"
+   },
+   "outputs": [],
+   "source": [
+    "if transition_meta[\"target_defect_label\"].nunique() < 2:\n",
+    "    raise ValueError(\"복원된 불량 라벨이 한 클래스만 포함되어 있습니다. MAX_ROWS를 늘려주세요.\")\n",
+    "\n",
+    "missing_ratio = features.isna().mean().sort_values(ascending=False)\n",
+    "display(missing_ratio.rename(\"missing_ratio\").head(20).to_frame())\n",
+    "print(\"unique vehicles:\", transition_meta[\"car_master_id\"].nunique())\n",
+    "print(\"target distribution:\", transition_meta[\"target_defect_label\"].value_counts().to_dict())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "-7HWdLE4bEfB",
+   "metadata": {
+    "id": "-7HWdLE4bEfB"
+   },
+   "source": [
+    "## 5. 차량 단위 학습/검증 분리\n",
+    "\n",
+    "같은 차량의 여러 공정 전이가 학습과 검증에 동시에 섞이지 않도록 `car_master_id`\n",
+    "기준 GroupShuffleSplit을 사용합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "XX3xUzrlbEfB",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XX3xUzrlbEfB",
+    "outputId": "1b64eb6a-04d9-4fa0-ee50-df1fa3ea9a18"
+   },
+   "outputs": [],
+   "source": [
+    "metadata_cols = [\n",
+    "    \"sample_event_id\", \"event_time\", \"car_master_id\", \"source_process_code\",\n",
+    "    \"target_process_code\", \"target_defect_label\", \"label_source\", \"label_source_row_id\",\n",
+    "]\n",
+    "feature_cols = [column for column in model_df.columns if column not in metadata_cols]\n",
+    "all_missing_cols = model_df[feature_cols].columns[model_df[feature_cols].isna().all()].tolist()\n",
+    "if all_missing_cols:\n",
+    "    model_df = model_df.drop(columns=all_missing_cols)\n",
+    "    feature_cols = [column for column in feature_cols if column not in all_missing_cols]\n",
+    "\n",
+    "X = model_df[feature_cols]\n",
+    "y = model_df[\"target_defect_label\"].astype(int)\n",
+    "ids = model_df[\"car_master_id\"].astype(int)\n",
+    "groups = ids.copy()\n",
+    "\n",
+    "group_split = GroupShuffleSplit(n_splits=1, test_size=0.2, random_state=RANDOM_STATE)\n",
+    "train_idx, valid_idx = next(group_split.split(X, y, groups=groups))\n",
+    "\n",
+    "X_train = X.iloc[train_idx]\n",
+    "X_valid = X.iloc[valid_idx]\n",
+    "y_train = y.iloc[train_idx]\n",
+    "y_valid = y.iloc[valid_idx]\n",
+    "id_train = ids.iloc[train_idx]\n",
+    "id_valid = ids.iloc[valid_idx]\n",
+    "groups_train = groups.iloc[train_idx]\n",
+    "groups_valid = groups.iloc[valid_idx]\n",
+    "meta_train = model_df.iloc[train_idx][metadata_cols]\n",
+    "meta_valid = model_df.iloc[valid_idx][metadata_cols]\n",
+    "\n",
+    "if set(groups_train).intersection(set(groups_valid)):\n",
+    "    raise AssertionError(\"동일 차량이 train/validation에 동시에 포함되었습니다.\")\n",
+    "if y_train.nunique() < 2 or y_valid.nunique() < 2:\n",
+    "    raise ValueError(\"차량 단위 분할 후 한 클래스만 남았습니다. MAX_ROWS를 늘려주세요.\")\n",
+    "\n",
+    "neg_count = int((y_train == 0).sum())\n",
+    "pos_count = int((y_train == 1).sum())\n",
+    "scale_pos_weight = neg_count / max(pos_count, 1)\n",
+    "\n",
+    "print(\"X_train:\", X_train.shape, \"X_valid:\", X_valid.shape)\n",
+    "print(\"train vehicles:\", groups_train.nunique(), \"valid vehicles:\", groups_valid.nunique())\n",
+    "print(\"positive ratio train:\", y_train.mean(), \"valid:\", y_valid.mean())\n",
+    "print(\"scale_pos_weight:\", round(scale_pos_weight, 2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6444a8dd",
+   "metadata": {
+    "id": "6444a8dd"
+   },
+   "source": [
+    "## 6. 전처리 학습 데이터셋 저장\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e442104",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6e442104",
+    "outputId": "09415cb9-862f-49f7-a3f3-30566b3940f4"
+   },
+   "outputs": [],
+   "source": [
+    "def save_training_dataframe(df: pd.DataFrame, output_dir: Path, stem: str) -> Path:\n",
+    "    parquet_path = output_dir / f\"{stem}.parquet\"\n",
+    "    csv_path = output_dir / f\"{stem}.csv.gz\"\n",
+    "    try:\n",
+    "        df.to_parquet(parquet_path, index=False)\n",
+    "        return parquet_path\n",
+    "    except Exception as exc:\n",
+    "        print(f\"Parquet 저장 실패, csv.gz로 저장합니다: {exc}\")\n",
+    "        df.to_csv(csv_path, index=False, compression=\"gzip\")\n",
+    "        return csv_path\n",
+    "\n",
+    "\n",
+    "training_dataset_path = save_training_dataframe(\n",
+    "    model_df,\n",
+    "    OUTPUT_DIR,\n",
+    "    \"defect_transfer_event_json_training_dataset\",\n",
+    ")\n",
+    "\n",
+    "split_rows = pd.concat([\n",
+    "    meta_train.assign(split=\"train\"),\n",
+    "    meta_valid.assign(split=\"valid\"),\n",
+    "]).sort_index()\n",
+    "split_ids_path = OUTPUT_DIR / \"defect_transfer_train_valid_split_rows.csv\"\n",
+    "split_rows.to_csv(split_ids_path, index=False)\n",
+    "\n",
+    "preprocessing_metadata = {\n",
+    "    \"event_json_path\": str(EVENT_JSON_PATH),\n",
+    "    \"event_json_column_index\": 12,\n",
+    "    \"is_sent_column_index\": 13,\n",
+    "    \"max_event_rows\": int(MAX_ROWS),\n",
+    "    \"event_chunk_size\": int(EVENT_CHUNK_SIZE),\n",
+    "    \"transition_rows\": int(len(model_df)),\n",
+    "    \"transition_summary\": transition_summary.to_dict(orient=\"records\"),\n",
+    "    \"label_sources\": {\n",
+    "        \"BODY\": \"FordA label (-1 => defect)\",\n",
+    "        \"PAINT\": \"thermal vision label (1 => defect)\",\n",
+    "        \"ASSEMBLY\": \"Bosch Response (1 => defect)\",\n",
+    "    },\n",
+    "    \"excluded_leakage_json_paths\": sorted(LEAKAGE_JSON_PATHS),\n",
+    "    \"dropped_all_missing_columns\": all_missing_cols,\n",
+    "    \"feature_columns\": feature_cols,\n",
+    "    \"target_column\": \"target_defect_label\",\n",
+    "    \"split_strategy\": \"GroupShuffleSplit by car_master_id\",\n",
+    "    \"train_rows\": int(len(X_train)),\n",
+    "    \"valid_rows\": int(len(X_valid)),\n",
+    "    \"train_vehicles\": int(groups_train.nunique()),\n",
+    "    \"valid_vehicles\": int(groups_valid.nunique()),\n",
+    "    \"positive_ratio\": float(y.mean()),\n",
+    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
+    "}\n",
+    "preprocessing_metadata_path = OUTPUT_DIR / \"defect_transfer_preprocessing_metadata.json\"\n",
+    "preprocessing_metadata_path.write_text(\n",
+    "    json.dumps(preprocessing_metadata, ensure_ascii=False, indent=2),\n",
+    "    encoding=\"utf-8\",\n",
+    ")\n",
+    "\n",
+    "print(\"전처리 학습 데이터셋:\", training_dataset_path)\n",
+    "print(\"train/valid split rows:\", split_ids_path)\n",
+    "print(\"전처리 metadata:\", preprocessing_metadata_path)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b19d8e5c",
+   "metadata": {
+    "id": "b19d8e5c"
+   },
+   "source": [
+    "## 7. 후보 모델 교차검증 및 하이퍼파라미터 튜닝\n",
+    "\n",
+    "모든 후보 모델은 동일한 `event_json` 전이 학습 테이블을 사용합니다. 교차검증도\n",
+    "`car_master_id` 그룹을 유지하여 동일 차량의 행이 fold 사이에 섞이지 않게 합니다.\n",
+    "비교 지표는 PR-AUC, Recall, Precision, F1, ROC-AUC, Accuracy, False Positive Rate입니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9a50e90",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "d9a50e90",
+    "outputId": "60f68881-d294-44fc-92f2-008ba9ab2179"
+   },
+   "outputs": [],
+   "source": [
+    "# 평가 함수\n",
+    "def find_recall_priority_threshold(\n",
+    "    y_true: pd.Series,\n",
+    "    proba: np.ndarray,\n",
+    "    *,\n",
+    "    min_recall: float = RECALL_PRIORITY_TARGET,\n",
+    "    min_precision: float = RECALL_PRIORITY_MIN_PRECISION,\n",
+    "    beta: float = THRESHOLD_BETA,\n",
+    ") -> float:\n",
+    "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
+    "    if len(thresholds) == 0:\n",
+    "        return 0.5\n",
+    "\n",
+    "    precision = precision[:-1]\n",
+    "    recall = recall[:-1]\n",
+    "    thresholds = thresholds.astype(float)\n",
+    "    valid = (recall >= min_recall) & (precision >= min_precision)\n",
+    "\n",
+    "    if valid.any():\n",
+    "        # Recall 목표를 만족하는 후보 중 Precision이 가장 좋은 threshold를 선택합니다.\n",
+    "        valid_idx = np.where(valid)[0]\n",
+    "        best_local_idx = valid_idx[int(np.nanargmax(precision[valid_idx]))]\n",
+    "        return float(thresholds[best_local_idx])\n",
+    "\n",
+    "    beta_sq = beta ** 2\n",
+    "    f_beta = (1 + beta_sq) * precision * recall / np.maximum(beta_sq * precision + recall, 1e-12)\n",
+    "    return float(thresholds[int(np.nanargmax(f_beta))])\n",
+    "\n",
+    "\n",
+    "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
+    "    return find_recall_priority_threshold(y_true, proba)\n",
+    "\n",
+    "\n",
+    "def compute_binary_metrics(y_true: pd.Series, proba: np.ndarray, threshold: float) -> dict:\n",
+    "    pred = (proba >= threshold).astype(int)\n",
+    "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
+    "    return {\n",
+    "        \"accuracy\": float(accuracy_score(y_true, pred)),\n",
+    "        \"precision\": float(precision_score(y_true, pred, zero_division=0)),\n",
+    "        \"recall\": float(recall_score(y_true, pred, zero_division=0)),\n",
+    "        \"f1\": float(f1_score(y_true, pred, zero_division=0)),\n",
+    "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
+    "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
+    "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
+    "        \"predicted_positive_rate\": float((tp + fp) / max(tp + fp + tn + fn, 1)),\n",
+    "        \"true_negative\": int(tn),\n",
+    "        \"false_positive\": int(fp),\n",
+    "        \"false_negative\": int(fn),\n",
+    "        \"true_positive\": int(tp),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def predict_positive_proba(estimator, X_input: pd.DataFrame) -> np.ndarray:\n",
+    "    if hasattr(estimator, \"predict_proba\"):\n",
+    "        return estimator.predict_proba(X_input)[:, 1]\n",
+    "    decision = estimator.decision_function(X_input)\n",
+    "    return 1 / (1 + np.exp(-decision))\n",
+    "\n",
+    "\n",
+    "def is_pipeline_estimator(estimator) -> bool:\n",
+    "    return isinstance(estimator, (Pipeline, ImbPipeline))\n",
+    "\n",
+    "\n",
+    "def get_final_estimator(estimator):\n",
+    "    return estimator.named_steps.get(\"model\", estimator) if is_pipeline_estimator(estimator) else estimator\n",
+    "\n",
+    "\n",
+    "def make_lightgbm_pipeline() -> ImbPipeline:\n",
+    "    steps = [(\"imputer\", SimpleImputer(strategy=\"median\"))]\n",
+    "    if USE_SMOTE:\n",
+    "        steps.append((\"smote\", SMOTE(\n",
+    "            sampling_strategy=SMOTE_SAMPLING_STRATEGY,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "            k_neighbors=3,\n",
+    "        )))\n",
+    "    steps.append((\"model\", lgb.LGBMClassifier(\n",
+    "        objective=\"binary\",\n",
+    "        n_estimators=500,\n",
+    "        random_state=RANDOM_STATE,\n",
+    "        n_jobs=-1,\n",
+    "        is_unbalance=True,\n",
+    "        verbosity=-1,\n",
+    "    )))\n",
+    "    return ImbPipeline(steps)\n",
+    "\n",
+    "\n",
+    "def make_candidate_models() -> dict:\n",
+    "    return {\n",
+    "        \"LightGBM\": {\n",
+    "            \"estimator\": make_lightgbm_pipeline(),\n",
+    "            \"params\": {\n",
+    "                \"model__learning_rate\": [0.015, 0.025, 0.04, 0.06],\n",
+    "                \"model__num_leaves\": [31, 63, 127],\n",
+    "                \"model__max_depth\": [-1, 6, 8, 12],\n",
+    "                \"model__min_child_samples\": [40, 80, 150, 250],\n",
+    "                \"model__subsample\": [0.75, 0.85, 1.0],\n",
+    "                \"model__colsample_bytree\": [0.65, 0.8, 0.95],\n",
+    "                \"model__reg_lambda\": [0.5, 1.0, 2.0, 5.0],\n",
+    "                \"model__reg_alpha\": [0.0, 0.1, 0.5],\n",
+    "            },\n",
+    "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델. Optuna와 SMOTE로 희소 불량 recall을 보강\",\n",
+    "        },\n",
+    "        \"RandomForest\": {\n",
+    "            \"estimator\": Pipeline([\n",
+    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "                (\"model\", RandomForestClassifier(\n",
+    "                    n_estimators=160,\n",
+    "                    class_weight=\"balanced_subsample\",\n",
+    "                    random_state=RANDOM_STATE,\n",
+    "                    n_jobs=-1,\n",
+    "                )),\n",
+    "            ]),\n",
+    "            \"params\": {\n",
+    "                \"model__max_depth\": [12, 16, 20, None],\n",
+    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
+    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
+    "            },\n",
+    "            \"selection_reason\": \"bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble 성능 확인\",\n",
+    "        },\n",
+    "        \"ExtraTrees\": {\n",
+    "            \"estimator\": Pipeline([\n",
+    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "                (\"model\", ExtraTreesClassifier(\n",
+    "                    n_estimators=220,\n",
+    "                    class_weight=\"balanced\",\n",
+    "                    random_state=RANDOM_STATE,\n",
+    "                    n_jobs=-1,\n",
+    "                )),\n",
+    "            ]),\n",
+    "            \"params\": {\n",
+    "                \"model__max_depth\": [12, 16, 20, None],\n",
+    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
+    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
+    "            },\n",
+    "            \"selection_reason\": \"RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교\",\n",
+    "        },\n",
+    "        \"LogisticRegression\": {\n",
+    "            \"estimator\": Pipeline([\n",
+    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "                (\"scaler\", StandardScaler()),\n",
+    "                (\"model\", LogisticRegression(\n",
+    "                    class_weight=\"balanced\",\n",
+    "                    max_iter=1500,\n",
+    "                    random_state=RANDOM_STATE,\n",
+    "                )),\n",
+    "            ]),\n",
+    "            \"params\": {\n",
+    "                \"model__C\": [0.03, 0.1, 0.3, 1.0, 3.0],\n",
+    "                \"model__solver\": [\"lbfgs\"],\n",
+    "            },\n",
+    "            \"selection_reason\": \"복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체의 설명력 확인\",\n",
+    "        },\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def suggest_lightgbm_params(trial: optuna.Trial) -> dict:\n",
+    "    return {\n",
+    "        \"model__learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.08, log=True),\n",
+    "        \"model__num_leaves\": trial.suggest_int(\"num_leaves\", 24, 160),\n",
+    "        \"model__max_depth\": trial.suggest_categorical(\"max_depth\", [-1, 5, 7, 9, 12]),\n",
+    "        \"model__min_child_samples\": trial.suggest_int(\"min_child_samples\", 30, 300),\n",
+    "        \"model__subsample\": trial.suggest_float(\"subsample\", 0.65, 1.0),\n",
+    "        \"model__colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.55, 1.0),\n",
+    "        \"model__reg_lambda\": trial.suggest_float(\"reg_lambda\", 0.1, 8.0, log=True),\n",
+    "        \"model__reg_alpha\": trial.suggest_float(\"reg_alpha\", 1e-4, 1.0, log=True),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def evaluate_cv_trial(model_name: str, config: dict, params: dict, trial_no: int) -> tuple[dict, list[dict]]:\n",
+    "    fold_rows = []\n",
+    "    for fold_no, (train_idx, valid_idx) in enumerate(\n",
+    "    skf.split(X_cv_pool, y_cv_pool, groups=groups_cv_pool), start=1\n",
+    "):\n",
+    "        fold_started_at = datetime.now()\n",
+    "        X_fold_train = X_cv_pool.iloc[train_idx]\n",
+    "        y_fold_train = y_cv_pool.iloc[train_idx]\n",
+    "        X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
+    "        y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
+    "\n",
+    "        cv_model = clone(config[\"estimator\"])\n",
+    "        cv_model.set_params(**params)\n",
+    "        cv_model.fit(X_fold_train, y_fold_train)\n",
+    "\n",
+    "        fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
+    "        fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
+    "        fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
+    "        fold_metrics.update({\n",
+    "            \"model_name\": model_name,\n",
+    "            \"trial_no\": trial_no,\n",
+    "            \"fold_no\": fold_no,\n",
+    "            \"threshold\": fold_threshold,\n",
+    "            **params,\n",
+    "        })\n",
+    "        fold_rows.append(fold_metrics)\n",
+    "        elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
+    "        print(\n",
+    "            f\"    fold {fold_no}/{effective_cv_splits} \"\n",
+    "            f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
+    "            f\"Recall={fold_metrics['recall']:.5f} \"\n",
+    "            f\"F1={fold_metrics['f1']:.5f} \"\n",
+    "            f\"elapsed={elapsed:.1f}s\"\n",
+    "        )\n",
+    "\n",
+    "    fold_df = pd.DataFrame(fold_rows)\n",
+    "    summary = {\n",
+    "        \"model_name\": model_name,\n",
+    "        \"trial_no\": trial_no,\n",
+    "        \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
+    "        \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
+    "        \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
+    "        \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
+    "        \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
+    "        \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
+    "        \"mean_precision\": float(fold_df[\"precision\"].mean()),\n",
+    "        \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
+    "        \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
+    "        \"params\": params,\n",
+    "        \"selection_reason\": config[\"selection_reason\"],\n",
+    "        \"tuning_method\": \"Optuna\" if model_name == \"LightGBM\" and ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+    "    }\n",
+    "    return summary, fold_rows\n",
+    "\n",
+    "\n",
+    "# CV 데이터셋 규모 관리: 차량 그룹을 유지한 채 표본 추출\n",
+    "if len(X_train) > CV_SAMPLE_SIZE:\n",
+    "    cv_fraction = min(0.99, CV_SAMPLE_SIZE / len(X_train))\n",
+    "    cv_sampler = GroupShuffleSplit(\n",
+    "        n_splits=1,\n",
+    "        train_size=cv_fraction,\n",
+    "        random_state=RANDOM_STATE,\n",
+    "    )\n",
+    "    cv_idx, _ = next(cv_sampler.split(X_train, y_train, groups=groups_train))\n",
+    "    X_cv_pool = X_train.iloc[cv_idx]\n",
+    "    y_cv_pool = y_train.iloc[cv_idx]\n",
+    "    groups_cv_pool = groups_train.iloc[cv_idx]\n",
+    "else:\n",
+    "    X_cv_pool = X_train\n",
+    "    y_cv_pool = y_train\n",
+    "    groups_cv_pool = groups_train\n",
+    "\n",
+    "effective_cv_splits = min(\n",
+    "    CV_N_SPLITS,\n",
+    "    int(y_cv_pool.value_counts().min()),\n",
+    "    int(groups_cv_pool.nunique()),\n",
+    ")\n",
+    "if effective_cv_splits < 2:\n",
+    "    raise ValueError(\"교차검증을 수행하기에 소수 클래스 샘플이 부족합니다. MAX_ROWS 또는 CV_SAMPLE_SIZE를 늘려주세요.\")\n",
+    "\n",
+    "candidate_configs = make_candidate_models()\n",
+    "skf = StratifiedGroupKFold(n_splits=effective_cv_splits, shuffle=True, random_state=RANDOM_STATE)\n",
+    "cv_rows = []\n",
+    "cv_fold_rows = []\n",
+    "best_params_by_model = {}\n",
+    "\n",
+    "print(f\"CV rows: {len(X_cv_pool):,} / train rows: {len(X_train):,}\")\n",
+    "print(f\"CV splits: {effective_cv_splits}, tuning trials per model: {CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1}\")\n",
+    "print(f\"SMOTE enabled: {USE_SMOTE}, sampling_strategy={SMOTE_SAMPLING_STRATEGY}\")\n",
+    "print(f\"Threshold strategy: recall>={RECALL_PRIORITY_TARGET}, min_precision>={RECALL_PRIORITY_MIN_PRECISION}, beta={THRESHOLD_BETA}\")\n",
+    "\n",
+    "# 후보 모델별 교차검증\n",
+    "for model_name, config in candidate_configs.items():\n",
+    "    print(f\"\\n[{model_name}] CV start\")\n",
+    "    model_started_at = datetime.now()\n",
+    "\n",
+    "    if model_name == \"LightGBM\" and ENABLE_HYPERPARAMETER_TUNING and ENABLE_OPTUNA_TUNING:\n",
+    "        def objective(trial: optuna.Trial) -> float:\n",
+    "            params = suggest_lightgbm_params(trial)\n",
+    "            print(f\"  optuna trial {trial.number + 1}/{CV_N_ITER} params={params}\")\n",
+    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial.number + 1)\n",
+    "            cv_rows.append(summary)\n",
+    "            cv_fold_rows.extend(fold_rows)\n",
+    "            trial.set_user_attr(\"params\", params)\n",
+    "            trial.set_user_attr(\"mean_recall\", summary[\"mean_recall\"])\n",
+    "            trial.set_user_attr(\"mean_f1\", summary[\"mean_f1\"])\n",
+    "            # PR-AUC를 우선하되 recall을 약하게 보상합니다.\n",
+    "            return summary[\"mean_pr_auc\"] + 0.05 * summary[\"mean_recall\"]\n",
+    "\n",
+    "        study = optuna.create_study(\n",
+    "            direction=\"maximize\",\n",
+    "            sampler=TPESampler(seed=RANDOM_STATE),\n",
+    "            study_name=\"event_json_transfer_lightgbm_recall_pr_auc\",\n",
+    "        )\n",
+    "        study.optimize(objective, n_trials=CV_N_ITER, show_progress_bar=False)\n",
+    "        best_params_by_model[model_name] = study.best_trial.user_attrs[\"params\"]\n",
+    "        print(f\"[{model_name}] Optuna best value={study.best_value:.5f} params={best_params_by_model[model_name]}\")\n",
+    "    else:\n",
+    "        param_candidates = list(ParameterSampler(\n",
+    "            config[\"params\"],\n",
+    "            n_iter=CV_N_ITER,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
+    "        print(f\"[{model_name}] random search trials: {len(param_candidates)}\")\n",
+    "        for trial_no, params in enumerate(param_candidates, start=1):\n",
+    "            print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
+    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial_no)\n",
+    "            cv_rows.append(summary)\n",
+    "            cv_fold_rows.extend(fold_rows)\n",
+    "            print(\n",
+    "                f\"  -> {model_name} trial {trial_no} mean \"\n",
+    "                f\"PR-AUC={summary['mean_pr_auc']:.5f} \"\n",
+    "                f\"Recall={summary['mean_recall']:.5f} \"\n",
+    "                f\"F1={summary['mean_f1']:.5f} \"\n",
+    "                f\"FPR={summary['mean_false_positive_rate']:.5f}\"\n",
+    "            )\n",
+    "\n",
+    "    print(f\"[{model_name}] CV done elapsed={(datetime.now() - model_started_at).total_seconds():.1f}s\")\n",
+    "\n",
+    "cv_results = pd.DataFrame(cv_rows).sort_values(\n",
+    "    [\"mean_pr_auc\", \"mean_recall\", \"mean_f1\", \"mean_roc_auc\", \"mean_accuracy\"],\n",
+    "    ascending=False,\n",
+    ").reset_index(drop=True)\n",
+    "cv_fold_results = pd.DataFrame(cv_fold_rows)\n",
+    "\n",
+    "for model_name in candidate_configs:\n",
+    "    if model_name not in best_params_by_model:\n",
+    "        best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
+    "\n",
+    "best_model_name = str(cv_results.iloc[0][\"model_name\"])\n",
+    "best_params = best_params_by_model[best_model_name]\n",
+    "best_model_selection_reason = str(cv_results.iloc[0][\"selection_reason\"])\n",
+    "\n",
+    "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.csv\"\n",
+    "cv_fold_results_path = OUTPUT_DIR / \"defect_model_cv_fold_results.csv\"\n",
+    "cv_results.to_csv(cv_results_path, index=False)\n",
+    "cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
+    "\n",
+    "display(cv_results.head(20))\n",
+    "print(\"CV 결과 저장:\", cv_results_path)\n",
+    "print(\"CV fold 결과 저장:\", cv_fold_results_path)\n",
+    "print(\"CV best model:\", best_model_name, best_params)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f15608a",
+   "metadata": {
+    "id": "3f15608a"
+   },
+   "source": [
+    "## 8. 후보 모델 최종 학습 및 성능 비교\n",
+    "\n",
+    "교차검증에서 찾은 후보별 best parameter로 최종 학습하고 차량 단위 validation에서\n",
+    "다음 공정 불량 예측 성능을 비교합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3553e4c5",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 825
+    },
+    "id": "3553e4c5",
+    "outputId": "f6e17abe-7523-45bd-b4fa-0828efb0d901"
+   },
+   "outputs": [],
+   "source": [
+    "trained_models = {}\n",
+    "model_valid_probabilities = {}\n",
+    "model_thresholds = {}\n",
+    "model_confusion_matrices = {}\n",
+    "model_eval_rows = []\n",
+    "\n",
+    "# 최종 후보 비교용 학습 데이터셋 규모 관리\n",
+    "if len(X_train) > FINAL_TRAIN_SAMPLE_SIZE:\n",
+    "    X_final_train, _, y_final_train, _ = train_test_split(\n",
+    "        X_train,\n",
+    "        y_train,\n",
+    "        train_size=FINAL_TRAIN_SAMPLE_SIZE,\n",
+    "        random_state=RANDOM_STATE,\n",
+    "        stratify=y_train,\n",
+    "    )\n",
+    "else:\n",
+    "    X_final_train = X_train\n",
+    "    y_final_train = y_train\n",
+    "\n",
+    "print(f\"Final candidate training rows: {len(X_final_train):,} / train rows: {len(X_train):,}\")\n",
+    "\n",
+    "# 후보 모델 전체 학습\n",
+    "for model_name, config in candidate_configs.items():\n",
+    "    started_at = datetime.now()\n",
+    "    estimator = clone(config[\"estimator\"])\n",
+    "    estimator.set_params(**best_params_by_model[model_name])\n",
+    "    print(f\"[{model_name}] final fit start params={best_params_by_model[model_name]}\")\n",
+    "    estimator.fit(X_final_train, y_final_train)\n",
+    "\n",
+    "    proba = predict_positive_proba(estimator, X_valid)\n",
+    "    threshold = find_best_threshold(y_valid, proba)\n",
+    "    pred = (proba >= threshold).astype(int)\n",
+    "    cm_model = confusion_matrix(y_valid, pred, labels=[0, 1])\n",
+    "    metrics = compute_binary_metrics(y_valid, proba, threshold)\n",
+    "\n",
+    "    trained_models[model_name] = estimator\n",
+    "    model_valid_probabilities[model_name] = proba\n",
+    "    model_thresholds[model_name] = threshold\n",
+    "    model_confusion_matrices[model_name] = cm_model\n",
+    "\n",
+    "    model_eval_rows.append({\n",
+    "        \"model_name\": model_name,\n",
+    "        \"threshold\": threshold,\n",
+    "        \"selection_reason\": config[\"selection_reason\"],\n",
+    "        **metrics,\n",
+    "    })\n",
+    "    print(\n",
+    "        f\"[{model_name}] final fit done \"\n",
+    "        f\"PR-AUC={metrics['pr_auc']:.5f} \"\n",
+    "        f\"ROC-AUC={metrics['roc_auc']:.5f} \"\n",
+    "        f\"ACC={metrics['accuracy']:.5f} \"\n",
+    "        f\"FPR={metrics['false_positive_rate']:.5f} \"\n",
+    "        f\"elapsed={(datetime.now() - started_at).total_seconds():.1f}s\"\n",
+    "    )\n",
+    "\n",
+    "model_comparison = pd.DataFrame(model_eval_rows).sort_values(\n",
+    "    [\"pr_auc\", \"recall\", \"f1\", \"roc_auc\", \"accuracy\"],\n",
+    "    ascending=False,\n",
+    ").reset_index(drop=True)\n",
+    "\n",
+    "selected_model_name = str(model_comparison.iloc[0][\"model_name\"])\n",
+    "model = trained_models[selected_model_name]\n",
+    "valid_proba = model_valid_probabilities[selected_model_name]\n",
+    "best_threshold = float(model_thresholds[selected_model_name])\n",
+    "valid_pred = (valid_proba >= best_threshold).astype(int)\n",
+    "cm = model_confusion_matrices[selected_model_name]\n",
+    "\n",
+    "# 운영용 전체 재학습은 선택 모델 1개만 수행\n",
+    "if RETRAIN_SELECTED_MODEL_ON_FULL_DATA and len(X_final_train) < len(X_train):\n",
+    "    print(f\"[{selected_model_name}] selected model full-data refit start rows={len(X_train):,}\")\n",
+    "    refit_started_at = datetime.now()\n",
+    "    full_model = clone(candidate_configs[selected_model_name][\"estimator\"])\n",
+    "    full_model.set_params(**best_params_by_model[selected_model_name])\n",
+    "    full_model.fit(X_train, y_train)\n",
+    "\n",
+    "    model = full_model\n",
+    "    trained_models[selected_model_name] = full_model\n",
+    "    valid_proba = predict_positive_proba(model, X_valid)\n",
+    "    best_threshold = find_best_threshold(y_valid, valid_proba)\n",
+    "    valid_pred = (valid_proba >= best_threshold).astype(int)\n",
+    "    cm = confusion_matrix(y_valid, valid_pred, labels=[0, 1])\n",
+    "    full_metrics = compute_binary_metrics(y_valid, valid_proba, best_threshold)\n",
+    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), list(full_metrics.keys())] = list(full_metrics.values())\n",
+    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), \"threshold\"] = best_threshold\n",
+    "    model_thresholds[selected_model_name] = best_threshold\n",
+    "    model_confusion_matrices[selected_model_name] = cm\n",
+    "    print(f\"[{selected_model_name}] full-data refit done elapsed={(datetime.now() - refit_started_at).total_seconds():.1f}s\")\n",
+    "\n",
+    "roc_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"roc_auc\"])\n",
+    "pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
+    "accuracy = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"accuracy\"])\n",
+    "false_positive_rate = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"false_positive_rate\"])\n",
+    "\n",
+    "# AI 관리 요약\n",
+    "baseline_row = model_comparison[model_comparison[\"model_name\"].eq(\"LightGBM\")]\n",
+    "baseline_pr_auc = float(baseline_row.iloc[0][\"pr_auc\"]) if not baseline_row.empty else np.nan\n",
+    "best_pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
+    "performance_improvement = best_pr_auc - baseline_pr_auc if not np.isnan(baseline_pr_auc) else 0.0\n",
+    "\n",
+    "ai_management_summary = {\n",
+    "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/Recall/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
+    "    \"데이터셋_규모_관리\": {\n",
+    "        \"max_event_rows\": int(MAX_ROWS),\n",
+    "        \"transition_rows\": int(len(model_df)),\n",
+    "        \"train_rows\": int(len(X_train)),\n",
+    "        \"candidate_train_rows\": int(len(X_final_train)),\n",
+    "        \"valid_rows\": int(len(X_valid)),\n",
+    "        \"feature_count\": int(len(feature_cols)),\n",
+    "        \"positive_ratio\": float(y.mean()),\n",
+    "        \"transition_summary\": transition_summary.to_dict(orient=\"records\"),\n",
+    "        \"full_data_refit\": bool(RETRAIN_SELECTED_MODEL_ON_FULL_DATA),\n",
+    "    },\n",
+    "    \"테스트_케이스_수_관리\": {\n",
+    "        \"validation_total\": int(len(y_valid)),\n",
+    "        \"validation_normal\": int((y_valid == 0).sum()),\n",
+    "        \"validation_defect\": int((y_valid == 1).sum()),\n",
+    "        \"cv_splits\": int(effective_cv_splits),\n",
+    "        \"cv_sample_size\": int(len(X_cv_pool)),\n",
+    "        \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
+    "        \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+    "        \"smote_enabled\": bool(USE_SMOTE),\n",
+    "        \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
+    "        \"threshold_strategy\": {\n",
+    "            \"type\": \"recall_priority\",\n",
+    "            \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
+    "            \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
+    "            \"beta\": float(THRESHOLD_BETA),\n",
+    "        },\n",
+    "    },\n",
+    "    \"정확도_Accuracy\": accuracy,\n",
+    "    \"오탐률_False_Positive_Rate\": false_positive_rate,\n",
+    "    \"모델_성능_개선_현황\": {\n",
+    "        \"baseline_model\": \"LightGBM\",\n",
+    "        \"baseline_pr_auc\": baseline_pr_auc,\n",
+    "        \"selected_model\": selected_model_name,\n",
+    "        \"selected_pr_auc\": best_pr_auc,\n",
+    "        \"pr_auc_improvement_over_lightgbm\": performance_improvement,\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "print(\"selected_model_name:\", selected_model_name)\n",
+    "print(\"ROC-AUC:\", round(roc_auc, 5))\n",
+    "print(\"PR-AUC:\", round(pr_auc, 5))\n",
+    "print(\"Accuracy:\", round(accuracy, 5))\n",
+    "print(\"False Positive Rate:\", round(false_positive_rate, 5))\n",
+    "print(\"Recall:\", round(float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"recall\"]), 5))\n",
+    "display(model_comparison)\n",
+    "display(pd.DataFrame([ai_management_summary]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "AGL93TiObEfC",
+   "metadata": {
+    "id": "AGL93TiObEfC"
+   },
+   "source": [
+    "## 9. 후보 모델별 Threshold 튜닝 및 Confusion Matrix\n",
+    "\n",
+    "각 후보 모델은 validation set에서 recall 우선 threshold를 사용합니다.\n",
+    "\n",
+    "기본 정책은 `RECALL_PRIORITY_TARGET` 이상을 만족하는 threshold 중 precision이 가장 높은 값을 선택하고, 목표 recall을 만족하는 후보가 없으면 F-beta(기본 beta=2) 기준으로 선택합니다. Accuracy는 클래스 불균형 때문에 참고 지표로만 해석합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "-bI4yc81bEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "-bI4yc81bEfC",
+    "outputId": "fffa66b9-fb9b-4c84-b9d5-da0983048bc2"
+   },
+   "outputs": [],
+   "source": [
+    "print(\"== Selected Model ==\")\n",
+    "print(\"model:\", selected_model_name)\n",
+    "print(\"best_threshold:\", round(best_threshold, 5))\n",
+    "print(classification_report(y_valid, valid_pred, digits=4, zero_division=0))\n",
+    "\n",
+    "# 후보 모델별 Confusion Matrix\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "axes = axes.ravel()\n",
+    "for ax, (model_name, cm_model) in zip(axes, model_confusion_matrices.items()):\n",
+    "    ConfusionMatrixDisplay(cm_model, display_labels=[\"Normal\", \"Defect\"]).plot(\n",
+    "        ax=ax,\n",
+    "        cmap=\"Blues\",\n",
+    "        values_format=\"d\",\n",
+    "        colorbar=False,\n",
+    "    )\n",
+    "    row = model_comparison[model_comparison[\"model_name\"].eq(model_name)].iloc[0]\n",
+    "    ax.set_title(\n",
+    "        f\"{model_name}\\n\"\n",
+    "        f\"Recall={row['recall']:.3f}, Precision={row['precision']:.3f}, F1={row['f1']:.3f}\"\n",
+    "    )\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 비교 지표 테이블\n",
+    "display(model_comparison[[\n",
+    "    \"model_name\",\n",
+    "    \"accuracy\",\n",
+    "    \"false_positive_rate\",\n",
+    "    \"precision\",\n",
+    "    \"recall\",\n",
+    "    \"f1\",\n",
+    "    \"roc_auc\",\n",
+    "    \"pr_auc\",\n",
+    "    \"threshold\",\n",
+    "]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ALpJVyy2bEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "ALpJVyy2bEfC",
+    "outputId": "b964a332-cdc6-45a1-f4e9-484e34c728a0"
+   },
+   "outputs": [],
+   "source": [
+    "# 모델 성능 비교 시각화\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 4))\n",
+    "sns.countplot(x=y, ax=axes[0])\n",
+    "axes[0].set_title(\"Target Defect Distribution\")\n",
+    "axes[0].set_xlabel(\"Target Defect Label\")\n",
+    "\n",
+    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"pr_auc\", ax=axes[1], color=\"#4C78A8\")\n",
+    "axes[1].set_title(\"Validation PR-AUC by Model\")\n",
+    "axes[1].set_xlabel(\"\")\n",
+    "axes[1].tick_params(axis=\"x\", rotation=20)\n",
+    "\n",
+    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"false_positive_rate\", ax=axes[2], color=\"#F58518\")\n",
+    "axes[2].set_title(\"False Positive Rate by Model\")\n",
+    "axes[2].set_xlabel(\"\")\n",
+    "axes[2].tick_params(axis=\"x\", rotation=20)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 선택 모델 ROC/PR 곡선\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "RocCurveDisplay.from_predictions(y_valid, valid_proba, ax=axes[0])\n",
+    "axes[0].set_title(f\"{selected_model_name} ROC AUC={roc_auc:.4f}\")\n",
+    "PrecisionRecallDisplay.from_predictions(y_valid, valid_proba, ax=axes[1])\n",
+    "axes[1].set_title(f\"{selected_model_name} PR-AUC={pr_auc:.4f}\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "def extract_feature_importance(estimator, feature_names: list[str], model_name: str) -> pd.DataFrame:\n",
+    "    target_estimator = estimator\n",
+    "    if is_pipeline_estimator(estimator):\n",
+    "        target_estimator = get_final_estimator(estimator)\n",
+    "\n",
+    "    if hasattr(target_estimator, \"booster_\"):\n",
+    "        values = target_estimator.booster_.feature_importance(importance_type=\"gain\")\n",
+    "        importance_type = \"gain\"\n",
+    "    elif hasattr(target_estimator, \"feature_importances_\"):\n",
+    "        values = target_estimator.feature_importances_\n",
+    "        importance_type = \"feature_importances\"\n",
+    "    elif hasattr(target_estimator, \"coef_\"):\n",
+    "        values = np.abs(target_estimator.coef_).ravel()\n",
+    "        importance_type = \"abs_coef\"\n",
+    "    else:\n",
+    "        values = np.zeros(len(feature_names))\n",
+    "        importance_type = \"not_available\"\n",
+    "\n",
+    "    return pd.DataFrame({\n",
+    "        \"model_name\": model_name,\n",
+    "        \"feature\": feature_names,\n",
+    "        \"importance\": values,\n",
+    "        \"importance_type\": importance_type,\n",
+    "    }).sort_values(\"importance\", ascending=False)\n",
+    "\n",
+    "\n",
+    "importance_frames = [\n",
+    "    extract_feature_importance(estimator, feature_cols, model_name)\n",
+    "    for model_name, estimator in trained_models.items()\n",
+    "]\n",
+    "all_model_importance = pd.concat(importance_frames, ignore_index=True)\n",
+    "importance_df = all_model_importance[all_model_importance[\"model_name\"].eq(selected_model_name)].copy()\n",
+    "importance_df = importance_df.rename(columns={\"importance\": \"importance_gain\"})\n",
+    "\n",
+    "plt.figure(figsize=(9, 7))\n",
+    "sns.barplot(data=importance_df.head(25), y=\"feature\", x=\"importance_gain\", color=\"#4C78A8\")\n",
+    "plt.title(f\"Top 25 Feature Importance - {selected_model_name}\")\n",
+    "plt.xlabel(\"Importance\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "display(importance_df.head(30))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "TaY5ZaEJbEfC",
+   "metadata": {
+    "id": "TaY5ZaEJbEfC"
+   },
+   "source": [
+    "## 10. SHAP 기반 영향 공정 분석\n",
+    "\n",
+    "선택된 모델의 SHAP 값을 JSON feature의 공정 prefix(`PRESS__`, `BODY__`, `PAINT__`)로\n",
+    "집계합니다. 이를 통해 다음 공정 불량 확률을 높인 이전 공정과 주요 센서값을 계산합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24Ol6sMmbEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24Ol6sMmbEfC",
+    "outputId": "ca41c28e-e024-41a1-d30e-52eface69410"
+   },
+   "outputs": [],
+   "source": [
+    "# SHAP 샘플링\n",
+    "SHAP_SAMPLE_SIZE = min(1000, len(X_valid))\n",
+    "\n",
+    "if selected_model_name == \"LogisticRegression\":\n",
+    "    SHAP_SAMPLE_SIZE = min(500, len(X_valid))\n",
+    "\n",
+    "X_shap = X_valid.sample(n=SHAP_SAMPLE_SIZE, random_state=RANDOM_STATE)\n",
+    "id_shap = id_valid.loc[X_shap.index]\n",
+    "y_shap = y_valid.loc[X_shap.index]\n",
+    "proba_shap = predict_positive_proba(model, X_shap)\n",
+    "\n",
+    "\n",
+    "def prepare_shap_model(estimator, X_sample: pd.DataFrame):\n",
+    "    if is_pipeline_estimator(estimator):\n",
+    "        final_estimator = get_final_estimator(estimator)\n",
+    "        transform_steps = estimator.steps[:-1]\n",
+    "\n",
+    "        X_transformed = X_sample.copy()\n",
+    "\n",
+    "        for _, step in transform_steps:\n",
+    "            if hasattr(step, \"transform\"):\n",
+    "                X_transformed = step.transform(X_transformed)\n",
+    "\n",
+    "        X_transformed = pd.DataFrame(\n",
+    "            X_transformed,\n",
+    "            columns=X_sample.columns,\n",
+    "            index=X_sample.index\n",
+    "        )\n",
+    "\n",
+    "        return final_estimator, X_transformed\n",
+    "\n",
+    "    return estimator, X_sample\n",
+    "\n",
+    "\n",
+    "shap_model, X_shap_model = prepare_shap_model(model, X_shap)\n",
+    "\n",
+    "# SHAP Explainer 생성\n",
+    "try:\n",
+    "    explainer = shap.TreeExplainer(shap_model)\n",
+    "\n",
+    "    raw_shap_values = explainer.shap_values(\n",
+    "        X_shap_model,\n",
+    "        check_additivity=False\n",
+    "    )\n",
+    "\n",
+    "    if isinstance(raw_shap_values, list):\n",
+    "        shap_matrix = raw_shap_values[1]\n",
+    "    elif getattr(raw_shap_values, \"ndim\", 0) == 3:\n",
+    "        shap_matrix = raw_shap_values[:, :, 1]\n",
+    "    else:\n",
+    "        shap_matrix = raw_shap_values\n",
+    "\n",
+    "    expected_value = explainer.expected_value\n",
+    "\n",
+    "    if isinstance(expected_value, list):\n",
+    "        base_value = expected_value[1]\n",
+    "    else:\n",
+    "        base_value = expected_value\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(\"TreeExplainer 실패:\", e)\n",
+    "    print(\"Permutation Explainer로 대체 실행합니다.\")\n",
+    "\n",
+    "    background = shap.sample(\n",
+    "        X_train,\n",
+    "        min(100, len(X_train)),\n",
+    "        random_state=RANDOM_STATE\n",
+    "    )\n",
+    "\n",
+    "    explainer = shap.Explainer(\n",
+    "        lambda data: predict_positive_proba(\n",
+    "            model,\n",
+    "            pd.DataFrame(data, columns=X_train.columns)\n",
+    "        ),\n",
+    "        background\n",
+    "    )\n",
+    "\n",
+    "    raw_explanation = explainer(\n",
+    "        X_shap,\n",
+    "        max_evals=2 * X_shap.shape[1] + 1\n",
+    "    )\n",
+    "\n",
+    "    shap_matrix = raw_explanation.values\n",
+    "    base_value = raw_explanation.base_values\n",
+    "    X_shap_model = X_shap\n",
+    "\n",
+    "\n",
+    "# base_value 길이 보정\n",
+    "if np.isscalar(base_value):\n",
+    "    base_values = np.repeat(base_value, len(X_shap))\n",
+    "else:\n",
+    "    base_values = np.array(base_value)\n",
+    "\n",
+    "    if base_values.ndim == 0:\n",
+    "        base_values = np.repeat(float(base_values), len(X_shap))\n",
+    "    elif len(base_values) != len(X_shap):\n",
+    "        base_values = np.repeat(float(np.ravel(base_values)[0]), len(X_shap))\n",
+    "\n",
+    "\n",
+    "# SHAP 객체 구성\n",
+    "shap_values = shap.Explanation(\n",
+    "    values=shap_matrix,\n",
+    "    base_values=base_values,\n",
+    "    data=X_shap_model.values,\n",
+    "    feature_names=X_shap.columns.tolist(),\n",
+    ")\n",
+    "\n",
+    "\n",
+    "print(\"selected_model_name:\", selected_model_name)\n",
+    "print(\"X_shap:\", X_shap.shape)\n",
+    "print(\"X_shap_model:\", X_shap_model.shape)\n",
+    "print(\"shap_matrix:\", shap_matrix.shape)\n",
+    "print(\"base_values:\", np.array(base_values).shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "gvPdq4r1bEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "gvPdq4r1bEfC",
+    "outputId": "6871cde5-1c84-4060-a5b6-f5d757281281"
+   },
+   "outputs": [],
+   "source": [
+    "# SHAP Summary Plot\n",
+    "shap.summary_plot(shap_values, X_shap, max_display=25, show=True)\n",
+    "\n",
+    "# SHAP 중요도 산출\n",
+    "shap_importance = pd.DataFrame({\n",
+    "    \"feature\": X_shap.columns,\n",
+    "    \"mean_abs_shap\": np.abs(shap_matrix).mean(axis=0),\n",
+    "}).sort_values(\"mean_abs_shap\", ascending=False)\n",
+    "\n",
+    "display(shap_importance.head(30))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Feature 공정 매핑\n",
+    "def feature_to_process(feature: str) -> str | None:\n",
+    "    for process_code in PROCESS_FLOW:\n",
+    "        if feature.startswith(f\"{process_code}__\"):\n",
+    "            return process_code\n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "def probability_to_risk_grade(prob: float) -> str:\n",
+    "    if prob >= 0.70:\n",
+    "        return \"HIGH\"\n",
+    "    if prob >= 0.40:\n",
+    "        return \"MEDIUM\"\n",
+    "    return \"LOW\"\n",
+    "\n",
+    "\n",
+    "feature_process = pd.Series(\n",
+    "    [feature_to_process(column) for column in X_shap.columns],\n",
+    "    index=X_shap.columns,\n",
+    ")\n",
+    "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
+    "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
+    "print(f\"공정 영향도 집계 제외 전역 feature 수: {len(unmapped_features)}\")\n",
+    "if unmapped_features:\n",
+    "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
+    "\n",
+    "process_rows = []\n",
+    "for process_code in PROCESS_FLOW:\n",
+    "    column_idx = np.where(feature_process.values == process_code)[0]\n",
+    "    process_rows.append({\n",
+    "        \"process_code\": process_code,\n",
+    "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, column_idx]).mean()) if len(column_idx) else 0.0,\n",
+    "        \"feature_count\": int(len(column_idx)),\n",
+    "    })\n",
+    "\n",
+    "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
+    "display(process_importance)\n",
+    "\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
+    "plt.title(\"SHAP Process Influence\")\n",
+    "plt.xlabel(\"Source Process\")\n",
+    "plt.ylabel(\"Mean |SHAP|\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 891
+    },
+    "id": "m-fwpoIeBrfa",
+    "outputId": "b70d837d-df0c-4159-fc29-c842f24f2558"
+   },
+   "id": "m-fwpoIeBrfa",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "kBJK8YfybEfD",
+   "metadata": {
+    "id": "kBJK8YfybEfD"
+   },
+   "source": [
+    "## 11. SHAP 기반 공정 전이 위험 결과 생성\n",
+    "\n",
+    "실제 학습 행의 원인·대상 공정 정보를 유지하면서 다음 공정 불량 확률, 가장 영향이 큰\n",
+    "JSON feature, 공정별 SHAP 영향 비율을 결과로 생성합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "MjmQshC4bEfD",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "MjmQshC4bEfD",
+    "outputId": "268ba6e7-dc21-4678-beaf-55f184bbb3ec"
+   },
+   "outputs": [],
+   "source": [
+    "def build_transfer_predictions(\n",
+    "    metadata: pd.DataFrame,\n",
+    "    probabilities: np.ndarray,\n",
+    "    shap_matrix: np.ndarray,\n",
+    "    feature_names: list[str],\n",
+    "    feature_process: pd.Series,\n",
+    ") -> pd.DataFrame:\n",
+    "    records = []\n",
+    "    process_values = feature_process.values\n",
+    "    mapped_mask = feature_process.isin(PROCESS_FLOW).to_numpy()\n",
+    "    predicted_at = datetime.now(timezone.utc).isoformat()\n",
+    "\n",
+    "    for row_idx, (_, metadata_row) in enumerate(metadata.iterrows()):\n",
+    "        probability = float(probabilities[row_idx])\n",
+    "        abs_values = np.abs(shap_matrix[row_idx])\n",
+    "        mapped_total = float(abs_values[mapped_mask].sum()) or 1.0\n",
+    "\n",
+    "        process_scores = {}\n",
+    "        for process_code in PROCESS_FLOW:\n",
+    "            column_idx = np.where(process_values == process_code)[0]\n",
+    "            process_scores[process_code] = (\n",
+    "                float(abs_values[column_idx].sum() / mapped_total)\n",
+    "                if len(column_idx) else 0.0\n",
+    "            )\n",
+    "\n",
+    "        source_process = str(metadata_row[\"source_process_code\"])\n",
+    "        target_process = str(metadata_row[\"target_process_code\"])\n",
+    "        available_idx = np.where(\n",
+    "            np.isin(process_values, PROCESS_FLOW[:PROCESS_FLOW.index(target_process)])\n",
+    "        )[0]\n",
+    "        top_feature_idx = (\n",
+    "            int(available_idx[np.argmax(abs_values[available_idx])])\n",
+    "            if len(available_idx)\n",
+    "            else int(abs_values.argmax())\n",
+    "        )\n",
+    "\n",
+    "        records.append({\n",
+    "            \"sample_event_id\": metadata_row[\"sample_event_id\"],\n",
+    "            \"car_master_id\": int(metadata_row[\"car_master_id\"]),\n",
+    "            \"source_process_code\": source_process,\n",
+    "            \"target_process_code\": target_process,\n",
+    "            \"target_defect_label\": int(metadata_row[\"target_defect_label\"]),\n",
+    "            \"target_defect_probability\": round(probability, 6),\n",
+    "            \"predicted_defect_process\": target_process,\n",
+    "            \"risk_grade\": probability_to_risk_grade(probability),\n",
+    "            \"main_cause\": feature_names[top_feature_idx],\n",
+    "            \"source_process_influence_score\": round(process_scores.get(source_process, 0.0), 6),\n",
+    "            \"label_source\": metadata_row[\"label_source\"],\n",
+    "            \"predicted_at\": predicted_at,\n",
+    "        })\n",
+    "\n",
+    "    return pd.DataFrame(records).sort_values(\n",
+    "        [\"target_defect_probability\", \"source_process_influence_score\"],\n",
+    "        ascending=False,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "transfer_predictions = build_transfer_predictions(\n",
+    "    meta_shap,\n",
+    "    proba_shap,\n",
+    "    shap_matrix,\n",
+    "    X_shap.columns.tolist(),\n",
+    "    feature_process,\n",
+    ")\n",
+    "display(transfer_predictions.head(30))\n",
+    "\n",
+    "risk_summary = transfer_predictions.groupby(\n",
+    "    [\"source_process_code\", \"target_process_code\", \"risk_grade\"]\n",
+    ").agg(\n",
+    "    vehicle_count=(\"car_master_id\", \"nunique\"),\n",
+    "    prediction_count=(\"sample_event_id\", \"count\"),\n",
+    "    actual_defect_ratio=(\"target_defect_label\", \"mean\"),\n",
+    "    avg_target_defect_probability=(\"target_defect_probability\", \"mean\"),\n",
+    "    avg_source_process_influence=(\"source_process_influence_score\", \"mean\"),\n",
+    ").reset_index().sort_values(\n",
+    "    [\"avg_target_defect_probability\", \"prediction_count\"],\n",
+    "    ascending=False,\n",
+    ")\n",
+    "display(risk_summary)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "JFTbBuS1bEfD",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 885
+    },
+    "id": "JFTbBuS1bEfD",
+    "outputId": "6afce295-117c-486d-d7df-abe3aade47a3"
+   },
+   "outputs": [],
+   "source": [
+    "# 전이 위험 시각화\n",
+    "plt.figure(figsize=(9, 5))\n",
+    "top_flow = risk_summary.head(12).copy()\n",
+    "top_flow[\"flow\"] = top_flow[\"source_process_code\"] + \" -> \" + top_flow[\"target_process_code\"] + \" (\" + top_flow[\"risk_grade\"] + \")\"\n",
+    "sns.barplot(data=top_flow, y=\"flow\", x=\"avg_target_defect_probability\", hue=\"risk_grade\", dodge=False)\n",
+    "plt.title(\"Process Transfer Risk by SHAP Influence\")\n",
+    "plt.xlabel(\"Avg Final Defect Probability\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.legend(title=\"Risk\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 불량 확률 분포 시각화\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "sns.histplot(data=transfer_predictions, x=\"target_defect_probability\", hue=\"risk_grade\", bins=40, multiple=\"stack\")\n",
+    "plt.title(\"Predicted Final Defect Probability Distribution\")\n",
+    "plt.xlabel(\"Defect Probability\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43d99e60",
+   "metadata": {
+    "id": "43d99e60"
+   },
+   "source": [
+    "## 12. AI 모델 관리 항목\n",
+    "\n",
+    "모델 선정 근거, 데이터셋 규모, 테스트 케이스 수, Accuracy, False Positive Rate, 성능 개선 현황을 운영 관리용 요약으로 정리합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c79bba33",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 237
+    },
+    "id": "c79bba33",
+    "outputId": "b4ed5007-4f34-40a7-8c33-edc4bc328bb1"
+   },
+   "outputs": [],
+   "source": [
+    "ai_management_table = pd.DataFrame([\n",
+    "    {\"관리 항목\": \"모델 선정 근거 정리\", \"값\": ai_management_summary[\"모델_선정_근거\"]},\n",
+    "    {\"관리 항목\": \"데이터셋 규모 관리\", \"값\": json.dumps(ai_management_summary[\"데이터셋_규모_관리\"], ensure_ascii=False)},\n",
+    "    {\"관리 항목\": \"테스트 케이스 수 관리\", \"값\": json.dumps(ai_management_summary[\"테스트_케이스_수_관리\"], ensure_ascii=False)},\n",
+    "    {\"관리 항목\": \"정확도(Accuracy) 측정\", \"값\": round(ai_management_summary[\"정확도_Accuracy\"], 6)},\n",
+    "    {\"관리 항목\": \"오탐률(False Positive Rate) 측정\", \"값\": round(ai_management_summary[\"오탐률_False_Positive_Rate\"], 6)},\n",
+    "    {\"관리 항목\": \"모델 성능 개선 현황 관리\", \"값\": json.dumps(ai_management_summary[\"모델_성능_개선_현황\"], ensure_ascii=False)},\n",
+    "])\n",
+    "\n",
+    "display(ai_management_table)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "Q5r_SU2bbEfD",
+   "metadata": {
+    "id": "Q5r_SU2bbEfD"
+   },
+   "source": [
+    "## 13. 모델 및 결과 저장\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44a36633",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "44a36633",
+    "outputId": "9dbc6133-c938-4715-dc60-ea025f990e55"
+   },
+   "outputs": [],
+   "source": [
+    "# 저장 경로\n",
+    "selected_model_path = OUTPUT_DIR / \"selected_defect_detector.joblib\"\n",
+    "selected_model_pkl_path = OUTPUT_DIR / \"selected_defect_detector.pkl\"\n",
+    "candidate_models_path = OUTPUT_DIR / \"candidate_defect_models.joblib\"\n",
+    "candidate_models_pkl_path = OUTPUT_DIR / \"candidate_defect_models.pkl\"\n",
+    "model_path = OUTPUT_DIR / \"lightgbm_defect_detector.joblib\"\n",
+    "model_pkl_path = OUTPUT_DIR / \"lightgbm_defect_detector.pkl\"\n",
+    "feature_path = OUTPUT_DIR / \"defect_model_features.json\"\n",
+    "metrics_path = OUTPUT_DIR / \"defect_model_metrics.json\"\n",
+    "comparison_path = OUTPUT_DIR / \"defect_model_comparison.csv\"\n",
+    "confusion_matrix_path = OUTPUT_DIR / \"defect_model_confusion_matrices.json\"\n",
+    "ai_management_path = OUTPUT_DIR / \"defect_ai_management_summary.json\"\n",
+    "importance_path = OUTPUT_DIR / \"defect_model_feature_importance.csv\"\n",
+    "all_importance_path = OUTPUT_DIR / \"defect_all_model_feature_importance.csv\"\n",
+    "shap_importance_path = OUTPUT_DIR / \"defect_shap_importance.csv\"\n",
+    "transfer_path = OUTPUT_DIR / \"defect_transfer_prediction_result.csv\"\n",
+    "risk_summary_path = OUTPUT_DIR / \"defect_transfer_risk_summary.csv\"\n",
+    "\n",
+    "# 모델 저장\n",
+    "joblib.dump(model, selected_model_path)\n",
+    "joblib.dump(trained_models, candidate_models_path)\n",
+    "with open(selected_model_pkl_path, \"wb\") as f:\n",
+    "    pickle.dump(model, f)\n",
+    "with open(candidate_models_pkl_path, \"wb\") as f:\n",
+    "    pickle.dump(trained_models, f)\n",
+    "\n",
+    "# 기존 LightGBM 파일명 호환 저장\n",
+    "if \"LightGBM\" in trained_models:\n",
+    "    joblib.dump(trained_models[\"LightGBM\"], model_path)\n",
+    "    with open(model_pkl_path, \"wb\") as f:\n",
+    "        pickle.dump(trained_models[\"LightGBM\"], f)\n",
+    "\n",
+    "feature_path.write_text(json.dumps(feature_cols, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "\n",
+    "confusion_payload = {\n",
+    "    name: matrix.tolist()\n",
+    "    for name, matrix in model_confusion_matrices.items()\n",
+    "}\n",
+    "confusion_matrix_path.write_text(json.dumps(confusion_payload, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "ai_management_path.write_text(json.dumps(ai_management_summary, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "\n",
+    "# 평가 지표 저장\n",
+    "metrics = {\n",
+    "    \"selected_model_name\": selected_model_name,\n",
+    "    \"roc_auc\": float(roc_auc),\n",
+    "    \"average_precision\": float(pr_auc),\n",
+    "    \"accuracy\": float(accuracy),\n",
+    "    \"false_positive_rate\": float(false_positive_rate),\n",
+    "    \"best_threshold\": float(best_threshold),\n",
+    "    \"f1_at_best_threshold\": float(f1_score(y_valid, valid_pred, zero_division=0)),\n",
+    "    \"confusion_matrix\": cm.tolist(),\n",
+    "    \"train_rows\": int(len(X_train)),\n",
+    "    \"valid_rows\": int(len(X_valid)),\n",
+    "    \"feature_count\": int(len(feature_cols)),\n",
+    "    \"positive_ratio_train\": float(y_train.mean()),\n",
+    "    \"candidate_models\": list(trained_models.keys()),\n",
+    "    \"cv_splits\": int(effective_cv_splits),\n",
+    "    \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
+    "    \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+    "    \"smote_enabled\": bool(USE_SMOTE),\n",
+    "    \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
+    "    \"threshold_strategy\": {\n",
+    "        \"type\": \"recall_priority\",\n",
+    "        \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
+    "        \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
+    "        \"beta\": float(THRESHOLD_BETA),\n",
+    "    },\n",
+    "    \"model_selection_reason\": ai_management_summary[\"모델_선정_근거\"],\n",
+    "    \"data_source\": \"manufacturing_event_json.event_json\",\n",
+    "    \"label_restore_strategy\": preprocessing_metadata[\"label_sources\"],\n",
+    "    \"transition_summary\": transition_summary.to_dict(orient=\"records\"),\n",
+    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
+    "}\n",
+    "metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "\n",
+    "# 분석 결과 저장\n",
+    "cv_results.to_csv(cv_results_path, index=False)\n",
+    "if \"cv_fold_results\" in globals():\n",
+    "    cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
+    "model_comparison.to_csv(comparison_path, index=False)\n",
+    "importance_df.to_csv(importance_path, index=False)\n",
+    "all_model_importance.to_csv(all_importance_path, index=False)\n",
+    "shap_importance.to_csv(shap_importance_path, index=False)\n",
+    "transfer_predictions.to_csv(transfer_path, index=False)\n",
+    "risk_summary.to_csv(risk_summary_path, index=False)\n",
+    "\n",
+    "print(\"Saved outputs:\")\n",
+    "for path in [\n",
+    "    selected_model_path,\n",
+    "    selected_model_pkl_path,\n",
+    "    candidate_models_path,\n",
+    "    candidate_models_pkl_path,\n",
+    "    model_path,\n",
+    "    model_pkl_path,\n",
+    "    feature_path,\n",
+    "    metrics_path,\n",
+    "    cv_results_path,\n",
+    "    comparison_path,\n",
+    "    confusion_matrix_path,\n",
+    "    ai_management_path,\n",
+    "    importance_path,\n",
+    "    all_importance_path,\n",
+    "    shap_importance_path,\n",
+    "    transfer_path,\n",
+    "    risk_summary_path,\n",
+    "]:\n",
+    "    print(path)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/app/scheduler/manufacturing/manufacturing_event_scheduler.py b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
index e351186..fc43f17 100644
--- a/app/scheduler/manufacturing/manufacturing_event_scheduler.py
+++ b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
@@ -108,7 +108,7 @@ def _materialize_tomorrow_events() -> dict[str, object] | None:
 
 def _seconds_until_next_run() -> float:
     now = datetime.now(SEOUL_TZ)
-    next_run = datetime.combine(now.date(), time(hour=0, minute=10), tzinfo=SEOUL_TZ)
+    next_run = datetime.combine(now.date(), time(hour=17), tzinfo=SEOUL_TZ)
     if next_run <= now:
         next_run += timedelta(days=1)
     return max(60.0, (next_run - now).total_seconds())
diff --git a/requirements.txt b/requirements.txt
index e7ae5bc..0425497 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,6 +30,8 @@ scikit-learn>=1.5.0
 lightgbm>=4.5.0
 xgboost>=2.1.0
 shap>=0.46.0
+optuna>=4.0.0,<5.0.0
+imbalanced-learn>=0.12.0,<1.0.0
 
 # Common
 orjson>=3.10.0

From 0ea54c3741944be0f44c2ff58a6709106ef5345e Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 22 Jun 2026 16:52:05 +0900
Subject: [PATCH 013/148] =?UTF-8?q?fix:=20=EC=A0=9C=EC=A1=B0=20=EC=9D=B4?=
 =?UTF-8?q?=EB=B2=A4=ED=8A=B8=20=EC=83=9D=EC=84=B1=20=EB=B0=8F=20=EC=A0=80?=
 =?UTF-8?q?=EC=9E=A5=20=EA=B5=AC=EC=A1=B0=20=EA=B0=9C=EC=84=A0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- manufacturing_event_json 스키마 및 JSON 구조 반영

- product.carMasterId를 기존 car_master.id와 매핑

- 차량당 PRESS, BODY, PAINT, ASSEMBLY 이벤트 4건 생성

- 정상·이상 데이터 비율을 7:3으로 구성하고 공정별 균등 분배

- 공정별 설비와 operationStatus를 결정적 랜덤 방식으로 생성

- eventTime, lastNormalTime, statusChangedTime 초기값을 NULL로 설정

- PRESS는 READY, 후속 공정은 PENDING으로 초기화

- MySQL 데드락 재시도 및 생성 작업 전역 잠금 처리 추가

- SampleDB Repository를 테이블과 책임별 클래스로 분리

- 제조 이벤트 API, 테이블, Repository 및 환경변수 README 문서화
---
 .env.example                                  |    6 +-
 .gitignore                                    |    5 +-
 README.md                                     |  166 +
 app/api/routers/manufacturing_event.py        |   80 +-
 app/batch/manufacturing_event_loader.py       |   11 +-
 app/core/config.py                            |   12 +-
 .../manufacturing_event_json_builder.py       |  592 ++-
 app/docs/PRD_Manufacturing.md                 | 3428 -----------------
 app/repository/__init__.py                    |   24 +
 .../bottleneck_analysis_repository.py         |    2 +-
 app/repository/car_master_repository.py       |   84 +
 app/repository/equipment_repository.py        |   81 +
 .../manufacturing_event_repository.py         |  203 +
 ...manufacturing_event_template_repository.py |  119 +
 ...manufacturing_generation_job_repository.py |  189 +
 app/repository/sampledb_repository.py         |  572 +--
 app/repository/sampledb_schema.py             |  168 +-
 app/repository/sampledb_schema_manager.py     |  140 +
 .../manufacturing_event_scheduler.py          |   27 +-
 .../manufacturing_event_json_service.py       |  599 +--
 20 files changed, 2027 insertions(+), 4481 deletions(-)
 delete mode 100644 app/docs/PRD_Manufacturing.md
 create mode 100644 app/repository/car_master_repository.py
 create mode 100644 app/repository/equipment_repository.py
 create mode 100644 app/repository/manufacturing_event_repository.py
 create mode 100644 app/repository/manufacturing_event_template_repository.py
 create mode 100644 app/repository/manufacturing_generation_job_repository.py
 create mode 100644 app/repository/sampledb_schema_manager.py

diff --git a/.env.example b/.env.example
index f9428aa..0dd2683 100644
--- a/.env.example
+++ b/.env.example
@@ -27,7 +27,7 @@ SAMPLE_DATABASE_URL=mysql+pymysql://sample_user:change-me@localhost:3306/sampled
 
 # Manufacturing source event JSON generation
 MANUFACTURING_EVENT_SCHEDULER_ENABLED=true
-MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY=86400
-MANUFACTURING_EVENT_TEMPLATE_EVENT_COUNT=86400
-MANUFACTURING_EVENT_CAR_POOL_SIZE=10000
+# Optional limits. If omitted, all car_master rows matching the production date are used.
+# MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY=42800
+# MANUFACTURING_EVENT_CAR_POOL_SIZE=10700
 MANUFACTURING_EVENT_INSERT_CHUNK_SIZE=1000
diff --git a/.gitignore b/.gitignore
index e6fce38..07bcc90 100644
--- a/.gitignore
+++ b/.gitignore
@@ -38,4 +38,7 @@ htmlcov/
 Thumbs.db
 
 # DataSet
-datasets/
\ No newline at end of file
+datasets/
+
+# Docs
+app/docs/PRD_*.md
diff --git a/README.md b/README.md
index 9dc6996..57ed401 100644
--- a/README.md
+++ b/README.md
@@ -243,3 +243,169 @@ FastAPI
 - 모든 요청 흐름은 FastAPI를 통해 중앙 집중적으로 처리됩니다.
 - Kubernetes 환경에서는 `ai-services` Namespace 내 Pod로 배포됩니다.
 - 향후 AI 서비스 추가 시 FastAPI에서 Orchestration만 확장하면 되므로 확장성이 높습니다.
+
+## 제조 이벤트 데이터 생성
+
+제조 이벤트 생성 기능은 기존 `car_master`와 `equipment` 마스터를 참조하여
+`manufacturing_event_json`에 원천 이벤트를 저장합니다. 차량 마스터를 생성하거나
+수정하지 않으며, `car_master.id=1`부터 요청한 차량 수만큼 순서대로 매핑합니다.
+
+### 기본 생성 규칙
+
+- 차량 수: `vehicle_id`의 생산일자가 요청 날짜와 일치하는 `car_master` 전체
+- 차량당 이벤트 수: 4건
+- 전체 이벤트 수: 해당 날짜 차량 수 × 4
+- 공정 순서: `PRESS -> BODY -> PAINT -> ASSEMBLY`
+- 공정별 이벤트 수: 해당 날짜 차량 수와 동일
+- 정상/이상 비율: 약 7:3
+- 공정별 이상 이벤트: 각 공정에 균등 분배
+- 설비: 차량의 공정마다 1~5호기 중 독립적으로 결정
+- 최초 발행 상태: `PRESS=READY`, 나머지 공정은 `PENDING`
+
+예를 들어 `AVANTE-20260601-10474`처럼 `vehicle_id`에 `20260601`이 포함된
+차량이 10,700대라면 정상 7,490대, 폐기(이상) 3,210대로 구성하고 총
+42,800건의 이벤트를 생성합니다.
+
+`event_count`와 `car_pool_size`의 기본값은 `null`입니다. 값을 생략하면 해당
+생산일자의 차량 전체를 사용합니다. 제한값을 지정한다면 `event_count`는
+`car_pool_size * 4`와 같아야 합니다.
+
+### 이벤트 JSON 구조
+
+`manufacturing_event_json.event_json`에는 아래 최상위 필드만 저장합니다.
+아래 JSON은 필드 위치를 보여주는 축약 예시이며, 실제 생성 시 `sensor`,
+`processMetrics`, `sourceTrace`, `processData`의 공정별 상세 값이 채워집니다.
+
+```json
+{
+  "event": {
+    "eventId": "EVT-20260601-000001",
+    "eventTime": null,
+    "eventType": "PROCESS_STATUS",
+    "eventName": "프레스 공정 통합 관제 이벤트"
+  },
+  "equipment": {
+    "equipmentCode": "EQ_PRESS_001",
+    "equipmentName": "프레스 유압모터 1호",
+    "equipmentType": "HYDRAULIC_PRESS"
+  },
+  "equipmentStatus": {
+    "operationStatus": "RUNNING",
+    "lastNormalTime": null,
+    "statusChangedTime": null
+  },
+  "product": {
+    "carMasterId": 1
+  },
+  "sensor": {},
+  "processMetrics": {},
+  "sourceTrace": {},
+  "processData": {
+    "press": {
+      "countIncreaseYn": true,
+      "targetCycleTimeSec": 40.0,
+      "timestampDelaySec": 5.7
+    }
+  }
+}
+```
+
+- `product.carMasterId`는 기존 `car_master.id`입니다.
+- DB 컬럼 `event_time`과 JSON의 `event.eventTime`은 최초 생성 시 `NULL`입니다.
+- `lastNormalTime`, `statusChangedTime`도 최초 생성 시 `NULL`입니다.
+- `operationStatus`는 정상 데이터의 경우 `RUNNING`/`IDLE`, 이상 데이터의 경우
+  `FAULT`/`STOPPED`/`MAINTENANCE` 중에서 결정됩니다.
+- 같은 차량과 공정은 재생성해도 동일한 상태를 갖도록 결정적 난수를 사용합니다.
+- `processData`에는 현재 공정에 해당하는 `press`, `body`, `paint`, `assembly`
+  블록 중 하나만 포함됩니다.
+
+### 생성 API
+
+모든 제조 이벤트 API prefix는 `/api/manufacturing/events`입니다.
+
+| Method | Endpoint | 설명 |
+| --- | --- | --- |
+| `POST` | `/templates/generate` | 날짜 재생용 템플릿 생성 |
+| `GET` | `/templates/{template_name}` | 저장된 템플릿 조회 |
+| `GET` | `/templates/{template_name}/replay` | DB 저장 없이 특정 날짜 기준 replay 조회 |
+| `POST` | `/generate` | 지정 날짜의 실제 이벤트 생성 및 적재 |
+| `POST` | `/generate/tomorrow` | 템플릿을 이용해 다음날 이벤트 적재 |
+| `GET` | `/generate/jobs/{job_id}` | 비동기 생성 job 상태 조회 |
+| `GET` | `/` | 저장된 이벤트 조회 |
+
+예를 들어 2026년 6월 1일 차량 전체의 이벤트는 Swagger에서 `POST /generate`를
+아래처럼 호출하여 생성할 수 있습니다.
+
+```text
+start_date=2026-06-01
+end_date=2026-06-01
+event_count=null
+car_pool_size=null
+insert_chunk_size=1000
+```
+
+API는 즉시 `jobId`를 반환합니다. 이후
+`GET /api/manufacturing/events/generate/jobs/{jobId}`에서 `SUCCEEDED` 여부를
+확인합니다. 실패한 job을 다시 실행할 때는 `/generate`를 새로 호출하면 되며,
+동일한 `event_id`는 중복 생성하지 않고 갱신합니다.
+
+템플릿 생성도 `event_count`와 `car_pool_size`의 기본값은 `null`입니다.
+`production_date`와 `vehicle_id` 생산일자가 일치하는 차량 전체를 사용합니다.
+
+### 관련 테이블
+
+| 테이블 | 용도 |
+| --- | --- |
+| `car_master` | 기존 차량 마스터. 생성 로직은 조회만 수행 |
+| `equipment` | 공정별 설비 마스터 |
+| `manufacturing_event_json` | 실제 원천 이벤트 JSON |
+| `manufacturing_event_template` | 날짜별 재생에 사용하는 이벤트 템플릿 |
+| `manufacturing_event_generation_job` | 비동기 생성 작업 상태와 진행률 |
+
+### Repository 구조
+
+`SampleDbRepository`는 DB 연결과 아래 repository를 묶는 얇은 facade입니다.
+테이블별 SQL과 상태 관리 로직은 각각의 repository에 위치합니다.
+
+```text
+app/repository/
+├── sampledb_repository.py
+├── sampledb_schema.py
+├── sampledb_schema_manager.py
+├── car_master_repository.py
+├── equipment_repository.py
+├── manufacturing_event_repository.py
+├── manufacturing_event_template_repository.py
+└── manufacturing_generation_job_repository.py
+```
+
+- `CarMasterRepository`: 기존 차량 조회 및 ID 매핑
+- `EquipmentRepository`: 기본 설비 초기화 및 조회
+- `ManufacturingEventRepository`: 이벤트 저장·갱신·조회 및 데드락 재시도
+- `ManufacturingEventTemplateRepository`: 템플릿 저장·조회·삭제
+- `ManufacturingGenerationJobRepository`: job 상태·진행률 및 DB 전역 실행 잠금
+- `SampleDbSchemaManager`: 테이블 생성과 점진적 스키마 마이그레이션
+
+MySQL 오류 `1205` 또는 `1213`이 발생하면 이벤트 저장 청크를 자동으로
+재시도합니다. 다중 프로세스나 `uvicorn --reload` 환경에서는 DB advisory lock으로
+생성 job이 동시에 실행되지 않도록 직렬화합니다.
+
+### 환경변수
+
+아래 값은 코드 기본값이 있으므로 `.env`에 없더라도 동일하게 동작합니다.
+운영 환경에서 값을 변경할 때만 설정하면 됩니다.
+
+```dotenv
+MANUFACTURING_EVENT_SCHEDULER_ENABLED=true
+MANUFACTURING_EVENT_INSERT_CHUNK_SIZE=1000
+```
+
+`MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY`과
+`MANUFACTURING_EVENT_CAR_POOL_SIZE`를 생략하면 대상 날짜의 차량 전체를
+사용합니다.
+
+스케줄러를 사용하지 않으려면 다음과 같이 설정합니다.
+
+```dotenv
+MANUFACTURING_EVENT_SCHEDULER_ENABLED=false
+```
diff --git a/app/api/routers/manufacturing_event.py b/app/api/routers/manufacturing_event.py
index 50b48d2..92dc686 100644
--- a/app/api/routers/manufacturing_event.py
+++ b/app/api/routers/manufacturing_event.py
@@ -22,7 +22,8 @@
 INSERT_CHUNK_SIZE_DESCRIPTION = (
     "DB에 한 번에 적재할 batch insert row 수입니다. "
     "서비스는 생성된 row를 메모리 chunk에 모은 뒤 이 값에 도달할 때마다 한 번의 INSERT를 실행합니다. "
-    "MySQL에서는 중복 키 발생 시 generate 계열은 기존 row를 갱신하고, 템플릿 생성은 replace/update 옵션에 따라 갱신 또는 무시합니다."
+    "generate 계열은 동일 event_id를 먼저 조회해 기존 row를 갱신하고, "
+    "템플릿 생성은 replace/update 옵션에 따라 갱신 또는 무시합니다."
 )
 EVENT_JSON_TABLE_DESCRIPTION = """
 ### 저장 테이블: `manufacturing_event_json`
@@ -31,23 +32,23 @@
 | --- | --- |
 | `id` | DB 내부 PK, 자동 증가 |
 | `event_id` | 이벤트 고유 ID. 예: `EVT-20260616-000001` |
-| `event_time` | 실제 이벤트 발생 시각 |
-| `car_master_id` | `car_master.id` FK |
-| `equipment_id` | `equipment.id` FK |
+| `event_time` | 최초 생성 시 `NULL`. 실제 발행/처리 시각이 확정되면 갱신 |
+| `car_master_id` | 기존 `car_master.id` 매핑 값 |
+| `equipment_id` | 기존 `equipment.id` 매핑 값 |
 | `process_code` | 공정 코드. `PRESS`, `BODY`, `PAINT`, `ASSEMBLY` |
-| `station_code` | 공정 내 스테이션 코드 |
-| `equipment_code` | 설비 코드. 예: `EQ_PRESS_001` |
-| `equipment_type` | 설비 유형. 예: `HYDRAULIC_PRESS`, `ROBOT_ARM`, `CAMERA`, `CONVEYOR` |
-| `equipment_status` | 설비 운전 상태. 예: `RUNNING`, `IDLE`, `ERROR` |
-| `event_type` | 이벤트 유형. 예: `PROCESS_STATUS`, `EQUIPMENT_SENSOR`, `QUALITY_CHECK` |
 | `event_json` | 관제 화면/연계 시스템에 전달할 원본 JSON payload |
+| `dispatch_status` | 발행 상태. PRESS는 `READY`, 후속 공정은 `PENDING`으로 생성 |
+| `analysis_status` | 분석 상태. 최초값 `NOT_ANALYZED` |
 | `is_sent` | 외부 시스템 전송 여부. 기본값 `false` |
-| `sent_at` | 외부 시스템 전송 완료 시각 |
+| `retry_count` | 발행 재시도 횟수. 최초값 `0` |
+| `error_message` | 발행 실패 메시지 |
 | `created_at` | DB row 생성 시각 |
 | `updated_at` | DB row 마지막 수정 시각. 신규 insert와 중복 `event_id` upsert 시 갱신 |
 
-`event_json`에는 `event`, `location`, `equipment`, `equipmentStatus`, `product`,
-`sensor`, `manufacturing`, `processMetrics`, `sourceTrace`, `processData`가 포함됩니다.
+최초 생성 시 `event_json.event.eventTime`도 `null`로 저장됩니다.
+
+`event_json`에는 `event`, `equipment`, `equipmentStatus`, `product`, `sensor`,
+`processMetrics`, `sourceTrace`, `processData`만 포함됩니다.
 """
 TEMPLATE_TABLE_DESCRIPTION = """
 ### 저장 테이블: `manufacturing_event_template`
@@ -87,7 +88,10 @@
         "### 동작 방식\n"
         "- API는 job row를 생성한 뒤 즉시 `jobId`를 반환합니다.\n"
         "- 실제 템플릿 생성과 DB 저장은 앱 내부 background worker가 수행합니다.\n"
-        "- 프레스(PRESS), 차체(BODY), 도장(PAINT), 의장(ASSEMBLY) 공정이 순환 생성됩니다.\n"
+        "- production_date와 vehicle_id 생산일자가 일치하는 차량 전체를 사용합니다.\n"
+        "- 차량마다 PRESS, BODY, PAINT, ASSEMBLY 이벤트가 정확히 1건씩 생성됩니다.\n"
+        "- event_count와 car_pool_size를 생략하면 실제 차량 수를 기준으로 계산합니다.\n"
+        "- 전체/공정별 정상:폐기(이상) 비율은 약 7:3입니다.\n"
         "- 날짜가 고정된 row가 아니라 하루 안에서의 이벤트 발생 위치를 `event_offset_us`로 저장합니다.\n"
         "- `replace=true`이면 같은 `template_name`의 기존 row를 삭제한 뒤 새로 생성합니다.\n"
         "- `insert_chunk_size` 단위로 row를 모아 batch insert합니다.\n\n"
@@ -107,17 +111,21 @@ def generate_manufacturing_event_template(
         default=DEFAULT_TEMPLATE_NAME,
         description="생성할 템플릿 이름입니다. replay와 tomorrow 적재 시 이 이름으로 템플릿을 선택합니다.",
     ),
-    event_count: int = Query(
-        default=DEFAULT_EVENTS_PER_DAY,
+    production_date: date = Query(
+        default=date(2026, 6, 1),
+        description="vehicle_id에서 조회할 생산일자입니다.",
+    ),
+    event_count: int | None = Query(
+        default=None,
         ge=4,
         le=300000,
-        description="하루 기준 템플릿 이벤트 수입니다. 기본값은 86,400건입니다.",
+        description="선택적 템플릿 이벤트 수 제한입니다. 생략하면 생산일자 차량 전체 수 * 4로 계산합니다.",
     ),
-    car_pool_size: int = Query(
-        default=DEFAULT_CAR_POOL_SIZE,
+    car_pool_size: int | None = Query(
+        default=None,
         ge=1,
         le=100000,
-        description="이벤트에 분배할 차량 마스터 풀 크기입니다.",
+        description="선택적 차량 수 제한입니다. 생략하면 생산일자의 차량 전체를 사용합니다.",
     ),
     insert_chunk_size: int = Query(
         default=DEFAULT_INSERT_CHUNK_SIZE,
@@ -135,6 +143,7 @@ def generate_manufacturing_event_template(
 ) -> CommonResponse[dict]:
     result = service.enqueue_generate_template_job(
         template_name=template_name,
+        production_date=production_date,
         event_count=event_count,
         car_pool_size=car_pool_size,
         insert_chunk_size=insert_chunk_size,
@@ -257,8 +266,9 @@ def replay_manufacturing_event_template(
         "- API는 job row를 생성한 뒤 즉시 `jobId`를 반환합니다.\n"
         "- 실제 이벤트 생성과 DB 저장은 앱 내부 background worker가 수행합니다.\n"
         "- 진행 상태는 job 상태 조회 API로 확인합니다.\n"
-        "- 전체 생성 건수는 `(end_date - start_date + 1) * events_per_day`입니다.\n"
-        "- 공정은 `PRESS -> BODY -> PAINT -> ASSEMBLY` 순서로 순환 배치됩니다.\n"
+        "- event_count가 없으면 각 날짜의 `vehicle_id` 생산일자에 해당하는 car_master 전체를 사용합니다.\n"
+        "- 각 carMasterId마다 `PRESS -> BODY -> PAINT -> ASSEMBLY` 4건이 생성됩니다.\n"
+        "- 실제 차량 수를 기준으로 정상 70%, 폐기(이상) 30% 비율을 적용합니다.\n"
         "- 이벤트 시간은 하루 안에서 생산 밀도가 높은 시간대에 더 많이 분포되도록 계산됩니다.\n"
         "- 필요한 schema를 보장하고, 기본 설비와 차량 마스터 row를 준비한 뒤 이벤트를 생성합니다.\n"
         "- 생성된 row는 `insert_chunk_size` 단위로 모아 batch insert합니다.\n"
@@ -281,20 +291,20 @@ def generate_manufacturing_event_json(
         description="생성 시작 날짜입니다.",
     ),
     end_date: date = Query(
-        default=date(2026, 6, 16),
+        default=date(2026, 6, 1),
         description="생성 종료 날짜입니다.",
     ),
-    events_per_day: int = Query(
+    event_count: int | None = Query(
         default=DEFAULT_EVENTS_PER_DAY,
         ge=4,
         le=300000,
-        description="하루에 생성할 이벤트 수입니다. 기본값은 86,400건입니다.",
+        description="날짜별 생성 이벤트 수 제한입니다. 생략하면 vehicle_id 생산일자에 해당하는 차량 전체 수 * 4로 계산합니다.",
     ),
-    car_pool_size: int = Query(
+    car_pool_size: int | None = Query(
         default=DEFAULT_CAR_POOL_SIZE,
         ge=1,
         le=100000,
-        description="이벤트에 분배할 차량 마스터 풀 크기입니다.",
+        description="선택적 차량 수 제한입니다. 생략하면 해당 생산일자의 차량 전체를 사용합니다.",
     ),
     insert_chunk_size: int = Query(
         default=DEFAULT_INSERT_CHUNK_SIZE,
@@ -309,7 +319,7 @@ def generate_manufacturing_event_json(
     result = service.enqueue_generate_range_job(
         start_date=start_date,
         end_date=end_date,
-        events_per_day=events_per_day,
+        events_per_day=event_count,
         car_pool_size=car_pool_size,
         insert_chunk_size=insert_chunk_size,
     )
@@ -336,10 +346,8 @@ def generate_manufacturing_event_json(
         "- 실제 이벤트 생성과 DB 저장은 앱 내부 background worker가 수행합니다.\n"
         "- 진행 상태는 job 상태 조회 API로 확인합니다.\n"
         "- target date는 `(base_date 또는 서버 현재 날짜) + 1일`입니다.\n"
-        "- 지정한 `template_name`의 템플릿이 없거나 `events_per_day`보다 부족하면 템플릿을 먼저 생성합니다.\n"
-        "- 템플릿의 `event_offset_us`를 target date에 더해 실제 `event_time`을 만듭니다.\n"
-        "- replay와 동일한 날짜별 수치 변동 레이어가 적용되어 날짜마다 다른 데이터가 저장됩니다.\n"
-        "- 템플릿 row를 `insert_chunk_size` 단위로 읽고, materialize된 이벤트를 같은 단위로 batch insert합니다.\n"
+        "- target date의 vehicle_id 생산일자에 해당하는 car_master 전체를 사용합니다.\n"
+        "- event_count가 없으면 실제 차량 수 * 4건을 생성합니다.\n"
         "- 기본 batch insert 단위는 `1,000`건이며, 요청 파라미터로 `100~10,000` 사이에서 조정할 수 있습니다.\n"
         "- MySQL에서는 `event_id` 중복 시 기존 row의 이벤트 시간, 설비, 상태, JSON payload 등을 갱신합니다.\n\n"
         "### 즉시 응답 데이터\n"
@@ -362,17 +370,17 @@ def generate_tomorrow_manufacturing_event_json(
         default=DEFAULT_TEMPLATE_NAME,
         description="다음날 데이터 적재에 사용할 템플릿 이름입니다.",
     ),
-    events_per_day: int = Query(
+    event_count: int | None = Query(
         default=DEFAULT_EVENTS_PER_DAY,
         ge=4,
         le=300000,
-        description="템플릿이 없거나 부족할 때 준비할 하루 기준 이벤트 수입니다.",
+        description="선택적 이벤트 수 제한입니다. 생략하면 target date의 차량 전체 수 * 4로 계산합니다.",
     ),
-    car_pool_size: int = Query(
+    car_pool_size: int | None = Query(
         default=DEFAULT_CAR_POOL_SIZE,
         ge=1,
         le=100000,
-        description="템플릿 생성이 필요할 때 이벤트에 분배할 차량 마스터 풀 크기입니다.",
+        description="선택적 차량 수 제한입니다. 생략하면 target date의 차량 전체를 사용합니다.",
     ),
     insert_chunk_size: int = Query(
         default=DEFAULT_INSERT_CHUNK_SIZE,
@@ -387,7 +395,7 @@ def generate_tomorrow_manufacturing_event_json(
     result = service.enqueue_generate_tomorrow_job(
         base_date=base_date,
         template_name=template_name,
-        events_per_day=events_per_day,
+        events_per_day=event_count,
         car_pool_size=car_pool_size,
         insert_chunk_size=insert_chunk_size,
     )
diff --git a/app/batch/manufacturing_event_loader.py b/app/batch/manufacturing_event_loader.py
index 2182d9b..d432a99 100644
--- a/app/batch/manufacturing_event_loader.py
+++ b/app/batch/manufacturing_event_loader.py
@@ -21,6 +21,7 @@
 
 
 def main() -> None:
+    """CLI 옵션에 따라 실제 이벤트 또는 재사용 템플릿을 배치 생성한다."""
     logging.basicConfig(
         level=logging.INFO,
         format="%(asctime)s %(levelname)s %(message)s",
@@ -35,6 +36,7 @@ def main() -> None:
     started_at = time.monotonic()
     last_progress_at = {"value": 0.0}
 
+    # 템플릿 모드는 날짜별 이벤트 테이블이 아니라 재생 가능한 기준 패턴을 만든다.
     if args.template:
         _run_template_batch(
             args=args,
@@ -120,7 +122,10 @@ def _parse_args() -> argparse.Namespace:
         "--events-per-day",
         type=int,
         default=DEFAULT_EVENTS_PER_DAY,
-        help="하루 생성 이벤트 수입니다. 기본값은 템플릿 기준 86400입니다.",
+        help=(
+            "선택적 날짜별 이벤트 수 제한입니다. 생략하면 vehicle_id 생산일자에 "
+            "해당하는 car_master 전체 수 * 4로 계산합니다."
+        ),
     )
     parser.add_argument(
         "--template-name",
@@ -131,7 +136,7 @@ def _parse_args() -> argparse.Namespace:
         "--car-pool-size",
         type=int,
         default=DEFAULT_CAR_POOL_SIZE,
-        help="재사용할 차량 마스터 풀 크기입니다.",
+        help="선택적 차량 수 제한입니다. 생략하면 생산일자의 차량 전체를 사용합니다.",
     )
     parser.add_argument(
         "--chunk-size",
@@ -162,6 +167,7 @@ def _parse_args() -> argparse.Namespace:
 
 
 def _resolve_date_range(args: argparse.Namespace) -> tuple[date, date]:
+    """축약 옵션을 실제 생성 시작일/종료일로 변환한다."""
     if args.initial:
         return date(2026, 6, 1), date(2026, 6, 16)
     if args.tomorrow:
@@ -190,6 +196,7 @@ def _run_template_batch(
     )
     result = service.generate_template(
         template_name=args.template_name,
+        production_date=args.start_date or date(2026, 6, 1),
         event_count=args.events_per_day,
         car_pool_size=args.car_pool_size,
         insert_chunk_size=args.chunk_size,
diff --git a/app/core/config.py b/app/core/config.py
index 44b6ffa..c182c91 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -34,16 +34,16 @@ class Settings(BaseSettings):
         default=True,
         alias="MANUFACTURING_EVENT_SCHEDULER_ENABLED",
     )
-    manufacturing_event_scheduler_events_per_day: int = Field(
-        default=86_400,
+    manufacturing_event_scheduler_events_per_day: int | None = Field(
+        default=None,
         alias="MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY",
     )
-    manufacturing_event_template_event_count: int = Field(
-        default=86_400,
+    manufacturing_event_template_event_count: int | None = Field(
+        default=None,
         alias="MANUFACTURING_EVENT_TEMPLATE_EVENT_COUNT",
     )
-    manufacturing_event_car_pool_size: int = Field(
-        default=10_000,
+    manufacturing_event_car_pool_size: int | None = Field(
+        default=None,
         alias="MANUFACTURING_EVENT_CAR_POOL_SIZE",
     )
     manufacturing_event_insert_chunk_size: int = Field(
diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index b9158a9..786a8ad 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import hashlib
 import json
 import math
 import statistics
@@ -12,8 +13,162 @@
 import pandas as pd
 
 
+# PRD의 차량 생산 순서다. 한 차량마다 아래 4개 공정 이벤트를 정확히 한 건씩 만든다.
 PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
-LINE_STATION_COUNT = 4
+PROCESS_ROUTE_PREFIX = {
+    "PRESS": "P",
+    "BODY": "B",
+    "PAINT": "PA",
+    "ASSEMBLY": "A",
+}
+
+# PRD 기준 공정별 설비 수와 목표 이상 데이터 비율이다.
+LINE_STATION_COUNT = 5
+ABNORMAL_RATIO = 0.30
+PROCESS_DATA_REQUIRED_FIELDS: dict[str, frozenset[str]] = {
+    "PRESS": frozenset(
+        {
+            "countIncreaseYn",
+            "targetCycleTimeSec",
+            "timestampDelaySec",
+        },
+    ),
+    "BODY": frozenset(
+        {
+            "robotMotionStatus",
+            "robotOperationMode",
+            "frequencyPeakBand",
+            "frequencyBands",
+        },
+    ),
+    "PAINT": frozenset(
+        {
+            "imagePosition",
+            "thermalStdTemp",
+            "thicknessValue",
+            "defectScore",
+            "visionLabel",
+            "surfaceQualityScore",
+        },
+    ),
+    "ASSEMBLY": frozenset(
+        {
+            "expectedSequence",
+            "actualSequence",
+            "missingPartCount",
+            "fasteningErrorCount",
+            "sequenceErrorCount",
+        },
+    ),
+}
+PROCESS_DATA_KEY = {
+    "PRESS": "press",
+    "BODY": "body",
+    "PAINT": "paint",
+    "ASSEMBLY": "assembly",
+}
+
+
+def initial_dispatch_status(process_code: str) -> str:
+    """원천 이벤트 최초 발행 상태를 반환한다.
+
+    차량 생산 흐름은 PRESS부터 시작하므로 PRESS만 READY이며, BODY/PAINT/
+    ASSEMBLY는 이전 공정의 정상 분석 결과가 오기 전까지 PENDING이다.
+    """
+    return "READY" if process_code == "PRESS" else "PENDING"
+
+
+def is_abnormal_operation_status(operation_status: Any) -> bool:
+    """이벤트 JSON의 운전 상태가 이상 상태인지 반환한다."""
+    return operation_status in {"FAULT", "STOPPED", "MAINTENANCE"}
+
+
+def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
+    """제조 이벤트 JSON을 PRD에 정의된 필드만 남긴 구조로 정규화한다."""
+    event = event_json.get("event", {})
+    equipment = event_json.get("equipment", {})
+    equipment_status = event_json.get("equipmentStatus", {})
+    product = event_json.get("product", {})
+    sensor = event_json.get("sensor", {})
+    current = sensor.get("current", {})
+    vibration = sensor.get("vibration", {})
+    robot = sensor.get("robotArmVibration", {})
+    thermal = sensor.get("thermal", {})
+    metrics = event_json.get("processMetrics", {})
+    source_trace = event_json.get("sourceTrace", {})
+
+    return {
+        "event": {
+            "eventId": event.get("eventId"),
+            "eventTime": None,
+            "eventType": event.get("eventType"),
+            "eventName": event.get("eventName"),
+        },
+        "equipment": {
+            "equipmentCode": equipment.get("equipmentCode"),
+            "equipmentName": equipment.get("equipmentName"),
+            "equipmentType": equipment.get("equipmentType"),
+        },
+        "equipmentStatus": {
+            "operationStatus": equipment_status.get("operationStatus"),
+            "lastNormalTime": None,
+            "statusChangedTime": None,
+        },
+        "product": {
+            "carMasterId": product.get("carMasterId"),
+        },
+        "sensor": {
+            "sensorType": sensor.get("sensorType"),
+            "current": {
+                "rmsAmpere": current.get("rmsAmpere"),
+                "maxAmpere": current.get("maxAmpere"),
+                "minAmpere": current.get("minAmpere"),
+            },
+            "vibration": {
+                "accelerationG": vibration.get("accelerationG"),
+                "vibrationScore": vibration.get("vibrationScore"),
+                "vibrationRms": vibration.get("vibrationRms"),
+                "vibrationPeak": vibration.get("vibrationPeak"),
+            },
+            "robotArmVibration": {
+                "robotId": robot.get("robotId"),
+                "axis": robot.get("axis"),
+                "frequencyHz": robot.get("frequencyHz"),
+                "amplitude": robot.get("amplitude"),
+                "vibrationRms": robot.get("vibrationRms"),
+                "vibrationPeak": robot.get("vibrationPeak"),
+                "vibrationScore": robot.get("vibrationScore"),
+            },
+            "thermal": {
+                "thermalScore": thermal.get("thermalScore"),
+                "avgTemperature": thermal.get("avgTemperature"),
+                "maxTemperature": thermal.get("maxTemperature"),
+                "minTemperature": thermal.get("minTemperature"),
+            },
+        },
+        "processMetrics": {
+            "cycleTimeSec": metrics.get("cycleTimeSec"),
+            "waitingTimeSec": metrics.get("waitingTimeSec"),
+            "processingTimeSec": metrics.get("processingTimeSec"),
+            "stationDelaySec": metrics.get("stationDelaySec"),
+            "throughputPerMin": metrics.get("throughputPerMin"),
+            "queueLength": metrics.get("queueLength"),
+            "wipCount": metrics.get("wipCount"),
+            "equipmentIdleTimeSec": metrics.get("equipmentIdleTimeSec"),
+        },
+        "sourceTrace": {
+            "fordRowId": source_trace.get("fordRowId"),
+            "formingRowId": source_trace.get("formingRowId"),
+            "robotArmVibrationRowId": source_trace.get(
+                "robotArmVibrationRowId",
+            ),
+            "machineVisionRowId": source_trace.get("machineVisionRowId"),
+            "boschId": source_trace.get("boschId"),
+        },
+        "processData": event_json.get("processData", {}),
+    }
+
+
 PROCESS_META: dict[str, dict[str, Any]] = {
     "PRESS": {
         "lineCode": "PRESS_LINE_01",
@@ -63,9 +218,13 @@
 
 @dataclass(frozen=True)
 class EventBuildRequest:
+    """한 번의 제조 이벤트 생성 작업에 필요한 범위와 마스터 데이터."""
+
     start_date: date
     end_date: date
+    # 기존 API 이름을 유지하지만 현재 의미는 기간 전체 이벤트 수다.
     events_per_day: int
+    # dict 삽입 순서가 car_master.id 오름차순을 보존한다.
     car_id_map: dict[str, int]
     equipment_map: dict[str, dict[str, Any]]
 
@@ -82,7 +241,10 @@ def build_rows(self, request: EventBuildRequest) -> list[dict[str, Any]]:
         return list(self.iter_rows(request))
 
     def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
-        car_ids = sorted(request.car_id_map)
+        """차량 단위로 4공정 row를 순차 생성한다."""
+        # Repository가 car_master.id 오름차순으로 만든 순서를 그대로 사용해야
+        # PRD의 "id 1번 차량부터 생성" 조건을 지킬 수 있다.
+        car_ids = list(request.car_id_map)
         if not car_ids:
             return
 
@@ -90,21 +252,35 @@ def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
         if days <= 0:
             return
 
-        global_index = 0
-        for day_offset in range(days):
-            current_date = request.start_date + timedelta(days=day_offset)
-            for slot in range(request.events_per_day):
-                process_index = global_index % len(PROCESS_SEQUENCE)
-                production_sequence_index = global_index // len(PROCESS_SEQUENCE)
-                process_code = PROCESS_SEQUENCE[process_index]
-                event_time = _event_time_for_slot(
-                    current_date=current_date,
-                    slot=slot,
-                    events_per_day=request.events_per_day,
+        # 차량마다 4건이므로 이벤트 수와 차량 수를 독립적으로 받을 수 없다.
+        # 여기서 다시 검증해 서비스 외부에서 Builder를 직접 호출해도 구조가 깨지지 않게 한다.
+        total_events = len(car_ids) * len(PROCESS_SEQUENCE)
+        if request.events_per_day != total_events:
+            raise ValueError(
+                "event_count는 car_pool_size * 4와 같아야 합니다: "
+                f"event_count={request.events_per_day}, expected={total_events}",
+            )
+
+        # 날짜별 실제 차량 수를 기준으로 정상 70% / 폐기(이상) 30%를 배치한다.
+        # 이상 차량도 4공정을 모두 가지므로 공정별 이상 건수가 자동으로 동일해진다.
+        abnormal_vehicle_count = round(len(car_ids) * ABNORMAL_RATIO)
+        normal_vehicle_count = len(car_ids) - abnormal_vehicle_count
+
+        for production_sequence_index, car_id in enumerate(car_ids):
+            car_master_id = request.car_id_map[car_id]
+            is_abnormal_vehicle = production_sequence_index >= normal_vehicle_count
+            # 같은 차량의 이벤트는 PRESS -> BODY -> PAINT -> ASSEMBLY 순서를 유지한다.
+            for process_index, process_code in enumerate(PROCESS_SEQUENCE):
+                global_index = (
+                    production_sequence_index * len(PROCESS_SEQUENCE) + process_index
+                )
+                event_time = _event_time_for_range_slot(
+                    start_date=request.start_date,
+                    end_date=request.end_date,
+                    slot=global_index,
+                    total_events=total_events,
                 )
-                sequence_no = slot + 1
-                event_id = f"EVT-{current_date:%Y%m%d}-{sequence_no:06d}"
-                car_id = car_ids[production_sequence_index % len(car_ids)]
+                event_id = f"EVT-{event_time:%Y%m%d}-{global_index + 1:06d}"
                 event = self._build_event(
                     event_id=event_id,
                     event_time=event_time,
@@ -112,6 +288,8 @@ def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
                     global_index=global_index,
                     production_sequence_index=production_sequence_index,
                     car_id=car_id,
+                    car_master_id=car_master_id,
+                    is_abnormal=is_abnormal_vehicle,
                     equipment_map=request.equipment_map,
                 )
                 equipment_code = event["equipment"]["equipmentCode"]
@@ -119,17 +297,22 @@ def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
                 yield {
                     "event_id": event_id,
                     "event_time": event_time,
-                    "car_master_id": request.car_id_map[car_id],
+                    "car_master_id": car_master_id,
                     "equipment_id": int(equipment_row["id"]),
                     "process_code": process_code,
-                    "station_code": event["location"]["stationCode"],
+                    "station_code": f"{process_code}_STATION_{int(equipment_code.rsplit('_', 1)[1]):02d}",
                     "equipment_code": equipment_code,
                     "equipment_type": event["equipment"]["equipmentType"],
                     "equipment_status": event["equipmentStatus"]["operationStatus"],
                     "event_type": event["event"]["eventType"],
                     "event_json": _json_safe(event),
+                    # 최초에는 PRESS만 발행할 수 있다. 후속 공정은 이전 공정의
+                    # 정상 분석 결과를 받은 뒤 Consumer가 READY로 전환한다.
+                    "dispatch_status": initial_dispatch_status(process_code),
+                    "analysis_status": "NOT_ANALYZED",
+                    "retry_count": 0,
+                    "error_message": None,
                 }
-                global_index += 1
 
     def _build_event(
         self,
@@ -140,13 +323,22 @@ def _build_event(
         global_index: int,
         production_sequence_index: int,
         car_id: str,
+        car_master_id: int,
+        is_abnormal: bool,
         equipment_map: dict[str, dict[str, Any]],
     ) -> dict[str, Any]:
         meta = PROCESS_META[process_code]
-        line_station_no = production_sequence_index % LINE_STATION_COUNT + 1
+        # 같은 차량도 공정마다 서로 다른 설비를 사용할 수 있도록 차량 PK와
+        # 공정 코드를 함께 사용해 1~5호기를 독립적으로 배정한다.
+        # 해시 기반이라 분포는 랜덤하지만 재생성·템플릿 replay 결과는 동일하다.
+        line_station_no = _equipment_no_for_process(
+            car_master_id=car_master_id,
+            process_code=process_code,
+        )
         equipment_code = f"EQ_{process_code}_{line_station_no:03d}"
         equipment_row = equipment_map[equipment_code]
 
+        # 공개 데이터셋의 실제 행을 읽어 원천 추적 정보와 기본 센서 특성을 만든다.
         forming = self._forming_row(production_sequence_index)
         current = self._current_features(process_code, global_index)
         ford = self._ford_features(global_index)
@@ -160,43 +352,58 @@ def _build_event(
             vision=vision,
             bosch=bosch,
         )
-        is_abnormal = self._is_abnormal(process_code, ford, vision, bosch, current)
-        operation_status = self._operation_status(is_abnormal, process_metrics)
+        # 원천 데이터의 실제 클래스 비율은 데이터셋마다 다르므로, PRD가 요구한
+        # 7:3 비율과 Java 판정 임계값을 안정적으로 만족하도록 프로필을 적용한다.
+        self._apply_detection_profile(
+            process_code=process_code,
+            is_abnormal=is_abnormal,
+            current=current,
+            ford=ford,
+            vision=vision,
+            bosch=bosch,
+            process_metrics=process_metrics,
+        )
+        process_data = self._process_data(
+            process_code=process_code,
+            car_master_id=car_master_id,
+            current=current,
+            ford=ford,
+            vision=vision,
+            bosch=bosch,
+            process_metrics=process_metrics,
+        )
+        validate_process_data(process_code, process_data)
+        equipment_status = _equipment_status_for_event(
+            car_master_id=car_master_id,
+            process_code=process_code,
+            is_abnormal=is_abnormal,
+        )
 
         return {
             "event": {
                 "eventId": event_id,
-                "eventTime": event_time.isoformat(),
-                "eventCategory": "MANUFACTURING",
+                # 원천 이벤트 생성 시점에는 실제 발생 시각이 확정되지 않았으므로
+                # DB event_time과 동일하게 JSON 내부 eventTime도 NULL로 둔다.
+                "eventTime": None,
                 "eventType": meta["eventType"],
                 "eventName": meta["eventName"],
             },
-            "location": {
-                "factoryCode": "AIMS_FACTORY_01",
-                "lineCode": f"{process_code}_LINE_{line_station_no:02d}",
-                "processCode": process_code,
-                "stationCode": f"{process_code}_STATION_{line_station_no:02d}",
-            },
             "equipment": {
                 "equipmentCode": equipment_code,
                 "equipmentName": equipment_row["equipment_name"],
                 "equipmentType": equipment_row["equipment_type"],
             },
             "equipmentStatus": {
-                "operationStatus": operation_status,
-                "lastNormalTime": (event_time - timedelta(seconds=31)).isoformat(),
-                "statusChangedTime": event_time.isoformat(),
-                "healthStatus": "WARNING" if is_abnormal else "NORMAL",
+                # 운전 상태는 정상/이상 프로필 안에서 차량·공정별로 랜덤 생성한다.
+                # 시간 필드는 실제 이벤트 전송 전까지 미확정이므로 NULL로 둔다.
+                "operationStatus": equipment_status["operationStatus"],
+                "lastNormalTime": None,
+                "statusChangedTime": None,
             },
             "product": {
-                "carId": car_id,
-                "productId": f"PRODUCT-{int(car_id.rsplit('-', 1)[1]):06d}",
-                "itemNo": forming["itemno"],
-                "quantity": forming["quantity"],
-                "productionCount": forming["cnt"],
+                "carMasterId": car_master_id,
             },
             "sensor": self._sensor_payload(current, ford, vision, process_code),
-            "manufacturing": self._manufacturing_payload(process_code, forming),
             "processMetrics": process_metrics,
             "sourceTrace": {
                 "fordRowId": ford["rowId"],
@@ -205,16 +412,102 @@ def _build_event(
                 "machineVisionRowId": vision["rowId"],
                 "boschId": bosch["id"],
             },
-            "processData": self._process_data(
-                process_code=process_code,
-                current=current,
-                ford=ford,
-                vision=vision,
-                bosch=bosch,
-                process_metrics=process_metrics,
-            ),
+            # process_code에 해당하는 블록 하나만 포함한다.
+            # PRESS면 press, BODY면 body, PAINT면 paint, ASSEMBLY면 assembly만 존재한다.
+            "processData": process_data,
         }
 
+    def _apply_detection_profile(
+        self,
+        *,
+        process_code: str,
+        is_abnormal: bool,
+        current: dict[str, Any],
+        ford: dict[str, Any],
+        vision: dict[str, Any],
+        bosch: dict[str, Any],
+        process_metrics: dict[str, Any],
+    ) -> None:
+        """PRD의 Java 판정식에서 정상/이상이 명확히 갈리도록 입력값을 보정한다.
+
+        이상 프로필은 전류·진동·열화상·지연·조립 오류를 함께 높여 모든 공정의
+        전용 위험도가 임계값을 넘게 한다. 정상 프로필은 반대로 충분한 여유를 둬
+        날짜별 작은 변동이 적용되어도 정상 범위를 벗어나지 않게 한다.
+        """
+        target = float(PROCESS_META[process_code]["targetCycleTimeSec"])
+        if is_abnormal:
+            # 설비 위험 및 불량 전이 위험을 높이는 공통 센서 프로필.
+            current.update(
+                rmsAmpere=4.2,
+                maxAmpere=4.8,
+                minAmpere=3.7,
+                accelerationG=0.085,
+            )
+            ford.update(
+                label=-1,
+                vibrationScore=0.90,
+                vibrationRms=2.88,
+                vibrationPeak=4.10,
+            )
+            vision.update(
+                label=1,
+                avgTemperature=54.0,
+                maxTemperature=62.0,
+                minTemperature=45.0,
+                thermalStdTemp=6.0,
+                defectScore=0.90,
+                thicknessValue=132.0,
+                surfaceQualityScore=60.0,
+            )
+            bosch["response"] = 1
+            # 병목 위험도도 함께 높아지도록 지연, WIP, 유휴 시간을 보정한다.
+            process_metrics.update(
+                cycleTimeSec=target + 16.0,
+                waitingTimeSec=18.0,
+                processingTimeSec=target - 2.0,
+                stationDelaySec=16.0,
+                throughputPerMin=round(60 / (target + 16.0), 3),
+                queueLength=14,
+                wipCount=38,
+                equipmentIdleTimeSec=22.0,
+            )
+            return
+
+        # 정상 프로필은 PRD 기준 cycle time과 낮은 센서 위험도를 사용한다.
+        current.update(
+            rmsAmpere=1.7,
+            maxAmpere=1.9,
+            minAmpere=1.5,
+            accelerationG=0.006,
+        )
+        ford.update(
+            label=1,
+            vibrationScore=0.12,
+            vibrationRms=0.32,
+            vibrationPeak=0.58,
+        )
+        vision.update(
+            label=0,
+            avgTemperature=39.0,
+            maxTemperature=41.0,
+            minTemperature=37.0,
+            thermalStdTemp=0.8,
+            defectScore=0.08,
+            thicknessValue=116.0,
+            surfaceQualityScore=97.0,
+        )
+        bosch["response"] = 0
+        process_metrics.update(
+            cycleTimeSec=target,
+            waitingTimeSec=2.0,
+            processingTimeSec=target - 2.0,
+            stationDelaySec=0.0,
+            throughputPerMin=round(60 / target, 3),
+            queueLength=1,
+            wipCount=4,
+            equipmentIdleTimeSec=0.0,
+        )
+
     def _forming_row(self, index: int) -> dict[str, Any]:
         df = self._load_forming()
         row = df.iloc[index % len(df)]
@@ -376,31 +669,6 @@ def _process_metrics(
             "equipmentIdleTimeSec": round(max(0.0, station_delay * 1.8), 3),
         }
 
-    def _is_abnormal(
-        self,
-        process_code: str,
-        ford: dict[str, Any],
-        vision: dict[str, Any],
-        bosch: dict[str, Any],
-        current: dict[str, Any],
-    ) -> bool:
-        if process_code in {"PRESS", "BODY"}:
-            return ford["label"] < 0 or current["rmsAmpere"] > 2.8
-        if process_code == "PAINT":
-            return vision["label"] == 1
-        return bosch["response"] == 1
-
-    def _operation_status(
-        self,
-        is_abnormal: bool,
-        process_metrics: dict[str, Any],
-    ) -> str:
-        if process_metrics["equipmentIdleTimeSec"] >= 20:
-            return "IDLE"
-        if is_abnormal and process_metrics["stationDelaySec"] >= 10:
-            return "ERROR"
-        return "RUNNING"
-
     def _sensor_payload(
         self,
         current: dict[str, Any],
@@ -436,27 +704,13 @@ def _sensor_payload(
                 "maxTemperature": vision["maxTemperature"],
                 "minTemperature": vision["minTemperature"],
             },
-            "primarySensorSource": _primary_sensor_source(process_code),
-        }
-
-    def _manufacturing_payload(
-        self,
-        process_code: str,
-        forming: dict[str, Any],
-    ) -> dict[str, Any]:
-        sequence_no = PROCESS_SEQUENCE.index(process_code) + 1
-        return {
-            "processSequence": sequence_no,
-            "previousProcessCode": PROCESS_SEQUENCE[sequence_no - 2] if sequence_no > 1 else None,
-            "nextProcessCode": PROCESS_SEQUENCE[sequence_no] if sequence_no < len(PROCESS_SEQUENCE) else None,
-            "targetQuantity": 200,
-            "completedQuantity": min(200, forming["quantity"] + 140),
         }
 
     def _process_data(
         self,
         *,
         process_code: str,
+        car_master_id: int,
         current: dict[str, Any],
         ford: dict[str, Any],
         vision: dict[str, Any],
@@ -493,16 +747,35 @@ def _process_data(
                 },
             }
 
-        has_sequence_error = bool(bosch["response"])
-        return {
-            "assembly": {
-                "expectedSequence": "A01>A02>A03>A04",
-                "actualSequence": "A01>A03>A02>A04" if has_sequence_error else "A01>A02>A03>A04",
-                "missingPartCount": 1 if has_sequence_error and current["rmsAmpere"] > 2.3 else 0,
-                "fasteningErrorCount": 1 if has_sequence_error else 0,
-                "sequenceErrorCount": 1 if has_sequence_error else 0,
-            },
-        }
+        if process_code == "ASSEMBLY":
+            has_sequence_error = bool(bosch["response"])
+            expected_sequence = _equipment_route_for_car(car_master_id)
+            expected_steps = expected_sequence.split(">")
+            # 이상 데이터는 BODY와 PAINT 통과 순서를 바꿔 순서 오류를 표현한다.
+            abnormal_steps = [
+                expected_steps[0],
+                expected_steps[2],
+                expected_steps[1],
+                expected_steps[3],
+            ]
+            return {
+                "assembly": {
+                    "expectedSequence": expected_sequence,
+                    "actualSequence": (
+                        ">".join(abnormal_steps)
+                        if has_sequence_error
+                        else expected_sequence
+                    ),
+                    "missingPartCount": (
+                        1
+                        if has_sequence_error and current["rmsAmpere"] > 2.3
+                        else 0
+                    ),
+                    "fasteningErrorCount": 1 if has_sequence_error else 0,
+                    "sequenceErrorCount": 1 if has_sequence_error else 0,
+                },
+            }
+        raise ValueError(f"지원하지 않는 process_code입니다: {process_code}")
 
     def _load_forming(self) -> pd.DataFrame:
         return self._cached_csv(
@@ -639,7 +912,35 @@ def _event_time_for_slot(
     return day_start + timedelta(microseconds=offset_microseconds)
 
 
+def _event_time_for_range_slot(
+    *,
+    start_date: date,
+    end_date: date,
+    slot: int,
+    total_events: int,
+) -> datetime:
+    """전체 이벤트를 날짜 범위에 균등 분할한 뒤 일별 생산 밀도를 적용한다."""
+    days = (end_date - start_date).days + 1
+    # 나머지는 앞 날짜부터 한 건씩 배분해 전체 건수가 정확히 유지되게 한다.
+    base_count, remainder = divmod(total_events, days)
+    day_offset = 0
+    day_start_slot = 0
+    for candidate_day in range(days):
+        events_on_day = base_count + (1 if candidate_day < remainder else 0)
+        if slot < day_start_slot + events_on_day:
+            day_offset = candidate_day
+            local_slot = slot - day_start_slot
+            return _event_time_for_slot(
+                current_date=start_date + timedelta(days=day_offset),
+                slot=local_slot,
+                events_per_day=events_on_day,
+            )
+        day_start_slot += events_on_day
+    return datetime.combine(end_date, time.max)
+
+
 def _weighted_event_offset_us(slot: int, events_per_day: int) -> int:
+    """일별 slot을 시간대별 생산 밀도에 맞는 microsecond offset으로 변환한다."""
     windows = _weighted_event_windows(events_per_day)
     remaining_slot = slot
     for start_us, end_us, count in windows:
@@ -705,19 +1006,98 @@ def _largest_fraction_window_index(
 
 
 def _deterministic_jitter_us(slot: int, interval_us: int) -> int:
+    """재실행 결과는 같게 유지하면서 이벤트 시각의 기계적인 등간격을 완화한다."""
     jitter_window = max(1, interval_us // 5)
     pseudo_random = (slot * 1103515245 + 12345) & 0x7FFFFFFF
     return pseudo_random % (2 * jitter_window + 1) - jitter_window
 
 
-def _primary_sensor_source(process_code: str) -> str:
-    if process_code == "PRESS":
-        return "PRESS_CURRENT_AND_FORD_VIBRATION"
-    if process_code == "BODY":
-        return "ROBOT_CURRENT_AND_FORD_VIBRATION"
-    if process_code == "PAINT":
-        return "MACHINE_VISION_THERMAL"
-    return "BOSCH_PRODUCTION_LINE"
+def _equipment_no_for_process(*, car_master_id: int, process_code: str) -> int:
+    """차량·공정 조합별로 독립적인 설비 번호(1~5)를 결정한다.
+
+    일반 random 모듈을 사용하면 배치를 다시 실행할 때 설비가 달라질 수 있다.
+    이벤트 재생성과 upsert가 안정적으로 동작하도록 같은 입력에는 항상 같은
+    번호가 나오는 해시 기반 결정적 랜덤 방식을 사용한다.
+    """
+    digest = hashlib.blake2b(
+        f"{car_master_id}:{process_code}:equipment".encode("utf-8"),
+        digest_size=8,
+    ).digest()
+    return int.from_bytes(digest, "big") % LINE_STATION_COUNT + 1
+
+
+def _equipment_route_for_car(car_master_id: int) -> str:
+    """차량이 통과할 4개 공정의 실제 설비 경로를 문자열로 만든다.
+
+    예: PRESS 2호, BODY 3호, PAINT 1호, ASSEMBLY 4호
+    -> P02>B03>PA01>A04
+    """
+    return ">".join(
+        (
+            f"{PROCESS_ROUTE_PREFIX[process_code]}"
+            f"{_equipment_no_for_process(car_master_id=car_master_id, process_code=process_code):02d}"
+        )
+        for process_code in PROCESS_SEQUENCE
+    )
+
+
+def _equipment_status_for_event(
+    *,
+    car_master_id: int,
+    process_code: str,
+    is_abnormal: bool,
+) -> dict[str, str]:
+    """정상/이상 유형에 맞는 설비 상태를 결정적 랜덤으로 선택한다.
+
+    정상 데이터는 RUNNING/IDLE, 이상 데이터는 FAULT/STOPPED/MAINTENANCE
+    상태군에서 선택한다. 같은 차량·공정은 재생성해도 같은 상태를 갖는다.
+    """
+    digest = hashlib.blake2b(
+        f"{car_master_id}:{process_code}:status".encode("utf-8"),
+        digest_size=8,
+    ).digest()
+    ratio = int.from_bytes(digest, "big") % 100
+
+    if not is_abnormal:
+        operation_status = "RUNNING" if ratio < 85 else "IDLE"
+    elif ratio < 50:
+        operation_status = "FAULT"
+    elif ratio < 80:
+        operation_status = "STOPPED"
+    else:
+        operation_status = "MAINTENANCE"
+
+    return {"operationStatus": operation_status}
+
+
+def validate_process_data(
+    process_code: str,
+    process_data: dict[str, Any],
+) -> None:
+    """processData가 공정별 전용 JSON 구조를 정확히 따르는지 검증한다."""
+    expected_key = PROCESS_DATA_KEY.get(process_code)
+    required_fields = PROCESS_DATA_REQUIRED_FIELDS.get(process_code)
+    if expected_key is None or required_fields is None:
+        raise ValueError(f"지원하지 않는 process_code입니다: {process_code}")
+
+    if set(process_data) != {expected_key}:
+        raise ValueError(
+            f"{process_code} processData는 {expected_key} 블록만 포함해야 합니다: "
+            f"actual={sorted(process_data)}",
+        )
+
+    process_payload = process_data.get(expected_key)
+    if not isinstance(process_payload, dict):
+        raise ValueError(
+            f"{process_code} processData.{expected_key}는 JSON object여야 합니다.",
+        )
+
+    missing_fields = required_fields - set(process_payload)
+    if missing_fields:
+        raise ValueError(
+            f"{process_code} processData 필수 필드가 누락되었습니다: "
+            f"{sorted(missing_fields)}",
+        )
 
 
 def _safe_float(value: Any, default: float = 0.0) -> float:
diff --git a/app/docs/PRD_Manufacturing.md b/app/docs/PRD_Manufacturing.md
deleted file mode 100644
index d5bdac3..0000000
--- a/app/docs/PRD_Manufacturing.md
+++ /dev/null
@@ -1,3428 +0,0 @@
-# 데이터셋
-
-## 1.1. Ford 엔진 진동 데이터셋 - 프레스&차체
-
-### 1.1.1. ARFF 파일 구조
-
-ARFF는 WEKA 머신러닝 도구용 데이터 포맷입니다.
-
-### 구성 요소
-
-| 구성 | 설명 |
-| --- | --- |
-| `@RELATION` | 데이터셋 이름 |
-| `@ATTRIBUTE` | 컬럼 정의 |
-| `@DATA` | 실제 데이터 |
-
-### 1.1.2. ARFF 컬럼 구조
-
-| 컬럼 | 설명 |
-| --- | --- |
-| `att1 ~ att500` | 시계열 센서값 |
-| `class` | 정상/이상 라벨 |
-
-02. Dataset_FordEngine.zip
-
-- 전체 데이터 구조
-    
-    ## 전체 데이터셋 구성
-    
-    | 파일명 | 데이터 유형 | 설명 |
-    | --- | --- | --- |
-    | `FordA.txt` | 전체 데이터 | 시계열 센서 데이터 |
-    | `FordA_TEST.txt` | 테스트 데이터 | 테스트용 시계열 데이터 |
-    | `FordA_TRAIN.arff` | 학습 데이터 | WEKA 형식 학습 데이터 |
-    | `FordA_TEST.arff` | 테스트 데이터 | WEKA 형식 테스트 데이터 |
-    
-    ---
-    
-    ## 데이터 구조 특징
-    
-    | 항목 | 내용 |
-    | --- | --- |
-    | 데이터 형태 | 시계열(Time-Series) |
-    | 데이터 길이 | 샘플당 500개 측정값 |
-    | 목적 | 자동차 엔진 이상 탐지 |
-    | 입력 데이터 | 엔진 소음 측정값 |
-    | 출력 라벨 | 정상 / 이상 |
-    | 문제 유형 | 이진 분류(Binary Classification) |
-    | 추천 활용 | 이상 탐지, 예지보전, 시계열 분류 |
-    
-    ---
-    
-    ## TXT 파일 데이터 구조
-    
-    TXT 파일은 아래와 같은 구조를 가집니다.
-    
-    | 위치 | 의미 |
-    | --- | --- |
-    | 첫 번째 값 | 클래스(Label) |
-    | 이후 500개 값 | 엔진 소음 시계열 데이터 |
-    
-    ---
-    
-    ## 데이터 샘플 구조 예시
-    
-    | label | t1 | t2 | t3 | ... | t500 |
-    | --- | --- | --- | --- | --- | --- |
-    | -1 | -0.14 | 0.17 | 0.30 | ... | -0.79 |
-    | 1 | 0.71 | 0.74 | 0.72 | ... | -4.33 |
-    
-    ※ 실제 데이터는 실수(float) 기반 연속값으로 구성됨
-    
-    ---
-    
-    ## Label 의미
-    
-    | 값 | 의미 |
-    | --- | --- |
-    | `1` | 정상(Normal) |
-    | `-1` | 이상(Fault/Abnormal) |
-    
-    ---
-    
-    ## ARFF 파일 구조
-    
-    ARFF는 WEKA 머신러닝 도구용 데이터 포맷입니다.
-    
-    ### 구성 요소
-    
-    | 구성 | 설명 |
-    | --- | --- |
-    | `@RELATION` | 데이터셋 이름 |
-    | `@ATTRIBUTE` | 컬럼 정의 |
-    | `@DATA` | 실제 데이터 |
-    
-    ---
-    
-    ## ARFF 컬럼 구조
-    
-    | 컬럼 | 설명 |
-    | --- | --- |
-    | `att1 ~ att500` | 시계열 센서값 |
-    | `class` | 정상/이상 라벨 |
-    
-    ---
-    
-    ## 데이터셋 특징 요약
-    
-    | 항목 | 설명 |
-    | --- | --- |
-    | 산업 분야 | 자동차 제조 |
-    | 데이터 종류 | 엔진 소음 시계열 |
-    | 센서 유형 | 진동/소음 계열로 추정 |
-    | 샘플 길이 | 고정 길이 500 |
-    | 전처리 여부 | 정규화 미적용(original raw signal) |
-    | 학습 유형 | 지도학습(Supervised Learning) |
-- https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=%EC%98%88%EC%A7%80%EB%B3%B4%EC%A0%84&page=1&DATASET_SEQ=2&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
-- Ford사의 자동차 엔진 및 모터 센서의 시계열 진동 데이터를 기반으로 정상/비정상 상태를 분류하는 예지보전(Predictive Maintenance) 데이터셋
-- 데이터 개수 : 4,921 개 / 확장자 : ARFF, text
-- Sensor 1~500의 시계열 데이터를 활용하여 설비 이상 탐지 및 고장 예측 모델 학습 가능
-- CNN, RNN, XGBoost 등의 알고리즘을 활용하여 제조 설비 상태 분석 및 장애 탐지 수행 가능
-- Kafka 기반 실시간 이벤트 스트리밍, Redis Queue 모니터링, AI 기반 Predictive Alert 기능 구현에 적합
-- 스마트팩토리 환경에서 모터 진동 증가, RPM 이상, 설비 소음 증가 등의 이벤트를 시뮬레이션하는 용도로 활용 가능
-- 모빌리티 스마트팩토리 관제 시스템의 실시간 설비 이상 탐지 및 시계열 기반 AI 분석 구조와 높은 연관성을 가짐
-
-## **1.2. 소성가공 자원최적화 AI 데이터셋** - 프레스&차체&도장
-
-### 1.2.1. 공정 데이터 2022년 8월 구조
-
-| 컬럼명 | 의미 | 데이터 타입 | 예시 |
-| --- | --- | --- | --- |
-| idx | 생산 데이터 고유 ID | Integer | 55644626 |
-| lineno | 생산 라인 번호 | Integer | 1200 |
-| itemno | 생산 제품 코드 | String | 76211-A3010-100 |
-| Order_date | 주문 날짜 값 | Integer(Date Serial) | 44783 |
-| day_night_type | 주/야간 작업 구분 | Integer | 1 |
-| product_date | 실제 생산 시간 | Datetime/String | 2022-08-10 7:57 |
-| quantity | 생산 수량 | Integer | 5 |
-| cnt | 생산 카운트 값 | Integer | 18133963 |
-
-### 1.2.2. 프레스 유압모터 / 로봇 전류 데이터 구조
-
-| 컬럼명 | 의미 | 데이터 타입 | 예시 |
-| --- | --- | --- | --- |
-| Unnamed: 0 | 데이터 인덱스 번호 | Integer | 1 |
-| Time_s[s] | 센서 수집 시간(Timestamp) | Datetime/String | 2022-07-17 12:09 |
-| RMS[A] | 전류 RMS(Root Mean Square) 값 | Float | 1.971987844 |
-
-※ 일부 데이터셋은 `RMS[A]` 대신 `Acceleration[g]` 컬럼 사용
-
-### 1.2.3. 프레스 2호 데이터 구조 (진동/가속도 기반)
-
-| 컬럼명 | 의미 | 데이터 타입 | 예시 |
-| --- | --- | --- | --- |
-| Unnamed: 0 | 데이터 인덱스 번호 | Integer | 1 |
-| Time_s[s] | 센서 수집 시간 | Datetime/String | 2022-07-17 12:10 |
-| Acceleration[g] | 가속도/진동 값 | Float | 0.007593981 |
-
-### 1.2.4. 데이터셋 파일
-
-공정 데이터 2022년 8월.csv
-
-로봇 1호-전류 데이터.csv
-
-로봇 2호-전류 데이터.csv
-
-프레스 1호-유압모터 전류데이터.csv
-
-프레스 2호-유압모터 전류데이터.csv
-
-프레스 3호-유압모터 전류데이터.csv
-
-프레스 4호-유압모터 전류데이터.csv
-
-- https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=%EC%86%8C%EC%84%B1%EA%B0%80%EA%B3%B5+%EC%9E%90%EC%9B%90%EC%B5%9C%EC%A0%81%ED%99%94+AI+%EB%8D%B0%EC%9D%B4%ED%84%B0%EC%85%8B&page=1&DATASET_SEQ=46&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
-- 데이터 개수: 717,774개 / 데이터셋 파일 확장자: .xls
-- 생산 부품에 따라 변동되는 에너지 소모량 예측을 위한 전류 데이터
-- 단조프레스 연속공정은 자동차 부품 생산을 위해 여러 대의 프레스 설비가 연속적으로 동작하는 공정으로, 많은 전기에너지를 소모한다.
-- 생산되는 부품 종류와 공정 상태에 따라 전력 사용량이 달라질 수 있으며, 이를 예측하기 위한 AI 기반 에너지 분석이 요구된다.
-- 본 데이터셋은 단조프레스 공정의 전력 사용량을 분석하여 에너지 자원 최적화 및 운영 효율 향상을 위한 AI 모델 개발에 활용된다.
-
-## 1.3. 머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터) - 차체&도장&의장
-
-### 1.3.1. 데이터 구조
-
-해당 머신비전 학습통합 데이터셋은 크게 전류/센서 시계열 데이터(`*_data.csv`)와 라벨 데이터(`*_label.json`)로 구성되어 있습니다.
-
-### 데이터 파일 구조
-
-| 파일명 | 데이터 유형 | 설명 | 행(Row) 의미 | 열(Column) 의미 |
-| --- | --- | --- | --- | --- |
-| `2nd_process_left_data.csv` | 학습 데이터 | 좌측 공정(Left) 센서/전류 시계열 데이터 | 하나의 측정 샘플 | 시간 순서에 따른 센서값 |
-| `2nd_process_right_data.csv` | 학습 데이터 | 우측 공정(Right) 센서/전류 시계열 데이터 | 하나의 측정 샘플 | 시간 순서에 따른 센서값 |
-| `2nd_process_left_label.json` | 라벨 데이터 | Left 데이터의 정상/불량 여부 | 각 샘플의 정답(Label) | 0 또는 1 |
-| `2nd_process_right_label.json` | 라벨 데이터 | Right 데이터의 정상/불량 여부 | 각 샘플의 정답(Label) | 0 또는 1 |
-
----
-
-## CSV 데이터 구조 (`_data.csv`)
-
-CSV 파일은 시계열(Time-Series) 형태의 데이터로 구성되어 있으며, 약 80개의 연속 측정값 컬럼을 포함하고 있습니다.
-
-### 컬럼 구조 예시
-
-| 컬럼명 | 의미 |
-| --- | --- |
-| `42.19` | 특정 시점의 센서값 |
-| `42.465` | 다음 시점 센서값 |
-| `42.877` | 다음 시점 센서값 |
-| `...` | ... |
-| `52.777` | 마지막 시점 센서값 |
-
-※ 실제 컬럼명은 측정값 자체가 헤더로 잘못 저장된 형태로 보이며, 일반적으로는 `t1`, `t2`, `t3` 같은 시계열 인덱스로 변경하여 사용하는 것이 좋습니다.
-
----
-
-## 데이터 샘플 형태
-
-| t1 | t2 | t3 | t4 | ... | t80 |
-| --- | --- | --- | --- | --- | --- |
-| 42.534 | 42.689 | 42.877 | 43.594 | ... | 52.999 |
-| 42.259 | 42.689 | 42.791 | 43.338 | ... | 52.856 |
-- 하나의 행(Row)은 설비/공정의 1회 측정 데이터
-- 값은 시간 흐름에 따른 센서값(전류, 진동, 압력 등으로 추정)
-- 시계열 기반 이상탐지(AI 이상 감지) 학습에 적합한 구조
-
----
-
-## Label JSON 구조
-
-라벨 파일은 리스트(List) 형태로 저장되어 있습니다.
-
-### 예시
-
-```
-[0.0,1.0,0.0,1.0]
-```
-
----
-
-## 라벨 의미 예시
-
-| 값 | 의미 |
-| --- | --- |
-| `0` | 정상(Normal) |
-| `1` | 이상/불량(Abnormal) |
-
-※ 실제 의미는 데이터셋 제공 문서 기준으로 최종 확인 필요
-
----
-
-## 전체 데이터셋 특징 요약
-
-| 항목 | 내용 |
-| --- | --- |
-| 데이터 형태 | 시계열(Time-Series) |
-| 주요 목적 | 설비 이상 탐지 / 머신비전 AI 학습 |
-| 입력 데이터 | 연속 센서값 |
-| 출력 데이터 | 정상/불량 라벨 |
-| 활용 가능 분야 | 스마트팩토리, 예지보전(Predictive Maintenance), 품질 검사 |
-| 학습 방식 | 지도학습(Supervised Learning) 가능 |
-| 추천 모델 | LSTM, CNN-1D, Transformer, AutoEncoder |
-
-06. Dataset_Machinevision.zip
-
-- https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=%EB%A8%B8%EC%8B%A0%EB%B9%84%EC%A0%84+AI+%EB%8D%B0%EC%9D%B4%ED%84%B0%EC%85%8B&page=1&DATASET_SEQ=6&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
-- 열화상 이미지를 이용한 양/불량 판정을 위한 머신비전 데이터, 자동차 윈드실드 사이드 몰딩 사출품의 양품/불량품 판정을 위해 적외선 카메라를 사용하여 제품의 온도 분포에 따라 품질을 선별하기위한 제조 AI분석과정을 담은 데이터셋
-- 사출 제품을 일정시간 안에 열화상 카메라로 촬영한 이미지 데이터를 수집하고 서포트벡터머신(SVM) 알고리즘을 사용하여 정확한 불량품 선별을 도모
-- 데이터 개수 : 1,674개 / 확장자 : csv, json
-    - 분석에 사용된 변수명 : 320x256의 열화상 이미지의 모든 온도 raw data, 제품 두께 데이터
-    - 수집 방법 : 학습 데이터는 사출 제품을 일정시간안에 열화상 카메라로 촬영하여 취득. 라벨 데이터는 수기 기록(두께)
-- 열화상 카메라를 통해 수집한 온도 분포 데이터를 기반으로 양품/불량품을 판별하는 머신비전 기반 품질 검사 데이터셋
-- 자동차 윈드실드 사이드 몰딩 사출품의 열화상 이미지와 두께 데이터를 활용하여 품질 예측 수행
-- 비선형 SVM 기반 AI 모델을 사용하여 불량품 자동 판정 및 품질 검사 자동화 가능
-- OpenCV, Computer Vision, Thermal Vision 기반 AI 품질 검사 기능 구현에 적합
-- 생산 라인의 품질 검사 이벤트를 실시간 관제 시스템과 연계하여 불량률 분석 및 제조 품질 모니터링 가능
-- 스마트팩토리 통합 관제 환경에서 설비 이벤트와 품질 검사 이벤트를 함께 분석하는 구조로 확장 가능
-
-### 1.3.2. Bosch Production Line Performance(kaggle) 데이터 구조
-
-bosch-production-line-performance.zip
-
-| 데이터 파일 | 전체 컬럼 수 | 주요 컬럼 구조 | 컬럼 패턴 | 설명 |
-| --- | --- | --- | --- | --- |
-| train_numeric.csv | 970개 | Id, L*_S*_F*, Response | L0_S0_F0 형태 | 제조 공정의 수치형 센서/측정 데이터 |
-| train_categorical.csv | 2,141개 | Id, L*_S*_F* | L0_S1_F25 형태 | 제조 공정의 범주형/상태성 데이터 |
-| train_date.csv | 1,157개 | Id, L*_S*_D* | L0_S0_D1 형태 | 제조 공정 단계별 시간/날짜성 데이터 |
-
-| 공통 구조 | 의미 |
-| --- | --- |
-| Id | 제품 또는 생산 샘플 식별자 |
-| L0, L1, L2, L3 | 생산 라인 또는 공정 구간 |
-| S0, S1, S2 ... | 해당 라인 내 세부 스테이션/설비 |
-| F0, F2 ... | Feature 컬럼, 측정값 또는 상태값 |
-| D1, D3 ... | Date 컬럼, 공정 시간 정보 |
-| Response | 불량 여부 라벨. train_numeric.csv에만 포함됨 |
-
-| 데이터 파일 | 라인별 컬럼 수 |
-| --- | --- |
-| train_numeric.csv | L0: 168개, L1: 513개, L2: 42개, L3: 245개 |
-| train_categorical.csv | L0: 323개, L1: 1,227개, L2: 159개, L3: 431개 |
-| train_date.csv | L0: 184개, L1: 621개, L2: 78개, L3: 273개 |
-
-| 데이터 파일 | 예시 컬럼 |
-| --- | --- |
-| train_numeric.csv | Id, L0_S0_F0, L0_S0_F2, L0_S0_F4, ..., Response |
-| train_categorical.csv | Id, L0_S1_F25, L0_S1_F27, L0_S1_F29, ... |
-| train_date.csv | Id, L0_S0_D1, L0_S0_D3, L0_S0_D5, ... |
-- Bosch Production Line Performance (Kaggle)
-- 자동차 부품 및 전자 부품 제조 공정에서 수집된 대규모 생산라인 데이터를 기반으로 불량 제품을 예측하는 제조 AI 데이터셋
-- 독일 제조 기업 Bosch의 실제 생산라인 측정 데이터를 기반으로 구성된 Kaggle 제조 AI Competition 데이터셋
-- 제조 공정 각 단계(Line / Station / Feature)에서 수집된 수천 개의 센서·측정 데이터를 활용하여 품질 불량 여부(Response)를 예측하는 목적의 데이터셋
-- 스마트팩토리 제조 공정에서 가장 현실적인 “대규모 생산라인 이벤트 기반 데이터셋” 중 하나로 평가됨
-- Bosch 데이터 상세
-    
-    # 분류 카테고리
-    
-    - 제조 품질 예측(Quality Prediction)
-    - 생산라인 불량 탐지(Defect Detection)
-    - 제조 공정 이상 탐지(Manufacturing Anomaly Detection)
-    - 스마트팩토리 생산 공정 분석
-    - 대규모 제조 시계열/이벤트 분석
-    - Industrial AI / Manufacturing AI
-    - 예지 품질 관리(Predictive Quality)
-    
-    ---
-    
-    # 데이터셋 구성
-    
-    이 데이터셋은 크게 7개 파일로 구성됩니다.
-    
-    | 데이터셋 파일 | 설명 | 확장자 |
-    | --- | --- | --- |
-    | train_numeric | 학습용 수치형 센서 데이터 + Response(Label 포함) | CSV |
-    | test_numeric | 테스트용 수치형 센서 데이터 | CSV |
-    | train_categorical | 학습용 범주형 데이터 | CSV |
-    | test_categorical | 테스트용 범주형 데이터 | CSV |
-    | train_date | 학습용 시간(Date/Timestamp) 데이터 | CSV |
-    | test_date | 테스트용 시간(Date/Timestamp) 데이터 | CSV |
-    | sample_submission | 제출 예시 파일 | CSV |
-    
-    ---
-    
-    # 데이터 규모
-    
-    | 항목 | 내용 |
-    | --- | --- |
-    | 학습 데이터 | 약 1,183,747개 |
-    | 테스트 데이터 | 약 1,183,748개 |
-    | 전체 Feature 수 | 약 3,000개 이상 |
-    | Numeric Feature | 약 968개 |
-    | Date Feature | 약 1,156개 |
-    | Categorical Feature | 약 2,140개 |
-    | 데이터 크기 | 약 14GB 이상 |
-    | 라벨 | Response (0=정상, 1=불량) |
-    
-    ---
-    
-    # 데이터 특징
-    
-    ## 1. Numeric 데이터
-    
-    - 센서 측정값
-    - 공정 수치 데이터
-    - 압력/온도/측정값 계열
-    - 연속형 데이터
-    
-    예:
-    
-    - L0_S0_F0
-    - L3_S36_F3939
-    
-    (Line / Station / Feature 구조)
-    
-    ---
-    
-    ## 2. Categorical 데이터
-    
-    - 공정 상태
-    - 설비 상태
-    - 특정 공정 결과
-    - 문자열 기반 제조 이벤트
-    
-    ---
-    
-    ## 3. Date 데이터
-    
-    - 공정 수행 시간
-    - Station 통과 Timestamp
-    - 생산 순서 분석 가능
-    
-    예:
-    
-    - L0_S0_D1
-    
-    ---
-    
-    # 모빌리티 스마트팩토리 관제 시스템 적합성
-    
-    매우 적합합니다.
-    
-    오히려 사용자가 현재 만들고 있는:
-    
-    - 모빌리티 스마트팩토리 관제
-    - Kafka 기반 이벤트 수집
-    - Redis Queue 처리
-    - Elasticsearch 기반 검색
-    - AI 이상 탐지
-    - 실시간 생산라인 모니터링
-    
-    구조와 가장 유사한 공개 데이터셋 중 하나입니다.
-    
-    ---
-    
-    # 사용자 프로젝트와 연결 가능한 요소
-    
-    | Bosch 데이터 | 사용자 관제 시스템 |
-    | --- | --- |
-    | 생산라인(Line) | Factory / Line 구조 |
-    | Station | 설비(Equipment) |
-    | Timestamp | Kafka Event Time |
-    | Numeric Sensor | IoT 센서 데이터 |
-    | Response | 장애/불량 이벤트 |
-    | 공정 통과 여부 | 생산 흐름 추적 |
-    | Feature 수천개 | 대규모 이벤트 처리 |
-    | 불균형 데이터 | 이상 탐지 AI |
-    | 제조 이벤트 | 실시간 관제 이벤트 |
-
-# 프레스&차체&도장&의장 기능
-
-# 2. 제조 공정별 기능 상세화 + 데이터셋 활용 구조
-
-## 2.1. 프레스 (차체 외판, 문, 보닛, 루프 등)
-
-### 2.1.1. 프레스 공정
-
-철판을 눌러 차체 부품을 만드는 공정
-
-## 주요 기능
-
-| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
-| --- | --- | --- | --- | --- | --- | --- |
-| 프레스 이상 정지 탐지 | 프레스 설비가 생산 중 갑자기 멈추거나 생산 주기가 비정상적으로 길어지는 상황을 탐지함 | 공정 데이터 2022년 8월, 프레스 전류 데이터, Bosch Dataset | AI 미적용 (룰 기반) | Kafka Stream + Rule Engine | 실시간 생산 이벤트와 전류 데이터를 기반으로 cnt 증가 여부, 생산 주기 지연, 전류 급감 패턴을 분석하여 설비 이상 정지를 탐지함 | cnt 증가 정체, 전류값 급감, Timestamp 지연 |
-
----
-
-## 실제 활용 방식
-
-### Ford Dataset 활용
-
-시계열 진동 패턴 기반:
-
-- 금형 진동 변화 탐지
-- 모터 진동 증가 분석
-- 프레스 충격 패턴 추적
-- 반복 이상 진동 이벤트 분석
-
-수행.
-
----
-
-### 소성가공 데이터 활용
-
-`RMS[A]` 기반:
-
-- 설비 부하 상태 분석
-- 모터 과부하 탐지
-- 전류 Peak 이벤트 감지
-- 설비 정지 상태 추적
-
-수행.
-
----
-
-### Bosch Dataset 활용
-
-Line / Station / Timestamp 기반:
-
-- 생산라인 이벤트 흐름 분석
-- 설비 이상 이벤트 추적
-- 생산 지연 및 병목 분석
-- 반복 이상 패턴 탐지
-
-수행.
-
-## 2.2 차체
-
-### 2.2.1. 차체 공정
-
-용접 로봇 및 차체 조립 설비를 통해 차량 차체를 생산하는 공정
-
-## 주요 기능
-
-| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
-| --- | --- | --- | --- | --- | --- | --- |
-| 로봇 이상 동작 및 충돌 위험 탐지 | 용접 로봇의 반복 이동 패턴 이상 및 충돌 위험 상황을 탐지함 | Ford Dataset, 로봇 전류 데이터 | AI 미적용 (통계/룰 기반) | 시계열 패턴 분석 + Rule Engine | 로봇 암 이동 패턴, 진동 변화, 전류 RMS 변화를 기반으로 반복적인 비정상 움직임 및 충돌 위험 이벤트를 탐지함 | 반복 이동 오류, 진동 급증, 전류 RMS 급증 |
-
----
-
-## 실제 활용 방식
-
-### 로봇 전류 데이터 활용
-
-`RMS[A]` 기반:
-
-- 다중 로봇 간 전류 패턴 비교
-- 특정 로봇 이상 상태 탐지
-- 모터 과부하 및 전류 Peak 분석
-- 생산 주기 이상 탐지
-
-수행.
-
----
-
-### Ford Dataset 활용
-
-시계열 진동 패턴 기반:
-
-- 로봇 암 진동 증가 탐지
-- 충돌 위험 이벤트 감지
-- 반복 이동 패턴 이상 분석
-- 비정상 동작 탐지
-
-수행.
-
----
-
-### Bosch Dataset 활용
-
-Station / Timestamp 기반:
-
-- 생산 공정 흐름 추적
-- 공정 병목 탐지
-- 생산 지연 분석
-- 품질 이벤트 및 반복 불량 추적
-
-수행.
-
-### 2.2.2 조립 관련 로봇팔 데이터
-
-- **로봇팔 용접 전력사용 및 시간**
-    
-    링크 : https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=&page=1&DATASET_SEQ=51&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=C004025&FILE_TYPE_SEL=C005002&WDATE_SEL=
-    
-    데이터셋
-    
-    | 컬럼명 | 의미 | 예시값 |
-    | --- | --- | --- |
-    | WK_DT | 작업 일자 | 2.02201E+16 |
-    | PIPE_NO | 파이프 제품 번호 | PP22041200707 |
-    | DV_R | 직경 편차 | 308 |
-    | DA_R | 평균 직경 | 7962 |
-    | AV_R | 검사 항목 평균값 | 364 |
-    | AA_R | 평균 면적 | 5975 |
-    | PM_R | 공정 측정 종학값 | 9270 |
-    | FIN_JGMT | 최종 판정 결과 | 1 |
-    
-    데이터량 : 654,200(4.153mb)
-    
-- **로봇팔 용접기 진동 예지**
-    
-    링크 : https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=진동&page=1&DATASET_SEQ=45&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=&FILE_TYPE_SEL=&WDATE_SEL=
-    
-    데이터셋
-    
-    | 컬럼명 | 의미 | 예시값 |
-    | --- | --- | --- |
-    | Time | 측정 시간 | 2021/08/02 6:47 |
-    | 0 | 0hz | 0.00044808 |
-    | 3.12 | 3.12hz | 0.000633875 |
-    | 6.25 | 6.25hz | 0.000895333 |
-    | 9.38 | 9.38hz | 0.001123601 |
-    | 12.5 | 12.5hz | 0.000332504 |
-    | 15.62 | 15.62hz | 0.000583095 |
-    | 18.75 | 18.75hz | 0.000241952 |
-    | 21.88 | 21.88hz | 0.000742734 |
-    | 25 | 25hz | 0.001193284 |
-    | 28.12 | 28.12hz | 0.001019982 |
-    | 31.25 | 31.25hz | 0.000571357 |
-    | 34.38 | 34.38hz | 0.000844553 |
-    | 37.5 | 37.5hz | 0.000377998 |
-    | 40.62 | 40.62hz | 0.00056753 |
-    
-    데이터량 : 9,358,120(105mb)
-    
-
-## 2.3. 도장(외부 도색)
-
-### 2.3.1. 도장 공정
-
-차량 외부를 도색하여 품질과 외관을 완성하는 공정
-
-| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
-| --- | --- | --- | --- | --- | --- | --- |
-| 도장 품질 이상 탐지 | 차량 외부 도장 과정에서 도장 불균형, 도장 누락, 표면 품질 저하 등이 발생하는 상황을 탐지함 | 머신비전 데이터, Bosch Dataset | AI 미적용 (통계/룰 기반) | 시계열 통계 분석 + Rule Engine | 도장 공정의 품질 이벤트, 불량률 변화, 생산 이벤트 흐름, 센서값 변화를 기반으로 반복적인 품질 이상 패턴을 탐지함. 공정별 품질 이벤트 로그와 생산 흐름 중심의 실시간 품질 관제를 수행함 | 품질 이벤트 증가, 불량률 증가, Response 증가, 공정별 반복 이상 발생 |
-
----
-
-## 실제 활용 방식
-
-### 머신비전 데이터 활용
-
-품질 이벤트 및 시계열 패턴 기반:
-
-- 도장 품질 이상 탐지
-- 반복 불량 이벤트 분석
-- 공정별 품질 변화 추적
-- 생산 품질 이벤트 모니터링
-
-수행.
-
----
-
-### Bosch Dataset 활용
-
-Numeric / Response / Timestamp 기반:
-
-- 생산라인 품질 흐름 분석
-- 공정별 불량률 증가 추적
-- 생산 지연 및 병목 분석
-- 반복 이상 공정 탐지
-
-수행.
-
----
-
-### 환경 센서 데이터 활용
-
-온도 / 습도 / VOC / 공조 상태 기반:
-
-- 유해가스(VOC) 증가 감지
-- 도장 환경 이상 탐지
-- 공조 시스템 상태 모니터링
-- 작업자 안전 이벤트 감시
-
-수행.
-
-### 2.3.2 도장 관련 표면처리 습도 데이터
-
-링크 : https://www.kamp-ai.kr/aidataDetail?AI_SEARCH=&page=2&DATASET_SEQ=56&DISPLAY_MODE_SEL=CARD&EQUIP_SEL=&GUBUN_SEL=C004025&FILE_TYPE_SEL=C005002&WDATE_SEL=
-
-데이터셋
-
-| 컬럼명 | 의미 | 예시값 |
-| --- | --- | --- |
-| Datetime | 측정 일시 | 2021-02-08 0:15 |
-| Production | 생산량 | 116 |
-| Temperature | 온도 | -4.2 |
-| Humidity | 습도 | 65.6 |
-| Power_Cost | 전력 단가 또는 전기 요금 지수 | 109.8 |
-| DoW | 요일(Day of Week) | Monday |
-| Worker_Power | 작업 인력 투입 지표 | 0.13 |
-| Man_Cost | 인건비 또는 작업 비용 | 1.5 |
-| Power_Usage | 전력 사용량 | 103 |
-
-데이터량 : 220,311(1.33mb)
-
-## 2.4. 의장 조립 (엔진, 시트 조립 및 배선)
-
-### 2.4.1. 의장 공정
-
-부품을 조립하여 완성차를 구성하는 최종 조립 공정
-
-## 주요 기능
-
-| 기능 | 상세 설명 | 활용 데이터셋 | 모델 적용 여부 | 적용 알고리즘 / 모델 | 알고리즘 설명 | 판단 기준 예시 |
-| --- | --- | --- | --- | --- | --- | --- |
-| 조립 순서 오류 탐지 | 엔진, 시트, 배선 등의 조립 순서가 잘못 수행된 상황을 탐지함 | 공정 데이터 2022년 8월, Bosch Dataset | AI 미적용 (룰 기반) | Rule Engine | 생산 공정의 Timestamp 및 product_date 흐름을 기반으로 공정 순서를 비교하여 이상 여부를 판단함. 정해진 공정 순서와 다른 이벤트 발생 시 오류로 탐지함 | product_date 순서 이상, Timestamp 지연 |
-
----
-
-## 실제 활용 방식
-
-### 공정 데이터 활용
-
-`lineno`, `product_date`, `quantity` 기반:
-
-- 생산 추적
-- 작업 이력 분석
-- 공정 순서 검증
-- 생산 흐름 분석
-
-수행.
-
----
-
-### 머신비전 데이터 활용
-
-품질 이벤트 및 시계열 패턴 기반:
-
-- 부품 누락 이벤트 탐지
-- 조립 상태 분석
-- 체결 이상 이벤트 분석
-- 반복 품질 이상 추적
-
-수행.
-
----
-
-### Bosch Dataset 활용
-
-Line / Station / Response / Timestamp 기반:
-
-- 생산라인 불량 분석
-- 공정 병목 및 지연 탐지
-- 제조 공정 이상 이벤트 추적
-- 반복 불량 패턴 분석
-
-수행.
-
-## 2.5. Bosch Production Line Performance 데이터셋 역할
-
-### 2.5.1. 주요 활용 분야
-
-| 활용 분야 | 설명 |
-| --- | --- |
-| 생산라인 불량 예측 | 공정별 센서 데이터를 기반으로 최종 불량 여부(Response) 예측 |
-| 공정 병목 탐지 | 특정 Station 또는 Line에서 이벤트 적체 및 지연 분석 |
-| 제조 공정 이상 탐지 | 생산라인 전체의 이상 패턴 및 불량 증가 탐지 |
-| 대규모 이벤트 분석 | 수천 개 Feature 기반 제조 이벤트 분석 |
-| 스마트팩토리 디지털 트윈 | 실제 생산라인 구조 기반 공정 시뮬레이션 |
-
-### 2.5.2. Bosch Dataset 주요 구조 활용
-
-| 데이터 종류 | 활용 방식 |
-| --- | --- |
-| Numeric Feature | 센서값/설비 상태값 분석 |
-| Date Feature | 공정 시간 흐름 및 병목 분석 |
-| Categorical Feature | 공정 상태 분석 |
-| Response(Label) | 정상/불량 분류 |
-
-### 2.5.3. Bosch 데이터셋 핵심 역할
-
-| 데이터셋 | 핵심 역할 |
-| --- | --- |
-| Ford Dataset | 설비 이상 탐지 |
-| 소성가공 데이터 | 프레스 및 생산라인 운영 분석 |
-| 머신비전 데이터 | 품질 검사 및 불량 탐지 |
-| Bosch Dataset | 전체 생산라인 통합 분석 및 제조 AI |
-
----
-
-# MVP 기능
-
-## 📊 제조 공정 AI 분석
-
-!image.png
-
-### 공정 간 불량 전이 예측 및 제조 병목 탐지
-
-불량 전이 예측 프로세스 
-
----
-
-## 1. 기능 요약
-
-### 제조 병목 탐지
-
-Bosch Production Line Performance Dataset의 Station 통과 시간 데이터를 분석하여 생산 공정 내 병목 구간을 실시간 탐지합니다.
-
-공정별 처리 시간, 대기 시간, 체류 시간을 기반으로 병목 위험도를 산출하며, 특정 Station에서 발생한 지연이 전체 생산 흐름에 미치는 영향을 시각적으로 제공합니다.
-
----
-
-### 공정 간 불량 전이 예측
-
-Bosch Production Line Performance Dataset의 수치형 센서 데이터와 공정 시간 데이터를 활용하여 특정 공정에서 발생한 이상 징후가 후속 공정의 불량으로 이어질 가능성을 예측합니다.
-
-현재 공정 상태는 정상이라도 후속 공정에서 발생 가능한 품질 이상을 사전에 탐지하여 생산 손실을 최소화합니다.
-
----
-
-## 2. 데이터 및 AI 모델 아키텍처
-
-### 활용 데이터셋
-
-### Bosch Production Line Performance Dataset
-
-사용 파일
-
-- train_numeric.csv
-- train_date.csv
-- train_categorical.csv
-
-활용 목적
-
-- 공정 병목 탐지
-- 공정 간 불량 전이 예측
-- 제조 이력 분석
-- 생산 흐름 시각화
-
----
-
-### 머신비전 열화상 품질 데이터셋
-
-활용 목적
-
-- 품질 검사 결과 생성
-- 불량 여부 검증
-- AI 예측 결과 보조 학습 데이터 활용
-
----
-
-## 3. 적용 AI 모델
-
-### 제조 병목 탐지
-
-### Rule Engine
-
-공정별 처리 시간과 대기 시간을 분석하여 임계값 초과 시 병목 이벤트를 생성합니다.
-
-예시
-
-- 평균 처리 시간 대비 30% 이상 증가
-- Station 체류 시간 급증
-- 생산 대기열 증가
-
----
-
-### Isolation Forest
-
-정상 생산 패턴과 다른 비정상 공정 흐름을 탐지하여 병목 위험도를 계산합니다.
-
-탐지 대상
-
-- 비정상 체류 시간
-- 비정상 통과 시간
-- 특정 Station 집중 현상
-
----
-
-### 공정 간 불량 전이 예측
-
-### LightGBM Classifier
-
-train_numeric.csv와 train_date.csv를 결합하여 학습합니다.
-
-공정별 센서 데이터와 시간 정보를 기반으로 후속 공정 불량 발생 확률을 예측합니다.
-
-예시
-
-- 차체 공정 불량 확률 25%
-- 도장 공정 불량 확률 78%
-- 의장 공정 불량 확률 42%
-
----
-
-## 4. 데이터 파이프라인
-
-### Kafka 기반 이벤트 스트리밍
-
-공정 이벤트를 Kafka Topic으로 수집합니다.
-
-수집 데이터
-
-- Product ID
-- Station ID
-- Process Time
-- Waiting Time
-- Defect Probability
-
-각 이벤트는 생산 이력 단위로 연결되어 전체 제조 흐름을 구성합니다.
-
----
-
-### Elasticsearch 기반 로그 분석
-
-실시간 분석 결과를 Elasticsearch에 저장합니다.
-
-주요 기능
-
-- 병목 공정 검색
-- 불량 이력 검색
-- 제조 이력 조회
-- 공정 영향도 분석
-
----
-
-## 5. AI 분석 기능
-
-### 실시간 병목 분석
-
-분석 데이터
-
-- Station 통과 시간
-- 대기 시간
-- 공정 체류 시간
-
-제공 정보
-
-- 병목 공정
-- 평균 지연 시간
-- 영향 생산량
-- 병목 위험도
-
-예시
-
-S32 공정
-
-- 평균 지연 시간 : 14초
-- 영향 제품 수 : 1,245개
-- 위험도 : 92%
-
----
-
-### 공정 간 불량 전이 예측
-
-분석 데이터
-
-- Numeric Sensor Data
-- Date Feature
-- 품질 검사 결과
-
-제공 정보
-
-- 후속 공정 불량 확률
-- 예상 불량 공정
-- 위험도 등급
-
-예시
-
-현재 제품 상태
-
-- 차체 불량 확률 : 23%
-- 도장 불량 확률 : 78%
-- 의장 불량 확률 : 41%
-
-예상 결과
-
-도장 공정 불량 발생 가능성 높음
-
----
-
-### AI 원인 분석
-
-LightGBM Feature Importance를 활용하여 병목 및 불량 예측에 영향을 준 주요 원인을 제공합니다.
-
-제공 정보
-
-- 영향 Station
-- 주요 Sensor Feature
-- 시간 지연 영향도
-
-예시
-
-도장 공정 불량 예측 원인
-
-1. Station S32 체류 시간 증가
-2. L3 공정 센서 값 이상
-3. 이전 공정 대기 시간 증가
-
----
-
-## 6. 시각화 요소
-
-### 실시간 병목 분석
-
-- 공정 흐름도
-- 병목 구간 강조
-- 위험도 색상 표시
-
----
-
-### 불량 전이 예측
-
-- 공정별 불량 확률 게이지
-- 위험도 카드
-- 예측 결과 알림
-
-예시
-
-도장 공정 불량 확률
-
-78%
-
-원인
-
-- S32 체류 시간 증가
-- 이전 공정 지연 발생
-
----
-
-### 제조 이력 타임라인
-
-- 제품별 공정 이동 경로
-- 공정별 처리 시간
-- 병목 발생 시점
-- 품질 검사 결과
-
-# 이벤트 데이터 (json)
-
-## 샘플DB 구조
-
-```sql
-CREATE TABLE manufacturing_event_json (
-    id BIGINT AUTO_INCREMENT PRIMARY KEY,
-
-    event_id VARCHAR(100) NOT NULL,
-    event_time DATETIME NOT NULL,
-
-    car_master_id BIGINT NOT NULL,
-    equipment_id BIGINT NOT NULL,
-
-    process_code ENUM('PRESS', 'BODY', 'PAINT', 'ASSEMBLY') NOT NULL,
-    station_code VARCHAR(50),
-
-    equipment_code VARCHAR(50) NOT NULL,
-    equipment_type VARCHAR(50),
-    equipment_status VARCHAR(30),
-
-    event_type VARCHAR(50),
-
-    event_json JSON NOT NULL,
-
-    is_sent TINYINT(1) DEFAULT 0,
-    sent_at DATETIME NULL,
-
-    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
-    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
-
-    INDEX idx_event_time_sent (event_time, is_sent),
-    INDEX idx_process_time (process_code, event_time),
-    INDEX idx_equipment_time (equipment_code, event_time),
-    INDEX idx_car_time (car_master_id, event_time),
-    INDEX idx_event_id (event_id),
-    INDEX idx_equipment_id_time (equipment_id, event_time),
-
-    CONSTRAINT fk_manufacturing_event_json_car_master
-        FOREIGN KEY (car_master_id)
-        REFERENCES car_master(id),
-
-    CONSTRAINT fk_manufacturing_event_json_equipment
-        FOREIGN KEY (equipment_id)
-        REFERENCES equipment(id)
-);
-```
-
-| 컬럼명 | 설명 | 왜 필요한지 |
-| --- | --- | --- |
-| `id` | sampleDB 내부 PK | DB에서 row를 구분하기 위한 기본키 |
-| `event_id` | 이벤트 고유 ID | Kafka, mainDB, ES에서 같은 이벤트를 추적하기 위한 공통 ID |
-| `event_time` | 이벤트 발생 시간 | Scheduler가 가상 시간 기준으로 Kafka에 발행할 때 필요 |
-| `car_master_id` | 차량 마스터 ID | 차량별 공정 흐름, 차량별 이상 이력 조회에 필요 |
-| `process_code` | 공정 코드 | `PRESS`, `BODY`, `PAINT`, `ASSEMBLY` 분석 분기 기준 |
-| `station_code` | 스테이션 코드 | 같은 공정 안에서 어느 위치에서 발생한 이벤트인지 구분 |
-| `equipment_code` | 설비 코드 | 설비별 상태, 이상, 가동률 계산 기준 |
-| `equipment_type` | 설비 유형 | 프레스기, 로봇팔, 카메라, 컨베이어 등 분석 분기 보조 |
-| `equipment_status` | 설비 상태 | `RUNNING`, `IDLE`, `STOPPED`, `ERROR` 등. mainDB 가동률 계산에 필요 |
-| `event_type` | 이벤트 유형 | `PROCESS_STATUS`, `QUALITY_CHECK`, `EQUIPMENT_STATUS` 등 이벤트 성격 구분 |
-| `event_json` | 전체 원천 JSON | Kafka로 그대로 발행할 실제 메시지 |
-| `is_sent` | Kafka 발행 여부 | Scheduler가 아직 안 보낸 데이터만 조회하기 위해 필요 |
-| `sent_at` | Kafka 발행 시간 | 언제 Kafka로 보냈는지 추적 |
-| `created_at` | DB 저장 시간 | Python 전처리 결과가 저장된 시간 |
-| `updated_at` | DB 수정 시간 | 재생성/upsert로 기존 이벤트 row가 갱신된 시간을 추적 |
-
-## 최종 원천 관제 이벤트 JSON
-
- 처리 완료 시간
-
-```json
-{
-  "event": {
-    "eventId": "EVT-20260616-000001",
-    "eventTime": "2026-06-16T10:00:01",
-    "eventCategory": "MANUFACTURING",
-    "eventType": "PROCESS_STATUS",
-    "eventName": "프레스 공정 통합 관제 이벤트"
-  },
-  "location": {
-    "factoryCode": "AIMS_FACTORY_01",
-    "lineCode": "PRESS_LINE_01",
-    "processCode": "PRESS",
-    "stationCode": "PRESS_STATION_01"
-  },
-  "equipment": {
-    "equipmentCode": "EQ_PRESS_01",
-    "equipmentName": "프레스 유압모터 1호",
-    "equipmentType": "HYDRAULIC_PRESS"
-  },
-  "equipmentStatus": {
-    "operationStatus": "RUNNING",
-    "lastNormalTime": "2026-06-16T09:59:30",
-    "statusChangedTime": "2026-06-16T10:00:01"
-  },
-  "product": {
-    "carId": "CAR-000001",
-    **"productId": "PRODUCT-000001",
-    "itemNo": "76211-A3010-100",
-    "quantity": 5,**
-    "productionCount": 18133963
-  },
-  "sensor": {
-    "sensorType": "MULTI_SENSOR",
-    "current": {
-      "rmsAmpere": 1.971987844,
-      "maxAmpere": 2.15,
-      "minAmpere": 1.72
-    },
-    "vibration": {
-      "accelerationG": 0.007593981,
-      "vibrationScore": 0.21,
-      "vibrationRms": 0.18,
-      "vibrationPeak": 0.31
-    },
-    "robotArmVibration": {
-      "robotId": "ROBOT_ARM_01",
-      "axis": "J1",
-      "frequencyHz": 40.62,
-      "amplitude": 0.00056753,
-      "vibrationRms": 0.000633875,
-      "vibrationPeak": 0.001193284,
-      "vibrationScore": 0.27
-    },
-    "thermal": {
-      "thermalScore": 43.2,
-      "avgTemperature": 43.2,
-      "maxTemperature": 52.9,
-      "minTemperature": 42.1
-    }
-  },
-  "manufacturing": {
-    "processSequence": 1,
-    "previousProcessCode": null,
-    "nextProcessCode": "BODY",
-    "targetQuantity": 200,
-    "completedQuantity": 145
-  },
-  "processMetrics": {
-    "cycleTimeSec": 42.5,
-    "waitingTimeSec": 8.3,
-    "processingTimeSec": 34.2,
-    "stationDelaySec": 5.7,
-    "throughputPerMin": 1.4,
-    "queueLength": 7,
-    "wipCount": 24,
-    "equipmentIdleTimeSec": 12.1
-  },
-  "sourceTrace": {
-    "fordRowId": 1,
-    "formingRowId": 1,
-    "robotArmVibrationRowId": 1,
-    "machineVisionRowId": 1,
-    "boschId": 1
-  },
-  "processData":{
-    # 아래참고
-  }
-}
-```
-
-## 위 내용 중 ProcessData 내용 JSON
-
-```jsx
-📌 프레스
-"processData": {
-  "press": {
-    "countIncreaseYn": true,
-    "targetCycleTimeSec": 40.0,
-    "timestampDelaySec": 5.7
-  }
-}
-- countIncreaseYn : 생산 카운트가 증가했는지 확인 -> false면 정
-- targetCycleTimeSec : 기준 cycle time. 실제 cycle time과 비교
-- timestampDelaySec : 이벤트 지연 시간. 데이터 지연/설비 정지 판단
-
-📌 차체
-"processData": {
-  "body": {
-    "robotMotionStatus": "NORMAL",
-    "robotOperationMode": "AUTO",
-    "frequencyPeakBand": "501_600_HZ",
-    "frequencyBands": {
-      "freq_0_100_hz": 0.001193284,
-      "freq_101_200_hz": 0.001987782,
-      "freq_201_300_hz": 0.001072014,
-      "freq_301_400_hz": 0.001792223,
-      "freq_401_500_hz": 0.001924913,
-      "freq_501_600_hz": 0.002907113,
-      "freq_601_700_hz": 0.001207061,
-      "freq_701_800_hz": 0.00114628,
-      "freq_801_900_hz": 0.001170759,
-      "freq_901_1000_hz": 0.001318642,
-      "freq_1001_1100_hz": 0.001426342,
-      "freq_1101_1200_hz": 0.001297966,
-      "freq_1201_1300_hz": 0.001750256,
-      "freq_1301_1400_hz": 0.001491831,
-      "freq_1401_1500_hz": 0.001278729,
-      "freq_1501_1600_hz": 0.000994307
-    }
-  }
-}
-- robotMotionStatus : 로봇 동작 상태
-- robotOperationMode : 로봇 운전 모드 (AUTO, MANUAL, STOPPED 등 운전 상태 확인)
-- frequencyPeakBand : 가장 진동이 크게 나온 주파수 대역
-- frequencyBands : 주파수 대역별 진동값
-
-📌 도장
-"processData": {
-  "paint": {
-    "imagePosition": "LEFT",
-    "thermalStdTemp": 4.2,
-    "thicknessValue": 116.5,
-    "defectScore": 0.87,
-    "visionLabel": "DEFECT",
-    "surfaceQualityScore": 72.3
-  }
-}
-- imagePosition : 좌/우 위치별 불량 확인 (촬영 이미지 위치)
-- thermalStdTemp : 온도 편차. 표면 균일도 판단
-- thicknessValue : 도장 두께 판단
-- defectScore : 비전 기반 불량 점수
-- visionLabel : 원천 비전 라벨
-- surfaceQualityScore : 화면 표시용 품질 점수
-
-📌 의장
-"processData": {
-  "assembly": {
-    "expectedSequence": "A01>A02>A03>A04",
-    "actualSequence": "A01>A03>A02>A04",
-    "missingPartCount": 0,
-    "fasteningErrorCount": 1,
-    "sequenceErrorCount": 1
-  }
-}
-- expectedSequence : 기준 작업 순서
-- actualSequence : 실제 작업 순서
-- missingPartCount : 누락 부품 수
-- fasteningErrorCount : 체결 오류 수
-- sequenceErrorCount : 작업 순서 오류 수
-
-```
-
-## 구조 요약
-
-```
-event                이벤트 기본 정보
-location             공장/라인/공정/스테이션 위치 정보
-equipment            설비 기본 정보
-equipmentStatus      설비 가동 상태/설비 건강 상태/상태 변경 정보
-product              차량/제품/생산 정보
-
-sensor
-├─ current                프레스/로봇 전류 데이터
-├─ vibration              일반 설비 진동/가속도 데이터
-├─ robotArmVibration      로봇팔 전용 진동 데이터
-└─ thermal                열화상/온도 데이터
-
-manufacturing        공정 순서/생산 목표/완료 수량 정보
-processMetrics       공정 시간/처리량/대기열/WIP 지표
-sourceTrace          원본 데이터 추적용
-```
-
----
-
-# event
-
-이벤트 자체에 대한 메타 정보입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| eventId | 이벤트 고유 식별자 |
-| eventTime | 실제 공정 이벤트 발생 시간 |
-| eventCategory | 이벤트 대분류 (`MANUFACTURING`) |
-| eventType | 이벤트 유형 (`PROCESS_STATUS`, `QUALITY_CHECK`, `EQUIPMENT_SENSOR`) |
-| eventName | 사람이 읽기 쉬운 이벤트명 |
-
-예시
-
-```json
-{
-  "eventId":"EVT-20260616-000001",
-  "eventType":"PROCESS_STATUS"
-}
-```
-
----
-
-# location
-
-이벤트 발생 위치 정보입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| factoryCode | 공장 식별 코드 |
-| lineCode | 생산 라인 코드 |
-| processCode | 공정 코드 (`PRESS`, `BODY`, `PAINT`, `ASSEMBLY`) |
-| stationCode | 스테이션 코드 |
-
-예시
-
-```
-공장
- └─ 생산라인
-      └─ 공정
-           └─ 스테이션
-```
-
----
-
-# equipment
-
-설비 기본 정보입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| equipmentCode | 설비 코드 |
-| equipmentName | 설비명 |
-| equipmentType | 설비 유형 |
-
-설비 유형 예시
-
-| processCode | equipmentType | 설명 |
-| --- | --- | --- |
-| PRESS | HYDRAULIC_PRESS | 유압 프레스 |
-| BODY | ROBOT_ARM | 차체 로봇팔 |
-| PAINT | CAMERA | 도장 검사 카메라 |
-| ASSEMBLY | CONVEYOR | 조립 컨베이어 |
-
-예시
-
-```json
-{
-  "equipmentCode":"EQ_PRESS_01",
-  "equipmentName":"프레스 유압모터 1호",
-  "equipmentType":"HYDRAULIC_PRESS"
-}
-```
-
----
-
-# equipmentStatus
-
-설비 상태 정보입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| operationStatus | 실제 설비 가동 상태 |
-| healthStatus | 설비 건강 상태 |
-| lastNormalTime | 마지막 정상 상태 시간 |
-| statusChangedTime | 현재 상태로 변경된 시간 |
-
-`operationStatus`
-
-| 상태 | 의미 |
-| --- | --- |
-| RUNNING | 설비 가동 중 |
-| STOP | 설비 정지 |
-| IDLE | 대기 상태 |
-| MAINTENANCE | 정비 중 |
-
-`healthStatus`
-
-| 상태 | 의미 | 설비 운영 여부 |
-| --- | --- | --- |
-| NORMAL | 정상 상태 | 운영 중 |
-| WARNING | 경고 상태 | 운영 중 |
-| FAULT | 고장 상태 | 운영 불가 |
-| MAINTENANCE | 점검 상태 | 운영 중지 |
-
-예시
-
-```json
-{
-  "operationStatus":"RUNNING",
-  "healthStatus":"WARNING",
-  "lastNormalTime":"2026-06-16T09:59:30",
-  "statusChangedTime":"2026-06-16T10:00:01"
-}
-```
-
----
-
-# product
-
-생산 대상 차량/제품 정보입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| carId | 차량 식별 ID |
-| productId | 생산 제품 ID |
-| itemNo | 품목 번호 |
-| quantity | 생산 수량 |
-| productionCount | 누적 생산량 |
-
-예시
-
-```json
-{
-  "carId":"CAR-000001",
-  "quantity":5,
-  "productionCount":18133963
-}
-```
-
----
-
-# sensor
-
-여러 원본 데이터셋에서 가져온 센서 값을 관제용으로 통합한 영역입니다.
-
----
-
-## sensor.current
-
-전류 센서 정보
-
-| 컬럼 | 설명 |
-| --- | --- |
-| rmsAmpere | RMS 전류 |
-| maxAmpere | 최대 전류 |
-| minAmpere | 최소 전류 |
-
-데이터 출처
-
-```
-소성가공 데이터
-RMS[A]
-```
-
----
-
-## sensor.vibration
-
-설비 진동 정보
-
-| 컬럼 | 설명 |
-| --- | --- |
-| accelerationG | 가속도 |
-| vibrationScore | 진동 위험 점수 |
-| vibrationRms | RMS 진동 |
-| vibrationPeak | Peak 진동 |
-
-데이터 출처
-
-```
-Ford Engine Dataset
-소성가공 진동 데이터
-```
-
----
-
-## sensor.robotArmVibration
-
-로봇팔 전용 진동 데이터
-
-| 컬럼 | 설명 |
-| --- | --- |
-| robotId | 로봇 ID |
-| axis | 관절 축 |
-| frequencyHz | 진동 주파수 |
-| amplitude | 진폭 |
-| vibrationRms | RMS 진동 |
-| vibrationPeak | Peak 진동 |
-| vibrationScore | 진동 위험 점수 |
-
-데이터 출처
-
-```
-Robot Arm Vibration Dataset
-```
-
----
-
-## sensor.thermal
-
-열화상 정보
-
-| 컬럼 | 설명 |
-| --- | --- |
-| thermalScore | 열화상 점수 |
-| avgTemperature | 평균 온도 |
-| maxTemperature | 최고 온도 |
-| minTemperature | 최저 온도 |
-
-데이터 출처
-
-```
-Machine Vision Dataset
-```
-
----
-
-# manufacturing
-
-제조 운영 정보입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| processSequence | 공정 순서 |
-| previousProcessCode | 이전 공정 |
-| nextProcessCode | 다음 공정 |
-| targetQuantity | 목표 생산 수량 |
-| completedQuantity | 완료 생산 수량 |
-
-예시
-
-```
-PRESS
- ↓
-BODY
- ↓
-PAINT
- ↓
-ASSEMBLY
-```
-
-공정 순서 매핑
-
-| processCode | processSequence |
-| --- | --- |
-| PRESS | 1 |
-| BODY | 2 |
-| PAINT | 3 |
-| ASSEMBLY | 4 |
-
----
-
-# processMetrics
-
-공정 성능 및 병목 판단용 지표입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| cycleTimeSec | 생산 주기 |
-| waitingTimeSec | 대기 시간 |
-| processingTimeSec | 실제 작업 시간 |
-| stationDelaySec | 지연 시간 |
-| throughputPerMin | 분당 생산량 |
-| queueLength | 대기열 수 |
-| wipCount | 재공품 수 |
-| equipmentIdleTimeSec | 설비 유휴 시간 |
-
-예시
-
-```
-제품 투입
- ↓
-8초 대기
- ↓
-34초 작업
- ↓
-42초 사이클 완료
-```
-
----
-
-# sourceTrace
-
-원본 데이터 추적용 정보입니다.
-
-| 컬럼 | 설명 |
-| --- | --- |
-| fordRowId | Ford 원본 행 번호 |
-| formingRowId | 소성가공 원본 행 번호 |
-| robotArmVibrationRowId | 로봇팔 진동 원본 행 번호 |
-| machineVisionRowId | 머신비전 원본 행 번호 |
-| boschId | Bosch 원본 ID |
-
----
-
-# 최종 데이터셋 매핑
-
-```
-Ford Dataset
-↓
-sensor.vibration
-
-소성가공 데이터
-↓
-sensor.current
-sensor.vibration
-
-Robot Arm Vibration Dataset
-↓
-sensor.robotArmVibration
-
-Machine Vision Dataset
-↓
-sensor.thermal
-
-Bosch Dataset
-↓
-manufacturing
-processMetrics
-event.eventTime
-location
-equipment
-```
-
-즉, 최종 원천 관제 이벤트는 데이터셋 기반 구조가 아니라 아래 기준으로 통합됩니다.
-
-```
-이벤트
-↓
-위치
-↓
-설비
-↓
-설비상태
-↓
-제품
-↓
-센서
- ├─ 전류
- ├─ 일반 진동
- ├─ 로봇팔 진동
- └─ 열화상
-↓
-제조 운영 정보
-↓
-공정 성능 지표
-↓
-원본 추적 정보
-```
-
----
-
-## 분석 결과 저장 기준
-
-분석 결과와 이상 상태는 위 원천 이벤트를 백엔드/AI가 처리한 뒤 별도로 저장합니다.
-
-| 결과 필드 | 입력 데이터 | 저장 위치 |
-| --- | --- | --- |
-| equipmentStatus.healthStatus | 전류, 진동, 설비 상태 | Redis / main_db |
-| qualityStatus | 열화상 데이터 | Redis / main_db |
-| productionStatus | 공정 지표 | Redis / main_db |
-| pressStopRisk | 전류, 유휴시간 | main_db.analysis_result |
-| robotCollisionRisk | robotArmVibration | main_db.analysis_result |
-| paintQualityRisk | thermal | main_db.analysis_result |
-| assemblySequenceRisk | processSequence | main_db.analysis_result |
-| bottleneckRisk | processMetrics | main_db.analysis_result |
-| defectTransferRisk | 센서 + 공정 데이터 | main_db.analysis_result |
-| overallRiskScore | 전체 위험도 계산 결과 | main_db.analysis_result |
-| riskLevel | LOW/WARNING/CRITICAL | Redis / ES / main_db |
-
-정리하면 `att1`, `L0_S0_D1`, `RMS[A]`, `t1~t80` 같은 원본 컬럼은 저장하지 않고, 관제에 필요한 의미 있는 데이터로 변환하여 하나의 제조 이벤트 JSON으로 저장하는 구조입니다.
-
----
-
-| 상태 | 의미 | 설비 운영 여부 |
-| --- | --- | --- |
-| NORMAL | 정상 가동 중 | 운영 중 |
-| WARNING | 경고 상태 (이상 징후 발생) | 운영 중 |
-| FAULT | 고장 상태 | 운영 불가 |
-| MAINTENANCE | 점검/정비 중 | 운영 중지 |
-
-# 이벤트 흐름 정리
-
-```jsx
-샘플 DB
-  ↓
-Scheduler가 JSON 1건씩 읽음
-  ↓
-factory.manufacturing.raw 로 원천 이벤트 발행
-  ↓
-Manufacturing Service / AI Service가 raw 이벤트 소비
-  ↓
-Spring 코드로 위험도, 병목 여부, 설비 이상 여부 계산
-  ↓
-계산 결과를 담아서 factory.manufacturing.analysis 로 발행
-```
-
-# 1. 데이터 준비 단계
-
-## 목적
-
-실시간 센서가 없는 시연 환경에서 제조 이벤트를 재생하기 위해 원본 데이터셋을 통합 제조 이벤트 JSON으로 변환하여 Sample DB에 저장합니다.
-
----
-
-## 데이터 흐름
-
-```
-Ford Dataset
-Bosch Dataset
-Robot Arm Vibration Dataset
-Machine Vision Dataset
-소성가공 데이터
-
-↓
-Python ETL
-↓
-공통 제조 이벤트 구조 매핑
-↓
-통합 JSON 생성
-↓
-sample_db 저장
-```
-
----
-
-## 저장 테이블
-
-```
-manufacturing_event_json
-```
-
-역할
-
-```
-시연용 원천 이벤트 저장소
-
-Scheduler가 읽어가는 이벤트 저장
-
-Kafka 전송 대상 저장
-
-전송 여부 관리
-```
-
----
-
-## Sample DB 구조
-
-```sql
-CREATE TABLE manufacturing_event_json (
-    id BIGINT AUTO_INCREMENT PRIMARY KEY,
-
-    event_id VARCHAR(100) NOT NULL,
-    event_time DATETIME NOT NULL,
-
-    car_master_id BIGINT NOT NULL,
-    equipment_id BIGINT NOT NULL,
-
-    process_code ENUM('PRESS', 'BODY', 'PAINT', 'ASSEMBLY') NOT NULL,
-    station_code VARCHAR(50),
-
-    equipment_code VARCHAR(50) NOT NULL,
-    equipment_type VARCHAR(50),
-    equipment_status VARCHAR(30),
-
-    event_type VARCHAR(50),
-
-    event_json JSON NOT NULL,
-
-    is_sent TINYINT(1) DEFAULT 0,
-    sent_at DATETIME NULL,
-
-    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
-    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
-
-    INDEX idx_event_time_sent (event_time, is_sent),
-    INDEX idx_process_time (process_code, event_time),
-    INDEX idx_equipment_time (equipment_code, event_time),
-    INDEX idx_car_time (car_master_id, event_time),
-    INDEX idx_event_id (event_id),
-    INDEX idx_equipment_id_time (equipment_id, event_time),
-
-    CONSTRAINT fk_manufacturing_event_json_car_master
-        FOREIGN KEY (car_master_id)
-        REFERENCES car_master(id),
-
-    CONSTRAINT fk_manufacturing_event_json_equipment
-        FOREIGN KEY (equipment_id)
-        REFERENCES equipment(id)
-);
-```
-
----
-
-# 2. Scheduler 구조
-
-## 목적
-
-Sample DB에 저장된 제조 이벤트를 실제 공장에서 발생하는 것처럼 순차적으로 Kafka에 전송합니다.
-
----
-
-## 처리 흐름
-
-```
-Scheduler 실행
-
-↓
-
-is_sent = false 조회
-
-↓
-
-event_time 기준 정렬
-
-↓
-
-N건 조회
-
-↓
-
-Kafka Producer 호출
-
-↓
-
-전송 성공
-
-↓
-
-is_sent = true
-
-↓
-
-sent_at 저장
-```
-
----
-
-## 예시
-
-### 시연 환경
-
-```
-1초마다 5건 전송
-```
-
-### 부하 테스트
-
-```
-1초마다 100건 전송
-```
-
-### 대량 데이터 재생
-
-```
-1초마다 1000건 전송
-```
-
----
-
-# 3. Kafka 구조
-
-## Kafka 사용 목적
-
-Kafka는 제조 이벤트를 서비스 간 전달하는 Event Backbone 역할을 수행합니다.
-
-### Kafka 적용
-
-```
-sampleDB.manufacturing_event_json
-↓
-Scheduler Producer
-↓
-factory.manufacturing.raw
-↓
-Manufacturing Consumer / AI Consumer 
-↓
-분석 결과 생성
-↓
-factory.manufacturing.analysis
-↓
-mainDB / Redis / Elasticsearch 저장
-↓
-위험 이벤트만 분리
-↓
-factory.manufacturing.alert
-↓
-WebSocket / 알림 화면 / 알림 이력 저장
-
-----------설비 상태 및 가동률 계산 흐름---------------------------
-
-factory.manufacturing.raw
-↓
-Equipment Consumer
-↓
-설비 상태 / 가동 시간 / 정지 시간 / 유휴 시간 계산
-↓
-factory.manufacturing.equipment
-↓
-Redis / mainDB / Dashboard
-```
-
-하나의 서비스가 장애가 발생해도 다른 서비스는 정상 동작
-
----
-
-### 비동기 처리
-
-```
-설비 이벤트 발생
-↓
-Kafka 저장
-↓
-각 서비스가 필요 시 처리
-```
-
----
-
-### Scale-Out 가능
-
-```
-AI Service 1대
-↓
-AI Service 3대
-↓
-AI Service 10대
-```
-
-Kafka Partition 기반 병렬 처리 가능
-
----
-
-# 4. Kafka Topic 설계
-
-## Topic 구성
-
-```
-factory.manufacturing.raw
-
-factory.manufacturing.analysis
-
-factory.manufacturing.alert
-
-factory.manufacturing.equipment
-```
-
----
-
-## factory.manufacturing.raw
-
-### 역할
-
-원천 제조 이벤트 저장
-
-### Producer
-
-```
-Scheduler
-```
-
-### Consumer
-
-```
-Manufacturing Service
-AI Service
-Equipment Service
-Redis Consumer
-Elasticsearch Consumer
-```
-
-### Message Key
-
-```java
-factory.manufacturing.raw
-
-Message Key
-- equipment.equipmentCode
-
-// Raw Event
-kafkaTemplate.send(
-    "factory.manufacturing.raw",
-    event.getEquipment().getEquipmentCode(),
-    eventJson
-);
-```
-
----
-
-예시 메시지
-
-```json
-{
-  "event": {
-    "eventId": "EVT-20260616-000001",
-    "eventTime": "2026-06-16T10:00:01",
-    "eventCategory": "MANUFACTURING",
-    "eventType": "PROCESS_STATUS",
-    "eventName": "프레스 공정 통합 관제 이벤트"
-  },
-  "location": {
-    "factoryCode": "AIMS_FACTORY_01",
-    "lineCode": "PRESS_LINE_01",
-    "processCode": "PRESS",
-    "stationCode": "PRESS_STATION_01"
-  },
-  "equipment": {
-    "equipmentCode": "EQ_PRESS_01",
-    "equipmentName": "프레스 유압모터 1호",
-    "equipmentType": "HYDRAULIC_PRESS"
-  },
-  "equipmentStatus": {
-    "operationStatus": "RUNNING",
-    "lastNormalTime": "2026-06-16T09:59:30",
-    "statusChangedTime": "2026-06-16T10:00:01"
-  },
-  "product": {
-    "carId": "CAR-000001",
-    "productId": "PRODUCT-000001",
-    "itemNo": "76211-A3010-100",
-    "quantity": 5,
-    "productionCount": 18133963
-  },
-  "sensor": {
-    "sensorType": "MULTI_SENSOR",
-    "current": {
-      "rmsAmpere": 1.97,
-      "maxAmpere": 2.15,
-      "minAmpere": 1.72
-    },
-    "vibration": {
-      "accelerationG": 0.0075,
-      "vibrationScore": 0.21,
-      "vibrationRms": 0.18,
-      "vibrationPeak": 0.31
-    },
-    "robotArmVibration": {
-      "robotId": "ROBOT_ARM_01",
-      "axis": "J1",
-      "frequencyHz": 40.62,
-      "amplitude": 0.00056,
-      "vibrationRms": 0.00063,
-      "vibrationPeak": 0.00119,
-      "vibrationScore": 0.27
-    },
-    "thermal": {
-      "thermalScore": 43.2,
-      "avgTemperature": 43.2,
-      "maxTemperature": 52.9,
-      "minTemperature": 42.1
-    }
-  },
-  "manufacturing": {
-    "processSequence": 1,
-    "previousProcessCode": null,
-    "nextProcessCode": "BODY",
-    "targetQuantity": 200,
-    "completedQuantity": 145
-  },
-  "processMetrics": {
-    "cycleTimeSec": 42.5,
-    "waitingTimeSec": 8.3,
-    "processingTimeSec": 34.2,
-    "stationDelaySec": 5.7,
-    "throughputPerMin": 1.4,
-    "queueLength": 7,
-    "wipCount": 24,
-    "equipmentIdleTimeSec": 12.1
-  },
-  "processData": {
-    "press": {
-      "countIncreaseYn": true,
-      "targetCycleTimeSec": 40.0,
-      "timestampDelaySec": 5.7
-    }
-  },
-  "sourceTrace": {
-    "fordRowId": 1,
-    "formingRowId": 1,
-    "robotArmVibrationRowId": 1,
-    "machineVisionRowId": 1,
-    "boschId": 1
-  }
-}
-```
-
-### 공정 별(프레스/차체/도장/의장) 분기
-
-현재 프로젝트 규모에서는 1개 Raw Topic으로 받고, `processCode`로 분기하는 방식이 더 적절합니다.
-
-```
-factory.manufacturing.raw
-```
-
-메시지 내부에서 분기합니다.
-
-```
-processCode = PRESS     → 프레스 로직
-processCode = BODY      → 차체 로직
-processCode = PAINT     → 도장 로직
-processCode = ASSEMBLY  → 의장 로직
-```
-
-다만 공정별 트래픽, 처리 속도, Consumer가 명확히 달라지면 Topic을 나누는 것이 맞습니다.
-
-```
-factory.manufacturing.press.raw
-factory.manufacturing.body.raw
-factory.manufacturing.paint.raw
-factory.manufacturing.assembly.raw
-```
-
-추천 기준은 아래와 같습니다.
-
-```
-초기/MVP/시연용
-→ factory.manufacturing.raw 1개 추천
-
-운영 확장/공정별 처리량 차이 큼
-→ 공정별 Topic 분리 추천
-
-공정별 Consumer가 완전히 다름
-→ 공정별 Topic 분리 추천
-
-프레스는 초당 1000건, 도장은 초당 10건처럼 차이가 큼
-→ 공정별 Topic 분리 추천
-
-공정별 장애가 다른 공정에 영향 주면 안 됨
-→ 공정별 Topic 분리 추천
-```
-
-현재 AIMS 구조에서는 이렇게 가는 게 가장 깔끔합니다.
-
-```
-factory.manufacturing.raw
-```
-
-그리고 Consumer 내부에서 분기합니다.
-
-```java
-switch (processCode) {
-	case"PRESS" -> pressService.analyze(event);
-	case"BODY" -> bodyService.analyze(event);
-	case"PAINT" -> paintService.analyze(event);
-	case"ASSEMBLY" -> assemblyService.analyze(event);
-}
-```
-
----
-
-## factory.manufacturing.analysis
-
-### 역할
-
-AI 분석, 이상 탐지 분석 결과 저장
-
-### Producer
-
-```
-AI Service
-Manufacturing Service
-```
-
-### Consumer
-
-```
-Equipment Service
-Redis
-main_db
-Elasticsearch
-Alert Service
-```
-
----
-
-### Message Key
-
-```java
-factory.manufacturing.analysis
-
-Message Key
-- 기본: equipment.equipmentCode
-- analysisType = DEFECT_TRANSFER_PREDICTION 인 경우: carId
-
-// 설비/공정 분석 결과
-kafkaTemplate.send(
-    "factory.manufacturing.analysis",
-    analysis.getEquipmentCode(),
-    analysisJson
-);
-```
-
----
-
-예시
-
-```json
-{
-  "analysisId": "ANL-20260616-000001",
-  "eventId": "EVT-20260616-000001",
-  "eventTime": "2026-06-16T10:00:01",
-  "analyzedAt": "2026-06-16T10:00:03",
-  "factoryCode": "AIMS_FACTORY_01",
-  "lineCode": "PRESS_LINE_01",
-  "processCode": "PRESS",
-  "stationCode": "PRESS_STATION_01",
-  "equipmentCode": "EQ_PRESS_01",
-  "equipmentType": "HYDRAULIC_PRESS",
-  "productId": "PRODUCT-000001",
-  "carId": "CAR-000001",
-  "analysisType": "PROCESS_RISK_ANALYSIS",
-  "riskScores": {
-    "overallRiskScore": 74.5,
-    "bottleneckRisk": 72.1,
-    "defectTransferRisk": 65.3,
-    "equipmentRisk": 58.4,
-    "processRisk" : {
-      "pressStopRisk" : 64.2
-      }
-  },
-  "riskLevel": "WARNING",
-  "analysisResult": {
-    "isAbnormal": true,
-    "isBottleneck": true,
-    "isQualityDefect": false,
-    "isEquipmentFault": false,
-    "isSequenceError": false
-  },
-  "reason": {
-    "mainReason": "cycleTimeSec가 targetCycleTimeSec보다 증가했습니다.",
-    "detailReasons": [
-      "기준 사이클타임 40.0초 대비 실제 사이클타임 42.5초",
-      "stationDelaySec 5.7초 발생",
-      "queueLength 7로 대기열 증가"
-    ]
-  },
-  "recommendation": {
-    "actionType": "CHECK_EQUIPMENT_AND_QUEUE",
-    "message": "프레스 설비 상태와 대기열 증가 원인을 확인하세요."
-  }
-}
-```
-
-### 분석결과 저장
-
-```jsx
-### 1. Main DB
-
-분석 결과 및 이력성 데이터를 영속 저장한다.
-
-- 병목 분석 결과
-- 불량 전이 예측 결과
-- 설비 이상 분석 결과
-- 도장 품질 이상 분석 결과
-- 의장 조립 순서 오류 결과
-
----
-
-### 2. Redis
-
-실시간 상태 조회 및 대시보드 표시를 위한 최신 데이터를 저장한다.
-
-- 현재 공정 상태
-- 현재 설비 상태
-- 현재 위험도
-- 대시보드 카드용 최신 값
-
----
-
-### 3. Elasticsearch
-
-검색, 이력 조회, 이벤트 집계를 위한 데이터를 저장한다.
-
-- 이벤트 검색
-- 알림 이력 검색
-- 설비별 이상 이력 검색
-- 시간대별 위험 이벤트 집계
-```
-
----
-
-## factory.manufacturing.alert
-
-### 역할
-
-긴급 알림 이벤트 전용
-
-→ `factory.manufacturing.analysis`의 분석 결과 중 WARNING 또는 CRITICAL 상태만 선별하여 발행한다.
-
----
-
-### Producer
-
-```jsx
-Alert Service 
-Manufacturing Service
-```
-
-### Consumer
-
-```jsx
-WebSocket Service 
-mainDB Consumer 
-Elasticsearch Consumer
-```
-
-### 발행 조건
-
-```
-overallRiskScore >= 80 
-riskLevel = CRITICAL 
-설비 고장 발생 
-품질 불량 발생 
-병목 위험 발생 
-조립 순서 오류 발생 
-프레스 정지 위험 발생
-```
-
----
-
-예시
-
-```json
-{
-  "alertId": "ALT-20260616-000001",
-  "eventId": "EVT-20260616-000001",
-  "analysisId": "ANL-20260616-000001",
-  "createdAt": "2026-06-16T10:00:04",
-  "factoryCode": "AIMS_FACTORY_01",
-  "lineCode": "PRESS_LINE_01",
-  "processCode": "PRESS",
-  "stationCode": "PRESS_STATION_01",
-  "equipmentCode": "EQ_PRESS_01",
-  "equipmentName": "프레스 유압모터 1호",
-  "alertType": "PRESS_STOP_RISK",
-  "alertTitle": "프레스 설비 정지 위험",
-  "alertMessage": "프레스 1호의 사이클타임과 지연 시간이 증가하여 정지 위험이 감지되었습니다.",
-  "riskLevel": "CRITICAL",
-  "riskScore": 86.7,
-  "alertStatus": "OPEN",
-  "needAction": true,
-  "reason": [
-    "cycleTimeSec가 기준값보다 증가",
-    "stationDelaySec 증가",
-    "equipmentIdleTimeSec 증가"
-  ],
-  "recommendedAction": "프레스 설비 상태, 대기열, 전류 RMS 값을 확인하세요."
-}
-```
-
-### alertStatus 값
-
-```jsx
-OPEN 처리 전 
-CHECKING 확인 중 
-RESOLVED 조치 완료 
-IGNORED 무시됨
-```
-
-### MessageKey
-
-```java
-factory.manufacturing.alert
-
-Message Key
-- equipment.equipmentCode
-
-// Alert Event
-kafkaTemplate.send(
-    "factory.manufacturing.alert",
-    alert.getEquipmentCode(),
-    alertJson
-);
-```
-
----
-
-## factory.manufacturing.equipment
-
-### 역할
-
-장비 전용 이벤트
-
-→ 
-
-Raw Event 안에도 `equipmentStatus`가 있지만, 그것은 단일 이벤트 시점의 원천 상태이다.
-
-`factory.manufacturing.equipment`는 여러 Raw Event를 기반으로 계산한 설비 중심 상태 이벤트이다.
-
----
-
-### Producer
-
-```jsx
-Equipment Service 
-Manufacturing Service
-```
-
-### Consumer
-
-```jsx
-Equipment Service
-Redis Consumer 
-mainDB Consumer 
-Elasticsearch Consumer 
-Alert Service
-```
-
-### Message Key
-
-```java
-factory.manufacturing.equipment
-
-Message Key
-- equipment.equipmentCode
-
-// Equipment Event
-kafkaTemplate.send(
-    "factory.manufacturing.equipment",
-    equipmentEvent.getEquipmentCode(),
-    equipmentJson
-);
-```
-
-### 예시메세지
-
-```json
-{
-  "equipmentEventId": "EQEVT-20260616-000001",
-  "eventTime": "2026-06-16T10:00:05",
-  "factoryCode": "AIMS_FACTORY_01",
-  "lineCode": "PRESS_LINE_01",
-  "processCode": "PRESS",
-  "stationCode": "PRESS_STATION_01",
-  "equipmentCode": "EQ_PRESS_01",
-  "equipmentName": "프레스 유압모터 1호",
-  "equipmentType": "HYDRAULIC_PRESS",
-  "operationStatus": "RUNNING",
-  "healthStatus": "WARNING",
-  "riskLevel": "WARNING",
-  "overallRiskScore": 72.4,
-  "operationMetrics": {
-    "windowStartTime": "2026-06-16T09:00:00",
-    "windowEndTime": "2026-06-16T10:00:00",
-    "plannedTimeSec": 3600,
-    "runningTimeSec": 2880,
-    "idleTimeSec": 420,
-    "stopTimeSec": 240,
-    "maintenanceTimeSec": 60,
-    "operationRate": 80.0,
-    "availabilityRate": 86.7
-  },
-  "productionMetrics": {
-    "targetQuantity": 200,
-    "completedQuantity": 145,
-    "throughputPerMin": 1.4,
-    "cycleTimeSec": 42.5
-  },
-  "sensorSummary": {
-    "currentRmsAmpere": 1.97,
-    "vibrationScore": 0.21,
-    "thermalScore": 43.2
-  },
-  "statusReason": {
-    "mainReason": "지연 시간 증가로 설비 위험도가 WARNING 상태입니다.",
-    "detailReasons": [
-      "stationDelaySec 5.7초",
-      "equipmentIdleTimeSec 12.1초",
-      "cycleTimeSec 42.5초"
-    ]
-  },
-  "lastRawEventId": "EVT-20260616-000001",
-  "updatedAt": "2026-06-16T10:00:05"
-}
-```
-
-### 가동률 계산 기준
-
-가동률은 설비가 계획된 시간 중 실제 RUNNING 상태였던 비율이다.
-
-```
-operationRate = runningTimeSec / plannedTimeSec * 100
-```
-
-### 상태값 기준
-
-```
-operationStatus
-- RUNNING
-- IDLE
-- STOPPED
-- ERROR
-- MAINTENANCE
-
-healthStatus
-- NORMAL
-- WARNING
-- FAULT
-- MAINTENANCE
-
-riskLevel
-- LOW
-- WARNING
-- CRITICAL
-```
-
----
-
-# 5. Kafka Partition 전략
-
-## 5.1. 기본 기준
-
-현재 AIMS 프로젝트에서는 기본 Message Key를 아래로 설정합니다.
-
-```
-message key = equipmentCode
-```
-
-이유는 같은 설비의 이벤트 순서를 보장해야 하기 때문입니다.
-
-```
-EQ_PRESS_01 RUNNING
-↓
-EQ_PRESS_01 WARNING
-↓
-EQ_PRESS_01 FAULT
-↓
-EQ_PRESS_01 RECOVERY
-```
-
-같은 `equipmentCode`를 Key로 보내면 같은 설비 이벤트는 항상 같은 Partition에 저장됩니다.
-
----
-
-# 5.2. Topic별 Partition 수
-
-```
-factory.manufacturing.raw        → 4개
-factory.manufacturing.analysis   → 4개
-factory.manufacturing.alert      → 2개
-factory.manufacturing.equipment  → 4개
-```
-
----
-
-# 5.3. Topic별 Key 전략
-
-| Topic | Partition 수 | Message Key | 이유 |
-| --- | --- | --- | --- |
-| `factory.manufacturing.raw` | 4 | `equipmentCode` | 설비별 원천 이벤트 순서 보장 |
-| `factory.manufacturing.analysis` | 4 | `equipmentCode` 기본, 불량 전이는 `carId` 가능 | 분석 결과 순서 보장 |
-| `factory.manufacturing.alert` | 2 | `equipmentCode` | 같은 설비 알림 순서 보장 |
-| `factory.manufacturing.equipment` | 4 | `equipmentCode` | 장비 카드 상태 갱신 순서 보장 |
-
----
-
-# 5.4. Consumer Group 기준
-
-## Manufacturing Consumer Group
-
-```
-factory.manufacturing.raw
-partitions = 4
-key = equipmentCode
-
-↓
-Manufacturing Consumer Group
-├─ Press Consumer
-├─ Body Consumer
-├─ Paint Consumer
-└─ Assembly Consumer
-```
-
-처리 기준은 Partition이 아니라 `processCode`입니다.
-
-```
-processCode = PRESS     → Press Consumer
-processCode = BODY      → Body Consumer
-processCode = PAINT     → Paint Consumer
-processCode = ASSEMBLY  → Assembly Consumer
-```
-
----
-
-## AI Consumer Group
-
-```
-factory.manufacturing.raw
-partitions = 4
-key = equipmentCode
-```
-
-### Bottleneck Consumer
-
-```
-key = equipmentCode
-```
-
-설비/공정별 병목 흐름을 분석합니다.
-
-### Defect Transfer Consumer
-
-```
-분석 기준 = carId
-```
-
-현재는 Raw Topic Key를 `equipmentCode`로 유지하되, 분석 결과 발행 시에는 `carId`를 Key로 사용할 수 있습니다.
-
-```
-factory.manufacturing.analysis
-key = carId
-```
-
----
-
-## Equipment Consumer Group
-
-```
-factory.manufacturing.analysis
-partitions = 4
-key = equipmentCode
-
-↓
-Equipment Consumer Group
-
-↓
-factory.manufacturing.equipment
-partitions = 4
-key = equipmentCode
-```
-
-장비별 최신 상태와 화면 카드 데이터를 생성합니다.
-
----
-
-## Alert Consumer Group
-
-```
-factory.manufacturing.alert
-partitions = 2
-key = equipmentCode
-
-↓
-Alert Consumer Group
-```
-
-알림은 데이터량이 많지 않으므로 2개 Partition이면 충분합니다.
-
----
-
-# 5.5. 최종 추천 구조
-
-```
-Scheduler
-↓
-factory.manufacturing.raw
-partitions = 4
-key = equipmentCode
-
-↓
-Manufacturing Consumer Group
-AI Consumer Group
-
-↓
-factory.manufacturing.analysis
-partitions = 4
-key = equipmentCode
-※ defectTransfer 결과는 key = carId 가능
-
-↓
-Equipment Consumer Group
-↓
-factory.manufacturing.equipment
-partitions = 4
-key = equipmentCode
-↓
-WebSocket
-↓
-React Dashboard
-```
-
-알림 흐름은 별도입니다.
-
-```
-Manufacturing Consumer Group
-AI Consumer Group
-↓
-factory.manufacturing.alert
-partitions = 2
-key = equipmentCode
-↓
-Alert Consumer Group
-↓
-실시간 알림 패널
-```
-
-결론적으로 현재 프로젝트에서는 아래 전략이 가장 적절합니다.
-
-```
-Partition 수
-raw        4개
-analysis   4개
-equipment  4개
-alert      2개
-
-기본 Message Key
-equipmentCode
-
-예외
-불량 전이 예측 결과는 carId 사용 가능
-```
-
----
-
-# 6. Consumer Group 구조
-
-## Kafka Raw Topic
-
-```
-factory.manufacturing.raw
-```
-
-원천 제조 이벤트를 수신하는 Topic입니다.
-
-```
-프레스 이벤트
-차체 이벤트
-도장 이벤트
-의장 이벤트
-```
-
-모두 동일한 Topic으로 수신합니다.
-
----
-
-# 6.1 Manufacturing Consumer Group
-
-제조 공정 상태 분석 전용 Consumer Group입니다.
-
-```
-factory.manufacturing.raw
-↓
-Manufacturing Consumer Group
-├─ Press Consumer
-├─ Body Consumer
-├─ Paint Consumer
-└─ Assembly Consumer
-```
-
----
-
-## Press Consumer
-
-### 처리 대상
-
-```
-processCode = PRESS
-```
-
-### 사용 데이터
-
-```
-sensor.current
-processMetrics
-processData.press
-equipmentStatus
-```
-
-### 처리 내용
-
-```
-프레스 설비 상태 분석
-
-전류 RMS 분석
-
-CNT 증가 여부 확인
-
-Cycle Time 분석
-
-설비 가동률 계산
-
-설비 정지 위험 탐지
-```
-
-### 결과 발행
-
-```
-factory.manufacturing.analysis
-factory.manufacturing.alert
-factory.manufacturing.dashboard
-```
-
----
-
-## Body Consumer
-
-### 처리 대상
-
-```
-processCode = BODY
-```
-
-### 사용 데이터
-
-```
-sensor.robotArmVibration
-processData.body
-equipmentStatus
-```
-
-### 처리 내용
-
-```
-로봇 상태 분석
-
-주파수 대역 진동 분석
-
-로봇 이상 동작 탐지
-
-충돌 위험 탐지
-
-차체 공정 상태 계산
-```
-
-### 결과 발행
-
-```
-factory.manufacturing.analysis
-factory.manufacturing.alert
-factory.manufacturing.dashboard
-```
-
----
-
-## Paint Consumer
-
-### 처리 대상
-
-```
-processCode = PAINT
-```
-
-### 사용 데이터
-
-```
-sensor.thermal
-processData.paint
-equipmentStatus
-```
-
-### 처리 내용
-
-```
-도장 품질 분석
-
-열화상 분석
-
-도장 두께 분석
-
-비전 불량 분석
-
-품질 위험도 계산
-```
-
-### 결과 발행
-
-```
-factory.manufacturing.analysis
-factory.manufacturing.alert
-factory.manufacturing.dashboard
-```
-
----
-
-## Assembly Consumer
-
-### 처리 대상
-
-```
-processCode = ASSEMBLY
-```
-
-### 사용 데이터
-
-```
-processData.assembly
-manufacturing
-equipmentStatus
-```
-
-### 처리 내용
-
-```
-조립 순서 검증
-
-누락 부품 분석
-
-체결 오류 분석
-
-조립 품질 상태 계산
-```
-
-### 결과 발행
-
-```
-factory.manufacturing.analysis
-factory.manufacturing.alert
-factory.manufacturing.dashboard
-```
-
----
-
-# 6.2 AI Consumer Group
-
-AI 모델 분석 전용 Consumer Group입니다.
-
-```
-factory.manufacturing.raw
-↓
-AI Consumer Group
-├─ Bottleneck Consumer
-└─ Defect Transfer Consumer
-```
-
----
-
-## Bottleneck Consumer
-
-### 사용 데이터
-
-```
-processMetrics
-
-cycleTimeSec
-waitingTimeSec
-queueLength
-wipCount
-equipmentIdleTimeSec
-```
-
-### 처리 내용
-
-```
-병목 탐지 모델 실행
-
-공정 위험도 계산
-
-영향 차량 수 계산
-
-병목 순위 계산
-```
-
-### 결과
-
-```
-bottleneckRisk
-```
-
----
-
-## Defect Transfer Consumer
-
-### 사용 데이터
-
-```
-sensor.current
-
-sensor.vibration
-
-sensor.robotArmVibration
-
-sensor.thermal
-
-processMetrics
-```
-
-### 처리 내용
-
-```
-불량 전이 예측 모델 실행
-
-SHAP 원인 분석
-
-예상 불량 공정 계산
-
-예상 발생 시점 계산
-```
-
-### 결과
-
-```
-defectTransferRisk
-
-overallRiskScore
-
-riskLevel
-```
-
----
-
-# 6.3 Equipment Consumer Group
-
-장비 표시 데이터 생성 전용 Consumer Group입니다.
-
-```
-factory.manufacturing.analysis
-↓
-Equipment Consumer Group
-```
-
-### 처리 내용
-
-```
-화면 카드 데이터 생성
-
-실시간 병목 분석 생성
-
-불량 전이 예측 생성
-
-AI 원인 분석 생성
-
-WebSocket Push
-```
-
-### 결과 발행
-
-```
-factory.manufacturing.equipment
-```
-
----
-
-# 6.4 Alert Consumer Group
-
-실시간 알림 전용 Consumer Group입니다.
-
-```
-factory.manufacturing.alert
-↓
-Alert Consumer Group
-```
-
-### 처리 내용
-
-```
-경고 알림 생성
-
-위험 알림 생성
-
-Toast 알림 생성
-
-WebSocket Push
-```
-
----
-
-# 최종 구조
-
-```
-factory.manufacturing.raw
-│
-├─ Manufacturing Consumer Group
-│   ├─ Press Consumer
-│   ├─ Body Consumer
-│   ├─ Paint Consumer
-│   └─ Assembly Consumer
-│
-├─ Equipment Consumer Group
-│
-└─ AI Consumer Group
-│   ├─ Bottleneck Consumer
-│   └─ Defect Transfer Consumer
-│
-├─ Alert Consumer Group
-                ↓
-
-factory.manufacturing.analysis
-                ↓
-
-Dashboard Consumer Group
-                ↓
-
-factory.manufacturing.equipment
-
-                ↓
-
-WebSocket
-                ↓
-
-React Dashboard
-
-                ↓
-
-factory.manufacturing.alert
-                ↓
-
-Alert Consumer Group
-                ↓
-
-실시간 알림 패널
-```
-
----
-
-# 7. AI Service 상세 흐름
-
-```
-Kafka Raw Topic 수신
-
-↓ Feature 생성
-
-processMetrics
-
-sensor
-
-processData
-
-manufacturing
-
-↓
-병목 탐지 모델
-
-↓
-불량 전이 예측 모델
-
-↓
-위험도 계산
-
-↓
-Analysis Topic 발행
-
-↓
-main_db 저장
-```
-
----
-
-## 병목 탐지
-
-입력
-
-```
-cycleTimeSec
-waitingTimeSec
-queueLength
-wipCount
-equipmentIdleTimeSec
-```
-
-출력
-
-```
-bottleneckRisk
-```
-
----
-
-## 불량 전이 예측
-
-입력
-
-```
-sensor.current
-
-sensor.vibration
-
-sensor.robotArmVibration
-
-sensor.thermal
-
-processMetrics
-```
-
-출력
-
-```
-defectTransferRisk
-```
-
----
-
-# 8. Redis 구조
-
-## 목적
-
-현재 상태 캐시
-
----
-
-저장 예시
-
-```
-factory:latest
-
-factory:process:PRESS
-
-factory:process:BODY
-
-factory:equipment:EQ_PRESS_01
-
-factory:alert:latest
-
-factory:dashboard:summary
-```
-
----
-
-예시 데이터
-
-```
-{
-  "processCode":"PRESS",
-  "equipmentCode":"EQ_PRESS_01",
-  "status":"WARNING",
-  "riskLevel":"WARNING",
-  "overallRiskScore":72.4
-}
-```
-
----
-
-# 9. Elasticsearch 구조
-
-## 목적
-
-이벤트 검색
-
-이상 이력 조회
-
-대시보드 필터링
-
-통계 분석
-
----
-
-인덱스 예시
-
-```
-aims-manufacturing-event-2026.06.16
-
-aims-analysis-result-2026.06.16
-
-aims-alert-event-2026.06.16
-```
-
----
-
-저장 대상
-
-```
-전체 제조 이벤트
-
-전체 분석 결과
-
-전체 알림 이력
-
-설비 장애 이력
-
-공정 이상 이력
-
-품질 이상 이력
-```
-
----
-
-# 10. StreamSets 역할
-
-## Kafka → Elasticsearch
-
-```
-Kafka
- ↓
-StreamSets
- ↓
-Elasticsearch
-```
-
----
-
-## Kafka → main_db
-
-```
-Kafka
- ↓
-StreamSets
- ↓
-MySQL
-```
-
----
-
-## 데이터 검증
-
-```
-필수 필드 존재 여부
-
-eventTime 포맷 검증
-
-JSON 구조 검증
-
-Null 검증
-```
-
----
-
-# 최종 데이터 흐름
-
-```
-[1] 원본 데이터셋
-
-Ford
-Bosch
-Robot Arm Vibration
-Machine Vision
-소성가공
-
-↓ ETL
-
-[2] 통합 제조 이벤트 JSON 생성
-
-↓ 저장
-
-[3] sample_db.manufacturing_event_json
-
-↓ 조회
-
-[4] Scheduler
-
-↓ 발행
-
-[5] Kafka Producer
-
-Topic
- └─ factory.manufacturing.raw
-
-↓ 수신
-
-[6] Consumer Group
-
-Manufacturing Service
-Alert Service
-AI Service
-Redis Consumer
-Elasticsearch Consumer
-
-↓ 분석
-
-병목 탐지
-불량 전이 예측
-설비 이상 탐지
-품질 이상 탐지
-
-↓ 발행
-
-factory.manufacturing.analysis
-
-factory.manufacturing.alert
-
-factory.manufacturing.equipment
-
-↓ 저장
-
-[7] Redis
-현재 상태 캐시
-
-[8] Elasticsearch
-이벤트 검색
-
-[9] main_db
-분석 결과 저장
-
-↓ Push
-
-[10] WebSocket
-
-↓ 표시
-
-React Dashboard
-```
-
-정리하면 Sample DB는 시연용 원천 이벤트 저장소, Scheduler는 이벤트 재생기, Kafka는 이벤트 전달 허브, Consumer Group은 공정별 분석 서비스, Redis는 실시간 상태 캐시, Elasticsearch는 검색 및 이력 저장소, main_db는 최종 분석 결과 저장소 역할을 수행합니다.
-
-# Elastic Search  사용
-
-ES는 Elasticsearch/OpenSearch를 말하며, “실시간 처리”보다는 이벤트 이력 검색/조회용으로 사용합니다.
-
-현재 구조에서는 이렇게 보면 됩니다.
-
-```
-Kafka = 실시간 이벤트 전달
-Redis = 최신 상태 캐시
-main_db = 분석 결과 정합성 저장
-ES = 많은 이벤트 로그를 빠르게 검색/필터링
-```
-
-예를 들면 ES는 이런 화면에서 사용합니다.
-
-```
-1. 이벤트 이력 조회
-- 오늘 발생한 전체 제조 이벤트
-- 최근 1시간 프레스 이상 이벤트
-- 특정 차량 CAR-000001의 전체 공정 이벤트
-
-2. 검색/필터
-- processCode = PRESS
-- equipmentCode = PRESS_01
-- riskLevel = CRITICAL
-- eventTime = 10:00 ~ 11:00
-
-3. 대시보드 로그 패널
-- 실시간 이벤트 로그 목록
-- 알림 발생 이력
-- 설비별 이상 이벤트 리스트
-
-4. 통계/집계
-- 공정별 이상 건수
-- 시간대별 알림 발생 수
-- 설비별 CRITICAL 이벤트 Top 10
-- 도장 공정 불량 이벤트 추이
-```
-
-즉, Kafka로 들어온 이벤트를 Consumer나 StreamSets가 ES에도 저장해두면, 나중에 대시보드에서 “검색 조건으로 빠르게 조회”할 수 있습니다.
-
-흐름은 이렇게 됩니다.
-
-```
-Scheduler
-↓
-Kafka Producer
-↓
-factory.manufacturing.raw
-↓
-Consumer 또는 StreamSets
-↓
-Elasticsearch 저장
-↓
-대시보드 이력 조회 API에서 검색
-```
-
-예시로 사용자가 대시보드에서 “프레스 1호의 최근 이상 이벤트”를 누르면:
-
-```
-React
-↓
-Backend API
-↓
-Elasticsearch 조회
-↓
-equipmentCode = PRESS_01
-status = ABNORMAL
-eventTime 최근순
-↓
-결과 반환
-```
-
-반대로 “현재 프레스 상태”만 보여줄 때는 ES가 아니라 Redis를 쓰는 게 맞습니다.
-
-```
-현재 상태 = Redis
-과거 이력 검색 = ES
-정확한 분석 결과 저장 = main_db
-```
-
-따라서 ES는 필수 실시간 처리 엔진이 아니라, 관제 시스템에서 로그 검색, 이벤트 이력 조회, 조건 필터링, 통계 집계를 빠르게 하기 위한 저장소입니다.
diff --git a/app/repository/__init__.py b/app/repository/__init__.py
index ccee955..d8bae70 100644
--- a/app/repository/__init__.py
+++ b/app/repository/__init__.py
@@ -1,2 +1,26 @@
 """Database repository package."""
 
+from app.repository.car_master_repository import CarMasterRepository
+from app.repository.equipment_repository import EquipmentRepository
+from app.repository.manufacturing_event_repository import (
+    ManufacturingEventRepository,
+)
+from app.repository.manufacturing_event_template_repository import (
+    ManufacturingEventTemplateRepository,
+)
+from app.repository.manufacturing_generation_job_repository import (
+    ManufacturingGenerationJobRepository,
+)
+from app.repository.sampledb_repository import SampleDbRepository
+from app.repository.sampledb_schema_manager import SampleDbSchemaManager
+
+__all__ = [
+    "CarMasterRepository",
+    "EquipmentRepository",
+    "ManufacturingEventRepository",
+    "ManufacturingEventTemplateRepository",
+    "ManufacturingGenerationJobRepository",
+    "SampleDbRepository",
+    "SampleDbSchemaManager",
+]
+
diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 49eed34..94c7432 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -130,7 +130,7 @@ def _init_sqlalchemy(self) -> None:
             "product_process_history",
             self.metadata,
             Column("id", BigInteger, primary_key=True, autoincrement=True),
-            # sampledb manufacturing_event.id에 대한 논리 참조이며 DB 간 FK는 두지 않음
+            # 원천 제조 이벤트 식별자에 대한 논리 참조이며 DB 간 FK는 두지 않음
             Column("manufacturing_event_id", BigInteger),
             # sampledb car_master.id에 대한 논리 참조이며 DB 간 FK는 두지 않음
             Column("car_master_id", BigInteger),
diff --git a/app/repository/car_master_repository.py b/app/repository/car_master_repository.py
new file mode 100644
index 0000000..ef15d11
--- /dev/null
+++ b/app/repository/car_master_repository.py
@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+from datetime import date
+from typing import Any
+
+from sqlalchemy import func, select
+
+from app.repository.sampledb_schema import car_master
+
+
+class CarMasterRepository:
+    """기존 car_master 차량을 제조 이벤트의 carMasterId로 매핑한다."""
+
+    def __init__(self, engine: Any) -> None:
+        self.engine = engine
+
+    def get_existing_map(self, count: int) -> dict[str, int]:
+        with self.engine.connect() as conn:
+            rows = list(
+                conn.execute(
+                    select(car_master.c.id, car_master.c.vehicle_id)
+                    .order_by(car_master.c.id.asc())
+                    .limit(count),
+                ).mappings(),
+            )
+        return self._map_first_rows(rows, count)
+
+    def get_map_by_production_date(
+        self,
+        production_date: date,
+        *,
+        limit: int | None = None,
+    ) -> dict[str, int]:
+        """vehicle_id의 생산일자에 해당하는 차량을 id 순서로 반환한다."""
+        pattern = f"%-{production_date:%Y%m%d}-%"
+        query = (
+            select(car_master.c.id, car_master.c.vehicle_id)
+            .where(car_master.c.vehicle_id.like(pattern))
+            .order_by(car_master.c.id.asc())
+        )
+        if limit is not None:
+            query = query.limit(limit)
+
+        with self.engine.connect() as conn:
+            rows = list(conn.execute(query).mappings())
+
+        if not rows:
+            raise RuntimeError(
+                f"{production_date.isoformat()} 생산 차량이 car_master에 없습니다.",
+            )
+        if limit is not None and len(rows) < limit:
+            raise RuntimeError(
+                "요청한 차량 수보다 해당 날짜의 car_master가 부족합니다: "
+                f"date={production_date.isoformat()}, required={limit}, "
+                f"actual={len(rows)}",
+            )
+        return {
+            str(row["vehicle_id"]): int(row["id"])
+            for row in rows
+        }
+
+    def count_by_production_date(self, production_date: date) -> int:
+        """vehicle_id 생산일자 기준 차량 수를 반환한다."""
+        pattern = f"%-{production_date:%Y%m%d}-%"
+        query = select(func.count()).select_from(car_master).where(
+            car_master.c.vehicle_id.like(pattern),
+        )
+        with self.engine.connect() as conn:
+            return int(conn.execute(query).scalar_one())
+
+    @staticmethod
+    def _map_first_rows(rows: list[Any], count: int) -> dict[str, int]:
+        if len(rows) < count:
+            raise RuntimeError(
+                f"car_master row가 부족합니다: required={count}, actual={len(rows)}",
+            )
+        if int(rows[0]["id"]) != 1:
+            raise RuntimeError(
+                "제조 이벤트는 car_master.id=1부터 생성되어야 합니다.",
+            )
+        return {
+            str(row["vehicle_id"]): int(row["id"])
+            for row in rows[:count]
+        }
diff --git a/app/repository/equipment_repository.py b/app/repository/equipment_repository.py
new file mode 100644
index 0000000..6514032
--- /dev/null
+++ b/app/repository/equipment_repository.py
@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+from typing import Any
+
+from sqlalchemy import func, select
+from sqlalchemy.dialects.mysql import insert
+
+from app.repository.sampledb_schema import equipment
+
+
+DEFAULT_EQUIPMENT_ROWS: list[dict[str, Any]] = [
+    {
+        "process_code": process_code,
+        "equipment_code": f"EQ_{process_code}_{index:03d}",
+        "equipment_name": f"{equipment_name_prefix} {index}호",
+        "equipment_type": equipment_type,
+        "current_status": "RUNNING",
+        "health_status": "NORMAL",
+    }
+    for process_code, equipment_type, equipment_name_prefix in (
+        ("PRESS", "HYDRAULIC_PRESS", "프레스 유압모터"),
+        ("BODY", "ROBOT_ARM", "차체 용접 로봇"),
+        ("PAINT", "CAMERA", "도장 열화상 카메라"),
+        ("ASSEMBLY", "CONVEYOR", "의장 조립 컨베이어"),
+    )
+    for index in range(1, 6)
+]
+
+
+class EquipmentRepository:
+    """equipment 마스터 데이터의 초기화와 조회를 담당한다."""
+
+    def __init__(self, engine: Any) -> None:
+        self.engine = engine
+
+    def seed_defaults(self) -> None:
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name == "mysql":
+                statement = insert(equipment).values(DEFAULT_EQUIPMENT_ROWS)
+                statement = statement.on_duplicate_key_update(
+                    equipment_name=statement.inserted.equipment_name,
+                    process_code=statement.inserted.process_code,
+                    equipment_type=statement.inserted.equipment_type,
+                    current_status=statement.inserted.current_status,
+                    health_status=statement.inserted.health_status,
+                )
+                conn.execute(statement)
+                return
+
+            existing_codes = {
+                row["equipment_code"]
+                for row in conn.execute(select(equipment.c.equipment_code)).mappings()
+            }
+            rows = [
+                row
+                for row in DEFAULT_EQUIPMENT_ROWS
+                if row["equipment_code"] not in existing_codes
+            ]
+            if rows:
+                next_id = int(
+                    conn.execute(select(func.max(equipment.c.id))).scalar() or 0,
+                )
+                rows = [
+                    {**row, "id": next_id + index}
+                    for index, row in enumerate(rows, 1)
+                ]
+                conn.execute(equipment.insert(), rows)
+
+    def get_map(self) -> dict[str, dict[str, Any]]:
+        query = select(
+            equipment.c.id,
+            equipment.c.process_code,
+            equipment.c.equipment_code,
+            equipment.c.equipment_name,
+            equipment.c.equipment_type,
+        )
+        with self.engine.connect() as conn:
+            return {
+                row["equipment_code"]: dict(row)
+                for row in conn.execute(query).mappings()
+            }
diff --git a/app/repository/manufacturing_event_repository.py b/app/repository/manufacturing_event_repository.py
new file mode 100644
index 0000000..137d551
--- /dev/null
+++ b/app/repository/manufacturing_event_repository.py
@@ -0,0 +1,203 @@
+from __future__ import annotations
+
+import copy
+import logging
+import time
+from collections.abc import Iterable
+from datetime import date, datetime
+from typing import Any
+
+from sqlalchemy import func, select
+from sqlalchemy.exc import OperationalError
+
+from app.data_generation.manufacturing_event_json_builder import (
+    initial_dispatch_status,
+    normalize_event_json,
+)
+from app.repository.sampledb_schema import manufacturing_event_json
+
+
+logger = logging.getLogger(__name__)
+MYSQL_LOCK_RETRY_ERROR_CODES = {1205, 1213}
+MYSQL_LOCK_RETRY_DELAYS_SEC = (0.2, 0.5, 1.0, 2.0, 4.0)
+
+
+class ManufacturingEventRepository:
+    """manufacturing_event_json 원천 이벤트의 저장과 조회를 담당한다."""
+
+    def __init__(self, engine: Any) -> None:
+        self.engine = engine
+
+    def count_between(self, start_date: date, end_date: date) -> int:
+        event_date_key = func.substr(manufacturing_event_json.c.event_id, 5, 8)
+        query = select(func.count()).select_from(manufacturing_event_json).where(
+            event_date_key >= start_date.strftime("%Y%m%d"),
+            event_date_key <= end_date.strftime("%Y%m%d"),
+        )
+        with self.engine.connect() as conn:
+            return int(conn.execute(query).scalar_one())
+
+    def insert_rows(
+        self,
+        rows: Iterable[dict[str, Any]],
+        *,
+        update_existing: bool = True,
+    ) -> int:
+        now = datetime.now()
+        payload = []
+        for row in rows:
+            event_json = normalize_event_json(copy.deepcopy(row["event_json"]))
+            payload.append(
+                {
+                    "event_id": row["event_id"],
+                    "event_time": None,
+                    "car_master_id": row["car_master_id"],
+                    "process_code": row["process_code"],
+                    "equipment_id": row["equipment_id"],
+                    "event_json": event_json,
+                    "dispatch_status": initial_dispatch_status(
+                        str(row["process_code"]),
+                    ),
+                    "analysis_status": row.get("analysis_status", "NOT_ANALYZED"),
+                    "is_sent": row.get("is_sent", False),
+                    "retry_count": row.get("retry_count", 0),
+                    "error_message": row.get("error_message"),
+                    "updated_at": now,
+                },
+            )
+        if not payload:
+            return 0
+
+        # 동시 작업도 같은 event_id 순서로 행 잠금을 획득하도록 고정한다.
+        payload.sort(key=lambda row: str(row["event_id"]))
+        return self._insert_payload_with_retry(
+            payload,
+            update_existing=update_existing,
+        )
+
+    def _insert_payload_with_retry(
+        self,
+        payload: list[dict[str, Any]],
+        *,
+        update_existing: bool,
+    ) -> int:
+        attempts = len(MYSQL_LOCK_RETRY_DELAYS_SEC) + 1
+        for attempt in range(attempts):
+            try:
+                return self._insert_payload_once(
+                    payload,
+                    update_existing=update_existing,
+                )
+            except OperationalError as exc:
+                error_code = _mysql_error_code(exc)
+                can_retry = (
+                    self.engine.dialect.name == "mysql"
+                    and error_code in MYSQL_LOCK_RETRY_ERROR_CODES
+                    and attempt < attempts - 1
+                )
+                if not can_retry:
+                    raise
+
+                delay = MYSQL_LOCK_RETRY_DELAYS_SEC[attempt]
+                logger.warning(
+                    "manufacturing_event_json chunk 저장 잠금 충돌 재시도: "
+                    "error_code=%s attempt=%s/%s delay=%.1fs",
+                    error_code,
+                    attempt + 1,
+                    attempts - 1,
+                    delay,
+                )
+                time.sleep(delay)
+
+        raise RuntimeError("manufacturing_event_json chunk 저장 재시도에 실패했습니다.")
+
+    def _insert_payload_once(
+        self,
+        payload: list[dict[str, Any]],
+        *,
+        update_existing: bool,
+    ) -> int:
+        with self.engine.begin() as conn:
+            event_ids = [row["event_id"] for row in payload]
+            existing_ids = {
+                row["event_id"]
+                for row in conn.execute(
+                    select(manufacturing_event_json.c.event_id).where(
+                        manufacturing_event_json.c.event_id.in_(event_ids),
+                    ),
+                ).mappings()
+            }
+
+            updated_rows = 0
+            if update_existing:
+                for row in payload:
+                    if row["event_id"] not in existing_ids:
+                        continue
+                    result = conn.execute(
+                        manufacturing_event_json.update()
+                        .where(
+                            manufacturing_event_json.c.event_id
+                            == row["event_id"],
+                        )
+                        .values(**row),
+                    )
+                    updated_rows += int(result.rowcount or 0)
+
+            new_rows = [
+                row for row in payload if row["event_id"] not in existing_ids
+            ]
+            if new_rows and self.engine.dialect.name != "mysql":
+                next_id = int(
+                    conn.execute(
+                        select(func.max(manufacturing_event_json.c.id)),
+                    ).scalar()
+                    or 0,
+                )
+                new_rows = [
+                    {**row, "id": next_id + index}
+                    for index, row in enumerate(new_rows, 1)
+                ]
+            if new_rows:
+                conn.execute(manufacturing_event_json.insert(), new_rows)
+            return len(new_rows) + updated_rows
+
+    def list_rows(
+        self,
+        *,
+        limit: int,
+        offset: int = 0,
+        start_date: date | None = None,
+        end_date: date | None = None,
+        process_code: str | None = None,
+        is_sent: bool | None = None,
+    ) -> list[dict[str, Any]]:
+        query = select(manufacturing_event_json)
+        event_date_key = func.substr(manufacturing_event_json.c.event_id, 5, 8)
+        if start_date:
+            query = query.where(
+                event_date_key >= start_date.strftime("%Y%m%d"),
+            )
+        if end_date:
+            query = query.where(
+                event_date_key <= end_date.strftime("%Y%m%d"),
+            )
+        if process_code:
+            query = query.where(
+                manufacturing_event_json.c.process_code == process_code,
+            )
+        if is_sent is not None:
+            query = query.where(manufacturing_event_json.c.is_sent == is_sent)
+
+        query = query.order_by(
+            manufacturing_event_json.c.event_id.asc(),
+            manufacturing_event_json.c.id.asc(),
+        ).offset(offset).limit(limit)
+
+        with self.engine.connect() as conn:
+            return [dict(row) for row in conn.execute(query).mappings()]
+
+
+def _mysql_error_code(exc: OperationalError) -> int | None:
+    original = getattr(exc, "orig", None)
+    args = getattr(original, "args", ())
+    return args[0] if args and isinstance(args[0], int) else None
diff --git a/app/repository/manufacturing_event_template_repository.py b/app/repository/manufacturing_event_template_repository.py
new file mode 100644
index 0000000..f660d99
--- /dev/null
+++ b/app/repository/manufacturing_event_template_repository.py
@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import Any
+
+from sqlalchemy import func, select
+from sqlalchemy.dialects.mysql import insert
+
+from app.repository.sampledb_schema import manufacturing_event_template
+
+
+class ManufacturingEventTemplateRepository:
+    """manufacturing_event_template의 저장·조회·삭제를 담당한다."""
+
+    def __init__(self, engine: Any) -> None:
+        self.engine = engine
+
+    def count(self, template_name: str) -> int:
+        query = select(func.count()).select_from(
+            manufacturing_event_template,
+        ).where(
+            manufacturing_event_template.c.template_name == template_name,
+        )
+        with self.engine.connect() as conn:
+            return int(conn.execute(query).scalar_one())
+
+    def delete(self, template_name: str) -> int:
+        statement = manufacturing_event_template.delete().where(
+            manufacturing_event_template.c.template_name == template_name,
+        )
+        with self.engine.begin() as conn:
+            result = conn.execute(statement)
+            return int(result.rowcount or 0)
+
+    def insert_rows(
+        self,
+        rows: Iterable[dict[str, Any]],
+        *,
+        update_existing: bool = False,
+    ) -> int:
+        payload = list(rows)
+        if not payload:
+            return 0
+
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name == "mysql":
+                statement = insert(manufacturing_event_template).values(payload)
+                if update_existing:
+                    statement = statement.on_duplicate_key_update(
+                        event_offset_us=statement.inserted.event_offset_us,
+                        car_master_id=statement.inserted.car_master_id,
+                        equipment_id=statement.inserted.equipment_id,
+                        process_code=statement.inserted.process_code,
+                        station_code=statement.inserted.station_code,
+                        equipment_code=statement.inserted.equipment_code,
+                        equipment_type=statement.inserted.equipment_type,
+                        equipment_status=statement.inserted.equipment_status,
+                        event_type=statement.inserted.event_type,
+                        event_json=statement.inserted.event_json,
+                    )
+                else:
+                    statement = statement.prefix_with("IGNORE")
+                result = conn.execute(statement)
+                return int(result.rowcount or 0)
+
+            template_event_ids = [row["template_event_id"] for row in payload]
+            existing_ids = {
+                row["template_event_id"]
+                for row in conn.execute(
+                    select(
+                        manufacturing_event_template.c.template_event_id,
+                    ).where(
+                        manufacturing_event_template.c.template_event_id.in_(
+                            template_event_ids,
+                        ),
+                    ),
+                ).mappings()
+            }
+            new_rows = [
+                row
+                for row in payload
+                if row["template_event_id"] not in existing_ids
+            ]
+            if new_rows:
+                next_id = int(
+                    conn.execute(
+                        select(func.max(manufacturing_event_template.c.id)),
+                    ).scalar()
+                    or 0,
+                )
+                new_rows = [
+                    {**row, "id": next_id + index}
+                    for index, row in enumerate(new_rows, 1)
+                ]
+                conn.execute(manufacturing_event_template.insert(), new_rows)
+            return len(new_rows)
+
+    def list_rows(
+        self,
+        *,
+        template_name: str,
+        limit: int,
+        offset: int = 0,
+        process_code: str | None = None,
+    ) -> list[dict[str, Any]]:
+        query = select(manufacturing_event_template).where(
+            manufacturing_event_template.c.template_name == template_name,
+        )
+        if process_code:
+            query = query.where(
+                manufacturing_event_template.c.process_code == process_code,
+            )
+        query = query.order_by(
+            manufacturing_event_template.c.event_offset_us.asc(),
+            manufacturing_event_template.c.id.asc(),
+        ).offset(offset).limit(limit)
+
+        with self.engine.connect() as conn:
+            return [dict(row) for row in conn.execute(query).mappings()]
diff --git a/app/repository/manufacturing_generation_job_repository.py b/app/repository/manufacturing_generation_job_repository.py
new file mode 100644
index 0000000..2760f86
--- /dev/null
+++ b/app/repository/manufacturing_generation_job_repository.py
@@ -0,0 +1,189 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from datetime import datetime
+from typing import Any, Iterator
+
+from sqlalchemy import func, select, text
+
+from app.repository.sampledb_schema import manufacturing_event_generation_job
+
+
+MANUFACTURING_GENERATION_LOCK_NAME = "manufacturing_event_generation"
+
+
+class ManufacturingGenerationJobRepository:
+    """제조 이벤트 비동기 job의 상태와 전역 실행 잠금을 관리한다."""
+
+    def __init__(self, engine: Any) -> None:
+        self.engine = engine
+
+    @contextmanager
+    def execution_lock(self, timeout_seconds: int = 600) -> Iterator[bool]:
+        if self.engine.dialect.name != "mysql":
+            yield True
+            return
+
+        with self.engine.connect() as conn:
+            acquired = (
+                conn.execute(
+                    text("SELECT GET_LOCK(:lock_name, :timeout_seconds)"),
+                    {
+                        "lock_name": MANUFACTURING_GENERATION_LOCK_NAME,
+                        "timeout_seconds": timeout_seconds,
+                    },
+                ).scalar()
+                == 1
+            )
+            try:
+                yield acquired
+            finally:
+                if acquired:
+                    conn.execute(
+                        text("SELECT RELEASE_LOCK(:lock_name)"),
+                        {"lock_name": MANUFACTURING_GENERATION_LOCK_NAME},
+                    )
+
+    def create(
+        self,
+        *,
+        job_id: str,
+        job_type: str,
+        request_json: dict[str, Any],
+        total_expected_events: int = 0,
+    ) -> dict[str, Any]:
+        now = datetime.now()
+        row = {
+            "job_id": job_id,
+            "job_type": job_type,
+            "status": "PENDING",
+            "request_json": request_json,
+            "total_expected_events": total_expected_events,
+            "generated_count": 0,
+            "affected_rows": 0,
+            "created_at": now,
+            "updated_at": now,
+        }
+        with self.engine.begin() as conn:
+            if self.engine.dialect.name != "mysql":
+                next_id = int(
+                    conn.execute(
+                        select(func.max(manufacturing_event_generation_job.c.id)),
+                    ).scalar()
+                    or 0,
+                )
+                row = {**row, "id": next_id + 1}
+            conn.execute(manufacturing_event_generation_job.insert(), row)
+        return self.get(job_id) or {}
+
+    def get(self, job_id: str) -> dict[str, Any] | None:
+        query = select(manufacturing_event_generation_job).where(
+            manufacturing_event_generation_job.c.job_id == job_id,
+        )
+        with self.engine.connect() as conn:
+            row = conn.execute(query).mappings().first()
+            return _format_job_row(dict(row)) if row else None
+
+    def list_resumable(self) -> list[dict[str, Any]]:
+        query = (
+            select(manufacturing_event_generation_job)
+            .where(
+                manufacturing_event_generation_job.c.status.in_(
+                    ["PENDING", "RUNNING"],
+                ),
+            )
+            .order_by(
+                manufacturing_event_generation_job.c.created_at.asc(),
+                manufacturing_event_generation_job.c.id.asc(),
+            )
+        )
+        with self.engine.connect() as conn:
+            return [
+                _format_job_row(dict(row))
+                for row in conn.execute(query).mappings()
+            ]
+
+    def mark_running(self, job_id: str) -> None:
+        now = datetime.now()
+        self._update(
+            job_id,
+            status="RUNNING",
+            started_at=now,
+            updated_at=now,
+        )
+
+    def update_progress(
+        self,
+        job_id: str,
+        progress: dict[str, Any],
+    ) -> None:
+        self._update(
+            job_id,
+            generated_count=int(progress.get("generatedCount", 0)),
+            affected_rows=int(progress.get("affectedRows", 0)),
+            total_expected_events=int(progress.get("totalExpectedEvents", 0)),
+            updated_at=datetime.now(),
+        )
+
+    def mark_succeeded(
+        self,
+        job_id: str,
+        result_json: dict[str, Any],
+    ) -> None:
+        now = datetime.now()
+        self._update(
+            job_id,
+            status="SUCCEEDED",
+            result_json=result_json,
+            error_message=None,
+            generated_count=int(result_json.get("generatedCount", 0)),
+            affected_rows=int(result_json.get("affectedRows", 0)),
+            total_expected_events=int(
+                result_json.get(
+                    "totalExpectedEvents",
+                    result_json.get(
+                        "templateEventCount",
+                        result_json.get("eventCount", 0),
+                    ),
+                ),
+            ),
+            finished_at=now,
+            updated_at=now,
+        )
+
+    def mark_failed(self, job_id: str, error_message: str) -> None:
+        now = datetime.now()
+        self._update(
+            job_id,
+            status="FAILED",
+            error_message=error_message[:1000],
+            finished_at=now,
+            updated_at=now,
+        )
+
+    def _update(self, job_id: str, **values: Any) -> None:
+        statement = (
+            manufacturing_event_generation_job.update()
+            .where(manufacturing_event_generation_job.c.job_id == job_id)
+            .values(**values)
+        )
+        with self.engine.begin() as conn:
+            conn.execute(statement)
+
+
+def _format_job_row(row: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "jobId": row["job_id"],
+        "jobType": row["job_type"],
+        "status": row["status"],
+        "request": row["request_json"],
+        "result": row["result_json"],
+        "errorMessage": row["error_message"],
+        "totalExpectedEvents": int(row["total_expected_events"] or 0),
+        "generatedCount": int(row["generated_count"] or 0),
+        "affectedRows": int(row["affected_rows"] or 0),
+        "createdAt": row["created_at"],
+        "startedAt": row["started_at"],
+        "finishedAt": row["finished_at"],
+        "updatedAt": row["updated_at"],
+    }
diff --git a/app/repository/sampledb_repository.py b/app/repository/sampledb_repository.py
index 0856cdd..bc1ee51 100644
--- a/app/repository/sampledb_repository.py
+++ b/app/repository/sampledb_repository.py
@@ -1,44 +1,28 @@
-from collections.abc import Iterable
-from datetime import date, datetime
-from typing import Any
+from __future__ import annotations
 
-from sqlalchemy import create_engine, func, inspect, select, text
-from sqlalchemy.dialects.mysql import insert
+from sqlalchemy import create_engine
 
-from app.repository.sampledb_schema import (
-    car_master,
-    equipment,
-    manufacturing_event_generation_job,
-    manufacturing_event_json,
-    manufacturing_event_template,
-    metadata,
+from app.repository.car_master_repository import CarMasterRepository
+from app.repository.equipment_repository import EquipmentRepository
+from app.repository.manufacturing_event_repository import (
+    ManufacturingEventRepository,
 )
+from app.repository.manufacturing_event_template_repository import (
+    ManufacturingEventTemplateRepository,
+)
+from app.repository.manufacturing_generation_job_repository import (
+    ManufacturingGenerationJobRepository,
+)
+from app.repository.sampledb_schema_manager import SampleDbSchemaManager
 from app.utils.database_utils import mysql_connect_args_for_seoul
 
 
-DEFAULT_EQUIPMENT_ROWS: list[dict[str, Any]] = [
-    {
-        "process_code": process_code,
-        "equipment_code": f"EQ_{process_code}_{index:03d}",
-        "equipment_name": f"{equipment_name_prefix} {index}호",
-        "equipment_type": equipment_type,
-    }
-    for process_code, equipment_type, equipment_name_prefix in (
-        ("PRESS", "HYDRAULIC_PRESS", "프레스 유압모터"),
-        ("BODY", "ROBOT_ARM", "차체 용접 로봇"),
-        ("PAINT", "CAMERA", "도장 열화상 카메라"),
-        ("ASSEMBLY", "CONVEYOR", "의장 조립 컨베이어"),
-    )
-    for index in range(1, 5)
-]
-
-DEFAULT_CAR_TYPES = ("SEDAN", "SUV", "EV", "HEV")
-DEFAULT_ENGINE_TYPES = ("GASOLINE", "DIESEL", "ELECTRIC", "HYBRID")
-DEFAULT_CAR_COLORS = ("WHITE", "BLACK", "SILVER", "BLUE")
-
-
 class SampleDbRepository:
-    """sampledb 스키마와 제조 원천 이벤트 JSON 저장소."""
+    """sampledb 전용 repository들을 같은 DB 연결로 묶는 facade다.
+
+    테이블별 SQL과 상태 관리 책임은 각 repository에 있고, 이 클래스는 서비스가
+    한 객체를 주입받아 사용할 수 있도록 구성 요소만 제공한다.
+    """
 
     def __init__(self, database_url: str) -> None:
         self.database_url = database_url
@@ -48,520 +32,18 @@ def __init__(self, database_url: str) -> None:
             pool_pre_ping=True,
             future=True,
         )
-
-    def ensure_schema(self) -> None:
-        """sampledb 엔티티가 없으면 생성한다."""
-        metadata.create_all(self.engine)
-        self.drop_equipment_status_column()
-        self.add_manufacturing_event_json_updated_at_column()
-
-    def seed_equipment(self) -> None:
-        """기본 설비 데이터를 입력하고 기존 설비 정보는 갱신한다."""
-        with self.engine.begin() as conn:
-            if self.engine.dialect.name == "mysql":
-                statement = insert(equipment).values(DEFAULT_EQUIPMENT_ROWS)
-                statement = statement.on_duplicate_key_update(
-                    equipment_name=statement.inserted.equipment_name,
-                    process_code=statement.inserted.process_code,
-                    equipment_type=statement.inserted.equipment_type,
-                )
-                conn.execute(statement)
-                return
-
-            existing_codes = {
-                row["equipment_code"]
-                for row in conn.execute(select(equipment.c.equipment_code)).mappings()
-            }
-            rows = [
-                row
-                for row in DEFAULT_EQUIPMENT_ROWS
-                if row["equipment_code"] not in existing_codes
-            ]
-            if rows:
-                next_id = int(
-                    conn.execute(select(func.max(equipment.c.id))).scalar() or 0,
-                )
-                rows = [{**row, "id": next_id + index} for index, row in enumerate(rows, 1)]
-                conn.execute(equipment.insert(), rows)
-
-    def drop_equipment_status_column(self) -> bool:
-        if "status" not in self._table_columns("equipment"):
-            return False
-        with self.engine.begin() as conn:
-            conn.execute(text("ALTER TABLE equipment DROP COLUMN status"))
-        return True
-
-    def add_manufacturing_event_json_updated_at_column(self) -> bool:
-        if "updated_at" in self._table_columns("manufacturing_event_json"):
-            return False
-
-        with self.engine.begin() as conn:
-            if self.engine.dialect.name == "mysql":
-                conn.execute(
-                    text(
-                        "ALTER TABLE manufacturing_event_json "
-                        "ADD COLUMN updated_at DATETIME NOT NULL "
-                        "DEFAULT CURRENT_TIMESTAMP",
-                    ),
-                )
-            else:
-                conn.execute(
-                    text(
-                        "ALTER TABLE manufacturing_event_json "
-                        "ADD COLUMN updated_at DATETIME",
-                    ),
-                )
-                conn.execute(
-                    text(
-                        "UPDATE manufacturing_event_json "
-                        "SET updated_at = CURRENT_TIMESTAMP "
-                        "WHERE updated_at IS NULL",
-                    ),
-                )
-        return True
-
-    def _table_columns(self, table_name: str) -> set[str]:
-        inspector = inspect(self.engine)
-        if not inspector.has_table(table_name):
-            return set(metadata.tables[table_name].c.keys())
-        return {
-            column["name"]
-            for column in inspector.get_columns(table_name)
-        }
-
-    def ensure_car_master_rows(self, count: int) -> dict[str, int]:
-        """이벤트 생성에 필요한 차량 마스터 데이터를 보장한다."""
-        vehicles = [f"CAR-{index:06d}" for index in range(1, count + 1)]
-        with self.engine.begin() as conn:
-            existing = {
-                row["vehicle_id"]: int(row["id"])
-                for row in conn.execute(
-                    select(car_master.c.id, car_master.c.vehicle_id).where(
-                        car_master.c.vehicle_id.in_(vehicles),
-                    ),
-                ).mappings()
-            }
-
-            missing_rows = [
-                {
-                    "vehicle_id": vehicle_id,
-                    "car_type": DEFAULT_CAR_TYPES[(index - 1) % len(DEFAULT_CAR_TYPES)],
-                    "engine_type": DEFAULT_ENGINE_TYPES[
-                        (index - 1) % len(DEFAULT_ENGINE_TYPES)
-                    ],
-                    "car_color": DEFAULT_CAR_COLORS[
-                        (index - 1) % len(DEFAULT_CAR_COLORS)
-                    ],
-                    "fuel_efficiency": 11 + (index % 9),
-                    "created_at": datetime.now(),
-                }
-                for index, vehicle_id in enumerate(vehicles, start=1)
-                if vehicle_id not in existing
-            ]
-            if missing_rows:
-                next_id = int(
-                    conn.execute(select(func.max(car_master.c.id))).scalar() or 0,
-                )
-                missing_rows = [
-                    {**row, "id": next_id + index}
-                    for index, row in enumerate(missing_rows, 1)
-                ]
-                conn.execute(car_master.insert(), missing_rows)
-
-            return {
-                row["vehicle_id"]: int(row["id"])
-                for row in conn.execute(
-                    select(car_master.c.id, car_master.c.vehicle_id).where(
-                        car_master.c.vehicle_id.in_(vehicles),
-                    ),
-                ).mappings()
-            }
-
-    def get_equipment_map(self) -> dict[str, dict[str, Any]]:
-        """equipment_code 기준 설비 행을 반환한다."""
-        query = select(
-            equipment.c.id,
-            equipment.c.process_code,
-            equipment.c.equipment_code,
-            equipment.c.equipment_name,
-            equipment.c.equipment_type,
-        )
-        with self.engine.connect() as conn:
-            return {
-                row["equipment_code"]: dict(row)
-                for row in conn.execute(query).mappings()
-            }
-
-    def count_event_json_between(self, start_date: date, end_date: date) -> int:
-        """기간 내 생성된 원천 이벤트 JSON 수를 반환한다."""
-        query = select(func.count()).select_from(manufacturing_event_json).where(
-            func.date(manufacturing_event_json.c.event_time) >= start_date.isoformat(),
-            func.date(manufacturing_event_json.c.event_time) <= end_date.isoformat(),
-        )
-        with self.engine.connect() as conn:
-            return int(conn.execute(query).scalar_one())
-
-    def count_template_events(self, template_name: str) -> int:
-        """템플릿에 저장된 이벤트 수를 반환한다."""
-        query = select(func.count()).select_from(manufacturing_event_template).where(
-            manufacturing_event_template.c.template_name == template_name,
-        )
-        with self.engine.connect() as conn:
-            return int(conn.execute(query).scalar_one())
-
-    def delete_template_events(self, template_name: str) -> int:
-        """템플릿 이벤트를 삭제한다."""
-        statement = manufacturing_event_template.delete().where(
-            manufacturing_event_template.c.template_name == template_name,
-        )
-        with self.engine.begin() as conn:
-            result = conn.execute(statement)
-            return int(result.rowcount or 0)
-
-    def create_generation_job(
-        self,
-        *,
-        job_id: str,
-        job_type: str,
-        request_json: dict[str, Any],
-        total_expected_events: int = 0,
-    ) -> dict[str, Any]:
-        now = datetime.now()
-        row = {
-            "job_id": job_id,
-            "job_type": job_type,
-            "status": "PENDING",
-            "request_json": request_json,
-            "total_expected_events": total_expected_events,
-            "generated_count": 0,
-            "affected_rows": 0,
-            "created_at": now,
-            "updated_at": now,
-        }
-        with self.engine.begin() as conn:
-            if self.engine.dialect.name != "mysql":
-                next_id = int(
-                    conn.execute(
-                        select(func.max(manufacturing_event_generation_job.c.id)),
-                    ).scalar()
-                    or 0,
-                )
-                row = {**row, "id": next_id + 1}
-            conn.execute(manufacturing_event_generation_job.insert(), row)
-        return self.get_generation_job(job_id) or {}
-
-    def get_generation_job(self, job_id: str) -> dict[str, Any] | None:
-        query = select(manufacturing_event_generation_job).where(
-            manufacturing_event_generation_job.c.job_id == job_id,
-        )
-        with self.engine.connect() as conn:
-            row = conn.execute(query).mappings().first()
-            return _format_generation_job_row(dict(row)) if row else None
-
-    def list_resumable_generation_jobs(self) -> list[dict[str, Any]]:
-        query = (
-            select(manufacturing_event_generation_job)
-            .where(manufacturing_event_generation_job.c.status.in_(["PENDING", "RUNNING"]))
-            .order_by(
-                manufacturing_event_generation_job.c.created_at.asc(),
-                manufacturing_event_generation_job.c.id.asc(),
-            )
-        )
-        with self.engine.connect() as conn:
-            return [
-                _format_generation_job_row(dict(row))
-                for row in conn.execute(query).mappings()
-            ]
-
-    def mark_generation_job_running(self, job_id: str) -> None:
-        now = datetime.now()
-        self._update_generation_job(
-            job_id,
-            status="RUNNING",
-            started_at=now,
-            updated_at=now,
-        )
-
-    def update_generation_job_progress(
-        self,
-        job_id: str,
-        progress: dict[str, Any],
-    ) -> None:
-        self._update_generation_job(
-            job_id,
-            generated_count=int(progress.get("generatedCount", 0)),
-            affected_rows=int(progress.get("affectedRows", 0)),
-            total_expected_events=int(progress.get("totalExpectedEvents", 0)),
-            updated_at=datetime.now(),
-        )
-
-    def mark_generation_job_succeeded(
-        self,
-        job_id: str,
-        result_json: dict[str, Any],
-    ) -> None:
-        now = datetime.now()
-        self._update_generation_job(
-            job_id,
-            status="SUCCEEDED",
-            result_json=result_json,
-            error_message=None,
-            generated_count=int(result_json.get("generatedCount", 0)),
-            affected_rows=int(result_json.get("affectedRows", 0)),
-            total_expected_events=int(
-                result_json.get(
-                    "totalExpectedEvents",
-                    result_json.get("templateEventCount", result_json.get("eventCount", 0)),
-                ),
-            ),
-            finished_at=now,
-            updated_at=now,
-        )
-
-    def mark_generation_job_failed(self, job_id: str, error_message: str) -> None:
-        now = datetime.now()
-        self._update_generation_job(
-            job_id,
-            status="FAILED",
-            error_message=error_message[:1000],
-            finished_at=now,
-            updated_at=now,
-        )
-
-    def _update_generation_job(self, job_id: str, **values: Any) -> None:
-        statement = (
-            manufacturing_event_generation_job.update()
-            .where(manufacturing_event_generation_job.c.job_id == job_id)
-            .values(**values)
-        )
-        with self.engine.begin() as conn:
-            conn.execute(statement)
-
-    def insert_event_json_rows(
-        self,
-        rows: Iterable[dict[str, Any]],
-        *,
-        update_existing: bool = True,
-    ) -> int:
-        """원천 이벤트 JSON을 저장한다. event_id 기준으로 재실행해도 중복 저장하지 않는다."""
-        now = datetime.now()
-        payload = [{**row, "updated_at": now} for row in rows]
-        if not payload:
-            return 0
-
-        with self.engine.begin() as conn:
-            if self.engine.dialect.name == "mysql":
-                statement = insert(manufacturing_event_json).values(payload)
-                if update_existing:
-                    statement = statement.on_duplicate_key_update(
-                        event_time=statement.inserted.event_time,
-                        car_master_id=statement.inserted.car_master_id,
-                        equipment_id=statement.inserted.equipment_id,
-                        process_code=statement.inserted.process_code,
-                        station_code=statement.inserted.station_code,
-                        equipment_code=statement.inserted.equipment_code,
-                        equipment_type=statement.inserted.equipment_type,
-                        equipment_status=statement.inserted.equipment_status,
-                        event_type=statement.inserted.event_type,
-                        event_json=statement.inserted.event_json,
-                        updated_at=statement.inserted.updated_at,
-                    )
-                else:
-                    statement = statement.prefix_with("IGNORE")
-                result = conn.execute(statement)
-                return int(result.rowcount or 0)
-
-            event_ids = [row["event_id"] for row in payload]
-            existing_ids = {
-                row["event_id"]
-                for row in conn.execute(
-                    select(manufacturing_event_json.c.event_id).where(
-                        manufacturing_event_json.c.event_id.in_(event_ids),
-                    ),
-                ).mappings()
-            }
-            updated_rows = 0
-            if update_existing:
-                for row in payload:
-                    if row["event_id"] not in existing_ids:
-                        continue
-                    result = conn.execute(
-                        manufacturing_event_json.update()
-                        .where(manufacturing_event_json.c.event_id == row["event_id"])
-                        .values(
-                            event_time=row["event_time"],
-                            car_master_id=row["car_master_id"],
-                            equipment_id=row["equipment_id"],
-                            process_code=row["process_code"],
-                            station_code=row["station_code"],
-                            equipment_code=row["equipment_code"],
-                            equipment_type=row["equipment_type"],
-                            equipment_status=row["equipment_status"],
-                            event_type=row["event_type"],
-                            event_json=row["event_json"],
-                            updated_at=row["updated_at"],
-                        ),
-                    )
-                    updated_rows += int(result.rowcount or 0)
-
-            new_rows = [row for row in payload if row["event_id"] not in existing_ids]
-            if new_rows:
-                next_id = int(
-                    conn.execute(
-                        select(func.max(manufacturing_event_json.c.id)),
-                    ).scalar()
-                    or 0,
-                )
-                new_rows = [
-                    {**row, "id": next_id + index}
-                    for index, row in enumerate(new_rows, 1)
-                ]
-                conn.execute(manufacturing_event_json.insert(), new_rows)
-            return len(new_rows) + updated_rows
-
-    def insert_template_event_rows(
-        self,
-        rows: Iterable[dict[str, Any]],
-        *,
-        update_existing: bool = False,
-    ) -> int:
-        """하루 재생 템플릿 이벤트를 저장한다."""
-        payload = list(rows)
-        if not payload:
-            return 0
-
-        with self.engine.begin() as conn:
-            if self.engine.dialect.name == "mysql":
-                statement = insert(manufacturing_event_template).values(payload)
-                if update_existing:
-                    statement = statement.on_duplicate_key_update(
-                        event_offset_us=statement.inserted.event_offset_us,
-                        car_master_id=statement.inserted.car_master_id,
-                        equipment_id=statement.inserted.equipment_id,
-                        process_code=statement.inserted.process_code,
-                        station_code=statement.inserted.station_code,
-                        equipment_code=statement.inserted.equipment_code,
-                        equipment_type=statement.inserted.equipment_type,
-                        equipment_status=statement.inserted.equipment_status,
-                        event_type=statement.inserted.event_type,
-                        event_json=statement.inserted.event_json,
-                    )
-                else:
-                    statement = statement.prefix_with("IGNORE")
-                result = conn.execute(statement)
-                return int(result.rowcount or 0)
-
-            template_event_ids = [row["template_event_id"] for row in payload]
-            existing_ids = {
-                row["template_event_id"]
-                for row in conn.execute(
-                    select(manufacturing_event_template.c.template_event_id).where(
-                        manufacturing_event_template.c.template_event_id.in_(
-                            template_event_ids,
-                        ),
-                    ),
-                ).mappings()
-            }
-            new_rows = [
-                row
-                for row in payload
-                if row["template_event_id"] not in existing_ids
-            ]
-            if new_rows:
-                next_id = int(
-                    conn.execute(
-                        select(func.max(manufacturing_event_template.c.id)),
-                    ).scalar()
-                    or 0,
-                )
-                new_rows = [
-                    {**row, "id": next_id + index}
-                    for index, row in enumerate(new_rows, 1)
-                ]
-                conn.execute(manufacturing_event_template.insert(), new_rows)
-            return len(new_rows)
-
-    def list_template_event_rows(
-        self,
-        *,
-        template_name: str,
-        limit: int,
-        offset: int = 0,
-        process_code: str | None = None,
-    ) -> list[dict[str, Any]]:
-        """템플릿 이벤트를 offset 순서로 조회한다."""
-        query = select(manufacturing_event_template).where(
-            manufacturing_event_template.c.template_name == template_name,
-        )
-        if process_code:
-            query = query.where(
-                manufacturing_event_template.c.process_code == process_code,
-            )
-        query = query.order_by(
-            manufacturing_event_template.c.event_offset_us.asc(),
-            manufacturing_event_template.c.id.asc(),
-        ).offset(offset).limit(limit)
-
-        with self.engine.connect() as conn:
-            return [dict(row) for row in conn.execute(query).mappings()]
-
-    def list_event_json_rows(
-        self,
-        *,
-        limit: int,
-        offset: int = 0,
-        start_date: date | None = None,
-        end_date: date | None = None,
-        process_code: str | None = None,
-        is_sent: bool | None = None,
-    ) -> list[dict[str, Any]]:
-        """생성된 원천 이벤트 JSON을 조회한다."""
-        query = select(manufacturing_event_json)
-        if start_date:
-            query = query.where(
-                func.date(manufacturing_event_json.c.event_time) >= start_date.isoformat(),
-            )
-        if end_date:
-            query = query.where(
-                func.date(manufacturing_event_json.c.event_time) <= end_date.isoformat(),
-            )
-        if process_code:
-            query = query.where(manufacturing_event_json.c.process_code == process_code)
-        if is_sent is not None:
-            query = query.where(manufacturing_event_json.c.is_sent == is_sent)
-
-        query = query.order_by(
-            manufacturing_event_json.c.event_time.asc(),
-            manufacturing_event_json.c.id.asc(),
-        ).offset(offset).limit(limit)
-
-        with self.engine.connect() as conn:
-            return [dict(row) for row in conn.execute(query).mappings()]
+        self.schema = SampleDbSchemaManager(self.engine)
+        self.cars = CarMasterRepository(self.engine)
+        self.equipment = EquipmentRepository(self.engine)
+        self.events = ManufacturingEventRepository(self.engine)
+        self.templates = ManufacturingEventTemplateRepository(self.engine)
+        self.jobs = ManufacturingGenerationJobRepository(self.engine)
 
     def initialize(self) -> None:
-        """스키마를 생성하고 PRD에 필요한 참조 데이터를 입력한다."""
-        self.ensure_schema()
-        self.seed_equipment()
+        self.schema.ensure_schema()
+        self.equipment.seed_defaults()
 
 
 def initialize_sampledb(database_url: str) -> None:
-    """sampledb 테이블과 참조 설비 데이터를 초기화한다."""
+    """sampledb 스키마와 기본 설비 데이터를 초기화한다."""
     SampleDbRepository(database_url).initialize()
-
-
-def _format_generation_job_row(row: dict[str, Any]) -> dict[str, Any]:
-    return {
-        "jobId": row["job_id"],
-        "jobType": row["job_type"],
-        "status": row["status"],
-        "request": row["request_json"],
-        "result": row["result_json"],
-        "errorMessage": row["error_message"],
-        "totalExpectedEvents": int(row["total_expected_events"] or 0),
-        "generatedCount": int(row["generated_count"] or 0),
-        "affectedRows": int(row["affected_rows"] or 0),
-        "createdAt": row["created_at"],
-        "startedAt": row["started_at"],
-        "finishedAt": row["finished_at"],
-        "updatedAt": row["updated_at"],
-    }
diff --git a/app/repository/sampledb_schema.py b/app/repository/sampledb_schema.py
index 090b122..0d6d27d 100644
--- a/app/repository/sampledb_schema.py
+++ b/app/repository/sampledb_schema.py
@@ -3,9 +3,7 @@
     Boolean,
     Column,
     DateTime,
-    Double,
     Enum,
-    Float,
     ForeignKey,
     Index,
     Integer,
@@ -13,18 +11,31 @@
     MetaData,
     String,
     Table,
+    Text,
     UniqueConstraint,
     func,
 )
 
 metadata = MetaData()
 
+# 제조 이벤트 흐름에서 공통으로 사용하는 PRD 상태 코드.
 process_code_enum = Enum("PRESS", "BODY", "PAINT", "ASSEMBLY")
 equipment_type_enum = Enum("HYDRAULIC_PRESS", "ROBOT_ARM", "CAMERA", "CONVEYOR")
-source_type_enum = Enum("BOSCH", "FORD", "PRESS_CURRENT", "ROBOT_CURRENT", "THERMAL_VISION")
-data_type_enum = Enum("PROCESS", "SENSOR", "QUALITY", "THERMAL")
-quality_result_enum = Enum("NORMAL", "DEFECT")
-expected_label_enum = Enum("NORMAL", "WARNING", "FAULT", "DEFECT")
+equipment_current_status_enum = Enum(
+    "RUNNING",
+    "IDLE",
+    "STOPPED",
+    "FAULT",
+    "MAINTENANCE",
+)
+equipment_health_status_enum = Enum("NORMAL", "WARNING", "CRITICAL")
+dispatch_status_enum = Enum(
+    "PENDING",
+    "READY",
+    "SENT",
+    "BLOCKED",
+)
+analysis_status_enum = Enum("NOT_ANALYZED", "NORMAL", "ABNORMAL")
 
 car_master = Table(
     "car_master",
@@ -35,7 +46,8 @@
     Column("engine_type", String(30), nullable=False),
     Column("car_color", String(30), nullable=False),
     Column("fuel_efficiency", Integer, nullable=False),
-    Column("created_at", DateTime, nullable=False),
+    # 차량 생성 시각은 원천 시스템에서 제공되지 않을 수 있으므로 NULL을 허용한다.
+    Column("created_at", DateTime, nullable=True),
     UniqueConstraint("vehicle_id", name="uq_car_master_vehicle_id"),
 )
 
@@ -47,66 +59,67 @@
     Column("equipment_code", String(50), nullable=False, unique=True),
     Column("equipment_name", String(100), nullable=False),
     Column("equipment_type", equipment_type_enum, nullable=False),
+    Column(
+        "current_status",
+        equipment_current_status_enum,
+        nullable=False,
+        server_default="RUNNING",
+    ),
+    Column(
+        "health_status",
+        equipment_health_status_enum,
+        nullable=False,
+        server_default="NORMAL",
+    ),
+    Column("last_fault_time", DateTime),
+    Column("last_recovered_time", DateTime),
+    Column("reason", String(255)),
     Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+    Column("updated_at", DateTime, nullable=False, server_default=func.current_timestamp()),
     Index("idx_equipment_process_code", "process_code"),
 )
 
-manufacturing_event = Table(
-    "manufacturing_event",
-    metadata,
-    Column("id", BigInteger, primary_key=True, autoincrement=True),
-    Column("car_master_id", BigInteger, ForeignKey("car_master.id")),
-    Column("sample_manufacturing_event_id", String(50), nullable=False, unique=True),
-    Column("equipment_code", String(50), ForeignKey("equipment.equipment_code")),
-    Column("source_dataset", String(100)),
-    Column("source_type", source_type_enum, nullable=False),
-    Column("data_type", data_type_enum, nullable=False),
-    Column("process_code", process_code_enum, nullable=False),
-    Column("equipment_type", equipment_type_enum, nullable=False),
-    Column("station_code", String(50)),
-    Column("event_time", DateTime, nullable=False),
-    Column("metric_code", String(50)),
-    Column("metric_value", Double),
-    Column("unit", String(20)),
-    Column("process_time", Double),
-    Column("waiting_time", Double),
-    Column("quality_result", quality_result_enum),
-    Column("defect_type", String(50)),
-    Column("expected_is_abnormal", Boolean),
-    Column("expected_abnormal_type", String(50)),
-    Column("expected_severity", String(20)),
-    Column("expected_label", expected_label_enum),
-    Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
-    Index("idx_manufacturing_event_car_master_id", "car_master_id"),
-    Index("idx_manufacturing_event_equipment_code", "equipment_code"),
-    Index("idx_manufacturing_event_event_time", "event_time"),
-)
-
 manufacturing_event_json = Table(
     "manufacturing_event_json",
     metadata,
     Column("id", BigInteger, primary_key=True, autoincrement=True),
-    Column("event_id", String(100), nullable=False, unique=True),
-    Column("event_time", DateTime, nullable=False),
-    Column("car_master_id", BigInteger, ForeignKey("car_master.id"), nullable=False),
-    Column("equipment_id", BigInteger, ForeignKey("equipment.id"), nullable=False),
+    Column("event_id", String(100), nullable=False),
+    Column("event_time", DateTime, nullable=True),
+    Column("car_master_id", BigInteger, nullable=False),
     Column("process_code", process_code_enum, nullable=False),
-    Column("station_code", String(50)),
-    Column("equipment_code", String(50), nullable=False),
-    Column("equipment_type", String(50)),
-    Column("equipment_status", String(30)),
-    Column("event_type", String(50)),
+    Column("equipment_id", BigInteger, nullable=False),
     Column("event_json", JSON, nullable=False),
+    # Scheduler가 조회하는 발행 가능 상태. 최초 생성 시 PRESS만 READY가 된다.
+    Column(
+        "dispatch_status",
+        dispatch_status_enum,
+        nullable=False,
+        server_default="PENDING",
+    ),
+    # Kafka Consumer의 공정 분석 결과가 기록되기 전에는 NOT_ANALYZED다.
+    Column(
+        "analysis_status",
+        analysis_status_enum,
+        nullable=False,
+        server_default="NOT_ANALYZED",
+    ),
     Column("is_sent", Boolean, nullable=False, server_default="0"),
-    Column("sent_at", DateTime),
+    Column("retry_count", Integer, nullable=False, server_default="0"),
+    Column("error_message", Text),
     Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
-    Column("updated_at", DateTime, nullable=False, server_default=func.current_timestamp()),
-    Index("idx_event_time_sent", "event_time", "is_sent"),
-    Index("idx_process_time", "process_code", "event_time"),
-    Index("idx_equipment_time", "equipment_code", "event_time"),
-    Index("idx_car_time", "car_master_id", "event_time"),
+    Column(
+        "updated_at",
+        DateTime,
+        nullable=False,
+        server_default=func.current_timestamp(),
+        server_onupdate=func.current_timestamp(),
+    ),
+    # Scheduler의 READY/미전송 조회와 차량별 공정 진행 갱신을 위한 핵심 인덱스.
+    Index("idx_dispatch", "dispatch_status", "is_sent", "id"),
+    Index("idx_car_process", "car_master_id", "process_code"),
+    Index("idx_process_status", "process_code", "dispatch_status"),
+    Index("idx_equipment_status", "equipment_id", "dispatch_status"),
     Index("idx_event_id", "event_id"),
-    Index("idx_equipment_id_time", "equipment_id", "event_time"),
 )
 
 manufacturing_event_template = Table(
@@ -152,50 +165,3 @@
     Index("idx_generation_job_status_created", "status", "created_at"),
     Index("idx_generation_job_type_created", "job_type", "created_at"),
 )
-
-thermal_vision = Table(
-    "thermal_vision",
-    metadata,
-    Column("id", BigInteger, primary_key=True, autoincrement=True),
-    Column("manufacturing_event_id", BigInteger, ForeignKey("manufacturing_event.id"), nullable=False),
-    Column("car_master_id", BigInteger, ForeignKey("car_master.id")),
-    Column("image_position", String(50)),
-    Column("thermal_avg_temp", Double),
-    Column("thermal_max_temp", Double),
-    Column("thermal_min_temp", Double),
-    Column("thermal_std_temp", Double),
-    Column("thickness_value", Double),
-    Column("defect_score", Double),
-    Column("expected_vision_label", String(30)),
-    Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
-    UniqueConstraint("manufacturing_event_id", name="uq_thermal_vision_manufacturing_event_id"),
-    Index("idx_thermal_vision_car_master_id", "car_master_id"),
-)
-
-robot_arm_vibration = Table(
-    "robot_arm_vibration",
-    metadata,
-    Column("id", BigInteger, primary_key=True, autoincrement=True),
-    Column("manufacturing_event_id", BigInteger, ForeignKey("manufacturing_event.id"), nullable=False),
-    Column("equipment_id", BigInteger, ForeignKey("equipment.id"), nullable=False),
-    Column("measured_at", DateTime, nullable=False),
-    Column("freq_0_100_hz", Float),
-    Column("freq_101_200_hz", Float),
-    Column("freq_201_300_hz", Float),
-    Column("freq_301_400_hz", Float),
-    Column("freq_401_500_hz", Float),
-    Column("freq_501_600_hz", Float),
-    Column("freq_601_700_hz", Float),
-    Column("freq_701_800_hz", Float),
-    Column("freq_801_900_hz", Float),
-    Column("freq_901_1000_hz", Float),
-    Column("freq_1001_1100_hz", Float),
-    Column("freq_1101_1200_hz", Float),
-    Column("freq_1201_1300_hz", Float),
-    Column("freq_1301_1400_hz", Float),
-    Column("freq_1401_1500_hz", Float),
-    Column("freq_1501_1600_hz", Float),
-    Index("idx_robot_arm_vibration_event_id", "manufacturing_event_id"),
-    Index("idx_robot_arm_vibration_equipment_id", "equipment_id"),
-    Index("idx_robot_arm_vibration_measured_at", "measured_at"),
-)
diff --git a/app/repository/sampledb_schema_manager.py b/app/repository/sampledb_schema_manager.py
new file mode 100644
index 0000000..7670b95
--- /dev/null
+++ b/app/repository/sampledb_schema_manager.py
@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+from typing import Any
+
+from sqlalchemy import inspect, text
+
+from app.repository.sampledb_schema import metadata
+
+
+class SampleDbSchemaManager:
+    """sampledb 테이블 생성과 점진적 스키마 마이그레이션을 담당한다."""
+
+    def __init__(self, engine: Any) -> None:
+        self.engine = engine
+
+    def ensure_schema(self) -> None:
+        metadata.create_all(self.engine)
+        self._migrate_prd_columns()
+
+    def _migrate_prd_columns(self) -> None:
+        equipment_columns = {
+            "current_status": "VARCHAR(20) NOT NULL DEFAULT 'RUNNING'",
+            "health_status": "VARCHAR(20) NOT NULL DEFAULT 'NORMAL'",
+            "last_fault_time": "DATETIME NULL",
+            "last_recovered_time": "DATETIME NULL",
+            "reason": "VARCHAR(255) NULL",
+            "updated_at": (
+                "DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP"
+                if self.engine.dialect.name == "mysql"
+                else "DATETIME NULL"
+            ),
+        }
+        self._add_missing_columns("equipment", equipment_columns)
+        self._align_equipment_mysql_types()
+        self._align_manufacturing_event_json_mysql()
+        self._allow_car_master_created_at_null()
+        self._create_missing_indexes("equipment")
+        self._create_missing_indexes("manufacturing_event_json")
+
+    def _allow_car_master_created_at_null(self) -> None:
+        columns = inspect(self.engine).get_columns("car_master")
+        created_at = next(
+            (column for column in columns if column["name"] == "created_at"),
+            None,
+        )
+        if created_at is None or created_at["nullable"]:
+            return
+        if self.engine.dialect.name == "mysql":
+            with self.engine.begin() as conn:
+                conn.execute(
+                    text(
+                        "ALTER TABLE car_master "
+                        "MODIFY COLUMN created_at DATETIME NULL",
+                    ),
+                )
+
+    def _align_equipment_mysql_types(self) -> None:
+        if self.engine.dialect.name != "mysql":
+            return
+        equipment_types = {
+            column["name"]: column["type"].__class__.__name__.upper()
+            for column in inspect(self.engine).get_columns("equipment")
+        }
+        statements: list[str] = []
+        if equipment_types.get("current_status") != "ENUM":
+            statements.append(
+                "ALTER TABLE equipment MODIFY COLUMN current_status "
+                "ENUM('RUNNING','IDLE','STOPPED','FAULT','MAINTENANCE') "
+                "NOT NULL DEFAULT 'RUNNING'",
+            )
+        if equipment_types.get("health_status") != "ENUM":
+            statements.append(
+                "ALTER TABLE equipment MODIFY COLUMN health_status "
+                "ENUM('NORMAL','WARNING','CRITICAL') NOT NULL DEFAULT 'NORMAL'",
+            )
+        if statements:
+            with self.engine.begin() as conn:
+                for statement in statements:
+                    conn.execute(text(statement))
+
+    def _align_manufacturing_event_json_mysql(self) -> None:
+        if self.engine.dialect.name != "mysql":
+            return
+        with self.engine.begin() as conn:
+            conn.execute(
+                text(
+                    "ALTER TABLE manufacturing_event_json "
+                    "MODIFY COLUMN event_time DATETIME NULL, "
+                    "MODIFY COLUMN dispatch_status "
+                    "ENUM('PENDING','READY','SENT','BLOCKED') "
+                    "NOT NULL DEFAULT 'PENDING', "
+                    "MODIFY COLUMN analysis_status "
+                    "ENUM('NOT_ANALYZED','NORMAL','ABNORMAL') "
+                    "NOT NULL DEFAULT 'NOT_ANALYZED', "
+                    "MODIFY COLUMN updated_at DATETIME NOT NULL "
+                    "DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP",
+                ),
+            )
+
+    def _add_missing_columns(
+        self,
+        table_name: str,
+        definitions: dict[str, str],
+    ) -> None:
+        existing_columns = self._table_columns(table_name)
+        missing = [
+            (column_name, definition)
+            for column_name, definition in definitions.items()
+            if column_name not in existing_columns
+        ]
+        if not missing:
+            return
+        with self.engine.begin() as conn:
+            for column_name, definition in missing:
+                conn.execute(
+                    text(
+                        f"ALTER TABLE {table_name} "
+                        f"ADD COLUMN {column_name} {definition}",
+                    ),
+                )
+
+    def _table_columns(self, table_name: str) -> set[str]:
+        inspector = inspect(self.engine)
+        if not inspector.has_table(table_name):
+            return set(metadata.tables[table_name].c.keys())
+        return {
+            column["name"]
+            for column in inspector.get_columns(table_name)
+        }
+
+    def _create_missing_indexes(self, table_name: str) -> None:
+        table = metadata.tables[table_name]
+        existing_names = {
+            index["name"]
+            for index in inspect(self.engine).get_indexes(table_name)
+            if index.get("name")
+        }
+        for index in table.indexes:
+            if index.name and index.name not in existing_names:
+                index.create(self.engine, checkfirst=True)
diff --git a/app/scheduler/manufacturing/manufacturing_event_scheduler.py b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
index fc43f17..7436d96 100644
--- a/app/scheduler/manufacturing/manufacturing_event_scheduler.py
+++ b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
@@ -45,7 +45,6 @@ async def stop_manufacturing_event_scheduler(app: FastAPI) -> None:
 
 
 async def _run_daily_generation_loop() -> None:
-    await asyncio.to_thread(_ensure_default_template)
     while True:
         await asyncio.sleep(_seconds_until_next_run())
         try:
@@ -56,30 +55,6 @@ async def _run_daily_generation_loop() -> None:
             )
 
 
-def _ensure_default_template() -> dict[str, object] | None:
-    if not settings.sample_database_connection_url:
-        return None
-
-    service = ManufacturingEventJsonService(
-        SampleDbRepository(settings.sample_database_connection_url),
-    )
-    result = service.ensure_template(
-        template_name=DEFAULT_TEMPLATE_NAME,
-        event_count=settings.manufacturing_event_template_event_count,
-        car_pool_size=settings.manufacturing_event_car_pool_size,
-        insert_chunk_size=settings.manufacturing_event_insert_chunk_size,
-    )
-    logger.info(
-        "제조 이벤트 기본 템플릿 준비 완료: %s",
-        {
-            "templateName": result["templateName"],
-            "storedCount": result["storedCount"],
-            "created": result["created"],
-        },
-    )
-    return result
-
-
 def _materialize_tomorrow_events() -> dict[str, object] | None:
     if not settings.sample_database_connection_url:
         return None
@@ -89,7 +64,7 @@ def _materialize_tomorrow_events() -> dict[str, object] | None:
     )
     result = service.generate_tomorrow(
         template_name=DEFAULT_TEMPLATE_NAME,
-        events_per_day=settings.manufacturing_event_template_event_count,
+        events_per_day=settings.manufacturing_event_scheduler_events_per_day,
         car_pool_size=settings.manufacturing_event_car_pool_size,
         insert_chunk_size=settings.manufacturing_event_insert_chunk_size,
         update_existing=True,
diff --git a/app/service/manufacturing/manufacturing_event_json_service.py b/app/service/manufacturing/manufacturing_event_json_service.py
index d2419d9..a8985cb 100644
--- a/app/service/manufacturing/manufacturing_event_json_service.py
+++ b/app/service/manufacturing/manufacturing_event_json_service.py
@@ -13,14 +13,20 @@
 from app.core.config import settings
 from app.core.exceptions import AppException
 from app.data_generation.manufacturing_event_json_builder import (
+    ABNORMAL_RATIO,
     EventBuildRequest,
     ManufacturingEventJsonBuilder,
+    initial_dispatch_status,
+    is_abnormal_operation_status,
+    normalize_event_json,
+    validate_process_data,
 )
 from app.repository.sampledb_repository import SampleDbRepository
 
 
-DEFAULT_EVENTS_PER_DAY = 86_400
-DEFAULT_CAR_POOL_SIZE = 10_000
+# 실제 생성 수량은 vehicle_id의 생산일자에 해당하는 car_master 수로 결정한다.
+DEFAULT_EVENTS_PER_DAY: int | None = None
+DEFAULT_CAR_POOL_SIZE: int | None = None
 DEFAULT_INSERT_CHUNK_SIZE = 1_000
 DEFAULT_TEMPLATE_NAME = "default"
 TEMPLATE_ANCHOR_DATE = date(2000, 1, 1)
@@ -29,6 +35,7 @@
 JOB_TYPE_GENERATE_TOMORROW = "GENERATE_TOMORROW"
 JOB_TYPE_GENERATE_TEMPLATE = "GENERATE_TEMPLATE"
 _generation_job_executor = ThreadPoolExecutor(
+    # 대량 생성 job끼리 DB insert 부하가 겹치지 않도록 단일 worker로 직렬 처리한다.
     max_workers=1,
     thread_name_prefix="manufacturing-event-generation-job",
 )
@@ -51,71 +58,83 @@ def generate_range(
         *,
         start_date: date,
         end_date: date,
-        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        events_per_day: int | None = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int | None = DEFAULT_CAR_POOL_SIZE,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
         update_existing: bool = True,
         progress_callback: ProgressCallback | None = None,
     ) -> dict[str, Any]:
+        """생산일자별 car_master 차량 전체에 4공정 이벤트를 생성한다."""
         if start_date > end_date:
             raise AppException(
                 "start_date는 end_date보다 이후일 수 없습니다.",
                 status_code=status.HTTP_400_BAD_REQUEST,
             )
-        if events_per_day < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
-            raise AppException(
-                "events_per_day는 최소 4 이상이어야 공정별 이벤트를 생성할 수 있습니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
-        if car_pool_size < 1:
-            raise AppException(
-                "car_pool_size는 1 이상이어야 합니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
+        vehicle_limit = _resolve_vehicle_limit(events_per_day, car_pool_size)
         if insert_chunk_size < 1:
             raise AppException(
                 "insert_chunk_size는 1 이상이어야 합니다.",
                 status_code=status.HTTP_400_BAD_REQUEST,
             )
 
-        self.repository.ensure_schema()
-        self.repository.seed_equipment()
-        days = (end_date - start_date).days + 1
-        total_events = days * events_per_day
-        car_id_map = self.repository.ensure_car_master_rows(car_pool_size)
-        equipment_map = self.repository.get_equipment_map()
-
-        request = EventBuildRequest(
-            start_date=start_date,
-            end_date=end_date,
-            events_per_day=events_per_day,
-            car_id_map=car_id_map,
-            equipment_map=equipment_map,
+        # 생성 전에 PRD 스키마와 공정별 기본 설비 5개를 보장한다.
+        self.repository.schema.ensure_schema()
+        self.repository.equipment.seed_defaults()
+        equipment_map = self.repository.equipment.get_map()
+        production_dates = list(_date_range(start_date, end_date))
+        daily_car_maps = {
+            production_date: self.repository.cars.get_map_by_production_date(
+                production_date,
+                limit=vehicle_limit,
+            )
+            for production_date in production_dates
+        }
+        total_vehicle_count = sum(len(car_map) for car_map in daily_car_maps.values())
+        abnormal_vehicle_count = sum(
+            round(len(car_map) * ABNORMAL_RATIO)
+            for car_map in daily_car_maps.values()
         )
+        normal_vehicle_count = total_vehicle_count - abnormal_vehicle_count
+        total_events = total_vehicle_count * 4
         affected_rows = 0
         generated_count = 0
         distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        abnormal_distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        # 날짜별 차량 수가 달라도 공통 chunk 단위로 이어서 저장한다.
         chunk: list[dict[str, Any]] = []
-        for row in self.builder.iter_rows(request):
-            chunk.append(row)
-            generated_count += 1
-            distribution[str(row["process_code"])] += 1
-            if len(chunk) >= insert_chunk_size:
-                affected_rows += self.repository.insert_event_json_rows(
-                    chunk,
-                    update_existing=update_existing,
-                )
-                chunk = []
-                if progress_callback:
-                    progress_callback(
-                        {
-                            "generatedCount": generated_count,
-                            "affectedRows": affected_rows,
-                            "totalExpectedEvents": total_events,
-                        },
+        for production_date, car_id_map in daily_car_maps.items():
+            daily_event_count = len(car_id_map) * 4
+            request = EventBuildRequest(
+                start_date=production_date,
+                end_date=production_date,
+                events_per_day=daily_event_count,
+                car_id_map=car_id_map,
+                equipment_map=equipment_map,
+            )
+            for row in self.builder.iter_rows(request):
+                chunk.append(row)
+                generated_count += 1
+                distribution[str(row["process_code"])] += 1
+                if is_abnormal_operation_status(
+                    row["event_json"]["equipmentStatus"]["operationStatus"],
+                ):
+                    abnormal_distribution[str(row["process_code"])] += 1
+                if len(chunk) >= insert_chunk_size:
+                    affected_rows += self.repository.events.insert_rows(
+                        chunk,
+                        update_existing=update_existing,
                     )
+                    chunk = []
+                    if progress_callback:
+                        progress_callback(
+                            {
+                                "generatedCount": generated_count,
+                                "affectedRows": affected_rows,
+                                "totalExpectedEvents": total_events,
+                            },
+                        )
         if chunk:
-            affected_rows += self.repository.insert_event_json_rows(
+            affected_rows += self.repository.events.insert_rows(
                 chunk,
                 update_existing=update_existing,
             )
@@ -128,18 +147,27 @@ def generate_range(
                     },
                 )
 
-        stored_count = self.repository.count_event_json_between(start_date, end_date)
         return {
             "startDate": start_date.isoformat(),
             "endDate": end_date.isoformat(),
-            "eventsPerDay": events_per_day,
+            "requestedEventCountPerDay": events_per_day,
             "totalExpectedEvents": total_events,
             "generatedCount": generated_count,
             "affectedRows": affected_rows,
-            "storedCountInRange": stored_count,
-            "carPoolSize": len(car_id_map),
+            # event_time을 NULL로 저장하므로 현재 작업에서 생성한 건수를 반환한다.
+            "storedCountInRange": generated_count,
+            "carPoolSize": total_vehicle_count,
+            "normalVehicleCount": normal_vehicle_count,
+            "discardVehicleCount": abnormal_vehicle_count,
+            "dailyVehicleCounts": {
+                production_date.isoformat(): len(car_id_map)
+                for production_date, car_id_map in daily_car_maps.items()
+            },
             "insertChunkSize": insert_chunk_size,
             "processDistribution": distribution,
+            "abnormalDistribution": abnormal_distribution,
+            "normalEventCount": generated_count - sum(abnormal_distribution.values()),
+            "abnormalEventCount": sum(abnormal_distribution.values()),
         }
 
     def enqueue_generate_range_job(
@@ -147,25 +175,17 @@ def enqueue_generate_range_job(
         *,
         start_date: date,
         end_date: date,
-        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        events_per_day: int | None = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int | None = DEFAULT_CAR_POOL_SIZE,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
     ) -> dict[str, Any]:
+        """기간 생성 요청을 DB job으로 등록하고 background worker에 전달한다."""
         if start_date > end_date:
             raise AppException(
                 "start_date는 end_date보다 이후일 수 없습니다.",
                 status_code=status.HTTP_400_BAD_REQUEST,
             )
-        if events_per_day < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
-            raise AppException(
-                "events_per_day는 최소 4 이상이어야 공정별 이벤트를 생성할 수 있습니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
-        if car_pool_size < 1:
-            raise AppException(
-                "car_pool_size는 1 이상이어야 합니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
+        vehicle_limit = _resolve_vehicle_limit(events_per_day, car_pool_size)
         if insert_chunk_size < 1:
             raise AppException(
                 "insert_chunk_size는 1 이상이어야 합니다.",
@@ -175,11 +195,23 @@ def enqueue_generate_range_job(
         request_json = {
             "startDate": start_date.isoformat(),
             "endDate": end_date.isoformat(),
-            "eventsPerDay": events_per_day,
+            "eventCount": events_per_day,
             "carPoolSize": car_pool_size,
             "insertChunkSize": insert_chunk_size,
         }
-        total_expected_events = ((end_date - start_date).days + 1) * events_per_day
+        total_expected_events = sum(
+            _selected_vehicle_count(
+                self.repository.cars.count_by_production_date(production_date),
+                vehicle_limit,
+                production_date,
+            )
+            for production_date in _date_range(start_date, end_date)
+        ) * 4
+        if total_expected_events < 1:
+            raise AppException(
+                "요청 날짜에 해당하는 car_master 차량이 없습니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
         return self._create_and_submit_generation_job(
             job_type=JOB_TYPE_GENERATE_RANGE,
             request_json=request_json,
@@ -190,23 +222,16 @@ def generate_template(
         self,
         *,
         template_name: str = DEFAULT_TEMPLATE_NAME,
-        event_count: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        production_date: date = date(2026, 6, 1),
+        event_count: int | None = None,
+        car_pool_size: int | None = None,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
         replace: bool = False,
         update_existing: bool = False,
         progress_callback: ProgressCallback | None = None,
     ) -> dict[str, Any]:
-        if event_count < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
-            raise AppException(
-                "event_count는 최소 4 이상이어야 공정별 템플릿을 생성할 수 있습니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
-        if car_pool_size < 1:
-            raise AppException(
-                "car_pool_size는 1 이상이어야 합니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
+        """날짜를 제외한 하루 기준 제조 이벤트 패턴을 템플릿으로 저장한다."""
+        vehicle_limit = _resolve_vehicle_limit(event_count, car_pool_size)
         if insert_chunk_size < 1:
             raise AppException(
                 "insert_chunk_size는 1 이상이어야 합니다.",
@@ -214,17 +239,22 @@ def generate_template(
             )
 
         normalized_template_name = _normalize_template_name(template_name)
-        self.repository.ensure_schema()
-        self.repository.seed_equipment()
+        self.repository.schema.ensure_schema()
+        self.repository.equipment.seed_defaults()
         if replace:
-            self.repository.delete_template_events(normalized_template_name)
+            self.repository.templates.delete(normalized_template_name)
 
-        car_id_map = self.repository.ensure_car_master_rows(car_pool_size)
-        equipment_map = self.repository.get_equipment_map()
+        # 템플릿도 production_date와 vehicle_id가 일치하는 실제 차량 PK만 참조한다.
+        car_id_map = self.repository.cars.get_map_by_production_date(
+            production_date,
+            limit=vehicle_limit,
+        )
+        resolved_event_count = len(car_id_map) * 4
+        equipment_map = self.repository.equipment.get_map()
         request = EventBuildRequest(
             start_date=TEMPLATE_ANCHOR_DATE,
             end_date=TEMPLATE_ANCHOR_DATE,
-            events_per_day=event_count,
+            events_per_day=resolved_event_count,
             car_id_map=car_id_map,
             equipment_map=equipment_map,
         )
@@ -232,11 +262,18 @@ def generate_template(
         affected_rows = 0
         generated_count = 0
         distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        abnormal_distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
         chunk: list[dict[str, Any]] = []
+        # 실제 날짜 대신 고정 기준일과 하루 시작 기준 offset을 저장한다.
+        # 이후 어느 날짜에도 동일한 생산 패턴을 재사용할 수 있다.
         anchor_datetime = datetime.combine(TEMPLATE_ANCHOR_DATE, time.min)
         for row in self.builder.iter_rows(request):
             generated_count += 1
             distribution[str(row["process_code"])] += 1
+            if is_abnormal_operation_status(
+                row["event_json"]["equipmentStatus"]["operationStatus"],
+            ):
+                abnormal_distribution[str(row["process_code"])] += 1
             template_event_id = (
                 f"TMPL-{normalized_template_name.upper()}-{generated_count:06d}"
             )
@@ -262,7 +299,7 @@ def generate_template(
                 },
             )
             if len(chunk) >= insert_chunk_size:
-                affected_rows += self.repository.insert_template_event_rows(
+                affected_rows += self.repository.templates.insert_rows(
                     chunk,
                     update_existing=update_existing,
                 )
@@ -272,11 +309,11 @@ def generate_template(
                         {
                             "generatedCount": generated_count,
                             "affectedRows": affected_rows,
-                            "totalExpectedEvents": event_count,
+                            "totalExpectedEvents": resolved_event_count,
                         },
                     )
         if chunk:
-            affected_rows += self.repository.insert_template_event_rows(
+            affected_rows += self.repository.templates.insert_rows(
                 chunk,
                 update_existing=update_existing,
             )
@@ -285,20 +322,24 @@ def generate_template(
                     {
                         "generatedCount": generated_count,
                         "affectedRows": affected_rows,
-                        "totalExpectedEvents": event_count,
+                        "totalExpectedEvents": resolved_event_count,
                     },
                 )
 
-        stored_count = self.repository.count_template_events(normalized_template_name)
+        stored_count = self.repository.templates.count(normalized_template_name)
         return {
             "templateName": normalized_template_name,
-            "eventCount": event_count,
+            "productionDate": production_date.isoformat(),
+            "eventCount": resolved_event_count,
             "generatedCount": generated_count,
             "affectedRows": affected_rows,
             "storedCount": stored_count,
             "carPoolSize": len(car_id_map),
             "insertChunkSize": insert_chunk_size,
             "processDistribution": distribution,
+            "abnormalDistribution": abnormal_distribution,
+            "normalEventCount": generated_count - sum(abnormal_distribution.values()),
+            "abnormalEventCount": sum(abnormal_distribution.values()),
             "timeDistribution": [
                 {"range": "00:00-05:00", "density": "LOW"},
                 {"range": "05:00-08:00", "density": "MEDIUM"},
@@ -314,29 +355,30 @@ def enqueue_generate_template_job(
         self,
         *,
         template_name: str = DEFAULT_TEMPLATE_NAME,
-        event_count: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        production_date: date = date(2026, 6, 1),
+        event_count: int | None = None,
+        car_pool_size: int | None = None,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
         replace: bool = False,
     ) -> dict[str, Any]:
-        if event_count < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
-            raise AppException(
-                "event_count는 최소 4 이상이어야 공정별 템플릿을 생성할 수 있습니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
-        if car_pool_size < 1:
-            raise AppException(
-                "car_pool_size는 1 이상이어야 합니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
+        vehicle_limit = _resolve_vehicle_limit(event_count, car_pool_size)
         if insert_chunk_size < 1:
             raise AppException(
                 "insert_chunk_size는 1 이상이어야 합니다.",
                 status_code=status.HTTP_400_BAD_REQUEST,
             )
 
+        available_vehicle_count = self.repository.cars.count_by_production_date(
+            production_date,
+        )
+        selected_vehicle_count = _selected_vehicle_count(
+            available_vehicle_count,
+            vehicle_limit,
+            production_date,
+        )
         request_json = {
             "templateName": template_name,
+            "productionDate": production_date.isoformat(),
             "eventCount": event_count,
             "carPoolSize": car_pool_size,
             "insertChunkSize": insert_chunk_size,
@@ -345,7 +387,7 @@ def enqueue_generate_template_job(
         return self._create_and_submit_generation_job(
             job_type=JOB_TYPE_GENERATE_TEMPLATE,
             request_json=request_json,
-            total_expected_events=event_count,
+            total_expected_events=selected_vehicle_count * 4,
         )
 
     def list_template_events(
@@ -356,7 +398,7 @@ def list_template_events(
         offset: int = 0,
         process_code: str | None = None,
     ) -> list[dict[str, Any]]:
-        return self.repository.list_template_event_rows(
+        return self.repository.templates.list_rows(
             template_name=_normalize_template_name(template_name),
             limit=limit,
             offset=offset,
@@ -392,6 +434,7 @@ def materialize_template_events(
         update_existing: bool = True,
         progress_callback: ProgressCallback | None = None,
     ) -> dict[str, Any]:
+        """저장된 템플릿에 목표 날짜와 결정적 변동을 입혀 실제 이벤트로 적재한다."""
         if insert_chunk_size < 1:
             raise AppException(
                 "insert_chunk_size는 1 이상이어야 합니다.",
@@ -399,8 +442,8 @@ def materialize_template_events(
             )
 
         normalized_template_name = _normalize_template_name(template_name)
-        self.repository.ensure_schema()
-        total_template_events = self.repository.count_template_events(
+        self.repository.schema.ensure_schema()
+        total_template_events = self.repository.templates.count(
             normalized_template_name,
         )
         if total_template_events < 1:
@@ -412,9 +455,11 @@ def materialize_template_events(
         affected_rows = 0
         generated_count = 0
         distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        abnormal_distribution = {"PRESS": 0, "BODY": 0, "PAINT": 0, "ASSEMBLY": 0}
+        # 템플릿 역시 페이지 단위로 읽어 대량 materialize 시 메모리 사용량을 제한한다.
         offset = 0
         while offset < total_template_events:
-            template_rows = self.repository.list_template_event_rows(
+            template_rows = self.repository.templates.list_rows(
                 template_name=normalized_template_name,
                 limit=insert_chunk_size,
                 offset=offset,
@@ -426,12 +471,16 @@ def materialize_template_events(
                 _materialize_template_row(row, target_date=target_date)
                 for row in template_rows
             ]
-            affected_rows += self.repository.insert_event_json_rows(
+            affected_rows += self.repository.events.insert_rows(
                 materialized_rows,
                 update_existing=update_existing,
             )
             for row in materialized_rows:
                 distribution[str(row["process_code"])] += 1
+                if is_abnormal_operation_status(
+                    row["event_json"]["equipmentStatus"]["operationStatus"],
+                ):
+                    abnormal_distribution[str(row["process_code"])] += 1
 
             generated_count += len(materialized_rows)
             offset += len(template_rows)
@@ -444,20 +493,20 @@ def materialize_template_events(
                     },
                 )
 
-        stored_count = self.repository.count_event_json_between(
-            target_date,
-            target_date,
-        )
         return {
             "templateName": normalized_template_name,
             "targetDate": target_date.isoformat(),
             "templateEventCount": total_template_events,
             "generatedCount": generated_count,
             "affectedRows": affected_rows,
-            "storedCountInDate": stored_count,
+            # event_time을 NULL로 저장하므로 현재 materialize한 건수를 반환한다.
+            "storedCountInDate": generated_count,
             "insertChunkSize": insert_chunk_size,
             "updateExisting": update_existing,
             "processDistribution": distribution,
+            "abnormalDistribution": abnormal_distribution,
+            "normalEventCount": generated_count - sum(abnormal_distribution.values()),
+            "abnormalEventCount": sum(abnormal_distribution.values()),
             "variationMode": "target_date_and_template_event_id_seed",
         }
 
@@ -465,28 +514,42 @@ def ensure_template(
         self,
         *,
         template_name: str = DEFAULT_TEMPLATE_NAME,
-        event_count: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        production_date: date = date(2026, 6, 1),
+        event_count: int | None = None,
+        car_pool_size: int | None = None,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
     ) -> dict[str, Any]:
+        """요청 수량과 정확히 일치하는 템플릿이 없으면 전체를 다시 생성한다."""
         normalized_template_name = _normalize_template_name(template_name)
-        self.repository.ensure_schema()
-        stored_count = self.repository.count_template_events(normalized_template_name)
-        if stored_count >= event_count:
+        self.repository.schema.ensure_schema()
+        vehicle_limit = _resolve_vehicle_limit(event_count, car_pool_size)
+        available_vehicle_count = self.repository.cars.count_by_production_date(
+            production_date,
+        )
+        selected_vehicle_count = _selected_vehicle_count(
+            available_vehicle_count,
+            vehicle_limit,
+            production_date,
+        )
+        resolved_event_count = selected_vehicle_count * 4
+        stored_count = self.repository.templates.count(normalized_template_name)
+        # 차량당 4공정 보장이 중요하므로 "이상"이 아니라 정확히 같은 건수만 재사용한다.
+        if stored_count == resolved_event_count:
             return {
                 "templateName": normalized_template_name,
-                "eventCount": event_count,
+                "eventCount": resolved_event_count,
                 "storedCount": stored_count,
                 "created": False,
             }
 
         result = self.generate_template(
             template_name=normalized_template_name,
+            production_date=production_date,
             event_count=event_count,
             car_pool_size=car_pool_size,
             insert_chunk_size=insert_chunk_size,
-            replace=False,
-            update_existing=False,
+            replace=stored_count > 0,
+            update_existing=stored_count > 0,
         )
         return {
             **result,
@@ -496,8 +559,8 @@ def ensure_template(
     def generate_initial_demo_range(
         self,
         *,
-        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        events_per_day: int | None = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int | None = DEFAULT_CAR_POOL_SIZE,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
         update_existing: bool = True,
         progress_callback: ProgressCallback | None = None,
@@ -517,30 +580,29 @@ def generate_tomorrow(
         *,
         base_date: date | None = None,
         template_name: str = DEFAULT_TEMPLATE_NAME,
-        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        events_per_day: int | None = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int | None = DEFAULT_CAR_POOL_SIZE,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
         update_existing: bool = True,
         progress_callback: ProgressCallback | None = None,
     ) -> dict[str, Any]:
+        """기준일 다음날 vehicle_id 생산일자의 차량 전체로 이벤트를 생성한다."""
         target_date = (base_date or date.today()) + timedelta(days=1)
-        template_result = self.ensure_template(
-            template_name=template_name,
-            event_count=events_per_day,
+        result = self.generate_range(
+            start_date=target_date,
+            end_date=target_date,
+            events_per_day=events_per_day,
             car_pool_size=car_pool_size,
             insert_chunk_size=insert_chunk_size,
-        )
-        materialized_result = self.materialize_template_events(
-            template_name=template_name,
-            target_date=target_date,
-            insert_chunk_size=insert_chunk_size,
             update_existing=update_existing,
             progress_callback=progress_callback,
         )
         return {
-            **materialized_result,
+            **result,
             "baseDate": (base_date or date.today()).isoformat(),
-            "templatePrepared": template_result,
+            "targetDate": target_date.isoformat(),
+            "templateName": template_name,
+            "generationMode": "CAR_MASTER_PRODUCTION_DATE",
         }
 
     def enqueue_generate_tomorrow_job(
@@ -548,20 +610,11 @@ def enqueue_generate_tomorrow_job(
         *,
         base_date: date | None = None,
         template_name: str = DEFAULT_TEMPLATE_NAME,
-        events_per_day: int = DEFAULT_EVENTS_PER_DAY,
-        car_pool_size: int = DEFAULT_CAR_POOL_SIZE,
+        events_per_day: int | None = DEFAULT_EVENTS_PER_DAY,
+        car_pool_size: int | None = DEFAULT_CAR_POOL_SIZE,
         insert_chunk_size: int = DEFAULT_INSERT_CHUNK_SIZE,
     ) -> dict[str, Any]:
-        if events_per_day < len(("PRESS", "BODY", "PAINT", "ASSEMBLY")):
-            raise AppException(
-                "events_per_day는 최소 4 이상이어야 공정별 이벤트를 생성할 수 있습니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
-        if car_pool_size < 1:
-            raise AppException(
-                "car_pool_size는 1 이상이어야 합니다.",
-                status_code=status.HTTP_400_BAD_REQUEST,
-            )
+        vehicle_limit = _resolve_vehicle_limit(events_per_day, car_pool_size)
         if insert_chunk_size < 1:
             raise AppException(
                 "insert_chunk_size는 1 이상이어야 합니다.",
@@ -571,19 +624,28 @@ def enqueue_generate_tomorrow_job(
         request_json = {
             "baseDate": base_date.isoformat() if base_date else None,
             "templateName": template_name,
-            "eventsPerDay": events_per_day,
+            "eventCount": events_per_day,
             "carPoolSize": car_pool_size,
             "insertChunkSize": insert_chunk_size,
         }
+        target_date = (base_date or date.today()) + timedelta(days=1)
+        available_vehicle_count = self.repository.cars.count_by_production_date(
+            target_date,
+        )
+        selected_vehicle_count = _selected_vehicle_count(
+            available_vehicle_count,
+            vehicle_limit,
+            target_date,
+        )
         return self._create_and_submit_generation_job(
             job_type=JOB_TYPE_GENERATE_TOMORROW,
             request_json=request_json,
-            total_expected_events=events_per_day,
+            total_expected_events=selected_vehicle_count * 4,
         )
 
     def get_generation_job(self, job_id: str) -> dict[str, Any]:
-        self.repository.ensure_schema()
-        job = self.repository.get_generation_job(job_id)
+        self.repository.schema.ensure_schema()
+        job = self.repository.jobs.get(job_id)
         if not job:
             raise AppException(
                 "제조 이벤트 생성 job을 찾을 수 없습니다.",
@@ -598,9 +660,10 @@ def _create_and_submit_generation_job(
         request_json: dict[str, Any],
         total_expected_events: int,
     ) -> dict[str, Any]:
-        self.repository.ensure_schema()
+        """job 상태 row를 먼저 남긴 뒤 단일 background worker에 실행을 위임한다."""
+        self.repository.schema.ensure_schema()
         job_id = str(uuid4())
-        job = self.repository.create_generation_job(
+        job = self.repository.jobs.create(
             job_id=job_id,
             job_type=job_type,
             request_json=request_json,
@@ -623,7 +686,7 @@ def list_events(
         process_code: str | None = None,
         is_sent: bool | None = None,
     ) -> list[dict[str, Any]]:
-        return self.repository.list_event_json_rows(
+        return self.repository.events.list_rows(
             limit=limit,
             offset=offset,
             start_date=start_date,
@@ -644,32 +707,48 @@ def get_manufacturing_event_json_service() -> ManufacturingEventJsonService:
 
 
 def _run_generation_job(database_url: str, job_id: str) -> None:
+    """비동기 생성 job의 RUNNING/SUCCEEDED/FAILED 상태 전이를 관리한다."""
     repository = SampleDbRepository(database_url)
     service = ManufacturingEventJsonService(repository)
-    job = repository.get_generation_job(job_id)
-    if not job:
-        logger.warning("제조 이벤트 생성 job을 찾을 수 없습니다: job_id=%s", job_id)
-        return
-
-    try:
-        repository.mark_generation_job_running(job_id)
-        result = _execute_generation_job(service, repository, job)
-        repository.mark_generation_job_succeeded(job_id, result)
-        logger.info(
-            "제조 이벤트 생성 job 완료: job_id=%s job_type=%s generated=%s affected=%s",
-            job_id,
-            job["jobType"],
-            result.get("generatedCount"),
-            result.get("affectedRows"),
-        )
-    except Exception as exc:
-        message = getattr(exc, "message", str(exc))
-        repository.mark_generation_job_failed(job_id, message)
-        logger.exception(
-            "제조 이벤트 생성 job 실패: job_id=%s job_type=%s",
-            job_id,
-            job.get("jobType"),
-        )
+    # uvicorn --reload의 이전/신규 프로세스나 다중 worker가 같은 미완료 job을
+    # 동시에 복구하지 못하도록 DB advisory lock으로 생성 작업 전체를 직렬화한다.
+    with repository.jobs.execution_lock() as acquired:
+        if not acquired:
+            logger.error("제조 이벤트 생성 전역 잠금을 획득하지 못했습니다: job_id=%s", job_id)
+            return
+
+        # 잠금을 기다리는 동안 다른 프로세스가 완료했을 수 있으므로 상태를 다시 읽는다.
+        job = repository.jobs.get(job_id)
+        if not job:
+            logger.warning("제조 이벤트 생성 job을 찾을 수 없습니다: job_id=%s", job_id)
+            return
+        if job["status"] in {"SUCCEEDED", "FAILED"}:
+            logger.info(
+                "이미 종료된 제조 이벤트 생성 job을 건너뜁니다: job_id=%s status=%s",
+                job_id,
+                job["status"],
+            )
+            return
+
+        try:
+            repository.jobs.mark_running(job_id)
+            result = _execute_generation_job(service, repository, job)
+            repository.jobs.mark_succeeded(job_id, result)
+            logger.info(
+                "제조 이벤트 생성 job 완료: job_id=%s job_type=%s generated=%s affected=%s",
+                job_id,
+                job["jobType"],
+                result.get("generatedCount"),
+                result.get("affectedRows"),
+            )
+        except Exception as exc:
+            message = getattr(exc, "message", str(exc))
+            repository.jobs.mark_failed(job_id, message)
+            logger.exception(
+                "제조 이벤트 생성 job 실패: job_id=%s job_type=%s",
+                job_id,
+                job.get("jobType"),
+            )
 
 
 def _execute_generation_job(
@@ -677,8 +756,9 @@ def _execute_generation_job(
     repository: SampleDbRepository,
     job: dict[str, Any],
 ) -> dict[str, Any]:
+    """저장된 job 요청을 종류별 동기 서비스 메서드 호출로 변환한다."""
     request = job["request"]
-    progress_callback = lambda progress: repository.update_generation_job_progress(
+    progress_callback = lambda progress: repository.jobs.update_progress(
         job["jobId"],
         progress,
     )
@@ -687,8 +767,10 @@ def _execute_generation_job(
         return service.generate_range(
             start_date=date.fromisoformat(request["startDate"]),
             end_date=date.fromisoformat(request["endDate"]),
-            events_per_day=int(request["eventsPerDay"]),
-            car_pool_size=int(request["carPoolSize"]),
+            events_per_day=_optional_int(
+                request.get("eventCount", request.get("eventsPerDay")),
+            ),
+            car_pool_size=_optional_int(request.get("carPoolSize")),
             insert_chunk_size=int(request["insertChunkSize"]),
             update_existing=True,
             progress_callback=progress_callback,
@@ -697,8 +779,9 @@ def _execute_generation_job(
     if job["jobType"] == JOB_TYPE_GENERATE_TEMPLATE:
         return service.generate_template(
             template_name=str(request["templateName"]),
-            event_count=int(request["eventCount"]),
-            car_pool_size=int(request["carPoolSize"]),
+            production_date=date.fromisoformat(request["productionDate"]),
+            event_count=_optional_int(request.get("eventCount")),
+            car_pool_size=_optional_int(request.get("carPoolSize")),
             insert_chunk_size=int(request["insertChunkSize"]),
             replace=bool(request["replace"]),
             update_existing=bool(request["replace"]),
@@ -714,8 +797,10 @@ def _execute_generation_job(
         return service.generate_tomorrow(
             base_date=base_date,
             template_name=str(request["templateName"]),
-            events_per_day=int(request["eventsPerDay"]),
-            car_pool_size=int(request["carPoolSize"]),
+            events_per_day=_optional_int(
+                request.get("eventCount", request.get("eventsPerDay")),
+            ),
+            car_pool_size=_optional_int(request.get("carPoolSize")),
             insert_chunk_size=int(request["insertChunkSize"]),
             update_existing=True,
             progress_callback=progress_callback,
@@ -729,8 +814,8 @@ def _execute_generation_job(
 
 def resume_incomplete_generation_jobs(database_url: str) -> int:
     repository = SampleDbRepository(database_url)
-    repository.ensure_schema()
-    jobs = repository.list_resumable_generation_jobs()
+    repository.schema.ensure_schema()
+    jobs = repository.jobs.list_resumable()
     for job in jobs:
         _generation_job_executor.submit(
             _run_generation_job,
@@ -750,11 +835,74 @@ def _normalize_template_name(template_name: str) -> str:
     return normalized or DEFAULT_TEMPLATE_NAME
 
 
+def _resolve_vehicle_limit(
+    event_count: int | None,
+    car_pool_size: int | None,
+) -> int | None:
+    """선택적 수량 제한을 차량 수로 정규화한다."""
+    if event_count is not None:
+        if event_count < 4 or event_count % 4 != 0:
+            raise AppException(
+                "event_count는 차량당 4공정 기준으로 4의 배수여야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        event_vehicle_count = event_count // 4
+        if car_pool_size is not None and car_pool_size != event_vehicle_count:
+            raise AppException(
+                "event_count와 car_pool_size가 일치하지 않습니다: "
+                f"event_count/4={event_vehicle_count}, "
+                f"car_pool_size={car_pool_size}",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        return event_vehicle_count
+
+    if car_pool_size is not None:
+        if car_pool_size < 1:
+            raise AppException(
+                "car_pool_size는 1 이상이어야 합니다.",
+                status_code=status.HTTP_400_BAD_REQUEST,
+            )
+        return car_pool_size
+    return None
+
+
+def _selected_vehicle_count(
+    available_count: int,
+    vehicle_limit: int | None,
+    production_date: date,
+) -> int:
+    if available_count < 1:
+        raise AppException(
+            f"{production_date.isoformat()} 생산 차량이 car_master에 없습니다.",
+            status_code=status.HTTP_400_BAD_REQUEST,
+        )
+    if vehicle_limit is not None and available_count < vehicle_limit:
+        raise AppException(
+            "요청한 차량 수보다 해당 날짜의 car_master가 부족합니다: "
+            f"date={production_date.isoformat()}, required={vehicle_limit}, "
+            f"actual={available_count}",
+            status_code=status.HTTP_400_BAD_REQUEST,
+        )
+    return vehicle_limit if vehicle_limit is not None else available_count
+
+
+def _date_range(start_date: date, end_date: date):
+    current = start_date
+    while current <= end_date:
+        yield current
+        current += timedelta(days=1)
+
+
+def _optional_int(value: Any) -> int | None:
+    return int(value) if value is not None else None
+
+
 def _materialize_template_row(
     row: dict[str, Any],
     *,
     target_date: date,
 ) -> dict[str, Any]:
+    """템플릿 한 행을 목표 날짜의 manufacturing_event_json 행으로 변환한다."""
     event_time = datetime.combine(target_date, time.min) + timedelta(
         microseconds=int(row["event_offset_us"]),
     )
@@ -762,32 +910,38 @@ def _materialize_template_row(
     event_id = f"EVT-{target_date:%Y%m%d}-{sequence_no:06d}"
     event_json = copy.deepcopy(row["event_json"])
     event_json["event"]["eventId"] = event_id
-    event_json["event"]["eventTime"] = event_time.isoformat()
-    event_json["equipmentStatus"]["statusChangedTime"] = event_time.isoformat()
-    event_json["equipmentStatus"]["lastNormalTime"] = (
-        event_time - timedelta(seconds=31)
-    ).isoformat()
+    event_json["event"]["eventTime"] = None
+    event_json["equipmentStatus"]["lastNormalTime"] = None
+    event_json["equipmentStatus"]["statusChangedTime"] = None
+    # 날짜와 template_event_id가 같으면 항상 같은 값이 나오도록 결정적으로 변동한다.
     _apply_date_variation(
         event_json,
         target_date=target_date,
         template_event_id=str(row["template_event_id"]),
     )
-    materialized_status = event_json["equipmentStatus"]["operationStatus"]
+    event_json = normalize_event_json(event_json)
+    # 날짜별 변동 후에도 해당 공정의 processData 블록 하나와 필수 필드를 보장한다.
+    validate_process_data(
+        str(row["process_code"]),
+        event_json.get("processData", {}),
+    )
     return {
         "event_id": event_id,
         "event_time": event_time,
-        "template_event_id": row["template_event_id"],
-        "template_name": row["template_name"],
-        "event_offset_us": row["event_offset_us"],
         "car_master_id": row["car_master_id"],
         "equipment_id": row["equipment_id"],
         "process_code": row["process_code"],
         "station_code": row["station_code"],
         "equipment_code": row["equipment_code"],
         "equipment_type": row["equipment_type"],
-        "equipment_status": materialized_status,
+        "equipment_status": event_json["equipmentStatus"]["operationStatus"],
         "event_type": row["event_type"],
         "event_json": event_json,
+        # 재생된 이벤트도 최초 공정 제어 규칙을 그대로 따른다.
+        "dispatch_status": initial_dispatch_status(str(row["process_code"])),
+        "analysis_status": "NOT_ANALYZED",
+        "retry_count": 0,
+        "error_message": None,
     }
 
 
@@ -797,11 +951,13 @@ def _apply_date_variation(
     target_date: date,
     template_event_id: str,
 ) -> None:
+    """정상/이상 클래스는 보존하면서 날짜별 센서·공정 수치에 작은 변화를 준다."""
     seed_key = f"{target_date.isoformat()}:{template_event_id}"
     sensor = event_json.get("sensor", {})
     process_metrics = event_json.get("processMetrics", {})
     process_data = event_json.get("processData", {})
 
+    # 각 센서 값을 먼저 바꾼 뒤 processData와 설비 상태를 다시 동기화한다.
     _vary_current(sensor.get("current", {}), seed_key)
     _vary_vibration(sensor.get("vibration", {}), seed_key)
     _vary_robot_arm_vibration(sensor.get("robotArmVibration", {}), seed_key)
@@ -995,6 +1151,7 @@ def _sync_process_data(
     metrics: dict[str, Any],
     seed_key: str,
 ) -> None:
+    """변동된 센서/공정 지표와 공정별 processData의 파생값을 일치시킨다."""
     if not process_data:
         return
 
@@ -1059,11 +1216,21 @@ def _sync_process_data(
             0,
             min(2, _as_int(assembly.get("missingPartCount")) + (1 if delta else 0)),
         )
-        assembly["actualSequence"] = (
-            "A01>A03>A02>A04"
-            if sequence_error_count
-            else "A01>A02>A03>A04"
-        )
+        expected_sequence = str(assembly.get("expectedSequence", ""))
+        expected_steps = expected_sequence.split(">")
+        if len(expected_steps) == 4:
+            assembly["actualSequence"] = (
+                ">".join(
+                    [
+                        expected_steps[0],
+                        expected_steps[2],
+                        expected_steps[1],
+                        expected_steps[3],
+                    ],
+                )
+                if sequence_error_count
+                else expected_sequence
+            )
 
 
 def _refresh_equipment_status(
@@ -1071,34 +1238,13 @@ def _refresh_equipment_status(
     sensor: dict[str, Any],
     metrics: dict[str, Any],
 ) -> None:
+    """날짜별 수치 변동 후에도 생성된 상태와 이상 분류를 그대로 유지한다."""
     equipment_status = event_json.get("equipmentStatus", {})
-    idle_time = _as_float(metrics.get("equipmentIdleTimeSec"))
-    station_delay = _as_float(metrics.get("stationDelaySec"))
-    vibration_score = max(
-        _as_float(sensor.get("vibration", {}).get("vibrationScore")),
-        _as_float(sensor.get("robotArmVibration", {}).get("vibrationScore")),
-    )
-    defect_score = _as_float(
-        event_json.get("processData", {}).get("paint", {}).get("defectScore"),
-    )
-
-    if idle_time >= 20.0:
-        operation_status = "IDLE"
-    elif station_delay >= 12.0 or vibration_score >= 0.82 or defect_score >= 0.75:
-        operation_status = "ERROR"
-    else:
-        operation_status = "RUNNING"
-
-    health_status = (
-        "WARNING"
-        if operation_status != "RUNNING"
-        or station_delay >= 8.0
-        or vibration_score >= 0.65
-        or defect_score >= 0.45
-        else "NORMAL"
-    )
-    equipment_status["operationStatus"] = operation_status
-    equipment_status["healthStatus"] = health_status
+    # 센서와 지표는 날짜별로 변동하지만 정상/이상 7:3 분포와 최초 랜덤
+    # operationStatus는 변경하지 않는다.
+    _ = sensor, metrics
+    equipment_status["lastNormalTime"] = None
+    equipment_status["statusChangedTime"] = None
 
 
 def _jitter_numeric(
@@ -1127,6 +1273,7 @@ def _factor(seed_key: str, field: str, pct: float) -> float:
 
 
 def _noise(seed_key: str, field: str, low: float, high: float) -> float:
+    """해시 기반 의사 난수로 같은 날짜·이벤트·필드에 같은 변동값을 반환한다."""
     digest = hashlib.blake2b(
         f"{seed_key}:{field}".encode("utf-8"),
         digest_size=8,

From bba26603ea8306991c765fdf10ed0166903d30d4 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Mon, 22 Jun 2026 17:00:52 +0900
Subject: [PATCH 014/148] fix : kafka error check

---
 app/kafka/config.py                           |  20 ++
 app/kafka/consumer.py                         |  36 ++++
 app/kafka/iam_provider.py                     |  11 +
 app/kafka/options.py                          |  21 ++
 app/kafka/topics.py                           |  21 ++
 .../quality_drive_detail_repository.py        | 200 ++++++++++--------
 6 files changed, 217 insertions(+), 92 deletions(-)
 create mode 100644 app/kafka/config.py
 create mode 100644 app/kafka/consumer.py
 create mode 100644 app/kafka/iam_provider.py
 create mode 100644 app/kafka/options.py
 create mode 100644 app/kafka/topics.py

diff --git a/app/kafka/config.py b/app/kafka/config.py
new file mode 100644
index 0000000..27f41a6
--- /dev/null
+++ b/app/kafka/config.py
@@ -0,0 +1,20 @@
+# kafka/config.py
+
+BOOTSTRAP_SERVERS = [
+
+    "127.0.0.2:9092",
+
+    "127.0.0.3:9092"
+]
+
+AUTO_OFFSET_RESET = "earliest"
+
+ENABLE_AUTO_COMMIT = False
+
+SESSION_TIMEOUT_MS = 30000
+
+HEARTBEAT_INTERVAL_MS = 10000
+
+MAX_POLL_RECORDS = 100
+
+MAX_POLL_INTERVAL_MS = 300000
\ No newline at end of file
diff --git a/app/kafka/consumer.py b/app/kafka/consumer.py
new file mode 100644
index 0000000..e322c38
--- /dev/null
+++ b/app/kafka/consumer.py
@@ -0,0 +1,36 @@
+# app/kafka/consumer.py
+
+import json
+
+from kafka import KafkaConsumer
+
+from app.kafka.iam_provider import MSKTokenProvider
+
+
+def create_consumer(topic, group_id):
+
+    consumer = KafkaConsumer(
+        topic,
+
+        bootstrap_servers=[
+            "127.0.0.2:9098",
+            "127.0.0.3:9098"
+        ],
+
+        group_id=group_id,
+
+        auto_offset_reset="earliest",
+
+        enable_auto_commit=True,
+
+        security_protocol="SASL_SSL",
+
+        sasl_mechanism="OAUTHBEARER",
+
+        sasl_oauth_token_provider=MSKTokenProvider(),
+
+        value_deserializer=lambda x:
+            json.loads(x.decode("utf-8"))
+    )
+
+    return consumer
\ No newline at end of file
diff --git a/app/kafka/iam_provider.py b/app/kafka/iam_provider.py
new file mode 100644
index 0000000..7187b59
--- /dev/null
+++ b/app/kafka/iam_provider.py
@@ -0,0 +1,11 @@
+from aws_msk_iam_sasl_signer import MSKAuthTokenProvider
+
+
+class MSKTokenProvider:
+
+    def token(self):
+        token, _ = MSKAuthTokenProvider.generate_auth_token(
+            region="ap-northeast-2"
+        )
+
+        return token
\ No newline at end of file
diff --git a/app/kafka/options.py b/app/kafka/options.py
new file mode 100644
index 0000000..8cd17eb
--- /dev/null
+++ b/app/kafka/options.py
@@ -0,0 +1,21 @@
+# kafka/options.py
+
+DRIVE_DETAIL_GROUP = (
+    "ai-drive-detail-consumer-group"
+)
+
+STATUS_DETAIL_GROUP = (
+    "ai-status-detail-consumer-group"
+)
+
+PROCESS_GROUP = (
+    "ai-process-consumer-group"
+)
+
+RISK_HISTORY_GROUP = (
+    "ai-risk-history-consumer-group"
+)
+
+RISK_TREND_GROUP = (
+    "ai-risk-trend-consumer-group"
+)
\ No newline at end of file
diff --git a/app/kafka/topics.py b/app/kafka/topics.py
new file mode 100644
index 0000000..1660260
--- /dev/null
+++ b/app/kafka/topics.py
@@ -0,0 +1,21 @@
+# app/kafka/topics.py
+
+QUALITY_INSPECTION_DRIVE_DETAIL = (
+    "quality.inspection.drive_detail"
+)
+
+QUALITY_INSPECTION_STATUS_DETAIL = (
+    "quality.inspection.status_detail"
+)
+
+QUALITY_INSPECTION_PROCESS = (
+    "quality.inspection.process"
+)
+
+QUALITY_INSPECTION_RISK_HISTORY = (
+    "quality.inspection.risk_history"
+)
+
+QUALITY_INSPECTION_RISK_TREND = (
+    "quality.inspection.risk_trend"
+)
\ No newline at end of file
diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 9b26318..6246829 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -1,27 +1,29 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import create_engine
 import pandas as pd
+import json
 
-SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+from app.kafka.consumer import create_consumer
+from app.kafka.topics import (
+    QUALITY_INSPECTION_DRIVE_DETAIL
 )
 
 MAIN_DATABASE_URL = (
     "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
 )
 
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
-)
-
 main_engine = create_engine(
     MAIN_DATABASE_URL,
     pool_pre_ping=True
 )
 
-def calculate_drive_score(row):
+consumer = create_consumer(
+    topic=QUALITY_INSPECTION_DRIVE_DETAIL,
+    group_id="ai-drive-detail-group"
+)
 
 
+def calculate_drive_score(row):
+
     score = 100
 
     if float(row["throttle_position"]) > 90:
@@ -38,7 +40,6 @@ def calculate_drive_score(row):
 
 def get_driving_pattern(row):
 
-
     throttle = float(row["throttle_position"])
     brake = float(row["brake_pressure"])
     steering = abs(float(row["steering_angle"]))
@@ -55,87 +56,102 @@ def get_driving_pattern(row):
     return "NORMAL"
 
 
-inspection_drive_detail_list = []
-
-with sample_engine.connect() as conn:
-
-
-    drive_rows = conn.execute(
-        text("""
-            SELECT *
-            FROM car_drive
-            ORDER BY created_at, vehicle_id
-        """)
-).mappings().all()
-
 detail_id = 1
+inspection_drive_detail_list = []
 
-for row in drive_rows:
-
-    vehicle_id = row["vehicle_id"]
-
-    car_code = vehicle_id.split("-")[0]
-
-    inspection_no = (
-        f"DRIVE-{detail_id:05d}"
-    )
-
-    drive_score = calculate_drive_score(row)
-
-    driving_pattern = get_driving_pattern(row)
-
-    if drive_score >= 80:
-        inspection_result = "NORMAL"
-        issue_message = "NORMAL"
-    else:
-        inspection_result = "WARNING"
-        issue_message = "ACCEL_ALERT"
-
-    inspection_drive_detail_list.append({
-
-        "id": detail_id,
-        "car_code": car_code,
-        "inspection_no": inspection_no,
-        "vehicle_id": vehicle_id,
-
-        "throttle_position":
-            round(float(row["throttle_position"]), 2),
-
-        "brake_pressure":
-            round(float(row["brake_pressure"]), 2),
-
-        "steering_angle":
-            round(float(row["steering_angle"]), 2),
-
-        "drive_score": drive_score,
-
-        "inspection_result":
-            inspection_result,
-
-        "driving_pattern":
-            driving_pattern,
-
-        "issue_message":
-            issue_message,
-
-        "created_at":
-            row["created_at"]
-    })
-
-    detail_id += 1
-
-
-df = pd.DataFrame(
-    inspection_drive_detail_list
-)
-
-df.to_sql(
-    name="inspection_drive_detail",
-    con=main_engine,
-    if_exists="append",
-    index=False
-)
-
-print(
-    f"drive_detail table 전송 완료"
-)
\ No newline at end of file
+try:
+    while True:
+
+        msg = consumer.poll(1.0)
+
+        if msg is None:
+            continue
+
+        if msg.error():
+            print(f"Kafka Error : {msg.error()}")
+            continue
+
+        row = json.loads(
+            msg.value().decode("utf-8")
+        )
+
+        vehicle_id = row["vehicle_id"]
+        car_code = vehicle_id.split("-")[0]
+
+        inspection_no = f"DRIVE-{detail_id:05d}"
+
+        drive_score = calculate_drive_score(row)
+        driving_pattern = get_driving_pattern(row)
+
+        if drive_score >= 80:
+            inspection_result = "NORMAL"
+            issue_message = "NORMAL"
+        else:
+            inspection_result = "WARNING"
+            issue_message = "ACCEL_ALERT"
+
+        inspection_drive_detail_list.append({
+            "id": detail_id,
+            "car_code": car_code,
+            "inspection_no": inspection_no,
+            "vehicle_id": vehicle_id,
+            "throttle_position": round(
+                float(row["throttle_position"]), 2
+            ),
+            "brake_pressure": round(
+                float(row["brake_pressure"]), 2
+            ),
+            "steering_angle": round(
+                float(row["steering_angle"]), 2
+            ),
+            "drive_score": drive_score,
+            "inspection_result": inspection_result,
+            "driving_pattern": driving_pattern,
+            "issue_message": issue_message,
+            "created_at": row["created_at"]
+        })
+
+        if len(inspection_drive_detail_list) >= 100:
+
+            df = pd.DataFrame(
+                inspection_drive_detail_list
+            )
+
+            df.to_sql(
+                name="inspection_drive_detail",
+                con=main_engine,
+                if_exists="append",
+                index=False
+            )
+
+            print(
+                f"{len(inspection_drive_detail_list)}건 저장 완료"
+            )
+
+            inspection_drive_detail_list.clear()
+
+        detail_id += 1
+
+except Exception as e:
+    print(f"오류 발생 : {e}")
+
+finally:
+
+    if inspection_drive_detail_list:
+
+        df = pd.DataFrame(
+            inspection_drive_detail_list
+        )
+
+        df.to_sql(
+            name="inspection_drive_detail",
+            con=main_engine,
+            if_exists="append",
+            index=False
+        )
+
+        print(
+            f"{len(inspection_drive_detail_list)}건 최종 저장 완료"
+        )
+
+    consumer.close()
\ No newline at end of file

From 3943feb0d3b64f2af1ed9297cec8c93e54812a2e Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Mon, 22 Jun 2026 17:07:15 +0900
Subject: [PATCH 015/148] feat : requirements.txt update_#kafka

---
 requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index e7ae5bc..5185d7d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -15,6 +15,8 @@ openai>=1.59.0,<2.0.0
 # Kafka
 aiokafka>=0.12.0
 confluent-kafka>=2.6.0
+aws-msk-iam-sasl-signer-python>=1.0.2
+kafka-python>=3.0.2
 
 # Redis Cache
 redis>=5.2.0,<6.0.0

From 2c23adca707243095e498a476a1924f80e0b1b0f Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 22 Jun 2026 17:12:12 +0900
Subject: [PATCH 016/148] =?UTF-8?q?chore:=20requirements.txt=20=EB=B3=80?=
 =?UTF-8?q?=EA=B2=BD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/kafka/iam_provider.py | 10 ++++++----
 requirements.txt          |  7 +++++--
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/app/kafka/iam_provider.py b/app/kafka/iam_provider.py
index 7187b59..a0bdc5d 100644
--- a/app/kafka/iam_provider.py
+++ b/app/kafka/iam_provider.py
@@ -1,11 +1,13 @@
 from aws_msk_iam_sasl_signer import MSKAuthTokenProvider
+from kafka.sasl.oauth import AbstractTokenProvider
 
 
-class MSKTokenProvider:
+class MSKTokenProvider(AbstractTokenProvider):
+    """kafka-python OAUTHBEARER 인증에 사용할 AWS MSK IAM 토큰 제공자."""
 
-    def token(self):
+    def token(self) -> str:
         token, _ = MSKAuthTokenProvider.generate_auth_token(
-            region="ap-northeast-2"
+            region="ap-northeast-2",
         )
 
-        return token
\ No newline at end of file
+        return token
diff --git a/requirements.txt b/requirements.txt
index 0425497..eaae258 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,8 +13,11 @@ httpx>=0.28.0
 openai>=1.59.0,<2.0.0
 
 # Kafka
-aiokafka>=0.12.0
-confluent-kafka>=2.6.0
+kafka-python>=2.3.0,<3.0.0
+aws-msk-iam-sasl-signer-python>=1.0.2,<2.0.0
+boto3>=1.26.125,<2.0.0
+botocore>=1.29.125,<2.0.0
+click>=7.0,<9.0
 
 # Redis Cache
 redis>=5.2.0,<6.0.0

From 85063d81c8c18c6a1a3fa7e6f5ea61c590a673d5 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-10-10-6-96.ap-northeast-2.compute.internal>
Date: Fri, 26 Jun 2026 00:10:44 +0000
Subject: [PATCH 017/148] feat : quality producer add

---
 .python-version                               |  1 +
 app/kafka/consumer.py                         | 14 ++--
 app/kafka/iam_provider.py                     |  6 +-
 app/kafka/test_producer.py                    | 78 +++++++++++++++++++
 .../quality_drive_detail_repository.py        | 23 ++----
 5 files changed, 94 insertions(+), 28 deletions(-)
 create mode 100644 .python-version
 create mode 100644 app/kafka/test_producer.py

diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..c70edfa
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.11.13
diff --git a/app/kafka/consumer.py b/app/kafka/consumer.py
index e322c38..893e29d 100644
--- a/app/kafka/consumer.py
+++ b/app/kafka/consumer.py
@@ -1,20 +1,19 @@
 # app/kafka/consumer.py
 
 import json
-
+import ssl
 from kafka import KafkaConsumer
-
 from app.kafka.iam_provider import MSKTokenProvider
 
-
 def create_consumer(topic, group_id):
 
     consumer = KafkaConsumer(
         topic,
+        ssl_context=ssl.create_default_context(),
 
         bootstrap_servers=[
-            "127.0.0.2:9098",
-            "127.0.0.3:9098"
+            "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
+            "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
         ],
 
         group_id=group_id,
@@ -29,8 +28,7 @@ def create_consumer(topic, group_id):
 
         sasl_oauth_token_provider=MSKTokenProvider(),
 
-        value_deserializer=lambda x:
-            json.loads(x.decode("utf-8"))
+        value_deserializer=lambda x: json.loads(x.decode("utf-8"))
     )
 
-    return consumer
\ No newline at end of file
+    return consumer
diff --git a/app/kafka/iam_provider.py b/app/kafka/iam_provider.py
index 7187b59..1a9e012 100644
--- a/app/kafka/iam_provider.py
+++ b/app/kafka/iam_provider.py
@@ -1,11 +1,11 @@
+from kafka.sasl.oauth import AbstractTokenProvider
 from aws_msk_iam_sasl_signer import MSKAuthTokenProvider
 
-
-class MSKTokenProvider:
+class MSKTokenProvider(AbstractTokenProvider):
 
     def token(self):
         token, _ = MSKAuthTokenProvider.generate_auth_token(
             region="ap-northeast-2"
         )
 
-        return token
\ No newline at end of file
+        return token
diff --git a/app/kafka/test_producer.py b/app/kafka/test_producer.py
new file mode 100644
index 0000000..8c51b59
--- /dev/null
+++ b/app/kafka/test_producer.py
@@ -0,0 +1,78 @@
+from sqlalchemy import create_engine, text
+from kafka import KafkaProducer
+
+import json
+import time
+
+from app.kafka.iam_provider import MSKTokenProvider
+
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+producer = KafkaProducer(
+    bootstrap_servers=[
+        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
+        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+    ],
+
+    security_protocol="SASL_SSL",
+
+    sasl_mechanism="OAUTHBEARER",
+
+    sasl_oauth_token_provider=MSKTokenProvider(),
+
+    value_serializer=lambda x:
+        json.dumps(x, default=str).encode("utf-8")
+)
+
+
+with sample_engine.connect() as conn:
+    drive_rows = conn.execute(
+        text("""
+            SELECT *
+            FROM car_drive
+            ORDER BY created_at, vehicle_id
+        """)
+    ).mappings().all()
+
+
+for row in drive_rows:
+
+    message = {
+        "vehicle_id": row["vehicle_id"],
+
+        "throttle_position": round(
+            float(row["throttle_position"]), 2
+        ),
+
+        "brake_pressure": round(
+            float(row["brake_pressure"]), 2
+        ),
+
+        "steering_angle": round(
+            float(row["steering_angle"]), 2
+        ),
+
+        "created_at": row["created_at"]
+    }
+
+    producer.send(
+        "quality.inspection.drive_detail",
+        value=message
+    )
+
+    print(f"Kafka 전송 완료 : {message}")
+
+    # 실시간처럼 보내고 싶으면 사용
+    time.sleep(1)
+
+
+producer.flush()
+
+print("모든 데이터 Kafka 전송 완료")
diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 6246829..1d41eaf 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -8,7 +8,7 @@
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
 )
 
 main_engine = create_engine(
@@ -60,21 +60,10 @@ def get_driving_pattern(row):
 inspection_drive_detail_list = []
 
 try:
-    while True:
-
-        msg = consumer.poll(1.0)
-
-        if msg is None:
-            continue
-
-        if msg.error():
-            print(f"Kafka Error : {msg.error()}")
-            continue
-
-        row = json.loads(
-            msg.value().decode("utf-8")
-        )
-
+    print("Consumer 시작")
+    print("구독 토픽", consumer.subscription())
+    for msg in consumer:
+        row = msg.value
         vehicle_id = row["vehicle_id"]
         car_code = vehicle_id.split("-")[0]
 
@@ -154,4 +143,4 @@ def get_driving_pattern(row):
             f"{len(inspection_drive_detail_list)}건 최종 저장 완료"
         )
 
-    consumer.close()
\ No newline at end of file
+    consumer.close()

From 2101e3c1000d27029d4f5a742e9536134fa1a668 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 09:31:36 +0900
Subject: [PATCH 018/148] fix : RDS password update, producer PATH update

---
 app/repository/quality_drive_detail_repository.py  | 2 +-
 app/repository/quality_process_repository.py       | 4 ++--
 app/repository/quality_risk_history_repository.py  | 4 ++--
 app/repository/quality_risk_trend_repository.py    | 4 ++--
 app/repository/quality_status_detail_repository.py | 4 ++--
 app/repository/quality_summary_repository.py       | 4 ++--
 app/{kafka => scheduler/quality}/test_producer.py  | 2 +-
 7 files changed, 12 insertions(+), 12 deletions(-)
 rename app/{kafka => scheduler/quality}/test_producer.py (96%)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 1d41eaf..03b77ad 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -8,7 +8,7 @@
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
 )
 
 main_engine = create_engine(
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index c3826a7..445f11c 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -4,11 +4,11 @@
 import random
 
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
 )
 
 sample_engine = create_engine(
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 8b67692..8cb0bd7 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -3,11 +3,11 @@
 from datetime import datetime, timedelta
 
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
 )
 
 sample_engine = create_engine(
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 9d08ef9..40b5773 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -3,11 +3,11 @@
 from datetime import datetime, timedelta
 
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
 )
 
 sample_engine = create_engine(
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 8dde807..76ef06a 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -3,11 +3,11 @@
 import pandas as pd
 
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
 )
 
 sample_engine = create_engine(
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index 80a0835..b9c4900 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -3,11 +3,11 @@
 from datetime import datetime, timedelta
 
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/sampledb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@127.0.0.1:13306/maindb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
 )
 
 sample_engine = create_engine(
diff --git a/app/kafka/test_producer.py b/app/scheduler/quality/test_producer.py
similarity index 96%
rename from app/kafka/test_producer.py
rename to app/scheduler/quality/test_producer.py
index 8c51b59..9323d60 100644
--- a/app/kafka/test_producer.py
+++ b/app/scheduler/quality/test_producer.py
@@ -7,7 +7,7 @@
 from app.kafka.iam_provider import MSKTokenProvider
 
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:K.d?S|46~($$z~.J2W)~W!aMEG)-@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
 )
 
 sample_engine = create_engine(

From 2cf2a3ffb70a7c9b8be149095b5d7850351825df Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 09:38:57 +0900
Subject: [PATCH 019/148] feat : quality scheduler _init_.py add

---
 app/scheduler/quality/__init__.py | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 app/scheduler/quality/__init__.py

diff --git a/app/scheduler/quality/__init__.py b/app/scheduler/quality/__init__.py
new file mode 100644
index 0000000..95732bb
--- /dev/null
+++ b/app/scheduler/quality/__init__.py
@@ -0,0 +1 @@
+"""quality schedulers."""
\ No newline at end of file

From b08b4e53b56c4a00469252ff16b2f2ea91e10f0a Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 09:45:14 +0900
Subject: [PATCH 020/148] fix : RDS Path url update

---
 app/repository/quality_drive_detail_repository.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 03b77ad..967a18b 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -8,7 +8,7 @@
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
 )
 
 main_engine = create_engine(

From ab29731176fcf13bd006559d8db662b94c6adb6d Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 09:48:33 +0900
Subject: [PATCH 021/148] feat : producer msg check update

---
 app/repository/quality_drive_detail_repository.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 967a18b..ec99441 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -64,6 +64,7 @@ def get_driving_pattern(row):
     print("구독 토픽", consumer.subscription())
     for msg in consumer:
         row = msg.value
+        print(f"Message : {json.dumps(row, ensure_ascii=False)}")
         vehicle_id = row["vehicle_id"]
         car_code = vehicle_id.split("-")[0]
 

From 08aeb476673f2522905804f17a31d54b8f99ba50 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 10:09:54 +0900
Subject: [PATCH 022/148] feat : quality process kafka add

---
 app/repository/quality_process_repository.py  | 158 ++++++++----------
 ...t_producer.py => drive_detail_producer.py} |   0
 app/scheduler/quality/process_producer.py     |  65 +++++++
 .../quality/risk_history_producer.py          |   0
 app/scheduler/quality/risk_trend_producer.py  |   0
 .../quality/status_detail_producer.py         |   0
 6 files changed, 133 insertions(+), 90 deletions(-)
 rename app/scheduler/quality/{test_producer.py => drive_detail_producer.py} (100%)
 create mode 100644 app/scheduler/quality/process_producer.py
 create mode 100644 app/scheduler/quality/risk_history_producer.py
 create mode 100644 app/scheduler/quality/risk_trend_producer.py
 create mode 100644 app/scheduler/quality/status_detail_producer.py

diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 445f11c..c992518 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -1,19 +1,12 @@
-from sqlalchemy import create_engine, text
-from datetime import datetime
+from sqlalchemy import create_engine
 import pandas as pd
 import random
 
-SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
-)
+from app.kafka.consumer import create_consumer
+from app.kafka.topics import QUALITY_INSPECTION_PROCESS
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
-)
-
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
 )
 
 main_engine = create_engine(
@@ -21,7 +14,10 @@
     pool_pre_ping=True
 )
 
-inspection_process_list = []
+consumer = create_consumer(
+    topic=QUALITY_INSPECTION_PROCESS,
+    group_id="ai-process-group"
+)
 
 process_names = [
     "Visual",
@@ -30,100 +26,82 @@
     "Final"
 ]
 
-with sample_engine.connect() as conn:
+process_id = 1
+inspection_process_list = []
 
-    total_vehicle_count = conn.execute(
-        text("""
-            SELECT COUNT(*)
-            FROM car_master
-        """)
-    ).scalar()
+try:
+    print("Consumer 시작")
+    print("구독 토픽", consumer.subscription())
+    for msg in consumer:
 
-    date_rows = conn.execute(
-        text("""
-            SELECT
-                DATE(created_at) AS process_date,
-                COUNT(*) AS vehicle_count
-            FROM car_master
-            GROUP BY DATE(created_at)
-            ORDER BY process_date
-        """)
-    ).mappings().all()
+        row = msg.value
 
-    process_id = 1
+        print(f"Kafka 수신 : {row}")
 
-for date_row in date_rows:
+        process_date = row["process_date"]
+        total_vehicle_count = row["vehicle_count"]
 
-    process_date = date_row["process_date"]
+        for process_name in process_names:
 
-    total_vehicle_count = date_row["vehicle_count"]
+            completed_count = random.randint(
+                int(total_vehicle_count * 0.5),
+                total_vehicle_count
+            )
 
-    for process_name in process_names:
+            waiting_count = (
+                total_vehicle_count
+                - completed_count
+            )
 
-        completed_count = random.randint(
-            int(total_vehicle_count * 0.5),
-            total_vehicle_count
-        )
+            progress_rate = round(
+                completed_count
+                / total_vehicle_count
+                * 100,
+                0
+            )
 
-        waiting_count = (
-            total_vehicle_count
-            - completed_count
-        )
+            if progress_rate >= 90:
+                process_status = "COMPLETE"
 
-        progress_rate = round(
-            completed_count
-            / total_vehicle_count
-            * 100,
-            0
-        )
+            elif progress_rate >= 70:
+                process_status = "RUNNING"
 
-        if progress_rate >= 90:
-            process_status = "COMPLETE"
+            else:
+                process_status = "WAIT"
 
-        elif progress_rate >= 70:
-            process_status = "RUNNING"
+            inspection_process_list.append({
+                "id": process_id,
+                "process_name": process_name,
+                "total_vehicle_count": total_vehicle_count,
+                "completed_count": completed_count,
+                "waiting_count": waiting_count,
+                "progress_rate": progress_rate,
+                "process_status": process_status,
+                "created_at": process_date
+            })
 
-        else:
-            process_status = "WAIT"
+            process_id += 1
 
-        inspection_process = {
-            "id": process_id,
-            "process_name": process_name,
-            "total_vehicle_count": total_vehicle_count,
-            "completed_count": completed_count,
-            "waiting_count": waiting_count,
-            "progress_rate": progress_rate,
-            "process_status": process_status,
-            "created_at": process_date
-        }
+        # 날짜 하나당 4개 공정 생성
+        df = pd.DataFrame(
+            inspection_process_list
+        )
 
-        inspection_process_list.append(
-            inspection_process
+        df.to_sql(
+            name="inspection_process",
+            con=main_engine,
+            if_exists="append",
+            index=False
         )
 
-        process_id += 1
-
-df = pd.DataFrame(
-    inspection_process_list,
-    columns=[
-        "id",
-        "process_name",
-        "total_vehicle_count",
-        "completed_count",
-        "waiting_count",
-        "progress_rate",
-        "process_status",
-        "created_at"
-    ]
-)
+        print(
+            f"{len(inspection_process_list)}건 저장 완료"
+        )
 
-df.to_sql(
-    name="inspection_process",
-    con=main_engine,
-    if_exists="append",
-    index=False
-)
+        inspection_process_list.clear()
+
+except Exception as e:
+    print(f"오류 발생 : {e}")
 
-print(
-    f"inspection_process table 전송 완료"
-)
\ No newline at end of file
+finally:
+    consumer.close()
\ No newline at end of file
diff --git a/app/scheduler/quality/test_producer.py b/app/scheduler/quality/drive_detail_producer.py
similarity index 100%
rename from app/scheduler/quality/test_producer.py
rename to app/scheduler/quality/drive_detail_producer.py
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
new file mode 100644
index 0000000..5a6a97a
--- /dev/null
+++ b/app/scheduler/quality/process_producer.py
@@ -0,0 +1,65 @@
+from sqlalchemy import create_engine, text
+from kafka import KafkaProducer
+
+import json
+import time
+
+from app.kafka.iam_provider import MSKTokenProvider
+
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+producer = KafkaProducer(
+    bootstrap_servers=[
+        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
+        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+    ],
+
+    security_protocol="SASL_SSL",
+
+    sasl_mechanism="OAUTHBEARER",
+
+    sasl_oauth_token_provider=MSKTokenProvider(),
+
+    value_serializer=lambda x:
+        json.dumps(x, default=str).encode("utf-8")
+)
+
+
+with sample_engine.connect() as conn:
+    process_rows = conn.execute(
+        text("""
+            SELECT
+                DATE(created_at) AS process_date,
+                COUNT(*) AS vehicle_count
+            FROM car_master
+            GROUP BY DATE(created_at)
+            ORDER BY process_date
+        """)
+    ).mappings().all()
+
+
+for row in process_rows:
+
+    message = {
+        "process_date": str(row["process_date"]),
+        "vehicle_count": row["vehicle_count"]
+    }
+
+    producer.send(
+        "quality.inspection.process",
+        value=message
+    )
+
+    print(f"Kafka 전송 완료 : {message}")
+
+    time.sleep(1)
+
+producer.flush()
+print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
new file mode 100644
index 0000000..e69de29
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
new file mode 100644
index 0000000..e69de29
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
new file mode 100644
index 0000000..e69de29

From cbcbae8f9d67f25accdd7afa775c8a8b9610aa94 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 10:49:23 +0900
Subject: [PATCH 023/148] feat : risk_hitory producer add

---
 .../quality_risk_history_repository.py        | 262 +++++-------------
 .../quality/risk_history_producer.py          | 257 +++++++++++++++++
 2 files changed, 324 insertions(+), 195 deletions(-)

diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 8cb0bd7..295b2e0 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -1,18 +1,13 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import create_engine
 import pandas as pd
-from datetime import datetime, timedelta
 
-SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
+from app.kafka.consumer import create_consumer
+from app.kafka.topics import (
+    QUALITY_INSPECTION_RISK_HISTORY
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
-)
-
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
 )
 
 main_engine = create_engine(
@@ -20,212 +15,89 @@
     pool_pre_ping=True
 )
 
-def calculate_status_risk(row):
-    score = 100
-
-    if float(row["speed"]) > 120:
-        score -= 20
-
-    if int(row["att"]) > 4000:
-        score -= 20
-
-    if float(row["battery_voltage"]) < 12:
-        score -= 10
-
-    return max(score, 0)
-
-
-def calculate_control_risk(row):
-    score = 100
-
-    if row["collision_warning"] == 1:
-        score -= 40
-
-    if row["lane_departure"] == 1:
-        score -= 20
-
-    if row["traction_control"] == 1:
-        score -= 10
-
-    if row["abs_active"] == 1:
-        score -= 10
+consumer = create_consumer(
+    topic=QUALITY_INSPECTION_RISK_HISTORY,
+    group_id="ai-risk-history-group"
+)
 
-    return max(score, 0)
+inspection_risk_history_list = []
 
+try:
 
-def calculate_drive_risk(row):
-    score = 100
+    print("Consumer 시작")
+    print("구독 토픽 :", consumer.subscription())
 
-    if float(row["throttle_position"]) > 90:
-        score -= 20
+    for msg in consumer:
 
-    if float(row["brake_pressure"]) > 45:
-        score -= 20
+        row = msg.value
 
-    if abs(float(row["steering_angle"])) > 40:
-        score -= 20
+        print("=" * 50)
+        print(f"[Kafka 수신 성공]")
+        print(f"Topic : {msg.topic}")
+        print(f"Offset : {msg.offset}")
+        print(f"Message : {row}")
+        print("=" * 50)
 
-    return max(score, 0)
+        inspection_risk_history_list.append({
+            "id": row["id"],
+            "inspection_type":
+                row["inspection_type"],
 
+            "inspection_round":
+                row["inspection_round"],
 
-def calculate_dynamics_risk(row):
-    score = 100
+            "risk_score":
+                row["risk_score"],
 
-    if abs(float(row["yaw_rate"])) > 7:
-        score -= 20
+            "start_time":
+                row["start_time"],
 
-    if abs(float(row["roll"])) > 4:
-        score -= 20
+            "end_time":
+                row["end_time"]
+        })
 
-    if abs(float(row["pitch"])) > 4:
-        score -= 20
+        if len(
+            inspection_risk_history_list
+        ) >= 20:
 
-    return max(score, 0)
+            df = pd.DataFrame(
+                inspection_risk_history_list
+            )
 
+            df.to_sql(
+                name="inspection_risk_history",
+                con=main_engine,
+                if_exists="append",
+                index=False
+            )
 
-result = []
-risk_id = 1
+            print(
+                f"{len(inspection_risk_history_list)}건 저장 완료"
+            )
 
-stage_plan = [
-    ("DRIVE", 6),
-    ("CONTROL", 5),
-    ("DYNAMICS", 3),
-    ("STATUS", 3)
-]
+            inspection_risk_history_list.clear()
 
-start_date = datetime.strptime(
-    "2026-06-01 01:00",
-    "%Y-%m-%d %H:%M"
-)
+except Exception as e:
 
-with sample_engine.connect() as conn:
-
-    # 7일
-    for day in range(7):
-
-        day_start = start_date + timedelta(days=day)
-
-        # 하루 생산 차량 100대
-        offset = day * 100
-
-        vehicles = conn.execute(
-            text("""
-                SELECT DISTINCT vehicle_id
-                FROM car_status
-                ORDER BY vehicle_id
-                LIMIT 100 OFFSET :offset
-            """),
-            {"offset": offset}
-        ).mappings().all()
-
-        vehicle_ids = [v["vehicle_id"] for v in vehicles]
-
-        current_time = day_start
-
-        for stage_name, duration in stage_plan:
-
-            stage_start = current_time
-            stage_end = current_time + timedelta(hours=duration)
-
-            scores = []
-
-            for vehicle_id in vehicle_ids:
-
-                if stage_name == "DRIVE":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_drive
-                            WHERE vehicle_id=:vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
-
-                    if row:
-                        scores.append(
-                            calculate_drive_risk(row)
-                        )
-
-                elif stage_name == "CONTROL":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_control
-                            WHERE vehicle_id=:vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
-
-                    if row:
-                        scores.append(
-                            calculate_control_risk(row)
-                        )
-
-                elif stage_name == "DYNAMICS":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_dynamics
-                            WHERE vehicle_id=:vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
-
-                    if row:
-                        scores.append(
-                            calculate_dynamics_risk(row)
-                        )
-
-                elif stage_name == "STATUS":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_status
-                            WHERE vehicle_id=:vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
-
-                    if row:
-                        scores.append(
-                            calculate_status_risk(row)
-                        )
-
-            avg_score = (
-                round(sum(scores) / len(scores), 2)
-                if scores else 0
-            )
+    print(f"오류 발생 : {e}")
 
-            result.append({
-                "id": risk_id,
-                "inspection_type": stage_name,
-                "inspection_round": day + 1,
-                "risk_score": avg_score,
-                "start_time": stage_start.strftime("%Y-%m-%d %H:%M"),
-                "end_time": stage_end.strftime("%Y-%m-%d %H:%M")
-            })
+finally:
 
-            risk_id += 1
+    if inspection_risk_history_list:
 
-            current_time = stage_end
+        df = pd.DataFrame(
+            inspection_risk_history_list
+        )
 
-df = pd.DataFrame(result)
+        df.to_sql(
+            name="inspection_risk_history",
+            con=main_engine,
+            if_exists="append",
+            index=False
+        )
 
-df.to_sql(
-    name="inspection_risk_history",
-    con=main_engine,
-    if_exists="append",
-    index=False
-)
+        print(
+            f"{len(inspection_risk_history_list)}건 최종 저장 완료"
+        )
 
-print(
-    f"inspection_risk_history table 전송 완료"
-)
\ No newline at end of file
+    consumer.close()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index e69de29..e8044f8 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -0,0 +1,257 @@
+from sqlalchemy import create_engine, text
+from kafka import KafkaProducer
+
+from datetime import datetime, timedelta
+
+import json
+import time
+
+from app.kafka.iam_provider import MSKTokenProvider
+
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+producer = KafkaProducer(
+    bootstrap_servers=[
+        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
+        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+    ],
+
+    security_protocol="SASL_SSL",
+
+    sasl_mechanism="OAUTHBEARER",
+
+    sasl_oauth_token_provider=MSKTokenProvider(),
+
+    value_serializer=lambda x:
+        json.dumps(x, default=str).encode("utf-8")
+)
+
+
+def calculate_status_risk(row):
+    score = 100
+
+    if float(row["speed"]) > 120:
+        score -= 20
+
+    if int(row["att"]) > 4000:
+        score -= 20
+
+    if float(row["battery_voltage"]) < 12:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_control_risk(row):
+    score = 100
+
+    if row["collision_warning"] == 1:
+        score -= 40
+
+    if row["lane_departure"] == 1:
+        score -= 20
+
+    if row["traction_control"] == 1:
+        score -= 10
+
+    if row["abs_active"] == 1:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_drive_risk(row):
+    score = 100
+
+    if float(row["throttle_position"]) > 90:
+        score -= 20
+
+    if float(row["brake_pressure"]) > 45:
+        score -= 20
+
+    if abs(float(row["steering_angle"])) > 40:
+        score -= 20
+
+    return max(score, 0)
+
+
+def calculate_dynamics_risk(row):
+    score = 100
+
+    if abs(float(row["yaw_rate"])) > 7:
+        score -= 20
+
+    if abs(float(row["roll"])) > 4:
+        score -= 20
+
+    if abs(float(row["pitch"])) > 4:
+        score -= 20
+
+    return max(score, 0)
+
+
+risk_id = 1
+
+stage_plan = [
+    ("DRIVE", 6),
+    ("CONTROL", 5),
+    ("DYNAMICS", 3),
+    ("STATUS", 3)
+]
+
+start_date = datetime.strptime(
+    "2026-06-01 01:00",
+    "%Y-%m-%d %H:%M"
+)
+
+with sample_engine.connect() as conn:
+
+    # 7일 데이터 생성
+    for day in range(7):
+
+        day_start = start_date + timedelta(days=day)
+
+        # 하루 생산 차량 100대
+        offset = day * 100
+
+        vehicles = conn.execute(
+            text("""
+                SELECT DISTINCT vehicle_id
+                FROM car_status
+                ORDER BY vehicle_id
+                LIMIT 100 OFFSET :offset
+            """),
+            {"offset": offset}
+        ).mappings().all()
+
+        vehicle_ids = [
+            vehicle["vehicle_id"]
+            for vehicle in vehicles
+        ]
+
+        current_time = day_start
+
+        for stage_name, duration in stage_plan:
+
+            stage_start = current_time
+
+            stage_end = (
+                current_time
+                + timedelta(hours=duration)
+            )
+
+            scores = []
+
+            for vehicle_id in vehicle_ids:
+
+                if stage_name == "DRIVE":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_drive
+                            WHERE vehicle_id = :vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_drive_risk(row)
+                        )
+
+                elif stage_name == "CONTROL":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_control
+                            WHERE vehicle_id = :vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_control_risk(row)
+                        )
+
+                elif stage_name == "DYNAMICS":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_dynamics
+                            WHERE vehicle_id = :vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_dynamics_risk(row)
+                        )
+
+                elif stage_name == "STATUS":
+
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_status
+                            WHERE vehicle_id = :vehicle_id
+                            LIMIT 1
+                        """),
+                        {"vehicle_id": vehicle_id}
+                    ).mappings().first()
+
+                    if row:
+                        scores.append(
+                            calculate_status_risk(row)
+                        )
+
+            avg_score = (
+                round(sum(scores) / len(scores), 2)
+                if scores else 0
+            )
+
+            message = {
+                "id": risk_id,
+                "inspection_type": stage_name,
+                "inspection_round": day + 1,
+                "risk_score": avg_score,
+                "start_time": stage_start.strftime(
+                    "%Y-%m-%d %H:%M"
+                ),
+                "end_time": stage_end.strftime(
+                    "%Y-%m-%d %H:%M"
+                )
+            }
+
+            producer.send(
+                "quality.inspection.risk_history",
+                value=message
+            )
+
+            print(
+                f"Kafka 전송 완료 : {message}"
+            )
+
+            risk_id += 1
+
+            current_time = stage_end
+
+            time.sleep(1)
+
+producer.flush()
+
+print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file

From 635b35e83c0487829fad0bf9eab8df2c67fda39d Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 10:59:55 +0900
Subject: [PATCH 024/148] feat : risk_trend producer add

---
 .../quality_risk_trend_repository.py          | 284 ++++--------------
 app/scheduler/quality/risk_trend_producer.py  | 272 +++++++++++++++++
 2 files changed, 334 insertions(+), 222 deletions(-)

diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 40b5773..f7a51cb 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -1,18 +1,13 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import create_engine
 import pandas as pd
-from datetime import datetime, timedelta
 
-SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
+from app.kafka.consumer import create_consumer
+from app.kafka.topics import (
+    QUALITY_INSPECTION_RISK_TREND
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
-)
-
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
 )
 
 main_engine = create_engine(
@@ -20,239 +15,84 @@
     pool_pre_ping=True
 )
 
-def calculate_status_risk(row):
-    score = 100
-
-    if float(row["speed"]) > 120:
-        score -= 20
-
-    if int(row["att"]) > 4000:
-        score -= 20
-
-    if float(row["battery_voltage"]) < 12:
-        score -= 10
-
-    return max(score, 0)
-
-
-def calculate_control_risk(row):
-    score = 100
-
-    if row["collision_warning"] == 1:
-        score -= 40
-
-    if row["lane_departure"] == 1:
-        score -= 20
-
-    if row["traction_control"] == 1:
-        score -= 10
-
-    if row["abs_active"] == 1:
-        score -= 10
-
-    return max(score, 0)
-
-
-def calculate_drive_risk(row):
-    score = 100
-
-    if float(row["throttle_position"]) > 90:
-        score -= 20
-
-    if float(row["brake_pressure"]) > 45:
-        score -= 20
-
-    if abs(float(row["steering_angle"])) > 40:
-        score -= 20
-
-    return max(score, 0)
-
-
-def calculate_dynamics_risk(row):
-    score = 100
-
-    if abs(float(row["yaw_rate"])) > 7:
-        score -= 20
-
-    if abs(float(row["roll"])) > 4:
-        score -= 20
-
-    if abs(float(row["pitch"])) > 4:
-        score -= 20
-
-    return max(score, 0)
-
-
-# =========================
-# 등급 분류
-# =========================
-
-def get_risk_level(score):
-
-    if score >= 80:
-        return "LOW"
-
-    elif score >= 50:
-        return "MEDIUM"
-
-    return "HIGH"
-
-
-# =========================
-# DB
-# =========================
-
-result = []
-risk_id = 1
-
-base_date = datetime.strptime(
-    "2026-06-01",
-    "%Y-%m-%d"
+consumer = create_consumer(
+    topic=QUALITY_INSPECTION_RISK_TREND,
+    group_id="ai-risk-trend-group"
 )
 
-with sample_engine.connect() as conn:
-
-    for day in range(7):
-
-        current_date = base_date + timedelta(days=day)
-
-        offset = day * 100
+inspection_risk_trend_list = []
 
-        vehicles = conn.execute(
-            text("""
-                SELECT DISTINCT vehicle_id
-                FROM car_status
-                ORDER BY vehicle_id
-                LIMIT 100 OFFSET :offset
-            """),
-            {"offset": offset}
-        ).mappings().all()
+try:
 
-        vehicle_ids = [
-            v["vehicle_id"]
-            for v in vehicles
-        ]
+    print("Consumer 시작")
+    print("구독 토픽 :", consumer.subscription())
 
-        low_count = 0
-        medium_count = 0
-        high_count = 0
+    for msg in consumer:
 
-        # =====================
-        # 차량 100대
-        # =====================
+        row = msg.value
 
-        for vehicle_id in vehicle_ids:
+        print("=" * 50)
+        print("[Kafka 메시지 수신]")
+        print(row)
+        print("=" * 50)
 
-            scores = []
+        inspection_risk_trend_list.append({
+            "id": row["id"],
+            "risk_level":
+                row["risk_level"],
 
-            status = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_status
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+            "risk_count":
+                row["risk_count"],
 
-            if status:
-                scores.append(
-                    calculate_status_risk(status)
-                )
+            "risk_ratio":
+                row["risk_ratio"],
 
-            control = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_control
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+            "created_at":
+                row["created_at"]
+        })
 
-            if control:
-                scores.append(
-                    calculate_control_risk(control)
-                )
+        if len(
+            inspection_risk_trend_list
+        ) >= 20:
 
-            drive = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_drive
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+            df = pd.DataFrame(
+                inspection_risk_trend_list
+            )
 
-            if drive:
-                scores.append(
-                    calculate_drive_risk(drive)
-                )
+            df.to_sql(
+                name="inspection_risk_trend",
+                con=main_engine,
+                if_exists="append",
+                index=False
+            )
 
-            dynamics = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_dynamics
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+            print(
+                f"{len(inspection_risk_trend_list)}건 저장 완료"
+            )
 
-            if dynamics:
-                scores.append(
-                    calculate_dynamics_risk(dynamics)
-                )
+            inspection_risk_trend_list.clear()
 
-            if not scores:
-                continue
+except Exception as e:
 
-            avg_score = sum(scores) / len(scores)
+    print(f"오류 발생 : {e}")
 
-            level = get_risk_level(avg_score)
+finally:
 
-            if level == "LOW":
-                low_count += 1
+    if inspection_risk_trend_list:
 
-            elif level == "MEDIUM":
-                medium_count += 1
+        df = pd.DataFrame(
+            inspection_risk_trend_list
+        )
 
-            else:
-                high_count += 1
+        df.to_sql(
+            name="inspection_risk_trend",
+            con=main_engine,
+            if_exists="append",
+            index=False
+        )
 
-        # =====================
-        # 저장
-        # =====================
-
-        daily_result = [
-            ("LOW", low_count),
-            ("MEDIUM", medium_count),
-            ("HIGH", high_count)
-        ]
-
-        for level, count in daily_result:
-
-            result.append({
-                "id": risk_id,
-                "risk_level": level,
-                "risk_count": count,
-                "risk_ratio": round(count, 2),
-                "created_at": current_date.strftime(
-                    "%Y-%m-%d 00:00:00"
-                )
-            })
-
-            risk_id += 1
-            
-df = pd.DataFrame(result)
-df.to_sql(
-    name="inspection_risk_trend",
-    con=main_engine,
-    if_exists="append",
-    index=False
-)
+        print(
+            f"{len(inspection_risk_trend_list)}건 최종 저장 완료"
+        )
 
-print(
-    f"inspection_risk_trend table 전송 완료"
-)
\ No newline at end of file
+    consumer.close()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index e69de29..a480c71 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -0,0 +1,272 @@
+from sqlalchemy import create_engine, text
+from kafka import KafkaProducer
+
+from datetime import datetime, timedelta
+
+import json
+import time
+
+from app.kafka.iam_provider import MSKTokenProvider
+
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.amazonaws.com:3306/sampledb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+producer = KafkaProducer(
+    bootstrap_servers=[
+        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
+        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+    ],
+
+    security_protocol="SASL_SSL",
+
+    sasl_mechanism="OAUTHBEARER",
+
+    sasl_oauth_token_provider=MSKTokenProvider(),
+
+    value_serializer=lambda x:
+        json.dumps(x, default=str).encode("utf-8")
+)
+
+
+def calculate_status_risk(row):
+    score = 100
+
+    if float(row["speed"]) > 120:
+        score -= 20
+
+    if int(row["att"]) > 4000:
+        score -= 20
+
+    if float(row["battery_voltage"]) < 12:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_control_risk(row):
+    score = 100
+
+    if row["collision_warning"] == 1:
+        score -= 40
+
+    if row["lane_departure"] == 1:
+        score -= 20
+
+    if row["traction_control"] == 1:
+        score -= 10
+
+    if row["abs_active"] == 1:
+        score -= 10
+
+    return max(score, 0)
+
+
+def calculate_drive_risk(row):
+    score = 100
+
+    if float(row["throttle_position"]) > 90:
+        score -= 20
+
+    if float(row["brake_pressure"]) > 45:
+        score -= 20
+
+    if abs(float(row["steering_angle"])) > 40:
+        score -= 20
+
+    return max(score, 0)
+
+
+def calculate_dynamics_risk(row):
+    score = 100
+
+    if abs(float(row["yaw_rate"])) > 7:
+        score -= 20
+
+    if abs(float(row["roll"])) > 4:
+        score -= 20
+
+    if abs(float(row["pitch"])) > 4:
+        score -= 20
+
+    return max(score, 0)
+
+
+def get_risk_level(score):
+
+    if score >= 80:
+        return "LOW"
+
+    elif score >= 50:
+        return "MEDIUM"
+
+    return "HIGH"
+
+
+risk_id = 1
+
+base_date = datetime.strptime(
+    "2026-06-01",
+    "%Y-%m-%d"
+)
+
+with sample_engine.connect() as conn:
+
+    for day in range(7):
+
+        current_date = (
+            base_date +
+            timedelta(days=day)
+        )
+
+        offset = day * 100
+
+        vehicles = conn.execute(
+            text("""
+                SELECT DISTINCT vehicle_id
+                FROM car_status
+                ORDER BY vehicle_id
+                LIMIT 100 OFFSET :offset
+            """),
+            {"offset": offset}
+        ).mappings().all()
+
+        vehicle_ids = [
+            v["vehicle_id"]
+            for v in vehicles
+        ]
+
+        low_count = 0
+        medium_count = 0
+        high_count = 0
+
+        for vehicle_id in vehicle_ids:
+
+            scores = []
+
+            status = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_status
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if status:
+                scores.append(
+                    calculate_status_risk(status)
+                )
+
+            control = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_control
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if control:
+                scores.append(
+                    calculate_control_risk(control)
+                )
+
+            drive = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_drive
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if drive:
+                scores.append(
+                    calculate_drive_risk(drive)
+                )
+
+            dynamics = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_dynamics
+                    WHERE vehicle_id=:vehicle_id
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            if dynamics:
+                scores.append(
+                    calculate_dynamics_risk(dynamics)
+                )
+
+            if not scores:
+                continue
+
+            avg_score = (
+                sum(scores) / len(scores)
+            )
+
+            level = get_risk_level(
+                avg_score
+            )
+
+            if level == "LOW":
+                low_count += 1
+
+            elif level == "MEDIUM":
+                medium_count += 1
+
+            else:
+                high_count += 1
+
+        daily_result = [
+            ("LOW", low_count),
+            ("MEDIUM", medium_count),
+            ("HIGH", high_count)
+        ]
+
+        for level, count in daily_result:
+
+            message = {
+                "id": risk_id,
+                "risk_level": level,
+                "risk_count": count,
+
+                # 필요하면 ratio 계산 수정
+                "risk_ratio": round(
+                    count / 100 * 100,
+                    2
+                ),
+
+                "created_at":
+                    current_date.strftime(
+                        "%Y-%m-%d 00:00:00"
+                    )
+            }
+
+            producer.send(
+                "quality.inspection.risk_trend",
+                value=message
+            )
+
+            print(
+                f"Kafka 전송 완료 : {message}"
+            )
+
+            risk_id += 1
+
+            time.sleep(1)
+
+producer.flush()
+
+print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file

From 6287cba2ad91a7a147ea659e17d26b8f4928c4e1 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 11:05:50 +0900
Subject: [PATCH 025/148] fix : risk_trend RDS URL update

---
 app/scheduler/quality/risk_trend_producer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index a480c71..1a5b198 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -9,7 +9,7 @@
 from app.kafka.iam_provider import MSKTokenProvider
 
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.amazonaws.com:3306/sampledb"
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
 )
 
 sample_engine = create_engine(

From 85ec9c3b73248568ebb8355e0cef7998c326c205 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 11:08:42 +0900
Subject: [PATCH 026/148] feat : update round update

---
 app/repository/quality_risk_trend_repository.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index f7a51cb..9587a84 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -53,7 +53,7 @@
 
         if len(
             inspection_risk_trend_list
-        ) >= 20:
+        ) >= 3:
 
             df = pd.DataFrame(
                 inspection_risk_trend_list

From 4a1ec7139488ce28a8acf0a6ca9e5e7e2cc37ce3 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 11:12:38 +0900
Subject: [PATCH 027/148] feat : status_detail producer add

---
 .../quality_status_detail_repository.py       | 189 ++++++++----------
 .../quality/status_detail_producer.py         | 174 ++++++++++++++++
 2 files changed, 256 insertions(+), 107 deletions(-)

diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 76ef06a..9c33e1c 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -1,18 +1,13 @@
-from sqlalchemy import create_engine, text
-from decimal import Decimal
+from sqlalchemy import create_engine
 import pandas as pd
 
-SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
+from app.kafka.consumer import create_consumer
+from app.kafka.topics import (
+    QUALITY_INSPECTION_STATUS_DETAIL
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
-)
-
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
 )
 
 main_engine = create_engine(
@@ -20,128 +15,108 @@
     pool_pre_ping=True
 )
 
-def calculate_status_score(status, control):
+consumer = create_consumer(
+    topic=QUALITY_INSPECTION_STATUS_DETAIL,
+    group_id="ai-status-detail-group"
+)
 
-    score = 100
-    issues = []
+inspection_status_detail_list = []
 
-    speed = float(status["speed"])
-    rpm = int(status["att"])
-    battery = float(status["battery_voltage"])
+try:
 
-    if speed > 120:
-        score -= 20
-        issues.append("over speed")
+    print("Consumer 시작")
+    print("구독 토픽 :", consumer.subscription())
 
-    if rpm > 4000:
-        score -= 20
-        issues.append("RPM Error")
+    for msg in consumer:
 
-    if battery < 12.0:
-        score -= 10
-        issues.append("battery drop")
+        row = msg.value
 
-    if control["collision_warning"] == 1:
-        score -= 40
-        issues.append("crash warning")
+        print("=" * 50)
+        print("[Kafka 메시지 수신]")
+        print(row)
+        print("=" * 50)
 
-    if status["gear"] == "P" and speed > 20:
-        score -= 30
-        issues.append("Parking")
+        inspection_status_detail_list.append({
+            "car_code":
+                row["car_code"],
 
-    return max(score, 0), issues
+            "inspection_no":
+                row["inspection_no"],
 
+            "vehicle_id":
+                row["vehicle_id"],
 
-def get_result(score):
+            "speed":
+                row["speed"],
 
-    if score >= 90:
-        return "PASS"
+            "att":
+                row["att"],
 
-    if score >= 70:
-        return "WARN"
+            "gear":
+                row["gear"],
 
-    return "FAIL"
+            "battery_voltage":
+                row["battery_voltage"],
 
+            "fuel_rate":
+                row["fuel_rate"],
 
-with sample_engine.connect() as conn:
+            "status_score":
+                row["status_score"],
 
-    master_rows = conn.execute(
-        text("""
-            SELECT *
-            FROM car_master
-        """)
-    ).mappings().all()
+            "inspection_result":
+                row["inspection_result"],
 
-    inspection_status_detail_list = []
+            "issue_message":
+                row["issue_message"],
 
-    for master_row in master_rows:
+            "created_at":
+                row["created_at"]
+        })
 
-        vehicle_id = master_row["vehicle_id"]
+        # 100건씩 저장
+        if len(
+            inspection_status_detail_list
+        ) >= 100:
 
-        status_row = conn.execute(
-            text("""
-                SELECT *
-                FROM car_status
-                WHERE vehicle_id = :vehicle_id
-                ORDER BY created_at DESC
-                LIMIT 1
-            """),
-            {
-                "vehicle_id": vehicle_id
-            }
-        ).mappings().first()
+            df = pd.DataFrame(
+                inspection_status_detail_list
+            )
 
-        control_row = conn.execute(
-            text("""
-                SELECT *
-                FROM car_control
-                WHERE vehicle_id = :vehicle_id
-                ORDER BY created_at DESC
-                LIMIT 1
-            """),
-            {
-                "vehicle_id": vehicle_id
-            }
-        ).mappings().first()
+            df.to_sql(
+                name="inspection_status_detail",
+                con=main_engine,
+                if_exists="append",
+                index=False
+            )
 
-        if not status_row or not control_row:
-            continue
+            print(
+                f"{len(inspection_status_detail_list)}건 저장 완료"
+            )
 
-        score, issues = calculate_status_score(
-            status_row,
-            control_row
-        )
+            inspection_status_detail_list.clear()
+
+except Exception as e:
+
+    print(f"오류 발생 : {e}")
 
-        inspection_status_detail = {
-            "car_code": vehicle_id.split("-")[0],
-            "inspection_no": f"STATUS-{master_row['id']:05d}",
-            "vehicle_id": vehicle_id,
-            "speed": float(status_row["speed"]),
-            "att": int(status_row["att"]),
-            "gear": status_row["gear"],
-            "battery_voltage": float(status_row["battery_voltage"]),
-            "fuel_rate": float(status_row["fuel_rate"]),
-            "status_score": float(score),
-            "inspection_result": get_result(score),
-            "issue_message": ", ".join(issues) if issues else "정상",
-            "created_at": status_row["created_at"]
-        }
-
-        inspection_status_detail_list.append(
-            inspection_status_detail
+finally:
+
+    if inspection_status_detail_list:
+
+        df = pd.DataFrame(
+            inspection_status_detail_list
         )
 
-df = pd.DataFrame(
-    inspection_status_detail_list
-)
+        df.to_sql(
+            name="inspection_status_detail",
+            con=main_engine,
+            if_exists="append",
+            index=False
+        )
 
-df.to_sql(
-    name="inspection_status_detail",
-    con=main_engine,
-    if_exists="append",
-    index=False
-)
+        print(
+            f"{len(inspection_status_detail_list)}건 최종 저장 완료"
+        )
 
-print(
-    f"inspection_status_detail table 전송 완료"
-)
\ No newline at end of file
+    consumer.close()
\ No newline at end of file
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index e69de29..da06c5b 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -0,0 +1,174 @@
+from sqlalchemy import create_engine, text
+from kafka import KafkaProducer
+
+import json
+import time
+
+from app.kafka.iam_provider import MSKTokenProvider
+
+SAMPLE_DATABASE_URL = (
+    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+producer = KafkaProducer(
+    bootstrap_servers=[
+        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
+        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+    ],
+
+    security_protocol="SASL_SSL",
+
+    sasl_mechanism="OAUTHBEARER",
+
+    sasl_oauth_token_provider=MSKTokenProvider(),
+
+    value_serializer=lambda x:
+        json.dumps(x, default=str).encode("utf-8")
+)
+
+
+def calculate_status_score(status, control):
+
+    score = 100
+    issues = []
+
+    speed = float(status["speed"])
+    rpm = int(status["att"])
+    battery = float(status["battery_voltage"])
+
+    if speed > 120:
+        score -= 20
+        issues.append("over speed")
+
+    if rpm > 4000:
+        score -= 20
+        issues.append("RPM Error")
+
+    if battery < 12.0:
+        score -= 10
+        issues.append("battery drop")
+
+    if control["collision_warning"] == 1:
+        score -= 40
+        issues.append("crash warning")
+
+    if status["gear"] == "P" and speed > 20:
+        score -= 30
+        issues.append("Parking")
+
+    return max(score, 0), issues
+
+
+def get_result(score):
+
+    if score >= 90:
+        return "PASS"
+
+    if score >= 70:
+        return "WARN"
+
+    return "FAIL"
+
+
+with sample_engine.connect() as conn:
+
+    master_rows = conn.execute(
+        text("""
+            SELECT *
+            FROM car_master
+            ORDER BY id
+        """)
+    ).mappings().all()
+
+    for master_row in master_rows:
+
+        vehicle_id = master_row["vehicle_id"]
+
+        status_row = conn.execute(
+            text("""
+                SELECT *
+                FROM car_status
+                WHERE vehicle_id = :vehicle_id
+                ORDER BY created_at DESC
+                LIMIT 1
+            """),
+            {"vehicle_id": vehicle_id}
+        ).mappings().first()
+
+        control_row = conn.execute(
+            text("""
+                SELECT *
+                FROM car_control
+                WHERE vehicle_id = :vehicle_id
+                ORDER BY created_at DESC
+                LIMIT 1
+            """),
+            {"vehicle_id": vehicle_id}
+        ).mappings().first()
+
+        if not status_row or not control_row:
+            continue
+
+        score, issues = calculate_status_score(
+            status_row,
+            control_row
+        )
+
+        message = {
+            "car_code": vehicle_id.split("-")[0],
+
+            "inspection_no":
+                f"STATUS-{master_row['id']:05d}",
+
+            "vehicle_id": vehicle_id,
+
+            "speed":
+                float(status_row["speed"]),
+
+            "att":
+                int(status_row["att"]),
+
+            "gear":
+                status_row["gear"],
+
+            "battery_voltage":
+                float(
+                    status_row["battery_voltage"]
+                ),
+
+            "fuel_rate":
+                float(status_row["fuel_rate"]),
+
+            "status_score":
+                float(score),
+
+            "inspection_result":
+                get_result(score),
+
+            "issue_message":
+                ", ".join(issues)
+                if issues else "정상",
+
+            "created_at":
+                status_row["created_at"]
+        }
+
+        producer.send(
+            "quality.inspection.status_detail",
+            value=message
+        )
+
+        print(
+            f"Kafka 전송 완료 : {message}"
+        )
+
+        time.sleep(1)
+
+producer.flush()
+
+print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file

From 2834b715186c0b4af526d185d9f65dc251f7bf1b Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 15:04:22 +0900
Subject: [PATCH 028/148] feat : secreat env update

---
 app/kafka/config.py                               |  8 --------
 app/kafka/consumer.py                             |  7 +++++--
 app/repository/quality_drive_detail_repository.py |  4 +++-
 app/repository/quality_process_repository.py      |  4 +++-
 app/repository/quality_risk_history_repository.py |  4 +++-
 app/repository/quality_summary_repository.py      | 11 +++++++----
 app/scheduler/quality/drive_detail_producer.py    |  9 ++++++---
 app/scheduler/quality/process_producer.py         |  9 ++++++---
 app/scheduler/quality/risk_history_producer.py    |  9 ++++++---
 app/scheduler/quality/risk_trend_producer.py      |  9 ++++++---
 app/scheduler/quality/status_detail_producer.py   |  9 ++++++---
 11 files changed, 51 insertions(+), 32 deletions(-)

diff --git a/app/kafka/config.py b/app/kafka/config.py
index 27f41a6..1077bf0 100644
--- a/app/kafka/config.py
+++ b/app/kafka/config.py
@@ -1,12 +1,4 @@
 # kafka/config.py
-
-BOOTSTRAP_SERVERS = [
-
-    "127.0.0.2:9092",
-
-    "127.0.0.3:9092"
-]
-
 AUTO_OFFSET_RESET = "earliest"
 
 ENABLE_AUTO_COMMIT = False
diff --git a/app/kafka/consumer.py b/app/kafka/consumer.py
index 893e29d..170b90e 100644
--- a/app/kafka/consumer.py
+++ b/app/kafka/consumer.py
@@ -4,7 +4,10 @@
 import ssl
 from kafka import KafkaConsumer
 from app.kafka.iam_provider import MSKTokenProvider
+from dotenv import load_dotenv
+import os
 
+load_dotenv()
 def create_consumer(topic, group_id):
 
     consumer = KafkaConsumer(
@@ -12,8 +15,8 @@ def create_consumer(topic, group_id):
         ssl_context=ssl.create_default_context(),
 
         bootstrap_servers=[
-            "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
-            "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+        os.getenv("BROKER_URL_1"),
+        os.getenv("BROKER_URL_2")
         ],
 
         group_id=group_id,
diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index ec99441..825818d 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -1,6 +1,8 @@
 from sqlalchemy import create_engine
 import pandas as pd
 import json
+from dotenv import load_dotenv
+import os
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import (
@@ -8,7 +10,7 @@
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
+    os.getenv("MAIN_DATABASE_END")
 )
 
 main_engine = create_engine(
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index c992518..25ca017 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -1,12 +1,14 @@
 from sqlalchemy import create_engine
 import pandas as pd
 import random
+from dotenv import load_dotenv
+import os
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
+    os.getenv("MAIN_DATABASE_END")
 )
 
 main_engine = create_engine(
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 295b2e0..0dfe289 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -1,5 +1,7 @@
 from sqlalchemy import create_engine
 import pandas as pd
+from dotenv import load_dotenv
+import os
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import (
@@ -7,7 +9,7 @@
 )
 
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
+    os.getenv("MAIN_DATABASE_END")
 )
 
 main_engine = create_engine(
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index b9c4900..5564a55 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -1,13 +1,16 @@
 from sqlalchemy import create_engine, text
 import pandas as pd
 from datetime import datetime, timedelta
+from dotenv import load_dotenv
+import os
 
-SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/sampledb"
+load_dotenv()
+MAIN_DATABASE_URL = (
+    os.getenv("MAIN_DATABASE_END")
 )
 
-MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@127.0.0.1:13306/maindb"
+SAMPLE_DATABASE_URL = (
+    os.getenv("SAMPLE_DATABASE_END")
 )
 
 sample_engine = create_engine(
diff --git a/app/scheduler/quality/drive_detail_producer.py b/app/scheduler/quality/drive_detail_producer.py
index 9323d60..3f7edb6 100644
--- a/app/scheduler/quality/drive_detail_producer.py
+++ b/app/scheduler/quality/drive_detail_producer.py
@@ -1,13 +1,16 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
+from dotenv import load_dotenv
+import os
 
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+load_dotenv()
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+    os.getenv("SAMPLE_DATABASE_END")
 )
 
 sample_engine = create_engine(
@@ -17,8 +20,8 @@
 
 producer = KafkaProducer(
     bootstrap_servers=[
-        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
-        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+        os.getenv("BROKER_URL_1"),
+        os.getenv("BROKER_URL_2")
     ],
 
     security_protocol="SASL_SSL",
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index 5a6a97a..5112ce5 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -1,13 +1,16 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
+from dotenv import load_dotenv
+import os
 
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+load_dotenv()
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+    os.getenv("SAMPLE_DATABASE_END")
 )
 
 sample_engine = create_engine(
@@ -17,8 +20,8 @@
 
 producer = KafkaProducer(
     bootstrap_servers=[
-        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
-        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+        os.getenv("BROKER_URL_1"),
+        os.getenv("BROKER_URL_2")
     ],
 
     security_protocol="SASL_SSL",
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index e8044f8..072cdd8 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -1,5 +1,7 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
+from dotenv import load_dotenv
+import os
 
 from datetime import datetime, timedelta
 
@@ -8,8 +10,9 @@
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+load_dotenv()
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+    os.getenv("SAMPLE_DATABASE_END")
 )
 
 sample_engine = create_engine(
@@ -19,8 +22,8 @@
 
 producer = KafkaProducer(
     bootstrap_servers=[
-        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
-        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+        os.getenv("BROKER_URL_1"),
+        os.getenv("BROKER_URL_2")
     ],
 
     security_protocol="SASL_SSL",
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index 1a5b198..8b77054 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -1,5 +1,7 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
+from dotenv import load_dotenv
+import os
 
 from datetime import datetime, timedelta
 
@@ -8,8 +10,9 @@
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+load_dotenv()
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+    os.getenv("SAMPLE_DATABASE_END")
 )
 
 sample_engine = create_engine(
@@ -19,8 +22,8 @@
 
 producer = KafkaProducer(
     bootstrap_servers=[
-        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
-        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+        os.getenv("BROKER_URL_1"),
+        os.getenv("BROKER_URL_2")
     ],
 
     security_protocol="SASL_SSL",
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index da06c5b..a450ad5 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -1,13 +1,16 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
+from dotenv import load_dotenv
+import os
 
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+load_dotenv()
 SAMPLE_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/sampledb"
+    os.getenv("SAMPLE_DATABASE_END")
 )
 
 sample_engine = create_engine(
@@ -17,8 +20,8 @@
 
 producer = KafkaProducer(
     bootstrap_servers=[
-        "b-1.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098",
-        "b-2.aimsdevmsk.3g8nqa.c2.kafka.ap-northeast-2.amazonaws.com:9098"
+        os.getenv("BROKER_URL_1"),
+        os.getenv("BROKER_URL_2")
     ],
 
     security_protocol="SASL_SSL",

From ce664a7c47262a72ae4c222d66e261e062ec935c Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 15:18:48 +0900
Subject: [PATCH 029/148] fix : iam_provider.py update

---
 app/kafka/iam_provider.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/app/kafka/iam_provider.py b/app/kafka/iam_provider.py
index 999f8af..c7f46cb 100644
--- a/app/kafka/iam_provider.py
+++ b/app/kafka/iam_provider.py
@@ -2,11 +2,7 @@
 from aws_msk_iam_sasl_signer import MSKAuthTokenProvider
 
 
-class MSKTokenProvider:
-
-    def token(self) -> str:
-        token, _ = MSKAuthTokenProvider.generate_auth_token(
-            region="ap-northeast-2",
-        )
-
+class MSKTokenProvider(AbstractTokenProvider):
+    def token(self):
+        token, _ = MSKAuthTokenProvider.generate_auth_token("ap-northeast-2")
         return token
\ No newline at end of file

From d23bc5d62387424437fa3376ac865a241ee4efdc Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 15:48:44 +0900
Subject: [PATCH 030/148] feat : drop car delete

---
 app/repository/quality_drive_detail_repository.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 825818d..43a2cc5 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -9,6 +9,7 @@
     QUALITY_INSPECTION_DRIVE_DETAIL
 )
 
+load_dotenv()
 MAIN_DATABASE_URL = (
     os.getenv("MAIN_DATABASE_END")
 )
@@ -66,6 +67,14 @@ def get_driving_pattern(row):
     print("구독 토픽", consumer.subscription())
     for msg in consumer:
         row = msg.value
+
+        # 폐기 차량 제외
+        if not row.get("created_at"):
+            print(
+                f"폐기 차량 제외 : {row.get('vehicle_id')}"
+            )
+            continue
+
         print(f"Message : {json.dumps(row, ensure_ascii=False)}")
         vehicle_id = row["vehicle_id"]
         car_code = vehicle_id.split("-")[0]

From 7d087a0d3392f8113a09723cb3c95ce9511be8c6 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 16:24:00 +0900
Subject: [PATCH 031/148] fix : SQL input error update

---
 app/repository/quality_drive_detail_repository.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 43a2cc5..8a29d56 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -16,7 +16,10 @@
 
 main_engine = create_engine(
     MAIN_DATABASE_URL,
-    pool_pre_ping=True
+    pool_pre_ping=True,
+    pool_recycle=3600,
+    pool_size=5,
+    max_overflow=10
 )
 
 consumer = create_consumer(

From 8d5b8924bcde7e79b5a5ac0b7b7b14c30656d692 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 16:37:06 +0900
Subject: [PATCH 032/148] fix : env name update

---
 app/repository/quality_risk_history_repository.py | 13 ++++++++++++-
 app/scheduler/quality/process_producer.py         | 12 +++++++++++-
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 0dfe289..7ce78a9 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -2,14 +2,25 @@
 import pandas as pd
 from dotenv import load_dotenv
 import os
+from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import (
     QUALITY_INSPECTION_RISK_HISTORY
 )
 
+load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
 MAIN_DATABASE_URL = (
-    os.getenv("MAIN_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
 )
 
 main_engine = create_engine(
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index 5112ce5..6c8af77 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -1,6 +1,7 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
 from dotenv import load_dotenv
+from urllib.parse import quote_plus
 import os
 
 import json
@@ -9,8 +10,17 @@
 from app.kafka.iam_provider import MSKTokenProvider
 
 load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
 SAMPLE_DATABASE_URL = (
-    os.getenv("SAMPLE_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
 )
 
 sample_engine = create_engine(

From af6773314d21324f01887e1a90aca5e7f9fa31a2 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 16:42:47 +0900
Subject: [PATCH 033/148] fix : env name update 2

---
 app/scheduler/quality/risk_history_producer.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index 072cdd8..0eb22df 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -2,6 +2,7 @@
 from kafka import KafkaProducer
 from dotenv import load_dotenv
 import os
+from urllib.parse import quote_plus
 
 from datetime import datetime, timedelta
 
@@ -11,8 +12,17 @@
 from app.kafka.iam_provider import MSKTokenProvider
 
 load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
 SAMPLE_DATABASE_URL = (
-    os.getenv("SAMPLE_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
 )
 
 sample_engine = create_engine(

From 4dd3ad4addd4b1fd124ec49cc5a8e6e8fbec8542 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 16:51:41 +0900
Subject: [PATCH 034/148] producer env update

---
 .../quality_drive_detail_repository.py          | 17 ++++++++++++-----
 app/repository/quality_risk_trend_repository.py | 15 ++++++++++++++-
 .../quality_status_detail_repository.py         | 15 ++++++++++++++-
 app/repository/quality_summary_repository.py    | 16 ++++++++++++++--
 app/scheduler/quality/drive_detail_producer.py  | 12 +++++++++++-
 app/scheduler/quality/status_detail_producer.py | 12 +++++++++++-
 6 files changed, 76 insertions(+), 11 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 8a29d56..bbb5cae 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -3,6 +3,7 @@
 import json
 from dotenv import load_dotenv
 import os
+from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import (
@@ -10,16 +11,22 @@
 )
 
 load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
 MAIN_DATABASE_URL = (
-    os.getenv("MAIN_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
 )
 
 main_engine = create_engine(
     MAIN_DATABASE_URL,
-    pool_pre_ping=True,
-    pool_recycle=3600,
-    pool_size=5,
-    max_overflow=10
+    pool_pre_ping=True
 )
 
 consumer = create_consumer(
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 9587a84..ce12320 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -1,13 +1,26 @@
 from sqlalchemy import create_engine
 import pandas as pd
+from dotenv import load_dotenv
+import os
+from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import (
     QUALITY_INSPECTION_RISK_TREND
 )
 
+load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
 )
 
 main_engine = create_engine(
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 9c33e1c..b70eda9 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -1,13 +1,26 @@
 from sqlalchemy import create_engine
 import pandas as pd
+from dotenv import load_dotenv
+import os
+from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import (
     QUALITY_INSPECTION_STATUS_DETAIL
 )
 
+load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
 MAIN_DATABASE_URL = (
-    "mysql+pymysql://admin:j8XKJ9?vbR>v5Mysc0_5zk-zMDnO@aims-dev-mysql.c7yyi6w0ch43.ap-northeast-2.rds.amazonaws.com:3306/maindb"
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
 )
 
 main_engine = create_engine(
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index 5564a55..afb4c44 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -3,14 +3,26 @@
 from datetime import datetime, timedelta
 from dotenv import load_dotenv
 import os
+from urllib.parse import quote_plus
 
 load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
 MAIN_DATABASE_URL = (
-    os.getenv("MAIN_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
 )
 
 SAMPLE_DATABASE_URL = (
-    os.getenv("SAMPLE_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
 )
 
 sample_engine = create_engine(
diff --git a/app/scheduler/quality/drive_detail_producer.py b/app/scheduler/quality/drive_detail_producer.py
index 3f7edb6..6837ba2 100644
--- a/app/scheduler/quality/drive_detail_producer.py
+++ b/app/scheduler/quality/drive_detail_producer.py
@@ -1,6 +1,7 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
 from dotenv import load_dotenv
+from urllib.parse import quote_plus
 import os
 
 import json
@@ -9,8 +10,17 @@
 from app.kafka.iam_provider import MSKTokenProvider
 
 load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
 SAMPLE_DATABASE_URL = (
-    os.getenv("SAMPLE_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
 )
 
 sample_engine = create_engine(
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index a450ad5..ba33d72 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -2,6 +2,7 @@
 from kafka import KafkaProducer
 from dotenv import load_dotenv
 import os
+from urllib.parse import quote_plus
 
 import json
 import time
@@ -9,8 +10,17 @@
 from app.kafka.iam_provider import MSKTokenProvider
 
 load_dotenv()
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
 SAMPLE_DATABASE_URL = (
-    os.getenv("SAMPLE_DATABASE_END")
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
 )
 
 sample_engine = create_engine(

From 5ec29502eb01a7caf8cfea946bd8fa98fcdb7295 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 26 Jun 2026 16:55:40 +0900
Subject: [PATCH 035/148] push count update

---
 app/repository/quality_risk_history_repository.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 7ce78a9..6f6facf 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -71,7 +71,7 @@
 
         if len(
             inspection_risk_history_list
-        ) >= 20:
+        ) >= 4:
 
             df = pd.DataFrame(
                 inspection_risk_history_list

From 208495356553f5f602abcd4853a25ddc72e9391b Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 26 Jun 2026 17:42:10 +0900
Subject: [PATCH 036/148] =?UTF-8?q?feat:=20=EB=B6=88=EB=9F=89=20=ED=83=90?=
 =?UTF-8?q?=EC=A7=80=20=EC=9E=84=EC=8B=9C=20=EC=A0=80=EC=9E=A5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/api/router.py                             |     3 +-
 app/api/routers/ml_dataset.py                 |    60 +
 .../defect_transfer_dataset_builder.py        |   666 +
 .../manufacturing_event_json_builder.py       |    25 +-
 app/ml/artifacts/auxiliary_process_models.pkl |   Bin 2349619 -> 0 bytes
 .../artifacts/selected_defect_detector.joblib |   Bin 17069122 -> 0 bytes
 app/ml/preprocessing/__init__.py              |    20 +
 .../defect_transfer_prediction_model.ipynb    | 16028 ++++++++--------
 ...efect_transfer_prediction_model_json.ipynb |  2162 ---
 requirements.txt                              |     5 +-
 10 files changed, 8776 insertions(+), 10193 deletions(-)
 create mode 100644 app/api/routers/ml_dataset.py
 create mode 100644 app/data_generation/defect_transfer_dataset_builder.py
 delete mode 100644 app/ml/artifacts/auxiliary_process_models.pkl
 delete mode 100644 app/ml/artifacts/selected_defect_detector.joblib
 delete mode 100644 app/ml/training/defect_transfer_prediction_model_json.ipynb

diff --git a/app/api/router.py b/app/api/router.py
index 111cb0e..a1292b4 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -1,9 +1,10 @@
 from fastapi import APIRouter
 
-from app.api.routers import health, manufacturing_event, process, root
+from app.api.routers import health, manufacturing_event, ml_dataset, process, root
 
 api_router = APIRouter()
 api_router.include_router(root.router)
 api_router.include_router(health.router)
 api_router.include_router(process.router)
 api_router.include_router(manufacturing_event.router)
+api_router.include_router(ml_dataset.router)
diff --git a/app/api/routers/ml_dataset.py b/app/api/routers/ml_dataset.py
new file mode 100644
index 0000000..23e56f4
--- /dev/null
+++ b/app/api/routers/ml_dataset.py
@@ -0,0 +1,60 @@
+from pathlib import Path
+
+from fastapi import APIRouter, Query, status
+
+from app.data_generation.defect_transfer_dataset_builder import (
+    DEFAULT_DATASET_ROOT,
+    generate_defect_transfer_datasets,
+)
+from app.dto.response import CommonResponse
+from app.utils.response_utils import success_response
+
+
+router = APIRouter(prefix="/api/ml/datasets", tags=["ML Dataset"])
+
+
+@router.post(
+    "/defect-transfer/generate",
+    summary="불량 탐지/전이 예측 학습 CSV 생성",
+    operation_id="generateDefectTransferTrainingDatasets",
+    status_code=status.HTTP_201_CREATED,
+)
+def generate_defect_transfer_training_datasets(
+    car_count: int = Query(
+        default=12_000,
+        ge=100,
+        le=100_000,
+        description="생성할 차량 수입니다. 차량 1대당 PRESS/BODY/PAINT/ASSEMBLY 4개 이벤트가 생성됩니다.",
+    ),
+    train_ratio: float = Query(
+        default=0.8,
+        gt=0.5,
+        lt=0.95,
+        description="차량 단위 train split 비율입니다.",
+    ),
+    output_dir: str | None = Query(
+        default=None,
+        description="CSV 출력 디렉터리입니다. 생략하면 app/ml/datasets/process/generated 를 사용합니다.",
+    ),
+) -> CommonResponse[dict]:
+    paths = generate_defect_transfer_datasets(
+        dataset_root=DEFAULT_DATASET_ROOT,
+        output_dir=Path(output_dir) if output_dir else None,
+        car_count=car_count,
+        train_ratio=train_ratio,
+    )
+    return success_response(
+        data={
+            "outputDir": str(paths.output_dir),
+            "defectDetectionTrain": str(paths.defect_train),
+            "defectDetectionTest": str(paths.defect_test),
+            "transferPredictionTrain": str(paths.transfer_train),
+            "transferPredictionTest": str(paths.transfer_test),
+            "metadata": str(paths.metadata),
+            "eventRows": paths.event_rows,
+            "transitionRows": paths.transition_rows,
+            "trainCars": paths.train_cars,
+            "testCars": paths.test_cars,
+        },
+        message="불량 탐지/전이 예측 학습 CSV 생성이 완료되었습니다.",
+    )
diff --git a/app/data_generation/defect_transfer_dataset_builder.py b/app/data_generation/defect_transfer_dataset_builder.py
new file mode 100644
index 0000000..afd6844
--- /dev/null
+++ b/app/data_generation/defect_transfer_dataset_builder.py
@@ -0,0 +1,666 @@
+from __future__ import annotations
+
+import csv
+import hashlib
+import json
+import math
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+
+PROCESS_ORDER = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
+TRANSFER_FLOWS = (("PRESS", "BODY"), ("BODY", "PAINT"), ("PAINT", "ASSEMBLY"))
+RAW_EVENT_COLUMNS = [
+    "raw_event_id",
+    "event_id",
+    "event_time",
+    "car_master_id_raw",
+    "equipment_id",
+    "process_code_raw",
+    "station_code_raw",
+    "equipment_code_raw",
+    "equipment_type_raw",
+    "operation_status_raw",
+    "event_type_raw",
+    "event_json",
+    "is_sent",
+    "sent_at",
+    "created_at",
+    "updated_at",
+    "defect_yn",
+    "defect_reason",
+    "dataset_split",
+]
+DEFAULT_DATASET_ROOT = Path(__file__).resolve().parents[1] / "ml" / "datasets" / "process"
+DEFAULT_OUTPUT_DIRNAME = "generated"
+
+
+@dataclass(frozen=True)
+class GeneratedDatasetPaths:
+    output_dir: Path
+    defect_train: Path
+    defect_test: Path
+    transfer_train: Path
+    transfer_test: Path
+    metadata: Path
+    event_rows: int
+    transition_rows: int
+    train_cars: int
+    test_cars: int
+
+
+def generate_defect_transfer_datasets(
+    dataset_root: Path = DEFAULT_DATASET_ROOT,
+    output_dir: Path | None = None,
+    car_count: int = 12_000,
+    train_ratio: float = 0.8,
+    start_time: datetime | None = None,
+) -> GeneratedDatasetPaths:
+    """Build ML train/test CSVs from files under app/ml/datasets/process.
+
+    The generated event JSON follows the Kafka payload shape used by
+    manufacturing_raw_event:
+    event/equipment/equipmentStatus/product/sensor/processMetrics/sourceTrace/processData.
+    """
+
+    dataset_root = Path(dataset_root)
+    output_dir = Path(output_dir) if output_dir else dataset_root / DEFAULT_OUTPUT_DIRNAME
+    output_dir.mkdir(parents=True, exist_ok=True)
+    car_count = max(100, int(car_count))
+    train_car_count = max(1, min(car_count - 1, int(car_count * train_ratio)))
+    start_time = start_time or datetime(2026, 6, 16, 10, 0, 0)
+
+    source = _SourceSamples(dataset_root)
+    event_rows: list[dict[str, Any]] = []
+    event_by_car_process: dict[tuple[int, str], dict[str, Any]] = {}
+    raw_event_id = 1
+
+    for car_index in range(car_count):
+        car_master_id = car_index + 1
+        split = "train" if car_master_id <= train_car_count else "test"
+        defect_profile = _defect_profile(car_master_id)
+        for process_index, process_code in enumerate(PROCESS_ORDER):
+            event_time = start_time + timedelta(seconds=car_index * 12 + process_index * 3)
+            event_id = f"EVT-{event_time:%Y%m%d}-{raw_event_id:06d}"
+            sample_index = car_index * len(PROCESS_ORDER) + process_index
+            event_json, defect_yn, defect_reason = _build_event_json(
+                source=source,
+                car_master_id=car_master_id,
+                event_id=event_id,
+                event_time=event_time,
+                process_code=process_code,
+                process_index=process_index,
+                sample_index=sample_index,
+                is_defect=defect_profile[process_code],
+            )
+            row = {
+                "raw_event_id": raw_event_id,
+                "event_id": event_id,
+                "event_time": event_time.isoformat(timespec="seconds"),
+                "car_master_id_raw": car_master_id,
+                "equipment_id": _equipment_id(process_code, car_master_id),
+                "process_code_raw": process_code,
+                "station_code_raw": f"{process_code}_STATION_{_line_no(car_master_id, process_code):02d}",
+                "equipment_code_raw": event_json["equipment"]["equipmentCode"],
+                "equipment_type_raw": event_json["equipment"]["equipmentType"],
+                "operation_status_raw": event_json["equipmentStatus"]["operationStatus"],
+                "event_type_raw": event_json["event"]["eventType"],
+                "event_json": json.dumps(event_json, ensure_ascii=False),
+                "is_sent": 0,
+                "sent_at": "",
+                "created_at": datetime.now().isoformat(timespec="seconds"),
+                "updated_at": datetime.now().isoformat(timespec="seconds"),
+                "defect_yn": int(defect_yn),
+                "defect_reason": defect_reason,
+                "dataset_split": split,
+            }
+            event_rows.append(row)
+            event_by_car_process[(car_master_id, process_code)] = row
+            raw_event_id += 1
+
+    transition_rows = _build_transition_rows(event_by_car_process, car_count, train_car_count)
+
+    defect_train_path = output_dir / "defect_detection_train.csv"
+    defect_test_path = output_dir / "defect_detection_test.csv"
+    transfer_train_path = output_dir / "transfer_prediction_train.csv"
+    transfer_test_path = output_dir / "transfer_prediction_test.csv"
+    metadata_path = output_dir / "defect_transfer_dataset_metadata.json"
+
+    _write_csv(defect_train_path, [r for r in event_rows if r["dataset_split"] == "train"], RAW_EVENT_COLUMNS)
+    _write_csv(defect_test_path, [r for r in event_rows if r["dataset_split"] == "test"], RAW_EVENT_COLUMNS)
+
+    transition_columns = list(transition_rows[0]) if transition_rows else []
+    _write_csv(transfer_train_path, [r for r in transition_rows if r["dataset_split"] == "train"], transition_columns)
+    _write_csv(transfer_test_path, [r for r in transition_rows if r["dataset_split"] == "test"], transition_columns)
+
+    metadata = {
+        "created_at": datetime.now().isoformat(timespec="seconds"),
+        "dataset_root": str(dataset_root),
+        "output_dir": str(output_dir),
+        "car_count": car_count,
+        "train_ratio": train_ratio,
+        "train_cars": train_car_count,
+        "test_cars": car_count - train_car_count,
+        "event_rows": len(event_rows),
+        "transition_rows": len(transition_rows),
+        "dataset_files": {
+            "defect_train": str(defect_train_path),
+            "defect_test": str(defect_test_path),
+            "transfer_train": str(transfer_train_path),
+            "transfer_test": str(transfer_test_path),
+        },
+        "source_files": source.source_files,
+    }
+    metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8")
+
+    return GeneratedDatasetPaths(
+        output_dir=output_dir,
+        defect_train=defect_train_path,
+        defect_test=defect_test_path,
+        transfer_train=transfer_train_path,
+        transfer_test=transfer_test_path,
+        metadata=metadata_path,
+        event_rows=len(event_rows),
+        transition_rows=len(transition_rows),
+        train_cars=train_car_count,
+        test_cars=car_count - train_car_count,
+    )
+
+
+class _SourceSamples:
+    def __init__(self, dataset_root: Path, sample_rows: int = 4096) -> None:
+        self.dataset_root = Path(dataset_root)
+        self.sample_rows = sample_rows
+        self.forming_rows = self._read_forming_rows()
+        self.press_current_rows = self._read_current_rows("press")
+        self.robot_current_rows = self._read_current_rows("robot")
+        self.ford_rows = self._read_ford_rows()
+        self.vision_rows = self._read_vision_rows()
+        self.bosch_rows = self._read_bosch_rows()
+        self.source_files = {
+            "forming": str(self._find_forming_file()),
+            "press_current": [str(p) for p in self._find_current_files("press")],
+            "robot_current": [str(p) for p in self._find_current_files("robot")],
+            "ford": str(self._find_file(lambda p: p.name == "FordA_TRAIN.txt")),
+            "vision": [str(p) for p in self.dataset_root.rglob("2nd_process_*_data.csv")],
+            "bosch": str(self._find_file(lambda p: p.name == "train_numeric.csv")),
+        }
+
+    def forming(self, index: int) -> dict[str, Any]:
+        return self.forming_rows[index % len(self.forming_rows)]
+
+    def current(self, process_code: str, index: int) -> dict[str, float]:
+        rows = self.robot_current_rows if process_code == "BODY" else self.press_current_rows
+        return rows[index % len(rows)]
+
+    def ford(self, index: int) -> dict[str, Any]:
+        return self.ford_rows[index % len(self.ford_rows)]
+
+    def vision(self, index: int) -> dict[str, Any]:
+        return self.vision_rows[index % len(self.vision_rows)]
+
+    def bosch(self, index: int) -> dict[str, Any]:
+        return self.bosch_rows[index % len(self.bosch_rows)]
+
+    def _find_file(self, predicate) -> Path:
+        for path in self.dataset_root.rglob("*"):
+            if path.is_file() and predicate(path):
+                return path
+        raise FileNotFoundError(f"Required source file was not found under {self.dataset_root}")
+
+    def _find_forming_file(self) -> Path:
+        return self._find_file(lambda p: p.suffix.lower() == ".csv" and "2022" in p.name)
+
+    def _find_current_files(self, kind: str) -> list[Path]:
+        files: list[Path] = []
+        for path in self.dataset_root.rglob("*.csv"):
+            if kind == "press" and "프레스" in path.name:
+                files.append(path)
+            if kind == "robot" and "로봇" in path.name:
+                files.append(path)
+        if not files:
+            raise FileNotFoundError(f"{kind} current CSV files were not found")
+        return sorted(files)
+
+    def _read_forming_rows(self) -> list[dict[str, Any]]:
+        path = self._find_forming_file()
+        rows: list[dict[str, Any]] = []
+        with path.open(encoding="utf-8-sig", newline="") as f:
+            for row in csv.DictReader(f):
+                rows.append(row)
+                if len(rows) >= self.sample_rows:
+                    break
+        return rows or [{"idx": "1", "itemno": "ITEM-001", "quantity": "1", "cnt": "1"}]
+
+    def _read_current_rows(self, kind: str) -> list[dict[str, float]]:
+        rows: list[dict[str, float]] = []
+        for path in self._find_current_files(kind):
+            with path.open(encoding="utf-8-sig", newline="") as f:
+                for row_no, row in enumerate(csv.DictReader(f), start=1):
+                    value = _to_float(row.get("RMS[A]"), 1.8)
+                    if value < 0.01:
+                        value = 1.65 + (row_no % 31) * 0.035
+                    rows.append(
+                        {
+                            "rowId": float(row.get("", row_no) or row_no),
+                            "rmsAmpere": value,
+                            "maxAmpere": value + 0.16,
+                            "minAmpere": max(0.0, value - 0.16),
+                            "accelerationG": abs(value - 1.8) / 25,
+                        },
+                    )
+                    if len(rows) >= self.sample_rows:
+                        return rows
+        return rows or [{"rowId": 1, "rmsAmpere": 1.8, "maxAmpere": 1.95, "minAmpere": 1.65, "accelerationG": 0.006}]
+
+    def _read_ford_rows(self) -> list[dict[str, Any]]:
+        path = self._find_file(lambda p: p.name == "FordA_TRAIN.txt")
+        rows: list[dict[str, Any]] = []
+        with path.open(encoding="utf-8", errors="ignore") as f:
+            for row_no, line in enumerate(f, start=1):
+                values = [_to_float(v, 0.0) for v in line.split()]
+                if len(values) < 20:
+                    continue
+                signal = values[1:501]
+                rms = math.sqrt(sum(v * v for v in signal) / len(signal))
+                peak = max(abs(v) for v in signal)
+                bands = _frequency_bands(signal)
+                rows.append(
+                    {
+                        "rowId": row_no,
+                        "label": int(values[0]),
+                        "vibrationRms": rms,
+                        "vibrationPeak": peak,
+                        "vibrationScore": min(0.99, rms / 3.2),
+                        "frequencyBands": bands,
+                        "frequencyPeakBand": max(bands, key=bands.get).replace("freq_", "").upper(),
+                    },
+                )
+                if len(rows) >= self.sample_rows:
+                    break
+        return rows or [{"rowId": 1, "label": 1, "vibrationRms": 0.2, "vibrationPeak": 0.4, "vibrationScore": 0.1, "frequencyBands": {}, "frequencyPeakBand": "0_100_HZ"}]
+
+    def _read_vision_rows(self) -> list[dict[str, Any]]:
+        rows: list[dict[str, Any]] = []
+        for data_path in sorted(self.dataset_root.rglob("2nd_process_*_data.csv")):
+            side = "LEFT" if "left" in data_path.name else "RIGHT"
+            label_path = data_path.with_name(data_path.name.replace("_data.csv", "_label.json"))
+            labels = json.loads(label_path.read_text(encoding="utf-8")) if label_path.exists() else []
+            with data_path.open(encoding="utf-8-sig", newline="") as f:
+                reader = csv.reader(f)
+                for row_no, row in enumerate(reader, start=1):
+                    values = [_to_float(v, 0.0) for v in row if str(v).strip()]
+                    if not values:
+                        continue
+                    avg = sum(values) / len(values)
+                    variance = sum((v - avg) ** 2 for v in values) / len(values)
+                    std = math.sqrt(variance)
+                    label = int(float(labels[(row_no - 1) % len(labels)])) if labels else 0
+                    defect_score = min(0.99, std / 7.0 + (0.35 if label else 0.04))
+                    rows.append(
+                        {
+                            "rowId": row_no,
+                            "imagePosition": side,
+                            "label": label,
+                            "avgTemperature": avg,
+                            "maxTemperature": max(values),
+                            "minTemperature": min(values),
+                            "thermalStdTemp": std,
+                            "defectScore": defect_score,
+                            "thicknessValue": 114.0 + (row_no % 19) * 0.45 + std,
+                            "surfaceQualityScore": max(0.0, 100.0 - defect_score * 38),
+                        },
+                    )
+                    if len(rows) >= self.sample_rows:
+                        return rows
+        return rows or [{"rowId": 1, "imagePosition": "LEFT", "label": 0, "avgTemperature": 42.0, "maxTemperature": 45.0, "minTemperature": 40.0, "thermalStdTemp": 1.0, "defectScore": 0.1, "thicknessValue": 116.0, "surfaceQualityScore": 96.0}]
+
+    def _read_bosch_rows(self) -> list[dict[str, Any]]:
+        path = self._find_file(lambda p: p.name == "train_numeric.csv")
+        rows: list[dict[str, Any]] = []
+        with path.open(encoding="utf-8-sig", newline="") as f:
+            for row in csv.DictReader(f):
+                numeric = [_to_float(v, math.nan) for k, v in row.items() if k not in {"Id", "Response"} and v not in {"", None}]
+                numeric = [v for v in numeric if not math.isnan(v)]
+                rows.append(
+                    {
+                        "id": int(_to_float(row.get("Id"), len(rows) + 1)),
+                        "response": int(_to_float(row.get("Response"), 0)),
+                        "meanNumericFeature": sum(numeric) / len(numeric) if numeric else 0.0,
+                        "nonNullFeatureCount": len(numeric),
+                    },
+                )
+                if len(rows) >= self.sample_rows:
+                    break
+        return rows or [{"id": 1, "response": 0, "meanNumericFeature": 0.0, "nonNullFeatureCount": 0}]
+
+
+def _build_event_json(
+    *,
+    source: _SourceSamples,
+    car_master_id: int,
+    event_id: str,
+    event_time: datetime,
+    process_code: str,
+    process_index: int,
+    sample_index: int,
+    is_defect: bool,
+) -> tuple[dict[str, Any], int, str]:
+    forming = source.forming(car_master_id - 1)
+    current = dict(source.current(process_code, sample_index))
+    ford = dict(source.ford(sample_index))
+    vision = dict(source.vision(sample_index))
+    bosch = dict(source.bosch(sample_index))
+    _apply_defect_profile(process_code, car_master_id, is_defect, current, ford, vision, bosch)
+    metrics = _process_metrics(process_code, sample_index, current, ford, vision, bosch, is_defect)
+    process_data = _process_data(process_code, car_master_id, current, ford, vision, bosch, metrics, is_defect)
+    defect_reason = _defect_reason(process_code, process_data, metrics, current, ford, vision, bosch, is_defect)
+    line_no = _line_no(car_master_id, process_code)
+    equipment = _equipment(process_code, line_no)
+    status_rand = _stable_float(car_master_id, process_code, "operation_status_rand")
+    if is_defect:
+        operation_status = "ERROR" if status_rand < 0.15 else "RUNNING"
+    else:
+        operation_status = "ERROR" if status_rand < 0.01 else "RUNNING"
+    is_error = (operation_status == "ERROR")
+    last_normal_time = event_time - timedelta(seconds=31 if is_error else 3)
+    event_json = {
+        "event": {
+            "eventId": event_id,
+            "eventTime": event_time.isoformat(timespec="seconds"),
+            "eventType": _event_type(process_code),
+            "eventName": _event_name(process_code),
+        },
+        "equipment": equipment,
+        "equipmentStatus": {
+            "operationStatus": operation_status,
+            "lastNormalTime": last_normal_time.isoformat(timespec="seconds") if is_error else None,
+            "statusChangedTime": event_time.isoformat(timespec="seconds") if is_error else None,
+        },
+        "product": {"carMasterId": car_master_id},
+        "sensor": _sensor(current, ford, vision),
+        "processMetrics": metrics,
+        "sourceTrace": {
+            "fordRowId": ford["rowId"],
+            "formingRowId": int(_to_float(forming.get("idx"), car_master_id)),
+            "robotArmVibrationRowId": int(_to_float(current.get("rowId"), sample_index + 1)),
+            "machineVisionRowId": vision["rowId"],
+            "boschId": bosch["id"],
+        },
+        "processData": process_data,
+    }
+    return _json_safe(event_json), int(is_defect), defect_reason
+
+
+def _build_transition_rows(event_by_car_process: dict[tuple[int, str], dict[str, Any]], car_count: int, train_car_count: int) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for car_master_id in range(1, car_count + 1):
+        split = "train" if car_master_id <= train_car_count else "test"
+        for source_process, target_process in TRANSFER_FLOWS:
+            source = event_by_car_process[(car_master_id, source_process)]
+            target = event_by_car_process[(car_master_id, target_process)]
+            source_payload = json.loads(source["event_json"])
+            target_payload = json.loads(target["event_json"])
+            source_metrics = source_payload["processMetrics"]
+            source_sensor = source_payload["sensor"]
+            row = {
+                "car_master_id": car_master_id,
+                "source_event_id": source["event_id"],
+                "target_event_id": target["event_id"],
+                "source_process_code": source_process,
+                "target_process_code": target_process,
+                "source_defect_yn": int(source["defect_yn"]),
+                "target_defect_yn": int(target["defect_yn"]),
+                "target_defect_reason": target["defect_reason"],
+                "source_cycle_time_sec": source_metrics["cycleTimeSec"],
+                "source_station_delay_sec": source_metrics["stationDelaySec"],
+                "source_queue_length": source_metrics["queueLength"],
+                "source_wip_count": source_metrics["wipCount"],
+                "source_current_rms_ampere": source_sensor["current"]["rmsAmpere"],
+                "source_vibration_score": source_sensor["vibration"]["vibrationScore"],
+                "source_thermal_score": source_sensor["thermal"]["thermalScore"],
+                "source_event_json": source["event_json"],
+                "target_event_json": target["event_json"],
+                "dataset_split": split,
+            }
+            rows.append(row)
+    return rows
+
+
+def _defect_profile(car_master_id: int) -> dict[str, bool]:
+    press = _stable_int(car_master_id, "PRESS") % 100 < 9
+    body = (_stable_int(car_master_id, "BODY") % 100 < 7) or (press and _stable_int(car_master_id, "PRESS_BODY") % 100 < 55)
+    paint = (_stable_int(car_master_id, "PAINT") % 100 < 8) or (body and _stable_int(car_master_id, "BODY_PAINT") % 100 < 48)
+    assembly = (_stable_int(car_master_id, "ASSEMBLY") % 100 < 6) or (paint and _stable_int(car_master_id, "PAINT_ASSEMBLY") % 100 < 45)
+    return {"PRESS": press, "BODY": body, "PAINT": paint, "ASSEMBLY": assembly}
+
+
+def _apply_defect_profile(
+    process_code: str,
+    car_master_id: int,
+    is_defect: bool,
+    current: dict[str, Any],
+    ford: dict[str, Any],
+    vision: dict[str, Any],
+    bosch: dict[str, Any],
+) -> None:
+    r1 = _stable_float(car_master_id, process_code, "defect_rand_1")
+    r2 = _stable_float(car_master_id, process_code, "defect_rand_2")
+    r3 = _stable_float(car_master_id, process_code, "defect_rand_3")
+
+    if is_defect:
+        current["rmsAmpere"] = round(3.0 + r1 * 1.5, 9)
+        current["maxAmpere"] = round(current["rmsAmpere"] + 0.2 + r2 * 0.2, 9)
+        current["minAmpere"] = round(max(0.0, current["rmsAmpere"] - 0.2 - r3 * 0.2), 9)
+        current["accelerationG"] = round(0.04 + r1 * 0.06, 9)
+        ford["label"] = -1
+        ford["vibrationScore"] = round(0.55 + r1 * 0.30, 6)
+        ford["vibrationRms"] = round(1.2 + r2 * 1.5, 9)
+        ford["vibrationPeak"] = round(2.0 + r3 * 2.0, 9)
+        vision["label"] = 1
+        vision["thermalStdTemp"] = round(3.5 + r1 * 3.5, 9)
+        vision["defectScore"] = round(0.50 + r2 * 0.30, 6)
+        vision["surfaceQualityScore"] = round(65.0 + r3 * 15.0, 3)
+        bosch["response"] = 1
+    else:
+        if r1 < 0.03:
+            current["rmsAmpere"] = round(2.8 + r2 * 0.8, 9)
+            current["maxAmpere"] = round(current["rmsAmpere"] + 0.2, 9)
+            current["minAmpere"] = round(max(0.0, current["rmsAmpere"] - 0.2), 9)
+        else:
+            current["rmsAmpere"] = round(float(current["rmsAmpere"]) * (0.9 + r2 * 0.2), 9)
+            current["maxAmpere"] = round(float(current["maxAmpere"]) * (0.9 + r2 * 0.2), 9)
+            current["minAmpere"] = round(float(current["minAmpere"]) * (0.9 + r2 * 0.2), 9)
+            
+        if r2 < 0.03:
+            ford["vibrationScore"] = round(0.45 + r3 * 0.20, 6)
+            ford["vibrationRms"] = round(1.0 + r1 * 0.8, 9)
+        else:
+            ford["vibrationScore"] = round(float(ford["vibrationScore"]) * (0.8 + r3 * 0.4), 6)
+            
+        if r3 < 0.03:
+            vision["defectScore"] = round(0.40 + r1 * 0.20, 6)
+            vision["surfaceQualityScore"] = round(75.0 + r2 * 10.0, 3)
+        else:
+            vision["defectScore"] = round(float(vision["defectScore"]) * (0.8 + r1 * 0.4), 6)
+            
+        bosch["response"] = 0
+
+
+def _process_metrics(process_code: str, index: int, current: dict[str, Any], ford: dict[str, Any], vision: dict[str, Any], bosch: dict[str, Any], is_defect: bool) -> dict[str, Any]:
+    target = {"PRESS": 40.0, "BODY": 52.0, "PAINT": 64.0, "ASSEMBLY": 58.0}[process_code]
+    anomaly = 0.0
+    if process_code in {"PRESS", "BODY"}:
+        anomaly = float(ford["vibrationScore"]) * 9
+    elif process_code == "PAINT":
+        anomaly = float(vision["defectScore"]) * 8
+    else:
+        anomaly = float(bosch["response"]) * 8
+    if is_defect:
+        anomaly += 8.0
+    cycle_time = target + anomaly + (index % 5) * 0.4
+    processing = max(1.0, cycle_time - (5 + index % 4))
+    waiting = cycle_time - processing + (index % 3)
+    delay = max(0.0, cycle_time - target)
+    return {
+        "cycleTimeSec": round(cycle_time, 3),
+        "waitingTimeSec": round(waiting, 3),
+        "processingTimeSec": round(processing, 3),
+        "stationDelaySec": round(delay, 3),
+        "throughputPerMin": round(60 / cycle_time, 3),
+        "queueLength": int(3 + delay // 2 + index % 5),
+        "wipCount": int(14 + delay // 1.5 + index % 9),
+        "equipmentIdleTimeSec": round(delay * 1.6, 3),
+    }
+
+
+def _process_data(
+    process_code: str,
+    car_master_id: int,
+    current: dict[str, Any],
+    ford: dict[str, Any],
+    vision: dict[str, Any],
+    bosch: dict[str, Any],
+    metrics: dict[str, Any],
+    is_defect: bool,
+) -> dict[str, Any]:
+    if process_code == "PRESS":
+        return {"press": {"countIncreaseYn": metrics["stationDelaySec"] < 10, "targetCycleTimeSec": 40.0, "timestampDelaySec": metrics["stationDelaySec"]}}
+    if process_code == "BODY":
+        body_rand = _stable_float(car_master_id, process_code, "body_motion_rand")
+        if is_defect:
+            robot_motion_status = "WARNING" if body_rand < 0.70 else "NORMAL"
+        else:
+            robot_motion_status = "WARNING" if body_rand < 0.05 else "NORMAL"
+        return {"body": {"robotMotionStatus": robot_motion_status, "robotOperationMode": "AUTO", "frequencyPeakBand": ford["frequencyPeakBand"], "frequencyBands": ford["frequencyBands"]}}
+    if process_code == "PAINT":
+        vision_rand = _stable_float(car_master_id, process_code, "vision_label_rand")
+        if is_defect:
+            vision_label = "DEFECT" if vision_rand < 0.70 else "NORMAL"
+        else:
+            vision_label = "DEFECT" if vision_rand < 0.05 else "NORMAL"
+        return {
+            "paint": {
+                "imagePosition": vision["imagePosition"],
+                "thermalStdTemp": round(vision["thermalStdTemp"], 3),
+                "thicknessValue": round(vision["thicknessValue"], 3),
+                "defectScore": round(vision["defectScore"], 4),
+                "visionLabel": vision_label,
+                "surfaceQualityScore": round(vision["surfaceQualityScore"], 3),
+            }
+        }
+    expected = "A01>A02>A03>A04"
+    seq_rand = _stable_float(car_master_id, process_code, "seq_rand")
+    missing_rand = _stable_float(car_master_id, process_code, "missing_rand")
+    fasten_rand = _stable_float(car_master_id, process_code, "fasten_rand")
+    
+    if is_defect:
+        seq_err = 1 if seq_rand < 0.65 else 0
+        missing_err = 1 if missing_rand < 0.60 else 0
+        fasten_err = 1 if fasten_rand < 0.70 else 0
+    else:
+        seq_err = 1 if seq_rand < 0.04 else 0
+        missing_err = 1 if missing_rand < 0.03 else 0
+        fasten_err = 1 if fasten_rand < 0.05 else 0
+        
+    actual = "A01>A03>A02>A04" if seq_err > 0 else expected
+    return {
+        "assembly": {
+            "expectedSequence": expected,
+            "actualSequence": actual,
+            "missingPartCount": missing_err,
+            "fasteningErrorCount": fasten_err,
+            "sequenceErrorCount": seq_err,
+        }
+    }
+
+
+def _defect_reason(process_code: str, process_data: dict[str, Any], metrics: dict[str, Any], current: dict[str, Any], ford: dict[str, Any], vision: dict[str, Any], bosch: dict[str, Any], is_defect: bool) -> str:
+    if not is_defect:
+        return "normal"
+    if process_code == "PRESS":
+        return "press_count_or_delay"
+    if process_code == "BODY":
+        return "body_robot_vibration"
+    if process_code == "PAINT":
+        return "paint_vision_or_thermal"
+    return "assembly_sequence_or_fastening"
+
+
+def _sensor(current: dict[str, Any], ford: dict[str, Any], vision: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "sensorType": "MULTI_SENSOR",
+        "current": {"rmsAmpere": round(current["rmsAmpere"], 9), "maxAmpere": round(current["maxAmpere"], 9), "minAmpere": round(current["minAmpere"], 9)},
+        "vibration": {"accelerationG": round(current["accelerationG"], 9), "vibrationScore": round(ford["vibrationScore"], 6), "vibrationRms": round(ford["vibrationRms"], 9), "vibrationPeak": round(ford["vibrationPeak"], 9)},
+        "robotArmVibration": {"robotId": "ROBOT_ARM_01", "axis": f"J{(int(current['rowId']) % 6) + 1}", "frequencyHz": round(40 + float(ford["vibrationScore"]) * 220, 3), "amplitude": round(float(ford["vibrationRms"]) / 1000, 9), "vibrationRms": round(float(ford["vibrationRms"]) / 900, 9), "vibrationPeak": round(float(ford["vibrationPeak"]) / 700, 9), "vibrationScore": round(ford["vibrationScore"], 6)},
+        "thermal": {"thermalScore": round(vision["avgTemperature"], 3), "avgTemperature": round(vision["avgTemperature"], 3), "maxTemperature": round(vision["maxTemperature"], 3), "minTemperature": round(vision["minTemperature"], 3)},
+    }
+
+
+def _event_type(process_code: str) -> str:
+    return {"PRESS": "PROCESS_STATUS", "BODY": "EQUIPMENT_SENSOR", "PAINT": "QUALITY_CHECK", "ASSEMBLY": "PROCESS_STATUS"}[process_code]
+
+
+def _event_name(process_code: str) -> str:
+    return {"PRESS": "프레스 공정 통합 관제 이벤트", "BODY": "차체 공정 로봇 관제 이벤트", "PAINT": "도장 공정 품질 관제 이벤트", "ASSEMBLY": "의장 공정 조립 관제 이벤트"}[process_code]
+
+
+def _equipment(process_code: str, line_no: int) -> dict[str, str]:
+    equipment_type = {"PRESS": "HYDRAULIC_PRESS", "BODY": "ROBOT_ARM", "PAINT": "CAMERA", "ASSEMBLY": "CONVEYOR"}[process_code]
+    name = {"PRESS": "프레스 유압모터", "BODY": "차체 용접 로봇", "PAINT": "도장 열화상 카메라", "ASSEMBLY": "의장 조립 컨베이어"}[process_code]
+    return {"equipmentCode": f"EQ_{process_code}_{line_no:03d}", "equipmentName": f"{name} {line_no}호", "equipmentType": equipment_type}
+
+
+def _equipment_id(process_code: str, car_master_id: int) -> int:
+    return (PROCESS_ORDER.index(process_code) * 10) + _line_no(car_master_id, process_code)
+
+
+def _line_no(car_master_id: int, process_code: str) -> int:
+    return _stable_int(car_master_id, process_code, "line") % 5 + 1
+
+
+def _frequency_bands(signal: list[float]) -> dict[str, float]:
+    names = ["freq_0_100_hz", "freq_101_200_hz", "freq_201_300_hz", "freq_301_400_hz", "freq_401_500_hz", "freq_501_600_hz", "freq_601_700_hz", "freq_701_800_hz", "freq_801_900_hz", "freq_901_1000_hz", "freq_1001_1100_hz", "freq_1101_1200_hz", "freq_1201_1300_hz", "freq_1301_1400_hz", "freq_1401_1500_hz", "freq_1501_1600_hz"]
+    chunk_size = max(1, len(signal) // len(names))
+    bands = {}
+    for idx, name in enumerate(names):
+        chunk = signal[idx * chunk_size : (idx + 1) * chunk_size] or [0.0]
+        bands[name] = round(math.sqrt(sum(v * v for v in chunk) / len(chunk)) / 700, 9)
+    return bands
+
+
+def _write_csv(path: Path, rows: list[dict[str, Any]], columns: list[str]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8-sig", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=columns, extrasaction="ignore")
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def _stable_int(*parts: Any) -> int:
+    digest = hashlib.blake2b(":".join(map(str, parts)).encode("utf-8"), digest_size=8).digest()
+    return int.from_bytes(digest, "big")
+
+
+def _stable_float(*parts: Any) -> float:
+    return (_stable_int(*parts) % 100000) / 100000.0
+
+
+def _to_float(value: Any, default: float = 0.0) -> float:
+    try:
+        result = float(value)
+    except (TypeError, ValueError):
+        return default
+    return result if not math.isnan(result) and not math.isinf(result) else default
+
+
+def _json_safe(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {k: _json_safe(v) for k, v in value.items()}
+    if isinstance(value, list):
+        return [_json_safe(v) for v in value]
+    if isinstance(value, float):
+        return None if math.isnan(value) or math.isinf(value) else value
+    return value
diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index 786a8ad..1bc4319 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -100,7 +100,7 @@ def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
     return {
         "event": {
             "eventId": event.get("eventId"),
-            "eventTime": None,
+            "eventTime": event.get("eventTime"),
             "eventType": event.get("eventType"),
             "eventName": event.get("eventName"),
         },
@@ -111,8 +111,8 @@ def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
         },
         "equipmentStatus": {
             "operationStatus": equipment_status.get("operationStatus"),
-            "lastNormalTime": None,
-            "statusChangedTime": None,
+            "lastNormalTime": equipment_status.get("lastNormalTime"),
+            "statusChangedTime": equipment_status.get("statusChangedTime"),
         },
         "product": {
             "carMasterId": product.get("carMasterId"),
@@ -171,32 +171,24 @@ def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
 
 PROCESS_META: dict[str, dict[str, Any]] = {
     "PRESS": {
-        "lineCode": "PRESS_LINE_01",
-        "stationCode": "PRESS_STATION_01",
         "equipmentType": "HYDRAULIC_PRESS",
         "eventType": "PROCESS_STATUS",
         "eventName": "프레스 공정 통합 관제 이벤트",
         "targetCycleTimeSec": 40.0,
     },
     "BODY": {
-        "lineCode": "BODY_LINE_01",
-        "stationCode": "BODY_STATION_01",
         "equipmentType": "ROBOT_ARM",
         "eventType": "EQUIPMENT_SENSOR",
         "eventName": "차체 공정 로봇 관제 이벤트",
         "targetCycleTimeSec": 52.0,
     },
     "PAINT": {
-        "lineCode": "PAINT_LINE_01",
-        "stationCode": "PAINT_STATION_01",
         "equipmentType": "CAMERA",
         "eventType": "QUALITY_CHECK",
         "eventName": "도장 공정 품질 관제 이벤트",
         "targetCycleTimeSec": 64.0,
     },
     "ASSEMBLY": {
-        "lineCode": "ASSEMBLY_LINE_01",
-        "stationCode": "ASSEMBLY_STATION_01",
         "equipmentType": "CONVEYOR",
         "eventType": "PROCESS_STATUS",
         "eventName": "의장 공정 조립 관제 이벤트",
@@ -383,8 +375,7 @@ def _build_event(
             "event": {
                 "eventId": event_id,
                 # 원천 이벤트 생성 시점에는 실제 발생 시각이 확정되지 않았으므로
-                # DB event_time과 동일하게 JSON 내부 eventTime도 NULL로 둔다.
-                "eventTime": None,
+                "eventTime": event_time.isoformat(),
                 "eventType": meta["eventType"],
                 "eventName": meta["eventName"],
             },
@@ -397,8 +388,12 @@ def _build_event(
                 # 운전 상태는 정상/이상 프로필 안에서 차량·공정별로 랜덤 생성한다.
                 # 시간 필드는 실제 이벤트 전송 전까지 미확정이므로 NULL로 둔다.
                 "operationStatus": equipment_status["operationStatus"],
-                "lastNormalTime": None,
-                "statusChangedTime": None,
+                "lastNormalTime": (
+                    (event_time - timedelta(seconds=31)).isoformat()
+                    if is_abnormal
+                    else None
+                ),
+                "statusChangedTime": event_time.isoformat() if is_abnormal else None,
             },
             "product": {
                 "carMasterId": car_master_id,
diff --git a/app/ml/artifacts/auxiliary_process_models.pkl b/app/ml/artifacts/auxiliary_process_models.pkl
deleted file mode 100644
index 81f089a7d22b7ad3ac2a7aa9b49f2f3fa1c9c6bb..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/app/ml/preprocessing/__init__.py b/app/ml/preprocessing/__init__.py
index 50cbb18..062d827 100644
--- a/app/ml/preprocessing/__init__.py
+++ b/app/ml/preprocessing/__init__.py
@@ -1,2 +1,22 @@
 """Data preprocessing package."""
 
+from app.ml.preprocessing.manufacturing_event_features import (
+    ADJACENT_TRANSITIONS,
+    PROCESS_ORDER,
+    build_adjacent_transition_training_frames,
+    build_event_feature_frame,
+    build_transition_training_frame,
+    build_vehicle_process_wide_frame,
+    infer_event_abnormal_label,
+)
+
+__all__ = [
+    "ADJACENT_TRANSITIONS",
+    "PROCESS_ORDER",
+    "build_adjacent_transition_training_frames",
+    "build_event_feature_frame",
+    "build_transition_training_frame",
+    "build_vehicle_process_wide_frame",
+    "infer_event_abnormal_label",
+]
+
diff --git a/app/ml/training/defect_transfer_prediction_model.ipynb b/app/ml/training/defect_transfer_prediction_model.ipynb
index 8b53290..2083b81 100644
--- a/app/ml/training/defect_transfer_prediction_model.ipynb
+++ b/app/ml/training/defect_transfer_prediction_model.ipynb
@@ -1,8118 +1,8118 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "873328b3",
+   "metadata": {
+    "id": "873328b3"
+   },
+   "source": [
+    "# 불량 탐지 및 SHAP 기반 공정 전이 위험 예측\n",
+    "\n",
+    "1. 라이브러리 import 및 Colab/Google Drive 로드\n",
+    "2. `app/ml/datasets/process/` 전체 데이터셋 정제 및 전처리\n",
+    "3. Bosch, Ford, 열화상, 소성가공 데이터 기반 보조 모델/피처 학습\n",
+    "4. 후보 모델 4종(LightGBM, RandomForest, ExtraTrees, LogisticRegression) 교차검증/튜닝\n",
+    "5. 최종 후보별 성능 비교, Accuracy, False Positive Rate, Confusion Matrix\n",
+    "6. SHAP 기반 영향 공정 분석 및 공정 간 전이 위험 결과 생성\n",
+    "7. AI 모델 관리 항목과 모델/결과 파일 저장\n",
+    "\n",
+    "> 현재 데이터에는 `BODY 불량 -> PAINT 불량` 같은 실제 전이 정답 라벨과 데이터셋 간 공통 제품 키가 없습니다. 따라서 이 노트북의 전이 예측은 별도 전이 라벨 학습이 아니라, `최종 불량 확률 + SHAP 영향 공정 + 보조 공정 위험 피처 + 공정 흐름 규칙` 기반 위험 확산 해석입니다.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "P44DGP2TDAt4",
+   "metadata": {
+    "id": "P44DGP2TDAt4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "hMAQMhIqbEe9",
+   "metadata": {
+    "id": "hMAQMhIqbEe9"
+   },
+   "outputs": [],
+   "source": [
+    "# Colab 환경에서 필요한 패키지 설치\n",
+    "import importlib.util\n",
+    "import subprocess\n",
+    "import sys\n",
+    "\n",
+    "def ensure_package(import_name: str, pip_name: str | None = None):\n",
+    "    pip_name = pip_name or import_name\n",
+    "    if importlib.util.find_spec(import_name) is None:\n",
+    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", pip_name, \"-q\"])\n",
+    "\n",
+    "ensure_package(\"lightgbm\")\n",
+    "ensure_package(\"shap\")\n",
+    "ensure_package(\"joblib\")\n",
+    "ensure_package(\"optuna\")\n",
+    "ensure_package(\"imblearn\", \"imbalanced-learn\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "XFBnJXPabHMi",
+   "metadata": {
+    "id": "XFBnJXPabHMi"
+   },
+   "outputs": [],
+   "source": [
+    "# CPU 병렬 사용\n",
+    "import lightgbm as lgb\n",
+    "\n",
+    "model = lgb.LGBMClassifier(\n",
+    "    n_estimators=300,\n",
+    "    learning_rate=0.05,\n",
+    "    num_leaves=31,\n",
+    "    n_jobs=-1,\n",
+    "    random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7a3d724",
+   "metadata": {
+    "id": "f7a3d724"
+   },
+   "source": [
+    "> 필요한 추가 패키지(`optuna`, `imbalanced-learn`)는 위 설치 셀에서 자동 확인/설치합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e37daef5",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "e37daef5",
+    "outputId": "d3014dc5-ad21-4f08-d5e8-65c43e05146f"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "873328b3",
-      "metadata": {
-        "id": "873328b3"
-      },
-      "source": [
-        "# 불량 탐지 및 SHAP 기반 공정 전이 위험 예측\n",
-        "\n",
-        "1. 라이브러리 import 및 Colab/Google Drive 로드\n",
-        "2. `app/ml/datasets/process/` 전체 데이터셋 정제 및 전처리\n",
-        "3. Bosch, Ford, 열화상, 소성가공 데이터 기반 보조 모델/피처 학습\n",
-        "4. 후보 모델 4종(LightGBM, RandomForest, ExtraTrees, LogisticRegression) 교차검증/튜닝\n",
-        "5. 최종 후보별 성능 비교, Accuracy, False Positive Rate, Confusion Matrix\n",
-        "6. SHAP 기반 영향 공정 분석 및 공정 간 전이 위험 결과 생성\n",
-        "7. AI 모델 관리 항목과 모델/결과 파일 저장\n",
-        "\n",
-        "> 현재 데이터에는 `BODY 불량 -> PAINT 불량` 같은 실제 전이 정답 라벨과 데이터셋 간 공통 제품 키가 없습니다. 따라서 이 노트북의 전이 예측은 별도 전이 라벨 학습이 아니라, `최종 불량 확률 + SHAP 영향 공정 + 보조 공정 위험 피처 + 공정 흐름 규칙` 기반 위험 확산 해석입니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "pandas 2.2.2\n",
+      "lightgbm 4.6.0\n",
+      "optuna 4.9.0\n",
+      "shap 0.52.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import gc\n",
+    "import json\n",
+    "import pickle\n",
+    "import re\n",
+    "import shutil\n",
+    "import warnings\n",
+    "from datetime import datetime, timezone\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import joblib\n",
+    "import lightgbm as lgb\n",
+    "import optuna\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import shap\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
+    "from optuna.samplers import TPESampler\n",
+    "from sklearn.base import clone\n",
+    "from sklearn.ensemble import ExtraTreesClassifier, IsolationForest, RandomForestClassifier\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import (\n",
+    "    ConfusionMatrixDisplay,\n",
+    "    PrecisionRecallDisplay,\n",
+    "    RocCurveDisplay,\n",
+    "    accuracy_score,\n",
+    "    average_precision_score,\n",
+    "    classification_report,\n",
+    "    confusion_matrix,\n",
+    "    f1_score,\n",
+    "    precision_score,\n",
+    "    precision_recall_curve,\n",
+    "    recall_score,\n",
+    "    roc_auc_score,\n",
+    ")\n",
+    "from sklearn.model_selection import ParameterSampler, StratifiedKFold, train_test_split\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "pd.set_option(\"display.max_columns\", 120)\n",
+    "pd.set_option(\"display.max_rows\", 80)\n",
+    "\n",
+    "RANDOM_STATE = 42\n",
+    "np.random.seed(RANDOM_STATE)\n",
+    "\n",
+    "print(\"pandas\", pd.__version__)\n",
+    "print(\"lightgbm\", lgb.__version__)\n",
+    "print(\"optuna\", optuna.__version__)\n",
+    "print(\"shap\", shap.__version__)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e3e0e4c",
+   "metadata": {
+    "id": "0e3e0e4c"
+   },
+   "source": [
+    "## 1. Google Drive 및 로컬 데이터셋 경로 설정\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "abb8856c",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "abb8856c",
+    "outputId": "d0b5cf16-76a8-4d8e-cf3f-ce1f188f80a2"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "P44DGP2TDAt4",
-      "metadata": {
-        "id": "P44DGP2TDAt4"
-      },
-      "source": []
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Mounted at /content/drive\n",
+      "PROJECT_ROOT: /content/drive/MyDrive\n",
+      "PROCESS_ROOT: /content/drive/MyDrive/aims_dataset\n",
+      "OUTPUT_DIR: /content/defect_transfer_outputs\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Drive 마운트\n",
+    "try:\n",
+    "    from google.colab import drive\n",
+    "    drive.mount(\"/content/drive\")\n",
+    "    IN_COLAB = True\n",
+    "except Exception:\n",
+    "    IN_COLAB = False\n",
+    "    print(\"Colab이 아니므로 Google Drive mount를 건너뜁니다.\")\n",
+    "\n",
+    "# 데이터 루트\n",
+    "PROJECT_ROOT = Path(\"/content/drive/MyDrive\") if IN_COLAB else Path.cwd()\n",
+    "PROCESS_ROOT = PROJECT_ROOT / \"aims_dataset\"\n",
+    "BOSCH_ROOT = PROCESS_ROOT / \"bosch-production-line-performance\"\n",
+    "THERMAL_ROOT = PROCESS_ROOT / \"머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)\"\n",
+    "FORD_ROOT = PROCESS_ROOT / \"Ford 엔진 진동 데이터셋\"\n",
+    "METAL_ROOT = PROCESS_ROOT / \"소성가공 자원최적화 AI 데이터셋\"\n",
+    "# 산출물 경로\n",
+    "OUTPUT_DIR = Path(\"/content/defect_transfer_outputs\") if IN_COLAB else Path(\"outputs/defect_transfer\")\n",
+    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Bosch 파일 경로\n",
+    "NUMERIC_PATH = BOSCH_ROOT / \"train_numeric.csv\"\n",
+    "DATE_PATH = BOSCH_ROOT / \"train_date.csv\"\n",
+    "CATEGORICAL_PATH = BOSCH_ROOT / \"train_categorical.csv\"\n",
+    "\n",
+    "# 열화상 파일 경로\n",
+    "THERMAL_LEFT_DATA_PATH = THERMAL_ROOT / \"2nd_process_left_data.csv\"\n",
+    "THERMAL_LEFT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_left_label.json\"\n",
+    "THERMAL_RIGHT_DATA_PATH = THERMAL_ROOT / \"2nd_process_right_data.csv\"\n",
+    "THERMAL_RIGHT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_right_label.json\"\n",
+    "\n",
+    "# 보조 데이터 파일 경로\n",
+    "FORD_TRAIN_PATH = FORD_ROOT / \"FordA_TRAIN.txt\"\n",
+    "FORD_TEST_PATH = FORD_ROOT / \"FordA_TEST.txt\"\n",
+    "METAL_PROCESS_PATH = METAL_ROOT / \"공정_데이터_2022년_8월.csv\"\n",
+    "METAL_SENSOR_PATHS = sorted([p for p in METAL_ROOT.glob(\"*.csv\") if p.name != \"공정_데이터_2022년_8월.csv\"])\n",
+    "\n",
+    "print(\"PROJECT_ROOT:\", PROJECT_ROOT)\n",
+    "print(\"PROCESS_ROOT:\", PROCESS_ROOT)\n",
+    "print(\"OUTPUT_DIR:\", OUTPUT_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lc2ZXZzwbEe_",
+   "metadata": {
+    "id": "lc2ZXZzwbEe_"
+   },
+   "source": [
+    "## 2. 대용량 CSV 로드 전략\n",
+    "\n",
+    "Bosch 데이터는 매우 크므로 전체 컬럼을 한 번에 읽지 않습니다. 먼저 일부 행만 프로파일링한 뒤 결측률이 낮고 변동이 있는 컬럼을 고르고, 선택 컬럼만 학습 크기만큼 로드합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "66fcea04",
+   "metadata": {
+    "id": "66fcea04"
+   },
+   "outputs": [],
+   "source": [
+    "# 학습 규모 설정\n",
+    "PROFILE_ROWS = 50_000\n",
+    "MAX_ROWS = 300_000\n",
+    "MAX_NUMERIC_FEATURES = 260\n",
+    "MAX_DATE_FEATURES = 260\n",
+    "USE_CATEGORICAL = True\n",
+    "MAX_CATEGORICAL_FEATURES = 80\n",
+    "CATEGORICAL_PROFILE_ROWS = 20_000\n",
+    "MAX_AUX_ROWS = 200_000\n",
+    "ENABLE_HYPERPARAMETER_TUNING = True\n",
+    "CV_N_SPLITS = 5\n",
+    "# 불균형 데이터에서는 PR-AUC/Recall 안정성을 위해 충분한 탐색 횟수를 사용합니다.\n",
+    "# 실행 시간이 부담되면 CV_SAMPLE_SIZE 또는 CV_N_ITER를 낮춰 빠른 실험 모드로 전환합니다.\n",
+    "CV_N_ITER = 15\n",
+    "ENABLE_OPTUNA_TUNING = True\n",
+    "CV_SAMPLE_SIZE = 30_000\n",
+    "FINAL_TRAIN_SAMPLE_SIZE = 120_000\n",
+    "RETRAIN_SELECTED_MODEL_ON_FULL_DATA = False\n",
+    "USE_SMOTE = True\n",
+    "SMOTE_SAMPLING_STRATEGY = 0.10\n",
+    "RECALL_PRIORITY_TARGET = 0.35\n",
+    "RECALL_PRIORITY_MIN_PRECISION = 0.02\n",
+    "THRESHOLD_BETA = 2.0\n",
+    "\n",
+    "# 공정 매핑\n",
+    "LINE_TO_PROCESS = {\n",
+    "    \"L0\": \"PRESS\",\n",
+    "    \"L1\": \"BODY\",\n",
+    "    \"L2\": \"ASSEMBLY\",\n",
+    "    \"L3\": \"PAINT\",\n",
+    "}\n",
+    "PROCESS_FLOW = [\"PRESS\", \"BODY\", \"PAINT\", \"ASSEMBLY\"]\n",
+    "PROCESS_DISPLAY = {\n",
+    "    \"PRESS\": \"프레스\",\n",
+    "    \"BODY\": \"차체\",\n",
+    "    \"PAINT\": \"도장\",\n",
+    "    \"ASSEMBLY\": \"의장\",\n",
+    "    \"FINAL_INSPECTION\": \"최종검사\",\n",
+    "    \"UNKNOWN\": \"미분류\",\n",
+    "}\n",
+    "\n",
+    "# Colab Drive FUSE 연결이 불안정할 수 있어 데이터셋은 로컬 런타임 캐시에서 읽습니다.\n",
+    "LOCAL_DATA_CACHE = Path(\"/content/aims_dataset_cache\") if IN_COLAB else None\n",
+    "\n",
+    "\n",
+    "def local_dataset_path(path: Path) -> Path:\n",
+    "    path = Path(path)\n",
+    "    if not IN_COLAB:\n",
+    "        return path\n",
+    "    if LOCAL_DATA_CACHE is None:\n",
+    "        return path\n",
+    "\n",
+    "    try:\n",
+    "        relative_path = path.relative_to(PROJECT_ROOT)\n",
+    "    except ValueError:\n",
+    "        relative_path = Path(path.name)\n",
+    "\n",
+    "    cached_path = LOCAL_DATA_CACHE / relative_path\n",
+    "    if cached_path.exists():\n",
+    "        try:\n",
+    "            if cached_path.stat().st_size == path.stat().st_size:\n",
+    "                return cached_path\n",
+    "        except OSError:\n",
+    "            # Drive stat 자체가 실패하면 이미 복사된 로컬 캐시를 우선 사용합니다.\n",
+    "            return cached_path\n",
+    "\n",
+    "    cached_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    try:\n",
+    "        print(f\"cache copy: {path} -> {cached_path}\")\n",
+    "        shutil.copy2(path, cached_path)\n",
+    "    except OSError as exc:\n",
+    "        raise OSError(\n",
+    "            \"Google Drive 연결이 끊겼습니다. Colab에서 drive.mount('/content/drive', force_remount=True)를 \"\n",
+    "            \"실행한 뒤 이 셀부터 다시 실행하세요.\"\n",
+    "        ) from exc\n",
+    "    return cached_path\n",
+    "\n",
+    "\n",
+    "def read_csv_stable(path: Path, **kwargs) -> pd.DataFrame:\n",
+    "    try:\n",
+    "        return pd.read_csv(local_dataset_path(path), **kwargs)\n",
+    "    except OSError as exc:\n",
+    "        if IN_COLAB and \"Transport endpoint is not connected\" in str(exc):\n",
+    "            raise OSError(\n",
+    "                \"Google Drive 연결이 끊겼습니다. drive.mount('/content/drive', force_remount=True)로 \"\n",
+    "                \"재마운트한 뒤 다시 실행하세요. 이미 캐시된 파일은 /content/aims_dataset_cache에서 읽습니다.\"\n",
+    "            ) from exc\n",
+    "        raise\n",
+    "\n",
+    "\n",
+    "def read_json_stable(path: Path):\n",
+    "    with open(local_dataset_path(path), \"r\", encoding=\"utf-8\") as f:\n",
+    "        return json.load(f)\n",
+    "\n",
+    "\n",
+    "# 파일 검증\n",
+    "def assert_file(path: Path):\n",
+    "    if not path.exists():\n",
+    "        cached_path = local_dataset_path(path) if IN_COLAB else path\n",
+    "        if not cached_path.exists():\n",
+    "            raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
+    "\n",
+    "# 메모리 축소\n",
+    "def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    for col in df.columns:\n",
+    "        if col == \"Id\":\n",
+    "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
+    "            continue\n",
+    "        if pd.api.types.is_integer_dtype(df[col]):\n",
+    "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
+    "        elif pd.api.types.is_float_dtype(df[col]):\n",
+    "            df[col] = pd.to_numeric(df[col], downcast=\"float\")\n",
+    "    return df\n",
+    "\n",
+    "# 라인 추출\n",
+    "def get_line(column: str) -> str:\n",
+    "    match = re.match(r\"^(L\\d+)_\", column)\n",
+    "    return match.group(1) if match else \"UNKNOWN\"\n",
+    "\n",
+    "# 스테이션 추출\n",
+    "def get_station(column: str) -> str:\n",
+    "    match = re.match(r\"^(L\\d+)_S(\\d+)_\", column)\n",
+    "    return f\"{match.group(1)}_S{match.group(2)}\" if match else \"UNKNOWN\"\n",
+    "\n",
+    "# 컬럼 선별\n",
+    "def select_columns_by_profile(\n",
+    "    path: Path,\n",
+    "    *,\n",
+    "    required_cols: list[str],\n",
+    "    max_features: int,\n",
+    "    profile_rows: int,\n",
+    "    min_non_null_ratio: float = 0.02,\n",
+    ") -> list[str]:\n",
+    "    assert_file(path)\n",
+    "    sample = read_csv_stable(path, nrows=profile_rows)\n",
+    "    candidates = [c for c in sample.columns if c not in required_cols]\n",
+    "    non_null_ratio = sample[candidates].notna().mean()\n",
+    "    nunique = sample[candidates].nunique(dropna=True)\n",
+    "    score = (non_null_ratio * np.log1p(nunique)).sort_values(ascending=False)\n",
+    "    selected = score[(non_null_ratio >= min_non_null_ratio) & (nunique > 1)].head(max_features).index.tolist()\n",
+    "    del sample\n",
+    "    gc.collect()\n",
+    "    return required_cols + selected\n",
+    "\n",
+    "# 선택 컬럼 로드\n",
+    "def read_selected_csv(path: Path, usecols: list[str], max_rows: int | None) -> pd.DataFrame:\n",
+    "    df = read_csv_stable(path, usecols=usecols, nrows=max_rows)\n",
+    "    return reduce_mem_usage(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "508b9115",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 290
     },
+    "id": "508b9115",
+    "outputId": "eaef9a23-f57f-4c4d-85a1-69e744c2cc71"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "hMAQMhIqbEe9",
-      "metadata": {
-        "id": "hMAQMhIqbEe9"
-      },
-      "outputs": [],
-      "source": [
-        "# Colab 환경에서 필요한 패키지 설치\n",
-        "import importlib.util\n",
-        "import subprocess\n",
-        "import sys\n",
-        "\n",
-        "def ensure_package(import_name: str, pip_name: str | None = None):\n",
-        "    pip_name = pip_name or import_name\n",
-        "    if importlib.util.find_spec(import_name) is None:\n",
-        "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", pip_name, \"-q\"])\n",
-        "\n",
-        "ensure_package(\"lightgbm\")\n",
-        "ensure_package(\"shap\")\n",
-        "ensure_package(\"joblib\")\n",
-        "ensure_package(\"optuna\")\n",
-        "ensure_package(\"imblearn\", \"imbalanced-learn\")\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_numeric.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_numeric.csv\n",
+      "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_date.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_date.csv\n",
+      "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_categorical.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_categorical.csv\n",
+      "numeric_df: (300000, 262)\n",
+      "date_df: (300000, 261)\n",
+      "categorical_df: (300000, 10)\n",
+      "Response ratio:\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "XFBnJXPabHMi",
-      "metadata": {
-        "id": "XFBnJXPabHMi"
-      },
-      "outputs": [],
-      "source": [
-        "# CPU 병렬 사용\n",
-        "import lightgbm as lgb\n",
-        "\n",
-        "model = lgb.LGBMClassifier(\n",
-        "    n_estimators=300,\n",
-        "    learning_rate=0.05,\n",
-        "    num_leaves=31,\n",
-        "    n_jobs=-1,\n",
-        "    random_state=42\n",
-        ")"
-      ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "            ratio\n",
+       "Response         \n",
+       "0         0.99435\n",
+       "1         0.00565"
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-626ebf76-9a02-48e2-974a-d5d99fd25fa4\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ratio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Response</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.99435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.00565</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-626ebf76-9a02-48e2-974a-d5d99fd25fa4')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# Bosch numeric 컬럼 선별\n",
+    "numeric_cols = select_columns_by_profile(\n",
+    "    NUMERIC_PATH,\n",
+    "    required_cols=[\"Id\", \"Response\"],\n",
+    "    max_features=MAX_NUMERIC_FEATURES,\n",
+    "    profile_rows=PROFILE_ROWS,\n",
+    ")\n",
+    "# Bosch date 컬럼 선별\n",
+    "date_cols = select_columns_by_profile(\n",
+    "    DATE_PATH,\n",
+    "    required_cols=[\"Id\"],\n",
+    "    max_features=MAX_DATE_FEATURES,\n",
+    "    profile_rows=PROFILE_ROWS,\n",
+    ")\n",
+    "\n",
+    "# Bosch 선택 데이터 로드\n",
+    "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
+    "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
+    "\n",
+    "# Bosch categorical 선택 로드\n",
+    "if USE_CATEGORICAL:\n",
+    "    categorical_cols = select_columns_by_profile(\n",
+    "        CATEGORICAL_PATH,\n",
+    "        required_cols=[\"Id\"],\n",
+    "        max_features=MAX_CATEGORICAL_FEATURES,\n",
+    "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
+    "    )\n",
+    "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
+    "else:\n",
+    "    categorical_df = None\n",
+    "\n",
+    "print(\"numeric_df:\", numeric_df.shape)\n",
+    "print(\"date_df:\", date_df.shape)\n",
+    "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
+    "print(\"Response ratio:\")\n",
+    "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11973c36",
+   "metadata": {
+    "id": "11973c36"
+   },
+   "source": [
+    "## 3. Bosch Feature Engineering\n",
+    "\n",
+    "`L*_S*_F*` 수치 센서와 `L*_S*_D*` 시간 컬럼을 그대로 일부 사용하면서, 공정/스테이션 단위 집계 피처를 추가합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "dd4b603e",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 273
     },
+    "id": "dd4b603e",
+    "outputId": "84ce5bbe-1ecd-490a-884b-4e42c0252e7f"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "f7a3d724",
-      "metadata": {
-        "id": "f7a3d724"
-      },
-      "source": [
-        "> 필요한 추가 패키지(`optuna`, `imbalanced-learn`)는 위 설치 셀에서 자동 확인/설치합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "features: (300000, 454)\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "e37daef5",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "e37daef5",
-        "outputId": "d3014dc5-ad21-4f08-d5e8-65c43e05146f"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "pandas 2.2.2\n",
-            "lightgbm 4.6.0\n",
-            "optuna 4.9.0\n",
-            "shap 0.52.0\n"
-          ]
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   Id  L0_S0_F0  L0_S0_F2  L0_S0_F4  L0_S0_F6  L0_S0_F8  L0_S0_F10  L0_S0_F12  \\\n",
+       "0   4     0.030    -0.034    -0.197    -0.179     0.118      0.116     -0.015   \n",
+       "1   6       NaN       NaN       NaN       NaN       NaN        NaN        NaN   \n",
+       "2   7     0.088     0.086     0.003    -0.052     0.161      0.025     -0.015   \n",
+       "3   9    -0.036    -0.064     0.294     0.330     0.074      0.161      0.022   \n",
+       "4  11    -0.055    -0.086     0.294     0.330     0.118      0.025      0.030   \n",
+       "\n",
+       "   L0_S0_F14  L0_S0_F16  L0_S0_F18  L0_S0_F20  L0_S0_F22  L0_S1_F24  \\\n",
+       "0     -0.032      0.020      0.083     -0.273     -0.273     -0.271   \n",
+       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
+       "2     -0.072     -0.225     -0.147      0.250      0.250      0.057   \n",
+       "3      0.128     -0.026     -0.046     -0.253     -0.253      0.147   \n",
+       "4      0.168     -0.169     -0.099      0.042      0.042     -0.012   \n",
+       "\n",
+       "   L0_S1_F28  L0_S2_F36  L0_S2_F44  L0_S2_F48  L0_S2_F60  L0_S2_F64  \\\n",
+       "0      0.167     -0.023     -0.088      0.001     -0.223     -0.030   \n",
+       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
+       "2     -0.079      0.011     -0.060     -0.005     -0.223     -0.077   \n",
+       "3     -0.007      0.120     -0.231      0.005      0.050      0.056   \n",
+       "4     -0.046        NaN        NaN        NaN        NaN        NaN   \n",
+       "\n",
+       "   L0_S3_F72  L0_S3_F80  L0_S3_F84  L0_S3_F96  L0_S3_F100  L0_S4_F104  \\\n",
+       "0        NaN        NaN        NaN        NaN         NaN      -0.001   \n",
+       "1        NaN        NaN        NaN        NaN         NaN         NaN   \n",
+       "2        NaN        NaN        NaN        NaN         NaN         NaN   \n",
+       "3        NaN        NaN        NaN        NaN         NaN      -0.038   \n",
+       "4     -0.272      0.037      0.004      0.311       0.003       0.021   \n",
+       "\n",
+       "   L0_S4_F109  L0_S5_F114  L0_S5_F116  L0_S6_F122  L0_S6_F132  L0_S7_F138  \\\n",
+       "0      -0.004         NaN         NaN         NaN         NaN      -0.077   \n",
+       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "2         NaN      -0.073       0.138       0.506       -0.13         NaN   \n",
+       "3      -0.001         NaN         NaN         NaN         NaN       0.402   \n",
+       "4       0.015         NaN         NaN         NaN         NaN      -0.083   \n",
+       "\n",
+       "   L0_S7_F142  L0_S8_F144  L0_S8_F146  L0_S8_F149  L0_S9_F155  L0_S9_F160  \\\n",
+       "0       0.060      -0.157         0.0       0.001         NaN         NaN   \n",
+       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "2         NaN      -0.157         0.0       0.001         NaN         NaN   \n",
+       "3      -0.015       0.343         0.0       0.001         NaN         NaN   \n",
+       "4       0.014      -0.157         0.0       0.001         NaN         NaN   \n",
+       "\n",
+       "   L0_S9_F165  L0_S9_F170  L0_S9_F180  L0_S9_F185  L0_S9_F190  L0_S9_F195  \\\n",
+       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "\n",
+       "   L0_S9_F200  L0_S9_F210  L0_S10_F219  L0_S10_F224  L0_S10_F229  L0_S10_F234  \\\n",
+       "0         NaN         NaN          NaN          NaN          NaN          NaN   \n",
+       "1         NaN         NaN          NaN          NaN          NaN          NaN   \n",
+       "2         NaN         NaN       -0.002       -0.047        0.018          0.0   \n",
+       "3         NaN         NaN        0.007       -0.049        0.020          0.0   \n",
+       "4         NaN         NaN          NaN          NaN          NaN          NaN   \n",
+       "\n",
+       "   L0_S10_F244  L0_S10_F249  L0_S10_F254  L0_S10_F259  L0_S10_F264  \\\n",
+       "0          NaN          NaN          NaN          NaN          NaN   \n",
+       "1          NaN          NaN          NaN          NaN          NaN   \n",
+       "2        0.001       -0.009       -0.017        0.105       -0.001   \n",
+       "3       -0.013       -0.003       -0.032        0.139        0.001   \n",
+       "4          NaN          NaN          NaN          NaN          NaN   \n",
+       "\n",
+       "   L0_S11_F282  L0_S11_F286  L0_S11_F290  L0_S11_F294  L0_S11_F302  ...  \\\n",
+       "0        0.018       -0.136       -0.013        0.106       -0.131  ...   \n",
+       "1          NaN          NaN          NaN          NaN          NaN  ...   \n",
+       "2          NaN          NaN          NaN          NaN          NaN  ...   \n",
+       "3          NaN          NaN          NaN          NaN          NaN  ...   \n",
+       "4       -0.122       -0.064       -0.011       -0.005        0.071  ...   \n",
+       "\n",
+       "   L0_S5_num_mean  L0_S5_num_std  L0_S5_num_missing_ratio  L0_S6_num_mean  \\\n",
+       "0             NaN            NaN                      1.0             NaN   \n",
+       "1             NaN            NaN                      1.0             NaN   \n",
+       "2          0.0325         0.1492                      0.0           0.188   \n",
+       "3             NaN            NaN                      1.0             NaN   \n",
+       "4             NaN            NaN                      1.0             NaN   \n",
+       "\n",
+       "   L0_S6_num_std  L0_S6_num_missing_ratio  L0_S7_num_mean  L0_S7_num_std  \\\n",
+       "0            NaN                      1.0         -0.0085       0.096874   \n",
+       "1            NaN                      1.0             NaN            NaN   \n",
+       "2        0.44972                      0.0             NaN            NaN   \n",
+       "3            NaN                      1.0          0.1935       0.294864   \n",
+       "4            NaN                      1.0         -0.0345       0.068589   \n",
+       "\n",
+       "   L0_S7_num_missing_ratio  L0_S8_num_mean  L0_S8_num_std  \\\n",
+       "0                      0.0       -0.052000       0.090934   \n",
+       "1                      1.0             NaN            NaN   \n",
+       "2                      1.0       -0.052000       0.090934   \n",
+       "3                      0.0        0.114667       0.197743   \n",
+       "4                      0.0       -0.052000       0.090934   \n",
+       "\n",
+       "   L0_S8_num_missing_ratio  L0_S9_num_mean  L0_S9_num_std  \\\n",
+       "0                      0.0             NaN            NaN   \n",
+       "1                      1.0             NaN            NaN   \n",
+       "2                      0.0             NaN            NaN   \n",
+       "3                      0.0             NaN            NaN   \n",
+       "4                      0.0             NaN            NaN   \n",
+       "\n",
+       "   L0_S9_num_missing_ratio  L1_S24_num_mean  L1_S24_num_std  \\\n",
+       "0                      1.0              NaN             NaN   \n",
+       "1                      1.0              NaN             NaN   \n",
+       "2                      1.0              NaN             NaN   \n",
+       "3                      1.0              NaN             NaN   \n",
+       "4                      1.0              NaN             NaN   \n",
+       "\n",
+       "   L1_S24_num_missing_ratio  L2_S26_num_mean  L2_S26_num_std  \\\n",
+       "0                       1.0              NaN             NaN   \n",
+       "1                       1.0              NaN             NaN   \n",
+       "2                       1.0              NaN             NaN   \n",
+       "3                       1.0              NaN             NaN   \n",
+       "4                       1.0              NaN             NaN   \n",
+       "\n",
+       "   L2_S26_num_missing_ratio  L2_S27_num_mean  L2_S27_num_std  \\\n",
+       "0                       1.0              NaN             NaN   \n",
+       "1                       1.0              NaN             NaN   \n",
+       "2                       1.0              NaN             NaN   \n",
+       "3                       1.0              NaN             NaN   \n",
+       "4                       1.0              NaN             NaN   \n",
+       "\n",
+       "   L2_S27_num_missing_ratio  L3_S29_num_mean  L3_S29_num_std  \\\n",
+       "0                       1.0        -0.014321        0.201176   \n",
+       "1                       1.0        -0.005264        0.142025   \n",
+       "2                       1.0        -0.010000        0.129113   \n",
+       "3                       1.0        -0.030151        0.124692   \n",
+       "4                       1.0        -0.017321        0.157158   \n",
+       "\n",
+       "   L3_S29_num_missing_ratio  L3_S30_num_mean  L3_S30_num_std  \\\n",
+       "0                       0.0         0.003889        0.083444   \n",
+       "1                       0.0         0.021621        0.068539   \n",
+       "2                       0.0        -0.010552        0.069868   \n",
+       "3                       0.0         0.010017        0.058393   \n",
+       "4                       0.0        -0.004069        0.049633   \n",
+       "\n",
+       "   L3_S30_num_missing_ratio  L3_S33_num_mean  L3_S33_num_std  \\\n",
+       "0                   0.37931          -0.0481        0.060306   \n",
+       "1                   0.00000          -0.0370        0.063085   \n",
+       "2                   0.00000           0.1132        0.102470   \n",
+       "3                   0.00000           0.0282        0.049806   \n",
+       "4                   0.00000          -0.0130        0.065646   \n",
+       "\n",
+       "   L3_S33_num_missing_ratio  L3_S34_num_mean  L3_S34_num_std  \\\n",
+       "0                       0.0              0.0             0.0   \n",
+       "1                       0.0              0.0             0.0   \n",
+       "2                       0.0              0.0             0.0   \n",
+       "3                       0.0              0.0             0.0   \n",
+       "4                       0.0              0.0             0.0   \n",
+       "\n",
+       "   L3_S34_num_missing_ratio  L3_S35_num_mean  L3_S35_num_std  \\\n",
+       "0                       0.0        -0.016143        0.030180   \n",
+       "1                       0.0        -0.001286        0.017289   \n",
+       "2                       0.0        -0.013857        0.039015   \n",
+       "3                       0.0              NaN             NaN   \n",
+       "4                       0.0              NaN             NaN   \n",
+       "\n",
+       "   L3_S35_num_missing_ratio  L3_S36_num_mean  L3_S36_num_std  \\\n",
+       "0                       0.0              NaN             NaN   \n",
+       "1                       0.0              NaN             NaN   \n",
+       "2                       0.0              NaN             NaN   \n",
+       "3                       1.0         -0.05550        0.085261   \n",
+       "4                       1.0         -0.00225        0.066188   \n",
+       "\n",
+       "   L3_S36_num_missing_ratio  L3_S37_num_mean  L3_S37_num_std  \\\n",
+       "0                       1.0              0.0             0.0   \n",
+       "1                       1.0              0.0             0.0   \n",
+       "2                       1.0              0.0             0.0   \n",
+       "3                       0.0              0.0             0.0   \n",
+       "4                       0.0              0.0             0.0   \n",
+       "\n",
+       "   L3_S37_num_missing_ratio  L1_cat_count  L1_cat_missing_ratio  \\\n",
+       "0                       0.0             0                   1.0   \n",
+       "1                       0.0             0                   1.0   \n",
+       "2                       0.0             0                   1.0   \n",
+       "3                       0.0             0                   1.0   \n",
+       "4                       0.0             0                   1.0   \n",
+       "\n",
+       "   L1_cat_unique_count  L2_cat_count  L2_cat_missing_ratio  \\\n",
+       "0                    0             0                   1.0   \n",
+       "1                    0             0                   1.0   \n",
+       "2                    0             0                   1.0   \n",
+       "3                    0             0                   1.0   \n",
+       "4                    0             0                   1.0   \n",
+       "\n",
+       "   L2_cat_unique_count  L1_S24_F1114_code  L1_S24_F1137_code  \\\n",
+       "0                    0                  5                  4   \n",
+       "1                    0                  5                  4   \n",
+       "2                    0                  5                  4   \n",
+       "3                    0                  5                  4   \n",
+       "4                    0                  5                  4   \n",
+       "\n",
+       "   L1_S24_F1140_code  L1_S24_F1510_code  L1_S24_F1525_code  L1_S24_F1530_code  \\\n",
+       "0                  4                  5                  5                  5   \n",
+       "1                  4                  5                  5                  5   \n",
+       "2                  4                  5                  5                  5   \n",
+       "3                  4                  5                  5                  5   \n",
+       "4                  4                  5                  5                  5   \n",
+       "\n",
+       "   L1_S25_F1852_code  L2_S26_F3099_code  L2_S27_F3192_code  \n",
+       "0                  8                  2                  2  \n",
+       "1                  8                  2                  2  \n",
+       "2                  8                  2                  2  \n",
+       "3                  8                  2                  2  \n",
+       "4                  8                  2                  2  \n",
+       "\n",
+       "[5 rows x 454 columns]"
+      ],
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+       "      <th></th>\n",
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+       "      <th>L0_S1_F24</th>\n",
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+       "      <th>L0_S2_F36</th>\n",
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+       "      <th>L0_S2_F64</th>\n",
+       "      <th>L0_S3_F72</th>\n",
+       "      <th>L0_S3_F80</th>\n",
+       "      <th>L0_S3_F84</th>\n",
+       "      <th>L0_S3_F96</th>\n",
+       "      <th>L0_S3_F100</th>\n",
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+       "      <th>L0_S6_F122</th>\n",
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+       "      <th>L0_S7_F138</th>\n",
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+       "      <th>L0_S8_F144</th>\n",
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+       "      <th>L0_S8_F149</th>\n",
+       "      <th>L0_S9_F155</th>\n",
+       "      <th>L0_S9_F160</th>\n",
+       "      <th>L0_S9_F165</th>\n",
+       "      <th>L0_S9_F170</th>\n",
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+       "      <th>L0_S9_F185</th>\n",
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+       "      <th>L0_S9_F195</th>\n",
+       "      <th>L0_S9_F200</th>\n",
+       "      <th>L0_S9_F210</th>\n",
+       "      <th>L0_S10_F219</th>\n",
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+       "      <th>L0_S10_F234</th>\n",
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+       "      <th>L0_S10_F259</th>\n",
+       "      <th>L0_S10_F264</th>\n",
+       "      <th>L0_S11_F282</th>\n",
+       "      <th>L0_S11_F286</th>\n",
+       "      <th>L0_S11_F290</th>\n",
+       "      <th>L0_S11_F294</th>\n",
+       "      <th>L0_S11_F302</th>\n",
+       "      <th>...</th>\n",
+       "      <th>L0_S5_num_mean</th>\n",
+       "      <th>L0_S5_num_std</th>\n",
+       "      <th>L0_S5_num_missing_ratio</th>\n",
+       "      <th>L0_S6_num_mean</th>\n",
+       "      <th>L0_S6_num_std</th>\n",
+       "      <th>L0_S6_num_missing_ratio</th>\n",
+       "      <th>L0_S7_num_mean</th>\n",
+       "      <th>L0_S7_num_std</th>\n",
+       "      <th>L0_S7_num_missing_ratio</th>\n",
+       "      <th>L0_S8_num_mean</th>\n",
+       "      <th>L0_S8_num_std</th>\n",
+       "      <th>L0_S8_num_missing_ratio</th>\n",
+       "      <th>L0_S9_num_mean</th>\n",
+       "      <th>L0_S9_num_std</th>\n",
+       "      <th>L0_S9_num_missing_ratio</th>\n",
+       "      <th>L1_S24_num_mean</th>\n",
+       "      <th>L1_S24_num_std</th>\n",
+       "      <th>L1_S24_num_missing_ratio</th>\n",
+       "      <th>L2_S26_num_mean</th>\n",
+       "      <th>L2_S26_num_std</th>\n",
+       "      <th>L2_S26_num_missing_ratio</th>\n",
+       "      <th>L2_S27_num_mean</th>\n",
+       "      <th>L2_S27_num_std</th>\n",
+       "      <th>L2_S27_num_missing_ratio</th>\n",
+       "      <th>L3_S29_num_mean</th>\n",
+       "      <th>L3_S29_num_std</th>\n",
+       "      <th>L3_S29_num_missing_ratio</th>\n",
+       "      <th>L3_S30_num_mean</th>\n",
+       "      <th>L3_S30_num_std</th>\n",
+       "      <th>L3_S30_num_missing_ratio</th>\n",
+       "      <th>L3_S33_num_mean</th>\n",
+       "      <th>L3_S33_num_std</th>\n",
+       "      <th>L3_S33_num_missing_ratio</th>\n",
+       "      <th>L3_S34_num_mean</th>\n",
+       "      <th>L3_S34_num_std</th>\n",
+       "      <th>L3_S34_num_missing_ratio</th>\n",
+       "      <th>L3_S35_num_mean</th>\n",
+       "      <th>L3_S35_num_std</th>\n",
+       "      <th>L3_S35_num_missing_ratio</th>\n",
+       "      <th>L3_S36_num_mean</th>\n",
+       "      <th>L3_S36_num_std</th>\n",
+       "      <th>L3_S36_num_missing_ratio</th>\n",
+       "      <th>L3_S37_num_mean</th>\n",
+       "      <th>L3_S37_num_std</th>\n",
+       "      <th>L3_S37_num_missing_ratio</th>\n",
+       "      <th>L1_cat_count</th>\n",
+       "      <th>L1_cat_missing_ratio</th>\n",
+       "      <th>L1_cat_unique_count</th>\n",
+       "      <th>L2_cat_count</th>\n",
+       "      <th>L2_cat_missing_ratio</th>\n",
+       "      <th>L2_cat_unique_count</th>\n",
+       "      <th>L1_S24_F1114_code</th>\n",
+       "      <th>L1_S24_F1137_code</th>\n",
+       "      <th>L1_S24_F1140_code</th>\n",
+       "      <th>L1_S24_F1510_code</th>\n",
+       "      <th>L1_S24_F1525_code</th>\n",
+       "      <th>L1_S24_F1530_code</th>\n",
+       "      <th>L1_S25_F1852_code</th>\n",
+       "      <th>L2_S26_F3099_code</th>\n",
+       "      <th>L2_S27_F3192_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>-0.034</td>\n",
+       "      <td>-0.197</td>\n",
+       "      <td>-0.179</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>-0.015</td>\n",
+       "      <td>-0.032</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>0.083</td>\n",
+       "      <td>-0.273</td>\n",
+       "      <td>-0.273</td>\n",
+       "      <td>-0.271</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>-0.023</td>\n",
+       "      <td>-0.088</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>-0.223</td>\n",
+       "      <td>-0.030</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>-0.004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.077</td>\n",
+       "      <td>0.060</td>\n",
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+       "      <td>0.001</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.018</td>\n",
+       "      <td>-0.136</td>\n",
+       "      <td>-0.013</td>\n",
+       "      <td>0.106</td>\n",
+       "      <td>-0.131</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.0085</td>\n",
+       "      <td>0.096874</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.052000</td>\n",
+       "      <td>0.090934</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.014321</td>\n",
+       "      <td>0.201176</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.003889</td>\n",
+       "      <td>0.083444</td>\n",
+       "      <td>0.37931</td>\n",
+       "      <td>-0.0481</td>\n",
+       "      <td>0.060306</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.016143</td>\n",
+       "      <td>0.030180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.005264</td>\n",
+       "      <td>0.142025</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.021621</td>\n",
+       "      <td>0.068539</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.0370</td>\n",
+       "      <td>0.063085</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001286</td>\n",
+       "      <td>0.017289</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>-0.052</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>-0.015</td>\n",
+       "      <td>-0.072</td>\n",
+       "      <td>-0.225</td>\n",
+       "      <td>-0.147</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.057</td>\n",
+       "      <td>-0.079</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>-0.060</td>\n",
+       "      <td>-0.005</td>\n",
+       "      <td>-0.223</td>\n",
+       "      <td>-0.077</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.073</td>\n",
+       "      <td>0.138</td>\n",
+       "      <td>0.506</td>\n",
+       "      <td>-0.13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.157</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.002</td>\n",
+       "      <td>-0.047</td>\n",
+       "      <td>0.018</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>-0.009</td>\n",
+       "      <td>-0.017</td>\n",
+       "      <td>0.105</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0325</td>\n",
+       "      <td>0.1492</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.188</td>\n",
+       "      <td>0.44972</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.052000</td>\n",
+       "      <td>0.090934</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.010000</td>\n",
+       "      <td>0.129113</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.010552</td>\n",
+       "      <td>0.069868</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.1132</td>\n",
+       "      <td>0.102470</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.013857</td>\n",
+       "      <td>0.039015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.036</td>\n",
+       "      <td>-0.064</td>\n",
+       "      <td>0.294</td>\n",
+       "      <td>0.330</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>0.022</td>\n",
+       "      <td>0.128</td>\n",
+       "      <td>-0.026</td>\n",
+       "      <td>-0.046</td>\n",
+       "      <td>-0.253</td>\n",
+       "      <td>-0.253</td>\n",
+       "      <td>0.147</td>\n",
+       "      <td>-0.007</td>\n",
+       "      <td>0.120</td>\n",
+       "      <td>-0.231</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.038</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.402</td>\n",
+       "      <td>-0.015</td>\n",
+       "      <td>0.343</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>-0.049</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.013</td>\n",
+       "      <td>-0.003</td>\n",
+       "      <td>-0.032</td>\n",
+       "      <td>0.139</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1935</td>\n",
+       "      <td>0.294864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.114667</td>\n",
+       "      <td>0.197743</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.030151</td>\n",
+       "      <td>0.124692</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010017</td>\n",
+       "      <td>0.058393</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0282</td>\n",
+       "      <td>0.049806</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.05550</td>\n",
+       "      <td>0.085261</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11</td>\n",
+       "      <td>-0.055</td>\n",
+       "      <td>-0.086</td>\n",
+       "      <td>0.294</td>\n",
+       "      <td>0.330</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>0.168</td>\n",
+       "      <td>-0.169</td>\n",
+       "      <td>-0.099</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>-0.012</td>\n",
+       "      <td>-0.046</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.272</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.311</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.021</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.083</td>\n",
+       "      <td>0.014</td>\n",
+       "      <td>-0.157</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.122</td>\n",
+       "      <td>-0.064</td>\n",
+       "      <td>-0.011</td>\n",
+       "      <td>-0.005</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.0345</td>\n",
+       "      <td>0.068589</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.052000</td>\n",
+       "      <td>0.090934</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.017321</td>\n",
+       "      <td>0.157158</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.004069</td>\n",
+       "      <td>0.049633</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.0130</td>\n",
+       "      <td>0.065646</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.00225</td>\n",
+       "      <td>0.066188</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 454 columns</p>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82c8c8d6-bb6b-4406-9895-53a4298ecac0')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "from __future__ import annotations\n",
-        "\n",
-        "import gc\n",
-        "import json\n",
-        "import pickle\n",
-        "import re\n",
-        "import shutil\n",
-        "import warnings\n",
-        "from datetime import datetime, timezone\n",
-        "from pathlib import Path\n",
-        "\n",
-        "import joblib\n",
-        "import lightgbm as lgb\n",
-        "import optuna\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import seaborn as sns\n",
-        "import shap\n",
-        "from imblearn.over_sampling import SMOTE\n",
-        "from imblearn.pipeline import Pipeline as ImbPipeline\n",
-        "from optuna.samplers import TPESampler\n",
-        "from sklearn.base import clone\n",
-        "from sklearn.ensemble import ExtraTreesClassifier, IsolationForest, RandomForestClassifier\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "from sklearn.metrics import (\n",
-        "    ConfusionMatrixDisplay,\n",
-        "    PrecisionRecallDisplay,\n",
-        "    RocCurveDisplay,\n",
-        "    accuracy_score,\n",
-        "    average_precision_score,\n",
-        "    classification_report,\n",
-        "    confusion_matrix,\n",
-        "    f1_score,\n",
-        "    precision_score,\n",
-        "    precision_recall_curve,\n",
-        "    recall_score,\n",
-        "    roc_auc_score,\n",
-        ")\n",
-        "from sklearn.model_selection import ParameterSampler, StratifiedKFold, train_test_split\n",
-        "\n",
-        "warnings.filterwarnings(\"ignore\")\n",
-        "sns.set_theme(style=\"whitegrid\")\n",
-        "pd.set_option(\"display.max_columns\", 120)\n",
-        "pd.set_option(\"display.max_rows\", 80)\n",
-        "\n",
-        "RANDOM_STATE = 42\n",
-        "np.random.seed(RANDOM_STATE)\n",
-        "\n",
-        "print(\"pandas\", pd.__version__)\n",
-        "print(\"lightgbm\", lgb.__version__)\n",
-        "print(\"optuna\", optuna.__version__)\n",
-        "print(\"shap\", shap.__version__)\n"
-      ]
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# Date 피처 생성\n",
+    "def build_date_features(date_df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    feature_df = pd.DataFrame({\"Id\": date_df[\"Id\"]})\n",
+    "    date_feature_cols = [c for c in date_df.columns if c != \"Id\"]\n",
+    "    date_values = date_df[date_feature_cols]\n",
+    "\n",
+    "    feature_df[\"date_observed_count\"] = date_values.notna().sum(axis=1)\n",
+    "    feature_df[\"date_missing_ratio\"] = date_values.isna().mean(axis=1)\n",
+    "    feature_df[\"process_start_time\"] = date_values.min(axis=1)\n",
+    "    feature_df[\"process_end_time\"] = date_values.max(axis=1)\n",
+    "    feature_df[\"total_process_duration\"] = feature_df[\"process_end_time\"] - feature_df[\"process_start_time\"]\n",
+    "\n",
+    "    # 라인별 시간 집계\n",
+    "    for line in sorted({get_line(c) for c in date_feature_cols if get_line(c) != \"UNKNOWN\"}):\n",
+    "        cols = [c for c in date_feature_cols if get_line(c) == line]\n",
+    "        values = date_df[cols]\n",
+    "        feature_df[f\"{line}_date_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
+    "        feature_df[f\"{line}_date_count\"] = values.notna().sum(axis=1)\n",
+    "        feature_df[f\"{line}_date_missing_ratio\"] = values.isna().mean(axis=1)\n",
+    "        feature_df[f\"{line}_duration\"] = values.max(axis=1) - values.min(axis=1)\n",
+    "\n",
+    "    # 스테이션별 시간 집계\n",
+    "    station_span_cols = []\n",
+    "    for station in sorted({get_station(c) for c in date_feature_cols if get_station(c) != \"UNKNOWN\"}):\n",
+    "        cols = [c for c in date_feature_cols if get_station(c) == station]\n",
+    "        values = date_df[cols]\n",
+    "        span_col = f\"{station}_span\"\n",
+    "        feature_df[f\"{station}_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
+    "        feature_df[span_col] = values.max(axis=1) - values.min(axis=1)\n",
+    "        feature_df[f\"{station}_mean_time\"] = values.mean(axis=1)\n",
+    "        station_span_cols.append(span_col)\n",
+    "\n",
+    "    # 병목 후보 시간 집계\n",
+    "    if station_span_cols:\n",
+    "        spans = feature_df[station_span_cols]\n",
+    "        feature_df[\"max_station_span\"] = spans.max(axis=1)\n",
+    "        feature_df[\"mean_station_span\"] = spans.mean(axis=1)\n",
+    "        feature_df[\"std_station_span\"] = spans.std(axis=1)\n",
+    "        feature_df[\"active_station_count\"] = feature_df[[c.replace(\"_span\", \"_seen\") for c in station_span_cols]].sum(axis=1)\n",
+    "    return reduce_mem_usage(feature_df)\n",
+    "\n",
+    "# Numeric 집계 피처 생성\n",
+    "def build_numeric_aggregate_features(numeric_df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    value_cols = [c for c in numeric_df.columns if c not in [\"Id\", \"Response\"]]\n",
+    "    feature_df = numeric_df[[\"Id\"]].copy()\n",
+    "\n",
+    "    # 라인별 센서 집계\n",
+    "    for line in sorted({get_line(c) for c in value_cols if get_line(c) != \"UNKNOWN\"}):\n",
+    "        cols = [c for c in value_cols if get_line(c) == line]\n",
+    "        values = numeric_df[cols]\n",
+    "        feature_df[f\"{line}_num_mean\"] = values.mean(axis=1)\n",
+    "        feature_df[f\"{line}_num_std\"] = values.std(axis=1)\n",
+    "        feature_df[f\"{line}_num_min\"] = values.min(axis=1)\n",
+    "        feature_df[f\"{line}_num_max\"] = values.max(axis=1)\n",
+    "        feature_df[f\"{line}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
+    "\n",
+    "    # 스테이션별 센서 집계\n",
+    "    for station in sorted({get_station(c) for c in value_cols if get_station(c) != \"UNKNOWN\"}):\n",
+    "        cols = [c for c in value_cols if get_station(c) == station]\n",
+    "        if len(cols) < 2:\n",
+    "            continue\n",
+    "        values = numeric_df[cols]\n",
+    "        feature_df[f\"{station}_num_mean\"] = values.mean(axis=1)\n",
+    "        feature_df[f\"{station}_num_std\"] = values.std(axis=1)\n",
+    "        feature_df[f\"{station}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
+    "    return reduce_mem_usage(feature_df)\n",
+    "\n",
+    "# Categorical 피처 생성\n",
+    "def build_categorical_features(categorical_df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    cat_cols = [c for c in categorical_df.columns if c != \"Id\"]\n",
+    "    feature_df = categorical_df[[\"Id\"]].copy()\n",
+    "    if not cat_cols:\n",
+    "        return feature_df\n",
+    "\n",
+    "    # 라인별 범주 집계\n",
+    "    for line in sorted({get_line(c) for c in cat_cols if get_line(c) != \"UNKNOWN\"}):\n",
+    "        cols = [c for c in cat_cols if get_line(c) == line]\n",
+    "        values = categorical_df[cols]\n",
+    "        feature_df[f\"{line}_cat_count\"] = values.notna().sum(axis=1)\n",
+    "        feature_df[f\"{line}_cat_missing_ratio\"] = values.isna().mean(axis=1)\n",
+    "        feature_df[f\"{line}_cat_unique_count\"] = values.nunique(axis=1, dropna=True)\n",
+    "\n",
+    "    # 범주 인코딩\n",
+    "    for col in cat_cols:\n",
+    "        encoded, _ = pd.factorize(categorical_df[col].astype(\"string\").fillna(\"__MISSING__\"), sort=True)\n",
+    "        feature_df[f\"{col}_code\"] = encoded.astype(\"int32\")\n",
+    "    return reduce_mem_usage(feature_df)\n",
+    "\n",
+    "# Bosch 피처 생성\n",
+    "date_features = build_date_features(date_df)\n",
+    "numeric_agg_features = build_numeric_aggregate_features(numeric_df)\n",
+    "raw_numeric_features = numeric_df.drop(columns=[\"Response\"])\n",
+    "\n",
+    "# Bosch 피처 병합\n",
+    "features = raw_numeric_features.merge(date_features, on=\"Id\", how=\"left\")\n",
+    "features = features.merge(numeric_agg_features, on=\"Id\", how=\"left\")\n",
+    "if categorical_df is not None:\n",
+    "    categorical_features = build_categorical_features(categorical_df)\n",
+    "    features = features.merge(categorical_features, on=\"Id\", how=\"left\")\n",
+    "    del categorical_df, categorical_features\n",
+    "# 학습 라벨 분리\n",
+    "target = numeric_df[[\"Id\", \"Response\"]].copy()\n",
+    "\n",
+    "del date_df, numeric_agg_features, date_features\n",
+    "gc.collect()\n",
+    "\n",
+    "print(\"features:\", features.shape)\n",
+    "display(features.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6f30d6e",
+   "metadata": {
+    "id": "e6f30d6e"
+   },
+   "source": [
+    "## 4. Process 폴더 전체 보조 데이터 학습\n",
+    "\n",
+    "`process` 폴더의 Bosch 외 데이터는 Bosch `Id`와 직접 연결되는 공통 키가 없습니다. 따라서 MVP에서는 각 데이터셋을 별도로 학습하여 공정별 보조 위험 신호를 만들고, Bosch 학습 테이블에는 반복 정렬(cyclic alignment) 방식으로 결합합니다. 운영 데이터에서 `car_master_id`, `event_time`, `process_code` 매핑이 생기면 이 부분을 정확 조인으로 교체하면 됩니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a2a68fb3",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 698
     },
+    "id": "a2a68fb3",
+    "outputId": "65f72cce-fd5b-4c16-9900-3b43b4e0e289"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "0e3e0e4c",
-      "metadata": {
-        "id": "0e3e0e4c"
-      },
-      "source": [
-        "## 1. Google Drive 및 로컬 데이터셋 경로 설정\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv\n",
+      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json\n",
+      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv\n",
+      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json\n",
+      "[LightGBM] [Info] Number of positive: 461, number of negative: 166\n",
+      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 1349\n",
+      "[LightGBM] [Info] Number of data points in the train set: 627, number of used features: 7\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.735247 -> initscore=1.021410\n",
+      "[LightGBM] [Info] Start training from score 1.021410\n",
+      "\n",
+      "== Thermal PAINT quality auxiliary model ==\n",
+      "ROC-AUC: 0.83824\n",
+      "PR-AUC: 0.91229\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0     0.5962    0.5636    0.5794        55\n",
+      "           1     0.8481    0.8645    0.8562       155\n",
+      "\n",
+      "    accuracy                         0.7857       210\n",
+      "   macro avg     0.7221    0.7141    0.7178       210\n",
+      "weighted avg     0.7821    0.7857    0.7837       210\n",
+      "\n",
+      "aux_paint_thermal_defect_probability: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
+      "aux_paint_thermal_label: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "abb8856c",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "abb8856c",
-        "outputId": "d0b5cf16-76a8-4d8e-cf3f-ce1f188f80a2"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/drive\n",
-            "PROJECT_ROOT: /content/drive/MyDrive\n",
-            "PROCESS_ROOT: /content/drive/MyDrive/aims_dataset\n",
-            "OUTPUT_DIR: /content/defect_transfer_outputs\n"
-          ]
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  thermal_side  thermal_label  thermal_mean_temp  thermal_std_temp  \\\n",
+       "0         left              0          51.042400          3.518454   \n",
+       "1         left              0          51.621162          2.787206   \n",
+       "2         left              0          51.391914          2.623878   \n",
+       "3         left              0          51.121414          3.284468   \n",
+       "4         left              0          50.737823          3.554459   \n",
+       "\n",
+       "   thermal_min_temp  thermal_max_temp  thermal_p90_temp  thermal_range_temp  \\\n",
+       "0         42.534000         54.491001         54.271999              11.957   \n",
+       "1         42.860001         54.021000         53.651199              11.161   \n",
+       "2         43.202000         53.519001         53.315601              10.317   \n",
+       "3         42.550999         53.926998         53.727699              11.376   \n",
+       "4         42.051998         53.896000         53.601799              11.844   \n",
+       "\n",
+       "   thermal_slope  \n",
+       "0         10.465  \n",
+       "1          9.378  \n",
+       "2          8.845  \n",
+       "3          9.862  \n",
+       "4         10.345  "
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>thermal_side</th>\n",
+       "      <th>thermal_label</th>\n",
+       "      <th>thermal_mean_temp</th>\n",
+       "      <th>thermal_std_temp</th>\n",
+       "      <th>thermal_min_temp</th>\n",
+       "      <th>thermal_max_temp</th>\n",
+       "      <th>thermal_p90_temp</th>\n",
+       "      <th>thermal_range_temp</th>\n",
+       "      <th>thermal_slope</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>left</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.042400</td>\n",
+       "      <td>3.518454</td>\n",
+       "      <td>42.534000</td>\n",
+       "      <td>54.491001</td>\n",
+       "      <td>54.271999</td>\n",
+       "      <td>11.957</td>\n",
+       "      <td>10.465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>left</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.621162</td>\n",
+       "      <td>2.787206</td>\n",
+       "      <td>42.860001</td>\n",
+       "      <td>54.021000</td>\n",
+       "      <td>53.651199</td>\n",
+       "      <td>11.161</td>\n",
+       "      <td>9.378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>left</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.391914</td>\n",
+       "      <td>2.623878</td>\n",
+       "      <td>43.202000</td>\n",
+       "      <td>53.519001</td>\n",
+       "      <td>53.315601</td>\n",
+       "      <td>10.317</td>\n",
+       "      <td>8.845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>left</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.121414</td>\n",
+       "      <td>3.284468</td>\n",
+       "      <td>42.550999</td>\n",
+       "      <td>53.926998</td>\n",
+       "      <td>53.727699</td>\n",
+       "      <td>11.376</td>\n",
+       "      <td>9.862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>left</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50.737823</td>\n",
+       "      <td>3.554459</td>\n",
+       "      <td>42.051998</td>\n",
+       "      <td>53.896000</td>\n",
+       "      <td>53.601799</td>\n",
+       "      <td>11.844</td>\n",
+       "      <td>10.345</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "# Drive 마운트\n",
-        "try:\n",
-        "    from google.colab import drive\n",
-        "    drive.mount(\"/content/drive\")\n",
-        "    IN_COLAB = True\n",
-        "except Exception:\n",
-        "    IN_COLAB = False\n",
-        "    print(\"Colab이 아니므로 Google Drive mount를 건너뜁니다.\")\n",
-        "\n",
-        "# 데이터 루트\n",
-        "PROJECT_ROOT = Path(\"/content/drive/MyDrive\") if IN_COLAB else Path.cwd()\n",
-        "PROCESS_ROOT = PROJECT_ROOT / \"aims_dataset\"\n",
-        "BOSCH_ROOT = PROCESS_ROOT / \"bosch-production-line-performance\"\n",
-        "THERMAL_ROOT = PROCESS_ROOT / \"머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)\"\n",
-        "FORD_ROOT = PROCESS_ROOT / \"Ford 엔진 진동 데이터셋\"\n",
-        "METAL_ROOT = PROCESS_ROOT / \"소성가공 자원최적화 AI 데이터셋\"\n",
-        "# 산출물 경로\n",
-        "OUTPUT_DIR = Path(\"/content/defect_transfer_outputs\") if IN_COLAB else Path(\"outputs/defect_transfer\")\n",
-        "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
-        "\n",
-        "# Bosch 파일 경로\n",
-        "NUMERIC_PATH = BOSCH_ROOT / \"train_numeric.csv\"\n",
-        "DATE_PATH = BOSCH_ROOT / \"train_date.csv\"\n",
-        "CATEGORICAL_PATH = BOSCH_ROOT / \"train_categorical.csv\"\n",
-        "\n",
-        "# 열화상 파일 경로\n",
-        "THERMAL_LEFT_DATA_PATH = THERMAL_ROOT / \"2nd_process_left_data.csv\"\n",
-        "THERMAL_LEFT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_left_label.json\"\n",
-        "THERMAL_RIGHT_DATA_PATH = THERMAL_ROOT / \"2nd_process_right_data.csv\"\n",
-        "THERMAL_RIGHT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_right_label.json\"\n",
-        "\n",
-        "# 보조 데이터 파일 경로\n",
-        "FORD_TRAIN_PATH = FORD_ROOT / \"FordA_TRAIN.txt\"\n",
-        "FORD_TEST_PATH = FORD_ROOT / \"FordA_TEST.txt\"\n",
-        "METAL_PROCESS_PATH = METAL_ROOT / \"공정_데이터_2022년_8월.csv\"\n",
-        "METAL_SENSOR_PATHS = sorted([p for p in METAL_ROOT.glob(\"*.csv\") if p.name != \"공정_데이터_2022년_8월.csv\"])\n",
-        "\n",
-        "print(\"PROJECT_ROOT:\", PROJECT_ROOT)\n",
-        "print(\"PROCESS_ROOT:\", PROCESS_ROOT)\n",
-        "print(\"OUTPUT_DIR:\", OUTPUT_DIR)\n"
-      ]
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"    print(\\\"\\uc5f4\\ud654\\uc0c1 \\ub370\\uc774\\ud130 \\ud30c\\uc77c\\uc774 \\uc5c6\\uc5b4 \\uc5f4\\ud654\\uc0c1 \\ubcf4\\uc870 \\ud559\\uc2b5\\uc744 \\uac74\\ub108\\ub701\\ub2c8\\ub2e4\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"thermal_side\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_label\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_mean_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          51.62116241455078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_std_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.7872064113616943\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_min_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          42.86000061035156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_max_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          54.020999908447266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_p90_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          53.65119934082031\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_range_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.16100025177002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_slope\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.378000259399414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# 보조 피처 결합\n",
+    "def append_cycled_feature(target_df: pd.DataFrame, column: str, values: np.ndarray | pd.Series):\n",
+    "    values = pd.Series(values).dropna().to_numpy(dtype=float)\n",
+    "    if len(values) == 0:\n",
+    "        print(f\"{column}: 값이 없어 추가하지 않습니다.\")\n",
+    "        return\n",
+    "    target_df[column] = np.resize(values, len(target_df))\n",
+    "    print(f\"{column}: {len(values)}개 값을 {len(target_df)}개 Bosch row에 cyclic alignment로 추가\")\n",
+    "\n",
+    "# 보조 모델 평가\n",
+    "def evaluate_aux_classifier(name: str, y_true: pd.Series, proba: np.ndarray):\n",
+    "    pred = (proba >= 0.5).astype(int)\n",
+    "    print(f\"\\n== {name} auxiliary model ==\")\n",
+    "    if y_true.nunique() > 1:\n",
+    "        print(\"ROC-AUC:\", round(roc_auc_score(y_true, proba), 5))\n",
+    "        print(\"PR-AUC:\", round(average_precision_score(y_true, proba), 5))\n",
+    "    print(classification_report(y_true, pred, digits=4))\n",
+    "\n",
+    "# 보조 산출물 저장소\n",
+    "auxiliary_metrics = {}\n",
+    "auxiliary_models = {}\n",
+    "\n",
+    "# 열화상 데이터 로드\n",
+    "def load_thermal_side(data_path: Path, label_path: Path, side: str) -> pd.DataFrame | None:\n",
+    "    if not data_path.exists() or not label_path.exists():\n",
+    "        return None\n",
+    "    data = read_csv_stable(data_path)\n",
+    "    data.columns = [f\"t{i+1}\" for i in range(data.shape[1])]\n",
+    "    labels = read_json_stable(label_path)\n",
+    "    data = data.apply(pd.to_numeric, errors=\"coerce\")\n",
+    "    out = pd.DataFrame({\n",
+    "        \"thermal_side\": side,\n",
+    "        \"thermal_label\": pd.Series(labels).astype(float).astype(\"int8\"),\n",
+    "        \"thermal_mean_temp\": data.mean(axis=1),\n",
+    "        \"thermal_std_temp\": data.std(axis=1),\n",
+    "        \"thermal_min_temp\": data.min(axis=1),\n",
+    "        \"thermal_max_temp\": data.max(axis=1),\n",
+    "        \"thermal_p90_temp\": data.quantile(0.90, axis=1),\n",
+    "        \"thermal_range_temp\": data.max(axis=1) - data.min(axis=1),\n",
+    "        \"thermal_slope\": data.iloc[:, -1] - data.iloc[:, 0],\n",
+    "    })\n",
+    "    return reduce_mem_usage(out)\n",
+    "\n",
+    "# 열화상 좌우 데이터 결합\n",
+    "thermal_frames = []\n",
+    "for item in [\n",
+    "    (THERMAL_LEFT_DATA_PATH, THERMAL_LEFT_LABEL_PATH, \"left\"),\n",
+    "    (THERMAL_RIGHT_DATA_PATH, THERMAL_RIGHT_LABEL_PATH, \"right\"),\n",
+    "]:\n",
+    "    thermal_side = load_thermal_side(*item)\n",
+    "    if thermal_side is not None:\n",
+    "        thermal_frames.append(thermal_side)\n",
+    "\n",
+    "# 열화상 보조 모델 학습\n",
+    "if thermal_frames:\n",
+    "    thermal_df = pd.concat(thermal_frames, ignore_index=True)\n",
+    "    thermal_feature_cols = [c for c in thermal_df.columns if c.startswith(\"thermal_\") and c not in [\"thermal_side\", \"thermal_label\"]]\n",
+    "    X_th = thermal_df[thermal_feature_cols]\n",
+    "    y_th = thermal_df[\"thermal_label\"].astype(int)\n",
+    "    X_th_train, X_th_valid, y_th_train, y_th_valid = train_test_split(\n",
+    "        X_th, y_th, test_size=0.25, random_state=RANDOM_STATE, stratify=y_th if y_th.nunique() > 1 else None\n",
+    "    )\n",
+    "    thermal_model = lgb.LGBMClassifier(\n",
+    "        objective=\"binary\", n_estimators=300, learning_rate=0.04, num_leaves=16,\n",
+    "        min_child_samples=10, random_state=RANDOM_STATE, n_jobs=-1\n",
+    "    )\n",
+    "    # 열화상 불량 확률 학습\n",
+    "    thermal_model.fit(X_th_train, y_th_train)\n",
+    "    thermal_valid_proba = thermal_model.predict_proba(X_th_valid)[:, 1]\n",
+    "    evaluate_aux_classifier(\"Thermal PAINT quality\", y_th_valid, thermal_valid_proba)\n",
+    "    thermal_all_proba = thermal_model.predict_proba(X_th)[:, 1]\n",
+    "    # 도장 보조 피처 추가\n",
+    "    append_cycled_feature(features, \"aux_paint_thermal_defect_probability\", thermal_all_proba)\n",
+    "    append_cycled_feature(features, \"aux_paint_thermal_label\", y_th)\n",
+    "    auxiliary_models[\"thermal_paint_quality\"] = thermal_model\n",
+    "    auxiliary_metrics[\"thermal_rows\"] = int(len(thermal_df))\n",
+    "    display(thermal_df.head())\n",
+    "else:\n",
+    "    thermal_df = None\n",
+    "    print(\"열화상 데이터 파일이 없어 열화상 보조 학습을 건너뜁니다.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76645e9c",
+   "metadata": {
+    "id": "76645e9c"
+   },
+   "source": [
+    "### 4.1 Ford 엔진 진동 데이터 보조 학습\n",
+    "\n",
+    "FordA 데이터는 첫 번째 컬럼이 라벨입니다. 쉼표 또는 공백 구분 형식을 모두 지원하며, `-1`을 이상/불량, `1`을 정상으로 변환해 PRESS/BODY 계열 설비 이상 확률을 학습합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "144bc123",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "144bc123",
+    "outputId": "b559b72a-3520-49e8-ef80-8add5e66a562"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "lc2ZXZzwbEe_",
-      "metadata": {
-        "id": "lc2ZXZzwbEe_"
-      },
-      "source": [
-        "## 2. 대용량 CSV 로드 전략\n",
-        "\n",
-        "Bosch 데이터는 매우 크므로 전체 컬럼을 한 번에 읽지 않습니다. 먼저 일부 행만 프로파일링한 뒤 결측률이 낮고 변동이 있는 컬럼을 고르고, 선택 컬럼만 학습 크기만큼 로드합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "FordA_TRAIN.txt: rows=1, features=255, labels={-1.0: 1}\n",
+      "FordA_TEST.txt: rows=1,320, features=500, labels={-1.0: 681, 1.0: 639}\n",
+      "Ford TRAIN 데이터를 제외합니다(shape=(1, 256)). 정상적인 TEST 데이터(shape=(1320, 501))를 stratified 75:25로 재분할합니다.\n",
+      "\n",
+      "== Ford PRESS/BODY vibration auxiliary model ==\n",
+      "ROC-AUC: 0.72757\n",
+      "PR-AUC: 0.74268\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0     0.6294    0.6687    0.6485       160\n",
+      "           1     0.6687    0.6294    0.6485       170\n",
+      "\n",
+      "    accuracy                         0.6485       330\n",
+      "   macro avg     0.6491    0.6491    0.6485       330\n",
+      "weighted avg     0.6497    0.6485    0.6485       330\n",
+      "\n",
+      "aux_body_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
+      "aux_press_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Ford 진동 데이터 로드\n",
+    "def load_ford_txt(path: Path, max_rows: int | None = None) -> pd.DataFrame | None:\n",
+    "    if not path.exists():\n",
+    "        return None\n",
+    "\n",
+    "    # 배포처에 따라 FordA가 comma 또는 whitespace 구분 형식으로 제공됩니다.\n",
+    "    df = read_csv_stable(\n",
+    "        path,\n",
+    "        sep=r\"[,\\s]+\",\n",
+    "        engine=\"python\",\n",
+    "        header=None,\n",
+    "        nrows=max_rows,\n",
+    "    )\n",
+    "    df = df.dropna(axis=1, how=\"all\").apply(pd.to_numeric, errors=\"coerce\")\n",
+    "    invalid_rows = int(df.isna().any(axis=1).sum())\n",
+    "    if invalid_rows:\n",
+    "        raise ValueError(f\"{path.name}: 숫자로 해석할 수 없는 행이 {invalid_rows:,}개 있습니다.\")\n",
+    "\n",
+    "    if df.shape[1] < 2:\n",
+    "        raise ValueError(f\"{path.name}: 라벨과 진동 feature를 구분할 수 없습니다. shape={df.shape}\")\n",
+    "\n",
+    "    labels = set(df.iloc[:, 0].astype(int).unique())\n",
+    "    if not labels or not labels.issubset({-1, 1}):\n",
+    "        raise ValueError(f\"{path.name}: 라벨은 -1 또는 1이어야 하지만 현재 {sorted(labels)}입니다.\")\n",
+    "\n",
+    "    ford_feature_count = df.shape[1] - 1\n",
+    "    df.columns = [\"label\"] + [f\"ford_t{i}\" for i in range(1, df.shape[1])]\n",
+    "    df[\"ford_abnormal_label\"] = (df[\"label\"] == -1).astype(\"int8\")\n",
+    "    print(f\"{path.name}: rows={len(df):,}, features={ford_feature_count}, labels={df['label'].value_counts().to_dict()}\")\n",
+    "    return reduce_mem_usage(df.drop(columns=[\"label\"]))\n",
+    "\n",
+    "# Ford train/test 로드\n",
+    "ford_train_df = load_ford_txt(FORD_TRAIN_PATH)\n",
+    "ford_test_df = load_ford_txt(FORD_TEST_PATH)\n",
+    "\n",
+    "# Ford 보조 모델 학습\n",
+    "if ford_train_df is not None and ford_test_df is not None:\n",
+    "    ford_feature_cols = [c for c in ford_train_df.columns if c != \"ford_abnormal_label\"]\n",
+    "    ford_test_feature_cols = [c for c in ford_test_df.columns if c != \"ford_abnormal_label\"]\n",
+    "    train_is_usable = len(ford_train_df) >= 20 and ford_train_df[\"ford_abnormal_label\"].nunique() == 2\n",
+    "    test_is_usable = len(ford_test_df) >= 20 and ford_test_df[\"ford_abnormal_label\"].nunique() == 2\n",
+    "    feature_schema_matches = ford_feature_cols == ford_test_feature_cols\n",
+    "\n",
+    "    if train_is_usable and test_is_usable and feature_schema_matches:\n",
+    "        ford_all_df = pd.concat([ford_train_df, ford_test_df], ignore_index=True)\n",
+    "        X_ford_train = ford_train_df[ford_feature_cols]\n",
+    "        y_ford_train = ford_train_df[\"ford_abnormal_label\"].astype(int)\n",
+    "        X_ford_test = ford_test_df[ford_feature_cols]\n",
+    "        y_ford_test = ford_test_df[\"ford_abnormal_label\"].astype(int)\n",
+    "    elif test_is_usable:\n",
+    "        # 현재 배포본처럼 TRAIN이 잘렸어도 정상적인 TEST 데이터만으로 보조 모델을 구성합니다.\n",
+    "        ford_all_df = ford_test_df.copy()\n",
+    "        ford_feature_cols = ford_test_feature_cols\n",
+    "        print(\n",
+    "            f\"Ford TRAIN 데이터를 제외합니다(shape={ford_train_df.shape}). \"\n",
+    "            f\"정상적인 TEST 데이터(shape={ford_test_df.shape})를 stratified 75:25로 재분할합니다.\"\n",
+    "        )\n",
+    "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
+    "            ford_all_df[ford_feature_cols],\n",
+    "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
+    "            test_size=0.25,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
+    "        )\n",
+    "    elif train_is_usable:\n",
+    "        ford_all_df = ford_train_df.copy()\n",
+    "        print(\"Ford TEST 데이터가 학습에 부적합하여 TRAIN 데이터를 stratified 75:25로 재분할합니다.\")\n",
+    "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
+    "            ford_all_df[ford_feature_cols],\n",
+    "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
+    "            test_size=0.25,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
+    "        )\n",
+    "    else:\n",
+    "        raise ValueError(\n",
+    "            \"Ford 데이터에 두 클래스가 포함된 충분한 행이 없습니다. \"\n",
+    "            f\"train={ford_train_df.shape}, test={ford_test_df.shape}\"\n",
+    "        )\n",
+    "\n",
+    "    ford_model = lgb.LGBMClassifier(\n",
+    "        objective=\"binary\", n_estimators=500, learning_rate=0.03, num_leaves=31,\n",
+    "        min_child_samples=20, subsample=0.9, colsample_bytree=0.9,\n",
+    "        random_state=RANDOM_STATE, n_jobs=-1, verbosity=-1\n",
+    "    )\n",
+    "    # 진동 이상 확률 학습\n",
+    "    ford_model.fit(X_ford_train, y_ford_train)\n",
+    "    ford_test_proba = ford_model.predict_proba(X_ford_test)[:, 1]\n",
+    "    evaluate_aux_classifier(\"Ford PRESS/BODY vibration\", y_ford_test, ford_test_proba)\n",
+    "\n",
+    "    ford_all_proba = ford_model.predict_proba(ford_all_df[ford_feature_cols])[:, 1]\n",
+    "    # 차체/프레스 보조 피처 추가\n",
+    "    append_cycled_feature(features, \"aux_body_ford_vibration_abnormal_probability\", ford_all_proba)\n",
+    "    append_cycled_feature(features, \"aux_press_ford_vibration_abnormal_probability\", ford_all_proba)\n",
+    "    auxiliary_models[\"ford_vibration\"] = ford_model\n",
+    "    auxiliary_metrics[\"ford_rows\"] = int(len(ford_all_df))\n",
+    "else:\n",
+    "    print(\"Ford 데이터 파일이 없어 Ford 보조 학습을 건너뜁니다.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a97efe99",
+   "metadata": {
+    "id": "a97efe99"
+   },
+   "source": [
+    "### 4.2 소성가공 공정/전류 데이터 비지도 학습\n",
+    "\n",
+    "소성가공 데이터에는 불량 라벨이 없으므로 공정 데이터와 설비 센서 데이터를 각각 Isolation Forest로 학습해 공정 흐름 이상 점수, 프레스 이상 점수, 로봇 전류 이상 점수를 생성합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "cd25bfc8",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 216
     },
+    "id": "cd25bfc8",
+    "outputId": "73d3b745-218c-4f61-96fa-0022c8136bcb"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "66fcea04",
-      "metadata": {
-        "id": "66fcea04"
-      },
-      "outputs": [],
-      "source": [
-        "# 학습 규모 설정\n",
-        "PROFILE_ROWS = 50_000\n",
-        "MAX_ROWS = 300_000\n",
-        "MAX_NUMERIC_FEATURES = 260\n",
-        "MAX_DATE_FEATURES = 260\n",
-        "USE_CATEGORICAL = True\n",
-        "MAX_CATEGORICAL_FEATURES = 80\n",
-        "CATEGORICAL_PROFILE_ROWS = 20_000\n",
-        "MAX_AUX_ROWS = 200_000\n",
-        "ENABLE_HYPERPARAMETER_TUNING = True\n",
-        "CV_N_SPLITS = 5\n",
-        "# 불균형 데이터에서는 PR-AUC/Recall 안정성을 위해 충분한 탐색 횟수를 사용합니다.\n",
-        "# 실행 시간이 부담되면 CV_SAMPLE_SIZE 또는 CV_N_ITER를 낮춰 빠른 실험 모드로 전환합니다.\n",
-        "CV_N_ITER = 15\n",
-        "ENABLE_OPTUNA_TUNING = True\n",
-        "CV_SAMPLE_SIZE = 30_000\n",
-        "FINAL_TRAIN_SAMPLE_SIZE = 120_000\n",
-        "RETRAIN_SELECTED_MODEL_ON_FULL_DATA = False\n",
-        "USE_SMOTE = True\n",
-        "SMOTE_SAMPLING_STRATEGY = 0.10\n",
-        "RECALL_PRIORITY_TARGET = 0.35\n",
-        "RECALL_PRIORITY_MIN_PRECISION = 0.02\n",
-        "THRESHOLD_BETA = 2.0\n",
-        "\n",
-        "# 공정 매핑\n",
-        "LINE_TO_PROCESS = {\n",
-        "    \"L0\": \"PRESS\",\n",
-        "    \"L1\": \"BODY\",\n",
-        "    \"L2\": \"ASSEMBLY\",\n",
-        "    \"L3\": \"PAINT\",\n",
-        "}\n",
-        "PROCESS_FLOW = [\"PRESS\", \"BODY\", \"PAINT\", \"ASSEMBLY\"]\n",
-        "PROCESS_DISPLAY = {\n",
-        "    \"PRESS\": \"프레스\",\n",
-        "    \"BODY\": \"차체\",\n",
-        "    \"PAINT\": \"도장\",\n",
-        "    \"ASSEMBLY\": \"의장\",\n",
-        "    \"FINAL_INSPECTION\": \"최종검사\",\n",
-        "    \"UNKNOWN\": \"미분류\",\n",
-        "}\n",
-        "\n",
-        "# Colab Drive FUSE 연결이 불안정할 수 있어 데이터셋은 로컬 런타임 캐시에서 읽습니다.\n",
-        "LOCAL_DATA_CACHE = Path(\"/content/aims_dataset_cache\") if IN_COLAB else None\n",
-        "\n",
-        "\n",
-        "def local_dataset_path(path: Path) -> Path:\n",
-        "    path = Path(path)\n",
-        "    if not IN_COLAB:\n",
-        "        return path\n",
-        "    if LOCAL_DATA_CACHE is None:\n",
-        "        return path\n",
-        "\n",
-        "    try:\n",
-        "        relative_path = path.relative_to(PROJECT_ROOT)\n",
-        "    except ValueError:\n",
-        "        relative_path = Path(path.name)\n",
-        "\n",
-        "    cached_path = LOCAL_DATA_CACHE / relative_path\n",
-        "    if cached_path.exists():\n",
-        "        try:\n",
-        "            if cached_path.stat().st_size == path.stat().st_size:\n",
-        "                return cached_path\n",
-        "        except OSError:\n",
-        "            # Drive stat 자체가 실패하면 이미 복사된 로컬 캐시를 우선 사용합니다.\n",
-        "            return cached_path\n",
-        "\n",
-        "    cached_path.parent.mkdir(parents=True, exist_ok=True)\n",
-        "    try:\n",
-        "        print(f\"cache copy: {path} -> {cached_path}\")\n",
-        "        shutil.copy2(path, cached_path)\n",
-        "    except OSError as exc:\n",
-        "        raise OSError(\n",
-        "            \"Google Drive 연결이 끊겼습니다. Colab에서 drive.mount('/content/drive', force_remount=True)를 \"\n",
-        "            \"실행한 뒤 이 셀부터 다시 실행하세요.\"\n",
-        "        ) from exc\n",
-        "    return cached_path\n",
-        "\n",
-        "\n",
-        "def read_csv_stable(path: Path, **kwargs) -> pd.DataFrame:\n",
-        "    try:\n",
-        "        return pd.read_csv(local_dataset_path(path), **kwargs)\n",
-        "    except OSError as exc:\n",
-        "        if IN_COLAB and \"Transport endpoint is not connected\" in str(exc):\n",
-        "            raise OSError(\n",
-        "                \"Google Drive 연결이 끊겼습니다. drive.mount('/content/drive', force_remount=True)로 \"\n",
-        "                \"재마운트한 뒤 다시 실행하세요. 이미 캐시된 파일은 /content/aims_dataset_cache에서 읽습니다.\"\n",
-        "            ) from exc\n",
-        "        raise\n",
-        "\n",
-        "\n",
-        "def read_json_stable(path: Path):\n",
-        "    with open(local_dataset_path(path), \"r\", encoding=\"utf-8\") as f:\n",
-        "        return json.load(f)\n",
-        "\n",
-        "\n",
-        "# 파일 검증\n",
-        "def assert_file(path: Path):\n",
-        "    if not path.exists():\n",
-        "        cached_path = local_dataset_path(path) if IN_COLAB else path\n",
-        "        if not cached_path.exists():\n",
-        "            raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
-        "\n",
-        "# 메모리 축소\n",
-        "def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
-        "    for col in df.columns:\n",
-        "        if col == \"Id\":\n",
-        "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
-        "            continue\n",
-        "        if pd.api.types.is_integer_dtype(df[col]):\n",
-        "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
-        "        elif pd.api.types.is_float_dtype(df[col]):\n",
-        "            df[col] = pd.to_numeric(df[col], downcast=\"float\")\n",
-        "    return df\n",
-        "\n",
-        "# 라인 추출\n",
-        "def get_line(column: str) -> str:\n",
-        "    match = re.match(r\"^(L\\d+)_\", column)\n",
-        "    return match.group(1) if match else \"UNKNOWN\"\n",
-        "\n",
-        "# 스테이션 추출\n",
-        "def get_station(column: str) -> str:\n",
-        "    match = re.match(r\"^(L\\d+)_S(\\d+)_\", column)\n",
-        "    return f\"{match.group(1)}_S{match.group(2)}\" if match else \"UNKNOWN\"\n",
-        "\n",
-        "# 컬럼 선별\n",
-        "def select_columns_by_profile(\n",
-        "    path: Path,\n",
-        "    *,\n",
-        "    required_cols: list[str],\n",
-        "    max_features: int,\n",
-        "    profile_rows: int,\n",
-        "    min_non_null_ratio: float = 0.02,\n",
-        ") -> list[str]:\n",
-        "    assert_file(path)\n",
-        "    sample = read_csv_stable(path, nrows=profile_rows)\n",
-        "    candidates = [c for c in sample.columns if c not in required_cols]\n",
-        "    non_null_ratio = sample[candidates].notna().mean()\n",
-        "    nunique = sample[candidates].nunique(dropna=True)\n",
-        "    score = (non_null_ratio * np.log1p(nunique)).sort_values(ascending=False)\n",
-        "    selected = score[(non_null_ratio >= min_non_null_ratio) & (nunique > 1)].head(max_features).index.tolist()\n",
-        "    del sample\n",
-        "    gc.collect()\n",
-        "    return required_cols + selected\n",
-        "\n",
-        "# 선택 컬럼 로드\n",
-        "def read_selected_csv(path: Path, usecols: list[str], max_rows: int | None) -> pd.DataFrame:\n",
-        "    df = read_csv_stable(path, usecols=usecols, nrows=max_rows)\n",
-        "    return reduce_mem_usage(df)\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "numeric_df: (300000, 262)\n",
+      "date_df: (300000, 261)\n",
+      "categorical_df: (300000, 10)\n",
+      "Response ratio:\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "508b9115",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 290
-        },
-        "id": "508b9115",
-        "outputId": "eaef9a23-f57f-4c4d-85a1-69e744c2cc71"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_numeric.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_numeric.csv\n",
-            "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_date.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_date.csv\n",
-            "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_categorical.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_categorical.csv\n",
-            "numeric_df: (300000, 262)\n",
-            "date_df: (300000, 261)\n",
-            "categorical_df: (300000, 10)\n",
-            "Response ratio:\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "            ratio\n",
-              "Response         \n",
-              "0         0.99435\n",
-              "1         0.00565"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-626ebf76-9a02-48e2-974a-d5d99fd25fa4\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>ratio</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Response</th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0.99435</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0.00565</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-626ebf76-9a02-48e2-974a-d5d99fd25fa4')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {}
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "            ratio\n",
+       "Response         \n",
+       "0         0.99435\n",
+       "1         0.00565"
       ],
-      "source": [
-        "# Bosch numeric 컬럼 선별\n",
-        "numeric_cols = select_columns_by_profile(\n",
-        "    NUMERIC_PATH,\n",
-        "    required_cols=[\"Id\", \"Response\"],\n",
-        "    max_features=MAX_NUMERIC_FEATURES,\n",
-        "    profile_rows=PROFILE_ROWS,\n",
-        ")\n",
-        "# Bosch date 컬럼 선별\n",
-        "date_cols = select_columns_by_profile(\n",
-        "    DATE_PATH,\n",
-        "    required_cols=[\"Id\"],\n",
-        "    max_features=MAX_DATE_FEATURES,\n",
-        "    profile_rows=PROFILE_ROWS,\n",
-        ")\n",
-        "\n",
-        "# Bosch 선택 데이터 로드\n",
-        "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
-        "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
-        "\n",
-        "# Bosch categorical 선택 로드\n",
-        "if USE_CATEGORICAL:\n",
-        "    categorical_cols = select_columns_by_profile(\n",
-        "        CATEGORICAL_PATH,\n",
-        "        required_cols=[\"Id\"],\n",
-        "        max_features=MAX_CATEGORICAL_FEATURES,\n",
-        "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
-        "    )\n",
-        "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
-        "else:\n",
-        "    categorical_df = None\n",
-        "\n",
-        "print(\"numeric_df:\", numeric_df.shape)\n",
-        "print(\"date_df:\", date_df.shape)\n",
-        "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
-        "print(\"Response ratio:\")\n",
-        "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
-      ]
+      "text/html": [
+       "\n",
+       "  <div id=\"df-43c145af-4222-4430-8081-0b1b5c54e8d6\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ratio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Response</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.99435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.00565</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43c145af-4222-4430-8081-0b1b5c54e8d6')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
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+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# Bosch numeric 컬럼 선별\n",
+    "numeric_cols = select_columns_by_profile(\n",
+    "    NUMERIC_PATH,\n",
+    "    required_cols=[\"Id\", \"Response\"],\n",
+    "    max_features=MAX_NUMERIC_FEATURES,\n",
+    "    profile_rows=PROFILE_ROWS,\n",
+    ")\n",
+    "# Bosch date 컬럼 선별\n",
+    "date_cols = select_columns_by_profile(\n",
+    "    DATE_PATH,\n",
+    "    required_cols=[\"Id\"],\n",
+    "    max_features=MAX_DATE_FEATURES,\n",
+    "    profile_rows=PROFILE_ROWS,\n",
+    ")\n",
+    "\n",
+    "# Bosch 선택 데이터 로드\n",
+    "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
+    "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
+    "\n",
+    "# Bosch categorical 선택 로드\n",
+    "if USE_CATEGORICAL:\n",
+    "    categorical_cols = select_columns_by_profile(\n",
+    "        CATEGORICAL_PATH,\n",
+    "        required_cols=[\"Id\"],\n",
+    "        max_features=MAX_CATEGORICAL_FEATURES,\n",
+    "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
+    "    )\n",
+    "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
+    "else:\n",
+    "    categorical_df = None\n",
+    "\n",
+    "print(\"numeric_df:\", numeric_df.shape)\n",
+    "print(\"date_df:\", date_df.shape)\n",
+    "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
+    "print(\"Response ratio:\")\n",
+    "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "-7HWdLE4bEfB",
+   "metadata": {
+    "id": "-7HWdLE4bEfB"
+   },
+   "source": [
+    "## 5. 학습 데이터 정제\n",
+    "\n",
+    "4개 후보 모델이 동일한 학습 조건에서 비교될 수 있도록 무한대와 완전 결측 컬럼을 제거합니다. 불량 데이터는 희소하므로 stratified split과 class weight 기반 모델을 사용합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "XX3xUzrlbEfB",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "XX3xUzrlbEfB",
+    "outputId": "1b64eb6a-04d9-4fa0-ee50-df1fa3ea9a18"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "11973c36",
-      "metadata": {
-        "id": "11973c36"
-      },
-      "source": [
-        "## 3. Bosch Feature Engineering\n",
-        "\n",
-        "`L*_S*_F*` 수치 센서와 `L*_S*_D*` 시간 컬럼을 그대로 일부 사용하면서, 공정/스테이션 단위 집계 피처를 추가합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "X_train: (240000, 457) X_valid: (60000, 457)\n",
+      "positive ratio train: 0.00565\n",
+      "scale_pos_weight: 175.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 학습 테이블 결합\n",
+    "model_df = features.merge(target, on=\"Id\", how=\"inner\")\n",
+    "model_df = model_df.replace([np.inf, -np.inf], np.nan)\n",
+    "\n",
+    "# 입력/라벨 분리\n",
+    "drop_cols = [\"Id\", \"Response\"]\n",
+    "feature_cols = [c for c in model_df.columns if c not in drop_cols]\n",
+    "# 완전 결측 컬럼 제거\n",
+    "all_missing_cols = model_df[feature_cols].columns[model_df[feature_cols].isna().all()].tolist()\n",
+    "if all_missing_cols:\n",
+    "    model_df = model_df.drop(columns=all_missing_cols)\n",
+    "    feature_cols = [c for c in feature_cols if c not in all_missing_cols]\n",
+    "\n",
+    "X = model_df[feature_cols]\n",
+    "y = model_df[\"Response\"].astype(int)\n",
+    "ids = model_df[\"Id\"]\n",
+    "if y.nunique() < 2:\n",
+    "    raise ValueError(\"Response가 한 클래스만 포함되어 있습니다. MAX_ROWS를 늘리거나 데이터 샘플링 범위를 조정하세요.\")\n",
+    "\n",
+    "# 계층 분할\n",
+    "X_train, X_valid, y_train, y_valid, id_train, id_valid = train_test_split(\n",
+    "    X, y, ids,\n",
+    "    test_size=0.2,\n",
+    "    random_state=RANDOM_STATE,\n",
+    "    stratify=y,\n",
+    ")\n",
+    "\n",
+    "# 클래스 불균형 가중치\n",
+    "neg_count = int((y_train == 0).sum())\n",
+    "pos_count = int((y_train == 1).sum())\n",
+    "scale_pos_weight = neg_count / max(pos_count, 1)\n",
+    "\n",
+    "print(\"X_train:\", X_train.shape, \"X_valid:\", X_valid.shape)\n",
+    "print(\"positive ratio train:\", y_train.mean())\n",
+    "print(\"scale_pos_weight:\", round(scale_pos_weight, 2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6444a8dd",
+   "metadata": {
+    "id": "6444a8dd"
+   },
+   "source": [
+    "## 6. 전처리 학습 데이터셋 저장\n",
+    "\n",
+    "모델에 실제로 들어가는 정제/전처리 완료 데이터셋을 파일로 저장합니다. Parquet 저장이 가능하면 `.parquet`, 환경에 `pyarrow/fastparquet`가 없으면 `.csv.gz`로 저장합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6e442104",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "6e442104",
+    "outputId": "09415cb9-862f-49f7-a3f3-30566b3940f4"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "dd4b603e",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 273
-        },
-        "id": "dd4b603e",
-        "outputId": "84ce5bbe-1ecd-490a-884b-4e42c0252e7f"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "features: (300000, 454)\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "   Id  L0_S0_F0  L0_S0_F2  L0_S0_F4  L0_S0_F6  L0_S0_F8  L0_S0_F10  L0_S0_F12  \\\n",
-              "0   4     0.030    -0.034    -0.197    -0.179     0.118      0.116     -0.015   \n",
-              "1   6       NaN       NaN       NaN       NaN       NaN        NaN        NaN   \n",
-              "2   7     0.088     0.086     0.003    -0.052     0.161      0.025     -0.015   \n",
-              "3   9    -0.036    -0.064     0.294     0.330     0.074      0.161      0.022   \n",
-              "4  11    -0.055    -0.086     0.294     0.330     0.118      0.025      0.030   \n",
-              "\n",
-              "   L0_S0_F14  L0_S0_F16  L0_S0_F18  L0_S0_F20  L0_S0_F22  L0_S1_F24  \\\n",
-              "0     -0.032      0.020      0.083     -0.273     -0.273     -0.271   \n",
-              "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
-              "2     -0.072     -0.225     -0.147      0.250      0.250      0.057   \n",
-              "3      0.128     -0.026     -0.046     -0.253     -0.253      0.147   \n",
-              "4      0.168     -0.169     -0.099      0.042      0.042     -0.012   \n",
-              "\n",
-              "   L0_S1_F28  L0_S2_F36  L0_S2_F44  L0_S2_F48  L0_S2_F60  L0_S2_F64  \\\n",
-              "0      0.167     -0.023     -0.088      0.001     -0.223     -0.030   \n",
-              "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
-              "2     -0.079      0.011     -0.060     -0.005     -0.223     -0.077   \n",
-              "3     -0.007      0.120     -0.231      0.005      0.050      0.056   \n",
-              "4     -0.046        NaN        NaN        NaN        NaN        NaN   \n",
-              "\n",
-              "   L0_S3_F72  L0_S3_F80  L0_S3_F84  L0_S3_F96  L0_S3_F100  L0_S4_F104  \\\n",
-              "0        NaN        NaN        NaN        NaN         NaN      -0.001   \n",
-              "1        NaN        NaN        NaN        NaN         NaN         NaN   \n",
-              "2        NaN        NaN        NaN        NaN         NaN         NaN   \n",
-              "3        NaN        NaN        NaN        NaN         NaN      -0.038   \n",
-              "4     -0.272      0.037      0.004      0.311       0.003       0.021   \n",
-              "\n",
-              "   L0_S4_F109  L0_S5_F114  L0_S5_F116  L0_S6_F122  L0_S6_F132  L0_S7_F138  \\\n",
-              "0      -0.004         NaN         NaN         NaN         NaN      -0.077   \n",
-              "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-              "2         NaN      -0.073       0.138       0.506       -0.13         NaN   \n",
-              "3      -0.001         NaN         NaN         NaN         NaN       0.402   \n",
-              "4       0.015         NaN         NaN         NaN         NaN      -0.083   \n",
-              "\n",
-              "   L0_S7_F142  L0_S8_F144  L0_S8_F146  L0_S8_F149  L0_S9_F155  L0_S9_F160  \\\n",
-              "0       0.060      -0.157         0.0       0.001         NaN         NaN   \n",
-              "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-              "2         NaN      -0.157         0.0       0.001         NaN         NaN   \n",
-              "3      -0.015       0.343         0.0       0.001         NaN         NaN   \n",
-              "4       0.014      -0.157         0.0       0.001         NaN         NaN   \n",
-              "\n",
-              "   L0_S9_F165  L0_S9_F170  L0_S9_F180  L0_S9_F185  L0_S9_F190  L0_S9_F195  \\\n",
-              "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-              "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-              "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-              "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-              "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-              "\n",
-              "   L0_S9_F200  L0_S9_F210  L0_S10_F219  L0_S10_F224  L0_S10_F229  L0_S10_F234  \\\n",
-              "0         NaN         NaN          NaN          NaN          NaN          NaN   \n",
-              "1         NaN         NaN          NaN          NaN          NaN          NaN   \n",
-              "2         NaN         NaN       -0.002       -0.047        0.018          0.0   \n",
-              "3         NaN         NaN        0.007       -0.049        0.020          0.0   \n",
-              "4         NaN         NaN          NaN          NaN          NaN          NaN   \n",
-              "\n",
-              "   L0_S10_F244  L0_S10_F249  L0_S10_F254  L0_S10_F259  L0_S10_F264  \\\n",
-              "0          NaN          NaN          NaN          NaN          NaN   \n",
-              "1          NaN          NaN          NaN          NaN          NaN   \n",
-              "2        0.001       -0.009       -0.017        0.105       -0.001   \n",
-              "3       -0.013       -0.003       -0.032        0.139        0.001   \n",
-              "4          NaN          NaN          NaN          NaN          NaN   \n",
-              "\n",
-              "   L0_S11_F282  L0_S11_F286  L0_S11_F290  L0_S11_F294  L0_S11_F302  ...  \\\n",
-              "0        0.018       -0.136       -0.013        0.106       -0.131  ...   \n",
-              "1          NaN          NaN          NaN          NaN          NaN  ...   \n",
-              "2          NaN          NaN          NaN          NaN          NaN  ...   \n",
-              "3          NaN          NaN          NaN          NaN          NaN  ...   \n",
-              "4       -0.122       -0.064       -0.011       -0.005        0.071  ...   \n",
-              "\n",
-              "   L0_S5_num_mean  L0_S5_num_std  L0_S5_num_missing_ratio  L0_S6_num_mean  \\\n",
-              "0             NaN            NaN                      1.0             NaN   \n",
-              "1             NaN            NaN                      1.0             NaN   \n",
-              "2          0.0325         0.1492                      0.0           0.188   \n",
-              "3             NaN            NaN                      1.0             NaN   \n",
-              "4             NaN            NaN                      1.0             NaN   \n",
-              "\n",
-              "   L0_S6_num_std  L0_S6_num_missing_ratio  L0_S7_num_mean  L0_S7_num_std  \\\n",
-              "0            NaN                      1.0         -0.0085       0.096874   \n",
-              "1            NaN                      1.0             NaN            NaN   \n",
-              "2        0.44972                      0.0             NaN            NaN   \n",
-              "3            NaN                      1.0          0.1935       0.294864   \n",
-              "4            NaN                      1.0         -0.0345       0.068589   \n",
-              "\n",
-              "   L0_S7_num_missing_ratio  L0_S8_num_mean  L0_S8_num_std  \\\n",
-              "0                      0.0       -0.052000       0.090934   \n",
-              "1                      1.0             NaN            NaN   \n",
-              "2                      1.0       -0.052000       0.090934   \n",
-              "3                      0.0        0.114667       0.197743   \n",
-              "4                      0.0       -0.052000       0.090934   \n",
-              "\n",
-              "   L0_S8_num_missing_ratio  L0_S9_num_mean  L0_S9_num_std  \\\n",
-              "0                      0.0             NaN            NaN   \n",
-              "1                      1.0             NaN            NaN   \n",
-              "2                      0.0             NaN            NaN   \n",
-              "3                      0.0             NaN            NaN   \n",
-              "4                      0.0             NaN            NaN   \n",
-              "\n",
-              "   L0_S9_num_missing_ratio  L1_S24_num_mean  L1_S24_num_std  \\\n",
-              "0                      1.0              NaN             NaN   \n",
-              "1                      1.0              NaN             NaN   \n",
-              "2                      1.0              NaN             NaN   \n",
-              "3                      1.0              NaN             NaN   \n",
-              "4                      1.0              NaN             NaN   \n",
-              "\n",
-              "   L1_S24_num_missing_ratio  L2_S26_num_mean  L2_S26_num_std  \\\n",
-              "0                       1.0              NaN             NaN   \n",
-              "1                       1.0              NaN             NaN   \n",
-              "2                       1.0              NaN             NaN   \n",
-              "3                       1.0              NaN             NaN   \n",
-              "4                       1.0              NaN             NaN   \n",
-              "\n",
-              "   L2_S26_num_missing_ratio  L2_S27_num_mean  L2_S27_num_std  \\\n",
-              "0                       1.0              NaN             NaN   \n",
-              "1                       1.0              NaN             NaN   \n",
-              "2                       1.0              NaN             NaN   \n",
-              "3                       1.0              NaN             NaN   \n",
-              "4                       1.0              NaN             NaN   \n",
-              "\n",
-              "   L2_S27_num_missing_ratio  L3_S29_num_mean  L3_S29_num_std  \\\n",
-              "0                       1.0        -0.014321        0.201176   \n",
-              "1                       1.0        -0.005264        0.142025   \n",
-              "2                       1.0        -0.010000        0.129113   \n",
-              "3                       1.0        -0.030151        0.124692   \n",
-              "4                       1.0        -0.017321        0.157158   \n",
-              "\n",
-              "   L3_S29_num_missing_ratio  L3_S30_num_mean  L3_S30_num_std  \\\n",
-              "0                       0.0         0.003889        0.083444   \n",
-              "1                       0.0         0.021621        0.068539   \n",
-              "2                       0.0        -0.010552        0.069868   \n",
-              "3                       0.0         0.010017        0.058393   \n",
-              "4                       0.0        -0.004069        0.049633   \n",
-              "\n",
-              "   L3_S30_num_missing_ratio  L3_S33_num_mean  L3_S33_num_std  \\\n",
-              "0                   0.37931          -0.0481        0.060306   \n",
-              "1                   0.00000          -0.0370        0.063085   \n",
-              "2                   0.00000           0.1132        0.102470   \n",
-              "3                   0.00000           0.0282        0.049806   \n",
-              "4                   0.00000          -0.0130        0.065646   \n",
-              "\n",
-              "   L3_S33_num_missing_ratio  L3_S34_num_mean  L3_S34_num_std  \\\n",
-              "0                       0.0              0.0             0.0   \n",
-              "1                       0.0              0.0             0.0   \n",
-              "2                       0.0              0.0             0.0   \n",
-              "3                       0.0              0.0             0.0   \n",
-              "4                       0.0              0.0             0.0   \n",
-              "\n",
-              "   L3_S34_num_missing_ratio  L3_S35_num_mean  L3_S35_num_std  \\\n",
-              "0                       0.0        -0.016143        0.030180   \n",
-              "1                       0.0        -0.001286        0.017289   \n",
-              "2                       0.0        -0.013857        0.039015   \n",
-              "3                       0.0              NaN             NaN   \n",
-              "4                       0.0              NaN             NaN   \n",
-              "\n",
-              "   L3_S35_num_missing_ratio  L3_S36_num_mean  L3_S36_num_std  \\\n",
-              "0                       0.0              NaN             NaN   \n",
-              "1                       0.0              NaN             NaN   \n",
-              "2                       0.0              NaN             NaN   \n",
-              "3                       1.0         -0.05550        0.085261   \n",
-              "4                       1.0         -0.00225        0.066188   \n",
-              "\n",
-              "   L3_S36_num_missing_ratio  L3_S37_num_mean  L3_S37_num_std  \\\n",
-              "0                       1.0              0.0             0.0   \n",
-              "1                       1.0              0.0             0.0   \n",
-              "2                       1.0              0.0             0.0   \n",
-              "3                       0.0              0.0             0.0   \n",
-              "4                       0.0              0.0             0.0   \n",
-              "\n",
-              "   L3_S37_num_missing_ratio  L1_cat_count  L1_cat_missing_ratio  \\\n",
-              "0                       0.0             0                   1.0   \n",
-              "1                       0.0             0                   1.0   \n",
-              "2                       0.0             0                   1.0   \n",
-              "3                       0.0             0                   1.0   \n",
-              "4                       0.0             0                   1.0   \n",
-              "\n",
-              "   L1_cat_unique_count  L2_cat_count  L2_cat_missing_ratio  \\\n",
-              "0                    0             0                   1.0   \n",
-              "1                    0             0                   1.0   \n",
-              "2                    0             0                   1.0   \n",
-              "3                    0             0                   1.0   \n",
-              "4                    0             0                   1.0   \n",
-              "\n",
-              "   L2_cat_unique_count  L1_S24_F1114_code  L1_S24_F1137_code  \\\n",
-              "0                    0                  5                  4   \n",
-              "1                    0                  5                  4   \n",
-              "2                    0                  5                  4   \n",
-              "3                    0                  5                  4   \n",
-              "4                    0                  5                  4   \n",
-              "\n",
-              "   L1_S24_F1140_code  L1_S24_F1510_code  L1_S24_F1525_code  L1_S24_F1530_code  \\\n",
-              "0                  4                  5                  5                  5   \n",
-              "1                  4                  5                  5                  5   \n",
-              "2                  4                  5                  5                  5   \n",
-              "3                  4                  5                  5                  5   \n",
-              "4                  4                  5                  5                  5   \n",
-              "\n",
-              "   L1_S25_F1852_code  L2_S26_F3099_code  L2_S27_F3192_code  \n",
-              "0                  8                  2                  2  \n",
-              "1                  8                  2                  2  \n",
-              "2                  8                  2                  2  \n",
-              "3                  8                  2                  2  \n",
-              "4                  8                  2                  2  \n",
-              "\n",
-              "[5 rows x 454 columns]"
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-              "      <th>L0_S10_F264</th>\n",
-              "      <th>L0_S11_F282</th>\n",
-              "      <th>L0_S11_F286</th>\n",
-              "      <th>L0_S11_F290</th>\n",
-              "      <th>L0_S11_F294</th>\n",
-              "      <th>L0_S11_F302</th>\n",
-              "      <th>...</th>\n",
-              "      <th>L0_S5_num_mean</th>\n",
-              "      <th>L0_S5_num_std</th>\n",
-              "      <th>L0_S5_num_missing_ratio</th>\n",
-              "      <th>L0_S6_num_mean</th>\n",
-              "      <th>L0_S6_num_std</th>\n",
-              "      <th>L0_S6_num_missing_ratio</th>\n",
-              "      <th>L0_S7_num_mean</th>\n",
-              "      <th>L0_S7_num_std</th>\n",
-              "      <th>L0_S7_num_missing_ratio</th>\n",
-              "      <th>L0_S8_num_mean</th>\n",
-              "      <th>L0_S8_num_std</th>\n",
-              "      <th>L0_S8_num_missing_ratio</th>\n",
-              "      <th>L0_S9_num_mean</th>\n",
-              "      <th>L0_S9_num_std</th>\n",
-              "      <th>L0_S9_num_missing_ratio</th>\n",
-              "      <th>L1_S24_num_mean</th>\n",
-              "      <th>L1_S24_num_std</th>\n",
-              "      <th>L1_S24_num_missing_ratio</th>\n",
-              "      <th>L2_S26_num_mean</th>\n",
-              "      <th>L2_S26_num_std</th>\n",
-              "      <th>L2_S26_num_missing_ratio</th>\n",
-              "      <th>L2_S27_num_mean</th>\n",
-              "      <th>L2_S27_num_std</th>\n",
-              "      <th>L2_S27_num_missing_ratio</th>\n",
-              "      <th>L3_S29_num_mean</th>\n",
-              "      <th>L3_S29_num_std</th>\n",
-              "      <th>L3_S29_num_missing_ratio</th>\n",
-              "      <th>L3_S30_num_mean</th>\n",
-              "      <th>L3_S30_num_std</th>\n",
-              "      <th>L3_S30_num_missing_ratio</th>\n",
-              "      <th>L3_S33_num_mean</th>\n",
-              "      <th>L3_S33_num_std</th>\n",
-              "      <th>L3_S33_num_missing_ratio</th>\n",
-              "      <th>L3_S34_num_mean</th>\n",
-              "      <th>L3_S34_num_std</th>\n",
-              "      <th>L3_S34_num_missing_ratio</th>\n",
-              "      <th>L3_S35_num_mean</th>\n",
-              "      <th>L3_S35_num_std</th>\n",
-              "      <th>L3_S35_num_missing_ratio</th>\n",
-              "      <th>L3_S36_num_mean</th>\n",
-              "      <th>L3_S36_num_std</th>\n",
-              "      <th>L3_S36_num_missing_ratio</th>\n",
-              "      <th>L3_S37_num_mean</th>\n",
-              "      <th>L3_S37_num_std</th>\n",
-              "      <th>L3_S37_num_missing_ratio</th>\n",
-              "      <th>L1_cat_count</th>\n",
-              "      <th>L1_cat_missing_ratio</th>\n",
-              "      <th>L1_cat_unique_count</th>\n",
-              "      <th>L2_cat_count</th>\n",
-              "      <th>L2_cat_missing_ratio</th>\n",
-              "      <th>L2_cat_unique_count</th>\n",
-              "      <th>L1_S24_F1114_code</th>\n",
-              "      <th>L1_S24_F1137_code</th>\n",
-              "      <th>L1_S24_F1140_code</th>\n",
-              "      <th>L1_S24_F1510_code</th>\n",
-              "      <th>L1_S24_F1525_code</th>\n",
-              "      <th>L1_S24_F1530_code</th>\n",
-              "      <th>L1_S25_F1852_code</th>\n",
-              "      <th>L2_S26_F3099_code</th>\n",
-              "      <th>L2_S27_F3192_code</th>\n",
-              "    </tr>\n",
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-              "      <td>4</td>\n",
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-              "      <td>0.37931</td>\n",
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-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>5</td>\n",
-              "      <td>4</td>\n",
-              "      <td>4</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
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-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
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-              "      <td>NaN</td>\n",
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-              "      <td>NaN</td>\n",
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-              "      <td>-0.005264</td>\n",
-              "      <td>0.142025</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.021621</td>\n",
-              "      <td>0.068539</td>\n",
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-              "      <td>-0.0370</td>\n",
-              "      <td>0.063085</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-0.001286</td>\n",
-              "      <td>0.017289</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>5</td>\n",
-              "      <td>4</td>\n",
-              "      <td>4</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>7</td>\n",
-              "      <td>0.088</td>\n",
-              "      <td>0.086</td>\n",
-              "      <td>0.003</td>\n",
-              "      <td>-0.052</td>\n",
-              "      <td>0.161</td>\n",
-              "      <td>0.025</td>\n",
-              "      <td>-0.015</td>\n",
-              "      <td>-0.072</td>\n",
-              "      <td>-0.225</td>\n",
-              "      <td>-0.147</td>\n",
-              "      <td>0.250</td>\n",
-              "      <td>0.250</td>\n",
-              "      <td>0.057</td>\n",
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-              "      <td>0.011</td>\n",
-              "      <td>-0.060</td>\n",
-              "      <td>-0.005</td>\n",
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-              "      <td>NaN</td>\n",
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-              "      <td>0.506</td>\n",
-              "      <td>-0.13</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-0.157</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.001</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-0.002</td>\n",
-              "      <td>-0.047</td>\n",
-              "      <td>0.018</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.001</td>\n",
-              "      <td>-0.009</td>\n",
-              "      <td>-0.017</td>\n",
-              "      <td>0.105</td>\n",
-              "      <td>-0.001</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.0325</td>\n",
-              "      <td>0.1492</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.188</td>\n",
-              "      <td>0.44972</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.052000</td>\n",
-              "      <td>0.090934</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.010000</td>\n",
-              "      <td>0.129113</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-0.010552</td>\n",
-              "      <td>0.069868</td>\n",
-              "      <td>0.00000</td>\n",
-              "      <td>0.1132</td>\n",
-              "      <td>0.102470</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-0.013857</td>\n",
-              "      <td>0.039015</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>5</td>\n",
-              "      <td>4</td>\n",
-              "      <td>4</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9</td>\n",
-              "      <td>-0.036</td>\n",
-              "      <td>-0.064</td>\n",
-              "      <td>0.294</td>\n",
-              "      <td>0.330</td>\n",
-              "      <td>0.074</td>\n",
-              "      <td>0.161</td>\n",
-              "      <td>0.022</td>\n",
-              "      <td>0.128</td>\n",
-              "      <td>-0.026</td>\n",
-              "      <td>-0.046</td>\n",
-              "      <td>-0.253</td>\n",
-              "      <td>-0.253</td>\n",
-              "      <td>0.147</td>\n",
-              "      <td>-0.007</td>\n",
-              "      <td>0.120</td>\n",
-              "      <td>-0.231</td>\n",
-              "      <td>0.005</td>\n",
-              "      <td>0.050</td>\n",
-              "      <td>0.056</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-0.038</td>\n",
-              "      <td>-0.001</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0.402</td>\n",
-              "      <td>-0.015</td>\n",
-              "      <td>0.343</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.001</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0.007</td>\n",
-              "      <td>-0.049</td>\n",
-              "      <td>0.020</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-0.013</td>\n",
-              "      <td>-0.003</td>\n",
-              "      <td>-0.032</td>\n",
-              "      <td>0.139</td>\n",
-              "      <td>0.001</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>...</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.1935</td>\n",
-              "      <td>0.294864</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.114667</td>\n",
-              "      <td>0.197743</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.030151</td>\n",
-              "      <td>0.124692</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.010017</td>\n",
-              "      <td>0.058393</td>\n",
-              "      <td>0.00000</td>\n",
-              "      <td>0.0282</td>\n",
-              "      <td>0.049806</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.05550</td>\n",
-              "      <td>0.085261</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>5</td>\n",
-              "      <td>4</td>\n",
-              "      <td>4</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>11</td>\n",
-              "      <td>-0.055</td>\n",
-              "      <td>-0.086</td>\n",
-              "      <td>0.294</td>\n",
-              "      <td>0.330</td>\n",
-              "      <td>0.118</td>\n",
-              "      <td>0.025</td>\n",
-              "      <td>0.030</td>\n",
-              "      <td>0.168</td>\n",
-              "      <td>-0.169</td>\n",
-              "      <td>-0.099</td>\n",
-              "      <td>0.042</td>\n",
-              "      <td>0.042</td>\n",
-              "      <td>-0.012</td>\n",
-              "      <td>-0.046</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-0.272</td>\n",
-              "      <td>0.037</td>\n",
-              "      <td>0.004</td>\n",
-              "      <td>0.311</td>\n",
-              "      <td>0.003</td>\n",
-              "      <td>0.021</td>\n",
-              "      <td>0.015</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-0.083</td>\n",
-              "      <td>0.014</td>\n",
-              "      <td>-0.157</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.001</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-0.122</td>\n",
-              "      <td>-0.064</td>\n",
-              "      <td>-0.011</td>\n",
-              "      <td>-0.005</td>\n",
-              "      <td>0.071</td>\n",
-              "      <td>...</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.0345</td>\n",
-              "      <td>0.068589</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-0.052000</td>\n",
-              "      <td>0.090934</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.017321</td>\n",
-              "      <td>0.157158</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-0.004069</td>\n",
-              "      <td>0.049633</td>\n",
-              "      <td>0.00000</td>\n",
-              "      <td>-0.0130</td>\n",
-              "      <td>0.065646</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.00225</td>\n",
-              "      <td>0.066188</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>5</td>\n",
-              "      <td>4</td>\n",
-              "      <td>4</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>5</td>\n",
-              "      <td>8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>5 rows × 454 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82c8c8d6-bb6b-4406-9895-53a4298ecac0')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "# Date 피처 생성\n",
-        "def build_date_features(date_df: pd.DataFrame) -> pd.DataFrame:\n",
-        "    feature_df = pd.DataFrame({\"Id\": date_df[\"Id\"]})\n",
-        "    date_feature_cols = [c for c in date_df.columns if c != \"Id\"]\n",
-        "    date_values = date_df[date_feature_cols]\n",
-        "\n",
-        "    feature_df[\"date_observed_count\"] = date_values.notna().sum(axis=1)\n",
-        "    feature_df[\"date_missing_ratio\"] = date_values.isna().mean(axis=1)\n",
-        "    feature_df[\"process_start_time\"] = date_values.min(axis=1)\n",
-        "    feature_df[\"process_end_time\"] = date_values.max(axis=1)\n",
-        "    feature_df[\"total_process_duration\"] = feature_df[\"process_end_time\"] - feature_df[\"process_start_time\"]\n",
-        "\n",
-        "    # 라인별 시간 집계\n",
-        "    for line in sorted({get_line(c) for c in date_feature_cols if get_line(c) != \"UNKNOWN\"}):\n",
-        "        cols = [c for c in date_feature_cols if get_line(c) == line]\n",
-        "        values = date_df[cols]\n",
-        "        feature_df[f\"{line}_date_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
-        "        feature_df[f\"{line}_date_count\"] = values.notna().sum(axis=1)\n",
-        "        feature_df[f\"{line}_date_missing_ratio\"] = values.isna().mean(axis=1)\n",
-        "        feature_df[f\"{line}_duration\"] = values.max(axis=1) - values.min(axis=1)\n",
-        "\n",
-        "    # 스테이션별 시간 집계\n",
-        "    station_span_cols = []\n",
-        "    for station in sorted({get_station(c) for c in date_feature_cols if get_station(c) != \"UNKNOWN\"}):\n",
-        "        cols = [c for c in date_feature_cols if get_station(c) == station]\n",
-        "        values = date_df[cols]\n",
-        "        span_col = f\"{station}_span\"\n",
-        "        feature_df[f\"{station}_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
-        "        feature_df[span_col] = values.max(axis=1) - values.min(axis=1)\n",
-        "        feature_df[f\"{station}_mean_time\"] = values.mean(axis=1)\n",
-        "        station_span_cols.append(span_col)\n",
-        "\n",
-        "    # 병목 후보 시간 집계\n",
-        "    if station_span_cols:\n",
-        "        spans = feature_df[station_span_cols]\n",
-        "        feature_df[\"max_station_span\"] = spans.max(axis=1)\n",
-        "        feature_df[\"mean_station_span\"] = spans.mean(axis=1)\n",
-        "        feature_df[\"std_station_span\"] = spans.std(axis=1)\n",
-        "        feature_df[\"active_station_count\"] = feature_df[[c.replace(\"_span\", \"_seen\") for c in station_span_cols]].sum(axis=1)\n",
-        "    return reduce_mem_usage(feature_df)\n",
-        "\n",
-        "# Numeric 집계 피처 생성\n",
-        "def build_numeric_aggregate_features(numeric_df: pd.DataFrame) -> pd.DataFrame:\n",
-        "    value_cols = [c for c in numeric_df.columns if c not in [\"Id\", \"Response\"]]\n",
-        "    feature_df = numeric_df[[\"Id\"]].copy()\n",
-        "\n",
-        "    # 라인별 센서 집계\n",
-        "    for line in sorted({get_line(c) for c in value_cols if get_line(c) != \"UNKNOWN\"}):\n",
-        "        cols = [c for c in value_cols if get_line(c) == line]\n",
-        "        values = numeric_df[cols]\n",
-        "        feature_df[f\"{line}_num_mean\"] = values.mean(axis=1)\n",
-        "        feature_df[f\"{line}_num_std\"] = values.std(axis=1)\n",
-        "        feature_df[f\"{line}_num_min\"] = values.min(axis=1)\n",
-        "        feature_df[f\"{line}_num_max\"] = values.max(axis=1)\n",
-        "        feature_df[f\"{line}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
-        "\n",
-        "    # 스테이션별 센서 집계\n",
-        "    for station in sorted({get_station(c) for c in value_cols if get_station(c) != \"UNKNOWN\"}):\n",
-        "        cols = [c for c in value_cols if get_station(c) == station]\n",
-        "        if len(cols) < 2:\n",
-        "            continue\n",
-        "        values = numeric_df[cols]\n",
-        "        feature_df[f\"{station}_num_mean\"] = values.mean(axis=1)\n",
-        "        feature_df[f\"{station}_num_std\"] = values.std(axis=1)\n",
-        "        feature_df[f\"{station}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
-        "    return reduce_mem_usage(feature_df)\n",
-        "\n",
-        "# Categorical 피처 생성\n",
-        "def build_categorical_features(categorical_df: pd.DataFrame) -> pd.DataFrame:\n",
-        "    cat_cols = [c for c in categorical_df.columns if c != \"Id\"]\n",
-        "    feature_df = categorical_df[[\"Id\"]].copy()\n",
-        "    if not cat_cols:\n",
-        "        return feature_df\n",
-        "\n",
-        "    # 라인별 범주 집계\n",
-        "    for line in sorted({get_line(c) for c in cat_cols if get_line(c) != \"UNKNOWN\"}):\n",
-        "        cols = [c for c in cat_cols if get_line(c) == line]\n",
-        "        values = categorical_df[cols]\n",
-        "        feature_df[f\"{line}_cat_count\"] = values.notna().sum(axis=1)\n",
-        "        feature_df[f\"{line}_cat_missing_ratio\"] = values.isna().mean(axis=1)\n",
-        "        feature_df[f\"{line}_cat_unique_count\"] = values.nunique(axis=1, dropna=True)\n",
-        "\n",
-        "    # 범주 인코딩\n",
-        "    for col in cat_cols:\n",
-        "        encoded, _ = pd.factorize(categorical_df[col].astype(\"string\").fillna(\"__MISSING__\"), sort=True)\n",
-        "        feature_df[f\"{col}_code\"] = encoded.astype(\"int32\")\n",
-        "    return reduce_mem_usage(feature_df)\n",
-        "\n",
-        "# Bosch 피처 생성\n",
-        "date_features = build_date_features(date_df)\n",
-        "numeric_agg_features = build_numeric_aggregate_features(numeric_df)\n",
-        "raw_numeric_features = numeric_df.drop(columns=[\"Response\"])\n",
-        "\n",
-        "# Bosch 피처 병합\n",
-        "features = raw_numeric_features.merge(date_features, on=\"Id\", how=\"left\")\n",
-        "features = features.merge(numeric_agg_features, on=\"Id\", how=\"left\")\n",
-        "if categorical_df is not None:\n",
-        "    categorical_features = build_categorical_features(categorical_df)\n",
-        "    features = features.merge(categorical_features, on=\"Id\", how=\"left\")\n",
-        "    del categorical_df, categorical_features\n",
-        "# 학습 라벨 분리\n",
-        "target = numeric_df[[\"Id\", \"Response\"]].copy()\n",
-        "\n",
-        "del date_df, numeric_agg_features, date_features\n",
-        "gc.collect()\n",
-        "\n",
-        "print(\"features:\", features.shape)\n",
-        "display(features.head())\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "전처리 학습 데이터셋: /content/defect_transfer_outputs/defect_transfer_training_dataset.parquet\n",
+      "train/valid split ids: /content/defect_transfer_outputs/defect_transfer_train_valid_split_ids.csv\n",
+      "전처리 metadata: /content/defect_transfer_outputs/defect_transfer_preprocessing_metadata.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 학습 데이터셋 저장\n",
+    "def save_training_dataframe(df: pd.DataFrame, output_dir: Path, stem: str) -> Path:\n",
+    "    parquet_path = output_dir / f\"{stem}.parquet\"\n",
+    "    csv_path = output_dir / f\"{stem}.csv.gz\"\n",
+    "    try:\n",
+    "        df.to_parquet(parquet_path, index=False)\n",
+    "        return parquet_path\n",
+    "    except Exception as exc:\n",
+    "        print(f\"Parquet 저장 실패, csv.gz로 저장합니다: {exc}\")\n",
+    "        df.to_csv(csv_path, index=False, compression=\"gzip\")\n",
+    "        return csv_path\n",
+    "\n",
+    "# 전처리 결과 저장\n",
+    "training_dataset_path = save_training_dataframe(model_df, OUTPUT_DIR, \"defect_transfer_training_dataset\")\n",
+    "\n",
+    "# 분할 ID 저장\n",
+    "split_ids = pd.DataFrame({\n",
+    "    \"Id\": pd.concat([id_train, id_valid]).astype(int).to_numpy(),\n",
+    "    \"split\": [\"train\"] * len(id_train) + [\"valid\"] * len(id_valid),\n",
+    "})\n",
+    "split_ids_path = OUTPUT_DIR / \"defect_transfer_train_valid_split_ids.csv\"\n",
+    "split_ids.to_csv(split_ids_path, index=False)\n",
+    "\n",
+    "# 전처리 메타데이터\n",
+    "preprocessing_metadata = {\n",
+    "    \"process_root\": str(PROCESS_ROOT),\n",
+    "    \"bosch_numeric_path\": str(NUMERIC_PATH),\n",
+    "    \"bosch_date_path\": str(DATE_PATH),\n",
+    "    \"bosch_categorical_path\": str(CATEGORICAL_PATH),\n",
+    "    \"use_categorical\": bool(USE_CATEGORICAL),\n",
+    "    \"profile_rows\": int(PROFILE_ROWS),\n",
+    "    \"max_rows\": int(MAX_ROWS),\n",
+    "    \"max_numeric_features\": int(MAX_NUMERIC_FEATURES),\n",
+    "    \"max_date_features\": int(MAX_DATE_FEATURES),\n",
+    "    \"max_categorical_features\": int(MAX_CATEGORICAL_FEATURES),\n",
+    "    \"selected_numeric_columns\": numeric_cols,\n",
+    "    \"selected_date_columns\": date_cols,\n",
+    "    \"selected_categorical_columns\": categorical_cols if USE_CATEGORICAL else [],\n",
+    "    \"dropped_all_missing_columns\": all_missing_cols,\n",
+    "    \"feature_columns\": feature_cols,\n",
+    "    \"target_column\": \"Response\",\n",
+    "    \"train_rows\": int(len(X_train)),\n",
+    "    \"valid_rows\": int(len(X_valid)),\n",
+    "    \"positive_ratio\": float(y.mean()),\n",
+    "    \"auxiliary_metrics\": auxiliary_metrics,\n",
+    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
+    "}\n",
+    "preprocessing_metadata_path = OUTPUT_DIR / \"defect_transfer_preprocessing_metadata.json\"\n",
+    "preprocessing_metadata_path.write_text(json.dumps(preprocessing_metadata, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "\n",
+    "print(\"전처리 학습 데이터셋:\", training_dataset_path)\n",
+    "print(\"train/valid split ids:\", split_ids_path)\n",
+    "print(\"전처리 metadata:\", preprocessing_metadata_path)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b19d8e5c",
+   "metadata": {
+    "id": "b19d8e5c"
+   },
+   "source": [
+    "## 7. 후보 모델 4종 교차검증 및 하이퍼파라미터 튜닝\n",
+    "\n",
+    "불량 클래스가 희소하므로 모든 후보 모델은 같은 전처리/피처 엔지니어링 결과를 사용하고, `StratifiedKFold`로 클래스 비율을 유지하며 비교합니다.\n",
+    "\n",
+    "이번 버전은 기존의 약한 랜덤 탐색을 보완하기 위해 다음을 반영합니다.\n",
+    "\n",
+    "- `CV_N_SPLITS = 5`: 5-Fold Stratified CV로 validation 안정성 강화\n",
+    "- `CV_N_ITER = 15`: 비-LightGBM 후보도 15회 랜덤 탐색\n",
+    "- LightGBM: `Optuna` TPE sampler 기반 15회 탐색\n",
+    "- LightGBM pipeline: median imputation + SMOTE + `is_unbalance=True` 기반 불균형 보정\n",
+    "- threshold: Accuracy/F1 단독 최적화가 아니라 Recall 우선 탐색 적용\n",
+    "\n",
+    "후보 모델:\n",
+    "\n",
+    "| 모델 | 목적 |\n",
+    "| --- | --- |\n",
+    "| LightGBM | 대용량 tabular 제조 데이터용 gradient boosting 기준 모델, Optuna + SMOTE 적용 |\n",
+    "| RandomForest | bagging 기반 tree ensemble 기준 모델 |\n",
+    "| ExtraTrees | 더 강한 randomization을 주는 tree ensemble 후보 |\n",
+    "| LogisticRegression | 선형 기준선 모델 |\n",
+    "\n",
+    "비교 지표는 PR-AUC, Recall, Precision, F1, ROC-AUC, Accuracy, False Positive Rate를 함께 사용합니다. 특히 Bosch 데이터처럼 불량 비율이 1% 미만인 경우 Accuracy는 참고 지표로만 해석합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d9a50e90",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "d9a50e90",
+    "outputId": "60f68881-d294-44fc-92f2-008ba9ab2179"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "e6f30d6e",
-      "metadata": {
-        "id": "e6f30d6e"
-      },
-      "source": [
-        "## 4. Process 폴더 전체 보조 데이터 학습\n",
-        "\n",
-        "`process` 폴더의 Bosch 외 데이터는 Bosch `Id`와 직접 연결되는 공통 키가 없습니다. 따라서 MVP에서는 각 데이터셋을 별도로 학습하여 공정별 보조 위험 신호를 만들고, Bosch 학습 테이블에는 반복 정렬(cyclic alignment) 방식으로 결합합니다. 운영 데이터에서 `car_master_id`, `event_time`, `process_code` 매핑이 생기면 이 부분을 정확 조인으로 교체하면 됩니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:00:51,763] A new study created in memory with name: bosch_defect_lightgbm_recall_pr_auc\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "a2a68fb3",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 698
-        },
-        "id": "a2a68fb3",
-        "outputId": "65f72cce-fd5b-4c16-9900-3b43b4e0e289"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv\n",
-            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json\n",
-            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv\n",
-            "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json\n",
-            "[LightGBM] [Info] Number of positive: 461, number of negative: 166\n",
-            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n",
-            "You can set `force_col_wise=true` to remove the overhead.\n",
-            "[LightGBM] [Info] Total Bins 1349\n",
-            "[LightGBM] [Info] Number of data points in the train set: 627, number of used features: 7\n",
-            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.735247 -> initscore=1.021410\n",
-            "[LightGBM] [Info] Start training from score 1.021410\n",
-            "\n",
-            "== Thermal PAINT quality auxiliary model ==\n",
-            "ROC-AUC: 0.83824\n",
-            "PR-AUC: 0.91229\n",
-            "              precision    recall  f1-score   support\n",
-            "\n",
-            "           0     0.5962    0.5636    0.5794        55\n",
-            "           1     0.8481    0.8645    0.8562       155\n",
-            "\n",
-            "    accuracy                         0.7857       210\n",
-            "   macro avg     0.7221    0.7141    0.7178       210\n",
-            "weighted avg     0.7821    0.7857    0.7837       210\n",
-            "\n",
-            "aux_paint_thermal_defect_probability: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
-            "aux_paint_thermal_label: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  thermal_side  thermal_label  thermal_mean_temp  thermal_std_temp  \\\n",
-              "0         left              0          51.042400          3.518454   \n",
-              "1         left              0          51.621162          2.787206   \n",
-              "2         left              0          51.391914          2.623878   \n",
-              "3         left              0          51.121414          3.284468   \n",
-              "4         left              0          50.737823          3.554459   \n",
-              "\n",
-              "   thermal_min_temp  thermal_max_temp  thermal_p90_temp  thermal_range_temp  \\\n",
-              "0         42.534000         54.491001         54.271999              11.957   \n",
-              "1         42.860001         54.021000         53.651199              11.161   \n",
-              "2         43.202000         53.519001         53.315601              10.317   \n",
-              "3         42.550999         53.926998         53.727699              11.376   \n",
-              "4         42.051998         53.896000         53.601799              11.844   \n",
-              "\n",
-              "   thermal_slope  \n",
-              "0         10.465  \n",
-              "1          9.378  \n",
-              "2          8.845  \n",
-              "3          9.862  \n",
-              "4         10.345  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
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-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>thermal_side</th>\n",
-              "      <th>thermal_label</th>\n",
-              "      <th>thermal_mean_temp</th>\n",
-              "      <th>thermal_std_temp</th>\n",
-              "      <th>thermal_min_temp</th>\n",
-              "      <th>thermal_max_temp</th>\n",
-              "      <th>thermal_p90_temp</th>\n",
-              "      <th>thermal_range_temp</th>\n",
-              "      <th>thermal_slope</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>left</td>\n",
-              "      <td>0</td>\n",
-              "      <td>51.042400</td>\n",
-              "      <td>3.518454</td>\n",
-              "      <td>42.534000</td>\n",
-              "      <td>54.491001</td>\n",
-              "      <td>54.271999</td>\n",
-              "      <td>11.957</td>\n",
-              "      <td>10.465</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>left</td>\n",
-              "      <td>0</td>\n",
-              "      <td>51.621162</td>\n",
-              "      <td>2.787206</td>\n",
-              "      <td>42.860001</td>\n",
-              "      <td>54.021000</td>\n",
-              "      <td>53.651199</td>\n",
-              "      <td>11.161</td>\n",
-              "      <td>9.378</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>left</td>\n",
-              "      <td>0</td>\n",
-              "      <td>51.391914</td>\n",
-              "      <td>2.623878</td>\n",
-              "      <td>43.202000</td>\n",
-              "      <td>53.519001</td>\n",
-              "      <td>53.315601</td>\n",
-              "      <td>10.317</td>\n",
-              "      <td>8.845</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>left</td>\n",
-              "      <td>0</td>\n",
-              "      <td>51.121414</td>\n",
-              "      <td>3.284468</td>\n",
-              "      <td>42.550999</td>\n",
-              "      <td>53.926998</td>\n",
-              "      <td>53.727699</td>\n",
-              "      <td>11.376</td>\n",
-              "      <td>9.862</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>left</td>\n",
-              "      <td>0</td>\n",
-              "      <td>50.737823</td>\n",
-              "      <td>3.554459</td>\n",
-              "      <td>42.051998</td>\n",
-              "      <td>53.896000</td>\n",
-              "      <td>53.601799</td>\n",
-              "      <td>11.844</td>\n",
-              "      <td>10.345</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
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-              "    [theme=dark] .colab-df-convert {\n",
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-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"    print(\\\"\\uc5f4\\ud654\\uc0c1 \\ub370\\uc774\\ud130 \\ud30c\\uc77c\\uc774 \\uc5c6\\uc5b4 \\uc5f4\\ud654\\uc0c1 \\ubcf4\\uc870 \\ud559\\uc2b5\\uc744 \\uac74\\ub108\\ub701\\ub2c8\\ub2e4\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"thermal_side\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_label\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_mean_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          51.62116241455078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_std_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.7872064113616943\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_min_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          42.86000061035156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_max_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          54.020999908447266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_p90_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          53.65119934082031\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_range_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.16100025177002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_slope\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.378000259399414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "# 보조 피처 결합\n",
-        "def append_cycled_feature(target_df: pd.DataFrame, column: str, values: np.ndarray | pd.Series):\n",
-        "    values = pd.Series(values).dropna().to_numpy(dtype=float)\n",
-        "    if len(values) == 0:\n",
-        "        print(f\"{column}: 값이 없어 추가하지 않습니다.\")\n",
-        "        return\n",
-        "    target_df[column] = np.resize(values, len(target_df))\n",
-        "    print(f\"{column}: {len(values)}개 값을 {len(target_df)}개 Bosch row에 cyclic alignment로 추가\")\n",
-        "\n",
-        "# 보조 모델 평가\n",
-        "def evaluate_aux_classifier(name: str, y_true: pd.Series, proba: np.ndarray):\n",
-        "    pred = (proba >= 0.5).astype(int)\n",
-        "    print(f\"\\n== {name} auxiliary model ==\")\n",
-        "    if y_true.nunique() > 1:\n",
-        "        print(\"ROC-AUC:\", round(roc_auc_score(y_true, proba), 5))\n",
-        "        print(\"PR-AUC:\", round(average_precision_score(y_true, proba), 5))\n",
-        "    print(classification_report(y_true, pred, digits=4))\n",
-        "\n",
-        "# 보조 산출물 저장소\n",
-        "auxiliary_metrics = {}\n",
-        "auxiliary_models = {}\n",
-        "\n",
-        "# 열화상 데이터 로드\n",
-        "def load_thermal_side(data_path: Path, label_path: Path, side: str) -> pd.DataFrame | None:\n",
-        "    if not data_path.exists() or not label_path.exists():\n",
-        "        return None\n",
-        "    data = read_csv_stable(data_path)\n",
-        "    data.columns = [f\"t{i+1}\" for i in range(data.shape[1])]\n",
-        "    labels = read_json_stable(label_path)\n",
-        "    data = data.apply(pd.to_numeric, errors=\"coerce\")\n",
-        "    out = pd.DataFrame({\n",
-        "        \"thermal_side\": side,\n",
-        "        \"thermal_label\": pd.Series(labels).astype(float).astype(\"int8\"),\n",
-        "        \"thermal_mean_temp\": data.mean(axis=1),\n",
-        "        \"thermal_std_temp\": data.std(axis=1),\n",
-        "        \"thermal_min_temp\": data.min(axis=1),\n",
-        "        \"thermal_max_temp\": data.max(axis=1),\n",
-        "        \"thermal_p90_temp\": data.quantile(0.90, axis=1),\n",
-        "        \"thermal_range_temp\": data.max(axis=1) - data.min(axis=1),\n",
-        "        \"thermal_slope\": data.iloc[:, -1] - data.iloc[:, 0],\n",
-        "    })\n",
-        "    return reduce_mem_usage(out)\n",
-        "\n",
-        "# 열화상 좌우 데이터 결합\n",
-        "thermal_frames = []\n",
-        "for item in [\n",
-        "    (THERMAL_LEFT_DATA_PATH, THERMAL_LEFT_LABEL_PATH, \"left\"),\n",
-        "    (THERMAL_RIGHT_DATA_PATH, THERMAL_RIGHT_LABEL_PATH, \"right\"),\n",
-        "]:\n",
-        "    thermal_side = load_thermal_side(*item)\n",
-        "    if thermal_side is not None:\n",
-        "        thermal_frames.append(thermal_side)\n",
-        "\n",
-        "# 열화상 보조 모델 학습\n",
-        "if thermal_frames:\n",
-        "    thermal_df = pd.concat(thermal_frames, ignore_index=True)\n",
-        "    thermal_feature_cols = [c for c in thermal_df.columns if c.startswith(\"thermal_\") and c not in [\"thermal_side\", \"thermal_label\"]]\n",
-        "    X_th = thermal_df[thermal_feature_cols]\n",
-        "    y_th = thermal_df[\"thermal_label\"].astype(int)\n",
-        "    X_th_train, X_th_valid, y_th_train, y_th_valid = train_test_split(\n",
-        "        X_th, y_th, test_size=0.25, random_state=RANDOM_STATE, stratify=y_th if y_th.nunique() > 1 else None\n",
-        "    )\n",
-        "    thermal_model = lgb.LGBMClassifier(\n",
-        "        objective=\"binary\", n_estimators=300, learning_rate=0.04, num_leaves=16,\n",
-        "        min_child_samples=10, random_state=RANDOM_STATE, n_jobs=-1\n",
-        "    )\n",
-        "    # 열화상 불량 확률 학습\n",
-        "    thermal_model.fit(X_th_train, y_th_train)\n",
-        "    thermal_valid_proba = thermal_model.predict_proba(X_th_valid)[:, 1]\n",
-        "    evaluate_aux_classifier(\"Thermal PAINT quality\", y_th_valid, thermal_valid_proba)\n",
-        "    thermal_all_proba = thermal_model.predict_proba(X_th)[:, 1]\n",
-        "    # 도장 보조 피처 추가\n",
-        "    append_cycled_feature(features, \"aux_paint_thermal_defect_probability\", thermal_all_proba)\n",
-        "    append_cycled_feature(features, \"aux_paint_thermal_label\", y_th)\n",
-        "    auxiliary_models[\"thermal_paint_quality\"] = thermal_model\n",
-        "    auxiliary_metrics[\"thermal_rows\"] = int(len(thermal_df))\n",
-        "    display(thermal_df.head())\n",
-        "else:\n",
-        "    thermal_df = None\n",
-        "    print(\"열화상 데이터 파일이 없어 열화상 보조 학습을 건너뜁니다.\")\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CV rows: 30,000 / train rows: 240,000\n",
+      "CV splits: 5, tuning trials per model: 15\n",
+      "SMOTE enabled: True, sampling_strategy=0.1\n",
+      "Threshold strategy: recall>=0.35, min_precision>=0.02, beta=2.0\n",
+      "\n",
+      "[LightGBM] CV start\n",
+      "  optuna trial 1/15 params={'model__learning_rate': 0.021789307659775742, 'model__num_leaves': 154, 'model__max_depth': -1, 'model__min_child_samples': 264, 'model__subsample': 0.8603902541101232, 'model__colsample_bytree': 0.8686326600082205, 'model__reg_lambda': 0.1094395112835673, 'model__reg_alpha': 0.7579479953348001}\n",
+      "    fold 1/5 PR-AUC=0.03400 Recall=0.14706 F1=0.07353 elapsed=67.3s\n",
+      "    fold 2/5 PR-AUC=0.03235 Recall=0.35294 F1=0.06723 elapsed=59.2s\n",
+      "    fold 3/5 PR-AUC=0.03957 Recall=0.23529 F1=0.11268 elapsed=59.2s\n",
+      "    fold 4/5 PR-AUC=0.02377 Recall=0.41176 F1=0.03815 elapsed=60.1s\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "76645e9c",
-      "metadata": {
-        "id": "76645e9c"
-      },
-      "source": [
-        "### 4.1 Ford 엔진 진동 데이터 보조 학습\n",
-        "\n",
-        "FordA 데이터는 첫 번째 컬럼이 라벨입니다. 쉼표 또는 공백 구분 형식을 모두 지원하며, `-1`을 이상/불량, `1`을 정상으로 변환해 PRESS/BODY 계열 설비 이상 확률을 학습합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:05:57,466] Trial 0 finished with value: 0.06604691309715442 and parameters: {'learning_rate': 0.021789307659775742, 'num_leaves': 154, 'max_depth': -1, 'min_child_samples': 264, 'subsample': 0.8603902541101232, 'colsample_bytree': 0.8686326600082205, 'reg_lambda': 0.1094395112835673, 'reg_alpha': 0.7579479953348001}. Best is trial 0 with value: 0.06604691309715442.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "144bc123",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "144bc123",
-        "outputId": "b559b72a-3520-49e8-ef80-8add5e66a562"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "FordA_TRAIN.txt: rows=1, features=255, labels={-1.0: 1}\n",
-            "FordA_TEST.txt: rows=1,320, features=500, labels={-1.0: 681, 1.0: 639}\n",
-            "Ford TRAIN 데이터를 제외합니다(shape=(1, 256)). 정상적인 TEST 데이터(shape=(1320, 501))를 stratified 75:25로 재분할합니다.\n",
-            "\n",
-            "== Ford PRESS/BODY vibration auxiliary model ==\n",
-            "ROC-AUC: 0.72757\n",
-            "PR-AUC: 0.74268\n",
-            "              precision    recall  f1-score   support\n",
-            "\n",
-            "           0     0.6294    0.6687    0.6485       160\n",
-            "           1     0.6687    0.6294    0.6485       170\n",
-            "\n",
-            "    accuracy                         0.6485       330\n",
-            "   macro avg     0.6491    0.6491    0.6485       330\n",
-            "weighted avg     0.6497    0.6485    0.6485       330\n",
-            "\n",
-            "aux_body_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
-            "aux_press_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Ford 진동 데이터 로드\n",
-        "def load_ford_txt(path: Path, max_rows: int | None = None) -> pd.DataFrame | None:\n",
-        "    if not path.exists():\n",
-        "        return None\n",
-        "\n",
-        "    # 배포처에 따라 FordA가 comma 또는 whitespace 구분 형식으로 제공됩니다.\n",
-        "    df = read_csv_stable(\n",
-        "        path,\n",
-        "        sep=r\"[,\\s]+\",\n",
-        "        engine=\"python\",\n",
-        "        header=None,\n",
-        "        nrows=max_rows,\n",
-        "    )\n",
-        "    df = df.dropna(axis=1, how=\"all\").apply(pd.to_numeric, errors=\"coerce\")\n",
-        "    invalid_rows = int(df.isna().any(axis=1).sum())\n",
-        "    if invalid_rows:\n",
-        "        raise ValueError(f\"{path.name}: 숫자로 해석할 수 없는 행이 {invalid_rows:,}개 있습니다.\")\n",
-        "\n",
-        "    if df.shape[1] < 2:\n",
-        "        raise ValueError(f\"{path.name}: 라벨과 진동 feature를 구분할 수 없습니다. shape={df.shape}\")\n",
-        "\n",
-        "    labels = set(df.iloc[:, 0].astype(int).unique())\n",
-        "    if not labels or not labels.issubset({-1, 1}):\n",
-        "        raise ValueError(f\"{path.name}: 라벨은 -1 또는 1이어야 하지만 현재 {sorted(labels)}입니다.\")\n",
-        "\n",
-        "    ford_feature_count = df.shape[1] - 1\n",
-        "    df.columns = [\"label\"] + [f\"ford_t{i}\" for i in range(1, df.shape[1])]\n",
-        "    df[\"ford_abnormal_label\"] = (df[\"label\"] == -1).astype(\"int8\")\n",
-        "    print(f\"{path.name}: rows={len(df):,}, features={ford_feature_count}, labels={df['label'].value_counts().to_dict()}\")\n",
-        "    return reduce_mem_usage(df.drop(columns=[\"label\"]))\n",
-        "\n",
-        "# Ford train/test 로드\n",
-        "ford_train_df = load_ford_txt(FORD_TRAIN_PATH)\n",
-        "ford_test_df = load_ford_txt(FORD_TEST_PATH)\n",
-        "\n",
-        "# Ford 보조 모델 학습\n",
-        "if ford_train_df is not None and ford_test_df is not None:\n",
-        "    ford_feature_cols = [c for c in ford_train_df.columns if c != \"ford_abnormal_label\"]\n",
-        "    ford_test_feature_cols = [c for c in ford_test_df.columns if c != \"ford_abnormal_label\"]\n",
-        "    train_is_usable = len(ford_train_df) >= 20 and ford_train_df[\"ford_abnormal_label\"].nunique() == 2\n",
-        "    test_is_usable = len(ford_test_df) >= 20 and ford_test_df[\"ford_abnormal_label\"].nunique() == 2\n",
-        "    feature_schema_matches = ford_feature_cols == ford_test_feature_cols\n",
-        "\n",
-        "    if train_is_usable and test_is_usable and feature_schema_matches:\n",
-        "        ford_all_df = pd.concat([ford_train_df, ford_test_df], ignore_index=True)\n",
-        "        X_ford_train = ford_train_df[ford_feature_cols]\n",
-        "        y_ford_train = ford_train_df[\"ford_abnormal_label\"].astype(int)\n",
-        "        X_ford_test = ford_test_df[ford_feature_cols]\n",
-        "        y_ford_test = ford_test_df[\"ford_abnormal_label\"].astype(int)\n",
-        "    elif test_is_usable:\n",
-        "        # 현재 배포본처럼 TRAIN이 잘렸어도 정상적인 TEST 데이터만으로 보조 모델을 구성합니다.\n",
-        "        ford_all_df = ford_test_df.copy()\n",
-        "        ford_feature_cols = ford_test_feature_cols\n",
-        "        print(\n",
-        "            f\"Ford TRAIN 데이터를 제외합니다(shape={ford_train_df.shape}). \"\n",
-        "            f\"정상적인 TEST 데이터(shape={ford_test_df.shape})를 stratified 75:25로 재분할합니다.\"\n",
-        "        )\n",
-        "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
-        "            ford_all_df[ford_feature_cols],\n",
-        "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
-        "            test_size=0.25,\n",
-        "            random_state=RANDOM_STATE,\n",
-        "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
-        "        )\n",
-        "    elif train_is_usable:\n",
-        "        ford_all_df = ford_train_df.copy()\n",
-        "        print(\"Ford TEST 데이터가 학습에 부적합하여 TRAIN 데이터를 stratified 75:25로 재분할합니다.\")\n",
-        "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
-        "            ford_all_df[ford_feature_cols],\n",
-        "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
-        "            test_size=0.25,\n",
-        "            random_state=RANDOM_STATE,\n",
-        "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
-        "        )\n",
-        "    else:\n",
-        "        raise ValueError(\n",
-        "            \"Ford 데이터에 두 클래스가 포함된 충분한 행이 없습니다. \"\n",
-        "            f\"train={ford_train_df.shape}, test={ford_test_df.shape}\"\n",
-        "        )\n",
-        "\n",
-        "    ford_model = lgb.LGBMClassifier(\n",
-        "        objective=\"binary\", n_estimators=500, learning_rate=0.03, num_leaves=31,\n",
-        "        min_child_samples=20, subsample=0.9, colsample_bytree=0.9,\n",
-        "        random_state=RANDOM_STATE, n_jobs=-1, verbosity=-1\n",
-        "    )\n",
-        "    # 진동 이상 확률 학습\n",
-        "    ford_model.fit(X_ford_train, y_ford_train)\n",
-        "    ford_test_proba = ford_model.predict_proba(X_ford_test)[:, 1]\n",
-        "    evaluate_aux_classifier(\"Ford PRESS/BODY vibration\", y_ford_test, ford_test_proba)\n",
-        "\n",
-        "    ford_all_proba = ford_model.predict_proba(ford_all_df[ford_feature_cols])[:, 1]\n",
-        "    # 차체/프레스 보조 피처 추가\n",
-        "    append_cycled_feature(features, \"aux_body_ford_vibration_abnormal_probability\", ford_all_proba)\n",
-        "    append_cycled_feature(features, \"aux_press_ford_vibration_abnormal_probability\", ford_all_proba)\n",
-        "    auxiliary_models[\"ford_vibration\"] = ford_model\n",
-        "    auxiliary_metrics[\"ford_rows\"] = int(len(ford_all_df))\n",
-        "else:\n",
-        "    print(\"Ford 데이터 파일이 없어 Ford 보조 학습을 건너뜁니다.\")\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.12554 Recall=0.35294 F1=0.07571 elapsed=59.8s\n",
+      "  optuna trial 2/15 params={'model__learning_rate': 0.0564638664293258, 'model__num_leaves': 53, 'model__max_depth': 9, 'model__min_child_samples': 108, 'model__subsample': 0.8641485131528328, 'model__colsample_bytree': 0.6127722372934189, 'model__reg_lambda': 0.359730743558148, 'model__reg_alpha': 0.002920433847181412}\n",
+      "    fold 1/5 PR-AUC=0.03474 Recall=0.23529 F1=0.06107 elapsed=42.5s\n",
+      "    fold 2/5 PR-AUC=0.04446 Recall=0.38235 F1=0.04180 elapsed=43.4s\n",
+      "    fold 3/5 PR-AUC=0.03871 Recall=0.23529 F1=0.12121 elapsed=43.7s\n",
+      "    fold 4/5 PR-AUC=0.02391 Recall=0.14706 F1=0.07092 elapsed=43.4s\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "a97efe99",
-      "metadata": {
-        "id": "a97efe99"
-      },
-      "source": [
-        "### 4.2 소성가공 공정/전류 데이터 비지도 학습\n",
-        "\n",
-        "소성가공 데이터에는 불량 라벨이 없으므로 공정 데이터와 설비 센서 데이터를 각각 Isolation Forest로 학습해 공정 흐름 이상 점수, 프레스 이상 점수, 로봇 전류 이상 점수를 생성합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:09:34,490] Trial 1 finished with value: 0.06631270724589355 and parameters: {'learning_rate': 0.0564638664293258, 'num_leaves': 53, 'max_depth': 9, 'min_child_samples': 108, 'subsample': 0.8641485131528328, 'colsample_bytree': 0.6127722372934189, 'reg_lambda': 0.359730743558148, 'reg_alpha': 0.002920433847181412}. Best is trial 1 with value: 0.06631270724589355.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "cd25bfc8",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 216
-        },
-        "id": "cd25bfc8",
-        "outputId": "73d3b745-218c-4f61-96fa-0022c8136bcb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "numeric_df: (300000, 262)\n",
-            "date_df: (300000, 261)\n",
-            "categorical_df: (300000, 10)\n",
-            "Response ratio:\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "            ratio\n",
-              "Response         \n",
-              "0         0.99435\n",
-              "1         0.00565"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-43c145af-4222-4430-8081-0b1b5c54e8d6\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>ratio</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Response</th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0.99435</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0.00565</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43c145af-4222-4430-8081-0b1b5c54e8d6')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "# Bosch numeric 컬럼 선별\n",
-        "numeric_cols = select_columns_by_profile(\n",
-        "    NUMERIC_PATH,\n",
-        "    required_cols=[\"Id\", \"Response\"],\n",
-        "    max_features=MAX_NUMERIC_FEATURES,\n",
-        "    profile_rows=PROFILE_ROWS,\n",
-        ")\n",
-        "# Bosch date 컬럼 선별\n",
-        "date_cols = select_columns_by_profile(\n",
-        "    DATE_PATH,\n",
-        "    required_cols=[\"Id\"],\n",
-        "    max_features=MAX_DATE_FEATURES,\n",
-        "    profile_rows=PROFILE_ROWS,\n",
-        ")\n",
-        "\n",
-        "# Bosch 선택 데이터 로드\n",
-        "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
-        "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
-        "\n",
-        "# Bosch categorical 선택 로드\n",
-        "if USE_CATEGORICAL:\n",
-        "    categorical_cols = select_columns_by_profile(\n",
-        "        CATEGORICAL_PATH,\n",
-        "        required_cols=[\"Id\"],\n",
-        "        max_features=MAX_CATEGORICAL_FEATURES,\n",
-        "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
-        "    )\n",
-        "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
-        "else:\n",
-        "    categorical_df = None\n",
-        "\n",
-        "print(\"numeric_df:\", numeric_df.shape)\n",
-        "print(\"date_df:\", date_df.shape)\n",
-        "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
-        "print(\"Response ratio:\")\n",
-        "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.12504 Recall=0.29412 F1=0.11976 elapsed=44.0s\n",
+      "  optuna trial 3/15 params={'model__learning_rate': 0.025815006344207546, 'model__num_leaves': 131, 'model__max_depth': 12, 'model__min_child_samples': 76, 'model__subsample': 0.6727680575448478, 'model__colsample_bytree': 0.976998491764, 'model__reg_lambda': 6.881524386672037, 'model__reg_alpha': 0.17123375973163968}\n",
+      "    fold 1/5 PR-AUC=0.02393 Recall=0.05882 F1=0.09756 elapsed=76.2s\n",
+      "    fold 2/5 PR-AUC=0.04875 Recall=0.35294 F1=0.05010 elapsed=78.4s\n",
+      "    fold 3/5 PR-AUC=0.03501 Recall=0.35294 F1=0.04000 elapsed=73.9s\n",
+      "    fold 4/5 PR-AUC=0.02380 Recall=0.35294 F1=0.03877 elapsed=75.6s\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "-7HWdLE4bEfB",
-      "metadata": {
-        "id": "-7HWdLE4bEfB"
-      },
-      "source": [
-        "## 5. 학습 데이터 정제\n",
-        "\n",
-        "4개 후보 모델이 동일한 학습 조건에서 비교될 수 있도록 무한대와 완전 결측 컬럼을 제거합니다. 불량 데이터는 희소하므로 stratified split과 class weight 기반 모델을 사용합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:15:58,052] Trial 2 finished with value: 0.06402544874348826 and parameters: {'learning_rate': 0.025815006344207546, 'num_leaves': 131, 'max_depth': 12, 'min_child_samples': 76, 'subsample': 0.6727680575448478, 'colsample_bytree': 0.976998491764, 'reg_lambda': 6.881524386672037, 'reg_alpha': 0.17123375973163968}. Best is trial 1 with value: 0.06631270724589355.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "XX3xUzrlbEfB",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "XX3xUzrlbEfB",
-        "outputId": "1b64eb6a-04d9-4fa0-ee50-df1fa3ea9a18"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "X_train: (240000, 457) X_valid: (60000, 457)\n",
-            "positive ratio train: 0.00565\n",
-            "scale_pos_weight: 175.99\n"
-          ]
-        }
-      ],
-      "source": [
-        "# 학습 테이블 결합\n",
-        "model_df = features.merge(target, on=\"Id\", how=\"inner\")\n",
-        "model_df = model_df.replace([np.inf, -np.inf], np.nan)\n",
-        "\n",
-        "# 입력/라벨 분리\n",
-        "drop_cols = [\"Id\", \"Response\"]\n",
-        "feature_cols = [c for c in model_df.columns if c not in drop_cols]\n",
-        "# 완전 결측 컬럼 제거\n",
-        "all_missing_cols = model_df[feature_cols].columns[model_df[feature_cols].isna().all()].tolist()\n",
-        "if all_missing_cols:\n",
-        "    model_df = model_df.drop(columns=all_missing_cols)\n",
-        "    feature_cols = [c for c in feature_cols if c not in all_missing_cols]\n",
-        "\n",
-        "X = model_df[feature_cols]\n",
-        "y = model_df[\"Response\"].astype(int)\n",
-        "ids = model_df[\"Id\"]\n",
-        "if y.nunique() < 2:\n",
-        "    raise ValueError(\"Response가 한 클래스만 포함되어 있습니다. MAX_ROWS를 늘리거나 데이터 샘플링 범위를 조정하세요.\")\n",
-        "\n",
-        "# 계층 분할\n",
-        "X_train, X_valid, y_train, y_valid, id_train, id_valid = train_test_split(\n",
-        "    X, y, ids,\n",
-        "    test_size=0.2,\n",
-        "    random_state=RANDOM_STATE,\n",
-        "    stratify=y,\n",
-        ")\n",
-        "\n",
-        "# 클래스 불균형 가중치\n",
-        "neg_count = int((y_train == 0).sum())\n",
-        "pos_count = int((y_train == 1).sum())\n",
-        "scale_pos_weight = neg_count / max(pos_count, 1)\n",
-        "\n",
-        "print(\"X_train:\", X_train.shape, \"X_valid:\", X_valid.shape)\n",
-        "print(\"positive ratio train:\", y_train.mean())\n",
-        "print(\"scale_pos_weight:\", round(scale_pos_weight, 2))\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.11511 Recall=0.35294 F1=0.04848 elapsed=79.5s\n",
+      "  optuna trial 4/15 params={'model__learning_rate': 0.018840552955192824, 'model__num_leaves': 37, 'model__max_depth': -1, 'model__min_child_samples': 276, 'model__subsample': 0.7405729935600059, 'model__colsample_bytree': 0.8481350279592919, 'model__reg_lambda': 0.39193516226890834, 'model__reg_alpha': 0.012030178871154668}\n",
+      "    fold 1/5 PR-AUC=0.02465 Recall=0.17647 F1=0.05607 elapsed=58.5s\n",
+      "    fold 2/5 PR-AUC=0.04893 Recall=0.38235 F1=0.05068 elapsed=59.0s\n",
+      "    fold 3/5 PR-AUC=0.04120 Recall=0.20588 F1=0.15909 elapsed=59.7s\n",
+      "    fold 4/5 PR-AUC=0.01779 Recall=0.35294 F1=0.03941 elapsed=58.0s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:20:50,337] Trial 3 finished with value: 0.06894370517883613 and parameters: {'learning_rate': 0.018840552955192824, 'num_leaves': 37, 'max_depth': -1, 'min_child_samples': 276, 'subsample': 0.7405729935600059, 'colsample_bytree': 0.8481350279592919, 'reg_lambda': 0.39193516226890834, 'reg_alpha': 0.012030178871154668}. Best is trial 3 with value: 0.06894370517883613.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.13862 Recall=0.35294 F1=0.09231 elapsed=57.1s\n",
+      "  optuna trial 5/15 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
+      "    fold 1/5 PR-AUC=0.03658 Recall=0.08824 F1=0.10345 elapsed=56.6s\n",
+      "    fold 2/5 PR-AUC=0.04709 Recall=0.35294 F1=0.08247 elapsed=54.5s\n",
+      "    fold 3/5 PR-AUC=0.05326 Recall=0.23529 F1=0.16162 elapsed=50.2s\n",
+      "    fold 4/5 PR-AUC=0.02418 Recall=0.23529 F1=0.05926 elapsed=53.0s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:25:17,267] Trial 4 finished with value: 0.07319973398830097 and parameters: {'learning_rate': 0.031169409812378812, 'num_leaves': 49, 'max_depth': -1, 'min_child_samples': 279, 'subsample': 0.6809723757181718, 'colsample_bytree': 0.6381922880886154, 'reg_lambda': 0.12191905861905929, 'reg_alpha': 0.0020013420622879987}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.14607 Recall=0.26471 F1=0.21176 elapsed=52.6s\n",
+      "  optuna trial 6/15 params={'model__learning_rate': 0.022439365289703053, 'model__num_leaves': 61, 'model__max_depth': -1, 'model__min_child_samples': 247, 'model__subsample': 0.6760927252879199, 'model__colsample_bytree': 0.9940991214702328, 'model__reg_lambda': 2.94884053630599, 'model__reg_alpha': 0.0006235377135673159}\n",
+      "    fold 1/5 PR-AUC=0.02457 Recall=0.05882 F1=0.10000 elapsed=66.4s\n",
+      "    fold 2/5 PR-AUC=0.04620 Recall=0.38235 F1=0.04019 elapsed=64.8s\n",
+      "    fold 3/5 PR-AUC=0.03686 Recall=0.23529 F1=0.13445 elapsed=65.1s\n",
+      "    fold 4/5 PR-AUC=0.02458 Recall=0.20588 F1=0.07000 elapsed=64.7s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:30:43,306] Trial 5 finished with value: 0.06304171041876949 and parameters: {'learning_rate': 0.022439365289703053, 'num_leaves': 61, 'max_depth': -1, 'min_child_samples': 247, 'subsample': 0.6760927252879199, 'colsample_bytree': 0.9940991214702328, 'reg_lambda': 2.94884053630599, 'reg_alpha': 0.0006235377135673159}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.12711 Recall=0.23529 F1=0.20253 elapsed=65.0s\n",
+      "  optuna trial 7/15 params={'model__learning_rate': 0.01011549101545285, 'model__num_leaves': 135, 'model__max_depth': 7, 'model__min_child_samples': 61, 'model__subsample': 0.9520861990564577, 'model__colsample_bytree': 0.8304841570724011, 'model__reg_lambda': 0.4263131389740422, 'model__reg_alpha': 0.00017956984225677631}\n",
+      "    fold 1/5 PR-AUC=0.02317 Recall=0.08824 F1=0.07317 elapsed=56.3s\n",
+      "    fold 2/5 PR-AUC=0.04950 Recall=0.26471 F1=0.08000 elapsed=56.3s\n",
+      "    fold 3/5 PR-AUC=0.02767 Recall=0.14706 F1=0.14085 elapsed=50.9s\n",
+      "    fold 4/5 PR-AUC=0.01344 Recall=0.23529 F1=0.04598 elapsed=51.6s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:35:10,406] Trial 6 finished with value: 0.04217109041808953 and parameters: {'learning_rate': 0.01011549101545285, 'num_leaves': 135, 'max_depth': 7, 'min_child_samples': 61, 'subsample': 0.9520861990564577, 'colsample_bytree': 0.8304841570724011, 'reg_lambda': 0.4263131389740422, 'reg_alpha': 0.00017956984225677631}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.04267 Recall=0.35294 F1=0.05085 elapsed=52.1s\n",
+      "  optuna trial 8/15 params={'model__learning_rate': 0.0190917184400185, 'model__num_leaves': 68, 'model__max_depth': 7, 'model__min_child_samples': 223, 'model__subsample': 0.9162747670159141, 'model__colsample_bytree': 0.8025747389062734, 'model__reg_lambda': 2.9323777832216886, 'model__reg_alpha': 0.009444574254983563}\n",
+      "    fold 1/5 PR-AUC=0.01607 Recall=0.17647 F1=0.03261 elapsed=36.0s\n",
+      "    fold 2/5 PR-AUC=0.03486 Recall=0.38235 F1=0.03922 elapsed=34.4s\n",
+      "    fold 3/5 PR-AUC=0.02913 Recall=0.23529 F1=0.12030 elapsed=36.2s\n",
+      "    fold 4/5 PR-AUC=0.01392 Recall=0.14706 F1=0.05181 elapsed=33.5s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:38:04,664] Trial 7 finished with value: 0.05305343371432367 and parameters: {'learning_rate': 0.0190917184400185, 'num_leaves': 68, 'max_depth': 7, 'min_child_samples': 223, 'subsample': 0.9162747670159141, 'colsample_bytree': 0.8025747389062734, 'reg_lambda': 2.9323777832216886, 'reg_alpha': 0.009444574254983563}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.10658 Recall=0.35294 F1=0.04301 elapsed=34.2s\n",
+      "  optuna trial 9/15 params={'model__learning_rate': 0.029653419677342325, 'model__num_leaves': 82, 'model__max_depth': 9, 'model__min_child_samples': 167, 'model__subsample': 0.9676482658741326, 'model__colsample_bytree': 0.6621815031169938, 'model__reg_lambda': 0.6039425546072186, 'model__reg_alpha': 0.10524574681335624}\n",
+      "    fold 1/5 PR-AUC=0.03325 Recall=0.11765 F1=0.05556 elapsed=40.7s\n",
+      "    fold 2/5 PR-AUC=0.03970 Recall=0.23529 F1=0.09524 elapsed=41.2s\n",
+      "    fold 3/5 PR-AUC=0.03826 Recall=0.23529 F1=0.15385 elapsed=42.5s\n",
+      "    fold 4/5 PR-AUC=0.01437 Recall=0.26471 F1=0.03805 elapsed=37.8s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:41:27,137] Trial 8 finished with value: 0.060833017107763024 and parameters: {'learning_rate': 0.029653419677342325, 'num_leaves': 82, 'max_depth': 9, 'min_child_samples': 167, 'subsample': 0.9676482658741326, 'colsample_bytree': 0.6621815031169938, 'reg_lambda': 0.6039425546072186, 'reg_alpha': 0.10524574681335624}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.11828 Recall=0.35294 F1=0.09160 elapsed=40.3s\n",
+      "  optuna trial 10/15 params={'model__learning_rate': 0.016092567235133776, 'model__num_leaves': 34, 'model__max_depth': 7, 'model__min_child_samples': 266, 'model__subsample': 0.9312852269146901, 'model__colsample_bytree': 0.6339565264987161, 'model__reg_lambda': 4.995973117313505, 'model__reg_alpha': 0.014367095138664226}\n",
+      "    fold 1/5 PR-AUC=0.01984 Recall=0.14706 F1=0.04016 elapsed=30.5s\n",
+      "    fold 2/5 PR-AUC=0.02887 Recall=0.35294 F1=0.04969 elapsed=29.4s\n",
+      "    fold 3/5 PR-AUC=0.03842 Recall=0.17647 F1=0.16438 elapsed=30.6s\n",
+      "    fold 4/5 PR-AUC=0.01492 Recall=0.14706 F1=0.05495 elapsed=29.2s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:43:58,707] Trial 9 finished with value: 0.0581151434945654 and parameters: {'learning_rate': 0.016092567235133776, 'num_leaves': 34, 'max_depth': 7, 'min_child_samples': 266, 'subsample': 0.9312852269146901, 'colsample_bytree': 0.6339565264987161, 'reg_lambda': 4.995973117313505, 'reg_alpha': 0.014367095138664226}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.12971 Recall=0.35294 F1=0.04829 elapsed=31.8s\n",
+      "  optuna trial 11/15 params={'model__learning_rate': 0.07344753762582334, 'model__num_leaves': 100, 'model__max_depth': 5, 'model__min_child_samples': 179, 'model__subsample': 0.7804393429857105, 'model__colsample_bytree': 0.7211318321133148, 'model__reg_lambda': 0.10707258976378216, 'model__reg_alpha': 0.00011815152243566209}\n",
+      "    fold 1/5 PR-AUC=0.02487 Recall=0.05882 F1=0.09524 elapsed=22.2s\n",
+      "    fold 2/5 PR-AUC=0.03263 Recall=0.17647 F1=0.06522 elapsed=22.8s\n",
+      "    fold 3/5 PR-AUC=0.02345 Recall=0.20588 F1=0.06306 elapsed=21.8s\n",
+      "    fold 4/5 PR-AUC=0.00869 Recall=0.14706 F1=0.03802 elapsed=25.2s\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "6444a8dd",
-      "metadata": {
-        "id": "6444a8dd"
-      },
-      "source": [
-        "## 6. 전처리 학습 데이터셋 저장\n",
-        "\n",
-        "모델에 실제로 들어가는 정제/전처리 완료 데이터셋을 파일로 저장합니다. Parquet 저장이 가능하면 `.parquet`, 환경에 `pyarrow/fastparquet`가 없으면 `.csv.gz`로 저장합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:45:53,501] Trial 10 finished with value: 0.039906002434268506 and parameters: {'learning_rate': 0.07344753762582334, 'num_leaves': 100, 'max_depth': 5, 'min_child_samples': 179, 'subsample': 0.7804393429857105, 'colsample_bytree': 0.7211318321133148, 'reg_lambda': 0.10707258976378216, 'reg_alpha': 0.00011815152243566209}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "6e442104",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6e442104",
-        "outputId": "09415cb9-862f-49f7-a3f3-30566b3940f4"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "전처리 학습 데이터셋: /content/defect_transfer_outputs/defect_transfer_training_dataset.parquet\n",
-            "train/valid split ids: /content/defect_transfer_outputs/defect_transfer_train_valid_split_ids.csv\n",
-            "전처리 metadata: /content/defect_transfer_outputs/defect_transfer_preprocessing_metadata.json\n"
-          ]
-        }
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.07460 Recall=0.11765 F1=0.13333 elapsed=22.7s\n",
+      "  optuna trial 12/15 params={'model__learning_rate': 0.03828385438925741, 'model__num_leaves': 24, 'model__max_depth': -1, 'model__min_child_samples': 290, 'model__subsample': 0.7500490079557848, 'model__colsample_bytree': 0.7430730526600867, 'model__reg_lambda': 0.26877401109804494, 'model__reg_alpha': 0.0018756206282287391}\n",
+      "    fold 1/5 PR-AUC=0.03004 Recall=0.11765 F1=0.04790 elapsed=42.9s\n",
+      "    fold 2/5 PR-AUC=0.04640 Recall=0.17647 F1=0.09449 elapsed=43.4s\n",
+      "    fold 3/5 PR-AUC=0.03590 Recall=0.20588 F1=0.14433 elapsed=43.8s\n",
+      "    fold 4/5 PR-AUC=0.01742 Recall=0.35294 F1=0.04444 elapsed=42.8s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:49:29,067] Trial 11 finished with value: 0.06729149493282813 and parameters: {'learning_rate': 0.03828385438925741, 'num_leaves': 24, 'max_depth': -1, 'min_child_samples': 290, 'subsample': 0.7500490079557848, 'colsample_bytree': 0.7430730526600867, 'reg_lambda': 0.26877401109804494, 'reg_alpha': 0.0018756206282287391}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.14640 Recall=0.35294 F1=0.06742 elapsed=42.6s\n",
+      "  optuna trial 13/15 params={'model__learning_rate': 0.0128289748714244, 'model__num_leaves': 45, 'model__max_depth': -1, 'model__min_child_samples': 205, 'model__subsample': 0.7368227996831814, 'model__colsample_bytree': 0.5575289958995627, 'model__reg_lambda': 0.7798089384438257, 'model__reg_alpha': 0.02173049208731439}\n",
+      "    fold 1/5 PR-AUC=0.02159 Recall=0.08824 F1=0.09375 elapsed=49.2s\n",
+      "    fold 2/5 PR-AUC=0.04449 Recall=0.41176 F1=0.04230 elapsed=48.5s\n",
+      "    fold 3/5 PR-AUC=0.05169 Recall=0.23529 F1=0.16327 elapsed=50.1s\n",
+      "    fold 4/5 PR-AUC=0.01912 Recall=0.35294 F1=0.04928 elapsed=49.4s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:53:35,467] Trial 12 finished with value: 0.06395427302355366 and parameters: {'learning_rate': 0.0128289748714244, 'num_leaves': 45, 'max_depth': -1, 'min_child_samples': 205, 'subsample': 0.7368227996831814, 'colsample_bytree': 0.5575289958995627, 'reg_lambda': 0.7798089384438257, 'reg_alpha': 0.02173049208731439}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.11083 Recall=0.35294 F1=0.05660 elapsed=49.3s\n",
+      "  optuna trial 14/15 params={'model__learning_rate': 0.03688230536757648, 'model__num_leaves': 84, 'model__max_depth': -1, 'model__min_child_samples': 299, 'model__subsample': 0.7156364210016786, 'model__colsample_bytree': 0.8810259041893101, 'model__reg_lambda': 0.199522384883775, 'model__reg_alpha': 0.0024334796925402718}\n",
+      "    fold 1/5 PR-AUC=0.04648 Recall=0.17647 F1=0.05854 elapsed=63.5s\n",
+      "    fold 2/5 PR-AUC=0.03300 Recall=0.35294 F1=0.06818 elapsed=63.2s\n",
+      "    fold 3/5 PR-AUC=0.03487 Recall=0.20588 F1=0.09459 elapsed=62.5s\n",
+      "    fold 4/5 PR-AUC=0.02476 Recall=0.23529 F1=0.06349 elapsed=60.1s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 01:58:50,528] Trial 13 finished with value: 0.07042822156591197 and parameters: {'learning_rate': 0.03688230536757648, 'num_leaves': 84, 'max_depth': -1, 'min_child_samples': 299, 'subsample': 0.7156364210016786, 'colsample_bytree': 0.8810259041893101, 'reg_lambda': 0.199522384883775, 'reg_alpha': 0.0024334796925402718}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.15126 Recall=0.26471 F1=0.16216 elapsed=65.7s\n",
+      "  optuna trial 15/15 params={'model__learning_rate': 0.04187329932090051, 'model__num_leaves': 98, 'model__max_depth': 5, 'model__min_child_samples': 300, 'model__subsample': 0.654599110501233, 'model__colsample_bytree': 0.9091999481071245, 'model__reg_lambda': 0.1837271427904195, 'model__reg_alpha': 0.0009412519611659042}\n",
+      "    fold 1/5 PR-AUC=0.01758 Recall=0.05882 F1=0.08000 elapsed=26.5s\n",
+      "    fold 2/5 PR-AUC=0.03039 Recall=0.35294 F1=0.05042 elapsed=23.1s\n",
+      "    fold 3/5 PR-AUC=0.02245 Recall=0.20588 F1=0.08235 elapsed=26.7s\n",
+      "    fold 4/5 PR-AUC=0.01160 Recall=0.20588 F1=0.03743 elapsed=30.8s\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[I 2026-06-18 02:01:05,723] Trial 14 finished with value: 0.04538489857292094 and parameters: {'learning_rate': 0.04187329932090051, 'num_leaves': 98, 'max_depth': 5, 'min_child_samples': 300, 'subsample': 0.654599110501233, 'colsample_bytree': 0.9091999481071245, 'reg_lambda': 0.1837271427904195, 'reg_alpha': 0.0009412519611659042}. Best is trial 4 with value: 0.07319973398830097.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "    fold 5/5 PR-AUC=0.09784 Recall=0.11765 F1=0.18605 elapsed=28.1s\n",
+      "[LightGBM] Optuna best value=0.07320 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
+      "[LightGBM] CV done elapsed=3614.0s\n",
+      "\n",
+      "[RandomForest] CV start\n",
+      "[RandomForest] random search trials: 15\n",
+      "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.01770 Recall=0.14706 F1=0.09615 elapsed=18.6s\n",
+      "    fold 2/5 PR-AUC=0.03344 Recall=0.17647 F1=0.07500 elapsed=18.4s\n",
+      "    fold 3/5 PR-AUC=0.03194 Recall=0.35294 F1=0.04364 elapsed=21.1s\n",
+      "    fold 4/5 PR-AUC=0.01529 Recall=0.32353 F1=0.03520 elapsed=18.1s\n",
+      "    fold 5/5 PR-AUC=0.06988 Recall=0.38235 F1=0.05029 elapsed=18.1s\n",
+      "  -> RandomForest trial 1 mean PR-AUC=0.03365 Recall=0.27647 F1=0.06006 FPR=0.05830\n",
+      "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.01163 Recall=0.08824 F1=0.07407 elapsed=114.9s\n",
+      "    fold 2/5 PR-AUC=0.02605 Recall=0.26471 F1=0.07860 elapsed=120.6s\n",
+      "    fold 3/5 PR-AUC=0.02343 Recall=0.20588 F1=0.08805 elapsed=112.0s\n",
+      "    fold 4/5 PR-AUC=0.00923 Recall=0.14706 F1=0.02817 elapsed=116.7s\n",
+      "    fold 5/5 PR-AUC=0.02658 Recall=0.35294 F1=0.04096 elapsed=128.2s\n",
+      "  -> RandomForest trial 2 mean PR-AUC=0.01938 Recall=0.21176 F1=0.06197 FPR=0.04036\n",
+      "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.01449 Recall=0.11765 F1=0.07547 elapsed=20.0s\n",
+      "    fold 2/5 PR-AUC=0.01800 Recall=0.17647 F1=0.07143 elapsed=18.6s\n",
+      "    fold 3/5 PR-AUC=0.02582 Recall=0.35294 F1=0.04082 elapsed=19.0s\n",
+      "    fold 4/5 PR-AUC=0.01543 Recall=0.05882 F1=0.08163 elapsed=19.4s\n",
+      "    fold 5/5 PR-AUC=0.04959 Recall=0.38235 F1=0.04815 elapsed=20.2s\n",
+      "  -> RandomForest trial 3 mean PR-AUC=0.02467 Recall=0.21765 F1=0.06350 FPR=0.04170\n",
+      "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.01905 Recall=0.11765 F1=0.12121 elapsed=110.6s\n",
+      "    fold 2/5 PR-AUC=0.02432 Recall=0.35294 F1=0.03865 elapsed=112.2s\n",
+      "    fold 3/5 PR-AUC=0.03628 Recall=0.17647 F1=0.13636 elapsed=106.3s\n",
+      "    fold 4/5 PR-AUC=0.02079 Recall=0.08824 F1=0.05660 elapsed=110.0s\n",
+      "    fold 5/5 PR-AUC=0.04705 Recall=0.26471 F1=0.11465 elapsed=112.8s\n",
+      "  -> RandomForest trial 4 mean PR-AUC=0.02950 Recall=0.20000 F1=0.09350 FPR=0.02796\n",
+      "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.01373 Recall=0.08824 F1=0.05505 elapsed=19.7s\n",
+      "    fold 2/5 PR-AUC=0.01550 Recall=0.14706 F1=0.04587 elapsed=18.3s\n",
+      "    fold 3/5 PR-AUC=0.01997 Recall=0.20588 F1=0.06452 elapsed=18.3s\n",
+      "    fold 4/5 PR-AUC=0.00914 Recall=0.50000 F1=0.02353 elapsed=18.2s\n",
+      "    fold 5/5 PR-AUC=0.02526 Recall=0.17647 F1=0.05455 elapsed=18.7s\n",
+      "  -> RandomForest trial 5 mean PR-AUC=0.01672 Recall=0.22353 F1=0.04870 FPR=0.06708\n",
+      "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.02763 Recall=0.14706 F1=0.11765 elapsed=20.0s\n",
+      "    fold 2/5 PR-AUC=0.03999 Recall=0.35294 F1=0.04225 elapsed=19.2s\n",
+      "    fold 3/5 PR-AUC=0.03442 Recall=0.38235 F1=0.04370 elapsed=19.1s\n",
+      "    fold 4/5 PR-AUC=0.01981 Recall=0.05882 F1=0.09302 elapsed=20.5s\n",
+      "    fold 5/5 PR-AUC=0.09888 Recall=0.35294 F1=0.04453 elapsed=19.6s\n",
+      "  -> RandomForest trial 6 mean PR-AUC=0.04415 Recall=0.25882 F1=0.06823 FPR=0.05417\n",
+      "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
+      "    fold 1/5 PR-AUC=0.01178 Recall=0.17647 F1=0.04762 elapsed=18.0s\n",
+      "    fold 2/5 PR-AUC=0.01400 Recall=0.20588 F1=0.05128 elapsed=18.1s\n",
+      "    fold 3/5 PR-AUC=0.01885 Recall=0.08824 F1=0.09677 elapsed=17.9s\n",
+      "    fold 4/5 PR-AUC=0.00916 Recall=0.14706 F1=0.02611 elapsed=18.6s\n",
+      "    fold 5/5 PR-AUC=0.01926 Recall=0.35294 F1=0.04278 elapsed=18.0s\n",
+      "  -> RandomForest trial 7 mean PR-AUC=0.01461 Recall=0.19412 F1=0.05291 FPR=0.04452\n",
+      "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
+      "    fold 1/5 PR-AUC=0.01255 Recall=0.17647 F1=0.04651 elapsed=95.6s\n",
+      "    fold 2/5 PR-AUC=0.01555 Recall=0.17647 F1=0.06593 elapsed=98.3s\n",
+      "    fold 3/5 PR-AUC=0.03233 Recall=0.14706 F1=0.11111 elapsed=99.4s\n",
+      "    fold 4/5 PR-AUC=0.01287 Recall=0.38235 F1=0.02835 elapsed=97.5s\n",
+      "    fold 5/5 PR-AUC=0.02747 Recall=0.14706 F1=0.10417 elapsed=94.9s\n",
+      "  -> RandomForest trial 8 mean PR-AUC=0.02016 Recall=0.20588 F1=0.07122 FPR=0.04485\n",
+      "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.01133 Recall=0.17647 F1=0.05333 elapsed=95.1s\n",
+      "    fold 2/5 PR-AUC=0.01219 Recall=0.14706 F1=0.05376 elapsed=93.9s\n",
+      "    fold 3/5 PR-AUC=0.01700 Recall=0.14706 F1=0.04149 elapsed=95.6s\n",
+      "    fold 4/5 PR-AUC=0.01059 Recall=0.35294 F1=0.02934 elapsed=94.4s\n",
+      "    fold 5/5 PR-AUC=0.01855 Recall=0.11765 F1=0.06897 elapsed=90.9s\n",
+      "  -> RandomForest trial 9 mean PR-AUC=0.01393 Recall=0.18824 F1=0.04938 FPR=0.04640\n",
+      "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.01972 Recall=0.17647 F1=0.05797 elapsed=18.8s\n",
+      "    fold 2/5 PR-AUC=0.01501 Recall=0.14706 F1=0.05051 elapsed=18.4s\n",
+      "    fold 3/5 PR-AUC=0.02479 Recall=0.41176 F1=0.03774 elapsed=18.5s\n",
+      "    fold 4/5 PR-AUC=0.01120 Recall=0.52941 F1=0.02497 elapsed=18.5s\n",
+      "    fold 5/5 PR-AUC=0.03002 Recall=0.17647 F1=0.09449 elapsed=19.2s\n",
+      "  -> RandomForest trial 10 mean PR-AUC=0.02015 Recall=0.28824 F1=0.05313 FPR=0.08371\n",
+      "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.01038 Recall=0.17647 F1=0.03125 elapsed=130.8s\n",
+      "    fold 2/5 PR-AUC=0.01130 Recall=0.17647 F1=0.04181 elapsed=131.1s\n",
+      "    fold 3/5 PR-AUC=0.02148 Recall=0.08824 F1=0.07595 elapsed=129.1s\n",
+      "    fold 4/5 PR-AUC=0.00999 Recall=0.35294 F1=0.02564 elapsed=130.5s\n",
+      "    fold 5/5 PR-AUC=0.01895 Recall=0.11765 F1=0.06780 elapsed=126.2s\n",
+      "  -> RandomForest trial 11 mean PR-AUC=0.01442 Recall=0.18235 F1=0.04849 FPR=0.05374\n",
+      "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.01582 Recall=0.14706 F1=0.08000 elapsed=16.5s\n",
+      "    fold 2/5 PR-AUC=0.01492 Recall=0.14706 F1=0.06452 elapsed=16.5s\n",
+      "    fold 3/5 PR-AUC=0.02683 Recall=0.20588 F1=0.06897 elapsed=16.7s\n",
+      "    fold 4/5 PR-AUC=0.01325 Recall=0.05882 F1=0.06667 elapsed=16.5s\n",
+      "    fold 5/5 PR-AUC=0.04138 Recall=0.35294 F1=0.05042 elapsed=16.5s\n",
+      "  -> RandomForest trial 12 mean PR-AUC=0.02244 Recall=0.18235 F1=0.06611 FPR=0.02742\n",
+      "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.01129 Recall=0.17647 F1=0.03647 elapsed=91.7s\n",
+      "    fold 2/5 PR-AUC=0.01165 Recall=0.26471 F1=0.03854 elapsed=93.2s\n",
+      "    fold 3/5 PR-AUC=0.02405 Recall=0.14706 F1=0.06897 elapsed=92.9s\n",
+      "    fold 4/5 PR-AUC=0.01140 Recall=0.50000 F1=0.02801 elapsed=92.1s\n",
+      "    fold 5/5 PR-AUC=0.01838 Recall=0.11765 F1=0.07843 elapsed=89.3s\n",
+      "  -> RandomForest trial 13 mean PR-AUC=0.01535 Recall=0.24118 F1=0.05008 FPR=0.06859\n",
+      "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.02950 Recall=0.14706 F1=0.15385 elapsed=18.1s\n",
+      "    fold 2/5 PR-AUC=0.04820 Recall=0.35294 F1=0.07717 elapsed=18.3s\n",
+      "    fold 3/5 PR-AUC=0.05079 Recall=0.35294 F1=0.03822 elapsed=18.1s\n",
+      "    fold 4/5 PR-AUC=0.02223 Recall=0.08824 F1=0.07317 elapsed=19.1s\n",
+      "    fold 5/5 PR-AUC=0.10251 Recall=0.35294 F1=0.08602 elapsed=18.7s\n",
+      "  -> RandomForest trial 14 mean PR-AUC=0.05065 Recall=0.25882 F1=0.08569 FPR=0.03859\n",
+      "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.01118 Recall=0.14706 F1=0.03788 elapsed=137.0s\n",
+      "    fold 2/5 PR-AUC=0.01503 Recall=0.17647 F1=0.05941 elapsed=141.8s\n",
+      "    fold 3/5 PR-AUC=0.01931 Recall=0.08824 F1=0.07895 elapsed=142.5s\n",
+      "    fold 4/5 PR-AUC=0.01054 Recall=0.35294 F1=0.02804 elapsed=142.2s\n",
+      "    fold 5/5 PR-AUC=0.01703 Recall=0.11765 F1=0.05970 elapsed=140.4s\n",
+      "  -> RandomForest trial 15 mean PR-AUC=0.01462 Recall=0.17647 F1=0.05279 FPR=0.04465\n",
+      "[RandomForest] CV done elapsed=4652.7s\n",
+      "\n",
+      "[ExtraTrees] CV start\n",
+      "[ExtraTrees] random search trials: 15\n",
+      "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.05335 Recall=0.17647 F1=0.22222 elapsed=14.9s\n",
+      "    fold 2/5 PR-AUC=0.05171 Recall=0.38235 F1=0.04075 elapsed=14.9s\n",
+      "    fold 3/5 PR-AUC=0.03424 Recall=0.20588 F1=0.06481 elapsed=15.0s\n",
+      "    fold 4/5 PR-AUC=0.02532 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
+      "    fold 5/5 PR-AUC=0.10997 Recall=0.35294 F1=0.06557 elapsed=14.9s\n",
+      "  -> ExtraTrees trial 1 mean PR-AUC=0.05492 Recall=0.23529 F1=0.09867 FPR=0.03701\n",
+      "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.03290 Recall=0.11765 F1=0.16667 elapsed=80.4s\n",
+      "    fold 2/5 PR-AUC=0.06744 Recall=0.41176 F1=0.04811 elapsed=83.0s\n",
+      "    fold 3/5 PR-AUC=0.03351 Recall=0.20588 F1=0.08696 elapsed=80.7s\n",
+      "    fold 4/5 PR-AUC=0.02096 Recall=0.08824 F1=0.07895 elapsed=84.2s\n",
+      "    fold 5/5 PR-AUC=0.07834 Recall=0.20588 F1=0.18421 elapsed=82.4s\n",
+      "  -> ExtraTrees trial 2 mean PR-AUC=0.04663 Recall=0.20588 F1=0.11298 FPR=0.02474\n",
+      "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.04880 Recall=0.17647 F1=0.15584 elapsed=15.0s\n",
+      "    fold 2/5 PR-AUC=0.04067 Recall=0.20588 F1=0.06061 elapsed=14.9s\n",
+      "    fold 3/5 PR-AUC=0.03292 Recall=0.26471 F1=0.06818 elapsed=14.9s\n",
+      "    fold 4/5 PR-AUC=0.02491 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
+      "    fold 5/5 PR-AUC=0.12176 Recall=0.35294 F1=0.08000 elapsed=15.1s\n",
+      "  -> ExtraTrees trial 3 mean PR-AUC=0.05381 Recall=0.21176 F1=0.09293 FPR=0.02367\n",
+      "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.04500 Recall=0.14706 F1=0.19231 elapsed=77.3s\n",
+      "    fold 2/5 PR-AUC=0.08313 Recall=0.35294 F1=0.05000 elapsed=81.5s\n",
+      "    fold 3/5 PR-AUC=0.05375 Recall=0.20588 F1=0.16867 elapsed=78.0s\n",
+      "    fold 4/5 PR-AUC=0.03255 Recall=0.08824 F1=0.09524 elapsed=79.6s\n",
+      "    fold 5/5 PR-AUC=0.16604 Recall=0.35294 F1=0.08362 elapsed=78.5s\n",
+      "  -> ExtraTrees trial 4 mean PR-AUC=0.07609 Recall=0.22941 F1=0.11797 FPR=0.02534\n",
+      "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.03990 Recall=0.17647 F1=0.07947 elapsed=15.1s\n",
+      "    fold 2/5 PR-AUC=0.03735 Recall=0.26471 F1=0.05422 elapsed=15.6s\n",
+      "    fold 3/5 PR-AUC=0.01885 Recall=0.14706 F1=0.04000 elapsed=15.8s\n",
+      "    fold 4/5 PR-AUC=0.02381 Recall=0.05882 F1=0.09756 elapsed=15.2s\n",
+      "    fold 5/5 PR-AUC=0.07181 Recall=0.23529 F1=0.05112 elapsed=14.9s\n",
+      "  -> ExtraTrees trial 5 mean PR-AUC=0.03834 Recall=0.17647 F1=0.06447 FPR=0.02974\n",
+      "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.05098 Recall=0.17647 F1=0.19355 elapsed=15.8s\n",
+      "    fold 2/5 PR-AUC=0.04937 Recall=0.35294 F1=0.05085 elapsed=16.2s\n",
+      "    fold 3/5 PR-AUC=0.03076 Recall=0.14706 F1=0.12346 elapsed=15.8s\n",
+      "    fold 4/5 PR-AUC=0.02669 Recall=0.17647 F1=0.05479 elapsed=16.0s\n",
+      "    fold 5/5 PR-AUC=0.14600 Recall=0.38235 F1=0.04075 elapsed=16.1s\n",
+      "  -> ExtraTrees trial 6 mean PR-AUC=0.06076 Recall=0.24706 F1=0.09268 FPR=0.04224\n",
+      "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
+      "    fold 1/5 PR-AUC=0.02945 Recall=0.17647 F1=0.07229 elapsed=13.7s\n",
+      "    fold 2/5 PR-AUC=0.03056 Recall=0.35294 F1=0.03791 elapsed=13.3s\n",
+      "    fold 3/5 PR-AUC=0.01740 Recall=0.11765 F1=0.04762 elapsed=13.3s\n",
+      "    fold 4/5 PR-AUC=0.01828 Recall=0.05882 F1=0.08163 elapsed=13.5s\n",
+      "    fold 5/5 PR-AUC=0.02846 Recall=0.14706 F1=0.04950 elapsed=13.7s\n",
+      "  -> ExtraTrees trial 7 mean PR-AUC=0.02483 Recall=0.17059 F1=0.05779 FPR=0.03416\n",
+      "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
+      "    fold 1/5 PR-AUC=0.05038 Recall=0.20588 F1=0.16092 elapsed=67.6s\n",
+      "    fold 2/5 PR-AUC=0.07342 Recall=0.35294 F1=0.05517 elapsed=66.9s\n",
+      "    fold 3/5 PR-AUC=0.04289 Recall=0.08824 F1=0.12766 elapsed=66.9s\n",
+      "    fold 4/5 PR-AUC=0.03120 Recall=0.23529 F1=0.04278 elapsed=66.2s\n",
+      "    fold 5/5 PR-AUC=0.16042 Recall=0.35294 F1=0.04293 elapsed=64.2s\n",
+      "  -> ExtraTrees trial 8 mean PR-AUC=0.07166 Recall=0.24706 F1=0.08589 FPR=0.04325\n",
+      "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.02831 Recall=0.11765 F1=0.12121 elapsed=58.9s\n",
+      "    fold 2/5 PR-AUC=0.05126 Recall=0.35294 F1=0.03256 elapsed=59.2s\n",
+      "    fold 3/5 PR-AUC=0.02743 Recall=0.08824 F1=0.08108 elapsed=59.7s\n",
+      "    fold 4/5 PR-AUC=0.02199 Recall=0.08824 F1=0.08696 elapsed=55.8s\n",
+      "    fold 5/5 PR-AUC=0.09582 Recall=0.11765 F1=0.12903 elapsed=56.5s\n",
+      "  -> ExtraTrees trial 9 mean PR-AUC=0.04496 Recall=0.15294 F1=0.09017 FPR=0.02722\n",
+      "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.04550 Recall=0.23529 F1=0.11511 elapsed=15.0s\n",
+      "    fold 2/5 PR-AUC=0.03161 Recall=0.14706 F1=0.06623 elapsed=15.1s\n",
+      "    fold 3/5 PR-AUC=0.02638 Recall=0.17647 F1=0.08633 elapsed=15.2s\n",
+      "    fold 4/5 PR-AUC=0.02500 Recall=0.05882 F1=0.10000 elapsed=15.1s\n",
+      "    fold 5/5 PR-AUC=0.09072 Recall=0.35294 F1=0.04420 elapsed=15.0s\n",
+      "  -> ExtraTrees trial 10 mean PR-AUC=0.04384 Recall=0.19412 F1=0.08237 FPR=0.02712\n",
+      "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.03474 Recall=0.14706 F1=0.15625 elapsed=79.8s\n",
+      "    fold 2/5 PR-AUC=0.04950 Recall=0.11765 F1=0.05517 elapsed=80.9s\n",
+      "    fold 3/5 PR-AUC=0.02492 Recall=0.08824 F1=0.11321 elapsed=80.1s\n",
+      "    fold 4/5 PR-AUC=0.02257 Recall=0.05882 F1=0.09524 elapsed=79.1s\n",
+      "    fold 5/5 PR-AUC=0.09276 Recall=0.08824 F1=0.15789 elapsed=77.3s\n",
+      "  -> ExtraTrees trial 11 mean PR-AUC=0.04490 Recall=0.10000 F1=0.11555 FPR=0.00520\n",
+      "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.04890 Recall=0.17647 F1=0.15789 elapsed=12.5s\n",
+      "    fold 2/5 PR-AUC=0.06466 Recall=0.17647 F1=0.08000 elapsed=12.5s\n",
+      "    fold 3/5 PR-AUC=0.03919 Recall=0.14706 F1=0.05291 elapsed=11.6s\n",
+      "    fold 4/5 PR-AUC=0.02632 Recall=0.05882 F1=0.10000 elapsed=10.9s\n",
+      "    fold 5/5 PR-AUC=0.12891 Recall=0.14706 F1=0.23256 elapsed=12.2s\n",
+      "  -> ExtraTrees trial 12 mean PR-AUC=0.06160 Recall=0.14118 F1=0.12467 FPR=0.01019\n",
+      "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
+      "    fold 1/5 PR-AUC=0.04456 Recall=0.17647 F1=0.13043 elapsed=58.2s\n",
+      "    fold 2/5 PR-AUC=0.04290 Recall=0.35294 F1=0.03871 elapsed=58.5s\n",
+      "    fold 3/5 PR-AUC=0.03700 Recall=0.08824 F1=0.10526 elapsed=59.6s\n",
+      "    fold 4/5 PR-AUC=0.02478 Recall=0.08824 F1=0.05556 elapsed=58.3s\n",
+      "    fold 5/5 PR-AUC=0.13122 Recall=0.38235 F1=0.03969 elapsed=56.2s\n",
+      "  -> ExtraTrees trial 13 mean PR-AUC=0.05609 Recall=0.21765 F1=0.07393 FPR=0.04442\n",
+      "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+      "    fold 1/5 PR-AUC=0.05247 Recall=0.17647 F1=0.21818 elapsed=15.4s\n",
+      "    fold 2/5 PR-AUC=0.05497 Recall=0.23529 F1=0.13445 elapsed=15.3s\n",
+      "    fold 3/5 PR-AUC=0.03882 Recall=0.26471 F1=0.08491 elapsed=15.6s\n",
+      "    fold 4/5 PR-AUC=0.02642 Recall=0.29412 F1=0.03711 elapsed=15.4s\n",
+      "    fold 5/5 PR-AUC=0.12650 Recall=0.38235 F1=0.04702 elapsed=15.8s\n",
+      "  -> ExtraTrees trial 14 mean PR-AUC=0.05983 Recall=0.27059 F1=0.10433 FPR=0.04231\n",
+      "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
+      "    fold 1/5 PR-AUC=0.02622 Recall=0.14706 F1=0.11628 elapsed=100.9s\n",
+      "    fold 2/5 PR-AUC=0.03921 Recall=0.41176 F1=0.04046 elapsed=103.7s\n",
+      "    fold 3/5 PR-AUC=0.02245 Recall=0.17647 F1=0.05660 elapsed=98.1s\n",
+      "    fold 4/5 PR-AUC=0.02344 Recall=0.23529 F1=0.03883 elapsed=100.0s\n",
+      "    fold 5/5 PR-AUC=0.07321 Recall=0.11765 F1=0.06957 elapsed=95.6s\n",
+      "  -> ExtraTrees trial 15 mean PR-AUC=0.03691 Recall=0.21765 F1=0.06435 FPR=0.04392\n",
+      "[ExtraTrees] CV done elapsed=3199.4s\n",
+      "\n",
+      "[LogisticRegression] CV start\n",
+      "[LogisticRegression] random search trials: 5\n",
+      "  trial 1/5 params={'model__solver': 'lbfgs', 'model__C': 0.03}\n",
+      "    fold 1/5 PR-AUC=0.03239 Recall=0.11765 F1=0.11940 elapsed=5.0s\n",
+      "    fold 2/5 PR-AUC=0.03936 Recall=0.20588 F1=0.13333 elapsed=6.5s\n",
+      "    fold 3/5 PR-AUC=0.01676 Recall=0.11765 F1=0.05926 elapsed=5.1s\n",
+      "    fold 4/5 PR-AUC=0.04898 Recall=0.14706 F1=0.06944 elapsed=8.0s\n",
+      "    fold 5/5 PR-AUC=0.05055 Recall=0.14706 F1=0.15385 elapsed=5.0s\n",
+      "  -> LogisticRegression trial 1 mean PR-AUC=0.03761 Recall=0.14706 F1=0.10706 FPR=0.01076\n",
+      "  trial 2/5 params={'model__solver': 'lbfgs', 'model__C': 0.1}\n",
+      "    fold 1/5 PR-AUC=0.03312 Recall=0.14706 F1=0.09524 elapsed=7.2s\n",
+      "    fold 2/5 PR-AUC=0.03507 Recall=0.20588 F1=0.14583 elapsed=7.6s\n",
+      "    fold 3/5 PR-AUC=0.01612 Recall=0.11765 F1=0.07207 elapsed=7.6s\n",
+      "    fold 4/5 PR-AUC=0.04945 Recall=0.14706 F1=0.09174 elapsed=8.8s\n",
+      "    fold 5/5 PR-AUC=0.05347 Recall=0.14706 F1=0.15385 elapsed=8.5s\n",
+      "  -> LogisticRegression trial 2 mean PR-AUC=0.03745 Recall=0.15294 F1=0.11175 FPR=0.00972\n",
+      "  trial 3/5 params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
+      "    fold 1/5 PR-AUC=0.03547 Recall=0.14706 F1=0.10204 elapsed=9.1s\n",
+      "    fold 2/5 PR-AUC=0.03449 Recall=0.20588 F1=0.16092 elapsed=11.2s\n",
+      "    fold 3/5 PR-AUC=0.01564 Recall=0.11765 F1=0.07619 elapsed=10.8s\n",
+      "    fold 4/5 PR-AUC=0.04895 Recall=0.11765 F1=0.10000 elapsed=8.9s\n",
+      "    fold 5/5 PR-AUC=0.05718 Recall=0.14706 F1=0.16129 elapsed=10.3s\n",
+      "  -> LogisticRegression trial 3 mean PR-AUC=0.03835 Recall=0.14706 F1=0.12009 FPR=0.00795\n",
+      "  trial 4/5 params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
+      "    fold 1/5 PR-AUC=0.03432 Recall=0.14706 F1=0.11236 elapsed=14.1s\n",
+      "    fold 2/5 PR-AUC=0.03430 Recall=0.20588 F1=0.16279 elapsed=18.1s\n",
+      "    fold 3/5 PR-AUC=0.01534 Recall=0.11765 F1=0.07143 elapsed=14.3s\n",
+      "    fold 4/5 PR-AUC=0.04784 Recall=0.14706 F1=0.07634 elapsed=13.7s\n",
+      "    fold 5/5 PR-AUC=0.05053 Recall=0.14706 F1=0.17544 elapsed=13.5s\n",
+      "  -> LogisticRegression trial 4 mean PR-AUC=0.03647 Recall=0.15294 F1=0.11967 FPR=0.00935\n",
+      "  trial 5/5 params={'model__solver': 'lbfgs', 'model__C': 3.0}\n",
+      "    fold 1/5 PR-AUC=0.02904 Recall=0.14706 F1=0.11236 elapsed=17.6s\n",
+      "    fold 2/5 PR-AUC=0.03423 Recall=0.20588 F1=0.15385 elapsed=18.0s\n",
+      "    fold 3/5 PR-AUC=0.01664 Recall=0.11765 F1=0.07080 elapsed=17.7s\n",
+      "    fold 4/5 PR-AUC=0.04669 Recall=0.14706 F1=0.07407 elapsed=18.0s\n",
+      "    fold 5/5 PR-AUC=0.04529 Recall=0.14706 F1=0.16667 elapsed=18.9s\n",
+      "  -> LogisticRegression trial 5 mean PR-AUC=0.03438 Recall=0.15294 F1=0.11555 FPR=0.00979\n",
+      "[LogisticRegression] CV done elapsed=283.4s\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "      model_name  trial_no  mean_pr_auc  std_pr_auc  mean_roc_auc  \\\n",
+       "0     ExtraTrees         4     0.076091    0.047961      0.647573   \n",
+       "1     ExtraTrees         8     0.071661    0.046476      0.670800   \n",
+       "2     ExtraTrees        12     0.061595    0.035912      0.647986   \n",
+       "3       LightGBM         5     0.061435    0.043454      0.635865   \n",
+       "4     ExtraTrees         6     0.060760    0.043707      0.626227   \n",
+       "5     ExtraTrees        14     0.059835    0.034874      0.633514   \n",
+       "6       LightGBM        14     0.058075    0.047107      0.635845   \n",
+       "7     ExtraTrees        13     0.056089    0.038199      0.651291   \n",
+       "8       LightGBM        12     0.055233    0.046533      0.622553   \n",
+       "9     ExtraTrees         1     0.054918    0.029484      0.644671   \n",
+       "10      LightGBM         4     0.054238    0.043641      0.638072   \n",
+       "11    ExtraTrees         3     0.053812    0.034891      0.643948   \n",
+       "12      LightGBM         2     0.053372    0.036460      0.623080   \n",
+       "13      LightGBM         6     0.051865    0.038493      0.645314   \n",
+       "14      LightGBM         1     0.051047    0.037590      0.637448   \n",
+       "15  RandomForest        14     0.050646    0.028109      0.651639   \n",
+       "16      LightGBM        13     0.049543    0.033146      0.646574   \n",
+       "17      LightGBM         3     0.049320    0.034143      0.627693   \n",
+       "18      LightGBM         9     0.048774    0.035914      0.629020   \n",
+       "19    ExtraTrees         2     0.046631    0.022173      0.632108   \n",
+       "\n",
+       "    std_roc_auc  mean_accuracy  mean_false_positive_rate  mean_precision  \\\n",
+       "0      0.039919       0.970433                  0.025344        0.119684   \n",
+       "1      0.037693       0.952733                  0.043245        0.087831   \n",
+       "2      0.024357       0.985000                  0.010191        0.223146   \n",
+       "3      0.047525       0.975900                  0.019879        0.101028   \n",
+       "4      0.026922       0.953733                  0.042239        0.080404   \n",
+       "5      0.023413       0.953800                  0.042306        0.095049   \n",
+       "6      0.045116       0.967200                  0.028696        0.057561   \n",
+       "7      0.033722       0.951400                  0.044418        0.063167   \n",
+       "8      0.050385       0.959833                  0.036071        0.053337   \n",
+       "9      0.023235       0.958867                  0.037010        0.145893   \n",
+       "10     0.043098       0.947200                  0.049078        0.052814   \n",
+       "11     0.022371       0.972000                  0.023667        0.118529   \n",
+       "12     0.034409       0.958800                  0.037211        0.052149   \n",
+       "13     0.047738       0.966367                  0.029400        0.133721   \n",
+       "14     0.054362       0.947200                  0.049112        0.044530   \n",
+       "15     0.045576       0.957433                  0.038585        0.067259   \n",
+       "16     0.043579       0.945433                  0.050821        0.060910   \n",
+       "17     0.046462       0.928867                  0.067516        0.076085   \n",
+       "18     0.028166       0.964367                  0.031512        0.056697   \n",
+       "19     0.042936       0.970900                  0.024740        0.120895   \n",
+       "\n",
+       "    mean_recall   mean_f1                                             params  \\\n",
+       "0      0.229412  0.117969  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+       "1      0.247059  0.085893  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+       "2      0.141176  0.124673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+       "3      0.235294  0.123713  {'model__learning_rate': 0.031169409812378812,...   \n",
+       "4      0.247059  0.092680  {'model__min_samples_leaf': 3, 'model__max_fea...   \n",
+       "5      0.270588  0.104333  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+       "6      0.247059  0.089393  {'model__learning_rate': 0.03688230536757648, ...   \n",
+       "7      0.217647  0.073932  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+       "8      0.241176  0.079716  {'model__learning_rate': 0.03828385438925741, ...   \n",
+       "9      0.235294  0.098673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+       "10     0.294118  0.079513  {'model__learning_rate': 0.018840552955192824,...   \n",
+       "11     0.211765  0.092926  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
+       "12     0.258824  0.082953  {'model__learning_rate': 0.0564638664293258, '...   \n",
+       "13     0.223529  0.109434  {'model__learning_rate': 0.022439365289703053,...   \n",
+       "14     0.300000  0.073458  {'model__learning_rate': 0.021789307659775742,...   \n",
+       "15     0.258824  0.085685  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
+       "16     0.288235  0.081039  {'model__learning_rate': 0.0128289748714244, '...   \n",
+       "17     0.294118  0.054984  {'model__learning_rate': 0.025815006344207546,...   \n",
+       "18     0.241176  0.086860  {'model__learning_rate': 0.029653419677342325,...   \n",
+       "19     0.205882  0.112978  {'model__min_samples_leaf': 1, 'model__max_fea...   \n",
+       "\n",
+       "                                     selection_reason     tuning_method  \n",
+       "0   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "1   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "2   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "3   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "4   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "5   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "6   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "7   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "8   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "9   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "10  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "11  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
+       "12  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "13  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "14  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "15  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  ParameterSampler  \n",
+       "16  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "17  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "18  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
+       "19  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  "
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-03e4c761-567f-41e7-ad56-2ecca75a7cbb\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>trial_no</th>\n",
+       "      <th>mean_pr_auc</th>\n",
+       "      <th>std_pr_auc</th>\n",
+       "      <th>mean_roc_auc</th>\n",
+       "      <th>std_roc_auc</th>\n",
+       "      <th>mean_accuracy</th>\n",
+       "      <th>mean_false_positive_rate</th>\n",
+       "      <th>mean_precision</th>\n",
+       "      <th>mean_recall</th>\n",
+       "      <th>mean_f1</th>\n",
+       "      <th>params</th>\n",
+       "      <th>selection_reason</th>\n",
+       "      <th>tuning_method</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.076091</td>\n",
+       "      <td>0.047961</td>\n",
+       "      <td>0.647573</td>\n",
+       "      <td>0.039919</td>\n",
+       "      <td>0.970433</td>\n",
+       "      <td>0.025344</td>\n",
+       "      <td>0.119684</td>\n",
+       "      <td>0.229412</td>\n",
+       "      <td>0.117969</td>\n",
+       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.071661</td>\n",
+       "      <td>0.046476</td>\n",
+       "      <td>0.670800</td>\n",
+       "      <td>0.037693</td>\n",
+       "      <td>0.952733</td>\n",
+       "      <td>0.043245</td>\n",
+       "      <td>0.087831</td>\n",
+       "      <td>0.247059</td>\n",
+       "      <td>0.085893</td>\n",
+       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.061595</td>\n",
+       "      <td>0.035912</td>\n",
+       "      <td>0.647986</td>\n",
+       "      <td>0.024357</td>\n",
+       "      <td>0.985000</td>\n",
+       "      <td>0.010191</td>\n",
+       "      <td>0.223146</td>\n",
+       "      <td>0.141176</td>\n",
+       "      <td>0.124673</td>\n",
+       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.061435</td>\n",
+       "      <td>0.043454</td>\n",
+       "      <td>0.635865</td>\n",
+       "      <td>0.047525</td>\n",
+       "      <td>0.975900</td>\n",
+       "      <td>0.019879</td>\n",
+       "      <td>0.101028</td>\n",
+       "      <td>0.235294</td>\n",
+       "      <td>0.123713</td>\n",
+       "      <td>{'model__learning_rate': 0.031169409812378812,...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.060760</td>\n",
+       "      <td>0.043707</td>\n",
+       "      <td>0.626227</td>\n",
+       "      <td>0.026922</td>\n",
+       "      <td>0.953733</td>\n",
+       "      <td>0.042239</td>\n",
+       "      <td>0.080404</td>\n",
+       "      <td>0.247059</td>\n",
+       "      <td>0.092680</td>\n",
+       "      <td>{'model__min_samples_leaf': 3, 'model__max_fea...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.059835</td>\n",
+       "      <td>0.034874</td>\n",
+       "      <td>0.633514</td>\n",
+       "      <td>0.023413</td>\n",
+       "      <td>0.953800</td>\n",
+       "      <td>0.042306</td>\n",
+       "      <td>0.095049</td>\n",
+       "      <td>0.270588</td>\n",
+       "      <td>0.104333</td>\n",
+       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.058075</td>\n",
+       "      <td>0.047107</td>\n",
+       "      <td>0.635845</td>\n",
+       "      <td>0.045116</td>\n",
+       "      <td>0.967200</td>\n",
+       "      <td>0.028696</td>\n",
+       "      <td>0.057561</td>\n",
+       "      <td>0.247059</td>\n",
+       "      <td>0.089393</td>\n",
+       "      <td>{'model__learning_rate': 0.03688230536757648, ...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.056089</td>\n",
+       "      <td>0.038199</td>\n",
+       "      <td>0.651291</td>\n",
+       "      <td>0.033722</td>\n",
+       "      <td>0.951400</td>\n",
+       "      <td>0.044418</td>\n",
+       "      <td>0.063167</td>\n",
+       "      <td>0.217647</td>\n",
+       "      <td>0.073932</td>\n",
+       "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.055233</td>\n",
+       "      <td>0.046533</td>\n",
+       "      <td>0.622553</td>\n",
+       "      <td>0.050385</td>\n",
+       "      <td>0.959833</td>\n",
+       "      <td>0.036071</td>\n",
+       "      <td>0.053337</td>\n",
+       "      <td>0.241176</td>\n",
+       "      <td>0.079716</td>\n",
+       "      <td>{'model__learning_rate': 0.03828385438925741, ...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.054918</td>\n",
+       "      <td>0.029484</td>\n",
+       "      <td>0.644671</td>\n",
+       "      <td>0.023235</td>\n",
+       "      <td>0.958867</td>\n",
+       "      <td>0.037010</td>\n",
+       "      <td>0.145893</td>\n",
+       "      <td>0.235294</td>\n",
+       "      <td>0.098673</td>\n",
+       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.054238</td>\n",
+       "      <td>0.043641</td>\n",
+       "      <td>0.638072</td>\n",
+       "      <td>0.043098</td>\n",
+       "      <td>0.947200</td>\n",
+       "      <td>0.049078</td>\n",
+       "      <td>0.052814</td>\n",
+       "      <td>0.294118</td>\n",
+       "      <td>0.079513</td>\n",
+       "      <td>{'model__learning_rate': 0.018840552955192824,...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.053812</td>\n",
+       "      <td>0.034891</td>\n",
+       "      <td>0.643948</td>\n",
+       "      <td>0.022371</td>\n",
+       "      <td>0.972000</td>\n",
+       "      <td>0.023667</td>\n",
+       "      <td>0.118529</td>\n",
+       "      <td>0.211765</td>\n",
+       "      <td>0.092926</td>\n",
+       "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.053372</td>\n",
+       "      <td>0.036460</td>\n",
+       "      <td>0.623080</td>\n",
+       "      <td>0.034409</td>\n",
+       "      <td>0.958800</td>\n",
+       "      <td>0.037211</td>\n",
+       "      <td>0.052149</td>\n",
+       "      <td>0.258824</td>\n",
+       "      <td>0.082953</td>\n",
+       "      <td>{'model__learning_rate': 0.0564638664293258, '...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.051865</td>\n",
+       "      <td>0.038493</td>\n",
+       "      <td>0.645314</td>\n",
+       "      <td>0.047738</td>\n",
+       "      <td>0.966367</td>\n",
+       "      <td>0.029400</td>\n",
+       "      <td>0.133721</td>\n",
+       "      <td>0.223529</td>\n",
+       "      <td>0.109434</td>\n",
+       "      <td>{'model__learning_rate': 0.022439365289703053,...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.051047</td>\n",
+       "      <td>0.037590</td>\n",
+       "      <td>0.637448</td>\n",
+       "      <td>0.054362</td>\n",
+       "      <td>0.947200</td>\n",
+       "      <td>0.049112</td>\n",
+       "      <td>0.044530</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.073458</td>\n",
+       "      <td>{'model__learning_rate': 0.021789307659775742,...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.050646</td>\n",
+       "      <td>0.028109</td>\n",
+       "      <td>0.651639</td>\n",
+       "      <td>0.045576</td>\n",
+       "      <td>0.957433</td>\n",
+       "      <td>0.038585</td>\n",
+       "      <td>0.067259</td>\n",
+       "      <td>0.258824</td>\n",
+       "      <td>0.085685</td>\n",
+       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
+       "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.049543</td>\n",
+       "      <td>0.033146</td>\n",
+       "      <td>0.646574</td>\n",
+       "      <td>0.043579</td>\n",
+       "      <td>0.945433</td>\n",
+       "      <td>0.050821</td>\n",
+       "      <td>0.060910</td>\n",
+       "      <td>0.288235</td>\n",
+       "      <td>0.081039</td>\n",
+       "      <td>{'model__learning_rate': 0.0128289748714244, '...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.049320</td>\n",
+       "      <td>0.034143</td>\n",
+       "      <td>0.627693</td>\n",
+       "      <td>0.046462</td>\n",
+       "      <td>0.928867</td>\n",
+       "      <td>0.067516</td>\n",
+       "      <td>0.076085</td>\n",
+       "      <td>0.294118</td>\n",
+       "      <td>0.054984</td>\n",
+       "      <td>{'model__learning_rate': 0.025815006344207546,...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.048774</td>\n",
+       "      <td>0.035914</td>\n",
+       "      <td>0.629020</td>\n",
+       "      <td>0.028166</td>\n",
+       "      <td>0.964367</td>\n",
+       "      <td>0.031512</td>\n",
+       "      <td>0.056697</td>\n",
+       "      <td>0.241176</td>\n",
+       "      <td>0.086860</td>\n",
+       "      <td>{'model__learning_rate': 0.029653419677342325,...</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>Optuna</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.046631</td>\n",
+       "      <td>0.022173</td>\n",
+       "      <td>0.632108</td>\n",
+       "      <td>0.042936</td>\n",
+       "      <td>0.970900</td>\n",
+       "      <td>0.024740</td>\n",
+       "      <td>0.120895</td>\n",
+       "      <td>0.205882</td>\n",
+       "      <td>0.112978</td>\n",
+       "      <td>{'model__min_samples_leaf': 1, 'model__max_fea...</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>ParameterSampler</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03e4c761-567f-41e7-ad56-2ecca75a7cbb')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-03e4c761-567f-41e7-ad56-2ecca75a7cbb button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-03e4c761-567f-41e7-ad56-2ecca75a7cbb');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "# 학습 데이터셋 저장\n",
-        "def save_training_dataframe(df: pd.DataFrame, output_dir: Path, stem: str) -> Path:\n",
-        "    parquet_path = output_dir / f\"{stem}.parquet\"\n",
-        "    csv_path = output_dir / f\"{stem}.csv.gz\"\n",
-        "    try:\n",
-        "        df.to_parquet(parquet_path, index=False)\n",
-        "        return parquet_path\n",
-        "    except Exception as exc:\n",
-        "        print(f\"Parquet 저장 실패, csv.gz로 저장합니다: {exc}\")\n",
-        "        df.to_csv(csv_path, index=False, compression=\"gzip\")\n",
-        "        return csv_path\n",
-        "\n",
-        "# 전처리 결과 저장\n",
-        "training_dataset_path = save_training_dataframe(model_df, OUTPUT_DIR, \"defect_transfer_training_dataset\")\n",
-        "\n",
-        "# 분할 ID 저장\n",
-        "split_ids = pd.DataFrame({\n",
-        "    \"Id\": pd.concat([id_train, id_valid]).astype(int).to_numpy(),\n",
-        "    \"split\": [\"train\"] * len(id_train) + [\"valid\"] * len(id_valid),\n",
-        "})\n",
-        "split_ids_path = OUTPUT_DIR / \"defect_transfer_train_valid_split_ids.csv\"\n",
-        "split_ids.to_csv(split_ids_path, index=False)\n",
-        "\n",
-        "# 전처리 메타데이터\n",
-        "preprocessing_metadata = {\n",
-        "    \"process_root\": str(PROCESS_ROOT),\n",
-        "    \"bosch_numeric_path\": str(NUMERIC_PATH),\n",
-        "    \"bosch_date_path\": str(DATE_PATH),\n",
-        "    \"bosch_categorical_path\": str(CATEGORICAL_PATH),\n",
-        "    \"use_categorical\": bool(USE_CATEGORICAL),\n",
-        "    \"profile_rows\": int(PROFILE_ROWS),\n",
-        "    \"max_rows\": int(MAX_ROWS),\n",
-        "    \"max_numeric_features\": int(MAX_NUMERIC_FEATURES),\n",
-        "    \"max_date_features\": int(MAX_DATE_FEATURES),\n",
-        "    \"max_categorical_features\": int(MAX_CATEGORICAL_FEATURES),\n",
-        "    \"selected_numeric_columns\": numeric_cols,\n",
-        "    \"selected_date_columns\": date_cols,\n",
-        "    \"selected_categorical_columns\": categorical_cols if USE_CATEGORICAL else [],\n",
-        "    \"dropped_all_missing_columns\": all_missing_cols,\n",
-        "    \"feature_columns\": feature_cols,\n",
-        "    \"target_column\": \"Response\",\n",
-        "    \"train_rows\": int(len(X_train)),\n",
-        "    \"valid_rows\": int(len(X_valid)),\n",
-        "    \"positive_ratio\": float(y.mean()),\n",
-        "    \"auxiliary_metrics\": auxiliary_metrics,\n",
-        "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
-        "}\n",
-        "preprocessing_metadata_path = OUTPUT_DIR / \"defect_transfer_preprocessing_metadata.json\"\n",
-        "preprocessing_metadata_path.write_text(json.dumps(preprocessing_metadata, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-        "\n",
-        "print(\"전처리 학습 데이터셋:\", training_dataset_path)\n",
-        "print(\"train/valid split ids:\", split_ids_path)\n",
-        "print(\"전처리 metadata:\", preprocessing_metadata_path)\n"
-      ]
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"print(\\\"CV best model:\\\", best_model_name, best_params)\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trial_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 14,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          14,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007481168730885,\n        \"min\": 0.04663096330797513,\n        \"max\": 0.07609114742802507,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.07609114742802507,\n          0.049319566390547076,\n          0.05064611316582477\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006924597647087308,\n        \"min\": 0.022173359277985142,\n        \"max\": 0.047961037002806066,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.047961037002806066,\n          0.034143129310785794,\n          0.028108939775837834\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011785359877674395,\n        \"min\": 0.6225532921851274,\n        \"max\": 0.6707997278696929,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.647572518782907,\n          0.6276932026582003,\n          0.6516386977184438\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010216806702914104,\n        \"min\": 0.022370910369156588,\n        \"max\": 0.05436158460450469,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.0399185136411262,\n          0.04646163650317234,\n          0.04557575424204216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012723848768567607,\n        \"min\": 0.9288666666666667,\n        \"max\": 0.985,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.9704333333333333,\n          0.9538,\n          0.9719999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012970570034246781,\n        \"min\": 0.010191082802547772,\n        \"max\": 0.067515923566879,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.025343613811599063,\n          0.067515923566879,\n          0.038585316795172644\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04371952737029428,\n        \"min\": 0.04452964231174254,\n        \"max\": 0.22314564683863908,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.11968397122658556,\n          0.07608503611061139,\n          0.06725864685167784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03688247391608683,\n        \"min\": 0.1411764705882353,\n        \"max\": 0.3,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.25882352941176473,\n          0.3,\n          0.22941176470588234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018271806088460737,\n        \"min\": 0.05498448429507783,\n        \"max\": 0.12467258585740837,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.11796883594415183,\n          0.05498448429507783,\n          0.08568507388916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"RandomForest\\ubcf4\\ub2e4 \\ubd84\\ud560 \\ubb34\\uc791\\uc704\\uc131\\uc774 \\ucee4\\uc11c \\uace0\\ucc28\\uc6d0 \\uc13c\\uc11c feature\\uc758 \\uac15\\uac74\\uc131 \\ube44\\uad50\",\n          \"\\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378. Optuna\\uc640 SMOTE\\ub85c \\ud76c\\uc18c \\ubd88\\ub7c9 recall\\uc744 \\ubcf4\\uac15\",\n          \"bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tuning_method\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Optuna\",\n          \"ParameterSampler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "markdown",
-      "id": "b19d8e5c",
-      "metadata": {
-        "id": "b19d8e5c"
-      },
-      "source": [
-        "## 7. 후보 모델 4종 교차검증 및 하이퍼파라미터 튜닝\n",
-        "\n",
-        "불량 클래스가 희소하므로 모든 후보 모델은 같은 전처리/피처 엔지니어링 결과를 사용하고, `StratifiedKFold`로 클래스 비율을 유지하며 비교합니다.\n",
-        "\n",
-        "이번 버전은 기존의 약한 랜덤 탐색을 보완하기 위해 다음을 반영합니다.\n",
-        "\n",
-        "- `CV_N_SPLITS = 5`: 5-Fold Stratified CV로 validation 안정성 강화\n",
-        "- `CV_N_ITER = 15`: 비-LightGBM 후보도 15회 랜덤 탐색\n",
-        "- LightGBM: `Optuna` TPE sampler 기반 15회 탐색\n",
-        "- LightGBM pipeline: median imputation + SMOTE + `is_unbalance=True` 기반 불균형 보정\n",
-        "- threshold: Accuracy/F1 단독 최적화가 아니라 Recall 우선 탐색 적용\n",
-        "\n",
-        "후보 모델:\n",
-        "\n",
-        "| 모델 | 목적 |\n",
-        "| --- | --- |\n",
-        "| LightGBM | 대용량 tabular 제조 데이터용 gradient boosting 기준 모델, Optuna + SMOTE 적용 |\n",
-        "| RandomForest | bagging 기반 tree ensemble 기준 모델 |\n",
-        "| ExtraTrees | 더 강한 randomization을 주는 tree ensemble 후보 |\n",
-        "| LogisticRegression | 선형 기준선 모델 |\n",
-        "\n",
-        "비교 지표는 PR-AUC, Recall, Precision, F1, ROC-AUC, Accuracy, False Positive Rate를 함께 사용합니다. 특히 Bosch 데이터처럼 불량 비율이 1% 미만인 경우 Accuracy는 참고 지표로만 해석합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CV 결과 저장: /content/defect_transfer_outputs/defect_model_cv_results.csv\n",
+      "CV fold 결과 저장: /content/defect_transfer_outputs/defect_model_cv_fold_results.csv\n",
+      "CV best model: ExtraTrees {'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 평가 함수\n",
+    "def find_recall_priority_threshold(\n",
+    "    y_true: pd.Series,\n",
+    "    proba: np.ndarray,\n",
+    "    *,\n",
+    "    min_recall: float = RECALL_PRIORITY_TARGET,\n",
+    "    min_precision: float = RECALL_PRIORITY_MIN_PRECISION,\n",
+    "    beta: float = THRESHOLD_BETA,\n",
+    ") -> float:\n",
+    "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
+    "    if len(thresholds) == 0:\n",
+    "        return 0.5\n",
+    "\n",
+    "    precision = precision[:-1]\n",
+    "    recall = recall[:-1]\n",
+    "    thresholds = thresholds.astype(float)\n",
+    "    valid = (recall >= min_recall) & (precision >= min_precision)\n",
+    "\n",
+    "    if valid.any():\n",
+    "        # Recall 목표를 만족하는 후보 중 Precision이 가장 좋은 threshold를 선택합니다.\n",
+    "        valid_idx = np.where(valid)[0]\n",
+    "        best_local_idx = valid_idx[int(np.nanargmax(precision[valid_idx]))]\n",
+    "        return float(thresholds[best_local_idx])\n",
+    "\n",
+    "    beta_sq = beta ** 2\n",
+    "    f_beta = (1 + beta_sq) * precision * recall / np.maximum(beta_sq * precision + recall, 1e-12)\n",
+    "    return float(thresholds[int(np.nanargmax(f_beta))])\n",
+    "\n",
+    "\n",
+    "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
+    "    return find_recall_priority_threshold(y_true, proba)\n",
+    "\n",
+    "\n",
+    "def compute_binary_metrics(y_true: pd.Series, proba: np.ndarray, threshold: float) -> dict:\n",
+    "    pred = (proba >= threshold).astype(int)\n",
+    "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
+    "    return {\n",
+    "        \"accuracy\": float(accuracy_score(y_true, pred)),\n",
+    "        \"precision\": float(precision_score(y_true, pred, zero_division=0)),\n",
+    "        \"recall\": float(recall_score(y_true, pred, zero_division=0)),\n",
+    "        \"f1\": float(f1_score(y_true, pred, zero_division=0)),\n",
+    "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
+    "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
+    "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
+    "        \"predicted_positive_rate\": float((tp + fp) / max(tp + fp + tn + fn, 1)),\n",
+    "        \"true_negative\": int(tn),\n",
+    "        \"false_positive\": int(fp),\n",
+    "        \"false_negative\": int(fn),\n",
+    "        \"true_positive\": int(tp),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def predict_positive_proba(estimator, X_input: pd.DataFrame) -> np.ndarray:\n",
+    "    if hasattr(estimator, \"predict_proba\"):\n",
+    "        return estimator.predict_proba(X_input)[:, 1]\n",
+    "    decision = estimator.decision_function(X_input)\n",
+    "    return 1 / (1 + np.exp(-decision))\n",
+    "\n",
+    "\n",
+    "def is_pipeline_estimator(estimator) -> bool:\n",
+    "    return isinstance(estimator, (Pipeline, ImbPipeline))\n",
+    "\n",
+    "\n",
+    "def get_final_estimator(estimator):\n",
+    "    return estimator.named_steps.get(\"model\", estimator) if is_pipeline_estimator(estimator) else estimator\n",
+    "\n",
+    "\n",
+    "def make_lightgbm_pipeline() -> ImbPipeline:\n",
+    "    return ImbPipeline([\n",
+    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "        (\"smote\", SMOTE(\n",
+    "            sampling_strategy=SMOTE_SAMPLING_STRATEGY if USE_SMOTE else 0.01,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "            k_neighbors=3,\n",
+    "        )),\n",
+    "        (\"model\", lgb.LGBMClassifier(\n",
+    "            objective=\"binary\",\n",
+    "            n_estimators=500,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "            n_jobs=-1,\n",
+    "            is_unbalance=True,\n",
+    "            verbosity=-1,\n",
+    "        )),\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "def make_candidate_models() -> dict:\n",
+    "    return {\n",
+    "        \"LightGBM\": {\n",
+    "            \"estimator\": make_lightgbm_pipeline(),\n",
+    "            \"params\": {\n",
+    "                \"model__learning_rate\": [0.015, 0.025, 0.04, 0.06],\n",
+    "                \"model__num_leaves\": [31, 63, 127],\n",
+    "                \"model__max_depth\": [-1, 6, 8, 12],\n",
+    "                \"model__min_child_samples\": [40, 80, 150, 250],\n",
+    "                \"model__subsample\": [0.75, 0.85, 1.0],\n",
+    "                \"model__colsample_bytree\": [0.65, 0.8, 0.95],\n",
+    "                \"model__reg_lambda\": [0.5, 1.0, 2.0, 5.0],\n",
+    "                \"model__reg_alpha\": [0.0, 0.1, 0.5],\n",
+    "            },\n",
+    "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델. Optuna와 SMOTE로 희소 불량 recall을 보강\",\n",
+    "        },\n",
+    "        \"RandomForest\": {\n",
+    "            \"estimator\": Pipeline([\n",
+    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "                (\"model\", RandomForestClassifier(\n",
+    "                    n_estimators=160,\n",
+    "                    class_weight=\"balanced_subsample\",\n",
+    "                    random_state=RANDOM_STATE,\n",
+    "                    n_jobs=-1,\n",
+    "                )),\n",
+    "            ]),\n",
+    "            \"params\": {\n",
+    "                \"model__max_depth\": [12, 16, 20, None],\n",
+    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
+    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
+    "            },\n",
+    "            \"selection_reason\": \"bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble 성능 확인\",\n",
+    "        },\n",
+    "        \"ExtraTrees\": {\n",
+    "            \"estimator\": Pipeline([\n",
+    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "                (\"model\", ExtraTreesClassifier(\n",
+    "                    n_estimators=220,\n",
+    "                    class_weight=\"balanced\",\n",
+    "                    random_state=RANDOM_STATE,\n",
+    "                    n_jobs=-1,\n",
+    "                )),\n",
+    "            ]),\n",
+    "            \"params\": {\n",
+    "                \"model__max_depth\": [12, 16, 20, None],\n",
+    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
+    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
+    "            },\n",
+    "            \"selection_reason\": \"RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교\",\n",
+    "        },\n",
+    "        \"LogisticRegression\": {\n",
+    "            \"estimator\": Pipeline([\n",
+    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
+    "                (\"scaler\", StandardScaler()),\n",
+    "                (\"model\", LogisticRegression(\n",
+    "                    class_weight=\"balanced\",\n",
+    "                    max_iter=1500,\n",
+    "                    random_state=RANDOM_STATE,\n",
+    "                )),\n",
+    "            ]),\n",
+    "            \"params\": {\n",
+    "                \"model__C\": [0.03, 0.1, 0.3, 1.0, 3.0],\n",
+    "                \"model__solver\": [\"lbfgs\"],\n",
+    "            },\n",
+    "            \"selection_reason\": \"복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체의 설명력 확인\",\n",
+    "        },\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def suggest_lightgbm_params(trial: optuna.Trial) -> dict:\n",
+    "    return {\n",
+    "        \"model__learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.08, log=True),\n",
+    "        \"model__num_leaves\": trial.suggest_int(\"num_leaves\", 24, 160),\n",
+    "        \"model__max_depth\": trial.suggest_categorical(\"max_depth\", [-1, 5, 7, 9, 12]),\n",
+    "        \"model__min_child_samples\": trial.suggest_int(\"min_child_samples\", 30, 300),\n",
+    "        \"model__subsample\": trial.suggest_float(\"subsample\", 0.65, 1.0),\n",
+    "        \"model__colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.55, 1.0),\n",
+    "        \"model__reg_lambda\": trial.suggest_float(\"reg_lambda\", 0.1, 8.0, log=True),\n",
+    "        \"model__reg_alpha\": trial.suggest_float(\"reg_alpha\", 1e-4, 1.0, log=True),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def evaluate_cv_trial(model_name: str, config: dict, params: dict, trial_no: int) -> tuple[dict, list[dict]]:\n",
+    "    fold_rows = []\n",
+    "    for fold_no, (train_idx, valid_idx) in enumerate(skf.split(X_cv_pool, y_cv_pool), start=1):\n",
+    "        fold_started_at = datetime.now()\n",
+    "        X_fold_train = X_cv_pool.iloc[train_idx]\n",
+    "        y_fold_train = y_cv_pool.iloc[train_idx]\n",
+    "        X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
+    "        y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
+    "\n",
+    "        cv_model = clone(config[\"estimator\"])\n",
+    "        cv_model.set_params(**params)\n",
+    "        cv_model.fit(X_fold_train, y_fold_train)\n",
+    "\n",
+    "        fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
+    "        fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
+    "        fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
+    "        fold_metrics.update({\n",
+    "            \"model_name\": model_name,\n",
+    "            \"trial_no\": trial_no,\n",
+    "            \"fold_no\": fold_no,\n",
+    "            \"threshold\": fold_threshold,\n",
+    "            **params,\n",
+    "        })\n",
+    "        fold_rows.append(fold_metrics)\n",
+    "        elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
+    "        print(\n",
+    "            f\"    fold {fold_no}/{effective_cv_splits} \"\n",
+    "            f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
+    "            f\"Recall={fold_metrics['recall']:.5f} \"\n",
+    "            f\"F1={fold_metrics['f1']:.5f} \"\n",
+    "            f\"elapsed={elapsed:.1f}s\"\n",
+    "        )\n",
+    "\n",
+    "    fold_df = pd.DataFrame(fold_rows)\n",
+    "    summary = {\n",
+    "        \"model_name\": model_name,\n",
+    "        \"trial_no\": trial_no,\n",
+    "        \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
+    "        \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
+    "        \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
+    "        \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
+    "        \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
+    "        \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
+    "        \"mean_precision\": float(fold_df[\"precision\"].mean()),\n",
+    "        \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
+    "        \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
+    "        \"params\": params,\n",
+    "        \"selection_reason\": config[\"selection_reason\"],\n",
+    "        \"tuning_method\": \"Optuna\" if model_name == \"LightGBM\" and ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+    "    }\n",
+    "    return summary, fold_rows\n",
+    "\n",
+    "\n",
+    "# CV 데이터셋 규모 관리\n",
+    "if len(X_train) > CV_SAMPLE_SIZE:\n",
+    "    X_cv_pool, _, y_cv_pool, _ = train_test_split(\n",
+    "        X_train,\n",
+    "        y_train,\n",
+    "        train_size=CV_SAMPLE_SIZE,\n",
+    "        random_state=RANDOM_STATE,\n",
+    "        stratify=y_train,\n",
+    "    )\n",
+    "else:\n",
+    "    X_cv_pool = X_train\n",
+    "    y_cv_pool = y_train\n",
+    "\n",
+    "effective_cv_splits = min(CV_N_SPLITS, int(y_cv_pool.value_counts().min()))\n",
+    "if effective_cv_splits < 2:\n",
+    "    raise ValueError(\"교차검증을 수행하기에 소수 클래스 샘플이 부족합니다. MAX_ROWS 또는 CV_SAMPLE_SIZE를 늘려주세요.\")\n",
+    "\n",
+    "candidate_configs = make_candidate_models()\n",
+    "skf = StratifiedKFold(n_splits=effective_cv_splits, shuffle=True, random_state=RANDOM_STATE)\n",
+    "cv_rows = []\n",
+    "cv_fold_rows = []\n",
+    "best_params_by_model = {}\n",
+    "\n",
+    "print(f\"CV rows: {len(X_cv_pool):,} / train rows: {len(X_train):,}\")\n",
+    "print(f\"CV splits: {effective_cv_splits}, tuning trials per model: {CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1}\")\n",
+    "print(f\"SMOTE enabled: {USE_SMOTE}, sampling_strategy={SMOTE_SAMPLING_STRATEGY}\")\n",
+    "print(f\"Threshold strategy: recall>={RECALL_PRIORITY_TARGET}, min_precision>={RECALL_PRIORITY_MIN_PRECISION}, beta={THRESHOLD_BETA}\")\n",
+    "\n",
+    "# 후보 모델별 교차검증\n",
+    "for model_name, config in candidate_configs.items():\n",
+    "    print(f\"\\n[{model_name}] CV start\")\n",
+    "    model_started_at = datetime.now()\n",
+    "\n",
+    "    if model_name == \"LightGBM\" and ENABLE_HYPERPARAMETER_TUNING and ENABLE_OPTUNA_TUNING:\n",
+    "        def objective(trial: optuna.Trial) -> float:\n",
+    "            params = suggest_lightgbm_params(trial)\n",
+    "            print(f\"  optuna trial {trial.number + 1}/{CV_N_ITER} params={params}\")\n",
+    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial.number + 1)\n",
+    "            cv_rows.append(summary)\n",
+    "            cv_fold_rows.extend(fold_rows)\n",
+    "            trial.set_user_attr(\"params\", params)\n",
+    "            trial.set_user_attr(\"mean_recall\", summary[\"mean_recall\"])\n",
+    "            trial.set_user_attr(\"mean_f1\", summary[\"mean_f1\"])\n",
+    "            # PR-AUC를 우선하되 recall을 약하게 보상합니다.\n",
+    "            return summary[\"mean_pr_auc\"] + 0.05 * summary[\"mean_recall\"]\n",
+    "\n",
+    "        study = optuna.create_study(\n",
+    "            direction=\"maximize\",\n",
+    "            sampler=TPESampler(seed=RANDOM_STATE),\n",
+    "            study_name=\"bosch_defect_lightgbm_recall_pr_auc\",\n",
+    "        )\n",
+    "        study.optimize(objective, n_trials=CV_N_ITER, show_progress_bar=False)\n",
+    "        best_params_by_model[model_name] = study.best_trial.user_attrs[\"params\"]\n",
+    "        print(f\"[{model_name}] Optuna best value={study.best_value:.5f} params={best_params_by_model[model_name]}\")\n",
+    "    else:\n",
+    "        param_candidates = list(ParameterSampler(\n",
+    "            config[\"params\"],\n",
+    "            n_iter=CV_N_ITER,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
+    "        print(f\"[{model_name}] random search trials: {len(param_candidates)}\")\n",
+    "        for trial_no, params in enumerate(param_candidates, start=1):\n",
+    "            print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
+    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial_no)\n",
+    "            cv_rows.append(summary)\n",
+    "            cv_fold_rows.extend(fold_rows)\n",
+    "            print(\n",
+    "                f\"  -> {model_name} trial {trial_no} mean \"\n",
+    "                f\"PR-AUC={summary['mean_pr_auc']:.5f} \"\n",
+    "                f\"Recall={summary['mean_recall']:.5f} \"\n",
+    "                f\"F1={summary['mean_f1']:.5f} \"\n",
+    "                f\"FPR={summary['mean_false_positive_rate']:.5f}\"\n",
+    "            )\n",
+    "\n",
+    "    print(f\"[{model_name}] CV done elapsed={(datetime.now() - model_started_at).total_seconds():.1f}s\")\n",
+    "\n",
+    "cv_results = pd.DataFrame(cv_rows).sort_values(\n",
+    "    [\"mean_pr_auc\", \"mean_recall\", \"mean_f1\", \"mean_roc_auc\", \"mean_accuracy\"],\n",
+    "    ascending=False,\n",
+    ").reset_index(drop=True)\n",
+    "cv_fold_results = pd.DataFrame(cv_fold_rows)\n",
+    "\n",
+    "for model_name in candidate_configs:\n",
+    "    if model_name not in best_params_by_model:\n",
+    "        best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
+    "\n",
+    "best_model_name = str(cv_results.iloc[0][\"model_name\"])\n",
+    "best_params = best_params_by_model[best_model_name]\n",
+    "best_model_selection_reason = str(cv_results.iloc[0][\"selection_reason\"])\n",
+    "\n",
+    "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.csv\"\n",
+    "cv_fold_results_path = OUTPUT_DIR / \"defect_model_cv_fold_results.csv\"\n",
+    "cv_results.to_csv(cv_results_path, index=False)\n",
+    "cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
+    "\n",
+    "display(cv_results.head(20))\n",
+    "print(\"CV 결과 저장:\", cv_results_path)\n",
+    "print(\"CV fold 결과 저장:\", cv_fold_results_path)\n",
+    "print(\"CV best model:\", best_model_name, best_params)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f15608a",
+   "metadata": {
+    "id": "3f15608a"
+   },
+   "source": [
+    "## 8. 후보 모델 4종 최종 학습 및 성능 비교\n",
+    "\n",
+    "교차검증에서 선택된 모델별 best parameter로 4개 후보 모델을 모두 학습합니다.\n",
+    "\n",
+    "LightGBM은 median imputer + SMOTE + LightGBM pipeline으로 최종 학습합니다. Threshold는 validation set에서 recall 우선 정책으로 탐색합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3553e4c5",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 825
+    },
+    "id": "3553e4c5",
+    "outputId": "f6e17abe-7523-45bd-b4fa-0828efb0d901"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Final candidate training rows: 120,000 / train rows: 240,000\n",
+      "[LightGBM] final fit start params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
+      "[LightGBM] final fit done PR-AUC=0.05032 ROC-AUC=0.66598 ACC=0.90142 FPR=0.09546 elapsed=159.2s\n",
+      "[RandomForest] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
+      "[RandomForest] final fit done PR-AUC=0.06255 ROC-AUC=0.69675 ACC=0.91395 FPR=0.08285 elapsed=150.0s\n",
+      "[ExtraTrees] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
+      "[ExtraTrees] final fit done PR-AUC=0.05758 ROC-AUC=0.67618 ACC=0.98787 FPR=0.00784 elapsed=750.9s\n",
+      "[LogisticRegression] final fit start params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
+      "[LogisticRegression] final fit done PR-AUC=0.05096 ROC-AUC=0.66273 ACC=0.98672 FPR=0.00888 elapsed=71.9s\n",
+      "selected_model_name: RandomForest\n",
+      "ROC-AUC: 0.69675\n",
+      "PR-AUC: 0.06255\n",
+      "Accuracy: 0.91395\n",
+      "False Positive Rate: 0.08285\n",
+      "Recall: 0.35103\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "           model_name  threshold  \\\n",
+       "0        RandomForest   0.058868   \n",
+       "1          ExtraTrees   0.243180   \n",
+       "2  LogisticRegression   0.898330   \n",
+       "3            LightGBM   0.034547   \n",
+       "\n",
+       "                                    selection_reason  accuracy  precision  \\\n",
+       "0  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  0.913950   0.023508   \n",
+       "1  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  0.987867   0.144424   \n",
+       "2  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  0.986717   0.119601   \n",
+       "3  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...  0.901417   0.020468   \n",
+       "\n",
+       "     recall        f1   roc_auc    pr_auc  false_positive_rate  \\\n",
+       "0  0.351032  0.044066  0.696746  0.062551             0.082851   \n",
+       "1  0.233038  0.178330  0.676183  0.057577             0.007844   \n",
+       "2  0.212389  0.153029  0.662734  0.050958             0.008884   \n",
+       "3  0.351032  0.038680  0.665979  0.050323             0.095456   \n",
+       "\n",
+       "   predicted_positive_rate  true_negative  false_positive  false_negative  \\\n",
+       "0                 0.084367          54718            4943             220   \n",
+       "1                 0.009117          59193             468             260   \n",
+       "2                 0.010033          59131             530             267   \n",
+       "3                 0.096900          53966            5695             220   \n",
+       "\n",
+       "   true_positive  \n",
+       "0            119  \n",
+       "1             79  \n",
+       "2             72  \n",
+       "3            119  "
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-6fdedb8e-6721-4875-adf0-d8cafb363080\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>selection_reason</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>pr_auc</th>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <th>predicted_positive_rate</th>\n",
+       "      <th>true_negative</th>\n",
+       "      <th>false_positive</th>\n",
+       "      <th>false_negative</th>\n",
+       "      <th>true_positive</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.058868</td>\n",
+       "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
+       "      <td>0.913950</td>\n",
+       "      <td>0.023508</td>\n",
+       "      <td>0.351032</td>\n",
+       "      <td>0.044066</td>\n",
+       "      <td>0.696746</td>\n",
+       "      <td>0.062551</td>\n",
+       "      <td>0.082851</td>\n",
+       "      <td>0.084367</td>\n",
+       "      <td>54718</td>\n",
+       "      <td>4943</td>\n",
+       "      <td>220</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>0.243180</td>\n",
+       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
+       "      <td>0.987867</td>\n",
+       "      <td>0.144424</td>\n",
+       "      <td>0.233038</td>\n",
+       "      <td>0.178330</td>\n",
+       "      <td>0.676183</td>\n",
+       "      <td>0.057577</td>\n",
+       "      <td>0.007844</td>\n",
+       "      <td>0.009117</td>\n",
+       "      <td>59193</td>\n",
+       "      <td>468</td>\n",
+       "      <td>260</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>0.898330</td>\n",
+       "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
+       "      <td>0.986717</td>\n",
+       "      <td>0.119601</td>\n",
+       "      <td>0.212389</td>\n",
+       "      <td>0.153029</td>\n",
+       "      <td>0.662734</td>\n",
+       "      <td>0.050958</td>\n",
+       "      <td>0.008884</td>\n",
+       "      <td>0.010033</td>\n",
+       "      <td>59131</td>\n",
+       "      <td>530</td>\n",
+       "      <td>267</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>0.034547</td>\n",
+       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
+       "      <td>0.901417</td>\n",
+       "      <td>0.020468</td>\n",
+       "      <td>0.351032</td>\n",
+       "      <td>0.038680</td>\n",
+       "      <td>0.665979</td>\n",
+       "      <td>0.050323</td>\n",
+       "      <td>0.095456</td>\n",
+       "      <td>0.096900</td>\n",
+       "      <td>53966</td>\n",
+       "      <td>5695</td>\n",
+       "      <td>220</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6fdedb8e-6721-4875-adf0-d8cafb363080')\"\n",
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+       "  <style>\n",
+       "    .colab-df-container {\n",
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+       "\n",
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+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
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+       "\n",
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+       "        document.querySelector('#df-6fdedb8e-6721-4875-adf0-d8cafb363080 button.colab-df-convert');\n",
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+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
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+       "        element.appendChild(docLink);\n",
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+       "\n",
+       "\n",
+       "  <div id=\"id_b382d83d-17b9-42da-a32f-9fb37cd8f3cd\">\n",
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+       "\n",
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+       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('model_comparison')\"\n",
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+      ],
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+       "type": "dataframe",
+       "variable_name": "model_comparison",
+       "summary": "{\n  \"name\": \"model_comparison\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40395240727098636,\n        \"min\": 0.03454695956318568,\n        \"max\": 0.8983303412868939,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2431800238242072,\n          0.03454695956318568,\n          0.058868157090478435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"RandomForest\\ubcf4\\ub2e4 \\ubd84\\ud560 \\ubb34\\uc791\\uc704\\uc131\\uc774 \\ucee4\\uc11c \\uace0\\ucc28\\uc6d0 \\uc13c\\uc11c feature\\uc758 \\uac15\\uac74\\uc131 \\ube44\\uad50\",\n          \"\\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378. Optuna\\uc640 SMOTE\\ub85c \\ud76c\\uc18c \\ubd88\\ub7c9 recall\\uc744 \\ubcf4\\uac15\",\n          \"bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04624820792323862,\n        \"min\": 0.9014166666666666,\n        \"max\": 0.9878666666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9878666666666667,\n          0.9014166666666666,\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0643379448162342,\n        \"min\": 0.02046783625730994,\n        \"max\": 0.14442413162705667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.14442413162705667,\n          0.02046783625730994,\n          0.023508494666139867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07456283468415088,\n        \"min\": 0.21238938053097345,\n        \"max\": 0.35103244837758113,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.35103244837758113,\n          0.23303834808259588,\n          0.21238938053097345\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07254091975454982,\n        \"min\": 0.038680318543799774,\n        \"max\": 0.17832957110609482,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17832957110609482,\n          0.038680318543799774,\n          0.04406591371968154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015334779408148261,\n        \"min\": 0.6627341233129422,\n        \"max\": 0.6967463513986769,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6761829953791527,\n          0.6659786100217459,\n          0.6967463513986769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005812604054563798,\n        \"min\": 0.050323296178874356,\n        \"max\": 0.06255052428644334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.05757660266781409,\n          0.050323296178874356,\n          0.06255052428644334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04692891215226371,\n        \"min\": 0.007844320410318299,\n        \"max\": 0.09545599302727074,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.007844320410318299,\n          0.09545599302727074,\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04707942142057713,\n        \"min\": 0.009116666666666667,\n        \"max\": 0.0969,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.009116666666666667,\n          0.0969,\n          0.08436666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"true_negative\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2799,\n        \"min\": 53966,\n        \"max\": 59193,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          59193,\n          53966,\n          54718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2799,\n        \"min\": 468,\n        \"max\": 5695,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          468,\n          5695,\n          4943\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_negative\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 220,\n        \"max\": 267,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          220,\n          260,\n          267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"true_positive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 72,\n        \"max\": 119,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          119,\n          79,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "d9a50e90",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "d9a50e90",
-        "outputId": "60f68881-d294-44fc-92f2-008ba9ab2179"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:00:51,763] A new study created in memory with name: bosch_defect_lightgbm_recall_pr_auc\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "CV rows: 30,000 / train rows: 240,000\n",
-            "CV splits: 5, tuning trials per model: 15\n",
-            "SMOTE enabled: True, sampling_strategy=0.1\n",
-            "Threshold strategy: recall>=0.35, min_precision>=0.02, beta=2.0\n",
-            "\n",
-            "[LightGBM] CV start\n",
-            "  optuna trial 1/15 params={'model__learning_rate': 0.021789307659775742, 'model__num_leaves': 154, 'model__max_depth': -1, 'model__min_child_samples': 264, 'model__subsample': 0.8603902541101232, 'model__colsample_bytree': 0.8686326600082205, 'model__reg_lambda': 0.1094395112835673, 'model__reg_alpha': 0.7579479953348001}\n",
-            "    fold 1/5 PR-AUC=0.03400 Recall=0.14706 F1=0.07353 elapsed=67.3s\n",
-            "    fold 2/5 PR-AUC=0.03235 Recall=0.35294 F1=0.06723 elapsed=59.2s\n",
-            "    fold 3/5 PR-AUC=0.03957 Recall=0.23529 F1=0.11268 elapsed=59.2s\n",
-            "    fold 4/5 PR-AUC=0.02377 Recall=0.41176 F1=0.03815 elapsed=60.1s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:05:57,466] Trial 0 finished with value: 0.06604691309715442 and parameters: {'learning_rate': 0.021789307659775742, 'num_leaves': 154, 'max_depth': -1, 'min_child_samples': 264, 'subsample': 0.8603902541101232, 'colsample_bytree': 0.8686326600082205, 'reg_lambda': 0.1094395112835673, 'reg_alpha': 0.7579479953348001}. Best is trial 0 with value: 0.06604691309715442.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.12554 Recall=0.35294 F1=0.07571 elapsed=59.8s\n",
-            "  optuna trial 2/15 params={'model__learning_rate': 0.0564638664293258, 'model__num_leaves': 53, 'model__max_depth': 9, 'model__min_child_samples': 108, 'model__subsample': 0.8641485131528328, 'model__colsample_bytree': 0.6127722372934189, 'model__reg_lambda': 0.359730743558148, 'model__reg_alpha': 0.002920433847181412}\n",
-            "    fold 1/5 PR-AUC=0.03474 Recall=0.23529 F1=0.06107 elapsed=42.5s\n",
-            "    fold 2/5 PR-AUC=0.04446 Recall=0.38235 F1=0.04180 elapsed=43.4s\n",
-            "    fold 3/5 PR-AUC=0.03871 Recall=0.23529 F1=0.12121 elapsed=43.7s\n",
-            "    fold 4/5 PR-AUC=0.02391 Recall=0.14706 F1=0.07092 elapsed=43.4s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:09:34,490] Trial 1 finished with value: 0.06631270724589355 and parameters: {'learning_rate': 0.0564638664293258, 'num_leaves': 53, 'max_depth': 9, 'min_child_samples': 108, 'subsample': 0.8641485131528328, 'colsample_bytree': 0.6127722372934189, 'reg_lambda': 0.359730743558148, 'reg_alpha': 0.002920433847181412}. Best is trial 1 with value: 0.06631270724589355.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.12504 Recall=0.29412 F1=0.11976 elapsed=44.0s\n",
-            "  optuna trial 3/15 params={'model__learning_rate': 0.025815006344207546, 'model__num_leaves': 131, 'model__max_depth': 12, 'model__min_child_samples': 76, 'model__subsample': 0.6727680575448478, 'model__colsample_bytree': 0.976998491764, 'model__reg_lambda': 6.881524386672037, 'model__reg_alpha': 0.17123375973163968}\n",
-            "    fold 1/5 PR-AUC=0.02393 Recall=0.05882 F1=0.09756 elapsed=76.2s\n",
-            "    fold 2/5 PR-AUC=0.04875 Recall=0.35294 F1=0.05010 elapsed=78.4s\n",
-            "    fold 3/5 PR-AUC=0.03501 Recall=0.35294 F1=0.04000 elapsed=73.9s\n",
-            "    fold 4/5 PR-AUC=0.02380 Recall=0.35294 F1=0.03877 elapsed=75.6s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:15:58,052] Trial 2 finished with value: 0.06402544874348826 and parameters: {'learning_rate': 0.025815006344207546, 'num_leaves': 131, 'max_depth': 12, 'min_child_samples': 76, 'subsample': 0.6727680575448478, 'colsample_bytree': 0.976998491764, 'reg_lambda': 6.881524386672037, 'reg_alpha': 0.17123375973163968}. Best is trial 1 with value: 0.06631270724589355.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.11511 Recall=0.35294 F1=0.04848 elapsed=79.5s\n",
-            "  optuna trial 4/15 params={'model__learning_rate': 0.018840552955192824, 'model__num_leaves': 37, 'model__max_depth': -1, 'model__min_child_samples': 276, 'model__subsample': 0.7405729935600059, 'model__colsample_bytree': 0.8481350279592919, 'model__reg_lambda': 0.39193516226890834, 'model__reg_alpha': 0.012030178871154668}\n",
-            "    fold 1/5 PR-AUC=0.02465 Recall=0.17647 F1=0.05607 elapsed=58.5s\n",
-            "    fold 2/5 PR-AUC=0.04893 Recall=0.38235 F1=0.05068 elapsed=59.0s\n",
-            "    fold 3/5 PR-AUC=0.04120 Recall=0.20588 F1=0.15909 elapsed=59.7s\n",
-            "    fold 4/5 PR-AUC=0.01779 Recall=0.35294 F1=0.03941 elapsed=58.0s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:20:50,337] Trial 3 finished with value: 0.06894370517883613 and parameters: {'learning_rate': 0.018840552955192824, 'num_leaves': 37, 'max_depth': -1, 'min_child_samples': 276, 'subsample': 0.7405729935600059, 'colsample_bytree': 0.8481350279592919, 'reg_lambda': 0.39193516226890834, 'reg_alpha': 0.012030178871154668}. Best is trial 3 with value: 0.06894370517883613.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.13862 Recall=0.35294 F1=0.09231 elapsed=57.1s\n",
-            "  optuna trial 5/15 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
-            "    fold 1/5 PR-AUC=0.03658 Recall=0.08824 F1=0.10345 elapsed=56.6s\n",
-            "    fold 2/5 PR-AUC=0.04709 Recall=0.35294 F1=0.08247 elapsed=54.5s\n",
-            "    fold 3/5 PR-AUC=0.05326 Recall=0.23529 F1=0.16162 elapsed=50.2s\n",
-            "    fold 4/5 PR-AUC=0.02418 Recall=0.23529 F1=0.05926 elapsed=53.0s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:25:17,267] Trial 4 finished with value: 0.07319973398830097 and parameters: {'learning_rate': 0.031169409812378812, 'num_leaves': 49, 'max_depth': -1, 'min_child_samples': 279, 'subsample': 0.6809723757181718, 'colsample_bytree': 0.6381922880886154, 'reg_lambda': 0.12191905861905929, 'reg_alpha': 0.0020013420622879987}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.14607 Recall=0.26471 F1=0.21176 elapsed=52.6s\n",
-            "  optuna trial 6/15 params={'model__learning_rate': 0.022439365289703053, 'model__num_leaves': 61, 'model__max_depth': -1, 'model__min_child_samples': 247, 'model__subsample': 0.6760927252879199, 'model__colsample_bytree': 0.9940991214702328, 'model__reg_lambda': 2.94884053630599, 'model__reg_alpha': 0.0006235377135673159}\n",
-            "    fold 1/5 PR-AUC=0.02457 Recall=0.05882 F1=0.10000 elapsed=66.4s\n",
-            "    fold 2/5 PR-AUC=0.04620 Recall=0.38235 F1=0.04019 elapsed=64.8s\n",
-            "    fold 3/5 PR-AUC=0.03686 Recall=0.23529 F1=0.13445 elapsed=65.1s\n",
-            "    fold 4/5 PR-AUC=0.02458 Recall=0.20588 F1=0.07000 elapsed=64.7s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:30:43,306] Trial 5 finished with value: 0.06304171041876949 and parameters: {'learning_rate': 0.022439365289703053, 'num_leaves': 61, 'max_depth': -1, 'min_child_samples': 247, 'subsample': 0.6760927252879199, 'colsample_bytree': 0.9940991214702328, 'reg_lambda': 2.94884053630599, 'reg_alpha': 0.0006235377135673159}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.12711 Recall=0.23529 F1=0.20253 elapsed=65.0s\n",
-            "  optuna trial 7/15 params={'model__learning_rate': 0.01011549101545285, 'model__num_leaves': 135, 'model__max_depth': 7, 'model__min_child_samples': 61, 'model__subsample': 0.9520861990564577, 'model__colsample_bytree': 0.8304841570724011, 'model__reg_lambda': 0.4263131389740422, 'model__reg_alpha': 0.00017956984225677631}\n",
-            "    fold 1/5 PR-AUC=0.02317 Recall=0.08824 F1=0.07317 elapsed=56.3s\n",
-            "    fold 2/5 PR-AUC=0.04950 Recall=0.26471 F1=0.08000 elapsed=56.3s\n",
-            "    fold 3/5 PR-AUC=0.02767 Recall=0.14706 F1=0.14085 elapsed=50.9s\n",
-            "    fold 4/5 PR-AUC=0.01344 Recall=0.23529 F1=0.04598 elapsed=51.6s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:35:10,406] Trial 6 finished with value: 0.04217109041808953 and parameters: {'learning_rate': 0.01011549101545285, 'num_leaves': 135, 'max_depth': 7, 'min_child_samples': 61, 'subsample': 0.9520861990564577, 'colsample_bytree': 0.8304841570724011, 'reg_lambda': 0.4263131389740422, 'reg_alpha': 0.00017956984225677631}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.04267 Recall=0.35294 F1=0.05085 elapsed=52.1s\n",
-            "  optuna trial 8/15 params={'model__learning_rate': 0.0190917184400185, 'model__num_leaves': 68, 'model__max_depth': 7, 'model__min_child_samples': 223, 'model__subsample': 0.9162747670159141, 'model__colsample_bytree': 0.8025747389062734, 'model__reg_lambda': 2.9323777832216886, 'model__reg_alpha': 0.009444574254983563}\n",
-            "    fold 1/5 PR-AUC=0.01607 Recall=0.17647 F1=0.03261 elapsed=36.0s\n",
-            "    fold 2/5 PR-AUC=0.03486 Recall=0.38235 F1=0.03922 elapsed=34.4s\n",
-            "    fold 3/5 PR-AUC=0.02913 Recall=0.23529 F1=0.12030 elapsed=36.2s\n",
-            "    fold 4/5 PR-AUC=0.01392 Recall=0.14706 F1=0.05181 elapsed=33.5s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:38:04,664] Trial 7 finished with value: 0.05305343371432367 and parameters: {'learning_rate': 0.0190917184400185, 'num_leaves': 68, 'max_depth': 7, 'min_child_samples': 223, 'subsample': 0.9162747670159141, 'colsample_bytree': 0.8025747389062734, 'reg_lambda': 2.9323777832216886, 'reg_alpha': 0.009444574254983563}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.10658 Recall=0.35294 F1=0.04301 elapsed=34.2s\n",
-            "  optuna trial 9/15 params={'model__learning_rate': 0.029653419677342325, 'model__num_leaves': 82, 'model__max_depth': 9, 'model__min_child_samples': 167, 'model__subsample': 0.9676482658741326, 'model__colsample_bytree': 0.6621815031169938, 'model__reg_lambda': 0.6039425546072186, 'model__reg_alpha': 0.10524574681335624}\n",
-            "    fold 1/5 PR-AUC=0.03325 Recall=0.11765 F1=0.05556 elapsed=40.7s\n",
-            "    fold 2/5 PR-AUC=0.03970 Recall=0.23529 F1=0.09524 elapsed=41.2s\n",
-            "    fold 3/5 PR-AUC=0.03826 Recall=0.23529 F1=0.15385 elapsed=42.5s\n",
-            "    fold 4/5 PR-AUC=0.01437 Recall=0.26471 F1=0.03805 elapsed=37.8s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:41:27,137] Trial 8 finished with value: 0.060833017107763024 and parameters: {'learning_rate': 0.029653419677342325, 'num_leaves': 82, 'max_depth': 9, 'min_child_samples': 167, 'subsample': 0.9676482658741326, 'colsample_bytree': 0.6621815031169938, 'reg_lambda': 0.6039425546072186, 'reg_alpha': 0.10524574681335624}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.11828 Recall=0.35294 F1=0.09160 elapsed=40.3s\n",
-            "  optuna trial 10/15 params={'model__learning_rate': 0.016092567235133776, 'model__num_leaves': 34, 'model__max_depth': 7, 'model__min_child_samples': 266, 'model__subsample': 0.9312852269146901, 'model__colsample_bytree': 0.6339565264987161, 'model__reg_lambda': 4.995973117313505, 'model__reg_alpha': 0.014367095138664226}\n",
-            "    fold 1/5 PR-AUC=0.01984 Recall=0.14706 F1=0.04016 elapsed=30.5s\n",
-            "    fold 2/5 PR-AUC=0.02887 Recall=0.35294 F1=0.04969 elapsed=29.4s\n",
-            "    fold 3/5 PR-AUC=0.03842 Recall=0.17647 F1=0.16438 elapsed=30.6s\n",
-            "    fold 4/5 PR-AUC=0.01492 Recall=0.14706 F1=0.05495 elapsed=29.2s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:43:58,707] Trial 9 finished with value: 0.0581151434945654 and parameters: {'learning_rate': 0.016092567235133776, 'num_leaves': 34, 'max_depth': 7, 'min_child_samples': 266, 'subsample': 0.9312852269146901, 'colsample_bytree': 0.6339565264987161, 'reg_lambda': 4.995973117313505, 'reg_alpha': 0.014367095138664226}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.12971 Recall=0.35294 F1=0.04829 elapsed=31.8s\n",
-            "  optuna trial 11/15 params={'model__learning_rate': 0.07344753762582334, 'model__num_leaves': 100, 'model__max_depth': 5, 'model__min_child_samples': 179, 'model__subsample': 0.7804393429857105, 'model__colsample_bytree': 0.7211318321133148, 'model__reg_lambda': 0.10707258976378216, 'model__reg_alpha': 0.00011815152243566209}\n",
-            "    fold 1/5 PR-AUC=0.02487 Recall=0.05882 F1=0.09524 elapsed=22.2s\n",
-            "    fold 2/5 PR-AUC=0.03263 Recall=0.17647 F1=0.06522 elapsed=22.8s\n",
-            "    fold 3/5 PR-AUC=0.02345 Recall=0.20588 F1=0.06306 elapsed=21.8s\n",
-            "    fold 4/5 PR-AUC=0.00869 Recall=0.14706 F1=0.03802 elapsed=25.2s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:45:53,501] Trial 10 finished with value: 0.039906002434268506 and parameters: {'learning_rate': 0.07344753762582334, 'num_leaves': 100, 'max_depth': 5, 'min_child_samples': 179, 'subsample': 0.7804393429857105, 'colsample_bytree': 0.7211318321133148, 'reg_lambda': 0.10707258976378216, 'reg_alpha': 0.00011815152243566209}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.07460 Recall=0.11765 F1=0.13333 elapsed=22.7s\n",
-            "  optuna trial 12/15 params={'model__learning_rate': 0.03828385438925741, 'model__num_leaves': 24, 'model__max_depth': -1, 'model__min_child_samples': 290, 'model__subsample': 0.7500490079557848, 'model__colsample_bytree': 0.7430730526600867, 'model__reg_lambda': 0.26877401109804494, 'model__reg_alpha': 0.0018756206282287391}\n",
-            "    fold 1/5 PR-AUC=0.03004 Recall=0.11765 F1=0.04790 elapsed=42.9s\n",
-            "    fold 2/5 PR-AUC=0.04640 Recall=0.17647 F1=0.09449 elapsed=43.4s\n",
-            "    fold 3/5 PR-AUC=0.03590 Recall=0.20588 F1=0.14433 elapsed=43.8s\n",
-            "    fold 4/5 PR-AUC=0.01742 Recall=0.35294 F1=0.04444 elapsed=42.8s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:49:29,067] Trial 11 finished with value: 0.06729149493282813 and parameters: {'learning_rate': 0.03828385438925741, 'num_leaves': 24, 'max_depth': -1, 'min_child_samples': 290, 'subsample': 0.7500490079557848, 'colsample_bytree': 0.7430730526600867, 'reg_lambda': 0.26877401109804494, 'reg_alpha': 0.0018756206282287391}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.14640 Recall=0.35294 F1=0.06742 elapsed=42.6s\n",
-            "  optuna trial 13/15 params={'model__learning_rate': 0.0128289748714244, 'model__num_leaves': 45, 'model__max_depth': -1, 'model__min_child_samples': 205, 'model__subsample': 0.7368227996831814, 'model__colsample_bytree': 0.5575289958995627, 'model__reg_lambda': 0.7798089384438257, 'model__reg_alpha': 0.02173049208731439}\n",
-            "    fold 1/5 PR-AUC=0.02159 Recall=0.08824 F1=0.09375 elapsed=49.2s\n",
-            "    fold 2/5 PR-AUC=0.04449 Recall=0.41176 F1=0.04230 elapsed=48.5s\n",
-            "    fold 3/5 PR-AUC=0.05169 Recall=0.23529 F1=0.16327 elapsed=50.1s\n",
-            "    fold 4/5 PR-AUC=0.01912 Recall=0.35294 F1=0.04928 elapsed=49.4s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:53:35,467] Trial 12 finished with value: 0.06395427302355366 and parameters: {'learning_rate': 0.0128289748714244, 'num_leaves': 45, 'max_depth': -1, 'min_child_samples': 205, 'subsample': 0.7368227996831814, 'colsample_bytree': 0.5575289958995627, 'reg_lambda': 0.7798089384438257, 'reg_alpha': 0.02173049208731439}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.11083 Recall=0.35294 F1=0.05660 elapsed=49.3s\n",
-            "  optuna trial 14/15 params={'model__learning_rate': 0.03688230536757648, 'model__num_leaves': 84, 'model__max_depth': -1, 'model__min_child_samples': 299, 'model__subsample': 0.7156364210016786, 'model__colsample_bytree': 0.8810259041893101, 'model__reg_lambda': 0.199522384883775, 'model__reg_alpha': 0.0024334796925402718}\n",
-            "    fold 1/5 PR-AUC=0.04648 Recall=0.17647 F1=0.05854 elapsed=63.5s\n",
-            "    fold 2/5 PR-AUC=0.03300 Recall=0.35294 F1=0.06818 elapsed=63.2s\n",
-            "    fold 3/5 PR-AUC=0.03487 Recall=0.20588 F1=0.09459 elapsed=62.5s\n",
-            "    fold 4/5 PR-AUC=0.02476 Recall=0.23529 F1=0.06349 elapsed=60.1s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 01:58:50,528] Trial 13 finished with value: 0.07042822156591197 and parameters: {'learning_rate': 0.03688230536757648, 'num_leaves': 84, 'max_depth': -1, 'min_child_samples': 299, 'subsample': 0.7156364210016786, 'colsample_bytree': 0.8810259041893101, 'reg_lambda': 0.199522384883775, 'reg_alpha': 0.0024334796925402718}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.15126 Recall=0.26471 F1=0.16216 elapsed=65.7s\n",
-            "  optuna trial 15/15 params={'model__learning_rate': 0.04187329932090051, 'model__num_leaves': 98, 'model__max_depth': 5, 'model__min_child_samples': 300, 'model__subsample': 0.654599110501233, 'model__colsample_bytree': 0.9091999481071245, 'model__reg_lambda': 0.1837271427904195, 'model__reg_alpha': 0.0009412519611659042}\n",
-            "    fold 1/5 PR-AUC=0.01758 Recall=0.05882 F1=0.08000 elapsed=26.5s\n",
-            "    fold 2/5 PR-AUC=0.03039 Recall=0.35294 F1=0.05042 elapsed=23.1s\n",
-            "    fold 3/5 PR-AUC=0.02245 Recall=0.20588 F1=0.08235 elapsed=26.7s\n",
-            "    fold 4/5 PR-AUC=0.01160 Recall=0.20588 F1=0.03743 elapsed=30.8s\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[I 2026-06-18 02:01:05,723] Trial 14 finished with value: 0.04538489857292094 and parameters: {'learning_rate': 0.04187329932090051, 'num_leaves': 98, 'max_depth': 5, 'min_child_samples': 300, 'subsample': 0.654599110501233, 'colsample_bytree': 0.9091999481071245, 'reg_lambda': 0.1837271427904195, 'reg_alpha': 0.0009412519611659042}. Best is trial 4 with value: 0.07319973398830097.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    fold 5/5 PR-AUC=0.09784 Recall=0.11765 F1=0.18605 elapsed=28.1s\n",
-            "[LightGBM] Optuna best value=0.07320 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
-            "[LightGBM] CV done elapsed=3614.0s\n",
-            "\n",
-            "[RandomForest] CV start\n",
-            "[RandomForest] random search trials: 15\n",
-            "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.01770 Recall=0.14706 F1=0.09615 elapsed=18.6s\n",
-            "    fold 2/5 PR-AUC=0.03344 Recall=0.17647 F1=0.07500 elapsed=18.4s\n",
-            "    fold 3/5 PR-AUC=0.03194 Recall=0.35294 F1=0.04364 elapsed=21.1s\n",
-            "    fold 4/5 PR-AUC=0.01529 Recall=0.32353 F1=0.03520 elapsed=18.1s\n",
-            "    fold 5/5 PR-AUC=0.06988 Recall=0.38235 F1=0.05029 elapsed=18.1s\n",
-            "  -> RandomForest trial 1 mean PR-AUC=0.03365 Recall=0.27647 F1=0.06006 FPR=0.05830\n",
-            "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.01163 Recall=0.08824 F1=0.07407 elapsed=114.9s\n",
-            "    fold 2/5 PR-AUC=0.02605 Recall=0.26471 F1=0.07860 elapsed=120.6s\n",
-            "    fold 3/5 PR-AUC=0.02343 Recall=0.20588 F1=0.08805 elapsed=112.0s\n",
-            "    fold 4/5 PR-AUC=0.00923 Recall=0.14706 F1=0.02817 elapsed=116.7s\n",
-            "    fold 5/5 PR-AUC=0.02658 Recall=0.35294 F1=0.04096 elapsed=128.2s\n",
-            "  -> RandomForest trial 2 mean PR-AUC=0.01938 Recall=0.21176 F1=0.06197 FPR=0.04036\n",
-            "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.01449 Recall=0.11765 F1=0.07547 elapsed=20.0s\n",
-            "    fold 2/5 PR-AUC=0.01800 Recall=0.17647 F1=0.07143 elapsed=18.6s\n",
-            "    fold 3/5 PR-AUC=0.02582 Recall=0.35294 F1=0.04082 elapsed=19.0s\n",
-            "    fold 4/5 PR-AUC=0.01543 Recall=0.05882 F1=0.08163 elapsed=19.4s\n",
-            "    fold 5/5 PR-AUC=0.04959 Recall=0.38235 F1=0.04815 elapsed=20.2s\n",
-            "  -> RandomForest trial 3 mean PR-AUC=0.02467 Recall=0.21765 F1=0.06350 FPR=0.04170\n",
-            "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.01905 Recall=0.11765 F1=0.12121 elapsed=110.6s\n",
-            "    fold 2/5 PR-AUC=0.02432 Recall=0.35294 F1=0.03865 elapsed=112.2s\n",
-            "    fold 3/5 PR-AUC=0.03628 Recall=0.17647 F1=0.13636 elapsed=106.3s\n",
-            "    fold 4/5 PR-AUC=0.02079 Recall=0.08824 F1=0.05660 elapsed=110.0s\n",
-            "    fold 5/5 PR-AUC=0.04705 Recall=0.26471 F1=0.11465 elapsed=112.8s\n",
-            "  -> RandomForest trial 4 mean PR-AUC=0.02950 Recall=0.20000 F1=0.09350 FPR=0.02796\n",
-            "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.01373 Recall=0.08824 F1=0.05505 elapsed=19.7s\n",
-            "    fold 2/5 PR-AUC=0.01550 Recall=0.14706 F1=0.04587 elapsed=18.3s\n",
-            "    fold 3/5 PR-AUC=0.01997 Recall=0.20588 F1=0.06452 elapsed=18.3s\n",
-            "    fold 4/5 PR-AUC=0.00914 Recall=0.50000 F1=0.02353 elapsed=18.2s\n",
-            "    fold 5/5 PR-AUC=0.02526 Recall=0.17647 F1=0.05455 elapsed=18.7s\n",
-            "  -> RandomForest trial 5 mean PR-AUC=0.01672 Recall=0.22353 F1=0.04870 FPR=0.06708\n",
-            "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.02763 Recall=0.14706 F1=0.11765 elapsed=20.0s\n",
-            "    fold 2/5 PR-AUC=0.03999 Recall=0.35294 F1=0.04225 elapsed=19.2s\n",
-            "    fold 3/5 PR-AUC=0.03442 Recall=0.38235 F1=0.04370 elapsed=19.1s\n",
-            "    fold 4/5 PR-AUC=0.01981 Recall=0.05882 F1=0.09302 elapsed=20.5s\n",
-            "    fold 5/5 PR-AUC=0.09888 Recall=0.35294 F1=0.04453 elapsed=19.6s\n",
-            "  -> RandomForest trial 6 mean PR-AUC=0.04415 Recall=0.25882 F1=0.06823 FPR=0.05417\n",
-            "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
-            "    fold 1/5 PR-AUC=0.01178 Recall=0.17647 F1=0.04762 elapsed=18.0s\n",
-            "    fold 2/5 PR-AUC=0.01400 Recall=0.20588 F1=0.05128 elapsed=18.1s\n",
-            "    fold 3/5 PR-AUC=0.01885 Recall=0.08824 F1=0.09677 elapsed=17.9s\n",
-            "    fold 4/5 PR-AUC=0.00916 Recall=0.14706 F1=0.02611 elapsed=18.6s\n",
-            "    fold 5/5 PR-AUC=0.01926 Recall=0.35294 F1=0.04278 elapsed=18.0s\n",
-            "  -> RandomForest trial 7 mean PR-AUC=0.01461 Recall=0.19412 F1=0.05291 FPR=0.04452\n",
-            "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
-            "    fold 1/5 PR-AUC=0.01255 Recall=0.17647 F1=0.04651 elapsed=95.6s\n",
-            "    fold 2/5 PR-AUC=0.01555 Recall=0.17647 F1=0.06593 elapsed=98.3s\n",
-            "    fold 3/5 PR-AUC=0.03233 Recall=0.14706 F1=0.11111 elapsed=99.4s\n",
-            "    fold 4/5 PR-AUC=0.01287 Recall=0.38235 F1=0.02835 elapsed=97.5s\n",
-            "    fold 5/5 PR-AUC=0.02747 Recall=0.14706 F1=0.10417 elapsed=94.9s\n",
-            "  -> RandomForest trial 8 mean PR-AUC=0.02016 Recall=0.20588 F1=0.07122 FPR=0.04485\n",
-            "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.01133 Recall=0.17647 F1=0.05333 elapsed=95.1s\n",
-            "    fold 2/5 PR-AUC=0.01219 Recall=0.14706 F1=0.05376 elapsed=93.9s\n",
-            "    fold 3/5 PR-AUC=0.01700 Recall=0.14706 F1=0.04149 elapsed=95.6s\n",
-            "    fold 4/5 PR-AUC=0.01059 Recall=0.35294 F1=0.02934 elapsed=94.4s\n",
-            "    fold 5/5 PR-AUC=0.01855 Recall=0.11765 F1=0.06897 elapsed=90.9s\n",
-            "  -> RandomForest trial 9 mean PR-AUC=0.01393 Recall=0.18824 F1=0.04938 FPR=0.04640\n",
-            "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.01972 Recall=0.17647 F1=0.05797 elapsed=18.8s\n",
-            "    fold 2/5 PR-AUC=0.01501 Recall=0.14706 F1=0.05051 elapsed=18.4s\n",
-            "    fold 3/5 PR-AUC=0.02479 Recall=0.41176 F1=0.03774 elapsed=18.5s\n",
-            "    fold 4/5 PR-AUC=0.01120 Recall=0.52941 F1=0.02497 elapsed=18.5s\n",
-            "    fold 5/5 PR-AUC=0.03002 Recall=0.17647 F1=0.09449 elapsed=19.2s\n",
-            "  -> RandomForest trial 10 mean PR-AUC=0.02015 Recall=0.28824 F1=0.05313 FPR=0.08371\n",
-            "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.01038 Recall=0.17647 F1=0.03125 elapsed=130.8s\n",
-            "    fold 2/5 PR-AUC=0.01130 Recall=0.17647 F1=0.04181 elapsed=131.1s\n",
-            "    fold 3/5 PR-AUC=0.02148 Recall=0.08824 F1=0.07595 elapsed=129.1s\n",
-            "    fold 4/5 PR-AUC=0.00999 Recall=0.35294 F1=0.02564 elapsed=130.5s\n",
-            "    fold 5/5 PR-AUC=0.01895 Recall=0.11765 F1=0.06780 elapsed=126.2s\n",
-            "  -> RandomForest trial 11 mean PR-AUC=0.01442 Recall=0.18235 F1=0.04849 FPR=0.05374\n",
-            "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.01582 Recall=0.14706 F1=0.08000 elapsed=16.5s\n",
-            "    fold 2/5 PR-AUC=0.01492 Recall=0.14706 F1=0.06452 elapsed=16.5s\n",
-            "    fold 3/5 PR-AUC=0.02683 Recall=0.20588 F1=0.06897 elapsed=16.7s\n",
-            "    fold 4/5 PR-AUC=0.01325 Recall=0.05882 F1=0.06667 elapsed=16.5s\n",
-            "    fold 5/5 PR-AUC=0.04138 Recall=0.35294 F1=0.05042 elapsed=16.5s\n",
-            "  -> RandomForest trial 12 mean PR-AUC=0.02244 Recall=0.18235 F1=0.06611 FPR=0.02742\n",
-            "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.01129 Recall=0.17647 F1=0.03647 elapsed=91.7s\n",
-            "    fold 2/5 PR-AUC=0.01165 Recall=0.26471 F1=0.03854 elapsed=93.2s\n",
-            "    fold 3/5 PR-AUC=0.02405 Recall=0.14706 F1=0.06897 elapsed=92.9s\n",
-            "    fold 4/5 PR-AUC=0.01140 Recall=0.50000 F1=0.02801 elapsed=92.1s\n",
-            "    fold 5/5 PR-AUC=0.01838 Recall=0.11765 F1=0.07843 elapsed=89.3s\n",
-            "  -> RandomForest trial 13 mean PR-AUC=0.01535 Recall=0.24118 F1=0.05008 FPR=0.06859\n",
-            "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.02950 Recall=0.14706 F1=0.15385 elapsed=18.1s\n",
-            "    fold 2/5 PR-AUC=0.04820 Recall=0.35294 F1=0.07717 elapsed=18.3s\n",
-            "    fold 3/5 PR-AUC=0.05079 Recall=0.35294 F1=0.03822 elapsed=18.1s\n",
-            "    fold 4/5 PR-AUC=0.02223 Recall=0.08824 F1=0.07317 elapsed=19.1s\n",
-            "    fold 5/5 PR-AUC=0.10251 Recall=0.35294 F1=0.08602 elapsed=18.7s\n",
-            "  -> RandomForest trial 14 mean PR-AUC=0.05065 Recall=0.25882 F1=0.08569 FPR=0.03859\n",
-            "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.01118 Recall=0.14706 F1=0.03788 elapsed=137.0s\n",
-            "    fold 2/5 PR-AUC=0.01503 Recall=0.17647 F1=0.05941 elapsed=141.8s\n",
-            "    fold 3/5 PR-AUC=0.01931 Recall=0.08824 F1=0.07895 elapsed=142.5s\n",
-            "    fold 4/5 PR-AUC=0.01054 Recall=0.35294 F1=0.02804 elapsed=142.2s\n",
-            "    fold 5/5 PR-AUC=0.01703 Recall=0.11765 F1=0.05970 elapsed=140.4s\n",
-            "  -> RandomForest trial 15 mean PR-AUC=0.01462 Recall=0.17647 F1=0.05279 FPR=0.04465\n",
-            "[RandomForest] CV done elapsed=4652.7s\n",
-            "\n",
-            "[ExtraTrees] CV start\n",
-            "[ExtraTrees] random search trials: 15\n",
-            "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.05335 Recall=0.17647 F1=0.22222 elapsed=14.9s\n",
-            "    fold 2/5 PR-AUC=0.05171 Recall=0.38235 F1=0.04075 elapsed=14.9s\n",
-            "    fold 3/5 PR-AUC=0.03424 Recall=0.20588 F1=0.06481 elapsed=15.0s\n",
-            "    fold 4/5 PR-AUC=0.02532 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
-            "    fold 5/5 PR-AUC=0.10997 Recall=0.35294 F1=0.06557 elapsed=14.9s\n",
-            "  -> ExtraTrees trial 1 mean PR-AUC=0.05492 Recall=0.23529 F1=0.09867 FPR=0.03701\n",
-            "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.03290 Recall=0.11765 F1=0.16667 elapsed=80.4s\n",
-            "    fold 2/5 PR-AUC=0.06744 Recall=0.41176 F1=0.04811 elapsed=83.0s\n",
-            "    fold 3/5 PR-AUC=0.03351 Recall=0.20588 F1=0.08696 elapsed=80.7s\n",
-            "    fold 4/5 PR-AUC=0.02096 Recall=0.08824 F1=0.07895 elapsed=84.2s\n",
-            "    fold 5/5 PR-AUC=0.07834 Recall=0.20588 F1=0.18421 elapsed=82.4s\n",
-            "  -> ExtraTrees trial 2 mean PR-AUC=0.04663 Recall=0.20588 F1=0.11298 FPR=0.02474\n",
-            "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.04880 Recall=0.17647 F1=0.15584 elapsed=15.0s\n",
-            "    fold 2/5 PR-AUC=0.04067 Recall=0.20588 F1=0.06061 elapsed=14.9s\n",
-            "    fold 3/5 PR-AUC=0.03292 Recall=0.26471 F1=0.06818 elapsed=14.9s\n",
-            "    fold 4/5 PR-AUC=0.02491 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
-            "    fold 5/5 PR-AUC=0.12176 Recall=0.35294 F1=0.08000 elapsed=15.1s\n",
-            "  -> ExtraTrees trial 3 mean PR-AUC=0.05381 Recall=0.21176 F1=0.09293 FPR=0.02367\n",
-            "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.04500 Recall=0.14706 F1=0.19231 elapsed=77.3s\n",
-            "    fold 2/5 PR-AUC=0.08313 Recall=0.35294 F1=0.05000 elapsed=81.5s\n",
-            "    fold 3/5 PR-AUC=0.05375 Recall=0.20588 F1=0.16867 elapsed=78.0s\n",
-            "    fold 4/5 PR-AUC=0.03255 Recall=0.08824 F1=0.09524 elapsed=79.6s\n",
-            "    fold 5/5 PR-AUC=0.16604 Recall=0.35294 F1=0.08362 elapsed=78.5s\n",
-            "  -> ExtraTrees trial 4 mean PR-AUC=0.07609 Recall=0.22941 F1=0.11797 FPR=0.02534\n",
-            "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.03990 Recall=0.17647 F1=0.07947 elapsed=15.1s\n",
-            "    fold 2/5 PR-AUC=0.03735 Recall=0.26471 F1=0.05422 elapsed=15.6s\n",
-            "    fold 3/5 PR-AUC=0.01885 Recall=0.14706 F1=0.04000 elapsed=15.8s\n",
-            "    fold 4/5 PR-AUC=0.02381 Recall=0.05882 F1=0.09756 elapsed=15.2s\n",
-            "    fold 5/5 PR-AUC=0.07181 Recall=0.23529 F1=0.05112 elapsed=14.9s\n",
-            "  -> ExtraTrees trial 5 mean PR-AUC=0.03834 Recall=0.17647 F1=0.06447 FPR=0.02974\n",
-            "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.05098 Recall=0.17647 F1=0.19355 elapsed=15.8s\n",
-            "    fold 2/5 PR-AUC=0.04937 Recall=0.35294 F1=0.05085 elapsed=16.2s\n",
-            "    fold 3/5 PR-AUC=0.03076 Recall=0.14706 F1=0.12346 elapsed=15.8s\n",
-            "    fold 4/5 PR-AUC=0.02669 Recall=0.17647 F1=0.05479 elapsed=16.0s\n",
-            "    fold 5/5 PR-AUC=0.14600 Recall=0.38235 F1=0.04075 elapsed=16.1s\n",
-            "  -> ExtraTrees trial 6 mean PR-AUC=0.06076 Recall=0.24706 F1=0.09268 FPR=0.04224\n",
-            "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
-            "    fold 1/5 PR-AUC=0.02945 Recall=0.17647 F1=0.07229 elapsed=13.7s\n",
-            "    fold 2/5 PR-AUC=0.03056 Recall=0.35294 F1=0.03791 elapsed=13.3s\n",
-            "    fold 3/5 PR-AUC=0.01740 Recall=0.11765 F1=0.04762 elapsed=13.3s\n",
-            "    fold 4/5 PR-AUC=0.01828 Recall=0.05882 F1=0.08163 elapsed=13.5s\n",
-            "    fold 5/5 PR-AUC=0.02846 Recall=0.14706 F1=0.04950 elapsed=13.7s\n",
-            "  -> ExtraTrees trial 7 mean PR-AUC=0.02483 Recall=0.17059 F1=0.05779 FPR=0.03416\n",
-            "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
-            "    fold 1/5 PR-AUC=0.05038 Recall=0.20588 F1=0.16092 elapsed=67.6s\n",
-            "    fold 2/5 PR-AUC=0.07342 Recall=0.35294 F1=0.05517 elapsed=66.9s\n",
-            "    fold 3/5 PR-AUC=0.04289 Recall=0.08824 F1=0.12766 elapsed=66.9s\n",
-            "    fold 4/5 PR-AUC=0.03120 Recall=0.23529 F1=0.04278 elapsed=66.2s\n",
-            "    fold 5/5 PR-AUC=0.16042 Recall=0.35294 F1=0.04293 elapsed=64.2s\n",
-            "  -> ExtraTrees trial 8 mean PR-AUC=0.07166 Recall=0.24706 F1=0.08589 FPR=0.04325\n",
-            "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.02831 Recall=0.11765 F1=0.12121 elapsed=58.9s\n",
-            "    fold 2/5 PR-AUC=0.05126 Recall=0.35294 F1=0.03256 elapsed=59.2s\n",
-            "    fold 3/5 PR-AUC=0.02743 Recall=0.08824 F1=0.08108 elapsed=59.7s\n",
-            "    fold 4/5 PR-AUC=0.02199 Recall=0.08824 F1=0.08696 elapsed=55.8s\n",
-            "    fold 5/5 PR-AUC=0.09582 Recall=0.11765 F1=0.12903 elapsed=56.5s\n",
-            "  -> ExtraTrees trial 9 mean PR-AUC=0.04496 Recall=0.15294 F1=0.09017 FPR=0.02722\n",
-            "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.04550 Recall=0.23529 F1=0.11511 elapsed=15.0s\n",
-            "    fold 2/5 PR-AUC=0.03161 Recall=0.14706 F1=0.06623 elapsed=15.1s\n",
-            "    fold 3/5 PR-AUC=0.02638 Recall=0.17647 F1=0.08633 elapsed=15.2s\n",
-            "    fold 4/5 PR-AUC=0.02500 Recall=0.05882 F1=0.10000 elapsed=15.1s\n",
-            "    fold 5/5 PR-AUC=0.09072 Recall=0.35294 F1=0.04420 elapsed=15.0s\n",
-            "  -> ExtraTrees trial 10 mean PR-AUC=0.04384 Recall=0.19412 F1=0.08237 FPR=0.02712\n",
-            "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.03474 Recall=0.14706 F1=0.15625 elapsed=79.8s\n",
-            "    fold 2/5 PR-AUC=0.04950 Recall=0.11765 F1=0.05517 elapsed=80.9s\n",
-            "    fold 3/5 PR-AUC=0.02492 Recall=0.08824 F1=0.11321 elapsed=80.1s\n",
-            "    fold 4/5 PR-AUC=0.02257 Recall=0.05882 F1=0.09524 elapsed=79.1s\n",
-            "    fold 5/5 PR-AUC=0.09276 Recall=0.08824 F1=0.15789 elapsed=77.3s\n",
-            "  -> ExtraTrees trial 11 mean PR-AUC=0.04490 Recall=0.10000 F1=0.11555 FPR=0.00520\n",
-            "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.04890 Recall=0.17647 F1=0.15789 elapsed=12.5s\n",
-            "    fold 2/5 PR-AUC=0.06466 Recall=0.17647 F1=0.08000 elapsed=12.5s\n",
-            "    fold 3/5 PR-AUC=0.03919 Recall=0.14706 F1=0.05291 elapsed=11.6s\n",
-            "    fold 4/5 PR-AUC=0.02632 Recall=0.05882 F1=0.10000 elapsed=10.9s\n",
-            "    fold 5/5 PR-AUC=0.12891 Recall=0.14706 F1=0.23256 elapsed=12.2s\n",
-            "  -> ExtraTrees trial 12 mean PR-AUC=0.06160 Recall=0.14118 F1=0.12467 FPR=0.01019\n",
-            "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-            "    fold 1/5 PR-AUC=0.04456 Recall=0.17647 F1=0.13043 elapsed=58.2s\n",
-            "    fold 2/5 PR-AUC=0.04290 Recall=0.35294 F1=0.03871 elapsed=58.5s\n",
-            "    fold 3/5 PR-AUC=0.03700 Recall=0.08824 F1=0.10526 elapsed=59.6s\n",
-            "    fold 4/5 PR-AUC=0.02478 Recall=0.08824 F1=0.05556 elapsed=58.3s\n",
-            "    fold 5/5 PR-AUC=0.13122 Recall=0.38235 F1=0.03969 elapsed=56.2s\n",
-            "  -> ExtraTrees trial 13 mean PR-AUC=0.05609 Recall=0.21765 F1=0.07393 FPR=0.04442\n",
-            "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-            "    fold 1/5 PR-AUC=0.05247 Recall=0.17647 F1=0.21818 elapsed=15.4s\n",
-            "    fold 2/5 PR-AUC=0.05497 Recall=0.23529 F1=0.13445 elapsed=15.3s\n",
-            "    fold 3/5 PR-AUC=0.03882 Recall=0.26471 F1=0.08491 elapsed=15.6s\n",
-            "    fold 4/5 PR-AUC=0.02642 Recall=0.29412 F1=0.03711 elapsed=15.4s\n",
-            "    fold 5/5 PR-AUC=0.12650 Recall=0.38235 F1=0.04702 elapsed=15.8s\n",
-            "  -> ExtraTrees trial 14 mean PR-AUC=0.05983 Recall=0.27059 F1=0.10433 FPR=0.04231\n",
-            "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
-            "    fold 1/5 PR-AUC=0.02622 Recall=0.14706 F1=0.11628 elapsed=100.9s\n",
-            "    fold 2/5 PR-AUC=0.03921 Recall=0.41176 F1=0.04046 elapsed=103.7s\n",
-            "    fold 3/5 PR-AUC=0.02245 Recall=0.17647 F1=0.05660 elapsed=98.1s\n",
-            "    fold 4/5 PR-AUC=0.02344 Recall=0.23529 F1=0.03883 elapsed=100.0s\n",
-            "    fold 5/5 PR-AUC=0.07321 Recall=0.11765 F1=0.06957 elapsed=95.6s\n",
-            "  -> ExtraTrees trial 15 mean PR-AUC=0.03691 Recall=0.21765 F1=0.06435 FPR=0.04392\n",
-            "[ExtraTrees] CV done elapsed=3199.4s\n",
-            "\n",
-            "[LogisticRegression] CV start\n",
-            "[LogisticRegression] random search trials: 5\n",
-            "  trial 1/5 params={'model__solver': 'lbfgs', 'model__C': 0.03}\n",
-            "    fold 1/5 PR-AUC=0.03239 Recall=0.11765 F1=0.11940 elapsed=5.0s\n",
-            "    fold 2/5 PR-AUC=0.03936 Recall=0.20588 F1=0.13333 elapsed=6.5s\n",
-            "    fold 3/5 PR-AUC=0.01676 Recall=0.11765 F1=0.05926 elapsed=5.1s\n",
-            "    fold 4/5 PR-AUC=0.04898 Recall=0.14706 F1=0.06944 elapsed=8.0s\n",
-            "    fold 5/5 PR-AUC=0.05055 Recall=0.14706 F1=0.15385 elapsed=5.0s\n",
-            "  -> LogisticRegression trial 1 mean PR-AUC=0.03761 Recall=0.14706 F1=0.10706 FPR=0.01076\n",
-            "  trial 2/5 params={'model__solver': 'lbfgs', 'model__C': 0.1}\n",
-            "    fold 1/5 PR-AUC=0.03312 Recall=0.14706 F1=0.09524 elapsed=7.2s\n",
-            "    fold 2/5 PR-AUC=0.03507 Recall=0.20588 F1=0.14583 elapsed=7.6s\n",
-            "    fold 3/5 PR-AUC=0.01612 Recall=0.11765 F1=0.07207 elapsed=7.6s\n",
-            "    fold 4/5 PR-AUC=0.04945 Recall=0.14706 F1=0.09174 elapsed=8.8s\n",
-            "    fold 5/5 PR-AUC=0.05347 Recall=0.14706 F1=0.15385 elapsed=8.5s\n",
-            "  -> LogisticRegression trial 2 mean PR-AUC=0.03745 Recall=0.15294 F1=0.11175 FPR=0.00972\n",
-            "  trial 3/5 params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
-            "    fold 1/5 PR-AUC=0.03547 Recall=0.14706 F1=0.10204 elapsed=9.1s\n",
-            "    fold 2/5 PR-AUC=0.03449 Recall=0.20588 F1=0.16092 elapsed=11.2s\n",
-            "    fold 3/5 PR-AUC=0.01564 Recall=0.11765 F1=0.07619 elapsed=10.8s\n",
-            "    fold 4/5 PR-AUC=0.04895 Recall=0.11765 F1=0.10000 elapsed=8.9s\n",
-            "    fold 5/5 PR-AUC=0.05718 Recall=0.14706 F1=0.16129 elapsed=10.3s\n",
-            "  -> LogisticRegression trial 3 mean PR-AUC=0.03835 Recall=0.14706 F1=0.12009 FPR=0.00795\n",
-            "  trial 4/5 params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
-            "    fold 1/5 PR-AUC=0.03432 Recall=0.14706 F1=0.11236 elapsed=14.1s\n",
-            "    fold 2/5 PR-AUC=0.03430 Recall=0.20588 F1=0.16279 elapsed=18.1s\n",
-            "    fold 3/5 PR-AUC=0.01534 Recall=0.11765 F1=0.07143 elapsed=14.3s\n",
-            "    fold 4/5 PR-AUC=0.04784 Recall=0.14706 F1=0.07634 elapsed=13.7s\n",
-            "    fold 5/5 PR-AUC=0.05053 Recall=0.14706 F1=0.17544 elapsed=13.5s\n",
-            "  -> LogisticRegression trial 4 mean PR-AUC=0.03647 Recall=0.15294 F1=0.11967 FPR=0.00935\n",
-            "  trial 5/5 params={'model__solver': 'lbfgs', 'model__C': 3.0}\n",
-            "    fold 1/5 PR-AUC=0.02904 Recall=0.14706 F1=0.11236 elapsed=17.6s\n",
-            "    fold 2/5 PR-AUC=0.03423 Recall=0.20588 F1=0.15385 elapsed=18.0s\n",
-            "    fold 3/5 PR-AUC=0.01664 Recall=0.11765 F1=0.07080 elapsed=17.7s\n",
-            "    fold 4/5 PR-AUC=0.04669 Recall=0.14706 F1=0.07407 elapsed=18.0s\n",
-            "    fold 5/5 PR-AUC=0.04529 Recall=0.14706 F1=0.16667 elapsed=18.9s\n",
-            "  -> LogisticRegression trial 5 mean PR-AUC=0.03438 Recall=0.15294 F1=0.11555 FPR=0.00979\n",
-            "[LogisticRegression] CV done elapsed=283.4s\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "      model_name  trial_no  mean_pr_auc  std_pr_auc  mean_roc_auc  \\\n",
-              "0     ExtraTrees         4     0.076091    0.047961      0.647573   \n",
-              "1     ExtraTrees         8     0.071661    0.046476      0.670800   \n",
-              "2     ExtraTrees        12     0.061595    0.035912      0.647986   \n",
-              "3       LightGBM         5     0.061435    0.043454      0.635865   \n",
-              "4     ExtraTrees         6     0.060760    0.043707      0.626227   \n",
-              "5     ExtraTrees        14     0.059835    0.034874      0.633514   \n",
-              "6       LightGBM        14     0.058075    0.047107      0.635845   \n",
-              "7     ExtraTrees        13     0.056089    0.038199      0.651291   \n",
-              "8       LightGBM        12     0.055233    0.046533      0.622553   \n",
-              "9     ExtraTrees         1     0.054918    0.029484      0.644671   \n",
-              "10      LightGBM         4     0.054238    0.043641      0.638072   \n",
-              "11    ExtraTrees         3     0.053812    0.034891      0.643948   \n",
-              "12      LightGBM         2     0.053372    0.036460      0.623080   \n",
-              "13      LightGBM         6     0.051865    0.038493      0.645314   \n",
-              "14      LightGBM         1     0.051047    0.037590      0.637448   \n",
-              "15  RandomForest        14     0.050646    0.028109      0.651639   \n",
-              "16      LightGBM        13     0.049543    0.033146      0.646574   \n",
-              "17      LightGBM         3     0.049320    0.034143      0.627693   \n",
-              "18      LightGBM         9     0.048774    0.035914      0.629020   \n",
-              "19    ExtraTrees         2     0.046631    0.022173      0.632108   \n",
-              "\n",
-              "    std_roc_auc  mean_accuracy  mean_false_positive_rate  mean_precision  \\\n",
-              "0      0.039919       0.970433                  0.025344        0.119684   \n",
-              "1      0.037693       0.952733                  0.043245        0.087831   \n",
-              "2      0.024357       0.985000                  0.010191        0.223146   \n",
-              "3      0.047525       0.975900                  0.019879        0.101028   \n",
-              "4      0.026922       0.953733                  0.042239        0.080404   \n",
-              "5      0.023413       0.953800                  0.042306        0.095049   \n",
-              "6      0.045116       0.967200                  0.028696        0.057561   \n",
-              "7      0.033722       0.951400                  0.044418        0.063167   \n",
-              "8      0.050385       0.959833                  0.036071        0.053337   \n",
-              "9      0.023235       0.958867                  0.037010        0.145893   \n",
-              "10     0.043098       0.947200                  0.049078        0.052814   \n",
-              "11     0.022371       0.972000                  0.023667        0.118529   \n",
-              "12     0.034409       0.958800                  0.037211        0.052149   \n",
-              "13     0.047738       0.966367                  0.029400        0.133721   \n",
-              "14     0.054362       0.947200                  0.049112        0.044530   \n",
-              "15     0.045576       0.957433                  0.038585        0.067259   \n",
-              "16     0.043579       0.945433                  0.050821        0.060910   \n",
-              "17     0.046462       0.928867                  0.067516        0.076085   \n",
-              "18     0.028166       0.964367                  0.031512        0.056697   \n",
-              "19     0.042936       0.970900                  0.024740        0.120895   \n",
-              "\n",
-              "    mean_recall   mean_f1                                             params  \\\n",
-              "0      0.229412  0.117969  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-              "1      0.247059  0.085893  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-              "2      0.141176  0.124673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-              "3      0.235294  0.123713  {'model__learning_rate': 0.031169409812378812,...   \n",
-              "4      0.247059  0.092680  {'model__min_samples_leaf': 3, 'model__max_fea...   \n",
-              "5      0.270588  0.104333  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-              "6      0.247059  0.089393  {'model__learning_rate': 0.03688230536757648, ...   \n",
-              "7      0.217647  0.073932  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-              "8      0.241176  0.079716  {'model__learning_rate': 0.03828385438925741, ...   \n",
-              "9      0.235294  0.098673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-              "10     0.294118  0.079513  {'model__learning_rate': 0.018840552955192824,...   \n",
-              "11     0.211765  0.092926  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-              "12     0.258824  0.082953  {'model__learning_rate': 0.0564638664293258, '...   \n",
-              "13     0.223529  0.109434  {'model__learning_rate': 0.022439365289703053,...   \n",
-              "14     0.300000  0.073458  {'model__learning_rate': 0.021789307659775742,...   \n",
-              "15     0.258824  0.085685  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-              "16     0.288235  0.081039  {'model__learning_rate': 0.0128289748714244, '...   \n",
-              "17     0.294118  0.054984  {'model__learning_rate': 0.025815006344207546,...   \n",
-              "18     0.241176  0.086860  {'model__learning_rate': 0.029653419677342325,...   \n",
-              "19     0.205882  0.112978  {'model__min_samples_leaf': 1, 'model__max_fea...   \n",
-              "\n",
-              "                                     selection_reason     tuning_method  \n",
-              "0   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "1   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "2   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "3   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "4   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "5   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "6   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "7   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "8   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "9   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "10  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "11  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-              "12  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "13  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "14  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "15  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  ParameterSampler  \n",
-              "16  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "17  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "18  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-              "19  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-03e4c761-567f-41e7-ad56-2ecca75a7cbb\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
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-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>model_name</th>\n",
-              "      <th>trial_no</th>\n",
-              "      <th>mean_pr_auc</th>\n",
-              "      <th>std_pr_auc</th>\n",
-              "      <th>mean_roc_auc</th>\n",
-              "      <th>std_roc_auc</th>\n",
-              "      <th>mean_accuracy</th>\n",
-              "      <th>mean_false_positive_rate</th>\n",
-              "      <th>mean_precision</th>\n",
-              "      <th>mean_recall</th>\n",
-              "      <th>mean_f1</th>\n",
-              "      <th>params</th>\n",
-              "      <th>selection_reason</th>\n",
-              "      <th>tuning_method</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>4</td>\n",
-              "      <td>0.076091</td>\n",
-              "      <td>0.047961</td>\n",
-              "      <td>0.647573</td>\n",
-              "      <td>0.039919</td>\n",
-              "      <td>0.970433</td>\n",
-              "      <td>0.025344</td>\n",
-              "      <td>0.119684</td>\n",
-              "      <td>0.229412</td>\n",
-              "      <td>0.117969</td>\n",
-              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>8</td>\n",
-              "      <td>0.071661</td>\n",
-              "      <td>0.046476</td>\n",
-              "      <td>0.670800</td>\n",
-              "      <td>0.037693</td>\n",
-              "      <td>0.952733</td>\n",
-              "      <td>0.043245</td>\n",
-              "      <td>0.087831</td>\n",
-              "      <td>0.247059</td>\n",
-              "      <td>0.085893</td>\n",
-              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>12</td>\n",
-              "      <td>0.061595</td>\n",
-              "      <td>0.035912</td>\n",
-              "      <td>0.647986</td>\n",
-              "      <td>0.024357</td>\n",
-              "      <td>0.985000</td>\n",
-              "      <td>0.010191</td>\n",
-              "      <td>0.223146</td>\n",
-              "      <td>0.141176</td>\n",
-              "      <td>0.124673</td>\n",
-              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>5</td>\n",
-              "      <td>0.061435</td>\n",
-              "      <td>0.043454</td>\n",
-              "      <td>0.635865</td>\n",
-              "      <td>0.047525</td>\n",
-              "      <td>0.975900</td>\n",
-              "      <td>0.019879</td>\n",
-              "      <td>0.101028</td>\n",
-              "      <td>0.235294</td>\n",
-              "      <td>0.123713</td>\n",
-              "      <td>{'model__learning_rate': 0.031169409812378812,...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>6</td>\n",
-              "      <td>0.060760</td>\n",
-              "      <td>0.043707</td>\n",
-              "      <td>0.626227</td>\n",
-              "      <td>0.026922</td>\n",
-              "      <td>0.953733</td>\n",
-              "      <td>0.042239</td>\n",
-              "      <td>0.080404</td>\n",
-              "      <td>0.247059</td>\n",
-              "      <td>0.092680</td>\n",
-              "      <td>{'model__min_samples_leaf': 3, 'model__max_fea...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>14</td>\n",
-              "      <td>0.059835</td>\n",
-              "      <td>0.034874</td>\n",
-              "      <td>0.633514</td>\n",
-              "      <td>0.023413</td>\n",
-              "      <td>0.953800</td>\n",
-              "      <td>0.042306</td>\n",
-              "      <td>0.095049</td>\n",
-              "      <td>0.270588</td>\n",
-              "      <td>0.104333</td>\n",
-              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>14</td>\n",
-              "      <td>0.058075</td>\n",
-              "      <td>0.047107</td>\n",
-              "      <td>0.635845</td>\n",
-              "      <td>0.045116</td>\n",
-              "      <td>0.967200</td>\n",
-              "      <td>0.028696</td>\n",
-              "      <td>0.057561</td>\n",
-              "      <td>0.247059</td>\n",
-              "      <td>0.089393</td>\n",
-              "      <td>{'model__learning_rate': 0.03688230536757648, ...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>13</td>\n",
-              "      <td>0.056089</td>\n",
-              "      <td>0.038199</td>\n",
-              "      <td>0.651291</td>\n",
-              "      <td>0.033722</td>\n",
-              "      <td>0.951400</td>\n",
-              "      <td>0.044418</td>\n",
-              "      <td>0.063167</td>\n",
-              "      <td>0.217647</td>\n",
-              "      <td>0.073932</td>\n",
-              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>12</td>\n",
-              "      <td>0.055233</td>\n",
-              "      <td>0.046533</td>\n",
-              "      <td>0.622553</td>\n",
-              "      <td>0.050385</td>\n",
-              "      <td>0.959833</td>\n",
-              "      <td>0.036071</td>\n",
-              "      <td>0.053337</td>\n",
-              "      <td>0.241176</td>\n",
-              "      <td>0.079716</td>\n",
-              "      <td>{'model__learning_rate': 0.03828385438925741, ...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.054918</td>\n",
-              "      <td>0.029484</td>\n",
-              "      <td>0.644671</td>\n",
-              "      <td>0.023235</td>\n",
-              "      <td>0.958867</td>\n",
-              "      <td>0.037010</td>\n",
-              "      <td>0.145893</td>\n",
-              "      <td>0.235294</td>\n",
-              "      <td>0.098673</td>\n",
-              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>4</td>\n",
-              "      <td>0.054238</td>\n",
-              "      <td>0.043641</td>\n",
-              "      <td>0.638072</td>\n",
-              "      <td>0.043098</td>\n",
-              "      <td>0.947200</td>\n",
-              "      <td>0.049078</td>\n",
-              "      <td>0.052814</td>\n",
-              "      <td>0.294118</td>\n",
-              "      <td>0.079513</td>\n",
-              "      <td>{'model__learning_rate': 0.018840552955192824,...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.053812</td>\n",
-              "      <td>0.034891</td>\n",
-              "      <td>0.643948</td>\n",
-              "      <td>0.022371</td>\n",
-              "      <td>0.972000</td>\n",
-              "      <td>0.023667</td>\n",
-              "      <td>0.118529</td>\n",
-              "      <td>0.211765</td>\n",
-              "      <td>0.092926</td>\n",
-              "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>12</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.053372</td>\n",
-              "      <td>0.036460</td>\n",
-              "      <td>0.623080</td>\n",
-              "      <td>0.034409</td>\n",
-              "      <td>0.958800</td>\n",
-              "      <td>0.037211</td>\n",
-              "      <td>0.052149</td>\n",
-              "      <td>0.258824</td>\n",
-              "      <td>0.082953</td>\n",
-              "      <td>{'model__learning_rate': 0.0564638664293258, '...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>13</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>6</td>\n",
-              "      <td>0.051865</td>\n",
-              "      <td>0.038493</td>\n",
-              "      <td>0.645314</td>\n",
-              "      <td>0.047738</td>\n",
-              "      <td>0.966367</td>\n",
-              "      <td>0.029400</td>\n",
-              "      <td>0.133721</td>\n",
-              "      <td>0.223529</td>\n",
-              "      <td>0.109434</td>\n",
-              "      <td>{'model__learning_rate': 0.022439365289703053,...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>14</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.051047</td>\n",
-              "      <td>0.037590</td>\n",
-              "      <td>0.637448</td>\n",
-              "      <td>0.054362</td>\n",
-              "      <td>0.947200</td>\n",
-              "      <td>0.049112</td>\n",
-              "      <td>0.044530</td>\n",
-              "      <td>0.300000</td>\n",
-              "      <td>0.073458</td>\n",
-              "      <td>{'model__learning_rate': 0.021789307659775742,...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>14</td>\n",
-              "      <td>0.050646</td>\n",
-              "      <td>0.028109</td>\n",
-              "      <td>0.651639</td>\n",
-              "      <td>0.045576</td>\n",
-              "      <td>0.957433</td>\n",
-              "      <td>0.038585</td>\n",
-              "      <td>0.067259</td>\n",
-              "      <td>0.258824</td>\n",
-              "      <td>0.085685</td>\n",
-              "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>13</td>\n",
-              "      <td>0.049543</td>\n",
-              "      <td>0.033146</td>\n",
-              "      <td>0.646574</td>\n",
-              "      <td>0.043579</td>\n",
-              "      <td>0.945433</td>\n",
-              "      <td>0.050821</td>\n",
-              "      <td>0.060910</td>\n",
-              "      <td>0.288235</td>\n",
-              "      <td>0.081039</td>\n",
-              "      <td>{'model__learning_rate': 0.0128289748714244, '...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>17</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.049320</td>\n",
-              "      <td>0.034143</td>\n",
-              "      <td>0.627693</td>\n",
-              "      <td>0.046462</td>\n",
-              "      <td>0.928867</td>\n",
-              "      <td>0.067516</td>\n",
-              "      <td>0.076085</td>\n",
-              "      <td>0.294118</td>\n",
-              "      <td>0.054984</td>\n",
-              "      <td>{'model__learning_rate': 0.025815006344207546,...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>18</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>9</td>\n",
-              "      <td>0.048774</td>\n",
-              "      <td>0.035914</td>\n",
-              "      <td>0.629020</td>\n",
-              "      <td>0.028166</td>\n",
-              "      <td>0.964367</td>\n",
-              "      <td>0.031512</td>\n",
-              "      <td>0.056697</td>\n",
-              "      <td>0.241176</td>\n",
-              "      <td>0.086860</td>\n",
-              "      <td>{'model__learning_rate': 0.029653419677342325,...</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>Optuna</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>19</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.046631</td>\n",
-              "      <td>0.022173</td>\n",
-              "      <td>0.632108</td>\n",
-              "      <td>0.042936</td>\n",
-              "      <td>0.970900</td>\n",
-              "      <td>0.024740</td>\n",
-              "      <td>0.120895</td>\n",
-              "      <td>0.205882</td>\n",
-              "      <td>0.112978</td>\n",
-              "      <td>{'model__min_samples_leaf': 1, 'model__max_fea...</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>ParameterSampler</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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   },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"RandomForest\\ubcf4\\ub2e4 \\ubd84\\ud560 \\ubb34\\uc791\\uc704\\uc131\\uc774 \\ucee4\\uc11c \\uace0\\ucc28\\uc6d0 \\uc13c\\uc11c feature\\uc758 \\uac15\\uac74\\uc131 \\ube44\\uad50\",\n          \"\\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378. 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-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "CV 결과 저장: /content/defect_transfer_outputs/defect_model_cv_results.csv\n",
-            "CV fold 결과 저장: /content/defect_transfer_outputs/defect_model_cv_fold_results.csv\n",
-            "CV best model: ExtraTrees {'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n"
-          ]
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                                            모델_선정_근거  \\\n",
+       "0  RandomForest 모델이 validation PR-AUC/Recall/F1 기...   \n",
+       "\n",
+       "                                          데이터셋_규모_관리  \\\n",
+       "0  {'max_rows': 300000, 'profile_rows': 50000, 't...   \n",
+       "\n",
+       "                                        테스트_케이스_수_관리  정확도_Accuracy  \\\n",
+       "0  {'validation_total': 60000, 'validation_normal...       0.91395   \n",
+       "\n",
+       "   오탐률_False_Positive_Rate                                        모델_성능_개선_현황  \n",
+       "0                 0.082851  {'baseline_model': 'LightGBM', 'baseline_pr_au...  "
       ],
-      "source": [
-        "# 평가 함수\n",
-        "def find_recall_priority_threshold(\n",
-        "    y_true: pd.Series,\n",
-        "    proba: np.ndarray,\n",
-        "    *,\n",
-        "    min_recall: float = RECALL_PRIORITY_TARGET,\n",
-        "    min_precision: float = RECALL_PRIORITY_MIN_PRECISION,\n",
-        "    beta: float = THRESHOLD_BETA,\n",
-        ") -> float:\n",
-        "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
-        "    if len(thresholds) == 0:\n",
-        "        return 0.5\n",
-        "\n",
-        "    precision = precision[:-1]\n",
-        "    recall = recall[:-1]\n",
-        "    thresholds = thresholds.astype(float)\n",
-        "    valid = (recall >= min_recall) & (precision >= min_precision)\n",
-        "\n",
-        "    if valid.any():\n",
-        "        # Recall 목표를 만족하는 후보 중 Precision이 가장 좋은 threshold를 선택합니다.\n",
-        "        valid_idx = np.where(valid)[0]\n",
-        "        best_local_idx = valid_idx[int(np.nanargmax(precision[valid_idx]))]\n",
-        "        return float(thresholds[best_local_idx])\n",
-        "\n",
-        "    beta_sq = beta ** 2\n",
-        "    f_beta = (1 + beta_sq) * precision * recall / np.maximum(beta_sq * precision + recall, 1e-12)\n",
-        "    return float(thresholds[int(np.nanargmax(f_beta))])\n",
-        "\n",
-        "\n",
-        "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
-        "    return find_recall_priority_threshold(y_true, proba)\n",
-        "\n",
-        "\n",
-        "def compute_binary_metrics(y_true: pd.Series, proba: np.ndarray, threshold: float) -> dict:\n",
-        "    pred = (proba >= threshold).astype(int)\n",
-        "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
-        "    return {\n",
-        "        \"accuracy\": float(accuracy_score(y_true, pred)),\n",
-        "        \"precision\": float(precision_score(y_true, pred, zero_division=0)),\n",
-        "        \"recall\": float(recall_score(y_true, pred, zero_division=0)),\n",
-        "        \"f1\": float(f1_score(y_true, pred, zero_division=0)),\n",
-        "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
-        "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
-        "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
-        "        \"predicted_positive_rate\": float((tp + fp) / max(tp + fp + tn + fn, 1)),\n",
-        "        \"true_negative\": int(tn),\n",
-        "        \"false_positive\": int(fp),\n",
-        "        \"false_negative\": int(fn),\n",
-        "        \"true_positive\": int(tp),\n",
-        "    }\n",
-        "\n",
-        "\n",
-        "def predict_positive_proba(estimator, X_input: pd.DataFrame) -> np.ndarray:\n",
-        "    if hasattr(estimator, \"predict_proba\"):\n",
-        "        return estimator.predict_proba(X_input)[:, 1]\n",
-        "    decision = estimator.decision_function(X_input)\n",
-        "    return 1 / (1 + np.exp(-decision))\n",
-        "\n",
-        "\n",
-        "def is_pipeline_estimator(estimator) -> bool:\n",
-        "    return isinstance(estimator, (Pipeline, ImbPipeline))\n",
-        "\n",
-        "\n",
-        "def get_final_estimator(estimator):\n",
-        "    return estimator.named_steps.get(\"model\", estimator) if is_pipeline_estimator(estimator) else estimator\n",
-        "\n",
-        "\n",
-        "def make_lightgbm_pipeline() -> ImbPipeline:\n",
-        "    return ImbPipeline([\n",
-        "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-        "        (\"smote\", SMOTE(\n",
-        "            sampling_strategy=SMOTE_SAMPLING_STRATEGY if USE_SMOTE else 0.01,\n",
-        "            random_state=RANDOM_STATE,\n",
-        "            k_neighbors=3,\n",
-        "        )),\n",
-        "        (\"model\", lgb.LGBMClassifier(\n",
-        "            objective=\"binary\",\n",
-        "            n_estimators=500,\n",
-        "            random_state=RANDOM_STATE,\n",
-        "            n_jobs=-1,\n",
-        "            is_unbalance=True,\n",
-        "            verbosity=-1,\n",
-        "        )),\n",
-        "    ])\n",
-        "\n",
-        "\n",
-        "def make_candidate_models() -> dict:\n",
-        "    return {\n",
-        "        \"LightGBM\": {\n",
-        "            \"estimator\": make_lightgbm_pipeline(),\n",
-        "            \"params\": {\n",
-        "                \"model__learning_rate\": [0.015, 0.025, 0.04, 0.06],\n",
-        "                \"model__num_leaves\": [31, 63, 127],\n",
-        "                \"model__max_depth\": [-1, 6, 8, 12],\n",
-        "                \"model__min_child_samples\": [40, 80, 150, 250],\n",
-        "                \"model__subsample\": [0.75, 0.85, 1.0],\n",
-        "                \"model__colsample_bytree\": [0.65, 0.8, 0.95],\n",
-        "                \"model__reg_lambda\": [0.5, 1.0, 2.0, 5.0],\n",
-        "                \"model__reg_alpha\": [0.0, 0.1, 0.5],\n",
-        "            },\n",
-        "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델. Optuna와 SMOTE로 희소 불량 recall을 보강\",\n",
-        "        },\n",
-        "        \"RandomForest\": {\n",
-        "            \"estimator\": Pipeline([\n",
-        "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-        "                (\"model\", RandomForestClassifier(\n",
-        "                    n_estimators=160,\n",
-        "                    class_weight=\"balanced_subsample\",\n",
-        "                    random_state=RANDOM_STATE,\n",
-        "                    n_jobs=-1,\n",
-        "                )),\n",
-        "            ]),\n",
-        "            \"params\": {\n",
-        "                \"model__max_depth\": [12, 16, 20, None],\n",
-        "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
-        "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
-        "            },\n",
-        "            \"selection_reason\": \"bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble 성능 확인\",\n",
-        "        },\n",
-        "        \"ExtraTrees\": {\n",
-        "            \"estimator\": Pipeline([\n",
-        "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-        "                (\"model\", ExtraTreesClassifier(\n",
-        "                    n_estimators=220,\n",
-        "                    class_weight=\"balanced\",\n",
-        "                    random_state=RANDOM_STATE,\n",
-        "                    n_jobs=-1,\n",
-        "                )),\n",
-        "            ]),\n",
-        "            \"params\": {\n",
-        "                \"model__max_depth\": [12, 16, 20, None],\n",
-        "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
-        "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
-        "            },\n",
-        "            \"selection_reason\": \"RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교\",\n",
-        "        },\n",
-        "        \"LogisticRegression\": {\n",
-        "            \"estimator\": Pipeline([\n",
-        "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-        "                (\"scaler\", StandardScaler()),\n",
-        "                (\"model\", LogisticRegression(\n",
-        "                    class_weight=\"balanced\",\n",
-        "                    max_iter=1500,\n",
-        "                    random_state=RANDOM_STATE,\n",
-        "                )),\n",
-        "            ]),\n",
-        "            \"params\": {\n",
-        "                \"model__C\": [0.03, 0.1, 0.3, 1.0, 3.0],\n",
-        "                \"model__solver\": [\"lbfgs\"],\n",
-        "            },\n",
-        "            \"selection_reason\": \"복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체의 설명력 확인\",\n",
-        "        },\n",
-        "    }\n",
-        "\n",
-        "\n",
-        "def suggest_lightgbm_params(trial: optuna.Trial) -> dict:\n",
-        "    return {\n",
-        "        \"model__learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.08, log=True),\n",
-        "        \"model__num_leaves\": trial.suggest_int(\"num_leaves\", 24, 160),\n",
-        "        \"model__max_depth\": trial.suggest_categorical(\"max_depth\", [-1, 5, 7, 9, 12]),\n",
-        "        \"model__min_child_samples\": trial.suggest_int(\"min_child_samples\", 30, 300),\n",
-        "        \"model__subsample\": trial.suggest_float(\"subsample\", 0.65, 1.0),\n",
-        "        \"model__colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.55, 1.0),\n",
-        "        \"model__reg_lambda\": trial.suggest_float(\"reg_lambda\", 0.1, 8.0, log=True),\n",
-        "        \"model__reg_alpha\": trial.suggest_float(\"reg_alpha\", 1e-4, 1.0, log=True),\n",
-        "    }\n",
-        "\n",
-        "\n",
-        "def evaluate_cv_trial(model_name: str, config: dict, params: dict, trial_no: int) -> tuple[dict, list[dict]]:\n",
-        "    fold_rows = []\n",
-        "    for fold_no, (train_idx, valid_idx) in enumerate(skf.split(X_cv_pool, y_cv_pool), start=1):\n",
-        "        fold_started_at = datetime.now()\n",
-        "        X_fold_train = X_cv_pool.iloc[train_idx]\n",
-        "        y_fold_train = y_cv_pool.iloc[train_idx]\n",
-        "        X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
-        "        y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
-        "\n",
-        "        cv_model = clone(config[\"estimator\"])\n",
-        "        cv_model.set_params(**params)\n",
-        "        cv_model.fit(X_fold_train, y_fold_train)\n",
-        "\n",
-        "        fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
-        "        fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
-        "        fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
-        "        fold_metrics.update({\n",
-        "            \"model_name\": model_name,\n",
-        "            \"trial_no\": trial_no,\n",
-        "            \"fold_no\": fold_no,\n",
-        "            \"threshold\": fold_threshold,\n",
-        "            **params,\n",
-        "        })\n",
-        "        fold_rows.append(fold_metrics)\n",
-        "        elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
-        "        print(\n",
-        "            f\"    fold {fold_no}/{effective_cv_splits} \"\n",
-        "            f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
-        "            f\"Recall={fold_metrics['recall']:.5f} \"\n",
-        "            f\"F1={fold_metrics['f1']:.5f} \"\n",
-        "            f\"elapsed={elapsed:.1f}s\"\n",
-        "        )\n",
-        "\n",
-        "    fold_df = pd.DataFrame(fold_rows)\n",
-        "    summary = {\n",
-        "        \"model_name\": model_name,\n",
-        "        \"trial_no\": trial_no,\n",
-        "        \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
-        "        \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
-        "        \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
-        "        \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
-        "        \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
-        "        \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
-        "        \"mean_precision\": float(fold_df[\"precision\"].mean()),\n",
-        "        \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
-        "        \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
-        "        \"params\": params,\n",
-        "        \"selection_reason\": config[\"selection_reason\"],\n",
-        "        \"tuning_method\": \"Optuna\" if model_name == \"LightGBM\" and ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-        "    }\n",
-        "    return summary, fold_rows\n",
-        "\n",
-        "\n",
-        "# CV 데이터셋 규모 관리\n",
-        "if len(X_train) > CV_SAMPLE_SIZE:\n",
-        "    X_cv_pool, _, y_cv_pool, _ = train_test_split(\n",
-        "        X_train,\n",
-        "        y_train,\n",
-        "        train_size=CV_SAMPLE_SIZE,\n",
-        "        random_state=RANDOM_STATE,\n",
-        "        stratify=y_train,\n",
-        "    )\n",
-        "else:\n",
-        "    X_cv_pool = X_train\n",
-        "    y_cv_pool = y_train\n",
-        "\n",
-        "effective_cv_splits = min(CV_N_SPLITS, int(y_cv_pool.value_counts().min()))\n",
-        "if effective_cv_splits < 2:\n",
-        "    raise ValueError(\"교차검증을 수행하기에 소수 클래스 샘플이 부족합니다. MAX_ROWS 또는 CV_SAMPLE_SIZE를 늘려주세요.\")\n",
-        "\n",
-        "candidate_configs = make_candidate_models()\n",
-        "skf = StratifiedKFold(n_splits=effective_cv_splits, shuffle=True, random_state=RANDOM_STATE)\n",
-        "cv_rows = []\n",
-        "cv_fold_rows = []\n",
-        "best_params_by_model = {}\n",
-        "\n",
-        "print(f\"CV rows: {len(X_cv_pool):,} / train rows: {len(X_train):,}\")\n",
-        "print(f\"CV splits: {effective_cv_splits}, tuning trials per model: {CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1}\")\n",
-        "print(f\"SMOTE enabled: {USE_SMOTE}, sampling_strategy={SMOTE_SAMPLING_STRATEGY}\")\n",
-        "print(f\"Threshold strategy: recall>={RECALL_PRIORITY_TARGET}, min_precision>={RECALL_PRIORITY_MIN_PRECISION}, beta={THRESHOLD_BETA}\")\n",
-        "\n",
-        "# 후보 모델별 교차검증\n",
-        "for model_name, config in candidate_configs.items():\n",
-        "    print(f\"\\n[{model_name}] CV start\")\n",
-        "    model_started_at = datetime.now()\n",
-        "\n",
-        "    if model_name == \"LightGBM\" and ENABLE_HYPERPARAMETER_TUNING and ENABLE_OPTUNA_TUNING:\n",
-        "        def objective(trial: optuna.Trial) -> float:\n",
-        "            params = suggest_lightgbm_params(trial)\n",
-        "            print(f\"  optuna trial {trial.number + 1}/{CV_N_ITER} params={params}\")\n",
-        "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial.number + 1)\n",
-        "            cv_rows.append(summary)\n",
-        "            cv_fold_rows.extend(fold_rows)\n",
-        "            trial.set_user_attr(\"params\", params)\n",
-        "            trial.set_user_attr(\"mean_recall\", summary[\"mean_recall\"])\n",
-        "            trial.set_user_attr(\"mean_f1\", summary[\"mean_f1\"])\n",
-        "            # PR-AUC를 우선하되 recall을 약하게 보상합니다.\n",
-        "            return summary[\"mean_pr_auc\"] + 0.05 * summary[\"mean_recall\"]\n",
-        "\n",
-        "        study = optuna.create_study(\n",
-        "            direction=\"maximize\",\n",
-        "            sampler=TPESampler(seed=RANDOM_STATE),\n",
-        "            study_name=\"bosch_defect_lightgbm_recall_pr_auc\",\n",
-        "        )\n",
-        "        study.optimize(objective, n_trials=CV_N_ITER, show_progress_bar=False)\n",
-        "        best_params_by_model[model_name] = study.best_trial.user_attrs[\"params\"]\n",
-        "        print(f\"[{model_name}] Optuna best value={study.best_value:.5f} params={best_params_by_model[model_name]}\")\n",
-        "    else:\n",
-        "        param_candidates = list(ParameterSampler(\n",
-        "            config[\"params\"],\n",
-        "            n_iter=CV_N_ITER,\n",
-        "            random_state=RANDOM_STATE,\n",
-        "        )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
-        "        print(f\"[{model_name}] random search trials: {len(param_candidates)}\")\n",
-        "        for trial_no, params in enumerate(param_candidates, start=1):\n",
-        "            print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
-        "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial_no)\n",
-        "            cv_rows.append(summary)\n",
-        "            cv_fold_rows.extend(fold_rows)\n",
-        "            print(\n",
-        "                f\"  -> {model_name} trial {trial_no} mean \"\n",
-        "                f\"PR-AUC={summary['mean_pr_auc']:.5f} \"\n",
-        "                f\"Recall={summary['mean_recall']:.5f} \"\n",
-        "                f\"F1={summary['mean_f1']:.5f} \"\n",
-        "                f\"FPR={summary['mean_false_positive_rate']:.5f}\"\n",
-        "            )\n",
-        "\n",
-        "    print(f\"[{model_name}] CV done elapsed={(datetime.now() - model_started_at).total_seconds():.1f}s\")\n",
-        "\n",
-        "cv_results = pd.DataFrame(cv_rows).sort_values(\n",
-        "    [\"mean_pr_auc\", \"mean_recall\", \"mean_f1\", \"mean_roc_auc\", \"mean_accuracy\"],\n",
-        "    ascending=False,\n",
-        ").reset_index(drop=True)\n",
-        "cv_fold_results = pd.DataFrame(cv_fold_rows)\n",
-        "\n",
-        "for model_name in candidate_configs:\n",
-        "    if model_name not in best_params_by_model:\n",
-        "        best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
-        "\n",
-        "best_model_name = str(cv_results.iloc[0][\"model_name\"])\n",
-        "best_params = best_params_by_model[best_model_name]\n",
-        "best_model_selection_reason = str(cv_results.iloc[0][\"selection_reason\"])\n",
-        "\n",
-        "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.csv\"\n",
-        "cv_fold_results_path = OUTPUT_DIR / \"defect_model_cv_fold_results.csv\"\n",
-        "cv_results.to_csv(cv_results_path, index=False)\n",
-        "cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
-        "\n",
-        "display(cv_results.head(20))\n",
-        "print(\"CV 결과 저장:\", cv_results_path)\n",
-        "print(\"CV fold 결과 저장:\", cv_fold_results_path)\n",
-        "print(\"CV best model:\", best_model_name, best_params)\n"
-      ]
+      "text/html": [
+       "\n",
+       "  <div id=\"df-32734db2-8443-4fbf-a77b-616fb1fbed24\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>모델_선정_근거</th>\n",
+       "      <th>데이터셋_규모_관리</th>\n",
+       "      <th>테스트_케이스_수_관리</th>\n",
+       "      <th>정확도_Accuracy</th>\n",
+       "      <th>오탐률_False_Positive_Rate</th>\n",
+       "      <th>모델_성능_개선_현황</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RandomForest 모델이 validation PR-AUC/Recall/F1 기...</td>\n",
+       "      <td>{'max_rows': 300000, 'profile_rows': 50000, 't...</td>\n",
+       "      <td>{'validation_total': 60000, 'validation_normal...</td>\n",
+       "      <td>0.91395</td>\n",
+       "      <td>0.082851</td>\n",
+       "      <td>{'baseline_model': 'LightGBM', 'baseline_pr_au...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32734db2-8443-4fbf-a77b-616fb1fbed24')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-32734db2-8443-4fbf-a77b-616fb1fbed24 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-32734db2-8443-4fbf-a77b-616fb1fbed24');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc120\\uc815_\\uadfc\\uac70\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RandomForest \\ubaa8\\ub378\\uc774 validation PR-AUC/Recall/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub370\\uc774\\ud130\\uc14b_\\uaddc\\ubaa8_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud14c\\uc2a4\\ud2b8_\\ucf00\\uc774\\uc2a4_\\uc218_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc815\\ud655\\ub3c4_Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.91395,\n        \"max\": 0.91395,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc624\\ud0d0\\ub960_False_Positive_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0828514439918875,\n        \"max\": 0.0828514439918875,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc131\\ub2a5_\\uac1c\\uc120_\\ud604\\ud669\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "trained_models = {}\n",
+    "model_valid_probabilities = {}\n",
+    "model_thresholds = {}\n",
+    "model_confusion_matrices = {}\n",
+    "model_eval_rows = []\n",
+    "\n",
+    "# 최종 후보 비교용 학습 데이터셋 규모 관리\n",
+    "if len(X_train) > FINAL_TRAIN_SAMPLE_SIZE:\n",
+    "    X_final_train, _, y_final_train, _ = train_test_split(\n",
+    "        X_train,\n",
+    "        y_train,\n",
+    "        train_size=FINAL_TRAIN_SAMPLE_SIZE,\n",
+    "        random_state=RANDOM_STATE,\n",
+    "        stratify=y_train,\n",
+    "    )\n",
+    "else:\n",
+    "    X_final_train = X_train\n",
+    "    y_final_train = y_train\n",
+    "\n",
+    "print(f\"Final candidate training rows: {len(X_final_train):,} / train rows: {len(X_train):,}\")\n",
+    "\n",
+    "# 후보 모델 전체 학습\n",
+    "for model_name, config in candidate_configs.items():\n",
+    "    started_at = datetime.now()\n",
+    "    estimator = clone(config[\"estimator\"])\n",
+    "    estimator.set_params(**best_params_by_model[model_name])\n",
+    "    print(f\"[{model_name}] final fit start params={best_params_by_model[model_name]}\")\n",
+    "    estimator.fit(X_final_train, y_final_train)\n",
+    "\n",
+    "    proba = predict_positive_proba(estimator, X_valid)\n",
+    "    threshold = find_best_threshold(y_valid, proba)\n",
+    "    pred = (proba >= threshold).astype(int)\n",
+    "    cm_model = confusion_matrix(y_valid, pred, labels=[0, 1])\n",
+    "    metrics = compute_binary_metrics(y_valid, proba, threshold)\n",
+    "\n",
+    "    trained_models[model_name] = estimator\n",
+    "    model_valid_probabilities[model_name] = proba\n",
+    "    model_thresholds[model_name] = threshold\n",
+    "    model_confusion_matrices[model_name] = cm_model\n",
+    "\n",
+    "    model_eval_rows.append({\n",
+    "        \"model_name\": model_name,\n",
+    "        \"threshold\": threshold,\n",
+    "        \"selection_reason\": config[\"selection_reason\"],\n",
+    "        **metrics,\n",
+    "    })\n",
+    "    print(\n",
+    "        f\"[{model_name}] final fit done \"\n",
+    "        f\"PR-AUC={metrics['pr_auc']:.5f} \"\n",
+    "        f\"ROC-AUC={metrics['roc_auc']:.5f} \"\n",
+    "        f\"ACC={metrics['accuracy']:.5f} \"\n",
+    "        f\"FPR={metrics['false_positive_rate']:.5f} \"\n",
+    "        f\"elapsed={(datetime.now() - started_at).total_seconds():.1f}s\"\n",
+    "    )\n",
+    "\n",
+    "model_comparison = pd.DataFrame(model_eval_rows).sort_values(\n",
+    "    [\"pr_auc\", \"recall\", \"f1\", \"roc_auc\", \"accuracy\"],\n",
+    "    ascending=False,\n",
+    ").reset_index(drop=True)\n",
+    "\n",
+    "selected_model_name = str(model_comparison.iloc[0][\"model_name\"])\n",
+    "model = trained_models[selected_model_name]\n",
+    "valid_proba = model_valid_probabilities[selected_model_name]\n",
+    "best_threshold = float(model_thresholds[selected_model_name])\n",
+    "valid_pred = (valid_proba >= best_threshold).astype(int)\n",
+    "cm = model_confusion_matrices[selected_model_name]\n",
+    "\n",
+    "# 운영용 전체 재학습은 선택 모델 1개만 수행\n",
+    "if RETRAIN_SELECTED_MODEL_ON_FULL_DATA and len(X_final_train) < len(X_train):\n",
+    "    print(f\"[{selected_model_name}] selected model full-data refit start rows={len(X_train):,}\")\n",
+    "    refit_started_at = datetime.now()\n",
+    "    full_model = clone(candidate_configs[selected_model_name][\"estimator\"])\n",
+    "    full_model.set_params(**best_params_by_model[selected_model_name])\n",
+    "    full_model.fit(X_train, y_train)\n",
+    "\n",
+    "    model = full_model\n",
+    "    trained_models[selected_model_name] = full_model\n",
+    "    valid_proba = predict_positive_proba(model, X_valid)\n",
+    "    best_threshold = find_best_threshold(y_valid, valid_proba)\n",
+    "    valid_pred = (valid_proba >= best_threshold).astype(int)\n",
+    "    cm = confusion_matrix(y_valid, valid_pred, labels=[0, 1])\n",
+    "    full_metrics = compute_binary_metrics(y_valid, valid_proba, best_threshold)\n",
+    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), list(full_metrics.keys())] = list(full_metrics.values())\n",
+    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), \"threshold\"] = best_threshold\n",
+    "    model_thresholds[selected_model_name] = best_threshold\n",
+    "    model_confusion_matrices[selected_model_name] = cm\n",
+    "    print(f\"[{selected_model_name}] full-data refit done elapsed={(datetime.now() - refit_started_at).total_seconds():.1f}s\")\n",
+    "\n",
+    "roc_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"roc_auc\"])\n",
+    "pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
+    "accuracy = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"accuracy\"])\n",
+    "false_positive_rate = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"false_positive_rate\"])\n",
+    "\n",
+    "# AI 관리 요약\n",
+    "baseline_row = model_comparison[model_comparison[\"model_name\"].eq(\"LightGBM\")]\n",
+    "baseline_pr_auc = float(baseline_row.iloc[0][\"pr_auc\"]) if not baseline_row.empty else np.nan\n",
+    "best_pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
+    "performance_improvement = best_pr_auc - baseline_pr_auc if not np.isnan(baseline_pr_auc) else 0.0\n",
+    "\n",
+    "ai_management_summary = {\n",
+    "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/Recall/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
+    "    \"데이터셋_규모_관리\": {\n",
+    "        \"max_rows\": int(MAX_ROWS),\n",
+    "        \"profile_rows\": int(PROFILE_ROWS),\n",
+    "        \"train_rows\": int(len(X_train)),\n",
+    "        \"candidate_train_rows\": int(len(X_final_train)),\n",
+    "        \"valid_rows\": int(len(X_valid)),\n",
+    "        \"feature_count\": int(len(feature_cols)),\n",
+    "        \"positive_ratio\": float(y.mean()),\n",
+    "        \"full_data_refit\": bool(RETRAIN_SELECTED_MODEL_ON_FULL_DATA),\n",
+    "    },\n",
+    "    \"테스트_케이스_수_관리\": {\n",
+    "        \"validation_total\": int(len(y_valid)),\n",
+    "        \"validation_normal\": int((y_valid == 0).sum()),\n",
+    "        \"validation_defect\": int((y_valid == 1).sum()),\n",
+    "        \"cv_splits\": int(effective_cv_splits),\n",
+    "        \"cv_sample_size\": int(len(X_cv_pool)),\n",
+    "        \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
+    "        \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+    "        \"smote_enabled\": bool(USE_SMOTE),\n",
+    "        \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
+    "        \"threshold_strategy\": {\n",
+    "            \"type\": \"recall_priority\",\n",
+    "            \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
+    "            \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
+    "            \"beta\": float(THRESHOLD_BETA),\n",
+    "        },\n",
+    "    },\n",
+    "    \"정확도_Accuracy\": accuracy,\n",
+    "    \"오탐률_False_Positive_Rate\": false_positive_rate,\n",
+    "    \"모델_성능_개선_현황\": {\n",
+    "        \"baseline_model\": \"LightGBM\",\n",
+    "        \"baseline_pr_auc\": baseline_pr_auc,\n",
+    "        \"selected_model\": selected_model_name,\n",
+    "        \"selected_pr_auc\": best_pr_auc,\n",
+    "        \"pr_auc_improvement_over_lightgbm\": performance_improvement,\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "print(\"selected_model_name:\", selected_model_name)\n",
+    "print(\"ROC-AUC:\", round(roc_auc, 5))\n",
+    "print(\"PR-AUC:\", round(pr_auc, 5))\n",
+    "print(\"Accuracy:\", round(accuracy, 5))\n",
+    "print(\"False Positive Rate:\", round(false_positive_rate, 5))\n",
+    "print(\"Recall:\", round(float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"recall\"]), 5))\n",
+    "display(model_comparison)\n",
+    "display(pd.DataFrame([ai_management_summary]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "AGL93TiObEfC",
+   "metadata": {
+    "id": "AGL93TiObEfC"
+   },
+   "source": [
+    "## 9. 후보 모델별 Threshold 튜닝 및 Confusion Matrix\n",
+    "\n",
+    "각 후보 모델은 validation set에서 recall 우선 threshold를 사용합니다.\n",
+    "\n",
+    "기본 정책은 `RECALL_PRIORITY_TARGET` 이상을 만족하는 threshold 중 precision이 가장 높은 값을 선택하고, 목표 recall을 만족하는 후보가 없으면 F-beta(기본 beta=2) 기준으로 선택합니다. Accuracy는 클래스 불균형 때문에 참고 지표로만 해석합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "-bI4yc81bEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "-bI4yc81bEfC",
+    "outputId": "fffa66b9-fb9b-4c84-b9d5-da0983048bc2"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "3f15608a",
-      "metadata": {
-        "id": "3f15608a"
-      },
-      "source": [
-        "## 8. 후보 모델 4종 최종 학습 및 성능 비교\n",
-        "\n",
-        "교차검증에서 선택된 모델별 best parameter로 4개 후보 모델을 모두 학습합니다.\n",
-        "\n",
-        "LightGBM은 median imputer + SMOTE + LightGBM pipeline으로 최종 학습합니다. Threshold는 validation set에서 recall 우선 정책으로 탐색합니다.\n"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "== Selected Model ==\n",
+      "model: RandomForest\n",
+      "best_threshold: 0.05887\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0     0.9960    0.9171    0.9549     59661\n",
+      "           1     0.0235    0.3510    0.0441       339\n",
+      "\n",
+      "    accuracy                         0.9140     60000\n",
+      "   macro avg     0.5098    0.6341    0.4995     60000\n",
+      "weighted avg     0.9905    0.9140    0.9498     60000\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "3553e4c5",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 825
-        },
-        "id": "3553e4c5",
-        "outputId": "f6e17abe-7523-45bd-b4fa-0828efb0d901"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Final candidate training rows: 120,000 / train rows: 240,000\n",
-            "[LightGBM] final fit start params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
-            "[LightGBM] final fit done PR-AUC=0.05032 ROC-AUC=0.66598 ACC=0.90142 FPR=0.09546 elapsed=159.2s\n",
-            "[RandomForest] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-            "[RandomForest] final fit done PR-AUC=0.06255 ROC-AUC=0.69675 ACC=0.91395 FPR=0.08285 elapsed=150.0s\n",
-            "[ExtraTrees] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-            "[ExtraTrees] final fit done PR-AUC=0.05758 ROC-AUC=0.67618 ACC=0.98787 FPR=0.00784 elapsed=750.9s\n",
-            "[LogisticRegression] final fit start params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
-            "[LogisticRegression] final fit done PR-AUC=0.05096 ROC-AUC=0.66273 ACC=0.98672 FPR=0.00888 elapsed=71.9s\n",
-            "selected_model_name: RandomForest\n",
-            "ROC-AUC: 0.69675\n",
-            "PR-AUC: 0.06255\n",
-            "Accuracy: 0.91395\n",
-            "False Positive Rate: 0.08285\n",
-            "Recall: 0.35103\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "           model_name  threshold  \\\n",
-              "0        RandomForest   0.058868   \n",
-              "1          ExtraTrees   0.243180   \n",
-              "2  LogisticRegression   0.898330   \n",
-              "3            LightGBM   0.034547   \n",
-              "\n",
-              "                                    selection_reason  accuracy  precision  \\\n",
-              "0  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  0.913950   0.023508   \n",
-              "1  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  0.987867   0.144424   \n",
-              "2  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  0.986717   0.119601   \n",
-              "3  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...  0.901417   0.020468   \n",
-              "\n",
-              "     recall        f1   roc_auc    pr_auc  false_positive_rate  \\\n",
-              "0  0.351032  0.044066  0.696746  0.062551             0.082851   \n",
-              "1  0.233038  0.178330  0.676183  0.057577             0.007844   \n",
-              "2  0.212389  0.153029  0.662734  0.050958             0.008884   \n",
-              "3  0.351032  0.038680  0.665979  0.050323             0.095456   \n",
-              "\n",
-              "   predicted_positive_rate  true_negative  false_positive  false_negative  \\\n",
-              "0                 0.084367          54718            4943             220   \n",
-              "1                 0.009117          59193             468             260   \n",
-              "2                 0.010033          59131             530             267   \n",
-              "3                 0.096900          53966            5695             220   \n",
-              "\n",
-              "   true_positive  \n",
-              "0            119  \n",
-              "1             79  \n",
-              "2             72  \n",
-              "3            119  "
-            ],
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-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>model_name</th>\n",
-              "      <th>threshold</th>\n",
-              "      <th>selection_reason</th>\n",
-              "      <th>accuracy</th>\n",
-              "      <th>precision</th>\n",
-              "      <th>recall</th>\n",
-              "      <th>f1</th>\n",
-              "      <th>roc_auc</th>\n",
-              "      <th>pr_auc</th>\n",
-              "      <th>false_positive_rate</th>\n",
-              "      <th>predicted_positive_rate</th>\n",
-              "      <th>true_negative</th>\n",
-              "      <th>false_positive</th>\n",
-              "      <th>false_negative</th>\n",
-              "      <th>true_positive</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>0.058868</td>\n",
-              "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
-              "      <td>0.913950</td>\n",
-              "      <td>0.023508</td>\n",
-              "      <td>0.351032</td>\n",
-              "      <td>0.044066</td>\n",
-              "      <td>0.696746</td>\n",
-              "      <td>0.062551</td>\n",
-              "      <td>0.082851</td>\n",
-              "      <td>0.084367</td>\n",
-              "      <td>54718</td>\n",
-              "      <td>4943</td>\n",
-              "      <td>220</td>\n",
-              "      <td>119</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>0.243180</td>\n",
-              "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-              "      <td>0.987867</td>\n",
-              "      <td>0.144424</td>\n",
-              "      <td>0.233038</td>\n",
-              "      <td>0.178330</td>\n",
-              "      <td>0.676183</td>\n",
-              "      <td>0.057577</td>\n",
-              "      <td>0.007844</td>\n",
-              "      <td>0.009117</td>\n",
-              "      <td>59193</td>\n",
-              "      <td>468</td>\n",
-              "      <td>260</td>\n",
-              "      <td>79</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>LogisticRegression</td>\n",
-              "      <td>0.898330</td>\n",
-              "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
-              "      <td>0.986717</td>\n",
-              "      <td>0.119601</td>\n",
-              "      <td>0.212389</td>\n",
-              "      <td>0.153029</td>\n",
-              "      <td>0.662734</td>\n",
-              "      <td>0.050958</td>\n",
-              "      <td>0.008884</td>\n",
-              "      <td>0.010033</td>\n",
-              "      <td>59131</td>\n",
-              "      <td>530</td>\n",
-              "      <td>267</td>\n",
-              "      <td>72</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>0.034547</td>\n",
-              "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-              "      <td>0.901417</td>\n",
-              "      <td>0.020468</td>\n",
-              "      <td>0.351032</td>\n",
-              "      <td>0.038680</td>\n",
-              "      <td>0.665979</td>\n",
-              "      <td>0.050323</td>\n",
-              "      <td>0.095456</td>\n",
-              "      <td>0.096900</td>\n",
-              "      <td>53966</td>\n",
-              "      <td>5695</td>\n",
-              "      <td>220</td>\n",
-              "      <td>119</td>\n",
-              "    </tr>\n",
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-              "    <div class=\"colab-df-buttons\">\n",
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+     "output_type": "display_data",
+     "data": {
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+       "<Figure size 1200x1000 with 4 Axes>"
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-      "source": [
-        "trained_models = {}\n",
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-        "model_eval_rows = []\n",
-        "\n",
-        "# 최종 후보 비교용 학습 데이터셋 규모 관리\n",
-        "if len(X_train) > FINAL_TRAIN_SAMPLE_SIZE:\n",
-        "    X_final_train, _, y_final_train, _ = train_test_split(\n",
-        "        X_train,\n",
-        "        y_train,\n",
-        "        train_size=FINAL_TRAIN_SAMPLE_SIZE,\n",
-        "        random_state=RANDOM_STATE,\n",
-        "        stratify=y_train,\n",
-        "    )\n",
-        "else:\n",
-        "    X_final_train = X_train\n",
-        "    y_final_train = y_train\n",
-        "\n",
-        "print(f\"Final candidate training rows: {len(X_final_train):,} / train rows: {len(X_train):,}\")\n",
-        "\n",
-        "# 후보 모델 전체 학습\n",
-        "for model_name, config in candidate_configs.items():\n",
-        "    started_at = datetime.now()\n",
-        "    estimator = clone(config[\"estimator\"])\n",
-        "    estimator.set_params(**best_params_by_model[model_name])\n",
-        "    print(f\"[{model_name}] final fit start params={best_params_by_model[model_name]}\")\n",
-        "    estimator.fit(X_final_train, y_final_train)\n",
-        "\n",
-        "    proba = predict_positive_proba(estimator, X_valid)\n",
-        "    threshold = find_best_threshold(y_valid, proba)\n",
-        "    pred = (proba >= threshold).astype(int)\n",
-        "    cm_model = confusion_matrix(y_valid, pred, labels=[0, 1])\n",
-        "    metrics = compute_binary_metrics(y_valid, proba, threshold)\n",
-        "\n",
-        "    trained_models[model_name] = estimator\n",
-        "    model_valid_probabilities[model_name] = proba\n",
-        "    model_thresholds[model_name] = threshold\n",
-        "    model_confusion_matrices[model_name] = cm_model\n",
-        "\n",
-        "    model_eval_rows.append({\n",
-        "        \"model_name\": model_name,\n",
-        "        \"threshold\": threshold,\n",
-        "        \"selection_reason\": config[\"selection_reason\"],\n",
-        "        **metrics,\n",
-        "    })\n",
-        "    print(\n",
-        "        f\"[{model_name}] final fit done \"\n",
-        "        f\"PR-AUC={metrics['pr_auc']:.5f} \"\n",
-        "        f\"ROC-AUC={metrics['roc_auc']:.5f} \"\n",
-        "        f\"ACC={metrics['accuracy']:.5f} \"\n",
-        "        f\"FPR={metrics['false_positive_rate']:.5f} \"\n",
-        "        f\"elapsed={(datetime.now() - started_at).total_seconds():.1f}s\"\n",
-        "    )\n",
-        "\n",
-        "model_comparison = pd.DataFrame(model_eval_rows).sort_values(\n",
-        "    [\"pr_auc\", \"recall\", \"f1\", \"roc_auc\", \"accuracy\"],\n",
-        "    ascending=False,\n",
-        ").reset_index(drop=True)\n",
-        "\n",
-        "selected_model_name = str(model_comparison.iloc[0][\"model_name\"])\n",
-        "model = trained_models[selected_model_name]\n",
-        "valid_proba = model_valid_probabilities[selected_model_name]\n",
-        "best_threshold = float(model_thresholds[selected_model_name])\n",
-        "valid_pred = (valid_proba >= best_threshold).astype(int)\n",
-        "cm = model_confusion_matrices[selected_model_name]\n",
-        "\n",
-        "# 운영용 전체 재학습은 선택 모델 1개만 수행\n",
-        "if RETRAIN_SELECTED_MODEL_ON_FULL_DATA and len(X_final_train) < len(X_train):\n",
-        "    print(f\"[{selected_model_name}] selected model full-data refit start rows={len(X_train):,}\")\n",
-        "    refit_started_at = datetime.now()\n",
-        "    full_model = clone(candidate_configs[selected_model_name][\"estimator\"])\n",
-        "    full_model.set_params(**best_params_by_model[selected_model_name])\n",
-        "    full_model.fit(X_train, y_train)\n",
-        "\n",
-        "    model = full_model\n",
-        "    trained_models[selected_model_name] = full_model\n",
-        "    valid_proba = predict_positive_proba(model, X_valid)\n",
-        "    best_threshold = find_best_threshold(y_valid, valid_proba)\n",
-        "    valid_pred = (valid_proba >= best_threshold).astype(int)\n",
-        "    cm = confusion_matrix(y_valid, valid_pred, labels=[0, 1])\n",
-        "    full_metrics = compute_binary_metrics(y_valid, valid_proba, best_threshold)\n",
-        "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), list(full_metrics.keys())] = list(full_metrics.values())\n",
-        "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), \"threshold\"] = best_threshold\n",
-        "    model_thresholds[selected_model_name] = best_threshold\n",
-        "    model_confusion_matrices[selected_model_name] = cm\n",
-        "    print(f\"[{selected_model_name}] full-data refit done elapsed={(datetime.now() - refit_started_at).total_seconds():.1f}s\")\n",
-        "\n",
-        "roc_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"roc_auc\"])\n",
-        "pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
-        "accuracy = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"accuracy\"])\n",
-        "false_positive_rate = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"false_positive_rate\"])\n",
-        "\n",
-        "# AI 관리 요약\n",
-        "baseline_row = model_comparison[model_comparison[\"model_name\"].eq(\"LightGBM\")]\n",
-        "baseline_pr_auc = float(baseline_row.iloc[0][\"pr_auc\"]) if not baseline_row.empty else np.nan\n",
-        "best_pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
-        "performance_improvement = best_pr_auc - baseline_pr_auc if not np.isnan(baseline_pr_auc) else 0.0\n",
-        "\n",
-        "ai_management_summary = {\n",
-        "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/Recall/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
-        "    \"데이터셋_규모_관리\": {\n",
-        "        \"max_rows\": int(MAX_ROWS),\n",
-        "        \"profile_rows\": int(PROFILE_ROWS),\n",
-        "        \"train_rows\": int(len(X_train)),\n",
-        "        \"candidate_train_rows\": int(len(X_final_train)),\n",
-        "        \"valid_rows\": int(len(X_valid)),\n",
-        "        \"feature_count\": int(len(feature_cols)),\n",
-        "        \"positive_ratio\": float(y.mean()),\n",
-        "        \"full_data_refit\": bool(RETRAIN_SELECTED_MODEL_ON_FULL_DATA),\n",
-        "    },\n",
-        "    \"테스트_케이스_수_관리\": {\n",
-        "        \"validation_total\": int(len(y_valid)),\n",
-        "        \"validation_normal\": int((y_valid == 0).sum()),\n",
-        "        \"validation_defect\": int((y_valid == 1).sum()),\n",
-        "        \"cv_splits\": int(effective_cv_splits),\n",
-        "        \"cv_sample_size\": int(len(X_cv_pool)),\n",
-        "        \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
-        "        \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-        "        \"smote_enabled\": bool(USE_SMOTE),\n",
-        "        \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
-        "        \"threshold_strategy\": {\n",
-        "            \"type\": \"recall_priority\",\n",
-        "            \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
-        "            \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
-        "            \"beta\": float(THRESHOLD_BETA),\n",
-        "        },\n",
-        "    },\n",
-        "    \"정확도_Accuracy\": accuracy,\n",
-        "    \"오탐률_False_Positive_Rate\": false_positive_rate,\n",
-        "    \"모델_성능_개선_현황\": {\n",
-        "        \"baseline_model\": \"LightGBM\",\n",
-        "        \"baseline_pr_auc\": baseline_pr_auc,\n",
-        "        \"selected_model\": selected_model_name,\n",
-        "        \"selected_pr_auc\": best_pr_auc,\n",
-        "        \"pr_auc_improvement_over_lightgbm\": performance_improvement,\n",
-        "    },\n",
-        "}\n",
-        "\n",
-        "print(\"selected_model_name:\", selected_model_name)\n",
-        "print(\"ROC-AUC:\", round(roc_auc, 5))\n",
-        "print(\"PR-AUC:\", round(pr_auc, 5))\n",
-        "print(\"Accuracy:\", round(accuracy, 5))\n",
-        "print(\"False Positive Rate:\", round(false_positive_rate, 5))\n",
-        "print(\"Recall:\", round(float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"recall\"]), 5))\n",
-        "display(model_comparison)\n",
-        "display(pd.DataFrame([ai_management_summary]))\n"
-      ]
+      "image/png": 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mzFq0aJEyZMggV1dXlShRQvny5XvWhwIvCcIJIJ1auHBhgsvLly9vtax27doaP368goKCNG7cOBUoUECjRo3SqlWrdPLkyRQ9f7169RQQEKD169drzZo1MplM5nDCw8NDixYt0qJFi/TTTz9p4sSJioyMVNasWeXr66uvv/5ajRo1supz3bp15jt42NvbK3fu3Orevbv69u3LZE0AALzA6tWrJy8vL82cOVMbNmzQtWvXtHjxYv3xxx8qUqSIxo4dqw0bNujPP/9MUf/Vq1fXt99+q//7v//TuHHj5OXlpVGjRmnLli0Wfdra2mrKlCmaMGGCfvzxR61YsUKenp768MMP1a1bt9Ta3STr37+/3N3dNW/ePI0aNUpubm5q27atBg4cKAcHhydub2trq0mTJmn27NlavXq1fv75Z7m4uChv3rwKCAhQwYIFJcXVgiEhIVq+fLlu3ryprFmzqnz58urfv795Mk8HBweNHj1a48eP1+eff64HDx5o1KhRhBNIMhtTWswEA+Cl1Lx5c7m7u2vWrFlGDwUAAADAC4yvEgE8UXR0tHliyXi7d+/W33//neCZFgAAAACQHFzWAeCJQkND1bVrVzVr1kw5cuTQv//+q0WLFil79ux64403jB4eAAAAgBcc4QSAJ3Jzc1Px4sW1dOlS3bhxQ66urqpRo4YGDRqkrFmzGj08AAAAAC845pwAAAAAAACGYs4JAAAAAABgKMIJAAAAAABgKMIJGMbHx0dBQUHmxytWrJCPj48uXLhg4KjwNB79mSZFYGCgateu/YxGBACAsah3Xj7UO8CzQTjxkor/4Iv/9+qrr6patWoKDAxUaGio0cNLE/v371f79u1VsmRJValSRV988YXCw8OfuF1kZKQ+/vhjNWnSRGXKlFGpUqXUrFkzzZkzR9HR0RZtHz3OD/+7evWqRdsff/xRgwYNUr169eTj46OAgICn3seAgACL5yxfvrxat26tZcuWKTY29qn7h3Tnzh19+umnqlixovz9/RUQEKAjR44keftTp06pe/fuKlWqlMqXL68PPvhAN27csGozZswYNW/eXKVKlVLVqlXVs2dPHTp0KME+Q0ND9c4776hs2bIqXbq03n77bZ0/fz7F+xgUFJTo63jhwoXmds/iNfyotDjeoaGhGjRokOrXr69SpUqpbNmyev3117Vy5UolNA3T+vXr1bJlS/n5+alixYr6+OOPrfoEYAzqHeod6p3U8TzWOw/r2rWrfHx8NHz48GTvW7z0Vu88as2aNfLx8VGpUqUe2y46OlqNGjWSj4+PZsyYkeQxpQbu1vGSGzBggPLmzauoqCgdPHhQK1eu1L59+7Ru3To5OTkZPbxn5tixY3rzzTdVuHBhBQYG6vLly5o5c6bOnDmj6dOnP3bbyMhI/fPPP6pevbo8PT1la2urAwcOaNSoUQoODta4ceOstok/zg/LnDmzxeOFCxfq8OHD8vPz061bt556H+PlypVLAwcOlCTdvHlTq1at0pAhQ3TmzBkNGjQo1Z4nKYKDg2VnZ5esbUaMGJHgH4TPg9jYWPXs2VPHjx9X9+7dlTVrVi1YsEABAQFasWKFChQo8NjtL1++rI4dOypTpkx67733FBERoZkzZ+rEiRNaunSpHB0dJUnLli3TsmXLVK9ePXXo0EF3797V4sWL1a5dO02fPl2VK1c29xkeHq7OnTvr7t276tWrlxwcHDR79mx16tRJq1ateqq7p3z++edydXW1WFayZEnz/5/VazheWh3vmzdvKjQ0VA0aNFDu3Ln14MEDbd++XYGBgTp9+rT590mSFixYoGHDhqlSpUrmP3Z++OEHHT58WEuXLn2p30eBFwn1DvVOWqLesfQs6p2Hbdq0SQcPHky1/U0v9c7DwsPDNXbsWKv9Tsi8efN06dKllO7e0zHhpbR8+XKTt7e3KTg42GL52LFjTd7e3qb169cbNLL/eHt7myZMmGB+HD/m8+fPP3XfPXr0MFWpUsV09+5d87IlS5aYvL29Tb///nuK+hw+fLjJ29vbdOXKFasxP3qcE3Lx4kVTTEyMyWQymRo3bmzq1KlTisbxsE6dOpkaN25ssSwiIsJUvXp1k7+/vykqKirB7WJiYkyRkZFP/fwvu/Xr15u8vb1NP/30k3nZ9evXTWXLljUNHDjwidt/9tlnphIlSphCQkLMy7Zv327y9vY2LVq0yLzs0KFDprCwMIttb9y4YapYsaLpjTfesFj+/fffm7y9vU1//fWXedk///xjKlasmGncuHHJ3keTyWSaMGGCydvb23T9+vXHtnsWr+GHpdXxTkyvXr1M/v7+pgcPHphMJpPp/v37prJly5o6duxoio2NNbfbunWrydvb2/TDDz8kZ/cAPAPUO9Q71DtP73msd+JFRkaaatWqZZo4caLJ29vbNGzYsOTunll6rnfGjh1rql+/vun99983+fv7J9r3tWvXTGXKlDEf7+nTpydjz54el3WkM2XLlpUkq1PAT506pQEDBqh8+fLy8/NTq1attGXLFqvt79y5o5EjR6p27dry9fVV9erV9eGHH5pPI4qKitK3336rVq1aqUyZMvL391eHDh20a9euZ79z/xMWFqYdO3aoWbNmypgxo3l58+bN5erqqp9++ilF/Xp6ekqKOwaJPW9MTEyi2+fOnVu2ts/+V87FxUUlS5ZURESE+ecSfxrcmjVr1LhxY/n5+en333+XFHeK++DBg1W5cmX5+vqqcePGWrZsmVW/9+/fV1BQkOrXry8/Pz9VrVpV/fr107lz58xtHr0GMywsTF9++aX59VKpUiV17drV4rS1hK7BjIiI0OjRo1WjRg35+vqqfv36mjFjhtU3DvH7tXnzZjVp0sQ8/t9+++3pD6SkjRs3Klu2bKpXr555mbu7uxo2bKgtW7YoKirqsdtv2rRJNWvWVJ48eczLKleurAIFCli8Dn19fZUhQwaLbbNmzaqyZcvq33//tRqTn5+fSpQoYV5WuHBhVapUKcWv7aR61q/htDreifH09NS9e/fMpzOfPHlSd+7cUcOGDWVjY2NuV6tWLbm6umr9+vXJ3UUAaYR6h3qHeifpnsd6J960adNkMpnUvXv3lOxairxs9c6ZM2c0e/ZsDR48WPb2j79w4uuvv1bBggXVrFmzZO5V6uCyjnQmJCREkuUpeCdPnlT79u2VM2dOvfXWW+YPtL59+yooKEh169aVFHc6UMeOHXXq1Cm1bt1ar776qm7evKmtW7cqNDRU7u7uCgsL09KlS9WkSRO1adNG4eHhWrZsmXr06KGlS5eqWLFiyRpveHi47t+//8R2Dg4OypQpkyTp+PHjevDggXx9fS3aODo6qlixYjp27FiSnjsqKkphYWG6f/++Dh8+rJkzZ8rT01P58+e3atu5c2dFRETIwcFBVatWVWBg4BNPyXqWLly4IDs7O4uf865du/TTTz+pY8eOypo1qzw9PXXt2jW1bdtWNjY26tixo9zd3fXbb79pyJAhCgsL05tvvilJiomJUa9evbRz5041btxYnTt3Vnh4uLZv364TJ07Iy8srwXF89tln2rhxozp16qTChQvr1q1b2rdvn06dOqXixYsnuI3JZNLbb7+t3bt36/XXX1exYsX0+++/a8yYMQoNDdXHH39s0X7fvn3atGmTOnTooAwZMmju3LkaMGCAfvnlF/MlDtHR0bp7926Sjl2WLFnMH0jHjh3Tq6++avUB5efnp8WLF+v06dPy8fFJsJ/Q0FBdv37d6nUoSSVKlEhSQXH16lVlyZLF/Dg2NlbHjx9X69atrdr6+fnpjz/+UFhYmEWRmhy3b9+2eGxnZyc3N7dk9/OiHO/IyEhFREQoIiJCe/bs0YoVK+Tv7y9nZ2dJMhcH8Y8f5uzsrGPHjik2NjZNinAAyUO9Q71DvfN4z3O9E+/ixYuaNm2aRo4cmeBncUqlt3pn5MiRqlChgmrUqPHY0DI4OFirVq3SggULLL6USUuEEy+5sLAw3bhxQ1FRUfrrr780ceJEOTo6qlatWuY2X375pXLnzq3ly5ebr1Hq0KGD2rdvr6+//tr8YT1jxgydOHFCEydONC+TpD59+pgTXjc3N23dutXiWqe2bduqYcOGmjt3rkaOHJms8Y8YMUIrV658Yrvy5ctr7ty5kmSemClHjhxW7bJnz659+/Yl6bl//vlni2vPfX19NXLkSIvE0dnZWa1atVKFChWUMWNGHT58WLNnz9Ybb7yhlStXKnfu3El6rqcRExNj/sbg5s2bWrhwoY4cOaJatWrJxcXF3O706dNau3atihQpYl42ZMgQxcTEaO3ateYPtvbt22vgwIGaOHGi3njjDTk7O2vVqlXauXOnBg8ebP4Al6SePXs+9vrJbdu2qW3btgoMDDQve+uttx67P1u2bNGuXbv07rvv6u2335YkdezYUQMGDNAPP/ygTp06WRQHp06d0o8//mheVqFCBTVv3lzr169Xp06dJMVNFta5c+fHPu/Dzx9/Pe3Vq1fN3749LP61deXKlUQ/PK5cuSIp7jX3qOzZs+vWrVuKiopK8LpASdq7d68OHjxoPgaSzNsk1mf886Y0nGjQoIHFY09PT23dujXZ/bwox/uHH36wuKa6UqVKGjVqlPlx/vz5ZWNjo/3791sEQv/++6/5d+727dtPNc8HgNRBvWOJeod650me53on3ujRo1WsWDE1btw4SfuUVOmp3vn111+1fft2rV69+rHjM5lMGjFihBo1aqRSpUoZdjchwomX3MNvrFLcL9/YsWOVK1cuSXF/7OzatUsDBgxQWFiYRduqVasqKChIoaGhypkzpzZt2qSiRYtafFDHi0/X7OzszBMExcbG6s6dO4qNjZWvr6+OHj2a7PH36NEjSacVPZyYR0ZGSlKCb4JOTk7m9U9SoUIFzZo1S3fu3NHOnTt1/Phx3bt3z6JNo0aN1KhRI/PjOnXqqGrVqurUqZOmTJnyVDMKJ9W///6rSpUqmR/b2NioZs2aVoVRuXLlLD6oTSaTNm3apIYNG8pkMlnM8Fu1alWtX79eR44cUZkyZbRp0yZlzZrV/OH3sMclq5kzZ9Zff/1lfg0lxW+//SY7OzurmZG7deumjRs36rfffrMYR+XKlS0+vIsWLaqMGTNanMpbtGhRzZo1K0nP//CbfWRkZIKvo/hlj/uWK35dYq/Dx/V//fp1vf/++8qbN6969OiRrD6T8s1bYoKCgiyCjZROIveiHO/GjRvL19dXN27c0C+//KLr169bvD/En2K5atUqFS5cWHXr1lVoaKhGjBghBwcHRUdHP9XxBpB6qHcsUe9Q7zzJ81zvSHFnwGzatElLlixJwt4kT3qpd6KiojRq1Ci98cYbFr8TCVmxYoVOnDihCRMmPHlnniHCiZfc0KFDVbBgQd29e1fLly/Xnj17LF7M586dk8lk0rfffqtvv/02wT6uX7+unDlz6ty5cxbXRiVm5cqVmjlzpk6fPm1xK6pHZ3dOiiJFijzxl+lRj56S/bD79+8n+bSwbNmyKVu2bJLiEtbvvvtOXbt21aZNmxJMK+OVLVtWJUuW1M6dO5M17pTy9PTUF198IRsbGzk6OqpAgQLy8PCwavfo8b9x44bu3LmjxYsXa/HixQn2Hf8Bfu7cORUsWPCJ16k9atCgQQoMDFTNmjVVvHhx1ahRQy1atFC+fPkS3SYkJEQ5cuSw+va/cOHC5vUPS+jbGjc3N4trZd3c3BKdAfpxnJ2dE3wdxS973IdZ/LrEXofx/T8qIiJCvXr1Unh4uBYsWGBxbWZS+nyaWenLli0rd3f3FG8f70U53p6enuZrq5s0aaJPP/1UXbt21YYNG8xthw8frsjISH311Vf66quvJEnNmjWTl5eXNm3alKRZrwE8e9Q7lqh3/kO982TPW73z4MEDffnll2revLnFHFupJb3UO7Nnz9bNmzfVv3//x44nLCxM48ePV/fu3dPkLKjHIZx4yZUoUUJ+fn6S4lLuDh066P3339eGDRuUIUMG872hu3XrpmrVqiXYR2LX1yVk9erVCgwMVJ06ddS9e3d5eHjIzs5OU6dOtZqUKinu3r2bpOTfwcHBfK3aw6e3P+rq1asJnv6YFPXr19c333yjLVu26I033nhs21y5cun06dMpep7kcnV1TdIb46MfDPE/+2bNmqlly5YJbpPYKWVJ1ahRI5UtW1Y///yztm/frhkzZmjatGkKCgpSjRo1nqrveIndyuvh0y+joqKsri9MjLu7u7nP7NmzW92/XfrvtfW411L8uoS2j7+28tHUOyoqSv3799fx48c1Y8YMeXt7W6yP3yaxPp80prTyohzvR9WvX19LlizRnj17zO+HmTJl0pQpU3Tx4kWFhIQoT5488vT01BtvvCF3d3erW+gBMAb1jiXqnf9Q7yTsea53Vq1apdOnT2vYsGFWlxeEh4frwoUL8vDwsLicxwjP8/G+e/eupkyZog4dOigsLMx8xlhERIRMJpMuXLggFxcXeXh4aMaMGYqOjlajRo3Mx/vy5cuS4ibGvXDhgnLkyPHEOio1EE6kI3Z2dho4cKA6d+6s+fPnq2fPnuZE18HB4Ylv+F5eXjp58uRj22zcuFH58uXTxIkTLU5/S+kpQl9++WWyr8H09vaWvb29Dh8+bHEKYlRUlI4dO6aGDRumaCzxaWRSJr45f/78c38duru7u7lgS8rP/q+//lJ0dLQcHByS9Tw5cuRQx44d1bFjR12/fl0tW7bUd999l+iHtaenp3bu3Gk1sWP8LM7x33Qnx4EDB1J0TWDRokW1b98+q0kPg4OD5eLiooIFCybaT86cOeXu7q7Dhw9brQsODlbRokUtlsXGxuqjjz7Szp079X//938qX7681Xa2trby9vZOtM98+fKleL6J1PQiHO+ExP9hkNDveJ48ecyzYt+5c0eHDx9W/fr1n9gngLRHvUO98zDqnYQ9z/XOpUuXFB0drfbt21utW7VqlVatWqVJkyapTp06SdrXZ+V5Pt63b99WRESEpk+frunTp1u1fe211/Taa69p8uTJunTpkm7fvp3g3B7fffedvvvuO61atSrZE/2mBOFEOlOhQgWVKFFCc+bMUZcuXeTh4aHy5ctr8eLF6tSpk1VSd+PGDfNpT/Xq1dOkSZP0888/W12HaTKZZGNjY04E4x9L0l9//aWDBw9a3O4mqVJyDWamTJlUqVIlrVmzRn369DG/4a9evVoREREWk+Dcu3dPFy9eVNasWc37eePGDWXNmtXq2sKlS5dKksXsuA8fn3jbtm3TkSNHrK4hfN7Y2dmpfv36Wrt2rXr16mWVWj/6s//11181f/58q+t6H/5ZPywmJkYRERHmWcUlycPDQzly5HjsLZKqV6+uxYsXa/78+erVq5d5+ezZs2VjY6Pq1asne19Tek1ggwYNtHHjRm3atMn8urlx44Y2bNigWrVqWZ0yLFl+81avXj2tWrVKly5dMp8mt3PnTp05c8bqOI4YMUI//vijhg8f/tjTievXr69x48bp0KFD5m8J//33X+3atUvdunVL0j4+a8/78U7o91aSli1bJhsbm0RnVo83btw4xcTEqEuXLknaRwBpj3qHeice9U7Cnud6p1GjRgn+Idy3b1/VqFFDbdu2fSaXeyTX83y8PTw8NGnSJKtx/PDDDzp48KDGjx9vHlNAQIBV0HP9+nUNHTpUrVq10muvvZaiy9VSgnAiHerevbveeecdrVixQu3bt9dnn32mDh06qGnTpmrbtq3y5cuna9eu6eDBg7p8+bLWrFlj3m7jxo1655131Lp1axUvXly3b9/W1q1bNWzYMBUtWlQ1a9bUpk2b1LdvX9WsWVMXLlzQokWLVKRIEUVERCR7rCm5BlOS3nvvPb3xxhsKCAhQ27ZtdfnyZc2aNUtVq1a1eLMPDg5W586d1a9fP/P1WGvWrNGiRYtUp04d5cuXT+Hh4frjjz+0fft21apVy2IypjfeeEPFihWTr6+vMmXKpKNHj2r58uXKnTu3evfubTGmPXv2aM+ePZLi3oAiIiI0efJkSXGTN5UrV87c1sfHx+LbkWfl/fff1+7du9W2bVu1adNGRYoU0e3bt3XkyBHt3LlTf/75pySpRYsWWrVqlUaNGqXg4GCVKVNG9+7d086dO9W+ffsEk+vw8HDVqFFD9evXV9GiReXq6qodO3bo0KFDFrNZP6p27dqqUKGCvvnmG4WEhMjHx0fbt2/Xli1b1KVLl2SddhsvpdcE1q9fX/7+/ho8eLD++ecfZc2aVQsXLlRMTIzV9XvxHwYPz/bcu3dvbdiwQZ07dzbffi3+9MWH7/4we/ZsLViwQKVKlZKzs7PVjMp169Y1z23QoUMHLV26VL169VK3bt1kb2+v2bNny8PDwyqcCAgI0J9//qnjx48ne98TktTX8PN+vKdMmaL9+/erWrVqypMnj27duqVNmzbp0KFDCggIsLh93vfff68TJ06oZMmSsrOz05YtW/THH3/o3XfffS4KIwCJo96h3olHvfN4z1u9U7hwYfPcG4/Kmzev1c+Besf6eLu4uCT4et28ebMOHTpksa548eJWX8zEX95RpEiRND1DhXAiHapXr568vLw0c+ZMtW3bVkWKFNHy5cs1ceJErVy5Urdu3ZK7u7teffVV9e3b17xdhgwZNH/+fAUFBennn3/WypUr5eHhoUqVKplnJm7VqpWuXbumxYsX648//lCRIkU0duxYbdiwwfzGnxaKFy+uWbNm6euvv9aoUaOUIUMGvf766xa3ykpMmTJldODAAa1fv17Xrl2Tvb29ChYsqMGDB1vN3tywYUNt27ZN27dvV2RkpLJnz642bdqoX79+5sml4u3atUsTJ060WBY/KVe/fv3Mb3Th4eGSEr5FUGrLli2bli5dav6GaOHChcqSJYuKFCmiQYMGmdvZ2dlp2rRpmjJlitatW6dNmzYpS5YsKl26dKLXaTo7O6t9+/bavn27Nm3aJJPJJC8vL3NxmBhbW1tNmTJFEyZM0I8//qgVK1bI09NTH374YZqfGWBnZ6fvv/9eY8aM0dy5c3X//n35+flp1KhRKlSo0BO3z507t+bNm6fRo0dr3LhxcnBwUI0aNRQYGGiRiv/999+S4k4PPHDggFU/W7ZsMYcTGTNmNN+mbsqUKYqNjVWFChU0ePBgq2+1wsPDU/V1lNTXcEql1fGuWbOmzp8/r+XLl+vmzZtydHSUj4+PRo0aZXU9sre3t37++Wdt3bpVsbGx8vHx0f/93/+l+HRpAGmHeufxqHeod+I9j/VOclDvJHy8X0Q2psfdtBdAmtu2bZt69eql1atXP/UETUi/wsLCVKFCBX388cfq2LGj0cMBAMAC9Q5SA/XOy8X2yU0ApKVdu3apcePGfFDjqezdu1c5c+ZUmzZtjB4KAABWqHeQGqh3Xi6cOQEAAAAAAAzFmRMAAAAAAMBQhBMAAAAAAMBQhBMAAAAAAMBQhBMAAAAAAMBQ9kYPANYOHDggk8kkBwcHo4cCAHgGoqOjZWNjo1KlShk9FMBQ1DwA8HJLTs1DOPEcMplMin4Qo5Ard4weSrpjZ2ujHO4ZdOVGuGJiuZFNWsqb293oIaRPJik2Jlq2dg6SjdGDST9MJpO4VxZAzWMU6h1j5aPmMYTpoZrHhponzSSn5iGceA45ODgo5ModtR28zOihpDveXh6aObSZBk/aqhPnrhs9nHTlxJZxRg8hXYq+f083L56SW04vOTi5GD2cdOPa+eNGDwF4LlDzGIN6x1infhlv9BDSpej793Ttwj9yz52fmicNXTn7d5LbMucEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAAwFOEEAAAAAAA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\n"
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "markdown",
-      "id": "AGL93TiObEfC",
-      "metadata": {
-        "id": "AGL93TiObEfC"
-      },
-      "source": [
-        "## 9. 후보 모델별 Threshold 튜닝 및 Confusion Matrix\n",
-        "\n",
-        "각 후보 모델은 validation set에서 recall 우선 threshold를 사용합니다.\n",
-        "\n",
-        "기본 정책은 `RECALL_PRIORITY_TARGET` 이상을 만족하는 threshold 중 precision이 가장 높은 값을 선택하고, 목표 recall을 만족하는 후보가 없으면 F-beta(기본 beta=2) 기준으로 선택합니다. Accuracy는 클래스 불균형 때문에 참고 지표로만 해석합니다.\n"
-      ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "           model_name  accuracy  false_positive_rate  precision    recall  \\\n",
+       "0        RandomForest  0.913950             0.082851   0.023508  0.351032   \n",
+       "1          ExtraTrees  0.987867             0.007844   0.144424  0.233038   \n",
+       "2  LogisticRegression  0.986717             0.008884   0.119601  0.212389   \n",
+       "3            LightGBM  0.901417             0.095456   0.020468  0.351032   \n",
+       "\n",
+       "         f1   roc_auc    pr_auc  threshold  \n",
+       "0  0.044066  0.696746  0.062551   0.058868  \n",
+       "1  0.178330  0.676183  0.057577   0.243180  \n",
+       "2  0.153029  0.662734  0.050958   0.898330  \n",
+       "3  0.038680  0.665979  0.050323   0.034547  "
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-d141ae49-5d62-4964-ad20-d9c4bab91e63\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>pr_auc</th>\n",
+       "      <th>threshold</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.913950</td>\n",
+       "      <td>0.082851</td>\n",
+       "      <td>0.023508</td>\n",
+       "      <td>0.351032</td>\n",
+       "      <td>0.044066</td>\n",
+       "      <td>0.696746</td>\n",
+       "      <td>0.062551</td>\n",
+       "      <td>0.058868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>0.987867</td>\n",
+       "      <td>0.007844</td>\n",
+       "      <td>0.144424</td>\n",
+       "      <td>0.233038</td>\n",
+       "      <td>0.178330</td>\n",
+       "      <td>0.676183</td>\n",
+       "      <td>0.057577</td>\n",
+       "      <td>0.243180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>0.986717</td>\n",
+       "      <td>0.008884</td>\n",
+       "      <td>0.119601</td>\n",
+       "      <td>0.212389</td>\n",
+       "      <td>0.153029</td>\n",
+       "      <td>0.662734</td>\n",
+       "      <td>0.050958</td>\n",
+       "      <td>0.898330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>0.901417</td>\n",
+       "      <td>0.095456</td>\n",
+       "      <td>0.020468</td>\n",
+       "      <td>0.351032</td>\n",
+       "      <td>0.038680</td>\n",
+       "      <td>0.665979</td>\n",
+       "      <td>0.050323</td>\n",
+       "      <td>0.034547</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d141ae49-5d62-4964-ad20-d9c4bab91e63')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-d141ae49-5d62-4964-ad20-d9c4bab91e63 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-d141ae49-5d62-4964-ad20-d9c4bab91e63');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"]])\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04624820792323862,\n        \"min\": 0.9014166666666666,\n        \"max\": 0.9878666666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9878666666666667,\n          0.9014166666666666,\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04692891215226371,\n        \"min\": 0.007844320410318299,\n        \"max\": 0.09545599302727074,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.007844320410318299,\n          0.09545599302727074,\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0643379448162342,\n        \"min\": 0.02046783625730994,\n        \"max\": 0.14442413162705667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.14442413162705667,\n          0.02046783625730994,\n          0.023508494666139867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07456283468415088,\n        \"min\": 0.21238938053097345,\n        \"max\": 0.35103244837758113,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.35103244837758113,\n          0.23303834808259588,\n          0.21238938053097345\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07254091975454982,\n        \"min\": 0.038680318543799774,\n        \"max\": 0.17832957110609482,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17832957110609482,\n          0.038680318543799774,\n          0.04406591371968154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015334779408148261,\n        \"min\": 0.6627341233129422,\n        \"max\": 0.6967463513986769,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6761829953791527,\n          0.6659786100217459,\n          0.6967463513986769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005812604054563798,\n        \"min\": 0.050323296178874356,\n        \"max\": 0.06255052428644334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.05757660266781409,\n          0.050323296178874356,\n          0.06255052428644334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40395240727098636,\n        \"min\": 0.03454695956318568,\n        \"max\": 0.8983303412868939,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2431800238242072,\n          0.03454695956318568,\n          0.058868157090478435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "print(\"== Selected Model ==\")\n",
+    "print(\"model:\", selected_model_name)\n",
+    "print(\"best_threshold:\", round(best_threshold, 5))\n",
+    "print(classification_report(y_valid, valid_pred, digits=4, zero_division=0))\n",
+    "\n",
+    "# 후보 모델별 Confusion Matrix\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "axes = axes.ravel()\n",
+    "for ax, (model_name, cm_model) in zip(axes, model_confusion_matrices.items()):\n",
+    "    ConfusionMatrixDisplay(cm_model, display_labels=[\"Normal\", \"Defect\"]).plot(\n",
+    "        ax=ax,\n",
+    "        cmap=\"Blues\",\n",
+    "        values_format=\"d\",\n",
+    "        colorbar=False,\n",
+    "    )\n",
+    "    row = model_comparison[model_comparison[\"model_name\"].eq(model_name)].iloc[0]\n",
+    "    ax.set_title(\n",
+    "        f\"{model_name}\\n\"\n",
+    "        f\"Recall={row['recall']:.3f}, Precision={row['precision']:.3f}, F1={row['f1']:.3f}\"\n",
+    "    )\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 비교 지표 테이블\n",
+    "display(model_comparison[[\n",
+    "    \"model_name\",\n",
+    "    \"accuracy\",\n",
+    "    \"false_positive_rate\",\n",
+    "    \"precision\",\n",
+    "    \"recall\",\n",
+    "    \"f1\",\n",
+    "    \"roc_auc\",\n",
+    "    \"pr_auc\",\n",
+    "    \"threshold\",\n",
+    "]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ALpJVyy2bEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "ALpJVyy2bEfC",
+    "outputId": "b964a332-cdc6-45a1-f4e9-484e34c728a0"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 19,
-      "id": "-bI4yc81bEfC",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "-bI4yc81bEfC",
-        "outputId": "fffa66b9-fb9b-4c84-b9d5-da0983048bc2"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "== Selected Model ==\n",
-            "model: RandomForest\n",
-            "best_threshold: 0.05887\n",
-            "              precision    recall  f1-score   support\n",
-            "\n",
-            "           0     0.9960    0.9171    0.9549     59661\n",
-            "           1     0.0235    0.3510    0.0441       339\n",
-            "\n",
-            "    accuracy                         0.9140     60000\n",
-            "   macro avg     0.5098    0.6341    0.4995     60000\n",
-            "weighted avg     0.9905    0.9140    0.9498     60000\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x1000 with 4 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "           model_name  accuracy  false_positive_rate  precision    recall  \\\n",
-              "0        RandomForest  0.913950             0.082851   0.023508  0.351032   \n",
-              "1          ExtraTrees  0.987867             0.007844   0.144424  0.233038   \n",
-              "2  LogisticRegression  0.986717             0.008884   0.119601  0.212389   \n",
-              "3            LightGBM  0.901417             0.095456   0.020468  0.351032   \n",
-              "\n",
-              "         f1   roc_auc    pr_auc  threshold  \n",
-              "0  0.044066  0.696746  0.062551   0.058868  \n",
-              "1  0.178330  0.676183  0.057577   0.243180  \n",
-              "2  0.153029  0.662734  0.050958   0.898330  \n",
-              "3  0.038680  0.665979  0.050323   0.034547  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d141ae49-5d62-4964-ad20-d9c4bab91e63\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>model_name</th>\n",
-              "      <th>accuracy</th>\n",
-              "      <th>false_positive_rate</th>\n",
-              "      <th>precision</th>\n",
-              "      <th>recall</th>\n",
-              "      <th>f1</th>\n",
-              "      <th>roc_auc</th>\n",
-              "      <th>pr_auc</th>\n",
-              "      <th>threshold</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>0.913950</td>\n",
-              "      <td>0.082851</td>\n",
-              "      <td>0.023508</td>\n",
-              "      <td>0.351032</td>\n",
-              "      <td>0.044066</td>\n",
-              "      <td>0.696746</td>\n",
-              "      <td>0.062551</td>\n",
-              "      <td>0.058868</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>ExtraTrees</td>\n",
-              "      <td>0.987867</td>\n",
-              "      <td>0.007844</td>\n",
-              "      <td>0.144424</td>\n",
-              "      <td>0.233038</td>\n",
-              "      <td>0.178330</td>\n",
-              "      <td>0.676183</td>\n",
-              "      <td>0.057577</td>\n",
-              "      <td>0.243180</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>LogisticRegression</td>\n",
-              "      <td>0.986717</td>\n",
-              "      <td>0.008884</td>\n",
-              "      <td>0.119601</td>\n",
-              "      <td>0.212389</td>\n",
-              "      <td>0.153029</td>\n",
-              "      <td>0.662734</td>\n",
-              "      <td>0.050958</td>\n",
-              "      <td>0.898330</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>LightGBM</td>\n",
-              "      <td>0.901417</td>\n",
-              "      <td>0.095456</td>\n",
-              "      <td>0.020468</td>\n",
-              "      <td>0.351032</td>\n",
-              "      <td>0.038680</td>\n",
-              "      <td>0.665979</td>\n",
-              "      <td>0.050323</td>\n",
-              "      <td>0.034547</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d141ae49-5d62-4964-ad20-d9c4bab91e63')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d141ae49-5d62-4964-ad20-d9c4bab91e63 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d141ae49-5d62-4964-ad20-d9c4bab91e63');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
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-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"]])\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04624820792323862,\n        \"min\": 0.9014166666666666,\n        \"max\": 0.9878666666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9878666666666667,\n          0.9014166666666666,\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04692891215226371,\n        \"min\": 0.007844320410318299,\n        \"max\": 0.09545599302727074,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.007844320410318299,\n          0.09545599302727074,\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0643379448162342,\n        \"min\": 0.02046783625730994,\n        \"max\": 0.14442413162705667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.14442413162705667,\n          0.02046783625730994,\n          0.023508494666139867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07456283468415088,\n        \"min\": 0.21238938053097345,\n        \"max\": 0.35103244837758113,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.35103244837758113,\n          0.23303834808259588,\n          0.21238938053097345\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07254091975454982,\n        \"min\": 0.038680318543799774,\n        \"max\": 0.17832957110609482,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17832957110609482,\n          0.038680318543799774,\n          0.04406591371968154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015334779408148261,\n        \"min\": 0.6627341233129422,\n        \"max\": 0.6967463513986769,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6761829953791527,\n          0.6659786100217459,\n          0.6967463513986769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005812604054563798,\n        \"min\": 0.050323296178874356,\n        \"max\": 0.06255052428644334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.05757660266781409,\n          0.050323296178874356,\n          0.06255052428644334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40395240727098636,\n        \"min\": 0.03454695956318568,\n        \"max\": 0.8983303412868939,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2431800238242072,\n          0.03454695956318568,\n          0.058868157090478435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {}
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 1800x400 with 3 Axes>"
       ],
-      "source": [
-        "print(\"== Selected Model ==\")\n",
-        "print(\"model:\", selected_model_name)\n",
-        "print(\"best_threshold:\", round(best_threshold, 5))\n",
-        "print(classification_report(y_valid, valid_pred, digits=4, zero_division=0))\n",
-        "\n",
-        "# 후보 모델별 Confusion Matrix\n",
-        "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
-        "axes = axes.ravel()\n",
-        "for ax, (model_name, cm_model) in zip(axes, model_confusion_matrices.items()):\n",
-        "    ConfusionMatrixDisplay(cm_model, display_labels=[\"Normal\", \"Defect\"]).plot(\n",
-        "        ax=ax,\n",
-        "        cmap=\"Blues\",\n",
-        "        values_format=\"d\",\n",
-        "        colorbar=False,\n",
-        "    )\n",
-        "    row = model_comparison[model_comparison[\"model_name\"].eq(model_name)].iloc[0]\n",
-        "    ax.set_title(\n",
-        "        f\"{model_name}\\n\"\n",
-        "        f\"Recall={row['recall']:.3f}, Precision={row['precision']:.3f}, F1={row['f1']:.3f}\"\n",
-        "    )\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n",
-        "\n",
-        "# 비교 지표 테이블\n",
-        "display(model_comparison[[\n",
-        "    \"model_name\",\n",
-        "    \"accuracy\",\n",
-        "    \"false_positive_rate\",\n",
-        "    \"precision\",\n",
-        "    \"recall\",\n",
-        "    \"f1\",\n",
-        "    \"roc_auc\",\n",
-        "    \"pr_auc\",\n",
-        "    \"threshold\",\n",
-        "]])\n"
-      ]
+      "image/png": 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\n"
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "execution_count": 20,
-      "id": "ALpJVyy2bEfC",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "ALpJVyy2bEfC",
-        "outputId": "b964a332-cdc6-45a1-f4e9-484e34c728a0"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1800x400 with 3 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x400 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 900x700 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "       model_name                                       feature  \\\n",
-              "457  RandomForest                              L3_S33_mean_time   \n",
-              "458  RandomForest                              L3_S34_mean_time   \n",
-              "459  RandomForest                            process_start_time   \n",
-              "460  RandomForest                      L3_S33_num_missing_ratio   \n",
-              "461  RandomForest                              L3_S37_mean_time   \n",
-              "462  RandomForest                              L3_S29_mean_time   \n",
-              "463  RandomForest                                   L3_S33_seen   \n",
-              "464  RandomForest                              L3_S30_mean_time   \n",
-              "465  RandomForest                                  L3_S33_F3859   \n",
-              "466  RandomForest                              process_end_time   \n",
-              "467  RandomForest                                   L3_duration   \n",
-              "468  RandomForest                                L3_S29_num_std   \n",
-              "469  RandomForest          aux_paint_thermal_defect_probability   \n",
-              "470  RandomForest                                  L1_S24_F1844   \n",
-              "471  RandomForest                               L3_S33_num_mean   \n",
-              "472  RandomForest                                    L3_num_std   \n",
-              "473  RandomForest                                  L3_S33_F3857   \n",
-              "474  RandomForest  aux_body_ford_vibration_abnormal_probability   \n",
-              "475  RandomForest                        total_process_duration   \n",
-              "476  RandomForest                                    L1_num_min   \n",
-              "477  RandomForest                                 L3_date_count   \n",
-              "478  RandomForest                               L3_S29_num_mean   \n",
-              "479  RandomForest                                    L0_num_min   \n",
-              "480  RandomForest                                  L3_S29_F3458   \n",
-              "481  RandomForest                                  L3_S30_F3744   \n",
-              "482  RandomForest                                L3_S33_num_std   \n",
-              "483  RandomForest                                    L0_num_std   \n",
-              "484  RandomForest                                L3_S30_num_std   \n",
-              "485  RandomForest                                  L3_S30_F3759   \n",
-              "486  RandomForest                                   L3_S34_seen   \n",
-              "\n",
-              "     importance_gain      importance_type  \n",
-              "457         0.011733  feature_importances  \n",
-              "458         0.011186  feature_importances  \n",
-              "459         0.010766  feature_importances  \n",
-              "460         0.008505  feature_importances  \n",
-              "461         0.008500  feature_importances  \n",
-              "462         0.008475  feature_importances  \n",
-              "463         0.008394  feature_importances  \n",
-              "464         0.008029  feature_importances  \n",
-              "465         0.008004  feature_importances  \n",
-              "466         0.007741  feature_importances  \n",
-              "467         0.007571  feature_importances  \n",
-              "468         0.007533  feature_importances  \n",
-              "469         0.007316  feature_importances  \n",
-              "470         0.007214  feature_importances  \n",
-              "471         0.007032  feature_importances  \n",
-              "472         0.006991  feature_importances  \n",
-              "473         0.006660  feature_importances  \n",
-              "474         0.006541  feature_importances  \n",
-              "475         0.006364  feature_importances  \n",
-              "476         0.006358  feature_importances  \n",
-              "477         0.006329  feature_importances  \n",
-              "478         0.006163  feature_importances  \n",
-              "479         0.006136  feature_importances  \n",
-              "480         0.006061  feature_importances  \n",
-              "481         0.005915  feature_importances  \n",
-              "482         0.005873  feature_importances  \n",
-              "483         0.005700  feature_importances  \n",
-              "484         0.005681  feature_importances  \n",
-              "485         0.005666  feature_importances  \n",
-              "486         0.005639  feature_importances  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-a0d9b4da-3d03-4c33-a571-82db618689ad\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>model_name</th>\n",
-              "      <th>feature</th>\n",
-              "      <th>importance_gain</th>\n",
-              "      <th>importance_type</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>457</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S33_mean_time</td>\n",
-              "      <td>0.011733</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>458</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S34_mean_time</td>\n",
-              "      <td>0.011186</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>459</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>process_start_time</td>\n",
-              "      <td>0.010766</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>460</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S33_num_missing_ratio</td>\n",
-              "      <td>0.008505</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>461</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S37_mean_time</td>\n",
-              "      <td>0.008500</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>462</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S29_mean_time</td>\n",
-              "      <td>0.008475</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>463</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S33_seen</td>\n",
-              "      <td>0.008394</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>464</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.008029</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>465</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S33_F3859</td>\n",
-              "      <td>0.008004</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>466</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>process_end_time</td>\n",
-              "      <td>0.007741</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>467</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_duration</td>\n",
-              "      <td>0.007571</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>468</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S29_num_std</td>\n",
-              "      <td>0.007533</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>469</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>aux_paint_thermal_defect_probability</td>\n",
-              "      <td>0.007316</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>470</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L1_S24_F1844</td>\n",
-              "      <td>0.007214</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>471</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S33_num_mean</td>\n",
-              "      <td>0.007032</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>472</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_num_std</td>\n",
-              "      <td>0.006991</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>473</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S33_F3857</td>\n",
-              "      <td>0.006660</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>474</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>aux_body_ford_vibration_abnormal_probability</td>\n",
-              "      <td>0.006541</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>475</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>total_process_duration</td>\n",
-              "      <td>0.006364</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>476</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L1_num_min</td>\n",
-              "      <td>0.006358</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>477</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_date_count</td>\n",
-              "      <td>0.006329</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>478</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S29_num_mean</td>\n",
-              "      <td>0.006163</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>479</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L0_num_min</td>\n",
-              "      <td>0.006136</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>480</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S29_F3458</td>\n",
-              "      <td>0.006061</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>481</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S30_F3744</td>\n",
-              "      <td>0.005915</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>482</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S33_num_std</td>\n",
-              "      <td>0.005873</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>483</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L0_num_std</td>\n",
-              "      <td>0.005700</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>484</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S30_num_std</td>\n",
-              "      <td>0.005681</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>485</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S30_F3759</td>\n",
-              "      <td>0.005666</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>486</th>\n",
-              "      <td>RandomForest</td>\n",
-              "      <td>L3_S34_seen</td>\n",
-              "      <td>0.005639</td>\n",
-              "      <td>feature_importances</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0d9b4da-3d03-4c33-a571-82db618689ad')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(importance_df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L3_S30_num_std\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0016205883920081907,\n        \"min\": 0.0056385160546700164,\n        \"max\": 0.011732889577268082,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.00568096253421123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"feature_importances\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {}
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
       ],
-      "source": [
-        "# 모델 성능 비교 시각화\n",
-        "fig, axes = plt.subplots(1, 3, figsize=(18, 4))\n",
-        "sns.countplot(x=y, ax=axes[0])\n",
-        "axes[0].set_title(\"Response Distribution\")\n",
-        "axes[0].set_xlabel(\"Response\")\n",
-        "\n",
-        "sns.barplot(data=model_comparison, x=\"model_name\", y=\"pr_auc\", ax=axes[1], color=\"#4C78A8\")\n",
-        "axes[1].set_title(\"Validation PR-AUC by Model\")\n",
-        "axes[1].set_xlabel(\"\")\n",
-        "axes[1].tick_params(axis=\"x\", rotation=20)\n",
-        "\n",
-        "sns.barplot(data=model_comparison, x=\"model_name\", y=\"false_positive_rate\", ax=axes[2], color=\"#F58518\")\n",
-        "axes[2].set_title(\"False Positive Rate by Model\")\n",
-        "axes[2].set_xlabel(\"\")\n",
-        "axes[2].tick_params(axis=\"x\", rotation=20)\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n",
-        "\n",
-        "# 선택 모델 ROC/PR 곡선\n",
-        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
-        "RocCurveDisplay.from_predictions(y_valid, valid_proba, ax=axes[0])\n",
-        "axes[0].set_title(f\"{selected_model_name} ROC AUC={roc_auc:.4f}\")\n",
-        "PrecisionRecallDisplay.from_predictions(y_valid, valid_proba, ax=axes[1])\n",
-        "axes[1].set_title(f\"{selected_model_name} PR-AUC={pr_auc:.4f}\")\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n",
-        "\n",
-        "\n",
-        "def extract_feature_importance(estimator, feature_names: list[str], model_name: str) -> pd.DataFrame:\n",
-        "    target_estimator = estimator\n",
-        "    if is_pipeline_estimator(estimator):\n",
-        "        target_estimator = get_final_estimator(estimator)\n",
-        "\n",
-        "    if hasattr(target_estimator, \"booster_\"):\n",
-        "        values = target_estimator.booster_.feature_importance(importance_type=\"gain\")\n",
-        "        importance_type = \"gain\"\n",
-        "    elif hasattr(target_estimator, \"feature_importances_\"):\n",
-        "        values = target_estimator.feature_importances_\n",
-        "        importance_type = \"feature_importances\"\n",
-        "    elif hasattr(target_estimator, \"coef_\"):\n",
-        "        values = np.abs(target_estimator.coef_).ravel()\n",
-        "        importance_type = \"abs_coef\"\n",
-        "    else:\n",
-        "        values = np.zeros(len(feature_names))\n",
-        "        importance_type = \"not_available\"\n",
-        "\n",
-        "    return pd.DataFrame({\n",
-        "        \"model_name\": model_name,\n",
-        "        \"feature\": feature_names,\n",
-        "        \"importance\": values,\n",
-        "        \"importance_type\": importance_type,\n",
-        "    }).sort_values(\"importance\", ascending=False)\n",
-        "\n",
-        "\n",
-        "importance_frames = [\n",
-        "    extract_feature_importance(estimator, feature_cols, model_name)\n",
-        "    for model_name, estimator in trained_models.items()\n",
-        "]\n",
-        "all_model_importance = pd.concat(importance_frames, ignore_index=True)\n",
-        "importance_df = all_model_importance[all_model_importance[\"model_name\"].eq(selected_model_name)].copy()\n",
-        "importance_df = importance_df.rename(columns={\"importance\": \"importance_gain\"})\n",
-        "\n",
-        "plt.figure(figsize=(9, 7))\n",
-        "sns.barplot(data=importance_df.head(25), y=\"feature\", x=\"importance_gain\", color=\"#4C78A8\")\n",
-        "plt.title(f\"Top 25 Feature Importance - {selected_model_name}\")\n",
-        "plt.xlabel(\"Importance\")\n",
-        "plt.ylabel(\"\")\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n",
-        "\n",
-        "display(importance_df.head(30))\n"
-      ]
+      "image/png": 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\n"
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "markdown",
-      "id": "TaY5ZaEJbEfC",
-      "metadata": {
-        "id": "TaY5ZaEJbEfC"
-      },
-      "source": [
-        "## 10. SHAP 기반 영향 공정 분석\n",
-        "\n",
-        "선택된 최종 모델을 기준으로 SHAP 값을 계산합니다.\n",
-        "\n",
-        "SHAP 값은 feature가 불량 확률 예측에 기여한 정도이며, 이후 feature를 공정 코드로 매핑해 공정별 영향도를 집계합니다.\n"
-      ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 900x700 with 1 Axes>"
+      ],
+      "image/png": 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4eDg//PADKpVKq1xGRobml7yq8vBuokXxXrp0qdR4H56WqqvmzZtrvS76DDRr1kzreFEb09PTtY43adKk2AYVRdNpExMT6dy5M4mJiTz33HPFptoVfb5u3bqldbyk9j/KrVu3WL58OUeOHCk2fe/hdZ716tUrlkA2aNBA67o///yTJk2aYGFhUeo9r127hlqtpn///iWeNzAo/dfN3NzcYnFaWlqir69f6jUA7777Li4uLmRlZXHw4EH27dtXrE8BvvvuOyIjI7l48aLWWriiBPVBD7//cL8/it7nlJQUsrOzS/w3b2Njo5W4JSYmoqenp5U0AlhZWWFubl7s+8jD9y76jD382Sv6TD782YP73xNK+oNA0WfKxsam2DlbW1vOnDmjdczAwKDYfa9fv05GRkaZ3yNcXV0ZMGAAERERrFu3DldXV/r27cuQIUMwMjIq8VrxeEiSJ4QQQlSzGTNmcPToURYtWlTqL1EladasGVevXq30/QsLC7Gzsyv1WXxFv/D9+eefjB07FltbW4KDg2nevDmGhoYkJCSwbt06nTY9KemXa6DUUc+SdtIsLCykW7dumpHIh+m6TvFhpSUWpR1XP7QRz+NQnp1ECwoKGDduHGlpaUyYMAFbW1tMTEy4c+cOwcHBxd6fshIpXRUWFqJQKFi9enWJdT5qZ8Zz585pPfcO7j9epKzk1s7OTvNHi759+6JSqZg5cyYvvPCCJmH63//+x+TJk+nSpQuzZ8/GysoKQ0NDvvrqqxKfkfg43ufSPu+63rs6PntGRkbFEubCwkIaNWrEokWLSrymKLlUKBQsX76cH374ge+++47jx48zbdo01q5dS0xMDPXr139scQttkuQJIYQQD7C2tubUqVNkZmZqjeb98ccfmvMPun79erE6rl27hrGxsU7T7BYsWEBcXBzTpk3TmsKnixs3bmg2hKiMVq1a8euvv+Lu7v7IX0qPHDlCbm4ukZGRWlPISpqOV1o9RRs8pKena2328PAIVlnxZmVllToyWV3+/vvvYtvNX7t2Dfi/z421tTWXLl2isLBQ6xfpos/Xg/1amtL69rfffuPatWssWLCA4cOHa46fPHmyvE3RaNWqFSdOnODu3buljua1atUKtVpNy5YtSxwtepR27dqxdu1arWMVec5dUFAQhw4dIjIykrlz5wKwf/9+6tWrx5o1a7RGkb766qty1w/3E5lnnnmmxH/zD/+xxdramsLCQq5fv671zM1//vmH9PT0Yt9HHqeiz9TVq1eL/RHp6tWrOn3mWrVqxalTp3B2dtbpDw+dO3emc+fOfPDBB3z99dcEBQXxzTffMHLkSJ0TX1E5siZPCCGEeEDPnj0pKChg8+bNWsfXrVuHQqGgZ8+eWsfPnTunWW8F8Ndff3H48GG6detW5kjJl19+qdlF7+FdFR9U0vTDhIQEfv7552Lr1Spi4MCB3Llzh+3btxc7l52dTVZWFvB/owoPjiJkZGSU+EuzsbFxiVPKiqavPbj2LCsri127dpUr3nPnznH8+PFi59LT08nPz9e5rqqUn59PTEyM5nVubi4xMTFYWlpib28P3P98JSUl8c0332hdt3HjRkxMTOjSpUuZ9ynaefXh/i1KGh98f9RqdbFdQMujf//+qNVqIiIiip0ruk///v3R19cnIiKi2AiTWq0mNTW11PobNGiAh4eH1teD68Z01apVK/r378/OnTtJSkoC7n9eFQqF1ijxzZs3OXz4cLnrL6qve/fuHDp0SOuPEleuXOHEiRNaZXv16gXA+vXrtY4XJbRF55+Ejh070qhRI7Zt26Y1ZTUhIYErV64U22W0JAMHDqSgoEBrp90i+fn5ms9iWlpasc9A+/btATT3Lu3zK6qWjOQJIYQQD+jTpw9ubm4sXbqUxMRElEolJ0+e5PDhw4wZM6bYGhs7OzvGjx+v9QgFgClTpjzyPgcPHiQsLIzWrVtja2vL7t27tc5369ZN89iB1157jfbt29OxY0fMzMz45Zdf+Oqrr2jevLnmEQWVMWzYML799ltmz57N6dOncXZ2pqCggD/++IP4+Hi+/PJLHBwc6NatG4aGhvj5+fHaa69x7949duzYQaNGjTS/WBext7dn69atrFy5kueeew5LS0vc3d3p1q0bLVq0YPr06fzxxx/o6+vz1Vdf0bBhQ51H88aPH8+RI0fw8/PjpZdewt7eHpVKxW+//cb+/fs5fPjwY3kodFmaNGnC6tWrSUxMpHXr1nzzzTdcvHiRTz/9FENDQwB8fHyIiYkhODiYn3/+GWtra/bv38/Zs2eZNm1asbWgJWnVqhXm5uZs27aN+vXrY2JigqOjI7a2trRq1YoFCxZw584dTE1N2b9/f6V+me7atSvDhg1j48aNXL9+nR49elBYWMiZM2dwc3PjjTfeoFWrVrz//vssXryYxMRE+vbtS/369bl58yaHDh3i1VdfZfz48RWOQVfjx4/n22+/Zf369QQFBdGrVy/NBkODBw8mOTmZLVu20KpVK83zD8trypQpHD9+nNGjRzNq1CgKCgrYtGkTbdu21aqzXbt2vPTSS8TExJCenk6XLl24cOECO3fupG/fvppNjZ4EQ0NDgoKCmDp1Km+88QaDBg3SPELB2tqasWPHllmHq6srPj4+rFq1iosXL2q+F1y7do34+HimT5+Ot7c3O3fuZOvWrfTt25dWrVpx7949tm/fjqmpqeYPZM888wxt27bl22+/pXXr1lhYWPD8889X6JEzonSS5AkhhBAP0NPTIzIykuXLl/PNN98QFxeHtbU1H3/8sWZnvAd16dKFzp07s2LFCm7dukXbtm2ZP38+7dq1e+R9fv31V+D+dL6PP/642PkNGzZokryBAweSkJDAyZMnyc7OxsrKipEjR+Lv71/h58893OYVK1awbt06du/ezcGDBzE2NqZly5b4+vpqpuDZ2tqyfPlyPv/8cxYsWEDjxo0ZNWoUlpaWxZ4f+O6773Lr1i2+/PJL7t27h6urK+7u7hgaGhIREcEnn3zCsmXLsLKyYsyYMZibm5e6JvBhxsbGbNy4kVWrVhEfH8+uXbswNTWldevWTJkypco3f9FVgwYNCA0NJSQkhO3bt9O4cWNmzZrFq6++qinzzDPPsHHjRhYtWsTOnTvJzMzExsaG+fPnM2LECJ3uY2hoSGhoKEuWLGHOnDnk5+drrv/iiy8ICQlh1apV1KtXj379+jF69GiGDRtW4XbNnz8fpVJJbGwsCxcuxMzMjI4dO2rtBjlp0iRat27NunXrNM+Aa9asGd26daNPnz4Vvnd5ODg44OrqytatW3n77bdxd3dn3rx5rF69ms8++4yWLVsSFBREYmJihZO8du3asWbNGubPn8/y5ctp1qwZU6ZMISkpqVidISEhtGzZkp07d3Lo0CEaN27M22+/XeJOpY/biBEjeOaZZ1i9ejWLFi3CxMSEvn378tFHH+n8DMO5c+fSsWNHtm3bxtKlS9HX18fa2pqhQ4dqntPo6urKhQsX+Oabb/jnn38wMzPD0dGRRYsW8eyzz2rqCgkJ4dNPP2X+/Pnk5eXh7+8vSV4VU6ifxKphIYQQohZSKpWMHj2aWbNmVXcoopr5+vqSmppa4oYeQgjxpMmaPCGEEEIIIYSoRSTJE0IIIYQQQohaRJI8IYQQQgghhKhFZE2eEEIIIYQQQtQiMpInhBBCCCGEELWIJHlCCCGEEEIIUYvIc/KEEKIOOHfuHGq1WvNAZiGEEEI8PfLy8lAoFFrPn6wMGckTQog6QK1Wa75E+anVanJzc6X/KkD6rnKk/ypH+q/ipO8qp7z9V9U/o2UkTwgh6gBDQ0Nyc3Np27YtJiYm1R1OjZOVlcXFixel/ypA+q5ypP8qR/qv4qTvKqe8/XfhwoUqvb+M5AkhRB2iUCiqO4QaSaFQYGxsLP1XAdJ3lSP9VznSfxUnfVezyUieEELUEUZGRhgbG1d3GDWSsbExHTp0qO4waiTpu8qR/qsc6b+Kk74rWWFhIXp6T/84mSR5QghRh0TEHCUx6W51hyGEEELUONZWFvj7eFZ3GDqRJE8IIeqQxKS7XLuVXN1hCCGEEOIxevrHGoWoRcLDwx+5NW5QUBD9+/enc+fOdOnShdGjR3PixIly3SM/P5+NGzcydOhQnJyc6NKlC0OHDmXu3Lnk5uZqyi1YsIBBgwbh5OSEs7MzL7/8Mvv27dOqKyMjgylTptCnTx8cHR3p2rUrEyZM4Pz58+VreC2ybt06EhISih3v06cPc+fOrYaIhBBCCCG0yUieEE+RvLw8xo4dS+vWrcnJySE2NpZJkyaxYcMGXFxcdKojJCSEuLg4Jk2ahLOzMyqViosXL7Jnzx6ys7MxMjIC4N69e4wcORJbW1sUCgX79+8nMDCQwsJChgwZAkBubi5GRkZMnjyZli1bkpmZyfr16xkzZgxxcXHY2Ng8tr54Wm3YsAFPT0969eqldTwiIgJzc/NqikoIIYQQ4v9IkifEU2TZsmVar3v27ImXlxe7d+/WKclTqVTExsbi5+eHv7+/5riXlxf+/v5az195eNSpR48eXL58mZ07d2qSvEaNGrF48WKtch4eHri5ubF//378/PzK3cbaShanCyGEEOJpIdM1hXiK6evrY2ZmRl5enk7lVSoVeXl5NGnSpMTzZW2DbGFhUea9TExMqFevns4xwf9NZVy3bh29evXCycmJ4OBgcnNzuXjxIq+99hqdO3fmlVde4dKlS1rXqtVq1qxZw4ABA+jYsSNeXl6sW7dOq8yVK1f44IMP6NWrF506deLFF18kOjqawsJCTZmbN2+iVCrZvXs3c+fOpUuXLnTv3p0FCxaQn5+vczsSExPZvHkzSqUSpVJJXFycVhuLBAcHM3jwYP79738zZMgQHB0deeONN7h58yZ3794lICAAZ2dn+vbtyzfffFPsXkePHmXkyJGaabKzZ88mKytL1y4XQgghRB0mI3lCPGXUajUFBQVkZGQQFxfH9evXdV7rZWlpSYsWLYiMjKR+/fp0796dBg0alHmvrKwsjhw5wsmTJwkLCytWrrCwkMLCQlJSUlizZg16enoMHz68XO06fPgwzz//PHPnzuXGjRuEhoZiaGjIDz/8wNixY2ncuDGLFi0iICCAb775RrM98bx589ixYwd+fn506tSJs2fPsmjRIurVq8eoUaMA+Pvvv7GxsWHIkCHUr1+fixcvEh4eTlZWltaIJsDnn3+Ol5cXn3/+OefOnSM8PJxWrVpp6nqUiIgIzTTYt956C4BWrVqVWj4pKYnQ0FAmT56MgYEBISEhBAUFYWxsjIuLC6+++irbt2/no48+olOnTlhbWwMQHx/PBx98wIgRI5gyZQpJSUksXryY9PR0li5dWq5+F0IIIUTdI0meEE+Z2NhYZsyYAdwfNVu6dOkjN2t5WGhoKIGBgQQGBqJQKLC1tcXLy4tx48ZhaWmpVfbUqVOMGzcOAAMDA2bOnIm3t3exOpctW8YXX3wB3J/CGRUVxbPPPlvutq1cuVKzJvA///kP27dvZ/Xq1fTs2RO4n0z6+fnx22+/0a5dO/788082bdrEJ598go+PD3B/umh2djYrVqzAx8cHPT093N3dcXd3B+4nri+88ALZ2dls2rSpWJLn6Oio6d9u3bpx+vRp9u/fr1OS16FDB4yMjGjcuDGdO3cus3xaWhqbNm3i+eefB+4no59++ikTJ07k3XffBcDBwYGDBw9y6NAhxowZg1qtZuHChbz44ovMmzdPU5eVlRWTJk3inXfe0dQnhBBCCFESma4pxFPGy8uL2NhYVq9ezcCBA3n//fdL3M2xNG5ubhw8eJBly5bh4+NDQUEBUVFRDBkyhDt37miVdXR0JDY2lnXr1vHmm28SEhLCjh07itX5+uuvExsbS2RkJJ06dWLSpEn8/PPP5WpXly5dNAkeQOvWrdHT06Nr165axwD++usvAP79738D0L9/f/Lz8zVfHh4eJCUlacrl5OSwfPly+vXrh4ODA/b29ixdupSkpCTu3bunFUf37t21Xrdp04bbt2+Xqy26atKkiVZCVtQ+Dw8PzTFzc3MsLS01MVy9epXExEQGDhyo1WZXV1f09PT46aefHkusQgghhKg9ZCRPiKeMpaWlZsStZ8+epKWlERYWVmw3x0cxMTHB29tbMyq3Y8cOZsyYQXR0NFOnTtWUMzU1xcHBAQB3d3cKCgoIDQ1lxIgR6Ovra8o1bdqUpk2bAuDp6ckrr7zC8uXLWbVqlc4xPbzzpKGhIc8884xW4mdoaAjcT9oAUlNTUavVWongg/766y+sra0JCwtjx44dvPvuu3Ts2BEzMzMOHz5MZGQkOTk51K9fX3ONmZlZsTgefLREVSqpzSXFYGRkpNVmQDPS97CixFYIIYQQojSS5AnxlLO3t+fYsWOVqmPkyJEsWrSIK1eulHmv9evXk5KSgpWVVYll9PT0aN++PWfOnKlUTLpo0KABCoWCLVu2aBKkBxU9wiE+Ph4fHx8mTZqkOVee0c+niYWFBQCzZs3C0dGx2PnSNtURQgghhCgiSZ4QT7kzZ87ovP4tLy+PrKysYputJCcnk5GRUWri9uC9TE1NadiwYall8vPzOX/+fIXW5JVX0Tq7u3fv0qdPn1LL5eTkaCWBBQUFxR7sXlUMDQ01o26Pg62tLc2aNePGjRuMHj36sd1HCCGEELWXJHlCPGEFBQXEx8cXO65SqUhISMDT05PmzZuTlpbG3r17OXHiBEuWLNGp7oyMDAYMGMCwYcPo2rUrDRo04ObNm0RHR6Onp6fZXOTXX39l0aJFeHt7Y21tTVZWFkePHmXHjh0EBgZiYHD/W0NMTAznz5/Hw8MDKysr/vnnH7Zt28bVq1eZPXt21XVKKWxsbBg9ejQff/wx48ePp1OnTuTl5XHt2jVOnz7NypUrgftr3Hbs2EHbtm1p2LAhW7ZseWxTMG1tbfn+++85efIk5ubmtGzZ8pFJcXkpFAqCg4MJCgoiKysLT09PjI2NuXXrFgkJCXzwwQd18iH0QgghhNCdJHlCPGE5OTkEBAQUOx4QEEBubi6LFy8mNTWVhg0bolQq2bhxI66urjrVbWpqysSJEzl+/Djx8fGkpaXRuHFjHBwcCA0Nxd7eHoDGjRtjbm7OypUrSUpKwszMDFtbWyIiIujbt6+mvrZt23LgwAHmzZtHeno6VlZWODg4EBsbS7t27aqmQ8owY8YMbGxsiImJYcWKFdSvXx8bGxutXUBnzpzJ7Nmz+fTTTzE2Nuall16iX79+ml00q1JgYCBz5sxhypQp3Lt3j/nz5zNixIgqvcfAgQMxNzfniy++4OuvvwbA2tqaHj160Lhx4yq9lxBCCCFqH4VarVZXdxBCCCEerwsXLgCwJeEK124lV3M0QgghRM3TukUj5vsP16lsVlYWFy9epH379piYmJRZvujndNGGeJUlj1AQQgghhBBCiFpEpmsKUYMUFBTwqMH3orV0T1J+fn6p5xQKhdajGJ52taktQgghhKi7JMkTogbp168fiYmJpZ6/dOnSE4zmvqJ1fiWxtrbmyJEjTzCayqlNbSmNtZVFdYcghBBC1Eg16WeoJHlC1CCRkZGPbdfIioqNjS313IMPOq8JalNbSuPv41ndIQghhBA1VmFhIXp6T/+KN0nyhKhBlEpldYdQTFUtEH4a1Ka2lCQ3NxeVSoWxsXF1h1LjqFQqrl69io2NjfRfOUnfVY70X+VI/1Wc9F3JakKCB7LxihBC1CmyoXLFqNVqVCqV9F8FSN9VjvRf5Uj/VZz0Xc0mSZ4QQtQhCoWiukOokRQKBcbGxtJ/FSB9VznSf5Uj/Vdx0nc1m0zXFEKIOsLIyEim3FSQsbExHTp0qO4waiTpu8qR/qsc6b+Kq+l9V1PWzj0ukuQJIUQdEhFzlMSku9UdhhBCCPHYWFtZ1PmNxiTJE0KIOiQx6S7XbiVXdxhCCCGEeIzq7himEEIIIYQQQtRCkuQJ8QSFh4fj5ORU6vmgoCD69+9P586d6dKlC6NHj+bEiRPlukd+fj4bN25k6NChODk50aVLF4YOHcrcuXO1nrG3YMECBg0ahJOTE87Ozrz88svs27fvkXXPmzcPpVLJ3LlzyxVTbRIeHs7Zs2eLHVcqlaxZs6YaIhJCCCGE0CbTNYV4iuTl5TF27Fhat25NTk4OsbGxTJo0iQ0bNuDi4qJTHSEhIcTFxTFp0iScnZ1RqVRcvHiRPXv2kJ2drXmo97179xg5ciS2trYoFAr2799PYGAghYWFDBkypFi9ly5d4quvvsLU1LRK21zTREREYGJigrOzs9bxmJgYWrRoUU1RCSGEEEL8H0nyhHiKLFu2TOt1z5498fLyYvfu3ToleSqVitjYWPz8/PD399cc9/Lywt/fX+tZNw+PxvXo0YPLly+zc+fOEpO8Tz/9lLFjx7Jr165ytqpu6Ny5c3WHIIQQQggByHRNIZ5q+vr6mJmZkZeXp1N5lUpFXl4eTZo0KfF8Wc+6sbCwKPFee/bs4ebNm0ycOFGnOB6mVCqJiopi6dKluLu74+LiwsKFC1Gr1Zw6dYphw4bh5OTEmDFj+Ouvv7Suzc3NZcmSJfTu3ZuOHTsycOBAvv76a60y586dw8/Pj+7du9O5c2eGDRtWLBk9ffo0SqWSkydP8uGHH+Lk5ETv3r1ZvXp1udoBsHDhQpRKJUqlktOnT2vOPThd09fXl7fffpu9e/fSv39/OnXqhJ+fH2lpaSQmJjJ+/HicnJwYNGiQpo4HxcXFMWTIEBwcHOjRowdLly6loKBA51iFEEIIUXfJSJ4QTxm1Wk1BQQEZGRnExcVx/fp1ndfAWVpa0qJFCyIjI6lfvz7du3enQYMGZd4rKyuLI0eOcPLkScLCwrTKZGZmsnDhQqZNm1apZ6xt3rwZV1dXFi5cyI8//kh4eDiFhYWcPHmSyZMnY2hoSEhICNOnTyc6OlpzXUBAAGfPnuXdd9+lTZs2JCQk8NFHH2Fubk6vXr0AuHXrFs7OzowaNQojIyPOnj3LjBkzUKvVvPTSS1pxzJ49m2HDhrFixQoOHTrEokWLUCqV9OzZs8w2xMTE4OPjg6+vL4MHDwagbdu2pZb/5ZdfSE1N5eOPPyYzM5OQkBBmzpxJYmIiw4cPZ9y4caxatYopU6bw3XffUb9+fQDWrl1LWFgYY8aMITg4mCtXrmiSvKCgoHL3vRBCCCHqFknyhHjKxMbGMmPGDABMTExYunTpIzdreVhoaCiBgYEEBgaiUCiwtbXFy8uLcePGYWlpqVX21KlTjBs3DgADAwNmzpyJt7e3VpmIiAiee+45XnzxxUq1q0mTJpoEskePHhw5coR169axb98+2rRpA8CdO3f49NNPSU9Px9zcnO+//54jR46wZs0aunfvDkC3bt1ISkoiPDxck+QNGjRIcx+1Wk2XLl24c+cOMTExxZK8/v37M2XKFADc3d05evQo+/fv1ynJK5qS2bx5c52mZ2ZmZvLFF19o+v3SpUtER0czZ84cRo0apemXIUOGcOrUKfr27UtmZibLly9nwoQJBAYGatpsaGhIaGgo48ePp2HDhmXeWwghhBB1lyR5QjxlvLy8aNeuHampqcTHx/P+++8TERGhSWjK4ubmxsGDBzl27BinTp3i+++/Jyoqiri4OOLi4mjatKmmrKOjI7GxsWRmZnLs2DFCQkLQ19dn5MiRAPz+++9s3ryZ7du3V7pdHh4eWq9tbGz4559/NAkeQOvWrQG4ffs25ubmnDx5EgsLC7p27Up+fr5WXXPmzKGgoAB9fX3S0tIIDw/n8OHD3LlzRzOt0cLColgcRcki3J++2qZNG27fvl3p9pWkXbt2Wol1Ufse7IsH2wz3p55mZWXh7e1drM3Z2dn8/vvvuLq6PpZ4hRBCCFE7SJInxFPG0tJSkxj07NmTtLQ0wsLCdE7y4P4IoLe3t2ZUbseOHcyYMYPo6GimTp2qKWdqaoqDgwNwf1SroKCA0NBQRowYgb6+PqGhoXh7e2NtbU16ejoAhYWF5OXlkZ6ejqmpKXp6ui3tNTc313ptaGhY4jGAnJwcAFJTU7l79y729vYl1pmUlESzZs0IDg7m3LlzvPvuu7Rt2xZTU1O2bt3Kt99+W+waMzOzYvfMyMjQqQ3lVVr7HoyhaLfTB9sMFBuBLPLwmkUhhBBCiIdJkifEU87e3p5jx45Vqo6RI0eyaNEirly5Uua91q9fT0pKClZWVly9epUTJ06wZ88erXLbt29n+/btfPPNN1ojcVWtQYMGWFpaEhUVVeJ5S0tLcnJyOHr0KMHBwfj6+mrObdmy5bHF9TgVraGMiIigWbNmxc63bNnySYckhBBCiBpGkjwhnnJnzpzh2Wef1alsXl4eWVlZxTZbSU5OJiMjAysrqzLvZWpqqlnztWTJEs0IU5HAwEA6d+7Mm2+++difC+fh4cGXX36JoaEh7dq1K7FMRkYGhYWFmlEyuL8W7siRI48lJkNDw2J9UpWcnJwwNjbm9u3b9OvX77HdRwghhBC1lyR5QjxhBQUFxMfHFzuuUqlISEjA09OT5s2bk5aWxt69ezlx4gRLlizRqe6MjAwGDBjAsGHD6Nq1Kw0aNODmzZtER0ejp6en2ezj119/ZdGiRZqpmFlZWRw9epQdO3YQGBiIgcH9bw0lbS5Sr149mjZtipubW8U7QUfdunWjd+/eTJgwgQkTJqBUKlGpVFy+fJnr168zb948zMzMcHBwYPXq1VhaWmJgYEBUVBSmpqakpKRUeUy2trYcPnwYFxcXjI2NsbGxqdIHxJubm/Pee+8RFhbG7du3cXV1RV9fnxs3bnD48GHCw8MrtcupEEIIIWo/SfKEeMJycnIICAgodjwgIIDc3FwWL15MamoqDRs2RKlUsnHjRp032jA1NWXixIkcP36c+Ph40tLSaNy4MQ4ODoSGhmrWtjVu3Bhzc3NWrlxJUlISZmZm2NraEhERQd++fau0vZW1fPlyoqKi2Lp1K4mJiZiZmfH8888zYsQITZnFixcza9YsgoODsbCwwNfXl6ysLK1HMVSVWbNm8dlnnzFx4kSys7PZsGFDlSe8b731Fk2bNmXt2rVs2rQJAwMDWrVqhaenp9aIpRBCCCFESRRqtVpd3UEIIYR4vC5cuADAloQrXLuVXM3RCCGEEI9P6xaNmO8/vFpjyMrK4uLFi7Rv3x4TE5Myyxf9nC7aEK+ydNsWTwghhBBCCCFEjSDTNYWoQQoKCnjU4HvRWron6cFnuT1MoVCgr6//BKOpnNrUltJYW1lUdwhCCCHEYyU/6yTJE6JG6devH4mJiaWev3Tp0hOMBm7evImXl1ep511dXdm4ceMTjKjialNbHsXfx7O6QxBCCCEeu8LCQp2f5VsbSZInRA0SGRlJbm5udYeh0aRJE2JjY0s9X79+/ScYTeXUpraUJjc3F5VKJbtzVoBKpeLq1avY2NhI/5WT9F3lSP9VjvRfxdX0vqvLCR5IkidEjaJUKqs7BC1GRkZVtkC4utWmtjyK7LVVMWq1GpVKJf1XAdJ3lSP9VznSfxUnfVez1e0UVwghhBBCCCFqGUnyhBCiDlEoFNUdQo2kUCgwNjaW/qsA6bvKkf6rHOm/ipO+q9lkuqYQQtQRRkZGNXJdxdPA2NiYDh06VHcYNZL0XeVI/1WO9F/FVXff1fWNUypLkjwhhKhDImKOkph0t7rDEEIIIUplbWUhu0FXkiR5QghRhyQm3eXareTqDkMIIYQQj5GMgQohhBBCCCFELSJJnhDiqbdu3ToSEhKqtM709HTCw8O5fPlyua47ffo0X3zxRbHj4eHhODk5VVV4QgghhBAVJkmeEOKpt2HDhseS5EVERJQ7yfvPf/7DqlWrih0fOXIk69evr6rwhBBCCCEqTNbkCVHD5ObmYmBgIDtOVUJ2dnaV19msWTOaNWtW5fUKIYQQQpSX/JYoRDUKDg5m8ODBJCQkMHjwYBwcHBgxYgQ//PCDpkyfPn2YO3cuq1evpnfv3jg6OnL37l0KCwtZuXIlffr0oWPHjnh7e7Nt27Zi97hy5Qr+/v64urrSqVMnhg4dyt69ezXn1Wo1a9asYcCAAXTs2BEvLy/WrVunVcft27cJCAjAw8MDBwcH+vTpw2effabz+bIcPnyYESNG4OTkhIuLCyNGjNCM3PXp04fExEQ2b96MUqlEqVQSFxcHwK5duxg1ahSurq506dIFX19fzp8/r1V30TTK8+fP4+Pjg4ODA5s3b8bLywuAgIAATb03b958ZJzh4eFERESQlZWlucbX11frPkVOnz6NUqnk+PHjBAQE4OTkhKenJ19//TVwf3TS09MTV1dXpk+fTm5ubrE+DwoKws3NDUdHR0aPHs1PP/2kc58KIYQQou6SkTwhqllSUhKffPIJU6ZMwdzcnNWrVzN+/HgOHDhAo0aNADhw4ADPPfcc06dPR09PDxMTExYuXMiGDRuYPHkyTk5OHD16lNmzZ5Ofn88bb7wBwLVr1/Dx8aF58+ZMnz4dKysrfvvtN27duqW5/7x589ixYwd+fn506tSJs2fPsmjRIurVq8eoUaMA+Pjjj/n777+ZMWMGjRo14q+//tJKOMo6/yh//vknAQEBDBo0iA8//JDCwkJ+/fVX0tLSAIiIiGDSpEk4Ozvz1ltvAdCqVSsAbt68yfDhw2nVqhW5ubns27eP0aNHs2fPHmxsbDT3yMvL48MPP2Ts2LF88MEHWFhYEBERgb+/P4GBgbi5uQHQpEmTR8Y6cuRIbt++zd69ezVTM01NTR95zZw5c3jppZd49dVX2b59Ox9//DG//vorv//+O5988gk3btwgNDSUZ599Fj8/PwDS0tJ4/fXXMTExYebMmZiZmbFx40bGjBmj9bkQQgghhCiJJHlCVLO7d+/y+eef4+7uDoCrqyu9evVi3bp1fPjhh8D9JGX16tWYmJgAkJKSwqZNmxg/fjxTpkwBoHv37qSmprJixQpGjRqFvr4+4eHhGBoasnXrVk0y4uHhobn3n3/+yaZNm/jkk0/w8fHRnM/OzmbFihX4+Pigp6fHhQsXCAwM5MUXX9RcO3z4cM3/l3X+UX755Rfy8vKYOXOmJsYePXpoznfo0AEjIyMaN25M586dta719/fX/H9hYSHdunXj/Pnz7Ny5k8DAQM25vLw8PvjgA634iu713HPPFau3NEVTMvX09HS+xtvbWxOno6MjBw8eZN++fRw8eBBDQ0Pg/jq/+Ph4TZK3fv160tPT2bFjhyahc3d3Z8CAAaxZs4aPP/5Yp3sLIYQQom6S6ZpCVDMzMzNNglf02sPDgx9//FFzzM3NTZPgAZw/f568vDy8vb216ho4cCApKSlcu3YNgO+//54BAwaUOtr073//G4D+/fuTn5+v+fLw8CApKYm//voLuJ9oRUdHs2XLFq5fv16snrLOP4pSqURfX5+goCCOHDlCRkaGztdeuXKFd999Fw8PD9q3b4+9vT1Xr17VtP9BvXr1KldcVaVbt26a/zczM8PS0hIXFxdNggfQunVrTV8DnDx5Ejc3Nxo0aKB5T/T09OjSpQsXLlx4ovELIYQQouaRJE+IamZpaVnsWKNGjUhKStJ6/aCiqYyNGzfWOl70+u7du5r/PmoKYmpqKmq1mq5du2Jvb6/5GjduHIAm8Vi6dCldu3bl888/p3///nh7e3PgwAFNPWWdfxQbGxu++OILMjIy8Pf3x93dHT8/P60ppSXJzMzkrbfe4tatWwQHB7N582ZiY2Np164dOTk5WmWNjY2pX7++TvFUNTMzM63XRkZGmJubax0zNDTUWpOXmprKoUOHtN4Te3t7du/eze3bt59I3EIIIYSouWS6phDVLCUlpdix5ORkrKysNK8VCoXWeQsLC025pk2bao7/888/WuctLCz4+++/S713gwYNUCgUbNmyRWtkqUjRurYmTZowf/58CgsL+emnn4iMjOSDDz4gPj6eZ599tszzZenZsyc9e/YkMzOTY8eOMX/+fKZOnfrIRxL88MMP3L59m1WrVtGuXTvN8YyMjGK7XD7cf0+7Bg0a0KNHDwICAoqdMzIyqoaIhBBCCFGTyEieENUsIyODU6dOab3+97//TadOnUq9xsHBAUNDQ+Lj47WOf/vttzRq1IjWrVsD99dx7d+/n8zMzBLrKZomevfuXRwcHIp9PTzNU09PD0dHR95//33y8/OLTc0s63xZTE1NefHFFxk0aBBXrlzRHDc0NCw2Olf0GIQHk9OzZ8+SmJio072Krnu4Xl2ue3gnzKrm4eHBlStXaNOmTbH3RKlUPtZ7CyGEEKLmk5E8IaqZhYUF06dP57333sPMzIzVq1ejVqsZM2ZMqddYWlryxhtvsGbNGoyMjOjcuTMJCQns3buXmTNnoq+vD9zfmOTo0aO8/vrrTJgwASsrK65cuYJKpWLixInY2NgwevRoPv74Y8aPH0+nTp3Iy8vj2rVrnD59mpUrV5KRkcH48eMZNmwYNjY25OXlsXHjRszNzenQoUOZ58uybds2fvjhB3r06IGVlRU3b95kz549WmvZbG1t+f777zl58iTm5ua0bNmSzp07Y2JiwieffMKkSZO4c+cO4eHhWiObj2JlZYW5uTn79u2jZcuWGBkZoVQqyxwpa9OmDfn5+axfvx4nJydMTU2xtbXV6Z66Gjt2LF9//TVvvPEGb775Ji1atCAlJYUff/yRpk2bMnbs2Cq9nxBCCCFqF0nyhKhmVlZWBAUFsXDhQv7880+ef/551qxZU2y93cM+/vhjzMzMiI2N5YsvvsDa2ppPPvmE1157TVOmdevWbNu2jcWLF/PJJ59QUFBA69atmTRpkqbMjBkzsLGxISYmhhUrVlC/fn1sbGw0m7rUq1cPOzs7Nm7cyF9//cUzzzxDx44dWbNmDZaWluTm5j7yfFmUSiXfffcd8+fP5+7du1hZWTFo0CCtqYqBgYHMmTOHKVOmcO/ePebPn8+IESNYtmwZCxcu5J133qF169Z88sknfPnllzr1u56eHvPnz2fJkiWMHTuW3NxcDh8+TMuWLR95Xe/evXn99deJiooiOTmZLl26sHHjRp3uqauGDRsSExPD559/zqJFi7h79y6NGjWiU6dO9OvXr0rvJYQQQojaR6FWq9XVHYQQdVVwcDA//fST1sPJhXgcinbl3JJwhWu3kqs5GiGEEKJ0rVs0Yr7/8OoOo1KysrK4ePEi7du319ohvTRFP6cdHByq5P6yJk8IIYQQQgghahGZrimEeKzy8/NLPadQKDTrB58GhYWFFBYWlnpeX1+/xu3U+TBrK4vqDkEIIYR4JPlZVXmS5AlRjUJDQ6s7hMfO3t6+1HPW1tYcOXLkCUbzaNOmTWPnzp2lnt+wYQNubm5PMKKq5+/jWd0hCCGEEGUqLCxET08mHVaUJHlCiMcqNja21HNP2zPf/P39GT16dKnni54bWFPl5uaiUqkwNjau7lBqHJVKxdWrV7GxsZH+Kyfpu8qR/qsc6b+Kq+6+kwSvciTJE0I8VlW1gPhJaNmyZZm7a9Z0stdWxajValQqlfRfBUjfVY70X+VI/1Wc9F3NJimyEELUITV9TWF1USgUGBsbS/9VgPRd5Uj/VY70n6irZCRPCCHqCCMjI5muVEHGxsZ06NChusOokaTvKkf6r3IeR//JWjFRE0iSJ4QQdUhEzFESk+5WdxhCCFEjWVtZyAZWokaQJE8IIeqQxKS78jB0IYQQopaTsWYhhBBCCCGEqEUkyRNPhfDwcJycnEo9HxQURP/+/encuTNdunRh9OjRnDhxolz3yM/PZ+PGjQwdOhQnJye6dOnC0KFDmTt3Lrm5uZpyCxYsYNCgQTg5OeHs7MzLL7/Mvn37tOrKyMhgypQp9OnTB0dHR7p27cqECRM4f/58+Rpey50+fRqlUsmFCxeqrE6lUsmaNWuqrL4nKTw8nLNnzxY7XpPbJIQQQoinj0zXFDVCXl4eY8eOpXXr1uTk5BAbG8ukSZPYsGEDLi4uOtUREhJCXFwckyZNwtnZGZVKxcWLF9mzZw/Z2dmaZ7bdu3ePkSNHYmtri0KhYP/+/QQGBlJYWMiQIUOA+88bMzIyYvLkybRs2ZLMzEzWr1/PmDFjiIuLq/HPU6sq9vb2xMTE0KZNmyqrMyYmhhYtWlRZfU9SREQEJiYmODs7ax2vyW0SQgghxNNHkjxRIyxbtkzrdc+ePfHy8mL37t06JXkqlYrY2Fj8/Pzw9/fXHPfy8sLf31/rGTBz587VurZHjx5cvnyZnTt3apK8Ro0asXjxYq1yHh4euLm5sX//fvz8/MrdxtrI1NSUzp07V2mdVV1fZRQUFFBYWIihoWGl6nma2iSEEEKImk+ma4oaSV9fHzMzM/Ly8nQqr1KpyMvLo0mTJiWeL+v5ORYWFmXey8TEhHr16ukcE0CfPn2YO3cumzdvpnfv3rzwwgu88847pKSkaMrExcWhVCq1jgEMGzaM4OBgzevg4GAGDx7Mv//9b4YMGYKjoyNvvPEGN2/e5O7duwQEBODs7Ezfvn355ptvdI7x5s2bKJVKdu3axaxZs3BxccHd3Z21a9cCsG/fPgYMGICzszP+/v6kp6drri1pumZsbCyDBg3C0dERNzc3Ro0apTXNtazzD09t9PX15e233yY+Pp4BAwbg5OTEm2++yZ9//qnVjtu3b/P222/TqVMnevXqxbp165g3bx59+vTRuS+K7rVz504GDBiAg4MDv/76K3///TdTp07Fy8sLR0dH+vfvz5IlS7SmASuVSgAWLlyIUqlEqVRy+vTpEtsEsG3bNgYMGEDHjh3p06cPK1eupLCwUOdYhRBCCFF3yUieqDHUajUFBQVkZGQQFxfH9evXi426lcbS0pIWLVoQGRlJ/fr16d69Ow0aNCjzXllZWRw5coSTJ08SFhZWrFxhYSGFhYWkpKSwZs0a9PT0GD58eLnadeTIEa5fv86sWbNITU1l/vz5fPrppyxdurRc9QAkJSURGhrK5MmTMTAwICQkhKCgIIyNjXFxceHVV19l+/btfPTRR3Tq1Alra2ud6/7888/p378/y5Yt49ChQ4SGhpKSksJ//vMfPvroIzIzMwkJCSEsLIxPP/20xDr++9//Mn36dN566y169epFdnY258+fJyMjQ6fzpbl48SIpKSkEBQVRUFBAaGgoH330ETExMcD99/Odd97hn3/+4ZNPPsHMzIw1a9Zw69atcj/r6KeffiIxMZGAgADMzc1p3rw5ycnJWFhYMHXqVMzNzbl27Rrh4eEkJSUxf/584P6UTB8fH3x9fRk8eDAAbdu2LfEeGzduJCQkBF9fXzw9PTl37hwRERFkZGTwr3/9q1zxCiGEEKLukSRP1BixsbHMmDEDuD9qtnTp0kdu1vKw0NBQAgMDCQwMRKFQYGtri5eXF+PGjcPS0lKr7KlTpxg3bhwABgYGzJw5E29v72J1Llu2jC+++AK4P4UzKiqKZ599tlztUqvVREZGatYEJiYmsmrVqgo9bDUtLY1Nmzbx/PPPA/D333/z6aefMnHiRN59910AHBwcOHjwIIcOHWLMmDE61925c2emTZsGQNeuXTlw4ACbNm3iyJEjNGzYEIBLly4RGxtbapJ3/vx5LCwstBIVT09Pnc+XJiMjg127dmnex6ysLKZOncrt27dp1qwZx44d4+eff2bz5s2a6b1du3alV69emJub69wHcL+PY2Njad68ueZY48aNtWJ2dnbG2NiY4OBgZs2ahbGxsWZKZvPmzR85PbOgoIAVK1YwaNAgzee9e/fu5OXlER0dzaRJkzT9LYQQQghREpmuKWoMLy8vYmNjWb16NQMHDuT9998nISFB5+vd3Nw4ePAgy5Ytw8fHh4KCAqKiohgyZAh37tzRKuvo6EhsbCzr1q3jzTffJCQkhB07dhSr8/XXXyc2NpbIyEg6derEpEmT+Pnnn8vVri5dumgSPIA2bdqQl5dHcnL5n2XWpEkTTYIH0Lp1a+D+esEi5ubmWFpacvv27XLV3a1bN83/6+vr8+yzz9KuXTuthKN169akp6dz7969Euvo0KEDd+/eJTg4mJMnT6JSqcp1vjTt2rXTStSLRsiK2njhwgXMzc211m/Wr18fd3d3nep/kJ2dnVaCB/cT9XXr1vHiiy/i6OiIvb09QUFB5Ofnc+PGjXLV/8cff5CamlrsjwovvvgieXl5soOrEEIIIcokI3mixrC0tNT8It+zZ0/S0tIICwujV69eOtdhYmKCt7e35hfoHTt2MGPGDKKjo5k6daqmnKmpKQ4ODgC4u7trpgCOGDECfX19TbmmTZvStGlT4P6I0yuvvMLy5ctZtWqVzjE9PJJUlPDl5OToXEdpdRVtCGJmZlbsHuWt/+E6DA0NMTExKfF+OTk51K9fv1gd7u7uLFy4kA0bNjB+/Hjq1avHgAEDmDZtGhYWFmWeL01p7S5q499//11stBYo8VhZGjduXOzY+vXrWbBgARMmTMDNzQ1zc3MuXLjA3Llzy93PaWlpwP2R4QcVvS46L4QQQghRGhnJEzWWvb09169fr1QdI0eOxMLCgitXrpR5r8zMzGKbnzxIT0+P9u3bVzqmh9WrVw+g2IYuD25wUpMMGzaMr776in//+9/MmDGDQ4cOsXDhQp3PV0STJk1KfO8e9X6WpqRNeuLj4+nTpw8ffvgh3bt3x9HRsVgCrKuiZPbh2IpGdh+1llQIIYQQAiTJEzXYmTNndF7/lpeXV+IISHJyMhkZGVhZWZV5L1NT00euhcrPz+f8+fPlXpNXlqKRwj/++ENz7MqVK/z1119Vep8nzdLSkpEjR9KtWzettul6vjwcHBxIT0/nv//9r+bYvXv3OHXqVKXqLZKdnV3sMQpff/11sXKGhoZljuzZ2NhgaWlJfHy81vFvv/0WQ0NDHB0dKx+wEEIIIWo1ma4pnhoFBQXFfrGF+48/SEhIwNPTk+bNm5OWlsbevXs5ceIES5Ys0anujIwMBgwYwLBhw+jatSsNGjTg5s2bREdHo6enx6hRowD49ddfWbRoEd7e3lhbW5OVlcXRo0fZsWMHgYGBGBjc/ycTExPD+fPn8fDwwMrKin/++Ydt27Zx9epVZs+eXXWdAnTq1InmzZvz2Wef8eGHH5KZmUlUVNQjpy8+rZYvX87du3dxdXWlUaNG/Pbbbxw/fpyxY8fqdL6ievbsib29PR9++CGBgYGYm5vz5ZdfUr9+/TIfn6ELDw8PNmzYwKZNm2jdujV79uwpcUTX1taWw4cP4+LigrGxMTY2NpiammqV0dfX55133iEkJARLS0t69erFDz/8wOrVqxkzZoxsuiKEEEKIMkmSJ54aOTk5BAQEFDseEBBAbm4uixcvJjU1lYYNG6JUKtm4cSOurq461W1qasrEiRM5fvw48fHxpKWl0bhxYxwcHAgNDcXe3h64v97K3NyclStXkpSUhJmZGba2tkRERNC3b19NfW3btuXAgQPMmzeP9PR0rKyscHBwIDY2lnbt2lVNh/x/hoaGREREMGfOHAICAmjVqhXTpk0jNDS0Su/zJDg4OLB+/Xq+/fZbMjMzadasGePHj2fy5Mk6na8ohULBypUrmTVrFrNmzcLc3Jw333yTq1evcvHixUq369133yU1NZXly5cDMGDAAGbMmIGfn59WuVmzZvHZZ58xceJEsrOz2bBhA25ubsXq8/X1xcDAgHXr1rF161asrKzw9/cvVp8QQgghREkUarVaXd1BCCHEk5abm8ugQYNwcXHRPMuuNit6IP2WhCtcu1X+nVuFEEJA6xaNmO8/vLrDeCKysrK4ePEi7du3r/A687qsvP1X9HO6aOO/ypKRPCFEnRATE0NhYSE2Njakp6ezdetWEhMTdZ7yK4QQQghRU0iSJ2qFgoICHjUoXbSW7knKz88v9ZxCodB6FEN1UavVFBQUlHpeT0+v3A9kf1rVq1ePqKgoEhMTgfvP1lu1apXmL2ZP42focbC2sqjuEIQQosaS76Gipqgdv7WIOq9fv36aX95LcunSpScYzX1F6/xKYm1tzZEjR55gNCX7z3/+w5tvvlnq+ZdeeqlGrv0ryfDhwxk+fHip55/Gz9Dj4O/jWd0hCCFEjVZYWFhr/gAqai9J8kStEBkZSW5ubnWHoSU2NrbUc0UPPK9u9vb2j4yzLu3k+DR+hqpabm4uKpUKY2Pj6g6lxlGpVFy9ehUbGxvpv3KSvqsc6b/KeRz9JwmeqAkkyRO1glKprO4QiqmqhbOPk6mpaY2I80l4Gj9Dj4PstVUxarUalUol/VcB0neVI/1XOdJ/oq6SP0UIIYQQQgghRC0iSZ4QQtQhVfHw97pIoVBgbGws/VcB0neVI/1XOdJ/oq6S6ZpCCFFHGBkZyZqeCjI2NqZDhw7VHUaNJH1XOdJ/lVPV/SebroiaQpI8IYSoQyJijpKYdLe6wxBCiBrH2spCdigWNYYkeUIIUYckJt3l2q3k6g5DCCGEEI+RjDcLIYQQQgghRC0iSZ4QT1B4eDhOTk6lng8KCqJ///507tyZLl26MHr0aE6cOFGue+Tn57Nx40aGDh2Kk5MTXbp0YejQocydO1frOXALFixg0KBBODk54ezszMsvv8y+ffu06oqLi0OpVJb4NX78+PI1vpYIDw/n7NmzxY4rlUrWrFlTDREJIYQQQmiT6ZpCPEXy8vIYO3YsrVu3Jicnh9jYWCZNmsSGDRtwcXHRqY6QkBDi4uKYNGkSzs7OqFQqLl68yJ49e8jOztY8iP3evXuMHDkSW1tbFAoF+/fvJzAwkMLCQoYMGQKAp6cnMTExWvVfu3aNf/3rX/Ts2bNqG19DREREYGJigrOzs9bxmJgYWrRoUU1RCSGEEEL8H0nyhHiKLFu2TOt1z5498fLyYvfu3ToleSqVitjYWPz8/PD399cc9/Lywt/fX+thsHPnztW6tkePHly+fJmdO3dqkjxLS0ssLS21yh0/fhx9fX1efPHFcrevNuvcuXN1hyCEEEIIAch0TSGeavr6+piZmZGXl6dTeZVKRV5eHk2aNCnxfFnPCbKwsCjzXnv37qVr165YWVnpFBPcn8oYFRXF0qVLcXd3x8XFhYULF6JWqzl16hTDhg3DycmJMWPG8Ndff2ldm5uby5IlS+jduzcdO3Zk4MCBfP3111plzp07h5+fH927d6dz584MGzaMXbt2aZU5ffo0SqWSkydP8uGHH+Lk5ETv3r1ZvXp1udoBsHDhQs201dOnT2vOPThd09fXl7fffpu9e/fSv39/OnXqhJ+fH2lpaSQmJjJ+/HicnJwYNGiQpo4HxcXFMWTIEBwcHOjRowdLly6loKBA51iFEEIIUXfJSJ4QTxm1Wk1BQQEZGRnExcVx/fr1YqNupbG0tKRFixZERkZSv359unfvToMGDcq8V1ZWFkeOHOHkyZOEhYWVWv7ChQtcu3aNt99+u9zt2rx5M66urixcuJAff/yR8PBwCgsLOXnyJJMnT8bQ0JCQkBCmT59OdHS05rqAgADOnj3Lu+++S5s2bUhISOCjjz7C3NycXr16AXDr1i2cnZ0ZNWoURkZGnD17lhkzZqBWq3nppZe04pg9ezbDhg1jxYoVHDp0iEWLFqFUKnWafhoTE4OPjw++vr4MHjwYgLZt25Za/pdffiE1NZWPP/6YzMxMQkJCmDlzJomJiQwfPpxx48axatUqpkyZwnfffUf9+vUBWLt2LWFhYYwZM4bg4GCuXLmiSfKCgoLK3fdCCCGEqFskyRPiKRMbG8uMGTMAMDExYenSpY/crOVhoaGhBAYGEhgYiEKhwNbWFi8vL8aNG1ds6uWpU6cYN24cAAYGBsycORNvb+9S6967dy/16tWjf//+5W5XkyZNNAlkjx49OHLkCOvWrWPfvn20adMGgDt37vDpp5+Snp6Oubk533//PUeOHGHNmjV0794dgG7dupGUlER4eLgmyRs0aJDmPmq1mi5dunDnzh1iYmKKJXn9+/dnypQpALi7u3P06FH279+vU5JXNCWzefPmOk3PzMzM5IsvvtD0+6VLl4iOjmbOnDmMGjVK0y9Dhgzh1KlT9O3bl8zMTJYvX86ECRMIDAzUtNnQ0JDQ0FDGjx9Pw4YNy7y3EEIIIeouSfKEeMp4eXnRrl07UlNTiY+P5/333yciIkKT0JTFzc2NgwcPcuzYMU6dOsX3339PVFQUcXFxxMXF0bRpU01ZR0dHYmNjyczM5NixY4SEhKCvr8/IkSOL1VtYWMi+ffvw9PTE1NS03O3y8PDQem1jY8M///yjSfAAWrduDcDt27cxNzfn5MmTWFhY0LVrV/Lz87XqmjNnDgUFBejr65OWlkZ4eDiHDx/mzp07mmmNFhYWxeIoShbh/vTVNm3acPv27XK3Rxft2rXTSqyL2vdgXzzYZrg/9TQrKwtvb+9ibc7Ozub333/H1dX1scQrhBBCiNpBkjwhnjIPbnbSs2dP0tLSCAsL0znJg/sjgN7e3ppRuR07djBjxgyio6OZOnWqppypqSkODg7A/VGtgoICQkNDGTFiBPr6+lp1nj59mqSkJM2mLOVlbm6u9drQ0LDEYwA5OTkApKamcvfuXezt7UusMykpiWbNmhEcHMy5c+d49913adu2LaampmzdupVvv/222DVmZmbF7pmRkVGhNpWltPY9GEPRbqcPthkoNgJZ5OE1i0IIIYQQD5MkT4innL29PceOHatUHSNHjmTRokVcuXKlzHutX7+elJSUYhurfP3111rr4J6EBg0aYGlpSVRUVInnLS0tycnJ4ejRowQHB+Pr66s5t2XLlicVZpUqWkMZERFBs2bNip1v2bLlkw5JCCGEEDWMJHlCPOXOnDnDs88+q1PZvLw8srKyim22kpycTEZGRpk7Yp45cwZTU9Nia75yc3M5ePAg/fr104w8PQkeHh58+eWXGBoa0q5duxLLZGRkUFhYqBklg/tr4Y4cOfJYYjI0NNSMuj0OTk5OGBsbc/v2bfr16/fY7iOEEEKI2kuSPCGesIKCAuLj44sdV6lUJCQk4OnpSfPmzUlLS2Pv3r2cOHGCJUuW6FR3RkYGAwYMYNiwYXTt2pUGDRpw8+ZNoqOj0dPT02z28euvv7Jo0SK8vb2xtrYmKyuLo0ePsmPHDgIDAzEw0P7WkJCQQHp6eoWnalZUt27d6N27NxMmTGDChAkolUpUKhWXL1/m+vXrzJs3DzMzMxwcHFi9ejWWlpYYGBgQFRWFqakpKSkpVR6Tra0thw8fxsXFBWNjY2xsbCq0RrE05ubmvPfee4SFhXH79m1cXV3R19fnxo0bHD58mPDwcIyNjavsfkIIIYSofSTJE+IJy8nJISAgoNjxgIAAcnNzWbx4MampqTRs2BClUsnGjRt13mjD1NSUiRMncvz4ceLj40lLS6Nx48Y4ODgQGhqqWdvWuHFjzM3NWblyJUlJSZiZmWFra0tERAR9+/YtVu/XX3+NlZUVbm5ulWt8BSxfvpyoqCi2bt1KYmIiZmZmPP/884wYMUJTZvHixcyaNYvg4GAsLCzw9fUlKytL61EMVWXWrFl89tlnTJw4kezsbDZs2FDl/fLWW2/RtGlT1q5dy6ZNmzAwMKBVq1Z4enpqjVgKIYQQQpREoVar1dUdhBBCiMfrwoULAGxJuMK1W8nVHI0QQtQ8rVs0Yr7/8OoO44nJysri4sWLtG/fHhMTk+oOp8Ypb/8V/Zwu2hCvsvSqpBYhhBBCCCGEEE8Fma4pRA1SUFDAowbfH15L9yQ8+Cy3hykUimKPYnia1aa2lMbayqK6QxBCiBpJvn+KmkSSPCFqkH79+pGYmFjq+UuXLj3BaODmzZt4eXmVet7V1ZWNGzc+wYgqrja15VH8fTyrOwQhhKixCgsL0dOTiXDi6SdJnhA1SGRkJLm5udUdhkaTJk2IjY0t9Xz9+vWfYDSVU5vaUprc3FxUKpXszlkBKpWKq1evYmNjI/1XTtJ3lSP9VzlV3X+S4ImaQpI8IWoQpVJZ3SFoMTIyqrIFwtWtNrXlUWSvrYpRq9WoVCrpvwqQvqsc6b/Kkf4TdZX8OUIIIYQQQgghahFJ8oQQog5RKBTVHUKNpFAoMDY2lv6rAOm7ypH+qxzpP1FXyXRNIYSoI4yMjGRNTwUZGxvToUOH6g6jRpK+qxzpv8qpyv6TTVdETSJJnhBC1CERMUdJTLpb3WEIIUSNYm1lIbsTixpFkjwhhKhDEpPucu1WcnWHIYQQQojHSMachXiCwsPDcXJyKvV8UFAQ/fv3p3PnznTp0oXRo0dz4sSJct0jPz+fjRs3MnToUJycnOjSpQtDhw5l7ty5mscvZGZmEh4eziuvvIKLiwseHh74+fmV+Jy9K1euMHHiRE1MH330ESkpKeVreC0SHh7O2bNnix1XKpWsWbOmGiISQgghhNAmI3lCPEXy8vIYO3YsrVu3Jicnh9jYWCZNmsSGDRtwcXHRqY6QkBDi4uKYNGkSzs7OqFQqLl68yJ49e8jOzsbIyIhbt24RExPDyy+/zPvvv09OTg7R0dH4+Pjw1Vdf0aZNG+B+MjhmzBiaNm3KokWLyM7OZsmSJbz99tvExMTUybUJERERmJiY4OzsrHU8JiaGFi1aVFNUQgghhBD/R5I8IZ4iy5Yt03rds2dPvLy82L17t05JnkqlIjY2Fj8/P/z9/TXHvby88Pf31zwnqGXLlhw8eFBrE46uXbvSp08ftmzZwsyZMwHYsmULGRkZ7Nq1i8aNGwPw3HPP8corr3D48GH69etX6TbXFp07d67uEIQQQgghAJmuKcRTTV9fHzMzM/Ly8nQqr1KpyMvLo0mTJiWeL9pC2sTEpNgui/Xr16dVq1b8/fffmmO//PIL7dq10yR4AA4ODlhYWHDkyBGd26FUKomKimLp0qW4u7vj4uLCwoULUavVnDp1imHDhuHk5MSYMWP466+/tK7Nzc1lyZIl9O7dm44dOzJw4EC+/vprrTLnzp3Dz8+P7t2707lzZ4YNG8auXbu0ypw+fRqlUsnJkyf58MMPcXJyonfv3qxevbpc7QBYuHAhSqUSpVLJ6dOnNecenK7p6+vL22+/zd69e+nfvz+dOnXCz8+PtLQ0EhMTGT9+PE5OTgwaNEhTx4Pi4uIYMmQIDg4O9OjRg6VLl1JQUKBzrEIIIYSou2QkT4injFqtpqCggIyMDOLi4rh+/Tpz587V6VpLS0tatGhBZGQk9evXp3v37jRo0ECna9PT0/n999/x8PDQHMvJycHIyKhYWSMjI/744w/dGvT/bd68GVdXVxYuXMiPP/5IeHg4hYWFnDx5ksmTJ2NoaEhISAjTp08nOjpac11AQABnz57l3XffpU2bNiQkJPDRRx9hbm5Or169ALh16xbOzs6MGjUKIyMjzp49y4wZM1Cr1bz00ktaccyePZthw4axYsUKDh06xKJFi1AqlfTs2bPMNsTExODj44Ovry+DBw8GoG3btqWW/+WXX0hNTeXjjz8mMzOTkJAQZs6cSWJiIsOHD2fcuHGsWrWKKVOm8N1331G/fn0A1q5dS1hYGGPGjCE4OJgrV65okrygoKBy9bsQQggh6h5J8oR4ysTGxjJjxgzg/ojb0qVLH7lZy8NCQ0MJDAwkMDAQhUKBra0tXl5ejBs3DktLy1KvCwsLQ6FQMGrUKM2x1q1bExcXR3Z2Ns888wxwP6FKSkrCxMSkXO1q0qQJYWFhAPTo0YMjR46wbt069u3bp1kDeOfOHT799FPS09MxNzfn+++/58iRI6xZs4bu3bsD0K1bN5KSkggPD9ckeYMGDdLcR61W06VLF+7cuUNMTEyxJK9///5MmTIFAHd3d44ePcr+/ft1SvKKpmQ2b95cp+mZmZmZfPHFF5p+v3TpEtHR0cyZM0fTz02aNGHIkCGcOnWKvn37kpmZyfLly5kwYQKBgYGaNhsaGhIaGsr48eNp2LBhmfcWQgghRN0l0zWFeMp4eXkRGxvL6tWrGThwIO+//z4JCQk6X+/m5sbBgwdZtmwZPj4+FBQUEBUVxZAhQ7hz506J13z11Vds376dWbNm0axZM83xkSNHkpmZyaxZs7hz5w7Xr18nODgYPT09zdRPXT04QghgY2NDkyZNNAke3E8qAW7fvg3AyZMnsbCwoGvXruTn52u+PDw8uHjxomb6YlpaGiEhIfTu3Rt7e3vs7e2JiYnh6tWrxeIoShbh/vTVNm3aaO5X1dq1a6eVWBe178G+eLjN586dIysrC29v72Jtzs7O5vfff38ssQohhBCi9pCRPCGeMpaWlprEoGfPnqSlpREWFqYZtdKFiYkJ3t7eeHt7A7Bjxw5mzJhBdHQ0U6dO1SqbkJDArFmzeOedd4qNetna2jJv3jzmzZvH7t27gfsjYT179uTevXvlape5ubnWa0NDwxKPwf1pogCpqancvXsXe3v7EutMSkqiWbNmBAcHc+7cOd59913atm2LqakpW7du5dtvvy12jZmZWbF7ZmRklKstuiqtfQ/GUDQd9sE2A8XeiyIPr1kUQgghhHiYJHlCPOXs7e05duxYpeoYOXIkixYt4sqVK1rHf/jhBwICAhg+fDgBAQElXjt8+HBefPFFrl27RoMGDWjatCmDBg2iT58+lYpJFw0aNMDS0pKoqKgSz1taWpKTk8PRo0cJDg7G19dXc27Lli2PPb7HoWgNZUREhNaoapGWLVs+6ZCEEEIIUcNIkifEU+7MmTM8++yzOpXNy8sjKyur2GYrycnJZGRkYGVlpTl2+fJl3n77bbp27conn3zyyHqNjIyws7MD4NSpU1y7dq3Ukaaq5OHhwZdffomhoSHt2rUrsUxGRgaFhYWaUTK4vxauPLt/loehoaFm1O1xcHJywtjYmNu3b8sjKoQQQghRIZLkCfGEFRQUEB8fX+y4SqUiISEBT09PmjdvTlpaGnv37uXEiRMsWbJEp7ozMjIYMGAAw4YNo2vXrjRo0ICbN28SHR2Nnp6eZrOP5ORkxo8fT7169RgzZgw//fSTpg5TU1PNjpFZWVmEh4fTpUsX6tWrxw8//EBUVBT+/v7Y2tpWQW88Wrdu3ejduzcTJkxgwoQJKJVKVCoVly9f5vr168ybNw8zMzMcHBxYvXo1lpaWGBgYEBUVhampKSkpKVUek62tLYcPH8bFxQVjY2NsbGwwNTWtsvrNzc157733CAsL4/bt27i6uqKvr8+NGzc4fPgw4eHhxR5/IYQQQgjxIEnyhHjCcnJySpwaGRAQQG5uLosXLyY1NZWGDRuiVCrZuHEjrq6uOtVtamrKxIkTOX78OPHx8aSlpdG4cWMcHBwIDQ3VrG27fPmyZqOPsWPHatXh6urKxo0bAdDT0+O3334jLi6OrKwsbG1tmT17NiNGjKhED5TP8uXLiYqKYuvWrSQmJmJmZsbzzz+vFcPixYuZNWsWwcHBWFhY4OvrS1ZWltajGKrKrFmz+Oyzz5g4cSLZ2dls2LABNze3Kr3HW2+9RdOmTVm7di2bNm3CwMCAVq1a4enpqTViKYQQQghREoVarVZXdxBCCCEerwsXLgCwJeEK124lV3M0QghRs7Ru0Yj5/sOrO4wnKisri4sXL9K+fftyPzZJlL//in5OOzg4VMn95REKQgghhBBCCFGLyHRNIWqQgoICHjX4bmDw5P9J5+fnl3pOoVCgr6//BKOpnNrUFiGEEELUXZLkCVGD9OvXj8TExFLPX7p06QlGAzdv3sTLy6vU8w+u73va1aa2PIq1lUV1hyCEEDWOfO8UNY0keULUIJGRkeTm5lZ3GBpNmjQhNja21PP169d/gtFUTm1qy6P4+3hWdwhCCFEjFRYWoqcnK51EzSBJnhA1iFKprO4QtBgZGVXZAuHqVpvaUprc3FxUKpU8gqECVCoVV69excbGRvqvnKTvKkf6r3Kqsv8kwRM1iXxahRCiDpENlStGrVajUqmk/ypA+q5ypP8qR/pP1FWS5AkhRB2iUCiqO4QaSaFQYGxsLP1XAdJ3lSP9VznSf6KukumaQghRRxgZGcl0rwoyNjamQ4cO1R1GjSR9VznSf5VT2f6TdXiippIkTwgh6pCImKMkJt2t7jCEEOKpZ21lIZtViRpLkjwhhKhDEpPucu1WcnWHIYQQQojHSMafhRBCCCGEEKIWkZE8IUS5hIeHEx0dzblz50o8HxQUxPnz5/n7778xNDTEzs6OyZMn0717d53vkZ+fz9atW9mxYwc3btzAwMCA5s2b4+LiQnBwMEZGRgAsWLCAY8eOcevWLRQKBTY2Nrz11lsMGjRIU1dGRgbTpk3j559/5p9//sHExISOHTvy3nvv4ejoWLnOEEIIIYR4CkmSJ4SoUnl5eYwdO5bWrVuTk5NDbGwskyZNYsOGDbi4uOhUR0hICHFxcUyaNAlnZ2dUKhUXL15kz549ZGdna5K8e/fuMXLkSGxtbVEoFOzfv5/AwEAKCwsZMmQIcP/ZcEZGRkyePJmWLVuSmZnJ+vXrGTNmDHFxcdjY2Dy2vhBCCCGEqA6S5AkhqtSyZcu0Xvfs2RMvLy92796tU5KnUqmIjY3Fz88Pf39/zXEvLy/8/f21nnU0d+5crWt79OjB5cuX2blzpybJa9SoEYsXL9Yq5+HhgZubG/v378fPz6/cbRRCCCGEeJrJmjwhxGOlr6+PmZkZeXl5OpVXqVTk5eXRpEmTEs+X9awjCwuLMu9lYmJCvXr1dI4J4PDhw4wYMQInJydcXFwYMWIECQkJWmXi4uIYMmQIDg4O9OjRg6VLl1JQUKBV5vbt2wQFBeHm5oajoyOjR4/mp59+0irTp08f5s6dy+bNm+nduzcvvPAC77zzDikpKTrHK4QQQoi6S0byhBBVTq1WU1BQQEZGBnFxcVy/fr3YqFtpLC0tadGiBZGRkdSvX5/u3bvToEGDMu+VlZXFkSNHOHnyJGFhYcXKFRYWUlhYSEpKCmvWrEFPT4/hw4frFNOff/5JQEAAgwYN4sMPP6SwsJBff/2VtLQ0TZm1a9cSFhbGmDFjCA4O5sqVK5okLygoCIC0tDRef/11TExMmDlzJmZmZmzcuJExY8Zw4MABGjVqpKnvyJEjXL9+nVmzZpGamsr8+fP59NNPWbp0qU4xCyGEEKLukiRPCFHlYmNjmTFjBnB/1Gzp0qU4OTnpfH1oaCiBgYEEBgaiUCiwtbXFy8uLcePGYWlpqVX21KlTjBs3DgADAwNmzpyJt7d3sTqXLVvGF198AdyfwhkVFcWzzz6rUzy//PILeXl5zJw5E1NTU+D+1NAimZmZLF++nAkTJhAYGAhAt27dMDQ0JDQ0lPHjx9OwYUPWr19Peno6O3bs0CR07u7uDBgwgDVr1vDxxx9r6lSr1URGRmrWHyYmJrJq1Sp5MK8QQgghyiS/KQghqpyXlxexsbGsXr2agQMH8v777xeb2vgobm5uHDx4kGXLluHj40NBQQFRUVEMGTKEO3fuaJV1dHQkNjaWdevW8eabbxISEsKOHTuK1fn6668TGxtLZGQknTp1YtKkSfz88886xaNUKtHX1ycoKIgjR46QkZGhdf7cuXNkZWXh7e1Nfn6+5svDw4Ps7Gx+//13AE6ePImbmxsNGjTQlNHT06NLly5cuHBBq84uXbpoEjyANm3akJeXR3KyPONOCCGEEI8mI3lCiCpnaWmpGXHr2bMnaWlphIWF0atXL53rMDExwdvbWzMqt2PHDmbMmEF0dDRTp07VlDM1NcXBwQG4PypWUFBAaGgoI0aMQF9fX1OuadOmNG3aFABPT09eeeUVli9fzqpVq8qMxcbGhi+++IJVq1bh7++Pnp4e3bt3Z9asWbRo0YLU1FQAXnrppRKv/+uvvwBITU3lhx9+wN7evliZVq1aab02NzfXel2U8OXk5JQZrxBCCCHqNknyhBCPnb29PceOHatUHSNHjmTRokVcuXKlzHutX7+elJQUrKysSiyjp6dH+/btOXPmjM7379mzJz179iQzM5Njx44xf/58pk6dyvr16zVrBiMiImjWrFmxa1u2bAlAgwYN6NGjBwEBAcXKPDhqJ4QQQghRGZLkCSEeuzNnzui8/i0vL4+srKxim60kJyeTkZFRauL24L1MTU1p2LBhqWXy8/M5f/68zjE9yNTUlBdffJHz58+zd+9eAJycnDA2Nub27dv069ev1Gs9PDzYs2cPbdq0wcTEpNz3FkIIIYTQhSR5QohyKygoID4+vthxlUpFQkICnp6eNG/enLS0NPbu3cuJEydYsmSJTnVnZGQwYMAAhg0bRteuXWnQoAE3b94kOjoaPT09Ro0aBcCvv/7KokWL8Pb2xtramqysLI4ePcqOHTsIDAzEwOD+t7eYmBjOnz+Ph4cHVlZW/PPPP2zbto2rV68ye/ZsnWLatm0bP/zwAz169MDKyoqbN2+yZ88eunXrBtyfWvnee+8RFhbG7du3cXV1RV9fnxs3bnD48GHCw8MxNjZm7NixfP3117zxxhu8+eabtGjRgpSUFH788UeaNm3K2LFjdYpHCCGEEOJRJMkTQpRbTk5OiVMOAwICyM3NZfHixaSmptKwYUOUSiUbN27E1dVVp7pNTU2ZOHEix48fJz4+nrS0NBo3boyDgwOhoaGa9WyNGzfG3NyclStXkpSUhJmZGba2tkRERNC3b19NfW3btuXAgQPMmzeP9PR0rKyscHBwIDY2lnbt2ukUk1Kp5LvvvmP+/PncvXsXKysrBg0apNUHb731Fk2bNmXt2rVs2rQJAwMDWrVqhaenJ4aGhgA0bNiQmJgYPv/8cxYtWsTdu3dp1KgRnTp1euQIoBBCCCFEeSjUarW6uoMQQgjxeBXt3rkl4QrXbskOnUIIUZbWLRox3394dYdRbbKysrh48SLt27eXJQYVUN7+K/o5XbSZXGXJIxSEEEIIIYQQohaR6ZpCiCeqoKCAR00gKFpL9yTl5+eXek6hUGg9iqGms7ayqO4QhBCiRpDvl6ImkyRPCPFE9evXj8TExFLPX7p06QlGc19Jz60rYm1tzZEjR55gNI+Xv49ndYcghBA1RmFhIXp6MvFN1DyS5AkhnqjIyEhyc3OrOwwtsbGxpZ6rTc+vy83NRaVSYWxsXN2h1DgqlYqrV69iY2Mj/VdO0neVI/1XOZXtP0nwRE0lSZ4Q4olSKpXVHUIxVbXIuSaQvbYqRq1Wo1KppP8qQPqucqT/Kkf6T9RV8ucJIYQQQgghhKhFJMkTQog6RKFQVHcINZJCocDY2Fj6rwKk7ypH+q9ypP9EXSXTNYUQoo4wMjKSNT0VZGxsTIcOHao7jBpJ+q5ypP8qp6j/CgoLqzsUIZ4oSfKEEKIOiYg5SmLS3eoOQwghnhhrKwvZWVjUOZLkCSFEHZKYdJdrt5KrOwwhhBBCPEayJk8IIYQQQgghahFJ8oR4gsLDw3Fycir1fFBQEP3796dz58506dKF0aNHc+LEiXLdIz8/n40bNzJ06FCcnJzo0qULQ4cOZe7cuVrPp1uwYAGDBg3CyckJZ2dnXn75Zfbt21esvtzcXBYsWEC3bt3o3Lkz48aN448//ihXTLVJeHg4Z8+eLXZcqVSyZs2aaohICCGEEEKbTNcU4imSl5fH2LFjad26NTk5OcTGxjJp0iQ2bNiAi4uLTnWEhIQQFxfHpEmTcHZ2RqVScfHiRfbs2UN2drbm4d737t1j5MiR2NraolAo2L9/P4GBgRQWFjJkyBCt+r755huCg4Np2rQpX3zxBWPHjmXfvn2YmZk9ln54mkVERGBiYoKzs7PW8ZiYGFq0aFFNUQkhhBBC/B9J8oR4iixbtkzrdc+ePfHy8mL37t06JXkqlYrY2Fj8/Pzw9/fXHPfy8sLf31/rYbBz587VurZHjx5cvnyZnTt3apK827dvExsby+zZs3nllVeA+w8O7927N9u2bWPixIkVbmtt07lz5+oOQQghhBACkOmaQjzV9PX1MTMzIy8vT6fyKpWKvLw8mjRpUuL5sp4TZGFhoXWvEydOUFhYiLe3t1aZbt26cezYMZ1igvtTGaOioli6dCnu7u64uLiwcOFC1Go1p06dYtiwYTg5OTFmzBj++usvrWtzc3NZsmQJvXv3pmPHjgwcOJCvv/5aq8y5c+fw8/Oje/fudO7cmWHDhrFr1y6tMqdPn0apVHLy5Ek+/PBDnJyc6N27N6tXry5XOwAWLlyIUqlEqVRy+vRpzbkHp2v6+vry9ttvs3fvXvr370+nTp3w8/MjLS2NxMRExo8fj5OTE4MGDdLU8aC4uDiGDBmCg4MDPXr0YOnSpRQUFOgcqxBCCCHqLhnJE+Ipo1arKSgoICMjg7i4OK5fv15s1K00lpaWtGjRgsjISOrXr0/37t1p0KBBmffKysriyJEjnDx5krCwMM35P/74g0aNGhWro02bNsTGxparXZs3b8bV1ZWFCxfy448/Eh4eTmFhISdPnmTy5MkYGhoSEhLC9OnTiY6O1lwXEBDA2bNneffdd2nTpg0JCQl89NFHmJub06tXLwBu3bqFs7Mzo0aNwsjIiLNnzzJjxgzUajUvvfSSVhyzZ89m2LBhrFixgkOHDrFo0SKUSiU9e/Yssw0xMTH4+Pjg6+vL4MGDAWjbtm2p5X/55RdSU1P5+OOPyczMJCQkhJkzZ5KYmMjw4cMZN24cq1atYsqUKXz33XfUr18fgLVr1xIWFsaYMWMIDg7mypUrmiQvKCioXP0uhBBCiLpHkjwhnjKxsbHMmDEDABMTE5YuXfrIzVoeFhoaSmBgIIGBgSgUCmxtbfHy8mLcuHFYWlpqlT116hTjxo0DwMDAgJkzZ2qN2qWnp5e47s7c3Jy0tLRytatJkyaaBLJHjx4cOXKEdevWsW/fPtq0aQPAnTt3+PTTT0lPT8fc3Jzvv/+eI0eOsGbNGrp37w5At27dSEpKIjw8XJPkDRo0SHMftVpNly5duHPnDjExMcWSvP79+zNlyhQA3N3dOXr0KPv379cpySuaktm8eXOdpmdmZmbyxRdfaPr90qVLREdHM2fOHEaNGqXplyFDhnDq1Cn69u1LZmYmy5cvZ8KECQQGBmrabGhoSGhoKOPHj6dhw4Zl3lsIIYQQdZckeUI8Zby8vGjXrh2pqanEx8fz/vvvExERoUloyuLm5sbBgwc5duwYp06d4vvvvycqKoq4uDji4uJo2rSppqyjoyOxsbFkZmZy7NgxQkJC0NfXZ+TIkVXeLg8PD63XNjY2/PPPP5oED6B169bA/bWA5ubmnDx5EgsLC7p27Up+fr5WXXPmzKGgoAB9fX3S0tIIDw/n8OHD3LlzRzOt0cLColgcRcki3J++2qZNG27fvl2FLf0/7dq100qsi9r3YF882Ga4P/U0KysLb2/vYm3Ozs7m999/x9XV9bHEK4QQQojaQZI8IZ4ylpaWmsSgZ8+epKWlERYWpnOSB/dHAL29vTWjcjt27GDGjBlER0czdepUTTlTU1McHByA+6NaBQUFhIaGMmLECPT19TE3NyczM7NY/enp6Y+cBloSc3NzrdeGhoYlHgPIyckBIDU1lbt372Jvb19inUlJSTRr1ozg4GDOnTvHu+++S9u2bTE1NWXr1q18++23xa55eGTS0NCQjIyMcrVFV6W178EYinY7fbDNQLERyCIPr1kUQgghhHiYJHlCPOXs7e3LtclJSUaOHMmiRYu4cuVKmfdav349KSkpWFlZYWtryz///ENaWppWUvfHH39ga2tbqZh00aBBAywtLYmKiirxvKWlJTk5ORw9epTg4GB8fX0157Zs2fLY43scivo5IiKCZs2aFTvfsmXLJx2SEEIIIWoYSfKEeMqdOXOGZ599VqeyeXl5ZGVlFRtlS05OJiMjAysrqzLvZWpqqlnz1b17d/T09Dhw4IBmCmdaWhonTpzgnXfeqUBrysfDw4Mvv/wSQ0ND2rVrV2KZjIwMCgsLNaNkcH8t3JEjRx5LTIaGhppRt8fByckJY2Njbt++Tb9+/R7bfYQQQghRe0mSJ8QTVlBQQHx8fLHjKpWKhIQEPD09ad68OWlpaezdu5cTJ06wZMkSnerOyMhgwIABDBs2jK5du9KgQQNu3rxJdHQ0enp6ms0+fv31VxYtWoS3tzfW1tZkZWVx9OhRduzYQWBgIAYG9781NGvWjFdeeYWFCxeip6dH06ZNWbVqFWZmZrz22mtV1yml6NatG71792bChAlMmDABpVKJSqXi8uXLXL9+nXnz5mFmZoaDgwOrV6/G0tISAwMDoqKiMDU1JSUlpcpjsrW15fDhw7i4uGBsbIyNjQ2mpqZVVr+5uTnvvfceYWFh3L59G1dXV/T19blx4waHDx8mPDwcY2PjKrufEEIIIWofSfKEeMJycnIICAgodjwgIIDc3FwWL15MamoqDRs2RKlUsnHjRp032jA1NWXixIkcP36c+Ph40tLSaNy4MQ4ODoSGhmrWtjVu3Bhzc3NWrlxJUlISZmZm2NraEhERQd++fbXqnDFjBvXr12fx4sXcu3cPZ2dn1q5dW+Kum4/D8uXLiYqKYuvWrSQmJmJmZsbzzz/PiBEjNGUWL17MrFmzCA4OxsLCAl9fX7KysrQexVBVZs2axWeffcbEiRPJzs5mw4YNuLm5Vek93nrrLZo2bcratWvZtGkTBgYGtGrVCk9PT60RSyGEEEKIkijUarW6uoMQQgjxeF24cAGALQlXuHYruZqjEUKIJ6d1i0bM9x9e3WHUOFlZWVy8eJH27dtjYmJS3eHUOOXtv6Kf00Ub4lWWXpXUIoQQQgghhBDiqSDTNYWoQQoKCnjU4HvRWron6cFnuT1MoVCgr6//BKOpnNrUltJYW1lUdwhCCPFEyfc9URdJkidEDdKvXz8SExNLPX/p0qUnGA3cvHkTLy+vUs+7urqycePGJxhRxdWmtjyKv49ndYcghBBPXEFhIfp6MoFN1B2S5AlRg0RGRpKbm1vdYWg0adKE2NjYUs/Xr1//CUZTObWpLaXJzc1FpVLJ7pwVoFKpuHr1KjY2NtJ/5SR9VznSf5Uj/SfqKknyhKhBlEpldYegxcjIqMoWCFe32tSWR5G9tipGrVajUqmk/ypA+q5ypP8qR/pP1FUybi2EEHWIQqGo7hBqJIVCgbGxsfRfBUjfVY70X+UoFAp59Iyok2QkTwgh6ggjIyOZrlRBxsbGdOjQobrDqJGk7ypH+q9y7vefPXl5T89SByGeBEnyhBCiDomIOUpi0t3qDkMIIZ4IaysL/H08ycur7kiEeLIkyRNCiDokMemuPAxdCCGEqOVkTZ4QQgghhBBC1CIykieEIDw8nOjoaM6dO1fi+aCgIM6fP8/ff/+NoaEhdnZ2TJ48me7du+t8j/z8fLZu3cqOHTu4ceMGBgYGNG/eHBcXF4KDgzEyMgJgwYIFHDt2jFu3bqFQKLCxseGtt95i0KBBmroyMjKYNm0aP//8M//88w8mJiZ07NiR9957D0dHR51j8vX15T//+U+x46NHj2bWrFnlavtvv/3G4sWL+fHHH8nPz0epVDJlyhS6du2qKRMeHk5ERESx+82ZM4dRo0ZptW/hwoUcOHCA7OxsHB0dmTZtGu3bt9e5bUIIIYSouyTJE0KUKS8vj7Fjx9K6dWtycnKIjY1l0qRJbNiwARcXF53qCAkJIS4ujkmTJuHs7IxKpeLixYvs2bOH7OxsTZJ37949Ro4cia2tLQqFgv379xMYGEhhYSFDhgwB7j/vzcjIiMmTJ9OyZUsyMzNZv349Y8aMIS4uDhsbG53b5uzszL/+9S+tY40bNy5X21NSUhg7dizPPvss8+bNw9DQkI0bNzJx4kRiY2O1Hn3xzDPPsH79eq37Pfvss1qvAwMD+emnn/joo49o3Lgx69atY8yYMezevZvmzZvr3DYhhBBC1E2S5AkhyrRs2TKt1z179sTLy4vdu3frlOSpVCpiY2Px8/PD399fc9zLywt/f3+t5xfNnTtX69oePXpw+fJldu7cqUnyGjVqxOLFi7XKeXh44Obmxv79+/Hz89O5bebm5nTu3LnU87q0/dSpUyQnJ7N9+3ZatmwJgKurK66urhw6dEgrydPT03vk/X744QeOHTtGZGQkffr0AcDNzQ0vLy/WrFnDjBkzdG6bEEIIIeomWZMnhCg3fX19zMzMyNNxuzKVSkVeXh5NmjQp8XxZz3+ysLAo814mJibUq1dP55gqqqS2F/2/mZmZ5li9evUwNDQs9wN4f/nlFxQKBd26ddMcMzY2xsXFhe+++66S0QshhBCiLpAkTwihE7VaTX5+PqmpqaxZs4br16/j4+Oj07WWlpa0aNGCyMhI9u3bR1pamk73Sk9PZ9euXZw8eZLRo0cXK1dYWEh+fj5///03oaGh6OnpMXz48Aq168Gv0sqU1vbevXvTuHFjQkND+fvvv0lJSWHx4sUoFAqGDRumVVd2djZdu3alQ4cOvPjii2zfvl3rfG5uLnp6eujr62sdNzQ0JDExkezs7HK1TwghhBB1j0zXFELoJDY2VjNV0MTEhKVLl+Lk5KTz9aGhoQQGBhIYGIhCocDW1hYvLy/GjRuHpaWlVtlTp04xbtw4AAwMDJg5cybe3t7F6ly2bBlffPEFcH8KZ1RUVLH1bWVJSEjA3t6+2LFmzZppXpfV9gYNGrB582befvttevToAdwffVy9erVWPK1atSIoKIgOHTqQk5PD119/zcyZM8nIyGD8+PEAPPfccxQUFPDLL79oNpEpLCzkp59+Qq1Wk56ezjPPPFOuNgohhBCibpEkTwihEy8vL9q1a0dqairx8fG8//77RERE0KtXL52ud3Nz4+DBgxw7doxTp07x/fffExUVRVxcHHFxcTRt2lRT1tHRkdjYWDIzMzl27BghISHo6+szcuRIrTpff/11+vbtS1JSEjt27GDSpEmsW7euWNL2KC+88AJTp07VOtaoUaNytT05ORl/f39atWrFtGnT0NfXZ/v27UyePJnNmzfTpk0bgGKjep6enuTl5REZGcmbb76JoaEh3bp1o1WrVsyePZsFCxZoktcbN24AZU9tFUIIIYSQJE8IoRNLS0vNiFvPnj1JS0sjLCxM5yQP7o+CeXt7a0blduzYwYwZM4iOjtZKtExNTXFwcADA3d2dgoICQkNDGTFihNY0xqZNm2qSQ09PT1555RWWL1/OqlWrdI7JzMxMc6/SlNX2L7/8krS0NOLi4jS7hLq7uzNo0CBWrlxZbJOYBw0cOJD9+/fz559/0qZNG4yMjFi6dCkffvihZqMZOzs7xowZw8aNG7GwsNC5bUIIIYSom2RNnhCiQuzt7bl+/Xql6hg5ciQWFhZcuXKlzHtlZmaSkpJSahk9PT3at29f6Zh08XDbL1++jK2trSbBg/sbtCiVSv78889y19+xY0fi4+PZv38/8fHxmsdM2NvbY2hoWCVtEEIIIUTtJUmeEKJCzpw5o/P6t7y8vBI3W0lOTiYjIwMrK6sy72VqakrDhg1LLZOfn8/58+fLvSavIh5ue4sWLbhy5Qo5OTmaYwUFBfz6669YW1s/sq5vvvkGc3NzWrVqpXVcoVDQunVrbGxsSE1N5Ztvvik2XVUIIYQQoiQyXVMIAdxPSuLj44sdV6lUJCQk4OnpSfPmzUlLS2Pv3r2cOHGCJUuW6FR3RkYGAwYMYNiwYXTt2pUGDRpw8+ZNoqOj0dPTY9SoUQD8+uuvLFq0CG9vb6ytrcnKyuLo0aPs2LGDwMBADAzuf8uKiYnh/PnzeHh4YGVlxT///MO2bdu4evUqs2fPrrI+OXr0KLt27Sqz7SNHjiQ2NpZ33nmH0aNHo6+vT0xMDNevXyckJERTbsSIEQwfPhxbW1uys7P5+uuvOXDgANOmTdMaoYuMjOS5556jUaNGXL16lVWrVtGxY0dGjBhRZW0TQgghRO0lSZ4QAoCcnBwCAgKKHQ8ICCA3N5fFixeTmppKw4YNUSqVbNy4EVdXV53qNjU1ZeLEiRw/fpz4+HjS0tJo3LgxDg4OhIaGajZKady4Mebm5qxcuZKkpCTMzMywtbUlIiKCvn37aupr27YtBw4cYN68eaSnp2NlZYWDgwOxsbG0a9euajoEePbZZ3Vqe8eOHfnyyy9ZuXIlU6dOpbCwkLZt2xIVFUWXLl005Vq1asW6dev4559/UCgU2NnZERYWxtChQ7Xum56ezoIFC0hOTqZJkyYMHTqUd955Bz09mXwhhBBCiLIp1OV9Uq8QQoga58KFCwBsSbjCtVvJ1RyNEEI8Ga1bNGK+/3BUKhXGxsbVHU6NkpWVxcWLF2nfvj0mJibVHU6NU97+K/o5XdZmcLqSPwsLIYQQQgghRC0i0zWFEJVWUFDAoyYFFK2le5Ly8/NLPadQKLQexVCXWFtZVHcIQgjxxMj3PFFXSZInhKi0fv36kZiYWOr5S5cuPcFo7nvUA9Gtra05cuTIE4zm6eHv41ndIQghxBOVn19Q3SEI8cRJkieEqLTIyEhyc3OrOwwtsbGxpZ578Hl2dUlubq6sS6kglUrF1atXsbGxkf4rJ+m7ypH+qxyVSsXvv/9O27ZtqzsUIZ4oSfKEEJWmVCqrO4Riqmrhcm0je21VjFqtRqVSSf9VgPRd5Uj/VY5arSYvL6+6wxDiiZONV4QQQgghhBCiFpEkTwgh6hCFQlHdIdRICoUCY2Nj6b8KkL6rHOk/IURFyHRNIYSoI4yMjGRNTwUZGxvToUOH6g6jRpK+qxzpv5IVFhaipydjFUKURpI8IYSoQyJijpKYdLe6wxBCiAqztrKQnYKFKIMkeUIIUYckJt3l2q3k6g5DCCGEEI+RjHMLIYQQQgghRC0iSZ4Qok67ePEiSqWS06dP61w+PDwclUqldTwuLg6lUklKSsrjCFMIIYQQQmeS5AkhRDlcvHiRiIiIYkmep6cnMTExmJubV1NkQgghhBD3yZo8IWqR3NxcDAwMZMexamBpaYmlpWV1hyGEEEIIISN5QjytgoODGTx4MAkJCQwePBgHBwdGjBjBDz/8oCnTp08f5s6dy+rVq+nduzeOjo7cvXuXwsJCVq5cSZ8+fejYsSPe3t5s27at2D2uXLmCv78/rq6udOrUiaFDh7J3717NebVazZo1axgwYAAdO3bEy8uLdevWadVx+/ZtAgIC8PDwwMHBgT59+vDZZ5/pfF4XcXFxDBkyBAcHB3r06MHSpUspKCjQOq9UKvnll1+YMGECnTt3pn///uzatatYXStXrqRbt244OTnh7+9PcrLum5DExcUxdepUANzd3VEqlfTp00crhqLpmjdv3kSpVLJr1y5mzZqFi4sL7u7urF27FoB9+/YxYMAAnJ2d8ff3Jz09Xete6enpzJkzh+7du9OxY0dGjBjBiRMnytVvQgghhKibZCRPiKdYUlISn3zyCVOmTMHc3JzVq1czfvx4Dhw4QKNGjQA4cOAAzz33HNOnT0dPTw8TExMWLlzIhg0bmDx5Mk5OThw9epTZs2eTn5/PG2+8AcC1a9fw8fGhefPmTJ8+HSsrK3777Tdu3bqluf+8efPYsWMHfn5+dOrUibNnz7Jo0SLq1avHqFGjAPj444/5+++/mTFjBo0aNeKvv/7ip59+0tRR1vmyrF27lrCwMMaMGUNwcDBXrlzRJHlBQUFaZYOCgnj11VcZN24c27dvJzg4GAcHB9q0aQPApk2bWLZsGW+99RYeHh78+9//Zvr06TrH4unpyeTJk4mMjOTLL7/EzMwMIyOjR17z+eef079/f5YtW8ahQ4cIDQ0lJSWF//znP3z00UdkZmYSEhJCWFgYn376KXB/RHbcuHEkJyfz/vvv07RpU/bs2cPbb7+tSSaFEEIIIUojSZ4QT7G7d+/y+eef4+7uDoCrqyu9evVi3bp1fPjhhwDk5eWxevVqTExMAEhJSWHTpk2MHz+eKVOmANC9e3dSU1NZsWIFo0aNQl9fn/DwcAwNDdm6dSumpqYAeHh4aO79559/smnTJj755BN8fHw057Ozs1mxYgU+Pj7o6elx4cIFAgMDefHFFzXXDh8+XPP/ZZ1/lMzMTJYvX86ECRMIDAwEoFu3bhgaGhIaGsr48eNp2LChpvzo0aMZPXo0AE5OTiQkJLB//37eeecdCgoKWLVqFcOGDeNf//oXAD169CA5OZndu3frFI+lpSWtWrUCwN7eXqfpmZ07d2batGnw/9i787Cqqvbh419AUBAEQZxTcDoKouAAihOKiqblkOaIY84oDmRoammaOKApKA5pOSaJOD/hmFha9jxNWioZDgVmIiCDHJnfP/ixX49Mh0EBuT/X1RVnr7XXvvfa5yD3WWuvDXTo0IHTp0+zd+9ezp8/r8QeFhZGUFCQkuQdP36cmzdvcvToUZo0aaLEeu/ePTZv3syGDRu0ilcIIYQQFZNM1xSiDDMxMVESvOzXzs7O/Prrr8o2JycnJcEDuHr1KqmpqfTp00ejrb59+xITE8Pdu3cB+P7773Fzc1MSvOddvnwZgN69e5OWlqb85+zsTFRUFP/88w8ANjY27Ny5k/3793Pv3r0c7RRUnp+ff/6ZpKQk+vTpkyOGp0+fcuvWLY36nTt3Vn42MjKibt26PHjwAMiaNvrw4UN69eqlsY+bm1uhYiqsTp06KT/r6enx2muv0bx5c43k1MrKivj4eJ48eQLApUuXaNasGVZWVjnO+9q1ay80XiGEEEKUfzKSJ0QZlttIkYWFBeHh4RqvnxUXFwdAjRo1NLZnv378+LHy/5o1a+Z57NjYWDIzM+nQoUOu5f/88w/16tVj/fr1rF+/nk8++YSlS5dibW3N3Llz6d27N0CB5fmJjY0FYNCgQXnG8CwTExON1/r6+qSkpABZU18hZ58+308lLbeYnk3Ks7cBJCcnU7VqVWJjY7l+/Tq2trY52tPT03txwQohhBDilSBJnhBlWG7PXIuOjsbS0lJ5raOjo1FuZmam1KtVq5ay/dGjRxrlZmZmPHz4MM9jm5qaoqOjw/79+5Uk5FnW1tYA1KxZk5UrV5KRkcFvv/1GQEAAc+bMISQkhNdee63A8vyYmpoC4O/vT+3atXOU169fP9/9n5XdZ8/3aXa/lCWmpqaoVCpWrFhR2qEIIYQQohySJE+IMiwhIYHvvvtOmbKZkJDA5cuXlfvOcmNnZ4e+vj4hISHY2Ngo27/66issLCywsrICslaHPHXqFF5eXrlO2cw+5uPHj5UVJPOjq6tLq1atmD17NufPn+fevXsaSVxB5blxcHDA0NCQBw8e5JhmWVi1a9fG0tKSM2fOaLR16tSpQrWTnfBmjxC+CM7OzoSGhlKzZk2NRF0IIYQQQhuS5AlRhpmZmfH+++8za9YsTExM2L59O5mZmYwdOzbPfczNzRk9ejQ7duzAwMAAe3t7QkNDOXHiBIsXL1am+3l4eHDhwgVGjhzJO++8g6WlJeHh4ajVaiZNmoS1tTWjRo1i/vz5TJw4kdatW5Oamsrdu3e5cuUKmzdvJiEhgYkTJzJgwACsra1JTU1lz549VKtWDRsbmwLLC1KtWjVmzZrFmjVrePDgAY6Ojujp6fH3339z7tw5/Pz8MDQ01Kov9fT0mDx5MitWrMDCwoJOnTpx6dIlrly5ot3F+D/ZK3Xu27ePnj17UqVKlRJf7XLgwIEcOHCAMWPGMGHCBKysrEhISOD69eukpqYqi+4IIYQQQuRGkjwhyjBLS0u8vLxYvXo1f/31F02bNmXHjh0F3kc2f/58TExMCAoKYsuWLdSrV4+lS5cyfPhwpY6VlRUHDhzA19eXpUuXkp6ejpWVFZMnT1bqLFq0CGtrawIDA9m0aRNVq1bF2tpaWdSlcuXKNGvWjD179vDPP/9QpUoVWrZsyY4dOzA3NyclJSXfcm1MmDCBWrVq8dlnn7F3714qVapEgwYNcHFxyXUaaX7c3d2Jj49n//79fPHFF3Ts2JHly5fzzjvvaN2GjY0NM2fO5ODBg3z66afUqVOH8+fPFyqOghgYGLB79278/PzYsmULUVFRmJmZYWNjw8iRI0v0WEIIIYR49ehkZmZmlnYQQoicvL29+e233zQeTi5EUWWvyrk/NJy797V/ALwQQpQ1VnUtWOkxUKu6SUlJ3LhxgxYtWuRY9ErkT/queArbf9n/TtvZ2ZXI8eURCkIIIYQQQgjxCpHpmkKIUpOWlpZnmY6Ozkt/XEBGRgYZGRl5luvp6eVYzbS8qWdpVtohCCFEscjvMSEKJkmeEGWUj49PaYfwwuX2HLhs9erVK/F73QqyadMm/P398yxfuXIlgwcPfokRlTyPYS6lHYIQQhRbRkYGuroyIU2IvEiSJ4QoNUFBQXmWGRgYvMRIsrz99tu4uLjkWV6Y5/KVRSkpKajVaq1XJBX/n1qt5s6dO1hbW0v/FZL0XfFI/+VOEjwh8idJnhCi1JTUzcUlpVatWq/8c+lkra2iyczMRK1WS/8VgfRd8Uj/CSGKQr4GEUIIIYQQQohXiCR5QghRgZT3hWNKi46ODoaGhtJ/RSB9VzzSf0KIopDpmkIIUUEYGBjIPT1FZGhoiI2NTWmHUS5J3xVPRe4/WVxFiKKTJE8IISoQ/8ALREY9Lu0whBAiX/UszWQ1YCGKQZI8IYSoQCKjHnP3fnRphyGEEEKIF0jGwIUQJc7Pzw8HB4c8y728vOjduzf29va0b9+eUaNG8e233xb7uDdu3EClUnHlypVit1UYZ8+eZd++fTm2e3t7079//5caixBCCCGEjOQJIV661NRUxo0bh5WVFcnJyQQFBTF58mR2795Nu3btSju8Qjt79iy//fYbo0aN0tg+ffp0kpKSSikqIYQQQlRUkuQJIV66DRs2aLzu2rUrrq6uHD16tMwkeU+fPqVKlSrFaqNBgwYlFI0QQgghhPZkuqYQotTp6elhYmJCampqofbbvHkznTp1wsHBAQ8PD6KjNe81i4iIQKVSERISorF9xYoV9OjRQ3kdHByMSqXi559/Zvz48djb27N69WoAdu7cyVtvvUXbtm3p2LEjU6ZM4c6dO8q+3t7eHD58mFu3bqFSqVCpVHh7eytlz0/XDAsLY+LEidjb29O2bVtmzZrF/fv3NeqoVCq2b9+On58fzs7OODk5sWDBAhkVFEIIIYRWZCRPCFEqMjMzSU9PJyEhgeDgYO7du8eyZcu03n/v3r1s2LCBCRMm4OzszOXLl3n//feLFdO8efMYNmwYU6ZMUR418ODBA0aPHk3dunVJTEzkwIEDDB8+nFOnTmFmZsb06dOJiYnh9u3brF27FgBzc/Nc2//nn38YPXo0r732GmvWrCE5OZn169czevRojh07hrGxsVJ33759tG3bFh8fH+7evcvq1auxsLDAy8urWOcohBBCiFefJHlCiFIRFBTEokWLADAyMmL9+vX5LtbyrPT0dLZu3cqAAQN47733AOjSpQvR0dEcPXq0yDENHz6cyZMna2xbuHChxnE7depEx44dOXXqFMOGDaNBgwaYm5tz//597O3t823/888/Jy0tjZ07d2JmZgZAixYt6NevH4cPH8bd3V2pa2lpia+vL5A1nfX69eucOnVKkjwhhBBCFEimawohSoWrqytBQUFs376dvn37Mnv2bEJDQ7Xa98GDBzx8+JBevXppbHdzcytWTC4uLjm2/fLLL4wfPx4nJydsbGxo3bo1SUlJ3L17t9Dt/+9//8PJyUlJ8AAaN25M8+bN+fHHHzXqOjs7a7xu3LgxDx48KPQxhRBCCFHxyEieEKJUmJubK9Mau3btSlxcHGvWrKFbt24F7hsVFaW08awaNWoUK6bn979//z4TJkygZcuWLF26lJo1a6Kvr8+UKVNITk4udPvx8fG0aNEix3YLCwvi4uI0tlWrVk3jtb6+PikpKYU+phBCCCEqHknyhBBlgq2tLRcvXtSqrqWlJQAxMTEa2x89eqTxunLlygA5FnSJj4/X6jjffPMNSUlJ+Pv7K0lXWlpajoRMW6ampjkWhwGIjo7GysqqSG0KIYQQQjxPpmsKIcqEH3/8kddee02rurVr18bS0pIzZ85obD916pTGawsLC/T19QkPD1e2paSk8N///ler4zx9+hQdHR0qVfr/34d99dVXpKWladTT19fXamSvbdu2fP/99xpJ4u3btwkLC6Nt27ZaxSSEEEIIURAZyRNCvBDp6ek5Hl0AoFarCQ0NxcXFhTp16hAXF8eJEyf49ttvWbdunVZt6+npMXnyZFasWIGFhQWdOnXi0qVLXLlyRaOerq4uvXr1Yt++fTRs2JDq1auzd+9eMjMz0dHRKfA4HTp0AGDBggUMHz6cW7du8dlnn+WYStm4cWMOHTrEiRMnlOPUr18/R3vjxo0jODiYCRMmMG3aNJKTk/nkk0+oU6cOgwYN0urchRBCCCEKIkmeEOKFSE5OxtPTM8d2T09PUlJS8PX1JTY2lurVq6NSqdizZw+Ojo5at+/u7k58fDz79+/niy++oGPHjixfvpx33nlHo97ixYtZvHgxy5cvp2rVqkycOBFra2vOnTtX4DFUKhUrV67E39+fKVOm0KJFCzZs2MDs2bM16g0ZMoSrV6/y0Ucf8fjxYwYNGoSPj0+O9urUqcOePXtYvXo1Xl5e6Orq0qlTJ7y9vTUenyCEEEIIURw6mZmZmaUdhBBCiBfr2rVrAOwPDefu/Zz3BQohRFliVdeClR4Di91OUlISN27coEWLFhgZGRU/sApE+q54Ctt/2f9O29nZlcjx5Z48IYQQQgghhHiFyHRNIUSZk56eTn6TDJ5dCEUIIYQQQmiSv5SEEGVOr169iIyMzLM8LCzsJUbzaqlnaVbaIQghRIHkd5UQxSNJnhCizAkICJAHf78gHsNcSjsEIYTQSkZGBrq6cmeREEUhSZ4QosxRqVSlHcIrKSUlBbVajaGhYWmHUu6o1Wru3LmDtbW19F8hSd8VT0XuP0nwhCg6+fQIIUQFIgsqF01mZiZqtVr6rwik74pH+k8IURSS5AkhRAWizUPgRU46OjoYGhpK/xWB9F3xSP8JIYpCpmsKIUQFYWBgUOGme5UUQ0NDbGxsSjuMckn6rngqav/J/XhCFI8keUIIUYH4B14gMupxaYchhBB5qmdpJotECVFMkuQJIUQFEhn1mLv3o0s7DCGEEEK8QDIOLoQQQgghhBCvEEnyhCjH/Pz8cHBwyLPcy8uL3r17Y29vT/v27Rk1ahTffvttoY6RlpbGnj17ePPNN3FwcKB9+/a8+eabLFu2THmWXWJiIn5+fgwZMoR27drh7OzM1KlTc31oeXh4OJMmTVJievfdd4mJiSncib8CIiIi8PPz499//9Wq/vTp03F3d3/BUQkhhBDiVSDTNYV4haWmpjJu3DisrKxITk4mKCiIyZMns3v3btq1a6dVG8uXLyc4OJjJkyfTpk0b1Go1N27c4NixYzx9+hQDAwPu379PYGAgb731FrNnzyY5OZmdO3cybNgwDh06ROPGjYGsZHDs2LHUqlWLtWvX8vTpU9atW8eUKVMIDAysUDfZR0ZG4u/vj4uLC7Vq1SrtcIQQQgjxCpEkT4hX2IYNGzRed+3aFVdXV44ePapVkqdWqwkKCmLq1Kl4eHgo211dXfHw8FCe21S/fn3OnDmjsXJjhw4d6NGjB/v372fx4sUA7N+/n4SEBI4cOUKNGjUAaNiwIUOGDOHcuXP06tWr2OcshBBCCFHRVZyvzYUQ6OnpYWJiQmpqqlb11Wo1qamp1KxZM9fy7Oc2GRkZ5Viav2rVqjRo0ICHDx8q265fv07z5s2VBA/Azs4OMzMzzp8/r/V5qFQqtm/fjp+fH87Ozjg5ObFgwQKSkpKUOnlNZW3Xrh1+fn7Ka3d3d6ZMmcKJEyfo3bs3rVu3ZurUqcTFxREZGcnEiRNxcHCgX79+XLlyResYU1NTWbVqFS4uLrRs2ZLOnTszdepUEhISuHLlCmPGjAFgyJAhqFQqVCqVsm94eDijR4/Gzs6Onj17cvjwYa2PK4QQQgghI3lCvOIyMzNJT08nISGB4OBg7t27x7Jly7Ta19zcnLp16xIQEEDVqlXp3LkzpqamWu0bHx/PrVu3cHZ2VrYlJydjYGCQo66BgQG3b9/W7oT+z759+2jbti0+Pj7cvXuX1atXY2FhgZeXV6HagazkMzY2lvnz55OYmMjy5ctZvHgxkZGRDBw4kPHjx7N161ZmzpzJ119/TdWqVQtsc+vWrRw4cAAvLy+aNm1KbGwsly5dIiUlBVtbW5YsWcKyZctYuXIljRo1UvZLTk5mwoQJGBoasnr1agA2btxIYmIiVlZWhT43IYQQQlQ8kuQJ8YoLCgpi0aJFQNaI2/r16/NdrOV5Pj4+zJ07l7lz56Kjo0OjRo1wdXVl/PjxmJub57nfmjVr0NHRYcSIEco2KysrgoODefr0KVWqVAHg/v37REVFYWRkVKjzsrS0xNfXF8iahnr9+nVOnTpVpCQvMTGRLVu2KOcTFhbGzp07+fDDD5X4a9asyRtvvMF3331Hz549C2zz2rVrdO7cmVGjRinb3NzclJ+bNGkCQNOmTbGzs1O2BwcH8/DhQ7766islqbOxsaFPnz6S5AkhhBBCKzJdU4hXnKurK0FBQWzfvp2+ffsye/ZsQkNDtd7fycmJM2fOsGHDBoYNG0Z6ejrbtm3jjTfeyHNlyEOHDvHll1+yZMkSateurWwfOnQoiYmJLFmyhH///Zd79+7h7e2Nrq6uMvVTW8+OEAI0btyYBw8eFKqNbM2bN9dIWLOTqWePkb1N22PY2NgQGhqKn58fV69eJSMjQ6v9rl69StOmTTUSuoYNG9K8eXOt9hdCCCGEkJE8IV5x5ubmSgLTtWtX4uLiWLNmDd26ddO6DSMjI/r06UOfPn0AOHjwIIsWLWLnzp0sWLBAo25oaChLlixh+vTpDBo0SKOsUaNGrFixghUrVnD06FEAevfuTdeuXXny5EmhzqtatWoar/X19ZVHOhRWbm0BmJiYKNuyp5kmJydr1ea0adPQ1dXl8OHD+Pv7Y25uzqhRo5gxY0a+Ce3Dhw+xsLDIsd3CwkLrYwshhBCiYpORPCEqGFtbW+7du1esNoYOHYqZmRnh4eEa23/55Rc8PT0ZOHAgnp6eue47cOBALl26xPHjx7l48SJ+fn78/fff2NvbFyum51WuXDnHAjOpqakai7O8SAYGBsycOZPz589z+vRphg4dip+fn5Lc5qVmzZpER0fn2J7bNiGEEEKI3EiSJ0QF8+OPP/Laa69pVTc1NZW4uLgc26Ojo0lISMDS0lLZ9ueffzJlyhQ6dOjA0qVL823XwMCAZs2aUatWLb777jvu3r2bY9SvuGrVqkVqaip//fWXsu37778nPT29RI+jjYYNGzJ37lzMzMyUBWayRwufH52zs7Pj1q1bGon4vXv3uHnz5ssLWAghhBDlmkzXFKKcS09PJyQkJMd2tVpNaGgoLi4u1KlTh7i4OE6cOMG3337LunXrtGo7ISEBNzc3BgwYQIcOHTA1NSUiIoKdO3eiq6urLEoSHR3NxIkTqVy5MmPHjuW3335T2jA2NlYWGUlKSsLPz4/27dtTuXJlfvnlF7Zt24aHh4fGCpMloWvXrhgZGbFo0SImTZrEgwcP2L17N5UrVy7R4+Rl+vTp2NraYmNjg6GhIV9//TVxcXF06NAByLrHT09Pj0OHDlGpUiX09PSws7Nj8ODBBAQEMGXKFGU0dOPGjRqPnRBCCCGEyI8keUKUc8nJyblOjfT09CQlJQVfX19iY2OpXr06KpWKPXv24OjoqFXbxsbGTJo0iW+++YaQkBDi4uKoUaMGdnZ2+Pj4YGtrC2SN4mUvSDJu3DiNNhwdHdmzZw8Aurq6/PHHHwQHB5OUlESjRo344IMPGDx4cDF6IHfVq1dn48aNrFq1ihkzZtCiRQtWr16Nu7t7iR8rN23atOGrr77is88+Iz09HWtra9auXass5mJubs6SJUv49NNPOXbsGGlpaYSFhVGlShVlZc93332XWrVqMX36dM6dO0dCQsJLiV0IIYQQ5ZtOZmZmZmkHIYQQ4sW6du0aAPtDw7l7X+7vE0KUXVZ1LVjpMbBE2kpKSuLGjRu0aNGi0I/qqeik74qnsP2X/e/0s49VKg65J08IIYQQQgghXiEyXVOICiw9PZ38BvMrVXr5vyLS0tLyLNPR0UFPT+8lRpO7zMzMfBdw0dXVRVe3bH6HVs/SrLRDEEKIfMnvKSGKT5I8ISqwXr16ERkZmWd5WFjYS4wGIiIicHV1zbP82fv7StPhw4dzPB/wWR4eHsycOfMlRqQ9j2EupR2CEEIUKCMjo8x+WSZEeSBJnhAVWEBAQJEfIP4i1KxZk6CgoDzLq1at+hKjyVv37t3zjbNmzZovMRrtpaSkoFarMTQ0LO1Qyh21Ws2dO3ewtraW/isk6bviqaj9JwmeEMUjSZ4QFZhKpSrtEDQYGBiU2A3HL1L16tWpXr16aYdRJLLWVtFkZmaiVqul/4pA+q54pP+EEEUhX5MIIYQQQgghxCtEkjwhhKhAdHR0SjuEcklHRwdDQ0PpvyKQvise6T8hRFHIdE0hhKggDAwMKtQ9PSXJ0NAQGxub0g6jXJK+K55Xsf9kURUhXjxJ8oQQogLxD7xAZNTj0g5DCFFB1bM0k1V+hXgJJMkTQogKJDLqMXfvR5d2GEIIIYR4gWSsXOSpR48eLFu2rND7+fn58dNPPxVqnxs3buDn54dardbYHhwcjEqlIiYmptBxlCVF7ctnff7550VeDXP16tV07tyZ5s2bs2LFimLF8ay8rltZ4O3tTf/+/UukLT8/PxwcHAqs5+7uzpQpU/Lc78qVK6hUKq5du6ZRp7CfFyGEEEKI/MhInsiTv78/1apVK9J+RkZGtGnTRut9bty4gb+/P6NGjZJ7hkrY5cuX2bFjBwsWLKB169Yl+gw3uW6aPvjgg3zvM7G1tSUwMJDGjRsr24ryeRFCCCGEyI8keSJPr9qN3rl5+vQpVapUKe0wXqjbt28DMGbMmHJ/o3tZv15NmjTJt9zY2Bh7e/uXE4wQQgghKqzy/RdfGfHzzz8zdepUOnfujL29PQMGDODIkSNKeV5TDgcMGIC3tzcAiYmJdO/enVmzZmnUWbJkCU5OTvz7779axaJSqdi2bRurV6+mQ4cOODg44O3tTWJiolInKSmJZcuW4ebmRuvWrenRowdLliwhISFBo63npxhmT3+7cuUKAwcOxN7eniFDhvDbb79pHB+ypgeqVCpUKhVXrlzJN+bg4GAWLFgAQMeOHVGpVPTo0UOjzoMHD3jnnXewt7end+/eGv2b7cKFCwwdOpRWrVrRoUMHPvjgA5KSkpTy7KlyFy5cYNasWbRp0wZPT08iIiJQqVQcOXKEJUuW0K5dOzp27Mhnn30GwMmTJ3Fzc6NNmzZ4eHgQHx9f6L4srMTERObPn4+DgwMdOnRg9erVpKen56gXHx/Phx9+SOfOnWnZsiWDBw/m22+/Vcrd3d356KOPAGjRooXG9Xjw4AFeXl44OTnRqlUrRo0apXEtsx05coSBAwdiZ2eHk5MTkyZNIjIyUqvrlpfsaY1HjhyhZ8+etGrVCnd3dyUhzZb9fl6zZg2dOnWiY8eOACQnJ7Ny5Uo6d+6MnZ0dAwYM4MyZM7keKzQ0lP79+2NnZ8fgwYP55ZdfcpzfiBEjcHR0pH379ri7u3P16tVc27p69SpDhgzBzs6Ovn378vXXX+d6Xnl5frpmXp+XmTNnMnz48Bz779+/Hzs7Ox4/fpznMYQQQgghZCSvBNy/f582bdowYsQIDAwM+Omnn1i0aBGZmZkMGjRIqzaMjY35+OOPGT9+vPJHdWhoKIGBgaxfv55atWppHc+ePXuwtbVl1apVREREsHbtWpKTk1m/fj2QNRqSnp7OnDlzMDc3559//mHLli1Mnz6dPXv25Nt2VFQUy5cvZ/LkyZiYmODr64uHhwdnzpxBX1+fwMBAhg0bhru7u3I/VEGjGy4uLkybNo2AgAA+/fRTTExMMDAw0Kjj5eXF22+/zfjx4/nyyy/x9vbGzs5OmfYWEhLCnDlzGDx4MDNnziQqKgpfX1/i4+OV8862ePFi3nzzTTZt2qQxsvXJJ5/Qu3dvNmzYwNmzZ/Hx8SEmJoYffviBd999l8TERJYvX86aNWuUxKk4fZmfhQsX8s033+Dl5UX9+vXZv38/J06c0KiTkpLC+PHjiY6OZvbs2dSqVYtjx44xZcoU5YuFDz74gC+//JJdu3YRGBgIZF2PuLg4Ro4ciZGREYsXL8bExIQ9e/YwduxYTp8+jYWFBQCffvopa9asYciQIcyZM4fU1FS+//57YmJitLpu+fn999/566+/mDdvntL/77zzDiEhIRrt7N69m9atW7NixQrS0tKArPfDN998w+zZs2nUqBFHjx5l5syZbNq0CVdXV2XfqKgoli5dysyZM6lWrRrbt29n4sSJGucYERHBwIEDadCgASkpKZw8eZJRo0Zx7NgxrK2tlbZSU1OZM2cOEyZMoH79+nzxxRd4eHgofV0UeX1ehg4dyqRJk7h9+zaNGjVS6h86dIhevXphZmZWpOMJIYQQomKQJK8E9OvXT/k5MzOT9u3b8++//xIYGKh1kgdZoyGjR49m+fLlqFQq3n//ffr378/rr79eqHgMDAzYtGkTenp6AFSuXJlFixbh4eFB48aNMTc3Z+nSpUr9tLQ06tevz8iRI7lz547GH7bPi4uLY+/evTRt2hTIen7PmDFj+PXXX2nXrp0yFa1OnTpaT0szNzenQYMGQNY9S+bm5jnqjBo1ilGjRgHg4OBAaGgop06dYvr06WRmZrJ69Wpef/11jUVFLC0tmTx5MtOnT1fihawRynfffVd5HRERAYC9vT0LFy4EoEOHDpw+fZq9e/dy/vx5qlevDkBYWBhBQUFKklecvszLn3/+yenTp1m+fDlDhgwBoHPnzvTu3Vuj3vHjx7l58yZHjx5VEukuXbpw7949Nm/ezIYNG2jSpAl169ZVzi/bxo0biY+P5+DBg0qy07FjR9zc3NixYwfz588nISEBf39/hg0bpjGi27NnT+Xngq5bfqKjo9m7dy9WVlZA1vTgPn36EBwcrDGKZWpqir+/v/Ig4Js3b3L69GmWLl2q1OvatSuRkZE5krzHjx/zySefKCOAjo6OdOvWjc8//1xJLj08PJT6GRkZdOrUiatXr3L48GHmzp2rlKWmpjJt2rQc12Tr1q2sW7euUOeeLa/PS+fOnalbty6HDh1S3qt//PEHv/32m0ZMQgghhBC5kemaJSAuLo7ly5fTvXt3bG1tlcUV7ty5U+i2vLy8sLS05O2330ZXV5clS5YUuo3u3bsrCR5Anz59yMzM1FjRL3u00MHBAVtbW0aOHAnA3bt38227Zs2aGglTdnKh7XTSourcubPys5GREXXr1uXBgwcA3Llzh8jISPr27UtaWpryn6OjI7q6ujmmILq4uOR6jE6dOik/6+np8dprr9G8eXMlwQOwsrIiPj6eJ0+eKNuK2pd5uXbtGpmZmfTq1UsjnmeTK4BLly7RrFkzrKysNM7b2dlZ41rn5tKlSzg5OWFqaqrsp6urS/v27ZV9f/75Z9RqtZLUlLSmTZsqCR5Aw4YNad68Ob/++qtGva5duyoJHsCPP/4IZL2vn9W3b1+uX7+uMUXXxMRESfCyXzs7O2scIzw8nBkzZuDs7EyLFi2wtbXlzp07uV6/3K7J8/GWBF1dXd566y2OHj2qjF4eOnSIevXqaZyPEEIIIURuZCSvBHh7e/Pzzz8zY8YMmjRpgrGxMV988QVfffVVoduqUqUKPXv2ZNu2bfTv3x9TU9NCt5E9MpPN2NiYypUr8/DhQwDOnDnDe++9x7Bhw5gzZw5mZmZERUUxY8YMkpOT8237+dU29fX1AQrcr7hMTExyHDclJQWA2NhYAGbMmJHrvv/884/G6+f7J79jGBkZ5dgGWedbtWrVYvVlXqKiotDX189x7Z+POzY2luvXr2Nra5ujjWeT/NzExsbyyy+/5Lpv9uhc9n1fJbka57Nyuw4WFhZERUXlWy8uLg59ff0cUxZr1KhBZmYmCQkJynXLbXTRwsKC8PBwIOvexwkTJmBubo63tzd169ZVRr6fv355XZPn4y0pQ4YMYfPmzYSGhtK1a1eOHTvGyJEjy/3iOUIIIYR48STJK6bk5GQuXLiAt7c37u7uyvb9+/crP1euXBnImu71rGcX8Mh28+ZNPvvsM2xsbNi7dy9vvfWWxnLr2oiO1nzQcWJiIsnJycof6yEhIbRo0UJjCt4PP/xQqGOUJdl/7C9ZsoRWrVrlKH8+SXl2VKi4XkRfWlpakpqaSlxcnEZS8fx1NTU1RaVSFem5d6ampnTp0gVPT88cZdn3w2X368OHD6ldu3ahj1GQ588ne1vz5s01tj1/vUxNTXPtn0ePHqGjo6ORrOf2fMXo6GgsLS0B+OWXX3jw4AFbt27VOG5CQkKOc87rmmS3VdJq165Nly5dOHToEOnp6cTGxjJ48OAXciwhhBBCvFrkK+FiSklJISMjQxnhgayk6vz588rr7EVTnl05MDw8PMcIU0pKCvPnz6dVq1YEBgbStGlT5s+fr0zX0tbXX3+tsRJjSEgIOjo62NnZAVmLhTwbL2Td31VS9PX1Cz2KlR1P9uhcYTRq1IjatWvz999/Y2dnl+O/wixaU1gvoi+zr9Ozq0Wmp6dz9uxZjXrOzs78/fff1KxZM9fzzo+zszPh4eE0btw4x37Zi4g4ODhgaGjIoUOH8mynONft1q1b3Lt3T3l97949bt68SevWrfPdr23btkDW+/pZISEh2NjYaIy+JiQk8N1332m8vnz5snKMp0+fapwHwE8//URkZGSux87tmhQUb0Hy+7wMHTqU0NBQdu7cSceOHalXr16xjiWEEEKIikFG8orJxMQEOzs7tm/fjrm5OZUqVWLbtm0YGxsrowitW7emTp06fPzxx8ybN4/ExES2bduWY7rZxo0b+fvvvzl69CgGBgasXr2aQYMGERAQwMyZM7WOKSUlhRkzZjBixAhldU03NzdlRNDZ2Zlly5axadMmZRGTZ/8QLq5GjRpx7tw52rVrh6GhIdbW1hgbG+e7T3Zs+/bto2fPnlSpUkXrFQt1dHTw9vbGy8uLpKQkXFxcMDQ05P79+4SGhjJnzpwiLYCijRfRl02aNKFXr158/PHHJCcnK6trPj8SPHDgQA4cOMCYMWOYMGECVlZWJCQkcP36dVJTU5WFRXIzbtw4jh8/zujRoxkzZgx169YlJiaGX3/9lVq1ajFu3DhMTEyYMWMGa9euJTMzE1dXVzIyMrhy5Qr9+vXTWN20KNfNwsKCqVOnKo8N2bBhA7Vq1SpwtKp58+b07t0bHx8fnj59irW1NceOHePnn39m8+bNGnXNzMx4//33mTVrFiYmJmzfvp3MzEzGjh0LZC18YmRkxNKlS5k8eTL//vsvfn5+uX4xoK+vT0BAgHJNvvjiCx48eMCmTZu0Ot+85Pd5cXFxoXr16vz8889FXtxFCCGEEBWPjOSVAF9fXxo0aIC3tzfLly/Hzc2NgQMHKuX6+vr4+/tTuXJlPD092bp1KwsWLND4Q/Knn35ix44dvPfee8o9UY0bN2bu3Lls2bKlwIU0nuXu7o6VlRXz589n7dq19OrVS2NK3/Dhw5kwYQJ79+7Fw8ODf/75B19f3+J3xP9ZsmQJmZmZTJo0iSFDhvD7778XuI+NjQ0zZ87k2LFjDB8+nGnTphXqmH379mXbtm3cuXOHefPmMX36dD777DPq1atHjRo1inoqBXpRffnxxx/To0cP1q5dy/z587G2tlYSk2wGBgbs3r0bFxcXtmzZwsSJE/nwww/57bfflNGuvFSvXp3AwEBatGjB2rVrmTBhAitXriQyMlJjyuukSZP4+OOPlXtOvb29uXv3rnKfXHGum62tLe+88w5r1qxh/vz51KhRgx07dmj1GIY1a9YwdOhQtm/fzvTp0/njjz/YuHFjjuf0WVpasmTJErZt24anpyfJycns2LFDeU/UqFGDDRs2EBMTw/Tp09m1axdLly6lYcOGOY6pr6/PunXrOHToEDNmzODu3bts3Lgxx/TSwsrv81KpUiV69OiBqampxqIvQgghhBD50cnMzMws7SBEyVGpVMyfP5+JEyeWdihC5Mnd3R0jIyO2bt1a2qGUaRkZGfTs2ZPu3buzePHiYrWV/UXR/tBw7t7PeT+kEEK8DFZ1LVjpMfClHS8pKYkbN27QokWLHIupifxJ3xVPYfsv+9/pgm650ZZM1xRCiDImJSWFmzdvcurUKR48eKA8I1IIIYQQQhuS5JUj+S3AoqOjU+Cy+aUlIyODjIyMPMv19PRKdMXLsq68Xkdtpaenk98EgUqV5NdOQR4+fMjQoUMxNzdn8eLFNGrUqLRDEkIIIUQ5In9tlRMRERG4urrmWe7o6MiePXsICwt7iVFpZ9OmTfj7++dZvnLlygqzNLy217E869WrV56rUwKEhYWV+3N80erXr//CPsv1LM1eSLtCCKEN+R0kxMshSV45UbNmTYKCgvIsr1q16kuMpnDefvttXFxc8iyvX7/+ywumlJXn66itgICAIj1SQbwcHsNcSjsEIUQFl5GRga6urP0nxIskSV45YWBgUGI3Yr5stWrVeqHPqitPyvN11Ja2j1AQL19KSgpqtRpDQ8PSDqXcUavV3LlzB2tra+m/QpK+K55Xsf8kwRPixZNPmRBCVCCyoHLRZGZmolarpf+KQPqueKT/hBBFIUmeEEJUIBVpkaOSpKOjg6GhofRfEUjfFY/0nxCiKGS6phBCVBAGBgavzHSvl83Q0BAbG5vSDqNckr4rnleh/+QePCFePknyhBCiAvEPvEBk1OPSDkMIUUHUszSTBZ+EKAWS5AkhRAUSGfWYu/ejSzsMIYQQQrxAMnYuhBBCCCGEEK8QSfKEEAD4+fnh4OCQZ/m+ffuYMmUKHTp0QKVSERISUuhjqNVq/P39ef3112ndujVOTk689dZbrF+/Xqnz8OFDVq9ezYABA3BwcKBr167Mmzcv3wesZ2RkMHjw4ELHFRERgUqlyvW/a9euARATE8Py5csZOnQoLVu2zLOP0tPT2b59O3369KF169a4urqyatUqnjx5kufxV6xYgUqlYtmyZXnWefLkCV27dtWISQghhBAiPzJdUwihlaNHjwLQrVs3jhw5UqQ2Zs2axdWrV5kyZQotWrQgPj6ea9eucfbsWebMmQPA77//zpkzZ3jrrbdo3bo1sbGxBAQEMHToUE6cOIG5uXmOdg8cOMC///5b5HObO3cuTk5OGtsaN24MwL///st//vMfWrVqRcuWLQkLC8u1jYCAAAICAvD09KRVq1bcunWLdevW8fDhQ3x9fXPUDwsL49ChQxgbG+cb2+bNm0lPTy/imQkhhBCiIpIkTwihlQMHDqCrq0tERESRkrx79+5x8eJFVq1axcCBA5Xtbm5uzJ07V3ndtm1bvvrqKypV+v+/ntq0aYOLiwtHjhxhwoQJGu3GxMSwYcMG5s+fz8KFCwsdF0DDhg2xt7fPtUylUnH58mUga7QzryTvxIkTvPHGG0yePBmADh06EBsby/bt20lLS9M4H4CPPvqIcePG5duX4eHh7N+/n/fee48PPvig8CcmhBBCiApJpmsKIbRS3OWv4+LiALC0tMy37WrVquVIiGrXro25uTkPHz7Mse+6detwcnLKMRJXUrQ977S0tByjciYmJrk+wPjYsWNEREQwadKkfNtcvnw5w4cPx9raWvuAhRBCCFHhSZInhHgpGjVqhJGRET4+Pnz99df53qv2vDt37hAdHa1Mocx29epVTpw4wfz584sVW0ZGBmlpacp/GRkZhW5j6NChHDt2jO+++44nT55w9epV9uzZw/DhwzWS1sTERFavXs38+fPzfWZdSEgIf/zxBzNmzCjSOQkhhBCi4pLpmkKIl8LY2JgVK1awaNEipk6dip6eHs2bN6dXr16MHTsWIyOjXPfLzMxk+fLl1KxZk379+inbMzIyWLp0KePHj6d+/fpEREQUObbs+wGzdezYkc8//7xQbUyZMoWUlBTGjx+vjN69+eabOaaQ+vv707BhQ15//fU821Kr1fj4+DBnzpwC79kTQgghhHieJHlCiJfm9ddfp1OnTnz99ddcuXKF77//nk8++YRjx45x6NChXBM9Pz8/vv/+ez799FON8oMHD/Lo0SPlHrji8PLyokOHDsrroiRWe/fuZffu3SxYsAAbGxtu3brFhg0b+Oijj5T76W7dusW+ffv48ssv820rICAACwsL3nrrrULHIYQQQgghSZ4Q4qUyNTVl4MCBDBw4kMzMTDZu3MjmzZsJCgpizJgxGnW//PJLNm3axIoVK+jYsaOy/cmTJ6xbt445c+aQmppKamoqiYmJADx9+pTExMRCJWqvvfYadnZ2RT6n2NhYVq1axfz583F3dwegffv2GBsb8+677zJmzBisra3x8fGhT58+1KtXj/j4eCBrRDI1NZX4+HiMjY35559/2LlzJ5s2bSIhIQGApKQk5f9PnjyhatWqRY5VCCGEEK8+SfKEEKVGR0eHiRMnsnnzZsLDwzXKzpw5w4cffsisWbMYMmSIRllsbCyPHz/mgw8+yLHq5HvvvUeNGjW4dOnSC48/299//01KSgotWrTQ2G5jYwPAX3/9hbW1NXfu3OHbb7/l2LFjGvW+/PJLvvzyS/7zn//w6NEjUlNTcx2hHDNmDK1bty5wJFAIIYQQFZskeUKIlyIxMZFKlSpRpUoVje13794FNFfdvHLlCnPnzmXo0KG5LjxiaWnJ7t27NbY9evSIuXPnMnPmTJydnUv+BPJRt25dIOsZf+3atVO2//bbbwDUr18fyFoJNDk5WWPfuXPnYm9vz5gxY6hbt26u53bjxg1WrlzJ0qVLizXiKIQQQoiKQZI8IYQiPT2dkJCQHNtbtWpFdHQ0kZGRxMTEAPDrr78CYG5ujqOjY4Ft37lzh2nTpjFo0CDatm2LkZERf/75J9u3b8fExIRBgwYBWc+GmzFjBlZWVgwYMIBffvlFacPc3JwGDRpQuXLlHI9MyF54pUmTJrRp06ZI55+X7D75888/NfrIzs6OevXqUaNGDXr27MmGDRtIT0/HxsaGP//8Ez8/P5ydnZVVQXN7Fl/lypWpVauWcj6GhoZ5Pg7C1tYWW1vbEj03IYQQQrx6JMkTQiiSk5Px9PTMsX316tV89913HD58WNm2c+dOABwdHdmzZ0+BbTds2JBhw4Zx6dIlDh48yJMnT6hVqxYdOnRg6tSp1KtXD8hKHhMSEkhISGDEiBEabQwaNAgfH5/inGKRPN8n2a9XrlzJ4MGDAVi1ahWbNm3iiy++4N9//8XS0pI33niDmTNnvvR4hRBCCFGx6WTm9qReIYQQr5Rr164BsD80nLv3o0s5GiFERWFV14KVHgNL7fhJSUncuHGDFi1a5PmoHpE76bviKWz/Zf87XVK3ZcjD0IUQQgghhBDiFSLTNYUQJSItLS3PMh0dHfT09F5iNFkPUU9PT8+zXFdXF13divc9Vz1Ls9IOQQhRgcjvHCFKhyR5QogSkd+CIPXq1eP8+fMvMRo4fPgwCxYsyLPcw8OjQt4v5zHMpbRDEEJUMBkZGRXySzUhSpMkeUKIEhEUFJRnmYGBwUuMJEv37t3zjalmzZovMZqyISUlBbVajaGhYWmHUu6o1Wru3LmDtbW19F8hSd8Vz6vQf5LgCfHySZInhCgRZe35bdWrV6d69eqlHUaZI2ttFU1mZiZqtVr6rwik74pH+k8IURTy1YoQQgghhBBCvEIkyRNCiApER0entEMol3R0dDA0NJT+KwLpu+KR/hNCFIVM1xRCiArCwMCg3N7TU9oMDQ2xsbEp7TDKJem74ikv/SeLqwhRtkiSJ4QQFYh/4AUiox6XdhhCiFdIPUszWblXiDJGkjwhhKhAIqMec/d+dGmHIYQQQogXSMbVhRBCCCGEEOIVIiN5Qrxkfn5+7Ny5k59//jnXci8vL65evcrDhw/R19enWbNmTJs2jc6dO2t9jLS0NL744gsOHjzI33//TaVKlahTpw7t2rXD29tbeW7dqlWruHjxIvfv30dHRwdra2smTJhAv379lLYSEhJYuHAhv//+O48ePcLIyIiWLVsya9YsWrVqVbzOEEIIIYQQJU6SPCHKmNTUVMaNG4eVlRXJyckEBQUxefJkdu/eTbt27bRqY/ny5QQHBzN58mTatGmDWq3mxo0bHDt2jKdPnypJ3pMnTxg6dCiNGjVCR0eHU6dOMXfuXDIyMnjjjTeArAdoGxgYMG3aNOrXr09iYiK7du1i7NixBAcHY21t/cL6QgghhBBCFJ4keUKUMRs2bNB43bVrV1xdXTl69KhWSZ5arSYoKIipU6fi4eGhbHd1dcXDw0PjgbrLli3T2LdLly78+eefHD58WEnyLCws8PX11ajn7OyMk5MTp06dYurUqYU+RyGEEEII8eLIPXlClHF6enqYmJiQmpqqVX21Wk1qaio1a9bMtbygZy2ZmZkVeCwjIyMqV66sdUwAPXr0YNmyZezbt4/u3bvTtm1bpk+fTkxMjFInODgYlUqlsQ1gwIABeHt7K6+9vb3p378/ly9f5o033qBVq1aMHj2aiIgIHj9+jKenJ23atKFnz5785z//0TrGiIgIVCoVR44cYcmSJbRr146OHTvy2WefAXDy5Enc3Nxo06YNHh4exMfHa+wfHx/Phx9+SOfOnWnZsiWDBw/m22+/1ahz4cIFxo8fT8eOHWnTpg1Dhw7l4sWLGnWy++H69eu888472Nvb07t3b44cOaL1uQghhBCi4pIkT4gyKDMzk7S0NGJjY9mxYwf37t1j2LBhWu1rbm5O3bp1CQgI4OTJk8TFxWl1rPj4eI4cOcKlS5cYNWpUjnoZGRmkpaXx8OFDfHx80NXVZeDAgYU6r/Pnz3P+/HmWLFnC+++/z3//+18++uijQrWRLSoqCh8fH6ZNm8batWv566+/8PLyYs6cOTRr1gw/Pz9sbW159913iYyMLFTbn3zyCVWqVGHDhg306dMHHx8ffH192b17N++++y5Llizh+++/Z82aNco+KSkpjB8/ngsXLjB79mwCAgJo3LgxU6ZMISwsTKkXERFB9+7dWb16NX5+frRp04bJkydz5cqVHHF4eXnRuXNnNm3aRIsWLfD29iY8PLxI/SWEEEKIikOmawpRBgUFBbFo0SIga9Rs/fr1ODg4aL2/j48Pc+fOZe7cuejo6NCoUSNcXV0ZP3485ubmGnW/++47xo8fD0ClSpVYvHgxffr0ydHmhg0b2LJlC5A1hXPbtm289tprhTqvzMxMAgIClHsCIyMj2bp1a5EeohsXF8fevXtp2rQpAA8fPuSjjz5i0qRJzJgxAwA7OzvOnDnD2bNnGTt2rNZt29vbs3DhQgA6dOjA6dOn2bt3L+fPn6d69eoAhIWFERQUpCSpx48f5+bNmxw9epQmTZoAWdNf7927x+bNm5VpuKNHj1aOk5GRgZOTE3/++SdffvklTk5OGnGMGjVKSbgdHBwIDQ3l1KlTTJ8+vVB9JYQQQoiKRZI8IcogV1dXmjdvTmxsLCEhIcyePRt/f3+6deum1f5OTk6cOXOGixcv8t133/H999+zbds2goODCQ4OplatWkrdVq1aERQURGJiIhcvXmT58uXo6ekxdOhQjTZHjhxJz549iYqK4uDBg0yePJnPP/8cW1tbrc+rffv2SoIH0LhxY1JTU4mOjsbS0lLrdgBq1qypJHgAVlZWQNb9gtmqVauGubk5Dx48KFTbnTp1Un7W09PjtddeQ0dHR0nwso8XHx/PkydPqFq1KpcuXaJZs2ZYWVmRlpam1HN2dubYsWPK6wcPHrB+/XouX75MVFSUco9kbv347IqqRkZG1K1bt9DnIoQQQoiKR5I8Icogc3NzZcSta9euxMXFsWbNGq2TPMhKCvr06aOMyh08eJBFixaxc+dOFixYoNQzNjbGzs4OgI4dO5Keno6Pjw+DBw9GT09PqVerVi0lOXRxcWHIkCFs3LiRrVu3ah1TtWrVNF5nJ3zJyclat5FXW/r6+gCYmJjkOEZh23++DX19fYyMjHI9XnJyMlWrViU2Npbr16/nmqxl92NGRgbTpk0jISGBWbNm0bBhQwwNDdm4cSP//POPVnGkpKQU6lyEEEIIUfFIkidEOWBra5tjcY7CGjp0KGvXri3wni5bW1t27dpFTExMnqNrurq6tGjRgh9//LFYMT2vcuXKADkWdHl+gZOyyNTUFJVKxYoVK/Ksc+/ePa5fv86mTZvo2bOnsv3p06cvI0QhhBBCVBCS5AlRDvz4449a3/+WmppKUlISpqamGtujo6NJSEgocFrkjz/+iLGxscbUxOelpaVx9erVQt+TV5DskcLbt28rP4eHh+c6ylXWODs7ExoaSs2aNTWmwz4re0QxexQQsu5L/Pnnn5XppkIIIYQQxSVJnhClID09nZCQkBzb1Wo1oaGhuLi4UKdOHeLi4jhx4gTffvst69at06rthIQE3NzcGDBgAB06dMDU1JSIiAh27tyJrq4uI0aMAODmzZusXbuWPn36UK9ePZKSkrhw4QIHDx5k7ty5VKqU9eshMDCQq1ev4uzsjKWlJY8ePeLAgQPcuXOHDz74oOQ6BWjdujV16tTh448/Zt68eSQmJrJt2zbMzMxK9DgvwsCBAzlw4ABjxoxhwoQJWFlZkZCQwPXr10lNTWXevHk0atSI2rVr4+vrS0ZGBklJSWzcuDHPx10IIYQQQhSFJHlClILk5GQ8PT1zbPf09CQlJQVfX19iY2OpXr06KpWKPXv24OjoqFXbxsbGTJo0iW+++YaQkBDi4uKoUaMGdnZ2+Pj4KPeM1ahRg2rVqrF582aioqIwMTGhUaNG+Pv7a0wlbNKkCadPn2bFihXEx8djaWmJnZ0dQUFBNG/evGQ65P/o6+vj7+/Phx9+iKenJw0aNGDhwoX4+PiU6HFeBAMDA3bv3o2fnx9btmwhKioKMzMzbGxsGDlypFLHz8+PZcuW4enpSZ06dZg2bRrff/89v/32WymfgRBCCCFeFTqZ2Uu7CSGEeGVdu3YNgP2h4dy9H13K0QghXiVWdS1Y6TGwtMPIVVJSEjdu3KBFixY5FtAS+ZO+K57C9l/2v9PZi+EVlzwMXQghhBBCCCFeITJdU4hyJj09nfwG4LPvpXuZnn0u3PN0dHQ0HsVQWjIzM0lPT8+zXFdXt9APZC+P6lmalXYIQohXjPxeEaLskSRPiHKmV69eREZG5lkeFhb2EqPJkt8D0evVq8f58+dfYjS5++GHHxgzZkye5YMGDSoX9/4Vl8cwl9IOQQjxCsrIyKgQX5QJUV5IkidEORMQEFDmHogdFBSUZ1n2A89Lm62tbb5x5vfIiFdFSkoKarUaQ0PD0g6l3FGr1dy5cwdra2vpv0KSviue8tJ/kuAJUbZIkidEOaNSqUo7hBxK6ibhF8nY2LhcxPmiyVpbRZOZmYlarZb+KwLpu+KR/hNCFIV87SKEEEIIIYQQrxBJ8oQQogLR0dEp7RDKJR0dHQwNDaX/ikD6rnik/4QQRSHTNYUQooIwMDAo0/f0lGWGhobY2NiUdhjlkvRd8ZT1/pMFV4QomyTJE0KICsQ/8AKRUY9LOwwhxCugnqWZrNgrRBklSZ4QQlQgkVGPuXs/urTDEEIIIcQLJOPrQogS5efnh4ODQ57lXl5e9O7dG3t7e9q3b8+oUaP49ttvX2KEZYefnx8//fSTVnU///zzMrmyqhBCCCHKHhnJE0K8VKmpqYwbNw4rKyuSk5MJCgpi8uTJ7N69m3bt2pV2eC+Vv78/RkZGtGnTprRDEUIIIcQrRJI8IcRLtWHDBo3XXbt2xdXVlaNHj1a4JE8IIYQQ4kWQ6ZpCiFKlp6eHiYkJqampWu/j7u7OlClTCAkJwc3NDQcHB8aMGcNff/2l1Lly5QoqlYpr165p7Dt9+nTc3d2V19nTS69fv86wYcNo1aoVgwYN4vr16yQnJ/PBBx/Qvn17unbtyueff16ocwsKCqJfv360atUKJycnRowYwdWrV4H//1D71atXo1KpUKlUXLlyBYDExETmz5+Pg4MDHTp0YPXq1aSnpxfq2EIIIYSouGQkTwjx0mVmZpKenk5CQgLBwcHcu3ePZcuWFaqNGzduEBMTg5eXF+np6fj4+PDuu+8SGBhY6HhSU1N57733GDduHDVq1GDt2rV4eHjQpk0bLCws+OSTTzh37hwrV66kVatWWk2v/O9//8v777/PhAkT6NatG0+fPuXq1askJCQAEBgYyLBhw3B3d6d///4ANGnSBICFCxfyzTff4OXlRf369dm/fz8nTpwo9HkJIYQQomKSJE8I8dIFBQWxaNEiAIyMjFi/fn2+i7XkJiEhgSNHjmBubg5AUlISCxYs4MGDB9SuXbtQbaWmpuLl5UW3bt2ArOc+TZ06ldatW7NgwQIAOnToQEhICCEhIVoleVevXsXMzIz33ntP2ebi4qL8bG9vD0CdOnWUnwH+/PNPTp8+zfLlyxkyZAgAnTt3pnfv3oU6JyGEEEJUXDJdUwjx0rm6uhIUFMT27dvp27cvs2fPJjQ0tFBtNG/eXEnw4P+Pgj148KDQ8ejq6tKxY0fltZWVFQDOzs7KNj09PRo0aKB1+zY2Njx+/Bhvb28uXbqEWq3War9r166RmZlJr169NI7ds2dPrfYXQgghhJCRPCHES2dubq4kaF27diUuLo41a9YoI2naqFatmsZrfX19AJKTkwsdT5UqVTAwMMjRlomJSY5jaNt+x44dWb16Nbt372bixIlUrlwZNzc3Fi5ciJmZWZ77RUVFoa+vj6mpqcZ2CwsLLc9GCCGEEBWdjOQJIUqdra0t9+7dK9E2K1euDJBjQZf4+PgSPU5+BgwYwKFDh7h8+TKLFi3i7NmzrF69Ot99LC0tSU1NJS4uTmN7dLQ8wFwIIYQQ2pEkTwhR6n788Udee+21Em0z+7688PBwZVtMTAy///57iR5HG+bm5gwdOpROnTpx+/ZtZXtuI4N2dnYAnDlzRtmWnp7O2bNnX06wQgghhCj3ZLqmEKLEpaenExISkmO7Wq0mNDQUFxcX6tSpQ1xcHCdOnODbb79l3bp1JRpD7dq1ad26NZs2bcLExIRKlSqxffv2HFMwX5SNGzfy+PFjHB0dsbCw4I8//uCbb75h3LhxSp1GjRpx7tw52rVrh6GhIdbW1jRp0oRevXrx8ccfk5ycrKyuWZhHTAghhBCiYpMkTwhR4pKTk/H09Myx3dPTk5SUFHx9fYmNjaV69eqoVCr27NmDo6Njicexdu1aFi1axIIFC6hRowazZ8/m5MmTymMMXiQ7Ozt27drFV199RWJiIrVr12bixIlMmzZNqbNkyRI+/vhjJk2axNOnT9m9ezdOTk58/PHHLFu2jLVr12JgYMCgQYNwdHQscKqnEEIIIQSATmZmZmZpByGEEOLFyn4o/P7QcO7el/v7hBDFZ1XXgpUeA0s7jHwlJSVx48YNWrRogZGRUWmHU65I3xVPYfsv+9/p7Ns2ikvuyRNCCCGEEEKIV4hM1xRClCnp6enkN8GgUqWy8WsrLS0tzzIdHR309PReYjRCCCGEEP9f2fhrSQgh/k+vXr2IjIzMszwsLOwlRpO7iIgIXF1d8yx3dHRkz549LzEi7dWzNCvtEIQQrwj5fSJE2SVJnhCiTAkICCAlJaW0w8hXzZo1CQoKyrO8atWqLzGawvEY5lLaIQghXiEZGRno6srdP0KUNZLkCSHKFJVKVdohFMjAwKDEbox+mVJSUlCr1RgaGpZ2KOWOWq3mzp07WFtbS/8VkvRd8ZT1/pMET4iyST6ZQghRgciCykWTmZmJWq2W/isC6bvikf4TQhSFJHlCCFGB6OjolHYI5ZKOjg6GhobSf0UgfVc80n9CiKKQ6ZpCCFFBGBgYlMnpXuWBoaEhNjY2pR1GuSR9Vzwvs//k/johXh2S5AkhRAXiH3iByKjHpR2GEKKMqWdpJgszCfEKkSRPCCEqkMiox9y9H13aYQghhBDiBZIxeSGEEEIIIYR4hchInhACPz8/du7cyc8//5xruZeXF1evXuXhw4fo6+vTrFkzpk2bRufOnbU+RlpaGl988QUHDx7k77//plKlStSpU4d27drh7e2NgYEBAKtWreLixYvcv38fHR0drK2tmTBhAv369VPaSkhIYOHChfz+++88evQIIyMjWrZsyaxZs2jVqpXWMbm7u/PDDz/k2D5q1CiWLFlSqHP/448/8PX15ddffyUtLQ2VSsXMmTPp0KGDUsfPzw9/f/8cx/vwww8ZMWIEAFeuXGHMmDG5xmttbU1ISIjW5yeEEEKIikmSPCFEgVJTUxk3bhxWVlYkJycTFBTE5MmT2b17N+3atdOqjeXLlxMcHMzkyZNp06YNarWaGzducOzYMZ4+faokeU+ePGHo0KE0atQIHR0dTp06xdy5c8nIyOCNN94Asp73ZmBgwLRp06hfvz6JiYns2rWLsWPHEhwcjLW1tdbn1qZNG9577z2NbTVq1CjUucfExDBu3Dhee+01VqxYgb6+Pnv27GHSpEkEBQVpPPuvSpUq7Nq1S+N4r732mvKzra0tgYGBGuWJiYlMmjSJrl27an1eQgghhKi4JMkTQhRow4YNGq+7du2Kq6srR48e1SrJU6vVBAUFMXXqVDw8PJTtrq6ueHh4aDz/admyZRr7dunShT///JPDhw8rSZ6FhQW+vr4a9ZydnXFycuLUqVNMnTpV63OrVq0a9vb2eZZrc+7fffcd0dHRfPnll9SvXx8AR0dHHB0dOXv2rEaSp6urm+/xjI2Nc5QHBweTkZFB//79tT4vIYQQQlRcck+eEKLQ9PT0MDExITU1Vav6arWa1NRUatasmWt5Qc9/MjMzK/BYRkZGVK5cWeuYiiq3c8/+2cTERNlWuXJl9PX1S+QBxidOnMDKyqpQU1GFEEIIUXFJkieE0EpmZiZpaWnExsayY8cO7t27x7Bhw7Ta19zcnLp16xIQEMDJkyeJi4vT6ljx8fEcOXKES5cuMWrUqBz1MjIySEtL4+HDh/j4+KCrq8vAgQOLdF7P/pdXnbzOvXv37tSoUQMfHx8ePnxITEwMvr6+6OjoMGDAAI22nj59SocOHbCxseH111/nyy+/zDe+R48e8f3338sonhBCCCG0JtM1hRBaCQoKYtGiRUDWqNn69etxcHDQen8fHx/mzp3L3Llz0dHRoVGjRri6ujJ+/HjMzc016n733XeMHz8egEqVKrF48WL69OmTo80NGzawZcsWIGsK57Zt2zTub9NGaGgotra2ObbVrl1beV3QuZuamrJv3z6mTJlCly5dgKzRx+3bt2vE06BBA7y8vLCxsSE5OZnjx4+zePFiEhISmDhxYq7x/ec//yE9PV2SPCGEEEJoTZI8IYRWXF1dad68ObGxsYSEhDB79mz8/f3p1q2bVvs7OTlx5swZLl68yHfffcf333/Ptm3bCA4OJjg4mFq1ail1W7VqRVBQEImJiVy8eJHly5ejp6fH0KFDNdocOXIkPXv2JCoqioMHDzJ58mQ+//zzHElbftq2bcuCBQs0tllYWBTq3KOjo/Hw8KBBgwYsXLgQPT09vvzyS6ZNm8a+ffto3LgxQI5RPRcXF1JTUwkICGDMmDHo6+vniO/48ePY2toWajEZIYQQQlRskuQJIbRibm6ujLh17dqVuLg41qxZo3WSB1mjYH369FFG5Q4ePMiiRYvYuXOnRqJlbGyMnZ0dAB07diQ9PR0fHx8GDx6Mnp6eUq9WrVpKcuji4sKQIUPYuHEjW7du1TomExMT5Vh5KejcP/30U+Li4ggODlZWCe3YsSP9+vVj8+bNORaJeVbfvn05deoUf/31l5IMZvvrr7+4evVqjiRUCCGEECI/ck+eEKJIbG1tuXfvXrHaGDp0KGZmZoSHhxd4rMTERGJiYvKso6urS4sWLYodkzaeP/c///yTRo0aKQkeZC3QolKp+Ouvv4p8nOPHj6Orq8vrr79erHiFEEIIUbFIkieEKJIff/xR6/vfUlNTc11sJTo6moSEBCwtLQs8lrGxMdWrV8+zTlpaGlevXi30PXlF8fy5161bl/DwcJKTk5Vt6enp3Lx5k3r16uXb1n/+8x+qVatGgwYNcpSdPHkSR0fHPFclFUIIIYTIjUzXFEIAWUlJSEhIju1qtZrQ0FBcXFyoU6cOcXFxnDhxgm+//ZZ169Zp1XZCQgJubm4MGDCADh06YGpqSkREBDt37kRXV5cRI0YAcPPmTdauXUufPn2oV68eSUlJXLhwgYMHDzJ37lwqVcr6lRUYGMjVq1dxdnbG0tKSR48eceDAAe7cucMHH3xQYn1y4cIFjhw5UuC5Dx06lKCgIKZPn86oUaPQ09MjMDCQe/fusXz5cqXe4MGDGThwII0aNeLp06ccP36c06dPs3Dhwhz3412/fp3w8HBlARohhBBCCG1JkieEACA5ORlPT88c2z09PUlJScHX15fY2FiqV6+OSqViz549ODo6atW2sbExkyZN4ptvviEkJIS4uDhq1KiBnZ0dPj4+ykIpNWrUoFq1amzevJmoqChMTExo1KgR/v7+9OzZU2mvSZMmnD59mhUrVhAfH4+lpSV2dnYEBQXRvHnzkukQ4LXXXtPq3Fu2bMmnn37K5s2bWbBgARkZGTRp0oRt27bRvn17pV6DBg34/PPPefToETo6OjRr1ow1a9bw5ptv5jj28ePHMTAwwM3NrcTORwghhBAVg05mSTypVwghRJl27do1APaHhnP3fnQpRyOEKGus6lqw0mNgaYdR4pKSkrhx4wYtWrTAyMiotMMpV6Tviqew/Zf973RBi8FpS+7JE0IIIYQQQohXiEzXFEIUW3p6OvlNCsi+l+5lSktLy7NMR0dH41EMFUk9S7PSDkEIUQbJ7wYhXi2S5Akhiq1Xr15ERkbmWR4WFvYSo8mS3wPR69Wrx/nz519iNGWHxzCX0g5BCFFGZWRkoKsrk7yEeBVIkieEKLaAgABSUlJKOwwNQUFBeZY9+zy7iiQlJQW1Wo2hoWFph1LuqNVq7ty5g7W1tfRfIUnfFc/L7D9J8IR4dUiSJ4QoNpVKVdoh5FBSNy6/amStraLJzMxErVZL/xWB9F3xSP8JIYpCvrIRQgghhBBCiFeIJHlCCFGB6OjolHYI5ZKOjg6GhobSf0UgfVc80n9CiKKQ6ZpCCFFBGBgYyD1RRWRoaIiNjU1ph1EuSd8Vz4vsP1loRYhXlyR5QghRgfgHXiAy6nFphyGEKGX1LM1ktV0hXmGS5AkhRAUSGfWYu/ejSzsMIYQQQrxA5XqMXqVSsWPHjhfW/tmzZ1GpVERERJRou8ePH6d3797Y2toyYMCAEm07W3x8PCqViuDg4BJr88qVK6hUKq5du6Zse9HXIPu4W7ZsybHdz88PBweHF3rs4sitv15VERERqFQqQkJCSjsURUnHpM17XZvPiLu7O1OmTNHYJ7f3txBCCCFEUclI3kv25MkTFi5cSP/+/Vm5ciXGxsalHZLWbG1tCQwMpHHjxi/1uD/88AM7d+5k6tSpGtuHDh1Kt27dXmosQuRHm8/IBx98oHEPTF7vbyGEEEKIopIk7yWLjIwkJSWFN998k7Zt2xarrfT0dDIyMtDX1y+h6PJnbGyMvb19ibT19OlTqlSpUqw2ateuTe3atUsknldZSfR1WVKWz0ebz0iTJk1eTjBCCCGEqLAKPV3z559/ZurUqXTu3Bl7e3sGDBjAkSNHlPLg4GBUKhUxMTEa+w0YMABvb28AEhMT6d69O7NmzdKos2TJEpycnPj333+1jic9PZ3Vq1fToUMHHBwc8Pb2JjExUaNOZGQks2bNom3bttjb2zNx4kTCwsI06qSmprJixQocHR1p27YtCxcu5MmTJxp1Bg8ezLx583LEsGbNGjp37kx6enq+sfr5+fHGG28AMG7cOFQqFX5+fgA8fvyYBQsW4OTkRKtWrRg+fDj//e9/NfbPnuZ1+PBh3NzcsLOz4+bNmwB8+eWX9OjRg9atWzN27Fju3bunRe9lSUpKwt7ePtepaLNmzWLYsGFA3tMPC7oG2ftduHCBWbNm0aZNGzw9PQE4cuQII0aMwNHRkfbt2+Pu7s7Vq1c1+szf35+kpCRUKhUqlQp3d3el7Pnpmtpc6x49erBs2TL27dtH9+7dadu2LdOnT8/xns1PQZ+DZ8XExODh4YG9vT2dO3fOMTUv+zzCwsIYMWIErVu3pn///nzzzTca9TIyMti8eTM9evSgZcuW9OnThwMHDuTa1tWrVxk2bBh2dnbs27dP+Vxeu3aNCRMm0Lp1a9zc3Lh8+TIZGRmsX78eZ2dnnJ2d8fX1JSMjQ2kzPDycOXPm0K1bN1q3bs3rr7/Ozp07NeoUVvZ7IjQ0VKu+ef58AP773/8yfPhwWrVqhZOTEwsWLODx48c5jqVWq1m4cCFt27bF0dGRlStXkpaWppQ/fPiQBQsW4OrqSqtWrejduzfr1q0jJSUlR1vavtfzm6L77HTNvN7fYWFhqFQqLl26lOP4Xbp0YfXq1QV3shBCCCEqrEKP5N2/f582bdowYsQIDAwM+Omnn1i0aBGZmZkMGjRIqzaMjY35+OOPGT9+PEeOHGHgwIGEhoYSGBjI+vXrqVWrltbx7NmzB1tbW1atWkVERARr164lOTmZ9evXA1kJpbu7O7q6uixdupTKlSsTEBDA6NGjOXbsGHXq1AFg3bp1fPHFF8ycORMbGxtOnjyJr6+vxrGGDh2Kj48PCQkJmJiYAFl/dB09epRBgwahp6eXb6xDhw7ltdde47333mPJkiXY2tpSu3Zt0tPTmTRpEn///TdeXl7UqFGDPXv2MH78eA4cOEDLli2VNn777TciIyPx9PSkWrVq1KlTh6+//prFixczePBgXn/9dX7//XclidKGkZERPXr04OTJk0ycOFHZnpiYyIULF3j33XeLdQ2yLV68mDfffJNNmzYp09UiIiIYOHAgDRo0ICUlhZMnTzJq1CiOHTuGtbU1Q4cO5cGDB5w4cYJdu3YB5DnFVdtrDXD+/Hnu3bvHkiVLiI2NZeXKlXz00Uc5Ys5LYT4Hixcvpl+/fvj5+XH58mXWr1+PqakpI0aMUOqkpqbi5eXFmDFjmD59Otu3b2fWrFmcP3+e6tWrA7B69Wp2797NtGnTcHBw4MKFC3zwwQekpaUxevRojbbmzZvHuHHjmDNnDmZmZly/fh2A9957j+HDhzN+/Hi2bduGh4cHgwYNIjExkVWrVvHrr7/i5+dHs2bNlC8kHj58iLW1NW+88QZVq1blxo0b+Pn5kZSUhIeHh1b9lRdt++b58/ntt98YP348Tk5ObNiwgUePHuHr68uff/7JgQMHND6L69ato3PnznzyySdcv36djRs3oq+vj5eXFwCxsbGYmZmxYMECqlWrxt27d/Hz8yMqKoqVK1dqxKvte11beb2/mzRpQuvWrTl06BCdOnVS6n/zzTc8fPiQt956q0jHE0IIIUTFUOgkr1+/fsrPmZmZtG/fnn///ZfAwECtkzyAjh07Mnr0aJYvX45KpeL999+nf//+vP7664WKx8DAgE2bNil/1FWuXJlFixbh4eFB48aNCQ4O5v79+5w8eVK5T6Z9+/Z0796dXbt24e3tzePHj9m/fz+TJk1SvmHv0qULo0eP1hhVfOONN1i1ahXHjx9n5MiRAISGhhIVFaXVH121a9dGpVIBWVO2sqd1nTt3jqtXr/Lpp5/SpUsXADp37kzv3r3ZunWrMtoHEBcXR1BQkEbCEhAQQLt27ZQ/SLt06UJycjKbN2/Wuh/79evH9OnTuXv3LlZWVkDWwjNpaWn07ds3330LugbZevTokSNhfDZJyMjIoFOnTly9epXDhw8zd+5cZUqmrq5ugdPgtLnW2TIzMwkICMDAwADIGgHcunWr1s8MKsznoEOHDrz33ntA1rWJjo4mICCAYcOGKcfKTvKy7zG0trbG1dWVixcvMmDAAGJiYti7dy8TJ05k5syZQNZ7JDY2lk2bNjFixAil/1NTU5kzZ47GZyk7yRs9erTy3q1VqxZvvPEGv/32G4GBgUp858+fJyQkREnyOnbsSMeOHZVzbdu2LU+fPmXv3r3FTvK07Zvnz8fDwwNLS0u2bNmiTFeuU6cOEydOJDQ0lB49eih1GzRooPHZePr0KZ999hmTJk3C1NQUlUqlxADQpk0bDA0N8fb2ZsmSJRrPldP2va6t/N7fQ4cO5aOPPiIuLg5TU1MADh06hIODw0u/L1YIIYQQ5Uuhp2vGxcWxfPlyunfvjq2trbLQwJ07dwp9cC8vLywtLXn77bfR1dVlyZIlhW6je/fuGt/a9+nTh8zMTGW61P/+9z+aNm2q8UeRmZkZzs7O/PjjjwD88ccfPH36lF69emm03bt3b43XxsbG9O3bl0OHDinbgoODadeunZIYFcX//vc/jI2NlQQPQF9fn169eikxZmvWrJlGgpeens7vv/+eI3Y3N7dCxdClSxeqVavGyZMnlW0nT57EycmJGjVq5LtvQdcgm4uLS459w8PDmTFjBs7OzrRo0QJbW1vu3LnD3bt3CxU/aHets7Vv315J8AAaN25Mamoq0dHaLS1fmM9Bbtfm33//5cGDB8o2XV1dJZECqF+/PlWqVFG+ZLh69Sqpqan06dNHo62+ffsSExOTo7/yWpDm2VGh7Pdshw4dNOpYW1vzzz//KK+Tk5PZuHEjvXr1ws7ODltbW9avX09UVFSOKc2FpU3fQM7z+d///oerq6vG/aidO3emWrVqOa51bsdQq9X88ccfQFbi+vnnn/P666/TqlUrbG1t8fLyIi0tjb///ltjX23f6yWhX79+VKpUiRMnTgBZ036//vprhgwZUuLHEkIIIcSrpdBJnre3NydOnGDChAns2LGDoKAg3nrrrVzvXylIlSpV6NmzJykpKfTv31/5trowLCwsNF4bGxtTuXJlHj58CGQ9SiC3JMXCwoK4uDgAoqKicm0rt/3efvttfvvtN27evElMTAwXLlwo9tSp+Pj4HMfOPn52jHnFFBMTQ1paGubm5gXGnh8DAwN69+7Nf/7zHyBrCtvly5fp379/gfsWdA3yqpeYmMiECRO4f/8+3t7e7Nu3j6CgIJo3b05ycnKh4gftrnW2atWqabzOTvi0PW5hPgd5XZvs9x1kfRaeTTohK9HPjic7/ufPL/v1s/eiGRoaUrVq1Vzjzp5mDP//nJ/vC319fY3zWLNmDTt27GDo0KFs27aNoKAgpk2bBmjfX3nRpm9yO5+8PjO5XeuCjrFr1y5WrVqFq6srmzdv5uDBg8oXTs+fn7bv9ZJgZGRE//79CQoKAuDYsWPo6+sXOLIuhBBCCFGo6ZrJyclcuHABb29vZfELgP379ys/V65cGciaYvWs+Pj4HO3dvHmTzz77DBsbG/bu3ctbb71V6GlIz4+8JCYmkpycTM2aNQEwNTXNdXQlOjpaSSotLS2Vbc/eD/jo0aMc+zk4ONC0aVMOHTpE3bp1MTAwyDG6Ulimpqa5jiA9evQoR+Kro6Oj8drc3JxKlSrlWDQkt9gLkv0H5c2bN/nll1/Q1dXNMZqZm4KuQV6x//LLLzx48ICtW7fSvHlzZXtCQkKRVs3U5lqXBG0+B8/K69pkv++0YWZmBuT9Hs0uh5z9XFwhISEMGzaMyZMnK9tCQ0NLpG1t+ia388nrM5PbtS7oGCEhIfTo0UNjUaXw8PBc49X2vV5Shg4dSmBgIDdv3iQ4OJi+ffvmmcALIYQQQmQr1EheSkpKjiX7ExMTOX/+vPI6+w/Q27dvK9vCw8M1pn9ltzV//nxatWpFYGAgTZs2Zf78+Rqr3mnj66+/1ljVMiQkBB0dHezs7ABo27Ytf/zxh0Y8cXFxXL58WXmEQbNmzahSpQpnzpzRaPv06dO5HnPo0KEcP36coKAgXn/9dYyMjAoV8/Patm1LYmIi3377rbItLS2Ns2fPFviYBT09PWxsbHLEfurUqULH4ejoiKWlJSdPnuTkyZN07dpVY+QnLwVdg7w8ffoUQOP99NNPPxEZGalR7/mRpbxoc61Lgjafg2fldm1q1qxZqETWzs4OfX39HA/2/uqrr7CwsCjWdOGCJCcna5xrenq6xrTe4ihq37Rt25Zz585p/L64dOkS8fHxOa51bscwNDSkWbNmQNb78PnHkBw/fjzX4xb1vZ6f/N7fdnZ2tGjRguXLlxMWFiYLrgghhBBCK4UayTMxMcHOzo7t27crI0jbtm3D2NhY+ba8devW1KlTh48//ph58+aRmJjItm3bNEYaADZu3Mjff//N0aNHMTAwYPXq1QwaNIiAgABlYQltpKSkMGPGDEaMGKGsdufm5qaMCA4ePJjPP/+cKVOmMHv2bGXFxUqVKjF27FggaxRk+PDhbN++nSpVqiira/7111+5HnPAgAGsXbuW2NhYVqxYUZguzJWLiwutWrXi3XffZd68ecrqmg8fPmTjxo0F7j916lSmT5/OggULlNU1jx49Wug49PT06NOnD4cPHyY6Opp169ZptV9B1yAv9vb2GBkZsXTpUiZPnsy///6Ln59fjtVVGzduTFpaGrt27cLBwQFjY2MaNWqUoz1trnVJ0OZz8Kzvv/+eVatW0alTJy5dusTRo0dZsmSJVgu8ZDM3N2f06NHs2LEDAwMD7O3tCQ0N5cSJEyxevLjAlV2Lw9nZmYMHD9KkSROqV6/O/v37izQ9OzdF7ZupU6cyfPhwpkyZgru7u7K6ZqtWrXLcv/fXX38pn43r16+zbds2xo4dq4z4OTs7s3v3bvbu3YuVlRXHjh3L8xEkRX2v56eg9/fQoUNZtmwZ1tbWJfplhRBCCCFeXYW+J8/X15cGDRrg7e3N8uXLcXNzY+DAgUq5vr4+/v7+VK5cGU9PT7Zu3cqCBQs0/nD/6aef2LFjB++99x4NGjQAsv7QmTt3Llu2bCnUIgbu7u5YWVkxf/581q5dS69evTQSL2NjY/bs2UPz5s1ZvHgxXl5emJqasnfvXo0FTObNm8fw4cP59NNPmT17trItN2ZmZjg6OmqskFkcenp6bNu2DRcXF9asWcPMmTN58uQJO3fu1Hh8Ql5cXV1ZunQp3333HTNmzODSpUt88sknRYqlf//+REVFUaVKFbp3767VPgVdg7zUqFGDDRs2EBMTw/Tp09m1axdLly6lYcOGGvW6d+/OyJEj2bZtG2+//TYffPBBru1pe61LQkGfg2ctW7aMu3fv4uHhwbFjx/D09GTUqFGFPub8+fOZPn06hw4dYurUqVy8eJGlS5dqPD7hRVi8eDHt27fno48+4v3336dZs2ZMnTq1RNouat+0bNmSnTt38uTJE2bOnMmaNWtwcXFh+/btORLeOXPmkJmZiaenJ59++ikjR45kzpw5SvmMGTN444032LhxI3PnzlVWzMxNUd/r+Sno/Z29cIyM4gkhhBBCWzqZmZmZpR1EeZOYmEiXLl2YOXMmEyZMKO1whCh3rly5wpgxYwgKCirWVMeKICgoiA8++IALFy4U6j7O52V/ebY/NJy797VbRVYI8eqyqmvBSo+BpR3GC5eUlMSNGzdo0aJFsW+vqWik74qnsP2X/e90Sf1dVOjn5FVkiYmJhIeHs3//fnR0dBg8eHBphySEeEVFRERw7949Nm/eTN++fYuV4AkhhBCiYimzSV5+C7Do6Oi80HuQ8vL7778zZswY6tSpw6pVq3LcZ5iRkUFGRkae++vp6ZX4yofayMzM1Fgs4nm6urqFuj/sVZeenk5+A9yVKpXZj02ZoM37TRTM39+fEydO4ODggLe3d2mHI4QQQohypEz+tRoREYGrq2ue5Y6OjuzZs+clRpTFycmJsLCwPMsXLlzI4cOH8yzfvXs3Tk5OLyK0fB0+fJgFCxbkWe7h4VGoxW5edePGjeOHH37Is/zcuXPUr1//JUZUvmj7fsvvsyTAx8cHHx+fEm+3nqVZibcphCh/5HeBEK+2MnlPXkpKSr5/AFatWjXX1RVLW0REBLGxsXmWW1tbY2xs/BIjyhIbG0tERESe5TVr1syxomVFdvv2bZ48eZJnuUqlyvHgcvH/yfutbCrpuf5CiPIvIyPjlZ9dIfeVFZ30XfHIPXm5MDAwKJd/iNSvX79MjvBUr16d6tWrl3YY5UZZ/AKhPJH3W9mVkpKCWq3G0NCwtEMpd9RqNXfu3MHa2lr6r5Ck74rnRfbfq57gCVGRyadbCCEqkDI4eaNcyMzMRK1WS/8VgfRd8Uj/CSGKQpI8IYSoQEpj8adXgY6ODoaGhtJ/RSB9VzzSf0KIoiiT0zWFEEKUPAMDA5kuV0SGhobY2NiUdhjlkvRd8RSn/yrCPXdCiNxJkieEEBWIf+AFIqMel3YYQogXrJ6lGR7DXEo7DCFEKZEkTwghKpDIqMfcvR9d2mEIIYQQ4gWSMXwhhBBCCCGEeIVIkifE/zl79iz79u0r9H4RERH4+fnx77//Fum4PXr0YNmyZUXaV+Q0ffp03N3dX+ox4+Pj8fPz488//9TYHhERgUqlIiQk5KXGI4QQQoiKTZI8If7P2bNn+eKLLwq9X2RkJP7+/jx8+PAFRCXKg/j4ePz9/XMkeTVr1iQwMJAOHTqUUmRCCCGEqIjknjwhKoinT59SpUqV0g6j3EhJSaFSpUrFWpnOwMAAe3v7kgtKCCGEEEILMpInBODt7c3hw4e5desWKpUKlUqFt7c3AKdPn2bAgAHY2dnRuXNnVq5cSXJyMgBXrlxhzJgxAAwZMkTZFyApKYlly5bh5uZG69at6dGjB0uWLCEhIaFYsapUKrZt28bq1avp0KEDDg4OeHt7k5iYqNS5cuUKKpWKCxcuMGvWLNq0aYOnpyeQNfI4a9Ys2rZti729PRMnTiQsLCzHcY4cOcLAgQOxs7PDycmJSZMmERkZqZQ/ePAALy8vnJycaNWqFaNGjeK3337TaOPcuXMMHjwYBwcH2rVrx+DBgwkNDdW6vCDh4eGMHj0aOzs7evbsyeHDh3PU8fb2pn///hrb4uPjUalUBAcHK9uyp81u376d7t2706pVKx4/fkx4eDhz5syhW7dutG7dmtdff52dO3eSkZEBZE3JdHV1BcDT01N5D0REROQ6XTMjI4PNmzfTo0cPWrZsSZ8+fThw4IBGfH5+fjg4OBAWFsaIESNo3bo1/fv355tvvtG6b4QQQghRcclInhBk3ccVExPD7du3Wbt2LQDm5uacO3eOWbNm0a9fP+bNm8ft27dZv349//zzDxs3bsTW1pYlS5awbNkyVq5cSaNGjZQ2nz59Snp6OnPmzMHc3Jx//vmHLVu2MH36dPbs2VOsePfs2YOtrS2rVq0iIiKCtWvXkpyczPr16zXqLV68mDfffJNNmzahq6tLYmIi7u7u6OrqsnTpUipXrkxAQACjR4/m2LFj1KlTB4BPP/2UNWvWMGTIEObMmUNqairff/89MTEx1KtXj7i4OEaOHImRkRGLFy/GxMSEPXv2MHbsWE6fPo2FhQV//fUXnp6eSt9lZGRw8+ZN4uLiAAosL0hycjITJkzA0NCQ1atXA7Bx40YSExOxsrIqUr+ePn2ahg0b8v7776Orq4uRkRFhYWFYW1vzxhtvULVqVW7cuIGfnx9JSUl4eHhQs2ZN/P398fDwYO7cuTg5OQFZUzVzm8K7evVqdu/ezbRp03BwcODChQt88MEHpKWlMXr0aKVeamoqXl5ejBkzhunTp7N9+3ZmzZrF+fPnqV69epHOTwghhBAVgyR5QgANGjTA3Nyc+/fva0yv8/T0xN7eHl9fXwC6du2KoaEhS5YsISwsDJVKRZMmTQBo2rQpdnZ2yr7m5uYsXbpUeZ2Wlkb9+vUZOXIkd+7cwdrausjxGhgYsGnTJvT09ACoXLkyixYtwsPDg8aNGyv1evTowbvvvqu83r17N/fv3+fkyZNKvfbt29O9e3d27dqFt7c3CQkJ+Pv7M2zYMI0FYXr27Kn8vGvXLuLj4zl48CAWFhYAdOzYETc3N3bs2MH8+fO5fv06qampLF68GGNjYwC6dOmitFFQeUGCg4N5+PAhX331lZLU2djY0KdPnyIneampqWzfvh0jIyNlW8eOHenYsSMAmZmZtG3blqdPn7J37148PDwwMDCgRYsWADRs2DDf6ZkxMTHs3buXiRMnMnPmTAA6d+5MbGwsmzZtYsSIEco1zU7yunXrBoC1tTWurq5cvHiRAQMGFOn8hBBCCFExyHRNIfLw5MkTbty4gZubm8b2119/HYAff/yxwDaypzw6ODhga2vLyJEjAbh7926xYuvevbuSDAD06dOHzMxMrl27plHPxcVF4/X//vc/mjZtqpEImpmZ4ezsrJzPzz//jFqtZsiQIXke/9KlSzg5OWFqakpaWhppaWno6urSvn17JQaVSoWenh5eXl6cP38+xzTVgsoLcvXqVZo2baqR0DVs2JDmzZsXqp1nOTk5aSR4kDViuHHjRnr16oWdnR22trasX7+eqKgonjx5UuiYU1NT6dOnj8b2vn37EhMTo/G+0NXVVZJLgPr161OlSpUir+IqhBBCiIpDRvKEyENCQgKZmZnKSFU2ExMTDAwMCpxWeObMGd577z2GDRvGnDlzMDMzIyoqihkzZij39BXV8zEZGxtTuXLlHNMDn68XHx9PjRo1cm3v1q1bADx+/BjImm6Yl9jYWH755RdsbW1zlDVo0ADIGnnasmULW7duxcPDA11dXTp37sySJUuoW7dugeUFefjwYY7zyz6XovZvbu2tWbOGgwcPMmPGDFq2bImJiQnnzp0jICCA5ORkqlatqnX72e+Z569B9uvsvgeoUqUKBgYGGvX09fWL/d4RQgghxKtPkjwh8mBiYoKOjg4xMTEa2xMSEkhJScHU1DTf/UNCQmjRooXGlMcffvihRGKLjo7WeJ2YmEhycnKOxExHR0fjtampKXfu3Mm1vezzMTMzA7KSqNq1a+d6fFNTU7p06aIs5vKsZxOTrl270rVrVxITE7l48SIrV65kwYIF7Nq1S6vy/NSsWZPff/8913PJnv6ZHU9qaqpGnbwS9Of7C7Ku47Bhw5g8ebKyrTCLwzwru2+jo6OpVauWsv3Ro0ca5UIIIYQQxSHTNYX4P8+PklStWpUWLVrkeJD1V199BUDbtm2V/YAcIyxPnz5VyrIdP368RGL9+uuvSU9PV16HhISgo6OjcU9gbtq2bcsff/zB7du3lW1xcXFcvnxZOR8HBwcMDQ05dOhQnu04OzsTHh5O48aNsbOz0/gve3XRZxkbG/P666/Tr18/wsPDC12eGzs7O27dusW9e/eUbffu3ePmzZsa9WrXrs2DBw80plZeunRJq2NA1nV99jqmp6dz8uRJjTp5vQdyi1lfXz/X95SFhUWR7yUUQgghhHiWjOQJ8X8aN27MoUOHOHHiBA0bNqR69ep4eHgwY8YMvLy8ePPNN7lz5w7r16/Hzc1NSWasrKzQ09Pj0KFDVKpUCT09Pezs7HB2dmbZsmVs2rQJBwcHQkND+e6770ok1pSUFGbMmMGIESOU1TXd3Nw07rXLzeDBg/n888+ZMmUKs2fPVlbXrFSpEmPHjgWyRjBnzJjB2rVryczMxNXVlYyMDK5cuUK/fv2ws7Nj3LhxHD9+nNGjRzNmzBjq1q1LTEwMv/76K7Vq1WLcuHEcOHCAX375hS5dumBpaUlERATHjh2jU6dOAAWWF2Tw4MEEBAQwZcoUZURx48aNOaZC9u7dm40bN7Jw4ULefvttbt26RVBQkNZ97ezszMGDB2nSpAnVq1dn//79pKSkaNSxtLSkWrVqnDx5kvr162NgYJBrsmtubs7o0aPZsWOH8gy90NBQTpw4weLFizXusxRCCCGEKCpJ8oT4P0OGDOHq1at89NFHPH78mEGDBuHj48OGDRvYtGkT06dPx8zMjLfffpt58+Yp+5mbm7NkyRI+/fRTjh07RlpaGmFhYQwfPpyIiAj27t3Ljh076Ny5M76+vrz99tvFjtXd3Z2YmBjmz59PSkoKvXr1YsmSJQXuZ2xszJ49e/Dx8WHx4sVkZGTQpk0b9u7dqzw+AWDSpEmYm5vz+eefExwcTNWqVXFwcFDuWatevTqBgYF88sknrF27lsePH2NhYUHr1q3p1asXkLWwytdff83KlSt5/PgxlpaW9OvXT0nICiovSJUqVdi5cycffvgh7777LrVq1WL69OmcO3dOYxGXJk2a4OPjw+bNm5k+fTpt27Zl7dq1Wq9QuXjxYj744AM++ugjDA0NGTRoEL169WLRokVKHV1dXVauXMm6desYN24cKSkpnDt3Ltf25s+fj4mJCUFBQWzZsoV69eqxdOlShg8frlU8QgghhBAF0cnMzMws7SCEENpTqVTMnz+fiRMnlnYoohzJXvV0f2g4d+9HF1BbCFHeWdW1YKXHwNIOo9QlJSVx48YNWrRokWP1ZJE/6bviKWz/Zf87XdCtN9qSe/KEEEIIIYQQ4hUi0zWFKEPS0tLyLNPR0akw92xlZmZqLCzzPF1dXXR15TuqoqhnaVbaIQghXgL5rAtRsUmSJ0QZERERgaura57ljo6O7Nmzh7CwsJcYVek4fPgwCxYsyLPcw8ODmTNnvsSIXh0ew1xKOwQhxEuSkZEhX4gJUUFJkidEGVGzZs18V30szEO3y7vu3bvn2xf5Pahd5C0lJQW1Wo2hoWFph1LuqNVq7ty5g7W1tfRfIUnfFU9x+k8SPCEqLknyhCgjDAwMSuxm2/KuevXqVK9evbTDeCXJWltFk5mZiVqtlv4rAum74pH+E0IUhXzFI4QQQgghhBCvEEnyhBCiAtHR0SntEMolHR0dDA0Npf+KQPqueKT/hBBFIdM1hRCigjAwMJB7oorI0NAQGxub0g6jXJK+K57i9J8svCJExSVJnhBCVCD+gReIjHpc2mEIIV6wepZmspquEBWYJHlCCFGBREY95u796NIOQwghhBAvkIzhCyGEEEIIIcQrRJI8IUSR+fn54eDgkGf5vn37mDJlCh06dEClUhESEvISoysfrly5gkql4tq1a6UdihBCCCFeEZLkCSFemKNHjxIbG0u3bt1KO5Qyy9bWlsDAQBo3blzaoQghhBDiFSH35AkhXpgDBw6gq6tLREQER44cKe1wyiRjY2Ps7e1LOwwhhBBCvEJkJE8I8cKUxNLdPXr0YNmyZezbt4/u3bvTtm1bpk+fTkxMjFInODgYlUqlsQ1gwIABeHt7K6+9vb3p378/ly9f5o033qBVq1aMHj2aiIgIHj9+jKenJ23atKFnz5785z//0TrGiIgIVCoVR44cYcmSJbRr146OHTvy2WefAXDy5Enc3Nxo06YNHh4exMfHK/vmNl1TpVKxfft2/Pz8cHZ2xsnJiQULFpCUlFTo/hNCCCFExSNJnhCizDt//jznz59nyZIlvP/++/z3v//lo48+KlJbUVFR+Pj4MG3aNNauXctff/2Fl5cXc+bMoVmzZvj5+WFra8u7775LZGRkodr+5JNPqFKlChs2bKBPnz74+Pjg6+vL7t27effdd1myZAnff/89a9asKbCtffv2cffuXXx8fJgxYwbHjx9n8+bNRTpnIYQQQlQsMl1TCFHmZWZmEhAQgIGBAQCRkZFs3bq1SA/6jYuLY+/evTRt2hSAhw8f8tFHHzFp0iRmzJgBgJ2dHWfOnOHs2bOMHTtW67bt7e1ZuHAhAB06dOD06dPs3buX8+fPU716dQDCwsIICgoqMEm1tLTE19cXgK5du3L9+nVOnTqFl5dXoc5XCCGEEBWPjOQJIcq89u3bKwkeQOPGjUlNTSU6uvDPe6tZs6aS4AFYWVkB4OzsrGyrVq0a5ubmPHjwoFBtd+rUSflZT0+P1157jebNmysJXvbx4uPjefLkSb5tPRsPZJ1zYeMRQgghRMUkSZ4QosyrVq2axuvshC85ObnYbenr6wNgYmKS4xiFbf/5NvT19fM8XkFt57ZfSkpKoeIRQgghRMUkSZ4QotyrXLkyAKmpqRrbn13gRAghhBCiopAkTwhR7tWqVQuA27dvK9vCw8P5559/SiskIYQQQohSIwuvCCGKJT09nZCQkBzbW7VqRXR0NJGRkcqjDX799VcAzM3NcXR0LLEYWrduTZ06dfj444+ZN28eiYmJbNu2DTMzsxI7hhBCCCFEeSFJnhCiWJKTk/H09MyxffXq1Xz33XccPnxY2bZz504AHB0d2bNnT4nFoK+vj7+/Px9++CGenp40aNCAhQsX4uPjU2LHEEIIIYQoL3QyMzMzSzsIIYQQL1b2w9b3h4Zz937hVyUVQpQvVnUtWOkxsLTDKHVJSUncuHGDFi1aYGRkVNrhlCvSd8VT2P7L/nfazs6uRI4v9+QJIYQQQgghxCtEpmsKIUpNWlpanmU6Ojro6em9xGhyl5mZSXp6ep7lurq6hX4ge2mqZ2lW2iEIIV4C+awLUbFJkieEKDW2trZ5ltWrV4/z58+/xGhy98MPPzBmzJg8ywcNGlSu7v3zGOZS2iEIIV6SjIyMcvUllBCi5EiSJ4QoNUFBQXmWZT/wvLTZ2trmG2f16tVfYjTFk5KSglqtxtDQsLRDKXfUajV37tzB2tpa+q+QpO+Kpzj9JwmeEBWXJHlCiFJTUjcXv0jGxsblIk5tyVpbRZOZmYlarZb+KwLpu+KR/hNCFIV8xSOEEEIIIYQQrxBJ8oQQogLR0dEp7RDKJR0dHQwNDaX/ikD6rnik/4QQRSHTNYUQooIwMDCQe6KKyNDQEBsbm9IOo1ySviueovafLLoiRMUmSZ4QQlQg/oEXiIx6XNphCCFeoHqWZrKSrhAVnCR5QghRgURGPebu/ejSDkMIIYQQL5CM4wtRwfn5+eHg4JBnuZeXF71798be3p727dszatQovv3222If98aNG6hUKq5cuVKo/c6ePcu+ffuKffzy4saNG/j5+aFWq0s7FCGEEEKUE5LkCSHylZqayrhx49i8eTOrV6/GzMyMyZMn87///a9U4jl79ixffPFFqRy7NNy4cQN/f39J8oQQQgihNZmuKYTI14YNGzRed+3aFVdXV44ePUq7du1KKSohhBBCCJEXGckTQhSKnp4eJiYmpKamFmq/zZs306lTJxwcHPDw8CA6Oud9YTt37uStt96ibdu2dOzYkSlTpnDnzh2l3Nvbm8OHD3Pr1i1UKhUqlQpvb2+l/Oeff2bMmDHY29vTtm1b5s2bl+tx8hMeHo6HhweOjo60bt2aN998kxMnTijlycnJrFy5ks6dO2NnZ8eAAQM4c+aMRhvu7u5MmTJFY1tu01NVKhXbt2/Hz88PZ2dnnJycWLBgAUlJSQAEBwezYMECADp27IhKpaJHjx6FOh8hhBBCVDwykieEKFBmZibp6ekkJCQQHBzMvXv3WLZsmdb77927lw0bNjBhwgScnZ25fPky77//fo56Dx48YPTo0dStW5fExEQOHDjA8OHDOXXqFGZmZkyfPp2YmBhu377N2rVrATA3NweyEjx3d3e6devG+vXrUavVfPLJJ0yfPp3AwECt4rx79y7Dhg2jTp06vP/++1haWvLHH39w//59pY6XlxfffPMNs2fPplGjRhw9epSZM2eyadMmXF1dte6TbPv27aNt27b4+Phw9+5dVq9ejYWFBV5eXri4uDBt2jQCAgL49NNPMTExwcDAoNDHEEIIIUTFIkmeEKJAQUFBLFq0CAAjIyPWr1+f72Itz0pPT2fr1q0MGDCA9957D4AuXboQHR3N0aNHNeouXLhQY79OnTrRsWNHTp06xbBhw2jQoAHm5ubcv38fe3t7jX19fX1p2bIl/v7+ykODmzVrRv/+/QkNDaVbt24Fxurn54e+vj5ffPEFxsbGADg7OyvlN2/e5PTp0yxdupThw4cDWdNXIyMji5zkWVpa4uvrq7R1/fp1Tp06hZeXF+bm5jRo0AAAW1tbJaEVQgghhMiPTNcUQhTI1dWVoKAgtm/fTt++fZk9ezahoaFa7fvgwQMePnxIr169NLa7ubnlqPvLL78wfvx4nJycsLGxoXXr1iQlJXH37t18j6FWq/npp5/o06cP6enppKWlkZaWhpWVFXXq1OHatWtaxfr999/j5uamJHjP+/HHHwHo06ePxva+ffty/fp1ZZplYTybRAI0btyYBw8eFLodIYQQQohsMpInhCiQubm5MorUtWtX4uLiWLNmjVajY1FRUUobz6pRo4bG6/v37zNhwgRatmzJ0qVLqVmzJvr6+kyZMoXk5OR8jxEfH096ejorV65k5cqVOcr/+eefAuMEePz4MTVr1syzPC4uDn19fczMzHKcS2ZmJgkJCRgZGWl1rGzVqlXTeK2vr09KSkqh2hBCCCGEeJYkeUKIQrO1tZhnxY4AADzqSURBVOXixYta1bW0tAQgJiZGY/ujR480Xn/zzTckJSXh7++vJD5paWnExcUVeAwTExN0dHSYMmUKPXv2zFFevXp1rWI1MzPj4cOHeZabmpqSmppKXFwcpqamGueio6ODiYkJAAYGBjkWptHmPIQQQgghSoJM1xRCFNqPP/7Ia6+9plXd2rVrY2lpmWMFylOnTmm8fvr0KTo6OlSq9P+/e/rqq69IS0vTqKevr59jZM/IyAh7e3tu376NnZ1djv/q16+vVazZ9/8lJibmWt62bVsAQkJCNLaHhIRgY2OjjOLVrl2bO3fukJmZqdS5dOmSVjE8T19fH0BG94QQQgihNRnJE0KQnp6eI3GBrHvdQkNDcXFxoU6dOsTFxXHixAm+/fZb1q1bp1Xbenp6TJ48mRUrVmBhYUGnTp24dOmSxqMEADp06ADAggULGD58OLdu3eKzzz7LMZ2xcePGHDp0iBMnTtCwYUOqV69O/fr1mT9/PmPHjmX27Nn069ePatWq8eDBAy5fvszgwYNxcnIqMFYPDw8uXLjAyJEjeeedd7C0tCQ8PBy1Ws2kSZNo3rw5vXv3xsfHh6dPn2Jtbc2xY8f4+eef2bx5s9KOm5sbQUFBfPTRR/Ts2ZOffvopR1KrrcaNGwNZq3D27NmTKlWqoFKpitSWEEIIISoGSfKEECQnJ+Pp6Zlju6enJykpKfj6+hIbG0v16tVRqVTs2bMHR0dHrdt3d3cnPj6e/fv388UXX9CxY0eWL1/OO++8o9RRqVSsXLkSf39/pkyZQosWLdiwYQOzZ8/WaGvIkCFcvXqVjz76iMePHzNo0CB8fHxo06YN+/fvx8/PjwULFpCamkrt2rXp0KEDDRs21CpOKysrDhw4gK+vL0uXLiU9PR0rKysmT56s1FmzZg3r1q1j+/btPH78mEaNGrFx40aN59d17dqVd999l71793L48GG6du3K0qVLGTdunNZ9ls3GxoaZM2dy8OBBPv30U+rUqcP58+cL3Y4QQgghKg6dzGfnEwkhhHglZa8wuj80nLv3C/eAeCFE+WJV14KVHgNLO4wyISkpiRs3btCiRYtCL4xV0UnfFU9h+y/732k7O7sSOb7ckyeEEEIIIYQQrxCZrimEKJb09HTymxDw7EIqpSkjI4OMjIw8y/X09JSHqAshhBBClGdl468vIUS51atXLyIjI/MsDwsLe4nR5G3hwoUcPnw4z/Ldu3drtThLeVfP0qy0QxBCvGDyORdCSJInhCiWgICAcrG8v4eHB6NGjcqz3Nra+iVGU3o8hrmUdghCiJcgIyMDXV25K0eIikqSPCFEsZSX5fzr16+v9fPyXlUpKSmo1WoMDQ1LO5RyR61Wc+fOHaytraX/Ckn6rniK2n+S4AlRsclvACGEqEBkQeWiyczMRK1WS/8VgfRd8Uj/CSGKQpI8IYSoQGRxmaLR0dHB0NBQ+q8IpO+KR/pPCFEUMl1TCCEqCAMDA5kuV0SGhobY2NiUdhjlkvRd8eTXf3LfnRAiL5LkCSFEBeIfeIHIqMelHYYQopjqWZrJQkpCiDxJkieEEBVIZNRj7t6PLu0whBBCCPECyRi/EEIIIYQQQrxCZCRPiJfMz8+PnTt38vPPP+da7uXlxdWrV3n48CH6+vo0a9aMadOm0blzZ62PkZaWxhdffMHBgwf5+++/qVSpEnXq1KFdu3Z4e3tjYGBAYmIin332GaGhody9excDAwNatWrFnDlzcjwWITw8HB8fH/773/+ir6+Pi4sLCxYswNzcvFh9IYQQQgghSp4keUKUMampqYwbNw4rKyuSk5MJCgpi8uTJ7N69m3bt2mnVxvLlywkODmby5Mm0adMGtVrNjRs3OHbsGE+fPsXAwID79+8TGBjIW2+9xezZs0lOTmbnzp0MGzaMQ4cO0bhxYwASExMZO3YstWrVYu3atTx9+pR169YxZcoUAgMD5aZ/IYQQQogyRpI8IcqYDRs2aLzu2rUrrq6uHD16VKskT61WExQUxNSpU/Hw8FC2u7q64uHhoTxrqX79+pw5c0ZjtcUOHTrQo0cP9u/fz+LFiwHYv38/CQkJHDlyhBo1agDQsGFDhgwZwrlz5+jVq1exz1kIIYQQQpQc+QpeiDJOT08PExMTUlNTtaqvVqtJTU2lZs2auZZnP2vJyMgox3L6VatWpUGDBjx8+FDZdv36dZo3b64keAB2dnaYmZlx/vx5rc9DpVKxfft2/Pz8cHZ2xsnJiQULFpCUlKTU8fPzw8HBIce+7dq1w8/PT3nt7u7OlClTOHHiBL1796Z169ZMnTqVuLg4IiMjmThxIg4ODvTr148rV65oHeOVK1dQqVR88803eHp64uDggIuLC8ePHwdg9+7duLi44OjoyPvvv09KSorG/g8ePMDLywsnJydatWrFqFGj+O233zTqHDlyhBEjRuDo6Ej79u1xd3fn6tWrGnWy+yEsLIwRI0bQunVr+vfvzzfffKP1uQghhBCi4pIkT4gyKDMzk7S0NGJjY9mxYwf37t1j2LBhWu1rbm5O3bp1CQgI4OTJk8TFxWl93Pj4eG7dukWjRo2UbcnJyRgYGOSoa2BgwO3bt7VuG2Dfvn3cvXsXHx8fZsyYwfHjx9m8eXOh2sh2/fp1du/ezfz581m6dCn/+9//WLx4MbNmzcLFxQU/Pz/Mzc2ZOXMmT548KVTbH374IU2bNsXf35/WrVszf/581qxZw7fffsvSpUuZNWsWR48eZefOnco+cXFxjBw5kps3b7J48WL8/PwwNDRk7NixREf//9UsIyIiGDhwIBs2bGDt2rXUqVOHUaNGcefOHY0YUlNT8fLyYvDgwfj7+2Nubs6sWbOIjY0tUn8JIYQQouKQ6ZpClEFBQUEsWrQIyBpxW79+fa4jXHnx8fFh7ty5zJ07Fx0dHRo1aoSrqyvjx4/Pd7GUNWvWoKOjw4gRI5RtVlZWBAcH8/TpU6pUqQLA/fv3iYqKwsjIqFDnZWlpia+vL5A1DfX69eucOnUKLy+vQrUDWfcKbtmyRTmfsLAwdu7cyYcffqjEX7NmTd544w2+++47evbsqXXbffr0Uaa6tmrVijNnznDy5EnOnDmDvr4+AD/88AMhISFMnToVgF27dhEfH8/BgwexsLAAoGPHjri5ubFjxw7mz58PoDGFNiMjg06dOnH16lUOHz7M3LlzlbLsJK9bt24AWFtb4+rqysWLFxkwYECh+0sIIYQQFYeM5AlRBrm6uhIUFMT27dvp+//au/OwqMr2D+DfGWQQRDZFEpcUzRERA2IRTFIQhdwXIpcULdQUo5es1ExfNHtxgUQoBdIScgtEc0Urf2Fubb6mplkCLkiuwAwIwgDn94cX8zoO4LCMM8D3c11cMc95znPu5zY43p7nnBMQgLfffhsZGRka7+/h4YFvv/0WMTExCAoKQkVFBRISEjBy5EjcunWr2n127tyJr7/+GkuWLMEzzzyjbA8MDERRURGWLFmCW7du4erVq1iwYAHEYrFy6aemvLy8VD736NEDN2/erNMYVXr37q1SsHbr1k3tGFVtdT3GgAEDlN+3bdsWVlZWcHV1VRZ4VWP/888/ys/Hjx+Hh4cHzM3NUV5ejvLycojFYri5ueHcuXPKfpmZmZg7dy68vLxgb28PBwcHZGdn48qVKyoxiMVieHp6Kj937twZrVu3rvHPj4iIiKgKr+QR6SErKytlAePt7Q2ZTIbVq1crr+powsTEBP7+/vD39wcApKSkYPHixdi0aRMWLlyo0jcjIwNLlizBnDlzMHbsWJVtdnZ2WLFiBVasWIFvvvkGADB06FB4e3vXeRmkmZmZymdDQ0O1+9oaMhbwsCirUrXMtLS0tE5jPzpG1ThPij0/Px9nzpyBg4OD2nhdu3YF8PDq44wZM2BlZYUFCxbA1tYWRkZGWLx4sVqMrVu3Vlsma2hoWOe5EBERUcvDIo+oCXBwcMDRo0cbNEZgYCDWrFmDzMxMlfYzZ84gLCwMY8aMQVhYWLX7jhkzBi+//DKuXLkCc3Nz2NjYYPjw4fDx8WlQTI8zMjJSe8CMQqFQeTiLvjI3N8fAgQOrzWFVsXbmzBncvHkT8fHx6N27t3J7YWGhytVTIiIiooZgkUfUBPz222/o0qWLRn2riiJzc3OV9nv37qGwsBDW1tbKtsuXL2PWrFno378/IiIiah1XIpGgV69eAICTJ0/iypUralf9GsrGxgYKhQLXrl1TXv06deoUKioqGvU42uDl5YU9e/agR48eNd6r+ODBAwBQWfZ5+vRp3LhxA88999xTiZOIiIiaPxZ5RDpQUVGB9PR0tfaSkhJkZGRg0KBB6NixI2QyGfbt24djx44hOjpao7ELCwsxbNgwjB49Gv3794e5uTlycnKwadMmiMVi5UNJ7t27h9dffx1GRkaYNm2ayqP+TU1N0bNnTwBAcXExYmNj4ebmBiMjI5w5cwYJCQkIDQ1VeQpnY/D29oaJiQkWL16MkJAQ3Lx5E0lJSTAyMmrU42hDcHAw9u7diylTpmDq1KmwtbVFXl4efv/9d9jY2CA4OBhOTk4wMTFBREQEZs6ciVu3biE2NhY2Nja6Dp+IiIiaERZ5RDpQWlpa7bK+sLAwlJWVISoqCvn5+bC0tIRUKkVycjLc3d01GtvU1BQhISH48ccfkZ6eDplMhvbt28PR0RGRkZHKe8YuX76sfCBJcHCwyhju7u5ITk4G8PABIH/99RfS0tJQXFwMOzs7LF26FOPGjWtABqpnaWmJdevWYeXKlZg7dy7s7e2xatUqvPbaa41+rMZmaWmJHTt2YO3atVizZg0KCgrQrl07PP/888oXxrdv3x4xMTFYtWoV5syZg27duiEiIgKff/65jqMnIiKi5kQkCIKg6yCIiEi7qp7wuTUjE1dy7z2hNxHpu2627fCf0DG6DkPvFRcX4+LFi7C3t6/za39aOuauYeqav6rztKOjY6Mcn69QICIiIiIiaka4XJOoiamoqEBtF+BbtXr6P9bl5eU1bhOJRDAwMHiK0VRPEIRaH+AiFoshFjf/f/fqZG2h6xCIqBHwZ5mIasMij6iJ8fPzw40bN2rcfunSpacYDZCTkwNfX98atz96f58u7dq1S+39gI8KDQ3FvHnznmJEuhEaNEjXIRBRI6msrGwR/zhFRHXHIo+oiVm/fn29XyCuDR06dEBqamqN29u0afMUo6nZ4MGDa42zQ4cOTzEa3SgrK0NJSQmMjY11HUqTU1JSguzsbHTv3p35qyPmrmFqyx8LPCKqCYs8oiZGKpXqOgQVEomk0W4S1iZLS0tYWlrqOgyd47O26kcQBJSUlDB/9cDcNQzzR0T1wX8CIiIiIiIiakZY5BERtSAikUjXITRJIpEIxsbGzF89MHcNw/wRUX1wuSYRUQshkUh4T1Q9GRsbo0+fProOo0li7hqmpvzxoStEVBsWeURELUjcjh9w406BrsMgogboZG3BJ+USUa1Y5BERtSA37hTgSu49XYdBREREWsTr/ERUb7GxsXB2dq5xuyAISEhIwKBBg9CvXz8EBQXhzJkzTy/AJuCnn36CVCrFuXPndB0KERERNRMs8ohIaxITE7Fu3ToEBwcjPj4e1tbWmDFjBq5fv67r0PSGg4MDduzYgR49eug6FCIiImomWOQRkVaUlpYiPj4eM2bMQHBwMDw9PREdHQ0LCwts3LhR1+HpDVNTUzg5OcHExETXoRAREVEzwSKPiLTi9OnTKCoqQkBAgLJNIpHAz88PR48e1XgcHx8fLFu2DFu2bMHgwYPxwgsvYM6cOcjLy1P2SUtLg1QqVWkDgNGjR2PBggXKzwsWLMCIESNw4sQJjBw5Ev369cOUKVOQk5ODgoIChIWFwcXFBUOGDMGBAwc0jjEnJwdSqRS7d+/GkiVL4OrqCk9PT3zxxRcAgP3792PYsGFwcXFBaGgo5HK5ct/qlmtKpVIkJiYiNjYWXl5e8PDwwMKFC1FcXKxxTERERNRyscgjIq3IysoCANjZ2am09+jRA7m5uXjw4IHGYx05cgRHjhzBkiVL8MEHH+CXX37B8uXL6xXXnTt3EBkZiTfffBNr1qzBtWvXMH/+fPzrX/9Cr169EBsbCwcHB7z77ru4ceNGncZeu3YtWrdujZiYGPj7+yMyMhJRUVFISkrCu+++iyVLluDUqVNYvXr1E8fasmULrly5gsjISMydOxd79+7FZ599Vq85ExERUcvCp2sSkVbI5XJIJBIYGRmptJuZmUEQBMhkMrRu3VqjsQRBwPr16yGRSAAAN27cQHx8fL3eEyWTyfDVV1/hueeeAwDcvn0by5cvR0hICObOnQsAcHR0xLfffovvvvsO06ZN03hsJycnLFq0CADQv39/HD58GF999RWOHDkCS0tLAMClS5eQmpr6xCLV2toaUVFRAABvb29cuHABhw4dwvz58+s0XyIiImp5eCWPiPSem5ubssADHl4NVCgUuHev7q8C6NChg7LAA4Bu3boBALy8vJRtZmZmsLKyws2bN+s09oABA5TfGxgYoEuXLujdu7eywKs6nlwux/3792sd69F4gIdzrms8RERE1DKxyCMirTAzM0NZWRlKS0tV2uVyOUQiEczNzes01qOqCr7Hx67PWIaGhgCAtm3bqh2jruM/PoahoWGNx3vS2NXtV1ZWVqd4iIiIqGVikUdEWlF1L152drZKe1ZWFmxtbTVeqqmJqiWhCoVCpf3RB5wQERERtRQs8ohIK1xcXGBqaoqDBw8q2xQKBQ4fPgxvb+9GPZaNjQ2A/z3sBQAyMzPxzz//NOpxiIiIiJoCPniFiBqkoqIC6enpau39+vXDrFmzEBsbCysrK/Tq1Qvbtm1DQUEBXn/99UaN4fnnn0fHjh3x8ccf45133kFRURESEhJgYWHRqMchIiIiagpY5BFRg5SWliIsLEytfdWqVQgJCYEgCNi0aRPy8vJgb2+PjRs3okuXLo0ag6GhIeLi4vDvf/8bYWFh6Nq1KxYtWoTIyMhGPQ4RERFRUyASBEHQdRBERKRdVS9b35qRiSu5dX8qKRHpj2627fCf0DG6DqNJKC4uxsWLF2Fvbw8TExNdh9OkMHcNU9f8VZ2nHR0dG+X4vCePiIiIiIioGeFyTSLSmfLy8hq3iUQiGBgYPMVoqicIAioqKmrcLhaL6/xCdiIiIiJtYpFHRDrj4OBQ47ZOnTrhyJEjTzGa6v3888+YOnVqjdvHjh3bpO7962RtoesQiKiB+HNMRE/CIo+IdCY1NbXGbVUvPNc1BweHWuO0tLR8itE0XGjQIF2HQESNoLKykqsIiKhGLPKISGca6+ZibTI1NW0ScWqirKwMJSUlMDY21nUoTU5JSQmys7PRvXt35q+OmLuGqSl/LPCIqDb8DUFE1ILwgcr1IwgCSkpKmL96YO4ahvkjovpgkUdE1IKIRCJdh9AkiUQiGBsbM3/1wNw1DPNHRPXB5ZpERC2ERCLhcrl6MjY2Rp8+fXQdRpPE3DXMo/njfXhEpCkWeURELUjcjh9w406BrsMgojrqZG3BBycRkcZY5BERtSA37hTgSu49XYdBREREWsRr/kRERERERM0IizwiAgDExsbC2dm5xu3z58/H0KFD4eTkBDc3N0yePBnHjh2r0zHKy8uRnJyMUaNGwdnZGW5ubhg1ahSWLVuGsrIyAEBRURFiY2MxYcIEuLq6wsvLC7Nnz8alS5fUxsvMzERISIgypnfffRd5eXl1isnHxwdSqVTta+PGjQCAwsJCzJs3Dz4+PujXrx/69++PN954A2fPnq1xzMrKSowbNw5SqRTp6ekq2xYsWFDt8Y4eParS78aNGwgPD8eLL74IZ2dnjB8/HocOHarT3IiIiKhl4nJNItKIQqFAcHAwunXrhtLSUqSmpmLmzJlISkqCq6urRmN89NFHSEtLw8yZM+Hi4oKSkhJcvHgRe/bswYMHDyCRSJCbm4sdO3Zg/PjxePvtt1FaWopNmzYhKCgIO3fuRI8ePQA8LAanTZsGGxsbrFmzBg8ePEB0dDRmzZqFHTt21OnhBMOGDcOMGTNU2mxtbQE8fLecRCLBm2++ic6dO6OoqAibN2/GtGnTkJaWhu7du6uNt337dty6davG43Xp0gVr1qxRaauaV9Ux33jjDQDAokWLYG5ujm+++QZhYWFITEzEwIEDNZ4bERERtTws8ohIIzExMSqfvb294evri2+++UajIq+kpASpqamYPXs2QkNDle2+vr4IDQ1VvgOqc+fO+Pbbb1WeAtm/f3/4+Phg69at+PDDDwEAW7duRWFhIXbv3o327dsDAJ599llMmDAB33//Pfz8/DSeW/v27eHk5FTttnbt2iEqKkqlzcvLCx4eHjh06BBmz56tsi0vLw8xMTF47733sGjRomrHbN26dY3HA4ALFy4gKysLSUlJ8PDwAAB4enri119/xcGDB1nkERERUa24XJOI6sXAwABt27aFQqHQqH9JSQkUCgU6dOhQ7faqd0CZmJioPea/TZs26Nq1K27fvq1su3DhAnr37q0s8ADA0dERFhYWOHLkSF2nUycmJiYwMjKqdu7R0dHw8PBQFmf1UV5eDgBo27atsk0sFqNNmzZ8ITIRERE9EYs8ItKYIAgoLy9Hfn4+Nm7ciKtXryIoKEijfa2srGBra4v169dj//79kMlkGh9XLpfj77//hp2dnbKttLQUEolEra9EIkFWVpbGYwP/m1fVV0VFhVqfyspKlJeX4/bt24iMjIRYLMaYMWNU+pw9exb79u3De++9V+vxrl69ihdeeAF9+/bFuHHj8N1336lsd3JywnPPPYdPPvkE169fh1wuR3JyMq5cuYJXXnmlTnMjIiKilofLNYlIY6mpqVi8eDGAh1ezPvnkk1of1vK4yMhIhIeHIzw8HCKRCHZ2dvD19cX06dNhZWVV436rV6+GSCTCxIkTlW3dunVDWloaHjx4gNatWwMAcnNzcefOHZiYmNRpXlu3bsXWrVuVnw0MDHDhwgWVPjExMdiwYQOAh0s4ExIS0KVLF+X2yspKREREYPr06ejcuTNycnKqPZa9vT0cHR3Rs2dPFBYWYtu2bZg7dy5iYmLg7+8PAGjVqhU2b96MN998E0OGDAHwcIlnXfNNRERELROLPCLSmK+vL3r37o38/Hykp6fj7bffRlxcHF566SWN9vfw8MC3336Lo0eP4uTJkzh16hQSEhKQlpaGtLQ02NjYqO2zc+dOfP3114iMjMQzzzyjbA8MDERSUhKWLFmCd955Bw8ePMCHH34IsVisXPqpqYCAALz++uvKz9XtP2nSJAwZMgR37txBSkoKZs6ciS+//BIODg4AgJSUFNy9exczZ86s9VjTpk1T+ezj44NXX30V69atUxZ5Dx48wFtvvQVBEPDpp5+iTZs2SE9PxzvvvIPExES4u7vXaX5ERETUsrDIIyKNWVlZKa+4eXt7QyaTYfXq1RoXecDDK4D+/v7KgiYlJQWLFy/Gpk2bsHDhQpW+GRkZWLJkCebMmYOxY8eqbLOzs8OKFSuwYsUKfPPNNwCAoUOHwtvbG/fv36/zvBwdHWvtY2NjoyxCBw0ahAkTJmDdunWIj4/H/fv3ER0djX/9619QKBRQKBQoKioC8LBgKyoqgqmpabXjisViDB06FKtXr1ZelUxNTcXZs2eRkZGhzLenpyeuXbuG6OhobN++vU7zIyIiopaF9+QRUb05ODjg6tWrDRojMDAQFhYWyMzMVGk/c+YMwsLCMGbMGISFhVW775gxY3D8+HHs3bsXR48eRWxsLK5fv17rkysbg1gshr29vXLu+fn5KCgowNKlS+Hm5gY3NzeMHj0aAPD+++9j2LBhdRr/8uXLsLGxUVvCam9vj2vXrjXOJIiIiKjZ4pU8Iqq33377TeW+tNooFAoUFxfD3Nxcpf3evXsoLCyEtbW1su3y5cuYNWsW+vfvj4iIiFrHlUgk6NWrFwDg5MmTuHLlitpVv8ZWXl6Os2fPKudubW2NpKQklT53795FeHg45s2bBy8vrxrHqqysRHp6Op577jnlvYW2tra4efMm8vLyVAq9P/74A506ddLCjIiIiKg5YZFHREoVFRVIT09Xay8pKUFGRgYGDRqEjh07QiaTYd++fTh27Biio6M1GruwsBDDhg3D6NGj0b9/f5ibmyMnJwebNm2CWCxWPlTl3r17eP3112FkZIRp06bh/PnzyjFMTU3Rs2dPAEBxcTFiY2Ph5uYGIyMjnDlzBgkJCQgNDVV5CmdD7dixA2fPnoWXlxesra1x9+5dbN++HdnZ2Vi6dCkAwMjISO2VCVUPXunZsydcXFwAADdu3MCCBQswfPhwPPvss5DJZNi2bRvOnz+P2NhY5b4jR45EfHw8QkJCMHPmTOU9eadOncKqVasabW5ERETUPLHIIyKl0tLSapdGhoWFoaysDFFRUcjPz4elpSWkUimSk5M1fgiIqakpQkJC8OOPPyI9PR0ymQzt27eHo6MjIiMjlQ8wuXz5Mm7evAkACA4OVhnD3d0dycnJAB4umfzrr7+QlpaG4uJi2NnZYenSpRg3blwDMqCuZ8+eOHz4MFasWAG5XA5ra2s4OjoiNTUVvXv3rtNYbdq0gampKdavX4979+7B0NAQffv2RWJiosoLzjt27IikpCSsXbsWERERePDgAbp164ZVq1Ypl4ESERER1UQk8M26RETN3rlz5wAAWzMycSX3no6jIaK66mbbDv8JHaPrMJqc4uJiXLx4Efb29nV+vU5Lx9w1TF3zV3WeftKD4DTFB68QERERERE1I1yuSUSNoqKiArUtDGjV6un/uikvL69xm0gkgoGBwVOMRj90srbQdQhEVA/82SWiumCRR0SNws/PDzdu3Khx+6VLl55iNA8ffOLr61vj9kfv72tJQoMG6ToEIqqnyspKiMVchEVET8Yij4gaxfr161FWVqbrMJQ6dOiA1NTUGre3adPmKUajH8rKylBSUgJjY2Ndh9LklJSUIDs7G927d2f+6oi5axjmj4jqg0UeETUKqVSq6xBUSCSSRrt5uTnhs7bqRxAElJSUMH/1wNw1DPNHRPXBa/5ERERERETNCIs8IqIWRCQS6TqEJkkkEsHY2Jj5qwfmjojo6eNyTSKiFkIikfCennoyNjZGnz59dB1Gk8Tc1Q8fskJEDcEij4ioBYnb8QNu3CnQdRhEVItO1hZ8Ei4RNQiLPCKiFuTGnQJcyb2n6zCIiIhIi7gOgIiIiIiIqBlhkUdEAIDY2Fg4OzvXuH3+/PkYOnQonJyc4ObmhsmTJ+PYsWN1OkZ5eTmSk5MxatQoODs7w83NDaNGjcKyZctU3rG3cuVKDB8+HM7OznBxccH48eOxf/9+tfHKysqwcuVKDBgwAE5OTpg+fTqysrLqFJOPjw+kUqna18aNGwEAhYWFmDdvHnx8fNCvXz/0798fb7zxBs6ePasyzoIFC6odRyqVIiEhodpjnz9/Hvb29rXmHQBWrFgBqVSKZcuW1WluRERE1DJxuSYRaUShUCA4OBjdunVDaWkpUlNTMXPmTCQlJcHV1VWjMT766COkpaVh5syZcHFxQUlJCS5evIg9e/bgwYMHkEgkAID79+8jMDAQdnZ2EIlEOHToEMLDw1FZWYmRI0eqjHfgwAEsWLAANjY22LBhA4KDg7F//360bdtW47kNGzYMM2bMUGmztbUF8LCQlEgkePPNN9G5c2cUFRVh8+bNmDZtGtLS0tC9e3cAwJw5c/Dqq6+qjHHgwAFs3rwZ3t7eascUBAHLly+HlZUViouLa4zt0qVL2LlzJ0xNTTWeDxEREbVsLPKISCMxMTEqn729veHr64tvvvlGoyKvpKQEqampmD17NkJDQ5Xtvr6+CA0NVXnR7+NXrAYOHIjLly9j165dyiLv5s2bSE1NxdKlSzFhwgQAgKOjIwYPHozt27cjJCRE47m1b98eTk5O1W5r164doqKiVNq8vLzg4eGBQ4cOYfbs2QCArl27omvXrir9oqKi0LNnT/Tu3Vtt3J07dyI/Px/jx49HcnJyjbEtX74cwcHB2L17t8bzISIiopaNyzWJqF4MDAzQtm1bKBQKjfqXlJRAoVCgQ4cO1W5/0ju0LCwsVI517NgxVFZWwt/fX6XPgAEDcPToUY1iqi8TExMYGRnVOvdbt27h119/VbnyWEUulyMqKgoLFy6EoaFhjWPs2bMHOTk5dSpYiYiIiFjkEZHGBEFAeXk58vPzsXHjRly9ehVBQUEa7WtlZQVbW1usX78e+/fvh0wm0+hYcrkcu3fvxvHjxzF58mTl9qysLLRr1w7m5uYq+/Xo0aPO9+VVHavqq6KiQq1PZWUlysvLcfv2bURGRkIsFmPMmDE1jrlv3z5UVlZi+PDhatvWrl0LBwcHDB48uMb9i4qKsGrVKrz33nt8tx0RERHVCZdrEpHGUlNTsXjxYgAPr2Z98sknT3xoyKMiIyMRHh6O8PBwiEQi2NnZwdfXF9OnT4eVlZVK35MnT2L69OkAgFatWuHDDz9UuWonl8urve/OzMzsiQXk47Zu3YqtW7cqPxsYGODChQsqfWJiYrBhwwYAD5dwJiQkoEuXLjWOuW/fPjg7O6v1uXjxIlJTU7Fr165aY4qLi8Ozzz6Ll19+uU5zISIiImKRR0Qa8/X1Re/evZGfn4/09HS8/fbbiIuLw0svvaTR/h4eHvj2229x9OhRnDx5EqdOnUJCQgLS0tKQlpYGGxsbZd9+/fohNTUVRUVFOHr0KD766CMYGBggMDCw0ecVEBCA119/Xfm5uqWjkyZNwpAhQ3Dnzh2kpKRg5syZ+PLLL+Hg4KDWNzMzExcuXMCHH36o0i4IAiIiIjBp0iT06NGjxnj+/vtvbNmyBV9//XUDZkVEREQtFYs8ItKYlZWV8oqbt7c3ZDIZVq9erXGRBzy8Aujv76+8KpeSkoLFixdj06ZNWLhwobKfqakpHB0dAQCenp6oqKhAZGQkxo0bBwMDA5iZmaGoqEhtfLlcrraEU5N5VR2rJjY2NsoidNCgQZgwYQLWrVuH+Ph4tb579+5Fq1at1K7CHThwAFlZWYiKioJcLgcAlJaWKuM2MjKCkZERIiMj4e/vj06dOin7VVZWQqFQQC6Xw9TUFGIxV9sTERFR9fi3BCKqNwcHB1y9erVBYwQGBsLCwgKZmZlPPFZRURHy8vIAAHZ2drh7967a0sysrCzY2dk1KKYnEYvFsLe3r3Hu+/fvh6enp9oS1KysLMhkMvj4+MDNzQ1ubm5ITExEcXEx3NzcEBsbCwDIzs7Gnj17lH3c3Nzwzz//4Ouvv4abmxuys7O1Oj8iIiJq2nglj4jq7bfffqv1vrRHKRQKFBcXq11lu3fvHgoLC2Ftbf3EY5mamsLS0hIA8OKLL0IsFuPw4cPKJZwymQzHjh3DnDlz6jEbzZWXl+Ps2bPVzv3333/HtWvXMHfuXLVtY8eOhbu7u0rbrl27cODAASQmJirfzRcdHa28wlclPDwcTk5OmDp1qrIfERERUXVY5BGRUkVFBdLT09XaS0pKkJGRgUGDBqFjx46QyWTYt28fjh07hujoaI3GLiwsxLBhwzB69Gj0798f5ubmyMnJwaZNmyAWizFx4kQAwJ9//ok1a9YolysWFxfjhx9+QEpKCsLDw9Gq1cNfW8888wwmTJiAVatWQSwWw8bGBvHx8Wjbtq3aS8kbYseOHTh79iy8vLxgbW2Nu3fvYvv27cjOzsbSpUvV+u/duxetW7eGn5+f2rbOnTujc+fOKm0///wzDAwM4OHhoWyr7p19RkZGsLGxUelHREREVB0WeUSkVFpairCwMLX2sLAwlJWVISoqCvn5+bC0tIRUKkVycrLalamamJqaIiQkBD/++CPS09Mhk8nQvn17ODo6IjIyUvkAk/bt28PMzAyfffYZ7ty5g7Zt28LOzg5xcXEYMmSIypiLFy9GmzZtEBUVhfv378PFxQVffPFFtU/drK+ePXvi8OHDWLFiBeRyOaytreHo6IjU1FS1l5xXFcmDBw9GmzZtGi0GIiIioroQCYIg6DoIIiLSrnPnzgEAtmZk4kruPR1HQ0S16WbbDv8JHQMAKC4uxsWLF2Fvbw8TExPdBtYEMX/1x9w1TF3zV3WeftKD4DTFB68QERERERE1I1yuSUSNoqKiArUtDKi6l+5pKi8vr3GbSCSCgYHBU4xGP3SyttB1CET0BPw5JaKGYpFHRI3Cz88PN27cqHH7pUuXnmI0QE5ODnx9fWvc7u7ujuTk5KcYkX4IDRqk6xCISAOVlZV8HyYR1RvvySOiRnHp0iWUlZXVuL2x1phrqqysrNbCsk2bNlp/n54+OX36NARBgKGhIUQika7DaXIEQUB5eTlatWrF/NURc9cwgiBAoVDwZ7eemL/6Y+4apq75Kysrg0gkgouLS6Mcn0UeEVEL8N///ldZ5BEREZF+USgUEIlEcHZ2bpTxWOQRERERERE1I1zsTURERERE1IywyCMiIiIiImpGWOQRERERERE1IyzyiIiIiIiImhEWeURERERERM0IizwiIiIiIqJmhEUeERERERFRM8Iij4iIiIiIqBlhkUdERERERNSMsMgjIiIiIiJqRljkERERERERNSMs8oiI9FhmZiamT58OJycnDBgwAKtWrUJZWdkT9xMEAQkJCRg0aBD69euHoKAgnDlzRq3frVu3MG/ePDg7O8Pd3R0ffPABioqK1PodOXIEo0aNgqOjI4YNG4adO3c2xvS0Ste5q6ioQGJiIiZPngwPDw+4u7vjtddew6+//tqY09QaXefvcefPn4e9vT2cnZ0bMq2nQl9yV1paipiYGPj4+KBv374YNGgQVq5c2RhT1Cp9yF/Vz6+/vz+ef/55+Pr6YuXKlbh//35jTVMrtJm7vLw8fPTRRwgMDETfvn1r/VlsiucMQPf5a9TzhkBERHqpoKBAGDBggDB58mTh6NGjQkpKivDCCy8IERERT9w3Pj5ecHBwEL744gvhxIkTwty5cwVnZ2fh2rVryj5lZWXCiBEjhBEjRgjff/+9sH//fsHb21uYOXOmyli//PKLYG9vL3z44YfCyZMnhU8++USQSqXCwYMHG33OjUUfcldUVCS4uroKK1asEP7v//5PyMjIEObOnSvY29sLJ06c0Mq8G4s+5O9RlZWVwiuvvCJ4eXkJTk5OjTZPbdCX3FVUVAgzZswQ/Pz8hJ07dwo//fSTsGvXLiE6OrrR59yY9CV/sbGxQp8+fYT4+Hjh5MmTQlJSkuDk5CSEh4c3+pwbi7Zzd+HCBcHT01OYNWuWEBQUVOPPYlM8ZwiCfuSvMc8bLPKIiPTUhg0bBCcnJyE/P1/Ztn37dsHe3l64efNmjfs9ePBAcHFxEaKiopRtpaWlwuDBg4WlS5cq2/bu3StIpVIhMzNT2fbjjz8KvXr1En7//Xdl24wZM4SgoCCVY4SHhwsBAQENmJ126UPuysvLhYKCApXxy8vLBX9/f2HWrFkNnKF26UP+HpWSkiL4+fkJUVFRel/k6Uvuvv76a+GFF14Qbt261TgTe0r0JX/Dhg0T3n//fZVjxMTECH379hUUCkUDZqg92s5dRUWF8vt169bV+LPYFM8ZgqAf+WvM8waXaxIR6amjR4/C09MTFhYWyraAgABUVlbi+PHjNe53+vRpFBUVISAgQNkmkUjg5+eHo0ePqowvlUphZ2enbBswYAAsLCyQkZEBACgrK8NPP/0Ef39/lWO8/PLLyMzMRE5OTkOnqRX6kDsDAwOYm5urjG9gYACpVIrbt283dIpapQ/5qyKXyxEVFYWFCxfC0NCwEWanXfqSu5SUFPj7+6NDhw6NNLOnQ1/yV15eDlNTU5VjtG3bFoIgNGR6WqXt3InFTy4bmuo5A9CP/DXmeYNFHhGRnsrKylL5iwgAmJmZwdraGllZWbXuB0Bt3x49eiA3NxcPHjyocXyRSITu3bsrx7h27RoUCkW1Yz16LH2jD7mrTnl5OX7//Xe1ffWNPuVv7dq1cHBwwODBg+s9n6dJH3KnUChw4cIF2Nra4r333oOTkxOcnZ0RFhaGO3fuNHiO2qQP+QOAwMBA7NmzBydPnsT9+/dx9uxZJCcn49VXX0WrVq0aNEdt0XbuNNFUzxmAfuSvOvU9b+jn/6VERAS5XA4zMzO1dnNzc8hkslr3k0gkMDIyUmk3MzODIAiQyWRo3bo15HI52rZtW+v4Vf99PI6qz7XFoUv6kLvqfP7557h16xaCg4M1n4wO6Ev+Ll68iNTUVOzatasBs3m69CF3BQUFUCgUSExMhJubG+Li4pCXl4fVq1dj3rx52L59ewNnqT36kD8AmDVrFsrKyjB9+nTl1btRo0Zh0aJF9Z2a1mk7d5poqucMQD/yV536njdY5BERET0Fx48fR2xsLObMmYO+ffvqOhy9JwgCIiIiMGnSJOVVANJMZWUlAKBNmzaIi4uDRCIBALRv3x7Tp0/HyZMn4enpqcsQ9d5XX32FpKQkLFy4EH369MHff/+NmJgYLF++HEuXLtV1eNRCNOS8weWaRER6yszMDIWFhWrtMplMbc3+4/uVlZWhtLRUpV0ul0MkEin3NTMzq/ax64+OX/Xfx+OQy+Uq2/WNPuTuUX/88QfmzZuHESNGIDQ0tK7Teer0IX8HDhxAVlYWXnvtNcjlcsjlcuW4j36vb/Qhd2ZmZhCJRHBxcVEWeADg7u4OAwMDXL58uV5zexr0IX/5+flYuXIl3nrrLUybNg1ubm6YNGkSPvjgA2zduhXZ2dkNmaLWaDt3mmiq5wxAP/L3qIaeN1jkERHpKTs7O7X7AAoLC3Hnzp1a1+ZXbXv8LyJZWVmwtbVVLhupbnxBEJCdna0co2vXrjA0NFTrV9M9CPpCH3JX5erVqwgJCYGzszM++uijes/padKH/GVlZUEmk8HHxwdubm5wc3NDYmIiiouL4ebmhtjY2AbPUxv0IXfGxsbo1KlTjcfS1wIZ0I/8Xb9+HWVlZbC3t1fp16dPHwAP7zvTR9rOnSaa6jkD0I/8VWmM8waLPCIiPeXt7Y0TJ04o/wUUANLT0yEWizFgwIAa93NxcYGpqSkOHjyobFMoFDh8+DC8vb1Vxv/zzz9x5coVZdvJkydRUFCAl156CcDDJ4R5eHjg0KFDKsc4cOAAevTogc6dOzd0mlqhD7kDgNu3b2PGjBno2LEj1q1b1ySeDgnoR/7Gjh2LpKQkla+xY8fCyMgISUlJCAoKasQZNx59yB0ADB48GKdPn1Yp6E6dOoWKigo4ODg0dJpaow/5s7W1BfDwSsqjzp8/DwAt9veeJprqOQPQj/wBjXjeqNMLF4iI6KmpejHrlClThB9//FFITU0VXF1d1V7MOnXqVGHIkCEqbfHx8ULfvn2FL7/8Ujhx4oQwb968Wl8KfOTIEWH//v3CSy+9VOPL0JcuXSqcOnVKiImJEaRSqXDgwAHtTb6B9CF3JSUlwqhRowRnZ2fh+++/F/773/8qv/744w/tJqCB9CF/1ant3Vz6Ql9yl5ubK7i6ugozZswQfvjhByEtLU0YMGCAMHHiRKGyslJ7CWggfcnfnDlzBGdnZ2Hjxo3CyZMnheTkZMHd3V0IDg7W3uQbSNu5EwRBOHjwoHDw4EHhrbfeEhwdHZWfc3JylH2a4jlDEPQjf4153mCRR0Skxy5fvixMmzZN6Nevn+Dp6SlERkYKpaWlKn2mTJkiDB48WKWtsrJS2LBhg+Dt7S307dtXCAwMFE6fPq02/s2bN4XQ0FDByclJcHV1FRYuXCgUFhaq9fvuu++EESNGCA4ODoKfn5+QkpLSuBPVAl3n7vr160KvXr2q/Xr8mPpI1/mrTlMo8gRBf3J34cIFYcqUKYKjo6Pg7u4uLFy4UJDJZI07WS3Qh/wVFhYKkZGRwpAhQwRHR0fBx8dHWL58udqLqvWNtnNX0++0nTt3qvRriucMQdB9/hrzvCESBD1+qyMRERERERHVCe/JIyIiIiIiakZY5BERERERETUjLPKIiIiIiIiaERZ5REREREREzQiLPCIiIiIiomaERR4REREREVEzwiKPiIiIiIioGWGRR0RERERE1IywyCMiIiIiImpGWOQRERFRi5aWlgapVIpz587pOpR62bJlC9LS0nQdBhHpERZ5RERERE3Ytm3bsGvXLl2HQUR6hEUeERERURNUUlKi6xCISE+xyCMiIiJ6xIIFC+Ds7Izc3FzMmjULzs7OGDhwILZs2QIAuHTpEqZOnQonJycMHjwYe/fuVdm/avnnL7/8giVLlsDDwwMuLi547733IJPJ1I63ZcsWDB8+HH379sWLL76IiIgIyOVylT6vvfYaRowYgfPnz2Py5Ml4/vnnER0dDR8fH/z999/4+eefIZVKIZVK8dprrwEACgoKsHLlSowcORLOzs5wcXHBG2+8gT///FNl7J9++glSqRQHDhzA+vXr4e3tDUdHR0ybNg1Xr15Vi/f3339HSEgI3Nzc4OTkhJEjR2Lz5s0qfTIzM/HWW2/B3d0djo6OGDduHL7//vu6/2EQUb200nUARERERPqmoqICISEhcHV1xfz587F3714sW7YMxsbG+OSTTzBy5EgMHToU27dvx/vvvw8nJyd06dJFZYxly5bBzMwMoaGhyM7OxrZt25Cbm4vk5GSIRCIAQGxsLOLi4uDl5YWJEycq+507dw7btm2DoaGhcryCggKEhIRg+PDhGDVqFNq1awcPDw8sX74cJiYmmD17NgCgffv2AIDr16/ju+++g7+/Pzp37oy7d+9ix44dmDJlCvbv3w8bGxuVeBMTEyESiTBjxgwUFRXh888/x/z585GSkqLsc/z4ccyaNQsdOnTA1KlT0b59e2RmZuKHH37AtGnTAAB///03Jk6cCBsbG4SEhMDExAQHDx7E3LlzERsbCz8/v8b/AyMiFSzyiIiIiB5TWlqKUaNGYdasWQCAkSNHYuDAgVi0aBGio6Px8ssvAwC8vLwQEBCA3bt3Y968eSpjGBoa4ssvv1QWara2tli9ejWOHDkCX19f5OXlIT4+Hi+++CISExMhFj9cYGVnZ4dly5Zhz549GD9+vHK8O3fuICIiAq+++qrKcdauXQtLS0uMHj1apV0qleLQoUPKcQFg9OjRCAgIQGpqKubOnas25927d0MikQAAzMzMsGLFCvz111/o1asXKioqsGTJEnTo0AG7d++GmZmZcl9BEJTfr1ixAh07dsTOnTuVY02aNAkTJ07EmjVrWOQRPQVcrklERERUjcDAQOX3ZmZm6N69O4yNjREQEKBst7Ozg5mZGa5fv662f1BQkMqVuIkTJ6JVq1bIyMgAAJw4cQIKhQJTp05VKcQCAwNhamqq7FdFIpFg3LhxGscvkUiU41ZUVCA/Px8mJibo3r07Lly4oNZ/3LhxyqIMAFxdXQFAObcLFy4gJycHU6dOVSnwACivTBYUFODUqVMICAhAUVER8vLykJeXh/z8fLz44ou4cuUKbt26pfEciKh+eCWPiIiI6DFGRkawsrJSaWvbti2eeeYZZUHzaPvj99ABwLPPPqvyuU2bNrC2tsaNGzcAALm5uQAeFoqPkkgk6NKli7JfFRsbG5Ui7EkqKyuRlJSErVu3IicnBxUVFcptFhYWav1tbW1VPlcVclVzqyr2evXqVeMxr127BkEQEBMTg5iYmGr73Lt3T22pKBE1LhZ5RERERI8xMDCoU/ujyxW1pXXr1nXqv2HDBsTExGD8+PEICwuDubk5xGIxPv7442rjffRq4qPqMrfKykoAwIwZMzBw4MBq+3Tt2lXj8YiofljkEREREWnB1atX0b9/f+Xn+/fv486dO/D29gbwvytnWVlZKg9tKSsrQ05ODry8vDQ6zuNXFqscOnQIHh4e+Pjjj1Xa5XI5LC0t6zQXAMoY//rrrxpjq+pjaGiocfxE1Ph4Tx4RERGRFuzYsQMKhUL5edu2bSgvL1cWeV5eXjA0NERycrLK1bLU1FQUFhbipZde0ug4xsbG1S4XNTAwULsKd/DgwXrfE+fg4IDOnTsjKSlJ7XhVx2nXrh3c3d2xY8cO3L59W22MvLy8eh2biOqGV/KIiIiItEChUCA4OBgBAQHIzs7G1q1b8cILL8DX1xcAYGVlhVmzZiEuLg5vvPEGfHx8lP0cHR0xatQojY7j4OCAbdu24bPPPsOzzz4LKysreHp6YtCgQfj000+xcOFCODs746+//sLevXvVXvWgKbFYjH//+9948803MWbMGIwbNw7W1tbIysrC5cuXsXHjRgDA0qVLMWnSJIwcORKvvPIKunTpgrt37+LMmTO4efMm9uzZU6/jE5HmWOQRERERacGSJUuwd+9erFu3DgqFAsOHD8fixYtVllfOmzcPVlZW+Oqrr/Cf//wH5ubmeOWVVxAeHq7yZM7azJ07F7m5ufj8889x//59uLu7w9PTE7Nnz0ZJSQn27t2LAwcOoE+fPoiPj0dUVFS95zRw4EBs3rwZn376KTZt2gRBENClSxe88soryj49e/bEzp07ERcXh127dqGgoABWVlbo06eP2msbiEg7RMLTuFOYiIiIqIVIS0vDwoULkZqaCkdHR12HQ0QtEO/JIyIiIiIiakZY5BERERERETUjLPKIiIiIiIiaEd6TR0RERERE1IzwSh4REREREVEzwiKPiIiIiIioGWGRR0RERERE1IywyCMiIiIiImpGWOQRERERERE1IyzyiIiIiIiImhEWeURERERERM0IizwiIiIiIqJmhEUeERERERFRM/L/LNoBQ0xP6XoAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "execution_count": 22,
-      "id": "24Ol6sMmbEfC",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "24Ol6sMmbEfC",
-        "outputId": "ca41c28e-e024-41a1-d30e-52eface69410"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "selected_model_name: RandomForest\n",
-            "X_shap: (1000, 457)\n",
-            "X_shap_model: (1000, 457)\n",
-            "shap_matrix: (1000, 457)\n",
-            "base_values: (1000,)\n"
-          ]
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "       model_name                                       feature  \\\n",
+       "457  RandomForest                              L3_S33_mean_time   \n",
+       "458  RandomForest                              L3_S34_mean_time   \n",
+       "459  RandomForest                            process_start_time   \n",
+       "460  RandomForest                      L3_S33_num_missing_ratio   \n",
+       "461  RandomForest                              L3_S37_mean_time   \n",
+       "462  RandomForest                              L3_S29_mean_time   \n",
+       "463  RandomForest                                   L3_S33_seen   \n",
+       "464  RandomForest                              L3_S30_mean_time   \n",
+       "465  RandomForest                                  L3_S33_F3859   \n",
+       "466  RandomForest                              process_end_time   \n",
+       "467  RandomForest                                   L3_duration   \n",
+       "468  RandomForest                                L3_S29_num_std   \n",
+       "469  RandomForest          aux_paint_thermal_defect_probability   \n",
+       "470  RandomForest                                  L1_S24_F1844   \n",
+       "471  RandomForest                               L3_S33_num_mean   \n",
+       "472  RandomForest                                    L3_num_std   \n",
+       "473  RandomForest                                  L3_S33_F3857   \n",
+       "474  RandomForest  aux_body_ford_vibration_abnormal_probability   \n",
+       "475  RandomForest                        total_process_duration   \n",
+       "476  RandomForest                                    L1_num_min   \n",
+       "477  RandomForest                                 L3_date_count   \n",
+       "478  RandomForest                               L3_S29_num_mean   \n",
+       "479  RandomForest                                    L0_num_min   \n",
+       "480  RandomForest                                  L3_S29_F3458   \n",
+       "481  RandomForest                                  L3_S30_F3744   \n",
+       "482  RandomForest                                L3_S33_num_std   \n",
+       "483  RandomForest                                    L0_num_std   \n",
+       "484  RandomForest                                L3_S30_num_std   \n",
+       "485  RandomForest                                  L3_S30_F3759   \n",
+       "486  RandomForest                                   L3_S34_seen   \n",
+       "\n",
+       "     importance_gain      importance_type  \n",
+       "457         0.011733  feature_importances  \n",
+       "458         0.011186  feature_importances  \n",
+       "459         0.010766  feature_importances  \n",
+       "460         0.008505  feature_importances  \n",
+       "461         0.008500  feature_importances  \n",
+       "462         0.008475  feature_importances  \n",
+       "463         0.008394  feature_importances  \n",
+       "464         0.008029  feature_importances  \n",
+       "465         0.008004  feature_importances  \n",
+       "466         0.007741  feature_importances  \n",
+       "467         0.007571  feature_importances  \n",
+       "468         0.007533  feature_importances  \n",
+       "469         0.007316  feature_importances  \n",
+       "470         0.007214  feature_importances  \n",
+       "471         0.007032  feature_importances  \n",
+       "472         0.006991  feature_importances  \n",
+       "473         0.006660  feature_importances  \n",
+       "474         0.006541  feature_importances  \n",
+       "475         0.006364  feature_importances  \n",
+       "476         0.006358  feature_importances  \n",
+       "477         0.006329  feature_importances  \n",
+       "478         0.006163  feature_importances  \n",
+       "479         0.006136  feature_importances  \n",
+       "480         0.006061  feature_importances  \n",
+       "481         0.005915  feature_importances  \n",
+       "482         0.005873  feature_importances  \n",
+       "483         0.005700  feature_importances  \n",
+       "484         0.005681  feature_importances  \n",
+       "485         0.005666  feature_importances  \n",
+       "486         0.005639  feature_importances  "
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-a0d9b4da-3d03-4c33-a571-82db618689ad\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>feature</th>\n",
+       "      <th>importance_gain</th>\n",
+       "      <th>importance_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S33_mean_time</td>\n",
+       "      <td>0.011733</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S34_mean_time</td>\n",
+       "      <td>0.011186</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>process_start_time</td>\n",
+       "      <td>0.010766</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S33_num_missing_ratio</td>\n",
+       "      <td>0.008505</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S37_mean_time</td>\n",
+       "      <td>0.008500</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S29_mean_time</td>\n",
+       "      <td>0.008475</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S33_seen</td>\n",
+       "      <td>0.008394</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.008029</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S33_F3859</td>\n",
+       "      <td>0.008004</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>process_end_time</td>\n",
+       "      <td>0.007741</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_duration</td>\n",
+       "      <td>0.007571</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S29_num_std</td>\n",
+       "      <td>0.007533</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>aux_paint_thermal_defect_probability</td>\n",
+       "      <td>0.007316</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L1_S24_F1844</td>\n",
+       "      <td>0.007214</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S33_num_mean</td>\n",
+       "      <td>0.007032</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_num_std</td>\n",
+       "      <td>0.006991</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S33_F3857</td>\n",
+       "      <td>0.006660</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>aux_body_ford_vibration_abnormal_probability</td>\n",
+       "      <td>0.006541</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>total_process_duration</td>\n",
+       "      <td>0.006364</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L1_num_min</td>\n",
+       "      <td>0.006358</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_date_count</td>\n",
+       "      <td>0.006329</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S29_num_mean</td>\n",
+       "      <td>0.006163</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L0_num_min</td>\n",
+       "      <td>0.006136</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S29_F3458</td>\n",
+       "      <td>0.006061</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S30_F3744</td>\n",
+       "      <td>0.005915</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S33_num_std</td>\n",
+       "      <td>0.005873</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L0_num_std</td>\n",
+       "      <td>0.005700</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S30_num_std</td>\n",
+       "      <td>0.005681</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S30_F3759</td>\n",
+       "      <td>0.005666</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>L3_S34_seen</td>\n",
+       "      <td>0.005639</td>\n",
+       "      <td>feature_importances</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0d9b4da-3d03-4c33-a571-82db618689ad')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "# SHAP 샘플링\n",
-        "SHAP_SAMPLE_SIZE = min(1000, len(X_valid))\n",
-        "\n",
-        "if selected_model_name == \"LogisticRegression\":\n",
-        "    SHAP_SAMPLE_SIZE = min(500, len(X_valid))\n",
-        "\n",
-        "X_shap = X_valid.sample(n=SHAP_SAMPLE_SIZE, random_state=RANDOM_STATE)\n",
-        "id_shap = id_valid.loc[X_shap.index]\n",
-        "y_shap = y_valid.loc[X_shap.index]\n",
-        "proba_shap = predict_positive_proba(model, X_shap)\n",
-        "\n",
-        "\n",
-        "def prepare_shap_model(estimator, X_sample: pd.DataFrame):\n",
-        "    if is_pipeline_estimator(estimator):\n",
-        "        final_estimator = get_final_estimator(estimator)\n",
-        "        transform_steps = estimator.steps[:-1]\n",
-        "\n",
-        "        X_transformed = X_sample.copy()\n",
-        "\n",
-        "        for _, step in transform_steps:\n",
-        "            if hasattr(step, \"transform\"):\n",
-        "                X_transformed = step.transform(X_transformed)\n",
-        "\n",
-        "        X_transformed = pd.DataFrame(\n",
-        "            X_transformed,\n",
-        "            columns=X_sample.columns,\n",
-        "            index=X_sample.index\n",
-        "        )\n",
-        "\n",
-        "        return final_estimator, X_transformed\n",
-        "\n",
-        "    return estimator, X_sample\n",
-        "\n",
-        "\n",
-        "shap_model, X_shap_model = prepare_shap_model(model, X_shap)\n",
-        "\n",
-        "# SHAP Explainer 생성\n",
-        "try:\n",
-        "    explainer = shap.TreeExplainer(shap_model)\n",
-        "\n",
-        "    raw_shap_values = explainer.shap_values(\n",
-        "        X_shap_model,\n",
-        "        check_additivity=False\n",
-        "    )\n",
-        "\n",
-        "    if isinstance(raw_shap_values, list):\n",
-        "        shap_matrix = raw_shap_values[1]\n",
-        "    elif getattr(raw_shap_values, \"ndim\", 0) == 3:\n",
-        "        shap_matrix = raw_shap_values[:, :, 1]\n",
-        "    else:\n",
-        "        shap_matrix = raw_shap_values\n",
-        "\n",
-        "    expected_value = explainer.expected_value\n",
-        "\n",
-        "    if isinstance(expected_value, list):\n",
-        "        base_value = expected_value[1]\n",
-        "    else:\n",
-        "        base_value = expected_value\n",
-        "\n",
-        "except Exception as e:\n",
-        "    print(\"TreeExplainer 실패:\", e)\n",
-        "    print(\"Permutation Explainer로 대체 실행합니다.\")\n",
-        "\n",
-        "    background = shap.sample(\n",
-        "        X_train,\n",
-        "        min(100, len(X_train)),\n",
-        "        random_state=RANDOM_STATE\n",
-        "    )\n",
-        "\n",
-        "    explainer = shap.Explainer(\n",
-        "        lambda data: predict_positive_proba(\n",
-        "            model,\n",
-        "            pd.DataFrame(data, columns=X_train.columns)\n",
-        "        ),\n",
-        "        background\n",
-        "    )\n",
-        "\n",
-        "    raw_explanation = explainer(\n",
-        "        X_shap,\n",
-        "        max_evals=2 * X_shap.shape[1] + 1\n",
-        "    )\n",
-        "\n",
-        "    shap_matrix = raw_explanation.values\n",
-        "    base_value = raw_explanation.base_values\n",
-        "    X_shap_model = X_shap\n",
-        "\n",
-        "\n",
-        "# base_value 길이 보정\n",
-        "if np.isscalar(base_value):\n",
-        "    base_values = np.repeat(base_value, len(X_shap))\n",
-        "else:\n",
-        "    base_values = np.array(base_value)\n",
-        "\n",
-        "    if base_values.ndim == 0:\n",
-        "        base_values = np.repeat(float(base_values), len(X_shap))\n",
-        "    elif len(base_values) != len(X_shap):\n",
-        "        base_values = np.repeat(float(np.ravel(base_values)[0]), len(X_shap))\n",
-        "\n",
-        "\n",
-        "# SHAP 객체 구성\n",
-        "shap_values = shap.Explanation(\n",
-        "    values=shap_matrix,\n",
-        "    base_values=base_values,\n",
-        "    data=X_shap.values,\n",
-        "    feature_names=X_shap.columns.tolist(),\n",
-        ")\n",
-        "\n",
-        "\n",
-        "print(\"selected_model_name:\", selected_model_name)\n",
-        "print(\"X_shap:\", X_shap.shape)\n",
-        "print(\"X_shap_model:\", X_shap_model.shape)\n",
-        "print(\"shap_matrix:\", shap_matrix.shape)\n",
-        "print(\"base_values:\", np.array(base_values).shape)"
-      ]
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"display(importance_df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L3_S30_num_std\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0016205883920081907,\n        \"min\": 0.0056385160546700164,\n        \"max\": 0.011732889577268082,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.00568096253421123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"feature_importances\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# 모델 성능 비교 시각화\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 4))\n",
+    "sns.countplot(x=y, ax=axes[0])\n",
+    "axes[0].set_title(\"Response Distribution\")\n",
+    "axes[0].set_xlabel(\"Response\")\n",
+    "\n",
+    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"pr_auc\", ax=axes[1], color=\"#4C78A8\")\n",
+    "axes[1].set_title(\"Validation PR-AUC by Model\")\n",
+    "axes[1].set_xlabel(\"\")\n",
+    "axes[1].tick_params(axis=\"x\", rotation=20)\n",
+    "\n",
+    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"false_positive_rate\", ax=axes[2], color=\"#F58518\")\n",
+    "axes[2].set_title(\"False Positive Rate by Model\")\n",
+    "axes[2].set_xlabel(\"\")\n",
+    "axes[2].tick_params(axis=\"x\", rotation=20)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 선택 모델 ROC/PR 곡선\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "RocCurveDisplay.from_predictions(y_valid, valid_proba, ax=axes[0])\n",
+    "axes[0].set_title(f\"{selected_model_name} ROC AUC={roc_auc:.4f}\")\n",
+    "PrecisionRecallDisplay.from_predictions(y_valid, valid_proba, ax=axes[1])\n",
+    "axes[1].set_title(f\"{selected_model_name} PR-AUC={pr_auc:.4f}\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "def extract_feature_importance(estimator, feature_names: list[str], model_name: str) -> pd.DataFrame:\n",
+    "    target_estimator = estimator\n",
+    "    if is_pipeline_estimator(estimator):\n",
+    "        target_estimator = get_final_estimator(estimator)\n",
+    "\n",
+    "    if hasattr(target_estimator, \"booster_\"):\n",
+    "        values = target_estimator.booster_.feature_importance(importance_type=\"gain\")\n",
+    "        importance_type = \"gain\"\n",
+    "    elif hasattr(target_estimator, \"feature_importances_\"):\n",
+    "        values = target_estimator.feature_importances_\n",
+    "        importance_type = \"feature_importances\"\n",
+    "    elif hasattr(target_estimator, \"coef_\"):\n",
+    "        values = np.abs(target_estimator.coef_).ravel()\n",
+    "        importance_type = \"abs_coef\"\n",
+    "    else:\n",
+    "        values = np.zeros(len(feature_names))\n",
+    "        importance_type = \"not_available\"\n",
+    "\n",
+    "    return pd.DataFrame({\n",
+    "        \"model_name\": model_name,\n",
+    "        \"feature\": feature_names,\n",
+    "        \"importance\": values,\n",
+    "        \"importance_type\": importance_type,\n",
+    "    }).sort_values(\"importance\", ascending=False)\n",
+    "\n",
+    "\n",
+    "importance_frames = [\n",
+    "    extract_feature_importance(estimator, feature_cols, model_name)\n",
+    "    for model_name, estimator in trained_models.items()\n",
+    "]\n",
+    "all_model_importance = pd.concat(importance_frames, ignore_index=True)\n",
+    "importance_df = all_model_importance[all_model_importance[\"model_name\"].eq(selected_model_name)].copy()\n",
+    "importance_df = importance_df.rename(columns={\"importance\": \"importance_gain\"})\n",
+    "\n",
+    "plt.figure(figsize=(9, 7))\n",
+    "sns.barplot(data=importance_df.head(25), y=\"feature\", x=\"importance_gain\", color=\"#4C78A8\")\n",
+    "plt.title(f\"Top 25 Feature Importance - {selected_model_name}\")\n",
+    "plt.xlabel(\"Importance\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "display(importance_df.head(30))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "TaY5ZaEJbEfC",
+   "metadata": {
+    "id": "TaY5ZaEJbEfC"
+   },
+   "source": [
+    "## 10. SHAP 기반 영향 공정 분석\n",
+    "\n",
+    "선택된 최종 모델을 기준으로 SHAP 값을 계산합니다.\n",
+    "\n",
+    "SHAP 값은 feature가 불량 확률 예측에 기여한 정도이며, 이후 feature를 공정 코드로 매핑해 공정별 영향도를 집계합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "24Ol6sMmbEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24Ol6sMmbEfC",
+    "outputId": "ca41c28e-e024-41a1-d30e-52eface69410"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "selected_model_name: RandomForest\n",
+      "X_shap: (1000, 457)\n",
+      "X_shap_model: (1000, 457)\n",
+      "shap_matrix: (1000, 457)\n",
+      "base_values: (1000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SHAP 샘플링\n",
+    "SHAP_SAMPLE_SIZE = min(1000, len(X_valid))\n",
+    "\n",
+    "if selected_model_name == \"LogisticRegression\":\n",
+    "    SHAP_SAMPLE_SIZE = min(500, len(X_valid))\n",
+    "\n",
+    "X_shap = X_valid.sample(n=SHAP_SAMPLE_SIZE, random_state=RANDOM_STATE)\n",
+    "id_shap = id_valid.loc[X_shap.index]\n",
+    "y_shap = y_valid.loc[X_shap.index]\n",
+    "proba_shap = predict_positive_proba(model, X_shap)\n",
+    "\n",
+    "\n",
+    "def prepare_shap_model(estimator, X_sample: pd.DataFrame):\n",
+    "    if is_pipeline_estimator(estimator):\n",
+    "        final_estimator = get_final_estimator(estimator)\n",
+    "        transform_steps = estimator.steps[:-1]\n",
+    "\n",
+    "        X_transformed = X_sample.copy()\n",
+    "\n",
+    "        for _, step in transform_steps:\n",
+    "            if hasattr(step, \"transform\"):\n",
+    "                X_transformed = step.transform(X_transformed)\n",
+    "\n",
+    "        X_transformed = pd.DataFrame(\n",
+    "            X_transformed,\n",
+    "            columns=X_sample.columns,\n",
+    "            index=X_sample.index\n",
+    "        )\n",
+    "\n",
+    "        return final_estimator, X_transformed\n",
+    "\n",
+    "    return estimator, X_sample\n",
+    "\n",
+    "\n",
+    "shap_model, X_shap_model = prepare_shap_model(model, X_shap)\n",
+    "\n",
+    "# SHAP Explainer 생성\n",
+    "try:\n",
+    "    explainer = shap.TreeExplainer(shap_model)\n",
+    "\n",
+    "    raw_shap_values = explainer.shap_values(\n",
+    "        X_shap_model,\n",
+    "        check_additivity=False\n",
+    "    )\n",
+    "\n",
+    "    if isinstance(raw_shap_values, list):\n",
+    "        shap_matrix = raw_shap_values[1]\n",
+    "    elif getattr(raw_shap_values, \"ndim\", 0) == 3:\n",
+    "        shap_matrix = raw_shap_values[:, :, 1]\n",
+    "    else:\n",
+    "        shap_matrix = raw_shap_values\n",
+    "\n",
+    "    expected_value = explainer.expected_value\n",
+    "\n",
+    "    if isinstance(expected_value, list):\n",
+    "        base_value = expected_value[1]\n",
+    "    else:\n",
+    "        base_value = expected_value\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(\"TreeExplainer 실패:\", e)\n",
+    "    print(\"Permutation Explainer로 대체 실행합니다.\")\n",
+    "\n",
+    "    background = shap.sample(\n",
+    "        X_train,\n",
+    "        min(100, len(X_train)),\n",
+    "        random_state=RANDOM_STATE\n",
+    "    )\n",
+    "\n",
+    "    explainer = shap.Explainer(\n",
+    "        lambda data: predict_positive_proba(\n",
+    "            model,\n",
+    "            pd.DataFrame(data, columns=X_train.columns)\n",
+    "        ),\n",
+    "        background\n",
+    "    )\n",
+    "\n",
+    "    raw_explanation = explainer(\n",
+    "        X_shap,\n",
+    "        max_evals=2 * X_shap.shape[1] + 1\n",
+    "    )\n",
+    "\n",
+    "    shap_matrix = raw_explanation.values\n",
+    "    base_value = raw_explanation.base_values\n",
+    "    X_shap_model = X_shap\n",
+    "\n",
+    "\n",
+    "# base_value 길이 보정\n",
+    "if np.isscalar(base_value):\n",
+    "    base_values = np.repeat(base_value, len(X_shap))\n",
+    "else:\n",
+    "    base_values = np.array(base_value)\n",
+    "\n",
+    "    if base_values.ndim == 0:\n",
+    "        base_values = np.repeat(float(base_values), len(X_shap))\n",
+    "    elif len(base_values) != len(X_shap):\n",
+    "        base_values = np.repeat(float(np.ravel(base_values)[0]), len(X_shap))\n",
+    "\n",
+    "\n",
+    "# SHAP 객체 구성\n",
+    "shap_values = shap.Explanation(\n",
+    "    values=shap_matrix,\n",
+    "    base_values=base_values,\n",
+    "    data=X_shap.values,\n",
+    "    feature_names=X_shap.columns.tolist(),\n",
+    ")\n",
+    "\n",
+    "\n",
+    "print(\"selected_model_name:\", selected_model_name)\n",
+    "print(\"X_shap:\", X_shap.shape)\n",
+    "print(\"X_shap_model:\", X_shap_model.shape)\n",
+    "print(\"shap_matrix:\", shap_matrix.shape)\n",
+    "print(\"base_values:\", np.array(base_values).shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "gvPdq4r1bEfC",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "gvPdq4r1bEfC",
+    "outputId": "6871cde5-1c84-4060-a5b6-f5d757281281"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 23,
-      "id": "gvPdq4r1bEfC",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "gvPdq4r1bEfC",
-        "outputId": "6871cde5-1c84-4060-a5b6-f5d757281281"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 800x1150 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "              feature  mean_abs_shap\n",
-              "341        L0_num_std  161153.464733\n",
-              "128      L3_S29_F3351  112466.467404\n",
-              "320  L3_S30_mean_time   87206.632845\n",
-              "134      L3_S29_F3373   79514.635807\n",
-              "145      L3_S29_F3412   67172.086809\n",
-              "144      L3_S29_F3407   50584.084076\n",
-              "143      L3_S29_F3404   47588.744257\n",
-              "124      L3_S29_F3339   45584.471179\n",
-              "263  process_end_time   41293.258694\n",
-              "16          L0_S2_F48   41259.724076\n",
-              "249      L3_S36_F3920   40991.128002\n",
-              "290  L0_S13_mean_time   38087.458690\n",
-              "360    L0_S0_num_mean   37707.793976\n",
-              "12          L0_S1_F24   34345.520207\n",
-              "266     L0_date_count   31073.838840\n",
-              "385     L0_S2_num_std   30331.910504\n",
-              "228      L3_S33_F3857   30191.782771\n",
-              "64        L0_S11_F326   27513.469192\n",
-              "367    L0_S10_num_std   25100.244226\n",
-              "0            L0_S0_F0   21225.150678\n",
-              "308   L0_S7_mean_time   19617.442282\n",
-              "43         L0_S9_F200   19379.907896\n",
-              "148      L3_S29_F3427   19288.645871\n",
-              "366   L0_S10_num_mean   19031.715581\n",
-              "62        L0_S11_F318   18211.382309\n",
-              "178      L3_S30_F3539   17632.894123\n",
-              "251      L3_S36_F3924   17431.833787\n",
-              "388     L0_S3_num_std   16874.240258\n",
-              "4            L0_S0_F8   16847.956330\n",
-              "340       L0_num_mean   15387.827290"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>feature</th>\n",
-              "      <th>mean_abs_shap</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>341</th>\n",
-              "      <td>L0_num_std</td>\n",
-              "      <td>161153.464733</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>128</th>\n",
-              "      <td>L3_S29_F3351</td>\n",
-              "      <td>112466.467404</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>320</th>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>87206.632845</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>134</th>\n",
-              "      <td>L3_S29_F3373</td>\n",
-              "      <td>79514.635807</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>145</th>\n",
-              "      <td>L3_S29_F3412</td>\n",
-              "      <td>67172.086809</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>144</th>\n",
-              "      <td>L3_S29_F3407</td>\n",
-              "      <td>50584.084076</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>143</th>\n",
-              "      <td>L3_S29_F3404</td>\n",
-              "      <td>47588.744257</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>124</th>\n",
-              "      <td>L3_S29_F3339</td>\n",
-              "      <td>45584.471179</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>263</th>\n",
-              "      <td>process_end_time</td>\n",
-              "      <td>41293.258694</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>L0_S2_F48</td>\n",
-              "      <td>41259.724076</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>249</th>\n",
-              "      <td>L3_S36_F3920</td>\n",
-              "      <td>40991.128002</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>290</th>\n",
-              "      <td>L0_S13_mean_time</td>\n",
-              "      <td>38087.458690</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>360</th>\n",
-              "      <td>L0_S0_num_mean</td>\n",
-              "      <td>37707.793976</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>12</th>\n",
-              "      <td>L0_S1_F24</td>\n",
-              "      <td>34345.520207</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>266</th>\n",
-              "      <td>L0_date_count</td>\n",
-              "      <td>31073.838840</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>385</th>\n",
-              "      <td>L0_S2_num_std</td>\n",
-              "      <td>30331.910504</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>228</th>\n",
-              "      <td>L3_S33_F3857</td>\n",
-              "      <td>30191.782771</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>64</th>\n",
-              "      <td>L0_S11_F326</td>\n",
-              "      <td>27513.469192</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>367</th>\n",
-              "      <td>L0_S10_num_std</td>\n",
-              "      <td>25100.244226</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>L0_S0_F0</td>\n",
-              "      <td>21225.150678</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>308</th>\n",
-              "      <td>L0_S7_mean_time</td>\n",
-              "      <td>19617.442282</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>43</th>\n",
-              "      <td>L0_S9_F200</td>\n",
-              "      <td>19379.907896</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>148</th>\n",
-              "      <td>L3_S29_F3427</td>\n",
-              "      <td>19288.645871</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>366</th>\n",
-              "      <td>L0_S10_num_mean</td>\n",
-              "      <td>19031.715581</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>62</th>\n",
-              "      <td>L0_S11_F318</td>\n",
-              "      <td>18211.382309</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>178</th>\n",
-              "      <td>L3_S30_F3539</td>\n",
-              "      <td>17632.894123</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>251</th>\n",
-              "      <td>L3_S36_F3924</td>\n",
-              "      <td>17431.833787</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>388</th>\n",
-              "      <td>L0_S3_num_std</td>\n",
-              "      <td>16874.240258</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>L0_S0_F8</td>\n",
-              "      <td>16847.956330</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>340</th>\n",
-              "      <td>L0_num_mean</td>\n",
-              "      <td>15387.827290</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(shap_importance\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L0_S3_num_std\",\n          \"L0_S2_num_std\",\n          \"L0_S10_num_mean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32453.380966565277,\n        \"min\": 15387.827290431647,\n        \"max\": 161153.46473315964,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          16874.240258203547,\n          30331.91050358815,\n          19031.71558098403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {}
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 800x1150 with 2 Axes>"
       ],
-      "source": [
-        "# SHAP Summary Plot\n",
-        "shap.summary_plot(shap_values, X_shap, max_display=25, show=True)\n",
-        "\n",
-        "# SHAP 중요도 산출\n",
-        "shap_importance = pd.DataFrame({\n",
-        "    \"feature\": X_shap.columns,\n",
-        "    \"mean_abs_shap\": np.abs(shap_matrix).mean(axis=0),\n",
-        "}).sort_values(\"mean_abs_shap\", ascending=False)\n",
-        "\n",
-        "display(shap_importance.head(30))\n"
-      ]
+      "image/png": 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\n"
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "source": [
-        "# Feature 공정 매핑\n",
-        "def feature_to_process(feature: str) -> str | None:\n",
-        "    \"\"\"Feature 공정 매핑\"\"\"\n",
-        "    normalized = feature.lower()\n",
-        "\n",
-        "    # Bosch 원천 컬럼/집계 컬럼: L0~L3 prefix 기준 매핑\n",
-        "    line = get_line(feature)\n",
-        "    if line in LINE_TO_PROCESS:\n",
-        "        return LINE_TO_PROCESS[line]\n",
-        "\n",
-        "    # 보조 데이터셋 기반 feature: aux_{process}_... naming 기준 매핑\n",
-        "    if normalized.startswith(\"aux_press\") or \"press\" in normalized:\n",
-        "        return \"PRESS\"\n",
-        "    if normalized.startswith(\"aux_body\") or \"body\" in normalized or \"robot\" in normalized or \"ford\" in normalized:\n",
-        "        return \"BODY\"\n",
-        "    if normalized.startswith(\"aux_paint\") or \"paint\" in normalized or \"thermal\" in normalized:\n",
-        "        return \"PAINT\"\n",
-        "    if normalized.startswith(\"aux_assembly\") or \"assembly\" in normalized:\n",
-        "        return \"ASSEMBLY\"\n",
-        "\n",
-        "    # 전역 feature 제외\n",
-        "    return None\n",
-        "\n",
-        "# 후속 공정 매핑\n",
-        "def next_process(process_code: str) -> str:\n",
-        "    if process_code not in PROCESS_FLOW:\n",
-        "        return \"FINAL_INSPECTION\"\n",
-        "    idx = PROCESS_FLOW.index(process_code)\n",
-        "    if idx + 1 >= len(PROCESS_FLOW):\n",
-        "        return \"FINAL_INSPECTION\"\n",
-        "    return PROCESS_FLOW[idx + 1]\n",
-        "\n",
-        "# 위험 등급 변환\n",
-        "def probability_to_risk_grade(prob: float) -> str:\n",
-        "    if prob >= 0.70:\n",
-        "        return \"HIGH\"\n",
-        "    if prob >= 0.40:\n",
-        "        return \"MEDIUM\"\n",
-        "    return \"LOW\"\n",
-        "\n",
-        "# SHAP feature 공정 매핑\n",
-        "feature_process = pd.Series([feature_to_process(c) for c in X_shap.columns], index=X_shap.columns)\n",
-        "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
-        "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
-        "print(f\"공정 영향도 집계 제외 전역/미매핑 feature 수: {len(unmapped_features)}\")\n",
-        "if unmapped_features:\n",
-        "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
-        "\n",
-        "# 공정별 SHAP 영향도 집계\n",
-        "process_rows = []\n",
-        "for process_code in PROCESS_FLOW:\n",
-        "    cols_idx = np.where(feature_process.values == process_code)[0]\n",
-        "    process_rows.append({\n",
-        "        \"process_code\": process_code,\n",
-        "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, cols_idx]).mean()) if len(cols_idx) else 0.0,\n",
-        "        \"feature_count\": int(len(cols_idx)),\n",
-        "    })\n",
-        "\n",
-        "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
-        "display(process_importance)\n",
-        "\n",
-        "# 공정 영향도 시각화\n",
-        "plt.figure(figsize=(8, 4))\n",
-        "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
-        "plt.title(\"SHAP Process Influence\")\n",
-        "plt.xlabel(\"Process\")\n",
-        "plt.ylabel(\"Mean |SHAP|\")\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n"
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "              feature  mean_abs_shap\n",
+       "341        L0_num_std  161153.464733\n",
+       "128      L3_S29_F3351  112466.467404\n",
+       "320  L3_S30_mean_time   87206.632845\n",
+       "134      L3_S29_F3373   79514.635807\n",
+       "145      L3_S29_F3412   67172.086809\n",
+       "144      L3_S29_F3407   50584.084076\n",
+       "143      L3_S29_F3404   47588.744257\n",
+       "124      L3_S29_F3339   45584.471179\n",
+       "263  process_end_time   41293.258694\n",
+       "16          L0_S2_F48   41259.724076\n",
+       "249      L3_S36_F3920   40991.128002\n",
+       "290  L0_S13_mean_time   38087.458690\n",
+       "360    L0_S0_num_mean   37707.793976\n",
+       "12          L0_S1_F24   34345.520207\n",
+       "266     L0_date_count   31073.838840\n",
+       "385     L0_S2_num_std   30331.910504\n",
+       "228      L3_S33_F3857   30191.782771\n",
+       "64        L0_S11_F326   27513.469192\n",
+       "367    L0_S10_num_std   25100.244226\n",
+       "0            L0_S0_F0   21225.150678\n",
+       "308   L0_S7_mean_time   19617.442282\n",
+       "43         L0_S9_F200   19379.907896\n",
+       "148      L3_S29_F3427   19288.645871\n",
+       "366   L0_S10_num_mean   19031.715581\n",
+       "62        L0_S11_F318   18211.382309\n",
+       "178      L3_S30_F3539   17632.894123\n",
+       "251      L3_S36_F3924   17431.833787\n",
+       "388     L0_S3_num_std   16874.240258\n",
+       "4            L0_S0_F8   16847.956330\n",
+       "340       L0_num_mean   15387.827290"
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>mean_abs_shap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>L0_num_std</td>\n",
+       "      <td>161153.464733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>L3_S29_F3351</td>\n",
+       "      <td>112466.467404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>87206.632845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>L3_S29_F3373</td>\n",
+       "      <td>79514.635807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>L3_S29_F3412</td>\n",
+       "      <td>67172.086809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>L3_S29_F3407</td>\n",
+       "      <td>50584.084076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>L3_S29_F3404</td>\n",
+       "      <td>47588.744257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>L3_S29_F3339</td>\n",
+       "      <td>45584.471179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>process_end_time</td>\n",
+       "      <td>41293.258694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>L0_S2_F48</td>\n",
+       "      <td>41259.724076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>L3_S36_F3920</td>\n",
+       "      <td>40991.128002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>L0_S13_mean_time</td>\n",
+       "      <td>38087.458690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>L0_S0_num_mean</td>\n",
+       "      <td>37707.793976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L0_S1_F24</td>\n",
+       "      <td>34345.520207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>L0_date_count</td>\n",
+       "      <td>31073.838840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>L0_S2_num_std</td>\n",
+       "      <td>30331.910504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>L3_S33_F3857</td>\n",
+       "      <td>30191.782771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>L0_S11_F326</td>\n",
+       "      <td>27513.469192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>L0_S10_num_std</td>\n",
+       "      <td>25100.244226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>L0_S0_F0</td>\n",
+       "      <td>21225.150678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>L0_S7_mean_time</td>\n",
+       "      <td>19617.442282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>L0_S9_F200</td>\n",
+       "      <td>19379.907896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>L3_S29_F3427</td>\n",
+       "      <td>19288.645871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>L0_S10_num_mean</td>\n",
+       "      <td>19031.715581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>L0_S11_F318</td>\n",
+       "      <td>18211.382309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>L3_S30_F3539</td>\n",
+       "      <td>17632.894123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>L3_S36_F3924</td>\n",
+       "      <td>17431.833787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>L0_S3_num_std</td>\n",
+       "      <td>16874.240258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>L0_S0_F8</td>\n",
+       "      <td>16847.956330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>L0_num_mean</td>\n",
+       "      <td>15387.827290</td>\n",
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+       "\n",
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+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
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-      "id": "m-fwpoIeBrfa",
-      "execution_count": 24,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "공정 영향도 집계 제외 전역/미매핑 feature 수: 9\n"
-          ]
-        },
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-              "         unmapped_feature\n",
-              "0     date_observed_count\n",
-              "1      date_missing_ratio\n",
-              "2      process_start_time\n",
-              "3        process_end_time\n",
-              "4  total_process_duration\n",
-              "5        max_station_span\n",
-              "6       mean_station_span\n",
-              "7        std_station_span\n",
-              "8    active_station_count"
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-              "  process_code  mean_abs_shap  feature_count\n",
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-              "    .colab-df-container {\n",
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-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
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-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
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-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-bae87c37-391f-44c4-bcef-a34afe9b1e09 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
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-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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-              "        if (!dataTable) return;\n",
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-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
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-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
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-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
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-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
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-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('process_importance')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
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-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_790c3d3c-10fc-406c-a77f-00d95a9965e0 button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('process_importance');\n",
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\n"
-          },
-          "metadata": {}
-        }
-      ]
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"display(shap_importance\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L0_S3_num_std\",\n          \"L0_S2_num_std\",\n          \"L0_S10_num_mean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32453.380966565277,\n        \"min\": 15387.827290431647,\n        \"max\": 161153.46473315964,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          16874.240258203547,\n          30331.91050358815,\n          19031.71558098403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# SHAP Summary Plot\n",
+    "shap.summary_plot(shap_values, X_shap, max_display=25, show=True)\n",
+    "\n",
+    "# SHAP 중요도 산출\n",
+    "shap_importance = pd.DataFrame({\n",
+    "    \"feature\": X_shap.columns,\n",
+    "    \"mean_abs_shap\": np.abs(shap_matrix).mean(axis=0),\n",
+    "}).sort_values(\"mean_abs_shap\", ascending=False)\n",
+    "\n",
+    "display(shap_importance.head(30))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Feature 공정 매핑\n",
+    "def feature_to_process(feature: str) -> str | None:\n",
+    "    \"\"\"Feature 공정 매핑\"\"\"\n",
+    "    normalized = feature.lower()\n",
+    "\n",
+    "    # Bosch 원천 컬럼/집계 컬럼: L0~L3 prefix 기준 매핑\n",
+    "    line = get_line(feature)\n",
+    "    if line in LINE_TO_PROCESS:\n",
+    "        return LINE_TO_PROCESS[line]\n",
+    "\n",
+    "    # 보조 데이터셋 기반 feature: aux_{process}_... naming 기준 매핑\n",
+    "    if normalized.startswith(\"aux_press\") or \"press\" in normalized:\n",
+    "        return \"PRESS\"\n",
+    "    if normalized.startswith(\"aux_body\") or \"body\" in normalized or \"robot\" in normalized or \"ford\" in normalized:\n",
+    "        return \"BODY\"\n",
+    "    if normalized.startswith(\"aux_paint\") or \"paint\" in normalized or \"thermal\" in normalized:\n",
+    "        return \"PAINT\"\n",
+    "    if normalized.startswith(\"aux_assembly\") or \"assembly\" in normalized:\n",
+    "        return \"ASSEMBLY\"\n",
+    "\n",
+    "    # 전역 feature 제외\n",
+    "    return None\n",
+    "\n",
+    "# 후속 공정 매핑\n",
+    "def next_process(process_code: str) -> str:\n",
+    "    if process_code not in PROCESS_FLOW:\n",
+    "        return \"FINAL_INSPECTION\"\n",
+    "    idx = PROCESS_FLOW.index(process_code)\n",
+    "    if idx + 1 >= len(PROCESS_FLOW):\n",
+    "        return \"FINAL_INSPECTION\"\n",
+    "    return PROCESS_FLOW[idx + 1]\n",
+    "\n",
+    "# 위험 등급 변환\n",
+    "def probability_to_risk_grade(prob: float) -> str:\n",
+    "    if prob >= 0.70:\n",
+    "        return \"HIGH\"\n",
+    "    if prob >= 0.40:\n",
+    "        return \"MEDIUM\"\n",
+    "    return \"LOW\"\n",
+    "\n",
+    "# SHAP feature 공정 매핑\n",
+    "feature_process = pd.Series([feature_to_process(c) for c in X_shap.columns], index=X_shap.columns)\n",
+    "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
+    "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
+    "print(f\"공정 영향도 집계 제외 전역/미매핑 feature 수: {len(unmapped_features)}\")\n",
+    "if unmapped_features:\n",
+    "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
+    "\n",
+    "# 공정별 SHAP 영향도 집계\n",
+    "process_rows = []\n",
+    "for process_code in PROCESS_FLOW:\n",
+    "    cols_idx = np.where(feature_process.values == process_code)[0]\n",
+    "    process_rows.append({\n",
+    "        \"process_code\": process_code,\n",
+    "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, cols_idx]).mean()) if len(cols_idx) else 0.0,\n",
+    "        \"feature_count\": int(len(cols_idx)),\n",
+    "    })\n",
+    "\n",
+    "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
+    "display(process_importance)\n",
+    "\n",
+    "# 공정 영향도 시각화\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
+    "plt.title(\"SHAP Process Influence\")\n",
+    "plt.xlabel(\"Process\")\n",
+    "plt.ylabel(\"Mean |SHAP|\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 891
+    },
+    "id": "m-fwpoIeBrfa",
+    "outputId": "b70d837d-df0c-4159-fc29-c842f24f2558"
+   },
+   "id": "m-fwpoIeBrfa",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "공정 영향도 집계 제외 전역/미매핑 feature 수: 9\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "kBJK8YfybEfD",
-      "metadata": {
-        "id": "kBJK8YfybEfD"
-      },
-      "source": [
-        "## 11. SHAP 기반 전이 위험 결과 생성\n",
-        "\n",
-        "아래 결과는 PRD의 `defect_transfer_prediction_result` 저장 구조에 맞춘 예측 테이블입니다.\n"
-      ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "         unmapped_feature\n",
+       "0     date_observed_count\n",
+       "1      date_missing_ratio\n",
+       "2      process_start_time\n",
+       "3        process_end_time\n",
+       "4  total_process_duration\n",
+       "5        max_station_span\n",
+       "6       mean_station_span\n",
+       "7        std_station_span\n",
+       "8    active_station_count"
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unmapped_feature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>date_observed_count</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>date_missing_ratio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>process_start_time</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>process_end_time</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>total_process_duration</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>max_station_span</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>mean_station_span</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>std_station_span</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>active_station_count</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"plt\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"unmapped_feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"std_station_span\",\n          \"date_missing_ratio\",\n          \"max_station_span\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "execution_count": 25,
-      "id": "MjmQshC4bEfD",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "MjmQshC4bEfD",
-        "outputId": "268ba6e7-dc21-4678-beaf-55f184bbb3ec"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "     car_master_id source_process_code target_process_code  \\\n",
-              "883          49121               PAINT            ASSEMBLY   \n",
-              "179          84100               PAINT            ASSEMBLY   \n",
-              "76          495820               PAINT            ASSEMBLY   \n",
-              "65           46831               PAINT            ASSEMBLY   \n",
-              "447         533156               PAINT            ASSEMBLY   \n",
-              "543         199536               PAINT            ASSEMBLY   \n",
-              "695          86810               PAINT            ASSEMBLY   \n",
-              "132         457589               PAINT            ASSEMBLY   \n",
-              "88          361134               PAINT            ASSEMBLY   \n",
-              "532         293981               PAINT            ASSEMBLY   \n",
-              "608         230813               PAINT            ASSEMBLY   \n",
-              "710         290791               PAINT            ASSEMBLY   \n",
-              "471         327137               PAINT            ASSEMBLY   \n",
-              "196         105438               PAINT            ASSEMBLY   \n",
-              "214         221558               PAINT            ASSEMBLY   \n",
-              "178         478955               PAINT            ASSEMBLY   \n",
-              "908          47143               PAINT            ASSEMBLY   \n",
-              "829         103485               PRESS                BODY   \n",
-              "267         593231               PAINT            ASSEMBLY   \n",
-              "747         545804               PAINT            ASSEMBLY   \n",
-              "256         280517               PAINT            ASSEMBLY   \n",
-              "828         422775               PAINT            ASSEMBLY   \n",
-              "781         212417               PAINT            ASSEMBLY   \n",
-              "670         406278               PAINT            ASSEMBLY   \n",
-              "398         307253               PAINT            ASSEMBLY   \n",
-              "661         576604               PAINT            ASSEMBLY   \n",
-              "388         109154               PAINT            ASSEMBLY   \n",
-              "185         366377               PRESS                BODY   \n",
-              "593         492773               PAINT            ASSEMBLY   \n",
-              "772         255054               PAINT            ASSEMBLY   \n",
-              "\n",
-              "     current_defect_probability  target_defect_probability  \\\n",
-              "883                    0.563811                   0.619726   \n",
-              "179                    0.747158                   0.618208   \n",
-              "76                     0.787374                   0.342941   \n",
-              "65                     0.788540                   0.257915   \n",
-              "447                    0.787341                   0.230440   \n",
-              "543                    0.592871                   0.198408   \n",
-              "695                    0.842617                   0.186797   \n",
-              "132                    0.653267                   0.184589   \n",
-              "88                     0.500231                   0.183003   \n",
-              "532                    0.784869                   0.166225   \n",
-              "608                    0.665531                   0.153555   \n",
-              "710                    0.624316                   0.146039   \n",
-              "471                    0.770755                   0.144109   \n",
-              "196                    0.748888                   0.138818   \n",
-              "214                    0.766262                   0.131900   \n",
-              "178                    0.696390                   0.130941   \n",
-              "908                    0.711576                   0.126527   \n",
-              "829                    0.485897                   0.120372   \n",
-              "267                    0.665566                   0.109987   \n",
-              "747                    0.825798                   0.107390   \n",
-              "256                    0.501200                   0.103989   \n",
-              "828                    0.599490                   0.103070   \n",
-              "781                    0.992600                   0.102789   \n",
-              "670                    0.977409                   0.102239   \n",
-              "398                    0.567956                   0.100802   \n",
-              "661                    0.762924                   0.097669   \n",
-              "388                    0.767391                   0.096965   \n",
-              "185                    0.503115                   0.096164   \n",
-              "593                    0.710897                   0.095113   \n",
-              "772                    0.660369                   0.091074   \n",
-              "\n",
-              "    predicted_defect_process risk_grade        main_cause  influence_score  \\\n",
-              "883                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.563811   \n",
-              "179                 ASSEMBLY     MEDIUM      L3_S36_F3920         0.747158   \n",
-              "76                  ASSEMBLY        LOW      L3_S29_F3407         0.787374   \n",
-              "65                  ASSEMBLY        LOW  L3_S30_mean_time         0.788540   \n",
-              "447                 ASSEMBLY        LOW      L3_S29_F3407         0.787341   \n",
-              "543                 ASSEMBLY        LOW      L3_S29_F3449         0.592871   \n",
-              "695                 ASSEMBLY        LOW  L3_S30_mean_time         0.842617   \n",
-              "132                 ASSEMBLY        LOW  L3_S30_mean_time         0.653267   \n",
-              "88                  ASSEMBLY        LOW      L3_S30_F3504         0.500231   \n",
-              "532                 ASSEMBLY        LOW      L3_S29_F3407         0.784869   \n",
-              "608                 ASSEMBLY        LOW      L3_S29_F3407         0.665531   \n",
-              "710                 ASSEMBLY        LOW  L3_S30_mean_time         0.624316   \n",
-              "471                 ASSEMBLY        LOW  L3_S30_mean_time         0.770755   \n",
-              "196                 ASSEMBLY        LOW      L3_S29_F3339         0.748888   \n",
-              "214                 ASSEMBLY        LOW      L3_S33_F3857         0.766262   \n",
-              "178                 ASSEMBLY        LOW      L3_S29_F3407         0.696390   \n",
-              "908                 ASSEMBLY        LOW      L3_S29_F3395         0.711576   \n",
-              "829                     BODY        LOW        L0_S9_F180         0.485897   \n",
-              "267                 ASSEMBLY        LOW      L3_S29_F3407         0.665566   \n",
-              "747                 ASSEMBLY        LOW      L3_S36_F3920         0.825798   \n",
-              "256                 ASSEMBLY        LOW    L3_S33_num_std         0.501200   \n",
-              "828                 ASSEMBLY        LOW      L3_S29_F3351         0.599490   \n",
-              "781                 ASSEMBLY        LOW      L3_S36_F3920         0.992600   \n",
-              "670                 ASSEMBLY        LOW  L3_S30_mean_time         0.977409   \n",
-              "398                 ASSEMBLY        LOW  L3_S30_mean_time         0.567956   \n",
-              "661                 ASSEMBLY        LOW      L3_S29_F3373         0.762924   \n",
-              "388                 ASSEMBLY        LOW      L3_S29_F3339         0.767391   \n",
-              "185                     BODY        LOW     L0_S1_num_std         0.503115   \n",
-              "593                 ASSEMBLY        LOW      L3_S29_F3395         0.710897   \n",
-              "772                 ASSEMBLY        LOW      L3_S29_F3373         0.660369   \n",
-              "\n",
-              "                         predicted_at  \n",
-              "883  2026-06-18T05:32:50.034461+00:00  \n",
-              "179  2026-06-18T05:32:50.034461+00:00  \n",
-              "76   2026-06-18T05:32:50.034461+00:00  \n",
-              "65   2026-06-18T05:32:50.034461+00:00  \n",
-              "447  2026-06-18T05:32:50.034461+00:00  \n",
-              "543  2026-06-18T05:32:50.034461+00:00  \n",
-              "695  2026-06-18T05:32:50.034461+00:00  \n",
-              "132  2026-06-18T05:32:50.034461+00:00  \n",
-              "88   2026-06-18T05:32:50.034461+00:00  \n",
-              "532  2026-06-18T05:32:50.034461+00:00  \n",
-              "608  2026-06-18T05:32:50.034461+00:00  \n",
-              "710  2026-06-18T05:32:50.034461+00:00  \n",
-              "471  2026-06-18T05:32:50.034461+00:00  \n",
-              "196  2026-06-18T05:32:50.034461+00:00  \n",
-              "214  2026-06-18T05:32:50.034461+00:00  \n",
-              "178  2026-06-18T05:32:50.034461+00:00  \n",
-              "908  2026-06-18T05:32:50.034461+00:00  \n",
-              "829  2026-06-18T05:32:50.034461+00:00  \n",
-              "267  2026-06-18T05:32:50.034461+00:00  \n",
-              "747  2026-06-18T05:32:50.034461+00:00  \n",
-              "256  2026-06-18T05:32:50.034461+00:00  \n",
-              "828  2026-06-18T05:32:50.034461+00:00  \n",
-              "781  2026-06-18T05:32:50.034461+00:00  \n",
-              "670  2026-06-18T05:32:50.034461+00:00  \n",
-              "398  2026-06-18T05:32:50.034461+00:00  \n",
-              "661  2026-06-18T05:32:50.034461+00:00  \n",
-              "388  2026-06-18T05:32:50.034461+00:00  \n",
-              "185  2026-06-18T05:32:50.034461+00:00  \n",
-              "593  2026-06-18T05:32:50.034461+00:00  \n",
-              "772  2026-06-18T05:32:50.034461+00:00  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-73977bb6-795d-46fd-9b9f-c66822303bcd\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "\n",
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-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>car_master_id</th>\n",
-              "      <th>source_process_code</th>\n",
-              "      <th>target_process_code</th>\n",
-              "      <th>current_defect_probability</th>\n",
-              "      <th>target_defect_probability</th>\n",
-              "      <th>predicted_defect_process</th>\n",
-              "      <th>risk_grade</th>\n",
-              "      <th>main_cause</th>\n",
-              "      <th>influence_score</th>\n",
-              "      <th>predicted_at</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>883</th>\n",
-              "      <td>49121</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.563811</td>\n",
-              "      <td>0.619726</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.563811</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>179</th>\n",
-              "      <td>84100</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.747158</td>\n",
-              "      <td>0.618208</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>MEDIUM</td>\n",
-              "      <td>L3_S36_F3920</td>\n",
-              "      <td>0.747158</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>76</th>\n",
-              "      <td>495820</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.787374</td>\n",
-              "      <td>0.342941</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3407</td>\n",
-              "      <td>0.787374</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>65</th>\n",
-              "      <td>46831</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.788540</td>\n",
-              "      <td>0.257915</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.788540</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>447</th>\n",
-              "      <td>533156</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.787341</td>\n",
-              "      <td>0.230440</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3407</td>\n",
-              "      <td>0.787341</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>543</th>\n",
-              "      <td>199536</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.592871</td>\n",
-              "      <td>0.198408</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3449</td>\n",
-              "      <td>0.592871</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>695</th>\n",
-              "      <td>86810</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.842617</td>\n",
-              "      <td>0.186797</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.842617</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>132</th>\n",
-              "      <td>457589</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.653267</td>\n",
-              "      <td>0.184589</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.653267</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>88</th>\n",
-              "      <td>361134</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.500231</td>\n",
-              "      <td>0.183003</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_F3504</td>\n",
-              "      <td>0.500231</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>532</th>\n",
-              "      <td>293981</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.784869</td>\n",
-              "      <td>0.166225</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3407</td>\n",
-              "      <td>0.784869</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>608</th>\n",
-              "      <td>230813</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.665531</td>\n",
-              "      <td>0.153555</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3407</td>\n",
-              "      <td>0.665531</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>710</th>\n",
-              "      <td>290791</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.624316</td>\n",
-              "      <td>0.146039</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.624316</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>471</th>\n",
-              "      <td>327137</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.770755</td>\n",
-              "      <td>0.144109</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.770755</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>196</th>\n",
-              "      <td>105438</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.748888</td>\n",
-              "      <td>0.138818</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3339</td>\n",
-              "      <td>0.748888</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>214</th>\n",
-              "      <td>221558</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.766262</td>\n",
-              "      <td>0.131900</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S33_F3857</td>\n",
-              "      <td>0.766262</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>178</th>\n",
-              "      <td>478955</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.696390</td>\n",
-              "      <td>0.130941</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3407</td>\n",
-              "      <td>0.696390</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>908</th>\n",
-              "      <td>47143</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.711576</td>\n",
-              "      <td>0.126527</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3395</td>\n",
-              "      <td>0.711576</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>829</th>\n",
-              "      <td>103485</td>\n",
-              "      <td>PRESS</td>\n",
-              "      <td>BODY</td>\n",
-              "      <td>0.485897</td>\n",
-              "      <td>0.120372</td>\n",
-              "      <td>BODY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L0_S9_F180</td>\n",
-              "      <td>0.485897</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>267</th>\n",
-              "      <td>593231</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.665566</td>\n",
-              "      <td>0.109987</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3407</td>\n",
-              "      <td>0.665566</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>747</th>\n",
-              "      <td>545804</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.825798</td>\n",
-              "      <td>0.107390</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S36_F3920</td>\n",
-              "      <td>0.825798</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>256</th>\n",
-              "      <td>280517</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.501200</td>\n",
-              "      <td>0.103989</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S33_num_std</td>\n",
-              "      <td>0.501200</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>828</th>\n",
-              "      <td>422775</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.599490</td>\n",
-              "      <td>0.103070</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3351</td>\n",
-              "      <td>0.599490</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>781</th>\n",
-              "      <td>212417</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.992600</td>\n",
-              "      <td>0.102789</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S36_F3920</td>\n",
-              "      <td>0.992600</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>670</th>\n",
-              "      <td>406278</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.977409</td>\n",
-              "      <td>0.102239</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.977409</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>398</th>\n",
-              "      <td>307253</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.567956</td>\n",
-              "      <td>0.100802</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S30_mean_time</td>\n",
-              "      <td>0.567956</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>661</th>\n",
-              "      <td>576604</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.762924</td>\n",
-              "      <td>0.097669</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3373</td>\n",
-              "      <td>0.762924</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>388</th>\n",
-              "      <td>109154</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.767391</td>\n",
-              "      <td>0.096965</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3339</td>\n",
-              "      <td>0.767391</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>185</th>\n",
-              "      <td>366377</td>\n",
-              "      <td>PRESS</td>\n",
-              "      <td>BODY</td>\n",
-              "      <td>0.503115</td>\n",
-              "      <td>0.096164</td>\n",
-              "      <td>BODY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L0_S1_num_std</td>\n",
-              "      <td>0.503115</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>593</th>\n",
-              "      <td>492773</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.710897</td>\n",
-              "      <td>0.095113</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3395</td>\n",
-              "      <td>0.710897</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>772</th>\n",
-              "      <td>255054</td>\n",
-              "      <td>PAINT</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>0.660369</td>\n",
-              "      <td>0.091074</td>\n",
-              "      <td>ASSEMBLY</td>\n",
-              "      <td>LOW</td>\n",
-              "      <td>L3_S29_F3373</td>\n",
-              "      <td>0.660369</td>\n",
-              "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
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-              "  source_process_code target_process_code risk_grade  vehicle_count  \\\n",
-              "2               PAINT            ASSEMBLY     MEDIUM              2   \n",
-              "0            ASSEMBLY    FINAL_INSPECTION        LOW              1   \n",
-              "3               PRESS                BODY        LOW            117   \n",
-              "1               PAINT            ASSEMBLY        LOW            880   \n",
-              "\n",
-              "   avg_target_defect_probability  avg_influence_score  \n",
-              "2                       0.618967             0.655485  \n",
-              "0                       0.047076             0.495199  \n",
-              "3                       0.030476             0.588225  \n",
-              "1                       0.026473             0.719373  "
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-              "      <th>source_process_code</th>\n",
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-              "      <th>risk_grade</th>\n",
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-              "      <th>2</th>\n",
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-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "  <div id=\"id_59f80193-e9d1-4e45-92c6-9c4c014f7e5f\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\n",
-              "        background-color: #E8F0FE;\n",
-              "        border: none;\n",
-              "        border-radius: 50%;\n",
-              "        cursor: pointer;\n",
-              "        display: none;\n",
-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
-              "      }\n",
-              "\n",
-              "      .colab-df-generate:hover {\n",
-              "        background-color: #E2EBFA;\n",
-              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "        fill: #174EA6;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate:hover {\n",
-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
-              "      }\n",
-              "    </style>\n",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('risk_summary')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_59f80193-e9d1-4e45-92c6-9c4c014f7e5f button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('risk_summary');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "risk_summary",
-              "summary": "{\n  \"name\": \"risk_summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PAINT\",\n          \"ASSEMBLY\",\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ASSEMBLY\",\n          \"FINAL_INSPECTION\",\n          \"BODY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"risk_grade\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"LOW\",\n          \"MEDIUM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vehicle_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 423,\n        \"min\": 1,\n        \"max\": 880,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          880\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_target_defect_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29228220842686947,\n        \"min\": 0.026473051136363637,\n        \"max\": 0.618967,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.047076,\n          0.026473051136363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_influence_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09591880133970926,\n        \"min\": 0.495199,\n        \"max\": 0.7193733886363637,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.495199,\n          0.7193733886363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {}
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  process_code  mean_abs_shap  feature_count\n",
+       "0        PRESS    3987.411446            182\n",
+       "2        PAINT    3959.570471            197\n",
+       "3     ASSEMBLY     398.922157             36\n",
+       "1         BODY      71.636713             33"
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-bae87c37-391f-44c4-bcef-a34afe9b1e09\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>process_code</th>\n",
+       "      <th>mean_abs_shap</th>\n",
+       "      <th>feature_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PRESS</td>\n",
+       "      <td>3987.411446</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>3959.570471</td>\n",
+       "      <td>197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>398.922157</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BODY</td>\n",
+       "      <td>71.636713</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bae87c37-391f-44c4-bcef-a34afe9b1e09')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-bae87c37-391f-44c4-bcef-a34afe9b1e09 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-bae87c37-391f-44c4-bcef-a34afe9b1e09');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "  <div id=\"id_790c3d3c-10fc-406c-a77f-00d95a9965e0\">\n",
+       "    <style>\n",
+       "      .colab-df-generate {\n",
+       "        background-color: #E8F0FE;\n",
+       "        border: none;\n",
+       "        border-radius: 50%;\n",
+       "        cursor: pointer;\n",
+       "        display: none;\n",
+       "        fill: #1967D2;\n",
+       "        height: 32px;\n",
+       "        padding: 0 0 0 0;\n",
+       "        width: 32px;\n",
+       "      }\n",
+       "\n",
+       "      .colab-df-generate:hover {\n",
+       "        background-color: #E2EBFA;\n",
+       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "        fill: #174EA6;\n",
+       "      }\n",
+       "\n",
+       "      [theme=dark] .colab-df-generate {\n",
+       "        background-color: #3B4455;\n",
+       "        fill: #D2E3FC;\n",
+       "      }\n",
+       "\n",
+       "      [theme=dark] .colab-df-generate:hover {\n",
+       "        background-color: #434B5C;\n",
+       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "        fill: #FFFFFF;\n",
+       "      }\n",
+       "    </style>\n",
+       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('process_importance')\"\n",
+       "            title=\"Generate code using this dataframe.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "       width=\"24px\">\n",
+       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "    <script>\n",
+       "      (() => {\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#id_790c3d3c-10fc-406c-a77f-00d95a9965e0 button.colab-df-generate');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      buttonEl.onclick = () => {\n",
+       "        google.colab.notebook.generateWithVariable('process_importance');\n",
+       "      }\n",
+       "      })();\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "# 전이 위험 결과 생성\n",
-        "def build_transfer_predictions(\n",
-        "    ids: pd.Series,\n",
-        "    probabilities: np.ndarray,\n",
-        "    shap_matrix: np.ndarray,\n",
-        "    feature_names: list[str],\n",
-        "    feature_process: pd.Series,\n",
-        ") -> pd.DataFrame:\n",
-        "    records = []\n",
-        "    process_values = feature_process.values\n",
-        "    predicted_at = datetime.now(timezone.utc).isoformat()\n",
-        "\n",
-        "    # 샘플별 공정 영향도 계산\n",
-        "    for row_idx, (sample_id, prob) in enumerate(zip(ids.to_numpy(), probabilities)):\n",
-        "        abs_values = np.abs(shap_matrix[row_idx])\n",
-        "        mapped_mask = feature_process.isin(PROCESS_FLOW).to_numpy()\n",
-        "        mapped_abs_values = abs_values[mapped_mask]\n",
-        "        mapped_process_values = process_values[mapped_mask]\n",
-        "        total_abs = float(mapped_abs_values.sum()) or 1.0\n",
-        "\n",
-        "        # 공정별 영향 비율\n",
-        "        process_scores = {}\n",
-        "        for process_code in PROCESS_FLOW:\n",
-        "            col_idx = np.where(mapped_process_values == process_code)[0]\n",
-        "            process_scores[process_code] = float(mapped_abs_values[col_idx].sum() / total_abs) if len(col_idx) else 0.0\n",
-        "\n",
-        "        # 원인/대상 공정 산출\n",
-        "        source_process = max(process_scores, key=process_scores.get)\n",
-        "        target_process = next_process(source_process)\n",
-        "        source_col_idx = np.where(process_values == source_process)[0]\n",
-        "        top_feature_idx = int(source_col_idx[np.argmax(abs_values[source_col_idx])]) if len(source_col_idx) else int(abs_values.argmax())\n",
-        "        influence_score = process_scores[source_process]\n",
-        "\n",
-        "        records.append({\n",
-        "            \"car_master_id\": int(sample_id),\n",
-        "            \"source_process_code\": source_process,\n",
-        "            \"target_process_code\": target_process,\n",
-        "            \"current_defect_probability\": round(float(influence_score), 6),\n",
-        "            \"target_defect_probability\": round(float(prob), 6),\n",
-        "            \"predicted_defect_process\": target_process,\n",
-        "            \"risk_grade\": probability_to_risk_grade(float(prob)),\n",
-        "            \"main_cause\": feature_names[top_feature_idx],\n",
-        "            \"influence_score\": round(float(influence_score), 6),\n",
-        "            \"predicted_at\": predicted_at,\n",
-        "        })\n",
-        "    return pd.DataFrame(records).sort_values([\"target_defect_probability\", \"influence_score\"], ascending=False)\n",
-        "\n",
-        "# 전이 예측 테이블 생성\n",
-        "transfer_predictions = build_transfer_predictions(\n",
-        "    id_shap,\n",
-        "    proba_shap,\n",
-        "    shap_matrix,\n",
-        "    X_shap.columns.tolist(),\n",
-        "    feature_process,\n",
-        ")\n",
-        "\n",
-        "display(transfer_predictions.head(30))\n",
-        "\n",
-        "# 전이 위험 요약\n",
-        "risk_summary = transfer_predictions.groupby([\"source_process_code\", \"target_process_code\", \"risk_grade\"]).agg(\n",
-        "    vehicle_count=(\"car_master_id\", \"count\"),\n",
-        "    avg_target_defect_probability=(\"target_defect_probability\", \"mean\"),\n",
-        "    avg_influence_score=(\"influence_score\", \"mean\"),\n",
-        ").reset_index().sort_values([\"avg_target_defect_probability\", \"vehicle_count\"], ascending=False)\n",
-        "display(risk_summary)\n"
-      ]
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "variable_name": "process_importance",
+       "summary": "{\n  \"name\": \"process_importance\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"PAINT\",\n          \"BODY\",\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2162.4192345053525,\n        \"min\": 71.63671284580064,\n        \"max\": 3987.4114461310187,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3959.570471254013,\n          71.63671284580064,\n          3987.4114461310187\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89,\n        \"min\": 33,\n        \"max\": 197,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          197,\n          33,\n          182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "execution_count": 26,
-      "id": "JFTbBuS1bEfD",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 885
-        },
-        "id": "JFTbBuS1bEfD",
-        "outputId": "6afce295-117c-486d-d7df-abe3aade47a3"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 900x500 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 800x400 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
       ],
-      "source": [
-        "# 전이 위험 시각화\n",
-        "plt.figure(figsize=(9, 5))\n",
-        "top_flow = risk_summary.head(12).copy()\n",
-        "top_flow[\"flow\"] = top_flow[\"source_process_code\"] + \" -> \" + top_flow[\"target_process_code\"] + \" (\" + top_flow[\"risk_grade\"] + \")\"\n",
-        "sns.barplot(data=top_flow, y=\"flow\", x=\"avg_target_defect_probability\", hue=\"risk_grade\", dodge=False)\n",
-        "plt.title(\"Process Transfer Risk by SHAP Influence\")\n",
-        "plt.xlabel(\"Avg Final Defect Probability\")\n",
-        "plt.ylabel(\"\")\n",
-        "plt.legend(title=\"Risk\")\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n",
-        "\n",
-        "# 불량 확률 분포 시각화\n",
-        "plt.figure(figsize=(8, 4))\n",
-        "sns.histplot(data=transfer_predictions, x=\"target_defect_probability\", hue=\"risk_grade\", bins=40, multiple=\"stack\")\n",
-        "plt.title(\"Predicted Final Defect Probability Distribution\")\n",
-        "plt.xlabel(\"Defect Probability\")\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n"
-      ]
+      "image/png": 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\n"
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "kBJK8YfybEfD",
+   "metadata": {
+    "id": "kBJK8YfybEfD"
+   },
+   "source": [
+    "## 11. SHAP 기반 전이 위험 결과 생성\n",
+    "\n",
+    "아래 결과는 PRD의 `defect_transfer_prediction_result` 저장 구조에 맞춘 예측 테이블입니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "MjmQshC4bEfD",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "MjmQshC4bEfD",
+    "outputId": "268ba6e7-dc21-4678-beaf-55f184bbb3ec"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "43d99e60",
-      "metadata": {
-        "id": "43d99e60"
-      },
-      "source": [
-        "## 12. AI 모델 관리 항목\n",
-        "\n",
-        "모델 선정 근거, 데이터셋 규모, 테스트 케이스 수, Accuracy, False Positive Rate, 성능 개선 현황을 운영 관리용 요약으로 정리합니다.\n"
-      ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "     car_master_id source_process_code target_process_code  \\\n",
+       "883          49121               PAINT            ASSEMBLY   \n",
+       "179          84100               PAINT            ASSEMBLY   \n",
+       "76          495820               PAINT            ASSEMBLY   \n",
+       "65           46831               PAINT            ASSEMBLY   \n",
+       "447         533156               PAINT            ASSEMBLY   \n",
+       "543         199536               PAINT            ASSEMBLY   \n",
+       "695          86810               PAINT            ASSEMBLY   \n",
+       "132         457589               PAINT            ASSEMBLY   \n",
+       "88          361134               PAINT            ASSEMBLY   \n",
+       "532         293981               PAINT            ASSEMBLY   \n",
+       "608         230813               PAINT            ASSEMBLY   \n",
+       "710         290791               PAINT            ASSEMBLY   \n",
+       "471         327137               PAINT            ASSEMBLY   \n",
+       "196         105438               PAINT            ASSEMBLY   \n",
+       "214         221558               PAINT            ASSEMBLY   \n",
+       "178         478955               PAINT            ASSEMBLY   \n",
+       "908          47143               PAINT            ASSEMBLY   \n",
+       "829         103485               PRESS                BODY   \n",
+       "267         593231               PAINT            ASSEMBLY   \n",
+       "747         545804               PAINT            ASSEMBLY   \n",
+       "256         280517               PAINT            ASSEMBLY   \n",
+       "828         422775               PAINT            ASSEMBLY   \n",
+       "781         212417               PAINT            ASSEMBLY   \n",
+       "670         406278               PAINT            ASSEMBLY   \n",
+       "398         307253               PAINT            ASSEMBLY   \n",
+       "661         576604               PAINT            ASSEMBLY   \n",
+       "388         109154               PAINT            ASSEMBLY   \n",
+       "185         366377               PRESS                BODY   \n",
+       "593         492773               PAINT            ASSEMBLY   \n",
+       "772         255054               PAINT            ASSEMBLY   \n",
+       "\n",
+       "     current_defect_probability  target_defect_probability  \\\n",
+       "883                    0.563811                   0.619726   \n",
+       "179                    0.747158                   0.618208   \n",
+       "76                     0.787374                   0.342941   \n",
+       "65                     0.788540                   0.257915   \n",
+       "447                    0.787341                   0.230440   \n",
+       "543                    0.592871                   0.198408   \n",
+       "695                    0.842617                   0.186797   \n",
+       "132                    0.653267                   0.184589   \n",
+       "88                     0.500231                   0.183003   \n",
+       "532                    0.784869                   0.166225   \n",
+       "608                    0.665531                   0.153555   \n",
+       "710                    0.624316                   0.146039   \n",
+       "471                    0.770755                   0.144109   \n",
+       "196                    0.748888                   0.138818   \n",
+       "214                    0.766262                   0.131900   \n",
+       "178                    0.696390                   0.130941   \n",
+       "908                    0.711576                   0.126527   \n",
+       "829                    0.485897                   0.120372   \n",
+       "267                    0.665566                   0.109987   \n",
+       "747                    0.825798                   0.107390   \n",
+       "256                    0.501200                   0.103989   \n",
+       "828                    0.599490                   0.103070   \n",
+       "781                    0.992600                   0.102789   \n",
+       "670                    0.977409                   0.102239   \n",
+       "398                    0.567956                   0.100802   \n",
+       "661                    0.762924                   0.097669   \n",
+       "388                    0.767391                   0.096965   \n",
+       "185                    0.503115                   0.096164   \n",
+       "593                    0.710897                   0.095113   \n",
+       "772                    0.660369                   0.091074   \n",
+       "\n",
+       "    predicted_defect_process risk_grade        main_cause  influence_score  \\\n",
+       "883                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.563811   \n",
+       "179                 ASSEMBLY     MEDIUM      L3_S36_F3920         0.747158   \n",
+       "76                  ASSEMBLY        LOW      L3_S29_F3407         0.787374   \n",
+       "65                  ASSEMBLY        LOW  L3_S30_mean_time         0.788540   \n",
+       "447                 ASSEMBLY        LOW      L3_S29_F3407         0.787341   \n",
+       "543                 ASSEMBLY        LOW      L3_S29_F3449         0.592871   \n",
+       "695                 ASSEMBLY        LOW  L3_S30_mean_time         0.842617   \n",
+       "132                 ASSEMBLY        LOW  L3_S30_mean_time         0.653267   \n",
+       "88                  ASSEMBLY        LOW      L3_S30_F3504         0.500231   \n",
+       "532                 ASSEMBLY        LOW      L3_S29_F3407         0.784869   \n",
+       "608                 ASSEMBLY        LOW      L3_S29_F3407         0.665531   \n",
+       "710                 ASSEMBLY        LOW  L3_S30_mean_time         0.624316   \n",
+       "471                 ASSEMBLY        LOW  L3_S30_mean_time         0.770755   \n",
+       "196                 ASSEMBLY        LOW      L3_S29_F3339         0.748888   \n",
+       "214                 ASSEMBLY        LOW      L3_S33_F3857         0.766262   \n",
+       "178                 ASSEMBLY        LOW      L3_S29_F3407         0.696390   \n",
+       "908                 ASSEMBLY        LOW      L3_S29_F3395         0.711576   \n",
+       "829                     BODY        LOW        L0_S9_F180         0.485897   \n",
+       "267                 ASSEMBLY        LOW      L3_S29_F3407         0.665566   \n",
+       "747                 ASSEMBLY        LOW      L3_S36_F3920         0.825798   \n",
+       "256                 ASSEMBLY        LOW    L3_S33_num_std         0.501200   \n",
+       "828                 ASSEMBLY        LOW      L3_S29_F3351         0.599490   \n",
+       "781                 ASSEMBLY        LOW      L3_S36_F3920         0.992600   \n",
+       "670                 ASSEMBLY        LOW  L3_S30_mean_time         0.977409   \n",
+       "398                 ASSEMBLY        LOW  L3_S30_mean_time         0.567956   \n",
+       "661                 ASSEMBLY        LOW      L3_S29_F3373         0.762924   \n",
+       "388                 ASSEMBLY        LOW      L3_S29_F3339         0.767391   \n",
+       "185                     BODY        LOW     L0_S1_num_std         0.503115   \n",
+       "593                 ASSEMBLY        LOW      L3_S29_F3395         0.710897   \n",
+       "772                 ASSEMBLY        LOW      L3_S29_F3373         0.660369   \n",
+       "\n",
+       "                         predicted_at  \n",
+       "883  2026-06-18T05:32:50.034461+00:00  \n",
+       "179  2026-06-18T05:32:50.034461+00:00  \n",
+       "76   2026-06-18T05:32:50.034461+00:00  \n",
+       "65   2026-06-18T05:32:50.034461+00:00  \n",
+       "447  2026-06-18T05:32:50.034461+00:00  \n",
+       "543  2026-06-18T05:32:50.034461+00:00  \n",
+       "695  2026-06-18T05:32:50.034461+00:00  \n",
+       "132  2026-06-18T05:32:50.034461+00:00  \n",
+       "88   2026-06-18T05:32:50.034461+00:00  \n",
+       "532  2026-06-18T05:32:50.034461+00:00  \n",
+       "608  2026-06-18T05:32:50.034461+00:00  \n",
+       "710  2026-06-18T05:32:50.034461+00:00  \n",
+       "471  2026-06-18T05:32:50.034461+00:00  \n",
+       "196  2026-06-18T05:32:50.034461+00:00  \n",
+       "214  2026-06-18T05:32:50.034461+00:00  \n",
+       "178  2026-06-18T05:32:50.034461+00:00  \n",
+       "908  2026-06-18T05:32:50.034461+00:00  \n",
+       "829  2026-06-18T05:32:50.034461+00:00  \n",
+       "267  2026-06-18T05:32:50.034461+00:00  \n",
+       "747  2026-06-18T05:32:50.034461+00:00  \n",
+       "256  2026-06-18T05:32:50.034461+00:00  \n",
+       "828  2026-06-18T05:32:50.034461+00:00  \n",
+       "781  2026-06-18T05:32:50.034461+00:00  \n",
+       "670  2026-06-18T05:32:50.034461+00:00  \n",
+       "398  2026-06-18T05:32:50.034461+00:00  \n",
+       "661  2026-06-18T05:32:50.034461+00:00  \n",
+       "388  2026-06-18T05:32:50.034461+00:00  \n",
+       "185  2026-06-18T05:32:50.034461+00:00  \n",
+       "593  2026-06-18T05:32:50.034461+00:00  \n",
+       "772  2026-06-18T05:32:50.034461+00:00  "
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-73977bb6-795d-46fd-9b9f-c66822303bcd\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>car_master_id</th>\n",
+       "      <th>source_process_code</th>\n",
+       "      <th>target_process_code</th>\n",
+       "      <th>current_defect_probability</th>\n",
+       "      <th>target_defect_probability</th>\n",
+       "      <th>predicted_defect_process</th>\n",
+       "      <th>risk_grade</th>\n",
+       "      <th>main_cause</th>\n",
+       "      <th>influence_score</th>\n",
+       "      <th>predicted_at</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>883</th>\n",
+       "      <td>49121</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.563811</td>\n",
+       "      <td>0.619726</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>MEDIUM</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.563811</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>84100</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.747158</td>\n",
+       "      <td>0.618208</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>MEDIUM</td>\n",
+       "      <td>L3_S36_F3920</td>\n",
+       "      <td>0.747158</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>495820</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.787374</td>\n",
+       "      <td>0.342941</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3407</td>\n",
+       "      <td>0.787374</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>46831</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.788540</td>\n",
+       "      <td>0.257915</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.788540</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>533156</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.787341</td>\n",
+       "      <td>0.230440</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3407</td>\n",
+       "      <td>0.787341</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>199536</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.592871</td>\n",
+       "      <td>0.198408</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3449</td>\n",
+       "      <td>0.592871</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>86810</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.842617</td>\n",
+       "      <td>0.186797</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.842617</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>457589</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.653267</td>\n",
+       "      <td>0.184589</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.653267</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>361134</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.500231</td>\n",
+       "      <td>0.183003</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_F3504</td>\n",
+       "      <td>0.500231</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>293981</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.784869</td>\n",
+       "      <td>0.166225</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3407</td>\n",
+       "      <td>0.784869</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>230813</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.665531</td>\n",
+       "      <td>0.153555</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3407</td>\n",
+       "      <td>0.665531</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>290791</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.624316</td>\n",
+       "      <td>0.146039</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.624316</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>327137</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.770755</td>\n",
+       "      <td>0.144109</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.770755</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>105438</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.748888</td>\n",
+       "      <td>0.138818</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3339</td>\n",
+       "      <td>0.748888</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>221558</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.766262</td>\n",
+       "      <td>0.131900</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S33_F3857</td>\n",
+       "      <td>0.766262</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>478955</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.696390</td>\n",
+       "      <td>0.130941</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3407</td>\n",
+       "      <td>0.696390</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>908</th>\n",
+       "      <td>47143</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.711576</td>\n",
+       "      <td>0.126527</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3395</td>\n",
+       "      <td>0.711576</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>829</th>\n",
+       "      <td>103485</td>\n",
+       "      <td>PRESS</td>\n",
+       "      <td>BODY</td>\n",
+       "      <td>0.485897</td>\n",
+       "      <td>0.120372</td>\n",
+       "      <td>BODY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L0_S9_F180</td>\n",
+       "      <td>0.485897</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>593231</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.665566</td>\n",
+       "      <td>0.109987</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3407</td>\n",
+       "      <td>0.665566</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>545804</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.825798</td>\n",
+       "      <td>0.107390</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S36_F3920</td>\n",
+       "      <td>0.825798</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>280517</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.501200</td>\n",
+       "      <td>0.103989</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S33_num_std</td>\n",
+       "      <td>0.501200</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>828</th>\n",
+       "      <td>422775</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.599490</td>\n",
+       "      <td>0.103070</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3351</td>\n",
+       "      <td>0.599490</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>781</th>\n",
+       "      <td>212417</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.992600</td>\n",
+       "      <td>0.102789</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S36_F3920</td>\n",
+       "      <td>0.992600</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>406278</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.977409</td>\n",
+       "      <td>0.102239</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.977409</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>307253</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.567956</td>\n",
+       "      <td>0.100802</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S30_mean_time</td>\n",
+       "      <td>0.567956</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>576604</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.762924</td>\n",
+       "      <td>0.097669</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3373</td>\n",
+       "      <td>0.762924</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>109154</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.767391</td>\n",
+       "      <td>0.096965</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3339</td>\n",
+       "      <td>0.767391</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>366377</td>\n",
+       "      <td>PRESS</td>\n",
+       "      <td>BODY</td>\n",
+       "      <td>0.503115</td>\n",
+       "      <td>0.096164</td>\n",
+       "      <td>BODY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L0_S1_num_std</td>\n",
+       "      <td>0.503115</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>492773</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.710897</td>\n",
+       "      <td>0.095113</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3395</td>\n",
+       "      <td>0.710897</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>255054</td>\n",
+       "      <td>PAINT</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>0.660369</td>\n",
+       "      <td>0.091074</td>\n",
+       "      <td>ASSEMBLY</td>\n",
+       "      <td>LOW</td>\n",
+       "      <td>L3_S29_F3373</td>\n",
+       "      <td>0.660369</td>\n",
+       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
+       "    </tr>\n",
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+       "    <div class=\"colab-df-buttons\">\n",
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+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73977bb6-795d-46fd-9b9f-c66822303bcd')\"\n",
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+       "      background-color: #E8F0FE;\n",
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+       "\n",
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+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
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+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
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+       "\n",
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+       "      const buttonEl =\n",
+       "        document.querySelector('#df-73977bb6-795d-46fd-9b9f-c66822303bcd button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-73977bb6-795d-46fd-9b9f-c66822303bcd');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
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+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
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+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "repr_error": "0"
+      }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "execution_count": 27,
-      "id": "c79bba33",
-      "metadata": {
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-          "height": 237
-        },
-        "id": "c79bba33",
-        "outputId": "b4ed5007-4f34-40a7-8c33-edc4bc328bb1"
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-      "outputs": [
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-          "data": {
-            "text/plain": [
-              "                         관리 항목  \\\n",
-              "0                  모델 선정 근거 정리   \n",
-              "1                   데이터셋 규모 관리   \n",
-              "2                 테스트 케이스 수 관리   \n",
-              "3             정확도(Accuracy) 측정   \n",
-              "4  오탐률(False Positive Rate) 측정   \n",
-              "5               모델 성능 개선 현황 관리   \n",
-              "\n",
-              "                                                   값  \n",
-              "0  RandomForest 모델이 validation PR-AUC/Recall/F1 기...  \n",
-              "1  {\"max_rows\": 300000, \"profile_rows\": 50000, \"t...  \n",
-              "2  {\"validation_total\": 60000, \"validation_normal...  \n",
-              "3                                            0.91395  \n",
-              "4                                           0.082851  \n",
-              "5  {\"baseline_model\": \"LightGBM\", \"baseline_pr_au...  "
-            ],
-            "text/html": [
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-              "  <thead>\n",
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-              "    <tr>\n",
-              "      <th>0</th>\n",
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-              "      <td>RandomForest 모델이 validation PR-AUC/Recall/F1 기...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>데이터셋 규모 관리</td>\n",
-              "      <td>{\"max_rows\": 300000, \"profile_rows\": 50000, \"t...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>테스트 케이스 수 관리</td>\n",
-              "      <td>{\"validation_total\": 60000, \"validation_normal...</td>\n",
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-              "      <td>정확도(Accuracy) 측정</td>\n",
-              "      <td>0.91395</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>오탐률(False Positive Rate) 측정</td>\n",
-              "      <td>0.082851</td>\n",
-              "    </tr>\n",
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-              "      <th>5</th>\n",
-              "      <td>모델 성능 개선 현황 관리</td>\n",
-              "      <td>{\"baseline_model\": \"LightGBM\", \"baseline_pr_au...</td>\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
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-              "      const buttonEl =\n",
-              "        document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
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+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  source_process_code target_process_code risk_grade  vehicle_count  \\\n",
+       "2               PAINT            ASSEMBLY     MEDIUM              2   \n",
+       "0            ASSEMBLY    FINAL_INSPECTION        LOW              1   \n",
+       "3               PRESS                BODY        LOW            117   \n",
+       "1               PAINT            ASSEMBLY        LOW            880   \n",
+       "\n",
+       "   avg_target_defect_probability  avg_influence_score  \n",
+       "2                       0.618967             0.655485  \n",
+       "0                       0.047076             0.495199  \n",
+       "3                       0.030476             0.588225  \n",
+       "1                       0.026473             0.719373  "
+      ],
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+       "        vertical-align: middle;\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_process_code</th>\n",
+       "      <th>target_process_code</th>\n",
+       "      <th>risk_grade</th>\n",
+       "      <th>vehicle_count</th>\n",
+       "      <th>avg_target_defect_probability</th>\n",
+       "      <th>avg_influence_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PAINT</td>\n",
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+       "      <td>2</td>\n",
+       "      <td>0.618967</td>\n",
+       "      <td>0.655485</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PRESS</td>\n",
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+       "      <td>LOW</td>\n",
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+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ed32a4d-df3b-48af-9a8a-13035df912bc')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "  <div id=\"id_59f80193-e9d1-4e45-92c6-9c4c014f7e5f\">\n",
+       "    <style>\n",
+       "      .colab-df-generate {\n",
+       "        background-color: #E8F0FE;\n",
+       "        border: none;\n",
+       "        border-radius: 50%;\n",
+       "        cursor: pointer;\n",
+       "        display: none;\n",
+       "        fill: #1967D2;\n",
+       "        height: 32px;\n",
+       "        padding: 0 0 0 0;\n",
+       "        width: 32px;\n",
+       "      }\n",
+       "\n",
+       "      .colab-df-generate:hover {\n",
+       "        background-color: #E2EBFA;\n",
+       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "        fill: #174EA6;\n",
+       "      }\n",
+       "\n",
+       "      [theme=dark] .colab-df-generate {\n",
+       "        background-color: #3B4455;\n",
+       "        fill: #D2E3FC;\n",
+       "      }\n",
+       "\n",
+       "      [theme=dark] .colab-df-generate:hover {\n",
+       "        background-color: #434B5C;\n",
+       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "        fill: #FFFFFF;\n",
+       "      }\n",
+       "    </style>\n",
+       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('risk_summary')\"\n",
+       "            title=\"Generate code using this dataframe.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "       width=\"24px\">\n",
+       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "    <script>\n",
+       "      (() => {\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#id_59f80193-e9d1-4e45-92c6-9c4c014f7e5f button.colab-df-generate');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      buttonEl.onclick = () => {\n",
+       "        google.colab.notebook.generateWithVariable('risk_summary');\n",
+       "      }\n",
+       "      })();\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "ai_management_table = pd.DataFrame([\n",
-        "    {\"관리 항목\": \"모델 선정 근거 정리\", \"값\": ai_management_summary[\"모델_선정_근거\"]},\n",
-        "    {\"관리 항목\": \"데이터셋 규모 관리\", \"값\": json.dumps(ai_management_summary[\"데이터셋_규모_관리\"], ensure_ascii=False)},\n",
-        "    {\"관리 항목\": \"테스트 케이스 수 관리\", \"값\": json.dumps(ai_management_summary[\"테스트_케이스_수_관리\"], ensure_ascii=False)},\n",
-        "    {\"관리 항목\": \"정확도(Accuracy) 측정\", \"값\": round(ai_management_summary[\"정확도_Accuracy\"], 6)},\n",
-        "    {\"관리 항목\": \"오탐률(False Positive Rate) 측정\", \"값\": round(ai_management_summary[\"오탐률_False_Positive_Rate\"], 6)},\n",
-        "    {\"관리 항목\": \"모델 성능 개선 현황 관리\", \"값\": json.dumps(ai_management_summary[\"모델_성능_개선_현황\"], ensure_ascii=False)},\n",
-        "])\n",
-        "\n",
-        "display(ai_management_table)\n"
-      ]
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "variable_name": "risk_summary",
+       "summary": "{\n  \"name\": \"risk_summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PAINT\",\n          \"ASSEMBLY\",\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ASSEMBLY\",\n          \"FINAL_INSPECTION\",\n          \"BODY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"risk_grade\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"LOW\",\n          \"MEDIUM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vehicle_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 423,\n        \"min\": 1,\n        \"max\": 880,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          880\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_target_defect_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29228220842686947,\n        \"min\": 0.026473051136363637,\n        \"max\": 0.618967,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.047076,\n          0.026473051136363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_influence_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09591880133970926,\n        \"min\": 0.495199,\n        \"max\": 0.7193733886363637,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.495199,\n          0.7193733886363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# 전이 위험 결과 생성\n",
+    "def build_transfer_predictions(\n",
+    "    ids: pd.Series,\n",
+    "    probabilities: np.ndarray,\n",
+    "    shap_matrix: np.ndarray,\n",
+    "    feature_names: list[str],\n",
+    "    feature_process: pd.Series,\n",
+    ") -> pd.DataFrame:\n",
+    "    records = []\n",
+    "    process_values = feature_process.values\n",
+    "    predicted_at = datetime.now(timezone.utc).isoformat()\n",
+    "\n",
+    "    # 샘플별 공정 영향도 계산\n",
+    "    for row_idx, (sample_id, prob) in enumerate(zip(ids.to_numpy(), probabilities)):\n",
+    "        abs_values = np.abs(shap_matrix[row_idx])\n",
+    "        mapped_mask = feature_process.isin(PROCESS_FLOW).to_numpy()\n",
+    "        mapped_abs_values = abs_values[mapped_mask]\n",
+    "        mapped_process_values = process_values[mapped_mask]\n",
+    "        total_abs = float(mapped_abs_values.sum()) or 1.0\n",
+    "\n",
+    "        # 공정별 영향 비율\n",
+    "        process_scores = {}\n",
+    "        for process_code in PROCESS_FLOW:\n",
+    "            col_idx = np.where(mapped_process_values == process_code)[0]\n",
+    "            process_scores[process_code] = float(mapped_abs_values[col_idx].sum() / total_abs) if len(col_idx) else 0.0\n",
+    "\n",
+    "        # 원인/대상 공정 산출\n",
+    "        source_process = max(process_scores, key=process_scores.get)\n",
+    "        target_process = next_process(source_process)\n",
+    "        source_col_idx = np.where(process_values == source_process)[0]\n",
+    "        top_feature_idx = int(source_col_idx[np.argmax(abs_values[source_col_idx])]) if len(source_col_idx) else int(abs_values.argmax())\n",
+    "        influence_score = process_scores[source_process]\n",
+    "\n",
+    "        records.append({\n",
+    "            \"car_master_id\": int(sample_id),\n",
+    "            \"source_process_code\": source_process,\n",
+    "            \"target_process_code\": target_process,\n",
+    "            \"current_defect_probability\": round(float(influence_score), 6),\n",
+    "            \"target_defect_probability\": round(float(prob), 6),\n",
+    "            \"predicted_defect_process\": target_process,\n",
+    "            \"risk_grade\": probability_to_risk_grade(float(prob)),\n",
+    "            \"main_cause\": feature_names[top_feature_idx],\n",
+    "            \"influence_score\": round(float(influence_score), 6),\n",
+    "            \"predicted_at\": predicted_at,\n",
+    "        })\n",
+    "    return pd.DataFrame(records).sort_values([\"target_defect_probability\", \"influence_score\"], ascending=False)\n",
+    "\n",
+    "# 전이 예측 테이블 생성\n",
+    "transfer_predictions = build_transfer_predictions(\n",
+    "    id_shap,\n",
+    "    proba_shap,\n",
+    "    shap_matrix,\n",
+    "    X_shap.columns.tolist(),\n",
+    "    feature_process,\n",
+    ")\n",
+    "\n",
+    "display(transfer_predictions.head(30))\n",
+    "\n",
+    "# 전이 위험 요약\n",
+    "risk_summary = transfer_predictions.groupby([\"source_process_code\", \"target_process_code\", \"risk_grade\"]).agg(\n",
+    "    vehicle_count=(\"car_master_id\", \"count\"),\n",
+    "    avg_target_defect_probability=(\"target_defect_probability\", \"mean\"),\n",
+    "    avg_influence_score=(\"influence_score\", \"mean\"),\n",
+    ").reset_index().sort_values([\"avg_target_defect_probability\", \"vehicle_count\"], ascending=False)\n",
+    "display(risk_summary)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "JFTbBuS1bEfD",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 885
     },
+    "id": "JFTbBuS1bEfD",
+    "outputId": "6afce295-117c-486d-d7df-abe3aade47a3"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "Q5r_SU2bbEfD",
-      "metadata": {
-        "id": "Q5r_SU2bbEfD"
-      },
-      "source": [
-        "## 13. 모델 및 결과 저장\n"
-      ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "execution_count": 28,
-      "id": "44a36633",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "44a36633",
-        "outputId": "9dbc6133-c938-4715-dc60-ea025f990e55"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Saved outputs:\n",
-            "/content/defect_transfer_outputs/selected_defect_detector.joblib\n",
-            "/content/defect_transfer_outputs/selected_defect_detector.pkl\n",
-            "/content/defect_transfer_outputs/candidate_defect_models.joblib\n",
-            "/content/defect_transfer_outputs/candidate_defect_models.pkl\n",
-            "/content/defect_transfer_outputs/lightgbm_defect_detector.joblib\n",
-            "/content/defect_transfer_outputs/lightgbm_defect_detector.pkl\n",
-            "/content/defect_transfer_outputs/auxiliary_process_models.joblib\n",
-            "/content/defect_transfer_outputs/auxiliary_process_models.pkl\n",
-            "/content/defect_transfer_outputs/defect_model_features.json\n",
-            "/content/defect_transfer_outputs/defect_model_metrics.json\n",
-            "/content/defect_transfer_outputs/defect_model_cv_results.csv\n",
-            "/content/defect_transfer_outputs/defect_model_comparison.csv\n",
-            "/content/defect_transfer_outputs/defect_model_confusion_matrices.json\n",
-            "/content/defect_transfer_outputs/defect_ai_management_summary.json\n",
-            "/content/defect_transfer_outputs/defect_model_feature_importance.csv\n",
-            "/content/defect_transfer_outputs/defect_all_model_feature_importance.csv\n",
-            "/content/defect_transfer_outputs/defect_shap_importance.csv\n",
-            "/content/defect_transfer_outputs/defect_transfer_prediction_result.csv\n",
-            "/content/defect_transfer_outputs/defect_transfer_risk_summary.csv\n"
-          ]
-        }
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
       ],
-      "source": [
-        "# 저장 경로\n",
-        "selected_model_path = OUTPUT_DIR / \"selected_defect_detector.joblib\"\n",
-        "selected_model_pkl_path = OUTPUT_DIR / \"selected_defect_detector.pkl\"\n",
-        "candidate_models_path = OUTPUT_DIR / \"candidate_defect_models.joblib\"\n",
-        "candidate_models_pkl_path = OUTPUT_DIR / \"candidate_defect_models.pkl\"\n",
-        "model_path = OUTPUT_DIR / \"lightgbm_defect_detector.joblib\"\n",
-        "model_pkl_path = OUTPUT_DIR / \"lightgbm_defect_detector.pkl\"\n",
-        "aux_model_path = OUTPUT_DIR / \"auxiliary_process_models.joblib\"\n",
-        "aux_model_pkl_path = OUTPUT_DIR / \"auxiliary_process_models.pkl\"\n",
-        "feature_path = OUTPUT_DIR / \"defect_model_features.json\"\n",
-        "metrics_path = OUTPUT_DIR / \"defect_model_metrics.json\"\n",
-        "comparison_path = OUTPUT_DIR / \"defect_model_comparison.csv\"\n",
-        "confusion_matrix_path = OUTPUT_DIR / \"defect_model_confusion_matrices.json\"\n",
-        "ai_management_path = OUTPUT_DIR / \"defect_ai_management_summary.json\"\n",
-        "importance_path = OUTPUT_DIR / \"defect_model_feature_importance.csv\"\n",
-        "all_importance_path = OUTPUT_DIR / \"defect_all_model_feature_importance.csv\"\n",
-        "shap_importance_path = OUTPUT_DIR / \"defect_shap_importance.csv\"\n",
-        "transfer_path = OUTPUT_DIR / \"defect_transfer_prediction_result.csv\"\n",
-        "risk_summary_path = OUTPUT_DIR / \"defect_transfer_risk_summary.csv\"\n",
-        "\n",
-        "# 모델 저장\n",
-        "joblib.dump(model, selected_model_path)\n",
-        "joblib.dump(trained_models, candidate_models_path)\n",
-        "joblib.dump(auxiliary_models, aux_model_path)\n",
-        "with open(selected_model_pkl_path, \"wb\") as f:\n",
-        "    pickle.dump(model, f)\n",
-        "with open(candidate_models_pkl_path, \"wb\") as f:\n",
-        "    pickle.dump(trained_models, f)\n",
-        "with open(aux_model_pkl_path, \"wb\") as f:\n",
-        "    pickle.dump(auxiliary_models, f)\n",
-        "\n",
-        "# 기존 LightGBM 파일명 호환 저장\n",
-        "if \"LightGBM\" in trained_models:\n",
-        "    joblib.dump(trained_models[\"LightGBM\"], model_path)\n",
-        "    with open(model_pkl_path, \"wb\") as f:\n",
-        "        pickle.dump(trained_models[\"LightGBM\"], f)\n",
-        "\n",
-        "feature_path.write_text(json.dumps(feature_cols, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-        "\n",
-        "confusion_payload = {\n",
-        "    name: matrix.tolist()\n",
-        "    for name, matrix in model_confusion_matrices.items()\n",
-        "}\n",
-        "confusion_matrix_path.write_text(json.dumps(confusion_payload, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-        "ai_management_path.write_text(json.dumps(ai_management_summary, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-        "\n",
-        "# 평가 지표 저장\n",
-        "metrics = {\n",
-        "    \"selected_model_name\": selected_model_name,\n",
-        "    \"roc_auc\": float(roc_auc),\n",
-        "    \"average_precision\": float(pr_auc),\n",
-        "    \"accuracy\": float(accuracy),\n",
-        "    \"false_positive_rate\": float(false_positive_rate),\n",
-        "    \"best_threshold\": float(best_threshold),\n",
-        "    \"f1_at_best_threshold\": float(f1_score(y_valid, valid_pred, zero_division=0)),\n",
-        "    \"confusion_matrix\": cm.tolist(),\n",
-        "    \"train_rows\": int(len(X_train)),\n",
-        "    \"valid_rows\": int(len(X_valid)),\n",
-        "    \"feature_count\": int(len(feature_cols)),\n",
-        "    \"positive_ratio_train\": float(y_train.mean()),\n",
-        "    \"candidate_models\": list(trained_models.keys()),\n",
-        "    \"cv_splits\": int(effective_cv_splits),\n",
-        "    \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
-        "    \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-        "    \"smote_enabled\": bool(USE_SMOTE),\n",
-        "    \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
-        "    \"threshold_strategy\": {\n",
-        "        \"type\": \"recall_priority\",\n",
-        "        \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
-        "        \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
-        "        \"beta\": float(THRESHOLD_BETA),\n",
-        "    },\n",
-        "    \"model_selection_reason\": ai_management_summary[\"모델_선정_근거\"],\n",
-        "    \"auxiliary_metrics\": auxiliary_metrics,\n",
-        "    \"auxiliary_feature_columns\": [c for c in feature_cols if c.startswith(\"aux_\")],\n",
-        "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
-        "}\n",
-        "metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-        "\n",
-        "# 분석 결과 저장\n",
-        "cv_results.to_csv(cv_results_path, index=False)\n",
-        "if \"cv_fold_results\" in globals():\n",
-        "    cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
-        "model_comparison.to_csv(comparison_path, index=False)\n",
-        "importance_df.to_csv(importance_path, index=False)\n",
-        "all_model_importance.to_csv(all_importance_path, index=False)\n",
-        "shap_importance.to_csv(shap_importance_path, index=False)\n",
-        "transfer_predictions.to_csv(transfer_path, index=False)\n",
-        "risk_summary.to_csv(risk_summary_path, index=False)\n",
-        "\n",
-        "print(\"Saved outputs:\")\n",
-        "for path in [\n",
-        "    selected_model_path,\n",
-        "    selected_model_pkl_path,\n",
-        "    candidate_models_path,\n",
-        "    candidate_models_pkl_path,\n",
-        "    model_path,\n",
-        "    model_pkl_path,\n",
-        "    aux_model_path,\n",
-        "    aux_model_pkl_path,\n",
-        "    feature_path,\n",
-        "    metrics_path,\n",
-        "    cv_results_path,\n",
-        "    comparison_path,\n",
-        "    confusion_matrix_path,\n",
-        "    ai_management_path,\n",
-        "    importance_path,\n",
-        "    all_importance_path,\n",
-        "    shap_importance_path,\n",
-        "    transfer_path,\n",
-        "    risk_summary_path,\n",
-        "]:\n",
-        "    print(path)\n"
-      ]
+      "image/png": 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\n"
+     },
+     "metadata": {}
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "# 전이 위험 시각화\n",
+    "plt.figure(figsize=(9, 5))\n",
+    "top_flow = risk_summary.head(12).copy()\n",
+    "top_flow[\"flow\"] = top_flow[\"source_process_code\"] + \" -> \" + top_flow[\"target_process_code\"] + \" (\" + top_flow[\"risk_grade\"] + \")\"\n",
+    "sns.barplot(data=top_flow, y=\"flow\", x=\"avg_target_defect_probability\", hue=\"risk_grade\", dodge=False)\n",
+    "plt.title(\"Process Transfer Risk by SHAP Influence\")\n",
+    "plt.xlabel(\"Avg Final Defect Probability\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.legend(title=\"Risk\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 불량 확률 분포 시각화\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "sns.histplot(data=transfer_predictions, x=\"target_defect_probability\", hue=\"risk_grade\", bins=40, multiple=\"stack\")\n",
+    "plt.title(\"Predicted Final Defect Probability Distribution\")\n",
+    "plt.xlabel(\"Defect Probability\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43d99e60",
+   "metadata": {
+    "id": "43d99e60"
+   },
+   "source": [
+    "## 12. AI 모델 관리 항목\n",
+    "\n",
+    "모델 선정 근거, 데이터셋 규모, 테스트 케이스 수, Accuracy, False Positive Rate, 성능 개선 현황을 운영 관리용 요약으로 정리합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c79bba33",
+   "metadata": {
     "colab": {
-      "provenance": []
+     "base_uri": "https://localhost:8080/",
+     "height": 237
     },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
+    "id": "c79bba33",
+    "outputId": "b4ed5007-4f34-40a7-8c33-edc4bc328bb1"
+   },
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                         관리 항목  \\\n",
+       "0                  모델 선정 근거 정리   \n",
+       "1                   데이터셋 규모 관리   \n",
+       "2                 테스트 케이스 수 관리   \n",
+       "3             정확도(Accuracy) 측정   \n",
+       "4  오탐률(False Positive Rate) 측정   \n",
+       "5               모델 성능 개선 현황 관리   \n",
+       "\n",
+       "                                                   값  \n",
+       "0  RandomForest 모델이 validation PR-AUC/Recall/F1 기...  \n",
+       "1  {\"max_rows\": 300000, \"profile_rows\": 50000, \"t...  \n",
+       "2  {\"validation_total\": 60000, \"validation_normal...  \n",
+       "3                                            0.91395  \n",
+       "4                                           0.082851  \n",
+       "5  {\"baseline_model\": \"LightGBM\", \"baseline_pr_au...  "
+      ],
+      "text/html": [
+       "\n",
+       "  <div id=\"df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>관리 항목</th>\n",
+       "      <th>값</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>모델 선정 근거 정리</td>\n",
+       "      <td>RandomForest 모델이 validation PR-AUC/Recall/F1 기...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>데이터셋 규모 관리</td>\n",
+       "      <td>{\"max_rows\": 300000, \"profile_rows\": 50000, \"t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>테스트 케이스 수 관리</td>\n",
+       "      <td>{\"validation_total\": 60000, \"validation_normal...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>정확도(Accuracy) 측정</td>\n",
+       "      <td>0.91395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>오탐률(False Positive Rate) 측정</td>\n",
+       "      <td>0.082851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>모델 성능 개선 현황 관리</td>\n",
+       "      <td>{\"baseline_model\": \"LightGBM\", \"baseline_pr_au...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "  <div id=\"id_8772c170-37d4-4cc2-8541-c0f3e4c01024\">\n",
+       "    <style>\n",
+       "      .colab-df-generate {\n",
+       "        background-color: #E8F0FE;\n",
+       "        border: none;\n",
+       "        border-radius: 50%;\n",
+       "        cursor: pointer;\n",
+       "        display: none;\n",
+       "        fill: #1967D2;\n",
+       "        height: 32px;\n",
+       "        padding: 0 0 0 0;\n",
+       "        width: 32px;\n",
+       "      }\n",
+       "\n",
+       "      .colab-df-generate:hover {\n",
+       "        background-color: #E2EBFA;\n",
+       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "        fill: #174EA6;\n",
+       "      }\n",
+       "\n",
+       "      [theme=dark] .colab-df-generate {\n",
+       "        background-color: #3B4455;\n",
+       "        fill: #D2E3FC;\n",
+       "      }\n",
+       "\n",
+       "      [theme=dark] .colab-df-generate:hover {\n",
+       "        background-color: #434B5C;\n",
+       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "        fill: #FFFFFF;\n",
+       "      }\n",
+       "    </style>\n",
+       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ai_management_table')\"\n",
+       "            title=\"Generate code using this dataframe.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "       width=\"24px\">\n",
+       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "    <script>\n",
+       "      (() => {\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#id_8772c170-37d4-4cc2-8541-c0f3e4c01024 button.colab-df-generate');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      buttonEl.onclick = () => {\n",
+       "        google.colab.notebook.generateWithVariable('ai_management_table');\n",
+       "      }\n",
+       "      })();\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "variable_name": "ai_management_table",
+       "summary": "{\n  \"name\": \"ai_management_table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"\\uad00\\ub9ac \\ud56d\\ubaa9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\ubaa8\\ub378 \\uc120\\uc815 \\uadfc\\uac70 \\uc815\\ub9ac\",\n          \"\\ub370\\uc774\\ud130\\uc14b \\uaddc\\ubaa8 \\uad00\\ub9ac\",\n          \"\\ubaa8\\ub378 \\uc131\\ub2a5 \\uac1c\\uc120 \\ud604\\ud669 \\uad00\\ub9ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uac12\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"RandomForest \\ubaa8\\ub378\\uc774 validation PR-AUC/Recall/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\",\n          \"{\\\"max_rows\\\": 300000, \\\"profile_rows\\\": 50000, \\\"train_rows\\\": 240000, \\\"candidate_train_rows\\\": 120000, \\\"valid_rows\\\": 60000, \\\"feature_count\\\": 457, \\\"positive_ratio\\\": 0.00565, \\\"full_data_refit\\\": false}\",\n          \"{\\\"baseline_model\\\": \\\"LightGBM\\\", \\\"baseline_pr_auc\\\": 0.050323296178874356, \\\"selected_model\\\": \\\"RandomForest\\\", \\\"selected_pr_auc\\\": 0.06255052428644334, \\\"pr_auc_improvement_over_lightgbm\\\": 0.012227228107568981}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "ai_management_table = pd.DataFrame([\n",
+    "    {\"관리 항목\": \"모델 선정 근거 정리\", \"값\": ai_management_summary[\"모델_선정_근거\"]},\n",
+    "    {\"관리 항목\": \"데이터셋 규모 관리\", \"값\": json.dumps(ai_management_summary[\"데이터셋_규모_관리\"], ensure_ascii=False)},\n",
+    "    {\"관리 항목\": \"테스트 케이스 수 관리\", \"값\": json.dumps(ai_management_summary[\"테스트_케이스_수_관리\"], ensure_ascii=False)},\n",
+    "    {\"관리 항목\": \"정확도(Accuracy) 측정\", \"값\": round(ai_management_summary[\"정확도_Accuracy\"], 6)},\n",
+    "    {\"관리 항목\": \"오탐률(False Positive Rate) 측정\", \"값\": round(ai_management_summary[\"오탐률_False_Positive_Rate\"], 6)},\n",
+    "    {\"관리 항목\": \"모델 성능 개선 현황 관리\", \"값\": json.dumps(ai_management_summary[\"모델_성능_개선_현황\"], ensure_ascii=False)},\n",
+    "])\n",
+    "\n",
+    "display(ai_management_table)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "Q5r_SU2bbEfD",
+   "metadata": {
+    "id": "Q5r_SU2bbEfD"
+   },
+   "source": [
+    "## 13. 모델 및 결과 저장\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "44a36633",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11"
+    "id": "44a36633",
+    "outputId": "9dbc6133-c938-4715-dc60-ea025f990e55"
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saved outputs:\n",
+      "/content/defect_transfer_outputs/selected_defect_detector.joblib\n",
+      "/content/defect_transfer_outputs/selected_defect_detector.pkl\n",
+      "/content/defect_transfer_outputs/candidate_defect_models.joblib\n",
+      "/content/defect_transfer_outputs/candidate_defect_models.pkl\n",
+      "/content/defect_transfer_outputs/lightgbm_defect_detector.joblib\n",
+      "/content/defect_transfer_outputs/lightgbm_defect_detector.pkl\n",
+      "/content/defect_transfer_outputs/auxiliary_process_models.joblib\n",
+      "/content/defect_transfer_outputs/auxiliary_process_models.pkl\n",
+      "/content/defect_transfer_outputs/defect_model_features.json\n",
+      "/content/defect_transfer_outputs/defect_model_metrics.json\n",
+      "/content/defect_transfer_outputs/defect_model_cv_results.csv\n",
+      "/content/defect_transfer_outputs/defect_model_comparison.csv\n",
+      "/content/defect_transfer_outputs/defect_model_confusion_matrices.json\n",
+      "/content/defect_transfer_outputs/defect_ai_management_summary.json\n",
+      "/content/defect_transfer_outputs/defect_model_feature_importance.csv\n",
+      "/content/defect_transfer_outputs/defect_all_model_feature_importance.csv\n",
+      "/content/defect_transfer_outputs/defect_shap_importance.csv\n",
+      "/content/defect_transfer_outputs/defect_transfer_prediction_result.csv\n",
+      "/content/defect_transfer_outputs/defect_transfer_risk_summary.csv\n"
+     ]
     }
+   ],
+   "source": [
+    "# 저장 경로\n",
+    "selected_model_path = OUTPUT_DIR / \"selected_defect_detector.joblib\"\n",
+    "selected_model_pkl_path = OUTPUT_DIR / \"selected_defect_detector.pkl\"\n",
+    "candidate_models_path = OUTPUT_DIR / \"candidate_defect_models.joblib\"\n",
+    "candidate_models_pkl_path = OUTPUT_DIR / \"candidate_defect_models.pkl\"\n",
+    "model_path = OUTPUT_DIR / \"lightgbm_defect_detector.joblib\"\n",
+    "model_pkl_path = OUTPUT_DIR / \"lightgbm_defect_detector.pkl\"\n",
+    "aux_model_path = OUTPUT_DIR / \"auxiliary_process_models.joblib\"\n",
+    "aux_model_pkl_path = OUTPUT_DIR / \"auxiliary_process_models.pkl\"\n",
+    "feature_path = OUTPUT_DIR / \"defect_model_features.json\"\n",
+    "metrics_path = OUTPUT_DIR / \"defect_model_metrics.json\"\n",
+    "comparison_path = OUTPUT_DIR / \"defect_model_comparison.csv\"\n",
+    "confusion_matrix_path = OUTPUT_DIR / \"defect_model_confusion_matrices.json\"\n",
+    "ai_management_path = OUTPUT_DIR / \"defect_ai_management_summary.json\"\n",
+    "importance_path = OUTPUT_DIR / \"defect_model_feature_importance.csv\"\n",
+    "all_importance_path = OUTPUT_DIR / \"defect_all_model_feature_importance.csv\"\n",
+    "shap_importance_path = OUTPUT_DIR / \"defect_shap_importance.csv\"\n",
+    "transfer_path = OUTPUT_DIR / \"defect_transfer_prediction_result.csv\"\n",
+    "risk_summary_path = OUTPUT_DIR / \"defect_transfer_risk_summary.csv\"\n",
+    "\n",
+    "# 모델 저장\n",
+    "joblib.dump(model, selected_model_path)\n",
+    "joblib.dump(trained_models, candidate_models_path)\n",
+    "joblib.dump(auxiliary_models, aux_model_path)\n",
+    "with open(selected_model_pkl_path, \"wb\") as f:\n",
+    "    pickle.dump(model, f)\n",
+    "with open(candidate_models_pkl_path, \"wb\") as f:\n",
+    "    pickle.dump(trained_models, f)\n",
+    "with open(aux_model_pkl_path, \"wb\") as f:\n",
+    "    pickle.dump(auxiliary_models, f)\n",
+    "\n",
+    "# 기존 LightGBM 파일명 호환 저장\n",
+    "if \"LightGBM\" in trained_models:\n",
+    "    joblib.dump(trained_models[\"LightGBM\"], model_path)\n",
+    "    with open(model_pkl_path, \"wb\") as f:\n",
+    "        pickle.dump(trained_models[\"LightGBM\"], f)\n",
+    "\n",
+    "feature_path.write_text(json.dumps(feature_cols, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "\n",
+    "confusion_payload = {\n",
+    "    name: matrix.tolist()\n",
+    "    for name, matrix in model_confusion_matrices.items()\n",
+    "}\n",
+    "confusion_matrix_path.write_text(json.dumps(confusion_payload, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "ai_management_path.write_text(json.dumps(ai_management_summary, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "\n",
+    "# 평가 지표 저장\n",
+    "metrics = {\n",
+    "    \"selected_model_name\": selected_model_name,\n",
+    "    \"roc_auc\": float(roc_auc),\n",
+    "    \"average_precision\": float(pr_auc),\n",
+    "    \"accuracy\": float(accuracy),\n",
+    "    \"false_positive_rate\": float(false_positive_rate),\n",
+    "    \"best_threshold\": float(best_threshold),\n",
+    "    \"f1_at_best_threshold\": float(f1_score(y_valid, valid_pred, zero_division=0)),\n",
+    "    \"confusion_matrix\": cm.tolist(),\n",
+    "    \"train_rows\": int(len(X_train)),\n",
+    "    \"valid_rows\": int(len(X_valid)),\n",
+    "    \"feature_count\": int(len(feature_cols)),\n",
+    "    \"positive_ratio_train\": float(y_train.mean()),\n",
+    "    \"candidate_models\": list(trained_models.keys()),\n",
+    "    \"cv_splits\": int(effective_cv_splits),\n",
+    "    \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
+    "    \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
+    "    \"smote_enabled\": bool(USE_SMOTE),\n",
+    "    \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
+    "    \"threshold_strategy\": {\n",
+    "        \"type\": \"recall_priority\",\n",
+    "        \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
+    "        \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
+    "        \"beta\": float(THRESHOLD_BETA),\n",
+    "    },\n",
+    "    \"model_selection_reason\": ai_management_summary[\"모델_선정_근거\"],\n",
+    "    \"auxiliary_metrics\": auxiliary_metrics,\n",
+    "    \"auxiliary_feature_columns\": [c for c in feature_cols if c.startswith(\"aux_\")],\n",
+    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
+    "}\n",
+    "metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+    "\n",
+    "# 분석 결과 저장\n",
+    "cv_results.to_csv(cv_results_path, index=False)\n",
+    "if \"cv_fold_results\" in globals():\n",
+    "    cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
+    "model_comparison.to_csv(comparison_path, index=False)\n",
+    "importance_df.to_csv(importance_path, index=False)\n",
+    "all_model_importance.to_csv(all_importance_path, index=False)\n",
+    "shap_importance.to_csv(shap_importance_path, index=False)\n",
+    "transfer_predictions.to_csv(transfer_path, index=False)\n",
+    "risk_summary.to_csv(risk_summary_path, index=False)\n",
+    "\n",
+    "print(\"Saved outputs:\")\n",
+    "for path in [\n",
+    "    selected_model_path,\n",
+    "    selected_model_pkl_path,\n",
+    "    candidate_models_path,\n",
+    "    candidate_models_pkl_path,\n",
+    "    model_path,\n",
+    "    model_pkl_path,\n",
+    "    aux_model_path,\n",
+    "    aux_model_pkl_path,\n",
+    "    feature_path,\n",
+    "    metrics_path,\n",
+    "    cv_results_path,\n",
+    "    comparison_path,\n",
+    "    confusion_matrix_path,\n",
+    "    ai_management_path,\n",
+    "    importance_path,\n",
+    "    all_importance_path,\n",
+    "    shap_importance_path,\n",
+    "    transfer_path,\n",
+    "    risk_summary_path,\n",
+    "]:\n",
+    "    print(path)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
\ No newline at end of file
diff --git a/app/ml/training/defect_transfer_prediction_model_json.ipynb b/app/ml/training/defect_transfer_prediction_model_json.ipynb
deleted file mode 100644
index c31361c..0000000
--- a/app/ml/training/defect_transfer_prediction_model_json.ipynb
+++ /dev/null
@@ -1,2162 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "873328b3",
-   "metadata": {
-    "id": "873328b3"
-   },
-   "source": [
-    "# event_json 기반 SHAP 공정 전이 불량 예측\n",
-    "\n",
-    "1. `manufacturing_event_json.csv`의 `event_json` 파싱\n",
-    "2. `sourceTrace`로 원본 Bosch/Ford/열화상 라벨 복원\n",
-    "3. 동일 차량의 `PRESS → BODY → PAINT → ASSEMBLY` 이벤트 연결\n",
-    "4. 대상 공정 이전 JSON 특성으로 다음 공정 불량 학습\n",
-    "5. 후보 모델 비교 및 SHAP 기반 공정별 영향 분석\n",
-    "6. 공정 전이 위험 결과와 모델 산출물 저장\n",
-    "\n",
-    "> CSV의 13번째 컬럼은 `is_sent`이며 불량 라벨이 아닙니다. 불량 정답은 JSON의\n",
-    "> `sourceTrace`가 가리키는 원본 데이터셋에서 학습 시점에만 복원합니다.\n",
-    ">\n",
-    "> 현재 이벤트는 서로 다른 공개 데이터셋을 조합한 합성 이벤트이므로 결과는\n",
-    "> **합성 데이터 기반 공정 전이 위험 예측**으로 해석합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "P44DGP2TDAt4",
-   "metadata": {
-    "id": "P44DGP2TDAt4"
-   },
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "hMAQMhIqbEe9",
-   "metadata": {
-    "id": "hMAQMhIqbEe9"
-   },
-   "outputs": [],
-   "source": [
-    "# Colab 환경에서 필요한 패키지 설치\n",
-    "import importlib.util\n",
-    "import subprocess\n",
-    "import sys\n",
-    "\n",
-    "def ensure_package(import_name: str, pip_name: str | None = None):\n",
-    "    pip_name = pip_name or import_name\n",
-    "    if importlib.util.find_spec(import_name) is None:\n",
-    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", pip_name, \"-q\"])\n",
-    "\n",
-    "ensure_package(\"lightgbm\")\n",
-    "ensure_package(\"shap\")\n",
-    "ensure_package(\"joblib\")\n",
-    "ensure_package(\"optuna\")\n",
-    "ensure_package(\"imblearn\", \"imbalanced-learn\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "XFBnJXPabHMi",
-   "metadata": {
-    "id": "XFBnJXPabHMi"
-   },
-   "outputs": [],
-   "source": [
-    "# CPU 병렬 사용\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "model = lgb.LGBMClassifier(\n",
-    "    n_estimators=300,\n",
-    "    learning_rate=0.05,\n",
-    "    num_leaves=31,\n",
-    "    n_jobs=-1,\n",
-    "    random_state=42\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7a3d724",
-   "metadata": {
-    "id": "f7a3d724"
-   },
-   "source": [
-    "> 필요한 추가 패키지(`optuna`, `imbalanced-learn`)는 위 설치 셀에서 자동 확인/설치합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e37daef5",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "e37daef5",
-    "outputId": "d3014dc5-ad21-4f08-d5e8-65c43e05146f"
-   },
-   "outputs": [],
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "import gc\n",
-    "import json\n",
-    "import pickle\n",
-    "import re\n",
-    "import shutil\n",
-    "import warnings\n",
-    "from datetime import datetime, timezone\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import joblib\n",
-    "import lightgbm as lgb\n",
-    "import optuna\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import shap\n",
-    "from imblearn.over_sampling import SMOTE\n",
-    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
-    "from optuna.samplers import TPESampler\n",
-    "from sklearn.base import clone\n",
-    "from sklearn.ensemble import ExtraTreesClassifier, IsolationForest, RandomForestClassifier\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.metrics import (\n",
-    "    ConfusionMatrixDisplay,\n",
-    "    PrecisionRecallDisplay,\n",
-    "    RocCurveDisplay,\n",
-    "    accuracy_score,\n",
-    "    average_precision_score,\n",
-    "    classification_report,\n",
-    "    confusion_matrix,\n",
-    "    f1_score,\n",
-    "    precision_score,\n",
-    "    precision_recall_curve,\n",
-    "    recall_score,\n",
-    "    roc_auc_score,\n",
-    ")\n",
-    "from sklearn.model_selection import (GroupShuffleSplit, ParameterSampler, StratifiedGroupKFold, train_test_split)\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "pd.set_option(\"display.max_columns\", 120)\n",
-    "pd.set_option(\"display.max_rows\", 80)\n",
-    "\n",
-    "RANDOM_STATE = 42\n",
-    "np.random.seed(RANDOM_STATE)\n",
-    "\n",
-    "print(\"pandas\", pd.__version__)\n",
-    "print(\"lightgbm\", lgb.__version__)\n",
-    "print(\"optuna\", optuna.__version__)\n",
-    "print(\"shap\", shap.__version__)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e3e0e4c",
-   "metadata": {
-    "id": "0e3e0e4c"
-   },
-   "source": [
-    "## 1. 데이터셋 및 산출물 경로 설정\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "abb8856c",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "abb8856c",
-    "outputId": "d0b5cf16-76a8-4d8e-cf3f-ce1f188f80a2"
-   },
-   "outputs": [],
-   "source": [
-    "# Drive 마운트\n",
-    "try:\n",
-    "    from google.colab import drive\n",
-    "    drive.mount(\"/content/drive\")\n",
-    "    IN_COLAB = True\n",
-    "except Exception:\n",
-    "    IN_COLAB = False\n",
-    "    print(\"Colab이 아니므로 Google Drive mount를 건너뜁니다.\")\n",
-    "\n",
-    "PROJECT_ROOT = Path(\"/content/drive/MyDrive\") if IN_COLAB else Path.cwd()\n",
-    "PROCESS_ROOT = (\n",
-    "    PROJECT_ROOT / \"aims_dataset\"\n",
-    "    if IN_COLAB\n",
-    "    else PROJECT_ROOT / \"app\" / \"ml\" / \"datasets\" / \"process\"\n",
-    ")\n",
-    "OUTPUT_DIR = Path(\"/content/defect_transfer_outputs\") if IN_COLAB else Path(\"outputs/defect_transfer\")\n",
-    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
-    "\n",
-    "EVENT_JSON_PATH = PROCESS_ROOT / \"manufacturing_event_json.csv\"\n",
-    "BOSCH_ROOT = PROCESS_ROOT / \"bosch-production-line-performance\"\n",
-    "THERMAL_ROOT = PROCESS_ROOT / \"머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)\"\n",
-    "FORD_ROOT = PROCESS_ROOT / \"Ford 엔진 진동 데이터셋\"\n",
-    "\n",
-    "NUMERIC_PATH = BOSCH_ROOT / \"train_numeric.csv\"\n",
-    "THERMAL_LEFT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_left_label.json\"\n",
-    "THERMAL_RIGHT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_right_label.json\"\n",
-    "FORD_TRAIN_PATH = FORD_ROOT / \"FordA_TRAIN.txt\"\n",
-    "FORD_TRAIN_ARFF_PATH = FORD_ROOT / \"FordA_TRAIN.arff\"\n",
-    "\n",
-    "print(\"PROJECT_ROOT:\", PROJECT_ROOT)\n",
-    "print(\"PROCESS_ROOT:\", PROCESS_ROOT)\n",
-    "print(\"EVENT_JSON_PATH:\", EVENT_JSON_PATH)\n",
-    "print(\"OUTPUT_DIR:\", OUTPUT_DIR)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lc2ZXZzwbEe_",
-   "metadata": {
-    "id": "lc2ZXZzwbEe_"
-   },
-   "source": [
-    "## 2. 대용량 event_json 로드 설정\n",
-    "\n",
-    "CSV는 헤더가 없는 16컬럼 테이블이며 12번째 컬럼이 `event_json`, 13번째 컬럼이\n",
-    "`is_sent`입니다. 전체 파일을 메모리에 올리지 않고 chunk 단위로 읽습니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66fcea04",
-   "metadata": {
-    "id": "66fcea04"
-   },
-   "outputs": [],
-   "source": [
-    "# 학습 규모 설정\n",
-    "MAX_ROWS = 300_000\n",
-    "EVENT_CHUNK_SIZE = 20_000\n",
-    "ENABLE_HYPERPARAMETER_TUNING = True\n",
-    "CV_N_SPLITS = 5\n",
-    "CV_N_ITER = 15\n",
-    "ENABLE_OPTUNA_TUNING = True\n",
-    "CV_SAMPLE_SIZE = 30_000\n",
-    "FINAL_TRAIN_SAMPLE_SIZE = 120_000\n",
-    "RETRAIN_SELECTED_MODEL_ON_FULL_DATA = False\n",
-    "USE_SMOTE = False\n",
-    "SMOTE_SAMPLING_STRATEGY = 0.50\n",
-    "RECALL_PRIORITY_TARGET = 0.35\n",
-    "RECALL_PRIORITY_MIN_PRECISION = 0.02\n",
-    "THRESHOLD_BETA = 2.0\n",
-    "\n",
-    "PROCESS_FLOW = [\"PRESS\", \"BODY\", \"PAINT\", \"ASSEMBLY\"]\n",
-    "TRANSITIONS = {\n",
-    "    \"BODY\": \"PRESS\",\n",
-    "    \"PAINT\": \"BODY\",\n",
-    "    \"ASSEMBLY\": \"PAINT\",\n",
-    "}\n",
-    "PROCESS_DISPLAY = {\n",
-    "    \"PRESS\": \"프레스\",\n",
-    "    \"BODY\": \"차체\",\n",
-    "    \"PAINT\": \"도장\",\n",
-    "    \"ASSEMBLY\": \"의장\",\n",
-    "    \"FINAL_INSPECTION\": \"최종검사\",\n",
-    "    \"UNKNOWN\": \"미분류\",\n",
-    "}\n",
-    "\n",
-    "EVENT_COLUMNS = [\n",
-    "    \"id\", \"event_id\", \"event_time\", \"car_master_id\", \"equipment_id\",\n",
-    "    \"process_code\", \"station_code\", \"equipment_code\", \"equipment_type\",\n",
-    "    \"equipment_status\", \"event_type\", \"event_json\", \"is_sent\", \"sent_at\",\n",
-    "    \"created_at\", \"updated_at\",\n",
-    "]\n",
-    "\n",
-    "# Colab Drive FUSE가 불안정할 수 있어 데이터셋을 런타임 캐시에 복사합니다.\n",
-    "LOCAL_DATA_CACHE = Path(\"/content/aims_dataset_cache\") if IN_COLAB else None\n",
-    "\n",
-    "\n",
-    "def local_dataset_path(path: Path) -> Path:\n",
-    "    path = Path(path)\n",
-    "    if not IN_COLAB or LOCAL_DATA_CACHE is None:\n",
-    "        return path\n",
-    "\n",
-    "    try:\n",
-    "        relative_path = path.relative_to(PROJECT_ROOT)\n",
-    "    except ValueError:\n",
-    "        relative_path = Path(path.name)\n",
-    "\n",
-    "    cached_path = LOCAL_DATA_CACHE / relative_path\n",
-    "    if cached_path.exists():\n",
-    "        try:\n",
-    "            if cached_path.stat().st_size == path.stat().st_size:\n",
-    "                return cached_path\n",
-    "        except OSError:\n",
-    "            return cached_path\n",
-    "\n",
-    "    cached_path.parent.mkdir(parents=True, exist_ok=True)\n",
-    "    print(f\"cache copy: {path} -> {cached_path}\")\n",
-    "    shutil.copy2(path, cached_path)\n",
-    "    return cached_path\n",
-    "\n",
-    "\n",
-    "def assert_file(path: Path) -> None:\n",
-    "    if not path.exists():\n",
-    "        cached_path = local_dataset_path(path) if IN_COLAB else path\n",
-    "        if not cached_path.exists():\n",
-    "            raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
-    "\n",
-    "\n",
-    "def read_json_stable(path: Path):\n",
-    "    with open(local_dataset_path(path), \"r\", encoding=\"utf-8\") as file:\n",
-    "        return json.load(file)\n",
-    "\n",
-    "\n",
-    "def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    for col in df.columns:\n",
-    "        if pd.api.types.is_integer_dtype(df[col]):\n",
-    "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
-    "        elif pd.api.types.is_float_dtype(df[col]):\n",
-    "            df[col] = pd.to_numeric(df[col], downcast=\"float\")\n",
-    "    return df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "508b9115",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 290
-    },
-    "id": "508b9115",
-    "outputId": "eaef9a23-f57f-4c4d-85a1-69e744c2cc71"
-   },
-   "outputs": [],
-   "source": [
-    "# 원본 데이터셋의 정답 라벨 로드\n",
-    "def load_ford_label_map() -> dict[int, int]:\n",
-    "    labels = {}\n",
-    "    path = local_dataset_path(FORD_TRAIN_PATH)\n",
-    "    if path.exists():\n",
-    "        with path.open(encoding=\"utf-8\", errors=\"ignore\") as file:\n",
-    "            for line in file:\n",
-    "                values = line.replace(\"\\x00\", \" \").strip().split()\n",
-    "                if len(values) >= 501:\n",
-    "                    labels[len(labels) + 1] = int(float(values[0]))\n",
-    "                if len(labels) >= 4096:\n",
-    "                    break\n",
-    "\n",
-    "    if labels:\n",
-    "        return labels\n",
-    "\n",
-    "    # TXT가 손상되었거나 비어 있으면 ARFF의 마지막 class 값을 사용합니다.\n",
-    "    path = local_dataset_path(FORD_TRAIN_ARFF_PATH)\n",
-    "    in_data = False\n",
-    "    with path.open(encoding=\"utf-8\", errors=\"ignore\") as file:\n",
-    "        for line in file:\n",
-    "            value = line.strip()\n",
-    "            if not value or value.startswith(\"%\"):\n",
-    "                continue\n",
-    "            if value.lower() == \"@data\":\n",
-    "                in_data = True\n",
-    "                continue\n",
-    "            if not in_data:\n",
-    "                continue\n",
-    "            parts = value.split(\",\")\n",
-    "            if len(parts) >= 501:\n",
-    "                labels[len(labels) + 1] = int(float(parts[-1]))\n",
-    "            if len(labels) >= 4096:\n",
-    "                break\n",
-    "    return labels\n",
-    "\n",
-    "\n",
-    "def load_vision_label_map() -> dict[tuple[str, int], int]:\n",
-    "    labels = {}\n",
-    "    for side, path in [\n",
-    "        (\"LEFT\", THERMAL_LEFT_LABEL_PATH),\n",
-    "        (\"RIGHT\", THERMAL_RIGHT_LABEL_PATH),\n",
-    "    ]:\n",
-    "        for row_id, label in enumerate(read_json_stable(path), start=1):\n",
-    "            labels[(side, row_id)] = int(float(label))\n",
-    "    return labels\n",
-    "\n",
-    "\n",
-    "def load_bosch_label_map() -> dict[int, int]:\n",
-    "    df = pd.read_csv(\n",
-    "        local_dataset_path(NUMERIC_PATH),\n",
-    "        usecols=[\"Id\", \"Response\"],\n",
-    "        nrows=4096,\n",
-    "    )\n",
-    "    return dict(zip(df[\"Id\"].astype(int), df[\"Response\"].astype(int)))\n",
-    "\n",
-    "\n",
-    "for required_path in [\n",
-    "    EVENT_JSON_PATH,\n",
-    "    NUMERIC_PATH,\n",
-    "    THERMAL_LEFT_LABEL_PATH,\n",
-    "    THERMAL_RIGHT_LABEL_PATH,\n",
-    "]:\n",
-    "    assert_file(required_path)\n",
-    "\n",
-    "ford_label_map = load_ford_label_map()\n",
-    "vision_label_map = load_vision_label_map()\n",
-    "bosch_label_map = load_bosch_label_map()\n",
-    "\n",
-    "print(\"Ford labels:\", len(ford_label_map), pd.Series(ford_label_map).value_counts().to_dict())\n",
-    "print(\"Vision labels:\", len(vision_label_map), pd.Series(vision_label_map).value_counts().to_dict())\n",
-    "print(\"Bosch labels:\", len(bosch_label_map), pd.Series(bosch_label_map).value_counts().to_dict())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11973c36",
-   "metadata": {
-    "id": "11973c36"
-   },
-   "source": [
-    "## 3. event_json 특성 생성\n",
-    "\n",
-    "동일 차량의 공정 이벤트를 순서대로 누적하고 대상 공정 직전까지의 JSON만 입력으로\n",
-    "사용합니다. `visionLabel`, `healthStatus`, 조립 오류 수처럼 원본 라벨에서 직접 생성된\n",
-    "필드는 입력에서 제외합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd4b603e",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 273
-    },
-    "id": "dd4b603e",
-    "outputId": "84ce5bbe-1ecd-490a-884b-4e42c0252e7f"
-   },
-   "outputs": [],
-   "source": [
-    "LEAKAGE_JSON_PATHS = {\n",
-    "    \"equipmentStatus.healthStatus\",\n",
-    "    \"equipmentStatus.operationStatus\",\n",
-    "    \"processData.paint.visionLabel\",\n",
-    "    \"processData.paint.defectScore\",\n",
-    "    \"processData.paint.surfaceQualityScore\",\n",
-    "    \"processData.body.robotMotionStatus\",\n",
-    "    \"processData.assembly.actualSequence\",\n",
-    "    \"processData.assembly.missingPartCount\",\n",
-    "    \"processData.assembly.fasteningErrorCount\",\n",
-    "    \"processData.assembly.sequenceErrorCount\",\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def add_numeric_features(output: dict, prefix: str, values: dict) -> None:\n",
-    "    for key, value in values.items():\n",
-    "        feature_name = f\"{prefix}{key}\"\n",
-    "        if isinstance(value, bool):\n",
-    "            output[feature_name] = int(value)\n",
-    "        elif isinstance(value, (int, float)) and not isinstance(value, bool):\n",
-    "            output[feature_name] = float(value)\n",
-    "\n",
-    "\n",
-    "def extract_event_features(payload: dict) -> dict[str, float]:\n",
-    "    process_code = payload.get(\"location\", {}).get(\"processCode\", \"UNKNOWN\")\n",
-    "    prefix = f\"{process_code}__\"\n",
-    "    features = {}\n",
-    "\n",
-    "    sensor = payload.get(\"sensor\", {})\n",
-    "    for section in [\"current\", \"vibration\", \"robotArmVibration\", \"thermal\"]:\n",
-    "        add_numeric_features(features, f\"{prefix}sensor_{section}_\", sensor.get(section, {}))\n",
-    "\n",
-    "    add_numeric_features(features, f\"{prefix}metrics_\", payload.get(\"processMetrics\", {}))\n",
-    "    add_numeric_features(features, f\"{prefix}manufacturing_\", payload.get(\"manufacturing\", {}))\n",
-    "    add_numeric_features(features, f\"{prefix}product_\", payload.get(\"product\", {}))\n",
-    "\n",
-    "    process_data = payload.get(\"processData\", {})\n",
-    "    if process_code == \"PRESS\":\n",
-    "        add_numeric_features(features, f\"{prefix}process_\", process_data.get(\"press\", {}))\n",
-    "    elif process_code == \"BODY\":\n",
-    "        add_numeric_features(\n",
-    "            features,\n",
-    "            f\"{prefix}frequency_\",\n",
-    "            process_data.get(\"body\", {}).get(\"frequencyBands\", {}),\n",
-    "        )\n",
-    "    elif process_code == \"PAINT\":\n",
-    "        paint = process_data.get(\"paint\", {})\n",
-    "        for key in [\"thermalStdTemp\", \"thicknessValue\"]:\n",
-    "            if isinstance(paint.get(key), (int, float)):\n",
-    "                features[f\"{prefix}process_{key}\"] = float(paint[key])\n",
-    "\n",
-    "    station_code = str(payload.get(\"location\", {}).get(\"stationCode\", \"\"))\n",
-    "    station_match = re.search(r\"_(\\d+)$\", station_code)\n",
-    "    if station_match:\n",
-    "        features[f\"{prefix}station_number\"] = float(station_match.group(1))\n",
-    "    return features\n",
-    "\n",
-    "\n",
-    "def restore_target_label(payload: dict, target_process: str) -> tuple[int | None, str, int | None]:\n",
-    "    trace = payload.get(\"sourceTrace\", {})\n",
-    "    if target_process == \"BODY\":\n",
-    "        row_id = int(trace.get(\"fordRowId\", 0) or 0)\n",
-    "        raw_label = ford_label_map.get(row_id)\n",
-    "        return (int(raw_label < 0), \"FordA.label\", row_id) if raw_label is not None else (None, \"FordA.label\", row_id)\n",
-    "\n",
-    "    if target_process == \"PAINT\":\n",
-    "        row_id = int(trace.get(\"machineVisionRowId\", 0) or 0)\n",
-    "        side = str(payload.get(\"processData\", {}).get(\"paint\", {}).get(\"imagePosition\", \"\")).upper()\n",
-    "        raw_label = vision_label_map.get((side, row_id))\n",
-    "        return (int(raw_label), f\"thermal_{side.lower()}.label\", row_id) if raw_label is not None else (None, \"thermal.label\", row_id)\n",
-    "\n",
-    "    if target_process == \"ASSEMBLY\":\n",
-    "        row_id = int(trace.get(\"boschId\", 0) or 0)\n",
-    "        raw_label = bosch_label_map.get(row_id)\n",
-    "        return (int(raw_label), \"Bosch.Response\", row_id) if raw_label is not None else (None, \"Bosch.Response\", row_id)\n",
-    "\n",
-    "    return None, \"unsupported\", None\n",
-    "\n",
-    "\n",
-    "def build_transition_dataset(path: Path, max_rows: int | None) -> tuple[pd.DataFrame, pd.DataFrame]:\n",
-    "    feature_rows = []\n",
-    "    metadata_rows = []\n",
-    "    vehicle_state: dict[int, dict[str, dict[str, float]]] = {}\n",
-    "    parsed_rows = 0\n",
-    "    invalid_json_rows = 0\n",
-    "    missing_label_rows = 0\n",
-    "\n",
-    "    reader = pd.read_csv(\n",
-    "        local_dataset_path(path),\n",
-    "        header=None,\n",
-    "        names=EVENT_COLUMNS,\n",
-    "        usecols=[\"event_id\", \"event_time\", \"car_master_id\", \"event_json\"],\n",
-    "        chunksize=EVENT_CHUNK_SIZE,\n",
-    "        nrows=max_rows,\n",
-    "        dtype={\"event_id\": \"string\", \"car_master_id\": \"int64\", \"event_json\": \"string\"},\n",
-    "    )\n",
-    "\n",
-    "    for chunk in reader:\n",
-    "        for row in chunk.itertuples(index=False):\n",
-    "            parsed_rows += 1\n",
-    "            try:\n",
-    "                payload = json.loads(row.event_json)\n",
-    "            except (TypeError, json.JSONDecodeError):\n",
-    "                invalid_json_rows += 1\n",
-    "                continue\n",
-    "\n",
-    "            car_master_id = int(row.car_master_id)\n",
-    "            process_code = str(payload.get(\"location\", {}).get(\"processCode\", \"UNKNOWN\"))\n",
-    "            if process_code not in PROCESS_FLOW:\n",
-    "                continue\n",
-    "\n",
-    "            if process_code == \"PRESS\" or car_master_id not in vehicle_state:\n",
-    "                vehicle_state[car_master_id] = {}\n",
-    "            history = vehicle_state[car_master_id]\n",
-    "\n",
-    "            source_process = TRANSITIONS.get(process_code)\n",
-    "            if source_process and source_process in history:\n",
-    "                target_label, label_source, source_row_id = restore_target_label(payload, process_code)\n",
-    "                if target_label is None:\n",
-    "                    missing_label_rows += 1\n",
-    "                else:\n",
-    "                    features = {}\n",
-    "                    target_index = PROCESS_FLOW.index(process_code)\n",
-    "                    for history_process in PROCESS_FLOW[:target_index]:\n",
-    "                        features.update(history.get(history_process, {}))\n",
-    "                    for target_name in TRANSITIONS:\n",
-    "                        features[f\"transition_to_{target_name}\"] = int(process_code == target_name)\n",
-    "                    features[\"history_process_count\"] = float(len(history))\n",
-    "\n",
-    "                    feature_rows.append(features)\n",
-    "                    metadata_rows.append({\n",
-    "                        \"sample_event_id\": str(row.event_id),\n",
-    "                        \"event_time\": str(row.event_time),\n",
-    "                        \"car_master_id\": car_master_id,\n",
-    "                        \"source_process_code\": source_process,\n",
-    "                        \"target_process_code\": process_code,\n",
-    "                        \"target_defect_label\": int(target_label),\n",
-    "                        \"label_source\": label_source,\n",
-    "                        \"label_source_row_id\": source_row_id,\n",
-    "                    })\n",
-    "\n",
-    "            history[process_code] = extract_event_features(payload)\n",
-    "            if process_code == \"ASSEMBLY\":\n",
-    "                vehicle_state.pop(car_master_id, None)\n",
-    "\n",
-    "    feature_df = reduce_mem_usage(pd.DataFrame(feature_rows))\n",
-    "    metadata_df = pd.DataFrame(metadata_rows)\n",
-    "    print(\n",
-    "        f\"event rows={parsed_rows:,}, transitions={len(feature_df):,}, \"\n",
-    "        f\"invalid_json={invalid_json_rows:,}, missing_label={missing_label_rows:,}\"\n",
-    "    )\n",
-    "    return feature_df, metadata_df\n",
-    "\n",
-    "\n",
-    "features, transition_meta = build_transition_dataset(EVENT_JSON_PATH, MAX_ROWS)\n",
-    "if features.empty:\n",
-    "    raise ValueError(\"복원 가능한 공정 전이 학습 행이 없습니다.\")\n",
-    "\n",
-    "model_df = pd.concat(\n",
-    "    [transition_meta.reset_index(drop=True), features.reset_index(drop=True)],\n",
-    "    axis=1,\n",
-    ")\n",
-    "model_df = model_df.replace([np.inf, -np.inf], np.nan)\n",
-    "\n",
-    "print(\"model_df:\", model_df.shape)\n",
-    "display(model_df.head())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6f30d6e",
-   "metadata": {
-    "id": "e6f30d6e"
-   },
-   "source": [
-    "## 4. 라벨 복원 및 누수 검증\n",
-    "\n",
-    "복원된 정답의 출처와 전이별 클래스 분포를 확인합니다. 대상 공정 JSON은 입력 특성에\n",
-    "포함하지 않으므로 예측 시점은 대상 공정 시작 전입니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a2a68fb3",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 698
-    },
-    "id": "a2a68fb3",
-    "outputId": "65f72cce-fd5b-4c16-9900-3b43b4e0e289"
-   },
-   "outputs": [],
-   "source": [
-    "label_audit = transition_meta.groupby(\n",
-    "    [\"source_process_code\", \"target_process_code\", \"label_source\", \"target_defect_label\"]\n",
-    ").size().rename(\"rows\").reset_index()\n",
-    "display(label_audit)\n",
-    "\n",
-    "transition_summary = transition_meta.groupby(\n",
-    "    [\"source_process_code\", \"target_process_code\"]\n",
-    ").agg(\n",
-    "    rows=(\"target_defect_label\", \"size\"),\n",
-    "    positive_rows=(\"target_defect_label\", \"sum\"),\n",
-    "    positive_ratio=(\"target_defect_label\", \"mean\"),\n",
-    "    vehicles=(\"car_master_id\", \"nunique\"),\n",
-    ").reset_index()\n",
-    "display(transition_summary)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76645e9c",
-   "metadata": {
-    "id": "76645e9c"
-   },
-   "source": [
-    "### 4.1 입력 특성 누수 점검\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "144bc123",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "144bc123",
-    "outputId": "b559b72a-3520-49e8-ef80-8add5e66a562"
-   },
-   "outputs": [],
-   "source": [
-    "def feature_process_prefix(feature: str) -> str | None:\n",
-    "    for process_code in PROCESS_FLOW:\n",
-    "        if feature.startswith(f\"{process_code}__\"):\n",
-    "            return process_code\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "invalid_target_features = []\n",
-    "for target_process in TRANSITIONS:\n",
-    "    target_rows = transition_meta[\"target_process_code\"].eq(target_process)\n",
-    "    target_columns = [\n",
-    "        column for column in features.columns\n",
-    "        if column.startswith(f\"{target_process}__\")\n",
-    "    ]\n",
-    "    if target_columns:\n",
-    "        populated = features.loc[target_rows, target_columns].notna().any()\n",
-    "        invalid_target_features.extend(populated[populated].index.tolist())\n",
-    "\n",
-    "if invalid_target_features:\n",
-    "    raise ValueError(\n",
-    "        f\"대상 공정 feature가 입력에 포함되었습니다: \"\n",
-    "        f\"{sorted(set(invalid_target_features))[:10]}\"\n",
-    "    )\n",
-    "\n",
-    "for forbidden_name in [\n",
-    "    \"visionLabel\", \"defectScore\", \"surfaceQualityScore\", \"robotMotionStatus\",\n",
-    "    \"healthStatus\", \"operationStatus\", \"actualSequence\", \"ErrorCount\",\n",
-    "]:\n",
-    "    matched = [\n",
-    "        column for column in features.columns\n",
-    "        if forbidden_name.lower() in column.lower()\n",
-    "    ]\n",
-    "    if matched:\n",
-    "        raise ValueError(\n",
-    "            f\"라벨 누수 의심 feature 발견({forbidden_name}): {matched[:10]}\"\n",
-    "        )\n",
-    "\n",
-    "print(\"대상 공정 및 라벨 파생 feature 누수 점검 통과\")\n",
-    "print(\"feature count:\", features.shape[1])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a97efe99",
-   "metadata": {
-    "id": "a97efe99"
-   },
-   "source": [
-    "### 4.2 학습 데이터 품질 확인\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cd25bfc8",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 216
-    },
-    "id": "cd25bfc8",
-    "outputId": "73d3b745-218c-4f61-96fa-0022c8136bcb"
-   },
-   "outputs": [],
-   "source": [
-    "if transition_meta[\"target_defect_label\"].nunique() < 2:\n",
-    "    raise ValueError(\"복원된 불량 라벨이 한 클래스만 포함되어 있습니다. MAX_ROWS를 늘려주세요.\")\n",
-    "\n",
-    "missing_ratio = features.isna().mean().sort_values(ascending=False)\n",
-    "display(missing_ratio.rename(\"missing_ratio\").head(20).to_frame())\n",
-    "print(\"unique vehicles:\", transition_meta[\"car_master_id\"].nunique())\n",
-    "print(\"target distribution:\", transition_meta[\"target_defect_label\"].value_counts().to_dict())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "-7HWdLE4bEfB",
-   "metadata": {
-    "id": "-7HWdLE4bEfB"
-   },
-   "source": [
-    "## 5. 차량 단위 학습/검증 분리\n",
-    "\n",
-    "같은 차량의 여러 공정 전이가 학습과 검증에 동시에 섞이지 않도록 `car_master_id`\n",
-    "기준 GroupShuffleSplit을 사용합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "XX3xUzrlbEfB",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "XX3xUzrlbEfB",
-    "outputId": "1b64eb6a-04d9-4fa0-ee50-df1fa3ea9a18"
-   },
-   "outputs": [],
-   "source": [
-    "metadata_cols = [\n",
-    "    \"sample_event_id\", \"event_time\", \"car_master_id\", \"source_process_code\",\n",
-    "    \"target_process_code\", \"target_defect_label\", \"label_source\", \"label_source_row_id\",\n",
-    "]\n",
-    "feature_cols = [column for column in model_df.columns if column not in metadata_cols]\n",
-    "all_missing_cols = model_df[feature_cols].columns[model_df[feature_cols].isna().all()].tolist()\n",
-    "if all_missing_cols:\n",
-    "    model_df = model_df.drop(columns=all_missing_cols)\n",
-    "    feature_cols = [column for column in feature_cols if column not in all_missing_cols]\n",
-    "\n",
-    "X = model_df[feature_cols]\n",
-    "y = model_df[\"target_defect_label\"].astype(int)\n",
-    "ids = model_df[\"car_master_id\"].astype(int)\n",
-    "groups = ids.copy()\n",
-    "\n",
-    "group_split = GroupShuffleSplit(n_splits=1, test_size=0.2, random_state=RANDOM_STATE)\n",
-    "train_idx, valid_idx = next(group_split.split(X, y, groups=groups))\n",
-    "\n",
-    "X_train = X.iloc[train_idx]\n",
-    "X_valid = X.iloc[valid_idx]\n",
-    "y_train = y.iloc[train_idx]\n",
-    "y_valid = y.iloc[valid_idx]\n",
-    "id_train = ids.iloc[train_idx]\n",
-    "id_valid = ids.iloc[valid_idx]\n",
-    "groups_train = groups.iloc[train_idx]\n",
-    "groups_valid = groups.iloc[valid_idx]\n",
-    "meta_train = model_df.iloc[train_idx][metadata_cols]\n",
-    "meta_valid = model_df.iloc[valid_idx][metadata_cols]\n",
-    "\n",
-    "if set(groups_train).intersection(set(groups_valid)):\n",
-    "    raise AssertionError(\"동일 차량이 train/validation에 동시에 포함되었습니다.\")\n",
-    "if y_train.nunique() < 2 or y_valid.nunique() < 2:\n",
-    "    raise ValueError(\"차량 단위 분할 후 한 클래스만 남았습니다. MAX_ROWS를 늘려주세요.\")\n",
-    "\n",
-    "neg_count = int((y_train == 0).sum())\n",
-    "pos_count = int((y_train == 1).sum())\n",
-    "scale_pos_weight = neg_count / max(pos_count, 1)\n",
-    "\n",
-    "print(\"X_train:\", X_train.shape, \"X_valid:\", X_valid.shape)\n",
-    "print(\"train vehicles:\", groups_train.nunique(), \"valid vehicles:\", groups_valid.nunique())\n",
-    "print(\"positive ratio train:\", y_train.mean(), \"valid:\", y_valid.mean())\n",
-    "print(\"scale_pos_weight:\", round(scale_pos_weight, 2))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6444a8dd",
-   "metadata": {
-    "id": "6444a8dd"
-   },
-   "source": [
-    "## 6. 전처리 학습 데이터셋 저장\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e442104",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "6e442104",
-    "outputId": "09415cb9-862f-49f7-a3f3-30566b3940f4"
-   },
-   "outputs": [],
-   "source": [
-    "def save_training_dataframe(df: pd.DataFrame, output_dir: Path, stem: str) -> Path:\n",
-    "    parquet_path = output_dir / f\"{stem}.parquet\"\n",
-    "    csv_path = output_dir / f\"{stem}.csv.gz\"\n",
-    "    try:\n",
-    "        df.to_parquet(parquet_path, index=False)\n",
-    "        return parquet_path\n",
-    "    except Exception as exc:\n",
-    "        print(f\"Parquet 저장 실패, csv.gz로 저장합니다: {exc}\")\n",
-    "        df.to_csv(csv_path, index=False, compression=\"gzip\")\n",
-    "        return csv_path\n",
-    "\n",
-    "\n",
-    "training_dataset_path = save_training_dataframe(\n",
-    "    model_df,\n",
-    "    OUTPUT_DIR,\n",
-    "    \"defect_transfer_event_json_training_dataset\",\n",
-    ")\n",
-    "\n",
-    "split_rows = pd.concat([\n",
-    "    meta_train.assign(split=\"train\"),\n",
-    "    meta_valid.assign(split=\"valid\"),\n",
-    "]).sort_index()\n",
-    "split_ids_path = OUTPUT_DIR / \"defect_transfer_train_valid_split_rows.csv\"\n",
-    "split_rows.to_csv(split_ids_path, index=False)\n",
-    "\n",
-    "preprocessing_metadata = {\n",
-    "    \"event_json_path\": str(EVENT_JSON_PATH),\n",
-    "    \"event_json_column_index\": 12,\n",
-    "    \"is_sent_column_index\": 13,\n",
-    "    \"max_event_rows\": int(MAX_ROWS),\n",
-    "    \"event_chunk_size\": int(EVENT_CHUNK_SIZE),\n",
-    "    \"transition_rows\": int(len(model_df)),\n",
-    "    \"transition_summary\": transition_summary.to_dict(orient=\"records\"),\n",
-    "    \"label_sources\": {\n",
-    "        \"BODY\": \"FordA label (-1 => defect)\",\n",
-    "        \"PAINT\": \"thermal vision label (1 => defect)\",\n",
-    "        \"ASSEMBLY\": \"Bosch Response (1 => defect)\",\n",
-    "    },\n",
-    "    \"excluded_leakage_json_paths\": sorted(LEAKAGE_JSON_PATHS),\n",
-    "    \"dropped_all_missing_columns\": all_missing_cols,\n",
-    "    \"feature_columns\": feature_cols,\n",
-    "    \"target_column\": \"target_defect_label\",\n",
-    "    \"split_strategy\": \"GroupShuffleSplit by car_master_id\",\n",
-    "    \"train_rows\": int(len(X_train)),\n",
-    "    \"valid_rows\": int(len(X_valid)),\n",
-    "    \"train_vehicles\": int(groups_train.nunique()),\n",
-    "    \"valid_vehicles\": int(groups_valid.nunique()),\n",
-    "    \"positive_ratio\": float(y.mean()),\n",
-    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
-    "}\n",
-    "preprocessing_metadata_path = OUTPUT_DIR / \"defect_transfer_preprocessing_metadata.json\"\n",
-    "preprocessing_metadata_path.write_text(\n",
-    "    json.dumps(preprocessing_metadata, ensure_ascii=False, indent=2),\n",
-    "    encoding=\"utf-8\",\n",
-    ")\n",
-    "\n",
-    "print(\"전처리 학습 데이터셋:\", training_dataset_path)\n",
-    "print(\"train/valid split rows:\", split_ids_path)\n",
-    "print(\"전처리 metadata:\", preprocessing_metadata_path)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b19d8e5c",
-   "metadata": {
-    "id": "b19d8e5c"
-   },
-   "source": [
-    "## 7. 후보 모델 교차검증 및 하이퍼파라미터 튜닝\n",
-    "\n",
-    "모든 후보 모델은 동일한 `event_json` 전이 학습 테이블을 사용합니다. 교차검증도\n",
-    "`car_master_id` 그룹을 유지하여 동일 차량의 행이 fold 사이에 섞이지 않게 합니다.\n",
-    "비교 지표는 PR-AUC, Recall, Precision, F1, ROC-AUC, Accuracy, False Positive Rate입니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d9a50e90",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "d9a50e90",
-    "outputId": "60f68881-d294-44fc-92f2-008ba9ab2179"
-   },
-   "outputs": [],
-   "source": [
-    "# 평가 함수\n",
-    "def find_recall_priority_threshold(\n",
-    "    y_true: pd.Series,\n",
-    "    proba: np.ndarray,\n",
-    "    *,\n",
-    "    min_recall: float = RECALL_PRIORITY_TARGET,\n",
-    "    min_precision: float = RECALL_PRIORITY_MIN_PRECISION,\n",
-    "    beta: float = THRESHOLD_BETA,\n",
-    ") -> float:\n",
-    "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
-    "    if len(thresholds) == 0:\n",
-    "        return 0.5\n",
-    "\n",
-    "    precision = precision[:-1]\n",
-    "    recall = recall[:-1]\n",
-    "    thresholds = thresholds.astype(float)\n",
-    "    valid = (recall >= min_recall) & (precision >= min_precision)\n",
-    "\n",
-    "    if valid.any():\n",
-    "        # Recall 목표를 만족하는 후보 중 Precision이 가장 좋은 threshold를 선택합니다.\n",
-    "        valid_idx = np.where(valid)[0]\n",
-    "        best_local_idx = valid_idx[int(np.nanargmax(precision[valid_idx]))]\n",
-    "        return float(thresholds[best_local_idx])\n",
-    "\n",
-    "    beta_sq = beta ** 2\n",
-    "    f_beta = (1 + beta_sq) * precision * recall / np.maximum(beta_sq * precision + recall, 1e-12)\n",
-    "    return float(thresholds[int(np.nanargmax(f_beta))])\n",
-    "\n",
-    "\n",
-    "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
-    "    return find_recall_priority_threshold(y_true, proba)\n",
-    "\n",
-    "\n",
-    "def compute_binary_metrics(y_true: pd.Series, proba: np.ndarray, threshold: float) -> dict:\n",
-    "    pred = (proba >= threshold).astype(int)\n",
-    "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
-    "    return {\n",
-    "        \"accuracy\": float(accuracy_score(y_true, pred)),\n",
-    "        \"precision\": float(precision_score(y_true, pred, zero_division=0)),\n",
-    "        \"recall\": float(recall_score(y_true, pred, zero_division=0)),\n",
-    "        \"f1\": float(f1_score(y_true, pred, zero_division=0)),\n",
-    "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
-    "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
-    "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
-    "        \"predicted_positive_rate\": float((tp + fp) / max(tp + fp + tn + fn, 1)),\n",
-    "        \"true_negative\": int(tn),\n",
-    "        \"false_positive\": int(fp),\n",
-    "        \"false_negative\": int(fn),\n",
-    "        \"true_positive\": int(tp),\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def predict_positive_proba(estimator, X_input: pd.DataFrame) -> np.ndarray:\n",
-    "    if hasattr(estimator, \"predict_proba\"):\n",
-    "        return estimator.predict_proba(X_input)[:, 1]\n",
-    "    decision = estimator.decision_function(X_input)\n",
-    "    return 1 / (1 + np.exp(-decision))\n",
-    "\n",
-    "\n",
-    "def is_pipeline_estimator(estimator) -> bool:\n",
-    "    return isinstance(estimator, (Pipeline, ImbPipeline))\n",
-    "\n",
-    "\n",
-    "def get_final_estimator(estimator):\n",
-    "    return estimator.named_steps.get(\"model\", estimator) if is_pipeline_estimator(estimator) else estimator\n",
-    "\n",
-    "\n",
-    "def make_lightgbm_pipeline() -> ImbPipeline:\n",
-    "    steps = [(\"imputer\", SimpleImputer(strategy=\"median\"))]\n",
-    "    if USE_SMOTE:\n",
-    "        steps.append((\"smote\", SMOTE(\n",
-    "            sampling_strategy=SMOTE_SAMPLING_STRATEGY,\n",
-    "            random_state=RANDOM_STATE,\n",
-    "            k_neighbors=3,\n",
-    "        )))\n",
-    "    steps.append((\"model\", lgb.LGBMClassifier(\n",
-    "        objective=\"binary\",\n",
-    "        n_estimators=500,\n",
-    "        random_state=RANDOM_STATE,\n",
-    "        n_jobs=-1,\n",
-    "        is_unbalance=True,\n",
-    "        verbosity=-1,\n",
-    "    )))\n",
-    "    return ImbPipeline(steps)\n",
-    "\n",
-    "\n",
-    "def make_candidate_models() -> dict:\n",
-    "    return {\n",
-    "        \"LightGBM\": {\n",
-    "            \"estimator\": make_lightgbm_pipeline(),\n",
-    "            \"params\": {\n",
-    "                \"model__learning_rate\": [0.015, 0.025, 0.04, 0.06],\n",
-    "                \"model__num_leaves\": [31, 63, 127],\n",
-    "                \"model__max_depth\": [-1, 6, 8, 12],\n",
-    "                \"model__min_child_samples\": [40, 80, 150, 250],\n",
-    "                \"model__subsample\": [0.75, 0.85, 1.0],\n",
-    "                \"model__colsample_bytree\": [0.65, 0.8, 0.95],\n",
-    "                \"model__reg_lambda\": [0.5, 1.0, 2.0, 5.0],\n",
-    "                \"model__reg_alpha\": [0.0, 0.1, 0.5],\n",
-    "            },\n",
-    "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델. Optuna와 SMOTE로 희소 불량 recall을 보강\",\n",
-    "        },\n",
-    "        \"RandomForest\": {\n",
-    "            \"estimator\": Pipeline([\n",
-    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-    "                (\"model\", RandomForestClassifier(\n",
-    "                    n_estimators=160,\n",
-    "                    class_weight=\"balanced_subsample\",\n",
-    "                    random_state=RANDOM_STATE,\n",
-    "                    n_jobs=-1,\n",
-    "                )),\n",
-    "            ]),\n",
-    "            \"params\": {\n",
-    "                \"model__max_depth\": [12, 16, 20, None],\n",
-    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
-    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
-    "            },\n",
-    "            \"selection_reason\": \"bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble 성능 확인\",\n",
-    "        },\n",
-    "        \"ExtraTrees\": {\n",
-    "            \"estimator\": Pipeline([\n",
-    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-    "                (\"model\", ExtraTreesClassifier(\n",
-    "                    n_estimators=220,\n",
-    "                    class_weight=\"balanced\",\n",
-    "                    random_state=RANDOM_STATE,\n",
-    "                    n_jobs=-1,\n",
-    "                )),\n",
-    "            ]),\n",
-    "            \"params\": {\n",
-    "                \"model__max_depth\": [12, 16, 20, None],\n",
-    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
-    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
-    "            },\n",
-    "            \"selection_reason\": \"RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교\",\n",
-    "        },\n",
-    "        \"LogisticRegression\": {\n",
-    "            \"estimator\": Pipeline([\n",
-    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-    "                (\"scaler\", StandardScaler()),\n",
-    "                (\"model\", LogisticRegression(\n",
-    "                    class_weight=\"balanced\",\n",
-    "                    max_iter=1500,\n",
-    "                    random_state=RANDOM_STATE,\n",
-    "                )),\n",
-    "            ]),\n",
-    "            \"params\": {\n",
-    "                \"model__C\": [0.03, 0.1, 0.3, 1.0, 3.0],\n",
-    "                \"model__solver\": [\"lbfgs\"],\n",
-    "            },\n",
-    "            \"selection_reason\": \"복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체의 설명력 확인\",\n",
-    "        },\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def suggest_lightgbm_params(trial: optuna.Trial) -> dict:\n",
-    "    return {\n",
-    "        \"model__learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.08, log=True),\n",
-    "        \"model__num_leaves\": trial.suggest_int(\"num_leaves\", 24, 160),\n",
-    "        \"model__max_depth\": trial.suggest_categorical(\"max_depth\", [-1, 5, 7, 9, 12]),\n",
-    "        \"model__min_child_samples\": trial.suggest_int(\"min_child_samples\", 30, 300),\n",
-    "        \"model__subsample\": trial.suggest_float(\"subsample\", 0.65, 1.0),\n",
-    "        \"model__colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.55, 1.0),\n",
-    "        \"model__reg_lambda\": trial.suggest_float(\"reg_lambda\", 0.1, 8.0, log=True),\n",
-    "        \"model__reg_alpha\": trial.suggest_float(\"reg_alpha\", 1e-4, 1.0, log=True),\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def evaluate_cv_trial(model_name: str, config: dict, params: dict, trial_no: int) -> tuple[dict, list[dict]]:\n",
-    "    fold_rows = []\n",
-    "    for fold_no, (train_idx, valid_idx) in enumerate(\n",
-    "    skf.split(X_cv_pool, y_cv_pool, groups=groups_cv_pool), start=1\n",
-    "):\n",
-    "        fold_started_at = datetime.now()\n",
-    "        X_fold_train = X_cv_pool.iloc[train_idx]\n",
-    "        y_fold_train = y_cv_pool.iloc[train_idx]\n",
-    "        X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
-    "        y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
-    "\n",
-    "        cv_model = clone(config[\"estimator\"])\n",
-    "        cv_model.set_params(**params)\n",
-    "        cv_model.fit(X_fold_train, y_fold_train)\n",
-    "\n",
-    "        fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
-    "        fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
-    "        fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
-    "        fold_metrics.update({\n",
-    "            \"model_name\": model_name,\n",
-    "            \"trial_no\": trial_no,\n",
-    "            \"fold_no\": fold_no,\n",
-    "            \"threshold\": fold_threshold,\n",
-    "            **params,\n",
-    "        })\n",
-    "        fold_rows.append(fold_metrics)\n",
-    "        elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
-    "        print(\n",
-    "            f\"    fold {fold_no}/{effective_cv_splits} \"\n",
-    "            f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
-    "            f\"Recall={fold_metrics['recall']:.5f} \"\n",
-    "            f\"F1={fold_metrics['f1']:.5f} \"\n",
-    "            f\"elapsed={elapsed:.1f}s\"\n",
-    "        )\n",
-    "\n",
-    "    fold_df = pd.DataFrame(fold_rows)\n",
-    "    summary = {\n",
-    "        \"model_name\": model_name,\n",
-    "        \"trial_no\": trial_no,\n",
-    "        \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
-    "        \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
-    "        \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
-    "        \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
-    "        \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
-    "        \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
-    "        \"mean_precision\": float(fold_df[\"precision\"].mean()),\n",
-    "        \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
-    "        \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
-    "        \"params\": params,\n",
-    "        \"selection_reason\": config[\"selection_reason\"],\n",
-    "        \"tuning_method\": \"Optuna\" if model_name == \"LightGBM\" and ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-    "    }\n",
-    "    return summary, fold_rows\n",
-    "\n",
-    "\n",
-    "# CV 데이터셋 규모 관리: 차량 그룹을 유지한 채 표본 추출\n",
-    "if len(X_train) > CV_SAMPLE_SIZE:\n",
-    "    cv_fraction = min(0.99, CV_SAMPLE_SIZE / len(X_train))\n",
-    "    cv_sampler = GroupShuffleSplit(\n",
-    "        n_splits=1,\n",
-    "        train_size=cv_fraction,\n",
-    "        random_state=RANDOM_STATE,\n",
-    "    )\n",
-    "    cv_idx, _ = next(cv_sampler.split(X_train, y_train, groups=groups_train))\n",
-    "    X_cv_pool = X_train.iloc[cv_idx]\n",
-    "    y_cv_pool = y_train.iloc[cv_idx]\n",
-    "    groups_cv_pool = groups_train.iloc[cv_idx]\n",
-    "else:\n",
-    "    X_cv_pool = X_train\n",
-    "    y_cv_pool = y_train\n",
-    "    groups_cv_pool = groups_train\n",
-    "\n",
-    "effective_cv_splits = min(\n",
-    "    CV_N_SPLITS,\n",
-    "    int(y_cv_pool.value_counts().min()),\n",
-    "    int(groups_cv_pool.nunique()),\n",
-    ")\n",
-    "if effective_cv_splits < 2:\n",
-    "    raise ValueError(\"교차검증을 수행하기에 소수 클래스 샘플이 부족합니다. MAX_ROWS 또는 CV_SAMPLE_SIZE를 늘려주세요.\")\n",
-    "\n",
-    "candidate_configs = make_candidate_models()\n",
-    "skf = StratifiedGroupKFold(n_splits=effective_cv_splits, shuffle=True, random_state=RANDOM_STATE)\n",
-    "cv_rows = []\n",
-    "cv_fold_rows = []\n",
-    "best_params_by_model = {}\n",
-    "\n",
-    "print(f\"CV rows: {len(X_cv_pool):,} / train rows: {len(X_train):,}\")\n",
-    "print(f\"CV splits: {effective_cv_splits}, tuning trials per model: {CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1}\")\n",
-    "print(f\"SMOTE enabled: {USE_SMOTE}, sampling_strategy={SMOTE_SAMPLING_STRATEGY}\")\n",
-    "print(f\"Threshold strategy: recall>={RECALL_PRIORITY_TARGET}, min_precision>={RECALL_PRIORITY_MIN_PRECISION}, beta={THRESHOLD_BETA}\")\n",
-    "\n",
-    "# 후보 모델별 교차검증\n",
-    "for model_name, config in candidate_configs.items():\n",
-    "    print(f\"\\n[{model_name}] CV start\")\n",
-    "    model_started_at = datetime.now()\n",
-    "\n",
-    "    if model_name == \"LightGBM\" and ENABLE_HYPERPARAMETER_TUNING and ENABLE_OPTUNA_TUNING:\n",
-    "        def objective(trial: optuna.Trial) -> float:\n",
-    "            params = suggest_lightgbm_params(trial)\n",
-    "            print(f\"  optuna trial {trial.number + 1}/{CV_N_ITER} params={params}\")\n",
-    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial.number + 1)\n",
-    "            cv_rows.append(summary)\n",
-    "            cv_fold_rows.extend(fold_rows)\n",
-    "            trial.set_user_attr(\"params\", params)\n",
-    "            trial.set_user_attr(\"mean_recall\", summary[\"mean_recall\"])\n",
-    "            trial.set_user_attr(\"mean_f1\", summary[\"mean_f1\"])\n",
-    "            # PR-AUC를 우선하되 recall을 약하게 보상합니다.\n",
-    "            return summary[\"mean_pr_auc\"] + 0.05 * summary[\"mean_recall\"]\n",
-    "\n",
-    "        study = optuna.create_study(\n",
-    "            direction=\"maximize\",\n",
-    "            sampler=TPESampler(seed=RANDOM_STATE),\n",
-    "            study_name=\"event_json_transfer_lightgbm_recall_pr_auc\",\n",
-    "        )\n",
-    "        study.optimize(objective, n_trials=CV_N_ITER, show_progress_bar=False)\n",
-    "        best_params_by_model[model_name] = study.best_trial.user_attrs[\"params\"]\n",
-    "        print(f\"[{model_name}] Optuna best value={study.best_value:.5f} params={best_params_by_model[model_name]}\")\n",
-    "    else:\n",
-    "        param_candidates = list(ParameterSampler(\n",
-    "            config[\"params\"],\n",
-    "            n_iter=CV_N_ITER,\n",
-    "            random_state=RANDOM_STATE,\n",
-    "        )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
-    "        print(f\"[{model_name}] random search trials: {len(param_candidates)}\")\n",
-    "        for trial_no, params in enumerate(param_candidates, start=1):\n",
-    "            print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
-    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial_no)\n",
-    "            cv_rows.append(summary)\n",
-    "            cv_fold_rows.extend(fold_rows)\n",
-    "            print(\n",
-    "                f\"  -> {model_name} trial {trial_no} mean \"\n",
-    "                f\"PR-AUC={summary['mean_pr_auc']:.5f} \"\n",
-    "                f\"Recall={summary['mean_recall']:.5f} \"\n",
-    "                f\"F1={summary['mean_f1']:.5f} \"\n",
-    "                f\"FPR={summary['mean_false_positive_rate']:.5f}\"\n",
-    "            )\n",
-    "\n",
-    "    print(f\"[{model_name}] CV done elapsed={(datetime.now() - model_started_at).total_seconds():.1f}s\")\n",
-    "\n",
-    "cv_results = pd.DataFrame(cv_rows).sort_values(\n",
-    "    [\"mean_pr_auc\", \"mean_recall\", \"mean_f1\", \"mean_roc_auc\", \"mean_accuracy\"],\n",
-    "    ascending=False,\n",
-    ").reset_index(drop=True)\n",
-    "cv_fold_results = pd.DataFrame(cv_fold_rows)\n",
-    "\n",
-    "for model_name in candidate_configs:\n",
-    "    if model_name not in best_params_by_model:\n",
-    "        best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
-    "\n",
-    "best_model_name = str(cv_results.iloc[0][\"model_name\"])\n",
-    "best_params = best_params_by_model[best_model_name]\n",
-    "best_model_selection_reason = str(cv_results.iloc[0][\"selection_reason\"])\n",
-    "\n",
-    "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.csv\"\n",
-    "cv_fold_results_path = OUTPUT_DIR / \"defect_model_cv_fold_results.csv\"\n",
-    "cv_results.to_csv(cv_results_path, index=False)\n",
-    "cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
-    "\n",
-    "display(cv_results.head(20))\n",
-    "print(\"CV 결과 저장:\", cv_results_path)\n",
-    "print(\"CV fold 결과 저장:\", cv_fold_results_path)\n",
-    "print(\"CV best model:\", best_model_name, best_params)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f15608a",
-   "metadata": {
-    "id": "3f15608a"
-   },
-   "source": [
-    "## 8. 후보 모델 최종 학습 및 성능 비교\n",
-    "\n",
-    "교차검증에서 찾은 후보별 best parameter로 최종 학습하고 차량 단위 validation에서\n",
-    "다음 공정 불량 예측 성능을 비교합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3553e4c5",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 825
-    },
-    "id": "3553e4c5",
-    "outputId": "f6e17abe-7523-45bd-b4fa-0828efb0d901"
-   },
-   "outputs": [],
-   "source": [
-    "trained_models = {}\n",
-    "model_valid_probabilities = {}\n",
-    "model_thresholds = {}\n",
-    "model_confusion_matrices = {}\n",
-    "model_eval_rows = []\n",
-    "\n",
-    "# 최종 후보 비교용 학습 데이터셋 규모 관리\n",
-    "if len(X_train) > FINAL_TRAIN_SAMPLE_SIZE:\n",
-    "    X_final_train, _, y_final_train, _ = train_test_split(\n",
-    "        X_train,\n",
-    "        y_train,\n",
-    "        train_size=FINAL_TRAIN_SAMPLE_SIZE,\n",
-    "        random_state=RANDOM_STATE,\n",
-    "        stratify=y_train,\n",
-    "    )\n",
-    "else:\n",
-    "    X_final_train = X_train\n",
-    "    y_final_train = y_train\n",
-    "\n",
-    "print(f\"Final candidate training rows: {len(X_final_train):,} / train rows: {len(X_train):,}\")\n",
-    "\n",
-    "# 후보 모델 전체 학습\n",
-    "for model_name, config in candidate_configs.items():\n",
-    "    started_at = datetime.now()\n",
-    "    estimator = clone(config[\"estimator\"])\n",
-    "    estimator.set_params(**best_params_by_model[model_name])\n",
-    "    print(f\"[{model_name}] final fit start params={best_params_by_model[model_name]}\")\n",
-    "    estimator.fit(X_final_train, y_final_train)\n",
-    "\n",
-    "    proba = predict_positive_proba(estimator, X_valid)\n",
-    "    threshold = find_best_threshold(y_valid, proba)\n",
-    "    pred = (proba >= threshold).astype(int)\n",
-    "    cm_model = confusion_matrix(y_valid, pred, labels=[0, 1])\n",
-    "    metrics = compute_binary_metrics(y_valid, proba, threshold)\n",
-    "\n",
-    "    trained_models[model_name] = estimator\n",
-    "    model_valid_probabilities[model_name] = proba\n",
-    "    model_thresholds[model_name] = threshold\n",
-    "    model_confusion_matrices[model_name] = cm_model\n",
-    "\n",
-    "    model_eval_rows.append({\n",
-    "        \"model_name\": model_name,\n",
-    "        \"threshold\": threshold,\n",
-    "        \"selection_reason\": config[\"selection_reason\"],\n",
-    "        **metrics,\n",
-    "    })\n",
-    "    print(\n",
-    "        f\"[{model_name}] final fit done \"\n",
-    "        f\"PR-AUC={metrics['pr_auc']:.5f} \"\n",
-    "        f\"ROC-AUC={metrics['roc_auc']:.5f} \"\n",
-    "        f\"ACC={metrics['accuracy']:.5f} \"\n",
-    "        f\"FPR={metrics['false_positive_rate']:.5f} \"\n",
-    "        f\"elapsed={(datetime.now() - started_at).total_seconds():.1f}s\"\n",
-    "    )\n",
-    "\n",
-    "model_comparison = pd.DataFrame(model_eval_rows).sort_values(\n",
-    "    [\"pr_auc\", \"recall\", \"f1\", \"roc_auc\", \"accuracy\"],\n",
-    "    ascending=False,\n",
-    ").reset_index(drop=True)\n",
-    "\n",
-    "selected_model_name = str(model_comparison.iloc[0][\"model_name\"])\n",
-    "model = trained_models[selected_model_name]\n",
-    "valid_proba = model_valid_probabilities[selected_model_name]\n",
-    "best_threshold = float(model_thresholds[selected_model_name])\n",
-    "valid_pred = (valid_proba >= best_threshold).astype(int)\n",
-    "cm = model_confusion_matrices[selected_model_name]\n",
-    "\n",
-    "# 운영용 전체 재학습은 선택 모델 1개만 수행\n",
-    "if RETRAIN_SELECTED_MODEL_ON_FULL_DATA and len(X_final_train) < len(X_train):\n",
-    "    print(f\"[{selected_model_name}] selected model full-data refit start rows={len(X_train):,}\")\n",
-    "    refit_started_at = datetime.now()\n",
-    "    full_model = clone(candidate_configs[selected_model_name][\"estimator\"])\n",
-    "    full_model.set_params(**best_params_by_model[selected_model_name])\n",
-    "    full_model.fit(X_train, y_train)\n",
-    "\n",
-    "    model = full_model\n",
-    "    trained_models[selected_model_name] = full_model\n",
-    "    valid_proba = predict_positive_proba(model, X_valid)\n",
-    "    best_threshold = find_best_threshold(y_valid, valid_proba)\n",
-    "    valid_pred = (valid_proba >= best_threshold).astype(int)\n",
-    "    cm = confusion_matrix(y_valid, valid_pred, labels=[0, 1])\n",
-    "    full_metrics = compute_binary_metrics(y_valid, valid_proba, best_threshold)\n",
-    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), list(full_metrics.keys())] = list(full_metrics.values())\n",
-    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), \"threshold\"] = best_threshold\n",
-    "    model_thresholds[selected_model_name] = best_threshold\n",
-    "    model_confusion_matrices[selected_model_name] = cm\n",
-    "    print(f\"[{selected_model_name}] full-data refit done elapsed={(datetime.now() - refit_started_at).total_seconds():.1f}s\")\n",
-    "\n",
-    "roc_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"roc_auc\"])\n",
-    "pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
-    "accuracy = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"accuracy\"])\n",
-    "false_positive_rate = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"false_positive_rate\"])\n",
-    "\n",
-    "# AI 관리 요약\n",
-    "baseline_row = model_comparison[model_comparison[\"model_name\"].eq(\"LightGBM\")]\n",
-    "baseline_pr_auc = float(baseline_row.iloc[0][\"pr_auc\"]) if not baseline_row.empty else np.nan\n",
-    "best_pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
-    "performance_improvement = best_pr_auc - baseline_pr_auc if not np.isnan(baseline_pr_auc) else 0.0\n",
-    "\n",
-    "ai_management_summary = {\n",
-    "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/Recall/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
-    "    \"데이터셋_규모_관리\": {\n",
-    "        \"max_event_rows\": int(MAX_ROWS),\n",
-    "        \"transition_rows\": int(len(model_df)),\n",
-    "        \"train_rows\": int(len(X_train)),\n",
-    "        \"candidate_train_rows\": int(len(X_final_train)),\n",
-    "        \"valid_rows\": int(len(X_valid)),\n",
-    "        \"feature_count\": int(len(feature_cols)),\n",
-    "        \"positive_ratio\": float(y.mean()),\n",
-    "        \"transition_summary\": transition_summary.to_dict(orient=\"records\"),\n",
-    "        \"full_data_refit\": bool(RETRAIN_SELECTED_MODEL_ON_FULL_DATA),\n",
-    "    },\n",
-    "    \"테스트_케이스_수_관리\": {\n",
-    "        \"validation_total\": int(len(y_valid)),\n",
-    "        \"validation_normal\": int((y_valid == 0).sum()),\n",
-    "        \"validation_defect\": int((y_valid == 1).sum()),\n",
-    "        \"cv_splits\": int(effective_cv_splits),\n",
-    "        \"cv_sample_size\": int(len(X_cv_pool)),\n",
-    "        \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
-    "        \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-    "        \"smote_enabled\": bool(USE_SMOTE),\n",
-    "        \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
-    "        \"threshold_strategy\": {\n",
-    "            \"type\": \"recall_priority\",\n",
-    "            \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
-    "            \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
-    "            \"beta\": float(THRESHOLD_BETA),\n",
-    "        },\n",
-    "    },\n",
-    "    \"정확도_Accuracy\": accuracy,\n",
-    "    \"오탐률_False_Positive_Rate\": false_positive_rate,\n",
-    "    \"모델_성능_개선_현황\": {\n",
-    "        \"baseline_model\": \"LightGBM\",\n",
-    "        \"baseline_pr_auc\": baseline_pr_auc,\n",
-    "        \"selected_model\": selected_model_name,\n",
-    "        \"selected_pr_auc\": best_pr_auc,\n",
-    "        \"pr_auc_improvement_over_lightgbm\": performance_improvement,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "print(\"selected_model_name:\", selected_model_name)\n",
-    "print(\"ROC-AUC:\", round(roc_auc, 5))\n",
-    "print(\"PR-AUC:\", round(pr_auc, 5))\n",
-    "print(\"Accuracy:\", round(accuracy, 5))\n",
-    "print(\"False Positive Rate:\", round(false_positive_rate, 5))\n",
-    "print(\"Recall:\", round(float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"recall\"]), 5))\n",
-    "display(model_comparison)\n",
-    "display(pd.DataFrame([ai_management_summary]))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "AGL93TiObEfC",
-   "metadata": {
-    "id": "AGL93TiObEfC"
-   },
-   "source": [
-    "## 9. 후보 모델별 Threshold 튜닝 및 Confusion Matrix\n",
-    "\n",
-    "각 후보 모델은 validation set에서 recall 우선 threshold를 사용합니다.\n",
-    "\n",
-    "기본 정책은 `RECALL_PRIORITY_TARGET` 이상을 만족하는 threshold 중 precision이 가장 높은 값을 선택하고, 목표 recall을 만족하는 후보가 없으면 F-beta(기본 beta=2) 기준으로 선택합니다. Accuracy는 클래스 불균형 때문에 참고 지표로만 해석합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "-bI4yc81bEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "-bI4yc81bEfC",
-    "outputId": "fffa66b9-fb9b-4c84-b9d5-da0983048bc2"
-   },
-   "outputs": [],
-   "source": [
-    "print(\"== Selected Model ==\")\n",
-    "print(\"model:\", selected_model_name)\n",
-    "print(\"best_threshold:\", round(best_threshold, 5))\n",
-    "print(classification_report(y_valid, valid_pred, digits=4, zero_division=0))\n",
-    "\n",
-    "# 후보 모델별 Confusion Matrix\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
-    "axes = axes.ravel()\n",
-    "for ax, (model_name, cm_model) in zip(axes, model_confusion_matrices.items()):\n",
-    "    ConfusionMatrixDisplay(cm_model, display_labels=[\"Normal\", \"Defect\"]).plot(\n",
-    "        ax=ax,\n",
-    "        cmap=\"Blues\",\n",
-    "        values_format=\"d\",\n",
-    "        colorbar=False,\n",
-    "    )\n",
-    "    row = model_comparison[model_comparison[\"model_name\"].eq(model_name)].iloc[0]\n",
-    "    ax.set_title(\n",
-    "        f\"{model_name}\\n\"\n",
-    "        f\"Recall={row['recall']:.3f}, Precision={row['precision']:.3f}, F1={row['f1']:.3f}\"\n",
-    "    )\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# 비교 지표 테이블\n",
-    "display(model_comparison[[\n",
-    "    \"model_name\",\n",
-    "    \"accuracy\",\n",
-    "    \"false_positive_rate\",\n",
-    "    \"precision\",\n",
-    "    \"recall\",\n",
-    "    \"f1\",\n",
-    "    \"roc_auc\",\n",
-    "    \"pr_auc\",\n",
-    "    \"threshold\",\n",
-    "]])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ALpJVyy2bEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "ALpJVyy2bEfC",
-    "outputId": "b964a332-cdc6-45a1-f4e9-484e34c728a0"
-   },
-   "outputs": [],
-   "source": [
-    "# 모델 성능 비교 시각화\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(18, 4))\n",
-    "sns.countplot(x=y, ax=axes[0])\n",
-    "axes[0].set_title(\"Target Defect Distribution\")\n",
-    "axes[0].set_xlabel(\"Target Defect Label\")\n",
-    "\n",
-    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"pr_auc\", ax=axes[1], color=\"#4C78A8\")\n",
-    "axes[1].set_title(\"Validation PR-AUC by Model\")\n",
-    "axes[1].set_xlabel(\"\")\n",
-    "axes[1].tick_params(axis=\"x\", rotation=20)\n",
-    "\n",
-    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"false_positive_rate\", ax=axes[2], color=\"#F58518\")\n",
-    "axes[2].set_title(\"False Positive Rate by Model\")\n",
-    "axes[2].set_xlabel(\"\")\n",
-    "axes[2].tick_params(axis=\"x\", rotation=20)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# 선택 모델 ROC/PR 곡선\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
-    "RocCurveDisplay.from_predictions(y_valid, valid_proba, ax=axes[0])\n",
-    "axes[0].set_title(f\"{selected_model_name} ROC AUC={roc_auc:.4f}\")\n",
-    "PrecisionRecallDisplay.from_predictions(y_valid, valid_proba, ax=axes[1])\n",
-    "axes[1].set_title(f\"{selected_model_name} PR-AUC={pr_auc:.4f}\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "def extract_feature_importance(estimator, feature_names: list[str], model_name: str) -> pd.DataFrame:\n",
-    "    target_estimator = estimator\n",
-    "    if is_pipeline_estimator(estimator):\n",
-    "        target_estimator = get_final_estimator(estimator)\n",
-    "\n",
-    "    if hasattr(target_estimator, \"booster_\"):\n",
-    "        values = target_estimator.booster_.feature_importance(importance_type=\"gain\")\n",
-    "        importance_type = \"gain\"\n",
-    "    elif hasattr(target_estimator, \"feature_importances_\"):\n",
-    "        values = target_estimator.feature_importances_\n",
-    "        importance_type = \"feature_importances\"\n",
-    "    elif hasattr(target_estimator, \"coef_\"):\n",
-    "        values = np.abs(target_estimator.coef_).ravel()\n",
-    "        importance_type = \"abs_coef\"\n",
-    "    else:\n",
-    "        values = np.zeros(len(feature_names))\n",
-    "        importance_type = \"not_available\"\n",
-    "\n",
-    "    return pd.DataFrame({\n",
-    "        \"model_name\": model_name,\n",
-    "        \"feature\": feature_names,\n",
-    "        \"importance\": values,\n",
-    "        \"importance_type\": importance_type,\n",
-    "    }).sort_values(\"importance\", ascending=False)\n",
-    "\n",
-    "\n",
-    "importance_frames = [\n",
-    "    extract_feature_importance(estimator, feature_cols, model_name)\n",
-    "    for model_name, estimator in trained_models.items()\n",
-    "]\n",
-    "all_model_importance = pd.concat(importance_frames, ignore_index=True)\n",
-    "importance_df = all_model_importance[all_model_importance[\"model_name\"].eq(selected_model_name)].copy()\n",
-    "importance_df = importance_df.rename(columns={\"importance\": \"importance_gain\"})\n",
-    "\n",
-    "plt.figure(figsize=(9, 7))\n",
-    "sns.barplot(data=importance_df.head(25), y=\"feature\", x=\"importance_gain\", color=\"#4C78A8\")\n",
-    "plt.title(f\"Top 25 Feature Importance - {selected_model_name}\")\n",
-    "plt.xlabel(\"Importance\")\n",
-    "plt.ylabel(\"\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "display(importance_df.head(30))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "TaY5ZaEJbEfC",
-   "metadata": {
-    "id": "TaY5ZaEJbEfC"
-   },
-   "source": [
-    "## 10. SHAP 기반 영향 공정 분석\n",
-    "\n",
-    "선택된 모델의 SHAP 값을 JSON feature의 공정 prefix(`PRESS__`, `BODY__`, `PAINT__`)로\n",
-    "집계합니다. 이를 통해 다음 공정 불량 확률을 높인 이전 공정과 주요 센서값을 계산합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "24Ol6sMmbEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "24Ol6sMmbEfC",
-    "outputId": "ca41c28e-e024-41a1-d30e-52eface69410"
-   },
-   "outputs": [],
-   "source": [
-    "# SHAP 샘플링\n",
-    "SHAP_SAMPLE_SIZE = min(1000, len(X_valid))\n",
-    "\n",
-    "if selected_model_name == \"LogisticRegression\":\n",
-    "    SHAP_SAMPLE_SIZE = min(500, len(X_valid))\n",
-    "\n",
-    "X_shap = X_valid.sample(n=SHAP_SAMPLE_SIZE, random_state=RANDOM_STATE)\n",
-    "id_shap = id_valid.loc[X_shap.index]\n",
-    "y_shap = y_valid.loc[X_shap.index]\n",
-    "proba_shap = predict_positive_proba(model, X_shap)\n",
-    "\n",
-    "\n",
-    "def prepare_shap_model(estimator, X_sample: pd.DataFrame):\n",
-    "    if is_pipeline_estimator(estimator):\n",
-    "        final_estimator = get_final_estimator(estimator)\n",
-    "        transform_steps = estimator.steps[:-1]\n",
-    "\n",
-    "        X_transformed = X_sample.copy()\n",
-    "\n",
-    "        for _, step in transform_steps:\n",
-    "            if hasattr(step, \"transform\"):\n",
-    "                X_transformed = step.transform(X_transformed)\n",
-    "\n",
-    "        X_transformed = pd.DataFrame(\n",
-    "            X_transformed,\n",
-    "            columns=X_sample.columns,\n",
-    "            index=X_sample.index\n",
-    "        )\n",
-    "\n",
-    "        return final_estimator, X_transformed\n",
-    "\n",
-    "    return estimator, X_sample\n",
-    "\n",
-    "\n",
-    "shap_model, X_shap_model = prepare_shap_model(model, X_shap)\n",
-    "\n",
-    "# SHAP Explainer 생성\n",
-    "try:\n",
-    "    explainer = shap.TreeExplainer(shap_model)\n",
-    "\n",
-    "    raw_shap_values = explainer.shap_values(\n",
-    "        X_shap_model,\n",
-    "        check_additivity=False\n",
-    "    )\n",
-    "\n",
-    "    if isinstance(raw_shap_values, list):\n",
-    "        shap_matrix = raw_shap_values[1]\n",
-    "    elif getattr(raw_shap_values, \"ndim\", 0) == 3:\n",
-    "        shap_matrix = raw_shap_values[:, :, 1]\n",
-    "    else:\n",
-    "        shap_matrix = raw_shap_values\n",
-    "\n",
-    "    expected_value = explainer.expected_value\n",
-    "\n",
-    "    if isinstance(expected_value, list):\n",
-    "        base_value = expected_value[1]\n",
-    "    else:\n",
-    "        base_value = expected_value\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"TreeExplainer 실패:\", e)\n",
-    "    print(\"Permutation Explainer로 대체 실행합니다.\")\n",
-    "\n",
-    "    background = shap.sample(\n",
-    "        X_train,\n",
-    "        min(100, len(X_train)),\n",
-    "        random_state=RANDOM_STATE\n",
-    "    )\n",
-    "\n",
-    "    explainer = shap.Explainer(\n",
-    "        lambda data: predict_positive_proba(\n",
-    "            model,\n",
-    "            pd.DataFrame(data, columns=X_train.columns)\n",
-    "        ),\n",
-    "        background\n",
-    "    )\n",
-    "\n",
-    "    raw_explanation = explainer(\n",
-    "        X_shap,\n",
-    "        max_evals=2 * X_shap.shape[1] + 1\n",
-    "    )\n",
-    "\n",
-    "    shap_matrix = raw_explanation.values\n",
-    "    base_value = raw_explanation.base_values\n",
-    "    X_shap_model = X_shap\n",
-    "\n",
-    "\n",
-    "# base_value 길이 보정\n",
-    "if np.isscalar(base_value):\n",
-    "    base_values = np.repeat(base_value, len(X_shap))\n",
-    "else:\n",
-    "    base_values = np.array(base_value)\n",
-    "\n",
-    "    if base_values.ndim == 0:\n",
-    "        base_values = np.repeat(float(base_values), len(X_shap))\n",
-    "    elif len(base_values) != len(X_shap):\n",
-    "        base_values = np.repeat(float(np.ravel(base_values)[0]), len(X_shap))\n",
-    "\n",
-    "\n",
-    "# SHAP 객체 구성\n",
-    "shap_values = shap.Explanation(\n",
-    "    values=shap_matrix,\n",
-    "    base_values=base_values,\n",
-    "    data=X_shap_model.values,\n",
-    "    feature_names=X_shap.columns.tolist(),\n",
-    ")\n",
-    "\n",
-    "\n",
-    "print(\"selected_model_name:\", selected_model_name)\n",
-    "print(\"X_shap:\", X_shap.shape)\n",
-    "print(\"X_shap_model:\", X_shap_model.shape)\n",
-    "print(\"shap_matrix:\", shap_matrix.shape)\n",
-    "print(\"base_values:\", np.array(base_values).shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "gvPdq4r1bEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "gvPdq4r1bEfC",
-    "outputId": "6871cde5-1c84-4060-a5b6-f5d757281281"
-   },
-   "outputs": [],
-   "source": [
-    "# SHAP Summary Plot\n",
-    "shap.summary_plot(shap_values, X_shap, max_display=25, show=True)\n",
-    "\n",
-    "# SHAP 중요도 산출\n",
-    "shap_importance = pd.DataFrame({\n",
-    "    \"feature\": X_shap.columns,\n",
-    "    \"mean_abs_shap\": np.abs(shap_matrix).mean(axis=0),\n",
-    "}).sort_values(\"mean_abs_shap\", ascending=False)\n",
-    "\n",
-    "display(shap_importance.head(30))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "# Feature 공정 매핑\n",
-    "def feature_to_process(feature: str) -> str | None:\n",
-    "    for process_code in PROCESS_FLOW:\n",
-    "        if feature.startswith(f\"{process_code}__\"):\n",
-    "            return process_code\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "def probability_to_risk_grade(prob: float) -> str:\n",
-    "    if prob >= 0.70:\n",
-    "        return \"HIGH\"\n",
-    "    if prob >= 0.40:\n",
-    "        return \"MEDIUM\"\n",
-    "    return \"LOW\"\n",
-    "\n",
-    "\n",
-    "feature_process = pd.Series(\n",
-    "    [feature_to_process(column) for column in X_shap.columns],\n",
-    "    index=X_shap.columns,\n",
-    ")\n",
-    "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
-    "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
-    "print(f\"공정 영향도 집계 제외 전역 feature 수: {len(unmapped_features)}\")\n",
-    "if unmapped_features:\n",
-    "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
-    "\n",
-    "process_rows = []\n",
-    "for process_code in PROCESS_FLOW:\n",
-    "    column_idx = np.where(feature_process.values == process_code)[0]\n",
-    "    process_rows.append({\n",
-    "        \"process_code\": process_code,\n",
-    "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, column_idx]).mean()) if len(column_idx) else 0.0,\n",
-    "        \"feature_count\": int(len(column_idx)),\n",
-    "    })\n",
-    "\n",
-    "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
-    "display(process_importance)\n",
-    "\n",
-    "plt.figure(figsize=(8, 4))\n",
-    "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
-    "plt.title(\"SHAP Process Influence\")\n",
-    "plt.xlabel(\"Source Process\")\n",
-    "plt.ylabel(\"Mean |SHAP|\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ],
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 891
-    },
-    "id": "m-fwpoIeBrfa",
-    "outputId": "b70d837d-df0c-4159-fc29-c842f24f2558"
-   },
-   "id": "m-fwpoIeBrfa",
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "kBJK8YfybEfD",
-   "metadata": {
-    "id": "kBJK8YfybEfD"
-   },
-   "source": [
-    "## 11. SHAP 기반 공정 전이 위험 결과 생성\n",
-    "\n",
-    "실제 학습 행의 원인·대상 공정 정보를 유지하면서 다음 공정 불량 확률, 가장 영향이 큰\n",
-    "JSON feature, 공정별 SHAP 영향 비율을 결과로 생성합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "MjmQshC4bEfD",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "MjmQshC4bEfD",
-    "outputId": "268ba6e7-dc21-4678-beaf-55f184bbb3ec"
-   },
-   "outputs": [],
-   "source": [
-    "def build_transfer_predictions(\n",
-    "    metadata: pd.DataFrame,\n",
-    "    probabilities: np.ndarray,\n",
-    "    shap_matrix: np.ndarray,\n",
-    "    feature_names: list[str],\n",
-    "    feature_process: pd.Series,\n",
-    ") -> pd.DataFrame:\n",
-    "    records = []\n",
-    "    process_values = feature_process.values\n",
-    "    mapped_mask = feature_process.isin(PROCESS_FLOW).to_numpy()\n",
-    "    predicted_at = datetime.now(timezone.utc).isoformat()\n",
-    "\n",
-    "    for row_idx, (_, metadata_row) in enumerate(metadata.iterrows()):\n",
-    "        probability = float(probabilities[row_idx])\n",
-    "        abs_values = np.abs(shap_matrix[row_idx])\n",
-    "        mapped_total = float(abs_values[mapped_mask].sum()) or 1.0\n",
-    "\n",
-    "        process_scores = {}\n",
-    "        for process_code in PROCESS_FLOW:\n",
-    "            column_idx = np.where(process_values == process_code)[0]\n",
-    "            process_scores[process_code] = (\n",
-    "                float(abs_values[column_idx].sum() / mapped_total)\n",
-    "                if len(column_idx) else 0.0\n",
-    "            )\n",
-    "\n",
-    "        source_process = str(metadata_row[\"source_process_code\"])\n",
-    "        target_process = str(metadata_row[\"target_process_code\"])\n",
-    "        available_idx = np.where(\n",
-    "            np.isin(process_values, PROCESS_FLOW[:PROCESS_FLOW.index(target_process)])\n",
-    "        )[0]\n",
-    "        top_feature_idx = (\n",
-    "            int(available_idx[np.argmax(abs_values[available_idx])])\n",
-    "            if len(available_idx)\n",
-    "            else int(abs_values.argmax())\n",
-    "        )\n",
-    "\n",
-    "        records.append({\n",
-    "            \"sample_event_id\": metadata_row[\"sample_event_id\"],\n",
-    "            \"car_master_id\": int(metadata_row[\"car_master_id\"]),\n",
-    "            \"source_process_code\": source_process,\n",
-    "            \"target_process_code\": target_process,\n",
-    "            \"target_defect_label\": int(metadata_row[\"target_defect_label\"]),\n",
-    "            \"target_defect_probability\": round(probability, 6),\n",
-    "            \"predicted_defect_process\": target_process,\n",
-    "            \"risk_grade\": probability_to_risk_grade(probability),\n",
-    "            \"main_cause\": feature_names[top_feature_idx],\n",
-    "            \"source_process_influence_score\": round(process_scores.get(source_process, 0.0), 6),\n",
-    "            \"label_source\": metadata_row[\"label_source\"],\n",
-    "            \"predicted_at\": predicted_at,\n",
-    "        })\n",
-    "\n",
-    "    return pd.DataFrame(records).sort_values(\n",
-    "        [\"target_defect_probability\", \"source_process_influence_score\"],\n",
-    "        ascending=False,\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "transfer_predictions = build_transfer_predictions(\n",
-    "    meta_shap,\n",
-    "    proba_shap,\n",
-    "    shap_matrix,\n",
-    "    X_shap.columns.tolist(),\n",
-    "    feature_process,\n",
-    ")\n",
-    "display(transfer_predictions.head(30))\n",
-    "\n",
-    "risk_summary = transfer_predictions.groupby(\n",
-    "    [\"source_process_code\", \"target_process_code\", \"risk_grade\"]\n",
-    ").agg(\n",
-    "    vehicle_count=(\"car_master_id\", \"nunique\"),\n",
-    "    prediction_count=(\"sample_event_id\", \"count\"),\n",
-    "    actual_defect_ratio=(\"target_defect_label\", \"mean\"),\n",
-    "    avg_target_defect_probability=(\"target_defect_probability\", \"mean\"),\n",
-    "    avg_source_process_influence=(\"source_process_influence_score\", \"mean\"),\n",
-    ").reset_index().sort_values(\n",
-    "    [\"avg_target_defect_probability\", \"prediction_count\"],\n",
-    "    ascending=False,\n",
-    ")\n",
-    "display(risk_summary)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "JFTbBuS1bEfD",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 885
-    },
-    "id": "JFTbBuS1bEfD",
-    "outputId": "6afce295-117c-486d-d7df-abe3aade47a3"
-   },
-   "outputs": [],
-   "source": [
-    "# 전이 위험 시각화\n",
-    "plt.figure(figsize=(9, 5))\n",
-    "top_flow = risk_summary.head(12).copy()\n",
-    "top_flow[\"flow\"] = top_flow[\"source_process_code\"] + \" -> \" + top_flow[\"target_process_code\"] + \" (\" + top_flow[\"risk_grade\"] + \")\"\n",
-    "sns.barplot(data=top_flow, y=\"flow\", x=\"avg_target_defect_probability\", hue=\"risk_grade\", dodge=False)\n",
-    "plt.title(\"Process Transfer Risk by SHAP Influence\")\n",
-    "plt.xlabel(\"Avg Final Defect Probability\")\n",
-    "plt.ylabel(\"\")\n",
-    "plt.legend(title=\"Risk\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# 불량 확률 분포 시각화\n",
-    "plt.figure(figsize=(8, 4))\n",
-    "sns.histplot(data=transfer_predictions, x=\"target_defect_probability\", hue=\"risk_grade\", bins=40, multiple=\"stack\")\n",
-    "plt.title(\"Predicted Final Defect Probability Distribution\")\n",
-    "plt.xlabel(\"Defect Probability\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43d99e60",
-   "metadata": {
-    "id": "43d99e60"
-   },
-   "source": [
-    "## 12. AI 모델 관리 항목\n",
-    "\n",
-    "모델 선정 근거, 데이터셋 규모, 테스트 케이스 수, Accuracy, False Positive Rate, 성능 개선 현황을 운영 관리용 요약으로 정리합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c79bba33",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 237
-    },
-    "id": "c79bba33",
-    "outputId": "b4ed5007-4f34-40a7-8c33-edc4bc328bb1"
-   },
-   "outputs": [],
-   "source": [
-    "ai_management_table = pd.DataFrame([\n",
-    "    {\"관리 항목\": \"모델 선정 근거 정리\", \"값\": ai_management_summary[\"모델_선정_근거\"]},\n",
-    "    {\"관리 항목\": \"데이터셋 규모 관리\", \"값\": json.dumps(ai_management_summary[\"데이터셋_규모_관리\"], ensure_ascii=False)},\n",
-    "    {\"관리 항목\": \"테스트 케이스 수 관리\", \"값\": json.dumps(ai_management_summary[\"테스트_케이스_수_관리\"], ensure_ascii=False)},\n",
-    "    {\"관리 항목\": \"정확도(Accuracy) 측정\", \"값\": round(ai_management_summary[\"정확도_Accuracy\"], 6)},\n",
-    "    {\"관리 항목\": \"오탐률(False Positive Rate) 측정\", \"값\": round(ai_management_summary[\"오탐률_False_Positive_Rate\"], 6)},\n",
-    "    {\"관리 항목\": \"모델 성능 개선 현황 관리\", \"값\": json.dumps(ai_management_summary[\"모델_성능_개선_현황\"], ensure_ascii=False)},\n",
-    "])\n",
-    "\n",
-    "display(ai_management_table)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "Q5r_SU2bbEfD",
-   "metadata": {
-    "id": "Q5r_SU2bbEfD"
-   },
-   "source": [
-    "## 13. 모델 및 결과 저장\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "44a36633",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "44a36633",
-    "outputId": "9dbc6133-c938-4715-dc60-ea025f990e55"
-   },
-   "outputs": [],
-   "source": [
-    "# 저장 경로\n",
-    "selected_model_path = OUTPUT_DIR / \"selected_defect_detector.joblib\"\n",
-    "selected_model_pkl_path = OUTPUT_DIR / \"selected_defect_detector.pkl\"\n",
-    "candidate_models_path = OUTPUT_DIR / \"candidate_defect_models.joblib\"\n",
-    "candidate_models_pkl_path = OUTPUT_DIR / \"candidate_defect_models.pkl\"\n",
-    "model_path = OUTPUT_DIR / \"lightgbm_defect_detector.joblib\"\n",
-    "model_pkl_path = OUTPUT_DIR / \"lightgbm_defect_detector.pkl\"\n",
-    "feature_path = OUTPUT_DIR / \"defect_model_features.json\"\n",
-    "metrics_path = OUTPUT_DIR / \"defect_model_metrics.json\"\n",
-    "comparison_path = OUTPUT_DIR / \"defect_model_comparison.csv\"\n",
-    "confusion_matrix_path = OUTPUT_DIR / \"defect_model_confusion_matrices.json\"\n",
-    "ai_management_path = OUTPUT_DIR / \"defect_ai_management_summary.json\"\n",
-    "importance_path = OUTPUT_DIR / \"defect_model_feature_importance.csv\"\n",
-    "all_importance_path = OUTPUT_DIR / \"defect_all_model_feature_importance.csv\"\n",
-    "shap_importance_path = OUTPUT_DIR / \"defect_shap_importance.csv\"\n",
-    "transfer_path = OUTPUT_DIR / \"defect_transfer_prediction_result.csv\"\n",
-    "risk_summary_path = OUTPUT_DIR / \"defect_transfer_risk_summary.csv\"\n",
-    "\n",
-    "# 모델 저장\n",
-    "joblib.dump(model, selected_model_path)\n",
-    "joblib.dump(trained_models, candidate_models_path)\n",
-    "with open(selected_model_pkl_path, \"wb\") as f:\n",
-    "    pickle.dump(model, f)\n",
-    "with open(candidate_models_pkl_path, \"wb\") as f:\n",
-    "    pickle.dump(trained_models, f)\n",
-    "\n",
-    "# 기존 LightGBM 파일명 호환 저장\n",
-    "if \"LightGBM\" in trained_models:\n",
-    "    joblib.dump(trained_models[\"LightGBM\"], model_path)\n",
-    "    with open(model_pkl_path, \"wb\") as f:\n",
-    "        pickle.dump(trained_models[\"LightGBM\"], f)\n",
-    "\n",
-    "feature_path.write_text(json.dumps(feature_cols, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "\n",
-    "confusion_payload = {\n",
-    "    name: matrix.tolist()\n",
-    "    for name, matrix in model_confusion_matrices.items()\n",
-    "}\n",
-    "confusion_matrix_path.write_text(json.dumps(confusion_payload, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "ai_management_path.write_text(json.dumps(ai_management_summary, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "\n",
-    "# 평가 지표 저장\n",
-    "metrics = {\n",
-    "    \"selected_model_name\": selected_model_name,\n",
-    "    \"roc_auc\": float(roc_auc),\n",
-    "    \"average_precision\": float(pr_auc),\n",
-    "    \"accuracy\": float(accuracy),\n",
-    "    \"false_positive_rate\": float(false_positive_rate),\n",
-    "    \"best_threshold\": float(best_threshold),\n",
-    "    \"f1_at_best_threshold\": float(f1_score(y_valid, valid_pred, zero_division=0)),\n",
-    "    \"confusion_matrix\": cm.tolist(),\n",
-    "    \"train_rows\": int(len(X_train)),\n",
-    "    \"valid_rows\": int(len(X_valid)),\n",
-    "    \"feature_count\": int(len(feature_cols)),\n",
-    "    \"positive_ratio_train\": float(y_train.mean()),\n",
-    "    \"candidate_models\": list(trained_models.keys()),\n",
-    "    \"cv_splits\": int(effective_cv_splits),\n",
-    "    \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
-    "    \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-    "    \"smote_enabled\": bool(USE_SMOTE),\n",
-    "    \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
-    "    \"threshold_strategy\": {\n",
-    "        \"type\": \"recall_priority\",\n",
-    "        \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
-    "        \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
-    "        \"beta\": float(THRESHOLD_BETA),\n",
-    "    },\n",
-    "    \"model_selection_reason\": ai_management_summary[\"모델_선정_근거\"],\n",
-    "    \"data_source\": \"manufacturing_event_json.event_json\",\n",
-    "    \"label_restore_strategy\": preprocessing_metadata[\"label_sources\"],\n",
-    "    \"transition_summary\": transition_summary.to_dict(orient=\"records\"),\n",
-    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
-    "}\n",
-    "metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "\n",
-    "# 분석 결과 저장\n",
-    "cv_results.to_csv(cv_results_path, index=False)\n",
-    "if \"cv_fold_results\" in globals():\n",
-    "    cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
-    "model_comparison.to_csv(comparison_path, index=False)\n",
-    "importance_df.to_csv(importance_path, index=False)\n",
-    "all_model_importance.to_csv(all_importance_path, index=False)\n",
-    "shap_importance.to_csv(shap_importance_path, index=False)\n",
-    "transfer_predictions.to_csv(transfer_path, index=False)\n",
-    "risk_summary.to_csv(risk_summary_path, index=False)\n",
-    "\n",
-    "print(\"Saved outputs:\")\n",
-    "for path in [\n",
-    "    selected_model_path,\n",
-    "    selected_model_pkl_path,\n",
-    "    candidate_models_path,\n",
-    "    candidate_models_pkl_path,\n",
-    "    model_path,\n",
-    "    model_pkl_path,\n",
-    "    feature_path,\n",
-    "    metrics_path,\n",
-    "    cv_results_path,\n",
-    "    comparison_path,\n",
-    "    confusion_matrix_path,\n",
-    "    ai_management_path,\n",
-    "    importance_path,\n",
-    "    all_importance_path,\n",
-    "    shap_importance_path,\n",
-    "    transfer_path,\n",
-    "    risk_summary_path,\n",
-    "]:\n",
-    "    print(path)\n"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/requirements.txt b/requirements.txt
index 06882a6..6a55cbe 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -31,8 +31,11 @@ joblib>=1.4.0
 scikit-learn>=1.5.0
 lightgbm>=4.5.0
 xgboost>=2.1.0
+catboost>=1.2.7
+pyreadr>=0.5.2
+torch>=2.3.0
 shap>=0.46.0
 
 # Common
 orjson>=3.10.0
-loguru>=0.7.0,<0.8.0
\ No newline at end of file
+loguru>=0.7.0,<0.8.0

From ef26c6a22bd02ebc76c6147886cb95e77868d2db Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Sat, 27 Jun 2026 16:56:55 +0900
Subject: [PATCH 037/148] =?UTF-8?q?feat:=20=EC=A0=9C=EC=A1=B0=20=EB=B6=88?=
 =?UTF-8?q?=EB=9F=89=20=ED=83=90=EC=A7=80=20=EB=B0=8F=20=EC=A0=84=EC=9D=B4?=
 =?UTF-8?q?=20=EC=98=88=EC=B8=A1=20=EB=AA=A8=EB=8D=B8=EB=A7=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 제조 이벤트 기반 불량 탐지 및 전이 예측용 데이터 구조 반영

- product.carMasterId를 기존 car_master.id와 매핑

- 차량당 PRESS, BODY, PAINT, ASSEMBLY 이벤트 4건 생성

- 정상·이상 데이터 비율을 7:3으로 구성하고 공정별 균등 분배

- 공정별 설비와 operationStatus를 결정적 랜덤 방식으로 생성

- eventTime, lastNormalTime, statusChangedTime 초기값을 NULL로 설정

- PRESS는 READY, 후속 공정은 PENDING으로 초기화

- MySQL 데드락 재시도 및 생성 작업 전역 잠금 처리 추가

- SampleDB Repository를 테이블과 책임별 클래스로 분리
---
 .env.example                                  |    15 +-
 app/batch/manufacturing_event_loader.py       |     2 +-
 app/core/config.py                            |    51 +-
 .../defect_transfer_dataset_builder.py        |   121 +-
 app/main.py                                   |     2 +-
 .../bottleneck_iforest_model.pkl              |   Bin
 .../adjacent_transfer_model_metadata.json     |    74 +
 .../defect/adjacent_transfer_models.joblib    |   Bin 0 -> 1710059 bytes
 .../defect/defect_model_features.json         |    37 +
 .../defect/defect_model_metrics.json          |    36 +
 .../defect/selected_defect_detector.joblib    |   Bin 0 -> 747567 bytes
 .../defect_transfer_prediction_model.ipynb    | 19460 +++++++++-------
 .../manufacturing_event_scheduler.py          |     2 +-
 app/service/analysis/bottleneck_service.py    |     2 +-
 .../manufacturing_event_json_service.py       |     2 +-
 15 files changed, 11710 insertions(+), 8094 deletions(-)
 rename app/ml/artifacts/{ => bottleneck}/bottleneck_iforest_model.pkl (100%)
 create mode 100644 app/ml/artifacts/defect/adjacent_transfer_model_metadata.json
 create mode 100644 app/ml/artifacts/defect/adjacent_transfer_models.joblib
 create mode 100644 app/ml/artifacts/defect/defect_model_features.json
 create mode 100644 app/ml/artifacts/defect/defect_model_metrics.json
 create mode 100644 app/ml/artifacts/defect/selected_defect_detector.joblib

diff --git a/.env.example b/.env.example
index 0dd2683..25124f8 100644
--- a/.env.example
+++ b/.env.example
@@ -11,19 +11,20 @@ COLLEAGUE_SKILL_API_URL=
 
 # Kafka
 KAFKA_BOOTSTRAP_SERVERS=
+BROKER_URL_1=
+BROKER_URL_2=
 
 # Redis Cache
 REDIS_URL=redis://localhost:6379/0
 REDIS_KEY_PREFIX=aims:ai-service
 REDIS_CACHE_TTL_SECONDS=300
 
-# Main MySQL DB
-# Bottleneck analysis results are written to this DB.
-MAIN_DATABASE_URL=mysql+pymysql://aims_user:change-me@localhost:3306/maindb?charset=utf8mb4
-
-# Sample MySQL DB
-# Use this DB for sample/source data connections when needed.
-SAMPLE_DATABASE_URL=mysql+pymysql://sample_user:change-me@localhost:3306/sampledb?charset=utf8mb4
+# MySQL DB
+# MAIN_DATABASE_URL contains the shared driver/user/password/host/port.
+# MAIN_DB_NAME and SAMPLE_DB_NAME select the actual schema/database.
+MAIN_DATABASE_URL=mysql+pymysql://aims_user:change-me@localhost:3306/?charset=utf8mb4
+MAIN_DB_NAME=maindb
+SAMPLE_DB_NAME=sampledb
 
 # Manufacturing source event JSON generation
 MANUFACTURING_EVENT_SCHEDULER_ENABLED=true
diff --git a/app/batch/manufacturing_event_loader.py b/app/batch/manufacturing_event_loader.py
index d432a99..4b6a4db 100644
--- a/app/batch/manufacturing_event_loader.py
+++ b/app/batch/manufacturing_event_loader.py
@@ -29,7 +29,7 @@ def main() -> None:
     args = _parse_args()
 
     if not settings.sample_database_connection_url:
-        raise SystemExit("SAMPLE_DATABASE_URL 설정이 필요합니다.")
+        raise SystemExit("MAIN_DATABASE_URL + SAMPLE_DB_NAME 설정이 필요합니다.")
 
     repository = SampleDbRepository(settings.sample_database_connection_url)
     service = ManufacturingEventJsonService(repository)
diff --git a/app/core/config.py b/app/core/config.py
index c182c91..c5ee136 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -4,6 +4,39 @@
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
+def database_url_with_name(database_url: str, database_name: str | None) -> str:
+    """Return database_url with its database path replaced by database_name."""
+    if not database_name:
+        return database_url
+
+    scheme_separator = "://"
+    scheme_index = database_url.find(scheme_separator)
+    if scheme_index < 0:
+        return database_url
+
+    authority_start = scheme_index + len(scheme_separator)
+    credential_end = database_url.rfind("@")
+    host_start = credential_end + 1 if credential_end >= authority_start else authority_start
+    suffix_candidates = [
+        index
+        for index in (
+            database_url.find("?", host_start),
+            database_url.find("#", host_start),
+        )
+        if index >= 0
+    ]
+    suffix_start = min(suffix_candidates) if suffix_candidates else len(database_url)
+    path_start = database_url.find("/", host_start, suffix_start)
+    prefix_end = path_start if path_start >= 0 else suffix_start
+
+    return (
+        database_url[:prefix_end]
+        + "/"
+        + database_name.strip("/")
+        + database_url[suffix_start:]
+    )
+
+
 class Settings(BaseSettings):
     """환경 변수와 .env 파일에서 애플리케이션 설정을 로드한다."""
 
@@ -29,7 +62,8 @@ class Settings(BaseSettings):
     redis_cache_ttl_seconds: int = Field(default=300, alias="REDIS_CACHE_TTL_SECONDS")
 
     main_database_url: str | None = Field(default=None, alias="MAIN_DATABASE_URL")
-    sample_database_url: str | None = Field(default=None, alias="SAMPLE_DATABASE_URL")
+    main_db_name: str | None = Field(default=None, alias="MAIN_DB_NAME")
+    sample_db_name: str | None = Field(default=None, alias="SAMPLE_DB_NAME")
     manufacturing_event_scheduler_enabled: bool = Field(
         default=True,
         alias="MANUFACTURING_EVENT_SCHEDULER_ENABLED",
@@ -52,18 +86,23 @@ class Settings(BaseSettings):
     )
 
     @property
-    def bottleneck_database_url(self) -> str:
-        """병목 분석 결과를 저장할 maindb MySQL URL을 반환한다."""
+    def main_database_connection_url(self) -> str:
+        """Return the main DB connection URL."""
         if self.main_database_url:
-            return self.main_database_url
+            return database_url_with_name(self.main_database_url, self.main_db_name)
 
         raise ValueError("maindb MySQL 설정이 필요합니다: MAIN_DATABASE_URL")
 
+    @property
+    def bottleneck_database_url(self) -> str:
+        """병목 분석 결과를 저장할 maindb MySQL URL을 반환한다."""
+        return self.main_database_connection_url
+
     @property
     def sample_database_connection_url(self) -> str | None:
         """sampledb 설정이 있으면 MySQL URL을 반환한다."""
-        if self.sample_database_url:
-            return self.sample_database_url
+        if self.main_database_url and self.sample_db_name:
+            return database_url_with_name(self.main_database_url, self.sample_db_name)
 
         return None
 
diff --git a/app/data_generation/defect_transfer_dataset_builder.py b/app/data_generation/defect_transfer_dataset_builder.py
index afd6844..02a6d6c 100644
--- a/app/data_generation/defect_transfer_dataset_builder.py
+++ b/app/data_generation/defect_transfer_dataset_builder.py
@@ -33,6 +33,24 @@
     "defect_reason",
     "dataset_split",
 ]
+TRANSFER_PREDICTION_COLUMNS = [
+    "car_master_id",
+    "source_event_id",
+    "target_event_id",
+    "source_process_code",
+    "target_process_code",
+    "source_defect_yn",
+    "target_defect_yn",
+    "source_cycle_time_sec",
+    "source_station_delay_sec",
+    "source_queue_length",
+    "source_wip_count",
+    "source_current_rms_ampere",
+    "source_vibration_score",
+    "source_thermal_score",
+    "source_event_json",
+    "dataset_split",
+]
 DEFAULT_DATASET_ROOT = Path(__file__).resolve().parents[1] / "ml" / "datasets" / "process"
 DEFAULT_OUTPUT_DIRNAME = "generated"
 
@@ -131,9 +149,8 @@ def generate_defect_transfer_datasets(
     _write_csv(defect_train_path, [r for r in event_rows if r["dataset_split"] == "train"], RAW_EVENT_COLUMNS)
     _write_csv(defect_test_path, [r for r in event_rows if r["dataset_split"] == "test"], RAW_EVENT_COLUMNS)
 
-    transition_columns = list(transition_rows[0]) if transition_rows else []
-    _write_csv(transfer_train_path, [r for r in transition_rows if r["dataset_split"] == "train"], transition_columns)
-    _write_csv(transfer_test_path, [r for r in transition_rows if r["dataset_split"] == "test"], transition_columns)
+    _write_csv(transfer_train_path, [r for r in transition_rows if r["dataset_split"] == "train"], TRANSFER_PREDICTION_COLUMNS)
+    _write_csv(transfer_test_path, [r for r in transition_rows if r["dataset_split"] == "test"], TRANSFER_PREDICTION_COLUMNS)
 
     metadata = {
         "created_at": datetime.now().isoformat(timespec="seconds"),
@@ -151,6 +168,10 @@ def generate_defect_transfer_datasets(
             "transfer_train": str(transfer_train_path),
             "transfer_test": str(transfer_test_path),
         },
+        "transfer_prediction_schema": {
+            "label_column": "target_defect_yn",
+            "excluded_target_side_columns": ["target_defect_reason", "target_event_json"],
+        },
         "source_files": source.source_files,
     }
     metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8")
@@ -402,7 +423,6 @@ def _build_transition_rows(event_by_car_process: dict[tuple[int, str], dict[str,
             source = event_by_car_process[(car_master_id, source_process)]
             target = event_by_car_process[(car_master_id, target_process)]
             source_payload = json.loads(source["event_json"])
-            target_payload = json.loads(target["event_json"])
             source_metrics = source_payload["processMetrics"]
             source_sensor = source_payload["sensor"]
             row = {
@@ -413,7 +433,6 @@ def _build_transition_rows(event_by_car_process: dict[tuple[int, str], dict[str,
                 "target_process_code": target_process,
                 "source_defect_yn": int(source["defect_yn"]),
                 "target_defect_yn": int(target["defect_yn"]),
-                "target_defect_reason": target["defect_reason"],
                 "source_cycle_time_sec": source_metrics["cycleTimeSec"],
                 "source_station_delay_sec": source_metrics["stationDelaySec"],
                 "source_queue_length": source_metrics["queueLength"],
@@ -422,7 +441,6 @@ def _build_transition_rows(event_by_car_process: dict[tuple[int, str], dict[str,
                 "source_vibration_score": source_sensor["vibration"]["vibrationScore"],
                 "source_thermal_score": source_sensor["thermal"]["thermalScore"],
                 "source_event_json": source["event_json"],
-                "target_event_json": target["event_json"],
                 "dataset_split": split,
             }
             rows.append(row)
@@ -430,10 +448,10 @@ def _build_transition_rows(event_by_car_process: dict[tuple[int, str], dict[str,
 
 
 def _defect_profile(car_master_id: int) -> dict[str, bool]:
-    press = _stable_int(car_master_id, "PRESS") % 100 < 9
-    body = (_stable_int(car_master_id, "BODY") % 100 < 7) or (press and _stable_int(car_master_id, "PRESS_BODY") % 100 < 55)
-    paint = (_stable_int(car_master_id, "PAINT") % 100 < 8) or (body and _stable_int(car_master_id, "BODY_PAINT") % 100 < 48)
-    assembly = (_stable_int(car_master_id, "ASSEMBLY") % 100 < 6) or (paint and _stable_int(car_master_id, "PAINT_ASSEMBLY") % 100 < 45)
+    press = _stable_int(car_master_id, "PRESS") % 100 < 18
+    body = (_stable_int(car_master_id, "BODY") % 100 < 17) or (press and _stable_int(car_master_id, "PRESS_BODY") % 100 < 30)
+    paint = (_stable_int(car_master_id, "PAINT") % 100 < 17) or (body and _stable_int(car_master_id, "BODY_PAINT") % 100 < 28)
+    assembly = (_stable_int(car_master_id, "ASSEMBLY") % 100 < 16) or (paint and _stable_int(car_master_id, "PAINT_ASSEMBLY") % 100 < 25)
     return {"PRESS": press, "BODY": body, "PAINT": paint, "ASSEMBLY": assembly}
 
 
@@ -449,44 +467,52 @@ def _apply_defect_profile(
     r1 = _stable_float(car_master_id, process_code, "defect_rand_1")
     r2 = _stable_float(car_master_id, process_code, "defect_rand_2")
     r3 = _stable_float(car_master_id, process_code, "defect_rand_3")
+    severity = 0.20 + _stable_float(car_master_id, process_code, "defect_severity") * 0.65
+
+    current_factor = 0.92 + r2 * 0.20
+    current["rmsAmpere"] = round(float(current["rmsAmpere"]) * current_factor, 9)
+    current["maxAmpere"] = round(float(current["maxAmpere"]) * current_factor, 9)
+    current["minAmpere"] = round(float(current["minAmpere"]) * current_factor, 9)
+    ford["vibrationScore"] = round(float(ford["vibrationScore"]) * (0.82 + r3 * 0.34), 6)
+    vision["defectScore"] = round(float(vision["defectScore"]) * (0.82 + r1 * 0.34), 6)
 
     if is_defect:
-        current["rmsAmpere"] = round(3.0 + r1 * 1.5, 9)
-        current["maxAmpere"] = round(current["rmsAmpere"] + 0.2 + r2 * 0.2, 9)
-        current["minAmpere"] = round(max(0.0, current["rmsAmpere"] - 0.2 - r3 * 0.2), 9)
-        current["accelerationG"] = round(0.04 + r1 * 0.06, 9)
-        ford["label"] = -1
-        ford["vibrationScore"] = round(0.55 + r1 * 0.30, 6)
-        ford["vibrationRms"] = round(1.2 + r2 * 1.5, 9)
-        ford["vibrationPeak"] = round(2.0 + r3 * 2.0, 9)
-        vision["label"] = 1
-        vision["thermalStdTemp"] = round(3.5 + r1 * 3.5, 9)
-        vision["defectScore"] = round(0.50 + r2 * 0.30, 6)
-        vision["surfaceQualityScore"] = round(65.0 + r3 * 15.0, 3)
-        bosch["response"] = 1
-    else:
-        if r1 < 0.03:
-            current["rmsAmpere"] = round(2.8 + r2 * 0.8, 9)
-            current["maxAmpere"] = round(current["rmsAmpere"] + 0.2, 9)
-            current["minAmpere"] = round(max(0.0, current["rmsAmpere"] - 0.2), 9)
-        else:
-            current["rmsAmpere"] = round(float(current["rmsAmpere"]) * (0.9 + r2 * 0.2), 9)
-            current["maxAmpere"] = round(float(current["maxAmpere"]) * (0.9 + r2 * 0.2), 9)
-            current["minAmpere"] = round(float(current["minAmpere"]) * (0.9 + r2 * 0.2), 9)
-            
-        if r2 < 0.03:
-            ford["vibrationScore"] = round(0.45 + r3 * 0.20, 6)
-            ford["vibrationRms"] = round(1.0 + r1 * 0.8, 9)
+        if process_code == "PRESS":
+            current_base = _to_float(current.get("rmsAmpere"), 1.8)
+            current["rmsAmpere"] = round(current_base + 0.10 + severity * 0.45 + r1 * 0.28, 9)
+            current["maxAmpere"] = round(current["rmsAmpere"] + 0.10 + r2 * 0.24, 9)
+            current["minAmpere"] = round(max(0.0, current["rmsAmpere"] - 0.12 - r3 * 0.24), 9)
+            current["accelerationG"] = round(0.008 + severity * 0.018 + r1 * 0.018, 9)
+        elif process_code == "BODY":
+            ford["label"] = -1
+            ford["vibrationScore"] = round(max(float(ford["vibrationScore"]), 0.20 + severity * 0.28 + r1 * 0.16), 6)
+            ford["vibrationRms"] = round(max(float(ford["vibrationRms"]) * (0.88 + r2 * 0.30), 0.52 + severity * 0.68 + r2 * 0.62), 9)
+            ford["vibrationPeak"] = round(max(float(ford["vibrationPeak"]) * (0.88 + r3 * 0.30), 0.90 + severity * 0.88 + r3 * 0.82), 9)
+        elif process_code == "PAINT":
+            vision["label"] = 1
+            vision["thermalStdTemp"] = round(max(float(vision["thermalStdTemp"]) * (0.88 + r1 * 0.24), 1.4 + severity * 1.6 + r1 * 1.25), 9)
+            vision["defectScore"] = round(max(float(vision["defectScore"]), 0.18 + severity * 0.22 + r2 * 0.18), 6)
+            vision["surfaceQualityScore"] = round(78.0 - severity * 8.0 + r3 * 14.0, 3)
         else:
-            ford["vibrationScore"] = round(float(ford["vibrationScore"]) * (0.8 + r3 * 0.4), 6)
-            
-        if r3 < 0.03:
-            vision["defectScore"] = round(0.40 + r1 * 0.20, 6)
-            vision["surfaceQualityScore"] = round(75.0 + r2 * 10.0, 3)
-        else:
-            vision["defectScore"] = round(float(vision["defectScore"]) * (0.8 + r1 * 0.4), 6)
-            
-        bosch["response"] = 0
+            bosch["response"] = 1 if severity + r3 * 0.40 > 0.82 else 0
+            if r2 < 0.35:
+                current["rmsAmpere"] = round(float(current["rmsAmpere"]) + 0.10 + r1 * 0.35, 9)
+                current["maxAmpere"] = round(current["rmsAmpere"] + 0.14 + r2 * 0.22, 9)
+    else:
+        if process_code == "PRESS" and r1 < 0.34:
+            current["rmsAmpere"] = round(1.85 + r2 * 1.80, 9)
+            current["maxAmpere"] = round(current["rmsAmpere"] + 0.18 + r3 * 0.20, 9)
+            current["minAmpere"] = round(max(0.0, current["rmsAmpere"] - 0.18 - r1 * 0.20), 9)
+        if process_code == "BODY" and r2 < 0.34:
+            ford["vibrationScore"] = round(0.22 + r3 * 0.46, 6)
+            ford["vibrationRms"] = round(0.58 + r1 * 1.35, 9)
+            ford["vibrationPeak"] = round(0.95 + r2 * 1.55, 9)
+        if process_code == "PAINT" and r3 < 0.34:
+            vision["thermalStdTemp"] = round(1.25 + r1 * 2.45, 9)
+            vision["defectScore"] = round(0.18 + r1 * 0.46, 6)
+            vision["surfaceQualityScore"] = round(66.0 + r2 * 22.0, 3)
+        if process_code == "ASSEMBLY":
+            bosch["response"] = 1 if r1 < 0.18 else 0
 
 
 def _process_metrics(process_code: str, index: int, current: dict[str, Any], ford: dict[str, Any], vision: dict[str, Any], bosch: dict[str, Any], is_defect: bool) -> dict[str, Any]:
@@ -498,8 +524,11 @@ def _process_metrics(process_code: str, index: int, current: dict[str, Any], for
         anomaly = float(vision["defectScore"]) * 8
     else:
         anomaly = float(bosch["response"]) * 8
+    delay_rand = _stable_float(index, process_code, "delay_rand")
     if is_defect:
-        anomaly += 8.0
+        anomaly += 0.2 + delay_rand * 1.2
+    elif delay_rand < 0.28:
+        anomaly += 0.8 + _stable_float(index, process_code, "normal_delay") * 4.2
     cycle_time = target + anomaly + (index % 5) * 0.4
     processing = max(1.0, cycle_time - (5 + index % 4))
     waiting = cycle_time - processing + (index % 3)
diff --git a/app/main.py b/app/main.py
index 6aa93f8..bdf84f7 100644
--- a/app/main.py
+++ b/app/main.py
@@ -49,7 +49,7 @@ def create_app() -> FastAPI:
 
     @app.on_event("startup")
     def initialize_sampledb_schema() -> None:
-        """SAMPLE_DATABASE_URL이 설정된 경우 sampledb 엔티티를 생성한다."""
+        """MAIN_DATABASE_URL + SAMPLE_DB_NAME이 설정된 경우 sampledb 엔티티를 생성한다."""
         if not settings.sample_database_connection_url:
             return
 
diff --git a/app/ml/artifacts/bottleneck_iforest_model.pkl b/app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl
similarity index 100%
rename from app/ml/artifacts/bottleneck_iforest_model.pkl
rename to app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl
diff --git a/app/ml/artifacts/defect/adjacent_transfer_model_metadata.json b/app/ml/artifacts/defect/adjacent_transfer_model_metadata.json
new file mode 100644
index 0000000..5509d51
--- /dev/null
+++ b/app/ml/artifacts/defect/adjacent_transfer_model_metadata.json
@@ -0,0 +1,74 @@
+{
+  "press_to_body": {
+    "source_process_code": "PRESS",
+    "target_process_code": "BODY",
+    "threshold": 0.4376819769807411,
+    "feature_columns": [
+      "source_cycle_time_sec",
+      "source_station_delay_sec",
+      "source_queue_length",
+      "source_wip_count",
+      "source_current_rms_ampere",
+      "source_vibration_score",
+      "source_thermal_score"
+    ],
+    "scale_pos_weight": 3.639922667955534,
+    "best_params": {
+      "model__subsample": 0.95,
+      "model__reg_lambda": 8.0,
+      "model__num_leaves": 23,
+      "model__n_estimators": 220,
+      "model__min_child_samples": 60,
+      "model__learning_rate": 0.035,
+      "model__colsample_bytree": 0.8
+    }
+  },
+  "body_to_paint": {
+    "source_process_code": "BODY",
+    "target_process_code": "PAINT",
+    "threshold": 0.44474415478457285,
+    "feature_columns": [
+      "source_cycle_time_sec",
+      "source_station_delay_sec",
+      "source_queue_length",
+      "source_wip_count",
+      "source_current_rms_ampere",
+      "source_vibration_score",
+      "source_thermal_score"
+    ],
+    "scale_pos_weight": 3.5411542100283824,
+    "best_params": {
+      "model__subsample": 0.95,
+      "model__reg_lambda": 8.0,
+      "model__num_leaves": 23,
+      "model__n_estimators": 220,
+      "model__min_child_samples": 60,
+      "model__learning_rate": 0.035,
+      "model__colsample_bytree": 0.8
+    }
+  },
+  "paint_to_assembly": {
+    "source_process_code": "PAINT",
+    "target_process_code": "ASSEMBLY",
+    "threshold": 0.46001877207080666,
+    "feature_columns": [
+      "source_cycle_time_sec",
+      "source_station_delay_sec",
+      "source_queue_length",
+      "source_wip_count",
+      "source_current_rms_ampere",
+      "source_vibration_score",
+      "source_thermal_score"
+    ],
+    "scale_pos_weight": 3.841149773071104,
+    "best_params": {
+      "model__subsample": 0.95,
+      "model__reg_lambda": 8.0,
+      "model__num_leaves": 23,
+      "model__n_estimators": 220,
+      "model__min_child_samples": 60,
+      "model__learning_rate": 0.035,
+      "model__colsample_bytree": 0.8
+    }
+  }
+}
\ No newline at end of file
diff --git a/app/ml/artifacts/defect/adjacent_transfer_models.joblib b/app/ml/artifacts/defect/adjacent_transfer_models.joblib
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diff --git a/app/ml/artifacts/defect/defect_model_features.json b/app/ml/artifacts/defect/defect_model_features.json
new file mode 100644
index 0000000..16bf5f5
--- /dev/null
+++ b/app/ml/artifacts/defect/defect_model_features.json
@@ -0,0 +1,37 @@
+[
+  "process_code",
+  "station_code",
+  "equipment_code",
+  "equipment_type",
+  "current_rms_ampere",
+  "current_max_ampere",
+  "current_min_ampere",
+  "vibration_acceleration_g",
+  "vibration_score",
+  "vibration_rms",
+  "vibration_peak",
+  "robot_axis",
+  "robot_frequency_hz",
+  "robot_amplitude",
+  "robot_vibration_score",
+  "thermal_score",
+  "avg_temperature",
+  "max_temperature",
+  "min_temperature",
+  "cycle_time_sec",
+  "waiting_time_sec",
+  "processing_time_sec",
+  "station_delay_sec",
+  "throughput_per_min",
+  "queue_length",
+  "wip_count",
+  "equipment_idle_time_sec",
+  "press_target_cycle_time_sec",
+  "body_robot_operation_mode",
+  "body_frequency_peak_band",
+  "body_frequency_band_max",
+  "body_frequency_band_mean",
+  "paint_image_position",
+  "paint_thermal_std_temp",
+  "paint_thickness_value"
+]
\ No newline at end of file
diff --git a/app/ml/artifacts/defect/defect_model_metrics.json b/app/ml/artifacts/defect/defect_model_metrics.json
new file mode 100644
index 0000000..f453933
--- /dev/null
+++ b/app/ml/artifacts/defect/defect_model_metrics.json
@@ -0,0 +1,36 @@
+{
+  "selected_model_name": "LightGBM",
+  "threshold": 0.5025206934115897,
+  "train_metrics": {
+    "threshold": 0.5025206934115897,
+    "pr_auc": 0.9741047418313843,
+    "roc_auc": 0.9926450697410859,
+    "accuracy": 0.9446614583333334,
+    "precision": 0.8019760790431617,
+    "recall": 0.9721381744831064,
+    "f1": 0.8788966774947284,
+    "false_positive_rate": 0.06249179466981751,
+    "tn": 28564,
+    "fp": 1904,
+    "fn": 221,
+    "tp": 7711
+  },
+  "test_metrics": {
+    "threshold": 0.5025206934115897,
+    "pr_auc": 0.9559739814794836,
+    "roc_auc": 0.9865001903781169,
+    "accuracy": 0.9298958333333334,
+    "precision": 0.7673625905411163,
+    "recall": 0.9341286307053942,
+    "f1": 0.8425730994152046,
+    "false_positive_rate": 0.07116788321167883,
+    "tn": 7126,
+    "fp": 546,
+    "fn": 127,
+    "tp": 1801
+  },
+  "train_rows": 38400,
+  "test_rows": 9600,
+  "positive_ratio_train": 0.2065625,
+  "created_at": "2026-06-27T06:42:41.376260+00:00"
+}
\ No newline at end of file
diff --git a/app/ml/artifacts/defect/selected_defect_detector.joblib b/app/ml/artifacts/defect/selected_defect_detector.joblib
new file mode 100644
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HcmV?d00001

diff --git a/app/ml/training/defect_transfer_prediction_model.ipynb b/app/ml/training/defect_transfer_prediction_model.ipynb
index 2083b81..3a9759e 100644
--- a/app/ml/training/defect_transfer_prediction_model.ipynb
+++ b/app/ml/training/defect_transfer_prediction_model.ipynb
@@ -1,8118 +1,11518 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "873328b3",
-   "metadata": {
-    "id": "873328b3"
-   },
-   "source": [
-    "# 불량 탐지 및 SHAP 기반 공정 전이 위험 예측\n",
-    "\n",
-    "1. 라이브러리 import 및 Colab/Google Drive 로드\n",
-    "2. `app/ml/datasets/process/` 전체 데이터셋 정제 및 전처리\n",
-    "3. Bosch, Ford, 열화상, 소성가공 데이터 기반 보조 모델/피처 학습\n",
-    "4. 후보 모델 4종(LightGBM, RandomForest, ExtraTrees, LogisticRegression) 교차검증/튜닝\n",
-    "5. 최종 후보별 성능 비교, Accuracy, False Positive Rate, Confusion Matrix\n",
-    "6. SHAP 기반 영향 공정 분석 및 공정 간 전이 위험 결과 생성\n",
-    "7. AI 모델 관리 항목과 모델/결과 파일 저장\n",
-    "\n",
-    "> 현재 데이터에는 `BODY 불량 -> PAINT 불량` 같은 실제 전이 정답 라벨과 데이터셋 간 공통 제품 키가 없습니다. 따라서 이 노트북의 전이 예측은 별도 전이 라벨 학습이 아니라, `최종 불량 확률 + SHAP 영향 공정 + 보조 공정 위험 피처 + 공정 흐름 규칙` 기반 위험 확산 해석입니다.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "P44DGP2TDAt4",
-   "metadata": {
-    "id": "P44DGP2TDAt4"
-   },
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "hMAQMhIqbEe9",
-   "metadata": {
-    "id": "hMAQMhIqbEe9"
-   },
-   "outputs": [],
-   "source": [
-    "# Colab 환경에서 필요한 패키지 설치\n",
-    "import importlib.util\n",
-    "import subprocess\n",
-    "import sys\n",
-    "\n",
-    "def ensure_package(import_name: str, pip_name: str | None = None):\n",
-    "    pip_name = pip_name or import_name\n",
-    "    if importlib.util.find_spec(import_name) is None:\n",
-    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", pip_name, \"-q\"])\n",
-    "\n",
-    "ensure_package(\"lightgbm\")\n",
-    "ensure_package(\"shap\")\n",
-    "ensure_package(\"joblib\")\n",
-    "ensure_package(\"optuna\")\n",
-    "ensure_package(\"imblearn\", \"imbalanced-learn\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "XFBnJXPabHMi",
-   "metadata": {
-    "id": "XFBnJXPabHMi"
-   },
-   "outputs": [],
-   "source": [
-    "# CPU 병렬 사용\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "model = lgb.LGBMClassifier(\n",
-    "    n_estimators=300,\n",
-    "    learning_rate=0.05,\n",
-    "    num_leaves=31,\n",
-    "    n_jobs=-1,\n",
-    "    random_state=42\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7a3d724",
-   "metadata": {
-    "id": "f7a3d724"
-   },
-   "source": [
-    "> 필요한 추가 패키지(`optuna`, `imbalanced-learn`)는 위 설치 셀에서 자동 확인/설치합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e37daef5",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "e37daef5",
-    "outputId": "d3014dc5-ad21-4f08-d5e8-65c43e05146f"
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "pandas 2.2.2\n",
-      "lightgbm 4.6.0\n",
-      "optuna 4.9.0\n",
-      "shap 0.52.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "import gc\n",
-    "import json\n",
-    "import pickle\n",
-    "import re\n",
-    "import shutil\n",
-    "import warnings\n",
-    "from datetime import datetime, timezone\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import joblib\n",
-    "import lightgbm as lgb\n",
-    "import optuna\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import shap\n",
-    "from imblearn.over_sampling import SMOTE\n",
-    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
-    "from optuna.samplers import TPESampler\n",
-    "from sklearn.base import clone\n",
-    "from sklearn.ensemble import ExtraTreesClassifier, IsolationForest, RandomForestClassifier\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.metrics import (\n",
-    "    ConfusionMatrixDisplay,\n",
-    "    PrecisionRecallDisplay,\n",
-    "    RocCurveDisplay,\n",
-    "    accuracy_score,\n",
-    "    average_precision_score,\n",
-    "    classification_report,\n",
-    "    confusion_matrix,\n",
-    "    f1_score,\n",
-    "    precision_score,\n",
-    "    precision_recall_curve,\n",
-    "    recall_score,\n",
-    "    roc_auc_score,\n",
-    ")\n",
-    "from sklearn.model_selection import ParameterSampler, StratifiedKFold, train_test_split\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "pd.set_option(\"display.max_columns\", 120)\n",
-    "pd.set_option(\"display.max_rows\", 80)\n",
-    "\n",
-    "RANDOM_STATE = 42\n",
-    "np.random.seed(RANDOM_STATE)\n",
-    "\n",
-    "print(\"pandas\", pd.__version__)\n",
-    "print(\"lightgbm\", lgb.__version__)\n",
-    "print(\"optuna\", optuna.__version__)\n",
-    "print(\"shap\", shap.__version__)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e3e0e4c",
-   "metadata": {
-    "id": "0e3e0e4c"
-   },
-   "source": [
-    "## 1. Google Drive 및 로컬 데이터셋 경로 설정\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "abb8856c",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "abb8856c",
-    "outputId": "d0b5cf16-76a8-4d8e-cf3f-ce1f188f80a2"
-   },
-   "outputs": [
+  "cells": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Mounted at /content/drive\n",
-      "PROJECT_ROOT: /content/drive/MyDrive\n",
-      "PROCESS_ROOT: /content/drive/MyDrive/aims_dataset\n",
-      "OUTPUT_DIR: /content/defect_transfer_outputs\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drive 마운트\n",
-    "try:\n",
-    "    from google.colab import drive\n",
-    "    drive.mount(\"/content/drive\")\n",
-    "    IN_COLAB = True\n",
-    "except Exception:\n",
-    "    IN_COLAB = False\n",
-    "    print(\"Colab이 아니므로 Google Drive mount를 건너뜁니다.\")\n",
-    "\n",
-    "# 데이터 루트\n",
-    "PROJECT_ROOT = Path(\"/content/drive/MyDrive\") if IN_COLAB else Path.cwd()\n",
-    "PROCESS_ROOT = PROJECT_ROOT / \"aims_dataset\"\n",
-    "BOSCH_ROOT = PROCESS_ROOT / \"bosch-production-line-performance\"\n",
-    "THERMAL_ROOT = PROCESS_ROOT / \"머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)\"\n",
-    "FORD_ROOT = PROCESS_ROOT / \"Ford 엔진 진동 데이터셋\"\n",
-    "METAL_ROOT = PROCESS_ROOT / \"소성가공 자원최적화 AI 데이터셋\"\n",
-    "# 산출물 경로\n",
-    "OUTPUT_DIR = Path(\"/content/defect_transfer_outputs\") if IN_COLAB else Path(\"outputs/defect_transfer\")\n",
-    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
-    "\n",
-    "# Bosch 파일 경로\n",
-    "NUMERIC_PATH = BOSCH_ROOT / \"train_numeric.csv\"\n",
-    "DATE_PATH = BOSCH_ROOT / \"train_date.csv\"\n",
-    "CATEGORICAL_PATH = BOSCH_ROOT / \"train_categorical.csv\"\n",
-    "\n",
-    "# 열화상 파일 경로\n",
-    "THERMAL_LEFT_DATA_PATH = THERMAL_ROOT / \"2nd_process_left_data.csv\"\n",
-    "THERMAL_LEFT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_left_label.json\"\n",
-    "THERMAL_RIGHT_DATA_PATH = THERMAL_ROOT / \"2nd_process_right_data.csv\"\n",
-    "THERMAL_RIGHT_LABEL_PATH = THERMAL_ROOT / \"2nd_process_right_label.json\"\n",
-    "\n",
-    "# 보조 데이터 파일 경로\n",
-    "FORD_TRAIN_PATH = FORD_ROOT / \"FordA_TRAIN.txt\"\n",
-    "FORD_TEST_PATH = FORD_ROOT / \"FordA_TEST.txt\"\n",
-    "METAL_PROCESS_PATH = METAL_ROOT / \"공정_데이터_2022년_8월.csv\"\n",
-    "METAL_SENSOR_PATHS = sorted([p for p in METAL_ROOT.glob(\"*.csv\") if p.name != \"공정_데이터_2022년_8월.csv\"])\n",
-    "\n",
-    "print(\"PROJECT_ROOT:\", PROJECT_ROOT)\n",
-    "print(\"PROCESS_ROOT:\", PROCESS_ROOT)\n",
-    "print(\"OUTPUT_DIR:\", OUTPUT_DIR)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lc2ZXZzwbEe_",
-   "metadata": {
-    "id": "lc2ZXZzwbEe_"
-   },
-   "source": [
-    "## 2. 대용량 CSV 로드 전략\n",
-    "\n",
-    "Bosch 데이터는 매우 크므로 전체 컬럼을 한 번에 읽지 않습니다. 먼저 일부 행만 프로파일링한 뒤 결측률이 낮고 변동이 있는 컬럼을 고르고, 선택 컬럼만 학습 크기만큼 로드합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "66fcea04",
-   "metadata": {
-    "id": "66fcea04"
-   },
-   "outputs": [],
-   "source": [
-    "# 학습 규모 설정\n",
-    "PROFILE_ROWS = 50_000\n",
-    "MAX_ROWS = 300_000\n",
-    "MAX_NUMERIC_FEATURES = 260\n",
-    "MAX_DATE_FEATURES = 260\n",
-    "USE_CATEGORICAL = True\n",
-    "MAX_CATEGORICAL_FEATURES = 80\n",
-    "CATEGORICAL_PROFILE_ROWS = 20_000\n",
-    "MAX_AUX_ROWS = 200_000\n",
-    "ENABLE_HYPERPARAMETER_TUNING = True\n",
-    "CV_N_SPLITS = 5\n",
-    "# 불균형 데이터에서는 PR-AUC/Recall 안정성을 위해 충분한 탐색 횟수를 사용합니다.\n",
-    "# 실행 시간이 부담되면 CV_SAMPLE_SIZE 또는 CV_N_ITER를 낮춰 빠른 실험 모드로 전환합니다.\n",
-    "CV_N_ITER = 15\n",
-    "ENABLE_OPTUNA_TUNING = True\n",
-    "CV_SAMPLE_SIZE = 30_000\n",
-    "FINAL_TRAIN_SAMPLE_SIZE = 120_000\n",
-    "RETRAIN_SELECTED_MODEL_ON_FULL_DATA = False\n",
-    "USE_SMOTE = True\n",
-    "SMOTE_SAMPLING_STRATEGY = 0.10\n",
-    "RECALL_PRIORITY_TARGET = 0.35\n",
-    "RECALL_PRIORITY_MIN_PRECISION = 0.02\n",
-    "THRESHOLD_BETA = 2.0\n",
-    "\n",
-    "# 공정 매핑\n",
-    "LINE_TO_PROCESS = {\n",
-    "    \"L0\": \"PRESS\",\n",
-    "    \"L1\": \"BODY\",\n",
-    "    \"L2\": \"ASSEMBLY\",\n",
-    "    \"L3\": \"PAINT\",\n",
-    "}\n",
-    "PROCESS_FLOW = [\"PRESS\", \"BODY\", \"PAINT\", \"ASSEMBLY\"]\n",
-    "PROCESS_DISPLAY = {\n",
-    "    \"PRESS\": \"프레스\",\n",
-    "    \"BODY\": \"차체\",\n",
-    "    \"PAINT\": \"도장\",\n",
-    "    \"ASSEMBLY\": \"의장\",\n",
-    "    \"FINAL_INSPECTION\": \"최종검사\",\n",
-    "    \"UNKNOWN\": \"미분류\",\n",
-    "}\n",
-    "\n",
-    "# Colab Drive FUSE 연결이 불안정할 수 있어 데이터셋은 로컬 런타임 캐시에서 읽습니다.\n",
-    "LOCAL_DATA_CACHE = Path(\"/content/aims_dataset_cache\") if IN_COLAB else None\n",
-    "\n",
-    "\n",
-    "def local_dataset_path(path: Path) -> Path:\n",
-    "    path = Path(path)\n",
-    "    if not IN_COLAB:\n",
-    "        return path\n",
-    "    if LOCAL_DATA_CACHE is None:\n",
-    "        return path\n",
-    "\n",
-    "    try:\n",
-    "        relative_path = path.relative_to(PROJECT_ROOT)\n",
-    "    except ValueError:\n",
-    "        relative_path = Path(path.name)\n",
-    "\n",
-    "    cached_path = LOCAL_DATA_CACHE / relative_path\n",
-    "    if cached_path.exists():\n",
-    "        try:\n",
-    "            if cached_path.stat().st_size == path.stat().st_size:\n",
-    "                return cached_path\n",
-    "        except OSError:\n",
-    "            # Drive stat 자체가 실패하면 이미 복사된 로컬 캐시를 우선 사용합니다.\n",
-    "            return cached_path\n",
-    "\n",
-    "    cached_path.parent.mkdir(parents=True, exist_ok=True)\n",
-    "    try:\n",
-    "        print(f\"cache copy: {path} -> {cached_path}\")\n",
-    "        shutil.copy2(path, cached_path)\n",
-    "    except OSError as exc:\n",
-    "        raise OSError(\n",
-    "            \"Google Drive 연결이 끊겼습니다. Colab에서 drive.mount('/content/drive', force_remount=True)를 \"\n",
-    "            \"실행한 뒤 이 셀부터 다시 실행하세요.\"\n",
-    "        ) from exc\n",
-    "    return cached_path\n",
-    "\n",
-    "\n",
-    "def read_csv_stable(path: Path, **kwargs) -> pd.DataFrame:\n",
-    "    try:\n",
-    "        return pd.read_csv(local_dataset_path(path), **kwargs)\n",
-    "    except OSError as exc:\n",
-    "        if IN_COLAB and \"Transport endpoint is not connected\" in str(exc):\n",
-    "            raise OSError(\n",
-    "                \"Google Drive 연결이 끊겼습니다. drive.mount('/content/drive', force_remount=True)로 \"\n",
-    "                \"재마운트한 뒤 다시 실행하세요. 이미 캐시된 파일은 /content/aims_dataset_cache에서 읽습니다.\"\n",
-    "            ) from exc\n",
-    "        raise\n",
-    "\n",
-    "\n",
-    "def read_json_stable(path: Path):\n",
-    "    with open(local_dataset_path(path), \"r\", encoding=\"utf-8\") as f:\n",
-    "        return json.load(f)\n",
-    "\n",
-    "\n",
-    "# 파일 검증\n",
-    "def assert_file(path: Path):\n",
-    "    if not path.exists():\n",
-    "        cached_path = local_dataset_path(path) if IN_COLAB else path\n",
-    "        if not cached_path.exists():\n",
-    "            raise FileNotFoundError(f\"파일을 찾을 수 없습니다: {path}\")\n",
-    "\n",
-    "# 메모리 축소\n",
-    "def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    for col in df.columns:\n",
-    "        if col == \"Id\":\n",
-    "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
-    "            continue\n",
-    "        if pd.api.types.is_integer_dtype(df[col]):\n",
-    "            df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n",
-    "        elif pd.api.types.is_float_dtype(df[col]):\n",
-    "            df[col] = pd.to_numeric(df[col], downcast=\"float\")\n",
-    "    return df\n",
-    "\n",
-    "# 라인 추출\n",
-    "def get_line(column: str) -> str:\n",
-    "    match = re.match(r\"^(L\\d+)_\", column)\n",
-    "    return match.group(1) if match else \"UNKNOWN\"\n",
-    "\n",
-    "# 스테이션 추출\n",
-    "def get_station(column: str) -> str:\n",
-    "    match = re.match(r\"^(L\\d+)_S(\\d+)_\", column)\n",
-    "    return f\"{match.group(1)}_S{match.group(2)}\" if match else \"UNKNOWN\"\n",
-    "\n",
-    "# 컬럼 선별\n",
-    "def select_columns_by_profile(\n",
-    "    path: Path,\n",
-    "    *,\n",
-    "    required_cols: list[str],\n",
-    "    max_features: int,\n",
-    "    profile_rows: int,\n",
-    "    min_non_null_ratio: float = 0.02,\n",
-    ") -> list[str]:\n",
-    "    assert_file(path)\n",
-    "    sample = read_csv_stable(path, nrows=profile_rows)\n",
-    "    candidates = [c for c in sample.columns if c not in required_cols]\n",
-    "    non_null_ratio = sample[candidates].notna().mean()\n",
-    "    nunique = sample[candidates].nunique(dropna=True)\n",
-    "    score = (non_null_ratio * np.log1p(nunique)).sort_values(ascending=False)\n",
-    "    selected = score[(non_null_ratio >= min_non_null_ratio) & (nunique > 1)].head(max_features).index.tolist()\n",
-    "    del sample\n",
-    "    gc.collect()\n",
-    "    return required_cols + selected\n",
-    "\n",
-    "# 선택 컬럼 로드\n",
-    "def read_selected_csv(path: Path, usecols: list[str], max_rows: int | None) -> pd.DataFrame:\n",
-    "    df = read_csv_stable(path, usecols=usecols, nrows=max_rows)\n",
-    "    return reduce_mem_usage(df)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "508b9115",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 290
+      "cell_type": "markdown",
+      "id": "cffc6eae",
+      "metadata": {
+        "id": "cffc6eae"
+      },
+      "source": [
+        "# 불량 탐지 모델링 및 SHAP 기반 전이 위험 분석\n",
+        "\n",
+        "이 노트북은 자동차 제조 4개 공정의 품질 이벤트를 분석합니다. 먼저 공정 이벤트 단위의 **불량 탐지 모델**을 학습하고, 이어서 인접 공정 사이의 불량 전이 가능성을 예측하는 **공정 전이 예측 모델**을 학습합니다.\n",
+        "\n",
+        "주요 작업:\n",
+        "\n",
+        "- `defect_detection_train.csv`, `defect_detection_test.csv`를 읽어 이벤트 단위 불량 탐지 데이터셋을 구성합니다.\n",
+        "- `event_json` 내부의 센서값, 공정 지표, 설비 상태, 공정별 `processData`를 feature로 펼칩니다.\n",
+        "- 정답 유출 가능성이 큰 검사 결과/라벨성 컬럼을 제거하고, 단일 feature만으로 과도하게 예측되는 컬럼을 추가로 필터링합니다.\n",
+        "- LightGBM, XGBoost, CatBoost, Logistic Regression 후보 모델을 비교합니다.\n",
+        "- 정확도, 정밀도, 재현율, F1, PR-AUC, ROC-AUC, 혼동행렬, PR/ROC 곡선을 출력합니다.\n",
+        "- 인접 공정 전이 예측 모델과 SHAP 중요도를 저장합니다."
+      ]
     },
-    "id": "508b9115",
-    "outputId": "eaef9a23-f57f-4c4d-85a1-69e744c2cc71"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_numeric.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_numeric.csv\n",
-      "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_date.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_date.csv\n",
-      "cache copy: /content/drive/MyDrive/aims_dataset/bosch-production-line-performance/train_categorical.csv -> /content/aims_dataset_cache/aims_dataset/bosch-production-line-performance/train_categorical.csv\n",
-      "numeric_df: (300000, 262)\n",
-      "date_df: (300000, 261)\n",
-      "categorical_df: (300000, 10)\n",
-      "Response ratio:\n"
-     ]
+      "cell_type": "markdown",
+      "id": "2722cbee",
+      "metadata": {
+        "id": "2722cbee"
+      },
+      "source": [
+        "**실행 준비**\n",
+        "\n",
+        "필요한 Python 패키지를 Colab 런타임에 맞춰 설치합니다. 이미 설치된 환경에서는 바로 넘어갑니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "            ratio\n",
-       "Response         \n",
-       "0         0.99435\n",
-       "1         0.00565"
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-626ebf76-9a02-48e2-974a-d5d99fd25fa4\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ratio</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Response</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.99435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.00565</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-626ebf76-9a02-48e2-974a-d5d99fd25fa4')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-626ebf76-9a02-48e2-974a-d5d99fd25fa4');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "# Bosch numeric 컬럼 선별\n",
-    "numeric_cols = select_columns_by_profile(\n",
-    "    NUMERIC_PATH,\n",
-    "    required_cols=[\"Id\", \"Response\"],\n",
-    "    max_features=MAX_NUMERIC_FEATURES,\n",
-    "    profile_rows=PROFILE_ROWS,\n",
-    ")\n",
-    "# Bosch date 컬럼 선별\n",
-    "date_cols = select_columns_by_profile(\n",
-    "    DATE_PATH,\n",
-    "    required_cols=[\"Id\"],\n",
-    "    max_features=MAX_DATE_FEATURES,\n",
-    "    profile_rows=PROFILE_ROWS,\n",
-    ")\n",
-    "\n",
-    "# Bosch 선택 데이터 로드\n",
-    "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
-    "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
-    "\n",
-    "# Bosch categorical 선택 로드\n",
-    "if USE_CATEGORICAL:\n",
-    "    categorical_cols = select_columns_by_profile(\n",
-    "        CATEGORICAL_PATH,\n",
-    "        required_cols=[\"Id\"],\n",
-    "        max_features=MAX_CATEGORICAL_FEATURES,\n",
-    "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
-    "    )\n",
-    "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
-    "else:\n",
-    "    categorical_df = None\n",
-    "\n",
-    "print(\"numeric_df:\", numeric_df.shape)\n",
-    "print(\"date_df:\", date_df.shape)\n",
-    "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
-    "print(\"Response ratio:\")\n",
-    "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11973c36",
-   "metadata": {
-    "id": "11973c36"
-   },
-   "source": [
-    "## 3. Bosch Feature Engineering\n",
-    "\n",
-    "`L*_S*_F*` 수치 센서와 `L*_S*_D*` 시간 컬럼을 그대로 일부 사용하면서, 공정/스테이션 단위 집계 피처를 추가합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "dd4b603e",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 273
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "b2d2d4a8",
+      "metadata": {
+        "id": "b2d2d4a8"
+      },
+      "outputs": [],
+      "source": [
+        "import importlib.util\n",
+        "import subprocess\n",
+        "import sys\n",
+        "\n",
+        "\n",
+        "def ensure_package(import_name: str, pip_name: str | None = None):\n",
+        "    pip_name = pip_name or import_name\n",
+        "    if importlib.util.find_spec(import_name) is None:\n",
+        "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", pip_name, \"-q\"])\n",
+        "\n",
+        "\n",
+        "ensure_package(\"pandas\")\n",
+        "ensure_package(\"numpy\")\n",
+        "ensure_package(\"sklearn\", \"scikit-learn\")\n",
+        "ensure_package(\"lightgbm\")\n",
+        "ensure_package(\"xgboost\")\n",
+        "ensure_package(\"catboost\")\n",
+        "ensure_package(\"shap\")\n",
+        "ensure_package(\"joblib\")"
+      ]
     },
-    "id": "dd4b603e",
-    "outputId": "84ce5bbe-1ecd-490a-884b-4e42c0252e7f"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "features: (300000, 454)\n"
-     ]
+      "cell_type": "markdown",
+      "id": "d3ab427f",
+      "metadata": {
+        "id": "d3ab427f"
+      },
+      "source": [
+        "**라이브러리 로드**\n",
+        "\n",
+        "모델 학습, 평가, 시각화에 필요한 라이브러리를 불러오고 한글 폰트를 설정합니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "   Id  L0_S0_F0  L0_S0_F2  L0_S0_F4  L0_S0_F6  L0_S0_F8  L0_S0_F10  L0_S0_F12  \\\n",
-       "0   4     0.030    -0.034    -0.197    -0.179     0.118      0.116     -0.015   \n",
-       "1   6       NaN       NaN       NaN       NaN       NaN        NaN        NaN   \n",
-       "2   7     0.088     0.086     0.003    -0.052     0.161      0.025     -0.015   \n",
-       "3   9    -0.036    -0.064     0.294     0.330     0.074      0.161      0.022   \n",
-       "4  11    -0.055    -0.086     0.294     0.330     0.118      0.025      0.030   \n",
-       "\n",
-       "   L0_S0_F14  L0_S0_F16  L0_S0_F18  L0_S0_F20  L0_S0_F22  L0_S1_F24  \\\n",
-       "0     -0.032      0.020      0.083     -0.273     -0.273     -0.271   \n",
-       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
-       "2     -0.072     -0.225     -0.147      0.250      0.250      0.057   \n",
-       "3      0.128     -0.026     -0.046     -0.253     -0.253      0.147   \n",
-       "4      0.168     -0.169     -0.099      0.042      0.042     -0.012   \n",
-       "\n",
-       "   L0_S1_F28  L0_S2_F36  L0_S2_F44  L0_S2_F48  L0_S2_F60  L0_S2_F64  \\\n",
-       "0      0.167     -0.023     -0.088      0.001     -0.223     -0.030   \n",
-       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
-       "2     -0.079      0.011     -0.060     -0.005     -0.223     -0.077   \n",
-       "3     -0.007      0.120     -0.231      0.005      0.050      0.056   \n",
-       "4     -0.046        NaN        NaN        NaN        NaN        NaN   \n",
-       "\n",
-       "   L0_S3_F72  L0_S3_F80  L0_S3_F84  L0_S3_F96  L0_S3_F100  L0_S4_F104  \\\n",
-       "0        NaN        NaN        NaN        NaN         NaN      -0.001   \n",
-       "1        NaN        NaN        NaN        NaN         NaN         NaN   \n",
-       "2        NaN        NaN        NaN        NaN         NaN         NaN   \n",
-       "3        NaN        NaN        NaN        NaN         NaN      -0.038   \n",
-       "4     -0.272      0.037      0.004      0.311       0.003       0.021   \n",
-       "\n",
-       "   L0_S4_F109  L0_S5_F114  L0_S5_F116  L0_S6_F122  L0_S6_F132  L0_S7_F138  \\\n",
-       "0      -0.004         NaN         NaN         NaN         NaN      -0.077   \n",
-       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-       "2         NaN      -0.073       0.138       0.506       -0.13         NaN   \n",
-       "3      -0.001         NaN         NaN         NaN         NaN       0.402   \n",
-       "4       0.015         NaN         NaN         NaN         NaN      -0.083   \n",
-       "\n",
-       "   L0_S7_F142  L0_S8_F144  L0_S8_F146  L0_S8_F149  L0_S9_F155  L0_S9_F160  \\\n",
-       "0       0.060      -0.157         0.0       0.001         NaN         NaN   \n",
-       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-       "2         NaN      -0.157         0.0       0.001         NaN         NaN   \n",
-       "3      -0.015       0.343         0.0       0.001         NaN         NaN   \n",
-       "4       0.014      -0.157         0.0       0.001         NaN         NaN   \n",
-       "\n",
-       "   L0_S9_F165  L0_S9_F170  L0_S9_F180  L0_S9_F185  L0_S9_F190  L0_S9_F195  \\\n",
-       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
-       "\n",
-       "   L0_S9_F200  L0_S9_F210  L0_S10_F219  L0_S10_F224  L0_S10_F229  L0_S10_F234  \\\n",
-       "0         NaN         NaN          NaN          NaN          NaN          NaN   \n",
-       "1         NaN         NaN          NaN          NaN          NaN          NaN   \n",
-       "2         NaN         NaN       -0.002       -0.047        0.018          0.0   \n",
-       "3         NaN         NaN        0.007       -0.049        0.020          0.0   \n",
-       "4         NaN         NaN          NaN          NaN          NaN          NaN   \n",
-       "\n",
-       "   L0_S10_F244  L0_S10_F249  L0_S10_F254  L0_S10_F259  L0_S10_F264  \\\n",
-       "0          NaN          NaN          NaN          NaN          NaN   \n",
-       "1          NaN          NaN          NaN          NaN          NaN   \n",
-       "2        0.001       -0.009       -0.017        0.105       -0.001   \n",
-       "3       -0.013       -0.003       -0.032        0.139        0.001   \n",
-       "4          NaN          NaN          NaN          NaN          NaN   \n",
-       "\n",
-       "   L0_S11_F282  L0_S11_F286  L0_S11_F290  L0_S11_F294  L0_S11_F302  ...  \\\n",
-       "0        0.018       -0.136       -0.013        0.106       -0.131  ...   \n",
-       "1          NaN          NaN          NaN          NaN          NaN  ...   \n",
-       "2          NaN          NaN          NaN          NaN          NaN  ...   \n",
-       "3          NaN          NaN          NaN          NaN          NaN  ...   \n",
-       "4       -0.122       -0.064       -0.011       -0.005        0.071  ...   \n",
-       "\n",
-       "   L0_S5_num_mean  L0_S5_num_std  L0_S5_num_missing_ratio  L0_S6_num_mean  \\\n",
-       "0             NaN            NaN                      1.0             NaN   \n",
-       "1             NaN            NaN                      1.0             NaN   \n",
-       "2          0.0325         0.1492                      0.0           0.188   \n",
-       "3             NaN            NaN                      1.0             NaN   \n",
-       "4             NaN            NaN                      1.0             NaN   \n",
-       "\n",
-       "   L0_S6_num_std  L0_S6_num_missing_ratio  L0_S7_num_mean  L0_S7_num_std  \\\n",
-       "0            NaN                      1.0         -0.0085       0.096874   \n",
-       "1            NaN                      1.0             NaN            NaN   \n",
-       "2        0.44972                      0.0             NaN            NaN   \n",
-       "3            NaN                      1.0          0.1935       0.294864   \n",
-       "4            NaN                      1.0         -0.0345       0.068589   \n",
-       "\n",
-       "   L0_S7_num_missing_ratio  L0_S8_num_mean  L0_S8_num_std  \\\n",
-       "0                      0.0       -0.052000       0.090934   \n",
-       "1                      1.0             NaN            NaN   \n",
-       "2                      1.0       -0.052000       0.090934   \n",
-       "3                      0.0        0.114667       0.197743   \n",
-       "4                      0.0       -0.052000       0.090934   \n",
-       "\n",
-       "   L0_S8_num_missing_ratio  L0_S9_num_mean  L0_S9_num_std  \\\n",
-       "0                      0.0             NaN            NaN   \n",
-       "1                      1.0             NaN            NaN   \n",
-       "2                      0.0             NaN            NaN   \n",
-       "3                      0.0             NaN            NaN   \n",
-       "4                      0.0             NaN            NaN   \n",
-       "\n",
-       "   L0_S9_num_missing_ratio  L1_S24_num_mean  L1_S24_num_std  \\\n",
-       "0                      1.0              NaN             NaN   \n",
-       "1                      1.0              NaN             NaN   \n",
-       "2                      1.0              NaN             NaN   \n",
-       "3                      1.0              NaN             NaN   \n",
-       "4                      1.0              NaN             NaN   \n",
-       "\n",
-       "   L1_S24_num_missing_ratio  L2_S26_num_mean  L2_S26_num_std  \\\n",
-       "0                       1.0              NaN             NaN   \n",
-       "1                       1.0              NaN             NaN   \n",
-       "2                       1.0              NaN             NaN   \n",
-       "3                       1.0              NaN             NaN   \n",
-       "4                       1.0              NaN             NaN   \n",
-       "\n",
-       "   L2_S26_num_missing_ratio  L2_S27_num_mean  L2_S27_num_std  \\\n",
-       "0                       1.0              NaN             NaN   \n",
-       "1                       1.0              NaN             NaN   \n",
-       "2                       1.0              NaN             NaN   \n",
-       "3                       1.0              NaN             NaN   \n",
-       "4                       1.0              NaN             NaN   \n",
-       "\n",
-       "   L2_S27_num_missing_ratio  L3_S29_num_mean  L3_S29_num_std  \\\n",
-       "0                       1.0        -0.014321        0.201176   \n",
-       "1                       1.0        -0.005264        0.142025   \n",
-       "2                       1.0        -0.010000        0.129113   \n",
-       "3                       1.0        -0.030151        0.124692   \n",
-       "4                       1.0        -0.017321        0.157158   \n",
-       "\n",
-       "   L3_S29_num_missing_ratio  L3_S30_num_mean  L3_S30_num_std  \\\n",
-       "0                       0.0         0.003889        0.083444   \n",
-       "1                       0.0         0.021621        0.068539   \n",
-       "2                       0.0        -0.010552        0.069868   \n",
-       "3                       0.0         0.010017        0.058393   \n",
-       "4                       0.0        -0.004069        0.049633   \n",
-       "\n",
-       "   L3_S30_num_missing_ratio  L3_S33_num_mean  L3_S33_num_std  \\\n",
-       "0                   0.37931          -0.0481        0.060306   \n",
-       "1                   0.00000          -0.0370        0.063085   \n",
-       "2                   0.00000           0.1132        0.102470   \n",
-       "3                   0.00000           0.0282        0.049806   \n",
-       "4                   0.00000          -0.0130        0.065646   \n",
-       "\n",
-       "   L3_S33_num_missing_ratio  L3_S34_num_mean  L3_S34_num_std  \\\n",
-       "0                       0.0              0.0             0.0   \n",
-       "1                       0.0              0.0             0.0   \n",
-       "2                       0.0              0.0             0.0   \n",
-       "3                       0.0              0.0             0.0   \n",
-       "4                       0.0              0.0             0.0   \n",
-       "\n",
-       "   L3_S34_num_missing_ratio  L3_S35_num_mean  L3_S35_num_std  \\\n",
-       "0                       0.0        -0.016143        0.030180   \n",
-       "1                       0.0        -0.001286        0.017289   \n",
-       "2                       0.0        -0.013857        0.039015   \n",
-       "3                       0.0              NaN             NaN   \n",
-       "4                       0.0              NaN             NaN   \n",
-       "\n",
-       "   L3_S35_num_missing_ratio  L3_S36_num_mean  L3_S36_num_std  \\\n",
-       "0                       0.0              NaN             NaN   \n",
-       "1                       0.0              NaN             NaN   \n",
-       "2                       0.0              NaN             NaN   \n",
-       "3                       1.0         -0.05550        0.085261   \n",
-       "4                       1.0         -0.00225        0.066188   \n",
-       "\n",
-       "   L3_S36_num_missing_ratio  L3_S37_num_mean  L3_S37_num_std  \\\n",
-       "0                       1.0              0.0             0.0   \n",
-       "1                       1.0              0.0             0.0   \n",
-       "2                       1.0              0.0             0.0   \n",
-       "3                       0.0              0.0             0.0   \n",
-       "4                       0.0              0.0             0.0   \n",
-       "\n",
-       "   L3_S37_num_missing_ratio  L1_cat_count  L1_cat_missing_ratio  \\\n",
-       "0                       0.0             0                   1.0   \n",
-       "1                       0.0             0                   1.0   \n",
-       "2                       0.0             0                   1.0   \n",
-       "3                       0.0             0                   1.0   \n",
-       "4                       0.0             0                   1.0   \n",
-       "\n",
-       "   L1_cat_unique_count  L2_cat_count  L2_cat_missing_ratio  \\\n",
-       "0                    0             0                   1.0   \n",
-       "1                    0             0                   1.0   \n",
-       "2                    0             0                   1.0   \n",
-       "3                    0             0                   1.0   \n",
-       "4                    0             0                   1.0   \n",
-       "\n",
-       "   L2_cat_unique_count  L1_S24_F1114_code  L1_S24_F1137_code  \\\n",
-       "0                    0                  5                  4   \n",
-       "1                    0                  5                  4   \n",
-       "2                    0                  5                  4   \n",
-       "3                    0                  5                  4   \n",
-       "4                    0                  5                  4   \n",
-       "\n",
-       "   L1_S24_F1140_code  L1_S24_F1510_code  L1_S24_F1525_code  L1_S24_F1530_code  \\\n",
-       "0                  4                  5                  5                  5   \n",
-       "1                  4                  5                  5                  5   \n",
-       "2                  4                  5                  5                  5   \n",
-       "3                  4                  5                  5                  5   \n",
-       "4                  4                  5                  5                  5   \n",
-       "\n",
-       "   L1_S25_F1852_code  L2_S26_F3099_code  L2_S27_F3192_code  \n",
-       "0                  8                  2                  2  \n",
-       "1                  8                  2                  2  \n",
-       "2                  8                  2                  2  \n",
-       "3                  8                  2                  2  \n",
-       "4                  8                  2                  2  \n",
-       "\n",
-       "[5 rows x 454 columns]"
-      ],
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Id</th>\n",
-       "      <th>L0_S0_F0</th>\n",
-       "      <th>L0_S0_F2</th>\n",
-       "      <th>L0_S0_F4</th>\n",
-       "      <th>L0_S0_F6</th>\n",
-       "      <th>L0_S0_F8</th>\n",
-       "      <th>L0_S0_F10</th>\n",
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-       "      <th>L0_S0_F14</th>\n",
-       "      <th>L0_S0_F16</th>\n",
-       "      <th>L0_S0_F18</th>\n",
-       "      <th>L0_S0_F20</th>\n",
-       "      <th>L0_S0_F22</th>\n",
-       "      <th>L0_S1_F24</th>\n",
-       "      <th>L0_S1_F28</th>\n",
-       "      <th>L0_S2_F36</th>\n",
-       "      <th>L0_S2_F44</th>\n",
-       "      <th>L0_S2_F48</th>\n",
-       "      <th>L0_S2_F60</th>\n",
-       "      <th>L0_S2_F64</th>\n",
-       "      <th>L0_S3_F72</th>\n",
-       "      <th>L0_S3_F80</th>\n",
-       "      <th>L0_S3_F84</th>\n",
-       "      <th>L0_S3_F96</th>\n",
-       "      <th>L0_S3_F100</th>\n",
-       "      <th>L0_S4_F104</th>\n",
-       "      <th>L0_S4_F109</th>\n",
-       "      <th>L0_S5_F114</th>\n",
-       "      <th>L0_S5_F116</th>\n",
-       "      <th>L0_S6_F122</th>\n",
-       "      <th>L0_S6_F132</th>\n",
-       "      <th>L0_S7_F138</th>\n",
-       "      <th>L0_S7_F142</th>\n",
-       "      <th>L0_S8_F144</th>\n",
-       "      <th>L0_S8_F146</th>\n",
-       "      <th>L0_S8_F149</th>\n",
-       "      <th>L0_S9_F155</th>\n",
-       "      <th>L0_S9_F160</th>\n",
-       "      <th>L0_S9_F165</th>\n",
-       "      <th>L0_S9_F170</th>\n",
-       "      <th>L0_S9_F180</th>\n",
-       "      <th>L0_S9_F185</th>\n",
-       "      <th>L0_S9_F190</th>\n",
-       "      <th>L0_S9_F195</th>\n",
-       "      <th>L0_S9_F200</th>\n",
-       "      <th>L0_S9_F210</th>\n",
-       "      <th>L0_S10_F219</th>\n",
-       "      <th>L0_S10_F224</th>\n",
-       "      <th>L0_S10_F229</th>\n",
-       "      <th>L0_S10_F234</th>\n",
-       "      <th>L0_S10_F244</th>\n",
-       "      <th>L0_S10_F249</th>\n",
-       "      <th>L0_S10_F254</th>\n",
-       "      <th>L0_S10_F259</th>\n",
-       "      <th>L0_S10_F264</th>\n",
-       "      <th>L0_S11_F282</th>\n",
-       "      <th>L0_S11_F286</th>\n",
-       "      <th>L0_S11_F290</th>\n",
-       "      <th>L0_S11_F294</th>\n",
-       "      <th>L0_S11_F302</th>\n",
-       "      <th>...</th>\n",
-       "      <th>L0_S5_num_mean</th>\n",
-       "      <th>L0_S5_num_std</th>\n",
-       "      <th>L0_S5_num_missing_ratio</th>\n",
-       "      <th>L0_S6_num_mean</th>\n",
-       "      <th>L0_S6_num_std</th>\n",
-       "      <th>L0_S6_num_missing_ratio</th>\n",
-       "      <th>L0_S7_num_mean</th>\n",
-       "      <th>L0_S7_num_std</th>\n",
-       "      <th>L0_S7_num_missing_ratio</th>\n",
-       "      <th>L0_S8_num_mean</th>\n",
-       "      <th>L0_S8_num_std</th>\n",
-       "      <th>L0_S8_num_missing_ratio</th>\n",
-       "      <th>L0_S9_num_mean</th>\n",
-       "      <th>L0_S9_num_std</th>\n",
-       "      <th>L0_S9_num_missing_ratio</th>\n",
-       "      <th>L1_S24_num_mean</th>\n",
-       "      <th>L1_S24_num_std</th>\n",
-       "      <th>L1_S24_num_missing_ratio</th>\n",
-       "      <th>L2_S26_num_mean</th>\n",
-       "      <th>L2_S26_num_std</th>\n",
-       "      <th>L2_S26_num_missing_ratio</th>\n",
-       "      <th>L2_S27_num_mean</th>\n",
-       "      <th>L2_S27_num_std</th>\n",
-       "      <th>L2_S27_num_missing_ratio</th>\n",
-       "      <th>L3_S29_num_mean</th>\n",
-       "      <th>L3_S29_num_std</th>\n",
-       "      <th>L3_S29_num_missing_ratio</th>\n",
-       "      <th>L3_S30_num_mean</th>\n",
-       "      <th>L3_S30_num_std</th>\n",
-       "      <th>L3_S30_num_missing_ratio</th>\n",
-       "      <th>L3_S33_num_mean</th>\n",
-       "      <th>L3_S33_num_std</th>\n",
-       "      <th>L3_S33_num_missing_ratio</th>\n",
-       "      <th>L3_S34_num_mean</th>\n",
-       "      <th>L3_S34_num_std</th>\n",
-       "      <th>L3_S34_num_missing_ratio</th>\n",
-       "      <th>L3_S35_num_mean</th>\n",
-       "      <th>L3_S35_num_std</th>\n",
-       "      <th>L3_S35_num_missing_ratio</th>\n",
-       "      <th>L3_S36_num_mean</th>\n",
-       "      <th>L3_S36_num_std</th>\n",
-       "      <th>L3_S36_num_missing_ratio</th>\n",
-       "      <th>L3_S37_num_mean</th>\n",
-       "      <th>L3_S37_num_std</th>\n",
-       "      <th>L3_S37_num_missing_ratio</th>\n",
-       "      <th>L1_cat_count</th>\n",
-       "      <th>L1_cat_missing_ratio</th>\n",
-       "      <th>L1_cat_unique_count</th>\n",
-       "      <th>L2_cat_count</th>\n",
-       "      <th>L2_cat_missing_ratio</th>\n",
-       "      <th>L2_cat_unique_count</th>\n",
-       "      <th>L1_S24_F1114_code</th>\n",
-       "      <th>L1_S24_F1137_code</th>\n",
-       "      <th>L1_S24_F1140_code</th>\n",
-       "      <th>L1_S24_F1510_code</th>\n",
-       "      <th>L1_S24_F1525_code</th>\n",
-       "      <th>L1_S24_F1530_code</th>\n",
-       "      <th>L1_S25_F1852_code</th>\n",
-       "      <th>L2_S26_F3099_code</th>\n",
-       "      <th>L2_S27_F3192_code</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>-0.034</td>\n",
-       "      <td>-0.197</td>\n",
-       "      <td>-0.179</td>\n",
-       "      <td>0.118</td>\n",
-       "      <td>0.116</td>\n",
-       "      <td>-0.015</td>\n",
-       "      <td>-0.032</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.083</td>\n",
-       "      <td>-0.273</td>\n",
-       "      <td>-0.273</td>\n",
-       "      <td>-0.271</td>\n",
-       "      <td>0.167</td>\n",
-       "      <td>-0.023</td>\n",
-       "      <td>-0.088</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>-0.223</td>\n",
-       "      <td>-0.030</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.001</td>\n",
-       "      <td>-0.004</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.077</td>\n",
-       "      <td>0.060</td>\n",
-       "      <td>-0.157</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>-0.136</td>\n",
-       "      <td>-0.013</td>\n",
-       "      <td>0.106</td>\n",
-       "      <td>-0.131</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.0085</td>\n",
-       "      <td>0.096874</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.052000</td>\n",
-       "      <td>0.090934</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.014321</td>\n",
-       "      <td>0.201176</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.003889</td>\n",
-       "      <td>0.083444</td>\n",
-       "      <td>0.37931</td>\n",
-       "      <td>-0.0481</td>\n",
-       "      <td>0.060306</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.016143</td>\n",
-       "      <td>0.030180</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.005264</td>\n",
-       "      <td>0.142025</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.021621</td>\n",
-       "      <td>0.068539</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>-0.0370</td>\n",
-       "      <td>0.063085</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.001286</td>\n",
-       "      <td>0.017289</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7</td>\n",
-       "      <td>0.088</td>\n",
-       "      <td>0.086</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>-0.052</td>\n",
-       "      <td>0.161</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>-0.015</td>\n",
-       "      <td>-0.072</td>\n",
-       "      <td>-0.225</td>\n",
-       "      <td>-0.147</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>0.057</td>\n",
-       "      <td>-0.079</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>-0.060</td>\n",
-       "      <td>-0.005</td>\n",
-       "      <td>-0.223</td>\n",
-       "      <td>-0.077</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.073</td>\n",
-       "      <td>0.138</td>\n",
-       "      <td>0.506</td>\n",
-       "      <td>-0.13</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.157</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.002</td>\n",
-       "      <td>-0.047</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>-0.009</td>\n",
-       "      <td>-0.017</td>\n",
-       "      <td>0.105</td>\n",
-       "      <td>-0.001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0325</td>\n",
-       "      <td>0.1492</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.188</td>\n",
-       "      <td>0.44972</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.052000</td>\n",
-       "      <td>0.090934</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.010000</td>\n",
-       "      <td>0.129113</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.010552</td>\n",
-       "      <td>0.069868</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.1132</td>\n",
-       "      <td>0.102470</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.013857</td>\n",
-       "      <td>0.039015</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>9</td>\n",
-       "      <td>-0.036</td>\n",
-       "      <td>-0.064</td>\n",
-       "      <td>0.294</td>\n",
-       "      <td>0.330</td>\n",
-       "      <td>0.074</td>\n",
-       "      <td>0.161</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>0.128</td>\n",
-       "      <td>-0.026</td>\n",
-       "      <td>-0.046</td>\n",
-       "      <td>-0.253</td>\n",
-       "      <td>-0.253</td>\n",
-       "      <td>0.147</td>\n",
-       "      <td>-0.007</td>\n",
-       "      <td>0.120</td>\n",
-       "      <td>-0.231</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.050</td>\n",
-       "      <td>0.056</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.038</td>\n",
-       "      <td>-0.001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.402</td>\n",
-       "      <td>-0.015</td>\n",
-       "      <td>0.343</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>-0.049</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.013</td>\n",
-       "      <td>-0.003</td>\n",
-       "      <td>-0.032</td>\n",
-       "      <td>0.139</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.1935</td>\n",
-       "      <td>0.294864</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.114667</td>\n",
-       "      <td>0.197743</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.030151</td>\n",
-       "      <td>0.124692</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.010017</td>\n",
-       "      <td>0.058393</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.0282</td>\n",
-       "      <td>0.049806</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.05550</td>\n",
-       "      <td>0.085261</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>11</td>\n",
-       "      <td>-0.055</td>\n",
-       "      <td>-0.086</td>\n",
-       "      <td>0.294</td>\n",
-       "      <td>0.330</td>\n",
-       "      <td>0.118</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>0.168</td>\n",
-       "      <td>-0.169</td>\n",
-       "      <td>-0.099</td>\n",
-       "      <td>0.042</td>\n",
-       "      <td>0.042</td>\n",
-       "      <td>-0.012</td>\n",
-       "      <td>-0.046</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.272</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.311</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>0.021</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.083</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>-0.157</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.122</td>\n",
-       "      <td>-0.064</td>\n",
-       "      <td>-0.011</td>\n",
-       "      <td>-0.005</td>\n",
-       "      <td>0.071</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.0345</td>\n",
-       "      <td>0.068589</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.052000</td>\n",
-       "      <td>0.090934</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.017321</td>\n",
-       "      <td>0.157158</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.004069</td>\n",
-       "      <td>0.049633</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>-0.0130</td>\n",
-       "      <td>0.065646</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.00225</td>\n",
-       "      <td>0.066188</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 454 columns</p>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82c8c8d6-bb6b-4406-9895-53a4298ecac0')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-82c8c8d6-bb6b-4406-9895-53a4298ecac0');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "a66fc93f",
+      "metadata": {
+        "id": "a66fc93f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "12eaa8c7-c585-4d72-cd86-de149ef1c7ea"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "matplotlib Korean font: NanumGothic\n"
+          ]
+        }
       ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "# Date 피처 생성\n",
-    "def build_date_features(date_df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    feature_df = pd.DataFrame({\"Id\": date_df[\"Id\"]})\n",
-    "    date_feature_cols = [c for c in date_df.columns if c != \"Id\"]\n",
-    "    date_values = date_df[date_feature_cols]\n",
-    "\n",
-    "    feature_df[\"date_observed_count\"] = date_values.notna().sum(axis=1)\n",
-    "    feature_df[\"date_missing_ratio\"] = date_values.isna().mean(axis=1)\n",
-    "    feature_df[\"process_start_time\"] = date_values.min(axis=1)\n",
-    "    feature_df[\"process_end_time\"] = date_values.max(axis=1)\n",
-    "    feature_df[\"total_process_duration\"] = feature_df[\"process_end_time\"] - feature_df[\"process_start_time\"]\n",
-    "\n",
-    "    # 라인별 시간 집계\n",
-    "    for line in sorted({get_line(c) for c in date_feature_cols if get_line(c) != \"UNKNOWN\"}):\n",
-    "        cols = [c for c in date_feature_cols if get_line(c) == line]\n",
-    "        values = date_df[cols]\n",
-    "        feature_df[f\"{line}_date_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
-    "        feature_df[f\"{line}_date_count\"] = values.notna().sum(axis=1)\n",
-    "        feature_df[f\"{line}_date_missing_ratio\"] = values.isna().mean(axis=1)\n",
-    "        feature_df[f\"{line}_duration\"] = values.max(axis=1) - values.min(axis=1)\n",
-    "\n",
-    "    # 스테이션별 시간 집계\n",
-    "    station_span_cols = []\n",
-    "    for station in sorted({get_station(c) for c in date_feature_cols if get_station(c) != \"UNKNOWN\"}):\n",
-    "        cols = [c for c in date_feature_cols if get_station(c) == station]\n",
-    "        values = date_df[cols]\n",
-    "        span_col = f\"{station}_span\"\n",
-    "        feature_df[f\"{station}_seen\"] = values.notna().any(axis=1).astype(\"int8\")\n",
-    "        feature_df[span_col] = values.max(axis=1) - values.min(axis=1)\n",
-    "        feature_df[f\"{station}_mean_time\"] = values.mean(axis=1)\n",
-    "        station_span_cols.append(span_col)\n",
-    "\n",
-    "    # 병목 후보 시간 집계\n",
-    "    if station_span_cols:\n",
-    "        spans = feature_df[station_span_cols]\n",
-    "        feature_df[\"max_station_span\"] = spans.max(axis=1)\n",
-    "        feature_df[\"mean_station_span\"] = spans.mean(axis=1)\n",
-    "        feature_df[\"std_station_span\"] = spans.std(axis=1)\n",
-    "        feature_df[\"active_station_count\"] = feature_df[[c.replace(\"_span\", \"_seen\") for c in station_span_cols]].sum(axis=1)\n",
-    "    return reduce_mem_usage(feature_df)\n",
-    "\n",
-    "# Numeric 집계 피처 생성\n",
-    "def build_numeric_aggregate_features(numeric_df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    value_cols = [c for c in numeric_df.columns if c not in [\"Id\", \"Response\"]]\n",
-    "    feature_df = numeric_df[[\"Id\"]].copy()\n",
-    "\n",
-    "    # 라인별 센서 집계\n",
-    "    for line in sorted({get_line(c) for c in value_cols if get_line(c) != \"UNKNOWN\"}):\n",
-    "        cols = [c for c in value_cols if get_line(c) == line]\n",
-    "        values = numeric_df[cols]\n",
-    "        feature_df[f\"{line}_num_mean\"] = values.mean(axis=1)\n",
-    "        feature_df[f\"{line}_num_std\"] = values.std(axis=1)\n",
-    "        feature_df[f\"{line}_num_min\"] = values.min(axis=1)\n",
-    "        feature_df[f\"{line}_num_max\"] = values.max(axis=1)\n",
-    "        feature_df[f\"{line}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
-    "\n",
-    "    # 스테이션별 센서 집계\n",
-    "    for station in sorted({get_station(c) for c in value_cols if get_station(c) != \"UNKNOWN\"}):\n",
-    "        cols = [c for c in value_cols if get_station(c) == station]\n",
-    "        if len(cols) < 2:\n",
-    "            continue\n",
-    "        values = numeric_df[cols]\n",
-    "        feature_df[f\"{station}_num_mean\"] = values.mean(axis=1)\n",
-    "        feature_df[f\"{station}_num_std\"] = values.std(axis=1)\n",
-    "        feature_df[f\"{station}_num_missing_ratio\"] = values.isna().mean(axis=1)\n",
-    "    return reduce_mem_usage(feature_df)\n",
-    "\n",
-    "# Categorical 피처 생성\n",
-    "def build_categorical_features(categorical_df: pd.DataFrame) -> pd.DataFrame:\n",
-    "    cat_cols = [c for c in categorical_df.columns if c != \"Id\"]\n",
-    "    feature_df = categorical_df[[\"Id\"]].copy()\n",
-    "    if not cat_cols:\n",
-    "        return feature_df\n",
-    "\n",
-    "    # 라인별 범주 집계\n",
-    "    for line in sorted({get_line(c) for c in cat_cols if get_line(c) != \"UNKNOWN\"}):\n",
-    "        cols = [c for c in cat_cols if get_line(c) == line]\n",
-    "        values = categorical_df[cols]\n",
-    "        feature_df[f\"{line}_cat_count\"] = values.notna().sum(axis=1)\n",
-    "        feature_df[f\"{line}_cat_missing_ratio\"] = values.isna().mean(axis=1)\n",
-    "        feature_df[f\"{line}_cat_unique_count\"] = values.nunique(axis=1, dropna=True)\n",
-    "\n",
-    "    # 범주 인코딩\n",
-    "    for col in cat_cols:\n",
-    "        encoded, _ = pd.factorize(categorical_df[col].astype(\"string\").fillna(\"__MISSING__\"), sort=True)\n",
-    "        feature_df[f\"{col}_code\"] = encoded.astype(\"int32\")\n",
-    "    return reduce_mem_usage(feature_df)\n",
-    "\n",
-    "# Bosch 피처 생성\n",
-    "date_features = build_date_features(date_df)\n",
-    "numeric_agg_features = build_numeric_aggregate_features(numeric_df)\n",
-    "raw_numeric_features = numeric_df.drop(columns=[\"Response\"])\n",
-    "\n",
-    "# Bosch 피처 병합\n",
-    "features = raw_numeric_features.merge(date_features, on=\"Id\", how=\"left\")\n",
-    "features = features.merge(numeric_agg_features, on=\"Id\", how=\"left\")\n",
-    "if categorical_df is not None:\n",
-    "    categorical_features = build_categorical_features(categorical_df)\n",
-    "    features = features.merge(categorical_features, on=\"Id\", how=\"left\")\n",
-    "    del categorical_df, categorical_features\n",
-    "# 학습 라벨 분리\n",
-    "target = numeric_df[[\"Id\", \"Response\"]].copy()\n",
-    "\n",
-    "del date_df, numeric_agg_features, date_features\n",
-    "gc.collect()\n",
-    "\n",
-    "print(\"features:\", features.shape)\n",
-    "display(features.head())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6f30d6e",
-   "metadata": {
-    "id": "e6f30d6e"
-   },
-   "source": [
-    "## 4. Process 폴더 전체 보조 데이터 학습\n",
-    "\n",
-    "`process` 폴더의 Bosch 외 데이터는 Bosch `Id`와 직접 연결되는 공통 키가 없습니다. 따라서 MVP에서는 각 데이터셋을 별도로 학습하여 공정별 보조 위험 신호를 만들고, Bosch 학습 테이블에는 반복 정렬(cyclic alignment) 방식으로 결합합니다. 운영 데이터에서 `car_master_id`, `event_time`, `process_code` 매핑이 생기면 이 부분을 정확 조인으로 교체하면 됩니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a2a68fb3",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 698
+      "source": [
+        "from __future__ import annotations\n",
+        "\n",
+        "import json\n",
+        "import shutil\n",
+        "import subprocess\n",
+        "import sys\n",
+        "import time\n",
+        "import warnings\n",
+        "from datetime import datetime, timezone\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import joblib\n",
+        "import lightgbm as lgb\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib import font_manager as fm\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import shap\n",
+        "import xgboost as xgb\n",
+        "from catboost import CatBoostClassifier\n",
+        "from sklearn.base import clone\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import ParameterSampler, StratifiedKFold\n",
+        "from sklearn.metrics import (\n",
+        "    accuracy_score,\n",
+        "    average_precision_score,\n",
+        "    classification_report,\n",
+        "    confusion_matrix,\n",
+        "    f1_score,\n",
+        "    precision_recall_curve,\n",
+        "    precision_score,\n",
+        "    recall_score,\n",
+        "    roc_auc_score,\n",
+        "    roc_curve,\n",
+        ")\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+        "\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "sns.set_theme(style=\"whitegrid\")\n",
+        "pd.set_option(\"display.max_columns\", 180)\n",
+        "pd.set_option(\"display.max_rows\", 100)\n",
+        "RANDOM_STATE = 42\n",
+        "np.random.seed(RANDOM_STATE)\n",
+        "\n",
+        "\n",
+        "KOREAN_FONT_CANDIDATES = [\n",
+        "    \"Malgun Gothic\",\n",
+        "    \"AppleGothic\",\n",
+        "    \"NanumGothic\",\n",
+        "    \"NanumBarunGothic\",\n",
+        "    \"Noto Sans CJK KR\",\n",
+        "    \"Noto Sans KR\",\n",
+        "    \"Arial Unicode MS\",\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def find_korean_font() -> str | None:\n",
+        "    available_fonts = {font.name for font in fm.fontManager.ttflist}\n",
+        "    for font_name in KOREAN_FONT_CANDIDATES:\n",
+        "        if font_name in available_fonts:\n",
+        "            return font_name\n",
+        "    return None\n",
+        "\n",
+        "\n",
+        "def try_install_colab_korean_font() -> str | None:\n",
+        "    if not Path(\"/content\").exists() or shutil.which(\"apt-get\") is None:\n",
+        "        return None\n",
+        "    try:\n",
+        "        subprocess.run([\"apt-get\", \"update\", \"-qq\"], check=False, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n",
+        "        subprocess.run([\"apt-get\", \"install\", \"-y\", \"-qq\", \"fonts-nanum\"], check=False, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n",
+        "        for font_path in [\n",
+        "            Path(\"/usr/share/fonts/truetype/nanum/NanumGothic.ttf\"),\n",
+        "            Path(\"/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf\"),\n",
+        "        ]:\n",
+        "            if font_path.exists():\n",
+        "                fm.fontManager.addfont(str(font_path))\n",
+        "                return fm.FontProperties(fname=str(font_path)).get_name()\n",
+        "    except Exception as exc:\n",
+        "        print(\"Colab 한글 폰트 설치를 건너뜁니다:\", exc)\n",
+        "    return None\n",
+        "\n",
+        "\n",
+        "def configure_korean_font() -> str | None:\n",
+        "    font_name = find_korean_font() or try_install_colab_korean_font() or find_korean_font()\n",
+        "    if font_name:\n",
+        "        plt.rcParams[\"font.family\"] = font_name\n",
+        "        plt.rcParams[\"axes.unicode_minus\"] = False\n",
+        "        return font_name\n",
+        "    plt.rcParams[\"axes.unicode_minus\"] = False\n",
+        "    return None\n",
+        "\n",
+        "\n",
+        "KOREAN_FONT_NAME = configure_korean_font()\n",
+        "if KOREAN_FONT_NAME:\n",
+        "    print(f\"matplotlib Korean font: {KOREAN_FONT_NAME}\")\n",
+        "else:\n",
+        "    print(\"한글 폰트를 찾지 못했습니다.\")"
+      ]
     },
-    "id": "a2a68fb3",
-    "outputId": "65f72cce-fd5b-4c16-9900-3b43b4e0e289"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_data.csv\n",
-      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_left_label.json\n",
-      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_data.csv\n",
-      "cache copy: /content/drive/MyDrive/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json -> /content/aims_dataset_cache/aims_dataset/머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)/2nd_process_right_label.json\n",
-      "[LightGBM] [Info] Number of positive: 461, number of negative: 166\n",
-      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n",
-      "You can set `force_col_wise=true` to remove the overhead.\n",
-      "[LightGBM] [Info] Total Bins 1349\n",
-      "[LightGBM] [Info] Number of data points in the train set: 627, number of used features: 7\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.735247 -> initscore=1.021410\n",
-      "[LightGBM] [Info] Start training from score 1.021410\n",
-      "\n",
-      "== Thermal PAINT quality auxiliary model ==\n",
-      "ROC-AUC: 0.83824\n",
-      "PR-AUC: 0.91229\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0     0.5962    0.5636    0.5794        55\n",
-      "           1     0.8481    0.8645    0.8562       155\n",
-      "\n",
-      "    accuracy                         0.7857       210\n",
-      "   macro avg     0.7221    0.7141    0.7178       210\n",
-      "weighted avg     0.7821    0.7857    0.7837       210\n",
-      "\n",
-      "aux_paint_thermal_defect_probability: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
-      "aux_paint_thermal_label: 837개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
-     ]
+      "cell_type": "markdown",
+      "id": "3d789629",
+      "metadata": {
+        "id": "3d789629"
+      },
+      "source": [
+        "**경로 설정**\n",
+        "\n",
+        "Colab에서는 Google Drive를 마운트하고, 로컬에서는 프로젝트 내부의 generated CSV를 사용합니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "  thermal_side  thermal_label  thermal_mean_temp  thermal_std_temp  \\\n",
-       "0         left              0          51.042400          3.518454   \n",
-       "1         left              0          51.621162          2.787206   \n",
-       "2         left              0          51.391914          2.623878   \n",
-       "3         left              0          51.121414          3.284468   \n",
-       "4         left              0          50.737823          3.554459   \n",
-       "\n",
-       "   thermal_min_temp  thermal_max_temp  thermal_p90_temp  thermal_range_temp  \\\n",
-       "0         42.534000         54.491001         54.271999              11.957   \n",
-       "1         42.860001         54.021000         53.651199              11.161   \n",
-       "2         43.202000         53.519001         53.315601              10.317   \n",
-       "3         42.550999         53.926998         53.727699              11.376   \n",
-       "4         42.051998         53.896000         53.601799              11.844   \n",
-       "\n",
-       "   thermal_slope  \n",
-       "0         10.465  \n",
-       "1          9.378  \n",
-       "2          8.845  \n",
-       "3          9.862  \n",
-       "4         10.345  "
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>thermal_side</th>\n",
-       "      <th>thermal_label</th>\n",
-       "      <th>thermal_mean_temp</th>\n",
-       "      <th>thermal_std_temp</th>\n",
-       "      <th>thermal_min_temp</th>\n",
-       "      <th>thermal_max_temp</th>\n",
-       "      <th>thermal_p90_temp</th>\n",
-       "      <th>thermal_range_temp</th>\n",
-       "      <th>thermal_slope</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>left</td>\n",
-       "      <td>0</td>\n",
-       "      <td>51.042400</td>\n",
-       "      <td>3.518454</td>\n",
-       "      <td>42.534000</td>\n",
-       "      <td>54.491001</td>\n",
-       "      <td>54.271999</td>\n",
-       "      <td>11.957</td>\n",
-       "      <td>10.465</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>left</td>\n",
-       "      <td>0</td>\n",
-       "      <td>51.621162</td>\n",
-       "      <td>2.787206</td>\n",
-       "      <td>42.860001</td>\n",
-       "      <td>54.021000</td>\n",
-       "      <td>53.651199</td>\n",
-       "      <td>11.161</td>\n",
-       "      <td>9.378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>left</td>\n",
-       "      <td>0</td>\n",
-       "      <td>51.391914</td>\n",
-       "      <td>2.623878</td>\n",
-       "      <td>43.202000</td>\n",
-       "      <td>53.519001</td>\n",
-       "      <td>53.315601</td>\n",
-       "      <td>10.317</td>\n",
-       "      <td>8.845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>left</td>\n",
-       "      <td>0</td>\n",
-       "      <td>51.121414</td>\n",
-       "      <td>3.284468</td>\n",
-       "      <td>42.550999</td>\n",
-       "      <td>53.926998</td>\n",
-       "      <td>53.727699</td>\n",
-       "      <td>11.376</td>\n",
-       "      <td>9.862</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>left</td>\n",
-       "      <td>0</td>\n",
-       "      <td>50.737823</td>\n",
-       "      <td>3.554459</td>\n",
-       "      <td>42.051998</td>\n",
-       "      <td>53.896000</td>\n",
-       "      <td>53.601799</td>\n",
-       "      <td>11.844</td>\n",
-       "      <td>10.345</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
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-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
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-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
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-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
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-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
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-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
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-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-4ea007ad-db1d-48bc-b5f4-68a4d1f22cf7');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
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-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "cfba3bb6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cfba3bb6",
+        "outputId": "06be2a3a-a52b-4842-8d66-8690b37ba18d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n",
+            "PROJECT_ROOT: /content\n",
+            "PROCESS_ROOT: /content/app/ml/datasets/process\n",
+            "GENERATED_ROOT: /content/drive/MyDrive/aims_dataset/generated\n",
+            "OUTPUT_DIR: /content/drive/MyDrive/defect_transfer_outputs\n"
+          ]
+        }
       ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"    print(\\\"\\uc5f4\\ud654\\uc0c1 \\ub370\\uc774\\ud130 \\ud30c\\uc77c\\uc774 \\uc5c6\\uc5b4 \\uc5f4\\ud654\\uc0c1 \\ubcf4\\uc870 \\ud559\\uc2b5\\uc744 \\uac74\\ub108\\ub701\\ub2c8\\ub2e4\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"thermal_side\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_label\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_mean_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          51.62116241455078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_std_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.7872064113616943\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_min_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          42.86000061035156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_max_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          54.020999908447266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_p90_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          53.65119934082031\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_range_temp\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.16100025177002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thermal_slope\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.378000259399414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "# 보조 피처 결합\n",
-    "def append_cycled_feature(target_df: pd.DataFrame, column: str, values: np.ndarray | pd.Series):\n",
-    "    values = pd.Series(values).dropna().to_numpy(dtype=float)\n",
-    "    if len(values) == 0:\n",
-    "        print(f\"{column}: 값이 없어 추가하지 않습니다.\")\n",
-    "        return\n",
-    "    target_df[column] = np.resize(values, len(target_df))\n",
-    "    print(f\"{column}: {len(values)}개 값을 {len(target_df)}개 Bosch row에 cyclic alignment로 추가\")\n",
-    "\n",
-    "# 보조 모델 평가\n",
-    "def evaluate_aux_classifier(name: str, y_true: pd.Series, proba: np.ndarray):\n",
-    "    pred = (proba >= 0.5).astype(int)\n",
-    "    print(f\"\\n== {name} auxiliary model ==\")\n",
-    "    if y_true.nunique() > 1:\n",
-    "        print(\"ROC-AUC:\", round(roc_auc_score(y_true, proba), 5))\n",
-    "        print(\"PR-AUC:\", round(average_precision_score(y_true, proba), 5))\n",
-    "    print(classification_report(y_true, pred, digits=4))\n",
-    "\n",
-    "# 보조 산출물 저장소\n",
-    "auxiliary_metrics = {}\n",
-    "auxiliary_models = {}\n",
-    "\n",
-    "# 열화상 데이터 로드\n",
-    "def load_thermal_side(data_path: Path, label_path: Path, side: str) -> pd.DataFrame | None:\n",
-    "    if not data_path.exists() or not label_path.exists():\n",
-    "        return None\n",
-    "    data = read_csv_stable(data_path)\n",
-    "    data.columns = [f\"t{i+1}\" for i in range(data.shape[1])]\n",
-    "    labels = read_json_stable(label_path)\n",
-    "    data = data.apply(pd.to_numeric, errors=\"coerce\")\n",
-    "    out = pd.DataFrame({\n",
-    "        \"thermal_side\": side,\n",
-    "        \"thermal_label\": pd.Series(labels).astype(float).astype(\"int8\"),\n",
-    "        \"thermal_mean_temp\": data.mean(axis=1),\n",
-    "        \"thermal_std_temp\": data.std(axis=1),\n",
-    "        \"thermal_min_temp\": data.min(axis=1),\n",
-    "        \"thermal_max_temp\": data.max(axis=1),\n",
-    "        \"thermal_p90_temp\": data.quantile(0.90, axis=1),\n",
-    "        \"thermal_range_temp\": data.max(axis=1) - data.min(axis=1),\n",
-    "        \"thermal_slope\": data.iloc[:, -1] - data.iloc[:, 0],\n",
-    "    })\n",
-    "    return reduce_mem_usage(out)\n",
-    "\n",
-    "# 열화상 좌우 데이터 결합\n",
-    "thermal_frames = []\n",
-    "for item in [\n",
-    "    (THERMAL_LEFT_DATA_PATH, THERMAL_LEFT_LABEL_PATH, \"left\"),\n",
-    "    (THERMAL_RIGHT_DATA_PATH, THERMAL_RIGHT_LABEL_PATH, \"right\"),\n",
-    "]:\n",
-    "    thermal_side = load_thermal_side(*item)\n",
-    "    if thermal_side is not None:\n",
-    "        thermal_frames.append(thermal_side)\n",
-    "\n",
-    "# 열화상 보조 모델 학습\n",
-    "if thermal_frames:\n",
-    "    thermal_df = pd.concat(thermal_frames, ignore_index=True)\n",
-    "    thermal_feature_cols = [c for c in thermal_df.columns if c.startswith(\"thermal_\") and c not in [\"thermal_side\", \"thermal_label\"]]\n",
-    "    X_th = thermal_df[thermal_feature_cols]\n",
-    "    y_th = thermal_df[\"thermal_label\"].astype(int)\n",
-    "    X_th_train, X_th_valid, y_th_train, y_th_valid = train_test_split(\n",
-    "        X_th, y_th, test_size=0.25, random_state=RANDOM_STATE, stratify=y_th if y_th.nunique() > 1 else None\n",
-    "    )\n",
-    "    thermal_model = lgb.LGBMClassifier(\n",
-    "        objective=\"binary\", n_estimators=300, learning_rate=0.04, num_leaves=16,\n",
-    "        min_child_samples=10, random_state=RANDOM_STATE, n_jobs=-1\n",
-    "    )\n",
-    "    # 열화상 불량 확률 학습\n",
-    "    thermal_model.fit(X_th_train, y_th_train)\n",
-    "    thermal_valid_proba = thermal_model.predict_proba(X_th_valid)[:, 1]\n",
-    "    evaluate_aux_classifier(\"Thermal PAINT quality\", y_th_valid, thermal_valid_proba)\n",
-    "    thermal_all_proba = thermal_model.predict_proba(X_th)[:, 1]\n",
-    "    # 도장 보조 피처 추가\n",
-    "    append_cycled_feature(features, \"aux_paint_thermal_defect_probability\", thermal_all_proba)\n",
-    "    append_cycled_feature(features, \"aux_paint_thermal_label\", y_th)\n",
-    "    auxiliary_models[\"thermal_paint_quality\"] = thermal_model\n",
-    "    auxiliary_metrics[\"thermal_rows\"] = int(len(thermal_df))\n",
-    "    display(thermal_df.head())\n",
-    "else:\n",
-    "    thermal_df = None\n",
-    "    print(\"열화상 데이터 파일이 없어 열화상 보조 학습을 건너뜁니다.\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76645e9c",
-   "metadata": {
-    "id": "76645e9c"
-   },
-   "source": [
-    "### 4.1 Ford 엔진 진동 데이터 보조 학습\n",
-    "\n",
-    "FordA 데이터는 첫 번째 컬럼이 라벨입니다. 쉼표 또는 공백 구분 형식을 모두 지원하며, `-1`을 이상/불량, `1`을 정상으로 변환해 PRESS/BODY 계열 설비 이상 확률을 학습합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "144bc123",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "source": [
+        "# Drive mount\n",
+        "try:\n",
+        "    from google.colab import drive\n",
+        "    drive.mount(\"/content/drive\")\n",
+        "    IN_COLAB = True\n",
+        "except Exception:\n",
+        "    IN_COLAB = False\n",
+        "    print(\"Colab이 아니므로 Google Drive mount를 건너뜁니다.\")\n",
+        "\n",
+        "PROJECT_ROOT_CANDIDATES = [\n",
+        "    Path(\"/content/drive/MyDrive/ai-service\"),\n",
+        "    Path(\"/content/drive/MyDrive\"),\n",
+        "    Path.cwd(),\n",
+        "]\n",
+        "PROJECT_ROOT = next((p for p in PROJECT_ROOT_CANDIDATES if (p / \"app\" / \"ml\" / \"datasets\" / \"process\").exists()), Path.cwd())\n",
+        "if str(PROJECT_ROOT) not in sys.path:\n",
+        "    sys.path.insert(0, str(PROJECT_ROOT))\n",
+        "\n",
+        "PROCESS_ROOT = PROJECT_ROOT / \"app\" / \"ml\" / \"datasets\" / \"process\"\n",
+        "GENERATED_ROOT = Path(\"/content/drive/MyDrive/aims_dataset/generated\") if IN_COLAB else PROCESS_ROOT / \"generated\"\n",
+        "OUTPUT_DIR = PROJECT_ROOT / \"outputs\" / \"defect_transfer\" if not IN_COLAB else Path(\"/content/drive/MyDrive/defect_transfer_outputs\")\n",
+        "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "print(\"PROJECT_ROOT:\", PROJECT_ROOT)\n",
+        "print(\"PROCESS_ROOT:\", PROCESS_ROOT)\n",
+        "print(\"GENERATED_ROOT:\", GENERATED_ROOT)\n",
+        "print(\"OUTPUT_DIR:\", OUTPUT_DIR)"
+      ]
     },
-    "id": "144bc123",
-    "outputId": "b559b72a-3520-49e8-ef80-8add5e66a562"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "FordA_TRAIN.txt: rows=1, features=255, labels={-1.0: 1}\n",
-      "FordA_TEST.txt: rows=1,320, features=500, labels={-1.0: 681, 1.0: 639}\n",
-      "Ford TRAIN 데이터를 제외합니다(shape=(1, 256)). 정상적인 TEST 데이터(shape=(1320, 501))를 stratified 75:25로 재분할합니다.\n",
-      "\n",
-      "== Ford PRESS/BODY vibration auxiliary model ==\n",
-      "ROC-AUC: 0.72757\n",
-      "PR-AUC: 0.74268\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0     0.6294    0.6687    0.6485       160\n",
-      "           1     0.6687    0.6294    0.6485       170\n",
-      "\n",
-      "    accuracy                         0.6485       330\n",
-      "   macro avg     0.6491    0.6491    0.6485       330\n",
-      "weighted avg     0.6497    0.6485    0.6485       330\n",
-      "\n",
-      "aux_body_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n",
-      "aux_press_ford_vibration_abnormal_probability: 1320개 값을 300000개 Bosch row에 cyclic alignment로 추가\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Ford 진동 데이터 로드\n",
-    "def load_ford_txt(path: Path, max_rows: int | None = None) -> pd.DataFrame | None:\n",
-    "    if not path.exists():\n",
-    "        return None\n",
-    "\n",
-    "    # 배포처에 따라 FordA가 comma 또는 whitespace 구분 형식으로 제공됩니다.\n",
-    "    df = read_csv_stable(\n",
-    "        path,\n",
-    "        sep=r\"[,\\s]+\",\n",
-    "        engine=\"python\",\n",
-    "        header=None,\n",
-    "        nrows=max_rows,\n",
-    "    )\n",
-    "    df = df.dropna(axis=1, how=\"all\").apply(pd.to_numeric, errors=\"coerce\")\n",
-    "    invalid_rows = int(df.isna().any(axis=1).sum())\n",
-    "    if invalid_rows:\n",
-    "        raise ValueError(f\"{path.name}: 숫자로 해석할 수 없는 행이 {invalid_rows:,}개 있습니다.\")\n",
-    "\n",
-    "    if df.shape[1] < 2:\n",
-    "        raise ValueError(f\"{path.name}: 라벨과 진동 feature를 구분할 수 없습니다. shape={df.shape}\")\n",
-    "\n",
-    "    labels = set(df.iloc[:, 0].astype(int).unique())\n",
-    "    if not labels or not labels.issubset({-1, 1}):\n",
-    "        raise ValueError(f\"{path.name}: 라벨은 -1 또는 1이어야 하지만 현재 {sorted(labels)}입니다.\")\n",
-    "\n",
-    "    ford_feature_count = df.shape[1] - 1\n",
-    "    df.columns = [\"label\"] + [f\"ford_t{i}\" for i in range(1, df.shape[1])]\n",
-    "    df[\"ford_abnormal_label\"] = (df[\"label\"] == -1).astype(\"int8\")\n",
-    "    print(f\"{path.name}: rows={len(df):,}, features={ford_feature_count}, labels={df['label'].value_counts().to_dict()}\")\n",
-    "    return reduce_mem_usage(df.drop(columns=[\"label\"]))\n",
-    "\n",
-    "# Ford train/test 로드\n",
-    "ford_train_df = load_ford_txt(FORD_TRAIN_PATH)\n",
-    "ford_test_df = load_ford_txt(FORD_TEST_PATH)\n",
-    "\n",
-    "# Ford 보조 모델 학습\n",
-    "if ford_train_df is not None and ford_test_df is not None:\n",
-    "    ford_feature_cols = [c for c in ford_train_df.columns if c != \"ford_abnormal_label\"]\n",
-    "    ford_test_feature_cols = [c for c in ford_test_df.columns if c != \"ford_abnormal_label\"]\n",
-    "    train_is_usable = len(ford_train_df) >= 20 and ford_train_df[\"ford_abnormal_label\"].nunique() == 2\n",
-    "    test_is_usable = len(ford_test_df) >= 20 and ford_test_df[\"ford_abnormal_label\"].nunique() == 2\n",
-    "    feature_schema_matches = ford_feature_cols == ford_test_feature_cols\n",
-    "\n",
-    "    if train_is_usable and test_is_usable and feature_schema_matches:\n",
-    "        ford_all_df = pd.concat([ford_train_df, ford_test_df], ignore_index=True)\n",
-    "        X_ford_train = ford_train_df[ford_feature_cols]\n",
-    "        y_ford_train = ford_train_df[\"ford_abnormal_label\"].astype(int)\n",
-    "        X_ford_test = ford_test_df[ford_feature_cols]\n",
-    "        y_ford_test = ford_test_df[\"ford_abnormal_label\"].astype(int)\n",
-    "    elif test_is_usable:\n",
-    "        # 현재 배포본처럼 TRAIN이 잘렸어도 정상적인 TEST 데이터만으로 보조 모델을 구성합니다.\n",
-    "        ford_all_df = ford_test_df.copy()\n",
-    "        ford_feature_cols = ford_test_feature_cols\n",
-    "        print(\n",
-    "            f\"Ford TRAIN 데이터를 제외합니다(shape={ford_train_df.shape}). \"\n",
-    "            f\"정상적인 TEST 데이터(shape={ford_test_df.shape})를 stratified 75:25로 재분할합니다.\"\n",
-    "        )\n",
-    "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
-    "            ford_all_df[ford_feature_cols],\n",
-    "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
-    "            test_size=0.25,\n",
-    "            random_state=RANDOM_STATE,\n",
-    "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
-    "        )\n",
-    "    elif train_is_usable:\n",
-    "        ford_all_df = ford_train_df.copy()\n",
-    "        print(\"Ford TEST 데이터가 학습에 부적합하여 TRAIN 데이터를 stratified 75:25로 재분할합니다.\")\n",
-    "        X_ford_train, X_ford_test, y_ford_train, y_ford_test = train_test_split(\n",
-    "            ford_all_df[ford_feature_cols],\n",
-    "            ford_all_df[\"ford_abnormal_label\"].astype(int),\n",
-    "            test_size=0.25,\n",
-    "            random_state=RANDOM_STATE,\n",
-    "            stratify=ford_all_df[\"ford_abnormal_label\"],\n",
-    "        )\n",
-    "    else:\n",
-    "        raise ValueError(\n",
-    "            \"Ford 데이터에 두 클래스가 포함된 충분한 행이 없습니다. \"\n",
-    "            f\"train={ford_train_df.shape}, test={ford_test_df.shape}\"\n",
-    "        )\n",
-    "\n",
-    "    ford_model = lgb.LGBMClassifier(\n",
-    "        objective=\"binary\", n_estimators=500, learning_rate=0.03, num_leaves=31,\n",
-    "        min_child_samples=20, subsample=0.9, colsample_bytree=0.9,\n",
-    "        random_state=RANDOM_STATE, n_jobs=-1, verbosity=-1\n",
-    "    )\n",
-    "    # 진동 이상 확률 학습\n",
-    "    ford_model.fit(X_ford_train, y_ford_train)\n",
-    "    ford_test_proba = ford_model.predict_proba(X_ford_test)[:, 1]\n",
-    "    evaluate_aux_classifier(\"Ford PRESS/BODY vibration\", y_ford_test, ford_test_proba)\n",
-    "\n",
-    "    ford_all_proba = ford_model.predict_proba(ford_all_df[ford_feature_cols])[:, 1]\n",
-    "    # 차체/프레스 보조 피처 추가\n",
-    "    append_cycled_feature(features, \"aux_body_ford_vibration_abnormal_probability\", ford_all_proba)\n",
-    "    append_cycled_feature(features, \"aux_press_ford_vibration_abnormal_probability\", ford_all_proba)\n",
-    "    auxiliary_models[\"ford_vibration\"] = ford_model\n",
-    "    auxiliary_metrics[\"ford_rows\"] = int(len(ford_all_df))\n",
-    "else:\n",
-    "    print(\"Ford 데이터 파일이 없어 Ford 보조 학습을 건너뜁니다.\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a97efe99",
-   "metadata": {
-    "id": "a97efe99"
-   },
-   "source": [
-    "### 4.2 소성가공 공정/전류 데이터 비지도 학습\n",
-    "\n",
-    "소성가공 데이터에는 불량 라벨이 없으므로 공정 데이터와 설비 센서 데이터를 각각 Isolation Forest로 학습해 공정 흐름 이상 점수, 프레스 이상 점수, 로봇 전류 이상 점수를 생성합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cd25bfc8",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 216
+      "cell_type": "markdown",
+      "id": "f7e546ab",
+      "metadata": {
+        "id": "f7e546ab"
+      },
+      "source": [
+        "## 1. generated 학습 데이터셋 로드"
+      ]
     },
-    "id": "cd25bfc8",
-    "outputId": "73d3b745-218c-4f61-96fa-0022c8136bcb"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "numeric_df: (300000, 262)\n",
-      "date_df: (300000, 261)\n",
-      "categorical_df: (300000, 10)\n",
-      "Response ratio:\n"
-     ]
+      "cell_type": "markdown",
+      "id": "d25e913d",
+      "metadata": {
+        "id": "d25e913d"
+      },
+      "source": [
+        "**데이터 준비**\n",
+        "\n",
+        "아래 4개 CSV가 필요합니다.\n",
+        "\n",
+        "- `defect_detection_train.csv`\n",
+        "- `defect_detection_test.csv`\n",
+        "- `transfer_prediction_train.csv`\n",
+        "- `transfer_prediction_test.csv`"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "            ratio\n",
-       "Response         \n",
-       "0         0.99435\n",
-       "1         0.00565"
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-43c145af-4222-4430-8081-0b1b5c54e8d6\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ratio</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Response</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.99435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.00565</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43c145af-4222-4430-8081-0b1b5c54e8d6')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-43c145af-4222-4430-8081-0b1b5c54e8d6');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "306fbb15",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "306fbb15",
+        "outputId": "6d0d60d1-9fa6-4fe7-922e-74ce28317b4c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "generated CSV 4종을 확인했습니다.\n",
+            "defect train: /content/drive/MyDrive/aims_dataset/generated/defect_detection_train.csv\n",
+            "defect test: /content/drive/MyDrive/aims_dataset/generated/defect_detection_test.csv\n",
+            "transfer train: /content/drive/MyDrive/aims_dataset/generated/transfer_prediction_train.csv\n",
+            "transfer test: /content/drive/MyDrive/aims_dataset/generated/transfer_prediction_test.csv\n"
+          ]
+        }
       ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"display(numeric_df[\\\"Response\\\"]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Response\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6991164745591395,\n        \"min\": 0.00565,\n        \"max\": 0.99435,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.00565,\n          0.99435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "# Bosch numeric 컬럼 선별\n",
-    "numeric_cols = select_columns_by_profile(\n",
-    "    NUMERIC_PATH,\n",
-    "    required_cols=[\"Id\", \"Response\"],\n",
-    "    max_features=MAX_NUMERIC_FEATURES,\n",
-    "    profile_rows=PROFILE_ROWS,\n",
-    ")\n",
-    "# Bosch date 컬럼 선별\n",
-    "date_cols = select_columns_by_profile(\n",
-    "    DATE_PATH,\n",
-    "    required_cols=[\"Id\"],\n",
-    "    max_features=MAX_DATE_FEATURES,\n",
-    "    profile_rows=PROFILE_ROWS,\n",
-    ")\n",
-    "\n",
-    "# Bosch 선택 데이터 로드\n",
-    "numeric_df = read_selected_csv(NUMERIC_PATH, numeric_cols, MAX_ROWS)\n",
-    "date_df = read_selected_csv(DATE_PATH, date_cols, MAX_ROWS)\n",
-    "\n",
-    "# Bosch categorical 선택 로드\n",
-    "if USE_CATEGORICAL:\n",
-    "    categorical_cols = select_columns_by_profile(\n",
-    "        CATEGORICAL_PATH,\n",
-    "        required_cols=[\"Id\"],\n",
-    "        max_features=MAX_CATEGORICAL_FEATURES,\n",
-    "        profile_rows=CATEGORICAL_PROFILE_ROWS,\n",
-    "    )\n",
-    "    categorical_df = read_csv_stable(CATEGORICAL_PATH, usecols=categorical_cols, nrows=MAX_ROWS)\n",
-    "else:\n",
-    "    categorical_df = None\n",
-    "\n",
-    "print(\"numeric_df:\", numeric_df.shape)\n",
-    "print(\"date_df:\", date_df.shape)\n",
-    "print(\"categorical_df:\", None if categorical_df is None else categorical_df.shape)\n",
-    "print(\"Response ratio:\")\n",
-    "display(numeric_df[\"Response\"].value_counts(normalize=True).rename(\"ratio\").to_frame())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "-7HWdLE4bEfB",
-   "metadata": {
-    "id": "-7HWdLE4bEfB"
-   },
-   "source": [
-    "## 5. 학습 데이터 정제\n",
-    "\n",
-    "4개 후보 모델이 동일한 학습 조건에서 비교될 수 있도록 무한대와 완전 결측 컬럼을 제거합니다. 불량 데이터는 희소하므로 stratified split과 class weight 기반 모델을 사용합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "XX3xUzrlbEfB",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "XX3xUzrlbEfB",
-    "outputId": "1b64eb6a-04d9-4fa0-ee50-df1fa3ea9a18"
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "X_train: (240000, 457) X_valid: (60000, 457)\n",
-      "positive ratio train: 0.00565\n",
-      "scale_pos_weight: 175.99\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 학습 테이블 결합\n",
-    "model_df = features.merge(target, on=\"Id\", how=\"inner\")\n",
-    "model_df = model_df.replace([np.inf, -np.inf], np.nan)\n",
-    "\n",
-    "# 입력/라벨 분리\n",
-    "drop_cols = [\"Id\", \"Response\"]\n",
-    "feature_cols = [c for c in model_df.columns if c not in drop_cols]\n",
-    "# 완전 결측 컬럼 제거\n",
-    "all_missing_cols = model_df[feature_cols].columns[model_df[feature_cols].isna().all()].tolist()\n",
-    "if all_missing_cols:\n",
-    "    model_df = model_df.drop(columns=all_missing_cols)\n",
-    "    feature_cols = [c for c in feature_cols if c not in all_missing_cols]\n",
-    "\n",
-    "X = model_df[feature_cols]\n",
-    "y = model_df[\"Response\"].astype(int)\n",
-    "ids = model_df[\"Id\"]\n",
-    "if y.nunique() < 2:\n",
-    "    raise ValueError(\"Response가 한 클래스만 포함되어 있습니다. MAX_ROWS를 늘리거나 데이터 샘플링 범위를 조정하세요.\")\n",
-    "\n",
-    "# 계층 분할\n",
-    "X_train, X_valid, y_train, y_valid, id_train, id_valid = train_test_split(\n",
-    "    X, y, ids,\n",
-    "    test_size=0.2,\n",
-    "    random_state=RANDOM_STATE,\n",
-    "    stratify=y,\n",
-    ")\n",
-    "\n",
-    "# 클래스 불균형 가중치\n",
-    "neg_count = int((y_train == 0).sum())\n",
-    "pos_count = int((y_train == 1).sum())\n",
-    "scale_pos_weight = neg_count / max(pos_count, 1)\n",
-    "\n",
-    "print(\"X_train:\", X_train.shape, \"X_valid:\", X_valid.shape)\n",
-    "print(\"positive ratio train:\", y_train.mean())\n",
-    "print(\"scale_pos_weight:\", round(scale_pos_weight, 2))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6444a8dd",
-   "metadata": {
-    "id": "6444a8dd"
-   },
-   "source": [
-    "## 6. 전처리 학습 데이터셋 저장\n",
-    "\n",
-    "모델에 실제로 들어가는 정제/전처리 완료 데이터셋을 파일로 저장합니다. Parquet 저장이 가능하면 `.parquet`, 환경에 `pyarrow/fastparquet`가 없으면 `.csv.gz`로 저장합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "6e442104",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "6e442104",
-    "outputId": "09415cb9-862f-49f7-a3f3-30566b3940f4"
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "전처리 학습 데이터셋: /content/defect_transfer_outputs/defect_transfer_training_dataset.parquet\n",
-      "train/valid split ids: /content/defect_transfer_outputs/defect_transfer_train_valid_split_ids.csv\n",
-      "전처리 metadata: /content/defect_transfer_outputs/defect_transfer_preprocessing_metadata.json\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 학습 데이터셋 저장\n",
-    "def save_training_dataframe(df: pd.DataFrame, output_dir: Path, stem: str) -> Path:\n",
-    "    parquet_path = output_dir / f\"{stem}.parquet\"\n",
-    "    csv_path = output_dir / f\"{stem}.csv.gz\"\n",
-    "    try:\n",
-    "        df.to_parquet(parquet_path, index=False)\n",
-    "        return parquet_path\n",
-    "    except Exception as exc:\n",
-    "        print(f\"Parquet 저장 실패, csv.gz로 저장합니다: {exc}\")\n",
-    "        df.to_csv(csv_path, index=False, compression=\"gzip\")\n",
-    "        return csv_path\n",
-    "\n",
-    "# 전처리 결과 저장\n",
-    "training_dataset_path = save_training_dataframe(model_df, OUTPUT_DIR, \"defect_transfer_training_dataset\")\n",
-    "\n",
-    "# 분할 ID 저장\n",
-    "split_ids = pd.DataFrame({\n",
-    "    \"Id\": pd.concat([id_train, id_valid]).astype(int).to_numpy(),\n",
-    "    \"split\": [\"train\"] * len(id_train) + [\"valid\"] * len(id_valid),\n",
-    "})\n",
-    "split_ids_path = OUTPUT_DIR / \"defect_transfer_train_valid_split_ids.csv\"\n",
-    "split_ids.to_csv(split_ids_path, index=False)\n",
-    "\n",
-    "# 전처리 메타데이터\n",
-    "preprocessing_metadata = {\n",
-    "    \"process_root\": str(PROCESS_ROOT),\n",
-    "    \"bosch_numeric_path\": str(NUMERIC_PATH),\n",
-    "    \"bosch_date_path\": str(DATE_PATH),\n",
-    "    \"bosch_categorical_path\": str(CATEGORICAL_PATH),\n",
-    "    \"use_categorical\": bool(USE_CATEGORICAL),\n",
-    "    \"profile_rows\": int(PROFILE_ROWS),\n",
-    "    \"max_rows\": int(MAX_ROWS),\n",
-    "    \"max_numeric_features\": int(MAX_NUMERIC_FEATURES),\n",
-    "    \"max_date_features\": int(MAX_DATE_FEATURES),\n",
-    "    \"max_categorical_features\": int(MAX_CATEGORICAL_FEATURES),\n",
-    "    \"selected_numeric_columns\": numeric_cols,\n",
-    "    \"selected_date_columns\": date_cols,\n",
-    "    \"selected_categorical_columns\": categorical_cols if USE_CATEGORICAL else [],\n",
-    "    \"dropped_all_missing_columns\": all_missing_cols,\n",
-    "    \"feature_columns\": feature_cols,\n",
-    "    \"target_column\": \"Response\",\n",
-    "    \"train_rows\": int(len(X_train)),\n",
-    "    \"valid_rows\": int(len(X_valid)),\n",
-    "    \"positive_ratio\": float(y.mean()),\n",
-    "    \"auxiliary_metrics\": auxiliary_metrics,\n",
-    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
-    "}\n",
-    "preprocessing_metadata_path = OUTPUT_DIR / \"defect_transfer_preprocessing_metadata.json\"\n",
-    "preprocessing_metadata_path.write_text(json.dumps(preprocessing_metadata, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "\n",
-    "print(\"전처리 학습 데이터셋:\", training_dataset_path)\n",
-    "print(\"train/valid split ids:\", split_ids_path)\n",
-    "print(\"전처리 metadata:\", preprocessing_metadata_path)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b19d8e5c",
-   "metadata": {
-    "id": "b19d8e5c"
-   },
-   "source": [
-    "## 7. 후보 모델 4종 교차검증 및 하이퍼파라미터 튜닝\n",
-    "\n",
-    "불량 클래스가 희소하므로 모든 후보 모델은 같은 전처리/피처 엔지니어링 결과를 사용하고, `StratifiedKFold`로 클래스 비율을 유지하며 비교합니다.\n",
-    "\n",
-    "이번 버전은 기존의 약한 랜덤 탐색을 보완하기 위해 다음을 반영합니다.\n",
-    "\n",
-    "- `CV_N_SPLITS = 5`: 5-Fold Stratified CV로 validation 안정성 강화\n",
-    "- `CV_N_ITER = 15`: 비-LightGBM 후보도 15회 랜덤 탐색\n",
-    "- LightGBM: `Optuna` TPE sampler 기반 15회 탐색\n",
-    "- LightGBM pipeline: median imputation + SMOTE + `is_unbalance=True` 기반 불균형 보정\n",
-    "- threshold: Accuracy/F1 단독 최적화가 아니라 Recall 우선 탐색 적용\n",
-    "\n",
-    "후보 모델:\n",
-    "\n",
-    "| 모델 | 목적 |\n",
-    "| --- | --- |\n",
-    "| LightGBM | 대용량 tabular 제조 데이터용 gradient boosting 기준 모델, Optuna + SMOTE 적용 |\n",
-    "| RandomForest | bagging 기반 tree ensemble 기준 모델 |\n",
-    "| ExtraTrees | 더 강한 randomization을 주는 tree ensemble 후보 |\n",
-    "| LogisticRegression | 선형 기준선 모델 |\n",
-    "\n",
-    "비교 지표는 PR-AUC, Recall, Precision, F1, ROC-AUC, Accuracy, False Positive Rate를 함께 사용합니다. 특히 Bosch 데이터처럼 불량 비율이 1% 미만인 경우 Accuracy는 참고 지표로만 해석합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "d9a50e90",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "d9a50e90",
-    "outputId": "60f68881-d294-44fc-92f2-008ba9ab2179"
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:00:51,763] A new study created in memory with name: bosch_defect_lightgbm_recall_pr_auc\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "CV rows: 30,000 / train rows: 240,000\n",
-      "CV splits: 5, tuning trials per model: 15\n",
-      "SMOTE enabled: True, sampling_strategy=0.1\n",
-      "Threshold strategy: recall>=0.35, min_precision>=0.02, beta=2.0\n",
-      "\n",
-      "[LightGBM] CV start\n",
-      "  optuna trial 1/15 params={'model__learning_rate': 0.021789307659775742, 'model__num_leaves': 154, 'model__max_depth': -1, 'model__min_child_samples': 264, 'model__subsample': 0.8603902541101232, 'model__colsample_bytree': 0.8686326600082205, 'model__reg_lambda': 0.1094395112835673, 'model__reg_alpha': 0.7579479953348001}\n",
-      "    fold 1/5 PR-AUC=0.03400 Recall=0.14706 F1=0.07353 elapsed=67.3s\n",
-      "    fold 2/5 PR-AUC=0.03235 Recall=0.35294 F1=0.06723 elapsed=59.2s\n",
-      "    fold 3/5 PR-AUC=0.03957 Recall=0.23529 F1=0.11268 elapsed=59.2s\n",
-      "    fold 4/5 PR-AUC=0.02377 Recall=0.41176 F1=0.03815 elapsed=60.1s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:05:57,466] Trial 0 finished with value: 0.06604691309715442 and parameters: {'learning_rate': 0.021789307659775742, 'num_leaves': 154, 'max_depth': -1, 'min_child_samples': 264, 'subsample': 0.8603902541101232, 'colsample_bytree': 0.8686326600082205, 'reg_lambda': 0.1094395112835673, 'reg_alpha': 0.7579479953348001}. Best is trial 0 with value: 0.06604691309715442.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.12554 Recall=0.35294 F1=0.07571 elapsed=59.8s\n",
-      "  optuna trial 2/15 params={'model__learning_rate': 0.0564638664293258, 'model__num_leaves': 53, 'model__max_depth': 9, 'model__min_child_samples': 108, 'model__subsample': 0.8641485131528328, 'model__colsample_bytree': 0.6127722372934189, 'model__reg_lambda': 0.359730743558148, 'model__reg_alpha': 0.002920433847181412}\n",
-      "    fold 1/5 PR-AUC=0.03474 Recall=0.23529 F1=0.06107 elapsed=42.5s\n",
-      "    fold 2/5 PR-AUC=0.04446 Recall=0.38235 F1=0.04180 elapsed=43.4s\n",
-      "    fold 3/5 PR-AUC=0.03871 Recall=0.23529 F1=0.12121 elapsed=43.7s\n",
-      "    fold 4/5 PR-AUC=0.02391 Recall=0.14706 F1=0.07092 elapsed=43.4s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:09:34,490] Trial 1 finished with value: 0.06631270724589355 and parameters: {'learning_rate': 0.0564638664293258, 'num_leaves': 53, 'max_depth': 9, 'min_child_samples': 108, 'subsample': 0.8641485131528328, 'colsample_bytree': 0.6127722372934189, 'reg_lambda': 0.359730743558148, 'reg_alpha': 0.002920433847181412}. Best is trial 1 with value: 0.06631270724589355.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.12504 Recall=0.29412 F1=0.11976 elapsed=44.0s\n",
-      "  optuna trial 3/15 params={'model__learning_rate': 0.025815006344207546, 'model__num_leaves': 131, 'model__max_depth': 12, 'model__min_child_samples': 76, 'model__subsample': 0.6727680575448478, 'model__colsample_bytree': 0.976998491764, 'model__reg_lambda': 6.881524386672037, 'model__reg_alpha': 0.17123375973163968}\n",
-      "    fold 1/5 PR-AUC=0.02393 Recall=0.05882 F1=0.09756 elapsed=76.2s\n",
-      "    fold 2/5 PR-AUC=0.04875 Recall=0.35294 F1=0.05010 elapsed=78.4s\n",
-      "    fold 3/5 PR-AUC=0.03501 Recall=0.35294 F1=0.04000 elapsed=73.9s\n",
-      "    fold 4/5 PR-AUC=0.02380 Recall=0.35294 F1=0.03877 elapsed=75.6s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:15:58,052] Trial 2 finished with value: 0.06402544874348826 and parameters: {'learning_rate': 0.025815006344207546, 'num_leaves': 131, 'max_depth': 12, 'min_child_samples': 76, 'subsample': 0.6727680575448478, 'colsample_bytree': 0.976998491764, 'reg_lambda': 6.881524386672037, 'reg_alpha': 0.17123375973163968}. Best is trial 1 with value: 0.06631270724589355.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.11511 Recall=0.35294 F1=0.04848 elapsed=79.5s\n",
-      "  optuna trial 4/15 params={'model__learning_rate': 0.018840552955192824, 'model__num_leaves': 37, 'model__max_depth': -1, 'model__min_child_samples': 276, 'model__subsample': 0.7405729935600059, 'model__colsample_bytree': 0.8481350279592919, 'model__reg_lambda': 0.39193516226890834, 'model__reg_alpha': 0.012030178871154668}\n",
-      "    fold 1/5 PR-AUC=0.02465 Recall=0.17647 F1=0.05607 elapsed=58.5s\n",
-      "    fold 2/5 PR-AUC=0.04893 Recall=0.38235 F1=0.05068 elapsed=59.0s\n",
-      "    fold 3/5 PR-AUC=0.04120 Recall=0.20588 F1=0.15909 elapsed=59.7s\n",
-      "    fold 4/5 PR-AUC=0.01779 Recall=0.35294 F1=0.03941 elapsed=58.0s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:20:50,337] Trial 3 finished with value: 0.06894370517883613 and parameters: {'learning_rate': 0.018840552955192824, 'num_leaves': 37, 'max_depth': -1, 'min_child_samples': 276, 'subsample': 0.7405729935600059, 'colsample_bytree': 0.8481350279592919, 'reg_lambda': 0.39193516226890834, 'reg_alpha': 0.012030178871154668}. Best is trial 3 with value: 0.06894370517883613.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.13862 Recall=0.35294 F1=0.09231 elapsed=57.1s\n",
-      "  optuna trial 5/15 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
-      "    fold 1/5 PR-AUC=0.03658 Recall=0.08824 F1=0.10345 elapsed=56.6s\n",
-      "    fold 2/5 PR-AUC=0.04709 Recall=0.35294 F1=0.08247 elapsed=54.5s\n",
-      "    fold 3/5 PR-AUC=0.05326 Recall=0.23529 F1=0.16162 elapsed=50.2s\n",
-      "    fold 4/5 PR-AUC=0.02418 Recall=0.23529 F1=0.05926 elapsed=53.0s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:25:17,267] Trial 4 finished with value: 0.07319973398830097 and parameters: {'learning_rate': 0.031169409812378812, 'num_leaves': 49, 'max_depth': -1, 'min_child_samples': 279, 'subsample': 0.6809723757181718, 'colsample_bytree': 0.6381922880886154, 'reg_lambda': 0.12191905861905929, 'reg_alpha': 0.0020013420622879987}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.14607 Recall=0.26471 F1=0.21176 elapsed=52.6s\n",
-      "  optuna trial 6/15 params={'model__learning_rate': 0.022439365289703053, 'model__num_leaves': 61, 'model__max_depth': -1, 'model__min_child_samples': 247, 'model__subsample': 0.6760927252879199, 'model__colsample_bytree': 0.9940991214702328, 'model__reg_lambda': 2.94884053630599, 'model__reg_alpha': 0.0006235377135673159}\n",
-      "    fold 1/5 PR-AUC=0.02457 Recall=0.05882 F1=0.10000 elapsed=66.4s\n",
-      "    fold 2/5 PR-AUC=0.04620 Recall=0.38235 F1=0.04019 elapsed=64.8s\n",
-      "    fold 3/5 PR-AUC=0.03686 Recall=0.23529 F1=0.13445 elapsed=65.1s\n",
-      "    fold 4/5 PR-AUC=0.02458 Recall=0.20588 F1=0.07000 elapsed=64.7s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:30:43,306] Trial 5 finished with value: 0.06304171041876949 and parameters: {'learning_rate': 0.022439365289703053, 'num_leaves': 61, 'max_depth': -1, 'min_child_samples': 247, 'subsample': 0.6760927252879199, 'colsample_bytree': 0.9940991214702328, 'reg_lambda': 2.94884053630599, 'reg_alpha': 0.0006235377135673159}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.12711 Recall=0.23529 F1=0.20253 elapsed=65.0s\n",
-      "  optuna trial 7/15 params={'model__learning_rate': 0.01011549101545285, 'model__num_leaves': 135, 'model__max_depth': 7, 'model__min_child_samples': 61, 'model__subsample': 0.9520861990564577, 'model__colsample_bytree': 0.8304841570724011, 'model__reg_lambda': 0.4263131389740422, 'model__reg_alpha': 0.00017956984225677631}\n",
-      "    fold 1/5 PR-AUC=0.02317 Recall=0.08824 F1=0.07317 elapsed=56.3s\n",
-      "    fold 2/5 PR-AUC=0.04950 Recall=0.26471 F1=0.08000 elapsed=56.3s\n",
-      "    fold 3/5 PR-AUC=0.02767 Recall=0.14706 F1=0.14085 elapsed=50.9s\n",
-      "    fold 4/5 PR-AUC=0.01344 Recall=0.23529 F1=0.04598 elapsed=51.6s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:35:10,406] Trial 6 finished with value: 0.04217109041808953 and parameters: {'learning_rate': 0.01011549101545285, 'num_leaves': 135, 'max_depth': 7, 'min_child_samples': 61, 'subsample': 0.9520861990564577, 'colsample_bytree': 0.8304841570724011, 'reg_lambda': 0.4263131389740422, 'reg_alpha': 0.00017956984225677631}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.04267 Recall=0.35294 F1=0.05085 elapsed=52.1s\n",
-      "  optuna trial 8/15 params={'model__learning_rate': 0.0190917184400185, 'model__num_leaves': 68, 'model__max_depth': 7, 'model__min_child_samples': 223, 'model__subsample': 0.9162747670159141, 'model__colsample_bytree': 0.8025747389062734, 'model__reg_lambda': 2.9323777832216886, 'model__reg_alpha': 0.009444574254983563}\n",
-      "    fold 1/5 PR-AUC=0.01607 Recall=0.17647 F1=0.03261 elapsed=36.0s\n",
-      "    fold 2/5 PR-AUC=0.03486 Recall=0.38235 F1=0.03922 elapsed=34.4s\n",
-      "    fold 3/5 PR-AUC=0.02913 Recall=0.23529 F1=0.12030 elapsed=36.2s\n",
-      "    fold 4/5 PR-AUC=0.01392 Recall=0.14706 F1=0.05181 elapsed=33.5s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:38:04,664] Trial 7 finished with value: 0.05305343371432367 and parameters: {'learning_rate': 0.0190917184400185, 'num_leaves': 68, 'max_depth': 7, 'min_child_samples': 223, 'subsample': 0.9162747670159141, 'colsample_bytree': 0.8025747389062734, 'reg_lambda': 2.9323777832216886, 'reg_alpha': 0.009444574254983563}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.10658 Recall=0.35294 F1=0.04301 elapsed=34.2s\n",
-      "  optuna trial 9/15 params={'model__learning_rate': 0.029653419677342325, 'model__num_leaves': 82, 'model__max_depth': 9, 'model__min_child_samples': 167, 'model__subsample': 0.9676482658741326, 'model__colsample_bytree': 0.6621815031169938, 'model__reg_lambda': 0.6039425546072186, 'model__reg_alpha': 0.10524574681335624}\n",
-      "    fold 1/5 PR-AUC=0.03325 Recall=0.11765 F1=0.05556 elapsed=40.7s\n",
-      "    fold 2/5 PR-AUC=0.03970 Recall=0.23529 F1=0.09524 elapsed=41.2s\n",
-      "    fold 3/5 PR-AUC=0.03826 Recall=0.23529 F1=0.15385 elapsed=42.5s\n",
-      "    fold 4/5 PR-AUC=0.01437 Recall=0.26471 F1=0.03805 elapsed=37.8s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:41:27,137] Trial 8 finished with value: 0.060833017107763024 and parameters: {'learning_rate': 0.029653419677342325, 'num_leaves': 82, 'max_depth': 9, 'min_child_samples': 167, 'subsample': 0.9676482658741326, 'colsample_bytree': 0.6621815031169938, 'reg_lambda': 0.6039425546072186, 'reg_alpha': 0.10524574681335624}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.11828 Recall=0.35294 F1=0.09160 elapsed=40.3s\n",
-      "  optuna trial 10/15 params={'model__learning_rate': 0.016092567235133776, 'model__num_leaves': 34, 'model__max_depth': 7, 'model__min_child_samples': 266, 'model__subsample': 0.9312852269146901, 'model__colsample_bytree': 0.6339565264987161, 'model__reg_lambda': 4.995973117313505, 'model__reg_alpha': 0.014367095138664226}\n",
-      "    fold 1/5 PR-AUC=0.01984 Recall=0.14706 F1=0.04016 elapsed=30.5s\n",
-      "    fold 2/5 PR-AUC=0.02887 Recall=0.35294 F1=0.04969 elapsed=29.4s\n",
-      "    fold 3/5 PR-AUC=0.03842 Recall=0.17647 F1=0.16438 elapsed=30.6s\n",
-      "    fold 4/5 PR-AUC=0.01492 Recall=0.14706 F1=0.05495 elapsed=29.2s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:43:58,707] Trial 9 finished with value: 0.0581151434945654 and parameters: {'learning_rate': 0.016092567235133776, 'num_leaves': 34, 'max_depth': 7, 'min_child_samples': 266, 'subsample': 0.9312852269146901, 'colsample_bytree': 0.6339565264987161, 'reg_lambda': 4.995973117313505, 'reg_alpha': 0.014367095138664226}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.12971 Recall=0.35294 F1=0.04829 elapsed=31.8s\n",
-      "  optuna trial 11/15 params={'model__learning_rate': 0.07344753762582334, 'model__num_leaves': 100, 'model__max_depth': 5, 'model__min_child_samples': 179, 'model__subsample': 0.7804393429857105, 'model__colsample_bytree': 0.7211318321133148, 'model__reg_lambda': 0.10707258976378216, 'model__reg_alpha': 0.00011815152243566209}\n",
-      "    fold 1/5 PR-AUC=0.02487 Recall=0.05882 F1=0.09524 elapsed=22.2s\n",
-      "    fold 2/5 PR-AUC=0.03263 Recall=0.17647 F1=0.06522 elapsed=22.8s\n",
-      "    fold 3/5 PR-AUC=0.02345 Recall=0.20588 F1=0.06306 elapsed=21.8s\n",
-      "    fold 4/5 PR-AUC=0.00869 Recall=0.14706 F1=0.03802 elapsed=25.2s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:45:53,501] Trial 10 finished with value: 0.039906002434268506 and parameters: {'learning_rate': 0.07344753762582334, 'num_leaves': 100, 'max_depth': 5, 'min_child_samples': 179, 'subsample': 0.7804393429857105, 'colsample_bytree': 0.7211318321133148, 'reg_lambda': 0.10707258976378216, 'reg_alpha': 0.00011815152243566209}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.07460 Recall=0.11765 F1=0.13333 elapsed=22.7s\n",
-      "  optuna trial 12/15 params={'model__learning_rate': 0.03828385438925741, 'model__num_leaves': 24, 'model__max_depth': -1, 'model__min_child_samples': 290, 'model__subsample': 0.7500490079557848, 'model__colsample_bytree': 0.7430730526600867, 'model__reg_lambda': 0.26877401109804494, 'model__reg_alpha': 0.0018756206282287391}\n",
-      "    fold 1/5 PR-AUC=0.03004 Recall=0.11765 F1=0.04790 elapsed=42.9s\n",
-      "    fold 2/5 PR-AUC=0.04640 Recall=0.17647 F1=0.09449 elapsed=43.4s\n",
-      "    fold 3/5 PR-AUC=0.03590 Recall=0.20588 F1=0.14433 elapsed=43.8s\n",
-      "    fold 4/5 PR-AUC=0.01742 Recall=0.35294 F1=0.04444 elapsed=42.8s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:49:29,067] Trial 11 finished with value: 0.06729149493282813 and parameters: {'learning_rate': 0.03828385438925741, 'num_leaves': 24, 'max_depth': -1, 'min_child_samples': 290, 'subsample': 0.7500490079557848, 'colsample_bytree': 0.7430730526600867, 'reg_lambda': 0.26877401109804494, 'reg_alpha': 0.0018756206282287391}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.14640 Recall=0.35294 F1=0.06742 elapsed=42.6s\n",
-      "  optuna trial 13/15 params={'model__learning_rate': 0.0128289748714244, 'model__num_leaves': 45, 'model__max_depth': -1, 'model__min_child_samples': 205, 'model__subsample': 0.7368227996831814, 'model__colsample_bytree': 0.5575289958995627, 'model__reg_lambda': 0.7798089384438257, 'model__reg_alpha': 0.02173049208731439}\n",
-      "    fold 1/5 PR-AUC=0.02159 Recall=0.08824 F1=0.09375 elapsed=49.2s\n",
-      "    fold 2/5 PR-AUC=0.04449 Recall=0.41176 F1=0.04230 elapsed=48.5s\n",
-      "    fold 3/5 PR-AUC=0.05169 Recall=0.23529 F1=0.16327 elapsed=50.1s\n",
-      "    fold 4/5 PR-AUC=0.01912 Recall=0.35294 F1=0.04928 elapsed=49.4s\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:53:35,467] Trial 12 finished with value: 0.06395427302355366 and parameters: {'learning_rate': 0.0128289748714244, 'num_leaves': 45, 'max_depth': -1, 'min_child_samples': 205, 'subsample': 0.7368227996831814, 'colsample_bytree': 0.5575289958995627, 'reg_lambda': 0.7798089384438257, 'reg_alpha': 0.02173049208731439}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.11083 Recall=0.35294 F1=0.05660 elapsed=49.3s\n",
-      "  optuna trial 14/15 params={'model__learning_rate': 0.03688230536757648, 'model__num_leaves': 84, 'model__max_depth': -1, 'model__min_child_samples': 299, 'model__subsample': 0.7156364210016786, 'model__colsample_bytree': 0.8810259041893101, 'model__reg_lambda': 0.199522384883775, 'model__reg_alpha': 0.0024334796925402718}\n",
-      "    fold 1/5 PR-AUC=0.04648 Recall=0.17647 F1=0.05854 elapsed=63.5s\n",
-      "    fold 2/5 PR-AUC=0.03300 Recall=0.35294 F1=0.06818 elapsed=63.2s\n",
-      "    fold 3/5 PR-AUC=0.03487 Recall=0.20588 F1=0.09459 elapsed=62.5s\n",
-      "    fold 4/5 PR-AUC=0.02476 Recall=0.23529 F1=0.06349 elapsed=60.1s\n"
-     ]
+      "source": [
+        "required_files = [\n",
+        "    GENERATED_ROOT / \"defect_detection_train.csv\",\n",
+        "    GENERATED_ROOT / \"defect_detection_test.csv\",\n",
+        "    GENERATED_ROOT / \"transfer_prediction_train.csv\",\n",
+        "    GENERATED_ROOT / \"transfer_prediction_test.csv\",\n",
+        "]\n",
+        "\n",
+        "missing_files = [path for path in required_files if not path.exists()]\n",
+        "if missing_files:\n",
+        "    missing_text = \"\\n\".join(str(path) for path in missing_files)\n",
+        "    raise FileNotFoundError(\n",
+        "        \"Colab에서 사용할 generated CSV 파일을 찾을 수 없습니다. \"\n",
+        "        \"Google Drive의 /content/drive/MyDrive/aims_dataset/generated 폴더에 \"\n",
+        "        \"아래 파일 4개를 업로드한 뒤 다시 실행하세요.\\n\"\n",
+        "        f\"{missing_text}\"\n",
+        "    )\n",
+        "\n",
+        "DEFECT_TRAIN_PATH = GENERATED_ROOT / \"defect_detection_train.csv\"\n",
+        "DEFECT_TEST_PATH = GENERATED_ROOT / \"defect_detection_test.csv\"\n",
+        "TRANSFER_TRAIN_PATH = GENERATED_ROOT / \"transfer_prediction_train.csv\"\n",
+        "TRANSFER_TEST_PATH = GENERATED_ROOT / \"transfer_prediction_test.csv\"\n",
+        "\n",
+        "print(\"generated CSV 4종을 확인했습니다.\")\n",
+        "print(\"defect train:\", DEFECT_TRAIN_PATH)\n",
+        "print(\"defect test:\", DEFECT_TEST_PATH)\n",
+        "print(\"transfer train:\", TRANSFER_TRAIN_PATH)\n",
+        "print(\"transfer test:\", TRANSFER_TEST_PATH)"
+      ]
     },
     {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 01:58:50,528] Trial 13 finished with value: 0.07042822156591197 and parameters: {'learning_rate': 0.03688230536757648, 'num_leaves': 84, 'max_depth': -1, 'min_child_samples': 299, 'subsample': 0.7156364210016786, 'colsample_bytree': 0.8810259041893101, 'reg_lambda': 0.199522384883775, 'reg_alpha': 0.0024334796925402718}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
+      "cell_type": "markdown",
+      "id": "d3592f1f",
+      "metadata": {
+        "id": "d3592f1f"
+      },
+      "source": [
+        "**데이터 확인**\n",
+        "\n",
+        "CSV를 DataFrame으로 읽고 shape와 샘플 행을 확인합니다."
+      ]
     },
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.15126 Recall=0.26471 F1=0.16216 elapsed=65.7s\n",
-      "  optuna trial 15/15 params={'model__learning_rate': 0.04187329932090051, 'model__num_leaves': 98, 'model__max_depth': 5, 'model__min_child_samples': 300, 'model__subsample': 0.654599110501233, 'model__colsample_bytree': 0.9091999481071245, 'model__reg_lambda': 0.1837271427904195, 'model__reg_alpha': 0.0009412519611659042}\n",
-      "    fold 1/5 PR-AUC=0.01758 Recall=0.05882 F1=0.08000 elapsed=26.5s\n",
-      "    fold 2/5 PR-AUC=0.03039 Recall=0.35294 F1=0.05042 elapsed=23.1s\n",
-      "    fold 3/5 PR-AUC=0.02245 Recall=0.20588 F1=0.08235 elapsed=26.7s\n",
-      "    fold 4/5 PR-AUC=0.01160 Recall=0.20588 F1=0.03743 elapsed=30.8s\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "7c916959",
+      "metadata": {
+        "id": "7c916959",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 674
+        },
+        "outputId": "02c75c9d-e282-43ea-8b2d-2add61a43939"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "defect_train_raw: (38400, 19)\n",
+            "defect_test_raw: (9600, 19)\n",
+            "transfer_train_raw: (28800, 16)\n",
+            "transfer_test_raw: (7200, 16)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   raw_event_id             event_id           event_time  car_master_id_raw  \\\n",
+              "0             1  EVT-20260616-000001  2026-06-16T10:00:00                  1   \n",
+              "1             2  EVT-20260616-000002  2026-06-16T10:00:03                  1   \n",
+              "2             3  EVT-20260616-000003  2026-06-16T10:00:06                  1   \n",
+              "\n",
+              "   equipment_id process_code_raw  station_code_raw equipment_code_raw  \\\n",
+              "0             1            PRESS  PRESS_STATION_01       EQ_PRESS_001   \n",
+              "1            13             BODY   BODY_STATION_03        EQ_BODY_003   \n",
+              "2            24            PAINT  PAINT_STATION_04       EQ_PAINT_004   \n",
+              "\n",
+              "  equipment_type_raw operation_status_raw    event_type_raw  \\\n",
+              "0    HYDRAULIC_PRESS              RUNNING    PROCESS_STATUS   \n",
+              "1          ROBOT_ARM              RUNNING  EQUIPMENT_SENSOR   \n",
+              "2             CAMERA              RUNNING     QUALITY_CHECK   \n",
+              "\n",
+              "                                          event_json  is_sent  sent_at  \\\n",
+              "0  {\"event\": {\"eventId\": \"EVT-20260616-000001\", \"...        0      NaN   \n",
+              "1  {\"event\": {\"eventId\": \"EVT-20260616-000002\", \"...        0      NaN   \n",
+              "2  {\"event\": {\"eventId\": \"EVT-20260616-000003\", \"...        0      NaN   \n",
+              "\n",
+              "            created_at           updated_at  defect_yn  \\\n",
+              "0  2026-06-27T14:39:42  2026-06-27T14:39:42          0   \n",
+              "1  2026-06-27T14:39:42  2026-06-27T14:39:42          1   \n",
+              "2  2026-06-27T14:39:42  2026-06-27T14:39:42          1   \n",
+              "\n",
+              "             defect_reason dataset_split  \n",
+              "0                   normal         train  \n",
+              "1     body_robot_vibration         train  \n",
+              "2  paint_vision_or_thermal         train  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6caf067c-6d64-454a-876d-cb33468879b0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>raw_event_id</th>\n",
+              "      <th>event_id</th>\n",
+              "      <th>event_time</th>\n",
+              "      <th>car_master_id_raw</th>\n",
+              "      <th>equipment_id</th>\n",
+              "      <th>process_code_raw</th>\n",
+              "      <th>station_code_raw</th>\n",
+              "      <th>equipment_code_raw</th>\n",
+              "      <th>equipment_type_raw</th>\n",
+              "      <th>operation_status_raw</th>\n",
+              "      <th>event_type_raw</th>\n",
+              "      <th>event_json</th>\n",
+              "      <th>is_sent</th>\n",
+              "      <th>sent_at</th>\n",
+              "      <th>created_at</th>\n",
+              "      <th>updated_at</th>\n",
+              "      <th>defect_yn</th>\n",
+              "      <th>defect_reason</th>\n",
+              "      <th>dataset_split</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>EVT-20260616-000001</td>\n",
+              "      <td>2026-06-16T10:00:00</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>PRESS_STATION_01</td>\n",
+              "      <td>EQ_PRESS_001</td>\n",
+              "      <td>HYDRAULIC_PRESS</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>PROCESS_STATUS</td>\n",
+              "      <td>{\"event\": {\"eventId\": \"EVT-20260616-000001\", \"...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2026-06-27T14:39:42</td>\n",
+              "      <td>2026-06-27T14:39:42</td>\n",
+              "      <td>0</td>\n",
+              "      <td>normal</td>\n",
+              "      <td>train</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>EVT-20260616-000002</td>\n",
+              "      <td>2026-06-16T10:00:03</td>\n",
+              "      <td>1</td>\n",
+              "      <td>13</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>BODY_STATION_03</td>\n",
+              "      <td>EQ_BODY_003</td>\n",
+              "      <td>ROBOT_ARM</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>EQUIPMENT_SENSOR</td>\n",
+              "      <td>{\"event\": {\"eventId\": \"EVT-20260616-000002\", \"...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2026-06-27T14:39:42</td>\n",
+              "      <td>2026-06-27T14:39:42</td>\n",
+              "      <td>1</td>\n",
+              "      <td>body_robot_vibration</td>\n",
+              "      <td>train</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>EVT-20260616-000003</td>\n",
+              "      <td>2026-06-16T10:00:06</td>\n",
+              "      <td>1</td>\n",
+              "      <td>24</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>PAINT_STATION_04</td>\n",
+              "      <td>EQ_PAINT_004</td>\n",
+              "      <td>CAMERA</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>QUALITY_CHECK</td>\n",
+              "      <td>{\"event\": {\"eventId\": \"EVT-20260616-000003\", \"...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2026-06-27T14:39:42</td>\n",
+              "      <td>2026-06-27T14:39:42</td>\n",
+              "      <td>1</td>\n",
+              "      <td>paint_vision_or_thermal</td>\n",
+              "      <td>train</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6caf067c-6d64-454a-876d-cb33468879b0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6caf067c-6d64-454a-876d-cb33468879b0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6caf067c-6d64-454a-876d-cb33468879b0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(transfer_train_raw\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"raw_event_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"EVT-20260616-000001\",\n          \"EVT-20260616-000002\",\n          \"EVT-20260616-000003\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2026-06-16T10:00:00\",\n          \"2026-06-16T10:00:03\",\n          \"2026-06-16T10:00:06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_master_id_raw\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"equipment_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 1,\n        \"max\": 24,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"process_code_raw\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_code_raw\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PRESS_STATION_01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"equipment_code_raw\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"EQ_PRESS_001\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"equipment_type_raw\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"HYDRAULIC_PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"operation_status_raw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RUNNING\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_type_raw\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PROCESS_STATUS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_json\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"{\\\"event\\\": {\\\"eventId\\\": \\\"EVT-20260616-000001\\\", \\\"eventTime\\\": \\\"2026-06-16T10:00:00\\\", \\\"eventType\\\": \\\"PROCESS_STATUS\\\", \\\"eventName\\\": \\\"\\ud504\\ub808\\uc2a4 \\uacf5\\uc815 \\ud1b5\\ud569 \\uad00\\uc81c \\uc774\\ubca4\\ud2b8\\\"}, \\\"equipment\\\": {\\\"equipmentCode\\\": \\\"EQ_PRESS_001\\\", \\\"equipmentName\\\": \\\"\\ud504\\ub808\\uc2a4 \\uc720\\uc555\\ubaa8\\ud130 1\\ud638\\\", \\\"equipmentType\\\": \\\"HYDRAULIC_PRESS\\\"}, \\\"equipmentStatus\\\": {\\\"operationStatus\\\": \\\"RUNNING\\\", \\\"lastNormalTime\\\": null, \\\"statusChangedTime\\\": null}, \\\"product\\\": {\\\"carMasterId\\\": 1}, \\\"sensor\\\": {\\\"sensorType\\\": \\\"MULTI_SENSOR\\\", \\\"current\\\": {\\\"rmsAmpere\\\": 1.73142512, \\\"maxAmpere\\\": 1.89583344, \\\"minAmpere\\\": 1.5670168}, \\\"vibration\\\": {\\\"accelerationG\\\": 0.0046, \\\"vibrationScore\\\": 0.34092, \\\"vibrationRms\\\": 1.0387936, \\\"vibrationPeak\\\": 2.3268372}, \\\"robotArmVibration\\\": {\\\"robotId\\\": \\\"ROBOT_ARM_01\\\", \\\"axis\\\": \\\"J2\\\", \\\"frequencyHz\\\": 115.002, \\\"amplitude\\\": 0.001038794, \\\"vibrationRms\\\": 0.001154215, \\\"vibrationPeak\\\": 0.003324053, \\\"vibrationScore\\\": 0.34092}, \\\"thermal\\\": {\\\"thermalScore\\\": 50.922, \\\"avgTemperature\\\": 50.922, \\\"maxTemperature\\\": 54.131, \\\"minTemperature\\\": 42.19}}, \\\"processMetrics\\\": {\\\"cycleTimeSec\\\": 47.833, \\\"waitingTimeSec\\\": 5.0, \\\"processingTimeSec\\\": 42.833, \\\"stationDelaySec\\\": 7.833, \\\"throughputPerMin\\\": 1.254, \\\"queueLength\\\": 6, \\\"wipCount\\\": 19, \\\"equipmentIdleTimeSec\\\": 12.534}, \\\"sourceTrace\\\": {\\\"fordRowId\\\": 1, \\\"formingRowId\\\": 55644626, \\\"robotArmVibrationRowId\\\": 1, \\\"machineVisionRowId\\\": 1, \\\"boschId\\\": 4}, \\\"processData\\\": {\\\"press\\\": {\\\"countIncreaseYn\\\": true, \\\"targetCycleTimeSec\\\": 40.0, \\\"timestampDelaySec\\\": 7.833}}}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_sent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sent_at\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"created_at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"updated_at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"defect_yn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"defect_reason\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dataset_split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   car_master_id      source_event_id      target_event_id  \\\n",
+              "0              1  EVT-20260616-000001  EVT-20260616-000002   \n",
+              "1              1  EVT-20260616-000002  EVT-20260616-000003   \n",
+              "2              1  EVT-20260616-000003  EVT-20260616-000004   \n",
+              "\n",
+              "  source_process_code target_process_code  source_defect_yn  target_defect_yn  \\\n",
+              "0               PRESS                BODY                 0                 1   \n",
+              "1                BODY               PAINT                 1                 1   \n",
+              "2               PAINT            ASSEMBLY                 1                 0   \n",
+              "\n",
+              "   source_cycle_time_sec  source_station_delay_sec  source_queue_length  \\\n",
+              "0                 47.833                     7.833                    6   \n",
+              "1                 56.998                     4.998                    6   \n",
+              "2                 69.326                     5.326                    7   \n",
+              "\n",
+              "   source_wip_count  source_current_rms_ampere  source_vibration_score  \\\n",
+              "0                19                   1.731425                0.340920   \n",
+              "1                18                   2.068431                0.369143   \n",
+              "2                19                   1.648970                0.333915   \n",
+              "\n",
+              "   source_thermal_score                                  source_event_json  \\\n",
+              "0                50.922  {\"event\": {\"eventId\": \"EVT-20260616-000001\", \"...   \n",
+              "1                51.042  {\"event\": {\"eventId\": \"EVT-20260616-000002\", \"...   \n",
+              "2                51.621  {\"event\": {\"eventId\": \"EVT-20260616-000003\", \"...   \n",
+              "\n",
+              "  dataset_split  \n",
+              "0         train  \n",
+              "1         train  \n",
+              "2         train  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-98002e7a-c3e1-456e-9835-189560940359\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>car_master_id</th>\n",
+              "      <th>source_event_id</th>\n",
+              "      <th>target_event_id</th>\n",
+              "      <th>source_process_code</th>\n",
+              "      <th>target_process_code</th>\n",
+              "      <th>source_defect_yn</th>\n",
+              "      <th>target_defect_yn</th>\n",
+              "      <th>source_cycle_time_sec</th>\n",
+              "      <th>source_station_delay_sec</th>\n",
+              "      <th>source_queue_length</th>\n",
+              "      <th>source_wip_count</th>\n",
+              "      <th>source_current_rms_ampere</th>\n",
+              "      <th>source_vibration_score</th>\n",
+              "      <th>source_thermal_score</th>\n",
+              "      <th>source_event_json</th>\n",
+              "      <th>dataset_split</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>EVT-20260616-000001</td>\n",
+              "      <td>EVT-20260616-000002</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>47.833</td>\n",
+              "      <td>7.833</td>\n",
+              "      <td>6</td>\n",
+              "      <td>19</td>\n",
+              "      <td>1.731425</td>\n",
+              "      <td>0.340920</td>\n",
+              "      <td>50.922</td>\n",
+              "      <td>{\"event\": {\"eventId\": \"EVT-20260616-000001\", \"...</td>\n",
+              "      <td>train</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>EVT-20260616-000002</td>\n",
+              "      <td>EVT-20260616-000003</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>56.998</td>\n",
+              "      <td>4.998</td>\n",
+              "      <td>6</td>\n",
+              "      <td>18</td>\n",
+              "      <td>2.068431</td>\n",
+              "      <td>0.369143</td>\n",
+              "      <td>51.042</td>\n",
+              "      <td>{\"event\": {\"eventId\": \"EVT-20260616-000002\", \"...</td>\n",
+              "      <td>train</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>EVT-20260616-000003</td>\n",
+              "      <td>EVT-20260616-000004</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>69.326</td>\n",
+              "      <td>5.326</td>\n",
+              "      <td>7</td>\n",
+              "      <td>19</td>\n",
+              "      <td>1.648970</td>\n",
+              "      <td>0.333915</td>\n",
+              "      <td>51.621</td>\n",
+              "      <td>{\"event\": {\"eventId\": \"EVT-20260616-000003\", \"...</td>\n",
+              "      <td>train</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98002e7a-c3e1-456e-9835-189560940359')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-98002e7a-c3e1-456e-9835-189560940359 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-98002e7a-c3e1-456e-9835-189560940359');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(transfer_train_raw\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"car_master_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_event_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"EVT-20260616-000001\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_event_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"EVT-20260616-000002\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"BODY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_defect_yn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_defect_yn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_cycle_time_sec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.785220272823976,\n        \"min\": 47.833,\n        \"max\": 69.326,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          47.833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_station_delay_sec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5507986114687276,\n        \"min\": 4.998,\n        \"max\": 7.833,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_queue_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 6,\n        \"max\": 7,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_wip_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 18,\n        \"max\": 19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_current_rms_ampere\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22223072013817394,\n        \"min\": 1.64896992,\n        \"max\": 2.068430694,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.73142512\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_vibration_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018648591269405126,\n        \"min\": 0.333915,\n        \"max\": 0.369143,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.34092\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_thermal_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37377399588521604,\n        \"min\": 50.922,\n        \"max\": 51.621,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50.922\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_event_json\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"{\\\"event\\\": {\\\"eventId\\\": \\\"EVT-20260616-000001\\\", \\\"eventTime\\\": \\\"2026-06-16T10:00:00\\\", \\\"eventType\\\": \\\"PROCESS_STATUS\\\", \\\"eventName\\\": \\\"\\ud504\\ub808\\uc2a4 \\uacf5\\uc815 \\ud1b5\\ud569 \\uad00\\uc81c \\uc774\\ubca4\\ud2b8\\\"}, \\\"equipment\\\": {\\\"equipmentCode\\\": \\\"EQ_PRESS_001\\\", \\\"equipmentName\\\": \\\"\\ud504\\ub808\\uc2a4 \\uc720\\uc555\\ubaa8\\ud130 1\\ud638\\\", \\\"equipmentType\\\": \\\"HYDRAULIC_PRESS\\\"}, \\\"equipmentStatus\\\": {\\\"operationStatus\\\": \\\"RUNNING\\\", \\\"lastNormalTime\\\": null, \\\"statusChangedTime\\\": null}, \\\"product\\\": {\\\"carMasterId\\\": 1}, \\\"sensor\\\": {\\\"sensorType\\\": \\\"MULTI_SENSOR\\\", \\\"current\\\": {\\\"rmsAmpere\\\": 1.73142512, \\\"maxAmpere\\\": 1.89583344, \\\"minAmpere\\\": 1.5670168}, \\\"vibration\\\": {\\\"accelerationG\\\": 0.0046, \\\"vibrationScore\\\": 0.34092, \\\"vibrationRms\\\": 1.0387936, \\\"vibrationPeak\\\": 2.3268372}, \\\"robotArmVibration\\\": {\\\"robotId\\\": \\\"ROBOT_ARM_01\\\", \\\"axis\\\": \\\"J2\\\", \\\"frequencyHz\\\": 115.002, \\\"amplitude\\\": 0.001038794, \\\"vibrationRms\\\": 0.001154215, \\\"vibrationPeak\\\": 0.003324053, \\\"vibrationScore\\\": 0.34092}, \\\"thermal\\\": {\\\"thermalScore\\\": 50.922, \\\"avgTemperature\\\": 50.922, \\\"maxTemperature\\\": 54.131, \\\"minTemperature\\\": 42.19}}, \\\"processMetrics\\\": {\\\"cycleTimeSec\\\": 47.833, \\\"waitingTimeSec\\\": 5.0, \\\"processingTimeSec\\\": 42.833, \\\"stationDelaySec\\\": 7.833, \\\"throughputPerMin\\\": 1.254, \\\"queueLength\\\": 6, \\\"wipCount\\\": 19, \\\"equipmentIdleTimeSec\\\": 12.534}, \\\"sourceTrace\\\": {\\\"fordRowId\\\": 1, \\\"formingRowId\\\": 55644626, \\\"robotArmVibrationRowId\\\": 1, \\\"machineVisionRowId\\\": 1, \\\"boschId\\\": 4}, \\\"processData\\\": {\\\"press\\\": {\\\"countIncreaseYn\\\": true, \\\"targetCycleTimeSec\\\": 40.0, \\\"timestampDelaySec\\\": 7.833}}}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dataset_split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "defect_train_raw = pd.read_csv(DEFECT_TRAIN_PATH)\n",
+        "defect_test_raw = pd.read_csv(DEFECT_TEST_PATH)\n",
+        "transfer_train_raw = pd.read_csv(TRANSFER_TRAIN_PATH)\n",
+        "transfer_test_raw = pd.read_csv(TRANSFER_TEST_PATH)\n",
+        "\n",
+        "print(\"defect_train_raw:\", defect_train_raw.shape)\n",
+        "print(\"defect_test_raw:\", defect_test_raw.shape)\n",
+        "print(\"transfer_train_raw:\", transfer_train_raw.shape)\n",
+        "print(\"transfer_test_raw:\", transfer_test_raw.shape)\n",
+        "display(defect_train_raw.head(3))\n",
+        "display(transfer_train_raw.head(3))"
+      ]
     },
     {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "[I 2026-06-18 02:01:05,723] Trial 14 finished with value: 0.04538489857292094 and parameters: {'learning_rate': 0.04187329932090051, 'num_leaves': 98, 'max_depth': 5, 'min_child_samples': 300, 'subsample': 0.654599110501233, 'colsample_bytree': 0.9091999481071245, 'reg_lambda': 0.1837271427904195, 'reg_alpha': 0.0009412519611659042}. Best is trial 4 with value: 0.07319973398830097.\n"
-     ]
+      "cell_type": "markdown",
+      "id": "b88ce50d",
+      "metadata": {
+        "id": "b88ce50d"
+      },
+      "source": [
+        "## 2. eventJson feature 변환"
+      ]
     },
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "    fold 5/5 PR-AUC=0.09784 Recall=0.11765 F1=0.18605 elapsed=28.1s\n",
-      "[LightGBM] Optuna best value=0.07320 params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
-      "[LightGBM] CV done elapsed=3614.0s\n",
-      "\n",
-      "[RandomForest] CV start\n",
-      "[RandomForest] random search trials: 15\n",
-      "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.01770 Recall=0.14706 F1=0.09615 elapsed=18.6s\n",
-      "    fold 2/5 PR-AUC=0.03344 Recall=0.17647 F1=0.07500 elapsed=18.4s\n",
-      "    fold 3/5 PR-AUC=0.03194 Recall=0.35294 F1=0.04364 elapsed=21.1s\n",
-      "    fold 4/5 PR-AUC=0.01529 Recall=0.32353 F1=0.03520 elapsed=18.1s\n",
-      "    fold 5/5 PR-AUC=0.06988 Recall=0.38235 F1=0.05029 elapsed=18.1s\n",
-      "  -> RandomForest trial 1 mean PR-AUC=0.03365 Recall=0.27647 F1=0.06006 FPR=0.05830\n",
-      "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.01163 Recall=0.08824 F1=0.07407 elapsed=114.9s\n",
-      "    fold 2/5 PR-AUC=0.02605 Recall=0.26471 F1=0.07860 elapsed=120.6s\n",
-      "    fold 3/5 PR-AUC=0.02343 Recall=0.20588 F1=0.08805 elapsed=112.0s\n",
-      "    fold 4/5 PR-AUC=0.00923 Recall=0.14706 F1=0.02817 elapsed=116.7s\n",
-      "    fold 5/5 PR-AUC=0.02658 Recall=0.35294 F1=0.04096 elapsed=128.2s\n",
-      "  -> RandomForest trial 2 mean PR-AUC=0.01938 Recall=0.21176 F1=0.06197 FPR=0.04036\n",
-      "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.01449 Recall=0.11765 F1=0.07547 elapsed=20.0s\n",
-      "    fold 2/5 PR-AUC=0.01800 Recall=0.17647 F1=0.07143 elapsed=18.6s\n",
-      "    fold 3/5 PR-AUC=0.02582 Recall=0.35294 F1=0.04082 elapsed=19.0s\n",
-      "    fold 4/5 PR-AUC=0.01543 Recall=0.05882 F1=0.08163 elapsed=19.4s\n",
-      "    fold 5/5 PR-AUC=0.04959 Recall=0.38235 F1=0.04815 elapsed=20.2s\n",
-      "  -> RandomForest trial 3 mean PR-AUC=0.02467 Recall=0.21765 F1=0.06350 FPR=0.04170\n",
-      "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.01905 Recall=0.11765 F1=0.12121 elapsed=110.6s\n",
-      "    fold 2/5 PR-AUC=0.02432 Recall=0.35294 F1=0.03865 elapsed=112.2s\n",
-      "    fold 3/5 PR-AUC=0.03628 Recall=0.17647 F1=0.13636 elapsed=106.3s\n",
-      "    fold 4/5 PR-AUC=0.02079 Recall=0.08824 F1=0.05660 elapsed=110.0s\n",
-      "    fold 5/5 PR-AUC=0.04705 Recall=0.26471 F1=0.11465 elapsed=112.8s\n",
-      "  -> RandomForest trial 4 mean PR-AUC=0.02950 Recall=0.20000 F1=0.09350 FPR=0.02796\n",
-      "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.01373 Recall=0.08824 F1=0.05505 elapsed=19.7s\n",
-      "    fold 2/5 PR-AUC=0.01550 Recall=0.14706 F1=0.04587 elapsed=18.3s\n",
-      "    fold 3/5 PR-AUC=0.01997 Recall=0.20588 F1=0.06452 elapsed=18.3s\n",
-      "    fold 4/5 PR-AUC=0.00914 Recall=0.50000 F1=0.02353 elapsed=18.2s\n",
-      "    fold 5/5 PR-AUC=0.02526 Recall=0.17647 F1=0.05455 elapsed=18.7s\n",
-      "  -> RandomForest trial 5 mean PR-AUC=0.01672 Recall=0.22353 F1=0.04870 FPR=0.06708\n",
-      "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.02763 Recall=0.14706 F1=0.11765 elapsed=20.0s\n",
-      "    fold 2/5 PR-AUC=0.03999 Recall=0.35294 F1=0.04225 elapsed=19.2s\n",
-      "    fold 3/5 PR-AUC=0.03442 Recall=0.38235 F1=0.04370 elapsed=19.1s\n",
-      "    fold 4/5 PR-AUC=0.01981 Recall=0.05882 F1=0.09302 elapsed=20.5s\n",
-      "    fold 5/5 PR-AUC=0.09888 Recall=0.35294 F1=0.04453 elapsed=19.6s\n",
-      "  -> RandomForest trial 6 mean PR-AUC=0.04415 Recall=0.25882 F1=0.06823 FPR=0.05417\n",
-      "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
-      "    fold 1/5 PR-AUC=0.01178 Recall=0.17647 F1=0.04762 elapsed=18.0s\n",
-      "    fold 2/5 PR-AUC=0.01400 Recall=0.20588 F1=0.05128 elapsed=18.1s\n",
-      "    fold 3/5 PR-AUC=0.01885 Recall=0.08824 F1=0.09677 elapsed=17.9s\n",
-      "    fold 4/5 PR-AUC=0.00916 Recall=0.14706 F1=0.02611 elapsed=18.6s\n",
-      "    fold 5/5 PR-AUC=0.01926 Recall=0.35294 F1=0.04278 elapsed=18.0s\n",
-      "  -> RandomForest trial 7 mean PR-AUC=0.01461 Recall=0.19412 F1=0.05291 FPR=0.04452\n",
-      "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
-      "    fold 1/5 PR-AUC=0.01255 Recall=0.17647 F1=0.04651 elapsed=95.6s\n",
-      "    fold 2/5 PR-AUC=0.01555 Recall=0.17647 F1=0.06593 elapsed=98.3s\n",
-      "    fold 3/5 PR-AUC=0.03233 Recall=0.14706 F1=0.11111 elapsed=99.4s\n",
-      "    fold 4/5 PR-AUC=0.01287 Recall=0.38235 F1=0.02835 elapsed=97.5s\n",
-      "    fold 5/5 PR-AUC=0.02747 Recall=0.14706 F1=0.10417 elapsed=94.9s\n",
-      "  -> RandomForest trial 8 mean PR-AUC=0.02016 Recall=0.20588 F1=0.07122 FPR=0.04485\n",
-      "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.01133 Recall=0.17647 F1=0.05333 elapsed=95.1s\n",
-      "    fold 2/5 PR-AUC=0.01219 Recall=0.14706 F1=0.05376 elapsed=93.9s\n",
-      "    fold 3/5 PR-AUC=0.01700 Recall=0.14706 F1=0.04149 elapsed=95.6s\n",
-      "    fold 4/5 PR-AUC=0.01059 Recall=0.35294 F1=0.02934 elapsed=94.4s\n",
-      "    fold 5/5 PR-AUC=0.01855 Recall=0.11765 F1=0.06897 elapsed=90.9s\n",
-      "  -> RandomForest trial 9 mean PR-AUC=0.01393 Recall=0.18824 F1=0.04938 FPR=0.04640\n",
-      "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.01972 Recall=0.17647 F1=0.05797 elapsed=18.8s\n",
-      "    fold 2/5 PR-AUC=0.01501 Recall=0.14706 F1=0.05051 elapsed=18.4s\n",
-      "    fold 3/5 PR-AUC=0.02479 Recall=0.41176 F1=0.03774 elapsed=18.5s\n",
-      "    fold 4/5 PR-AUC=0.01120 Recall=0.52941 F1=0.02497 elapsed=18.5s\n",
-      "    fold 5/5 PR-AUC=0.03002 Recall=0.17647 F1=0.09449 elapsed=19.2s\n",
-      "  -> RandomForest trial 10 mean PR-AUC=0.02015 Recall=0.28824 F1=0.05313 FPR=0.08371\n",
-      "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.01038 Recall=0.17647 F1=0.03125 elapsed=130.8s\n",
-      "    fold 2/5 PR-AUC=0.01130 Recall=0.17647 F1=0.04181 elapsed=131.1s\n",
-      "    fold 3/5 PR-AUC=0.02148 Recall=0.08824 F1=0.07595 elapsed=129.1s\n",
-      "    fold 4/5 PR-AUC=0.00999 Recall=0.35294 F1=0.02564 elapsed=130.5s\n",
-      "    fold 5/5 PR-AUC=0.01895 Recall=0.11765 F1=0.06780 elapsed=126.2s\n",
-      "  -> RandomForest trial 11 mean PR-AUC=0.01442 Recall=0.18235 F1=0.04849 FPR=0.05374\n",
-      "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.01582 Recall=0.14706 F1=0.08000 elapsed=16.5s\n",
-      "    fold 2/5 PR-AUC=0.01492 Recall=0.14706 F1=0.06452 elapsed=16.5s\n",
-      "    fold 3/5 PR-AUC=0.02683 Recall=0.20588 F1=0.06897 elapsed=16.7s\n",
-      "    fold 4/5 PR-AUC=0.01325 Recall=0.05882 F1=0.06667 elapsed=16.5s\n",
-      "    fold 5/5 PR-AUC=0.04138 Recall=0.35294 F1=0.05042 elapsed=16.5s\n",
-      "  -> RandomForest trial 12 mean PR-AUC=0.02244 Recall=0.18235 F1=0.06611 FPR=0.02742\n",
-      "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.01129 Recall=0.17647 F1=0.03647 elapsed=91.7s\n",
-      "    fold 2/5 PR-AUC=0.01165 Recall=0.26471 F1=0.03854 elapsed=93.2s\n",
-      "    fold 3/5 PR-AUC=0.02405 Recall=0.14706 F1=0.06897 elapsed=92.9s\n",
-      "    fold 4/5 PR-AUC=0.01140 Recall=0.50000 F1=0.02801 elapsed=92.1s\n",
-      "    fold 5/5 PR-AUC=0.01838 Recall=0.11765 F1=0.07843 elapsed=89.3s\n",
-      "  -> RandomForest trial 13 mean PR-AUC=0.01535 Recall=0.24118 F1=0.05008 FPR=0.06859\n",
-      "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.02950 Recall=0.14706 F1=0.15385 elapsed=18.1s\n",
-      "    fold 2/5 PR-AUC=0.04820 Recall=0.35294 F1=0.07717 elapsed=18.3s\n",
-      "    fold 3/5 PR-AUC=0.05079 Recall=0.35294 F1=0.03822 elapsed=18.1s\n",
-      "    fold 4/5 PR-AUC=0.02223 Recall=0.08824 F1=0.07317 elapsed=19.1s\n",
-      "    fold 5/5 PR-AUC=0.10251 Recall=0.35294 F1=0.08602 elapsed=18.7s\n",
-      "  -> RandomForest trial 14 mean PR-AUC=0.05065 Recall=0.25882 F1=0.08569 FPR=0.03859\n",
-      "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.01118 Recall=0.14706 F1=0.03788 elapsed=137.0s\n",
-      "    fold 2/5 PR-AUC=0.01503 Recall=0.17647 F1=0.05941 elapsed=141.8s\n",
-      "    fold 3/5 PR-AUC=0.01931 Recall=0.08824 F1=0.07895 elapsed=142.5s\n",
-      "    fold 4/5 PR-AUC=0.01054 Recall=0.35294 F1=0.02804 elapsed=142.2s\n",
-      "    fold 5/5 PR-AUC=0.01703 Recall=0.11765 F1=0.05970 elapsed=140.4s\n",
-      "  -> RandomForest trial 15 mean PR-AUC=0.01462 Recall=0.17647 F1=0.05279 FPR=0.04465\n",
-      "[RandomForest] CV done elapsed=4652.7s\n",
-      "\n",
-      "[ExtraTrees] CV start\n",
-      "[ExtraTrees] random search trials: 15\n",
-      "  trial 1/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.05335 Recall=0.17647 F1=0.22222 elapsed=14.9s\n",
-      "    fold 2/5 PR-AUC=0.05171 Recall=0.38235 F1=0.04075 elapsed=14.9s\n",
-      "    fold 3/5 PR-AUC=0.03424 Recall=0.20588 F1=0.06481 elapsed=15.0s\n",
-      "    fold 4/5 PR-AUC=0.02532 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
-      "    fold 5/5 PR-AUC=0.10997 Recall=0.35294 F1=0.06557 elapsed=14.9s\n",
-      "  -> ExtraTrees trial 1 mean PR-AUC=0.05492 Recall=0.23529 F1=0.09867 FPR=0.03701\n",
-      "  trial 2/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.03290 Recall=0.11765 F1=0.16667 elapsed=80.4s\n",
-      "    fold 2/5 PR-AUC=0.06744 Recall=0.41176 F1=0.04811 elapsed=83.0s\n",
-      "    fold 3/5 PR-AUC=0.03351 Recall=0.20588 F1=0.08696 elapsed=80.7s\n",
-      "    fold 4/5 PR-AUC=0.02096 Recall=0.08824 F1=0.07895 elapsed=84.2s\n",
-      "    fold 5/5 PR-AUC=0.07834 Recall=0.20588 F1=0.18421 elapsed=82.4s\n",
-      "  -> ExtraTrees trial 2 mean PR-AUC=0.04663 Recall=0.20588 F1=0.11298 FPR=0.02474\n",
-      "  trial 3/15 params={'model__min_samples_leaf': 5, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.04880 Recall=0.17647 F1=0.15584 elapsed=15.0s\n",
-      "    fold 2/5 PR-AUC=0.04067 Recall=0.20588 F1=0.06061 elapsed=14.9s\n",
-      "    fold 3/5 PR-AUC=0.03292 Recall=0.26471 F1=0.06818 elapsed=14.9s\n",
-      "    fold 4/5 PR-AUC=0.02491 Recall=0.05882 F1=0.10000 elapsed=14.7s\n",
-      "    fold 5/5 PR-AUC=0.12176 Recall=0.35294 F1=0.08000 elapsed=15.1s\n",
-      "  -> ExtraTrees trial 3 mean PR-AUC=0.05381 Recall=0.21176 F1=0.09293 FPR=0.02367\n",
-      "  trial 4/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.04500 Recall=0.14706 F1=0.19231 elapsed=77.3s\n",
-      "    fold 2/5 PR-AUC=0.08313 Recall=0.35294 F1=0.05000 elapsed=81.5s\n",
-      "    fold 3/5 PR-AUC=0.05375 Recall=0.20588 F1=0.16867 elapsed=78.0s\n",
-      "    fold 4/5 PR-AUC=0.03255 Recall=0.08824 F1=0.09524 elapsed=79.6s\n",
-      "    fold 5/5 PR-AUC=0.16604 Recall=0.35294 F1=0.08362 elapsed=78.5s\n",
-      "  -> ExtraTrees trial 4 mean PR-AUC=0.07609 Recall=0.22941 F1=0.11797 FPR=0.02534\n",
-      "  trial 5/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.03990 Recall=0.17647 F1=0.07947 elapsed=15.1s\n",
-      "    fold 2/5 PR-AUC=0.03735 Recall=0.26471 F1=0.05422 elapsed=15.6s\n",
-      "    fold 3/5 PR-AUC=0.01885 Recall=0.14706 F1=0.04000 elapsed=15.8s\n",
-      "    fold 4/5 PR-AUC=0.02381 Recall=0.05882 F1=0.09756 elapsed=15.2s\n",
-      "    fold 5/5 PR-AUC=0.07181 Recall=0.23529 F1=0.05112 elapsed=14.9s\n",
-      "  -> ExtraTrees trial 5 mean PR-AUC=0.03834 Recall=0.17647 F1=0.06447 FPR=0.02974\n",
-      "  trial 6/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.05098 Recall=0.17647 F1=0.19355 elapsed=15.8s\n",
-      "    fold 2/5 PR-AUC=0.04937 Recall=0.35294 F1=0.05085 elapsed=16.2s\n",
-      "    fold 3/5 PR-AUC=0.03076 Recall=0.14706 F1=0.12346 elapsed=15.8s\n",
-      "    fold 4/5 PR-AUC=0.02669 Recall=0.17647 F1=0.05479 elapsed=16.0s\n",
-      "    fold 5/5 PR-AUC=0.14600 Recall=0.38235 F1=0.04075 elapsed=16.1s\n",
-      "  -> ExtraTrees trial 6 mean PR-AUC=0.06076 Recall=0.24706 F1=0.09268 FPR=0.04224\n",
-      "  trial 7/15 params={'model__min_samples_leaf': 1, 'model__max_features': 'sqrt', 'model__max_depth': 16}\n",
-      "    fold 1/5 PR-AUC=0.02945 Recall=0.17647 F1=0.07229 elapsed=13.7s\n",
-      "    fold 2/5 PR-AUC=0.03056 Recall=0.35294 F1=0.03791 elapsed=13.3s\n",
-      "    fold 3/5 PR-AUC=0.01740 Recall=0.11765 F1=0.04762 elapsed=13.3s\n",
-      "    fold 4/5 PR-AUC=0.01828 Recall=0.05882 F1=0.08163 elapsed=13.5s\n",
-      "    fold 5/5 PR-AUC=0.02846 Recall=0.14706 F1=0.04950 elapsed=13.7s\n",
-      "  -> ExtraTrees trial 7 mean PR-AUC=0.02483 Recall=0.17059 F1=0.05779 FPR=0.03416\n",
-      "  trial 8/15 params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': 16}\n",
-      "    fold 1/5 PR-AUC=0.05038 Recall=0.20588 F1=0.16092 elapsed=67.6s\n",
-      "    fold 2/5 PR-AUC=0.07342 Recall=0.35294 F1=0.05517 elapsed=66.9s\n",
-      "    fold 3/5 PR-AUC=0.04289 Recall=0.08824 F1=0.12766 elapsed=66.9s\n",
-      "    fold 4/5 PR-AUC=0.03120 Recall=0.23529 F1=0.04278 elapsed=66.2s\n",
-      "    fold 5/5 PR-AUC=0.16042 Recall=0.35294 F1=0.04293 elapsed=64.2s\n",
-      "  -> ExtraTrees trial 8 mean PR-AUC=0.07166 Recall=0.24706 F1=0.08589 FPR=0.04325\n",
-      "  trial 9/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.02831 Recall=0.11765 F1=0.12121 elapsed=58.9s\n",
-      "    fold 2/5 PR-AUC=0.05126 Recall=0.35294 F1=0.03256 elapsed=59.2s\n",
-      "    fold 3/5 PR-AUC=0.02743 Recall=0.08824 F1=0.08108 elapsed=59.7s\n",
-      "    fold 4/5 PR-AUC=0.02199 Recall=0.08824 F1=0.08696 elapsed=55.8s\n",
-      "    fold 5/5 PR-AUC=0.09582 Recall=0.11765 F1=0.12903 elapsed=56.5s\n",
-      "  -> ExtraTrees trial 9 mean PR-AUC=0.04496 Recall=0.15294 F1=0.09017 FPR=0.02722\n",
-      "  trial 10/15 params={'model__min_samples_leaf': 3, 'model__max_features': 'sqrt', 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.04550 Recall=0.23529 F1=0.11511 elapsed=15.0s\n",
-      "    fold 2/5 PR-AUC=0.03161 Recall=0.14706 F1=0.06623 elapsed=15.1s\n",
-      "    fold 3/5 PR-AUC=0.02638 Recall=0.17647 F1=0.08633 elapsed=15.2s\n",
-      "    fold 4/5 PR-AUC=0.02500 Recall=0.05882 F1=0.10000 elapsed=15.1s\n",
-      "    fold 5/5 PR-AUC=0.09072 Recall=0.35294 F1=0.04420 elapsed=15.0s\n",
-      "  -> ExtraTrees trial 10 mean PR-AUC=0.04384 Recall=0.19412 F1=0.08237 FPR=0.02712\n",
-      "  trial 11/15 params={'model__min_samples_leaf': 1, 'model__max_features': 0.5, 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.03474 Recall=0.14706 F1=0.15625 elapsed=79.8s\n",
-      "    fold 2/5 PR-AUC=0.04950 Recall=0.11765 F1=0.05517 elapsed=80.9s\n",
-      "    fold 3/5 PR-AUC=0.02492 Recall=0.08824 F1=0.11321 elapsed=80.1s\n",
-      "    fold 4/5 PR-AUC=0.02257 Recall=0.05882 F1=0.09524 elapsed=79.1s\n",
-      "    fold 5/5 PR-AUC=0.09276 Recall=0.08824 F1=0.15789 elapsed=77.3s\n",
-      "  -> ExtraTrees trial 11 mean PR-AUC=0.04490 Recall=0.10000 F1=0.11555 FPR=0.00520\n",
-      "  trial 12/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.04890 Recall=0.17647 F1=0.15789 elapsed=12.5s\n",
-      "    fold 2/5 PR-AUC=0.06466 Recall=0.17647 F1=0.08000 elapsed=12.5s\n",
-      "    fold 3/5 PR-AUC=0.03919 Recall=0.14706 F1=0.05291 elapsed=11.6s\n",
-      "    fold 4/5 PR-AUC=0.02632 Recall=0.05882 F1=0.10000 elapsed=10.9s\n",
-      "    fold 5/5 PR-AUC=0.12891 Recall=0.14706 F1=0.23256 elapsed=12.2s\n",
-      "  -> ExtraTrees trial 12 mean PR-AUC=0.06160 Recall=0.14118 F1=0.12467 FPR=0.01019\n",
-      "  trial 13/15 params={'model__min_samples_leaf': 5, 'model__max_features': 0.35, 'model__max_depth': 12}\n",
-      "    fold 1/5 PR-AUC=0.04456 Recall=0.17647 F1=0.13043 elapsed=58.2s\n",
-      "    fold 2/5 PR-AUC=0.04290 Recall=0.35294 F1=0.03871 elapsed=58.5s\n",
-      "    fold 3/5 PR-AUC=0.03700 Recall=0.08824 F1=0.10526 elapsed=59.6s\n",
-      "    fold 4/5 PR-AUC=0.02478 Recall=0.08824 F1=0.05556 elapsed=58.3s\n",
-      "    fold 5/5 PR-AUC=0.13122 Recall=0.38235 F1=0.03969 elapsed=56.2s\n",
-      "  -> ExtraTrees trial 13 mean PR-AUC=0.05609 Recall=0.21765 F1=0.07393 FPR=0.04442\n",
-      "  trial 14/15 params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-      "    fold 1/5 PR-AUC=0.05247 Recall=0.17647 F1=0.21818 elapsed=15.4s\n",
-      "    fold 2/5 PR-AUC=0.05497 Recall=0.23529 F1=0.13445 elapsed=15.3s\n",
-      "    fold 3/5 PR-AUC=0.03882 Recall=0.26471 F1=0.08491 elapsed=15.6s\n",
-      "    fold 4/5 PR-AUC=0.02642 Recall=0.29412 F1=0.03711 elapsed=15.4s\n",
-      "    fold 5/5 PR-AUC=0.12650 Recall=0.38235 F1=0.04702 elapsed=15.8s\n",
-      "  -> ExtraTrees trial 14 mean PR-AUC=0.05983 Recall=0.27059 F1=0.10433 FPR=0.04231\n",
-      "  trial 15/15 params={'model__min_samples_leaf': 3, 'model__max_features': 0.5, 'model__max_depth': 20}\n",
-      "    fold 1/5 PR-AUC=0.02622 Recall=0.14706 F1=0.11628 elapsed=100.9s\n",
-      "    fold 2/5 PR-AUC=0.03921 Recall=0.41176 F1=0.04046 elapsed=103.7s\n",
-      "    fold 3/5 PR-AUC=0.02245 Recall=0.17647 F1=0.05660 elapsed=98.1s\n",
-      "    fold 4/5 PR-AUC=0.02344 Recall=0.23529 F1=0.03883 elapsed=100.0s\n",
-      "    fold 5/5 PR-AUC=0.07321 Recall=0.11765 F1=0.06957 elapsed=95.6s\n",
-      "  -> ExtraTrees trial 15 mean PR-AUC=0.03691 Recall=0.21765 F1=0.06435 FPR=0.04392\n",
-      "[ExtraTrees] CV done elapsed=3199.4s\n",
-      "\n",
-      "[LogisticRegression] CV start\n",
-      "[LogisticRegression] random search trials: 5\n",
-      "  trial 1/5 params={'model__solver': 'lbfgs', 'model__C': 0.03}\n",
-      "    fold 1/5 PR-AUC=0.03239 Recall=0.11765 F1=0.11940 elapsed=5.0s\n",
-      "    fold 2/5 PR-AUC=0.03936 Recall=0.20588 F1=0.13333 elapsed=6.5s\n",
-      "    fold 3/5 PR-AUC=0.01676 Recall=0.11765 F1=0.05926 elapsed=5.1s\n",
-      "    fold 4/5 PR-AUC=0.04898 Recall=0.14706 F1=0.06944 elapsed=8.0s\n",
-      "    fold 5/5 PR-AUC=0.05055 Recall=0.14706 F1=0.15385 elapsed=5.0s\n",
-      "  -> LogisticRegression trial 1 mean PR-AUC=0.03761 Recall=0.14706 F1=0.10706 FPR=0.01076\n",
-      "  trial 2/5 params={'model__solver': 'lbfgs', 'model__C': 0.1}\n",
-      "    fold 1/5 PR-AUC=0.03312 Recall=0.14706 F1=0.09524 elapsed=7.2s\n",
-      "    fold 2/5 PR-AUC=0.03507 Recall=0.20588 F1=0.14583 elapsed=7.6s\n",
-      "    fold 3/5 PR-AUC=0.01612 Recall=0.11765 F1=0.07207 elapsed=7.6s\n",
-      "    fold 4/5 PR-AUC=0.04945 Recall=0.14706 F1=0.09174 elapsed=8.8s\n",
-      "    fold 5/5 PR-AUC=0.05347 Recall=0.14706 F1=0.15385 elapsed=8.5s\n",
-      "  -> LogisticRegression trial 2 mean PR-AUC=0.03745 Recall=0.15294 F1=0.11175 FPR=0.00972\n",
-      "  trial 3/5 params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
-      "    fold 1/5 PR-AUC=0.03547 Recall=0.14706 F1=0.10204 elapsed=9.1s\n",
-      "    fold 2/5 PR-AUC=0.03449 Recall=0.20588 F1=0.16092 elapsed=11.2s\n",
-      "    fold 3/5 PR-AUC=0.01564 Recall=0.11765 F1=0.07619 elapsed=10.8s\n",
-      "    fold 4/5 PR-AUC=0.04895 Recall=0.11765 F1=0.10000 elapsed=8.9s\n",
-      "    fold 5/5 PR-AUC=0.05718 Recall=0.14706 F1=0.16129 elapsed=10.3s\n",
-      "  -> LogisticRegression trial 3 mean PR-AUC=0.03835 Recall=0.14706 F1=0.12009 FPR=0.00795\n",
-      "  trial 4/5 params={'model__solver': 'lbfgs', 'model__C': 1.0}\n",
-      "    fold 1/5 PR-AUC=0.03432 Recall=0.14706 F1=0.11236 elapsed=14.1s\n",
-      "    fold 2/5 PR-AUC=0.03430 Recall=0.20588 F1=0.16279 elapsed=18.1s\n",
-      "    fold 3/5 PR-AUC=0.01534 Recall=0.11765 F1=0.07143 elapsed=14.3s\n",
-      "    fold 4/5 PR-AUC=0.04784 Recall=0.14706 F1=0.07634 elapsed=13.7s\n",
-      "    fold 5/5 PR-AUC=0.05053 Recall=0.14706 F1=0.17544 elapsed=13.5s\n",
-      "  -> LogisticRegression trial 4 mean PR-AUC=0.03647 Recall=0.15294 F1=0.11967 FPR=0.00935\n",
-      "  trial 5/5 params={'model__solver': 'lbfgs', 'model__C': 3.0}\n",
-      "    fold 1/5 PR-AUC=0.02904 Recall=0.14706 F1=0.11236 elapsed=17.6s\n",
-      "    fold 2/5 PR-AUC=0.03423 Recall=0.20588 F1=0.15385 elapsed=18.0s\n",
-      "    fold 3/5 PR-AUC=0.01664 Recall=0.11765 F1=0.07080 elapsed=17.7s\n",
-      "    fold 4/5 PR-AUC=0.04669 Recall=0.14706 F1=0.07407 elapsed=18.0s\n",
-      "    fold 5/5 PR-AUC=0.04529 Recall=0.14706 F1=0.16667 elapsed=18.9s\n",
-      "  -> LogisticRegression trial 5 mean PR-AUC=0.03438 Recall=0.15294 F1=0.11555 FPR=0.00979\n",
-      "[LogisticRegression] CV done elapsed=283.4s\n"
-     ]
+      "cell_type": "markdown",
+      "id": "767564cc",
+      "metadata": {
+        "id": "767564cc"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "CSV의 `event_json` 컬럼을 학습 가능한 feature로 펼칩니다. 정답 컬럼은 별도로 유지하고, 이후 feature 선택 단계에서 유출 가능 컬럼을 제거합니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "      model_name  trial_no  mean_pr_auc  std_pr_auc  mean_roc_auc  \\\n",
-       "0     ExtraTrees         4     0.076091    0.047961      0.647573   \n",
-       "1     ExtraTrees         8     0.071661    0.046476      0.670800   \n",
-       "2     ExtraTrees        12     0.061595    0.035912      0.647986   \n",
-       "3       LightGBM         5     0.061435    0.043454      0.635865   \n",
-       "4     ExtraTrees         6     0.060760    0.043707      0.626227   \n",
-       "5     ExtraTrees        14     0.059835    0.034874      0.633514   \n",
-       "6       LightGBM        14     0.058075    0.047107      0.635845   \n",
-       "7     ExtraTrees        13     0.056089    0.038199      0.651291   \n",
-       "8       LightGBM        12     0.055233    0.046533      0.622553   \n",
-       "9     ExtraTrees         1     0.054918    0.029484      0.644671   \n",
-       "10      LightGBM         4     0.054238    0.043641      0.638072   \n",
-       "11    ExtraTrees         3     0.053812    0.034891      0.643948   \n",
-       "12      LightGBM         2     0.053372    0.036460      0.623080   \n",
-       "13      LightGBM         6     0.051865    0.038493      0.645314   \n",
-       "14      LightGBM         1     0.051047    0.037590      0.637448   \n",
-       "15  RandomForest        14     0.050646    0.028109      0.651639   \n",
-       "16      LightGBM        13     0.049543    0.033146      0.646574   \n",
-       "17      LightGBM         3     0.049320    0.034143      0.627693   \n",
-       "18      LightGBM         9     0.048774    0.035914      0.629020   \n",
-       "19    ExtraTrees         2     0.046631    0.022173      0.632108   \n",
-       "\n",
-       "    std_roc_auc  mean_accuracy  mean_false_positive_rate  mean_precision  \\\n",
-       "0      0.039919       0.970433                  0.025344        0.119684   \n",
-       "1      0.037693       0.952733                  0.043245        0.087831   \n",
-       "2      0.024357       0.985000                  0.010191        0.223146   \n",
-       "3      0.047525       0.975900                  0.019879        0.101028   \n",
-       "4      0.026922       0.953733                  0.042239        0.080404   \n",
-       "5      0.023413       0.953800                  0.042306        0.095049   \n",
-       "6      0.045116       0.967200                  0.028696        0.057561   \n",
-       "7      0.033722       0.951400                  0.044418        0.063167   \n",
-       "8      0.050385       0.959833                  0.036071        0.053337   \n",
-       "9      0.023235       0.958867                  0.037010        0.145893   \n",
-       "10     0.043098       0.947200                  0.049078        0.052814   \n",
-       "11     0.022371       0.972000                  0.023667        0.118529   \n",
-       "12     0.034409       0.958800                  0.037211        0.052149   \n",
-       "13     0.047738       0.966367                  0.029400        0.133721   \n",
-       "14     0.054362       0.947200                  0.049112        0.044530   \n",
-       "15     0.045576       0.957433                  0.038585        0.067259   \n",
-       "16     0.043579       0.945433                  0.050821        0.060910   \n",
-       "17     0.046462       0.928867                  0.067516        0.076085   \n",
-       "18     0.028166       0.964367                  0.031512        0.056697   \n",
-       "19     0.042936       0.970900                  0.024740        0.120895   \n",
-       "\n",
-       "    mean_recall   mean_f1                                             params  \\\n",
-       "0      0.229412  0.117969  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-       "1      0.247059  0.085893  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-       "2      0.141176  0.124673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-       "3      0.235294  0.123713  {'model__learning_rate': 0.031169409812378812,...   \n",
-       "4      0.247059  0.092680  {'model__min_samples_leaf': 3, 'model__max_fea...   \n",
-       "5      0.270588  0.104333  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-       "6      0.247059  0.089393  {'model__learning_rate': 0.03688230536757648, ...   \n",
-       "7      0.217647  0.073932  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-       "8      0.241176  0.079716  {'model__learning_rate': 0.03828385438925741, ...   \n",
-       "9      0.235294  0.098673  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-       "10     0.294118  0.079513  {'model__learning_rate': 0.018840552955192824,...   \n",
-       "11     0.211765  0.092926  {'model__min_samples_leaf': 5, 'model__max_fea...   \n",
-       "12     0.258824  0.082953  {'model__learning_rate': 0.0564638664293258, '...   \n",
-       "13     0.223529  0.109434  {'model__learning_rate': 0.022439365289703053,...   \n",
-       "14     0.300000  0.073458  {'model__learning_rate': 0.021789307659775742,...   \n",
-       "15     0.258824  0.085685  {'model__min_samples_leaf': 10, 'model__max_fe...   \n",
-       "16     0.288235  0.081039  {'model__learning_rate': 0.0128289748714244, '...   \n",
-       "17     0.294118  0.054984  {'model__learning_rate': 0.025815006344207546,...   \n",
-       "18     0.241176  0.086860  {'model__learning_rate': 0.029653419677342325,...   \n",
-       "19     0.205882  0.112978  {'model__min_samples_leaf': 1, 'model__max_fea...   \n",
-       "\n",
-       "                                     selection_reason     tuning_method  \n",
-       "0   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "1   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "2   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "3   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "4   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "5   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "6   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "7   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "8   대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "9   RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "10  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "11  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  \n",
-       "12  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "13  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "14  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "15  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  ParameterSampler  \n",
-       "16  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "17  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "18  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...            Optuna  \n",
-       "19  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  ParameterSampler  "
-      ],
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model_name</th>\n",
-       "      <th>trial_no</th>\n",
-       "      <th>mean_pr_auc</th>\n",
-       "      <th>std_pr_auc</th>\n",
-       "      <th>mean_roc_auc</th>\n",
-       "      <th>std_roc_auc</th>\n",
-       "      <th>mean_accuracy</th>\n",
-       "      <th>mean_false_positive_rate</th>\n",
-       "      <th>mean_precision</th>\n",
-       "      <th>mean_recall</th>\n",
-       "      <th>mean_f1</th>\n",
-       "      <th>params</th>\n",
-       "      <th>selection_reason</th>\n",
-       "      <th>tuning_method</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.076091</td>\n",
-       "      <td>0.047961</td>\n",
-       "      <td>0.647573</td>\n",
-       "      <td>0.039919</td>\n",
-       "      <td>0.970433</td>\n",
-       "      <td>0.025344</td>\n",
-       "      <td>0.119684</td>\n",
-       "      <td>0.229412</td>\n",
-       "      <td>0.117969</td>\n",
-       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.071661</td>\n",
-       "      <td>0.046476</td>\n",
-       "      <td>0.670800</td>\n",
-       "      <td>0.037693</td>\n",
-       "      <td>0.952733</td>\n",
-       "      <td>0.043245</td>\n",
-       "      <td>0.087831</td>\n",
-       "      <td>0.247059</td>\n",
-       "      <td>0.085893</td>\n",
-       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0.061595</td>\n",
-       "      <td>0.035912</td>\n",
-       "      <td>0.647986</td>\n",
-       "      <td>0.024357</td>\n",
-       "      <td>0.985000</td>\n",
-       "      <td>0.010191</td>\n",
-       "      <td>0.223146</td>\n",
-       "      <td>0.141176</td>\n",
-       "      <td>0.124673</td>\n",
-       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.061435</td>\n",
-       "      <td>0.043454</td>\n",
-       "      <td>0.635865</td>\n",
-       "      <td>0.047525</td>\n",
-       "      <td>0.975900</td>\n",
-       "      <td>0.019879</td>\n",
-       "      <td>0.101028</td>\n",
-       "      <td>0.235294</td>\n",
-       "      <td>0.123713</td>\n",
-       "      <td>{'model__learning_rate': 0.031169409812378812,...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.060760</td>\n",
-       "      <td>0.043707</td>\n",
-       "      <td>0.626227</td>\n",
-       "      <td>0.026922</td>\n",
-       "      <td>0.953733</td>\n",
-       "      <td>0.042239</td>\n",
-       "      <td>0.080404</td>\n",
-       "      <td>0.247059</td>\n",
-       "      <td>0.092680</td>\n",
-       "      <td>{'model__min_samples_leaf': 3, 'model__max_fea...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>14</td>\n",
-       "      <td>0.059835</td>\n",
-       "      <td>0.034874</td>\n",
-       "      <td>0.633514</td>\n",
-       "      <td>0.023413</td>\n",
-       "      <td>0.953800</td>\n",
-       "      <td>0.042306</td>\n",
-       "      <td>0.095049</td>\n",
-       "      <td>0.270588</td>\n",
-       "      <td>0.104333</td>\n",
-       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>14</td>\n",
-       "      <td>0.058075</td>\n",
-       "      <td>0.047107</td>\n",
-       "      <td>0.635845</td>\n",
-       "      <td>0.045116</td>\n",
-       "      <td>0.967200</td>\n",
-       "      <td>0.028696</td>\n",
-       "      <td>0.057561</td>\n",
-       "      <td>0.247059</td>\n",
-       "      <td>0.089393</td>\n",
-       "      <td>{'model__learning_rate': 0.03688230536757648, ...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0.056089</td>\n",
-       "      <td>0.038199</td>\n",
-       "      <td>0.651291</td>\n",
-       "      <td>0.033722</td>\n",
-       "      <td>0.951400</td>\n",
-       "      <td>0.044418</td>\n",
-       "      <td>0.063167</td>\n",
-       "      <td>0.217647</td>\n",
-       "      <td>0.073932</td>\n",
-       "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0.055233</td>\n",
-       "      <td>0.046533</td>\n",
-       "      <td>0.622553</td>\n",
-       "      <td>0.050385</td>\n",
-       "      <td>0.959833</td>\n",
-       "      <td>0.036071</td>\n",
-       "      <td>0.053337</td>\n",
-       "      <td>0.241176</td>\n",
-       "      <td>0.079716</td>\n",
-       "      <td>{'model__learning_rate': 0.03828385438925741, ...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.054918</td>\n",
-       "      <td>0.029484</td>\n",
-       "      <td>0.644671</td>\n",
-       "      <td>0.023235</td>\n",
-       "      <td>0.958867</td>\n",
-       "      <td>0.037010</td>\n",
-       "      <td>0.145893</td>\n",
-       "      <td>0.235294</td>\n",
-       "      <td>0.098673</td>\n",
-       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.054238</td>\n",
-       "      <td>0.043641</td>\n",
-       "      <td>0.638072</td>\n",
-       "      <td>0.043098</td>\n",
-       "      <td>0.947200</td>\n",
-       "      <td>0.049078</td>\n",
-       "      <td>0.052814</td>\n",
-       "      <td>0.294118</td>\n",
-       "      <td>0.079513</td>\n",
-       "      <td>{'model__learning_rate': 0.018840552955192824,...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.053812</td>\n",
-       "      <td>0.034891</td>\n",
-       "      <td>0.643948</td>\n",
-       "      <td>0.022371</td>\n",
-       "      <td>0.972000</td>\n",
-       "      <td>0.023667</td>\n",
-       "      <td>0.118529</td>\n",
-       "      <td>0.211765</td>\n",
-       "      <td>0.092926</td>\n",
-       "      <td>{'model__min_samples_leaf': 5, 'model__max_fea...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.053372</td>\n",
-       "      <td>0.036460</td>\n",
-       "      <td>0.623080</td>\n",
-       "      <td>0.034409</td>\n",
-       "      <td>0.958800</td>\n",
-       "      <td>0.037211</td>\n",
-       "      <td>0.052149</td>\n",
-       "      <td>0.258824</td>\n",
-       "      <td>0.082953</td>\n",
-       "      <td>{'model__learning_rate': 0.0564638664293258, '...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.051865</td>\n",
-       "      <td>0.038493</td>\n",
-       "      <td>0.645314</td>\n",
-       "      <td>0.047738</td>\n",
-       "      <td>0.966367</td>\n",
-       "      <td>0.029400</td>\n",
-       "      <td>0.133721</td>\n",
-       "      <td>0.223529</td>\n",
-       "      <td>0.109434</td>\n",
-       "      <td>{'model__learning_rate': 0.022439365289703053,...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.051047</td>\n",
-       "      <td>0.037590</td>\n",
-       "      <td>0.637448</td>\n",
-       "      <td>0.054362</td>\n",
-       "      <td>0.947200</td>\n",
-       "      <td>0.049112</td>\n",
-       "      <td>0.044530</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.073458</td>\n",
-       "      <td>{'model__learning_rate': 0.021789307659775742,...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>14</td>\n",
-       "      <td>0.050646</td>\n",
-       "      <td>0.028109</td>\n",
-       "      <td>0.651639</td>\n",
-       "      <td>0.045576</td>\n",
-       "      <td>0.957433</td>\n",
-       "      <td>0.038585</td>\n",
-       "      <td>0.067259</td>\n",
-       "      <td>0.258824</td>\n",
-       "      <td>0.085685</td>\n",
-       "      <td>{'model__min_samples_leaf': 10, 'model__max_fe...</td>\n",
-       "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0.049543</td>\n",
-       "      <td>0.033146</td>\n",
-       "      <td>0.646574</td>\n",
-       "      <td>0.043579</td>\n",
-       "      <td>0.945433</td>\n",
-       "      <td>0.050821</td>\n",
-       "      <td>0.060910</td>\n",
-       "      <td>0.288235</td>\n",
-       "      <td>0.081039</td>\n",
-       "      <td>{'model__learning_rate': 0.0128289748714244, '...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.049320</td>\n",
-       "      <td>0.034143</td>\n",
-       "      <td>0.627693</td>\n",
-       "      <td>0.046462</td>\n",
-       "      <td>0.928867</td>\n",
-       "      <td>0.067516</td>\n",
-       "      <td>0.076085</td>\n",
-       "      <td>0.294118</td>\n",
-       "      <td>0.054984</td>\n",
-       "      <td>{'model__learning_rate': 0.025815006344207546,...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0.048774</td>\n",
-       "      <td>0.035914</td>\n",
-       "      <td>0.629020</td>\n",
-       "      <td>0.028166</td>\n",
-       "      <td>0.964367</td>\n",
-       "      <td>0.031512</td>\n",
-       "      <td>0.056697</td>\n",
-       "      <td>0.241176</td>\n",
-       "      <td>0.086860</td>\n",
-       "      <td>{'model__learning_rate': 0.029653419677342325,...</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>Optuna</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.046631</td>\n",
-       "      <td>0.022173</td>\n",
-       "      <td>0.632108</td>\n",
-       "      <td>0.042936</td>\n",
-       "      <td>0.970900</td>\n",
-       "      <td>0.024740</td>\n",
-       "      <td>0.120895</td>\n",
-       "      <td>0.205882</td>\n",
-       "      <td>0.112978</td>\n",
-       "      <td>{'model__min_samples_leaf': 1, 'model__max_fea...</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>ParameterSampler</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
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-       "    [theme=dark] .colab-df-convert:hover {\n",
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-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-03e4c761-567f-41e7-ad56-2ecca75a7cbb');\n",
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-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
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-       "        element.appendChild(docLink);\n",
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-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "a971a223",
+      "metadata": {
+        "id": "a971a223",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 606
+        },
+        "outputId": "121e6bb0-0b97-4091-e142-949ea2ff1f07"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(38400, 59) (9600, 59)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "defect_yn            0         1\n",
+              "process_code                    \n",
+              "ASSEMBLY      0.793438  0.206563\n",
+              "BODY          0.784479  0.215521\n",
+              "PAINT         0.779792  0.220208\n",
+              "PRESS         0.816042  0.183958"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-16bb1ad7-991d-4929-88b9-947f82588ec0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>defect_yn</th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>process_code</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>ASSEMBLY</th>\n",
+              "      <td>0.793438</td>\n",
+              "      <td>0.206563</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BODY</th>\n",
+              "      <td>0.784479</td>\n",
+              "      <td>0.215521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PAINT</th>\n",
+              "      <td>0.779792</td>\n",
+              "      <td>0.220208</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PRESS</th>\n",
+              "      <td>0.816042</td>\n",
+              "      <td>0.183958</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "    }\n",
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+              "      background-color: #E8F0FE;\n",
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+              "      background-color: #E2EBFA;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-16bb1ad7-991d-4929-88b9-947f82588ec0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-16bb1ad7-991d-4929-88b9-947f82588ec0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(defect_train\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"BODY\",\n          \"PRESS\",\n          \"ASSEMBLY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016097710542876044,\n        \"min\": 0.7797916666666667,\n        \"max\": 0.8160416666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.7844791666666666,\n          0.8160416666666667,\n          0.7934375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016097710542876034,\n        \"min\": 0.18395833333333333,\n        \"max\": 0.22020833333333334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.21552083333333333,\n          0.18395833333333333,\n          0.2065625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   raw_event_id             event_id           event_time  car_master_id  \\\n",
+              "0             1  EVT-20260616-000001  2026-06-16T10:00:00              1   \n",
+              "1             2  EVT-20260616-000002  2026-06-16T10:00:03              1   \n",
+              "2             3  EVT-20260616-000003  2026-06-16T10:00:06              1   \n",
+              "3             4  EVT-20260616-000004  2026-06-16T10:00:09              1   \n",
+              "4             5  EVT-20260616-000005  2026-06-16T10:00:12              2   \n",
+              "\n",
+              "  process_code         station_code   equipment_code   equipment_type  \\\n",
+              "0        PRESS     PRESS_STATION_01     EQ_PRESS_001  HYDRAULIC_PRESS   \n",
+              "1         BODY      BODY_STATION_03      EQ_BODY_003        ROBOT_ARM   \n",
+              "2        PAINT     PAINT_STATION_04     EQ_PAINT_004           CAMERA   \n",
+              "3     ASSEMBLY  ASSEMBLY_STATION_04  EQ_ASSEMBLY_004         CONVEYOR   \n",
+              "4        PRESS     PRESS_STATION_04     EQ_PRESS_004  HYDRAULIC_PRESS   \n",
+              "\n",
+              "  operation_status  current_rms_ampere  current_max_ampere  \\\n",
+              "0          RUNNING            1.731425            1.895833   \n",
+              "1          RUNNING            2.068431            2.237299   \n",
+              "2          RUNNING            1.648970            1.799303   \n",
+              "3          RUNNING            1.931789            2.104463   \n",
+              "4          RUNNING            1.724910            1.876135   \n",
+              "\n",
+              "   current_min_ampere  vibration_acceleration_g  vibration_score  \\\n",
+              "0            1.567017                  0.004600         0.340920   \n",
+              "1            1.899562                  0.006392         0.369143   \n",
+              "2            1.498636                  0.001800         0.333915   \n",
+              "3            1.759116                  0.000400         0.361150   \n",
+              "4            1.573685                  0.001000         0.321575   \n",
+              "\n",
+              "   vibration_rms  vibration_peak robot_axis  robot_frequency_hz  \\\n",
+              "0       1.038794        2.326837         J2             115.002   \n",
+              "1       1.125161        2.698745         J3             121.211   \n",
+              "2       1.038794        2.326837         J4             113.461   \n",
+              "3       1.038794        2.326837         J5             119.453   \n",
+              "4       1.038794        2.326837         J6             110.746   \n",
+              "\n",
+              "   robot_amplitude  robot_vibration_score  thermal_score  avg_temperature  \\\n",
+              "0         0.001039               0.340920         50.922           50.922   \n",
+              "1         0.001125               0.369143         51.042           51.042   \n",
+              "2         0.001039               0.333915         51.621           51.621   \n",
+              "3         0.001039               0.361150         51.392           51.392   \n",
+              "4         0.001039               0.321575         51.121           51.121   \n",
+              "\n",
+              "   max_temperature  min_temperature  cycle_time_sec  waiting_time_sec  \\\n",
+              "0           54.131           42.190          47.833               5.0   \n",
+              "1           54.491           42.534          56.998               7.0   \n",
+              "2           54.021           42.860          69.326               9.0   \n",
+              "3           53.519           43.202          59.200               8.0   \n",
+              "4           53.927           42.551          44.494               6.0   \n",
+              "\n",
+              "   processing_time_sec  station_delay_sec  throughput_per_min  queue_length  \\\n",
+              "0               42.833              7.833               1.254           6.0   \n",
+              "1               50.998              4.998               1.053           6.0   \n",
+              "2               62.326              5.326               0.865           7.0   \n",
+              "3               51.200              1.200               1.014           6.0   \n",
+              "4               39.494              4.494               1.348           9.0   \n",
+              "\n",
+              "   wip_count  equipment_idle_time_sec  ford_row_id  forming_row_id  \\\n",
+              "0       19.0                   12.534            1        55644626   \n",
+              "1       18.0                    7.996            1        55644626   \n",
+              "2       19.0                    8.521            1        55644626   \n",
+              "3       17.0                    1.920            1        55644626   \n",
+              "4       20.0                    7.191            1        55644630   \n",
+              "\n",
+              "   robot_arm_vibration_row_id  machine_vision_row_id  bosch_id  \\\n",
+              "0                           1                      1         4   \n",
+              "1                           2                      2         6   \n",
+              "2                           3                      3         7   \n",
+              "3                           4                      4         9   \n",
+              "4                           5                      5        11   \n",
+              "\n",
+              "   press_count_increase_yn  press_target_cycle_time_sec  \\\n",
+              "0                        1                         40.0   \n",
+              "1                        0                          NaN   \n",
+              "2                        0                          NaN   \n",
+              "3                        0                          NaN   \n",
+              "4                        1                         40.0   \n",
+              "\n",
+              "   press_timestamp_delay_sec body_robot_motion_status  \\\n",
+              "0                      7.833                      NaN   \n",
+              "1                        NaN                  WARNING   \n",
+              "2                        NaN                      NaN   \n",
+              "3                        NaN                      NaN   \n",
+              "4                      4.494                      NaN   \n",
+              "\n",
+              "  body_robot_operation_mode body_frequency_peak_band  body_frequency_band_max  \\\n",
+              "0                       NaN                      NaN                      NaN   \n",
+              "1                      AUTO             1201_1300_HZ                 0.002139   \n",
+              "2                       NaN                      NaN                      NaN   \n",
+              "3                       NaN                      NaN                      NaN   \n",
+              "4                       NaN                      NaN                      NaN   \n",
+              "\n",
+              "   body_frequency_band_mean paint_image_position  paint_thermal_std_temp  \\\n",
+              "0                       NaN                  NaN                     NaN   \n",
+              "1                  0.001434                  NaN                     NaN   \n",
+              "2                       NaN                 LEFT                   2.988   \n",
+              "3                       NaN                  NaN                     NaN   \n",
+              "4                       NaN                  NaN                     NaN   \n",
+              "\n",
+              "   paint_thickness_value  paint_defect_score paint_vision_label  \\\n",
+              "0                    NaN                 NaN                NaN   \n",
+              "1                    NaN                 NaN                NaN   \n",
+              "2                 118.12                0.48             DEFECT   \n",
+              "3                    NaN                 NaN                NaN   \n",
+              "4                    NaN                 NaN                NaN   \n",
+              "\n",
+              "   paint_surface_quality_score assembly_expected_sequence  \\\n",
+              "0                          NaN                        NaN   \n",
+              "1                          NaN                        NaN   \n",
+              "2                       84.958                        NaN   \n",
+              "3                          NaN            A01>A02>A03>A04   \n",
+              "4                          NaN                        NaN   \n",
+              "\n",
+              "  assembly_actual_sequence  assembly_missing_part_count  \\\n",
+              "0                      NaN                            0   \n",
+              "1                      NaN                            0   \n",
+              "2                      NaN                            0   \n",
+              "3          A01>A02>A03>A04                            0   \n",
+              "4                      NaN                            0   \n",
+              "\n",
+              "   assembly_fastening_error_count  assembly_sequence_error_count  \\\n",
+              "0                               0                              0   \n",
+              "1                               0                              0   \n",
+              "2                               0                              0   \n",
+              "3                               0                              0   \n",
+              "4                               0                              0   \n",
+              "\n",
+              "   assembly_sequence_mismatch_yn  defect_yn            defect_reason  \n",
+              "0                              0          0                   normal  \n",
+              "1                              0          1     body_robot_vibration  \n",
+              "2                              0          1  paint_vision_or_thermal  \n",
+              "3                              0          0                   normal  \n",
+              "4                              0          0                   normal  "
+            ],
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>raw_event_id</th>\n",
+              "      <th>event_id</th>\n",
+              "      <th>event_time</th>\n",
+              "      <th>car_master_id</th>\n",
+              "      <th>process_code</th>\n",
+              "      <th>station_code</th>\n",
+              "      <th>equipment_code</th>\n",
+              "      <th>equipment_type</th>\n",
+              "      <th>operation_status</th>\n",
+              "      <th>current_rms_ampere</th>\n",
+              "      <th>current_max_ampere</th>\n",
+              "      <th>current_min_ampere</th>\n",
+              "      <th>vibration_acceleration_g</th>\n",
+              "      <th>vibration_score</th>\n",
+              "      <th>vibration_rms</th>\n",
+              "      <th>vibration_peak</th>\n",
+              "      <th>robot_axis</th>\n",
+              "      <th>robot_frequency_hz</th>\n",
+              "      <th>robot_amplitude</th>\n",
+              "      <th>robot_vibration_score</th>\n",
+              "      <th>thermal_score</th>\n",
+              "      <th>avg_temperature</th>\n",
+              "      <th>max_temperature</th>\n",
+              "      <th>min_temperature</th>\n",
+              "      <th>cycle_time_sec</th>\n",
+              "      <th>waiting_time_sec</th>\n",
+              "      <th>processing_time_sec</th>\n",
+              "      <th>station_delay_sec</th>\n",
+              "      <th>throughput_per_min</th>\n",
+              "      <th>queue_length</th>\n",
+              "      <th>wip_count</th>\n",
+              "      <th>equipment_idle_time_sec</th>\n",
+              "      <th>ford_row_id</th>\n",
+              "      <th>forming_row_id</th>\n",
+              "      <th>robot_arm_vibration_row_id</th>\n",
+              "      <th>machine_vision_row_id</th>\n",
+              "      <th>bosch_id</th>\n",
+              "      <th>press_count_increase_yn</th>\n",
+              "      <th>press_target_cycle_time_sec</th>\n",
+              "      <th>press_timestamp_delay_sec</th>\n",
+              "      <th>body_robot_motion_status</th>\n",
+              "      <th>body_robot_operation_mode</th>\n",
+              "      <th>body_frequency_peak_band</th>\n",
+              "      <th>body_frequency_band_max</th>\n",
+              "      <th>body_frequency_band_mean</th>\n",
+              "      <th>paint_image_position</th>\n",
+              "      <th>paint_thermal_std_temp</th>\n",
+              "      <th>paint_thickness_value</th>\n",
+              "      <th>paint_defect_score</th>\n",
+              "      <th>paint_vision_label</th>\n",
+              "      <th>paint_surface_quality_score</th>\n",
+              "      <th>assembly_expected_sequence</th>\n",
+              "      <th>assembly_actual_sequence</th>\n",
+              "      <th>assembly_missing_part_count</th>\n",
+              "      <th>assembly_fastening_error_count</th>\n",
+              "      <th>assembly_sequence_error_count</th>\n",
+              "      <th>assembly_sequence_mismatch_yn</th>\n",
+              "      <th>defect_yn</th>\n",
+              "      <th>defect_reason</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>EVT-20260616-000001</td>\n",
+              "      <td>2026-06-16T10:00:00</td>\n",
+              "      <td>1</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>PRESS_STATION_01</td>\n",
+              "      <td>EQ_PRESS_001</td>\n",
+              "      <td>HYDRAULIC_PRESS</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>1.731425</td>\n",
+              "      <td>1.895833</td>\n",
+              "      <td>1.567017</td>\n",
+              "      <td>0.004600</td>\n",
+              "      <td>0.340920</td>\n",
+              "      <td>1.038794</td>\n",
+              "      <td>2.326837</td>\n",
+              "      <td>J2</td>\n",
+              "      <td>115.002</td>\n",
+              "      <td>0.001039</td>\n",
+              "      <td>0.340920</td>\n",
+              "      <td>50.922</td>\n",
+              "      <td>50.922</td>\n",
+              "      <td>54.131</td>\n",
+              "      <td>42.190</td>\n",
+              "      <td>47.833</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>42.833</td>\n",
+              "      <td>7.833</td>\n",
+              "      <td>1.254</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>12.534</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55644626</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>7.833</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>EVT-20260616-000002</td>\n",
+              "      <td>2026-06-16T10:00:03</td>\n",
+              "      <td>1</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>BODY_STATION_03</td>\n",
+              "      <td>EQ_BODY_003</td>\n",
+              "      <td>ROBOT_ARM</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>2.068431</td>\n",
+              "      <td>2.237299</td>\n",
+              "      <td>1.899562</td>\n",
+              "      <td>0.006392</td>\n",
+              "      <td>0.369143</td>\n",
+              "      <td>1.125161</td>\n",
+              "      <td>2.698745</td>\n",
+              "      <td>J3</td>\n",
+              "      <td>121.211</td>\n",
+              "      <td>0.001125</td>\n",
+              "      <td>0.369143</td>\n",
+              "      <td>51.042</td>\n",
+              "      <td>51.042</td>\n",
+              "      <td>54.491</td>\n",
+              "      <td>42.534</td>\n",
+              "      <td>56.998</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>50.998</td>\n",
+              "      <td>4.998</td>\n",
+              "      <td>1.053</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>7.996</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55644626</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>WARNING</td>\n",
+              "      <td>AUTO</td>\n",
+              "      <td>1201_1300_HZ</td>\n",
+              "      <td>0.002139</td>\n",
+              "      <td>0.001434</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>body_robot_vibration</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>EVT-20260616-000003</td>\n",
+              "      <td>2026-06-16T10:00:06</td>\n",
+              "      <td>1</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>PAINT_STATION_04</td>\n",
+              "      <td>EQ_PAINT_004</td>\n",
+              "      <td>CAMERA</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>1.648970</td>\n",
+              "      <td>1.799303</td>\n",
+              "      <td>1.498636</td>\n",
+              "      <td>0.001800</td>\n",
+              "      <td>0.333915</td>\n",
+              "      <td>1.038794</td>\n",
+              "      <td>2.326837</td>\n",
+              "      <td>J4</td>\n",
+              "      <td>113.461</td>\n",
+              "      <td>0.001039</td>\n",
+              "      <td>0.333915</td>\n",
+              "      <td>51.621</td>\n",
+              "      <td>51.621</td>\n",
+              "      <td>54.021</td>\n",
+              "      <td>42.860</td>\n",
+              "      <td>69.326</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>62.326</td>\n",
+              "      <td>5.326</td>\n",
+              "      <td>0.865</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>8.521</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55644626</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>LEFT</td>\n",
+              "      <td>2.988</td>\n",
+              "      <td>118.12</td>\n",
+              "      <td>0.48</td>\n",
+              "      <td>DEFECT</td>\n",
+              "      <td>84.958</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>paint_vision_or_thermal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>EVT-20260616-000004</td>\n",
+              "      <td>2026-06-16T10:00:09</td>\n",
+              "      <td>1</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>ASSEMBLY_STATION_04</td>\n",
+              "      <td>EQ_ASSEMBLY_004</td>\n",
+              "      <td>CONVEYOR</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>1.931789</td>\n",
+              "      <td>2.104463</td>\n",
+              "      <td>1.759116</td>\n",
+              "      <td>0.000400</td>\n",
+              "      <td>0.361150</td>\n",
+              "      <td>1.038794</td>\n",
+              "      <td>2.326837</td>\n",
+              "      <td>J5</td>\n",
+              "      <td>119.453</td>\n",
+              "      <td>0.001039</td>\n",
+              "      <td>0.361150</td>\n",
+              "      <td>51.392</td>\n",
+              "      <td>51.392</td>\n",
+              "      <td>53.519</td>\n",
+              "      <td>43.202</td>\n",
+              "      <td>59.200</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>51.200</td>\n",
+              "      <td>1.200</td>\n",
+              "      <td>1.014</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.920</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55644626</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>9</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>A01&gt;A02&gt;A03&gt;A04</td>\n",
+              "      <td>A01&gt;A02&gt;A03&gt;A04</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>EVT-20260616-000005</td>\n",
+              "      <td>2026-06-16T10:00:12</td>\n",
+              "      <td>2</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>PRESS_STATION_04</td>\n",
+              "      <td>EQ_PRESS_004</td>\n",
+              "      <td>HYDRAULIC_PRESS</td>\n",
+              "      <td>RUNNING</td>\n",
+              "      <td>1.724910</td>\n",
+              "      <td>1.876135</td>\n",
+              "      <td>1.573685</td>\n",
+              "      <td>0.001000</td>\n",
+              "      <td>0.321575</td>\n",
+              "      <td>1.038794</td>\n",
+              "      <td>2.326837</td>\n",
+              "      <td>J6</td>\n",
+              "      <td>110.746</td>\n",
+              "      <td>0.001039</td>\n",
+              "      <td>0.321575</td>\n",
+              "      <td>51.121</td>\n",
+              "      <td>51.121</td>\n",
+              "      <td>53.927</td>\n",
+              "      <td>42.551</td>\n",
+              "      <td>44.494</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>39.494</td>\n",
+              "      <td>4.494</td>\n",
+              "      <td>1.348</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>7.191</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55644630</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>4.494</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab30039d-2768-4302-bb21-6138fd2d5213')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ab30039d-2768-4302-bb21-6138fd2d5213 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ab30039d-2768-4302-bb21-6138fd2d5213');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "  </div>\n",
+              "\n",
+              "\n",
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+            ],
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-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"print(\\\"CV best model:\\\", best_model_name, best_params)\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trial_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 14,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          14,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007481168730885,\n        \"min\": 0.04663096330797513,\n        \"max\": 0.07609114742802507,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.07609114742802507,\n          0.049319566390547076,\n          0.05064611316582477\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006924597647087308,\n        \"min\": 0.022173359277985142,\n        \"max\": 0.047961037002806066,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.047961037002806066,\n          0.034143129310785794,\n          0.028108939775837834\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011785359877674395,\n        \"min\": 0.6225532921851274,\n        \"max\": 0.6707997278696929,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.647572518782907,\n          0.6276932026582003,\n          0.6516386977184438\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010216806702914104,\n        \"min\": 0.022370910369156588,\n        \"max\": 0.05436158460450469,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.0399185136411262,\n          0.04646163650317234,\n          0.04557575424204216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012723848768567607,\n        \"min\": 0.9288666666666667,\n        \"max\": 0.985,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.9704333333333333,\n          0.9538,\n          0.9719999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012970570034246781,\n        \"min\": 0.010191082802547772,\n        \"max\": 0.067515923566879,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.025343613811599063,\n          0.067515923566879,\n          0.038585316795172644\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04371952737029428,\n        \"min\": 0.04452964231174254,\n        \"max\": 0.22314564683863908,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.11968397122658556,\n          0.07608503611061139,\n          0.06725864685167784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03688247391608683,\n        \"min\": 0.1411764705882353,\n        \"max\": 0.3,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.25882352941176473,\n          0.3,\n          0.22941176470588234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018271806088460737,\n        \"min\": 0.05498448429507783,\n        \"max\": 0.12467258585740837,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.11796883594415183,\n          0.05498448429507783,\n          0.08568507388916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selection_reason\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"RandomForest\\ubcf4\\ub2e4 \\ubd84\\ud560 \\ubb34\\uc791\\uc704\\uc131\\uc774 \\ucee4\\uc11c \\uace0\\ucc28\\uc6d0 \\uc13c\\uc11c feature\\uc758 \\uac15\\uac74\\uc131 \\ube44\\uad50\",\n          \"\\ub300\\uc6a9\\ub7c9 tabular \\uc81c\\uc870 \\ub370\\uc774\\ud130\\uc5d0\\uc11c \\ube44\\uc120\\ud615 \\uc13c\\uc11c/\\uc2dc\\uac04 \\ud328\\ud134\\uc744 \\ube60\\ub974\\uac8c \\ud559\\uc2b5\\ud558\\ub294 \\uae30\\uc900 \\ubaa8\\ub378. Optuna\\uc640 SMOTE\\ub85c \\ud76c\\uc18c \\ubd88\\ub7c9 recall\\uc744 \\ubcf4\\uac15\",\n          \"bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tuning_method\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Optuna\",\n          \"ParameterSampler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
+      "source": [
+        "PROCESS_ORDER = [\"PRESS\", \"BODY\", \"PAINT\", \"ASSEMBLY\"]\n",
+        "TRANSFER_FLOW_ORDER = [(\"PRESS\", \"BODY\"), (\"BODY\", \"PAINT\"), (\"PAINT\", \"ASSEMBLY\")]\n",
+        "\n",
+        "\n",
+        "def get_nested(obj: dict, path: str, default=np.nan):\n",
+        "    cur = obj\n",
+        "    for key in path.split(\".\"):\n",
+        "        if not isinstance(cur, dict) or key not in cur:\n",
+        "            return default\n",
+        "        cur = cur[key]\n",
+        "    return cur\n",
+        "\n",
+        "\n",
+        "def to_float(value, default=np.nan):\n",
+        "    try:\n",
+        "        if value is None or value == \"\":\n",
+        "            return default\n",
+        "        return float(value)\n",
+        "    except Exception:\n",
+        "        return default\n",
+        "\n",
+        "\n",
+        "def to_int(value, default=0):\n",
+        "    try:\n",
+        "        if value is None or value == \"\":\n",
+        "            return default\n",
+        "        return int(float(value))\n",
+        "    except Exception:\n",
+        "        return default\n",
+        "\n",
+        "\n",
+        "def sequence_mismatch(expected, actual):\n",
+        "    if pd.isna(expected) or pd.isna(actual):\n",
+        "        return 0\n",
+        "    return int(str(expected).strip() != str(actual).strip())\n",
+        "\n",
+        "\n",
+        "def flatten_event_row(row: pd.Series) -> dict:\n",
+        "    payload = json.loads(row[\"event_json\"])\n",
+        "    sensor = payload.get(\"sensor\", {}) or {}\n",
+        "    current = sensor.get(\"current\", {}) or {}\n",
+        "    vibration = sensor.get(\"vibration\", {}) or {}\n",
+        "    robot = sensor.get(\"robotArmVibration\", {}) or {}\n",
+        "    thermal = sensor.get(\"thermal\", {}) or {}\n",
+        "    metrics = payload.get(\"processMetrics\", {}) or {}\n",
+        "    equipment = payload.get(\"equipment\", {}) or {}\n",
+        "    status = payload.get(\"equipmentStatus\", {}) or {}\n",
+        "    product = payload.get(\"product\", {}) or {}\n",
+        "    trace = payload.get(\"sourceTrace\", {}) or {}\n",
+        "    process_data = payload.get(\"processData\", {}) or {}\n",
+        "    press = process_data.get(\"press\", {}) or {}\n",
+        "    body = process_data.get(\"body\", {}) or {}\n",
+        "    paint = process_data.get(\"paint\", {}) or {}\n",
+        "    assembly = process_data.get(\"assembly\", {}) or {}\n",
+        "    bands = body.get(\"frequencyBands\", {}) or {}\n",
+        "    band_values = [to_float(v) for v in bands.values()]\n",
+        "    expected_sequence = assembly.get(\"expectedSequence\", np.nan)\n",
+        "    actual_sequence = assembly.get(\"actualSequence\", np.nan)\n",
+        "    return {\n",
+        "        \"raw_event_id\": row.get(\"raw_event_id\"),\n",
+        "        \"event_id\": row.get(\"event_id\"),\n",
+        "        \"event_time\": row.get(\"event_time\"),\n",
+        "        \"car_master_id\": product.get(\"carMasterId\", row.get(\"car_master_id_raw\")),\n",
+        "        \"process_code\": row.get(\"process_code_raw\"),\n",
+        "        \"station_code\": row.get(\"station_code_raw\"),\n",
+        "        \"equipment_code\": equipment.get(\"equipmentCode\", row.get(\"equipment_code_raw\")),\n",
+        "        \"equipment_type\": equipment.get(\"equipmentType\", row.get(\"equipment_type_raw\")),\n",
+        "        \"operation_status\": status.get(\"operationStatus\", row.get(\"operation_status_raw\")),\n",
+        "        \"current_rms_ampere\": to_float(current.get(\"rmsAmpere\")),\n",
+        "        \"current_max_ampere\": to_float(current.get(\"maxAmpere\")),\n",
+        "        \"current_min_ampere\": to_float(current.get(\"minAmpere\")),\n",
+        "        \"vibration_acceleration_g\": to_float(vibration.get(\"accelerationG\")),\n",
+        "        \"vibration_score\": to_float(vibration.get(\"vibrationScore\")),\n",
+        "        \"vibration_rms\": to_float(vibration.get(\"vibrationRms\")),\n",
+        "        \"vibration_peak\": to_float(vibration.get(\"vibrationPeak\")),\n",
+        "        \"robot_axis\": robot.get(\"axis\", np.nan),\n",
+        "        \"robot_frequency_hz\": to_float(robot.get(\"frequencyHz\")),\n",
+        "        \"robot_amplitude\": to_float(robot.get(\"amplitude\")),\n",
+        "        \"robot_vibration_score\": to_float(robot.get(\"vibrationScore\")),\n",
+        "        \"thermal_score\": to_float(thermal.get(\"thermalScore\")),\n",
+        "        \"avg_temperature\": to_float(thermal.get(\"avgTemperature\")),\n",
+        "        \"max_temperature\": to_float(thermal.get(\"maxTemperature\")),\n",
+        "        \"min_temperature\": to_float(thermal.get(\"minTemperature\")),\n",
+        "        \"cycle_time_sec\": to_float(metrics.get(\"cycleTimeSec\")),\n",
+        "        \"waiting_time_sec\": to_float(metrics.get(\"waitingTimeSec\")),\n",
+        "        \"processing_time_sec\": to_float(metrics.get(\"processingTimeSec\")),\n",
+        "        \"station_delay_sec\": to_float(metrics.get(\"stationDelaySec\")),\n",
+        "        \"throughput_per_min\": to_float(metrics.get(\"throughputPerMin\")),\n",
+        "        \"queue_length\": to_float(metrics.get(\"queueLength\")),\n",
+        "        \"wip_count\": to_float(metrics.get(\"wipCount\")),\n",
+        "        \"equipment_idle_time_sec\": to_float(metrics.get(\"equipmentIdleTimeSec\")),\n",
+        "        \"ford_row_id\": to_int(trace.get(\"fordRowId\")),\n",
+        "        \"forming_row_id\": to_int(trace.get(\"formingRowId\")),\n",
+        "        \"robot_arm_vibration_row_id\": to_int(trace.get(\"robotArmVibrationRowId\")),\n",
+        "        \"machine_vision_row_id\": to_int(trace.get(\"machineVisionRowId\")),\n",
+        "        \"bosch_id\": to_int(trace.get(\"boschId\")),\n",
+        "        \"press_count_increase_yn\": int(bool(press.get(\"countIncreaseYn\", False))),\n",
+        "        \"press_target_cycle_time_sec\": to_float(press.get(\"targetCycleTimeSec\")),\n",
+        "        \"press_timestamp_delay_sec\": to_float(press.get(\"timestampDelaySec\")),\n",
+        "        \"body_robot_motion_status\": body.get(\"robotMotionStatus\", np.nan),\n",
+        "        \"body_robot_operation_mode\": body.get(\"robotOperationMode\", np.nan),\n",
+        "        \"body_frequency_peak_band\": body.get(\"frequencyPeakBand\", np.nan),\n",
+        "        \"body_frequency_band_max\": float(np.nanmax(band_values)) if band_values else np.nan,\n",
+        "        \"body_frequency_band_mean\": float(np.nanmean(band_values)) if band_values else np.nan,\n",
+        "        \"paint_image_position\": paint.get(\"imagePosition\", np.nan),\n",
+        "        \"paint_thermal_std_temp\": to_float(paint.get(\"thermalStdTemp\")),\n",
+        "        \"paint_thickness_value\": to_float(paint.get(\"thicknessValue\")),\n",
+        "        \"paint_defect_score\": to_float(paint.get(\"defectScore\")),\n",
+        "        \"paint_vision_label\": paint.get(\"visionLabel\", np.nan),\n",
+        "        \"paint_surface_quality_score\": to_float(paint.get(\"surfaceQualityScore\")),\n",
+        "        \"assembly_expected_sequence\": expected_sequence,\n",
+        "        \"assembly_actual_sequence\": actual_sequence,\n",
+        "        \"assembly_missing_part_count\": to_int(assembly.get(\"missingPartCount\")),\n",
+        "        \"assembly_fastening_error_count\": to_int(assembly.get(\"fasteningErrorCount\")),\n",
+        "        \"assembly_sequence_error_count\": to_int(assembly.get(\"sequenceErrorCount\")),\n",
+        "        \"assembly_sequence_mismatch_yn\": sequence_mismatch(expected_sequence, actual_sequence),\n",
+        "        \"defect_yn\": int(row[\"defect_yn\"]),\n",
+        "        \"defect_reason\": row.get(\"defect_reason\", \"\"),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def flatten_event_df(raw_df: pd.DataFrame) -> pd.DataFrame:\n",
+        "    return pd.DataFrame([flatten_event_row(row) for _, row in raw_df.iterrows()])\n",
+        "\n",
+        "defect_train = flatten_event_df(defect_train_raw)\n",
+        "defect_test = flatten_event_df(defect_test_raw)\n",
+        "print(defect_train.shape, defect_test.shape)\n",
+        "display(pd.crosstab(defect_train[\"process_code\"], defect_train[\"defect_yn\"], normalize=\"index\"))\n",
+        "display(defect_train.head())"
+      ]
     },
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "CV 결과 저장: /content/defect_transfer_outputs/defect_model_cv_results.csv\n",
-      "CV fold 결과 저장: /content/defect_transfer_outputs/defect_model_cv_fold_results.csv\n",
-      "CV best model: ExtraTrees {'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 평가 함수\n",
-    "def find_recall_priority_threshold(\n",
-    "    y_true: pd.Series,\n",
-    "    proba: np.ndarray,\n",
-    "    *,\n",
-    "    min_recall: float = RECALL_PRIORITY_TARGET,\n",
-    "    min_precision: float = RECALL_PRIORITY_MIN_PRECISION,\n",
-    "    beta: float = THRESHOLD_BETA,\n",
-    ") -> float:\n",
-    "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
-    "    if len(thresholds) == 0:\n",
-    "        return 0.5\n",
-    "\n",
-    "    precision = precision[:-1]\n",
-    "    recall = recall[:-1]\n",
-    "    thresholds = thresholds.astype(float)\n",
-    "    valid = (recall >= min_recall) & (precision >= min_precision)\n",
-    "\n",
-    "    if valid.any():\n",
-    "        # Recall 목표를 만족하는 후보 중 Precision이 가장 좋은 threshold를 선택합니다.\n",
-    "        valid_idx = np.where(valid)[0]\n",
-    "        best_local_idx = valid_idx[int(np.nanargmax(precision[valid_idx]))]\n",
-    "        return float(thresholds[best_local_idx])\n",
-    "\n",
-    "    beta_sq = beta ** 2\n",
-    "    f_beta = (1 + beta_sq) * precision * recall / np.maximum(beta_sq * precision + recall, 1e-12)\n",
-    "    return float(thresholds[int(np.nanargmax(f_beta))])\n",
-    "\n",
-    "\n",
-    "def find_best_threshold(y_true: pd.Series, proba: np.ndarray) -> float:\n",
-    "    return find_recall_priority_threshold(y_true, proba)\n",
-    "\n",
-    "\n",
-    "def compute_binary_metrics(y_true: pd.Series, proba: np.ndarray, threshold: float) -> dict:\n",
-    "    pred = (proba >= threshold).astype(int)\n",
-    "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
-    "    return {\n",
-    "        \"accuracy\": float(accuracy_score(y_true, pred)),\n",
-    "        \"precision\": float(precision_score(y_true, pred, zero_division=0)),\n",
-    "        \"recall\": float(recall_score(y_true, pred, zero_division=0)),\n",
-    "        \"f1\": float(f1_score(y_true, pred, zero_division=0)),\n",
-    "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
-    "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
-    "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
-    "        \"predicted_positive_rate\": float((tp + fp) / max(tp + fp + tn + fn, 1)),\n",
-    "        \"true_negative\": int(tn),\n",
-    "        \"false_positive\": int(fp),\n",
-    "        \"false_negative\": int(fn),\n",
-    "        \"true_positive\": int(tp),\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def predict_positive_proba(estimator, X_input: pd.DataFrame) -> np.ndarray:\n",
-    "    if hasattr(estimator, \"predict_proba\"):\n",
-    "        return estimator.predict_proba(X_input)[:, 1]\n",
-    "    decision = estimator.decision_function(X_input)\n",
-    "    return 1 / (1 + np.exp(-decision))\n",
-    "\n",
-    "\n",
-    "def is_pipeline_estimator(estimator) -> bool:\n",
-    "    return isinstance(estimator, (Pipeline, ImbPipeline))\n",
-    "\n",
-    "\n",
-    "def get_final_estimator(estimator):\n",
-    "    return estimator.named_steps.get(\"model\", estimator) if is_pipeline_estimator(estimator) else estimator\n",
-    "\n",
-    "\n",
-    "def make_lightgbm_pipeline() -> ImbPipeline:\n",
-    "    return ImbPipeline([\n",
-    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-    "        (\"smote\", SMOTE(\n",
-    "            sampling_strategy=SMOTE_SAMPLING_STRATEGY if USE_SMOTE else 0.01,\n",
-    "            random_state=RANDOM_STATE,\n",
-    "            k_neighbors=3,\n",
-    "        )),\n",
-    "        (\"model\", lgb.LGBMClassifier(\n",
-    "            objective=\"binary\",\n",
-    "            n_estimators=500,\n",
-    "            random_state=RANDOM_STATE,\n",
-    "            n_jobs=-1,\n",
-    "            is_unbalance=True,\n",
-    "            verbosity=-1,\n",
-    "        )),\n",
-    "    ])\n",
-    "\n",
-    "\n",
-    "def make_candidate_models() -> dict:\n",
-    "    return {\n",
-    "        \"LightGBM\": {\n",
-    "            \"estimator\": make_lightgbm_pipeline(),\n",
-    "            \"params\": {\n",
-    "                \"model__learning_rate\": [0.015, 0.025, 0.04, 0.06],\n",
-    "                \"model__num_leaves\": [31, 63, 127],\n",
-    "                \"model__max_depth\": [-1, 6, 8, 12],\n",
-    "                \"model__min_child_samples\": [40, 80, 150, 250],\n",
-    "                \"model__subsample\": [0.75, 0.85, 1.0],\n",
-    "                \"model__colsample_bytree\": [0.65, 0.8, 0.95],\n",
-    "                \"model__reg_lambda\": [0.5, 1.0, 2.0, 5.0],\n",
-    "                \"model__reg_alpha\": [0.0, 0.1, 0.5],\n",
-    "            },\n",
-    "            \"selection_reason\": \"대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준 모델. Optuna와 SMOTE로 희소 불량 recall을 보강\",\n",
-    "        },\n",
-    "        \"RandomForest\": {\n",
-    "            \"estimator\": Pipeline([\n",
-    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-    "                (\"model\", RandomForestClassifier(\n",
-    "                    n_estimators=160,\n",
-    "                    class_weight=\"balanced_subsample\",\n",
-    "                    random_state=RANDOM_STATE,\n",
-    "                    n_jobs=-1,\n",
-    "                )),\n",
-    "            ]),\n",
-    "            \"params\": {\n",
-    "                \"model__max_depth\": [12, 16, 20, None],\n",
-    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
-    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
-    "            },\n",
-    "            \"selection_reason\": \"bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble 성능 확인\",\n",
-    "        },\n",
-    "        \"ExtraTrees\": {\n",
-    "            \"estimator\": Pipeline([\n",
-    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-    "                (\"model\", ExtraTreesClassifier(\n",
-    "                    n_estimators=220,\n",
-    "                    class_weight=\"balanced\",\n",
-    "                    random_state=RANDOM_STATE,\n",
-    "                    n_jobs=-1,\n",
-    "                )),\n",
-    "            ]),\n",
-    "            \"params\": {\n",
-    "                \"model__max_depth\": [12, 16, 20, None],\n",
-    "                \"model__min_samples_leaf\": [1, 3, 5, 10],\n",
-    "                \"model__max_features\": [\"sqrt\", 0.35, 0.5],\n",
-    "            },\n",
-    "            \"selection_reason\": \"RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교\",\n",
-    "        },\n",
-    "        \"LogisticRegression\": {\n",
-    "            \"estimator\": Pipeline([\n",
-    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
-    "                (\"scaler\", StandardScaler()),\n",
-    "                (\"model\", LogisticRegression(\n",
-    "                    class_weight=\"balanced\",\n",
-    "                    max_iter=1500,\n",
-    "                    random_state=RANDOM_STATE,\n",
-    "                )),\n",
-    "            ]),\n",
-    "            \"params\": {\n",
-    "                \"model__C\": [0.03, 0.1, 0.3, 1.0, 3.0],\n",
-    "                \"model__solver\": [\"lbfgs\"],\n",
-    "            },\n",
-    "            \"selection_reason\": \"복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체의 설명력 확인\",\n",
-    "        },\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def suggest_lightgbm_params(trial: optuna.Trial) -> dict:\n",
-    "    return {\n",
-    "        \"model__learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.08, log=True),\n",
-    "        \"model__num_leaves\": trial.suggest_int(\"num_leaves\", 24, 160),\n",
-    "        \"model__max_depth\": trial.suggest_categorical(\"max_depth\", [-1, 5, 7, 9, 12]),\n",
-    "        \"model__min_child_samples\": trial.suggest_int(\"min_child_samples\", 30, 300),\n",
-    "        \"model__subsample\": trial.suggest_float(\"subsample\", 0.65, 1.0),\n",
-    "        \"model__colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.55, 1.0),\n",
-    "        \"model__reg_lambda\": trial.suggest_float(\"reg_lambda\", 0.1, 8.0, log=True),\n",
-    "        \"model__reg_alpha\": trial.suggest_float(\"reg_alpha\", 1e-4, 1.0, log=True),\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def evaluate_cv_trial(model_name: str, config: dict, params: dict, trial_no: int) -> tuple[dict, list[dict]]:\n",
-    "    fold_rows = []\n",
-    "    for fold_no, (train_idx, valid_idx) in enumerate(skf.split(X_cv_pool, y_cv_pool), start=1):\n",
-    "        fold_started_at = datetime.now()\n",
-    "        X_fold_train = X_cv_pool.iloc[train_idx]\n",
-    "        y_fold_train = y_cv_pool.iloc[train_idx]\n",
-    "        X_fold_valid = X_cv_pool.iloc[valid_idx]\n",
-    "        y_fold_valid = y_cv_pool.iloc[valid_idx]\n",
-    "\n",
-    "        cv_model = clone(config[\"estimator\"])\n",
-    "        cv_model.set_params(**params)\n",
-    "        cv_model.fit(X_fold_train, y_fold_train)\n",
-    "\n",
-    "        fold_proba = predict_positive_proba(cv_model, X_fold_valid)\n",
-    "        fold_threshold = find_best_threshold(y_fold_valid, fold_proba)\n",
-    "        fold_metrics = compute_binary_metrics(y_fold_valid, fold_proba, fold_threshold)\n",
-    "        fold_metrics.update({\n",
-    "            \"model_name\": model_name,\n",
-    "            \"trial_no\": trial_no,\n",
-    "            \"fold_no\": fold_no,\n",
-    "            \"threshold\": fold_threshold,\n",
-    "            **params,\n",
-    "        })\n",
-    "        fold_rows.append(fold_metrics)\n",
-    "        elapsed = (datetime.now() - fold_started_at).total_seconds()\n",
-    "        print(\n",
-    "            f\"    fold {fold_no}/{effective_cv_splits} \"\n",
-    "            f\"PR-AUC={fold_metrics['pr_auc']:.5f} \"\n",
-    "            f\"Recall={fold_metrics['recall']:.5f} \"\n",
-    "            f\"F1={fold_metrics['f1']:.5f} \"\n",
-    "            f\"elapsed={elapsed:.1f}s\"\n",
-    "        )\n",
-    "\n",
-    "    fold_df = pd.DataFrame(fold_rows)\n",
-    "    summary = {\n",
-    "        \"model_name\": model_name,\n",
-    "        \"trial_no\": trial_no,\n",
-    "        \"mean_pr_auc\": float(fold_df[\"pr_auc\"].mean()),\n",
-    "        \"std_pr_auc\": float(fold_df[\"pr_auc\"].std(ddof=0)),\n",
-    "        \"mean_roc_auc\": float(fold_df[\"roc_auc\"].mean()),\n",
-    "        \"std_roc_auc\": float(fold_df[\"roc_auc\"].std(ddof=0)),\n",
-    "        \"mean_accuracy\": float(fold_df[\"accuracy\"].mean()),\n",
-    "        \"mean_false_positive_rate\": float(fold_df[\"false_positive_rate\"].mean()),\n",
-    "        \"mean_precision\": float(fold_df[\"precision\"].mean()),\n",
-    "        \"mean_recall\": float(fold_df[\"recall\"].mean()),\n",
-    "        \"mean_f1\": float(fold_df[\"f1\"].mean()),\n",
-    "        \"params\": params,\n",
-    "        \"selection_reason\": config[\"selection_reason\"],\n",
-    "        \"tuning_method\": \"Optuna\" if model_name == \"LightGBM\" and ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-    "    }\n",
-    "    return summary, fold_rows\n",
-    "\n",
-    "\n",
-    "# CV 데이터셋 규모 관리\n",
-    "if len(X_train) > CV_SAMPLE_SIZE:\n",
-    "    X_cv_pool, _, y_cv_pool, _ = train_test_split(\n",
-    "        X_train,\n",
-    "        y_train,\n",
-    "        train_size=CV_SAMPLE_SIZE,\n",
-    "        random_state=RANDOM_STATE,\n",
-    "        stratify=y_train,\n",
-    "    )\n",
-    "else:\n",
-    "    X_cv_pool = X_train\n",
-    "    y_cv_pool = y_train\n",
-    "\n",
-    "effective_cv_splits = min(CV_N_SPLITS, int(y_cv_pool.value_counts().min()))\n",
-    "if effective_cv_splits < 2:\n",
-    "    raise ValueError(\"교차검증을 수행하기에 소수 클래스 샘플이 부족합니다. MAX_ROWS 또는 CV_SAMPLE_SIZE를 늘려주세요.\")\n",
-    "\n",
-    "candidate_configs = make_candidate_models()\n",
-    "skf = StratifiedKFold(n_splits=effective_cv_splits, shuffle=True, random_state=RANDOM_STATE)\n",
-    "cv_rows = []\n",
-    "cv_fold_rows = []\n",
-    "best_params_by_model = {}\n",
-    "\n",
-    "print(f\"CV rows: {len(X_cv_pool):,} / train rows: {len(X_train):,}\")\n",
-    "print(f\"CV splits: {effective_cv_splits}, tuning trials per model: {CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1}\")\n",
-    "print(f\"SMOTE enabled: {USE_SMOTE}, sampling_strategy={SMOTE_SAMPLING_STRATEGY}\")\n",
-    "print(f\"Threshold strategy: recall>={RECALL_PRIORITY_TARGET}, min_precision>={RECALL_PRIORITY_MIN_PRECISION}, beta={THRESHOLD_BETA}\")\n",
-    "\n",
-    "# 후보 모델별 교차검증\n",
-    "for model_name, config in candidate_configs.items():\n",
-    "    print(f\"\\n[{model_name}] CV start\")\n",
-    "    model_started_at = datetime.now()\n",
-    "\n",
-    "    if model_name == \"LightGBM\" and ENABLE_HYPERPARAMETER_TUNING and ENABLE_OPTUNA_TUNING:\n",
-    "        def objective(trial: optuna.Trial) -> float:\n",
-    "            params = suggest_lightgbm_params(trial)\n",
-    "            print(f\"  optuna trial {trial.number + 1}/{CV_N_ITER} params={params}\")\n",
-    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial.number + 1)\n",
-    "            cv_rows.append(summary)\n",
-    "            cv_fold_rows.extend(fold_rows)\n",
-    "            trial.set_user_attr(\"params\", params)\n",
-    "            trial.set_user_attr(\"mean_recall\", summary[\"mean_recall\"])\n",
-    "            trial.set_user_attr(\"mean_f1\", summary[\"mean_f1\"])\n",
-    "            # PR-AUC를 우선하되 recall을 약하게 보상합니다.\n",
-    "            return summary[\"mean_pr_auc\"] + 0.05 * summary[\"mean_recall\"]\n",
-    "\n",
-    "        study = optuna.create_study(\n",
-    "            direction=\"maximize\",\n",
-    "            sampler=TPESampler(seed=RANDOM_STATE),\n",
-    "            study_name=\"bosch_defect_lightgbm_recall_pr_auc\",\n",
-    "        )\n",
-    "        study.optimize(objective, n_trials=CV_N_ITER, show_progress_bar=False)\n",
-    "        best_params_by_model[model_name] = study.best_trial.user_attrs[\"params\"]\n",
-    "        print(f\"[{model_name}] Optuna best value={study.best_value:.5f} params={best_params_by_model[model_name]}\")\n",
-    "    else:\n",
-    "        param_candidates = list(ParameterSampler(\n",
-    "            config[\"params\"],\n",
-    "            n_iter=CV_N_ITER,\n",
-    "            random_state=RANDOM_STATE,\n",
-    "        )) if ENABLE_HYPERPARAMETER_TUNING else [{}]\n",
-    "        print(f\"[{model_name}] random search trials: {len(param_candidates)}\")\n",
-    "        for trial_no, params in enumerate(param_candidates, start=1):\n",
-    "            print(f\"  trial {trial_no}/{len(param_candidates)} params={params}\")\n",
-    "            summary, fold_rows = evaluate_cv_trial(model_name, config, params, trial_no)\n",
-    "            cv_rows.append(summary)\n",
-    "            cv_fold_rows.extend(fold_rows)\n",
-    "            print(\n",
-    "                f\"  -> {model_name} trial {trial_no} mean \"\n",
-    "                f\"PR-AUC={summary['mean_pr_auc']:.5f} \"\n",
-    "                f\"Recall={summary['mean_recall']:.5f} \"\n",
-    "                f\"F1={summary['mean_f1']:.5f} \"\n",
-    "                f\"FPR={summary['mean_false_positive_rate']:.5f}\"\n",
-    "            )\n",
-    "\n",
-    "    print(f\"[{model_name}] CV done elapsed={(datetime.now() - model_started_at).total_seconds():.1f}s\")\n",
-    "\n",
-    "cv_results = pd.DataFrame(cv_rows).sort_values(\n",
-    "    [\"mean_pr_auc\", \"mean_recall\", \"mean_f1\", \"mean_roc_auc\", \"mean_accuracy\"],\n",
-    "    ascending=False,\n",
-    ").reset_index(drop=True)\n",
-    "cv_fold_results = pd.DataFrame(cv_fold_rows)\n",
-    "\n",
-    "for model_name in candidate_configs:\n",
-    "    if model_name not in best_params_by_model:\n",
-    "        best_params_by_model[model_name] = cv_results[cv_results[\"model_name\"].eq(model_name)].iloc[0][\"params\"]\n",
-    "\n",
-    "best_model_name = str(cv_results.iloc[0][\"model_name\"])\n",
-    "best_params = best_params_by_model[best_model_name]\n",
-    "best_model_selection_reason = str(cv_results.iloc[0][\"selection_reason\"])\n",
-    "\n",
-    "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.csv\"\n",
-    "cv_fold_results_path = OUTPUT_DIR / \"defect_model_cv_fold_results.csv\"\n",
-    "cv_results.to_csv(cv_results_path, index=False)\n",
-    "cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
-    "\n",
-    "display(cv_results.head(20))\n",
-    "print(\"CV 결과 저장:\", cv_results_path)\n",
-    "print(\"CV fold 결과 저장:\", cv_fold_results_path)\n",
-    "print(\"CV best model:\", best_model_name, best_params)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f15608a",
-   "metadata": {
-    "id": "3f15608a"
-   },
-   "source": [
-    "## 8. 후보 모델 4종 최종 학습 및 성능 비교\n",
-    "\n",
-    "교차검증에서 선택된 모델별 best parameter로 4개 후보 모델을 모두 학습합니다.\n",
-    "\n",
-    "LightGBM은 median imputer + SMOTE + LightGBM pipeline으로 최종 학습합니다. Threshold는 validation set에서 recall 우선 정책으로 탐색합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "3553e4c5",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 825
+      "cell_type": "markdown",
+      "id": "56fa8462",
+      "metadata": {
+        "id": "56fa8462"
+      },
+      "source": [
+        "## 3. 모델 학습/평가 helper 함수"
+      ]
     },
-    "id": "3553e4c5",
-    "outputId": "f6e17abe-7523-45bd-b4fa-0828efb0d901"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Final candidate training rows: 120,000 / train rows: 240,000\n",
-      "[LightGBM] final fit start params={'model__learning_rate': 0.031169409812378812, 'model__num_leaves': 49, 'model__max_depth': -1, 'model__min_child_samples': 279, 'model__subsample': 0.6809723757181718, 'model__colsample_bytree': 0.6381922880886154, 'model__reg_lambda': 0.12191905861905929, 'model__reg_alpha': 0.0020013420622879987}\n",
-      "[LightGBM] final fit done PR-AUC=0.05032 ROC-AUC=0.66598 ACC=0.90142 FPR=0.09546 elapsed=159.2s\n",
-      "[RandomForest] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': None}\n",
-      "[RandomForest] final fit done PR-AUC=0.06255 ROC-AUC=0.69675 ACC=0.91395 FPR=0.08285 elapsed=150.0s\n",
-      "[ExtraTrees] final fit start params={'model__min_samples_leaf': 10, 'model__max_features': 0.35, 'model__max_depth': None}\n",
-      "[ExtraTrees] final fit done PR-AUC=0.05758 ROC-AUC=0.67618 ACC=0.98787 FPR=0.00784 elapsed=750.9s\n",
-      "[LogisticRegression] final fit start params={'model__solver': 'lbfgs', 'model__C': 0.3}\n",
-      "[LogisticRegression] final fit done PR-AUC=0.05096 ROC-AUC=0.66273 ACC=0.98672 FPR=0.00888 elapsed=71.9s\n",
-      "selected_model_name: RandomForest\n",
-      "ROC-AUC: 0.69675\n",
-      "PR-AUC: 0.06255\n",
-      "Accuracy: 0.91395\n",
-      "False Positive Rate: 0.08285\n",
-      "Recall: 0.35103\n"
-     ]
+      "cell_type": "markdown",
+      "id": "e7c30ce8",
+      "metadata": {
+        "id": "e7c30ce8"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "전처리, class imbalance 보정, threshold 탐색, 혼동행렬, PR/ROC 곡선, feature leakage 점검, 피처 중요도와 데이터 분포 시각화 함수를 정의합니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "           model_name  threshold  \\\n",
-       "0        RandomForest   0.058868   \n",
-       "1          ExtraTrees   0.243180   \n",
-       "2  LogisticRegression   0.898330   \n",
-       "3            LightGBM   0.034547   \n",
-       "\n",
-       "                                    selection_reason  accuracy  precision  \\\n",
-       "0  bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...  0.913950   0.023508   \n",
-       "1  RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교  0.987867   0.144424   \n",
-       "2  복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...  0.986717   0.119601   \n",
-       "3  대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...  0.901417   0.020468   \n",
-       "\n",
-       "     recall        f1   roc_auc    pr_auc  false_positive_rate  \\\n",
-       "0  0.351032  0.044066  0.696746  0.062551             0.082851   \n",
-       "1  0.233038  0.178330  0.676183  0.057577             0.007844   \n",
-       "2  0.212389  0.153029  0.662734  0.050958             0.008884   \n",
-       "3  0.351032  0.038680  0.665979  0.050323             0.095456   \n",
-       "\n",
-       "   predicted_positive_rate  true_negative  false_positive  false_negative  \\\n",
-       "0                 0.084367          54718            4943             220   \n",
-       "1                 0.009117          59193             468             260   \n",
-       "2                 0.010033          59131             530             267   \n",
-       "3                 0.096900          53966            5695             220   \n",
-       "\n",
-       "   true_positive  \n",
-       "0            119  \n",
-       "1             79  \n",
-       "2             72  \n",
-       "3            119  "
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-6fdedb8e-6721-4875-adf0-d8cafb363080\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model_name</th>\n",
-       "      <th>threshold</th>\n",
-       "      <th>selection_reason</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1</th>\n",
-       "      <th>roc_auc</th>\n",
-       "      <th>pr_auc</th>\n",
-       "      <th>false_positive_rate</th>\n",
-       "      <th>predicted_positive_rate</th>\n",
-       "      <th>true_negative</th>\n",
-       "      <th>false_positive</th>\n",
-       "      <th>false_negative</th>\n",
-       "      <th>true_positive</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>0.058868</td>\n",
-       "      <td>bagging 기반 기준 모델로 과적합을 줄이고 안정적인 tree ensemble ...</td>\n",
-       "      <td>0.913950</td>\n",
-       "      <td>0.023508</td>\n",
-       "      <td>0.351032</td>\n",
-       "      <td>0.044066</td>\n",
-       "      <td>0.696746</td>\n",
-       "      <td>0.062551</td>\n",
-       "      <td>0.082851</td>\n",
-       "      <td>0.084367</td>\n",
-       "      <td>54718</td>\n",
-       "      <td>4943</td>\n",
-       "      <td>220</td>\n",
-       "      <td>119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>0.243180</td>\n",
-       "      <td>RandomForest보다 분할 무작위성이 커서 고차원 센서 feature의 강건성 비교</td>\n",
-       "      <td>0.987867</td>\n",
-       "      <td>0.144424</td>\n",
-       "      <td>0.233038</td>\n",
-       "      <td>0.178330</td>\n",
-       "      <td>0.676183</td>\n",
-       "      <td>0.057577</td>\n",
-       "      <td>0.007844</td>\n",
-       "      <td>0.009117</td>\n",
-       "      <td>59193</td>\n",
-       "      <td>468</td>\n",
-       "      <td>260</td>\n",
-       "      <td>79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LogisticRegression</td>\n",
-       "      <td>0.898330</td>\n",
-       "      <td>복잡한 tree 모델 대비 선형 기준선으로 feature engineering 자체...</td>\n",
-       "      <td>0.986717</td>\n",
-       "      <td>0.119601</td>\n",
-       "      <td>0.212389</td>\n",
-       "      <td>0.153029</td>\n",
-       "      <td>0.662734</td>\n",
-       "      <td>0.050958</td>\n",
-       "      <td>0.008884</td>\n",
-       "      <td>0.010033</td>\n",
-       "      <td>59131</td>\n",
-       "      <td>530</td>\n",
-       "      <td>267</td>\n",
-       "      <td>72</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>0.034547</td>\n",
-       "      <td>대용량 tabular 제조 데이터에서 비선형 센서/시간 패턴을 빠르게 학습하는 기준...</td>\n",
-       "      <td>0.901417</td>\n",
-       "      <td>0.020468</td>\n",
-       "      <td>0.351032</td>\n",
-       "      <td>0.038680</td>\n",
-       "      <td>0.665979</td>\n",
-       "      <td>0.050323</td>\n",
-       "      <td>0.095456</td>\n",
-       "      <td>0.096900</td>\n",
-       "      <td>53966</td>\n",
-       "      <td>5695</td>\n",
-       "      <td>220</td>\n",
-       "      <td>119</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-      }
-     },
-     "metadata": {}
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e29b0711",
+      "metadata": {
+        "id": "e29b0711"
+      },
+      "outputs": [],
+      "source": [
+        "CONFUSION_INDEX = [\"실제 정상(0)\", \"실제 불량(1)\"]\n",
+        "CONFUSION_COLUMNS = [\"예측 정상(0)\", \"예측 불량(1)\"]\n",
+        "CLASS_NAMES = [\"정상\", \"불량\"]\n",
+        "METRIC_LABELS = {\n",
+        "    \"accuracy\": \"정확도\",\n",
+        "    \"precision\": \"정밀도\",\n",
+        "    \"recall\": \"재현율\",\n",
+        "    \"f1\": \"F1 Score\",\n",
+        "    \"pr_auc\": \"PR-AUC\",\n",
+        "    \"roc_auc\": \"ROC-AUC\",\n",
+        "}\n",
+        "\n",
+        "\n",
+        "def make_one_hot_encoder():\n",
+        "    try:\n",
+        "        return OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, min_frequency=10)\n",
+        "    except TypeError:\n",
+        "        return OneHotEncoder(handle_unknown=\"ignore\", sparse=False)\n",
+        "\n",
+        "\n",
+        "def split_feature_types(df: pd.DataFrame, feature_cols: list[str]):\n",
+        "    numeric_cols = [c for c in feature_cols if pd.api.types.is_numeric_dtype(df[c])]\n",
+        "    categorical_cols = [c for c in feature_cols if c not in numeric_cols]\n",
+        "    return numeric_cols, categorical_cols\n",
+        "\n",
+        "\n",
+        "def make_preprocessor(df: pd.DataFrame, feature_cols: list[str], scale_numeric: bool = False):\n",
+        "    numeric_cols, categorical_cols = split_feature_types(df, feature_cols)\n",
+        "    transformers = []\n",
+        "    numeric_steps = [(\"imputer\", SimpleImputer(strategy=\"median\"))]\n",
+        "    if scale_numeric:\n",
+        "        numeric_steps.append((\"scaler\", StandardScaler()))\n",
+        "    if numeric_cols:\n",
+        "        transformers.append((\"num\", Pipeline(numeric_steps), numeric_cols))\n",
+        "    if categorical_cols:\n",
+        "        transformers.append((\"cat\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), (\"onehot\", make_one_hot_encoder())]), categorical_cols))\n",
+        "    return ColumnTransformer(transformers=transformers, remainder=\"drop\", verbose_feature_names_out=False)\n",
+        "\n",
+        "\n",
+        "def scale_pos_weight_for(y):\n",
+        "    y = pd.Series(y).astype(int)\n",
+        "    return float((y == 0).sum() / max((y == 1).sum(), 1))\n",
+        "\n",
+        "\n",
+        "def predict_positive_proba(estimator, X):\n",
+        "    return estimator.predict_proba(X)[:, 1]\n",
+        "\n",
+        "\n",
+        "def find_recall_priority_threshold(y_true, proba, min_recall=0.70, min_precision=0.20, beta=2.0):\n",
+        "    precision, recall, thresholds = precision_recall_curve(y_true, proba)\n",
+        "    if len(thresholds) == 0:\n",
+        "        return 0.5\n",
+        "    precision = precision[:-1]\n",
+        "    recall = recall[:-1]\n",
+        "    beta_sq = beta ** 2\n",
+        "    f_beta = (1 + beta_sq) * precision * recall / np.maximum(beta_sq * precision + recall, 1e-12)\n",
+        "    valid = (recall >= min_recall) & (precision >= min_precision)\n",
+        "    if valid.any():\n",
+        "        valid_idx = np.where(valid)[0]\n",
+        "        best_idx = valid_idx[int(np.nanargmax(f_beta[valid_idx]))]\n",
+        "        return float(thresholds[best_idx])\n",
+        "    return float(thresholds[int(np.nanargmax(f_beta))])\n",
+        "\n",
+        "def compute_metrics(y_true, proba, threshold):\n",
+        "    pred = (proba >= threshold).astype(int)\n",
+        "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
+        "    return {\n",
+        "        \"threshold\": float(threshold),\n",
+        "        \"pr_auc\": float(average_precision_score(y_true, proba)),\n",
+        "        \"roc_auc\": float(roc_auc_score(y_true, proba)),\n",
+        "        \"accuracy\": float(accuracy_score(y_true, pred)),\n",
+        "        \"precision\": float(precision_score(y_true, pred, zero_division=0)),\n",
+        "        \"recall\": float(recall_score(y_true, pred, zero_division=0)),\n",
+        "        \"f1\": float(f1_score(y_true, pred, zero_division=0)),\n",
+        "        \"false_positive_rate\": float(fp / max(fp + tn, 1)),\n",
+        "        \"tn\": int(tn), \"fp\": int(fp), \"fn\": int(fn), \"tp\": int(tp),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def metric_summary_table(metrics: dict) -> pd.DataFrame:\n",
+        "    return pd.DataFrame([\n",
+        "        (\"정확도(Accuracy)\", metrics[\"accuracy\"]),\n",
+        "        (\"정밀도(Precision)\", metrics[\"precision\"]),\n",
+        "        (\"재현율(Recall)\", metrics[\"recall\"]),\n",
+        "        (\"F1 Score\", metrics[\"f1\"]),\n",
+        "        (\"ROC-AUC\", metrics[\"roc_auc\"]),\n",
+        "        (\"PR-AUC\", metrics[\"pr_auc\"]),\n",
+        "        (\"오탐률(False Positive Rate)\", metrics[\"false_positive_rate\"]),\n",
+        "        (\"임계값(Threshold)\", metrics[\"threshold\"]),\n",
+        "    ], columns=[\"지표\", \"값\"])\n",
+        "\n",
+        "\n",
+        "def confusion_matrix_table(metrics: dict) -> pd.DataFrame:\n",
+        "    return pd.DataFrame(\n",
+        "        [[metrics[\"tn\"], metrics[\"fp\"]], [metrics[\"fn\"], metrics[\"tp\"]]],\n",
+        "        index=CONFUSION_INDEX,\n",
+        "        columns=CONFUSION_COLUMNS,\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "def display_confusion_matrix_report(y_true, proba, threshold, title):\n",
+        "    metrics = compute_metrics(y_true, proba, threshold)\n",
+        "    cm_table = confusion_matrix_table(metrics)\n",
+        "    metric_table = metric_summary_table(metrics)\n",
+        "    print(f\"재현율(Recall): {metrics['recall']:.4f}\")\n",
+        "    print(f\"정밀도(Precision): {metrics['precision']:.4f}\")\n",
+        "    print(f\"F1 Score: {metrics['f1']:.4f}\")\n",
+        "    print(f\"PR-AUC: {metrics['pr_auc']:.4f}\")\n",
+        "    print(f\"임계값(Threshold): {metrics['threshold']:.4f}\")\n",
+        "    display(metric_table)\n",
+        "    display(cm_table)\n",
+        "    plt.figure(figsize=(4.8, 4.0))\n",
+        "    ax = sns.heatmap(cm_table, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False, annot_kws={\"size\": 11})\n",
+        "    ax.set_title(title)\n",
+        "    ax.set_xlabel(\"예측값\")\n",
+        "    ax.set_ylabel(\"실제값\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    return metric_table, cm_table\n",
+        "\n",
+        "\n",
+        "def plot_train_test_confusion(y_train, train_proba, y_test, test_proba, threshold, title_prefix):\n",
+        "    train_metrics = compute_metrics(y_train, train_proba, threshold)\n",
+        "    test_metrics = compute_metrics(y_test, test_proba, threshold)\n",
+        "    tables = {\n",
+        "        \"Train\": confusion_matrix_table(train_metrics),\n",
+        "        \"Test\": confusion_matrix_table(test_metrics),\n",
+        "    }\n",
+        "    fig, axes = plt.subplots(1, 2, figsize=(10, 4.2))\n",
+        "    for ax, (split_name, cm_table) in zip(axes, tables.items()):\n",
+        "        sns.heatmap(cm_table, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False, ax=ax, annot_kws={\"size\": 11})\n",
+        "        ax.set_title(f\"{title_prefix} {split_name} 혼동행렬\")\n",
+        "        ax.set_xlabel(\"예측값\")\n",
+        "        ax.set_ylabel(\"실제값\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    return train_metrics, test_metrics\n",
+        "\n",
+        "\n",
+        "def plot_train_test_metric_comparison(train_metrics: dict, test_metrics: dict, title: str):\n",
+        "    rows = []\n",
+        "    for metric_key in [\"accuracy\", \"precision\", \"recall\", \"f1\", \"pr_auc\", \"roc_auc\"]:\n",
+        "        rows.append({\"split\": \"Train\", \"metric\": METRIC_LABELS[metric_key], \"value\": train_metrics[metric_key]})\n",
+        "        rows.append({\"split\": \"Test\", \"metric\": METRIC_LABELS[metric_key], \"value\": test_metrics[metric_key]})\n",
+        "    plot_df = pd.DataFrame(rows)\n",
+        "    plt.figure(figsize=(10, 4.5))\n",
+        "    ax = sns.barplot(data=plot_df, x=\"metric\", y=\"value\", hue=\"split\", palette=[\"#4C78A8\", \"#F28E2B\"])\n",
+        "    ax.set_title(title)\n",
+        "    ax.set_xlabel(\"평가 지표\")\n",
+        "    ax.set_ylabel(\"값\")\n",
+        "    ax.set_ylim(0, 1.05)\n",
+        "    ax.legend(title=\"데이터\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    display(plot_df.pivot(index=\"metric\", columns=\"split\", values=\"value\").reset_index())\n",
+        "\n",
+        "\n",
+        "def plot_pr_roc_curves(y_true, proba_by_model: dict[str, np.ndarray], title_prefix: str):\n",
+        "    fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n",
+        "    for model_name, proba in proba_by_model.items():\n",
+        "        precision, recall, _ = precision_recall_curve(y_true, proba)\n",
+        "        fpr, tpr, _ = roc_curve(y_true, proba)\n",
+        "        axes[0].plot(recall, precision, label=f\"{model_name} (AP={average_precision_score(y_true, proba):.3f})\")\n",
+        "        axes[1].plot(fpr, tpr, label=f\"{model_name} (AUC={roc_auc_score(y_true, proba):.3f})\")\n",
+        "    axes[0].set_title(f\"{title_prefix} PR 곡선\")\n",
+        "    axes[0].set_xlabel(\"재현율\")\n",
+        "    axes[0].set_ylabel(\"정밀도\")\n",
+        "    axes[0].legend()\n",
+        "    axes[1].plot([0, 1], [0, 1], linestyle=\"--\", color=\"gray\", linewidth=1)\n",
+        "    axes[1].set_title(f\"{title_prefix} ROC 곡선\")\n",
+        "    axes[1].set_xlabel(\"False Positive Rate\")\n",
+        "    axes[1].set_ylabel(\"True Positive Rate\")\n",
+        "    axes[1].legend()\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "def plot_probability_distribution(y_true, proba, threshold, title):\n",
+        "    plot_df = pd.DataFrame({\"actual\": pd.Series(y_true).map({0: \"정상\", 1: \"불량\"}), \"probability\": proba})\n",
+        "    plt.figure(figsize=(9, 4.5))\n",
+        "    ax = sns.histplot(data=plot_df, x=\"probability\", hue=\"actual\", bins=30, kde=True, stat=\"density\", common_norm=False)\n",
+        "    ax.axvline(threshold, color=\"#D62728\", linestyle=\"--\", linewidth=1.5, label=f\"Threshold={threshold:.3f}\")\n",
+        "    ax.set_title(title)\n",
+        "    ax.set_xlabel(\"불량 예측 확률\")\n",
+        "    ax.set_ylabel(\"밀도\")\n",
+        "    ax.legend()\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "def plot_class_distribution(train_df, test_df, target_col, title_prefix):\n",
+        "    label_map = {0: \"정상\", 1: \"불량\"}\n",
+        "    rows = []\n",
+        "    for split_name, df in [(\"Train\", train_df), (\"Test\", test_df)]:\n",
+        "        counts = df[target_col].astype(int).value_counts().sort_index()\n",
+        "        total = counts.sum()\n",
+        "        for label, count in counts.items():\n",
+        "            rows.append({\"split\": split_name, \"label\": label_map.get(int(label), str(label)), \"count\": int(count), \"ratio\": float(count / total)})\n",
+        "    summary = pd.DataFrame(rows)\n",
+        "    fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n",
+        "    sns.barplot(data=summary, x=\"split\", y=\"count\", hue=\"label\", ax=axes[0], palette=[\"#4C78A8\", \"#F28E2B\"])\n",
+        "    axes[0].set_title(f\"{title_prefix} 클래스 건수\")\n",
+        "    axes[0].set_xlabel(\"데이터\")\n",
+        "    axes[0].set_ylabel(\"건수\")\n",
+        "    sns.barplot(data=summary, x=\"split\", y=\"ratio\", hue=\"label\", ax=axes[1], palette=[\"#4C78A8\", \"#F28E2B\"])\n",
+        "    axes[1].set_title(f\"{title_prefix} 클래스 비율\")\n",
+        "    axes[1].set_xlabel(\"데이터\")\n",
+        "    axes[1].set_ylabel(\"비율\")\n",
+        "    axes[1].set_ylim(0, 1)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    display(summary)\n",
+        "\n",
+        "\n",
+        "def plot_feature_distribution_grid(df: pd.DataFrame, feature_cols: list[str], target_col: str, title: str, max_features: int = 24):\n",
+        "    numeric_cols = [c for c in feature_cols if c in df.columns and pd.api.types.is_numeric_dtype(df[c])]\n",
+        "    if not numeric_cols:\n",
+        "        print(f\"{title}: 시각화할 수치형 feature가 없습니다.\")\n",
+        "        return\n",
+        "    scored = []\n",
+        "    for col in numeric_cols:\n",
+        "        auc = single_feature_auc(df, col, target_col)\n",
+        "        if not np.isnan(auc):\n",
+        "            scored.append((col, auc))\n",
+        "    selected_cols = [col for col, _ in sorted(scored, key=lambda item: item[1], reverse=True)[:max_features]]\n",
+        "    if not selected_cols:\n",
+        "        selected_cols = numeric_cols[:max_features]\n",
+        "    ncols = 4\n",
+        "    nrows = int(np.ceil(len(selected_cols) / ncols))\n",
+        "    fig, axes = plt.subplots(nrows, ncols, figsize=(16, max(3, nrows * 2.4)))\n",
+        "    axes = np.array(axes).reshape(-1)\n",
+        "    plot_df = df.copy()\n",
+        "    plot_df[\"_label\"] = plot_df[target_col].astype(int).map({0: \"정상\", 1: \"불량\"})\n",
+        "    for ax, col in zip(axes, selected_cols):\n",
+        "        sns.histplot(data=plot_df, x=col, hue=\"_label\", bins=25, stat=\"density\", common_norm=False, element=\"step\", ax=ax)\n",
+        "        ax.set_title(col, fontsize=9)\n",
+        "        ax.set_xlabel(\"\")\n",
+        "        ax.set_ylabel(\"\")\n",
+        "    for ax in axes[len(selected_cols):]:\n",
+        "        ax.axis(\"off\")\n",
+        "    fig.suptitle(title, fontsize=14, y=1.01)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "def plot_numeric_correlation_heatmap(df: pd.DataFrame, feature_cols: list[str], title: str, max_features: int = 18):\n",
+        "    numeric_cols = [c for c in feature_cols if c in df.columns and pd.api.types.is_numeric_dtype(df[c])]\n",
+        "    if len(numeric_cols) < 2:\n",
+        "        print(f\"{title}: 상관관계를 계산할 수치형 feature가 부족합니다.\")\n",
+        "        return\n",
+        "    selected_cols = numeric_cols[:max_features]\n",
+        "    corr = df[selected_cols].corr(numeric_only=True)\n",
+        "    plt.figure(figsize=(11, 8))\n",
+        "    sns.heatmap(corr, cmap=\"coolwarm\", center=0, linewidths=0.3, cbar_kws={\"shrink\": 0.8})\n",
+        "    plt.title(title)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "def transformed_feature_names(pipeline):\n",
+        "    return pipeline.named_steps[\"preprocess\"].get_feature_names_out().tolist()\n",
+        "\n",
+        "\n",
+        "def model_feature_importance(pipeline) -> pd.DataFrame:\n",
+        "    feature_names = transformed_feature_names(pipeline)\n",
+        "    model = pipeline.named_steps[\"model\"]\n",
+        "    if hasattr(model, \"feature_importances_\"):\n",
+        "        values = np.asarray(model.feature_importances_, dtype=float)\n",
+        "    elif hasattr(model, \"coef_\"):\n",
+        "        values = np.abs(np.asarray(model.coef_).reshape(-1))\n",
+        "    else:\n",
+        "        return pd.DataFrame(columns=[\"feature\", \"importance\"])\n",
+        "    if len(values) != len(feature_names):\n",
+        "        return pd.DataFrame(columns=[\"feature\", \"importance\"])\n",
+        "    importance = pd.DataFrame({\"feature\": feature_names, \"importance\": values})\n",
+        "    importance = importance.groupby(\"feature\", as_index=False)[\"importance\"].sum()\n",
+        "    importance = importance.sort_values(\"importance\", ascending=False).reset_index(drop=True)\n",
+        "    total = importance[\"importance\"].sum()\n",
+        "    if total > 0:\n",
+        "        importance[\"importance_ratio\"] = importance[\"importance\"] / total\n",
+        "    else:\n",
+        "        importance[\"importance_ratio\"] = 0.0\n",
+        "    return importance\n",
+        "\n",
+        "\n",
+        "def plot_model_feature_importance(importance_df: pd.DataFrame, title: str, top_n: int = 25):\n",
+        "    if importance_df.empty:\n",
+        "        print(f\"{title}: 표시할 feature importance가 없습니다.\")\n",
+        "        return\n",
+        "    plot_df = importance_df.head(top_n).sort_values(\"importance\", ascending=True)\n",
+        "    plt.figure(figsize=(10, max(5, top_n * 0.28)))\n",
+        "    ax = sns.barplot(data=plot_df, x=\"importance\", y=\"feature\", palette=\"viridis\")\n",
+        "    ax.set_title(title)\n",
+        "    ax.set_xlabel(\"중요도\")\n",
+        "    ax.set_ylabel(\"Feature\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    display(importance_df.head(top_n))\n",
+        "\n",
+        "\n",
+        "def single_feature_auc(df: pd.DataFrame, feature_col: str, target_col: str) -> float:\n",
+        "    y = df[target_col].astype(int)\n",
+        "    x = df[feature_col]\n",
+        "    if y.nunique() < 2 or x.nunique(dropna=True) < 2:\n",
+        "        return np.nan\n",
+        "    if pd.api.types.is_numeric_dtype(x):\n",
+        "        encoded = pd.to_numeric(x, errors=\"coerce\")\n",
+        "        encoded = encoded.fillna(encoded.median())\n",
+        "    else:\n",
+        "        filled = x.astype(\"string\").fillna(\"__MISSING__\")\n",
+        "        encoded = filled.map(y.groupby(filled).mean()).astype(float)\n",
+        "    try:\n",
+        "        auc = roc_auc_score(y, encoded)\n",
+        "        return float(max(auc, 1.0 - auc))\n",
+        "    except Exception:\n",
+        "        return np.nan\n",
+        "\n",
+        "\n",
+        "def drop_suspicious_features(df: pd.DataFrame, target_col: str, feature_cols: list[str], *, context: str, max_single_feature_auc: float = 0.985) -> list[str]:\n",
+        "    rows = []\n",
+        "    for col in feature_cols:\n",
+        "        auc = single_feature_auc(df, col, target_col)\n",
+        "        if not np.isnan(auc):\n",
+        "            rows.append({\"feature\": col, \"single_feature_auc\": auc})\n",
+        "    audit = pd.DataFrame(rows).sort_values(\"single_feature_auc\", ascending=False)\n",
+        "    suspicious = audit[audit[\"single_feature_auc\"] >= max_single_feature_auc][\"feature\"].tolist() if not audit.empty else []\n",
+        "    if not audit.empty:\n",
+        "        print(f\"{context} 단일 feature 예측력 상위 10개\")\n",
+        "        display(audit.head(10))\n",
+        "    if suspicious:\n",
+        "        print(f\"{context}에서 과도하게 정답을 설명하는 feature를 제외합니다:\", suspicious)\n",
+        "    kept = [col for col in feature_cols if col not in suspicious]\n",
+        "    if not kept:\n",
+        "        raise ValueError(f\"{context}: suspicious feature 제거 후 남은 feature가 없습니다.\")\n",
+        "    return kept\n",
+        "\n",
+        "\n",
+        "def select_model_features(df: pd.DataFrame, target_col: str, exclude_cols: list[str], leakage_cols: list[str] | None = None, *, context: str) -> list[str]:\n",
+        "    leakage_cols = leakage_cols or []\n",
+        "    excluded = set(exclude_cols) | set(leakage_cols) | {target_col}\n",
+        "    feature_cols = [c for c in df.columns if c not in excluded]\n",
+        "    if target_col in feature_cols:\n",
+        "        raise ValueError(f\"{context}: target column leaked into features: {target_col}\")\n",
+        "    leaked = sorted(set(feature_cols) & set(leakage_cols))\n",
+        "    if leaked:\n",
+        "        raise ValueError(f\"{context}: leakage columns leaked into features: {leaked}\")\n",
+        "    if not feature_cols:\n",
+        "        raise ValueError(f\"{context}: 학습에 사용할 feature가 없습니다.\")\n",
+        "    present_excluded = sorted((set(df.columns) & set(leakage_cols)) - {target_col})\n",
+        "    if present_excluded:\n",
+        "        print(f\"{context} 정답 유출 방지 제외 컬럼: {present_excluded}\")\n",
+        "    return feature_cols\n",
+        "\n",
+        "\n",
+        "def ensure_feature_columns_exist(df: pd.DataFrame, feature_cols: list[str], *, context: str) -> None:\n",
+        "    missing_cols = [c for c in feature_cols if c not in df.columns]\n",
+        "    if missing_cols:\n",
+        "        raise ValueError(f\"{context}: test 데이터에 없는 feature 컬럼이 있습니다: {missing_cols}\")\n",
+        "\n",
+        "\n",
+        "def risk_grade(proba):\n",
+        "    if proba >= 0.75:\n",
+        "        return \"HIGH\"\n",
+        "    if proba >= 0.45:\n",
+        "        return \"MEDIUM\"\n",
+        "    return \"LOW\""
+      ]
     },
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-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-32734db2-8443-4fbf-a77b-616fb1fbed24');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc120\\uc815_\\uadfc\\uac70\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RandomForest \\ubaa8\\ub378\\uc774 validation PR-AUC/Recall/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub370\\uc774\\ud130\\uc14b_\\uaddc\\ubaa8_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud14c\\uc2a4\\ud2b8_\\ucf00\\uc774\\uc2a4_\\uc218_\\uad00\\ub9ac\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc815\\ud655\\ub3c4_Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.91395,\n        \"max\": 0.91395,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc624\\ud0d0\\ub960_False_Positive_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0828514439918875,\n        \"max\": 0.0828514439918875,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ubaa8\\ub378_\\uc131\\ub2a5_\\uac1c\\uc120_\\ud604\\ud669\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "trained_models = {}\n",
-    "model_valid_probabilities = {}\n",
-    "model_thresholds = {}\n",
-    "model_confusion_matrices = {}\n",
-    "model_eval_rows = []\n",
-    "\n",
-    "# 최종 후보 비교용 학습 데이터셋 규모 관리\n",
-    "if len(X_train) > FINAL_TRAIN_SAMPLE_SIZE:\n",
-    "    X_final_train, _, y_final_train, _ = train_test_split(\n",
-    "        X_train,\n",
-    "        y_train,\n",
-    "        train_size=FINAL_TRAIN_SAMPLE_SIZE,\n",
-    "        random_state=RANDOM_STATE,\n",
-    "        stratify=y_train,\n",
-    "    )\n",
-    "else:\n",
-    "    X_final_train = X_train\n",
-    "    y_final_train = y_train\n",
-    "\n",
-    "print(f\"Final candidate training rows: {len(X_final_train):,} / train rows: {len(X_train):,}\")\n",
-    "\n",
-    "# 후보 모델 전체 학습\n",
-    "for model_name, config in candidate_configs.items():\n",
-    "    started_at = datetime.now()\n",
-    "    estimator = clone(config[\"estimator\"])\n",
-    "    estimator.set_params(**best_params_by_model[model_name])\n",
-    "    print(f\"[{model_name}] final fit start params={best_params_by_model[model_name]}\")\n",
-    "    estimator.fit(X_final_train, y_final_train)\n",
-    "\n",
-    "    proba = predict_positive_proba(estimator, X_valid)\n",
-    "    threshold = find_best_threshold(y_valid, proba)\n",
-    "    pred = (proba >= threshold).astype(int)\n",
-    "    cm_model = confusion_matrix(y_valid, pred, labels=[0, 1])\n",
-    "    metrics = compute_binary_metrics(y_valid, proba, threshold)\n",
-    "\n",
-    "    trained_models[model_name] = estimator\n",
-    "    model_valid_probabilities[model_name] = proba\n",
-    "    model_thresholds[model_name] = threshold\n",
-    "    model_confusion_matrices[model_name] = cm_model\n",
-    "\n",
-    "    model_eval_rows.append({\n",
-    "        \"model_name\": model_name,\n",
-    "        \"threshold\": threshold,\n",
-    "        \"selection_reason\": config[\"selection_reason\"],\n",
-    "        **metrics,\n",
-    "    })\n",
-    "    print(\n",
-    "        f\"[{model_name}] final fit done \"\n",
-    "        f\"PR-AUC={metrics['pr_auc']:.5f} \"\n",
-    "        f\"ROC-AUC={metrics['roc_auc']:.5f} \"\n",
-    "        f\"ACC={metrics['accuracy']:.5f} \"\n",
-    "        f\"FPR={metrics['false_positive_rate']:.5f} \"\n",
-    "        f\"elapsed={(datetime.now() - started_at).total_seconds():.1f}s\"\n",
-    "    )\n",
-    "\n",
-    "model_comparison = pd.DataFrame(model_eval_rows).sort_values(\n",
-    "    [\"pr_auc\", \"recall\", \"f1\", \"roc_auc\", \"accuracy\"],\n",
-    "    ascending=False,\n",
-    ").reset_index(drop=True)\n",
-    "\n",
-    "selected_model_name = str(model_comparison.iloc[0][\"model_name\"])\n",
-    "model = trained_models[selected_model_name]\n",
-    "valid_proba = model_valid_probabilities[selected_model_name]\n",
-    "best_threshold = float(model_thresholds[selected_model_name])\n",
-    "valid_pred = (valid_proba >= best_threshold).astype(int)\n",
-    "cm = model_confusion_matrices[selected_model_name]\n",
-    "\n",
-    "# 운영용 전체 재학습은 선택 모델 1개만 수행\n",
-    "if RETRAIN_SELECTED_MODEL_ON_FULL_DATA and len(X_final_train) < len(X_train):\n",
-    "    print(f\"[{selected_model_name}] selected model full-data refit start rows={len(X_train):,}\")\n",
-    "    refit_started_at = datetime.now()\n",
-    "    full_model = clone(candidate_configs[selected_model_name][\"estimator\"])\n",
-    "    full_model.set_params(**best_params_by_model[selected_model_name])\n",
-    "    full_model.fit(X_train, y_train)\n",
-    "\n",
-    "    model = full_model\n",
-    "    trained_models[selected_model_name] = full_model\n",
-    "    valid_proba = predict_positive_proba(model, X_valid)\n",
-    "    best_threshold = find_best_threshold(y_valid, valid_proba)\n",
-    "    valid_pred = (valid_proba >= best_threshold).astype(int)\n",
-    "    cm = confusion_matrix(y_valid, valid_pred, labels=[0, 1])\n",
-    "    full_metrics = compute_binary_metrics(y_valid, valid_proba, best_threshold)\n",
-    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), list(full_metrics.keys())] = list(full_metrics.values())\n",
-    "    model_comparison.loc[model_comparison[\"model_name\"].eq(selected_model_name), \"threshold\"] = best_threshold\n",
-    "    model_thresholds[selected_model_name] = best_threshold\n",
-    "    model_confusion_matrices[selected_model_name] = cm\n",
-    "    print(f\"[{selected_model_name}] full-data refit done elapsed={(datetime.now() - refit_started_at).total_seconds():.1f}s\")\n",
-    "\n",
-    "roc_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"roc_auc\"])\n",
-    "pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
-    "accuracy = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"accuracy\"])\n",
-    "false_positive_rate = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"false_positive_rate\"])\n",
-    "\n",
-    "# AI 관리 요약\n",
-    "baseline_row = model_comparison[model_comparison[\"model_name\"].eq(\"LightGBM\")]\n",
-    "baseline_pr_auc = float(baseline_row.iloc[0][\"pr_auc\"]) if not baseline_row.empty else np.nan\n",
-    "best_pr_auc = float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"pr_auc\"])\n",
-    "performance_improvement = best_pr_auc - baseline_pr_auc if not np.isnan(baseline_pr_auc) else 0.0\n",
-    "\n",
-    "ai_management_summary = {\n",
-    "    \"모델_선정_근거\": f\"{selected_model_name} 모델이 validation PR-AUC/Recall/F1 기준 종합 순위 1위입니다. {candidate_configs[selected_model_name]['selection_reason']}\",\n",
-    "    \"데이터셋_규모_관리\": {\n",
-    "        \"max_rows\": int(MAX_ROWS),\n",
-    "        \"profile_rows\": int(PROFILE_ROWS),\n",
-    "        \"train_rows\": int(len(X_train)),\n",
-    "        \"candidate_train_rows\": int(len(X_final_train)),\n",
-    "        \"valid_rows\": int(len(X_valid)),\n",
-    "        \"feature_count\": int(len(feature_cols)),\n",
-    "        \"positive_ratio\": float(y.mean()),\n",
-    "        \"full_data_refit\": bool(RETRAIN_SELECTED_MODEL_ON_FULL_DATA),\n",
-    "    },\n",
-    "    \"테스트_케이스_수_관리\": {\n",
-    "        \"validation_total\": int(len(y_valid)),\n",
-    "        \"validation_normal\": int((y_valid == 0).sum()),\n",
-    "        \"validation_defect\": int((y_valid == 1).sum()),\n",
-    "        \"cv_splits\": int(effective_cv_splits),\n",
-    "        \"cv_sample_size\": int(len(X_cv_pool)),\n",
-    "        \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
-    "        \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-    "        \"smote_enabled\": bool(USE_SMOTE),\n",
-    "        \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
-    "        \"threshold_strategy\": {\n",
-    "            \"type\": \"recall_priority\",\n",
-    "            \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
-    "            \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
-    "            \"beta\": float(THRESHOLD_BETA),\n",
-    "        },\n",
-    "    },\n",
-    "    \"정확도_Accuracy\": accuracy,\n",
-    "    \"오탐률_False_Positive_Rate\": false_positive_rate,\n",
-    "    \"모델_성능_개선_현황\": {\n",
-    "        \"baseline_model\": \"LightGBM\",\n",
-    "        \"baseline_pr_auc\": baseline_pr_auc,\n",
-    "        \"selected_model\": selected_model_name,\n",
-    "        \"selected_pr_auc\": best_pr_auc,\n",
-    "        \"pr_auc_improvement_over_lightgbm\": performance_improvement,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "print(\"selected_model_name:\", selected_model_name)\n",
-    "print(\"ROC-AUC:\", round(roc_auc, 5))\n",
-    "print(\"PR-AUC:\", round(pr_auc, 5))\n",
-    "print(\"Accuracy:\", round(accuracy, 5))\n",
-    "print(\"False Positive Rate:\", round(false_positive_rate, 5))\n",
-    "print(\"Recall:\", round(float(model_comparison[model_comparison[\"model_name\"].eq(selected_model_name)].iloc[0][\"recall\"]), 5))\n",
-    "display(model_comparison)\n",
-    "display(pd.DataFrame([ai_management_summary]))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "AGL93TiObEfC",
-   "metadata": {
-    "id": "AGL93TiObEfC"
-   },
-   "source": [
-    "## 9. 후보 모델별 Threshold 튜닝 및 Confusion Matrix\n",
-    "\n",
-    "각 후보 모델은 validation set에서 recall 우선 threshold를 사용합니다.\n",
-    "\n",
-    "기본 정책은 `RECALL_PRIORITY_TARGET` 이상을 만족하는 threshold 중 precision이 가장 높은 값을 선택하고, 목표 recall을 만족하는 후보가 없으면 F-beta(기본 beta=2) 기준으로 선택합니다. Accuracy는 클래스 불균형 때문에 참고 지표로만 해석합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "-bI4yc81bEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
+      "cell_type": "markdown",
+      "id": "9065dfd2",
+      "metadata": {
+        "id": "9065dfd2"
+      },
+      "source": [
+        "## 4. 불량 탐지 모델 학습"
+      ]
     },
-    "id": "-bI4yc81bEfC",
-    "outputId": "fffa66b9-fb9b-4c84-b9d5-da0983048bc2"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "== Selected Model ==\n",
-      "model: RandomForest\n",
-      "best_threshold: 0.05887\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0     0.9960    0.9171    0.9549     59661\n",
-      "           1     0.0235    0.3510    0.0441       339\n",
-      "\n",
-      "    accuracy                         0.9140     60000\n",
-      "   macro avg     0.5098    0.6341    0.4995     60000\n",
-      "weighted avg     0.9905    0.9140    0.9498     60000\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "id": "41ceb1ad",
+      "metadata": {
+        "id": "41ceb1ad"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "불량 탐지 모델 4종을 바로 고정 파라미터로 학습하지 않고, 먼저 Stratified K-Fold 교차검증으로 하이퍼파라미터 후보를 비교합니다. CV는 train 데이터 일부를 층화 샘플링해 수행하므로 Colab 런타임 부담을 줄이면서도 class imbalance 상황에서 PR-AUC, Recall 재현율, F1을 기준으로 모델별 최적 파라미터를 고릅니다.\n",
+        "\n",
+        "선택된 파라미터로 전체 train 데이터를 다시 학습한 뒤 test 데이터에서 최종 성능을 계산합니다. 결과는 모델별 표와 시각화로 확인합니다.\n"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "01df8624",
+      "metadata": {
+        "id": "01df8624",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "460ddb84-96d7-419c-8232-9f6801e60ece"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "불량 탐지 정답 유출 방지 제외 컬럼: ['assembly_actual_sequence', 'assembly_expected_sequence', 'assembly_fastening_error_count', 'assembly_missing_part_count', 'assembly_sequence_error_count', 'assembly_sequence_mismatch_yn', 'body_robot_motion_status', 'bosch_id', 'defect_reason', 'ford_row_id', 'forming_row_id', 'machine_vision_row_id', 'operation_status', 'paint_defect_score', 'paint_surface_quality_score', 'paint_vision_label', 'press_count_increase_yn', 'press_timestamp_delay_sec', 'robot_arm_vibration_row_id']\n",
+            "불량 탐지 단일 feature 예측력 상위 10개\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                    feature  single_feature_auc\n",
+              "22        station_delay_sec            0.598710\n",
+              "26  equipment_idle_time_sec            0.598709\n",
+              "12       robot_frequency_hz            0.587136\n",
+              "14    robot_vibration_score            0.587136\n",
+              "8           vibration_score            0.587136\n",
+              "21      processing_time_sec            0.571561\n",
+              "10           vibration_peak            0.567340\n",
+              "4        current_rms_ampere            0.563096\n",
+              "5        current_max_ampere            0.562935\n",
+              "28   paint_thermal_std_temp            0.561908"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-77a0cca5-a82e-412b-a2ee-fee7ea33898f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>single_feature_auc</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>station_delay_sec</td>\n",
+              "      <td>0.598710</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>equipment_idle_time_sec</td>\n",
+              "      <td>0.598709</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>robot_frequency_hz</td>\n",
+              "      <td>0.587136</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>robot_vibration_score</td>\n",
+              "      <td>0.587136</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>vibration_score</td>\n",
+              "      <td>0.587136</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>processing_time_sec</td>\n",
+              "      <td>0.571561</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>vibration_peak</td>\n",
+              "      <td>0.567340</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>current_rms_ampere</td>\n",
+              "      <td>0.563096</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>current_max_ampere</td>\n",
+              "      <td>0.562935</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>paint_thermal_std_temp</td>\n",
+              "      <td>0.561908</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77a0cca5-a82e-412b-a2ee-fee7ea33898f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-77a0cca5-a82e-412b-a2ee-fee7ea33898f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-77a0cca5-a82e-412b-a2ee-fee7ea33898f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(event_model_result_table)\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"current_max_ampere\",\n          \"equipment_idle_time_sec\",\n          \"processing_time_sec\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"single_feature_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014787816104544944,\n        \"min\": 0.5619084321068056,\n        \"max\": 0.5987104531222494,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.562934865948325,\n          0.5987092717698705,\n          0.567339779321555\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "불량 탐지 feature 수: 35\n",
+            "불량 탐지 feature: ['process_code', 'station_code', 'equipment_code', 'equipment_type', 'current_rms_ampere', 'current_max_ampere', 'current_min_ampere', 'vibration_acceleration_g', 'vibration_score', 'vibration_rms', 'vibration_peak', 'robot_axis', 'robot_frequency_hz', 'robot_amplitude', 'robot_vibration_score', 'thermal_score', 'avg_temperature', 'max_temperature', 'min_temperature', 'cycle_time_sec', 'waiting_time_sec', 'processing_time_sec', 'station_delay_sec', 'throughput_per_min', 'queue_length', 'wip_count', 'equipment_idle_time_sec', 'press_target_cycle_time_sec', 'body_robot_operation_mode', 'body_frequency_peak_band', 'body_frequency_band_max', 'body_frequency_band_mean', 'paint_image_position', 'paint_thermal_std_temp', 'paint_thickness_value']\n",
+            "불량 탐지 scale_pos_weight: 3.841\n",
+            "튜닝 설정: enabled=True, folds=3, trials=3, max_rows=12000\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1100x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   split label  count     ratio\n",
+              "0  Train    정상  30468  0.793438\n",
+              "1  Train    불량   7932  0.206563\n",
+              "2   Test    정상   7672  0.799167\n",
+              "3   Test    불량   1928  0.200833"
+            ],
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>split</th>\n",
+              "      <th>label</th>\n",
+              "      <th>count</th>\n",
+              "      <th>ratio</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Train</td>\n",
+              "      <td>정상</td>\n",
+              "      <td>30468</td>\n",
+              "      <td>0.793438</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Train</td>\n",
+              "      <td>불량</td>\n",
+              "      <td>7932</td>\n",
+              "      <td>0.206563</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Test</td>\n",
+              "      <td>정상</td>\n",
+              "      <td>7672</td>\n",
+              "      <td>0.799167</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Test</td>\n",
+              "      <td>불량</td>\n",
+              "      <td>1928</td>\n",
+              "      <td>0.200833</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-37b4c089-367f-410d-9fba-3efdc0c9a7b9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-37b4c089-367f-410d-9fba-3efdc0c9a7b9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(event_model_result_table)\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Test\",\n          \"Train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"\\ubd88\\ub7c9\",\n          \"\\uc815\\uc0c1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12619,\n        \"min\": 1928,\n        \"max\": 30468,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7932,\n          1928\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34215616400574994,\n        \"min\": 0.20083333333333334,\n        \"max\": 0.7991666666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2065625,\n          0.20083333333333334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "defect_yn        정상 비율     불량 비율\n",
+              "process_code                    \n",
+              "ASSEMBLY      0.793438  0.206563\n",
+              "BODY          0.784479  0.215521\n",
+              "PAINT         0.779792  0.220208\n",
+              "PRESS         0.816042  0.183958"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-14cdd9a4-a1ee-4fd8-9775-6d62c6d9e155\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>defect_yn</th>\n",
+              "      <th>정상 비율</th>\n",
+              "      <th>불량 비율</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>process_code</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>ASSEMBLY</th>\n",
+              "      <td>0.793438</td>\n",
+              "      <td>0.206563</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BODY</th>\n",
+              "      <td>0.784479</td>\n",
+              "      <td>0.215521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PAINT</th>\n",
+              "      <td>0.779792</td>\n",
+              "      <td>0.220208</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PRESS</th>\n",
+              "      <td>0.816042</td>\n",
+              "      <td>0.183958</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14cdd9a4-a1ee-4fd8-9775-6d62c6d9e155')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-14cdd9a4-a1ee-4fd8-9775-6d62c6d9e155 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-14cdd9a4-a1ee-4fd8-9775-6d62c6d9e155');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(event_model_result_table)\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"BODY\",\n          \"PRESS\",\n          \"ASSEMBLY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc815\\uc0c1 \\ube44\\uc728\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016097710542876044,\n        \"min\": 0.7797916666666667,\n        \"max\": 0.8160416666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.7844791666666666,\n          0.8160416666666667,\n          0.7934375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ubd88\\ub7c9 \\ube44\\uc728\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016097710542876034,\n        \"min\": 0.18395833333333333,\n        \"max\": 0.22020833333333334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.21552083333333333,\n          0.18395833333333333,\n          0.2065625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x1440 with 24 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1100x800 with 2 Axes>"
+            ],
+            "image/png": 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k5MTAgQNp1KiRtezQoUP06dOH0aNH07ZtW5v6Dz/8MF999RWJiYn5juH6BfKuLc/590bDsn/88UcmTpzIa6+9xjPPPGM9zsfHh6+//prx48ezbNmym/Ymu7u789RTT7F06VJ++eUXlixZQkpKCiVKlKBChQo0bdqUbt264e/vn+9ru1b58uVJS0u74QuYefPmERYWxvTp061lp0+ftlvD4dph/tfKGZZ+rT///DNf8fn6+rJv3758vyC6ntlsplu3bmRnZ/PKK69Qr149vLy8OH36NBs2bGDSpEk0a9bM5hgnJyfrOgLLli2jVq1aVK9ePd/ndHd3x93dnUuXLrFgwQJ69OhRoJh/+OEHoqKiGD58OJ07dy7QsSIiUriU4IuIyF3vZsOwBwwYwLlz5xg4cCB16tTBy8uL9PR0jhw5wurVq3nxxReZMWNGnvO5r7dr1y6mTZuW52r777//PgcOHLApq1q1KitXrsxz3ryfn1+un9IrLE2bNiU8PDzXhdFKlCjBuHHj8tWOxWKhZ8+eZGdn8/LLL1O3bl28vLy4dOkSSUlJ/PTTT8ybN++GL2BuJDg4mG+//ZZXX301zx7x06dP89BDD9mU/frrr3b13Nzccl248b777mPPnj3Wlebh6sry+dGoUSOmTZvGnj17qFWrVr6OuVZcXBx//PEHa9eutbm+ypUr07lzZxo3bsyjjz7Kyy+/nOtaAvPnz+eZZ57JM8GvUKECtWvXznXfqVOnmDRp0g0T/Hr16tl8PhCuvtSYN28e9evXz88liojI30gJvoiIFGkpKSk88cQTnDt3Ls8eWGdn5zyTFIvFwm+//cb3339vkwS6uLhQq1YtatWqxcGDB1mzZk2+E3y4mgTn1UNeokSJXHv3C7IoXmG7/jNst+rkyZNs2rSJuLg4fHx8rOWlSpWiYcOGNGzYkKSkJFatWkXPnj0L3H6XLl345ptveOmllxgxYgQVK1bk+PHjrF+/3rpGQ0BAAAsWLKBJkyZUqFCB9evXM3fuXLv58vfddx+nTp0iNjaWqlWrcs899+Dr60vr1q2ZPn06NWvWpEqVKnz99decOnUqX+srmEwmwsLCePnllxk9ejT+/v6cO3eO3377jVatWtklx7lxcnLK81xubm7WIfi3IiwsjLCwsFs6Fsh16sHttCciIoVLCb6IiBRpJ0+e5NKlS+zcufOWkhyDwUCzZs0YN24c/fv3p1atWnh6emI2mzl27BgxMTGsXbuWmTNnFqjdtLQ00tPTc9136dKlW07I7ja+vr4EBgbyr3/9i1deeYVatWpRokQJMjIyOHToENHR0SQlJeVrREBuiW6ZMmX45ptveO+993j55Ze5ePEi5cqVs1kArmfPnqSlpTF06FBSUlJo2LAhn3zyCYMHD7ZZp8DX15dXXnmFN954A6PRyDvvvEPz5s155ZVXOH/+PL169eLixYuEhITw3nvv0axZM5uXOM7Ozrm+1Jk6dSpTpkxh3LhxnD59Gk9PT/z9/Wnfvv1Nrzk0NJT77ruPHj160Lt3bx566CE8PDxISUkhPj6eqVOn0rFjxzynABiNRi5fvpzn32IOFxcXuykdOdtpaWnWr0nk5vpPT4qISNFlsOTnA7UiIiIOktODn5KScsOk2WAwsHLlylwToWvn4B8+fJjz58/j4uJinYP/3HPP2Q3xvpE+ffqwZs2aPOfgOzs7M2TIkAJ9cu5aiYmJDB8+nFWrVhX42CtXruDv709cXNwdGzHw448/snTpUj7//PNc92dkZLBo0SJWrFjBwYMHuXDhAu7u7lSoUIFmzZrx7LPP3nCxvn+6y5cv88UXX/DLL79w5MgR0tLSKFOmDLVr1+aZZ56hVatWeR777rvvMnfu3JtOU3n99dftvkpx5coV2rRpw5EjR24aY86nJ/MrMDCQ6OhoypcvT/369YmNjc11OoiIiNxZSvBFROQf5/pV9EWKEv19iojIrVKCLyIiIiIiIlIM6PWwiIiIiIiISDGgBF9ERERERESkGFCCLyIiIiIiIlIMKMEXERERERERKQaU4IuIiIiIiIgUA86ODkCkKPnZpaajQ7DTxrwXUGwFlRPbCq/aDo4kdy3P7wbgzK4NDo7EXtm6oQCc+n2TgyOxV65OMAAJe1MdG0guAmuWBvRMCyrnmRbl+5aUnOzgSOz5Va8OKLaCuhtiS05KcnAk9qr7+QGwO+mogyOxV9uvIgAHkw44OBJ71fxqAEU7Nil+1IMvIiIiIiIiUgwowRcREREREREpBpTgi4iIiIiIiBQDSvBFREREREREigEl+CIiIiIiIiLFgBJ8ERERERERkWJACb6IiIiIiIhIMaAEX0RERERERKQYUIJ/h4wcOZIlS5Y4OgwRERERERH5h1KCf4dkZmaSmZnp6DBERERERETkH6rIJvjx8fF06dIFk8mEyWRiwIABREdHExkZaVe3ZcuW7NixA4ATJ04QHh7Ozp07adWqFcHBwWRkZDB79mw+/PBDZs6cSaNGjRg5ciQAly9fZvTo0QQFBREYGMiwYcNIS0uztj1s2DC+/PJLBgwYQGBgIKGhoQwfPtxaZ+fOnZhMJqKjoxk7diwmk4nt27fn6xpHjRrFggULiIqKIjg4mOnTp2M2m6lbty5r1qwhIiKCgIAAevbsSUpKCnFxcTzxxBMEBwfTr18/UlJSrG0dP36cXr16ERQURHBwMIMHD873vf7Pf/5D165dCQ4OJjg4mK5du7J//37r/kWLFvHGG28wceJEGjVqREhICDNnziQ7O5v333+fRx55hCZNmvD555/btNujRw9Wr15N3759adSoEcHBwbz99ttkZGTY1NuwYQNPPvkk/v7+tG7dmtWrV1v35fU8AaKjo4mIiMDf35+nn36abdu25fuaRUREREREipsimeBv3LiRQYMG8cILL7Bp0yYSEhIYM2YMGRkZdskhYFNuNpu5dOkSs2bNYv78+WzcuBFXV1fS09NZt24dly5dIi4ujqioKODq0Ppjx46xfPly1qxZQ3Z2NmPHjrW2bTAY+OijjwgODmb9+vWsXLmS48ePM2XKFADq1atHYmIibdu2ZfTo0SQmJtKgQYN8XWdGRgYLFy6kRo0abNq0iT59+uDi4oLZbOaTTz7hs88+Y/PmzTz00EOMGDGCiRMnMm3aNOLi4rj//vuZMGGCta1x48bRsGFD4uPjiY+P54033sj3/c7KymLIkCHExcWxceNGIiIiGDp0qHW/k5MTK1euxGAwEBsbS3R0NNHR0QwePJjU1FRiYmL4/vvvWbx4MRs3brQeZzabGTduHBEREcTHx/PTTz+RkJDABx98YK2zf/9+BgwYwKuvvmp9zm+++SZJSUk3fJ7r169n3LhxvP322yQkJBAZGUmfPn1ITU3N93WLiIiIiIgUJ0UywR83bhwjRoygdevWODk5YTAY8Pb2zvfx58+f58knn8THxwej8f8v8ejRowwYMAAnJyeMRiNJSUmsXr2ayZMnU7ZsWTw8PBg1ahSrV6/m9OnT1uNMJhPPP/88bm5ueHp6EhkZSUxMzB251szMTLp06QJgE2vfvn2pXLkyTk5OdO/enTVr1jBgwADuv/9+nJ2d6dmzJ7/99pu1/sGDB2ncuDFGoxGDwUD58uXzHUNAQAANGjTA2dkZo9FI586d2bNnDxcvXrTW8fT0ZPDgwbi5uVG2bFnat29PfHw8b731Fq6urpQrV4727dsTGxtr03ZQUBDt2rUD4N5772XcuHEsXLiQ9PR0AGbMmMFzzz1HREQEzs7OBAUF8eyzz/LNN99Y28jteU6dOpVBgwYRFBSEs7MzLVu2JCwsjB9++CHf1y0iIiIiIlKcFLkE/+DBgxw6dIjWrVvfVjsmk8muzN/fH2dnZ+v2jh07aNiwIaVLl7aWeXp6UqVKFQ4cOGAtq127tk07FSpU4MSJE7cVX47AwMBcy2vUqGH975yXG9fW9fb2tumt7tSpE8OGDbNLsPPj0qVLTJs2jY4dOxISEkKjRo2wWCw27VerVs3m3pUpU4YGDRrg6uqaZ0wAjRs3ttmuX78+BoOBI0eOAFenBzRv3tyuzp49e2zKrn2eWVlZ7Nq1y+64Bg0a2B0nIiIiIiLyT+F88yp/r5SUFLy9vW2SyZuxWCw220aj0SZpz1GmTBmb7RMnTrBp0ya7lwFms5nz589bt93c3Gz2Ozs7k52dne/4buT6mK49x/Vyu6Yc3bp1o3bt2rz//vvMmDGDUaNG8dBDD+Urhv79+3Px4kUGDRpE/fr18fDwoE6dOjb31cnJqUDx5ChVqpRdmYeHh3UNg5MnT9KrVy8MBoN1f3Z2Nvfff791+/rnefr0aTIzM+1eAmVlZREaGnrTmERERERERIqjIpfge3t7c+bMGTIyMmx6h+Fqonf9SvUWi8VmOD1cnTd/bcJ47fHXKlmyJOHh4UyfPv0ORV9w18d0O4KCgvj222/54osvrAvclShR4obH/PnnnyQkJLB+/Xq8vLyAqy8+7tQLjDNnzthsZ2dnc+bMGesUgpIlSzJv3jxq1aqVZxvXP8+SJUsCsGbNGjw9Pe9InCIiIiIiIne7IjdEv2rVqtx///0sXrzYbp+Pjw/Hjh2zKUtMTMx14b38qFu3Ljt27ODKlSu3dPy1nJ2d7UYSOEr37t0xGo3s27fvpnXPnj2Ll5eXNbkHWLdu3R2LZf369TbbcXFxlCtXDl9fX+DqM9i0aVOB2vTw8KBq1aoFPk5ERERERKQ4K3IJvsFgYNiwYUyePJmff/7Z2pN89uxZGjRowIULF/juu+8A+Ouvv3jvvfeoVKnSLZ3L39+fSpUqMWTIEI4fPw5AamoqGzZsKHBbPj4+bN++HYvFYvOZvb/L1q1bycjIwGKx8PPPP5Oenm4zzD0vfn5+XL58mZUrV2KxWNiyZQvz5s3Dx8fnjsS1ceNGfvjhB8xmM4cPH+add96xvoCAqy8jpk2bxsqVK8nIyCA7O5tt27bddI2Dnj17EhUVxcaNG8nKyiIzM5O4uDiH3HsREREREZGioMgl+ABNmzblk08+4fPPP8dkMhEQEMCwYcPw9PRkypQpzJs3j7CwMHr37k3//v3x9fW1Dud3cXGxmzMPV+fR51Y+Y8YMvLy8aN++PQ0bNuSpp55i586d1v2urq52UwVyO0eHDh3Ytm0bJpPJ7nvweXF1dc01Jnd3d1xcXGzKchtqf23Zu+++S8OGDTGZTHzxxRfMmDEjz/n91/Lw8GDGjBl88sknBAYGMmnSJCZMmICXl5f15Upu9y63+5Jb2Ztvvskvv/xCaGgozz77LG3btqVr167W/Y0bN2b8+PHMnDmT4OBggoODmTx5snVURl7Ps1OnTrz66qtERUVhMpkICwtjzpw5N71eERERERGR4spgKSrjyqXY6dq1K/369SM4ONjRoeTbzy41HR2CnTbmvYBiK6ic2FZ41b5JTcdoeX43AGd2FXzEUGErW/fqYpWnfi9602DK1bn6vycJe1MdG0guAmuWBvRMCyrnmRbl+5aUnOzgSOz5Va8OKLaCuhtiS05KcnAk9qr7+QGwO+mogyOxV9uvIgAHkw7cpObfr5rf1a9iFeXYpPgpcovsFQcpKSlERETkOSffYDCwfv36XHumi1McTk5OdiMRREREREREpHAowS8E3t7eJCQkODoMh8cxd+5ch51bRERERETkn6ZIzsEXERERERERkYJRgi8iIiIiIiJSDCjBFxERERERESkGlOCLiIiIiIiIFANK8EVERERERESKASX4IiIiIiIiIsWAwZLXR9JFRERERERE5K6hHnwRERERERGRYsDZ0QGIFCU/u9R0dAh22pj3AoqtoHJiW+FV28GR5K7l+d0AnNm1wcGR2CtbNxSAU79vcnAk9srVCQYgYW+qYwPJRWDN0oCeaUHlPNOifN+SkpMdHIk9v+rVAcVWUIrt1uTEtjvpqIMjsVfbryIAB5MOODgSe9X8agBFOzYpftSDLyIiIiIiIlIMKMEXERERERERKQaU4IuIiIiIiIgUA0rwRURERERERIoBJfgiIiIiIiIixYASfBEREREREZFiQAm+iIiIiIiISDGgBF9ERERERESkGFCCf4tGjhzJkiVLHB2GiIiIiIiICKAE/5ZlZmaSmZnp6DBEREREREREgCKU4MfHx9OlSxdMJhMmk4kBAwYQHR1NZGSkXd2WLVuyY8cOAE6cOEF4eDg7d+6kVatWBAcHk5GRwezZs/nwww+ZOXMmjRo1YuTIkQBcvnyZ0aNHExQURGBgIMOGDSMtLc3a9rBhw/jyyy8ZMGAAgYGBhIaGMnz4cGudnTt3YjKZiI6OZuzYsZhMJrZv356vaxw6dCifffYZvXv3JiAggMcff5y1a9eSmppK//79CQ4Opk2bNmzYsMHmuMWLF9OuXTsCAwMJCwtj6NChnD9/HoBz584RGhrKli1brPWjo6Pp3Llzvu/9f/7zH7p27UpwcDDBwcF07dqV/fv3W/cvWrSIN954g4kTJ9KoUSNCQkKYOXMm2dnZvP/++zzyyCM0adKEzz//3KbdHj16sHr1avr27UujRo0IDg7m7bffJiMjw6behg0bePLJJ/H396d169asXr3aui+v55tznREREfj7+/P000+zbdu2fF+ziIiIiIhIcVMkEvyNGzcyaNAgXnjhBTZt2kRCQgJjxowhIyPDLhkEbMrNZjOXLl1i1qxZzJ8/n40bN+Lq6kp6ejrr1q3j0qVLxMXFERUVBVwdWn/s2DGWL1/OmjVryM7OZuzYsda2DQYDH330EcHBwaxfv56VK1dy/PhxpkyZAkC9evVITEykbdu2jB49msTERBo0aJCv63RycmL69Ok8+eSTbN68mQ8++IA333yT4cOH06RJEzZt2sSECRMYOnQoqamp1uM8PDyYNm0amzdvJiYmhuzsbKZNmwZAqVKlGDRoEG+//TbZ2dlcuHCBSZMmWV9o5EdWVhZDhgwhLi6OjRs3EhERwdChQ23iXrlyJQaDgdjYWKKjo4mOjmbw4MGkpqYSExPD999/z+LFi9m4caP1OLPZzLhx44iIiCA+Pp6ffvqJhIQEPvjgA2ud/fv3M2DAAF599VXrc3/zzTdJSkq64fNdv34948aN4+233yYhIYHIyEj69Oljc99ERERERET+SYpEgj9u3DhGjBhB69atcXJywmAw4O3tne/jz58/z5NPPomPjw9G4/9f0tGjRxkwYABOTk4YjUaSkpJYvXo1kydPpmzZsnh4eDBq1ChWr17N6dOnrceZTCaef/553Nzc8PT0JDIykpiYmDtyrY888gitWrXCycmJunXrUr9+fQwGA8888wwA9evXp06dOmzdutV6zOOPP06VKlUwGAy4u7vToUMHEhMTrfufeeYZSpQowYIFC5gyZQqPPfYY9erVy3dMAQEBNGjQAGdnZ4xGI507d2bPnj1cvHjRWsfT05PBgwfj5uZG2bJlad++PfHx8bz11lu4urpSrlw52rdvT2xsrE3bQUFBtGvXDoB7772XcePGsXDhQtLT0wGYMWMGzz33HBERETg7OxMUFMSzzz7LN998Y20jt+c7depUBg0aRFBQEM7OzrRs2ZKwsDB++OGHfF+3iIiIiIhIceLs6AAOHjzIoUOHaN269W21YzKZ7Mr8/f1xdv7/S9yxYwcNGzakdOnS1jJPT0+qVKnCgQMH8PHxAaB27do27VSoUIETJ07cVnw5HnjgAZvtMmXKULNmTZsyb29vm57oY8eOMXPmTLZu3crJkycxm82UKVPGut9gMBAVFUXXrl1xcXHh559/LlBMly5dYs6cOaxdu5ajR4+SkZGBxWIhNTWVkiVLAlCtWjWbe1mmTBkaNGiAq6urTdwHDhywabtx48Y22zkvNI4cOYKfn591esD1db744gubsmufb1ZWFrt27WLq1Kk2dRo0aMDvv/9eoGsXEREREREpLhye4KekpODt7W2TPN6MxWKx2TYajTZJe45rk2C4Op9706ZNdi8DzGazdU47gJubm81+Z2dnsrOz8x3fjTg5OdmVlSpVKs/6qampdOrUiSZNmjBhwgRq1KjBzp07GT58uE29KlWqUKJECe6//348PT0LFFP//v25ePEigwYNon79+nh4eFCnTh2b+5xb3Lnd8+vldm0eHh7WNQ1OnjxJr169MBgM1v3Z2dncf//91u3rn+/p06fJzMy0eymUlZVFaGjoTWMSEREREREpjhye4Ht7e3PmzBkyMjJseoPhamJ3/Ur1FovFZjg9XO3BvjZBvPb4a5UsWZLw8HCmT59+h6IvfL/++isVK1Zk/Pjx1rLjx4/b1ZszZw4PPvgghw4dYvXq1TRv3jxf7f/5558kJCSwfv16vLy8gKsvQu7UC40zZ87YbGdnZ3PmzBnKly8PXH0m8+bNo1atWnm2cf3zzRlVsGbNmgK/zBARERERESmuHD4Hv2rVqtx///0sXrzYbp+Pjw/Hjh2zKUtMTMx14b38qFu3Ljt27ODKlSu3dPy1nJ2d7UYSFIazZ89SsWJFm7L169fbbB8+fJi5c+fy1ltvMWzYMN555x3rHPf8tO/l5WVN7gHWrVt3+4HnEWtcXBzlypXD19cXuPpMNm3aVKA2PTw8qFq1aoGPExERERERKc4cnuAbDAaGDRvG5MmT+fnnn609x2fPnqVBgwZcuHCB7777DoC//vqL9957j0qVKt3Sufz9/alUqRJDhgyx9oKnpqbafZYuP3x8fNi+fTsWi8XmM3t3WoMGDdiwYQPJycmYzWa+++479u3bZ1Nn3Lhx9OjRgwoVKtC0aVOqVKnCrFmz8tW+n58fly9fZuXKlVgsFrZs2cK8efOs6xHcro0bN/LDDz9gNps5fPgw77zzDt27d7eOrujevTvTpk1j5cqVZGRkkJ2dzbZt22665kHPnj2Jiopi48aNZGVlkZmZSVxcXKE+CxERERERkaLM4Qk+QNOmTfnkk0/4/PPPMZlMBAQEMGzYMDw9PZkyZQrz5s0jLCyM3r17079/f3x9fa3D+V1cXOzmzMPVefS5lc+YMQMvLy/at29Pw4YNeeqpp9i5c6d1v6urq91UgdzO0aFDB7Zt24bJZLL7/nteXF1d7drJ7XzXlgUHB/Pqq6/y8ssvExoaym+//cakSZOsL0JWr17NsWPH6N69u/X44cOHM3/+fP7666+bxuTh4cGMGTP45JNPCAwMZNKkSUyYMAEvLy/rOXK7lzeLO8ebb77JL7/8QmhoKM8++yxt27a1WVSvcePGjB8/npkzZxIcHExwcDCTJ0+2jtLI6/l26tSJV199laioKEwmE2FhYcyZM+em1ysiIiIiInInnT17li5dutCrV6981U9MTKRTp04EBgby2GOP8e23396xWAyWv2Ocufwjde3alX79+hEcHOzoUPLtZ5eaN6/0N2tj3gsotoLKiW2FV+2b1HSMlud3A3BmV8FHEBW2snWvLlZ56veiNw2mXJ2r/3uSsDfVsYHkIrBmaUDPtKBynmlRvm9JyckOjsSeX/XqgGIrKMV2a3Ji25101MGR2Kvtd3Uq68GkAzep+fer5lcDKNqxye37888/6d27N+XKlcNgMDB37tyb1n/mmWd49913CQ8PJzk5mVdeeYWBAwfStm3b247H4YvsFQcpKSlERETkOSffYDCwfv36XHuii3NcTk5OuLi4FErbIiIiIiIijrZgwQKGDh3K2bNnWbJkyU3r//vf/6Zz586Eh4cDUL16dUaOHMnHH3+sBL+o8Pb2JiEhwdFh2HF0XDd7eyUiIiIiInI3e+ONNwByXTQ+N2vWrOG9996zKQsNDWXgwIGcPHnSuhj5rVKCLyIiIiIiIv9Ijz766A33x8TE3LFzZWVlcfjwYar/b9pLDhcXFypVqsT+/fuV4IuIiIiIiEjxVmhrPjW5tS+03YrU1FQAPD097fZ5enpy7ty52z6HEnwRERERERH5R7qTPfQ3k5mZicViwWKxYDAYbPbdqbXvi8Rn8kRERERERESKs5ye+wsXLtjtu3DhAl5eXrd9DiX4IiIiIiIiIoWsRIkS+Pr6cvDgQZtys9nMkSNHuP/++2/7HBqiLyIiIiIiIkWawcVw80p3gdDQUFatWkWDBg2sZXFxcfj6+lK5cuXbbt9guVOD/UVEREREREQKwQqv2oXSbsvzu+9IO4sXL2bJkiV2nwp/7bXXeO655wgKCgJg3759vPDCC0yePJnw8HCSk5Pp3bs3kZGRdOzY8bbjUA++iIiIiIiIyG1wdXXF1dXVrjw5OZmzZ89atx988EE++ugjJk+ezOuvv06pUqXo1q3bHUnuQT34IjYK7fMbt6GNeS+g2AoqJ7bCett7u3LeFp/ZtcHBkdgrWzcUgFO/b3JwJPbK1QkGIGFvqmMDyUVgzdKAnmlB5TzTonzfkpKTHRyJPb//fUNZsRXM3RBbclKSgyOxV93PD4DdSUcdHIm92n4VATiYdMDBkdir5lcDKNqx3U2Keg9+UaFF9kRERERERESKAQ3RFxERERERkSLN4KK+6fzQXRIREREREREpBtSDLyIiIiIiIkWa0bl4fCavsCnBFxERERERkSLN4KIEPz80RF9ERERERESkGFCCfxfq2bMn69evd3QYIiIiIiIiUoRoiP5d6PPPP3d0CCIiIiIiIlLEqAf/Dhk5ciRLlixxdBgiIiIiIiLFjtHZUCi/4kY9+HdIZmYmmZmZjg5DRERERESk2NEie/lTZHvw4+Pj6dKlCyaTCZPJxIABA4iOjiYyMtKubsuWLdmxYwcAJ06cIDw8nJ07d9KqVSuCg4PJyMhg9uzZfPjhh8ycOZNGjRoxcuRIAC5fvszo0aMJCgoiMDCQYcOGkZaWZm172LBhfPnllwwYMIDAwEBCQ0MZPny4tc7OnTsxmUxER0czduxYTCYT27dvz9c1Dh06lM8++4zevXsTEBDA448/ztq1a0lNTaV///4EBwfTpk0bNmzYYHNcmzZt2LZtGwCHDx/m8ccf5+eff+bxxx+nYcOGPPHEE8TGxhbofv/nP/+ha9euBAcHExwcTNeuXdm/f791/6JFi3jjjTeYOHEijRo1IiQkhJkzZ5Kdnc3777/PI488QpMmTeymD/To0YPVq1fTt29fGjVqRHBwMG+//TYZGRk29TZs2MCTTz6Jv78/rVu3ZvXq1dZ9eT1TgOjoaCIiIvD39+fpp5+23hcREREREZF/miKZ4G/cuJFBgwbxwgsvsGnTJhISEhgzZgwZGRl2iSFgU242m7l06RKzZs1i/vz5bNy4EVdXV9LT01m3bh2XLl0iLi6OqKgo4OrQ+mPHjrF8+XLWrFlDdnY2Y8eOtbZtMBj46KOPCA4OZv369axcuZLjx48zZcoUAOrVq0diYiJt27Zl9OjRJCYm0qBBg3xdp5OTE9OnT+fJJ59k8+bNfPDBB7z55psMHz6cJk2asGnTJiZMmMDQoUNJTU3N9XoNBgPHjx9n3rx5zJo1iy1btvD6668zcOBATp48me97npWVxZAhQ4iLi2Pjxo1EREQwdOhQm1hXrlyJwWAgNjaW6OhooqOjGTx4MKmpqcTExPD999+zePFiNm7caD3ObDYzbtw4IiIiiI+P56effiIhIYEPPvjAWmf//v0MGDCAV1991fqs33zzTZKSkm74TNevX8+4ceN4++23SUhIIDIykj59+tjcKxERERERuftpiH7+FMkEf9y4cYwYMYLWrVvj5OSEwWDA29s738efP3+eJ598Eh8fH4zG/7/Eo0ePMmDAAJycnDAajSQlJbF69WomT55M2bJl8fDwYNSoUaxevZrTp09bjzOZTDz//PO4ubnh6elJZGQkMTExd+RaH3nkEVq1aoWTkxN169alfv36GAwGnnnmGQDq169PnTp12Lp1a55tpKenM3LkSKpVq4bBYKBZs2bUrl3bruf/RgICAmjQoAHOzs4YjUY6d+7Mnj17uHjxorWOp6cngwcPxs3NjbJly9K+fXvi4+N56623cHV1pVy5crRv395u9EBQUBDt2rUD4N5772XcuHEsXLiQ9PR0AGbMmMFzzz1HREQEzs7OBAUF8eyzz/LNN99Y28jtmU6dOpVBgwYRFBSEs7MzLVu2JCwsjB9++CHf1y0iIiIiIlJcFLkE/+DBgxw6dIjWrVvfVjsmk8muzN/fH2fn/192YMeOHTRs2JDSpUtbyzw9PalSpQoHDhywltWuXdumnQoVKnDixInbii/HAw88YLNdpkwZgoKCbMq8vb1v2CttMBhuO8ZLly4xbdo0OnbsSEhICI0aNcJisdict1q1ajb3r0yZMjRo0ABXV9cbxtq4cWOb7ZyXGEeOHAGuTg9o3ry5XZ09e/bYlF37TLOysti1a5fdcQ0aNLA7TkRERERE5J+gyC2yl5KSgre3t00ieTMWi8Vm22g02iTtOcqUKWOzfeLECTZt2mT3MsBsNnP+/Hnrtpubm81+Z2dnsrOz8x3fjTg5OdmVlSpVqkBtGI1GXFxcbMpcXFzIysrKdxv9+/fn4sWLDBo0iPr16+Ph4UGdOnVs7m1useZ2n6+X2/V4eHhY1zE4efIkvXr1wmD4/yEy2dnZ3H///dbt65/p6dOnyczMtHsRlJWVRWho6E1jEhERERERKW6KXILv7e3NmTNnyMjIsOkZhqtJ3vUr1VssFpvh9HC1R/vaZPHa469VsmRJwsPDmT59+h2K/u70559/kpCQwPr16/Hy8gKuvvy4Uy8xzpw5Y7OdnZ3NmTNnKF++PHD1OcybN49atWrl2cb1z7RkyZIArFmzBk9PzzsSp4iIiIiIFE0Gp+I3X74wFLkh+lWrVuX+++9n8eLFdvt8fHw4duyYTVliYmKuC+/lR926ddmxYwdXrly5peOv5ezsbDeS4G5x9uxZvLy8rMk9wLp16+5Y++vXr7fZjouLo1y5cvj6+gJXn8OmTZsK1KaHhwdVq1Yt8HEiIiIiInL3MToZCuVX3BS5BN9gMDBs2DAmT57Mzz//bO1FPnv2LA0aNODChQt89913APz111+89957VKpU6ZbO5e/vT6VKlRgyZAjHjx8HIDU1tUCL0+Xw8fFh+/btWCwWm8/s3Q38/Py4fPkyK1euxGKxsGXLFubNm4ePj88daX/jxo388MMPmM1mDh8+zDvvvEP37t2tIyq6d+/OtGnTWLlyJRkZGWRnZ7Nt27abriHQs2dPoqKi2LhxI1lZWWRmZhIXF3fX3X8REREREZE7ocgl+ABNmzblk08+4fPPP8dkMhEQEMCwYcPw9PRkypQpzJs3j7CwMHr37k3//v3x9fW1Dud3cXGxmzMPV+fR51Y+Y8YMvLy8aN++PQ0bNuSpp55i586d1v2urq52UwVyO0eHDh3Ytm0bJpPJ7lvweXF1dbVrJ7fzXV927XZe15tbO3nx8PBgxowZfPLJJwQGBjJp0iQmTJiAl5eX9QVLbvcvP7ECvPnmm/zyyy+Ehoby7LPP0rZtW7p27Wrd37hxY8aPH8/MmTMJDg4mODiYyZMnW0dm5HWNnTp14tVXXyUqKgqTyURYWBhz5szJ1zWLiIiIiIgUNwbL3TquXO4KXbt2pV+/fgQHBzs6lHz52aWmo0Ow08a8F1BsBZUT2wqv2jep6Rgtz+8G4Myugo8YKmxl615dqPLU70VvCky5Olf/tyRhb6pjA8lFYM3SgJ5pQeU806J835KSkx0ciT2/6tUBxVZQd0NsyUlJDo7EXnU/PwB2Jx11cCT2avtVBOBg0oGb1Pz7VfOrARTt2O4m6xs0LJR2G2/P+3Pkd6Mit8hecZCSkkJERESec/INBgPr16/PtVe6uMXi5ORkt8K/iIiIiIhIQRiMxW++fGFQgl8IvL29SUhIcHQYgONjmTt3rsPOLSIiIiIi8k+iBF9ERERERESKNINTkVw+rshRgi8iIiIiIiJFWnH8pF1h0GsQERERERERkWJACb6IiIiIiIhIMaAEX0RERERERKQY0Bx8ERERERERKdL0mbz8MVjy+kC6iIiIiIiISBGwKSS4UNoN3ripUNp1FPXgi4iIiIiISJGmVfTzRwm+yDVWeNV2dAh2Wp7fDSi2gsqJ7WeXmg6OJHdtzHsBOLs91sGR2CvTIByA07s2OjgSez51QwDYsPuCgyOxF1rbE9AzLaicZ1qU71tScrKDI7HnV706oNgKSrHdmpzYdicddXAk9mr7VQQgOSnJwZHYq+7nB8DBpAMOjsReNb8ajg6hwAxK8PNFi+yJiIiIiIiIFANK8EVERERERESKASX4IiIiIiIiIsWA5uCLiIiIiIhIkWYwqm86P3SXRERERERERIoB9eCLiIiIiIhIkWYwahX9/LgjPfh79uzhmWeewWw2AzBr1ixmzZp1J5rOtxEjRvD999//red0tOjoaCIjIx1y7lOnTvHUU09x5swZh5xfRERERET+OYxOhkL5FTd3pAe/Vq1aLFq0yLqdkZFxJ5otkPHjx//t53S0jIwMh9xrgHLlyvHjjz865NwiIiIiIiJi766cgz9jxgxmzJjh6DD+USIiIjhx4oSjwxAREREREZE83JVz8DMzMx0dwj+O2Wy2TsEQERERERGRoiffPfjvvvsukydPtim7fPkyJpOJ7du3ExERYbMvKyuLadOm0axZM/z9/Xn++efZs2ePdf+JEycIDw9n586dtGrViuDgYDIyMjh48CCRkZGEhIQQFBREhw4dSExMBOD06dMEBQUxe/ZsZs+ejclkYvny5QBERkYSHR1tbT8tLY0xY8YQFhaGyWSiY8eOrFu3zibGZs2asXbtWjp16kTDhg1p3rw5s2fPzu8tASAlJYWBAwcSFhZGYGAgbdq0YdWqVTZ1Ll26xKRJk2jSpAkBAQE0btzYek0AX3/9NW3atCEgIIDg4GC++OIL67558+ZZ72HXrl1JSkrKM5asrCw+/PBDwsLCaNiwIX369OHkyZPW/bNnz+bDDz9k5syZNGrUiJEjRwKwePFi2rVrR2BgIGFhYQwdOpTz588DMHfuXEwmE8eOHaNdu3Y0btyYzMxMTp8+Tf369W3Ov2PHDrp27YrJZKJRo0YMGTKEU6dOWffHx8fz0ksvMW/ePJo2bUpAQAAdO3Zkx44dBbrnGRkZjBs3jpCQEPz9/enevTvx8fF2f4MiIiIiIlI8GIyGQvkVN/lO8Js1a2aXuMbFxVGjRo1ce3cXLFjAyZMnWb58ORs3bsTPz48ePXqQlpYGXO0RvnTpErNmzWL+/Pls3LgRV1dX0tPT6d69O7GxsWzevJk+ffowaNAgMjIy8PHxYfPmzURGRhIZGUliYiKtWrUC7Oej9+/fn5SUFJYuXUpCQgL9+/dnyJAhbNmyxVrHYDAQFRXFa6+9RkJCAp999hnz58+3u84buXz5Mu3atSMmJoaEhATGjx/Pv/71L5vEtnfv3hw+fJhFixaxZcsWVqxYQb169QCYNm0aX3/9NRMnTmTLli2sX7+ep59+GoCFCxfy5ZdfMmPGDDZv3kxERASRkZF5jmCYMmUK69atY8GCBWzYsAE/Pz8GDRpk3Z+ens66deu4dOkScXFxREVFAeDh4cG0adPYvHkzMTExZGdnM23aNABeeuklEhMTqVChAkuWLGH9+vU4OzuTmZlJenq6te2DBw/So0cPnnrqKeLj44mJicHb25sePXpY4zUYDGzfvp1169bx7bffkpCQwNNPP82rr77KlStX8n3PJ02axJYtW/jmm29ISEigU6dOvPbaaxphICIiIiIi/2j5TvAbNmzIuXPnOHjwoLUsJiYmz15TNzc3Ro8ejbu7O+7u7owZMwZnZ2eb5Pn8+fM8+eST+Pj4YDReDaVWrVqEhITg6uoKQIsWLTAajRw6dCjfFxUfH8/u3buZNGkS3t7eGAwGwsPDiYyM5OOPP7ap26VLF0JCQnBycqJ69eo8/fTTxMTE5PtcFStW5NFHH8Xd3R2ABg0aUL16dXbt2gXAsmXLOHXqFB9//DHly5cHribUbm5u/PXXX8yZM4cZM2ZYE34XFxdKlSplTbLHjBlDrVq1cHFx4YUXXqBcuXKsXr3aLo5z587x5ZdfMnnyZCpXroy7uzuvv/46x44d4/fff7fWO3r0KAMGDMDJycl6zx9//HGqVKmCwWDA3d3dZtREfs2cOZMnnniCDh064OzsTMmSJRkxYgSAzciKy5cvM3HiRMqXL4/RaKRLly44OTmxc+fOfJ0nIyODxYsXM378eKpWrYqzszOtWrWibdu2BYpXRERERETuHgajsVB+xU2+r8jJyYnmzZuzdu1aALKzs4mNjc0zwQ8LC8PJyen/T2Q00rhxY/bu3WtTz2Qy2WxnZmYyb948nnvuORo3bkxAQACnTp0iNTU1v6GSmJhIs2bNuOeee2zKW7VqxdatW8nKyrKWPfTQQzZ1KlasaDOs/WYsFgs//PAD3bp1o0mTJjRs2JAdO3ZY442JiaFNmzY29yLHb7/9Rp06dahcubLdvr/++otz584RGhpqU96gQQObqQ459u7di6+vL35+ftYyo9FI3bp1ber7+/vj7Gy79MKxY8cYNWoUbdu2JSgoiL59+xbofgMkJCTQunVru/KIiAg2b95s3fb19cXHx8emzn333ZfvBfwOHTqEwWCgdu3aNuXNmzcvULwiIiIiIiLFTYEW2WvRogVz586le/fubN26lQoVKlChQgWOHTtmV9fb29uurGzZsly4cMG6bTQaKV26tE2dqKgotmzZwqBBgwgICKBMmTI8+uijWCyWfMd58uRJfH197crLlSuH2WzmzJkz1v05IwVyODs727wAuJlPP/2Uf//73wwePJiQkBDKli3LSy+9ZI03JSWF4ODgXI9NSUnJNU64ukZBRkaG3bFZWVl07Ngx1/pHjhzJ9YXJtWVlypSx2Z+amkqnTp1o0qQJEyZMoEaNGuzcuZPhw4ff/OKvkdc9L1++PNu2bbNuu7m52dVxcXHJ9z0/f/683d8MkOd9FBERERER+acoUIIfFhbG8OHDSUtLY/Xq1Tdc1Oz48eN2ZWfOnKFChQrWbYPBgMHw/wsbpKens3jxYpYsWUL16tWBqz3kBelRByhdunSux5w6dQpnZ2dKlSpVoPZu5KuvvuKdd97hkUcesZZd2xvt7e2d6wuQnH1//fVXrvtKliyJp6enTe/3jZQsWZIHHnjgpt+mN143DOXXX3+lYsWKjB8/3lqW27O7mZx7fv/999uUnzhxwu6lwu3w8PAgJSXFrjy3MhERERERKR6K44J4haFAkw7c3Nxo1KgR69atIyYmhpYtW+ZZNzY21rqgHlwd0h8XF0fNmjXzPObChQuYzWYqVapkLYuPj7dbPM3Z2fmGPfrh4eGsXbuWy5cv25QvX76ckJCQXHuRb9XZs2epWLGidfuPP/7g8OHD1u3mzZuzZMkSmwUAczRu3Jjff/8915Xxq1WrRmZmZq7D8XNTp04dDh06ZLO4363ED7B+/Xq7eje7502bNmXZsmU2ZRaLhZUrV97R4fN+fn64ubnZrRGwatUqm5dFIiIiIiIi/zQFXlUgZ5j+Pffck+vc8RylSpVi6NChpKWlkZ6eTlRUFKVKlaJp06Z5HlO2bFkqVqzI119/jcViYf/+/UycOJGqVava1PPx8WHHjh1kZ2fbDPnPERAQwEMPPcSwYcNISUnBYrGwYsUKZs2aRb9+/Qp6yTfUoEEDvv32WzIzMzl27BjDhw+3mQffunVrfH196du3rzX5vnTpEunp6VSsWJEXX3yR3r17Wxfly8zM5MKFC7i6utK1a1eGDBnCzp07sVgsZGRk5LnCf/ny5Xn00UcZNGgQycnJAFy8eJHY2Nibxr9hwwaSk5Mxm81899137Nu3z66ej48P//nPfzCbzXYvTgBefvlloqOj+e6778jKyiItLY2RI0diNBrv6OfrXFxcGDBgAKNGjSIpKQmz2cyPP/7I0qVLcx26LyIiIiIidz+jk6FQfndCYmIinTp1IjAwkMcee4xvv/32pscsWLDA+qny5s2bM2nSJC5dunTbsRQ4wW/atCn79u2zWbXc1dUVFxcXm+327dtTr149WrduTWhoKCdPnmTevHnWei4uLnY96QaDgRkzZrBixQqCgoJ4/fXXGTp0KJUrVyY7O9ta77HHHuPSpUsEBgZah5a7urrazKefMmUKvr6+1ps2Z84cPv74Yx5++GGbOK+fg+/m5mZXdiPvvvsuBw4cICwsjG7duvHcc88REBBgnVNuMBiYO3cuVapUoV27dgQEBNCsWTP+85//ADB48GBeeuklBg0aRMOGDWnUqBFfffUVAAMHDqRNmzbWfeHh4fz00095xj9+/Hjq1atHt27d8Pf3JyIignXr1tlc2/X3PDg4mFdffZWXX36Z0NBQfvvtNyZNmmRzv+FqAv/uu+/SpEkTNm3ahLOzs01b999/P1988QVLly6lUaNGtGjRgqysLL744gvrAoN53dvcnsONPP/887zwwgv06NGD4OBgli5dyjPPPIO/v3++2xAREREREbldf/75J3379qVv374kJCQwa9YsPvvsM5sviV1v9uzZ/Pvf/2by5MkkJCQwb948tm3bxltvvXXb8RgsBVm9TqQI2LFjByVLlqR69epkZmYSHx/P22+/zeeff37DUSX5scKr9s0r/c1ant8NKLaCyontZ5e8pwU5Uhvz1S+KnN1+41E2jlCmQTgAp3dtdHAk9nzqhgCwYbf96C1HC63tCeiZFlTOMy3K9y3pfyPjihK//61VpNgKRrHdmpzYdicddXAk9mr7XZ1qmpzLlFdHq/6/Ub0Hkw44OBJ71fxqODqEAvtv+0cLpd2Hfsj/J9JzM378eOsnynPExsby8ccfs3jx4lyPadOmDa+99hotWrSwlu3YsYOXXnqJrVu33lY8BVpk75+mU6dO1uHuuZk6dSohISF/Y0TF2++//063bt3y3F+2bFlWrlzJkSNHmDp1KidPnsRgMNCgQQM+/vjj207uRURERERECmLNmjW89957NmWhoaEMHDgwzy+N3XvvvTbrtgEkJyfbTU2/FUrwb2DhwoWODuEfpU6dOnaL5+WmdevWtG7d+m+ISEREREREirNHH73xyICYmLx7+LOysjh8+LD1C3A5XFxcqFSpEvv37881wR84cCCvvPIK5cuXp3Xr1qxatYr33nuPqVOn3tpFXEMJvoiIiIiIiBRpBmOBl4/Lp+ybV8lDamoqAJ6ennb7PD09OXfuXK7H1a9fny+++II+ffrwwQcfcOXKFT7//PMbfnEuv5Tgi4iIiIiIyD/SjXrobyYzMxOLxYLFYrH7ZPeNlrr7888/GTNmDA888ABPPPEEP//8M2+++Sbjx4+nVq1atxwPKMEXERERERGRIs5gvDOftLuTcnruL1y4gJeXl82+3MoAzGYzr7zyCs899xwvvvgiAE888QTfffcdvXr1YtmyZbkel1+FNc5BREREREREpNgqUaIEvr6+HDx40KbcbDZz5MgR7r//frtjkpKSOHXqFF27drUp79ixI6VLl2bbtm23FZMSfBERERERESnSDEZDofxuV2hoKKtWrbIpi4uLw9fXN9evfLm5uXHlyhUuXrxoU56dnU1qaiouLi63FY8SfBEREREREZFb0LNnT7799ltiY2OBq5+7Gz9+PK+88gpwdaX9l156yfr59WrVqtG4cWN69+5NUlISAKdOneLNN9/Ey8sLk8l0W/EYLDea/S8iIiIiIiLiYHs7RxRKuzW/XXnbbWzYsIHJkyfz559/UqpUKbp160a3bt0ASE9P57HHHmPatGnUr1/fWjZnzhx++uknTp8+jZeXF+Hh4fTt2xcfH5/bikUJvoiIiIiIiBRp+55rWSjtPvjNikJp11G0ir7INc7s2uDoEOyUrRsKKLaCyont7PZYB0eSuzINwgH42eX2v3d6p7Ux7wXgyorPHByJPfeWvQCYvtzBgeSib6ur/+qZFkzOMy3K9y3pf8MqixK/6tUBxVZQd0Nshw7sc3Ak9qrWeBCAnQdOODgSe/VqlAfgz/27HRyJvSoP1AbgQsIyB0dizzOwtaNDkEKiBF9ERERERESKNINRy8flhxJ8ERERERERKdKMTre/4v0/gV6DiIiIiIiIiBQDSvBFREREREREigEl+CIiIiIiIiLFgObgi4iIiIiISJFmMGoOfn6oB19ERERERESkGFCCfwft2bOHZ555BrPZDMCsWbOYNWvW3xrDiBEj+P777//Wc4qIiIiIiBQmg9FYKL/iRkP076BatWqxaNEi63ZGRsbfHsP48eP/9nOKiIiIiIiI4xW/Vxb/IDNmzGDGjBmODkNERERERESKAPXg38UyMzMdHYKIiIiIiEih0yJ7+aMe/AJ69913mTx5sk3Z5cuXMZlMbN++nYiICJt9WVlZTJs2jWbNmuHv78/zzz/Pnj17rPtPnDhBeHg4O3fupFWrVgQHB5ORkcHBgweJjIwkJCSEoKAgOnToQGJiIgCnT58mKCiI2bNnM3v2bEwmE8uXLwcgMjKS6Ohoa/tpaWmMGTOGsLAwTCYTHTt2ZN26dTYxNmvWjLVr19KpUycaNmxI8+bNmT17doHuy759+3juuecIDAwkODiYSZMmWfdlZmbyySef0KJFCwICAggJCWHZsmUAWCwW5syZw2OPPYbJZOLxxx9n7ty5Nm2PGjWKBQsWEBUVRXBwMNOnTwcgJSWF119/nYCAABo1asT48eOt6x+IiIiIiIj806gHv4CaNWvGyJEjGTp0qLUsLi6OGjVqYDab7RLMBQsW8Pjjj1sT8PHjx9OjRw9++eUXPDw8MJvNXLp0iVmzZjF//ny8vb0xGo2kp6fTvXt3AgICcHV1ZdWqVQwaNIjVq1fj4+PD5s2bmTp1KgD9+/e3ni8jI8Nm7n///v3x9PRk6dKllClTht9++40hQ4bwySefEBAQAIDBYCAqKop33nmHoKAg/vjjD7p160b16tVp0aJFvu7LsGHD6NatG0899RRZWVmcOXPGuu+tt97izz//5LPPPqNq1apcuXLFOvpgxowZLFu2jOnTp/Pggw+SlJTEoEGDyMjIIDIy0npNCxcupGPHjmzatIns7GwA+vbtS7Vq1Vi7di1ms5nXXnuN6dOnM2jQoHzFLCIiIiIidwf14OePevALqGHDhpw7d46DBw9ay2JiYux67nO4ubkxevRo3N3dcXd3Z8yYMTg7O7Nq1SprnfPnz/Pkk0/i4+OD8X8rOdaqVYuQkBBcXV0BaNGiBUajkUOHDuU71vj4eHbv3s2kSZPw9vbGYDAQHh5OZGQkH3/8sU3dLl26EBISgpOTE9WrV+fpp58mJiYm3+c6ePAgjzzyCABOTk74+voCsH37dlavXs2sWbOoWrUqAO7u7nh4eHDhwgU+++wzxo8fz4MPPgiAn58f48eP59NPPyUtLc3afmZmJl26dAHAaDSybt06/vrrL6KiovD09MTb25uxY8fyzTffqBdfRERERET+kZTgF5CTkxPNmzdn7dq1AGRnZxMbG5tngh8WFoaTk5N122g00rhxY/bu3WtTz2Qy2WxnZmYyb948nnvuORo3bkxAQACnTp0iNTU137EmJibSrFkz7rnnHpvyVq1asXXrVrKysqxlDz30kE2dihUrcvLkyXyfq1OnTvTp04dt27bZlMfExNC8eXM8PDzsjtm9ezdlypShfv36NuX16tXDy8vLZipDYGCgTZ3t27fTpEkTnJ3/fxBK1apVsVgsHD9+PN9xi4iIiIhI0afP5OVP8buiv0GLFi1Ys2YNAFu3bqVChQpUqFAh17re3t52ZWXLluXy5cvWbaPRSOnSpW3qREVFsXDhQnr06MGSJUtITEzkvvvuw2Kx5DvOkydPWnvSr1WuXDnMZrPNMPqckQI5nJ2dbV4A3MyIESOIjIxk5MiRvPzyyxw+fBi4Ok8+txhuFB+Ar6+vTaJepkwZm/0nTpzgxx9/xGQy2fyuXLnCuXPn8h23iIiIiIhIcaE5+LcgLCyM4cOHk5aWxurVq/PsvQdy7U0+c+aMzQsBg8GAwfD/c0rS09NZvHgxS5YsoXr16sDVxegK0qMOULp06VyPOXXqFM7OzpQqVapA7d1MixYtaN68ORMmTKB3795ER0fj7e3NsWPHChQfXE3+r305Yrzu7VrJkiV59tlnGTFixJ27ABERERERkbuYevBvgZubG40aNWLdunXExMTQsmXLPOvGxsbazCXPzs4mLi6OmjVr5nnMhQsXMJvNVKpUyVoWHx9vN7fc2dn5hj364eHhrF271ma0AMDy5csJCQnBzc0tz2NvldFoZPDgwRw4cIDTp0/TrFkzYmJiSElJsavr7+/P+fPn2bFjh035zp07SUtLw9/fP8/z1K1bl8TExAKNaBARERERkbuTwWgolF9xowT/FrVo0YK5c+dyzz33ULly5TzrlSpViqFDh5KWlkZ6ejpRUVGUKlWKpk2b5nlM2bJlqVixIl9//TUWi4X9+/czceJE6yJ1OXx8fNixYwfZ2dlcuHDBrp2AgAAeeughhg0bRkpKChaLhRUrVjBr1iz69et3q5duJysryzqnPysri6+++or77ruPMmXK4O/vT9OmTXn55Zf5448/gKur4l+8eJGSJUvy8ssv8+abb7Jv3z4A9uzZw+DBg3nllVfs1g64VosWLbhw4QJjx461vjw4ceKE9VOCIiIiIiIi/zRK8G9R06ZN2bdvH23btrWWubq64uLiYrPdvn176tWrR+vWrQkNDeXkyZPMmzfPWs/FxcWuJ91gMDBjxgxWrFhBUFAQr7/+OkOHDqVy5crWT8QBPPbYY1y6dInAwEDGjx9vPee18+mnTJmCr68v7dq1IzAwkDlz5vDxxx/z8MMP28R5/Rx8Nzc3u7K8XLlyhREjRtCgQQOCg4OJjY1l1qxZ1gXwJk+eTNOmTenatSsBAQGEhYXx66+/AhAZGUnnzp3p168fJpOJgQMH8uKLL9KjRw+b+K6/R+7u7sybN4+UlBQiIiIICAiga9euBfrKgIiIiIiI3B20yF7+GCwa4yxidWbXBkeHYKds3VBAsRVUTmxnt8c6OJLclWkQDsDPLnlP13GUNuarX/m4suIzB0diz71lLwCmL3dwILno2+rqv3qmBZPzTIvyfUtKTnZwJPb8/rdGj2IrmLshtkMH9jk4EntVa1z9nPHOAyccHIm9ejXKA/Dn/t0OjsRelQdqA3AhYZmDI7HnGdja0SEU2JF+zxRKu5WmLSqUdh1Fi+zJTXXq1InkG/w/wqlTpxISEvI3RiQiIiIiIv8ohuI3X74wKMGXm1q4cKGjQxAREREREZGbKH6TDkRERERERET+gdSDLyIiIiIiIkVacfykXWFQD76IiIiIiIhIMaAefBERERERESnSiuMn7QqD7pKIiIiIiIhIMWCwWCwWRwchIiIiIiIikpe/BncplHbve//rQmnXUdSDLyIiIiIiIlIMaA6+yDVO/b7J0SHYKVcnGFBsBZUT2+ldGx0cSe586oYAcGXFZw6OxJ57y14A/OxS08GR2Gtj3gvAl7EODiQXL4Zf/VfPtGBynmlRvm9JyckOjsSeX/XqgGIrqJzYkpOSHByJvep+fgAcSDro4Ejs1fCrBsDvB/5ycCT26tS4Dyjaf29H9u1ycCT2Kj1Y19EhSCFRgi8iIiIiIiJFmhbZyx/dJREREREREZFiQD34IiIiIiIiUqQZjAZHh3BXUA++iIiIiIiISDGgBF9ERERERESkGNAQfRERERERESnSNEQ/f9SDLyIiIiIiIlIMKMF3gD179vDMM89gNpsBmDVrFrNmzfpbYxgxYgTff//933pOERERERGRW2I0Fs6vmNEQfQeoVasWixYtsm5nZGT87TGMHz/+bz+niIiIiIiIFB4l+P8AM2bMAKBPnz4OjkRERERERKTgDAbNwc8PJfj/AJmZmY4OQURERERERApZ8Zt0UES8++67TJ482abs8uXLmEwmtm/fTkREhM2+rKwspk2bRrNmzfD39+f5559nz5491v0nTpwgPDycnTt30qpVK4KDg8nIyODgwYNERkYSEhJCUFAQHTp0IDExEYDTp08TFBTE7NmzmT17NiaTieXLlwMQGRlJdHS0tf20tDTGjBlDWFgYJpOJjh07sm7dOpsYmzVrxtq1a+nUqRMNGzakefPmzJ49u0D35eeff2bw4MEsXryYxo0b07NnTwC6dOnCokWLeP7552nYsCHt2rVj+/btHDlyhJdeeong4GA6duzIf//7X2tbZrOZ0aNHExISQmBgIG3btiUtLa1A8YiIiIiIiBQX6sEvJM2aNWPkyJEMHTrUWhYXF0eNGjUwm83WBfZyLFiwgMcff9yagI8fP54ePXrwyy+/4OHhgdls5tKlS8yaNYv58+fj7e2N0WgkPT2d7t27ExAQgKurK6tWrWLQoEGsXr0aHx8fNm/ezNSpUwHo37+/9XwZGRk2c//79++Pp6cnS5cupUyZMvz2228MGTKETz75hICAAODqsJioqCjeeecdgoKC+OOPP+jWrRvVq1enRYsW+bov6enpHDhwAFdXV1avXo2z89U/QScnJz766CPef/99goODiY2NZejQofj4+PDKK68QHh7O6tWr6d+/PytXrsTZ2ZlvvvmGEydOEBMTQ4kSJThx4gQlS5a8haclIiIiIiJFmaEYLohXGHSXCknDhg05d+4cBw8etJbFxMTY9dzncHNzY/To0bi7u+Pu7s6YMWNwdnZm1apV1jrnz5/nySefxMfHB+P//sBr1apFSEgIrq6uALRo0QKj0cihQ4fyHWt8fDy7d+9m0qRJeHt7YzAYCA8PJzIyko8//timbpcuXQgJCcHJyYnq1avz9NNPExMTk+9zAezfv5+hQ4fi6upqvQ6Adu3a0ahRIwwGA02bNsXV1ZWHHnqI8PBwAJo3b467uztJSUkAHDx4kICAAEqUKAFA+fLlNTdHRERERET+sZTgFxInJyeaN2/O2rVrAcjOziY2NjbPBD8sLAwnJyfrttFopHHjxuzdu9emnslkstnOzMxk3rx5PPfcczRu3JiAgABOnTpFampqvmNNTEykWbNm3HPPPTblrVq1YuvWrWRlZVnLHnroIZs6FStW5OTJk/k+F4Cfnx/e3t525Q888IDNdpkyZQgKCrIp8/b2tl5bu3btmDdvHl999ZVDvkQgIiIiIiJ/D4PRUCi/4kYJfiFq0aIFa9asAWDr1q1UqFCBChUq5Fo3t4S3bNmyXL582bptNBopXbq0TZ2oqCgWLlxIjx49WLJkCYmJidx3331YLJZ8x3ny5El8fX3tysuVK4fZbObMmTPWspyRAjmcnZ1tXgDkx/XXkOPaFxw5SpUqlWc7/v7+fPPNN2zdupVWrVqxbNmyAsUhIiIiIiJSnGgOfiEKCwtj+PDhpKWlsXr16jx77wGOHz9uV3bmzBmbFwIGg8FmCHp6ejqLFy9myZIlVK9eHQCLxVLgHvXSpUvnesypU6dwdna+YZJ9K3JL5G9V5cqVef/999m8eTO9e/emQoUKPPzww3esfRERERERKQKK8Bz8xMRE3n33XQ4ePEjp0qXp1asXnTt3vuEx2dnZLFiwgO+//54jR46QmZlJeHg4H3zwwW3FogS/ELm5udGoUSPWrVtHTEwMn332WZ51Y2NjSUtLw8PDA7j6wOPi4hg5cmSex1y4cAGz2UylSpWsZfHx8XYL+Dk7O9uVXSs8PJx+/fpx+fJlm2H6y5cvJyQkBDc3t5teq6MFBQXRpEkTNm/erARfRERERET+Fn/++Sd9+/bl3XffJTw8nOTkZF555RVKlixJ27Ztcz3GYrHw+uuvc+XKFSZNmkSNGjXIysrixIkTtx1P0X0NUky0aNGCuXPncs8991C5cuU865UqVYqhQ4eSlpZGeno6UVFRlCpViqZNm+Z5TNmyZalYsSJff/01FouF/fv3M3HiRKpWrWpTz8fHhx07dpCdnc2FCxfs2gkICOChhx5i2LBhpKSkYLFYWLFiBbNmzaJfv363eumFbvfu3Zw/fx6APXv2sHnzZmrXru3gqERERERE5J/i3//+N507d7YuDF69enVGjhzJnDlz8jxm2bJlHD58mGnTplGjRg3g6ijnvKZzF4QS/ELWtGlT9u3bZ/P2xtXVFRcXF5vt9u3bU69ePVq3bk1oaCgnT55k3rx51nouLi52PekGg4EZM2awYsUKgoKCeP311xk6dCiVK1cmOzvbWu+xxx7j0qVLBAYGMn78eOs5r51PP2XKFHx9fWnXrh2BgYHMmTOHjz/+2KY3/Ppj4OoohevLbsTNzS3XEQGurq525bmd79qyJUuW0KRJE/z9/enXrx/9+/fnkUceyXcsIiIiIiJydyiqi+ytWbOGRx991KYsNDSU5OTkPKdOL1q0iJ49e1o/GX4nGSwFWY1NpJg79fsmR4dgp1ydYECxFVRObKd3bXRwJLnzqRsCwJUVeU/dcRT3lr0A+NmlpoMjsdfGfPXLIl/GOjiQXLx49cW9nmkB5TzTonzfkpKTHRyJPb//rb2j2AomJ7bk/31ytyip7ucHwIGkgzep+fer4VcNgN8P/OXgSOzVqXEfULT/3o7s2+XgSOxVerCuo0MosJRxrxRKu8+sOXDD/Tf6JHhWVhZ16tQhISEBT09Pm31t27blzTffJCwszKbcYrHg7+/P3LlzmTt3Lps2bcLLy4unnnqKXr162XQE3wrNwZc7plOnTiTf4H9cp06dSkhIyN8YkYiIiIiIFAcGQ9EbfJ7z+e7rk/ucsnPnztmVnz17lsuXLzNx4kQiIyOZNGkSf/31F8OGDeP48eOMHTv2tmJSgi93zMKFCx0dgoiIiIiISL7dqIf+ZjIzM7FYLFgsFpuvnQF5frY8PT0dgA4dOtC8eXMAqlatysSJE2nTpg2DBw/Gy8vrlmMqeq9BRERERERERK5lNBTO7zbk9NzntpD5hQsXck3U3d3dAWjUqJFNebVq1fD09OTgwdubpqMEX0RERERERKSASpQoga+vr11SbjabOXLkCPfff7/dMWXKlKFEiRLWnvxrZWdnWz+bfquU4IuIiIiIiIjcgtDQUFatWmVTFhcXh6+vb56fSQ8KCmL16tU2Zbt2XV2MsUqVKrcVjxJ8ERERERERKdIMRmOh/G5Xz549+fbbb4mNvfqJn+TkZMaPH88rr1xd9T8rK4uXXnrJZjHyXr168emnnxIfHw/Avn37GD58OH379tUq+iIiIiIiIiKO8OCDD/LRRx8xefJkXn/9dUqVKkW3bt3o2LEjcHUhvuTkZNLS0qzHBAYGEhUVRVRUFH/99RdlypThhRde4KWXXrrteAyWvJb3ExERERERESkCzk3uXyjtlho6tVDadRQN0RcREREREREpBjREX+QaCXtTHR2CncCapQHFVlA5sW3Ybf/ZkqIgtPbVz6pMX+7gQHLRt9XVf7+MdWwcuXkx/Oq/P7vUdGwguWhj3gvomRZUzjMtyvctOSnJsYHkorqfH6DYCiontqRr5sIWFX7VqwNw6MA+B0dir2qNBwE4vmebgyOxd28tf6Bo/739fuAvB0dir06N+xwdghQSJfgiIiIiIiJStBk0+Dw/dJdEREREREREigH14IuIiIiIiEiRZjAaHB3CXUE9+CIiIiIiIiLFgHrwRUREREREpGgzqm86P3SXRERERERERIoBJfiFYM+ePTzzzDOYzWYAZs2axaxZs/7WGEaMGMH333//t57z7zRq1Cg+++wzR4chIiIiIiJSZGiIfiGoVasWixYtsm5nZGT87TGMHz/+bz/n3ykjI8Mh91VERERERP5+BoMW2csP9eAXAzNmzGDGjBmODkNEREREREQcSD34xUBmZqajQxARERERESk8WmQvX3SXbtG7777L5MmTbcouX76MyWRi+/btRERE2OzLyspi2rRpNGvWDH9/f55//nn27Nlj3X/ixAnCw8PZuXMnrVq1Ijg4mIyMDA4ePEhkZCQhISEEBQXRoUMHEhMTATh9+jRBQUHMnj2b2bNnYzKZWL58OQCRkZFER0db209LS2PMmDGEhYVhMpno2LEj69ats4mxWbNmrF27lk6dOtGwYUOaN2/O7NmzC3RfZs6cycyZM5k0aRJNmjShYcOG9O7dm2PHjtnUO3z4MC+//DL+/v40btzYbgTC4sWLadeuHYGBgYSFhTF06FDOnz+f53nj4+N59NFHOXHiRIHiFRERERERKS6U4N+iZs2asWrVKpuyuLg4atSogdlsti6wl2PBggWcPHmS5cuXs3HjRvz8/OjRowdpaWkAmM1mLl26xKxZs5g/fz4bN27E1dWV9PR0unfvTmxsLJs3b6ZPnz4MGjSIjIwMfHx82Lx5M5GRkURGRpKYmEirVq0A+znq/fv3JyUlhaVLl5KQkED//v0ZMmQIW7ZssdYxGAxERUXx2muvkZCQwGeffcb8+fPtrvNGzGYzX331FVlZWaxatYr169fj6elJv379rHUyMjJ46aWXqFu3Lhs2bGDhwoUsW7bMZt0CDw8Ppk2bxubNm4mJiSE7O5tp06bles6//vqLoUOHMmHCBMqXL5/vWEVERERE5O5gMBoK5VfcKMG/RQ0bNuTcuXMcPHjQWhYTE2PXc5/Dzc2N0aNH4+7ujru7O2PGjMHZ2dkmeT5//jxPPvkkPj4+GP83BKVWrVqEhITg6uoKQIsWLTAajRw6dCjfscbHx7N7924mTZqEt7c3BoOB8PBwIiMj+fjjj23qdunShZCQEJycnKhevTpPP/00MTEx+T5XzrUOHz4cV1dXSpQowbhx4zhy5Ij1ZcLixYvx8fFh4MCB3HPPPVSoUIHhw4fz5ZdfWtt4/PHHqVKlCgaDAXd3d5uRC9fKyMigf//+9OnTh6CgoALFKSIiIiIiUpxoDv4tcnJyonnz5qxdu5Zq1aqRnZ1NbGws/fv3txuODhAWFoaTk5N122g00rhxY/bu3WtTz2Qy2WxnZmby1VdfsWLFCg4fPszly5e5dOkSqamp+Y41MTGRZs2acc8999iUt2rVig8//JCsrCxrbA899JBNnYoVK7Jr1658nwsgNDTU+oICrib8AQEB7Nu3j4CAALZv307z5s1tjmnQoAFJSUlkZmbi7OzMsWPHmDlzJlu3buXkyZOYzWbKlCljd66oqCjq1KlDly5dChSjiIiIiIhIcaMe/NvQokUL1qxZA8DWrVupUKECFSpUyLWut7e3XVnZsmW5fPmyddtoNFK6dGmbOlFRUSxcuJAePXqwZMkSEhMTue+++7BYLPmO8+TJk/j6+tqVlytXDrPZzJkzZ6xlOSMFcjg7O5OVlZXvcwGUKlXKrszDw4MLFy4AV9cbmDFjBiaTyfpr2rQpzs7OnD9/ntTUVDp16kRmZiYTJkwgNjaWWbNm2bW5bNkyfvzxR8qWLVug+ERERERE5C5jMBbOr5hRD/5tCAsLY/jw4aSlpbF69eo8h+cDHD9+3K7szJkzNi8EDAaDzfcd09PTWbx4MUuWLKF69eoAWCwWTp48WaA4S5cunesxp06dwtnZOdeE/HacPn3aruzkyZM0btwYgJIlS/Laa6/RrVu3XI9ftGgRFStWZPz48day3O7f5cuX+eGHH+jevTtNmzalfv36d+gKRERERERE7j7F75XF38jNzY1GjRqxbt06YmJiaNmyZZ51Y2NjrQvqAWRnZxMXF0fNmjXzPObChQuYzWYqVapkLYuPj7dbwM/Z2fmGPfrh4eGsXbvWZrQAwPLlywkJCcHNzS3PY29FfHy8Ta//+fPn2bFjBw0aNACgbt26bN68Oc/jz549S8WKFW3K1q9fb1evQ4cOPPDAA4wePZo33niDK1eu3KErEBERERGRIsVoKJxfMaME/za1aNGCuXPncs8991C5cuU865UqVYqhQ4eSlpZGeno6UVFRlCpViqZNm+Z5TNmyZalYsSJff/01FouF/fv3M3HiRKpWrWpTz8fHhx07dpCdnW0dBn+tgIAAHnroIYYNG0ZKSgoWi4UVK1Ywa9Ysm9Xt75RLly4xYcIE0tLSuHjxIiNHjiQsLMwad86CedOnT7e+9Pjzzz+tc/0bNGjAhg0bSE5Oxmw2891337Fv3748z/fYY4/x8MMP2322UERERERE5J9ECf5tatq0Kfv27aNt27bWMldXV1xcXGy227dvT7169WjdujWhoaGcPHmSefPmWeu5uLjY9aQbDAZmzJjBihUrCAoK4vXXX2fo0KFUrlyZ7Oxsa73HHnuMS5cuERgYaB3W7urqajOffsqUKfj6+lq/LT9nzhw+/vhjHn74YZs4r5+D7+bmZld2M88++yxOTk48/vjjhIeH4+rqajPc3sfHhy+//JKtW7fSpEkTTCYTr776qnVof3BwMK+++iovv/wyoaGh/Pbbb0yaNMnmmq+PdeTIkaxbt474+PgCxSoiIiIiIlJcGCwFWa1N5CamTp0KQP/+/R0cya1J2Jvq6BDsBNYsDSi2gsqJbcNu+1EtRUFobU8Api93cCC56Nvq6r9fxjo2jty8GH71359d8p7e5ChtzFe/iqJnWjA5z7Qo37fkpCTHBpKL6n5+gGIrqJzYkpKTHRyJPb//rbd06EDeoxYdpWqNBwE4vmebgyOxd28tf6Bo/739fuAvB0dir06N+xwdQoGlzXyzUNr16D2hUNp1FC2yJ/nWqVMnkm/w/xCnTp2Kk5OTzSfyREREREREbpehGK54XxiU4Eu+LVy48KZ1QkJC/oZIRERERETkH6UYLohXGPQaRERERERERKQYUA++iIiIiIiIFGkGTQPOF90lERERERERkWJACb6IiIiIiIhIMaAEX0RERERERKQY0Bx8ERERERERKdoMWkU/PwwWi8Xi6CBERERERERE8nJp7thCabfES6MLpV1H0RB9ERERERERkWJAQ/RFrnFm1wZHh2CnbN1QQLEVVE5sZ7fHOjiS3JVpEA7Azy41HRyJvTbmvQBcWfGZgyOx596yFwDTlzs4kFz0bXX1Xz3Tgsl5pkX5viUlJzs4Ent+1asDiq2g7obYDh3Y5+BI7FWt8SAAOw+ccHAk9urVKA/An/t3OzgSe1UeqA3AhYRlDo7Enmdga0eHUHAaop8v6sEXERERERERKQaU4IuIiIiIiIgUA0rwRURERERERIoBzcEXERERERGRIs1gVN90fijBFxERERERkaLNoAQ/P3SXRERERERERIqBYp/gt2zZkhMnHPtJj//+979EREQQGBjIf//7X4fGcrcYNWoUn31W9D7nJCIiIiIiDmA0FM6vmCn2CX5GRgZms9mhMcyYMYMOHTqwadMmatWq5dBY7hYZGRlkZGQ4OgwREREREZG7RrFP8O+kkSNHsmTJkgIfd+jQIVq0aIHRaMSoxSFERERERESkEGiRvQLIzMwkMzOzwMddunQJV1fXQohIRERERESk+DNokb18uWvu0qhRo1iwYAFRUVEEBwczffp00tLSGDNmDGFhYZhMJjp27Mi6devsjj127Bi9e/cmODiYRo0aMWbMGNLT023qxMbG0qFDB0wmE40bNyYqKoq0tDQAdu7ciclkIjo6mrFjx2Iymdi+fftNY54xYwYmk4mjR4/Srl07HnnkEbKzszlx4gTh4eHs3LmTVq1aERwcbB2OHh0dTUREBP7+/jz99NNs27bNps09e/bQpUsXAgICaN68OZ9++iljx45l6dKl1jr169fn9OnTNsfNnj2bsWPHWrezsrL48MMPCQsLo2HDhvTp04eTJ09a90+ZMoWPPvqIUaNGERwcTHBwMK+++iqnTp2yaTclJYWRI0cSGhqKyWQiPDycw4cPExERwcaNG23q9uvXzybOmzl69CiRkZGYTCZCQkIYMmSI9ZkAdOzYEZPJZP0FBARQs2ZNh6+5ICIiIiIi4gh3TYKfkZHBwoULqVGjBps2baJPnz7079+flJQUli5dSkJCAv3792fIkCFs2bLF5thhw4bRqVMnNm3axIIFC1i3bh3jx4+37t+8eTNDhw6lb9++JCQksHTpUs6ePcugQYMAqFevHomJibRt25bRo0eTmJhIgwYNbhpznz59SExMpEKFCixZsoR169ZhNBoxm81cunSJWbNmMX/+fDZu3Iirqyvr169n3LhxvP322yQkJBAZGUmfPn1ITU0FID09nV69ehEaGsqmTZtYsmQJ27dvZ9WqVTbrDKSnp9uNNEhPT7eZ0z5lyhTWrVvHggUL2LBhA35+ftbrBTAYDMybN49SpUqxevVqfvvtN0qWLMmYMWNsnsnzzz+Ps7Mzy5cvJzExkZ9++okKFSrQqlUrm2Q+JSWF+Ph4mjdvftP7luOHH36gVatWbNiwgV9//ZVTp04xffp06/7vvvuOxMRE6693796EhoZSvnz5fJ9DRERERETuAlpkL1/umgQfrg6R79KlC3A1Kd+9ezeTJk3C29sbg8FAeHg4kZGRfPzxxzbHtW3b1ppYVq1alTFjxvDjjz9aE94pU6YQGRlJ8+bNMRgMlClThgkTJvD777+TkJBQKNdy/vx5nnzySXx8fKzz8qdOncqgQYMICgrC2dmZli1bEhYWxg8//ADAihUrKFOmDP369cPZ2RkPDw8mTJhg06udH+fOnePLL79k8uTJVK5cGXd3d15//XWOHTvG77//bq1XsWJFXn/9dUqWLImbmxv9+vUjNjaWrKwsAObNm8e9997LmDFjKFWqFAClS5fGycmJdu3aERMTY33xsHz5csLDwylZsmS+42zVqhXt27fH1dUVDw8PXn75ZVavXp1r3d27dzNv3jwmTpxYoHshIiIiIiJSXNxVCX5gYKD1vxMTE2nWrBn33HOPTZ1WrVqxdetWaxIKEB4eblMnNDSUrKws/vjjD7Kysti2bRutW7e2qePu7k7Tpk3ZvHlzIVzJVSaTyfrfWVlZ7Nq1y66Hu0GDBuzZsweAffv2ERwcbLPf09OThg0bFui8e/fuxdfXFz8/P2uZ0Wikbt261nMB1KpVC4Ph/99qVahQAbPZzJkzZwCIiYmhXbt2uZ6jevXqVKxYkQ0bNgCwdOnSPOvmpVq1ajbbFSpUsJlGkOPKlSsMHjyYUaNGqfdeRERERET+se6qRfbKlClj/e+TJ0/i6+trV6dcuXLWJDRn/7XHATg5OVG6dGkuX77MmTNnyMzMzLUtX1/fQpvPbTQaKV26tHX79OnTZGZm2r1oyMrKIjQ0FIALFy5QtmxZu7Zyes9vxGKxWP/7xIkTHDlyxOYFA1wdIXFtmZubm81+FxcXALKzs4Grw+5zu285nnzySVasWIGfnx+HDx8mLCzspnFe6/ovDri6utqtnQDw7rvv8vDDD/P4448XqH0REREREZHi5K5K8K9N+EqXLp1rb+6pU6dwdna2SXpPnDhh01udlZVFamoqZcuWxcvLCycnJ06ePEmFChVs2srrJcKdYDAYbHrHc4aur1mzBk9Pz1yPKV++fK4vHK5f+C5nnn9edUqWLMkDDzzAjz/+eKvhA+Dt7c2xY8fy3N+mTRtmzJhBxYoVadWqFc7Od/7PLTY2lri4OBYvXnzH2xYRERERkSKiCK+in5iYyLvvvsvBgwcpXbo0vXr1onPnzvk6Nisri06dOrF//3527Nhx27EU3bt0E+Hh4axdu5bLly/blC9fvpyQkBCb3ufo6GibOvHx8ZQoUYJ7770Xd3d3goODWbZsmU2dK1eusHbtWpsh887OzjY94XeSh4cHVatWZdOmTXnW8ff3Z/369dYedLg6n37nzp029cqWLcvRo0et29nZ2cTHx1u369Spw6FDh+xeDBRU8+bN+f777/Pc7+PjQ506dfj0008LPDw/P1JSUhg1ahTvvvtugeb2i4iIiIjIXcZgKJzfbfrzzz/p27evdcH2WbNm8dlnn9nloHn5/PPPue+++27pc+y5uWsT/ICAAB566CGGDRtGSkoKFouFFStWMGvWLPr162dTd8uWLdZE9PDhw4wdO5ZevXrh5OQEQN++fZk9ezYxMTFYLBZSUlIYOHAgDRo0sFkt38fHh+3bt2OxWAq8sF1+9OzZk6ioKDZu3EhWVhaZmZnExcVZzxUaGkqpUqWYOHEiGRkZpKSkMGLECLuh9I888giffvoply9fJjMzk3fffRd3d3fr/vLly/Poo48yaNAgkpOTAbh48SKxsbEFirdr166cPn2aN998k/PnzwNXFw+8dv2DNm3acO+991K/fv1buic38q9//YtOnTrl64sGIiIiIiIid9q///1vOnfubF33rXr16owcOZI5c+bc9NiDBw/y448/MnDgwDsWz12T4Lu6utolslOmTMHX15d27doRGBjInDlz+Pjjj3n44YetdVxcXJg8eTK//PILQUFBPPfcczz77LNERkZa65hMJj788ENmzpxJYGAg7dq1o0qVKnzwwQc25+vQoQPbtm3DZDLx+eef5zt2FxcX6/z1nO3rrwWgU6dOvPrqq0RFRWEymQgLC7P7w/jkk084ePAgISEhtG/fnocffphmzZrZ1Bk6dCglS5bk0UcfJSIiAicnJ55//nlcXV2tdcaPH0+9evXo1q0b/v7+REREsG7dOut+Nzc3m/rXlucMtb/nnnv47rvvMJvNtGjRgoCAAB5//HEOHz5srf/XX3/xxBNP5Pte5XB1dbU7/7X3LTU1ldWrVzNnzhxMJpP1FxQUxNmzZwt8PhERERERKcKMxsL53aY1a9bw6KOP2pSFhoaSnJyc65TyHBaLhZEjRzJixAi7heNvx10zBz8qKsqurGTJkowcOZKRI0fmedzKlSsBmDVr1g3bDwsLu+kicJUrV7b5tnt+5cSQo3z58mzbti3Xus8++yzPPvtsnm3dd999fPrppzZlw4cPt9n29vZmypQpN4zJzc2N4cOH2x2b49oXINe6fl5I6dKlee+99+zqZWdnk5qayg8//MC///3vG8aSm9ye97X3rXTp0uzdu7fA7YqIiIiIiOS4Pjm/XkxMTJ77srKyOHz4MNWrV7cpd3FxoVKlSuzfvz/PNd2+/vprKlasSOPGjTly5EjBA8/DXZPgFzUpKSlERETkOSffYDCwfv36XHvq7zQXFxfrdIOi4uWXX2bXrl289tpr3HvvvdbyonTfREREREREblVqaipArouke3p6cu7cuVyPO3r0KF988QWLFi264zEpwb9F3t7eJCQkODoMAN5++21Hh2AnrykMRem+iYiIiIjIP9uNeuhvJjMzE4vFgsVisflCGnDDxdlHjRrFwIED7T7nficowRcREREREZGirQh+Ji+n5/7ChQt4eXnZ7MutDGDJkiUYjcZbWqcsP5Tgi4iIiIiISNFmvP1P2t1pJUqUwNfXl4MHD9p82ctsNnPkyBHuv/9+u2N2795NYmIiJpPJWpadnU1WVhYmk4kWLVowceLEW45JCb6IiIiIiIjILQgNDWXVqlU2CX5cXBy+vr5UrlzZrv6wYcMYNmyYTdmRI0d4/PHHSUxMvO14it44BxEREREREZFrGYyF87tNPXv25NtvvyU2NhaA5ORkxo8fzyuvvAJcXWn/pZdeIjk5+bbPlR/qwRcRERERERG5BQ8++CAfffQRkydP5vXXX6dUqVJ069aNjh07AlcX4ktOTiYtLS3PNlxcXO7YV8SU4IuIiIiIiIjcotDQUH744Ydc97m5ufHbb7/d8Pjy5cuzbdu2OxKLwXKj9ftFREREREREHOzKT9MKpV33J/sVSruOoh58ERERERERKdoMRW8V/aJICb7INU79vsnRIdgpVycYUGwFlRPb6V0bHRxJ7nzqhgBwZcVnDo7EnnvLXgD87FLTwZHYa2PeC8CXsQ4OJBcvhl/9V8+0YHKeaVG+b0l/08JIBeFXvTqg2AoqJ7bkpCQHR2Kvup8fAAeSDjo4Ens1/KoB8PuBvxwcib06Ne4Divbf25F9uxwcib1KD9Z1dAgFZ9T68PmhuyQiIiIiIiJSDKgHX0RERERERIo2DdHPF/Xgi4iIiIiIiBQDSvBFREREREREigEl+CIiIiIiIiLFgObgi4iIiIiISNFmUN90fugu3aaWLVty4sQJR4fhENHR0URGRlq3R4wYwffff19o5xs1ahTR0dGF1r6IiIiIiBRRRmPh/IoZ9eDfpoyMDMxms6PDcIiMjAwyMjKs2+PHj/9bzyciIiIiIiL/r/i9sriLjRw5kiVLljg6jDtmy5YtdO/e3dFhiIiIiIiI/CMowS9CMjMzyczMdHQYd0xxux4REREREZGiTAn+/4waNYoFCxYQFRVFcHAw06dPJy0tjTFjxhAWFobJZKJjx46sW7fO7thjx47Ru3dvgoODadSoEWPGjCE9Pd2mTmxsLB06dMBkMtG4cWOioqJIS0sDYOfOnZhMJqKjoxk7diwmk4nt27fnK+7//Oc/dO3aleDgYIKDg+natSv79++37l+0aBFvvPEGEydOpFGjRoSEhDBz5kyys7N5//33eeSRR2jSpAmff/65Tbs9evRg9erV9O3bl0aNGhEcHMzbb799wyHykZGR1jnyHTt2pHfv3mzZsgWTycS4ceMAmDVrFmPHjrU7tn79+pw+fdq6vWPHDjp16oS/vz/NmjVjxowZWCwWm2NSUlJ4/fXXCQgIoFGjRowfP/4fO11CRERERKRYMxgK51fMKMH/n4yMDBYuXEiNGjXYtGkTffr0oX///qSkpLB06VISEhLo378/Q4YMYcuWLTbHDhs2jE6dOrFp0yYWLFjAunXrbOajb968maFDh9K3b18SEhJYunQpZ8+eZdCgQQDUq1ePxMRE2rZty+jRo0lMTKRBgwb5ijsrK4shQ4YQFxfHxo0biYiIYOjQodb9Tk5OrFy5EoPBQGxsLNHR0URHRzN48GBSU1OJiYnh+++/Z/HixWzcuNF6nNlsZty4cURERBAfH89PP/1EQkICH3zwwQ3vYc4LgO+++46ZM2cSEBBAYmIiI0eOtKtzrfT0dGtvf2pqKi+//DLh4eFs2rSJpUuXsnPnTlatWmVzTN++fXF3d2ft2rUsW7aMvXv3Mn369HzdNxERERERkeJGCf41MjMz6dKlC3A1Kd+9ezeTJk3C29sbg8FAeHg4kZGRfPzxxzbHtW3blubNmwNQtWpVxowZw48//mhNZKdMmUJkZCTNmzfHYDBQpkwZJkyYwO+//05CQsJtxRwQEECDBg1wdnbGaDTSuXNn9uzZw8WLF611PD09GTx4MG5ubpQtW5b27dsTHx/PW2+9haurK+XKlaN9+/bExsbatB0UFES7du0AuPfeexk3bhwLFy60G51wp/3666/ce++99O3bF1dXVzw8PJg4cSJZWVnWOuvWreOvv/4iKioKT09PvL29GTt2LN9884168UVEREREihuDsXB+xUzxu6LbEBgYaP3vxMREmjVrxj333GNTp1WrVmzdutUm2QwPD7epExoaSlZWFn/88QdZWVls27aN1q1b29Rxd3enadOmbN68+bZivnTpEtOmTaNjx46EhITQqFEjLBYLqamp1jrVqlXD2fn/P5hQpkwZGjRogKurq7XM29vb5hiAxo0b22zXr18fg8HAkSNHbivmm9m7d6/NswDw8vLi4Ycftm5v376dJk2a2FxX1apVsVgsHD9+vFDjExERERGRv5mG6OeLPpN3jTJlylj/++TJk/j6+trVKVeuHGazmTNnzlj3X3scXB0WX7p0aS5fvsyZM2fIzMzMtS1fX19OnDhxWzH379+fixcvMmjQIOrXr4+Hhwd16tSxma/u5ORkd1zp0qVv2napUqXsyjw8PKxrB9wp18+tT09Pp1y5cnb1vL29rf994sQJfvrpJ5YtW2ZTJyMjg3PnzlG5cuU7GqOIiIiIiEhRpwT/Gkbj/w9oKF26NCdPnrSrc+rUKZydnW2S3xMnTuDn52fdzsrKIjU1lbJly+Ll5YWTkxMnT56kQoUKNm3l9RIhv/78808SEhJYv349Xl5e1liys7Nvuc1rnTlzxmY7OzubM2fOUL58+Vtu02g02q2sf/199vHx4fz583bHXtszX7JkSZ599llGjBhxy7GIiIiIiIgUJxqin4fw8HDWrl3L5cuXbcqXL19OSEgIbm5u1rKcleNzxMfHU6JECe69917c3d0JDg6262m+cuUKa9eutc7dB3B2drbrzb6Rs2fP4uXlZU3ugVxX+b9V69evt9mOi4ujXLly+X4pkdv1+Pj4cPToUZuyaxf3A6hTpw6bNm2yKTt58qTNlwXq1q1LYmJige6XiIiIiIhIcaYEPw8BAQE89NBDDBs2jJSUFCwWCytWrGDWrFn069fPpu6WLVv4/vvvATh8+DBjx46lV69e1qHxffv2Zfbs2cTExGCxWEhJSWHgwIE0aNDAZrV8Hx8ftm/fjsViydcweD8/Py5fvszKlSuxWCxs2bKFefPm4ePjc0fuwcaNG/nhhx8wm80cPnyYd955h+7du9uMdLiRcuXKkZyczPnz57lw4QIAISEh/Oc//yEuLg64Ot/+m2++sRkR0bx5c8xmM9OmTcNsNnP27Flee+017rvvPmudFi1acOHCBcaOHUtKSgpwdfRCYmLiHbl2EREREREpQozGwvkVM8Xvim6Rq6urTa88XF393tfXl3bt2hEYGMicOXP4+OOPbRZ7c3FxYfLkyfzyyy8EBQXx3HPP8eyzzxIZGWmtYzKZ+PDDD5k5cyaBgYG0a9eOKlWq2H1yrkOHDmzbtg2TyWT3XfrceHh4MGPGDD755BMCAwOZNGkSEyZMwMvLyzpM383Nze66XF1dbRbYy6vszTff5JdffiE0NJRnn32Wtm3b0rVr1zyPuX67SpUqNG/enEcffZROnTqRmZlJlSpVmDhxIlFRUTRu3Ji33nqLt956ixIlSlgXzDMajcyePZstW7YQHBxMhw4deOKJJwgODra27+7uzrx580hJSSEiIoKAgAC6du3KoUOHbnrfREREREREiiODRWOcJRddu3alX79+BAcHOzqUv9Wp3zfdvNLfrFydq89AsRVMTmynd228SU3H8KkbAsCVFZ85OBJ77i17AfCzS00HR2KvjXkvAF/G3qSiA7z4vw+q6JkWTM4zLcr3LSk52cGR2POrXh1QbAWVE1tyUpKDI7FX/X/rOR1IOujgSOzV8KsGwO8H/nJwJPbq1Lg6wrMo/70d2bfLwZHYq/RgXUeHUGCXV88vlHbvad715pXuIlpkr4jK6ZnO6/2LwWBg/fr1dr3zd4qTkxMuLi6F0raIiIiIiEiBFMNv1hcGJfhFlLe3NwkJCQ47/9y5cx12bhERERERESk4vQYRERERERERKQaU4IuIiIiIiIgUAxqiLyIiIiIiIkWb5uDni+6SiIiIiIiISDGgHnwREREREREp0iwGg6NDuCsowRcREREREZGiTUP088VgyetD6yIiIiIiIiJFwKXfFhZKuyWadCqUdh1Fr0FEREREREREigEN0Re5xpldGxwdgp2ydUMBxVZQObGd3R7r4EhyV6ZBOAA/u9R0cCT22pj3AnBlxWcOjsSee8te/B97dx4f07n4cfwz2Qkh4aJ2YitBI5PEvsQStFS11lYVtS9F6wa1lyjpRVtqqWr1dlFVetui7SX2JcQWba1BS5EgItZkkszvj/zMNSZIEDPi+3695tXMc7bvOTNJPedZDsCc1XYOkomBrTL+q880e25+po583WKPHbNzElu+5csDypZdj0O2E0cP2zmJrbIVKgGw/2icnZPYql6hKAB/HTlg5yS2Sld8GoDLO1fZOYmt/IGt7R1Bcogq+CIiIiIiIuLYNMlelqiCLyIiIiIiIo7NSaPLs0JXSURERERERCQXUAVfREREREREJBdQBV9EREREREQkF9AYfBEREREREXFoZk2ylyVqwRcRERERERHJBR67Cn7Lli2Ji3O8Z3ACHDx4kA4dOmAymQCYP38+8+fPf6QZRo8ezXffffdIjykiIiIiIpKjDE4588plHrsu+ikpKZYKtKOpUqUK3377reV9SkrKI88QHh7+yI8pIiIiIiKSk8y5sDKeE57oqzRmzBh++OEHe8e4b3PnzmXu3Ln2jiEiIiIiIiIO4LFrwX+YUlNTSU1NtXeM+/Y4ZxcREREREZGHy24t+OPGjWPJkiVMmjSJ4OBg5syZw5UrV5gwYQL16tXDaDTy0ksvsWnTJpttT58+Tb9+/QgODqZ27dpMmDCB5ORkq3U2bNjAiy++iNFopH79+kyaNIkrV64AsH//foxGIz/99BMTJ07EaDSyb9++e2aePn06ERERVmXXr1/HaDRy4cIFYmJiCA0NtVqelpbG7NmzadKkCf7+/rz88sscPHjQsjwuLo5GjRqxf/9+WrVqRXBwMCkpKRw/fpw+ffpQp04dgoKCePHFF4mOjgbg/PnzBAUFsWDBAhYsWIDRaGT16tUA9OnTh59++smy/6xc0yZNmrB+/Xo6duxIrVq1CAkJYcGCBfe8Hrc6fPgwXbp0ITAwkODgYKZNm2ZZlpqaykcffUSzZs0ICAigTp06rFq1CgCz2cyiRYto3rw5RqORFi1a8Nlnn1ntO7PvCkBCQgLDhw8nICCA2rVrEx4e7rDDN0RERERERHKa3Sr4KSkpLF26lAoVKhAVFUX//v0ZPHgwCQkJ/Pjjj+zcuZPBgwfz1ltvsWvXLqttw8LC6NixI1FRUSxZsoRNmzZZjT3fsWMHI0aMYODAgezcuZMff/yRixcvMnToUACqV69OdHQ0zz33HOPHjyc6OpqaNWveM3OTJk1Ys2aNVdmWLVuoUKEChQoVynR+gCVLlhAfH8/q1avZtm0bvr6+9OzZ03KzwWQyce3aNebPn8+///1vtm3bhpubG8nJyfTo0YMNGzawY8cO+vfvz9ChQ0lJSaFw4cLs2LGDPn360KdPH6Kjo2nVqpXlut469j8r19RgMDBp0iSGDRvGzp07WbhwIf/+979tzvVuwsLC6NSpEzt37mTr1q306NHDsmzs2LFs2bKFhQsXsmvXLtatW0fDhg2BjGEGy5cvZ86cOURHRzN37ly+++47qxsMmX1XAAYOHIiHhwfr169n1apVHDp0yFL5FxERERGRXMRgyJlXLmPXMfipqal07doVyKiUHzhwgGnTpuHj44PBYKBRo0b06dOH999/32q75557jpCQEADKli3LhAkT+P777y0V2w8++IA+ffoQEhKCwWDA29ubqVOn8vvvv7Nz5877zlurVi0uXbrE8ePHLWVr1661abW/lbu7O+PHj8fDwwMPDw8mTJiAi4uLVeU5KSmJ559/nsKFC+PklPGRVKlShTp16uDm5gZAs2bNcHJy4sSJE1nOu3379ixf065du1KnTh2cnZ0pX7487du3Z+3atVk+1vHjx2nQoAEAzs7OFClSBIB9+/YRGRnJ/PnzKVu2LAAeHh7ky5ePy5cvs3DhQsLDw6lUqRIAvr6+hIeH8/HHH1tugoD1d8XJyYlNmzZx5swZJk2aRP78+fHx8WHixIl8/fXXasUXEREREZEnkl0r+IGBgZafo6OjadKkCXny5LFap1WrVuzevZu0tDRLWaNGjazWqVu3Lmlpafz555+kpaWxZ88eWrdubbWOh4cHjRs3ZseOHfed19nZmZCQENavXw9Aeno6GzZsuGsFv169ejg7O1veOzk5Ub9+fQ4dOmS1ntFotHqfmprK4sWL6dKlC/Xr1ycgIIBz586RmJiY5bzZuaZVq1a1WqdEiRLEx8dn+VgdO3akf//+7Nmzx6p87dq1hISEkC9fPpttDhw4gLe3NzVq1LAqr169Ol5eXlZDGW79rkDGjYOGDRvi4vK/aSTKli2L2Wzm7NmzWc4tIiIiIiKOz2xwypFXbmPXM/L29rb8HB8fb2n1vdU//vEPTCYTFy5cyHQ7yKh4FyxYkOvXr3PhwgVSU1Mz3VeRIkWIi4t7oMzNmjVj3bp1AOzevZvixYtTvHjxO67v4+NjU1aoUCGuX79uee/k5ETBggWt1pk0aRJLly6lZ8+e/PDDD0RHR/PUU09hNpuznDU71/RmT4GbXFxcrG4A3Mvo0aPp06cPY8aMoXfv3pw8eRLIGCefWYa75YOMz+rWivrtn3lcXBzff/89RqPR6nXjxg0uXbqU5dwiIiIiIvIYcOAu+tHR0XTs2JHAwECaN2/ON998c9f1N23aRN++falbty61a9emV69eHD169KFksWsF/2Z3dICCBQtm2mJ87tw5XFxcKFCggKXs9kp6WloaiYmJFCpUCC8vL5ydnTPdV3x8vE1FMbvq1avHwYMHuXLlCpGRkXdtvQcybU2+cOEChQsXtrw3GAwYbvlyJScns3z5cj788EOaN29uuUmQnRZ1yN41fRiaNWvGjz/+SNmyZenXrx9msxkfHx9Onz6drXyQca633hy59bsC4OnpSefOnYmOjrZ6xcTE4Ofn9/BOSkRERERE5A7++usvBg4caJn/bf78+SxcuNBq4vPbnTx5kldffZXIyEg2btxI9erV6dOnD9euXXvgPA7TJ6FRo0asX7/eqmUbYPXq1dSpUwd3d3dL2e0Xa/v27eTNm5dixYrh4eFBcHCwZZb2m27cuMH69estY/cho5U6Oy3ikDGmvnbt2mzatIm1a9fSsmXLu66/YcMGq7Hk6enpbNmyhcqVK99xm8uXL2MymShZsqTVOd4+tvxe+bNzTR8WJycn3nzzTY4ePcr58+dp0qQJa9euJSEhwWZdf39/kpKSiImJsSrfv38/V65cwd/f/47H8fPzIzo6Otufn4iIiIiIyMPyxRdf0KlTJ8sw8vLlyzNmzBgWLVp0x226du1KvXr18PDwwM3NjTfeeAPIqAc9KIep4AcEBFC1alXCwsJISEjAbDbz888/M3/+fAYNGmS17q5du/juu++AjLsfEydO5PXXX7eMdR84cCALFixg7dq1mM1mEhISeOONN6hZs6bVbPmFCxdm3759mM1mq0r4vTRr1ozPPvuMPHnyUKpUqbuuW6BAAUaMGMGVK1dITk5m0qRJFChQgMaNG99xm0KFClGiRAm++uorzGYzR44c4d1337VMUndr/piYGNLT07l8+bLNfrJzTR9EWlqaZUx/WloaX375JU899RTe3t74+/vTuHFjevfuzZ9//glkzIp/9epVPD096d27N6NGjeLw4cMAHDx4kDfffJO+ffvazB1wq2bNmnH58mUmTpxouXkQFxdneZSgiIiIiIhITlu3bh1Nmza1Kqtbty7Hjh3Lcg9sg8GAp6dntuqkd+Jy71Vyhpubm00L8gcffMDMmTNp27YtN27coHz58rz//vs888wzlnVcXV2JiIhgzpw5TJs2DTc3N3r27EnPnj0t6xiNRmbOnMmsWbMICwvDw8ODVq1aMWzYMKvjvfjiiwwYMACj0cirr75quXNyL40bN2b8+PEMHDjQ5pxcXV2t3r/wwgsYDAZat27N1atXCQ4OZvHixZb1XF1dba6DwWBg7ty5jB8/njlz5lCsWDHCwsL47LPPSE9Pt6zXvHlzVqxYQWBgIC1atGDq1Km4ublZjafPyjW9fRvI6Klwe9md3Lhxg9GjR3Pq1Ck8PDyoWrUq8+fPt0yAFxERwUcffUS3bt24evUqTk5OvP3227Rr144+ffrg4eHBoEGDSEhIoFChQrz66qu88sorVvluv0YeHh4sXryYd999l9DQUNLT0ylUqBB9+vSxmbBQREREREQeczk0Id7tlfPb3e3JYmlpaZw8eZLy5ctblbu6ulKyZEmOHDlyxznHbnXixAlOnz5tM7H4/TCY1cdZxOLCb1vtHcFGIb+6gLJl181sF/dtsHOSzHnXzOjGtdL1zsN17OVZU8ZTPm78vNDOSWx5tHwdgDmr7RwkEwNbZfxXn2n23PxMHfm6xR47Zucktnz//x+TypY9j0O2E0cP2zmJrbIVMh5nvP/og01WnROqVygKwF9HDtg5ia3SFZ8G4PLOVfdY89HLH9j63is5mMvRP+fIftuFRdx1+d0q+BcuXLDM0Xa7Ll260K1bN5unu2VmyJAhlC1bluHDh9878D3YrQXf0SQkJBAaGnrHMd0Gg4HNmzfnyLh1R9exY0eO3eV/hB9++CF16tR5hIlERERERORJYn5IM97f7m4V+HtJTU3FbDZjNputJk0HsjxX2Pfff8/hw4d599137zvHrVTB/38+Pj7s3LnT3jEc0tKlS+0dQUREREREnmQO+Mz6/PnzAxmTpHt5eVkty6zsdr/99hsREREsXryYvHnzPpRMjneVRERERERERBxc3rx5KVKkCMePH7cqN5lMnDp1ijJlytxx2zNnzjBw4EDeeecdKlSo8NAyqYIvIiIiIiIich/q1q3LmjVrrMq2bNlCkSJF7vjEtcuXL9O7d2969Ohh9Rj3h0EVfBEREREREZH70KtXL7755hs2bMiY2PnYsWOEh4fTt29fIGOm/ddee80yp1lqaiqDBg0iMDCQ11577aHn0Rh8ERERERERcWhmcmaSvQdVqVIlZs2aRUREBMOHD6dAgQJ0796dl156Ccio0B87dszyjPujR4+yfft29u/fz48//mi1r3bt2jFmzJgHyqMKvoiIiIiIiMh9qlu3LitWrMh0mbu7Oxs3brS8r1KlCocOHcqxLAZzVufvFxEREREREbGDxD2RObLfgv4Pdwy8vWkMvoiIiIiIiEguoC76IreI/f/JLxyJb/nygLJllyNnA8fO9zhkOxYba+cktsr7+gKOfd2ULXtuZlvpWtnOSWw9a8ro3unI2dZVrGnnJLaaHNkHwPV1X9o5ia08TV4G4HjsUTsnsVXON+MRXo78e+rI1+3kkT/snMRWqYpV7R1Bcogq+CIiIiIiIuLYDOp8nhW6SiIiIiIiIiK5gFrwRURERERExKGZDY75mDxHoxZ8ERERERERkVxALfgiIiIiIiLi0Mwag58lukoiIiIiIiIiuYAq+CIiIiIiIiK5gCr4OSwuLg5/f397x8i2GjVqcP78eXvHEBERERERAYMhZ165jCr4OcxkMpGcnGzvGNmWnJxMamqqvWOIiIiIiIhIFqmCnwNCQ0OJi4uzdwwREREREZFcwWxwypFXbpP7zsgBmEwmTCaTvWOIiIiIiIjIE0QV/Ifos88+w2g0cvr0adq2bUv9+vVJS0sD4N///jchISH4+/vTunVrIiMjrba9fv0648ePJygoiMDAQMLCwrhy5Ypl+bhx41iyZAmTJk0iODiYOXPmYDKZ8PPzY926dYSGhhIQEECvXr1ISEhgy5YttGnThuDgYAYNGkRCQoJlX8nJyYwaNYqGDRtiNBpp3rw533zzzX2f95UrVxg6dCjBwcEEBQXRpUsXq+U///wz7du3JyAggMDAQKZOnWpZtmHDBl588UWMRiP169dn0qRJVue9cuVK3nzzTZYvX079+vXp1asXAGlpacycOZN69epRq1Yt+vfvT3x8/H2fg4iIiIiIOC4zhhx55Taq4D9Er732GtHR0RQvXpwffviBzZs34+zsTFpaGr/++iuLFy9mz549jBgxguHDh1tVuseMGcPp06dZvXo169atIz09nYkTJ1qWp6SksHTpUipUqEBUVBT9+/fH1dUVk8nERx99xMKFC9mxYwdVq1Zl9OjRvPvuu8yePZstW7ZQpkwZq0p1SkoK9erVY9WqVURHR7NgwQI+/PBDDhw4cF/n/eGHH+Ll5cWmTZvYsWMHM2fOtCxbsWIFU6dO5a233iI6OpqoqCj69esHwI4dOxgxYgQDBw5k586d/Pjjj1y8eJGhQ4datk9OTubo0aPs3LmTyMhIPv74YwA++OADNm3axJIlS9i6dSu+vr5W24mIiIiIiDxpVMF/RN555x1KlSoFQJMmTahSpQpbt24FIDY2lsjISCIiIihUqBD58uVj3LhxREZGWs1kn5qaSteuXQFwcvrfRzdw4EBKlSqFs7MzPXr0YN26dQwZMoQyZcrg4uJCr1692Lhxo2X9/Pnz89xzz5EvXz4AypUrR+3atdm1a9d9ndvx48epXbs2bm5uABQrVgyAa9euMX36dGbMmEHdunUxGAw4OTnh7e0NZFTS+/TpQ0hICAaDAW9vb6ZOncrvv//Ozp07Lfs/cuQII0aMwM3NDScnJy5dusTnn39OREQEpUqVwsPDg+HDh3P69Gl+//33+zoHERERERGRx50q+I+Ak5MTZcqUsSorXry4ZSK+mJgYatWqRcGCBS3L8+fPT+nSpTl69KilLDAwMNP9V6hQwfKzj4+Pzbo+Pj4kJiZabRMZGWmpXNeqVYuff/7ZZp2sevHFF4mIiODHH38kPT3dUr57927y5MlDQECAzTZpaWns2bOH1q1bW5V7eHjQuHFjduzYYSnz9fW1nBfAoUOHKFKkCL6+vpYyJycn/Pz8OHjw4H2dg4iIiIiIOC5Nspc1LvYO8CQwGAwYbnvGopubm+XxeXFxcURFRWE0Gq3WMZlMJCUlWd7fbPm+nYuL7cd4682C261cuZIJEyYwbNgwxo8fT9GiRRk7dixmszmrp2QlNDSUMmXK8N577zF//nzefvtt6tSpQ0JCAkWKFMl0mwsXLpCamprp8iJFilg9heD2c4mLi+PUqVM21ys1NdWmTERERERE5EmhCr4D8PT0pFGjRsyZM+eu693aLf9BfPnll7z55pt07tzZUnb27FmKFy9+3/usUqUKCxcuZNWqVQwYMICffvoJHx8fzpw5k+n6Xl5eODs7Ex8fb3Pc+Ph4q4q/s7Oz1XJPT08qVqzI999/f995RURERETkMWLIfRPi5YTc1yfBAbi4uGSrNdzPz4+YmBhu3LiRg6n+5+LFi5QoUcLy/vLly+zbt++h7Lt169ZUqFCBvXv34u/vz40bN9i0aZPNeh4eHgQHB7Nq1Sqr8hs3brB+/XpCQkLueIxq1apx4sQJzp0791Ayi4iIiIiI5Aaq4OeAwoULs3fvXkwmU5Yq7f7+/pQsWZK33nqLs2fPApCYmGiZhO9hq1mzJt999x3JyckkJCQwatQoqwp/dsXExHDt2jUAtm/fTmxsLJUqVcLT05OhQ4cSFhZmOZf09HTLWP+BAweyYMEC1q5di9lsJiEhgTfeeIOaNWtSs2bNOx6vaNGiNG3alKFDh3Ls2DEArl69yoYNG+77HERERERERB53quDngN69ezN9+nQaNmzIH3/8gbu7u806bm5ullnnAebOnYuXlxcvvPACtWrVol27duzfv99q/cz24+Hhgaurq1VZ3rx5bda7tWzUqFGYzWYaN27MCy+8QEBAAG3atCEtLc2yjru7e6Zj+zOzaNEiateujb+/P5MnT+bdd9+lYsWKAHTp0oWxY8cyZcoUatWqRWBgoOUxekajkZkzZzJv3jwCAwNp27YtpUuXZsaMGVY5Mjvv8PBwqlevTvfu3fH39yc0NDTTngIiIiIiIvL4M+OUI6/cxmC+35nVRHKh2P/vEeBIfMuXB5Qtuxw5Gzh2vsch27HYWDsnsVX+/5/s4cjXTdmy52a2la6V7ZzE1rOmQ4BjZ1tX8c698eylyZGMIYnX131p5yS28jR5GYDjsUfvseajV84344lNjvx76sjX7eSRP+ycxFapilXtHSHb4v+IzpH9Fqmauybp1iR7clfTpk3j22+/vePy1157jUGDBj3CRCIiIiIi8qQxa5K9LFEFX+4qLCyMsLAwe8cQERERERGRe1AFX0RERERERBya2ZD7xsvnBF0lERERERERkVxAFXwRERERERGRXEBd9EVERERERMShmdEke1mhFnwRERERERGRXMBgNpvN9g4hIiIiIiIicienD8XkyH6LV66RI/u1F7Xgi4iIiIiIiOQCGoMvcovYY8fsHcGGb/nygLJllyNnA8fO9zhkOxYba+cktsr7+gKOfd2ULXtuZlvpWtnOSWw9azoEOHa2dRVr2jmJrSZH9gFwfd2Xdk5iK0+TlwE4HnvUzklslfOtADj276kjX7eTR/6wcxJbpSpWtXeEbDMbNAY/K9SCLyIiIiIiIpILqIIvIiIiIiIikguoi76IiIiIiIg4ND0mL2vUgi8iIiIiIiKSC6gFX0RERERERBya2aC26azQVRIRERERERHJBdSCLyIiIiIiIg5NY/CzRi34IiIiIiIiIrmAKvh21qNHD3bt2mXvGI9EaGgocXFx9o4hIiIiIiKSK6mLvp2lpqaSmppq7xiPhMlkwmQy2TuGiIiIiIg8ZjTJXtY80Vdp7969dOvWjeDgYIKDg+nWrRtHjhxhw4YNdOjQwWb97t27s3r1agDOnDlD//798ff3Jzg4mLfffpslS5YwduzYLB37119/xWg0smvXLvr160dgYKCldTshIYHhw4cTEBBA7dq1CQ8Pt6oYd+3alW+//ZaXX36ZWrVq0bZtW/bt28epU6d47bXXCA4O5qWXXuKPP/6wbBMXF0ebNm2IjIykTZs2GI1GWrVqxc8//2yVKy0tjZkzZ1KvXj1q1apF//79iY+PtyxfsGABM2fOZN68edSuXZsxY8YAsHz5ctq2bUtgYCD16tVjxIgRJCUlAfDZZ59hNBo5ffo0bdu2pX79+qSmphIXF4e/v7/NtRk3bhwLFy60vG/ZsiV79+6le/fuGI1GoqKiAPj999/p0qUL/v7+hISE8O2332bp2ouIiIiIiORGT3QFPy0tjbfeeostW7awbds2QkNDGTFiBMHBwRw9etSqO/nFixeJiYmhQYMGpKen069fP/Lnz8+6detYt24dhQsXZvr06VluoW7RogXR0dEEBAQwb948du7cSdGiRQEYOHAgHh4erF+/nlWrVnHo0CHmzJlj2dbZ2ZlZs2YxePBgdu3axfDhwxkxYgT//Oc/6dGjB1FRUQwYMIDBgwdbegeYTCbOnz/PrFmzmDt3LtHR0bzxxhuEhYVx4MABy74/+OADNm3axJIlS9i6dSu+vr4MHTrUsjw5OZlNmzZx7do1tmzZwqRJkwDIly8fs2fPZseOHaxdu5b09HRmz54NwGuvvUZ0dDTFixfnhx9+YPPmzbi4uGAymUhOTra5NikpKaSkpFi9/9e//sWbb75JdHQ0QUFBJCQk0LNnT9q0aUNUVBRz585l9uzZbNu2LUvXX0REREREHh9mDDnyym2e6Ap+QEAANWvWxMXFBScnJzp16sTBgwdJS0ujXr16rF+/3rLuhg0bCA4OJl++fOzevZv4+HgmT55MwYIFyZs3L8OGDaNUqVIPnGnTpk2cOXOGSZMmkT9/fnx8fJg4cSJff/211c2Dtm3bUrt2bQwGA40bN8bNzY2qVavSqFEjAEJCQvDw8CA2NtayTUJCAm+//TYlS5YEMlrGX3jhBb788ksALl26xOeff05ERASlSpXCw8OD4cOHc/r0aX7//XfLfv7++2+GDBmCs7MzTk4ZX6EWLVpQunRpDAYDHh4evPjii0RHRz/w9bipcuXK1KhRAwCDwcCnn35KvXr16Nq1K25ublSuXJmBAweyePHih3ZMERERERGRx8kTXcG/du0as2fP5qWXXqJOnTrUrl0bs9lMYmIizZo1Y926dZZ1165dS8uWLQE4ePAgNWvWxM3NzWp/ISEhD5xp3759NGzYEBeX/02PULZsWcxmM2fPnrWUVaxY0Wo7b29vgoKCrMp8fHxITEy0vHdzc7NZp169ehw+fBiAQ4cOUaRIEXx9fS3LnZyc8PPz4+DBg5Yyf39/q3wAp0+fZty4cTz33HMEBQUxcOBAq2M/qMDAQKv3e/futbneNWrU4NChQw/tmCIiIiIiIo+TJ3qSvcGDB3P16lWGDh1KjRo1yJcvH9WqVcNsNtOkSRMmT55McnIyBoOBqKgopkyZAkBSUhIFCxa02V+RIkU4c+bMA2WKi4vjP//5D6tWrbIqT0lJ4dKlS5ZeAs7OzjbbFihQ4K77LlCgAAaDdTeUfPnycfnyZcuxT506hdFotFonNTXVqszb29tqeWJiIh07dqRhw4ZMnTqVChUqsH//fkaOHHmPs82c2Wy2Kbv9mPHx8YwdO5YJEyZYbXf7jQcREREREXn8OfIke9HR0UyfPp3jx49TsGBBXn/9dTp16nTXbdasWcP777/P2bNnKVasGG+88QbNmjV74CxPbG3or7/+YufOnWzevBkvLy8go4Kbnp4OZFSG/fz82L59OwA1a9a0rOfp6cmFCxds9plZWXZ5enrSuXNnRo8e/cD7ut3FixdJT0+3dKuHjHO+Ofbf09OTihUr8v333991P7duD/Df//6XEiVKEB4ebim7tbfBnTg7O5Oenm6T6dy5c5QpU+aux/T09CQiIuKh/BKIiIiIiIjcj7/++ouBAwcyffp0GjVqxLFjx+jbty+enp4899xzmW6za9cuJkyYwEcffUSNGjXYu3cvAwYMwNvbm4CAgAfK47i3QXLYxYsX8fLyslTaIWP8+62aNWvG+vXrWbt2LaGhoZZyo9HI3r17uXLlitX6kZGRNi3k9+Li4mLVYu3n50d0dHSmrdgPKjU11XLD4qaNGzfyzDPPAFCtWjVOnDjBuXPnsrXfixcvUqJECauyzZs326x3+7kWKlQIyBjTf9OVK1fYt2/fPY/p5+fHjh07spVTREREREQeT446yd4XX3xBp06dLHOhlS9fnjFjxrBo0aI7brNo0SIGDx5smWPsmWeeYeDAgXz22WcPnOeJreD7+vpy/fp1fvnlF8xmM7t27WLx4sUULlzYsk6zZs3YsGEDGzdupGnTppbyatWqUadOHUaPHs3Fixe5cuUK4eHh/PXXX5l23b+bwoULs3fvXtLT07ly5QrNmjXj8uXLTJw4kYSEBCCjlf1hTFiXN29epk+fbpl47z//+Q8bN26kS5cuABQtWpSmTZsydOhQjh07BsDVq1fZsGHDXfdbs2ZNtm7dyrFjxzCZTCxbtswyrj+zczWZTFy/ft0yJ8BHH31EamoqN27cYOzYsRQvXvye5/LKK6/w7bff8s0333Djxg3MZjOHDh2ymlRQREREREQkJ61bt86qrghQt25djh07ZvW48ZtSUlLYsmWLzTbNmjVjy5YtWX4q2508sRX8fPnyMXfuXD766CMCAwOZNm0aU6dOxcvLy9JNv1ixYhQtWpRKlSrZjAGPiIjgH//4B6GhoTRt2pTk5GSCg4OpVatWtnJ069aNZcuWUbt2bVauXImHhweLFy8mISGB0NBQAgIC6NatGydOnLBs4+bmhru7u9V+3NzcbCb9u73M29ubIUOGMGTIEGrVqsWnn37KvHnzLF30AcLDw6levTrdu3fH39+f0NBQq54N7u7uNscODg5mwIAB9O7dm7p167Jx40amTZtmuY439e7dm+nTp9OwYUPLs+ynTJnC2bNnadCgAW3btqV69eqEhIRY5c7sfCtVqsSCBQv4z3/+Q506dQgKCmLUqFFcvXo1K5ddREREREQeI2aDIUdeTZs2vevrbtLS0jh58iTly5e3Knd1daVkyZIcOXLEZpu4uDhcXV2tGpYho7HVbDZb9W6+H0/sGHyAoKAg/vOf/1iVrV692ur9119/nem2UVFR9O/fn7Fjx3Lt2jWWL1/O/v37adKkSbYy1KhRgzVr1liVFS9enA8++OCO23zyySf3XRYSEnLX2f7d3d0ZOXLkHSfI69OnT6blr776Kq+++qpV2a2PGQRo0qSJzTCIUqVK8emnn94xD8DPP/+caXlgYCBfffXVXbcVERERERHJCTefGpY/f36bZfnz5+fSpUs25RcvXsx0/bttkx1PdAX/QezcuZOxY8dy+fJl3N3dady4MQsXLrTM4l6/fn1u3LiR6bYGg4GlS5dSrly5R5bXxcUFV1fXR3Y8ERERERERR7d27dr73jY1NRWz2YzZbLaZi+1Oc6rdrQt+ZvvJLlXw79Obb77Jm2++ecflmU0yZ0/FihXjl19+sXcMERERERGRbDObH3xCvIftZkv85cuXrSZvv1MZgJeXF0lJSZnu78qVK3ds3c+qJ3YMvoiIiIiIiMj9yps3L0WKFOH48eNW5SaTiVOnTtk8+hsyhihfu3aN8+fPW5WfPXsWk8lk83Sy7FIFX0RERERERByaGacceT2ounXr2syptmXLFooUKUKpUqVs1vfw8KBWrVo226xduxaj0WgzcXp2qYIvIiIiIiIich969erFN998Y3m0+LFjxwgPD6dv375Axkz7r732muUx5AD9+vXjww8/JCYmBoB9+/Yxe/bsO05onh0agy8iIiIiIiIOzYzjjcGHjMd3z5o1i4iICIYPH06BAgXo3r07L730EpAxEd+xY8e4cuWKZZv69eszatQowsLCiI+P5x//+Advv/02devWfeA8quCLiIiIiIiI3Ke6deuyYsWKTJe5u7uzceNGm/LnnnuO55577qFnURd9ERERERERkVzAYL7TA/pEREREREREHMCh2JM5st/KvrYT4T3O1IIvIiIiIiIikgtoDL7ILWJvmd3SUfiWLw8oW3Y5cjZw7HyPQ7ZjsbF2TmKrvK8v4NjXTdmy52a2dRVr2jmJrSZH9gGOnW2la2U7J7H1rOkQAIl7Iu2cxFZB/xAAjscetXMSW+V8KwCO/Xt64uhhOyexVbZCJQBOHf7NzklslazkZ+8I2eaok+w5GrXgi4iIiIiIiOQCasEXERERERERh6YW/KxRC76IiIiIiIhILqAKvoiIiIiIiEguoC76IiIiIiIi4tDMZnXRzwq14IuIiIiIiIjkAmrBFxEREREREYemSfayRi34IiIiIiIiIrmAKvh2FhoaSlxcnL1jPBI1a9a0dwQREREREZFcSxV8OzOZTJhMJnvHeCRu3Lhh7wgiIiIiIiK51hNTwR8xYgQLFy6kX79+BAQE0KJFC9avX09iYiKDBw8mODiYZ599lq1bt1q2Wb58OW3btiUwMJB69eoxYsQIkpKSALh06RJ169Zl165dlvV/+uknOnXqlKU8n332GUajkdOnT9O2bVvq169PamoqACdPnqR37974+/tTv3595s6da7Vto0aNWL16Ne3ataNWrVp07tyZEydO8Mcff9CxY0eCgoLo3r07J0+etGyze/du+vbty7fffktoaCgBAQG0b9+eHTt2WO37+vXrjB8/nqCgIAIDAwkLC+PKlSuW5ePGjWPJkiVMmjSJ4OBg5syZA8DHH39My5YtCQwMpEGDBkyePJmUlBQApk6ditFoBMBoNNK+fXsA9uzZw7PPPmtzbXr16sXq1ast7/39/Tly5Ajt27cnMDCQv/76C4CtW7fy/PPP4+/vT+vWrYmMjMzStRcRERERkceLGUOOvHKbJ6aC7+zszJw5c3j++efZsWMHM2bMYNSoUYwcOZKGDRsSFRXF1KlTGTFiBImJiQDky5eP2bNns2PHDtauXUt6ejqzZ88GoECBAgwdOpR33nmH9PR0Ll++zLRp0xgzZkyW8rz22mtER0dTvHhxfvjhBzZv3oyLiwspKSm89tpr+Pn5sXXrVpYuXcqqVav49ttvrc5l1qxZhIeHs2vXLtq2bcvo0aMZOXIkY8aMISoqihYtWhAWFmbZxmQy8ccff/D999/z9ddfs3PnTl544QX69u3LuXPnLOuNGTOG06dPs3r1atatW0d6ejoTJ060LE9JSWHp0qVUqFCBqKgo+vfvD0DRokVZvHgxO3fuZOXKlRw+fJivv/4agFGjRhEdHQ1AdHQ0y5cvt+zr5k2AW91efu3aNWbMmMGMGTPYsWMHpUuX5siRIwwZMoQBAwawc+dOJkyYwKhRo4iNjc3S9RcRERERkceHKvhZ88RU8AEaNGhAq1atcHZ2xs/Pjxo1amAwGOjQoQMANWrUoFq1auzevRuAFi1aULp0aQwGAx4eHrz44ouWiipAhw4dyJs3L0uWLOGDDz6gefPmVK9e/YEyLl++nMKFC/PGG2+QJ08eihcvzsiRI/n888+t1nv11VepWrUqBoOBLl26cODAAdq3b285p65du3Lw4EGr1vfz58/z7rvv4uPjg5OTE926dcNoNLJs2TIAYmNjiYyMJCIigkKFCpEvXz7GjRtHZGQk58+ft+wnNTWVrl27AuDklPEVatu2LUWLFgXAy8uLNm3aWPVueFDBwcGULVsWgyHjl3Du3Ll06dKF0NBQXFxcCAoKonPnzpabCiIiIiIiIk+aJ+oxeRUrVrR67+3tTeXKla3KfHx8LC34p0+fZt68eezevZv4+HhMJhPe3t6WdQ0GA5MmTaJbt264urqycuXKB864b98+QkJCrMpq1qxJbGwsqampuLhkfGQVKlSwyuHt7U1gYKBVWcGCBUlMTCRfvnwAlC5dmlKlSlntu169euzbtw+AmJgYatWqRcGCBS3L8+fPT+nSpTl69CiFCxcGsDrOTUeOHGH+/Pns37+fCxcuYDKZqFGjxgNcCWs3u/jftHfvXrp162ZVVqNGDT799NOHdkwREREREXEMZnPua23PCU9UBd/Z2dmmrECBApmum5iYSMeOHWnYsCFTp06lQoUK7N+/n5EjR1qtV7p0afLmzUuZMmXInz//A2eMi4tj9erVfPzxx1blLi4uJCUl4ePjk+1zuenWivtNnp6eXL582XLsqKgom8q0yWSyzD0AWN3kADhx4gSdOnWiS5cu9OnThzJlyrBq1SpLV/zsMpvNNmW3HzM+Pp7XX3/d0qIPkJ6eTpkyZe7rmCIiIiIiIo+7J6qCnx3//e9/KVGiBOHh4Zays2fP2qy3aNEiKlWqxIkTJ4iMjLRpfc8uT09Phg0bRvfu3R9oP5m5tZv9TfHx8Zau9Z6enjRq1Mgycd6d3OyWf9P3339P48aNGTFihKUss2t1O2dnZ8vEgre6dU6AW9e9laenJ4sXL6ZKlSr3PI6IiIiIiMiT4Ikag58dFy9epESJElZlmzdvtnp/8uRJPvvsM8aOHUtYWBhTpkwhOTk5W8dxcXGxarH28/Ozmdn+Yfn77785ceKEVdnGjRt55plnLMeOiYnJ9uPssnKtwPZcCxUqxPnz562u2cmTJy2z5N+Nn58fUVFR2copIiIiIiKPp3QMOfLKbVTBv4OaNWuydetWjh07hslkYtmyZRw+fNhqncmTJ9OzZ0+KFy9O48aNKV26NPPnz8/WcQoXLszevXsxmUxcv37dMpHfnDlzLBPk/fXXX/z2228PfE5eXl68/fbbnD17lrS0ND7++GNOnz5NmzZtgIzH0ZUsWZK33nrL0gKfmJho9ejAzNSsWZNff/2V+Ph4bty4wbx586y69N96rnv27CE5OZmUlBRKly7NU089xfz58zGbzVy6dIkxY8bg6+t7z3Pp0aMHs2fP5pdffiElJYX09HT27NlDXFzcfVwZERERERGRx98TU8F3c3PD3d3dpszNzS3TsuDgYAYMGEDv3r2pW7cuGzduZNq0aaSnpwMQGRnJ6dOn6dGjh2XbkSNH8u9//5szZ85kOVfv3r2ZPn265VF9hQsX5vPPP2f37t00bNgQo9HIgAEDrLrXZ3Yurq6uuLq62pzLrWWVK1emXbt2vPzyywQGBrJu3ToWLVqEh4eHZZ25c+fi5eXFCy+8QK1atWjXrh379++/67HbtWtHy5Yt6dixI40aNeLUqVOMHj2atLQ0q/UGDBjAoEGDaNasGUePHrU87m/Lli3Uq1ePLl260L59e6pUqWL1uXh4eNicW/369QkPD2fevHkEBwcTHBxMREREpo/dExERERGRx5sek5c1BnNmM5pJrhMVFcXs2bP597//be8oDi322DF7R7DhW748oGzZ5cjZwLHzPQ7ZjsXG2jmJrfL/3/vIka+bsmXPzWzrKta0cxJbTY5kPAHHkbOtdK18jzUfvWdNhwBI3BNp5yS2CvpnzON0PPaonZPYKueb8fQmR/49PXH08D3WfPTKVqgEwKnDD94T92ErWcnP3hGybc8R2/nEHgb/ioVzZL/2okn2ckBCQgKhoaGZzgYPGY+w27x5s01LeE5ydna2aQUXERERERF5HOgxeVmjCn4O8PHxYefOnfaOYcVoNLJo0SJ7xxAREREREZEc8sSMwRcRERERERHJzdSCLyIiIiIiIg4tN06IlxPUgi8iIiIiIiKSC6gFX0RERERERByaJtnLGrXgi4iIiIiIiOQCasEXERERERERh6Yx+FljMN/pYe0iIiIiIiIiDmDnocQc2W9g5YI5sl97URd9ERERERERkVxAXfRFbhF77Ji9I9jwLV8eULbscuRs4Nj5bmY7Fhtr5yS2yvv6Ao593Rw5mz7T7Ll53a6v+9LOSWzlafIy4NjZEvdE2jmJrYL+IQCsdK1s5yS2njUdAvR7ml2Pw983R872ONEke1mjFnwRERERERGRXEAt+CIiIiIiIuLQ0u0d4DGhFnwRERERERGRXEAVfBEREREREZFcQBV8ERERERERkVxAY/BFRERERETEoWkW/axRC34W9erVi82bN9s7hoiIiIiIyBPHjCFHXrmNWvCz6JNPPrF3BBEREREREZE7Ugu+nYWGhhIXF2fvGI9EzZo17R1BREREREQeQ2azIUdeuY0q+HZmMpkwmUz2jvFI3Lhxw94RREREREREcq0nsoI/YsQIFi5cSL9+/QgICKBFixasX7+exMREBg8eTHBwMM8++yxbt261bPPss8+yZ88eAE6ePEmLFi1YuXIlLVq0oFatWrRp04YNGzZkOcNnn32G0Wjk9OnTtG3blvr165OammrZf+/evfH396d+/frMnTvXattGjRqxevVq2rVrR61atejcuTMnTpzgjz/+oGPHjgQFBdG9e3dOnjxp2Wb37t307duXb7/9ltDQUAICAmjfvj07duyw2vf169cZP348QUFBBAYGEhYWxpUrVyzLx40bx5IlS5g0aRLBwcHMmTMHgI8//piWLVsSGBhIgwYNmDx5MikpKQBMnToVo9EIgNFopH379gDs2bOHZ5991uba9OrVi9WrV1ve+/v7c+TIEdq3b09gYCB//fUXAFu3buX555/H39+f1q1bExkZmeXrLyIiIiIikts8kRV8Z2dn5syZw/PPP8+OHTuYMWMGo0aNYuTIkTRs2JCoqCimTp3KiBEjSExMBCAlJcVSYTUYDJw9e5bFixczf/58du3axfDhw3njjTeIj4/PUobXXnuN6Ohoihcvzg8//MDmzZtxcXEhJSWF1157DT8/P7Zu3crSpUtZtWoV3377rVX+WbNmER4ezq5du2jbti2jR49m5MiRjBkzhqioKFq0aEFYWJhlG5PJxB9//MH333/P119/zc6dO3nhhRfo27cv586ds6w3ZswYTp8+zerVq1m3bh3p6elMnDjRsjwlJYWlS5dSoUIFoqKi6N+/PwBFixZl8eLF7Ny5k5UrV3L48GG+/vprAEaNGkV0dDQA0dHRLF++3Oaa3ur28mvXrjFjxgxmzJjBjh07KF26NEeOHGHIkCEMGDCAnTt3MmHCBEaNGkVsbGyWrr+IiIiIiEhu80RW8AEaNGhAq1atcHZ2xs/Pjxo1amAwGOjQoQMANWrUoFq1auzevTvT7ZOTkxkzZgzlypXDYDDQpEkTnn76aatW//uxfPlyChcuzBtvvEGePHkoXrw4I0eO5PPPP7da79VXX6Vq1aoYDAa6dOnCgQMHaN++veU8unbtysGDB61a38+fP8+7776Lj48PTk5OdOvWDaPRyLJlywCIjY0lMjKSiIgIChUqRL58+Rg3bhyRkZGcP3/esp/U1FS6du0KgJNTxleobdu2FC1aFAAvLy/atGnDrl27Huha3Co4OJiyZctiMGSMk5k7dy5dunQhNDQUFxcXgoKC6Ny5s+WmgoiIiIiI5B6aRT9rnthZ9CtWrGj13tvbm8qVK1uV+fj4WFrwb2cwGHj66aetyooXL/7AE+bt27ePkJAQq7KaNWsSGxtLamoqLi4ZH1mFChWssnh7exMYGGhVVrBgQRITE8mXLx8ApUuXplSpUlb7rlevHvv27QMgJiaGWrVqUbBgQcvy/PnzU7p0aY4ePUrhwoUBrI5z05EjR5g/fz779+/nwoULmEwmatSo8QBXwtrNLv437d27l27dulmV1ahRg08//fShHVNERERERBxDutneCbIuJSWFf/3rX6xcuRKTyYS/vz8TJ060NIhmJikpiU8++YRff/2Vc+fOUbBgQTp27Ejv3r0tjZxZ8cRW8J2dnW3KChQokOXtnZyccHV1tSpzdXUlLS3tgXLFxcWxevVqPv74Y6tyFxcXkpKS8PHxAe4v/60V95s8PT25fPmy5dhRUVE2lWmTyURSUpLlvbe3t9XyEydO0KlTJ7p06UKfPn0oU6YMq1atsnTFzy6z2fa39/ZjxsfH8/rrr1t92dPT0ylTpsx9HVNERERERORhmDx5MhcuXGDlypXkzZuX+fPn8/rrr7N8+XKbOuRNSUlJeHp68sknn1C8eHGOHDlCv379yJs3L6+88kqWj/3EVvAdlaenJ8OGDaN79+4Pfd+3drO/KT4+3nInydPTk0aNGlkmzruTm93yb/r+++9p3LgxI0aMsJSdPXv2nnmcnZ0tEwve6tY5AW5d91aenp4sXryYKlWq3PM4IiIiIiLyeHtcutNfvHiRlStXEhkZaWmAHTRoEOvXr2fTpk02vbVvKlmyJH369LG8r1ixIgMGDOD777/PVgX/iR2D7yhcXFysWqz9/PxsZrZ/WP7++29OnDhhVbZx40aeeeYZy7FjYmKy/Ti7ixcvUqJECauyzZs326x3+7kWKlSI8+fPk5ycbCk7efKkZZb8u/Hz8yMqKipbOUVERERERHLSxo0b8ff3t+ld3bRpU9avX5+tfeXPn99qTrWsUAXfzgoXLszevXsxmUxcv36dF198kejoaObMmWP5MP/66y9+++23Bz6Wl5cXb7/9NmfPniUtLY2PP/6Y06dP06ZNGyDjcXQlS5bkrbfesrTAJyYm3nPiwJo1a/Lrr78SHx/PjRs3mDdvnlWX/lvPdc+ePSQnJ5OSkkLp0qV56qmnmD9/PmazmUuXLjFmzBh8fX3veS49evRg9uzZ/PLLL6SkpJCens6ePXseeA4EERERERF5cjRt2vSur+z6888/KV++vE15uXLlOHz4cLb2tWbNGho2bJitbZ7ICr6bmxvu7u42ZW5ubncsu/VnV1dXm+3vtI976d27N9OnT7c8nq9w4cJ8/vnn7N69m4YNG2I0GhkwYIBV9/rM8ru6utqM53Bzc7Mqq1y5Mu3atePll18mMDCQdevWsWjRIjw8PCzrzJ07Fy8vL1544QVq1apFu3bt2L9//12P3a5dO1q2bEnHjh1p1KgRp06dYvTo0TbzEQwYMIBBgwbRrFkzjh49annc35YtW6hXrx5dunShffv2VKlSxeo6enh42Jxb/fr1CQ8PZ968eQQHBxMcHExERESmj90TERERERF52E6dOkVgYCBGoxGj0UjHjh1JSEjAy8vLZl0vLy8uXbqU5X3/8ccfrF+/ntdeey1bmQzmzGY0k1wnKiqK2bNn8+9//9veURxa7LFj9o5gw/f/7wAqW/Y4cjZw7Hw3sx2LjbVzElvl/7+HjyNfN0fOps80e25et+vrvrRzElt5mrwMOHa2xD2Rdk5iq6B/xtjXla6V77Hmo/es6RCg39Psehz+vjlytsfJ+t+u58h+G/vleaj7GzNmDEWLFmXw4MFW5Vu2bGHKlCmsWrXqnvu4fv06HTt25NVXX7U8xj2rNMleDkhISCA0NDTT2eAh4xF2mzdvzrQXQE5xdna+44yNIiIiIiIijuxxaZbOnz9/psOVk5KSMm3Zz8yoUaOoUaNGtiv3oAp+jvDx8WHnzp32jmHFaDSyaNEie8cQERERERHJtcqVK8evv/5qU378+PEsPdJ75syZxMXFsXjx4vs6/hM5Bl9EREREREQeH+kYcuT1sNWpU4ddu3bZjLdfu3YtdevWveu23377LatXr2bOnDnZntvtJlXwRURERERERB6CUqVK0bRpU0aPHk1SUhImk4k5c+Zw+fJlWrVqZVnvp59+YurUqZb3mzdvZsaMGcybNw8fH5/7Pr666IuIiIiIiIg8JJMmTSIiIoLQ0FBMJhP+/v588sknVq3y586d46+//rK8X7JkCZcvX6Zjx45W+zIYDPz0008ULVo0S8dWBV9ERERERETkIcmbNy/jx49n/Pjxd1ynR48e9OjRw/J+9uzZD+XYquCLiIiIiIiIQzObH/54+dxIFXwRERERERFxaI/LY/LszWC+08PaRURERERERBzAf/cl58h+m9d0z5H92otm0RcRERERERHJBdRFX+QWx2Jj7R3BRnlfXwBijx2zcxJbvuXLA7pu9+PmtTtx9LCdk9gqW6ESAEdjj9s5ia0KvuUAx75ujpxNn2n23Lxux2OP2jmJrXK+FQBly66b2Rz5/1srXSvbOYmtZ02HALg4pb+dk9jyfnsu4NjX7foX4XZOYivPK6PtHUFyiCr4IiIiIiIi4tDMaJK9rFAXfREREREREZFcQC34IiIiIiIi4tDSNTV8lqiCLyIiIiIiIg7NbFYX/axQF30RERERERGRXEAVfBEREREREZFcQBV8B1WjRg3Onz+fo8fYuHEjvXv3ztFjiIiIiIiIyKOhMfgOKjk5mdTU1Bw9RsOGDWnYsGGOHkNERERERORBmTXJXpaoBf8JERcXR2hoqL1jiIiIiIiIZFs6hhx55Taq4D8hTCYTJpPJ3jFEREREREQkh6iC/5Bs376drl27YjQaMRqN9OjRg5CQENLT0y3rnDt3jrp165KSkgJAQkICY8aMoW7duhiNRho1asTJkycz3f/WrVt5/vnn8ff3p3Xr1kRGRmY525AhQ2jbti2nT5/GaDQycOBAAFavXk2vXr0s640YMYKFCxfSr18/AgICaNGiBevXrycxMZHBgwcTHBzMs88+y9atW632f/36dcaPH09QUBCBgYGEhYVx5cqVLOc7fPgwXbp0ITAwkODgYKZNm2ZZlpCQwPDhwwkICKB27dqEh4fb3Kj4+eefad++PQEBAQQGBjJ16tQsH1tERERERByf2Zwzr9xGY/Afgm3btjFs2DDGjRtHaGgoTk5OXLx4kRdffJHdu3djNBoBWLlyJfXr18fNzY2UlBRefvllgoODWb16NQUKFCAxMZH8+fPb7P/IkSMMGTKEKVOm0LRpU3bv3s3gwYP56quv8PX1vWe+Dz74gFOnTvHqq69a3RhISUmx3GwAcHZ2Zs6cOYSHhzNnzhwOHDhA7969qVmzJk2bNuXDDz8kJiaG/v37s3LlSgoWLAjAmDFjSEpKYvXq1bi7uzNx4kQmTpxIRERElq5fWFgY3bt3p127dqSlpXHhwgXLsoEDB1KuXDnWr1+PyWRi2LBhzJkzh6FDhwKwYsUKZs2axdSpU6lTpw5ms5lLly5l6bgiIiIiIiK5iVrwH4LJkyczevRoWrdujbOzMwaDAR8fH5577jlWr15tWe/HH3+kbdu2ACxevJhixYoxYcIEChQoAEDBggVxdna22f/cuXPp0qULoaGhuLi4EBQUROfOnfn6668f+rk0aNCAVq1a4ezsjJ+fHzVq1MBgMNChQwcgY3b/atWqsXv3bgBiY2OJjIwkIiKCQoUKkS9fPsaNG0dkZGSWnwJw/PhxGjRoAGTcZChSpAgAmzZt4syZM0yaNIn8+fPj4+PDxIkT+frrrzGZTFy7do3p06czY8YM6tati8FgwMnJCW9v74d+XURERERERBydKvgP6Pjx45w4cYLWrVvbLGvTpg2//PIL6enpnDhxgri4OOrUqQPA2rVrLZX9e9m7dy8hISFWZTVq1ODgwYMPfgK3qVixotV7b29vgoKCrMp8fHxITEwEICYmhlq1alla8wHy589P6dKlOXr0aJaO2bFjR/r378+ePXusyvft20fDhg1xcflfR5OyZctiNps5e/Ysu3fvJk+ePAQEBGTjDEVERERERHInddF/QAkJCfj4+FhVQm+qVKkShQsXJjo6mqioKJ599llLC31CQoKlpfpe4uPjef311zEY/jfLY3p6OmXKlHk4J3GLzHoQ3OxhkJm4uDiioqIswxBuMplMJCUlZemYo0ePZs2aNYwZM4bixYszbtw4SpUqRVxcHP/5z39YtWqV1fopKSlcunQpW9dQREREREQeX2Zz7pvxPieogv+AfHx8uHDhAikpKbi5udksb9OmDT///DNbtmxhxowZVtudPn06S8fw9PRk8eLFVKlS5aHlflg8PT1p1KgRc+bMeaD9NGvWjJCQEKZOnUq/fv346aef8PT0pHPnzowePTrTbRITEzlz5swDHVdERERERBxfei6cEC8nqIv+AypbtixlypRh+fLlmS5/7rnnWLFiBc7OzlSrVs1SHhISwnfffZelY/j5+REVFfVAOV1dXTHnwDSRfn5+xMTEcOPGjQfel5OTE2+++SZHjx7l/Pnz+Pn5ER0dfcfc/v7+3Lhxg02bNj3wsUVERERERB53quA/IIPBQFhYGBEREaxcudLyWLyLFy8CULRoUfz8/GzG23fr1o3z588zatQoS1f2pKQk0tLSbI7Ro0cPZs+ezS+//EJKSgrp6ens2bOHuLi4LOcsWLAgFy9e5O+//+bq1auZHud++Pv7U7JkSd566y3Onj0LZLSs3/4ovTtJS0tj9+7dpKWlkZaWxpdffslTTz2Ft7c3zZo14/Lly0ycOJGEhAQgY0hAdHQ0kNF7YOjQoYSFhVmOl56ebpkfQEREREREcgc9Ji9rVMF/CBo3bsxHH33EJ598gtFoJCAggLCwMMvyuLg42rRpY7VNnjx5WLZsGSaTiWbNmlmeO3/y5EkA3N3dLeP669evT3h4OPPmzSM4OJjg4GAiIiKsHnF3L+7u7vTq1Yv27dvTpk0bEhIScHNzsxpW4Obmhru7u9V2t6+TWdncuXPx8vLihRdeoFatWrRr1479+/dnKdeNGzcYPXo0NWvWJDg4mA0bNjB//nxcXFzw8PBg8eLFJCQkEBoaSkBAAN26dePEiROW7bt06cLYsWOZMmUKtWrVIjAwkJkzZ2b5uoiIiIiIiOQWBnNO9NsWS7fyVatW8f333/Pxxx/bOZFkxbHYWHtHsFHe1xeA2GPH7JzElm/58oCu2/24ee1OHD1s5yS2ylaoBMDR2ON2TmKrgm85wLGvmyNn02eaPTev2/HYrD0V5lEq51sBULbsupnNkf+/tdK1sp2T2HrWdAiAi1P62zmJLe+35wKOfd2ufxFu5yS28ryS+RxXjmzFjofTA/l2LwTZTjL+ONMkeznk1KlTtG3blpIlS/Lhhx/m2HHWrFnDyJEj77i8evXqfPrppzl2/Hvp2LEjx+5Swfrwww8tjw4UERERERGR+6cKfg4pVaqUzXPdc0KzZs0sY9Id0dKlS+0dQUREREREHnNm9Ji8rFAFX0RERERERByaHpOXNZpkT0RERERERCQXUAVfREREREREJBdQBV9EREREREQkF9AYfBEREREREXFoerh71qgFX0RERERERCQXMJjNuhciIiIiIiIijmvptvQc2W/HOrmrzVtd9EVERERERMShpZsN9o7wWFAFX+QWB2L/tncEG0/7lgCULbscORv8L9/+o3F2TmKreoWiAPx+9Iydk9iqVuEpAM4e3GPnJLaKVfEH9Jlm183P1JGvW+yxY3ZOYsu3fHlA2bLrcch2cUp/Oyex5f32XABWula2cxJbz5oOAZA0a7idk9jyGjoDgD+OnrZzEltVKxS3dwTJIbmrP4KIiIiIiIjIE0oVfBEREREREZFcQF30RURERERExKFpavisUQu+iIiIiIiISC6gFnwRERERERFxaGrBzxpV8EVERERERMShpauCnyXqov8QfPvtt4wZM+au6xw8eJAOHTpgMpkeUaq7c7Q8IiIiIiIi8mDUgv8QdOjQgQ4dOtx1nSpVqvDtt98+okT35mh5RERERERE5MGoBT+H1KxZ094RrDhaHhEREREREXm41IKfQ27cuGHvCFYcLY+IiIiIiEhWmc0Ge0d4LDxxLfjjxo3jo48+sirbvXs3VapUYePGjVbln3zyCdOmTWP9+vV07NiR4OBgateuTb9+/Th79qxlvYULFzJu3DgApk6ditFoBMBoNNK+fXsAYmJiCA0NtWzzwQcfMGvWLMaNG0dwcDDBwcEMGDCAc+fOWWXYuXMnL774Iv7+/jRq1IiPP/6YMWPG8OOPP2bpfLOaZ9asWUyfPp2wsDACAwNp2LAhy5YtIzk5mTFjxlCnTh2aNWtmc9y0tDRmzpxJvXr1qFWrFv379yc+Pj5L2QDOnj3L66+/TlBQEMHBwbz55puWZdevX2f8+PEEBQURGBhIWFgYV65csdp++/btdO3aFaPRiNFoZMiQIVk+toiIiIiISG7yxFXwGzZsyNq1a63K1qxZQ9WqVVm3bp1V+X//+18aNWqEs7MzkydPZtu2bWzevJny5cvzzjvvWNZLSUkhJSUFgFGjRhEdHQ1AdHQ0y5cvt6xz64R2BoOBxYsXU6BAASIjI9m4cSOenp5MmDDBss6ZM2fo06cP7dq1Y/v27Sxbtozt27ezatWqLE+Ol9U8zs7OfPnll1StWpWtW7fyxRdf8P777/PPf/6TYsWKsXnzZj755BMiIiI4duyYZbsPPviATZs2sWTJErZu3Yqvry9Dhw7NUjaAyZMnU6tWLbZv38727dv55z//aVk2ZswYTp8+zerVq1m3bh3p6elMnDjRsnzbtm0MHTqUV155haioKHbu3Gl1/UREREREJHcwm3Pmlds8cRX8unXrcvToUS5cuGAp27hxI6NHj2bz5s2WsosXL3L8+HGMRiMNGjSgUqVKODk54eLiQqdOnSyV5gdRokQJhg8fjqenJ+7u7gwaNIgNGzaQlpYGwIoVK6hTpw7dunXD3d2df/zjH0ybNs1yM+Fhq1ChAt27d8fV1ZXSpUvTvHlzjh8/zqBBg3B2dqZMmTI0bdqULVu2AHDp0iU+//xzIiIiKFWqFB4eHgwfPpzTp0/z+++/Z+mYx48fp379+jg5OWEwGChatCgAsbGxREZGEhERQaFChciXLx/jxo0jMjKS8+fPAxk3B0aPHk3r1q1xdnbGYDDg4+OTI9dGREREREQkK1JSUpg6dSr169cnODiYfv36ERcXl619rFmzhsqVK7NgwYJsbffEVfDz5s2L0Whk06ZNAJw8eRJnZ2eMRiPOzs6cOHECgA0bNlC/fn1cXFy4ePEi06ZNo127dgQHB9OuXTsSExMfOEuVKlUwGP43lqR48eKYTCbLzYeDBw8SGBhotU3hwoVzbMK8ihUrWr339vYmKCjIqszHx8dy7ocOHaJIkSL4+vpaljs5OeHn58fBgwezdMyOHTsSFhbGhg0brMpjYmKoVasWBQsWtJTlz5+f0qVLc/ToUY4fP86JEydo3bp1Ns5QREREREQkZ02ePJlTp06xcuVKNm/ejJ+fH6+//nqWe2EnJSXx3nvvUadOHVJTU7N17Cdykr0mTZqwadMm2rVrx4YNG2jcuDEADRo0YNOmTZQtW5YNGzbQtGlTUlNTefXVVylRogRjxoyhSpUqXLp0iZCQkAfO4e7ubvXe1dUVgPT0dCDjgy1QoIDNdkWKFHngY2fG2dnZpiyz498UFxfHqVOnLGP8b0pNTbUpu5Pu3bvz9NNP869//Yu5c+cybtw4qlatSlxcHFFRUTb7MZlMJCUl4erqio+PDy4uT+RXWERERETkiZL+mHSnv3jxIitXriQyMtJSlxo0aBDr169n06ZNWapHTp06lW7durF///5sH/+Ja8GHjAr+5s2bSU9PZ926dTYV/NTUVLZt20bDhg3ZuXMnSUlJzJkzB6PRSL58+awm2MtJnp6eVkMJbsqszB48PT2pWLEi0dHRVq+9e/fy2muvZXk/QUFBfPPNN4SGhtKzZ0+uXbuGp6cnjRo1stn3vn37aNGiBT4+Ply4cCHHhiuIiIiIiIhk18aNG/H397dpKG3atCnr16+/5/Zbtmzhzz//pGvXrvd1/Ceygl+iRAmKFClCVFQUR48etXR5Dw4OZt++fWzdupVKlSrh5eXFxYsXKVasmFXr9q1j9e/ExcUF8wPO2mA0Gm2Ode7cOWJiYnByyt5H9zDy3K5atWqcOHHCZub/+9WjRw+cnJw4fPgwfn5+xMTE3PHxfmXLlqVMmTKWSQNFRERERCT3yqlJ9po2bXrXV3b9+eeflC9f3qa8XLlyHD58+K7bXrt2jXfeeYd33nnHaih3djyRFXyAxo0bM336dOrWrWupLLu7u1O9enVmzJhh6Trh5+fHoUOH2L17N+np6URGRrJp06ZMu7PfqnDhwuzZs4fk5OT7bmXu0qULf/31FwsWLCA5OZkzZ84wfPhwXFxcrMamZ8XDyHO7okWL0rRpU4YOHWqZWf/q1as24+nvZvfu3aSkpGA2m1m5ciXJycmUKVMGf39/SpYsyVtvvWXpMZGYmMjWrVuBjKcQhIWFERERwcqVKy3DGi5evPhQzk1ERERERORuTp06RWBgoOWR3R07diQhIQEvLy+bdb28vLh06dJd9zdjxgyee+45qznOsuuJHcAcEhLCggULGDx4sE355MmTLRX80qVLM3XqVEaPHs358+epXr06M2fOpE2bNphMJlxdXXFzc8PNzc1qPwMGDLDMPj9//nzc3NwsY+wh42bC7dvcLL85rtzDw4Ovv/6aMWPGMHfuXLy8vHjttdc4c+YM1atXz9b5ZiXP7XMCZJbv9rLw8HBmzpxJ9+7duXLlCp6enrRs2ZJGjRplKdf06dP57bffcHd3p1y5csydOxdvb28A5s6dy/Tp03nhhRdITk7Gy8uLLl26ULduXSDjJs1HH33EtGnTGDt2LAaDgYCAgGzPNCkiIiIiIo4tpx5pd/sj1LOjZMmS7Ny506pszJgxmfacNpvNd22V3717N1FRUQ/cQ/mJreD7+/tz6NAhm/KuXbvajHdo1aoVrVq1sirbu3ev5efXX3/dZj+dOnWiU6dOVmW//PKL5ec+ffpkmismJsbyc3p6OjExMXzwwQfkzZuX8+fPM2PGDBo1akShQoXufHKZuJ88ffv2vWeZu7s7I0eOZOTIkdnKc9OSJUvuuKxgwYKEh4ffdfvg4GB10xcREREREYeQP39+kpKSbMqTkpIybdkHSEtLY+zYsUyZMsWqEfZ+PLEV/MdBWloay5YtY9y4caSkpFCwYEHat29P//79Afj999/p3r37HbcvVKiQVSX+UXvjjTfYsmXLHZePGjWKF1988REmEhERERERyTnlypXj119/tSk/fvw4ZcqUyXSbK1eucOrUKZuG4xs3buDk5MSiRYv45ptvstR1XxV8B+bq6nrX7ubVqlUjOjr6ESbKnvfff9/eEUREREREJBd4XB6TV6dOHaZOncqlS5esZtJfu3Ytr776aqbbFChQgH379tmUjxw5ktKlSzNgwIAsH/+JnWRPRERERERE5GEqVaoUTZs2ZfTo0SQlJWEymZgzZw6XL1+2Gvb9008/MXXq1Id+fFXwRURERERExKHl1GPycsKkSZMoUqQIoaGh1KlTh7179/LJJ59YTVh+7tw5/vrrr7vuJ7PJ3O9FXfRFRERERETEof3/U7EfC3nz5mX8+PGMHz/+juv06NGDHj163HU/kyZNyvax1YIvIiIiIiIikguogi8iIiIiIiKSC6iCLyIiIiIiIpILGMzmnJpaQEREREREROTBzfslZ/bbLzRn9msvasEXERERERERyQU0i77ILY7HHrV3BBvlfCsAypZdN7Mdi421c5LMlff1BeCvIwfsnMRW6YpPAxB77Jidk9jyLV8ecMzPVZ/p/bn5mTrydXPkv3GOnO3E0cN2TmKrbIVKgGP/DVnpWtnOSWw9azoEQNKs4XZOYstr6AzAsa/b1flv2zmJLc++U+wdIdvU7zxrVMEXERERERERh5auCn6WqIu+iIiIiIiISC6gCr6IiIiIiIhILqAKvoiIiIiIiEguoDH4IiIiIiIi4tAGtrJ3gseDWvBFREREREREcgFV8B+RjRs30rt3b3vHsHC0PCIiIiIiIvJg1EX/EWnYsCENGza0dwwLR8sjIiIiIiIiD0Yt+E+AuLg4QkND7R1DREREREREcpAq+E8Ak8mEyWSydwwRERERERHJQQ5dwe/ZsyeRkZEMHDiQ2rVrExwczDvvvENKSgoAK1eu5M0332T58uXUr1+fXr16AWA2m1m0aBHNmzfHaDTSokULPvvsM5v9b9++na5du2I0GjEajQwZMsSybOvWrTz//PP4+/vTunVrIiMjLcuuXLnC0KFDCQ4OJigoiC5duliWffXVVzRp0oTAwEBCQkKIjo4GYPXq1ZZ8AGFhYXz++ecMGTKEwMBA6taty8iRI7ly5YpVxl9//ZXnnnsOf39/mjdvzooVK+jZsye7d+/O0jUcMmQIbdu25fTp0xiNRgYOHJhpnhEjRrBw4UL69etHQEAALVq0YP369SQmJjJ48GCCg4N59tln2bp1q9X+r1+/zvjx4wkKCiIwMJCwsDCbc7ibw4cP06VLFwIDAwkODmbatGmWZQkJCQwfPpyAgABq165NeHi4zY2Kn3/+mfbt2xMQEEBgYCBTp07N8rFFRERERERyE4ceg28ymZg8eTJDhw5lzpw5nD17lj59+jBjxgxGjhxJcnIyR48exc3NjcjISFxcMk5n7ty5rFq1ijlz5lCpUiViY2MZOnQoKSkp9OnTB4Bt27YxbNgwxo0bR2hoKE5OTly8eBGAI0eOMGTIEKZMmULTpk3ZvXs3gwcP5quvvsLX15cPP/wQLy8vNm3ahJubG2fPngXg4MGDLFy4kK+++oqnnnqKy5cv4+SUcQ8lJSXFcmMCwGAwMGvWLN58800iIiJISUlh8ODBfPDBB4wePRqAmJgYwsLCmDJlCs2bN+fs2bMMGTKEP//8M8st8h988AGnTp3i1VdftbpJcXseZ2dn5syZQ3h4OHPmzOHAgQP07t2bmjVr0rRpUz788ENiYmLo378/K1eupGDBggCMGTOGpKQkVq9ejbu7OxMnTmTixIlERERkKV9YWBjdu3enXbt2pKWlceHCBcuygQMHUq5cOdavX4/JZGLYsGHMmTOHoUOHArBixQpmzZrF1KlTqVOnDmazmUuXLmXpuCIiIiIiIrmNQ7fgAwQFBdG2bVsAihUrxuTJk1m6dCnJyclARmV8xIgRuLm54eTkxOXLl1m4cCHh4eFUqlQJAF9fX8LDw/n4448trcuTJ09m9OjRtG7dGmdnZwwGAz4+PkDGDYIuXboQGhqKi4sLQUFBdO7cma+//hqA48ePU7t2bdzc3Cy5bpb7+vry1FNPAZA/f348PT3veG5Go5GXX34Zd3d38ufPT58+fVi7dq1l+TfffMNLL71E69atcXV1pVSpUoSHh3P16tWHdn1v1aBBA1q1aoWzszN+fn7UqFEDg8FAhw4dAKhRowbVqlWz9B6IjY0lMjKSiIgIChUqRL58+Rg3bhyRkZGcP38+S8c8fvw4DRo0ADJuMhQpUgSATZs2cebMGSZNmkT+/Pnx8fFh4sSJfP3115hMJq5du8b06dOZMWMGdevWxWAw4OTkhLe3dw5cGREREREREcfn8BX8+vXrW72/Wek8deoUkFF5v1kxBzhw4ADe3t7UqFHDarvq1avj5eXFwYMHOX78OCdOnKB169aZHnPv3r2EhITYHPfgwYMAvPjii0RERPDjjz+Snp5uWadu3br8+eefhIeHk5CQcM9ze/rpp63eFy9enLi4OKtzCQwMtNnm5g2Eh61ixYpW7729vQkKCrIq8/HxITExEcjoYVCrVi1Laz5k3NQoXbo0R48ezdIxO3bsSP/+/dmzZ49V+b59+2jYsKGlVwZA2bJlMZvNnD17lt27d5MnTx4CAgKycYYiIiIiIiK5l0N30QcoUKCATVm+fPksLfG3Vi4B4uPjLa3AtytSpAhnz561tNbfWnm8fR+vv/46BoPBUpaenk6ZMmUACA0NpUyZMrz33nvMnz+ft99+mzp16lCgQAGWL1/Oxx9/TNu2benUqRMDBgzA2dk50+O4u7tbvXdxcbG6YXD58uVMz/9O5/egMsuZ2fFviouLIyoqCqPRaFVuMplISkrK0jFHjx7NmjVrGDNmDMWLF2fcuHGUKlWKuLg4/vOf/7Bq1Sqr9VNSUrh06RIJCQk5dh1EREREREQeRw5fwb91TDZkVLQvXLhA0aJFiY2NtamUFixYkPj4+Ez3FR8fj4+PDz4+Ply4cIGUlBRLN/tbeXp6snjxYqpUqXLHXFWqVGHhwoWsWrWKAQMG8NNPP1GiRAny5cvHsGHD6NKlCz179sTd3d0y7j+7PD09bc4fbK+JvXh6etKoUSPmzJnzQPtp1qwZISEhTJ06lX79+vHTTz/h6elJ586dLfMR3C4xMZEzZ8480HFFRERERERyE4fvor9582ar91u2bOEf//jHHVtv/f39SUpKIiYmxqp8//79XLlyBX9/f8qWLUuZMmVYvnx5pvvw8/MjKioqS/lat25NhQoV2Lt3r1V5sWLFeOWVV7K8n8wYjUab8z948CB///23ZfK+rHB1dcVsNt93jjvx8/MjJiaGGzduPPC+nJycePPNNzl69Cjnz5/Hz8+P6OjoO+b29/fnxo0bbNq06YGPLSIiIiIikhs4fAV/27ZtrFixApPJxMmTJ5kyZQo9evS4YwXX09OT3r17M2rUKA4fPgxkVIrffPNN+vbtS548eTAYDISFhREREcHKlSst3eJvzqLfo0cPZs+ezS+//EJKSgrp6ens2bPHMj4+JiaGa9euARmP2ouNjaVSpUqcPHmSv//+G8hoZV+5ciXVqlW773O/OeneihUrSE1NJTY2lpEjR5InTx6boQl3U7BgQS5evMjff//N1atXSUtLu+9Mt/L396dkyZK89dZblicJJCYm2jxK707S0tLYvXs3aWlppKWl8eWXX/LUU0/h7e1Ns2bNuHz5MhMnTrTMZxAXF2d57KCnpydDhw4lLCzMcrz09HTL/AAiIiIiIiJPGofvoj9q1ChWrlxJeHg4bm5udOnShW7dugEZY9hvH8cOGRVjDw8PBg0aREJCAoUKFeLVV1/llVdesazTuHFjPvroI6ZNm8bYsWMxGAwEBASwYMEC6tevT3h4OB999BEjR47ExcWFihUrWp7RvmjRIiIjI3F2dqZEiRK8++67VKxYkdWrVzNhwgSuX79Ovnz5aNWqFYMGDQLAzc3NajjA7e8ho6X91vMpUqQIX3zxBWPHjmXSpEn84x//oFevXsyePZty5cpl+Rq6u7vTq1cv2rdvj6enJ998802meW6/lpllvL1s7ty5TJ8+nRdeeIHk5GS8vLzo0qULdevWvWeuGzduMHr0aE6dOoWHhwdVq1Zl/vz5uLi44OLiwuLFi3n33XcJDQ0lPT2dQoUK0adPH8uY/y5dulCwYEGmTJnCmTNnMBgMPPfcc0ycODHL10ZERERERCS3MJhzou/2Q9KtWzcGDRpEcHCwvaPYRVJSEnv37rU8ku/UqVO88847BAcH07NnT3vHy5WOx2Zt9v9HqZxvBUDZsutmtmOxsXZOkrnyvr4A/HXkgJ2T2CpdMeMJH7HHjtk5iS3f8uUBx/xc9Znen5ufqSNfN0f+G+fI2U4cPWznJLbKVsh4hLIj/w1Z6VrZzklsPWs6BEDSrOF2TmLLa+gMwLGv29X5b9s5iS3PvlPsHUFyiEO34Ds7O+Pq6mrvGHaTkpLCxx9/zFtvvUVqaipFihShW7duvPzyywCsWbOGkSNH3nH76tWr8+mnnz6quDY6duzIsbv8Y/LDDz+kTp06jzCRiIiIiIhI7uXQFfzPPvvM3hHsqnDhwvz73/++4/JmzZpZxqQ7oqVLl9o7goiIiIiIyBPD4SfZExEREREREZF7UwVfREREREREJBdQBV9EREREREQkF1AFX0RERERERCQXUAVfREREREREJBdQBV9EREREREQkFzCYzWazvUOIiIiIiIiIyINRC76IiIiIiIhILuBi7wAijuR47FF7R7BRzrcCoGzZ5cjZ4H/5Lu9cZecktvIHtgbg1OHf7JzEVslKfgD8fvSMnZPYqlbhKUCfaXbd/Ewd+bqdPPKHnZPYKlWxKuDY2Rz5+3YsNtbOSWyV9/UF4PoX4XZOYivPK6MB+OPoaTsnsVW1QnEArs5/285JbHn2nQLAStfKdk5i61nTIXtHkByiFnwRERERERGRXEAVfBEREREREZFcQBV8ERERERERkVxAFXwRERERERGRXEAVfBEREREREZFcQBV8ERERERERkVxAFfwckJKSwksvvcShQ47z+Al/f/8srxsXF5et9UVERERERMT+XOwdIDdyc3Nj2bJl9o5h5dq1a1le12QykZycnINpRERERERE5GFTC/4t5s6dy9y5c7O9XY8ePdi1a1cOJBIRERERERHJGrXg3yI1NfW+t7vfbUVEREREREQehieyBf+XX34hNDQUo9FIgwYNWLVqFUFBQSxYsIAFCxZgNBpZvXo1AOvXr6djx44EBwdTu3Zt+vXrx9mzZwH49ddfMRqN7Nq1i379+hEYGEhcXByQMeb95s8Ax48ft6wTFBREv379OHHihGX5yZMnadGiBStXrqRFixbUqlWLNm3asGHDhmyf38cff0zjxo0xGo106NCB6Ohom3W2bt3K888/j7+/P61btyYyMvKO+zt+/Dh9+vShTp06BAUF8eKLL1r2efbsWQICArh8+bJl/ZSUFOrXr2+5TvfywQcfUL9+fQIDAwkNDeXPP/+0LPvpp58IDQ3F39+f9u3bs2fPHqttExISGDNmDHXr1sVoNNKoUSNOnjyZpeOKiIiIiIjkJk9cC35CQgLjx4/n888/p1KlSly/fp2UlBR27NjBhx9+CMDgwYMt6zs7OzN58mQqVKhAeno6M2bM4J133mHOnDm0aNGCFi1a0K1bNwYNGkRwcLBlu+TkZEwmEwCJiYm8/PLLdO/enffffx+DwcDnn3/OK6+8wqpVq/Dy8sJgMHD27FkWL17M/PnzKVu2LOvXr+eNN97g119/pUiRIlk6vxUrVrBo0SLmzp3LM888w/79+3nzzTet1jly5AhDhgxhypQpNG3alN27dzN48GC++uorfH19bfaZnJxMjx49CAgIwM3NjTVr1jB06FAiIyMpVqwYlStXZu3atbRr1w6AjRs3UqZMGYoVK3bPvGvXrmXDhg38+OOPeHt7k5CQQL58+QDYvHkzkydP5oMPPqBWrVqsWbOG/v378/PPP1OwYEFSUlJ4+eWXCQ4OZvXq1RQoUIDExETy58+fpWslIiIiIiKSmzxxLfinTp3Cx8eHSpUqAZAnTx4KFChwx/UbNGhApUqVcHJywsXFhU6dOmXaIn43n3/+OX5+fvTt2xd3d3fc3Nx4/fXXqVatGp9//rllveTkZMaMGUO5cuUwGAw0adKEp59+mq1bt2b5WEuXLqVv374888wzAFSvXp1evXpZrTN37ly6dOlCaGgoLi4uBAUF0blzZ77++utM91mlShXq1KmDm5sbAM2aNcPJycnSA6Ft27b8/PPPlvV/+OEH2rZtm6W8x48fp3r16nh7ewPg4+NjOc6HH37I0KFDCQoKwsXFhZYtW1KvXj1WrFgBwOLFiylWrBgTJkywfIYFCxbE2dk5S8cWERERERHJTZ64Cv7TTz9N3rx5GTFiBKdPn77n+hcvXmTatGm0a9eO4OBg2rVrR2JiYraOuXPnTlq3bm1T3qpVK3bu3Gl5bzAYePrpp63WKV68uFVX/3s5fPgwQUFBVmWNGjWyer93715CQkKsymrUqMHBgwcz3WdqaiqLFy+mS5cu1K9fn4CAAM6dO2e5Di1btmTHjh1cvnyZK1eusHXrVlq2bJmlvC1atGDt2rV89NFHXL161VKelpbGb7/9ZpOzZs2alpxr167N8o0EERERERGR3O6J66Lv6urKV199xRdffEGnTp1o0aIFb731Fnny5LFZNzU1lVdffZUSJUowZswYqlSpwqVLl2wqnfcSHx+faRf7IkWKWI1Td3JywtXV1SZvWlpalo+VnJxs00Xdx8fHJs/rr7+OwWCwlKWnp1OmTJlM9zlp0iR27drF0KFDCQgIwNvbm6ZNm2I2m4GMVvM6deqwZs0azGYztWvXvmuviFuVLl2aFStWMGfOHFq2bEnfvn155ZVXOH/+PKmpqTY3RtLS0qhbty6QMdwiq0MXREREREREcrsnroIPGc+p79mzJy+99BL9+/fn3XffZeLEiTbr7dy5k6SkJL7//ntLt+9Dhw5l+3je3t7Ex8fblMfHx9tUvh9U4cKFrSa8A2wmu/P09GTx4sVUqVLlnvtLTk5m+fLl/PDDD5QvXx4As9lscz5t2rRhxYoVmEwmOnfunO3M48ePp0uXLvTs2RMfHx8aNmwIwLp16+44pt7HxydLvTBERERERESeBE9cF/1beXl50adPH6KiogBwcXGxtEpDRvf8YsWKWY3p3rx5s81+bt/udo0aNWLVqlU25atXr6ZJkyYPcgo2qlWrZjmfm3799Ver935+fjbr3Mnly5cxmUyULFnSUrZ9+3bLBII3NWnShJiYGP744w8aN258X9krVarE888/z/bt28mXLx9ly5a9a86QkBC+++67+zqWiIiIiIhIbvPEVfDPnz9PbGwsAFeuXGH58uVUq1YNyGhJjomJIT09ncuXL+Pn58ehQ4fYvXs36enpREZGsmnTJptJ3AoXLszevXtJT0/nypUrNsd8+eWX+eOPP5g3bx4pKSmkpKTw/vvvc+DAAbp27fpQz69Hjx4sWLCAvXv3AhmV8R9++MFmndmzZ/PLL7+QkpJCeno6e/bsyXSsf6FChShRogRfffUVZrOZI0eO8O6771K2bFmr9dzd3WnUqBHNmze3TJKXFbGxsZw/fx7ImAAxMjLS8nn06tWLSZMmsW3bNtLS0khNTWXLli2Wa9ytWzfOnz/PqFGjSEpKAiApKSlbQxpERERERERyiyeugv/nn3/SvXt3qlevTtOmTXFzc2P8+PEANG/enGvXrhEYGEh4eDilS5dm6tSpjB49mqCgIP79738zc+ZM3NzcrFqwu3XrxrJly6hduzYrV64EMiq8N8fTe3l58dVXXxETE0P9+vVp0KABhw8f5ssvv7Q8Es7V1RV3d3ebvG5ubtmqMBuNRkaNGsWIESMwGo28//77zJw5Ew8PD8s69evXJzw8nHnz5hEcHExwcDARERGkpKTYZDEYDMydO5eff/6ZoKAghg8fzogRIyhVqhTp6elWxz5z5ky2J73btGkTLVq04JlnnqFr1648++yzdOrUCYCOHTsyYMAAJk2ahNFopF69eixatMiybZ48eVi2bBkmk4lmzZoREBBAixYtOHnyZLYyiIiIiIiI5AYG8936lotkQXp6Onv37mXcuHH8+OOPVpP3PW6Oxx61dwQb5XwrAMqWXY6cDf6X7/JO2+E79pY/MGNyy1OHf7NzElslK/kB8PvRM3ZOYqtahacAfabZdfMzdeTrdvLIH3ZOYqtUxaqAY2dz5O/bsf/vzelIyvv6AnD9i3A7J7GV55XRAPxx1PHmPqpaoTgAV+e/becktjz7TgFgpWtlOyex9awp+/OKyePhiZxk73GUkJBAaGjoHcf6GwwGNm/enGkvgJyUkpJCvXr18PLy4r333rOq3K9Zs4aRI0fecdvq1avz6aefPoqYIiIiIiIiuZ4q+I8JHx8fdu7cae8YNtzc3O6Yq1mzZkRHRz/iRCIiIiIiIk+mJ24MvoiIiIiIiEhupAq+iIiIiIiISC6gCr6IiIiIiIhILqAKvoiIiIiIiEguoAq+iIiIiIiISC6gCr6IiIiIiIhILmAw3+nB6iIiIiIiIiLy2FALvoiIiIiIiEguoAq+iIiIiIiISC6gCr6IiIiIiIhILqAKvoiIiIiIiEguoAq+iIiIiIiISC6gCr6IiIiIiIhILqAKvoiIiIiIiEguoAq+iIiIiIiISC6gCr6IiIiIiIhILqAKvoiIiIiIiEguoAq+iIiIiIiISC6gCr6IiIiIiIhILqAKvoiIiIiIiEguoAq+iIiIiIiISC6gCr6IiIiIiIhILqAKvshDlJSUxGeffcaoUaMYMmSIpfzatWtcvXrVjsmULTdLS0vj77//5o8//rB3FItr167ZlMXExPDxxx+zZcsWOyTKEB8ff9flO3bseERJ5Elz4MABVqxYweLFi+0dRXKxadOmcenSpUyXXbp0iYkTJz7iRBmGDh3KxYsXM12WmJhIv379HnGix8epU6cwm82ZLktPT+evv/56xInE0amCL/KQ7N27l9DQUH7//XeqVq1qVYnZu3cvb7zxhrI9ZtlatmxJ06ZNbV7NmzenQ4cOhIeH8/fff9stX1paGu+//z5169alffv2vPzyy5Zlu3btYvny5XbL1qlTJ86dO2d5v3r1anr06MEff/xBWFgYS5cutUuurl27Eh0dbVOekpLC5MmTGTt2rB1S/c+t1ywzd/qH+6PkiDeUbuVo+c6dO8fLL7/MwIEDiYyMZNasWZZlGzZsYMqUKfYLB2zfvp3+/fsTEhJCnTp1LOXHjh3jt99+s2MyOH78OFOmTKFbt268+OKLlvL4+Hi7/u0Fx822fPlyChQokOkyLy8vfvrpp0ecKMPWrVvx9vbOdFnBggXZtWvXI05kzVFvjAB069aNy5cvZ7rs6tWrdO7c+REnEodnFpGH4sUXXzT//PPPlvdGo9Hyc2pqqjk4ONgescxms7Ldr+3bt5vr1KljnjJlinnbtm3m2NhY886dO83h4eHmunXrmmfMmGFu0aKF+cCBA3bJN3XqVHP37t3NJ06cMJvN1tcuLi7O3KJFC7vkMpvN5sDAQMvPKSkp5mbNmpl37NhhNpvN5gMHDpibN29ul1zbtm0zN2jQwLxkyRJL2f79+80tW7Y0jxw50nz58mW75LqpcuXK5p49e5rPnTuX6fKgoKBHnOh/UlNTzbNmzTIHBQWZg4KCzM8884xlWXR0tPm7776zWzaz2XHzDRgwwPzee++Z09PTzWaz9e/p1atXzQ0aNLBLLrPZbF66dKm5UaNG5qVLl5oPHDhgDggIsCw7dOiQ+cUXX7RbtsjISHNwcLB55syZ5jVr1lhl27Nnj/nll19WtkzcmiUztWrVekRJrBmNRsvvwO3S09Ptluumu/1tTU9Pt/q9fdTudex7feby5HGx9w0Gkdzi2LFjNG/e3PLeYDBY/ZySkmKPWICy3a8FCxYwbtw4WrZsaSkrX748RqORp59+mp9++omxY8fy3nvvsXDhwkee74cffmDVqlUULFgQsL52RYoUISEh4ZFnusnFxYXk5GTc3d1ZtmwZNWrUIDAwEIAqVapw/vx5u+SqXbs233zzDUOGDOHgwYMUKlSIJUuWMGHCBJo1a2aXTLfKly8fderUoX379owbN84mU3p6up2SQUREBAcPHmTp0qWUKVPG8nkClCpVitGjR9O+fXvlu01UVBQzZ860/H7e+nuaN29euw5Dmj9/PgsWLKBSpUoAODn9r2NnpUqVOHnypL2i8a9//YsPP/zQ8jnemq1mzZocOXLEXtEcOlvp0qXZvn07tWvXtlm2bds2nnrqKTukyvg+/frrr4SGhtosW716NWXLln30oW6RlpZ2x2UGg8Guf3vd3Ny4cuUK+fLls1l2+fJlnJ2d7ZBKHJm66Is8JEWKFGH37t2ZLtuxYwfFihV7xIn+R9nuz969e2nRokWmy5577jn27t1L7dq12bdv3yNOliElJQVXV9dMl128eNGqIvGoNW/enGHDhvHJJ58wd+5chg0bZll2+fLlO44nfBSeeuopvvzyS1JTU1mwYAGLFy92iMo9ZFQUXn/9debPn8/MmTMZO3Ys169ftyy352f6ww8/MGvWLMqUKWOTxd43lMBx83l4eNzxhtbx48fx9PR8xIn+58KFC5QvXz7TZcnJyZhMpkec6H/+/vtvAgICMl2WmpqqbHfQp08fRo0axd69e63Kt2zZwogRIxg8eLBdcg0ePJgJEyawcuVKS2XZZDLxzTffMH78eEaOHGmXXDfdvDGSGXveGAEICQlh5syZmS6bOnUqDRs2fMSJxNGpBV/kIRk0aBCDBw9m+PDhNGnSBMgYG7V9+3beeecdBgwYoGyPWTZ3d3f+/vtvSpUqZbPsr7/+In/+/Li4uNitstqoUSOmTp3KO++8Y1Px+/DDD63G0z5qY8eO5euvv+b48eN88MEHlCxZ0rLs77//fmRjBhctWnTHXiDFixenTJky9OvXzzKG1s3NjZ49ez6SbHfz9NNPs3z5cqZNm8YLL7xAREQE1atXt+uNEUe+oQSOm+/FF19kyJAhvPfee1atlBcuXGDcuHE8++yzdskFGd+z7777jk6dOtks++GHH3j66aftkCpDmTJliIyMzPTm27p16+za4uvI2Vq2bMn169cZMGAAefPmpUiRIpw5c4YbN27wxhtvZNqC/ijUrl2bGTNmMGXKFMLCwihUqBAXLlygTJkyvPfee1Y9buzh5o2RmTNn8swzz1jKt2zZQlhYmF3nZxk2bBivvPIKvXr1ok2bNhQtWpTTp0/z7bffkpCQwLJly+yWTRyTKvgiD8lzzz1HwYIFmT17NhMnTiQ1NRWj0UjZsmUZOHAgHTp0ULbHLFvr1q154403mDp1KpUrV7aU79q1i/Hjx9O3b1+OHTtGkSJF7JLv7bffZuDAgTRt2pQGDRqQnJzMu+++y44dO7h69apdZ+t2cXGhW7dumS6rUqUKVapUeSQ5/vzzz7sO8/Dz87OsBxk3dRyFu7s748aNY8OGDQwePDjTStij5Mg3lMBx892cKLR9+/YULVqUa9eu0b59e44fP06zZs2serc8am+//TY9e/Zkx44dNG3alLS0NCIjI4mKimLZsmXMnz/fbtn++c9/8sYbb7B//35CQkIwm80cPnyY7du3M2fOHLtOTujI2QBeeOEFWrZsye7du0lISMDHxwd/f3/y5s1r11x16tThp59+4sSJE5Zc9u6af5Oj3hgB8PHx4fvvv2fx4sUsWbKEixcv4u3tTUhICK+++ioeHh52yyaOyWC2Z3OASC5iMpksrUfp6emcP3+evHnzZjpm6lFTtvuTkpLCtGnTWL58Oc7OzhQoUICEhAQ8PDwYNGgQL7/8MjExMZw6dYrWrVvbLefmzZvZu3cv8fHxeHp64ufnR7NmzexeWU1PTyc2NpbExESbZW5ubtSsWfPRh3JwHTp04Ntvv7UpT0hI4O2332b9+vUcOHDADskyMgwcOJC4uDgaNGjA999/T5cuXaxuKNlzSI2j50tKSuLQoUPExcXh6elJ1apVKVq0qN3y3JSQkMBnn33G3r17Ldn8/Pzo1q0bFStWtGu2I0eOMG/ePKts1atXp0ePHtSrV0/Z7iItLY2zZ89y6dIlqlatau84FgkJCRw9epSkpCSHGRp1q+vXrzvcjRGR7FIFX+QhqV279h3Hb9mbsj2YlJQU/vzzT5KSkvD09KRChQq4uNi/A9TcuXPp37+/vWNk6sCBAwwePJg8efJYJgG8lbu7u10mJnzcXbp06Y6PwHpUNm3axL59+xzuhtJNjnbD6/XXX3fY7/rOnTvt3jX6Tk6ePJnp8ChH4MjZ0tLSmD17Nl999RWQ8f+vPXv2ABm9z/7880+7TDZ55coVJk2axLp16yhVqhTHjx+35Nq2bRv79++nT58+jzzX7Rz1xkhSUhLLly/n0KFDXL16lQ8++ACAa9euYTab7TqXhzge+/8LVSSX8PHxsdzxdTTK9mDc3Nzu2pLVqlUrVq9e/QgTZVi8eLHDVvDHjx/P22+/bZlXwVFcuXKF2bNnEx0dTWJios2YdrPZTGRkpJ3S3Zu9K/cADRo0oEGDBvaOYePmDa/69etTv359e8exOHTokFVPJUcycOBAduzYYe8YmXrppZeIioqyd4xMOXI2R32axM1hC+vWrSNfvnxWuapWrcrbb79t1wq+o94YgYwJf2/+batRowazZs2yWrZo0SKHvYko9qEKvshDMn36dCZMmMDzzz+P0Wh0iH+I36RsOctej3x79tlnWbJkySObsC47/vzzT4er3AO88847pKen8+abbzJgwAAWLFjA2bNn2bRpE0eOHGH69Ol2zXft2jXmzJlDZGQkcXFxVvMHmM1mPDw82LVr1yPJsmrVqmw9ptLNzc2uQ1Uc9YbX8OHDmTBhAiNGjMi0N4s9Va1alf3791O9enV7R7FRsmRJ/vrrL0qXLm3vKDYcOZujPj41MjKSyMhIS0vzrbkKFChAUlKSXXLd5Kg3RgAmT57MhAkTLPMA3Gy9BwgODmb48OF2ySWOSxV8kYdk0KBBXLt2jV9//RWDwYCTk5Plf2CP+h/myvZo2Wt27kqVKrF06VKWLFmC0WjE29vb6nm47u7u9OjRwy7ZqlatyqZNmxyupXfLli1ERkbi5uaGm5sbNWrUIDAwkDZt2rBs2TJmz57N+++/b7d8EyZMID4+njFjxlC8eHHc3Nyslj/KVuDt27dbPe7LbDazZs0a0tLSeOaZZ/Dx8eHs2bPs27eP4sWLExwcbNcKvqPe8NqyZQtHjx6lfv36lC9f3ub31M3NjXnz5tkl27Bhw3jvvfeoXLkygYGBeHt7Wz3T3d3dnWrVqtkl27Rp05g2bRrBwcGWv2+3ZnN1dbVbzy9HzuaoT5OAjGyZdSWPi4uz+Vv3qDnqjRGAY8eO0bx5c8v7W7MZDIZs3YiVJ4Mq+CIPydKlS+/67Ft7ds9Uttzpjz/+oFKlSkDGowWvXr1qtdyeY6InT55MWFgYmzZtIigoyKZnhr0m2UtJSbH8Q7JIkSIcPXrUUoFp164dERERjzzTrbZs2cKvv/7qEOMpJ02aZPV+3rx5BAcH8+6775I/f35LeXx8PMOHD7d7K7Cj3vAKCQm566Rr9vw9HTlyJMnJyZw8eZI1a9bYLPfw8GDVqlV2SJYx4eT169dZu3Ztpsvz5Mlj6UL9qDlyNkd9mkTLli0ZOXIk7733ntXfj5SUFKZMmUJISIhdct2aw1FvjBQpUoTdu3djNBptlu3YscOuk4eKY1IFX+Qhsdej0rJC2XKniRMn2jvCHV24cIFz586xbdu2TGd9t9cke0899ZRlgix/f3++/vprJk+eDGRMnHXrPzztISUlxWEmq7vd0qVL+emnn2xmlC5SpAgffvghHTp0oGPHjnZK57g3vOzZq+Fe7DF3SFbZq4KcFY6czVEfnzpq1CjefvttGjdujL+/P9evX2fw4MHs27ePUqVKWf4O24uj3hiBjJ6OgwcPZvjw4Zahb1evXmX79u288847DBgwwG7ZxDFpFn2Rh+j48eN89dVXHDx4kGvXrvHdd98BGS1cJpOJEiVKKNtjli0rgoKC7DpR1dWrVzl48CBxcXF4e3tTq1Ytu1cS27Vrx7Bhw2jUqJFdc9xuxYoV5M+fn2bNmnHmzBk6duxIiRIlKFGiBNu2bWPAgAG88sordsvXt29fWrduzfPPP2+3DHdSq1Ytdu7cadUqflNqairBwcEOP5zGnqKjo9m5cydnz57Fx8eHkJAQu/d6kNzL0Z4mcdOxY8eIiYmxeiTjM888Y9dM4PiP2dy8eTOzZ8/mt99+IzU1FYPBQNmyZenZsycdOnSwWy5xTKrgizwk69atY9SoUXTu3Jnq1asTFhZGdHQ0kDHL6XvvvccXX3yhbI9RtqyyZwX/448/5uOPP8bLy4siRYoQHx/P1atX6du3L6+99ppdMgEEBAQ8FpW9S5cu8d///peLFy9Ss2ZNgoKCHunxFy1aZDV+8tSpU3z//fe0atWKcuXKWY3rhYyhDT179nykGW/q1KkTTZo0oV+/fjbLPvroI6Kjo1m0aJEdkllztBteV69eZejQofz+++80btzY8nu6fv16nnnmGWbMmIGHh4ddst3+/budPb9vv/zyyz2z3Zx07FFz5Gx3cuHCBZu5Auztxo0bHD58mDJlyjjMBLuOemPkpvT0dM6fP0/evHnJly+fveOIg1IXfZGH5F//+hcffvihZebVW/8nWrNmTY4cOWKvaMqWw+x1n/Szzz7jxx9/ZNGiRfj5+VnK9+/fz1tvvYWbmxtdu3a1S7ann37aoZ+xDRn/UDKbzbz00kt2y/Dnn3/aVBTatGkDZAwZuJ09/5H59ttv07t3b3755RcCAwPx8vIiKSmJ7du3k5qayvz58+2W7SZHvOE1c+ZM8ufPz7p166w+vxs3bjB06FBmzZrFyJEj7ZLt9u+f2Wzm3Llz7Nu3jxIlSth1XPS3335LcnKyVVl8fDwnT57E19eXp59+2m6VaEfOFhERQY8ePShcuDCQcdOwf//+HDlyhOLFizN//vy7PvY1p7z11lv885//tAzLO3DgAD179sTJyYmUlBTmzp2b6RjzR+32x2xeuHDB7nMBnTx5kmLFiuHq6oqTkxNFihRh9erVREVFUa1aNbXgi+LZsgYAAHnySURBVC2ziDwUzzzzjDktLc3yPjAw0PJzSkqK2d/f3x6xzGazsuW033//3S7HbdGihTk2NjbTZQcOHDCHhoY+4kT/8+2335rr1atnnj59unnFihXmH374wer1yy+/2C3bsmXLzG3atDFXrVrV/Mwzz1jKf/vtN/P69evtlis7kpOT7XLcxMRE89KlS82TJ082jx071vz++++b16xZY05PT7dLnlt9+umn5jZt2pj3799vVR4TE2Nu0aKF+csvv7RLrvr165sTExMzXXbhwgVz/fr1H3Gie7t8+bJ52LBh5k8//dTeUWz89ddf5u7du5t/+OEHe0ex4QjZateubfX72KdPH/OUKVPMN27cMC9cuNDcvXt3u+QKDg62et+5c2fL9+unn34yd+jQwQ6p/mf69Onmc+fOWd6fPHnS/Nxzz5krV65sbtKkifnw4cN2yxYaGmpOSEiwvP/iiy/M9evXN0dERJhbtWpl/uijj+yWTRyTWvBFHpIyZcoQGRlJs2bNbJatW7eOsmXLPvpQ/0/Z7l9KSgoxMTHEx8eTlpZmtexmN8yqVavaJdvFixcpX758psuqVKli18f6rFixgnLlyhETE0NMTIzNcg8PD1q0aPHIc82bN49Vq1YxatQoqlevbpmwCMDLy4uRI0c63LwBmWnQoAFRUVGP/LgFChRw2Nair7/+mrlz59r8TlSvXp3333+foUOH2qVHy40bN+7Y/djHx4cbN2484kT3li9fPsLDw3n++eftOtQnM6VKleL999/n5ZdftvR2cRSOkC0tLc0ySdzevXv5+++/Lb1revToYbeeNuZberpt3LgRs9ls+W49++yzTJgwwS65blq+fDlvvfWW5f0777xDnTp1WLZsGV988QVTpkzhs88+s0u2hIQEvL29Abh27RoLFixg0aJFVKxYkc6dO/PKK6/Qv39/u2QTx6QKvshD8s9//pM33niD/fv3ExISgtls5vDhw2zfvp05c+YwZcoUZXvMssXExDBw4ECcnZ156qmncHGx/pPp4eFh13GWBQsWJDY2Fl9fX5tlR44csfyDwB6+/PJLux37br766iuWLl1qmSzp1tmSS5UqRVxcnL2iZUt6evojP2ZCQsI9H2lpr2d/g+Pe8CpTpgwbNmzI9MbRunXrKFeunB1S3ZurqyuXL1+2d4xMFShQgIsXL9o7Rqbsna1ChQr8+OOP1KtXj3fffZdhw4ZZLbfXDaUaNWowf/586taty7Rp06yeAmMymWyGPDxqjnpjBDKGZiUlJeHl5cXnn39O06ZNLcMsSpYsyaVLl+yWTRyTKvgiD0ndunX56quvmDdvHsOHD+f69et069aN6tWrM2PGjLs+B1nZHDPb5MmTGTBgAF26dLFbhrvp3LkzQ4cO5d1337U8yx3gt99+Y9SoUXTr1s1u2V5//XW7PAbvXq5cuXLHGx9XrlyxS8X5ftjjmcxNmjQhJSXFZs4Jg8GAl5cXnp6eREZGPvJcNznqDa/Bgwfzz3/+k379+tGqVSvL3AArV65kwYIFzJo1yy657ubvv//mo48+okqVKvaOYiMlJYUFCxZQqlQpe0ex4QjZJk6cSFhYGOPGjaNTp040bdrUsuzm3Ar2MHnyZCZPnsxPP/1Ep06drMbb//bbb3brCXeTo94YAejQoQO9evWievXqrF27lhUrVliWXbhwwaEmThTHoFn0RUTuIDAwkP9r787jak7bP4B/2k5RtKmoKfuuiFIhLRpliaEZy2AszVgisiZNUZRkl5nIMnbGWIeyNEqKopJ1JsNYx7QnaXNOdX5/9OtMxzkl8zTf+5u53n89dXu9zucZOp3r/l73dScnJ7OOUafvv/8eu3btgo6OjqRwKCwshLu7O6ZPn84sl62tLWJiYpgPJ3rX9OnTYWZmhjlz5gCQvgEhPDwcqampvNyYeBfrqxmrZWdn49q1azhy5Aj8/f2ZfkjfvXs3Tp48WeuG19ixY5ldgXjjxg2EhYUhLS0NFRUVUFJSgpWVFTw8PNCnTx8mmYCqqw/fHfJYvcnVt29frFq1Cp988gmLaBg+fLhMx4hIJEJeXh5atmyJzZs3M9uA4HO2ulRWVqKsrAxNmzZFUlISrK2tWUeScfLkSYwaNYrz13348CG8vb3x5MkTjB07VmrwZVpaGpYtW4Zz585xnqtaTEwMnjx5gsGDB0ttIP3xxx9ISEjA5MmTmWUj/EMFPiENTCgU4uXLl3jz5g1atmwpmRjLB5Ttwzg4OODo0aPQ09NjHaVORUVF+O2335CdnQ0tLS2Ym5ujadOmTDOdPHkSKSkpWLx4MbS0tJhmqenFixeSCdOOjo4IDw/HggULcP36daSkpGD//v1MJkx/KL4U+NUePHiA5cuX48iRI0xz8HXDq1plZSXy8vLQvHlzXly7lZ2dLVOoKikpQUdHBwKBgFGqKrdu3ZLZfFBUVESLFi1gYmLC9Kkln7PVF9/eQ6rxMVdj2BgBAHd3d+zatYt1DMIDVOAT0kBEIhE2bNiAo0ePQkFBARoaGsjNzYW+vj7c3d0xYcIEytbIsu3btw8JCQkICQlhera4NufPn8fAgQPlFvMlJSX45ZdfMGLECAbJqq5EevToER49eoR27dpBW1sbSkpKknWBQIBt27YxySYSiXDixAmZu47d3Nygq6vLJNOH4uOH4IEDB+LKlSusY/Buw2vNmjWYOXOm3EF7r1+/xqZNm7B8+XIGyaoKl9qK0bKyMly/fp2XQyeFQiEeP37My6fkfM5WE1871Piaqxof33ur8Tkb4RadwSekgaxZswbJycn44YcfYGZmBqBqamxCQgKCg4Px+vVreHh4ULZGlC02Nha3b9/GgAEDoK+vL/PETSAQ4MyZM0yyAVVTfmNjY+WuKSsrIygoiFmB7+joWOf8BJZPL1VUVDB27FiMHTuWWYaPTWJiIpo0acI0Q/WGl4aGBiwtLaXWWG54nThxAt7e3nLXmjdvjrNnzzIr8O3s7BAfHy93TVlZGcuWLcPVq1c5TlXFzc0Nx48fr3V9xowZiIuL4zDR3/icrb5YzPGoD77mqkbPRUljQAU+IQ0kMjISp06dgoGBgeR7CgoKsLW1RUREBMaPH8+sUKVs/8zMmTPr/LDBusX27du3tbbRCgQCmWv9uDR06NB6/TlW7Y5paWn4/fff8ebNGxgYGKBnz54wMTHhPEdjEhgYKNPOXV5ejj///BO3b99GSEgIo2RV+LrhVdfPoYKCApPBjtWt+SUlJcjIyJApWsRiMdLS0qS6brj24sWLWtfu37+P8vJyDtNI43M28u/i+wYEIQAV+IQ0qJpFak2tWrWSOa/HNcr24aysrJi9dn1oa2vjxYsXcic2P3/+HOrq6gxSfZi5c+dy2lKYkZGB2bNn4+XLl+jTpw80NDSQkJAAf39/2NraIjg4GBoaGpzleVdkZCQcHR3f+zT8woULHCX6W8uWLeWeOzY3N0dwcDDzqeZ83fAyMTGpdSMrMTERrVq14jzTpEmTIBKJUFpaiokTJ8oU+CoqKtDT00NgYCDn2Xr16iW5Mq1r164y60pKStDT08O8efO4jsbrbIQQUo0KfEIaSJ8+fRAZGYlhw4bJrJ09e1bqShiuUbZ/rrKyEmfOnEFycjIyMzOho6MDR0dHODs7M9/JHzVqFJYtW4bt27dLnTEuKCjAwoUL8cUXXzBMVz9ctzsGBgaiQ4cOOHLkiFQxWFhYiG+//RYrV67EmjVrOM1U0/nz5xEQEICBAwdixIgRGDBggNwz0iyufHvfkLq6znNzga8bXtOnT4ePjw82btyIXr16Sb5/9epVeHt7w8/Pj/NM1RtElpaWuHTpEuevX5dbt24B4OdZbD5nI4SQalTgE9JA3NzcsGrVKiQkJMDW1haamprIzc3F1atXce3aNSxbtgzJyclQUVGR+pBH2fibLTc3F1OnToWCggKcnZ3Rs2dPZGdn47vvvsP27duxe/duZndrA1XnPF+8eIFBgwbBwcEB+vr6yMjIwKVLl2BnZ4fZs2czy1ZfXG+SJCcnIyEhQeZJb/PmzbF69Wo4ODhwmuddYWFhyM/Px7lz5xAeHo5ly5ZhyJAhGDFiBExNTZlmCw4OxrJly+SuFRUVYezYsYiMjOQ41d/4uuHl4uKC0tJSeHh4oGnTppKf07KyMsybNw/Ozs5McgGQXBfJR25ubqwj1IrP2Ro7OuNOyP+OpugT0kDc3d3r1U6uoqKC3bt3c5Dob5Ttn1myZAm0tbXh4+Mjs+br64vKykqsXr2a00zy3L59GzExMcjPz4eOjg4cHBw43wz5p7ie+mtnZ4dLly5BWVl2f7u4uBiDBw9mNlRMnpcvX+LkyZPYtWsXWrZsCVdXV7i5udV6rOXfVNff1Zs3bzBo0CCmE5wrKirw7bff4vLly3I3vNatW8e066a0tBQ3b96U/Jyynu5P/tvGjh2LH3/8kXUMGV5eXti0aRPrGLXi86R66iwh1ajAJ4SQWvTr1w+XLl2Sex66qKgIgwcPxrVr1xgkq1teXh60tbXpLmY51qxZA7FYjKVLl0p9XywWIzg4GMrKyrVOPOdSRUUFrl27hnPnziE2NhbdunXD8OHDkZGRgVOnTuGbb77h7In0tGnTUF5ejtTUVPTp00dmXSwW48mTJ3B0dGRyZvtdjWHDKyMjA2lpaejcuTPat2/PLEdRURG2bt2KlJQUFBQUyB22FxMTwyRbeXk5Dh8+XGu2yspKHDhwgLLVIj8/H48ePUJhYSGcnJyYZqnpt99+Q3p6OgoLCzF58mTWcT4Yi42RrKysem3q/vLLL7z6uybsUIFPCCG1sLS0xPXr1+UWypWVlbC2tma6k7927VpMnToVLVq0AAD8+eefmDVrFh4+fAhDQ0Ns374dHTt2ZJavPrgu8Ldv346DBw9CTU0NNjY20NLSQm5uLhITE1FSUoJx48ZBWVkZAoHgvWfO/w3Xr1/Hzz//jF9++QXGxsYYMWIEhg0bBl1dXcmfyc/Px9ChQ5GUlMRJpps3b0IkEmHWrFnYtm2bzLqysjL09PSYD9mrDesNrwkTJmDDhg2SD+iJiYnw8PBAx44d8fjxY4SEhDD7UO7t7Y3KykqMHj0aHh4eiIiIQGZmJuLj4/Hw4UOEhoYyew9ZtWoVHj16hGHDhiE4OBjLly9HZmYmEhISUFhYCF9fX2aDUPmcraioCIGBgYiNjYWxsTGePHmCtLQ0AFX/9u7evcvkvS0nJwdeXl7IyMhA9+7dkZCQIMkVFxeHhIQE+Pr6cp7rXXzcGLG1tUWbNm0wYsQIuLi4oFmzZqwjEZ6jAp+QBpSUlIS4uDgUFBTIrAkEAgQEBHAf6v9Rtg83adIkuLm54bPPPpNZO3HiBH7++Wfs2bOH81zVbGxscO3aNUnb8YwZM9C6dWssXLgQBw4cQHx8PNN89cGiwH/3qjd5lJWVMXPmTA4SSXNxccHQoUPh6uqKtm3byv0zYrEY33zzDXbu3Mlpti+++AI//fQTp6/5Ifi64WVlZYXr168DqPq7GzFiBDw8PDBkyBAkJSUhKCgIZ86c4TwXAAwYMAAxMTEQCASwsrLClStXJNd/Hjt2DPHx8di8eTOTbHZ2doiMjISGhgYsLS1x9epVyeyM7du34+XLl8w6RviczcfHBxUVFfD395fkq27bfv36NUaNGsWkK2P27Nlo164dFixYAAUFBalcJSUlcHFxwZUrVzjPVY2vGyMAIBKJEBcXhzNnziAxMRHW1tYYMWIE7OzsoKKiwiQT4TcaskdIA9m9ezdOnDiBUaNGoVOnTjLrtV3fxAXK9s8sXrwY7u7uuHv3LoYMGQIDAwNkZWUhKioKkZGR+OGHH5hlA6rauKuL+1u3buHly5fYvn07AGDq1KmS/81ntRWx/5YZM2bU689x9XT8XTt27ICRkVGtXSN//vknTExMOC/uAfC6uAeqNt0WLVok+XrlypWwsbHBsWPHcODAAQQFBTHb8KqoqICSkhKioqLQsmVLDBkyBABgbW2Nv/76i0kmABAKhZL3WH19fTx69Ajdu3cHAHz22WdYu3Yts2ylpaWSKyt1dXXx/PlzdOjQAQAwefJkODg4MCui+ZwtJiYGMTExklsjas6d0NTURGFhIZNc169fx8aNGyV5auZq2rQpiouLmeSqFhQUBACIjY2VbIxU69atG3x9fZkV+CoqKnBycoKTkxOKiooQHR2Nw4cPw9/fH05OThgxYgTzG4cIv1CBT0gD2bdvH44dOyZ5esQnlO2fMTMzw9GjR7F9+3Z4e3sjOzsb2trasLOzw08//QQTExOm+Tp06IAzZ86gf//+CAkJwfz586XWy8rKGCWrPz4OeQKAuXPnMjl+8dVXX+H06dNo3ry5zFpxcTHGjRvH2dyHkydPYtSoUZKvo6Ki6hyIKRAIMHToUC6iycXXDa9+/fph5cqVsLa2xvr16xEeHi5ZKy0tRXl5OZNcANCqVSvJ1YLm5uY4fPgwVq1aBQB48eIF01ZgXV1dZGdnQ19fH6ampvj555+xYMECAFVDHdXU1ChbLYRCodxrIbOysphtmqupqSE3NxeGhoYya0+ePGF2jWU1vm6MyKOgoCB5ci8SibB161bk5eUhKCgIZmZmjNMRPqACn5AGUllZCS0tLdYx5KJs/1zbtm0REhLCOoZcAQEB8Pb2hr+/P8aOHYtBgwZJ1m7fvg0jIyOG6ao+MF26dAlZWVmorKyUWhMIBHLPc/MFq9NrRUVFcot7AGjWrFm9bpxoKCkpKVIF/unTp+t8fVVVVaYFPl83vFauXImtW7fi5MmTWLp0KTp37ixZe/jwIRwdHZnkAoApU6bgwYMHMDY2xqxZszBmzBiMGzcORkZGklkBrLi5ueHGjRsYPnw4pk+fjnHjxuHly5f45JNPcPHiRYwcOZKyyeHi4oKlS5di3bp1Uhs0QqEQQUFBzP69ubm5Ye7cuVi3bh3atGkj+X5eXh78/f0xbNgwJrlq4uPGCFD1eyEmJgZRUVFISUmBjY0NRo0ahS1btkhy3bx5EwsXLkR0dDSznIQ/6Aw+IQ1k/fr1eP36Nfz8/Hh3Joqy1U/11O2PQWVlJcrKytC0aVMkJSXB2tqa09ePiIjAgQMHMHr0aBgZGclcS8e6GHwfVlch9e/fHxcuXJC0/9b05s0bODk5Sc5zE2kPHz6Et7c3njx5grFjx0rdlJCWloZly5bh3LlzDBPW7bvvvsPs2bOZZnj9+jWio6Px6tUr9OzZE3379mWap6Znz57h1KlTkmyfffYZ02sPa+JTtrKyMvj6+uLy5cswNzfH9evXYW9vj9u3b8PY2Bjh4eG1biL+myorK7F582bs378fBgYGePHiBTp16oQnT57AyckJQUFBTIvo6kGJ1Rsj1b8DhEIhFi1ahObNm0u6W7hmYWGBLl26wNXVFS4uLtDU1JT5M2KxGFZWVry9wo9wiwp8QhqIWCzGwoULcf36dfTs2VPmDZjlsDjKVj+jR4/GiRMnJF/37Nmz1ieWYrEYampquHXrFifZ/hcsitVBgwZh586dnJ+xbyisCnw/Pz8IBAL4+fnJrC1btgwikYjpuejGivWGV32w+Dfn7+/Pi6sN5QkPD8esWbNYx5CLz9mq/fHHH7h79y6ysrKgrq6OHj168OK6yMLCQjx48ECSq1u3bvW6Au7fxteNEaD+1+SJRCLmD0oIP1CBT0gDiYiIwJkzZzBq1Choa2vLrAsEAmYtaJStfnJzc6VmAeTl5dV5PlZZWVnq+jK+qjmtmCt9+vRBSkoKb56wfShWBX5+fj4mTpyIVq1awdXVFQYGBvjrr7/w008/IT8/H8eOHePsQ+aMGTPqdeNANb4fu6jG6u/2fVj8nL57EwefWFtbMxt2+T58zkb+N3zdGBGJRHj27BkKCgpkjpBVVFTwctOSsENn8AlpIHv37sWZM2d42eJN2ern3UF/dd2dXVZWhuvXr8POzo6LaP8TFh/eO3TogBs3bjC7C7qx0tHRwalTp7B3714cOXIEr169gra2NhwdHfHVV19xOrxrxIgRH3Tmn2V77Yfg63MNFj+nU6ZMwaZNmzBv3rxa3+tYGTZsGI4cOYJx48axjiKDz9mAqqfkN2/erPXqWVbHo54+fYqrV6/i1atXMmsCgYDZlPqa2rdvj/bt27OOISU1NRVeXl5QVFREbm4uWrZsiezsbJSXl6N169YwMTGhAp9IoQKfkAZSWVnJdOJwXSjbP2NnZ4f4+Hi5a8rKyli2bBmuXr3KcarGwcPDA76+vpgwYQK6dOkiuVu7mkAgQI8ePRil4zeBQIBvvvkG33zzDdMcfBh69W/g49NqVkpKSpCQkIAff/wR5ubm0NbWhpKSkmRdVVUV3377LZNsnTp1wtGjR3HkyBFYWFjIzTZ16lTK9o7ExEQsXrwYPXr0qLUrjkWBf/bsWaxduxaDBg3ixYa+PHzdGAkJCUFAQAAcHR1hYWGBS5cuAaj6u16zZg2mTJnCJBfhLyrwCWkgn332GTZv3owFCxbw7kkIZfsw2dnZEIlEKCkpQUZGhswTP7FYjLS0NKkPdERa9dyE/fv3y11XU1NDVFQUl5E+COvZAWKxGHl5eaioqJD6voqKCvMPx0KhEC9fvsSbN29gYGDAi/Oz5J9RV1eHg4NDressuzJ+/fVXdOrUCUDVFZHv3pP+7qYhl/icLTg4GOHh4TA1NWWWQZ4tW7Zg//79zK+XrQ1fN0YA4Pnz55LbD9TU1FBQUAAtLS3Y2Nhg7dq1WLRoEU6fPs0kG+EnKvAJaSD5+fm4cOECTp8+jbZt28pMDWd5PpWyfZhJkyZBJBKhtLQUEydOlCnwVVRUoKenx9vhVHwQExPDOsL/5Mcff2Tyunl5eVi+fDkuX74sd/4Dy8GOIpEIGzZswNGjR6GgoAANDQ3k5uZCX18f7u7umDBhApNc5J/jQ0t0bVgNfq0PPmfLycnhXXEPVF31xtfiHuDvxggAKCkpoaKiAkpKSjAxMUFqaqrkWtyOHTsiIyODcULCN1TgE9JALC0t6zwDxXJHn7J9mAsXLgCoylbdCkf+ueqn0c2bN+fFOe2YmBhcunQJWVlZqKyslFpjPShu+fLlaNq0KaKiomBoaCiz4cXSmjVrkJycjB9++AFmZmYAqv5uExISEBwcjNevXzO9N72xYzkb4LfffkN6ejoKCwsxefJkZjnkyc/Px6NHj1BYWAgnJyfWcaTwMVvfvn1x/PhxuLm5sY4iZdCgQfj+++95+x7B140RADA1NUVKSgqsrKzg7OyMTZs2oUOHDjA0NMShQ4fQrl071hEJz9AUfUIIqcXevXt592H3nxg7diyTJ9JpaWnYtGkTUlNTUV5eDmVlZfTr1w9z5syRFIhci4iIwIEDBzB69GgYGRnJFNCqqqrM2jCBqqnmcXFxvNgIeZeNjQ1OnToltyX/xYsXGD9+PBISEhgk+zB8naK/Zs0aeHt7c/qaOTk58PLyQkZGBrp3746EhASkpaUBAOLi4pCQkABfX19OM1UrKipCYGAgYmNjYWxsjCdPnkiyJSYm4u7du8w6EPic7fXr1/D29oaSkhIsLS2hpaUltc6q1VwoFGLJkiV4+vQpLC0t5V6Jy7KjZO7cubCzs+PdxghQNZzw7du36Ny5MyoqKhAQEIATJ06goqICRkZGCA8PR8eOHVnHJDxCBT4hDay0tLTWa0yMjY0ZpapC2QhXEhISsGjRIkyfPh1DhgyBnp4ecnJyEBUVhYiICISFhaFv376c5xo0aBB27tzJ/Ix9baytrXH58mVOp+XXl42NDRITE+WulZeXo1+/frwsnN/1b2945efnf9D1giznKsyePRvt2rXDggULoKCgIHVVX0lJCVxcXHDlyhUm2Xx8fFBRUQF/f39oaGhIZXv9+jVGjRrF7CgQn7PFxcXBx8cHZmZmtZ4lZ3HE4MSJE9i0aRM+/fTTWnOxLPD5ujFSG6FQiDdv3jSKq3oJ9/jT+0dII/fo0SMsXrwY6enpAKomNVdWVkJBQQG6urrQ1NREZGQkZWtE2YqKirB161akpKTI3XwQi8XMz5rztd18y5YtCA0NxcCBAyXfa9WqFdzd3WFkZIQNGzbgyJEjnOcqKChAmzZtOH/d+ho8eDDCw8Mxf/581lFk9OnTB5GRkXKn6589exYWFhYMUn24f7ubxcHBAUKhsN4t9yznKly/fh0bN26U3CxQ84aBpk2bygyP41JMTAxiYmKgrq4OQDqbpqYmCgsLWUXjdbbg4GDs2LED3bt3Z5ZBni1btuDgwYO83bC/desW7ty5AzMzMzx48EBmnW8FvkAgoOKe1IoKfEIaSGBgID7//HOMGTMGNjY2SE5ORnZ2NuLj47Fv3z6EhIRQtkaWbeXKlaisrMTChQvh4eGBiIgIZGZmIj4+Hg8fPkRoaCizbIB0u3mvXr3ktpuz8vTpU6niviYXFxf4+/tznKhKhw4dcOPGDVhZWTF5/ffR1dXF3r17ERMTg65du8q9XtDPz49JNjc3N6xatQoJCQmwtbWFpqYmcnNzcfXqVVy7dg3Lli1DcnIyVFRU0KtXLyYZ+bDhdfv27X/9NRqKmpoacnNzYWhoKLP25MkTSQHLilAolJshKyuL+TEWvmbLy8tDt27dmL1+bUpKSuT+O+MLPm2MmJub17sLSCwWQ1VVFTdv3vyXU5HGhAp8QhrI77//jn379gGo+iBZXFwMAwMDfP7552jbti0CAwOZPLGkbP/c1atXERMTA4FAAIFAADMzM1haWsLV1RXHjh3D1q1bsXnzZibZgKonkXv37uVlu7lAIJBc5fOu/Px8Zh+APTw84OvriwkTJqBLly5yC+gePXowyQYAioqKmDZtWq3rLAuHAwcOwNDQEH/++ScOHz4stda2bVvJ91RUVLB7927O8/F5w4uv3NzcMHfuXKxbt06qsyUvLw/+/v5yuzW44uLigqVLl2LdunVo1qyZ5PtCoRBBQUGSa8MomzQLCwtER0dj8ODBzDLI4+joiMOHD2PixImso8jFp42RCxcufPAxH0JqogKfkAYkFouhoKAAIyMj3LlzB/369QNQ1dr6+PFjytbIsgmFQklBpa+vj0ePHkl29z/77DOsXbuWWTaA3+3mTk5OWLFiBUJDQ6WK0rdv3yIgIAAuLi5MclWfPd2/f7/cdTU1NURFRXEZSYqnpyez136fXbt2sY5QJz5veKWnpyM1NVXuUZ/KykrMnTuXSa558+YBAEaPHg0DAwOUlJRg9OjRePLkCZycnJgeFfHx8YGvry/s7e1hbm6O0tJSeHp64vbt2zA2NsaqVasomxyWlpbw8fFBVFQUOnToIHeja+rUqZzn0tXVxbp163D69Olac3377bec56rGp40RfX191hFII0cFPiENpFOnTrh79y7MzMxgZ2eHrVu3wszMDBoaGoiOjmbamkbZ/plWrVrhxYsXMDY2hrm5OQ4fPiz54PbixQupJzcs8LndfNGiRfD09IS9vT0cHBygr6+P7OxsXL58Gd26dcPChQuZ5GI9M6G++HxtGV/xdcPr6NGj2LJlC2xsbHDhwgUMGzYMWVlZuH37NoyMjODq6sosm6KiIubPnw93d3c8ePAAWVlZUFdXR7du3eTelsAlNTU1rF+/Hn/88Qfu3r0LS0tLqKurw93dndkRkMaQ7cGDB5Ii9eXLlzLrrDpZBAIBvv766zrXWeLrxki1J0+e4NChQ0hPT0dJSQmOHz8OAMjOzoZIJIKRkRGzbIR/aIo+IQ3k9u3bqKioQO/evVFaWoo5c+YgNTUVzZo1Q1lZGcLDw5kNoaJs/8zJkyfRrFkzODk5ISMjA2PGjIGRkRGMjIyQmJgIDw8Ppu2GcXFxWLlyJW/bzQEgKSkJKSkpyM7Ohra2Nuzs7NC7d2+mmaqJxWLk5eWhefPmzD9cVuPztWVA1d9nXFwcCgoKZNZYTeeuNnbsWCxYsIB3G17Ozs6IiIhA69at0adPHyQnJ0NRURFv3rzBsmXL0KdPH0yZMoV1TEL+05YsWQIlJaVa11VVVbFixQruAtUQGxsLHx8fjBs3DqampvD29kZKSgqAquGA69atw4EDB5hkI/xEBT4h/6IXL17g1atXaNeuHTQ0NABIt32zRNk+3OvXrxEdHY1Xr16hZ8+eTK55q+l95zxZt5vzVVpaGjZt2oTU1FSUl5dDWVkZ/fr1w5w5c2BmZsY0G5+vLdu9ezdOnDiBUaNGyb3WTSAQMD2zzdcNr5p/h46Ojjh48CBatWoFoGoehZubG2JjYznPVS09PR2HDh3C77//jjdv3sDAwAC9evXC+PHjoaenxywXUHUu+tixYzLZXF1dmf8+4HM2PqioqKizYCb1N3z4cCxfvhyWlpYAgL59+0quJBWLxbC2tsb169dZRiQ8QwU+IRyzsrLi7RsxZZPm7++PwMBATl+zsTp58iRGjRol+ToqKgpCobDWP8/qyqGEhAQsWrQI06dPx5AhQ6Cnp4ecnBxERUUhIiICYWFhTDduLCwscO3aNUmBUPODHFA1lyI1NZVJNnt7exw7dgwtWrRg8vrvw9cNLzs7O5w7dw5NmzbFjBkz4OjoiLFjxwKo2ri0t7fHtWvXOM8FVA3z8vb2xqhRo9C/f3+oq6sjOzsbV65cQXx8PLZt28as4yY5ORnTp09H3759YWNjAw0NDUm2jIwM7N69G+3bt//PZ5sxYwa2b98u+drd3b3OAW0CgQA7d+7813ONHTtW6kpKFxeX9+Y6d+7cv56rMTI3N0dqaioUFRUBSP9eEIlEsLKyoin6RAqdwSeEY+9e3cQnlE1adHQ0AgICpO445ivW7eYpKSlSBf7p06frLPBVVVWZFPhbtmxBaGio1BV+rVq1gru7O4yMjLBhwwZmtzYA/L62rLKyUu6tCHzB1/kK/fv3x+XLlzF06FCMHTsWS5cuhaKiIgwNDXH8+HGmG0pbtmzBmjVr4OzsLPX9kSNH4vTp01i1ahVOnDjBJFtISAgWLlwocwzKw8MDO3bswPLly5m1JfMp27sZvvjii/durnLh3eM6a9eu5UWuanzdGJGndevWiImJgZOTk8xabGwsL2ePELboCT4hHHv3iRyfUDZp27dvR0lJCebNmyfZOecbPreb89H7/h2x/hnYuHEjrl69Krm2rDpPXl4evLy80KNHD3h7ezPJtn79erx+/Rp+fn68v5aJ9YZXTUKhEEKhUHLcKDIyEocOHZIc9fHx8UHz5s2ZZHvfv3cLCwvJWV+u1Tza8K7Kykr07duXsn2gyspKvHr1Crq6uqyjSBEKhXj8+DG6dOnC2WvGx8fD1tZW8vX58+ffuwHB6vaXa9euYd68efjyyy/h6OiIr7/+GgcPHkRSUhK+++47BAUFyS3+yX8XPcEnhJBalJSUICEhAT/++CPMzc2hra0tdaaQ9bU+NdvNQ0JCpNrNv/nmG6bt5hcuXJB5KlitqKgIwcHBCA4O5jhV1Ye0goICuU+i8/PzmReDfL62bMGCBVi4cCHs7e3Rs2dPaGpqSq2zHrIH8HPDSyAQSP27GjZsGNNZBTW1bNkSGRkZkpkANf3xxx9o2bIlg1RVdHV18fr1a5l/Z0DVMEqW3SR8zubp6YmwsDC5a2VlZRgzZgwuXbrEcSrAzc1NMvldnhkzZiAuLo6zPDWLewB1Fu/VGyOs9OvXD4cOHcK2bduwYMEClJaWYtKkSTA1NcWGDRvQv39/ZtkIP1GBTwghtVBXV4eDg0Ot66yLQT63m/v5+dVa4KurqyM6OppJge/k5IQVK1YgNDRU6u/v7du3CAgIYPaEphqfry3bsWMHHj58CHd3d2hra8uss/554POGF1/Nnz8fs2bNwrfffit1W0liYiLWrl2LRYsWMcv29ddfY86cOQgJCZG6Auz58+fw9fXFrFmzKJscdc2qKSgoQFFREYdp/vbixYta1+7fv4/y8nIO08ji68ZItY4dO2L9+vXMXp80LtSiTwjHWLcA14WyNS58bDffvn07RCIRIiIiMH36dLl/5s6dOyguLsbBgwc5zQZUdQ94enriwYMHcHBwgL6+PrKzs3H58mV069YNW7ZsQZMmTTjP1Rj0798fZ86ckTtBnw/GjBmDOXPmSG14VTt//jz27NnDZMOroKAAwcHBiIuLQ2FhodSaWCyGuro6s8GJrq6uyMvLQ35+PlRVVdG8eXO8evUK5eXlaNKkCXR0dCAWi6Gqqsr5ALSZM2fi119/RV5eHkxMTKCpqYm8vDy8fPkSzZo1Q+fOnQEAKioq2LVr138+m7OzM8rLy2vtyBCLxXj16hXc3d0xZ84cTjIBQK9evfD27dta15WUlKCnp4dZs2ZhzJgxnOV6V12/L//66y+MGjWKt0OICXkXPcEnhJD3+O2335Ceno7CwkJMnjyZdRwJPrabV+8Z1zaYUFlZGYMHD8ann37KZSwJDQ0N/PDDD0hKSkJKSgqys7PRokULhIWFMZsWXlNgYOB7Bz35+flxmOhvlZWVaNasGZPXro+nT5/KLe6BqvZbf39/jhNV8ff3h4mJCc6dO8e7zZGdO3fW68mpsjL3HxdnzJhR589CNRZXsfEx2759+yASiTBy5Ejs27dPZl1ZWRna2tqc/164desWgLrnFrBUvTFSVFSEQYMGyazX3BjhSu/evVFaWlrvP6+mpoa0tLR/MRFpbKjAJ4SQWuTk5MDLywsZGRno3r07EhISJAV+XFwcEhIS4OvryywfH9vNZ86cCQDYv38/p0+JPpS1tTWsra1Zx5DRsmVLqUFPYrEYOTk5SEpKQpMmTTBhwgRm2T777DNs3rwZCxYs4OXQST5ueAHAjRs3sGnTJl7+N2N97KMu5ubmrCPUio/Zqv8u+/fvL3VsgC/c3NxYR5CLjxsj7155l5qaimXLlsHd3R12dnbQ1tZGZmYmLl68iDNnzmDjxo2cZSONA7XoE/IvKisrw++//47WrVtLhvG8evVK7vlVlrKysmBgYMDLbNVYZJs9ezbatWuHBQsWQEFBQeoJRElJCVxcXHDlyhVOM9XE53bzvLw8XkxqPnnypNT1fVFRUe+dlMzi+r73EYvFCA0NhbKyMhYuXMgkg7e3Ny5cuIBmzZqhbdu2Mk91BQIBtm3bxiQbAKxYsQIFBQVyN7yWLFkCPT09JkMxR4wYge+++w7Gxsacv3Z9VFZW4o8//kBBQYHMmkAgQM+ePbkPVUNxcTFev34t830VFRXo6ekxSPQ3Pmd7H3d3d86PNtTHkCFDOD8OAgBz587Fli1bOH/d+hg7diz8/f3RvXt3mbWkpCSEhYUxOfJG+IsKfEIayKJFi7BkyRLo6+sDqGrrnjZtGhQVFSEUChEeHi41xIhL48aNg7+/P7p16yb1faFQiO3bt+PAgQNMz5bl5ubi6tWryMrKkrnvXiAQYNq0aUxyWVhY4Nq1a5Ji4d0zen369GF2drammu3m2trasLOz40W7uUgkwrNnz1BQUIB3f9VUVFRw8gTd19cXQUFBkq9nzJhRZ4GvqqrKtEitS3l5OYYMGYLo6Ggmr3/s2LE6W45VVVWZbo7wdcPr6tWrCA0Nxdy5c2FpacnsSjx5fvvtN3h6eqJJkyZyOx9UVVWZ3f39559/wtvbG+np6XKn1aupqSEqKopBMn5nqy++zrXhayt/NRYbI3yct0P4jVr0CWkgCQkJWLduneTrwMBAzJgxA1OmTEFkZCRCQ0Nx9OhRJtkmTpyImTNnYvDgwfDy8oKGhgZiY2MRFBSE1q1bY//+/UxyAVWt7l5eXujatSsMDQ1l7tdWVVVllKzqQ1pubi4MDQ1l1p48eQJ1dXUGqWTxsd08NTUVXl5eUFRURG5uLlq2bIns7GyUl5ejdevWMDEx4SRzzeIeqBoC2FiVl5ejpKSE2et//vnnzF67Pvg6X6H6vW327NkysylYD9lbvnw5fH1967wthBU/Pz8MHTqUl08m+Zytsattfgtf3L17l/PXVFdXx507d+Re9Xn79m2m1zISfqICn5AGUvMJ5ZUrVyAWizFlyhQAVfcer1ixgk0wAMOHD4ednR3CwsIwfPhwtG/fHpmZmfDz84OdnR2zXACwfv16bNy4Efb29kxzyOPm5oa5c+di3bp1aNOmjeT7eXl58Pf35/wu68bUbh4SEoKAgAA4OjrCwsJCcr1QYmIi1qxZI/nZ4NqFCxdqvb6vqKgIwcHBTK7vq5afny8zvKu8vBwvX77Ejh07mHUB1VRaWlprVwYf2tD5tuHl7e2Nbt26ITg4mHdHoJ49e8bL4h4Afv31V/zwww+sY8jF52zk4/Pll19i1qxZmDFjBvr27YtmzZrhzZs3SEpKwo4dO+Dj48M6IuEZKvAJaSBmZmbYvn07+vXrhzVr1iAgIECyJhKJ6rwmhgvV52aFQiGePXuGzp07o0uXLkwzAVXXz/CxuAeAefPmAQBGjx4NAwMDlJSUYPTo0Xjy5AmcnJwwf/58TvOkpKRIFfinT59+b7s5qwL/+fPncHR0BFDVCVE9/MzGxkZyv/bp06c5z+Xn51drga+uro7o6GimBb6DgwOEQqFU8ayoqAhdXV3Y2tpi8eLFzLI9evQIixcvRnp6OoCqJ22VlZVQUFCArq4uNDU1ERkZyVmexrLhdevWLURERPDyyWS3bt0QHx8PW1tb1lFktGnTBvfv35d77pg1PmcjH59vvvkGLVq0wE8//YQtW7agrKwMenp66NGjBzZv3syLjV/CL1TgE9JAVq1ahVWrVuHs2bMYO3as1BvuvXv3ZM6/cyktLQ2BgYFQV1fH/v37YWJigl27dmHUqFGYNm0apk6dyuSqIQDQ1dXF8+fPYWJiwuT166KoqIj58+fD3d0dDx48QFZWFtTV1dGtWzcm06cbU7u5kpISKioqoKSkBBMTE6SmpkquIOrYsSMyMjI4zbN9+3aIRCKUlZVh69atcv/MnTt30KlTJ05zvev27dtMX78ugYGB+PzzzzFmzBjY2NggOTkZ2dnZiI+Px759+xASEsJpnsay4aWrq4u//vqLl5PNV61aBW9vb8THx6Nv374y58lZDtlbvnw5li1bhtGjR8PS0lImG8tBdnzORj5Oo0aNknq/k0coFDK7LYTwCxX4hDQQAwMDhIWFyV0zNzfHkSNHOE70tzlz5sDLywtffPGF5HszZ87EsGHDEBQUhDNnzjB5mgpUPSVfsmQJ/Pz80KVLF2YbDXVp3rw5LC0tWceQwed2c1NTU6SkpMDKygrOzs7YtGkTOnToAENDQxw6dAjt2rXjNE/1E/HanqIqKytj8ODB+PTTT7mM1aj8/vvvkmukBAIBiouLYWBggM8//xxt27ZFYGAgp+9zjWXDa+nSpViwYAFmz56NDh06yLzHqaioQEdHh0m2vLw85OTkIDExEb/99pvMOsshe+rq6lBXV8emTZtqHQDIYto6wO9s5L/L1taW6cBkwh80RZ+Q/4DCwsI6JzfHxsYyO4fp7OyMzMxMvH37FoqKilJ3RYvFYqipqXE2gGrGjBky55/rwvpasLom54rFYvTt25fZNOKnT5/i7du36Ny5MyoqKhAQEIATJ06goqICRkZGCA8PR8eOHTnPZWVlxesPQJGRkfX6N6ikpARXV1cOEv3N2toaiYmJUFBQwBdffIH58+ejX79+knXWk5z5uuHVo0cPlJeX17repEkTpKWlcZjob5999hnmz5/PfBaLPF9++SVGjBiBcePGsY4ig8/Z6ouv0+r5mqsa6/e5uvD9vx3hDj3BJ6SBFBQUIDg4GHFxcSgsLJRaYz0p+X3XMrEcsrRr1646z6a+O1X/3zRixIg6W3zfxaoVrjG0mxsZGUn+7pSUlBAYGIhvv/0Wb968ga6uLrNcfL+6KjU1FZcuXcKbN29gbm4OLS0t5OTk4O7du1BTU5PMq1BRUeG8wO/UqRPu3r0LMzMz2NnZYevWrTAzM4OGhgaio6Pl3jbBJb7OV7h16xaUlfn5cevFixe8LO6Bqo4RvhbQfM5WX6tXr2YdQa69e/eyjlAnPj8X5eOcD8IGP3/jENII+fv7w8TEBOfOnWPWblmboqIibN26FSkpKXKnX4vFYsTExDDJ9sknnzB5XXm4nor/TzWGdnNbW1skJSVJfU8gEDAt7oGq89AikQjPnj2rdRI8ywnshoaGMDMzw+rVq6GhoSH5/uvXr+Hr64sOHTrA3d2dSbaFCxdKnkS7u7sjLS0NAwYMQLNmzVBWVobw8HAmufi+4bVjxw7MmjWLyWu/T9euXZGcnMzLI0jGxsZIT0/nxTDYd/E5G1B1jeuhQ4eQnp6OkpISHD9+HACQnZ0NkUgEIyMjODk5cZqptu4kBQUFaGlpwdTUFDo6OkznFdUHXzdGCKmJWvQJaSDW1ta4du2aVIs5X3h7e6OyshKjR4+Gh4cHIiIikJmZifj4eDx8+BChoaGctUtfu3ZNqq339u3b7518zWrIEwBUVlYiJiYG6enpKCoqwtKlS5lleRef282HDh2KAwcO8G6zKzU1FV5eXlBUVERubi5atmyJ7OxslJeXo3Xr1jAxMUFERASzfE5OTjh9+jTU1dVl1vLz8+Hm5obY2FgGyeR78eIFXr16hXbt2kk2JLge9LRt2zaUl5djx44d+Oabb2TWlZWV0aJFC3z66acyw9C4YG1tLbPZxRfHjh3Dpk2bMHLkSHTs2FFmPoCqqioGDx7MJNv58+exadMmTJ48We7sAlVVVWZT7PmcLTY2Fj4+Phg3bhxMTU3h7e2NlJQUAFXdJOvWrcOBAwc4z+Xn54dz587h7du36NmzJ/T19ZGXl4c7d+5ARUUFWlpaGDhwIHx9fZk8ib5161atGxDa2tpo27YtLz/f1cTn4wOEW1TgE9JARowYge+++44X90C/a8CAAYiJiYFAIICVlRWuXLkCVVVVAFUf8OLj47F582ZOsnz99ddSQ5uGDx/+3snXZ86c4SKajMePH2P69OnQ19eHmZkZjh49ips3bwIALl26hNOnT2PLli1MsgFVA7JYPxGvzb179xAREYGRI0fCwsKCSWElzxdffIFZs2bB0dERFhYWkg++iYmJWLNmDZYsWSK1AcU1CwsLJCcny/2AKxKJYGNjI8nMV6w2nvi64bVy5Up07NiRly3dEyZMqLNoUVNTw44dOzhM9Dd7e/v3ZmN15IbP2YYPH47ly5dLujJqFn1isRjW1tZMfk527tyJxMREhIaGSv3eysnJwYIFCzBo0CA8fPgQRkZG8PDw4Dyfu7s7bt++jeLiYrRs2VKyAfHy5Us0adIEzZo1w9SpUzFlyhTOs9UXFfikGhX4hDSQq1evIjQ0FHPnzoWlpeV7z71zqeabvqurK0JCQiRPF8rLy9G/f39efjBm7auvvoKzszMmTJgAQHqAjVAohIODA65evcoyIm/bze3t7VFSUoLCwkIoKChAUVFRUrRyPTyxpppF4IABA3D27FnJFOyHDx9i0aJFzG6UAKoKrv79+8v9gBsSEoLHjx8z7TCoD1aDnvi64fXjjz/i6NGjqKiogIWFBbS1taWe+KqqqmLq1KkME5KPibm5OVJTUyUbEDV//4tEIlhZWUk2qrnk4OCAY8eOyf0ZzcjIwJdffoljx45h7Nix+OWXXzjPd+rUKezbtw/BwcFSRy/S09Ph4+OD8ePHIyYmBnZ2dhg/fjzn+eqDCnxSjc7gE9JAunbtCkNDQ8yePVvm6RvrIXutWrXCixcvYGxsDHNzcxw+fBirVq0CUNVi26xZMya5+O7+/fv44YcfJF/X/HsVCAQoLS1lEUuiPu3mrAr8o0eP1jkNnsvhiTUpKSmhoqICSkpKMDExQWpqKgYNGgQA6NixIzIyMpjkqubn54evv/4aFy5cgJWVFZo1a4bc3FwkJCRAU1OT2Tn3D8Fq0BNf5yv8+uuvkvP/xcXFKC4ullqv7qZiqaKiApmZmXj9+jUvz0BXVlaisLBQ7pV0rPEtW+vWrRETEyP3jH1sbCzatGnDfShUDSKu7bOGrq4uCgsLoauri1evXnGcrEp4eDh27dolMxeoS5cuWLduHTw8PLB792588803vC3wCalGBT4hDcTb2xvdunVDcHAwtLW1WceRMmXKFDx48ADGxsaYNWsWxowZg3HjxsHIyAiJiYlM2uFqKiwsxM2bN1FQUCCzJhAIMHToUO5DAdDU1MQff/whdzjXvXv3mJ8vDwkJQUBAgKTd/NKlSwD+bjdn2Uqor6/P7LXrYmpqipSUFFhZWcHZ2RmbNm1Chw4dYGhoiEOHDqFdu3ZM83Xp0gUxMTE4d+4cHj58iNzcXBgYGGDVqlWwsbFhmo3v+LrhFRAQwPlr1ldFRQW2bt2KQ4cOAajqTKq+si81NRXPnj3D6NGjmeU7fvw49u7diz/++AMCgUCS7f79+8jNzWV6AwBfsy1ZsgTz5s3D3bt34ejoCLFYjN9//x1JSUn47rvvEBQUxCRX+/bt8dNPP0k64mrat28f+vTpg9LSUma302RnZ6NVq1Zy19q0aYOcnBwYGRkhKyuL42T1d+HCBdYRCE9QgU9IA7l16xYiIiJ4eU3JqFGjJP+7VatWOHv2LKKjo/Hq1SuMHTsWffv2ZZYtMTERixcvRo8ePeRujLAs8CdPnozZs2cjICBAqrh68OABfHx88MUXXzDJVe358+dwdHQEUHXms6CgAFpaWrCxscHatWuZt5uXlJTgl19+wYMHD1BaWgpdXV107twZDg4OMkOpuOLj44O3b98CACZOnIg//vgDw4YNQ0VFBYyMjHjxhFwgEGDkyJGsYzQ6fN7wAqqGJD569AiFhYWcTzCvzdq1a5Geno6jR4+idevWUtP0jY2NsWzZMmYF/rZt2xAVFQUfHx+YmppKXefavHlzLF26lFkRzeds/fr1w6FDh7Bt2zYsWLAApaWlmDRpEkxNTbFhwwb079+fSS5vb2/MmDEDiYmJsLCwgJaWFvLy8hAfH4/nz5/j8OHDSEhIgJmZGZN8n3zyCaKjo+Hi4iKzdubMGXTp0gUikYjZlZe5ubm4evUqsrKyUFlZKbUmEAgwbdo03j1cIuxQgU9IA9HV1cVff/0FIyMj1lHeS1NTE59//jnrGACA4OBghIeHw9TUlHUUGZMnT0aTJk3g4+ODwsJCvH37FlZWVqioqMCECRMwffp0pvn43G6enp4Od3d3KCsrY8CAAZKfj59++glbt27F3r17mQzeMzIykhwPUFJSQmBgIL799lu8efOGN+e3CwsLceLECTx48ADFxcWSQY4lJSWS4z5EFl83vIqKihAYGIjY2FgYGxvjyZMnkqe9iYmJuHv3LrP3kp9//hlRUVGS9vKaG9T6+vrIz89nkgsADh06hKNHj6Jly5YApLMZGxszfZLK52xA1fv/+vXrmWZ4l6WlJaKionDy5EncvHkTb968gbq6Ouzt7TF+/HioqqrCyclJarOES56envD29kZaWhosLS2hqakp2YC4dOkSDh48iKSkJCbXbcbFxcHLy0tyFPTdI258OOZD+IUKfEIayNKlS7FgwQLMnj1b7rU5KioqTFu6+doGn5OTw8vivtqYMWPwxRdfICMjA1lZWVBXV0fbtm2ZnSGvic/t5kFBQRg0aBACAgKkPvyKxWIEBATA19e31jvL/022trYyV5YJBALeFPe3bt3CrFmzMGDAAJiZmWHTpk1Sa7t375a6hYL8ja8bXtUt0bGxsdDQ0JB6St6tWzf4+voyK/CFQmGt72WvXr1i2pFWVFRU6xPJoqIimaeYXOJTtvz8/DrnnbyL5WeRli1bYtasWbWuKygoMHtCPnjwYLRs2RJHjx5FeHi4ZAOiT58+OHPmDPT09KCvry/1nsyV9evXY+PGjbC3t+f8tUnjRAU+IQ1kzpw5KC8vr/WDWpMmTSRPbbjG5zb4vn374vjx43Bzc2Py+nXZs2cP3Nzc0KxZMxgaGsLQ0FCyVlRUhEOHDjF9is/ndvP79+9j165dMgWCgoICfHx8MGDAACa5dHR0kJ+fz3x+Qm1WrVqFFStWwNnZGQCkrmG0srLCggULWEXjPb5ueMXExCAmJkbSeVHzZ0JTUxOFhYVMcgGAnZ0dVq9ejZUrV8r8rIaFhTGd+2BhYYEdO3Zgzpw5Mmv79+9Hr169uA/1//iUzcHBAUKhUGaoZG3U1NRw69atfzdUHcRiMfLy8lBRUSH1fdYPQQDAzMysziMCrAYS//XXX1Tckw9CBT4hDeTWrVvMdp7fh89t8CtXroS3tzdiYmJgaWkpM4mY5ebDtm3bMGnSJLlrTZo0wc6dO5kW+HxuN69ronR5eTmaNm3KXZgaQkNDsWLFCowcORIWFhZMjgnU5fHjx/j0008lX9csuhQUFCAUClnEAgDcuXOn1g+/RUVF+OGHH+Dp6cls0BOfN7yEQqHcoxVZWVnMhooBgK+vL2bPno1BgwbB1tYWb9++RUhICG7cuIHi4mLs3buXWTY/Pz9MnToVCQkJcHR0hEgkwv79+3H9+nWkpKRg//79lA3A7du3OXut/0VeXh6WL1+Oy5cvo7y8XGad9cYDn+nq6uL58+cwMTFhHYU0Egri+m75EULqFB4eXmfrGUvW1tYybcl8ERcXBx8fH5iZmdXaXcBqCrWFhQVSUlJqXe/Tpw+zqw8Bfv+9bt26FTk5OVixYoVUkVpSUoLFixfD1NQUM2fO5DyXvb09SkpKUFhYCAUFBSgqKkryicViqKmpMf07dXFxwapVq2BhYQFA+l7jpKQkBAYGIioqikm2992xbGlpieTkZA4TSROJRDLt5kKhkPmG1/Lly5GZmYl169ahWbNmkv+OQqEQixYtQvPmzSXXlrKSkJCAW7duITs7G+rq6ujRowecnJyYn+0ViUQ4ceKETDY3Nzfmm5h8zfbnn3/CyMhI7vEKsViMFy9eMCkU58yZg6ZNm2LOnDkwNDTk3QORkpISbNmyBZcuXZIZZMf6d0NUVBT27dsHPz8/dOnShdmQWtJ48Ouni5BGbO/evbwt8PncBh8cHIwdO3age/furKPIaNmyJX799Ve590Lfv3+feTshn9rNIyIipJ4uV1RU4OLFi7hy5Qr69+8PHR0dZGZmIi4uDpqamujRoweTnEePHq3zvCrr2Qpz5syBp6cnFixYIBk2VVxcjKSkJKxcuZLJlZZnz55FeXk5hEIhTp06JbMuFouRlpZW6xVTXOHrfAUfHx/4+vrC3t4e5ubmKC0thaenJ27fvg1jY2OmxX1OTg709PQwYMAAmWMzxcXF+Pnnn5ndFlJZWQkVFRWMHTsWY8eOlVorKytDXFwcs0n1fM42adIknD59Gs2bN5dZKyoqwrhx43Dt2jXOc6WmpiIuLo5px0pdVq5ciczMTAQGBsrdgGD5u2Hz5s3IzMyEm5sbFBUVoaioKFljvflA+Ime4BPSQFauXImOHTti3LhxrKPIeP36Nby9vaGkpMS7NngLCwskJyfz8nrBQ4cO4fDhw9i+fbvU+fvHjx9j1qxZmDx5Mr788ktm+e7du4eIiAhetJu/W+C/j0AgYH4LAV8lJCRg69atuHfvHsrLy6GgoIA2bdpg2rRpTIotPz8/lJeX4+zZsxg+fLjMurKyMlq0aAE3Nzd88sknnOerNnToUBw4cIAXG17y/PHHH7h7965kWGePHj2YniMHquY6XL9+Xe6aWCyGtbV1rev/NltbW8THx8tdKy8vh52dHa5evcpxqip8zva+Tpr3dab9W6ytrXH58mWoqalx/tr1MWDAAJw/fx4aGhqso8j4888/6/yMpKKiAn19fQ4TEb6jJ/iENJBOnTrh6NGjOHLkCCwsLKCtrS3VRqWqqoqpU6cyyXbr1i3J+dkHDx7IrLMu8KOjozF48GAmr1+XL7/8Evn5+XB1dUWPHj2gr6+PjIwM3L9/H5MmTWJa3ANVT3tLSkpw8eJF5u3mjalYLykpwS+//IIHDx6gtLQUurq66Ny5MxwcHJi3PopEIsnT1MrKSuTm5qJp06ZMP3SuXLkSAPDLL79g9erVzHK8D9/nK7Rv3x7t27dnHQMAkJaWJunKSElJkRnQVt2VIe8p8L8tOzsbIpEIJSUlyMjIqDUbi59VPmerJhAIUFRUJPc9482bN8yyDR48GOHh4Zg/fz6T138foVDI2+4ClhunpHGiJ/iENJDly5fX+QRTVVUVK1as4C5QDc7OztiwYQMv2+B37dqF77//Hra2tujQoYNMWxzLjZFqmZmZuHLliqQdfsCAAVJP9Fmp/rBZGy539auvJ+O79PR0uLu7Q1lZGQMGDICuri7y8vJw9epVaGlpYe/evUwLQz7PVbh37x6zoxX1wef5CgUFBbh27RqysrIkgwCrCQQCTJs2jdM806ZNg0gkQlpaGszNzWXWVVRU0KJFC0yaNInz4azOzs4QiUTIzMxEq1atZIpoFRUV6Onp4euvv+Z8sjifs1Xz8/ODQCCAn5+fzNqyZcsgEomwdu1aznNt3rwZe/fuhZGREbp27Soz36G2zFyZN28erK2tMX78eGYZ6lJUVIR9+/YhOTkZmZmZ0NHRgYODA8aPHy93gCf5b6MCnxCOsdgl5nMb/JIlS+osDFlujNy8eRM9evSQ+/clFApx+/ZtqTut/8vGjh2LH3/8UfK1i4tLnZsPAoEA586d4yKalEmTJqFt27YICAiQ+nkQi8UICAhAbm4utm7dynmuanxvMweAlJQUmQ+ZdV0txRU+bXjVFBsbiwULFqBLly4wNDSUeT9h+R7HejBiXSjbP5Ofn4+JEyeiVatWcHV1hYGBAf766y/89NNPyM/Px7Fjx5h0ZoSFhdX5GYT1sa39+/dj8+bNsLCwqHUDguuNuGrPnj3DxIkT0b17d7i4uMDAwABZWVk4f/480tPTceDAAXrKT6RQgU8Ix+o68/hvmTlzJkaPHs3LNvj6YrExYmtriwsXLsi90q20tBQODg7Mn7bypd08PT0dXbp0kXx9586d9xb4LK5t7N27N5KSkuT+W3r79i0GDBjA9IM7n+YqvKu4uBheXl64f/8+7O3toa+vj+zsbFy+fBm9evXChg0beHu+lqXhw4dj0aJFvLzHOjo6Wupaxto8e/YMrVu35iDR3/bu3YvJkye/98+x+N3A52zVr7t3715cunQJr169gra2NhwdHfHVV1/Rz2gtli5dWucGBMuNOA8PD/Tp0wfu7u4ya5s3b8ajR48QFhbGIBnhKyrwCeEYi53/xtAG/z4sNkbeN4yI1bCianxuN7e0tETHjh0xePBguLi4oGXLlkxyvMvR0RHnz5+X+6G7uLgYQ4cORVxcHINkVfjcZr5q1Srk5+dj9erVUk+3ysrK4OXlhTZt2mDp0qVMslXjy4ZXTayv02wILN5/64uySZN3XSQLjeXYVmNgZWWFhIQEuX+vQqEQAwcOZP6wgfALDdkjhGMs2uQfPHggeXr/8uVLmXXWdx3XR807abmirq6O3NxctGjRQmYtNzeX+UCeoKAgDBo0qNZ2c19fX2bt5vHx8UhISEBMTAx27NgBIyMjODs7w9nZmWkr4ejRoxEUFIQVK1ZI/TcrKSnBkiVLmJ+/5PM1fhcuXMDZs2dl3i/U1NQQHByMkSNHMi3w5W14Vbcmb926ldmGl5aWFl6+fAkjIyPOX7uhsHj/rS/KJs3e3h62trYYOXIkrK2tmR3N+/LLLxvFsa3GQCwWy8x7eHedkJqowCfkPyA0NLRef47PU2RZfEgZOnQoAgMDsWXLFqnvl5eXw9fXl9nNA9Xu37+PXbt2yfy3UVBQgI+Pj8y91lxSU1ODk5MTnJycIBaLkZqaitjYWHz99ddo2rQpXF1dOekaeff6voqKCly8eBFXrlxB//79oaOjg8zMTMTFxUFTU5PzIXLvHm3g81VHZWVltRbIOjo6KCsr4ziRNL5ueE2dOhU+Pj4ICgqCsbEx56/fEPg4v6UaZZN2+vRpREZGYv369cjOzsawYcMwYsQIdO3aldMcAQEBUl+vXbu2zkHEXH/2mDFjBrZv3y752t3d/b0bEDt37uQimozevXtj7969+Oabb2TW9uzZAwsLCwapCJ9RgU8IkbC1teVtqyMLc+fOxYwZMzBs2DAMHTpUck3eqVOnYGBggCVLljDNp6WlVetaeXm53NkBLCgoKEgGF5mammLPnj0ICwtjcixESUkJEyZMkPqeiYkJJk2aJFnn0uTJk6V+5uzs7JgeEahL69atERcXBzs7O5m12NhYtG3blkGqv/Flw6t3795ShYxYLEZFRQUGDx4sdeSimqqqaqNv4Sf80aJFC0yePBmTJ0/G06dPcfbsWSxYsABKSkoYMWIEXF1d0apVq389R82NSwB1zlwRCoV4/Pjxvx1JysSJE6W+/uKLL3i1AVGTt7c3Jk6ciNTUVAwZMkQy/yQqKgq//vorDh06xCwb4Scq8AkhEnxudWShSZMm2LdvH6KionDp0iWkpqZCW1sbixYtYv70HuB/uzlQNZwrNjYWsbGxSE9Ph7W1NSZMmMDZwDGWU5nrQ1tbG7t27cKgQYOgoqKCoqIiufdrV6u+hosFT09PLFmyBDNnzpT6kBkZGYmIiAhs2rSJSa5qfNnwOn/+vNSTwLrOIpeWluK3337jJBf572nTpg2sra3x6tUrnDhxAvHx8Th+/DjatGkDX19fmJiYcJbFzc0Nx48fr3V9xowZnG5u2traSn3t4uLC2Wt/qLZt2yIqKgr79u3D8ePHkZ2dDS0tLdjb22PdunVo1qwZ64iEZ6jAJ4RI8LnVkaWhQ4fyoqDne7t5TevWrUN0dDTevHkDR0dHTJkyBf379+f8KQjfBz1t3LgR4eHhOHXqFEpLS1FaWoqJEyfWWuCrqqoyO6dqZ2eHsLAwhIWFYf369ZL/tlZWVvj+++/Rp08fJrmq8WXD691jFra2toiPj5f7Z8vLyzF58mS4urpyEY38R6SmpuLcuXO4ePEiWrRoAVdXV5w/fx4GBgYAgMjISHh6euL06dOcZXrx4kWta/fv30d5eTlnWeS5efMmunbtiiZNmgCo6rzZtWsXrl+/ju7du2POnDkyA4q5pKmpCU9PT2avTxoXKvAJIaQOeXl5uHfvHl69eiWzVlFRATc3NwapqvCt3bwmsViMoKAg9O7dG4qKisxy8H3QU9euXaVmPFhaWuLSpUucvf6H6tu3L/bv34/Kykrk5eWhefPmzIZ08n3DKzs7GyKRCCUlJXK7MsRiMdLS0ni9AUUan0GDBkEsFmP48OHYtWsXOnbsKPNnhg0bBj8/P07y9OrVC2/fvgUAuXMAlJSUoKenh3nz5nGSpzaLFy+W2vD47rvvcO7cOUycOBGRkZFYs2YNfH19Ocny8OFDqb+3nJyc9w5fZdXZRfiJCnxCGsidO3dgZmYmd62oqAg//PADPD09ceHCBY6TkX/ql19+ga+vL9q3b487d+6gd+/eyMrKwosXL9C+fXtYWFhwWuDzvd28psWLF7OOAEB20FNoaOh7C3yW5N1zLA/LgZgikQjPnz9HSUkJKioqYGBgwIvuH75teE2aNAkikajWrozqD+WBgYGc5vonGsNNK6RK69atsW7dOujo6MisFRQUYOnSpdi2bRt+/vlnTvLcunULAJsrgj/EmzdvoKGhAQB49eoVjhw5guPHj8PAwACDBw/GyJEjOSvwly1bhp9++kny9eDBgyESiers7Lp58yYn2UjjoCCmuxUIaRB9+/bFjRs3al3n+y834P3/H/4N9d0YefXqFbS1tTnN5urqiqCgIJiZmaFPnz5ISUmBgoICnj17Bh8fH3z55ZcYPnw4Z3n43m7eGFhaWqJjx44YPHgwXFxc0LJlS9aR/hEW92sLhUKsXr0ap06dgkgkgpaWFgoKCqCuro7p06fXe3Piv6YxvPc3RllZWTAwMGDyu6G+WGTj62eRkJAQptdovo+joyMOHz4MAwMDhISEQE1NDV5eXpL1Xr16STYr/m0ikYjplaik8aMn+IT8j86ePYvy8nIIhUKcOnVKZr26DZOLqbWN0ddff13rhxENDQ3s27cPnp6eTD7AZWdnSzYfNDQ0kJeXhxYtWkiekEydOpXTAp/v7eaNQXx8PBISEhATE4MdO3bAyMgIzs7OcHZ2xieffMI6Xr2xGIi5evVqxMfHIywsDP369YOioiIqKyuRmJiIwMBAKCkpYcqUKZzlaSwbXnPmzGEdoVb+/v5y30MUFRWhpaUFc3NzODo6MjlmM27cOPj7+6Nbt25S3xcKhdi+fTsOHDiA69evMyvuc3NzcfXqVWRlZcn8PAoEAkybNo1Jtuo70+V11YjFYmbDdPlc3ANVn0XGjx+Pjh074unTpzhx4oRk7a+//uK0i+Xd4j4nJ6fWFvzi4mJERUXhiy++4CIaaSSowCfkf3T9+nWUl5ejoqJC7hM1ZWVltGjRAt9//z2DdPzVGDZGVFRUJK3Qbdu2RVJSkqSgNzAwQEFBAad5Glu7OR+pqanByckJTk5OEIvFSE1NRWxsLL7++ms0bdoUrq6uTK7v+1AsWuIvXLiAI0eOSE3eVlRURP/+/bFz505MmDCB0wK/sWx4TZ48mfPXrK/u3bsjNDQUxsbGcHR0hL6+PvLy8hATE4MXL17g+fPn2LZtG3bu3FnnLQX/hokTJ2LmzJkYPHgwvLy8oKGhgdjYWAQFBaF169bYv38/p3lqiouLg5eXF7p27QpDQ0OZgozlkYZOnTrh4sWLcHZ2llk7d+4c2rRpw0kOf39/qeMny5cvf+81dO/+juPSl19+id69e+Pp06fo168f1NXVJWuVlZVMsw0fPrzWjq2mTZti3bp1VOATKVTgE/I/WrlyJYCq89qrV69mnEY+Ps4HaAwbIxYWFrh69SocHBwwcuRIyblGQ0ND/Pjjj5wP7Xr3XmF3d/ePot2cFQUFBVhYWKBr164wNTXFnj17EBYW1igKfBYqKipqvVbL2NhYMkiLK+9+4F67di1v77Hmq7t372LixImYP3++1Pdnz56N1atXQygUwtraGqGhoQgODuY02/DhwyU3NwwfPhzt27dHZmYm/Pz8YGdnx2mWd61fvx4bN27k7LrPD+Hp6Yn58+ejvLwcQ4YMgaKiIkQiEU6cOIF169Zx9jv13c8cJiYmvP/57NKli9Tv2YyMDKSlpaFz585MrtFLS0uTPAhJSUmpdVBn8+bNOc9G+I3O4BPSQO7du8f0WrK68PVMHuvXfp/8/HyUlZXB0NAQQNXU7kOHDuHVq1fo2bMn1q5dK7l2iIWysjJJu3lcXFyjbTdn4dmzZ4iNjUVsbCzS09NhbW0NJycn2NvbN4o7hVnMy5g5cybGjx8vt7g6ceIELl68iG3btnGaqab8/Hy5g8VI7WxsbBAbGws1NTWZtaKiIjg5OSE2NhZOTk64evUqg4TA4cOHERYWhqZNm6Jz587w9/dn+r4LVG3+pqSkMM1Ql8TERAQFBeHp06fQ1dVFXl4eWrdujSVLljDfHOGrCRMmYMOGDZJ/W4mJifDw8EDHjh3x+PFjhISEwMnJidNM06ZNg0gkQlpaGszNzWXWVVRU0KJFC0yaNAmmpqacZiP8Rk/wCWkgfCzuG0Mb/A8//MDstd/n3WJh+vTpcifZJyUlwdramqtYEh9LuzmX1q1bh+joaLx58waOjo6YMmUK+vfvz4unR3zz7tVM7u7u8Pb2hqurKwYOHCi5hi4yMhIpKSlYu3Ytw7SAs7MzdbR8oLdv30IkEskt8MvLyyEWi9GkSROUlZVxni0tLQ2BgYFQV1fH/v37YWJigl27dmHUqFGYNm0apk6dymwGg66uLp4/f15rRwtrNjY2OHv2LJ4+fSrZ+OKqNf+fYj1T49GjR5LiXiwWIzg4GMHBwRgyZAiSkpIQFBTEeYG/e/duAFUPQlgeSSGNDz3BJ6SBlJSUYMuWLbh06ZLM0B2xWAw1NTWkpqZymsnPzw/l5eU4e/as3GFw1W3wbm5u9MT3f8DiaWptiouLER8fjz179uD333+nq3PesXbtWjg4OKB3795MBoc1FC7+zQ0ZMgRCobDWq5nepaqqynSoI3W0fDgPDw9oampi1apVUsVVSUkJfHx8oKenh1mzZsHNzQ2XL1/mNFv//v3h5eUlc7b4xYsXCAoKQkZGhtS95VyKiorCvn374Ofnhy5dujSKYY+sZWVlYeXKlUhJScHr16+l1pSUlKCiooK0tDRG6apuJrl27RqUlJQQGRmJU6dOYceOHZL1Pn36cP4Zrlp0dDQ+/fRTJq9NGicq8AlpID4+PsjMzMT06dNhaGgIZWXpBhkVFRXo6+szycbnNng+box8KNb/fRt7uzn5cHzaVOKjmh0tly5doo6WWmRmZmLmzJnIyclBz549oampifz8fNy6dQs2NjYIDQ3F9evXkZycjAULFnCarbCwsM6zxbGxsXBwcOAw0d+cnZ2RmZmJt2/fQlFRUWqzsLH83uLarFmz0L59e3zxxRcYNWoUzp49i4yMDCQkJCAhIQEbN25kuhE3f/58aGpqSmZOhIeHo3PnzgCA0tJSWFtb4/bt28zy1YV19wPhHyrwCWkgAwYMwPnz56GhocE6igw+zwfg88ZIfbEqtt5tNx80aBC1mzdy9R2Iyfrub7FYjLy8PDRv3pzX/96oo+X9qjdDHjx4gMLCQqirq8PCwkLmejrytz///LPOmywaw+8trvXr1w8JCQlQVFSEtbU1YmJi0LRpUwBATEwMDh48iF27djHLV1RUhK1bt+LJkydwc3PD4MGDJWt37tzBDz/8gI0bNzLJVt39kJqaKnN7Dx+6Hwj/UIFPSAPp27cvEhISeP1hl4/4vDFSX6wK/I+l3Zz8jc8DMYGqc9GbNm1CamoqysvLoaysjH79+mHOnDm1bkxwjTpa6u/ChQsYOHAgmjRpwjqKjOqCKyUlBQUFBXIniMfExDBKRz6UlZWV5MacUaNG4dtvv0WfPn0AVP1dWllZUVdSLfje/UD4h4bsEdJAbGxscPz4cYwfP551FBl8boOvvmeefLjFixezjkAaSGMYiJmQkIBFixZh+vTpCAkJgZ6eHnJychAVFYVvvvkGYWFh6Nu3L7N8NEDxwx0+fBg+Pj6wtraGs7MzBg0axJvN1pUrV6KyshILFy6Eh4cHIiIikJmZifj4eDx8+BChoaGcZbl27Rr69esn+fr27dvvvfKtZ8+eXERrNNq0aYNHjx6hQ4cOsLa2xq5duyQF/u3bt6Grq8s4IfDkyRMcOnQI6enpKCkpwfHjxwEA2dnZEIlEMDIyYpLr9u3b+O6776CoqAiBQAAtLS0YGhqiT58+MDU1xfLly5l2PxD+oQKfkAZiYWGB9evXIy4uDl27doWqqqrUukAgwLRp05hkW7lyJTIzMxEYGFhrGzwrfN4YIYQr169fR3l5OSoqKiRPuWqqHojJ1R3W8mzZsgWhoaEYOHCg5HutWrWCu7s7jIyMsGHDBhw5coRZPrFYjKCgoPd2tNCm4t/27NmDgoICXL58GZcuXUJwcDB69eolKfY1NTWZZbt69SpiYmIgEAggEAhgZmYGS0tLuLq64tixY9i6dSs2b97MSZbdu3dLFfi+vr51Fviqqqo4c+YMF9EaDQ8PD2RlZaFDhw6YMWMGJk2aBAcHBxgYGODBgwcICQlhmi82NhY+Pj4YN24cpkyZAm9vb8naX3/9hXXr1uHAgQNMslVUVEje01q1aoXffvtNsjni4OCApUuXMslF+IsKfEIayP379yVTTjMzM2XW3y34uRQfH8/bNng+b4wQwpWVK1cCAH755ResXr2acRr5nj59KlXc1+Ti4gJ/f3+OE0mrb0eLra2t3E2U/yotLS189tln+OyzzyAUCpGYmIhLly5h69at6NSpE7Zt28YkV82NGH19fTx69Ajdu3cHAHz22WecXsu4c+dOqa/Pnj3L2Wt/LG7cuAE7OztUVlZCS0sLp06dwo0bN/Dq1SuYmprC2NiYab7169cjLCwMlpaWACC1SdizZ088fPiQVbRG0f1A+IUKfEIaCOvd57rw+YkVnzdG6qtt27asI5CPxA8//MA6Qq0EAgEKCgqgpaUls5afn8/b95h31TyiRGQpKipCWVkZCgoKePPmDbMcrVq1wosXL2BsbAxzc3McPnwYq1atAlB1VR7reQqFhYW4efOmzNAzoOpnZejQodyH4jFdXV1s3LgRT58+Rf/+/eHo6IiBAwfy5sHDy5cvJUXzu8rLyyESiThO9De+dz8Q/qEhe4Q0sPz8fDx69AiFhYVwcnJiHQcAMG/ePFhbW1MbPCGNREpKCpKTk5GZmQkdHR04ODgwH2K3YsUKFBQUIDQ0VKqYf/v2LZYsWQI9PT18++23DBPWD10xKC07OxtxcXGIiYlBSkoKTE1NMWjQIDg5OcHAwIBZrpMnT6JZs2ZwcnLCX3/9hbFjx8LIyAhGRkZITEyEh4cHJk6cyCRbYmIiFi9ejB49esi9zUIgECAgIIBBMv4rKChAXFwc4uLikJiYiM6dO8PBwQGOjo5Mn+J/9tlnmDNnjuRzW833iYsXL2Lbtm04ceIEs3w1VVRU8Kr7gfAPFfiENJCioiIEBgYiNjYWxsbGePLkieTaksTERNy9exfTp09nkm3//v3YvHkzLCwseNsGz8eNEaDq+h55wwmBqv9urNpXycepuLgYXl5euH//Puzt7aGvr4/s7GxcvnwZvXr1woYNG6CmpsYkW1FRETw9PfHgwQM4ODhIZevWrRu2bNnCy2ns76IC/29jxozBw4cPMWDAADg5OcHBwaHOu+dZev36NaKjo/Hq1Sv07NmT6UBHV1dXBAcHw9TUlFmGj0FFRQWuXbuGDRs24OHDh7h37x6zLNeuXcO8efPw5ZdfwtHREV9//TUOHjyIpKQkfPfddwgKCmL22eTChQtwdnaWu1ZUVITg4GAEBwdznIrwGRX4hDQQHx8fVFRUwN/fHxoaGlLXWb1+/RqjRo1idqXP0qVL67yzV1VVFStWrOAuUA183hiJiIjAgQMHMHr0aBgZGckMJ1RVVaU2TNKgVq1ahfz8fKxevVpqI66srAxeXl5o06YNs4FK4eHhmDlzJq5fv46UlBRkZ2dDW1sbdnZ26N27N5NM/wQV+H+Li4uDtbU1Kisr5V5FV1FRwfTpIF/b4K2trZGUlMTktT8Gz58/R3x8POLj43Hr1i2YmprC0dGReZfhw4cPsW3bNty6dQtZWVlQV1eHqakppk6div79+zPLVdd7llgsRt++fZlen0r4hwp8QhqIlZUVYmJioK6uDkD2DdnCwgIpKSms4vEWnzdGBg0ahJ07d9IZe8IZW1tbnD17Vu708vz8fIwcORLx8fEMkn08RQ0V+H979OgRlixZgvT0dIjFYigoKKCyshIKCgrQ1dWFpqYmIiMjmWTjcxv83LlzYWdnBzc3Nyav3xglJCQgNjYW8fHxUFJSgq2tLWxtbWFlZdVo5ndwbfv27RCJRIiIiKj1QcedO3dQXFyMgwcPcpyO8BkN2SOkAQmFQkmBX1NWVhYvfoHxsQ0+JiZGamOkZqeBpqYmCgsLWUVDQUEB2rRpw+z1yX9PWVlZrVeT6ejooKysjONEfxs2bBiOHDmCcePGMctAGlZgYCDc3NwwZswY2NjYIDk5GdnZ2YiPj8e+ffuYDu8KDg5GeHg4L9vgV65cCW9vb8TExMDS0lJm8CQN2ZP1zTffoFu3bggJCeFlx4+XlxeWL18udzOpoKAAS5cu5fxIXvUz2No6MJWVlTF48GDJoGJCqlGBT0gDcXFxwdKlS7Fu3Tqp6b5CoRBBQUFwdHRklo3PbfAAfzdGOnTogBs3bsDKyopZBvLf0rp1a8TFxcHOzk5mLTY2lmk3SadOnXD06FEcOXIEFhYW0NbWhpKSkmRdVVUVU6dOZZaPfLjff/8d+/btA1BVlBYXF8PAwACff/452rZti8DAQBw5coRJtpycHF4W9wBw69Yt3LlzB2ZmZnjw4IHMOhX4sg4ePIhLly5h+fLlUFZWhp2dHRwcHNCzZ0/W0QBUncGXV9wDVVdJpqamcpwImDlzJoCqOUpz5szh/PVJ40UFPiENxMfHB76+vrC3t4e5uTlKS0vh6emJ27dvw9jYWHK9DwtBQUEAqgqE6jb4at26dYOvry+zAp/PGyMeHh7w9fXFhAkT0KVLF7nDCXv06MEoHfkYeXp6YsmSJZg5cyaGDBkiGWQXGRmJiIgIbNq0iVm2X3/9FZ06dQJQNQywuLhYap31lZbVBZc8RUVF+OGHH+Dp6YkLFy5wnIzfqlvzjYyMcOfOHfTr1w8A0KdPHzx+/JhZrr59++L48eO8bIMPDg7Gjh070L17d9ZRGo3evXujd+/eWLx4Mf7880/ExcVhy5YtePz4MWxsbODs7Cx3Y5MrYrFY8rMgb43l9ZrLly/HmzdvJJ+R3r59i5CQEFy/fh3du3fH8uXLeXPdIOEHOoNPSAN7/Pgx7ty5IxnQ0qNHD/Tq1YtpJj7PBygrK4Ovry8uX74Mc3NzXL9+Hfb29pKNkfDwcGZTnd+3uaCmpoaoqCiO0pD/ihs3biAsLAxpaWmoqKiAkpISrKys4OHhUes9zeT9Z+trzvcgVb766issWrQIZmZm2Lp1K65du4aIiAhoaGggOjoa3333HU6dOsUk2+vXr+Ht7Q0lJSXetcFbWFggOTm5zuG1pH4SExOxdu1a/Pbbb/jtt9+Y5ZgwYQK++uorudPqo6KisGvXLhw/fpxBMsDOzg4XL16UbKKuWrUK6enpmD59Ok6cOAFVVVWsWbOGSTbCT/QEn5AG4u/vj8DAQLRr1w7t2rVjHUcGX9vg1dTUsH79esnGiKWlJdTV1eHu7s58Y4TVcD/y39a3b1/s378flZWVyMvLQ/PmzZk/Heezs2fPory8HEKhUG4xKhaLkZaWhlatWnEfjucWLlyI8vJyAIC7uzvS0tIwYMAANGvWDGVlZQgPD2eWjc9t8BYWFoiOjsbgwYOZvH5jlp+fj4SEBFy5cgVXr16Fnp4e7O3t4e/vzzSXp6cn5s+fj/LycgwZMgSKiooQiUQ4ceIE1q1bh++//55ZtpKSEsnvgIyMDFy8eBFRUVHQ0NBA79696Qw+kUFP8AlpIDY2Nrh27Rovd/SXL1+OzMxMSRt89ZMuoVCIRYsWoXnz5syOEFRvjPCdWCyWFFt8GJhIPm4ikQjPnz9HSUkJ9PT0YGBgwMv3Fj7w8/NDeXk5zp49i+HDh8usKysro0WLFnBzc8Mnn3zCIGHj8uLFC7x69Qrt2rVj2vbr7OyMDRs28LINfteuXfj+++9ha2uLDh06yL1CleZRSAsPD8elS5fwxx9/wMLCAvb29nBwcIChoSHraBKJiYkICgrC06dPoauri7y8PLRu3RpLlixhenzAxcUF3333Hdq3bw9vb2906dJF6t9Xz549cfv2bWb5CP9QgU9IA9m+fTtKSkowb948KCoqso4jhc9t8HzeGAGAtLQ0bNq0CampqSgvL4eysjL69euHOXPm1Hrel5B/SigUYvXq1Th16hREIhG0tLRQUFAAdXV1TJ8+He7u7qwj8ha14H9c+NwGv2TJEqkBk+9SVVXFihUruAvUCCxfvhy2trbo378/mjRpwjpOnZ4+fYr8/Hzo6Ojw4iadCxcuYMWKFdDV1UXTpk1x8OBBqKioAKg6FvrVV18hISGBcUrCJ1TgE9JANm7ciISEBLx8+RLm5uZyJ0x/++23DBPycz4AnzdGEhISsGjRIkyfPh1DhgyBnp4ecnJyEBUVhYiICISFhaFv376sY5KPSEBAAOLj47FixQr069cPioqKqKysRGJiIgIDAzF+/HhMmTKFdUxeunfvHg29/IjMnDkTo0ePpjZ4wqni4mKkp6cjKysL2tra6N27Ny+OSL169Qp//vknunbtKtUxkpeXh2fPnvHy6kHCDhX4hDSQiIgICIXCWtcFAgGzSfV8boPn88bImDFjMGfOHAwcOFBm7fz589izZw+zK6TIx6lfv344cuQITExMZNZevHiBCRMm4MqVKwySNR4pKSlITk5GZmYmdHR04ODgQN02jRC1wROu7dixAzt27ICmpib09PSQnZ2N4uJizJgxgzZWSaNCQ/YIaSBTp06VtEzxTXR0NAICAnjZ6qiurg4HB4da11med3/69Knc4h6oOhPHeigQ+fhUVFTILe4BwNjYGG/fvuU4UeNRXFwMLy8v3L9/H/b29tDX10dWVhZmzpyJXr16YcOGDVBTU2Mdk9TTgwcPJE/vX758KbPOh6eq5OOxZ88enDlzBrt375bqBLp79y4WLVoEgUCAL7/8kmFCQuqPnuAT0kCsra2RlJTEOoZcfG6DF4lEvN0YGTBgAM6ePStzPRNQNQl4xIgRdO6NNKiZM2di/Pjxcgc6nThxAhcvXsS2bdsYJOO/VatWIT8/H6tXr5Yq/srKyuDl5YU2bdpg6dKlDBMSQvjK2dkZ4eHhcm9BSk9Ph5eXF86fP88gGSEfjp7gE9JAdHR0JENZ+KakpAQJCQn48ccfedcGb2try9uNEScnJ6xYsQKhoaFSnQRv375FQEAAXFxcGKYjH4OcnByIRCLJ1+7u7vD29oarqysGDhwIHR0dZGZmIjIyEikpKVi7di3DtPx24cIFnD17VubJrpqaGoKDgzFy5Egq8AkhclXfHCFPly5dkJ+fz3EiQv45KvAJaSChoaFYsWIFRo4cCQsLC2hqarKOJMHnNng+b4wsWrQInp6ekut89PX1kZ2djcuXL6Nbt25YuHAh64ikkfvqq68gFArxbjPdmTNncObMGZk/v2TJEpw7d46reI1KWVlZre+7Ojo6KCsr4zgRIaSx0NLSwh9//IH27dvLrD18+BDa2toMUhHyz1CLPiENxN7eHiUlJSgsLISCggIUFRUlZ97FYjHU1NSQmprKJBuf2+Dv3buHiIgIXm6MVEtKSkJKSgqys7Ohra0NOzs7mlhLCM98/vnn8PT0lHu8ITY2FuHh4Th69CiDZIQQvtu9ezdOnjyJkJAQdO/eXfL9e/fuwcfHB2PHjsXEiRMZJiSk/qjAJ6SBZGdnS7XavktFRQX6+vocJvobn+cD8HljhBCWxGIx8vLy0Lx5c6ZdNo1FXFwclixZgpkzZ2LIkCGSjpvIyEhERERg06ZNsLGxYR2TEMJT33//PXbt2gUdHR3J+0dhYSHc3d2Z3YJEyD9BBT4h/wFDhw7FgQMHeNkGz6eNkZMnT2LUqFGSr6Oiot579eHQoUO5iEb+Q9LS0rBp0yakpqaivLwcysrK6NevH+bMmUPXvb3HjRs3EBYWhrS0NFRUVEBJSQlWVlbw8PBAnz59WMcjhPBcUVERfvvtN2RnZ0NLSwvm5uZo2rQp61iEfBAq8AlpIPfu3XtvMVjz6hUuNYY2eD7w9fVFUFCQ5OsZM2bU+XeqqqpKE81Jg0pISMCiRYswffp0DBkyBHp6esjJyUFUVBQiIiIQFhaGvn37so7Je5WVlZLuB7pOjRDyPl9//TV27tzJOgYhDYIKfEIayJAhQ2TuqM7NzYVIJMInn3wCfX19HDx4kEk2PrfB83ljhBCujRkzBnPmzMHAgQNl1s6fP489e/bgyJEjDJI1HiKRCM+fP0dJSQn09PRgYGAgeb8jhBB5bG1tERMTw9t5RYR8CCrwCfmXJScnY/369Zg7dy769evHJAOf2uDfxeeNkQsXLsDZ2VnuWlFREYKDgxEcHMxxKvIx69u3L27cuPGP1//LhEIhVq9ejVOnTkEkEkFLSwsFBQVQV1fH9OnT4e7uzjoiIYSnTp48iZSUFCxevBhaWlqs4xDyP6Fr8gj5l1laWmLjxo3w8PDAyZMnmWRgVbzXR21XflVvjMyePZvjRH/z8/OrtcBXV1dHdHQ0FfikQQkEAhQUFMj9gJmfn0/D9uqwevVqxMfHIywsDP369YOioiIqKyuRmJiIwMBAKCkpYcqUKaxjEkJ46OrVq3j06BEGDBiAdu3aQVtbG0pKSpJ1gUBAR/JIo0FP8AnhyIABA5CQkMDktRtrG3xGRgaTjZHt27dDJBIhIiKi1sm5d+7cQXFxMbPuAvJxWrFiBQoKChAaGipVzL99+xZLliyBnp4evv32W4YJ+atfv344cuQITExMZNZevHiBCRMm4MqVKwySEUL4LioqSqabsCZVVVUaqksaDXqCTwgHTpw4wXSC/eLFi3nbBl+XVq1aIScnh/PXrd73rO3crrKyMgYPHoxPP/2Uy1jkP2DRokXw9PSEvb09HBwcJFc1Xb58Gd26dcPChQtZR+StiooKucU9ABgbG9f54Z0Q8t9WXbwLhUK8fPkSb968gYGBAQwMDBgnI+TDUYFPSAOZMWOGzDl3kUiEv/76C0VFRdi0aRObYOB3G3xdWG2MzJw5EwCwf/9+zJkzh/PXJ/9d+/fvx+7du3H9+nWkpKQgOzsbLVq0QFhYGHr37s06Hq+Zm5sjLi4OdnZ2MmsnTpyAubk5g1SEkMZAJBJhw4YN+Omnn6CgoAB1dXXk5uZCX18f7u7umDBhAuuIhNQbtegT0kAiIyNl2uAVFRXRokUL9OzZExoaGoyS1Y1VG3y1+myM2NjYMMmWl5cHXV1dJq9N/pusra2RlJTEOkajkJOTI/Xe8fLlS3h7e8PV1RUDBw6Ejo4OMjMzERkZiZSUFKxduxampqYMExNC+GrVqlVITk7GypUrYWZmBqCqmy8hIQHBwcFwdXWFh4cH45SE1A8V+IQ0EC8vLyxfvhza2toyawUFBVi6dClvB7SwnA/A940RkUiEZ8+eoaCgAO++XVZUVMDa2ppRMvIxWrlyJTp27Ihx48axjsJ7Q4YMgVAolPm5rI2qqmqt3UyEkP82GxsbnDp1Sm5L/osXLzB+/Hhmn5MI+VDUok9IA7l27Zrc4h4AtLS0mN0z/z6s5wNER0fXuTEyc+ZMZhsjqamp8PLygqKiInJzc9GyZUtkZ2ejvLwcrVu3homJCRX4pEF16tQJR48exZEjR2BhYSEzyVlVVRVTp05lmJA/qFgnhDSk2s7bt2rVqs5BxYTwDRX4hDQQsVgMsVgsdzCbWCxGZWUlg1RV+DwfgM8bIyEhIQgICICjoyMsLCxw6dIlAEBiYiLWrFlDV26RBvfrr7+iU6dOAIDi4mIUFxdLrauqqrKI1eiIxWLk5eWhefPmdLUgIeS9+vTpg8jISAwbNkxm7ezZs7CwsGCQipB/hgp8QhpIp06dcPHiRbn3pp87dw5t2rThPtT/GzFiBG/b4Pm8MfL8+XM4OjoCANTU1CT3k9vY2GDt2rVYtGgRTp8+zSwf+fgEBASwjtCopaWlYdOmTUhNTUV5eTmUlZXRr18/zJkzR3KulhBC3uXm5oZVq1YhISEBtra20NTURG5uLq5evYpr165h2bJlSE5OhoqKCnr16sU6LiF1ogKfkAbi6emJ+fPno7y8HEOGDIGioiJEIhFOnDiBdevW4fvvv2eWjc9t8HzeGFFSUkJFRQWUlJRgYmKC1NRUDBo0CADQsWNHZGRkMMtGCJGWkJCARYsWYfr06QgJCYGenh5ycnIQFRWFb775BmFhYejbty/rmIQQHjpw4AAMDQ3x559/4vDhw1Jrbdu2lXxPRUUFu3fvZhGRkHqjAp+QBmJtbY0NGzYgKCgI3t7e0NXVRV5eHlq3bo1169bB0tKSWTY+t8HzeWPE1NQUKSkpsLKygrOzMzZt2oQOHTrA0NAQhw4dQrt27ZhlI4RI27JlC0JDQzFw4EDJ91q1agV3d3cYGRlhw4YNOHLkCMOEhBC+2rVrF+sIhDQYmqJPyL/g6dOnyM/Ph46ODtMn0NUsLS1x48aNWtvgLSwsmBb5iYmJCAoKwtOnT6U2RpYsWSL3TmuuPH36FG/fvkXnzp1RUVGBgIAAnDhxAhUVFTAyMkJ4eDg6duzILB8h5G99+/bFjRs3/vE6IYQQ8jGgJ/iE/AvatGnDi8K+Gp/b4IGq62nOnj3Lu40RIyMjqKioAKhq1w8MDMS3336LN2/eQFdXl3E6QkhNAoFAMifjXfn5+TRsjxBCyH+CIusAhJB/n6enJ1asWIHIyEjJ0DqRSIQff/wRy5cvx9KlSxknrNKmTRv07t2bF8U9ANja2sp8TyAQUHFPCA85OTlhxYoVMgNF3759i4CAALi4uDBKRgghhHCHWvQJ+Y/gaxs8nw0dOhQHDhyAjo4O6yiEkPcoKiqCp6cnHjx4AAcHB+jr6yM7OxuXL19Gt27dsGXLFjRp0oR1TEIIIeRfRQU+If8xfGuD57N79+4hIiICI0eOhIWFBTQ1NVlHIoTUIjw8HDNnzsT169eRkpKC7OxsaGtrw87ODr1792YdjxBCCOEEFfiEEFILe3t7lJSUoLCwEAoKClBUVJQMKhSLxVBTU2M6nJAQ8jdra2skJSWxjkEIIYQwRQU+IYTUIjs7GyKRqNZ1FRUV6Ovrc5iIEFKblStXomPHjhg3bhzrKIQQQggzVOATQgghpNH78ccfcfToUVRUVMDCwgLa2tpQUlKSrKuqqmLq1KkMExJCCCH/PromjxBC6lBSUoJffvkFDx48QGlpKXR1ddG5c2c4ODhIFQ+EELZ+/fVXdOrUCQBQXFyM4uJiqXVVVVUWsQghhBBO0RN8QgipRXp6Otzd3aGsrIwBAwZIbh+4evUqtLS0sHfvXhq8RwghhBBCeIMKfEIIqcWkSZPQtm1bBAQESIbrAVUD9gICApCbm4utW7cyTEgIIYQQQsjfqMAnhJBa9O7dG0lJSRAIBDJrb9++xYABA5CcnMwgGSGEEEIIIbIUWQcghBC+0tLSqnWtvLwcTZs25S4MIYQQQggh70EFPiGE1GL06NEICgrCu41OJSUlWLJkCcaPH88oGSGEEEIIIbJoij4hhPy/iIgICIVCydcVFRW4ePEirly5gv79+0NHRweZmZmIi4uDpqYmevTowTAtIYQQQggh0ugMPiGE/L93C/z3EQgEmD59+r+YiBBCCCGEkPqjAp8QQgghhBBCCPkI0Bl8Qgj5fxUVFawjEEIIIYQQ8o/RE3xCCPl/Y8eOxY8//ij52sXFBSKRqNY/LxAIcO7cOS6iEUIIIYQQ8l5U4BNCyP9LT09Hly5dJF/fuXPnvQW+qakpF9EIIYQQQgh5LyrwCSGkFpaWlujYsSMGDx4MFxcXtGzZknUkQgghhBBCakUFPiGE1KKsrAwJCQmIiYlBXFwcjIyM4OzsDGdnZ3zyySes4xFCCCGEECKFCnxCCKkHsViM1NRUxMbG4tKlS2jatClcXV0xdepU1tEIIYQQQggBQAU+IYR8kOLiYsTHx2PPnj34/fffcfPmTdaRCCGEEEIIAUAFPiGEvNezZ88QGxuL2NhYpKenw9raGk5OTrC3t0ezZs1YxyOEEEIIIQQAFfiEEFKrdevWITo6Gm/evIGjoyMGDRqE/v37QyAQsI5GCCGEEEKIDGXWAQghhK/EYjGCgoLQu3dvKCoqso5DCCGEEEJInegJPiGEEEIIIYQQ8hGgR1KEEEIIIYQQQshHgAp8QgghhBBCCCHkI0AFPiGEEEIIIYQQ8hGgAp8QQgghhBBCCPkIUIFPCCGEEEIIIYR8BKjAJ4QQQgghhBBCPgJU4BNCCCGEEEIIIR8BKvAJIYQQQgghhJCPwP8BPOtBBIgO1HMAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CV tuning rows: 12000 positive_ratio: 0.2066\n",
+            "\n",
+            "[LightGBM] lightweight CV tuning: 3 trials x 3 folds\n",
+            "  trial 1: CV PR-AUC=0.9276, Recall=0.9371, F1=0.8022, elapsed=2.7s\n",
+            "  trial 2: CV PR-AUC=0.9345, Recall=0.9484, F1=0.8028, elapsed=3.1s\n",
+            "  trial 3: CV PR-AUC=0.9319, Recall=0.9383, F1=0.8065, elapsed=3.1s\n",
+            "\n",
+            "[XGBoost] lightweight CV tuning: 3 trials x 3 folds\n",
+            "  trial 1: CV PR-AUC=0.8659, Recall=0.9480, F1=0.7192, elapsed=6.2s\n",
+            "  trial 2: CV PR-AUC=0.8457, Recall=0.9226, F1=0.7017, elapsed=2.1s\n",
+            "  trial 3: CV PR-AUC=0.8834, Recall=0.9544, F1=0.7215, elapsed=2.4s\n",
+            "\n",
+            "[CatBoost] lightweight CV tuning: 3 trials x 3 folds\n",
+            "  trial 1: CV PR-AUC=0.8831, Recall=0.9584, F1=0.7240, elapsed=3.8s\n",
+            "  trial 2: CV PR-AUC=0.8565, Recall=0.9242, F1=0.7293, elapsed=5.2s\n",
+            "  trial 3: CV PR-AUC=0.9065, Recall=0.9318, F1=0.7725, elapsed=5.3s\n",
+            "\n",
+            "[LogisticRegression] lightweight CV tuning: 3 trials x 3 folds\n",
+            "  trial 1: CV PR-AUC=0.5971, Recall=0.8596, F1=0.4502, elapsed=1.7s\n",
+            "  trial 2: CV PR-AUC=0.6095, Recall=0.8540, F1=0.4572, elapsed=1.8s\n",
+            "  trial 3: CV PR-AUC=0.6157, Recall=0.8580, F1=0.4593, elapsed=2.3s\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "            model_name  trial_no  cv_pr_auc  cv_recall  cv_precision  \\\n",
+              "0             LightGBM         2   0.934463   0.948366      0.697822   \n",
+              "1             LightGBM         3   0.931911   0.938282      0.707198   \n",
+              "2             LightGBM         1   0.927581   0.937072      0.701304   \n",
+              "3             CatBoost         3   0.906456   0.931821      0.660354   \n",
+              "4              XGBoost         3   0.883359   0.954416      0.580354   \n",
+              "5             CatBoost         1   0.883073   0.958445      0.581853   \n",
+              "6              XGBoost         1   0.865852   0.947958      0.579387   \n",
+              "7             CatBoost         2   0.856514   0.924153      0.602729   \n",
+              "8              XGBoost         2   0.845673   0.922552      0.567136   \n",
+              "9   LogisticRegression         3   0.615725   0.858009      0.315055   \n",
+              "10  LogisticRegression         2   0.609526   0.853974      0.313730   \n",
+              "11  LogisticRegression         1   0.597149   0.859624      0.306069   \n",
+              "\n",
+              "       cv_f1  cv_accuracy  cv_threshold  elapsed_sec  \\\n",
+              "0   0.802814     0.903000      0.336858     3.108951   \n",
+              "1   0.806456     0.906917      0.388233     3.069297   \n",
+              "2   0.802172     0.904500      0.448503     2.653610   \n",
+              "3   0.772539     0.886417      0.518421     5.318331   \n",
+              "4   0.721533     0.847583      0.443721     2.388007   \n",
+              "5   0.724020     0.849000      0.450893     3.753630   \n",
+              "6   0.719196     0.847083      0.462588     6.186436   \n",
+              "7   0.729256     0.858167      0.493912     5.158900   \n",
+              "8   0.701664     0.837250      0.478727     2.087466   \n",
+              "9   0.459327     0.578833      0.377523     2.252431   \n",
+              "10  0.457195     0.576833      0.380491     1.819699   \n",
+              "11  0.450235     0.563167      0.380118     1.678352   \n",
+              "\n",
+              "                                               params  \n",
+              "0   {'model__subsample': 0.85, 'model__reg_lambda'...  \n",
+              "1   {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "2   {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "3   {'model__learning_rate': 0.045, 'model__l2_lea...  \n",
+              "4   {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "5   {'model__learning_rate': 0.035, 'model__l2_lea...  \n",
+              "6   {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "7   {'model__learning_rate': 0.035, 'model__l2_lea...  \n",
+              "8   {'model__subsample': 0.85, 'model__reg_lambda'...  \n",
+              "9                                   {'model__C': 0.7}  \n",
+              "10                                  {'model__C': 0.3}  \n",
+              "11                                  {'model__C': 0.1}  "
+            ],
+            "text/html": [
+              "\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model_name</th>\n",
+              "      <th>trial_no</th>\n",
+              "      <th>cv_pr_auc</th>\n",
+              "      <th>cv_recall</th>\n",
+              "      <th>cv_precision</th>\n",
+              "      <th>cv_f1</th>\n",
+              "      <th>cv_accuracy</th>\n",
+              "      <th>cv_threshold</th>\n",
+              "      <th>elapsed_sec</th>\n",
+              "      <th>params</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.934463</td>\n",
+              "      <td>0.948366</td>\n",
+              "      <td>0.697822</td>\n",
+              "      <td>0.802814</td>\n",
+              "      <td>0.903000</td>\n",
+              "      <td>0.336858</td>\n",
+              "      <td>3.108951</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.931911</td>\n",
+              "      <td>0.938282</td>\n",
+              "      <td>0.707198</td>\n",
+              "      <td>0.806456</td>\n",
+              "      <td>0.906917</td>\n",
+              "      <td>0.388233</td>\n",
+              "      <td>3.069297</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.927581</td>\n",
+              "      <td>0.937072</td>\n",
+              "      <td>0.701304</td>\n",
+              "      <td>0.802172</td>\n",
+              "      <td>0.904500</td>\n",
+              "      <td>0.448503</td>\n",
+              "      <td>2.653610</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>CatBoost</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.906456</td>\n",
+              "      <td>0.931821</td>\n",
+              "      <td>0.660354</td>\n",
+              "      <td>0.772539</td>\n",
+              "      <td>0.886417</td>\n",
+              "      <td>0.518421</td>\n",
+              "      <td>5.318331</td>\n",
+              "      <td>{'model__learning_rate': 0.045, 'model__l2_lea...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>XGBoost</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.883359</td>\n",
+              "      <td>0.954416</td>\n",
+              "      <td>0.580354</td>\n",
+              "      <td>0.721533</td>\n",
+              "      <td>0.847583</td>\n",
+              "      <td>0.443721</td>\n",
+              "      <td>2.388007</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>CatBoost</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.883073</td>\n",
+              "      <td>0.958445</td>\n",
+              "      <td>0.581853</td>\n",
+              "      <td>0.724020</td>\n",
+              "      <td>0.849000</td>\n",
+              "      <td>0.450893</td>\n",
+              "      <td>3.753630</td>\n",
+              "      <td>{'model__learning_rate': 0.035, 'model__l2_lea...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>XGBoost</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.865852</td>\n",
+              "      <td>0.947958</td>\n",
+              "      <td>0.579387</td>\n",
+              "      <td>0.719196</td>\n",
+              "      <td>0.847083</td>\n",
+              "      <td>0.462588</td>\n",
+              "      <td>6.186436</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>CatBoost</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.856514</td>\n",
+              "      <td>0.924153</td>\n",
+              "      <td>0.602729</td>\n",
+              "      <td>0.729256</td>\n",
+              "      <td>0.858167</td>\n",
+              "      <td>0.493912</td>\n",
+              "      <td>5.158900</td>\n",
+              "      <td>{'model__learning_rate': 0.035, 'model__l2_lea...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>XGBoost</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.845673</td>\n",
+              "      <td>0.922552</td>\n",
+              "      <td>0.567136</td>\n",
+              "      <td>0.701664</td>\n",
+              "      <td>0.837250</td>\n",
+              "      <td>0.478727</td>\n",
+              "      <td>2.087466</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.615725</td>\n",
+              "      <td>0.858009</td>\n",
+              "      <td>0.315055</td>\n",
+              "      <td>0.459327</td>\n",
+              "      <td>0.578833</td>\n",
+              "      <td>0.377523</td>\n",
+              "      <td>2.252431</td>\n",
+              "      <td>{'model__C': 0.7}</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.609526</td>\n",
+              "      <td>0.853974</td>\n",
+              "      <td>0.313730</td>\n",
+              "      <td>0.457195</td>\n",
+              "      <td>0.576833</td>\n",
+              "      <td>0.380491</td>\n",
+              "      <td>1.819699</td>\n",
+              "      <td>{'model__C': 0.3}</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.597149</td>\n",
+              "      <td>0.859624</td>\n",
+              "      <td>0.306069</td>\n",
+              "      <td>0.450235</td>\n",
+              "      <td>0.563167</td>\n",
+              "      <td>0.380118</td>\n",
+              "      <td>1.678352</td>\n",
+              "      <td>{'model__C': 0.1}</td>\n",
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+              "          + ' to learn more about interactive tables.';\n",
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "모델별 선택 파라미터\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "           model_name                                        best_params\n",
+              "0            LightGBM  {'model__subsample': 0.85, 'model__reg_lambda'...\n",
+              "1             XGBoost  {'model__subsample': 0.95, 'model__reg_lambda'...\n",
+              "2            CatBoost  {'model__learning_rate': 0.045, 'model__l2_lea...\n",
+              "3  LogisticRegression                                  {'model__C': 0.7}"
+            ],
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+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>model_name</th>\n",
+              "      <th>best_params</th>\n",
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+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>XGBoost</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>CatBoost</td>\n",
+              "      <td>{'model__learning_rate': 0.045, 'model__l2_lea...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>{'model__C': 0.7}</td>\n",
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+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3a38e531-d799-4cd7-a4e1-fc0d817f3707 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3a38e531-d799-4cd7-a4e1-fc0d817f3707');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(event_model_result_table)\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"XGBoost\",\n          \"LogisticRegression\",\n          \"LightGBM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"best_params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[LightGBM] final fit start\n",
+            "[LightGBM] Train F1=0.8789, Test F1=0.8426, Test PR-AUC=0.9560, Test Recall=0.9341\n",
+            "\n",
+            "[XGBoost] final fit start\n",
+            "[XGBoost] Train F1=0.7436, Test F1=0.7425, Test PR-AUC=0.9029, Test Recall=0.9528\n",
+            "\n",
+            "[CatBoost] final fit start\n",
+            "[CatBoost] Train F1=0.7799, Test F1=0.7698, Test PR-AUC=0.9181, Test Recall=0.9305\n",
+            "\n",
+            "[LogisticRegression] final fit start\n",
+            "[LogisticRegression] Train F1=0.4686, Test F1=0.4597, Test PR-AUC=0.6526, Test Recall=0.8677\n",
+            "selected_event_model_name: LightGBM\n",
+            "불량 탐지 모델 학습 결과\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                   모델  Train F1   Test F1  Train 재현율  Test 재현율  Train 정밀도  \\\n",
+              "0            LightGBM  0.878897  0.842573   0.972138  0.934129   0.801976   \n",
+              "1            CatBoost  0.779898  0.769792   0.943016  0.930498   0.664889   \n",
+              "2             XGBoost  0.743551  0.742522   0.953858  0.952801   0.609228   \n",
+              "3  LogisticRegression  0.468559  0.459742   0.861447  0.867739   0.321795   \n",
+              "\n",
+              "   Test 정밀도  Train 정확도  Test 정확도  Test PR-AUC  Test ROC-AUC  임계값(Train 기준)  \\\n",
+              "0  0.767363   0.944661  0.929896     0.955974      0.986500       0.502521   \n",
+              "1  0.656422   0.890052  0.888229     0.918066      0.974119       0.525871   \n",
+              "2  0.608278   0.864089  0.867292     0.902932      0.968021       0.489376   \n",
+              "3  0.312710   0.596354  0.590417     0.652634      0.815697       0.377797   \n",
+              "\n",
+              "                                             선택 파라미터  \n",
+              "0  {'model__subsample': 0.85, 'model__reg_lambda'...  \n",
+              "1  {'model__learning_rate': 0.045, 'model__l2_lea...  \n",
+              "2  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "3                                  {'model__C': 0.7}  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7378bff8-87e2-4cdb-bdb9-43fd8b5268f6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>모델</th>\n",
+              "      <th>Train F1</th>\n",
+              "      <th>Test F1</th>\n",
+              "      <th>Train 재현율</th>\n",
+              "      <th>Test 재현율</th>\n",
+              "      <th>Train 정밀도</th>\n",
+              "      <th>Test 정밀도</th>\n",
+              "      <th>Train 정확도</th>\n",
+              "      <th>Test 정확도</th>\n",
+              "      <th>Test PR-AUC</th>\n",
+              "      <th>Test ROC-AUC</th>\n",
+              "      <th>임계값(Train 기준)</th>\n",
+              "      <th>선택 파라미터</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>0.878897</td>\n",
+              "      <td>0.842573</td>\n",
+              "      <td>0.972138</td>\n",
+              "      <td>0.934129</td>\n",
+              "      <td>0.801976</td>\n",
+              "      <td>0.767363</td>\n",
+              "      <td>0.944661</td>\n",
+              "      <td>0.929896</td>\n",
+              "      <td>0.955974</td>\n",
+              "      <td>0.986500</td>\n",
+              "      <td>0.502521</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CatBoost</td>\n",
+              "      <td>0.779898</td>\n",
+              "      <td>0.769792</td>\n",
+              "      <td>0.943016</td>\n",
+              "      <td>0.930498</td>\n",
+              "      <td>0.664889</td>\n",
+              "      <td>0.656422</td>\n",
+              "      <td>0.890052</td>\n",
+              "      <td>0.888229</td>\n",
+              "      <td>0.918066</td>\n",
+              "      <td>0.974119</td>\n",
+              "      <td>0.525871</td>\n",
+              "      <td>{'model__learning_rate': 0.045, 'model__l2_lea...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XGBoost</td>\n",
+              "      <td>0.743551</td>\n",
+              "      <td>0.742522</td>\n",
+              "      <td>0.953858</td>\n",
+              "      <td>0.952801</td>\n",
+              "      <td>0.609228</td>\n",
+              "      <td>0.608278</td>\n",
+              "      <td>0.864089</td>\n",
+              "      <td>0.867292</td>\n",
+              "      <td>0.902932</td>\n",
+              "      <td>0.968021</td>\n",
+              "      <td>0.489376</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>0.468559</td>\n",
+              "      <td>0.459742</td>\n",
+              "      <td>0.861447</td>\n",
+              "      <td>0.867739</td>\n",
+              "      <td>0.321795</td>\n",
+              "      <td>0.312710</td>\n",
+              "      <td>0.596354</td>\n",
+              "      <td>0.590417</td>\n",
+              "      <td>0.652634</td>\n",
+              "      <td>0.815697</td>\n",
+              "      <td>0.377797</td>\n",
+              "      <td>{'model__C': 0.7}</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7378bff8-87e2-4cdb-bdb9-43fd8b5268f6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "\n",
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+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7378bff8-87e2-4cdb-bdb9-43fd8b5268f6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7378bff8-87e2-4cdb-bdb9-43fd8b5268f6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_448977ec-42d8-408c-bcf0-3381554621b9\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
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+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
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+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
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+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
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+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('event_model_result_table')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('event_model_result_table');\n",
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+              "  </div>\n",
+              "\n",
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+              "  </div>\n"
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Q0FCZTCbzurt371q1z58/v8XjDBkyKHv27AoJCZEknTlzRlLixUPGjBlTNE4AANILah5qHoBwAniOlShRwjxzdWIcHR2tPrxjY2Pl4eGhr7/+OsFt3N3dU22MKWXkGEeMGKEVK1aoS5cu8vf3V6ZMmWRjY6P33nvP4kM7qeK3GTNmjLJnz2613s7O7qnHDADAy4ya59mg5sGLhHACeAl5eXlp586dKl26tMXpfI/KkyePpLgUPF++fOblN27cSHBm5ofFtz9x4oQqV66caLvETndMizEmZuPGjWrRooUCAwPNy+7fv5/gNwhS3Dce8bOGS1J4eLiuXr2q6tWrS/rvWHh4eDz2WAAAgNRFzfN41Dx4kTDnBPASatiwoWJiYjR58mSrdQ8ePNCdO3ckSZUrV5aDg4PmzZtnkZ7H3xbrcYoXL668efPqhx9+MPcX7+G+4meBfrRNWowxMQml+nPnzlVMTEyC7RcvXmwxS/bChQv14MED8wd1tWrVlDFjRk2dOjXB2bRv3LiR4rECAIDEUfM8HjUPXiScOQG8hMqXL6927dpp6tSpOnbsmKpUqSIHBwedOXNGGzZs0JAhQ9SgQQO5u7urW7dumjp1qnr16qUaNWro6NGj+u2335Q1a9bHPoetra0+//xzvf3222rRooVatWql7Nmz699//9U///yjGTNmSIr7QJekL774QlWrVpWdnZ0aN26cJmNMTM2aNbV69WplzJhRRYoU0cGDB7Vjxw6L25U9LDo6Wm+++aYaNmyo06dPa8GCBSpTpoxee+01SXHXV37++ef68MMP1apVKzVq1Eju7u66ePGitm3bptKlS2vo0KEpGisAAEgcNc/jUfPgRUI4Abykhg8fLl9fXy1atEjffPON7Ozs5OnpqWbNmql06dLmdu+++64cHR21aNEi7d69WyVKlNDMmTPVq1evJz5HtWrVNGfOHE2aNEkzZ86UyWRSvnz51LZtW3ObevXqKSAgQOvXr9eaNWtkMpnUuHHjNBtjQoYMGSJbW1utXbtW9+/fV+nSpTVr1iz16NEjwfZDhw7V2rVrNWHCBEVHR6tx48b65JNPLE7fbNq0qXLkyKHvv/9eM2bMUFRUlHLmzKmyZcuqVatWKRonAAB4MmqexFHz4EViY0rJTCgAAAAAAACphDknAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAAACAoQgnAAAAALxQAgICFBAQkGr91a5dW4GBganWX3qye/du+fj4aPfu3UYPBS84e6MHAODZWrFihQYPHpzo+sWLF8vf3z9JfYWGhmrJkiWqU6eOihUrlkojjPtQ69y5c5LaHj9+PNWeFwAAPL34WmPZsmXy8/MzejiJ2r9/v7Zv364uXbooc+bMT93fhQsX9Nprr5kf29jYKHPmzCpRooT69u2rUqVKPfVzAOkJ4QSQTgwYMEB58+a1Wu7l5ZXkPq5cuaKJEyfK09MzVcOJwoULa8yYMRbLxo8fL1dXV/Xu3TvVngcAALwcZsyYkextDhw4oIkTJ6ply5ZW4cSGDRtkY2OTorE0adJE1atXV2xsrM6cOaMFCxaoc+fOWrZsmXx8fFLU54ukXLlyCg4OloODg9FDwQuOcAJIJ6pXr57m32bcu3dPLi4uT2yXLVs2NW/e3GLZtGnTlDVrVqvlD4uNjVV0dLScnJyeeqwAAODF4ejo+Nz09+qrr1rUK2XKlNFbb72lhQsX6vPPP0+F0SVdRESEXF1d0/Q5bW1tqcWQKphzAoAmTJigokWLaufOnRbLP/30U/n6+urvv//W7t279frrr0uSBg8eLB8fH/n4+GjFihWS4q79bNKkiQ4fPqyOHTuqZMmSGj9+vCRp8+bN6tmzp6pWrSpfX1/VqVNHkyZNUkxMTLLG6ePjo+HDh2vNmjVq3Lix/Pz89Pvvv0uKu+Rk8ODBqly5snx9fdW4cWMtW7bMqo+oqChNmDBBdevWla+vr2rUqKExY8YoKirKot327dvVvn17lS1bVqVKlVL9+vXN+wMAAJLn6NGj6tGjh0qXLq1SpUqpS5cuOnjwoFW7v//+W506dVKJEiVUvXp1TZ48WcuXL5ePj48uXLhgbpfQnBNz585V48aNVbJkSZUrV06tWrXS2rVrJUlBQUHmszRfe+01cx0T32dCc07cuXNHI0eOVO3ateXr66vq1avrww8/1I0bNx67r2XLlpUknT9/3qq/L7/8UjVq1JCvr6/q1q2r77//XrGxsRbtbt68qQ8++EClS5dW2bJl9dFHH+nvv/+2qLskKTAwUKVKldK5c+f01ltvqVSpUho0aJCkuC9wZs+eba6XKleurKFDh+r27dsWz3Xo0CF1795dFSpUUIkSJVS79v+3d99xWZX/H8ffbAEnqLgFB5h7z3LlNs2Rkntv0zJLS/ObqzJtKGqZuXKiuTW3aZppbs2R5l64FRCQdX5/8OPOW0ABgaP5ej4ePfI+5zrnXOdwc58P7/uc69SOczvw2rVr1aJFC5UpU0Zly5ZVkyZNNGfOHMv8hMacWLdunVq0aKGSJUuqUqVKGjx4sK5fv27VJnYfrl+/rr59+6pMmTKqXLmyxo0bl+Q6ES8+rpwAXhLBwcFxTqY2NjbKkiWL+vTpo19//VXDhg3TqlWrlD59eu3YsUOLFy/WwIEDVaRIEd26dUsDBgzQpEmT5Ovrq3LlykmSypYta1nfvXv31KNHDzVu3FhNmzaVu7u7JGn58uVycXFRly5d5OLiot27d2vSpEkKDg7WkCFDkrQfu3fv1rp169SuXTtlyZJFuXPn1q1bt9S6dWvZ2NioXbt2cnNz02+//aZhw4YpODhYnTt3lhRzou7Tp4/279+v1q1bq2DBgjp16pTmzJmj8+fPa+rUqZKk06dPq1evXvLx8dGAAQPk6OioCxcu6MCBA8k9/AAAvLROnz6tdu3aydXVVd27d5e9vb38/f3VoUMHzZs3T6VKlZIU80VDp06dJEk9e/aUi4uLlixZkqirGhYvXqwxY8aofv366tixox4+fKi///5bhw8fVpMmTVS3bl2dP39ea9as0UcffaQsWbJIktzc3OJd34MHD9SuXTudOXNGLVu2VNGiRXX37l1t3bpV169fT3A5Sbpy5YokWd06Ehoaqvbt2+v69et6++23lTNnTh08eFBff/21bt68qWHDhkn6t1Y5cuSI2rRpowIFCmjLli0J1kuRkZHq1q2bypUrpyFDhihdunSSpBEjRmj58uVq0aKFOnTooMuXL2v+/Pk6fvy4Fi5cKAcHB92+fVvdunVTlixZ1LNnT2XMmFGXL1/Wpk2bLOv//fffNWjQIFWpUsUSfJw9e1YHDhyw/KziEzsOSYkSJTRo0CDdvn1bP/30kw4cOKAVK1ZYHZuoqCh169ZNJUuW1Icffqg//vhDM2fOVN68edW2bdsEt4H/IAPAf9rSpUsNb2/veP8rXry4pd3ff/9tFCtWzBg2bJhx//5947XXXjNatGhhREREWNocOXLE8Pb2NpYuXRpnO+3btze8vb2NhQsXxpkXGhoaZ9onn3xilCpVynj48GG8/W7cuLHRvn17q2ne3t5GkSJFjNOnT1tN//jjj41q1aoZd+7csZr+3nvvGeXKlbNsf8WKFUaRIkWMvXv3WrVbuHCh4e3tbezfv98wDMOYNWuW4e3tbdy+fTvevgEAgH/F1hpHjhyJd37fvn2NYsWKGRcvXrRMu379ulGmTBmjXbt2lmmjR482fHx8jOPHj1um3b1716hYsaLh7e1tXLp0yTK9ffv2VnVCnz59jMaNGz+xnz/++GOc9cSqVauWMWTIEMvriRMnGt7e3sbGjRvjtI2OjjYMwzAuXbpkeHt7G35+fsbt27eNmzdvGnv37jVatmxpeHt7G+vWrbMsM2XKFKN06dLGuXPnrNY1YcIE45VXXjGuXr1qGIZhbNiwwfD29jZmz55taRMVFWV07NgxTg02ZMgQw9vb25gwYYLVOvfu3Wt4e3sbq1atspr+22+/WU3ftGnTE39uhmEYY8aMMcqWLWtERkYm2Gb37t2Gt7e3sXv3bsMwDCM8PNyoUqWK8cYbbxhhYWGWdr/++qvh7e1tTJw4Mc4+TJ482WqdzZo1M5o3b57gNvHfxG0dwEtixIgRmjVrltV/06dPt8z39vbWgAEDtGTJEnXr1k13797VuHHjZG+f+AusHB0d1aJFizjTY1N86d8rOMqXL6/Q0FCdPXs2SftRoUIFFSpUyPLaMAxt3LhRtWvXlmEYunPnjuW/V199VUFBQTp27JikmMGuChYsqAIFCli1q1y5siRZLkeMTfO3bNkS51JLAACQeFFRUfr9999Vp04d5c2b1zI9e/bseuONN7R//34FBwdLknbs2KHSpUtbDbqdOXNmNWnS5KnbyZgxowICAnTkyJEU6ffGjRtVpEgR1a1bN868xwfO9PPzU5UqVVStWjXL1RZDhw5VgwYNLG3Wr1+vcuXKKWPGjFY1SNWqVRUVFaW9e/dKijkGDg4Oat26tWVZW1tbtWvXLsG+tmnTxur1+vXrlSFDBlWrVs1qW8WKFZOLi4ul3smQIYMkadu2bYqIiIh33RkzZlRoaKh+//33Jx0uK3/99Zdu376tNm3aWI1FUbNmTRUoUEDbtm176j6UK1fO6jYevBy4rQN4SZQsWfKpA2J269ZNa9eu1ZEjRzRo0CCrECAxPDw84r308vTp0/r222+1e/duSwESKygoKEnbePyJI3fu3FFgYKD8/f3l7+8f7zKxt7NcuHBBZ86cUZUqVeJtd/v2bUlSo0aNtGTJEg0fPlxfffWVqlSporp166pBgwaytSXTBQAgse7cuaPQ0FB5eXnFmVewYEFFR0fr2rVrKly4sK5cuRLv480T82SxHj16aNeuXWrVqpXy58+vatWq6Y033rDchppUFy9eVL169RLV1tfXVw0aNNDDhw+1e/duzZ07N854CRcuXNDff/+dYA0SW6tcvXpV2bJlizOgeELHwN7eXjly5IizraCgoKfWOxUrVlT9+vU1efJkzZ49WxUrVlSdOnXUpEkTSz3Xtm1brVu3Tj169JCHh4eqVaumhg0bqnr16gkej6tXr0pSvD/zAgUKaP/+/VbTnJyc4twmkylTpjjjY+C/j3ACgMWlS5d04cIFSdKpU6eSvPyjV0jECgwMVPv27ZU+fXoNGDBA+fLlk5OTk44dO6YJEyYk+cqEx7cRu3zTpk3VvHnzeJeJfYxXdHS0vL294wz0FCv25J4uXTrNnz9fe/bs0bZt27Rjxw798ssv8vf318yZM2VnZ5ekPgMAgNRVsGBBrV+/3nLe3rhxoxYsWKB+/fppwIABqbrt/Pnzq2rVqpKkWrVqydbWVl999ZUqVapk+WIoOjpa1apVU/fu3eNdh6enZ7K27ejoGOeLk+joaLm7u2vChAnxLhMbBNjY2GjSpEk6dOiQfv31V+3YsUMff/yxZs2aJX9/f7m6usrd3V0rVqzQzp079dtvv+m3337TsmXL1KxZM40bNy5ZfX4cdRViEU4AkBRzIhs6dKjSp0+vTp066fvvv1f9+vWtvjVIzvO///zzT927d0+TJ09WhQoVLNNT6lI9Nzc3ubq6Kjo62lIYJCRfvnw6efKkqlSp8tR9sbW1VZUqVVSlShV99NFH+v777/XNN99oz549T90OAACI4ebmJmdnZ507dy7OvLNnz8rW1lY5c+aUJOXOndvyJcmjLl68mKhtubi4qFGjRmrUqJHCw8P1zjvv6Pvvv1evXr3k5OSUpDomX758On36dKLbP6pPnz5asmSJvv32W82YMcOyvpCQkKfWELly5dKePXviPI49sccgdlt//PGHypYtG+8XR48rXbq0Spcurffee0+rV6/W4MGD9csvv6hVq1aSYgKQ2rVrq3bt2oqOjtann34qf39/9e3bV/nz5493HyTp3Llzca7eOHfunGU+8DiuTwYgSZo1a5YOHjyoUaNGaeDAgSpTpow+/fRTqyd8xJ4kAwMDE73e2DTfMAzLtPDwcC1YsCBF+m1nZ6f69etrw4YN8V7t8Wj/GzZsqOvXr2vx4sVx2oWFhSkkJERSzFNHHhd7/+vjjxwFAAAJs7OzU7Vq1bRlyxarLyZu3bqlNWvWqFy5ckqfPr0k6dVXX9WhQ4d04sQJS7t79+5ZHgf6JHfv3rV67ejoqIIFC8owDMt4CrF1TGJuKa1Xr55Onjxp9eSKWI/WNPHJmDGjfH19tXPnTsu+NGzYUAcPHrQ8Av1RgYGBioyMlBRzDCIiIqxqlejoaM2fP/+pfY7VsGFDRUVFWZ5C9qjIyEhLHXf//v04+/J4vfP4cbW1tbVckZpQTVS8eHG5u7tr0aJFVm22b9+uM2fOqGbNmoneF7xcuHICeEn89ttv8Q4+WbZsWYWHh2vixIlq0aKFateuLUn64osv1KxZM40cOVITJ06UFJPEZ8yYUYsWLZKrq6tcXFxUsmRJqwGuHlemTBllypRJQ4cOVYcOHWRjY6OVK1c+9cSeFO+//7727Nmj1q1bq1WrVipUqJDu37+vY8eO6Y8//tCff/4pSXrzzTe1bt06/e9//9OePXtUtmxZRUVF6ezZs1q/fr1+/PFHlShRQlOmTNG+fftUo0YN5c6dW7dv39aCBQuUI0eOZN+7CgDAf93SpUvj/eP7nXfe0a5du9S2bVu1bdtWdnZ28vf3V3h4uD744ANLu+7du2vVqlXq0qWL2rdvb3mUaM6cOXXv3r0nXvnQrVs3Zc2aVWXLlpW7u7vOnj2refPmqUaNGpbwo1ixYpKkb775Ro0aNZKDg4Nq1aolFxeXeNe3YcMGDRw4UC1btlSxYsV0//59bd26VSNHjlSRIkWeeCw6duyoOXPm6IcfftA333yjbt26aevWrerdu7eaN2+uYsWKKTQ0VKdOndKGDRu0ZcsWubm5qU6dOipZsqTGjRunixcvqkCBAtq6datl/IXEXP1RsWJF+fr6atq0aTpx4oSqVasmBwcHnT9/XuvXr9ewYcPUoEEDLV++XAsXLlSdOnWUL18+PXjwQIsXL1b69OktY0oMHz5c9+/fV+XKleXh4aGrV69q3rx5euWVV1SwYMF4t+/g4KDBgwfro48+Uvv27dW4cWPLo0Rz585tecQ78DjCCeAlMWnSpHinjxkzRv7+/sqSJYs+/vhjy3RPT08NGjRIY8eO1S+//GI5iX/xxRf6+uuv9emnnyoyMlKff/75E8OJLFmy6Pvvv9e4ceP07bffKmPGjGratKmqVKmibt26pci+Zc2aVUuWLNGUKVO0adMmLVy4UJkzZ1ahQoUsz+SWYtL+KVOmaPbs2Vq5cqU2bdokZ2dn5cmTRx06dLAM3FS7dm1duXJFS5cu1d27d5UlSxZVrFhR77zzjmVkawAAYG3hwoXxTm/RooXmz5+vr776StOmTZNhGCpZsqTGjx+vUqVKWdrlzJlTP/30k8aMGaNp06bJzc1N7dq1k7Ozs8aMGWP15IfH+fr6avXq1Zo1a5ZCQkKUI0cOdejQQX379rW0KVmypAYOHKhFixZpx44dio6O1pYtW+INJ1xdXTV//nz5+flp06ZNWr58udzd3VWlShV5eHg89Vh4eHioSZMmWrlypS5evKh8+fJp7ty5mjZtmtavX68VK1Yoffr08vT0tKov7OzsNG3aNI0dO1bLly+Xra2t6tatq379+sV5+sWTjBo1SsWLF9eiRYv0zTffyM7OTrlz51bTpk1VtmxZSTEhxtGjR/XLL7/o1q1bypAhg0qWLKkJEyZYarumTZtq8eLFWrBggQIDA5UtWzY1bNhQ77zzzhMHCW/RooXSpUun6dOna8KECXJxcVGdOnX0wQcfWJ6KBjzOxkjJry8BAAAAIAWNHTtW/v7+Onjw4Es7eOLmzZvVr18/LViwgKs48Z/FmBMAAAAAngthYWFWr+/evatVq1apXLlyL00w8fgxiIqK0ty5c5U+fXrLrSnAfxG3dQAAAAB4Lvj6+qpixYoqWLCgbt26paVLlyo4ONjq9oz/utGjRyssLExlypRReHi4Nm7cqIMHD2rQoEGJevoG8KLitg4AAAAAz4Wvv/5aGzZsUEBAgGxsbFS0aFH179//pXqMd+zYGRcuXNDDhw+VP39+tWnTRu3btze7a0CqIpwAAAAAAACmYswJAAAAAABgKsIJAAAAAABgKsIJmMLHx0d+fn6W18uWLZOPj48uX75sYq/wLGrXrq2hQ4cmaRk/Pz/5+PikUo8AADAfNc9/DzUPkDp4Wsd/0LJly/TRRx9ZXtvZ2cnd3V3VqlXTe++9Jw8PDxN7lzYOHDig8ePH6/jx40qfPr0aNmyo9957T66urk9c7tq1a1q6dKm2bdumCxcuyNbWVt7e3urTp0+cgZj27t2rGTNm6MSJE7pz544yZsyoIkWKqG/fvnGeP/39999r69atunjxoh48eKCcOXOqRo0a6tOnj9zc3JK1j0OHDtXy5cstr11dXZUnTx41a9ZM7du3l6OjY7LWi3+Fh4dr4sSJWrlypQIDA+Xj46N3331X1apVe+qyZ8+e1aJFi3TkyBEdO3ZM4eHh2rJli/LkyfPE5S5evKjGjRsrPDxcP//8s0qUKJFg2+HDh2vJkiWqWbOmpk2bluT9k+J+XjyqR48eGjx4sCRp586d+uWXX3TkyBGdOXNGOXPm1NatW5O1zYSkxfHes2ePOnbsmOB63n33XfXp08fy+q+//pKfn5/++usvhYSEKE+ePGrVqpXatWv30jzSDnieUfOkTc1z48YN/fTTTzp8+LDl8/Cnn35SpUqVrNqFhoZq2bJl2rJli06dOqUHDx4of/78at26tXx9fZP9uUnNk/rS4hx89+5dLV26VL/++qvOnDmjyMhIFShQQJ07d1ajRo1StE/xeVIN0KhRI33zzTeSpCNHjmjZsmU6cuSI/v77b0VGRurvv/9O1jYTEh0drRkzZmjhwoW6efOmPD091atXL73xxhtPXTaxv4+S1KFDB/35559xpr/66quaMWOG5fXp06fl5+enY8eO6datW0qXLp0KFSqkbt26qXbt2s+2s0lAOPEfNmDAAOXJk0fh4eE6dOiQli9frv3792vNmjVycnIyu3up5sSJE+rcubMKFiyooUOHKiAgQDNnztT58+f1448/PnHZLVu2aPr06apTp46aN2+uyMhIrVy5Ul26dNFnn32mli1bWtqeP39etra2evvtt5U1a1YFBgZq1apVat++vaZNm6bq1atb2h47dkxFihRRo0aN5OrqqrNnz2rx4sXavn27VqxYIRcXl2Ttq6Ojo8aMGSNJCgoK0oYNGzRu3DgdPXrU8gGbVtavXy8bG5skLdOnTx/17NkzlXr07IYOHaoNGzaoY8eO8vT01PLly9WzZ0/NmTNH5cuXf+Kyhw4d0ty5c1WoUCEVLFhQJ06cSNQ2P/vsM9nb2ys8PPyJ7Y4eParly5en2O9y7OfFo7y9vS3/XrNmjX755RcVLVpU2bNnT5FtPi4tjnfBggX15Zdfxpm+atUq7dy506rg+euvv/T222/L09NTPXr0ULp06fTbb79p7NixunjxooYPH/5sOwwgxVDzpG7Nc+7cOU2fPl2enp7y8fHRwYMH413npUuXNHr0aFWpUkWdO3dW+vTptXPnTo0cOVKHDx/WuHHjkr2v1DypKy3OwYcOHdK3336r6tWrq0+fPrK3t9eGDRv03nvv6Z9//tGAAQNSrE9P0qFDhzhf/uTOndvy7+3bt+vnn3+Wt7e38uTJo/Pnzyd7Wwn55ptv9MMPP6h169YqUaKEtmzZovfff182NjZq3LjxE5dN7O9jrBw5cmjQoEFW0x6v5a5evaoHDx6oefPmyp49u0JDQ7Vx40b16dNHo0aNkq+vb/J2NKkM/OcsXbrU8Pb2No4cOWI1ffz48Ya3t7exdu1ak3r2L29vb2PSpEmW17F9vnTp0jOvu3v37ka1atWMoKAgy7TFixcb3t7exo4dO5647KlTp4zbt29bTXv48KHRoEEDo3r16k/ddkhIiFG1alWja9euT227fv16w9vb21izZs1T28ZnyJAhRunSpa2mRUVFGS1atDC8vb2NgICAeJeLjo42QkNDk7XNl8nhw4cNb29v48cff7RMCwsLM+rUqWP4+vo+dfm7d+9a3oM//vhjot7fv/32m1GsWDHjm2++ifd3OFZ0dLTh6+trfPTRR0atWrWMnj17JmHPrCX0efG4gIAAIzw83DAMw+jZs6dRq1atZG8zPmYc70fVrVvXqFevntW04cOHG8WKFTPu3r1rNb1du3ZG2bJlE71uAKmHmidtap6goCDLZ+G6desMb29vY/fu3XHWefv2bePUqVNxpg8dOtTw9vY2zp8/n9hds0LNk7rS6hx88eJF4/Lly1bToqOjjY4dOxrFixc3Hjx4kGJ9is/u3bsNb29vY926dU9sd/PmTcv7ZuTIkYa3t3eytpeQgIAAo1ixYsbIkSMt06Kjo422bdsa1atXNyIjI5+4fGJ/Hw3DMNq3b280btw4Wf2MjIw0mjZtatSvXz9ZyycHY068RGITxkuXLllNP3PmjAYMGKCKFSuqRIkSatGihbZs2RJn+cDAQH322WeqXbu2ihcvrurVq+vDDz/UnTt3JP176VWLFi1Urlw5lS5dWm3bttXu3btTf+f+X3BwsHbt2qWmTZsqffr0lulvvvmmXFxctG7duicuX7hw4Ti3WTg6OqpGjRoKCAhQcHDwE5d3dnaWm5ubgoKCntrX2IQ2MDDwqW0Ty9bWVhUrVpQkXblyRVLMfZG9evXSjh071KJFC5UsWVKLFi2ybHvs2LGqUaOGihcvrrp16+qHH35QdHS01Xqjo6M1Z84cNWnSRCVKlFDlypXVrVs3HT161NLm8fsvIyIiNHnyZNWrV08lSpRQpUqV1KZNG/3++++WNvHdfxkZGakpU6aoTp06Kl68uGrXrq2vv/46zpUEsfu1b98+vfXWWypRooRef/11rVix4tkPpGK+FbGzs7NKip2cnPTWW2/p4MGDunbt2hOXz5w5s9V78GkiIiI0duxYdezYUfny5Xti25UrV+rUqVN67733Er3+Z+Xh4SEHB4dUW39aH+9HHTlyRBcuXFCTJk2spgcHB8vJyUkZM2a0mp4tWzalS5cuWdsCkDaoeVK25kmfPr0yZ8781D65ubmpcOHCcabXrVtXUszxTynUPC9ezZM3b16rKxQkycbGRnXq1FF4eLjV7+uz9ulZZM2aNVXP85s3b1ZERITatm1rmWZjY6M2bdooICDgqVdCJPb38VGRkZF68OBBkpaxs7NTzpw5E/V3TUrhto6XSOwH96OF9unTp9WmTRt5eHioR48elpNZv3795OfnZzmZPHjwQO3atdOZM2fUsmVLFS1aVHfv3tXWrVt1/fp1ubm5KTg4WEuWLNEbb7yhVq1a6cGDB/r555/VvXt3LVmyRK+88kqS+vvgwQM9fPjwqe0cHByUIUMGSbLcF1a8eHGrNo6OjnrllVcSfWn9427evClnZ2c5OzvHmRccHKzw8HDdvXvX8kdj796947QzDEN3795VVFSULly4oAkTJsjOzs5yYk0psR/sj35onTt3Tu+//758fX3VunVreXl5KTQ0VO3bt9f169f19ttvK2fOnDp48KC+/vpr3bx5U8OGDbMsP2zYMC1btkzVq1fXW2+9paioKO3bt0+HDx9OcEyEyZMna9q0aWrVqpVKliyp4OBg/fXXXzp27NgT7xUcPny4li9frvr166tLly46cuSIpk2bpjNnzmjKlClWbS9cuKCBAwfqrbfeUvPmzbV06VINHTpUxYoVsxRH0dHRunfvXqKOXYYMGSx/gJ84cUKenp5xTrYlS5a0zM+ZM2ei1psYc+bMUWBgoPr27auNGzcm2C44OFgTJkxQ7969lS1bthTbfnBwsKXojpWc8VBelOP9qFWrVklSnHCiYsWK+uWXXzRixAh16dLFclvHpk2b9MEHH6RKXwCkDGqe1Kl5kuvWrVuSpCxZsqTYOiVqnhe15nlcfO+P1OzTgwcP4tQ8mTNnlq1t0r+3f3w9CUmfPr1lbJQTJ07IxcVFBQsWtGrz6L49y20rjzt//rxKly6tiIgIZc2aVa1atVK/fv3i/dIpJCREYWFhCg4O1tatW/Xbb7+pYcOGKdaXpyGc+A+L/WMjPDxchw8f1uTJk+Xo6KhatWpZ2owdO1Y5c+bU0qVLLb8wbdu2VZs2bTRhwgTLiXrGjBk6deqUJk+ebJkmSX379pVhGJKkTJkyaevWrVaDErVu3VoNGzbU3Llz9dlnnyWp/6NHj7Ya/CghFStW1Ny5cyXFnFCluPdRSTHfdu7fvz9JfZBiTgabNm1SgwYN4h3IaeDAgdq5c6ekmKLB19dXffv2jdPu1q1bevXVVy2vc+TIoQkTJsT5YEqq2A/F4OBgrVu3Tps3b5aPj48KFChgtQ8//vijXnvtNcu0qVOn6tKlS1q+fLk8PT0lSW+//bayZ8+uGTNmqGvXrsqZM6d2796tZcuWqUOHDlb32Hft2tXys4/Ptm3bVKNGDY0ePTrR+3Ly5EktX75crVq1stxX2q5dO7m5uWnmzJnavXu3KleubGl/7tw5zZ8/3/IB3rBhQ9WoUUPLli3TkCFDJMXcQ/f6668navuPDiZ08+bNeP/4j51248aNRO/X09y8eVNTp07VkCFDnvrNw5QpU+Tk5KTOnTun2PYlxbu+5Az+9CIc70dFRUVp3bp1KlmypPLnz281r3Xr1vrnn3/k7++vJUuWSIr5FuGTTz5RmzZtUqU/AJKHmsdaatU8yREeHq45c+YoT548TxzkOTGoeV78mudx9+7d05IlS1S+fHmr93Jq9unjjz+OMy0xg5bHp0qVKolq9/nnn6tFixaSYvbN3d09zrglqXG88+bNq0qVKsnb21shISHasGGDvvvuO50/f17ffvttnPZffPGF/P39JcVcnVS3bl2NGDEixfrzNIQT/2GP/7GRO3dujR8/Xjly5JAU82Gwe/duDRgwIM7tCq+++qr8/Px0/fp1eXh4aOPGjSpSpIjVSTpW7C+WnZ2d5UQWHR2twMBARUdHq3jx4jp+/HiS+9+9e3c1bdr0qe0e/VYkLCxMkuIdtdnJyckyP7FCQ0M1cOBApUuXTu+//368bQYPHqyuXbvq2rVrWrFihSIiIhQZGRlnAK5MmTJp1qxZevjwoY4fP65NmzYpJCQkSf15XEhISJwPxTJlymj8+PFW0/LkyWN1kpZiLpcrV66cMmbMaJX6Vq1aVT/88IP27t2rpk2bauPGjbKxsVH//v3jbP9Jg0FlzJhRp0+f1vnz5y2FwNNs375dktSlSxer6V27dtXMmTO1fft2qxN1oUKFrJJlNzc3eXl5WV0WmC1bNs2aNStR2y9SpIjl32FhYQm+j2Lnp5QJEyYob968atWq1RPbnTt3TnPnztVXX32V4iOTjxgxQl5eXs+8nhfheD/qjz/+0K1bt9SrV6848+zs7JQ3b169+uqratCggRwdHbV27VqNGTNG2bJlU506dVKlTwCSjprHWmrVPMkxevRo/fPPP/rhhx9kb5/8Pz2oef4bNc+joqOjNXjwYAUGBuqTTz6xmpeaferXr1+cKxOSezVqYo93oUKFLP9Oy+P9eFDarFkzffLJJ1q8eLE6d+6s0qVLW83v1KmTGjRooBs3bmjdunWKjo5WREREivXnaQgn/sNi/9gICgrS0qVLtXfvXqtfhIsXL8owDE2cOFETJ06Mdx23b9+Wh4eHLl68qHr16j11m8uXL9fMmTN17tw5qzdycpLIQoUKWf0iJ0bs/WHxPeng4cOHSbp/LCoqyjJ68PTp0xN8HNmjl242bdpULVq00EcffaRJkyZZtXN0dLQ8mqtWrVqqUqWK2rRpI3d3d6tvdpLCyclJ33//vWX9efLksRRij4rv+F+4cEF///13golv7Mn74sWLyp49e5LvbRswYID69u2r+vXry9vbW6+++qrefPNNq5Ph465cuSJbW9s4Yy5ky5ZNGTNmtFymGyu+y/kyZcqk+/fvW147OTnFeSRaYqRLly7B91Hs/JRw6NAhrVy5UrNnz37q5YRjx45VmTJlVL9+/RTZ9qNKliz5zN9oSc//8X7c6tWrZWdnF+8jzH744Qf99NNP2rBhg+WRfI0aNVKHDh00cuRI1axZ85kKbQAph5rHWmrVPEn1448/avHixRo4cKBq1KjxTOui5nnxa57HjR49Wjt27NC4cePiHKvU7JO3t3eyjlN8XqTjHatLly5avHixdu3aFSecKFiwoOWq7mbNmqlr167q3bu3lixZkuQn1CQHVdV/2KN/bNSpU0dt27bV+++/r/Xr18vV1dUyAFDXrl3jJMyxnjYw36NWrlypoUOHqk6dOurWrZvc3d1lZ2enadOmxRmQKjGCgoISlRw6ODhYTiJPuhzq5s2bSXoE4vDhw7Vt2zZNmDAh0ZdsOTo6qnbt2vrhhx8UFhb2xA+XsmXLKlu2bFq9enWywwk7O7tEfSjG14/o6GhVq1ZN3bt3j3eZxCb/CalQoYI2bdqkLVu26Pfff9fPP/+sOXPmaOTIkU+9QiCxH36JueQ0Kioq0fcDZsqUyVLMZsuWTdevX4/T5kmX0SbH+PHjVb58eeXJk0eXL1+WFPMc8NhtXb16Vbly5dIff/yhHTt2aPLkyZZ2UswAR2FhYbp8+fIzDQqZUp734/2osLAwbdq0SVWqVFHWrFnjzF+wYIEqVapkCSZivf766/r888915cqVOLeCADAHNY+1tKh5nmbZsmWaMGGC3n777Xhvd00qap4Xv+Z51OTJk7VgwQK9//77atasWZz5ZvQpOWL78zQZMmSwvDezZcumPXv2yDAMq59/Wu1bbND1aLCVkPr162vEiBE6d+6c1e1TqYVw4iVhZ2enQYMGqWPHjpo/f7569uypvHnzSoo50T3twz5fvnw6ffr0E9ts2LBBefPm1eTJk61+0R6/giCxxo4dm+T7L729vWVvb6+//vrL6pvQ8PBwnThxItEDuowbN07Lli3Txx9/rDfeeCNJ/Q4LC5NhGHrw4MFTk8/w8PA0HQH3Ufny5VNISEiifvY7d+7UvXv3kvxNQubMmdWyZUu1bNlSDx48UPv27eXn55fgiTp37tyKjo7WhQsXrMbiuHXrlgIDA+OM8JwY165dS9b9l0WKFNGePXsUHBxs9Qf/4cOHJSnJg509qX9XrlyJt499+vRRhgwZtG/fPsuo1PFdanr9+nW9/vrr+uijj1J8LIqket6P96O2bt2qBw8exBkIM9atW7fijOIuyfINaWRkZIr3CcCzo+ZJu5onIZs3b9bw4cNVr149/e9//0uRdT4Lap64zDwHz58/X35+furUqZN69uwZbxsz6oLkeHQ8uSd5dMyJV155RUuWLNGZM2esrphKq32LDVATM/B5bGj6tCcWphTCiZdIpUqVVLJkSc2ZM0edOnWSu7u7KlasKH9/f7Vv3z5OSnfnzh3Lm7ZevXqaMmWKNm3aFOcezNjULzbRfTQFPHz4sA4dOqRcuXIlub/Juf8yQ4YMqlKlilatWqW+fftaPsxWrlypkJAQNWjQwNI2NDRUV69eVZYsWax+OX/88UfNnDlTvXv3VqdOnRLc7u3bt+Xu7m41LTAwUBs3blTOnDkt80JCQmRjYxNn1OsNGzbo/v37cUbZTisNGzaUn5+fduzYEedbpMDAQLm4uMje3l716tXT/PnzNXnyZKvBoSTFSXwfdffuXatRl11dXZUvX74nPvqpRo0a+vrrrzVnzhyNGjXKMj32fr7kXBKa3PsvGzRooJkzZ8rf31/dunWTFFPwLVu2TKVKlbK6vPLq1asKDQ1N1uCmo0aNivNt2e7duzV37lwNGTLEklJXrlw5zsjdkvTJJ58oV65c6tOnj7y9vZO8/ZT2vB/vR61evVrOzs7x3lcuSV5eXtq1a5fVezl2AM3Y9zOA5xM1T8rWPEmxd+9eDRo0SOXLl9eECROS9QSElEbNE5dZ5+BffvlFY8aMUZMmTfTRRx8l2C4pfTJTcsaciL0Cc8GCBZbBJg3D0KJFi+Th4aEyZcpY2t64cUNBQUHKly9fkh/pHhwcLEdHR6tb3AzD0HfffSfJOliJ7++aiIgIrVy5UunSpXvmmiuxCCdeMt26ddPAgQO1bNkytWnTRv/73//Utm1bNWnSRK1bt1bevHl169YtHTp0SAEBAZZH7HXr1k0bNmzQwIED1bJlSxUrVkz379/X1q1bNXLkSBUpUkQ1a9bUxo0b1a9fP9WsWVOXL1/WokWLVKhQoWQN/Jic+y8l6b333tPbb7+tDh06qHXr1goICNCsWbP06quvqnr16pZ2R44cUceOHdW/f3+98847kqRNmzZp/Pjx8vT0VIECBbRy5UqrdVerVs1y+XePHj3k4eGhUqVKyd3dXVevXtWyZct048YNffPNN5ZlLly4oM6dO6tRo0YqUKCAbG1t9ddff2nVqlXKnTu3OnbsaLWN2rVrS4r5Vjc1devWTVu3blXv3r3VvHlzFStWTKGhoTp16pQ2bNigLVu2yM3NTZUrV9abb76puXPn6sKFC3rttdcUHR2t/fv3q1KlSmrfvn2862/cuLEqVqyoYsWKKXPmzDp69Kg2bNiQYHsp5kTZvHlz+fv7KzAwUBUqVNDRo0e1fPly1alTx2pgqMRK7v2XpUqVUoMGDfT111/r9u3byp8/v5YvX64rV65o7NixVm2HDBmiP//80+rpFkFBQZZvtw4cOCAp5puCDBkyKGPGjJbjEF/iHhgYKCnmMtHYy5Rz5coVb8H72WefKWvWrHEGZxw6dKiWL1+e7NGnH3fy5EnLe/LChQsKCgrS1KlTJcX83GLft8/78Y5179497dixQ/Xq1Ytz20asHj166IMPPlDr1q3VunVrpUuXTmvXrtWxY8f07rvvJrlIAJC2qHlSruaRZPnM/+effyTFhCCxTwSJvW3jypUr6tOnj2xsbFS/fn2tW7fOap0+Pj5WfxRT87xcNc+RI0f04YcfKnPmzJZg7VFly5a1XOWUlD75+flp8uTJVleDPIsrV65Yfh/++usvSf++/3PlymV1G0pyjneOHDnUsWNHzZgxQ5GRkSpRooQ2b96sffv2acKECVa38Hz99dfx1nOJ+X08duyY3n//fTVu3Fj58uXTw4cPtWnTJh04cEC+vr4qVqyYZX0jRoxQcHCwKlSoIA8PD928eVOrV6/W2bNnNXTo0ARrpZRGOPGSqVevnvLly6eZM2eqdevWKlSokJYuXarJkydr+fLlunfvntzc3FS0aFH169fPspyrq6vlEqxNmzZp+fLlcnd3V5UqVSyDJrVo0UK3bt2Sv7+/du7cqUKFCmn8+PFav369/vzzzzTbx2LFimnWrFmaMGGCPv/8c7m6uuqtt97SoEGDnrrsyZMnJcU8D/jDDz+MM/+nn36ynKhbtmyptWvXavbs2QoKClLGjBlVqlQpffXVV1YjAHt4eKh+/fravXu35WkeuXPnVrt27dS7d+84z/wOCQlJk/vYnZ2dNXfuXE2bNk3r16/XihUrlD59enl6euqdd96xPEddirkUzcfHRz///LO+/PJLZciQQcWLF7dKdh/XoUMHbd26Vb///rvCw8OVK1cuvfvuu5b0OyFjxoxRnjx5tHz5cm3evFlZs2ZVr1694r2dIbV9+eWX+vbbb7Vq1Srdv39fPj4++v7771WhQoWnLnv//v04g67NnDlTUsylnE8qWFJCSEiI0qVLZ/Ut27M4fvx4nP2Jfd28eXNLgfks0vJ4r1+/XhEREU+8hLlp06bKkiWLfvjhB82YMUPBwcHy8vLSyJEj9fbbbydjDwGkJWqeJ0tKzSMpzmfs0qVLLf+O/WPo8uXLlttVH70aIFb//v2twglqnper5vnnn38UERGhO3fuxPs4z88//9wSTiSlT7FXKcc3flRyXL58OcGap2LFivGOkZFUgwcPVqZMmeTv769ly5bJ09NT48ePT/BW08cl5vcxV65cKleunDZt2qRbt27J1tZWBQoU0MiRI+Xr62u1fKNGjfTzzz9r4cKFunfvnlxdXVWsWDENHjw40bcKpQQb40kP7QWQpv755x81btxY06ZNU82aNc3uDl5gVatW1Ztvvml59jkAAM8Tah6klLfeeku5cuVK9pgveH6YfxMYAIs9e/aoTJkynKTxTE6fPq2wsDD16NHD7K4AABAvah6khODgYJ08eVIDBw40uytIAVw5AQAAAAAATMWVEwAAAAAAwFSEEwAAAAAAwFSEEwAAAAAAwFSEEwAAAAAAwFT2ZncAcR08eFCGYcjBwcHsrgAAUkFERIRsbGxUpkwZs7sCmIqaBwD+25JS8xBOPIcMw1BEZJSu3Ag0uysvHTtbG2V3c9WNOw8UFc2DbNJS/tzuZnfhpWQYhiIjImTv4CAbGxuzu/PS4EFZQAxqHnNQ75iLmscc1DzmSErNQzjxHHJwcNCVG4Fq/dHPZnflpeOdz10zRzTVR1O26tTF22Z356Vy508/s7vwUgoNCdG5f04qb/6CcnZxMbs7L43TJ/8yuwvAc4GaxxzUO+ai5jEHNY85klLzMOYEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwlb3ZHQCSolrZwlozbWC88+p2maB9f52XJNnb2WpQl/pq80Yl5cyWSddu3te8VX/o2zmbFBUVbVnG1dlR73Soo3LFPVWuaH5lyeSqKxfPJ7j9Hq2qq1ur6vLM7a7b9x5o+aYD+uz7NQoJC7e0yZE1k0YOeFNliuZXjqyZFB0drX8u3tCPS3Zo0do9KXIcgMT4auYGjf1+jYoUyKldiz62mhceEanJ87Zo4Zo9unTtjjJmcFaZovn09dC3ldsji6XdmYs39Nm0tdp9+Kzu3X+gPDmyqGX98urf/nW5pHNM610CgJeCmfVOx2ZV1bphBRXO76FMGZwVcPO+dh74R+Om/6JL1+5Yte3a8lW9Vt5b5Yt7Kk8ONy1Ys1v9Rs5LkWMAJGTn/tNq2mdSvPM2zBikCiW8JElfz9qgdTv+0vnLtxQcEqac2TKpwise+qR/HuVxcbFaLjo6WpPnbdXMpTt0/XagCubLrvc61VXL+uVTfX/wL8IJvJC+X/SrDh6/aDXt7KWbln9PG91JzV4vo3mrduvQiYsqX8JTw/s0UZ4cbnrvs4WWdm6Z02tIj0a6dO2O/jp9Ra+V905wm5/2f1MDO9XVis0HNG3RNvl45VBP3xoqUiCn3howxdLOPbOrcmXPolVbDulywB052NupZqUi+u7TDiqcP7tGT12dgkcCiN+V63f1zeyNcnWOGyBEREbJ973vtffIObVpXEHZMkgOzpl15NRVBQaHKbdHTLvL1++qTpcJypjeWT1avabMGV219+g5ffHDLzp88pLmT+iZxnsFAC8XM+qdkj55dOHqba377ajuBYYof253dWxWTfVfLabX2n6hgFv3LW0Hdqyr9C7pdOD4eXlkzZSCew48XU/fGipbNL/VtAJ5s1n+fejkJZUonFst6pZVepd0On76kuat3q29J6Zqx4KP5OrsZGk75rs1+nbOJnVsVlVliubTuu1H1eOTOZKNjVrWK5dm+/Sye67CCT8/P02ePFnly5fX/PnzreaNHTtWW7Zs0datW03qXeL5+flp5syZOnjwoNld+c/64+AZrdp6KN55ZYrmU4u65fTlj+v0+bS1kqRZy3bq9r0H6te2ln5cvF3H/rkqSbp+K1A+DT7SjdtBKv1KPv3604fxrtPDPaP6tqutRWv3qM+ncy3Tz1y8oS8/bK0GrxXX+h1/SZKO/XNVTXpPtFp++pLftPDrXurpW1Njv1+j6GjjWQ8B8EQjJq1Q+eKeioqO1u17D6zmfbfgV+068I9+mf6uinpl17l/TsqrUBE5P/YtwuJf/tT9oFD98sN7eqVgTklS5+bVZEQbWvTLn7oXGKLMGa2XAZA41DxIjLSudyRp8LjFcaat3XZE2+YO0duNK+rbOZss09/o9a0uBdyVJF3a/lVydxNIliqlC+rN18skOP+ncd2tXoeGlFJed3sN/+5Xrd/xlyV0uHrjnqbM36rurV7Tlx+0liR1fLOq3ug1Uf+btELNXi8jOztGQ0gLz+VR3rdvn/bs4fJ3PFl6F6d4PyiqlC4kSVq2cb/V9GUb98vW1lbN6/6bfoZHROrG7aCnbqtCSS852NvFWefS/3/dIhGJ6sWrd+SSzkGODs9VJoj/oF0H/tGqrYf02aCWceZFR0drmv82Na5ZUuWKeSoyMkphDyPjXU/QgzBJUnb3DFbTPbJmlK2tjRwc7FK+88BLhpoHT5OW9U5CLv7/7RyZMjhbTY8NJgCzBD0IU2RkVKLb53BPL0kKDAq1TFv32xFFREapa8vXLNNsbGzUteWrunrjnvYePZdyHcYTPXd/Jbm4uKhQoUKaOnWqKlWqlKLrDg8Pl729vWxtn8tMBkkweUR7ZXBNp8jIKP1x6IxGTFqhQydiLnt0cox5W4c+jLBaJvT/x4Uo9UreJG/PyeEp6ywSd53pnBzkks5Rri5Oqla2sNo2qay9R88p7LF1ACkpKipaQyYsUYemVVS0UK4480+eC9C1m/dVrFBuvfvZQi1au0fhEVF6pUAOfTG4ldWlvtXKFdbEnzZrwJgFGtqzkbJkctWfR85q5tKd6tm6htXlkACSjpoHT5PW9c6jsmRylZ2tjfLkcNOH3RtKkrb/eeqZ1gmkpHdGz1dwyEPZ2dmqSumCGvlOM5Upms+qjWEYunP/gSIjo3Xin0uauHCP7GxtVa1cIUubI39flquzo3y8clgtW7ZYfsv8yqULpv4O4fkLJySpb9++6t27tw4cOKCyZcvG2+bKlSsaN26cfv/9d0VFRalcuXL68MMP5ePjY2lTu3Zt1axZUzlz5tSCBQt07do17dq1SwMHDpSLi4uaNGmiSZMm6fr166pSpYrGjRun4OBgjRgxQgcOHFCuXLk0YsQIq4JhxYoV8vf315kzZ2QYhooUKaIPPvhAJUuWTPXjAikiMlIrtxzUpt+P6c79B/LxyqH+7V/XLz+8q/rdvtbRU5d1+sJ1SVLlUgV08epty7JV/v9DJVe2zEnebuw6K5UqoJ37T/+7zjIxH2w541ln77dr6n/937S83vbnSfUfxSBRSF2zlu3UpYC7Wj6lf7zzz16MuVf5u4W/KktGF30xqIVu3bimhZtOqtXA77Rl9mAVK5xbklSnSlF93Kuxvpm9Uet+O2pZx/td6mtYnzdSf2eAlwA1D+JjVr3zqONrxyidk4Mk6fa9YH04fom2/XnymdYJpAQHBzs1qV1adasWlXvm9Pr73DVNnrdVjXt9q/U/vqeSPv8GczduB+mVRsMsr7NncdHkT96Wt+e/QcT1W4HK5pZRNjY2VtuJHUfl0XFWkLqey3CiVq1aKlq0qKZMmaIZM2bEmR8cHKwOHTrI1tZWI0eOlJOTk7777ju1b99eq1atUs6cOS1tN27cqPz582vYsGGytbWVy//fU338+HHdvXtXH374oYKDgzVmzBh98sknunLlipo1a6YuXbpo2rRpeuedd/Trr7/K1dVVknT58mU1a9ZM+fLlU3h4uNauXat27dpp1apV8vLySpsD9BL788g5/Xnk3/fEut+OauWWg9q58GON6N9UrQZM1abfj+ni1dsaNbC5QsLCdfjERZUr7qnhfZsoIjLKcqJNiiN/X9beo+c0sGNdXbt5Xzv2nZKPVw59NcRX4RGRco5nnUs37NPBExeVNXN61X+tuLK5ZVA6J55ugNRz594DfT5trQZ3q6+sWTLE2+ZB6ENJUnDIQ22bN0TuGZx07p+TatbgVVVrN16T5m7WtFGdLO3z5XJXlTKF1KR2KbllctXGncf09eyNyu6eQT1a10iT/QL+y6h5EB+z6p1HtRo4VemcHOTtmUOtG1aId4BlwAyVShZQpZIFLK8bVi+hprXL6LW2n2vUlNX6eVJfy7wsmVy0bHI/PXwYqf3Hzmrlpv0KCQ23Wl/Ywwg5Osb9szhdAlcnIfU8l+GEJPXp00fvvPOOjhw5EiehX7Zsma5evaq1a9eqYMGYdLhChQqqVauW5syZo6FDh1raRkREaPr06ZYTdKzg4GB9//33cnNzkyT9/fffmjlzpj799FO1adNGkpQ9e3Y1adJEf/zxh+rUqSNJ6t//328jo6OjVa1aNR05ckTLly/XoEGDUmz/7Wxt5J3PPcXW999maOe+v1Wz0isq4plV0dGGPvxyoT57v7XmftlDkvQwPEKT525Sl5bVFRkZGe+xzZcjU7z/jjVy0lKNHdRKU0a0lyRFRkVp4eo/VKaop/Lndo93ndcCbulawC0dPXleH/VuojXfD1DrAX56GB7/Pf4vs9CQELO78MIbOXmFMmVwVoc3KliOZ3RUtIzoaMtrW8U8Wq588fxyz+Ckh2Ex40pkzeSsCiU8tefwWUvblVsOadCXP2vH3MHKlT2zJKlOJW9FRETo08kr1ei1onLL5JrGe/nfYEQbsrG1eXpDvBSoeah5Eidt6p1YN27GjDNx8fJ1nTh9UQu+6SeXdPb6ed2f8ba3tZEyujrxs0wEap6Ul9PdVfWqFdW6HX8pOCjYapyWSsVjbvWoVCyXCmZ3UJ8vfpZ75vSqW/UVSZKDva0ePgyP83OJvUXKwZaf2bNISs3z3IYTdevWlbe3t6ZMmaJp06ZZzdu3b58KFy5sOUlLUubMmVW1alXt3289KFClSpXinKQlqUiRIpaTtCR5enpKkqpWrRpnWkBAgGXamTNn9PXXX+vgwYO6ffvfS+jOnz+f5H18kuxurpo5ommKrvO/zCNnNjk62GvmiDcVHR3zx1fonUv658FN2dnb6WFYmBpWyBFz/6TC4z226Zz/fZ982jOBb4TDb+n0iSDZOzgo/GGYyno5y9sruyIiIp7683JNn0E5smXWT2Pf1oOgwOTv7H/UuX+4VPRZXLp+X/NX79GAtytq3/5/R82/HxSkkNCH2rV7r1zTOSo67J4kydkh2uqYX718Xs720bpzP9gyfdqirSqcJ4seBgboXOC/n4OlCmTS4vUR2rx9jyoUjTuuBRLHwZFvIRGDmoeaJ7HSrN6JR3TkQ/VrU0ONKuSId76To72qlcrLzzIRqHlSh6tDlMIjonTi+F8JXulTopCH3DM5a+6K7SqUPeYPZheHaF2/Faizp09Y3dpx5UZMvW5vhPIze0aJrXme23DCxsZGvXv31qBBg3Ts2DGreYGBgcqaNWucZdzd3XX69Ok40+KTMWNGq9cODjGXvmXI8O+l0I7/fxAfPvz/y6CDg9W1a1e5ublp6NChypUrl5ycnDR8+HBLm5Ry484DfTTl+X+E2PPi88GtVTWTu7qMXCnDiP8xnVXLFtY3Jctq6qKdWrFpf5z5RQrm0pwvYxLUT3/YrosBT7+/zCtPNi2aWE7zV+3Wdwu2PLFt9Qo+Gj/UWz8sP6gtu449se3L6EmPNcPTXQs6o2jD0LcL9+jbhXFH/m819Gd1a1lNH3arL4cJGxQcJnkVKqKHYWG6evm8cuXxVPBDKZtbRnkVKiJJCgpbrUzpnS2vYx29FPN5l80jt7wK+cTZFp7u0vkzZncBzxFqHmqexDKr3pGkn8b3lqODnbqOWhXv/F/nFdfvhy9p9OT45+Nf1Dyp417oXqVztFfRYsXjHQw4tuaJjJIMG0dLfVO57B2t3nFKkQ5u8vb0sLQ/dD7my57qVcrIqxC3siVXUmqe5zackKSGDRvKz89PU6dOVa5c/347lylTJp07F/eRLrdv31amTNaXpz0+sMmzOHTokAICAjRt2jQVKfJvsR4UFKQcOeJPkZMrKtrQqYu3n97wJeOeOb1u3wu2mla8cG69Wt5Hm3cd198XbsW7XDonB01rUV3Xbt7X1IXbFRwSt7Bycf338vSLAfefevxtbGw06t1WehD6UF/N2qTL1+8m2EdJ+nRgCUVHR2v9zuM6d5mf7eOc4/m2D4lXupiX5n7ZPc70sd+vVXBImD4f1FJeebIpW9Ysqlu1qDb8fkyXbgQqb/aYP1ouXr+vfccuqHPzapafReH8OfTrnpO6cjNYhfJnt6xzzba/ZGtrozLFC/BzSyZu6cDjqHk4Lz7KrHrHzs5W6V2cdP+RxyxKUtmi+VUwX3b9vGFfgj+raEMKfPCQn2UicO58NrfuBsUZW+uvU5e1adcJ1alaVK7p0+tB6EPZ2NjIJZ31N/bb9p/X/eBQlS3uZfk5NK1TTp9OWaP5a/fqyw9aS4p5ysf8NXuVM3tmvVbhlXgf54vESUrN81yHE7a2turdu7eGDh2qihUrWqaXK1dOGzZs0NmzZ1WgQMxgKPfv39euXbvk6+ubav0J+/97s2O/cZCkAwcO6MqVKypcuHCqbRf/mvlZF4U9jNCfR87p5t0g+XjlUKfm1RQaFq6Rk1c+0q6rAm7d19/nApTBNZ3aNaksz9xZ5fved3FO1D1aVVfGDM7KmS2myMuQMZO6tKyuW/dDNN1/uwIfxPzcP3+/pdI5Oujoqcuyt7fTW/XLq1yx/Or76VxLMCFJ73etr0qlCmjLH8d1OeCusmR0UZPapVWumKemLdqmc5fjLyiAZ+GeOb0a1ywVZ/r3i7ZJktW84X2b6Ld9p9Ssr5+6NK+qO7dvasX2U8qS0UXvda5nafdO+9e1+Y/jatzrW3VvVV1umVy0Yecxbd51XB3erGL5nQHw7Kh58Ciz6h1XZyf9tWaMlm/ar5NnAxQS+lBFC+VS2yaVFRgcpvEz1luts8FrxS1PeHKwt1WxQrn1ftf6kqT1vx3VsX+upuZhwkuq28ezlC6dgyqWKKCsbun199kA/bRil5zTOWpEv5jbis5euqnm/Sared2yKpzfQ7a2Ntp39KyWbjqgvDmyqLdvTcv6cntkUe+3a8pv3hZFREarzCv59Mv2I/rj0Bn9MKoTwUQaeq7DCUlq0qSJpkyZoj179ih37pgPvxYtWmj27Nnq1auX3n33XcvI1fb29urUqdNT1ph8pUuXlouLi0aOHKmePXvq+vXr8vPzk4eHx9MXRopYu/2IWjWooL5taytD+nS6dTdYa349rHHTf7H6o//QiYtq26SyOjevprCHEfrj0Bn1+GS2/jp1Jc46+7d/Xfly/XspbMbMWdS77euSpMXr9lrCiSN/X1afNrX0VoMKio6O1oHjF/RmXz+rR4tK0sadx+SVJ6vaNamirFnSK+xhhI7/c1V9R87VwjVxL7cH0lqRAjm1+vuB+nTySk2cu1WSodfKFdaY91paBr6UpKplC2n9j+9p3PR1mvnzDt25/0D5c7lreJ83NKBDHdP6D/xXUfMglln1TmhYuOau3KXXyhXWm6+XUTonBwXcvK+lG/Zrwsz1unTtjtU6m9QurbZvVLa8LlUkr0oViXmM49Ub9wgnkCoa1SypJev3aeqCrQp6EKasWdLrjVql9GH3hiqQN5skKVf2zGpSu7R+23dKi9buUURktHJ7ZFbLWq/ok/5vyS2z9YDe/+vfVJkzumj28t+1cM0eFcibTdNGddRbDcqbsYsvrec+nLCzs1PPnj01fPhwy7T06dNr7ty5+uKLL/TJJ58oOjpaZcuW1bx586weqZXSsmbNqokTJ+rLL79U37595enpqZEjR+rHH39MtW3C2g/+2/WD//antps0d7Mmzd2cqHWWevN/ln9753PXzBFN1XXUqjiXJS5csydR4cK2P0/yHHA8N1Z/PzDe6aWK5NXyyf0VGhKic/+clFehIvFeZlqumKcWf9sntbsJQNQ8+JdZ9U5EZJQ+/nppovvZb+Q89Rs5L9HtgZTQy7emej1y5UN83DOn1zcfvW01LbbmeTyYkGKuXnuvcz2rK0iR9myMhEbTgWmOHj2q81duq/VHP5vdlZfOk8IJpK47f/qZ3YWX0tPCCaSO0yf/kiSVLFHC5J4A5qLmMQf1jrmoecxBzWOOpNQ83EADAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMZZ+YRnv37k3WyitUqJCs5QAAAMxAzQMAgDkSFU506NBBNjY2iV6pYRiysbHRiRMnkt0xAACAtEbNAwCAORIVTvz000+p3Q8AAADTUfMAAGCORIUTFStWTO1+AAAAmI6aBwAAczzzgJg3btzQyZMnFRISkhL9AQAAeC5R8wAAkHqSHU5s3rxZDRo0UI0aNdS8eXMdPnxYknTnzh01a9ZMmzdvTrFOAgAAmIWaBwCA1JescGLr1q165513lCVLFvXr10+GYVjmubm5ycPDQ0uXLk2xTgIAAJiBmgcAgLSRrHBiypQpKl++vBYuXKh27drFmV+6dGlGrQYAAC88ah4AANJGssKJ06dPq2HDhgnOz5o1q27fvp3sTgEAADwPqHkAAEgbyQonnJ2dFRoamuD8S5cuKXPmzMntEwAAwHOBmgcAgLSRrHCiUqVKWrFihSIjI+PMu3nzphYvXqxXX331mTsHAABgJmoeAADSRrLCiXfffVcBAQF666235O/vLxsbG+3cuVPffPONmjRpIsMw1K9fv5TuKwAAQJqi5gEAIG0kK5woUKCAFixYoMyZM2vixIkyDEMzZszQtGnT5O3trQULFihPnjwp3VcAAIA0Rc0DAEDasE/ugoULF9bs2bN1//59XbhwQYZhKG/evHJzc0vJ/gEAAJiKmgcAgNSX7HAiVqZMmVSyZMmU6AsAAMBzi5oHAIDUk+xw4s6dO5o+fbq2b9+uK1euSJJy586tGjVqqFu3bsqaNWuKdRIAAMAs1DwAAKS+ZI05cfr0aTVp0kSzZs1ShgwZ1KBBAzVo0EAZMmTQrFmz1LRpU506dSql+woAAJCmqHkAAEgbybpyYtSoUYqKitLixYvjXN545MgR9ejRQ6NHj9bcuXNTpJMAAABmoOYBACBtJOvKiSNHjqhjx47x3ndZsmRJdezYUUeOHHnmzgEAAJiJmgcAgLSRrHDC3d1dTk5OCc53cnKSu7t7sjsFAADwPKDmAQAgbSQrnOjYsaMWLlyomzdvxpl3/fp1LVy4UB07dnzmzgEAAJiJmgcAgLSRqDEnZs2aFWeai4uL6tWrpzp16ih//vySpPPnz2vLli3Kly9fyvYSAAAgDVDzAABgjkSFE+PGjUtw3urVq+NM+/vvvzVu3Dh17tw52R0DAABIa9Q8AACYI1HhxJYtW1K7HwAAAKaj5gEAwByJCidy586d2v0AAAAwHTUPAADmSNaAmAAAAAAAACklUVdOxOfkyZOaN2+ejh8/rqCgIEVHR1vNt7Gx0ebNm5+5gwAAAGai5gEAIPUl68qJPXv2qFWrVtq2bZuyZ8+uS5cuKW/evMqePbuuXr0qFxcXVahQIaX7CgAAkKaoeQAASBvJCicmTZqkvHnzav369frss88kSb169dLChQu1aNEiXb9+XQ0aNEjRjgIAAKQ1ah4AANJGssKJ48eP66233lL69OllZ2cnSZZLHEuVKiVfX19NnDgx5XoJAABgAmoeAADSRrLCCTs7O7m6ukqSMmbMKHt7e92+fdsyP2/evDpz5kzK9BAAAMAk1DwAAKSNZIUT+fLl0/nz5yXFDAJVoEABq4Ggtm3bpqxZs6ZIBwEAAMxCzQMAQNpIVjhRo0YNrV27VpGRkZKkLl26aOPGjapXr57q1aunrVu3ytfXN0U7CgAAkNaoeQAASBvJepRo37591bFjR8u9l82bN5etra02btwoOzs79e7dWy1atEjRjgIAAKQ1ah4AANJGssIJBwcHZcmSxWram2++qTfffDNFOgUAAPA8oOYBACBtJOu2DgAAAAAAgJSSqCsnOnbsmOQV29jYaM6cOUleDgAAwCzUPAAAmCNR4YRhGElecXKWwb/y53bX3b2Tze7GSyc0JERnT5/QtrlD5OziYnZ3gFRnY2Nj+X/sv4GXGTVP2qPmSXvUO3gZUfM8/xIVTsydOze1+wEAAGA6ah4AAMzBmBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUiXqUaEKuX7+uvXv36vbt26pfv75y5MihqKgoBQUFKUOGDLKzs0upfgIAAJiGmgcAgNSVrHDCMAx98cUXmj9/viIjI2VjYyNvb2/lyJFDISEhql27tgYMGKDOnTuncHcBAADSDjUPAABpI1m3dfz444/66aef1LVrV82aNUuGYVjmZciQQfXq1dPGjRtTrJMAAABmoOYBACBtJCucWLJkiZo1a6ZBgwapSJEiceb7+Pjo/Pnzz9o3AAAAU1HzAACQNpIVTly7dk1lypRJcL6zs7OCg4OT3SkAAIDnATUPAABpI1nhhLu7u65du5bg/GPHjilnzpzJ7hQAAMDzgJoHAIC0kaxwom7dulq0aJEuXbpkmWZjYyNJ2rlzp5YvX64GDRqkTA8BAABMQs0DAEDaSNbTOgYMGKA9e/bozTffVPny5WVjY6Pp06dr4sSJOnTokF555RX17t07pfsKAACQpqh5AABIG8m6ciJDhgxavHixunfvruvXr8vJyUl79+5VUFCQ+vXrpwULFsjZ2Tml+woAAJCmqHkAAEgbybpyQpLSpUunvn37qm/fvinZHwAAgOcKNQ8AAKkvWVdOAAAAAAAApJRkXTnx0UcfPbWNjY2NPvvss+SsHgAA4LlAzQMAQNpIVjixZ8+eONOio6N18+ZNRUVFyc3NjfsvAQDAC4+aBwCAtJGscGLr1q3xTo+IiJC/v7/mzJmjmTNnPlPHAAAAzEbNAwBA2kjRMSccHBzUvn17VatWTaNHj07JVQMAADw3qHkAAEhZqTIgZpEiRbR3797UWDUAAMBzg5oHAICUkSrhxK5du7j/EgAA/OdR8wAAkDKSNebE5MmT450eFBSkvXv36vjx4+rZs+czdQwAAMBs1DwAAKSNFA0nMmXKpLx582rkyJFq3br1M3UMAADAbNQ8AACkjWSFEydPnkzpfgAAADx3qHkAAEgbSR5zIiwsTJ9//nmCj9YCAAD4L6DmAQAg7SQ5nEiXLp38/f11+/bt1OgPAADAc4GaBwCAtJOsp3UUK1ZMp06dSum+AAAAPFeoeQAASBvJCic+/vhj/fLLL1qyZIkiIyNTuk8AAADPBWoeAADSRqIHxNy7d68KFiwoNzc3DR06VDY2NhoxYoTGjBkjDw8POTk5WbW3sbHRqlWrUrzDAAAAqYmaBwCAtJfocKJjx44aP3683njjDWXOnFmZM2eWl5dXavYNAAAgzVHzAACQ9hIdThiGIcMwJElz585NtQ4BAACYiZoHAIC0l6wxJwAAAAAAAFJKksIJGxub1OoHAADAc4OaBwCAtJXo2zok6YMPPtAHH3yQqLY2NjY6fvx4sjoFAABgJmoeAADSVpLCiapVq8rT0zOVugIAAPB8oOYBACBtJSmcaNasmZo0aZJafQEAAHguUPMAAJC2GBATAAAAAACYinACAAAAAACYinACAAAAAACYKtFjTpw8eTI1+wEAAPBcoOYBACDtceUEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwFeEEAAAAAAAwlb3ZHQBSw4FjF7Rw7R7t3HdKF6/dUZZMrqpQwlPDer+hQvk9rNpGR0dr1rLfNXvZTv1z4bocHexUwjuPPh/cSiW881i185u3RTOX7tT1W/dVMF92vde5nt6qXz6tdw9ItL6fztXCtXsSnL//52GSpIjIKE2a/osWrtmjazfvK2e2TGrftIre7VRX9vZ2adVdAEASJafmmbn0N/1z4YZcnZ1U3DuPxr7XwlLzfPHDWo2bvi7B7a378T1VLlUwVfcJSKqn1TvH1o6Rk720dOsJ7f9+p/4+d10PQh/KK082dWpeTZ2bV5OdHd/bm41wAv9JE3/apD2Hz+rNOmVUrFBu3bgdqOmLt6tmh3HaOHOwihbKZWnbf9R8LVm/V283rqROzSrr0qVLCrgXpVt3g6zWOXrqan07Z5M6NauqMkXz65ffjqjH8NmysZFa1iOgwPOpc4tqqlHRx2qaIen9zxcpX0435cyWSWfvXVX/MQu1ZttRtW9aWaVfyad9R89r7PdrdDngjr4d1taczgMAnio5Nc9b9cupSdUCcs3krhNnr1vVPG/UKi2vPNnibGf01NV6EPpQZYvmT5P9ApLiafVOruyZdfCvs/p24W69Wraw+rarrQyu6bT1jxMaPM5f+/46p+8+7WhO52Hx3IcTfn5+mjx5siTJxsZGrq6uypUrlypUqKB27dqpYMGkJ7e///67vvjiC507d07p0qXTvn37UrS/1apVU9myZVNsnUi6vm1ra/qYznJ0+Pct3rxuWVVr85m+nbNJP4zuJElavumAFq7do7lf9tAbtUopNCREZ09nVIHCr8jZxcWy7NUb9zRl/lZ1b1Vd4z9sLUnq2KyqGvf6ViMmrlCz18uStuK5VLFkAVUsWcBq2h+HzigkLFxvNawgSTpx7qZW/3pEH3RroI97vyFJ6tryNblndtWUBb+qe+saKl44d5r3HXjZUPMgOZJT87xeqbDOnj4Rp96RpOKFc8f5zL8ccFdXb9xTxzerWG0HeF4kpt7J5pZBcz5tpjo1q1re911avKr+o+Zp/urd+qBbQxXIGzeYQ9p5If6aSpcunfz9/bVo0SJNmjRJLVq00K5du/Tmm29q5cqVSV7fRx99pBw5cmj27NmaNWtWivZ18uTJOnjwYIquE0lXqVSBOCfPgvmyq0iBnDp1PsAybeqCrSpXLL/eqFVK0dHRCgkNj3d9v2w/oojIKHV76zXLNBsbG3Vt+Zqu3rinP4+eS50dAVLBz+v3ycbGRq3+/5akw6evS5Ja1Ctn1a5FvXIyDEPLN+1P8z4CLytqHiRVcmue0IcRid7G0o37ZBiGWjWokGL9BlLb4/WOe2ZXFcidJU67xjVLSZL+PhcQZx7S1gsRTtja2qp06dIqXbq0qlWrpi5dumjlypUqV66chg0bpkuXLiV6XQ8ePND169dVv359lS9fXiVKlEjFnuN5YhiGbt4JkntmV0lSYHCo9h+7oDJF82vUlFXKX+sDFWowXK2GLtGqrYetlj3692W5OjvKxyuH1fRyxfL///zEvwcBM0VERmnF5gOqWNJL+XK5//+0aEmSs5ODVVvndI6SpMMneH8DaYWaBynhaTWPT6MRqttvniq//YWWbzrw1PUtWb9PuT2yqGrZQqnddSBFxFfvJOTG7UBJsvy+wDwvRDgRHycnJ33yySeKiIjQkiVLLNOXLVumJk2aqESJEnrttdf0zTffKCoqyjIv9tLDYcOGycfHR0OHDpUU8yE+Y8YM1a9fX8WLF9frr7+u2bNnx9numTNn1L9/f1WsWFGlSpVS06ZNtWbNGkmSj0/MfU5ffvmlfHx85OPjoz17Eh6YBWlr8bq9unrjnprXjfl2+PyVWzIMQ8s27tf8VX/o03eaafLwNsqcIZ36jFqgzbuOW5YNuH1f2dwyysbGxmqdHlkzSZKu3byfdjsCPIMtfxzXnfsPrL79yueRUZK0+/BZq7Z/HDojSbp6816a9Q9AXNQ8SKqn1TzDezfWiB7V5Z7ZVd2GzbKqeR534sw1HTt9RS3rl4tTBwHPq/jqnfiER0Tq+4W/Kn8ud8ZTeQ680DeNFSpUSB4eHpZLCmfNmqXx48erU6dOGjp0qM6cOWM5UQ8ePFg1a9bUrFmz1KVLF/Xp00c1a9aUm5ubJGns2LFasmSJevfurVKlSunAgQOaMGGCnJyc1KZNG0nS+fPn5evrq5w5c2rYsGHKli2bTp06patXr0qS/P395evrqw4dOuiNN96w9BHmO3U+QB98uVgVSnipTeNKkqTgkIeSpDv3H2jTrMEqX9xToSEh8s5hp7eHLdeEmetVp2pRSVJYWIScHOP+uqT7/2lhSbg0EjDTzxv2ycHeTs3rlLFMq1wyj/LkyKIRE5fLJZ2jSr2ST/v/Oq8xU1fL3s6W9zfwHKDmQWIlpuYpViC7zp4+oQ4t6qpym3FWNc/jlqzfK0nc0oEXSnz1Tnw+/HKxTp4LkP+3fXg62XPghQ4nJClnzpy6deuWgoODNWnSJHXv3l2DBg2SJFWrVk0ODg764osv1K1bN7m5ual48eKSpHz58ql06dKSpIsXL2revHkaOXKkfH19JUlVq1ZVWFiYpkyZIl9fX9na2srPz08ODg5auHCh0qdPb2kXK3Z9OXPmtPw7uQzDUGhIyDOtAzFu3A5S64FTlcE1naZ92lbhD8MkSbZGzLdL+XK6qViB7AoNCdHDsFC5pHPQ65W8tWLrEQUFBsne3k6ODrYKexge52cSEhYzRoW9nQ0/Lzz3HoQ81C/bj6hGBW85O9pa3vNODvaaMbKt3vn8Z3Uc8qMkycnRXsN6NdKkeVvlnM6B93cKM6IN2djyDSSShpoHT5PYmudhWKgkyd42WnWqFNGyTQctNc+jDMPQkvV7VcQrhwrmzsLPCS+E+OodSZb3fez/py7cpjkrdunDbvX1Whkv3t+pJCk1zwsfThiGIRsbGx08eFAhISFq0KCBIiMjLfNjT7inT59WxYoV413Hrl27JEn16tWLs+z06dN17do15c6dW7t371b9+vUtJ+nUFBkRobOnT6T6dv7rgkPC9c74dbp7/4GmDGmkkLtXdfZuzLc+D4NiPoAyONvFOdaOtpGKiIzS8WN/Kb2Lo5zto3X9VqDOnDpudUnjlRsx96jZR4fw88Jzb8MfZxQaFqFXS2SP8351tQvRjGENde7qPQWFhMsrZ2Y5Odrp03sPVKJgVt7fqcDB0dHsLuAFQ82DJ0lOzXPl0nnZGw+tap5HHT59XZcD7qpXi3L8jPDCeFK9I8W873/5/bQ+n71TzWr4qGmVXLy/U1lia54XPpwICAiQp6en7t69K0lq3rx5vO2uXbuW4Dru3r0rwzBUuXLlBJfNnTu37t27p+zZsz97pxPB3sFB+Ty5PPJZhD2MUJvBP+ryzSD5f9VT5Ytb30dWQFJ2t190NzhCBQq/IikmSb1y6bxCImyVztFexUuUkK2trSqXu6vVO04pytFd3p4elnUcOh9zeW2NKmVVoLBXmu0bkBw7f9glV2dHtW9ZVy7/P9hl7Hs+d15POaVzVkHvf9tv2X1C0YahhjXKWn5HkDIunvvH7C7gBUTNg4QkteZ59LP/YfRhq5rnUT+sOiEbGxt1e7ue8njEfcoB8DyKr96R/q15jl0K07ifdqlR9RKa/Gm7OO97pKyk1DwvdDhx+vRpXb9+Xc2bN1emTDEDE06ePFk5cuSI0zZPnjwJridTpkyysbHRggUL5ODgEGe+l1fMH52ZM2fWjRs3Uqj3T2ZjYxPnudNIvKioaHX7ZK72H7ug+V/10msV4//DqkW9cvp+0TbtPnpBtSrFtLkXFKbNu//WaxV85Pr/3xi9WaecPp28WvPW7NX4D1tLivkGa/7aP5Ure2a9VvEV2dnxwYbn1627Qdqx/7Ra1i8vd7fMceY7pXO2+swJDQvXhFmblSNrRr3dpKqcXdKlYW//+7ilA0lFzYOEJKfmqVwiJrwIDovWht+PW9U8sSIio7Rm+1FVLl1Ahb1yp/p+ACnhafXOoVMBGvztJlUtU0gzPusqJ8e4n4NIWUmpeV7YcOLhw4caPXq0HB0d1apVK2XMmFHOzs4KCAhQ3bp1k7SuKlWqSJLu3bun2rVrP7Hdhg0bNHjw4AQvc3RwcNDDhw+TtH2kvOHfLtO6346qwWvFdff+A/n/8qfVfN9GMZe7vte5nlZsPqBOQ2aob9tacna004yff1NEZJQ+6dvE0j63Rxb1blNLfnM3KyIySmWL5tfa7Yf1x8Ez+mF0J4IJPPeWbTqgyKhotWpQPt75vf43T7lzuMnHK4eCHoRp/urdOn/llvy/6aMMrgQTgJmoefAkyal5erR6VREhgVr7x2pFPlbzxErs0w6A58mT6p3LAXc11G+LZGOjprVLa8Xmg1bzixXOreKFCeLM9EKEE9HR0Tp06JAkKSQkRKdOnZK/v78uXbqkL774wvINwYABAzR+/HgFBASoYsWKsrOz06VLl7Rlyxb5+fnJ2dk53vV7eXmpXbt2+vDDD9WtWzeVKlVKEREROn/+vPbs2aOpU6dKkvr3769t27apbdu26t69u7Jly6YzZ84oNDRUPXr0kCQVKFBAW7ZsUfny5eXs7CwvL680uV8T1o6euixJWr/jL63f8Vec+bEn6uzuGbVu+iB9MnG5pi74VRGRUSrmlVXTRnZSCW/rb54+7d9UmTM4a/by37VwzR4VyJtN00Z14qSNF8LP6/cqm1sG1axYJN75JX3yaMmG/Zq9/Helc3JQldIFNX10Z5XwSfgbWAApj5oHSZWcmmf6kp0Kj4hQheJemj66S5yaR5KWrI952kGz15/8tAPgefKkeufitTsKDo0ZzP6DLxfHmT+kR0PCCZPZGIZhmN2JJ/Hz89PkyZMtr11cXJQnTx5VqFBB7dq1U8GCBa3ar127VrNmzdLp06dlb2+vfPnyqWbNmurXr5/s7e0VGBioChUq6PPPP1eLFi0syxmGofnz58vf31/nzp2Tq6urvLy81KBBA3Xu3NnS7p9//tFXX32lP//8U1FRUfL09FTPnj3VqFEjSdK+ffv02Wef6cyZMwoLC9NPP/2kSpUqJWmfjx49KkOS9yslkn7A8ExCQ2IGtixQ+BUuMcVLgfe8OU6dOCpJKlmCz3n8i5oHaYXPfryMeN+bIyk1z3MfTryMOFGbhw8tvGx4z5uDcAKIQc1jDj778TLifW+OpNQ83CgPAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMRTgBAAAAAABMZW92BxBXRESEDMPQqRNHze7KS8eINiRJF8/9IxtbG5N7A6Q+3vPmiAgPl40Nxxug5jEHn/14GfG+N0dSah7CiedQ7A+PX5m0Z2NrI0dHR7O7AaQZ3vPmsLGxIZwARM1jFj778TLifW+OpNQ8NoZhGKncHwAAAAAAgAQx5gQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QQAAAAAADAV4QReCH5+fvLx8VG7du3izBs7dqxq165tQq+Szs/PT2XKlDG7G3jBxL7/fXx8VKRIEZUrV05NmjTRqFGjdObMmWSt8/fff1eTJk1UvHhxlS9fPsX7e+DAgRRdJwC8LKh58DKj5nm52ZvdASAp9u3bpz179qhSpUpmdwVIU+nSpdOcOXMkSQ8ePNCpU6fk7++vxYsXa+zYsXrzzTeTtL6PPvpIPj4++t///icnJ6cU7evkyZPl4uKismXLpuh6AeBlQs2DlxU1z8uLKyfwwnBxcVHJkiU1derUFF93eHi4oqOjU3y9QEqxtbVV6dKlVbp0aVWrVk1dunTRypUrVa5cOQ0bNkyXLl1K9LoePHig69evq379+ipfvrxKlCiRij0HACQVNQ9eZtQ8Ly/CCbxQ+vbtq927dz/x8qkrV65owIABKleunEqXLq1u3brp77//tmpTu3ZtjRo1StOnT1etWrVUsmRJ3bt3Tx06dFCvXr20Zs0a1atXT6VKlVLv3r11//59XblyRd26dVOZMmXUuHFj7dmzx2qdK1asUJs2bVSxYkVVqFBBHTp00JEjR1LlOACS5OTkpE8++UQRERFasmSJZfqyZcvUpEkTlShRQq+99pq++eYbRUVFWebFpvvDhg2Tj4+Phg4dKkkyDEMzZsxQ/fr1Vbx4cb3++uuaPXt2nO2eOXNG/fv3V8WKFVWqVCk1bdpUa9askST5+PhIkr788kvLZZmP/64AAJ6Omgf4FzXPy4HbOvBCqVWrlooWLaopU6ZoxowZceYHBwerQ4cOsrW11ciRI+Xk5KTvvvtO7du316pVq5QzZ05L240bNyp//vwaNmyYbG1t5eLiIkk6fvy47t69qw8//FDBwcEaM2aMPvnkE125ckXNmjVTly5dNG3aNL3zzjv69ddf5erqKkm6fPmymjVrpnz58ik8PFxr165Vu3bttGrVKnl5eaXNAcJLp1ChQvLw8NDBgwclSbNmzdL48ePVqVMnDR06VGfOnLGcqAcPHqyaNWtq1qxZ6tKli/r06aOaNWvKzc1NUsy9zEuWLFHv3r1VqlQpHThwQBMmTJCTk5PatGkjSTp//rx8fX2VM2dODRs2TNmyZdOpU6d09epVSZK/v798fX3VoUMHvfHGG5Y+AgCShpoHsEbN899HOIEXTp8+ffTOO+/oyJEjKlmypNW8ZcuW6erVq1q7dq0KFiwoSapQoYJq1aqlOXPmWNJSSYqIiND06dMtJ+hYwcHB+v777y0fXn///bdmzpypTz/91PJhlT17djVp0kR//PGH6tSpI0nq37+/ZR3R0dGqVq2ajhw5ouXLl2vQoEEpfyCA/5czZ07dunVLwcHBmjRpkrp37255z1WrVk0ODg764osv1K1bN7m5ual48eKSpHz58ql06dKSpIsXL2revHkaOXKkfH19JUlVq1ZVWFiYpkyZIl9fX9na2srPz08ODg5auHCh0qdPb2kXK3Z9OXPmtPwbAJA81DyANWqe/zZu68ALp27duvL29taUKVPizNu3b58KFy5sOUlLUubMmVW1alXt37/fqm2lSpXinKQlqUiRIpaTtCR5enpKsv4wip0WEBBgmXbmzBn169dPVatW1SuvvKJixYrp3LlzOn/+fHJ2E0g0wzBkY2OjgwcPKiQkRA0aNFBkZKTlv9gT7unTpxNcx65duyRJ9erVi7PszZs3de3aNUnS7t27Vb9+fctJGgCQeqh5AGvUPP9tXDmBF46NjY169+6tQYMG6dixY1bzAgMDlTVr1jjLuLu7x/mQcnd3j3f9GTNmtHrt4OAgScqQIYNlmqOjoyTp4cOHkmK+eejatavc3Nw0dOhQ5cqVS05OTho+fLilDZBaAgIC5Onpqbt370qSmjdvHm+72JNtfO7evSvDMFS5cuUEl82dO7fu3bun7NmzP3unAQBPRc0DWKPm+W8jnMALqWHDhvLz89PUqVOVK1cuy/RMmTLp3Llzcdrfvn1bmTJlsppmY2OTYv05dOiQAgICNG3aNBUpUsQyPSgoSDly5Eix7QCPO336tK5fv67mzZtb3uOTJ0+O932XJ0+eBNeTKVMm2djYaMGCBZbi9FGx9xBnzpxZN27cSKHeAwCehpoHiEHN899HOIEXkq2trXr37q2hQ4eqYsWKlunlypXThg0bdPbsWRUoUECSdP/+fe3atctyT1lqCAsLkySrD7gDBw7oypUrKly4cKptFy+3hw8favTo0XJ0dFSrVq2UMWNGOTs7KyAgQHXr1k3SuqpUqSJJunfvnmrXrv3Edhs2bNDgwYMTvMzRwcGBb88AIIVQ8wDUPC8Lwgm8sJo0aaIpU6Zoz549yp07tySpRYsWmj17tnr16qV3333XMnK1vb29OnXqlGp9KV26tFxcXDRy5Ej17NlT169fl5+fnzw8PFJtm3i5REdH69ChQ5KkkJAQnTp1Sv7+/rp06ZK++OILyzcEAwYM0Pjx4xUQEKCKFSvKzs5Oly5d0pYtW+Tn5ydnZ+d41+/l5aV27drpww8/VLdu3VSqVClFRETo/Pnz2rNnj6ZOnSopZhC0bdu2qW3bturevbuyZcumM2fOKDQ0VD169JAkFShQQFu2bFH58uXl7OwsLy8v7tcEgGdAzYOXCTXPy4twAi8sOzs79ezZU8OHD7dMS58+vebOnasvvvhCn3zyiaKjo1W2bFnNmzfP6pFaKS1r1qyaOHGivvzyS/Xt21eenp4aOXKkfvzxx1TbJl4uYWFhlm/CXFxclCdPHlWpUkWTJ0+2Ggyta9eu8vDw0KxZszRv3jzZ29srX758qlmzZryXLj5q+PDh8vLykr+/v6ZMmSJXV1d5eXmpQYMGljaenp5atGiRvvrqK40cOVJRUVHy9PRUz549LW1GjBihzz77TD169FBYWJh++uknVapUKYWPCAC8PKh58DKh5nl52RiGYZjdCQAAAAAA8PLiUaIAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMA4lW7dm0NHTrU8nrPnj3y8fHRnj17TOyVtcf7mBAfHx/5+fklef3Lli2Tj4+Pjh49mpzuxcvPz08+Pj4ptj4AAPBsqHmoefB8IJwAnkOxJ4jY/0qUKKH69etr1KhRunXrltndS5Lt27cn6yQJAAD++6h5AMSyN7sDABI2YMAA5cmTR+Hh4dq/f78WLlyo7du3a82aNXJ2dk7TvlSoUEFHjhyRg4NDkpbbvn275s+fr3feeSeVegYAAF501DwACCeA51j16tVVokQJSVKrVq2UOXNmzZo1S1u2bNEbb7wR7zIhISFycXFJ8b7Y2trKyckpxdcLAABAzQOA2zqAF0jlypUlSZcvX5YkDR06VGXKlNHFixfVo0cPlSlTRoMHD5YkRUdHa/bs2WrcuLFKlCihqlWrasSIEbp//77VOg3D0NSpU1W9enWVKlVKHTp00OnTp+NsO6H7Lw8fPqwePXqoQoUKKl26tJo0aaI5c+ZY+jd//nxJsrpkM1ZK9zGxrly5ok8//VT169dXyZIlValSJQ0YMMByXB8XFhamESNGqFKlSipbtqw+/PDDOH2UYr4xadu2rUqXLq0yZcqoZ8+ez9RPAABeVtQ81Dx4+XDlBPACuXjxoiQpc+bMlmmRkZHq1q2bypUrpyFDhihdunSSpBEjRmj58uVq0aKFOnTooMuXL2v+/Pk6fvy4Fi5caLlUceLEifruu+9Uo0YN1ahRQ8eOHVPXrl0VERHx1P78/vvv6tWrl7Jnz66OHTsqa9asOnPmjLZt26ZOnTrJ19dXN27c0O+//64vv/wyzvJp0cf4HD16VAcPHlTjxo2VI0cOXblyRQsXLlTHjh21du3aOJePjho1ShkzZlT//v117tw5LVy4UFevXtXcuXNlY2MjSVqxYoWGDh2qV199VYMHD1ZoaKgWLlyotm3bavny5cqTJ0+y+goAwMuImoeaBy8hA8BzZ+nSpYa3t7exa9cu4/bt28a1a9eMtWvXGhUrVjRKlixpBAQEGIZhGEOGDDG8vb2NCRMmWC2/d+9ew9vb21i1apXV9N9++81q+u3bt41ixYoZPXv2NKKjoy3tvv76a8Pb29sYMmSIZdru3bsNb29vY/fu3YZhGEZkZKRRu3Zto1atWsb9+/ettvPoukaOHGl4e3vH2cfU6GNCvL29jUmTJlleh4aGxmlz8OBBw9vb21i+fLllWuzPoXnz5kZ4eLhl+vTp0w1vb29j8+bNhmEYRnBwsFG+fHlj+PDhVuu8efOmUa5cOavpkyZNivd4AADwMqLmoeYBYnFbB/Ac69y5s6pUqaIaNWrovffek6urqyZPniwPDw+rdm3atLF6vX79emXIkEHVqlXTnTt3LP8VK1ZMLi4ulssUd+3apYiICLVv396ShktSp06dntq348eP6/Lly+rYsaMyZsxoNe/RdSUkLfqYkNhvWiQpIiJCd+/eVb58+ZQxY0YdP348TntfX1+rQbHatGkje3t7bd++3dLHwMBANW7c2GpfbG1tVapUqefqUWQAADyPqHmoeQBu6wCeYyNGjJCXl5fs7OyUNWtWeXl5ydbWOlO0t7dXjhw5rKZduHBBQUFBqlKlSrzrvX37tiTp6tWrkiRPT0+r+W5ubsqUKdMT+3bp0iVJkre3d6L3J637mJCwsDBNmzZNy5Yt0/Xr12UYhmVeUFBQnPb58+e3eu3q6qps2bLpypUrkqTz589LSrh4SJ8+fbL6CQDAy4Kah5oHIJwAnmMlS5a0jFydEEdHxzgn7+joaLm7u2vChAnxLuPm5pZifUwuM/s4evRoLVu2TJ06dVLp0qWVIUMG2djY6L333rM6aSdW7DJffvmlsmXLFme+nZ3dM/cZAID/Mmqe1EHNgxcJ4QTwH5QvXz798ccfKlu2rNXlfI/LlSuXpJgUPG/evJbpd+7ciXdk5kfFtj916pSqVq2aYLuELndMiz4mZMOGDWrWrJmGDh1qmfbw4cN4v0GQYr7xiB01XJIePHigmzdvqnr16pL+PRbu7u5PPBYAACBlUfM8GTUPXiSMOQH8BzVs2FBRUVGaOnVqnHmRkZEKDAyUJFWtWlUODg6aN2+eVXoe+1isJylWrJjy5Mmjn376ybK+WI+uK3YU6MfbpEUfExJfqj937lxFRUXF297f399qlOyFCxcqMjLScqJ+7bXXlD59ek2bNi3e0bTv3LmT7L4CAICEUfM8GTUPXiRcOQH8B1WsWFG+vr6aNm2aTpw4oWrVqsnBwUHnz5/X+vXrNWzYMDVo0EBubm7q2rWrpk2bpl69eqlGjRo6fvy4fvvtN2XJkuWJ27C1tdWnn36qPn36qFmzZmrRooWyZcums2fP6p9//tGMGTMkxZzQJWnMmDF69dVXZWdnp8aNG6dJHxNSs2ZNrVy5UunTp1ehQoV06NAh7dq1y+pxZY+KiIhQ586d1bBhQ507d04LFixQuXLl9Prrr0uKub/y008/1YcffqgWLVqoUaNGcnNz09WrV7V9+3aVLVtWI0aMSFZfAQBAwqh5noyaBy8SwgngP2rUqFEqXry4Fi1apG+++UZ2dnbKnTu3mjZtqrJly1ravfvuu3J0dNSiRYu0Z88elSxZUjNnzlSvXr2euo3XXntNc+bM0ZQpUzRz5kwZhqG8efOqdevWljb16tVThw4dtHbtWq1atUqGYahx48Zp1sf4DBs2TLa2tlq9erUePnyosmXLatasWerevXu87UeMGKHVq1dr0qRJioiIUOPGjTV8+HCryzebNGmi7Nmz64cfftCMGTMUHh4uDw8PlS9fXi1atEhWPwEAwNNR8ySMmgcvEhsjOSOhAAAAAAAApBDGnAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKYinAAAAAAAAKb6P+KJ0qfovfMuAAAAAElFTkSuQmCC\n"
-     },
-     "metadata": {}
+      "source": [
+        "EVENT_TARGET = \"defect_yn\"\n",
+        "EVENT_LEAKAGE_COLS = [\n",
+        "    \"defect_yn\",\n",
+        "    \"target_defect_yn\",\n",
+        "    \"defect_reason\",\n",
+        "    \"operation_status\",\n",
+        "    \"operation_status_raw\",\n",
+        "    \"quality_status\",\n",
+        "    \"paint_vision_label\",\n",
+        "    \"paint_defect_score\",\n",
+        "    \"paint_surface_quality_score\",\n",
+        "    \"assembly_missing_part_count\",\n",
+        "    \"assembly_fastening_error_count\",\n",
+        "    \"assembly_sequence_error_count\",\n",
+        "    \"assembly_sequence_mismatch_yn\",\n",
+        "    \"press_count_increase_yn\",\n",
+        "    \"press_timestamp_delay_sec\",\n",
+        "    \"body_robot_motion_status\",\n",
+        "    \"assembly_expected_sequence\",\n",
+        "    \"assembly_actual_sequence\",\n",
+        "    \"ford_row_id\",\n",
+        "    \"forming_row_id\",\n",
+        "    \"robot_arm_vibration_row_id\",\n",
+        "    \"machine_vision_row_id\",\n",
+        "    \"bosch_id\",\n",
+        "]\n",
+        "EVENT_EXCLUDE_COLS = [\n",
+        "    \"raw_event_id\",\n",
+        "    \"event_id\",\n",
+        "    \"event_time\",\n",
+        "    \"car_master_id\",\n",
+        "    \"defect_reason\",\n",
+        "    EVENT_TARGET,\n",
+        "] + EVENT_LEAKAGE_COLS\n",
+        "\n",
+        "event_feature_cols = select_model_features(defect_train, EVENT_TARGET, EVENT_EXCLUDE_COLS, EVENT_LEAKAGE_COLS, context=\"불량 탐지\")\n",
+        "event_feature_cols = drop_suspicious_features(defect_train, EVENT_TARGET, event_feature_cols, context=\"불량 탐지\")\n",
+        "ensure_feature_columns_exist(defect_test, event_feature_cols, context=\"불량 탐지\")\n",
+        "\n",
+        "X_event_train = defect_train[event_feature_cols]\n",
+        "y_event_train = defect_train[EVENT_TARGET].astype(int)\n",
+        "X_event_test = defect_test[event_feature_cols]\n",
+        "y_event_test = defect_test[EVENT_TARGET].astype(int)\n",
+        "event_scale_pos_weight = scale_pos_weight_for(y_event_train)\n",
+        "\n",
+        "ENABLE_HYPERPARAMETER_TUNING = True\n",
+        "CV_FOLDS = 3\n",
+        "TUNING_TRIALS_PER_MODEL = 3\n",
+        "MAX_TUNING_ROWS = 12_000\n",
+        "\n",
+        "print(\"불량 탐지 feature 수:\", len(event_feature_cols))\n",
+        "print(\"불량 탐지 feature:\", event_feature_cols)\n",
+        "print(\"불량 탐지 scale_pos_weight:\", round(event_scale_pos_weight, 3))\n",
+        "print(f\"튜닝 설정: enabled={ENABLE_HYPERPARAMETER_TUNING}, folds={CV_FOLDS}, trials={TUNING_TRIALS_PER_MODEL}, max_rows={MAX_TUNING_ROWS}\")\n",
+        "\n",
+        "plot_class_distribution(defect_train, defect_test, EVENT_TARGET, \"불량 탐지\")\n",
+        "display(pd.crosstab(defect_train[\"process_code\"], defect_train[EVENT_TARGET], normalize=\"index\").rename(columns={0: \"정상 비율\", 1: \"불량 비율\"}))\n",
+        "plot_feature_distribution_grid(defect_train, event_feature_cols, EVENT_TARGET, \"불량 탐지 주요 수치형 feature 분포\", max_features=24)\n",
+        "plot_numeric_correlation_heatmap(defect_train, event_feature_cols, \"불량 탐지 수치형 feature 상관관계\", max_features=18)\n",
+        "\n",
+        "\n",
+        "def make_event_model_configs(scale_pos_weight: float):\n",
+        "    return {\n",
+        "        \"LightGBM\": {\n",
+        "            \"estimator\": lgb.LGBMClassifier(\n",
+        "                objective=\"binary\",\n",
+        "                n_estimators=220,\n",
+        "                learning_rate=0.045,\n",
+        "                num_leaves=15,\n",
+        "                min_child_samples=80,\n",
+        "                subsample=0.85,\n",
+        "                colsample_bytree=0.75,\n",
+        "                reg_lambda=8.0,\n",
+        "                scale_pos_weight=scale_pos_weight,\n",
+        "                random_state=RANDOM_STATE,\n",
+        "                n_jobs=-1,\n",
+        "                verbosity=-1,\n",
+        "            ),\n",
+        "            \"scale_numeric\": False,\n",
+        "            \"param_distributions\": {\n",
+        "                \"model__n_estimators\": [160, 220, 280],\n",
+        "                \"model__learning_rate\": [0.035, 0.045, 0.06],\n",
+        "                \"model__num_leaves\": [11, 15, 23],\n",
+        "                \"model__min_child_samples\": [60, 80, 120],\n",
+        "                \"model__subsample\": [0.75, 0.85, 0.95],\n",
+        "                \"model__colsample_bytree\": [0.70, 0.80, 0.90],\n",
+        "                \"model__reg_lambda\": [4.0, 8.0, 12.0],\n",
+        "            },\n",
+        "        },\n",
+        "        \"XGBoost\": {\n",
+        "            \"estimator\": xgb.XGBClassifier(\n",
+        "                objective=\"binary:logistic\",\n",
+        "                eval_metric=\"aucpr\",\n",
+        "                tree_method=\"hist\",\n",
+        "                n_estimators=220,\n",
+        "                learning_rate=0.045,\n",
+        "                max_depth=3,\n",
+        "                min_child_weight=8,\n",
+        "                subsample=0.85,\n",
+        "                colsample_bytree=0.75,\n",
+        "                reg_lambda=8.0,\n",
+        "                scale_pos_weight=scale_pos_weight,\n",
+        "                random_state=RANDOM_STATE,\n",
+        "                n_jobs=-1,\n",
+        "            ),\n",
+        "            \"scale_numeric\": False,\n",
+        "            \"param_distributions\": {\n",
+        "                \"model__n_estimators\": [160, 220, 280],\n",
+        "                \"model__learning_rate\": [0.035, 0.045, 0.06],\n",
+        "                \"model__max_depth\": [2, 3, 4],\n",
+        "                \"model__min_child_weight\": [5, 8, 12],\n",
+        "                \"model__subsample\": [0.75, 0.85, 0.95],\n",
+        "                \"model__colsample_bytree\": [0.70, 0.80, 0.90],\n",
+        "                \"model__reg_lambda\": [4.0, 8.0, 12.0],\n",
+        "            },\n",
+        "        },\n",
+        "        \"CatBoost\": {\n",
+        "            \"estimator\": CatBoostClassifier(\n",
+        "                loss_function=\"Logloss\",\n",
+        "                eval_metric=\"PRAUC\",\n",
+        "                iterations=220,\n",
+        "                learning_rate=0.045,\n",
+        "                depth=4,\n",
+        "                l2_leaf_reg=10.0,\n",
+        "                scale_pos_weight=scale_pos_weight,\n",
+        "                random_seed=RANDOM_STATE,\n",
+        "                thread_count=2,\n",
+        "                verbose=False,\n",
+        "                allow_writing_files=False,\n",
+        "            ),\n",
+        "            \"scale_numeric\": False,\n",
+        "            \"param_distributions\": {\n",
+        "                \"model__iterations\": [160, 220, 280],\n",
+        "                \"model__learning_rate\": [0.035, 0.045, 0.06],\n",
+        "                \"model__depth\": [3, 4, 5],\n",
+        "                \"model__l2_leaf_reg\": [6.0, 10.0, 14.0],\n",
+        "            },\n",
+        "        },\n",
+        "        \"LogisticRegression\": {\n",
+        "            \"estimator\": LogisticRegression(class_weight=\"balanced\", max_iter=1000, C=0.3, random_state=RANDOM_STATE),\n",
+        "            \"scale_numeric\": True,\n",
+        "            \"param_distributions\": {\n",
+        "                \"model__C\": [0.1, 0.3, 0.7],\n",
+        "            },\n",
+        "        },\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "def make_model_pipeline(config, train_df, feature_cols):\n",
+        "    return Pipeline([\n",
+        "        (\"preprocess\", make_preprocessor(train_df, feature_cols, scale_numeric=config.get(\"scale_numeric\", False))),\n",
+        "        (\"model\", clone(config[\"estimator\"])),\n",
+        "    ])\n",
+        "\n",
+        "\n",
+        "def sample_param_candidates(param_distributions: dict, n_iter: int, random_state: int):\n",
+        "    if not param_distributions:\n",
+        "        return [{}]\n",
+        "    return list(ParameterSampler(param_distributions, n_iter=n_iter, random_state=random_state))\n",
+        "\n",
+        "\n",
+        "def make_tuning_sample(X: pd.DataFrame, y: pd.Series, max_rows: int):\n",
+        "    if len(X) <= max_rows:\n",
+        "        return X, y\n",
+        "    sampled_parts = []\n",
+        "    for label, label_index in y.groupby(y).groups.items():\n",
+        "        label_index = pd.Index(label_index)\n",
+        "        n_rows = max(1, int(round(len(label_index) / len(y) * max_rows)))\n",
+        "        sampled_parts.append(label_index.to_series().sample(n=min(n_rows, len(label_index)), random_state=RANDOM_STATE))\n",
+        "    sampled_index = pd.Index(pd.concat(sampled_parts).tolist())\n",
+        "    if len(sampled_index) > max_rows:\n",
+        "        sampled_index = sampled_index.to_series().sample(max_rows, random_state=RANDOM_STATE).index\n",
+        "    sampled_index = sampled_index.sort_values()\n",
+        "    return X.loc[sampled_index], y.loc[sampled_index]\n",
+        "\n",
+        "\n",
+        "def tune_event_model_cv(model_name: str, config: dict, X: pd.DataFrame, y: pd.Series, feature_cols: list[str]):\n",
+        "    candidates = sample_param_candidates(config.get(\"param_distributions\", {}), TUNING_TRIALS_PER_MODEL, RANDOM_STATE)\n",
+        "    cv = StratifiedKFold(n_splits=CV_FOLDS, shuffle=True, random_state=RANDOM_STATE)\n",
+        "    rows = []\n",
+        "    best_row = None\n",
+        "    best_score = (-np.inf, -np.inf, -np.inf)\n",
+        "    print(f\"\\n[{model_name}] lightweight CV tuning: {len(candidates)} trials x {CV_FOLDS} folds\")\n",
+        "    for trial_no, params in enumerate(candidates, start=1):\n",
+        "        fold_metrics = []\n",
+        "        started_at = time.perf_counter()\n",
+        "        for train_idx, valid_idx in cv.split(X, y):\n",
+        "            X_fold_train = X.iloc[train_idx]\n",
+        "            y_fold_train = y.iloc[train_idx]\n",
+        "            X_fold_valid = X.iloc[valid_idx]\n",
+        "            y_fold_valid = y.iloc[valid_idx]\n",
+        "            estimator = make_model_pipeline(config, X_fold_train, feature_cols)\n",
+        "            estimator.set_params(**params)\n",
+        "            estimator.fit(X_fold_train, y_fold_train)\n",
+        "            valid_proba = predict_positive_proba(estimator, X_fold_valid)\n",
+        "            threshold = find_recall_priority_threshold(y_fold_valid, valid_proba, min_recall=0.65, min_precision=0.20)\n",
+        "            fold_metrics.append(compute_metrics(y_fold_valid, valid_proba, threshold))\n",
+        "        elapsed_sec = time.perf_counter() - started_at\n",
+        "        row = {\n",
+        "            \"model_name\": model_name,\n",
+        "            \"trial_no\": trial_no,\n",
+        "            \"params\": params,\n",
+        "            \"cv_pr_auc\": float(np.mean([m[\"pr_auc\"] for m in fold_metrics])),\n",
+        "            \"cv_recall\": float(np.mean([m[\"recall\"] for m in fold_metrics])),\n",
+        "            \"cv_precision\": float(np.mean([m[\"precision\"] for m in fold_metrics])),\n",
+        "            \"cv_f1\": float(np.mean([m[\"f1\"] for m in fold_metrics])),\n",
+        "            \"cv_accuracy\": float(np.mean([m[\"accuracy\"] for m in fold_metrics])),\n",
+        "            \"cv_threshold\": float(np.mean([m[\"threshold\"] for m in fold_metrics])),\n",
+        "            \"elapsed_sec\": elapsed_sec,\n",
+        "        }\n",
+        "        rows.append(row)\n",
+        "        score = (row[\"cv_pr_auc\"], row[\"cv_recall\"], row[\"cv_f1\"])\n",
+        "        if score > best_score:\n",
+        "            best_score = score\n",
+        "            best_row = row\n",
+        "        print(f\"  trial {trial_no}: CV PR-AUC={row['cv_pr_auc']:.4f}, Recall={row['cv_recall']:.4f}, F1={row['cv_f1']:.4f}, elapsed={elapsed_sec:.1f}s\")\n",
+        "    return best_row, rows\n",
+        "\n",
+        "\n",
+        "event_model_configs = make_event_model_configs(event_scale_pos_weight)\n",
+        "event_best_params = {model_name: {} for model_name in event_model_configs}\n",
+        "event_cv_rows = []\n",
+        "X_event_tune, y_event_tune = make_tuning_sample(X_event_train, y_event_train, MAX_TUNING_ROWS)\n",
+        "print(\"CV tuning rows:\", len(X_event_tune), \"positive_ratio:\", round(float(y_event_tune.mean()), 4))\n",
+        "\n",
+        "if ENABLE_HYPERPARAMETER_TUNING:\n",
+        "    for model_name, config in event_model_configs.items():\n",
+        "        best_row, rows = tune_event_model_cv(model_name, config, X_event_tune, y_event_tune, event_feature_cols)\n",
+        "        event_cv_rows.extend(rows)\n",
+        "        event_best_params[model_name] = best_row[\"params\"] if best_row else {}\n",
+        "\n",
+        "event_cv_results = pd.DataFrame(event_cv_rows)\n",
+        "if not event_cv_results.empty:\n",
+        "    display(\n",
+        "        event_cv_results.sort_values([\"cv_pr_auc\", \"cv_recall\", \"cv_f1\"], ascending=False)\n",
+        "        [[\"model_name\", \"trial_no\", \"cv_pr_auc\", \"cv_recall\", \"cv_precision\", \"cv_f1\", \"cv_accuracy\", \"cv_threshold\", \"elapsed_sec\", \"params\"]]\n",
+        "        .reset_index(drop=True)\n",
+        "    )\n",
+        "print(\"모델별 선택 파라미터\")\n",
+        "display(pd.DataFrame([{\"model_name\": model_name, \"best_params\": params} for model_name, params in event_best_params.items()]))\n",
+        "\n",
+        "event_candidate_models = {}\n",
+        "event_eval_rows = []\n",
+        "event_train_proba_by_model = {}\n",
+        "event_test_proba_by_model = {}\n",
+        "\n",
+        "for model_name, config in event_model_configs.items():\n",
+        "    print(f\"\\n[{model_name}] final fit start\")\n",
+        "    estimator = make_model_pipeline(config, defect_train, event_feature_cols)\n",
+        "    estimator.set_params(**event_best_params.get(model_name, {}))\n",
+        "    estimator.fit(X_event_train, y_event_train)\n",
+        "    train_proba = predict_positive_proba(estimator, X_event_train)\n",
+        "    test_proba = predict_positive_proba(estimator, X_event_test)\n",
+        "    threshold = find_recall_priority_threshold(y_event_train, train_proba, min_recall=0.65, min_precision=0.20)\n",
+        "    train_metrics = compute_metrics(y_event_train, train_proba, threshold)\n",
+        "    test_metrics = compute_metrics(y_event_test, test_proba, threshold)\n",
+        "    event_candidate_models[model_name] = estimator\n",
+        "    event_train_proba_by_model[model_name] = train_proba\n",
+        "    event_test_proba_by_model[model_name] = test_proba\n",
+        "    event_eval_rows.append({\n",
+        "        \"model_name\": model_name,\n",
+        "        \"threshold\": threshold,\n",
+        "        \"best_params\": event_best_params.get(model_name, {}),\n",
+        "        **{f\"train_{k}\": v for k, v in train_metrics.items()},\n",
+        "        **{f\"test_{k}\": v for k, v in test_metrics.items()},\n",
+        "    })\n",
+        "    print(f\"[{model_name}] Train F1={train_metrics['f1']:.4f}, Test F1={test_metrics['f1']:.4f}, Test PR-AUC={test_metrics['pr_auc']:.4f}, Test Recall={test_metrics['recall']:.4f}\")\n",
+        "\n",
+        "event_model_comparison = pd.DataFrame(event_eval_rows).sort_values([\"test_pr_auc\", \"test_recall\", \"test_f1\"], ascending=False).reset_index(drop=True)\n",
+        "selected_event_model_name = str(event_model_comparison.iloc[0][\"model_name\"])\n",
+        "selected_event_threshold = float(event_model_comparison.iloc[0][\"threshold\"])\n",
+        "selected_event_model = event_candidate_models[selected_event_model_name]\n",
+        "print(\"selected_event_model_name:\", selected_event_model_name)\n",
+        "\n",
+        "event_model_result_table = event_model_comparison[[\n",
+        "    \"model_name\",\n",
+        "    \"train_f1\", \"test_f1\",\n",
+        "    \"train_recall\", \"test_recall\",\n",
+        "    \"train_precision\", \"test_precision\",\n",
+        "    \"train_accuracy\", \"test_accuracy\",\n",
+        "    \"test_pr_auc\", \"test_roc_auc\",\n",
+        "    \"threshold\",\n",
+        "    \"best_params\",\n",
+        "]].rename(columns={\n",
+        "    \"model_name\": \"모델\",\n",
+        "    \"train_f1\": \"Train F1\",\n",
+        "    \"test_f1\": \"Test F1\",\n",
+        "    \"train_recall\": \"Train 재현율\",\n",
+        "    \"test_recall\": \"Test 재현율\",\n",
+        "    \"train_precision\": \"Train 정밀도\",\n",
+        "    \"test_precision\": \"Test 정밀도\",\n",
+        "    \"train_accuracy\": \"Train 정확도\",\n",
+        "    \"test_accuracy\": \"Test 정확도\",\n",
+        "    \"test_pr_auc\": \"Test PR-AUC\",\n",
+        "    \"test_roc_auc\": \"Test ROC-AUC\",\n",
+        "    \"threshold\": \"임계값(Train 기준)\",\n",
+        "    \"best_params\": \"선택 파라미터\",\n",
+        "})\n",
+        "print(\"불량 탐지 모델 학습 결과\")\n",
+        "display(event_model_result_table)"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "           model_name  accuracy  false_positive_rate  precision    recall  \\\n",
-       "0        RandomForest  0.913950             0.082851   0.023508  0.351032   \n",
-       "1          ExtraTrees  0.987867             0.007844   0.144424  0.233038   \n",
-       "2  LogisticRegression  0.986717             0.008884   0.119601  0.212389   \n",
-       "3            LightGBM  0.901417             0.095456   0.020468  0.351032   \n",
-       "\n",
-       "         f1   roc_auc    pr_auc  threshold  \n",
-       "0  0.044066  0.696746  0.062551   0.058868  \n",
-       "1  0.178330  0.676183  0.057577   0.243180  \n",
-       "2  0.153029  0.662734  0.050958   0.898330  \n",
-       "3  0.038680  0.665979  0.050323   0.034547  "
-      ],
-      "text/html": [
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-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model_name</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>false_positive_rate</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>0.913950</td>\n",
-       "      <td>0.082851</td>\n",
-       "      <td>0.023508</td>\n",
-       "      <td>0.351032</td>\n",
-       "      <td>0.044066</td>\n",
-       "      <td>0.696746</td>\n",
-       "      <td>0.062551</td>\n",
-       "      <td>0.058868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>ExtraTrees</td>\n",
-       "      <td>0.987867</td>\n",
-       "      <td>0.007844</td>\n",
-       "      <td>0.144424</td>\n",
-       "      <td>0.233038</td>\n",
-       "      <td>0.178330</td>\n",
-       "      <td>0.676183</td>\n",
-       "      <td>0.057577</td>\n",
-       "      <td>0.243180</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LogisticRegression</td>\n",
-       "      <td>0.986717</td>\n",
-       "      <td>0.008884</td>\n",
-       "      <td>0.119601</td>\n",
-       "      <td>0.212389</td>\n",
-       "      <td>0.153029</td>\n",
-       "      <td>0.662734</td>\n",
-       "      <td>0.050958</td>\n",
-       "      <td>0.898330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>0.901417</td>\n",
-       "      <td>0.095456</td>\n",
-       "      <td>0.020468</td>\n",
-       "      <td>0.351032</td>\n",
-       "      <td>0.038680</td>\n",
-       "      <td>0.665979</td>\n",
-       "      <td>0.050323</td>\n",
-       "      <td>0.034547</td>\n",
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-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"]])\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"LightGBM\",\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04624820792323862,\n        \"min\": 0.9014166666666666,\n        \"max\": 0.9878666666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9878666666666667,\n          0.9014166666666666,\n          0.91395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04692891215226371,\n        \"min\": 0.007844320410318299,\n        \"max\": 0.09545599302727074,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.007844320410318299,\n          0.09545599302727074,\n          0.0828514439918875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0643379448162342,\n        \"min\": 0.02046783625730994,\n        \"max\": 0.14442413162705667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.14442413162705667,\n          0.02046783625730994,\n          0.023508494666139867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07456283468415088,\n        \"min\": 0.21238938053097345,\n        \"max\": 0.35103244837758113,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.35103244837758113,\n          0.23303834808259588,\n          0.21238938053097345\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07254091975454982,\n        \"min\": 0.038680318543799774,\n        \"max\": 0.17832957110609482,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17832957110609482,\n          0.038680318543799774,\n          0.04406591371968154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015334779408148261,\n        \"min\": 0.6627341233129422,\n        \"max\": 0.6967463513986769,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6761829953791527,\n          0.6659786100217459,\n          0.6967463513986769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pr_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005812604054563798,\n        \"min\": 0.050323296178874356,\n        \"max\": 0.06255052428644334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.05757660266781409,\n          0.050323296178874356,\n          0.06255052428644334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40395240727098636,\n        \"min\": 0.03454695956318568,\n        \"max\": 0.8983303412868939,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2431800238242072,\n          0.03454695956318568,\n          0.058868157090478435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "print(\"== Selected Model ==\")\n",
-    "print(\"model:\", selected_model_name)\n",
-    "print(\"best_threshold:\", round(best_threshold, 5))\n",
-    "print(classification_report(y_valid, valid_pred, digits=4, zero_division=0))\n",
-    "\n",
-    "# 후보 모델별 Confusion Matrix\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
-    "axes = axes.ravel()\n",
-    "for ax, (model_name, cm_model) in zip(axes, model_confusion_matrices.items()):\n",
-    "    ConfusionMatrixDisplay(cm_model, display_labels=[\"Normal\", \"Defect\"]).plot(\n",
-    "        ax=ax,\n",
-    "        cmap=\"Blues\",\n",
-    "        values_format=\"d\",\n",
-    "        colorbar=False,\n",
-    "    )\n",
-    "    row = model_comparison[model_comparison[\"model_name\"].eq(model_name)].iloc[0]\n",
-    "    ax.set_title(\n",
-    "        f\"{model_name}\\n\"\n",
-    "        f\"Recall={row['recall']:.3f}, Precision={row['precision']:.3f}, F1={row['f1']:.3f}\"\n",
-    "    )\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# 비교 지표 테이블\n",
-    "display(model_comparison[[\n",
-    "    \"model_name\",\n",
-    "    \"accuracy\",\n",
-    "    \"false_positive_rate\",\n",
-    "    \"precision\",\n",
-    "    \"recall\",\n",
-    "    \"f1\",\n",
-    "    \"roc_auc\",\n",
-    "    \"pr_auc\",\n",
-    "    \"threshold\",\n",
-    "]])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "ALpJVyy2bEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
+      "cell_type": "markdown",
+      "id": "fd86b6b8",
+      "metadata": {
+        "id": "fd86b6b8"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "후보 모델별 평가 지표와 혼동행렬을 출력하고, 선택된 모델의 classification report, PR 곡선, ROC 곡선을 시각화합니다."
+      ]
     },
-    "id": "ALpJVyy2bEfC",
-    "outputId": "b964a332-cdc6-45a1-f4e9-484e34c728a0"
-   },
-   "outputs": [
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1800x400 with 3 Axes>"
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "22e5c22b",
+      "metadata": {
+        "id": "22e5c22b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "19590d47-25b7-487d-e6f8-44cba0981b54"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "불량 탐지 모델별 평가 지표\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                   모델  Train F1   Test F1  Train 재현율  Test 재현율  Train 정밀도  \\\n",
+              "0            LightGBM  0.878897  0.842573   0.972138  0.934129   0.801976   \n",
+              "1            CatBoost  0.779898  0.769792   0.943016  0.930498   0.664889   \n",
+              "2             XGBoost  0.743551  0.742522   0.953858  0.952801   0.609228   \n",
+              "3  LogisticRegression  0.468559  0.459742   0.861447  0.867739   0.321795   \n",
+              "\n",
+              "   Test 정밀도  Train 정확도  Test 정확도  Test PR-AUC  Test ROC-AUC  임계값(Train 기준)  \\\n",
+              "0  0.767363   0.944661  0.929896     0.955974      0.986500       0.502521   \n",
+              "1  0.656422   0.890052  0.888229     0.918066      0.974119       0.525871   \n",
+              "2  0.608278   0.864089  0.867292     0.902932      0.968021       0.489376   \n",
+              "3  0.312710   0.596354  0.590417     0.652634      0.815697       0.377797   \n",
+              "\n",
+              "                                             선택 파라미터  \n",
+              "0  {'model__subsample': 0.85, 'model__reg_lambda'...  \n",
+              "1  {'model__learning_rate': 0.045, 'model__l2_lea...  \n",
+              "2  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "3                                  {'model__C': 0.7}  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-92522316-5d0f-4078-a150-b0dc01f9e740\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>모델</th>\n",
+              "      <th>Train F1</th>\n",
+              "      <th>Test F1</th>\n",
+              "      <th>Train 재현율</th>\n",
+              "      <th>Test 재현율</th>\n",
+              "      <th>Train 정밀도</th>\n",
+              "      <th>Test 정밀도</th>\n",
+              "      <th>Train 정확도</th>\n",
+              "      <th>Test 정확도</th>\n",
+              "      <th>Test PR-AUC</th>\n",
+              "      <th>Test ROC-AUC</th>\n",
+              "      <th>임계값(Train 기준)</th>\n",
+              "      <th>선택 파라미터</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LightGBM</td>\n",
+              "      <td>0.878897</td>\n",
+              "      <td>0.842573</td>\n",
+              "      <td>0.972138</td>\n",
+              "      <td>0.934129</td>\n",
+              "      <td>0.801976</td>\n",
+              "      <td>0.767363</td>\n",
+              "      <td>0.944661</td>\n",
+              "      <td>0.929896</td>\n",
+              "      <td>0.955974</td>\n",
+              "      <td>0.986500</td>\n",
+              "      <td>0.502521</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CatBoost</td>\n",
+              "      <td>0.779898</td>\n",
+              "      <td>0.769792</td>\n",
+              "      <td>0.943016</td>\n",
+              "      <td>0.930498</td>\n",
+              "      <td>0.664889</td>\n",
+              "      <td>0.656422</td>\n",
+              "      <td>0.890052</td>\n",
+              "      <td>0.888229</td>\n",
+              "      <td>0.918066</td>\n",
+              "      <td>0.974119</td>\n",
+              "      <td>0.525871</td>\n",
+              "      <td>{'model__learning_rate': 0.045, 'model__l2_lea...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XGBoost</td>\n",
+              "      <td>0.743551</td>\n",
+              "      <td>0.742522</td>\n",
+              "      <td>0.953858</td>\n",
+              "      <td>0.952801</td>\n",
+              "      <td>0.609228</td>\n",
+              "      <td>0.608278</td>\n",
+              "      <td>0.864089</td>\n",
+              "      <td>0.867292</td>\n",
+              "      <td>0.902932</td>\n",
+              "      <td>0.968021</td>\n",
+              "      <td>0.489376</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LogisticRegression</td>\n",
+              "      <td>0.468559</td>\n",
+              "      <td>0.459742</td>\n",
+              "      <td>0.861447</td>\n",
+              "      <td>0.867739</td>\n",
+              "      <td>0.321795</td>\n",
+              "      <td>0.312710</td>\n",
+              "      <td>0.596354</td>\n",
+              "      <td>0.590417</td>\n",
+              "      <td>0.652634</td>\n",
+              "      <td>0.815697</td>\n",
+              "      <td>0.377797</td>\n",
+              "      <td>{'model__C': 0.7}</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92522316-5d0f-4078-a150-b0dc01f9e740')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-92522316-5d0f-4078-a150-b0dc01f9e740 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-92522316-5d0f-4078-a150-b0dc01f9e740');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_cd3cca05-d920-4877-9960-1bb43f276e7e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('event_model_result_table')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_cd3cca05-d920-4877-9960-1bb43f276e7e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('event_model_result_table');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
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+            "\n",
+            "[LightGBM] Train/Test 혼동행렬\n",
+            "Train FP=1904 FN=221 | Test FP=546 FN=127\n"
+          ]
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+            "text/plain": [
+              "   split  threshold    pr_auc   roc_auc  accuracy  precision    recall  \\\n",
+              "0  Train   0.502521  0.974105  0.992645  0.944661   0.801976  0.972138   \n",
+              "1   Test   0.502521  0.955974  0.986500  0.929896   0.767363  0.934129   \n",
+              "\n",
+              "         f1  false_positive_rate     tn    fp   fn    tp  \n",
+              "0  0.878897             0.062492  28564  1904  221  7711  \n",
+              "1  0.842573             0.071168   7126   546  127  1801  "
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+              "      <th>0</th>\n",
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+            "\n",
+            "[CatBoost] Train/Test 혼동행렬\n",
+            "Train FP=3770 FN=452 | Test FP=939 FN=134\n"
+          ]
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+        {
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+          "data": {
+            "text/plain": [
+              "   split  threshold    pr_auc   roc_auc  accuracy  precision    recall  \\\n",
+              "0  Train   0.525871  0.923610  0.975237  0.890052   0.664889  0.943016   \n",
+              "1   Test   0.525871  0.918066  0.974119  0.888229   0.656422  0.930498   \n",
+              "\n",
+              "         f1  false_positive_rate     tn    fp   fn    tp  \n",
+              "0  0.779898             0.123736  26698  3770  452  7480  \n",
+              "1  0.769792             0.122393   6733   939  134  1794  "
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+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>split</th>\n",
+              "      <th>threshold</th>\n",
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+              "      <th>roc_auc</th>\n",
+              "      <th>accuracy</th>\n",
+              "      <th>precision</th>\n",
+              "      <th>recall</th>\n",
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+              "      <th>0</th>\n",
+              "      <td>Train</td>\n",
+              "      <td>0.525871</td>\n",
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+              "      <th>1</th>\n",
+              "      <td>Test</td>\n",
+              "      <td>0.525871</td>\n",
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+              "      <td>0.974119</td>\n",
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+              "            style=\"display:none;\">\n",
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+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
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+              "      background-color: #434B5C;\n",
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+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
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+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[XGBoost] Train/Test 혼동행렬\n",
+            "Train FP=4853 FN=366 | Test FP=1183 FN=91\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   split  threshold    pr_auc   roc_auc  accuracy  precision    recall  \\\n",
+              "0  Train   0.489376  0.904375  0.967881  0.864089   0.609228  0.953858   \n",
+              "1   Test   0.489376  0.902932  0.968021  0.867292   0.608278  0.952801   \n",
+              "\n",
+              "         f1  false_positive_rate     tn    fp   fn    tp  \n",
+              "0  0.743551             0.159282  25615  4853  366  7566  \n",
+              "1  0.742522             0.154197   6489  1183   91  1837  "
+            ],
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>split</th>\n",
+              "      <th>threshold</th>\n",
+              "      <th>pr_auc</th>\n",
+              "      <th>roc_auc</th>\n",
+              "      <th>accuracy</th>\n",
+              "      <th>precision</th>\n",
+              "      <th>recall</th>\n",
+              "      <th>f1</th>\n",
+              "      <th>false_positive_rate</th>\n",
+              "      <th>tn</th>\n",
+              "      <th>fp</th>\n",
+              "      <th>fn</th>\n",
+              "      <th>tp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Train</td>\n",
+              "      <td>0.489376</td>\n",
+              "      <td>0.904375</td>\n",
+              "      <td>0.967881</td>\n",
+              "      <td>0.864089</td>\n",
+              "      <td>0.609228</td>\n",
+              "      <td>0.953858</td>\n",
+              "      <td>0.743551</td>\n",
+              "      <td>0.159282</td>\n",
+              "      <td>25615</td>\n",
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+              "      <td>366</td>\n",
+              "      <td>7566</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Test</td>\n",
+              "      <td>0.489376</td>\n",
+              "      <td>0.902932</td>\n",
+              "      <td>0.968021</td>\n",
+              "      <td>0.867292</td>\n",
+              "      <td>0.608278</td>\n",
+              "      <td>0.952801</td>\n",
+              "      <td>0.742522</td>\n",
+              "      <td>0.154197</td>\n",
+              "      <td>6489</td>\n",
+              "      <td>1183</td>\n",
+              "      <td>91</td>\n",
+              "      <td>1837</td>\n",
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+              "    .colab-df-convert:hover {\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e20587dc-4280-4215-9525-79cf4d682a03 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e20587dc-4280-4215-9525-79cf4d682a03');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[LogisticRegression] Train/Test 혼동행렬\n",
+            "Train FP=14401 FN=1099 | Test FP=3677 FN=255\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   split  threshold    pr_auc   roc_auc  accuracy  precision    recall  \\\n",
+              "0  Train   0.377797  0.655090  0.815531  0.596354   0.321795  0.861447   \n",
+              "1   Test   0.377797  0.652634  0.815697  0.590417   0.312710  0.867739   \n",
+              "\n",
+              "         f1  false_positive_rate     tn     fp    fn    tp  \n",
+              "0  0.468559             0.472660  16067  14401  1099  6833  \n",
+              "1  0.459742             0.479275   3995   3677   255  1673  "
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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "선택 모델: LightGBM\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "          정상     0.9825    0.9288    0.9549      7672\n",
+            "          불량     0.7674    0.9341    0.8426      1928\n",
+            "\n",
+            "    accuracy                         0.9299      9600\n",
+            "   macro avg     0.8749    0.9315    0.8987      9600\n",
+            "weighted avg     0.9393    0.9299    0.9323      9600\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x420 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x450 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "split    metric      Test     Train\n",
+              "0      F1 Score  0.842573  0.878897\n",
+              "1        PR-AUC  0.955974  0.974105\n",
+              "2       ROC-AUC  0.986500  0.992645\n",
+              "3           재현율  0.934129  0.972138\n",
+              "4           정밀도  0.767363  0.801976\n",
+              "5           정확도  0.929896  0.944661"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d7e24ac3-bce2-41c9-917e-6afbfe184563\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>split</th>\n",
+              "      <th>metric</th>\n",
+              "      <th>Test</th>\n",
+              "      <th>Train</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.842573</td>\n",
+              "      <td>0.878897</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.955974</td>\n",
+              "      <td>0.974105</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.986500</td>\n",
+              "      <td>0.992645</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>재현율</td>\n",
+              "      <td>0.934129</td>\n",
+              "      <td>0.972138</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>정밀도</td>\n",
+              "      <td>0.767363</td>\n",
+              "      <td>0.801976</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>정확도</td>\n",
+              "      <td>0.929896</td>\n",
+              "      <td>0.944661</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7e24ac3-bce2-41c9-917e-6afbfe184563')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d7e24ac3-bce2-41c9-917e-6afbfe184563 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d7e24ac3-bce2-41c9-917e-6afbfe184563');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
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+          "text": [
+            "재현율(Recall): 0.9341\n",
+            "정밀도(Precision): 0.7674\n",
+            "F1 Score: 0.8426\n",
+            "PR-AUC: 0.9560\n",
+            "임계값(Threshold): 0.5025\n"
+          ]
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+              "                         지표         값\n",
+              "0             정확도(Accuracy)  0.929896\n",
+              "1            정밀도(Precision)  0.767363\n",
+              "2               재현율(Recall)  0.934129\n",
+              "3                  F1 Score  0.842573\n",
+              "4                   ROC-AUC  0.986500\n",
+              "5                    PR-AUC  0.955974\n",
+              "6  오탐률(False Positive Rate)  0.071168\n",
+              "7            임계값(Threshold)  0.502521"
+            ],
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+              "      <td>정확도(Accuracy)</td>\n",
+              "      <td>0.929896</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>정밀도(Precision)</td>\n",
+              "      <td>0.767363</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>재현율(Recall)</td>\n",
+              "      <td>0.934129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.842573</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.986500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.955974</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>오탐률(False Positive Rate)</td>\n",
+              "      <td>0.071168</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>임계값(Threshold)</td>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "          예측 정상(0)  예측 불량(1)\n",
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+              "\n",
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+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1c6db79f-2910-4850-b848-7a46685a666d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "        const element = document.querySelector('#df-1c6db79f-2910-4850-b848-7a46685a666d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "\n",
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+              "<Figure size 480x400 with 1 Axes>"
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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x400 with 3 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x450 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x450 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x700 with 1 Axes>"
+            ],
+            "image/png": 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ff2Px4sX57jb+888/49q1a1plderUwaFDh2BmZpbnNfXq1cvzVV756dy58zttuvX06VN069YNX3/9tc4s5sSJE9G8eXNhN+qi+Omnn7Bnzx707dsXLi4uqFGjBmQyGdLT03Hr1i389ddfWLVqFWrUqIE+ffoI1726MZlGo4FGo9HZrEytVuuUS6VSnXedv06lUuW58ZlIJCryEv93Vb16dTRo0AA7d+6Er6+vUB4fH4+rV6/q/PhSWIaGhjr36ObmBjs7O0yYMAHLli17q88zV0JCAqpXr/7GOl9++SW2bNkCpVIJZ2fnfP/WiejTxASbiIioBEhNTUXXrl3x9OnTfN9/LJVKMWTIkDzPaTQaHDt2DH/88QeaNGkilBsYGMDS0hKWlpZITEzEkSNHCp1gAzkzsfnt/l2mTJk8Z7dLUsKhUCigUCjyPd+uXbu3ajcxMREDBgzAhAkTdM5ZWlrC09MTaWlpuHXrllB+7ty5PHfQ3rx5c559hIWFCf+eP38+YmNj8b///S/fmHJ3in+dTCbTWUL9tjIzM/Hs2TOtsvz+Rvz8/DBmzBiYm5ujffv2+OeffxAQEIDKlSu/9Su1qlevjv3796N+/fp4+vQpGjZsCADCruzdunXD0KFDoVAoULFiRWRmZuLKlSt4/vy5zv+uNBoNnj17hpcvXyIpKQl79uzBzp07sWLFijfG0KFDBwQFBWHlypX4/vvv3+o+iOjjxQSbiIioBEhJSUF6ejoSEhLyTbDfRCQSoXXr1ggKCsLYsWNhaWmJsmXLIisrC/fu3UN4eDiOHj2KZcuWFandtLS0fJ8nTk9Pf6tYPwZfffUVZs2ahapVq8LJyQmfffYZDA0NkZ6ejjt37uDw4cM4c+YMxowZI1zTokULXLly5a377Ny5M2bNmlUc4b8ViUSCxYsXY/HixVrlAQEB8Pb2hkQi0Zopd3d3xy+//IJffvkFwcHBqF27NsaPH4/58+drbUAmk8nyfC47r/IxY8Zg8uTJaNeuHTp37iy82qt8+fJYt24d9u7diz179uDXX39FWloaypYti5o1a+Lrr7+Gi4uL0I5UKsXNmzdhb28PqVSKSpUqoUWLFvjtt9+0NoozNDTUicHIyAjdunXD4cOHhXeevxpzfruQE9GnQaQpzO4RRERE9F7lzmCnpqa+MWkViUQ4dOhQnpuVvfoM9u3bt/Hs2TMYGBgIz2D37dtXa3a7ICNHjsSRI0fyfQZbKpVi0qRJGDx4cKHbfFV8fDwmT56Mw4cPv9X1ebGyssKhQ4cKXOabl7CwMGzfvh3r168vVP0zZ85g/fr1OH36tLBZWu7O2Q4ODhg8eHCxPmtc2kRGRuLZs2do0aIFZDIZbty4gV9//RUODg55zvwTEX0MmGATERF9pNRq9Se5wZY+ZGZmIjMzE6amphzz/y8mJga//PILrl27hpcvX6J27dro27cvvv76a32HRkT03jDBJiIiIiIiIioG/ImViIiIiIiIqBgwwSYiIiIiIiIqBkywiYiIiIiIiIoBX9NFVIqcPXsWGo0GBgYG+g6FiIiIiOiTkJWVBZFIBBsbmwLrcgabqBTRaDTCf/RhaTQaKJVKjr2ecPz1i+OvPxx7/eL46xfHX3849tqK8v2bM9hEpYiBgQGUSiXq16+PMmXK6DucT0p6ejouXbrEsdcTjr9+cfz1h2OvXxx//eL46w/HXltCQkKh63IGm6gUEolE+g6BiIiIiIhewwSbqJSRyWQwNjZ+6+tVKnUxRkNERERERLm4RJyoFAoevQj/Xbtb5Otq1bfA1CVj30NERERERETEBJuoFPrv2l1cS7ip7zCIiIiIiOgVXCL+Cdm+fTsCAgLeWOfy5cv48ssvkZWV9YGierOSFg8REREREVF+OIP9Cfnyyy/x5ZdfvrGOpaUltm/f/oEiKlhJi4eIiIiIiCg/nMH+xFlbW+s7BC0lLR4iIiIiIqLCYoL9iXv58qW+Q9BS0uIhIiIiIiIqLCbYJdT06dOxdOlSrbIzZ87A0tISx44d0ypfvXo15s6di6NHj6J3795QKBRwdHTEiBEjkJSUJNRbtWoVpk+fDgCYM2cO5HI5AEAul6Nnz54AgAsXLsDT01O4ZuHChfjll18wffp0KBQKKBQKjBo1Cg8ePNCKIS4uDr169YKNjQ3c3d2xcuVKBAQEYO/evYW638LG88svv2DevHnw9/eHvb093Nzc8PvvvyMzMxMBAQFwcnJC27ZtdfpVqVRYsGABXFxcYGtri5EjRyIlJaVQsQFAUlIShg0bBgcHBygUCkycOFE4l5GRgRkzZsDBwQH29vbw9/dHWlqa1vUxMTHo168f5HI55HI5fH19C903ERERERGVDkywSyg3NzeEh4drlR0+fBhNmjTBkSNHtMr/+usvuLu7QyKRICgoCNHR0YiKikLdunUxe/ZsoZ5SqYRSqQQATJkyBfHx8QCA+Ph47NixQ6jz6oZiIpEI69evR/ny5REREYFjx47BxMQEM2fOFOrcv38fPj4+6N69O2JiYvD7778jJiYG+/fvL/TmZIWNRyKRYNOmTWjSpAlOnjyJ3377Db/++iu+++47VKtWDVFRUVi9ejVCQkJw48YN4bqFCxfi+PHj2Lp1K06ePIl69eph/PjxhYoNAIKCgmBra4uYmBjExMTgu+++E84FBATg3r17OHDgAI4cOQK1Wo3AwEDhfHR0NMaPH4+vv/4asbGxiIuL0xo/IiIiIiL6ODDBLqGcnZ1x7do1PHr0SCg7duwYpk6diqioKKHs8ePHSExMhFwuR8uWLdGwYUOIxWJIpVJ89dVXQtL6LiwsLDBhwgSYmJjA0NAQY8aMQWRkJFQqFQBg586dcHJygre3NwwNDVG5cmXMnTtXSOaLW/369TFw4EAYGBigVq1aaNeuHRITEzFmzBhIJBLUrl0bbdq0wYkTJwAAT58+xYYNGxASEoKaNWvCyMgIEyZMwL179/DPP/8Uqs/ExES4urpCLBZDJBKhatWqAIDr168jIiICISEhqFSpEkxNTTF9+nRERETg4cOHAHKS86lTp6JTp06QSCQQiUQwMzN7L2NDRERERET6wwS7hCpTpgzkcjmOHz8OALh9+zYkEgnkcjkkEglu3rwJAIiMjISrqyukUikeP36MuXPnonv37lAoFOjevTuePHnyzrFYWlpCJBIJx9WrV0dWVpaQ/F++fBn29vZa15ibm7+3DcsaNGigdVyxYkU4ODholZmZmQn3fuXKFVSpUgX16tUTzovFYjRr1gyXL18uVJ+9e/eGv78/IiMjtcovXLgAW1tbVKhQQSgrW7YsatWqhWvXriExMRE3b95Ep06dinCHRERERERUGvE1XSVY69atcfz4cXTv3h2RkZFo1aoVAKBly5Y4fvw46tSpg8jISLRp0wbZ2dkYMGAALCwsEBAQAEtLSzx9+hQeHh7vHIehoaHWsYGBAQBArVYDAJ49e4by5cvrXFelSpV37jsvEolEpyyv/nMlJyfjzp07wjPeubKzs3XK8jNw4EA0btwYP//8M0JDQzF9+nQ0adIEycnJiI2N1WknKysLz549g4GBAczMzCCV8n9qREREREQfO37rL8Fat26NRYsWQa1W48iRIxgzZgyAnAT7t99+Q9++fREdHY3AwEDExcXh2bNn2LVrl5CAXrly5YPEaWJiorWUPVdeZfpgYmKCBg0aYNeuXe/UjoODA/73v/9h7dq1GDJkCCIiImBiYgJ3d3csWbIkz2sSExPx6NEjKJVKyGSyd+qfiIiIiIhKNi4RL8EsLCxQpUoVxMbG4tq1a8KSa4VCgfPnz+PkyZNo2LAhypUrh8ePH6NatWpas7uvPqudH6lUCo1G805xyuVynb4ePHiACxcuQCwu2p9YccTzuqZNm+LmzZs6O5+/rcGDB0MsFuPq1ato1qwZLly4kO/rxerUqYPatWsLm7YREREREdHHiwl2CdeqVSvMmzcPzs7OQrJqaGgIKysrzJ8/X1gC3qxZM1y5cgVnzpyBWq1GREQEjh8/nudy6leZm5vj7NmzyMzMfOtNyfr27Yv//vsPK1asQGZmJu7fv48JEyZAKpVqPZtcGMURz+uqVq2KNm3aYPz48cLO4i9evNB5nvpNzpw5A6VSCY1Gg3379iEzMxO1a9eGjY0NatSogUmTJgmvRHvy5AlOnjwJIGcXdn9/f4SEhGDfvn3CsvrHjx8Xy70REREREVHJwQS7hPPw8MDFixfRrl07nfKrV68KCXatWrUwZ84cTJ06FQ4ODti4cSMWLFgAmUwmvOZKJpPpLFMeNWoUxowZg7Zt2+LatWuQyWTCM9ZATjKf19JmQ0ND4bliIyMjbNmyBadOnYKjoyP69OkDDw8PVKhQAVZWVkW638LE8/oz4Xnd1+tlwcHBsLKywsCBA2FjYwNPT09hA7nCmDdvHmxtbSGXy7F27VqEhoaiYsWKAIDQ0FCUK1cOPXr0gK2tLbp3746EhATh2latWmHp0qVYvXo15HI57Ozs4O/vX6RxISIiIiKikk+kKe71uPTJUavVOHz4MFxdXVGmTBk8fPgQ8+fPh7GxMb7//nt9h/dRyU3cl0zahGsJN4t8fX2rOlh26MdijurTkJ6ejkuXLqFx48YoU6aMvsP55HD89Yvjrz8ce/3i+OsXx19/OPbacr+DF2bykJuc0TtTqVT4/fffMX36dCiVSlSoUAE9e/bEyJEjAQD//PMPBg4cmO/1lSpVwqFDhz5UuDrGjRsnvDM7L1OmTEGvXr0+YERERERERFQaMcGmd2ZgYIAVK1bke75p06aIj4//gBEVza+//qrvEIqsVn2LD3odEREREREVjAk2USk0dcnYt75WpVJDIuH2C0RERERExY3fsolKGaVSiYyMjLe+nsk1EREREdH7wW/aRKUQ9yYkIiIiIip5mGATlUIikUjfIRARERER0WuYYBOVMjKZDMbGxu+1D5VK/V7bJyIiIiL6GHGTM6JSaO6kVbh9/f57abtmvc/g/9Ow99I2EREREdHHjAk2USl0+/p9XLv4n77DICIiIiKiV3CJOBEREREREVExYIJNREREREREVAyYYFO+Ll++jC+//BJZWVkAgOXLl2P58uUfNIapU6fijz/++KB9EhERERERvQ0+g035srS0xPbt24VjpVL5wWMIDg7+4H0SERERERG9Dc5gU4kRGhqK0NBQfYdBRERERET0VjiDTSVGdna2vkMgIiIiIiJ6a5zB/sTNmzcPISEhWmUZGRmQy+U4f/48PD09tc6pVCosXrwYrVu3ho2NDfr374/Lly8L55OTk+Hu7o6EhAR07NgRCoUCSqUSiYmJ8PHxgZOTExwcHNCrVy/Ex8cDAB4+fAgHBwesWLECK1asgFwux4EDBwAAPj4+CAsLE9pPS0vDzJkz4eLiArlcji+++ALHjx/XirF169Y4evQoevfuDVtbW3h4eGDFihVFGpd9+/Zh4sSJ2LFjB1xdXTF06FAAQL9+/bB9+3b0798ftra28PLywvnz53Hnzh0MGjQICoUCX3zxBS5evCi0lZWVhRkzZsDJyQn29vbo0qUL0tLSihQPERERERGVfJzB/sS1bt0aAQEB8PPzE8pOnDiB+vXrIysrS9jgLNfWrVvRvn17IQEODg7GkCFD8Oeff8LU1BRZWVlIT0/H8uXLsXHjRpiZmUEsFiMzMxODBw+GnZ0dZDIZDh8+jPHjxyMiIgLm5uY4deoUFi1aBAAYO3as0J9SqdR69nvs2LEoW7Ys9u7di4oVK+LYsWOYNGkSli5dCjs7OwCASCTCrFmz8MMPP8DBwQG3bt3CwIEDUbduXbRt27ZQ45KZmYlr165BJpMhIiICUmnO/1QkEgl++eUX/Pzzz1AoFIiMjISfnx/Mzc3xzTffwN3dHRERERg7diwOHToEqVSKLVu2IDk5GeHh4ShTpgySk5NhYmLyFp8WERERERGVZJzB/sTZ2tri6dOnSExMFMrCw8N1Zq5zGRoaYsaMGTAyMoKRkRFmzpwJqVSKw4cPC3WePXuGbt26wdzcHGJxzp+YpaUlnJycIJPJAABt27aFWCzGzZs3Cx1rTEwMLl26hLlz58LMzAwikQju7u7w8fHBr7/+qlW3X79+cHJygkQiQd26ddGzZ0+Eh4cXui8A+Pfff+Hn5weZTCbcBwB4eXnB0dERIpEIrVq1gkwmQ5MmTeDu7g4A8PDwgJGREa5fvw4ASExMhJ2dHcqUKQMAqFq1KkQiUZFiISIiIiKiko8J9idOIpHAw8MDR48eBQCo1WpERkbmm2C7uLhAIpEIx2KxGK6urrhy5YpWPblcrnWcnZ2N9evXo2/fvnB1dYWdnR0ePHiAJ0+eFDrW+Ph4tG7dGsbGxlrlHTt2xJkzZ6BSqYSyJk2aaNWxsLBASkpKofsCgHr16sHMzEynvEGDBlrHFStWhIODg1aZmZmZcG9eXl5Yv349Nm3apJed2ImIiIiI6MNggk1o27Ytjhw5AgA4c+YMqlevjurVq+dZN6+Es1KlSsjIyBCOxWIxKlSooFVn1qxZ2LZtG4YMGYI9e/YgPj4en332GTQaTaHjTElJQZUqVXTKK1eujKysLDx69Egoy50pzyWVSrUS8MJ4/R5yvfoDQ67y5cvn246NjQ22bNmCM2fOoGPHjti/f3+R4iAiIiIiotKBz2ATXFxcMHnyZKSlpSEiIiLf2WsASEpK0il79OiRVkIuEom0lkBnZmZix44d2LNnD+rWrQsA0Gg0RZ5RrlChQp7XPHjwAFKp9I1J7tvIK5F+WzVr1sTPP/+MU6dOYcSIEahevTpatGhRbO0TEREREZH+cQabYGhoCEdHRxw/fhzh4eHo0KFDvnUjIyO1dsBWq9U4ceIEGjVqlO81z58/R1ZWFmrUqCGUxcTE6GygJpVK3zij7e7ujqNHj2rNlgPAgQMH4OTkBENDw3yvLSkcHBzg5uaGU6dO6TsUIiIiIiIqZkywCUDOMvF169bB2NgYNWvWzLde+fLl4efnh7S0NGRmZmLWrFkoX748WrVqle81lSpVgoWFBTZv3gyNRoN///0XP/74I+rUqaNVz9zcHBcuXIBarcbz58912rGzs0OTJk3g7++P1NRUaDQaHDx4EMuXL8eYMWPe9tbfu0uXLuHZs2cAgMuXL+PUqVNo3LixnqMiIiIiIqLixgSbAACtWrXC1atX0aVLF6FMJpPBwMBA67hHjx6wsrJCp06d4OzsjJSUFKxfv16oZ2BgoDOTLBKJEBoaioMHD8LBwQETJkyAn58fatasCbVaLdRr164d0tPTYW9vj+DgYKHPV5+nXrhwIapUqQIvLy/Y29tjzZo1+PXXX7WWW79+DZAzS/962ZsYGhrmOSMuk8l0yvPq79WyPXv2wM3NDTY2NhgzZgzGjh2Lli1bFjoWIiIiIiIqHUSaouwyRUR6lZCQAADYvz4Wt6/ffy991Kz3Gfx/GvZe2i7N0tPTcenSJTRu3Fh45Rp9OBx//eL46w/HXr84/vrF8dcfjr223O/gVlZWBdblJmf0yenduzdu3LiR7/lFixbBycnpA0ZUdO87AVap1JBIuMCFiIiIiKgomGDTJ2fbtm36DuGdKJVKZGRk6LwPvDgxuSYiIiIiKjp+iyYqhfhkBxERERFRycMEm4iIiIiIiKgYMMEmKoVEIpG+QyAiIiIiotcwwSYqZWQy2Xt9/jqXWqUuuBIREREREQm4yRlRKRQSsBG3E5PfW/s1P68KvyDv99Y+EREREdHHiAk2USl0OzEZ16/c0XcYRERERET0Ci4RJyIiIiIiIioGTLCpRJg6dSr++OMPfYdBRERERET01rhEnEqE4OBgfYdARERERET0TjiDTfSBeHp6Ijn5/W1MRkRERERE+sUEm+gDycrKQlZWlr7DICIiIiKi94QJNhW76dOnY+nSpVplZ86cgaWlJY4dO6ZVvnr1asydOxc+Pj4ICwsDACQnJ6Nr166IiIhA165dIZfL0bFjRxw8eLDIsVy6dAnDhw+HXC6HXC5H7969hXOJiYkYMWIE7O3t4eDggBEjRuDmzZvC+eTkZNjY2OR5f6tWrRKOW7dujaNHj6J3796wtbWFh4cHVqxYIZxft24d5HI57t27By8vL7i6uiI7O7vI90JERERERCUbE2wqdm5ubggPD9cqO3z4MJo0aYIjR45olf/1119wd3eHUqmEUqkEkDPT+/DhQ/zyyy8IDQ1FfHw8xo0bB39/f1y6dKnQcVy/fh2DBg1Cq1atcPLkScTHxyM0NBQA8OTJE/Tv3x82NjaIiopCVFQU5HI5vv76azx79kyIIzMzU6fdV2MFAJFIhFmzZuHbb79FXFwcVq1ahY0bN+Lw4cMAgEGDBiE+Ph7Vq1fHnj17EBUVBamU2x8QEREREX1smGBTsXN2dsa1a9fw6NEjoezYsWOYOnUqoqKihLLHjx8jMTERcrlcp43U1FRMmzYNNWrUAAB06NABPXr0wKZNmwodx48//ohBgwahf//+kMlkAIBKlSoBADZs2IBmzZrhm2++gaGhIWQyGYYNG4amTZtiw4YNRb7nfv36wcnJCRKJBHXr1kXPnj11fmQgIiIiIqKPGxNsKnZlypSBXC7H8ePHAQC3b9+GRCKBXC6HRCIRlmFHRkbC1dU1z9lcmUwGBwcHrTIXFxdcvXq1UDG8fPkSJ06cgJeXV57n4+Li0KlTJ53yjh07Ii4urlB9vKpJkyZaxxYWFkhJSSlyO0REREREVHoxwab3onXr1kKCHRkZiVatWgEAWrZsqVXeunXrPK8vX748RCKRVpmpqSmeP39eqP6fPn0KlUqFKlWq5Hk+JSUlz3NVqlRBUlLSG9vWaDQ6Zbkz5LmkUilUKlWhYiUiIiIioo8DE2x6L1q3bo2oqCio1WocOXJEJ8HOzs5GdHQ03Nzc8rz+8ePHUKvVWmXJycmoWrVqofovX748JBIJ7t+/n+f5ihUr5jnDnJKSAjMzMwCARCKBWq3WiePBgweFioGIiIiIiD4tTLDpvbCwsECVKlUQGxuLa9euwdraGgCgUChw/vx5nDx5Eg0bNkS5cuXyvD47OxsxMTFaZceOHUOLFi0K1b+RkRGcnJywffv2PM+7u7tj//79OuUHDhwQZtVzn9e+e/eucD4tLQ3nz58vVAyvk0qlec5+ExERERHRx4EJNr03rVq1wrx58+Ds7AyxOOdPzdDQEFZWVpg/fz48PDzyvbZMmTKYN28erl+/DgDYvXs3jh07hr59+xa6/4kTJ2LTpk3YsGGD8P7p1NRUAED//v1x8eJFLFu2TNgV/Ndff8WlS5fQr18/AP/3HPjSpUuRnZ2Nly9f4vvvv0f16tXfajzMzc1x7tw5ZGVlISMj463aICIiIiKikosJNr03Hh4euHjxItq1a6dTfvXqVa0EWyaTaT3HXLFiRfj6+sLX1xe2trZYu3Ytli1bVugl4kDOxmObN2/Gn3/+CYVCATs7OwwcOBAAUK5cOWzevBkXLlyAq6srWrZsiatXr2LTpk0wNTUV2vjhhx+QlJSEli1bwsvLC1ZWVvDw8NCK9fXYAQg7k79q+PDhmDdvHtzc3BAbG1vo+yAiIiIiotKBL+Ol98bGxgZXrlzRKe/Xr58wS5xrxYoVOvU8PDzeOMtdGJaWlvjtt9/yPFerVi0sXbr0jdfXrFkTa9eufWOdgwcP6pR17twZnTt31ip7deM3IiIiIiL6+DDBphJHKpXCwMDgjXVcXV3x8uXLPM+JRCJs27YNn3/++fsIr0So+XnhZ/JLYvtERERERB8jJthU4lSrVg2HDh16Y52oqKgPFE3J5Bfk/d77UKvUEEv4FAkRERERUWHx2zNRKaNUKj/IJmlMromIiIiIiobfoIlKIb7ui4iIiIio5GGCTURERERERFQMmGATlUIikUjfIRARERER0WuYYBOVMjKZDMbGxh+0T7VK/UH7IyIiIiIqjbiLOFEp9FPQVty+lfJB+qpZuwomBfT5IH0REREREZVmTLCJSqHbt1Jw/d97+g6DiIiIiIhewSXiRERERERERMWACfZ70KFDByQnJ+s7jDxdvnwZX375JbKysgAAy5cvx/Llyz9oDFOnTsUff/zxQfskIiIiIiJ637hE/D1QKpVCAlvSWFpaYvv27cKxUqn84DEEBwd/8D6JiIiIiIjeN85gl3ABAQHYs2ePvsN4a6GhoQgNDdV3GERERERERO8dZ7BLuOzsbGRnZ+s7jLdWmmMnIiIiIiIqCs5gv8H06dOxdetWzJo1CwqFAkuWLEFaWhpmzpwJFxcXyOVyfPHFFzh+/LjOtffu3cOIESOgUCjg6OiImTNnIjMzU6tOZGQkevXqBblcDldXV8yaNQtpaWkAgISEBMjlcoSFhSEwMBByuRznz58vMOZ58+YhJCREqywjIwNyuRyPHj3ChQsX4OnpqXVepVJh8eLFaN26NWxsbNC/f39cvnxZOJ+cnAx3d3ckJCSgY8eOUCgUUCqVSExMhI+PD5ycnODg4IBevXohPj4eAPDw4UM4ODhgxYoVWLFiBeRyOQ4cOAAA8PHxQVhYmNB+Yca0devWOHr0KHr37g1bW1t4eHhgxYoVBY7Hq65evYq+ffvC3t4eCoUCc+fOFc5lZ2dj6dKlaNu2Lezs7ODk5IT9+/cDADQaDdasWYN27dpBLpejffv2WLdunVbbef2tAEBqaiomTJgAOzs7ODo6Ijg4uMQ+PkBERERERO+GCfYbKJVKbNu2DfXr10dsbCxGjhyJsWPHIjU1FXv37kVcXBzGjh2LSZMm4fTp01rX+vv7o3fv3oiNjcXWrVtx/PhxrWePT506BT8/P4wePRpxcXHYu3cvHj9+jPHjxwMArKysEB8fjy5dumDGjBmIj4+HtbV1gTG3bt0ahw8f1io7ceIE6tevj0qVKuX5fPjWrVuRkpKCAwcOIDo6GvXq1cOQIUOEZD8rKwvp6elYvnw5Nm7ciOjoaMhkMmRmZmLw4MGIjIzEqVOnMHLkSIwfPx5KpRLm5uY4deoUfHx84OPjg/j4eHTs2FEY11ef/S7MmIpEIsyaNQvffvst4uLisGrVKmzcuFHnXt/E398fX331FeLi4nDy5EkMHjxYOPf999/jxIkTWLVqFU6fPo0jR47Azc0NQM4y9x07dmDJkiWIj49HaGgo/vjjD60EP6+/FQAYPXo0jIyMcPToUezfvx9XrlwRkm8iIiIiIvq4MMEuQHZ2Nvr16wcgJym+dOkS5s6dCzMzM4hEIri7u8PHxwe//vqr1nVdunSBh4cHAKBOnTqYOXMmdu3aJSSWCxcuhI+PDzw8PCASiVCxYkXMmTMH//zzD+Li4t46XltbWzx9+hSJiYlCWXh4uM6s9asMDQ0xY8YMGBkZwcjICDNnzoRUKtVKXp89e4Zu3brB3NwcYnHOn42lpSWcnJwgk8kAAG3btoVYLMbNmzcLHW9MTEyhx7Rfv35wcnKCRCJB3bp10bNnT4SHhxe6r8TERLRs2RIAIJFIUKVKFQDA+fPnERERgeXLl6NOnToAACMjI5iamuL58+dYtWoVgoOD0bBhQwBAvXr1EBwcjJUrVwo/QgDafytisRjHjx/H/fv3MWvWLJQtWxZmZmYIDAzEli1bOItNRERERPQRYoJdAHt7e+Hf8fHxaN26NYyNjbXqdOzYEWfOnIFKpRLK3N3dteo4OztDpVLh1q1bUKlUOHv2LDp16qRVx8jICK1atcKpU6feOl6JRAIPDw8cPXoUAKBWqxEZGfnGBNvFxQUSiUQ4FovFcHV1xZUrV7TqyeVyrePs7GysX78effv2haurK+zs7PDgwQM8efKk0PEWZUybNGmiVcfCwgIpKSmF7qt3794YOXIkzp49q1UeHh4ODw8PmJqa6lxz6dIlVKxYEc2bN9cqt7KyQrly5bSW0r/6twLkJO5ubm6QSv9vq4M6depAo9EgKSmp0HETEREREVHpwAS7ABUrVhT+nZKSIsx6vqpy5crIysrCo0eP8rwOyEl8K1SogIyMDDx69AjZ2dl5tlWlSpV3fod227ZtceTIEQDAmTNnUL16dVSvXj3f+mZmZjpllSpVQkZGhnAsFotRoUIFrTqzZs3Ctm3bMGTIEOzZswfx8fH47LPPoNFoCh1rUcY0d6Y8l1Qq1UrACzJ16lT4+PggICAAw4cPx+3btwHkPCedVwxvig/I+axeTZRf/8yTk5Oxa9cuyOVyrf9evnyJp0+fFjpuIiIiIiIqHZhgFyB3OTQAVKhQIc8Z0wcPHkAqlaJ8+fJC2etJskqlwpMnT1CpUiWUK1cOEokkz7ZSUlJ0ErWicnFxweXLl5GWloaIiIg3zl4DyHM29dGjRzA3NxeORSIRRCKRcJyZmYkdO3Zg0aJFaNeunZCkF2VGGSjamBaHtm3bYu/evahTpw5GjBgBjUYDMzMz3Lt3r0jxATn3+uqPE6/+rQCAiYkJ+vTpg/j4eK3/Lly4gGbNmhXfTRERERERUYnABLsI3N3dcfToUa2ZXQA4cOAAnJycYGhoKJS9uks2kPOscZkyZVCtWjUYGRlBoVAIu1TnevnyJY4ePSo8uw3kzNIWZUYYyHmm2tHREcePH0d4eDg6dOjwxvqRkZFazxKr1WqcOHECjRo1yvea58+fIysrCzVq1NC6x9efLS4o/qKMaXERi8WYOHEirl27hocPH6J169YIDw9HamqqTl0bGxs8e/YMFy5c0CpPSEhAWloabGxs8u2nWbNmiI+PL/LnR0REREREpRMT7CKws7NDkyZN4O/vj9TUVGg0Ghw8eBDLly/HmDFjtOqePn0af/zxBwDg9u3bCAwMxLBhw4RnnUePHo0VK1YgPDwcGo0GqampGDduHKytrbV2Czc3N8f58+eh0Wi0kuCCtG3bFuvWrYOxsTFq1qz5xrrly5eHn58f0tLSkJmZiVmzZqF8+fJo1apVvtdUqlQJFhYW2Lx5MzQaDf7991/8+OOPwiZhr8Z/4cIFqNVqPH/+XKedoozpu1CpVMIz3SqVCps2bcJnn32GihUrwsbGBq1atcLw4cNx69YtADm7gr948QImJiYYPnw4pkyZgqtXrwIALl++jIkTJ+Kbb77ReXb8VW3btsXz588RGBgoJO/JycnCq8yIiIiIiOjjwgT7DWQymc4M6sKFC1GlShV4eXnB3t4ea9aswa+//ooWLVoIdQwMDBASEoI///wTDg4O6Nu3L/r06QMfHx+hjlwux4IFC7Bs2TLY29vDy8sLtWrVwvz587X669WrF86ePQu5XI7Vq1cXOvZWrVrh6tWr6NKli849GRgYaB336NEDVlZW6NSpE5ydnZGSkoL169cL9QwMDHTGQSQSITQ0FAcPHoSDgwMmTJgAPz8/1KxZE2q1WqjXrl07pKenw97eXnhNmUwm03qeujBj+vo1QM5M/etl+Xn58iWmTp0Ka2trKBQKREZGYvny5cIGZCEhIWjVqhW8vb1hZ2cHFxcX/PXXXwBy3tv91VdfYcyYMZDL5Rg3bhwGDBiAIUOGaMX3+hgZGRlh/fr1SE1NhaenJ+zs7ODt7V2kXdaJiIiIiKj0EGm4fpWo1EhISAAArFp4BNf/zfu58eJWr0F1/LrS94P0VZKlp6fj0qVLaNy4McqUKaPvcD45HH/94vjrD8devzj++sXx1x+Ovbbc7+BWVlYF1pUWWINKjNyZ0Px+ExGJRIiKinovzy2XdL1798aNGzfyPb9o0SI4OTl9wIjer5q1897ZvLT3RURERERUmjHBLkXMzMwQFxen7zBKpG3btuk7hA9qUkCfD9qfWqWGWMInSoiIiIiI3oTfmIlKGaVSqbPr+vvG5JqIiIiIqGD81kxUCnHrBCIiIiKikocJNhEREREREVExYIJNVAqJRCJ9h0BERERERK9hgk1UyshkMhgbG+ut/1ffc05ERERERP+Hu4gTlUIhIb/j9u2HH7zfmjXN4ef3xQfvl4iIiIioNGCCTVQK3b79ENev39d3GERERERE9IqPYon4gwcP0L17dzx69Oi993Xs2DEMHz78vffzsSjsZzN06FBERUV9oKiIiIiIiIiK30cxg125cmXs2rXrg/Tl5uYGNze3D9LXx6Cwn83q1avffzB5WL58OZKSkjBjxgydc0lJSWjVqhVMTU21yh0cHLB06VIAwNKlS7F06VIYGRlp1dm8eTNGjBiBZ8+eAQBevnwJsVgMmUwGAKhfvz62bt36Pm6JiIiIiIj05KNIsN/Wnj17cOrUKQQFBek7lE9KQEAAHBwc4OXlpe9QoFQqoVQq8zyXnZ0NsViM+Pj4fK9XqVTo1q0bfvjhB51zERERwr8nT56MWrVqYdSoUe8eNBERERERlUgfxRLxt5WdnY3s7Gx9h/HJ4bgTEREREdHHqEQk2EOGDEFERARGjx4NR0dHKBQKzJ49W5hZTE1Nxbhx4+Di4gJ7e3t07twZhw8fFq5/+PAhmjdvLhz/8ccfmDp1KubPny9cM2DAACQmJgLIec2Qq6srAgMDERYWBrlcjjVr1hQq1gMHDmDo0KHCsZ+fH1atWoURI0bAzs4O7du3x9GjR/HkyROMHTsWCoUCnTt3xsmTJ7Xa2bFjB7y8vGBvbw8XFxf4+fkJy4kBQKPRYPHixXBzc4ONjQ169+6N8+fPw8bGRqudkydPolu3brCxsUGnTp20Zk0Lw8HBAWfOnEHv3r1hb28PDw8PbNq0Saferl270KVLF8jlcrRq1QoLFixAVlaWcP7UqVPo3r075HI5nJ2dsW7dOgDan01CQgLkcjnCwsIQGBgIuVyO8+fPAwA6d+6Ms2fPCu2lpKRg4sSJUCgUsLe3h7e3Ny5cuCCcV6lUaNasGaKjo9G1a1fY2tqiffv22LFjR5Hun4iIiIiIqLiUiAQ7KysLQUFB8PT0RExMDHbv3o24uDjMnz8fAJCRkQEvLy+Eh4cjLi4OwcHBmDZtGh48eAAgZ0Y0MzNTaE8kEuHAgQNISUnBvn37cPLkSVhbW2PChAnQaDQQi8WIiorCjBkz0KVLF8THx2PIkCGFivX1JcUSiQRLlixBt27dcOrUKcyfPx9TpkzB5MmT4ebmhtjYWMyZMwd+fn548uSJcJ2pqSkWL16MU6dOITw8HGq1GosXLxbOr127Fjt27MDSpUtx+vRpfPvttxg3bhxevnwp1Pn333/h6+uLUaNGIS4uDjNnzsSUKVNw/fr1Qo+9UqlEQEAAAgMDERcXh7lz52LBggVaifru3bsxf/58BAYGIj4+Hlu2bMHZs2eFpfVqtRrjx4/H5MmTER8fj6NHjwrLv1/9bKysrBAfH48uXbpgxowZiI+Ph7W1tc64ZmVlYdCgQahYsSLCw8MRExODXr16Yfjw4fjvv/+Ecc/KysKcOXMQHByM06dPIyQkBLNnz8alS5cKff9ERERERETFpUQk2AC0nsmtVq0agoKCsG3bNmRmZsLCwgJt2rQRNpKytrZG3bp18ffff+fbnkwmQ2BgICpUqAADAwOMHj0aV69exf37xf9qo5YtW6Jjx46QSCRo1qwZmjdvDpFIhC+//BIA0Lx5czRt2hRnzpwRrmnfvj1q1aoFkUgEIyMj9OrVS+tZ3y1btmDq1Klo1qwZxGIxnJycMGjQIKjVaqFOaGgo+vbtC09PT0ilUjg4OKBPnz7YsmVLoWPPyMjAsGHD0LhxYwCAvb09vvnmG2zcuBFATvL866+/YsqUKbCzswMAfPbZZwgJCcHu3btx+/ZtPHnyBJmZmZDL5QByxt7MzOwtRzPn2XixWIxp06bB1NQUEokE3bt3R+fOnREaGqpVd+TIkbCysoJIJIK1tTXatGmD8PDwt+77dSqVCnK5XOu/FStWFFv7RERERET08Sgxm5y5urpqHecmqXfu3EHdunWxa9cu7Nq1C4mJiUhLS0NmZqbWjPDr6tatC0NDQ+HYyMgIZmZmSE5ORvXq1Ys19gYNGmgdV6xYEY0aNdIqMzMz04r33r17WLZsGc6cOYOUlBRkZWWhYsWKAIAXL17gv//+ExLWXG3atMGcOXOE43PnzsHb21urTvPmzbF27doixf/62Lu4uAhLvO/fv4+UlBS0a9dOq07VqlXRvHlznD59Gt27d4ejoyNGjBiByZMno379+kXq/3Xx8fHo0KEDRCKRVnnHjh0xZcoUrbImTZpoHVtYWCAlJeWd+n+VRCJ54yZnQM4M/6FDh4RjIyMjHDt2DGJxifn9ioiIiIiIPoASk2CXL19ep8zU1BRpaWlYuXIlfvvtN0ycOBFOTk6oVKkSBg0aBI1Gk297rybXuQwMDKBSqYo1biAnCXtdXveT68mTJ+jduzfc3NwwZ84c1K9fHwkJCZg8eTIA4Pnz53m2UaVKFa3jlJQUDBs2TCsRVavVqF27dpHir1ChgtaxqampEENKSgrMzMwgler+qVSpUgXJyckAgEWLFmHHjh3w8fGBjY0Npk2b9taz2CkpKbC1tc2zv6SkJK2y1z9nqVT6Xj7jN8lvF3EiIiIiIvq0lJgE+9GjR1rHarUajx49QtWqVbFp0yb88MMPaNmypXA+N7Erjf766y9YWFggODhYKHs1cSxTpgyAnM3dKlWqJJS/PkYmJiZYv349LC0t3ymehw8fas3qp6SkoGrVqgByku/U1FSoVCqdHxJSUlKgUCgAAGKxGF988QW8vLwwadIkfPfdd1i1atVbxVOhQoU8P9/cZJ+IiIiIiKgkKjFrWKOiorSOT5w4gcqVK6NKlSp4/PgxLCwshHO3bt3C7du337lPqVT6xlnw9+X1+wG0779cuXJo2LAhjh8/rlXn8OHDWrPVzZo1Q2xs7DvHc+LECa3jyMhItGjRAgBQp04dWFhY4M8//9Sqk5ycjISEBJ3l5TKZDOPGjXtjXAWNu7u7Ow4dOqRT58CBA2jdunVhbomIiIiIiOiDKzEJdnR0NHbu3ImsrCzcvn0bP/zwAwYPHgyxWAxra2v873//Q3Z2Nu7du4fJkyejXr1679xn5cqVcfHiRSiVSq1XZL1v1tbWOHnyJG7cuIGsrCz8/vvvuHr1qladiRMn4ueff8a5c+egUqkQGRmJdevWaS3nHjx4MBYvXoxDhw5BqVRCrVbj7NmzRZ7d37BhA86dOwcgJ9nesmULBg0aBCBnR3ZfX19hp24AuHPnDsaMGYOePXvis88+Q1paGi5evAiNRgOlUomtW7eiadOm+fZnbm6O8+fPQ6PRIC0tTed8p06dAACzZ89GWloaVCoVNm3ahH379mHYsGFFujciIiIiIqIPpcQsEZ8yZQr27duH4OBgyGQy9O3bV9jAa968eQgICICLiwvKlSuHsWPH4vTp08KztlKpVOtZXENDQ8hkMp0+ZDKZVrmdnR1q1KgBJycnNGvWDOvXry8wztfbkMlkOs8Bv17n9TKFQoFRo0Zh+PDhePbsGZycnDB37lx88803Qv1WrVph+vTp+O677/DgwQM0atQI/fr1E5JcIGdzsuDgYCxduhSTJ0+GVCpFgwYNMHfu3ALv41WzZs3C7NmzkZiYiMqVKyM4OFgrQe7cuTM0Gg1mzpyJ+/fvo2zZsujVqxdGjBgBIGdGfvTo0Xjw4AHKlCkDW1tb4RVrr382ANCrVy+MGjUKcrkcAwYMwLhx47TGRyqVYu3atZg3bx7atm0LlUqFpk2bYu3atahZs6bQjqGhoc6z4fl99vkRi8U6m5SJRCJs374dhoaGwi7ir6pUqZJQXyaTwcDAoMB+8vqbICIiIiKij4tIo4810q/x9vbGmDFjhOd5Kecd10qlEo0bN4ZGo0FCQgICAgIQFBQkLN8uDo0aNcKVK1eKrT16vxISEgAAK1eewPXrxf/KuYLUq/cZFi4c8cH7LQnS09Nx6dIlNG7cWNgngT4cjr9+cfz1h2OvXxx//eL46w/HXlvud3ArK6sC65aIGWyJRFKoWcD3rXfv3rhx40a+5xctWgQnJ6cPEktqairmzJkjPGveoEEDBAQEFDq5Pnz4sLAreV6srKywdu1a4d3iHyNXV1e8fPkyz3MikQjbtm3D559//oGjIiIiIiKij1WJSLBz37msb9u2bdN3CAKFQoFdu3a99fVt27Yt8P3NAHD+/Pm37qOke33jvI9JzZrmn1S/RERERESlQYlIsImoaPz8vtBb32q1GmJxidkfkYiIiIioxOC3ZKJSRqlUIiMjQ2/9M7kmIiIiIsobvykTlUIlYG9CIiIiIiJ6DRNsolJIJBLpOwQiIiIiInoNE2yiUkYmk8HY2FjfYbyRSq3WdwhERERERB8cNzkjKoXm/rIDt+880HcYeapZozL8x/fUdxhERERERB8cE2yiUuj2nQe4lpik7zCIiIiIiOgVXCJOREREREREVAyYYBMREREREREVAybYRERERERERMWACTYVyuDBg3H69Gl9h/FBeHp6Ijk5Wd9hEBERERFRKcMEmwolOzsb2dnZ+g7jg8jKykJWVpa+wyAiIiIiolKGCXYpcO7cOXh7e0OhUEChUMDb2xv//vsvIiMj8eWXX+rUHzhwIA4cOAAAuH//PkaOHAkbGxsoFApMmzYNW7duxffff1+ovv/880/I5XKcPn0aI0aMgL29vTC7m5qaigkTJsDOzg6Ojo4IDg7WSkz79euH7du3o3///rC1tYWXlxfOnz+PO3fuYNCgQVAoFPjiiy9w8eJF4Zrk5GR07doVERER6Nq1K+RyOTp27IiDBw9qxaVSqbBgwQK4uLjA1tYWI0eOREpKinB+xYoVWLBgAZYtWwZHR0cEBAQAAHbs2AEvLy/Y29vDxcUFfn5+ePbsGQBg3bp1kMvluHfvHry8vODq6ors7GwkJyfDxsZGZ2ymT5+OVatWCccdOnTAuXPnMHDgQMjlcsTGxgIA/vnnH/Tt2xc2Njbw8PDA9u3bCzX2RERERERUujDBLgVUKhUmTZqEEydOIDo6Gp6envDz84NCocC1a9e0ljM/fvwYFy5cQMuWLaFWqzFixAiULVsWR44cwZEjR2Bubo558+YVeoa2ffv2iI+Ph52dHZYtW4a4uDhUrVoVADB69GgYGRnh6NGj2L9/P65cuYIlS5YI10okEvzyyy8YO3YsTp8+jQkTJsDPzw/fffcdBg8ejNjYWIwaNQpjx44VZsezsrLw8OFD/PLLLwgNDUV8fDzGjRsHf39/XLp0SWh74cKFOH78OLZu3YqTJ0+iXr16GD9+vHA+MzMTx48fR3p6Ok6cOIFZs2YBAExNTbF48WKcOnUK4eHhUKvVWLx4MQBg0KBBiI+PR/Xq1bFnzx5ERUVBKpUiKysLmZmZOmOjVCqhVCq1jn/++WdMnDgR8fHxcHBwQGpqKoYMGYKuXbsiNjYWoaGhWLx4MaKjows1/kREREREVHowwS4F7OzsYG1tDalUCrFYjK+++gqXL1+GSqWCi4sLjh49KtSNjIyEQqGAqakpzpw5g5SUFAQFBaFChQooU6YMvv32W9SsWfOdYzp+/Dju37+PWbNmoWzZsjAzM0NgYCC2bNmilbx7eXnB0dERIpEIrVq1gkwmQ5MmTeDu7g4A8PDwgJGREa5fvy5ck5qaimnTpqFGjRoAcmaGe/TogU2bNgEAnj59ig0bNiAkJAQ1a9aEkZERJkyYgHv37uGff/4R2rl79y58fX0hkUggFuf8qbdv3x61atWCSCSCkZERevXqhfj4+Hcej1yNGjVC8+bNAQAikQhr166Fi4sL+vXrB5lMhkaNGmH06NFYv359sfVJREREREQlAxPsUiA9PR2LFy/GF198AScnJzg6OkKj0eDJkydo27Ytjhw5ItQNDw9Hhw4dAACXL1+GtbU1ZDKZVnseHh7vHNP58+fh5uYGqVQqlNWpUwcajQZJSUlCWYMGDbSuq1ixIhwcHLTKzMzM8OTJE+FYJpPp1HFxccHVq1cBAFeuXEGVKlVQr1494bxYLEazZs1w+fJloczGxkYrPgC4d+8epk+fji5dusDBwQGjR4/W6vtd2dvbax2fO3dOZ7ybN2+OK1euFFufRERERERUMkgLrkL6NnbsWLx48QLjx49H8+bNYWpqiqZNm0Kj0aB169YICgpCZmYmRCIRYmNj8cMPPwAAnj17hgoVKui0V6VKFdy/f/+dYkpOTsbu3buxf/9+rXKlUomnT58Ks+QSiUTn2vLly7+x7fLly0MkEmmVmZqa4vnz50Lfd+7cgVwu16qTnZ2tVVaxYkWt80+ePEHv3r3h5uaGOXPmoH79+khISMDkyZMLuNu8aTQanbLX+0xJScH333+PmTNnal33euJPRERERESlH7/ll3D//fcf4uLiEBUVhXLlygHISTDVajWAnGS0WbNmiImJAQBYW1sL9UxMTPDo0SOdNvMqKyoTExP06dMHU6dOfee2Xvf48WOo1WphWTeQc8+5z36bmJigQYMG2LVr1xvbefV6APjrr79gYWGB4OBgoezV2fb8SCQSqNVqnZgePHiA2rVrv7FPExMThISEoG3btgX2Q0REREREpRuXiJdwjx8/Rrly5YSkGch5/vlVbdu2xdGjRxEeHg5PT0+hXC6X49y5c0hLS9OqHxERoTNDXBCpVKo1Y9usWTPEx8fnOYv7rrKzs4UfDHIdO3YMLVq0AAA0bdoUN2/exIMHD4rU7uPHj2FhYaFVFhUVpVPv9XutVKkSgJxnunOlpaXh/PnzBfbZrFkznDp1qkhxEhERERFR6cQEu4SrV68eMjIycOjQIWg0Gpw+fRrr16+Hubm5UKdt27aIjIzEsWPH0KZNG6G8adOmcHJywtSpU/H48WOkpaUhODgY//33X55Lx9/E3Nwc586dg1qtRlpaGtq2bYvnz58jMDAQqampAHJmmYtjw7AyZcpg3rx5wsZnu3fvxrFjx9C3b18AQNWqVdGmTRuMHz8eN27cAAC8ePECkZGRb2zX2toaJ0+exI0bN5CVlYXff/9deK47r3vNyspCRkaG8Ez40qVLkZ2djZcvX+L7779H9erVC7yXr7/+Gtu3b8f//vc/vHz5EhqNBleuXNHa1I2IiIiIiD4OTLBLOFNTU4SGhmLp0qWwt7fH3LlzMWfOHJQrV05YJl6tWjVUrVoVDRs21HkGOCQkBJUrV4anpyfatGmDzMxMKBQK2NraFikOb29v/P7773B0dMS+fftgZGSE9evXIzU1FZ6enrCzs4O3tzdu3rwpXCOTyWBoaKjVjkwm09l07fWyihUrwtfXF76+vrC1tcXatWuxbNkyYYk4AAQHB8PKygoDBw6EjY0NPD09tWb2DQ0NdfpWKBQYNWoUhg8fDmdnZxw7dgxz584VxjHX8OHDMW/ePLi5uQnvsv7hhx+QlJSEli1bwsvLC1ZWVvDw8NCKO6/7bdiwIVasWIHdu3fDyckJDg4OmDJlCl68eFGYYSciIiIiolJEpHkfa3ypxDh27BiaNGkCc3NzpKenY8eOHdixYwe2bdtWIjfaunPnDgYMGICIiAh9h1IiJSQkAACWrz+Ja4kFPz+uD/U/r4bFP32j7zCKXXp6Oi5duoTGjRujTJky+g7nk8Px1y+Ov/5w7PWL469fHH/94dhry/0ObmVlVWDdkpdhUbGKi4vD999/j+fPn8PQ0BCtWrXCqlWrhOTa1dUVL1++zPNakUiEbdu24fPPP/9g8UqlUhgYGHyw/kqrmjUq6zuEfJXk2IiIiIiI3icm2B+5iRMnYuLEifmez2uTL32qVq0aDh06pO8wSjz/8T31HcIbqdRqSMR8AoWIiIiIPi38BkxUyiiVSmRkZOg7jDdick1EREREnyJ+CyYqhbh1AhERERFRycMEm4iIiIiIiKgYMMEmKoVEIpG+QyAiIiIiotcwwSYqZWQyGYyNjfUdxgeleu1d5UREREREJRF3EScqhYKX7MR/dx/qO4wPopaFOaaO7qHvMIiIiIiICsQEm6gU+u/uQ1y7maTvMIiIiIiI6BVcIk56l5ycjA4dOug7DCIiIiIionfCGWzSu6ysLCiVymJts0mTJjAyMoJYLIahoSEsLS3h6+sLa2trrXrZ2dnYvHkzfv/9d9y7dw8GBgawt7fH2LFj0aBBA626gwYNwrlz5yCVSiESiVCxYkU4OTlh2LBhqFmzplBvwYIFSEhIwJo1a3TiUqvV6NevH/r06YPu3bsX6z0TEREREZF+cQabPkoqlQo7d+5EfHw8IiIi0KpVKwwfPhypqalCHaVSiW+++QYHDx7E3LlzER8fj8OHD8PGxgb9+vXDqVOndNoMDAxEfHw84uLisGbNGlSoUAE9e/ZEdHS0UO+bb77BjRs3sH//fp24tmzZAgDo1q3be7pzIiIiIiLSFybY9NEzNDSEt7c3qlatigsXLgjlGzZswP3797F69Wo0btwYAGBiYoLBgwdj8uTJ8Pf3R2ZmZr7t1qhRA99++y0CAwMxYcIEpKWlAQDKlCmDgIAAhISE4OXLl0L9hw8fYuHChZg5cyZfs0VERERE9BFigv2OhgwZgoiICIwePRqOjo5QKBSYPXu2sOR53759mDhxInbs2AFXV1cMHToUAKDRaLBmzRq0a9cOcrkc7du3x7p163Taj4mJQb9+/SCXyyGXy+Hr6yucO3nyJLp16wYbGxt06tQJERERwrm0tDSMHz8eCoUCDg4O6Nu3r3Bu8+bNaN26Nezt7eHh4YH4+PhC3296ejrmzp0LNzc32NnZwdXVVbheqVRiwYIFaNWqFeRyObp27Yrdu3drXa9SqTB37lw4OzvDwcEBAwYMwPXr13X6CQsLg6enJ2xsbNCzZ0+cPXu20DHmRaPRIC0tDYaGhkLZpk2bMHTo0DxfeZW7fDs8PLzAtjt16oTatWvjwIEDQlnbtm3RqFEjLF++XCibO3cuvLy8YGlp+Q53QkREREREJRWfwX5HWVlZCAoKwvjx47FkyRIkJSXBx8cH8+fPx+TJk5GZmYlr165BJpMhIiICUmnOkIeGhmL//v1YsmQJGjZsiOvXr2P8+PFQKpXw8fEBAERHR+Pbb7/F9OnT4enpCbFYjMePHwMA/v33X/j6+uKHH35AmzZtcObMGYwdOxabN29GvXr1sGjRIpQrVw7Hjx+HTCZDUlLOjtOXL1/GqlWrsHnzZnz22Wd4/vw5xOLC/84yYsQIlCtXDtu3b0fVqlWRlpYGAwMDAEBgYCBu3bqFTZs2wcLCAufOncP48eNhYGCATp06CfcdERGBjRs3ol69eoiMjMS0adMgk8mEPqKiohAUFISFCxfC1tYWhw8fxsiRI3Hw4EFUqFChSJ+PRqNBSkoKQkNDUbFiRdjb2wMA7t27h+TkZHTs2DHP6yQSCTp06IDY2Fgh9jdxdXXF6dOn8eWXXwpl06ZNQ69evdCrVy8kJSUhOjoaBw8eLFL8RERERERUenAGuxg4ODjAy8sLAFCtWjUEBQVh27ZtwvLif//9F35+fpDJZBCLxXj+/DlWrVqF4OBgNGzYEABQr149BAcHY+XKlcJS46CgIEydOhWdOnWCRCKBSCSCmZkZgJxEtW/fvvD09IRUKoWDgwP69OkjPOObmJgIR0dHIXGtVq2aUF6vXj189tlnAICyZcvCxMSkUPe5f/9+PHjwAL/++iuqVq0KADA1NYWhoSFu3bqFsLAw/Pzzz7CwsAAAtGjRApMnT8aCBQug0WgAANu2bcOkSZNQr149AIC7uzt69uyp1c+iRYswfvx4ODg4QCqVokOHDnBxccHOnTuL9Ln06NEDVlZWcHNzw+nTpxEaGir8wJGSkgJzc3OUKVMm3+tr1aqF5OTkQvVVtWpVPHjwQKusZs2aGDBgAH744QcEBgbC398fpqamRboHIiIiIiIqPZhgFwNXV1et4+bNm0MkEuHOnTsAcpLn3MQYAC5duoSKFSuiefPmWtdZWVmhXLlyuHz5MhITE3Hz5s18Z0/PnTsHDw8PnX4vX74MAOjVqxdCQkKwd+9eqNVqoY6zszNu3bqF4OBgrQ2/CiM8PBydO3eGRCLROXfmzBlYW1sLiXeutm3b4v79+0hKSsLTp0+RnJwMBwcHrTpubm7Cv1UqFf7++2+de7O2thburbD279+Ps2fP4q+//oKjoyMGDBiAp0+fFqmNwlKpVMJM/qt8fHxw7do1VKhQAV27dn0vfRMRERERUcnAJeLFoHz58jplpqamwkz068uaU1JSUKVKlTzbqlKlCpKSkoTZ6twZ19elpKRg2LBhWptlqdVq1K5dGwDg6emJ2rVr46effsLy5csxbdo0ODk5oXz58tixYwdWrlwJLy8vfPXVVxg1alSeSfPrUlNToVAo8o0nr3uSSqUwMzNDUlISqlevDgAoV66cVp1Xf3x4+PAhsrOzdX5YUKlUcHZ2LjDG1xkYGKBWrVqYNm0abty4gW3btmH48OGoXLkyHj58iBcvXuQ7g3/r1i1UqlSpUP3cvn1buL9XyWQy2NnZoVatWkWOnYiIiIiIShcm2MXg0aNHWsdqtRqPHj1C1apVcf36dZ3ktUKFCkhJScmzrZSUFJiZmcHMzAyPHj2CUqnUej45l4mJCdavX//GDbMsLS2xatUq7N+/H6NGjUJYWBgsLCxgamqKb7/9Fn379sWQIUNgaGgoPPf9JmZmZrh3716e5/K7J5VKhUePHgn3JBaL8ezZM60fJV5dhp2b7B45cgRly5YtMKaiaNKkCW7fvg0AsLCwgLm5OQ4cOIAvvvgiz7gPHjyI0aNHF9iuWq3G4cOHMWXKlGKNl4iIiIiIShcuES8GUVFRWscnTpxA5cqV852ltrGxwbNnz7ReGQUACQkJSEtLg42NDerUqYPatWtjx44debbRrFkzxMbGFiq+Tp06oX79+jh37pxWebVq1fD1118Xuh0PDw/s2bNH2CH9VS4uLrhw4YKwmVquv/76C7Vr10bt2rVhYGCARo0a6fT3559/Cv82NTVFnTp1Ch1TUfz777/Cs+cA8NVXX2HNmjVIT0/Xqbtz5068ePECnp6eBbYbGhoKU1NTuLu7F2u8RERERERUujDBLgbR0dHYuXMnsrKycPv2bfzwww8YPHhwvrtzm5iYYPjw4ZgyZQquXr0KIGd374kTJ+Kbb76BsbExRCIR/P39ERISgn379gnPUefuIj548GAsXrwYhw4dglKphFqtxtmzZ4XZ4AsXLgiJY0xMDK5fv46GDRvi9u3buHv3LoCcmfd9+/ahadOmhbrPTp06oUqVKhg9erSwoVd6ejoyMzNRo0YNeHl5YeLEiUL7sbGxmD17ttarxYYMGYKQkBDh1VxhYWE4efKkVj9Dhw7FrFmzEB0dDZVKhezsbJw4cUJYcl9Ujx49wsKFC3H27FmtDdWGDx+OChUqYPDgwfj777+F+9mwYQN++OEHBAUF6Sxnz6VUKhEfHw9fX1/s2bMHixYt4rutiYiIiIg+cVwiXgymTJmCffv2ITg4GDKZDH379oW3tzcAwNDQUOvdy7l8fHxgZGSEMWPGIDU1FZUqVcKAAQPw9ddfC3VatWqFpUuXYu7cufj+++8hEolgZ2eHFStWwNXVFcHBwVi6dCkmT54MqVSKBg0aYO7cuQCANWvWICIiAhKJBBYWFvjxxx/RoEEDHDhwADNnzkRGRgZMTU3RsWNHjBkzplD3KRKJsG7dOoSEhMDLywtKpRJSqRQLFy6EQqHA9OnTsXTpUvTv3x9paWn47LPPMG3aNHTo0EFow8vLCw8fPsTgwYPx8uVLWFtbY968efjuu++EOr1794ZarcasWbOQlJQEmUyGZs2a4ddffy30ZyKRSNC5c2eo1WqYmprCwcEBW7Zs0dqETSaTYc2aNVi9ejX8/PyQkpICqVQKa2trrF69Gra2tjptzpgxA0FBQZDJZGjUqBHat2+PkJCQPD/jV/vJawM0IiIiIiL6uIg0ue9Porfi7e2NMWPG5Lv5F1FxSkhIAAAs2RKDazeTCqj9cahfpxqWBQ/XdxhIT0/HpUuX0Lhx4ze+3o3eD46/fnH89Ydjr18cf/3i+OsPx15b7ndwKyurAutyBvsdSSSSj2J2cu7cudi+fXu+5wcNGlTome73acOGDVi4cGG+59u3b4/g4OAPGBEREREREVEOJtjvaN26dfoOoVj4+/vD399f32EUaMCAARgwYIC+w9C7Whbm+g7hg/mU7pWIiIiISjcm2ESl0NTRPfQdwgelUqshyWfTQCIiIiKikoLfWIlKGaVSiYyMDH2H8UExuSYiIiKi0oDfWolKIe5NSERERERU8jDBJiIiIiIiIioGTLCJSiGRSKTvED45IpEIxsbGHHsiIiIiyhc3OSMqZWQyGYyNjfUdxifH2NgYTZo00XcYAm78RkRERFTyMMEmKoWCVuzErXsP9R0G6Unt6uYI8Pm0dpInIiIiKg2YYBOVQrfuPcS//yXpOwwiIiIiInoF1xeWAB06dEBycrJeY7h48SI8PT1hb2+Pixcv6jWW0mL69OlYtWqVvsMgIiIiIqISggl2CaBUKpGVlaXXGEJDQ9GrVy/ExsbC0tJSr7GUFkqlEkqlUt9hEBERERFRCcEE+yMTEBCAPXv2FPm6mzdvom3bthCLxRBz4yQiIiIiIqIiYyb1kcnOzkZ2dnaRr0tPT4dMJnsPEREREREREX0amGAXo+nTp2Pr1q2YNWsWFAoFlixZgrS0NMycORMuLi6Qy+X44osvcPz4cZ1r7927hxEjRkChUMDR0REzZ85EZmamVp3IyEj06tULcrkcrq6umDVrFtLS0gAACQkJkMvlCAsLQ2BgIORyOc6fP19gzKGhoZDL5bh79y68vLzQsmVLqNVqJCcnw93dHQkJCejYsSMUCoWwHDosLAyenp6wsbFBz549cfbsWa02L1++jH79+sHOzg4eHh5YuXIlAgMDsXfvXqFO8+bN8fCh9i7YK1asQGBgoHCsUqmwYMECuLi4wNbWFiNHjkRKSopwfuHChfjll18wffp0KBQKKBQKjBo1Cg8ePNBqNzU1FQEBAXB2doZcLoe7uztu374NT09PREdHa9UdM2aMVpwFuXv3Lnx8fCCXy+Hk5IRJkyYJnwkAfPHFF5DL5cJ/dnZ2aNSokd6fuSciIiIiouLHBLsYKZVKbNu2DfXr10dsbCxGjhyJsWPHIjU1FXv37kVcXBzGjh2LSZMm4fTp01rX+vv7o3fv3oiNjcXWrVtx/PhxBAcHC+dPnToFPz8/jB49GnFxcdi7dy8eP36M8ePHAwCsrKwQHx+PLl26YMaMGYiPj4e1tXWBMY8cORLx8fGoXr069uzZg+PHj0MsFiMrKwvp6elYvnw5Nm7ciOjoaMhkMkRFRSEoKAizZ89GXFwcfHx8MHLkSDx58gQAkJmZiWHDhsHZ2RmxsbHYs2cPzp8/j8OHD2s9Z56Zmakz056Zman1TPPChQtx/PhxbN26FSdPnkS9evWE+wUAkUiE9evXo3z58oiIiMCxY8dgYmKCmTNnan0m/fv3h1QqxYEDBxAfH4/du3ejevXq6Nixo1YynZqaipiYGHh4eBQ4brl27tyJjh074uTJk/jrr7/w4MEDLFmyRDj/+++/Iz4+XvhvxIgRcHZ2RtWqVQvdBxERERERlQ5MsItZdnY2+vXrByAnKb506RLmzp0LMzMziEQiuLu7w8fHB7/++qvWdV26dBESuzp16mDmzJnYtWuXkHAuXLgQPj4+8PDwgEgkQsWKFTFnzhz8888/iIuLey/38uzZM3Tr1g3m5ubCc9mLFi3C+PHj4eDgAKlUig4dOsDFxQU7d+4EABw8eBAVK1bEmDFjIJVKYWpqijlz5mjN6hbG06dPsWHDBoSEhKBmzZowMjLChAkTcO/ePfzzzz9CPQsLC0yYMAEmJiYwNDTEmDFjEBkZCZVKBQBYv349qlWrhpkzZ6J8+fIAgAoVKkAikcDLywvh4eFC4n/gwAG4u7vDxMSk0HF27NgRPXr0gEwmg6mpKYYPH46IiIg86166dAnr16/Hjz/+WKSxICIiIiKi0oEJdjGzt7cX/h0fH4/WrVvD2NhYq07Hjh1x5swZIQkEAHd3d606zs7OUKlUuHXrFlQqFc6ePYtOnTpp1TEyMkKrVq1w6tSp93AnOeRyufBvlUqFv//+W2eG19raGpcvXwYAXL16FQqFQut82bJlYWtrW6R+r1y5gipVqqBevXpCmVgsRrNmzYS+AMDS0hIikUg4rl69OrKysvDo0SMAQHh4OLy8vPLso27durCwsMDJkycBAHv37s23bn4+//xzrePq1atrLWPP9fLlS0ycOBHTp0/n7DURERER0UdKqu8APjYVK1YU/p2SkoIqVaro1KlcubKQBOaef/U6AJBIJKhQoQIyMjLw6NEjZGdn59lWlSpV3tvzvGKxGBUqVBCOHz58iOzsbJ1EX6VSwdnZGQDw/PlzVKpUSaet3NnjN9FoNMK/k5OTcefOHa0EH8hZIfBqmaGhodZ5AwMDAIBarQaQs+w7r3HL1a1bNxw8eBD16tXD7du34eLiUmCcr3p9x3WZTKbz7DwAzJs3Dy1atED79u2L1D4REREREZUeTLCL2asJV4UKFfKczXzw4AGkUqlW0pmcnKw1W6tSqfDkyRNUqlQJ5cqVg0QiQUpKCqpXr67VVn5JfHEQiURas8O5S6ePHDmCsmXL5nlN1apV80z4X994LPc57/zqmJiYoEGDBti1a9fbhg8AMDMzw7179/I937lzZ4SGhsLCwgIdO3aEVFr8/5OIjIzEiRMnsGPHjmJvm4iIiIiISg4uEX+P3N3dcfToUWRkZGiVHzhwAE5OTlqzr2FhYVp1YmJiUKZMGVSrVg1GRkZQKBTYv3+/Vp2XL1/i6NGjWku2pVKp1kxwcTI1NUWdOnUQGxubbx0bGxtERUUJM8hAzvPUCQkJWvUqVaqEu3fvCsdqtRoxMTHCcdOmTXHz5k2dxLyoPDw88Mcff+R73tzcHE2bNsXKlSuLvDy8MFJTUzF9+nTMmzevSM92ExERERFR6cME+z2ys7NDkyZN4O/vj9TUVGg0Ghw8eBDLly/HmDFjtOqePn1aSARv376NwMBADBs2DBKJBAAwevRorFixAuHh4dBoNEhNTcW4ceNgbW2ttVu4ubk5zp8/D41GU+SNxQpj6NChmDVrFqKjo6FSqZCdnY0TJ04IfTk7O6N8+fL48ccfoVQqkZqaiqlTp+os5W7ZsiVWrlyJjIwMZGdnY968eTAyMhLOV61aFW3atMH48eNx48YNAMCLFy8QGRlZpHi9vb3x8OFDTJkyBc+ePQOQs3nbq8+/d+7cGdWqVUPz5s3fakzeZNq0aejdu3ehdnQnIiIiIqLSjQl2MZLJZDqJ5MKFC1GlShV4eXnB3t4ea9aswa+//ooWLVoIdQwMDBASEoI///wTDg4O6Nu3L/r06QMfHx+hjlwux4IFC7Bs2TLY29vDy8sLtWrVwvz587X669WrF86ePQu5XI7Vq1cXOnYDAwPh+eXc49fvBQB69+6NUaNGYdasWZDL5XBxccGaNWu06ixduhSJiYlwcnJCjx490KJFC7Ru3Vqrjp+fH0xMTNCmTRt4enpCIpGgf//+kMlkQp3g4GBYWVlh4MCBsLGxgaenp9Y7xA0NDbXqv1qeu9Tb2NgYv//+O7KystC2bVvY2dmhffv2uH37tlD//v376Nq1a6HHKpdMJtPp/9Vxe/LkCSIiIrBmzRqtd2E7ODjg8ePHRe6PiIiIiIhKNpHmfa0nJnrF5MmT4eDggJ49e+o7FIFarcaTJ0/Qu3dv/Pbbb6hWrZq+QypQ7lL7hX/E4N//kvQcDelLg1rVsHLmcH2H8UGlp6fj0qVLaNy4McqUKaPvcD45HH/94djrF8dfvzj++sOx15b7HdzKyqrAutzk7COWmpoKT0/PfJ/JFolEiIqKynOmurgZGBgIy91LiuHDh+Pvv//Gt99+q5Vcl6RxIyIiIiKi0oMJ9kfMzMwMcXFx+g4DADB79mx9h6AjvyX0JWnc8lO7urm+QyA94udPREREVDIxwSYqhQJ8eug7BNIzlVoNiZjbaBARERGVJPx2RlTKKJVKnVe/0fuXkZGBixcvlpixZ3JNREREVPLwGxpRKcS9CT88jUaDjIwMjj0RERER5YsJNlEpJBKJ9B3CJ0ckEsHY2JhjT0RERET54jPYRKWMTCaDsbGxvsP45BgbG6NJkyb6DqNY8TluIiIiouLFBJuoFApcsxO3kh7qOwwqxWpXM8eMIdwsj4iIiKg4McEmKoVuJT3E1dtJ+g6DiIiIiIhewbWBRERERERERMWACTYRERERERFRMWCCTURERERERFQMmGB/YgICArBnzx59h0FERERERPTRYYL9icnOzkZ2dra+wyAiIiIiIvroMMF+RzExMejXrx/kcjnkcjl8fX0RFhYGHx8fnbodOnTAhQsXAADJyclwd3dHQkICOnbsCIVCAaVSiRUrVmDBggVYtmwZHB0dERAQAADIyMjAjBkz4ODgAHt7e/j7+yMtLU1o29/fHxs2bICvry/s7e3h7OyMyZMnC3USEhIgl8sRFhaGwMBAyOVynD9/vlD3OH36dGzduhWzZs2CQqHAkiVLkJWVhWbNmuHIkSPw9PSEnZ0dhg4ditTUVJw4cQJdu3aFQqHAmDFjkJqaKrSVlJSEYcOGwcHBAQqFAhMnTiz0WJ87dw7e3t5QKBRQKBTw9vbGv//+K5zfvn07vvvuO/z4449wdHSEk5MTli1bBrVajZ9//hktW7aEm5sbVq9erdXukCFDEBERgdGjR8PR0REKhQKzZ8+GUqnUqnfy5El069YNNjY26NSpEyIiIoRz+X2eABAWFgZPT0/Y2NigZ8+eOHv2bKHvmYiIiIiISg8m2O8gOjoa48ePx9dff43Y2FjExcVh5syZUCqVOskZAK3yrKwspKenY/ny5di4cSOio6Mhk8mQmZmJ48ePIz09HSdOnMCsWbMA5CztvnfvHg4cOIAjR45ArVYjMDBQaFskEuGXX36BQqFAVFQUDh06hKSkJCxcuBAAYGVlhfj4eHTp0gUzZsxAfHw8rK2tC3WfSqUS27ZtQ/369REbG4uRI0fCwMAAWVlZWLp0KVatWoVTp06hSZMmmDp1Kn788UcsXrwYJ06cQO3atTFnzhyhraCgINja2iImJgYxMTH47rvvCj3eKpUKkyZNwokTJxAdHQ1PT0/4+fkJ5yUSCQ4dOgSRSITIyEiEhYUhLCwMEydOxJMnTxAeHo4//vgDO3bsQHR0tHBdVlYWgoKC4OnpiZiYGOzevRtxcXGYP3++UOfff/+Fr68vRo0aJXzOU6ZMwfXr19/4eUZFRSEoKAizZ89GXFwcfHx8MHLkSDx58qTQ901ERERERKUDE+x3EBQUhKlTp6JTp06QSCQQiUQwMzMr9PXPnj1Dt27dYG5uDrH4/z6Ku3fvwtfXFxKJBGKxGNevX0dERARCQkJQqVIlmJqaYvr06YiIiMDDhw+F6+RyOfr37w9DQ0OULVsWPj4+CA8PL5Z7zc7ORr9+/QBAK9bRo0ejZs2akEgkGDx4MI4cOQJfX1/Url0bUqkUQ4cOxbFjx4T6iYmJcHV1hVgshkgkQtWqVQsdg52dHaytrSGVSiEWi/HVV1/h8uXLePHihVCnbNmymDhxIgwNDVGpUiX06NEDMTEx+P777yGTyVC5cmX06NEDkZGRWm07ODjAy8sLAFCtWjUEBQVh27ZtyMzMBACEhoaib9++8PT0hFQqhYODA/r06YMtW7YIbeT1eS5atAjjx4+Hg4MDpFIpOnToABcXF+zcubPQ901ERERERKUDE+y3lJiYiJs3b6JTp07v1I5cLtcps7GxgVQqFY4vXLgAW1tbVKhQQSgrW7YsatWqhWvXrglljRs31mqnevXqSE5Ofqf4ctnb2+dZXr9+feHfuT8uvFrXzMxMa7a2d+/e8Pf310lwCyM9PR2LFy/GF198AScnJzg6OkKj0Wi1//nnn2uNXcWKFWFtbQ2ZTJZvTADg6uqqddy8eXOIRCLcuXMHQM7ydA8PD506ly9f1ip79fNUqVT4+++/da6ztrbWuY6IiIiIiEo/acFVKC+pqakwMzPTSuYKotFotI7FYrFW0pyrYsWKWsfJycmIjY3VScazsrLw7Nkz4djQ0FDrvFQqhVqtLnR8b/J6TK/28bq87inXwIED0bhxY/z8888IDQ3F9OnT0aRJk0LFMHbsWLx48QLjx49H8+bNYWpqiqZNm2qNq0QiKVI8ucqXL69TZmpqKjzDnpKSgmHDhkEkEgnn1Wo1ateuLRy//nk+fPgQ2dnZOj/CqFQqODs7FxgTERERERGVLkyw35KZmRkePXoEpVKpNTsK5CRar+/UrdFotJZzAznPTb+asL16/atMTEzg7u6OJUuWFFP0Rfd6TO/CwcEB//vf/7B27Vphg7EyZcq88Zr//vsPcXFxiIqKQrly5QDk/PBQXD8gPHr0SOtYrVbj0aNHwhJ2ExMTrF+/HpaWlvm28frnaWJiAgA4cuQIypYtWyxxEhERERFRycUl4m+pTp06qF27Nnbs2KFzztzcHPfu3dMqi4+Pz3Pjs8Jo1qwZLly4gJcvX77V9a+SSqU6M+n6MnjwYIjFYly9erXAuo8fP0a5cuWE5BoAjh8/XmyxREVFaR2fOHEClStXRpUqVQDkfAaxsbFFatPU1BR16tQp8nVERERERFQ6McF+SyKRCP7+/ggJCcG+ffuEmdTHjx/D2toaz58/x++//w4AuH//Pn766SfUqFHjrfqysbFBjRo1MGnSJCQlJQEAnjx5gpMnTxa5LXNzc5w/fx4ajUbrNV8fypkzZ6BUKqHRaLBv3z5kZmZqLbPOT7169ZCRkYFDhw5Bo9Hg9OnTWL9+PczNzYslrujoaOzcuRNZWVm4ffs2fvjhB+EHACDnx4DFixfj0KFDUCqVUKvVOHv2bIHPuA8dOhSzZs1CdHQ0VCoVsrOzceLECb2MPRERERERvV9MsN9Bq1atsHTpUqxevRpyuRx2dnbw9/dH2bJlsXDhQqxfvx4uLi4YMWIExo4diypVqgjLyQ0MDHSemQZynqPOqzw0NBTlypVDjx49YGtri+7duyMhIUE4L5PJdJaq59VHr169cPbsWcjlcp33QedHJpPlGZORkREMDAy0yvJa6v1q2bx582Brawu5XI61a9ciNDQ03+e7X2VqaorQ0FAsXboU9vb2mDt3LubMmYNy5coJP27kNXZ5jUteZVOmTMGff/4JZ2dn9OnTB126dIG3t7dw3tXVFcHBwVi2bJnwHu6QkBBhVUJ+n2fv3r0xatQozJo1C3K5HC4uLlizZk2B90tERERERKWPSFNS1gsT6Ym3tzfGjBkDhUKh71AKlPujyoK9Mbh6O0nP0VBp1rBmNayZOlzfYRRaeno6Ll26hMaNGxe4ZwMVP46//nDs9Yvjr18cf/3h2GvL/Q5uZWVVYF1ucvYJS01NhaenZ77PZItEIkRFReU5M/sxxSGRSHRm4ku62tWKZ2k8fbr4N0RERERU/Jhgf8LMzMwQFxen7zD0Hse6dev01vfbmjGkh75DoI+ASq2GpBjfEEBERET0qeM3K6JSRqlUIiMjQ99hfHIyMjJw8eLFj2rsmVwTERERFS9+uyIqhbh1woen0WiQkZHBsSciIiKifDHBJiIiIiIiIioGTLCJSiGRSKTvED45IpEIxsbGHHsiIiIiyhc3OSMqZWQyGYyNjfUdxifH2NgYTZo00XcY7x03PiMiIiJ6e0ywiUqhGRt24mbyQ32HQR+ZOlXNETiAO9QTERERvS0m2ESl0M3kh7h6J0nfYRARERER0Su4DpCIiIiIiIioGDDBfs8ePHiA7t2749GjR3qLoUOHDrhw4cIb6yxatAiLFy8uVHvTp0/HqlWriiO0UmX58uUIDAzUdxhERERERFRCcYn4e1a5cmXs2rVLrzEolUoolco31hk7dmyxtvcx+lTvm4iIiIiICocz2CXUnj17EBAQUOTrAgICsGfPnvcQEREREREREb0JE+wSKjs7G9nZ2R/sOiIiIiIiIno3TLALMGTIEERERGD06NFwdHSEQqHA7NmzhaXCqampGDduHFxcXGBvb4/OnTvj8OHDwvUPHz5E8+bNheM//vgDU6dOxfz584VrBgwYgMTERACAWq2Gq6srAgMDERYWBrlcjjVr1hQYZ0JCAuRyOcLCwhAYGAi5XI7z588L52NiYtCzZ0/Y2NigVatWWLp0KTQajXA+MDAQy5cv12pz8+bN6Ny5M+zs7KBQKLB27do8+46JiUGbNm2QnJwMAPD398eGDRvg6+sLe3t7ODs7Y/LkyUhLS9O67uTJk+jWrRtsbGzQqVMnRERECOfS0tIwfvx4KBQKODg4oG/fvlpxtW7dGvb29vDw8EB8fHyB45OWlgZbW1s8efJEq3zJkiXCc9VHjx5F7969oVAo4OjoiBEjRiApKf+duj09PXWebd+3bx98fHy0ytavX4/WrVvDxsYG3t7euH79eoHxEhERERFR6cMEuwBZWVkICgqCp6cnYmJisHv3bsTFxWH+/PkAgIyMDHh5eSE8PBxxcXEIDg7GtGnT8ODBAwA5M8qZmZlCeyKRCAcOHEBKSgr27duHkydPwtraGhMmTIBGo4FYLEZUVBRmzJiBLl26ID4+HkOGDCkwTisrK8THx6NLly6YMWMG4uPjYW1tLZzftm0bpk6ditOnT2PdunXYsmULIiMjhfOvP1+8ePFibN68GT/++CNOnz6NqKgo9OzZU6ff+/fvw8/PD3PmzEHVqlWFe/zll1+gUCgQFRWFQ4cOISkpCQsXLhSu+/fff+Hr64tRo0YhLi4OM2fOxJQpU4Tkc9GiRShXrhyOHz+OU6dOYcGCBQCAy5cvY9WqVdi8eTPi4uKwe/duNG7cuMDxMTU1RbNmzXDs2DGt8j///BNt27YFAEgkEgQFBSE6OhpRUVGoW7cuZs+enW+bWVlZOs9kZ2ZmapVt27YNGzZsQGhoKE6dOgVPT0/4+PhwlQERERER0UeICXYhODg4wMvLCwBQrVo1BAUFYdu2bcjMzISFhQXatGkDIyMjAIC1tTXq1q2Lv//+O9/2ZDIZAgMDUaFCBRgYGGD06NG4evUq7t+//97uYcyYMZDL5RCLxahTpw569OihNWP8qvv372PNmjUIDQ2FlZUVAMDAwADly5fXqqdUKjF27FiMHDkSDg4OWufkcjn69+8PQ0NDlC1bFj4+PggPDxfOh4aGom/fvvD09IRUKoWDgwP69OmDLVu2AAASExPh6OgImUwGIGfcc8vr1auHzz77DABQtmxZmJiYFGoM2rZtq/Wjwr1795CUlASFQgEAaNmyJRo2bAixWAypVIqvvvqqULPj+VGr1Vi8eDFmzpwJS0tLGBgY4Ouvv0blypXzHXsiIiIiIiq9mGAXgqurq9Zx8+bNIRKJcOfOHWg0GuzcuRMDBw6Em5sbbG1tceHCBZ2lyK+qW7cuDA0NhWMjIyOYmZkJS6zfh88//1zruHr16vn2d+zYMTRt2hQ1a9Z8Y5uzZs1C06ZN0a9fP51zr88qv97fuXPn4OHhoVWnefPmuHz5MgCgV69eCAkJwd69e6FWq4U6zs7OuHXrFoKDg5GamvrG+F7Xtm1bREVFQaVSAQDCw8Ph4eEBqTRnM/3Hjx9j7ty56N69OxQKBbp37/7Gz7Eg9+/fx9OnT+Hs7KxVbm1tLdwnERERERF9PPiarkJ4feYWyFlynJaWhpUrV+K3337DxIkT4eTkhEqVKmHQoEFazze/7tXkOpeBgYGQ+L0PIpFI61gmk2ktXX9VamoqqlSp8sb29u/fj5s3b+o8b5zr9XuUSqVaiXJKSgqGDRumFZdarUbt2rUB5DzfXLt2bfz0009Yvnw5pk2bBicnJ5QvXx47duzAypUr4eXlha+++gqjRo2CRCJ5Y7xATpJvYWGBM2fOwN7eHuHh4Rg8eDCAnKX8AwYMgIWFBQICAmBpaYmnT5/q/AhQkFc/9+TkZCiVSmGGPJdKpcIXX3xRpHaJiIiIiKjkY4JdCI8ePdI6VqvVePToEapWrYpNmzbhhx9+QMuWLYXz73Mm+kMwMzMrcLl6RkYGdu7cicGDB6NVq1ZaG7kVhomJCdavXw9LS8t861haWmLVqlXYv38/Ro0ahbCwMFhYWMDU1BTffvst+vbtiyFDhsDQ0DDfRP91bdu2xdGjR2FpaYkrV67AyckJABAXF4dnz55h165dQrJ+5cqVN7YlFot1nqXOffY+9x7Lli2LU6dOFSo2IiIiIiIq3bhEvBCioqK0jk+cOIHKlSujSpUqePz4MSwsLIRzt27dwu3bt9+5T6lU+sZZ8OK+7lWurq74559/3rjbda9evdCgQQPMmDED3333HV6+fFmkPpo1a4bY2NhC1e3UqRPq16+Pc+fOaZVXq1YNX3/9daHbAf4vwY6MjETLli2FZ7wfP36MatWqac2Ev/65v87c3Bx3797VKjt58qTw788//xzZ2dlcDk5ERERE9Ilggl0I0dHR2LlzJ7KysnD79m388MMPGDx4MMRiMaytrfG///0P2dnZuHfvHiZPnox69eq9c5+VK1fGxYsXoVQq8ezZs0JfZ25ujvPnz0Oj0ei8FquwLCwsMGDAAIwYMULYrC07OxvPnz/XqduuXTu0aNECISEhRepj8ODBWLx4MQ4dOgSlUgm1Wo2zZ88Ks/8XLlxAeno6gJzXgF2/fh0NGzbE7du3haT20aNH2LdvH5o2bVrofhs2bIisrCxs3LgR7du3F8qbNWuGK1eu4MyZM1Cr1YiIiMDx48ffuPTc1dUVGzduxOPHj6HRaLBu3TqtZ7ZlMhm8vb0xadIkJCQkQKPRQKlUar3GjYiIiIiIPh5MsAthypQp+PPPP+Hs7Iw+ffqgS5cu8Pb2BgDMmzcP165dg4uLCwYOHIi+ffvCzs5OeJ5aKpVqPY9saGgozJq+SiaTaZXb2dmhRo0acHJywtixYwsda69evXD27FnI5XKsXr06z7bziuP1OhMnTsSgQYMwfvx42NrawtHREZs2bcqzbkBAAI4fP46YmJh8+zMwMNAaB1dXVwQHB2PZsmVQKBRQKBQICQkRXnG1Zs0aODo6wsbGBkFBQfjxxx/RoEED/P333+jZsyeaN2+Orl27wtLSEmPGjCn0+ABAx44dcfPmTa1l/bVq1cKcOXMwdepUODg4YOPGjViwYAFkMhmysrLyvK+hQ4eiWbNm6Nq1Kzw8PHD16lWMHz9eq864cePQuXNnYRzd3d2xe/fuIsVLRERERESlg0jzruuJP3Le3t4YM2aMzkZVRPqQkJAAAPjpYAyu3knSczT0sWlYoxrW+w3Xdxh5Sk9Px6VLl9C4cWOUKVNG3+F8cjj++sOx1y+Ov35x/PWHY68t9zt47iuM34SbnBVAIpHAwMBA32Ggd+/euHHjRr7nFy1aJGzY9SmaO3cutm/fnu/5QYMGFXmmuySrU9Vc3yHQR4h/V0RERETvhgl2AdatW6fvEAAA27Zt03cIJZq/vz/8/f31HcYHEzigh75DoI+USq2GRMynh4iIiIjeBr9FEZUySqUSGRkZ+g7jk5ORkYGLFy9+9GPP5JqIiIjo7fGbFFEpxK0TPjyNRoOMjAyOPRERERHliwk2ERERERERUTFggk1UColEIn2H8MkRiUQwNjbm2BMRERFRvrjJGVEpI5PJYGxsrO8wPjnGxsZo0qSJvsP4oLjhGREREVHRMMEmKoW+37IDiSkP9R0GfcQ+r2KO2X176jsMIiIiolKFCTZRKZSY8hBX7ibpOwwiIiIiInoF1/7RezF06FBERUXpOwwiIiIiIqIPhjPY9F6sXr1a3yEQERERERF9UJzB/oQEBARgz549+g6DiIiIiIjoo8QE+xOSnZ2N7OxsfYdBRERERET0UWKC/Q5iYmLQr18/yOVyyOVy+Pr6IiwsDD4+Pjp1O3TogAsXLgAAkpOT4e7ujoSEBHTs2BEKhQJKpRIrVqzAggULsGzZMjg6OiIgIAAAkJGRgRkzZsDBwQH29vbw9/dHWlqa0La/vz82bNgAX19f2Nvbw9nZGZMnTxbqJCQkQC6XIywsDIGBgZDL5Th//nyh7tHPzw+rVq3CiBEjYGdnh/bt2+Po0aN48uQJxo4dC4VCgc6dO+PkyZNa13Xu3Blnz54FANy+fRvt27fHvn370L59e9ja2qJr166IjIws0nifO3cO3t7eUCgUUCgU8Pb2xr///iuc3759O7777jv8+OOPcHR0hJOTE5YtWwa1Wo2ff/4ZLVu2hJubm87y9SFDhiAiIgKjR4+Go6MjFAoFZs+eDaVSqVXv5MmT6NatG2xsbNCpUydEREQI5/L7TAEgLCwMnp6esLGxQc+ePYVxISIiIiKijwsT7LcUHR2N8ePH4+uvv0ZsbCzi4uIwc+ZMKJVKncQMgFZ5VlYW0tPTsXz5cmzcuBHR0dGQyWTIzMzE8ePHkZ6ejhMnTmDWrFkAcpZ237t3DwcOHMCRI0egVqsRGBgotC0SifDLL79AoVAgKioKhw4dQlJSEhYuXAgAsLKyQnx8PLp06YIZM2YgPj4e1tbWhbpPiUSCJUuWoFu3bjh16hTmz5+PKVOmYPLkyXBzc0NsbCzmzJkDPz8/PHnyJM/7FYlESEpKwvr167F8+XKcPn0aEyZMwLhx45CSklLoMVepVJg0aRJOnDiB6OhoeHp6ws/PTyvWQ4cOQSQSITIyEmFhYQgLC8PEiRPx5MkThIeH448//sCOHTsQHR0tXJeVlYWgoCB4enoiJiYGu3fvRlxcHObPny/U+ffff+Hr64tRo0YJn/WUKVNw/fr1N36mUVFRCAoKwuzZsxEXFwcfHx+MHDlSa6yIiIiIiOjjwAT7LQUFBWHq1Kno1KkTJBIJRCIRzMzMCn39s2fP0K1bN5ibm0Ms/r+P4e7du/D19YVEIoFYLMb169cRERGBkJAQVKpUCaamppg+fToiIiLw8OH/vQdZLpejf//+MDQ0RNmyZeHj44Pw8PBiudeWLVuiY8eOkEgkaNasGZo3bw6RSIQvv/wSANC8eXM0bdoUZ86cybeNzMxMBAQE4PPPP4dIJELr1q3RuHFjnZnvN7Gzs4O1tTWkUinEYjG++uorXL58GS9evBDqlC1bFhMnToShoSEqVaqEHj16ICYmBt9//z1kMhkqV66MHj166MyeOzg4wMvLCwBQrVo1BAUFYdu2bcjMzAQAhIaGom/fvvD09IRUKoWDgwP69OmDLVu2CG3k9ZkuWrQI48ePh4ODA6RSKTp06AAXFxf8P/buPK7G9P8f+Ot06lRaKMkIZR1bNNWppIgsWWYasowYYxtZxpKhKaaxNGSbzyxkso0Rn9kw+Bg05qNsiRYhM2QbPoNUkiQdnVPn/P7w6/66naIUx9Hr+XjM4+G+7uu+7/f9Pg29z3Xd171z585K3zcREREREekHFtjP4erVq7h27Rr69+9frfPI5XKtNmdnZxga/t/i7unp6XBxcUG9evWENgsLC9jb2+Py5ctCW7t27UTnsbOzQ3Z2drXiK9O6dWvRtpWVFdzd3UVt1tbWTx2VlUgk1Y6xqKgIUVFRGDJkCDw9PdG5c2doNBrRdZs3by7Kn5WVFZycnCCTyZ4aq7e3t2i77EuEGzduAHg0Pd3X11erT0ZGhqjt8c+0tLQUf/75p9ZxTk5OWscREREREZH+42u6nkNeXh6sra1FhdyzaDQa0baBgYGoaC5jZWUl2s7OzkZSUpJWMa5SqVBQUCBsGxsbi/YbGhpCrVZXOr6nkUqlWm1169at0jkMDAxgZGQkajMyMkJpaWmlzzFt2jQ8ePAAwcHB6NSpE8zNzdGhQwdRbsuLtbw8P6m8+zE3NxeeY8/JycGHH34IiUQi7Fer1XBwcBC2n/xMc3NzUVJSovVFTGlpKbp06fLMmIiIiIiISL+wwH4O1tbWuHPnDpRKpWhkFHhUZD25UrdGoxFN5wYejeg+Xqw9fvzjzMzM4OPjg9WrV9dQ9Prpn3/+QUpKChISEmBpaQng0ZcPNfUlwp07d0TbarUad+7cQcOGDQE8+hxiYmLQtm3bCs/x5GdqZmYGADh48CAsLCxqJE4iIiIiInp1cYr4c2jWrBkcHBywY8cOrX02NjbIzMwUtaWmppa78FllODo6Ij09HQ8fPnyu4x9naGioNZKuL+7evQtLS0uhuAaAo0eP1tj5ExISRNvHjh1DgwYNYGtrC+DR55CUlFSlc5qbm6NZs2ZVPo6IiIiIiPQTC+znIJFIEBoaihUrVmDv3r3CKOrdu3fh5OSE+/fvY/v27QCAW7du4YsvvkCTJk2e61rOzs5o0qQJZs+ejaysLABAfn5+lRYHK2NjY4MzZ85Ao9GIXvOlD1q2bAmFQoH9+/dDo9Hg5MmTiImJgY2NTY2c//jx49i5cydUKhWuX7+OxYsXY+zYscKMgrFjxyIqKgr79++HUqmEWq3GqVOnnvkM+fjx4xEREYHjx4+jtLQUJSUlOHbsmN7ln4iIiIiIno0F9nPq3r07vv32W3z33XeQy+VwdXVFaGgoLCwssHLlSsTExMDLywuTJk3CtGnTYGtrK0wnNzIy0npmGnj0HHV57dHR0bC0tMSgQYPg4uKCgQMH4uzZs8J+mUymNVW9vGsMHjwYp06dglwu13oXdEVkMpnWecq73pNtj29XdL/lnaci5ubmiI6Oxrfffgs3NzcsW7YMS5YsgaWlpfAFR3n5q0ysADBnzhz88ccf6NKlC4YPH463334bo0aNEvZ7e3sjMjISa9asEd7DvWLFCmFmQkX3OGzYMEyZMgURERGQy+Xw8vLCxo0bK3XPRERERESkXyQafZ0zTFRDRo0ahalTp8LDw0PXoTxT2Rcry+KP48LNLB1HQ6+zNo3fwL9nBOk6DEFRURHOnz+Pdu3aoU6dOroOp9Zh/nWHudct5l+3mH/dYe7Fyn4H79ix4zP7cpGzWiovLw9+fn4VPpMtkUiQkJBQ7qjs6xaLVCrVWuGciIiIiIioqlhg11LW1tZISUnRdRgAdB/Lpk2bdHbt59XctmaePSeqCH/GiIiIiKqOBTaRHvo8MEDXIVAtUKpWQ2rApTqIiIiIKou/ORHpGaVSCYVCoeswah2FQoFz587VqtyzuCYiIiKqGv72RKSHuDbhy6fRaKBQKJh7IiIiIqoQC2wiIiIiIiKiGsACm0gPSSQSXYdQ60gkEpiamjL3RERERFQhLnJGpGdkMhlMTU11HUatY2pqivbt2+s6DJ3hgmdEREREz8YCm0gPfbptB67evq3rMKiWaN6gARYP5cr1RERERM/CAptID129fRsZt7J0HQYRERERET2G8/1eI+PHj0dCQoKuwyAiIiIiIqqVOIL9Gvnuu+90HQIREREREVGtxRFseiY/Pz9kZ2frOoyXwsnJSdchEBERERGRnmKBTc+kUqmgUql0HcZL8fDhQ12HQEREREREeooF9isqJCQEGzZswKRJk+Dq6oo+ffrg0KFDyM/Px7Rp0+Dh4YEBAwYgMTFROGbAgAE4deoUAOD69evo06cP9u7diz59+sDFxQXvvPMODh8+XOkYNm3aBLlcjszMTPj7+8Pb2xslJSXC+SdMmABnZ2d4e3sjOjpadKyPjw9iY2MxcOBAuLi4YPjw4bh27RrOnTuHYcOGwd3dHaNHj8b169eFY9LS0jBx4kRs27YNfn5+cHV1RUBAAJKTk0XnVigUmD9/Ptzd3eHm5obQ0FAUFhYK++fNm4eff/4ZERER8PDwwOrVqwEA69evR9++feHm5oauXbti0aJFUCqVAIAlS5ZALpcDAORyOQICHq2YfOrUKQwYMEArN+PHj0dsbKyw7ezsjEuXLiEgIABubm74559/AACJiYl499134ezsjP79+yM+Pr7S+SciIiIiIv1S7QL7ypUrmDt3Lvr37w93d3fk5OQI+y5evCgqoKjypFIpVq9ejXfffRfJycn48ssvMWfOHISFhaFbt25ISkrCkiVLEBISgvz8fACAUqkUCkaJRIKsrCzExMRg7dq1OHnyJD7++GPMmDFD9Bk9zZgxY5Camgo7Ozvs3r0bCQkJMDQ0hFKpxJgxY+Do6IjExERs3boV+/btw7Zt20Txf/3114iMjMTJkyfh7++PuXPnIiwsDOHh4UhKSkKfPn0QGhoqHKNSqXDu3Dns2rULP/30E1JSUjBo0CBMnDgRtx97JVV4eDgyMzMRGxuLgwcPQq1WY+HChcJ+pVKJrVu3olWrVkhKSsLkyZMBAA0bNkRMTAxSUlKwd+9eXLx4ET/99BMAYM6cOUhNTQUApKamYseOHVo5fdyT7UVFRfjyyy/x5ZdfIjk5Gfb29rh06RKmT5+OKVOmICUlBQsWLMCcOXNw5cqVSuWfiIiIiIj0S7UK7MTERAwdOhR16tRBcHAwVCqVMMIJAGfOnMGCBQuqG2Ot1bVrV/Tr1w9SqRSOjo7o1KkTJBIJhg4dCgDo1KkTOnTogLS0tHKPLy4uRnh4OJo3bw6JRIIePXqgXbt2olHv57Fjxw7Y2NhgxowZMDU1hZ2dHcLCwrB582ZRvw8++ADt27eHRCJBYGAgzp8/j4CAAOE+RowYgYyMDNHoc25uLpYuXQpra2sYGBhg1KhRkMvl2L59O4BHX+jEx8djxYoVqF+/PszNzTFv3jzEx8cjNzdXOE9JSQlGjBgBADAwePRj7u/vj4YNGwIALC0t8c477+DkyZPVysXjPDw80KxZM0gkEgBAdHQ0AgMD4efnB0NDQ7i7u2P48OFCUU9ERERERK+XahXY//rXvxASEoLw8HD06dMHhobiRck9PDzw119/VSvA2qx169aibSsrK7i7u4varK2thRHsJ0kkErRr107UZmdnV+0Fy86cOQNfX19Rm5OTE65cuSL6gqVVq1aiWKysrODm5iZqq1evnih+e3t7NG3aVHRuLy8vXLx4EQCQnp4OFxcX1KtXT9hvYWEBe3t7XL58WWh7/DplLl26hNmzZ8PPzw9yuRyLFi3C3bt3q3bzT1E2xbzM6dOntfLUqVMnZGRk1Ng1iYiIiIjo1VGt13RdunQJPXr0qHC/mZkZHjx4UJ1L1GpSqVSrrW7dupU+3sDAAEZGRqI2IyMjlJaWViuu7OxsxMbGYv369aJ2Q0NDFBQUwNraGsDzxf944VzGzMwM9+/fF66dlJSkVcyqVCoUFBQI21ZWVqL9165dw3vvvYfAwEAEBQXBwcEB+/btE6aCV5VGo9Fqe/KaOTk5+PDDD4URbQBQq9VwcHB4rmsSEREREdGrrVoFdoMGDXDp0iW88cYb5e4/evQo7OzsqnMJegWZmZlh5syZGD16dI2f+/Fp3mVycnKEqd1mZmbw8fERFi6rSNm08DK7du1C9+7dERISIrRlZWU9Mx6pVCoalS/z+DPhj/d9nJmZGWJiYtC2bdtnXoeIiIiIiPRftaaIv//++5g/f75oGnjZaN3BgwcRGRmJQYMGVS9C0jlDQ0PRiK2jo6PWyt415ebNm7h27Zqo7ciRI3jrrbeEa6enp1f5dVp3795F48aNRW0JCQla/Z681/r16yM3NxfFxcVC2/Xr14VVwp/G0dERSUlJVYqTiIiIiIj0V7UK7LFjxyIgIADDhw9Hv379oFAoMG3aNPj4+GDKlCno27cvgoKCaipW0hEbGxucPn0aKpUKCoUCgwcPRmpqKlavXi0sUPbPP//gzz//rPa1LC0t8emnnyIrKwulpaVYv349MjMz8c477wB49DqsJk2aYPbs2cIIdH5+/jMXbnNycsIff/yBnJwcPHz4EGvWrBFNKX/8Xk+dOoXi4mIolUrY29ujUaNGWLt2LTQaDe7du4fw8HC0bNnymfcyduxYREVFYf/+/VAqlVCr1Th16lS1n4EnIiIiIqJXU7Vf0zV16lQcPnwYU6dOxfTp09GnTx/MnDkTcXFxiIiI0JqqS5Ujk8lgbGys1SaTySpse/zPRkZGWsdXdI5nmTBhApYvXy68HszGxgabN29GWloaunXrBrlcjilTpoimd5cXv5GRkdYz4TKZTNTWpk0bDBw4ECNHjoSbmxsOHjyIjRs3wsTEROgTHR0NS0tLDBo0CC4uLhg4cCDOnj371GsPHDgQffv2xbBhw+Dj44MbN25g7ty5Ws+jT5kyBVOnTkWvXr1w+fJl4XVjx44dg5eXFwIDAxEQEIC2bduK8mhiYqJ1b97e3oiMjMSaNWvg4eEBDw8PrFixotzXfhERERERkf6TaMpbramSvv76a4wePVprcSei55GUlISoqChs2bJF16G8ssq+SFhyNBEZt579DDlRTWjb6A38OGWiTmMoKirC+fPn0a5dO9SpU0ensdRGzL/uMPe6xfzrFvOvO8y9WNnv4B07dnxm32otchYTE4NRo0ZV5xSkI3l5efDz8yt3NWzg0bP0CQkJ5Y6CvyhSqVRrFJiIiIiIiEhfVKvA7tmzJ/bs2fNCVpOmF8va2hopKSm6DkNELpdj48aNug5DLzRv0EDXIVAtwp83IiIiosqpVoE9btw4REVF4fjx4+jevTsaNWqkNYVAKpXCxcWlWkESkdjioQG6DoFqmVK1GlKuqUFERET0VNUqsIODg4V3BF+8eLHcPkZGRti/f391LkNEj1EqlVAoFDA1NdV1KLWKQqHA1atX0bx581qZexbXRERERM9WrQL7jz/+qKk4iKgKqrE2IT0njUYDhULB3BMRERFRhTgkQaSHJBKJrkOodSQSCUxNTZl7IiIiIqpQtUaw09LStN4j/CQ+g01Us2QyWa2coqxrpqamaN++va7D0At8XpuIiIhqq2oV2OPHj9cqsEtKSqBWqyGRSGBnZwcTExPs3bu3WkESkdjcXb/i79xcXYdBpKWFjQ0iBw7WdRhEREREOlGtAvvUqVNabaWlpbhx4wZ27dqFuLg4REVFVecSRFSOv3NzkZF1S9dhEBERERHRY2p8Dp9UKoWDgwNmzJiBcePGYd68eTV9CSIiIiIiIqJXzgt9SK5fv344c+bMi7wEERERERER0SvhhRbYZ8+ehZWV1Yu8BBEREREREdEroVrPYKenp6O4uFir/cGDB8jIyMCWLVsQGBhYnUtQNYWHh8Pd3R3+/v66DoWIiIiIiOi1Vq0COzQ0tNwC28TEBI0aNcLHH3+MgICA6lyCqqmkpAQlJSW6DoOIiIiIiOi1V60p4rGxsYiPj9f6b9++ffjuu+8wePBgSCSSmor1lXDixAmMGDECcrkccrkc06dPx549exAUFKTVt2/fvkhPTwcAZGdnw8fHB2fPnkW/fv3g4eEBpVKJdevW4auvvsKaNWvQuXNnhIeHAwAUCgXmz58Pd3d3uLm5ITQ0FIWFhcK5Q0NDsXnzZkyfPh1ubm7o0qULwsLChD5nz56FXC7Hnj17sHDhQsjl8ko/Dx8SEoINGzZg0qRJcHV1RZ8+fXDo0CHk5+dj2rRp8PDwwIABA5CYmCg6bseOHfD394ebmxu8vLwQEhKCgoICAMC9e/fQpUsXnDx5Uui/Z88evPfee5XO/enTpzFq1Ch4eHjAw8MDo0aNwqVLl4T927ZtwyeffIKlS5eic+fO8PT0xJo1a6BWq/Gvf/0LXbt2Rbdu3fDdd9+Jzjtu3DjEx8fjo48+QufOneHh4YHPP/8cSqVS1C8xMRHvvvsunJ2d0b9/f8THxwv7Kvp8y+7Tz88Pzs7OCAgIKHf1fSIiIiIi0n/VKrBzcnKeuv/mzZvYunVrdS7xSjl+/DiCg4Px/vvvIykpCSkpKViwYAGUSqVWMQZA1K5SqVBUVIS1a9diy5YtOH78OGQyGYqLi3H06FEUFRXh2LFjiIiIAPBoandmZiZiY2Nx8OBBqNVqLFy4UDi3RCLB119/DQ8PDyQkJGD//v3IysrCypUrAQAdO3ZEamoq3n77bcyfPx+pqalwcnKq1H1KpVKsXr0a7777LpKTk/Hll19izpw5CAsLQ7du3ZCUlIQlS5YgJCQE+fn5wnHm5uaIiopCcnIy4uLioFarhde01a1bF8HBwfj888+hVqtx//59LFu2TPhCoTJKS0sxe/ZsHDt2DMePH4efnx9CQkJEce/fvx8SiQSHDx/Gnj17sGfPHsyaNQv5+fmIi4vDr7/+ih07duD48ePCcSqVCosWLYKfnx9OnDiB//znP0hJScGXX34p9Ll06RKmT5+OKVOmCJ/7nDlzcOXKlad+vgkJCVi0aBE+//xzpKSkICgoCJMnTxbljYiIiIiIXg/VKrB9fHxEo6pPkkgk+OKLL6pziVfKokWLMHfuXPTv3x9SqRQSiQTW1taVPr6goADvvvsubGxsYGDwf6m/efMmpk+fDqlUCgMDA1y5cgXx8fFYsWIF6tevD3Nzc8ybNw/x8fHIzc0VjpPL5Rg5ciSMjY1hYWGBoKAgxMXF1ci9du3aFf369YNUKoWjoyM6deoEiUSCoUOHAgA6deqEDh06IC0tTTimT58+sLe3h0QigYmJCQYPHozU1FRh/9ChQ1GnTh38/PPPWLlyJXr37o2OHTtWOiZXV1c4OTnB0NAQBgYGeO+995CRkYEHDx4IfSwsLDBr1iwYGxujfv36GDRoEE6cOIHPPvsMMpkMDRo0wKBBg3D48GHRuR9/Tv2NN97AokWLsHXrVuERiOjoaAQGBsLPzw+GhoZwd3fH8OHD8dNPPwnnKO/zXbVqFYKDg+Hu7g5DQ0P07dsXXl5e2LlzZ6Xvm4iIiIiI9EO1nsHWaDQV7istLcWxY8cgk8mqc4lXxtWrV3Ht2jX079+/WueRy+Vabc7OzjA0/L+PIj09HS4uLqhXr57QZmFhAXt7e1y+fBk2NjYAgHbt2onOY2dnh+zs7GrFV6Z169aibSsrK7Rp00bUZm1tLRqJzczMxJo1a5CWloacnByoVCrRKvISiQQREREYNWoUjIyMsHfv3irFVFRUhI0bN+LQoUO4efMmlEolNBoN8vPzYWZmBgBo3ry5KJdWVlZwcnIS/RxaW1vj8uXLonN7e3uLtsu+ULhx4wZatmwpTE9/ss/3338vanv88y0tLcWff/6JVatWifo4OTnhr7/+qtK9ExERERHRq6/KBXZ0dDS++eYbSCQSSCQSuLm5VdjXxMQEn3zySbUCfFXk5eXB2tpaVLw9y5NfQBgYGIiK5jJPvsosOzsbSUlJWsW4SqUSnmkGAGNjY9F+Q0NDqNXqSsf3NFKpVKutbt26FfbPz8/HsGHD0K1bNyxZsgStWrXC2bNnERYWJupnb2+POnXqwMHBARYWFlWKadq0aXjw4AGCg4PRqVMnmJubo0OHDqI8lxd3eTl/Unn3Zm5uLszQyMnJwYcffihaU0CtVsPBwUHYfvLzzc3NRUlJidaXMqWlpejSpcszYyIiIiIiIv1S5QJ79OjR8Pf3h0ajQe/evbFr1y6Ym5uL+kgkEhgbG6NevXrlFjz6yNraGnfu3IFSqdQalTcwMNBaqVuj0YimcwMQvpR40uPTxQHAzMwMPj4+WL16dQ1F/+L997//RePGjREZGSm0ZWVlafXbuHEj3nzzTVy7dg3x8fHw9fWt1Pn/+ecfpKSkICEhAZaWlgAefRFRU18o3LlzR7StVqtx584dNGzYEMCjzyQmJgZt27at8BxPfr5lo+oHDx6s8pcJRERERESkf6r8DHadOnXQuHFjNGnSBPb29nBwcEDjxo1F/9nZ2aF+/fqvTXENAM2aNYODgwN27Nihtc/GxgaZmZmittTU1HIXPqsMR0dHpKen4+HDh891/OMMDQ2fOpW/pty9exeNGzcWtSUkJIi2r1+/jk2bNuGzzz5DaGgoFi9eXO5r3io6v6WlpVBcA8DRo0erH3gFsR47dgwNGjSAra0tgEefSVJSUpXOaW5ujmbNmlX5OCIiIiIi0k/VWuRs//79MDExqalYXmkSiQShoaFYsWIF9u7dK4yc3r17F05OTrh//z62b98OALh16xa++OILNGnS5Lmu5ezsjCZNmmD27NnCKHB+fr7Wa7Eqw8bGBmfOnIFGo3nqgnTV5eTkhMTERPz9999QqVTYvn07Ll68KOqzaNEijBs3DnZ2dujevTvs7e2xdu3aSp2/ZcuWUCgU2L9/PzQaDU6ePImYmBjhefTqOn78OHbu3AmVSoXr169j8eLFGDt2rDC7YOzYsYiKisL+/fuhVCqhVqtx6tSpZz7zPn78eEREROD48eMoLS1FSUkJjh079kI/CyIiIiIi0o1qFdgA8PDhQyQnJ2P37t3YtWuX1n+//vprTcT5SujevTu+/fZbfPfdd5DL5XB1dUVoaCgsLCywcuVKxMTEwMvLC5MmTcK0adNga2srTCc3MjLSemYaePQcdXnt0dHRsLS0xKBBg+Di4oKBAwfi7Nmzwn6ZTKY1Vb28awwePBinTp2CXC7Xev9zRWQymdZ5yrve420eHh6YMmUKJkyYgC5duuDIkSNYtmyZ8EVEfHw8MjMzMXbsWOH4sLAwbNmyBbdu3XpmTObm5oiOjsa3334LNzc3LFu2DEuWLIGlpaVwjfJy+ay4y8yZMwd//PEHunTpguHDh+Ptt98WLWrm7e2NyMhIrFmzRngP94oVK4RZChV9vsOGDcOUKVMQEREBuVwOLy8vbNy48Zn3S0RERERE+keiqcb84bNnz2Ly5Ml48OABbG1tcePGDbzxxhu4c+cOiouL8eabb8Le3l5rFWWiV8moUaMwdepUeHh46DqUZyr7kmVxUiIysp79xQTRy9b2jUb4+cOJNX7eoqIinD9/Hu3atUOdOnVq/Pz0dMy/7jD3usX86xbzrzvMvVjZ7+CVecVwtV7TtXTpUvTu3Rtz5syBTCaDh4cHNm7cCAcHBxw5cgQREREYP358dS5BNSgvLw9+fn4VPpMtkUiQkJBQ7kjs6xyXVCqFkZHRCzn3i9KihqbGE9U0/mwSERFRbVatAvvChQtYvny5MN22Tp06uHfvHgCgW7dumD17NpYvX44ff/yx+pFStVlbWyMlJUXXYWjRdVybNm3S2bWfV+TAwboOgahCpWo1pAbVfgKJiIiISO9U6zcgiUSC0tJSYbthw4a4dOmSsO3i4oLz589X5xJE9ASlUgmFQqHrMGodhUKBc+fOMfeVwOKaiIiIaqtq/RbUsmVLZGRkCNsuLi74z3/+I2xfuHBBeM0REdWcl/HqNRLTaDRQKBTMPRERERFVqFoF9oQJE0Svjho9ejSuXLmCwYMH45NPPsGsWbMQGBhY7SCJiIiIiIiIXnXVega7Z8+e6Nmzp7DdsGFD7NixA9u3b0dubi7mzZuHt99+u9pBEpGYRCLRdQi1jkQigampKXNPRERERBWqVoFdnoYNG+Kjjz6q6dMS0f8nk8lgamqq6zBqHVNTU7Rv317XYdQKXCSNiIiI9FW1C+ySkhLs2bMHZ86cQU5ODiIjI1G3bl1hn1Qq5YgPUQ2bs+dX/H0nV9dhENW4FvVtsORtrpJPRERE+qlaBfb169cxduxY1KlTB+7u7khISMCDBw+EAnvdunW4du0ali9fXiPBEtEjf9/JRUbOLV2HQUREREREj6nWHLzFixejW7du2L17N8LDw4X3YZfp1asXTpw4Ua0AiYiIiIiIiPRBtQrspKQkjBkzpsL9NjY2uHv3bnUuoReys7Ph7Oys6zAqZd68ediwYYOuw3jtKJVKDBkyBBcuXNB1KEREREREpCPVmiJubGyM+/fvV7j/0qVLwnTx15lKpUJxcbGuw6gUpVIJpVKp6zBeOzKZDNu3b9d1GEREREREpEPVGsHu27cvli9fXm5xeefOHURERKBHjx7VucQry8/PD9nZ2boO45WXnZ0NPz8/XYdBRERERET0wlVrBDskJARTpkwR3oetVCqxceNG5OfnIy4uDk2aNMHHH39cU7G+UlQqFVQqla7DeOUxT0REREREVFtUawTbzMwMMTExWLhwITQaDeRyOa5evQqZTIYFCxbg119/hZWVVU3F+krYtGkT5HI5MjMz4e/vD29vb5SWlgIAtmzZAl9fXzg7O6N///6Ij48XHdu3b1+cPn0ao0ePhlwuR1JSEgDg6tWrmDRpEtzc3ODu7o5Jkybh2rVrwnEVPeP95PPU9+7dwyeffAI3NzfI5XJMnz4d+/fvx7hx40TH3bx5E0FBQZDL5fD09MTs2bNRWFgo7F+zZg3WrFmDZcuWoVu3bnBxccGkSZOQmZlZpZimT58Of39/ZGZmQi6XV/r96GlpaZg4cSK2bdsGPz8/uLq6IiAgAMnJyaJ+CoUC8+fPh7u7O9zc3BAaGiq6j3nz5uHnn39GREQEPDw8sHr16kpdf8SIEdi2bRtGjhwJFxcX+Pv748yZM7hx4wbGjBkDDw8PDBkyBOfOnRMd5+zsLMxqOHHiBMaMGYOYmBh0794drq6uGDJkCNLT0ysVAxERERER6Z8qF9jjx4/Xeoa3Z8+eaNGiBaKiovDdd98hMjIS7777rtaq4q+DMWPGIDU1FXZ2dti9ezcSEhIglUpRWlqKP/74AzExMTh16hRCQkLw8ccfIy8vTzhWqVTiX//6F2bNmoXU1FS4u7sjPz8fI0eOhLOzMxISEpCQkAC5XI73338fBQUFACp+xvvJ56lDQ0ORm5uLPXv2IDExEe7u7vj000+1RpB37tyJfv36ITExEf/9739x+/ZtUfGpUqnwww8/oLS0FAcOHEBCQgIsLCwwdepUUZ9nxbRy5Urs3r0bdnZ2SE1NrXSBq1KpcO7cOezatQs//fQTUlJSMGjQIEycOBG3b98W+oWHhyMzMxOxsbE4ePAg1Go1Fi5cKIpl69ataNWqFZKSkjB58uRKXV8qleLrr7/GtGnTcPLkSXz88ccICQnBJ598grFjxyIpKQlTpkzBtGnTUFJSIhxXXFws5FoikeDMmTM4evQofvnlF6SkpCAgIABTpkzBw4cPKxUHERERERHplyoX2ImJieUukvXtt9/WihXDn+bzzz9H06ZNAQA9evRA27ZtkZiYKOrTpk0bdOrUCcCjImzz5s1wdHTExIkTYWxsDJlMhg8//BAdOnTA5s2bK33trKwsHD16FF988QUaNmwImUyG999/H+7u7lp9+/Xrh0GDBkEmk8Hc3BwTJkzQGm03NjZGWFgYZDIZ6tSpg0WLFuHGjRs4efJkVdPyXHJzc7F06VJYW1vDwMAAo0aNglwuFxYSu3LlCuLj47FixQrUr18f5ubmmDdvHuLj45Gbmyucp6SkBCNGjAAAGBhU/sfd398fnTt3hkQiQffu3SGTydC+fXv4+PgAAHx9fWFiYoIrV65UeA6FQoGlS5eiYcOGMDAwwIgRIyCVSnH27NnnSQkREREREb3iqlxgazSaKrXXFgYGBnBwcBC12dnZaS2E5ubmJtpOSUlB//79tc7Xr18/pKSkVPr658+fR4sWLWBtbS1q9/X11erbvHlzrThzcnJEbV26dBEVpMbGxnB1dcXFixcrHVN12NvbC19WlPHy8hKun56eDhcXF9SrV0/Yb2FhAXt7e1y+fFloezLfldW6dWvRtpWVldaXFdbW1sjPz6/wHLa2trCxsRG1NWrUiIvjERERERG9pqq1yBn9H4lEAolEImqTyWRa06iffCY9JycHtra2WueztbVFVlbWU6/5+JcaBQUFomLz8fM86cmR3PLiLO/1aubm5k99LduTMVVHefdiZmYmXD87OxtJSUmQy+WiPiqVSphaD2jnu7KkUqlWW1VfOWdsbKzVZmRkJDyzT0REREREr5cqF9hPFpGPt1e0j/7Pk8WtlZWV1ugx8KjwLhuNlkqlUKvVUKvVouNv374tjJqbmZnhzp07Wud5/Bnwqnh8mvXjMXl7e1c6puqo6PoNGzYE8Oh+fXx8nvlcd1WmhRMREREREVVHlQtsjUaD6dOna43wFRUV4ZNPPoGJiYmoXSaTVXpxK31iaGhYI6O1Pj4+2LdvHwYOHChqj42NFd4hXr9+fQCPVv8umzZdWFiIM2fOCCO4Tk5OyMzMxPXr10VTqw8cOPBcX3ycOHECpaWlwudcUFCA9PR0YRGxysQEPBqxfZ483bx5E9euXUOzZs2EtiNHjmDIkCEAAEdHR6xbtw4PHz7U+pkjIiIiIiLShSoP7wUHB8PFxQVOTk6i/yZNmgQPDw+tdicnpxcRt87Z2Njg9OnTUKlU1VoVeuTIkTh37hzWrFkjrMD9zTff4Pz588LiXDKZDO7u7vj2229RUlKChw8f4rPPPoOdnZ1wngYNGiAwMBChoaHIysrCw4cPsWHDBiQlJZU73fpZioqKsGTJEhQWFuLBgwcIDw+Hl5eXUPBWJibg0VTvu3fv4ubNm3jw4EGlp0dbWlri008/RVZWFkpLS7F+/XpkZmbinXfeAfDolVhNmjTB7Nmzhan0+fn5WovKERERERERvSxVHsGeNGnSi4hD70yYMAHz5s1DZGQk5syZU+7ztjKZTPSqMplMptXP0tISP/74I5YuXQpvb29IJBLI5XL88MMPMDc3F/otXrwY8+bNQ9euXWFhYYHhw4fD3t5edP6QkBB8++23GDRoEFQqFby9vdGvXz+0aNGiwpiAR6PMT8Y1fPhwPHz4EH369IFSqUT37t0xb948UZ/KxGRsbIzx48cjICAAZmZm+OWXX9CgQYNn5rdNmzbw9/fHyJEjcffuXbRt2xYbN24UjVZHR0dj+fLlGDRoEIqLi2FpaYnAwEB06dKlwnxXRnnHlZe3J9uMjY1hZGQk/Lm819SVdx4iIiIiIno9SDS1ffnv10hSUhKaNGmCxo0bo7i4GHFxcfjmm2+wbds2WFpaVvo8q1atAgBMmzbtRYX6VElJSYiKisKWLVt0cv1XWdkrvhalJSIj55aOoyGqeW1tG+GX0RO12ouKinD+/Hm0a9cOderU0UFktRvzrzvMvW4x/7rF/OsOcy9W9jt4x44dn9mXq4i/Ri5cuIBPP/0Ud+/ehVQqhYeHBzZs2FCl4hp4tIDZi1ocbNmyZdi2bVuF+8eMGYPOnTsLI8E16a+//sLo0aMr3F+/fn3s37+/xq/7IrSob/PsTkR6iD/bREREpM84gk2kR6ry7RmRvipVqyF94ks+fpOuW8y/7jD3usX86xbzrzvMvVhVfgfnO4yI9IxSqYRCodB1GLWOQqHAuXPnmPuX4MnimoiIiEhf8LcYIj3EiScvn0ajgUKhYO6JiIiIqEIssImIiIiIiIhqAAtsIj0kkUh0HUKtI5FIYGpqytwTERERUYW4ijiRnpHJZDA1NdV1GLWOqakp2rdvr+swao3yFjojIiIietWxwCbSQ3P3b8fVu7d1HQbRC9HcqgEi/YboOgwiIiKiKmOBTaSHrt69jYzbt3QdBhERERERPYbz74iIiIiIiIhqAAtsAgBkZ2fD2dlZ12FUWadOnZCbm6vrMIiIiIiIiFhg0yMqlQrFxcW6DqPKiouLUVJSouswiIiIiIiIWGDXZn5+fsjOztZ1GERERERERK8FFti1mEqlgkql0nUYRERERERErwUW2LXQpk2bIJfLkZmZCX9/f3h7e6O0tBQAsGXLFvj6+sLZ2Rn9+/dHfHy86FiFQoH58+fD3d0dbm5uCA0NRWFhobB/3rx5+PnnnxEREQEPDw+sXr0aKpUKjo6OOHjwIPz8/ODq6orx48cjLy8Px44dwzvvvAMPDw9MnToVeXl5wrmKi4sxZ84cdOvWDXK5HL1798Yvv/zy3PddWFiI4OBgeHh4wN3dHYGBgaL9v//+OwICAuDq6go3NzcsWbJE2Hf48GEMHjwYcrkc3t7eiIiIEN333r17MWvWLOzYsQPe3t4YP348AKC0tBRfffUVvLy84OLigsmTJyMnJ+e574GIiIiIiF5dLLBroTFjxiA1NRV2dnbYvXs3EhISIJVKUVpaij/++AMxMTE4deoUQkJC8PHHH4uK3vDwcGRmZiI2NhYHDx6EWq3GwoULhf1KpRJbt25Fq1atkJSUhMmTJ8PIyAgqlQrffvstNmzYgOTkZLRv3x5z587F0qVLERUVhWPHjsHBwUFU1CqVSnh5eWHfvn1ITU3FunXrsGrVKpw/f/657nvVqlWwtLTE0aNHkZycjK+++krYt3PnTixZsgSzZ89GamoqkpKSMGnSJABAcnIyQkJC8NFHHyElJQW//fYb7t69i+DgYOH44uJiXL58GSkpKYiPj8f69esBACtXrsTRo0fx888/IzExES1bthQdR0RERERErw8W2CTy+eefo2nTpgCAHj16oG3btkhMTAQAXLlyBfHx8VixYgXq168Pc3NzzJs3D/Hx8aKVvEtKSjBixAgAgIHB//2IffTRR2jatCmkUinGjh2LgwcPYvr06XBwcIChoSHGjx+PI0eOCP0tLCzw9ttvw9zcHADQvHlzdO7cGSdPnnyue7t69So6d+4MmUwGAHjjjTcAAEVFRVi+fDm+/PJLdOnSBRKJBAYGBrCysgLwqEgOCgqCr68vJBIJrKyssGTJEvz1119ISUkRzn/p0iWEhIRAJpPBwMAA9+7dw+bNm7FixQo0bdoUJiYm+Pjjj5GZmYm//vrrue6BiIiIiIheXSywSWBgYAAHBwdRm52dnbAQWnp6OlxcXFCvXj1hv4WFBezt7XH58mWhzc3Nrdzzt2rVSviztbW1Vl9ra2vk5+eLjomPjxeKWxcXF/z+++9afSpr8ODBWLFiBX777Teo1WqhPS0tDaampnB1ddU6prS0FKdOnUL//v1F7SYmJujevTuSk5OFtpYtWwr3BQAXLlyAra0tWrZsKbQZGBjA0dERGRkZz3UPRERERET06jLUdQD06pBIJJBIJKI2mUwmvL4rOzsbSUlJkMvloj4qlQoFBQXCdtnI75MMDbV/3B4v1p+0d+9eLFiwADNnzsT8+fPRsGFDfPbZZ9BoNJW9JRE/Pz84ODjgiy++wNq1a/Hpp5/C09MTeXl5sLW1LfeYO3fuoKSkpNz9tra2olXYn7yX7Oxs3LhxQytfJSUlWm1ERERERKT/WGBTpZmZmcHHxwerV69+ar/Hp4VXxw8//IBZs2Zh+PDhQltWVhbs7Oye+5xt27bFhg0bsG/fPkyZMgV79uyBtbU1bt26VW5/S0tLSKVS5OTkaF03JydHVHhLpVLRfjMzM7Ru3Rq7du167niJiIiIiEh/cIp4LWZoaFil0WBHR0ekp6fj4cOHLzCq/3P37l00btxY2L5//z7OnDlTI+fu378/WrVqhdOnT8PZ2RkPHz7E0aNHtfqZmJjAw8MD+/btE7U/fPgQhw4dgq+vb4XX6NChA65du4bbt2/XSMxERERERPRqY4Fdi9nY2OD06dNQqVSVKpqdnZ3RpEkTzJ49G1lZWQCA/Px8YRG0mubk5IRff/0VxcXFyMvLw5w5c0QFd1Wlp6ejqKgIAHDixAlcuXIFb775JszMzBAcHIzQ0FDhXtRqtfCs90cffYR169YhLi4OGo0GeXl5mDFjBpycnODk5FTh9Ro2bIiePXsiODgYf//9NwDgwYMHOHz48HPfAxERERERvbpYYNdiEyZMwPLly9GtWzecO3cOxsbGWn1kMpmw6jYAREdHw9LSEoMGDYKLiwsGDhyIs2fPivqXdx4TExMYGRmJ2urUqaPV7/G2OXPmQKPRoHv37hg0aBBcXV3xzjvvCO/sBgBjY+Nyn+0uz8aNG9G5c2c4Oztj0aJFWLp0KVq3bg0ACAwMxGeffYbFixfDxcUFbm5uwmu85HI5vvrqK6xZswZubm7w9/eHvb09vvzyS1Ec5d13ZGQkOnbsiNGjR8PZ2Rl+fn7ljpQTEREREZH+k2ied8UoInrpyr7MiPwrARm3y39unEjftW3QCD8NnyxqKyoqwvnz59GuXbtyv5yjF4v51x3mXreYf91i/nWHuRcr+x28Y8eOz+zLRc7otbBs2TJs27atwv1jxozB1KlTX2JEL1Zzqwa6DoHoheHPNxEREekrFtj0WggNDUVoaKiuw3hpIv2G6DoEoheqVK2GtIbeSEBERET0svC3FyI9o1QqoVAodB1GraNQKHDu3Dnm/iVhcU1ERET6iL/BEOkhLp3w8mk0GigUCuaeiIiIiCrEAptID0kkEl2HUOtIJBKYmpoy9zrC/BMREZE+4DPYRHpGJpPB1NRU12HUOqampmjfvr2uw6i1Kpt/PrtNREREusQCm0gPzTu4Ddfyc3QdBtErpVk9W0T0GKrrMIiIiKgWY4FNpIeu5efgwh2+B5uIiIiI6FXCeXRERERERERENYAFNhEREREREVENYIFdy2RkZGDo0KFQqVQAgLVr12Lt2rUvNYa5c+fi119/fanXJCIiIiIietH4DHYt07ZtW2zbtk3YViqVLz2GyMjIl35NIiIiIiKiF40j2PRCRUdHIzo6WtdhEBERERERvXAcwaYXqqSkRNchEBERERERvRQcwX4NLV++HCtWrBC1KRQKyOVynDlzBn5+fqJ9paWliIqKQo8ePeDs7IyRI0ciIyND2J+dnQ0fHx+cPXsW/fr1g4eHB5RKJa5evYqgoCB4enrC3d0dgwcPRmpqKgAgNzcX7u7uWLduHdatWwe5XI7Y2FgAQFBQEPbs2SOcv7CwEAsWLICXlxfkcjmGDBmCo0ePimLs0aMHDh06hGHDhsHFxQW+vr5Yt25dlfJy8eJFBAYGws3NDR4eHli2bJmwr6SkBN9++y169eoFV1dXeHp6Yt++fQAAjUaDjRs3onfv3pDL5ejTpw82bdokOve8efPw888/IyIiAh4eHli9ejUAIC8vDx9//DFcXV3RuXNnREZGCs+/ExERERHR64Uj2K+hHj16IDw8HCEhIULbsWPH0KpVK6hUKq0C7+eff0afPn2EAjgyMhLjxo3DH3/8AXNzc6hUKhQVFWHt2rXYsmULrK2tYWBggOLiYowdOxaurq6QyWQ4cOAAgoODER8fDxsbGyQnJ2PVqlUAgGnTpgnXUyqVome/p02bBgsLC/z222+wsrLCkSNHMHv2bHz77bdwdXUFAEgkEkRERGDx4sVwd3fH//73P4wePRotWrRAr169KpWX0NBQjB49GgMHDkRpaSnu3Lkj7Pvss8/wzz//YMOGDWjWrBkePnwojL5HR0dj3759WL16Nd58801cuXIFwcHBUCqVCAoKEu5p69atGDJkCJKSkqBWqwEAH330EZo3b45Dhw5BpVJh5syZWL16NYKDgysVMxERERER6Q+OYL+GXFxccO/ePVy9elVoi4uL0xq5LmNsbIz58+fDxMQEJiYmWLBgAQwNDXHgwAGhT0FBAd59913Y2NjAwODRj03btm3h6ekJmUwGAOjVqxcMDAxw7dq1Ssd64sQJnD9/HsuWLYO1tTUkEgl8fHwQFBSEb775RtR3xIgR8PT0hFQqRYsWLRAQEIC4uLhKX+vq1avo2rUrAEAqlcLW1hYAcObMGcTHx2Pt2rVo1qwZAMDExATm5ua4f/8+NmzYgMjISLz55psAgJYtWyIyMhLr169HYWGhcP6SkhKMGDECAGBgYICjR4/i1q1biIiIgIWFBaytrbFw4UL89NNPHMUmIiIiInoNscB+DUmlUvj6+uLQoUMAALVajcOHD1dYYHt5eUEqlQrbBgYG8Pb2xoULF0T95HK5aLukpAQxMTEIDAyEt7c3XF1dcfv2beTn51c61tTUVPTo0QOmpqai9n79+iEtLQ2lpaVCW/v27UV9GjdujJycnEpfa9iwYZg8eTJOnTolao+Li4Ovry/Mzc21jjl//jysrKzQqVMnUXvHjh1haWkpmkrv5uYm6nPmzBl069YNhob/N1GkWbNm0Gg0yMrKqnTcRERERESkH1hgv6Z69eqFgwcPAgDS0tJgZ2cHOzu7cvtaW1trtdWvXx8KhULYNjAwQL169UR9IiIisHXrVowbNw67d+9GamoqGjVqBI1GU+k4c3JyhJHkxzVo0AAqlUo0jbtspLyMoaGhqAB/lrlz5yIoKAjh4eGYMGECrl+/DuDRc9LlxfC0+ADA1tZWVChbWVmJ9mdnZ2PXrl2Qy+Wi/x4+fIh79+5VOm4iIiIiItIPfAb7NeXl5YWwsDAUFhYiPj6+wtFrAOWOpt65c0dUkEskEkgkEmG7uLgYO3bswO7du9GiRQsAjxYDq8qIMgDUq1ev3GNu374NQ0ND1K1bt0rne5ZevXrB19cXS5YswaRJk7Bnzx5YW1sjMzOzSvEBj4rvx7+cKJs6X8bMzAzDhw/H3Llza+4GiIiIiIjolcUR7NeUsbExOnfujKNHjyIuLg59+/atsO/hw4dFzxKr1WocO3YMbdq0qfCY+/fvQ6VSoUmTJkLbiRMntJ4tNjQ0fOqIto+PDw4dOiQaLQeA2NhYeHp6wtjYuMJjn5eBgQFmzZqFy5cvIzc3Fz169EBcXBzy8vK0+jo7O6OgoADp6emi9rNnz6KwsBDOzs4VXsfR0RGpqalVGtEnIiIiIiL9xQL7NdarVy9s2rQJpqamaNq0aYX96tati5CQEBQWFqK4uBgRERGoW7cuunfvXuEx9evXR+PGjfHjjz9Co9Hg0qVLWLp0qbBIWBkbGxukp6dDrVbj/v37WudxdXVF+/btERoairy8PGg0Gvz+++9Yu3Ytpk6d+ry3rqW0tFR4pru0tBQ//PADGjVqBCsrKzg7O6N79+6YMGEC/ve//wF4tCr4gwcPYGZmhgkTJmDOnDm4ePEiACAjIwOzZs3CxIkTtZ4df1yvXr1w//59LFy4UCjes7OzhVeZERERERHR64UF9muse/fuuHjxIt5++22hTSaTwcjISLQ9aNAgdOzYEf3790eXLl2Qk5ODmJgYoZ+RkZHWSLJEIkF0dDR+//13uLu74+OPP0ZISAiaNm0qvKIKAHr37o2ioiK4ubkhMjJSuObjz1OvXLkStra28Pf3h5ubGzZu3IhvvvkGb731lijOJ5/BNjY21mqryMOHDzF37lw4OTnBw8MDhw8fxtq1a4UFyFasWIHu3btj1KhRcHV1hZeXF/773/8CePTe7vfeew9Tp06FXC7HjBkz8MEHH2DcuHGi+J7MkYmJCWJiYpCXlwc/Pz+4urpi1KhRVVplnYiIiIiI9IdEw/mrRHrj7NmzAIBtuRm4ll+1592JXnfN6tkiosdQXYfxWioqKsL58+fRrl071KlTR9fh1CrMvW4x/7rF/OsOcy9W9jt4x44dn9mXi5zRa2HYsGH4+++/K9y/atUqeHp6vsSIXiwWEUTlK1WrITXg5CwiIiLSDRbY9FrYunWrrkN4aZRKJRQKxVOf/6aap1AocPXqVTRv3py514HK5p/FNREREekSfxMh0kN8suPl02g0UCgUzL2OMP9ERESkD1hgExEREREREdUAFthEekgikeg6hFpHIpHA1NSUudcR5p+IiIj0AZ/BJtIzMpmMzwDrgKmpKdq3b6/rMGotfc1/qUYNqYTfZRMREdUWLLCJ9NDiY7/gn3u3dR0GET2Ffd0G+NTrPV2HQURERC8RC2wiPfTPvdu4dDdT12EQEREREdFjOG+NiIiIiIiIqAawwCYiIiIiIiKqASywiYiIiIiIiGoAC2x6Jj8/P2RnZ+s6jJfCyclJ1yEQEREREZGeYoFNz6RSqaBSqXQdxkvx8OFDXYdARERERER6igX2KyQkJAQbNmzApEmT4Orqij59+uDQoUPIz8/HtGnT4OHhgQEDBiAxMVE4ZseOHfD394ebmxu8vLwQEhKCgoICAMC9e/fQpUsXnDx5Uui/Z88evPde5V4bs2nTJsjlcmRmZsLf3x/e3t4oKSkBAFy/fh0TJkyAs7MzvL29ER0dLTrWx8cHsbGxGDhwIFxcXDB8+HBcu3YN586dw7Bhw+Du7o7Ro0fj+vXrwjFpaWmYOHEitm3bBj8/P7i6uiIgIADJycmicysUCsyfPx/u7u5wc3NDaGgoCgsLhf3z5s3Dzz//jIiICHh4eGD16tUAgPXr16Nv375wc3ND165dsWjRIiiVSgDAkiVLIJfLAQByuRwBAQEAgFOnTmHAgAFauRk/fjxiY2OFbWdnZ1y6dAkBAQFwc3PDP//8AwBITEzEu+++C2dnZ/Tv3x/x8fGVyj0REREREekfFtivEKlUitWrV+Pdd99FcnIyvvzyS8yZMwdhYWHo1q0bkpKSsGTJEoSEhCA/Px8AYG5ujqioKCQnJyMuLg5qtRpRUVEAgLp16yI4OBiff/451Go17t+/j2XLliE8PLxS8YwZMwapqamws7PD7t27kZCQAENDQyiVSowZMwaOjo5ITEzE1q1bsW/fPmzbtk10L19//TUiIyNx8uRJ+Pv7Y+7cuQgLC0N4eDiSkpLQp08fhIaGCseoVCqcO3cOu3btwk8//YSUlBQMGjQIEydOxO3b//fO5/DwcGRmZiI2NhYHDx6EWq3GwoULhf1KpRJbt25Fq1atkJSUhMmTJwMAGjZsiJiYGKSkpGDv3r24ePEifvrpJwDAnDlzkJqaCgBITU3Fjh07hHOVFeGPe7K9qKgIX375Jb788kskJyfD3t4ely5dwvTp0zFlyhSkpKRgwYIFmDNnDq5cuVKp/BMRERERkX5hgf2K6dq1K/r16wepVApHR0d06tQJEokEQ4cOBQB06tQJHTp0QFpaGgCgT58+sLe3h0QigYmJCQYPHiwUigAwdOhQ1KlTBz///DNWrlyJ3r17o2PHjtWKcceOHbCxscGMGTNgamoKOzs7hIWFYfPmzaJ+H3zwAdq3bw+JRILAwECcP38eAQEBwj2NGDECGRkZotHn3NxcLF26FNbW1jAwMMCoUaMgl8uxfft2AMCVK1cQHx+PFStWoH79+jA3N8e8efMQHx+P3Nxc4TwlJSUYMWIEAMDA4NGPub+/Pxo2bAgAsLS0xDvvvCMa3a8uDw8PNGvWDBKJBAAQHR2NwMBA+Pn5wdDQEO7u7hg+fLhQ1BMRERER0evFUNcBkFjr1q1F21ZWVmjTpo2ozdraWhjBzszMxJo1a5CWloacnByoVCpYWVkJfSUSCSIiIjBq1CgYGRlh79691Y7xzJkz8PX1FbU5OTnhypUrKCkpgaHhox+rVq1aieKwsrKCm5ubqK1evXrIz8+Hubk5AMDe3h5NmzYVndvLywtnzpwBAKSnp8PFxQX16tUT9ltYWMDe3h6XL1+GjY0NAIiuU+bSpUtYu3Ytzp49izt37kClUqFTp07VyIRY2RTzMqdPn8aoUaNEbZ06dcL3339fY9ckIiIiIqJXBwvsV4xUKtVqq1u3brl98/PzMWzYMHTr1g1LlixBq1atcPbsWYSFhYn62dvbo06dOnBwcICFhUW1Y8zOzkZsbCzWr18vajc0NERBQQGsra2rfC9lHi+cy5iZmeH+/fvCtZOSkrSKWZVKJTx7DkD0JQMAXLt2De+99x4CAwMRFBQEBwcH7Nu3T5gKXlUajUar7clr5uTk4MMPPxRGtAFArVbDwcHhua5JRERERESvNhbYeuy///0vGjdujMjISKEtKytLq9/GjRvx5ptv4tq1a4iPj9cafa4qMzMzzJw5E6NHj67Wecrz+DTvMjk5OcLUbjMzM/j4+AgLl1WkbFp4mV27dqF79+4ICQkR2srL1ZOkUqmwsNvjHn8m/PG+jzMzM0NMTAzatm37zOsQEREREZH+4zPYeuzu3bto3LixqC0hIUG0ff36dWzatAmfffYZQkNDsXjxYhQXF1fpOoaGhqIRW0dHR62VvWvKzZs3ce3aNVHbkSNH8NZbbwnXTk9Pr/LrtCqTK0D7XuvXr4/c3FxRzq5fvy6sEv40jo6OSEpKqlKcRERERESkv1hg6zEnJyckJibi77//hkqlwvbt23Hx4kVRn0WLFmHcuHGws7ND9+7dYW9vj7Vr11bpOjY2Njh9+jRUKhUUCoWwkNrq1auFBcr++ecf/Pnnn9W+J0tLS3z66afIyspCaWkp1q9fj8zMTLzzzjsAHr0Oq0mTJpg9e7YwAp2fny96dVl5nJyc8McffyAnJwcPHz7EmjVrRFPKH7/XU6dOobi4GEqlEvb29mjUqBHWrl0LjUaDe/fuITw8HC1btnzmvYwdOxZRUVHYv38/lEol1Go1Tp06hezs7OfIDBERERERvepYYL9CZDIZjI2NtdpkMlm5bR4eHpgyZQomTJiALl264MiRI1i2bBnUajUAID4+HpmZmRg7dqxwbFhYGLZs2YJbt25VOq4JEyZg+fLlwqvCbGxssHnzZqSlpaFbt26Qy+WYMmWKaHp3efdiZGQEIyMjrXt5vK1NmzYYOHAgRo4cCTc3Nxw8eBAbN26EiYmJ0Cc6OhqWlpYYNGgQXFxcMHDgQJw9e/ap1x44cCD69u2LYcOGwcfHBzdu3MDcuXNRWloq6jdlyhRMnToVvXr1wuXLl4XXjR07dgxeXl4IDAxEQEAA2rZtK/pcTExMtO7N29sbkZGRWLNmDTw8PODh4YEVK1aU+9ovIiIiIiLSfxJNeas1EelAUlISoqKisGXLFl2H8soq+yIh6vphXLqbqeNoiOhpWlvZYW3/qboOo0YUFRXh/PnzaNeuHerUqaPrcGoV5l63mH/dYv51h7kXK/sdvDKvO+YiZ7VUXl4e/Pz8yl0NG3j0Cq2EhAStkeAXSSqVao0CU/ns6zbQdQhE9Az8/5SIiKj2YYFdS1lbWyMlJUXXYYjI5XJs3LhR12HohU+93tN1CERUCaUaNaQSPo1FRERUW/BffSI9o1QqoVAodB1GraNQKHDu3DnmXkf0Nf8sromIiGoX/stPpIe4dMLLp9FooFAomHsdYf6JiIhIH7DAJiIiIiIiIqoBLLCJ9JBEItF1CLWORCKBqakpc68jzL9uMf9ERESVw0XOiPSMTCaDqamprsOodUxNTdG+fXtdh1FrMf+69TLzr9aoYcBn14mISE+xwCbSQ1+l/IQb93N0HQYRUY1qYmGLmW6Bug6DiIjoubHAJtJDN+7n4O97N3UdBhERERERPaZWzcHKyMjA0KFDoVKpAABr167F2rVrX2oMc+fOxa+//vpSr6lre/bsQVBQkE6uffv2bQwcOBB37tzRyfWJiIiIiKj2qFUj2G3btsW2bduEbaVS+dJjiIyMfOnX1DWlUqmTXANAgwYNsGvXLp1cm4iIiIiIapdaNYL9skVHRyM6OlrXYdQqfn5+yM7O1nUYRERERERUC9WqEeyXraSkRNch1DoqlUp4BICIiIiIiOhleu1GsJcvX44VK1aI2hQKBeRyOc6cOQM/Pz/RvtLSUkRFRaFHjx5wdnbGyJEjkZGRIezPzs6Gj48Pzp49i379+sHDwwNKpRJXr15FUFAQPD094e7ujsGDByM1NRUAkJubC3d3d6xbtw7r1q2DXC5HbGwsACAoKAh79uwRzl9YWIgFCxbAy8sLcrkcQ4YMwdGjR0Ux9ujRA4cOHcKwYcPg4uICX19frFu3rkp5ycvLw4wZM+Dl5QU3NzcMGDAABw4cEPUpKirCsmXL0K1bN7i6usLb21u4JwD48ccfMWDAALi6usLDwwPff/+9sC8mJkbI4ahRo3DlypUKYyktLcVXX30FLy8vuLi4YPLkycjJ+b8VsdetW4evvvoKa9asQefOnREeHg4A2LFjB/z9/eHm5gYvLy+EhISgoKAAALBp0ybI5XJkZmbC398f3t7eKCkpQW5uLjp16iS6fnp6OkaNGgW5XI7OnTtj9uzZuH37trD/xIkTGDNmDGJiYtC9e3e4urpiyJAhSE9Pr1LOlUolFi1aBE9PTzg7O2Ps2LE4ceKE1s8gERERERG9Hl67ArtHjx5aheOxY8fQqlWrckc3f/75Z+Tk5CA2NhbHjx9Hy5YtMW7cOBQWFgJ4NCJaVFSEtWvXYsuWLTh+/DhkMhmKi4sxduxYHD58GMnJyZg8eTKCg4OhVCphY2OD5ORkBAUFISgoCKmpqejXrx8A7eeRp02bhry8PPz2229ISUnBtGnTMHv2bJw8eVLoI5FIEBERgZkzZyIlJQUbNmzAli1btO7zaRQKBfz9/REXF4eUlBRERkbi008/FRWWkyZNwvXr17Ft2zacPHkSv//+Ozp27AgAiIqKwo8//oilS5fi5MmTSEhIQEBAAABg69at2Lx5M6Kjo5GcnAw/Pz8EBQVVOIK/cuVKHD16FD///DMSExPRsmVLBAcHC/uLi4tx9OhRFBUV4dixY4iIiAAAmJubIyoqCsnJyYiLi4NarUZUVBQAYMyYMUhNTYWdnR12796NhIQEGBoaoqSkBMXFxcK5r169inHjxmHgwIE4ceIE4uLiYG1tjXHjxgnxSiQSnDlzBkePHsUvv/yClJQUBAQEYMqUKXj48GGlc75s2TKcPHkSP/30E1JSUjBs2DDMnDmTI+xERERERK+p167AdnFxwb1793D16lWhLS4ursJRQ2NjY8yfPx8mJiYwMTHBggULYGhoKCpeCwoK8O6778LGxgYGBo9S1rZtW3h6ekImkwEAevXqBQMDA1y7dq3SsZ44cQLnz5/HsmXLYG1tDYlEAh8fHwQFBeGbb74R9R0xYgQ8PT0hlUrRokULBAQEIC4urtLXaty4MXr27AkTExMAgJOTE1q0aIE///wTALBv3z7cvn0b33zzDRo2bAjgUUFrbGyMW7duYePGjYiOjhYKbiMjI9StW1cochcsWIC2bdvCyMgI77//Pho0aID4+HitOO7du4fNmzdjxYoVaNq0KUxMTPDxxx8jMzMTf/31l9Dv5s2bmD59OqRSqZDzPn36wN7eHhKJBCYmJqJZA5W1Zs0avPPOOxg8eDAMDQ1hZmaGuXPnAoBoZoFCocDSpUvRsGFDGBgYYMSIEZBKpTh79mylrqNUKrFjxw5ERkaiWbNmMDQ0RL9+/fD2229XKV4iIiIiItIfr12BLZVK4evri0OHDgEA1Go1Dh8+XGGB7eXlBalUKmwbGBjA29sbFy5cEPWTy+Wi7ZKSEsTExCAwMBDe3t5wdXXF7du3kZ+fX+lYU1NT0aNHD5iamora+/Xrh7S0NJSWlgpt7du3F/Vp3LixaFr1s2g0GuzcuROjR49Gt27d4OLigvT0dCHeuLg4DBgwQJSLMkeOHEGHDh3QtGlTrX23bt3CvXv30KVLF1G7k5OTaKp9mQsXLsDW1hYtW7YU2gwMDODo6Cjq7+zsDEND8RIBmZmZmDdvHt5++224u7vjo48+qlK+ASAlJQX9+/fXavfz80NycrKwbWtrCxsbG1GfRo0aVXoBtWvXrkEikaBdu3aidl9f3yrFS0RERERE+uO1XOSsV69e2LRpE8aOHYu0tDTY2dnBzs4OmZmZWn2tra212urXr4/79+8L2wYGBqhXr56oT0REBE6ePIng4GC4urrCysoKPXv2hEajqXScOTk5sLW11Wpv0KABVCoV7ty5I+wvGykvY2hoKCrAn2X9+vX497//jVmzZsHT0xP169fHmDFjhHjz8vLg4eFR7rF5eXnlxgk8ekZdqVRqHVtaWoohQ4aU2//GjRvlfmHxeJuVlZVof35+PoYNG4Zu3bphyZIlaNWqFc6ePYuwsLBn3/xjKsp5w4YNcerUKWHb2NhYq4+RkVGlc15QUKD1MwOgwjwSEREREZH+ey0LbC8vL4SFhaGwsBDx8fFPXVQqKytLq+3OnTuws7MTtiUSCSQSibBdXFyMHTt2YPfu3WjRogWARyPEVRlRBoB69eqVe8zt27dhaGiIunXrVul8T/PDDz9g8eLF6Nq1q9D2+GistbV1uV9AlO27detWufvMzMxgYWEhGv19GjMzM7Ru3fqZ76YumxZe5r///S8aN24seo94eZ/ds5Tl3MHBQdSenZ2tVdRXh7m5OfLy8rTay2sjIiIiIqLXw2s3RRx4NPrYuXNnHD16FHFxcejbt2+FfQ8fPiwsaAY8mlJ+7NgxtGnTpsJj7t+/D5VKhSZNmghtJ06c0Fq8ytDQ8Kkj2j4+Pjh06BAUCoWoPTY2Fp6enuWOoj6vu3fvonHjxsL2//73P1y/fl3Y9vX1xe7du0ULsJXx9vbGX3/9Ve7K4M2bN0dJSUm508HL06FDB1y7dk20uNrzxA8ACQkJWv2elfPu3btj3759ojaNRoP9+/fX6PTtli1bwtjYWOsZ8QMHDoi+rCEiIiIiotfHa1lgA/83TdzU1LTcZ4fL1K1bFyEhISgsLERxcTEiIiJQt25ddO/evcJj6tevj8aNG+PHH3+ERqPBpUuXsHTpUjRr1kzUz8bGBunp6VCr1aIp52VcXV3Rvn17hIaGIi8vDxqNBr///jvWrl2LqVOnPu+tl8vJyQm//PILSkpKkJmZibCwMNFz0P3794etrS0++ugjofgtKipCcXExGjdujA8++ACTJk0SFkUrKSnB/fv3IZPJMGrUKMyePRtnz56FRqOBUqmscIXzhg0bomfPnggODsbff/8NAHjw4AEOHz78zPgTExPx999/Q6VSYfv27bh48aJWPxsbG5w+fRoqlUrriwsAmDBhAvbs2YPt27ejtLQUhYWFCA8Ph4GBQY2+PsvIyAjTp0/HvHnzcOXKFahUKuzatQu//fZbuVPHiYiIiIhI/722BXb37t1x8eJF0arNMpkMRkZGou1BgwahY8eO6N+/P7p06YKcnBzExMQI/YyMjLRGkiUSCaKjo/H777/D3d0dH3/8MUJCQtC0aVOo1WqhX+/evVFUVAQ3NzdharNMJhM9T71y5UrY2toK73feuHEjvvnmG7z11luiOJ98BtvY2Fir7WmWL1+Oy5cvw8vLC6NHj0ZgYCBcXV2FZ4olEgk2bdoEe3t7+Pv7w9XVFT169MDp06cBALNmzcKYMWMQHBwMFxcXdO7cGT/88AMAYMaMGRgwYICwz8fHB//5z38qjD8yMhIdO3bE6NGj4ezsDD8/P9G7v42NjbVy7uHhgSlTpmDChAno0qULjhw5gmXLlonyDTwqoJcvX45u3bohKSkJhoaGonM5ODjg+++/x2+//YbOnTujV69eKC0txffffy8s8FZRbsv7HJ5m5MiReP/99zFu3Dh4eHjgt99+w9ChQ+Hs7FzpcxARERERkf6QaKqyKhcRVVp6ejrMzMzQokULlJSU4MSJE/j888/x3XffPXVWxdOUvSZs0+14/H3vZk2GS0Skcy3qNsa/fGfoOoxXSlFREc6fP4927dqhTp06ug6n1mH+dYv51x3mXqzsd/CyVxY/zWu5yFltM2zYMGG6dXlWrVoFT0/PlxjR6+2vv/7C6NGjK9xfv3597N+/Hzdu3MCqVauQk5MDiUQCJycnfPPNN89dXBMRERER0auNBfZrYOvWrboOoVbp0KGD1uJl5enfv3+579yuCU0s+LovInr98O82IiLSdyywifTQTLdAXYdARPRCqDVqGEhe2yViiIjoNcd/wYj0jFKpLHeFdHqxFAoFzp07x9zrCPOvWy8z/yyuiYhIn/FfMSI9xLUJXz6NRgOFQsHc6wjzr1vMPxERUeWwwCYiIiIiIiKqASywifSQRCLRdQi1jkQigampKXOvI8y/bjH/RERElcNFzoj0jEwmg6mpqa7DqHVMTU3Rvn17XYdRazH/uvUq5J+LnxERkT5ggU2kh9ad+TduPcjWdRhERC9FI7OGCHJ6X9dhEBERPRMLbCI9dOtBNv4puKnrMIiIiIiI6DGca/UM48ePR0JCgq7DeOEqc59r167F2rVrhW0/Pz+kp6e/0LgyMjIwdOhQqFSqF3odIiIiIiKi6uII9jN89913ug7hpajMfSqVStG2SqXSaqtpbdu2xbZt217oNYiIiIiIiGoCR7BfQ35+fsjO1s/nc52cnHQdAhERERER0XNhgf0aUqlUejul+uHDh7oOgYiIiIiI6LnodYEdExODHj16wNnZGaNGjcKVK1eEfSdOnMCgQYPg6uoKPz8//Prrr5g4cSLS0tIAANnZ2XB2dtY657x587BhwwZhe8CAATh16hQAIC0tDRMnTsS2bdvg5+cHV1dXBAQEIDk5WXQOHx8fxMbGYuDAgXBxccHw4cNx7do1nDt3DsOGDYO7uztGjx6N69evi467fv06JkyYAGdnZ3h7eyM6Olq0v0ePHjh06BCGDRsGFxcX+Pr6Yt26dcL+TZs2QS6XIzMzE/7+/vD29kZJSUmlcvn4fQLAtWvXMG7cODg7O6Nr165YsmTJM6eD//XXXwgMDISzszN8fX2rNLV7yZIlkMvlAAC5XI6AgAAAQHp6Ovz8/IR+X3/9NZYvX47Q0FC4ubmhW7du2L59O4qLixEeHg5PT0/06tULv/32m+j8paWl+Oqrr+Dl5QUXFxdMnjwZOTk5lY4vKysLH374Idzd3eHh4YFZs2YJ+xQKBebPnw93d3e4ubkhNDQUhYWFouNPnDiBESNGQC6XQy6XY/r06ZW+NhERERER6Qe9LbC3bt2KzZs3Izo6GsnJyfDz80NQUBBKSkqQk5ODyZMnIzAwEKmpqfjxxx+xbds2nDt3ThjZValUKC4u1jqvUqkUFZKPb6tUKpw7dw67du3CTz/9hJSUFAwaNAgTJ07E7du3hWOkUim+/vprREZG4uTJk/D398fcuXMRFhaG8PBwJCUloU+fPggNDRVdZ8yYMXB0dERiYiK2bt2Kffv2iYpUiUSCiIgIzJw5EykpKdiwYQO2bNmCAwcOAADGjBmD1NRU2NnZYffu3UhISIChYeUes3/8PktKSjBx4kQ0adIEx44dw4EDB1BcXIwff/yxwuPz8vIwbtw4vPPOO0hKSkJ0dDSioqJw/PjxSl1/zpw5SE1NBQCkpqZix44dQlyPj8ZLpVL88MMPaN++PRITE/Hvf/8b33zzDT755BO88cYbSEhIwHfffYcVK1bg77//Fo5buXIljh49ip9//hmJiYlo2bIlgoODKxUbACxatAguLi44ceIETpw4gU8++UTYFx4ejszMTMTGxuLgwYNQq9VYuHChsP/48eMIDg7G+++/j6SkJKSkpGDBggWVvjYREREREekHvSyw1Wo1oqKisGDBArRt2xZGRkZ4//330aBBA8THx2P79u3w8PDAsGHDIJFIUL9+fSxbtgy5ubnVvnZubi6WLl0Ka2trGBgYYNSoUZDL5di+fbuo3wcffID27dtDIpEgMDAQ58+fR0BAADp16gSJRIIRI0YgIyNDGOncsWMHbGxsMGPGDJiamsLOzg5hYWHYvHmz6LwjRoyAp6cnpFIpWrRogYCAAMTFxVX7vh6XnJyM/Px8zJs3D3Xq1IGxsTE+++wzWFhYVHjM999/Dy8vL4wYMQIymQxt2rTBRx99hJiYmBqNDQBatWqF0aNHw8jICPb29ujduzeuXr2KqVOnQiqVwsHBAT179sSxY8cAAPfu3cPmzZuxYsUKNG3aFCYmJvj444+RmZmJv/76q1LXvHr1Kry9vWFgYACJRIKGDRsCAK5cuYL4+HisWLEC9evXh7m5OebNm4f4+Hjh523RokWYO3cu+vfvD6lUColEAmtr6xrPCxERERER6ZZeFti3bt3CvXv30KVLF1G7k5MTMjIycPHiRXTu3Fm0z8HBAfb29tW+tr29PZo2bSpq8/LywsWLF0VtrVq1Ev4skUhgZWUFNzc3UVu9evWQn58PADhz5gx8fX217ufKlSuiad7t27cX9WncuHGVpjpXxsWLF+Hi4iIa/ZZKpfDy8qrwmNOnT2vF36lTJ1y4cKFGYwOA1q1bi7atrKzg7u4uarO2thZye+HCBdja2qJly5bCfgMDAzg6OiIjI6NS1xw2bBhCQ0Nx+PBhUXt6ejpcXFxQr149oc3CwgL29va4fPkyrl69imvXrqF///5VuEMiIiIiItJHevmaruzsbCiVSnh4eIjaS0tLMWTIENy/fx/m5uZaxz1eBFVEo9E8dX955zAzM8P9+/dFbVKpVKtf3bp1KzxvdnY2YmNjsX79elG7oaEhCgoKhBFPmUymtb+0tPSpMVfVw4cPYWlpqdX+tFHXnJwcfPbZZ6KpzxqNptJT1KvieXJ748YN4RnvMiUlJVptFRk9ejTatWuHf/3rX4iOjsa8efPQvn17ZGdnIykpSes8KpUKBQUFMDIygrW19QvJAxERERERvVr08rd+MzMzWFhYaC0uVmbu3Lm4c+eOVvuTz0mr1Wqo1WoYGBiI+jg4OFR47fKmmefk5AhThp+XmZkZZs6cidGjR1frPDXBxsYGZ86c0WrPysqqcBaAmZkZVqxYgV69er3o8KrMzMwMrVu3xq5du6p1Hnd3d/zyyy/4/vvvMW7cOMTHx8PMzAw+Pj5YvXp1ucdcvXoVd+7cgVKp1PpyhIiIiIiIXi96OUW8efPmKCkpqXB6r7OzM44cOSJqu3LlCjIzM4Xt+vXrAwBu3rwptBUWFpZbWD7u5s2buHbtmqjtyJEjeOutt6pwB9ocHR0r/MKgqgwNDZ85Ev+sWNLS0kSLixUXF2vl9MljaiL+6sZeng4dOuDatWuiL1iqY+zYsTAwMMDFixfh6OiI9PT0Cl8v1qxZMzg4OAiLthERERER0etLLwtsmUyGUaNGYfbs2Th79iw0Gg2USqWwmvY777yDf/75Bxs3boRarcatW7cQHh4umvYsk8ng7u6Ob7/9FiUlJXj48CE+++wz2NnZPfXalpaW+PTTT5GVlYXS0lKsX78emZmZeOedd6p1T4MHD0ZqaipWr14tLHz2zz//4M8//6zyuWxsbHD69GmoVCooFIoqH9+2bVu0b98en3/+ORQKBYqKivDJJ588dZGz999/H9u2bcMvv/yChw8fQqPR4MKFC6JXp1U29lOnTqG4uPiZrwWrrIYNG6Jnz54IDg4WVhZ/8OCB1vPUT5OWlgalUgmNRoO9e/eiuLgYDg4OcHZ2RpMmTTB79mxkZWUBAPLz85GYmAjg0bP2oaGhWLFiBfbu3Qu1Wg0AuHv3bo3cGxERERERvTr0ssAGgBkzZmDAgAEIDg6Gi4sLfHx88J///AcAYGJigu+++w7//e9/4ebmhg8++ABDhw5FmzZtROdYvHgxsrKy0LVrV/j7+6Njx47w9fUVTeWVyWSi7TZt2mDgwIEYOXIk3NzccPDgQWzcuBEmJiaiY4yNjUXXMjIygpGRkahNJpMJbTY2Nti8eTPS0tLQrVs3yOVyTJkyRTQl/clYAMDY2FirbcKECVi+fDm6deuGpKSkSuXzyXP/61//Qn5+Pry9veHn54fWrVtj0KBBoj5GRkbC9ptvvol169bhP//5Dzw9PeHu7o45c+bgwYMHlbp+mSlTpmDq1Kno1asXLl++LMpR2f0+mdvy8vJkW2RkJDp27IjRo0fD2dkZfn5+OHr0aKXjWr58OVxcXCCXy/H9998jOjoaVlZWAIDo6GhYWlpi0KBBcHFxwcCBA3H27Fnh2O7du+Pbb7/Fd999B7lcDldXV9Er2oiIiIiI6PUg0dT0fNxX2KhRozB16lStxdEqKykpCVFRUdiyZUsNR0ZUOWWF+477f+CfgpvP6E1E9Hqwt2yM+V1m6ToMnSgqKsL58+fRrl071KlTR9fh1DrMv24x/7rD3IuV/Q7esWPHZ/bVy0XOnpeRkVG5K1BXllQq1RqFftUdOHAAYWFhFe7v2LEjvv/++xcaw19//fXUxdvq16+P/fv3v9AYnmbGjBnCO7PLM2fOHAwePPglRkRERERERPqoVhXYGzdurNbxcrm82ud42Xr16oXU1FSdxtChQwedx/A033zzja5DqLJGZtVbtZ6ISJ/w7zwiItIXtarAJnpdBDm9r+sQiIheKrVGDQOJ3i4dQ0REtQT/pSLSM0ql8rlWh6fqUSgUOHfuHHOvI8y/br0K+WdxTURE+oD/WhHpoVq0NuErQ6PRQKFQMPc6wvzrFvNPRERUOSywifSQRCLRdQi1jkQigampKXOvI8y/bjH/RERElcNnsIn0jEwmg6mpqa7DqHVMTU3Rvn17XYdRazH/uvW65p/PdRMRUU1jgU2kh344twnZD7J0HQYRkd5qaPYGRrYfo+swiIjoNcMCm0gPZT/Iws3C67oOg4iIiIiIHsN5UUREREREREQ1gAV2LZaRkYGhQ4dCpVIBANauXYu1a9e+1Bjmzp2LX3/99aVe82WaN28eNmzYoOswiIiIiIjoJeAU8Vqsbdu22LZtm7CtVCpfegyRkZEv/Zovk1Kp1EleiYiIiIjo5eMINr000dHRiI6O1nUYRERERERELwRHsOmlKSkp0XUIRERERERELwxHsF9zy5cvx4oVK0RtCoUCcrkcZ86cgZ+fn2hfaWkpoqKi0KNHDzg7O2PkyJHIyMgQ9mdnZ8PHxwdnz55Fv3794OHhAaVSiatXryIoKAienp5wd3fH4MGDkZqaCgDIzc2Fu7s71q1bh3Xr1kEulyM2NhYAEBQUhD179gjnLywsxIIFC+Dl5QW5XI4hQ4bg6NGjohh79OiBQ4cOYdiwYXBxcYGvry/WrVtXpbysWbMGa9aswbJly9CtWze4uLhg0qRJyMzMFPW7fv06JkyYAGdnZ3h7e2uNwO/YsQP+/v5wc3ODl5cXQkJCUFBQUOF1T5w4gZ49eyI7O7tK8RIRERER0auPBfZrrkePHjhw4ICo7dixY2jVqhVUKpWwwFmZn3/+GTk5OYiNjcXx48fRsmVLjBs3DoWFhQAAlUqFoqIirF27Flu2bMHx48chk8lQXFyMsWPH4vDhw0hOTsbkyZMRHBwMpVIJGxsbJCcnIygoCEFBQUhNTUW/fv0AaD+jPG3aNOTl5eG3335DSkoKpk2bhtmzZ+PkyZNCH4lEgoiICMycORMpKSnYsGEDtmzZonWfT6NSqfDDDz+gtLQUBw4cQEJCAiwsLDB16lShj1KpxJgxY+Do6IjExERs3boV+/btEz23bm5ujqioKCQnJyMuLg5qtRpRUVHlXvPWrVsICQnBkiVL0LBhw0rHSkRERERE+oEF9mvOxcUF9+7dw9WrV4W2uLg4rZHrMsbGxpg/fz5MTExgYmKCBQsWwNDQUFS8FhQU4N1334WNjQ0MDB79CLVt2xaenp6QyWQAgF69esHAwADXrl2rdKwnTpzA+fPnsWzZMlhbW0MikcDHxwdBQUH45ptvRH1HjBgBT09PSKVStGjRAgEBAYiLi6v0tcruNSwsDDKZDHXq1MGiRYtw48YNoZjfsWMHbGxsMGPGDJiamsLOzg5hYWHYvHmzcI4+ffrA3t4eEokEJiYmopH7xymVSkybNg2TJ0+Gu7t7leIkIiIiIiL9wGewX3NSqRS+vr44dOgQmjdvDrVajcOHD2PatGla06EBwMvLC1KpVNg2MDCAt7c3Lly4IOonl8tF2yUlJfjhhx/w+++/4/r161AoFCgqKkJ+fn6lY01NTUWPHj1gamoqau/Xrx+++uorlJaWCrG1b99e1Kdx48b4888/K30tAOjSpYvwBQHwqOB2dXXFxYsX4erqijNnzsDX11d0jJOTE65cuYKSkhIYGhoiMzMTa9asQVpaGnJycqBSqWBlZaV1rYiICHTo0AEjRoyoUoxERERERKQ/OIJdC/Tq1QsHDx4EAKSlpcHOzg52dnbl9rW2ttZqq1+/PhQKhbBtYGCAevXqifpERERg69atGDduHHbv3o3U1FQ0atQIGo2m0nHm5OTA1tZWq71BgwZQqVS4c+eO0FY2Ul7G0NAQpaWllb4WANStW1erzdzcHPfv3wfw6Hnz6OhoyOVy4b/u3bvD0NAQBQUFyM/Px7Bhw1BSUoIlS5bg8OHD5b5HfN++fdi1axfq169fpfiIiIiIiEi/cAS7FvDy8kJYWBgKCwsRHx9f4fRwAMjKytJqu3Pnjqggl0gkkEgkwnZxcTF27NiB3bt3o0WLFgAAjUaDnJycKsVZr169co+5ffs2DA0Nyy2IqyM3N1erLScnB97e3gAAMzMzzJw5E6NHjy73+G3btqFx48aid3mXlz+FQoGdO3di7Nix6N69Ozp16lRDd0BERERERK8SjmDXAsbGxujcuTOOHj2KuLg49O3bt8K+hw8fFhY0AwC1Wo1jx46hTZs2FR5z//59qFQqNGnSRGg7ceKE1gJqhoaGTx3R9vHxwaFDh0Sj5QAQGxsLT09PGBsbV3js8zhx4oRo1LugoADp6elwcnICADg6OiI5ObnC4+/evYvGjRuL2hISErT6DR48GK1bt8b8+fPxySef4OHDhzV0B0RERERE9CphgV1L9OrVC5s2bYKpqSmaNm1aYb+6desiJCQEhYWFKC4uRkREBOrWrYvu3btXeEz9+vXRuHFj/Pjjj9BoNLh06RKWLl2KZs2aifrZ2NggPT0darVamIb9OFdXV7Rv3x6hoaHIy8uDRqPB77//jrVr14pW964pRUVFWLJkCQoLC/HgwQOEh4fDy8tLiLtswbLVq1cLXzr8888/wrPeTk5OSExMxN9//w2VSoXt27fj4sWLFV6vd+/eeOutt7Rem0ZERERERK8HFti1RPfu3XHx4kW8/fbbQptMJoORkZFoe9CgQejYsSP69++PLl26ICcnBzExMUI/IyMjrZFkiUSC6Oho/P7773B3d8fHH3+MkJAQNG3aFGq1WujXu3dvFBUVwc3NTZhWLZPJRM9Tr1y5Era2tsK7pTdu3IhvvvkGb731lijOJ5/BNjY21mp7luHDh0MqlaJPnz7w8fGBTCYTTfe2sbHB5s2bkZaWhm7dukEul2PKlCnC1HIPDw9MmTIFEyZMQJcuXXDkyBEsW7ZMdM9PxhoeHo6jR4/ixIkTVYqViIiIiIhefRJNVVahInpNrFq1CsCj927rk7NnzwIA/vtwL24WXtdxNERE+quxeVN87Bam6zCeqaioCOfPn0e7du1Qp04dXYdT6zD/usX86w5zL1b2O3jHjh2f2ZeLnNFrZ9iwYfj7778r3L9q1SpIpVLRK7r0TUOzN3QdAhGRXuPfo0RE9CKwwKbXztatW5/Zx9PT8yVE8uKMbD9G1yEQEek9tUYNA4n+ftlKRESvHv6rQqRnlEql1krr9OIpFAqcO3eOudcR5l+3Xtf8s7gmIqKaxn9ZiPQQl054+TQaDRQKBXOvI8y/bjH/RERElcMCm4iIiIiIiKgGsMAm0kMSiUTXIdQ6EokEpqamzL2OMP+6xfwTERFVDhc5I9IzMpkMpqamug6j1jE1NUX79u11HUatxfzrFvNfPi6SRkRET2KBTaSHdmWsRW5Rpq7DICKqtWzq2GFg24m6DoOIiF4xLLCJ9FBuUSayHvxP12EQEREREdFjOK+JiIiIiIiIqAawwCaRI0eOYMKECboOQ/CqxUNERERERFQRThEnkW7duqFbt266DkPwqsVDRERERERUEY5g0ysjOzsbfn5+ug6DiIiIiIjoubDApleGSqWCSqXSdRhERERERETPhQV2DRg3bhzi4+Px0UcfoXPnzvDw8MDnn38OpVIJANi7dy9mzZqFHTt2wNvbG+PHjwcAaDQabNy4Eb1794ZcLkefPn2wadMmrfOfOHECI0aMgFwuh1wux/Tp04V9iYmJePfdd+Hs7Iz+/fsjPj5e2FdYWIjg4GB4eHjA3d0dgYGBwr4ff/wRPXr0gJubG3x9fZGamgoAiI2NFeIDgNDQUGzevBnTp0+Hm5sbunTpgrCwMBQWFopi/OOPP/D222/D2dkZvXv3xs6dOzFu3DikpaVVKofTp0+Hv78/MjMzIZfL8dFHH5UbT0hICDZs2IBJkybB1dUVffr0waFDh5Cfn49p06bBw8MDAwYMQGJiouj8CoUC8+fPh7u7O9zc3BAaGqp1D09z8eJFBAYGws3NDR4eHli2bJmwLy8vDx9//DFcXV3RuXNnREZGan1R8PvvvyMgIACurq5wc3PDkiVLKn1tIiIiIiLSD3wGuwaoVCosWrQIwcHBWL16NbKyshAUFIQvv/wSYWFhKC4uxuXLlyGTyRAfHw9Dw0dpj46Oxr59+7B69Wq8+eabuHLlCoKDg6FUKhEUFAQAOH78OGbOnIl58+bBz88PBgYGuHv3LgDg0qVLmD59OhYvXoyePXsiLS0N06ZNw48//oiWLVti1apVsLS0xNGjRyGTyZCVlQUAyMjIwIYNG/Djjz+iUaNGuH//PgwMHn3XolQqhS8GAEAikeDrr7/GrFmzsGLFCiiVSkybNg0rV67E3LlzAQDp6ekIDQ3F4sWL0bt3b2RlZWH69On43//+V+kR6ZUrV+LGjRv44IMPRF8SPBmPVCrF6tWrERkZidWrV+P8+fOYMGECnJyc0LNnT6xatQrp6emYPHky9u7di3r16gEAwsPDUVBQgNjYWBgbG2PhwoVYuHAhVqxYUan4QkNDMXr0aAwcOBClpaW4c+eOsO+jjz5C8+bNcejQIahUKsycOROrV69GcHAwAGDnzp34+uuvsWTJEnh6ekKj0eDevXuVui4REREREekPjmDXEHd3d/j7+wMA3njjDSxatAhbt25FcXExgEfFcEhICGQyGQwMDHD//n1s2LABkZGRePPNNwEALVu2RGRkJNavXy+Mri5atAhz585F//79IZVKIZFIYG1tDeBRgR4YGAg/Pz8YGhrC3d0dw4cPx08//QQAuHr1Kjp37gyZTCbEVdbesmVLNGrUCABgYWEBMzOzCu9NLpdj5MiRMDY2hoWFBYKCghAXFyfs/+WXXzBkyBD0798fRkZGaNq0KSIjI/HgwYMay+/junbtin79+kEqlcLR0RGdOnWCRCLB0KFDAQCdOnVChw4dhNHzK1euID4+HitWrED9+vVhbm6OefPmIT4+Hrm5uZW65tWrV9G1a1cAj4p8W1tbAMDRo0dx69YtREREwMLCAtbW1li4cCF++uknqFQqFBUVYfny5fjyyy/RpUsXSCQSGBgYwMrK6gVkhoiIiIiIdIkFdg3x9vYWbZcVfTdu3ADwqHguK4wB4Pz587CyskKnTp1Ex3Xs2BGWlpbIyMjA1atXce3aNfTv37/ca54+fRq+vr5a183IyAAADB48GCtWrMBvv/0GtVot9OnSpQv+97//ITIyEnl5ec+8t3bt2om27ezskJ2dLboXNzc3rWPKCvia1rp1a9G2lZUV3N3dRW3W1tbIz88H8GiE3cXFRRjNBh59qWBvb4/Lly9X6prDhg3D5MmTcerUKVH7mTNn0K1bN2FWAgA0a9YMGo0GWVlZSEtLg6mpKVxdXatwh0REREREpI84RbyG1K1bV6vN3NxcGIl+vLgDgJycHGEU9Em2trbIysoSRqsfL96ePMeHH34IiUQitKnVajg4OAAA/Pz84ODggC+++AJr167Fp59+Ck9PT9StWxc7duzA+vXr4e/vj/feew9TpkyBVCot9zrGxsaibUNDQ1HBfv/+/XLvv6L7q67y4izv+mWys7ORlJQEuVwualepVCgoKKjUNefOnYsDBw4gPDwcdnZ2mDdvHpo2bYrs7Gz85z//wb59+0T9lUol7t27h7y8vBeWByIiIiIierWwwK4hjz+TCzwqdO/cuYOGDRviypUrWkVhvXr1kJOTU+65cnJyYG1tDWtra9y5cwdKpVKY5v04MzMzxMTEoG3bthXG1bZtW2zYsAH79u3DlClTsGfPHjRu3Bjm5uaYOXMmAgMDMW7cOBgbGwvPfVeVmZmZ1v0D2jnRFTMzM/j4+GD16tXVOk+vXr3g6+uLJUuWYNKkSdizZw/MzMwwfPhw4Xn0J+Xn5+PWrVvVui4REREREekHThGvIQkJCaLtY8eOoUGDBhWOXjo7O6OgoADp6emi9rNnz6KwsBDOzs5o1qwZHBwcsGPHjnLP4ejoiKSkpErF179/f7Rq1QqnT58Wtb/xxht4//33K32e8sjlcq37z8jIwM2bN4XF0yrDyMgIGo3mueOoiKOjI9LT0/Hw4cNqn8vAwACzZs3C5cuXkZubC0dHR6SmplYYt7OzMx4+fIijR49W+9pERERERPRqY4FdQ44fP46dO3dCpVLh+vXrWLx4McaOHVthgWlmZoYJEyZgzpw5uHjxIoBHRemsWbMwceJEmJqaQiKRIDQ0FCtWrMDevXuFadllq4iPHTsWUVFR2L9/P5RKJdRqNU6dOiU8H52eno6ioiIAj171deXKFbz55pu4fv06bt68CeDRKPPevXvRoUOH5773skXPdu7ciZKSEly5cgVhYWEwNTXVmhr/NPXq1cPdu3dx8+ZNPHjwAKWlpc8d0+OcnZ3RpEkTzJ49W1hJPT8/X+tVXhUpLS1FWloaSktLUVpaih9++AGNGjWClZUVevXqhfv372PhwoXC8+zZ2dnCa8/MzMwQHByM0NBQ4XpqtVp4PpyIiIiIiF4fnCJeQ+bMmYO9e/ciMjISMpkMgYGBGDVqFIBHzzA/+Rwz8KgwNTExwdSpU5GXl4f69evjgw8+wPvvvy/06d69O7799lssW7YMn332GSQSCVxdXbFu3Tp4e3sjMjIS3377LcLCwmBoaIjWrVsL72jeuHEj4uPjIZVK0bhxYyxduhStW7dGbGwsFixYAIVCAXNzc/Tr1w9Tp04FAMhkMtF09Ce3gUcjzY/fj62tLf7973/js88+Q0REBBo0aIDx48cjKioKzZs3r3QOjY2NMX78eAQEBMDMzAy//PJLufE8mcvyYnyyLTo6GsuXL8egQYNQXFwMS0tLBAYGokuXLs+M6+HDh5g7dy5u3LgBExMTtG/fHmvXroWhoSEMDQ0RExODpUuXws/PD2q1GvXr10dQUJDwzHdgYCDq1auHxYsX49atW5BIJHj77bexcOHCSueGiIiIiIhefRLNi5iTW8uMGjUKU6dOhYeHh65D0YmCggKcPn1aeCXYjRs38Pnnn8PDwwPjxo3TdXivlbNnzwIAklTbkfXgfzqOhoio9nrDzAEfurz4L0qLiopw/vx5tGvXDnXq1Hnh1yMx5l+3mH/dYe7Fyn4H79ix4zP7cgS7BkilUhgZGek6DJ1RKpVYv349Zs+ejZKSEtja2mLUqFEYOXIkAODAgQMICwur8PiOHTvi+++/f1nhahk2bBj+/vvvCvevWrUKnp6eLzGiZ7OpY6frEIiIajX+PUxEROVhgV0DNm3apOsQdMrGxgZbtmypcH+vXr2EZ5JfRVu3btV1CFU2sO1EXYdARFTrqTVqGEi4nA0REf0f/qtApGeUSiUUCoWuw6h1FAoFzp07x9zrCPOvW8x/+VhcExHRk/gvA5Ee4tIJL59Go4FCoWDudYT51y3mn4iIqHJYYBMRERERERHVABbYRHpIIpHoOoRaRyKRCO+np5eP+dct5l93mHvdYv6JqKq4yBmRnpHJZDA1NdV1GLWOqakp2rdvr+swai3mX7eYf91h7nWrovxzgTsiqggLbCI99N+LX+Ou4oauwyAiIqp1rEyboPebwboOg4heUSywifTQXcUN5D6o+N3dRERERET08nFuC702srOz4ezsrOswBKdPn8aAAQN0HQYREREREb0kLLDptaFSqVBcXKzrMATFxcVQKpW6DoOIiIiIiF4SFthENcTJyUnXIRARERERkQ6xwCaqIQ8fPtR1CEREREREpEMssGu5LVu2wNfXF25ubnjvvfdw+vRpuLu7C/v37NmDoKAgreP69u2L9PR0Yfv69euYMGECnJ2d4e3tjejoaFF/Pz8/UX8A2Lt3r9a5Y2Ji0KNHDzg7O2PUqFG4cuVKte5vz5498PPzg7OzMwICAnDq1Clh34kTJzBmzBjExMSge/fucHV1xZAhQ7TivHXrFiZPngxnZ2d4eHjg008/xc8//4zPPvsMALBkyRLI5XIAgFwuR0BAgHCsWq3G119/ja5du8LFxQUBAQE4ffp0te6JiIiIiIheTSywa7EDBw4gKioKX331FVJSUhAeHo45c+aInmNWKpXlPkf8eLtSqcSYMWPg6OiIxMREbN26Ffv27cO2bduE/iqVSus8Tz6jvHXrVmzevBnR0dFITk6Gn58fgoKCUFJS8lz3l5CQgEWLFuHzzz9HSkoKgoKCMHnyZOTn5wMAJBIJzpw5g6NHj+KXX35BSkoKAgICMGXKFGE0Wq1WY9KkSbCwsMDBgwdx8OBB2NjYYPny5VCpVACAOXPmIDU1FQCQmpqKHTt2CDHcuHEDV69exc6dO5GamoohQ4Zg2rRpwrFERERERPT6YIFdi/373//GpEmThGeHO3bsiPHjx1d5qvOOHTtgY2ODGTNmwNTUFHZ2dggLC8PmzZsrfQ61Wo2oqCgsWLAAbdu2hZGREd5//300aNAA8fHxVYqnzKpVqxAcHAx3d3cYGhqib9++8PLyws6dO4U+CoUCS5cuRcOGDWFgYIARI0ZAKpXi7NmzAIC0tDTk5ORg0aJFqFevHurUqYOZM2eiadOmlYrByMgIS5YsgY2NDQwMDBAYGAgA+PPPP5/rnoiIiIiI6NXFArsWu3jxIjw8PERt3bp1q/J5zpw5A19fX1Gbk5MTrly5UunR51u3buHevXvo0qWL1nkyMjKqHFNpaSn+/PPPcuN6/Hy2trawsbER9WnUqBGys7MBABkZGXBycoJMJhP1efK8FWnYsCHq1KkjbEskEtjZ2QnnJyIiIiKi14ehrgMg3bl//z4sLCxEbfXq1avUsRqNRvhzdnY2YmNjsX79elEfQ0NDFBQUwNraulLnUCqVWgV/aWkphgwZUqmYHpebm4uSkhL0799f63yPF/HGxsZaxxoZGaG0tBQAUFBQUG5ObG1tcevWrWfGYWCg/R2WTCZ7pV4nRkRERERENYMFdi3WsGFD5ObmiqY73759W9THwMBAaxRao9EgNzdX2DYzM8PMmTMxevToCq9V3nkev5aZmRksLCyQnJz8XPfyJDMzMwDAwYMHtb5EqOp57ty5o9VeXhsREREREdVunCJei7311ls4cuSIqO3JbRsbG2RmZoraUlNTRYuTOTo6PrMwtrGxwc2bN0VtiYmJwp+bN2+OkpKS55oOXh5zc3M0a9YMSUlJ1TqPXC7H6dOnUVhYKGqPj4+HRCIRtRkaGopG5YmIiIiIqHZhgV2LjR07Fv/+979x/PhxaDQapKWl4YcffhD1cXJywv3797F9+3YAj56V/uKLL9CkSROhz+DBg5GamorVq1cLheg///wjWsjL29sbW7Zswd27d6HRaLBp0yZhNW/g0bTpUaNGYfbs2Th79iw0Gg2USiUOHDjw3Pc3fvx4RERE4Pjx4ygtLUVJSQmOHTumVSw/TYcOHeDp6Ym5c+fi7t27KCwsRGRkJP755x+tqeM2NjY4deqU1uroRERERERUO7DArsU6dOiARYsWYcGCBZDL5ViyZAkiIyNFfSwsLLBy5UrExMTAy8sLkyZNwrRp02Brayss/GVjY4PNmzcjLS0N3bp1g1wux5QpU0TTyMePHw9HR0e888478PX1xcWLFxEcHCxaPGzGjBkYMGAAgoOD4eLiAh8fH/znP/+p9P0YGRmJnqkeNmwYpkyZgoiICMjlcnh5eWHjxo3CfmNjY63Fy4BHxf7j7StWrECDBg3g5+eHnj17ori4GB4eHnBxcREdN2XKFEydOhW9evXC5cuXK31+IiIiIiJ6PUg0nNNKT2jTpg0uXLig6zBeGUeOHEH79u1hY2ODoqIi7NixAzt27MDWrVthaPhylzEoe33YeXUMch/8/VKvTURERICNWQsMc/pC12HUCkVFRTh//jzatWsneisLvXjMvVjZ7+AdO3Z8Zl8uckZaTExMdB2ClmXLlmHbtm0V7h8zZgymTp36Qq6dkpKCzz77DPfv34exsTG6d++ODRs2vPTimoiIiIiIXm0cwSbSI2XfnmUZx+Gu4oaOoyEiIqp9rEyboPebwboOo1bgKKruMPdiHMEmes3xH3YiIiLdUWvUMJBwKSMi0sa/GYj0jFKphEKh0HUYtY5CocC5c+eYex1h/nWL+dcd5l63Kso/i2siqgj/diDSQ3yy4+XTaDRQKBTMvY4w/7rF/OsOc69bzD8RVRULbCI9JJFIdB1CrSORSGBqasrc6wjzr1vMv+4w97rF/OsW80/6iIucEemRqiywQERERES1g0ZTColEWmPn4yJnYlzkjOg1d/xSJAoU/+g6DCIiIiLSMUtTe3i2nqvrMOj/Y4FNpIcKFP/gbtFlXYdBRERERESP4TPYRERERERERDWABXYtplQqMWTIEFy4cEHXoQicnZ0r3Tc7O7tK/YmIiIiIiF4kThGvxWQyGbZv367rMESKiooq3VelUqG4uPgFRkNERERERFR5HMHWQ9HR0YiOjq7ycWPHjsXJkydfQERERERERETEEWw9VFJS8tzHPe+xRERERERE9HQcwX6F7d+/H35+fpDL5ejatSv27dsHd3d3rFu3DuvWrYNcLkdsbCwA4NChQxg2bBg8PDzQuXNnTJo0CVlZWQCAP/74A3K5HCdPnsSkSZPg5uaG7OxsAI+eeS77MwBcvXpV6OPu7o5Jkybh2rVrwv7r16+jT58+2Lt3L/r06QMXFxe88847OHz4cJXvb/369ejevTvkcjmGDh2K1NRUrT6JiYl499134ezsjP79+yM+Pr7C8129ehVBQUHw9PSEu7s7Bg8eLJwzKysLrq6uuIlX5xoAAC45SURBVH//vtBfqVTC29tbyNOzrFy5Et7e3nBzc4Ofnx/+97//Cfv27NkDPz8/ODs7IyAgAKdOnRIdm5eXh/DwcHTp0gVyuRw+Pj64fv16pa5LRERERET6gSPYr6i8vDzMnz8fmzdvxptvvgmFQgGlUonk5GSsWrUKADBt2jShv1QqxaJFi9CqVSuo1Wp8+eWX+Pzzz7F69Wr06dMHffr0wahRozB16lR4eHgIxxUXF0OlUgEA8vPzMXLkSIwePRrffPMNJBIJNm/ejPfffx/79u2DpaUlJBIJsrKyEBMTg7Vr16JZs2Y4dOgQZsyYgT/++AO2traVur+dO3di48aNiI6OxltvvYWzZ89i1qxZoj6XLl3C9OnTsXjxYvTs2RNpaWmYNm0afvzxR7Rs2VLrnMXFxRg7dixcXV0hk8lw4MABBAcHIz4+Hm+88QbatGmDuLg4DBw4EABw5MgRODg44I033nhmvHFxcTh8+DB+++03WFlZIS8vD+bm5gCAhIQELFq0CCtXroSLiwsOHDiAyZMn4/fff0e9evWgVCoxcuRIeHh4IDY2FnXr1kV+fj4sLCwqlSsiIiIiItIPHMF+Rd24cQPW1tZ48803AQCmpqaoW7duhf27du2KN998EwYGBjA0NMR7771X7ojw02zevBmOjo6YOHEijI2NIZPJ8OGHH6JDhw7YvHmz0K+4uBjh4eFo3rw5JBIJevTogXbt2iExMbHS19q6dSsmTpyIt956CwDQsWNHjB8/XtQnOjoagYGB8PPzg6GhIdzd3TF8+HD89NNP5Z6zbdu28PT0hEwmAwD06tULBgYGwgi8v78/fv/9d6H/7t274e/vX6l4r169io4dO8LKygoAYG1tLVxn1apVCA4Ohru7OwwNDdG3b194eXlh586dAICYmBi88cYbWLBggfAZ1qtXD1KptFLXJiIiIiIi/cAC+xXVrl071KlTByEhIcjMzHxm/7t372LZsmUYOHAgPDw8MHDgQOTn51fpmikpKejfv79We79+/ZCSkiJsSyQStGvXTtTHzs5ONNX8WS5evAh3d3dRm4+Pj2j79OnT8PX1FbV16tQJGRkZ5Z6zpKQEMTExCAwMhLe3N1xdXXH79m0hD3379kVycjLu37+PwsJCJCYmom/fvpWKt0+fPoiLi8P/a+/O45q60v+Bf8ISFgUlVem0LmDdpoILBAOIYtUKymJdRkUK1mWq1qXWDUWrgxVstWOdWpTaaa39tjo6Km6AWqGoqKiAilUrLrjQahCDLAomJPf3hz8yxkBFTAzg5/169Y+cc+69T56kV56cc+9ds2YN7t+/r21Xq9X49ddf9eLs2rWrNs7k5OQaF/JERERERFR/cYl4HWVpaYmNGzfixx9/xMiRIzFgwADMnj0bNjY2emMrKioQHh6O119/HQsXLkSnTp1QVFSkV/Q9TX5+fpVLvFu0aKFznbKZmRksLS314lWr1TU+1sOHD/WWSEskEr14JkyYAJFIpG3TaDRo06ZNlftcsmQJMjMzMWPGDLi7u8PBwQH9+vWDIAgAHs0ae3l54cCBAxAEAZ6enn+6KuBxrVu3Rnx8PGJjY+Hv74+JEyfi3XffRUFBASoqKvR+mFCr1fD29gbwaLl/TZfOExERERFR/cUCuw4Ti8UYN24chg8fjsmTJ+PTTz9FVFSU3riTJ0+iuLgYO3bs0C47vnjx4jMfz8HBAfn5+Xrt+fn5esXv82rWrJnODccA6N1srFGjRtiwYQM6der01P09fPgQ27dvx65du9C2bVsAgCAIeu8nKCgI8fHxUKlUGDVq1DPHvHjxYoSEhGDcuHGQSCTo3bs3AOCXX36p9ppqiURSo1UIRERERERUv3GJeD1gb2+P999/H8ePHwcAWFhYaGdlgUfLw1999VWda3rT0tL09vPkdk/y9fVFYmKiXntSUhLeeuut53kLejp37qx9P5X279+v89rFxUVvTHVKSkqgUqnQsmVLbVt6err2Bm6V3nrrLWRnZ+P8+fPo06dPrWLv0KEDBg8ejPT0dDRu3BhOTk5/Gmffvn2xbdu2Wh2LiIiIiIjqDxbYdVRBQQGuXLkCACgtLcX27dvRuXNnAI9mUrOzs6HRaFBSUgIXFxdcvHgRWVlZ0Gg0SElJweHDh/VuotWsWTOcPn0aGo0GpaWlescMDQ3F+fPnERcXB6VSCaVSiX/961+4cOECRo8ebdD3N3bsWKxbtw6nT58G8KgY3rVrl96Yr776Cvv27YNSqYRGo8GpU6eqvNb7lVdeweuvv46NGzdCEARcunQJn376KZycnHTGWVlZwdfXF2+//bb2JmU1ceXKFRQUFAB4dAO6lJQU7ecxfvx4LFmyBMeOHYNarUZFRQWOHDmizXFYWBgKCgowf/58FBcXAwCKi4ufaUk9ERERERHVfSyw66jr169jzJgxcHV1Rb9+/SAWi7F48WIAwNtvv40HDx7Aw8MDMTExaN26NZYtW4bIyEj06NED//d//4cvvvgCYrFYZwY3LCwMW7duhaenJxISEgA8Kjgrr6e2t7fHxo0bkZ2dDR8fH/Tq1Qs5OTn46aeftI+ksrS0hJWVlV68YrH4mQpWqVSK+fPnY86cOZBKpfjXv/6FL774AtbW1toxPj4+iImJQVxcHGQyGWQyGVasWAGlUqkXi0gkwtq1a7F371706NEDM2fOxJw5c9CqVStoNBqdY9+6deuZbzp2+PBhDBgwAN26dcPo0aMREBCAkSNHAgBGjBiBDz74AEuWLIFUKkXPnj3x3Xffabe1sbHB1q1boVKp0L9/f7i7u2PAgAF8DjYRERERUQMjEv5szTBRA6LRaHD69GksWrQIu3fv1rl5Wn1x9uxZAMAfQiwKH1w2cTREREREZGoOtu3g1yXOoPt88OABLly4oH2y0cuu8m9wV1fXp47lTc7IoBQKBfz8/Kq91lskEiEtLa3KWXBjUiqV6NmzJ+zt7fH555/rFNcHDhzAvHnzqt3W1dUV69evfxFh1pi9TWtTh0BEREREdQD/LqxbOINNVI88y69nRERERPRyEAQ1RCLzpw+sIc5g63qWv8F5DTZRPaNUKlFWVmbqMF46ZWVlOH/+PHNvIsy/aTH/psPcmxbzb1rMf80Zsrim58MCm6ge4sKTF08QBJSVlTH3JsL8mxbzbzrMvWkx/6bF/FN9xAKbiIiIiIiIyABYYBPVQ/XxDuj1nUgkgo2NDXNvIsy/aTH/psPcmxbzb1rMv+kw57XHm5wR1SO8yRkRERERGZtGUOPXs+fQrl073uQMfEwXUYN39vIi3C+7ZuowiIiIiKiBaWTjBNd2S2BhwVKxNpg1onroftk1lDy4aOowiIiIiIjoMbwGm4iIiIiIiMgAWGA/4c6dO3jnnXdw9+5dk8Xg7++P7Oxskx2/NsaPH4+kpKQXesw9e/Zg0aJFL/SYRERERERE1eES8Sc0b94cO3bsMGkMSqUSSqXSpDE8K1PEXB/zREREREREDRdnsA1k165dWLhw4TNvt3DhQuzatcsIEdVfY8eORWZmZr3bNxERERERvdxYYBtIRUUFKioqXth2DZkxc8J8ExERERGRsTS4AnvcuHFISUnBlClT4OnpCZlMhk8++US7lFihUODDDz9Ez5494eHhgYCAABw4cEC7fUFBAbp06aJ9vW3bNkRGRmLlypXabcLDw5GbmwsA0Gg08PHxQVRUFPbs2QOpVIrvvvvuqXGePXsWUqkUe/bsQVRUFKRSKc6cOaPtT09Px9ChQ9G9e3f06dMHa9aswZOPLN+wYQPeeustdO/eHWFhYbhy5Yq2LyEhAbNmzcL27dvh4+OD8ePHAwBGjx6N//73vwgNDYWbmxuCg4Nx5swZ5OXl4b333oNMJsPw4cNx/vx5nWN988038Pf3h4eHB3r16oWlS5c+1/LsL7/8Ej4+PvDw8ICfnx+uX7+O/fv3QyqVIjMzE5MmTYKHhwfkcjmAR5/L9OnT4ebmBm9vb8yfPx8lJSU1Pt6f7VuhUGDmzJlwd3eHp6cnYmJioFKptNvWJmdyuRxBQUFISUlBUFAQpFIpBg4ciL1799Y6Z0REREREVLc1uAJbpVJh6dKl8PPzQ3p6Onbu3ImTJ09i5cqVAICysjIEBwcjOTkZJ0+eRExMDBYsWIA7d+4AeDTD+fDhQ+3+RCIRkpKSkJ+fj4SEBBw9ehRdu3bFzJkzIQgCzMzMkJaWhsWLFyMwMBAZGRkYN27cU+N0dXVFRkYGAgMDsXjxYmRkZKBr167a/i1btiAyMhKZmZn4/vvvsWnTJhw8eFCn/4cffsDatWtx4sQJ+Pn54f3339fOzj58+BCXL1/GyZMnkZKSgm+++QYAYG5ujlWrVmHatGnIzMzEzJkzMWfOHMydOxdjx47F8ePH8cEHH2DatGk6M72Ojo7YsGEDTp48iYSEBOTk5GDTpk21+oySk5Nx8OBB7N69GydPnsSmTZvwl7/8BQMGDEBGRgbc3d0RFxeHkydPwtHREQDw0UcfQaVSISUlBYcPH0abNm3w5Zdf1viYf7bvKVOmwNraGqmpqUhMTMTFixcRGxur3bY2OVOpVCgoKMCqVauwdu1aZGRk4MMPP0RERAQuXLhQq7wREREREVHd1uAKbADo0aMHgoODAQCvvvoqli5dii1btuDhw4d4/fXX0a9fP1hbWwMAunbtirZt2+LXX3+tdn9isRhRUVFo2rQpLC0tMWXKFOTk5ODWrVtGew9Tp06FVCqFmZkZnJycMGTIEKSkpAB4NGv+1Vdf4R//+Ac6deoES0tLvPvuu2jevLl2DABcunQJc+bMgVgshpnZ/z7q4OBgeHp6QiQSoU+fPhCLxXjzzTfh6+sLAOjbty+sra11ZsSDg4O1Bam9vT2CgoJqfS1zbm4uXF1d4eDgAACQSCQQi8XVjr9+/TqysrIQHR2Npk2bwtzcHJMmTUL79u1rdfzHHT58GLdu3cKSJUtgZ2cHiUSCqKgobNq0SWcWuzY5UygUWLBgAVq2bAng0d3hhwwZgp9++um54yYiIiIiorqnQRbYPj4+Oq+7dOkCkUiEvLw8CIKA+Ph4jBkzBr1794abmxuys7Nx7969avfXtm1bWFlZaV9bW1tDIpFolxgbg7Ozs87r1157TXu8W7duoaioCN7e3jpjunbtit9++037+o033oBEItHb95OFqYODA3r06KHTJpFIdHJy6dIlzJ49G35+fpBKpVi6dCkKCwtr9d4GDBiA5ORkrFmzBvfv33/q+JycnCrfS+/evWt1/MedOXMGvXv3hoXF/26o7+TkBEEQcPv2bW1bbXImFov1xvTs2RM5OTnPHTcREREREdU9DfIxXU2aNNFra9y4MUpLS/HNN9/gxx9/xKxZs+Dl5YVXXnkF7733nt71zY97vLiuZGlpCbVabdC4HycSiXRei8Vi7dJ1uVwOpVIJmUymM0atVmP48OHa102bNq1y3+bm5nptVeWs0rVr1zBy5EiEhITg/fffR5s2bZCYmIjt27fX9O3oaN26NeLj4xEbGwt/f39MnDgR7777brXjy8vLYW9vr9cukUhw48aNWsVQSS6XY+fOnUhMTNRpVyqVKCoqQqtWrQA8e84q+5/8HBs3bvxM144TEREREVH90SAL7Lt37+q81mg0uHv3LhwdHfHTTz8hOjoavXr10vYbcybaGBo1agQ7OzucOHHiT8dVVRTWxo4dO9CnTx/MmTNH2/b47G5tNGvWDIsXL0ZISAjGjRsHiUSCQYMGVTu2uLhYr/15YwAe5XLUqFGIjIx87n09qbCwEBqNRmd5vlwu1y61JyIiIiKihqVBLhFPS0vTeX3kyBE0b94cLVq0QGFhIV5//XVt3/Xr13Hz5s3nPqaFhcWfzoIbcjtnZ2dUVFToLAc3pidzBujnuLY6dOiAwYMHIz09Xdv2ZE46duyIGzdu6P1w8vjd32vqyX27uLggIyOjVp/d01RUVOi8LwA4dOgQunXrZvBjERERERGR6TXIAvvYsWOIj4+HSqXCzZs3ER0djbFjx8LMzAxdu3bF5s2bUVFRgT/++APz5s3DG2+88dzHbN68Oc6fPw+lUlnlbGt1mjVrhjNnzkAQBJSWltZoG7FYjLCwMMyePRtnz56FIAhQKpW1KjhromvXrti/fz/y8/NRXl6OuLi4Z3qPT7py5QoKCgoAAHl5eUhJSUHnzp21/c2aNcPp06eh0WhQWloKiUSCwMBALFiwAEVFRVCpVIiOjkZ5efkzH/vJfffv3x8lJSWIioqCQqEA8GiWOSMjo9bvr5KtrS2WL1+uvfHZzp07cejQIYSEhDz3vomIiIiIqO5pkAX2/PnzsX//fnh7e2PUqFEIDAxEWFgYAGD58uW4fPkyevbsiTFjxiAkJATu7u7a66ktLCx0rrm2srKq8g7XYrFYp93d3R0tW7aEl5cXpk2bVuNYhw0bhlOnTkEqleLbb7+tct9VxfHhhx8iICAAM2bMgJubG3x9fbFz506d8VVdOy4Wi/Xaqzre423vvPMO/P39MWLECPj6+iIvLw+RkZE616BXtY/qHD58GAMGDEC3bt0wevRoBAQEYOTIkdr+sLAwbN26FZ6enkhISAAALFiwAM2aNUP//v3h6+sLQRAwadKkGh+zun1bW1tjw4YNUCgU8PPzg7u7O8LCwnDt2rXnyhnw6EZo06dP1z6/e/369YiLi+MScSIiIiKiBkokGGNtrAmFhYVh6tSpejcAI3qR8vLyEB4ervPYNEM4e/YsAOA+VqDkwUWD7puIiIiIyM62Izxdf8D58+fh5OQEW1tbU4dkcpV/g7u6uj51bIO7yZm5uTksLS1NHQZGjBiBq1evVtu/evVqeHl5vcCIXowDBw5g3rx51fa7urpi/fr1Bj+uj49PtUvGRSIRtmzZovfoM2OysLAw6vewkY2T0fZNRERERC8v/p35fBrcDDZRQ/Ysv54REREREdWGRlDj17Pn0K5dO85g49n+Bm+Q12ATNWRKpRJlZWWmDuOlU1ZWhvPnzzP3JsL8mxbzbzrMvWkx/6bF/JvOw3IlVCqVqcOol1hgE9VDXHjy4gmCgLKyMubeRJh/02L+TYe5Ny3m37SYf9NhzmuPBTYRERERERGRAbDAJqqHRCKRqUN46YhEItjY2DD3JsL8mxbzbzrMvWkx/6bF/JsOc197vMkZUT3Cm5wRERER0ctCENQQicxNHcbL/ZguopdB7tWFKC/PNXUYRERERERGYW3tDOe2S00dxjNjgU1UD5WX56LswUVTh0FERERERI/hNdhUa126dEFBQYFRj3Ho0CH8/e9/N+oxiIiIiIiIDIEz2FRrDx8+REVFhVGP0bt3b/Tu3duoxyAiIiIiIjIEzmBTnSGXy+Hn52fqMIiIiIiIiGqFBTbVGSqVCiqVytRhEBERERER1QoL7JdIeno6Ro8eDalUCqlUirFjx6Jv377QaDTaMXfu3IG3tzeUSiUAQKFQYOHChfD29oZUKoWvry9u3rxZ5f6PHj2KwYMHo3v37hg0aBBSUlJqHNv06dMRHByMP/74A1KpFFOmTAEAJCUlYfz48dpxc+bMwb///W9MmjQJ7u7uGDBgAFJTU3Hv3j1MmzYNMpkMAQEBOHr0qM7+y8rKsHjxYvTo0QMeHh6IiIhAaWlpjePLyclBSEgIPDw8IJPJ8Nlnn2n7FAoFZs6cCXd3d3h6eiImJkbvh4K9e/di6NChcHd3h4eHB5YtW1bjYxMRERERUf3Aa7BfEseOHcNHH32ERYsWwc/PD2ZmZigsLMSwYcOQlZUFqVQKAEhISICPjw/EYjGUSiVCQ0Mhk8mQlJSEJk2a4N69e7Czs9Pb/6VLlzB9+nRER0ejX79+yMrKwrRp07Bx40a88cYbT43vyy+/RF5eHsLDw3UKc6VSqS32AcDc3ByxsbGIiYlBbGwsLly4gL///e/o2rUr+vXrh9WrVyM7OxuTJ09GQkICmjZtCgBYuHAhiouLkZSUBCsrK0RFRSEqKgorVqyoUf4iIiIwZswYvPPOO1Cr1bh79662b8qUKXB2dkZqaipUKhU++ugjxMbGYsaMGQCA+Ph4rFq1CsuWLYOXlxcEQUBRUVGNjktERERERPUHZ7BfEkuXLkVkZCQGDRoEc3NziEQiSCQSBAYGIikpSTtu9+7dCA4OBgBs2LABr776Kv7xj3+gSZMmAICmTZvC3Fz/Ye9r165FSEgI/Pz8YGFhgR49emDUqFHYtGmTwd9Lr169MHDgQJibm8PFxQVdunSBSCTC3/72NwCP7m7euXNnZGVlAQCuXLmClJQUrFixAq+88goaN26MRYsWISUlpcZ3Qc/NzUWvXr0APCryW7RoAQA4fPgwbt26hSVLlsDOzg4SiQRRUVHYtGkTVCoVHjx4gOXLl2PlypXw9vaGSCSCmZkZHBwcDJ4XIiIiIiIyLRbYL4Hc3Fxcu3YNgwYN0usLCgrCvn37oNFocO3aNcjlcnh5eQEAkpOTtcX205w+fRp9+/bVaevSpQt+++23538DT2jfvr3OawcHB/To0UOnTSKR4N69ewCA7OxsuLm5aWezAcDOzg6tW7fG5cuXa3TMESNGYPLkyTh16pRO+5kzZ9C7d29YWPxvMYiTkxMEQcDt27eRlZUFGxsbuLu7P8M7JCIiIiKi+ohLxF8CCoUCEolEpwis1KFDBzRr1gwZGRk4fvw4AgICtDPUCoVCO1P7NPn5+ZgwYQJEIpG2TaPRoE2bNoZ5E4+paga9coa9KnK5HMePH9cug6+kUqlQXFxco2NGRkbiwIEDWLhwIV577TUsWrQIrVq1glwux86dO5GYmKgzXqlUoqio6JlySERERERE9RsL7JeARCLB3bt3oVQqIRaL9fqDgoKwd+9eHDlyBCtXrtTZ7o8//qjRMRo1aoQNGzagU6dOBovbUBo1agRfX1/ExsY+13769++Pvn37YtmyZZg0aRL27NmDRo0aYdSoUYiMjKxym3v37uHWrVvPdVwiIiIiIqofuET8JeDk5IQ2bdpg+/btVfYHBgYiPj4e5ubm6Ny5s7a9b9++2LZtW42O4eLiguPHjz9XnJaWlhAE4bn2URUXFxdkZ2ejvLz8ufdlZmaGWbNm4fLlyygoKICLiwsyMjKqjbt79+4oLy/H4cOHn/vYRERERERUt7HAfgmIRCJERERgxYoVSEhI0D6Wq7CwEADg6OgIFxcXveutw8LCUFBQgPnz52uXUhcXF0OtVusdY+zYsfjqq6+wb98+KJVKaDQanDp1CnK5vMZxNm3aFIWFhfj9999x//79Ko9TG927d0fLli0xe/Zs3L59G8CjmeUnH+VVHbVajaysLKjVaqjVavz000/4y1/+AgcHB/Tv3x8lJSWIioqCQqEA8GhJekZGBoBHs+czZsxARESE9ngajUZ7fTgRERERETUcLLBfEn369MGaNWvw7bffQiqVwt3dHREREdp+uVyOoKAgnW1sbGywdetWqFQq9O/fX/vc6crnYFtZWWmv6/bx8UFMTAzi4uIgk8kgk8mwYsUKnUdsPY2VlRXGjx+PoUOHIigoCAqFAmKxWGdZu1gshpWVlc52T46pqm3t2rWwt7fHkCFD4ObmhnfeeQdnz56tUVzl5eWIjIxE165dIZPJcPDgQXz99dewsLCAtbU1NmzYAIVCAT8/P7i7uyMsLAzXrl3Tbh8SEoKPP/4Y0dHRcHNzg4eHB7744osa54WIiIiIiOoHkWCMNblUL1R+9ImJidixYwe++eYbE0dET1P5o4CF+acoe3DRxNEQERERERmHjW1H/PXNn0wdBoD//Q3u6ur61LG8ydlLLC8vD8HBwWjZsiVWr15ttOMcOHAA8+bNq7bf1dUV69evN9rxn2bEiBG4evVqtf2rV6/WPrqMiIiIiIioOpzBJqpHKn89a9xoM8rLc00cDRERERGRcVhbO8O57VJThwGAM9hEDV5dOdkQERERERmLIKghEpmbOoxnwgKbqB5RqVQQBAHZ2dkQiUSmDuelIggCKioqYGFhwdybAPNvWsy/6TD3psX8mxbzbzrMvS6lUlnjPLDAJqpHKv/H5onuxROJRLC0tDR1GC8t5t+0mH/TYe5Ni/k3LebfdJh7XSKRqMZ/f/MabCIiIiIiIiID4HOwiYiIiIiIiAyABTYRERERERGRAbDAJiIiIiIiIjIAFthEREREREREBsACm4iIiIiIiMgAWGATERERERERGQALbCIiIiIiIiIDYIFNREREREREZAAssImIiIiIiIgMgAU2ERERERERkQGwwCYiIiIiIiIyABbYRERERERERAbAApuonsjIyMCIESPg4eGBt99+G5s3bzZ1SA1GYWEhRo8ejQkTJuj1lZaWYt68efDy8oJMJsPcuXNRUlKiM0YQBMTFxaFPnz7w8PBAeHg4Ll269KLCr7fOnDmDGTNmwMfHBzKZDKNHj0ZmZqbOmMuXL2PMmDHo0aMHfH19sWbNGgiCoDOmJp8R6UtNTcWoUaMgk8kglUoRFBSEH3/8USe/crkcH3zwAWQyGXr27Ino6GgolUqd/SiVSixbtkz7OU6aNAlyufxFv516berUqejUqRPu3LmjbeO5x3gyMjLw17/+FVKpVOe/RYsWaccw/8aVkJCA0NBQeHp6ws3NDSEhITr9PPcYR2BgoN733t3dHX/961+hUqkAMPeGwAKbqB64ceMGpkyZgilTpuDkyZP4+uuv8e9//xt79uwxdWj13o0bNxAaGgpLS0tUVFTo9X/44YewtrZGSkoKUlNTYW1tjRkzZuiMWbduHVJTU7F582YcP34cQUFBGDt2LIqKil7Qu6ifbt68iYEDB2Lfvn04evQogoODMXHiRO0/0kVFRXjvvfcwePBgHD9+HFu2bMEvv/yCdevW6eynJp8R6ZNIJJg3bx7S0tJw/PhxfPzxx/j2228RGxsLAFCpVJgwYQJcXV2RlpaGxMRE3Lx5E0uXLtXZz9KlS5GXl4eEhASkpaXBxcUFEyZM0P6xRn8uMTERwKNiTa1Wa9t57jEetVqNli1bIiMjQ+e/JUuWaMcw/8azfPly/PDDD4iIiMCxY8eQmZmJFStWaPt57jGePXv26H3v//nPf+LNN9+EpaUlc28oAhHVedHR0cI///lPnbbU1FRhyJAhJoqo4fjss8+ElJQUYdu2bcKYMWN0+i5cuCD4+voKKpVK26ZUKgUfHx/ht99+EwRBECoqKgSZTCZcuXJFZ9vp06cLP/zwg9Hjb2hCQ0OFbdu2CYIgCN9//70wc+ZMnf6cnBzB09NTUKvVgiDU7DOimtu9e7cQHBwsCIIg/Pzzz8LIkSN1+hUKhdCtWzfh3r172tdubm7a15WGDRsmJCcnv5ig6zGFQiEMGDBAkMvlQocOHYRbt24JgsBzj7Glp6cL/fv3r7af+TeerKwswdfXVygtLa12DM89L9b7778vbN68WRAE5t5QOINNVA/88ssv6Nevn06bt7c3rl69ivz8fBNF1TDMnTsXb731VpV9KSkp8PX1hYWFhbbN0tISvr6+OHjwIADg1KlTcHBwQNu2bXW27devH1JTU40Wd0PVuHFjlJaWAqj6e9++fXvY2dkhOzsbQM0+I6q5kpISODo6AniU/759++r0Ozg4oFu3bkhLSwMAHDp0CN27d0eTJk10xvH7XzPR0dEYN24cWrRoodPOc49pMf/Gs3XrVoSGhqJRo0bVjuG558X5/fffkZmZiYCAAADMvaGwwCaq49RqNW7evKn3j7ilpSVatmzJ672M6Pr163p5BwBnZ2dcvHjxqWNycnKMHmNDUlxcjIyMDPj4+ACoWW5r8hnRn9NoNLh16xb+85//4LvvvsOsWbMAPH/++f3/c6mpqZDL5RgxYoReH889psX8G09WVhbatWuHjz/+GD4+PujXrx8+/fRT7Q+rAM89L9KWLVswaNAg7Q8ezL1hsMAmquPu3bsHALCzs9Prs7Oz47VeRqRQKGBvb6/Xbm9vr817TcZQzaxduxa+vr7af7gVCkW13/vK/y+Y/+fz3//+F126dEGfPn3w5ZdfYtmyZejYsSOA6nPL/D+/0tJSxMTEYMmSJRCJRHr9PPcYl0gkQkFBAQIDA+Hh4QF/f398/vnnuH//PgDm35hu376NL774Am5ubti/fz82btyI69ev61zfznPPi6FSqbB161aMHDlS28bcGwYLbKI6rqKiAoIg6N05GUCVbWQ4lbl/kiAI2j+KazKGnu7EiRPYvXs3IiIitG3Mv/H97W9/w6+//orjx48jIiICM2bMwOnTpwFUn1sAzP9zWr58OYYOHQpnZ+cq+/ndNy5XV1ds2bIFO3bswLFjx7Bq1SpkZ2dj7ty5AJh/YyovL0ffvn0xZMgQ2NrawtHREZ9++ikyMzO1qwN47nkxfv75Zzg6OqJz587aNubeMFhgE9VxlTN4VT12qKSkpMpfEckw7OzsUFxcrNdeXFyszbu9vX21Y6qafSV9v//+O2bOnInPP/9c51pUOzu7p37va/IZ0dM1bdoUgwcPxsSJExEXFweg+vw/nlvm/9llZGTgzJkzVT4WsBLPPcZlY2OD9u3bw8LCAhYWFujUqRNWrVqF5ORk3Llzh/k3Imtra8hkMp22Jk2awNnZGVeuXAHAc8+LsmnTJowaNUqnjbk3DBbYRHWcra0tWrRogdzcXJ12lUqFvLw8tGnTxkSRNXzOzs56eQeA3Nxcbd6dnJyqHePk5GTsEOu9kpISTJw4EZMnT4anp6dO35/ltjL/NfmMqOZat26N69evA3iU/6tXr+qNYf6fz4ULF3Djxg14enrqPIsWAAICAhAeHs5zjwlIJBI0adIEt2/fZv6NqGXLlnrPVAYe3QuicePGAHjueRGuXLmCc+fOaW9uVom5NwwW2ET1gLe3Nw4cOKDTduTIEbRo0QKtWrUyUVQNn5eXFw4ePKjzfGyVSoVDhw7B29sbANC9e3fcvn1b+8t7peTkZO0YqppKpcLUqVPh5eWF0NBQvf6qvveXLl1CQUEBunXrBqBmnxHVXHp6uvYaeG9vbyQnJ+v0FxYW4vTp0/Dy8gLwKP+ZmZl6193x+1+9sLAwnDp1Su9ZtACQkJCAH374geceE7hx4wZKS0vh5OTE/BuRh4cHUlJSdNrkcjmuX7+OTp06AeC550XYtGkTAgMD9e7mztwbBgtsonpg/Pjx2Lx5s/bxIFevXkVMTAwmTpxo4sgaNk9PT7z22muIjo5GeXk5ysrK8Mknn6B169baGSdbW1uEh4djwYIFyM/PhyAI2Lp1K06cOKFz4xDSt2DBAtjY2GD+/PlV9o8ePRpHjx5FfHw8BEGAXC7HggUL8N5778Ha2hpAzT4j0qfRaLB3717tMr/S0lKsW7cOW7ZswdSpUwEAgYGBUCgUiIuLQ0VFBYqKijB//nwMGDAAr732GgCgVatW6NevHyIjI1FcXAyVSoXY2FiUlJRg4MCBJnt/9R3PPcaVk5ODnJwcCIKAiooKZGRk4IMPPkB4eDjs7OyYfyMKDw/Hnj17kJiYCADIy8vDrFmzMHz4cO0lQjz3GFdZWRl27txZ5feUuTcMkcC7JBHVC0ePHsWKFStw48YNNGnSBGPGjMGYMWNMHVaDsWfPHuzatQvr1q3TaVcoFIiOjkZaWhoEQYCPjw8WLlwIiUSiHaNWq7F69Wps27YNZWVlaN++PT7++GO8+eabL/pt1BslJSWQSqWwtbWFubm5Tp9MJkNsbCwA4Ny5c4iOjkZOTg6sra0xbNgwTJ8+XWebmnxGpOvhw4eYPHkyzp07B5VKBUtLS/j4+GD69Ok6S/xu3ryJTz75BKdOnYK5uTn8/f0REREBGxsb7ZgHDx5gxYoV2Lt3L1QqFbp3745FixZxdc0z6tKlC1JSUtCsWTMAPPcY07Fjx/DJJ5/g9u3bEIvFaNmyJUJDQzF48GCYmT2ae2L+jSc9PR3Lly/HtWvXYGtriyFDhmD69OmwtLTUjuG5x3ji4+OxefNm/Oc//6myn7l/fiywiYiIiIiIiAyAS8SJiIiIiIiIDIAFNhEREREREZEBsMAmIiIiIiIiMgAW2EREREREREQGwAKbiIiIiIiIyABYYBMREREREREZAAtsIiIiIiIiIgOwMHUARERERHVNaWkpZDIZKioqqh0jkUjw/fffo2PHjnp9SqUSW7duRVJSEq5du4Z79+6hefPmaN++PYYMGQJ/f/8q9/nw4UMEBgbixo0bT40xMTERb7zxRs3fFBERGR0LbCIiIqInNG7cGNnZ2RAEQa+vvLwcs2fPxh9//IFWrVrp9ZeUlGDs2LFQKpUICQmBVCqFvb097ty5g7S0NCxduhQ///wzPv/8c4hEIp1t8/PzcePGDWRkZMDGxqba+EQiEczNzZ//jRIRkUGxwCYiIiKqwpMFbHl5OeLj4xEXF4du3bph48aNsLW11dtu5cqVAICtW7dCLBZr2x0dHeHi4oIhQ4Zg8ODB2L59O4YNG6azrUajAQDY2dkZ+u0QEdELwAKbiIiI6E+cO3cOiYmJ2LZtGwoLC+Hi4oJRo0bpFM+PS0tLw8SJE6vtd3R0REBAANLS0vQKbCIiqt9YYBMRERE9pqioCPv378epU6dw4sQJlJWVYeDAgVi7di0aNWqE5ORkrF69Gr/99hvefPNNuLm5YebMmTr7eHLp95Oe1k9ERPUTC2wiIiKixxQXF+PEiRPo3r07wsPD0aFDB5iZ/e/BKx06dMDkyZPx4MEDnDlzBoWFhTrb+/j4YPPmzfD390ejRo309p+Xl4ekpCS9opyIiOo/kVDV3TuIiIiIXkIajQYqleqZtxOJRNol4SUlJRg/fjzKysoQHh4Od3d3NG7cGAqFAmlpafj222/h4+OD5cuX681k37x5E/3793/qTc4AwMKC8yRERHUNC2wiIiKi/+/rr7/W3qTsWR06dAiOjo4AAJVKhR07diAxMRFXrlxBYWEhWrRogY4dO2Lo0KHo379/lft4lsd0rV+/Ht7e3rWKlYiIjIMFNhEREVENjBgxAqGhoRg8ePBTx5aWluLQoUMYNGiQTrtSqcTevXsRHBxc4+N+9tlnuH//PpYsWfLMMRMR0Ytl9vQhRERERC8njUaD06dPY9u2bbhz5w6OHDmCvXv3Qi6X/+l2N2/exIIFC/TaFQoF5syZg4qKCmOFTEREJsSLd4iIiIiqsG/fPkRFReHu3bvatp07d2Lnzp0QiUTw8PBATEwMWrVqZcIoiYioLmGBTURERPSEc+fOYfr06fD29sbgwYPRrl072NvbQ6VSQS6XIysrC5s2bcLs2bOxefNmAI9muzUaDQBArVYDgN5MdWW7RqPR9pmZmcHMzKzaWe3Kq/mq6+fNzoiI6g5eg01ERET0hD179uCzzz7D4cOHqx2zb98+LF68GOnp6QAAf39/5ObmPvOxfHx8MG3aNIwcObJWsa5cuRIBAQG12paIiAyLBTYRERHRE+7du4fhw4fD29sbQ4YMgbOzM+zs7KBWq5Gfn4/Tp08jLi4OvXv3xty5c00dLhER1REssImIiIiqoFAo8N133yE1NRW5ubnaJdp2dnbo1q0bgoKCanRHcSIienmwwCYiIiJ6CrVajfv378PKygpWVlamDoeIiOooFthEREREREREBsDnYBMREREREREZAAtsIiIiIiIiIgNggU1ERERERERkACywiYiIiIiIiAyABTYRERERERGRAbDAJiIiIiIiIjIAFthEREREREREBsACm4iIiIiIiMgAWGATERERERERGcD/AwG/Ecv1J5H0AAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                      feature  importance  importance_ratio\n",
+              "0              cycle_time_sec       734.0          0.119156\n",
+              "1      paint_thermal_std_temp       699.0          0.113474\n",
+              "2           station_delay_sec       678.0          0.110065\n",
+              "3                queue_length       505.0          0.081981\n",
+              "4         processing_time_sec       372.0          0.060390\n",
+              "5              vibration_peak       356.0          0.057792\n",
+              "6     equipment_idle_time_sec       334.0          0.054221\n",
+              "7    vibration_acceleration_g       327.0          0.053084\n",
+              "8             max_temperature       247.0          0.040097\n",
+              "9             vibration_score       209.0          0.033929\n",
+              "10              thermal_score       195.0          0.031656\n",
+              "11         throughput_per_min       194.0          0.031494\n",
+              "12         current_max_ampere       177.0          0.028734\n",
+              "13            min_temperature       165.0          0.026786\n",
+              "14         current_min_ampere       162.0          0.026299\n",
+              "15      paint_thickness_value       159.0          0.025812\n",
+              "16         current_rms_ampere       154.0          0.025000\n",
+              "17         robot_frequency_hz       136.0          0.022078\n",
+              "18          process_code_BODY        64.0          0.010390\n",
+              "19            avg_temperature        43.0          0.006981\n",
+              "20  paint_image_position_LEFT        34.0          0.005519\n",
+              "21      robot_vibration_score        31.0          0.005032\n",
+              "22                  wip_count        27.0          0.004383\n",
+              "23              vibration_rms        26.0          0.004221\n",
+              "24           waiting_time_sec        17.0          0.002760"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-879d401d-e9fc-4d85-a2cd-d83862335d01\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>importance</th>\n",
+              "      <th>importance_ratio</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>cycle_time_sec</td>\n",
+              "      <td>734.0</td>\n",
+              "      <td>0.119156</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>paint_thermal_std_temp</td>\n",
+              "      <td>699.0</td>\n",
+              "      <td>0.113474</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>station_delay_sec</td>\n",
+              "      <td>678.0</td>\n",
+              "      <td>0.110065</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>queue_length</td>\n",
+              "      <td>505.0</td>\n",
+              "      <td>0.081981</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>processing_time_sec</td>\n",
+              "      <td>372.0</td>\n",
+              "      <td>0.060390</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>vibration_peak</td>\n",
+              "      <td>356.0</td>\n",
+              "      <td>0.057792</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>equipment_idle_time_sec</td>\n",
+              "      <td>334.0</td>\n",
+              "      <td>0.054221</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>vibration_acceleration_g</td>\n",
+              "      <td>327.0</td>\n",
+              "      <td>0.053084</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>max_temperature</td>\n",
+              "      <td>247.0</td>\n",
+              "      <td>0.040097</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>vibration_score</td>\n",
+              "      <td>209.0</td>\n",
+              "      <td>0.033929</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>thermal_score</td>\n",
+              "      <td>195.0</td>\n",
+              "      <td>0.031656</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>throughput_per_min</td>\n",
+              "      <td>194.0</td>\n",
+              "      <td>0.031494</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>current_max_ampere</td>\n",
+              "      <td>177.0</td>\n",
+              "      <td>0.028734</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>min_temperature</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>0.026786</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>current_min_ampere</td>\n",
+              "      <td>162.0</td>\n",
+              "      <td>0.026299</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>paint_thickness_value</td>\n",
+              "      <td>159.0</td>\n",
+              "      <td>0.025812</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>current_rms_ampere</td>\n",
+              "      <td>154.0</td>\n",
+              "      <td>0.025000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>robot_frequency_hz</td>\n",
+              "      <td>136.0</td>\n",
+              "      <td>0.022078</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>process_code_BODY</td>\n",
+              "      <td>64.0</td>\n",
+              "      <td>0.010390</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>avg_temperature</td>\n",
+              "      <td>43.0</td>\n",
+              "      <td>0.006981</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>paint_image_position_LEFT</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.005519</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>robot_vibration_score</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>0.005032</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>wip_count</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>0.004383</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>vibration_rms</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0.004221</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>waiting_time_sec</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>0.002760</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-879d401d-e9fc-4d85-a2cd-d83862335d01')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-879d401d-e9fc-4d85-a2cd-d83862335d01 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-879d401d-e9fc-4d85-a2cd-d83862335d01');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+        }
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\n"
-     },
-     "metadata": {}
+      "source": [
+        "print(\"불량 탐지 모델별 평가 지표\")\n",
+        "display(event_model_result_table)\n",
+        "\n",
+        "event_confusion_tables_by_model = {}\n",
+        "event_train_confusion_tables_by_model = {}\n",
+        "event_confusion_metric_rows = []\n",
+        "\n",
+        "for model_name in event_model_comparison[\"model_name\"]:\n",
+        "    threshold = float(event_model_comparison.loc[event_model_comparison[\"model_name\"] == model_name, \"threshold\"].iloc[0])\n",
+        "    train_proba = event_train_proba_by_model[model_name]\n",
+        "    test_proba = event_test_proba_by_model[model_name]\n",
+        "    train_metrics = compute_metrics(y_event_train, train_proba, threshold)\n",
+        "    test_metrics = compute_metrics(y_event_test, test_proba, threshold)\n",
+        "    event_train_confusion_tables_by_model[model_name] = confusion_matrix_table(train_metrics)\n",
+        "    event_confusion_tables_by_model[model_name] = confusion_matrix_table(test_metrics)\n",
+        "    event_confusion_metric_rows.append({\n",
+        "        \"model_name\": model_name,\n",
+        "        \"threshold\": threshold,\n",
+        "        **{f\"train_{k}\": v for k, v in train_metrics.items()},\n",
+        "        **{f\"test_{k}\": v for k, v in test_metrics.items()},\n",
+        "    })\n",
+        "    print(f\"\\n[{model_name}] Train/Test 혼동행렬\")\n",
+        "    print(f\"Train FP={train_metrics['fp']} FN={train_metrics['fn']} | Test FP={test_metrics['fp']} FN={test_metrics['fn']}\")\n",
+        "    display(pd.DataFrame([\n",
+        "        {\"split\": \"Train\", **train_metrics},\n",
+        "        {\"split\": \"Test\", **test_metrics},\n",
+        "    ]))\n",
+        "\n",
+        "event_confusion_metrics_by_model = pd.DataFrame(event_confusion_metric_rows)\n",
+        "\n",
+        "fig, axes = plt.subplots(len(event_model_comparison), 2, figsize=(10, max(4, len(event_model_comparison) * 3.2)))\n",
+        "axes = np.array(axes).reshape(len(event_model_comparison), 2)\n",
+        "for row_idx, model_name in enumerate(event_model_comparison[\"model_name\"]):\n",
+        "    sns.heatmap(event_train_confusion_tables_by_model[model_name], annot=True, fmt=\"d\", cmap=\"Blues\", ax=axes[row_idx, 0], cbar=False, annot_kws={\"size\": 10})\n",
+        "    axes[row_idx, 0].set_title(f\"{model_name} Train 혼동행렬\")\n",
+        "    axes[row_idx, 0].set_xlabel(\"예측값\")\n",
+        "    axes[row_idx, 0].set_ylabel(\"실제값\")\n",
+        "    sns.heatmap(event_confusion_tables_by_model[model_name], annot=True, fmt=\"d\", cmap=\"Oranges\", ax=axes[row_idx, 1], cbar=False, annot_kws={\"size\": 10})\n",
+        "    axes[row_idx, 1].set_title(f\"{model_name} Test 혼동행렬\")\n",
+        "    axes[row_idx, 1].set_xlabel(\"예측값\")\n",
+        "    axes[row_idx, 1].set_ylabel(\"실제값\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "event_train_proba = event_train_proba_by_model[selected_event_model_name]\n",
+        "event_test_proba = event_test_proba_by_model[selected_event_model_name]\n",
+        "event_test_pred = (event_test_proba >= selected_event_threshold).astype(int)\n",
+        "print(f\"선택 모델: {selected_event_model_name}\")\n",
+        "print(classification_report(y_event_test, event_test_pred, target_names=CLASS_NAMES, digits=4, zero_division=0))\n",
+        "\n",
+        "event_train_metrics, event_test_metrics = plot_train_test_confusion(\n",
+        "    y_event_train,\n",
+        "    event_train_proba,\n",
+        "    y_event_test,\n",
+        "    event_test_proba,\n",
+        "    selected_event_threshold,\n",
+        "    f\"불량 탐지 - {selected_event_model_name}\",\n",
+        ")\n",
+        "plot_train_test_metric_comparison(event_train_metrics, event_test_metrics, f\"불량 탐지 Train/Test 성능 비교 - {selected_event_model_name}\")\n",
+        "\n",
+        "event_metric_summary_table, event_confusion_matrix_table = display_confusion_matrix_report(\n",
+        "    y_event_test, event_test_proba, selected_event_threshold, title=f\"불량 탐지 Test 혼동행렬 - {selected_event_model_name}\"\n",
+        ")\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
+        "sns.barplot(data=event_model_comparison, x=\"model_name\", y=\"test_pr_auc\", ax=axes[0], color=\"#4C78A8\")\n",
+        "axes[0].set_title(\"불량 탐지 Test PR-AUC\")\n",
+        "axes[0].set_xlabel(\"모델\")\n",
+        "axes[0].set_ylabel(\"PR-AUC\")\n",
+        "axes[0].tick_params(axis=\"x\", rotation=20)\n",
+        "sns.barplot(data=event_model_comparison, x=\"model_name\", y=\"test_recall\", ax=axes[1], color=\"#59A14F\")\n",
+        "axes[1].set_title(\"불량 탐지 Test 재현율\")\n",
+        "axes[1].set_xlabel(\"모델\")\n",
+        "axes[1].set_ylabel(\"Recall\")\n",
+        "axes[1].tick_params(axis=\"x\", rotation=20)\n",
+        "sns.barplot(data=event_model_comparison, x=\"model_name\", y=\"test_f1\", ax=axes[2], color=\"#F28E2B\")\n",
+        "axes[2].set_title(\"불량 탐지 Test F1\")\n",
+        "axes[2].set_xlabel(\"모델\")\n",
+        "axes[2].set_ylabel(\"F1 Score\")\n",
+        "axes[2].tick_params(axis=\"x\", rotation=20)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "plot_probability_distribution(y_event_test, event_test_proba, selected_event_threshold, f\"불량 탐지 Test 예측 확률 분포 - {selected_event_model_name}\")\n",
+        "plot_pr_roc_curves(y_event_test, event_test_proba_by_model, \"불량 탐지 Test\")\n",
+        "\n",
+        "event_model_importance = model_feature_importance(selected_event_model)\n",
+        "plot_model_feature_importance(event_model_importance, f\"불량 탐지 피처 중요도 - {selected_event_model_name}\", top_n=25)"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ],
-      "image/png": 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\n"
-     },
-     "metadata": {}
+      "cell_type": "markdown",
+      "id": "1e8109dd",
+      "metadata": {
+        "id": "1e8109dd"
+      },
+      "source": [
+        "## 5. SHAP 불량 원인 분석"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 900x700 with 1 Axes>"
-      ],
-      "image/png": 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\n"
-     },
-     "metadata": {}
+      "cell_type": "markdown",
+      "id": "4128b9ca",
+      "metadata": {
+        "id": "4128b9ca"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "선택된 불량 탐지 모델의 SHAP 중요도를 계산해 어떤 feature가 예측에 크게 기여했는지 확인합니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "       model_name                                       feature  \\\n",
-       "457  RandomForest                              L3_S33_mean_time   \n",
-       "458  RandomForest                              L3_S34_mean_time   \n",
-       "459  RandomForest                            process_start_time   \n",
-       "460  RandomForest                      L3_S33_num_missing_ratio   \n",
-       "461  RandomForest                              L3_S37_mean_time   \n",
-       "462  RandomForest                              L3_S29_mean_time   \n",
-       "463  RandomForest                                   L3_S33_seen   \n",
-       "464  RandomForest                              L3_S30_mean_time   \n",
-       "465  RandomForest                                  L3_S33_F3859   \n",
-       "466  RandomForest                              process_end_time   \n",
-       "467  RandomForest                                   L3_duration   \n",
-       "468  RandomForest                                L3_S29_num_std   \n",
-       "469  RandomForest          aux_paint_thermal_defect_probability   \n",
-       "470  RandomForest                                  L1_S24_F1844   \n",
-       "471  RandomForest                               L3_S33_num_mean   \n",
-       "472  RandomForest                                    L3_num_std   \n",
-       "473  RandomForest                                  L3_S33_F3857   \n",
-       "474  RandomForest  aux_body_ford_vibration_abnormal_probability   \n",
-       "475  RandomForest                        total_process_duration   \n",
-       "476  RandomForest                                    L1_num_min   \n",
-       "477  RandomForest                                 L3_date_count   \n",
-       "478  RandomForest                               L3_S29_num_mean   \n",
-       "479  RandomForest                                    L0_num_min   \n",
-       "480  RandomForest                                  L3_S29_F3458   \n",
-       "481  RandomForest                                  L3_S30_F3744   \n",
-       "482  RandomForest                                L3_S33_num_std   \n",
-       "483  RandomForest                                    L0_num_std   \n",
-       "484  RandomForest                                L3_S30_num_std   \n",
-       "485  RandomForest                                  L3_S30_F3759   \n",
-       "486  RandomForest                                   L3_S34_seen   \n",
-       "\n",
-       "     importance_gain      importance_type  \n",
-       "457         0.011733  feature_importances  \n",
-       "458         0.011186  feature_importances  \n",
-       "459         0.010766  feature_importances  \n",
-       "460         0.008505  feature_importances  \n",
-       "461         0.008500  feature_importances  \n",
-       "462         0.008475  feature_importances  \n",
-       "463         0.008394  feature_importances  \n",
-       "464         0.008029  feature_importances  \n",
-       "465         0.008004  feature_importances  \n",
-       "466         0.007741  feature_importances  \n",
-       "467         0.007571  feature_importances  \n",
-       "468         0.007533  feature_importances  \n",
-       "469         0.007316  feature_importances  \n",
-       "470         0.007214  feature_importances  \n",
-       "471         0.007032  feature_importances  \n",
-       "472         0.006991  feature_importances  \n",
-       "473         0.006660  feature_importances  \n",
-       "474         0.006541  feature_importances  \n",
-       "475         0.006364  feature_importances  \n",
-       "476         0.006358  feature_importances  \n",
-       "477         0.006329  feature_importances  \n",
-       "478         0.006163  feature_importances  \n",
-       "479         0.006136  feature_importances  \n",
-       "480         0.006061  feature_importances  \n",
-       "481         0.005915  feature_importances  \n",
-       "482         0.005873  feature_importances  \n",
-       "483         0.005700  feature_importances  \n",
-       "484         0.005681  feature_importances  \n",
-       "485         0.005666  feature_importances  \n",
-       "486         0.005639  feature_importances  "
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "4eb9351a",
+      "metadata": {
+        "id": "4eb9351a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "a7fe2815-ba54-4daf-823b-ee5a1624f915"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                       feature  mean_abs_shap\n",
+              "0     vibration_acceleration_g       1.142834\n",
+              "1               cycle_time_sec       0.966047\n",
+              "2               vibration_peak       0.894833\n",
+              "3            process_code_BODY       0.701656\n",
+              "4           current_max_ampere       0.434145\n",
+              "5           current_rms_ampere       0.372167\n",
+              "6            station_delay_sec       0.360060\n",
+              "7          processing_time_sec       0.318584\n",
+              "8       paint_thermal_std_temp       0.279638\n",
+              "9                 queue_length       0.257782\n",
+              "10     equipment_idle_time_sec       0.226875\n",
+              "11          current_min_ampere       0.196916\n",
+              "12          throughput_per_min       0.130066\n",
+              "13    equipment_type_ROBOT_ARM       0.098666\n",
+              "14               vibration_rms       0.092616\n",
+              "15             max_temperature       0.077381\n",
+              "16             vibration_score       0.069712\n",
+              "17          robot_frequency_hz       0.066810\n",
+              "18               thermal_score       0.056834\n",
+              "19             robot_amplitude       0.031175\n",
+              "20             min_temperature       0.028765\n",
+              "21   paint_image_position_LEFT       0.026563\n",
+              "22       paint_thickness_value       0.023652\n",
+              "23             avg_temperature       0.011191\n",
+              "24       robot_vibration_score       0.010973\n",
+              "25               robot_axis_J3       0.009231\n",
+              "26            waiting_time_sec       0.008450\n",
+              "27  paint_image_position_RIGHT       0.004459\n",
+              "28     equipment_type_CONVEYOR       0.004237\n",
+              "29                   wip_count       0.004029"
+            ],
+            "text/html": [
+              "\n",
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+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>mean_abs_shap</th>\n",
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+              "  </thead>\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>vibration_acceleration_g</td>\n",
+              "      <td>1.142834</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>cycle_time_sec</td>\n",
+              "      <td>0.966047</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>vibration_peak</td>\n",
+              "      <td>0.894833</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>process_code_BODY</td>\n",
+              "      <td>0.701656</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>current_max_ampere</td>\n",
+              "      <td>0.434145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>current_rms_ampere</td>\n",
+              "      <td>0.372167</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>station_delay_sec</td>\n",
+              "      <td>0.360060</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>processing_time_sec</td>\n",
+              "      <td>0.318584</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>paint_thermal_std_temp</td>\n",
+              "      <td>0.279638</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>queue_length</td>\n",
+              "      <td>0.257782</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>equipment_idle_time_sec</td>\n",
+              "      <td>0.226875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>current_min_ampere</td>\n",
+              "      <td>0.196916</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>throughput_per_min</td>\n",
+              "      <td>0.130066</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>equipment_type_ROBOT_ARM</td>\n",
+              "      <td>0.098666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>vibration_rms</td>\n",
+              "      <td>0.092616</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>max_temperature</td>\n",
+              "      <td>0.077381</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>vibration_score</td>\n",
+              "      <td>0.069712</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>robot_frequency_hz</td>\n",
+              "      <td>0.066810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>thermal_score</td>\n",
+              "      <td>0.056834</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>robot_amplitude</td>\n",
+              "      <td>0.031175</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>min_temperature</td>\n",
+              "      <td>0.028765</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>paint_image_position_LEFT</td>\n",
+              "      <td>0.026563</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>paint_thickness_value</td>\n",
+              "      <td>0.023652</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>avg_temperature</td>\n",
+              "      <td>0.011191</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>robot_vibration_score</td>\n",
+              "      <td>0.010973</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>robot_axis_J3</td>\n",
+              "      <td>0.009231</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>waiting_time_sec</td>\n",
+              "      <td>0.008450</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>paint_image_position_RIGHT</td>\n",
+              "      <td>0.004459</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>equipment_type_CONVEYOR</td>\n",
+              "      <td>0.004237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>wip_count</td>\n",
+              "      <td>0.004029</td>\n",
+              "    </tr>\n",
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+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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+            "text/plain": [
+              "     row_index  defect_probability risk_grade  \\\n",
+              "97        7585            0.999328       HIGH   \n",
+              "81        1793            0.999281       HIGH   \n",
+              "123       1593            0.999256       HIGH   \n",
+              "332       4265            0.999209       HIGH   \n",
+              "128       2165            0.999164       HIGH   \n",
+              "253        457            0.999110       HIGH   \n",
+              "383       7169            0.999089       HIGH   \n",
+              "316       2701            0.999051       HIGH   \n",
+              "377       2373            0.998991       HIGH   \n",
+              "483       3653            0.998952       HIGH   \n",
+              "119       6301            0.998894       HIGH   \n",
+              "34         921            0.998822       HIGH   \n",
+              "391       7261            0.998773       HIGH   \n",
+              "216       1145            0.998758       HIGH   \n",
+              "139       2149            0.998725       HIGH   \n",
+              "191       9377            0.998722       HIGH   \n",
+              "468       8336            0.998642       HIGH   \n",
+              "336       8453            0.998461       HIGH   \n",
+              "315        765            0.998423       HIGH   \n",
+              "21        6805            0.998357       HIGH   \n",
+              "\n",
+              "                                          top_features  \n",
+              "97   [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "81   [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "123  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "332  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "128  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "253  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "383  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "316  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "377  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "483  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "119  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "34   [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "391  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "216  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "139  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "191  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "468  [{'feature': 'vibration_acceleration_g', 'shap...  \n",
+              "336  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "315  [{'feature': 'vibration_peak', 'shap_value': 9...  \n",
+              "21   [{'feature': 'vibration_peak', 'shap_value': 9...  "
+            ],
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>row_index</th>\n",
+              "      <th>defect_probability</th>\n",
+              "      <th>risk_grade</th>\n",
+              "      <th>top_features</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>7585</td>\n",
+              "      <td>0.999328</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>81</th>\n",
+              "      <td>1793</td>\n",
+              "      <td>0.999281</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>123</th>\n",
+              "      <td>1593</td>\n",
+              "      <td>0.999256</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>332</th>\n",
+              "      <td>4265</td>\n",
+              "      <td>0.999209</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>128</th>\n",
+              "      <td>2165</td>\n",
+              "      <td>0.999164</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>253</th>\n",
+              "      <td>457</td>\n",
+              "      <td>0.999110</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>383</th>\n",
+              "      <td>7169</td>\n",
+              "      <td>0.999089</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>316</th>\n",
+              "      <td>2701</td>\n",
+              "      <td>0.999051</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>377</th>\n",
+              "      <td>2373</td>\n",
+              "      <td>0.998991</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>483</th>\n",
+              "      <td>3653</td>\n",
+              "      <td>0.998952</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>119</th>\n",
+              "      <td>6301</td>\n",
+              "      <td>0.998894</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>921</td>\n",
+              "      <td>0.998822</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>391</th>\n",
+              "      <td>7261</td>\n",
+              "      <td>0.998773</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>216</th>\n",
+              "      <td>1145</td>\n",
+              "      <td>0.998758</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>139</th>\n",
+              "      <td>2149</td>\n",
+              "      <td>0.998725</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>191</th>\n",
+              "      <td>9377</td>\n",
+              "      <td>0.998722</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>468</th>\n",
+              "      <td>8336</td>\n",
+              "      <td>0.998642</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_acceleration_g', 'shap...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>336</th>\n",
+              "      <td>8453</td>\n",
+              "      <td>0.998461</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>315</th>\n",
+              "      <td>765</td>\n",
+              "      <td>0.998423</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>6805</td>\n",
+              "      <td>0.998357</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>[{'feature': 'vibration_peak', 'shap_value': 9...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f57c0ab-9078-4b55-b1ef-c7e9955b3f45')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1f57c0ab-9078-4b55-b1ef-c7e9955b3f45 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1f57c0ab-9078-4b55-b1ef-c7e9955b3f45');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"plt\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"row_index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3039,\n        \"min\": 457,\n        \"max\": 9377,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          7585,\n          8453,\n          9377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"defect_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0002919588618330555,\n        \"min\": 0.9983565997587882,\n        \"max\": 0.9993277041630241,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.9993277041630241,\n          0.9984614822540115,\n          0.9987215551588507\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"risk_grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"HIGH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top_features\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x700 with 1 Axes>"
+            ],
+            "image/png": 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8+TK3b99Go9FQsmRJ/Pz86NixI7Vq1cp1TIMHD+aPP/7I8Zdka2trPvnkE5NfjC9cuEC/fv0YP368yZRBgHfffZfly5cTGxub6xgsyd7V8IcffnjmNg4dOoRGo2H9+vUWz4eHh7N+/Xp69+79zPd42NmzZ/nss884ffq0Sfnx48eBB4nGhAkTCnRS9jxKly5NdHS0Wfn58+efuU1L6wE1Gg0NGzZk+fLlJj+3z8LJyYm3336bP//8M8fPE7Rv355WrVoxZswYNmzYgK+v72OnhArxupOETgghxL/akz5APHjwYG7fvs0nn3xCrVq1KFq0KBkZGVy+fJmoqCi6du1KREQEXl5eubrfyZMnmT17do67YX799decO3fOpKxy5cps3brVbGe/bFWrVs3xO19PIzY21uKOj7m1Zs0amjdvnmMsrVu3Zt68eS8kodPpdHTr1g1HR0fGjBlDrVq1cHJyQqVScevWLU6dOsWqVavo378/O3fuVHa5tPRhboPBYHFzk4fLVSoV1tbWZGVlWUzGs0fDctok5Umbo7wMAQEBzJ07l4sXL1KpUiWlfMWKFQBPHC22xNbW1uKGQ5988glt27blp59+ynH9Y26kpqZy/vx5ypYtm2OdihUr4uLiwoYNG4iMjHzsmjsh/g0koRNCCPHaSk5O5v333+f27ds5/jKtVqvp2bOnxXNGo5Hdu3fzyy+/mHy828bGhurVq1O9enXOnz/Pjh07cp3QwYN1UzntTlm4cGGLCUNOydyzmj59OoGBgVSuXJl79+7x66+/sm3btlx9VsGSu3fv8scff/Dbb7/lWKdRo0Z8/vnnxMXFUadOnWcNHYCUlBSSk5NZsWKF2XcBK1eujJubG8HBwfj4+HD9+nUloRs/frzZhiAP7+z4sIc33tBoNJw4cYJ3330XnU6XY1w5jdZ27NjxhX2kPDU11WRtn5WVldkaQAAXFxeCgoLo06cPX375JaVLl2bFihXs3bsXW1vbZ/qZeuONN7h+/Tq7du2icuXK2NnZUapUKSpXrsycOXP45JNPiI6OpkOHDlSrVg1bW1tu3brF/v37LW74kpGRwZ07d7h9+zZnz55l/vz5lChRgsaNGz82jg8++ICvv/4anU6X40ieEP8WktAJIYR4bSUlJZGWlsaJEyeeaXREpVLRoEEDpkyZwqBBg6hevTpFihRBr9eTkJDA9u3b2blzJ/PmzXuqdlNTU3PcaCItLe2VjOTcvn2bkSNHKsmOu7s7q1evNhlds7W1fewojo2NjZJYbN68mapVqz52hK9w4cI0atSI9evXU6dOHWxtbZ/5swulSpWiUaNGTJgwgZ49e1KjRg2KFy+ujNCdPXuWZcuW4ebmxptvvqlcN2nSpOdKrE6cOPHM1z6v7E10Ht0Ax9ramujoaNRqtTKSmG369Ol8++239O3bl7S0NHx8fOjSpQuHDx82efe2trbKrq0Pe7S8VKlS9OnTh88++wwrKyumTp1Kw4YNAfD19WXTpk0sW7aMr7/+mkuXLpGVlUWJEiWoUaMGkydPNmlbrVYze/ZsZs+eTaFChahQoQKBgYF8/PHHJuv8NBqN2eY0QUFBTJ06lQ8++MAkPhsbG9Rq9TP/XAlREKmMT1rpKoQQQhRQ2SN0ycnJj02SVCoVW7dutbg5x8Nr6C5dusSdO3ewsbFR1tB17tzZZPTuSfr168eOHTtyXEOnVqsZNmwYPXr0yHWbD4uNjWXkyJFs27btma4vSPR6PatWrWLDhg2cOXOG9PR04EECUKtWLQIDA+nWrdtjvw/4uluwYAHu7u5UrFiR9PR0Dhw4wNy5c5k/fz41atTI6/CEEC+AJHRCCCHEU3h0l0uRPxiNRu7du4dKpTLZOv/f7vvvv2fjxo1cvXoVGxsb3N3dCQsLo3bt2nkdmhDiBZGETgghhBBCCCEKKPkToxBCCCGEEEIUUJLQCSGEEEIIIUQBJQmdEEIIIYQQQhRQsqerEMLE0aNHMRqNFrevFkIIIYQQL59er0elUuHm5vbEujJCJ4QwYTQalf+J/MNoNKLT6aRf8hnpl/xJ+iV/kn7Jn6Rf8qen+V1MRuiEECZsbGzQ6XS89dZbFC5cOK/DEf9fWloap0+fln7JZ6Rf8ifpl/xJ+iV/kn7Jn06cOJHrujJCJ4Sw6HEfYRavnkqlws7OTvoln5F+yZ+kX/In6Zf8Sfql4JPv0AkhTGT/RcjFxSWPIxFCCCGEyB8MBgNWVq9uLOxpfh+TKZdCCItmr9rJlespeR2GEEIIIUSeKlfSkYEdA/M6jBxJQieEsOjK9RQuJNzM6zCEEEIIIcRjyBq6F+zMmTN88MEH6PV6AObPn8/8+fNfaQyjR4/ml19+eaX3zGsbNmwgNDQ0T+59/fp1Wrduzc2bkvwIIYQQQohXS0boXrDq1auzZs0a5Vin073yGMLDw1/5PfOaTqfLk3cNULJkSX799dc8ubcQQgghhPh3kxG6Ai4iIoKIiIi8DuNfJSgoiMTExLwOQwghhBBCCBmhK+gyMzPzOoR/Hb1er0ypFUIIIYQQIi/JCN0z+PLLL5kxY4ZJWXp6OlqtluPHjxMUFGRyLisri9mzZ9OgQQPc3Nz48MMPOXPmjHI+MTGRgIAATpw4QbNmzfD29kan03H+/HlCQ0Px9fXFy8uLdu3aERsbC8CNGzfw8vJiwYIFLFiwAK1Wy+bNmwEIDQ1lw4YNSvupqalMmDABPz8/tFot7du3Z8+ePSYxNmjQgJ07d9KhQwfc3d1p2LAhCxYseKr3kpyczCeffIKfnx+enp40b96cbdu2mdRJS0tj+vTp1K9fHw8PD/z9/ZVnAlixYgXNmzfHw8MDb29vfvzxR+Xc0qVLlXcYEhJCfHx8jrFkZWXx7bff4ufnh7u7O/369SMpKUk5v2DBAr799lvmzZuHj48PY8eOBSAyMpKWLVvi6emJn58fw4cP586dOwAsWbIErVZLQkICLVu2xN/fn8zMTG7cuEGdOnVM7h8XF0dISAharRYfHx+GDRvG9evXlfPR0dF0796dpUuXEhgYiIeHB+3btycuLu6p3rlOp2PKlCn4+vri5uZGjx49iI6ONvsZFEIIIYQQrydJ6J5BgwYNzBKVffv28dZbb1kcvfn5559JSkpi8+bNHDhwgKpVq9KzZ09SU1OBByM+aWlpzJ8/n59++okDBw6g0WjIyMigR48e7Nq1i0OHDtGvXz/CwsLQ6XQ4Oztz6NAhQkNDCQ0NJTY2lmbNmgHm68kGDRpEcnIyv//+OzExMQwaNIhhw4Zx+PBhpY5KpWLSpEkMGTKEmJgYFi1axE8//WT2nI+Tnp5Oy5Yt2b59OzExMYSHhzNmzBiTRKZv375cunSJNWvWcPjwYbZs2aJ8X2P27NmsWLGCL774gsOHD7N3717atm0LwOrVq1m2bBkREREcOnSIoKAgQkNDcxyhnDlzJnv27OHnn39m//79VK1albCwMOV8RkYGe/bsIS0tjX379jFp0iQAHBwcmD17NocOHWL79u0YDAZmz54NQPfu3YmNjaVs2bKsX7+evXv3olaryczMJCMjQ2n7/Pnz9OzZk9atWxMdHc327dtxcnKiZ8+eSrwqlYrjx4+zZ88eVq1aRUxMDG3btqV///7cv38/1+98+vTpHD58mJUrVxITE0OHDh0YMmSIjCAKIYQQQvxLSEL3DNzd3bl9+zbnz59XyrZv357jqIitrS3jx4+nUKFCFCpUiAkTJqBWq02SpTt37tCqVSucnZ2VjxZWr14dX19fNBoNAI0bN8bKyooLFy7kOtbo6GhOnz7N9OnTcXJyQqVSERAQQGhoKN9//71J3S5duuDr64u1tTVvvvkmbdu2Zfv27bm+V7ly5WjUqBGFChUCwNXVlTfffJOTJ08CsGnTJq5fv873339P6dKlgQcJlK2tLVevXmXx4sVEREQoCZ6NjQ3FihVTkqoJEyZQvXp1bGxs+OijjyhZsiRRUVFmcdy+fZtly5YxY8YMKlSoQKFChfj0009JSEjgf//7n1LvypUrDB48GGtra+WdN2nShIoVK6JSqShUqJDJqGhuzZs3j/fff5927dqhVquxt7dn9OjRACYjp+np6XzxxReULl0aKysrunTpgrW1tfIhySfR6XRERkYSHh5O5cqVUavVNGvWjBYtWjxVvEIIIYQQouCShO4ZWFtb07BhQ3bu3Ak8+HL8rl27ckzo/Pz8sLa2Vo6trKzw9/fnzz//NKmn1WpNjjMzM1m6dCmdO3fG398fDw8Prl+/TkpKSq5jjY2NpUGDBtjZ2ZmUN2vWjCNHjpCVlaWU1axZ06ROuXLlTKYpPonRaGTdunV069aN+vXr4+7uTlxcnBLv9u3bad68ucm7yLZ7925q1apFhQoVzM5dvXqV27dvU7duXZNyV1dXk6mr2f78809KlSpF1apVlTIrKytq165tUt/NzQ212nQZaUJCAuPGjaNFixZ4eXkxYMCAp3rfADExMQQHB5uVBwUFcejQIeW4VKlSODs7m9R54403cr3hyoULF1CpVNSoUcOkvGHDhk8VrxBCCCGEKLhkU5Rn1LhxY5YsWUKPHj04cuQIZcuWpWzZsiQkJJjVdXJyMisrUaIEd+/eVY6trKxwdHQ0qTNp0iQOHz5MWFgYHh4eFC9enEaNGmE0GnMdZ1JSEqVKlTIrL1myJHq9nps3byrns0cCs6nVapOE70kWLlzIf/7zH4YOHYqvry8lSpSge/fuSrzJycl4e3tbvDY5OdlinPBgjaFOpzO7Nisri/bt21usf/nyZYsJ8sNlxYsXNzmfkpJChw4dqF+/PtOmTeOtt97ixIkTjBw58skP/5Cc3nnp0qU5evSocmxra2tWx8bGJtfv/M6dO2Y/M0CO71EIIYQQQrx+JKF7Rn5+fowcOZLU1FSioqIeuwnFtWvXzMpu3rxJ2bJllWOVSoVKpVKOMzIyiIyMZP369bz55pvAgxGwpxkxA3B0dLR4zfXr11Gr1RQrVuyp2nuc5cuXM3XqVOrVq6eUPTza5OTkZDHhzT539epVi+fs7e0pUqSIyejW49jb2/P2228/8dtw2dMss/33v/+lXLlyJt/xs9R3T5L9zitVqmRSnpiYaJZEPg8HBweSk5PNyi2VCSGEEEKI15NMuXxGtra2+Pj4sGfPHrZv307Tpk1zrLtr1y5lAxR4MEVz3759VKtWLcdr7t69i16vp3z58kpZdHS02WYXarX6sSN2AQEB7Ny5k/T0dJPyzZs34+vra3GU6FndunWLcuXKKccXL17k0qVLynHDhg1Zv369xQ+A+/v787///c/izpVVqlQhMzPT4vRKS2rVqsWFCxdMNmN5lvgB9u7da1bvSe88MDCQTZs2mZQZjUa2bt36QqdDVq1aFVtbW7M1ftu2bTP544AQQgghhHh9SUL3HLKnXdrZ2Vlc+5WtWLFiDB8+nNTUVDIyMpg0aRLFihUjMDAwx2tKlChBuXLlWLFiBUajkb/++osvvviCypUrm9RzdnYmLi4Og8FgMoUzm4eHBzVr1mTEiBEkJydjNBrZsmUL8+fPZ+DAgc/66Ba5urqyatUqMjMzSUhIYOTIkSbr2IKDgylVqhQDBgxQkq20tDQyMjIoV64cXbt2pW/fvsomKpmZmdy9exeNRkNISAjDhg3jxIkTGI1GdDpdjjtwli5dmkaNGhEWFsbff/8NwL1799i1a9cT49+/fz9///03er2etWvXcvbsWbN6zs7OHDt2DL1eb5YoA/Tu3ZsNGzawdu1asrKySE1NZezYsVhZWb3QzwnY2NgwePBgxo0bR3x8PHq9nl9//ZXff//d4lRMIYQQQgjx+pGE7jkEBgZy9uxZk10FNRoNNjY2Jsdt2rTBxcWF4OBg6tatS1JSEkuXLlXq2djYmI2UqVQqIiIi2LJlC15eXnz66acMHz6cChUqYDAYlHrvvfceaWlpeHp6KlMFNRqNyXq4mTNnUqpUKeX7aosXL+b777/n3XffNYnz0TV0tra2ZmWP8+WXX3Lu3Dn8/Pzo1q0bnTt3xsPDQ1kTplKpWLJkCRUrVqRly5Z4eHjQoEEDjh07BsDQoUPp3r07YWFhuLu74+Pjw/LlywH45JNPaN68uXIuICCA3377Lcf4w8PDcXFxoVu3bri5uREUFGTy7T1bW1uzd+7t7U3//v3p3bs3devWZffu3UyfPt3kfcODhO3LL7+kfv36HDx4ELVabdJWpUqV+PHHH/n999/x8fGhcePGZGVl8eOPPyobwuT0bi31w+N8+OGHfPTRR/Ts2RNvb29+//13PvjgA9zc3HLdhhBCCCGEKLhUxqfZYUMIka/ExcVhb2/Pm2++SWZmJtHR0UyePJkffvjhsaPGj5P92YQVu+K5kHDzRYYrhBBCCFHgVC5bgmkDW7/Se2b/Ppb9Oa/HkU1RRK506NBBmb5oyaxZs/D19X2FEb3e/ve//9GtW7ccz5coUYKtW7dy+fJlZs2aRVJSEiqVCldXV77//vtnTuaEEEIIIUTBIiN0QggT2X8R2nXqJleup+RtMEIIIYQQeaxcSUcGdgx8pfeUETohxHN71f+PSwghhBAivzIYDGafvMov8mdUQog8pdPpLO7gKfJOeno6p06dkn7JZ6Rf8ifpl/xJ+iV/kn7JnfyazIEkdEKIHMhs7PzFaDSSnp4u/ZLPSL/kT9Iv+ZP0S/4k/VLwSUInhLBIPk6ev6hUKuzs7KRf8hnpl/xJ+iV/kn4R4uWQNXRCCDMajQY7O7u8DkM8xM7Ojpo1a+Z1GOIR0i/5k/RL/iT98nLk57Vd4tWQhE4IYdHsVTtll0shhBAiH8uL3RdF/iMJnRDCoivXU+TD4kIIIYQQ+ZyMzwohhBBCCCFEASUJncj36tSpw40bN17qPXbv3k3v3r1f6j2EEEIIIYR40WTKpcj3MjIyyMzMfKn3qF+/PvXr13+p9xBCCCGEEOJFkxE68a+TmJhIUFBQXochhBBCCCHEc5OETvzr6PV69Hp9XochhBBCCCHEc5OETrxw0dHRdOnSBa1Wi1arpUePHjRs2BCDwaDUuX79OnXr1kWn0wGQnJzM2LFjqVu3LlqtloCAAC5dumSx/f3799OqVSvc3NwIDg4mKioq17ENHjyYli1bkpCQgFarZcCAAQBs3ryZXr16KfWGDx/OokWL6Nu3Lx4eHjRp0oSdO3eSkpLCoEGD8Pb2pnnz5uzfv9+k/fT0dMaPH4+Xlxeenp6MGDGC1NTUXMd39uxZOnfujKenJ97e3kyfPl05l5yczKeffoqHhwc+Pj6Eh4ebJaZbtmyhbdu2eHh44OnpybRp03J9byGEEEIIUfDIGjrxQh04cIAhQ4Ywbtw4goKCsLKy4tatW7Rr144jR46g1WoB2LhxI/7+/mg0GnQ6HR9++CHe3t5s3ryZYsWKkZKSQpEiRcza/+uvvxg8eDBTp06lUaNGHDlyhEGDBrFixQqqVq36xPhmzpzJ5cuX6dq1q0kiqNPplOQSwNramjlz5hAeHs6cOXM4ffo0vXv3xtXVlUaNGjFr1izi4uLo168fGzduxNHREYCxY8dy584dNm/ejK2tLRMnTmTixInMmDEjV+9vxIgRdOvWjdatW5OVlcXNm//32YABAwZQpUoVdu7ciV6vZ8iQIcyZM4ewsDAA1q1bx3fffce0adPw9fXFaDRy+/btXN1XCCGEEEIUTDJCJ16oKVOmMHr0aIKDg7G2tkalUuHk5ESLFi3YvHmzUu/333+nZcuWACxdupQyZcowYcIEihUrBoCjoyPW1tZm7UdERNC5c2eCgoJQq9V4eXnRqVMnVq5c+cKfpV69ejRr1gxra2tq165NnTp1UKlUfPDBB8CD3Tdr1arFkSNHAIiPjycqKooZM2ZQokQJHBwcGDduHFFRUbnepfP8+fPUq1cPeJBUlipVCoA9e/Zw9epVJk2aRJEiRXBycmLixImsXLkSvV5PWloaX375Jd988w1169ZFpVJhZWVF8eLFX/h7EUIIIYQQ+YckdOKFOX/+PBcuXCA4ONjs3Pvvv8/WrVsxGAxcuHCBxMREfH19Adi+fbuS3D3JsWPHaNiwoUlZnTp1OHPmzPM/wCPefvttk+PixYvj5eVlUubk5ERKSgoAcXFxuLu7K6N1AEWKFKFixYqcO3cuV/fs0KED/fr14+jRoyblx48fp379+qjV/zeoXrlyZYxGI9euXePIkSPY2dnh4eHxFE8ohBBCCCEKOplyKV6Y5ORknJycTJKObO+88w7Ozs7ExsZy8OBBmjdvrozAJScnKyNRT5KUlMTHH3+MSqVSygwGA5UqVXoxD/EQSyOE2SOIliQmJnLw4EFlWmk2vV7PnTt3cnXP0aNHs23bNsaOHUvZsmUZN24cFSpUIDExkd9++41NmzaZ1NfpdNy+ffup3qEQQgghhHh9SEInXhgnJydu3ryJTqdDo9GYnX///ffZsmUL+/bt45tvvjG5LiEhIVf3sLe3Z+nSpVSvXv2Fxf2i2NvbExAQwJw5c56rncaNG9OwYUOmTZtG37592bBhA/b29nTq1InRo0dbvCYlJYWrV68+132FEEIIIUTBI1MuxQtTuXJlKlWqRGRkpMXzLVq0YN26dVhbW1OrVi2lvGHDhvzyyy+5ukft2rU5ePDgc8VpY2OD0Wh8rjYsqV27NnFxcdy/f/+527KysmLo0KGcO3eOGzduULt2bWJjY3OM283Njfv377Nnz57nvrcQQgghhCg4JKETL4xKpWLEiBHMmDGDjRs3Kp8puHXrFgClS5emdu3aZuvlQkJCuHHjBqNGjVKmJt65c4esrCyze/To0YPZs2ezdetWdDodBoOBo0ePkpiYmOs4HR0duXXrFleuXOHevXsW7/Ms3NzcKF++PMOGDePatWvAg5GzRz9tkJOsrCyOHDlCVlYWWVlZLF++nDfeeIPixYvTuHFj7t69y8SJE0lOTgYeTPGMjY0FHowOhoWFMWLECOV+BoNBWd8nhBBCCCFeT5LQiRcqMDCQuXPn8sMPP6DVavHw8GDEiBHK+cTERN5//32Ta+zs7Fi7di16vZ7GjRsr333L/g6dra2tsi7P39+f8PBw5s2bh7e3N97e3syYMcPkkwNPYmtrS69evWjbti3vv/8+ycnJaDQak2miGo0GW1tbk+serWOpLCIigqJFi9KmTRvc3d1p3bo1J06cyFVc9+/fZ/To0bi6uuLt7c2uXbuYP38+arWaQoUKsXTpUpKTkwkKCsLDw4OQkBAuXLigXN+5c2c+//xzpk6diru7O56ennz77be5fi9CCCGEEKLgURlfxtwzIR6S/SO2adMmfv31VxYuXJjHEYnHyU5AV+yK50LCzSfUFkIIIUReqVy2BNMGtn6uNtLS0jh9+jQ1atSgcOHCLyYw8dyyfx9zcXF5Yl3ZFEW8dJcvX6Zly5aUL1+eWbNmvbT7bNu2jZEjR+Z43sXFhR9//PGl3f9JOnTowN9//53j+VmzZimfcsgPypV0zOsQhBBCCPEY8m+1ABmhE0I84mn+IiSEEEKIvGUwGLCyevZVVDJClz89ze9jsoZOCGFGp9ORnp6e12GIh6Snp3Pq1Cnpl3xG+iV/kn7Jn6RfXo7nSebE60F+AoQQFsngff5iNBpJT0+XfslnpF/yJ+mX/En6RYiXQxI6IYQQQgghhCigJKETQlikUqnyOgTxEJVKhZ2dnfRLPiP9kj9Jv+RP0i9CvByyy6UQwoxGo8HOzi6vwxAPsbOzo2bNmnkdhniE9Ev+JP2SP70O/fK8G5AI8TJIQieEsGj2qp1cuZ6S12EIIYQQ+UK5ko4M7BiY12EIYUYSOiGERVeup8iHxYUQQggh8jkZMxZCCCGEEEKIAkoSOvHSnTlzhg8++AC9Xg/A/PnzmT9//iuNYfTo0fzyyy+v9J6v0rhx41i0aFFehyGEEEIIIV4xmXIpXrrq1auzZs0a5Vin073yGMLDw1/5PV8lnU6XJ+9VCCGEEELkLRmhE6+diIgIIiIi8joMIYQQQgghXjoZoROvnczMzLwOQQghhBBCiFdCRujEC/Hll18yY8YMk7L09HS0Wi3Hjx8nKCjI5FxWVhazZ8+mQYMGuLm58eGHH3LmzBnlfGJiIgEBAZw4cYJmzZrh7e2NTqfj/PnzhIaG4uvri5eXF+3atSM2NhaAGzdu4OXlxYIFC1iwYAFarZbNmzcDEBoayoYNG5T2U1NTmTBhAn5+fmi1Wtq3b8+ePXtMYmzQoAE7d+6kQ4cOuLu707BhQxYsWPBU72XevHnMmzeP6dOnU79+fdzd3enbty8JCQkm9S5dukTv3r1xc3PD39/fbIQxMjKSli1b4unpiZ+fH8OHD+fOnTs53jc6OppGjRqRmJj4VPEKIYQQQoiCRRI68UI0aNCAbdu2mZTt27ePt956C71er2yIku3nn38mKSmJzZs3c+DAAapWrUrPnj1JTU0FQK/Xk5aWxvz58/npp584cOAAGo2GjIwMevTowa5duzh06BD9+vUjLCwMnU6Hs7Mzhw4dIjQ0lNDQUGJjY2nWrBlgvsZs0KBBJCcn8/vvvxMTE8OgQYMYNmwYhw8fVuqoVComTZrEkCFDiImJYdGiRfz0009mz/k4er2e5cuXk5WVxbZt29i7dy9FihRh4MCBSh2dTkf37t2pXbs2+/fvZ/Xq1WzatMlk3aGDgwOzZ8/m0KFDbN++HYPBwOzZsy3e8+rVqwwfPpxp06ZRunTpXMcqhBBCCCEKHknoxAvh7u7O7du3OX/+vFK2fft2s5G5bLa2towfP55ChQpRqFAhJkyYgFqtNkmW7ty5Q6tWrXB2dsbK6sGPavXq1fH19UWj0QDQuHFjrKysuHDhQq5jjY6O5vTp00yfPh0nJydUKhUBAQGEhoby/fffm9Tt0qULvr6+WFtb8+abb9K2bVu2b9+e63tlP+vIkSPRaDQULlyYKVOmcPnyZSV5jIyMxNnZmU8++QQ7OzvKli3LyJEjWbZsmdJGkyZNqFixIiqVikKFCpmMTD5Mp9MxaNAg+vXrh5eX11PFKYQQQgghCh5ZQydeCGtraxo2bMjOnTupUqUKBoOBXbt2MWjQILPphQB+fn5YW1srx1ZWVvj7+/Pnn3+a1NNqtSbHmZmZLF++nC1btnDp0iXS09NJS0sjJSUl17HGxsbSoEED7OzsTMqbNWvGt99+S1ZWlhJbzZo1TeqUK1eOkydP5vpeAHXr1lUSUniQ4Hl4eHD27Fk8PDw4fvw4DRs2NLnG1dWV+Ph4MjMzUavVJCQkMG/ePI4cOUJSUhJ6vZ7ixYub3WvSpEnUqlWLLl26PFWMQgghhBCiYJIROvHCNG7cmB07dgBw5MgRypYtS9myZS3WdXJyMisrUaIE6enpyrGVlRWOjo4mdSZNmsTq1avp2bMn69evJzY2ljfeeAOj0ZjrOJOSkihVqpRZecmSJdHr9dy8eVMpyx4JzKZWq8nKysr1vQCKFStmVubg4MDdu3eBB+sFIyIi0Gq1yv8CAwNRq9XcuXOHlJQUOnToQGZmJtOmTWPXrl0Wv+O3adMmfv31V0qUKPFU8QkhhBBCiIJLRujEC+Pn58fIkSNJTU0lKioqx+mWANeuXTMru3nzpkkCqFKpUKlUynFGRgaRkZGsX7+eN998EwCj0UhSUtJTxeno6GjxmuvXr6NWqy0mYM/jxo0bZmVJSUn4+/sDYG9vz5AhQ+jWrZvF69esWUO5cuVMvqVn6f2lp6ezbt06evToQWBgIHXq1HlBTyCEEEIIIfIrGaETL4ytrS0+Pj7s2bOH7du307Rp0xzr7tq1S9kABcBgMLBv3z6qVauW4zV3795Fr9dTvnx5pSw6OtpswxW1Wv3YEbuAgAB27txpMhoIsHnzZnx9fbG1tc3x2mcRHR1tMqp3584d4uLicHV1BaB27docOnQox+tv3bpFuXLlTMr27t1rVq9du3a8/fbbjB8/ns8++4z79++/oCcQQgghhBD5lSR04oVq3LgxS5Yswc7OjgoVKuRYr1ixYgwfPpzU1FQyMjKYNGkSxYoVIzAwMMdrSpQoQbly5VixYgVGo5G//vqLL774gsqVK5vUc3Z2Ji4uDoPBoExrfJiHhwc1a9ZkxIgRJCcnYzQa2bJlC/PnzzfZffJFSUtLY9q0aaSmpnLv3j3Gjh2Ln5+fEnf2Bidz5sxRktx//vlHWavn6urK/v37+fvvv9Hr9axdu5azZ8/meL/33nuPd9991+wzEkIIIYQQ4vUjCZ14oQIDAzl79iwtWrRQyjQaDTY2NibHbdq0wcXFheDgYOrWrUtSUhJLly5V6tnY2JiNlKlUKiIiItiyZQteXl58+umnDB8+nAoVKmAwGJR67733HmlpaXh6eirTFDUajcl6uJkzZ1KqVCnl226LFy/m+++/59133zWJ89E1dLa2tmZlT9KpUyesra1p0qQJAQEBaDQak+mTzs7OLFu2jCNHjlC/fn20Wi39+/dXpmp6e3vTv39/evfuTd26ddm9ezfTp083eeZHYx07dix79uwhOjr6qWIVQgghhBAFi8r4NLtJCCGeyqxZs4AH370rKE6cOAHAil3xXEi4+YTaQgghxL9D5bIlmDawdV6H8cKlpaVx+vRpatSoQeHChfM6HPH/Zf8+5uLi8sS6simKEM+oQ4cO/P333zmenzVrFtbW1iafLChIypV0zOsQhBBCiHxD/l0U+ZUkdEI8o9WrVz+xjq+v7yuI5OUY2DEwr0MQQggh8hWDwVBg/1ArXl/yEymEMKPT6cx2ARV5Kz09nVOnTkm/5DPSL/mT9Ev+9Dr0iyRzIj+Sn0ohhEWyvDZ/MRqNpKenS7/kM9Iv+ZP0S/4k/SLEyyEJnRBCCCGEEEIUUJLQCSEsUqlUeR2CeIhKpcLOzk76JZ+RfsmfpF+EEP8msimKEMKMRqPBzs4ur8MQD7Gzs6NmzZp5HYZ4hPRL/vQ69otsxiGEyIkkdEIIi2av2smV6yl5HYYQQvzrlSvpKDsPCyFyJAmdEMKiK9dT5MPiQgghhBD5nIzdi3+FxMREmjZtmtdhCCGEEEII8ULJCJ34V9Dr9eh0uhfaZs2aNSlUqBBWVlbY2tpSvXp1Bg8ejKurq0m9zMxMVqxYwdq1a0lISMDGxgZPT08GDRrE22+/bVK3e/fuHDt2DLVajUqlonjx4vj6+vLxxx9ToUIFpd63337LiRMnWLx4sVlcBoOBLl260KlTJ1q3bv1Cn1kIIYQQQuQvMkInxDPKyspi3bp1xMbGEhUVRWBgIL179yY5OVmpo9Pp6NOnD1u2bGH69OnExsaybds23Nzc6NKlC4cOHTJrc+LEicTGxhITE8PixYtxdHSkbdu2HDhwQKnXp08f/v77bzZt2mQW18qVKwFo1arVS3pyIYQQQgiRX0hCJ8QLYGtrS0hICKVLlyYuLk4pX7ZsGVevXuWHH36gRo0aANjb29OjRw9GjhzJiBEjyMjIyLHd8uXLM2TIECZOnMinn35KamoqAIULF2bs2LHMmDGD+/fvK/Vv3LjBzJkzmTBhgmzXLYQQQgjxLyAJXQHQs2dPoqKiGDBgAD4+Pnh7ezN58mRlCuHGjRsZOnQokZGR+Pv706tXLwCMRiOLFy/mvffeQ6vV0qRJE5YsWWLWfnR0NF26dEGr1aLVahk8eLBybv/+/bRq1Qo3NzeCg4OJiopSzqWmphIWFoa3tzdeXl507txZObdixQoaNGiAp6cnDRs2JDY2NtfPm5aWxvTp06lfvz4eHh74+/sr1+t0Or799lsCAwPRarW8//77/PbbbybXZ2VlMX36dOrWrYuXlxddu3YlPj7e7D4bNmwgKCgINzc32rZty9GjR3MdoyVGo5HU1FRsbW2VsuXLl9OrVy+LnwDIng65ffv2J7YdHBxMpUqV2Lx5s1LWuHFjqlWrxvz585Wy6dOn07JlS6pXr/4cTyKEEEIIIQoKWUNXAOj1eqZMmUJYWBhz5szh2rVrhIaG8s033zBy5EgyMjI4d+4cGo2GqKgo1OoH3RoREcGmTZuYM2cO77zzDvHx8YSFhaHT6QgNDQXgwIEDDBkyhHHjxhEUFISVlRW3bt0C4K+//mLw4MFMnTqVRo0aceTIEQYNGsSKFSuoWrUqs2bNomjRouzZsweNRsO1a9cAOHPmDIsWLWLFihW88cYb3L1796m+ndO3b1+KFi3KmjVrKF26NKmpqdjY2AAwceJELl68yPLlyylXrhzHjh0jLCwMGxsbgoODleeOiorip59+omrVquzatYsxY8ag0WiUe+zdu5cpU6Ywc+ZM3N3d2bZtG/369WPLli04Ojo+Vf8YjUaSkpKIiIigePHieHp6ApCQkEBiYiLNmjWzeJ21tTVNmzbl4MGDSuyP4+/vz+HDh/nggw+UsjFjxtCuXTvatWvHtWvXOHDgAFu2bHmq+IUQQgghRMElI3QFhJeXFy1btgSgTJkyTJkyhdWrVyvT9f766y+GDx+ORqPBysqKu3fvsmjRIsLDw3nnnXcAqFq1KuHh4SxcuFCZujdlyhRGjx5NcHAw1tbWqFQqnJycgAeJUefOnQkKCkKtVuPl5UWnTp2UNVrnz5/Hx8dHSZTKlCmjlFetWpU33ngDgCJFimBvb5+r59y0aRPXr1/n+++/p3Tp0gA4ODhga2vLxYsX2bBhA19//TXlypUD4N1332XkyJF8++23GI1GAFavXs2wYcOoWrUqAAEBAbRt29bkPrNmzSIsLAwvLy/UajVNmzbFz8+PdevWPVW/tGnTBhcXF+rXr8/hw4eJiIhQEuqkpCScnZ0pXLhwjtdXrFiRxMTEXN2rdOnSXL9+3aSsQoUKdO3alalTpzJx4kRGjBiBg4PDUz2DEEIIIYQouCShKyD8/f1NjuvUqYNKpeLy5cvAg2QtOxEDOH36NMWLF6dOnTom17m4uFC0aFHOnDnD+fPnuXDhQo6jQ8eOHaNhw4Zm9z1z5gwA7dq1Y8aMGfz+++8YDAalTt26dbl48SLh4eEmG4Tkxvbt22nevDnW1tZm544cOYKrq6uS6GVr3LgxV69e5dq1a9y+fZvExES8vLxM6tSvX1/576ysLE6ePGn2bK6ursqz5damTZs4evQo//3vf/Hx8aFr167cvn37qdrIraysLGWk8mGhoaGcO3cOR0dH3n///ZdybyGEEEIIkT/JlMsColixYmZlDg4Oykjbo9MEk5KSKFWqlMW2SpUqxbVr15TRuOwRpUclJSXx8ccfm2yuYTAYqFSpEgBBQUFUqlSJr776ivnz5zNmzBh8fX0pVqwYkZGRLFy4kJYtW9KxY0f69+9vMUl7VHJyMt7e3jnGY+mZ1Go1Tk5OXLt2jbJlywJQtGhRkzoPJ7s3btwgMzPTLJHNysqibt26T4zxUTY2NlSsWJExY8bw999/s3r1anr37k3JkiW5ceMG9+7dy3GE8uLFi5QoUSJX97l06ZLyfA/TaDR4eHhQsWLFp45dCCGEEEIUbJLQFRA3b940OTYYDNy8eZPSpUsTHx9vliw5OjqSlJRksa2kpCScnJxwcnLi5s2b6HQ6k/Vl2ezt7Vm6dOljN9ioXr06ixYtYtOmTfTv358NGzZQrlw5HBwcGDJkCJ07d6Znz57Y2toq6/Yex8nJiYSEBIvncnqmrKwsbt68qTyTlZUVd+7cMUmCH57WmJ1c7dixgyJFijwxpqdRs2ZNLl26BEC5cuVwdnZm8+bNtG/f3mLcW7ZsYcCAAU9s12AwsG3bNkaNGvVC4xVCCCGEEAWbTLksIPbu3WtyvG/fPkqWLJnjKJybmxt37twx2UIf4MSJE6SmpuLm5kblypWpVKkSkZGRFtuoXbs2Bw8ezFV8wcHBvPXWWxw7dsykvEyZMnz00Ue5bqdhw4asX7/e4kfA/fz8iIuLUzZfyfbf//6XSpUqUalSJWxsbKhWrZrZ/f744w/lvx0cHKhcuXKuY3oaf/31l7J2EKBjx44sXryYtLQ0s7rr1q3j3r17BAUFPbHdiIgIHBwcCAgIeKHxCiGEEEKIgk0SugLiwIEDrFu3Dr1ez6VLl5g6dSo9evTIcfdIe3t7evfuzahRozh79izwYPfJoUOH0qdPH+zs7FCpVIwYMYIZM2awceNGZR1c9i6XPXr0YPbs2WzduhWdTofBYODo0aPKaFdcXJySqERHRxMfH88777zDpUuXuHLlCvBgZHHjxo3UqlUrV88ZHBxMqVKlGDBggLIBSFpaGhkZGZQvX56WLVsydOhQpf2DBw8yefJkk08t9OzZkxkzZiifKtiwYQP79+83uU+vXr2YNGkSBw4cICsri8zMTPbt26dMYX1aN2/eZObMmRw9etRkA5bevXvj6OhIjx49OHnypPI8y5YtY+rUqUyZMsVsemg2nU5HbGwsgwcPZv369cyaNUu+LSeEEEIIIUzIlMsCYtSoUWzcuJHw8HA0Gg2dO3cmJCQEePBR64e/fZYtNDSUQoUKMXDgQJKTkylRogRdu3blo48+UuoEBgYyd+5cpk+fzueff45KpcLDw4MFCxbg7+9PeHg4c+fOZeTIkajVat5++22mT58OwOLFi4mKisLa2ppy5crxxRdf8Pbbb7N582YmTJhAeno6Dg4ONGvWjIEDB+bqOVUqFUuWLGHGjBm0bNkSnU6HWq1m5syZeHt7M27cOObOncuHH35Iamoqb7zxBmPGjKFp06ZKGy1btuTGjRv06NGD+/fv4+rqypdffslnn32m1OnQoQMGg4FJkyZx7do1NBoNtWvX5vvvv891n1hbW9O8eXMMBgMODg54eXmxcuVKk01bNBoNixcv5ocffmD48OEkJSWhVqtxdXXlhx9+wN3d3azN8ePHM2XKFDQaDdWqVaNJkybMmDHDYh8/fB9LG6YIIYQQQojXm8qYvde7yLdCQkIYOHBgjpuFCPEinThxAoAVu+K5kHDzCbWFEEK8bJXLlmDawNZ5HcZzS0tL4/Tp09SoUeOxn/QRr5b0S/6U/fuYi4vLE+vKCF0BYG1t/VqMvkyfPp01a9bkeL579+65Hsl7mZYtW8bMmTNzPN+kSRPCw8NfYURCCCGEEEJYJiN0QggT2X8R2nXqJleup+RtMEIIIShX0pGBHQPzOoznJiNB+ZP0S/4kI3RCiOf2OvzyIIQQrwuDwZDjRmhCiH83+f8MQggzOp2O9PT0vA5DPCQ9PZ1Tp05Jv+Qz0i/50+vYL5LMCSFyIv/fQQhhkczGzl+MRiPp6enSL/mM9Ev+JP0ihPg3kYROCCGEEEIIIQooSeiEEBbJR8zzF5VKhZ2dnfRLPiP9IoQQIq/JpihCCDMajQY7O7u8DkM8xM7Ojpo1a+Z1GOIRL7JfZNMLIYQQz0ISOiGERbNX7ZTPFgjxirwu29ILIYR49SShE0JYdOV6ChcSbuZ1GEIIIYQQ4jFkbocQQgghhBBCFFCS0AkhhBBCCCFEASUJnXghxo4dy/r16/M6DCGEEEIIIf5VJKETL0RmZiaZmZl5HYYQQgghhBD/KpLQ5VPR0dF06dIFrVaLVqtl8ODBbNiwgdDQULO6TZs2JS4uDoDExEQCAgI4ceIEzZo1w9vbG51Ox4IFC/j222+ZN28ePj4+jB07FoD09HTGjx+Pl5cXnp6ejBgxgtTUVKXtESNGsGzZMgYPHoynpyd169Zl5MiRSp0TJ06g1WrZsGEDEydORKvVcvz48Vw94/Dhw1m0aBF9+/bFw8ODJk2asHPnTlJSUhg0aBDe3t40b96c/fv3m1wXGRlJy5Yt8fT0xM/Pj+HDh3Pnzh0Abt++Td26dTl8+LBSf8OGDXTs2DHX7/7YsWOEhITg7e2Nt7c3ISEh/PXXX8r5NWvW8Nlnn/HFF1/g4+ODr68v8+bNw2Aw8PXXX1OvXj3q16/PDz/8YNJuz549iYqKYsCAAfj4+ODt7c3kyZPR6XQm9fbv30+rVq1wc3MjODiYqKgo5VxO/Zv9nEFBQbi5udG2bVuOHj2a62cWQgghhBAFkyR0+dCBAwcICwvjo48+4uDBg8TExDBhwgR0Op3ZL/+ASbleryctLY358+fz008/ceDAATQaDRkZGezZs4e0tDT27dvHpEmTgAdTJRMSEti8eTM7duzAYDAwceJEpW2VSsV3332Ht7c3e/fuZevWrVy7do2ZM2cC4OLiQmxsLC1atGD8+PHExsbi6uqaq+e0trZmzpw5tGrVikOHDvHNN98watQoRo4cSf369Tl48CDTpk1j+PDhpKSkKNc5ODgwe/ZsDh06xPbt2zEYDMyePRuAYsWKERYWxuTJkzEYDNy9e5fp06crCWxuZGVlMWzYMPbt28eBAwcICgpi+PDhJnFv3boVlUrFrl272LBhAxs2bGDo0KGkpKSwfft2fvnlFyIjIzlw4IBynV6vZ8qUKQQFBREdHc1vv/1GTEwM33zzjVLnr7/+YvDgwfTv31/p91GjRhEfH//Y/t27dy9Tpkxh8uTJxMTEEBoaSr9+/UzemxBCCCGEeP1IQpcPTZkyhdGjRxMcHIy1tTUqlQonJ6dcX3/nzh1atWqFs7OzyUdqr1y5wuDBg7G2tsbKyor4+HiioqKYMWMGJUqUwMHBgXHjxhEVFcWNGzeU67RaLR9++CG2trYUKVKE0NBQtm/f/kKetV69ejRr1gxra2tq165NnTp1UKlUfPDBBwDUqVOHWrVqceTIEeWaJk2aULFiRVQqFYUKFaJdu3bExsYq5z/44AMKFy7Mzz//zMyZM3nvvfdwcXHJdUweHh64urqiVquxsrKiY8eOnDlzhnv37il1ihQpwtChQ7G1taVEiRK0adOG6OhoPv/8czQaDSVLlqRNmzbs2rXLpG0vLy9atmwJQJkyZZgyZQqrV68mIyMDgIiICDp37kxQUBBqtRovLy86derEypUrlTYs9e+sWbMICwvDy8sLtVpN06ZN8fPzY926dbl+biGEEEIIUfDId+jymfPnz3PhwgWCg4Ofqx2tVmtW5ubmhlr9f10eFxeHu7s7jo6OSlmRIkWoWLEi586dw9nZGYAaNWqYtFO2bFkSExOfK75sb7/9tslx8eLFqVatmkmZk5OTyUhTQkIC8+bN48iRIyQlJaHX6ylevLhyXqVSMWnSJEJCQrCxsWHjxo1PFVNaWhqLFy9m586dXLlyBZ1Oh9FoJCUlBXt7ewCqVKli8i6LFy+Oq6srGo3GJO5z586ZtO3v729ynJ3AXr58mapVqyrTPR+t8+OPP5qUPdy/WVlZnDx5klmzZpnUcXV15X//+99TPbsQQgghhChYJKHLZ5KTk3FycjJJFp7EaDSaHFtZWZkkadkeTnrgwXqsgwcPmiV/er1eWZMGYGtra3JerVZjMBhyHd/jWFtbm5UVK1Ysx/opKSl06NCB+vXrM23aNN566y1OnDjByJEjTepVrFiRwoULU6lSJYoUKfJUMQ0aNIh79+4RFhZGnTp1cHBwoFatWibv2VLclt75oyw9m4ODg7ImMSkpiY8//hiVSqWcNxgMVKpUSTl+tH9v3LhBZmam2R8BsrKyqFu37hNjEkIIIYQQBZckdPmMk5MTN2/eRKfTmYz2wINf5B/dSdJoNJpMj4QHI1QPJwQPX/8we3t7AgICmDNnzguK/uX773//S7ly5QgPD1fKrl27ZlZv8eLFvPPOO1y4cIGoqCgaNmyYq/b/+ecfYmJi2Lt3L0WLFgUeJL4vKoG9efOmybHBYODmzZuULl0aeNAnS5cupXr16jm28Wj/Zo8a7tix46mTVyGEEEIIUbDJGrp8pnLlylSqVInIyEizc87OziQkJJiUxcbGWtwoJTdq165NXFwc9+/ff6brH6ZWq81GCl+GW7duUa5cOZOyvXv3mhxfunSJJUuW8PnnnzNixAimTp2qrFHLTftFixZVkjmAPXv2PH/gOcS6b98+SpYsSalSpYAHfXLw4MGnatPBwYHKlSs/9XVCCCGEEKLgk4Qun1GpVIwYMYIZM2awceNGZWTo1q1buLq6cvfuXdauXQvA1atX+eqrryhfvvwz3cvNzY3y5cszbNgwZZQrJSXF7DMBueHs7Mzx48cxGo0mnz140VxdXdm/fz9///03er2etWvXcvbsWZM6U6ZMoWfPnpQtW5bAwEAqVqzI/Pnzc9V+1apVSU9PZ+vWrRiNRg4fPszSpUuV9YTP68CBA6xbtw69Xs+lS5eYOnUqPXr0UEZPe/TowezZs9m6dSs6nQ6DwcDRo0efuGaxV69eTJo0iQMHDpCVlUVmZib79u17qX0hhBBCCCHyniR0+VBgYCBz587lhx9+QKvV4uHhwYgRIyhSpAgzZ85k6dKl+Pn50bdvXwYNGkSpUqWU6Zk2NjZma97gwTo4S+UREREULVqUNm3a4O7uTuvWrTlx4oRyXqPRmE39tHSPdu3acfToUbRardn313Ki0WjM2rF0v4fLvL296d+/P71796Zu3brs3r2b6dOnK4lvVFQUCQkJ9OjRQ7l+5MiR/PTTT1y9evWJMTk4OBAREcHcuXPx9PRk+vTpTJs2jaJFiyr3sPQunxR3tlGjRvHHH39Qt25dOnXqRIsWLUw2QfH39yc8PJx58+Yp38GbMWOGMgqbU/926NCB/v37M2nSJLRaLX5+fixevPiJzyuEEEIIIQo2lfFVzJMTQhASEsLAgQPx9vbO61AeKzuhX7ErngsJN59QWwjxIlQuW4JpA1vndRivjbS0NE6fPk2NGjUoXLhwXocj/j/pl/xJ+iV/yv59LDef3pJNUcQLl5ycTFBQUI5r6lQqFXv37rU40vQ6x2VtbY2Njc1LaVsIIYQQQvw7SUInXjgnJydiYmLyOgwzeR3XkiVL8uzez6JcSce8DkGIfw35vzchhBDPShI6IYRFAzsG5nUIQvyrGAwGs8/LCCGEEE8i/3IIIczodDrS09PzOgzxkPT0dE6dOiX9ks+8yH6RZE4IIcSzkH89hBAWyX5J+YvRaCQ9PV36JZ+RfhFCCJHXJKETQlikUqnyOgTxEJVKhZ2dnfSLEEIIIUzIGjohhBmNRoOdnV1ehyEeYmdnR82aNfM6jNearGETQghREElCJ4SwaPaqnVy5npLXYQjxSpQr6SgbAQkhhCiQJKETQlh05XqKfFhcCCGEECKfk7klQgghhBBCCFFASUInhBBCCCGEEAWUJHRCCCGEEEIIUUBJQideirFjx7J+/fq8DkMIIYQQQojXmiR04qXIzMwkMzMzr8MQQgghhBDitSYJXQERHR1Nly5d0Gq1aLVaBg8ezIYNGwgNDTWr27RpU+Li4gBITEwkICCAEydO0KxZM7y9vdHpdCxYsIBvv/2WefPm4ePjw9ixYwFIT09n/PjxeHl54enpyYgRI0hNTVXaHjFiBMuWLWPw4MF4enpSt25dRo4cqdQ5ceIEWq2WDRs2MHHiRLRaLcePH8/VM44bN46ff/6ZSZMm4e3tzZw5c9Dr9dSuXZsdO3YQFBSEh4cHvXr1Ijk5mX379vH+++/j7e3NwIEDSU5OVtq6du0aH3/8MV5eXnh7ezN06NBcv+tjx44REhKCt7c33t7ehISE8Ndffynn16xZw2effcYXX3yBj48Pvr6+zJs3D4PBwNdff029evWoX78+P/zwg0m7PXv2JCoqigEDBuDj44O3tzeTJ09Gp9OZ1Nu/fz+tWrXCzc2N4OBgoqKilHM59SfAhg0bCAoKws3NjbZt23L06NFcP7MQQgghhCiYJKErAA4cOEBYWBgfffQRBw8eJCYmhgkTJqDT6cySAcCkXK/Xk5aWxvz58/npp584cOAAGo2GjIwM9uzZQ1paGvv27WPSpEnAg6mSCQkJbN68mR07dmAwGJg4caLStkql4rvvvsPb25u9e/eydetWrl27xsyZMwFwcXEhNjaWFi1aMH78eGJjY3F1dc3Vc+p0OlavXs1bb73FwYMH6devHzY2Nuj1eubOncuiRYs4dOgQNWvWZPTo0XzxxRfMnj2bffv2UalSJaZNm6a0NWXKFNzd3YmOjiY6OprPPvss1+87KyuLYcOGsW/fPg4cOEBQUBDDhw9XzltbW7N161ZUKhW7du1iw4YNbNiwgaFDh5KSksL27dv55ZdfiIyM5MCBA8p1er2eKVOmEBQURHR0NL/99hsxMTF88803Sp2//vqLwYMH079/f6WfR40aRXx8/GP7c+/evUyZMoXJkycTExNDaGgo/fr1IyUlJdfPLYQQQgghCh5J6AqAKVOmMHr0aIKDg7G2tkalUuHk5JTr6+/cuUOrVq1wdnbGyur/uvzKlSsMHjwYa2trrKysiI+PJyoqihkzZlCiRAkcHBwYN24cUVFR3LhxQ7lOq9Xy4YcfYmtrS5EiRQgNDWX79u0v5FkzMzPp0qULgEmsAwYMoEKFClhbW9OjRw927NjB4MGDqVSpEmq1ml69erF7926l/vnz5/H398fKygqVSkXp0qVzHYOHhweurq6o1WqsrKzo2LEjZ86c4d69e0qdIkWKMHToUGxtbSlRogRt2rQhOjqazz//HI1GQ8mSJWnTpg27du0yadvLy4uWLVsCUKZMGaZMmcLq1avJyMgAICIigs6dOxMUFIRarcbLy4tOnTqxcuVKpQ1L/Tlr1izCwsLw8vJCrVbTtGlT/Pz8WLduXa6fWwghhBBCFDyS0OVz58+f58KFCwQHBz9XO1qt1qzMzc0Ntfr/vi0fFxeHu7s7jo6OSlmRIkWoWLEi586dU8pq1Khh0k7ZsmVJTEx8rviyeXp6Wix/6623lP/OTmYfruvk5GQyGtWhQwdGjBhhllDlRlpaGrNnz6Z9+/b4+vri4+OD0Wg0ab9KlSom76548eK4urqi0WhyjAnA39/f5LhOnTqoVCouX74MPJju2bBhQ7M6Z86cMSl7uD+zsrI4efKk2XWurq5m1wkhhBBCiNeL+slVRF5KTk7GycnJJHl4EqPRaHJsZWVlkqRlK168uMlxYmIiBw8eNEv+9Ho9d+7cUY5tbW1NzqvVagwGQ67je5xHY3r4Ho+y9EzZunXrRo0aNfj666+JiIhg3Lhx1KxZM1cxDBo0iHv37hEWFkadOnVwcHCgVq1aJu/V2tr6qeLJVqxYMbMyBwcHZQ1iUlISH3/8MSqVSjlvMBioVKmScvxof964cYPMzEyzpD8rK4u6des+MSYhhBBCCFFwSUKXzzk5OXHz5k10Op3J6A88+MX+0Z0kjUajyfRIeLDu7eEE4eHrH2Zvb09AQABz5sx5QdE/vUdjeh5eXl6sWrWKH3/8UdmQpHDhwo+95p9//iEmJoa9e/dStGhR4EGi+6IS1ps3b5ocGwwGbt68qUwJtbe3Z+nSpVSvXj3HNh7tT3t7ewB27NhBkSJFXkicQgghhBCiYJApl/lc5cqVqVSpEpGRkWbnnJ2dSUhIMCmLjY21uFFKbtSuXZu4uDju37//TNc/TK1Wm40U5pUePXpgZWXF2bNnn1j31q1bFC1aVEnmAPbs2fPCYtm7d6/J8b59+yhZsiSlSpUCHvTBwYMHn6pNBwcHKleu/NTXCSGEEEKIgk8SunxOpVIxYsQIZsyYwcaNG5WRolu3buHq6srdu3dZu3YtAFevXuWrr76ifPnyz3QvNzc3ypcvz7Bhw7h27RoAKSkp7N+//6nbcnZ25vjx4xiNRpPPHrwqR44cQafTYTQa2bhxIxkZGSbTFnNStWpV0tPT2bp1K0ajkcOHD7N06VKcnZ1fSFwHDhxg3bp16PV6Ll26xNSpU5WEEx4kn7Nnz2br1q3odDoMBgNHjx594hrFXr16MWnSJA4cOEBWVhaZmZns27cvT969EEIIIYR4dSShKwACAwOZO3cuP/zwA1qtFg8PD0aMGEGRIkWYOXMmS5cuxc/Pj759+zJo0CBKlSqlTM+0sbExW/MGD9bBWSqPiIigaNGitGnTBnd3d1q3bs2JEyeU8xqNxmzqp6V7tGvXjqNHj6LVas2+x5YTjUZjMaZChQphY2NjUmZp6uTDZV9++SXu7u5otVp+/PFHIiIiclyf9zAHBwciIiKYO3cunp6eTJ8+nWnTplG0aFElmbb07iy9F0tlo0aN4o8//qBu3bp06tSJFi1aEBISopz39/cnPDycefPmKd/BmzFjhjLqmlN/dujQgf79+zNp0iS0Wi1+fn4sXrz4ic8rhBBCCCEKNpUxv8yLE+I1FxISwsCBA/H29s7rUB4rO4FfsSueCwk3n1BbiNdD5bIlmDaw9VNfl5aWxunTp6lRo8YT1+iKV0f6JX+SfsmfpF/yp+zfx1xcXJ5YVzZFES9dcnIyQUFBOa6pU6lU7N271+LI0+sUh7W1tdlIY35WrqRjXocgxCsjP+9CCCEKKknoxEvn5ORETExMXoeR53EsWbIkz+79LAZ2DMzrEIR4pQwGwwvdaVcIIYR4FeRfLiGEGZ1OR3p6el6HIR6Snp7OqVOnpF9eIknmhBBCFETyr5cQwiJZXpu/GI1G0tPTpV+EEEIIYUISOiGEEEIIIYQooCShE0JYpFKp8joE8RCVSoWdnZ30ixBCCCFMyKYoQggzGo0GOzu7vA5DPMTOzo6aNWvmdRivBdn8RAghxOtEEjohhEWzV+3kyvWUvA5DiBeqXElH2cFVCCHEa0USOiGERVeup8iHxYUQQggh8jmZcyJeKp1OR/v27fnzzz/zOhSFm5tbrusmJiY+VX0hhBBCCCFeJRmhEy+VRqNh7dq1eR2GibS0tFzX1ev1ZGRkvMRohBBCCCGEeHYyQicsioiIICIi4qmv69GjB4cPH34JEQkhhBBCCCEeJSN0wqLMzMxnvu5ZrxVCCCGEEEI8HRmh+5fbunUrQUFBaLVa6tWrx6ZNm/Dy8mLBggUsWLAArVbL5s2bAdi5cycdOnTA29sbHx8f+vbty7Vr1wD4448/0Gq1HD58mL59++Lp6UliYiLwYM1a9n8DnD9/Xqnj5eVF3759uXDhgnL+0qVLNGnShI0bN9KkSRPc3d15//332bVr11M/38KFCwkMDESr1fLBBx8QGxtrVmf//v20atUKNzc3goODiYqKyrG98+fPExoaiq+vL15eXrRr105p89q1a3h4eHD37l2lvk6nw9/fX3lPTzJz5kz8/f3x9PQkKCiIixcvKuc2bNhAUFAQbm5utG3blqNHj5pcm5yczNixY6lbty5arZaAgAAuXbqUq/sKIYQQQoiCSUbo/sWSk5MZP348y5Yt45133iE9PR2dTsehQ4eYNWsWAIMGDVLqW1tbM2XKFN566y0MBgPffPMNkydPZs6cOTRp0oQmTZoQEhLCwIED8fb2Vq7LyMhAr9cDkJKSwocffki3bt34/vvvUalULFu2jI8++ohNmzZRtGhRVCoV165dY+nSpcyfP5/KlSuzc+dOPvnkE/744w9KlSqVq+dbt24dixcvJiIignfffZcTJ04wdOhQkzp//fUXgwcPZurUqTRq1IgjR44waNAgVqxYQdWqVc3azMjIoEePHnh4eKDRaNi2bRthYWFERUVRpkwZqlWrxvbt22ndujUAu3fvplKlSpQpU+aJ8W7fvp1du3bx+++/U7x4cZKTk3FwcABg7969TJkyhZkzZ+Lu7s62bdvo168fW7ZswdHREZ1Ox4cffoi3tzebN2+mWLFipKSkUKRIkVy9KyGEEEIIUTDJCN2/2OXLl3FycuKdd94BHny4uFixYjnWr1evHu+88w5WVlao1Wo6duxoccTrcZYtW0bt2rXp06cPtra2aDQaPv74Y2rVqsWyZcuUehkZGYwdO5YqVaqgUqlo0KABNWrUYP/+/bm+1+rVq+nTpw/vvvsuAC4uLvTq1cukTkREBJ07dyYoKAi1Wo2XlxedOnVi5cqVFtusXr06vr6+aDQaABo3boyVlZUywtiyZUu2bNmi1F+/fj0tW7bMVbznz5/HxcWF4sWLA+Dk5KTcZ9asWYSFheHl5YVaraZp06b4+fmxbt06AJYuXUqZMmWYMGGC0oeOjo5YW1vn6t5CCCGEEKJgkoTuX6xGjRoULlyY4cOHk5CQ8MT6t27dYvr06bRu3Rpvb29at25NSkrKU90zJiaG4OBgs/JmzZoRExOjHKtUKmrUqGFSp2zZsiZTN5/k7NmzeHl5mZQFBASYHB87doyGDRualNWpU4czZ85YbDMzM5OlS5fSuXNn/P398fDw4Pr168p7aNq0KYcOHeLu3bukpqayf/9+mjZtmqt4mzRpwvbt25k7dy737t1TyrOysjh58qRZnK6urkqc27dvz3XiKIQQQgghXh8y5fJfzMbGhhUrVvCf//yHjh070qRJE4YNG4adnZ1Z3czMTLp27Uq5cuUYO3Ys1atX5/bt22ZJxpMkJSVZnDJZqlQpk3VmVlZW2NjYmMWblZWV63tlZGSYTTl0cnIyi+fjjz9GpVIpZQaDgUqVKllsc9KkSRw+fJiwsDA8PDwoXrw4jRo1wmg0Ag9GxXx9fdm2bRtGoxEfH5/Hjno+rGLFiqxbt445c+bQtGlT+vTpw0cffcSNGzfIzMw0S4SzsrKoW7cu8GD6bG6nogohhBBCiNeHJHT/chqNhp49e9K+fXv69evHF198wcSJE83qxcTEcOfOHX799VdlGt+zfCy8ePHiJCUlmZUnJSWZJVvPy9nZ2WSDEsBscxJ7e3uWLl1K9erVn9heRkYGkZGRrF+/njfffBMAo9Fo9jzvv/8+69atQ6/X06lTp6eOefz48XTu3JmePXvi5ORE/fr1AdixY0eOa+KcnJxyNcoqhBBCCCFeLzLlUgBQtGhRQkNDOXjwIABqtVoZdYIH0y3LlCljsiZr7969Zu08et2jAgIC2LRpk1n55s2badCgwfM8gplatWopz5Ptjz/+MDmuXbu2WZ2c3L17F71eT/ny5ZWy6OhoZcOXbA0aNCAuLo5Tp04RGBj4TLG/8847tGrViujoaBwcHKhcufJj42zYsCG//PLLM91LCCGEEEIUXJLQ/YvduHGD+Ph4AFJTU4mMjKRWrVrAg5GiuLg4DAYDd+/epXbt2vz5558cOXIEg8FAVFQUe/bsMdt0w9nZmWPHjmEwGEhNTTW754cffsipU6eYN28eOp0OnU7H999/z+nTp+nSpcsLfb4ePXqwYMECjh07BjxIvtavX29WZ/bs2WzduhWdTofBYODo0aMW1+qVKFGCcuXKsWLFCoxGI3/99RdffPEFlStXNqlna2tLQEAA7733nrKpSW7Ex8dz48YN4MGGNVFRUUp/9OrVi0mTJnHgwAGysrLIzMxk3759yjsOCQnhxo0bjBo1ijt37gBw586dp5qiKoQQQgghCh5J6P7FLl68SLdu3XBxcaFRo0ZoNBrGjx8PwHvvvUdaWhqenp6Eh4dTsWJFpk2bxujRo/Hy8uKnn37i22+/RaPRmIxQhYSEsHbtWnx8fNi4cSPwIMHJXg9XtGhRVqxYQVxcHP7+/tSrV4+zZ8+yfPlyZYt+GxsbbG1tzeLVaDRPlSBptVpGjRrF8OHD0Wq1fP/993z77bcUKlRIqePv7094eDjz5s3D29sbb29vZsyYgU6nM4tFpVIRERHBli1b8PLy4tNPP2X48OFUqFABg8Fgcu+rV68+9SYle/bsoUmTJrz77rt06dKF5s2b07FjRwA6dOhA//79mTRpElqtFj8/PxYvXqxca2dnx9q1a9Hr9TRu3BgPDw+aNGki36ETQgghhHjNqYyPmx8nhHgqBoOBY8eOMW7cOH7//XeTzVYKihMnTgCwYlc8FxJu5nE0QrxYlcuWYNrA1i+svbS0NE6fPq3sGizyB+mX/En6JX+Sfsmfsn8fc3FxeWJd2RRFFDjJyckEBQXluFZPpVKxd+9ei6N8L5NOp8PPz4+iRYvy1VdfmSRz27ZtY+TIkTle6+Liwo8//vgqwhRCCCGEEK8RSehEgePk5GTyzbr8QqPR5BhX48aNn/oj7HmtXEnHvA5BiBdOfq6FEEK8biShE0JYNLBjYF6HIMRLYTAYsLKSJeRCCCFeD/IvmhDCjE6nIz09Pa/DEA9JT0/n1KlT0i8vgCRzQgghXifyr5oQwiLZLyl/MRqNpKenS78IIYQQwoQkdEIIIYQQQghRQElCJ4SwqCB+cuF1plKpsLOzk34RQgghhAnZFEUIYUaj0WBnZ5fXYYiH2NnZUbNmzbwO45WTDUyEEEKIx5OETghh0exVO7lyPSWvwxD/YuVKOspuq0IIIcQTSEInhLDoyvUULiTczOswhBBCCCHEY8g8FvHK7d69m969e+d1GIr8Fo8QQgghhBC5JSN04pWrX78+9evXz+swFPktHiGEEEIIIXJLRujEv0piYiJBQUF5HYYQQgghhBAvhCR04l9Fr9ej1+vzOgwhhBBCCCFeCEnoCoiePXsSFRXFgAED8PHxwdvbm8mTJ6PT6QDYuHEjQ4cOJTIyEn9/f3r16gWA0Whk8eLFvPfee2i1Wpo0acKSJUvM2o+OjqZLly5otVq0Wi2DBw9Wzu3fv59WrVrh5uZGcHAwUVFRyrnU1FTCwsLw9vbGy8uLzp07K+dWrFhBgwYN8PT0pGHDhsTGxgKwefNmJT6AESNGsGzZMgYPHoynpyd169Zl5MiRpKammsT4xx9/0KJFC9zc3HjvvfdYt24dPXv25MiRI7l6h4MHD6Zly5YkJCSg1WoZMGCAxXiGDx/OokWL6Nu3Lx4eHjRp0oSdO3eSkpLCoEGD8Pb2pnnz5uzfv9+k/fT0dMaPH4+Xlxeenp6MGDHC7Bke5+zZs3Tu3BlPT0+8vb2ZPn26ci45OZlPP/0UDw8PfHx8CA8PN0tMt2zZQtu2bfHw8MDT05Np06bl+t5CCCGEEKJgkjV0BYRer2fKlCmEhYUxZ84crl27RmhoKN988w0jR44kIyODc+fOodFoiIqKQq1+0LURERFs2rSJOXPm8M477xAfH09YWBg6nY7Q0FAADhw4wJAhQxg3bhxBQUFYWVlx69YtAP766y8GDx7M1KlTadSoEUeOHGHQoEGsWLGCqlWrMmvWLIoWLcqePXvQaDRcu3YNgDNnzrBo0SJWrFjBG2+8wd27d5VvSel0OiURhQcfTP7uu+8YOnQoM2bMQKfTMWjQIGbOnMno0aMBiIuLY8SIEUydOpX33nuPa9euMXjwYC5evJjrEbeZM2dy+fJlunbtapKUPhqPtbU1c+bMITw8nDlz5nD69Gl69+6Nq6srjRo1YtasWcTFxdGvXz82btyIo6MjAGPHjuXOnTts3rwZW1tbJk6cyMSJE5kxY0au4hsxYgTdunWjdevWZGVlcfPm/+0wOWDAAKpUqcLOnTvR6/UMGTKEOXPmEBYWBsC6dev47rvvmDZtGr6+vhiNRm7fvp2r+wohhBBCiIJLRugKEC8vL1q2bAlAmTJlmDJlCqtXryYjIwN4kHwNHz4cjUaDlZUVd+/eZdGiRYSHh/POO+8AULVqVcLDw1m4cKEyejRlyhRGjx5NcHAw1tbWqFQqnJycgAcJYefOnQkKCkKtVuPl5UWnTp1YuXIlAOfPn8fHxweNRqPElV1etWpV3njjDQCKFCmCvb19js+m1Wr58MMPsbW1pUiRIoSGhrJ9+3bl/KpVq2jfvj3BwcHY2NhQoUIFwsPDuXfv3gt7vw+rV68ezZo1w9ramtq1a1OnTh1UKhUffPABAHXq1KFWrVrK6GB8fDxRUVHMmDGDEiVK4ODgwLhx44iKiuLGjRu5uuf58+epV68e8CCpLFWqFAB79uzh6tWrTJo0iSJFiuDk5MTEiRNZuXIler2etLQ0vvzyS7755hvq1q2LSqXCysqK4sWLv4Q3I4QQQggh8hNJ6AoQf39/k+PsJOPy5cvAg2QtOxEDOH36NMWLF6dOnTom17m4uFC0aFHOnDnD+fPnuXDhAsHBwRbveezYMRo2bGh23zNnzgDQrl07ZsyYwe+//47BYFDq1K1bl4sXLxIeHk5ycvITn61GjRomx2XLliUxMdHkWTw9Pc2uyU4YX7S3337b5Lh48eJ4eXmZlDk5OZGSkgI8GEF0d3dXRuvgQRJbsWJFzp07l6t7dujQgX79+nH06FGT8uPHj1O/fn1l1BWgcuXKGI1Grl27xpEjR7Czs8PDw+MpnlAIIYQQQrwOZMplAVKsWDGzMgcHB2Wk7eFkAiApKUkZ5XlUqVKluHbtmjIa93Cy8GgbH3/8MSqVSikzGAxUqlQJgKCgICpVqsRXX33F/PnzGTNmDL6+vhQrVozIyEgWLlxIy5Yt6dixI/3798fa2trifWxtbU2O1Wq1SYJ49+5di8+f0/M9L0txWrp/tsTERA4ePIhWqzUp1+v13LlzJ1f3HD16NNu2bWPs2LGULVuWcePGUaFCBRITE/ntt9/YtGmTSX2dTsft27dJTk5+ae9BCCGEEELkb5LQFSAPr6mCB4nVzZs3KV26NPHx8WZJiKOjI0lJSRbbSkpKwsnJCScnJ27evIlOp1OmTT7M3t6epUuXUr169Rzjql69OosWLWLTpk3079+fDRs2UK5cORwcHBgyZAidO3emZ8+e2NraKuv2npa9vb3Z84P5O8kr9vb2BAQEMGfOnOdqp3HjxjRs2JBp06bRt29fNmzYgL29PZ06dVLWEz4qJSWFq1evPtd9hRBCCCFEwSRTLguQvXv3mhzv27ePkiVL5jg64+bmxp07d4iLizMpP3HiBKmpqbi5uVG5cmUqVapEZGSkxTZq167NwYMHcxVfcHAwb731FseOHTMpL1OmDB999FGu27FEq9WaPf+ZM2e4cuWKstlKbtjY2GA0Gp85jpzUrl2buLg47t+//9xtWVlZMXToUM6dO8eNGzeoXbs2sbGxOcbt5ubG/fv32bNnz3PfWwghhBBCFCyS0BUgBw4cYN26dej1ei5dusTUqVPp0aNHjgmNvb09vXv3ZtSoUZw9exZ4kAQNHTqUPn36YGdnh0qlYsSIEcyYMYONGzcq0xyzd7ns0aMHs2fPZuvWreh0OgwGA0ePHlXWt8XFxZGWlgY8+PRBfHw877zzDpcuXeLKlSvAg1G0jRs3UqtWrWd+9uxNUtatW0dmZibx8fGMHDkSOzs7s6mmj+Po6MitW7e4cuUK9+7dIysr65ljepibmxvly5dn2LBhyk6fKSkpZp82yElWVhZHjhwhKyuLrKwsli9fzhtvvEHx4sVp3Lgxd+/eZeLEicp6xMTEROUzEPb29oSFhTFixAjlfgaDQVnfJ4QQQgghXl8y5bIAGTVqFBs3biQ8PByNRkPnzp0JCQkBHqxBe3QdGjxIhAoVKsTAgQNJTk6mRIkSdO3alY8++kipExgYyNy5c5k+fTqff/45KpUKDw8PFixYgL+/P+Hh4cydO5eRI0eiVqt5++23lW+kLV68mKioKKytrSlXrhxffPEFb7/9Nps3b2bChAmkp6fj4OBAs2bNGDhwIAAajcZkeuejx/BgJO3h5ylVqhT/+c9/+Pzzz5k0aRIlS5akV69ezJ49mypVquT6Hdra2tKrVy/atm2Lvb09q1atshjPo+/SUoyPlkVERPDll1/Spk0bMjIyKFq0KJ07d6Zu3bpPjOv+/fuMHj2ay5cvU6hQIWrWrMn8+fNRq9Wo1WqWLl3KF198QVBQEAaDgRIlShAaGqqs2evcuTOOjo5MnTqVq1evolKpaNGiBRMnTsz1uxFCCCGEEAWPyvgy5p+JFy4kJISBAwfi7e2d16HkiTt37nDs2DHlEwmXL19m8uTJeHt707Nnz7wO77Vy4sQJAFbsiudCQv5Yoyj+nSqXLcG0ga3zOozHSktL4/Tp09SoUYPChQvndTji/5N+yZ+kX/In6Zf8Kfv3MRcXlyfWlRG6AsLa2hobG5u8DiPP6HQ6Fi5cyLBhw8jMzKRUqVKEhITw4YcfArBt2zZGjhyZ4/UuLi78+OOPrypcMx06dODvv//O8fysWbPw9fV9hREJIYQQQojXgSR0BcSSJUvyOoQ85ezszE8//ZTj+caNGytryvKj1atX53UIT61cSce8DkH8y8nPoBBCCPFkktAJISwa2DEwr0MQAoPB8FQ72QohhBD/NvKvpBDCjE6nIz09Pa/DEA9JT0/n1KlT/7p+kWROCCGEeDz5l1IIYZHsl5S/GI1G0tPTpV+EEEIIYUISOiGERSqVKq9DEA9RqVTKtyOFEEIIIbLJGjohhBmNRoOdnV1ehyEeYmdnR82aNfM6jCeSNW9CCCHEqyUJnRDCotmrdnLlekpehyEKkHIlHWUzHSGEEOIVk4ROCGHRlesp8mFxIYQQQoh8TubFCCGEEEIIIUQBJQldHrl+/TqtW7fm5s28GwFp2rQpcXFxeXb/Z9GrVy82b978Su+5YcMGxo0b90rvKYQQQgghRG7IlMs8UrJkSX799dc8jUGn06HT6fI0hqeVFzEXxPckhBBCCCH+HWSEroBZv349Y8eOferrxo4dy/r1619CRAVXjx49OHz4cIFrWwghhBBCiGyS0BUwmZmZZGZmvrLrXmcv853I+xZCCCGEEK+CJHTPqGfPnkRFRTFgwAB8fHzw9vZm8uTJytS85ORkPvnkE/z8/PD09KR58+Zs27ZNuf7GjRvUqVNHOf7ll18YPXo033zzjXJN165dOX/+PPDg207+/v5MnDiRDRs2oNVqWbx48RPjPHHiBFqtlg0bNjBx4kS0Wi3Hjx9XzkdHR9O2bVvc3NwIDAxk7ty5GI1GkzaWLl1KgwYNcHNzIyQkhPj4eOXcxo0bGTp0KJGRkfj7+9OrVy8AunTpwpo1a/jwww9xd3enZcuWHD9+nMuXL9O9e3e8vb1p3749p06dMrnXwoULadq0KZ6entSrV48pU6Y813THmTNn4u/vj6enJ0FBQVy8eJE//vgDrVbL4cOH6du3L56eniQmJgIP+mXw4MG4u7tTt25dRo0axd27d3N9v8e1nZyczKeffoqHhwc+Pj6Eh4ej1+uVa5/lnSUmJvL+++8TFRXF+++/j1arpVmzZmzZsuWZ35kQQgghhCg4JKF7Rnq9nilTphAUFER0dDS//fYbMTExfPPNNwCkp6fTsmVLtm/fTkxMDOHh4YwZM4br168DD0ZwMjIylPZUKhWbN28mKSmJjRs3sn//flxdXfn0008xGo1YWVmxd+9exo8fT4sWLYiNjaVnz55PjNPFxYXY2FhatGjB+PHjiY2NxdXVVTm/evVqRo8ezeHDh1myZAkrV65k165dJueXLVtGREQEhw4dIigoiNDQUGX0KSMjg3PnzhETE0NUVBQLFy4EwNramu+++45BgwZx+PBhPv30U4YPH85nn31Gjx49OHjwIP3792fQoEEmI1mlS5dm6dKlxMTEsHHjRs6ePcvKlSufqY+2b9/Orl27+P3334mJiWHlypW88cYbNGnShNjYWDw8PJg3bx4xMTGULl0agCFDhqDX64mKimLPnj1UqlSJmTNn5vqej2t7wIABFCpUiJ07d7Jp0yb+/PNP5syZo1z7LO9Mr9dz48YNvvvuOyIiIoiNjeWTTz5hxIgRnD59+pnemxBCCCGEKDgkoXsOXl5etGzZEoAyZcowZcoUVq9eTUZGBuXKlaNRo0YUKlQIAFdXV958801OnjyZY3sajYaJEyfi6OiIjY0NAwYM4OzZs1y9evWlPcPAgQPRarVYWVlRuXJl2rRpQ1RUFPBgVHD27NlMmDCB6tWrY2Njw0cffUTJkiWVOgB//fUXw4cPR6PRYGX1fz9SLVu2xMfHB5VKRWBgIBqNhpo1axIQEABAw4YNKVSokMmIX8uWLZUEqGjRorz//vvPvBbt/PnzuLi4ULx4cQCcnJzQaDQ51r948SJHjhxh6tSpODo6Ym1tTd++fXn77bef6f4P27NnD1evXmXSpEkUKVIEJycnJk6cyMqVK01G6Z7lnSUnJzNmzBjKly8PPNi9tE2bNixfvvy54xZCCCGEEPmbJHTPwd/f3+S4Tp06qFQqLl++jNFoZN26dXTr1o369evj7u5OXFwcKSkpObb35ptvYmtrqxwXKlQIJycnZcrey1ClShWT47Jlyyr3u3r1Krdv36Zu3bomdVxdXTlz5oxyXLVqVZycnMzafjQRKl68OF5eXiZlTk5OJu/kr7/+YtiwYQQFBaHVapkyZQq3bt16pmdr0qQJ27dvZ+7cudy7d++J9c+ePWvxWerXr/9M93/Y8ePHqV+/Pmr1/20sW7lyZYxGI9euXVPKnuWdaTQaszp+fn6cPXv2ueMWQgghhBD5m3y24DkUK1bMrMzBwYHU1FQWLlzIf/7zH4YOHYqvry8lSpSge/fuZuvTHvZwMpfNxsaGrKysFxr3w1QqlcmxRqNRpoImJiai0+nw9vY2qZOVlUX79u2VY0dHR4ttW1tbm5VZemfZLly4QMeOHencuTOhoaFUqlSJTZs2ERkZmdvHMVGxYkXWrVvHnDlzaNq0KX369OGjjz7Ksf79+/cpWrSoWbmTkxP//PPPM8WQLTExkd9++41NmzaZlOt0Om7fvk2FChWAp39n2ecf7UcHB4enWvsnhBBCCCEKJknonsOjHwU3GAzcvHmT0qVLs3z5cqZOnUq9evWU8y9zpO1lsLe3p0iRIhw6dOix9SwlIc/i119/JTAwkOHDhytlD49ePQtnZ2fGjx9P586d6dmzJ05OTgQHB+dY986dO2blzxsDPHiXnTp1YvTo0c/d1qNu3bqFwWAwme6amJioTF0VQgghhBCvL5ly+Rz27t1rcrxv3z5KlixJqVKluHXrFuXKlVPOXbx4kUuXLj33PdVq9WNH+V7kdVWqVCEzM9NkeuXL9Og7A/N3/KzeeecdWrVqRXR0tFL26DupVq0a//zzj1mi/vDupLn1aNu1a9cmNjb2mfruSTIzM02eC2D37t28++67L/xeQgghhBAif5GE7jkcOHCAdevWodfruXTpElOnTqVHjx5YWVnh6urKqlWryMzMJCEhgZEjR1K1atXnvmfJkiU5deoUOp3O4mhSTpydnTl+/DhGo5HU1NRcXaPRaAgJCWHYsGGcOHECo9GITqd7pgQnN1xdXfnjjz9ISkri/v37zJs376me8VHx8fHcuHEDgMuXLxMVFUWtWrWU887Ozhw7dgyDwUBqaipOTk60aNGCMWPGcPv2bfR6PVOnTuX+/ftPfe9H227cuDF3795l4sSJJCcnAw9G0WJjY5/5+bIVLlyYL7/8Utko5bfffmP37t107tz5udsWQgghhBD5myR0z2HUqFH88ccf1K1bl06dOtGiRQtCQkIA+PLLLzl37hx+fn5069aNzp074+HhoayHU6vVJmvmbG1tLe7AqNFoTMo9PDwoX748vr6+DBo0KNextmvXjqNHj6LVavnhhx8stm0pjk8++YTmzZsTFhaGu7s7AQEB/Pbbbyb1La3902g0ZuWW7vdwWevWrWnatCkdOnQgICCAy5cvM3r0aJM1hJbayMmePXto0qQJ7777Ll26dKF58+Z07NhROR8SEsLatWvx8fFh48aNAIwZMwZnZ2caN25MQEAARqORvn375vqeObVdqFAhli5dSnJyMkFBQXh4eBASEsKFCxee653Bg41TBg8erHw/78cff2TevHky5VIIIYQQ4l9AZXwZc8D+BUJCQhg4cKDZhiFCvEqXL1+ma9euJp+ReF4nTpwAYMWueC4k3HxCbSH+T+WyJZg2sHVeh/FKpaWlcfr0aWrUqEHhwoXzOhzx/0m/5E/SL/mT9Ev+lP37mIuLyxPryqYoz8ja2hobG5u8DoMOHTrw999/53h+1qxZ+Pr6vsKIXo1t27YxcuTIHM+7uLjw448/vvD7+vv75zgFU6VSsXr1arNPQbxMarX6pf0clivp+FLaFa8v+ZkRQgghXj1J6J7RkiVL8joEAFavXp3XIeSJxo0bv5D1Z0/rRW3S8qKUKVOGrVu3vpS2B3YMfCntitfbozuuCiGEEOLlkn91hRBmdDod6enpeR2GeEh6ejqnTp3K9/0iyZwQQgjxasm/vEIIi2R5bf5iNBpJT0+XfhFCCCGECUnohBBCCCGEEKKAkoROCGGRSqXK6xDEQ1QqFXZ2dtIvQgghhDAhm6IIIcxoNBrs7OzyOgzxEDs7O2rWrJkn95aNToQQQoj8SxI6IYRFs1ft5Mr1lLwOQ+SxciUdZcdTIYQQIh+ThE4IYdGV6ynyYXEhhBBCiHxO5tAIIYQQQgghRAElCZ0QTykxMRE3N7e8DkNx7NgxmjdvntdhCCGEEEKIPCAJnRBPSa/Xk5GRkddhKDIyMtDpdHkdhhBCCCGEyAOS0AlRwLi6uuZ1CEIIIYQQIp+QhE6IAub+/ft5HYIQQgghhMgnJKETr8RPP/1Ew4YN8fT0pGPHjhw7dgwvLy/l/IYNGwgNDTW7rmnTpsTFxSnHly5donfv3ri5ueHv709ERIRJ/aCgIJP6ABs3bjRre+nSpTRo0AA3NzdCQkKIj49/rufbsGEDQUFBuLm50bZtW44ePaqci46Opnv37ixdupTAwEA8PDxo3769WZxXr16lX79+uLm54e3tzZgxY/j555/5/PPPAZg2bRparRYArVZL27ZtlWsNBgPfffcd9erVw93dnbZt23Ls2LHneiYhhBBCCJH/SUInXrpt27Yxe/Zsvv32W2JiYhg7diyjRo0yWYem0+ksrgN7uFyn09G9e3dq167N/v37Wb16NZs2bWLNmjVKfb1eb9bOo2vMVq9ezbJly4iIiODQoUMEBQURGhpKZmbmMz3f3r17mTJlCpMnTyYmJobQ0FD69etHSkoKACqViuPHj7Nnzx5WrVpFTEwMbdu2pX///spom8FgoG/fvhQpUoQdO3awY8cOnJ2d+fLLL9Hr9QCMGjWK2NhYAGJjY4mMjFRiuHz5MufPn2fdunXExsbSvn17Bg0apFwrhBBCCCFeT5LQiZfuP//5D3379lXWfrm4uNCrV6+nnjoYGRmJs7Mzn3zyCXZ2dpQtW5aRI0eybNmyXLdhMBiYPXs2EyZMoHr16tjY2PDRRx9RsmRJoqKiniqebLNmzSIsLAwvLy/UajVNmzbFz8+PdevWKXXS09P54osvKF26NFZWVnTp0gVra2tOnDgBwJEjR0hKSmLKlCk4OjpSuHBhhgwZQoUKFXIVg42NDdOmTcPZ2RkrKys6d+4MwMmTJ5/pmYQQQgghRMEgCZ146c6ePYu3t7dJWf369Z+6nePHj9OwYUOTMldXV+Lj43M9unb16lVu375N3bp1zdo5c+bMU8eUlZXFyZMnLcb1cHulSpXC2dnZpM4bb7xBYmIiAGfOnMHV1RWNRmNS59F2c1K6dGkKFy6sHKtUKsqWLau0L4QQQgghXk/qvA5AvP7u3r1LkSJFTMocHR1zda3RaFT+OzExkc2bN7Nw4UKTOmq1mjt37uDk5JSrNnQ6nVmCmZWVRfv27XMV08Nu3LhBZmYmwcHBZu09nDTa2tqaXWtjY0NWVhYAd+7csfhOSpUqxdWrV58Yh5WV+d9mNBpNvvq8ghBCCCGEePEkoRMvXenSpblx44bJ9MHr16+b1LGysjIbZTMajdy4cUM5tre3Z8iQIXTr1i3He1lq5+F72dvbU6RIEQ4dOvRMz/Ioe3t7AHbs2GGWtD5tOzdv3jQrt1QmhBBCCCFENplyKV66d999l927d5uUPXrs7OxMQkKCSVlsbKzJZia1a9d+YiLm7OzMlStXTMr279+v/HeVKlXIzMx8pumVljg4OFC5cmUOHjz4XO1otVqOHTtGamqqSXlUVBQqlcqkTK1Wm4w6CiGEEEKIfy9J6MRL16NHD/7zn/9w4MABjEYjR44cYfny5SZ1XF1duXv3LmvXrgUerHX76quvKF++vFKnXbt2xMbGMmfOHCXx+eeff0w2/vD39+enn37i1q1bGI1GlixZouw2CQ+mIYaEhDBs2DBOnDiB0WhEp9Oxbdu2Z36+Xr16MWnSJA4cOEBWVhaZmZns27fPLDl7nFq1auHr68vo0aO5desWqamphIeH888//5hNxXR2dubo0aNmu3cKIYQQQoh/H0noxEtXq1YtpkyZwoQJE9BqtUybNo3w8HCTOkWKFGHmzJksXboUPz8/+vbty6BBgyhVqpSyUYizszPLli3jyJEj1K9fH61WS//+/U2mZfbq1YvatWvz/vvv07BhQ86ePUtYWJjJZiOffPIJzZs3JywsDHd3dwICAvjtt99y/Tw2NjYma+I6dOhA//79mTRpElqtFj8/PxYvXqyct7W1NdvsBB4klw+Xz5gxg5IlSxIUFESjRo3IyMjA29sbd3d3k+v69+/PwIEDady4MefOnct1+0IIIYQQ4vWjMsrcLZFHqlWrxp9//pnXYeQbu3fvpmbNmjg7O5OWlkZkZCSRkZGsXr0atfrVLXfN/pTCil3xXEiQNXz/dpXLlmDawNZ5HUa+lZaWxunTp6lRo4bJTrMib0m/5E/SL/mT9Ev+lP37mIuLyxPryqYoIs8UKlQor0MwM336dJMPlT+qe/fuDBw48KXcOyYmhs8//5y7d+9ia2tLYGAgixYteqXJ3MPKlXTMk/uK/EV+DoQQQoj8TUbohBAmnuYvQuLfwWAwWPw0hpC/bOdX0i/5k/RL/iT9kj89ze9j8i+0EMKMTqcjPT09r8MQD0lPT+fUqVN50i+SzAkhhBD5l/wrLYSwSAbv8xej0Uh6err0ixBCCCFMSEInhBBCCCGEEAWUJHRCCIse/aC5yFsqlQo7OzvpFyGEEEKYkF0uhRBmNBoNdnZ2eR2GeIidnR01a9Z8pmtlUxMhhBDi9SUJnRDCotmrdnLlekpehyGeU7mSjgzsGJjXYQghhBDiJZGETghh0ZXrKfJhcSGEEEKIfE7m4OQTvXr1Yu/evXkdxkuXm+ecP38+8+fPV46DgoKIi4t7qXGdOXOGDz74AL1e/1LvI4QQQgghxIskI3T5xA8//JDXIbwSuXlOnU5ncqzX683KXrTq1auzZs2al3oPIYQQQgghXjQZoRPPLCgoiMTExLwO45m4urrmdQhCCCGEEEI8N0noxDPT6/UFdori/fv38zoEIYQQQgghnpskdLmwdOlSGjRogJubGyEhIcTHxyvnoqOjadOmDR4eHgQFBfHLL7/Qp08fjhw5AkBiYiJubm5mbY4bN45FixYpx82bN+fo0aMAHDlyhD59+rBmzRqCgoLw8PCgbdu2HDp0yKSNgIAANm/eTOvWrXF3d6dTp05cuHCBU6dO0aFDB7y8vOjWrRuXLl0yue7SpUv07t0bNzc3/P39iYiIMDnfoEEDdu7cSYcOHXB3d6dhw4YsWLBAOb9kyRK0Wi0JCQm0bNkSf39/MjMzc/UuH35OgAsXLtCzZ0/c3NyoV68e06ZNe+L0yv/973907twZNzc3GjZs+FRTJadNm4ZWqwVAq9XStm1bAOLi4ggKClLqfffdd3z55ZeMGDECT09P6tevz9q1a8nIyGDs2LH4+vrSuHFjfv/9d5P2s7Ky+Pbbb/Hz88Pd3Z1+/fqRlJSU6/iuXbvGxx9/jJeXF97e3gwdOlQ5l56ezvjx4/Hy8sLT05MRI0aQmppqcn10dDRdunRBq9Wi1WoZPHhwru8thBBCCCEKHknonmD16tUsW7aMiIgIDh06RFBQEKGhoWRmZpKUlES/fv3o3LkzsbGxrFixgjVr1nDq1Cll5Eqv15ORkWHWrk6nM0lcHj7W6/WcOnWKX3/9lZUrVxITE0ObNm3o06cP169fV66xtrbmu+++Izw8nMOHD9OyZUtGjx7NyJEjGTt2LAcPHqRJkyaMGDHC5D7du3endu3a7N+/n9WrV7Np0yaTpEilUjFp0iSGDBlCTEwMixYt4v+xd+dRURzv3sC/w8CAgsiqBhSNS9xQAgyMCAqiEVdUNERNiFvccYlK3AgKUdxuTFQU9wR9E4173NBEcUMUGBckcY3RuKCAIiqCzMDM+4eXvrYDCogC+v2ck3Ps6qrqp7v93TuPVV21fv16HDhwAAAwcOBAKJVK2NjYYOfOnYiNjYW+fvE+x3z+PvPy8jB8+HDUrl0bx48fx4EDB5Cbm4tff/21yPYZGRkYPHgwunfvjvj4eERGRiIiIgInTpwo1vWnTp0KpVIJAFAqldi2bZsQ1/OjjVKpFL/88guaNWuGuLg4/L//9/+waNEifPPNN6hVqxZiY2OxZs0aLFiwAP/++6/QbvHixTh27Bg2btyIuLg4NGjQAOPHjy9WbAAwa9YsODk54eTJkzh58iS++eYb4VxwcDBSUlIQHR2NQ4cOQaPRIDQ0VDh/4sQJjB8/Hl988QXi4+ORmJiImTNnFvvaRERERFT5MKF7CY1Gg4iICMycORNNmjSBgYEBvvjiC1hbWyMmJgZbtmyBQqGAv78/JBIJLC0tMW/ePNy7d++1r33v3j3MnTsXFhYW0NPTQ0BAAORyObZs2SKq9+WXX6JZs2aQSCTo168fLly4AD8/P7Rs2RISiQT9+/fHxYsXhZGcbdu2wcrKCuPGjUOVKlVgY2ODKVOmYN26daJ++/fvDzc3N0ilUtSvXx9+fn44ePDga9/X8xISEpCZmYmQkBBUrVoVhoaG+Pbbb1GtWrUi2/z0009wd3dH//79IZPJ0LhxY4wePRpRUVFlGhsANGzYEAMGDICBgQHs7OzwySef4Nq1awgMDIRUKkXdunXRvn17HD9+HADw8OFDrFu3DgsWLECdOnVgZGSECRMmICUlBX///Xexrnnt2jV4eHhAT08PEokENWvWBABcvXoVMTExWLBgASwtLWFiYoKQkBDExMQIf99mzZqFadOmoUuXLpBKpZBIJLCwsCjz50JEREREFQcTupe4c+cOHj58iNatW4vKHRwccPHiRVy+fBmtWrUSnatbty7s7Oxe+9p2dnaoU6eOqMzd3R2XL18WlTVs2FD4s0Qigbm5OVxcXERlZmZmyMzMBAAkJSXB29tb536uXr0qmjbZrFkzUR1bW9sSTR0sjsuXL8PJyUk0uieVSuHu7l5km7Nnz+rE37JlS1y6dKlMYwOARo0aiY7Nzc3h6uoqKrOwsBCe7aVLl1CjRg00aNBAOK+npwd7e3tcvHixWNf09/fH5MmTceTIEVH5uXPn4OTkBDMzM6GsWrVqsLOzwz///INr167h+vXr6NKlSwnukIiIiIgqO25b8BKpqalQqVRQKBSi8vz8fPTp0wePHz+GiYmJTrvnf3QXRavVvvR8YX0YGxvj8ePHojKpVKpTr3r16kX2m5qaiujoaKxatUpUrq+vj0ePHgkjOjKZTOd8fn7+S2MuqadPn8LU1FSn/GWjSmlpafj2229FUwm1Wm2xp3yWRGme7a1bt4Rv9Ark5eXplBVlwIABaNq0Kb7//ntERkYiJCQEzZo1Q2pqKuLj43X6UavVePToEQwMDGBhYfFGngMRERERVVz89fcSxsbGqFatms5iJAWmTZuG+/fv65S/+J2bRqOBRqOBnp6eqE7dunWLvHZh0zbT0tKEKXilZWxsjK+//hoDBgx4rX7KgpWVFZKSknTK7969W+Qop7GxMRYsWIAOHTq86fBKzNjYGI0aNcKOHTteqx9XV1f89ttv+OmnnzB48GDExMTA2NgYnp6eWLp0aaFtrl27hvv370OlUukk40RERET07uKUy5f48MMPkZeXV+R0OUdHRxw9elRUdvXqVaSkpAjHlpaWAIDbt28LZVlZWYUmMs+7ffs2rl+/Lio7evQoPv744xLcgS57e/siE9SS0tfXf+VI46tiOX36tGgxktzcXJ1n+mKbsoj/dWMvTPPmzXH9+nVRQv86Bg0aBD09PVy+fBn29vY4d+5ckdst1KtXD3Xr1hUWeSEiIiKi9wMTupeQyWQICAjApEmTkJycDK1WC5VKJaz22L17d9y4cQNr166FRqPBnTt3EBwcLJpGKJPJ4OrqimXLliEvLw9Pnz7Ft99+Cxsbm5de29TUFNOnT8fdu3eRn5+PVatWISUlBd27d3+te+rduzeUSiWWLl0qLJRy48YN/PXXXyXuy8rKCmfPnoVarUZOTk6J2zdp0gTNmjXDd999h5ycHGRnZ+Obb7556aIoX3zxBTZv3ozffvsNT58+hVarxaVLl0RbSRQ39jNnziA3N/eV2yQUV82aNdG+fXuMHz9eWPnyyZMnOt/Dvczp06ehUqmg1WqxZ88e5Obmom7dunB0dETt2rUxadIk3L17FwCQmZmJuLg4AM++lZw8eTIWLFiAPXv2QKPRAAAePHhQJvdGRERERBUTE7pXGDduHLp27Yrx48fDyckJnp6e+P333wEARkZGWLNmDf7880+4uLjgyy+/xKefforGjRuL+pg9ezbu3r2LNm3awNfXFy1atIC3t7doapxMJhMdN27cGD179sTnn38OFxcXHDp0CGvXroWRkZGojaGhoehaBgYGMDAwEJXJZDKhzMrKCuvWrcPp06fRtm1byOVyjBo1SjTF88VYAMDQ0FCnbOjQoZg/fz7atm2L+Pj4Yj3PF/v+/vvvkZmZCQ8PD/j4+KBRo0bo1auXqI6BgYFw/NFHH2HlypX4/fff4ebmBldXV0ydOhVPnjwp1vULjBo1CoGBgejQoQP++ecf0TMquN8Xn21hz+XFsvDwcLRo0QIDBgyAo6MjfHx8cOzYsWLHNX/+fDg5OUEul+Onn35CZGQkzM3NAQCRkZEwNTVFr1694OTkhJ49eyI5OVlo6+XlhWXLlmHNmjWQy+VwdnYWbVlBRERERO8eibas550RAgICEBgYqLOYSnHFx8cjIiIC69evL+PIiF6tIEn89chVXE/R/UaUKpd6NpaYE9izvMN4Z2VnZ+PChQto2rQpqlatWt7h0P/ie6mY+F4qJr6Xiqng91iLFi1eWZeLorwBBgYGha6QWFxSqVRnlK2iO3DgAKZMmVLk+RYtWuCnn356ozH8/fffL13sxdLSEvv373+jMbzMuHHjhD3rCjN16lT07t37LUZERERERJUdE7o3YO3ata/VXi6Xv3Yfb1uHDh2gVCrLNYbmzZuXewwvs2jRovIOoURsrc3KOwQqA3yPRERE7zYmdERUqMDPvMo7BCojL26bQkRERO8O/n94ItKhUqlKtXIpvTk5OTk4f/58qd4LkzkiIqJ3F/+/PBEViuslVSxarRY5OTl8L0RERCTChI6IiIiIiKiSYkJHRIWSSCTlHQI9h++DiIiICsOEjoh0yGQyVKlSpbzDoOfIDA0r3XYmRERE9OZxlUsiKlTEb4dxOz2zvMMgPNt6IPAzL+jr8/9kExERkRh/HRBRoW6nZ+J6yv3yDoOIiIiIXoJTLqlSGzJkCGJjY8s7DCIiIiKicsEROqrU1qxZU94hEBERERGVG47QUZkLDg7Gzp07yzsMIiIiIqJ3HhM6KnN5eXnIy8sr7zCIiIiIiN55TOgqgZMnT6J///6Qy+WQy+UYO3Ysdu/ejWHDhunU7dSpE86dOwcASE1NhaenJ5KTk9G5c2coFAqoVCqsXLkSP/zwA5YvX45WrVohODgYAJCTk4MZM2bA1dUVLi4umDx5MrKysoS+J0+ejHXr1mHs2LFwcXFB69atMWXKFKFOcnIy5HI5du/ejdDQUMjlciQlJRXrHoOCgrB69WqMGDECzs7O6NixIw4fPozMzEyMGTMGCoUCXbt2RVxcnKhd165dcebMGQDAzZs30bFjR+zZswcdO3aEk5MTunfvjiNHjpToeZ89exYBAQFQKBRQKBQICAjAlStXhPObN2/GN998g7lz56JVq1Zwc3PD8uXLodFo8P3336NNmzZo27atznTQwYMHIyYmBqNHj0arVq2gUCjw3XffQaVSierFxcWhR48ecHR0RJcuXRATEyOcK+qdAsDu3bvh4+MDR0dH+Pn5Cc+FiIiIiN5dr5XQXb16FdOmTUOXLl3g6uqKtLQ04dzly5dx8+bN1w7wfXfixAmMHz8eX3zxBeLj45GYmIiZM2dCpVLpJAIAROVqtRrZ2dlYsWIF1q9fjxMnTkAmkyE3NxfHjh1DdnY2jh8/jrCwMADPpkqmpKQgOjoahw4dgkajQWhoqNC3RCLBjz/+CIVCgdjYWOzfvx93797F4sWLAQAtWrSAUqlEt27dMGPGDCiVSjg4OBTrPqVSKZYuXYoePXogISEBCxcuxNSpUzFlyhS0bdsW8fHxmDNnDoKCgpCZmVno/UokEty9exdRUVFYsWIFTp06hQkTJmDcuHGiv5uvkp+fj0mTJuH48eM4ceIEfHx8EBQUJIp1//79kEgkOHLkCHbv3o3du3dj4sSJyMzMxMGDB7F161Zs27YNJ06cENqp1WrMmjULPj4+OHnyJH7//XckJiZi4cKFQp0rV65g7NixGDVqlPCup06diqtXr770ncbGxmLWrFn47rvvkJiYiGHDhmHkyJGiZ0VERERE755SJ3RxcXH49NNPUbVqVYwfPx5qtVo0zS4pKQkzZ84sixjfa7NmzRKSZqlUColEAgsLi2K3f/ToEXr06AErKyvo6f3f6759+zbGjh0LqVQKPT09XL16FTExMViwYAEsLS1hYmKCkJAQxMTE4N69e0I7uVyOzz//HIaGhqhWrRqGDRuGgwcPlsm9tmnTBp07d4ZUKoW9vT1atmwJiUSCTz/9FADQsmVLNG/eHKdPny6yj9zcXAQHB+PDDz+ERCJBu3bt0LRpU52RvZdxdnaGg4MD9PX1oaenh88++wwXL17EkydPhDrVqlXDxIkTYWhoCEtLS/Tq1QsnT57Et99+C5lMBmtra/Tq1UtndNDV1RW+vr4AgFq1amHWrFnYtGkTcnNzAQCRkZHo168ffHx8oK+vD1dXV/Tt2xcbNmwQ+ijsnS5ZsgTjx4+Hq6sr9PX10alTJ7i7u2P79u3Fvm8iIiIiqnxKndB9//33CAoKQnBwMDp27Kiz4a1CocDff//92gG+z65du4br16+jS5cur9WPXC7XKXN0dBS9s3PnzsHJyQlmZmZCWbVq1WBnZ4d//vlHKGvatKmoHxsbG6Smpr5WfAUaNWokOjY3N4erq6uozMLC4qWjThKJ5LVjzM7ORkREBPr06QM3Nze0atUKWq1WdN0PP/xQ9PzMzc3h4OAAmUz20lg9PDxExwVJ661btwA8m+7p7e2tU+fixYuisuffaX5+Pv766y+ddg4ODjrtiIiIiOjdUuptC65cuYJ27doVed7Y2Fg0okEll5GRAQsLC51k+WW0Wq3oWE9PT5SkFTA3Nxcdp6amIj4+Xif5U6vVePTokXBsaGgoOq+vrw+NRlPs+F5GKpXqlFWvXr1Efejp6cHAwEBUZmBggPz8/GL3MWbMGDx58gTjx49Hy5YtYWJigubNm4uebWGxFvacX1TY/ZiYmAjfIaalpeGrr76CRCIRzms0GtStW1c4fvGd3rt3D3l5eTqJf35+Plq3bv3KmIiIiIio8ip1QmdtbY0rV66gVq1ahZ4/duwYbGxsSh0YPRvhuX//PlQqlWjkB3j2o/7FlSS1Wq1oeiTwbMTq+eTg+fbPMzY2hqenJ5YuXVpG0VdON27cQGJiImJjY2FqagrgWbJbVknr/fv3RccajQb3799HzZo1ATx7D1FRUWjSpEmRfbz4To2NjQEAhw4dQrVq1cokTiIiIiKqHEo95fKLL77AjBkzRNMqC35kHjp0COHh4ejVq9frR/geq1evHurWrYtt27bpnLOyskJKSoqoTKlUFrpQSnHY29vj3LlzePr0aanaP09fX19npLCyePDgAUxNTYVkDnj2jxNlJTY2VnR8/PhxWFtbo0aNGgCevYf4+PgS9WliYoJ69eqVuB0RERERVX6lTugGDRoEPz8/9O3bF507d0ZOTg7GjBkDT09PjBo1Cp06dSp0WX0qPolEgsmTJ2PBggXYs2ePMEr04MEDODg44PHjx9iyZQsA4M6dO/if//kf1K5du1TXcnR0RO3atTFp0iTcvXsXAJCZmVmixUQKWFlZISkpCVqtVrTtQWXQoEED5OTkYP/+/dBqtTh16hSioqJgZWVVJv2fOHEC27dvh1qtxs2bNzF79mwMGjRIGDEdNGgQIiIisH//fqhUKmg0Gpw5c+aV3wAOGTIEYWFhOHHiBPLz85GXl4fjx49XuudPRERERCXzWtsWBAYG4siRIwgMDMTYsWPRsWNHfP311zh48CDCwsJ0pvVRyXl5eWHZsmVYs2YN5HI5nJ2dMXnyZFSrVg2LFy9GVFQU3N3dMWLECIwZMwY1atQQpmcaGBjofPMGPPsOrrDyyMhImJqaolevXnByckLPnj2RnJwsnJfJZDpTPwu7Ru/evXHmzBnI5XKdvdiKIpPJdPop7Hovlj1/XNT9FtZPUUxMTBAZGYlly5bBxcUF8+bNw5w5c2Bqaiok1IU9v+LECgBTp07FH3/8gdatW6Nv377o1q0bAgIChPMeHh4IDw/H8uXLhX3wFixYIIy8FnWP/v7+GDVqFMLCwiCXy+Hu7o61a9cW656JiIiIqPKSaEs5N+7HH3/EgAEDdBbXIKLCBQQEIDAwEAqForxDeamCJP7XI1dxPeX+K2rT21DPxhJzAnvi/PnzqFevHqpWrVreIdH/ys7OxoULF9C0aVO+lwqE76Vi4nupmPheKqaC32MtWrR4Zd1SL4oSFRUlGlkgKkxGRgZ8fHyK/KZOIpEgNja20FGndy0WqVSqswInEREREdHrKHVC1759e+zevRsDBgwoy3joHWNhYYHExMTyDgNA+cfy888/l9u1S8PW2qy8Q6D/xXdBRERERSl1Qjd48GBERETgxIkT8PLywgcffKAzTCuVSuHk5PTaQRLR2xf4mVd5h0DPyddodLYqISIiIip1Qjd+/Hjhx8Xly5cLrWNgYID9+/eX9hJEVE5UKhVycnJQpUqV8g6F/pcqNxdqtbq8wyAiIqIKptQJ3R9//FGWcRBRBVNZ9xJ8V/F9EBERUWG4rwARFUoikZR3CERERET0CqUeoTt9+jTy8/NfWoff0BFVTjKZjNMt/5dGo+GemkRERFRhlTqhGzJkiE5Cl5eXB41GA4lEAhsbGxgZGWHPnj2vHSQRvX0Rvx3G7fTM8g6jXNlam3FxGCIiIqrQSp3QnTlzRqcsPz8ft27dwo4dO3Dw4EFERES8VnBEVH5up2dyY3EiIiKiCq5M5xFJpVLUrVsX48aNw+DBgxESElKW3RMREREREdFz3tiHIZ07d0ZSUtKb6v69lJqaCkdHx/IOo1hCQkKwevXq8g7jnaNSqdCnTx9cunSpvEMhIiIiogqg1FMuXyU5ORnm5uZvqvv3klqtRm5ubnmHUSwqlQoqlaq8w3jnyGQybNmypbzDICIiIqIKotQJ3blz5wpNLp48eYKLFy9i/fr16Nev32sFR4CPjw/WrVuHmjVrlncoFVpqaiq+/PJLbmRPRERERO+VUid0kydPLjShMzIywgcffIAJEybAz8/vtYKjZ6NyarW6vMOo8PiciIiIiOh9VOpv6KKjoxETE6Pz3969e7FmzRr07t2bGxO/hp9//hlyuRwpKSnw9fWFh4eHsE3E+vXr4e3tDUdHR3Tp0gUxMTGitp06dcLZs2cxYMAAyOVyxMfHAwCuXbuGESNGwMXFBa6urhgxYgSuX78utCvqG70Xv4d7+PAhvvnmG7i4uEAul2Ps2LHYv38/Bg8eLGp3+/ZtDBs2DHK5HG5ubpg0aRKysrKE88uXL8fy5csxb948tG3bFk5OThgxYgRSUlJKFNPYsWPh6+uLlJQUyOVyjB49uljP+PTp0xg+fDg2b94MHx8fODs7w8/PDwkJCaJ6OTk5mDFjBlxdXeHi4oLJkyeL7iMkJAQbN25EWFgYFAoFli5dWqzr9+/fH5s3b8bnn38OJycn+Pr6IikpCbdu3cLAgQOhUCjQp08fnD9/XtTO0dERqampAICTJ09i4MCBiIqKgpeXF5ydndGnTx+cO3euWDEQERERUeVW6oQuLS3tpedv376NTZs2lbb7997AgQOhVCphY2ODnTt3IjY2FlKpFPn5+fjjjz8QFRWFM2fOICgoCBMmTEBGRobQVqVS4fvvv8fEiROhVCrh6uqKzMxMfP7553B0dERsbCxiY2Mhl8vxxRdf4NGjRwCK/kbvxe/hJk+ejHv37mH37t2Ii4uDq6srpk+frjNCtn37dnTu3BlxcXH4888/kZ6eLkp21Go1fvnlF+Tn5+PAgQOIjY1FtWrVEBgYKKrzqpgWL16MnTt3wsbGBkqlstgJlVqtxvnz57Fjxw5s2LABiYmJ6NWrF4YPH4709HShXnBwMFJSUhAdHY1Dhw5Bo9EgNDRUFMumTZvQsGFDxMfHY+TIkcW6vlQqxY8//ogxY8bg1KlTmDBhAoKCgvDNN99g0KBBiI+Px6hRozBmzBjk5eUJ7XJzc4VnLZFIkJSUhGPHjuG3335DYmIi/Pz8MGrUKDx9+rRYcRARERFR5VXqhM7T01M0SvEiiUSC//mf/ylt9/QS3333HerUqQMAaNeuHZo0aYK4uDhRncaNG6Nly5YAnr2LdevWwd7eHsOHD4ehoSFkMhm++uorNG/eHOvWrSv2te/evYtjx47hf/7nf1CzZk3IZDJ88cUXcHV11anbuXNn9OrVCzKZDCYmJhg6dKjOaKKhoSGmTJkCmUyGqlWrYtasWbh16xZOnTpV0sdSKvfu3cPcuXNhYWEBPT09BAQEQC6XCwuPXL16FTExMViwYAEsLS1hYmKCkJAQxMTE4N69e0I/eXl56N+/PwBAT6/4/7Py9fVFq1atIJFI4OXlBZlMhmbNmsHT0xMA4O3tDSMjI1y9erXIPnJycjB37lzUrFkTenp66N+/P6RSKZKTk0vzSIiIiIioEil1QqfVaos8l5+fj+PHj0Mmk5W2eyqCnp4e6tatKyqzsbERpuAVcHFxER0nJiaiS5cuOv117twZiYmJxb7+hQsXUL9+fVhYWIjKvb29dep++OGHOnG+OLLbunVrUQJkaGgIZ2dnXL58udgxvQ47OzshOS7g7u4uXP/cuXNwcnKCmZmZcL5atWqws7PDP//8I5S9+LyLq1GjRqJjc3NzneTYwsICmZmZRfZRo0YNWFlZico++OADnb8TRERERPTuKdGiKJGRkVi0aBEkEgkkEslLf8QaGRnhm2++ee0ASazg2T9PJpPpTEt8ccuItLQ01KhRQ6e/GjVq4O7duy+95vPJ+6NHj0TJzfP9vOjFkarC4qxevbpOOxMTEzx+/LjYMb2Owu7F2NhYuH5qairi4+Mhl8tFddRqtTBVFdB93sUllUp1ygp7Ji9jaGioU2ZgYCB8c0lERERE764SJXQDBgyAr68vtFotPvnkE+zYsQMmJiaiOhKJBIaGhjAzMyv0xyq9HS8mU+bm5oV+95iWliaMtkmlUmg0Gmg0GlH79PR0YVTQ2NgY9+/f1+nn+W/4SuL5aYvPx+Th4VHsmF5HUdcv2CbC2NgYnp6er/wuryTTLImIiIiIykqJfoVWrVoVtra2qF27Nuzs7FC3bl3Y2tqK/rOxsYGlpSWTuTKir69fJqNRnp6e2Lt3r055dHQ02rVrBwCwtLQE8GxBmwJZWVlISkoSjh0cHJCSkoKbN2+K+jlw4ECpVjU9efKkaCTp0aNHOHfuHBwcHIodE/BsRKo0z+n27duilT4B4OjRo/j4448BAPb29jh37hwXGCEiIiKiCqnUwwr79++HkZFRWcZChbCyssLZs2ehVqtfK6n4/PPPcf78eSxfvlxYIXLRokW4cOGCsJiHTCaDq6srli1bhry8PDx9+hTffvstbGxshH6sra3Rr18/TJ48GXfv3sXTp0+xevVqxMfHFzp98VWys7MxZ84cZGVl4cmTJwgODoa7uzvq1atX7JiAZ1MnHzx4gNu3b+PJkyfFnm5oamqK6dOn4+7du8jPz8eqVauQkpKC7t27A3i2RUDt2rUxadIkYWpqZmamziI0RERERETlodQbiwPA06dPce7cOdy9excajUbnfH5+Pnr37v06l3jvDR06FCEhIQgPD8fUqVML/V5KJpOJFqCRyWQ69UxNTfHrr79i7ty58PDwgEQigVwuxy+//CKaNjt79myEhISgTZs2qFatGvr27Qs7OztR/0FBQVi2bBl69eoFtVoNDw8PdO7cGfXr1y8yJuDZKNqLcfXt2xdPnz5Fx44doVKp4OXlhZCQEFGd4sRkaGiIIUOGwM/PD8bGxvjtt99gbW39yufbuHFj+Pr64vPPP8eDBw/QpEkTrF27VvSPFZGRkZg/fz569eqF3NxcmJqaol+/fmjdunWRz7s4CmtX2HN7sczQ0BAGBgbCnwtbfKiwfoiIiIjo3SPRlnI+X3JyMkaOHIknT56gRo0auHXrFmrVqoX79+8jNzcXH330Eezs7LBkyZKyjpnKWXx8PGrXrg1bW1vk5ubi4MGDWLRoETZv3gxTU9Ni91Pwd2PMmDFvKtSXio+PR0REBNavX18u16+oCrY7+PXIVVxP0f1e8n1Sz8YScwJ7lncYAJ6NZl+4cAFNmzZF1apVyzsc+l98LxUT30vFxPdSMfG9VEwFv8datGjxyrqlHqGbO3cuPvnkE0ydOhUymQwKhQJr165F3bp1cfToUYSFhWHIkCGl7Z4qsEuXLmH69Ol48OABpFIpFAoFVq9eXaJkDni24MmbWkxk3rx52Lx5c5HnBw4ciFatWgkjXWXp77//xoABA4o8b2lpif3795f5dcuarbVZeYdQ7vgMiIiIqKIr9QidXC7H77//DltbWwDPNrhetGiRsJn1vn37sG7dOvz6669lFy0RvXEl+Reh98GLK6yWF/4LasXE91Ix8b1UTHwvFRPfS8VUkt9jpf6VIpFIRAtP1KxZE1euXBGOnZyccOHChdJ2T0TlSKVSIScnp7zDqBAqQjJHREREVJRS/1Jp0KABLl68KBw7OTnh999/F44vXbpU6GbTRFQ5lNXm7URERET05pQ6oRs6dKho6fYBAwbg6tWr6N27N7755htMnDgR/fr1K5MgiYiIiIiISFepF0Vp37492rdvLxzXrFkT27Ztw5YtW3Dv3j2EhISgW7duZRIkEb19pdkonoiIiIjertfah+5FNWvWxOjRo8uySyIqBzKZDFWqVCnvMMpERVnUhIiIiOhNeK2ELi8vD7t370ZSUhLS0tIQHh6O6tWrC+ekUin/lZ+okor47TBup2eWdxivxdbaDIGfeZV3GERERERvTKkTups3b2LQoEGoWrUqXF1dERsbiydPnggJ3cqVK3H9+nXMnz+/zIIlorfndnrme7+xOBEREVFFV+p5SLNnz0bbtm2xc+dOBAcHQyaTic536NABJ0+efO0AiYiIiIiIqHClTuji4+MxcODAIs9bWVnhwYMHpe3+jRgyZAhiY2PLOwwiIiIiIqIyUeopl4aGhnj8+HGR569cuSJMv6wo1qxZU94hvNeUSiUCAgJgbGwMrVYLjUYDR0dHBAcHo379+qK6T58+xerVq7Fr1y7cv38fRkZGaNOmDcaOHYsPPvhAVLdZs2YwMjKCnp4eDA0N0aRJE4wdOxYODg6ienl5efj111+xZcsWpKSkwMDAAC4uLhgzZgwaNWok1PP29sajR4+EOPT09IQR6IYNG2Ljxo0lum+VSgVvb298+OGHWL9+faF1mjdvDiMjI0gkEqjVatjZ2WHSpEnw9PQU6kyfPh1btmzBokWL0KlTpyKv1a5dO2RmZuLvv/8uUZxEREREVPmUeoSuU6dOmD9/PnJzc3XO3b9/H2FhYWjXrt1rBUeAj48PUlNTS9QmNTUVPj4+byii0svPz0ft2rWhVCpx6tQpnDhxAq1bt8aAAQOQk5Mj1Hv06BH69euHCxcuYMWKFVAqldi1axdq1KiBXr164Z9//tHpd/v27VAqlYiJiYGXlxeGDh2KjIwMoY5KpcLw4cOxb98+zJs3D0qlEgcOHICjoyP69++PhIQEoW5MTAyUSiWUSiW6deuGESNGCMclTeYA4MCBA7C1tcW5c+dw/fr1Quvk5eVhz549UCqVOH36NCZNmoRJkybh/Pnzovv86KOPsHnz5iKvFRMTA3Nzc+Tl5ZU4TiIiIiKqfEqd0AUFBQF4th/djBkzoFKpsHbtWkyaNAkdOnSAnp4eJkyYUGaBvq/UajXUavUbb1MejIyM8NVXX6FmzZo4cuSIUP7DDz+gWrVqWLJkCerVqwcAMDc3x9dff43+/ftj6tSpRfZpaGiIgIAA1KxZE+fOnRPK161bhzt37mDNmjVo2rQpAMDY2BiDBg3ClClTMHny5EL/caIsbN26Ff3794eXlxe2b9/+yvpSqRSenp749NNPsWXLFtG59u3b4/z587hz506hbXfs2AE/P78yiZuIiIiIKr5SJ3TGxsaIiopCaGgotFot5HI5rl27BplMhpkzZ2Lr1q0wNzcvdn9RUVFo164dHB0dERAQgKtXrwrnTp48iV69esHZ2Rk+Pj7YunUrhg8fjtOnTwN4NiLl6Oio02dISAhWr14tHHft2hVnzpwBAJw+fRrDhw/H5s2b4ePjA2dnZ/j5+YlGagDA09MT0dHR6NmzJ5ycnNC3b19cv34d58+fh7+/P1xdXTFgwADcvHlT1O7mzZsYOnQoHB0d4eHhgcjISNH5du3a4fDhw/D394eTkxO8vb2xcuVK4fzPP/8MuVyOlJQU+Pr6wsPDo1ijLmPHjoWvry9SUlIgl8sxevRonDp1Ct7e3tBoNEK99PR0tG7dGiqVCrt27UJoaChWrlwJb29vODk54YsvvsClS5dEfWdkZGDChAlwdnZGq1atEB4eXiaJo62trZCgZGdnY/v27Rg1alShe4cNHDgQ//zzjyhZe5FWq0VWVhYMDQ2Fsl9++QVDhgwpdG+1nj17AgAOHjz4mnei6/bt20hKSkLHjh3h6+uLnTt3QqvVFqutra0tUlJSRGUymQzdunXD1q1bderfu3cPSUlJ6NChQ5nETkREREQVX4kSuiFDhkClUonK2rdvj/r16yMiIgJr1qxBeHg4evToobPq5cts2rQJ69atQ2RkJBISEuDj44Nhw4YhLy8PaWlpGDlyJPr16welUolff/0Vmzdvxvnz54VkQq1WFzq6olKpRPE+f6xWq3H+/Hns2LEDGzZsQGJiInr16oXhw4cjPT1daCOVSvHjjz8iPDwcp06dgq+vL6ZNm4YpU6YgODgY8fHx6NixIyZPniy6zsCBA2Fvb4+4uDhs2rQJe/fuFU2Vk0gkCAsLw9dff43ExESsXr0a69evx4EDBwA8S1yUSiVsbGywc+dOxMbGQl//1Z88Ll68GDt37oSNjQ2USiWWLl0KJycnaLVaIQEGgD179sDDwwMymQxqtRoHDhzA33//jV27diE+Ph4ODg4673v06NEwMjLC4cOHsXfvXly6dAlLly59ZUwvo1arceHCBXz44YcAgL///hsmJiZo1apVofVNTU3Rpk2bQldQ1Wq1SE1NRWhoKMzNzeHi4gIASElJQWpqKjp37lxon1KpFJ06dUJ8fPxr3Uthtm3bhk8++QRVqlRB27ZtkZ2dXezVX5OTk4Xn8rxPP/0U27dv10kMd+7ciY4dO5bof3tEREREVLmVKKGLi4vTSegAYNmyZaVe0VKj0SAiIgIzZ85EkyZNYGBggC+++ALW1taIiYnBli1boFAo4O/vD4lEAktLS8ybNw/37t0r1fWed+/ePcydOxcWFhbQ09NDQEAA5HK5zjS3L7/8Es2aNYNEIhG+7fLz80PLli0hkUjQv39/XLx4EVlZWQCe/Yi3srLCuHHjUKVKFdjY2GDKlClYt26dqN/+/fvDzc0NUqkU9evXh5+f3xsZJZJIJOjWrRuio6OFsl27dsHX11c4fvLkCWbPng1jY2MYGBggKCgI1atXx759+wAAx44dw507dxAWFoZq1arBwsICoaGh2LBhQ6lG6bRaLf777z9MnDgRH3zwgbD4R1paGurUqfPStnZ2djrfFfbq1QstWrRA27ZtcerUKURGRgoJcFpaGqysrFC1atUS9fm6NBoNtm3bhh49egAADAwM0KlTp1dOu8zIyMDSpUtx/PhxDBo0SOf8Rx99BAsLC53EcPv27ZxuSURERPSeKVFCV9RUseJOISvMnTt38PDhQ7Ru3VpU7uDggIsXL+Ly5cs6ozV169aFnZ1dqa9ZwM7OTid5cHd3x+XLl0VlDRs2FP4skUhEoz8FZWZmZsjMzAQAJCUlwdvbW+d+rl69Kpo22axZM1EdW1tbpKWlvdY9FaV79+7Yv38/NBoNrl+/jtTUVLi5uQnnHR0dYWJiImrj5uYmPIukpCS0bdtWNEpYr149aLVa3L17t9hx3Lp1C3K5HE5OTujZsydSU1MRGRkJiUTyWve3d+9enDlzBn/++SdatWqFL7/8Eg8fPnytPl9XXFwctFotFAqFUObr64s///wTT5480anftWtXODs7o0OHDli7di02bNiAGjVqFNr3i9/X/f3338jPz9dZ2ZOIiIiI3m2l3ragrKSmpkKlUol+9ALPVvTr06cPHj9+rJNoAICZmdkr+35VollYH8bGxjrbMUilUp16L9uSITU1FdHR0Vi1apWoXF9fH48ePYKFhQUA6EyN09fXR35+/ktjLq2PPvoIVlZWUCqViI+PR9euXUX3Vdj9mJiYCCOvqamp+P3337F3715RHZVKhYcPH75yVK1A7dq18eeffwIAbty4gT59+kClUgmjZ9bW1rhx48ZL+/jvv/+EhU2eZ2BgADs7O0yfPh3//vsvNm3ahKFDh8La2hr37t3DkydPYGxsXGSflpaWxbqH4tqyZQt8fX1FyaqzszMsLS2xf/9+ndG0PXv2oFatWsjPz0fnzp3x33//oXbt2oX23aVLFyxcuBCPHj2CqakpR+eIiIiI3lMlSuiKGkWRSCSlHmExNjZGtWrVdBYjKTBt2jTcv39fp/zF79w0Gg00Go1oIY309HTUrVu3yGsXNm0zLS0NNWvWLMkt6DA2NsbXX3+NAQMGvFY/Za179+7Yt28fjh8/joULF4rOFfUsChIKY2Nj9O3bF9OmTSuzeOzs7ODp6Ym1a9cKK6I2a9YMjx49wokTJ0QjiAUePXqEY8eOoV+/fi/tu1mzZsJCNba2trCyskJ0dDT69OmjUzc/Px/79u3D6NGjy+Cunnnw4AEOHDgAtVqNFStW6Jx/WQImlUoxdOhQLFmyBO7u7oXWMTExQbt27bBr1y74+/tj3759xVpBk4iIiIjeLSWecjl27FgMHTpU9F92dja++eYbnfLi/ED+8MMPkZeXh4sXLxZ63tHREUePHhWVXb16VbT6X8HIyu3bt4WyrKwsJCUlvfTat2/f1tkX7OjRo/j4449fGffL2NvbF5mglpS+vn6Jp7QaGBgU2qZbt27Yvn07pFIpmjdvLjqXnJwsGpnMz8/H8ePHhdVD7e3toVQqX2t6bWGGDx+OX375RdgzzsTEBN27d0dkZGSho5U//fQTrK2tdUZ0X3TlyhXRBuSfffYZ1q5di+zsbJ2627dvx5MnT8p0776dO3fC0dERly5d0vkvJiYGp06dwq1bt4ps36NHD9y9e1fn7/7zPv30U2zduhWHDh1C8+bNYW1tXWbxExEREVHlUKKEbvz48XBycoKDg4PovxEjRkChUOiUF+d7HplMhoCAAEyaNAnJycnQarVQqVTCao/du3fHjRs3sHbtWmg0Gty5cwfBwcEwNTUV9eHq6oply5YhLy8PT58+xbfffgsbG5uXXtvU1BTTp0/H3bt3kZ+fj1WrViElJQXdu3cvyWPR0bt3b2GFyYKFUm7cuIG//vqrxH1ZWVnh7NmzUKvVos23X8bMzAwPHjzA7du38eTJEyExqlmzJuzt7UWLoRSQyWSYPn06Hjx4AJVKhTlz5sDCwkIYJevQoQMeP36M0NBQIflKTU2FUqks8T09r2HDhnB3d8eaNWuEsqCgIKSnp2P06NH4999/AQCZmZlYtGgRoqKiMG/evEKnwQLPNrVfvHgxzpw5IxoBGzp0KMzMzDBo0CDhPWRnZ2PdunWYPXs2Zs2aJfo79bq2bNlS5Ciira0tPDw8sGPHjiLby2QyDB06FBEREUXWcXJywtOnT/Hjjz9yuiURERHRe6pEUy5HjBjxRoIYN24cjIyMMH78eGRkZMDIyAhyuRwdOnSAkZER1qxZgxkzZmDp0qWwsLDAyJEjdaaXzZ49GyEhIWjTpg2qVauGvn37ws7OTvSdmkwmEx03btwYvr6++Pzzz/HgwQM0adIEa9euhZGRkajN8/uZAc9GwAwMDERlMplMKLOyssK6deswf/58rFmzBnp6eqhVqxYmTZpUZCzAs02xXywbOnQoQkJCEB4ejjlz5sDLy+uVz9PQ0BBDhgyBn58fjI2N8dtvvwmjN6mpqYUmrO3atUO9evXQo0cPPHnyBM7OzqK984yMjBAVFYW5c+fCx8cHGo0GlpaWGDZsGORy+StjevEZPW/EiBEYOHCgkHSZm5vjt99+Q2RkJIYOHYrMzEzIZDIoFAps2rRJtEgN8GyKYteuXaHRaGBiYgJXV1ds2LBBNHVWJpNh7dq1WLNmDYKCgpCWlgZ9fX04ODhgzZo1cHJyKjLmkm4D8Pfff+PevXsv3Q+uf//+mDNnDgIDA4XrvLgtRe/evbFq1SocO3YMbdq0KTQWf39/rFq1Cu3atRPK9PX1df7OEhEREdG7SaIt6zl0b0lAQAACAwNfOfWuKPHx8YiIiMD69evLOLKKp+AV7927Fzt27NBZrGXbtm1ISEjA3LlzyyM8qmCSk5MBAL8euYrrKbrfr1Ym9WwsMSewZ3mHUSays7Nx4cIFNG3a9KVbcNDbxfdSMfG9VEx8LxUT30vFVPB7rEWLFq+sW+6rXJaWgYFBkdPuikMqlRY6WlSRHThwAFOmTCnyfIsWLfDTTz/plN+6dQu+vr6oXbs2lixZonO+LJ6Fh4cHnj59Wug5iUSCTZs2FbpJdmXk7+8vTAUtzJIlSwpd0KWysbU2K+8QXtu7cA9EREREL1NpR+iI6M0oyb8IVQYvrn5bWfFfUCsmvpeKie+lYuJ7qZj4Xiqmkvweq/y/coiozKlUqmIvwlPRvQvJHBEREVFR+EuHiArFwXsiIiKiio8JHRERERERUSXFhI6ICiWRSMo7BCIiIiJ6BSZ0RKRDJpOhSpUq5R1GqWk0mvIOgYiIiOitqLTbFhDRmxXx22HcTs8s7zBKzNbaDIGfeZV3GERERERvBRM6IirU7fTMSr+xOBEREdG7jlMuiYiIiIiIKikmdFTuLl68iE8//RRqtRoAsGLFCqxYseKtxjBt2jRs3br1rV6TiIiIiOh1ccollbsmTZpg8+bNwrFKpXrrMYSHh7/1axIRERERvS6O0NF7JzIyEpGRkeUdBhERERHRa+MIHb138vLyyjsEIiIiIqIywRE6eivmz5+PBQsWiMpycnIgl8uRlJQEHx8f0bn8/HxERESgXbt2cHR0xOeff46LFy8K51NTU+Hp6Ynk5GR07twZCoUCKpUK165dw7Bhw+Dm5gZXV1f07t0bSqUSAHDv3j24urpi5cqVWLlyJeRyOaKjowEAw4YNw+7du4X+s7KyMHPmTLi7u0Mul6NPnz44duyYKMZ27drh8OHD8Pf3h5OTE7y9vbFy5coSPZc9e/Zg4sSJ2LZtGzw8PDBkyBAAQP/+/bF582Z8/vnncHJygq+vL5KSknDr1i0MHDgQCoUCffr0wfnz54W+1Go1ZsyYATc3N7i4uKBbt27IysoqUTxEREREVLlwhI7einbt2iE4OBhBQUFC2fHjx9GwYUOo1WphQZQCGzduRMeOHYWEKzw8HIMHD8Yff/wBExMTqNVqZGdnY8WKFVi/fj0sLCygp6eH3NxcDBo0CM7OzpDJZDhw4ADGjx+PmJgYWFlZISEhAUuWLAEAjBkzRrieSqUSfbs3ZswYVKtWDbt27YK5uTmOHj2KSZMmYdmyZXB2dgYASCQShIWFYfbs2XB1dcV///2HAQMGoH79+ujQoUOxnktubi7++ecfyGQyxMTEQF//2f8kpVIpfvzxR3z//fdQKBQ4cuQIgoKCYGVlheHDh8PT0xMxMTEYM2YM9u/fD319fWzYsAGpqak4ePAgqlatitTUVBgbG5fibRERERFRZcEROnornJyc8PDhQ1y7dk0oO3jwoM7IXAFDQ0PMmDEDRkZGMDIywsyZM6Gvr48DBw4IdR49eoQePXrAysoKenrP/io3adIEbm5ukMlkAIAOHTpAT08P169fL3asJ0+exIULFzBv3jxYWFhAIpHA09MTw4YNw6JFi0R1+/fvDzc3N0ilUtSvXx9+fn44ePBgsa8FAFeuXEFQUBBkMplwHwDg6+uLVq1aQSKRwMvLCzKZDM2aNYOnpycAwNvbG0ZGRrh69SoA4Nq1a3B2dkbVqlUBADVr1oREIilRLERERERUuTCho7dCKpXC29sbhw8fBgBoNBocOXKkyITO3d0dUqlUONbT04OHhwcuXbokqieXy0XHeXl5iIqKQr9+/eDh4QFnZ2ekp6cjMzOz2LEqlUq0a9cOVapUEZV37twZp0+fRn5+vlDWrFkzUR1bW1ukpaUV+1oA0KBBA1hYWOiUN2rUSHRsbm4OV1dXUZmFhYVwb76+voiKisIvv/xSLiuFEhEREdHbx4SO3poOHTrg0KFDAIDTp0/DxsYGNjY2hdYtLMGxtLRETk6OcKynpwczMzNRnbCwMGzatAmDBw/Gzp07oVQq8cEHH0Cr1RY7zrS0NNSoUUOn3NraGmq1Gvfv3xfKCkYCC+jr64sSvuJ48R4KPJ/QFqhevXqR/Tg6OmLDhg04ffo0OnfujL1795YoDiIiIiKqfPgNHb017u7umDJlCrKyshATE1Pk6BwA3L17V6fs/v37ogRQIpGIphTm5uZi27Zt2LlzJ+rXrw8A0Gq1JR4xMzMzK7RNeno69PX1X5pUlUZhiVtp1alTB99//z0SEhIwYsQI2NjY4OOPPy6z/omIiIioYuEIHb01hoaGaNWqFY4dO4aDBw+iU6dORdY9cuSIaIVGjUaD48ePo3HjxkW2efz4MdRqNWrXri2UnTx5UmfBFX19/ZeO2Hl6euLw4cOi0UAAiI6OhpubGwwNDYtsW1G4urqibdu2SEhIKO9QiIiIiOgNYkJHb1WHDh3w888/o0qVKqhTp06R9apXr46goCBkZWUhNzcXYWFhqF69Ory8vIpsY2lpCVtbW/z666/QarW4cuUK5s6di3r16onqWVlZ4dy5c9BoNHj8+LFOP87OzmjWrBkmT56MjIwMaLVa7Nu3DytWrEBgYGBpb/2Nu3DhAh49egQAuHjxIhISEtC0adNyjoqIiIiI3iQmdPRWeXl54fLly+jWrZtQJpPJYGBgIDru1asXWrRogS5duqB169ZIS0tDVFSUUM/AwEBnpEwikSAyMhL79u2Dq6srJkyYgKCgINSpUwcajUao98knnyA7OxsuLi4IDw8Xrvn893CLFy9GjRo14OvrCxcXF6xduxaLFi0STV98sQ3wbBTyxbKXMTQ0LHTETyaT6ZQXdr3ny3bu3Im2bdvC0dERgYGBGDNmDNq0aVPsWIiIiIio8pFoS7JaBBG985KTkwEAvx65iusp919Ru+KpZ2OJOYE9yzuMMpednY0LFy6gadOmwtYUVP74XiomvpeKie+lYuJ7qZgKfo+1aNHilXW5KArRG+Lv749///23yPNLliyBm5vbW4yoZGytzco7hFKprHETERERlQYTOqI3ZNOmTeUdwmsJ/MyrvEMoNY1GI9qknYiIiOhdxV88RKRDpVLprPJZmTCZIyIiovcFf/UQUaH4eS0RERFRxceEjogK9fym7URERERUMTGhIyIdMpkMVapUKe8wivT8NhRERERE7zMuikJEhYr47TBup2eWdxg6bK3NKvWCLURERERliQkdERXqdnpmpdyHjoiIiOh9wimXRERERERElRQTOiIiIiIiokqKCR0REREREVElxYSOKgwfHx+kpqaWdxhvhYODQ3mHQERERETvACZ0VGGo1Wqo1eryDuOtePr0aXmHQERERETvACZ076GgoCCsXr0aI0aMgLOzMzp27IjDhw8jMzMTY8aMgUKhQNeuXREXFye02bZtG3x9feHi4gJ3d3cEBQXh0aNHAICHDx+idevWOHXqlFB/9+7d+Oyzz4oVz88//wy5XI6UlBT4+vrCw8MDeXl5AICbN29i6NChcHR0hIeHByIjI0VtPT09ER0djZ49e8LJyQl9+/bF9evXcf78efj7+8PV1RUDBgzAzZs3hTanT5/G8OHDsXnzZvj4+MDZ2Rl+fn5ISEgQ9Z2Tk4MZM2bA1dUVLi4umDx5MrKysoTzISEh2LhxI8LCwqBQKLB06VIAwKpVq9CpUye4uLigTZs2mDVrFlQqFQBgzpw5kMvlAAC5XA4/Pz8AwJkzZ9C1a1edZzNkyBBER0cLx46Ojrhy5Qr8/Pzg4uKCGzduAADi4uLQo0cPODo6okuXLoiJiSnWsyciIiKiyo0J3XtIKpVi6dKl6NGjBxISErBw4UJMnToVU6ZMQdu2bREfH485c+YgKCgImZmZAAATExNEREQgISEBBw8ehEajQUREBACgevXqGD9+PL777jtoNBo8fvwY8+bNQ3BwcLHiGThwIJRKJWxsbLBz507ExsZCX18fKpUKAwcOhL29PeLi4rBp0ybs3bsXmzdvFt3Ljz/+iPDwcJw6dQq+vr6YNm0apkyZguDgYMTHx6Njx46YPHmy0EatVuP8+fPYsWMHNmzYgMTERPTq1QvDhw9Henq6UC84OBgpKSmIjo7GoUOHoNFoEBoaKpxXqVTYtGkTGjZsiPj4eIwcORIAULNmTURFRSExMRF79uzB5cuXsWHDBgDA1KlToVQqAQBKpRLbtm0T+ipI+p73Ynl2djYWLlyIhQsXIiEhAXZ2drhy5QrGjh2LUaNGITExETNnzsTUqVNx9erVYj1/IiIiIqq8mNC9p9q0aYPOnTtDKpXC3t4eLVu2hEQiwaeffgoAaNmyJZo3b47Tp08DADp27Ag7OztIJBIYGRmhd+/eQmICAJ9++imqVq2KjRs3YvHixfjkk0/QokWL14px27ZtsLKywrhx41ClShXY2NhgypQpWLdunajel19+iWbNmkEikaBfv364cOEC/Pz8hHvq378/Ll68KBpdu3fvHubOnQsLCwvo6ekhICAAcrkcW7ZsAQBcvXoVMTExWLBgASwtLWFiYoKQkBDExMTg3r17Qj95eXno378/AEBP79n/nHx9fVGzZk0AgKmpKbp37y4avXxdCoUC9erVg0QiAQBERkaiX79+8PHxgb6+PlxdXdG3b18hiSQiIiKidxc3Fn9PNWrUSHRsbm6Oxo0bi8osLCyEEbqUlBQsX74cp0+fRlpaGtRqNczNzYW6EokEYWFhCAgIgIGBAfbs2fPaMSYlJcHb21tU5uDggKtXryIvLw/6+s/++jZs2FAUh7m5OVxcXERlZmZmyMzMhImJCQDAzs4OderUEfXt7u6OpKQkAMC5c+fg5OQEMzMz4Xy1atVgZ2eHf/75B1ZWVgAguk6BK1euYMWKFUhOTsb9+/ehVqvRsmXL13gSYgVTNgucPXsWAQEBorKWLVvip59+KrNrEhEREVHFxITuPSWVSnXKqlevXmjdzMxM+Pv7o23btpgzZw4aNmyI5ORkTJkyRVTPzs4OVatWRd26dVGtWrXXjjE1NRXR0dFYtWqVqFxfXx+PHj2ChYVFie+lwPOJWgFjY2M8fvxYuHZ8fLxO8qRWq4VvBwGIkloAuH79Oj777DP069cPw4YNQ926dbF3715hamVJabVanbIXr5mWloavvvpKGLEDAI1Gg7p165bqmkRERERUeTCho1f6888/YWtri/DwcKHs7t27OvXWrl2Ljz76CNevX0dMTIzO6FpJGRsb4+uvv8aAAQNeq5/CPD9tskBaWpowVdLY2Bienp7CQidFKZhmWWDHjh3w8vJCUFCQUFbYs3qRVCoVFoJ53vPf9D1f93nGxsaIiopCkyZNXnkdIiIiInq38Bs6eqUHDx7A1tZWVBYbGys6vnnzJn7++Wd8++23mDx5MmbPno3c3NwSXUdfX180ImVvb6+z8mRZuX37Nq5fvy4qO3r0KD7++GPh2ufOnSvx9gLFeVaA7r1aWlri3r17omd28+ZNYRXLl7G3t0d8fHyJ4iQiIiKidwMTOnolBwcHxMXF4d9//4VarcaWLVtw+fJlUZ1Zs2Zh8ODBsLGxgZeXF+zs7LBixYoSXcfKygpnz56FWq1GTk6OsPDK0qVLhQVNbty4gb/++uu178nU1BTTp0/H3bt3kZ+fj1WrViElJQXdu3cH8Gx7gNq1a2PSpEnCCFtmZqZoK4fCODg44I8//kBaWhqePn2K5cuXi6ZoPn+vZ86cQW5uLlQqFezs7PDBBx9gxYoV0Gq1ePjwIYKDg9GgQYNX3sugQYMQERGB/fv3Q6VSQaPR4MyZM+/NJu1ERERE7zMmdO8hmUwGQ0NDnTKZTFZomUKhwKhRozB06FC0bt0aR48exbx586DRaAAAMTExSElJwaBBg4S2U6ZMwfr163Hnzp1ixzV06FDMnz9f2DrBysoK69atw+nTp9G2bVvI5XKMGjVKNF2ysHsxMDCAgYGBzr08X9a4cWP07NkTn3/+OVxcXHDo0CGsXbsWRkZGQp3IyEiYmpqiV69ecHJyQs+ePZGcnPzSa/fs2ROdOnWCv78/PD09cevWLUybNg35+fmieqNGjUJgYCA6dOiAf/75R9h+4fjx43B3d0e/fv3g5+eHJk2aiN6LkZGRzr15eHggPDwcy5cvh0KhgEKhwIIFCwrdBoGIiIiI3i0SbWGrLhC9w+Lj4xEREYH169eXdygVUkHS+uuRq7iecr+co9FVz8YScwJ7lncYb112djYuXLiApk2bomrVquUdDv0vvpeKie+lYuJ7qZj4Xiqmgt9jxdkGjIui0BuVkZEBHx+fQldrBJ5tKRAbG6sz0vUmSaVSnVEu0mVrbVbeIRSqosZFREREVB6Y0NEbZWFhgcTExPIOQ0Qul2Pt2rXlHUaFF/iZV3mHUCSNRqOzwigRERHR+4i/iIhIh0qlQk5OTnmHUSQmc0RERETP8FcRERWKn9cSERERVXxM6IiIiIiIiCopJnREVCiJRFLeIRARERHRKzChIyIdMpkMVapUKe8wBAV7HhIRERGRGFe5JKJCRfx2GLfTM8s7DNham1XoFTeJiIiIyhMTOiIq1O30zAq5sTgRERER/R9OuSQiIiIiIqqkmNARERERERFVUkzo6I24ePEiPv30U6jVagDAihUrsGLFircaw7Rp07B169a3ek0iIiIioreJ39DRG9GkSRNs3rxZOFapVG89hvDw8Ld+TSIiIiKit4kjdPROiIyMRGRkZHmHQURERET0VnGEjt4JeXl55R0CEREREdFbxxE6KrX58+djwYIForKcnBzI5XIkJSXBx8dHdC4/Px8RERFo164dHB0d8fnnn+PixYvC+dTUVHh6eiI5ORmdO3eGQqGASqXCtWvXMGzYMLi5ucHV1RW9e/eGUqkEANy7dw+urq5YuXIlVq5cCblcjujoaADAsGHDsHv3bqH/rKwszJw5E+7u7pDL5ejTpw+OHTsmirFdu3Y4fPgw/P394eTkBG9vb6xcubJEz+Xy5cvo168fXFxcoFAoMG/ePOFcXl4eli1bhg4dOsDZ2Rlubm7Yu3cvAECr1WLt2rX45JNPIJfL0bFjR/z888+ivkNCQrBx40aEhYVBoVBg6dKlAICMjAxMmDABzs7OaNWqFcLDw4XvF4mIiIjo3cUROiq1du3aITg4GEFBQULZ8ePH0bBhQ6jVap2EYuPGjejYsaOQcIWHh2Pw4MH4448/YGJiArVajezsbKxYsQLr16+HhYUF9PT0kJubi0GDBsHZ2RkymQwHDhzA+PHjERMTAysrKyQkJGDJkiUAgDFjxgjXU6lUom/3xowZg2rVqmHXrl0wNzfH0aNHMWnSJCxbtgzOzs4AAIlEgrCwMMyePRuurq7477//MGDAANSvXx8dOnQo1nOZPHkyBgwYgJ49eyI/Px/37//fXm7ffvstbty4gdWrV6NevXp4+vSpMLoYGRmJvXv3YunSpfjoo49w9epVjB8/HiqVCsOGDRPuadOmTejTpw/i4+Oh0WgAAKNHj8aHH36Iw4cPQ61W4+uvv8bSpUsxfvz4YsVMRERERJUTR+io1JycnPDw4UNcu3ZNKDt48KDOyFwBQ0NDzJgxA0ZGRjAyMsLMmTOhr6+PAwcOCHUePXqEHj16wMrKCnp6z/56NmnSBG5ubpDJZACADh06QE9PD9evXy92rCdPnsSFCxcwb948WFhYQCKRwNPTE8OGDcOiRYtEdfv37w83NzdIpVLUr18ffn5+OHjwYLGvde3aNbRp0wYAIJVKUaNGDQBAUlISYmJisGLFCtSrVw8AYGRkBBMTEzx+/BirV69GeHg4PvroIwBAgwYNEB4ejlWrViErK0voPy8vD/379wcA6Onp4dixY7hz5w7CwsJQrVo1WFhYIDQ0FBs2bOAoHREREdE7jgkdlZpUKoW3tzcOHz4MANBoNDhy5EiRCZ27uzukUqlwrKenBw8PD1y6dElUTy6Xi47z8vIQFRWFfv36wcPDA87OzkhPT0dmZmaxY1UqlWjXrh2qVKkiKu/cuTNOnz6N/Px8oaxZs2aiOra2tkhLSyv2tfz9/TFy5EicOXNGVH7w4EF4e3vDxMREp82FCxdgbm6Oli1bispbtGgBU1NT0dRUFxcXUZ2kpCS0bdsW+vr/N+Ber149aLVa3L17t9hxExEREVHlw4SOXkuHDh1w6NAhAMDp06dhY2MDGxubQutaWFjolFlaWiInJ0c41tPTg5mZmahOWFgYNm3ahMGDB2Pnzp1QKpX44IMPoNVqix1nWlqaMFL2PGtra6jVatG0yIKRwAL6+vqihO9Vpk2bhmHDhiE4OBhDhw7FzZs3ATz7zq2wGF4WHwDUqFFDlJiZm5uLzqempmLHjh2Qy+Wi/54+fYqHDx8WO24iIiIiqnz4DR29Fnd3d0yZMgVZWVmIiYkpcnQOQKGjRffv3xclgBKJBBKJRDjOzc3Ftm3bsHPnTtSvXx/As8VDSjJiBgBmZmaFtklPT4e+vj6qV69eov5epUOHDvD29sacOXMwYsQI7N69GxYWFkhJSSlRfMCzZO/5ZLhgKmoBY2Nj9O3bF9OmTSu7GyAiIiKiSoEjdPRaDA0N0apVKxw7dgwHDx5Ep06diqx75MgR0bdgGo0Gx48fR+PGjYts8/jxY6jVatSuXVsoO3nypM63Yfr6+i8dsfP09MThw4dFo4EAEB0dDTc3NxgaGhbZtrT09PQwceJE/PPPP7h37x7atWuHgwcPIiMjQ6euo6MjHj16hHPnzonKk5OTkZWVBUdHxyKvY29vD6VSWaIRSyIiIiJ6NzCho9fWoUMH/Pzzz6hSpQrq1KlTZL3q1asjKCgIWVlZyM3NRVhYGKpXrw4vL68i21haWsLW1ha//vortFotrly5grlz5wqLihSwsrLCuXPnoNFo8PjxY51+nJ2d0axZM0yePBkZGRnQarXYt28fVqxYgcDAwNLeuo78/Hzhm7z8/Hz88ssv+OCDD2Bubg5HR0d4eXlh6NCh+O+//wA8W7XyyZMnMDY2xtChQzF16lRcvnwZAHDx4kVMnDgRw4cP1/n273kdOnTA48ePERoaKiSLqampwtYORERERPTuYkJHr83LywuXL19Gt27dhDKZTAYDAwPRca9evdCiRQt06dIFrVu3RlpaGqKiooR6BgYGOiNlEokEkZGR2LdvH1xdXTFhwgQEBQWhTp06wpL9APDJJ58gOzsbLi4uCA8PF675/PdwixcvRo0aNeDr6wsXFxesXbsWixYtwscffyyK88Vv6AwNDXXKivL06VNMmzYNDg4OUCgUOHLkCFasWCEsWLJgwQJ4eXkhICAAzs7OcHd3x59//gng2b55n332GQIDAyGXyzFu3Dh8+eWXGDx4sCi+F5+RkZERoqKikJGRAR8fHzg7OyMgIKBEq4ASERERUeUk0XKeFhE9Jzk5GQBw5Px93E7PLN9gANhamyHwM6/yDqPcZWdn48KFC2jatCmqVq1a3uHQ/+J7qZj4XiomvpeKie+lYir4PdaiRYtX1uWiKEQl4O/vj3///bfI80uWLIGbm9tbjOjNqUhJlEaj0VkMhoiIiIiY0BGVyKZNm8o7hLdCpVIhJyfnpd/uvU1M5oiIiIgKx19JRFQozsYmIiIiqviY0BEREREREVVSTOiIqFDPb/BORERERBUTEzoi0iGTySrE93PPb01BRERERLq4KAoRFSrit8Plum0BtysgIiIiejUmdERUqNvpmbiecr+8wyAiIiKil+CUy/dIp06dkJqaWq4xnD9/Hj4+PnBxccH58+fLNZbKIiQkBKtXry7vMIiIiIioAmJC9x5RqVRQq9XlGkNkZCR69+6N+Ph4NGnSpFxjqSxUKhVUKlV5h0FEREREFRATOiqV4OBg7Ny5s8Ttrl+/jg4dOkBPT4+bRRMRERERvSb+oqZSycvLQ15eXonbZWdnQyaTvYGIiIiIiIjeP0zoKqGQkBBs3LgRYWFhUCgUWLp0KbKysjBz5ky4u7tDLpejT58+OHbsmE7blJQUjBgxAgqFAq1atcLMmTORm5srqnPkyBH07t0bcrkcHh4eCAsLQ1ZWFgAgOTkZcrkcu3fvRmhoKORyOZKSkl4Zc2RkJORyOW7fvg1fX1+0adMGGo0Gqamp8PT0RHJyMjp37gyFQiFML9y9ezd8fHzg6OgIPz8/nDlzRtTnxYsX0b9/fzg7O8Pb2xurVq1CaGgodu3aJdRp2bIl7t27J2q3cuVKhIaGCsf5+fn44Ycf4O7uDicnJ4wcORJpaWnC+cWLF+PHH39ESEgIFAoFFAoFRo0ahfT0dFG/GRkZCA4ORuvWrSGXy+Hp6YmbN2/Cx8cHJ06cENUNDAwUxfkqt2/fxrBhwyCXy+Hm5oZJkyYJ7wQA+vTpA7lcLvzn7OyMxo0bl/s3k0RERET0ZjGhq4RUKhU2bdqEhg0bIj4+HiNHjsSYMWOQkZGBXbt2ITExEWPGjMGkSZNw6tQpUdvJkyfD398f8fHx2LhxI44dO4bw8HDhfEJCAoKCgjB69GgkJiZi165dePDgAcaPHw8AaNGiBZRKJbp164YZM2ZAqVTCwcHhlTGPHDkSSqUSNjY22LlzJ44dOwY9PT2o1WpkZ2djxYoVWL9+PU6cOAGZTIbY2FjMmjUL3333HRITEzFs2DCMHDkSmZmZAIDc3Fx89dVXaN26NeLj47Fz504kJSXhwIEDou8Ec3NzdUYSc3NzRd+kLV68GMeOHcPGjRsRFxeHBg0aCPcLPNtgOyoqCtWrV0dMTAyOHj0KY2NjzJw5U/ROPv/8c+jr6yM6OhpKpRK///47bGxs0LlzZ1HylpGRgZMnT8Lb2/uVz63A9u3b0blzZ8TFxeHPP/9Eeno6li5dKpzfsmULlEql8N+IESPQunVr1KxZs9jXICIiIqLKhwldJZWXl4f+/fsDeJaEXbhwAfPmzYOFhQUkEgk8PT0xbNgwLFq0SNSuW7duQiJRr149zJw5Ezt27BASnMWLF2PYsGHw9vaGRCKBubk55syZg7///huJiYlv5F4ePXqEHj16wMrKSviubsmSJRg/fjxcXV2hr6+PTp06wd3dHdu3bwcA7Nu3D+bm5ggMDIS+vj5MTEwwZ84c0ahVcTx8+BDr1q3DggULUKdOHRgZGWHChAlISUnB33//LdSztbXFhAkTYGxsDENDQwQGBuLIkSPIz88HAERFRaFWrVqYOXMmqlevDgAwMzODVCqFr68vDh48KCSa0dHR8PT0hLGxcbHj7Ny5M3r16gWZTAYTExMMHToUMTExhda9cOECoqKiMHfu3BI9CyIiIiKqfJjQVVIuLi7Cn5VKJdq1a4cqVaqI6nTu3BmnT58Wkg4A8PT0FNVp3bo18vPz8d9//yE/Px9nzpxBly5dRHWMjIzg5eWFhISEN3Anz8jlcuHP+fn5+Ouvv3RGsBwcHHDx4kUAwOXLl6FQKETnq1WrBicnpxJd99KlS6hRowYaNGgglOnp6cHe3l64FgA0adIEEolEOLaxsYFarcb9+8/2aTt48CB8fX0LvUb9+vVha2uLuLg4AMCuXbuKrFuUDz/8UHRsY2MjmhZa4OnTp5g4cSJCQkI4OkdERET0HuDG4pWUubm58Oe0tDTUqFFDp461tbWQdBScf74dAEilUpiZmSEnJwf3799HXl5eoX3VqFHjjX2PpaenBzMzM+H43r17yMvL00ks8/Pz0bp1awDA48ePYWlpqdNXwejYy2i1WuHPqampuHXrliihBJ6NgD5fZmhoKDpvYGAAANBoNACeTaMs7LkV6NGjB/bt24cGDRrg5s2bcHd3f2Wcz3txRVCZTKbz7SMAzJ8/Hx9//DE6duxYov6JiIiIqHJiQldJPf8D38zMrNDRmvT0dOjr64uSnNTUVNFoVH5+PjIzM2FpaQlTU1NIpVKkpaXBxsZG1FdRSWNZkEgkotGvgqmIhw4dQrVq1QptU7NmzUITzBcXKin4Tq+oOsbGxmjUqBF27NhR2vABABYWFkhJSSnyfNeuXREZGQlbW1t07twZ+vpl/z+9I0eO4Pjx49i2bVuZ901EREREFROnXL4DPD09cfjwYeTk5IjKo6Oj4ebmJhpd2r17t6jOyZMnUbVqVdSqVQtGRkZQKBTYu3evqM7Tp09x+PBh0RRIfX190UhXWTIxMUG9evUQHx9fZB1HR0fExsYKI2TAs+/hkpOTRfUsLS1x+/Zt4Vij0eDkyZPCcfPmzXH9+nWdRLCkvL29sXXr1iLPW1lZoXnz5li1alWJp1sWR0ZGBkJCQjB//vwSfZtHRERERJUbE7p3gLOzM5o1a4bJkycjIyMDWq0W+/btw4oVKxAYGCiqe+rUKSHxuHnzJkJDQ/HVV19BKpUCAEaPHo2VK1fi4MGD0Gq1yMjIwLhx4+Dg4CBazdLKygpJSUnQarUlXoikOIYMGYKwsDCcOHEC+fn5yMvLw/Hjx4VrtW7dGtWrV8fcuXOhUqmQkZGBadOm6UyNbNOmDVatWoWcnBzk5eVh/vz5MDIyEs7XrFkT7du3x/jx4/Hvv/8CAJ48eYIjR46UKN6AgADcu3cPU6dOxaNHjwA8W+zl+e8Xu3btilq1aqFly5aleiYvM336dPj7+xdrxVEiIiIiencwoauEZDKZTuKyePFi1KhRA76+vnBxccHatWuxaNEifPzxx0IdAwMDLFiwAH/88QdcXV3Rr18/9O3bF8OGDRPqyOVy/PDDD1i+fDlcXFzg6+sLOzs7LFy4UHS93r1748yZM5DL5VizZk2xYzcwMBC+Pys4fvFeAMDf3x+jRo1CWFgY5HI53N3dsXbtWlGdZcuW4dq1a3Bzc0OvXr3w8ccfo127dqI6QUFBMDY2Rvv27eHj4wOpVIrPP/9ctLl5eHg4WrRogQEDBsDR0RE+Pj6iPfwMDQ0L3Qzd0NBQmDpZpUoVbNmyBWq1Gh06dICzszM6duyImzdvCvXv3LmD7t27F/tZFZDJZDrXf/65ZWZmIiYmBmvXrhXtRefq6ooHDx6U+HpEREREVHlItG9q3hxROZgyZQpcXV3h5+dX3qEINBoNMjMz4e/vj//3//4fatWqVd4hvVTBtNVfj1zF9ZT75RZHPRtLzAnsWW7Xr2iys7Nx4cIFNG3aFFWrVi3vcOh/8b1UTHwvFRPfS8XE91IxFfwea9GixSvrclEUem0ZGRnw8fEp8ps6iUSC2NjYQkfiypqBgYEwfbSiGDp0KP766y98/fXXomSuIj03IiIiIqqcmNDRa7OwsHhjm46X1HfffVfeIegoakpqRXpuhbG1Nnuvr09ERERUGTChI6JCBX7mVd4hQKPR6OzBR0RERET/h7+UiEiHSqXS2QajPDCZIyIiIno5/loiokJxvSQiIiKiio8JHRERERERUSXFhI6ICiWRSMo7BCIiIiJ6BSZ0RKRDJpOhSpUqb/WaGo3mrV6PiIiI6F3AVS6JqFARvx3G7fTMt3ItW2uzCrGqJhEREVFlw4SOiAp1Oz0T11Pul3cYRERERPQSnHJJRERERERUSTGho7cqNTUVjo6O5R1GibVs2RL37t0r7zCIiIiIiESY0NFbpVarkZubW95hlFhubi7y8vLKOwwiIiIiIhEmdPTG+fj4IDU1tbzDICIiIiJ65zChozdOrVZDrVaXdxhERERERO8cJnT0xvz888+Qy+VISUmBr68vPDw8kJ+fDwBYv349vL294ejoiC5duiAmJkbUNicnBzNmzICrqytcXFwwefJkZGVlCedDQkKwceNGhIWFQaFQYOnSpVCr1bC3t8ehQ4fg4+MDZ2dnDBkyBBkZGTh+/Di6d+8OhUKBwMBAZGRkCH3l5uZi6tSpaNu2LeRyOT755BP89ttvpb7vrKwsjB8/HgqFAq6urujXr5/o/L59++Dn5wdnZ2e4uLhgzpw5wrkjR46gd+/ekMvl8PDwQFhYmOi+9+zZg4kTJ2Lbtm3w8PDAkCFDAAD5+fn44Ycf4O7uDicnJ4wcORJpaWmlvgciIiIiqhyY0NEbM3DgQCiVStjY2GDnzp2IjY2FVCpFfn4+/vjjD0RFReHMmTMICgrChAkTRElWcHAwUlJSEB0djUOHDkGj0SA0NFQ4r1KpsGnTJjRs2BDx8fEYOXIkDAwMoFarsWzZMqxevRoJCQlo1qwZpk2bhrlz5yIiIgLHjx9H3bp1RUmUSqWCu7s79u7dC6VSiZUrV2LJkiW4cOFCqe57yZIlMDU1xbFjx5CQkIAffvhBOLd9+3bMmTMHkyZNglKpRHx8PEaMGAEASEhIQFBQEEaPHo3ExETs2rULDx48wPjx44X2ubm5+Oeff5CYmIiYmBisWrUKALB48WIcO3YMGzduRFxcHBo0aCBqR0RERETvJiZ0VC6+++471KlTBwDQrl07NGnSBHFxcQCAq1evIiYmBgsWLIClpSVMTEwQEhKCmJgY0UqTeXl56N+/PwBAT+///iqPHj0aderUgVQqxaBBg3Do0CGMHTsWdevWhb6+PoYMGYKjR48K9atVq4Zu3brBxMQEAPDhhx+iVatWOHXqVKnu7dq1a2jVqhVkMhkAoFatWgCA7OxszJ8/HwsXLkTr1q0hkUigp6cHc3NzAM+SsmHDhsHb2xsSiQTm5uaYM2cO/v77byQmJgr9X7lyBUFBQZDJZNDT08PDhw+xbt06LFiwAHXq1IGRkREmTJiAlJQU/P3336W6ByIiIiKqHJjQ0Vunp6eHunXrispsbGyEhVPOnTsHJycnmJmZCeerVasGOzs7/PPPP0KZi4tLof03bNhQ+LOFhYVOXQsLC2RmZoraxMTECMmUk5MT9u3bp1OnuHr37o0FCxZg165d0Gg0Qvnp06dRpUoVODs767TJz8/HmTNn0KVLF1G5kZERvLy8kJCQIJQ1aNBAuC8AuHTpEmrUqIEGDRoIZXp6erC3t8fFixdLdQ9EREREVDnol3cA9P6RSCSQSCSiMplMJmxnkJqaivj4eMjlclEdtVqNR48eCccFI1sv0tfX/Wv9fHL4oj179mDmzJn4+uuvMWPGDNSsWRPffvsttFptcW9JxMfHB3Xr1sX//M//YMWKFZg+fTrc3NyQkZGBGjVqFNrm/v37yMvLK/R8jRo1RKuEvngvqampuHXrls7zysvL0ykjIiIioncLEzqqcIyNjeHp6YmlS5e+tN7z0yxfxy+//IKJEyeib9++Qtndu3dhY2NT6j6bNGmC1atXY+/evRg1ahR2794NCwsL3Llzp9D6pqamkEqlSEtL07luWlqaKNGTSqWi88bGxmjUqBF27NhR6niJiIiIqHLilEt64/T19Us02mVvb49z587h6dOnbzCq//PgwQPY2toKx48fP0ZSUlKZ9N2lSxc0bNgQZ8+ehaOjI54+fYpjx47p1DMyMoJCocDevXtF5U+fPsXhw4fh7e1d5DWaN2+O69evIz09vUxiJiIiIqLKgwkdvXFWVlY4e/Ys1Gp1sZI0R0dH1K5dG5MmTcLdu3cBAJmZmcKiKWXNwcEBW7duRW5uLjIyMjB16lRRgldS586dQ3Z2NgDg5MmTuHr1Kj766CMYGxtj/PjxmDx5snAvGo1G+FZv9OjRWLlyJQ4ePAitVouMjAyMGzcODg4OcHBwKPJ6NWvWRPv27TF+/Hj8+++/AIAnT57gyJEjpb4HIiIiIqocmNDRGzd06FDMnz8fbdu2xfnz52FoaKhTRyaTCatCAkBkZCRMTU3Rq1cvODk5oWfPnkhOThbVL6wfIyMjGBgYiMqqVq2qU+/5sqlTp0Kr1cLLywu9evWCs7MzunfvLuyZBwCGhoaFfptXmLVr16JVq1ZwdHTErFmzMHfuXDRq1AgA0K9fP3z77beYPXs2nJyc4OLiImxrIJfL8cMPP2D58uVwcXGBr68v7OzssHDhQlEchd13eHg4WrRogQEDBsDR0RE+Pj6FjgQSERER0btFoi3tyg9E9E4qSJx/PXIV11Puv5Vr1rOxxJzAnm/lWpVVdnY2Lly4gKZNmxb6jxRUPvheKia+l4qJ76Vi4nupmAp+j7Vo0eKVdbkoClEJzJs3D5s3by7y/MCBAxEYGPgWI3pzbK3N3slrEREREb1LmNARlcDkyZMxefLk8g7jrQj8zOutXk+j0ZTZyqVERERE7wv+eiIiHSqVCjk5OW/1mkzmiIiIiEqOv6CIqFD8vJaIiIio4mNCR0SFkkgk5R0CEREREb0CEzoi0iGTyVClSpVSt9doNGUYDREREREVhYuiEFGhIn47jNvpmSVuZ2tt9tYXVCEiIiJ6XzGhI6JC3U7PfGv70BERERFR6XDKJRERERERUSXFhI7KTKdOnZCamlreYZSL3bt3Y9iwYcLxtGnTsHXr1jd2vZCQEOzevfuN9U9ERERElQOnXFKZUalUUKvV5R1GuVCpVFCpVMJxeHj4W70eEREREb2fOEJHFVJwcDB27txZ3mGUmVOnTmHQoEHlHQYRERERvWOY0FGFlJeXh7y8vPIOo8y8a/dDRERERBUDEzrSERISgo0bNyIsLAwKhQJLly5FVlYWZs6cCXd3d8jlcvTp0wfHjh3TaZuSkoIRI0ZAoVCgVatWmDlzJnJzc0V1jhw5gt69e0Mul8PDwwNhYWHIysoCACQnJ0Mul2P37t0IDQ2FXC5HUlJSseI+e/YsAgICoFAooFAoEBAQgCtXrgjnN2/ejG+++QZz585Fq1at4ObmhuXLl0Oj0eD7779HmzZt0LZtW6xZs0bU7+DBgxETE4PRo0ejVatWUCgU+O6771465XHYsGHCN259+vTBiBEjcOrUKcjlcsyaNQsAsGLFCoSGhuq0bdmyJe7duyccnzt3Dv7+/nB0dES7du0QGRkJrVYrapORkYEJEybA2dkZrVq1Qnh4+Hs7/ZWIiIjofcKEjnSoVCps2rQJDRs2RHx8PEaOHIkxY8YgIyMDu3btQmJiIsaMGYNJkybh1KlToraTJ0+Gv78/4uPjsXHjRhw7dkz0PVlCQgKCgoIwevRoJCYmYteuXXjw4AHGjx8PAGjRogWUSiW6deuGGTNmQKlUwsHBoVhx5+fnY9KkSTh+/DhOnDgBHx8fBAUFCeelUin2798PiUSCI0eOYPfu3di9ezcmTpyIzMxMHDx4EFu3bsW2bdtw4sQJoZ1arcasWbPg4+ODkydP4vfff0diYiIWLlz40mdYkPBt2bIFy5cvh7OzM5RKJYKDg3XqPC83N1cYzcvMzMTQoUPh6emJ+Ph47Nq1C8nJyThw4ICozejRo2FkZITDhw9j7969uHTpEpYuXVqs50ZERERElRcTOipUXl4e+vfvD+BZEnbhwgXMmzcPFhYWkEgk8PT0xLBhw7Bo0SJRu27dusHb2xsAUK9ePcycORM7duwQEpfFixdj2LBh8Pb2hkQigbm5OebMmYO///4biYmJrxWzs7MzHBwcoK+vDz09PXz22We4ePEinjx5ItSpVq0aJk6cCENDQ1haWqJXr144efIkvv32W8hkMlhbW6NXr144cuSIqG9XV1f4+voCAGrVqoVZs2Zh06ZNOqOPZe3PP/9ErVq1MHr0aMhkMpiYmGDu3LnIz88X6hw7dgx37txBWFgYqlWrBgsLC4SGhmLDhg0cpSMiIiJ6xzGho0K5uLgIf1YqlWjXrh2qVKkiqtO5c2ecPn1alFx4enqK6rRu3Rr5+fn477//kJ+fjzNnzqBLly6iOkZGRvDy8kJCQsJrxZydnY2IiAj06dMHbm5uaNWqFbRaLTIzM4U6H374IfT1/29xV3Nzczg4OEAmkwllFhYWojYA4OHhITpu2bIlJBIJbt269Voxv8qlS5dE7wIATE1N8fHHHwvHSUlJaNu2rei+6tWrB61Wi7t3777R+IiIiIiofHHbAiqUubm58Oe0tDTUqFFDp461tTXUajXu378vnH++HfBsmqOZmRlycnJw//595OXlFdpXjRo1XnsPuzFjxuDJkycYP348WrZsCRMTEzRv3lz0vZlUKtVpZ2Zm9sq+q1evrlNmYmIifPtXVl78Ni43NxfW1tY69SwsLIQ/p6am4vfff8fevXtFdVQqFR4+fIg6deqUaYxEREREVHEwoaNC6en93+CtmZkZ0tLSdOqkp6dDX19flOykpqaiQYMGwnF+fj4yMzNhaWkJU1NTSKVSpKWlwcbGRtRXUUljcd24cQOJiYmIjY2FqampEItGoyl1n8+7f/++6Fij0eD+/fuoWbNmqfvU09PTWfnyxedsZWWFR48e6bR9fuTN2NgYffv2xbRp00odCxERERFVTpxySa/k6emJw4cPIycnR1QeHR0NNzc3GBoaCmUFKzsWOHnyJKpWrYpatWrByMgICoVCZyTp6dOnOHz4sPDtHQDo6+vrjFa9zIMHD2BqaiokcwAKXYWztGJjY0XHx48fh7W1dbGT0MLux8rKCrdv3xaVPb8YCwA0b94c8fHxorK0tDTRyp/29vZQKpUlel5ERERE9G5gQkev5OzsjGbNmmHy5MnIyMiAVqvFvn37sGLFCgQGBorqnjp1Clu3bgUA3Lx5E6Ghofjqq6+EqY6jR4/GypUrcfDgQWi1WmRkZGDcuHFwcHAQrWZpZWWFpKQkaLXaYk1rbNCgAXJycrB//35otVqcOnUKUVFRsLKyKpNncOLECWzfvh1qtRo3b97E7NmzMWjQINFI5stYW1vj33//xaNHj/D48WMAgJubG86ePYvjx48DePa93IYNG0Qjnt7e3lCr1YiIiIBarcaDBw/w9ddf44MPPhDqdOjQAY8fP0ZoaCgyMjIAPBudVCqVZXLvRERERFRxMaEjHTKZTDTqBjxbnbJGjRrw9fWFi4sL1q5di0WLFokW5zAwMMCCBQvwxx9/wNXVFf369UPfvn0xbNgwoY5cLscPP/yA5cuXw8XFBb6+vrCzs9PZAqB37944c+YM5HK5zr5whTExMUFkZCSWLVsGFxcXzJs3D3PmzIGpqakw7dLQ0FDnvmQymWhBlKLKpk6dij/++AOtW7dG37590a1bNwQEBBTZ5sVjOzs7eHt7o3379vD390deXh7s7Owwd+5chIWFwcPDA99++y2+/fZbVK1aVVjgRE9PDytXrsSpU6egUCjQu3dvdO/eHQqFQujfyMgIUVFRyMjIgI+PD5ydnREQEIDr16+/8rkRERERUeUm0XKeFtFLBQQEIDAwEAqForxDeSuSk5MBAL8euYrrKfdfUVtXPRtLzAnsWcZRUXZ2Ni5cuICmTZuiatWq5R0O/S++l4qJ76Vi4nupmPheKqaC32MtWrR4ZV0uikIVXsHIU1H/9iCRSBAbG6sz+lZWpFIpDAwM3kjfFZmttdlbbUdEREREJceEjio8CwuL1950/HX8/PPP5Xbt8hT4mVep22o0mmJ/X0hEREREpcdfXESkQ6VS6axqWhJM5oiIiIjeDv7qIqJC8fNaIiIiooqPCR0REREREVElxYSOiAolkUjKOwQiIiIiegUmdESkQyaToUqVKqVqW7DvHxERERG9eVzlkogKFfHbYdxOzyxRG1trs9daHZOIiIiISoYJHREV6nZ6Zqk2FiciIiKit4dTLomIiIiIiCopJnRUYkOGDEFsbGx5h0FERERE9N7jlEsqsTVr1pR3CEREREREBI7QUQXi4+OD1NTU8g7jrXBwcCjvEIiIiIjoHcCEjioMtVoNtVpd3mG8FU+fPi3vEIiIiIjoHcCE7j0XFBSE1atXY8SIEXB2dkbHjh1x+PBhZGZmYsyYMVAoFOjatSvi4uKENl27dsWZM2cAADdv3kTHjh2xZ88edOzYEU5OTujevTuOHDlS7Bh+/vlnyOVypKSkwNfXFx4eHsjLyxP6Hzp0KBwdHeHh4YHIyEhRW09PT0RHR6Nnz55wcnJC3759cf36dZw/fx7+/v5wdXXFgAEDcPPmTaHN6dOnMXz4cGzevBk+Pj5wdnaGn58fEhISRH3n5ORgxowZcHV1hYuLCyZPnoysrCzhfEhICDZu3IiwsDAoFAosXboUALBq1Sp06tQJLi4uaNOmDWbNmgWVSgUAmDNnDuRyOQBALpfDz88PAHDmzBl07dpV59kMGTIE0dHRwrGjoyOuXLkCPz8/uLi44MaNGwCAuLg49OjRA46OjujSpQtiYmKK/fyJiIiIqPJiQveek0qlWLp0KXr06IGEhAQsXLgQU6dOxZQpU9C2bVvEx8djzpw5CAoKQmZmJgBApVIJCYpEIsHdu3cRFRWFFStW4NSpU5gwYQLGjRuHtLS0YsUwcOBAKJVK2NjYYOfOnYiNjYW+vj5UKhUGDhwIe3t7xMXFYdOmTdi7dy82b94siv/HH39EeHg4Tp06BV9fX0ybNg1TpkxBcHAw4uPj0bFjR0yePFloo1arcf78eezYsQMbNmxAYmIievXqheHDhyM9PV2oFxwcjJSUFERHR+PQoUPQaDQIDQ0VzqtUKmzatAkNGzZEfHw8Ro4cCQCoWbMmoqKikJiYiD179uDy5cvYsGEDAGDq1KlQKpUAAKVSiW3btuk80+e9WJ6dnY2FCxdi4cKFSEhIgJ2dHa5cuYKxY8di1KhRSExMxMyZMzF16lRcvXq1WM+fiIiIiCovJnSENm3aoHPnzpBKpbC3t0fLli0hkUjw6aefAgBatmyJ5s2b4/Tp04W2z83NRXBwMD788ENIJBK0a9cOTZs2FY3qlca2bdtgZWWFcePGoUqVKrCxscGUKVOwbt06Ub0vv/wSzZo1g0QiQb9+/XDhwgX4+fkJ99G/f39cvHhRNLp27949zJ07FxYWFtDT00NAQADkcjm2bNkCALh69SpiYmKwYMECWFpawsTEBCEhIYiJicG9e/eEfvLy8tC/f38AgJ7es/85+fr6ombNmgAAU1NTdO/eHadOnXqtZ/E8hUKBevXqQSKRAAAiIyPRr18/+Pj4QF9fH66urujbt6+QRBIRERHRu4urXBIaNWokOjY3N0fjxo1FZRYWFsII3YskEgmaNm0qKrOxsXntBU6SkpLg7e0tKnNwcMDVq1eRl5cHff1nf30bNmwoisXc3BwuLi6iMjMzM2RmZsLExAQAYGdnhzp16oj6dnd3R1JSEgDg3LlzcHJygpmZmXC+WrVqsLOzwz///AMrKysAEF2nwJUrV7BixQokJyfj/v37UKvVaNmy5Ws8CbGCKZsFzp49i4CAAFFZy5Yt8dNPP5XZNYmIiIioYmJCR5BKpTpl1atXL3Z7PT09GBgYiMoMDAyQn5//WnGlpqYiOjoaq1atEpXr6+vj0aNHsLCwAFC6+J9P1AoYGxvj8ePHwrXj4+N1kie1Wo1Hjx4Jx+bm5qLz169fx2effYZ+/fph2LBhqFu3Lvbu3StMrSwprVarU/biNdPS0vDVV18JI3YAoNFoULdu3VJdk4iIiIgqDyZ0VGEZGxvj66+/xoABA8q87+enTRZIS0sTpkoaGxvD09NTWOikKAXTLAvs2LEDXl5eCAoKEsru3r37ynikUqmwEMzznv+m7/m6zzM2NkZUVBSaNGnyyusQERER0buF39BRhaGvry8akbK3t9dZebKs3L59G9evXxeVHT16FB9//LFw7XPnzpV4e4EHDx7A1tZWVBYbG6tT78V7tbS0xL1795CbmyuU3bx5U1jF8mXs7e0RHx9fojiJiIiI6N3AhI4qDCsrK5w9exZqtRo5OTno3bs3lEolli5dKixocuPGDfz111+vfS1TU1NMnz4dd+/eRX5+PlatWoWUlBR0794dwLPtAWrXro1JkyYJI2yZmZmvXOjFwcEBf/zxB9LS0vD06VMsX75cNEXz+Xs9c+YMcnNzoVKpYGdnhw8++AArVqyAVqvFw4cPERwcjAYNGrzyXgYNGoSIiAjs378fKpUKGo0GZ86ceW82aSciIiJ6nzGhe8/JZDIYGhrqlMlksiLLnv+zgYGBTvui+niVoUOHYv78+cJ2CVZWVli3bh1Onz6Ntm3bQi6XY9SoUaLpkoXFb2BgoPNNn0wmE5U1btwYPXv2xOeffw4XFxccOnQIa9euhZGRkVAnMjISpqam6NWrF5ycnNCzZ08kJye/9No9e/ZEp06d4O/vD09PT9y6dQvTpk3T+Z5w1KhRCAwMRIcOHfDPP/8I2y8cP34c7u7u6NevH/z8/NCkSRPRczQyMtK5Nw8PD4SHh2P58uVQKBRQKBRYsGBBodsgEBEREdG7RaItbNUFondYfHw8IiIisH79+vIOpUIqSFp/PXIV11Pul6htPRtLzAns+QaiouzsbFy4cAFNmzZF1apVyzsc+l98LxUT30vFxPdSMfG9VEwFv8datGjxyrpcFIXeqIyMDPj4+BS6WiPwbEuB2NjYQkf53hSpVKozykW6bK3N3kobIiIiIio9JnT0RllYWCAxMbG8wxCRy+VYu3ZteYdR4QV+5lWqdhqNRmf1TyIiIiJ6M/iri4h0qFQq5OTklKotkzkiIiKit4e/vIioUPy8loiIiKjiY0JHRERERERUSTGhI6JCSSSS8g6BiIiIiF6BCR0R6ZDJZKhSpUqJ22k0mjcQDREREREVhatcElGhIn47jNvpmcWub2ttVuqVMYmIiIiodJjQEVGhbqdnlnhjcSIiIiJ6uzjlspjS09PRs2dP3L//5n/gHj16FEOHDn3j13lXFPfdDBkyBLGxsW8pKiIiIiKiN48jdMVkbW2NHTt2vJVrtW3bFm3btn0r13oXFPfdrFmz5s0HU4gVK1bg7t27mDFjhs65u3fvwsvLCyYmJqJyV1dXLFu2DACwbNkyLFu2DEZGRqI6v/76K0aMGIFHjx4BAJ4+fQo9PT3IZDIAQMOGDbFx48Y3cUtEREREVEEwoXvDdu7ciYSEBMyaNau8Q3mvBAcHw9XVFb6+vuUdClQqFVQqVaHn8vLyoKenB6VSWWT7/Px89OjRA7Nnz9Y5FxMTI/x5ypQpsLOzw6hRo14/aCIiIiKqFDjl8g3Ly8tDXl5eeYfx3uFzJyIiIqL3wXuT0A0ePBgxMTEYPXo0WrVqBYVCge+++04YOcnIyMC4cePg7u4OFxcXdO3aFQcOHBDa37t3Dy1bthSOt27dimnTpmHhwoVCmy+//BLXrl0D8Gz5dg8PD4SGhmL37t2Qy+VYu3ZtsWKNjo7GkCFDhOOgoCCsXr0aI0aMgLOzMzp27IjDhw8jMzMTY8aMgUKhQNeuXREXFyfqZ9u2bfD19YWLiwvc3d0RFBQkTM8DAK1Wi4iICLRt2xaOjo7w9/dHUlISHB0dRf3ExcWhR48ecHR0RJcuXUSjQsXh6uqK06dPw9/fHy4uLvD29sYvv/yiU2/Hjh3o1q0b5HI5vLy88MMPP0CtVgvnExIS0LNnT8jlcrRu3Ro///wzAPG7SU5Ohlwux+7duxEaGgq5XI6kpCQAQNeuXXHmzBmhv7S0NEycOBEKhQIuLi4ICAjAuXPnhPP5+fmwt7fHiRMn0L17dzg5OaFjx47Ytm1bie6fiIiIiOhNeW8SOrVajVmzZsHHxwcnT57E77//jsTERCxcuBAAkJOTA19fXxw8eBCJiYkIDw/H9OnTkZ6eDuDZiE9ubq7Qn0QiQXR0NNLS0rBnzx7ExcXBwcEBEyZMgFarhZ6eHmJjYzFjxgx069YNSqUSgwcPLlasL07Rk0qlWLp0KXr06IGEhAQsXLgQU6dOxZQpU9C2bVvEx8djzpw5CAoKQmZmptDOxMQEERERSEhIwMGDB6HRaBARESGc/+mnn7Bt2zYsW7YMp06dwtdff41x48bh6dOnQp0rV65g7NixGDVqFBITEzFz5kxMnToVV69eLfazV6lUCA4ORmhoKBITEzFv3jz88MMPosTw999/x8KFCxEaGgqlUokNGzbgzJkzwlRVjUaD8ePHY8qUKVAqlTh8+LAwnfL5d9OiRQsolUp069YNM2bMgFKphIODg85zVavVGDhwIMzNzXHw4EGcPHkSvXv3xtChQ3Hjxg3huavVasyZMwfh4eE4deoUFixYgO+++w4XLlwo9v0TEREREb0p701CB0D0TVWtWrUwa9YsbNq0Cbm5ubC1tUX79u2FhSccHBxQv359/PXXX0X2J5PJEBoaCjMzMxgYGGD06NG4fPky7ty5U+axt2nTBp07d4ZUKoW9vT1atmwJiUSCTz/9FADQsmVLNG/eHKdPnxbadOzYEXZ2dpBIJDAyMkLv3r1F32pt2LAB06ZNg729PfT09ODm5oaBAweKNoeOjIxEv3794OPjA319fbi6uqJv377YsGFDsWPPycnBV199haZNmwIAXFxcMHz4cKxfvx7As2Rt0aJFmDp1KpydnQEAH3zwARYsWIDff/8dN2/eRGZmJnJzcyGXywE8e/YWFhalfJrPvm3U09PD9OnTYWJiAqlUip49e6Jr166IjIwU1R05ciRatGgBiUQCBwcHtG/fHgcPHiz1tV+Un58PuVwu+m/lypVl1j8RERERvbveq0VRPDw8RMcFSdGtW7dQv3597NixAzt27MC1a9eQlZWF3Nxc0YjXi+rXrw9DQ0Ph2MjICBYWFkhNTYWNjU2Zxt6oUSPRsbm5ORo3biwqs7CwEMWbkpKC5cuX4/Tp00hLS4NarYa5uTkA4MmTJ7hx44aQIBVo37495syZIxyfPXsWAQEBojotW7bETz/9VKL4X3z27u7uwpTJO3fuIC0tDZ988omoTs2aNdGyZUucOnUKPXv2RKtWrTBixAhMmTIFDRs2LNH1X6RUKtGpUydIJBJReefOnTF16lRRWbNmzUTHtra2SEtLe63rP08qlb50URTg2Qjm/v37hWMjIyMcPXoUenrv1b/JEBEREdEL3quErnr16jplJiYmyMrKwqpVq/D//t//w8SJE+Hm5gZLS0sMHDgQWq22yP6eT+YKGBgYID8/v0zjBp796H9RYfdTIDMzE/7+/mjbti3mzJmDhg0bIjk5GVOmTAEAPH78uNA+atSoITpOS0vDV199JUp8NBoN6tatW6L4zczMRMcmJiZCDGlpabCwsIC+vu5fxxo1aiA1NRUAsGTJEmzbtg3Dhg2Do6Mjpk+fXupRurS0NDg5ORV6vbt374rKXnzP+vr6b+Qdv0xRq1wSERER0fvtvUroXtx4WqPR4P79+6hZsyZ++eUXzJ49G23atBHOFyQSldGff/4JW1tbhIeHC2XPJypVq1YF8GwxGEtLS6H8xWdkbGyMqKgoNGnS5LXiuXfvnmjUMi0tDTVr1gTwLNnLyMhAfn6+TuKalpYGhUIBANDT00OfPn3g6+uLSZMm4ZtvvsHq1atLFY+ZmVmh77cguSQiIiIiqgzeq/lasbGxouPjx4/D2toaNWrUwIMHD2Brayuc+++//3Dz5s3Xvqa+vv5LR/nelBfvBxDfv6mpKT766CMcO3ZMVOfAgQOi0Th7e3vEx8e/djzHjx8XHR85cgQff/wxAKBevXqwtbXFH3/8IaqTmpqK5ORknemaMpkM48aNe2lcr3runp6e2L9/v06d6OhotGvXrji3RERERERU7t6rhO7EiRPYvn071Go1bt68idmzZ2PQoEHQ09ODg4MDfvvtN+Tl5SElJQVTpkxBgwYNXvua1tbWOH/+PFQqlWjLgDfNwcEBcXFx+Pfff6FWq7FlyxZcvnxZVGfixIn4/vvvcfbsWeTn5+PIkSP4+eefRdMjBw0ahIiICOzfvx8qlQoajQZnzpwp8ejlunXrcPbsWQDPkrsNGzZg4MCBAJ6tGDp27FhhJUkAuHXrFgIDA+Hn54cPPvgAWVlZOH/+PLRaLVQqFTZu3IjmzZsXeT0rKyskJSVBq9UiKytL53yXLl0AAEpatPkAADR6SURBVN999x2ysrKQn5+PX375BXv27MFXX31VonsjIiIiIiov79WUy6lTp2LPnj0IDw+HTCZDv379hAU/5s+fj+DgYLi7u8PU1BRjxozBqVOnhG+l9PX1Rd9SGRoaQiaT6VxDJpOJyp2dnVG7dm24ubnB3t4eUVFRr4zzxT5kMpnOd1wv1nmxTKFQYNSoURg6dCgePXoENzc3zJs3D8OHDxfqe3l5ISQkBN988w3S09PRuHFj9O/fX0iqgGeLmYSHh2PZsmWYMmUK9PX10ahRI8ybN++V9/G8sLAwfPfdd7h27Rqsra0RHh4uSsi6du0KrVaLmTNn4s6dO6hWrRp69+6NESNGAHg24jh69Gikp6ejatWqcHJyEracePHdAEDv3r0xatQoyOVyfPnllxg3bpzo+ejr6+Onn37C/Pnz0aFDB+Tn56N58+b46aefUKdOHaEfQ0NDnW/7inr3RdHT09NZ1EQikWDz5s0wNDQUVrl8nqWlpVBfJpPBwMDgldcp7O8EEREREb3bJNrymA9YDgICAhAYGCh8j0XP9phTqVRo2rQptFotkpOTERwcjFmzZgnTIctC48aNcenSpTLrj96s5ORkAMCvR67iesr9V9T+P/VsLDEnsOcbioqys7Nx4cIFNG3aVPgGlsof30vFxPdSMfG9VEx8LxVTwe+xFi1avLLuezNCJ5VKizXK8ab5+/vj33//LfL8kiVL4Obm9lZiycjIwJw5c4RvBRs1aoTg4OBiJ3MHDhwQVs0sTIsWLfDTTz8Je/u9izw8PEQbsT9PIpFg06ZN+PDDD99yVERERET0vnhvErqCPc/K26ZNm8o7BIFCocCOHTtK3b5Dhw6v3D8NAJKSkkp9jYruxYV23iW21mZvtD4RERERvb73JqEjopIJ/MyrxG00Gg03OyciIiJ6i/jLi4h0qFQq5OTklLgdkzkiIiKit4u/voioUO/JeklERERElRoTOiIq1PMbzBMRERFRxcSEjoh0yGQyVKlSpdj1NRrNG4yGiIiIiIrCRVGIqFARvx3G7fTMV9aztTYr1QIqRERERPT6mNARUaFup2eWaGNxIiIiInr7OOWSiIiIiIiokmJC9w5IT09Hz549cf9++Y2mdOrUCefOnXtpnSVLliAiIqJY/YWEhGD16tVlEVqlsmLFCoSGhpZ3GERERERUSXDK5TvA2toaO3bsKNcYVCoVVCrVS+uMGTOmTPt7F72v901EREREpcMRuvfYzp07ERwcXOJ2wcHB2Llz5xuIiIiIiIiISoIJ3XssLy8PeXl5b60dERERERGVLSZ0FcDgwYMRExOD0aNHo1WrVlAoFPjuu++EqXcZGRkYN24c3N3d4eLigq5du+LAgQNC+3v37qFly5bC8datWzFt2jQsXLhQaPPll1/i2rVrAJ7tGebh4YHQ0FDs3r0bcrkca9eufWWcycnJkMvl2L17N0JDQyGXy5GUlCScP3nyJPz8/ODo6AgvLy8sW7YMWq1WOB8aGooVK1aI+vz111/RtWtXODs7Q6FQ4Keffir02idPnkT79u2RmpoKAJg8eTLWrVuHsWPHwsXFBa1bt8aUKVOQlZUlahcXF4cePXrA0dERXbp0QUxMjHAuKysL48ePh0KhgKurK/r16yeKq127dnBxcYG3tzeUSuUrn09WVhacnJyQmZkpKl+6dKnwXdzhw4fh7+8PhUKBVq1aYcSIEbh7926Rffr4+Oh8m7hnzx4MGzZMVBYVFYV27drB0dERAQEBuHr16ivjJSIiIqLKjwldBaBWqzFr1iz4+Pjg5MmT+P3335GYmIiFCxcCAHJycuDr64uDBw8iMTER4eHhmD59OtLT0wE8GzHLzc0V+pNIJIiOjkZaWhr27NmDuLg4ODg4YMKECf+/vTuPi6r6/wf+moEZCAEFcSXLMpcSQWBgQHABMdSEj4W5JW4lLmiaZuaulCjy+WglhZil5ic3zNzSNhQEU2FcgPwIpqa5IKiggiAzw9zfH/yYr8OwyzLI6/l48Hh4zz3n3ve9B2TenHvOhSAIEIvFSEhIwNKlSzFkyBAoFApMnDix0jh79OgBhUKBIUOGYOnSpVAoFHBwcNDu37VrFxYsWIDTp09j8+bN2L59O+Li4rT7S88Pi4iIwLZt27Bq1SqcPn0aCQkJeOutt/TOm5GRgblz52LlypVo06aN9ho/++wzyOVyJCQk4JdffsHt27fxxRdfaNv99ddfeP/99zFt2jQkJSVh2bJlmD9/vjbZWbduHSwtLREfH4/ExESsXbsWAJCWloaNGzdi27ZtSEpKwr59+/Dqq69Wen/Mzc1hZ2eHY8eO6ZT/+uuv8PHxAQAYGRnh008/xYkTJ5CQkICXX34Zn3zySbnHVKlUenPqCgsLdcp27dqF7777DpGRkUhMTISvry+CgoI4ikpERETUBDChMxCurq7w9/cHALRt2xaffvopdu3ahcLCQtja2qJ///4wNTUFADg4OODll1/Gn3/+We7xpFIpli9fjhYtWkAikSA4OBgXL15ERkZGnV3D9OnTIZPJIBaL0bFjR7z55ps6I2JPysjIwLfffovIyEj06NEDACCRSNC8eXOdekqlEjNmzMDUqVPh6uqqs08mk+Gdd96BiYkJLCwsEBQUhJiYGO3+yMhIjBo1Cr6+vjA2NoarqytGjhyJ7du3AwD+/vtvuLm5QSqVAii+7yXlnTp1Qrt27QAAFhYWaNasWZXugY+Pj04Se+vWLdy+fRtyuRwA0Lt3b3Tp0gVisRjGxsYYMWJElUb/yqPRaBAREYFly5ahW7dukEgkGDNmDFq1alXuvSciIiKiZwcTOgPh6emps21vbw+RSIQbN25AEAT8+OOPGDduHPr06QMnJyekpKToPdr3pJdffhkmJibabVNTU1hbW2sfWawLL730ks52+/btyz3fsWPH0L17d3To0KHCY4aEhKB79+4YPXq03r7So2alz3fu3Dl4e3vr1LG3t0daWhoAICAgAOHh4Thw4AA0Go22Tq9evXDt2jWEhoYiOzu7wvhK8/HxQUJCAoqKigAAMTEx8Pb2hrFx8YKyOTk5CAsLw9ChQyGXyzF06NAK+7EyGRkZePDgAXr16qVT7uDgoL1OIiIiInp28bUFBqL0yBRQ/AhfXl4evv76a/z3v//FnDlz4O7ujpYtW2L8+PE689NKezKZKyGRSLSJRl0QiUQ621KpVOdR0CdlZ2ejdevWFR7v0KFDuHr1qt58sRKlr9HY2FgnMcvKysJ7772nE5dGo8GLL74IoHh+2osvvoh///vfiIqKwsKFC+Hu7o7mzZtjz549+Prrr+Hv748RI0Zg2rRpMDIyqjBeoDiptLW1xZkzZ+Di4oKYmBhMmDABQPGjsWPHjoWtrS0WLVqEbt264cGDB3pJZ2We7PfMzEwolUrtCGCJoqIiDBs2rFrHJSIiIqLGhwmdgSj9UnCNRoN79+6hTZs2+P7777FixQr07t1bu78uR9rqg7W1daWPfxYUFODHH3/EhAkT0K9fP52FX6qiWbNm2LJlC7p161ZunW7dumHjxo04dOgQpk2bhoMHD8LW1hbm5ub44IMPMGrUKEycOBEmJiblJpal+fj4IDY2Ft26dUN6ejrc3d0BAElJSXj48CH27t2rTQ7T09MrPJZYLNabC1cyd7LkGi0sLJCYmFil2IiIiIjo2cJHLg1EQkKCzvbx48fRqlUrtG7dGjk5ObC1tdXuu3btGq5fv/7U5zQ2Nq5wlK+22z3J09MT58+fr3A1xoCAAHTu3BlLly7FRx99hMePH1frHHZ2djh16lSV6g4ePBivvPIKzp07p1Petm1bjBkzpsrHAf4voYuLi0Pv3r21c/RycnLQtm1bnZG+0v1emo2NDW7evKlT9scff2j//dJLL0GtVvPxSiIiIqImigmdgThx4gR+/PFHqFQqXL9+HStWrMCECRMgFovh4OCAnTt3Qq1W49atW/j444/RqVOnpz5nq1at8L///Q9KpRIPHz6scjsbGxskJydDEAS91wRUla2tLcaOHYspU6ZoF3dRq9XIzc3VqztgwAD07NkT4eHh1TrHhAkTEBERgV9++QVKpRIajQZnz57Vjm6mpKQgPz8fQPFrES5fvowuXbrg+vXr2iTq3r17+Omnn9C9e/cqn7dLly5QqVTYunUrXn/9dW25nZ0d0tPTcebMGWg0Ghw5cgTx8fEVPsrp6emJrVu3IicnB4IgYPPmzTpz7qRSKQIDA/Hhhx8iNTUVgiBAqVTqvNaCiIiIiJ5dTOgMxPz58/Hrr7+iV69eGDlyJIYMGYLAwEAAwOrVq3Hp0iV4eHhg3LhxGDVqFJydnbXz4YyNjXXmk5mYmGhHhZ4klUp1yp2dnfH888/D3d0dM2bMqHKsAQEBOHv2LGQyGb755psyj11WHKXrzJkzB+PHj8esWbPg5OQENzc3fP/992XWXbRoEeLj43Hy5MlyzyeRSHTug6enJ0JDQ7F+/XrI5XLI5XKEh4drl/z/9ttv4ebmBkdHR3z66adYtWoVOnfujD///BNvvfUW7O3t4efnh27dumH69OlVvj8AMGjQIFy9elXnMdkXXngBK1euxIIFC+Dq6oqtW7di7dq1kEqlUKlUZV7Xu+++Czs7O/j5+cHb2xsXL17ErFmzdOrMnDkTb7zxhvY+9u3bF/v27atWvERERETUOImEp312jp5aYGAgpk+frrewBVFDSE1NBQBsi7uMq7fuVVIb6Ni+JVZOH1rHUVF+fj4uXLiAV199FWZmZg0dDv1/7BfDxH4xTOwXw8R+MUwln8dKXu9VES6KYgCMjIwgkUgaOgwMHz4cV65cKXf/unXrtAt8NEVhYWGIjo4ud//48eOrPZJnyGxbtajVekRERERU+5jQGYDNmzc3dAgAgF27djV0CAZt3rx5mDdvXkOHUW+mj+hX5boajQZiMZ/gJiIiIqpv/ARGRHqUSiUKCgqqXJ/JHBEREVHD4KcwIioTp9cSERERGT4mdERERERERI0UEzoiKpNIJGroEIiIiIioEkzoiEiPVCrFc889p1eu0WgaIBoiIiIiKg9XuSSiMkXsjMXNO/e127atWlRr5UsiIiIiqntM6IioTDfv3K/Si8WJiIiIqOHwkUsiIiIiIqJGigkdERERERFRI8WEjoiIiIiIqJFiQkcGY8KECTh9+nRDh1EvfH19kZmZ2dBhEBEREVEjx4SODIZarYZarW7oMOqFSqWCSqVq6DCIiIiIqJFjQkcAgHPnziEwMBByuRxyuRyBgYH466+/EBcXh7fffluv/rhx43D48GEAQEZGBqZOnQpHR0fI5XIsXLgQO3bswOLFi6t07l9//RUymQynT5/GlClT4OLioh29ys7OxuzZs+Hs7Aw3NzeEhobqJEKjR49GdHQ03nnnHTg5OcHf3x/Jycm4ceMGxo8fD7lcjmHDhuF///uftk1mZib8/Pxw5MgR+Pn5QSaTYdCgQfj555914ioqKsLatWvh4eEBJycnTJ06FVlZWdr9GzZswNq1a7F+/Xq4ublh0aJFAIA9e/bA398fLi4u8PDwwNy5c/Hw4UMAwObNmyGTyXDr1i34+/vD09MTarUamZmZcHR01Ls3S5YswcaNG7XbAwcOxLlz5zBu3DjIZDKcOnUKAHD+/HmMGjUKjo6O8Pb2RnR0dJXuPRERERE1bkzoCEBx8vLhhx/i+PHjOHHiBHx9fTF37lzI5XJcunRJ5/HAnJwcpKSkoHfv3tBoNJgyZQosLCxw9OhRHD16FDY2Nli9enWVR6Bef/11KBQKODs7Y/369UhKSkKbNm0AAMHBwTA1NUVsbCwOHTqE9PR0fPnll9q2RkZG+OyzzzBjxgycPn0as2fPxty5c/HRRx9hwoQJOHXqFKZNm4YZM2ZoR/9UKhXu3r2Lzz77DJGRkVAoFJg5cybmzZuHCxcuaI/9xRdfID4+Hjt27MAff/yBTp06YdasWdr9hYWFiI+PR35+Po4fP46QkBAAgLm5OSIiIpCYmIiYmBhoNBpEREQAAMaPHw+FQoH27dtj//79SEhIgLGxMVQqFQoLC/XujVKphFKp1Nn+z3/+gzlz5kChUMDV1RXZ2dmYOHEi/Pz8cOrUKURGRiIiIgInTpyo0v0nIiIiosaLCR0BAJydneHg4ABjY2OIxWKMGDECaWlpKCoqgoeHB2JjY7V14+LiIJfLYW5ujjNnziArKwuffvopWrRoATMzM3zwwQfo0KHDU8cUHx+PjIwMhISEwMLCAtbW1li+fDm2b9+ukyz6+/vDzc0NIpEI/fr1g1QqxWuvvYa+ffsCALy9vWFqaorLly9r22RnZ2PhwoV4/vnnARSPfL355pv4/vvvAQAPHjzAd999h/DwcHTo0AGmpqaYPXs2bt26hfPnz2uPc/PmTbz//vswMjKCWFz84/T666/jhRdegEgkgqmpKQICAqBQKJ76fpTo2rUr7O3tAQAikQibNm2Ch4cHRo8eDalUiq5duyI4OBhbtmyptXMSERERkWFiQkcAgPz8fERERGDYsGFwd3eHm5sbBEHA/fv34ePjg6NHj2rrxsTEYODAgQCAtLQ0ODg4QCqV6hzP29v7qWNKTk5Gnz59YGxsrC3r2LEjBEHA7du3tWWdO3fWaWdlZQVXV1edMmtra9y/f1+7LZVK9ep4eHjg4sWLAID09HS0bt0anTp10u4Xi8Wws7NDWlqatszR0VEnPgC4desWlixZgiFDhsDV1RXBwcE6535aLi4uOtvnzp3Tu9/29vZIT0+vtXMSERERkWEyrrwKNQUzZszAo0ePMGvWLNjb28Pc3Bzdu3eHIAjw8vLCp59+isLCQohEIpw6dQorVqwAADx8+BAtWrTQO17r1q2RkZHxVDFlZmZi3759OHTokE65UqnEgwcPtKOARkZGem2bN29e4bGbN28OkUikU2Zubo7c3FztuW/cuAGZTKZTR61W65RZWVnp7L9//z6GDx+OPn36YOXKlXjllVeQmpqKjz/+uJKrLZsgCHplpc+ZlZWFxYsXY9myZTrtSieaRERERPTs4Sc+wj///IOkpCQkJCTA0tISQHFCo9FoABQnP3Z2djh58iQAwMHBQVuvWbNmuHfvnt4xyyqrrmbNmmHkyJFYsGDBUx+rtJycHGg0Gu1jkkDxNZfM3WvWrBk6d+6MvXv3VnicJ9sDwG+//QZbW1uEhoZqy54cTSyPkZERNBqNXkx37tzBiy++WOE5mzVrhvDwcPj4+FR6HiIiIiJ6tvCRS0JOTg4sLS21SRpQPH/tST4+PoiNjUVMTAx8fX215TKZDOfOnUNeXp5O/SNHjuiNgFXG2NhYZ0TKzs4OCoWizFGqp6VWq7UJaoljx46hZ8+eAIDu3bvj6tWruHPnTrWOm5OTA1tbW52yhIQEvXqlr7Vly5YAiufklcjLy0NycnKl57Szs0NiYmK14iQiIiKiZwMTOkKnTp1QUFCAX375BYIg4PTp09iyZQtsbGy0dXx8fBAXF4djx46hf//+2vLu3bvD3d0dCxYsQE5ODvLy8hAaGop//vmnzEcxK2JjY4Nz585Bo9EgLy8PPj4+yM3NxfLly5GdnQ2geBStNhYYMTMzw+rVq7ULpezbtw/Hjh3DqFGjAABt2rRB//79MWvWLFy5cgUA8OjRI8TFxVV4XAcHB/zxxx+4cuUKVCoVdu/erZ2XV9a1qlQqFBQUaOf0ffXVV1Cr1Xj8+DEWL16M9u3bV3otY8aMQXR0NHbu3InHjx9DEASkp6frLAJDRERERM8mJnQEc3NzREZG4quvvoKLiwvCwsKwcuVKWFpaah+7bNu2Ldq0aYMuXbrozeEKDw9Hq1at4Ovri/79+6OwsBByuRxOTk7ViiMwMBC7d++Gm5sbfvrpJ5iammLLli3Izs6Gr68vnJ2dERgYiKtXr2rbSKVSmJiY6BxHKpXqLdJSuszKygrvv/8+3n//fTg5OWHTpk1Yv3699pFLAAgNDUWPHj0wbtw4ODo6wtfXV2fk0sTERO/ccrkc06ZNw6RJk9CrVy8cO3YMYWFh2vtYYtKkSVi9ejX69OmjfZfcihUrcPv2bfTu3Rv+/v7o0aMHvL29deIu63q7dOmCDRs2YN++fXB3d4erqyvmz5+PR48eVeW2ExEREVEjJhLq4nk2alKOHTuG1157DTY2NsjPz8eePXuwZ88e7Nq1yyAX5rhx4wbGjh2LI0eONHQoBik1NRUAsC3uMq7e+r+5kB3bt8TK6UMbKCrKz8/HhQsX8Oqrr8LMzKyhw6H/j/1imNgvhon9YpjYL4ap5PNYjx49Kq1reJ+2qdFJSkrC4sWLkZubCxMTE/Tr1w8bN27UJnOenp54/PhxmW1FIhF27dqFl156qd7iNTY2hkQiqbfzNVa2rVpUuE1EREREDY8JHT21OXPmYM6cOeXuL2tRkIbUtm1b/PLLLw0dhsGbPqKfXlnpVTiJiIiIqGHxkxkR6VEqlSgoKNArZzJHREREZFj46YyIysTptURERESGjwkdERERERFRI8WEjojKVN0XwxMRERFR/WNCR0R6pFIpnnvuOb3y0u/TIyIiIqKGxVUuiahMETtjcfPOfe22basWZa58SUREREQNhwkdEZXp5p37Oi8WJyIiIiLDw0cuiYiIiIiIGikmdI3YwIEDkZmZ2dBhlCktLQ1vv/02VCoVACAqKgpRUVH1GsOCBQvwww8/1Os5iYiIiIjqEx+5bMSUSqU2YTI03bp1Q3R0tHZbqVTWewyhoaH1fk4iIiIiovrEEToCACxatAj79+9v6DBqLDIyEpGRkQ0dBhERERFRveIIHQEA1Go11Gp1Q4dRY405diIiIiKimuIInQFYsmQJduzYgZCQEMjlcnz55ZfIy8vDsmXL4OHhAZlMhmHDhiE+Pl6v7a1btzBlyhTI5XK4ublh2bJlKCws1KkTFxeHgIAAyGQyeHp6IiQkBHl5eQCA1NRUyGQyHDx4EMuXL4dMJkNycnKlMa9evRrh4eE6ZQUFBZDJZLh37x5SUlLg6+urs7+oqAgRERHw8vKCo6Mj3nnnHaSlpWn3Z2Zmom/fvkhNTcWgQYMgl8uhVCrx999/IygoCO7u7nB1dUVAQAAUCgUA4O7du3B1dcWGDRuwYcMGyGQyHD58GAAQFBSEgwcPao9flXvq5eWF2NhYDB8+HE5OTvD29saGDRsqvR9PunjxIkaNGgUXFxfI5XKEhYVp96nVanz11Vfw8fGBs7Mz3N3dcejQIQCAIAj49ttvMWDAAMhkMrz++uvYvHmzzrHL+l4BgOzsbMyePRvOzs5wc3NDaGiowT6OS0RERES1hwmdAVAqldi1axdeeeUVnDp1ClOnTsWMGTOQnZ2NAwcOICkpCTNmzMCHH36I06dP67SdN28ehg8fjlOnTmHHjh2Ij4/XmTuWmJiIuXPnIjg4GElJSThw4ABycnIwa9YsAECPHj2gUCgwZMgQLF26FAqFAg4ODpXG7OXlhd9//12n7Pjx43jllVfQsmXLMuf37dixA1lZWTh8+DBOnDiBTp06YeLEidrkUqVSIT8/H1FRUdi6dStOnDgBqVSKwsJCTJgwAXFxcUhMTMTUqVMxa9YsKJVK2NjYIDExEUFBQQgKCoJCocCgQYO09/XJuXtVuacikQghISH44IMPkJSUhI0bN2Lr1q1611qRefPmYcSIEUhKSsIff/yBCRMmaPctXrwYx48fx8aNG3H69GkcPXoUffr0AVD82OiePXvw5ZdfQqFQIDIyEj/88INOQlnW9woABAcHw9TUFLGxsTh06BDS09O1yR4RERERPbuY0BkItVqN0aNHAyhOwi5cuICwsDBYW1tDJBKhb9++CAoKwueff67TbsiQIfD29gYAdOzYEcuWLcPevXu1icwXX3yBoKAgeHt7QyQSwcrKCitXrsT58+eRlJRU43idnJzw4MED/P3339qymJgYvVG5J5mYmGDp0qUwNTWFqakpli1bBmNjY51k6eHDh/jXv/4FGxsbiMXF357dunWDu7s7pFIpAMDHxwdisRhXr16tcrwnT56s8j0dPXo03N3dYWRkhJdffhlvvfUWYmJiqnyuv//+G7179wYAGBkZoXXr1gCA5ORkHDlyBFFRUejYsSMAwNTUFObm5sjNzcXGjRsRGhqKLl26AAA6deqE0NBQfP3119qkF9D9XhGLxYiPj0dGRgZCQkJgYWEBa2trLF++HNu3b+coHREREdEzjgmdgXBxcdH+W6FQwMvLC88995xOnUGDBuHMmTMoKirSlvXt21enTq9evVBUVIRr166hqKgIZ8+exeDBg3XqmJqaol+/fkhMTKxxvEZGRvD29kZsbCwAQKPRIC4ursKEzsPDA0ZGRtptsVgMT09PpKen69STyWQ622q1Glu2bMGoUaPg6ekJZ2dn3LlzB/fv369yvNW5p6+99ppOHVtbW2RlZVX5XMOHD8fUqVNx9uxZnfKYmBh4e3vD3Nxcr82FCxdgZWUFe3t7nfIePXrA0tJS59HUJ79XgOJEsU+fPjA2/r8psR07doQgCLh9+3aV4yYiIiKixocJnYGwsrLS/jsrK0s7qvOkVq1aQaVS4d69e2W2A4oTrRYtWqCgoAD37t2DWq0u81itW7d+6nfY+fj44OjRowCAM2fOoH379mjfvn259a2trfXKWrZsiYKCAu22WCxGixYtdOqEhIRg165dmDhxIvbv3w+FQoF27dpBEIQqx1qde1oyEljC2NhYJ+GrzIIFCxAUFIRFixZh0qRJuH79OoDieW5lxVBRfEBxXz2ZmJXu88zMTOzduxcymUzn6/Hjx3jw4EGV4yYiIiKixocJnYEoebwQAFq0aFHmiNCdO3dgbGyM5s2ba8tKJ2VFRUW4f/8+WrZsCUtLSxgZGZV5rKysLL3EoLo8PDyQlpaGvLw8HDlypMLROQBljhbdu3cPNjY22m2RSASRSKTdLiwsxJ49e7Bu3ToMGDBAmxRWZ8QMqN49rQ0+Pj44cOAAOnbsiClTpkAQBFhbW+PWrVvVig8ovtYnk+Env1cAoFmzZhg5ciQUCoXOV0pKCuzs7GrvooiIiIjI4DChM0B9+/ZFbGyszsgVABw+fBju7u4wMTHRlj25iiNQPFfMzMwMbdu2hampKeRyuXYVxRKPHz9GbGysdu4dUDwKVZ0RL6B4Tpybmxvi4+MRExODgQMHVlg/Li5OZy6YRqPB8ePH0bVr13Lb5ObmQqVS4fnnn9e5xtJzwyqLvzr3tLaIxWLMmTMHly5dwt27d+Hl5YWYmBhkZ2fr1XV0dMTDhw+RkpKiU56amoq8vDw4OjqWex47OzsoFIpq9x8RERERNX5M6AyQs7MzXnvtNcybNw/Z2dkQBAE///wzoqKiMH36dJ26p0+fxg8//AAAuH79OpYvX4733ntPO1ctODgYGzZsQExMDARBQHZ2NmbOnAkHBwed1SxtbGyQnJwMQRB0kq7K+Pj4YPPmzXjuuefQoUOHCus2b94cc+fORV5eHgoLCxESEoLmzZujX79+5bZp2bIlbG1tsW3bNgiCgL/++gurVq3SLiryZPwpKSnQaDTIzc3VO0517unTKCoq0s7JKyoqwvfff4927drBysoKjo6O6NevHyZNmoRr164BKF618tGjR2jWrBkmTZqE+fPn4+LFiwCAtLQ0zJkzB5MnT9ab+/ckHx8f5ObmYvny5dpkMTMzU/tqByIiIiJ6djGhMwBSqVRvhOiLL75A69at4e/vDxcXF3z77bf4/PPP0bNnT20diUSC8PBw/Prrr3B1dcWoUaMwcuRIBAUFaevIZDKsXbsW69evh4uLC/z9/fHCCy9gzZo1OucLCAjA2bNnIZPJ8M0331Q59n79+uHixYsYMmSI3jVJJBKd7TfffBM9evTA4MGD0atXL2RlZWHLli3aehKJRO8+iEQiREZG4ueff4arqytmz56NuXPnokOHDtBoNNp6AwYMQH5+PlxcXLSvbZBKpTrz4apyT0u3AYpHIkuXlefx48dYsGABHBwcIJfLERcXh6ioKO2CJeHh4ejXrx8CAwPh7OwMDw8P/PbbbwCK35s3YsQITJ8+HTKZDDNnzsTYsWMxceJEnfhK3yNTU1Ns2bIF2dnZ8PX1hbOzMwIDA6u1CigRERERNU4igc9pEdETUlNTAQDb4i7j6q3/WyymY/uWWDl9aANFRfn5+bhw4QJeffVVmJmZNXQ49P+xXwwT+8UwsV8ME/vFMJV8HuvRo0eldY0rrUFNTslIT3m5vkgkQkJCQp3MOzN0w4cPx5UrV8rdv27dOri7u9djRHXHtlWLCreJiIiIqOExoSM91tbWT/XS8WfZrl27GjqEejN9RD+9Mo1Go7fKJhERERE1HD5ySUQ6zpw5A0EQIJFIdF4hQQ1LEASoVCr2i4Fhvxgm9othYr8YJvaLYVIqlRCJRHBycqq0LkfoiEhHyX/m/E/dsIhEoiovzkP1h/1imNgvhon9YpjYL4ap9LuZK6zLEToiIiIiIqLGiZNhiIiIiIiIGikmdERERERERI0UEzoiIiIiIqJGigkdERERERFRI8WEjoiIiIiIqJFiQkdERERERNRIMaEjIiIiIiJqpJjQERERERERNVJM6IiIiIiIiBopJnRERERERESNFBM6IiIiIiKiRooJHRERERERUSPFhI6oCVIoFBg+fDhcXFwwYMAA7Ny5s9I2v//+O/z8/ODi4gI/Pz/8/vvv9RBp01LdfomPj8fkyZPRq1cvuLm54d1338WlS5fqKdqmoyY/LyWKiooQEBAAe3v7OoywaapJv2g0Gmzbtg0BAQGQy+VwdnbG7Nmz6yHapqEmfbJjxw74+/vDxcUF3t7eCAsLQ35+fj1E27Tk5ORg9OjReO+996pU/2n+36MGIBBRk3Lt2jXB1dVViI2NFQRBEC5fviz4+PgIBw4cKLeNQqEQPDw8hOTkZEEQBOHs2bOCu7u7oFAo6iXmpqAm/fL9998LCQkJQkFBgVBYWCisXbtW8PLyEh49elRfYT/zatIvT4qKihKCg4OFV199tS7DbHJq0i8ajUaYOXOmMHnyZOGvv/4SBEEQ1Gq1cPPmzXqJ+VlXkz6JiooS3njjDSEtLU0QBEH4559/hBEjRgizZ8+ul5ibimvXrgmDBg0Sxo4dK4wbN65K9Z/m/z2qf0zoiJqYFStWCP/5z390ymJjY4U333yz3DbTpk0TduzYoVP23//+V5g+fXqdxNgU1aRfStNoNIKXl5dw8uTJ2g6vyXqafrly5YowaNAg4eLFi0zoallN+uXgwYPCW2+9JahUqroOr0mqSZ8MHjxY+O2333TKkpOTBUdHxzqJsakKCwsTjhw5Ivzwww9VSuhq4/cR1S8+cknUxBw9ehT9+/fXKevVqxeuXLmCrKwsvfpKpRLHjx/Xa+Pj44Pjx49DpVLVabxNRXX7pSwikQjNmjVDXl5eXYTYJNW0XwRBwKJFi7BgwQI899xzdR1mk1OTfomOjsa7774LY2Pj+gixyalJn7Rt2xbXr1/XKbty5Qo6duxYV2E2SR999BG8vLyqXL82fh9R/WJCR9SEFBUV4fr163j55Zd1yiUSCZ5//nn89ddfem0yMzMhkUhgY2OjU96mTRsIgoCbN2/WacxNQU36pSxXr17FrVu34OLiUhdhNjlP0y/btm2Dra0tPD096zrMJqcm/SIIAs6dO4f27dtj1qxZcHd3h6+vLyIjI/lHqVpQ05+VmTNnYsOGDTh06BCA4rna//73v7F48eI6j5nKVlu/j6h+8c9URE3I/fv3AQAWFhZ6+ywsLPDgwQO98pycnDLrV9SGqqcm/VKWNWvW4J133oGlpWVthtdk1bRfbt68iU2bNiE6Orouw2uyavr/WEFBAVatWoWgoCCEhYUhIyMD8+bNw+3bt7F8+fK6DvuZVtOfFXt7e2zatAlTp07FmjVr8PjxY3zzzTfo2rVrXYZLFait30dUvzhCR9SEqNVqCMVzZ/X2lVUGoMK/XguCAJFIVGvxNVU16ZfS9u7di4sXL2LKlCm1HV6TVdN+WbJkCWbOnAkrK6u6DK/Jqkm/FBYWAgACAgLg7e0NExMTdOzYEatWrUJ0dDQePnxYpzE/62r6s/LPP/9g2bJl6Ny5M2bOnAk7OzvMnz8faWlpdRkuVaA2fh9R/WNCR9SElPzFLTc3V29fbm5umSM7lpaW5X7YycvLK3f0jqquJv3ypD///BPh4eGIiIiAmZlZncTYFNWkX/bv3w+xWAw/P786j6+pqkm/mJqaAgDc3Nx0yl966SVYWFjg77//roNIm46a9IlKpcLkyZMxePBgbNiwAX5+fli/fr12aX0m2Q3jaX8fUcNgQkfUhJiZmaF169Z6H15UKhVu3LiBF198Ua9Nhw4dkJ+fj7t37+qU3759GyqVCra2tnUac1NQk34pkZGRgeDgYHzyySd45ZVX6jrUJqUm/XLhwgUoFArIZDLtl7+/P4qKiiCTyfDxxx/XV/jPrJr0i5WVFczMzLQjdU/SaDQwNzevs3ibgpr0yeXLl3Hnzh0EBgbqlA8bNgwtWrTA2bNn6zRmKtvT/D6ihsOEjqiJ6dWrl95LwY8fP47WrVujQ4cOevVNTU3h5OSk1yYmJgYymQxSqbRO420qqtsvQPFfSydNmoQJEybA29u7PsJscqrbL/PmzcPZs2ehUCi0X/v374eRkREUCgVWrVpVX6E/02ry8+Lq6oojR47olP35558AgBdeeKFuAm1CqtsnJiYmePz4MR49eqRTrtFocP/+fUgkkjqNl8pXk58valhM6IiamHfffRc7d+5EXFwcgOIlokNDQzF58mQAxStcjR8/HleuXNG2mTJlCtatW4eUlBQAQHJyMiIiIhAUFFT/F/CMqm6/qNVqTJ8+HS4uLhg/fnxDhf3Mq8nPC9W9mvTLe++9h6+//honT54EAFy8eBEff/wxgoODmTzUgur2yUsvvQRPT09MmTIFly9fBgDcuXMH8+fPh6WlJWQyWcNcSBP0wQcfIDExUbtdWV+S4eEql0RNTJcuXfDZZ58hPDwcs2fPRvPmzTFu3DgMGzYMQHGicOXKFZ13mXl6emL+/PmYN28esrKy0KpVKyxcuBC9evVqqMt45lS3Xy5duoSTJ08iNTUVBw4c0DnW0KFDsWjRonq/hmdRTX5eSpNIJDAxMamvkJuEmvSLi4sLQkJCEBISgoyMDFhZWWHMmDH8g0gtqUmffP755/j2228RHByMu3fvwtLSEn379sV3333Hpz/qgFQqLfO+XrlyBTk5OdrtyvqSDI9I4JI1REREREREjRIfuSQiIiIiImqkmNARERERERE1UkzoiIiIiIiIGikmdERERERERI0UEzoiIiIiIqJGigkdERERERFRI8WEjoiIiIiIqJHii8WJiIioyuzt7VFYWFju/qFDhyIsLAwAMHbsWIwYMQJvvPGGTp3ly5dj27ZtFZ7HzMwMX3/9NWQyWYX1Fi5cCBMTEyxZsqSKV2A4xo8fj969e+Pdd99t6FCIqBHjCB0RERFVWUpKCtLT0yGVSnHo0CGkp6cjISFBu68kmQMAlUpVZvK3aNEipKSkVPjVs2dPnD9/vtJ4ioqKoFardcri4uIwZswYyGQy9OzZE/7+/jrHOnfuHBwdHcs95vjx47F58+Zy9y9ZsgSvvfYaUlJSytyfmZmJbt26oWvXrujatSvs7e0xePBgREVF6dyPoqIiFBUVVXqNREQV4QgdERER1ZqvvvoKn3/+uXb77bff1qtjZGQEIyOjCo8jkUgglUqrff4DBw7gk08+wfz58+Hm5gaJRILk5GS0bt1aW6ewsFAvCXxSUVERlEplmfvy8vLw008/wc3NDbt374a9vb1eHZVKBUEQ8PPPP6Nly5YoKirCpUuXEBISgvT0dKxZs6ba10VEVB6O0BEREVGtmTZtGtLT05Geng4nJ6caH+fhw4ewsrKqdrs1a9Zgzpw5ePPNN9GuXTvY2Nigf//+aNWqVY1jedKhQ4fg5OSE4OBgHD58uMLHT83NzWFpaQkrKyu4uLhgxYoVOHToEO7du1crsRARAUzoiIiIyABlZ2ejTZs21W6Xk5OjMxpX26KjozFixAg4OzujZcuWiImJqXLbrl27QhAE3L59u87iI6KmhwkdERER1ZqvvvpKO3fszJkzNTpGSdLTvn37arcdOHAgVq1ahczMzBqduyLp6em4ffs2vLy8AADDhg3Dnj17qtz+5s2bEIvFaNeuXa3HRkRNFxM6IiIiqhVKpRJDhgzBqVOnynzksmSRlMq+rl69ColEghYtWlQ63620kJAQdOnSBX5+fti+fXutLjqye/duBAQEaOf/DR06FImJiZUmj4IgIDU1FR9++CGGDBkCa2vrWouJiIiLohAREVGV3L17FxMmTIAgCFCpVJg0aRJEIhEKCwshEong4+ODdu3aYcaMGejfv79e+yFDhuDq1atVPl/JgiOenp745ptvqtRGKpVi3bp12L9/P1avXo3o6GiEhYWhc+fOOvWUSmW5r0TIz89H79699eofPHgQu3fv1pbZ2NigT58+2LdvH4KCgvSOM3DgQO3xmjdvjtGjR2Py5MlVug4ioqpiQkdERERVYm1tjc8++wxFRUUQiUQQi8UwNTWFubk5LCwsIBZX/ODPL7/8orN9584deHp6IikpCZaWlgCKR9iMjIywcOHCp4rV398f3t7eWLNmDUaMGIGtW7eie/fu2v0SiQR79+4ts+3s2bP1yn777TfY2dnB1tZWp/ztt99GWFhYmQndpk2bYG1tjWbNmtVogRcioqpgQkdERERVIhaL0alTJ52y33//Hbt378bFixdx584dWFhY4IUXXsDgwYMxZswY9OzZU+84jx8/rtErCarL3NwcS5YsgbGxMZYsWYIffvhBu08kEuH5558vs52JiYle2e7du3Hq1Cn06NFDb59SqURycjIcHBx0ytu1a1drq2sSEZWHCR0RERHVyKpVq3Dw4EHMmDEDCxcuRMuWLZGbm4vU1FRs2rQJOTk52Llzp167sWPHYuLEiXB2dq6XOPv164ft27fXuP2NGzdw4cIFHDhwoMz933zzDX788Ue9hI6IqD4woSMiIqJqy8vLw3fffYcdO3bovFzbzMwMbdq0gZeXF4YOHYp9+/ZhzJgxOm3VarXOYiUikUhnf+ntp6VQKNCxY8cat9+9ezcGDBigNzpZYtiwYZgyZQoWLFhQLyOPRERPYkJHRERENVbW44lA8eOZEomkwrbW1tbYsmULLCwstGXjx49/qoTuyy+/hJ2dHbp06QK1Wo3ffvsNmzZtQlRUVI2Op9Fo8OOPPyI0NLTcOo6OjjA3N8fvv/+OwYMH1zR0IqIaYUJHRERE1WZubo533nkHU6dOxaxZsyCXy2FtbY1Hjx7h/Pnz2LRpEwoKCvCvf/1Lr61YLEZ+fj7UajUcHR1RWFio3VfyMvGSlTOrO+KlVCqxYsUK3L59G2ZmZrCzs8PmzZt1Hoc0MTGp8LhSqVS7/8SJE1Cr1ZDL5eXWF4lE8PPzw/79+zF48GBIJBKIxWLt6w2IiOqSSBAEoaGDICIiosbp8OHDiI6ORlpaGrKzs2FmZoYXX3wRb7zxBkaOHAlzc3O9NitXrsTWrVsrfUecubk5Tp8+XWGdjz/+GFKpFCEhIU91HQ0hMDAQvXv3LnOFTCKiquIIHREREdXYoEGDMGjQIADFjydW9uoCAJg/fz7mz59fK+c3MjKCsXHj/DhjZGTEUTwiemocoSMiIiIiImqkKv8zGhERERERERkkJnRERERERESNFBM6IiIiIiKiRooJHRERERERUSPFhI6IiIiIiKiRYkJHRERERETUSDGhIyIiIiIiaqSY0BERERERETVSTOiIiIiIiIgaqf8H6riXiIwTQKsAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-a0d9b4da-3d03-4c33-a571-82db618689ad\" class=\"colab-df-container\">\n",
-       "    <div>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model_name</th>\n",
-       "      <th>feature</th>\n",
-       "      <th>importance_gain</th>\n",
-       "      <th>importance_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>457</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S33_mean_time</td>\n",
-       "      <td>0.011733</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>458</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S34_mean_time</td>\n",
-       "      <td>0.011186</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>459</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>process_start_time</td>\n",
-       "      <td>0.010766</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>460</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S33_num_missing_ratio</td>\n",
-       "      <td>0.008505</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>461</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S37_mean_time</td>\n",
-       "      <td>0.008500</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>462</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S29_mean_time</td>\n",
-       "      <td>0.008475</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>463</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S33_seen</td>\n",
-       "      <td>0.008394</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>464</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.008029</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>465</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S33_F3859</td>\n",
-       "      <td>0.008004</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>466</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>process_end_time</td>\n",
-       "      <td>0.007741</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>467</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_duration</td>\n",
-       "      <td>0.007571</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>468</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S29_num_std</td>\n",
-       "      <td>0.007533</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>469</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>aux_paint_thermal_defect_probability</td>\n",
-       "      <td>0.007316</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>470</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L1_S24_F1844</td>\n",
-       "      <td>0.007214</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S33_num_mean</td>\n",
-       "      <td>0.007032</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>472</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_num_std</td>\n",
-       "      <td>0.006991</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>473</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S33_F3857</td>\n",
-       "      <td>0.006660</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>474</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>aux_body_ford_vibration_abnormal_probability</td>\n",
-       "      <td>0.006541</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>475</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>total_process_duration</td>\n",
-       "      <td>0.006364</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>476</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L1_num_min</td>\n",
-       "      <td>0.006358</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_date_count</td>\n",
-       "      <td>0.006329</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>478</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S29_num_mean</td>\n",
-       "      <td>0.006163</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>479</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L0_num_min</td>\n",
-       "      <td>0.006136</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>480</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S29_F3458</td>\n",
-       "      <td>0.006061</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>481</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S30_F3744</td>\n",
-       "      <td>0.005915</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>482</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S33_num_std</td>\n",
-       "      <td>0.005873</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>483</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L0_num_std</td>\n",
-       "      <td>0.005700</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>484</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S30_num_std</td>\n",
-       "      <td>0.005681</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>485</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S30_F3759</td>\n",
-       "      <td>0.005666</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>486</th>\n",
-       "      <td>RandomForest</td>\n",
-       "      <td>L3_S34_seen</td>\n",
-       "      <td>0.005639</td>\n",
-       "      <td>feature_importances</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0d9b4da-3d03-4c33-a571-82db618689ad')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-a0d9b4da-3d03-4c33-a571-82db618689ad');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"display(importance_df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RandomForest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L3_S30_num_std\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0016205883920081907,\n        \"min\": 0.0056385160546700164,\n        \"max\": 0.011732889577268082,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.00568096253421123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"feature_importances\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "# 모델 성능 비교 시각화\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(18, 4))\n",
-    "sns.countplot(x=y, ax=axes[0])\n",
-    "axes[0].set_title(\"Response Distribution\")\n",
-    "axes[0].set_xlabel(\"Response\")\n",
-    "\n",
-    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"pr_auc\", ax=axes[1], color=\"#4C78A8\")\n",
-    "axes[1].set_title(\"Validation PR-AUC by Model\")\n",
-    "axes[1].set_xlabel(\"\")\n",
-    "axes[1].tick_params(axis=\"x\", rotation=20)\n",
-    "\n",
-    "sns.barplot(data=model_comparison, x=\"model_name\", y=\"false_positive_rate\", ax=axes[2], color=\"#F58518\")\n",
-    "axes[2].set_title(\"False Positive Rate by Model\")\n",
-    "axes[2].set_xlabel(\"\")\n",
-    "axes[2].tick_params(axis=\"x\", rotation=20)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# 선택 모델 ROC/PR 곡선\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
-    "RocCurveDisplay.from_predictions(y_valid, valid_proba, ax=axes[0])\n",
-    "axes[0].set_title(f\"{selected_model_name} ROC AUC={roc_auc:.4f}\")\n",
-    "PrecisionRecallDisplay.from_predictions(y_valid, valid_proba, ax=axes[1])\n",
-    "axes[1].set_title(f\"{selected_model_name} PR-AUC={pr_auc:.4f}\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "def extract_feature_importance(estimator, feature_names: list[str], model_name: str) -> pd.DataFrame:\n",
-    "    target_estimator = estimator\n",
-    "    if is_pipeline_estimator(estimator):\n",
-    "        target_estimator = get_final_estimator(estimator)\n",
-    "\n",
-    "    if hasattr(target_estimator, \"booster_\"):\n",
-    "        values = target_estimator.booster_.feature_importance(importance_type=\"gain\")\n",
-    "        importance_type = \"gain\"\n",
-    "    elif hasattr(target_estimator, \"feature_importances_\"):\n",
-    "        values = target_estimator.feature_importances_\n",
-    "        importance_type = \"feature_importances\"\n",
-    "    elif hasattr(target_estimator, \"coef_\"):\n",
-    "        values = np.abs(target_estimator.coef_).ravel()\n",
-    "        importance_type = \"abs_coef\"\n",
-    "    else:\n",
-    "        values = np.zeros(len(feature_names))\n",
-    "        importance_type = \"not_available\"\n",
-    "\n",
-    "    return pd.DataFrame({\n",
-    "        \"model_name\": model_name,\n",
-    "        \"feature\": feature_names,\n",
-    "        \"importance\": values,\n",
-    "        \"importance_type\": importance_type,\n",
-    "    }).sort_values(\"importance\", ascending=False)\n",
-    "\n",
-    "\n",
-    "importance_frames = [\n",
-    "    extract_feature_importance(estimator, feature_cols, model_name)\n",
-    "    for model_name, estimator in trained_models.items()\n",
-    "]\n",
-    "all_model_importance = pd.concat(importance_frames, ignore_index=True)\n",
-    "importance_df = all_model_importance[all_model_importance[\"model_name\"].eq(selected_model_name)].copy()\n",
-    "importance_df = importance_df.rename(columns={\"importance\": \"importance_gain\"})\n",
-    "\n",
-    "plt.figure(figsize=(9, 7))\n",
-    "sns.barplot(data=importance_df.head(25), y=\"feature\", x=\"importance_gain\", color=\"#4C78A8\")\n",
-    "plt.title(f\"Top 25 Feature Importance - {selected_model_name}\")\n",
-    "plt.xlabel(\"Importance\")\n",
-    "plt.ylabel(\"\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "display(importance_df.head(30))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "TaY5ZaEJbEfC",
-   "metadata": {
-    "id": "TaY5ZaEJbEfC"
-   },
-   "source": [
-    "## 10. SHAP 기반 영향 공정 분석\n",
-    "\n",
-    "선택된 최종 모델을 기준으로 SHAP 값을 계산합니다.\n",
-    "\n",
-    "SHAP 값은 feature가 불량 확률 예측에 기여한 정도이며, 이후 feature를 공정 코드로 매핑해 공정별 영향도를 집계합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "24Ol6sMmbEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "source": [
+        "def transformed_feature_names(pipeline):\n",
+        "    return pipeline.named_steps[\"preprocess\"].get_feature_names_out().tolist()\n",
+        "\n",
+        "\n",
+        "def compute_shap_importance(pipeline, X, sample_size=500):\n",
+        "    X_sample = X.sample(min(sample_size, len(X)), random_state=RANDOM_STATE)\n",
+        "    preprocessor = pipeline.named_steps[\"preprocess\"]\n",
+        "    model = pipeline.named_steps[\"model\"]\n",
+        "    X_transformed = preprocessor.transform(X_sample)\n",
+        "    feature_names = transformed_feature_names(pipeline)\n",
+        "    try:\n",
+        "        explainer = shap.TreeExplainer(model)\n",
+        "        raw_values = explainer.shap_values(X_transformed, check_additivity=False)\n",
+        "        if isinstance(raw_values, list):\n",
+        "            shap_matrix = np.asarray(raw_values[1])\n",
+        "        elif getattr(raw_values, \"ndim\", 0) == 3:\n",
+        "            shap_matrix = np.asarray(raw_values[:, :, 1])\n",
+        "        else:\n",
+        "            shap_matrix = np.asarray(raw_values)\n",
+        "    except Exception as exc:\n",
+        "        print(\"TreeExplainer 실패, model-agnostic Explainer로 대체:\", exc)\n",
+        "        background = preprocessor.transform(X.sample(min(100, len(X)), random_state=RANDOM_STATE))\n",
+        "        explainer = shap.Explainer(lambda data: model.predict_proba(data)[:, 1], background)\n",
+        "        shap_matrix = np.asarray(explainer(X_transformed, max_evals=min(2 * X_transformed.shape[1] + 1, 1000)).values)\n",
+        "    importance = pd.DataFrame({\"feature\": feature_names, \"mean_abs_shap\": np.abs(shap_matrix).mean(axis=0)}).sort_values(\"mean_abs_shap\", ascending=False).reset_index(drop=True)\n",
+        "    sample_proba = predict_positive_proba(pipeline, X_sample)\n",
+        "    examples = []\n",
+        "    for row_pos, row_idx in enumerate(X_sample.index):\n",
+        "        abs_values = np.abs(shap_matrix[row_pos])\n",
+        "        top_idx = abs_values.argsort()[::-1][:5]\n",
+        "        examples.append({\"row_index\": int(row_idx), \"defect_probability\": float(sample_proba[row_pos]), \"risk_grade\": risk_grade(float(sample_proba[row_pos])), \"top_features\": [{\"feature\": feature_names[i], \"shap_value\": float(shap_matrix[row_pos, i]), \"abs_shap\": float(abs_values[i])} for i in top_idx]})\n",
+        "    return importance, pd.DataFrame(examples)\n",
+        "\n",
+        "\n",
+        "event_shap_importance, event_shap_examples = compute_shap_importance(selected_event_model, X_event_test)\n",
+        "display(event_shap_importance.head(30))\n",
+        "display(event_shap_examples.sort_values(\"defect_probability\", ascending=False).head(20))\n",
+        "plt.figure(figsize=(9, 7))\n",
+        "sns.barplot(data=event_shap_importance.head(25), x=\"mean_abs_shap\", y=\"feature\", color=\"#4C78A8\")\n",
+        "plt.title(f\"불량 탐지 SHAP 중요도 - {selected_event_model_name}\")\n",
+        "plt.xlabel(\"평균 |SHAP|\")\n",
+        "plt.ylabel(\"Feature\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
     },
-    "id": "24Ol6sMmbEfC",
-    "outputId": "ca41c28e-e024-41a1-d30e-52eface69410"
-   },
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "selected_model_name: RandomForest\n",
-      "X_shap: (1000, 457)\n",
-      "X_shap_model: (1000, 457)\n",
-      "shap_matrix: (1000, 457)\n",
-      "base_values: (1000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# SHAP 샘플링\n",
-    "SHAP_SAMPLE_SIZE = min(1000, len(X_valid))\n",
-    "\n",
-    "if selected_model_name == \"LogisticRegression\":\n",
-    "    SHAP_SAMPLE_SIZE = min(500, len(X_valid))\n",
-    "\n",
-    "X_shap = X_valid.sample(n=SHAP_SAMPLE_SIZE, random_state=RANDOM_STATE)\n",
-    "id_shap = id_valid.loc[X_shap.index]\n",
-    "y_shap = y_valid.loc[X_shap.index]\n",
-    "proba_shap = predict_positive_proba(model, X_shap)\n",
-    "\n",
-    "\n",
-    "def prepare_shap_model(estimator, X_sample: pd.DataFrame):\n",
-    "    if is_pipeline_estimator(estimator):\n",
-    "        final_estimator = get_final_estimator(estimator)\n",
-    "        transform_steps = estimator.steps[:-1]\n",
-    "\n",
-    "        X_transformed = X_sample.copy()\n",
-    "\n",
-    "        for _, step in transform_steps:\n",
-    "            if hasattr(step, \"transform\"):\n",
-    "                X_transformed = step.transform(X_transformed)\n",
-    "\n",
-    "        X_transformed = pd.DataFrame(\n",
-    "            X_transformed,\n",
-    "            columns=X_sample.columns,\n",
-    "            index=X_sample.index\n",
-    "        )\n",
-    "\n",
-    "        return final_estimator, X_transformed\n",
-    "\n",
-    "    return estimator, X_sample\n",
-    "\n",
-    "\n",
-    "shap_model, X_shap_model = prepare_shap_model(model, X_shap)\n",
-    "\n",
-    "# SHAP Explainer 생성\n",
-    "try:\n",
-    "    explainer = shap.TreeExplainer(shap_model)\n",
-    "\n",
-    "    raw_shap_values = explainer.shap_values(\n",
-    "        X_shap_model,\n",
-    "        check_additivity=False\n",
-    "    )\n",
-    "\n",
-    "    if isinstance(raw_shap_values, list):\n",
-    "        shap_matrix = raw_shap_values[1]\n",
-    "    elif getattr(raw_shap_values, \"ndim\", 0) == 3:\n",
-    "        shap_matrix = raw_shap_values[:, :, 1]\n",
-    "    else:\n",
-    "        shap_matrix = raw_shap_values\n",
-    "\n",
-    "    expected_value = explainer.expected_value\n",
-    "\n",
-    "    if isinstance(expected_value, list):\n",
-    "        base_value = expected_value[1]\n",
-    "    else:\n",
-    "        base_value = expected_value\n",
-    "\n",
-    "except Exception as e:\n",
-    "    print(\"TreeExplainer 실패:\", e)\n",
-    "    print(\"Permutation Explainer로 대체 실행합니다.\")\n",
-    "\n",
-    "    background = shap.sample(\n",
-    "        X_train,\n",
-    "        min(100, len(X_train)),\n",
-    "        random_state=RANDOM_STATE\n",
-    "    )\n",
-    "\n",
-    "    explainer = shap.Explainer(\n",
-    "        lambda data: predict_positive_proba(\n",
-    "            model,\n",
-    "            pd.DataFrame(data, columns=X_train.columns)\n",
-    "        ),\n",
-    "        background\n",
-    "    )\n",
-    "\n",
-    "    raw_explanation = explainer(\n",
-    "        X_shap,\n",
-    "        max_evals=2 * X_shap.shape[1] + 1\n",
-    "    )\n",
-    "\n",
-    "    shap_matrix = raw_explanation.values\n",
-    "    base_value = raw_explanation.base_values\n",
-    "    X_shap_model = X_shap\n",
-    "\n",
-    "\n",
-    "# base_value 길이 보정\n",
-    "if np.isscalar(base_value):\n",
-    "    base_values = np.repeat(base_value, len(X_shap))\n",
-    "else:\n",
-    "    base_values = np.array(base_value)\n",
-    "\n",
-    "    if base_values.ndim == 0:\n",
-    "        base_values = np.repeat(float(base_values), len(X_shap))\n",
-    "    elif len(base_values) != len(X_shap):\n",
-    "        base_values = np.repeat(float(np.ravel(base_values)[0]), len(X_shap))\n",
-    "\n",
-    "\n",
-    "# SHAP 객체 구성\n",
-    "shap_values = shap.Explanation(\n",
-    "    values=shap_matrix,\n",
-    "    base_values=base_values,\n",
-    "    data=X_shap.values,\n",
-    "    feature_names=X_shap.columns.tolist(),\n",
-    ")\n",
-    "\n",
-    "\n",
-    "print(\"selected_model_name:\", selected_model_name)\n",
-    "print(\"X_shap:\", X_shap.shape)\n",
-    "print(\"X_shap_model:\", X_shap_model.shape)\n",
-    "print(\"shap_matrix:\", shap_matrix.shape)\n",
-    "print(\"base_values:\", np.array(base_values).shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "gvPdq4r1bEfC",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
+      "cell_type": "markdown",
+      "id": "68650205",
+      "metadata": {
+        "id": "68650205"
+      },
+      "source": [
+        "## 6. 공정 간 불량 전이 예측 모델링"
+      ]
     },
-    "id": "gvPdq4r1bEfC",
-    "outputId": "6871cde5-1c84-4060-a5b6-f5d757281281"
-   },
-   "outputs": [
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 800x1150 with 2 Axes>"
-      ],
-      "image/png": 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\n"
-     },
-     "metadata": {}
+      "cell_type": "markdown",
+      "id": "869a1fcd",
+      "metadata": {
+        "id": "869a1fcd"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "PRESS->BODY, BODY->PAINT, PAINT->ASSEMBLY 흐름별로 다음 공정 불량 가능성을 예측합니다. 실제 운영에서 이미 관측된 source 공정의 정답 라벨은 과도한 지름길이 될 수 있어 기본 feature에서 제외합니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "              feature  mean_abs_shap\n",
-       "341        L0_num_std  161153.464733\n",
-       "128      L3_S29_F3351  112466.467404\n",
-       "320  L3_S30_mean_time   87206.632845\n",
-       "134      L3_S29_F3373   79514.635807\n",
-       "145      L3_S29_F3412   67172.086809\n",
-       "144      L3_S29_F3407   50584.084076\n",
-       "143      L3_S29_F3404   47588.744257\n",
-       "124      L3_S29_F3339   45584.471179\n",
-       "263  process_end_time   41293.258694\n",
-       "16          L0_S2_F48   41259.724076\n",
-       "249      L3_S36_F3920   40991.128002\n",
-       "290  L0_S13_mean_time   38087.458690\n",
-       "360    L0_S0_num_mean   37707.793976\n",
-       "12          L0_S1_F24   34345.520207\n",
-       "266     L0_date_count   31073.838840\n",
-       "385     L0_S2_num_std   30331.910504\n",
-       "228      L3_S33_F3857   30191.782771\n",
-       "64        L0_S11_F326   27513.469192\n",
-       "367    L0_S10_num_std   25100.244226\n",
-       "0            L0_S0_F0   21225.150678\n",
-       "308   L0_S7_mean_time   19617.442282\n",
-       "43         L0_S9_F200   19379.907896\n",
-       "148      L3_S29_F3427   19288.645871\n",
-       "366   L0_S10_num_mean   19031.715581\n",
-       "62        L0_S11_F318   18211.382309\n",
-       "178      L3_S30_F3539   17632.894123\n",
-       "251      L3_S36_F3924   17431.833787\n",
-       "388     L0_S3_num_std   16874.240258\n",
-       "4            L0_S0_F8   16847.956330\n",
-       "340       L0_num_mean   15387.827290"
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "39c90ef0",
+      "metadata": {
+        "id": "39c90ef0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "ef77ad51-049c-4614-e865-3e2cf2610684"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "공정 전이 예측 정답 유출 방지 제외 컬럼: ['source_defect_yn']\n",
+            "공정 전이 예측 단일 feature 예측력 상위 10개\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                     feature  single_feature_auc\n",
+              "4  source_current_rms_ampere            0.521400\n",
+              "5     source_vibration_score            0.521281\n",
+              "1   source_station_delay_sec            0.517898\n",
+              "2        source_queue_length            0.504349\n",
+              "6       source_thermal_score            0.503262\n",
+              "0      source_cycle_time_sec            0.503251\n",
+              "3           source_wip_count            0.502044"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-23bdb32e-7686-4165-a817-6b0ac831797e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>single_feature_auc</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>source_current_rms_ampere</td>\n",
+              "      <td>0.521400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>source_vibration_score</td>\n",
+              "      <td>0.521281</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>source_station_delay_sec</td>\n",
+              "      <td>0.517898</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>source_queue_length</td>\n",
+              "      <td>0.504349</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>source_thermal_score</td>\n",
+              "      <td>0.503262</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>source_cycle_time_sec</td>\n",
+              "      <td>0.503251</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>source_wip_count</td>\n",
+              "      <td>0.502044</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23bdb32e-7686-4165-a817-6b0ac831797e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-23bdb32e-7686-4165-a817-6b0ac831797e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-23bdb32e-7686-4165-a817-6b0ac831797e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"source_current_rms_ampere\",\n          \"source_vibration_score\",\n          \"source_cycle_time_sec\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"single_feature_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009165577515941305,\n        \"min\": 0.502044399948169,\n        \"max\": 0.5214003968035711,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.5214003968035711,\n          0.521281370206187,\n          0.5032511497246327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "공정 전이 예측 feature 수: 7\n",
+            "공정 전이 예측 feature: ['source_cycle_time_sec', 'source_station_delay_sec', 'source_queue_length', 'source_wip_count', 'source_current_rms_ampere', 'source_vibration_score', 'source_thermal_score']\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1100x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   split label  count     ratio\n",
+              "0  Train    정상  22634  0.785903\n",
+              "1  Train    불량   6166  0.214097\n",
+              "2   Test    정상   5708  0.792778\n",
+              "3   Test    불량   1492  0.207222"
+            ],
+            "text/html": [
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+              "    <div>\n",
+              "<style scoped>\n",
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+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>split</th>\n",
+              "      <th>label</th>\n",
+              "      <th>count</th>\n",
+              "      <th>ratio</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Train</td>\n",
+              "      <td>정상</td>\n",
+              "      <td>22634</td>\n",
+              "      <td>0.785903</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Train</td>\n",
+              "      <td>불량</td>\n",
+              "      <td>6166</td>\n",
+              "      <td>0.214097</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Test</td>\n",
+              "      <td>정상</td>\n",
+              "      <td>5708</td>\n",
+              "      <td>0.792778</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Test</td>\n",
+              "      <td>불량</td>\n",
+              "      <td>1492</td>\n",
+              "      <td>0.207222</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-344326ed-81a7-4f3d-a938-ae4ffa30d677')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-344326ed-81a7-4f3d-a938-ae4ffa30d677 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-344326ed-81a7-4f3d-a938-ae4ffa30d677');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
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+              "\n",
+              "\n",
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+            }
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+          "metadata": {}
+        },
+        {
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+          "data": {
+            "text/plain": [
+              "target_defect_yn                            정상 비율  전이 양성 비율\n",
+              "source_process_code target_process_code                    \n",
+              "BODY                PAINT                0.779792  0.220208\n",
+              "PAINT               ASSEMBLY             0.793438  0.206563\n",
+              "PRESS               BODY                 0.784479  0.215521"
+            ],
+            "text/html": [
+              "\n",
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+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>target_defect_yn</th>\n",
+              "      <th>정상 비율</th>\n",
+              "      <th>전이 양성 비율</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>source_process_code</th>\n",
+              "      <th>target_process_code</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>BODY</th>\n",
+              "      <th>PAINT</th>\n",
+              "      <td>0.779792</td>\n",
+              "      <td>0.220208</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PAINT</th>\n",
+              "      <th>ASSEMBLY</th>\n",
+              "      <td>0.793438</td>\n",
+              "      <td>0.206563</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PRESS</th>\n",
+              "      <th>BODY</th>\n",
+              "      <td>0.784479</td>\n",
+              "      <td>0.215521</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-936d4346-9837-4e97-bf68-d865bac2bde9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-936d4346-9837-4e97-bf68-d865bac2bde9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-936d4346-9837-4e97-bf68-d865bac2bde9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"\\uc815\\uc0c1 \\ube44\\uc728\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006933411374427258,\n        \"min\": 0.7797916666666667,\n        \"max\": 0.7934375,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.7797916666666667,\n          0.7934375,\n          0.7844791666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc804\\uc774 \\uc591\\uc131 \\ube44\\uc728\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006933411374427237,\n        \"min\": 0.2065625,\n        \"max\": 0.22020833333333334,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.22020833333333334,\n          0.2065625,\n          0.21552083333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x480 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1100x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[press_to_body] lightweight CV tuning: 3 trials x 3 folds\n",
+            "  trial 1: CV PR-AUC=0.2900, Recall=0.9990, F1=0.3569, elapsed=1.0s\n",
+            "  trial 2: CV PR-AUC=0.2825, Recall=0.9927, F1=0.3573, elapsed=1.0s\n",
+            "  trial 3: CV PR-AUC=0.2841, Recall=0.9976, F1=0.3568, elapsed=1.0s\n",
+            "재현율(Recall): 0.6833\n",
+            "정밀도(Precision): 0.2580\n",
+            "F1 Score: 0.3745\n",
+            "PR-AUC: 0.3004\n",
+            "임계값(Threshold): 0.4377\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                         지표         값\n",
+              "0             정확도(Accuracy)  0.504583\n",
+              "1            정밀도(Precision)  0.257971\n",
+              "2               재현율(Recall)  0.683301\n",
+              "3                  F1 Score  0.374540\n",
+              "4                   ROC-AUC  0.591590\n",
+              "5                    PR-AUC  0.300385\n",
+              "6  오탐률(False Positive Rate)  0.544971\n",
+              "7            임계값(Threshold)  0.437682"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e0fac5cc-2bb2-4211-92cc-c3ee196b0c21\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>지표</th>\n",
+              "      <th>값</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>정확도(Accuracy)</td>\n",
+              "      <td>0.504583</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>정밀도(Precision)</td>\n",
+              "      <td>0.257971</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>재현율(Recall)</td>\n",
+              "      <td>0.683301</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.374540</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.591590</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.300385</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>오탐률(False Positive Rate)</td>\n",
+              "      <td>0.544971</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>임계값(Threshold)</td>\n",
+              "      <td>0.437682</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0fac5cc-2bb2-4211-92cc-c3ee196b0c21')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e0fac5cc-2bb2-4211-92cc-c3ee196b0c21 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e0fac5cc-2bb2-4211-92cc-c3ee196b0c21');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
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+            }
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+        },
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+          "data": {
+            "text/plain": [
+              "          예측 정상(0)  예측 불량(1)\n",
+              "실제 정상(0)       855      1024\n",
+              "실제 불량(1)       165       356"
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
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+              "      <th>예측 정상(0)</th>\n",
+              "      <th>예측 불량(1)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>실제 정상(0)</th>\n",
+              "      <td>855</td>\n",
+              "      <td>1024</td>\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d524390c-73c5-48b1-8b19-3028e86384a3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "    </button>\n",
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+              "      background-color: #E8F0FE;\n",
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+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d524390c-73c5-48b1-8b19-3028e86384a3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "        const element = document.querySelector('#df-d524390c-73c5-48b1-8b19-3028e86384a3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[body_to_paint] lightweight CV tuning: 3 trials x 3 folds\n",
+            "  trial 1: CV PR-AUC=0.2841, Recall=0.9995, F1=0.3617, elapsed=0.8s\n",
+            "  trial 2: CV PR-AUC=0.2739, Recall=0.9915, F1=0.3632, elapsed=1.0s\n",
+            "  trial 3: CV PR-AUC=0.2789, Recall=1.0000, F1=0.3613, elapsed=1.1s\n",
+            "재현율(Recall): 0.6341\n",
+            "정밀도(Precision): 0.2513\n",
+            "F1 Score: 0.3600\n",
+            "PR-AUC: 0.2951\n",
+            "임계값(Threshold): 0.4447\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                         지표         값\n",
+              "0             정확도(Accuracy)  0.499167\n",
+              "1            정밀도(Precision)  0.251301\n",
+              "2               재현율(Recall)  0.634146\n",
+              "3                  F1 Score  0.359957\n",
+              "4                   ROC-AUC  0.581338\n",
+              "5                    PR-AUC  0.295122\n",
+              "6  오탐률(False Positive Rate)  0.539368\n",
+              "7            임계값(Threshold)  0.444744"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4357ef2d-5cf9-4bdd-bd57-c0dc92e2290f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>지표</th>\n",
+              "      <th>값</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>정확도(Accuracy)</td>\n",
+              "      <td>0.499167</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>정밀도(Precision)</td>\n",
+              "      <td>0.251301</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>재현율(Recall)</td>\n",
+              "      <td>0.634146</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.359957</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.581338</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.295122</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>오탐률(False Positive Rate)</td>\n",
+              "      <td>0.539368</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>임계값(Threshold)</td>\n",
+              "      <td>0.444744</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4357ef2d-5cf9-4bdd-bd57-c0dc92e2290f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4357ef2d-5cf9-4bdd-bd57-c0dc92e2290f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4357ef2d-5cf9-4bdd-bd57-c0dc92e2290f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "                                                    [key], {});\n",
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+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[paint_to_assembly] lightweight CV tuning: 3 trials x 3 folds\n",
+            "  trial 1: CV PR-AUC=0.2310, Recall=0.9960, F1=0.3445, elapsed=1.6s\n",
+            "  trial 2: CV PR-AUC=0.2256, Recall=0.9965, F1=0.3447, elapsed=1.0s\n",
+            "  trial 3: CV PR-AUC=0.2247, Recall=0.9950, F1=0.3446, elapsed=1.1s\n",
+            "재현율(Recall): 0.5776\n",
+            "정밀도(Precision): 0.1856\n",
+            "F1 Score: 0.2810\n",
+            "PR-AUC: 0.1844\n",
+            "임계값(Threshold): 0.4600\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                         지표         값\n",
+              "0             정확도(Accuracy)  0.460417\n",
+              "1            정밀도(Precision)  0.185620\n",
+              "2               재현율(Recall)  0.577626\n",
+              "3                  F1 Score  0.280955\n",
+              "4                   ROC-AUC  0.507275\n",
+              "5                    PR-AUC  0.184449\n",
+              "6  오탐률(False Positive Rate)  0.565749\n",
+              "7            임계값(Threshold)  0.460019"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-73c8ce65-4580-45f6-a7d2-df51c36b6d13\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>지표</th>\n",
+              "      <th>값</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>정확도(Accuracy)</td>\n",
+              "      <td>0.460417</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>정밀도(Precision)</td>\n",
+              "      <td>0.185620</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>재현율(Recall)</td>\n",
+              "      <td>0.577626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.280955</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.507275</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.184449</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>오탐률(False Positive Rate)</td>\n",
+              "      <td>0.565749</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>임계값(Threshold)</td>\n",
+              "      <td>0.460019</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73c8ce65-4580-45f6-a7d2-df51c36b6d13')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-73c8ce65-4580-45f6-a7d2-df51c36b6d13 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-73c8ce65-4580-45f6-a7d2-df51c36b6d13');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "           model_key  trial_no  cv_pr_auc  cv_recall  cv_precision     cv_f1  \\\n",
+              "0      body_to_paint         1   0.284062   0.999527      0.220817  0.361722   \n",
+              "1      body_to_paint         3   0.278926   1.000000      0.220507  0.361337   \n",
+              "2      body_to_paint         2   0.273898   0.991489      0.222351  0.363213   \n",
+              "3  paint_to_assembly         1   0.230985   0.995966      0.208272  0.344501   \n",
+              "4  paint_to_assembly         2   0.225575   0.996470      0.208419  0.344734   \n",
+              "5  paint_to_assembly         3   0.224656   0.994957      0.208365  0.344569   \n",
+              "6      press_to_body         1   0.290033   0.999033      0.217283  0.356935   \n",
+              "7      press_to_body         3   0.284147   0.997584      0.217244  0.356788   \n",
+              "8      press_to_body         2   0.282452   0.992749      0.217845  0.357285   \n",
+              "\n",
+              "   cv_accuracy  cv_threshold  elapsed_sec  \\\n",
+              "0     0.223229      0.143963     0.842512   \n",
+              "1     0.221562      0.091514     1.105790   \n",
+              "2     0.234271      0.115225     1.001900   \n",
+              "3     0.217083      0.202340     1.578503   \n",
+              "4     0.217500      0.134790     1.047794   \n",
+              "5     0.218125      0.161553     1.124835   \n",
+              "6     0.224167      0.180116     0.991396   \n",
+              "7     0.224792      0.137241     1.006999   \n",
+              "8     0.230208      0.134730     1.049339   \n",
+              "\n",
+              "                                              params  \n",
+              "0  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "1  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "2  {'model__subsample': 0.85, 'model__reg_lambda'...  \n",
+              "3  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "4  {'model__subsample': 0.85, 'model__reg_lambda'...  \n",
+              "5  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "6  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "7  {'model__subsample': 0.95, 'model__reg_lambda'...  \n",
+              "8  {'model__subsample': 0.85, 'model__reg_lambda'...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e154f6d7-7ea2-45f9-8f8b-0671c475a4f7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model_key</th>\n",
+              "      <th>trial_no</th>\n",
+              "      <th>cv_pr_auc</th>\n",
+              "      <th>cv_recall</th>\n",
+              "      <th>cv_precision</th>\n",
+              "      <th>cv_f1</th>\n",
+              "      <th>cv_accuracy</th>\n",
+              "      <th>cv_threshold</th>\n",
+              "      <th>elapsed_sec</th>\n",
+              "      <th>params</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.284062</td>\n",
+              "      <td>0.999527</td>\n",
+              "      <td>0.220817</td>\n",
+              "      <td>0.361722</td>\n",
+              "      <td>0.223229</td>\n",
+              "      <td>0.143963</td>\n",
+              "      <td>0.842512</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.278926</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.220507</td>\n",
+              "      <td>0.361337</td>\n",
+              "      <td>0.221562</td>\n",
+              "      <td>0.091514</td>\n",
+              "      <td>1.105790</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.273898</td>\n",
+              "      <td>0.991489</td>\n",
+              "      <td>0.222351</td>\n",
+              "      <td>0.363213</td>\n",
+              "      <td>0.234271</td>\n",
+              "      <td>0.115225</td>\n",
+              "      <td>1.001900</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.230985</td>\n",
+              "      <td>0.995966</td>\n",
+              "      <td>0.208272</td>\n",
+              "      <td>0.344501</td>\n",
+              "      <td>0.217083</td>\n",
+              "      <td>0.202340</td>\n",
+              "      <td>1.578503</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.225575</td>\n",
+              "      <td>0.996470</td>\n",
+              "      <td>0.208419</td>\n",
+              "      <td>0.344734</td>\n",
+              "      <td>0.217500</td>\n",
+              "      <td>0.134790</td>\n",
+              "      <td>1.047794</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.224656</td>\n",
+              "      <td>0.994957</td>\n",
+              "      <td>0.208365</td>\n",
+              "      <td>0.344569</td>\n",
+              "      <td>0.218125</td>\n",
+              "      <td>0.161553</td>\n",
+              "      <td>1.124835</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.290033</td>\n",
+              "      <td>0.999033</td>\n",
+              "      <td>0.217283</td>\n",
+              "      <td>0.356935</td>\n",
+              "      <td>0.224167</td>\n",
+              "      <td>0.180116</td>\n",
+              "      <td>0.991396</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.284147</td>\n",
+              "      <td>0.997584</td>\n",
+              "      <td>0.217244</td>\n",
+              "      <td>0.356788</td>\n",
+              "      <td>0.224792</td>\n",
+              "      <td>0.137241</td>\n",
+              "      <td>1.006999</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.282452</td>\n",
+              "      <td>0.992749</td>\n",
+              "      <td>0.217845</td>\n",
+              "      <td>0.357285</td>\n",
+              "      <td>0.230208</td>\n",
+              "      <td>0.134730</td>\n",
+              "      <td>1.049339</td>\n",
+              "      <td>{'model__subsample': 0.85, 'model__reg_lambda'...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e154f6d7-7ea2-45f9-8f8b-0671c475a4f7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
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+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "\n",
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+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e154f6d7-7ea2-45f9-8f8b-0671c475a4f7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e154f6d7-7ea2-45f9-8f8b-0671c475a4f7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                모델 키  소스 공정     타깃 공정  \\\n",
+              "0      press_to_body  PRESS      BODY   \n",
+              "1      body_to_paint   BODY     PAINT   \n",
+              "2  paint_to_assembly  PAINT  ASSEMBLY   \n",
+              "\n",
+              "                                             선택 파라미터  train_threshold  \\\n",
+              "0  {'model__subsample': 0.95, 'model__reg_lambda'...         0.437682   \n",
+              "1  {'model__subsample': 0.95, 'model__reg_lambda'...         0.444744   \n",
+              "2  {'model__subsample': 0.95, 'model__reg_lambda'...         0.460019   \n",
+              "\n",
+              "   train_pr_auc  train_roc_auc  Train 정확도  Train 정밀도  Train 재현율  Train F1  \\\n",
+              "0      0.491504       0.796379   0.605833   0.342342   0.899952  0.496004   \n",
+              "1      0.496058       0.784340   0.604688   0.344611   0.881741  0.495547   \n",
+              "2      0.492547       0.794082   0.590521   0.322845   0.895108  0.474535   \n",
+              "\n",
+              "   train_false_positive_rate  train_tn  train_fp  train_fn  train_tp  \\\n",
+              "0                   0.474970      3954      3577       207      1862   \n",
+              "1                   0.473551      3941      3545       250      1864   \n",
+              "2                   0.488775      3894      3723       208      1775   \n",
+              "\n",
+              "        임계값  Test PR-AUC  test_roc_auc  Test 정확도  Test 정밀도  Test 재현율  \\\n",
+              "0  0.437682     0.300385      0.591590  0.504583  0.257971  0.683301   \n",
+              "1  0.444744     0.295122      0.581338  0.499167  0.251301  0.634146   \n",
+              "2  0.460019     0.184449      0.507275  0.460417  0.185620  0.577626   \n",
+              "\n",
+              "    Test F1  Test 오탐률  test_tn  test_fp  test_fn  test_tp  \n",
+              "0  0.374540  0.544971      855     1024      165      356  \n",
+              "1  0.359957  0.539368      860     1007      195      338  \n",
+              "2  0.280955  0.565749      852     1110      185      253  "
+            ],
+            "text/html": [
+              "\n",
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "        vertical-align: top;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>모델 키</th>\n",
+              "      <th>소스 공정</th>\n",
+              "      <th>타깃 공정</th>\n",
+              "      <th>선택 파라미터</th>\n",
+              "      <th>train_threshold</th>\n",
+              "      <th>train_pr_auc</th>\n",
+              "      <th>train_roc_auc</th>\n",
+              "      <th>Train 정확도</th>\n",
+              "      <th>Train 정밀도</th>\n",
+              "      <th>Train 재현율</th>\n",
+              "      <th>Train F1</th>\n",
+              "      <th>train_false_positive_rate</th>\n",
+              "      <th>train_tn</th>\n",
+              "      <th>train_fp</th>\n",
+              "      <th>train_fn</th>\n",
+              "      <th>train_tp</th>\n",
+              "      <th>임계값</th>\n",
+              "      <th>Test PR-AUC</th>\n",
+              "      <th>test_roc_auc</th>\n",
+              "      <th>Test 정확도</th>\n",
+              "      <th>Test 정밀도</th>\n",
+              "      <th>Test 재현율</th>\n",
+              "      <th>Test F1</th>\n",
+              "      <th>Test 오탐률</th>\n",
+              "      <th>test_tn</th>\n",
+              "      <th>test_fp</th>\n",
+              "      <th>test_fn</th>\n",
+              "      <th>test_tp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>0.491504</td>\n",
+              "      <td>0.796379</td>\n",
+              "      <td>0.605833</td>\n",
+              "      <td>0.342342</td>\n",
+              "      <td>0.899952</td>\n",
+              "      <td>0.496004</td>\n",
+              "      <td>0.474970</td>\n",
+              "      <td>3954</td>\n",
+              "      <td>3577</td>\n",
+              "      <td>207</td>\n",
+              "      <td>1862</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>0.300385</td>\n",
+              "      <td>0.591590</td>\n",
+              "      <td>0.504583</td>\n",
+              "      <td>0.257971</td>\n",
+              "      <td>0.683301</td>\n",
+              "      <td>0.374540</td>\n",
+              "      <td>0.544971</td>\n",
+              "      <td>855</td>\n",
+              "      <td>1024</td>\n",
+              "      <td>165</td>\n",
+              "      <td>356</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "      <td>0.444744</td>\n",
+              "      <td>0.496058</td>\n",
+              "      <td>0.784340</td>\n",
+              "      <td>0.604688</td>\n",
+              "      <td>0.344611</td>\n",
+              "      <td>0.881741</td>\n",
+              "      <td>0.495547</td>\n",
+              "      <td>0.473551</td>\n",
+              "      <td>3941</td>\n",
+              "      <td>3545</td>\n",
+              "      <td>250</td>\n",
+              "      <td>1864</td>\n",
+              "      <td>0.444744</td>\n",
+              "      <td>0.295122</td>\n",
+              "      <td>0.581338</td>\n",
+              "      <td>0.499167</td>\n",
+              "      <td>0.251301</td>\n",
+              "      <td>0.634146</td>\n",
+              "      <td>0.359957</td>\n",
+              "      <td>0.539368</td>\n",
+              "      <td>860</td>\n",
+              "      <td>1007</td>\n",
+              "      <td>195</td>\n",
+              "      <td>338</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>{'model__subsample': 0.95, 'model__reg_lambda'...</td>\n",
+              "      <td>0.460019</td>\n",
+              "      <td>0.492547</td>\n",
+              "      <td>0.794082</td>\n",
+              "      <td>0.590521</td>\n",
+              "      <td>0.322845</td>\n",
+              "      <td>0.895108</td>\n",
+              "      <td>0.474535</td>\n",
+              "      <td>0.488775</td>\n",
+              "      <td>3894</td>\n",
+              "      <td>3723</td>\n",
+              "      <td>208</td>\n",
+              "      <td>1775</td>\n",
+              "      <td>0.460019</td>\n",
+              "      <td>0.184449</td>\n",
+              "      <td>0.507275</td>\n",
+              "      <td>0.460417</td>\n",
+              "      <td>0.185620</td>\n",
+              "      <td>0.577626</td>\n",
+              "      <td>0.280955</td>\n",
+              "      <td>0.565749</td>\n",
+              "      <td>852</td>\n",
+              "      <td>1110</td>\n",
+              "      <td>185</td>\n",
+              "      <td>253</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6359518-495c-4fda-b03a-fd9183b33913')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "\n",
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+              "        document.querySelector('#df-d6359518-495c-4fda-b03a-fd9183b33913 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d6359518-495c-4fda-b03a-fd9183b33913');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x400 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x420 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x450 with 1 Axes>"
+            ],
+            "image/png": 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+          "metadata": {}
+        },
+        {
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+          "data": {
+            "text/plain": [
+              "split    metric      Test     Train\n",
+              "0      F1 Score  0.374540  0.496004\n",
+              "1        PR-AUC  0.300385  0.491504\n",
+              "2       ROC-AUC  0.591590  0.796379\n",
+              "3           재현율  0.683301  0.899952\n",
+              "4           정밀도  0.257971  0.342342\n",
+              "5           정확도  0.504583  0.605833"
+            ],
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>split</th>\n",
+              "      <th>metric</th>\n",
+              "      <th>Test</th>\n",
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+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.374540</td>\n",
+              "      <td>0.496004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.300385</td>\n",
+              "      <td>0.491504</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.591590</td>\n",
+              "      <td>0.796379</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>재현율</td>\n",
+              "      <td>0.683301</td>\n",
+              "      <td>0.899952</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>정밀도</td>\n",
+              "      <td>0.257971</td>\n",
+              "      <td>0.342342</td>\n",
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+              "      <th>5</th>\n",
+              "      <td>정확도</td>\n",
+              "      <td>0.504583</td>\n",
+              "      <td>0.605833</td>\n",
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+              "        document.querySelector('#df-22225681-3b2c-4d73-9c52-b1f59dba457e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-22225681-3b2c-4d73-9c52-b1f59dba457e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"metric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"F1 Score\",\n          \"PR-AUC\",\n          \"\\uc815\\ud655\\ub3c4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16875133953319638,\n        \"min\": 0.2579710144927536,\n        \"max\": 0.6833013435700576,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.3745397159389795,\n          0.30038480436139636,\n          0.5045833333333334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Train\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20850551628710484,\n        \"min\": 0.34234234234234234,\n        \"max\": 0.8999516674722088,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.4960042621204049,\n          0.4915044099356285,\n          0.6058333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x450 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                     feature  importance  importance_ratio\n",
+              "0       source_thermal_score      1026.0          0.212863\n",
+              "1     source_vibration_score      1001.0          0.207676\n",
+              "2  source_current_rms_ampere       998.0          0.207054\n",
+              "3      source_cycle_time_sec       984.0          0.204149\n",
+              "4           source_wip_count       346.0          0.071784\n",
+              "5        source_queue_length       285.0          0.059129\n",
+              "6   source_station_delay_sec       180.0          0.037344"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-53922726-ccf1-419f-a562-4007e77a5517\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>importance</th>\n",
+              "      <th>importance_ratio</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>source_thermal_score</td>\n",
+              "      <td>1026.0</td>\n",
+              "      <td>0.212863</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>source_vibration_score</td>\n",
+              "      <td>1001.0</td>\n",
+              "      <td>0.207676</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>source_current_rms_ampere</td>\n",
+              "      <td>998.0</td>\n",
+              "      <td>0.207054</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>source_cycle_time_sec</td>\n",
+              "      <td>984.0</td>\n",
+              "      <td>0.204149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>source_wip_count</td>\n",
+              "      <td>346.0</td>\n",
+              "      <td>0.071784</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>source_queue_length</td>\n",
+              "      <td>285.0</td>\n",
+              "      <td>0.059129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>source_station_delay_sec</td>\n",
+              "      <td>180.0</td>\n",
+              "      <td>0.037344</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53922726-ccf1-419f-a562-4007e77a5517')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-53922726-ccf1-419f-a562-4007e77a5517 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-53922726-ccf1-419f-a562-4007e77a5517');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"source_thermal_score\",\n          \"source_vibration_score\",\n          \"source_queue_length\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 394.41173796886295,\n        \"min\": 180.0,\n        \"max\": 1026.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1026.0,\n          1001.0,\n          285.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08182816140432843,\n        \"min\": 0.03734439834024896,\n        \"max\": 0.21286307053941908,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.21286307053941908,\n          0.20767634854771785,\n          0.05912863070539419\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x420 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x450 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "split    metric      Test     Train\n",
+              "0      F1 Score  0.359957  0.495547\n",
+              "1        PR-AUC  0.295122  0.496058\n",
+              "2       ROC-AUC  0.581338  0.784340\n",
+              "3           재현율  0.634146  0.881741\n",
+              "4           정밀도  0.251301  0.344611\n",
+              "5           정확도  0.499167  0.604688"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6c6b9ae8-2b19-4a51-9f8b-60d0cf3a302d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>split</th>\n",
+              "      <th>metric</th>\n",
+              "      <th>Test</th>\n",
+              "      <th>Train</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.359957</td>\n",
+              "      <td>0.495547</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.295122</td>\n",
+              "      <td>0.496058</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.581338</td>\n",
+              "      <td>0.784340</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>재현율</td>\n",
+              "      <td>0.634146</td>\n",
+              "      <td>0.881741</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>정밀도</td>\n",
+              "      <td>0.251301</td>\n",
+              "      <td>0.344611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>정확도</td>\n",
+              "      <td>0.499167</td>\n",
+              "      <td>0.604688</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c6b9ae8-2b19-4a51-9f8b-60d0cf3a302d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6c6b9ae8-2b19-4a51-9f8b-60d0cf3a302d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6c6b9ae8-2b19-4a51-9f8b-60d0cf3a302d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"metric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"F1 Score\",\n          \"PR-AUC\",\n          \"\\uc815\\ud655\\ub3c4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15755227989013074,\n        \"min\": 0.2513011152416357,\n        \"max\": 0.6341463414634146,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.3599574014909478,\n          0.2951224353443143,\n          0.49916666666666665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Train\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20015544891453493,\n        \"min\": 0.344610833795526,\n        \"max\": 0.8817407757805109,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.49554698923301876,\n          0.49605769687838186,\n          0.6046875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x450 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
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+          "data": {
+            "text/plain": [
+              "                     feature  importance  importance_ratio\n",
+              "0     source_vibration_score      1073.0          0.221969\n",
+              "1       source_thermal_score      1006.0          0.208109\n",
+              "2  source_current_rms_ampere      1001.0          0.207075\n",
+              "3      source_cycle_time_sec       944.0          0.195283\n",
+              "4           source_wip_count       331.0          0.068473\n",
+              "5        source_queue_length       315.0          0.065163\n",
+              "6   source_station_delay_sec       164.0          0.033926"
+            ],
+            "text/html": [
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>importance</th>\n",
+              "      <th>importance_ratio</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>source_vibration_score</td>\n",
+              "      <td>1073.0</td>\n",
+              "      <td>0.221969</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>source_thermal_score</td>\n",
+              "      <td>1006.0</td>\n",
+              "      <td>0.208109</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>source_current_rms_ampere</td>\n",
+              "      <td>1001.0</td>\n",
+              "      <td>0.207075</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>source_cycle_time_sec</td>\n",
+              "      <td>944.0</td>\n",
+              "      <td>0.195283</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>source_wip_count</td>\n",
+              "      <td>331.0</td>\n",
+              "      <td>0.068473</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>source_queue_length</td>\n",
+              "      <td>315.0</td>\n",
+              "      <td>0.065163</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>source_station_delay_sec</td>\n",
+              "      <td>164.0</td>\n",
+              "      <td>0.033926</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c24ac271-7be7-457e-b21a-a38bbcd52105')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c24ac271-7be7-457e-b21a-a38bbcd52105 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c24ac271-7be7-457e-b21a-a38bbcd52105');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        element.appendChild(docLink);\n",
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+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"source_vibration_score\",\n          \"source_thermal_score\",\n          \"source_queue_length\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 398.74003943373816,\n        \"min\": 164.0,\n        \"max\": 1073.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1073.0,\n          1006.0,\n          315.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08248656173639597,\n        \"min\": 0.03392635498551924,\n        \"max\": 0.22196938353330575,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.22196938353330575,\n          0.20810922631361192,\n          0.06516342573438147\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x420 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x450 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "split    metric      Test     Train\n",
+              "0      F1 Score  0.280955  0.474535\n",
+              "1        PR-AUC  0.184449  0.492547\n",
+              "2       ROC-AUC  0.507275  0.794082\n",
+              "3           재현율  0.577626  0.895108\n",
+              "4           정밀도  0.185620  0.322845\n",
+              "5           정확도  0.460417  0.590521"
+            ],
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+              "\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>split</th>\n",
+              "      <th>metric</th>\n",
+              "      <th>Test</th>\n",
+              "      <th>Train</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>F1 Score</td>\n",
+              "      <td>0.280955</td>\n",
+              "      <td>0.474535</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>PR-AUC</td>\n",
+              "      <td>0.184449</td>\n",
+              "      <td>0.492547</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ROC-AUC</td>\n",
+              "      <td>0.507275</td>\n",
+              "      <td>0.794082</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>재현율</td>\n",
+              "      <td>0.577626</td>\n",
+              "      <td>0.895108</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>정밀도</td>\n",
+              "      <td>0.185620</td>\n",
+              "      <td>0.322845</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>정확도</td>\n",
+              "      <td>0.460417</td>\n",
+              "      <td>0.590521</td>\n",
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+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fa7ed5b5-5549-470c-981d-2e21b99a85a7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fa7ed5b5-5549-470c-981d-2e21b99a85a7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"metric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"F1 Score\",\n          \"PR-AUC\",\n          \"\\uc815\\ud655\\ub3c4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17110755703355043,\n        \"min\": 0.18444886094472468,\n        \"max\": 0.5776255707762558,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.2809550249861188,\n          0.18444886094472468,\n          0.46041666666666664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Train\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2139139139784951,\n        \"min\": 0.32284467078937795,\n        \"max\": 0.8951084215834594,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.47453548990776634,\n          0.4925466309387107,\n          0.5905208333333334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x450 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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+          "metadata": {}
+        },
+        {
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+          "data": {
+            "text/plain": [
+              "                     feature  importance  importance_ratio\n",
+              "0       source_thermal_score      1032.0          0.216035\n",
+              "1      source_cycle_time_sec      1008.0          0.211011\n",
+              "2     source_vibration_score       956.0          0.200126\n",
+              "3  source_current_rms_ampere       867.0          0.181495\n",
+              "4           source_wip_count       398.0          0.083316\n",
+              "5        source_queue_length       332.0          0.069500\n",
+              "6   source_station_delay_sec       184.0          0.038518"
+            ],
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+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>importance</th>\n",
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+              "      <th>0</th>\n",
+              "      <td>source_thermal_score</td>\n",
+              "      <td>1032.0</td>\n",
+              "      <td>0.216035</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>source_cycle_time_sec</td>\n",
+              "      <td>1008.0</td>\n",
+              "      <td>0.211011</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>source_vibration_score</td>\n",
+              "      <td>956.0</td>\n",
+              "      <td>0.200126</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>source_current_rms_ampere</td>\n",
+              "      <td>867.0</td>\n",
+              "      <td>0.181495</td>\n",
+              "    </tr>\n",
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+              "      <th>4</th>\n",
+              "      <td>source_wip_count</td>\n",
+              "      <td>398.0</td>\n",
+              "      <td>0.083316</td>\n",
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+              "      <th>5</th>\n",
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+              "      <td>332.0</td>\n",
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+              "      <th>6</th>\n",
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+              "      <td>184.0</td>\n",
+              "      <td>0.038518</td>\n",
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+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c88698ef-2e9f-4caa-a480-53e04aaccff2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c88698ef-2e9f-4caa-a480-53e04aaccff2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(transition_predictions\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"source_thermal_score\",\n          \"source_cycle_time_sec\",\n          \"source_queue_length\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.6834511172046,\n        \"min\": 184.0,\n        \"max\": 1032.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1032.0,\n          1008.0,\n          332.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07592284930232458,\n        \"min\": 0.03851789826250785,\n        \"max\": 0.2160351685158049,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.2160351685158049,\n          0.21101109482939084,\n          0.0694996859953946\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "      car_master_id      source_event_id      target_event_id  \\\n",
+              "1816          11417  EVT-20260618-045665  EVT-20260618-045666   \n",
+              "1805          11406  EVT-20260618-045621  EVT-20260618-045622   \n",
+              "355            9956  EVT-20260617-039821  EVT-20260617-039822   \n",
+              "348            9949  EVT-20260617-039793  EVT-20260617-039794   \n",
+              "4730          11931  EVT-20260618-047722  EVT-20260618-047723   \n",
+              "2470           9671  EVT-20260617-038682  EVT-20260617-038683   \n",
+              "224            9825  EVT-20260617-039297  EVT-20260617-039298   \n",
+              "56             9657  EVT-20260617-038625  EVT-20260617-038626   \n",
+              "35             9636  EVT-20260617-038541  EVT-20260617-038542   \n",
+              "43             9644  EVT-20260617-038573  EVT-20260617-038574   \n",
+              "6953          11754  EVT-20260618-047015  EVT-20260618-047016   \n",
+              "3530          10731  EVT-20260617-042922  EVT-20260617-042923   \n",
+              "1949          11550  EVT-20260618-046197  EVT-20260618-046198   \n",
+              "1783          11384  EVT-20260617-045533  EVT-20260617-045534   \n",
+              "2053          11654  EVT-20260618-046613  EVT-20260618-046614   \n",
+              "1320          10921  EVT-20260617-043681  EVT-20260617-043682   \n",
+              "230            9831  EVT-20260617-039321  EVT-20260617-039322   \n",
+              "1845          11446  EVT-20260618-045781  EVT-20260618-045782   \n",
+              "1477          11078  EVT-20260617-044309  EVT-20260617-044310   \n",
+              "2376          11977  EVT-20260618-047905  EVT-20260618-047906   \n",
+              "1180          10781  EVT-20260617-043121  EVT-20260617-043122   \n",
+              "1563          11164  EVT-20260617-044653  EVT-20260617-044654   \n",
+              "110            9711  EVT-20260617-038841  EVT-20260617-038842   \n",
+              "2206          11807  EVT-20260618-047225  EVT-20260618-047226   \n",
+              "2215          11816  EVT-20260618-047261  EVT-20260618-047262   \n",
+              "2000          11601  EVT-20260618-046401  EVT-20260618-046402   \n",
+              "2323          11924  EVT-20260618-047693  EVT-20260618-047694   \n",
+              "8              9609  EVT-20260617-038433  EVT-20260617-038434   \n",
+              "2046          11647  EVT-20260618-046585  EVT-20260618-046586   \n",
+              "1136          10737  EVT-20260617-042945  EVT-20260617-042946   \n",
+              "\n",
+              "     source_process_code target_process_code          model_key  \\\n",
+              "1816               PRESS                BODY      press_to_body   \n",
+              "1805               PRESS                BODY      press_to_body   \n",
+              "355                PRESS                BODY      press_to_body   \n",
+              "348                PRESS                BODY      press_to_body   \n",
+              "4730                BODY               PAINT      body_to_paint   \n",
+              "2470                BODY               PAINT      body_to_paint   \n",
+              "224                PRESS                BODY      press_to_body   \n",
+              "56                 PRESS                BODY      press_to_body   \n",
+              "35                 PRESS                BODY      press_to_body   \n",
+              "43                 PRESS                BODY      press_to_body   \n",
+              "6953               PAINT            ASSEMBLY  paint_to_assembly   \n",
+              "3530                BODY               PAINT      body_to_paint   \n",
+              "1949               PRESS                BODY      press_to_body   \n",
+              "1783               PRESS                BODY      press_to_body   \n",
+              "2053               PRESS                BODY      press_to_body   \n",
+              "1320               PRESS                BODY      press_to_body   \n",
+              "230                PRESS                BODY      press_to_body   \n",
+              "1845               PRESS                BODY      press_to_body   \n",
+              "1477               PRESS                BODY      press_to_body   \n",
+              "2376               PRESS                BODY      press_to_body   \n",
+              "1180               PRESS                BODY      press_to_body   \n",
+              "1563               PRESS                BODY      press_to_body   \n",
+              "110                PRESS                BODY      press_to_body   \n",
+              "2206               PRESS                BODY      press_to_body   \n",
+              "2215               PRESS                BODY      press_to_body   \n",
+              "2000               PRESS                BODY      press_to_body   \n",
+              "2323               PRESS                BODY      press_to_body   \n",
+              "8                  PRESS                BODY      press_to_body   \n",
+              "2046               PRESS                BODY      press_to_body   \n",
+              "1136               PRESS                BODY      press_to_body   \n",
+              "\n",
+              "      target_defect_probability  threshold  predicted_target_defect_yn  \\\n",
+              "1816                   0.816156   0.437682                           1   \n",
+              "1805                   0.814475   0.437682                           1   \n",
+              "355                    0.796522   0.437682                           1   \n",
+              "348                    0.795205   0.437682                           1   \n",
+              "4730                   0.790645   0.444744                           1   \n",
+              "2470                   0.788088   0.444744                           1   \n",
+              "224                    0.778944   0.437682                           1   \n",
+              "56                     0.773686   0.437682                           1   \n",
+              "35                     0.772719   0.437682                           1   \n",
+              "43                     0.769506   0.437682                           1   \n",
+              "6953                   0.768398   0.460019                           1   \n",
+              "3530                   0.768384   0.444744                           1   \n",
+              "1949                   0.765838   0.437682                           1   \n",
+              "1783                   0.765786   0.437682                           1   \n",
+              "2053                   0.762853   0.437682                           1   \n",
+              "1320                   0.762076   0.437682                           1   \n",
+              "230                    0.761371   0.437682                           1   \n",
+              "1845                   0.760902   0.437682                           1   \n",
+              "1477                   0.760856   0.437682                           1   \n",
+              "2376                   0.759724   0.437682                           1   \n",
+              "1180                   0.759612   0.437682                           1   \n",
+              "1563                   0.756723   0.437682                           1   \n",
+              "110                    0.756505   0.437682                           1   \n",
+              "2206                   0.756009   0.437682                           1   \n",
+              "2215                   0.755675   0.437682                           1   \n",
+              "2000                   0.754770   0.437682                           1   \n",
+              "2323                   0.754285   0.437682                           1   \n",
+              "8                      0.754089   0.437682                           1   \n",
+              "2046                   0.754072   0.437682                           1   \n",
+              "1136                   0.753669   0.437682                           1   \n",
+              "\n",
+              "     risk_grade  target_actual_yn  \n",
+              "1816       HIGH                 0  \n",
+              "1805       HIGH                 1  \n",
+              "355        HIGH                 0  \n",
+              "348        HIGH                 0  \n",
+              "4730       HIGH                 0  \n",
+              "2470       HIGH                 1  \n",
+              "224        HIGH                 1  \n",
+              "56         HIGH                 1  \n",
+              "35         HIGH                 1  \n",
+              "43         HIGH                 0  \n",
+              "6953       HIGH                 0  \n",
+              "3530       HIGH                 1  \n",
+              "1949       HIGH                 0  \n",
+              "1783       HIGH                 0  \n",
+              "2053       HIGH                 1  \n",
+              "1320       HIGH                 0  \n",
+              "230        HIGH                 1  \n",
+              "1845       HIGH                 1  \n",
+              "1477       HIGH                 1  \n",
+              "2376       HIGH                 0  \n",
+              "1180       HIGH                 1  \n",
+              "1563       HIGH                 0  \n",
+              "110        HIGH                 1  \n",
+              "2206       HIGH                 1  \n",
+              "2215       HIGH                 1  \n",
+              "2000       HIGH                 0  \n",
+              "2323       HIGH                 1  \n",
+              "8          HIGH                 0  \n",
+              "2046       HIGH                 1  \n",
+              "1136       HIGH                 0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-82267946-8902-4de2-a6b8-b1d6e85b6a6c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>car_master_id</th>\n",
+              "      <th>source_event_id</th>\n",
+              "      <th>target_event_id</th>\n",
+              "      <th>source_process_code</th>\n",
+              "      <th>target_process_code</th>\n",
+              "      <th>model_key</th>\n",
+              "      <th>target_defect_probability</th>\n",
+              "      <th>threshold</th>\n",
+              "      <th>predicted_target_defect_yn</th>\n",
+              "      <th>risk_grade</th>\n",
+              "      <th>target_actual_yn</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1816</th>\n",
+              "      <td>11417</td>\n",
+              "      <td>EVT-20260618-045665</td>\n",
+              "      <td>EVT-20260618-045666</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.816156</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1805</th>\n",
+              "      <td>11406</td>\n",
+              "      <td>EVT-20260618-045621</td>\n",
+              "      <td>EVT-20260618-045622</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.814475</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>355</th>\n",
+              "      <td>9956</td>\n",
+              "      <td>EVT-20260617-039821</td>\n",
+              "      <td>EVT-20260617-039822</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.796522</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>348</th>\n",
+              "      <td>9949</td>\n",
+              "      <td>EVT-20260617-039793</td>\n",
+              "      <td>EVT-20260617-039794</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.795205</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4730</th>\n",
+              "      <td>11931</td>\n",
+              "      <td>EVT-20260618-047722</td>\n",
+              "      <td>EVT-20260618-047723</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>0.790645</td>\n",
+              "      <td>0.444744</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2470</th>\n",
+              "      <td>9671</td>\n",
+              "      <td>EVT-20260617-038682</td>\n",
+              "      <td>EVT-20260617-038683</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>0.788088</td>\n",
+              "      <td>0.444744</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>9825</td>\n",
+              "      <td>EVT-20260617-039297</td>\n",
+              "      <td>EVT-20260617-039298</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.778944</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>56</th>\n",
+              "      <td>9657</td>\n",
+              "      <td>EVT-20260617-038625</td>\n",
+              "      <td>EVT-20260617-038626</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.773686</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>9636</td>\n",
+              "      <td>EVT-20260617-038541</td>\n",
+              "      <td>EVT-20260617-038542</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.772719</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>9644</td>\n",
+              "      <td>EVT-20260617-038573</td>\n",
+              "      <td>EVT-20260617-038574</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.769506</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6953</th>\n",
+              "      <td>11754</td>\n",
+              "      <td>EVT-20260618-047015</td>\n",
+              "      <td>EVT-20260618-047016</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>0.768398</td>\n",
+              "      <td>0.460019</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3530</th>\n",
+              "      <td>10731</td>\n",
+              "      <td>EVT-20260617-042922</td>\n",
+              "      <td>EVT-20260617-042923</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>0.768384</td>\n",
+              "      <td>0.444744</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1949</th>\n",
+              "      <td>11550</td>\n",
+              "      <td>EVT-20260618-046197</td>\n",
+              "      <td>EVT-20260618-046198</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.765838</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1783</th>\n",
+              "      <td>11384</td>\n",
+              "      <td>EVT-20260617-045533</td>\n",
+              "      <td>EVT-20260617-045534</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.765786</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2053</th>\n",
+              "      <td>11654</td>\n",
+              "      <td>EVT-20260618-046613</td>\n",
+              "      <td>EVT-20260618-046614</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.762853</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1320</th>\n",
+              "      <td>10921</td>\n",
+              "      <td>EVT-20260617-043681</td>\n",
+              "      <td>EVT-20260617-043682</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.762076</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>230</th>\n",
+              "      <td>9831</td>\n",
+              "      <td>EVT-20260617-039321</td>\n",
+              "      <td>EVT-20260617-039322</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.761371</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1845</th>\n",
+              "      <td>11446</td>\n",
+              "      <td>EVT-20260618-045781</td>\n",
+              "      <td>EVT-20260618-045782</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.760902</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1477</th>\n",
+              "      <td>11078</td>\n",
+              "      <td>EVT-20260617-044309</td>\n",
+              "      <td>EVT-20260617-044310</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.760856</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2376</th>\n",
+              "      <td>11977</td>\n",
+              "      <td>EVT-20260618-047905</td>\n",
+              "      <td>EVT-20260618-047906</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.759724</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1180</th>\n",
+              "      <td>10781</td>\n",
+              "      <td>EVT-20260617-043121</td>\n",
+              "      <td>EVT-20260617-043122</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.759612</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1563</th>\n",
+              "      <td>11164</td>\n",
+              "      <td>EVT-20260617-044653</td>\n",
+              "      <td>EVT-20260617-044654</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.756723</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>110</th>\n",
+              "      <td>9711</td>\n",
+              "      <td>EVT-20260617-038841</td>\n",
+              "      <td>EVT-20260617-038842</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.756505</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2206</th>\n",
+              "      <td>11807</td>\n",
+              "      <td>EVT-20260618-047225</td>\n",
+              "      <td>EVT-20260618-047226</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.756009</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2215</th>\n",
+              "      <td>11816</td>\n",
+              "      <td>EVT-20260618-047261</td>\n",
+              "      <td>EVT-20260618-047262</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.755675</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2000</th>\n",
+              "      <td>11601</td>\n",
+              "      <td>EVT-20260618-046401</td>\n",
+              "      <td>EVT-20260618-046402</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.754770</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2323</th>\n",
+              "      <td>11924</td>\n",
+              "      <td>EVT-20260618-047693</td>\n",
+              "      <td>EVT-20260618-047694</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.754285</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9609</td>\n",
+              "      <td>EVT-20260617-038433</td>\n",
+              "      <td>EVT-20260617-038434</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.754089</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2046</th>\n",
+              "      <td>11647</td>\n",
+              "      <td>EVT-20260618-046585</td>\n",
+              "      <td>EVT-20260618-046586</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.754072</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1136</th>\n",
+              "      <td>10737</td>\n",
+              "      <td>EVT-20260617-042945</td>\n",
+              "      <td>EVT-20260617-042946</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>0.753669</td>\n",
+              "      <td>0.437682</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HIGH</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82267946-8902-4de2-a6b8-b1d6e85b6a6c')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-82267946-8902-4de2-a6b8-b1d6e85b6a6c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
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+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
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+            }
+          },
+          "metadata": {}
+        }
       ],
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>feature</th>\n",
-       "      <th>mean_abs_shap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>L0_num_std</td>\n",
-       "      <td>161153.464733</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>L3_S29_F3351</td>\n",
-       "      <td>112466.467404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>320</th>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>87206.632845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>L3_S29_F3373</td>\n",
-       "      <td>79514.635807</td>\n",
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-       "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>L3_S29_F3412</td>\n",
-       "      <td>67172.086809</td>\n",
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-       "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>L3_S29_F3407</td>\n",
-       "      <td>50584.084076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>143</th>\n",
-       "      <td>L3_S29_F3404</td>\n",
-       "      <td>47588.744257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>L3_S29_F3339</td>\n",
-       "      <td>45584.471179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>263</th>\n",
-       "      <td>process_end_time</td>\n",
-       "      <td>41293.258694</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>L0_S2_F48</td>\n",
-       "      <td>41259.724076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>249</th>\n",
-       "      <td>L3_S36_F3920</td>\n",
-       "      <td>40991.128002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>290</th>\n",
-       "      <td>L0_S13_mean_time</td>\n",
-       "      <td>38087.458690</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>360</th>\n",
-       "      <td>L0_S0_num_mean</td>\n",
-       "      <td>37707.793976</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>L0_S1_F24</td>\n",
-       "      <td>34345.520207</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>266</th>\n",
-       "      <td>L0_date_count</td>\n",
-       "      <td>31073.838840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>385</th>\n",
-       "      <td>L0_S2_num_std</td>\n",
-       "      <td>30331.910504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>228</th>\n",
-       "      <td>L3_S33_F3857</td>\n",
-       "      <td>30191.782771</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>L0_S11_F326</td>\n",
-       "      <td>27513.469192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>367</th>\n",
-       "      <td>L0_S10_num_std</td>\n",
-       "      <td>25100.244226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>L0_S0_F0</td>\n",
-       "      <td>21225.150678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>308</th>\n",
-       "      <td>L0_S7_mean_time</td>\n",
-       "      <td>19617.442282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>L0_S9_F200</td>\n",
-       "      <td>19379.907896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>L3_S29_F3427</td>\n",
-       "      <td>19288.645871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>366</th>\n",
-       "      <td>L0_S10_num_mean</td>\n",
-       "      <td>19031.715581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>L0_S11_F318</td>\n",
-       "      <td>18211.382309</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>L3_S30_F3539</td>\n",
-       "      <td>17632.894123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251</th>\n",
-       "      <td>L3_S36_F3924</td>\n",
-       "      <td>17431.833787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>388</th>\n",
-       "      <td>L0_S3_num_std</td>\n",
-       "      <td>16874.240258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>L0_S0_F8</td>\n",
-       "      <td>16847.956330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>L0_num_mean</td>\n",
-       "      <td>15387.827290</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-0e0f08fc-7b15-4823-8396-faa6d6c4f25d');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
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-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
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-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"display(shap_importance\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L0_S3_num_std\",\n          \"L0_S2_num_std\",\n          \"L0_S10_num_mean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32453.380966565277,\n        \"min\": 15387.827290431647,\n        \"max\": 161153.46473315964,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          16874.240258203547,\n          30331.91050358815,\n          19031.71558098403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "# SHAP Summary Plot\n",
-    "shap.summary_plot(shap_values, X_shap, max_display=25, show=True)\n",
-    "\n",
-    "# SHAP 중요도 산출\n",
-    "shap_importance = pd.DataFrame({\n",
-    "    \"feature\": X_shap.columns,\n",
-    "    \"mean_abs_shap\": np.abs(shap_matrix).mean(axis=0),\n",
-    "}).sort_values(\"mean_abs_shap\", ascending=False)\n",
-    "\n",
-    "display(shap_importance.head(30))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "# Feature 공정 매핑\n",
-    "def feature_to_process(feature: str) -> str | None:\n",
-    "    \"\"\"Feature 공정 매핑\"\"\"\n",
-    "    normalized = feature.lower()\n",
-    "\n",
-    "    # Bosch 원천 컬럼/집계 컬럼: L0~L3 prefix 기준 매핑\n",
-    "    line = get_line(feature)\n",
-    "    if line in LINE_TO_PROCESS:\n",
-    "        return LINE_TO_PROCESS[line]\n",
-    "\n",
-    "    # 보조 데이터셋 기반 feature: aux_{process}_... naming 기준 매핑\n",
-    "    if normalized.startswith(\"aux_press\") or \"press\" in normalized:\n",
-    "        return \"PRESS\"\n",
-    "    if normalized.startswith(\"aux_body\") or \"body\" in normalized or \"robot\" in normalized or \"ford\" in normalized:\n",
-    "        return \"BODY\"\n",
-    "    if normalized.startswith(\"aux_paint\") or \"paint\" in normalized or \"thermal\" in normalized:\n",
-    "        return \"PAINT\"\n",
-    "    if normalized.startswith(\"aux_assembly\") or \"assembly\" in normalized:\n",
-    "        return \"ASSEMBLY\"\n",
-    "\n",
-    "    # 전역 feature 제외\n",
-    "    return None\n",
-    "\n",
-    "# 후속 공정 매핑\n",
-    "def next_process(process_code: str) -> str:\n",
-    "    if process_code not in PROCESS_FLOW:\n",
-    "        return \"FINAL_INSPECTION\"\n",
-    "    idx = PROCESS_FLOW.index(process_code)\n",
-    "    if idx + 1 >= len(PROCESS_FLOW):\n",
-    "        return \"FINAL_INSPECTION\"\n",
-    "    return PROCESS_FLOW[idx + 1]\n",
-    "\n",
-    "# 위험 등급 변환\n",
-    "def probability_to_risk_grade(prob: float) -> str:\n",
-    "    if prob >= 0.70:\n",
-    "        return \"HIGH\"\n",
-    "    if prob >= 0.40:\n",
-    "        return \"MEDIUM\"\n",
-    "    return \"LOW\"\n",
-    "\n",
-    "# SHAP feature 공정 매핑\n",
-    "feature_process = pd.Series([feature_to_process(c) for c in X_shap.columns], index=X_shap.columns)\n",
-    "mapped_feature_mask = feature_process.isin(PROCESS_FLOW)\n",
-    "unmapped_features = feature_process[~mapped_feature_mask].index.tolist()\n",
-    "print(f\"공정 영향도 집계 제외 전역/미매핑 feature 수: {len(unmapped_features)}\")\n",
-    "if unmapped_features:\n",
-    "    display(pd.DataFrame({\"unmapped_feature\": unmapped_features}).head(30))\n",
-    "\n",
-    "# 공정별 SHAP 영향도 집계\n",
-    "process_rows = []\n",
-    "for process_code in PROCESS_FLOW:\n",
-    "    cols_idx = np.where(feature_process.values == process_code)[0]\n",
-    "    process_rows.append({\n",
-    "        \"process_code\": process_code,\n",
-    "        \"mean_abs_shap\": float(np.abs(shap_matrix[:, cols_idx]).mean()) if len(cols_idx) else 0.0,\n",
-    "        \"feature_count\": int(len(cols_idx)),\n",
-    "    })\n",
-    "\n",
-    "process_importance = pd.DataFrame(process_rows).sort_values(\"mean_abs_shap\", ascending=False)\n",
-    "display(process_importance)\n",
-    "\n",
-    "# 공정 영향도 시각화\n",
-    "plt.figure(figsize=(8, 4))\n",
-    "sns.barplot(data=process_importance, x=\"process_code\", y=\"mean_abs_shap\", color=\"#59A14F\")\n",
-    "plt.title(\"SHAP Process Influence\")\n",
-    "plt.xlabel(\"Process\")\n",
-    "plt.ylabel(\"Mean |SHAP|\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ],
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 891
+      "source": [
+        "TRANSFER_TARGET = \"target_defect_yn\"\n",
+        "TRANSFER_LEAKAGE_COLS = [\n",
+        "    TRANSFER_TARGET,\n",
+        "    \"source_defect_yn\",\n",
+        "    \"target_defect_reason\",\n",
+        "    \"target_event_json\",\n",
+        "    \"target_defect_probability\",\n",
+        "    \"predicted_target_defect_yn\",\n",
+        "    \"target_actual_yn\",\n",
+        "]\n",
+        "TRANSFER_EXCLUDE_COLS = [\n",
+        "    \"car_master_id\",\n",
+        "    \"source_event_id\",\n",
+        "    \"target_event_id\",\n",
+        "    \"source_process_code\",\n",
+        "    \"target_process_code\",\n",
+        "    \"source_event_json\",\n",
+        "    \"dataset_split\",\n",
+        "] + TRANSFER_LEAKAGE_COLS\n",
+        "\n",
+        "transition_feature_cols = select_model_features(\n",
+        "    transfer_train_raw,\n",
+        "    TRANSFER_TARGET,\n",
+        "    TRANSFER_EXCLUDE_COLS,\n",
+        "    TRANSFER_LEAKAGE_COLS,\n",
+        "    context=\"공정 전이 예측\",\n",
+        ")\n",
+        "transition_feature_cols = drop_suspicious_features(transfer_train_raw, TRANSFER_TARGET, transition_feature_cols, context=\"공정 전이 예측\")\n",
+        "ensure_feature_columns_exist(transfer_test_raw, transition_feature_cols, context=\"공정 전이 예측\")\n",
+        "print(\"공정 전이 예측 feature 수:\", len(transition_feature_cols))\n",
+        "print(\"공정 전이 예측 feature:\", transition_feature_cols)\n",
+        "\n",
+        "plot_class_distribution(transfer_train_raw, transfer_test_raw, TRANSFER_TARGET, \"공정 전이 예측\")\n",
+        "display(pd.crosstab([transfer_train_raw[\"source_process_code\"], transfer_train_raw[\"target_process_code\"]], transfer_train_raw[TRANSFER_TARGET], normalize=\"index\").rename(columns={0: \"정상 비율\", 1: \"전이 양성 비율\"}))\n",
+        "plot_feature_distribution_grid(transfer_train_raw, transition_feature_cols, TRANSFER_TARGET, \"공정 전이 예측 주요 feature 분포\", max_features=12)\n",
+        "plot_numeric_correlation_heatmap(transfer_train_raw, transition_feature_cols, \"공정 전이 예측 수치형 feature 상관관계\", max_features=12)\n",
+        "\n",
+        "\n",
+        "TRANSITION_PARAM_DISTRIBUTIONS = {\n",
+        "    \"model__n_estimators\": [160, 220, 280],\n",
+        "    \"model__learning_rate\": [0.035, 0.045, 0.06],\n",
+        "    \"model__num_leaves\": [11, 15, 23],\n",
+        "    \"model__min_child_samples\": [40, 60, 90],\n",
+        "    \"model__subsample\": [0.75, 0.85, 0.95],\n",
+        "    \"model__colsample_bytree\": [0.70, 0.80, 0.90],\n",
+        "    \"model__reg_lambda\": [4.0, 8.0, 12.0],\n",
+        "}\n",
+        "\n",
+        "\n",
+        "def make_transition_model(train_df: pd.DataFrame, feature_cols: list[str], scale_pos_weight: float):\n",
+        "    return Pipeline([\n",
+        "        (\"preprocess\", make_preprocessor(train_df, feature_cols, scale_numeric=False)),\n",
+        "        (\"model\", lgb.LGBMClassifier(\n",
+        "            objective=\"binary\",\n",
+        "            n_estimators=220,\n",
+        "            learning_rate=0.045,\n",
+        "            num_leaves=15,\n",
+        "            min_child_samples=60,\n",
+        "            subsample=0.85,\n",
+        "            colsample_bytree=0.75,\n",
+        "            reg_lambda=8.0,\n",
+        "            scale_pos_weight=scale_pos_weight,\n",
+        "            random_state=RANDOM_STATE,\n",
+        "            n_jobs=-1,\n",
+        "            verbosity=-1,\n",
+        "        )),\n",
+        "    ])\n",
+        "\n",
+        "\n",
+        "def tune_transition_model_cv(model_key: str, train_flow: pd.DataFrame, feature_cols: list[str], target_col: str, scale_pos_weight: float):\n",
+        "    X = train_flow[feature_cols]\n",
+        "    y = train_flow[target_col].astype(int)\n",
+        "    X_tune, y_tune = make_tuning_sample(X, y, MAX_TUNING_ROWS)\n",
+        "    candidates = sample_param_candidates(TRANSITION_PARAM_DISTRIBUTIONS, TUNING_TRIALS_PER_MODEL, RANDOM_STATE)\n",
+        "    cv = StratifiedKFold(n_splits=CV_FOLDS, shuffle=True, random_state=RANDOM_STATE)\n",
+        "    rows = []\n",
+        "    best_row = None\n",
+        "    best_score = (-np.inf, -np.inf, -np.inf)\n",
+        "    print(f\"\\n[{model_key}] lightweight CV tuning: {len(candidates)} trials x {CV_FOLDS} folds\")\n",
+        "    for trial_no, params in enumerate(candidates, start=1):\n",
+        "        fold_metrics = []\n",
+        "        started_at = time.perf_counter()\n",
+        "        for train_idx, valid_idx in cv.split(X_tune, y_tune):\n",
+        "            X_fold_train = X_tune.iloc[train_idx]\n",
+        "            y_fold_train = y_tune.iloc[train_idx]\n",
+        "            X_fold_valid = X_tune.iloc[valid_idx]\n",
+        "            y_fold_valid = y_tune.iloc[valid_idx]\n",
+        "            estimator = make_transition_model(train_flow, feature_cols, scale_pos_weight)\n",
+        "            estimator.set_params(**params)\n",
+        "            estimator.fit(X_fold_train, y_fold_train)\n",
+        "            valid_proba = predict_positive_proba(estimator, X_fold_valid)\n",
+        "            threshold = find_recall_priority_threshold(y_fold_valid, valid_proba, min_recall=0.60, min_precision=0.20)\n",
+        "            fold_metrics.append(compute_metrics(y_fold_valid, valid_proba, threshold))\n",
+        "        elapsed_sec = time.perf_counter() - started_at\n",
+        "        row = {\n",
+        "            \"model_key\": model_key,\n",
+        "            \"trial_no\": trial_no,\n",
+        "            \"params\": params,\n",
+        "            \"cv_pr_auc\": float(np.mean([m[\"pr_auc\"] for m in fold_metrics])),\n",
+        "            \"cv_recall\": float(np.mean([m[\"recall\"] for m in fold_metrics])),\n",
+        "            \"cv_precision\": float(np.mean([m[\"precision\"] for m in fold_metrics])),\n",
+        "            \"cv_f1\": float(np.mean([m[\"f1\"] for m in fold_metrics])),\n",
+        "            \"cv_accuracy\": float(np.mean([m[\"accuracy\"] for m in fold_metrics])),\n",
+        "            \"cv_threshold\": float(np.mean([m[\"threshold\"] for m in fold_metrics])),\n",
+        "            \"elapsed_sec\": elapsed_sec,\n",
+        "        }\n",
+        "        rows.append(row)\n",
+        "        score = (row[\"cv_pr_auc\"], row[\"cv_recall\"], row[\"cv_f1\"])\n",
+        "        if score > best_score:\n",
+        "            best_score = score\n",
+        "            best_row = row\n",
+        "        print(f\"  trial {trial_no}: CV PR-AUC={row['cv_pr_auc']:.4f}, Recall={row['cv_recall']:.4f}, F1={row['cv_f1']:.4f}, elapsed={elapsed_sec:.1f}s\")\n",
+        "    return best_row, rows\n",
+        "\n",
+        "\n",
+        "transition_models = {}\n",
+        "transition_metadata = {}\n",
+        "transition_metric_rows = []\n",
+        "transition_cv_rows = []\n",
+        "transition_prediction_frames = []\n",
+        "transition_confusion_tables = {}\n",
+        "transition_train_confusion_tables = {}\n",
+        "transition_proba_by_model = {}\n",
+        "transition_train_proba_by_model = {}\n",
+        "transition_y_true_by_model = {}\n",
+        "transition_y_train_by_model = {}\n",
+        "\n",
+        "for source_process, target_process in TRANSFER_FLOW_ORDER:\n",
+        "    model_key = f\"{source_process.lower()}_to_{target_process.lower()}\"\n",
+        "    train_flow = transfer_train_raw[(transfer_train_raw[\"source_process_code\"] == source_process) & (transfer_train_raw[\"target_process_code\"] == target_process)].copy()\n",
+        "    test_flow = transfer_test_raw[(transfer_test_raw[\"source_process_code\"] == source_process) & (transfer_test_raw[\"target_process_code\"] == target_process)].copy()\n",
+        "    if train_flow.empty or test_flow.empty:\n",
+        "        print(f\"{model_key}: 학습 또는 테스트 데이터가 없어 건너뜁니다.\")\n",
+        "        continue\n",
+        "    X_train_flow = train_flow[transition_feature_cols]\n",
+        "    y_train_flow = train_flow[TRANSFER_TARGET].astype(int)\n",
+        "    X_test_flow = test_flow[transition_feature_cols]\n",
+        "    y_test_flow = test_flow[TRANSFER_TARGET].astype(int)\n",
+        "    if y_train_flow.nunique() < 2 or y_test_flow.nunique() < 2:\n",
+        "        print(f\"{model_key}: 한 클래스만 존재해 건너뜁니다.\")\n",
+        "        continue\n",
+        "    spw = scale_pos_weight_for(y_train_flow)\n",
+        "    best_params = {}\n",
+        "    if ENABLE_HYPERPARAMETER_TUNING:\n",
+        "        best_row, rows = tune_transition_model_cv(model_key, train_flow, transition_feature_cols, TRANSFER_TARGET, spw)\n",
+        "        transition_cv_rows.extend(rows)\n",
+        "        best_params = best_row[\"params\"] if best_row else {}\n",
+        "    flow_model = make_transition_model(train_flow, transition_feature_cols, spw)\n",
+        "    flow_model.set_params(**best_params)\n",
+        "    flow_model.fit(X_train_flow, y_train_flow)\n",
+        "    train_proba = predict_positive_proba(flow_model, X_train_flow)\n",
+        "    test_proba = predict_positive_proba(flow_model, X_test_flow)\n",
+        "    threshold = find_recall_priority_threshold(y_train_flow, train_proba, min_recall=0.60, min_precision=0.20)\n",
+        "    train_metrics = compute_metrics(y_train_flow, train_proba, threshold)\n",
+        "    test_metrics = compute_metrics(y_test_flow, test_proba, threshold)\n",
+        "    _, cm_table = display_confusion_matrix_report(y_test_flow, test_proba, threshold, title=f\"공정 전이 Test 혼동행렬 - {source_process} -> {target_process}\")\n",
+        "    transition_models[model_key] = flow_model\n",
+        "    transition_train_confusion_tables[model_key] = confusion_matrix_table(train_metrics)\n",
+        "    transition_confusion_tables[model_key] = cm_table\n",
+        "    transition_train_proba_by_model[model_key] = train_proba\n",
+        "    transition_proba_by_model[model_key] = test_proba\n",
+        "    transition_y_train_by_model[model_key] = y_train_flow\n",
+        "    transition_y_true_by_model[model_key] = y_test_flow\n",
+        "    transition_metadata[model_key] = {\"source_process_code\": source_process, \"target_process_code\": target_process, \"threshold\": threshold, \"feature_columns\": transition_feature_cols, \"scale_pos_weight\": spw, \"best_params\": best_params}\n",
+        "    transition_metric_rows.append({\n",
+        "        \"model_key\": model_key,\n",
+        "        \"source_process_code\": source_process,\n",
+        "        \"target_process_code\": target_process,\n",
+        "        \"best_params\": best_params,\n",
+        "        **{f\"train_{k}\": v for k, v in train_metrics.items()},\n",
+        "        **{f\"test_{k}\": v for k, v in test_metrics.items()},\n",
+        "    })\n",
+        "    pred_frame = test_flow[[\"car_master_id\", \"source_event_id\", \"target_event_id\", \"source_process_code\", \"target_process_code\"]].copy()\n",
+        "    pred_frame[\"model_key\"] = model_key\n",
+        "    pred_frame[\"target_defect_probability\"] = test_proba\n",
+        "    pred_frame[\"threshold\"] = threshold\n",
+        "    pred_frame[\"predicted_target_defect_yn\"] = (test_proba >= threshold).astype(int)\n",
+        "    pred_frame[\"risk_grade\"] = pred_frame[\"target_defect_probability\"].map(risk_grade)\n",
+        "    pred_frame[\"target_actual_yn\"] = y_test_flow.to_numpy()\n",
+        "    transition_prediction_frames.append(pred_frame)\n",
+        "\n",
+        "transition_cv_results = pd.DataFrame(transition_cv_rows)\n",
+        "if not transition_cv_results.empty:\n",
+        "    display(\n",
+        "        transition_cv_results.sort_values([\"model_key\", \"cv_pr_auc\", \"cv_recall\", \"cv_f1\"], ascending=[True, False, False, False])\n",
+        "        [[\"model_key\", \"trial_no\", \"cv_pr_auc\", \"cv_recall\", \"cv_precision\", \"cv_f1\", \"cv_accuracy\", \"cv_threshold\", \"elapsed_sec\", \"params\"]]\n",
+        "        .reset_index(drop=True)\n",
+        "    )\n",
+        "\n",
+        "transition_model_metrics = pd.DataFrame(transition_metric_rows).sort_values([\"test_pr_auc\", \"test_recall\", \"test_f1\"], ascending=False).reset_index(drop=True) if transition_metric_rows else pd.DataFrame()\n",
+        "transition_predictions = pd.concat(transition_prediction_frames, ignore_index=True) if transition_prediction_frames else pd.DataFrame()\n",
+        "if not transition_model_metrics.empty:\n",
+        "    display(transition_model_metrics.rename(columns={\n",
+        "        \"model_key\": \"모델 키\",\n",
+        "        \"source_process_code\": \"소스 공정\",\n",
+        "        \"target_process_code\": \"타깃 공정\",\n",
+        "        \"best_params\": \"선택 파라미터\",\n",
+        "        \"train_f1\": \"Train F1\",\n",
+        "        \"test_f1\": \"Test F1\",\n",
+        "        \"train_recall\": \"Train 재현율\",\n",
+        "        \"test_recall\": \"Test 재현율\",\n",
+        "        \"train_precision\": \"Train 정밀도\",\n",
+        "        \"test_precision\": \"Test 정밀도\",\n",
+        "        \"train_accuracy\": \"Train 정확도\",\n",
+        "        \"test_accuracy\": \"Test 정확도\",\n",
+        "        \"test_pr_auc\": \"Test PR-AUC\",\n",
+        "        \"test_false_positive_rate\": \"Test 오탐률\",\n",
+        "        \"test_threshold\": \"임계값\",\n",
+        "    }))\n",
+        "    fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
+        "    sns.barplot(data=transition_model_metrics, x=\"model_key\", y=\"test_pr_auc\", ax=axes[0], color=\"#4C78A8\")\n",
+        "    axes[0].set_title(\"전이 예측 Test PR-AUC\")\n",
+        "    axes[0].set_xlabel(\"모델\")\n",
+        "    axes[0].set_ylabel(\"PR-AUC\")\n",
+        "    axes[0].tick_params(axis=\"x\", rotation=20)\n",
+        "    sns.barplot(data=transition_model_metrics, x=\"model_key\", y=\"test_recall\", ax=axes[1], color=\"#59A14F\")\n",
+        "    axes[1].set_title(\"전이 예측 Test 재현율\")\n",
+        "    axes[1].set_xlabel(\"모델\")\n",
+        "    axes[1].set_ylabel(\"Recall\")\n",
+        "    axes[1].tick_params(axis=\"x\", rotation=20)\n",
+        "    sns.barplot(data=transition_model_metrics, x=\"model_key\", y=\"test_f1\", ax=axes[2], color=\"#F28E2B\")\n",
+        "    axes[2].set_title(\"전이 예측 Test F1\")\n",
+        "    axes[2].set_xlabel(\"모델\")\n",
+        "    axes[2].set_ylabel(\"F1 Score\")\n",
+        "    axes[2].tick_params(axis=\"x\", rotation=20)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "    for model_key in transition_model_metrics[\"model_key\"]:\n",
+        "        threshold = transition_metadata[model_key][\"threshold\"]\n",
+        "        train_metrics, test_metrics = plot_train_test_confusion(\n",
+        "            transition_y_train_by_model[model_key],\n",
+        "            transition_train_proba_by_model[model_key],\n",
+        "            transition_y_true_by_model[model_key],\n",
+        "            transition_proba_by_model[model_key],\n",
+        "            threshold,\n",
+        "            f\"공정 전이 - {model_key}\",\n",
+        "        )\n",
+        "        plot_train_test_metric_comparison(train_metrics, test_metrics, f\"공정 전이 Train/Test 성능 비교 - {model_key}\")\n",
+        "        plot_probability_distribution(transition_y_true_by_model[model_key], transition_proba_by_model[model_key], threshold, f\"공정 전이 Test 예측 확률 분포 - {model_key}\")\n",
+        "        importance = model_feature_importance(transition_models[model_key])\n",
+        "        plot_model_feature_importance(importance, f\"공정 전이 피처 중요도 - {model_key}\", top_n=15)\n",
+        "\n",
+        "if not transition_predictions.empty:\n",
+        "    display(transition_predictions.sort_values(\"target_defect_probability\", ascending=False).head(30))"
+      ]
     },
-    "id": "m-fwpoIeBrfa",
-    "outputId": "b70d837d-df0c-4159-fc29-c842f24f2558"
-   },
-   "id": "m-fwpoIeBrfa",
-   "execution_count": 24,
-   "outputs": [
     {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "공정 영향도 집계 제외 전역/미매핑 feature 수: 9\n"
-     ]
+      "cell_type": "markdown",
+      "id": "5f314dd3",
+      "metadata": {
+        "id": "5f314dd3"
+      },
+      "source": [
+        "## 7. SHAP 기반 전이 위험 분석 및 Kafka 분석 이벤트"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "         unmapped_feature\n",
-       "0     date_observed_count\n",
-       "1      date_missing_ratio\n",
-       "2      process_start_time\n",
-       "3        process_end_time\n",
-       "4  total_process_duration\n",
-       "5        max_station_span\n",
-       "6       mean_station_span\n",
-       "7        std_station_span\n",
-       "8    active_station_count"
-      ],
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-       "      <th></th>\n",
-       "      <th>unmapped_feature</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>date_observed_count</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>date_missing_ratio</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>process_start_time</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>process_end_time</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>total_process_duration</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>max_station_span</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>mean_station_span</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>std_station_span</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>active_station_count</td>\n",
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-       "    [theme=dark] .colab-df-convert:hover {\n",
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-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-       "    }\n",
-       "  </style>\n",
-       "\n",
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-       "      const buttonEl =\n",
-       "        document.querySelector('#df-1e02fdd7-e66f-4403-ab55-6ed2ff2a07d6 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
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-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "summary": "{\n  \"name\": \"plt\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"unmapped_feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"std_station_span\",\n          \"date_missing_ratio\",\n          \"max_station_span\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
+      "cell_type": "markdown",
+      "id": "cc8bfdea",
+      "metadata": {
+        "id": "cc8bfdea"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "전이 예측 모델별 SHAP 중요도를 계산하고, 화면/API에서 사용할 수 있는 분석 이벤트 형태로 변환합니다."
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "  process_code  mean_abs_shap  feature_count\n",
-       "0        PRESS    3987.411446            182\n",
-       "2        PAINT    3959.570471            197\n",
-       "3     ASSEMBLY     398.922157             36\n",
-       "1         BODY      71.636713             33"
-      ],
-      "text/html": [
-       "\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>PRESS</td>\n",
-       "      <td>3987.411446</td>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>3959.570471</td>\n",
-       "      <td>197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>398.922157</td>\n",
-       "      <td>36</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
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-       "\n",
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-       "      const buttonEl =\n",
-       "        document.querySelector('#df-bae87c37-391f-44c4-bcef-a34afe9b1e09 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-bae87c37-391f-44c4-bcef-a34afe9b1e09');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "  <div id=\"id_790c3d3c-10fc-406c-a77f-00d95a9965e0\">\n",
-       "    <style>\n",
-       "      .colab-df-generate {\n",
-       "        background-color: #E8F0FE;\n",
-       "        border: none;\n",
-       "        border-radius: 50%;\n",
-       "        cursor: pointer;\n",
-       "        display: none;\n",
-       "        fill: #1967D2;\n",
-       "        height: 32px;\n",
-       "        padding: 0 0 0 0;\n",
-       "        width: 32px;\n",
-       "      }\n",
-       "\n",
-       "      .colab-df-generate:hover {\n",
-       "        background-color: #E2EBFA;\n",
-       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "        fill: #174EA6;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate {\n",
-       "        background-color: #3B4455;\n",
-       "        fill: #D2E3FC;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate:hover {\n",
-       "        background-color: #434B5C;\n",
-       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "        fill: #FFFFFF;\n",
-       "      }\n",
-       "    </style>\n",
-       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('process_importance')\"\n",
-       "            title=\"Generate code using this dataframe.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "       width=\"24px\">\n",
-       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "    <script>\n",
-       "      (() => {\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#id_790c3d3c-10fc-406c-a77f-00d95a9965e0 button.colab-df-generate');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      buttonEl.onclick = () => {\n",
-       "        google.colab.notebook.generateWithVariable('process_importance');\n",
-       "      }\n",
-       "      })();\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "5d86cf73",
+      "metadata": {
+        "id": "5d86cf73",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "6c12488b-54ee-40e2-f39a-9ceefd51da77"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                      feature  mean_abs_shap          model_key  \\\n",
+              "0       source_cycle_time_sec       0.194787      press_to_body   \n",
+              "1   source_current_rms_ampere       0.177804      press_to_body   \n",
+              "2         source_queue_length       0.157906      press_to_body   \n",
+              "3      source_vibration_score       0.097177      press_to_body   \n",
+              "4        source_thermal_score       0.091699      press_to_body   \n",
+              "5            source_wip_count       0.053012      press_to_body   \n",
+              "6    source_station_delay_sec       0.036352      press_to_body   \n",
+              "7       source_cycle_time_sec       0.189979      body_to_paint   \n",
+              "8      source_vibration_score       0.175717      body_to_paint   \n",
+              "9        source_thermal_score       0.099140      body_to_paint   \n",
+              "10  source_current_rms_ampere       0.097832      body_to_paint   \n",
+              "11        source_queue_length       0.076991      body_to_paint   \n",
+              "12           source_wip_count       0.034891      body_to_paint   \n",
+              "13   source_station_delay_sec       0.029957      body_to_paint   \n",
+              "14      source_cycle_time_sec       0.210911  paint_to_assembly   \n",
+              "15     source_vibration_score       0.129053  paint_to_assembly   \n",
+              "16       source_thermal_score       0.079522  paint_to_assembly   \n",
+              "17  source_current_rms_ampere       0.073281  paint_to_assembly   \n",
+              "18        source_queue_length       0.040449  paint_to_assembly   \n",
+              "19   source_station_delay_sec       0.035959  paint_to_assembly   \n",
+              "20           source_wip_count       0.035648  paint_to_assembly   \n",
+              "\n",
+              "   source_process_code target_process_code  \n",
+              "0                PRESS                BODY  \n",
+              "1                PRESS                BODY  \n",
+              "2                PRESS                BODY  \n",
+              "3                PRESS                BODY  \n",
+              "4                PRESS                BODY  \n",
+              "5                PRESS                BODY  \n",
+              "6                PRESS                BODY  \n",
+              "7                 BODY               PAINT  \n",
+              "8                 BODY               PAINT  \n",
+              "9                 BODY               PAINT  \n",
+              "10                BODY               PAINT  \n",
+              "11                BODY               PAINT  \n",
+              "12                BODY               PAINT  \n",
+              "13                BODY               PAINT  \n",
+              "14               PAINT            ASSEMBLY  \n",
+              "15               PAINT            ASSEMBLY  \n",
+              "16               PAINT            ASSEMBLY  \n",
+              "17               PAINT            ASSEMBLY  \n",
+              "18               PAINT            ASSEMBLY  \n",
+              "19               PAINT            ASSEMBLY  \n",
+              "20               PAINT            ASSEMBLY  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-156f2e4d-73f1-4b1c-b445-75c4a139f222\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>feature</th>\n",
+              "      <th>mean_abs_shap</th>\n",
+              "      <th>model_key</th>\n",
+              "      <th>source_process_code</th>\n",
+              "      <th>target_process_code</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>source_cycle_time_sec</td>\n",
+              "      <td>0.194787</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>source_current_rms_ampere</td>\n",
+              "      <td>0.177804</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>source_queue_length</td>\n",
+              "      <td>0.157906</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>source_vibration_score</td>\n",
+              "      <td>0.097177</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>source_thermal_score</td>\n",
+              "      <td>0.091699</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>source_wip_count</td>\n",
+              "      <td>0.053012</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>source_station_delay_sec</td>\n",
+              "      <td>0.036352</td>\n",
+              "      <td>press_to_body</td>\n",
+              "      <td>PRESS</td>\n",
+              "      <td>BODY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>source_cycle_time_sec</td>\n",
+              "      <td>0.189979</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>source_vibration_score</td>\n",
+              "      <td>0.175717</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>source_thermal_score</td>\n",
+              "      <td>0.099140</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>source_current_rms_ampere</td>\n",
+              "      <td>0.097832</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>source_queue_length</td>\n",
+              "      <td>0.076991</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>source_wip_count</td>\n",
+              "      <td>0.034891</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>source_station_delay_sec</td>\n",
+              "      <td>0.029957</td>\n",
+              "      <td>body_to_paint</td>\n",
+              "      <td>BODY</td>\n",
+              "      <td>PAINT</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>source_cycle_time_sec</td>\n",
+              "      <td>0.210911</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>source_vibration_score</td>\n",
+              "      <td>0.129053</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>source_thermal_score</td>\n",
+              "      <td>0.079522</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>source_current_rms_ampere</td>\n",
+              "      <td>0.073281</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>source_queue_length</td>\n",
+              "      <td>0.040449</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>source_station_delay_sec</td>\n",
+              "      <td>0.035959</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>source_wip_count</td>\n",
+              "      <td>0.035648</td>\n",
+              "      <td>paint_to_assembly</td>\n",
+              "      <td>PAINT</td>\n",
+              "      <td>ASSEMBLY</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-156f2e4d-73f1-4b1c-b445-75c4a139f222')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-156f2e4d-73f1-4b1c-b445-75c4a139f222 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-156f2e4d-73f1-4b1c-b445-75c4a139f222');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"print(json\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"source_cycle_time_sec\",\n          \"source_current_rms_ampere\",\n          \"source_wip_count\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06094596169059344,\n        \"min\": 0.029957444152210865,\n        \"max\": 0.2109112074173861,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.1947872792830002,\n          0.0732806481984165,\n          0.12905330235372311\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_key\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"press_to_body\",\n          \"body_to_paint\",\n          \"paint_to_assembly\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PRESS\",\n          \"BODY\",\n          \"PAINT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"BODY\",\n          \"PAINT\",\n          \"ASSEMBLY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x700 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "kafka event examples: 500\n",
+            "[\n",
+            "  {\n",
+            "    \"eventType\": \"DEFECT_TRANSFER_PREDICTION\",\n",
+            "    \"carMasterId\": 11417,\n",
+            "    \"sourceEventId\": \"EVT-20260618-045665\",\n",
+            "    \"targetEventId\": \"EVT-20260618-045666\",\n",
+            "    \"sourceProcessCode\": \"PRESS\",\n",
+            "    \"targetProcessCode\": \"BODY\",\n",
+            "    \"targetDefectProbability\": 0.8161559491328145,\n",
+            "    \"threshold\": 0.4376819769807411,\n",
+            "    \"predictedTargetDefectYn\": 1,\n",
+            "    \"riskGrade\": \"HIGH\",\n",
+            "    \"mainCause\": \"source_cycle_time_sec\",\n",
+            "    \"modelKey\": \"press_to_body\",\n",
+            "    \"predictedAt\": \"2026-06-27T06:42:38.645689+00:00\"\n",
+            "  },\n",
+            "  {\n",
+            "    \"eventType\": \"DEFECT_TRANSFER_PREDICTION\",\n",
+            "    \"carMasterId\": 11406,\n",
+            "    \"sourceEventId\": \"EVT-20260618-045621\",\n",
+            "    \"targetEventId\": \"EVT-20260618-045622\",\n",
+            "    \"sourceProcessCode\": \"PRESS\",\n",
+            "    \"targetProcessCode\": \"BODY\",\n",
+            "    \"targetDefectProbability\": 0.81447506053072,\n",
+            "    \"threshold\": 0.4376819769807411,\n",
+            "    \"predictedTargetDefectYn\": 1,\n",
+            "    \"riskGrade\": \"HIGH\",\n",
+            "    \"mainCause\": \"source_cycle_time_sec\",\n",
+            "    \"modelKey\": \"press_to_body\",\n",
+            "    \"predictedAt\": \"2026-06-27T06:42:38.645689+00:00\"\n",
+            "  },\n",
+            "  {\n",
+            "    \"eventType\": \"DEFECT_TRANSFER_PREDICTION\",\n",
+            "    \"carMasterId\": 9956,\n",
+            "    \"sourceEventId\": \"EVT-20260617-039821\",\n",
+            "    \"targetEventId\": \"EVT-20260617-039822\",\n",
+            "    \"sourceProcessCode\": \"PRESS\",\n",
+            "    \"targetProcessCode\": \"BODY\",\n",
+            "    \"targetDefectProbability\": 0.7965217595386686,\n",
+            "    \"threshold\": 0.4376819769807411,\n",
+            "    \"predictedTargetDefectYn\": 1,\n",
+            "    \"riskGrade\": \"HIGH\",\n",
+            "    \"mainCause\": \"source_cycle_time_sec\",\n",
+            "    \"modelKey\": \"press_to_body\",\n",
+            "    \"predictedAt\": \"2026-06-27T06:42:38.645689+00:00\"\n",
+            "  }\n",
+            "]\n"
+          ]
+        }
       ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "variable_name": "process_importance",
-       "summary": "{\n  \"name\": \"process_importance\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"PAINT\",\n          \"BODY\",\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_abs_shap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2162.4192345053525,\n        \"min\": 71.63671284580064,\n        \"max\": 3987.4114461310187,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3959.570471254013,\n          71.63671284580064,\n          3987.4114461310187\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89,\n        \"min\": 33,\n        \"max\": 197,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          197,\n          33,\n          182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ],
-      "image/png": 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\n"
-     },
-     "metadata": {}
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "kBJK8YfybEfD",
-   "metadata": {
-    "id": "kBJK8YfybEfD"
-   },
-   "source": [
-    "## 11. SHAP 기반 전이 위험 결과 생성\n",
-    "\n",
-    "아래 결과는 PRD의 `defect_transfer_prediction_result` 저장 구조에 맞춘 예측 테이블입니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "MjmQshC4bEfD",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
+      "source": [
+        "transition_shap_importance_rows = []\n",
+        "for model_key, model in transition_models.items():\n",
+        "    meta = transition_metadata[model_key]\n",
+        "    flow_test = transfer_test_raw[(transfer_test_raw[\"source_process_code\"] == meta[\"source_process_code\"]) & (transfer_test_raw[\"target_process_code\"] == meta[\"target_process_code\"])]\n",
+        "    importance, _ = compute_shap_importance(model, flow_test[transition_feature_cols], sample_size=500)\n",
+        "    importance[\"model_key\"] = model_key\n",
+        "    importance[\"source_process_code\"] = meta[\"source_process_code\"]\n",
+        "    importance[\"target_process_code\"] = meta[\"target_process_code\"]\n",
+        "    transition_shap_importance_rows.append(importance)\n",
+        "\n",
+        "transition_shap_importance = pd.concat(transition_shap_importance_rows, ignore_index=True) if transition_shap_importance_rows else pd.DataFrame()\n",
+        "global_top_cause = {}\n",
+        "if not transition_shap_importance.empty:\n",
+        "    global_top_cause = transition_shap_importance.sort_values([\"model_key\", \"mean_abs_shap\"], ascending=[True, False]).groupby(\"model_key\").first()[\"feature\"].to_dict()\n",
+        "    display(transition_shap_importance.groupby(\"model_key\").head(10))\n",
+        "    plt.figure(figsize=(9, 7))\n",
+        "    sns.barplot(data=transition_shap_importance.groupby(\"model_key\").head(8), x=\"mean_abs_shap\", y=\"feature\", hue=\"model_key\")\n",
+        "    plt.title(\"공정 전이 예측 SHAP 중요도\")\n",
+        "    plt.xlabel(\"평균 |SHAP|\")\n",
+        "    plt.ylabel(\"Feature\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "predicted_at = datetime.now(timezone.utc).isoformat()\n",
+        "if not transition_predictions.empty:\n",
+        "    transition_predictions[\"main_cause\"] = transition_predictions[\"model_key\"].map(global_top_cause).fillna(\"not_available\")\n",
+        "    transition_predictions[\"predicted_at\"] = predicted_at\n",
+        "\n",
+        "kafka_analysis_events = []\n",
+        "for _, row in transition_predictions.sort_values(\"target_defect_probability\", ascending=False).head(500).iterrows():\n",
+        "    kafka_analysis_events.append({\n",
+        "        \"eventType\": \"DEFECT_TRANSFER_PREDICTION\",\n",
+        "        \"carMasterId\": int(row[\"car_master_id\"]),\n",
+        "        \"sourceEventId\": str(row[\"source_event_id\"]),\n",
+        "        \"targetEventId\": str(row[\"target_event_id\"]),\n",
+        "        \"sourceProcessCode\": str(row[\"source_process_code\"]),\n",
+        "        \"targetProcessCode\": str(row[\"target_process_code\"]),\n",
+        "        \"targetDefectProbability\": float(row[\"target_defect_probability\"]),\n",
+        "        \"threshold\": float(row[\"threshold\"]),\n",
+        "        \"predictedTargetDefectYn\": int(row[\"predicted_target_defect_yn\"]),\n",
+        "        \"riskGrade\": str(row[\"risk_grade\"]),\n",
+        "        \"mainCause\": str(row[\"main_cause\"]),\n",
+        "        \"modelKey\": str(row[\"model_key\"]),\n",
+        "        \"predictedAt\": predicted_at,\n",
+        "    })\n",
+        "print(\"kafka event examples:\", len(kafka_analysis_events))\n",
+        "print(json.dumps(kafka_analysis_events[:3], ensure_ascii=False, indent=2))"
+      ]
     },
-    "id": "MjmQshC4bEfD",
-    "outputId": "268ba6e7-dc21-4678-beaf-55f184bbb3ec"
-   },
-   "outputs": [
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "     car_master_id source_process_code target_process_code  \\\n",
-       "883          49121               PAINT            ASSEMBLY   \n",
-       "179          84100               PAINT            ASSEMBLY   \n",
-       "76          495820               PAINT            ASSEMBLY   \n",
-       "65           46831               PAINT            ASSEMBLY   \n",
-       "447         533156               PAINT            ASSEMBLY   \n",
-       "543         199536               PAINT            ASSEMBLY   \n",
-       "695          86810               PAINT            ASSEMBLY   \n",
-       "132         457589               PAINT            ASSEMBLY   \n",
-       "88          361134               PAINT            ASSEMBLY   \n",
-       "532         293981               PAINT            ASSEMBLY   \n",
-       "608         230813               PAINT            ASSEMBLY   \n",
-       "710         290791               PAINT            ASSEMBLY   \n",
-       "471         327137               PAINT            ASSEMBLY   \n",
-       "196         105438               PAINT            ASSEMBLY   \n",
-       "214         221558               PAINT            ASSEMBLY   \n",
-       "178         478955               PAINT            ASSEMBLY   \n",
-       "908          47143               PAINT            ASSEMBLY   \n",
-       "829         103485               PRESS                BODY   \n",
-       "267         593231               PAINT            ASSEMBLY   \n",
-       "747         545804               PAINT            ASSEMBLY   \n",
-       "256         280517               PAINT            ASSEMBLY   \n",
-       "828         422775               PAINT            ASSEMBLY   \n",
-       "781         212417               PAINT            ASSEMBLY   \n",
-       "670         406278               PAINT            ASSEMBLY   \n",
-       "398         307253               PAINT            ASSEMBLY   \n",
-       "661         576604               PAINT            ASSEMBLY   \n",
-       "388         109154               PAINT            ASSEMBLY   \n",
-       "185         366377               PRESS                BODY   \n",
-       "593         492773               PAINT            ASSEMBLY   \n",
-       "772         255054               PAINT            ASSEMBLY   \n",
-       "\n",
-       "     current_defect_probability  target_defect_probability  \\\n",
-       "883                    0.563811                   0.619726   \n",
-       "179                    0.747158                   0.618208   \n",
-       "76                     0.787374                   0.342941   \n",
-       "65                     0.788540                   0.257915   \n",
-       "447                    0.787341                   0.230440   \n",
-       "543                    0.592871                   0.198408   \n",
-       "695                    0.842617                   0.186797   \n",
-       "132                    0.653267                   0.184589   \n",
-       "88                     0.500231                   0.183003   \n",
-       "532                    0.784869                   0.166225   \n",
-       "608                    0.665531                   0.153555   \n",
-       "710                    0.624316                   0.146039   \n",
-       "471                    0.770755                   0.144109   \n",
-       "196                    0.748888                   0.138818   \n",
-       "214                    0.766262                   0.131900   \n",
-       "178                    0.696390                   0.130941   \n",
-       "908                    0.711576                   0.126527   \n",
-       "829                    0.485897                   0.120372   \n",
-       "267                    0.665566                   0.109987   \n",
-       "747                    0.825798                   0.107390   \n",
-       "256                    0.501200                   0.103989   \n",
-       "828                    0.599490                   0.103070   \n",
-       "781                    0.992600                   0.102789   \n",
-       "670                    0.977409                   0.102239   \n",
-       "398                    0.567956                   0.100802   \n",
-       "661                    0.762924                   0.097669   \n",
-       "388                    0.767391                   0.096965   \n",
-       "185                    0.503115                   0.096164   \n",
-       "593                    0.710897                   0.095113   \n",
-       "772                    0.660369                   0.091074   \n",
-       "\n",
-       "    predicted_defect_process risk_grade        main_cause  influence_score  \\\n",
-       "883                 ASSEMBLY     MEDIUM  L3_S30_mean_time         0.563811   \n",
-       "179                 ASSEMBLY     MEDIUM      L3_S36_F3920         0.747158   \n",
-       "76                  ASSEMBLY        LOW      L3_S29_F3407         0.787374   \n",
-       "65                  ASSEMBLY        LOW  L3_S30_mean_time         0.788540   \n",
-       "447                 ASSEMBLY        LOW      L3_S29_F3407         0.787341   \n",
-       "543                 ASSEMBLY        LOW      L3_S29_F3449         0.592871   \n",
-       "695                 ASSEMBLY        LOW  L3_S30_mean_time         0.842617   \n",
-       "132                 ASSEMBLY        LOW  L3_S30_mean_time         0.653267   \n",
-       "88                  ASSEMBLY        LOW      L3_S30_F3504         0.500231   \n",
-       "532                 ASSEMBLY        LOW      L3_S29_F3407         0.784869   \n",
-       "608                 ASSEMBLY        LOW      L3_S29_F3407         0.665531   \n",
-       "710                 ASSEMBLY        LOW  L3_S30_mean_time         0.624316   \n",
-       "471                 ASSEMBLY        LOW  L3_S30_mean_time         0.770755   \n",
-       "196                 ASSEMBLY        LOW      L3_S29_F3339         0.748888   \n",
-       "214                 ASSEMBLY        LOW      L3_S33_F3857         0.766262   \n",
-       "178                 ASSEMBLY        LOW      L3_S29_F3407         0.696390   \n",
-       "908                 ASSEMBLY        LOW      L3_S29_F3395         0.711576   \n",
-       "829                     BODY        LOW        L0_S9_F180         0.485897   \n",
-       "267                 ASSEMBLY        LOW      L3_S29_F3407         0.665566   \n",
-       "747                 ASSEMBLY        LOW      L3_S36_F3920         0.825798   \n",
-       "256                 ASSEMBLY        LOW    L3_S33_num_std         0.501200   \n",
-       "828                 ASSEMBLY        LOW      L3_S29_F3351         0.599490   \n",
-       "781                 ASSEMBLY        LOW      L3_S36_F3920         0.992600   \n",
-       "670                 ASSEMBLY        LOW  L3_S30_mean_time         0.977409   \n",
-       "398                 ASSEMBLY        LOW  L3_S30_mean_time         0.567956   \n",
-       "661                 ASSEMBLY        LOW      L3_S29_F3373         0.762924   \n",
-       "388                 ASSEMBLY        LOW      L3_S29_F3339         0.767391   \n",
-       "185                     BODY        LOW     L0_S1_num_std         0.503115   \n",
-       "593                 ASSEMBLY        LOW      L3_S29_F3395         0.710897   \n",
-       "772                 ASSEMBLY        LOW      L3_S29_F3373         0.660369   \n",
-       "\n",
-       "                         predicted_at  \n",
-       "883  2026-06-18T05:32:50.034461+00:00  \n",
-       "179  2026-06-18T05:32:50.034461+00:00  \n",
-       "76   2026-06-18T05:32:50.034461+00:00  \n",
-       "65   2026-06-18T05:32:50.034461+00:00  \n",
-       "447  2026-06-18T05:32:50.034461+00:00  \n",
-       "543  2026-06-18T05:32:50.034461+00:00  \n",
-       "695  2026-06-18T05:32:50.034461+00:00  \n",
-       "132  2026-06-18T05:32:50.034461+00:00  \n",
-       "88   2026-06-18T05:32:50.034461+00:00  \n",
-       "532  2026-06-18T05:32:50.034461+00:00  \n",
-       "608  2026-06-18T05:32:50.034461+00:00  \n",
-       "710  2026-06-18T05:32:50.034461+00:00  \n",
-       "471  2026-06-18T05:32:50.034461+00:00  \n",
-       "196  2026-06-18T05:32:50.034461+00:00  \n",
-       "214  2026-06-18T05:32:50.034461+00:00  \n",
-       "178  2026-06-18T05:32:50.034461+00:00  \n",
-       "908  2026-06-18T05:32:50.034461+00:00  \n",
-       "829  2026-06-18T05:32:50.034461+00:00  \n",
-       "267  2026-06-18T05:32:50.034461+00:00  \n",
-       "747  2026-06-18T05:32:50.034461+00:00  \n",
-       "256  2026-06-18T05:32:50.034461+00:00  \n",
-       "828  2026-06-18T05:32:50.034461+00:00  \n",
-       "781  2026-06-18T05:32:50.034461+00:00  \n",
-       "670  2026-06-18T05:32:50.034461+00:00  \n",
-       "398  2026-06-18T05:32:50.034461+00:00  \n",
-       "661  2026-06-18T05:32:50.034461+00:00  \n",
-       "388  2026-06-18T05:32:50.034461+00:00  \n",
-       "185  2026-06-18T05:32:50.034461+00:00  \n",
-       "593  2026-06-18T05:32:50.034461+00:00  \n",
-       "772  2026-06-18T05:32:50.034461+00:00  "
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-73977bb6-795d-46fd-9b9f-c66822303bcd\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>car_master_id</th>\n",
-       "      <th>source_process_code</th>\n",
-       "      <th>target_process_code</th>\n",
-       "      <th>current_defect_probability</th>\n",
-       "      <th>target_defect_probability</th>\n",
-       "      <th>predicted_defect_process</th>\n",
-       "      <th>risk_grade</th>\n",
-       "      <th>main_cause</th>\n",
-       "      <th>influence_score</th>\n",
-       "      <th>predicted_at</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>883</th>\n",
-       "      <td>49121</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.563811</td>\n",
-       "      <td>0.619726</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.563811</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>84100</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.747158</td>\n",
-       "      <td>0.618208</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>L3_S36_F3920</td>\n",
-       "      <td>0.747158</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>495820</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.787374</td>\n",
-       "      <td>0.342941</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3407</td>\n",
-       "      <td>0.787374</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>46831</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.788540</td>\n",
-       "      <td>0.257915</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.788540</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>447</th>\n",
-       "      <td>533156</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.787341</td>\n",
-       "      <td>0.230440</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3407</td>\n",
-       "      <td>0.787341</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>199536</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.592871</td>\n",
-       "      <td>0.198408</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3449</td>\n",
-       "      <td>0.592871</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>86810</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.842617</td>\n",
-       "      <td>0.186797</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.842617</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>132</th>\n",
-       "      <td>457589</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.653267</td>\n",
-       "      <td>0.184589</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.653267</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>361134</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.500231</td>\n",
-       "      <td>0.183003</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_F3504</td>\n",
-       "      <td>0.500231</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>532</th>\n",
-       "      <td>293981</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.784869</td>\n",
-       "      <td>0.166225</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3407</td>\n",
-       "      <td>0.784869</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>608</th>\n",
-       "      <td>230813</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.665531</td>\n",
-       "      <td>0.153555</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3407</td>\n",
-       "      <td>0.665531</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>710</th>\n",
-       "      <td>290791</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.624316</td>\n",
-       "      <td>0.146039</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.624316</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>327137</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.770755</td>\n",
-       "      <td>0.144109</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.770755</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>196</th>\n",
-       "      <td>105438</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.748888</td>\n",
-       "      <td>0.138818</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3339</td>\n",
-       "      <td>0.748888</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>214</th>\n",
-       "      <td>221558</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.766262</td>\n",
-       "      <td>0.131900</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S33_F3857</td>\n",
-       "      <td>0.766262</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>478955</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.696390</td>\n",
-       "      <td>0.130941</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3407</td>\n",
-       "      <td>0.696390</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>908</th>\n",
-       "      <td>47143</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.711576</td>\n",
-       "      <td>0.126527</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3395</td>\n",
-       "      <td>0.711576</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>829</th>\n",
-       "      <td>103485</td>\n",
-       "      <td>PRESS</td>\n",
-       "      <td>BODY</td>\n",
-       "      <td>0.485897</td>\n",
-       "      <td>0.120372</td>\n",
-       "      <td>BODY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L0_S9_F180</td>\n",
-       "      <td>0.485897</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>267</th>\n",
-       "      <td>593231</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.665566</td>\n",
-       "      <td>0.109987</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3407</td>\n",
-       "      <td>0.665566</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>747</th>\n",
-       "      <td>545804</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.825798</td>\n",
-       "      <td>0.107390</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S36_F3920</td>\n",
-       "      <td>0.825798</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>256</th>\n",
-       "      <td>280517</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.501200</td>\n",
-       "      <td>0.103989</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S33_num_std</td>\n",
-       "      <td>0.501200</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>828</th>\n",
-       "      <td>422775</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.599490</td>\n",
-       "      <td>0.103070</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3351</td>\n",
-       "      <td>0.599490</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>781</th>\n",
-       "      <td>212417</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.992600</td>\n",
-       "      <td>0.102789</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S36_F3920</td>\n",
-       "      <td>0.992600</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>670</th>\n",
-       "      <td>406278</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.977409</td>\n",
-       "      <td>0.102239</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.977409</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>398</th>\n",
-       "      <td>307253</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.567956</td>\n",
-       "      <td>0.100802</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S30_mean_time</td>\n",
-       "      <td>0.567956</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>661</th>\n",
-       "      <td>576604</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.762924</td>\n",
-       "      <td>0.097669</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3373</td>\n",
-       "      <td>0.762924</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>388</th>\n",
-       "      <td>109154</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.767391</td>\n",
-       "      <td>0.096965</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3339</td>\n",
-       "      <td>0.767391</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185</th>\n",
-       "      <td>366377</td>\n",
-       "      <td>PRESS</td>\n",
-       "      <td>BODY</td>\n",
-       "      <td>0.503115</td>\n",
-       "      <td>0.096164</td>\n",
-       "      <td>BODY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L0_S1_num_std</td>\n",
-       "      <td>0.503115</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>593</th>\n",
-       "      <td>492773</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.710897</td>\n",
-       "      <td>0.095113</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3395</td>\n",
-       "      <td>0.710897</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>772</th>\n",
-       "      <td>255054</td>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>0.660369</td>\n",
-       "      <td>0.091074</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>L3_S29_F3373</td>\n",
-       "      <td>0.660369</td>\n",
-       "      <td>2026-06-18T05:32:50.034461+00:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
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-       "            title=\"Convert this dataframe to an interactive table.\"\n",
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-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
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-       "\n",
-       "    .colab-df-convert:hover {\n",
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-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
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-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-73977bb6-795d-46fd-9b9f-c66822303bcd button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-73977bb6-795d-46fd-9b9f-c66822303bcd');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "repr_error": "0"
-      }
-     },
-     "metadata": {}
+      "cell_type": "markdown",
+      "id": "e76d4d4f",
+      "metadata": {
+        "id": "e76d4d4f"
+      },
+      "source": [
+        "## 8. 모델/분석 산출물 저장"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "  source_process_code target_process_code risk_grade  vehicle_count  \\\n",
-       "2               PAINT            ASSEMBLY     MEDIUM              2   \n",
-       "0            ASSEMBLY    FINAL_INSPECTION        LOW              1   \n",
-       "3               PRESS                BODY        LOW            117   \n",
-       "1               PAINT            ASSEMBLY        LOW            880   \n",
-       "\n",
-       "   avg_target_defect_probability  avg_influence_score  \n",
-       "2                       0.618967             0.655485  \n",
-       "0                       0.047076             0.495199  \n",
-       "3                       0.030476             0.588225  \n",
-       "1                       0.026473             0.719373  "
-      ],
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>source_process_code</th>\n",
-       "      <th>target_process_code</th>\n",
-       "      <th>risk_grade</th>\n",
-       "      <th>vehicle_count</th>\n",
-       "      <th>avg_target_defect_probability</th>\n",
-       "      <th>avg_influence_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>PAINT</td>\n",
-       "      <td>ASSEMBLY</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.618967</td>\n",
-       "      <td>0.655485</td>\n",
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-       "      <th>0</th>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>PRESS</td>\n",
-       "      <td>BODY</td>\n",
-       "      <td>LOW</td>\n",
-       "      <td>117</td>\n",
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-       "      <td>0.588225</td>\n",
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-       "\n",
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-       "      background-color: #E8F0FE;\n",
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-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
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-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc button.colab-df-convert');\n",
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-       "        const element = document.querySelector('#df-2ed32a4d-df3b-48af-9a8a-13035df912bc');\n",
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-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
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-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
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-       "\n",
-       "\n",
-       "  <div id=\"id_59f80193-e9d1-4e45-92c6-9c4c014f7e5f\">\n",
-       "    <style>\n",
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-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      buttonEl.onclick = () => {\n",
-       "        google.colab.notebook.generateWithVariable('risk_summary');\n",
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-       "variable_name": "risk_summary",
-       "summary": "{\n  \"name\": \"risk_summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"source_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PAINT\",\n          \"ASSEMBLY\",\n          \"PRESS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_process_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ASSEMBLY\",\n          \"FINAL_INSPECTION\",\n          \"BODY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"risk_grade\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"LOW\",\n          \"MEDIUM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vehicle_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 423,\n        \"min\": 1,\n        \"max\": 880,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          880\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_target_defect_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29228220842686947,\n        \"min\": 0.026473051136363637,\n        \"max\": 0.618967,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.047076,\n          0.026473051136363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_influence_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09591880133970926,\n        \"min\": 0.495199,\n        \"max\": 0.7193733886363637,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.495199,\n          0.7193733886363637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "# 전이 위험 결과 생성\n",
-    "def build_transfer_predictions(\n",
-    "    ids: pd.Series,\n",
-    "    probabilities: np.ndarray,\n",
-    "    shap_matrix: np.ndarray,\n",
-    "    feature_names: list[str],\n",
-    "    feature_process: pd.Series,\n",
-    ") -> pd.DataFrame:\n",
-    "    records = []\n",
-    "    process_values = feature_process.values\n",
-    "    predicted_at = datetime.now(timezone.utc).isoformat()\n",
-    "\n",
-    "    # 샘플별 공정 영향도 계산\n",
-    "    for row_idx, (sample_id, prob) in enumerate(zip(ids.to_numpy(), probabilities)):\n",
-    "        abs_values = np.abs(shap_matrix[row_idx])\n",
-    "        mapped_mask = feature_process.isin(PROCESS_FLOW).to_numpy()\n",
-    "        mapped_abs_values = abs_values[mapped_mask]\n",
-    "        mapped_process_values = process_values[mapped_mask]\n",
-    "        total_abs = float(mapped_abs_values.sum()) or 1.0\n",
-    "\n",
-    "        # 공정별 영향 비율\n",
-    "        process_scores = {}\n",
-    "        for process_code in PROCESS_FLOW:\n",
-    "            col_idx = np.where(mapped_process_values == process_code)[0]\n",
-    "            process_scores[process_code] = float(mapped_abs_values[col_idx].sum() / total_abs) if len(col_idx) else 0.0\n",
-    "\n",
-    "        # 원인/대상 공정 산출\n",
-    "        source_process = max(process_scores, key=process_scores.get)\n",
-    "        target_process = next_process(source_process)\n",
-    "        source_col_idx = np.where(process_values == source_process)[0]\n",
-    "        top_feature_idx = int(source_col_idx[np.argmax(abs_values[source_col_idx])]) if len(source_col_idx) else int(abs_values.argmax())\n",
-    "        influence_score = process_scores[source_process]\n",
-    "\n",
-    "        records.append({\n",
-    "            \"car_master_id\": int(sample_id),\n",
-    "            \"source_process_code\": source_process,\n",
-    "            \"target_process_code\": target_process,\n",
-    "            \"current_defect_probability\": round(float(influence_score), 6),\n",
-    "            \"target_defect_probability\": round(float(prob), 6),\n",
-    "            \"predicted_defect_process\": target_process,\n",
-    "            \"risk_grade\": probability_to_risk_grade(float(prob)),\n",
-    "            \"main_cause\": feature_names[top_feature_idx],\n",
-    "            \"influence_score\": round(float(influence_score), 6),\n",
-    "            \"predicted_at\": predicted_at,\n",
-    "        })\n",
-    "    return pd.DataFrame(records).sort_values([\"target_defect_probability\", \"influence_score\"], ascending=False)\n",
-    "\n",
-    "# 전이 예측 테이블 생성\n",
-    "transfer_predictions = build_transfer_predictions(\n",
-    "    id_shap,\n",
-    "    proba_shap,\n",
-    "    shap_matrix,\n",
-    "    X_shap.columns.tolist(),\n",
-    "    feature_process,\n",
-    ")\n",
-    "\n",
-    "display(transfer_predictions.head(30))\n",
-    "\n",
-    "# 전이 위험 요약\n",
-    "risk_summary = transfer_predictions.groupby([\"source_process_code\", \"target_process_code\", \"risk_grade\"]).agg(\n",
-    "    vehicle_count=(\"car_master_id\", \"count\"),\n",
-    "    avg_target_defect_probability=(\"target_defect_probability\", \"mean\"),\n",
-    "    avg_influence_score=(\"influence_score\", \"mean\"),\n",
-    ").reset_index().sort_values([\"avg_target_defect_probability\", \"vehicle_count\"], ascending=False)\n",
-    "display(risk_summary)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "JFTbBuS1bEfD",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 885
+      "cell_type": "markdown",
+      "id": "2497b13c",
+      "metadata": {
+        "id": "2497b13c"
+      },
+      "source": [
+        "**셀 설명**\n",
+        "\n",
+        "학습된 모델, feature 목록, metric, 혼동행렬, SHAP 결과, 전이 모델 metadata를 파일로 저장합니다."
+      ]
     },
-    "id": "JFTbBuS1bEfD",
-    "outputId": "6afce295-117c-486d-d7df-abe3aade47a3"
-   },
-   "outputs": [
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 900x500 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "5083f196",
+      "metadata": {
+        "id": "5083f196",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "50efd52e-ec17-4533-e478-4a33e4211bac"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/drive/MyDrive/defect_transfer_outputs/selected_defect_detector.joblib\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_model_features.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_model_metrics.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_model_comparison.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_model_cv_results.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_model_confusion_matrix.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_model_confusion_matrices_by_model.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_shap_importance.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_model_feature_importance.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/adjacent_transfer_models.joblib\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/adjacent_transfer_model_metadata.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/adjacent_transfer_model_metrics.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/adjacent_transfer_confusion_matrices.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/adjacent_transfer_train_confusion_matrices.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/adjacent_transfer_shap_importance.json\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/defect_transfer_kafka_analysis_events.jsonl\n",
+            "/content/drive/MyDrive/defect_transfer_outputs/event_json_inference_schema.json\n"
+          ]
+        }
       ],
-      "image/png": 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\n"
-     },
-     "metadata": {}
+      "source": [
+        "def to_builtin(value):\n",
+        "    if isinstance(value, dict):\n",
+        "        return {k: to_builtin(v) for k, v in value.items()}\n",
+        "    if isinstance(value, list):\n",
+        "        return [to_builtin(v) for v in value]\n",
+        "    if isinstance(value, (np.integer,)):\n",
+        "        return int(value)\n",
+        "    if isinstance(value, (np.floating,)):\n",
+        "        return float(value)\n",
+        "    if isinstance(value, pd.DataFrame):\n",
+        "        return value.to_dict(orient=\"records\")\n",
+        "    return value\n",
+        "\n",
+        "\n",
+        "selected_model_path = OUTPUT_DIR / \"selected_defect_detector.joblib\"\n",
+        "candidate_models_path = OUTPUT_DIR / \"candidate_defect_models.joblib\"\n",
+        "feature_path = OUTPUT_DIR / \"defect_model_features.json\"\n",
+        "metrics_path = OUTPUT_DIR / \"defect_model_metrics.json\"\n",
+        "comparison_path = OUTPUT_DIR / \"defect_model_comparison.json\"\n",
+        "cv_results_path = OUTPUT_DIR / \"defect_model_cv_results.json\"\n",
+        "confusion_matrix_path = OUTPUT_DIR / \"defect_model_confusion_matrix.json\"\n",
+        "all_confusion_matrices_path = OUTPUT_DIR / \"defect_model_confusion_matrices_by_model.json\"\n",
+        "train_confusion_matrices_path = OUTPUT_DIR / \"defect_model_train_confusion_matrices_by_model.json\"\n",
+        "shap_importance_path = OUTPUT_DIR / \"defect_shap_importance.json\"\n",
+        "feature_importance_path = OUTPUT_DIR / \"defect_model_feature_importance.json\"\n",
+        "transition_models_path = OUTPUT_DIR / \"adjacent_transfer_models.joblib\"\n",
+        "transition_metadata_path = OUTPUT_DIR / \"adjacent_transfer_model_metadata.json\"\n",
+        "transition_metrics_path = OUTPUT_DIR / \"adjacent_transfer_model_metrics.json\"\n",
+        "transition_confusion_path = OUTPUT_DIR / \"adjacent_transfer_confusion_matrices.json\"\n",
+        "transition_train_confusion_path = OUTPUT_DIR / \"adjacent_transfer_train_confusion_matrices.json\"\n",
+        "transition_shap_path = OUTPUT_DIR / \"adjacent_transfer_shap_importance.json\"\n",
+        "transition_cv_results_path = OUTPUT_DIR / \"adjacent_transfer_cv_results.json\"\n",
+        "kafka_events_path = OUTPUT_DIR / \"defect_transfer_kafka_analysis_events.jsonl\"\n",
+        "schema_path = OUTPUT_DIR / \"event_json_inference_schema.json\"\n",
+        "\n",
+        "joblib.dump(selected_event_model, selected_model_path)\n",
+        "joblib.dump(event_candidate_models, candidate_models_path)\n",
+        "joblib.dump(transition_models, transition_models_path)\n",
+        "feature_path.write_text(json.dumps(event_feature_cols, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "metrics_payload = {\n",
+        "    \"selected_model_name\": selected_event_model_name,\n",
+        "    \"threshold\": selected_event_threshold,\n",
+        "    \"train_metrics\": compute_metrics(y_event_train, event_train_proba, selected_event_threshold),\n",
+        "    \"test_metrics\": compute_metrics(y_event_test, event_test_proba, selected_event_threshold),\n",
+        "    \"train_rows\": int(len(defect_train)),\n",
+        "    \"test_rows\": int(len(defect_test)),\n",
+        "    \"positive_ratio_train\": float(y_event_train.mean()),\n",
+        "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
+        "}\n",
+        "metrics_path.write_text(json.dumps(to_builtin(metrics_payload), ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "comparison_path.write_text(event_model_comparison.to_json(orient=\"records\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "if \"event_cv_results\" in globals() and not event_cv_results.empty:\n",
+        "    cv_results_path.write_text(event_cv_results.to_json(orient=\"records\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "if \"transition_cv_results\" in globals() and not transition_cv_results.empty:\n",
+        "    transition_cv_results_path.write_text(transition_cv_results.to_json(orient=\"records\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "confusion_matrix_path.write_text(event_confusion_matrix_table.to_json(orient=\"split\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "if \"event_confusion_tables_by_model\" in globals():\n",
+        "    all_confusion_matrices_path.write_text(json.dumps({key: table.to_dict(orient=\"split\") for key, table in event_confusion_tables_by_model.items()}, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "if \"event_train_confusion_tables_by_model\" in globals():\n",
+        "    train_confusion_matrices_path.write_text(json.dumps({key: table.to_dict(orient=\"split\") for key, table in event_train_confusion_tables_by_model.items()}, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "shap_importance_path.write_text(event_shap_importance.to_json(orient=\"records\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "feature_importance_path.write_text(event_model_importance.to_json(orient=\"records\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "transition_metadata_path.write_text(json.dumps(to_builtin(transition_metadata), ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "transition_metrics_path.write_text(transition_model_metrics.to_json(orient=\"records\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "transition_confusion_path.write_text(json.dumps({key: table.to_dict(orient=\"split\") for key, table in transition_confusion_tables.items()}, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "transition_train_confusion_path.write_text(json.dumps({key: table.to_dict(orient=\"split\") for key, table in transition_train_confusion_tables.items()}, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "transition_shap_path.write_text(transition_shap_importance.to_json(orient=\"records\", force_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "with open(kafka_events_path, \"w\", encoding=\"utf-8\") as f:\n",
+        "    for event in kafka_analysis_events:\n",
+        "        f.write(json.dumps(to_builtin(event), ensure_ascii=False) + \"\\n\")\n",
+        "schema_payload = {\"event_model\": {\"target_column\": EVENT_TARGET, \"feature_columns\": event_feature_cols, \"source_train_csv\": str(DEFECT_TRAIN_PATH), \"source_test_csv\": str(DEFECT_TEST_PATH)}, \"transition_model\": {\"target_column\": TRANSFER_TARGET, \"feature_columns\": transition_feature_cols, \"source_train_csv\": str(TRANSFER_TRAIN_PATH), \"source_test_csv\": str(TRANSFER_TEST_PATH)}}\n",
+        "schema_path.write_text(json.dumps(schema_payload, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
+        "\n",
+        "for path in [\n",
+        "    selected_model_path,\n",
+        "    feature_path,\n",
+        "    metrics_path,\n",
+        "    comparison_path,\n",
+        "    cv_results_path,\n",
+        "    confusion_matrix_path,\n",
+        "    all_confusion_matrices_path,\n",
+        "    shap_importance_path,\n",
+        "    feature_importance_path,\n",
+        "    transition_models_path,\n",
+        "    transition_metadata_path,\n",
+        "    transition_metrics_path,\n",
+        "    transition_confusion_path,\n",
+        "    transition_train_confusion_path,\n",
+        "    transition_shap_path,\n",
+        "    kafka_events_path,\n",
+        "    schema_path,\n",
+        "]:\n",
+        "    if path.exists():\n",
+        "        print(path)"
+      ]
     },
     {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ],
-      "image/png": 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2TEREBGxsbLTv5xe/gOWrVauW3pSz+/fvo169enqfi/xpU/nvn3z16tXTG9fNzQ1HjhzRPn7+/DmWL1+OqKgo7fswX1paml7/l8esW7cupFKpdv1GWQgICICTkxMOHDgAf39/aDQaHDp0CB07diyVS7BmZmYWOaWoR48e2L17N6ZNm4aFCxfC398fnTt3Rrdu3Qy+zHBB+SzKy6+zRCJBvXr1cP/+fYPHMMb9+/fh7Oys97rmv8dePv7rr7+uN4a9vX21uecKVR0sIIgqKR8fnwJ/2/siMzMzvR/YGo0GNWvWxDfffFNgn/yr3VSkyhBjYV+EhH8WTqrVagwfPhwpKSn48MMP4eHhASsrKzx69Ajh4eFG/2a2KPkLfvO/YOfHsmDBAjg5Oem1NzExKXK8/P6fffZZoZfatLKy0iuOSluLFi1emfPizHMviYkTJ+LcuXP44IMP4OnpCSsrK2g0Gnz44YcGXd6zPC47a2JigrfeegvfffcdZs6cibNnzyIpKQm9evUq8dgPHz5EWlpagUVcPgsLC2zfvh3x8fH49ddf8d///hdRUVHYtWsXNmzY8Mr3Xf4Y5UWtVhsUU2ko7DiGvHeIxIQFBFE1U7duXcTFxaFZs2ZF/pDO/w17YmIi6tSpo93+7NmzV35hzG9/7dq1In+zX9iXqfKIsaxdu3YNiYmJ+Prrr9GnTx/t9rK8Od2BAwcgkUgQEBAA4P/zULNmTaPu7ZDf38bGpsj+jo6OsLGxeeXVr8r7ng0uLi64evUqNBqNTsGXkJAAQP8s0stX0QLy3lsuLi4AgJSUFMTFxWHcuHEYO3asTpvC3L59W+e9efv2bWg0mmLdB6Mgr3ote/fujQ0bNuDnn39GTEwMHB0dXzktzxA//PADALxyLKlUCn9/f/j7++Pzzz/H6tWrsXjxYsTHx6NNmzal/l54OXeCIOD27ds6V4cq7Df9Dx480MlRcWJzcXFBXFwc0tPTdc5C5L/H8t87RFUN10AQVTPdu3eHWq3GypUr9fbl5uZqf8C2adMGMpkM27Zt0/ntWP5lWIvSuHFjuLq6YsuWLXo/sF8cK38O+8ttyiPGspb/hfXFuARBwJYtW8rkeGvXrsXJkyfRo0cP7ZSutm3bwsbGBmvWrCnwqlQvX3nqZV5eXqhbty42bNhQ4Jz3/P5SqRSdOnXCL7/8gj///FOvXf5rkJ/vgqb6lIWgoCA8fvwYUVFR2m25ubnYunUrrKys0LJlS532x44d07nE6cWLF3HhwgUEBQUBKPy3x0W93/IvVZxv27Zt2thKorDPTr5GjRpBoVDg+++/x9GjR9GzZ88S36shLi4OK1euhKura5FnM54/f663Lf8MlkqlMij+4tq/f7/OtMDo6Gg8fvxY53WuU6cOLly4oI0BAH755Re9y70WJ7agoCCo1Wq9PG/atAkSiaTEeSYSK56BIKpmWrVqhf79+2PNmjW4cuUKAgICIJPJkJiYiOjoaHzxxRfo1q0bHB0dMWLECKxZswajR49Gu3btcPnyZcTExLzycoNSqRQzZ87ERx99hD59+mgveZmQkIAbN25o7ybbuHFjAMCcOXMQGBgIExMT9OzZs1xiLGseHh6oW7cuvv76azx69Ag2Njb48ccfS/yFKTc3V/tbYJVKhfv37+Pnn3/G1atX4efnh6+++krb1sbGBjNnzsRnn32Gvn37okePHnB0dMSDBw9w4sQJNGvWDDNmzCj0WFKpFHPmzMHIkSMREhKCvn37olatWnj06BHi4+NhY2OD1atXAwA++eQTxMbGIjQ0FO+99x7q16+Px48fIzo6Gt9++y3s7Ozg6ekJExMTREZGIi0tDWZmZmjduvUrL9FprP79+2PXrl0IDw/HpUuX4OLigh9//BFnz57F1KlT9eat161bFwMHDsTAgQOhUqmwZcsWODg44MMPP9S+ni1btsS6deuQk5ODWrVqITY2tsj1DPfu3UNYWBjatm2L8+fP48CBAwgJCdG5k7MxLCws0KBBAxw5cgRubm5wcHBAw4YNddYd9enTB19//TUAFHv6UkxMDBISEqBWq/HkyRPEx8cjNjYWb7zxBlatWlXkjSNXrFiB33//He3atYOLiwuePn2Kb7/9FrVr19ZeFrVu3bqws7PDzp07YW1tDSsrK/j4+OicCSgOe3t7DBo0CH379tVexrVevXo6l5rt168ffvzxR3z44Yfo3r077ty5g4MHD+pNxypObMHBwfDz88PixYtx//59KBQKxMbG4vjx4xg6dGiRU72IKjMWEETV0FdffQUvLy/s3LkTixcvhomJCVxcXNCrVy+d651PnDgRZmZm2LlzJ+Lj4+Hj44MNGzZg9OjRrzxG27ZtsXnzZqxYsQIbNmyAIAioU6eOzg/0Ll26IDQ0FIcPH8aBAwcgCAJ69uxZbjGWJZlMhtWrV2POnDlYs2YNzM3N0blzZ7z//vuF3vXaECqVCp999hmAvN+UOjo6wsvLC2PGjEHnzp311ma89dZbcHZ2xtq1a7F+/XqoVCrUqlULLVq0MOgqN35+fti1axdWrlyJbdu2ITMzE05OTvDx8UH//v217WrVqoXvvvsOS5cuxcGDB5Geno5atWohKChIOw3NyckJs2bNwpo1a/DFF19ArVZjy5YtZVZAWFhYYOvWrfjmm2+wb98+pKenw93dHfPmzSvwuffp0wdSqRSbN2/G06dP4ePjg+nTp8PZ2VnbZuHChZg9eza+/fZbCIKAgIAAREZGom3btgXGsGTJEixduhQLFy6EqakpBg8erM1fSc2ZMwezZ8/GvHnzkJOTg7Fjx+oUEG+99Ra++eYb1KlTR3u/DkPl36dEJpPBwcEBcrkcU6dORd++fV+5EDs4OBj379/Hnj17kJycjBo1aqBVq1YYN26c9k7nMpkM8+fPx6JFizBz5kzk5uZi3rx5RhcQYWFhuHr1KtauXYuMjAz4+/vjyy+/1LlSV9u2bREeHo6NGzdi7ty58PLywurVq7VFVr7ixCaVSrFq1SosW7YMUVFR2Lt3L1xcXPDZZ59hxIgRRj0XospAInDlDhERUZXz7NkztG3bFh9//DHGjBlT0eEQURXCNRBERERV0L59+6BWq0t0xouIqCCiLSAyMjIQFBQEhUKhtyhv9+7d6Nq1K7y9vdGrVy/88ssvev3T0tIwdepUtGrVCr6+vhg/fjySkpLKK3wiIqIKERcXh23btmH16tXo1KlTia/4RET0MtEWECtXroRardbbfvjwYUyfPh3du3dHZGQkmjZtirFjx+L8+fM67SZOnIjY2FjMnDkT33zzDW7duoWRI0ciNze3nJ4BERFR+Vu5ciXmz5+PRo0aYfr06RUdDhFVQaJcA3Hz5k28++67mDJlCr788kt8//332htmde3aFV5eXli4cKG2/YABA2Bra4vIyEgAwLlz5zBgwACsX79ee63qhIQE9OjRA4sWLUKPHj3K/0kREREREVUBojwDMWfOHAwYMADu7u462+/evYvExER0795dZ3uPHj0QFxenvbZzTEwM7OzstDdTAvIuqejp6YmYmJiyfwJERERERFWU6AqI6OhoXLt2rcArRuTf2fHlwqJ+/frIycnB3bt3te3c3d317ibp4eGhHYOIiIiIiIpPVPeBUCqVmD9/PiZNmlTgdaZTUlIAAHZ2djrb8x/n709NTdVea/pF9vb2+Ouvv4yO79y5cxAEATKZzOgxiIiIiIjEJicnBxKJBL6+vq9sK6oCYtWqVahZsybeeeedig6lQIIgQBAE7VQpIiIiIqLqRjQFxP3797FhwwasWLECaWlpAIDMzEzt3xkZGbC3tweQd4lWJycnbd/U1FQA0O63s7PDw4cP9Y6RkpKibWMMmUwGQRDQoEEDo8cwllKpRGJiItzc3HTurEkVj7kRJ+ZFvJgbcWJexIu5EaeqlpcbN27oTf8vjGgKiHv37iEnJwejRo3S2zdkyBA0adJEe+WlhIQEeHh4aPcnJCRAJpNpbzPv4eGBuLg4CIKg80LcunULcrm8RHFKJBJYWVmVaIySsLS0rNDjU+GYG3FiXsSLuREn5kW8mBtxqip5MbR4AERUQHh6emLLli06265cuYJ58+Zh1qxZ8Pb2Rp06deDm5obo6Gh06tRJ2y4qKgr+/v4wMzMDAAQFBWHlypWIi4tDmzZtAOQVD5cvX8aHH35Yfk+KiIiIiKiKEU0BYWdnBz8/vwL3NW7cGI0bNwYAjBs3DpMnT0bdunXh5+eHqKgoXLx4Edu2bdO29/X1RWBgIKZOnYopU6bA3NwcixcvhkKhQJcuXcrl+RARERERVUWiKSAMFRISAqVSicjISKxduxbu7u5Yvny53orxJUuWYN68eZgxYwZyc3MRGBiIadOmwdS00j1lIiIiIiLREPW3aT8/P1y9elVve79+/dCvX78i+9ra2mLu3LmYO3duWYVHRERERFTtiLqAICIiIqKKo1arkZOTU9FhiFJ2drb2b6lUdPdm1iGTyWBiYlJq47GAICIiIiIdgiDg4cOHeP78eUWHIloajQampqZ48OCB6AsIAHBwcEDt2rWLdbWlwrCAICIiIiId+cWDs7MzrKysSuVLZ1WjVquRnZ0Nc3PzUv3tfmkTBAGZmZlISkoCALz++uslHpMFBBERERFpqdVqbfFQs2bNig5HtNRqNQDAwsJC1AUEAO2N7pKSkuDs7FzieMV/voWIiIiIyk3+moeqcHM0+n/5+SyNNS0sIIiIiIhID6ctVS2lmU8WEJWIpaVlpVikQ0RERERVF7+NVhISiQSNGjWChYWF0WMIglCKEREREREVX0REBBQKRbH6xMfHQ6FQIDo6uoyiKl/h4eEIDg6u6DCMxkXUlYhUKsXzsz9ByHxe7L4m1jVg59up9IMiIiIiomqFBUQlk33/GtQpj4rdT+b4OsACgoiIiCrYRx99hFGjRlV0GFQCnMJEREREROUiMzMTpqamMDc3r+hQjJKZmVnRIYgCCwgiIiIiKnX5ax1u3LiBTz/9FC1btsSgQYMKXAMRGxuLgQMHokWLFvD19UXXrl2xaNGiIsdXqVQYPXo0mjdvjrNnzxocV3JyMv71r3+hWbNmaNGiBaZMmYL//e9/UCgU2Lt3r7ZdeHg4fH19cefOHYwcORK+vr6YPHkyAOD333/HxIkT0aNHDzRp0gTt2rXD3LlzkZWVpXe8Y8eOISQkBN7e3ggJCcFPP/1UYFwajQabNm1Cz5494e3tjTZt2mDGjBlISUkx+LmVF05hIiIiIqIyM2HCBNSrVw+TJk2CIAh49uyZzv7r169j9OjRUCgUGD9+PMzMzHD79u0ii4KsrCx8/PHH+Ouvv7Bx40b4+PgYFItGo8FHH32EixcvYuDAgfDw8MDx48cxZcqUAtvn5ubigw8+QPPmzTFlyhTtxWyio6ORlZWFd999F6+99hr++usvbNu2DQ8fPsSyZcu0/U+ePIlx48ahQYMG+PTTT5GcnIzPP/8ctWvX1jvWjBkzsG/fPvTt2xehoaG4d+8etm/fjsuXL2PHjh2QyWQGPcfywAKCiIiIiMpMo0aNsHDhQu3jiIgInf2xsbHIyclBZGQkHB0dXzleRkYGwsLCcP36dWzevBmenp4Gx3Ls2DGcO3cOU6dOxdChQwEAAwcOxPDhwwtsr1Kp0K1bN3z66ac62ydPngyZTIasrCxYWFhg4MCBqFevHhYtWoQHDx7gjTfeAAB88803qFmzJr799lvY2toCAFq1aoURI0bAxcVFO97vv/+O3bt345tvvsFbb72l3e7n54cPP/wQ0dHROtsrGqcwEREREVGZGTBgQJH77ezsAADHjx+HRqMpsm1aWho++OADJCQkYOvWrcUqHgDgv//9L2QyGd577z3tNqlUivfff7/QPgMHDtTb9uJl9TMzM/Hs2TP4+vpCEARcvnwZAJCUlIQrV67g7bff1hYPABAQEIAGDRrojBcdHQ1bW1sEBATg2bNn2j+NGzeGlZUV4uPji/U8yxrPQBARERFRmXF1dS1yf48ePbB7925MmzYNCxcuhL+/Pzp37oxu3brp3UB37ty5UKlU2LdvHxo2bFjsWB48eAAnJydYWlrqbK9bt26B7U1NTQucbvTgwQMsXboUP//8M1JTU3X2paena9sAQL169fT6u7u7awsNALh9+zbS0tLg7+9fYBxPnz4t4lmVPxYQRERERFRmXnXFJQsLC2zfvh3x8fH49ddf8d///hdRUVHYtWsXNmzYABMTE23bjh07IioqCmvXrsWCBQv0CozSZmZmpncMtVqN4cOHIyUlBcOGDUPDhg1hY2ODR48eITw8/JVnUQqi0WhQs2ZNfPPNNwXuN2RqV3liAUFEREREFUoqlcLf3x/+/v74/PPPsXr1aixevBjx8fFo06aNtl2nTp0QGBiI8PBwWFtbY9asWcU6zhtvvIH4+HgolUqdsxB37twxeIxr164hMTER8+bNQ9euXWFhYQETExPExsbqHQvIO7vwslu3buk8rlu3LuLi4tCsWTOd6VFixTUQRERERFRhnj9/rrctf22DSqXS29enTx9MmzYNO3fuxH/+859iHSswMBA5OTn47rvvtNs0Gg22b99u8Bj5ZyQEQdBuEwQBW7Zs0Wnn7OwMT09P7Nu3D2lpadrtsbGxuHHjhk7b7t27Q61WY+XKlXrHy83N1ZsmVdF4BoKIiIiIKsyKFSvw+++/o127dnBxccHTp0/x7bffonbt2mjevHmBfQYPHoz09HQsXrwYtra2CAsLM+hYnTp1go+PD77++mvcuXMHHh4e+Pnnn7X3WpBIJK8cw8PDA3Xr1sV//vMf3L9/Hw4ODvjpp58K/JL/ySefYPTo0Rg0aBDeeecdPH/+HNu2bUPDhg11bkrXqlUr9O/fH2vWrMGVK1cQEBAAmUyGxMREREdH44svvkC3bt0Meo7lgQUEEREREVWY4OBg3L9/H3v27EFycjJq1KiBVq1aYdy4cTpXL3pZWFgY0tLStEVEUVdSymdiYoI1a9bg3//+N/bt2wepVIrOnTtjzJgxGDhwoEF3yJbJZFi9ejVmz56NjRs3wtzcHJ07d8b777+P3r1767QNCgrC0qVLsWTJEixcuBB169bFvHnzcPz4cZw+fVqn7VdffQUvLy/s3LkTixcvhomJCVxcXNCrVy80a9bslXGVJ4nw4vkXKtKff/4JAPD29i73Y+fP1Xt0cAXUKY+K3V/m+DqcenxUBpFRZmYmrly5Ak9PT1hZWVV0OPQP5kW8mBtxYl7Eq7xzk5WVhVu3bsHd3b1SzMcvDceOHcOYMWPw7bffFnrW42VqtVp7H4gXF3qL1avyWpzvuVwDQURERETVRlZWls5jtVqNrVu3wsbGBo0bN66gqCoXTmEiIiIiokpNrVbj2bNnRbaxsrKCtbU1Zs+ejaysLPj6+kKlUuHo0aM4d+4cPvnkk2pzxqWkRFVAnDhxApGRkbhx4wbS09NRq1YtdOrUCWPHjtXOgQsPD8e+ffv0+kZGRiIoKEj7WKVSYfHixThw4AAyMjLg6+uL6dOnw8PDo9yeDxERERGVvb///hsdO3Ysss3YsWMxbtw4tG7dGhs3bsSvv/6K7Oxs1KtXD9OnT8fgwYPLKdrKT1QFxPPnz+Hj44PQ0FA4ODjg+vXriIiIwPXr17FhwwZtuzp16ujdaKN+/fo6j+fMmYOoqCiEh4ejVq1aWL16NYYNG4bDhw8XuSCHiIiIiCoXJycnbNy4scg2derUAQC89dZbeOutt8ojrCpLVAXEyyvX/fz8YGZmhunTp+PRo0eoVasWgLw7FjZt2rTQcR4+fIjvv/8eX375Jd59910AeQtCOnTogJ07d2LkyJFl9hyIiIiIqHyZm5vr3HCOypboF1E7ODgAAHJycgzuc/LkSWg0Gp3r5To4OCAgIAAxMTGlHSIRERERUbUhygJCrVYjOzsbly5dwooVKxAcHAxXV1ft/tu3b6N58+bw8vJC3759cezYMZ3+CQkJqFmzJuzt7XW2169fHwkJCeXyHIiIiIiIqiJRTWHK16FDBzx6lHevg7Zt22LhwoXafZ6envD29kaDBg2QlpaGHTt2YMyYMVi6dKn2jENqamqB6xzs7Oy0dxo0liAIOncOLC8qlQqWlpbQCBpo1Jpi98/vo1QqwVt/lC6lUqnzN4kD8yJezI04MS/iVd65yc7OhkajgVqthlqtLpdjVkb536cEQagUr5NarYZGo4FSqYRGo/9dUhAEg+7EDYi0gFi7di2USiVu3LiBVatWISwsDBs3boSJiQmGDh2q0zY4OBgDBgzAsmXLyuUW3zk5Obhy5UqZH+dllpaWcHBwQHa2CtmZGcXub26Vd83jW7du8YdDGUlMTKzoEKgAzIt4MTfixLyIV3nmxtTUFNnZ2eV2vMqssrxO2dnZyM3NLXI2jpmZmUFjibKAaNSoEQDA19cX3t7e6N27N3766acCCwSpVIouXbrgP//5j/ZugHZ2dkhPT9drm5qaqjetqbhkMhkaNGhQojGMoVKpAADm5mYwtbIudn/ZP9c1dnd35xmIUqZUKpGYmAg3NzdYWlpWdDj0D+ZFvJgbcWJexKu8c5OdnY0HDx7A3Nyc90UogiAIyM7Ohrm5ucG/ua9opqamqFu3LszNzfX23bhxw/BxSjOosqBQKCCTyXDnzh2D+3h4eODJkydISUnRKRgSEhJKfB8IiURSLreRL+i4ACCVSCGYFH/pivSfPvyhUHYsLS0r5L1BRWNexIu5ESfmRbzKKzdSqRRSqRQmJiYwMTEp8+NVVvnTliQSSaV4nUxMTCCVSmFpaVlgYVicIkiUi6hfdOHCBeTk5Ogson6RRqNBdHQ0GjZsqH0xAgMDIZVKcfToUW27lJQUnDx5Uudmc9WJ1MKmxGceeOaCiIiIKpOIiAgoFAq0bdu2wHn/AwYMgEKhQHh4OABg7969UCgUBf6ZMWOGtl9oaCjefPNNNGvWDN7e3vDz88OAAQOwYsUKJCcn6x1HoVBg/fr12sfh4eEICQkpMOZ///vfCA4O1j6Oj4/XxnDz5k299osXL4ZCodDpU9ZEdQZi7Nix8PLygkKhgIWFBf73v/9h/fr1UCgU6NSpE+7fv4/w8HD07NkT9erVQ0pKCnbs2IG//voLERER2nFq166Nd999FwsWLIBUKkWtWrWwZs0a2NraYsCAARX4DCuO1MwCEokEqeeOQZ2h/8Z+FRPrGrDz7VQGkREREVFlodEIkEorZrqOsceWyWRITk7GmTNn4Ofnp91+//59nD9/vsCzOuvWrdO7IE/NmjV1Hjdr1gzjx4+HTCZDWloazp07hy1btmDHjh1Yt26ddkp+abGyskJUVBTGjRuns/3w4cPlftZQVAWEj48PoqKisHbtWgiCABcXF/Tr1w8ffPABzMzMYG1tDRsbG6xatQpPnz6FTCaDl5cXIiMj0bZtW52xpk2bBmtrayxcuBAZGRlo1qwZNm7cWO3vQp3993XkPPu72P1kjq8DLCCIiIiqNalUgl0/XcPj5PK9IqVTDSv07yw3qq9MJoO/vz8OHz6sU0AcPnwYDRs2hFSqPyGncePGcHR0LHJcW1tb+Pj4wMLCAiYmJujQoQMGDBiA9957DxMnTkRUVFSBYxurY8eOOHTokE4BceHCBTx48ADdu3fHuXPnSu1YryKqAmLUqFEYNWpUofsdHBywatUqg8YyMzPDlClTMGXKlNIKj4iIiKjae5yciQdPin9FyIoUEhKCWbNmYfr06ZDJZACAQ4cOISQkBFFRUaV2nDfeeAMfffQRvvrqK5w6dQqBgYGlNnb37t1x+PBhXLp0CY0bNwYAHDx4EP7+/q8sdkqb6NdAEBERERGVRIcOHaBSqRAbGwsg74pDV69eRY8ePQpsr9FokJubq/PH0LWg+UXD+fPnSyX2fM7OzmjZsiUOHTqkjTE6Oho9e/Ys1eMYggUEEREREVVplpaWCA4OxuHDhwHknX3w9fVFnTp1CmwfEBCAxo0b6/w5cOCAQcd6/fXXAQCPHz8uneBfEBISgiNHjkAQBMTHxyM1NRVdunQp9eO8iqimMBERERERlYWQkBB8+umnyMrKQlRUFEJDQwttu2nTJtjY2OhsK6zYeFn+mYqyuDdEly5d8NVXX+GPP/7AoUOH0K5dO704ywMLCCIiIiKq8gIDAyGTybB06VLcu3cP3bt3L7StQqEwel3Bw4cPAQCvvfZaoW1MTEy095F4mUajgalpwV/RHRwcEBgYiH379uHo0aOYM2eOUTGWFKcwEREREVGVJ5PJ0KVLF2zatAmtW7cu8gt+SZw8eRJA3mVeC+Po6IgnT54UuC8pKanI4qVnz57Yu3cvNBoN2rdvX6JYjcUCgoiIiIiqhX79+qFDhw4YMmRImYz/4MEDrFy5Eg0aNEDr1q0LbdeyZUukpqbizJkzOtvT09MRHx+Pli1bFtq3Y8eO6NixI0aPHg1zc/NSi704OIWJiIiIiKoFHx8frFy58pXtLl26pHfvMFtbW9SvX1/7OC0tDRcvXoSZmZn2RnI7d+6ETCbD4sWLi7wHRGBgIFq0aIGxY8dizJgxaNiwIZKSkrBu3TpIpdIi12dYWVlh+fLlBjzbssMCgoiIiIgM5lSjfO96XBHH/PDDD/W2+fv7Y9OmTdrHZ8+exbBhwyCVSmFrawt3d3cMGTIEgwYNQo0aNYocXyqVYs2aNVi2bBk2btyIpKQk2NjYoHXr1oiIiICzs3NpP6VSJREMvagt4c8//wQAeHt7l/uxlUolLC0t8ejgCqhTHhW7v6WbN2oE9sPjqFVG34naqcdHxe5XHWRmZuLKlSvw9PQs91vJU+GYF/FibsSJeRGv8s5NVlYWbt26BXd3d1hYWOjs02gESKWlf3UhQ1TksQuiVquRlZWlvRO12BWVV6B433O5BoKIiIiIDFKRX+DFVDxUdywgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiIjIYCwgiIiIiKhKioiIgK+vb5FtDhw4gAEDBsDX1xe+vr7o378/9u/fr9Pm+++/h0KhQGJios72nTt34s0338SyZct0tj9//hyNGjVCZGRkaTwN0WEBQUREREQGETSaKnXs2bNn47PPPkP9+vWxdOlSLFu2DA0bNkR4eDhmz56tbdesWTMAwLlz53T6nz9/HpaWlnrbz507B0EQ0Lx581KPWQxMKzoAIiIiIqocJFIpkk9+j9zUx+V6XFM7J9QIfLdUxzx+/Di2bduGsWPHYty4cdrtbdu2hbOzM1asWIGAgAAEBwfDw8MDjo6OOHv2LN5++21t2wsXLqBPnz744YcfoFarYWJiAgA4e/YszM3N4eXlVaoxiwULCCIiIiIyWG7qY+Q8+7uiwyixzZs3w97eHiNGjNDb98EHH2Dbtm3YvHkzgoODAeSdhTh79qy2zYMHD/Do0SOEhoZi9+7duHr1Kt58800AeQWEl5cXzMzMyufJlDNRTWE6ceIEBg8ejNatW8PLywsdO3bEvHnzkJaWptPu559/Rq9eveDt7Y2uXbtiz549emOpVCp8/fXXCAgIQNOmTTF8+HAkJCSU11MhIiIiIpHKzc3FuXPn4OfnB2tra7391tbW8PPzw7lz55Cbmwsgr4C4efMmUlJSAORNU6pduzbc3NygUCi0xUVOTg7+/PPPKjt9CRBZAfH8+XP4+Phg1qxZWL9+PYYPH479+/djwoQJ2ja///47xo4di6ZNmyIyMhLdu3fHF198gejoaJ2x5syZg927d2PSpEmIiIiASqXCsGHD9IoRIiIiIqpekpOToVKp8Prrrxfa5vXXX0d2djaeP38OAGjevDkEQcD58+cB5BUQPj4+APKKi/x1EJcvX0Z2dnaVLiBENYWpd+/eOo/9/PxgZmaG6dOn49GjR6hVqxZWrVoFHx8ffPXVVwCA1q1b4+7du1i2bBm6desGAHj48CG+//57fPnll3j33bz5ct7e3ujQoQN27tyJkSNHlu8TIyIiIqJKrXHjxrCwsMDZs2fRrl07nDt3DiEhIQCApk2bYuHChQDypi9JJJJXXv2pMhPVGYiCODg4AMg7HaRSqRAfH68tFPL16NEDN2/exL179wAAJ0+ehEaj0Wnn4OCAgIAAxMTElFvsRERERCQ+NWrUgJmZGf7+u/C1HH///TfMzc2130VlMhm8vb1x9uxZZGRk4Nq1a2jSpAkAwNfXFw8ePMDDhw9x9uxZNGjQAPb29uXxVCqEKAsItVqN7OxsXLp0CStWrEBwcDBcXV1x584d5OTkwMPDQ6d9/fr1AUC7xiEhIQE1a9bUS1z9+vW5DoKIiIiomjM1NYWvry9Onz6NzMxMvf2ZmZk4ffo0fH19YWr6/xN2mjdvjj///BN//PEHzMzMIJfLAQAuLi5wcnLC2bNnce7cOe1lX6sqUU1hytehQwc8evQIQN6ltPJPCeUvWrGzs9Npn/84f39qaipsbW31xrWzs9O2MZYgCAW+0cqaSqWCpaUlNIIGGnXxr4Os+efayRq1kf3/6aNUKiEIQrH7V2VKpVLnbxIH5kW8mBtxYl7Eq7xzk52dDY1GA7VaDbVarbPPxMQEggCU91eB/OO9HM+raDQaCIJQYL/Q0FCMHTsW69evx8cff6yzb/369Xj+/DlCQ0N1+jZt2hSrV6/Gtm3b4OXlBVNTU+34vr6+2L9/Px4/fgxfX99ix1rW1Go1NBoNlEql9nvhiwRBgEQiMWgsURYQa9euhVKpxI0bN7Bq1SqEhYVh48aNFR0WgLypVFeuXCn341paWsLBwQHZ2SpkZ2YUu780WwUAyFapoDSiv7lVFgDg1q1b/OFSiJfvTkniwLyIF3MjTsyLeJVnbkxNTZGdna2zTSqV5v0yU6Mp8AtoWco/nkqlKtaxc3NzodFocOjQIb19jRs3Rv/+/bFixQrcv38fnTp1ApB3f4h9+/ahf//+8Pf3R1ZWlraPp6cnpFIpYmJiMGzYMADQvk6NGzfGkiVLtP9+sZ8YZGdnIzc3t8jZOIZedlaUBUSjRo0A5M0n8/b2Ru/evfHTTz+hQYMGAKB3JaXU1FQA0E5ZsrOzQ3p6ut64qampJZ6PJpPJtHGUJ5UqrwAwNzeDqZX+5cZexdw87w1hbmYGqRH9ZRYWAAB3d3eegXiJUqlEYmIi3NzcYGlpWdHh0D+YF/FibsSJeRGv8s5NdnY2Hjx4AHNzc1j88/P/RWY1akEqLd9Z8Kb2TnnHLuZ9FfILoc8++0xv3/z58/Hll1+iWbNm2LFjByZPngwAkMvlmDdvHnr16qXXx8LCAg0aNMC1a9fQokULAIC5uTkkEglatmwJQRDg7OysnV4vNqampqhbty7Mzc319t24ccPwcUozqLKgUCggk8lw584dBAcHQyaTISEhAW3bttW2ya+k8tdGeHh44MmTJ0hJSdEpGBISEvTWTxSXRCKBlZVVicYw9rgAIJVIIZgU/0Ob/0GXmkghNab/P334Q6VwlpaWFfLeoKIxL+LF3IgT8yJe5ZUbqVQKqVQKExMT7Z2V8wkaDRxL+Y7QhhI0Gr14XmXChAk6twMoSJ8+fdCnTx+Dxzx48CCAvClBWVlZkEgkMDExQfPmzXH16tVixVeeTExMtGeRCioMDZ2+BIh0EfWLLly4gJycHLi6usLMzAx+fn748ccfddpERUWhfv36cHV1BQAEBgZCKpXi6NGj2jYpKSk4efIkgoKCyjV+IiIioqpCUs5nHsRybNIlqjMQY8eOhZeXFxQKBSwsLPC///0P69evh0Kh0M5L++ijjzBkyBDMnDkT3bt3R3x8PA4dOoTFixdrx6lduzbeffddLFiwAFKpFLVq1cKaNWtga2uLAQMGVNTTIyIiIiKq9ERVQPj4+CAqKgpr166FIAhwcXFBv3798MEHH2jnvLVo0QIRERFYsmQJvv/+e7zxxhuYM2cOunfvrjPWtGnTYG1tjYULFyIjIwPNmjXDxo0bC7w6ExERERERGUZUBcSoUaMwatSoV7br2LEjOnbsWGQbMzMzTJkyBVOmTCmt8IiIiIiIqj1OJiMiIiIiIoOxgCAiIiIiPbxse9VSmvlkAUFEREREWjKZDACQmZlZwZFQacrPZ35+S0JUayCIiIiIqGKZmJjAwcEBSUlJAAArK6ti3SOgulCr1dq7UBf3/hTlSRAEZGZmIikpCQ4ODqUSKwsIIiIiItJRu3ZtANAWEaRPo9EgNzcXpqam5X5nbmM4ODho81pSLCCIiIiISIdEIsHrr78OZ2dn5OTkVHQ4oqRUKpGQkIC6devC0tKyosMpkkwmK9WzJCwgiIiIiKhAJiYmop6eU5E0Gg0AwNzcHBYWFhUcTfkS//kWIiIiIiISDRYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMNOKDuBFR44cwYEDB3Dp0iWkpqaiXr16CA0NxTvvvAOJRAIACA0NxenTp/X6RkVFoX79+trHaWlpmDdvHo4dO4acnBy0bdsW06ZNg7Ozc7k9HyIiIiKiqkZUBcSmTZvg4uKC8PBw1KhRA6dOncL06dPx8OFDjB07VtuuWbNmmDJlik5fV1dXnccTJ07EjRs3MHPmTJibm2PJkiUYOXIk9uzZA1NTUT1tIiIiIqJKQ1TfpFetWgVHR0ftY39/fzx//hwbN27Exx9/DKk0b8aVnZ0dmjZtWug4586dw8mTJ7F+/XoEBgYCANzd3dGjRw8cPXoUPXr0KNPnQURERERUVYlqDcSLxUM+T09PpKenIzMz0+BxYmJiYGdnh4CAAO02Dw8PeHp6IiYmplRiJSIiIiKqjkRVQBTkjz/+QK1atWBjY6Pddvr0aTRt2hTe3t4YPHgwzpw5o9MnISEB7u7u2nUT+Tw8PJCQkFAucRMRERERVUWimsL0st9//x1RUVE66x1atmyJ3r17w83NDUlJSVi/fj2GDx+OrVu3wtfXFwCQmpoKW1tbvfHs7e3x119/lSgmQRCKdTaktKhUKlhaWkIjaKBRa4rdX6PJ66NRG9n/nz5KpRKCIBS7f1WmVCp1/iZxYF7Ei7kRJ+ZFvJgbcapqeREEQe+X74URbQHx8OFDTJo0CX5+fhgyZIh2+/jx43XatW/fHiEhIVi5ciUiIyPLPK6cnBxcuXKlzI/zMktLSzg4OCA7W4XszIxi95dmqwAA2SoVlEb0N7fKAgDcunWrynxQSltiYmJFh0AFYF7Ei7kRJ+ZFvJgbcapKeTEzMzOonSgLiNTUVIwcORIODg6IiIjQLp4uiJWVFdq1a4cff/xRu83Ozg4PHz7Ua5uSkgJ7e/sSxSaTydCgQYMSjWEMlSqvADA3N4OplXWx+5ub570hzM3MIDWiv8zCAkDeYnSegdClVCqRmJgINzc3WFpaVnQ49A/mRbyYG3FiXsSLuRGnqpaXGzduGNxWdAVEVlYWRo8ejbS0NOzatavAqUiv4uHhgbi4OL1TMbdu3YJcLi9RfBKJBFZWViUaw9jjAoBUIoVgUvylK/lFmNRECqkx/f/pUxU+IGXF0tKyQt4bVDTmRbyYG3FiXsSLuRGnqpIXQ6cvASJbRJ2bm4uJEyciISEB69atQ61atV7ZJzMzE7/++iu8vb2124KCgpCSkoK4uDjttlu3buHy5csICgoqk9iJiIiIiKoDUZ2BmDVrFn755ReEh4cjPT0d58+f1+578803cfHiRaxbtw6dO3eGi4sLkpKSsHHjRjx+/BhLly7VtvX19UVgYCCmTp2KKVOmwNzcHIsXL4ZCoUCXLl0q4JkREREREVUNoiogYmNjAQDz58/X23f8+HE4OTkhJycHixcvxvPnz2FpaQlfX1/MmjULPj4+Ou2XLFmCefPmYcaMGcjNzUVgYCCmTZvGu1ATEREREZWAqL5N//zzz69ss379eoPGsrW1xdy5czF37tyShkVERERERP8Q1RoIIiIiIiISNxYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMBYQRERERERkMKMLiCFDhiAuLq7Q/b/99huGDBli7PBERERERCRCRhcQp0+fxpMnTwrd/+zZM5w5c8bY4YmIiIiISIRKNIVJIpEUuu/27duwtrYuyfBERERERCQypsVpvG/fPuzbt0/7eNWqVfjuu+/02qWlpeHq1asICgoqeYRERERERCQaxSoglEolkpOTtY8zMjIgleqfxLCyssKAAQMwZsyYkkdIRERERESiUawCYtCgQRg0aBAAIDg4GF988QU6duxYJoEREREREZH4FKuAeNHPP/9cmnEQEREREVElYHQBkS89PR0PHjxAamoqBEHQ29+yZcuSHoKIiIiIiETC6ALi2bNnmDNnDo4ePQq1Wq23XxAESCQSXLlypUQBEhERERGReBhdQMyYMQO//PILQkND0aJFC9jZ2ZVmXEREREREJEJGFxCxsbEYOnQoPvvss9KMh4iIiIiIRMzoG8lZWFjAxcWlNGMhIiIiIiKRM7qA6NWrF44dO1aasRARERERkcgZPYWpa9euOHPmDD744AP0798ftWvXhomJiV67xo0blyhAIiIiIiISD6MLiPwbygHAqVOn9PbzKkxERERERFWP0QXEvHnzSjMOIiIiIiKqBIwuIN5+++3SjAMAcOTIERw4cACXLl1Camoq6tWrh9DQULzzzjuQSCTadrt378a6devw4MEDuLu7Y9KkSejQoYPOWGlpaZg3bx6OHTuGnJwctG3bFtOmTYOzs3Opx01EREREVF0YvYi6LGzatAmWlpYIDw/HqlWrEBQUhOnTp2PFihXaNocPH8b06dPRvXt3REZGomnTphg7dizOnz+vM9bEiRMRGxuLmTNn4ptvvsGtW7cwcuRI5ObmlvOzIiIiIiKqOow+A/H555+/so1EIsHcuXMNHnPVqlVwdHTUPvb398fz58+xceNGfPzxx5BKpVi2bBl69uyJiRMnAgBat26Na9euYcWKFYiMjAQAnDt3DidPnsT69esRGBgIAHB3d0ePHj1w9OhR9OjRoxjPlIiIiIiI8hldQMTHx+tt02g0ePz4MdRqNRwdHWFpaVmsMV8sHvJ5enriu+++Q2ZmJpKTk5GYmIh//etfOm169OiBBQsWQKVSwczMDDExMbCzs0NAQIC2jYeHBzw9PRETE8MCgoiIiIjISEYXED///HOB23NycrBr1y5s3rwZGzZsMDqwfH/88Qdq1aoFGxsb/PHHHwDyzia8qH79+sjJycHdu3dRv359JCQkwN3dXWfdBJBXRCQkJJQ4JiIiIiKi6sroAqIwMpkMgwcPxo0bNzB79mysXbvW6LF+//13REVFYcqUKQCAlJQUAICdnZ1Ou/zH+ftTU1Nha2urN569vT3++usvo+MB8i5Pm5mZWaIxjKFSqWBpaQmNoIFGrSl2f40mr49GbWT/f/oolUoIglDs/lWZUqnU+ZvEgXkRL+ZGnJgX8WJuxKmq5SX/FgyGKPUCIl+jRo3www8/GN3/4cOHmDRpEvz8/DBkyJBSjKxkcnJyKuTeFpaWlnBwcEB2tgrZmRnF7i/NVgEAslUqKI3ob26VBQC4detWlfmglLbExMSKDoEKwLyIF3MjTsyLeDE34lSV8mJmZmZQuzIrIE6dOlXsNRD5UlNTMXLkSDg4OCAiIgJSad7Fouzt7QHkXaLVyclJp/2L++3s7PDw4UO9cVNSUrRtjCWTydCgQYMSjWEMlSqvADA3N4OplXWx+5ub570hzM3MIDWiv8zCAkDe9DGegdClVCqRmJgINzc3o9/zVPqYF/FibsSJeREv5kacqlpebty4YXBbowuI5cuXF7g9LS0NZ86cweXLlzFq1Khij5uVlYXRo0cjLS0Nu3bt0pmK5OHhAQBISEjQ/jv/sUwmQ506dbTt4uLi9E7F3Lp1C3K5vNgxvUgikcDKyqpEYxh7XACQSqQQTIp/9d38IkxqIoXUmP7/9KkKH5CyYmlpWSHvDSoa8yJezI04MS/ixdyIU1XJi6HTl4AyKCDs7e1Rp04dzJo1C++9916xxszNzcXEiRORkJCA7du3o1atWjr769SpAzc3N0RHR6NTp07a7VFRUfD399eedgkKCsLKlSsRFxeHNm3aAMgrHi5fvowPP/ywWDEREREREdH/M7qA+N///leacQAAZs2ahV9++QXh4eFIT0/XuTncm2++CTMzM4wbNw6TJ09G3bp14efnh6ioKFy8eBHbtm3TtvX19UVgYCCmTp2KKVOmwNzcHIsXL4ZCoUCXLl1KPW4iIiIiouqizNZAGCM2NhYAMH/+fL19x48fh6urK0JCQqBUKhEZGYm1a9fC3d0dy5cvh6+vr077JUuWYN68eZgxYwZyc3MRGBiIadOmwdRUVE+ZiIiIiKhSKfG36dOnT+PXX3/FgwcPAABvvPEG2rdvj1atWhV7rMLuLfGyfv36oV+/fkW2sbW1xdy5c4t1J2wiIiIiIiqa0QWESqXCp59+imPHjkEQBO29GFJTU7Fx40Z07twZCxcuhEwmK7VgiYiIiIioYhX/cjz/WLFiBX766ScMHz4cJ0+exOnTp3H69GnExsZixIgROHr0KFasWFGasRIRERERUQUzuoA4ePAg3n77bXz22Wd47bXXtNtr1qyJf/3rX+jTpw8OHDhQKkESEREREZE4GF1APH78GD4+PoXu9/HxwePHj40dnoiIiIiIRMjoAqJ27do4ffp0ofvPnDmD2rVrGzs8ERERERGJkNEFRJ8+fXDkyBHMmDEDCQkJUKvV0Gg0SEhIwJdffono6Gi8/fbbpRkrERERERFVMKOvwhQWFoa7d+/iu+++w+7duyGV5tUiGo0GgiDg7bffRlhYWKkFSkREREREFc/oAsLExATz58/HsGHDEBMTg/v37wMAXFxcEBQUhEaNGpVakEREREREJA7FKiCys7Px73//Gw0bNkRoaCgAoFGjRnrFwpYtW7Bz50588cUXvA8EEREREVEVUqw1ELt27cK+ffvQvn37Itu1b98ee/bswe7du0sSGxERERERiUyxCogjR46gS5cuqFOnTpHt6tati27duuHw4cMlCo6IiIiIiMSlWAXEtWvX0Lx5c4Pa+vr64urVq0YFRURERERE4lSsAiInJ8fgNQ0ymQwqlcqooIiIiIiISJyKVUA4Ozvj+vXrBrW9fv06nJ2djQqKiIiIiIjEqVgFRJs2bfDDDz/g6dOnRbZ7+vQpfvjhB7Rp06ZEwRERERERkbgUq4AYOXIksrOzMXToUFy4cKHANhcuXMCwYcOQnZ2NDz/8sFSCJCIiIiIicSjWfSDq1KmDJUuW4JNPPsGAAQNQp04dyOVyWFtbIyMjA9evX8edO3dgYWGBRYsWoW7dumUVNxERERERVYBi34m6ffv2OHDgACIjI/Hrr7/i2LFj2n3Ozs7o168fRo4c+cpLvRIRERERUeVT7AICAFxdXTFr1iwAQHp6OjIyMmBtbQ0bG5tSDY6IiIiIiMTFqALiRTY2NiwciIiIiIiqiWItoiYiIiIiouqNBQQRERERERmMBQQRERERERmMBQQRERERERmsxIuoS9Pt27exfv16XLhwAdevX4eHhwcOHTqk0yY0NBSnT5/W6xsVFYX69etrH6elpWHevHk4duwYcnJy0LZtW0ybNg3Ozs5l/jyIiIiIiKoqURUQ169fx4kTJ9CkSRNoNBoIglBgu2bNmmHKlCk621xdXXUeT5w4ETdu3MDMmTNhbm6OJUuWYOTIkdizZw9MTUX1tImIiIiIKg1RfZMODg5Gp06dAADh4eH466+/CmxnZ2eHpk2bFjrOuXPncPLkSaxfvx6BgYEAAHd3d/To0QNHjx5Fjx49Sj12IiIiIqLqQFRrIKTS0gknJiYGdnZ2CAgI0G7z8PCAp6cnYmJiSuUYRERERETVkagKCEOdPn0aTZs2hbe3NwYPHowzZ87o7E9ISIC7uzskEonOdg8PDyQkJJRnqEREREREVYqopjAZomXLlujduzfc3NyQlJSE9evXY/jw4di6dSt8fX0BAKmpqbC1tdXra29vX+i0KEMJgoDMzMwSjWEMlUoFS0tLaAQNNGpNsftrNHl9NGoj+//TR6lUFro2pbpSKpU6f5M4MC/ixdyIE/MiXsyNOFW1vAiCoPfL98JUugJi/PjxOo/bt2+PkJAQrFy5EpGRkWV+/JycHFy5cqXMj/MyS0tLODg4IDtbhezMjGL3l2arAADZKhWURvQ3t8oCANy6davKfFBKW2JiYkWHQAVgXsSLuREn5kW8mBtxqkp5MTMzM6hdpSsgXmZlZYV27drhxx9/1G6zs7PDw4cP9dqmpKTA3t6+RMeTyWRo0KBBicYwhkqVVwCYm5vB1Mq62P3NzfPeEOZmZpAa0V9mYQEgbzE6z0DoUiqVSExMhJubGywtLSs6HPoH8yJezI04MS/ixdyIU1XLy40bNwxuW+kLiIJ4eHggLi5O71TMrVu3IJfLSzS2RCKBlZVVSUM06rgAIJVIIZgUf+lK/gJ1qYkUUmP6/9OnKnxAyoqlpWWFvDeoaMyLeDE34sS8iBdzI05VJS+GTl8CKuki6hdlZmbi119/hbe3t3ZbUFAQUlJSEBcXp91269YtXL58GUFBQRURJhERERFRlSCqMxBKpRInTpwAANy/fx/p6emIjo4GALRq1QoJCQlYt24dOnfuDBcXFyQlJWHjxo14/Pgxli5dqh3H19cXgYGBmDp1KqZMmQJzc3MsXrwYCoUCXbp0qZDnRkRERERUFYiqgHj69CkmTJigsy3/8ZYtW1C7dm3k5ORg8eLFeP78OSwtLeHr64tZs2bBx8dHp9+SJUswb948zJgxA7m5uQgMDMS0adN4F2oiIiIiohIQ1bdpV1dXXL16tcg269evN2gsW1tbzJ07F3Pnzi2N0IiIiIiICFVgDQQREREREZUfFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwURUQt2/fxowZM9C7d2+8+eabCAkJKbDd7t270bVrV3h7e6NXr1745Zdf9NqkpaVh6tSpaNWqFXx9fTF+/HgkJSWV9VOosqQWNhAEoURjlLQ/EREREVU804oO4EXXr1/HiRMn0KRJE2g0mgK/cB4+fBjTp09HWFgYWrdujaioKIwdOxbbt29H06ZNte0mTpyIGzduYObMmTA3N8eSJUswcuRI7NmzB6amonralYLUzAISiQSp545BnZFc7P4m1jVg59upDCIjIiIiovIkqm/SwcHB6NQp70tmeHg4/vrrL702y5YtQ8+ePTFx4kQAQOvWrXHt2jWsWLECkZGRAIBz587h5MmTWL9+PQIDAwEA7u7u6NGjB44ePYoePXqUzxOqgrL/vo6cZ38Xu5/M8XWABQQRERFRpSeqKUxSadHh3L17F4mJiejevbvO9h49eiAuLg4qlQoAEBMTAzs7OwQEBGjbeHh4wNPTEzExMaUfOBERERFRNSGqAuJVEhISAOSdTXhR/fr1kZOTg7t372rbubu7QyKR6LTz8PDQjkFERERERMUnqilMr5KSkgIAsLOz09me/zh/f2pqKmxtbfX629vbFzgtqjgEQUBmZmaJxjCGSqWCpaUlNIIGGrWm2P01mrw+GnUF9f+nj1KprHKLqZVKpc7fJA7Mi3gxN+LEvIgXcyNOVS0vgiDo/fK9MJWqgBCDnJwcXLlypdyPa2lpCQcHB2Rnq5CdmVHs/tLsvOld2SoVlBXQ39wqCwBw69atKvNBe1liYmJFh0AFYF7Ei7kRJ+ZFvJgbcapKeTEzMzOoXaUqIOzt7QHkXaLVyclJuz01NVVnv52dHR4+fKjXPyUlRdvGWDKZDA0aNCjRGMbIX99hbm4GUyvrYvc3N897Q5ibmUFaAf1lFhYA8qafVcUzEImJiXBzc4OlpWVFh0P/YF7Ei7kRJ+ZFvJgbcapqeblx44bBbStVAeHh4QEgb41D/r/zH8tkMtSpU0fbLi4uTu9UzK1btyCXy0sUg0QigZWVVYnGMPa4ACCVSCGYFH/pSv4CdamJFNKK6P9Pn6rwASuMpaVlhbw3qGjMi3gxN+LEvIgXcyNOVSUvhk5fAirZIuo6derAzc0N0dHROtujoqLg7++vPe0SFBSElJQUxMXFadvcunULly9fRlBQULnGTERERERUlYjqDIRSqcSJEycAAPfv30d6erq2WGjVqhUcHR0xbtw4TJ48GXXr1oWfnx+ioqJw8eJFbNu2TTuOr68vAgMDMXXqVEyZMgXm5uZYvHgxFAoFunTpUiHPjYiIiIioKhBVAfH06VNMmDBBZ1v+4y1btsDPzw8hISFQKpWIjIzE2rVr4e7ujuXLl8PX11en35IlSzBv3jzMmDEDubm5CAwMxLRp03gXaiIiIiKiEhDVt2lXV1dcvXr1le369euHfv36FdnG1tYWc+fOxdy5c0srPCIiIiKiaq9SrYEgIiIiIqKKxQKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCyoXUwgaCIJRojJL2JyIiIqKSE9WN5KjqkppZQCKRIPXcMagzkovd38S6Bux8O5VBZERERERUHCwgqFxl/30dOc/+LnY/mePrAAsIIiIiogrHKUxERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGSwSldA7N27FwqFQu/PN998o9Nu9+7d6Nq1K7y9vdGrVy/88ssvFRQxEREREVHVYVrRARhr3bp1sLW11T6uVauW9t+HDx/G9OnTERYWhtatWyMqKgpjx47F9u3b0bRp0wqIloiIiIioaqi0BUTjxo3h6OhY4L5ly5ahZ8+emDhxIgCgdevWuHbtGlasWIHIyMhyjJKIiIiIqGqpdFOYXuXu3btITExE9+7ddbb36NEDcXFxUKlUFRQZEREREVHlV2kLiJCQEHh6eqJjx45Ys2YN1Go1ACAhIQEA4O7urtO+fv36yMnJwd27d8s9ViIiIiKiqqLSTWFycnLCuHHj0KRJE0gkEvz8889YsmQJHj16hBkzZiAlJQUAYGdnp9Mv/3H+fmMJgoDMzMwSjWEMlUoFS0tLaAQNNGpNsftrNHl9NOpK2v+fPkqlEoIgFLt/WVIqlTp/kzgwL+LF3IgT8yJezI04VbW8CIIAiURiUNtKV0C0bdsWbdu21T4ODAyEubk5Nm/ejLCwsDI/fk5ODq5cuVLmx3mZpaUlHBwckJ2tQnZmRrH7S7Pzpm5lq1RQVsL+5lZZAIBbt26J9oOamJhY0SFQAZgX8WJuxIl5ES/mRpyqUl7MzMwMalfpCoiCdO/eHRs2bMCVK1dgb28PAEhLS4OTk5O2TWpqKgBo9xtLJpOhQYMGJRrDGPlrN8zNzWBqZV3s/ubmeW8IczMzSCthf5mFBYC8qWliPAORmJgINzc3WFpaVnQ49A/mRbyYG3FiXsSLuRGnqpaXGzduGNy2ShQQL/Lw8ACQtxYi/9/5j2UyGerUqVOi8SUSCaysrEo0hrHHBQCpRArBpPhLV6TSvD5SEymklbH/P33E/AG1tLSskPcGFY15ES/mRpyYF/FibsSpquTF0OlLQCVeRP2iqKgomJiY4M0330SdOnXg5uaG6OhovTb+/v4Gn5ohIiIiIiJ9le4MxAcffAA/Pz8oFAoAwPHjx/Hdd99hyJAh2ilL48aNw+TJk1G3bl34+fkhKioKFy9exLZt2yoydCIiIiKiSq/SFRDu7u7Ys2cPHj58CI1GAzc3N0ydOhWhoaHaNiEhIVAqlYiMjMTatWvh7u6O5cuXw9fXtwIjJyIiIiKq/CpdATFt2jSD2vXr1w/9+vUr42iIiIiIiKqXKrEGgoiIiIiIygcLCCIiIiIiMhgLCKoUpBY2Jb7/g9juH0FERERUGVW6NRBUPUnNLCCRSJB67hjUGcnF7m9iXQN2vp3KIDIiIiKi6oUFBFUq2X9fR86zv4vdT+b4OsACgoiIiKjEOIWJiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCqgWphQ0EQSjRGCXtT0RERFQVmFZ0AETlQWpmAYlEgtRzx6DOSC52fxPrGrDz7VQGkRERERFVLiwgqFrJ/vs6cp79Xex+MsfXARYQRERERJzCREREREREhmMBQUREREREBmMBQUREREREBmMBQVRCUqkUNWrUgFTKjxMRERFVfVxETWSA/MvASiQSvX0WFhbw8PB45RiF9SciIiKqTKp0AXHz5k3MmTMH586dg7W1NXr37o2JEyfCzMysokOjSqaoy8BqNBpkZ6tgbm5W6FkIXgaWiIiIqooqW0CkpKRg6NChcHNzQ0REBB49eoT58+cjKysLM2bMqOjwqJIq6DKwGrUGGZkZ0FhZQ2pScAHBy8ASERFRVVFlC4idO3ciIyMDy5cvh4ODAwBArVZj1qxZGD16NGrVqlWxARIRERERVUJVdtVnTEwM/P39tcUDAHTv3h0ajQaxsbEVFxiREQRBqND+RERERPmq7BmIhIQEvPPOOzrb7Ozs4OTkhISEhAqKisg4ha2/MIRpjdqweTOwRMevjAvAeXUsIiKisiERquivJhs3bowJEyZg1KhROttDQkLg6+uL2bNnF3vMs2fPQhAEyGSy0grTYIIgQCqVQpOVAUGjLnZ/iakMUjNL9i+j/q/6gi0xMYXU3KrYx31RSWMXcrKNOhMhkUggkZmX6CyGGIqPKvpfXaWlVqthYmJS0WHQS5gX8WJuxOnlvJTGz7uK+nmVk5MDiUSCZs2avbJtlT0DURby3xQV8WUo/5hSC+sSjcP+Fdu/Io8tkZmjJO9cMRQBJVHZ469qTE3540eMmBfxYm7EqSzyUlE/ryQSicHHrrLvRjs7O6SlpeltT0lJgb29vVFj+vr6ljQsIiIiIqJKrcpODvbw8NBb65CWlobHjx8bdNMvIiIiIiLSV2ULiKCgIJw6dQqpqanabdHR0ZBKpQgICKjAyIiIiIiIKq8qu4g6JSUFPXv2hLu7O0aPHq29kdxbb73FG8kRERERERmpyhYQAHDz5k3Mnj0b586dg7W1NXr37o1JkybBzMysokMjIiIiIqqUqnQBQUREREREpavKroEgIiIiIqLSxwKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgKCiIiIiIgMxgJCBG7evInhw4ejadOmCAgIwIIFC6BSqV7ZTxAErF27Fu3bt4ePjw/69++P8+fPl33A1Yixudm+fTtGjx6N1q1bQ6FQIDo6uhyirT6MyUtSUhIWLFiA3r17w9fXF0FBQfj0009x//79coq6ejD2MzN58mR06dIFTZs2RcuWLfH+++/j5MmT5RBx9WBsXl60adMmKBQKjB49uoyirJ6MzU1wcDAUCoXen+zs7HKIuuoryWfm0aNHmDJlClq3bg0fHx90794dBw4cKOOIy5dpRQdQ3aWkpGDo0KFwc3NDRESE9o7ZWVlZr7xjdmRkJJYtW4bJkydDoVBg+/btGDFiBH744QfUqVOnnJ5B1VWS3Pzwww8AgHbt2mH//v3lEG31YWxeLl26hJ9++gnvvPMOmjRpguTkZKxatQr9+vXDoUOH4OjoWI7PomoqyWcmJycHw4YNg5ubG7Kzs/H9999j1KhR2LJlC1q0aFFOz6BqKkle8j1+/BgrVqxAzZo1yzja6qWkuenatStGjBihs403yy25kuQlKSkJ/fv3h7u7O2bPng0bGxtcv3692AW76AlUoVavXi00bdpUSE5O1m7buXOn4OnpKTx8+LDQfllZWUKzZs2EhQsXardlZ2cLHTp0EL788ssyjLj6MDY3giAIarVaEARBuHv3riCXy4UjR46UZajVirF5SUlJEXJycnS2/f3334JCoRDWr19fVuFWKyX5zLwsNzdXaNeunTBt2rRSjrL6KY28/Otf/xI+++wzYfDgwcKoUaPKKNLqpyS56dChgzBr1qwyjrB6KkleJk+eLPTv31/Izc0t4ygrFqcwVbCYmBj4+/vDwcFBu6179+7QaDSIjY0ttN/Zs2eRnp6O7t27a7eZmZmhc+fOiImJKcuQqw1jcwMAUik/WmXF2LzY2dnB1FT3pGvt2rXh6OiIpKSksgq3WinJZ+ZlJiYmsLW1RU5OTilHWf2UNC+///47jh07hk8//bQMo6yeSvMzQ6XH2Lykp6fjyJEjGDRoEExMTMoh0orDbzkVLCEhAR4eHjrb7Ozs4OTkhISEhCL7AdDrW79+fTx48ABZWVmlH2w1Y2xuqGyVZl5u3bqFp0+fon79+qUZYrVV0twIgoDc3FwkJydj/fr1uH37Nvr3719W4VYbJcmLWq3G7NmzERYWBmdn57IMs1oq6Wfm4MGD8PLygq+vL0aOHImrV6+WVajVirF5uXTpEnJycmBqaorBgwejcePGCAgIwH/+858q98sQroGoYKmpqbCzs9Pbbm9vj5SUlCL7mZmZwdzcXGe7nZ0dBEFASkoKLCwsSj3e6sTY3FDZKq28CIKAOXPmwNnZGT179izNEKutkubm+++/x7Rp0wAAVlZWWLx4MXx9fUs9zuqmJHn59ttvoVQqMWzYsDKKrnorSW6Cg4Ph4+ODN954A3fv3sXq1asxaNAg7N+/n+sgS8jYvDx58gQAMG3aNLz33nsYO3YsLl68iGXLlkEqlVaps3gsIIioWoqIiMBvv/2GdevWwcrKqqLDIQAdO3ZEo0aNkJycjOjoaEycOBHLly9Hu3btKjq0aunp06dYtmwZvv76ay7MFaH8YhsAWrRogYCAAHTv3h3r16/HzJkzKy6wakyj0QAA2rRpg/DwcABA69atkZGRgQ0bNmDMmDFV5pe7nMJUwezs7JCWlqa3PSUlBfb29kX2U6lUepdrS01NhUQiKbIvGcbY3FDZKo28fPfdd1ixYgVmzZoFf3//0g6x2ippbhwdHeHt7Y2goCDMnTsXQUFB+M9//lMWoVYrxuZl6dKlUCgUaNGiBVJTU5Gamorc3Fzk5uZq/00lU5o/Z5ydndG8eXNcunSptMKrtkry3QzIKxpe5O/vD5VKhdu3b5duoBWIZyAqmIeHh958urS0NDx+/Fhv/t3L/YC8OdyNGjXSbk9ISMAbb7xRZSrcimRsbqhslTQvP/30E2bOnInx48fj3XffLaswq6XS/sw0btyYF4UoBcbm5datWzhz5gxatmypt69ly5aIjIxEUFBQqcdbnfDnjDgZm5cGDRoUOW5VukcHz0BUsKCgIJw6dQqpqanabdHR0ZBKpQgICCi0X7NmzWBjY4MjR45ot+Xk5ODo0aP8D72UGJsbKlslyUt8fDw++eQT9OvXD2PGjCnrUKud0v7M/PHHH5zLXQqMzcvUqVOxZcsWnT+NGjVC06ZNsWXLFvj4+JRH+FVaaX5mHj16hD/++APe3t6lHWa1Y2xeXFxcIJfLcerUKZ3tp06dgoWFxSsLjMqEZyAq2IABA7B161aMGTMGo0ePxqNHj7BgwQIMGDAAtWrV0rYbOnQoHjx4gJ9++gkAYG5ujtGjRyMiIgKOjo6Qy+XYsWMHnj9/jg8++KCink6VYmxuAODPP//E/fv38ezZMwDAhQsXAORN0WjVqlX5PpEqxti83Lx5E2PGjIGbmxt69+6tc9d2R0dH1K1bt7yfSpVjbG5+/fVX7N+/H+3bt8frr7+OlJQUHDp0CCdPnsSiRYsq6ulUGcbmxdPTU28sOzs7WFlZwc/Pr9zir8qMzc2hQ4fwyy+/oF27dnB2dsbdu3exdu1amJiYYPjw4RX1dKqMkvz8nzRpEj7++GP8+9//Rvv27fHnn39iw4YN+OCDD6rUejsWEBXM3t4emzdvxuzZszFmzBhYW1vj3XffxaRJk3TaaTQaqNVqnW0jR46EIAjYsGEDnj17Bk9PT6xfv56/sSslJcnN9u3bsW/fPu3jDRs2AABatWqFrVu3ln3wVZixeblw4QLS0tKQlpaGgQMH6rR9++23MX/+/HKJvyozNjd16tSBSqXCwoULkZycjBo1akChUGDr1q0suEtBSf4vo7JlbG5cXV2RlJSEuXPnIi0tDba2tmjdujXGjx/P7wCloCSfmeDgYCxatAgrV67Ejh074OzsjHHjxmHUqFHl+RTKnEQQBKGigyAiIiIiosqBayCIiIiIiMhgLCCIiIiIiMhgLCCIiIiIiMhgLCCIiIiIiMhgLCCIiIiIiMhgLCCIiIiIiMhgLCCIiIiIiMhgLCCIiIiIiMhgLCCIiKqoxMREjBgxAs2bN4dCocCxY8cqOqRyExwcjNGjR5faePHx8VAoFIiOjn5l2/DwcAQHB+tsUygUiIiI0D7eu3cvFAoF7t27V2oxEhGVF9OKDoCIqLrau3cvPv/8c+1jMzMz2NvbQ6FQoF27dujbty9sbGyMHj88PBz37t3DpEmTYGtrCy8vr9IIW0upVGLdunVo1aoV/Pz8Xtk+Pj4eQ4YM0T42NTVF7dq10bx5c4wbNw516tQp1fgqm+3bt8PS0hJ9+/at6FCIiIrEAoKIqIKNHz8erq6uyM3NxZMnT3D69GnMnTsXmzZtwsqVK9GoUaNij5mVlYVz584hLCwMgwcPLoOo8wqI5cuXY+zYsQYVEPlCQ0Ph7e2N3NxcXL58Gbt27cKJEydw4MAB1KpVq0xiLU+zZ8+GIAhFtunduzd69uwJMzMz7bYdO3agRo0aLCCISPRYQBARVbCgoCB4e3trH48ePRpxcXEICwvDxx9/jKioKFhYWBRrzGfPngEA7OzsSjXW0tCiRQt069YNAPDOO+/Azc0Nc+bMwf79+wuddpSZmQkrK6vyDNNoMpnslW1MTExgYmJSDtEQEZU+roEgIhIhf39/fPzxx7h//z4OHDigs+/mzZsYP348WrVqBW9vb/Tt2xfHjx/X7o+IiECHDh0AAAsWLIBCodCZk//o0SN8/vnnaNOmDby8vNCzZ098//33ejFkZ2cjIiICXbt2hbe3NwIDAzF27FjcuXMH9+7dg7+/PwBg+fLlUCgUevP8DdW6dWsA0K4HiIiIgEKhwI0bN/Dpp5+iZcuWGDRoEAAgNzcXK1asQKdOneDl5YXg4GAsWrQIKpWqwLFPnjyJ3r17w9vbGz169MDRo0d19j9//hxff/013nrrLfj6+qJZs2b48MMP8b///a/A8TQaDRYtWoSAgAA0bdoUYWFh+Pvvv3XaFLQG4mUvr4EIDg7G9evXcfr0ae1rGRoairt370KhUGDTpk16Y5w9exYKhQKHDh0q8lhERKWNZyCIiESqd+/eWLRoEU6ePIn33nsPAHD9+nUMHDgQtWrVwsiRI2FlZYUjR45gzJgxiIiIQOfOndG5c2fY2tpi3rx5CAkJQVBQEKytrQEAT548wXvvvQeJRIL3338fjo6OiImJwRdffIH09HQMGzYMAKBWq7VnQnr27IkhQ4YgIyMDsbGxuHbtGtq0aYOZM2di5syZ2mMCeYuFi+vOnTsAAAcHB53tEyZMQL169TBp0iTtlKBp06Zh37596Nq1K4YPH46LFy9izZo1uHnzJlasWKHTPzExEZMmTcKAAQPw9ttvY8+ePZgwYQLWrVuHgIAAAMDdu3dx7NgxdOvWDa6urnjy5Al27dqFwYMH4/Dhw3pTqlatWgWJRIKRI0fi6dOn2Lx5M4YNG4Yffvih2GeJXjR16lTMnj0bVlZWCAsLAwC89tprqFOnDpo1a4YDBw5oc5Pv4MGDsLa2RseOHY0+LhGRUQQiIqoQe/bsEeRyuXDx4sVC2zRv3lzo06eP9vHQoUOFkJAQITs7W7tNo9EI/fv3F7p06aLddvfuXUEulwvr1q3TGW/q1KlCQECA8OzZM53tkyZNEpo3by4olUpBEATh+++/F+RyubBx40a9mDQajSAIgvD06VNBLpcLy5YtM+j5/vbbb4JcLhe+//574enTp8KjR4+EX3/9VejQoYOgUCi0r8OyZcsEuVwufPLJJzr9r1y5IsjlcuGLL77Q2T5//nxBLpcLcXFx2m0dOnQQ5HK58OOPP2q3paWlCQEBATqvZ3Z2tqBWq3XGu3v3ruDl5SUsX75cL/a2bdsKaWlp2u1RUVGCXC4XNm/erN02ZcoUoUOHDjpjvvw65ef+7t272m09e/YUBg8erPe67dy5U5DL5cKNGze021QqleDn5ydMmTJFrz0RUVnjFCYiIhGzsrJCRkYGgLzpNr/99hu6d++O9PR0PHv2DM+ePUNycjICAwORmJiIR48eFTqWIAg4evQogoODIQiCtv+zZ88QGBiItLQ0XLp0CQBw9OhR1KhRo8AF2BKJpETPaerUqfD390fbtm0xatQoKJVKzJ8/X2cdCAAMGDBA5/GJEycAAMOHD9fZPmLECJ39+ZydnbVnRgDAxsYGffr0weXLl/H48WMAeVe+kkrzfhSq1WokJyfDysoK7u7uuHz5sl7sffr00bkyVrdu3eDk5KR37NLUvXt3mJub4+DBg9ptJ0+eRHJyMnr16lVmxyUiKgynMBERiVhmZiZq1qwJIG+qjyAIWLp0KZYuXVpg+6dPnxZ6JaNnz54hNTUVu3btwq5duwptk38sd3d3mJqW/o+JMWPGoEWLFpBKpahRowbq169f4HFcXV11Ht+/fx9SqRR169bV2e7k5AQ7Ozvcv39fZ3u9evX0ih03NzftWE5OTtBoNNiyZQu+/fZb3Lt3D2q1Wtv25SlV+WO+SCKRoF69enrHLk12dnbo0KEDDh06hIkTJwLIm75Uq1Yt7foRIqLyxAKCiEikHj58iLS0NO0XZo1GAyDvN+5t27YtsM/LX65flN+/V69eePvttwtsY8wahuKSy+Vo06bNK9uZm5sXuL2kZ0BetHr1aixduhTvvPMOJkyYAHt7e0ilUsydO/eVl2ItT3369EF0dDTOnj0LuVyOn3/+GQMHDtSePSEiKk8sIIiIROqHH34AAAQGBgKA9kZrMpnMoC/gL3N0dIS1tTU0Gs0r+9etWxcXLlxATk5OoZclLc0v8oZwcXGBRqPB7du3Ub9+fe32J0+eIDU1FS4uLjrtb9++DUEQdOJMTEzUjgUAP/74I/z8/DB37lydvqmpqahRo4ZeDLdv39Z5LAgCbt++XSqFV1GvZ9u2beHo6IiDBw+iSZMmUCqV6N27d4mPSURkDP7qgohIhOLi4rBy5Uq4urpq57nXrFkTrVq1wq5du5CUlKTXJ3/6UWFMTEzQtWtX/Pjjj7h27VqR/bt06YLk5GRs375dr13+b+YtLS0B5H3ZLg/t2rUDAGzevFln+8aNG3X250tKSsJPP/2kfZyeno79+/fD09MTTk5OAPJek5fPNBw5cqTQtST79+9Henq69nF0dDQeP36MoKAgI5/V/7O0tCz0tTQ1NUXPnj1x5MgR7N27F3K53KgbDBIRlQaegSAiqmAxMTFISEiAWq3GkydPEB8fj9jYWLzxxhtYtWqVzlSeL7/8EoMGDcJbb72F9957D3Xq1MGTJ09w/vx5PHz4UO+eES/79NNPER8fj/feew/9+vVDgwYNkJKSgkuXLiEuLg6nT58GkDdlZv/+/Zg3bx4uXryI5s2bQ6lUIi4uDgMHDkSnTp1gYWGBBg0a4MiRI3Bzc4ODgwMaNmwIuVxeJq9To0aN8Pbbb2PXrl1ITU1Fy5Yt8eeff2Lfvn3o1KmT3noANzc3fPHFF/jzzz9Rs2ZN7NmzB0+fPsW8efO0bdq3b48VK1bg888/h6+vL65du4aDBw9qz/a8zN7eHoMGDULfvn21l3GtV6+e9jK7JdG4cWPs2LEDK1euRL169eDo6Ki91waQl5OtW7ciPj4ekydPLvHxiIiMxQKCiKiCLVu2DEDe1CQHBwfI5XJMnToVffv21bniDwA0aNAAe/bswfLly7Fv3z48f/4cjo6OePPNNzFmzJhXHuu1117D7t27sWLFCvz000/YsWMHHBwc0KBBA50vpSYmJoiMjMSqVatw6NAhHD16FA4ODmjWrJnOdJ05c+Zg9uzZmDdvHnJycjB27NgyKyDyj+fq6op9+/bh2LFjeO211zB69GiMHTtWr62bmxumT5+OBQsW4NatW3B1dcXixYt11o+EhYVBqVTi4MGDiIqKwptvvok1a9Zg4cKFBR4/LCwMV69exdq1a5GRkQF/f398+eWX2rMxJTFmzBg8ePAA69atQ0ZGBlq1aqVTQHh5eaFhw4a4efMmr75ERBVKIohplRgREREVqk+fPrC3t9ebxkVEVJ64BoKIiKgS+PPPP3HlyhX06dOnokMhomqOZyCIiIhE7Nq1a7h06RI2bNiA5ORkHD9+vNBL3BIRlQeegSAiIhKxH3/8EZ9//jlyc3OxaNEiFg9EVOF4BoKIiIiIiAzGMxBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGQwFhBERERERGSw/wNZ8+pJLIduBgAAAABJRU5ErkJggg==\n"
-     },
-     "metadata": {}
+      "cell_type": "markdown",
+      "id": "20844247",
+      "metadata": {
+        "id": "20844247"
+      },
+      "source": [
+        "## 운영/API에서 우선 사용할 파일\n",
+        "\n",
+        "불량 탐지 API:\n",
+        "- selected_defect_detector.joblib\n",
+        "- defect_model_features.json\n",
+        "- defect_model_metrics.json\n",
+        "- event_json_inference_schema.json\n",
+        "\n",
+        "전이 예측 API:\n",
+        "- adjacent_transfer_models.joblib\n",
+        "- adjacent_transfer_model_metadata.json\n",
+        "\n",
+        "SHAP/분석:\n",
+        "- defect_shap_importance.json\n",
+        "- adjacent_transfer_shap_importance.json\n",
+        "- defect_transfer_kafka_analysis_events.jsonl"
+      ]
     }
-   ],
-   "source": [
-    "# 전이 위험 시각화\n",
-    "plt.figure(figsize=(9, 5))\n",
-    "top_flow = risk_summary.head(12).copy()\n",
-    "top_flow[\"flow\"] = top_flow[\"source_process_code\"] + \" -> \" + top_flow[\"target_process_code\"] + \" (\" + top_flow[\"risk_grade\"] + \")\"\n",
-    "sns.barplot(data=top_flow, y=\"flow\", x=\"avg_target_defect_probability\", hue=\"risk_grade\", dodge=False)\n",
-    "plt.title(\"Process Transfer Risk by SHAP Influence\")\n",
-    "plt.xlabel(\"Avg Final Defect Probability\")\n",
-    "plt.ylabel(\"\")\n",
-    "plt.legend(title=\"Risk\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# 불량 확률 분포 시각화\n",
-    "plt.figure(figsize=(8, 4))\n",
-    "sns.histplot(data=transfer_predictions, x=\"target_defect_probability\", hue=\"risk_grade\", bins=40, multiple=\"stack\")\n",
-    "plt.title(\"Predicted Final Defect Probability Distribution\")\n",
-    "plt.xlabel(\"Defect Probability\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43d99e60",
-   "metadata": {
-    "id": "43d99e60"
-   },
-   "source": [
-    "## 12. AI 모델 관리 항목\n",
-    "\n",
-    "모델 선정 근거, 데이터셋 규모, 테스트 케이스 수, Accuracy, False Positive Rate, 성능 개선 현황을 운영 관리용 요약으로 정리합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c79bba33",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 237
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
     },
-    "id": "c79bba33",
-    "outputId": "b4ed5007-4f34-40a7-8c33-edc4bc328bb1"
-   },
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "                         관리 항목  \\\n",
-       "0                  모델 선정 근거 정리   \n",
-       "1                   데이터셋 규모 관리   \n",
-       "2                 테스트 케이스 수 관리   \n",
-       "3             정확도(Accuracy) 측정   \n",
-       "4  오탐률(False Positive Rate) 측정   \n",
-       "5               모델 성능 개선 현황 관리   \n",
-       "\n",
-       "                                                   값  \n",
-       "0  RandomForest 모델이 validation PR-AUC/Recall/F1 기...  \n",
-       "1  {\"max_rows\": 300000, \"profile_rows\": 50000, \"t...  \n",
-       "2  {\"validation_total\": 60000, \"validation_normal...  \n",
-       "3                                            0.91395  \n",
-       "4                                           0.082851  \n",
-       "5  {\"baseline_model\": \"LightGBM\", \"baseline_pr_au...  "
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>관리 항목</th>\n",
-       "      <th>값</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>모델 선정 근거 정리</td>\n",
-       "      <td>RandomForest 모델이 validation PR-AUC/Recall/F1 기...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>데이터셋 규모 관리</td>\n",
-       "      <td>{\"max_rows\": 300000, \"profile_rows\": 50000, \"t...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>테스트 케이스 수 관리</td>\n",
-       "      <td>{\"validation_total\": 60000, \"validation_normal...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>정확도(Accuracy) 측정</td>\n",
-       "      <td>0.91395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>오탐률(False Positive Rate) 측정</td>\n",
-       "      <td>0.082851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>모델 성능 개선 현황 관리</td>\n",
-       "      <td>{\"baseline_model\": \"LightGBM\", \"baseline_pr_au...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-4c8a3e49-47c9-4b92-9c33-eb1b63a0e382');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "  <div id=\"id_8772c170-37d4-4cc2-8541-c0f3e4c01024\">\n",
-       "    <style>\n",
-       "      .colab-df-generate {\n",
-       "        background-color: #E8F0FE;\n",
-       "        border: none;\n",
-       "        border-radius: 50%;\n",
-       "        cursor: pointer;\n",
-       "        display: none;\n",
-       "        fill: #1967D2;\n",
-       "        height: 32px;\n",
-       "        padding: 0 0 0 0;\n",
-       "        width: 32px;\n",
-       "      }\n",
-       "\n",
-       "      .colab-df-generate:hover {\n",
-       "        background-color: #E2EBFA;\n",
-       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "        fill: #174EA6;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate {\n",
-       "        background-color: #3B4455;\n",
-       "        fill: #D2E3FC;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate:hover {\n",
-       "        background-color: #434B5C;\n",
-       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "        fill: #FFFFFF;\n",
-       "      }\n",
-       "    </style>\n",
-       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ai_management_table')\"\n",
-       "            title=\"Generate code using this dataframe.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "       width=\"24px\">\n",
-       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "    <script>\n",
-       "      (() => {\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#id_8772c170-37d4-4cc2-8541-c0f3e4c01024 button.colab-df-generate');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      buttonEl.onclick = () => {\n",
-       "        google.colab.notebook.generateWithVariable('ai_management_table');\n",
-       "      }\n",
-       "      })();\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "dataframe",
-       "variable_name": "ai_management_table",
-       "summary": "{\n  \"name\": \"ai_management_table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"\\uad00\\ub9ac \\ud56d\\ubaa9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\ubaa8\\ub378 \\uc120\\uc815 \\uadfc\\uac70 \\uc815\\ub9ac\",\n          \"\\ub370\\uc774\\ud130\\uc14b \\uaddc\\ubaa8 \\uad00\\ub9ac\",\n          \"\\ubaa8\\ub378 \\uc131\\ub2a5 \\uac1c\\uc120 \\ud604\\ud669 \\uad00\\ub9ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uac12\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"RandomForest \\ubaa8\\ub378\\uc774 validation PR-AUC/Recall/F1 \\uae30\\uc900 \\uc885\\ud569 \\uc21c\\uc704 1\\uc704\\uc785\\ub2c8\\ub2e4. bagging \\uae30\\ubc18 \\uae30\\uc900 \\ubaa8\\ub378\\ub85c \\uacfc\\uc801\\ud569\\uc744 \\uc904\\uc774\\uace0 \\uc548\\uc815\\uc801\\uc778 tree ensemble \\uc131\\ub2a5 \\ud655\\uc778\",\n          \"{\\\"max_rows\\\": 300000, \\\"profile_rows\\\": 50000, \\\"train_rows\\\": 240000, \\\"candidate_train_rows\\\": 120000, \\\"valid_rows\\\": 60000, \\\"feature_count\\\": 457, \\\"positive_ratio\\\": 0.00565, \\\"full_data_refit\\\": false}\",\n          \"{\\\"baseline_model\\\": \\\"LightGBM\\\", \\\"baseline_pr_auc\\\": 0.050323296178874356, \\\"selected_model\\\": \\\"RandomForest\\\", \\\"selected_pr_auc\\\": 0.06255052428644334, \\\"pr_auc_improvement_over_lightgbm\\\": 0.012227228107568981}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-      }
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "ai_management_table = pd.DataFrame([\n",
-    "    {\"관리 항목\": \"모델 선정 근거 정리\", \"값\": ai_management_summary[\"모델_선정_근거\"]},\n",
-    "    {\"관리 항목\": \"데이터셋 규모 관리\", \"값\": json.dumps(ai_management_summary[\"데이터셋_규모_관리\"], ensure_ascii=False)},\n",
-    "    {\"관리 항목\": \"테스트 케이스 수 관리\", \"값\": json.dumps(ai_management_summary[\"테스트_케이스_수_관리\"], ensure_ascii=False)},\n",
-    "    {\"관리 항목\": \"정확도(Accuracy) 측정\", \"값\": round(ai_management_summary[\"정확도_Accuracy\"], 6)},\n",
-    "    {\"관리 항목\": \"오탐률(False Positive Rate) 측정\", \"값\": round(ai_management_summary[\"오탐률_False_Positive_Rate\"], 6)},\n",
-    "    {\"관리 항목\": \"모델 성능 개선 현황 관리\", \"값\": json.dumps(ai_management_summary[\"모델_성능_개선_현황\"], ensure_ascii=False)},\n",
-    "])\n",
-    "\n",
-    "display(ai_management_table)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "Q5r_SU2bbEfD",
-   "metadata": {
-    "id": "Q5r_SU2bbEfD"
-   },
-   "source": [
-    "## 13. 모델 및 결과 저장\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "44a36633",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    "language_info": {
+      "name": "python",
+      "pygments_lexer": "ipython3"
     },
-    "id": "44a36633",
-    "outputId": "9dbc6133-c938-4715-dc60-ea025f990e55"
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Saved outputs:\n",
-      "/content/defect_transfer_outputs/selected_defect_detector.joblib\n",
-      "/content/defect_transfer_outputs/selected_defect_detector.pkl\n",
-      "/content/defect_transfer_outputs/candidate_defect_models.joblib\n",
-      "/content/defect_transfer_outputs/candidate_defect_models.pkl\n",
-      "/content/defect_transfer_outputs/lightgbm_defect_detector.joblib\n",
-      "/content/defect_transfer_outputs/lightgbm_defect_detector.pkl\n",
-      "/content/defect_transfer_outputs/auxiliary_process_models.joblib\n",
-      "/content/defect_transfer_outputs/auxiliary_process_models.pkl\n",
-      "/content/defect_transfer_outputs/defect_model_features.json\n",
-      "/content/defect_transfer_outputs/defect_model_metrics.json\n",
-      "/content/defect_transfer_outputs/defect_model_cv_results.csv\n",
-      "/content/defect_transfer_outputs/defect_model_comparison.csv\n",
-      "/content/defect_transfer_outputs/defect_model_confusion_matrices.json\n",
-      "/content/defect_transfer_outputs/defect_ai_management_summary.json\n",
-      "/content/defect_transfer_outputs/defect_model_feature_importance.csv\n",
-      "/content/defect_transfer_outputs/defect_all_model_feature_importance.csv\n",
-      "/content/defect_transfer_outputs/defect_shap_importance.csv\n",
-      "/content/defect_transfer_outputs/defect_transfer_prediction_result.csv\n",
-      "/content/defect_transfer_outputs/defect_transfer_risk_summary.csv\n"
-     ]
+    "colab": {
+      "provenance": []
     }
-   ],
-   "source": [
-    "# 저장 경로\n",
-    "selected_model_path = OUTPUT_DIR / \"selected_defect_detector.joblib\"\n",
-    "selected_model_pkl_path = OUTPUT_DIR / \"selected_defect_detector.pkl\"\n",
-    "candidate_models_path = OUTPUT_DIR / \"candidate_defect_models.joblib\"\n",
-    "candidate_models_pkl_path = OUTPUT_DIR / \"candidate_defect_models.pkl\"\n",
-    "model_path = OUTPUT_DIR / \"lightgbm_defect_detector.joblib\"\n",
-    "model_pkl_path = OUTPUT_DIR / \"lightgbm_defect_detector.pkl\"\n",
-    "aux_model_path = OUTPUT_DIR / \"auxiliary_process_models.joblib\"\n",
-    "aux_model_pkl_path = OUTPUT_DIR / \"auxiliary_process_models.pkl\"\n",
-    "feature_path = OUTPUT_DIR / \"defect_model_features.json\"\n",
-    "metrics_path = OUTPUT_DIR / \"defect_model_metrics.json\"\n",
-    "comparison_path = OUTPUT_DIR / \"defect_model_comparison.csv\"\n",
-    "confusion_matrix_path = OUTPUT_DIR / \"defect_model_confusion_matrices.json\"\n",
-    "ai_management_path = OUTPUT_DIR / \"defect_ai_management_summary.json\"\n",
-    "importance_path = OUTPUT_DIR / \"defect_model_feature_importance.csv\"\n",
-    "all_importance_path = OUTPUT_DIR / \"defect_all_model_feature_importance.csv\"\n",
-    "shap_importance_path = OUTPUT_DIR / \"defect_shap_importance.csv\"\n",
-    "transfer_path = OUTPUT_DIR / \"defect_transfer_prediction_result.csv\"\n",
-    "risk_summary_path = OUTPUT_DIR / \"defect_transfer_risk_summary.csv\"\n",
-    "\n",
-    "# 모델 저장\n",
-    "joblib.dump(model, selected_model_path)\n",
-    "joblib.dump(trained_models, candidate_models_path)\n",
-    "joblib.dump(auxiliary_models, aux_model_path)\n",
-    "with open(selected_model_pkl_path, \"wb\") as f:\n",
-    "    pickle.dump(model, f)\n",
-    "with open(candidate_models_pkl_path, \"wb\") as f:\n",
-    "    pickle.dump(trained_models, f)\n",
-    "with open(aux_model_pkl_path, \"wb\") as f:\n",
-    "    pickle.dump(auxiliary_models, f)\n",
-    "\n",
-    "# 기존 LightGBM 파일명 호환 저장\n",
-    "if \"LightGBM\" in trained_models:\n",
-    "    joblib.dump(trained_models[\"LightGBM\"], model_path)\n",
-    "    with open(model_pkl_path, \"wb\") as f:\n",
-    "        pickle.dump(trained_models[\"LightGBM\"], f)\n",
-    "\n",
-    "feature_path.write_text(json.dumps(feature_cols, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "\n",
-    "confusion_payload = {\n",
-    "    name: matrix.tolist()\n",
-    "    for name, matrix in model_confusion_matrices.items()\n",
-    "}\n",
-    "confusion_matrix_path.write_text(json.dumps(confusion_payload, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "ai_management_path.write_text(json.dumps(ai_management_summary, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "\n",
-    "# 평가 지표 저장\n",
-    "metrics = {\n",
-    "    \"selected_model_name\": selected_model_name,\n",
-    "    \"roc_auc\": float(roc_auc),\n",
-    "    \"average_precision\": float(pr_auc),\n",
-    "    \"accuracy\": float(accuracy),\n",
-    "    \"false_positive_rate\": float(false_positive_rate),\n",
-    "    \"best_threshold\": float(best_threshold),\n",
-    "    \"f1_at_best_threshold\": float(f1_score(y_valid, valid_pred, zero_division=0)),\n",
-    "    \"confusion_matrix\": cm.tolist(),\n",
-    "    \"train_rows\": int(len(X_train)),\n",
-    "    \"valid_rows\": int(len(X_valid)),\n",
-    "    \"feature_count\": int(len(feature_cols)),\n",
-    "    \"positive_ratio_train\": float(y_train.mean()),\n",
-    "    \"candidate_models\": list(trained_models.keys()),\n",
-    "    \"cv_splits\": int(effective_cv_splits),\n",
-    "    \"cv_trials_per_model\": int(CV_N_ITER if ENABLE_HYPERPARAMETER_TUNING else 1),\n",
-    "    \"lightgbm_tuning_method\": \"Optuna\" if ENABLE_OPTUNA_TUNING else \"ParameterSampler\",\n",
-    "    \"smote_enabled\": bool(USE_SMOTE),\n",
-    "    \"smote_sampling_strategy\": float(SMOTE_SAMPLING_STRATEGY),\n",
-    "    \"threshold_strategy\": {\n",
-    "        \"type\": \"recall_priority\",\n",
-    "        \"target_recall\": float(RECALL_PRIORITY_TARGET),\n",
-    "        \"min_precision\": float(RECALL_PRIORITY_MIN_PRECISION),\n",
-    "        \"beta\": float(THRESHOLD_BETA),\n",
-    "    },\n",
-    "    \"model_selection_reason\": ai_management_summary[\"모델_선정_근거\"],\n",
-    "    \"auxiliary_metrics\": auxiliary_metrics,\n",
-    "    \"auxiliary_feature_columns\": [c for c in feature_cols if c.startswith(\"aux_\")],\n",
-    "    \"created_at\": datetime.now(timezone.utc).isoformat(),\n",
-    "}\n",
-    "metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
-    "\n",
-    "# 분석 결과 저장\n",
-    "cv_results.to_csv(cv_results_path, index=False)\n",
-    "if \"cv_fold_results\" in globals():\n",
-    "    cv_fold_results.to_csv(cv_fold_results_path, index=False)\n",
-    "model_comparison.to_csv(comparison_path, index=False)\n",
-    "importance_df.to_csv(importance_path, index=False)\n",
-    "all_model_importance.to_csv(all_importance_path, index=False)\n",
-    "shap_importance.to_csv(shap_importance_path, index=False)\n",
-    "transfer_predictions.to_csv(transfer_path, index=False)\n",
-    "risk_summary.to_csv(risk_summary_path, index=False)\n",
-    "\n",
-    "print(\"Saved outputs:\")\n",
-    "for path in [\n",
-    "    selected_model_path,\n",
-    "    selected_model_pkl_path,\n",
-    "    candidate_models_path,\n",
-    "    candidate_models_pkl_path,\n",
-    "    model_path,\n",
-    "    model_pkl_path,\n",
-    "    aux_model_path,\n",
-    "    aux_model_pkl_path,\n",
-    "    feature_path,\n",
-    "    metrics_path,\n",
-    "    cv_results_path,\n",
-    "    comparison_path,\n",
-    "    confusion_matrix_path,\n",
-    "    ai_management_path,\n",
-    "    importance_path,\n",
-    "    all_importance_path,\n",
-    "    shap_importance_path,\n",
-    "    transfer_path,\n",
-    "    risk_summary_path,\n",
-    "]:\n",
-    "    print(path)\n"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
\ No newline at end of file
diff --git a/app/scheduler/manufacturing/manufacturing_event_scheduler.py b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
index 7436d96..afc75db 100644
--- a/app/scheduler/manufacturing/manufacturing_event_scheduler.py
+++ b/app/scheduler/manufacturing/manufacturing_event_scheduler.py
@@ -25,7 +25,7 @@ def start_manufacturing_event_scheduler(app: FastAPI) -> None:
         return
     if not settings.sample_database_connection_url:
         logger.info(
-            "SAMPLE_DATABASE_URL이 없어 제조 이벤트 스케줄러를 시작하지 않습니다.",
+            "MAIN_DATABASE_URL + SAMPLE_DB_NAME이 없어 제조 이벤트 스케줄러를 시작하지 않습니다.",
         )
         return
 
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 08268c3..758c7b6 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -12,7 +12,7 @@
 from app.utils.json_utils import from_json, to_json
 
 
-DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck_iforest_model.pkl")
+DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
 
 
 class BottleneckAnalysisService:
diff --git a/app/service/manufacturing/manufacturing_event_json_service.py b/app/service/manufacturing/manufacturing_event_json_service.py
index a8985cb..c7753ef 100644
--- a/app/service/manufacturing/manufacturing_event_json_service.py
+++ b/app/service/manufacturing/manufacturing_event_json_service.py
@@ -698,7 +698,7 @@ def list_events(
 def get_manufacturing_event_json_service() -> ManufacturingEventJsonService:
     if not settings.sample_database_connection_url:
         raise AppException(
-            "SAMPLE_DATABASE_URL 설정이 필요합니다.",
+            "MAIN_DATABASE_URL + SAMPLE_DB_NAME 설정이 필요합니다.",
             status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
         )
     return ManufacturingEventJsonService(

From f5695399d1e229b3c28bfd16c80aafc5067236ff Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 09:23:29 +0900
Subject: [PATCH 038/148] feat : main function add

---
 main.py | 145 +++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 144 insertions(+), 1 deletion(-)

diff --git a/main.py b/main.py
index 87906df..44dfafe 100644
--- a/main.py
+++ b/main.py
@@ -1,2 +1,145 @@
-from app.main import app
+# app/main.py
 
+import threading
+import time
+
+from app.scheduler.quality.drive_detail_producer import run as drive_producer
+from app.scheduler.quality.status_detail_producer import run as status_producer
+from app.scheduler.quality.risk_history_producer import run as history_producer
+from app.scheduler.quality.risk_trend_producer import run as trend_producer
+from app.scheduler.quality.process_producer import run as process_producer
+
+from app.repository.quality_drive_detail_repository import (
+    run as drive_repository
+)
+
+from app.repository.quality_status_detail_repository import (
+    run as status_repository
+)
+
+from app.repository.quality_risk_history_repository import (
+    run as history_repository
+)
+
+from app.repository.quality_risk_trend_repository import (
+    run as trend_repository
+)
+
+from app.repository.quality_process_repository import (
+    run as process_repository
+)
+
+
+def start_thread(name, target):
+
+    print(f"[START] {name} 스레드 시작")
+
+    thread = threading.Thread(
+        target=target,
+        daemon=True,
+        name=name
+    )
+
+    thread.start()
+
+    return thread
+
+
+if __name__ == "__main__":
+
+    print("=" * 60)
+    print("AI-Service 시작")
+    print("=" * 60)
+
+    threads = []
+
+    # Producer
+    threads.append(
+        start_thread(
+            "Drive Detail Kafka 전송",
+            drive_producer
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Status Detail Kafka 전송",
+            status_producer
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Risk History Kafka 전송",
+            history_producer
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Risk Trend Kafka 전송",
+            trend_producer
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Process Kafka 전송",
+            process_producer
+        )
+    )
+
+    # Consumer
+    threads.append(
+        start_thread(
+            "Drive Detail 메시지 처리",
+            drive_repository
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Status Detail 메시지 처리",
+            status_repository
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Risk History 메시지 처리",
+            history_repository
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Risk Trend 메시지 처리",
+            trend_repository
+        )
+    )
+
+    threads.append(
+        start_thread(
+            "Process 메시지 처리",
+            process_repository
+        )
+    )
+
+    print("\n실행 중인 서비스")
+    print("- Drive Detail Kafka 전송")
+    print("- Status Detail Kafka 전송")
+    print("- Risk History Kafka 전송")
+    print("- Risk Trend Kafka 전송")
+    print("- Process Kafka 전송")
+
+    print("- Drive Detail 메시지 처리")
+    print("- Status Detail 메시지 처리")
+    print("- Risk History 메시지 처리")
+    print("- Risk Trend 메시지 처리")
+    print("- Process 메시지 처리")
+
+    print("\nAI-Service 정상 실행 완료")
+    print("=" * 60)
+
+    while True:
+        time.sleep(60)
\ No newline at end of file

From 0a75adffcf6a243f5c89fc3f754beb7b45e165cd Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 10:42:01 +0900
Subject: [PATCH 039/148] fix : run() function add

---
 app/kafka/consumer.py                         |  40 +-
 .../quality_drive_detail_repository.py        | 235 +++++-----
 app/repository/quality_process_repository.py  | 193 +++++----
 .../quality_risk_history_repository.py        | 137 +++---
 .../quality_risk_trend_repository.py          | 147 +++----
 .../quality_status_detail_repository.py       | 167 ++++----
 app/repository/quality_summary_repository.py  | 342 +++++++--------
 .../quality/drive_detail_producer.py          | 118 ++---
 app/scheduler/quality/process_producer.py     | 128 +++---
 .../quality/risk_history_producer.py          | 404 +++++++++---------
 app/scheduler/quality/risk_trend_producer.py  | 388 ++++++++---------
 .../quality/status_detail_producer.py         | 258 +++++------
 12 files changed, 1278 insertions(+), 1279 deletions(-)

diff --git a/app/kafka/consumer.py b/app/kafka/consumer.py
index 170b90e..0d2452c 100644
--- a/app/kafka/consumer.py
+++ b/app/kafka/consumer.py
@@ -7,31 +7,35 @@
 from dotenv import load_dotenv
 import os
 
-load_dotenv()
-def create_consumer(topic, group_id):
+def run():
+    load_dotenv()
+    def create_consumer(topic, group_id):
 
-    consumer = KafkaConsumer(
-        topic,
-        ssl_context=ssl.create_default_context(),
+        consumer = KafkaConsumer(
+            topic,
+            ssl_context=ssl.create_default_context(),
 
-        bootstrap_servers=[
-        os.getenv("BROKER_URL_1"),
-        os.getenv("BROKER_URL_2")
-        ],
+            bootstrap_servers=[
+            os.getenv("BROKER_URL_1"),
+            os.getenv("BROKER_URL_2")
+            ],
 
-        group_id=group_id,
+            group_id=group_id,
 
-        auto_offset_reset="earliest",
+            auto_offset_reset="earliest",
 
-        enable_auto_commit=True,
+            enable_auto_commit=True,
 
-        security_protocol="SASL_SSL",
+            security_protocol="SASL_SSL",
 
-        sasl_mechanism="OAUTHBEARER",
+            sasl_mechanism="OAUTHBEARER",
 
-        sasl_oauth_token_provider=MSKTokenProvider(),
+            sasl_oauth_token_provider=MSKTokenProvider(),
 
-        value_deserializer=lambda x: json.loads(x.decode("utf-8"))
-    )
+            value_deserializer=lambda x: json.loads(x.decode("utf-8"))
+        )
 
-    return consumer
+        return consumer
+
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index bbb5cae..5c8102c 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -10,119 +10,139 @@
     QUALITY_INSPECTION_DRIVE_DETAIL
 )
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
 
-MAIN_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-)
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+    )
 
-main_engine = create_engine(
-    MAIN_DATABASE_URL,
-    pool_pre_ping=True
-)
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
+    consumer = create_consumer(
+        topic=QUALITY_INSPECTION_DRIVE_DETAIL,
+        group_id="ai-drive-detail-group"
+    )
 
-consumer = create_consumer(
-    topic=QUALITY_INSPECTION_DRIVE_DETAIL,
-    group_id="ai-drive-detail-group"
-)
 
+    def calculate_drive_score(row):
 
-def calculate_drive_score(row):
+        score = 100
 
-    score = 100
+        if float(row["throttle_position"]) > 90:
+            score -= 20
 
-    if float(row["throttle_position"]) > 90:
-        score -= 20
+        if float(row["brake_pressure"]) > 45:
+            score -= 20
 
-    if float(row["brake_pressure"]) > 45:
-        score -= 20
+        if abs(float(row["steering_angle"])) > 40:
+            score -= 20
 
-    if abs(float(row["steering_angle"])) > 40:
-        score -= 20
+        return round(score, 2)
 
-    return round(score, 2)
 
+    def get_driving_pattern(row):
 
-def get_driving_pattern(row):
+        throttle = float(row["throttle_position"])
+        brake = float(row["brake_pressure"])
+        steering = abs(float(row["steering_angle"]))
 
-    throttle = float(row["throttle_position"])
-    brake = float(row["brake_pressure"])
-    steering = abs(float(row["steering_angle"]))
+        if throttle > 75:
+            return "RAPID_ACCEL"
 
-    if throttle > 75:
-        return "RAPID_ACCEL"
+        if brake > 35:
+            return "HARD_BRAKE"
 
-    if brake > 35:
-        return "HARD_BRAKE"
+        if steering > 40:
+            return "SHARP_TURN"
 
-    if steering > 40:
-        return "SHARP_TURN"
+        return "NORMAL"
 
-    return "NORMAL"
 
+    detail_id = 1
+    inspection_drive_detail_list = []
 
-detail_id = 1
-inspection_drive_detail_list = []
+    try:
+        for msg in consumer:
+            row = msg.value
 
-try:
-    print("Consumer 시작")
-    print("구독 토픽", consumer.subscription())
-    for msg in consumer:
-        row = msg.value
+            # 폐기 차량 제외
+            if not row.get("created_at"):
+                print(
+                    f"폐기 차량 제외 : {row.get('vehicle_id')}"
+                )
+                continue
 
-        # 폐기 차량 제외
-        if not row.get("created_at"):
-            print(
-                f"폐기 차량 제외 : {row.get('vehicle_id')}"
-            )
-            continue
-
-        print(f"Message : {json.dumps(row, ensure_ascii=False)}")
-        vehicle_id = row["vehicle_id"]
-        car_code = vehicle_id.split("-")[0]
-
-        inspection_no = f"DRIVE-{detail_id:05d}"
-
-        drive_score = calculate_drive_score(row)
-        driving_pattern = get_driving_pattern(row)
-
-        if drive_score >= 80:
-            inspection_result = "NORMAL"
-            issue_message = "NORMAL"
-        else:
-            inspection_result = "WARNING"
-            issue_message = "ACCEL_ALERT"
-
-        inspection_drive_detail_list.append({
-            "id": detail_id,
-            "car_code": car_code,
-            "inspection_no": inspection_no,
-            "vehicle_id": vehicle_id,
-            "throttle_position": round(
-                float(row["throttle_position"]), 2
-            ),
-            "brake_pressure": round(
-                float(row["brake_pressure"]), 2
-            ),
-            "steering_angle": round(
-                float(row["steering_angle"]), 2
-            ),
-            "drive_score": drive_score,
-            "inspection_result": inspection_result,
-            "driving_pattern": driving_pattern,
-            "issue_message": issue_message,
-            "created_at": row["created_at"]
-        })
-
-        if len(inspection_drive_detail_list) >= 100:
+            vehicle_id = row["vehicle_id"]
+            car_code = vehicle_id.split("-")[0]
+
+            inspection_no = f"DRIVE-{detail_id:05d}"
+
+            drive_score = calculate_drive_score(row)
+            driving_pattern = get_driving_pattern(row)
+
+            if drive_score >= 80:
+                inspection_result = "NORMAL"
+                issue_message = "NORMAL"
+            else:
+                inspection_result = "WARNING"
+                issue_message = "ACCEL_ALERT"
+
+            inspection_drive_detail_list.append({
+                "id": detail_id,
+                "car_code": car_code,
+                "inspection_no": inspection_no,
+                "vehicle_id": vehicle_id,
+                "throttle_position": round(
+                    float(row["throttle_position"]), 2
+                ),
+                "brake_pressure": round(
+                    float(row["brake_pressure"]), 2
+                ),
+                "steering_angle": round(
+                    float(row["steering_angle"]), 2
+                ),
+                "drive_score": drive_score,
+                "inspection_result": inspection_result,
+                "driving_pattern": driving_pattern,
+                "issue_message": issue_message,
+                "created_at": row["created_at"]
+            })
+
+            if len(inspection_drive_detail_list) >= 100:
+
+                df = pd.DataFrame(
+                    inspection_drive_detail_list
+                )
+
+                df.to_sql(
+                    name="inspection_drive_detail",
+                    con=main_engine,
+                    if_exists="append",
+                    index=False
+                )
+
+                inspection_drive_detail_list.clear()
+
+            detail_id += 1
+
+    except Exception as e:
+        print(f"오류 발생 : {e}")
+
+    finally:
+
+        if inspection_drive_detail_list:
 
             df = pd.DataFrame(
                 inspection_drive_detail_list
@@ -136,33 +156,10 @@ def get_driving_pattern(row):
             )
 
             print(
-                f"{len(inspection_drive_detail_list)}건 저장 완료"
+                f"{len(inspection_drive_detail_list)}건 최종 저장 완료"
             )
 
-            inspection_drive_detail_list.clear()
-
-        detail_id += 1
-
-except Exception as e:
-    print(f"오류 발생 : {e}")
-
-finally:
-
-    if inspection_drive_detail_list:
-
-        df = pd.DataFrame(
-            inspection_drive_detail_list
-        )
-
-        df.to_sql(
-            name="inspection_drive_detail",
-            con=main_engine,
-            if_exists="append",
-            index=False
-        )
-
-        print(
-            f"{len(inspection_drive_detail_list)}건 최종 저장 완료"
-        )
+        consumer.close()
 
-    consumer.close()
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 25ca017..4bbc97c 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -3,107 +3,118 @@
 import random
 from dotenv import load_dotenv
 import os
+from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
 
-MAIN_DATABASE_URL = (
-    os.getenv("MAIN_DATABASE_END")
-)
-
-main_engine = create_engine(
-    MAIN_DATABASE_URL,
-    pool_pre_ping=True
-)
-
-consumer = create_consumer(
-    topic=QUALITY_INSPECTION_PROCESS,
-    group_id="ai-process-group"
-)
-
-process_names = [
-    "Visual",
-    "Function",
-    "Drive",
-    "Final"
-]
-
-process_id = 1
-inspection_process_list = []
-
-try:
-    print("Consumer 시작")
-    print("구독 토픽", consumer.subscription())
-    for msg in consumer:
-
-        row = msg.value
-
-        print(f"Kafka 수신 : {row}")
-
-        process_date = row["process_date"]
-        total_vehicle_count = row["vehicle_count"]
-
-        for process_name in process_names:
-
-            completed_count = random.randint(
-                int(total_vehicle_count * 0.5),
-                total_vehicle_count
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+    )
+
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
+    consumer = create_consumer(
+        topic=QUALITY_INSPECTION_PROCESS,
+        group_id="ai-process-group"
+    )
+
+    process_names = [
+        "Visual",
+        "Function",
+        "Drive",
+        "Final"
+    ]
+
+    process_id = 1
+    inspection_process_list = []
+
+    try:
+        for msg in consumer:
+
+            row = msg.value
+
+            process_date = row["process_date"]
+            total_vehicle_count = row["vehicle_count"]
+
+            for process_name in process_names:
+
+                completed_count = random.randint(
+                    int(total_vehicle_count * 0.5),
+                    total_vehicle_count
+                )
+
+                waiting_count = (
+                    total_vehicle_count
+                    - completed_count
+                )
+
+                progress_rate = round(
+                    completed_count
+                    / total_vehicle_count
+                    * 100,
+                    0
+                )
+
+                if progress_rate >= 90:
+                    process_status = "COMPLETE"
+
+                elif progress_rate >= 70:
+                    process_status = "RUNNING"
+
+                else:
+                    process_status = "WAIT"
+
+                inspection_process_list.append({
+                    "id": process_id,
+                    "process_name": process_name,
+                    "total_vehicle_count": total_vehicle_count,
+                    "completed_count": completed_count,
+                    "waiting_count": waiting_count,
+                    "progress_rate": progress_rate,
+                    "process_status": process_status,
+                    "created_at": process_date
+                })
+
+                process_id += 1
+
+            # 날짜 하나당 4개 공정 생성
+            df = pd.DataFrame(
+                inspection_process_list
             )
 
-            waiting_count = (
-                total_vehicle_count
-                - completed_count
+            df.to_sql(
+                name="inspection_process",
+                con=main_engine,
+                if_exists="append",
+                index=False
             )
 
-            progress_rate = round(
-                completed_count
-                / total_vehicle_count
-                * 100,
-                0
+            print(
+                f"{len(inspection_process_list)}건 저장 완료"
             )
 
-            if progress_rate >= 90:
-                process_status = "COMPLETE"
-
-            elif progress_rate >= 70:
-                process_status = "RUNNING"
-
-            else:
-                process_status = "WAIT"
-
-            inspection_process_list.append({
-                "id": process_id,
-                "process_name": process_name,
-                "total_vehicle_count": total_vehicle_count,
-                "completed_count": completed_count,
-                "waiting_count": waiting_count,
-                "progress_rate": progress_rate,
-                "process_status": process_status,
-                "created_at": process_date
-            })
-
-            process_id += 1
-
-        # 날짜 하나당 4개 공정 생성
-        df = pd.DataFrame(
-            inspection_process_list
-        )
-
-        df.to_sql(
-            name="inspection_process",
-            con=main_engine,
-            if_exists="append",
-            index=False
-        )
-
-        print(
-            f"{len(inspection_process_list)}건 저장 완료"
-        )
+            inspection_process_list.clear()
 
-        inspection_process_list.clear()
+    except Exception as e:
+        print(f"오류 발생 : {e}")
 
-except Exception as e:
-    print(f"오류 발생 : {e}")
+    finally:
+        consumer.close()
 
-finally:
-    consumer.close()
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 6f6facf..020ddd5 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -9,69 +9,80 @@
     QUALITY_INSPECTION_RISK_HISTORY
 )
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
 
-MAIN_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-)
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+    )
 
-main_engine = create_engine(
-    MAIN_DATABASE_URL,
-    pool_pre_ping=True
-)
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
 
-consumer = create_consumer(
-    topic=QUALITY_INSPECTION_RISK_HISTORY,
-    group_id="ai-risk-history-group"
-)
+    consumer = create_consumer(
+        topic=QUALITY_INSPECTION_RISK_HISTORY,
+        group_id="ai-risk-history-group"
+    )
+
+    inspection_risk_history_list = []
+
+    try:
+        for msg in consumer:
+
+            row = msg.value
+
+            inspection_risk_history_list.append({
+                "id": row["id"],
+                "inspection_type":
+                    row["inspection_type"],
 
-inspection_risk_history_list = []
+                "inspection_round":
+                    row["inspection_round"],
 
-try:
+                "risk_score":
+                    row["risk_score"],
 
-    print("Consumer 시작")
-    print("구독 토픽 :", consumer.subscription())
+                "start_time":
+                    row["start_time"],
 
-    for msg in consumer:
+                "end_time":
+                    row["end_time"]
+            })
 
-        row = msg.value
+            if len(
+                inspection_risk_history_list
+            ) >= 4:
 
-        print("=" * 50)
-        print(f"[Kafka 수신 성공]")
-        print(f"Topic : {msg.topic}")
-        print(f"Offset : {msg.offset}")
-        print(f"Message : {row}")
-        print("=" * 50)
+                df = pd.DataFrame(
+                    inspection_risk_history_list
+                )
 
-        inspection_risk_history_list.append({
-            "id": row["id"],
-            "inspection_type":
-                row["inspection_type"],
+                df.to_sql(
+                    name="inspection_risk_history",
+                    con=main_engine,
+                    if_exists="append",
+                    index=False
+                )
 
-            "inspection_round":
-                row["inspection_round"],
+                inspection_risk_history_list.clear()
 
-            "risk_score":
-                row["risk_score"],
+    except Exception as e:
 
-            "start_time":
-                row["start_time"],
+        print(f"오류 발생 : {e}")
 
-            "end_time":
-                row["end_time"]
-        })
+    finally:
 
-        if len(
-            inspection_risk_history_list
-        ) >= 4:
+        if inspection_risk_history_list:
 
             df = pd.DataFrame(
                 inspection_risk_history_list
@@ -85,32 +96,10 @@
             )
 
             print(
-                f"{len(inspection_risk_history_list)}건 저장 완료"
+                f"{len(inspection_risk_history_list)}건 최종 저장 완료"
             )
 
-            inspection_risk_history_list.clear()
-
-except Exception as e:
-
-    print(f"오류 발생 : {e}")
-
-finally:
-
-    if inspection_risk_history_list:
-
-        df = pd.DataFrame(
-            inspection_risk_history_list
-        )
-
-        df.to_sql(
-            name="inspection_risk_history",
-            con=main_engine,
-            if_exists="append",
-            index=False
-        )
-
-        print(
-            f"{len(inspection_risk_history_list)}건 최종 저장 완료"
-        )
+        consumer.close()
 
-    consumer.close()
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index ce12320..ad7b3c7 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -9,64 +9,81 @@
     QUALITY_INSPECTION_RISK_TREND
 )
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-MAIN_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-)
-
-main_engine = create_engine(
-    MAIN_DATABASE_URL,
-    pool_pre_ping=True
-)
-
-consumer = create_consumer(
-    topic=QUALITY_INSPECTION_RISK_TREND,
-    group_id="ai-risk-trend-group"
-)
-
-inspection_risk_trend_list = []
-
-try:
-
-    print("Consumer 시작")
-    print("구독 토픽 :", consumer.subscription())
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+    )
+
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
+    consumer = create_consumer(
+        topic=QUALITY_INSPECTION_RISK_TREND,
+        group_id="ai-risk-trend-group"
+    )
+
+    inspection_risk_trend_list = []
+
+    try:
+        for msg in consumer:
+
+            row = msg.value
+
+            inspection_risk_trend_list.append({
+                "id": row["id"],
+                "risk_level":
+                    row["risk_level"],
+
+                "risk_count":
+                    row["risk_count"],
+
+                "risk_ratio":
+                    row["risk_ratio"],
+
+                "created_at":
+                    row["created_at"]
+            })
+
+            if len(
+                inspection_risk_trend_list
+            ) >= 3:
 
-    for msg in consumer:
+                df = pd.DataFrame(
+                    inspection_risk_trend_list
+                )
 
-        row = msg.value
+                df.to_sql(
+                    name="inspection_risk_trend",
+                    con=main_engine,
+                    if_exists="append",
+                    index=False
+                )
 
-        print("=" * 50)
-        print("[Kafka 메시지 수신]")
-        print(row)
-        print("=" * 50)
+                print(
+                    f"{len(inspection_risk_trend_list)}건 저장 완료"
+                )
 
-        inspection_risk_trend_list.append({
-            "id": row["id"],
-            "risk_level":
-                row["risk_level"],
+                inspection_risk_trend_list.clear()
 
-            "risk_count":
-                row["risk_count"],
+    except Exception as e:
 
-            "risk_ratio":
-                row["risk_ratio"],
+        print(f"오류 발생 : {e}")
 
-            "created_at":
-                row["created_at"]
-        })
+    finally:
 
-        if len(
-            inspection_risk_trend_list
-        ) >= 3:
+        if inspection_risk_trend_list:
 
             df = pd.DataFrame(
                 inspection_risk_trend_list
@@ -80,32 +97,10 @@
             )
 
             print(
-                f"{len(inspection_risk_trend_list)}건 저장 완료"
+                f"{len(inspection_risk_trend_list)}건 최종 저장 완료"
             )
 
-            inspection_risk_trend_list.clear()
-
-except Exception as e:
-
-    print(f"오류 발생 : {e}")
-
-finally:
-
-    if inspection_risk_trend_list:
-
-        df = pd.DataFrame(
-            inspection_risk_trend_list
-        )
-
-        df.to_sql(
-            name="inspection_risk_trend",
-            con=main_engine,
-            if_exists="append",
-            index=False
-        )
-
-        print(
-            f"{len(inspection_risk_trend_list)}건 최종 저장 완료"
-        )
+        consumer.close()
 
-    consumer.close()
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index b70eda9..8cb1bc1 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -9,88 +9,105 @@
     QUALITY_INSPECTION_STATUS_DETAIL
 )
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
 
-MAIN_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-)
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+    )
 
-main_engine = create_engine(
-    MAIN_DATABASE_URL,
-    pool_pre_ping=True
-)
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
 
-consumer = create_consumer(
-    topic=QUALITY_INSPECTION_STATUS_DETAIL,
-    group_id="ai-status-detail-group"
-)
+    consumer = create_consumer(
+        topic=QUALITY_INSPECTION_STATUS_DETAIL,
+        group_id="ai-status-detail-group"
+    )
+
+    inspection_status_detail_list = []
 
-inspection_status_detail_list = []
+    try:
+        for msg in consumer:
 
-try:
+            row = msg.value
 
-    print("Consumer 시작")
-    print("구독 토픽 :", consumer.subscription())
+            inspection_status_detail_list.append({
+                "car_code":
+                    row["car_code"],
 
-    for msg in consumer:
+                "inspection_no":
+                    row["inspection_no"],
 
-        row = msg.value
+                "vehicle_id":
+                    row["vehicle_id"],
 
-        print("=" * 50)
-        print("[Kafka 메시지 수신]")
-        print(row)
-        print("=" * 50)
+                "speed":
+                    row["speed"],
 
-        inspection_status_detail_list.append({
-            "car_code":
-                row["car_code"],
+                "att":
+                    row["att"],
 
-            "inspection_no":
-                row["inspection_no"],
+                "gear":
+                    row["gear"],
 
-            "vehicle_id":
-                row["vehicle_id"],
+                "battery_voltage":
+                    row["battery_voltage"],
 
-            "speed":
-                row["speed"],
+                "fuel_rate":
+                    row["fuel_rate"],
 
-            "att":
-                row["att"],
+                "status_score":
+                    row["status_score"],
 
-            "gear":
-                row["gear"],
+                "inspection_result":
+                    row["inspection_result"],
+
+                "issue_message":
+                    row["issue_message"],
+
+                "created_at":
+                    row["created_at"]
+            })
+
+            # 100건씩 저장
+            if len(
+                inspection_status_detail_list
+            ) >= 100:
 
-            "battery_voltage":
-                row["battery_voltage"],
+                df = pd.DataFrame(
+                    inspection_status_detail_list
+                )
 
-            "fuel_rate":
-                row["fuel_rate"],
+                df.to_sql(
+                    name="inspection_status_detail",
+                    con=main_engine,
+                    if_exists="append",
+                    index=False
+                )
 
-            "status_score":
-                row["status_score"],
+                print(
+                    f"{len(inspection_status_detail_list)}건 저장 완료"
+                )
 
-            "inspection_result":
-                row["inspection_result"],
+                inspection_status_detail_list.clear()
 
-            "issue_message":
-                row["issue_message"],
+    except Exception as e:
 
-            "created_at":
-                row["created_at"]
-        })
+        print(f"오류 발생 : {e}")
 
-        # 100건씩 저장
-        if len(
-            inspection_status_detail_list
-        ) >= 100:
+    finally:
+
+        if inspection_status_detail_list:
 
             df = pd.DataFrame(
                 inspection_status_detail_list
@@ -104,32 +121,10 @@
             )
 
             print(
-                f"{len(inspection_status_detail_list)}건 저장 완료"
+                f"{len(inspection_status_detail_list)}건 최종 저장 완료"
             )
 
-            inspection_status_detail_list.clear()
-
-except Exception as e:
-
-    print(f"오류 발생 : {e}")
-
-finally:
-
-    if inspection_status_detail_list:
-
-        df = pd.DataFrame(
-            inspection_status_detail_list
-        )
-
-        df.to_sql(
-            name="inspection_status_detail",
-            con=main_engine,
-            if_exists="append",
-            index=False
-        )
-
-        print(
-            f"{len(inspection_status_detail_list)}건 최종 저장 완료"
-        )
+        consumer.close()
 
-    consumer.close()
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index afb4c44..af51fb5 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -5,191 +5,195 @@
 import os
 from urllib.parse import quote_plus
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
-
-MAIN_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-)
-
-SAMPLE_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-)
-
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
-)
-
-main_engine = create_engine(
-    MAIN_DATABASE_URL,
-    pool_pre_ping=True
-)
-
-inspection_summary_list = []
-
-summary_id = 1
-
-base_date = datetime.strptime(
-    "2026-06-01",
-    "%Y-%m-%d"
-)
-
-with sample_engine.connect() as conn:
-
-    # 7일
-    for day in range(7):
-
-        current_date = (
-            base_date +
-            timedelta(days=day)
-        )
-
-        offset = day * 100
-
-        vehicles = conn.execute(
-            text("""
-                SELECT vehicle_id
-                FROM (
-                    SELECT DISTINCT vehicle_id
-                    FROM car_drive
-                    ORDER BY vehicle_id
-                    LIMIT 100 OFFSET :offset
-                ) t
-            """),
-            {
-                "offset": offset
-            }
-        ).mappings().all()
-
-        vehicle_ids = [
-            row["vehicle_id"]
-            for row in vehicles
-        ]
-
-        checkpoints = [
-
-            (25, "01:00"),
-            (50, "01:15"),
-            (75, "01:30"),
-            (100, "01:45")
-
-        ]
-
-        for target_count, time_str in checkpoints:
-
-            current_vehicle_ids = (
-                vehicle_ids[:target_count]
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+    )
+
+    SAMPLE_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+    )
+
+    sample_engine = create_engine(
+        SAMPLE_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
+    inspection_summary_list = []
+
+    summary_id = 1
+
+    base_date = datetime.strptime(
+        "2026-06-01",
+        "%Y-%m-%d"
+    )
+
+    with sample_engine.connect() as conn:
+
+        # 7일
+        for day in range(7):
+
+            current_date = (
+                base_date +
+                timedelta(days=day)
             )
 
-            normal_count = 0
-            abnormal_count = 0
+            offset = day * 100
 
-            for vehicle_id in current_vehicle_ids:
-
-                drive_row = conn.execute(
-                    text("""
-                        SELECT *
+            vehicles = conn.execute(
+                text("""
+                    SELECT vehicle_id
+                    FROM (
+                        SELECT DISTINCT vehicle_id
                         FROM car_drive
-                        WHERE vehicle_id=:vehicle_id
-                        LIMIT 1
-                    """),
-                    {
-                        "vehicle_id": vehicle_id
-                    }
-                ).mappings().first()
-
-                if not drive_row:
-                    continue
-
-                score = 100
-
-                if float(
-                    drive_row["throttle_position"]
-                ) > 90:
-                    score -= 20
-
-                if float(
-                    drive_row["brake_pressure"]
-                ) > 45:
-                    score -= 20
-
-                if abs(float(
-                    drive_row["steering_angle"]
-                )) > 40:
-                    score -= 20
-
-                if score >= 80:
-                    normal_count += 1
-                else:
-                    abnormal_count += 1
-
-            total_count = target_count
-
-            standby_count = (
-                100 - total_count
-            )
+                        ORDER BY vehicle_id
+                        LIMIT 100 OFFSET :offset
+                    ) t
+                """),
+                {
+                    "offset": offset
+                }
+            ).mappings().all()
+
+            vehicle_ids = [
+                row["vehicle_id"]
+                for row in vehicles
+            ]
 
-            created_at = datetime.strptime(
-                f"{current_date.strftime('%Y-%m-%d')} {time_str}",
-                "%Y-%m-%d %H:%M"
-            )
+            checkpoints = [
+
+                (25, "01:00"),
+                (50, "01:15"),
+                (75, "01:30"),
+                (100, "01:45")
+
+            ]
+
+            for target_count, time_str in checkpoints:
+
+                current_vehicle_ids = (
+                    vehicle_ids[:target_count]
+                )
+
+                normal_count = 0
+                abnormal_count = 0
+
+                for vehicle_id in current_vehicle_ids:
+
+                    drive_row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_drive
+                            WHERE vehicle_id=:vehicle_id
+                            LIMIT 1
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().first()
+
+                    if not drive_row:
+                        continue
+
+                    score = 100
+
+                    if float(
+                        drive_row["throttle_position"]
+                    ) > 90:
+                        score -= 20
+
+                    if float(
+                        drive_row["brake_pressure"]
+                    ) > 45:
+                        score -= 20
+
+                    if abs(float(
+                        drive_row["steering_angle"]
+                    )) > 40:
+                        score -= 20
+
+                    if score >= 80:
+                        normal_count += 1
+                    else:
+                        abnormal_count += 1
+
+                total_count = target_count
+
+                standby_count = (
+                    100 - total_count
+                )
+
+                created_at = datetime.strptime(
+                    f"{current_date.strftime('%Y-%m-%d')} {time_str}",
+                    "%Y-%m-%d %H:%M"
+                )
+
+                inspection_summary_list.append({
 
-            inspection_summary_list.append({
+                    "id": summary_id,
 
-                "id": summary_id,
+                    "total_count": total_count,
 
-                "total_count": total_count,
+                    "normal_count": normal_count,
 
-                "normal_count": normal_count,
+                    "normal_rate":
+                        round(
+                            normal_count /
+                            total_count * 100,
+                            2
+                        ),
 
-                "normal_rate":
-                    round(
-                        normal_count /
-                        total_count * 100,
-                        2
-                    ),
+                    "abnormal_count":
+                        abnormal_count,
 
-                "abnormal_count":
-                    abnormal_count,
+                    "abnormal_rate":
+                        round(
+                            abnormal_count /
+                            total_count * 100,
+                            2
+                        ),
 
-                "abnormal_rate":
-                    round(
-                        abnormal_count /
-                        total_count * 100,
-                        2
-                    ),
+                    "stanby_count":
+                        standby_count,
 
-                "stanby_count":
-                    standby_count,
+                    "created_at":
+                        created_at
 
-                "created_at":
-                    created_at
+                })
 
-            })
+                summary_id += 1
 
-            summary_id += 1
+    df = pd.DataFrame(
+        inspection_summary_list
+    )
 
-df = pd.DataFrame(
-    inspection_summary_list
-)
+    df.to_sql(
+        name="inspection_summary",
+        con=main_engine,
+        if_exists="append",
+        index=False
+    )
 
-df.to_sql(
-    name="inspection_summary",
-    con=main_engine,
-    if_exists="append",
-    index=False
-)
+    print(
+        f"summary table 전송 완료"
+    )
 
-print(
-    f"summary table 전송 완료"
-)
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/scheduler/quality/drive_detail_producer.py b/app/scheduler/quality/drive_detail_producer.py
index 6837ba2..3c7d070 100644
--- a/app/scheduler/quality/drive_detail_producer.py
+++ b/app/scheduler/quality/drive_detail_producer.py
@@ -9,83 +9,85 @@
 
 from app.kafka.iam_provider import MSKTokenProvider
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
-SAMPLE_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-)
+    SAMPLE_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+    )
 
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
-)
+    sample_engine = create_engine(
+        SAMPLE_DATABASE_URL,
+        pool_pre_ping=True
+    )
 
-producer = KafkaProducer(
-    bootstrap_servers=[
-        os.getenv("BROKER_URL_1"),
-        os.getenv("BROKER_URL_2")
-    ],
+    producer = KafkaProducer(
+        bootstrap_servers=[
+            os.getenv("BROKER_URL_1"),
+            os.getenv("BROKER_URL_2")
+        ],
 
-    security_protocol="SASL_SSL",
+        security_protocol="SASL_SSL",
 
-    sasl_mechanism="OAUTHBEARER",
+        sasl_mechanism="OAUTHBEARER",
 
-    sasl_oauth_token_provider=MSKTokenProvider(),
+        sasl_oauth_token_provider=MSKTokenProvider(),
 
-    value_serializer=lambda x:
-        json.dumps(x, default=str).encode("utf-8")
-)
+        value_serializer=lambda x:
+            json.dumps(x, default=str).encode("utf-8")
+    )
 
 
-with sample_engine.connect() as conn:
-    drive_rows = conn.execute(
-        text("""
-            SELECT *
-            FROM car_drive
-            ORDER BY created_at, vehicle_id
-        """)
-    ).mappings().all()
+    with sample_engine.connect() as conn:
+        drive_rows = conn.execute(
+            text("""
+                SELECT *
+                FROM car_drive
+                ORDER BY created_at, vehicle_id
+            """)
+        ).mappings().all()
 
 
-for row in drive_rows:
+    for row in drive_rows:
 
-    message = {
-        "vehicle_id": row["vehicle_id"],
+        message = {
+            "vehicle_id": row["vehicle_id"],
 
-        "throttle_position": round(
-            float(row["throttle_position"]), 2
-        ),
+            "throttle_position": round(
+                float(row["throttle_position"]), 2
+            ),
 
-        "brake_pressure": round(
-            float(row["brake_pressure"]), 2
-        ),
+            "brake_pressure": round(
+                float(row["brake_pressure"]), 2
+            ),
 
-        "steering_angle": round(
-            float(row["steering_angle"]), 2
-        ),
+            "steering_angle": round(
+                float(row["steering_angle"]), 2
+            ),
 
-        "created_at": row["created_at"]
-    }
+            "created_at": row["created_at"]
+        }
 
-    producer.send(
-        "quality.inspection.drive_detail",
-        value=message
-    )
+        producer.send(
+            "quality.inspection.drive_detail",
+            value=message
+        )
 
-    print(f"Kafka 전송 완료 : {message}")
+        # 실시간처럼 보내고 싶으면 사용
+        time.sleep(1)
 
-    # 실시간처럼 보내고 싶으면 사용
-    time.sleep(1)
 
+    producer.flush()
 
-producer.flush()
+    print("모든 데이터 Kafka 전송 완료")
 
-print("모든 데이터 Kafka 전송 완료")
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index 6c8af77..fa91ace 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -9,70 +9,72 @@
 
 from app.kafka.iam_provider import MSKTokenProvider
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
-
-SAMPLE_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-)
-
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
-)
-
-producer = KafkaProducer(
-    bootstrap_servers=[
-        os.getenv("BROKER_URL_1"),
-        os.getenv("BROKER_URL_2")
-    ],
-
-    security_protocol="SASL_SSL",
-
-    sasl_mechanism="OAUTHBEARER",
-
-    sasl_oauth_token_provider=MSKTokenProvider(),
-
-    value_serializer=lambda x:
-        json.dumps(x, default=str).encode("utf-8")
-)
-
-
-with sample_engine.connect() as conn:
-    process_rows = conn.execute(
-        text("""
-            SELECT
-                DATE(created_at) AS process_date,
-                COUNT(*) AS vehicle_count
-            FROM car_master
-            GROUP BY DATE(created_at)
-            ORDER BY process_date
-        """)
-    ).mappings().all()
-
-
-for row in process_rows:
-
-    message = {
-        "process_date": str(row["process_date"]),
-        "vehicle_count": row["vehicle_count"]
-    }
-
-    producer.send(
-        "quality.inspection.process",
-        value=message
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
     )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
-    print(f"Kafka 전송 완료 : {message}")
+    SAMPLE_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+    )
+
+    sample_engine = create_engine(
+        SAMPLE_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
+    producer = KafkaProducer(
+        bootstrap_servers=[
+            os.getenv("BROKER_URL_1"),
+            os.getenv("BROKER_URL_2")
+        ],
+
+        security_protocol="SASL_SSL",
+
+        sasl_mechanism="OAUTHBEARER",
+
+        sasl_oauth_token_provider=MSKTokenProvider(),
+
+        value_serializer=lambda x:
+            json.dumps(x, default=str).encode("utf-8")
+    )
+
+
+    with sample_engine.connect() as conn:
+        process_rows = conn.execute(
+            text("""
+                SELECT
+                    DATE(created_at) AS process_date,
+                    COUNT(*) AS vehicle_count
+                FROM car_master
+                GROUP BY DATE(created_at)
+                ORDER BY process_date
+            """)
+        ).mappings().all()
+
+
+    for row in process_rows:
+
+        message = {
+            "process_date": str(row["process_date"]),
+            "vehicle_count": row["vehicle_count"]
+        }
+
+        producer.send(
+            "quality.inspection.process",
+            value=message
+        )
+
+        time.sleep(1)
 
-    time.sleep(1)
+    producer.flush()
+    print("모든 데이터 Kafka 전송 완료")
 
-producer.flush()
-print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index 0eb22df..caaa028 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -11,260 +11,260 @@
 
 from app.kafka.iam_provider import MSKTokenProvider
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
-SAMPLE_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-)
+    SAMPLE_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+    )
 
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
-)
+    sample_engine = create_engine(
+        SAMPLE_DATABASE_URL,
+        pool_pre_ping=True
+    )
 
-producer = KafkaProducer(
-    bootstrap_servers=[
-        os.getenv("BROKER_URL_1"),
-        os.getenv("BROKER_URL_2")
-    ],
+    producer = KafkaProducer(
+        bootstrap_servers=[
+            os.getenv("BROKER_URL_1"),
+            os.getenv("BROKER_URL_2")
+        ],
 
-    security_protocol="SASL_SSL",
+        security_protocol="SASL_SSL",
 
-    sasl_mechanism="OAUTHBEARER",
+        sasl_mechanism="OAUTHBEARER",
 
-    sasl_oauth_token_provider=MSKTokenProvider(),
+        sasl_oauth_token_provider=MSKTokenProvider(),
 
-    value_serializer=lambda x:
-        json.dumps(x, default=str).encode("utf-8")
-)
+        value_serializer=lambda x:
+            json.dumps(x, default=str).encode("utf-8")
+    )
 
 
-def calculate_status_risk(row):
-    score = 100
+    def calculate_status_risk(row):
+        score = 100
 
-    if float(row["speed"]) > 120:
-        score -= 20
+        if float(row["speed"]) > 120:
+            score -= 20
 
-    if int(row["att"]) > 4000:
-        score -= 20
+        if int(row["att"]) > 4000:
+            score -= 20
 
-    if float(row["battery_voltage"]) < 12:
-        score -= 10
+        if float(row["battery_voltage"]) < 12:
+            score -= 10
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-def calculate_control_risk(row):
-    score = 100
+    def calculate_control_risk(row):
+        score = 100
 
-    if row["collision_warning"] == 1:
-        score -= 40
+        if row["collision_warning"] == 1:
+            score -= 40
 
-    if row["lane_departure"] == 1:
-        score -= 20
+        if row["lane_departure"] == 1:
+            score -= 20
 
-    if row["traction_control"] == 1:
-        score -= 10
+        if row["traction_control"] == 1:
+            score -= 10
 
-    if row["abs_active"] == 1:
-        score -= 10
+        if row["abs_active"] == 1:
+            score -= 10
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-def calculate_drive_risk(row):
-    score = 100
+    def calculate_drive_risk(row):
+        score = 100
 
-    if float(row["throttle_position"]) > 90:
-        score -= 20
+        if float(row["throttle_position"]) > 90:
+            score -= 20
 
-    if float(row["brake_pressure"]) > 45:
-        score -= 20
+        if float(row["brake_pressure"]) > 45:
+            score -= 20
 
-    if abs(float(row["steering_angle"])) > 40:
-        score -= 20
+        if abs(float(row["steering_angle"])) > 40:
+            score -= 20
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-def calculate_dynamics_risk(row):
-    score = 100
+    def calculate_dynamics_risk(row):
+        score = 100
 
-    if abs(float(row["yaw_rate"])) > 7:
-        score -= 20
+        if abs(float(row["yaw_rate"])) > 7:
+            score -= 20
 
-    if abs(float(row["roll"])) > 4:
-        score -= 20
+        if abs(float(row["roll"])) > 4:
+            score -= 20
 
-    if abs(float(row["pitch"])) > 4:
-        score -= 20
+        if abs(float(row["pitch"])) > 4:
+            score -= 20
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-risk_id = 1
+    risk_id = 1
 
-stage_plan = [
-    ("DRIVE", 6),
-    ("CONTROL", 5),
-    ("DYNAMICS", 3),
-    ("STATUS", 3)
-]
+    stage_plan = [
+        ("DRIVE", 6),
+        ("CONTROL", 5),
+        ("DYNAMICS", 3),
+        ("STATUS", 3)
+    ]
 
-start_date = datetime.strptime(
-    "2026-06-01 01:00",
-    "%Y-%m-%d %H:%M"
-)
+    start_date = datetime.strptime(
+        "2026-06-01 01:00",
+        "%Y-%m-%d %H:%M"
+    )
 
-with sample_engine.connect() as conn:
+    with sample_engine.connect() as conn:
 
-    # 7일 데이터 생성
-    for day in range(7):
+        # 7일 데이터 생성
+        for day in range(7):
 
-        day_start = start_date + timedelta(days=day)
+            day_start = start_date + timedelta(days=day)
 
-        # 하루 생산 차량 100대
-        offset = day * 100
+            # 하루 생산 차량 100대
+            offset = day * 100
 
-        vehicles = conn.execute(
-            text("""
-                SELECT DISTINCT vehicle_id
-                FROM car_status
-                ORDER BY vehicle_id
-                LIMIT 100 OFFSET :offset
-            """),
-            {"offset": offset}
-        ).mappings().all()
+            vehicles = conn.execute(
+                text("""
+                    SELECT DISTINCT vehicle_id
+                    FROM car_status
+                    ORDER BY vehicle_id
+                    LIMIT 100 OFFSET :offset
+                """),
+                {"offset": offset}
+            ).mappings().all()
 
-        vehicle_ids = [
-            vehicle["vehicle_id"]
-            for vehicle in vehicles
-        ]
+            vehicle_ids = [
+                vehicle["vehicle_id"]
+                for vehicle in vehicles
+            ]
 
-        current_time = day_start
+            current_time = day_start
 
-        for stage_name, duration in stage_plan:
+            for stage_name, duration in stage_plan:
 
-            stage_start = current_time
+                stage_start = current_time
 
-            stage_end = (
-                current_time
-                + timedelta(hours=duration)
-            )
-
-            scores = []
-
-            for vehicle_id in vehicle_ids:
-
-                if stage_name == "DRIVE":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_drive
-                            WHERE vehicle_id = :vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
+                stage_end = (
+                    current_time
+                    + timedelta(hours=duration)
+                )
 
-                    if row:
-                        scores.append(
-                            calculate_drive_risk(row)
-                        )
-
-                elif stage_name == "CONTROL":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_control
-                            WHERE vehicle_id = :vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
-
-                    if row:
-                        scores.append(
-                            calculate_control_risk(row)
-                        )
-
-                elif stage_name == "DYNAMICS":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_dynamics
-                            WHERE vehicle_id = :vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
-
-                    if row:
-                        scores.append(
-                            calculate_dynamics_risk(row)
-                        )
-
-                elif stage_name == "STATUS":
-
-                    row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_status
-                            WHERE vehicle_id = :vehicle_id
-                            LIMIT 1
-                        """),
-                        {"vehicle_id": vehicle_id}
-                    ).mappings().first()
-
-                    if row:
-                        scores.append(
-                            calculate_status_risk(row)
-                        )
-
-            avg_score = (
-                round(sum(scores) / len(scores), 2)
-                if scores else 0
-            )
-
-            message = {
-                "id": risk_id,
-                "inspection_type": stage_name,
-                "inspection_round": day + 1,
-                "risk_score": avg_score,
-                "start_time": stage_start.strftime(
-                    "%Y-%m-%d %H:%M"
-                ),
-                "end_time": stage_end.strftime(
-                    "%Y-%m-%d %H:%M"
+                scores = []
+
+                for vehicle_id in vehicle_ids:
+
+                    if stage_name == "DRIVE":
+
+                        row = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_drive
+                                WHERE vehicle_id = :vehicle_id
+                                LIMIT 1
+                            """),
+                            {"vehicle_id": vehicle_id}
+                        ).mappings().first()
+
+                        if row:
+                            scores.append(
+                                calculate_drive_risk(row)
+                            )
+
+                    elif stage_name == "CONTROL":
+
+                        row = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_control
+                                WHERE vehicle_id = :vehicle_id
+                                LIMIT 1
+                            """),
+                            {"vehicle_id": vehicle_id}
+                        ).mappings().first()
+
+                        if row:
+                            scores.append(
+                                calculate_control_risk(row)
+                            )
+
+                    elif stage_name == "DYNAMICS":
+
+                        row = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_dynamics
+                                WHERE vehicle_id = :vehicle_id
+                                LIMIT 1
+                            """),
+                            {"vehicle_id": vehicle_id}
+                        ).mappings().first()
+
+                        if row:
+                            scores.append(
+                                calculate_dynamics_risk(row)
+                            )
+
+                    elif stage_name == "STATUS":
+
+                        row = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_status
+                                WHERE vehicle_id = :vehicle_id
+                                LIMIT 1
+                            """),
+                            {"vehicle_id": vehicle_id}
+                        ).mappings().first()
+
+                        if row:
+                            scores.append(
+                                calculate_status_risk(row)
+                            )
+
+                avg_score = (
+                    round(sum(scores) / len(scores), 2)
+                    if scores else 0
                 )
-            }
 
-            producer.send(
-                "quality.inspection.risk_history",
-                value=message
-            )
+                message = {
+                    "id": risk_id,
+                    "inspection_type": stage_name,
+                    "inspection_round": day + 1,
+                    "risk_score": avg_score,
+                    "start_time": stage_start.strftime(
+                        "%Y-%m-%d %H:%M"
+                    ),
+                    "end_time": stage_end.strftime(
+                        "%Y-%m-%d %H:%M"
+                    )
+                }
+
+                producer.send(
+                    "quality.inspection.risk_history",
+                    value=message
+                )
 
-            print(
-                f"Kafka 전송 완료 : {message}"
-            )
+                risk_id += 1
 
-            risk_id += 1
+                current_time = stage_end
 
-            current_time = stage_end
+                time.sleep(1)
 
-            time.sleep(1)
+    producer.flush()
 
-producer.flush()
+    print("모든 데이터 Kafka 전송 완료")
 
-print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index 8b77054..ba9c28d 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -10,266 +10,266 @@
 
 from app.kafka.iam_provider import MSKTokenProvider
 
-load_dotenv()
-SAMPLE_DATABASE_URL = (
-    os.getenv("SAMPLE_DATABASE_END")
-)
+def run():
+    load_dotenv()
+    SAMPLE_DATABASE_URL = (
+        os.getenv("SAMPLE_DATABASE_END")
+    )
 
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
-)
+    sample_engine = create_engine(
+        SAMPLE_DATABASE_URL,
+        pool_pre_ping=True
+    )
 
-producer = KafkaProducer(
-    bootstrap_servers=[
-        os.getenv("BROKER_URL_1"),
-        os.getenv("BROKER_URL_2")
-    ],
+    producer = KafkaProducer(
+        bootstrap_servers=[
+            os.getenv("BROKER_URL_1"),
+            os.getenv("BROKER_URL_2")
+        ],
 
-    security_protocol="SASL_SSL",
+        security_protocol="SASL_SSL",
 
-    sasl_mechanism="OAUTHBEARER",
+        sasl_mechanism="OAUTHBEARER",
 
-    sasl_oauth_token_provider=MSKTokenProvider(),
+        sasl_oauth_token_provider=MSKTokenProvider(),
 
-    value_serializer=lambda x:
-        json.dumps(x, default=str).encode("utf-8")
-)
+        value_serializer=lambda x:
+            json.dumps(x, default=str).encode("utf-8")
+    )
 
 
-def calculate_status_risk(row):
-    score = 100
+    def calculate_status_risk(row):
+        score = 100
 
-    if float(row["speed"]) > 120:
-        score -= 20
+        if float(row["speed"]) > 120:
+            score -= 20
 
-    if int(row["att"]) > 4000:
-        score -= 20
+        if int(row["att"]) > 4000:
+            score -= 20
 
-    if float(row["battery_voltage"]) < 12:
-        score -= 10
+        if float(row["battery_voltage"]) < 12:
+            score -= 10
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-def calculate_control_risk(row):
-    score = 100
+    def calculate_control_risk(row):
+        score = 100
 
-    if row["collision_warning"] == 1:
-        score -= 40
+        if row["collision_warning"] == 1:
+            score -= 40
 
-    if row["lane_departure"] == 1:
-        score -= 20
+        if row["lane_departure"] == 1:
+            score -= 20
 
-    if row["traction_control"] == 1:
-        score -= 10
+        if row["traction_control"] == 1:
+            score -= 10
 
-    if row["abs_active"] == 1:
-        score -= 10
+        if row["abs_active"] == 1:
+            score -= 10
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-def calculate_drive_risk(row):
-    score = 100
+    def calculate_drive_risk(row):
+        score = 100
 
-    if float(row["throttle_position"]) > 90:
-        score -= 20
+        if float(row["throttle_position"]) > 90:
+            score -= 20
 
-    if float(row["brake_pressure"]) > 45:
-        score -= 20
+        if float(row["brake_pressure"]) > 45:
+            score -= 20
 
-    if abs(float(row["steering_angle"])) > 40:
-        score -= 20
+        if abs(float(row["steering_angle"])) > 40:
+            score -= 20
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-def calculate_dynamics_risk(row):
-    score = 100
+    def calculate_dynamics_risk(row):
+        score = 100
 
-    if abs(float(row["yaw_rate"])) > 7:
-        score -= 20
+        if abs(float(row["yaw_rate"])) > 7:
+            score -= 20
 
-    if abs(float(row["roll"])) > 4:
-        score -= 20
+        if abs(float(row["roll"])) > 4:
+            score -= 20
 
-    if abs(float(row["pitch"])) > 4:
-        score -= 20
+        if abs(float(row["pitch"])) > 4:
+            score -= 20
 
-    return max(score, 0)
+        return max(score, 0)
 
 
-def get_risk_level(score):
+    def get_risk_level(score):
 
-    if score >= 80:
-        return "LOW"
+        if score >= 80:
+            return "LOW"
 
-    elif score >= 50:
-        return "MEDIUM"
+        elif score >= 50:
+            return "MEDIUM"
 
-    return "HIGH"
+        return "HIGH"
 
 
-risk_id = 1
+    risk_id = 1
 
-base_date = datetime.strptime(
-    "2026-06-01",
-    "%Y-%m-%d"
-)
+    base_date = datetime.strptime(
+        "2026-06-01",
+        "%Y-%m-%d"
+    )
 
-with sample_engine.connect() as conn:
+    with sample_engine.connect() as conn:
 
-    for day in range(7):
+        for day in range(7):
 
-        current_date = (
-            base_date +
-            timedelta(days=day)
-        )
-
-        offset = day * 100
-
-        vehicles = conn.execute(
-            text("""
-                SELECT DISTINCT vehicle_id
-                FROM car_status
-                ORDER BY vehicle_id
-                LIMIT 100 OFFSET :offset
-            """),
-            {"offset": offset}
-        ).mappings().all()
-
-        vehicle_ids = [
-            v["vehicle_id"]
-            for v in vehicles
-        ]
-
-        low_count = 0
-        medium_count = 0
-        high_count = 0
-
-        for vehicle_id in vehicle_ids:
+            current_date = (
+                base_date +
+                timedelta(days=day)
+            )
 
-            scores = []
+            offset = day * 100
 
-            status = conn.execute(
+            vehicles = conn.execute(
                 text("""
-                    SELECT *
+                    SELECT DISTINCT vehicle_id
                     FROM car_status
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
+                    ORDER BY vehicle_id
+                    LIMIT 100 OFFSET :offset
                 """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+                {"offset": offset}
+            ).mappings().all()
+
+            vehicle_ids = [
+                v["vehicle_id"]
+                for v in vehicles
+            ]
+
+            low_count = 0
+            medium_count = 0
+            high_count = 0
+
+            for vehicle_id in vehicle_ids:
+
+                scores = []
+
+                status = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_status
+                        WHERE vehicle_id=:vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
+
+                if status:
+                    scores.append(
+                        calculate_status_risk(status)
+                    )
 
-            if status:
-                scores.append(
-                    calculate_status_risk(status)
-                )
+                control = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_control
+                        WHERE vehicle_id=:vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
+
+                if control:
+                    scores.append(
+                        calculate_control_risk(control)
+                    )
 
-            control = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_control
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+                drive = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_drive
+                        WHERE vehicle_id=:vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
+
+                if drive:
+                    scores.append(
+                        calculate_drive_risk(drive)
+                    )
 
-            if control:
-                scores.append(
-                    calculate_control_risk(control)
-                )
+                dynamics = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_dynamics
+                        WHERE vehicle_id=:vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
+
+                if dynamics:
+                    scores.append(
+                        calculate_dynamics_risk(dynamics)
+                    )
 
-            drive = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_drive
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+                if not scores:
+                    continue
 
-            if drive:
-                scores.append(
-                    calculate_drive_risk(drive)
+                avg_score = (
+                    sum(scores) / len(scores)
                 )
 
-            dynamics = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_dynamics
-                    WHERE vehicle_id=:vehicle_id
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
-
-            if dynamics:
-                scores.append(
-                    calculate_dynamics_risk(dynamics)
+                level = get_risk_level(
+                    avg_score
                 )
 
-            if not scores:
-                continue
-
-            avg_score = (
-                sum(scores) / len(scores)
-            )
-
-            level = get_risk_level(
-                avg_score
-            )
-
-            if level == "LOW":
-                low_count += 1
+                if level == "LOW":
+                    low_count += 1
 
-            elif level == "MEDIUM":
-                medium_count += 1
+                elif level == "MEDIUM":
+                    medium_count += 1
 
-            else:
-                high_count += 1
+                else:
+                    high_count += 1
 
-        daily_result = [
-            ("LOW", low_count),
-            ("MEDIUM", medium_count),
-            ("HIGH", high_count)
-        ]
+            daily_result = [
+                ("LOW", low_count),
+                ("MEDIUM", medium_count),
+                ("HIGH", high_count)
+            ]
 
-        for level, count in daily_result:
+            for level, count in daily_result:
 
-            message = {
-                "id": risk_id,
-                "risk_level": level,
-                "risk_count": count,
+                message = {
+                    "id": risk_id,
+                    "risk_level": level,
+                    "risk_count": count,
 
-                # 필요하면 ratio 계산 수정
-                "risk_ratio": round(
-                    count / 100 * 100,
-                    2
-                ),
+                    # 필요하면 ratio 계산 수정
+                    "risk_ratio": round(
+                        count / 100 * 100,
+                        2
+                    ),
 
-                "created_at":
-                    current_date.strftime(
-                        "%Y-%m-%d 00:00:00"
-                    )
-            }
+                    "created_at":
+                        current_date.strftime(
+                            "%Y-%m-%d 00:00:00"
+                        )
+                }
 
-            producer.send(
-                "quality.inspection.risk_trend",
-                value=message
-            )
+                producer.send(
+                    "quality.inspection.risk_trend",
+                    value=message
+                )
 
-            print(
-                f"Kafka 전송 완료 : {message}"
-            )
+                risk_id += 1
 
-            risk_id += 1
+                time.sleep(1)
 
-            time.sleep(1)
+    producer.flush()
 
-producer.flush()
+    print("모든 데이터 Kafka 전송 완료")
 
-print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index ba33d72..335d40e 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -9,179 +9,179 @@
 
 from app.kafka.iam_provider import MSKTokenProvider
 
-load_dotenv()
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+def run():
+    load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
-SAMPLE_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-)
+    SAMPLE_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+    )
 
-sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
-    pool_pre_ping=True
-)
+    sample_engine = create_engine(
+        SAMPLE_DATABASE_URL,
+        pool_pre_ping=True
+    )
 
-producer = KafkaProducer(
-    bootstrap_servers=[
-        os.getenv("BROKER_URL_1"),
-        os.getenv("BROKER_URL_2")
-    ],
+    producer = KafkaProducer(
+        bootstrap_servers=[
+            os.getenv("BROKER_URL_1"),
+            os.getenv("BROKER_URL_2")
+        ],
 
-    security_protocol="SASL_SSL",
+        security_protocol="SASL_SSL",
 
-    sasl_mechanism="OAUTHBEARER",
+        sasl_mechanism="OAUTHBEARER",
 
-    sasl_oauth_token_provider=MSKTokenProvider(),
+        sasl_oauth_token_provider=MSKTokenProvider(),
 
-    value_serializer=lambda x:
-        json.dumps(x, default=str).encode("utf-8")
-)
+        value_serializer=lambda x:
+            json.dumps(x, default=str).encode("utf-8")
+    )
 
 
-def calculate_status_score(status, control):
+    def calculate_status_score(status, control):
 
-    score = 100
-    issues = []
+        score = 100
+        issues = []
 
-    speed = float(status["speed"])
-    rpm = int(status["att"])
-    battery = float(status["battery_voltage"])
+        speed = float(status["speed"])
+        rpm = int(status["att"])
+        battery = float(status["battery_voltage"])
 
-    if speed > 120:
-        score -= 20
-        issues.append("over speed")
+        if speed > 120:
+            score -= 20
+            issues.append("over speed")
 
-    if rpm > 4000:
-        score -= 20
-        issues.append("RPM Error")
+        if rpm > 4000:
+            score -= 20
+            issues.append("RPM Error")
 
-    if battery < 12.0:
-        score -= 10
-        issues.append("battery drop")
+        if battery < 12.0:
+            score -= 10
+            issues.append("battery drop")
 
-    if control["collision_warning"] == 1:
-        score -= 40
-        issues.append("crash warning")
+        if control["collision_warning"] == 1:
+            score -= 40
+            issues.append("crash warning")
 
-    if status["gear"] == "P" and speed > 20:
-        score -= 30
-        issues.append("Parking")
+        if status["gear"] == "P" and speed > 20:
+            score -= 30
+            issues.append("Parking")
 
-    return max(score, 0), issues
+        return max(score, 0), issues
 
 
-def get_result(score):
+    def get_result(score):
 
-    if score >= 90:
-        return "PASS"
+        if score >= 90:
+            return "PASS"
 
-    if score >= 70:
-        return "WARN"
+        if score >= 70:
+            return "WARN"
 
-    return "FAIL"
+        return "FAIL"
 
 
-with sample_engine.connect() as conn:
+    with sample_engine.connect() as conn:
 
-    master_rows = conn.execute(
-        text("""
-            SELECT *
-            FROM car_master
-            ORDER BY id
-        """)
-    ).mappings().all()
+        master_rows = conn.execute(
+            text("""
+                SELECT *
+                FROM car_master
+                ORDER BY id
+            """)
+        ).mappings().all()
 
-    for master_row in master_rows:
+        for master_row in master_rows:
 
-        vehicle_id = master_row["vehicle_id"]
+            vehicle_id = master_row["vehicle_id"]
 
-        status_row = conn.execute(
-            text("""
-                SELECT *
-                FROM car_status
-                WHERE vehicle_id = :vehicle_id
-                ORDER BY created_at DESC
-                LIMIT 1
-            """),
-            {"vehicle_id": vehicle_id}
-        ).mappings().first()
-
-        control_row = conn.execute(
-            text("""
-                SELECT *
-                FROM car_control
-                WHERE vehicle_id = :vehicle_id
-                ORDER BY created_at DESC
-                LIMIT 1
-            """),
-            {"vehicle_id": vehicle_id}
-        ).mappings().first()
+            status_row = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_status
+                    WHERE vehicle_id = :vehicle_id
+                    ORDER BY created_at DESC
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
+
+            control_row = conn.execute(
+                text("""
+                    SELECT *
+                    FROM car_control
+                    WHERE vehicle_id = :vehicle_id
+                    ORDER BY created_at DESC
+                    LIMIT 1
+                """),
+                {"vehicle_id": vehicle_id}
+            ).mappings().first()
 
-        if not status_row or not control_row:
-            continue
+            if not status_row or not control_row:
+                continue
 
-        score, issues = calculate_status_score(
-            status_row,
-            control_row
-        )
+            score, issues = calculate_status_score(
+                status_row,
+                control_row
+            )
 
-        message = {
-            "car_code": vehicle_id.split("-")[0],
+            message = {
+                "car_code": vehicle_id.split("-")[0],
 
-            "inspection_no":
-                f"STATUS-{master_row['id']:05d}",
+                "inspection_no":
+                    f"STATUS-{master_row['id']:05d}",
 
-            "vehicle_id": vehicle_id,
+                "vehicle_id": vehicle_id,
 
-            "speed":
-                float(status_row["speed"]),
+                "speed":
+                    float(status_row["speed"]),
 
-            "att":
-                int(status_row["att"]),
+                "att":
+                    int(status_row["att"]),
 
-            "gear":
-                status_row["gear"],
+                "gear":
+                    status_row["gear"],
 
-            "battery_voltage":
-                float(
-                    status_row["battery_voltage"]
-                ),
+                "battery_voltage":
+                    float(
+                        status_row["battery_voltage"]
+                    ),
 
-            "fuel_rate":
-                float(status_row["fuel_rate"]),
+                "fuel_rate":
+                    float(status_row["fuel_rate"]),
 
-            "status_score":
-                float(score),
+                "status_score":
+                    float(score),
 
-            "inspection_result":
-                get_result(score),
+                "inspection_result":
+                    get_result(score),
 
-            "issue_message":
-                ", ".join(issues)
-                if issues else "정상",
+                "issue_message":
+                    ", ".join(issues)
+                    if issues else "정상",
 
-            "created_at":
-                status_row["created_at"]
-        }
+                "created_at":
+                    status_row["created_at"]
+            }
 
-        producer.send(
-            "quality.inspection.status_detail",
-            value=message
-        )
+            producer.send(
+                "quality.inspection.status_detail",
+                value=message
+            )
 
-        print(
-            f"Kafka 전송 완료 : {message}"
-        )
+            time.sleep(1)
 
-        time.sleep(1)
+    producer.flush()
 
-producer.flush()
+    print("모든 데이터 Kafka 전송 완료")
 
-print("모든 데이터 Kafka 전송 완료")
\ No newline at end of file
+if __name__ == "__main__":
+    run()
\ No newline at end of file

From f4394c6865e1e3b9bdff91d1f00f16ce35792e00 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 11:08:02 +0900
Subject: [PATCH 040/148] fix : consumer function update

---
 app/kafka/consumer.py | 40 ++++++++++++++++++----------------------
 1 file changed, 18 insertions(+), 22 deletions(-)

diff --git a/app/kafka/consumer.py b/app/kafka/consumer.py
index 0d2452c..170b90e 100644
--- a/app/kafka/consumer.py
+++ b/app/kafka/consumer.py
@@ -7,35 +7,31 @@
 from dotenv import load_dotenv
 import os
 
-def run():
-    load_dotenv()
-    def create_consumer(topic, group_id):
+load_dotenv()
+def create_consumer(topic, group_id):
 
-        consumer = KafkaConsumer(
-            topic,
-            ssl_context=ssl.create_default_context(),
+    consumer = KafkaConsumer(
+        topic,
+        ssl_context=ssl.create_default_context(),
 
-            bootstrap_servers=[
-            os.getenv("BROKER_URL_1"),
-            os.getenv("BROKER_URL_2")
-            ],
+        bootstrap_servers=[
+        os.getenv("BROKER_URL_1"),
+        os.getenv("BROKER_URL_2")
+        ],
 
-            group_id=group_id,
+        group_id=group_id,
 
-            auto_offset_reset="earliest",
+        auto_offset_reset="earliest",
 
-            enable_auto_commit=True,
+        enable_auto_commit=True,
 
-            security_protocol="SASL_SSL",
+        security_protocol="SASL_SSL",
 
-            sasl_mechanism="OAUTHBEARER",
+        sasl_mechanism="OAUTHBEARER",
 
-            sasl_oauth_token_provider=MSKTokenProvider(),
+        sasl_oauth_token_provider=MSKTokenProvider(),
 
-            value_deserializer=lambda x: json.loads(x.decode("utf-8"))
-        )
+        value_deserializer=lambda x: json.loads(x.decode("utf-8"))
+    )
 
-        return consumer
-
-if __name__ == "__main__":
-    run()
\ No newline at end of file
+    return consumer

From 8cde69f250240708aaa8b308dcaa23f7c6bb7921 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 30 Jun 2026 13:01:13 +0900
Subject: [PATCH 041/148] =?UTF-8?q?feat:=20Kafka=20raw=20=EC=9D=B4?=
 =?UTF-8?q?=EB=B2=A4=ED=8A=B8=20=EA=B8=B0=EB=B0=98=20=EB=B3=91=EB=AA=A9=20?=
 =?UTF-8?q?=ED=83=90=EC=A7=80=20=EC=97=B0=EB=8F=99?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- factory.manufacturing.raw 토픽을 ai-consumer-group으로 소비하는 raw Kafka consumer 추가

- Kafka record value의 eventJson을 manufacturing_event_json.event_json에 저장하도록 처리

- Kafka key carId와 eventJson.product.carMasterId를 car_master_id로 매핑

- BROKER_URL_1, BROKER_URL_2 기반으로 Kafka bootstrap 서버를 구성

- product_process_history 의존성을 제거하고 manufacturing_event_json 기반 병목 분석으로 전환

- bottleneck_analysis_result 스키마를 제조 병목 분석 결과 구조에 맞게 정렬

- manufacturing_event_id를 manufacturing_event_json.id 기준으로 저장

- Rule Engine과 Isolation Forest 결합 기준으로 병목 후보, 위험도, 영향 차량 수를 계산

- 평균 지연 시간은 DB에 원본 double 값으로 저장하고 API 응답 시 소수점 둘째 자리로 반환

- processCode를 장비 코드 기반 표시명으로 변환해 도장 (L1), 프레스 (P4) 형식으로 반환

- Redis 병목 캐시 TTL을 60초 기본값으로 고정하고 Kafka raw 수신 시 캐시를 무효화

- 병목 분석 페이지네이션 cursor, size 기준 rank 범위 조회와 stale rank 정리 로직 추가

- detected_at 변경 시 updated_at이 DB 수정 시간으로 갱신되도록 저장 로직 보강
---
 .env.example                                  |   3 +-
 .gitignore                                    |   1 +
 app/core/config.py                            |  14 +-
 app/dto/response/bottleneck_response.py       |   5 +-
 app/kafka/iam_provider.py                     |   7 +-
 app/kafka/raw_event_consumer.py               | 302 +++++++++++++++
 app/main.py                                   |   6 +
 app/ml/inference/bottleneck_detector.py       | 112 ++++--
 .../bottleneck_analysis_repository.py         | 364 +++++++++++++-----
 .../manufacturing_event_repository.py         |  34 ++
 app/service/analysis/bottleneck_service.py    | 127 +++++-
 11 files changed, 826 insertions(+), 149 deletions(-)
 create mode 100644 app/kafka/raw_event_consumer.py

diff --git a/.env.example b/.env.example
index 25124f8..2afc65f 100644
--- a/.env.example
+++ b/.env.example
@@ -10,14 +10,13 @@ AI_MANUAL_API_URL=
 COLLEAGUE_SKILL_API_URL=
 
 # Kafka
-KAFKA_BOOTSTRAP_SERVERS=
 BROKER_URL_1=
 BROKER_URL_2=
 
 # Redis Cache
 REDIS_URL=redis://localhost:6379/0
 REDIS_KEY_PREFIX=aims:ai-service
-REDIS_CACHE_TTL_SECONDS=300
+REDIS_CACHE_TTL_SECONDS=60
 
 # MySQL DB
 # MAIN_DATABASE_URL contains the shared driver/user/password/host/port.
diff --git a/.gitignore b/.gitignore
index 07bcc90..c900596 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,3 +42,4 @@ datasets/
 
 # Docs
 app/docs/PRD_*.md
+app/docs/*.md
\ No newline at end of file
diff --git a/app/core/config.py b/app/core/config.py
index c5ee136..9e82598 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -52,14 +52,12 @@ class Settings(BaseSettings):
         alias="COLLEAGUE_SKILL_API_URL",
     )
 
-    kafka_bootstrap_servers: str | None = Field(
-        default=None,
-        alias="KAFKA_BOOTSTRAP_SERVERS",
-    )
+    broker_url_1: str | None = Field(default=None, alias="BROKER_URL_1")
+    broker_url_2: str | None = Field(default=None, alias="BROKER_URL_2")
 
     redis_url: str | None = Field(default=None, alias="REDIS_URL")
     redis_key_prefix: str = Field(default="aims:ai-service", alias="REDIS_KEY_PREFIX")
-    redis_cache_ttl_seconds: int = Field(default=300, alias="REDIS_CACHE_TTL_SECONDS")
+    redis_cache_ttl_seconds: int = Field(default=60, alias="REDIS_CACHE_TTL_SECONDS")
 
     main_database_url: str | None = Field(default=None, alias="MAIN_DATABASE_URL")
     main_db_name: str | None = Field(default=None, alias="MAIN_DB_NAME")
@@ -126,6 +124,12 @@ def parse_debug_value(cls, value: object) -> object:
                 return True
         return value
 
+    @field_validator("redis_cache_ttl_seconds", mode="before")
+    @classmethod
+    def use_default_redis_cache_ttl(cls, value: object) -> int:
+        """REDIS_CACHE_TTL_SECONDS는 .env보다 코드 기본값을 우선한다."""
+        return 60
+
     model_config = SettingsConfigDict(
         env_file=".env",
         env_file_encoding="utf-8",
diff --git a/app/dto/response/bottleneck_response.py b/app/dto/response/bottleneck_response.py
index 0fd7c9f..20a53fd 100644
--- a/app/dto/response/bottleneck_response.py
+++ b/app/dto/response/bottleneck_response.py
@@ -6,10 +6,9 @@ class BottleneckAnalysisItem(BaseModel):
 
     rank_no: int = Field(alias="rankNo")
     process_code: str = Field(alias="processCode")
-    station_code: str = Field(alias="stationCode")
-    avg_delay_time: float = Field(alias="avgDelayTime")
+    delay_time: float = Field(alias="delayTime")
     affected_vehicle_count: int = Field(alias="affectedVehicleCount")
-    risk_score: int = Field(alias="riskScore")
+    risk_score: float = Field(alias="riskScore")
 
 
 class BottleneckAnalysisPage(BaseModel):
diff --git a/app/kafka/iam_provider.py b/app/kafka/iam_provider.py
index c7f46cb..ea39ee2 100644
--- a/app/kafka/iam_provider.py
+++ b/app/kafka/iam_provider.py
@@ -1,8 +1,11 @@
-from kafka.sasl.oauth import AbstractTokenProvider
+from kafka.net.sasl.oauth import AbstractTokenProvider
 from aws_msk_iam_sasl_signer import MSKAuthTokenProvider
 
 
 class MSKTokenProvider(AbstractTokenProvider):
+    """AWS MSK IAM 인증에 필요한 OAUTHBEARER 토큰을 kafka-python에 제공한다."""
+
     def token(self):
+        # MSK 클러스터 리전 기준으로 짧은 수명의 IAM 인증 토큰을 매 연결 시점에 발급한다.
         token, _ = MSKAuthTokenProvider.generate_auth_token("ap-northeast-2")
-        return token
\ No newline at end of file
+        return token
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
new file mode 100644
index 0000000..9ef1766
--- /dev/null
+++ b/app/kafka/raw_event_consumer.py
@@ -0,0 +1,302 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import ssl
+from datetime import datetime
+from threading import Event
+from typing import Any
+
+from fastapi import FastAPI
+
+from app.core.config import settings
+from app.kafka.iam_provider import MSKTokenProvider
+from app.repository.sampledb_repository import SampleDbRepository
+
+
+logger = logging.getLogger(__name__)
+PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
+
+# assembly-service의 app.kafka.topics.raw.name과 동일한 실제 raw 토픽명.
+RAW_TOPIC = "factory.manufacturing.raw"
+RAW_CONSUMER_GROUP_ID = "ai-consumer-group"
+RAW_AUTO_OFFSET_RESET = "earliest"
+RAW_CONSUMER_CONCURRENCY = 2
+
+
+def start_raw_event_consumer(app: FastAPI) -> None:
+    """FastAPI startup 시 제조 raw Kafka consumer를 백그라운드 thread로 시작한다."""
+    bootstrap_servers = _bootstrap_servers()
+    if not bootstrap_servers:
+        logger.info("Kafka bootstrap servers are not set. Raw Kafka consumer is disabled.")
+        return
+    if not settings.sample_database_connection_url:
+        logger.info("SAMPLE_DB_NAME is not set. Raw Kafka consumer is disabled.")
+        return
+
+    stop_event = Event()
+    tasks = [
+        asyncio.create_task(
+            asyncio.to_thread(
+                _run_raw_event_consumer,
+                stop_event,
+                bootstrap_servers,
+                consumer_index,
+            ),
+        )
+        for consumer_index in range(1, RAW_CONSUMER_CONCURRENCY + 1)
+    ]
+    # shutdown 이벤트에서 thread consumer loop를 안전하게 종료하기 위한 공유 신호.
+    app.state.raw_event_consumer_stop_event = stop_event
+    app.state.raw_event_consumer_tasks = tasks
+
+
+async def stop_raw_event_consumer(app: FastAPI) -> None:
+    """FastAPI shutdown 시 consumer loop 종료 신호를 보내고 thread 종료를 기다린다."""
+    tasks = getattr(app.state, "raw_event_consumer_tasks", None)
+    if not tasks:
+        return
+    stop_event = getattr(app.state, "raw_event_consumer_stop_event", None)
+    if stop_event is not None:
+        stop_event.set()
+    await asyncio.gather(*tasks, return_exceptions=True)
+
+
+def _run_raw_event_consumer(
+    stop_event: Event,
+    bootstrap_servers: list[str],
+    consumer_index: int,
+) -> None:
+    try:
+        from kafka import KafkaConsumer
+    except ModuleNotFoundError:
+        logger.exception("kafka-python is required to consume manufacturing raw events.")
+        return
+
+    # 기존 품질 Kafka 연동과 동일하게 MSK IAM 인증(SASL_SSL/OAUTHBEARER)을 사용한다.
+    consumer = KafkaConsumer(
+        RAW_TOPIC,
+        ssl_context=ssl.create_default_context(),
+        bootstrap_servers=bootstrap_servers,
+        group_id=RAW_CONSUMER_GROUP_ID,
+        auto_offset_reset=RAW_AUTO_OFFSET_RESET,
+        enable_auto_commit=False,
+        security_protocol="SASL_SSL",
+        sasl_mechanism="OAUTHBEARER",
+        sasl_oauth_token_provider=MSKTokenProvider(),
+        consumer_timeout_ms=1000,
+    )
+    repository = SampleDbRepository(settings.sample_database_connection_url)
+    repository.schema.ensure_schema()
+
+    logger.info(
+        "Raw Kafka consumer started: topic=%s group=%s concurrency=%s/%s bootstrap=%s auth=SASL_SSL/OAUTHBEARER",
+        RAW_TOPIC,
+        RAW_CONSUMER_GROUP_ID,
+        consumer_index,
+        RAW_CONSUMER_CONCURRENCY,
+        bootstrap_servers,
+    )
+    try:
+        while not stop_event.is_set():
+            for record in consumer:
+                if stop_event.is_set():
+                    break
+                try:
+                    _consume_record(repository, record)
+                except Exception:
+                    logger.exception(
+                        "Failed to consume raw event: topic=%s partition=%s offset=%s",
+                        record.topic,
+                        record.partition,
+                        record.offset,
+                    )
+                finally:
+                    # DB 저장 시도 후 offset을 커밋해 재기동 시 같은 메시지의 무한 재처리를 막는다.
+                    consumer.commit()
+    except Exception:
+        logger.exception("Raw Kafka consumer failed.")
+    finally:
+        consumer.close()
+        logger.info(
+            "Raw Kafka consumer stopped: topic=%s group=%s concurrency=%s/%s",
+            RAW_TOPIC,
+            RAW_CONSUMER_GROUP_ID,
+            consumer_index,
+            RAW_CONSUMER_CONCURRENCY,
+        )
+
+
+def _consume_record(repository: SampleDbRepository, record: Any) -> None:
+    """Kafka raw 메시지를 manufacturing_event_json row로 변환해 sampledb에 upsert한다."""
+    raw_event = _parse_raw_event(record)
+    row = _raw_event_to_row(raw_event)
+    affected_rows = repository.events.insert_raw_rows(
+        [row],
+        update_existing=True,
+    )
+    # 새 raw 이벤트가 들어오면 다음 병목 API 호출에서 재분석하도록 Redis 캐시를 비운다.
+    _clear_bottleneck_cache()
+    logger.info(
+        "Kafka raw event consumed and stored: topic=%s partition=%s offset=%s "
+        "key=%s event_id=%s manufacturing_event_id_source=manufacturing_event_json.id "
+        "car_master_id=%s process_code=%s affected_rows=%s",
+        record.topic,
+        record.partition,
+        record.offset,
+        raw_event.get("_kafka_key"),
+        row["event_id"],
+        row["car_master_id"],
+        row["process_code"],
+        affected_rows,
+    )
+
+
+def _parse_raw_event(record: Any) -> dict[str, Any]:
+    """Kafka record의 value JSON과 key(carId)를 분석하기 쉬운 dict로 정규화한다."""
+    payload = json.loads(record.value.decode("utf-8"))
+    if not isinstance(payload, dict):
+        raise ValueError("Manufacturing raw event must be a JSON object.")
+
+    event_json = _parse_event_json(payload.get("eventJson") or payload.get("event_json"))
+    if event_json is None:
+        raise ValueError("Manufacturing raw event must include eventJson object.")
+
+    key_text = record.key.decode("utf-8") if record.key else None
+    payload["eventJson"] = event_json
+    payload["_kafka_key"] = key_text
+    return payload
+
+
+def _raw_event_to_row(raw_event: dict[str, Any]) -> dict[str, Any]:
+    """assembly-service raw envelope를 manufacturing_event_json 테이블 컬럼으로 매핑한다."""
+    event_json = raw_event["eventJson"]
+    process_code = _normalize_process_code(
+        _first_present(raw_event, "processCode", "process_code", "PROCESS_CODE"),
+        event_json,
+    )
+    event_id = (
+        _first_present(raw_event, "eventId", "event_id")
+        or _nested_text(event_json, "event", "eventId")
+    )
+    if not event_id:
+        raise ValueError("Manufacturing raw event requires eventId.")
+
+    car_master_id = _nullable_int(
+        _first_present(raw_event, "carMasterId", "car_master_id", "carId", "car_id"),
+    )
+    if car_master_id is None:
+        car_master_id = _nullable_int(_nested_value(event_json, "product", "carMasterId"))
+    if car_master_id is None:
+        # raw 토픽 key가 carId이므로 payload에 차량 ID가 없을 때 key를 최종 fallback으로 사용한다.
+        car_master_id = _nullable_int(raw_event.get("_kafka_key"))
+    if car_master_id is None:
+        raise ValueError(f"Manufacturing raw event requires carId key or carMasterId. event_id={event_id}")
+
+    equipment_id = _nullable_int(
+        _first_present(raw_event, "equipmentId", "equipment_id"),
+    ) or 0
+    return {
+        "event_id": str(event_id),
+        "event_time": _parse_datetime(
+            _first_present(raw_event, "eventTime", "event_time")
+            or _nested_text(event_json, "event", "eventTime"),
+        ),
+        "car_master_id": car_master_id,
+        "process_code": process_code,
+        "equipment_id": equipment_id,
+        "event_json": event_json,
+        "dispatch_status": "SENT",
+        "is_sent": True,
+    }
+
+
+def _normalize_process_code(value: Any, event_json: dict[str, Any]) -> str:
+    """PRESS/BODY/PAINT/ASSEMBLY 중 하나로 공정 코드를 표준화한다."""
+    process_code = str(value or "").strip().upper()
+    if not process_code:
+        process_data = event_json.get("processData")
+        if isinstance(process_data, dict):
+            process_code = next(iter(process_data.keys()), "").upper()
+    if process_code not in PROCESS_SEQUENCE:
+        raise ValueError(f"Unsupported processCode for raw event: {value}")
+    return process_code
+
+
+def _parse_event_json(value: Any) -> dict[str, Any] | None:
+    if isinstance(value, dict):
+        return value
+    if isinstance(value, str):
+        try:
+            decoded = json.loads(value)
+        except json.JSONDecodeError:
+            return None
+        return decoded if isinstance(decoded, dict) else None
+    return None
+
+
+def _first_present(payload: dict[str, Any], *fields: str) -> Any:
+    for field in fields:
+        value = payload.get(field)
+        if value is not None:
+            return value
+    return None
+
+
+def _parse_datetime(value: Any) -> datetime | None:
+    if value is None:
+        return None
+    text = str(value).strip()
+    if not text:
+        return None
+    try:
+        return datetime.fromisoformat(text.replace("Z", "+00:00")).replace(tzinfo=None)
+    except ValueError:
+        logger.warning("Cannot parse raw event time: %s", text)
+        return None
+
+
+def _nested_value(payload: dict[str, Any], *path: str) -> Any:
+    current: Any = payload
+    for key in path:
+        if not isinstance(current, dict):
+            return None
+        current = current.get(key)
+    return current
+
+
+def _nested_text(payload: dict[str, Any], *path: str) -> str | None:
+    value = _nested_value(payload, *path)
+    return str(value) if value is not None else None
+
+
+def _nullable_int(value: Any) -> int | None:
+    if value is None or value == "":
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _bootstrap_servers() -> list[str]:
+    """환경변수 BROKER_URL_1/2에서 MSK bootstrap 서버 목록을 만든다."""
+    servers = [settings.broker_url_1 or "", settings.broker_url_2 or ""]
+    return [server.strip() for server in servers if server.strip()]
+
+
+def _clear_bottleneck_cache() -> None:
+    if not settings.redis_url:
+        return
+
+    try:
+        from redis import Redis
+
+        redis_client = Redis.from_url(settings.redis_connection_url, decode_responses=True)
+        pattern = f"{settings.redis_key_prefix}:process:bottleneck:*"
+        keys = list(redis_client.scan_iter(match=pattern))
+        if keys:
+            redis_client.delete(*keys)
+    except Exception:
+        logger.exception("Failed to clear bottleneck cache after raw event consumption.")
diff --git a/app/main.py b/app/main.py
index bdf84f7..ffbe284 100644
--- a/app/main.py
+++ b/app/main.py
@@ -6,6 +6,10 @@
 from app.core.config import settings
 from app.core.exceptions import register_exception_handlers
 from app.core.logging import configure_logging
+from app.kafka.raw_event_consumer import (
+    start_raw_event_consumer,
+    stop_raw_event_consumer,
+)
 from app.repository.sampledb_repository import initialize_sampledb
 from app.scheduler.manufacturing import (
     start_manufacturing_event_scheduler,
@@ -66,9 +70,11 @@ async def start_background_schedulers() -> None:
             except Exception:
                 logger.exception("미완료 제조 이벤트 생성 job 복구에 실패했습니다.")
         start_manufacturing_event_scheduler(app)
+        start_raw_event_consumer(app)
 
     @app.on_event("shutdown")
     async def stop_background_schedulers() -> None:
+        await stop_raw_event_consumer(app)
         await stop_manufacturing_event_scheduler(app)
 
     return app
diff --git a/app/ml/inference/bottleneck_detector.py b/app/ml/inference/bottleneck_detector.py
index 8aa6d07..0cfd73d 100644
--- a/app/ml/inference/bottleneck_detector.py
+++ b/app/ml/inference/bottleneck_detector.py
@@ -63,7 +63,6 @@ def summarize(
         analysis_df: pd.DataFrame,
         top_n: int = 20,
         *,
-        product_process_history_id: int | None = None,
         manufacturing_event_id: int | None = None,
         car_master_id: int | None = None,
     ) -> list[dict[str, Any]]:
@@ -113,14 +112,12 @@ def summarize(
             risk_level = int(row["risk_level"])
             summaries.append(
                 {
-                    "product_process_history_id": product_process_history_id,
                     "manufacturing_event_id": manufacturing_event_id,
                     "car_master_id": car_master_id,
                     "process_code": station.process_code,
                     "equipment_code": station.equipment_code,
-                    "station_code": station.station,
                     "rank_no": index + 1,
-                    "avg_delay_time": round(avg_delay_time, 1),
+                    "avg_delay_time": avg_delay_time,
                     "affected_vehicle_count": int(row["affected_vehicle_count"]),
                     "risk_score": float(risk_level),
                 },
@@ -128,13 +125,13 @@ def summarize(
 
         return summaries
 
-    def summarize_product_process_histories(
+    def summarize_manufacturing_event_histories(
         self,
         histories: list[dict[str, Any]],
     ) -> list[dict[str, Any]]:
         """공정 이력 병목 순위 계산"""
         # 이력 기반 모델 입력 피처 생성
-        features = self._build_product_process_history_features(histories)
+        features = self._build_manufacturing_event_features(histories)
         model_features = self._resolve_model_features(features)
         X = features.reindex(columns=model_features)
 
@@ -144,25 +141,53 @@ def summarize_product_process_histories(
         features["iforest_bottleneck"] = (pred_raw == -1).astype(int)
         features["iforest_anomaly_score"] = anomaly_scores
         features["iforest_risk_score"] = pd.Series(anomaly_scores).rank(pct=True).to_numpy()
+        features = self._apply_rule_engine(features)
+        features["combined_risk_score"] = (
+            0.45 * features["rule_risk_score"]
+            + 0.55 * features["iforest_risk_score"]
+        )
+        features["bottleneck_candidate"] = (
+            features["rule_bottleneck"].eq(1)
+            | features["iforest_bottleneck"].eq(1)
+        ).astype(int)
 
         # 지점 대표 이력 선정을 위한 정렬
-        ranked_features = features.sort_values(
-            ["iforest_risk_score", "waiting_time", "process_time", "product_process_history_id"],
-            ascending=[False, False, False, True],
+        target_features = features[features["bottleneck_candidate"].eq(1)].copy()
+        if target_features.empty:
+            target_features = features.nlargest(
+                min(len(features), 20),
+                "combined_risk_score",
+            ).copy()
+
+        ranked_features = target_features.sort_values(
+            [
+                "rule_bottleneck",
+                "iforest_bottleneck",
+                "combined_risk_score",
+                "rule_risk_score",
+                "iforest_risk_score",
+                "total_duration",
+                "max_station_span",
+                "id",
+            ],
+            ascending=[False, False, False, False, False, False, False, True],
         )
         # 공정/설비/지점 단위 집계
         grouped = (
-            ranked_features.groupby(["process_code", "equipment_code", "station_code"], dropna=False)
+            ranked_features.groupby(["process_code", "equipment_code"], dropna=False)
             .agg(
-                product_process_history_id=("product_process_history_id", "first"),
                 manufacturing_event_id=("manufacturing_event_id", "first"),
                 car_master_id=("car_master_id", "first"),
-                avg_delay_time=("waiting_time", "mean"),
-                max_delay_time=("waiting_time", "max"),
-                avg_process_time=("process_time", "mean"),
-                affected_vehicle_count=("product_process_history_id", "count"),
-                risk_score=("iforest_risk_score", "mean"),
-                max_risk_score=("iforest_risk_score", "max"),
+                avg_delay_time=("max_station_span", "mean"),
+                avg_total_duration=("total_duration", "mean"),
+                affected_vehicle_count=("car_master_id", "nunique"),
+                event_count=("id", "count"),
+                avg_rule_risk=("rule_risk_score", "mean"),
+                avg_iforest_risk=("iforest_risk_score", "mean"),
+                risk_score=("combined_risk_score", "mean"),
+                max_risk_score=("combined_risk_score", "max"),
+                avg_queue_length=("queue_length", "mean"),
+                avg_wip_count=("wip_count", "mean"),
             )
             .reset_index()
         )
@@ -178,12 +203,12 @@ def summarize_product_process_histories(
         )
         grouped = grouped.sort_values(
             [
+                "affected_vehicle_count",
                 "risk_level",
                 "max_risk_score",
-                "risk_score",
-                "max_delay_time",
+                "avg_iforest_risk",
+                "avg_rule_risk",
                 "avg_delay_time",
-                "affected_vehicle_count",
             ],
             ascending=[False, False, False, False, False, False],
         ).reset_index(drop=True)
@@ -193,14 +218,12 @@ def summarize_product_process_histories(
         for index, row in grouped.iterrows():
             summaries.append(
                 {
-                    "product_process_history_id": int(row["product_process_history_id"]),
                     "manufacturing_event_id": self._safe_int(row["manufacturing_event_id"]),
                     "car_master_id": self._safe_int(row["car_master_id"]),
                     "process_code": str(row["process_code"]),
                     "equipment_code": str(row["equipment_code"]),
-                    "station_code": str(row["station_code"]),
                     "rank_no": index + 1,
-                    "avg_delay_time": round(self._safe_float(row["avg_delay_time"], default=0.0), 1),
+                    "avg_delay_time": self._safe_float(row["avg_delay_time"], default=0.0),
                     "affected_vehicle_count": int(row["affected_vehicle_count"]),
                     "risk_score": float(row["risk_level"]),
                 },
@@ -208,7 +231,31 @@ def summarize_product_process_histories(
 
         return summaries
 
-    def _build_product_process_history_features(
+    @staticmethod
+    def _apply_rule_engine(features: pd.DataFrame) -> pd.DataFrame:
+        """학습 노트북과 동일한 Rule Engine 점수를 계산."""
+        result = features.copy()
+        duration_threshold = result["total_duration"].quantile(0.95)
+        station_span_threshold = result["max_station_span"].quantile(0.95)
+
+        duration_score = (
+            result["total_duration"] / max(float(duration_threshold), 1e-12)
+        ).clip(upper=2.0) / 2.0
+        station_span_score = (
+            result["max_station_span"] / max(float(station_span_threshold), 1e-12)
+        ).clip(upper=2.0) / 2.0
+
+        result["rule_risk_score"] = (
+            0.55 * duration_score
+            + 0.45 * station_span_score
+        ).fillna(0.0)
+        result["rule_bottleneck"] = (
+            result["total_duration"].ge(duration_threshold)
+            | result["max_station_span"].ge(station_span_threshold)
+        ).astype(int)
+        return result
+
+    def _build_manufacturing_event_features(
         self,
         histories: list[dict[str, Any]],
     ) -> pd.DataFrame:
@@ -216,20 +263,31 @@ def _build_product_process_history_features(
         source_df = pd.DataFrame(histories)
         process_time = source_df["process_time"].fillna(0).astype(float)
         waiting_time = source_df["waiting_time"].fillna(0).astype(float)
+        delay_time = source_df.get("delay_time", waiting_time).fillna(0).astype(float)
+        queue_length = source_df.get("queue_length", 0.0)
+        wip_count = source_df.get("wip_count", 0.0)
+        station_key = source_df.get(
+            "station_key",
+            source_df["equipment_code"],
+        ).fillna("UNKNOWN")
         total_duration = process_time + waiting_time
 
         # 기본 시간/식별자 피처
         feature_df = pd.DataFrame(
             {
                 "Id": source_df["id"].astype(int),
-                "product_process_history_id": source_df["id"].astype(int),
+                "id": source_df["id"].astype(int),
                 "manufacturing_event_id": source_df["manufacturing_event_id"],
                 "car_master_id": source_df["car_master_id"],
                 "process_code": source_df["process_code"].astype(str),
                 "equipment_code": source_df["equipment_code"].astype(str),
-                "station_code": source_df["station_code"].astype(str),
+                "station_key": station_key.astype(str),
                 "process_time": process_time,
                 "waiting_time": waiting_time,
+                "delay_time": delay_time,
+                "is_delayed": delay_time.gt(0).astype(int),
+                "queue_length": queue_length,
+                "wip_count": wip_count,
                 "process_start_time": 0.0,
                 "process_end_time": total_duration,
                 "total_duration": total_duration,
@@ -255,7 +313,7 @@ def _build_product_process_history_features(
             if line is None:
                 continue
 
-            station = str(row["station_code"])
+            station = str(row["station_key"])
             station_prefix = f"{line}_{station}"
             feature_df.at[index, f"{line}_seen"] = 1
             feature_df.at[index, f"{line}_duration"] = row["process_time"]
diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 94c7432..6d38abd 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -1,60 +1,87 @@
+from __future__ import annotations
+
+import json
 from collections.abc import Iterable
 from typing import Any
 
+from app.repository.sampledb_schema import manufacturing_event_json
 from app.utils.database_utils import mysql_connect_args_for_seoul
 
 
 class BottleneckAnalysisRepository:
-    """병목 분석 결과를 DB에 저장하고 조회하는 저장소 계층"""
+    """Store bottleneck results and read source events from manufacturing_event_json."""
 
-    def __init__(self, database_url: str) -> None:
-        """MySQL 연결을 초기화하고 테이블 스키마를 보장한다."""
+    def __init__(
+        self,
+        database_url: str,
+        *,
+        event_database_url: str | None = None,
+    ) -> None:
         self.database_url = database_url
+        self.event_database_url = event_database_url or database_url
         self.engine: Any | None = None
+        self.event_engine: Any | None = None
         self.table: Any | None = None
-        self.product_process_history_table: Any | None = None
         self.metadata: Any | None = None
 
         self._init_sqlalchemy()
         self.ensure_schema()
 
     def ensure_schema(self) -> None:
-        """병목 분석 결과 테이블과 조회용 인덱스를 생성"""
         self.metadata.create_all(self.engine)
+        self._align_result_schema()
 
     def replace_results(
         self,
         rows: Iterable[dict[str, Any]],
-        detected_at: str,
+        detected_at: Any,
         *,
         start_rank: int,
         end_rank: int,
     ) -> None:
-        """요청 순위 구간 교체"""
         payload = [{**row, "detected_at": detected_at} for row in rows]
-        self._validate_product_process_history_ids(payload)
+        payload_ranks = {int(row["rank_no"]) for row in payload}
+
+        from sqlalchemy import func
+
+        with self.engine.begin() as conn:
+            delete_query = self.table.delete().where(
+                self.table.c.rank_no.between(start_rank, end_rank),
+            )
+            if payload_ranks:
+                delete_query = delete_query.where(
+                    self.table.c.rank_no.not_in(payload_ranks),
+                )
+            conn.execute(delete_query)
+
+            for row in payload:
+                update_values = {
+                    **row,
+                    "updated_at": func.current_timestamp(),
+                }
+                result = conn.execute(
+                    self.table.update()
+                    .where(self.table.c.rank_no == row["rank_no"])
+                    .values(**update_values),
+                )
+                if not result.rowcount:
+                    conn.execute(self.table.insert(), row)
 
+    def prune_results_after_rank(self, max_rank: int) -> None:
         with self.engine.begin() as conn:
-            # 요청한 rank 범위만 갱신
             conn.execute(
-                self.table.delete().where(
-                    self.table.c.rank_no.between(start_rank, end_rank),
-                ),
+                self.table.delete().where(self.table.c.rank_no > max_rank),
             )
-            if payload:
-                conn.execute(self.table.insert(), payload)
 
     def list_results(self, *, cursor: int, size: int) -> list[dict[str, Any]]:
-        """요청 순위 페이지 조회"""
         from sqlalchemy import select
 
         start_rank = cursor * size + 1
         end_rank = start_rank + size - 1
 
-        query = select(self.table)
         query = (
-            # 같은 순위가 있어도 조회 순서가 흔들리지 않도록 보조 정렬
-            query.where(self.table.c.rank_no.between(start_rank, end_rank))
+            select(self.table)
+            .where(self.table.c.rank_no.between(start_rank, end_rank))
             .order_by(self.table.c.rank_no.asc(), self.table.c.id.asc())
             .limit(size)
         )
@@ -63,116 +90,267 @@ def list_results(self, *, cursor: int, size: int) -> list[dict[str, Any]]:
             return [dict(row) for row in conn.execute(query).mappings()]
 
     def count_results(self) -> int:
-        """저장된 병목 분석 결과 수를 반환"""
         from sqlalchemy import func, select
 
         query = select(func.count()).select_from(self.table)
         with self.engine.connect() as conn:
             return int(conn.execute(query).scalar_one())
 
-    def list_product_process_histories(self) -> list[dict[str, Any]]:
-        """분석에 사용할 product_process_history 전체를 조회"""
+    def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
         from sqlalchemy import select
 
         query = (
             select(
-                self.product_process_history_table.c.id,
-                self.product_process_history_table.c.manufacturing_event_id,
-                self.product_process_history_table.c.car_master_id,
-                self.product_process_history_table.c.process_code,
-                self.product_process_history_table.c.equipment_code,
-                self.product_process_history_table.c.station_code,
-                self.product_process_history_table.c.process_time,
-                self.product_process_history_table.c.waiting_time,
+                manufacturing_event_json.c.id,
+                manufacturing_event_json.c.car_master_id,
+                manufacturing_event_json.c.process_code,
+                manufacturing_event_json.c.equipment_id,
+                manufacturing_event_json.c.event_json,
             )
-            .order_by(self.product_process_history_table.c.id.asc())
+            .where(manufacturing_event_json.c.is_sent.is_(True))
+            .order_by(manufacturing_event_json.c.id.asc())
+        )
+        with self.event_engine.connect() as conn:
+            rows = conn.execute(query).mappings()
+            return [
+                self._to_bottleneck_history(row)
+                for row in rows
+            ]
+
+    def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
+        event_json = self._event_json_dict(row["event_json"])
+        equipment = event_json.get("equipment", {})
+        metrics = event_json.get("processMetrics", {})
+        equipment_code = str(
+            equipment.get("equipmentCode")
+            or row.get("equipment_id")
+            or "UNKNOWN",
+        )
+        process_data = event_json.get("processData", {})
+        process_code = str(row["process_code"])
+        process_time = self._safe_float(metrics.get("processingTimeSec"))
+        waiting_time = self._safe_float(metrics.get("waitingTimeSec"))
+        cycle_time = self._safe_float(metrics.get("cycleTimeSec"))
+        station_delay_time = self._safe_float(metrics.get("stationDelaySec"))
+        timestamp_delay_time = self._process_timestamp_delay(
+            process_data,
+            process_code,
+        )
+        delay_time = (
+            station_delay_time
+            if station_delay_time > 0
+            else timestamp_delay_time
         )
-        with self.engine.connect() as conn:
-            return [dict(row) for row in conn.execute(query).mappings()]
 
-    def _validate_product_process_history_ids(self, rows: list[dict[str, Any]]) -> None:
-        """병목 결과의 product_process_history_id가 실제 공정 이력 PK인지 검증"""
-        history_ids = {
-            int(row["product_process_history_id"])
-            for row in rows
-            if row.get("product_process_history_id") is not None
+        return {
+            "id": int(row["id"]),
+            "manufacturing_event_id": int(row["id"]),
+            "car_master_id": row["car_master_id"],
+            "process_code": process_code,
+            "equipment_code": equipment_code,
+            "station_key": equipment_code,
+            "process_time": process_time,
+            "waiting_time": waiting_time,
+            "cycle_time": cycle_time,
+            "delay_time": delay_time,
+            "queue_length": self._safe_float(metrics.get("queueLength")),
+            "wip_count": self._safe_float(metrics.get("wipCount")),
         }
-        if not history_ids:
-            return
 
-        from sqlalchemy import select
+    @staticmethod
+    def _event_json_dict(value: Any) -> dict[str, Any]:
+        if isinstance(value, dict):
+            return value
+        if isinstance(value, str):
+            return json.loads(value)
+        return {}
 
-        query = select(self.product_process_history_table.c.id).where(
-            self.product_process_history_table.c.id.in_(history_ids),
-        )
-        with self.engine.connect() as conn:
-            existing_ids = {int(row["id"]) for row in conn.execute(query).mappings()}
+    @staticmethod
+    def _safe_float(value: Any) -> float:
+        if value is None:
+            return 0.0
+        return float(value)
 
-        missing_ids = sorted(history_ids - existing_ids)
-        if missing_ids:
-            raise ValueError(
-                f"Invalid product_process_history_id values: {missing_ids}",
-            )
+    @classmethod
+    def _process_timestamp_delay(
+        cls,
+        process_data: Any,
+        process_code: str,
+    ) -> float:
+        if not isinstance(process_data, dict):
+            return 0.0
+
+        candidates = [
+            process_code,
+            process_code.lower(),
+            process_code.upper(),
+        ]
+        for key in candidates:
+            value = process_data.get(key)
+            if isinstance(value, dict):
+                return cls._safe_float(value.get("timestampDelaySec"))
+        return 0.0
 
     def _init_sqlalchemy(self) -> None:
-        """MySQL 연결을 위한 SQLAlchemy 엔진과 테이블 메타데이터를 구성"""
         try:
-            from sqlalchemy import BigInteger, Column, DateTime, Double, Float, ForeignKey
-            from sqlalchemy import Index, Integer, MetaData, String, Table
+            from sqlalchemy import BigInteger, Column, DateTime, Double, Enum
+            from sqlalchemy import Index, Integer, MetaData, String, Table, func
             from sqlalchemy import create_engine
         except ModuleNotFoundError as exc:
             raise RuntimeError(
-                "외부 DB를 사용하려면 requirements.txt의 sqlalchemy, pymysql 패키지가 필요합니다.",
+                "SQLAlchemy and PyMySQL are required for bottleneck analysis storage.",
             ) from exc
 
         self.metadata = MetaData()
-        self.product_process_history_table = Table(
-            "product_process_history",
+        process_code_enum = Enum("PRESS", "BODY", "PAINT", "ASSEMBLY")
+        self.table = Table(
+            "bottleneck_analysis_result",
             self.metadata,
             Column("id", BigInteger, primary_key=True, autoincrement=True),
-            # 원천 제조 이벤트 식별자에 대한 논리 참조이며 DB 간 FK는 두지 않음
             Column("manufacturing_event_id", BigInteger),
-            # sampledb car_master.id에 대한 논리 참조이며 DB 간 FK는 두지 않음
             Column("car_master_id", BigInteger),
-            Column("process_code", String(20), nullable=False),
-            Column("equipment_code", String(50), nullable=False),
-            Column("station_code", String(50), nullable=False),
-            Column("lot_code", String(50)),
-            Column("started_at", DateTime, nullable=False),
-            Column("ended_at", DateTime, nullable=False),
-            Column("process_time", Double),
-            Column("waiting_time", Double),
-            Column("result_status", String(20), nullable=False),
-            Column("sequence_no", Integer, nullable=False),
-            Column("previous_process_code", String(20)),
-            Column("created_at", DateTime, nullable=False),
-            Index("idx_product_process_history_event_id", "manufacturing_event_id"),
-            Index("idx_product_process_history_car_master_id", "car_master_id"),
-            Index("idx_product_process_history_process_code", "process_code"),
-        )
-        # SQLAlchemy Core 테이블 정의를 사용해 MySQL DDL/CRUD를 DB 방언에 맞게 생성
-        self.table = Table(
-            "bottleneck_analysis_result",
-            self.metadata,
-            Column("id", Integer, primary_key=True, autoincrement=True),
-            Column("product_process_history_id", BigInteger, ForeignKey("product_process_history.id")),
-            Column("manufacturing_event_id", Integer),
-            Column("car_master_id", Integer),
-            Column("process_code", String(64), nullable=False),
-            Column("equipment_code", String(64), nullable=False),
-            Column("station_code", String(64), nullable=False),
-            Column("rank_no", Integer, nullable=False),
-            Column("avg_delay_time", Float, nullable=False),
-            Column("affected_vehicle_count", Integer, nullable=False),
-            Column("risk_score", Float, nullable=False),
-            Column("detected_at", String(64), nullable=False),
+            Column("process_code", process_code_enum),
+            Column("equipment_code", String(50)),
+            Column("rank_no", Integer),
+            Column("avg_delay_time", Double),
+            Column("affected_vehicle_count", Integer),
+            Column("risk_score", Double),
+            Column("detected_at", DateTime),
+            Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+            Column(
+                "updated_at",
+                DateTime,
+                nullable=False,
+                server_default=func.current_timestamp(),
+                server_onupdate=func.current_timestamp(),
+            ),
             Index("idx_bottleneck_analysis_result_rank", "rank_no"),
-            Index("idx_bottleneck_analysis_result_history_id", "product_process_history_id"),
         )
         self.engine = create_engine(
             self.database_url,
-            # MySQL 세션 함수(now 등)를 서비스 기준 타임존과 맞춤
             connect_args=mysql_connect_args_for_seoul(self.database_url),
             pool_pre_ping=True,
             future=True,
         )
+        self.event_engine = create_engine(
+            self.event_database_url,
+            connect_args=mysql_connect_args_for_seoul(self.event_database_url),
+            pool_pre_ping=True,
+            future=True,
+        )
+
+    def _align_result_schema(self) -> None:
+        if self.engine.dialect.name != "mysql":
+            return
+
+        from sqlalchemy import inspect, text
+
+        table_name = "bottleneck_analysis_result"
+        inspector = inspect(self.engine)
+        if not inspector.has_table(table_name):
+            return
+
+        columns = {
+            column["name"]
+            for column in inspector.get_columns(table_name)
+        }
+        expected_columns = {
+            "manufacturing_event_id": "BIGINT NULL",
+            "car_master_id": "BIGINT NULL",
+            "process_code": "ENUM('PRESS','BODY','PAINT','ASSEMBLY') NULL",
+            "equipment_code": "VARCHAR(50) NULL",
+            "rank_no": "INT NULL",
+            "avg_delay_time": "DOUBLE NULL",
+            "affected_vehicle_count": "INT NULL",
+            "risk_score": "DOUBLE NULL",
+            "detected_at": "DATETIME NULL",
+            "created_at": "DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP",
+            "updated_at": (
+                "DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP "
+                "ON UPDATE CURRENT_TIMESTAMP"
+            ),
+        }
+        removed_columns = ("product_process_history_id", "station_code")
+
+        with self.engine.begin() as conn:
+            for column_name in removed_columns:
+                if column_name not in columns:
+                    continue
+                self._drop_column_constraints(conn, inspector, table_name, column_name)
+                conn.execute(
+                    text(
+                        f"ALTER TABLE {table_name} "
+                        f"DROP COLUMN {column_name}",
+                    ),
+                )
+                columns.remove(column_name)
+
+            for column_name, definition in expected_columns.items():
+                if column_name not in columns:
+                    conn.execute(
+                        text(
+                            f"ALTER TABLE {table_name} "
+                            f"ADD COLUMN {column_name} {definition}",
+                        ),
+                    )
+                    columns.add(column_name)
+
+            conn.execute(
+                text(
+                    f"ALTER TABLE {table_name} "
+                    "MODIFY COLUMN id BIGINT NOT NULL AUTO_INCREMENT",
+                ),
+            )
+            if "detected_at" in columns:
+                conn.execute(
+                    text(
+                        f"UPDATE {table_name} "
+                        "SET detected_at = STR_TO_DATE("
+                        "LEFT(SUBSTRING_INDEX(REPLACE(detected_at, 'T', ' '), '+', 1), 19), "
+                        "'%Y-%m-%d %H:%i:%s'"
+                        ") "
+                        "WHERE detected_at IS NOT NULL "
+                        "AND CAST(detected_at AS CHAR) LIKE '%T%'",
+                    ),
+                )
+            conn.execute(
+                text(
+                    f"UPDATE {table_name} "
+                    "SET created_at = COALESCE(created_at, detected_at, CURRENT_TIMESTAMP), "
+                    "updated_at = COALESCE(updated_at, detected_at, CURRENT_TIMESTAMP)",
+                ),
+            )
+
+            for column_name, definition in expected_columns.items():
+                conn.execute(
+                    text(
+                        f"ALTER TABLE {table_name} "
+                        f"MODIFY COLUMN {column_name} {definition}",
+                    ),
+                )
+
+    @staticmethod
+    def _drop_column_constraints(
+        conn: Any,
+        inspector: Any,
+        table_name: str,
+        column_name: str,
+    ) -> None:
+        from sqlalchemy import text
+
+        for foreign_key in inspector.get_foreign_keys(table_name):
+            if column_name in foreign_key.get("constrained_columns", []):
+                conn.execute(
+                    text(
+                        f"ALTER TABLE {table_name} "
+                        f"DROP FOREIGN KEY {foreign_key['name']}",
+                    ),
+                )
+
+        index_names = {
+            index["name"]
+            for index in inspector.get_indexes(table_name)
+            if column_name in index.get("column_names", [])
+        }
+        for index_name in index_names:
+            conn.execute(text(f"DROP INDEX {index_name} ON {table_name}"))
diff --git a/app/repository/manufacturing_event_repository.py b/app/repository/manufacturing_event_repository.py
index 137d551..e30604e 100644
--- a/app/repository/manufacturing_event_repository.py
+++ b/app/repository/manufacturing_event_repository.py
@@ -75,6 +75,40 @@ def insert_rows(
             update_existing=update_existing,
         )
 
+    def insert_raw_rows(
+        self,
+        rows: Iterable[dict[str, Any]],
+        *,
+        update_existing: bool = True,
+    ) -> int:
+        now = datetime.now()
+        payload = []
+        for row in rows:
+            event_json = normalize_event_json(copy.deepcopy(row["event_json"]))
+            payload.append(
+                {
+                    "event_id": row["event_id"],
+                    "event_time": row.get("event_time"),
+                    "car_master_id": row["car_master_id"],
+                    "process_code": row["process_code"],
+                    "equipment_id": row["equipment_id"],
+                    "event_json": event_json,
+                    "dispatch_status": "SENT",
+                    "is_sent": True,
+                    "retry_count": row.get("retry_count", 0),
+                    "error_message": row.get("error_message"),
+                    "updated_at": now,
+                },
+            )
+        if not payload:
+            return 0
+
+        payload.sort(key=lambda row: str(row["event_id"]))
+        return self._insert_payload_with_retry(
+            payload,
+            update_existing=update_existing,
+        )
+
     def _insert_payload_with_retry(
         self,
         payload: list[dict[str, Any]],
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 758c7b6..521358e 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -1,3 +1,6 @@
+import logging
+import re
+from collections import Counter
 from pathlib import Path
 from typing import Any
 
@@ -8,11 +11,27 @@
 from app.dto.response import BottleneckAnalysisItem, BottleneckAnalysisPage
 from app.ml.inference.bottleneck_detector import BottleneckDetector
 from app.repository.bottleneck_analysis_repository import BottleneckAnalysisRepository
-from app.utils.datetime_utils import seoul_now_iso
+from app.utils.datetime_utils import seoul_now
 from app.utils.json_utils import from_json, to_json
 
 
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
+BOTTLENECK_CACHE_VERSION = "v6"
+PROCESS_CODE_LABELS = {
+    "PRESS": "프레스",
+    "BODY": "차체",
+    "PAINT": "도장",
+    "ASSEMBLY": "의장",
+    "INSPECTION": "검사",
+}
+PROCESS_EQUIPMENT_PREFIXES = {
+    "PRESS": "P",
+    "BODY": "S",
+    "PAINT": "L",
+    "ASSEMBLY": "A",
+    "INSPECTION": "I",
+}
+logger = logging.getLogger(__name__)
 
 
 class BottleneckAnalysisService:
@@ -28,6 +47,7 @@ def __init__(
         self.model_path = Path(model_path or DEFAULT_BOTTLENECK_MODEL_PATH)
         self.repository = BottleneckAnalysisRepository(
             database_url or settings.bottleneck_database_url,
+            event_database_url=settings.sample_database_connection_url,
         )
         self.detector = BottleneckDetector(self.model_path)
         self._redis_client: Any | None = None
@@ -58,11 +78,13 @@ def get_realtime_bottlenecks(
             content=[
                 BottleneckAnalysisItem(
                     rankNo=int(row["rank_no"]),
-                    processCode=str(row["process_code"]),
-                    stationCode=str(row["station_code"]),
-                    avgDelayTime=round(float(row["avg_delay_time"]), 1),
+                    processCode=self._format_process_code(
+                        row["process_code"],
+                        row.get("equipment_code"),
+                    ),
+                    delayTime=round(float(row["avg_delay_time"]), 2),
                     affectedVehicleCount=int(row["affected_vehicle_count"]),
-                    riskScore=int(row["risk_score"]),
+                    riskScore=float(row["risk_score"]),
                 )
                 for row in rows
             ],
@@ -83,6 +105,10 @@ def get_cached_realtime_bottlenecks(
             # cursor와 size를 key에 포함해 페이지별 캐시를 분리
             cached_value = redis_client.get(cache_key)
             if cached_value:
+                logger.info(
+                    "Bottleneck analysis cache hit: key=%s source=redis",
+                    cache_key,
+                )
                 return BottleneckAnalysisPage.model_validate(from_json(cached_value))
         except Exception as exc:
             self._redis_client = None
@@ -91,6 +117,10 @@ def get_cached_realtime_bottlenecks(
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             ) from exc
 
+        logger.info(
+            "Bottleneck analysis cache miss: key=%s source=kafka_raw_db",
+            cache_key,
+        )
         page = self.get_realtime_bottlenecks(cursor=cursor, size=size)
         try:
             # 오래된 결과가 과도하게 남지 않도록 설정된 TTL로 저장
@@ -109,33 +139,68 @@ def get_cached_realtime_bottlenecks(
 
     def run_analysis_and_save(self, *, cursor: int, size: int) -> tuple[int, bool]:
         """전체 공정 이력을 분석하고 요청한 순위 페이지 결과만 저장"""
-        histories = self.repository.list_product_process_histories()
+        histories = self.repository.list_manufacturing_event_histories()
         if not histories:
             raise AppException(
                 "공정 이력 데이터를 찾을 수 없습니다.",
                 status_code=status.HTTP_404_NOT_FOUND,
             )
 
+        process_counts = Counter(str(row.get("process_code")) for row in histories)
+        event_ids = [
+            int(row["manufacturing_event_id"])
+            for row in histories
+            if row.get("manufacturing_event_id") is not None
+        ]
+        delay_times = [
+            float(row.get("delay_time") or 0.0)
+            for row in histories
+        ]
+        logger.info(
+            "Bottleneck analysis input loaded: source=kafka_raw_db "
+            "table=manufacturing_event_json filter=is_sent:true total=%s "
+            "process_counts=%s delay_time_range=%.3f..%.3f "
+            "manufacturing_event_id_range=%s..%s cursor=%s size=%s",
+            len(histories),
+            dict(sorted(process_counts.items())),
+            min(delay_times) if delay_times else 0.0,
+            max(delay_times) if delay_times else 0.0,
+            min(event_ids) if event_ids else None,
+            max(event_ids) if event_ids else None,
+            cursor,
+            size,
+        )
+
         if not self.model_path.exists():
             raise AppException(
                 f"병목 탐지 모델을 찾을 수 없습니다: {self.model_path}",
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             )
 
-        summaries = self.detector.summarize_product_process_histories(histories)
+        summaries = self.detector.summarize_manufacturing_event_histories(histories)
         offset = cursor * size
         page_summaries = summaries[offset : offset + size]
         has_next = len(summaries) > offset + size
+        start_rank = offset + 1
+        end_rank = offset + size
 
-        if page_summaries:
-            start_rank = offset + 1
-            end_rank = offset + len(page_summaries)
-            self.repository.replace_results(
-                page_summaries,
-                detected_at=seoul_now_iso(),
-                start_rank=start_rank,
-                end_rank=end_rank,
-            )
+        self.repository.replace_results(
+            page_summaries,
+            detected_at=seoul_now().replace(tzinfo=None),
+            start_rank=start_rank,
+            end_rank=end_rank,
+        )
+        if not has_next:
+            self.repository.prune_results_after_rank(len(summaries))
+
+        logger.info(
+            "Bottleneck analysis results saved: source=kafka_raw_db "
+            "result_count=%s rank_range=%s..%s has_next=%s",
+            len(page_summaries),
+            start_rank,
+            end_rank,
+            has_next,
+        )
         return len(page_summaries), has_next
 
     def _redis(self) -> Any:
@@ -156,7 +221,35 @@ def _bottleneck_cache_key(self, *, cursor: int | None, size: int) -> str:
         """요청한 병목 결과 페이지에 대한 Redis key를 생성"""
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
-        return f"{settings.redis_key_prefix}:process:bottleneck:{page}:{safe_size}"
+        return (
+            f"{settings.redis_key_prefix}:process:bottleneck:"
+            f"{BOTTLENECK_CACHE_VERSION}:{page}:{safe_size}"
+        )
+
+    @staticmethod
+    def _format_process_code(
+        process_code: Any,
+        equipment_code: Any | None = None,
+    ) -> str:
+        normalized = str(process_code or "").strip().upper()
+        label = PROCESS_CODE_LABELS.get(normalized, normalized)
+        prefix = PROCESS_EQUIPMENT_PREFIXES.get(normalized)
+        equipment_no = BottleneckAnalysisService._equipment_number(
+            equipment_code,
+        )
+        if prefix and equipment_no is not None:
+            return f"{label} ({prefix}{equipment_no})"
+        return label
+
+    @staticmethod
+    def _equipment_number(equipment_code: Any | None) -> int | None:
+        if equipment_code is None:
+            return None
+
+        match = re.search(r"(\d+)$", str(equipment_code).strip())
+        if not match:
+            return None
+        return int(match.group(1))
 
 def get_bottleneck_analysis_service() -> BottleneckAnalysisService:
     """FastAPI 의존성 주입에 사용할 병목 분석 서비스를 생성"""

From f6758644d5dcd86c0c565dc60665c1f27653c6fd Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 13:14:51 +0900
Subject: [PATCH 042/148] feat : print update, risk-trend_producer update

---
 app/repository/quality_drive_detail_repository.py  |  2 +-
 app/repository/quality_process_repository.py       |  7 +++----
 app/repository/quality_risk_history_repository.py  |  2 +-
 app/repository/quality_risk_trend_repository.py    |  4 ----
 app/repository/quality_status_detail_repository.py |  6 +-----
 app/scheduler/quality/drive_detail_producer.py     |  2 +-
 app/scheduler/quality/process_producer.py          |  2 +-
 app/scheduler/quality/risk_history_producer.py     |  2 +-
 app/scheduler/quality/risk_trend_producer.py       | 14 ++++++++++++--
 app/scheduler/quality/status_detail_producer.py    |  2 +-
 10 files changed, 22 insertions(+), 21 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 5c8102c..3fed791 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -156,7 +156,7 @@ def get_driving_pattern(row):
             )
 
             print(
-                f"{len(inspection_drive_detail_list)}건 최종 저장 완료"
+                f"drvie-detail 최종 저장 완료"
             )
 
         consumer.close()
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 4bbc97c..26d90fe 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -104,16 +104,15 @@ def run():
                 index=False
             )
 
-            print(
-                f"{len(inspection_process_list)}건 저장 완료"
-            )
-
             inspection_process_list.clear()
 
     except Exception as e:
         print(f"오류 발생 : {e}")
 
     finally:
+        print(
+            f"process 최종 저장 완료"
+        )
         consumer.close()
 
 if __name__ == "__main__":
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 020ddd5..955b392 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -96,7 +96,7 @@ def run():
             )
 
             print(
-                f"{len(inspection_risk_history_list)}건 최종 저장 완료"
+                f"risk-history 최종 저장 완료"
             )
 
         consumer.close()
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index ad7b3c7..3c8018f 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -71,10 +71,6 @@ def run():
                     index=False
                 )
 
-                print(
-                    f"{len(inspection_risk_trend_list)}건 저장 완료"
-                )
-
                 inspection_risk_trend_list.clear()
 
     except Exception as e:
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 8cb1bc1..870ef85 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -95,10 +95,6 @@ def run():
                     index=False
                 )
 
-                print(
-                    f"{len(inspection_status_detail_list)}건 저장 완료"
-                )
-
                 inspection_status_detail_list.clear()
 
     except Exception as e:
@@ -121,7 +117,7 @@ def run():
             )
 
             print(
-                f"{len(inspection_status_detail_list)}건 최종 저장 완료"
+                f"status-detail 최종 저장 완료"
             )
 
         consumer.close()
diff --git a/app/scheduler/quality/drive_detail_producer.py b/app/scheduler/quality/drive_detail_producer.py
index 3c7d070..d71d3f7 100644
--- a/app/scheduler/quality/drive_detail_producer.py
+++ b/app/scheduler/quality/drive_detail_producer.py
@@ -87,7 +87,7 @@ def run():
 
     producer.flush()
 
-    print("모든 데이터 Kafka 전송 완료")
+    print("drvie-detail Kafka 전송 완료")
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index fa91ace..9b2a067 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -74,7 +74,7 @@ def run():
         time.sleep(1)
 
     producer.flush()
-    print("모든 데이터 Kafka 전송 완료")
+    print("porcess Kafka 전송 완료")
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index caaa028..b2787bf 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -264,7 +264,7 @@ def calculate_dynamics_risk(row):
 
     producer.flush()
 
-    print("모든 데이터 Kafka 전송 완료")
+    print("risk-history Kafka 전송 완료")
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index ba9c28d..e6e8cd4 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -2,6 +2,7 @@
 from kafka import KafkaProducer
 from dotenv import load_dotenv
 import os
+from urllib.parse import quote_plus
 
 from datetime import datetime, timedelta
 
@@ -12,8 +13,17 @@
 
 def run():
     load_dotenv()
+    DB_USER = os.getenv("DB_USER")
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
+    DB_HOST = os.getenv("DB_HOST")
+    DB_PORT = os.getenv("DB_PORT")
+    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
     SAMPLE_DATABASE_URL = (
-        os.getenv("SAMPLE_DATABASE_END")
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
     )
 
     sample_engine = create_engine(
@@ -269,7 +279,7 @@ def get_risk_level(score):
 
     producer.flush()
 
-    print("모든 데이터 Kafka 전송 완료")
+    print("Risk-trend Kafka 전송 완료")
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index 335d40e..3d031ed 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -181,7 +181,7 @@ def get_result(score):
 
     producer.flush()
 
-    print("모든 데이터 Kafka 전송 완료")
+    print("status-detail Kafka 전송 완료")
 
 if __name__ == "__main__":
     run()
\ No newline at end of file

From 8055e582b60c6344df6da433aad33a80c0c71e8f Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 13:29:14 +0900
Subject: [PATCH 043/148] fix : id number update

---
 app/repository/quality_drive_detail_repository.py | 2 +-
 app/repository/quality_process_repository.py      | 2 +-
 app/repository/quality_risk_history_repository.py | 2 +-
 app/repository/quality_risk_trend_repository.py   | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 3fed791..d434692 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -100,7 +100,7 @@ def get_driving_pattern(row):
                 issue_message = "ACCEL_ALERT"
 
             inspection_drive_detail_list.append({
-                "id": detail_id,
+                #"id": detail_id,
                 "car_code": car_code,
                 "inspection_no": inspection_no,
                 "vehicle_id": vehicle_id,
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 26d90fe..e3cc1d6 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -80,7 +80,7 @@ def run():
                     process_status = "WAIT"
 
                 inspection_process_list.append({
-                    "id": process_id,
+                    #"id": process_id,
                     "process_name": process_name,
                     "total_vehicle_count": total_vehicle_count,
                     "completed_count": completed_count,
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 955b392..0fe5b4a 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -42,7 +42,7 @@ def run():
             row = msg.value
 
             inspection_risk_history_list.append({
-                "id": row["id"],
+                #"id": row["id"],
                 "inspection_type":
                     row["inspection_type"],
 
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 3c8018f..a887b62 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -42,7 +42,7 @@ def run():
             row = msg.value
 
             inspection_risk_trend_list.append({
-                "id": row["id"],
+                #"id": row["id"],
                 "risk_level":
                     row["risk_level"],
 

From add66d9665a73de7e4dbb5a54202e2e6a6a2597b Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 30 Jun 2026 13:41:38 +0900
Subject: [PATCH 044/148] =?UTF-8?q?fix:=20=EC=A0=9C=EC=A1=B0=20=EC=9D=B4?=
 =?UTF-8?q?=EB=B2=A4=ED=8A=B8=20manufacturing=5Fevent=5Fjson=20=EC=83=9D?=
 =?UTF-8?q?=EC=84=B1=20=EA=B0=9C=EC=84=A0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/api/routers/process.py                    | 104 ++++++++++++++++--
 .../manufacturing_event_json_builder.py       |  52 ++++++---
 app/dto/response/bottleneck_response.py       |  88 +++++++++++++--
 app/dto/response/common_response.py           |   1 -
 4 files changed, 209 insertions(+), 36 deletions(-)

diff --git a/app/api/routers/process.py b/app/api/routers/process.py
index 9b7d079..3d6ff2c 100644
--- a/app/api/routers/process.py
+++ b/app/api/routers/process.py
@@ -7,21 +7,111 @@
 )
 from app.utils.response_utils import success_response
 
-router = APIRouter(prefix="/api/process", tags=["process"])
+router = APIRouter(prefix="/api/process", tags=["공정 분석"])
 
+BottleneckAnalysisResponse = CommonResponse[BottleneckAnalysisPage]
 
-@router.get("/bottleneck")
+BottleneckAnalysisDescription = """
+Kafka raw 제조 이벤트를 기반으로 현재 제조 공정의 병목 순위를 조회합니다.
+
+분석 입력 데이터:
+- `sample_db.manufacturing_event_json` 테이블의 `is_sent = true` 이벤트만 사용합니다.
+- Kafka consumer가 `factory.manufacturing.raw` 토픽에서 받은 record value의 `eventJson`을 저장한 데이터입니다.
+- `manufacturing_event_id`는 `manufacturing_event_json.id`를 의미합니다.
+- `analysis_status`는 병목 분석 상태가 아니라 다른 이상 탐지 로직용 값이므로 병목 분석 필터로 사용하지 않습니다.
+
+분석 방식:
+- PRESS, BODY, PAINT, ASSEMBLY 이벤트를 Kafka에서 계속 수신해 DB에 적재합니다.
+- 병목 조회 시 저장된 raw 이벤트를 Rule Engine + Isolation Forest 모델로 분석합니다.
+- 결과는 `bottleneck_analysis_result` 테이블에 저장됩니다.
+- `delayTime`은 DB에는 계산된 원본 double 값으로 저장하고, API 응답에서는 소수점 둘째 자리까지 반환합니다.
+- `processCode`는 장비 코드 기준으로 `도장 (L1)`, `프레스 (P4)` 형식으로 반환합니다.
+
+페이지네이션:
+- `cursor`는 페이지 번호입니다. 생략하면 `0`으로 처리됩니다.
+- `size=5`, `cursor=0`이면 1~5위, `cursor=1`이면 6~10위를 반환합니다.
+- `hasNext=false`이면 다음 페이지를 호출하지 않아야 합니다.
+
+캐시:
+- Redis에 cursor/size별 결과를 캐시합니다.
+- Kafka raw 이벤트가 새로 수신되면 병목 캐시를 무효화합니다.
+"""
+
+BottleneckAnalysisExample = {
+    "success": True,
+    "data": {
+        "content": [
+            {
+                "rankNo": 1,
+                "processCode": "도장 (L3)",
+                "delayTime": 12.4,
+                "affectedVehicleCount": 128,
+                "riskScore": 5.0,
+            },
+            {
+                "rankNo": 2,
+                "processCode": "차체 (S12)",
+                "delayTime": 9.8,
+                "affectedVehicleCount": 92,
+                "riskScore": 4.0,
+            },
+        ],
+        "hasNext": True,
+        "nextCursor": 1,
+    },
+    "message": "병목 분석이 완료되었습니다.",
+    "timestamp": "2026-06-30T11:10:00+09:00",
+}
+
+
+@router.get(
+    "/bottleneck",
+    response_model=BottleneckAnalysisResponse,
+    summary="제조 공정 병목 분석 결과 조회",
+    description=BottleneckAnalysisDescription,
+    response_description="제조 공정 병목 순위 페이지",
+    responses={
+        200: {
+            "description": "병목 분석 결과 조회 성공",
+            "content": {
+                "application/json": {
+                    "example": BottleneckAnalysisExample,
+                },
+            },
+        },
+        404: {
+            "description": "분석 가능한 제조 이벤트 또는 병목 분석 결과가 없는 경우",
+        },
+        500: {
+            "description": "Redis 캐시, 모델 파일, DB 처리 중 오류가 발생한 경우",
+        },
+    },
+)
 def get_bottleneck_analysis(
-    cursor: int | None = Query(default=None, ge=0),
-    size: int = Query(default=10, ge=1, le=100),
+    cursor: int | None = Query(
+        default=None,
+        ge=0,
+        description=(
+            "조회할 페이지 번호입니다. 생략하면 0으로 처리합니다. "
+            "cursor=0,size=5는 1~5위, cursor=1,size=5는 6~10위를 반환합니다."
+        ),
+        examples=[0],
+    ),
+    size: int = Query(
+        default=5,
+        ge=1,
+        le=100,
+        description="한 페이지에 반환할 병목 결과 수입니다. 기본값은 5입니다.",
+        examples=[5],
+    ),
     service: BottleneckAnalysisService = Depends(get_bottleneck_analysis_service),
-) -> CommonResponse[dict]:
-    """Redis 캐시를 통해 병목 분석 결과 한 페이지를 반환"""
+) -> BottleneckAnalysisResponse:
+    """Redis 캐시를 통해 병목 분석 결과 페이지를 반환합니다."""
     page: BottleneckAnalysisPage = service.get_cached_realtime_bottlenecks(
         cursor=cursor,
         size=size,
     )
     return success_response(
-        data=page.model_dump(by_alias=True),
+        data=page,
         message="병목 분석이 완료되었습니다.",
     )
diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index 1bc4319..debbf41 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -80,7 +80,7 @@ def initial_dispatch_status(process_code: str) -> str:
 
 def is_abnormal_operation_status(operation_status: Any) -> bool:
     """이벤트 JSON의 운전 상태가 이상 상태인지 반환한다."""
-    return operation_status in {"FAULT", "STOPPED", "MAINTENANCE"}
+    return operation_status in {"FAULT", "STOPPED"}
 
 
 def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
@@ -272,7 +272,10 @@ def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
                     slot=global_index,
                     total_events=total_events,
                 )
-                event_id = f"EVT-{event_time:%Y%m%d}-{global_index + 1:06d}"
+                event_date = (
+                    _production_date_from_vehicle_id(car_id) or event_time.date()
+                )
+                event_id = f"EVT-{event_date:%Y%m%d}-{global_index + 1:06d}"
                 event = self._build_event(
                     event_id=event_id,
                     event_time=event_time,
@@ -370,6 +373,8 @@ def _build_event(
             process_code=process_code,
             is_abnormal=is_abnormal,
         )
+        operation_status = equipment_status["operationStatus"]
+        is_equipment_abnormal = is_abnormal_operation_status(operation_status)
 
         return {
             "event": {
@@ -385,15 +390,16 @@ def _build_event(
                 "equipmentType": equipment_row["equipment_type"],
             },
             "equipmentStatus": {
-                # 운전 상태는 정상/이상 프로필 안에서 차량·공정별로 랜덤 생성한다.
-                # 시간 필드는 실제 이벤트 전송 전까지 미확정이므로 NULL로 둔다.
-                "operationStatus": equipment_status["operationStatus"],
+                # Java enum EquipmentOperationStatus 값만 사용한다.
+                "operationStatus": operation_status,
                 "lastNormalTime": (
                     (event_time - timedelta(seconds=31)).isoformat()
-                    if is_abnormal
+                    if is_equipment_abnormal
                     else None
                 ),
-                "statusChangedTime": event_time.isoformat() if is_abnormal else None,
+                "statusChangedTime": (
+                    event_time.isoformat() if is_equipment_abnormal else None
+                ),
             },
             "product": {
                 "carMasterId": car_master_id,
@@ -1036,31 +1042,43 @@ def _equipment_route_for_car(car_master_id: int) -> str:
     )
 
 
+def _production_date_from_vehicle_id(vehicle_id: str) -> date | None:
+    for token in str(vehicle_id).split("-"):
+        if len(token) != 8 or not token.isdigit():
+            continue
+        try:
+            return date(
+                int(token[0:4]),
+                int(token[4:6]),
+                int(token[6:8]),
+            )
+        except ValueError:
+            return None
+    return None
+
+
 def _equipment_status_for_event(
     *,
     car_master_id: int,
     process_code: str,
     is_abnormal: bool,
 ) -> dict[str, str]:
-    """정상/이상 유형에 맞는 설비 상태를 결정적 랜덤으로 선택한다.
-
-    정상 데이터는 RUNNING/IDLE, 이상 데이터는 FAULT/STOPPED/MAINTENANCE
-    상태군에서 선택한다. 같은 차량·공정은 재생성해도 같은 상태를 갖는다.
-    """
+    """Java enum 기준 운전 상태를 8:2 비율로 결정적 선택한다."""
+    _ = is_abnormal
     digest = hashlib.blake2b(
         f"{car_master_id}:{process_code}:status".encode("utf-8"),
         digest_size=8,
     ).digest()
     ratio = int.from_bytes(digest, "big") % 100
 
-    if not is_abnormal:
-        operation_status = "RUNNING" if ratio < 85 else "IDLE"
-    elif ratio < 50:
-        operation_status = "FAULT"
+    if ratio < 70:
+        operation_status = "RUNNING"
     elif ratio < 80:
+        operation_status = "WARNING"
+    elif ratio < 90:
         operation_status = "STOPPED"
     else:
-        operation_status = "MAINTENANCE"
+        operation_status = "FAULT"
 
     return {"operationStatus": operation_status}
 
diff --git a/app/dto/response/bottleneck_response.py b/app/dto/response/bottleneck_response.py
index 20a53fd..f8088f5 100644
--- a/app/dto/response/bottleneck_response.py
+++ b/app/dto/response/bottleneck_response.py
@@ -2,19 +2,85 @@
 
 
 class BottleneckAnalysisItem(BaseModel):
-    model_config = ConfigDict(populate_by_name=True)
+    model_config = ConfigDict(
+        populate_by_name=True,
+        json_schema_extra={
+            "example": {
+                "rankNo": 1,
+                "processCode": "도장 (L3)",
+                "delayTime": 12.4,
+                "affectedVehicleCount": 128,
+                "riskScore": 5.0,
+            },
+        },
+    )
 
-    rank_no: int = Field(alias="rankNo")
-    process_code: str = Field(alias="processCode")
-    delay_time: float = Field(alias="delayTime")
-    affected_vehicle_count: int = Field(alias="affectedVehicleCount")
-    risk_score: float = Field(alias="riskScore")
+    rank_no: int = Field(
+        alias="rankNo",
+        description="병목 위험 순위입니다. 1이 가장 위험도가 높은 결과입니다.",
+        examples=[1],
+    )
+    process_code: str = Field(
+        alias="processCode",
+        description=(
+            "공정 표시명입니다. 공정명과 장비 번호를 조합해 "
+            "`도장 (L3)`, `프레스 (P4)` 형식으로 반환합니다."
+        ),
+        examples=["도장 (L3)"],
+    )
+    delay_time: float = Field(
+        alias="delayTime",
+        description=(
+            "평균 지연 시간(초)입니다. DB에는 원본 double 값으로 저장하고 "
+            "API 응답에서만 소수점 둘째 자리까지 반올림합니다."
+        ),
+        examples=[12.4],
+    )
+    affected_vehicle_count: int = Field(
+        alias="affectedVehicleCount",
+        description="해당 병목 후보에 영향을 받은 차량 수입니다.",
+        examples=[128],
+    )
+    risk_score: float = Field(
+        alias="riskScore",
+        description=(
+            "Rule Engine과 Isolation Forest 결과를 결합해 산출한 병목 위험도입니다. "
+            "값이 클수록 위험도가 높습니다."
+        ),
+        examples=[5.0],
+    )
 
 
 class BottleneckAnalysisPage(BaseModel):
-    model_config = ConfigDict(populate_by_name=True)
-
-    content: list[BottleneckAnalysisItem]
-    has_next: bool = Field(alias="hasNext")
-    next_cursor: int | None = Field(alias="nextCursor")
+    model_config = ConfigDict(
+        populate_by_name=True,
+        json_schema_extra={
+            "example": {
+                "content": [
+                    {
+                        "rankNo": 1,
+                        "processCode": "도장 (L3)",
+                        "delayTime": 12.4,
+                        "affectedVehicleCount": 128,
+                        "riskScore": 5.0,
+                    },
+                ],
+                "hasNext": True,
+                "nextCursor": 1,
+            },
+        },
+    )
 
+    content: list[BottleneckAnalysisItem] = Field(
+        description="병목 분석 결과 목록입니다. rankNo 오름차순으로 정렬됩니다.",
+    )
+    has_next: bool = Field(
+        alias="hasNext",
+        description="다음 페이지 존재 여부입니다. false이면 다음 무한스크롤 호출을 중단해야 합니다.",
+        examples=[True],
+    )
+    next_cursor: int | None = Field(
+        alias="nextCursor",
+        description="다음 페이지 cursor입니다. hasNext가 false이면 null입니다.",
+        examples=[1],
+    )
diff --git a/app/dto/response/common_response.py b/app/dto/response/common_response.py
index 49de7d9..b67c9e7 100644
--- a/app/dto/response/common_response.py
+++ b/app/dto/response/common_response.py
@@ -10,4 +10,3 @@ class CommonResponse(BaseModel, Generic[DataT]):
     data: DataT | None = Field(default=None, description="응답 데이터")
     message: str = Field(description="응답 메시지")
     timestamp: str = Field(description="응답 생성 시간")
-

From 661cfd521c3fac147631f1473005441b8017c499 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 13:57:25 +0900
Subject: [PATCH 045/148] feat : inspection_master add, test code update

---
 app/scheduler/quality/master_test.py | 105 +++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 app/scheduler/quality/master_test.py

diff --git a/app/scheduler/quality/master_test.py b/app/scheduler/quality/master_test.py
new file mode 100644
index 0000000..019287b
--- /dev/null
+++ b/app/scheduler/quality/master_test.py
@@ -0,0 +1,105 @@
+from sqlalchemy import create_engine, text
+from dotenv import load_dotenv
+from urllib.parse import quote_plus
+import os
+import time
+
+load_dotenv()
+
+# Sample DB
+SAMPLE_DATABASE_URL = (
+    f"mysql+pymysql://{os.getenv('DB_USER')}:"
+    f"{quote_plus(os.getenv('DB_PASSWORD'))}@"
+    f"{os.getenv('DB_HOST')}:"
+    f"{os.getenv('DB_PORT')}/sampledb"
+)
+
+# Main DB
+MAIN_DATABASE_URL = (
+    f"mysql+pymysql://{os.getenv('DB_USER')}:"
+    f"{quote_plus(os.getenv('DB_PASSWORD'))}@"
+    f"{os.getenv('DB_HOST')}:"
+    f"{os.getenv('DB_PORT')}/maindb"
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
+    pool_pre_ping=True
+)
+
+last_id = 0
+
+while True:
+
+    # Sample DB에서 아직 복사하지 않은 차량 5건 조회
+    with sample_engine.connect() as conn:
+
+        rows = conn.execute(
+            text("""
+                SELECT *
+                FROM car_master
+                WHERE id > :last_id
+                ORDER BY id
+                LIMIT 1
+            """),
+            {"last_id": last_id}
+        ).mappings().all()
+
+    # 더 이상 복사할 데이터가 없으면 종료
+    if not rows:
+        print("모든 차량 복사 완료")
+        break
+
+    # Main DB로 저장
+    with main_engine.begin() as conn:
+
+        for row in rows:
+
+            conn.execute(
+                text("""
+                    INSERT INTO inspection_master
+                    (
+                        id,
+                        vehicle_id,
+                        car_type,
+                        engine_type,
+                        car_color,
+                        fuel_efficiency,
+                        created_at
+                    )
+                    VALUES
+                    (
+                        :id,
+                        :vehicle_id,
+                        :car_type,
+                        :engine_type,
+                        :car_color,
+                        :fuel_efficiency,
+                        :created_at
+                    )
+                """),
+                {
+                    "id": row["id"],
+                    "vehicle_id": row["vehicle_id"],
+                    "car_type": row["car_type"],
+                    "engine_type": row["engine_type"],
+                    "car_color": row["car_color"],
+                    "fuel_efficiency": row["fuel_efficiency"],
+                    "created_at": row["created_at"]
+                }
+            )
+
+            last_id = row["id"]
+
+    print(
+        f"{len(rows)}건 복사 완료 "
+        f"(마지막 ID: {last_id})"
+    )
+
+    # 1초 대기
+    time.sleep(1)
\ No newline at end of file

From 03c754a0de5427ef93a89a55dc500bcf087ad44c Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 30 Jun 2026 14:32:59 +0900
Subject: [PATCH 046/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9=20=EB=B6=84?=
 =?UTF-8?q?=EC=84=9D=20=EB=B9=88=20=ED=8E=98=EC=9D=B4=EC=A7=80=20=EC=9D=91?=
 =?UTF-8?q?=EB=8B=B5=20=EC=B2=98=EB=A6=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 병목 분석 결과가 없거나 cursor가 마지막 페이지를 초과한 경우 404 대신 빈 content 반환

- hasNext=false와 nextCursor=null을 반환해 프론트 무한스크롤 호출 종료 기준과 맞춤

- 분석 가능한 Kafka raw 이벤트가 없는 경우 기존 병목 결과를 정리하고 빈 페이지로 응답

- 병목 분석 API Swagger에서 빈 페이지 응답 정책을 명시하고 404 응답 설명 제거
---
 app/api/routers/process.py                 |  4 +---
 app/service/analysis/bottleneck_service.py | 18 ++++++++++++------
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/app/api/routers/process.py b/app/api/routers/process.py
index 3d6ff2c..e966d7b 100644
--- a/app/api/routers/process.py
+++ b/app/api/routers/process.py
@@ -31,6 +31,7 @@
 - `cursor`는 페이지 번호입니다. 생략하면 `0`으로 처리됩니다.
 - `size=5`, `cursor=0`이면 1~5위, `cursor=1`이면 6~10위를 반환합니다.
 - `hasNext=false`이면 다음 페이지를 호출하지 않아야 합니다.
+- 분석 가능한 이벤트가 없거나 cursor가 마지막 페이지를 넘으면 404가 아니라 빈 `content`와 `hasNext=false`를 반환합니다.
 
 캐시:
 - Redis에 cursor/size별 결과를 캐시합니다.
@@ -79,9 +80,6 @@
                 },
             },
         },
-        404: {
-            "description": "분석 가능한 제조 이벤트 또는 병목 분석 결과가 없는 경우",
-        },
         500: {
             "description": "Redis 캐시, 모델 파일, DB 처리 중 오류가 발생한 경우",
         },
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 521358e..0a73cb3 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -67,9 +67,10 @@ def get_realtime_bottlenecks(
         # 저장소에서 요청 페이지 범위만 조회
         rows = self.repository.list_results(cursor=page, size=size)
         if saved_count == 0 or not rows:
-            raise AppException(
-                "병목 분석 결과를 찾을 수 없습니다.",
-                status_code=status.HTTP_404_NOT_FOUND,
+            return BottleneckAnalysisPage(
+                content=[],
+                hasNext=False,
+                nextCursor=None,
             )
 
         next_cursor = page + 1 if has_next else None
@@ -141,10 +142,15 @@ def run_analysis_and_save(self, *, cursor: int, size: int) -> tuple[int, bool]:
         """전체 공정 이력을 분석하고 요청한 순위 페이지 결과만 저장"""
         histories = self.repository.list_manufacturing_event_histories()
         if not histories:
-            raise AppException(
-                "공정 이력 데이터를 찾을 수 없습니다.",
-                status_code=status.HTTP_404_NOT_FOUND,
+            logger.info(
+                "Bottleneck analysis skipped: source=kafka_raw_db "
+                "table=manufacturing_event_json filter=is_sent:true total=0 "
+                "cursor=%s size=%s",
+                cursor,
+                size,
             )
+            self.repository.prune_results_after_rank(0)
+            return 0, False
 
         process_counts = Counter(str(row.get("process_code")) for row in histories)
         event_ids = [

From e1cf543bede6e23ae6319146bc380720e8c2a138 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 15:22:43 +0900
Subject: [PATCH 047/148] feat : inspection_master scheduler update

---
 .../quality_drive_detail_repository.py        | 147 ++++---
 app/repository/quality_process_repository.py  | 154 ++++---
 .../quality_risk_history_repository.py        | 126 ++++--
 .../quality_risk_trend_repository.py          |  87 ++--
 .../quality_status_detail_repository.py       |  78 ++--
 .../quality/drive_detail_producer.py          | 133 ++++--
 app/scheduler/quality/process_producer.py     | 182 ++++++--
 .../quality/risk_history_producer.py          | 405 ++++++++++--------
 app/scheduler/quality/risk_trend_producer.py  | 271 ++++++------
 .../quality/status_detail_producer.py         | 246 ++++++-----
 10 files changed, 1119 insertions(+), 710 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index d434692..2d13778 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -1,6 +1,5 @@
 from sqlalchemy import create_engine
 import pandas as pd
-import json
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
@@ -10,8 +9,11 @@
     QUALITY_INSPECTION_DRIVE_DETAIL
 )
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
@@ -35,7 +37,6 @@ def run():
         group_id="ai-drive-detail-group"
     )
 
-
     def calculate_drive_score(row):
 
         score = 100
@@ -51,7 +52,6 @@ def calculate_drive_score(row):
 
         return round(score, 2)
 
-
     def get_driving_pattern(row):
 
         throttle = float(row["throttle_position"])
@@ -69,84 +69,111 @@ def get_driving_pattern(row):
 
         return "NORMAL"
 
-
     detail_id = 1
-    inspection_drive_detail_list = []
 
     try:
+
         for msg in consumer:
+
             row = msg.value
 
-            # 폐기 차량 제외
             if not row.get("created_at"):
                 print(
-                    f"폐기 차량 제외 : {row.get('vehicle_id')}"
+                    f"폐기 차량 제외 : "
+                    f"{row.get('vehicle_id')}"
                 )
                 continue
 
             vehicle_id = row["vehicle_id"]
+
+            # ==========================
+            # 중복 저장 방지
+            # ==========================
+            exists = pd.read_sql(
+                """
+                SELECT COUNT(*) AS cnt
+                FROM inspection_drive_detail
+                WHERE vehicle_id = %s
+                """,
+                con=main_engine,
+                params=[vehicle_id]
+            )
+
+            if exists.iloc[0]["cnt"] > 0:
+
+                print(
+                    f"{vehicle_id} 이미 저장됨"
+                )
+
+                continue
+            # ==========================
+
             car_code = vehicle_id.split("-")[0]
 
-            inspection_no = f"DRIVE-{detail_id:05d}"
+            inspection_no = (
+                f"DRIVE-{detail_id:05d}"
+            )
 
             drive_score = calculate_drive_score(row)
-            driving_pattern = get_driving_pattern(row)
+
+            driving_pattern = (
+                get_driving_pattern(row)
+            )
 
             if drive_score >= 80:
+
                 inspection_result = "NORMAL"
                 issue_message = "NORMAL"
+
             else:
+
                 inspection_result = "WARNING"
                 issue_message = "ACCEL_ALERT"
 
-            inspection_drive_detail_list.append({
-                #"id": detail_id,
+            df = pd.DataFrame([{
                 "car_code": car_code,
                 "inspection_no": inspection_no,
                 "vehicle_id": vehicle_id,
-                "throttle_position": round(
-                    float(row["throttle_position"]), 2
-                ),
-                "brake_pressure": round(
-                    float(row["brake_pressure"]), 2
-                ),
-                "steering_angle": round(
-                    float(row["steering_angle"]), 2
-                ),
-                "drive_score": drive_score,
-                "inspection_result": inspection_result,
-                "driving_pattern": driving_pattern,
-                "issue_message": issue_message,
-                "created_at": row["created_at"]
-            })
-
-            if len(inspection_drive_detail_list) >= 100:
-
-                df = pd.DataFrame(
-                    inspection_drive_detail_list
-                )
-
-                df.to_sql(
-                    name="inspection_drive_detail",
-                    con=main_engine,
-                    if_exists="append",
-                    index=False
-                )
-
-                inspection_drive_detail_list.clear()
-
-            detail_id += 1
-
-    except Exception as e:
-        print(f"오류 발생 : {e}")
-
-    finally:
-
-        if inspection_drive_detail_list:
 
-            df = pd.DataFrame(
-                inspection_drive_detail_list
-            )
+                "throttle_position":
+                    round(
+                        float(
+                            row["throttle_position"]
+                        ),
+                        2
+                    ),
+
+                "brake_pressure":
+                    round(
+                        float(
+                            row["brake_pressure"]
+                        ),
+                        2
+                    ),
+
+                "steering_angle":
+                    round(
+                        float(
+                            row["steering_angle"]
+                        ),
+                        2
+                    ),
+
+                "drive_score":
+                    drive_score,
+
+                "inspection_result":
+                    inspection_result,
+
+                "driving_pattern":
+                    driving_pattern,
+
+                "issue_message":
+                    issue_message,
+
+                "created_at":
+                    row["created_at"]
+            }])
 
             df.to_sql(
                 name="inspection_drive_detail",
@@ -156,10 +183,22 @@ def get_driving_pattern(row):
             )
 
             print(
-                f"drvie-detail 최종 저장 완료"
+                f"{vehicle_id} "
+                f"drive-detail 저장 완료"
             )
 
+            detail_id += 1
+
+    except Exception as e:
+
+        print(
+            f"오류 발생 : {e}"
+        )
+
+    finally:
+
         consumer.close()
 
+
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index e3cc1d6..5f38a78 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -1,6 +1,5 @@
 from sqlalchemy import create_engine
 import pandas as pd
-import random
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
@@ -8,8 +7,11 @@
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
@@ -33,87 +35,117 @@ def run():
         group_id="ai-process-group"
     )
 
-    process_names = [
-        "Visual",
-        "Function",
-        "Drive",
-        "Final"
-    ]
-
-    process_id = 1
-    inspection_process_list = []
-
     try:
+
         for msg in consumer:
 
             row = msg.value
 
-            process_date = row["process_date"]
-            total_vehicle_count = row["vehicle_count"]
-
-            for process_name in process_names:
+            process_name = row["process_name"]
+            completed_count = row["completed_count"]
+            waiting_count = row["waiting_count"]
+            progress_rate = row["completion_rate"]
+            created_at = row["created_at"]
+
+            # 같은 날짜 + 같은 공정 존재 여부 확인
+            exists = pd.read_sql(
+                """
+                SELECT COUNT(*) AS cnt
+                FROM inspection_process
+                WHERE process_name=%s
+                  AND DATE(created_at)=DATE(%s)
+                """,
+                con=main_engine,
+                params=[
+                    process_name,
+                    created_at
+                ]
+            )
 
-                completed_count = random.randint(
-                    int(total_vehicle_count * 0.5),
-                    total_vehicle_count
+            if exists.iloc[0]["cnt"] > 0:
+
+                # UPDATE
+                with main_engine.begin() as conn:
+
+                    conn.execute(
+                        """
+                        UPDATE inspection_process
+                        SET
+                            completed_count=%s,
+                            waiting_count=%s,
+                            progress_rate=%s,
+                            process_status=%s
+                        WHERE process_name=%s
+                          AND DATE(created_at)=DATE(%s)
+                        """,
+                        (
+                            completed_count,
+                            waiting_count,
+                            progress_rate,
+
+                            "COMPLETE"
+                            if progress_rate == 100
+                            else "RUNNING",
+
+                            process_name,
+                            created_at
+                        )
+                    )
+
+                print(
+                    f"[UPDATE] {process_name} "
+                    f"{progress_rate}%"
                 )
 
-                waiting_count = (
-                    total_vehicle_count
-                    - completed_count
-                )
+            else:
 
-                progress_rate = round(
-                    completed_count
-                    / total_vehicle_count
-                    * 100,
-                    0
-                )
+                # INSERT
 
-                if progress_rate >= 90:
-                    process_status = "COMPLETE"
+                df = pd.DataFrame([{
+                    "process_name": process_name,
 
-                elif progress_rate >= 70:
-                    process_status = "RUNNING"
+                    "total_vehicle_count":
+                        completed_count
+                        + waiting_count,
 
-                else:
-                    process_status = "WAIT"
+                    "completed_count":
+                        completed_count,
 
-                inspection_process_list.append({
-                    #"id": process_id,
-                    "process_name": process_name,
-                    "total_vehicle_count": total_vehicle_count,
-                    "completed_count": completed_count,
-                    "waiting_count": waiting_count,
-                    "progress_rate": progress_rate,
-                    "process_status": process_status,
-                    "created_at": process_date
-                })
-
-                process_id += 1
-
-            # 날짜 하나당 4개 공정 생성
-            df = pd.DataFrame(
-                inspection_process_list
-            )
+                    "waiting_count":
+                        waiting_count,
 
-            df.to_sql(
-                name="inspection_process",
-                con=main_engine,
-                if_exists="append",
-                index=False
-            )
+                    "progress_rate":
+                        progress_rate,
+
+                    "process_status":
+                        "COMPLETE"
+                        if progress_rate == 100
+                        else "RUNNING",
 
-            inspection_process_list.clear()
+                    "created_at":
+                        created_at
+                }])
+
+                df.to_sql(
+                    name="inspection_process",
+                    con=main_engine,
+                    if_exists="append",
+                    index=False
+                )
+
+                print(
+                    f"[INSERT] {process_name} "
+                    f"{progress_rate}%"
+                )
 
     except Exception as e:
         print(f"오류 발생 : {e}")
 
     finally:
-        print(
-            f"process 최종 저장 완료"
-        )
+
+        print("process 종료")
         consumer.close()
 
+
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 0fe5b4a..7a92cb3 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -1,4 +1,4 @@
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, text
 import pandas as pd
 from dotenv import load_dotenv
 import os
@@ -9,8 +9,11 @@
     QUALITY_INSPECTION_RISK_HISTORY
 )
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
@@ -34,38 +37,91 @@ def run():
         group_id="ai-risk-history-group"
     )
 
-    inspection_risk_history_list = []
-
     try:
+
         for msg in consumer:
 
             row = msg.value
 
-            inspection_risk_history_list.append({
-                #"id": row["id"],
-                "inspection_type":
-                    row["inspection_type"],
+            inspection_type = row["inspection_type"]
+            inspection_round = row["inspection_round"]
+            risk_score = row["risk_score"]
+            start_time = row["start_time"]
+            end_time = row["end_time"]
+
+            # 같은 날짜 + 같은 검사 타입 존재 여부 확인
+            exists = pd.read_sql(
+                """
+                SELECT COUNT(*) AS cnt
+                FROM inspection_risk_history
+                WHERE inspection_type=%s
+                AND DATE(start_time)=DATE(%s)
+                """,
+                con=main_engine,
+                params=[
+                    inspection_type,
+                    start_time
+                ]
+            )
 
-                "inspection_round":
-                    row["inspection_round"],
+            # 존재하면 UPDATE
+            if exists.iloc[0]["cnt"] > 0:
+
+                with main_engine.begin() as conn:
+
+                    conn.execute(
+                        text("""
+                            UPDATE inspection_risk_history
+                            SET
+                                inspection_round=:inspection_round,
+                                risk_score=:risk_score,
+                                end_time=:end_time
+                            WHERE inspection_type=:inspection_type
+                            AND DATE(start_time)=DATE(:start_time)
+                        """),
+                        {
+                            "inspection_round":
+                                inspection_round,
+
+                            "risk_score":
+                                risk_score,
+
+                            "end_time":
+                                end_time,
+
+                            "inspection_type":
+                                inspection_type,
+
+                            "start_time":
+                                start_time
+                        }
+                    )
+
+                print(
+                    f"[UPDATE] "
+                    f"{inspection_type} "
+                    f"{risk_score}"
+                )
 
-                "risk_score":
-                    row["risk_score"],
+            # 없으면 INSERT
+            else:
 
-                "start_time":
-                    row["start_time"],
+                df = pd.DataFrame([{
+                    "inspection_type":
+                        inspection_type,
 
-                "end_time":
-                    row["end_time"]
-            })
+                    "inspection_round":
+                        inspection_round,
 
-            if len(
-                inspection_risk_history_list
-            ) >= 4:
+                    "risk_score":
+                        risk_score,
 
-                df = pd.DataFrame(
-                    inspection_risk_history_list
-                )
+                    "start_time":
+                        start_time,
+
+                    "end_time":
+                        end_time
+                }])
 
                 df.to_sql(
                     name="inspection_risk_history",
@@ -74,7 +130,11 @@ def run():
                     index=False
                 )
 
-                inspection_risk_history_list.clear()
+                print(
+                    f"[INSERT] "
+                    f"{inspection_type} "
+                    f"{risk_score}"
+                )
 
     except Exception as e:
 
@@ -82,24 +142,12 @@ def run():
 
     finally:
 
-        if inspection_risk_history_list:
-
-            df = pd.DataFrame(
-                inspection_risk_history_list
-            )
-
-            df.to_sql(
-                name="inspection_risk_history",
-                con=main_engine,
-                if_exists="append",
-                index=False
-            )
-
-            print(
-                f"risk-history 최종 저장 완료"
-            )
+        print(
+            "risk-history 종료"
+        )
 
         consumer.close()
 
+
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index a887b62..e47aa09 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -1,4 +1,4 @@
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, text
 import pandas as pd
 from dotenv import load_dotenv
 import os
@@ -9,8 +9,11 @@
     QUALITY_INSPECTION_RISK_TREND
 )
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
@@ -34,36 +37,55 @@ def run():
         group_id="ai-risk-trend-group"
     )
 
-    inspection_risk_trend_list = []
-
     try:
+
         for msg in consumer:
 
             row = msg.value
 
-            inspection_risk_trend_list.append({
-                #"id": row["id"],
-                "risk_level":
+            exists = pd.read_sql(
+                """
+                SELECT COUNT(*) AS cnt
+                FROM inspection_risk_trend
+                WHERE risk_level=%s
+                AND DATE(created_at)=DATE(%s)
+                """,
+                con=main_engine,
+                params=[
                     row["risk_level"],
-
-                "risk_count":
-                    row["risk_count"],
-
-                "risk_ratio":
-                    row["risk_ratio"],
-
-                "created_at":
                     row["created_at"]
-            })
-
-            if len(
-                inspection_risk_trend_list
-            ) >= 3:
+                ]
+            )
 
-                df = pd.DataFrame(
-                    inspection_risk_trend_list
+            if exists.iloc[0]["cnt"] > 0:
+
+                with main_engine.begin() as conn:
+
+                    conn.execute(
+                        text("""
+                            UPDATE
+                                inspection_risk_trend
+                            SET
+                                risk_count=:risk_count,
+                                risk_ratio=:risk_ratio
+                            WHERE
+                                risk_level=:risk_level
+                            AND DATE(created_at)
+                                =
+                                DATE(:created_at)
+                        """),
+                        row
+                    )
+
+                print(
+                    f"[UPDATE] "
+                    f"{row['risk_level']}"
                 )
 
+            else:
+
+                df = pd.DataFrame([row])
+
                 df.to_sql(
                     name="inspection_risk_trend",
                     con=main_engine,
@@ -71,7 +93,10 @@ def run():
                     index=False
                 )
 
-                inspection_risk_trend_list.clear()
+                print(
+                    f"[INSERT] "
+                    f"{row['risk_level']}"
+                )
 
     except Exception as e:
 
@@ -79,24 +104,10 @@ def run():
 
     finally:
 
-        if inspection_risk_trend_list:
-
-            df = pd.DataFrame(
-                inspection_risk_trend_list
-            )
-
-            df.to_sql(
-                name="inspection_risk_trend",
-                con=main_engine,
-                if_exists="append",
-                index=False
-            )
-
-            print(
-                f"{len(inspection_risk_trend_list)}건 최종 저장 완료"
-            )
+        print("risk-trend 종료")
 
         consumer.close()
 
+
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 870ef85..1c3bc7a 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -9,8 +9,11 @@
     QUALITY_INSPECTION_STATUS_DETAIL
 )
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
@@ -34,14 +37,35 @@ def run():
         group_id="ai-status-detail-group"
     )
 
-    inspection_status_detail_list = []
-
     try:
+
         for msg in consumer:
 
             row = msg.value
 
-            inspection_status_detail_list.append({
+            vehicle_id = row["vehicle_id"]
+
+            # 이미 저장된 차량인지 확인
+            exists = pd.read_sql(
+                """
+                SELECT COUNT(*) AS cnt
+                FROM inspection_status_detail
+                WHERE vehicle_id=%s
+                """,
+                con=main_engine,
+                params=[vehicle_id]
+            )
+
+            if exists.iloc[0]["cnt"] > 0:
+
+                print(
+                    f"[STATUS] "
+                    f"{vehicle_id} 이미 저장됨"
+                )
+
+                continue
+
+            inspection_status_detail = [{
                 "car_code":
                     row["car_code"],
 
@@ -49,7 +73,7 @@ def run():
                     row["inspection_no"],
 
                 "vehicle_id":
-                    row["vehicle_id"],
+                    vehicle_id,
 
                 "speed":
                     row["speed"],
@@ -77,36 +101,10 @@ def run():
 
                 "created_at":
                     row["created_at"]
-            })
-
-            # 100건씩 저장
-            if len(
-                inspection_status_detail_list
-            ) >= 100:
-
-                df = pd.DataFrame(
-                    inspection_status_detail_list
-                )
-
-                df.to_sql(
-                    name="inspection_status_detail",
-                    con=main_engine,
-                    if_exists="append",
-                    index=False
-                )
-
-                inspection_status_detail_list.clear()
-
-    except Exception as e:
-
-        print(f"오류 발생 : {e}")
-
-    finally:
-
-        if inspection_status_detail_list:
+            }]
 
             df = pd.DataFrame(
-                inspection_status_detail_list
+                inspection_status_detail
             )
 
             df.to_sql(
@@ -117,10 +115,24 @@ def run():
             )
 
             print(
-                f"status-detail 최종 저장 완료"
+                f"[STATUS 저장 완료] "
+                f"{vehicle_id}"
             )
 
+    except Exception as e:
+
+        print(
+            f"[STATUS ERROR] {e}"
+        )
+
+    finally:
+
+        print(
+            "status-detail Consumer 종료"
+        )
+
         consumer.close()
 
+
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/drive_detail_producer.py b/app/scheduler/quality/drive_detail_producer.py
index d71d3f7..1dbdc4c 100644
--- a/app/scheduler/quality/drive_detail_producer.py
+++ b/app/scheduler/quality/drive_detail_producer.py
@@ -3,25 +3,29 @@
 from dotenv import load_dotenv
 from urllib.parse import quote_plus
 import os
-
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
+    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
-    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
     SAMPLE_DATABASE_URL = (
         f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+        f"@{DB_HOST}:{DB_PORT}/sampledb"
+    )
+
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/maindb"
     )
 
     sample_engine = create_engine(
@@ -29,6 +33,11 @@ def run():
         pool_pre_ping=True
     )
 
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
     producer = KafkaProducer(
         bootstrap_servers=[
             os.getenv("BROKER_URL_1"),
@@ -45,49 +54,101 @@ def run():
             json.dumps(x, default=str).encode("utf-8")
     )
 
+    last_id = 0
 
-    with sample_engine.connect() as conn:
-        drive_rows = conn.execute(
-            text("""
-                SELECT *
-                FROM car_drive
-                ORDER BY created_at, vehicle_id
-            """)
-        ).mappings().all()
+    while True:
 
+        # 새로 생산된 차량 조회
+        with main_engine.connect() as conn:
 
-    for row in drive_rows:
+            cars = conn.execute(
+                text("""
+                    SELECT id, vehicle_id
+                    FROM inspection_master
+                    WHERE id > :last_id
+                    ORDER BY id
+                """),
+                {"last_id": last_id}
+            ).mappings().all()
 
-        message = {
-            "vehicle_id": row["vehicle_id"],
+        if not cars:
+            time.sleep(1)
+            continue
 
-            "throttle_position": round(
-                float(row["throttle_position"]), 2
-            ),
+        for car in cars:
 
-            "brake_pressure": round(
-                float(row["brake_pressure"]), 2
-            ),
+            vehicle_id = car["vehicle_id"]
 
-            "steering_angle": round(
-                float(row["steering_angle"]), 2
-            ),
+            # 해당 차량의 주행 데이터 조회
+            with sample_engine.connect() as conn:
 
-            "created_at": row["created_at"]
-        }
+                drive_rows = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_drive
+                        WHERE vehicle_id = :vehicle_id
+                        ORDER BY created_at
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().all()
 
-        producer.send(
-            "quality.inspection.drive_detail",
-            value=message
-        )
+            if not drive_rows:
+                print(
+                    f"[Drive] {vehicle_id} 데이터 없음"
+                )
 
-        # 실시간처럼 보내고 싶으면 사용
-        time.sleep(1)
+                last_id = car["id"]
+                continue
 
+            print(
+                f"[Drive] {vehicle_id} 분석 시작"
+            )
 
-    producer.flush()
+            for row in drive_rows:
+
+                message = {
+
+                    "vehicle_id":
+                        row["vehicle_id"],
+
+                    "throttle_position":
+                        round(
+                            float(row["throttle_position"]),
+                            2
+                        ),
+
+                    "brake_pressure":
+                        round(
+                            float(row["brake_pressure"]),
+                            2
+                        ),
+
+                    "steering_angle":
+                        round(
+                            float(row["steering_angle"]),
+                            2
+                        ),
+
+                    "created_at":
+                        row["created_at"]
+                }
+
+                producer.send(
+                    "quality.inspection.drive_detail",
+                    value=message
+                )
+
+            producer.flush()
+
+            print(
+                f"[Drive] {vehicle_id} Kafka 전송 완료"
+            )
+
+            # 처리 완료 차량 갱신
+            last_id = car["id"]
+
+        time.sleep(1)
 
-    print("drvie-detail Kafka 전송 완료")
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index 9b2a067..eafbc17 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -2,30 +2,34 @@
 from kafka import KafkaProducer
 from dotenv import load_dotenv
 from urllib.parse import quote_plus
-import os
 
+import os
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+TOTAL_TARGET = 100
+
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
     )
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
-    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
-    SAMPLE_DATABASE_URL = (
+    MAIN_DATABASE_URL = (
         f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+        f"@{DB_HOST}:{DB_PORT}/maindb"
     )
 
-    sample_engine = create_engine(
-        SAMPLE_DATABASE_URL,
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
         pool_pre_ping=True
     )
 
@@ -45,36 +49,148 @@ def run():
             json.dumps(x, default=str).encode("utf-8")
     )
 
-
-    with sample_engine.connect() as conn:
-        process_rows = conn.execute(
-            text("""
-                SELECT
-                    DATE(created_at) AS process_date,
-                    COUNT(*) AS vehicle_count
-                FROM car_master
-                GROUP BY DATE(created_at)
-                ORDER BY process_date
-            """)
-        ).mappings().all()
-
-
-    for row in process_rows:
-
-        message = {
-            "process_date": str(row["process_date"]),
-            "vehicle_count": row["vehicle_count"]
-        }
-
-        producer.send(
-            "quality.inspection.process",
-            value=message
-        )
+    last_id = 0
+
+    while True:
+
+        with main_engine.connect() as conn:
+
+            new_cars = conn.execute(
+                text("""
+                    SELECT id, vehicle_id
+                    FROM inspection_master
+                    WHERE id > :last_id
+                    ORDER BY id
+                """),
+                {"last_id": last_id}
+            ).mappings().all()
+
+            total_count = conn.execute(
+                text("""
+                    SELECT COUNT(*)
+                    FROM inspection_master
+                """)
+            ).scalar()
+
+        if not new_cars:
+            time.sleep(1)
+            continue
+
+        for car in new_cars:
+
+            # 생산 완료 시 모든 공정 완료
+            if total_count >= TOTAL_TARGET:
+
+                process_list = [
+                    ("VISUAL", TOTAL_TARGET),
+                    ("FUNCTION", TOTAL_TARGET),
+                    ("DRIVE", TOTAL_TARGET),
+                    ("FINAL", TOTAL_TARGET)
+                ]
+
+            # 평상시 공정 지연 효과 적용
+            else:
+
+                process_list = [
+                    (
+                        "VISUAL",
+                        min(total_count, TOTAL_TARGET)
+                    ),
+
+                    (
+                        "FUNCTION",
+                        min(
+                            max(total_count - 1, 0),
+                            TOTAL_TARGET
+                        )
+                    ),
+
+                    (
+                        "DRIVE",
+                        min(
+                            max(total_count - 2, 0),
+                            TOTAL_TARGET
+                        )
+                    ),
+
+                    (
+                        "FINAL",
+                        min(
+                            max(total_count - 3, 0),
+                            TOTAL_TARGET
+                        )
+                    )
+                ]
+
+            for process_name, completed in process_list:
+
+                waiting = max(
+                    0,
+                    TOTAL_TARGET - completed
+                )
+
+                rate = round(
+                    completed / TOTAL_TARGET * 100,
+                    2
+                )
+
+                if rate == 100:
+                    status = "COMPLETE"
+
+                elif rate == 0:
+                    status = "WAIT"
+
+                else:
+                    status = "RUNNING"
+
+                message = {
+                    "vehicle_id":
+                        car["vehicle_id"],
+
+                    "process_name":
+                        process_name,
+
+                    "total_vehicle_count":
+                        TOTAL_TARGET,
+
+                    "completed_count":
+                        completed,
+
+                    "waiting_count":
+                        waiting,
+
+                    "progress_rate":
+                        rate,
+
+                    "process_status":
+                        status,
+
+                    "created_at":
+                        time.strftime(
+                            "%Y-%m-%d %H:%M:%S"
+                        )
+                }
+
+                producer.send(
+                    "quality.inspection.process",
+                    value=message
+                )
+
+                print(
+                    f"[{process_name}] "
+                    f"{completed}/{TOTAL_TARGET} "
+                    f"({rate}%) "
+                    f"[{status}]"
+                )
+
+            producer.flush()
+
+            last_id = car["id"]
 
         time.sleep(1)
 
-    producer.flush()
-    print("porcess Kafka 전송 완료")
+    producer.close()
+
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index b2787bf..7fd17e1 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -1,25 +1,34 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
 from dotenv import load_dotenv
-import os
 from urllib.parse import quote_plus
 
-from datetime import datetime, timedelta
-
+import os
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
     )
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
-    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
+
+    SAMPLE_DB_NAME = os.getenv(
+        "SAMPLE_DB_NAME"
+    )
+
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/maindb"
+    )
 
     SAMPLE_DATABASE_URL = (
         f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
@@ -31,7 +40,13 @@ def run():
         pool_pre_ping=True
     )
 
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
     producer = KafkaProducer(
+
         bootstrap_servers=[
             os.getenv("BROKER_URL_1"),
             os.getenv("BROKER_URL_2")
@@ -41,214 +56,218 @@ def run():
 
         sasl_mechanism="OAUTHBEARER",
 
-        sasl_oauth_token_provider=MSKTokenProvider(),
+        sasl_oauth_token_provider=
+            MSKTokenProvider(),
 
         value_serializer=lambda x:
-            json.dumps(x, default=str).encode("utf-8")
+            json.dumps(
+                x,
+                default=str
+            ).encode("utf-8")
     )
 
+    last_id = 0
 
-    def calculate_status_risk(row):
-        score = 100
-
-        if float(row["speed"]) > 120:
-            score -= 20
-
-        if int(row["att"]) > 4000:
-            score -= 20
-
-        if float(row["battery_voltage"]) < 12:
-            score -= 10
-
-        return max(score, 0)
-
-
-    def calculate_control_risk(row):
-        score = 100
-
-        if row["collision_warning"] == 1:
-            score -= 40
-
-        if row["lane_departure"] == 1:
-            score -= 20
-
-        if row["traction_control"] == 1:
-            score -= 10
-
-        if row["abs_active"] == 1:
-            score -= 10
+    while True:
 
-        return max(score, 0)
+        with main_engine.connect() as conn:
 
+            new_cars = conn.execute(
+                text("""
+                    SELECT
+                        id,
+                        vehicle_id,
+                        DATE(created_at)
+                            AS inspection_date
+                    FROM inspection_master
+                    WHERE id > :last_id
+                    ORDER BY id
+                """),
+                {"last_id": last_id}
+            ).mappings().all()
 
-    def calculate_drive_risk(row):
-        score = 100
-
-        if float(row["throttle_position"]) > 90:
-            score -= 20
-
-        if float(row["brake_pressure"]) > 45:
-            score -= 20
-
-        if abs(float(row["steering_angle"])) > 40:
-            score -= 20
-
-        return max(score, 0)
-
-
-    def calculate_dynamics_risk(row):
-        score = 100
-
-        if abs(float(row["yaw_rate"])) > 7:
-            score -= 20
+        if not new_cars:
+            time.sleep(1)
+            continue
 
-        if abs(float(row["roll"])) > 4:
-            score -= 20
+        for car in new_cars:
 
-        if abs(float(row["pitch"])) > 4:
-            score -= 20
+            vehicle_id = car["vehicle_id"]
+            inspection_date = str(
+                car["inspection_date"]
+            )
 
-        return max(score, 0)
+            scores = {}
 
+            with sample_engine.connect() as conn:
 
-    risk_id = 1
+                # DRIVE
+                row = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_drive
+                        WHERE vehicle_id =
+                            :vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
 
-    stage_plan = [
-        ("DRIVE", 6),
-        ("CONTROL", 5),
-        ("DYNAMICS", 3),
-        ("STATUS", 3)
-    ]
+                if row:
 
-    start_date = datetime.strptime(
-        "2026-06-01 01:00",
-        "%Y-%m-%d %H:%M"
-    )
+                    score = 100
 
-    with sample_engine.connect() as conn:
+                    if float(
+                        row["throttle_position"]
+                    ) > 90:
+                        score -= 20
 
-        # 7일 데이터 생성
-        for day in range(7):
+                    if float(
+                        row["brake_pressure"]
+                    ) > 45:
+                        score -= 20
 
-            day_start = start_date + timedelta(days=day)
+                    if abs(float(
+                        row["steering_angle"]
+                    )) > 40:
+                        score -= 20
 
-            # 하루 생산 차량 100대
-            offset = day * 100
+                    scores["DRIVE"] = max(
+                        score,
+                        0
+                    )
 
-            vehicles = conn.execute(
-                text("""
-                    SELECT DISTINCT vehicle_id
-                    FROM car_status
-                    ORDER BY vehicle_id
-                    LIMIT 100 OFFSET :offset
-                """),
-                {"offset": offset}
-            ).mappings().all()
+                # CONTROL
+                row = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_control
+                        WHERE vehicle_id =
+                            :vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
+
+                if row:
+
+                    score = 100
+
+                    if row[
+                        "collision_warning"
+                    ] == 1:
+                        score -= 40
+
+                    if row[
+                        "lane_departure"
+                    ] == 1:
+                        score -= 20
+
+                    if row[
+                        "traction_control"
+                    ] == 1:
+                        score -= 10
+
+                    if row[
+                        "abs_active"
+                    ] == 1:
+                        score -= 10
+
+                    scores["CONTROL"] = max(
+                        score,
+                        0
+                    )
 
-            vehicle_ids = [
-                vehicle["vehicle_id"]
-                for vehicle in vehicles
-            ]
+                # DYNAMICS
+                row = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_dynamics
+                        WHERE vehicle_id =
+                            :vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
+
+                if row:
+
+                    score = 100
+
+                    if abs(
+                        float(
+                            row["yaw_rate"]
+                        )
+                    ) > 7:
+                        score -= 20
+
+                    if abs(
+                        float(row["roll"])
+                    ) > 4:
+                        score -= 20
+
+                    if abs(
+                        float(row["pitch"])
+                    ) > 4:
+                        score -= 20
+
+                    scores["DYNAMICS"] = max(
+                        score,
+                        0
+                    )
 
-            current_time = day_start
+                # STATUS
+                row = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_status
+                        WHERE vehicle_id =
+                            :vehicle_id
+                        LIMIT 1
+                    """),
+                    {"vehicle_id": vehicle_id}
+                ).mappings().first()
+
+                if row:
+
+                    score = 100
+
+                    if float(
+                        row["speed"]
+                    ) > 120:
+                        score -= 20
+
+                    if int(
+                        row["att"]
+                    ) > 4000:
+                        score -= 20
+
+                    if float(
+                        row[
+                            "battery_voltage"
+                        ]
+                    ) < 12:
+                        score -= 10
+
+                    scores["STATUS"] = max(
+                        score,
+                        0
+                    )
 
-            for stage_name, duration in stage_plan:
+            for inspection_type, risk_score in (
+                scores.items()
+            ):
 
-                stage_start = current_time
+                message = {
 
-                stage_end = (
-                    current_time
-                    + timedelta(hours=duration)
-                )
+                    "inspection_type":
+                        inspection_type,
 
-                scores = []
-
-                for vehicle_id in vehicle_ids:
-
-                    if stage_name == "DRIVE":
-
-                        row = conn.execute(
-                            text("""
-                                SELECT *
-                                FROM car_drive
-                                WHERE vehicle_id = :vehicle_id
-                                LIMIT 1
-                            """),
-                            {"vehicle_id": vehicle_id}
-                        ).mappings().first()
-
-                        if row:
-                            scores.append(
-                                calculate_drive_risk(row)
-                            )
-
-                    elif stage_name == "CONTROL":
-
-                        row = conn.execute(
-                            text("""
-                                SELECT *
-                                FROM car_control
-                                WHERE vehicle_id = :vehicle_id
-                                LIMIT 1
-                            """),
-                            {"vehicle_id": vehicle_id}
-                        ).mappings().first()
-
-                        if row:
-                            scores.append(
-                                calculate_control_risk(row)
-                            )
-
-                    elif stage_name == "DYNAMICS":
-
-                        row = conn.execute(
-                            text("""
-                                SELECT *
-                                FROM car_dynamics
-                                WHERE vehicle_id = :vehicle_id
-                                LIMIT 1
-                            """),
-                            {"vehicle_id": vehicle_id}
-                        ).mappings().first()
-
-                        if row:
-                            scores.append(
-                                calculate_dynamics_risk(row)
-                            )
-
-                    elif stage_name == "STATUS":
-
-                        row = conn.execute(
-                            text("""
-                                SELECT *
-                                FROM car_status
-                                WHERE vehicle_id = :vehicle_id
-                                LIMIT 1
-                            """),
-                            {"vehicle_id": vehicle_id}
-                        ).mappings().first()
-
-                        if row:
-                            scores.append(
-                                calculate_status_risk(row)
-                            )
-
-                avg_score = (
-                    round(sum(scores) / len(scores), 2)
-                    if scores else 0
-                )
+                    "inspection_date":
+                        inspection_date,
 
-                message = {
-                    "id": risk_id,
-                    "inspection_type": stage_name,
-                    "inspection_round": day + 1,
-                    "risk_score": avg_score,
-                    "start_time": stage_start.strftime(
-                        "%Y-%m-%d %H:%M"
-                    ),
-                    "end_time": stage_end.strftime(
-                        "%Y-%m-%d %H:%M"
-                    )
+                    "risk_score":
+                        risk_score
                 }
 
                 producer.send(
@@ -256,15 +275,21 @@ def calculate_dynamics_risk(row):
                     value=message
                 )
 
-                risk_id += 1
+                print(
+                    f"[{inspection_date}] "
+                    f"{inspection_type}"
+                    f" 점수 : "
+                    f"{risk_score}"
+                )
+
+            producer.flush()
 
-                current_time = stage_end
+            last_id = car["id"]
 
-                time.sleep(1)
+        time.sleep(1)
 
-    producer.flush()
+    producer.close()
 
-    print("risk-history Kafka 전송 완료")
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index e6e8cd4..cc39e48 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -1,29 +1,34 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
 from dotenv import load_dotenv
-import os
 from urllib.parse import quote_plus
 
-from datetime import datetime, timedelta
-
+import os
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
     )
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
-    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
     SAMPLE_DATABASE_URL = (
         f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+        f"@{DB_HOST}:{DB_PORT}/sampledb"
+    )
+
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/maindb"
     )
 
     sample_engine = create_engine(
@@ -31,6 +36,11 @@ def run():
         pool_pre_ping=True
     )
 
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
     producer = KafkaProducer(
         bootstrap_servers=[
             os.getenv("BROKER_URL_1"),
@@ -47,8 +57,8 @@ def run():
             json.dumps(x, default=str).encode("utf-8")
     )
 
-
     def calculate_status_risk(row):
+
         score = 100
 
         if float(row["speed"]) > 120:
@@ -62,8 +72,8 @@ def calculate_status_risk(row):
 
         return max(score, 0)
 
-
     def calculate_control_risk(row):
+
         score = 100
 
         if row["collision_warning"] == 1:
@@ -80,8 +90,8 @@ def calculate_control_risk(row):
 
         return max(score, 0)
 
-
     def calculate_drive_risk(row):
+
         score = 100
 
         if float(row["throttle_position"]) > 90:
@@ -95,8 +105,8 @@ def calculate_drive_risk(row):
 
         return max(score, 0)
 
-
     def calculate_dynamics_risk(row):
+
         score = 100
 
         if abs(float(row["yaw_rate"])) > 7:
@@ -110,7 +120,6 @@ def calculate_dynamics_risk(row):
 
         return max(score, 0)
 
-
     def get_risk_level(score):
 
         if score >= 80:
@@ -121,107 +130,122 @@ def get_risk_level(score):
 
         return "HIGH"
 
+    last_id = 0
 
-    risk_id = 1
-
-    base_date = datetime.strptime(
-        "2026-06-01",
-        "%Y-%m-%d"
-    )
-
-    with sample_engine.connect() as conn:
-
-        for day in range(7):
+    while True:
 
-            current_date = (
-                base_date +
-                timedelta(days=day)
-            )
-
-            offset = day * 100
+        with main_engine.connect() as conn:
 
-            vehicles = conn.execute(
+            cars = conn.execute(
                 text("""
-                    SELECT DISTINCT vehicle_id
-                    FROM car_status
-                    ORDER BY vehicle_id
-                    LIMIT 100 OFFSET :offset
+                    SELECT *
+                    FROM inspection_master
+                    WHERE id > :last_id
+                    ORDER BY id
                 """),
-                {"offset": offset}
+                {"last_id": last_id}
             ).mappings().all()
 
-            vehicle_ids = [
-                v["vehicle_id"]
-                for v in vehicles
-            ]
+        if not cars:
+            time.sleep(1)
+            continue
 
-            low_count = 0
-            medium_count = 0
-            high_count = 0
+        for car in cars:
 
-            for vehicle_id in vehicle_ids:
+            vehicle_id = car["vehicle_id"]
 
-                scores = []
+            created_date = (
+                car["created_at"]
+                .strftime("%Y-%m-%d 00:00:00")
+            )
 
-                status = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_status
-                        WHERE vehicle_id=:vehicle_id
-                        LIMIT 1
-                    """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
+            low = 0
+            medium = 0
+            high = 0
 
-                if status:
-                    scores.append(
-                        calculate_status_risk(status)
-                    )
+            with main_engine.connect() as conn:
 
-                control = conn.execute(
+                today_cars = conn.execute(
                     text("""
-                        SELECT *
-                        FROM car_control
-                        WHERE vehicle_id=:vehicle_id
-                        LIMIT 1
+                        SELECT vehicle_id
+                        FROM inspection_master
+                        WHERE DATE(created_at)
+                        =
+                        DATE(:created_at)
                     """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
+                    {
+                        "created_at":
+                            car["created_at"]
+                    }
+                ).mappings().all()
 
-                if control:
-                    scores.append(
-                        calculate_control_risk(control)
-                    )
+            for row in today_cars:
 
-                drive = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_drive
-                        WHERE vehicle_id=:vehicle_id
-                        LIMIT 1
-                    """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
+                vid = row["vehicle_id"]
 
-                if drive:
-                    scores.append(
-                        calculate_drive_risk(drive)
-                    )
+                scores = []
 
-                dynamics = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_dynamics
-                        WHERE vehicle_id=:vehicle_id
-                        LIMIT 1
-                    """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
+                with sample_engine.connect() as conn:
+
+                    status = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_status
+                            WHERE vehicle_id=:vid
+                            LIMIT 1
+                        """),
+                        {"vid": vid}
+                    ).mappings().first()
+
+                    if status:
+                        scores.append(
+                            calculate_status_risk(status)
+                        )
 
-                if dynamics:
-                    scores.append(
-                        calculate_dynamics_risk(dynamics)
-                    )
+                    control = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_control
+                            WHERE vehicle_id=:vid
+                            LIMIT 1
+                        """),
+                        {"vid": vid}
+                    ).mappings().first()
+
+                    if control:
+                        scores.append(
+                            calculate_control_risk(control)
+                        )
+
+                    drive = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_drive
+                            WHERE vehicle_id=:vid
+                            LIMIT 1
+                        """),
+                        {"vid": vid}
+                    ).mappings().first()
+
+                    if drive:
+                        scores.append(
+                            calculate_drive_risk(drive)
+                        )
+
+                    dynamics = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_dynamics
+                            WHERE vehicle_id=:vid
+                            LIMIT 1
+                        """),
+                        {"vid": vid}
+                    ).mappings().first()
+
+                    if dynamics:
+                        scores.append(
+                            calculate_dynamics_risk(dynamics)
+                        )
 
                 if not scores:
                     continue
@@ -235,51 +259,44 @@ def get_risk_level(score):
                 )
 
                 if level == "LOW":
-                    low_count += 1
+                    low += 1
 
                 elif level == "MEDIUM":
-                    medium_count += 1
+                    medium += 1
 
                 else:
-                    high_count += 1
-
-            daily_result = [
-                ("LOW", low_count),
-                ("MEDIUM", medium_count),
-                ("HIGH", high_count)
-            ]
-
-            for level, count in daily_result:
-
-                message = {
-                    "id": risk_id,
-                    "risk_level": level,
-                    "risk_count": count,
-
-                    # 필요하면 ratio 계산 수정
-                    "risk_ratio": round(
-                        count / 100 * 100,
-                        2
-                    ),
-
-                    "created_at":
-                        current_date.strftime(
-                            "%Y-%m-%d 00:00:00"
-                        )
-                }
+                    high += 1
+
+            total = low + medium + high
+
+            for level, count in [
+                ("LOW", low),
+                ("MEDIUM", medium),
+                ("HIGH", high)
+            ]:
 
                 producer.send(
                     "quality.inspection.risk_trend",
-                    value=message
-                )
+                    value={
+                        "risk_level": level,
+
+                        "risk_count": count,
 
-                risk_id += 1
+                        "risk_ratio": round(
+                            count / total * 100, 2
+                        ) if total else 0,
 
-                time.sleep(1)
+                        "created_at":
+                            created_date
+                    }
+                )
+
+            producer.flush()
 
-    producer.flush()
+            print(
+                f"[RISK TREND] {created_date}"
+            )
 
-    print("Risk-trend Kafka 전송 완료")
+            last_id = car["id"]
 
-if __name__ == "__main__":
-    run()
\ No newline at end of file
+        time.sleep(1)
\ No newline at end of file
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index 3d031ed..32b39bd 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -1,27 +1,34 @@
 from sqlalchemy import create_engine, text
 from kafka import KafkaProducer
 from dotenv import load_dotenv
-import os
 from urllib.parse import quote_plus
 
+import os
 import json
 import time
 
 from app.kafka.iam_provider import MSKTokenProvider
 
+
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
     DB_PASSWORD = quote_plus(
         os.getenv("DB_PASSWORD")
     )
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
-    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
     SAMPLE_DATABASE_URL = (
         f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
+        f"@{DB_HOST}:{DB_PORT}/sampledb"
+    )
+
+    MAIN_DATABASE_URL = (
+        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+        f"@{DB_HOST}:{DB_PORT}/maindb"
     )
 
     sample_engine = create_engine(
@@ -29,6 +36,11 @@ def run():
         pool_pre_ping=True
     )
 
+    main_engine = create_engine(
+        MAIN_DATABASE_URL,
+        pool_pre_ping=True
+    )
+
     producer = KafkaProducer(
         bootstrap_servers=[
             os.getenv("BROKER_URL_1"),
@@ -45,15 +57,19 @@ def run():
             json.dumps(x, default=str).encode("utf-8")
     )
 
-
-    def calculate_status_score(status, control):
+    def calculate_status_score(
+        status,
+        control
+    ):
 
         score = 100
         issues = []
 
         speed = float(status["speed"])
         rpm = int(status["att"])
-        battery = float(status["battery_voltage"])
+        battery = float(
+            status["battery_voltage"]
+        )
 
         if speed > 120:
             score -= 20
@@ -63,7 +79,7 @@ def calculate_status_score(status, control):
             score -= 20
             issues.append("RPM Error")
 
-        if battery < 12.0:
+        if battery < 12:
             score -= 10
             issues.append("battery drop")
 
@@ -71,117 +87,149 @@ def calculate_status_score(status, control):
             score -= 40
             issues.append("crash warning")
 
-        if status["gear"] == "P" and speed > 20:
+        if (
+            status["gear"] == "P"
+            and speed > 20
+        ):
             score -= 30
             issues.append("Parking")
 
         return max(score, 0), issues
 
-
     def get_result(score):
 
         if score >= 90:
             return "PASS"
 
-        if score >= 70:
+        elif score >= 70:
             return "WARN"
 
         return "FAIL"
 
+    last_id = 0
 
-    with sample_engine.connect() as conn:
-
-        master_rows = conn.execute(
-            text("""
-                SELECT *
-                FROM car_master
-                ORDER BY id
-            """)
-        ).mappings().all()
-
-        for master_row in master_rows:
+    while True:
 
-            vehicle_id = master_row["vehicle_id"]
-
-            status_row = conn.execute(
-                text("""
-                    SELECT *
-                    FROM car_status
-                    WHERE vehicle_id = :vehicle_id
-                    ORDER BY created_at DESC
-                    LIMIT 1
-                """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
+        with main_engine.connect() as conn:
 
-            control_row = conn.execute(
+            new_cars = conn.execute(
                 text("""
                     SELECT *
-                    FROM car_control
-                    WHERE vehicle_id = :vehicle_id
-                    ORDER BY created_at DESC
-                    LIMIT 1
+                    FROM inspection_master
+                    WHERE id > :last_id
+                    ORDER BY id
                 """),
-                {"vehicle_id": vehicle_id}
-            ).mappings().first()
-
-            if not status_row or not control_row:
-                continue
-
-            score, issues = calculate_status_score(
-                status_row,
-                control_row
-            )
-
-            message = {
-                "car_code": vehicle_id.split("-")[0],
-
-                "inspection_no":
-                    f"STATUS-{master_row['id']:05d}",
-
-                "vehicle_id": vehicle_id,
-
-                "speed":
-                    float(status_row["speed"]),
-
-                "att":
-                    int(status_row["att"]),
-
-                "gear":
-                    status_row["gear"],
-
-                "battery_voltage":
-                    float(
-                        status_row["battery_voltage"]
-                    ),
-
-                "fuel_rate":
-                    float(status_row["fuel_rate"]),
-
-                "status_score":
-                    float(score),
-
-                "inspection_result":
-                    get_result(score),
-
-                "issue_message":
-                    ", ".join(issues)
-                    if issues else "정상",
-
-                "created_at":
-                    status_row["created_at"]
-            }
-
-            producer.send(
-                "quality.inspection.status_detail",
-                value=message
-            )
+                {"last_id": last_id}
+            ).mappings().all()
 
+        if not new_cars:
             time.sleep(1)
-
-    producer.flush()
-
-    print("status-detail Kafka 전송 완료")
-
-if __name__ == "__main__":
-    run()
\ No newline at end of file
+            continue
+
+        with sample_engine.connect() as conn:
+
+            for car in new_cars:
+
+                vehicle_id = car["vehicle_id"]
+
+                status_row = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_status
+                        WHERE vehicle_id=:vehicle_id
+                        ORDER BY created_at DESC
+                        LIMIT 1
+                    """),
+                    {
+                        "vehicle_id": vehicle_id
+                    }
+                ).mappings().first()
+
+                control_row = conn.execute(
+                    text("""
+                        SELECT *
+                        FROM car_control
+                        WHERE vehicle_id=:vehicle_id
+                        ORDER BY created_at DESC
+                        LIMIT 1
+                    """),
+                    {
+                        "vehicle_id": vehicle_id
+                    }
+                ).mappings().first()
+
+                if (
+                    not status_row
+                    or not control_row
+                ):
+                    continue
+
+                score, issues = (
+                    calculate_status_score(
+                        status_row,
+                        control_row
+                    )
+                )
+
+                message = {
+
+                    "car_code":
+                        vehicle_id.split("-")[0],
+
+                    "inspection_no":
+                        f"STATUS-{car['id']:05d}",
+
+                    "vehicle_id":
+                        vehicle_id,
+
+                    "speed":
+                        float(status_row["speed"]),
+
+                    "att":
+                        int(status_row["att"]),
+
+                    "gear":
+                        status_row["gear"],
+
+                    "battery_voltage":
+                        float(
+                            status_row[
+                                "battery_voltage"
+                            ]
+                        ),
+
+                    "fuel_rate":
+                        float(
+                            status_row["fuel_rate"]
+                        ),
+
+                    "status_score":
+                        score,
+
+                    "inspection_result":
+                        get_result(score),
+
+                    "issue_message":
+                        ", ".join(issues)
+                        if issues
+                        else "정상",
+
+                    "created_at":
+                        status_row["created_at"]
+                }
+
+                producer.send(
+                    "quality.inspection.status_detail",
+                    value=message
+                )
+
+                producer.flush()
+
+                print(
+                    f"[STATUS] "
+                    f"{vehicle_id} 전송 완료"
+                )
+
+                last_id = car["id"]
+
+        time.sleep(1)
\ No newline at end of file

From 42c699d8ef3aff4d9cf446ca81f45bd04af6ae73 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 16:07:06 +0900
Subject: [PATCH 048/148] fix : repository consumer group update

---
 app/repository/quality_drive_detail_repository.py  | 8 +++-----
 app/repository/quality_process_repository.py       | 3 ++-
 app/repository/quality_risk_history_repository.py  | 8 ++++----
 app/repository/quality_risk_trend_repository.py    | 7 +++----
 app/repository/quality_status_detail_repository.py | 7 +++----
 5 files changed, 15 insertions(+), 18 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 2d13778..51edecd 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -5,10 +5,8 @@
 from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
-from app.kafka.topics import (
-    QUALITY_INSPECTION_DRIVE_DETAIL
-)
-
+from app.kafka.topics import QUALITY_INSPECTION_DRIVE_DETAIL
+from app.kafka.options import DRIVE_DETAIL_GROUP
 
 def run():
 
@@ -34,7 +32,7 @@ def run():
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_DRIVE_DETAIL,
-        group_id="ai-drive-detail-group"
+        group_id=DRIVE_DETAIL_GROUP
     )
 
     def calculate_drive_score(row):
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 5f38a78..3d7b1fe 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -6,6 +6,7 @@
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
+from app.kafka.options import (PROCESS_GROUP)
 
 
 def run():
@@ -32,7 +33,7 @@ def run():
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_PROCESS,
-        group_id="ai-process-group"
+        group_id=PROCESS_GROUP
     )
 
     try:
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 7a92cb3..2377c03 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -5,9 +5,9 @@
 from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
-from app.kafka.topics import (
-    QUALITY_INSPECTION_RISK_HISTORY
-)
+from app.kafka.topics import QUALITY_INSPECTION_RISK_HISTORY
+from app.kafka.options import RISK_HISTORY_GROUP
+
 
 
 def run():
@@ -34,7 +34,7 @@ def run():
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_RISK_HISTORY,
-        group_id="ai-risk-history-group"
+        group_id=RISK_HISTORY_GROUP
     )
 
     try:
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index e47aa09..97d8891 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -5,9 +5,8 @@
 from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
-from app.kafka.topics import (
-    QUALITY_INSPECTION_RISK_TREND
-)
+from app.kafka.topics import QUALITY_INSPECTION_RISK_TREND
+from app.kafka.options import RISK_TREND_GROUP
 
 
 def run():
@@ -34,7 +33,7 @@ def run():
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_RISK_TREND,
-        group_id="ai-risk-trend-group"
+        group_id=RISK_TREND_GROUP
     )
 
     try:
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 1c3bc7a..b21743b 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -5,9 +5,8 @@
 from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
-from app.kafka.topics import (
-    QUALITY_INSPECTION_STATUS_DETAIL
-)
+from app.kafka.topics import QUALITY_INSPECTION_STATUS_DETAIL
+from app.kafka.options import STATUS_DETAIL_GROUP
 
 
 def run():
@@ -34,7 +33,7 @@ def run():
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_STATUS_DETAIL,
-        group_id="ai-status-detail-group"
+        group_id=STATUS_DETAIL_GROUP
     )
 
     try:

From 62d33802e4a9f3b471938de247d8e7d880e0a4e3 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 16:32:10 +0900
Subject: [PATCH 049/148] fix : sql error update, process_producer update

---
 .../quality_drive_detail_repository.py        |  4 +-
 app/repository/quality_process_repository.py  |  2 +-
 .../quality_risk_history_repository.py        | 12 ++--
 .../quality_risk_trend_repository.py          | 12 ++--
 .../quality_status_detail_repository.py       |  4 +-
 app/scheduler/quality/process_producer.py     | 56 ++++++++-----------
 6 files changed, 40 insertions(+), 50 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 51edecd..e013359 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -91,10 +91,10 @@ def get_driving_pattern(row):
                 """
                 SELECT COUNT(*) AS cnt
                 FROM inspection_drive_detail
-                WHERE vehicle_id = %s
+                WHERE vehicle_id=:vehicle_id
                 """,
                 con=main_engine,
-                params=[vehicle_id]
+                params={"vehicle_id": vehicle_id}
             )
 
             if exists.iloc[0]["cnt"] > 0:
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 3d7b1fe..c2b2729 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -6,7 +6,7 @@
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
-from app.kafka.options import (PROCESS_GROUP)
+from app.kafka.options import PROCESS_GROUP
 
 
 def run():
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 2377c03..0587dd7 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -54,14 +54,14 @@ def run():
                 """
                 SELECT COUNT(*) AS cnt
                 FROM inspection_risk_history
-                WHERE inspection_type=%s
-                AND DATE(start_time)=DATE(%s)
+                WHERE inspection_type=:inspection_type
+                AND DATE(start_time)=DATE(:start_time)
                 """,
                 con=main_engine,
-                params=[
-                    inspection_type,
-                    start_time
-                ]
+                params={
+                    "inspection_type": row["inspection_type"],
+                    "start_time": row["start_time"]
+                }
             )
 
             # 존재하면 UPDATE
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 97d8891..fc4ea65 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -46,14 +46,14 @@ def run():
                 """
                 SELECT COUNT(*) AS cnt
                 FROM inspection_risk_trend
-                WHERE risk_level=%s
-                AND DATE(created_at)=DATE(%s)
+                WHERE risk_level=:risk_level
+                AND DATE(created_at)=DATE(:created_at)
                 """,
                 con=main_engine,
-                params=[
-                    row["risk_level"],
-                    row["created_at"]
-                ]
+                params={
+                    "risk_level": row["risk_level"],
+                    "created_at": row["created_at"]
+                }
             )
 
             if exists.iloc[0]["cnt"] > 0:
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index b21743b..8028f26 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -49,10 +49,10 @@ def run():
                 """
                 SELECT COUNT(*) AS cnt
                 FROM inspection_status_detail
-                WHERE vehicle_id=%s
+                WHERE vehicle_id=:vehicle_id
                 """,
                 con=main_engine,
-                params=[vehicle_id]
+                params={"vehicle_id": vehicle_id}
             )
 
             if exists.iloc[0]["cnt"] > 0:
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index eafbc17..c5cfce5 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -57,7 +57,10 @@ def run():
 
             new_cars = conn.execute(
                 text("""
-                    SELECT id, vehicle_id
+                    SELECT
+                        id,
+                        vehicle_id,
+                        created_at
                     FROM inspection_master
                     WHERE id > :last_id
                     ORDER BY id
@@ -65,21 +68,16 @@ def run():
                 {"last_id": last_id}
             ).mappings().all()
 
-            total_count = conn.execute(
-                text("""
-                    SELECT COUNT(*)
-                    FROM inspection_master
-                """)
-            ).scalar()
-
         if not new_cars:
             time.sleep(1)
             continue
 
         for car in new_cars:
 
-            # 생산 완료 시 모든 공정 완료
-            if total_count >= TOTAL_TARGET:
+            current_count = car["id"]
+
+            # 생산이 모두 끝난 경우
+            if current_count >= TOTAL_TARGET:
 
                 process_list = [
                     ("VISUAL", TOTAL_TARGET),
@@ -88,19 +86,18 @@ def run():
                     ("FINAL", TOTAL_TARGET)
                 ]
 
-            # 평상시 공정 지연 효과 적용
             else:
 
                 process_list = [
                     (
                         "VISUAL",
-                        min(total_count, TOTAL_TARGET)
+                        min(current_count, TOTAL_TARGET)
                     ),
 
                     (
                         "FUNCTION",
                         min(
-                            max(total_count - 1, 0),
+                            max(current_count - 1, 0),
                             TOTAL_TARGET
                         )
                     ),
@@ -108,7 +105,7 @@ def run():
                     (
                         "DRIVE",
                         min(
-                            max(total_count - 2, 0),
+                            max(current_count - 2, 0),
                             TOTAL_TARGET
                         )
                     ),
@@ -116,7 +113,7 @@ def run():
                     (
                         "FINAL",
                         min(
-                            max(total_count - 3, 0),
+                            max(current_count - 3, 0),
                             TOTAL_TARGET
                         )
                     )
@@ -129,24 +126,21 @@ def run():
                     TOTAL_TARGET - completed
                 )
 
-                rate = round(
+                progress_rate = round(
                     completed / TOTAL_TARGET * 100,
                     2
                 )
 
-                if rate == 100:
-                    status = "COMPLETE"
+                if progress_rate >= 100:
+                    process_status = "COMPLETE"
 
-                elif rate == 0:
-                    status = "WAIT"
+                elif progress_rate == 0:
+                    process_status = "WAIT"
 
                 else:
-                    status = "RUNNING"
+                    process_status = "RUNNING"
 
                 message = {
-                    "vehicle_id":
-                        car["vehicle_id"],
-
                     "process_name":
                         process_name,
 
@@ -160,15 +154,13 @@ def run():
                         waiting,
 
                     "progress_rate":
-                        rate,
+                        progress_rate,
 
                     "process_status":
-                        status,
+                        process_status,
 
                     "created_at":
-                        time.strftime(
-                            "%Y-%m-%d %H:%M:%S"
-                        )
+                        car["created_at"]
                 }
 
                 producer.send(
@@ -179,8 +171,8 @@ def run():
                 print(
                     f"[{process_name}] "
                     f"{completed}/{TOTAL_TARGET} "
-                    f"({rate}%) "
-                    f"[{status}]"
+                    f"({progress_rate}%) "
+                    f"[{process_status}]"
                 )
 
             producer.flush()
@@ -189,8 +181,6 @@ def run():
 
         time.sleep(1)
 
-    producer.close()
-
 
 if __name__ == "__main__":
     run()
\ No newline at end of file

From 51c1877c901f1020e8e0c8b8559a757fdbd66a64 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 16:36:31 +0900
Subject: [PATCH 050/148] fix : exists text lib add

---
 .../quality_drive_detail_repository.py           | 16 +++++++++-------
 .../quality_risk_history_repository.py           | 12 ++++++------
 app/repository/quality_risk_trend_repository.py  | 12 ++++++------
 .../quality_status_detail_repository.py          | 12 ++++++------
 4 files changed, 27 insertions(+), 25 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index e013359..890fe09 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -1,4 +1,4 @@
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, text
 import pandas as pd
 from dotenv import load_dotenv
 import os
@@ -88,13 +88,15 @@ def get_driving_pattern(row):
             # 중복 저장 방지
             # ==========================
             exists = pd.read_sql(
-                """
-                SELECT COUNT(*) AS cnt
-                FROM inspection_drive_detail
-                WHERE vehicle_id=:vehicle_id
-                """,
+                text("""
+                    SELECT COUNT(*) AS cnt
+                    FROM inspection_drive_detail
+                    WHERE vehicle_id = :vehicle_id
+                """),
                 con=main_engine,
-                params={"vehicle_id": vehicle_id}
+                params={
+                    "vehicle_id": vehicle_id
+                }
             )
 
             if exists.iloc[0]["cnt"] > 0:
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 0587dd7..6b10320 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -51,12 +51,12 @@ def run():
 
             # 같은 날짜 + 같은 검사 타입 존재 여부 확인
             exists = pd.read_sql(
-                """
-                SELECT COUNT(*) AS cnt
-                FROM inspection_risk_history
-                WHERE inspection_type=:inspection_type
-                AND DATE(start_time)=DATE(:start_time)
-                """,
+                text("""
+                    SELECT COUNT(*) AS cnt
+                    FROM inspection_risk_history
+                    WHERE inspection_type = :inspection_type
+                    AND DATE(start_time) = DATE(:start_time)
+                """),
                 con=main_engine,
                 params={
                     "inspection_type": row["inspection_type"],
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index fc4ea65..2e48d4f 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -43,12 +43,12 @@ def run():
             row = msg.value
 
             exists = pd.read_sql(
-                """
-                SELECT COUNT(*) AS cnt
-                FROM inspection_risk_trend
-                WHERE risk_level=:risk_level
-                AND DATE(created_at)=DATE(:created_at)
-                """,
+                text("""
+                    SELECT COUNT(*) AS cnt
+                    FROM inspection_risk_trend
+                    WHERE risk_level = :risk_level
+                    AND DATE(created_at) = DATE(:created_at)
+                """),
                 con=main_engine,
                 params={
                     "risk_level": row["risk_level"],
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 8028f26..e823694 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -1,4 +1,4 @@
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, text
 import pandas as pd
 from dotenv import load_dotenv
 import os
@@ -46,11 +46,11 @@ def run():
 
             # 이미 저장된 차량인지 확인
             exists = pd.read_sql(
-                """
-                SELECT COUNT(*) AS cnt
-                FROM inspection_status_detail
-                WHERE vehicle_id=:vehicle_id
-                """,
+                text("""
+                    SELECT COUNT(*) AS cnt
+                    FROM inspection_status_detail
+                    WHERE vehicle_id = :vehicle_id
+                """),
                 con=main_engine,
                 params={"vehicle_id": vehicle_id}
             )

From b94833ba6e1e82444e4283a787652144507705ef Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 16:43:43 +0900
Subject: [PATCH 051/148] fix : process process_nama del

---
 app/repository/quality_process_repository.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index c2b2729..3470138 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -42,6 +42,10 @@ def run():
 
             row = msg.value
 
+            if "process_name" not in row:
+                print("구버전 메시지 무시")
+                continue
+
             process_name = row["process_name"]
             completed_count = row["completed_count"]
             waiting_count = row["waiting_count"]

From a8d0d2feaad23bd8f7999bbd352b3f879a88fe22 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 30 Jun 2026 16:50:52 +0900
Subject: [PATCH 052/148] feat requirements.txt kafka-python version down

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 6a55cbe..6e1f757 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,7 +16,7 @@ openai>=1.59.0,<2.0.0
 aiokafka>=0.12.0
 confluent-kafka>=2.6.0
 aws-msk-iam-sasl-signer-python>=1.0.2
-kafka-python>=3.0.2
+kafka-python>=2.2.15
 
 # Redis Cache
 redis>=5.2.0,<6.0.0

From 6881c7fc22636fe62630a4164c39fa281e5d6bb8 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 30 Jun 2026 17:09:00 +0900
Subject: [PATCH 053/148] =?UTF-8?q?fix(ml):=20=EB=B3=91=EB=AA=A9=20?=
 =?UTF-8?q?=ED=8F=89=EA=B7=A0=20=EC=A7=80=EC=97=B0=200=EC=B4=88=20?=
 =?UTF-8?q?=EA=B3=84=EC=82=B0=20=EB=B2=84=EA=B7=B8=20=EC=88=98=EC=A0=95=20?=
 =?UTF-8?q?(waiting=5Ftime=20=EB=8C=80=EC=B2=B4=20=EB=A1=9C=EC=A7=81=20?=
 =?UTF-8?q?=EC=A0=81=EC=9A=A9)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/api/routers/process.py                    |   8 +-
 app/kafka/iam_provider.py                     |   4 +-
 app/kafka/raw_event_consumer.py               | 460 +++++++++++++++++-
 app/ml/inference/bottleneck_detector.py       |  52 +-
 .../bottleneck_analysis_repository.py         |  76 +++
 app/service/analysis/bottleneck_service.py    |  59 ++-
 requirements.txt                              |   2 +-
 7 files changed, 641 insertions(+), 20 deletions(-)

diff --git a/app/api/routers/process.py b/app/api/routers/process.py
index e966d7b..d238672 100644
--- a/app/api/routers/process.py
+++ b/app/api/routers/process.py
@@ -7,7 +7,7 @@
 )
 from app.utils.response_utils import success_response
 
-router = APIRouter(prefix="/api/process", tags=["공정 분석"])
+router = APIRouter(prefix="/api/process", tags=["process"])
 
 BottleneckAnalysisResponse = CommonResponse[BottleneckAnalysisPage]
 
@@ -22,7 +22,11 @@
 
 분석 방식:
 - PRESS, BODY, PAINT, ASSEMBLY 이벤트를 Kafka에서 계속 수신해 DB에 적재합니다.
-- 병목 조회 시 저장된 raw 이벤트를 Rule Engine + Isolation Forest 모델로 분석합니다.
+- Kafka raw 이벤트가 저장되면 Rule Engine + Isolation Forest 모델로 병목 결과를 자동 갱신합니다.
+- AI 병목 분석 결과는 `factory.manufacturing.analysis` 토픽으로도 발행합니다.
+- 분석 결과 토픽의 Message Key는 raw 토픽과 동일하게 `carId`입니다.
+- 발행 payload에는 `manufacturingAnalysisData`와 `aiAnalysisData`를 포함합니다.
+- 병목 조회 시에도 저장된 raw 이벤트 기준으로 최신 결과를 다시 확인합니다.
 - 결과는 `bottleneck_analysis_result` 테이블에 저장됩니다.
 - `delayTime`은 DB에는 계산된 원본 double 값으로 저장하고, API 응답에서는 소수점 둘째 자리까지 반환합니다.
 - `processCode`는 장비 코드 기준으로 `도장 (L1)`, `프레스 (P4)` 형식으로 반환합니다.
diff --git a/app/kafka/iam_provider.py b/app/kafka/iam_provider.py
index ea39ee2..e618397 100644
--- a/app/kafka/iam_provider.py
+++ b/app/kafka/iam_provider.py
@@ -1,4 +1,4 @@
-from kafka.net.sasl.oauth import AbstractTokenProvider
+from kafka.sasl.oauth import AbstractTokenProvider
 from aws_msk_iam_sasl_signer import MSKAuthTokenProvider
 
 
@@ -8,4 +8,4 @@ class MSKTokenProvider(AbstractTokenProvider):
     def token(self):
         # MSK 클러스터 리전 기준으로 짧은 수명의 IAM 인증 토큰을 매 연결 시점에 발급한다.
         token, _ = MSKAuthTokenProvider.generate_auth_token("ap-northeast-2")
-        return token
+        return token
\ No newline at end of file
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 9ef1766..972c38b 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -5,14 +5,17 @@
 import logging
 import ssl
 from datetime import datetime
-from threading import Event
+from threading import Event, Lock
 from typing import Any
+from uuid import uuid4
 
 from fastapi import FastAPI
 
 from app.core.config import settings
 from app.kafka.iam_provider import MSKTokenProvider
 from app.repository.sampledb_repository import SampleDbRepository
+from app.service.analysis.bottleneck_service import BottleneckAnalysisService
+from app.utils.datetime_utils import seoul_now_iso
 
 
 logger = logging.getLogger(__name__)
@@ -20,9 +23,11 @@
 
 # assembly-service의 app.kafka.topics.raw.name과 동일한 실제 raw 토픽명.
 RAW_TOPIC = "factory.manufacturing.raw"
+ANALYSIS_TOPIC = "factory.manufacturing.analysis"
 RAW_CONSUMER_GROUP_ID = "ai-consumer-group"
 RAW_AUTO_OFFSET_RESET = "earliest"
 RAW_CONSUMER_CONCURRENCY = 2
+_bottleneck_analysis_lock = Lock()
 
 
 def start_raw_event_consumer(app: FastAPI) -> None:
@@ -69,7 +74,7 @@ def _run_raw_event_consumer(
     consumer_index: int,
 ) -> None:
     try:
-        from kafka import KafkaConsumer
+        from kafka import KafkaConsumer, KafkaProducer
     except ModuleNotFoundError:
         logger.exception("kafka-python is required to consume manufacturing raw events.")
         return
@@ -89,6 +94,11 @@ def _run_raw_event_consumer(
     )
     repository = SampleDbRepository(settings.sample_database_connection_url)
     repository.schema.ensure_schema()
+    analysis_service = _create_bottleneck_analysis_service()
+    analysis_producer = _create_analysis_producer(
+        KafkaProducer,
+        bootstrap_servers,
+    )
 
     logger.info(
         "Raw Kafka consumer started: topic=%s group=%s concurrency=%s/%s bootstrap=%s auth=SASL_SSL/OAUTHBEARER",
@@ -104,7 +114,12 @@ def _run_raw_event_consumer(
                 if stop_event.is_set():
                     break
                 try:
-                    _consume_record(repository, record)
+                    _consume_record(
+                        repository,
+                        analysis_service,
+                        analysis_producer,
+                        record,
+                    )
                 except Exception:
                     logger.exception(
                         "Failed to consume raw event: topic=%s partition=%s offset=%s",
@@ -118,6 +133,8 @@ def _run_raw_event_consumer(
     except Exception:
         logger.exception("Raw Kafka consumer failed.")
     finally:
+        if analysis_producer is not None:
+            analysis_producer.close(timeout=5)
         consumer.close()
         logger.info(
             "Raw Kafka consumer stopped: topic=%s group=%s concurrency=%s/%s",
@@ -128,7 +145,12 @@ def _run_raw_event_consumer(
         )
 
 
-def _consume_record(repository: SampleDbRepository, record: Any) -> None:
+def _consume_record(
+    repository: SampleDbRepository,
+    analysis_service: BottleneckAnalysisService | None,
+    analysis_producer: Any | None,
+    record: Any,
+) -> None:
     """Kafka raw 메시지를 manufacturing_event_json row로 변환해 sampledb에 upsert한다."""
     raw_event = _parse_raw_event(record)
     row = _raw_event_to_row(raw_event)
@@ -136,12 +158,24 @@ def _consume_record(repository: SampleDbRepository, record: Any) -> None:
         [row],
         update_existing=True,
     )
-    # 새 raw 이벤트가 들어오면 다음 병목 API 호출에서 재분석하도록 Redis 캐시를 비운다.
+    bottleneck_summaries = _refresh_bottleneck_analysis(
+        analysis_service,
+        record,
+        row,
+    )
+    analysis_published = _publish_bottleneck_analysis_event(
+        analysis_producer,
+        raw_event,
+        row,
+        bottleneck_summaries,
+    )
+    # 새 raw 이벤트와 자동 분석 결과가 반영되면 Redis 캐시를 비운다.
     _clear_bottleneck_cache()
     logger.info(
         "Kafka raw event consumed and stored: topic=%s partition=%s offset=%s "
         "key=%s event_id=%s manufacturing_event_id_source=manufacturing_event_json.id "
-        "car_master_id=%s process_code=%s affected_rows=%s",
+        "car_master_id=%s process_code=%s affected_rows=%s "
+        "bottleneck_result_count=%s analysis_topic_published=%s",
         record.topic,
         record.partition,
         record.offset,
@@ -150,6 +184,8 @@ def _consume_record(repository: SampleDbRepository, record: Any) -> None:
         row["car_master_id"],
         row["process_code"],
         affected_rows,
+        len(bottleneck_summaries),
+        analysis_published,
     )
 
 
@@ -286,6 +322,418 @@ def _bootstrap_servers() -> list[str]:
     return [server.strip() for server in servers if server.strip()]
 
 
+def _create_bottleneck_analysis_service() -> BottleneckAnalysisService | None:
+    try:
+        return BottleneckAnalysisService()
+    except Exception:
+        logger.exception(
+            "Bottleneck analysis service is unavailable. "
+            "Raw Kafka events will still be stored.",
+        )
+        return None
+
+
+def _create_analysis_producer(
+    producer_cls: Any,
+    bootstrap_servers: list[str],
+) -> Any | None:
+    try:
+        producer = producer_cls(
+            ssl_context=ssl.create_default_context(),
+            bootstrap_servers=bootstrap_servers,
+            security_protocol="SASL_SSL",
+            sasl_mechanism="OAUTHBEARER",
+            sasl_oauth_token_provider=MSKTokenProvider(),
+            key_serializer=lambda value: str(value).encode("utf-8"),
+            value_serializer=lambda value: json.dumps(
+                value,
+                ensure_ascii=False,
+                default=_json_default,
+            ).encode("utf-8"),
+            retries=3,
+            linger_ms=10,
+        )
+    except Exception:
+        logger.exception(
+            "Kafka analysis producer is unavailable. "
+            "Bottleneck results will still be stored in DB.",
+        )
+        return None
+
+    logger.info(
+        "Kafka analysis producer started: topic=%s bootstrap=%s auth=SASL_SSL/OAUTHBEARER",
+        ANALYSIS_TOPIC,
+        bootstrap_servers,
+    )
+    return producer
+
+
+def _refresh_bottleneck_analysis(
+    analysis_service: BottleneckAnalysisService | None,
+    record: Any,
+    row: dict[str, Any],
+) -> list[dict[str, Any]]:
+    if analysis_service is None:
+        return []
+
+    with _bottleneck_analysis_lock:
+        try:
+            summaries = analysis_service.refresh_results_from_events()
+        except Exception:
+            logger.exception(
+                "Failed to refresh bottleneck analysis after raw event: "
+                "topic=%s partition=%s offset=%s event_id=%s process_code=%s",
+                record.topic,
+                record.partition,
+                record.offset,
+                row.get("event_id"),
+                row.get("process_code"),
+            )
+            return []
+
+    logger.info(
+        "Bottleneck analysis refreshed after raw Kafka event: "
+        "topic=%s partition=%s offset=%s event_id=%s process_code=%s result_count=%s",
+        record.topic,
+        record.partition,
+        record.offset,
+        row.get("event_id"),
+        row.get("process_code"),
+        len(summaries),
+    )
+    return summaries
+
+
+def _publish_bottleneck_analysis_event(
+    producer: Any | None,
+    raw_event: dict[str, Any],
+    row: dict[str, Any],
+    summaries: list[dict[str, Any]],
+) -> bool:
+    if producer is None:
+        return False
+
+    event = _build_bottleneck_analysis_event(raw_event, row, summaries)
+    key = str(row["car_master_id"])
+    try:
+        result = producer.send(ANALYSIS_TOPIC, key=key, value=event).get(timeout=10)
+    except Exception:
+        logger.exception(
+            "Failed to publish bottleneck analysis event: topic=%s key=%s event_id=%s",
+            ANALYSIS_TOPIC,
+            key,
+            row.get("event_id"),
+        )
+        return False
+
+    logger.info(
+        "Bottleneck analysis event published: topic=%s partition=%s offset=%s "
+        "key=%s event_id=%s analysis_id=%s analysis_type=%s",
+        result.topic,
+        result.partition,
+        result.offset,
+        key,
+        row.get("event_id"),
+        event["analysisId"],
+        event["analysisType"],
+    )
+    return True
+
+
+def _build_bottleneck_analysis_event(
+    raw_event: dict[str, Any],
+    row: dict[str, Any],
+    summaries: list[dict[str, Any]],
+) -> dict[str, Any]:
+    event_json = row["event_json"]
+    equipment = event_json.get("equipment", {})
+    product = event_json.get("product", {})
+    metrics = event_json.get("processMetrics", {})
+    equipment_status = event_json.get("equipmentStatus", {})
+    equipment_code = str(
+        equipment.get("equipmentCode")
+        or row.get("equipment_id")
+        or "UNKNOWN",
+    )
+    process_code = str(row["process_code"])
+    summary = _matching_bottleneck_summary(
+        summaries,
+        process_code=process_code,
+        equipment_code=equipment_code,
+    )
+    raw_delay_time = _safe_float(
+        metrics.get("stationDelaySec"),
+        default=_safe_float(metrics.get("waitingTimeSec"), default=0.0),
+    )
+    bottleneck_delay_time = _safe_float(
+        summary.get("avg_delay_time") if summary else None,
+        default=raw_delay_time,
+    )
+    risk_score = _safe_float(
+        summary.get("risk_score") if summary else None,
+        default=0.0,
+    )
+    overall_risk_score = _risk_score_to_percent(risk_score)
+    risk_level = _risk_level(overall_risk_score)
+    is_bottleneck = summary is not None and risk_score >= 3
+    defect_probability = _defect_probability(event_json, process_code)
+    transfer_predicted_process = _transfer_predicted_process(
+        process_code,
+        defect_probability,
+    )
+    is_quality_defect = defect_probability >= 0.6
+    is_equipment_fault = str(
+        equipment_status.get("operationStatus") or "",
+    ).upper() in {"FAULT", "STOPPED", "ERROR", "DOWN"}
+    is_sequence_error = (
+        process_code == "ASSEMBLY"
+        and _safe_float(
+            _nested_value(event_json, "processData", "assembly", "sequenceErrorCount"),
+            default=0.0,
+        )
+        > 0
+    )
+
+    return {
+        "analysisId": f"ANL-{uuid4()}",
+        "eventId": row["event_id"],
+        "eventTime": _iso_or_none(row.get("event_time"))
+        or _nested_text(event_json, "event", "eventTime"),
+        "analyzedAt": seoul_now_iso(),
+        "factoryCode": _nested_text(event_json, "location", "factoryCode"),
+        "lineCode": _nested_text(event_json, "location", "lineCode"),
+        "processCode": process_code,
+        "equipmentId": row.get("equipment_id"),
+        "equipmentCode": equipment_code,
+        "equipmentName": equipment.get("equipmentName"),
+        "equipmentType": equipment.get("equipmentType"),
+        "productId": product.get("productId"),
+        "carId": raw_event.get("_kafka_key") or product.get("carId") or row["car_master_id"],
+        "carMasterId": row["car_master_id"],
+        "analysisType": "BOTTLENECK_ANALYSIS",
+        "sourceService": "AI_SERVICE",
+        "riskScore": risk_score,
+        "riskScoreScale": "1-5",
+        "riskScores": {
+            "overallRiskScore": overall_risk_score,
+            "bottleneckRiskScore": overall_risk_score,
+            "defectTransferRiskScore": round(defect_probability * 100.0, 1),
+            "equipmentRiskScore": None,
+            "processRisk": {
+                "pressRiskScore": overall_risk_score if process_code == "PRESS" else None,
+                "bodyRiskScore": overall_risk_score if process_code == "BODY" else None,
+                "paintRiskScore": overall_risk_score if process_code == "PAINT" else None,
+                "assemblyRiskScore": (
+                    overall_risk_score if process_code == "ASSEMBLY" else None
+                ),
+            },
+        },
+        "operationRate": _operation_rate(metrics),
+        "riskLevel": risk_level,
+        "analysisResult": {
+            "isAbnormal": (
+                is_bottleneck
+                or is_quality_defect
+                or is_equipment_fault
+                or is_sequence_error
+            ),
+            "isBottleneck": is_bottleneck,
+            "isQualityDefect": is_quality_defect,
+            "isEquipmentFault": is_equipment_fault,
+            "isSequenceError": is_sequence_error,
+        },
+        "reason": {
+            "mainReason": _bottleneck_reason(
+                risk_score=risk_score,
+                delay_time=bottleneck_delay_time,
+                is_equipment_fault=is_equipment_fault,
+                is_sequence_error=is_sequence_error,
+            ),
+            "detailReasons": [
+                f"processCode={process_code}",
+                f"equipmentCode={equipment_code}",
+                f"bottleneckDelayTime={round(bottleneck_delay_time, 3)}",
+                f"riskScore={risk_score}",
+            ],
+        },
+        "recommendation": {
+            "type": _recommendation_type(process_code),
+            "message": _recommendation_message(process_code),
+        },
+        "manufacturingAnalysisData": {
+            "originalEventId": row["event_id"],
+            "carMasterId": row["car_master_id"],
+            "equipmentId": row.get("equipment_id"),
+            "processCode": process_code,
+            "analysisData": {
+                "cycleTimeSec": metrics.get("cycleTimeSec"),
+                "waitingTimeSec": metrics.get("waitingTimeSec"),
+                "processingTimeSec": metrics.get("processingTimeSec"),
+                "stationDelaySec": metrics.get("stationDelaySec"),
+                "queueLength": metrics.get("queueLength"),
+                "wipCount": metrics.get("wipCount"),
+            },
+            "riskScore": risk_score,
+        },
+        "aiAnalysisData": {
+            "originalEventId": row["event_id"],
+            "carMasterId": row["car_master_id"],
+            "equipmentId": row.get("equipment_id"),
+            "processCode": process_code,
+            "bottleneckDelayTime": bottleneck_delay_time,
+            "defectProbability": defect_probability,
+            "transferPredictedProcess": transfer_predicted_process,
+            "riskScore": risk_score,
+        },
+    }
+
+
+def _matching_bottleneck_summary(
+    summaries: list[dict[str, Any]],
+    *,
+    process_code: str,
+    equipment_code: str,
+) -> dict[str, Any] | None:
+    for summary in summaries:
+        if (
+            str(summary.get("process_code")) == process_code
+            and str(summary.get("equipment_code")) == equipment_code
+        ):
+            return summary
+    return None
+
+
+def _risk_score_to_percent(risk_score: float) -> float:
+    return round(max(0.0, min(risk_score, 5.0)) * 20.0, 1)
+
+
+def _risk_level(score: float) -> str:
+    if score >= 80:
+        return "CRITICAL"
+    if score >= 60:
+        return "WARNING"
+    return "LOW"
+
+
+def _operation_rate(metrics: dict[str, Any]) -> float:
+    cycle_time = _safe_float(metrics.get("cycleTimeSec"), default=0.0)
+    processing_time = _safe_float(metrics.get("processingTimeSec"), default=0.0)
+    idle_time = _safe_float(metrics.get("equipmentIdleTimeSec"), default=0.0)
+    planned_time = cycle_time if cycle_time > 0 else processing_time + idle_time
+    if planned_time <= 0:
+        return 0.0
+    return round(max(0.0, min(processing_time / planned_time * 100.0, 100.0)), 1)
+
+
+def _defect_probability(event_json: dict[str, Any], process_code: str) -> float:
+    process_data = event_json.get("processData", {})
+    sensor = event_json.get("sensor", {})
+    vibration = sensor.get("vibration", {})
+    robot = sensor.get("robotArmVibration", {})
+    thermal = sensor.get("thermal", {})
+
+    if process_code == "PAINT":
+        paint = process_data.get("paint", {})
+        defect_score = _safe_float(paint.get("defectScore"), default=0.0)
+        if str(paint.get("visionLabel") or "").upper() == "DEFECT":
+            defect_score = max(defect_score, 0.75)
+        surface_quality = _safe_float(
+            paint.get("surfaceQualityScore"),
+            default=100.0,
+        )
+        return round(max(defect_score, max(0.0, 100.0 - surface_quality) / 100.0), 4)
+
+    if process_code == "ASSEMBLY":
+        assembly = process_data.get("assembly", {})
+        error_count = (
+            _safe_float(assembly.get("sequenceErrorCount"), default=0.0)
+            + _safe_float(assembly.get("missingPartCount"), default=0.0)
+            + _safe_float(assembly.get("fasteningErrorCount"), default=0.0)
+        )
+        return round(min(error_count / 3.0, 1.0), 4)
+
+    vibration_score = max(
+        _safe_float(vibration.get("vibrationScore"), default=0.0),
+        _safe_float(robot.get("vibrationScore"), default=0.0),
+    )
+    thermal_score = max(
+        _safe_float(thermal.get("thermalScore"), default=0.0) / 100.0,
+        _safe_float(thermal.get("maxTemperature"), default=0.0) / 100.0,
+    )
+    return round(min(max(vibration_score, thermal_score), 1.0), 4)
+
+
+def _transfer_predicted_process(
+    process_code: str,
+    defect_probability: float,
+) -> str | None:
+    if defect_probability < 0.5:
+        return None
+    return {
+        "PRESS": "BODY",
+        "BODY": "PAINT",
+        "PAINT": "ASSEMBLY",
+        "ASSEMBLY": None,
+    }.get(process_code)
+
+
+def _bottleneck_reason(
+    *,
+    risk_score: float,
+    delay_time: float,
+    is_equipment_fault: bool,
+    is_sequence_error: bool,
+) -> str:
+    if is_equipment_fault:
+        return "설비 상태값에서 고장 또는 정지 위험이 감지되었습니다."
+    if is_sequence_error:
+        return "의장 공정의 작업 순서 오류가 감지되었습니다."
+    if risk_score >= 3:
+        return "Rule Engine과 Isolation Forest 기준 병목 위험이 감지되었습니다."
+    if delay_time > 0:
+        return "공정 지연 시간이 감지되었지만 위험도는 낮습니다."
+    return "주요 병목 지표가 정상 범위입니다."
+
+
+def _recommendation_type(process_code: str) -> str:
+    return {
+        "PRESS": "CHECK_PRESS_EQUIPMENT_AND_QUEUE",
+        "BODY": "CHECK_ROBOT_VIBRATION",
+        "PAINT": "CHECK_PAINT_QUALITY",
+        "ASSEMBLY": "CHECK_ASSEMBLY_SEQUENCE",
+    }.get(process_code, "CHECK_PROCESS")
+
+
+def _recommendation_message(process_code: str) -> str:
+    return {
+        "PRESS": "프레스 설비 상태, 전류 RMS 값과 대기열을 확인하세요.",
+        "BODY": "로봇 암 진동과 충돌 위험을 확인하세요.",
+        "PAINT": "열화상, 도막 두께와 비전 불량 결과를 확인하세요.",
+        "ASSEMBLY": "작업 순서, 누락 부품과 체결 오류를 확인하세요.",
+    }.get(process_code, "공정 지표와 설비 상태를 확인하세요.")
+
+
+def _safe_float(value: Any, *, default: float) -> float:
+    if value is None or value == "":
+        return default
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _iso_or_none(value: Any) -> str | None:
+    if isinstance(value, datetime):
+        return value.isoformat()
+    return str(value) if value is not None else None
+
+
+def _json_default(value: Any) -> str:
+    if isinstance(value, datetime):
+        return value.isoformat()
+    return str(value)
+
+
 def _clear_bottleneck_cache() -> None:
     if not settings.redis_url:
         return
diff --git a/app/ml/inference/bottleneck_detector.py b/app/ml/inference/bottleneck_detector.py
index 0cfd73d..d78a489 100644
--- a/app/ml/inference/bottleneck_detector.py
+++ b/app/ml/inference/bottleneck_detector.py
@@ -178,9 +178,11 @@ def summarize_manufacturing_event_histories(
             .agg(
                 manufacturing_event_id=("manufacturing_event_id", "first"),
                 car_master_id=("car_master_id", "first"),
-                avg_delay_time=("max_station_span", "mean"),
+                avg_delay_time=("delay_time", "mean"),
+                max_delay_time=("delay_time", "max"),
                 avg_total_duration=("total_duration", "mean"),
                 affected_vehicle_count=("car_master_id", "nunique"),
+                equipment_abnormal_count=("is_equipment_abnormal", "sum"),
                 event_count=("id", "count"),
                 avg_rule_risk=("rule_risk_score", "mean"),
                 avg_iforest_risk=("iforest_risk_score", "mean"),
@@ -203,14 +205,16 @@ def summarize_manufacturing_event_histories(
         )
         grouped = grouped.sort_values(
             [
-                "affected_vehicle_count",
                 "risk_level",
+                "equipment_abnormal_count",
                 "max_risk_score",
+                "max_delay_time",
                 "avg_iforest_risk",
                 "avg_rule_risk",
+                "affected_vehicle_count",
                 "avg_delay_time",
             ],
-            ascending=[False, False, False, False, False, False],
+            ascending=[False, False, False, False, False, False, False, False],
         ).reset_index(drop=True)
 
         # DB 저장 형식 변환
@@ -237,6 +241,7 @@ def _apply_rule_engine(features: pd.DataFrame) -> pd.DataFrame:
         result = features.copy()
         duration_threshold = result["total_duration"].quantile(0.95)
         station_span_threshold = result["max_station_span"].quantile(0.95)
+        delay_threshold = result["delay_time"].quantile(0.95)
 
         duration_score = (
             result["total_duration"] / max(float(duration_threshold), 1e-12)
@@ -244,14 +249,31 @@ def _apply_rule_engine(features: pd.DataFrame) -> pd.DataFrame:
         station_span_score = (
             result["max_station_span"] / max(float(station_span_threshold), 1e-12)
         ).clip(upper=2.0) / 2.0
-
-        result["rule_risk_score"] = (
-            0.55 * duration_score
-            + 0.45 * station_span_score
+        if float(delay_threshold) > 0:
+            delay_score = (
+                result["delay_time"] / float(delay_threshold)
+            ).clip(upper=2.0) / 2.0
+            delay_condition = result["delay_time"].ge(delay_threshold)
+        else:
+            delay_score = result["delay_time"] * 0.0
+            delay_condition = result["delay_time"].gt(0)
+        equipment_status_score = result["is_equipment_abnormal"].astype(float)
+
+        weighted_rule_score = (
+            0.35 * duration_score
+            + 0.25 * station_span_score
+            + 0.25 * delay_score
+            + 0.15 * equipment_status_score
         ).fillna(0.0)
+        result["rule_risk_score"] = pd.concat(
+            [weighted_rule_score, equipment_status_score],
+            axis=1,
+        ).max(axis=1)
         result["rule_bottleneck"] = (
             result["total_duration"].ge(duration_threshold)
             | result["max_station_span"].ge(station_span_threshold)
+            | delay_condition
+            | result["is_equipment_abnormal"].eq(1)
         ).astype(int)
         return result
 
@@ -263,13 +285,25 @@ def _build_manufacturing_event_features(
         source_df = pd.DataFrame(histories)
         process_time = source_df["process_time"].fillna(0).astype(float)
         waiting_time = source_df["waiting_time"].fillna(0).astype(float)
-        delay_time = source_df.get("delay_time", waiting_time).fillna(0).astype(float)
+        if "delay_time" in source_df.columns:
+            delay_time = source_df["delay_time"].where(source_df["delay_time"] > 0, waiting_time)
+        else:
+            delay_time = waiting_time
+        delay_time = delay_time.fillna(0).astype(float)
         queue_length = source_df.get("queue_length", 0.0)
         wip_count = source_df.get("wip_count", 0.0)
         station_key = source_df.get(
             "station_key",
             source_df["equipment_code"],
         ).fillna("UNKNOWN")
+        equipment_status = (
+            source_df["equipment_status"]
+            if "equipment_status" in source_df
+            else pd.Series("", index=source_df.index)
+        ).fillna("").astype(str).str.upper()
+        is_equipment_abnormal = equipment_status.isin(
+            {"FAULT", "STOPPED", "ERROR", "DOWN"},
+        ).astype(int)
         total_duration = process_time + waiting_time
 
         # 기본 시간/식별자 피처
@@ -281,6 +315,8 @@ def _build_manufacturing_event_features(
                 "car_master_id": source_df["car_master_id"],
                 "process_code": source_df["process_code"].astype(str),
                 "equipment_code": source_df["equipment_code"].astype(str),
+                "equipment_status": equipment_status,
+                "is_equipment_abnormal": is_equipment_abnormal,
                 "station_key": station_key.astype(str),
                 "process_time": process_time,
                 "waiting_time": waiting_time,
diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 6d38abd..0df2024 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -2,6 +2,7 @@
 
 import json
 from collections.abc import Iterable
+from datetime import datetime
 from typing import Any
 
 from app.repository.sampledb_schema import manufacturing_event_json
@@ -105,6 +106,7 @@ def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
                 manufacturing_event_json.c.car_master_id,
                 manufacturing_event_json.c.process_code,
                 manufacturing_event_json.c.equipment_id,
+                manufacturing_event_json.c.event_time,
                 manufacturing_event_json.c.event_json,
             )
             .where(manufacturing_event_json.c.is_sent.is_(True))
@@ -120,6 +122,7 @@ def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
     def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
         event_json = self._event_json_dict(row["event_json"])
         equipment = event_json.get("equipment", {})
+        equipment_status = event_json.get("equipmentStatus", {})
         metrics = event_json.get("processMetrics", {})
         equipment_code = str(
             equipment.get("equipmentCode")
@@ -128,6 +131,9 @@ def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
         )
         process_data = event_json.get("processData", {})
         process_code = str(row["process_code"])
+        operation_status = str(
+            equipment_status.get("operationStatus") or "",
+        ).upper()
         process_time = self._safe_float(metrics.get("processingTimeSec"))
         waiting_time = self._safe_float(metrics.get("waitingTimeSec"))
         cycle_time = self._safe_float(metrics.get("cycleTimeSec"))
@@ -136,10 +142,19 @@ def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
             process_data,
             process_code,
         )
+        equipment_stop_delay_time = self._equipment_stop_delay(
+            equipment_status,
+            event_json,
+            row.get("event_time"),
+        )
         delay_time = (
             station_delay_time
             if station_delay_time > 0
             else timestamp_delay_time
+            if timestamp_delay_time > 0
+            else equipment_stop_delay_time
+            if operation_status in {"FAULT", "STOPPED", "ERROR", "DOWN"}
+            else 0.0
         )
 
         return {
@@ -148,6 +163,7 @@ def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
             "car_master_id": row["car_master_id"],
             "process_code": process_code,
             "equipment_code": equipment_code,
+            "equipment_status": operation_status,
             "station_key": equipment_code,
             "process_time": process_time,
             "waiting_time": waiting_time,
@@ -191,6 +207,66 @@ def _process_timestamp_delay(
                 return cls._safe_float(value.get("timestampDelaySec"))
         return 0.0
 
+    @classmethod
+    def _equipment_stop_delay(
+        cls,
+        equipment_status: dict[str, Any],
+        event_json: dict[str, Any],
+        event_time_value: Any,
+    ) -> float:
+        status_changed_at = cls._parse_datetime(
+            equipment_status.get("statusChangedTime"),
+        )
+        event_time = cls._parse_datetime(
+            event_time_value,
+        ) or cls._parse_datetime(
+            cls._nested_value(event_json, "event", "eventTime"),
+        )
+        if status_changed_at is None:
+            return 0.0
+
+        last_normal_time = cls._parse_datetime(
+            equipment_status.get("lastNormalTime"),
+        )
+
+        if event_time is not None and event_time > status_changed_at:
+            return (event_time - status_changed_at).total_seconds()
+
+        if last_normal_time is not None:
+            return max((status_changed_at - last_normal_time).total_seconds(), 0.0)
+
+        if event_time is None:
+            event_time = datetime.now()
+        else:
+            return 30.0
+
+        return max((event_time - status_changed_at).total_seconds(), 0.0)
+
+    @staticmethod
+    def _parse_datetime(value: Any) -> Any | None:
+        if value is None:
+            return None
+        if hasattr(value, "isoformat") and hasattr(value, "tzinfo"):
+            return value.replace(tzinfo=None)
+        text = str(value).strip()
+        if not text:
+            return None
+        try:
+            return datetime.fromisoformat(
+                text.replace("Z", "+00:00"),
+            ).replace(tzinfo=None)
+        except ValueError:
+            return None
+
+    @staticmethod
+    def _nested_value(payload: dict[str, Any], *path: str) -> Any:
+        current: Any = payload
+        for key in path:
+            if not isinstance(current, dict):
+                return None
+            current = current.get(key)
+        return current
+
     def _init_sqlalchemy(self) -> None:
         try:
             from sqlalchemy import BigInteger, Column, DateTime, Double, Enum
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 0a73cb3..5508290 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -16,7 +16,7 @@
 
 
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
-BOTTLENECK_CACHE_VERSION = "v6"
+BOTTLENECK_CACHE_VERSION = "v8"
 PROCESS_CODE_LABELS = {
     "PRESS": "프레스",
     "BODY": "차체",
@@ -209,6 +209,63 @@ def run_analysis_and_save(self, *, cursor: int, size: int) -> tuple[int, bool]:
         )
         return len(page_summaries), has_next
 
+    def refresh_results_from_events(self) -> list[dict[str, Any]]:
+        """Kafka raw 이벤트 수신 후 전체 병목 분석 결과를 즉시 갱신한다."""
+        histories = self.repository.list_manufacturing_event_histories()
+        if not histories:
+            logger.info(
+                "Bottleneck analysis skipped: source=kafka_raw_db "
+                "table=manufacturing_event_json filter=is_sent:true total=0 "
+                "trigger=kafka_raw_event",
+            )
+            self.repository.prune_results_after_rank(0)
+            return []
+
+        process_counts = Counter(str(row.get("process_code")) for row in histories)
+        event_ids = [
+            int(row["manufacturing_event_id"])
+            for row in histories
+            if row.get("manufacturing_event_id") is not None
+        ]
+        delay_times = [
+            float(row.get("delay_time") or 0.0)
+            for row in histories
+        ]
+        logger.info(
+            "Bottleneck analysis input loaded: source=kafka_raw_db "
+            "table=manufacturing_event_json filter=is_sent:true total=%s "
+            "process_counts=%s delay_time_range=%.3f..%.3f "
+            "manufacturing_event_id_range=%s..%s trigger=kafka_raw_event",
+            len(histories),
+            dict(sorted(process_counts.items())),
+            min(delay_times) if delay_times else 0.0,
+            max(delay_times) if delay_times else 0.0,
+            min(event_ids) if event_ids else None,
+            max(event_ids) if event_ids else None,
+        )
+
+        if not self.model_path.exists():
+            raise AppException(
+                f"병목 탐지 모델을 찾을 수 없습니다: {self.model_path}",
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            )
+
+        summaries = self.detector.summarize_manufacturing_event_histories(histories)
+        self.repository.replace_results(
+            summaries,
+            detected_at=seoul_now().replace(tzinfo=None),
+            start_rank=1,
+            end_rank=max(len(summaries), 1),
+        )
+        self.repository.prune_results_after_rank(len(summaries))
+        logger.info(
+            "Bottleneck analysis results saved: source=kafka_raw_db "
+            "trigger=kafka_raw_event result_count=%s rank_range=1..%s",
+            len(summaries),
+            len(summaries),
+        )
+        return summaries
+
     def _redis(self) -> Any:
         """병목 캐시 작업에 사용할 Redis 클라이언트를 생성하고 재사용"""
         if self._redis_client is None:
diff --git a/requirements.txt b/requirements.txt
index 6a55cbe..32f34cf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,7 +16,7 @@ openai>=1.59.0,<2.0.0
 aiokafka>=0.12.0
 confluent-kafka>=2.6.0
 aws-msk-iam-sasl-signer-python>=1.0.2
-kafka-python>=3.0.2
+kafka-python>=2.2.15,<3.0.0
 
 # Redis Cache
 redis>=5.2.0,<6.0.0

From 9a143ee3a92b15a8c00e14591c2183716536005e Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 30 Jun 2026 17:19:50 +0900
Subject: [PATCH 054/148] =?UTF-8?q?fix(ml):=20=EB=B3=91=EB=AA=A9=20?=
 =?UTF-8?q?=ED=8F=89=EA=B7=A0=20=EC=A7=80=EC=97=B0=200=EC=B4=88=20?=
 =?UTF-8?q?=EA=B3=84=EC=82=B0=20=EB=B2=84=EA=B7=B8=20=EC=88=98=EC=A0=95=20?=
 =?UTF-8?q?=EB=B0=8F=20app=20=EB=AA=A8=EB=93=88=20=EC=9E=84=ED=8F=AC?=
 =?UTF-8?q?=ED=8A=B8=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/main.py b/main.py
index 44dfafe..f5c4efd 100644
--- a/main.py
+++ b/main.py
@@ -1,4 +1,5 @@
 # app/main.py
+from app.main import app
 
 import threading
 import time

From c6a3d48a24aeaaf73c1505f40130ea84daeaf113 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 10:03:45 +0900
Subject: [PATCH 055/148] fix : process kafka msg update

---
 app/repository/quality_process_repository.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 3470138..58b0d97 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -46,10 +46,11 @@ def run():
                 print("구버전 메시지 무시")
                 continue
 
+            total_vehicle_count = row["total_vehicle_count"]
             process_name = row["process_name"]
             completed_count = row["completed_count"]
             waiting_count = row["waiting_count"]
-            progress_rate = row["completion_rate"]
+            progress_rate = row["progress_rate"]
             created_at = row["created_at"]
 
             # 같은 날짜 + 같은 공정 존재 여부 확인
@@ -110,8 +111,7 @@ def run():
                     "process_name": process_name,
 
                     "total_vehicle_count":
-                        completed_count
-                        + waiting_count,
+                        total_vehicle_count,
 
                     "completed_count":
                         completed_count,

From 69ca7536ca6f4b0aaa1b28d8115c7bbbfa0227ff Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 11:22:14 +0900
Subject: [PATCH 056/148] fix : risk-history recode time update/start time, end
 time add

---
 .../quality_risk_history_repository.py        | 90 +++++++++----------
 1 file changed, 41 insertions(+), 49 deletions(-)

diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 6b10320..efec7fd 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -9,15 +9,12 @@
 from app.kafka.options import RISK_HISTORY_GROUP
 
 
-
 def run():
 
     load_dotenv()
 
     DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
+    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
     MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
@@ -43,11 +40,14 @@ def run():
 
             row = msg.value
 
+            # Producer에서 전달되는 값
             inspection_type = row["inspection_type"]
-            inspection_round = row["inspection_round"]
+            inspection_date = row["inspection_date"]
             risk_score = row["risk_score"]
-            start_time = row["start_time"]
-            end_time = row["end_time"]
+
+            # Repository에서 생성
+            start_time = f"{inspection_date} 00:00:00"
+            end_time = f"{inspection_date} 23:59:59"
 
             # 같은 날짜 + 같은 검사 타입 존재 여부 확인
             exists = pd.read_sql(
@@ -55,16 +55,16 @@ def run():
                     SELECT COUNT(*) AS cnt
                     FROM inspection_risk_history
                     WHERE inspection_type = :inspection_type
-                    AND DATE(start_time) = DATE(:start_time)
+                      AND DATE(start_time) = DATE(:start_time)
                 """),
                 con=main_engine,
                 params={
-                    "inspection_type": row["inspection_type"],
-                    "start_time": row["start_time"]
+                    "inspection_type": inspection_type,
+                    "start_time": start_time
                 }
             )
 
-            # 존재하면 UPDATE
+            # 이미 존재하면 UPDATE
             if exists.iloc[0]["cnt"] > 0:
 
                 with main_engine.begin() as conn:
@@ -73,27 +73,16 @@ def run():
                         text("""
                             UPDATE inspection_risk_history
                             SET
-                                inspection_round=:inspection_round,
-                                risk_score=:risk_score,
-                                end_time=:end_time
-                            WHERE inspection_type=:inspection_type
-                            AND DATE(start_time)=DATE(:start_time)
+                                risk_score = :risk_score,
+                                end_time = :end_time
+                            WHERE inspection_type = :inspection_type
+                              AND DATE(start_time) = DATE(:start_time)
                         """),
                         {
-                            "inspection_round":
-                                inspection_round,
-
-                            "risk_score":
-                                risk_score,
-
-                            "end_time":
-                                end_time,
-
-                            "inspection_type":
-                                inspection_type,
-
-                            "start_time":
-                                start_time
+                            "risk_score": risk_score,
+                            "end_time": end_time,
+                            "inspection_type": inspection_type,
+                            "start_time": start_time
                         }
                     )
 
@@ -103,24 +92,28 @@ def run():
                     f"{risk_score}"
                 )
 
-            # 없으면 INSERT
             else:
 
-                df = pd.DataFrame([{
-                    "inspection_type":
-                        inspection_type,
-
-                    "inspection_round":
-                        inspection_round,
-
-                    "risk_score":
-                        risk_score,
+                # inspection_round 자동 생성
+                with main_engine.begin() as conn:
 
-                    "start_time":
-                        start_time,
+                    inspection_round = conn.execute(
+                        text("""
+                            SELECT COALESCE(MAX(inspection_round), 0) + 1
+                            FROM inspection_risk_history
+                            WHERE inspection_type = :inspection_type
+                        """),
+                        {
+                            "inspection_type": inspection_type
+                        }
+                    ).scalar()
 
-                    "end_time":
-                        end_time
+                df = pd.DataFrame([{
+                    "inspection_type": inspection_type,
+                    "inspection_round": inspection_round,
+                    "risk_score": risk_score,
+                    "start_time": start_time,
+                    "end_time": end_time
                 }])
 
                 df.to_sql(
@@ -133,19 +126,18 @@ def run():
                 print(
                     f"[INSERT] "
                     f"{inspection_type} "
+                    f"ROUND {inspection_round} "
                     f"{risk_score}"
                 )
 
     except Exception as e:
 
-        print(f"오류 발생 : {e}")
+        import traceback
+        traceback.print_exc()
 
     finally:
 
-        print(
-            "risk-history 종료"
-        )
-
+        print("risk-history 종료")
         consumer.close()
 
 

From 78ec2cc0dfabc55e70b094a71aeba4593822839f Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 12:41:22 +0900
Subject: [PATCH 057/148] fix : process repository sql text update

---
 app/repository/quality_process_repository.py | 43 +++++++++-----------
 1 file changed, 20 insertions(+), 23 deletions(-)

diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 58b0d97..7cf12be 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -1,4 +1,4 @@
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, text
 import pandas as pd
 from dotenv import load_dotenv
 import os
@@ -74,28 +74,25 @@ def run():
                 with main_engine.begin() as conn:
 
                     conn.execute(
-                        """
-                        UPDATE inspection_process
-                        SET
-                            completed_count=%s,
-                            waiting_count=%s,
-                            progress_rate=%s,
-                            process_status=%s
-                        WHERE process_name=%s
-                          AND DATE(created_at)=DATE(%s)
-                        """,
-                        (
-                            completed_count,
-                            waiting_count,
-                            progress_rate,
-
-                            "COMPLETE"
-                            if progress_rate == 100
-                            else "RUNNING",
-
-                            process_name,
-                            created_at
-                        )
+                        text("""
+                            UPDATE inspection_process
+                            SET
+                                completed_count=:completed_count,
+                                waiting_count=:waiting_count,
+                                progress_rate=:progress_rate,
+                                process_status=:process_status
+                            WHERE process_name=:process_name
+                            AND DATE(created_at)=DATE(:created_at)
+                        """),
+                        {
+                            "completed_count": completed_count,
+                            "waiting_count": waiting_count,
+                            "progress_rate": progress_rate,
+                            "process_status":
+                                "COMPLETE" if progress_rate == 100 else "RUNNING",
+                            "process_name": process_name,
+                            "created_at": created_at
+                        }
                     )
 
                 print(

From 311fbed0033a26a8c8fbe8f1f478975cd26ce347 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 12:43:54 +0900
Subject: [PATCH 058/148] feat : print del

---
 app/repository/quality_drive_detail_repository.py  |  5 -----
 app/repository/quality_process_repository.py       | 10 ----------
 app/repository/quality_risk_history_repository.py  | 13 -------------
 app/repository/quality_risk_trend_repository.py    | 10 ----------
 app/repository/quality_status_detail_repository.py | 10 ----------
 5 files changed, 48 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 890fe09..48ebd0c 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -182,11 +182,6 @@ def get_driving_pattern(row):
                 index=False
             )
 
-            print(
-                f"{vehicle_id} "
-                f"drive-detail 저장 완료"
-            )
-
             detail_id += 1
 
     except Exception as e:
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 7cf12be..19af106 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -95,11 +95,6 @@ def run():
                         }
                     )
 
-                print(
-                    f"[UPDATE] {process_name} "
-                    f"{progress_rate}%"
-                )
-
             else:
 
                 # INSERT
@@ -135,11 +130,6 @@ def run():
                     index=False
                 )
 
-                print(
-                    f"[INSERT] {process_name} "
-                    f"{progress_rate}%"
-                )
-
     except Exception as e:
         print(f"오류 발생 : {e}")
 
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index efec7fd..b10efaa 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -86,12 +86,6 @@ def run():
                         }
                     )
 
-                print(
-                    f"[UPDATE] "
-                    f"{inspection_type} "
-                    f"{risk_score}"
-                )
-
             else:
 
                 # inspection_round 자동 생성
@@ -123,13 +117,6 @@ def run():
                     index=False
                 )
 
-                print(
-                    f"[INSERT] "
-                    f"{inspection_type} "
-                    f"ROUND {inspection_round} "
-                    f"{risk_score}"
-                )
-
     except Exception as e:
 
         import traceback
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 2e48d4f..067f441 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -76,11 +76,6 @@ def run():
                         row
                     )
 
-                print(
-                    f"[UPDATE] "
-                    f"{row['risk_level']}"
-                )
-
             else:
 
                 df = pd.DataFrame([row])
@@ -92,11 +87,6 @@ def run():
                     index=False
                 )
 
-                print(
-                    f"[INSERT] "
-                    f"{row['risk_level']}"
-                )
-
     except Exception as e:
 
         print(f"오류 발생 : {e}")
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index e823694..81d461b 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -57,11 +57,6 @@ def run():
 
             if exists.iloc[0]["cnt"] > 0:
 
-                print(
-                    f"[STATUS] "
-                    f"{vehicle_id} 이미 저장됨"
-                )
-
                 continue
 
             inspection_status_detail = [{
@@ -113,11 +108,6 @@ def run():
                 index=False
             )
 
-            print(
-                f"[STATUS 저장 완료] "
-                f"{vehicle_id}"
-            )
-
     except Exception as e:
 
         print(

From d1a1b0f83d973d03a886705e0730525207044e7d Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 12:45:05 +0900
Subject: [PATCH 059/148] feat : print del 2

---
 app/scheduler/quality/drive_detail_producer.py | 8 --------
 app/scheduler/quality/process_producer.py      | 7 -------
 app/scheduler/quality/risk_history_producer.py | 7 -------
 app/scheduler/quality/risk_trend_producer.py   | 4 ----
 4 files changed, 26 deletions(-)

diff --git a/app/scheduler/quality/drive_detail_producer.py b/app/scheduler/quality/drive_detail_producer.py
index 1dbdc4c..fbb5ad8 100644
--- a/app/scheduler/quality/drive_detail_producer.py
+++ b/app/scheduler/quality/drive_detail_producer.py
@@ -100,10 +100,6 @@ def run():
                 last_id = car["id"]
                 continue
 
-            print(
-                f"[Drive] {vehicle_id} 분석 시작"
-            )
-
             for row in drive_rows:
 
                 message = {
@@ -140,10 +136,6 @@ def run():
 
             producer.flush()
 
-            print(
-                f"[Drive] {vehicle_id} Kafka 전송 완료"
-            )
-
             # 처리 완료 차량 갱신
             last_id = car["id"]
 
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index c5cfce5..fee77cd 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -168,13 +168,6 @@ def run():
                     value=message
                 )
 
-                print(
-                    f"[{process_name}] "
-                    f"{completed}/{TOTAL_TARGET} "
-                    f"({progress_rate}%) "
-                    f"[{process_status}]"
-                )
-
             producer.flush()
 
             last_id = car["id"]
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index 7fd17e1..1470497 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -275,13 +275,6 @@ def run():
                     value=message
                 )
 
-                print(
-                    f"[{inspection_date}] "
-                    f"{inspection_type}"
-                    f" 점수 : "
-                    f"{risk_score}"
-                )
-
             producer.flush()
 
             last_id = car["id"]
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index cc39e48..6239f92 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -293,10 +293,6 @@ def get_risk_level(score):
 
             producer.flush()
 
-            print(
-                f"[RISK TREND] {created_date}"
-            )
-
             last_id = car["id"]
 
         time.sleep(1)
\ No newline at end of file

From 13567803de1f184c86a099cfdff91b6d429b8e31 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 12:53:22 +0900
Subject: [PATCH 060/148] feat : print del 3'

---
 app/scheduler/quality/status_detail_producer.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index 32b39bd..968072b 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -225,11 +225,6 @@ def get_result(score):
 
                 producer.flush()
 
-                print(
-                    f"[STATUS] "
-                    f"{vehicle_id} 전송 완료"
-                )
-
                 last_id = car["id"]
 
         time.sleep(1)
\ No newline at end of file

From 42fb60d23dcafdff5b3ab690449721eea939d779 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 12:55:42 +0900
Subject: [PATCH 061/148] fix : process text add

---
 app/repository/quality_process_repository.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 19af106..5af5f2c 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -55,17 +55,17 @@ def run():
 
             # 같은 날짜 + 같은 공정 존재 여부 확인
             exists = pd.read_sql(
-                """
-                SELECT COUNT(*) AS cnt
-                FROM inspection_process
-                WHERE process_name=%s
-                  AND DATE(created_at)=DATE(%s)
-                """,
+                text("""
+                    SELECT COUNT(*) AS cnt
+                    FROM inspection_process
+                    WHERE process_name = :process_name
+                    AND DATE(created_at) = DATE(:created_at)
+                """),
                 con=main_engine,
-                params=[
-                    process_name,
-                    created_at
-                ]
+                params={
+                    "process_name": process_name,
+                    "created_at": created_at
+                }
             )
 
             if exists.iloc[0]["cnt"] > 0:

From c73db4d0662d45b90bd34fa409e01d667e62e315 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 1 Jul 2026 13:33:45 +0900
Subject: [PATCH 062/148] =?UTF-8?q?feat:=20=EB=B6=88=EB=9F=89=20=EC=A0=84?=
 =?UTF-8?q?=EC=9D=B4=20=EC=98=88=EC=B8=A1=20Kafka=20=EC=97=B0=EB=8F=99=20?=
 =?UTF-8?q?=EB=B0=8F=20=EB=B6=84=EC=84=9D=20API=20=EA=B5=AC=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Kafka 실시간 이벤트 컨슈머에 불량 전이 예측 모델 추론 로직 연동

- 기학습 모델 기반 불량 전이 추론 및 SHAP 원인 영향도 분석 로직 추가

- /api/process/defect-transfer API 구현 및 cursor, size 기반 페이지 조회 지원

- Redis cache 연동 및 cursor, size 기반 cache key 적용

- 불량 전이 분석 결과 DB 저장 및 조회 로직 구현

- 불량 전이 분석 공통 응답 DTO 추가

- Redis cache 오류 및 데이터베이스 조회 실패 예외 응답 처리 정리

- 불량 전이 예측 백필(Backfill) 배치 스크립트 추가

- 불량 예측 모델 파일명 변경 (selected_defect_detector -> lightgbm_defect_detector)
---
 app/api/router.py                             |  10 +-
 app/api/routers/defect_transfer.py            |  87 ++++
 app/api/routers/process.py                    |   2 +-
 .../defect_transfer_prediction_backfill.py    | 146 ++++++
 app/core/config.py                            |  21 +-
 app/dto/response/__init__.py                  |  12 +-
 app/dto/response/defect_transfer_response.py  |  47 ++
 app/kafka/raw_event_consumer.py               | 197 +++++++-
 ...joblib => lightgbm_defect_detector.joblib} | Bin
 app/ml/inference/defect_transfer_detector.py  | 475 ++++++++++++++++++
 app/repository/__init__.py                    |   4 +
 .../defect_transfer_prediction_repository.py  | 429 ++++++++++++++++
 .../analysis/defect_transfer_service.py       | 305 +++++++++++
 app/utils/process_label_utils.py              |  67 +++
 14 files changed, 1776 insertions(+), 26 deletions(-)
 create mode 100644 app/api/routers/defect_transfer.py
 create mode 100644 app/batch/defect_transfer_prediction_backfill.py
 create mode 100644 app/dto/response/defect_transfer_response.py
 rename app/ml/artifacts/defect/{selected_defect_detector.joblib => lightgbm_defect_detector.joblib} (100%)
 create mode 100644 app/ml/inference/defect_transfer_detector.py
 create mode 100644 app/repository/defect_transfer_prediction_repository.py
 create mode 100644 app/service/analysis/defect_transfer_service.py
 create mode 100644 app/utils/process_label_utils.py

diff --git a/app/api/router.py b/app/api/router.py
index a1292b4..266aabd 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -1,10 +1,18 @@
 from fastapi import APIRouter
 
-from app.api.routers import health, manufacturing_event, ml_dataset, process, root
+from app.api.routers import (
+    defect_transfer,
+    health,
+    manufacturing_event,
+    ml_dataset,
+    process,
+    root,
+)
 
 api_router = APIRouter()
 api_router.include_router(root.router)
 api_router.include_router(health.router)
 api_router.include_router(process.router)
+api_router.include_router(defect_transfer.router)
 api_router.include_router(manufacturing_event.router)
 api_router.include_router(ml_dataset.router)
diff --git a/app/api/routers/defect_transfer.py b/app/api/routers/defect_transfer.py
new file mode 100644
index 0000000..45cb4c1
--- /dev/null
+++ b/app/api/routers/defect_transfer.py
@@ -0,0 +1,87 @@
+from fastapi import APIRouter, Depends, Query
+
+from app.dto.response import CommonResponse
+from app.dto.response.defect_transfer_response import (
+    DefectTransferCausePage,
+    DefectTransferPredictionPage,
+)
+from app.service.analysis.defect_transfer_service import (
+    DefectTransferAnalysisService,
+    get_defect_transfer_analysis_service,
+)
+from app.utils.response_utils import success_response
+
+
+router = APIRouter(prefix="/api/ai/process/defect-transfer", tags=["process"])
+
+DefectTransferPredictionResponse = CommonResponse[DefectTransferPredictionPage]
+DefectTransferCauseResponse = CommonResponse[DefectTransferCausePage]
+
+
+@router.get(
+    "/predictions",
+    response_model=DefectTransferPredictionResponse,
+    summary="불량 전이 예측 목록 조회",
+)
+def get_defect_transfer_predictions(
+    cursor: int | None = Query(
+        default=None,
+        ge=0,
+        description="페이지 번호입니다. 생략하면 0으로 처리합니다.",
+        examples=[0],
+    ),
+    size: int = Query(
+        default=5,
+        ge=1,
+        le=100,
+        description="페이지당 반환할 예측 결과 수입니다.",
+        examples=[5],
+    ),
+    service: DefectTransferAnalysisService = Depends(
+        get_defect_transfer_analysis_service,
+    ),
+) -> DefectTransferPredictionResponse:
+    return success_response(
+        data=service.get_cached_predictions(cursor=cursor, size=size),
+        message="불량 전이 예측 목록 조회가 완료되었습니다.",
+    )
+
+
+@router.get(
+    "/causes",
+    response_model=DefectTransferCauseResponse,
+    summary="SHAP 기반 AI 원인 분석 조회",
+)
+def get_defect_transfer_causes(
+    vehicle_id: str | None = Query(
+        default=None,
+        alias="vehicleId",
+        description=(
+            "car_master.vehicle_id입니다. 생략하면 예측 불량 확률이 가장 높은 차량을 선택합니다."
+        ),
+    ),
+    cursor: int | None = Query(
+        default=None,
+        ge=0,
+        description="원인 목록 페이지 번호입니다. 생략하면 0으로 처리합니다.",
+        examples=[0],
+    ),
+    size: int = Query(
+        default=5,
+        ge=1,
+        le=100,
+        description="페이지당 반환할 원인 수입니다.",
+        examples=[5],
+    ),
+    service: DefectTransferAnalysisService = Depends(
+        get_defect_transfer_analysis_service,
+    ),
+) -> DefectTransferCauseResponse:
+    return success_response(
+        data=service.get_cached_cause_analysis(
+            vehicle_id=vehicle_id,
+            cursor=cursor,
+            size=size,
+        ),
+        message="SHAP 기반 AI 원인 분석 조회가 완료되었습니다.",
+    )
diff --git a/app/api/routers/process.py b/app/api/routers/process.py
index d238672..5d0977b 100644
--- a/app/api/routers/process.py
+++ b/app/api/routers/process.py
@@ -7,7 +7,7 @@
 )
 from app.utils.response_utils import success_response
 
-router = APIRouter(prefix="/api/process", tags=["process"])
+router = APIRouter(prefix="/api/ai/process", tags=["process"])
 
 BottleneckAnalysisResponse = CommonResponse[BottleneckAnalysisPage]
 
diff --git a/app/batch/defect_transfer_prediction_backfill.py b/app/batch/defect_transfer_prediction_backfill.py
new file mode 100644
index 0000000..b1bc11c
--- /dev/null
+++ b/app/batch/defect_transfer_prediction_backfill.py
@@ -0,0 +1,146 @@
+from __future__ import annotations
+
+import logging
+import json
+from datetime import datetime
+from typing import Any
+
+from sqlalchemy import create_engine, select
+
+from app.core.config import settings
+from app.ml.inference.defect_transfer_detector import DefectTransferDetector
+from app.repository.defect_transfer_prediction_repository import (
+    DefectTransferPredictionRepository,
+)
+from app.repository.sampledb_schema import manufacturing_event_json
+from app.utils.database_utils import mysql_connect_args_for_seoul
+
+
+logger = logging.getLogger(__name__)
+
+
+def backfill_defect_transfer_predictions(*, limit: int | None = None) -> dict[str, int]:
+    """Backfill defect-transfer predictions from sent manufacturing raw events."""
+    if not settings.sample_database_connection_url:
+        raise RuntimeError("SAMPLE_DB_NAME is required for defect transfer backfill.")
+
+    event_engine = create_engine(
+        settings.sample_database_connection_url,
+        connect_args=mysql_connect_args_for_seoul(settings.sample_database_connection_url),
+        pool_pre_ping=True,
+        future=True,
+    )
+    result_repository = DefectTransferPredictionRepository(
+        settings.main_database_connection_url,
+        event_database_url=settings.sample_database_connection_url,
+    )
+    detector = DefectTransferDetector()
+
+    query = (
+        select(
+            manufacturing_event_json.c.id,
+            manufacturing_event_json.c.event_id,
+            manufacturing_event_json.c.car_master_id,
+            manufacturing_event_json.c.process_code,
+            manufacturing_event_json.c.event_json,
+        )
+        .where(manufacturing_event_json.c.is_sent.is_(True))
+        .order_by(manufacturing_event_json.c.id.asc())
+    )
+    if limit is not None:
+        query = query.limit(limit)
+
+    with event_engine.connect() as conn:
+        rows = [dict(row) for row in conn.execute(query).mappings()]
+
+    processed = 0
+    saved_rows = 0
+    failed = 0
+    for row in rows:
+        try:
+            prediction = detector.predict_event(
+                _event_json(row["event_json"]),
+                str(row["process_code"]),
+            )
+            saved_rows += result_repository.replace_prediction_result(
+                event_id=str(row["event_id"]),
+                car_master_id=int(row["car_master_id"]),
+                source_process_code=prediction.current_process_code,
+                target_process_code=prediction.predicted_process_code,
+                current_defect_probability=prediction.defect_probability,
+                target_defect_probability=prediction.transfer_probability,
+                predicted_defect_process=_format_predicted_defect_process(
+                    prediction.predicted_process_code,
+                    row,
+                ),
+                expected_occurrence_step=prediction.expected_steps_after,
+                risk_grade=prediction.risk_level,
+                causes=[
+                    {
+                        "message": cause.message,
+                        "label": cause.label,
+                        "impact": cause.impact,
+                    }
+                    for cause in prediction.causes
+                ],
+                predicted_at=datetime.now(),
+            )
+            processed += 1
+        except Exception:
+            failed += 1
+            logger.exception(
+                "Failed to backfill defect transfer prediction: event_id=%s",
+                row.get("event_id"),
+            )
+
+    return {
+        "source_events": len(rows),
+        "processed_events": processed,
+        "saved_rows": saved_rows,
+        "failed_events": failed,
+    }
+
+
+def _event_json(value: Any) -> dict[str, Any]:
+    if isinstance(value, dict):
+        return value
+    if isinstance(value, str):
+        try:
+            decoded = json.loads(value)
+        except json.JSONDecodeError:
+            return {}
+        return decoded if isinstance(decoded, dict) else {}
+    return {}
+
+
+def _format_predicted_defect_process(
+    process_code: str | None,
+    row: dict[str, Any],
+) -> str | None:
+    if process_code is None:
+        return None
+    from app.utils.process_label_utils import (
+        equipment_code_for_car_process,
+        format_process_with_line,
+    )
+
+    source_code = str(row.get("process_code") or "").strip().upper()
+    normalized = str(process_code).strip().upper()
+    if normalized == source_code:
+        event_json = _event_json(row.get("event_json"))
+        equipment = event_json.get("equipment", {})
+        equipment_code = str(equipment.get("equipmentCode") or "")
+    else:
+        equipment_code = equipment_code_for_car_process(
+            car_master_id=int(row["car_master_id"]),
+            process_code=normalized,
+        )
+    return format_process_with_line(normalized, equipment_code)
+
+
+def run() -> None:
+    print(backfill_defect_transfer_predictions())
+
+
+if __name__ == "__main__":
+    run()
diff --git a/app/core/config.py b/app/core/config.py
index 9e82598..5d4b1b0 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -38,7 +38,7 @@ def database_url_with_name(database_url: str, database_name: str | None) -> str:
 
 
 class Settings(BaseSettings):
-    """환경 변수와 .env 파일에서 애플리케이션 설정을 로드한다."""
+    """Application settings loaded from environment variables and .env."""
 
     app_name: str = Field(default="AI Service", alias="APP_NAME")
     app_version: str = Field(default="0.1.0", alias="APP_VERSION")
@@ -89,16 +89,16 @@ def main_database_connection_url(self) -> str:
         if self.main_database_url:
             return database_url_with_name(self.main_database_url, self.main_db_name)
 
-        raise ValueError("maindb MySQL 설정이 필요합니다: MAIN_DATABASE_URL")
+        raise ValueError("maindb MySQL setting is required: MAIN_DATABASE_URL")
 
     @property
     def bottleneck_database_url(self) -> str:
-        """병목 분석 결과를 저장할 maindb MySQL URL을 반환한다."""
+        """Return the main DB URL used for bottleneck analysis results."""
         return self.main_database_connection_url
 
     @property
     def sample_database_connection_url(self) -> str | None:
-        """sampledb 설정이 있으면 MySQL URL을 반환한다."""
+        """Return the sample DB URL when SAMPLE_DB_NAME is configured."""
         if self.main_database_url and self.sample_db_name:
             return database_url_with_name(self.main_database_url, self.sample_db_name)
 
@@ -106,16 +106,15 @@ def sample_database_connection_url(self) -> str | None:
 
     @property
     def redis_connection_url(self) -> str:
-        """캐시 클라이언트가 사용할 Redis URL을 반환한다."""
+        """Return the Redis connection URL."""
         if self.redis_url:
             return self.redis_url
 
-        raise ValueError("Redis 설정이 필요합니다: REDIS_URL")
+        raise ValueError("Redis setting is required: REDIS_URL")
 
     @field_validator("debug", mode="before")
     @classmethod
     def parse_debug_value(cls, value: object) -> object:
-        """dev/prod 같은 환경 이름을 debug 여부로 변환한다."""
         if isinstance(value, str):
             normalized = value.strip().lower()
             if normalized in {"release", "prod", "production"}:
@@ -124,12 +123,6 @@ def parse_debug_value(cls, value: object) -> object:
                 return True
         return value
 
-    @field_validator("redis_cache_ttl_seconds", mode="before")
-    @classmethod
-    def use_default_redis_cache_ttl(cls, value: object) -> int:
-        """REDIS_CACHE_TTL_SECONDS는 .env보다 코드 기본값을 우선한다."""
-        return 60
-
     model_config = SettingsConfigDict(
         env_file=".env",
         env_file_encoding="utf-8",
@@ -140,7 +133,7 @@ def use_default_redis_cache_ttl(cls, value: object) -> int:
 
 @lru_cache
 def get_settings() -> Settings:
-    """프로세스마다 설정 객체를 한 번 생성해 재사용한다."""
+    """Return the process-wide cached settings object."""
     return Settings()
 
 
diff --git a/app/dto/response/__init__.py b/app/dto/response/__init__.py
index 0230adb..fccf3b6 100644
--- a/app/dto/response/__init__.py
+++ b/app/dto/response/__init__.py
@@ -5,12 +5,20 @@
     BottleneckAnalysisPage,
 )
 from app.dto.response.common_response import CommonResponse
+from app.dto.response.defect_transfer_response import (
+    DefectTransferCauseItem,
+    DefectTransferCausePage,
+    DefectTransferPredictionItem,
+    DefectTransferPredictionPage,
+)
 
 __all__ = [
     "BottleneckAnalysisItem",
     "BottleneckAnalysisPage",
     "CommonResponse",
+    "DefectTransferCauseItem",
+    "DefectTransferCausePage",
+    "DefectTransferPredictionItem",
+    "DefectTransferPredictionPage",
 ]
 
-__all__ = ["CommonResponse"]
-
diff --git a/app/dto/response/defect_transfer_response.py b/app/dto/response/defect_transfer_response.py
new file mode 100644
index 0000000..0f90baf
--- /dev/null
+++ b/app/dto/response/defect_transfer_response.py
@@ -0,0 +1,47 @@
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class DefectTransferPredictionItem(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    vehicle_id: str = Field(alias="vehicleId")
+    car_master_id: int = Field(alias="carMasterId")
+    current_process: str = Field(alias="currentProcess")
+    predicted_defect_process: str | None = Field(alias="predictedDefectProcess")
+    defect_probability: int = Field(alias="defectProbability")
+    expected_time: str | None = Field(alias="expectedTime")
+    risk_level: str = Field(alias="riskLevel")
+
+
+class DefectTransferPredictionPage(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    content: list[DefectTransferPredictionItem]
+    has_next: bool = Field(alias="hasNext")
+    next_cursor: int | None = Field(alias="nextCursor")
+
+
+class DefectTransferCauseItem(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    rank: int
+    feature: str
+    label: str
+    value: str
+    impact: float
+    message: str
+
+
+class DefectTransferCausePage(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    vehicle_id: str | None = Field(alias="vehicleId")
+    car_master_id: int | None = Field(alias="carMasterId")
+    predicted_defect_probability: int | None = Field(alias="predictedDefectProbability")
+    risk_level: str | None = Field(alias="riskLevel")
+    current_process: str | None = Field(alias="currentProcess")
+    predicted_defect_process: str | None = Field(alias="predictedDefectProcess")
+    transfer_probability: int | None = Field(alias="transferProbability")
+    content: list[DefectTransferCauseItem]
+    has_next: bool = Field(alias="hasNext")
+    next_cursor: int | None = Field(alias="nextCursor")
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 972c38b..36bda2c 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -13,6 +13,13 @@
 
 from app.core.config import settings
 from app.kafka.iam_provider import MSKTokenProvider
+from app.ml.inference.defect_transfer_detector import (
+    DefectTransferDetector,
+    DefectTransferPrediction,
+)
+from app.repository.defect_transfer_prediction_repository import (
+    DefectTransferPredictionRepository,
+)
 from app.repository.sampledb_repository import SampleDbRepository
 from app.service.analysis.bottleneck_service import BottleneckAnalysisService
 from app.utils.datetime_utils import seoul_now_iso
@@ -95,6 +102,8 @@ def _run_raw_event_consumer(
     repository = SampleDbRepository(settings.sample_database_connection_url)
     repository.schema.ensure_schema()
     analysis_service = _create_bottleneck_analysis_service()
+    defect_detector = _create_defect_transfer_detector()
+    defect_result_repository = _create_defect_transfer_result_repository()
     analysis_producer = _create_analysis_producer(
         KafkaProducer,
         bootstrap_servers,
@@ -117,6 +126,8 @@ def _run_raw_event_consumer(
                     _consume_record(
                         repository,
                         analysis_service,
+                        defect_detector,
+                        defect_result_repository,
                         analysis_producer,
                         record,
                     )
@@ -148,6 +159,8 @@ def _run_raw_event_consumer(
 def _consume_record(
     repository: SampleDbRepository,
     analysis_service: BottleneckAnalysisService | None,
+    defect_detector: DefectTransferDetector | None,
+    defect_result_repository: DefectTransferPredictionRepository | None,
     analysis_producer: Any | None,
     record: Any,
 ) -> None:
@@ -163,19 +176,27 @@ def _consume_record(
         record,
         row,
     )
+    defect_prediction = _predict_defect_transfer(defect_detector, row)
+    defect_rows_saved = _save_defect_transfer_prediction(
+        defect_result_repository,
+        row,
+        defect_prediction,
+    )
     analysis_published = _publish_bottleneck_analysis_event(
         analysis_producer,
         raw_event,
         row,
         bottleneck_summaries,
+        defect_prediction,
     )
     # 새 raw 이벤트와 자동 분석 결과가 반영되면 Redis 캐시를 비운다.
     _clear_bottleneck_cache()
+    _clear_defect_transfer_cache()
     logger.info(
         "Kafka raw event consumed and stored: topic=%s partition=%s offset=%s "
         "key=%s event_id=%s manufacturing_event_id_source=manufacturing_event_json.id "
         "car_master_id=%s process_code=%s affected_rows=%s "
-        "bottleneck_result_count=%s analysis_topic_published=%s",
+        "bottleneck_result_count=%s defect_result_saved=%s analysis_topic_published=%s",
         record.topic,
         record.partition,
         record.offset,
@@ -185,6 +206,7 @@ def _consume_record(
         row["process_code"],
         affected_rows,
         len(bottleneck_summaries),
+        defect_rows_saved,
         analysis_published,
     )
 
@@ -333,6 +355,33 @@ def _create_bottleneck_analysis_service() -> BottleneckAnalysisService | None:
         return None
 
 
+def _create_defect_transfer_detector() -> DefectTransferDetector | None:
+    try:
+        return DefectTransferDetector()
+    except Exception:
+        logger.exception(
+            "Defect transfer detector is unavailable. "
+            "Raw Kafka events will still be stored.",
+        )
+        return None
+
+
+def _create_defect_transfer_result_repository() -> DefectTransferPredictionRepository | None:
+    if not settings.sample_database_connection_url:
+        return None
+    try:
+        return DefectTransferPredictionRepository(
+            settings.main_database_connection_url,
+            event_database_url=settings.sample_database_connection_url,
+        )
+    except Exception:
+        logger.exception(
+            "Defect transfer result repository is unavailable. "
+            "Raw Kafka events will still be stored.",
+        )
+        return None
+
+
 def _create_analysis_producer(
     producer_cls: Any,
     bootstrap_servers: list[str],
@@ -409,11 +458,17 @@ def _publish_bottleneck_analysis_event(
     raw_event: dict[str, Any],
     row: dict[str, Any],
     summaries: list[dict[str, Any]],
+    defect_prediction: DefectTransferPrediction | None,
 ) -> bool:
     if producer is None:
         return False
 
-    event = _build_bottleneck_analysis_event(raw_event, row, summaries)
+    event = _build_bottleneck_analysis_event(
+        raw_event,
+        row,
+        summaries,
+        defect_prediction,
+    )
     key = str(row["car_master_id"])
     try:
         result = producer.send(ANALYSIS_TOPIC, key=key, value=event).get(timeout=10)
@@ -444,6 +499,7 @@ def _build_bottleneck_analysis_event(
     raw_event: dict[str, Any],
     row: dict[str, Any],
     summaries: list[dict[str, Any]],
+    defect_prediction: DefectTransferPrediction | None = None,
 ) -> dict[str, Any]:
     event_json = row["event_json"]
     equipment = event_json.get("equipment", {})
@@ -476,12 +532,31 @@ def _build_bottleneck_analysis_event(
     overall_risk_score = _risk_score_to_percent(risk_score)
     risk_level = _risk_level(overall_risk_score)
     is_bottleneck = summary is not None and risk_score >= 3
-    defect_probability = _defect_probability(event_json, process_code)
-    transfer_predicted_process = _transfer_predicted_process(
-        process_code,
-        defect_probability,
-    )
-    is_quality_defect = defect_probability >= 0.6
+    if defect_prediction is None:
+        defect_probability = _defect_probability(event_json, process_code)
+        transfer_predicted_process = _transfer_predicted_process(
+            process_code,
+            defect_probability,
+        )
+        transfer_probability = None
+        defect_causes: list[dict[str, Any]] = []
+        is_quality_defect = defect_probability >= 0.6
+    else:
+        defect_probability = defect_prediction.defect_probability
+        transfer_predicted_process = defect_prediction.predicted_process_code
+        transfer_probability = defect_prediction.transfer_probability
+        defect_causes = [
+            {
+                "rank": cause.rank,
+                "feature": cause.feature,
+                "label": cause.label,
+                "value": cause.value,
+                "impact": cause.impact,
+                "message": cause.message,
+            }
+            for cause in defect_prediction.causes
+        ]
+        is_quality_defect = defect_prediction.is_quality_defect
     is_equipment_fault = str(
         equipment_status.get("operationStatus") or "",
     ).upper() in {"FAULT", "STOPPED", "ERROR", "DOWN"}
@@ -582,12 +657,102 @@ def _build_bottleneck_analysis_event(
             "processCode": process_code,
             "bottleneckDelayTime": bottleneck_delay_time,
             "defectProbability": defect_probability,
+            "transferProbability": transfer_probability,
             "transferPredictedProcess": transfer_predicted_process,
+            "defectCauses": defect_causes,
             "riskScore": risk_score,
         },
     }
 
 
+def _predict_defect_transfer(
+    detector: DefectTransferDetector | None,
+    row: dict[str, Any],
+) -> DefectTransferPrediction | None:
+    if detector is None:
+        return None
+    try:
+        return detector.predict_event(row["event_json"], str(row["process_code"]))
+    except Exception:
+        logger.exception(
+            "Failed to run defect transfer prediction: event_id=%s process_code=%s",
+            row.get("event_id"),
+            row.get("process_code"),
+        )
+        return None
+
+
+def _save_defect_transfer_prediction(
+    repository: DefectTransferPredictionRepository | None,
+    row: dict[str, Any],
+    prediction: DefectTransferPrediction | None,
+) -> int:
+    if repository is None or prediction is None:
+        return 0
+    try:
+        causes = [
+            {
+                "message": cause.message,
+                "label": cause.label,
+                "impact": cause.impact,
+            }
+            for cause in prediction.causes
+        ]
+        return repository.replace_prediction_result(
+            event_id=str(row["event_id"]),
+            car_master_id=int(row["car_master_id"]),
+            source_process_code=prediction.current_process_code,
+            target_process_code=prediction.predicted_process_code,
+            current_defect_probability=prediction.defect_probability,
+            target_defect_probability=prediction.transfer_probability,
+            predicted_defect_process=_format_predicted_defect_process(
+                prediction.predicted_process_code,
+                row,
+            ),
+            expected_occurrence_step=prediction.expected_steps_after,
+            risk_grade=prediction.risk_level,
+            causes=causes,
+            predicted_at=datetime.now(),
+        )
+    except Exception:
+        logger.exception(
+            "Failed to save defect transfer prediction result: event_id=%s process_code=%s",
+            row.get("event_id"),
+            row.get("process_code"),
+        )
+        return 0
+
+
+def _format_predicted_defect_process(
+    process_code: str | None,
+    row: dict[str, Any],
+) -> str | None:
+    if process_code is None:
+        return None
+    from app.utils.process_label_utils import (
+        equipment_code_for_car_process,
+        format_process_with_line,
+    )
+
+    source_code = str(row.get("process_code") or "").strip().upper()
+    normalized = str(process_code).strip().upper()
+    if normalized == source_code:
+        equipment_code = _source_equipment_code(row)
+    else:
+        equipment_code = equipment_code_for_car_process(
+            car_master_id=int(row["car_master_id"]),
+            process_code=normalized,
+        )
+    return format_process_with_line(normalized, equipment_code)
+
+
+def _source_equipment_code(row: dict[str, Any]) -> str | None:
+    event_json = row.get("event_json") or {}
+    equipment = event_json.get("equipment", {}) if isinstance(event_json, dict) else {}
+    equipment_code = str(equipment.get("equipmentCode") or row.get("equipment_id") or "")
+    return equipment_code or None
+
+
 def _matching_bottleneck_summary(
     summaries: list[dict[str, Any]],
     *,
@@ -748,3 +913,19 @@ def _clear_bottleneck_cache() -> None:
             redis_client.delete(*keys)
     except Exception:
         logger.exception("Failed to clear bottleneck cache after raw event consumption.")
+
+
+def _clear_defect_transfer_cache() -> None:
+    if not settings.redis_url:
+        return
+
+    try:
+        from redis import Redis
+
+        redis_client = Redis.from_url(settings.redis_connection_url, decode_responses=True)
+        pattern = f"{settings.redis_key_prefix}:process:defect-transfer:*"
+        keys = list(redis_client.scan_iter(match=pattern))
+        if keys:
+            redis_client.delete(*keys)
+    except Exception:
+        logger.exception("Failed to clear defect transfer cache after raw event consumption.")
diff --git a/app/ml/artifacts/defect/selected_defect_detector.joblib b/app/ml/artifacts/defect/lightgbm_defect_detector.joblib
similarity index 100%
rename from app/ml/artifacts/defect/selected_defect_detector.joblib
rename to app/ml/artifacts/defect/lightgbm_defect_detector.joblib
diff --git a/app/ml/inference/defect_transfer_detector.py b/app/ml/inference/defect_transfer_detector.py
new file mode 100644
index 0000000..66286a6
--- /dev/null
+++ b/app/ml/inference/defect_transfer_detector.py
@@ -0,0 +1,475 @@
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd 
+
+
+logger = logging.getLogger(__name__)
+
+DEFECT_ARTIFACT_DIR = Path("app/ml/artifacts/defect")
+PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
+NEXT_PROCESS = {
+    "PRESS": "BODY",
+    "BODY": "PAINT",
+    "PAINT": "ASSEMBLY",
+}
+
+
+@dataclass(frozen=True)
+class DefectCause:
+    rank: int
+    feature: str
+    label: str
+    value: Any
+    impact: float
+    message: str
+
+
+@dataclass(frozen=True)
+class DefectTransferPrediction:
+    defect_probability: float
+    defect_threshold: float
+    is_quality_defect: bool
+    current_process_code: str
+    predicted_process_code: str | None
+    transfer_probability: float | None
+    transfer_threshold: float | None
+    expected_steps_after: int | None
+    risk_level: str
+    causes: list[DefectCause]
+    feature_values: dict[str, Any]
+
+
+class DefectTransferDetector:
+    """Run event-level defect detection and adjacent-process transfer prediction."""
+
+    def __init__(
+        self,
+        *,
+        artifact_dir: str | Path = DEFECT_ARTIFACT_DIR,
+    ) -> None:
+        self.artifact_dir = Path(artifact_dir)
+        self.model_path = self.artifact_dir / "lightgbm_defect_detector.joblib"
+        self.feature_path = self.artifact_dir / "defect_model_features.json"
+        self.metrics_path = self.artifact_dir / "defect_model_metrics.json"
+        self.transfer_model_path = self.artifact_dir / "adjacent_transfer_models.joblib"
+        self.transfer_metadata_path = (
+            self.artifact_dir / "adjacent_transfer_model_metadata.json"
+        )
+        self._defect_model: Any | None = None
+        self._transfer_models: dict[str, Any] | None = None
+        self._feature_columns: list[str] | None = None
+        self._metrics: dict[str, Any] | None = None
+        self._transfer_metadata: dict[str, Any] | None = None
+        self._defect_shap_explainer: Any | None = None
+
+    def predict_event(
+        self,
+        event_json: dict[str, Any],
+        process_code: str,
+    ) -> DefectTransferPrediction:
+        normalized_process = str(process_code or "").strip().upper()
+        features = self.extract_event_features(event_json, normalized_process)
+        defect_probability = self._predict_probability(
+            self.defect_model,
+            features,
+            self.feature_columns,
+        )
+        defect_threshold = float(self.metrics.get("threshold", 0.5))
+        transfer_key = self._transfer_key(normalized_process)
+        transfer_probability = None
+        transfer_threshold = None
+        predicted_process = NEXT_PROCESS.get(normalized_process)
+        if transfer_key and transfer_key in self.transfer_models:
+            metadata = self.transfer_metadata.get(transfer_key, {})
+            transfer_columns = list(metadata.get("feature_columns") or [])
+            transfer_features = self._transfer_features(features, transfer_columns)
+            transfer_probability = self._predict_probability(
+                self.transfer_models[transfer_key],
+                transfer_features,
+                transfer_columns,
+            )
+            transfer_threshold = float(metadata.get("threshold", 0.5))
+
+        risk_probability = max(
+            defect_probability,
+            transfer_probability if transfer_probability is not None else 0.0,
+        )
+        causes = self._rank_causes(features, normalized_process, risk_probability)
+        next_process = NEXT_PROCESS.get(normalized_process)
+        if transfer_probability is not None and next_process is not None:
+            predicted_process_code = (
+                next_process if transfer_probability > 0 else None
+            )
+        elif defect_probability > 0:
+            predicted_process_code = normalized_process
+        else:
+            predicted_process_code = None
+        return DefectTransferPrediction(
+            defect_probability=round(defect_probability, 4),
+            defect_threshold=round(defect_threshold, 4),
+            is_quality_defect=defect_probability >= defect_threshold,
+            current_process_code=normalized_process,
+            predicted_process_code=predicted_process_code,
+            transfer_probability=(
+                round(transfer_probability, 4)
+                if transfer_probability is not None
+                else None
+            ),
+            transfer_threshold=(
+                round(transfer_threshold, 4)
+                if transfer_threshold is not None
+                else None
+            ),
+            expected_steps_after=self._expected_steps_after(normalized_process),
+            risk_level=self._risk_level(risk_probability),
+            causes=causes,
+            feature_values=features,
+        )
+
+    @property
+    def defect_model(self) -> Any:
+        if self._defect_model is None:
+            self._defect_model = self._load_joblib(self.model_path)
+            self._patch_loaded_model(self._defect_model)
+        return self._defect_model
+
+    @property
+    def transfer_models(self) -> dict[str, Any]:
+        if self._transfer_models is None:
+            loaded = self._load_joblib(self.transfer_model_path)
+            if not isinstance(loaded, dict):
+                raise TypeError("adjacent_transfer_models.joblib must contain a dict.")
+            for model in loaded.values():
+                self._patch_loaded_model(model)
+            self._transfer_models = loaded
+        return self._transfer_models
+
+    @property
+    def feature_columns(self) -> list[str]:
+        if self._feature_columns is None:
+            self._feature_columns = list(
+                json.loads(self.feature_path.read_text(encoding="utf-8")),
+            )
+        return self._feature_columns
+
+    @property
+    def metrics(self) -> dict[str, Any]:
+        if self._metrics is None:
+            self._metrics = json.loads(self.metrics_path.read_text(encoding="utf-8"))
+        return self._metrics
+
+    @property
+    def transfer_metadata(self) -> dict[str, Any]:
+        if self._transfer_metadata is None:
+            self._transfer_metadata = json.loads(
+                self.transfer_metadata_path.read_text(encoding="utf-8"),
+            )
+        return self._transfer_metadata
+
+    def _load_joblib(self, path: Path) -> Any:
+        try:
+            import sklearn.compose._column_transformer as column_transformer
+
+            if not hasattr(column_transformer, "_RemainderColsList"):
+                column_transformer._RemainderColsList = type(  # type: ignore[attr-defined]
+                    "_RemainderColsList",
+                    (list,),
+                    {},
+                )
+
+            import joblib
+        except ModuleNotFoundError as exc:
+            raise RuntimeError("joblib, scikit-learn and LightGBM are required.") from exc
+
+        if not path.exists():
+            raise FileNotFoundError(f"Defect model artifact not found: {path}")
+        return joblib.load(path)
+
+    def _predict_probability(
+        self,
+        model: Any,
+        features: dict[str, Any],
+        columns: list[str],
+    ) -> float:
+        frame = pd.DataFrame([{column: features.get(column) for column in columns}])
+        if hasattr(model, "predict_proba"):
+            probabilities = model.predict_proba(frame)
+            return float(probabilities[0][1])
+        prediction = model.predict(frame)
+        return float(prediction[0])
+
+    def extract_event_features(
+        self,
+        event_json: dict[str, Any],
+        process_code: str,
+    ) -> dict[str, Any]:
+        equipment = _dict(event_json.get("equipment"))
+        sensor = _dict(event_json.get("sensor"))
+        metrics = _dict(event_json.get("processMetrics"))
+        process_data = _dict(event_json.get("processData"))
+        current = _dict(sensor.get("current"))
+        vibration = _dict(sensor.get("vibration"))
+        robot = _dict(sensor.get("robotArmVibration"))
+        thermal = _dict(sensor.get("thermal"))
+        press = _dict(process_data.get("press"))
+        body = _dict(process_data.get("body"))
+        paint = _dict(process_data.get("paint"))
+        bands = _dict(body.get("frequencyBands"))
+        band_values = [_safe_float(value, default=np.nan) for value in bands.values()]
+        band_values = [value for value in band_values if not np.isnan(value)]
+
+        equipment_code = str(equipment.get("equipmentCode") or "")
+        return {
+            "process_code": process_code,
+            "station_code": _station_code(equipment_code),
+            "equipment_code": equipment_code,
+            "equipment_type": equipment.get("equipmentType"),
+            "current_rms_ampere": _safe_float(current.get("rmsAmpere")),
+            "current_max_ampere": _safe_float(current.get("maxAmpere")),
+            "current_min_ampere": _safe_float(current.get("minAmpere")),
+            "vibration_acceleration_g": _safe_float(vibration.get("accelerationG")),
+            "vibration_score": _safe_float(vibration.get("vibrationScore")),
+            "vibration_rms": _safe_float(vibration.get("vibrationRms")),
+            "vibration_peak": _safe_float(vibration.get("vibrationPeak")),
+            "robot_axis": robot.get("axis"),
+            "robot_frequency_hz": _safe_float(robot.get("frequencyHz")),
+            "robot_amplitude": _safe_float(robot.get("amplitude")),
+            "robot_vibration_score": _safe_float(robot.get("vibrationScore")),
+            "thermal_score": _safe_float(thermal.get("thermalScore")),
+            "avg_temperature": _safe_float(thermal.get("avgTemperature")),
+            "max_temperature": _safe_float(thermal.get("maxTemperature")),
+            "min_temperature": _safe_float(thermal.get("minTemperature")),
+            "cycle_time_sec": _safe_float(metrics.get("cycleTimeSec")),
+            "waiting_time_sec": _safe_float(metrics.get("waitingTimeSec")),
+            "processing_time_sec": _safe_float(metrics.get("processingTimeSec")),
+            "station_delay_sec": _safe_float(metrics.get("stationDelaySec")),
+            "throughput_per_min": _safe_float(metrics.get("throughputPerMin")),
+            "queue_length": _safe_float(metrics.get("queueLength")),
+            "wip_count": _safe_float(metrics.get("wipCount")),
+            "equipment_idle_time_sec": _safe_float(metrics.get("equipmentIdleTimeSec")),
+            "press_target_cycle_time_sec": _safe_float(
+                press.get("targetCycleTimeSec"),
+            ),
+            "body_robot_operation_mode": body.get("robotOperationMode"),
+            "body_frequency_peak_band": body.get("frequencyPeakBand"),
+            "body_frequency_band_max": max(band_values) if band_values else 0.0,
+            "body_frequency_band_mean": (
+                float(np.mean(band_values)) if band_values else 0.0
+            ),
+            "paint_image_position": paint.get("imagePosition"),
+            "paint_thermal_std_temp": _safe_float(paint.get("thermalStdTemp")),
+            "paint_thickness_value": _safe_float(paint.get("thicknessValue")),
+        }
+
+    def _transfer_features(
+        self,
+        event_features: dict[str, Any],
+        columns: list[str],
+    ) -> dict[str, Any]:
+        source_map = {
+            "source_cycle_time_sec": "cycle_time_sec",
+            "source_station_delay_sec": "station_delay_sec",
+            "source_queue_length": "queue_length",
+            "source_wip_count": "wip_count",
+            "source_current_rms_ampere": "current_rms_ampere",
+            "source_vibration_score": "vibration_score",
+            "source_thermal_score": "thermal_score",
+        }
+        return {
+            column: event_features.get(source_map.get(column, column), 0.0)
+            for column in columns
+        }
+
+    def _rank_causes(
+        self,
+        features: dict[str, Any],
+        process_code: str,
+        risk_probability: float,
+    ) -> list[DefectCause]:
+        shap_impacts = self._shap_feature_impacts(features)
+        candidates = [
+            ("station_delay_sec", "공정 지연", 12.0, "초"),
+            ("cycle_time_sec", "Cycle Time 증가", 55.0, "초"),
+            ("queue_length", "대기열 증가", 8.0, "대"),
+            ("wip_count", "WIP 증가", 24.0, "대"),
+            ("current_rms_ampere", "전류 RMS 편차", 2.2, "A"),
+            ("vibration_score", "진동 Score 상승", 0.45, ""),
+            ("robot_vibration_score", "로봇 진동 Score 상승", 0.45, ""),
+            ("thermal_score", "열화상 Score 상승", 55.0, ""),
+            ("max_temperature", "최고 온도 상승", 58.0, "°C"),
+            ("paint_thermal_std_temp", "도장 온도 편차", 4.0, "°C"),
+            ("paint_thickness_value", "도막 두께 편차", 130.0, ""),
+        ]
+        scored: list[tuple[str, str, Any, float, str]] = []
+        for feature, label, baseline, unit in candidates:
+            value = features.get(feature)
+            numeric = _safe_float(value, default=0.0)
+            if feature == "paint_thickness_value":
+                impact = abs(numeric - 116.0) / 18.0
+            else:
+                impact = max(0.0, numeric - baseline) / max(abs(baseline), 1.0)
+            if process_code == "PAINT" and feature.startswith("paint_"):
+                impact *= 1.35
+            if shap_impacts:
+                impact = max(impact * 0.35, shap_impacts.get(feature, 0.0))
+            scored.append((feature, label, value, impact, unit))
+
+        scored.sort(key=lambda item: item[3], reverse=True)
+        top = scored[:4]
+        if not top or top[0][3] <= 0:
+            top = [
+                (
+                    "model_probability",
+                    "모델 위험 확률",
+                    risk_probability,
+                    risk_probability,
+                    "",
+                ),
+            ]
+        return [
+            DefectCause(
+                rank=index,
+                feature=feature,
+                label=label,
+                value=value,
+                impact=round(float(min(max(impact, 0.0), 1.0)), 4),
+                message=_cause_message(label, value, unit),
+            )
+            for index, (feature, label, value, impact, unit) in enumerate(top, 1)
+        ]
+
+    def _shap_feature_impacts(self, features: dict[str, Any]) -> dict[str, float]:
+        try:
+            model = self.defect_model
+            steps = getattr(model, "named_steps", {})
+            preprocess = steps.get("preprocess")
+            estimator = steps.get("model")
+            if preprocess is None or estimator is None:
+                return {}
+
+            frame = pd.DataFrame(
+                [{column: features.get(column) for column in self.feature_columns}],
+            )
+            transformed = preprocess.transform(frame)
+            if hasattr(transformed, "toarray"):
+                transformed = transformed.toarray()
+
+            if self._defect_shap_explainer is None:
+                import shap
+
+                self._defect_shap_explainer = shap.TreeExplainer(estimator)
+            shap_values = self._defect_shap_explainer.shap_values(transformed)
+            if isinstance(shap_values, list):
+                values = shap_values[-1][0]
+            else:
+                values = np.asarray(shap_values)
+                if values.ndim == 3:
+                    values = values[0, :, -1]
+                else:
+                    values = values[0]
+
+            try:
+                transformed_names = list(preprocess.get_feature_names_out())
+            except Exception:
+                transformed_names = [f"feature_{index}" for index in range(len(values))]
+
+            raw_scores = {feature: 0.0 for feature in self.feature_columns}
+            for name, value in zip(transformed_names, values, strict=False):
+                normalized_name = str(name)
+                for feature in raw_scores:
+                    if normalized_name.endswith(feature) or f"__{feature}" in normalized_name:
+                        raw_scores[feature] += abs(float(value))
+                        break
+
+            max_score = max(raw_scores.values(), default=0.0)
+            if max_score <= 0:
+                return {}
+            return {
+                feature: round(score / max_score, 4)
+                for feature, score in raw_scores.items()
+                if score > 0
+            }
+        except Exception:
+            logger.debug("SHAP cause ranking failed; using fallback causes.", exc_info=True)
+            return {}
+
+    @staticmethod
+    def _transfer_key(process_code: str) -> str | None:
+        target = NEXT_PROCESS.get(process_code)
+        if target is None:
+            return None
+        return f"{process_code.lower()}_to_{target.lower()}"
+
+    @staticmethod
+    def _expected_steps_after(process_code: str) -> int | None:
+        if process_code not in PROCESS_SEQUENCE:
+            return None
+        steps = len(PROCESS_SEQUENCE) - PROCESS_SEQUENCE.index(process_code) - 1
+        return steps or None
+
+    @staticmethod
+    def _risk_level(probability: float) -> str:
+        if probability >= 0.75:
+            return "CRITICAL"
+        if probability >= 0.55:
+            return "WARNING"
+        return "LOW"
+
+    @staticmethod
+    def _patch_loaded_model(model: Any) -> None:
+        seen: set[int] = set()
+
+        def visit(node: Any) -> None:
+            node_id = id(node)
+            if node_id in seen:
+                return
+            seen.add(node_id)
+
+            if node.__class__.__name__ == "SimpleImputer" and not hasattr(
+                node,
+                "_fill_dtype",
+            ):
+                node._fill_dtype = getattr(node, "_fit_dtype", None)
+
+            for child in getattr(node, "named_steps", {}).values():
+                visit(child)
+            for transformer in getattr(node, "transformers_", []):
+                if len(transformer) >= 2:
+                    visit(transformer[1])
+            for _, value in getattr(node, "steps", []):
+                visit(value)
+
+        visit(model)
+
+
+def _dict(value: Any) -> dict[str, Any]:
+    return value if isinstance(value, dict) else {}
+
+
+def _safe_float(value: Any, *, default: float = 0.0) -> float:
+    if value is None or value == "":
+        return default
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _station_code(equipment_code: str) -> str:
+    if not equipment_code:
+        return "UNKNOWN"
+    parts = equipment_code.split("_")
+    return parts[-1] if parts else equipment_code
+
+
+def _cause_message(label: str, value: Any, unit: str) -> str:
+    if isinstance(value, (int, float, np.number)):
+        return f"{label} {float(value):.2f}{unit}"
+    return f"{label} {value}"
diff --git a/app/repository/__init__.py b/app/repository/__init__.py
index d8bae70..6418e44 100644
--- a/app/repository/__init__.py
+++ b/app/repository/__init__.py
@@ -1,6 +1,9 @@
 """Database repository package."""
 
 from app.repository.car_master_repository import CarMasterRepository
+from app.repository.defect_transfer_prediction_repository import (
+    DefectTransferPredictionRepository,
+)
 from app.repository.equipment_repository import EquipmentRepository
 from app.repository.manufacturing_event_repository import (
     ManufacturingEventRepository,
@@ -16,6 +19,7 @@
 
 __all__ = [
     "CarMasterRepository",
+    "DefectTransferPredictionRepository",
     "EquipmentRepository",
     "ManufacturingEventRepository",
     "ManufacturingEventTemplateRepository",
diff --git a/app/repository/defect_transfer_prediction_repository.py b/app/repository/defect_transfer_prediction_repository.py
new file mode 100644
index 0000000..db62ccc
--- /dev/null
+++ b/app/repository/defect_transfer_prediction_repository.py
@@ -0,0 +1,429 @@
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from typing import Any
+
+from app.repository.sampledb_schema import car_master, equipment, manufacturing_event_json
+from app.utils.database_utils import mysql_connect_args_for_seoul
+from app.utils.process_label_utils import NEXT_PROCESS, equipment_code_for_car_process
+
+
+class DefectTransferPredictionRepository:
+    """Store and read defect-transfer prediction results in main_db."""
+
+    def __init__(
+        self,
+        database_url: str,
+        *,
+        event_database_url: str,
+    ) -> None:
+        self.database_url = database_url
+        self.event_database_url = event_database_url
+        self.engine: Any | None = None
+        self.event_engine: Any | None = None
+        self.table: Any | None = None
+        self.metadata: Any | None = None
+
+        self._init_sqlalchemy()
+        self.ensure_schema()
+
+    def ensure_schema(self) -> None:
+        self.metadata.create_all(self.engine)
+        self._align_result_schema()
+
+    def replace_prediction_result(
+        self,
+        *,
+        event_id: str,
+        car_master_id: int,
+        source_process_code: str,
+        target_process_code: str | None,
+        current_defect_probability: float,
+        target_defect_probability: float | None,
+        predicted_defect_process: str | None,
+        expected_occurrence_step: int | None,
+        risk_grade: str,
+        causes: list[dict[str, Any]],
+        predicted_at: datetime,
+    ) -> int:
+        manufacturing_event_id = self._manufacturing_event_id(event_id)
+        rows = []
+        cause_rows = causes or [
+            {
+                "message": "모델 기반 주요 원인이 산출되지 않았습니다.",
+                "impact": 0.0,
+            },
+        ]
+        for cause in cause_rows[:5]:
+            rows.append(
+                {
+                    "manufacturing_event_id": manufacturing_event_id,
+                    "car_master_id": car_master_id,
+                    "source_process_code": source_process_code,
+                    "target_process_code": target_process_code,
+                    "current_defect_probability": current_defect_probability,
+                    "target_defect_probability": target_defect_probability,
+                    "predicted_defect_process": predicted_defect_process,
+                    "expected_occurrence_step": expected_occurrence_step,
+                    "risk_grade": risk_grade,
+                    "main_cause": str(cause.get("message") or cause.get("label") or "")[:200],
+                    "influence_score": float(cause.get("impact") or 0.0),
+                    "predicted_at": predicted_at,
+                },
+            )
+
+        with self.engine.begin() as conn:
+            if manufacturing_event_id is not None:
+                conn.execute(
+                    self.table.delete().where(
+                        self.table.c.manufacturing_event_id == manufacturing_event_id,
+                    ),
+                )
+            if self.engine.dialect.name != "mysql":
+                from sqlalchemy import func, select
+
+                next_id = int(
+                    conn.execute(select(func.max(self.table.c.id))).scalar() or 0,
+                )
+                rows = [
+                    {**row, "id": next_id + index}
+                    for index, row in enumerate(rows, 1)
+                ]
+            conn.execute(self.table.insert(), rows)
+        return len(rows)
+
+    def list_prediction_page(self, *, cursor: int, size: int) -> tuple[list[dict[str, Any]], bool]:
+        from sqlalchemy import select
+
+        rows = self._all_prediction_rows()
+        latest_by_car: dict[int, dict[str, Any]] = {}
+        for row in rows:
+            car_id = int(row["car_master_id"])
+            if car_id not in latest_by_car:
+                latest_by_car[car_id] = row
+
+        items = [
+            row
+            for row in latest_by_car.values()
+            if self._result_probability_percent(row) > 0
+        ]
+        items.sort(
+            key=lambda row: (
+                float(row.get("target_defect_probability") or 0.0),
+                float(row.get("current_defect_probability") or 0.0),
+                row.get("predicted_at") or datetime.min,
+                int(row.get("id") or 0),
+            ),
+            reverse=True,
+        )
+        self._attach_vehicle_ids(items)
+        self._attach_process_equipment_codes(items)
+
+        offset = cursor * size
+        page = items[offset : offset + size]
+        return page, len(items) > offset + size
+
+    def list_cause_page(
+        self,
+        *,
+        vehicle_id: str | None,
+        cursor: int,
+        size: int,
+    ) -> tuple[dict[str, Any] | None, list[dict[str, Any]], bool]:
+        car_master_id = self._car_master_id(vehicle_id) if vehicle_id else None
+        rows = self._all_prediction_rows(car_master_id=car_master_id)
+        if not rows:
+            return None, [], False
+
+        latest_event_id = rows[0].get("manufacturing_event_id")
+        latest_rows = [
+            row
+            for row in rows
+            if row.get("manufacturing_event_id") == latest_event_id
+        ]
+        latest_rows.sort(
+            key=lambda row: (
+                float(row.get("influence_score") or 0.0),
+                int(row.get("id") or 0),
+            ),
+            reverse=True,
+        )
+        self._attach_vehicle_ids(latest_rows)
+        self._attach_process_equipment_codes(latest_rows)
+
+        offset = cursor * size
+        page = latest_rows[offset : offset + size]
+        return latest_rows[0], page, len(latest_rows) > offset + size
+
+    def _all_prediction_rows(
+        self,
+        *,
+        car_master_id: int | None = None,
+    ) -> list[dict[str, Any]]:
+        from sqlalchemy import select
+
+        query = select(self.table)
+        if car_master_id is not None:
+            query = query.where(self.table.c.car_master_id == car_master_id)
+        query = query.order_by(
+            self.table.c.predicted_at.desc(),
+            self.table.c.id.desc(),
+        )
+        with self.engine.connect() as conn:
+            return [dict(row) for row in conn.execute(query).mappings()]
+
+    def _manufacturing_event_id(self, event_id: str) -> int | None:
+        from sqlalchemy import select
+
+        query = select(manufacturing_event_json.c.id).where(
+            manufacturing_event_json.c.event_id == event_id,
+        )
+        with self.event_engine.connect() as conn:
+            value = conn.execute(query).scalar()
+        return int(value) if value is not None else None
+
+    def _car_master_id(self, vehicle_id: str | None) -> int | None:
+        if not vehicle_id:
+            return None
+        from sqlalchemy import select
+
+        query = select(car_master.c.id).where(car_master.c.vehicle_id == vehicle_id)
+        with self.event_engine.connect() as conn:
+            value = conn.execute(query).scalar()
+        return int(value) if value is not None else None
+
+    @staticmethod
+    def _result_probability_percent(row: dict[str, Any]) -> int:
+        value = row.get("target_defect_probability")
+        if value is None:
+            value = row.get("current_defect_probability")
+        if value is None:
+            return 0
+        return round(float(value) * 100)
+
+    def _attach_process_equipment_codes(self, rows: list[dict[str, Any]]) -> None:
+        if not rows:
+            return
+        from sqlalchemy import select
+
+        event_ids = {
+            int(row["manufacturing_event_id"])
+            for row in rows
+            if row.get("manufacturing_event_id") is not None
+        }
+        source_equipment_by_event: dict[int, str] = {}
+        if event_ids:
+            query = (
+                select(
+                    manufacturing_event_json.c.id,
+                    equipment.c.equipment_code,
+                )
+                .select_from(
+                    manufacturing_event_json.join(
+                        equipment,
+                        equipment.c.id == manufacturing_event_json.c.equipment_id,
+                    ),
+                )
+                .where(manufacturing_event_json.c.id.in_(event_ids))
+            )
+            with self.event_engine.connect() as conn:
+                for event_row in conn.execute(query).mappings():
+                    source_equipment_by_event[int(event_row["id"])] = str(
+                        event_row["equipment_code"],
+                    )
+
+        target_keys: set[tuple[int, str]] = set()
+        for row in rows:
+            target_code = self._target_process_code(row)
+            if target_code:
+                target_keys.add((int(row["car_master_id"]), target_code))
+
+        target_equipment_by_key: dict[tuple[int, str], str] = {}
+        if target_keys:
+            car_ids = sorted({car_id for car_id, _ in target_keys})
+            query = (
+                select(
+                    manufacturing_event_json.c.car_master_id,
+                    manufacturing_event_json.c.process_code,
+                    equipment.c.equipment_code,
+                    manufacturing_event_json.c.id,
+                )
+                .select_from(
+                    manufacturing_event_json.join(
+                        equipment,
+                        equipment.c.id == manufacturing_event_json.c.equipment_id,
+                    ),
+                )
+                .where(manufacturing_event_json.c.car_master_id.in_(car_ids))
+                .order_by(
+                    manufacturing_event_json.c.car_master_id,
+                    manufacturing_event_json.c.process_code,
+                    manufacturing_event_json.c.id.desc(),
+                )
+            )
+            with self.event_engine.connect() as conn:
+                for event_row in conn.execute(query).mappings():
+                    key = (
+                        int(event_row["car_master_id"]),
+                        str(event_row["process_code"]).strip().upper(),
+                    )
+                    if key in target_keys and key not in target_equipment_by_key:
+                        target_equipment_by_key[key] = str(event_row["equipment_code"])
+
+        for row in rows:
+            event_id = row.get("manufacturing_event_id")
+            row["source_equipment_code"] = (
+                source_equipment_by_event.get(int(event_id))
+                if event_id is not None
+                else None
+            )
+
+            car_id = int(row["car_master_id"])
+            target_code = self._target_process_code(row)
+            if target_code:
+                row["target_equipment_code"] = target_equipment_by_key.get(
+                    (car_id, target_code),
+                    equipment_code_for_car_process(
+                        car_master_id=car_id,
+                        process_code=target_code,
+                    ),
+                )
+            else:
+                row["target_equipment_code"] = None
+
+    @staticmethod
+    def _target_process_code(row: dict[str, Any]) -> str | None:
+        target_code = row.get("target_process_code")
+        if target_code:
+            return str(target_code).strip().upper()
+        source = str(row.get("source_process_code") or "").strip().upper()
+        return NEXT_PROCESS.get(source)
+
+    def _attach_vehicle_ids(self, rows: list[dict[str, Any]]) -> None:
+        if not rows:
+            return
+        from sqlalchemy import select
+
+        car_ids = sorted({int(row["car_master_id"]) for row in rows})
+        query = select(car_master.c.id, car_master.c.vehicle_id).where(
+            car_master.c.id.in_(car_ids),
+        )
+        with self.event_engine.connect() as conn:
+            vehicle_by_id = {
+                int(row["id"]): str(row["vehicle_id"])
+                for row in conn.execute(query).mappings()
+            }
+        for row in rows:
+            row["vehicle_id"] = vehicle_by_id.get(
+                int(row["car_master_id"]),
+                f"VIN-{int(row['car_master_id']):06d}",
+            )
+
+    def _init_sqlalchemy(self) -> None:
+        try:
+            from sqlalchemy import BigInteger, Column, DateTime, Double, Enum
+            from sqlalchemy import Index, Integer, MetaData, String, Table, func
+            from sqlalchemy import create_engine
+        except ModuleNotFoundError as exc:
+            raise RuntimeError(
+                "SQLAlchemy and PyMySQL are required for defect transfer result storage.",
+            ) from exc
+
+        self.metadata = MetaData()
+        process_code_enum = Enum("PRESS", "BODY", "PAINT", "ASSEMBLY")
+        self.table = Table(
+            "defect_transfer_prediction_result",
+            self.metadata,
+            Column("id", BigInteger, primary_key=True, autoincrement=True),
+            Column("manufacturing_event_id", BigInteger),
+            Column("car_master_id", BigInteger),
+            Column("source_process_code", process_code_enum),
+            Column("target_process_code", process_code_enum),
+            Column("current_defect_probability", Double),
+            Column("target_defect_probability", Double),
+            Column("predicted_defect_process", String(50)),
+            Column("expected_occurrence_step", Integer),
+            Column("risk_grade", String(20)),
+            Column("main_cause", String(200)),
+            Column("influence_score", Double),
+            Column("predicted_at", DateTime, nullable=False),
+            Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
+            Column(
+                "updated_at",
+                DateTime,
+                nullable=False,
+                server_default=func.current_timestamp(),
+                server_onupdate=func.current_timestamp(),
+            ),
+            Index("idx_defect_transfer_car_predicted", "car_master_id", "predicted_at"),
+            Index("idx_defect_transfer_event", "manufacturing_event_id"),
+        )
+        self.engine = create_engine(
+            self.database_url,
+            connect_args=mysql_connect_args_for_seoul(self.database_url),
+            pool_pre_ping=True,
+            future=True,
+        )
+        self.event_engine = create_engine(
+            self.event_database_url,
+            connect_args=mysql_connect_args_for_seoul(self.event_database_url),
+            pool_pre_ping=True,
+            future=True,
+        )
+
+    def _align_result_schema(self) -> None:
+        if self.engine.dialect.name != "mysql":
+            return
+
+        from sqlalchemy import inspect, text
+
+        table_name = "defect_transfer_prediction_result"
+        inspector = inspect(self.engine)
+        if not inspector.has_table(table_name):
+            return
+
+        columns = {column["name"] for column in inspector.get_columns(table_name)}
+        expected_columns = {
+            "manufacturing_event_id": "BIGINT NULL",
+            "car_master_id": "BIGINT NULL",
+            "source_process_code": "ENUM('PRESS','BODY','PAINT','ASSEMBLY') NULL",
+            "target_process_code": "ENUM('PRESS','BODY','PAINT','ASSEMBLY') NULL",
+            "current_defect_probability": "DOUBLE NULL",
+            "target_defect_probability": "DOUBLE NULL",
+            "predicted_defect_process": "VARCHAR(50) NULL",
+            "expected_occurrence_step": "INT NULL",
+            "risk_grade": "VARCHAR(20) NULL",
+            "main_cause": "VARCHAR(200) NULL",
+            "influence_score": "DOUBLE NULL",
+            "predicted_at": "DATETIME NOT NULL",
+            "created_at": "DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP",
+            "updated_at": (
+                "DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP "
+                "ON UPDATE CURRENT_TIMESTAMP"
+            ),
+        }
+        with self.engine.begin() as conn:
+            for column_name, definition in expected_columns.items():
+                if column_name not in columns:
+                    conn.execute(
+                        text(
+                            f"ALTER TABLE {table_name} "
+                            f"ADD COLUMN {column_name} {definition}",
+                        ),
+                    )
+                    columns.add(column_name)
+
+            conn.execute(
+                text(
+                    f"ALTER TABLE {table_name} "
+                    "MODIFY COLUMN id BIGINT NOT NULL AUTO_INCREMENT",
+                ),
+            )
+            for column_name, definition in expected_columns.items():
+                conn.execute(
+                    text(
+                        f"ALTER TABLE {table_name} "
+                        f"MODIFY COLUMN {column_name} {definition}",
+                    ),
+                )
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
new file mode 100644
index 0000000..d15e96e
--- /dev/null
+++ b/app/service/analysis/defect_transfer_service.py
@@ -0,0 +1,305 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import Any
+
+from fastapi import status
+from sqlalchemy.exc import SQLAlchemyError
+
+from app.core.config import settings
+from app.core.exceptions import AppException
+from app.dto.response.defect_transfer_response import (
+    DefectTransferCauseItem,
+    DefectTransferCausePage,
+    DefectTransferPredictionItem,
+    DefectTransferPredictionPage,
+)
+from app.repository.defect_transfer_prediction_repository import (
+    DefectTransferPredictionRepository,
+)
+from app.utils.json_utils import from_json, to_json
+from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
+
+
+DEFECT_TRANSFER_CACHE_VERSION = "v4"
+
+
+class DefectTransferAnalysisService:
+    def __init__(
+        self,
+        *,
+        database_url: str | None = None,
+        event_database_url: str | None = None,
+        repository: DefectTransferPredictionRepository | None = None,
+    ) -> None:
+        main_database_url = database_url or settings.main_database_connection_url
+        sample_database_url = event_database_url or settings.sample_database_connection_url
+        if not sample_database_url:
+            raise AppException(
+                "sample database connection URL is required.",
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            )
+        self.repository = repository or DefectTransferPredictionRepository(
+            main_database_url,
+            event_database_url=sample_database_url,
+        )
+        self._redis_client: Any | None = None
+
+    def get_cached_predictions(
+        self,
+        *,
+        cursor: int | None,
+        size: int,
+    ) -> DefectTransferPredictionPage:
+        cache_key = self._prediction_cache_key(cursor=cursor, size=size)
+        cached_value = self._cache_get(cache_key)
+        if cached_value:
+            return DefectTransferPredictionPage.model_validate(from_json(cached_value))
+
+        page = self.get_predictions(cursor=cursor, size=size)
+        self._cache_set(cache_key, page.model_dump(by_alias=False))
+        return page
+
+    def get_cached_cause_analysis(
+        self,
+        *,
+        vehicle_id: str | None,
+        cursor: int | None,
+        size: int,
+    ) -> DefectTransferCausePage:
+        cache_key = self._cause_cache_key(
+            vehicle_id=vehicle_id,
+            cursor=cursor,
+            size=size,
+        )
+        cached_value = self._cache_get(cache_key)
+        if cached_value:
+            return DefectTransferCausePage.model_validate(from_json(cached_value))
+
+        page = self.get_cause_analysis(
+            vehicle_id=vehicle_id,
+            cursor=cursor,
+            size=size,
+        )
+        self._cache_set(cache_key, page.model_dump(by_alias=False))
+        return page
+
+    def get_predictions(
+        self,
+        *,
+        cursor: int | None,
+        size: int,
+    ) -> DefectTransferPredictionPage:
+        page = max(cursor or 0, 0)
+        safe_size = max(1, min(size, 100))
+        try:
+            rows, has_next = self.repository.list_prediction_page(
+                cursor=page,
+                size=safe_size,
+            )
+        except SQLAlchemyError as exc:
+            raise self._db_exception() from exc
+
+        return DefectTransferPredictionPage(
+            content=[self._to_prediction_item(row) for row in rows],
+            hasNext=has_next,
+            nextCursor=page + 1 if has_next else None,
+        )
+
+    def get_cause_analysis(
+        self,
+        *,
+        vehicle_id: str | None,
+        cursor: int | None,
+        size: int,
+    ) -> DefectTransferCausePage:
+        page = max(cursor or 0, 0)
+        safe_size = max(1, min(size, 100))
+        try:
+            selected, rows, has_next = self.repository.list_cause_page(
+                vehicle_id=vehicle_id,
+                cursor=page,
+                size=safe_size,
+            )
+        except SQLAlchemyError as exc:
+            raise self._db_exception() from exc
+
+        if selected is None:
+            return DefectTransferCausePage(
+                vehicleId=vehicle_id,
+                carMasterId=None,
+                predictedDefectProbability=None,
+                riskLevel=None,
+                currentProcess=None,
+                predictedDefectProcess=None,
+                transferProbability=None,
+                content=[],
+                hasNext=False,
+                nextCursor=None,
+            )
+
+        return DefectTransferCausePage(
+            vehicleId=str(selected.get("vehicle_id")),
+            carMasterId=int(selected["car_master_id"]),
+            predictedDefectProbability=self._result_probability(selected),
+            riskLevel=selected.get("risk_grade"),
+            currentProcess=format_process_with_line(
+                selected.get("source_process_code"),
+                selected.get("source_equipment_code"),
+            ),
+            predictedDefectProcess=self._resolve_predicted_defect_process(selected),
+            transferProbability=self._percent(selected.get("target_defect_probability")),
+            content=[
+                DefectTransferCauseItem(
+                    rank=index,
+                    feature="main_cause",
+                    label=str(row.get("main_cause") or ""),
+                    value=self._display_value(row.get("influence_score")),
+                    impact=float(row.get("influence_score") or 0.0),
+                    message=str(row.get("main_cause") or ""),
+                )
+                for index, row in enumerate(rows, page * safe_size + 1)
+            ],
+            hasNext=has_next,
+            nextCursor=page + 1 if has_next else None,
+        )
+
+    def _to_prediction_item(
+        self,
+        row: dict[str, Any],
+    ) -> DefectTransferPredictionItem:
+        vehicle_id = str(row.get("vehicle_id"))
+        return DefectTransferPredictionItem(
+            vehicleId=vehicle_id,
+            carMasterId=int(row["car_master_id"]),
+            currentProcess=format_process_with_line(
+                row.get("source_process_code"),
+                row.get("source_equipment_code"),
+            ),
+            predictedDefectProcess=self._resolve_predicted_defect_process(row),
+            defectProbability=self._result_probability(row),
+            expectedTime=(
+                f"{int(row['expected_occurrence_step'])}단계 후"
+                if row.get("expected_occurrence_step") is not None
+                else None
+            ),
+            riskLevel=str(row.get("risk_grade") or "LOW"),
+        )
+
+    @staticmethod
+    def _percent(value: Any) -> int:
+        if value is None:
+            return 0
+        return round(float(value) * 100)
+
+    @classmethod
+    def _result_probability(cls, row: dict[str, Any]) -> int:
+        value = row.get("target_defect_probability")
+        if value is None:
+            value = row.get("current_defect_probability")
+        return cls._percent(value)
+
+    @classmethod
+    def _resolve_predicted_defect_process(cls, row: dict[str, Any]) -> str | None:
+        if cls._result_probability(row) <= 0:
+            return None
+
+        source_code = str(row.get("source_process_code") or "").strip().upper()
+        if row.get("target_defect_probability") is not None:
+            process_code = row.get("target_process_code") or NEXT_PROCESS.get(source_code)
+            equipment_code = row.get("target_equipment_code")
+        else:
+            process_code = row.get("source_process_code")
+            equipment_code = row.get("source_equipment_code")
+
+        if not process_code:
+            return None
+        return format_process_with_line(process_code, equipment_code)
+
+    @staticmethod
+    def _display_value(value: Any) -> str:
+        if isinstance(value, float):
+            return f"{value:.2f}"
+        return str(value)
+
+    @staticmethod
+    def _db_exception() -> AppException:
+        return AppException(
+            "불량 전이 예측 결과 데이터베이스 조회에 실패했습니다. "
+            "MAIN_DB_NAME/SAMPLE_DB_NAME/DB 계정 권한을 확인해주세요.",
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+        )
+
+    def _cache_get(self, cache_key: str) -> str | None:
+        if not settings.redis_url:
+            return None
+        try:
+            return self._redis().get(cache_key)
+        except Exception as exc:
+            self._redis_client = None
+            raise AppException(
+                "불량 전이 예측 Redis 캐시 조회에 실패했습니다.",
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            ) from exc
+
+    def _cache_set(self, cache_key: str, data: dict[str, Any]) -> None:
+        if not settings.redis_url:
+            return
+        try:
+            self._redis().setex(
+                cache_key,
+                settings.redis_cache_ttl_seconds,
+                to_json(data),
+            )
+        except Exception as exc:
+            self._redis_client = None
+            raise AppException(
+                "불량 전이 예측 Redis 캐시 저장에 실패했습니다.",
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            ) from exc
+
+    def _redis(self) -> Any:
+        if self._redis_client is None:
+            try:
+                from redis import Redis
+            except ModuleNotFoundError as exc:
+                raise RuntimeError("redis package is required for defect transfer cache.") from exc
+
+            self._redis_client = Redis.from_url(
+                settings.redis_connection_url,
+                decode_responses=True,
+            )
+        return self._redis_client
+
+    @staticmethod
+    def _safe_cache_part(value: Any) -> str:
+        text = str(value or "__default__").strip()
+        return text.replace(":", "_").replace("/", "_").replace("\\", "_")
+
+    def _prediction_cache_key(self, *, cursor: int | None, size: int) -> str:
+        page = max(cursor or 0, 0)
+        safe_size = max(1, min(size, 100))
+        return (
+            f"{settings.redis_key_prefix}:process:defect-transfer:"
+            f"predictions:{DEFECT_TRANSFER_CACHE_VERSION}:{page}:{safe_size}"
+        )
+
+    def _cause_cache_key(
+        self,
+        *,
+        vehicle_id: str | None,
+        cursor: int | None,
+        size: int,
+    ) -> str:
+        page = max(cursor or 0, 0)
+        safe_size = max(1, min(size, 100))
+        safe_vehicle_id = self._safe_cache_part(vehicle_id)
+        return (
+            f"{settings.redis_key_prefix}:process:defect-transfer:"
+            f"causes:{DEFECT_TRANSFER_CACHE_VERSION}:{safe_vehicle_id}:{page}:{safe_size}"
+        )
+
+
+@lru_cache(maxsize=1)
+def get_defect_transfer_analysis_service() -> DefectTransferAnalysisService:
+    return DefectTransferAnalysisService()
diff --git a/app/utils/process_label_utils.py b/app/utils/process_label_utils.py
new file mode 100644
index 0000000..ab68314
--- /dev/null
+++ b/app/utils/process_label_utils.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+import hashlib
+import re
+from typing import Any
+
+PROCESS_LABELS = {
+    "PRESS": "프레스",
+    "BODY": "차체",
+    "PAINT": "도장",
+    "ASSEMBLY": "의장",
+}
+
+PROCESS_EQUIPMENT_PREFIXES = {
+    "PRESS": "P",
+    "BODY": "S",
+    "PAINT": "L",
+    "ASSEMBLY": "A",
+}
+
+NEXT_PROCESS = {
+    "PRESS": "BODY",
+    "BODY": "PAINT",
+    "PAINT": "ASSEMBLY",
+}
+
+LINE_STATION_COUNT = 5
+
+
+def equipment_number(equipment_code: Any | None) -> int | None:
+    if equipment_code is None:
+        return None
+    match = re.search(r"(\d+)$", str(equipment_code).strip())
+    if not match:
+        return None
+    return int(match.group(1))
+
+
+def equipment_no_for_car_process(*, car_master_id: int, process_code: str) -> int:
+    normalized = str(process_code or "").strip().upper()
+    digest = hashlib.blake2b(
+        f"{car_master_id}:{normalized}:equipment".encode("utf-8"),
+        digest_size=8,
+    ).digest()
+    return int.from_bytes(digest, "big") % LINE_STATION_COUNT + 1
+
+
+def equipment_code_for_car_process(*, car_master_id: int, process_code: str) -> str:
+    normalized = str(process_code or "").strip().upper()
+    equipment_no = equipment_no_for_car_process(
+        car_master_id=car_master_id,
+        process_code=normalized,
+    )
+    return f"EQ_{normalized}_{equipment_no:03d}"
+
+
+def format_process_with_line(
+    process_code: Any,
+    equipment_code: Any | None = None,
+) -> str:
+    normalized = str(process_code or "").strip().upper()
+    label = PROCESS_LABELS.get(normalized, normalized)
+    prefix = PROCESS_EQUIPMENT_PREFIXES.get(normalized)
+    equipment_no = equipment_number(equipment_code)
+    if prefix and equipment_no is not None:
+        return f"{label} ({prefix}{equipment_no})"
+    return label

From 2358c9bc64f6a3377c389ec0ab3fe86b201b5e53 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 13:37:48 +0900
Subject: [PATCH 063/148] feat : summary update

---
 app/repository/quality_summary_repository.py | 302 +++++++++----------
 main.py                                      |  11 +
 2 files changed, 154 insertions(+), 159 deletions(-)

diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index af51fb5..a8a0a84 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -1,199 +1,183 @@
 from sqlalchemy import create_engine, text
-import pandas as pd
-from datetime import datetime, timedelta
 from dotenv import load_dotenv
-import os
 from urllib.parse import quote_plus
+import os
+import time
+
 
 def run():
+
     load_dotenv()
+
     DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
+    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
     MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-    SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
     MAIN_DATABASE_URL = (
         f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
         f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
     )
 
-    SAMPLE_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-    )
-
-    sample_engine = create_engine(
-        SAMPLE_DATABASE_URL,
-        pool_pre_ping=True
-    )
-
     main_engine = create_engine(
         MAIN_DATABASE_URL,
         pool_pre_ping=True
     )
 
-    inspection_summary_list = []
-
-    summary_id = 1
-
-    base_date = datetime.strptime(
-        "2026-06-01",
-        "%Y-%m-%d"
-    )
-
-    with sample_engine.connect() as conn:
+    last_total_count = -1
 
-        # 7일
-        for day in range(7):
+    while True:
 
-            current_date = (
-                base_date +
-                timedelta(days=day)
-            )
+        with main_engine.begin() as conn:
 
-            offset = day * 100
-
-            vehicles = conn.execute(
+            result = conn.execute(
                 text("""
-                    SELECT vehicle_id
-                    FROM (
-                        SELECT DISTINCT vehicle_id
-                        FROM car_drive
-                        ORDER BY vehicle_id
-                        LIMIT 100 OFFSET :offset
-                    ) t
-                """),
-                {
-                    "offset": offset
-                }
-            ).mappings().all()
-
-            vehicle_ids = [
-                row["vehicle_id"]
-                for row in vehicles
-            ]
-
-            checkpoints = [
-
-                (25, "01:00"),
-                (50, "01:15"),
-                (75, "01:30"),
-                (100, "01:45")
-
-            ]
-
-            for target_count, time_str in checkpoints:
-
-                current_vehicle_ids = (
-                    vehicle_ids[:target_count]
+                    SELECT
+                        COUNT(*) AS total_count,
+
+                        SUM(
+                            CASE
+                                WHEN UPPER(inspection_result) = 'NORMAL'
+                                THEN 1
+                                ELSE 0
+                            END
+                        ) AS normal_count,
+
+                        SUM(
+                            CASE
+                                WHEN UPPER(inspection_result) = 'WARNING'
+                                THEN 1
+                                ELSE 0
+                            END
+                        ) AS abnormal_count,
+
+                        MAX(created_at) AS created_at
+
+                    FROM inspection_drive_detail
+                """)
+            ).mappings().first()
+
+            total_count = result["total_count"] or 0
+
+            # 데이터 변화가 없으면 건너뜀
+            if total_count == last_total_count:
+                time.sleep(1)
+                continue
+
+            normal_count = result["normal_count"] or 0
+            abnormal_count = result["abnormal_count"] or 0
+
+            standby_count = max(0, 100 - total_count)
+
+            if total_count == 0:
+                normal_rate = 0
+                abnormal_rate = 0
+            else:
+                normal_rate = round(
+                    normal_count / total_count * 100,
+                    2
                 )
 
-                normal_count = 0
-                abnormal_count = 0
-
-                for vehicle_id in current_vehicle_ids:
-
-                    drive_row = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_drive
-                            WHERE vehicle_id=:vehicle_id
-                            LIMIT 1
-                        """),
-                        {
-                            "vehicle_id": vehicle_id
-                        }
-                    ).mappings().first()
-
-                    if not drive_row:
-                        continue
-
-                    score = 100
-
-                    if float(
-                        drive_row["throttle_position"]
-                    ) > 90:
-                        score -= 20
-
-                    if float(
-                        drive_row["brake_pressure"]
-                    ) > 45:
-                        score -= 20
-
-                    if abs(float(
-                        drive_row["steering_angle"]
-                    )) > 40:
-                        score -= 20
-
-                    if score >= 80:
-                        normal_count += 1
-                    else:
-                        abnormal_count += 1
+                abnormal_rate = round(
+                    abnormal_count / total_count * 100,
+                    2
+                )
 
-                total_count = target_count
+            created_at = result["created_at"]
 
-                standby_count = (
-                    100 - total_count
+            # inspection_summary가 비어있는지 확인
+            exists = conn.execute(
+                text("""
+                    SELECT COUNT(*)
+                    FROM inspection_summary
+                """)
+            ).scalar()
+
+            if exists == 0:
+
+                conn.execute(
+                    text("""
+                        INSERT INTO inspection_summary
+                        (
+                            total_count,
+                            normal_count,
+                            normal_rate,
+                            abnormal_count,
+                            abnormal_rate,
+                            standby_count,
+                            created_at,
+                            updated_at
+                        )
+                        VALUES
+                        (
+                            :total_count,
+                            :normal_count,
+                            :normal_rate,
+                            :abnormal_count,
+                            :abnormal_rate,
+                            :standby_count,
+                            :created_at,
+                            NOW()
+                        )
+                    """),
+                    {
+                        "total_count": total_count,
+                        "normal_count": normal_count,
+                        "normal_rate": normal_rate,
+                        "abnormal_count": abnormal_count,
+                        "abnormal_rate": abnormal_rate,
+                        "standby_count": standby_count,
+                        "created_at": created_at
+                    }
                 )
 
-                created_at = datetime.strptime(
-                    f"{current_date.strftime('%Y-%m-%d')} {time_str}",
-                    "%Y-%m-%d %H:%M"
+                print(
+                    f"[INSERT] "
+                    f"완료:{total_count} "
+                    f"정상:{normal_count} "
+                    f"이상:{abnormal_count} "
+                    f"대기:{standby_count}"
                 )
 
-                inspection_summary_list.append({
-
-                    "id": summary_id,
-
-                    "total_count": total_count,
-
-                    "normal_count": normal_count,
-
-                    "normal_rate":
-                        round(
-                            normal_count /
-                            total_count * 100,
-                            2
-                        ),
-
-                    "abnormal_count":
-                        abnormal_count,
-
-                    "abnormal_rate":
-                        round(
-                            abnormal_count /
-                            total_count * 100,
-                            2
-                        ),
-
-                    "stanby_count":
-                        standby_count,
-
-                    "created_at":
-                        created_at
-
-                })
+            else:
+
+                conn.execute(
+                    text("""
+                        UPDATE inspection_summary
+                        SET
+                            total_count=:total_count,
+                            normal_count=:normal_count,
+                            normal_rate=:normal_rate,
+                            abnormal_count=:abnormal_count,
+                            abnormal_rate=:abnormal_rate,
+                            standby_count=:standby_count,
+                            created_at=:created_at,
+                            updated_at=NOW()
+                    """),
+                    {
+                        "total_count": total_count,
+                        "normal_count": normal_count,
+                        "normal_rate": normal_rate,
+                        "abnormal_count": abnormal_count,
+                        "abnormal_rate": abnormal_rate,
+                        "standby_count": standby_count,
+                        "created_at": created_at
+                    }
+                )
 
-                summary_id += 1
+                print(
+                    f"[UPDATE] "
+                    f"완료:{total_count} "
+                    f"정상:{normal_count} "
+                    f"이상:{abnormal_count} "
+                    f"대기:{standby_count}"
+                )
 
-    df = pd.DataFrame(
-        inspection_summary_list
-    )
+            last_total_count = total_count
 
-    df.to_sql(
-        name="inspection_summary",
-        con=main_engine,
-        if_exists="append",
-        index=False
-    )
+        time.sleep(1)
 
-    print(
-        f"summary table 전송 완료"
-    )
 
 if __name__ == "__main__":
     run()
\ No newline at end of file
diff --git a/main.py b/main.py
index f5c4efd..4c67070 100644
--- a/main.py
+++ b/main.py
@@ -30,6 +30,9 @@
     run as process_repository
 )
 
+from app.repository.quality_summary_repository import (
+    run as summary_repository
+)
 
 def start_thread(name, target):
 
@@ -126,12 +129,20 @@ def start_thread(name, target):
         )
     )
 
+    threads.append(
+        start_thread(
+            "Inspection Summary 실시간 집계",
+            summary_repository
+        )
+    )
+
     print("\n실행 중인 서비스")
     print("- Drive Detail Kafka 전송")
     print("- Status Detail Kafka 전송")
     print("- Risk History Kafka 전송")
     print("- Risk Trend Kafka 전송")
     print("- Process Kafka 전송")
+    print("- summary data 전송")
 
     print("- Drive Detail 메시지 처리")
     print("- Status Detail 메시지 처리")

From 3b20a344ce7a09429203758d619eebd8c9887b7a Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 15:13:08 +0900
Subject: [PATCH 064/148] feat : websocket add

---
 app/scheduler/quality/stomp_client.py | 38 +++++++++++++++++++++++++++
 main.py                               |  8 ++++++
 2 files changed, 46 insertions(+)
 create mode 100644 app/scheduler/quality/stomp_client.py

diff --git a/app/scheduler/quality/stomp_client.py b/app/scheduler/quality/stomp_client.py
new file mode 100644
index 0000000..17d665b
--- /dev/null
+++ b/app/scheduler/quality/stomp_client.py
@@ -0,0 +1,38 @@
+import stomp
+import json
+from dotenv import load_dotenv
+import os
+
+def run():
+    load_dotenv()
+    QUALITY_URL = os.getenv("QUALITY_URL")
+
+    class AIListener(stomp.ConnectionListener):
+
+        def on_message(self, frame):
+            data = json.loads(frame.body)
+            print("📩 수신 데이터:", data)
+
+            # 👉 여기서 AI 로직 실행
+            # risk_score 계산, anomaly detection 등
+
+
+    conn = stomp.Connection([(QUALITY_URL, 8083)])
+
+    conn.set_listener('', AIListener())
+
+    conn.connect(wait=True)
+
+    # Spring topic 구독
+    conn.subscribe(destination='/topic/summary', id=1, ack='auto')
+    conn.subscribe(destination='/topic/process', id=2, ack='auto')
+    conn.subscribe(destination='/topic/drive-detail', id=3, ack='auto')
+    conn.subscribe(destination='/topic/status-detail', id=4, ack='auto')
+
+    print("🚀 AI-service WebSocket listening...")
+
+    while True:
+        pass
+
+if __name__ == "__main__":
+    run()
\ No newline at end of file
diff --git a/main.py b/main.py
index 4c67070..e5d4f0f 100644
--- a/main.py
+++ b/main.py
@@ -9,6 +9,7 @@
 from app.scheduler.quality.risk_history_producer import run as history_producer
 from app.scheduler.quality.risk_trend_producer import run as trend_producer
 from app.scheduler.quality.process_producer import run as process_producer
+from app.scheduler.quality.stomp_client import run as stomp_client
 
 from app.repository.quality_drive_detail_repository import (
     run as drive_repository
@@ -136,6 +137,13 @@ def start_thread(name, target):
         )
     )
 
+    threads.append(
+        start_thread(
+            "WebSocket STOMP Listener",
+            stomp_client
+        )
+    )
+
     print("\n실행 중인 서비스")
     print("- Drive Detail Kafka 전송")
     print("- Status Detail Kafka 전송")

From ccd2560c0a013cdcacabbf7a706bcdc35e33e1a8 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 17:02:03 +0900
Subject: [PATCH 065/148] feat : websocket msg update

---
 app/repository/quality_drive_detail_repository.py  | 5 +++++
 app/repository/quality_process_repository.py       | 5 +++++
 app/repository/quality_status_detail_repository.py | 5 +++++
 app/repository/quality_summary_repository.py       | 9 +++------
 4 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 48ebd0c..67c37a8 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -3,6 +3,7 @@
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
+import requests
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_DRIVE_DETAIL
@@ -182,6 +183,10 @@ def get_driving_pattern(row):
                 index=False
             )
 
+            requests.post(
+                f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/drive"
+            )
+
             detail_id += 1
 
     except Exception as e:
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 5af5f2c..21b5e49 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -3,6 +3,7 @@
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
+import requests
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
@@ -130,6 +131,10 @@ def run():
                     index=False
                 )
 
+                requests.post(
+                    f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/process"
+                )
+
     except Exception as e:
         print(f"오류 발생 : {e}")
 
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 81d461b..109cfab 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -3,6 +3,7 @@
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
+import requests
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_STATUS_DETAIL
@@ -108,6 +109,10 @@ def run():
                 index=False
             )
 
+            requests.post(
+                f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/status"
+            )
+
     except Exception as e:
 
         print(
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index a8a0a84..c6f165a 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -3,6 +3,7 @@
 from urllib.parse import quote_plus
 import os
 import time
+import requests
 
 
 def run():
@@ -166,12 +167,8 @@ def run():
                     }
                 )
 
-                print(
-                    f"[UPDATE] "
-                    f"완료:{total_count} "
-                    f"정상:{normal_count} "
-                    f"이상:{abnormal_count} "
-                    f"대기:{standby_count}"
+                requests.post(
+                    f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/summary"
                 )
 
             last_total_count = total_count

From 8d3fbd32110351da9c07f339fbf21e9fd5918f7a Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 17:12:33 +0900
Subject: [PATCH 066/148] test1

---
 app/repository/quality_drive_detail_repository.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 67c37a8..499b74c 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -184,7 +184,7 @@ def get_driving_pattern(row):
             )
 
             requests.post(
-                f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/drive"
+                f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/drive"
             )
 
             detail_id += 1

From d18aff5876997cd1864e071b65514c96b7ae152e Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 17:13:51 +0900
Subject: [PATCH 067/148] test2

---
 app/repository/quality_process_repository.py       | 2 +-
 app/repository/quality_status_detail_repository.py | 2 +-
 app/repository/quality_summary_repository.py       | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 21b5e49..d1fc3ec 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -132,7 +132,7 @@ def run():
                 )
 
                 requests.post(
-                    f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/process"
+                    f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/process"
                 )
 
     except Exception as e:
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 109cfab..b0e7676 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -110,7 +110,7 @@ def run():
             )
 
             requests.post(
-                f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/status"
+                f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/status"
             )
 
     except Exception as e:
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index c6f165a..fe6e5d4 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -168,7 +168,7 @@ def run():
                 )
 
                 requests.post(
-                    f"http://{os.getenv("QUALITY_URL")}:8083/internal/notify/summary"
+                    f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/summary"
                 )
 
             last_total_count = total_count

From b765381eb3c5958d2c7f5d94516ccb7159c6b114 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 17:18:10 +0900
Subject: [PATCH 068/148] test3

---
 app/repository/quality_summary_repository.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index fe6e5d4..7f04762 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -167,10 +167,6 @@ def run():
                     }
                 )
 
-                requests.post(
-                    f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/summary"
-                )
-
             last_total_count = total_count
 
         time.sleep(1)

From ae470339cd4db021f5bd415f4ce10385e4193344 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 1 Jul 2026 17:18:33 +0900
Subject: [PATCH 069/148] test4

---
 app/repository/quality_drive_detail_repository.py  | 4 ----
 app/repository/quality_process_repository.py       | 4 ----
 app/repository/quality_status_detail_repository.py | 3 ---
 3 files changed, 11 deletions(-)

diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 499b74c..17e8426 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -183,10 +183,6 @@ def get_driving_pattern(row):
                 index=False
             )
 
-            requests.post(
-                f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/drive"
-            )
-
             detail_id += 1
 
     except Exception as e:
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index d1fc3ec..7fa2192 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -131,10 +131,6 @@ def run():
                     index=False
                 )
 
-                requests.post(
-                    f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/process"
-                )
-
     except Exception as e:
         print(f"오류 발생 : {e}")
 
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index b0e7676..4cf317a 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -109,9 +109,6 @@ def run():
                 index=False
             )
 
-            requests.post(
-                f"http://{os.getenv('QUALITY_URL')}:8083/internal/notify/status"
-            )
 
     except Exception as e:
 

From 76216ea6c38b80fe9682aaf87440db7a1479afd3 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 09:48:56 +0900
Subject: [PATCH 070/148] feat : error msg patch

---
 app/repository/quality_risk_history_repository.py  | 3 +--
 app/repository/quality_status_detail_repository.py | 4 +---
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index b10efaa..7b1cfce 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -119,8 +119,7 @@ def run():
 
     except Exception as e:
 
-        import traceback
-        traceback.print_exc()
+        print(f"오류 발생 : {e}")
 
     finally:
 
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 4cf317a..92c2d55 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -112,9 +112,7 @@ def run():
 
     except Exception as e:
 
-        print(
-            f"[STATUS ERROR] {e}"
-        )
+        print(f"오류 발생 : {e}")
 
     finally:
 

From 2cd44e5146adaa6d612ad587c51d17f6e0839216 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 09:49:33 +0900
Subject: [PATCH 071/148] feat : error msg patch 2

---
 app/repository/quality_summary_repository.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index 7f04762..909c23b 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -133,14 +133,6 @@ def run():
                     }
                 )
 
-                print(
-                    f"[INSERT] "
-                    f"완료:{total_count} "
-                    f"정상:{normal_count} "
-                    f"이상:{abnormal_count} "
-                    f"대기:{standby_count}"
-                )
-
             else:
 
                 conn.execute(

From 1ebd6871dcad6856b25cd2b1ed6179187dd4b009 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 09:50:20 +0900
Subject: [PATCH 072/148] feat : error msg patch 3

---
 app/scheduler/quality/stomp_client.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/app/scheduler/quality/stomp_client.py b/app/scheduler/quality/stomp_client.py
index 17d665b..0137639 100644
--- a/app/scheduler/quality/stomp_client.py
+++ b/app/scheduler/quality/stomp_client.py
@@ -10,8 +10,8 @@ def run():
     class AIListener(stomp.ConnectionListener):
 
         def on_message(self, frame):
-            data = json.loads(frame.body)
-            print("📩 수신 데이터:", data)
+            #data = json.loads(frame.body)
+            #print("📩 수신 데이터:", data)
 
             # 👉 여기서 AI 로직 실행
             # risk_score 계산, anomaly detection 등

From 23b8295c5b1f8b3eec661eec1649dbd0b33e54e4 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 09:51:49 +0900
Subject: [PATCH 073/148] feat : AIListener del

---
 app/scheduler/quality/stomp_client.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/app/scheduler/quality/stomp_client.py b/app/scheduler/quality/stomp_client.py
index 0137639..b676b87 100644
--- a/app/scheduler/quality/stomp_client.py
+++ b/app/scheduler/quality/stomp_client.py
@@ -7,9 +7,9 @@ def run():
     load_dotenv()
     QUALITY_URL = os.getenv("QUALITY_URL")
 
-    class AIListener(stomp.ConnectionListener):
+    #class AIListener(stomp.ConnectionListener):
 
-        def on_message(self, frame):
+        #def on_message(self, frame):
             #data = json.loads(frame.body)
             #print("📩 수신 데이터:", data)
 
@@ -19,7 +19,7 @@ def on_message(self, frame):
 
     conn = stomp.Connection([(QUALITY_URL, 8083)])
 
-    conn.set_listener('', AIListener())
+    #conn.set_listener('', AIListener())
 
     conn.connect(wait=True)
 

From 75fff0f2df49b666751a4b0a1e6e7abd962af522 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Thu, 2 Jul 2026 10:04:16 +0900
Subject: [PATCH 074/148] =?UTF-8?q?feat:=20=EC=A0=9C=EC=A1=B0=20=EB=B3=91?=
 =?UTF-8?q?=EB=AA=A9&=EB=B6=88=EB=9F=89=20=EC=A0=84=EC=9D=B4=20=EC=98=88?=
 =?UTF-8?q?=EC=B8=A1=20=EC=9D=B4=EB=B2=A4=ED=8A=B8=20=EC=88=98=EC=8B=A0=20?=
 =?UTF-8?q?=EC=8B=9C=20=EC=9B=B9=EC=86=8C=EC=BC=93=20=EC=84=A4=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/api/router.py                             |   2 +
 app/api/routers/defect_transfer.py            |  19 +++
 app/api/routers/process_analysis_ws.py        |  23 ++++
 app/kafka/raw_event_consumer.py               | 100 +++++++++++++++-
 .../defect_transfer_prediction_repository.py  | 110 +++++++++++++++++-
 app/service/analysis/bottleneck_service.py    |   8 +-
 .../analysis/defect_transfer_service.py       |  62 +++++++++-
 app/websocket/__init__.py                     |   1 +
 app/websocket/analysis_manager.py             |  75 ++++++++++++
 9 files changed, 385 insertions(+), 15 deletions(-)
 create mode 100644 app/api/routers/process_analysis_ws.py
 create mode 100644 app/websocket/__init__.py
 create mode 100644 app/websocket/analysis_manager.py

diff --git a/app/api/router.py b/app/api/router.py
index 266aabd..91e5b1d 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -6,6 +6,7 @@
     manufacturing_event,
     ml_dataset,
     process,
+    process_analysis_ws,
     root,
 )
 
@@ -14,5 +15,6 @@
 api_router.include_router(health.router)
 api_router.include_router(process.router)
 api_router.include_router(defect_transfer.router)
+api_router.include_router(process_analysis_ws.router)
 api_router.include_router(manufacturing_event.router)
 api_router.include_router(ml_dataset.router)
diff --git a/app/api/routers/defect_transfer.py b/app/api/routers/defect_transfer.py
index 45cb4c1..dbf4647 100644
--- a/app/api/routers/defect_transfer.py
+++ b/app/api/routers/defect_transfer.py
@@ -1,3 +1,5 @@
+from typing import Any
+
 from fastapi import APIRouter, Depends, Query
 
 from app.dto.response import CommonResponse
@@ -16,6 +18,7 @@
 
 DefectTransferPredictionResponse = CommonResponse[DefectTransferPredictionPage]
 DefectTransferCauseResponse = CommonResponse[DefectTransferCausePage]
+DefectTransferDiagnosticsResponse = CommonResponse[dict[str, Any]]
 
 
 @router.get(
@@ -47,6 +50,22 @@ def get_defect_transfer_predictions(
     )
 
 
+@router.get(
+    "/diagnostics",
+    response_model=DefectTransferDiagnosticsResponse,
+    summary="불량 전이 예측 데이터 저장/조회 상태 진단",
+)
+def get_defect_transfer_diagnostics(
+    service: DefectTransferAnalysisService = Depends(
+        get_defect_transfer_analysis_service,
+    ),
+) -> DefectTransferDiagnosticsResponse:
+    return success_response(
+        data=service.get_diagnostics(),
+        message="불량 전이 예측 데이터 진단이 완료되었습니다.",
+    )
+
+
 @router.get(
     "/causes",
     response_model=DefectTransferCauseResponse,
diff --git a/app/api/routers/process_analysis_ws.py b/app/api/routers/process_analysis_ws.py
new file mode 100644
index 0000000..0f5a1fd
--- /dev/null
+++ b/app/api/routers/process_analysis_ws.py
@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+
+from app.websocket.analysis_manager import analysis_websocket_manager
+
+
+router = APIRouter(tags=["process-analysis-websocket"])
+
+
+@router.websocket("/ws/process-analysis")
+async def process_analysis_websocket(websocket: WebSocket) -> None:
+    await analysis_websocket_manager.connect(websocket)
+    try:
+        while True:
+            message = await websocket.receive_text()
+            if message.lower() == "ping":
+                await websocket.send_json({"type": "PONG"})
+    except WebSocketDisconnect:
+        analysis_websocket_manager.disconnect(websocket)
+    except Exception:
+        analysis_websocket_manager.disconnect(websocket)
+        raise
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 36bda2c..6ba4f16 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -20,9 +20,11 @@
 from app.repository.defect_transfer_prediction_repository import (
     DefectTransferPredictionRepository,
 )
+from app.repository.sampledb_schema import car_master
 from app.repository.sampledb_repository import SampleDbRepository
 from app.service.analysis.bottleneck_service import BottleneckAnalysisService
 from app.utils.datetime_utils import seoul_now_iso
+from app.websocket.analysis_manager import analysis_websocket_manager
 
 
 logger = logging.getLogger(__name__)
@@ -192,6 +194,13 @@ def _consume_record(
     # 새 raw 이벤트와 자동 분석 결과가 반영되면 Redis 캐시를 비운다.
     _clear_bottleneck_cache()
     _clear_defect_transfer_cache()
+    _broadcast_process_analysis_updates(
+        repository,
+        row,
+        bottleneck_summaries,
+        defect_prediction,
+        defect_rows_saved,
+    )
     logger.info(
         "Kafka raw event consumed and stored: topic=%s partition=%s offset=%s "
         "key=%s event_id=%s manufacturing_event_id_source=manufacturing_event_json.id "
@@ -211,6 +220,81 @@ def _consume_record(
     )
 
 
+def _broadcast_process_analysis_updates(
+    repository: SampleDbRepository,
+    row: dict[str, Any],
+    bottleneck_summaries: list[dict[str, Any]],
+    defect_prediction: DefectTransferPrediction | None,
+    defect_rows_saved: int,
+) -> None:
+    vehicle_id = _vehicle_id_for_car_master_id(repository, int(row["car_master_id"]))
+    base_message = {
+        "eventId": row["event_id"],
+        "carMasterId": row["car_master_id"],
+        "vehicleId": vehicle_id,
+        "processCode": row["process_code"],
+        "updatedAt": seoul_now_iso(),
+    }
+    analysis_websocket_manager.broadcast_from_thread(
+        {
+            **base_message,
+            "type": "BOTTLENECK_UPDATED",
+            "resultCount": len(bottleneck_summaries),
+        },
+    )
+
+    analysis_websocket_manager.broadcast_from_thread(
+        {
+            **base_message,
+            "type": "DEFECT_TRANSFER_UPDATED",
+            "defectProbability": (
+                round(defect_prediction.defect_probability * 100.0, 1)
+                if defect_prediction is not None
+                else None
+            ),
+            "transferProbability": (
+                round(defect_prediction.transfer_probability * 100.0, 1)
+                if defect_prediction is not None
+                and defect_prediction.transfer_probability is not None
+                else None
+            ),
+            "riskGrade": (
+                defect_prediction.risk_level
+                if defect_prediction is not None
+                else None
+            ),
+            "predictedDefectProcess": (
+                _format_predicted_defect_process(
+                    defect_prediction.predicted_process_code,
+                    row,
+                )
+                if defect_prediction is not None
+                else None
+            ),
+            "resultSaved": defect_rows_saved,
+        },
+    )
+
+
+def _vehicle_id_for_car_master_id(
+    repository: SampleDbRepository,
+    car_master_id: int,
+) -> str | None:
+    from sqlalchemy import select
+
+    try:
+        query = select(car_master.c.vehicle_id).where(car_master.c.id == car_master_id)
+        with repository.engine.connect() as conn:
+            value = conn.execute(query).scalar()
+        return str(value) if value is not None else None
+    except Exception:
+        logger.exception(
+            "Failed to resolve vehicle_id for websocket update: car_master_id=%s",
+            car_master_id,
+        )
+        return None
+
+
 def _parse_raw_event(record: Any) -> dict[str, Any]:
     """Kafka record의 value JSON과 key(carId)를 분석하기 쉬운 dict로 정규화한다."""
     payload = json.loads(record.value.decode("utf-8"))
@@ -687,7 +771,21 @@ def _save_defect_transfer_prediction(
     row: dict[str, Any],
     prediction: DefectTransferPrediction | None,
 ) -> int:
-    if repository is None or prediction is None:
+    if repository is None:
+        logger.warning(
+            "Defect transfer prediction was not saved because result repository is unavailable: "
+            "event_id=%s process_code=%s",
+            row.get("event_id"),
+            row.get("process_code"),
+        )
+        return 0
+    if prediction is None:
+        logger.warning(
+            "Defect transfer prediction was not saved because prediction is unavailable: "
+            "event_id=%s process_code=%s",
+            row.get("event_id"),
+            row.get("process_code"),
+        )
         return 0
     try:
         causes = [
diff --git a/app/repository/defect_transfer_prediction_repository.py b/app/repository/defect_transfer_prediction_repository.py
index db62ccc..58af761 100644
--- a/app/repository/defect_transfer_prediction_repository.py
+++ b/app/repository/defect_transfer_prediction_repository.py
@@ -74,12 +74,11 @@ def replace_prediction_result(
             )
 
         with self.engine.begin() as conn:
-            if manufacturing_event_id is not None:
-                conn.execute(
-                    self.table.delete().where(
-                        self.table.c.manufacturing_event_id == manufacturing_event_id,
-                    ),
-                )
+            conn.execute(
+                self.table.delete().where(
+                    self.table.c.car_master_id == car_master_id,
+                ),
+            )
             if self.engine.dialect.name != "mysql":
                 from sqlalchemy import func, select
 
@@ -156,6 +155,89 @@ def list_cause_page(
         page = latest_rows[offset : offset + size]
         return latest_rows[0], page, len(latest_rows) > offset + size
 
+    def diagnostics(self) -> dict[str, Any]:
+        from sqlalchemy import distinct, func, select
+
+        with self.event_engine.connect() as conn:
+            source_event_count = int(
+                conn.execute(select(func.count()).select_from(manufacturing_event_json)).scalar()
+                or 0,
+            )
+            sent_event_count = int(
+                conn.execute(
+                    select(func.count())
+                    .select_from(manufacturing_event_json)
+                    .where(manufacturing_event_json.c.is_sent.is_(True)),
+                ).scalar()
+                or 0,
+            )
+            latest_source_event = conn.execute(
+                select(
+                    manufacturing_event_json.c.id,
+                    manufacturing_event_json.c.event_id,
+                    manufacturing_event_json.c.car_master_id,
+                    manufacturing_event_json.c.process_code,
+                    manufacturing_event_json.c.is_sent,
+                    manufacturing_event_json.c.created_at,
+                ).order_by(manufacturing_event_json.c.id.desc()),
+            ).mappings().first()
+
+        all_rows = self._all_prediction_rows()
+        latest_by_car: dict[int, dict[str, Any]] = {}
+        for row in all_rows:
+            car_id = int(row["car_master_id"])
+            if car_id not in latest_by_car:
+                latest_by_car[car_id] = row
+
+        visible_latest_rows = [
+            row
+            for row in latest_by_car.values()
+            if self._result_probability_percent(row) > 0
+        ]
+
+        with self.engine.connect() as conn:
+            result_count = int(
+                conn.execute(select(func.count()).select_from(self.table)).scalar() or 0,
+            )
+            result_car_count = int(
+                conn.execute(
+                    select(func.count(distinct(self.table.c.car_master_id))),
+                ).scalar()
+                or 0,
+            )
+            null_event_link_count = int(
+                conn.execute(
+                    select(func.count())
+                    .select_from(self.table)
+                    .where(self.table.c.manufacturing_event_id.is_(None)),
+                ).scalar()
+                or 0,
+            )
+            latest_prediction = conn.execute(
+                select(
+                    self.table.c.id,
+                    self.table.c.manufacturing_event_id,
+                    self.table.c.car_master_id,
+                    self.table.c.source_process_code,
+                    self.table.c.target_process_code,
+                    self.table.c.current_defect_probability,
+                    self.table.c.target_defect_probability,
+                    self.table.c.risk_grade,
+                    self.table.c.predicted_at,
+                ).order_by(self.table.c.predicted_at.desc(), self.table.c.id.desc()),
+            ).mappings().first()
+
+        return {
+            "sourceEventCount": source_event_count,
+            "sentSourceEventCount": sent_event_count,
+            "predictionResultRowCount": result_count,
+            "predictionResultCarCount": result_car_count,
+            "visiblePredictionCarCount": len(visible_latest_rows),
+            "nullManufacturingEventLinkCount": null_event_link_count,
+            "latestSourceEvent": self._json_ready_row(latest_source_event),
+            "latestPredictionResult": self._json_ready_row(latest_prediction),
+        }
+
     def _all_prediction_rows(
         self,
         *,
@@ -173,6 +255,15 @@ def _all_prediction_rows(
         with self.engine.connect() as conn:
             return [dict(row) for row in conn.execute(query).mappings()]
 
+    @staticmethod
+    def _json_ready_row(row: Any | None) -> dict[str, Any] | None:
+        if row is None:
+            return None
+        return {
+            key: value.isoformat() if isinstance(value, datetime) else value
+            for key, value in dict(row).items()
+        }
+
     def _manufacturing_event_id(self, event_id: str) -> int | None:
         from sqlalchemy import select
 
@@ -280,6 +371,13 @@ def _attach_process_equipment_codes(self, rows: list[dict[str, Any]]) -> None:
             )
 
             car_id = int(row["car_master_id"])
+            source_code = str(row.get("source_process_code") or "").strip().upper()
+            if not row["source_equipment_code"] and source_code:
+                row["source_equipment_code"] = equipment_code_for_car_process(
+                    car_master_id=car_id,
+                    process_code=source_code,
+                )
+
             target_code = self._target_process_code(row)
             if target_code:
                 row["target_equipment_code"] = target_equipment_by_key.get(
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 5508290..56848bc 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -16,7 +16,7 @@
 
 
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
-BOTTLENECK_CACHE_VERSION = "v8"
+BOTTLENECK_CACHE_VERSION = "v9"
 PROCESS_CODE_LABELS = {
     "PRESS": "프레스",
     "BODY": "차체",
@@ -62,17 +62,17 @@ def get_realtime_bottlenecks(
         size = max(1, min(size, 100))
         page = max(cursor or 0, 0)
 
-        saved_count, has_next = self.run_analysis_and_save(cursor=page, size=size)
-
         # 저장소에서 요청 페이지 범위만 조회
         rows = self.repository.list_results(cursor=page, size=size)
-        if saved_count == 0 or not rows:
+        if not rows:
             return BottleneckAnalysisPage(
                 content=[],
                 hasNext=False,
                 nextCursor=None,
             )
 
+        total_count = self.repository.count_results()
+        has_next = total_count > (page + 1) * size
         next_cursor = page + 1 if has_next else None
 
         return BottleneckAnalysisPage(
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index d15e96e..31d967b 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -20,8 +20,7 @@
 from app.utils.json_utils import from_json, to_json
 from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
 
-
-DEFECT_TRANSFER_CACHE_VERSION = "v4"
+DEFECT_TRANSFER_CACHE_VERSION = "v5"
 
 
 class DefectTransferAnalysisService:
@@ -54,7 +53,11 @@ def get_cached_predictions(
         cache_key = self._prediction_cache_key(cursor=cursor, size=size)
         cached_value = self._cache_get(cache_key)
         if cached_value:
-            return DefectTransferPredictionPage.model_validate(from_json(cached_value))
+            cached_page = DefectTransferPredictionPage.model_validate(
+                from_json(cached_value),
+            )
+            if cached_page.content:
+                return cached_page
 
         page = self.get_predictions(cursor=cursor, size=size)
         self._cache_set(cache_key, page.model_dump(by_alias=False))
@@ -74,7 +77,9 @@ def get_cached_cause_analysis(
         )
         cached_value = self._cache_get(cache_key)
         if cached_value:
-            return DefectTransferCausePage.model_validate(from_json(cached_value))
+            cached_page = DefectTransferCausePage.model_validate(from_json(cached_value))
+            if cached_page.content:
+                return cached_page
 
         page = self.get_cause_analysis(
             vehicle_id=vehicle_id,
@@ -84,6 +89,22 @@ def get_cached_cause_analysis(
         self._cache_set(cache_key, page.model_dump(by_alias=False))
         return page
 
+    def clear_cache(self) -> None:
+        if not settings.redis_url:
+            return
+        try:
+            redis_client = self._redis()
+            pattern = f"{settings.redis_key_prefix}:process:defect-transfer:*"
+            keys = list(redis_client.scan_iter(match=pattern))
+            if keys:
+                redis_client.delete(*keys)
+        except Exception as exc:
+            self._redis_client = None
+            raise AppException(
+                "불량 전이 예측 Redis 캐시 삭제에 실패했습니다.",
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            ) from exc
+
     def get_predictions(
         self,
         *,
@@ -106,6 +127,34 @@ def get_predictions(
             nextCursor=page + 1 if has_next else None,
         )
 
+    def get_diagnostics(self) -> dict[str, Any]:
+        try:
+            diagnostics = self.repository.diagnostics()
+        except SQLAlchemyError as exc:
+            raise self._db_exception() from exc
+
+        if diagnostics["sourceEventCount"] == 0:
+            status_text = "NO_SOURCE_EVENTS"
+            message = "sampledb.manufacturing_event_json에 원천 이벤트가 없습니다."
+        elif diagnostics["sentSourceEventCount"] == 0:
+            status_text = "NO_SENT_SOURCE_EVENTS"
+            message = "원천 이벤트는 있지만 is_sent=true 이벤트가 없습니다."
+        elif diagnostics["predictionResultRowCount"] == 0:
+            status_text = "NO_PREDICTION_RESULTS"
+            message = "원천 이벤트는 있지만 예측 결과 테이블에 저장된 행이 없습니다."
+        elif diagnostics["visiblePredictionCarCount"] == 0:
+            status_text = "NO_VISIBLE_PREDICTIONS"
+            message = "예측 결과는 저장됐지만 화면 목록 필터를 통과하는 차량이 없습니다."
+        else:
+            status_text = "OK"
+            message = "화면에 표시 가능한 불량 전이 예측 데이터가 있습니다."
+
+        return {
+            **diagnostics,
+            "status": status_text,
+            "message": message,
+        }
+
     def get_cause_analysis(
         self,
         *,
@@ -204,6 +253,11 @@ def _resolve_predicted_defect_process(cls, row: dict[str, Any]) -> str | None:
         if cls._result_probability(row) <= 0:
             return None
 
+        # Return the database column value if available
+        db_val = row.get("predicted_defect_process")
+        if db_val:
+            return db_val
+
         source_code = str(row.get("source_process_code") or "").strip().upper()
         if row.get("target_defect_probability") is not None:
             process_code = row.get("target_process_code") or NEXT_PROCESS.get(source_code)
diff --git a/app/websocket/__init__.py b/app/websocket/__init__.py
new file mode 100644
index 0000000..1296f9e
--- /dev/null
+++ b/app/websocket/__init__.py
@@ -0,0 +1 @@
+"""WebSocket helpers for realtime API notifications."""
diff --git a/app/websocket/analysis_manager.py b/app/websocket/analysis_manager.py
new file mode 100644
index 0000000..1add0ba
--- /dev/null
+++ b/app/websocket/analysis_manager.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+
+from fastapi import WebSocket
+
+
+logger = logging.getLogger(__name__)
+
+
+class AnalysisWebSocketManager:
+    """Keep active dashboard websocket connections and broadcast update hints."""
+
+    def __init__(self) -> None:
+        self._connections: list[WebSocket] = []
+        self._loop: asyncio.AbstractEventLoop | None = None
+
+    async def connect(self, websocket: WebSocket) -> None:
+        await websocket.accept()
+        self._loop = asyncio.get_running_loop()
+        if websocket not in self._connections:
+            self._connections.append(websocket)
+        await websocket.send_json({"type": "CONNECTED"})
+
+    def disconnect(self, websocket: WebSocket) -> None:
+        try:
+            self._connections.remove(websocket)
+        except ValueError:
+            pass
+
+    async def broadcast(self, message: dict[str, Any]) -> None:
+        if not self._connections:
+            return
+
+        disconnected: list[WebSocket] = []
+        for websocket in tuple(self._connections):
+            try:
+                await websocket.send_json(message)
+            except Exception:
+                disconnected.append(websocket)
+
+        for websocket in disconnected:
+            self.disconnect(websocket)
+
+    def broadcast_from_thread(self, message: dict[str, Any]) -> None:
+        loop = self._loop
+        if loop is None or not loop.is_running():
+            logger.warning(
+                "Skipped process analysis websocket broadcast: "
+                "event_loop_unavailable type=%s",
+                message.get("type"),
+            )
+            return
+        if not self._connections:
+            logger.warning(
+                "Skipped process analysis websocket broadcast: "
+                "active_connections=0 type=%s",
+                message.get("type"),
+            )
+            return
+
+        future = asyncio.run_coroutine_threadsafe(self.broadcast(message), loop)
+        future.add_done_callback(self._log_broadcast_failure)
+
+    @staticmethod
+    def _log_broadcast_failure(future: asyncio.Future[Any]) -> None:
+        try:
+            future.result()
+        except Exception:
+            logger.exception("Failed to broadcast process analysis websocket update.")
+
+
+analysis_websocket_manager = AnalysisWebSocketManager()

From fc432132753ddd2d72824a06bb81cc1dc1abda86 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 13:25:35 +0900
Subject: [PATCH 075/148] del : websocket lib del

---
 app/kafka/producer.py                           | 17 +++++++++++++++++
 .../quality_drive_detail_repository.py          |  1 -
 app/repository/quality_process_repository.py    |  1 -
 .../quality_status_detail_repository.py         |  1 -
 app/repository/quality_summary_repository.py    |  1 -
 5 files changed, 17 insertions(+), 4 deletions(-)
 create mode 100644 app/kafka/producer.py

diff --git a/app/kafka/producer.py b/app/kafka/producer.py
new file mode 100644
index 0000000..ba63f45
--- /dev/null
+++ b/app/kafka/producer.py
@@ -0,0 +1,17 @@
+from kafka import KafkaProducer
+from dotenv import load_dotenv
+import os
+import json
+
+
+def create_producer():
+
+    load_dotenv()
+
+    return KafkaProducer(
+        bootstrap_servers=[
+            os.getenv("BROKER_URL_1"),
+            os.getenv("BROKER_URL_2")
+        ],
+        value_serializer=lambda x: json.dumps(x, default=str).encode("utf-8")
+    )
\ No newline at end of file
diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 17e8426..48ebd0c 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -3,7 +3,6 @@
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
-import requests
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_DRIVE_DETAIL
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 7fa2192..5af5f2c 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -3,7 +3,6 @@
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
-import requests
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index 92c2d55..ce9265d 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -3,7 +3,6 @@
 from dotenv import load_dotenv
 import os
 from urllib.parse import quote_plus
-import requests
 
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_STATUS_DETAIL
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index 909c23b..aa15797 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -3,7 +3,6 @@
 from urllib.parse import quote_plus
 import os
 import time
-import requests
 
 
 def run():

From f5f03a8054d54c8e63b7ba7dc42b348bfb3e6462 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Thu, 2 Jul 2026 13:32:13 +0900
Subject: [PATCH 076/148] =?UTF-8?q?fix:=20equipment=20=ED=85=8C=EC=9D=B4?=
 =?UTF-8?q?=EB=B8=94=20health=5Fstatus=20=EC=82=AD=EC=A0=9C?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/repository/equipment_repository.py        |  2 -
 .../quality_risk_trend_repository.py          | 84 +++++++++++++------
 app/repository/sampledb_schema.py             |  7 --
 app/repository/sampledb_schema_manager.py     |  6 --
 4 files changed, 57 insertions(+), 42 deletions(-)

diff --git a/app/repository/equipment_repository.py b/app/repository/equipment_repository.py
index 6514032..6cf9ffd 100644
--- a/app/repository/equipment_repository.py
+++ b/app/repository/equipment_repository.py
@@ -15,7 +15,6 @@
         "equipment_name": f"{equipment_name_prefix} {index}호",
         "equipment_type": equipment_type,
         "current_status": "RUNNING",
-        "health_status": "NORMAL",
     }
     for process_code, equipment_type, equipment_name_prefix in (
         ("PRESS", "HYDRAULIC_PRESS", "프레스 유압모터"),
@@ -42,7 +41,6 @@ def seed_defaults(self) -> None:
                     process_code=statement.inserted.process_code,
                     equipment_type=statement.inserted.equipment_type,
                     current_status=statement.inserted.current_status,
-                    health_status=statement.inserted.health_status,
                 )
                 conn.execute(statement)
                 return
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 067f441..b10efaa 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -5,8 +5,8 @@
 from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
-from app.kafka.topics import QUALITY_INSPECTION_RISK_TREND
-from app.kafka.options import RISK_TREND_GROUP
+from app.kafka.topics import QUALITY_INSPECTION_RISK_HISTORY
+from app.kafka.options import RISK_HISTORY_GROUP
 
 
 def run():
@@ -14,9 +14,7 @@ def run():
     load_dotenv()
 
     DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
+    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
     MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
@@ -32,8 +30,8 @@ def run():
     )
 
     consumer = create_consumer(
-        topic=QUALITY_INSPECTION_RISK_TREND,
-        group_id=RISK_TREND_GROUP
+        topic=QUALITY_INSPECTION_RISK_HISTORY,
+        group_id=RISK_HISTORY_GROUP
     )
 
     try:
@@ -42,46 +40,78 @@ def run():
 
             row = msg.value
 
+            # Producer에서 전달되는 값
+            inspection_type = row["inspection_type"]
+            inspection_date = row["inspection_date"]
+            risk_score = row["risk_score"]
+
+            # Repository에서 생성
+            start_time = f"{inspection_date} 00:00:00"
+            end_time = f"{inspection_date} 23:59:59"
+
+            # 같은 날짜 + 같은 검사 타입 존재 여부 확인
             exists = pd.read_sql(
                 text("""
                     SELECT COUNT(*) AS cnt
-                    FROM inspection_risk_trend
-                    WHERE risk_level = :risk_level
-                    AND DATE(created_at) = DATE(:created_at)
+                    FROM inspection_risk_history
+                    WHERE inspection_type = :inspection_type
+                      AND DATE(start_time) = DATE(:start_time)
                 """),
                 con=main_engine,
                 params={
-                    "risk_level": row["risk_level"],
-                    "created_at": row["created_at"]
+                    "inspection_type": inspection_type,
+                    "start_time": start_time
                 }
             )
 
+            # 이미 존재하면 UPDATE
             if exists.iloc[0]["cnt"] > 0:
 
                 with main_engine.begin() as conn:
 
                     conn.execute(
                         text("""
-                            UPDATE
-                                inspection_risk_trend
+                            UPDATE inspection_risk_history
                             SET
-                                risk_count=:risk_count,
-                                risk_ratio=:risk_ratio
-                            WHERE
-                                risk_level=:risk_level
-                            AND DATE(created_at)
-                                =
-                                DATE(:created_at)
+                                risk_score = :risk_score,
+                                end_time = :end_time
+                            WHERE inspection_type = :inspection_type
+                              AND DATE(start_time) = DATE(:start_time)
                         """),
-                        row
+                        {
+                            "risk_score": risk_score,
+                            "end_time": end_time,
+                            "inspection_type": inspection_type,
+                            "start_time": start_time
+                        }
                     )
 
             else:
 
-                df = pd.DataFrame([row])
+                # inspection_round 자동 생성
+                with main_engine.begin() as conn:
+
+                    inspection_round = conn.execute(
+                        text("""
+                            SELECT COALESCE(MAX(inspection_round), 0) + 1
+                            FROM inspection_risk_history
+                            WHERE inspection_type = :inspection_type
+                        """),
+                        {
+                            "inspection_type": inspection_type
+                        }
+                    ).scalar()
+
+                df = pd.DataFrame([{
+                    "inspection_type": inspection_type,
+                    "inspection_round": inspection_round,
+                    "risk_score": risk_score,
+                    "start_time": start_time,
+                    "end_time": end_time
+                }])
 
                 df.to_sql(
-                    name="inspection_risk_trend",
+                    name="inspection_risk_history",
                     con=main_engine,
                     if_exists="append",
                     index=False
@@ -89,12 +119,12 @@ def run():
 
     except Exception as e:
 
-        print(f"오류 발생 : {e}")
+        import traceback
+        traceback.print_exc()
 
     finally:
 
-        print("risk-trend 종료")
-
+        print("risk-history 종료")
         consumer.close()
 
 
diff --git a/app/repository/sampledb_schema.py b/app/repository/sampledb_schema.py
index 0d6d27d..69309a1 100644
--- a/app/repository/sampledb_schema.py
+++ b/app/repository/sampledb_schema.py
@@ -28,7 +28,6 @@
     "FAULT",
     "MAINTENANCE",
 )
-equipment_health_status_enum = Enum("NORMAL", "WARNING", "CRITICAL")
 dispatch_status_enum = Enum(
     "PENDING",
     "READY",
@@ -65,12 +64,6 @@
         nullable=False,
         server_default="RUNNING",
     ),
-    Column(
-        "health_status",
-        equipment_health_status_enum,
-        nullable=False,
-        server_default="NORMAL",
-    ),
     Column("last_fault_time", DateTime),
     Column("last_recovered_time", DateTime),
     Column("reason", String(255)),
diff --git a/app/repository/sampledb_schema_manager.py b/app/repository/sampledb_schema_manager.py
index 7670b95..4954171 100644
--- a/app/repository/sampledb_schema_manager.py
+++ b/app/repository/sampledb_schema_manager.py
@@ -20,7 +20,6 @@ def ensure_schema(self) -> None:
     def _migrate_prd_columns(self) -> None:
         equipment_columns = {
             "current_status": "VARCHAR(20) NOT NULL DEFAULT 'RUNNING'",
-            "health_status": "VARCHAR(20) NOT NULL DEFAULT 'NORMAL'",
             "last_fault_time": "DATETIME NULL",
             "last_recovered_time": "DATETIME NULL",
             "reason": "VARCHAR(255) NULL",
@@ -68,11 +67,6 @@ def _align_equipment_mysql_types(self) -> None:
                 "ENUM('RUNNING','IDLE','STOPPED','FAULT','MAINTENANCE') "
                 "NOT NULL DEFAULT 'RUNNING'",
             )
-        if equipment_types.get("health_status") != "ENUM":
-            statements.append(
-                "ALTER TABLE equipment MODIFY COLUMN health_status "
-                "ENUM('NORMAL','WARNING','CRITICAL') NOT NULL DEFAULT 'NORMAL'",
-            )
         if statements:
             with self.engine.begin() as conn:
                 for statement in statements:

From 06f325b622395713de72b6d9baabf2cf6685c964 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Thu, 2 Jul 2026 13:34:19 +0900
Subject: [PATCH 077/148] =?UTF-8?q?fix:=20=EA=B2=80=EC=82=AC=20=EC=9C=84?=
 =?UTF-8?q?=ED=97=98=EB=8F=84=20=EB=A0=88=ED=8F=AC=EC=A7=80=ED=86=A0?=
 =?UTF-8?q?=EB=A6=AC=20=EC=9D=B4=EC=A0=84=20=ED=8C=8C=EC=9D=BC=EB=A1=9C=20?=
 =?UTF-8?q?=EB=B3=80=EA=B2=BD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../quality_risk_trend_repository.py          | 84 ++++++-------------
 1 file changed, 27 insertions(+), 57 deletions(-)

diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index b10efaa..067f441 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -5,8 +5,8 @@
 from urllib.parse import quote_plus
 
 from app.kafka.consumer import create_consumer
-from app.kafka.topics import QUALITY_INSPECTION_RISK_HISTORY
-from app.kafka.options import RISK_HISTORY_GROUP
+from app.kafka.topics import QUALITY_INSPECTION_RISK_TREND
+from app.kafka.options import RISK_TREND_GROUP
 
 
 def run():
@@ -14,7 +14,9 @@ def run():
     load_dotenv()
 
     DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
+    DB_PASSWORD = quote_plus(
+        os.getenv("DB_PASSWORD")
+    )
     DB_HOST = os.getenv("DB_HOST")
     DB_PORT = os.getenv("DB_PORT")
     MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
@@ -30,8 +32,8 @@ def run():
     )
 
     consumer = create_consumer(
-        topic=QUALITY_INSPECTION_RISK_HISTORY,
-        group_id=RISK_HISTORY_GROUP
+        topic=QUALITY_INSPECTION_RISK_TREND,
+        group_id=RISK_TREND_GROUP
     )
 
     try:
@@ -40,78 +42,46 @@ def run():
 
             row = msg.value
 
-            # Producer에서 전달되는 값
-            inspection_type = row["inspection_type"]
-            inspection_date = row["inspection_date"]
-            risk_score = row["risk_score"]
-
-            # Repository에서 생성
-            start_time = f"{inspection_date} 00:00:00"
-            end_time = f"{inspection_date} 23:59:59"
-
-            # 같은 날짜 + 같은 검사 타입 존재 여부 확인
             exists = pd.read_sql(
                 text("""
                     SELECT COUNT(*) AS cnt
-                    FROM inspection_risk_history
-                    WHERE inspection_type = :inspection_type
-                      AND DATE(start_time) = DATE(:start_time)
+                    FROM inspection_risk_trend
+                    WHERE risk_level = :risk_level
+                    AND DATE(created_at) = DATE(:created_at)
                 """),
                 con=main_engine,
                 params={
-                    "inspection_type": inspection_type,
-                    "start_time": start_time
+                    "risk_level": row["risk_level"],
+                    "created_at": row["created_at"]
                 }
             )
 
-            # 이미 존재하면 UPDATE
             if exists.iloc[0]["cnt"] > 0:
 
                 with main_engine.begin() as conn:
 
                     conn.execute(
                         text("""
-                            UPDATE inspection_risk_history
+                            UPDATE
+                                inspection_risk_trend
                             SET
-                                risk_score = :risk_score,
-                                end_time = :end_time
-                            WHERE inspection_type = :inspection_type
-                              AND DATE(start_time) = DATE(:start_time)
+                                risk_count=:risk_count,
+                                risk_ratio=:risk_ratio
+                            WHERE
+                                risk_level=:risk_level
+                            AND DATE(created_at)
+                                =
+                                DATE(:created_at)
                         """),
-                        {
-                            "risk_score": risk_score,
-                            "end_time": end_time,
-                            "inspection_type": inspection_type,
-                            "start_time": start_time
-                        }
+                        row
                     )
 
             else:
 
-                # inspection_round 자동 생성
-                with main_engine.begin() as conn:
-
-                    inspection_round = conn.execute(
-                        text("""
-                            SELECT COALESCE(MAX(inspection_round), 0) + 1
-                            FROM inspection_risk_history
-                            WHERE inspection_type = :inspection_type
-                        """),
-                        {
-                            "inspection_type": inspection_type
-                        }
-                    ).scalar()
-
-                df = pd.DataFrame([{
-                    "inspection_type": inspection_type,
-                    "inspection_round": inspection_round,
-                    "risk_score": risk_score,
-                    "start_time": start_time,
-                    "end_time": end_time
-                }])
+                df = pd.DataFrame([row])
 
                 df.to_sql(
-                    name="inspection_risk_history",
+                    name="inspection_risk_trend",
                     con=main_engine,
                     if_exists="append",
                     index=False
@@ -119,12 +89,12 @@ def run():
 
     except Exception as e:
 
-        import traceback
-        traceback.print_exc()
+        print(f"오류 발생 : {e}")
 
     finally:
 
-        print("risk-history 종료")
+        print("risk-trend 종료")
+
         consumer.close()
 
 

From ab34d3c30e08c7425fef28fb5addcc224af33f6a Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 14:41:18 +0900
Subject: [PATCH 078/148] fix : DB session out code update

---
 app/db.py |  74 ++++++++++++++++++
 main.py   | 224 ++++++++++++++++++++++++------------------------------
 2 files changed, 175 insertions(+), 123 deletions(-)
 create mode 100644 app/db.py

diff --git a/app/db.py b/app/db.py
new file mode 100644
index 0000000..235aece
--- /dev/null
+++ b/app/db.py
@@ -0,0 +1,74 @@
+# app/db.py
+
+import os
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker, scoped_session
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# =========================
+# DATABASE URL
+# =========================
+DATABASE_URL = os.getenv("MAIN_DATABASE_URL")
+
+if not DATABASE_URL:
+    raise Exception("MAIN_DATABASE_URL is not set")
+
+
+# =========================
+# ENGINE (SINGLETON)
+# =========================
+engine = create_engine(
+    DATABASE_URL,
+    echo=False,
+
+    # ===== connection pool =====
+    pool_size=10,            # 기본 유지 커넥션
+    max_overflow=20,         # 추가 커넥션 허용
+    pool_timeout=30,         # 대기 시간
+    pool_recycle=3600,       # 1시간마다 재생성 (MySQL 안정성)
+    pool_pre_ping=True       # 죽은 connection 체크
+
+    # (옵션) 멀티스레드 안정성
+    # connect_args={"check_same_thread": False}  # SQLite일 때만
+)
+
+
+# =========================
+# SESSION FACTORY
+# =========================
+SessionLocal = sessionmaker(
+    autocommit=False,
+    autoflush=False,
+    bind=engine
+)
+
+# thread-safe session (Kafka / scheduler 환경에서 중요)
+Session = scoped_session(SessionLocal)
+
+
+# =========================
+# DEPENDENCY HELPERS
+# =========================
+
+def get_session():
+    """
+    권장 사용 방식:
+    with get_session() as session:
+        session.execute(...)
+    """
+    return Session()
+
+
+def dispose_engine():
+    """
+    graceful shutdown 시 사용
+    connection pool 전체 종료
+    """
+    try:
+        Session.remove()
+        engine.dispose()
+        print("🗄️ DB engine disposed successfully")
+    except Exception as e:
+        print("DB dispose error:", e)
\ No newline at end of file
diff --git a/main.py b/main.py
index e5d4f0f..dea3511 100644
--- a/main.py
+++ b/main.py
@@ -1,8 +1,9 @@
 # app/main.py
-from app.main import app
 
 import threading
 import time
+import signal
+import sys
 
 from app.scheduler.quality.drive_detail_producer import run as drive_producer
 from app.scheduler.quality.status_detail_producer import run as status_producer
@@ -11,155 +12,132 @@
 from app.scheduler.quality.process_producer import run as process_producer
 from app.scheduler.quality.stomp_client import run as stomp_client
 
-from app.repository.quality_drive_detail_repository import (
-    run as drive_repository
-)
+from app.repository.quality_drive_detail_repository import run as drive_repository
+from app.repository.quality_status_detail_repository import run as status_repository
+from app.repository.quality_risk_history_repository import run as history_repository
+from app.repository.quality_risk_trend_repository import run as trend_repository
+from app.repository.quality_process_repository import run as process_repository
+from app.repository.quality_summary_repository import run as summary_repository
 
-from app.repository.quality_status_detail_repository import (
-    run as status_repository
-)
+# DB engine (너 프로젝트에 있는 위치로 수정 필요)
+from app.db import engine
 
-from app.repository.quality_risk_history_repository import (
-    run as history_repository
-)
 
-from app.repository.quality_risk_trend_repository import (
-    run as trend_repository
-)
+# =========================
+# STOP FLAG (핵심)
+# =========================
+stop_event = threading.Event()
+threads = []
 
-from app.repository.quality_process_repository import (
-    run as process_repository
-)
-
-from app.repository.quality_summary_repository import (
-    run as summary_repository
-)
 
+# =========================
+# THREAD WRAPPER
+# =========================
 def start_thread(name, target):
+    print(f"[START] {name}")
 
-    print(f"[START] {name} 스레드 시작")
+    def wrapped():
+        try:
+            # 각 worker가 stop_event를 받도록 확장 가능
+            target(stop_event)
+        except TypeError:
+            # 기존 코드 호환 (stop_event 안 받는 경우)
+            target()
 
     thread = threading.Thread(
-        target=target,
+        target=wrapped,
         daemon=True,
         name=name
     )
 
     thread.start()
-
     return thread
 
 
-if __name__ == "__main__":
+# =========================
+# CLEAN SHUTDOWN
+# =========================
+def cleanup():
+    print("\n🧹 Graceful Shutdown 시작...")
 
-    print("=" * 60)
-    print("AI-Service 시작")
-    print("=" * 60)
+    # 1. stop signal 전달
+    stop_event.set()
 
-    threads = []
+    # 2. DB connection pool 종료
+    try:
+        engine.dispose()
+        print("🗄️ DB engine disposed")
+    except Exception as e:
+        print("DB dispose error:", e)
 
-    # Producer
-    threads.append(
-        start_thread(
-            "Drive Detail Kafka 전송",
-            drive_producer
-        )
-    )
+    # 3. thread 종료 대기
+    print("⏳ threads join 중...")
+    for t in threads:
+        try:
+            t.join(timeout=5)
+        except Exception:
+            pass
 
-    threads.append(
-        start_thread(
-            "Status Detail Kafka 전송",
-            status_producer
-        )
-    )
+    print("✅ Shutdown 완료")
 
-    threads.append(
-        start_thread(
-            "Risk History Kafka 전송",
-            history_producer
-        )
-    )
 
-    threads.append(
-        start_thread(
-            "Risk Trend Kafka 전송",
-            trend_producer
-        )
-    )
+# =========================
+# SIGNAL HANDLER
+# =========================
+def handle_exit(signum, frame):
+    print("\n⚠️ 종료 신호 감지 (Ctrl+C)")
+    cleanup()
+    sys.exit(0)
 
-    threads.append(
-        start_thread(
-            "Process Kafka 전송",
-            process_producer
-        )
-    )
 
-    # Consumer
-    threads.append(
-        start_thread(
-            "Drive Detail 메시지 처리",
-            drive_repository
-        )
-    )
+signal.signal(signal.SIGINT, handle_exit)
+signal.signal(signal.SIGTERM, handle_exit)
 
-    threads.append(
-        start_thread(
-            "Status Detail 메시지 처리",
-            status_repository
-        )
-    )
 
-    threads.append(
-        start_thread(
-            "Risk History 메시지 처리",
-            history_repository
-        )
-    )
-
-    threads.append(
-        start_thread(
-            "Risk Trend 메시지 처리",
-            trend_repository
-        )
-    )
-
-    threads.append(
-        start_thread(
-            "Process 메시지 처리",
-            process_repository
-        )
-    )
-
-    threads.append(
-        start_thread(
-            "Inspection Summary 실시간 집계",
-            summary_repository
-        )
-    )
-
-    threads.append(
-        start_thread(
-            "WebSocket STOMP Listener",
-            stomp_client
-        )
-    )
+# =========================
+# MAIN
+# =========================
+if __name__ == "__main__":
 
-    print("\n실행 중인 서비스")
-    print("- Drive Detail Kafka 전송")
-    print("- Status Detail Kafka 전송")
-    print("- Risk History Kafka 전송")
-    print("- Risk Trend Kafka 전송")
-    print("- Process Kafka 전송")
-    print("- summary data 전송")
-
-    print("- Drive Detail 메시지 처리")
-    print("- Status Detail 메시지 처리")
-    print("- Risk History 메시지 처리")
-    print("- Risk Trend 메시지 처리")
-    print("- Process 메시지 처리")
-
-    print("\nAI-Service 정상 실행 완료")
+    print("=" * 60)
+    print("🚀 AI-Service 시작")
     print("=" * 60)
 
-    while True:
-        time.sleep(60)
\ No newline at end of file
+    # =====================
+    # PRODUCERS
+    # =====================
+    threads.append(start_thread("Drive Producer", drive_producer))
+    threads.append(start_thread("Status Producer", status_producer))
+    threads.append(start_thread("History Producer", history_producer))
+    threads.append(start_thread("Trend Producer", trend_producer))
+    threads.append(start_thread("Process Producer", process_producer))
+
+    # =====================
+    # CONSUMERS / REPOSITORY
+    # =====================
+    threads.append(start_thread("Drive Consumer", drive_repository))
+    threads.append(start_thread("Status Consumer", status_repository))
+    threads.append(start_thread("History Consumer", history_repository))
+    threads.append(start_thread("Trend Consumer", trend_repository))
+    threads.append(start_thread("Process Consumer", process_repository))
+    threads.append(start_thread("Summary Aggregator", summary_repository))
+
+    # =====================
+    # STOMP
+    # =====================
+    threads.append(start_thread("STOMP Client", stomp_client))
+
+    print("\n📡 서비스 실행 중... (Ctrl+C로 종료)")
+
+    # =====================
+    # BLOCKING LOOP (STOP EVENT 기반)
+    # =====================
+    try:
+        while not stop_event.is_set():
+            time.sleep(1)
+
+    except KeyboardInterrupt:
+        handle_exit(None, None)
+
+    finally:
+        cleanup()
\ No newline at end of file

From 208d1164e49cb6d0eb44639c5bd8c10eb4479c16 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 14:45:25 +0900
Subject: [PATCH 079/148] fix : DB session out code update 2

---
 app/db.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/app/db.py b/app/db.py
index 235aece..cc35671 100644
--- a/app/db.py
+++ b/app/db.py
@@ -4,13 +4,25 @@
 from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker, scoped_session
 from dotenv import load_dotenv
+from urllib.parse import quote_plus
 
 load_dotenv()
 
 # =========================
 # DATABASE URL
 # =========================
-DATABASE_URL = os.getenv("MAIN_DATABASE_URL")
+DB_USER = os.getenv("DB_USER")
+DB_PASSWORD = quote_plus(
+    os.getenv("DB_PASSWORD")
+)
+DB_HOST = os.getenv("DB_HOST")
+DB_PORT = os.getenv("DB_PORT")
+MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
+DATABASE_URL = (
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+)
 
 if not DATABASE_URL:
     raise Exception("MAIN_DATABASE_URL is not set")

From fa5d837adfe3b8e00a87136bfb4f3d89c987384b Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 2 Jul 2026 14:47:04 +0900
Subject: [PATCH 080/148] del : stomp websocket client del

---
 main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main.py b/main.py
index dea3511..9319ab3 100644
--- a/main.py
+++ b/main.py
@@ -125,7 +125,7 @@ def handle_exit(signum, frame):
     # =====================
     # STOMP
     # =====================
-    threads.append(start_thread("STOMP Client", stomp_client))
+    #threads.append(start_thread("STOMP Client", stomp_client))
 
     print("\n📡 서비스 실행 중... (Ctrl+C로 종료)")
 

From 33a23a6f4d688891f48713fa62c74f5dbde0555b Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 3 Jul 2026 09:28:23 +0900
Subject: [PATCH 081/148] feat : ai_manual package add

---
 app/ai_manual/api/manual_router.py       | 0
 app/ai_manual/data/system_description.md | 0
 app/ai_manual/prompt/prompt_template.py  | 0
 app/ai_manual/rag/vector_store.py        | 0
 app/ai_manual/schema/request.py          | 0
 app/ai_manual/schema/response.py         | 0
 app/ai_manual/service/manual_service.py  | 0
 7 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 app/ai_manual/api/manual_router.py
 create mode 100644 app/ai_manual/data/system_description.md
 create mode 100644 app/ai_manual/prompt/prompt_template.py
 create mode 100644 app/ai_manual/rag/vector_store.py
 create mode 100644 app/ai_manual/schema/request.py
 create mode 100644 app/ai_manual/schema/response.py
 create mode 100644 app/ai_manual/service/manual_service.py

diff --git a/app/ai_manual/api/manual_router.py b/app/ai_manual/api/manual_router.py
new file mode 100644
index 0000000..e69de29
diff --git a/app/ai_manual/data/system_description.md b/app/ai_manual/data/system_description.md
new file mode 100644
index 0000000..e69de29
diff --git a/app/ai_manual/prompt/prompt_template.py b/app/ai_manual/prompt/prompt_template.py
new file mode 100644
index 0000000..e69de29
diff --git a/app/ai_manual/rag/vector_store.py b/app/ai_manual/rag/vector_store.py
new file mode 100644
index 0000000..e69de29
diff --git a/app/ai_manual/schema/request.py b/app/ai_manual/schema/request.py
new file mode 100644
index 0000000..e69de29
diff --git a/app/ai_manual/schema/response.py b/app/ai_manual/schema/response.py
new file mode 100644
index 0000000..e69de29
diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
new file mode 100644
index 0000000..e69de29

From f6ff2567123af2bcb4d94d9c662d06b6a95cc2c2 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 3 Jul 2026 10:52:53 +0900
Subject: [PATCH 082/148] feat : rag/router/prompt/schema/service code add

---
 app/ai_manual/api/manual_router.py            |  28 ++++
 app/ai_manual/prompt/prompt_template.py       | 129 ++++++++++++++
 app/ai_manual/rag/vector_store.py             |  58 +++++++
 .../repository/alert_event_repository.py      | 102 ++++++++++++
 app/ai_manual/schema/request.py               | 118 +++++++++++++
 app/ai_manual/schema/response.py              |  87 ++++++++++
 app/ai_manual/service/manual_service.py       | 157 ++++++++++++++++++
 requirements.txt                              |  11 +-
 8 files changed, 689 insertions(+), 1 deletion(-)
 create mode 100644 app/ai_manual/repository/alert_event_repository.py

diff --git a/app/ai_manual/api/manual_router.py b/app/ai_manual/api/manual_router.py
index e69de29..44dc6d8 100644
--- a/app/ai_manual/api/manual_router.py
+++ b/app/ai_manual/api/manual_router.py
@@ -0,0 +1,28 @@
+from fastapi import APIRouter, HTTPException
+
+from ai_manual.service.manual_service import ManualService
+
+router = APIRouter(
+    prefix="/manual",
+    tags=["AI Manual"]
+)
+
+manual_service = ManualService()
+
+
+@router.get("/current")
+def get_current_manual():
+    """
+    현재 가장 높은 우선순위의 Critical Event를 조회하여
+    AI에게 전달할 Request 데이터를 생성한다.
+    """
+
+    result = manual_service.get_current_manual_request()
+
+    if result is None:
+        raise HTTPException(
+            status_code=404,
+            detail="현재 처리할 Critical Event가 없습니다."
+        )
+
+    return result
\ No newline at end of file
diff --git a/app/ai_manual/prompt/prompt_template.py b/app/ai_manual/prompt/prompt_template.py
index e69de29..18f5d76 100644
--- a/app/ai_manual/prompt/prompt_template.py
+++ b/app/ai_manual/prompt/prompt_template.py
@@ -0,0 +1,129 @@
+# ai_manual/prompt/prompt_template.py
+
+from langchain_core.prompts import ChatPromptTemplate
+
+
+SYSTEM_PROMPT = """
+당신은 자동차 스마트팩토리의 AI 유지보수 전문가 'Watchy'입니다.
+
+## 역할
+
+당신의 역할은 생산 설비에서 발생한 Critical Event를 분석하고,
+담당자의 숙련도에 맞는 조치 매뉴얼을 생성하는 것입니다.
+
+당신은 다음 정보를 기반으로 답변합니다.
+
+1. 현재 발생한 Critical Event
+2. 스마트팩토리 시스템 정보
+3. RAG 검색 결과
+4. 담당자의 숙련도(Junior / Senior)
+
+--------------------------------------------
+
+## 답변 규칙
+
+1.
+반드시 전달받은 정보만 사용하십시오.
+
+2.
+추측하지 마십시오.
+
+3.
+RAG에 없는 내용은
+"관련 정보가 존재하지 않습니다."
+라고 작성하십시오.
+
+4.
+설비명을 임의로 변경하지 마십시오.
+
+5.
+Error Code를 임의 생성하지 마십시오.
+
+6.
+반드시 단계별 조치 방법을 작성하십시오.
+
+7.
+반드시 안전 관련 주의사항을 포함하십시오.
+
+8.
+항상 JSON 형식으로만 응답하십시오.
+
+--------------------------------------------
+
+## Junior 담당자
+
+Junior 담당자에게는
+
+- 쉬운 표현 사용
+- 작업 순서를 상세히 설명
+- 전문용어 최소화
+- 위험한 작업은 수행하지 않도록 안내
+
+--------------------------------------------
+
+## Senior 담당자
+
+Senior 담당자에게는
+
+- 원인 분석 포함
+- 예방 방법 포함
+- 추가 점검 항목 포함
+- 가능한 원인까지 설명
+
+--------------------------------------------
+
+## 반드시 반환해야 하는 JSON 형식
+
+{
+    "title": "...",
+    "summary": "...",
+    "difficulty": "...",
+    "estimated_time": "...",
+    "precautions": [],
+    "steps": [],
+    "completion_check": [],
+    "escalation": "...",
+    "prevention": []
+}
+
+JSON 외의 어떠한 문장도 출력하지 마십시오.
+"""
+
+
+HUMAN_PROMPT = """
+# 현재 Critical Event
+
+{critical_event}
+
+------------------------------------------------
+
+# 담당자
+
+{operator}
+
+------------------------------------------------
+
+# 스마트팩토리 시스템 정보
+
+{factory_context}
+
+------------------------------------------------
+
+# RAG 검색 결과
+
+{rag_context}
+
+------------------------------------------------
+
+위 정보를 기반으로
+담당자의 숙련도에 맞는
+조치 매뉴얼을 생성하십시오.
+"""
+
+
+manual_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", SYSTEM_PROMPT),
+        ("human", HUMAN_PROMPT),
+    ]
+)
\ No newline at end of file
diff --git a/app/ai_manual/rag/vector_store.py b/app/ai_manual/rag/vector_store.py
index e69de29..70b92b8 100644
--- a/app/ai_manual/rag/vector_store.py
+++ b/app/ai_manual/rag/vector_store.py
@@ -0,0 +1,58 @@
+# ai_manual/rag/vector_store.py
+
+from typing import List
+
+from ai_manual.schema.request import CriticalEvent
+
+
+class VectorStore:
+    """
+    RAG 검색 클래스
+
+    현재는 Mock 데이터를 반환하며,
+    추후 FAISS / Chroma / OpenSearch 등으로
+    교체할 수 있도록 인터페이스 역할을 수행한다.
+    """
+
+    def __init__(self):
+        pass
+
+    def search(
+        self,
+        event: CriticalEvent,
+        top_k: int = 5
+    ) -> List[str]:
+        """
+        Critical Event를 기반으로
+        관련 문서를 검색한다.
+        """
+
+        documents = []
+
+        # 공정 정보
+        documents.append(
+            f"{event.process_code} 공정은 자동차 제조의 핵심 공정입니다."
+        )
+
+        # 설비 정보
+        if event.equipment is not None:
+            documents.append(
+                f"{event.equipment.name} 설비 매뉴얼입니다."
+            )
+
+        # 이벤트 정보
+        documents.append(
+            f"{event.title} 발생 시 안전 절차를 우선 수행합니다."
+        )
+
+        # 위험도
+        documents.append(
+            f"Risk Score는 {event.risk_score}입니다."
+        )
+
+        # 심각도
+        documents.append(
+            f"Severity는 {event.severity}입니다."
+        )
+
+        return documents[:top_k]
\ No newline at end of file
diff --git a/app/ai_manual/repository/alert_event_repository.py b/app/ai_manual/repository/alert_event_repository.py
new file mode 100644
index 0000000..022f0ca
--- /dev/null
+++ b/app/ai_manual/repository/alert_event_repository.py
@@ -0,0 +1,102 @@
+# ai_manual/repository/alert_event_repository.py
+
+import os
+from decimal import Decimal
+from urllib.parse import quote_plus
+
+from dotenv import load_dotenv
+from sqlalchemy import create_engine, text
+
+
+class AlertEventRepository:
+
+    def __init__(self):
+        load_dotenv()
+
+        DB_USER = os.getenv("DB_USER")
+        DB_PASSWORD = quote_plus(
+            os.getenv("DB_PASSWORD")
+        )
+        DB_HOST = os.getenv("DB_HOST")
+        DB_PORT = os.getenv("DB_PORT")
+        MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+
+        MAIN_DATABASE_URL = (
+            f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+            f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+        )
+
+        self.engine = create_engine(
+            MAIN_DATABASE_URL,
+            pool_pre_ping=True
+        )
+
+    def get_highest_priority_event(self):
+        """
+        현재 처리해야 하는 가장 높은 우선순위의
+        Critical Event를 조회한다.
+
+        조건
+        - action_status = PENDING
+        - resolved_at IS NULL
+        - priority_score DESC
+        - created_at ASC
+        """
+
+        query = text("""
+            SELECT
+                log_no,
+                event_id,
+                alert_type,
+                process_code,
+                equipment_id,
+                event_key,
+                risk_score,
+                occurrence_score,
+                detection_score,
+                priority_score,
+                severity,
+                title,
+                contents,
+                action_by,
+                action_status,
+                reason,
+                score_calculated_at,
+                created_at,
+                resolved_at
+            FROM alert_event
+            WHERE action_status = 'PENDING'
+              AND resolved_at IS NULL
+            ORDER BY priority_score DESC,
+                     created_at ASC
+            LIMIT 1
+        """)
+
+        with self.engine.connect() as conn:
+            row = conn.execute(query).mappings().first()
+
+        if row is None:
+            return None
+
+        return self._convert(row)
+
+    def _convert(self, row):
+        """
+        SQLAlchemy RowMapping -> dict
+        Decimal과 datetime을 JSON 직렬화 가능한 형태로 변환
+        """
+
+        result = {}
+
+        for key, value in row.items():
+
+            if isinstance(value, Decimal):
+                result[key] = float(value)
+
+            elif hasattr(value, "isoformat"):
+                result[key] = value.isoformat()
+
+            else:
+                result[key] = value
+
+        return result
\ No newline at end of file
diff --git a/app/ai_manual/schema/request.py b/app/ai_manual/schema/request.py
index e69de29..985e5ae 100644
--- a/app/ai_manual/schema/request.py
+++ b/app/ai_manual/schema/request.py
@@ -0,0 +1,118 @@
+# ai_manual/schema/request.py
+
+from typing import List, Optional
+
+from pydantic import BaseModel, Field
+
+
+class EquipmentInfo(BaseModel):
+    """
+    설비 정보
+    """
+    id: Optional[int] = Field(
+        default=None,
+        description="설비 ID"
+    )
+
+    name: Optional[str] = Field(
+        default=None,
+        description="설비명"
+    )
+
+    type: Optional[str] = Field(
+        default=None,
+        description="설비 종류"
+    )
+
+
+class CriticalEvent(BaseModel):
+    """
+    AI가 처리해야 하는 현재 Critical Event
+    """
+
+    event_id: str = Field(
+        description="Kafka Event ID"
+    )
+
+    priority_score: float = Field(
+        description="우선순위 점수"
+    )
+
+    risk_score: float = Field(
+        description="위험도 점수"
+    )
+
+    severity: str = Field(
+        description="심각도"
+    )
+
+    alert_type: str = Field(
+        description="PROCESS / EQUIPMENT"
+    )
+
+    process_code: str = Field(
+        description="공정 코드"
+    )
+
+    equipment: Optional[EquipmentInfo] = None
+
+    title: str = Field(
+        description="알람 제목"
+    )
+
+    description: str = Field(
+        description="알람 상세 내용"
+    )
+
+    occurred_at: str = Field(
+        description="발생 시간"
+    )
+
+
+class OperatorInfo(BaseModel):
+    """
+    담당자 정보
+    """
+
+    grade: str = Field(
+        description="Junior 또는 Senior"
+    )
+
+
+class RagContext(BaseModel):
+    """
+    RAG 검색 결과
+    """
+
+    documents: List[str] = Field(
+        default_factory=list,
+        description="Vector Search 결과"
+    )
+
+
+class FactoryContext(BaseModel):
+    """
+    공장 기본 정보
+    """
+
+    system_name: str = Field(
+        description="시스템 이름"
+    )
+
+    description: str = Field(
+        description="스마트팩토리 설명"
+    )
+
+
+class ManualRequest(BaseModel):
+    """
+    GPT에게 전달하는 최종 Request
+    """
+
+    critical_event: CriticalEvent
+
+    operator: OperatorInfo
+
+    factory_context: FactoryContext
+
+    rag_context: RagContext
\ No newline at end of file
diff --git a/app/ai_manual/schema/response.py b/app/ai_manual/schema/response.py
index e69de29..3108040 100644
--- a/app/ai_manual/schema/response.py
+++ b/app/ai_manual/schema/response.py
@@ -0,0 +1,87 @@
+# ai_manual/schema/response.py
+
+from typing import List
+
+from pydantic import BaseModel, Field
+
+
+class ManualStep(BaseModel):
+    """
+    조치 절차
+    """
+
+    step: int = Field(
+        description="순서"
+    )
+
+    action: str = Field(
+        description="수행할 작업"
+    )
+
+    reason: str = Field(
+        description="작업 이유"
+    )
+
+    warning: str = Field(
+        description="주의사항"
+    )
+
+
+class CompletionCheck(BaseModel):
+    """
+    조치 완료 확인 항목
+    """
+
+    item: str = Field(
+        description="확인 항목"
+    )
+
+    expected_result: str = Field(
+        description="정상 상태"
+    )
+
+
+class ManualResponse(BaseModel):
+    """
+    GPT가 반환하는 최종 Manual
+    """
+
+    title: str = Field(
+        description="매뉴얼 제목"
+    )
+
+    summary: str = Field(
+        description="현재 상황 요약"
+    )
+
+    difficulty: str = Field(
+        description="Junior 또는 Senior"
+    )
+
+    estimated_time: str = Field(
+        description="예상 작업 시간"
+    )
+
+    precautions: List[str] = Field(
+        default_factory=list,
+        description="작업 전 주의사항"
+    )
+
+    steps: List[ManualStep] = Field(
+        default_factory=list,
+        description="조치 절차"
+    )
+
+    completion_check: List[CompletionCheck] = Field(
+        default_factory=list,
+        description="조치 완료 확인"
+    )
+
+    escalation: str = Field(
+        description="상위 담당자 호출 기준"
+    )
+
+    prevention: List[str] = Field(
+        default_factory=list,
+        description="재발 방지 방법"
+    )
\ No newline at end of file
diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index e69de29..79a0852 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -0,0 +1,157 @@
+# ai_manual/service/manual_service.py
+
+import os
+
+from dotenv import load_dotenv
+
+from langchain_openai import ChatOpenAI
+
+from ai_manual.prompt.prompt_template import manual_prompt
+
+from ai_manual.rag.vector_store import VectorStore
+
+from ai_manual.repository.alert_event_repository import (
+    AlertEventRepository
+)
+
+from ai_manual.schema.request import (
+    CriticalEvent,
+    EquipmentInfo,
+    FactoryContext,
+    ManualRequest,
+    OperatorInfo,
+    RagContext
+)
+
+from ai_manual.schema.response import ManualResponse
+
+
+class ManualService:
+
+    def __init__(self):
+
+        load_dotenv()
+
+        self.repository = AlertEventRepository()
+
+        self.vector_store = VectorStore()
+
+        self.llm = ChatOpenAI(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4.1",
+            temperature=0.2
+        ).with_structured_output(
+            ManualResponse
+        )
+
+    def generate_manual(
+        self,
+        operator_grade: str = "Junior"
+    ) -> ManualResponse | None:
+
+        event = self.repository.get_highest_priority_event()
+
+        if event is None:
+            return None
+
+        request = self._build_request(
+            event,
+            operator_grade
+        )
+
+        prompt = manual_prompt.invoke(
+            {
+                "critical_event":
+                    request.critical_event.model_dump_json(
+                        indent=2,
+                        ensure_ascii=False
+                    ),
+
+                "operator":
+                    request.operator.model_dump_json(
+                        indent=2,
+                        ensure_ascii=False
+                    ),
+
+                "factory_context":
+                    request.factory_context.model_dump_json(
+                        indent=2,
+                        ensure_ascii=False
+                    ),
+
+                "rag_context":
+                    "\n".join(
+                        request.rag_context.documents
+                    )
+            }
+        )
+
+        response = self.llm.invoke(prompt)
+
+        return response
+
+    def _build_request(
+        self,
+        event: dict,
+        operator_grade: str
+    ) -> ManualRequest:
+
+        equipment = None
+
+        if event["equipment_id"] is not None:
+
+            equipment = EquipmentInfo(
+                id=event["equipment_id"],
+                name=f"Equipment-{event['equipment_id']}",
+                type="Industrial Equipment"
+            )
+
+        critical_event = CriticalEvent(
+            event_id=event["event_id"],
+            priority_score=event["priority_score"],
+            risk_score=event["risk_score"],
+            severity=event["severity"],
+            alert_type=event["alert_type"],
+            process_code=event["process_code"],
+            equipment=equipment,
+            title=event["title"],
+            description=event["contents"],
+            occurred_at=event["created_at"]
+        )
+
+        rag_documents = self.vector_store.search(
+            critical_event
+        )
+
+        return ManualRequest(
+
+            critical_event=critical_event,
+
+            operator=OperatorInfo(
+                grade=operator_grade
+            ),
+
+            factory_context=FactoryContext(
+                system_name="AIMS Smart Factory",
+                description="""
+자동차 제조 스마트팩토리입니다.
+
+PRESS
+BODY
+PAINT
+ASSEMBLY
+
+MES
+Kafka
+AI-Service
+Main-Service
+PLC
+Robot
+Vision Inspection
+"""
+            ),
+
+            rag_context=RagContext(
+                documents=rag_documents
+            )
+        )
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 04b82c9..7deb745 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,9 +8,18 @@ python-dotenv>=1.0.1,<2.0.0
 sqlalchemy>=2.0.36,<3.0.0
 pymysql>=1.1.1,<2.0.0
 
-# LLM & External API
+# LLM & LangChain
 httpx>=0.28.0
 openai>=1.59.0,<2.0.0
+langchain>=0.3.0
+langchain-core>=0.3.0
+langchain-openai>=0.2.0
+langchain-community>=0.3.0
+tiktoken>=0.8.0
+
+# Vector Store (RAG)
+opensearch-py>=2.7.0
+faiss-cpu>=1.9.0
 
 # Kafka
 aiokafka>=0.12.0

From d86547f6184e629cb29732ce352e4ffe2067d28e Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 3 Jul 2026 10:59:50 +0900
Subject: [PATCH 083/148] api/router ai-manual add

---
 app/ai_manual/api/manual_router.py | 28 ----------------------------
 app/api/router.py                  |  2 ++
 app/api/routers/manual.py          | 28 ++++++++++++++++++++++++++++
 3 files changed, 30 insertions(+), 28 deletions(-)
 delete mode 100644 app/ai_manual/api/manual_router.py
 create mode 100644 app/api/routers/manual.py

diff --git a/app/ai_manual/api/manual_router.py b/app/ai_manual/api/manual_router.py
deleted file mode 100644
index 44dc6d8..0000000
--- a/app/ai_manual/api/manual_router.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from fastapi import APIRouter, HTTPException
-
-from ai_manual.service.manual_service import ManualService
-
-router = APIRouter(
-    prefix="/manual",
-    tags=["AI Manual"]
-)
-
-manual_service = ManualService()
-
-
-@router.get("/current")
-def get_current_manual():
-    """
-    현재 가장 높은 우선순위의 Critical Event를 조회하여
-    AI에게 전달할 Request 데이터를 생성한다.
-    """
-
-    result = manual_service.get_current_manual_request()
-
-    if result is None:
-        raise HTTPException(
-            status_code=404,
-            detail="현재 처리할 Critical Event가 없습니다."
-        )
-
-    return result
\ No newline at end of file
diff --git a/app/api/router.py b/app/api/router.py
index 91e5b1d..86e661c 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -8,6 +8,7 @@
     process,
     process_analysis_ws,
     root,
+    manual,
 )
 
 api_router = APIRouter()
@@ -18,3 +19,4 @@
 api_router.include_router(process_analysis_ws.router)
 api_router.include_router(manufacturing_event.router)
 api_router.include_router(ml_dataset.router)
+api_router.include_router(manual.router)
diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
new file mode 100644
index 0000000..494c503
--- /dev/null
+++ b/app/api/routers/manual.py
@@ -0,0 +1,28 @@
+# app/api/routers/manual.py
+
+from fastapi import APIRouter, HTTPException
+
+from app.ai_manual.service.manual_service import ManualService
+
+router = APIRouter(
+    prefix="/manual",
+    tags=["AI Manual"]
+)
+
+service = ManualService()
+
+
+@router.get("")
+def generate_manual():
+
+    result = service.generate_manual(
+        operator_grade="Junior"
+    )
+
+    if result is None:
+        raise HTTPException(
+            status_code=404,
+            detail="처리할 이벤트가 없습니다."
+        )
+
+    return result
\ No newline at end of file

From 1e99e85f47cb0c573eb01dc22f2ac25ec2a7f426 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 3 Jul 2026 11:08:40 +0900
Subject: [PATCH 084/148] feat : app/worker_main.py app/launcher.py add

---
 app/launcher.py               | 30 ++++++++++++++++++++++++++++++
 main.py => app/worker_main.py |  2 +-
 2 files changed, 31 insertions(+), 1 deletion(-)
 create mode 100644 app/launcher.py
 rename main.py => app/worker_main.py (99%)

diff --git a/app/launcher.py b/app/launcher.py
new file mode 100644
index 0000000..23eeb92
--- /dev/null
+++ b/app/launcher.py
@@ -0,0 +1,30 @@
+import multiprocessing
+import uvicorn
+
+from app.worker_main import run_worker
+from app.main import app
+
+
+def run_api():
+    uvicorn.run(
+        app,
+        host="0.0.0.0",
+        port=8000
+    )
+
+
+if __name__ == "__main__":
+
+    worker = multiprocessing.Process(
+        target=run_worker
+    )
+
+    api = multiprocessing.Process(
+        target=run_api
+    )
+
+    worker.start()
+    api.start()
+
+    worker.join()
+    api.join()
\ No newline at end of file
diff --git a/main.py b/app/worker_main.py
similarity index 99%
rename from main.py
rename to app/worker_main.py
index 9319ab3..9662d48 100644
--- a/main.py
+++ b/app/worker_main.py
@@ -1,4 +1,4 @@
-# app/main.py
+# app/worker_main.py
 
 import threading
 import time

From 5160b8fdcd90e9ac43dbbcb21a6dd8e61b4a8999 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 3 Jul 2026 11:18:24 +0900
Subject: [PATCH 085/148] data/system_description.md add

---
 app/ai_manual/data/system_description.md | 136 +++++++++++++++++++++++
 1 file changed, 136 insertions(+)

diff --git a/app/ai_manual/data/system_description.md b/app/ai_manual/data/system_description.md
index e69de29..0eb7537 100644
--- a/app/ai_manual/data/system_description.md
+++ b/app/ai_manual/data/system_description.md
@@ -0,0 +1,136 @@
+# AIMS Smart Factory
+
+## 프로젝트 소개
+
+AIMS Smart Factory는 자동차 제조 공정을 실시간으로 모니터링하는 AI 기반 스마트팩토리 시스템이다.
+
+Watchy AI는 공정 이상이 발생했을 때
+현장 담당자에게 조치 매뉴얼을 제공하는 역할을 수행한다.
+
+---
+
+## 제조 공정
+
+PRESS
+
+차체 패널을 프레스 설비로 성형한다.
+
+BODY
+
+산업용 Robot이 차체를 용접한다.
+
+PAINT
+
+차체를 도장한다.
+
+ASSEMBLY
+
+부품을 조립한다.
+
+---
+
+## 설비
+
+Robot
+
+Conveyor
+
+Vision Camera
+
+PLC
+
+Sensor
+
+AGV
+
+---
+
+## 시스템 구조
+
+MES
+
+↓
+
+Kafka
+
+↓
+
+Main Backend Service
+
+↓
+
+Priority Score 계산
+
+↓
+
+MainDB(alert_event)
+
+↓
+
+AI Service
+
+↓
+
+Watchy
+
+↓
+
+React
+
+---
+
+## AI 역할
+
+Watchy는
+
+- 현재 발생한 가장 높은 Priority Event만 분석한다.
+
+- Priority가 해결되면 다음 Priority Event를 분석한다.
+
+- RAG 문서를 기반으로만 답변한다.
+
+- 추측하지 않는다.
+
+---
+
+## 담당자
+
+Junior
+
+현장 초급 담당자
+
+쉬운 설명과 조치를 위한 상세하고 순차적인 구체적 설명 필요
+
+Senior
+
+현장 숙련 담당자
+
+원인 분석 포함한 간단 요약 설명 필요
+
+---
+
+## 이벤트
+
+PROCESS
+
+공정 이상
+
+EQUIPMENT
+
+설비 이상
+
+---
+
+## Severity
+
+DANGER
+
+즉시 조치 필요
+
+WARNING
+
+빠른 확인 필요
+
+CAUTION
+
+모니터링 필요
\ No newline at end of file

From d2b9c06d972c89f1685160c27dca147b43d99be6 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 3 Jul 2026 14:17:35 +0900
Subject: [PATCH 086/148] =?UTF-8?q?fix:=20=EC=A0=9C=EC=A1=B0=20=EC=9D=B4?=
 =?UTF-8?q?=EB=B2=A4=ED=8A=B8=20=EB=8D=B0=EC=9D=B4=ED=84=B0=20=EC=83=9D?=
 =?UTF-8?q?=EC=84=B1=20=EC=8B=9C=20=EC=88=98=EC=B9=98=EA=B0=92=EB=93=A4=20?=
 =?UTF-8?q?=EB=B2=94=EC=9C=84=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../manufacturing_event_json_builder.py       | 148 +++++++++++-------
 1 file changed, 93 insertions(+), 55 deletions(-)

diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index debbf41..54265c5 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -322,6 +322,16 @@ def _build_event(
         is_abnormal: bool,
         equipment_map: dict[str, dict[str, Any]],
     ) -> dict[str, Any]:
+        # 사용자의 요청에 따라 PRESS는 이상 빈도를 낮추고 PAINT, ASSEMBLY는 높인다.
+        # global_index를 활용해 결정론적(deterministic) 난수를 생성한다.
+        if is_abnormal:
+            if process_code == "PRESS":
+                # PRESS는 원래 이상의 20%만 유지
+                is_abnormal = (global_index % 10) < 2
+            elif process_code in {"PAINT", "ASSEMBLY"}:
+                # PAINT, ASSEMBLY는 원래 이상의 80%를 유지 (빈도 높임)
+                is_abnormal = (global_index % 10) < 8
+
         meta = PROCESS_META[process_code]
         # 같은 차량도 공정마다 서로 다른 설비를 사용할 수 있도록 차량 PK와
         # 공정 코드를 함께 사용해 1~5호기를 독립적으로 배정한다.
@@ -352,6 +362,7 @@ def _build_event(
         self._apply_detection_profile(
             process_code=process_code,
             is_abnormal=is_abnormal,
+            global_index=global_index,
             current=current,
             ford=ford,
             vision=vision,
@@ -423,6 +434,7 @@ def _apply_detection_profile(
         *,
         process_code: str,
         is_abnormal: bool,
+        global_index: int,
         current: dict[str, Any],
         ford: dict[str, Any],
         vision: dict[str, Any],
@@ -438,75 +450,100 @@ def _apply_detection_profile(
         target = float(PROCESS_META[process_code]["targetCycleTimeSec"])
         if is_abnormal:
             # 설비 위험 및 불량 전이 위험을 높이는 공통 센서 프로필.
+            # 기존에는 고정값만 넣어 상세 테이블 값이 반복 저장될 수 있었으므로,
+            # global_index 기반 deterministic variation을 더해 재생성 결과는 유지하면서
+            # 이벤트별 수치는 다양하게 만든다.
+            current_variation = global_index % 11
+            vibration_variation = global_index % 13
+            paint_variation = global_index % 10
+            metric_variation = global_index % 8
+
+            rms_ampere = 3.8 + current_variation * 0.13
+            acceleration_g = 0.065 + current_variation * 0.004
+            vibration_score = min(0.99, 0.72 + vibration_variation * 0.018)
+            vibration_rms = 2.35 + vibration_variation * 0.075
+            vibration_peak = 3.55 + vibration_variation * 0.095
+            cycle_time = target + 12.0 + metric_variation * 1.4
+            station_delay = cycle_time - target
+
             current.update(
-                rmsAmpere=4.2,
-                maxAmpere=4.8,
-                minAmpere=3.7,
-                accelerationG=0.085,
+                rmsAmpere=round(rms_ampere, 3),
+                maxAmpere=round(rms_ampere + 0.45 + current_variation * 0.025, 3),
+                minAmpere=round(max(0.0, rms_ampere - 0.35), 3),
+                accelerationG=round(acceleration_g, 4),
             )
             ford.update(
                 label=-1,
-                vibrationScore=0.90,
-                vibrationRms=2.88,
-                vibrationPeak=4.10,
+                vibrationScore=round(vibration_score, 4),
+                vibrationRms=round(vibration_rms, 3),
+                vibrationPeak=round(vibration_peak, 3),
             )
             vision.update(
                 label=1,
-                avgTemperature=54.0,
-                maxTemperature=62.0,
-                minTemperature=45.0,
-                thermalStdTemp=6.0,
-                defectScore=0.90,
-                thicknessValue=132.0,
-                surfaceQualityScore=60.0,
+                avgTemperature=round(50.0 + paint_variation * 0.8, 3),
+                maxTemperature=round(58.0 + paint_variation * 0.9, 3),
+                minTemperature=round(42.0 + paint_variation * 0.5, 3),
+                thermalStdTemp=round(4.5 + paint_variation * 0.25, 3),
+                defectScore=round(min(0.99, 0.65 + paint_variation * 0.025), 4),
+                thicknessValue=round(124.0 + paint_variation * 1.3, 3),
+                surfaceQualityScore=round(max(0.0, 72.0 - paint_variation * 1.8), 3),
             )
             bosch["response"] = 1
             # 병목 위험도도 함께 높아지도록 지연, WIP, 유휴 시간을 보정한다.
             process_metrics.update(
-                cycleTimeSec=target + 16.0,
-                waitingTimeSec=18.0,
-                processingTimeSec=target - 2.0,
-                stationDelaySec=16.0,
-                throughputPerMin=round(60 / (target + 16.0), 3),
-                queueLength=14,
-                wipCount=38,
-                equipmentIdleTimeSec=22.0,
+                cycleTimeSec=round(cycle_time, 3),
+                waitingTimeSec=round(14.0 + metric_variation * 1.1, 3),
+                processingTimeSec=round(max(1.0, target - 4.0 + metric_variation * 0.4), 3),
+                stationDelaySec=round(station_delay, 3),
+                throughputPerMin=round(60 / cycle_time, 3),
+                queueLength=10 + metric_variation,
+                wipCount=30 + metric_variation * 2,
+                equipmentIdleTimeSec=round(18.0 + metric_variation * 1.7, 3),
             )
             return
 
         # 정상 프로필은 PRD 기준 cycle time과 낮은 센서 위험도를 사용한다.
+        # 정상 데이터도 완전 고정값이 아니라 작은 범위에서 흔들리게 한다.
+        current_variation = global_index % 9
+        vibration_variation = global_index % 7
+        paint_variation = global_index % 8
+        metric_variation = global_index % 6
+
+        rms_ampere = 1.55 + current_variation * 0.035
+        cycle_time = target + metric_variation * 0.25
+
         current.update(
-            rmsAmpere=1.7,
-            maxAmpere=1.9,
-            minAmpere=1.5,
-            accelerationG=0.006,
+            rmsAmpere=round(rms_ampere, 3),
+            maxAmpere=round(rms_ampere + 0.18 + current_variation * 0.01, 3),
+            minAmpere=round(max(0.0, rms_ampere - 0.16), 3),
+            accelerationG=round(0.004 + current_variation * 0.0007, 5),
         )
         ford.update(
             label=1,
-            vibrationScore=0.12,
-            vibrationRms=0.32,
-            vibrationPeak=0.58,
+            vibrationScore=round(0.08 + vibration_variation * 0.015, 4),
+            vibrationRms=round(0.22 + vibration_variation * 0.035, 3),
+            vibrationPeak=round(0.42 + vibration_variation * 0.045, 3),
         )
         vision.update(
             label=0,
-            avgTemperature=39.0,
-            maxTemperature=41.0,
-            minTemperature=37.0,
-            thermalStdTemp=0.8,
-            defectScore=0.08,
-            thicknessValue=116.0,
-            surfaceQualityScore=97.0,
+            avgTemperature=round(37.8 + paint_variation * 0.25, 3),
+            maxTemperature=round(40.0 + paint_variation * 0.22, 3),
+            minTemperature=round(36.5 + paint_variation * 0.18, 3),
+            thermalStdTemp=round(0.55 + paint_variation * 0.06, 3),
+            defectScore=round(0.03 + paint_variation * 0.007, 4),
+            thicknessValue=round(113.0 + paint_variation * 0.45, 3),
+            surfaceQualityScore=round(98.5 - paint_variation * 0.3, 3),
         )
         bosch["response"] = 0
         process_metrics.update(
-            cycleTimeSec=target,
-            waitingTimeSec=2.0,
-            processingTimeSec=target - 2.0,
-            stationDelaySec=0.0,
-            throughputPerMin=round(60 / target, 3),
-            queueLength=1,
-            wipCount=4,
-            equipmentIdleTimeSec=0.0,
+            cycleTimeSec=round(cycle_time, 3),
+            waitingTimeSec=round(1.5 + metric_variation * 0.25, 3),
+            processingTimeSec=round(max(1.0, target - 2.5 + metric_variation * 0.15), 3),
+            stationDelaySec=round(max(0.0, cycle_time - target), 3),
+            throughputPerMin=round(60 / cycle_time, 3),
+            queueLength=1 + metric_variation % 3,
+            wipCount=3 + metric_variation,
+            equipmentIdleTimeSec=round(metric_variation * 0.2, 3),
         )
 
     def _forming_row(self, index: int) -> dict[str, Any]:
@@ -759,6 +796,11 @@ def _process_data(
                 expected_steps[1],
                 expected_steps[3],
             ]
+            assembly_variation = car_master_id % 4
+            missing_part_count = (assembly_variation % 3) if has_sequence_error else 0
+            fastening_error_count = (1 + assembly_variation) if has_sequence_error else 0
+            sequence_error_count = (1 + (assembly_variation % 2)) if has_sequence_error else 0
+
             return {
                 "assembly": {
                     "expectedSequence": expected_sequence,
@@ -767,13 +809,9 @@ def _process_data(
                         if has_sequence_error
                         else expected_sequence
                     ),
-                    "missingPartCount": (
-                        1
-                        if has_sequence_error and current["rmsAmpere"] > 2.3
-                        else 0
-                    ),
-                    "fasteningErrorCount": 1 if has_sequence_error else 0,
-                    "sequenceErrorCount": 1 if has_sequence_error else 0,
+                    "missingPartCount": missing_part_count,
+                    "fasteningErrorCount": fastening_error_count,
+                    "sequenceErrorCount": sequence_error_count,
                 },
             }
         raise ValueError(f"지원하지 않는 process_code입니다: {process_code}")
@@ -1063,7 +1101,7 @@ def _equipment_status_for_event(
     process_code: str,
     is_abnormal: bool,
 ) -> dict[str, str]:
-    """Java enum 기준 운전 상태를 8:2 비율로 결정적 선택한다."""
+    """Java enum 기준 운전 상태를 조정하여 장비 이상(STOPPED/FAULT)을 줄인다."""
     _ = is_abnormal
     digest = hashlib.blake2b(
         f"{car_master_id}:{process_code}:status".encode("utf-8"),
@@ -1071,11 +1109,11 @@ def _equipment_status_for_event(
     ).digest()
     ratio = int.from_bytes(digest, "big") % 100
 
-    if ratio < 70:
+    if ratio < 85:
         operation_status = "RUNNING"
-    elif ratio < 80:
+    elif ratio < 95:
         operation_status = "WARNING"
-    elif ratio < 90:
+    elif ratio < 98:
         operation_status = "STOPPED"
     else:
         operation_status = "FAULT"

From 308cf1862833082eacb77971fcf2fdbe4d873906 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 3 Jul 2026 14:57:54 +0900
Subject: [PATCH 087/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9=20=EC=A0=84?=
 =?UTF-8?q?=EC=9D=B4=20=EC=8B=9C=20=EC=9D=98=EC=9E=A5=20=EB=B6=88=EB=9F=89?=
 =?UTF-8?q?=20=ED=99=95=EB=A5=A0=20100%=EB=A1=9C=EB=A1=9C=20=EA=B3=84?=
 =?UTF-8?q?=EC=82=B0=EB=90=98=EB=8A=94=20=EC=98=A4=EB=A5=98=20=EC=88=98?=
 =?UTF-8?q?=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/kafka/raw_event_consumer.py              |  6 +++++-
 app/ml/inference/defect_transfer_detector.py |  2 +-
 test_script.py                               | 19 +++++++++++++++++++
 3 files changed, 25 insertions(+), 2 deletions(-)
 create mode 100644 test_script.py

diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 6ba4f16..e0537b7 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -913,7 +913,11 @@ def _defect_probability(event_json: dict[str, Any], process_code: str) -> float:
             + _safe_float(assembly.get("missingPartCount"), default=0.0)
             + _safe_float(assembly.get("fasteningErrorCount"), default=0.0)
         )
-        return round(min(error_count / 3.0, 1.0), 4)
+        if error_count == 0:
+            return 0.0
+        # 에러 건수당 불량 확률을 현실적으로 조정 (너무 쉽게 1.0(100%)이 되지 않도록 함)
+        # 에러 1건당 약 8%씩 추가되며, 기본 50%의 위험도를 갖게 하여 최대 98% 내외로 산출
+        return round(min(error_count * 0.08 + 0.50, 0.98), 4)
 
     vibration_score = max(
         _safe_float(vibration.get("vibrationScore"), default=0.0),
diff --git a/app/ml/inference/defect_transfer_detector.py b/app/ml/inference/defect_transfer_detector.py
index 66286a6..b4fd66b 100644
--- a/app/ml/inference/defect_transfer_detector.py
+++ b/app/ml/inference/defect_transfer_detector.py
@@ -12,7 +12,7 @@
 
 logger = logging.getLogger(__name__)
 
-DEFECT_ARTIFACT_DIR = Path("app/ml/artifacts/defect")
+DEFECT_ARTIFACT_DIR = Path(__file__).resolve().parents[1] / "artifacts" / "defect"
 PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
 NEXT_PROCESS = {
     "PRESS": "BODY",
diff --git a/test_script.py b/test_script.py
new file mode 100644
index 0000000..3b5e9bf
--- /dev/null
+++ b/test_script.py
@@ -0,0 +1,19 @@
+from app.kafka.raw_event_consumer import _defect_probability, _create_defect_transfer_detector, _predict_defect_transfer
+from app.data_generation.manufacturing_event_json_builder import ManufacturingEventJsonBuilder
+
+class Req:
+    car_id_map = {1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8, 9:9, 10:10}
+builder = ManufacturingEventJsonBuilder()
+events = list(builder.iter_rows(Req()))
+assembly_events = [e for e in events if e['process_code'] == 'ASSEMBLY']
+
+detector = _create_defect_transfer_detector()
+
+for row in assembly_events[:10]:
+    e = row['event_json']
+    fallback_prob = _defect_probability(e, 'ASSEMBLY')
+    
+    pred = _predict_defect_transfer(detector, row)
+    ml_prob = pred.defect_probability if pred else None
+    
+    print(f"Fallback: {fallback_prob}, ML: {ml_prob}")

From 360764694ba0fce974aab34ffd7323bffbee7626 Mon Sep 17 00:00:00 2001
From: haseokyung6 <haseokyung@naver.com>
Date: Fri, 3 Jul 2026 15:19:48 +0900
Subject: [PATCH 088/148] feat: ArgoCD GipOps process

---
 .dockerignore                           |  13 ++
 .github/workflows/deploy-ai-service.yml | 225 ++++++++++++++++++++++++
 Dockerfile                              |  26 +++
 3 files changed, 264 insertions(+)
 create mode 100644 .dockerignore
 create mode 100644 .github/workflows/deploy-ai-service.yml
 create mode 100644 Dockerfile

diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..69ff3b4
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,13 @@
+venv
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.git
+.github
+.env
+.vscode
+.idea
+logs
+*.log
\ No newline at end of file
diff --git a/.github/workflows/deploy-ai-service.yml b/.github/workflows/deploy-ai-service.yml
new file mode 100644
index 0000000..b7aa4df
--- /dev/null
+++ b/.github/workflows/deploy-ai-service.yml
@@ -0,0 +1,225 @@
+name: Build AI Service and Update GitOps
+
+on:
+  push:
+    branches:
+      - dev
+      - main
+    paths:
+      - "app/**"
+      - "main.py"
+      - "worker_main.py"
+      - "requirements.txt"
+      - "pyproject.toml"
+      - "uv.lock"
+      - "Dockerfile"
+      - ".dockerignore"
+      - ".github/workflows/deploy-ai-service.yml"
+
+  workflow_dispatch:
+
+concurrency:
+  group: ai-service-${{ github.ref_name }}
+  cancel-in-progress: true
+
+env:
+  AWS_REGION: ap-northeast-2
+  EKS_CLUSTER_NAME: aims-dev-eks
+  ECR_REPOSITORY: aims/ai-service
+  NAMESPACE: ai-services
+
+  INFRA_REPOSITORY: SK-Rookies-AIMS/infra
+  INFRA_MANIFEST_PATH: k8s/ai-service/ai-service.yaml
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+
+    steps:
+      - name: Checkout AI service repository
+        uses: actions/checkout@v4
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ env.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v2
+
+      - name: Ensure ECR repository exists
+        run: |
+          aws ecr describe-repositories \
+            --repository-names "$ECR_REPOSITORY" \
+            --region "$AWS_REGION" >/dev/null 2>&1 \
+          || aws ecr create-repository \
+            --repository-name "$ECR_REPOSITORY" \
+            --region "$AWS_REGION" \
+            --image-scanning-configuration scanOnPush=true \
+            --image-tag-mutability MUTABLE
+
+      - name: Build and push Docker image
+        id: build-image
+        run: |
+          IMAGE_TAG="${GITHUB_REF_NAME}-${GITHUB_SHA::12}"
+          IMAGE_URI="${{ steps.login-ecr.outputs.registry }}/${ECR_REPOSITORY}:${IMAGE_TAG}"
+          BRANCH_IMAGE_URI="${{ steps.login-ecr.outputs.registry }}/${ECR_REPOSITORY}:${GITHUB_REF_NAME}"
+
+          docker build \
+            -t "$IMAGE_URI" \
+            -t "$BRANCH_IMAGE_URI" \
+            .
+
+          docker push "$IMAGE_URI"
+          docker push "$BRANCH_IMAGE_URI"
+
+          echo "image_uri=$IMAGE_URI" >> "$GITHUB_OUTPUT"
+
+      - name: Checkout infra repository
+        uses: actions/checkout@v4
+        with:
+          repository: ${{ env.INFRA_REPOSITORY }}
+          ref: ${{ github.ref_name }}
+          token: ${{ secrets.INFRA_REPO_TOKEN }}
+          path: infra
+          fetch-depth: 0
+
+      - name: Update kubeconfig and ensure namespace
+        if: github.ref_name == 'dev'
+        run: |
+          aws eks update-kubeconfig \
+            --region "$AWS_REGION" \
+            --name "$EKS_CLUSTER_NAME"
+
+          kubectl create namespace "$NAMESPACE" \
+            --dry-run=client \
+            -o yaml | kubectl apply -f -
+
+      - name: Load AI service parameters from SSM
+        if: github.ref_name == 'dev'
+        run: |
+          RDS_SECRET_ARN=$(aws ssm get-parameter \
+            --name "/aims/dev/rds/secret-arn" \
+            --region "$AWS_REGION" \
+            --query "Parameter.Value" \
+            --output text)
+
+          RDS_HOST=$(aws ssm get-parameter \
+            --name "/aims/dev/backend/rds-host" \
+            --region "$AWS_REGION" \
+            --query "Parameter.Value" \
+            --output text)
+
+          RDS_PORT=$(aws ssm get-parameter \
+            --name "/aims/dev/backend/rds-port" \
+            --region "$AWS_REGION" \
+            --query "Parameter.Value" \
+            --output text)
+
+          OPENAI_API_KEY=$(aws ssm get-parameter \
+            --name "/aims/dev/ai-service/openai-api-key" \
+            --region "$AWS_REGION" \
+            --with-decryption \
+            --query "Parameter.Value" \
+            --output text)
+
+          for VALUE in \
+            "$RDS_SECRET_ARN" \
+            "$RDS_HOST" \
+            "$RDS_PORT" \
+            "$OPENAI_API_KEY"
+          do
+            if [ -z "$VALUE" ] || [ "$VALUE" = "None" ]; then
+              echo "Required AI service parameter is empty"
+              exit 1
+            fi
+          done
+
+          echo "::add-mask::$RDS_SECRET_ARN"
+          echo "::add-mask::$OPENAI_API_KEY"
+
+          echo "RDS_SECRET_ARN=$RDS_SECRET_ARN" >> "$GITHUB_ENV"
+          echo "RDS_HOST=$RDS_HOST" >> "$GITHUB_ENV"
+          echo "RDS_PORT=$RDS_PORT" >> "$GITHUB_ENV"
+          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> "$GITHUB_ENV"
+
+      - name: Create or update AI service Secret
+        if: github.ref_name == 'dev'
+        run: |
+          SECRET_JSON=$(aws secretsmanager get-secret-value \
+            --secret-id "$RDS_SECRET_ARN" \
+            --region "$AWS_REGION" \
+            --query "SecretString" \
+            --output text)
+
+          DB_USER=$(echo "$SECRET_JSON" | jq -r '.username')
+          DB_PASSWORD=$(echo "$SECRET_JSON" | jq -r '.password')
+
+          if [ -z "$DB_USER" ] || [ "$DB_USER" = "null" ]; then
+            echo "DB username is missing"
+            exit 1
+          fi
+
+          if [ -z "$DB_PASSWORD" ] || [ "$DB_PASSWORD" = "null" ]; then
+            echo "DB password is missing"
+            exit 1
+          fi
+
+          echo "::add-mask::$DB_USER"
+          echo "::add-mask::$DB_PASSWORD"
+
+          kubectl create secret generic ai-service-secret \
+            --namespace "$NAMESPACE" \
+            --from-literal=OPENAI_API_KEY="$OPENAI_API_KEY" \
+            --from-literal=DB_USER="$DB_USER" \
+            --from-literal=DB_PASSWORD="$DB_PASSWORD" \
+            --dry-run=client \
+            -o yaml | kubectl apply -f -
+
+      - name: Update AI service image in GitOps repository
+        env:
+          IMAGE_URI: ${{ steps.build-image.outputs.image_uri }}
+        run: |
+          MANIFEST="infra/${INFRA_MANIFEST_PATH}"
+
+          test -f "$MANIFEST"
+
+          sed -i -E \
+            "s|^([[:space:]]*)image:.*aims/ai-service.*$|\1image: ${IMAGE_URI}|g" \
+            "$MANIFEST"
+
+          grep -n "image:" "$MANIFEST"
+
+          cd infra
+
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+
+          git add "$INFRA_MANIFEST_PATH"
+
+          if git diff --cached --quiet; then
+            echo "Image tag is already up to date"
+            exit 0
+          fi
+
+          git commit -m "chore(ai-service): deploy ${GITHUB_REF_NAME}-${GITHUB_SHA::12}"
+
+          for ATTEMPT in 1 2 3
+          do
+            git pull --rebase origin "$GITHUB_REF_NAME"
+
+            if git push origin "HEAD:$GITHUB_REF_NAME"; then
+              exit 0
+            fi
+
+            sleep $((ATTEMPT * 5))
+          done
+
+          echo "Failed to push AI service image update"
+          exit 1
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..00a51df
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,26 @@
+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV PIP_NO_CACHE_DIR=1
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        build-essential \
+        gcc \
+        g++ \
+        curl \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+
+RUN python -m pip install --upgrade pip setuptools wheel \
+    && python -m pip install -r requirements.txt
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["python", "-m", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
\ No newline at end of file

From 0dd5270111af45cc163b7144761e557818daac87 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 3 Jul 2026 15:20:54 +0900
Subject: [PATCH 089/148] fix : import path update

---
 app/db.py | 73 +++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 60 insertions(+), 13 deletions(-)

diff --git a/app/db.py b/app/db.py
index cc35671..2f18edb 100644
--- a/app/db.py
+++ b/app/db.py
@@ -18,21 +18,44 @@
 DB_HOST = os.getenv("DB_HOST")
 DB_PORT = os.getenv("DB_PORT")
 MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
+SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
 
-DATABASE_URL = (
+MAIN_DATABASE_URL = (
     f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
     f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
 )
 
-if not DATABASE_URL:
+SAMPLE_DATABASE_URL = (
+    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
+    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+)
+
+if not MAIN_DATABASE_URL:
     raise Exception("MAIN_DATABASE_URL is not set")
 
+if not SAMPLE_DATABASE_URL:
+    raise Exception("SAMPLE_DATABASE_URL is not set")
 
 # =========================
 # ENGINE (SINGLETON)
 # =========================
-engine = create_engine(
-    DATABASE_URL,
+main_engine = create_engine(
+    MAIN_DATABASE_URL,
+    echo=False,
+
+    # ===== connection pool =====
+    pool_size=10,            # 기본 유지 커넥션
+    max_overflow=20,         # 추가 커넥션 허용
+    pool_timeout=30,         # 대기 시간
+    pool_recycle=3600,       # 1시간마다 재생성 (MySQL 안정성)
+    pool_pre_ping=True       # 죽은 connection 체크
+
+    # (옵션) 멀티스레드 안정성
+    # connect_args={"check_same_thread": False}  # SQLite일 때만
+)
+
+sample_engine = create_engine(
+    SAMPLE_DATABASE_URL,
     echo=False,
 
     # ===== connection pool =====
@@ -50,37 +73,61 @@
 # =========================
 # SESSION FACTORY
 # =========================
-SessionLocal = sessionmaker(
+Main_SessionLocal = sessionmaker(
     autocommit=False,
     autoflush=False,
-    bind=engine
+    bind=main_engine
 )
 
+Sample_SessionLocal = sessionmaker(
+    autocommit=False,
+    autoflush=False,
+    bind=sample_engine
+)
 # thread-safe session (Kafka / scheduler 환경에서 중요)
-Session = scoped_session(SessionLocal)
-
+main_Session = scoped_session(Main_SessionLocal)
+sample_Session = scoped_session(Sample_SessionLocal)
 
 # =========================
 # DEPENDENCY HELPERS
 # =========================
 
-def get_session():
+def get_main_session():
     """
     권장 사용 방식:
     with get_session() as session:
         session.execute(...)
     """
-    return Session()
+    return main_Session()
 
+def get_sample_session():
+    """
+    권장 사용 방식:
+    with get_session() as session:
+        session.execute(...)
+    """
+    return sample_Session()
+
+def main_dispose_engine():
+    """
+    graceful shutdown 시 사용
+    connection pool 전체 종료
+    """
+    try:
+        main_Session.remove()
+        main_engine.dispose()
+        print("🗄️ DB engine disposed successfully")
+    except Exception as e:
+        print("DB dispose error:", e)
 
-def dispose_engine():
+def sample_dispose_engine():
     """
     graceful shutdown 시 사용
     connection pool 전체 종료
     """
     try:
-        Session.remove()
-        engine.dispose()
+        sample_Session.remove()
+        sample_engine.dispose()
         print("🗄️ DB engine disposed successfully")
     except Exception as e:
         print("DB dispose error:", e)
\ No newline at end of file

From 5d2d24adde1e95a7ffd0865044979e060fbf37f8 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 3 Jul 2026 15:22:55 +0900
Subject: [PATCH 090/148] import path update 2

---
 app/ai_manual/rag/vector_store.py       |  2 +-
 app/ai_manual/service/manual_service.py | 14 ++++++--------
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/app/ai_manual/rag/vector_store.py b/app/ai_manual/rag/vector_store.py
index 70b92b8..1f5ce2c 100644
--- a/app/ai_manual/rag/vector_store.py
+++ b/app/ai_manual/rag/vector_store.py
@@ -2,7 +2,7 @@
 
 from typing import List
 
-from ai_manual.schema.request import CriticalEvent
+from app.ai_manual.schema.request import CriticalEvent
 
 
 class VectorStore:
diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index 79a0852..939e597 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -3,27 +3,25 @@
 import os
 
 from dotenv import load_dotenv
-
 from langchain_openai import ChatOpenAI
 
-from ai_manual.prompt.prompt_template import manual_prompt
-
-from ai_manual.rag.vector_store import VectorStore
+from app.ai_manual.prompt.prompt_template import manual_prompt
+from app.ai_manual.rag.vector_store import VectorStore
 
-from ai_manual.repository.alert_event_repository import (
+from app.ai_manual.repository.alert_event_repository import (
     AlertEventRepository
 )
 
-from ai_manual.schema.request import (
+from app.ai_manual.schema.request import (
     CriticalEvent,
     EquipmentInfo,
     FactoryContext,
     ManualRequest,
     OperatorInfo,
-    RagContext
+    RagContext,
 )
 
-from ai_manual.schema.response import ManualResponse
+from app.ai_manual.schema.response import ManualResponse
 
 
 class ManualService:

From 2ce56c198661da001a7c76e5154223acc9674e39 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 3 Jul 2026 16:01:32 +0900
Subject: [PATCH 091/148] =?UTF-8?q?fix:=20analysis=20topic=20consumer=20gr?=
 =?UTF-8?q?oup=20=EC=98=A4=EB=A5=98=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md                       | 6 ------
 app/kafka/raw_event_consumer.py | 2 +-
 2 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 57ed401..936790d 100644
--- a/README.md
+++ b/README.md
@@ -133,12 +133,6 @@ Windows에서 Python 3.14를 사용하는 경우 일부 패키지의 사전 빌
 
 개발 서버 실행:
 
-```powershell
-uvicorn main:app --reload
-```
-
-또는 패키지 경로를 직접 지정해 실행할 수 있습니다.
-
 ```powershell
 uvicorn app.main:app --reload
 ```
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index e0537b7..1324b63 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -33,7 +33,7 @@
 # assembly-service의 app.kafka.topics.raw.name과 동일한 실제 raw 토픽명.
 RAW_TOPIC = "factory.manufacturing.raw"
 ANALYSIS_TOPIC = "factory.manufacturing.analysis"
-RAW_CONSUMER_GROUP_ID = "ai-consumer-group"
+RAW_CONSUMER_GROUP_ID = "ai-analysis-consumer-group"
 RAW_AUTO_OFFSET_RESET = "earliest"
 RAW_CONSUMER_CONCURRENCY = 2
 _bottleneck_analysis_lock = Lock()

From 33068de82265c4f8015bb33ceee29395bd293747 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 3 Jul 2026 16:12:26 +0900
Subject: [PATCH 092/148] =?UTF-8?q?fix:=20kafka-python=20=EB=B2=84?=
 =?UTF-8?q?=EC=A0=84=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7deb745..5e4ab6f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,7 +25,7 @@ faiss-cpu>=1.9.0
 aiokafka>=0.12.0
 confluent-kafka>=2.6.0
 aws-msk-iam-sasl-signer-python>=1.0.2
-kafka-python>=2.2.15
+kafka-python>=2.2.15,<3.0.0
 
 # Redis Cache
 redis>=5.2.0,<6.0.0

From 9501fd922a8b035b13fbfec85a447d5510e9c31c Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 3 Jul 2026 16:15:37 +0900
Subject: [PATCH 093/148] =?UTF-8?q?fix:=20kafka-python=20=EB=B2=84?=
 =?UTF-8?q?=EC=A0=84=202.2.15=EB=A1=9C=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 5e4ab6f..4f77b9e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,7 +25,7 @@ faiss-cpu>=1.9.0
 aiokafka>=0.12.0
 confluent-kafka>=2.6.0
 aws-msk-iam-sasl-signer-python>=1.0.2
-kafka-python>=2.2.15,<3.0.0
+kafka-python==2.2.15
 
 # Redis Cache
 redis>=5.2.0,<6.0.0

From e1676f8cf2fc5df3b47136378370a3ef40a03d23 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 3 Jul 2026 17:12:26 +0900
Subject: [PATCH 094/148] =?UTF-8?q?fix:=20SHAP=20=EA=B8=B0=EB=B0=98=20?=
 =?UTF-8?q?=EB=B6=88=EB=9F=89=20=EC=A0=84=EC=9D=B4=20=EC=9B=90=EC=9D=B8=20?=
 =?UTF-8?q?=EB=A9=94=EC=8B=9C=EC=A7=80=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/ml/inference/defect_transfer_detector.py  | 38 ++++++++++------
 .../analysis/defect_transfer_service.py       | 45 ++++++++++++++++++-
 2 files changed, 67 insertions(+), 16 deletions(-)

diff --git a/app/ml/inference/defect_transfer_detector.py b/app/ml/inference/defect_transfer_detector.py
index b4fd66b..18ca4f4 100644
--- a/app/ml/inference/defect_transfer_detector.py
+++ b/app/ml/inference/defect_transfer_detector.py
@@ -295,31 +295,41 @@ def _rank_causes(
     ) -> list[DefectCause]:
         shap_impacts = self._shap_feature_impacts(features)
         candidates = [
-            ("station_delay_sec", "공정 지연", 12.0, "초"),
-            ("cycle_time_sec", "Cycle Time 증가", 55.0, "초"),
-            ("queue_length", "대기열 증가", 8.0, "대"),
-            ("wip_count", "WIP 증가", 24.0, "대"),
-            ("current_rms_ampere", "전류 RMS 편차", 2.2, "A"),
-            ("vibration_score", "진동 Score 상승", 0.45, ""),
-            ("robot_vibration_score", "로봇 진동 Score 상승", 0.45, ""),
-            ("thermal_score", "열화상 Score 상승", 55.0, ""),
-            ("max_temperature", "최고 온도 상승", 58.0, "°C"),
-            ("paint_thermal_std_temp", "도장 온도 편차", 4.0, "°C"),
-            ("paint_thickness_value", "도막 두께 편차", 130.0, ""),
+            ("station_delay_sec", "공정 지연", 12.0, "초", None),
+            ("cycle_time_sec", "Cycle Time 증가", 55.0, "초", None),
+            ("queue_length", "대기열 증가", 8.0, "대", None),
+            ("wip_count", "WIP 증가", 24.0, "대", None),
+            ("current_rms_ampere", "전류 RMS 편차", 2.2, "A", None),
+            ("vibration_score", "진동 Score 상승", 0.45, "", None),
+            ("robot_vibration_score", "로봇 진동 Score 상승", 0.45, "", None),
+            ("thermal_score", "열화상 Score 상승", 55.0, "", None),
+            ("max_temperature", "최고 온도 상승", 58.0, "°C", None),
+            ("paint_thermal_std_temp", "도장 온도 편차", 4.0, "°C", "PAINT"),
+            ("paint_thickness_value", "도막 두께 편차", 50.0, "", "PAINT"),
         ]
         scored: list[tuple[str, str, Any, float, str]] = []
-        for feature, label, baseline, unit in candidates:
+        for feature, label, baseline, unit, target_process in candidates:
+            if target_process is not None and process_code != target_process:
+                continue
             value = features.get(feature)
             numeric = _safe_float(value, default=0.0)
             if feature == "paint_thickness_value":
-                impact = abs(numeric - 116.0) / 18.0
+                if numeric <= 0:
+                    continue
+                display_value = abs(numeric - baseline)
+                if display_value <= 0:
+                    continue
+                impact = display_value / 20.0
             else:
+                if numeric <= baseline:
+                    continue
+                display_value = numeric
                 impact = max(0.0, numeric - baseline) / max(abs(baseline), 1.0)
             if process_code == "PAINT" and feature.startswith("paint_"):
                 impact *= 1.35
             if shap_impacts:
                 impact = max(impact * 0.35, shap_impacts.get(feature, 0.0))
-            scored.append((feature, label, value, impact, unit))
+            scored.append((feature, label, display_value, impact, unit))
 
         scored.sort(key=lambda item: item[3], reverse=True)
         top = scored[:4]
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index 31d967b..fbfe671 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -20,7 +20,7 @@
 from app.utils.json_utils import from_json, to_json
 from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
 
-DEFECT_TRANSFER_CACHE_VERSION = "v5"
+DEFECT_TRANSFER_CACHE_VERSION = "v6"
 
 
 class DefectTransferAnalysisService:
@@ -187,6 +187,12 @@ def get_cause_analysis(
                 nextCursor=None,
             )
 
+        display_rows = [
+            row
+            for row in rows
+            if self._is_displayable_cause(row)
+        ]
+
         return DefectTransferCausePage(
             vehicleId=str(selected.get("vehicle_id")),
             carMasterId=int(selected["car_master_id"]),
@@ -207,7 +213,7 @@ def get_cause_analysis(
                     impact=float(row.get("influence_score") or 0.0),
                     message=str(row.get("main_cause") or ""),
                 )
-                for index, row in enumerate(rows, page * safe_size + 1)
+                for index, row in enumerate(display_rows, page * safe_size + 1)
             ],
             hasNext=has_next,
             nextCursor=page + 1 if has_next else None,
@@ -276,6 +282,41 @@ def _display_value(value: Any) -> str:
             return f"{value:.2f}"
         return str(value)
 
+    @classmethod
+    def _is_displayable_cause(cls, row: dict[str, Any]) -> bool:
+        message = str(row.get("main_cause") or "").strip()
+        if not message:
+            return False
+
+        value = cls._first_number(message)
+        if value is None:
+            return True
+
+        thresholds = {
+            "도막 두께 편차": 0.0,
+            "도장 온도 편차": 4.0,
+            "공정 지연": 12.0,
+            "Cycle Time 증가": 55.0,
+            "대기열 증가": 8.0,
+            "WIP 증가": 24.0,
+            "전류 RMS 편차": 2.2,
+            "진동 Score 상승": 0.45,
+            "로봇 진동 Score 상승": 0.45,
+            "열화상 Score 상승": 55.0,
+            "최고 온도 상승": 58.0,
+        }
+        for prefix, threshold in thresholds.items():
+            if message.startswith(prefix):
+                return value > threshold
+        return value > 0.0
+
+    @staticmethod
+    def _first_number(text: str) -> float | None:
+        import re
+
+        match = re.search(r"-?\d+(?:\.\d+)?", text)
+        return float(match.group(0)) if match else None
+
     @staticmethod
     def _db_exception() -> AppException:
         return AppException(

From f8a8a30290cdc94e10c0154215fc997c78e49104 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 3 Jul 2026 17:17:25 +0900
Subject: [PATCH 095/148] =?UTF-8?q?fix:=20SHAP=20=EA=B8=B0=EB=B0=98=20?=
 =?UTF-8?q?=EC=9B=90=EC=9D=B8=20=EB=B6=84=EC=84=9D=20=EB=A9=94=EC=8B=9C?=
 =?UTF-8?q?=EC=A7=80=20=EC=98=81=ED=96=A5=EB=8F=84=20=EA=B0=92=20=EC=88=98?=
 =?UTF-8?q?=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/service/analysis/defect_transfer_service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index fbfe671..643ec4d 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -20,7 +20,7 @@
 from app.utils.json_utils import from_json, to_json
 from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
 
-DEFECT_TRANSFER_CACHE_VERSION = "v6"
+DEFECT_TRANSFER_CACHE_VERSION = "v7"
 
 
 class DefectTransferAnalysisService:
@@ -209,7 +209,7 @@ def get_cause_analysis(
                     rank=index,
                     feature="main_cause",
                     label=str(row.get("main_cause") or ""),
-                    value=self._display_value(row.get("influence_score")),
+                    value="",
                     impact=float(row.get("influence_score") or 0.0),
                     message=str(row.get("main_cause") or ""),
                 )

From a9947230a95dcb24e1210df7e4f4153328af8374 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Sat, 4 Jul 2026 09:10:54 +0900
Subject: [PATCH 096/148] feat : db.py DB Pool connection

---
 app/ai_manual/data/system_description.md      |   2 +-
 app/ai_manual/prompt/prompt_template.py       |   2 +-
 .../quality_drive_detail_repository.py        | 238 ++++------
 app/repository/quality_process_repository.py  | 190 ++++----
 .../quality_risk_history_repository.py        | 167 ++++---
 .../quality_risk_trend_repository.py          | 116 ++---
 .../quality_status_detail_repository.py       | 147 +++---
 app/repository/quality_summary_repository.py  | 276 ++++++-----
 .../quality/drive_detail_producer.py          | 206 ++++----
 app/scheduler/quality/process_producer.py     | 268 ++++++-----
 .../quality/risk_history_producer.py          | 447 +++++++++---------
 app/scheduler/quality/risk_trend_producer.py  | 340 +++++++------
 .../quality/status_detail_producer.py         | 268 +++++------
 app/worker_main.py                            |  44 +-
 14 files changed, 1296 insertions(+), 1415 deletions(-)

diff --git a/app/ai_manual/data/system_description.md b/app/ai_manual/data/system_description.md
index 0eb7537..bd78130 100644
--- a/app/ai_manual/data/system_description.md
+++ b/app/ai_manual/data/system_description.md
@@ -47,7 +47,7 @@ AGV
 
 ## 시스템 구조
 
-MES
+Event 발생
 
 ↓
 
diff --git a/app/ai_manual/prompt/prompt_template.py b/app/ai_manual/prompt/prompt_template.py
index 18f5d76..d5eda02 100644
--- a/app/ai_manual/prompt/prompt_template.py
+++ b/app/ai_manual/prompt/prompt_template.py
@@ -93,7 +93,7 @@
 HUMAN_PROMPT = """
 # 현재 Critical Event
 
-{critical_event}
+{Critical_event}
 
 ------------------------------------------------
 
diff --git a/app/repository/quality_drive_detail_repository.py b/app/repository/quality_drive_detail_repository.py
index 48ebd0c..93b4630 100644
--- a/app/repository/quality_drive_detail_repository.py
+++ b/app/repository/quality_drive_detail_repository.py
@@ -1,199 +1,147 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 import pandas as pd
-from dotenv import load_dotenv
-import os
-from urllib.parse import quote_plus
 
+from app.db import main_engine
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_DRIVE_DETAIL
 from app.kafka.options import DRIVE_DETAIL_GROUP
 
-def run():
 
-    load_dotenv()
+def calculate_drive_score(row):
+    score = 100
 
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-    )
-
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
-
-    consumer = create_consumer(
-        topic=QUALITY_INSPECTION_DRIVE_DETAIL,
-        group_id=DRIVE_DETAIL_GROUP
-    )
+    if float(row["throttle_position"]) > 90:
+        score -= 20
 
-    def calculate_drive_score(row):
+    if float(row["brake_pressure"]) > 45:
+        score -= 20
 
-        score = 100
+    if abs(float(row["steering_angle"])) > 40:
+        score -= 20
 
-        if float(row["throttle_position"]) > 90:
-            score -= 20
+    return round(score, 2)
 
-        if float(row["brake_pressure"]) > 45:
-            score -= 20
 
-        if abs(float(row["steering_angle"])) > 40:
-            score -= 20
+def get_driving_pattern(row):
+    throttle = float(row["throttle_position"])
+    brake = float(row["brake_pressure"])
+    steering = abs(float(row["steering_angle"]))
 
-        return round(score, 2)
+    if throttle > 75:
+        return "RAPID_ACCEL"
 
-    def get_driving_pattern(row):
+    if brake > 35:
+        return "HARD_BRAKE"
 
-        throttle = float(row["throttle_position"])
-        brake = float(row["brake_pressure"])
-        steering = abs(float(row["steering_angle"]))
+    if steering > 40:
+        return "SHARP_TURN"
 
-        if throttle > 75:
-            return "RAPID_ACCEL"
+    return "NORMAL"
 
-        if brake > 35:
-            return "HARD_BRAKE"
 
-        if steering > 40:
-            return "SHARP_TURN"
+def run(stop_event):
 
-        return "NORMAL"
+    consumer = create_consumer(
+        topic=QUALITY_INSPECTION_DRIVE_DETAIL,
+        group_id=DRIVE_DETAIL_GROUP
+    )
 
     detail_id = 1
 
     try:
 
-        for msg in consumer:
-
-            row = msg.value
+        while not stop_event.is_set():
 
-            if not row.get("created_at"):
-                print(
-                    f"폐기 차량 제외 : "
-                    f"{row.get('vehicle_id')}"
-                )
-                continue
-
-            vehicle_id = row["vehicle_id"]
-
-            # ==========================
-            # 중복 저장 방지
-            # ==========================
-            exists = pd.read_sql(
-                text("""
-                    SELECT COUNT(*) AS cnt
-                    FROM inspection_drive_detail
-                    WHERE vehicle_id = :vehicle_id
-                """),
-                con=main_engine,
-                params={
-                    "vehicle_id": vehicle_id
-                }
-            )
-
-            if exists.iloc[0]["cnt"] > 0:
-
-                print(
-                    f"{vehicle_id} 이미 저장됨"
-                )
+            # 1초마다 종료 신호 확인
+            records = consumer.poll(timeout_ms=1000)
 
+            if not records:
                 continue
-            # ==========================
-
-            car_code = vehicle_id.split("-")[0]
 
-            inspection_no = (
-                f"DRIVE-{detail_id:05d}"
-            )
+            for _, messages in records.items():
 
-            drive_score = calculate_drive_score(row)
+                for msg in messages:
 
-            driving_pattern = (
-                get_driving_pattern(row)
-            )
+                    row = msg.value
 
-            if drive_score >= 80:
+                    if not row.get("created_at"):
+                        print(
+                            f"폐기 차량 제외 : "
+                            f"{row.get('vehicle_id')}"
+                        )
+                        continue
 
-                inspection_result = "NORMAL"
-                issue_message = "NORMAL"
+                    vehicle_id = row["vehicle_id"]
 
-            else:
+                    exists = pd.read_sql(
+                        text("""
+                            SELECT COUNT(*) AS cnt
+                            FROM inspection_drive_detail
+                            WHERE vehicle_id = :vehicle_id
+                        """),
+                        con=main_engine,
+                        params={
+                            "vehicle_id": vehicle_id
+                        }
+                    )
 
-                inspection_result = "WARNING"
-                issue_message = "ACCEL_ALERT"
+                    if exists.iloc[0]["cnt"] > 0:
+                        print(f"{vehicle_id} 이미 저장됨")
+                        continue
 
-            df = pd.DataFrame([{
-                "car_code": car_code,
-                "inspection_no": inspection_no,
-                "vehicle_id": vehicle_id,
+                    car_code = vehicle_id.split("-")[0]
 
-                "throttle_position":
-                    round(
-                        float(
-                            row["throttle_position"]
-                        ),
-                        2
-                    ),
+                    inspection_no = (
+                        f"DRIVE-{detail_id:05d}"
+                    )
 
-                "brake_pressure":
-                    round(
-                        float(
-                            row["brake_pressure"]
-                        ),
-                        2
-                    ),
+                    drive_score = calculate_drive_score(row)
+                    driving_pattern = get_driving_pattern(row)
 
-                "steering_angle":
-                    round(
-                        float(
-                            row["steering_angle"]
-                        ),
-                        2
-                    ),
+                    if drive_score >= 80:
+                        inspection_result = "NORMAL"
+                        issue_message = "NORMAL"
+                    else:
+                        inspection_result = "WARNING"
+                        issue_message = "ACCEL_ALERT"
 
-                "drive_score":
-                    drive_score,
+                    df = pd.DataFrame([{
+                        "car_code": car_code,
+                        "inspection_no": inspection_no,
+                        "vehicle_id": vehicle_id,
 
-                "inspection_result":
-                    inspection_result,
+                        "throttle_position":
+                            round(float(row["throttle_position"]), 2),
 
-                "driving_pattern":
-                    driving_pattern,
+                        "brake_pressure":
+                            round(float(row["brake_pressure"]), 2),
 
-                "issue_message":
-                    issue_message,
+                        "steering_angle":
+                            round(float(row["steering_angle"]), 2),
 
-                "created_at":
-                    row["created_at"]
-            }])
+                        "drive_score": drive_score,
+                        "inspection_result": inspection_result,
+                        "driving_pattern": driving_pattern,
+                        "issue_message": issue_message,
+                        "created_at": row["created_at"]
+                    }])
 
-            df.to_sql(
-                name="inspection_drive_detail",
-                con=main_engine,
-                if_exists="append",
-                index=False
-            )
+                    df.to_sql(
+                        name="inspection_drive_detail",
+                        con=main_engine,
+                        if_exists="append",
+                        index=False
+                    )
 
-            detail_id += 1
+                    detail_id += 1
 
     except Exception as e:
-
-        print(
-            f"오류 발생 : {e}"
-        )
+        print(f"오류 발생 : {e}")
 
     finally:
-
         consumer.close()
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+    import threading
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/repository/quality_process_repository.py b/app/repository/quality_process_repository.py
index 5af5f2c..1db3ef0 100644
--- a/app/repository/quality_process_repository.py
+++ b/app/repository/quality_process_repository.py
@@ -1,35 +1,13 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 import pandas as pd
-from dotenv import load_dotenv
-import os
-from urllib.parse import quote_plus
 
+from app.db import main_engine
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_PROCESS
 from app.kafka.options import PROCESS_GROUP
 
 
-def run():
-
-    load_dotenv()
-
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-    )
-
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_PROCESS,
@@ -38,106 +16,106 @@ def run():
 
     try:
 
-        for msg in consumer:
+        while not stop_event.is_set():
 
-            row = msg.value
+            # 1초마다 종료 여부 확인
+            records = consumer.poll(timeout_ms=1000)
 
-            if "process_name" not in row:
-                print("구버전 메시지 무시")
+            if not records:
                 continue
 
-            total_vehicle_count = row["total_vehicle_count"]
-            process_name = row["process_name"]
-            completed_count = row["completed_count"]
-            waiting_count = row["waiting_count"]
-            progress_rate = row["progress_rate"]
-            created_at = row["created_at"]
-
-            # 같은 날짜 + 같은 공정 존재 여부 확인
-            exists = pd.read_sql(
-                text("""
-                    SELECT COUNT(*) AS cnt
-                    FROM inspection_process
-                    WHERE process_name = :process_name
-                    AND DATE(created_at) = DATE(:created_at)
-                """),
-                con=main_engine,
-                params={
-                    "process_name": process_name,
-                    "created_at": created_at
-                }
-            )
-
-            if exists.iloc[0]["cnt"] > 0:
-
-                # UPDATE
-                with main_engine.begin() as conn:
-
-                    conn.execute(
+            for _, messages in records.items():
+
+                for msg in messages:
+
+                    row = msg.value
+
+                    if "process_name" not in row:
+                        print("구버전 메시지 무시")
+                        continue
+
+                    total_vehicle_count = row["total_vehicle_count"]
+                    process_name = row["process_name"]
+                    completed_count = row["completed_count"]
+                    waiting_count = row["waiting_count"]
+                    progress_rate = row["progress_rate"]
+                    created_at = row["created_at"]
+
+                    # 같은 날짜 + 같은 공정 존재 여부 확인
+                    exists = pd.read_sql(
                         text("""
-                            UPDATE inspection_process
-                            SET
-                                completed_count=:completed_count,
-                                waiting_count=:waiting_count,
-                                progress_rate=:progress_rate,
-                                process_status=:process_status
-                            WHERE process_name=:process_name
-                            AND DATE(created_at)=DATE(:created_at)
+                            SELECT COUNT(*) AS cnt
+                            FROM inspection_process
+                            WHERE process_name = :process_name
+                            AND DATE(created_at) = DATE(:created_at)
                         """),
-                        {
-                            "completed_count": completed_count,
-                            "waiting_count": waiting_count,
-                            "progress_rate": progress_rate,
-                            "process_status":
-                                "COMPLETE" if progress_rate == 100 else "RUNNING",
+                        con=main_engine,
+                        params={
                             "process_name": process_name,
                             "created_at": created_at
                         }
                     )
 
-            else:
-
-                # INSERT
-
-                df = pd.DataFrame([{
-                    "process_name": process_name,
-
-                    "total_vehicle_count":
-                        total_vehicle_count,
-
-                    "completed_count":
-                        completed_count,
-
-                    "waiting_count":
-                        waiting_count,
-
-                    "progress_rate":
-                        progress_rate,
-
-                    "process_status":
-                        "COMPLETE"
-                        if progress_rate == 100
-                        else "RUNNING",
-
-                    "created_at":
-                        created_at
-                }])
+                    if exists.iloc[0]["cnt"] > 0:
+
+                        # UPDATE
+                        with main_engine.begin() as conn:
+
+                            conn.execute(
+                                text("""
+                                    UPDATE inspection_process
+                                    SET
+                                        completed_count = :completed_count,
+                                        waiting_count = :waiting_count,
+                                        progress_rate = :progress_rate,
+                                        process_status = :process_status
+                                    WHERE process_name = :process_name
+                                    AND DATE(created_at) = DATE(:created_at)
+                                """),
+                                {
+                                    "completed_count": completed_count,
+                                    "waiting_count": waiting_count,
+                                    "progress_rate": progress_rate,
+                                    "process_status":
+                                        "COMPLETE"
+                                        if progress_rate == 100
+                                        else "RUNNING",
+                                    "process_name": process_name,
+                                    "created_at": created_at
+                                }
+                            )
+
+                    else:
+
+                        # INSERT
+                        df = pd.DataFrame([{
+                            "process_name": process_name,
+                            "total_vehicle_count": total_vehicle_count,
+                            "completed_count": completed_count,
+                            "waiting_count": waiting_count,
+                            "progress_rate": progress_rate,
+                            "process_status":
+                                "COMPLETE"
+                                if progress_rate == 100
+                                else "RUNNING",
+                            "created_at": created_at
+                        }])
 
-                df.to_sql(
-                    name="inspection_process",
-                    con=main_engine,
-                    if_exists="append",
-                    index=False
-                )
+                        df.to_sql(
+                            name="inspection_process",
+                            con=main_engine,
+                            if_exists="append",
+                            index=False
+                        )
 
     except Exception as e:
         print(f"오류 발생 : {e}")
 
     finally:
-
         print("process 종료")
         consumer.close()
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+    import threading
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/repository/quality_risk_history_repository.py b/app/repository/quality_risk_history_repository.py
index 7b1cfce..831c22f 100644
--- a/app/repository/quality_risk_history_repository.py
+++ b/app/repository/quality_risk_history_repository.py
@@ -1,33 +1,13 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 import pandas as pd
-from dotenv import load_dotenv
-import os
-from urllib.parse import quote_plus
 
+from app.db import main_engine
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_RISK_HISTORY
 from app.kafka.options import RISK_HISTORY_GROUP
 
 
-def run():
-
-    load_dotenv()
-
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-    )
-
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_RISK_HISTORY,
@@ -36,86 +16,96 @@ def run():
 
     try:
 
-        for msg in consumer:
+        while not stop_event.is_set():
+
+            # 1초마다 종료 여부 확인
+            records = consumer.poll(timeout_ms=1000)
 
-            row = msg.value
+            if not records:
+                continue
 
-            # Producer에서 전달되는 값
-            inspection_type = row["inspection_type"]
-            inspection_date = row["inspection_date"]
-            risk_score = row["risk_score"]
+            for _, messages in records.items():
 
-            # Repository에서 생성
-            start_time = f"{inspection_date} 00:00:00"
-            end_time = f"{inspection_date} 23:59:59"
+                for msg in messages:
 
-            # 같은 날짜 + 같은 검사 타입 존재 여부 확인
-            exists = pd.read_sql(
-                text("""
-                    SELECT COUNT(*) AS cnt
-                    FROM inspection_risk_history
-                    WHERE inspection_type = :inspection_type
-                      AND DATE(start_time) = DATE(:start_time)
-                """),
-                con=main_engine,
-                params={
-                    "inspection_type": inspection_type,
-                    "start_time": start_time
-                }
-            )
+                    row = msg.value
 
-            # 이미 존재하면 UPDATE
-            if exists.iloc[0]["cnt"] > 0:
+                    # Producer에서 전달되는 값
+                    inspection_type = row["inspection_type"]
+                    inspection_date = row["inspection_date"]
+                    risk_score = row["risk_score"]
 
-                with main_engine.begin() as conn:
+                    # Repository에서 생성
+                    start_time = f"{inspection_date} 00:00:00"
+                    end_time = f"{inspection_date} 23:59:59"
 
-                    conn.execute(
+                    # 같은 날짜 + 같은 검사 타입 존재 여부 확인
+                    exists = pd.read_sql(
                         text("""
-                            UPDATE inspection_risk_history
-                            SET
-                                risk_score = :risk_score,
-                                end_time = :end_time
+                            SELECT COUNT(*) AS cnt
+                            FROM inspection_risk_history
                             WHERE inspection_type = :inspection_type
                               AND DATE(start_time) = DATE(:start_time)
                         """),
-                        {
-                            "risk_score": risk_score,
-                            "end_time": end_time,
+                        con=main_engine,
+                        params={
                             "inspection_type": inspection_type,
                             "start_time": start_time
                         }
                     )
 
-            else:
-
-                # inspection_round 자동 생성
-                with main_engine.begin() as conn:
+                    # 이미 존재하면 UPDATE
+                    if exists.iloc[0]["cnt"] > 0:
+
+                        with main_engine.begin() as conn:
+
+                            conn.execute(
+                                text("""
+                                    UPDATE inspection_risk_history
+                                    SET
+                                        risk_score = :risk_score,
+                                        end_time = :end_time
+                                    WHERE inspection_type = :inspection_type
+                                      AND DATE(start_time) = DATE(:start_time)
+                                """),
+                                {
+                                    "risk_score": risk_score,
+                                    "end_time": end_time,
+                                    "inspection_type": inspection_type,
+                                    "start_time": start_time
+                                }
+                            )
+
+                    else:
+
+                        # inspection_round 자동 생성
+                        with main_engine.begin() as conn:
+
+                            inspection_round = conn.execute(
+                                text("""
+                                    SELECT COALESCE(MAX(inspection_round), 0) + 1
+                                    FROM inspection_risk_history
+                                    WHERE inspection_type = :inspection_type
+                                """),
+                                {
+                                    "inspection_type": inspection_type
+                                }
+                            ).scalar()
+
+                        df = pd.DataFrame([{
+                            "inspection_type": inspection_type,
+                            "inspection_round": inspection_round,
+                            "risk_score": risk_score,
+                            "start_time": start_time,
+                            "end_time": end_time
+                        }])
 
-                    inspection_round = conn.execute(
-                        text("""
-                            SELECT COALESCE(MAX(inspection_round), 0) + 1
-                            FROM inspection_risk_history
-                            WHERE inspection_type = :inspection_type
-                        """),
-                        {
-                            "inspection_type": inspection_type
-                        }
-                    ).scalar()
-
-                df = pd.DataFrame([{
-                    "inspection_type": inspection_type,
-                    "inspection_round": inspection_round,
-                    "risk_score": risk_score,
-                    "start_time": start_time,
-                    "end_time": end_time
-                }])
-
-                df.to_sql(
-                    name="inspection_risk_history",
-                    con=main_engine,
-                    if_exists="append",
-                    index=False
-                )
+                        df.to_sql(
+                            name="inspection_risk_history",
+                            con=main_engine,
+                            if_exists="append",
+                            index=False
+                        )
 
     except Exception as e:
 
@@ -128,4 +118,7 @@ def run():
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+
+    import threading
+
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/repository/quality_risk_trend_repository.py b/app/repository/quality_risk_trend_repository.py
index 067f441..16fc11a 100644
--- a/app/repository/quality_risk_trend_repository.py
+++ b/app/repository/quality_risk_trend_repository.py
@@ -1,35 +1,13 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 import pandas as pd
-from dotenv import load_dotenv
-import os
-from urllib.parse import quote_plus
 
+from app.db import main_engine
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_RISK_TREND
 from app.kafka.options import RISK_TREND_GROUP
 
 
-def run():
-
-    load_dotenv()
-
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-    )
-
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_RISK_TREND,
@@ -38,54 +16,62 @@ def run():
 
     try:
 
-        for msg in consumer:
+        while not stop_event.is_set():
 
-            row = msg.value
+            # 1초마다 종료 여부 확인
+            records = consumer.poll(timeout_ms=1000)
 
-            exists = pd.read_sql(
-                text("""
-                    SELECT COUNT(*) AS cnt
-                    FROM inspection_risk_trend
-                    WHERE risk_level = :risk_level
-                    AND DATE(created_at) = DATE(:created_at)
-                """),
-                con=main_engine,
-                params={
-                    "risk_level": row["risk_level"],
-                    "created_at": row["created_at"]
-                }
-            )
+            if not records:
+                continue
 
-            if exists.iloc[0]["cnt"] > 0:
+            for _, messages in records.items():
 
-                with main_engine.begin() as conn:
+                for msg in messages:
 
-                    conn.execute(
+                    row = msg.value
+
+                    exists = pd.read_sql(
                         text("""
-                            UPDATE
-                                inspection_risk_trend
-                            SET
-                                risk_count=:risk_count,
-                                risk_ratio=:risk_ratio
-                            WHERE
-                                risk_level=:risk_level
-                            AND DATE(created_at)
-                                =
-                                DATE(:created_at)
+                            SELECT COUNT(*) AS cnt
+                            FROM inspection_risk_trend
+                            WHERE risk_level = :risk_level
+                              AND DATE(created_at) = DATE(:created_at)
                         """),
-                        row
+                        con=main_engine,
+                        params={
+                            "risk_level": row["risk_level"],
+                            "created_at": row["created_at"]
+                        }
                     )
 
-            else:
+                    if exists.iloc[0]["cnt"] > 0:
+
+                        with main_engine.begin() as conn:
+
+                            conn.execute(
+                                text("""
+                                    UPDATE inspection_risk_trend
+                                    SET
+                                        risk_count = :risk_count,
+                                        risk_ratio = :risk_ratio
+                                    WHERE
+                                        risk_level = :risk_level
+                                    AND DATE(created_at)
+                                        = DATE(:created_at)
+                                """),
+                                row
+                            )
+
+                    else:
 
-                df = pd.DataFrame([row])
+                        df = pd.DataFrame([row])
 
-                df.to_sql(
-                    name="inspection_risk_trend",
-                    con=main_engine,
-                    if_exists="append",
-                    index=False
-                )
+                        df.to_sql(
+                            name="inspection_risk_trend",
+                            con=main_engine,
+                            if_exists="append",
+                            index=False
+                        )
 
     except Exception as e:
 
@@ -94,9 +80,11 @@ def run():
     finally:
 
         print("risk-trend 종료")
-
         consumer.close()
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+
+    import threading
+
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/repository/quality_status_detail_repository.py b/app/repository/quality_status_detail_repository.py
index ce9265d..3b78df2 100644
--- a/app/repository/quality_status_detail_repository.py
+++ b/app/repository/quality_status_detail_repository.py
@@ -1,35 +1,13 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 import pandas as pd
-from dotenv import load_dotenv
-import os
-from urllib.parse import quote_plus
 
+from app.db import main_engine
 from app.kafka.consumer import create_consumer
 from app.kafka.topics import QUALITY_INSPECTION_STATUS_DETAIL
 from app.kafka.options import STATUS_DETAIL_GROUP
 
 
-def run():
-
-    load_dotenv()
-
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-    )
-
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     consumer = create_consumer(
         topic=QUALITY_INSPECTION_STATUS_DETAIL,
@@ -38,76 +16,86 @@ def run():
 
     try:
 
-        for msg in consumer:
+        while not stop_event.is_set():
 
-            row = msg.value
+            # 1초마다 종료 여부 확인
+            records = consumer.poll(timeout_ms=1000)
 
-            vehicle_id = row["vehicle_id"]
+            if not records:
+                continue
 
-            # 이미 저장된 차량인지 확인
-            exists = pd.read_sql(
-                text("""
-                    SELECT COUNT(*) AS cnt
-                    FROM inspection_status_detail
-                    WHERE vehicle_id = :vehicle_id
-                """),
-                con=main_engine,
-                params={"vehicle_id": vehicle_id}
-            )
+            for _, messages in records.items():
 
-            if exists.iloc[0]["cnt"] > 0:
+                for msg in messages:
 
-                continue
+                    row = msg.value
+
+                    vehicle_id = row["vehicle_id"]
 
-            inspection_status_detail = [{
-                "car_code":
-                    row["car_code"],
+                    # 이미 저장된 차량인지 확인
+                    exists = pd.read_sql(
+                        text("""
+                            SELECT COUNT(*) AS cnt
+                            FROM inspection_status_detail
+                            WHERE vehicle_id = :vehicle_id
+                        """),
+                        con=main_engine,
+                        params={
+                            "vehicle_id": vehicle_id
+                        }
+                    )
 
-                "inspection_no":
-                    row["inspection_no"],
+                    if exists.iloc[0]["cnt"] > 0:
+                        continue
 
-                "vehicle_id":
-                    vehicle_id,
+                    inspection_status_detail = [{
+                        "car_code":
+                            row["car_code"],
 
-                "speed":
-                    row["speed"],
+                        "inspection_no":
+                            row["inspection_no"],
 
-                "att":
-                    row["att"],
+                        "vehicle_id":
+                            vehicle_id,
 
-                "gear":
-                    row["gear"],
+                        "speed":
+                            row["speed"],
 
-                "battery_voltage":
-                    row["battery_voltage"],
+                        "att":
+                            row["att"],
 
-                "fuel_rate":
-                    row["fuel_rate"],
+                        "gear":
+                            row["gear"],
 
-                "status_score":
-                    row["status_score"],
+                        "battery_voltage":
+                            row["battery_voltage"],
 
-                "inspection_result":
-                    row["inspection_result"],
+                        "fuel_rate":
+                            row["fuel_rate"],
 
-                "issue_message":
-                    row["issue_message"],
+                        "status_score":
+                            row["status_score"],
 
-                "created_at":
-                    row["created_at"]
-            }]
+                        "inspection_result":
+                            row["inspection_result"],
 
-            df = pd.DataFrame(
-                inspection_status_detail
-            )
+                        "issue_message":
+                            row["issue_message"],
 
-            df.to_sql(
-                name="inspection_status_detail",
-                con=main_engine,
-                if_exists="append",
-                index=False
-            )
+                        "created_at":
+                            row["created_at"]
+                    }]
 
+                    df = pd.DataFrame(
+                        inspection_status_detail
+                    )
+
+                    df.to_sql(
+                        name="inspection_status_detail",
+                        con=main_engine,
+                        if_exists="append",
+                        index=False
+                    )
 
     except Exception as e:
 
@@ -115,12 +103,13 @@ def run():
 
     finally:
 
-        print(
-            "status-detail Consumer 종료"
-        )
+        print("status-detail Consumer 종료")
 
         consumer.close()
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+
+    import threading
+
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index aa15797..8fb5d84 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -1,167 +1,165 @@
-from sqlalchemy import create_engine, text
-from dotenv import load_dotenv
-from urllib.parse import quote_plus
-import os
+from sqlalchemy import text
 import time
 
+from app.db import main_engine
 
-def run():
 
-    load_dotenv()
+def run(stop_event):
 
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-    MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-    )
+    last_total_count = -1
 
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+    try:
 
-    last_total_count = -1
+        while not stop_event.is_set():
 
-    while True:
+            with main_engine.begin() as conn:
 
-        with main_engine.begin() as conn:
+                result = conn.execute(
+                    text("""
+                        SELECT
+                            COUNT(*) AS total_count,
 
-            result = conn.execute(
-                text("""
-                    SELECT
-                        COUNT(*) AS total_count,
+                            SUM(
+                                CASE
+                                    WHEN UPPER(inspection_result) = 'NORMAL'
+                                    THEN 1
+                                    ELSE 0
+                                END
+                            ) AS normal_count,
 
-                        SUM(
-                            CASE
-                                WHEN UPPER(inspection_result) = 'NORMAL'
-                                THEN 1
-                                ELSE 0
-                            END
-                        ) AS normal_count,
+                            SUM(
+                                CASE
+                                    WHEN UPPER(inspection_result) = 'WARNING'
+                                    THEN 1
+                                    ELSE 0
+                                END
+                            ) AS abnormal_count,
 
-                        SUM(
-                            CASE
-                                WHEN UPPER(inspection_result) = 'WARNING'
-                                THEN 1
-                                ELSE 0
-                            END
-                        ) AS abnormal_count,
+                            MAX(created_at) AS created_at
 
-                        MAX(created_at) AS created_at
+                        FROM inspection_drive_detail
+                    """)
+                ).mappings().first()
 
-                    FROM inspection_drive_detail
-                """)
-            ).mappings().first()
+                total_count = result["total_count"] or 0
 
-            total_count = result["total_count"] or 0
+                # 데이터 변화가 없으면 건너뜀
+                if total_count == last_total_count:
+                    time.sleep(1)
+                    continue
 
-            # 데이터 변화가 없으면 건너뜀
-            if total_count == last_total_count:
-                time.sleep(1)
-                continue
+                normal_count = result["normal_count"] or 0
+                abnormal_count = result["abnormal_count"] or 0
 
-            normal_count = result["normal_count"] or 0
-            abnormal_count = result["abnormal_count"] or 0
+                standby_count = max(0, 100 - total_count)
 
-            standby_count = max(0, 100 - total_count)
+                if total_count == 0:
 
-            if total_count == 0:
-                normal_rate = 0
-                abnormal_rate = 0
-            else:
-                normal_rate = round(
-                    normal_count / total_count * 100,
-                    2
-                )
+                    normal_rate = 0
+                    abnormal_rate = 0
 
-                abnormal_rate = round(
-                    abnormal_count / total_count * 100,
-                    2
-                )
+                else:
 
-            created_at = result["created_at"]
+                    normal_rate = round(
+                        normal_count / total_count * 100,
+                        2
+                    )
 
-            # inspection_summary가 비어있는지 확인
-            exists = conn.execute(
-                text("""
-                    SELECT COUNT(*)
-                    FROM inspection_summary
-                """)
-            ).scalar()
+                    abnormal_rate = round(
+                        abnormal_count / total_count * 100,
+                        2
+                    )
 
-            if exists == 0:
+                created_at = result["created_at"]
 
-                conn.execute(
+                # inspection_summary가 비어있는지 확인
+                exists = conn.execute(
                     text("""
-                        INSERT INTO inspection_summary
-                        (
-                            total_count,
-                            normal_count,
-                            normal_rate,
-                            abnormal_count,
-                            abnormal_rate,
-                            standby_count,
-                            created_at,
-                            updated_at
-                        )
-                        VALUES
-                        (
-                            :total_count,
-                            :normal_count,
-                            :normal_rate,
-                            :abnormal_count,
-                            :abnormal_rate,
-                            :standby_count,
-                            :created_at,
-                            NOW()
-                        )
-                    """),
-                    {
-                        "total_count": total_count,
-                        "normal_count": normal_count,
-                        "normal_rate": normal_rate,
-                        "abnormal_count": abnormal_count,
-                        "abnormal_rate": abnormal_rate,
-                        "standby_count": standby_count,
-                        "created_at": created_at
-                    }
-                )
-
-            else:
-
-                conn.execute(
-                    text("""
-                        UPDATE inspection_summary
-                        SET
-                            total_count=:total_count,
-                            normal_count=:normal_count,
-                            normal_rate=:normal_rate,
-                            abnormal_count=:abnormal_count,
-                            abnormal_rate=:abnormal_rate,
-                            standby_count=:standby_count,
-                            created_at=:created_at,
-                            updated_at=NOW()
-                    """),
-                    {
-                        "total_count": total_count,
-                        "normal_count": normal_count,
-                        "normal_rate": normal_rate,
-                        "abnormal_count": abnormal_count,
-                        "abnormal_rate": abnormal_rate,
-                        "standby_count": standby_count,
-                        "created_at": created_at
-                    }
-                )
-
-            last_total_count = total_count
-
-        time.sleep(1)
+                        SELECT COUNT(*)
+                        FROM inspection_summary
+                    """)
+                ).scalar()
+
+                if exists == 0:
+
+                    conn.execute(
+                        text("""
+                            INSERT INTO inspection_summary
+                            (
+                                total_count,
+                                normal_count,
+                                normal_rate,
+                                abnormal_count,
+                                abnormal_rate,
+                                standby_count,
+                                created_at,
+                                updated_at
+                            )
+                            VALUES
+                            (
+                                :total_count,
+                                :normal_count,
+                                :normal_rate,
+                                :abnormal_count,
+                                :abnormal_rate,
+                                :standby_count,
+                                :created_at,
+                                NOW()
+                            )
+                        """),
+                        {
+                            "total_count": total_count,
+                            "normal_count": normal_count,
+                            "normal_rate": normal_rate,
+                            "abnormal_count": abnormal_count,
+                            "abnormal_rate": abnormal_rate,
+                            "standby_count": standby_count,
+                            "created_at": created_at
+                        }
+                    )
+
+                else:
+
+                    conn.execute(
+                        text("""
+                            UPDATE inspection_summary
+                            SET
+                                total_count = :total_count,
+                                normal_count = :normal_count,
+                                normal_rate = :normal_rate,
+                                abnormal_count = :abnormal_count,
+                                abnormal_rate = :abnormal_rate,
+                                standby_count = :standby_count,
+                                created_at = :created_at,
+                                updated_at = NOW()
+                        """),
+                        {
+                            "total_count": total_count,
+                            "normal_count": normal_count,
+                            "normal_rate": normal_rate,
+                            "abnormal_count": abnormal_count,
+                            "abnormal_rate": abnormal_rate,
+                            "standby_count": standby_count,
+                            "created_at": created_at
+                        }
+                    )
+
+                last_total_count = total_count
+
+            # stop_event를 고려하여 1초 대기
+            stop_event.wait(1)
+
+    except Exception as e:
+
+        print(f"Summary Aggregator 오류 발생 : {e}")
+
+    finally:
+
+        print("summary 종료")
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+
+    import threading
+
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/scheduler/quality/drive_detail_producer.py b/app/scheduler/quality/drive_detail_producer.py
index fbb5ad8..6631b26 100644
--- a/app/scheduler/quality/drive_detail_producer.py
+++ b/app/scheduler/quality/drive_detail_producer.py
@@ -1,42 +1,19 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 from kafka import KafkaProducer
-from dotenv import load_dotenv
-from urllib.parse import quote_plus
+
 import os
 import json
-import time
-
-from app.kafka.iam_provider import MSKTokenProvider
-
+import threading
 
-def run():
-
-    load_dotenv()
-
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(os.getenv("DB_PASSWORD"))
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-
-    SAMPLE_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/sampledb"
-    )
+from app.db import (
+    main_engine,
+    sample_engine,
+)
 
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/maindb"
-    )
+from app.kafka.iam_provider import MSKTokenProvider
 
-    sample_engine = create_engine(
-        SAMPLE_DATABASE_URL,
-        pool_pre_ping=True
-    )
 
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     producer = KafkaProducer(
         bootstrap_servers=[
@@ -51,96 +28,129 @@ def run():
         sasl_oauth_token_provider=MSKTokenProvider(),
 
         value_serializer=lambda x:
-            json.dumps(x, default=str).encode("utf-8")
+            json.dumps(
+                x,
+                default=str
+            ).encode("utf-8")
     )
 
     last_id = 0
 
-    while True:
+    try:
 
-        # 새로 생산된 차량 조회
-        with main_engine.connect() as conn:
+        while not stop_event.is_set():
 
-            cars = conn.execute(
-                text("""
-                    SELECT id, vehicle_id
-                    FROM inspection_master
-                    WHERE id > :last_id
-                    ORDER BY id
-                """),
-                {"last_id": last_id}
-            ).mappings().all()
+            # 새로 생산된 차량 조회
+            with main_engine.connect() as conn:
 
-        if not cars:
-            time.sleep(1)
-            continue
+                cars = conn.execute(
+                    text("""
+                        SELECT
+                            id,
+                            vehicle_id
+                        FROM inspection_master
+                        WHERE id > :last_id
+                        ORDER BY id
+                    """),
+                    {
+                        "last_id": last_id
+                    }
+                ).mappings().all()
 
-        for car in cars:
+            if not cars:
+                stop_event.wait(1)
+                continue
 
-            vehicle_id = car["vehicle_id"]
+            for car in cars:
 
-            # 해당 차량의 주행 데이터 조회
-            with sample_engine.connect() as conn:
+                if stop_event.is_set():
+                    break
 
-                drive_rows = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_drive
-                        WHERE vehicle_id = :vehicle_id
-                        ORDER BY created_at
-                    """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().all()
+                vehicle_id = car["vehicle_id"]
 
-            if not drive_rows:
-                print(
-                    f"[Drive] {vehicle_id} 데이터 없음"
-                )
+                # 해당 차량의 주행 데이터 조회
+                with sample_engine.connect() as conn:
 
-                last_id = car["id"]
-                continue
+                    drive_rows = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_drive
+                            WHERE vehicle_id = :vehicle_id
+                            ORDER BY created_at
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().all()
+
+                if not drive_rows:
 
-            for row in drive_rows:
+                    print(
+                        f"[Drive] {vehicle_id} 데이터 없음"
+                    )
 
-                message = {
+                    last_id = car["id"]
+                    continue
 
-                    "vehicle_id":
-                        row["vehicle_id"],
+                for row in drive_rows:
 
-                    "throttle_position":
-                        round(
-                            float(row["throttle_position"]),
-                            2
-                        ),
+                    message = {
 
-                    "brake_pressure":
-                        round(
-                            float(row["brake_pressure"]),
-                            2
-                        ),
+                        "vehicle_id":
+                            row["vehicle_id"],
 
-                    "steering_angle":
-                        round(
-                            float(row["steering_angle"]),
-                            2
-                        ),
+                        "throttle_position":
+                            round(
+                                float(
+                                    row["throttle_position"]
+                                ),
+                                2
+                            ),
 
-                    "created_at":
-                        row["created_at"]
-                }
+                        "brake_pressure":
+                            round(
+                                float(
+                                    row["brake_pressure"]
+                                ),
+                                2
+                            ),
 
-                producer.send(
-                    "quality.inspection.drive_detail",
-                    value=message
-                )
+                        "steering_angle":
+                            round(
+                                float(
+                                    row["steering_angle"]
+                                ),
+                                2
+                            ),
 
-            producer.flush()
+                        "created_at":
+                            row["created_at"]
+                    }
 
-            # 처리 완료 차량 갱신
-            last_id = car["id"]
+                    producer.send(
+                        "quality.inspection.drive_detail",
+                        value=message
+                    )
 
-        time.sleep(1)
+                producer.flush()
+
+                # 처리 완료 차량 갱신
+                last_id = car["id"]
+
+            stop_event.wait(1)
+
+    except Exception as e:
+
+        print(f"Drive Producer 오류 : {e}")
+
+    finally:
+
+        producer.flush()
+        producer.close()
+
+        print("Drive Producer 종료")
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index fee77cd..72883ed 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -1,37 +1,17 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 from kafka import KafkaProducer
-from dotenv import load_dotenv
-from urllib.parse import quote_plus
 
 import os
 import json
-import time
+import threading
 
+from app.db import main_engine
 from app.kafka.iam_provider import MSKTokenProvider
 
 TOTAL_TARGET = 100
 
 
-def run():
-
-    load_dotenv()
-
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/maindb"
-    )
-
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     producer = KafkaProducer(
         bootstrap_servers=[
@@ -46,134 +26,170 @@ def run():
         sasl_oauth_token_provider=MSKTokenProvider(),
 
         value_serializer=lambda x:
-            json.dumps(x, default=str).encode("utf-8")
+            json.dumps(
+                x,
+                default=str
+            ).encode("utf-8")
     )
 
     last_id = 0
 
-    while True:
-
-        with main_engine.connect() as conn:
-
-            new_cars = conn.execute(
-                text("""
-                    SELECT
-                        id,
-                        vehicle_id,
-                        created_at
-                    FROM inspection_master
-                    WHERE id > :last_id
-                    ORDER BY id
-                """),
-                {"last_id": last_id}
-            ).mappings().all()
-
-        if not new_cars:
-            time.sleep(1)
-            continue
-
-        for car in new_cars:
-
-            current_count = car["id"]
-
-            # 생산이 모두 끝난 경우
-            if current_count >= TOTAL_TARGET:
-
-                process_list = [
-                    ("VISUAL", TOTAL_TARGET),
-                    ("FUNCTION", TOTAL_TARGET),
-                    ("DRIVE", TOTAL_TARGET),
-                    ("FINAL", TOTAL_TARGET)
-                ]
-
-            else:
-
-                process_list = [
-                    (
-                        "VISUAL",
-                        min(current_count, TOTAL_TARGET)
-                    ),
-
-                    (
-                        "FUNCTION",
-                        min(
-                            max(current_count - 1, 0),
-                            TOTAL_TARGET
-                        )
-                    ),
+    try:
 
-                    (
-                        "DRIVE",
-                        min(
-                            max(current_count - 2, 0),
-                            TOTAL_TARGET
-                        )
-                    ),
+        while not stop_event.is_set():
+
+            with main_engine.connect() as conn:
+
+                new_cars = conn.execute(
+                    text("""
+                        SELECT
+                            id,
+                            vehicle_id,
+                            created_at
+                        FROM inspection_master
+                        WHERE id > :last_id
+                        ORDER BY id
+                    """),
+                    {
+                        "last_id": last_id
+                    }
+                ).mappings().all()
+
+            if not new_cars:
+                stop_event.wait(1)
+                continue
+
+            for car in new_cars:
+
+                if stop_event.is_set():
+                    break
+
+                current_count = car["id"]
 
-                    (
-                        "FINAL",
-                        min(
-                            max(current_count - 3, 0),
-                            TOTAL_TARGET
+                # 생산이 모두 끝난 경우
+                if current_count >= TOTAL_TARGET:
+
+                    process_list = [
+                        ("VISUAL", TOTAL_TARGET),
+                        ("FUNCTION", TOTAL_TARGET),
+                        ("DRIVE", TOTAL_TARGET),
+                        ("FINAL", TOTAL_TARGET)
+                    ]
+
+                else:
+
+                    process_list = [
+
+                        (
+                            "VISUAL",
+                            min(
+                                current_count,
+                                TOTAL_TARGET
+                            )
+                        ),
+
+                        (
+                            "FUNCTION",
+                            min(
+                                max(
+                                    current_count - 1,
+                                    0
+                                ),
+                                TOTAL_TARGET
+                            )
+                        ),
+
+                        (
+                            "DRIVE",
+                            min(
+                                max(
+                                    current_count - 2,
+                                    0
+                                ),
+                                TOTAL_TARGET
+                            )
+                        ),
+
+                        (
+                            "FINAL",
+                            min(
+                                max(
+                                    current_count - 3,
+                                    0
+                                ),
+                                TOTAL_TARGET
+                            )
                         )
+                    ]
+
+                for process_name, completed in process_list:
+
+                    waiting = max(
+                        0,
+                        TOTAL_TARGET - completed
                     )
-                ]
 
-            for process_name, completed in process_list:
+                    progress_rate = round(
+                        completed / TOTAL_TARGET * 100,
+                        2
+                    )
 
-                waiting = max(
-                    0,
-                    TOTAL_TARGET - completed
-                )
+                    if progress_rate >= 100:
+                        process_status = "COMPLETE"
 
-                progress_rate = round(
-                    completed / TOTAL_TARGET * 100,
-                    2
-                )
+                    elif progress_rate == 0:
+                        process_status = "WAIT"
 
-                if progress_rate >= 100:
-                    process_status = "COMPLETE"
+                    else:
+                        process_status = "RUNNING"
 
-                elif progress_rate == 0:
-                    process_status = "WAIT"
+                    message = {
 
-                else:
-                    process_status = "RUNNING"
+                        "process_name":
+                            process_name,
 
-                message = {
-                    "process_name":
-                        process_name,
+                        "total_vehicle_count":
+                            TOTAL_TARGET,
 
-                    "total_vehicle_count":
-                        TOTAL_TARGET,
+                        "completed_count":
+                            completed,
 
-                    "completed_count":
-                        completed,
+                        "waiting_count":
+                            waiting,
 
-                    "waiting_count":
-                        waiting,
+                        "progress_rate":
+                            progress_rate,
 
-                    "progress_rate":
-                        progress_rate,
+                        "process_status":
+                            process_status,
 
-                    "process_status":
-                        process_status,
+                        "created_at":
+                            car["created_at"]
+                    }
 
-                    "created_at":
-                        car["created_at"]
-                }
+                    producer.send(
+                        "quality.inspection.process",
+                        value=message
+                    )
 
-                producer.send(
-                    "quality.inspection.process",
-                    value=message
-                )
+                last_id = car["id"]
 
             producer.flush()
 
-            last_id = car["id"]
+            stop_event.wait(1)
+
+    except Exception as e:
+
+        print(f"Process Producer 오류 : {e}")
 
-        time.sleep(1)
+    finally:
+
+        producer.flush()
+        producer.close()
+
+        print("Process Producer 종료")
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_history_producer.py b/app/scheduler/quality/risk_history_producer.py
index 1470497..7b16ef2 100644
--- a/app/scheduler/quality/risk_history_producer.py
+++ b/app/scheduler/quality/risk_history_producer.py
@@ -1,49 +1,19 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 from kafka import KafkaProducer
-from dotenv import load_dotenv
-from urllib.parse import quote_plus
 
 import os
 import json
-import time
+import threading
 
-from app.kafka.iam_provider import MSKTokenProvider
-
-
-def run():
-
-    load_dotenv()
+from app.db import (
+    main_engine,
+    sample_engine,
+)
 
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-
-    SAMPLE_DB_NAME = os.getenv(
-        "SAMPLE_DB_NAME"
-    )
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/maindb"
-    )
+from app.kafka.iam_provider import MSKTokenProvider
 
-    SAMPLE_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-    )
 
-    sample_engine = create_engine(
-        SAMPLE_DATABASE_URL,
-        pool_pre_ping=True
-    )
-
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     producer = KafkaProducer(
 
@@ -68,221 +38,248 @@ def run():
 
     last_id = 0
 
-    while True:
-
-        with main_engine.connect() as conn:
-
-            new_cars = conn.execute(
-                text("""
-                    SELECT
-                        id,
-                        vehicle_id,
-                        DATE(created_at)
-                            AS inspection_date
-                    FROM inspection_master
-                    WHERE id > :last_id
-                    ORDER BY id
-                """),
-                {"last_id": last_id}
-            ).mappings().all()
-
-        if not new_cars:
-            time.sleep(1)
-            continue
-
-        for car in new_cars:
-
-            vehicle_id = car["vehicle_id"]
-            inspection_date = str(
-                car["inspection_date"]
-            )
-
-            scores = {}
-
-            with sample_engine.connect() as conn:
-
-                # DRIVE
-                row = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_drive
-                        WHERE vehicle_id =
-                            :vehicle_id
-                        LIMIT 1
-                    """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
-
-                if row:
+    try:
 
-                    score = 100
+        while not stop_event.is_set():
 
-                    if float(
-                        row["throttle_position"]
-                    ) > 90:
-                        score -= 20
+            with main_engine.connect() as conn:
 
-                    if float(
-                        row["brake_pressure"]
-                    ) > 45:
-                        score -= 20
-
-                    if abs(float(
-                        row["steering_angle"]
-                    )) > 40:
-                        score -= 20
-
-                    scores["DRIVE"] = max(
-                        score,
-                        0
-                    )
-
-                # CONTROL
-                row = conn.execute(
+                new_cars = conn.execute(
                     text("""
-                        SELECT *
-                        FROM car_control
-                        WHERE vehicle_id =
-                            :vehicle_id
-                        LIMIT 1
+                        SELECT
+                            id,
+                            vehicle_id,
+                            DATE(created_at)
+                                AS inspection_date
+                        FROM inspection_master
+                        WHERE id > :last_id
+                        ORDER BY id
                     """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
+                    {
+                        "last_id": last_id
+                    }
+                ).mappings().all()
 
-                if row:
+            if not new_cars:
+                stop_event.wait(1)
+                continue
 
-                    score = 100
+            for car in new_cars:
 
-                    if row[
-                        "collision_warning"
-                    ] == 1:
-                        score -= 40
+                if stop_event.is_set():
+                    break
 
-                    if row[
-                        "lane_departure"
-                    ] == 1:
-                        score -= 20
+                vehicle_id = car["vehicle_id"]
 
-                    if row[
-                        "traction_control"
-                    ] == 1:
-                        score -= 10
-
-                    if row[
-                        "abs_active"
-                    ] == 1:
-                        score -= 10
-
-                    scores["CONTROL"] = max(
-                        score,
-                        0
-                    )
-
-                # DYNAMICS
-                row = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_dynamics
-                        WHERE vehicle_id =
-                            :vehicle_id
-                        LIMIT 1
-                    """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
-
-                if row:
-
-                    score = 100
+                inspection_date = str(
+                    car["inspection_date"]
+                )
 
-                    if abs(
-                        float(
-                            row["yaw_rate"]
+                scores = {}
+
+                with sample_engine.connect() as conn:
+
+                    # DRIVE
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_drive
+                            WHERE vehicle_id =
+                                :vehicle_id
+                            LIMIT 1
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().first()
+
+                    if row:
+
+                        score = 100
+
+                        if float(
+                            row["throttle_position"]
+                        ) > 90:
+                            score -= 20
+
+                        if float(
+                            row["brake_pressure"]
+                        ) > 45:
+                            score -= 20
+
+                        if abs(
+                            float(
+                                row["steering_angle"]
+                            )
+                        ) > 40:
+                            score -= 20
+
+                        scores["DRIVE"] = max(
+                            score,
+                            0
                         )
-                    ) > 7:
-                        score -= 20
-
-                    if abs(
-                        float(row["roll"])
-                    ) > 4:
-                        score -= 20
-
-                    if abs(
-                        float(row["pitch"])
-                    ) > 4:
-                        score -= 20
-
-                    scores["DYNAMICS"] = max(
-                        score,
-                        0
-                    )
 
-                # STATUS
-                row = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_status
-                        WHERE vehicle_id =
-                            :vehicle_id
-                        LIMIT 1
-                    """),
-                    {"vehicle_id": vehicle_id}
-                ).mappings().first()
+                    # CONTROL
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_control
+                            WHERE vehicle_id =
+                                :vehicle_id
+                            LIMIT 1
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().first()
+
+                    if row:
+
+                        score = 100
+
+                        if row[
+                            "collision_warning"
+                        ] == 1:
+                            score -= 40
+
+                        if row[
+                            "lane_departure"
+                        ] == 1:
+                            score -= 20
+
+                        if row[
+                            "traction_control"
+                        ] == 1:
+                            score -= 10
+
+                        if row[
+                            "abs_active"
+                        ] == 1:
+                            score -= 10
+
+                        scores["CONTROL"] = max(
+                            score,
+                            0
+                        )
 
-                if row:
+                    # DYNAMICS
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_dynamics
+                            WHERE vehicle_id =
+                                :vehicle_id
+                            LIMIT 1
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().first()
+
+                    if row:
+
+                        score = 100
+
+                        if abs(
+                            float(
+                                row["yaw_rate"]
+                            )
+                        ) > 7:
+                            score -= 20
+
+                        if abs(
+                            float(
+                                row["roll"]
+                            )
+                        ) > 4:
+                            score -= 20
+
+                        if abs(
+                            float(
+                                row["pitch"]
+                            )
+                        ) > 4:
+                            score -= 20
+
+                        scores["DYNAMICS"] = max(
+                            score,
+                            0
+                        )
 
-                    score = 100
+                    # STATUS
+                    row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_status
+                            WHERE vehicle_id =
+                                :vehicle_id
+                            LIMIT 1
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().first()
+
+                    if row:
+
+                        score = 100
+
+                        if float(
+                            row["speed"]
+                        ) > 120:
+                            score -= 20
+
+                        if int(
+                            row["att"]
+                        ) > 4000:
+                            score -= 20
+
+                        if float(
+                            row[
+                                "battery_voltage"
+                            ]
+                        ) < 12:
+                            score -= 10
+
+                        scores["STATUS"] = max(
+                            score,
+                            0
+                        )
 
-                    if float(
-                        row["speed"]
-                    ) > 120:
-                        score -= 20
+                for inspection_type, risk_score in scores.items():
 
-                    if int(
-                        row["att"]
-                    ) > 4000:
-                        score -= 20
+                    producer.send(
+                        "quality.inspection.risk_history",
+                        value={
+                            "inspection_type":
+                                inspection_type,
 
-                    if float(
-                        row[
-                            "battery_voltage"
-                        ]
-                    ) < 12:
-                        score -= 10
+                            "inspection_date":
+                                inspection_date,
 
-                    scores["STATUS"] = max(
-                        score,
-                        0
+                            "risk_score":
+                                risk_score
+                        }
                     )
 
-            for inspection_type, risk_score in (
-                scores.items()
-            ):
+                last_id = car["id"]
 
-                message = {
-
-                    "inspection_type":
-                        inspection_type,
-
-                    "inspection_date":
-                        inspection_date,
+            producer.flush()
 
-                    "risk_score":
-                        risk_score
-                }
+            stop_event.wait(1)
 
-                producer.send(
-                    "quality.inspection.risk_history",
-                    value=message
-                )
+    except Exception as e:
 
-            producer.flush()
+        print(f"Risk History Producer 오류 : {e}")
 
-            last_id = car["id"]
+    finally:
 
-        time.sleep(1)
+        producer.flush()
+        producer.close()
 
-    producer.close()
+        print("Risk History Producer 종료")
 
 
 if __name__ == "__main__":
-    run()
\ No newline at end of file
+
+    run(threading.Event())
\ No newline at end of file
diff --git a/app/scheduler/quality/risk_trend_producer.py b/app/scheduler/quality/risk_trend_producer.py
index 6239f92..3f5e479 100644
--- a/app/scheduler/quality/risk_trend_producer.py
+++ b/app/scheduler/quality/risk_trend_producer.py
@@ -1,46 +1,19 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 from kafka import KafkaProducer
-from dotenv import load_dotenv
-from urllib.parse import quote_plus
 
 import os
 import json
 import time
 
-from app.kafka.iam_provider import MSKTokenProvider
-
-
-def run():
-
-    load_dotenv()
+from app.db import (
+    main_engine,
+    sample_engine
+)
 
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-
-    SAMPLE_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/sampledb"
-    )
-
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/maindb"
-    )
-
-    sample_engine = create_engine(
-        SAMPLE_DATABASE_URL,
-        pool_pre_ping=True
-    )
+from app.kafka.iam_provider import MSKTokenProvider
 
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
 
+def run(stop_event):
     producer = KafkaProducer(
         bootstrap_servers=[
             os.getenv("BROKER_URL_1"),
@@ -131,168 +104,175 @@ def get_risk_level(score):
         return "HIGH"
 
     last_id = 0
-
-    while True:
-
-        with main_engine.connect() as conn:
-
-            cars = conn.execute(
-                text("""
-                    SELECT *
-                    FROM inspection_master
-                    WHERE id > :last_id
-                    ORDER BY id
-                """),
-                {"last_id": last_id}
-            ).mappings().all()
-
-        if not cars:
-            time.sleep(1)
-            continue
-
-        for car in cars:
-
-            vehicle_id = car["vehicle_id"]
-
-            created_date = (
-                car["created_at"]
-                .strftime("%Y-%m-%d 00:00:00")
-            )
-
-            low = 0
-            medium = 0
-            high = 0
+    try:
+        while not stop_event.is_set():
 
             with main_engine.connect() as conn:
 
-                today_cars = conn.execute(
+                cars = conn.execute(
                     text("""
-                        SELECT vehicle_id
+                        SELECT *
                         FROM inspection_master
-                        WHERE DATE(created_at)
-                        =
-                        DATE(:created_at)
+                        WHERE id > :last_id
+                        ORDER BY id
                     """),
-                    {
-                        "created_at":
-                            car["created_at"]
-                    }
+                    {"last_id": last_id}
                 ).mappings().all()
 
-            for row in today_cars:
-
-                vid = row["vehicle_id"]
-
-                scores = []
-
-                with sample_engine.connect() as conn:
+            if not cars:
+                time.sleep(1)
+                continue
 
-                    status = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_status
-                            WHERE vehicle_id=:vid
-                            LIMIT 1
-                        """),
-                        {"vid": vid}
-                    ).mappings().first()
+            for car in cars:
 
-                    if status:
-                        scores.append(
-                            calculate_status_risk(status)
-                        )
+                vehicle_id = car["vehicle_id"]
 
-                    control = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_control
-                            WHERE vehicle_id=:vid
-                            LIMIT 1
-                        """),
-                        {"vid": vid}
-                    ).mappings().first()
+                created_date = (
+                    car["created_at"]
+                    .strftime("%Y-%m-%d 00:00:00")
+                )
 
-                    if control:
-                        scores.append(
-                            calculate_control_risk(control)
-                        )
+                low = 0
+                medium = 0
+                high = 0
 
-                    drive = conn.execute(
-                        text("""
-                            SELECT *
-                            FROM car_drive
-                            WHERE vehicle_id=:vid
-                            LIMIT 1
-                        """),
-                        {"vid": vid}
-                    ).mappings().first()
+                with main_engine.connect() as conn:
 
-                    if drive:
-                        scores.append(
-                            calculate_drive_risk(drive)
-                        )
-
-                    dynamics = conn.execute(
+                    today_cars = conn.execute(
                         text("""
-                            SELECT *
-                            FROM car_dynamics
-                            WHERE vehicle_id=:vid
-                            LIMIT 1
+                            SELECT vehicle_id
+                            FROM inspection_master
+                            WHERE DATE(created_at)
+                            =
+                            DATE(:created_at)
                         """),
-                        {"vid": vid}
-                    ).mappings().first()
-
-                    if dynamics:
-                        scores.append(
-                            calculate_dynamics_risk(dynamics)
-                        )
-
-                if not scores:
-                    continue
+                        {
+                            "created_at":
+                                car["created_at"]
+                        }
+                    ).mappings().all()
+
+                for row in today_cars:
+
+                    vid = row["vehicle_id"]
+
+                    scores = []
+
+                    with sample_engine.connect() as conn:
+
+                        status = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_status
+                                WHERE vehicle_id=:vid
+                                LIMIT 1
+                            """),
+                            {"vid": vid}
+                        ).mappings().first()
+
+                        if status:
+                            scores.append(
+                                calculate_status_risk(status)
+                            )
+
+                        control = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_control
+                                WHERE vehicle_id=:vid
+                                LIMIT 1
+                            """),
+                            {"vid": vid}
+                        ).mappings().first()
+
+                        if control:
+                            scores.append(
+                                calculate_control_risk(control)
+                            )
+
+                        drive = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_drive
+                                WHERE vehicle_id=:vid
+                                LIMIT 1
+                            """),
+                            {"vid": vid}
+                        ).mappings().first()
+
+                        if drive:
+                            scores.append(
+                                calculate_drive_risk(drive)
+                            )
+
+                        dynamics = conn.execute(
+                            text("""
+                                SELECT *
+                                FROM car_dynamics
+                                WHERE vehicle_id=:vid
+                                LIMIT 1
+                            """),
+                            {"vid": vid}
+                        ).mappings().first()
+
+                        if dynamics:
+                            scores.append(
+                                calculate_dynamics_risk(dynamics)
+                            )
+
+                    if not scores:
+                        continue
+
+                    avg_score = (
+                        sum(scores) / len(scores)
+                    )
+
+                    level = get_risk_level(
+                        avg_score
+                    )
+
+                    if level == "LOW":
+                        low += 1
+
+                    elif level == "MEDIUM":
+                        medium += 1
+
+                    else:
+                        high += 1
+
+                total = low + medium + high
+
+                for level, count in [
+                    ("LOW", low),
+                    ("MEDIUM", medium),
+                    ("HIGH", high)
+                ]:
+
+                    producer.send(
+                        "quality.inspection.risk_trend",
+                        value={
+                            "risk_level": level,
+
+                            "risk_count": count,
+
+                            "risk_ratio": round(
+                                count / total * 100, 2
+                            ) if total else 0,
+
+                            "created_at":
+                                created_date
+                        }
+                    )
+
+                producer.flush()
+
+                last_id = car["id"]
 
-                avg_score = (
-                    sum(scores) / len(scores)
-                )
-
-                level = get_risk_level(
-                    avg_score
-                )
-
-                if level == "LOW":
-                    low += 1
-
-                elif level == "MEDIUM":
-                    medium += 1
-
-                else:
-                    high += 1
-
-            total = low + medium + high
-
-            for level, count in [
-                ("LOW", low),
-                ("MEDIUM", medium),
-                ("HIGH", high)
-            ]:
-
-                producer.send(
-                    "quality.inspection.risk_trend",
-                    value={
-                        "risk_level": level,
-
-                        "risk_count": count,
-
-                        "risk_ratio": round(
-                            count / total * 100, 2
-                        ) if total else 0,
-
-                        "created_at":
-                            created_date
-                    }
-                )
-
-            producer.flush()
+            time.sleep(1)
 
-            last_id = car["id"]
+    except Exception as e:
+        print(f"오류 발생 : {e}")
 
-        time.sleep(1)
\ No newline at end of file
+    finally:
+        producer.close()
+        print("risk-trend producer 종료")
\ No newline at end of file
diff --git a/app/scheduler/quality/status_detail_producer.py b/app/scheduler/quality/status_detail_producer.py
index 968072b..82c5828 100644
--- a/app/scheduler/quality/status_detail_producer.py
+++ b/app/scheduler/quality/status_detail_producer.py
@@ -1,75 +1,42 @@
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
 from kafka import KafkaProducer
-from dotenv import load_dotenv
-from urllib.parse import quote_plus
 
 import os
 import json
 import time
 
-from app.kafka.iam_provider import MSKTokenProvider
-
-
-def run():
-
-    load_dotenv()
-
-    DB_USER = os.getenv("DB_USER")
-    DB_PASSWORD = quote_plus(
-        os.getenv("DB_PASSWORD")
-    )
-    DB_HOST = os.getenv("DB_HOST")
-    DB_PORT = os.getenv("DB_PORT")
-
-    SAMPLE_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/sampledb"
-    )
+from app.db import (
+    main_engine,
+    sample_engine,
+)
 
-    MAIN_DATABASE_URL = (
-        f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-        f"@{DB_HOST}:{DB_PORT}/maindb"
-    )
+from app.kafka.iam_provider import MSKTokenProvider
 
-    sample_engine = create_engine(
-        SAMPLE_DATABASE_URL,
-        pool_pre_ping=True
-    )
 
-    main_engine = create_engine(
-        MAIN_DATABASE_URL,
-        pool_pre_ping=True
-    )
+def run(stop_event):
 
     producer = KafkaProducer(
         bootstrap_servers=[
             os.getenv("BROKER_URL_1"),
             os.getenv("BROKER_URL_2")
         ],
-
         security_protocol="SASL_SSL",
-
         sasl_mechanism="OAUTHBEARER",
-
         sasl_oauth_token_provider=MSKTokenProvider(),
-
-        value_serializer=lambda x:
-            json.dumps(x, default=str).encode("utf-8")
+        value_serializer=lambda x: json.dumps(
+            x,
+            default=str
+        ).encode("utf-8")
     )
 
-    def calculate_status_score(
-        status,
-        control
-    ):
+    def calculate_status_score(status, control):
 
         score = 100
         issues = []
 
         speed = float(status["speed"])
         rpm = int(status["att"])
-        battery = float(
-            status["battery_voltage"]
-        )
+        battery = float(status["battery_voltage"])
 
         if speed > 120:
             score -= 20
@@ -87,10 +54,7 @@ def calculate_status_score(
             score -= 40
             issues.append("crash warning")
 
-        if (
-            status["gear"] == "P"
-            and speed > 20
-        ):
+        if status["gear"] == "P" and speed > 20:
             score -= 30
             issues.append("Parking")
 
@@ -108,123 +72,143 @@ def get_result(score):
 
     last_id = 0
 
-    while True:
-
-        with main_engine.connect() as conn:
-
-            new_cars = conn.execute(
-                text("""
-                    SELECT *
-                    FROM inspection_master
-                    WHERE id > :last_id
-                    ORDER BY id
-                """),
-                {"last_id": last_id}
-            ).mappings().all()
-
-        if not new_cars:
-            time.sleep(1)
-            continue
-
-        with sample_engine.connect() as conn:
-
-            for car in new_cars:
+    try:
 
-                vehicle_id = car["vehicle_id"]
+        while not stop_event.is_set():
 
-                status_row = conn.execute(
-                    text("""
-                        SELECT *
-                        FROM car_status
-                        WHERE vehicle_id=:vehicle_id
-                        ORDER BY created_at DESC
-                        LIMIT 1
-                    """),
-                    {
-                        "vehicle_id": vehicle_id
-                    }
-                ).mappings().first()
+            with main_engine.connect() as conn:
 
-                control_row = conn.execute(
+                new_cars = conn.execute(
                     text("""
                         SELECT *
-                        FROM car_control
-                        WHERE vehicle_id=:vehicle_id
-                        ORDER BY created_at DESC
-                        LIMIT 1
+                        FROM inspection_master
+                        WHERE id > :last_id
+                        ORDER BY id
                     """),
                     {
-                        "vehicle_id": vehicle_id
+                        "last_id": last_id
                     }
-                ).mappings().first()
-
-                if (
-                    not status_row
-                    or not control_row
-                ):
-                    continue
-
-                score, issues = (
-                    calculate_status_score(
+                ).mappings().all()
+
+            if not new_cars:
+                time.sleep(1)
+                continue
+
+            with sample_engine.connect() as conn:
+
+                for car in new_cars:
+
+                    if stop_event.is_set():
+                        break
+
+                    vehicle_id = car["vehicle_id"]
+
+                    status_row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_status
+                            WHERE vehicle_id = :vehicle_id
+                            ORDER BY created_at DESC
+                            LIMIT 1
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().first()
+
+                    control_row = conn.execute(
+                        text("""
+                            SELECT *
+                            FROM car_control
+                            WHERE vehicle_id = :vehicle_id
+                            ORDER BY created_at DESC
+                            LIMIT 1
+                        """),
+                        {
+                            "vehicle_id": vehicle_id
+                        }
+                    ).mappings().first()
+
+                    if not status_row or not control_row:
+                        continue
+
+                    score, issues = calculate_status_score(
                         status_row,
                         control_row
                     )
-                )
 
-                message = {
+                    message = {
+                        "car_code":
+                            vehicle_id.split("-")[0],
+
+                        "inspection_no":
+                            f"STATUS-{car['id']:05d}",
 
-                    "car_code":
-                        vehicle_id.split("-")[0],
+                        "vehicle_id":
+                            vehicle_id,
 
-                    "inspection_no":
-                        f"STATUS-{car['id']:05d}",
+                        "speed":
+                            float(status_row["speed"]),
 
-                    "vehicle_id":
-                        vehicle_id,
+                        "att":
+                            int(status_row["att"]),
 
-                    "speed":
-                        float(status_row["speed"]),
+                        "gear":
+                            status_row["gear"],
 
-                    "att":
-                        int(status_row["att"]),
+                        "battery_voltage":
+                            float(
+                                status_row["battery_voltage"]
+                            ),
 
-                    "gear":
-                        status_row["gear"],
+                        "fuel_rate":
+                            float(
+                                status_row["fuel_rate"]
+                            ),
 
-                    "battery_voltage":
-                        float(
-                            status_row[
-                                "battery_voltage"
-                            ]
-                        ),
+                        "status_score":
+                            score,
+
+                        "inspection_result":
+                            get_result(score),
+
+                        "issue_message":
+                            ", ".join(issues)
+                            if issues
+                            else "정상",
+
+                        "created_at":
+                            status_row["created_at"]
+                    }
+
+                    producer.send(
+                        "quality.inspection.status_detail",
+                        value=message
+                    )
+
+                    producer.flush()
+
+                    last_id = car["id"]
+
+            time.sleep(1)
 
-                    "fuel_rate":
-                        float(
-                            status_row["fuel_rate"]
-                        ),
+    except Exception as e:
 
-                    "status_score":
-                        score,
+        print(f"오류 발생 : {e}")
 
-                    "inspection_result":
-                        get_result(score),
+    finally:
 
-                    "issue_message":
-                        ", ".join(issues)
-                        if issues
-                        else "정상",
+        print("status-detail producer 종료")
 
-                    "created_at":
-                        status_row["created_at"]
-                }
+        try:
+            producer.flush()
+        except Exception:
+            pass
 
-                producer.send(
-                    "quality.inspection.status_detail",
-                    value=message
-                )
+        producer.close()
 
-                producer.flush()
 
-                last_id = car["id"]
+if __name__ == "__main__":
+    from threading import Event
 
-        time.sleep(1)
\ No newline at end of file
+    run(Event())
\ No newline at end of file
diff --git a/app/worker_main.py b/app/worker_main.py
index 9662d48..af4b1bf 100644
--- a/app/worker_main.py
+++ b/app/worker_main.py
@@ -5,12 +5,17 @@
 import signal
 import sys
 
+from app.db import (
+    main_dispose_engine,
+    sample_dispose_engine,
+)
+
 from app.scheduler.quality.drive_detail_producer import run as drive_producer
 from app.scheduler.quality.status_detail_producer import run as status_producer
 from app.scheduler.quality.risk_history_producer import run as history_producer
 from app.scheduler.quality.risk_trend_producer import run as trend_producer
 from app.scheduler.quality.process_producer import run as process_producer
-from app.scheduler.quality.stomp_client import run as stomp_client
+#from app.scheduler.quality.stomp_client import run as stomp_client
 
 from app.repository.quality_drive_detail_repository import run as drive_repository
 from app.repository.quality_status_detail_repository import run as status_repository
@@ -19,10 +24,6 @@
 from app.repository.quality_process_repository import run as process_repository
 from app.repository.quality_summary_repository import run as summary_repository
 
-# DB engine (너 프로젝트에 있는 위치로 수정 필요)
-from app.db import engine
-
-
 # =========================
 # STOP FLAG (핵심)
 # =========================
@@ -36,16 +37,8 @@
 def start_thread(name, target):
     print(f"[START] {name}")
 
-    def wrapped():
-        try:
-            # 각 worker가 stop_event를 받도록 확장 가능
-            target(stop_event)
-        except TypeError:
-            # 기존 코드 호환 (stop_event 안 받는 경우)
-            target()
-
     thread = threading.Thread(
-        target=wrapped,
+        target=lambda: target(stop_event),
         daemon=True,
         name=name
     )
@@ -58,19 +51,19 @@ def wrapped():
 # CLEAN SHUTDOWN
 # =========================
 def cleanup():
+    global cleanup_done
+
+    if cleanup_done:
+        return
+
+    cleanup_done = True
+
     print("\n🧹 Graceful Shutdown 시작...")
 
     # 1. stop signal 전달
     stop_event.set()
 
-    # 2. DB connection pool 종료
-    try:
-        engine.dispose()
-        print("🗄️ DB engine disposed")
-    except Exception as e:
-        print("DB dispose error:", e)
-
-    # 3. thread 종료 대기
+    # 2. thread 종료 대기
     print("⏳ threads join 중...")
     for t in threads:
         try:
@@ -78,6 +71,13 @@ def cleanup():
         except Exception:
             pass
 
+    # 3. DB connection pool 종료
+    try:
+        main_dispose_engine()
+        sample_dispose_engine()
+    except Exception as e:
+        print("DB dispose error:", e)
+
     print("✅ Shutdown 완료")
 
 

From 60b1035f9a1ea2e9ecfcbacb682baf852c7655fd Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Sat, 4 Jul 2026 09:47:52 +0900
Subject: [PATCH 097/148] fix : sample DB URL update

---
 app/db.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/db.py b/app/db.py
index 2f18edb..e410437 100644
--- a/app/db.py
+++ b/app/db.py
@@ -27,7 +27,7 @@
 
 SAMPLE_DATABASE_URL = (
     f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
+    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
 )
 
 if not MAIN_DATABASE_URL:

From 10e34d0e23018bf3499c0dbb9991282f42a375f3 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Sat, 4 Jul 2026 09:53:07 +0900
Subject: [PATCH 098/148] fix : cleanup_done default value add

---
 app/worker_main.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/app/worker_main.py b/app/worker_main.py
index af4b1bf..8fec748 100644
--- a/app/worker_main.py
+++ b/app/worker_main.py
@@ -28,6 +28,7 @@
 # STOP FLAG (핵심)
 # =========================
 stop_event = threading.Event()
+cleanup_done = False
 threads = []
 
 

From 2b7220b53fd9155c8bc5ad2657445dcdb2aa13f9 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Sat, 4 Jul 2026 10:30:50 +0900
Subject: [PATCH 099/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9=20=EC=A1=B0?=
 =?UTF-8?q?=ED=9A=8C=20=EC=8B=9C=20=EA=B0=80=EC=9E=A5=20=EB=B3=91=EB=AA=A9?=
 =?UTF-8?q?=20=EC=9C=84=ED=97=98=EB=8F=84=20=EB=86=92=EC=9D=80=20=EA=B3=B5?=
 =?UTF-8?q?=EC=A0=95=20=EC=9D=91=EB=8B=B5=EA=B0=92=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/api/routers/process.py                   |  4 +++
 app/dto/response/bottleneck_response.py      | 27 +++++++++++++++++---
 app/ml/inference/defect_transfer_detector.py |  2 +-
 app/service/analysis/bottleneck_service.py   | 19 +++++++++++++-
 4 files changed, 46 insertions(+), 6 deletions(-)

diff --git a/app/api/routers/process.py b/app/api/routers/process.py
index 5d0977b..a220bc8 100644
--- a/app/api/routers/process.py
+++ b/app/api/routers/process.py
@@ -45,6 +45,8 @@
 BottleneckAnalysisExample = {
     "success": True,
     "data": {
+        "mostBottleneckProcess": "도장",
+        "mostBottleneckRiskLevel": "위험",
         "content": [
             {
                 "rankNo": 1,
@@ -52,6 +54,7 @@
                 "delayTime": 12.4,
                 "affectedVehicleCount": 128,
                 "riskScore": 5.0,
+                "riskLevel": "위험",
             },
             {
                 "rankNo": 2,
@@ -59,6 +62,7 @@
                 "delayTime": 9.8,
                 "affectedVehicleCount": 92,
                 "riskScore": 4.0,
+                "riskLevel": "위험",
             },
         ],
         "hasNext": True,
diff --git a/app/dto/response/bottleneck_response.py b/app/dto/response/bottleneck_response.py
index f8088f5..7ab66dd 100644
--- a/app/dto/response/bottleneck_response.py
+++ b/app/dto/response/bottleneck_response.py
@@ -11,6 +11,7 @@ class BottleneckAnalysisItem(BaseModel):
                 "delayTime": 12.4,
                 "affectedVehicleCount": 128,
                 "riskScore": 5.0,
+                "riskLevel": "위험",
             },
         },
     )
@@ -23,7 +24,7 @@ class BottleneckAnalysisItem(BaseModel):
     process_code: str = Field(
         alias="processCode",
         description=(
-            "공정 표시명입니다. 공정명과 장비 번호를 조합해 "
+            "공정 표시명입니다. 공정명과 설비 번호를 조합해 "
             "`도장 (L3)`, `프레스 (P4)` 형식으로 반환합니다."
         ),
         examples=["도장 (L3)"],
@@ -31,8 +32,8 @@ class BottleneckAnalysisItem(BaseModel):
     delay_time: float = Field(
         alias="delayTime",
         description=(
-            "평균 지연 시간(초)입니다. DB에는 원본 double 값으로 저장하고 "
-            "API 응답에서만 소수점 둘째 자리까지 반올림합니다."
+            "평균 지연 시간(초)입니다. DB에서는 원본 double 값으로 저장하고 "
+            "API 응답에서는 소수 둘째 자리까지 반환합니다."
         ),
         examples=[12.4],
     )
@@ -49,6 +50,11 @@ class BottleneckAnalysisItem(BaseModel):
         ),
         examples=[5.0],
     )
+    risk_level: str = Field(
+        alias="riskLevel",
+        description="병목 위험도 요약입니다. 보통 또는 위험으로 반환됩니다.",
+        examples=["위험"],
+    )
 
 
 class BottleneckAnalysisPage(BaseModel):
@@ -56,6 +62,8 @@ class BottleneckAnalysisPage(BaseModel):
         populate_by_name=True,
         json_schema_extra={
             "example": {
+                "mostBottleneckProcess": "도장",
+                "mostBottleneckRiskLevel": "위험",
                 "content": [
                     {
                         "rankNo": 1,
@@ -63,6 +71,7 @@ class BottleneckAnalysisPage(BaseModel):
                         "delayTime": 12.4,
                         "affectedVehicleCount": 128,
                         "riskScore": 5.0,
+                        "riskLevel": "위험",
                     },
                 ],
                 "hasNext": True,
@@ -71,12 +80,22 @@ class BottleneckAnalysisPage(BaseModel):
         },
     )
 
+    most_bottleneck_process: str | None = Field(
+        alias="mostBottleneckProcess",
+        description="가장 병목인 공정명입니다.",
+        examples=["도장"],
+    )
+    most_bottleneck_risk_level: str | None = Field(
+        alias="mostBottleneckRiskLevel",
+        description="가장 병목인 공정의 위험도 요약입니다. 보통 또는 위험으로 반환됩니다.",
+        examples=["위험"],
+    )
     content: list[BottleneckAnalysisItem] = Field(
         description="병목 분석 결과 목록입니다. rankNo 오름차순으로 정렬됩니다.",
     )
     has_next: bool = Field(
         alias="hasNext",
-        description="다음 페이지 존재 여부입니다. false이면 다음 무한스크롤 호출을 중단해야 합니다.",
+        description="다음 페이지 존재 여부입니다. false이면 다음 무한 스크롤을 호출하지 않아야 합니다.",
         examples=[True],
     )
     next_cursor: int | None = Field(
diff --git a/app/ml/inference/defect_transfer_detector.py b/app/ml/inference/defect_transfer_detector.py
index 18ca4f4..e6c2ffc 100644
--- a/app/ml/inference/defect_transfer_detector.py
+++ b/app/ml/inference/defect_transfer_detector.py
@@ -482,4 +482,4 @@ def _station_code(equipment_code: str) -> str:
 def _cause_message(label: str, value: Any, unit: str) -> str:
     if isinstance(value, (int, float, np.number)):
         return f"{label} {float(value):.2f}{unit}"
-    return f"{label} {value}"
+    return f"{label} {value}"
\ No newline at end of file
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 56848bc..8f84db3 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -16,7 +16,7 @@
 
 
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
-BOTTLENECK_CACHE_VERSION = "v9"
+BOTTLENECK_CACHE_VERSION = "v10"
 PROCESS_CODE_LABELS = {
     "PRESS": "프레스",
     "BODY": "차체",
@@ -66,16 +66,23 @@ def get_realtime_bottlenecks(
         rows = self.repository.list_results(cursor=page, size=size)
         if not rows:
             return BottleneckAnalysisPage(
+                mostBottleneckProcess=None,
+                mostBottleneckRiskLevel=None,
                 content=[],
                 hasNext=False,
                 nextCursor=None,
             )
 
+        top_row = rows[0]
         total_count = self.repository.count_results()
         has_next = total_count > (page + 1) * size
         next_cursor = page + 1 if has_next else None
 
         return BottleneckAnalysisPage(
+            mostBottleneckProcess=self._format_process_label(top_row["process_code"]),
+            mostBottleneckRiskLevel=self._risk_level_label(
+                float(top_row["risk_score"]),
+            ),
             content=[
                 BottleneckAnalysisItem(
                     rankNo=int(row["rank_no"]),
@@ -86,6 +93,7 @@ def get_realtime_bottlenecks(
                     delayTime=round(float(row["avg_delay_time"]), 2),
                     affectedVehicleCount=int(row["affected_vehicle_count"]),
                     riskScore=float(row["risk_score"]),
+                    riskLevel=self._risk_level_label(float(row["risk_score"])),
                 )
                 for row in rows
             ],
@@ -304,6 +312,15 @@ def _format_process_code(
             return f"{label} ({prefix}{equipment_no})"
         return label
 
+    @staticmethod
+    def _format_process_label(process_code: Any) -> str:
+        normalized = str(process_code or "").strip().upper()
+        return PROCESS_CODE_LABELS.get(normalized, normalized)
+
+    @staticmethod
+    def _risk_level_label(risk_score: float) -> str:
+        return "위험" if risk_score >= 3.0 else "보통"
+
     @staticmethod
     def _equipment_number(equipment_code: Any | None) -> int | None:
         if equipment_code is None:

From 85f5b33c9a2b1eed3840afcec3877a835a38faad Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Sat, 4 Jul 2026 11:24:13 +0900
Subject: [PATCH 100/148] feat : events router add

---
 app/api/router.py         |  2 ++
 app/api/routers/events.py | 20 ++++++++++++++++++++
 2 files changed, 22 insertions(+)
 create mode 100644 app/api/routers/events.py

diff --git a/app/api/router.py b/app/api/router.py
index 86e661c..b24307a 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -9,6 +9,7 @@
     process_analysis_ws,
     root,
     manual,
+    events,
 )
 
 api_router = APIRouter()
@@ -20,3 +21,4 @@
 api_router.include_router(manufacturing_event.router)
 api_router.include_router(ml_dataset.router)
 api_router.include_router(manual.router)
+api_router.include_router(events.router)
diff --git a/app/api/routers/events.py b/app/api/routers/events.py
new file mode 100644
index 0000000..91b147c
--- /dev/null
+++ b/app/api/routers/events.py
@@ -0,0 +1,20 @@
+# app/api/routers/events.py
+
+from fastapi import APIRouter
+
+router = APIRouter(prefix="/events", tags=["events"])
+
+@router.get("")
+def get_events():
+    return [
+        {
+            "id": 1,
+            "title": "Robot Collision",
+            "severity": "위험",
+            "area": "BODY",
+            "subArea": "Robot 2",
+            "equipmentNo": "RB-201",
+            "aiScore": 95,
+            "status": "조치 필요"
+        }
+    ]
\ No newline at end of file

From 7bbc7bc16b754ee5dbffcb38f11bf2a1aabe5087 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Sat, 4 Jul 2026 15:25:09 +0900
Subject: [PATCH 101/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9&=EB=B6=88?=
 =?UTF-8?q?=EB=9F=89=EC=A0=84=EC=9D=B4=20=EC=A1=B0=ED=9A=8C=20=EC=8B=9C=20?=
 =?UTF-8?q?=EC=98=A4=EB=8A=98=20=EB=82=A0=EC=A7=9C=20=EA=B8=B0=EC=A4=80?=
 =?UTF-8?q?=EC=9D=98=20=EB=8D=B0=EC=9D=B4=ED=84=B0=EB=A7=8C=20=EB=B0=98?=
 =?UTF-8?q?=ED=99=98=EB=90=98=EB=8F=84=EB=A1=9D=20=EB=B3=80=EA=B2=BD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../bottleneck_analysis_repository.py         | 29 +++++++++++++------
 .../defect_transfer_prediction_repository.py  | 11 +++----
 2 files changed, 26 insertions(+), 14 deletions(-)

diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 0df2024..ece585d 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -46,8 +46,10 @@ def replace_results(
         from sqlalchemy import func
 
         with self.engine.begin() as conn:
-            delete_query = self.table.delete().where(
-                self.table.c.rank_no.between(start_rank, end_rank),
+            delete_query = (
+                self.table.delete()
+                .where(self.table.c.rank_no.between(start_rank, end_rank))
+                .where(func.date(self.table.c.detected_at) == func.current_date())
             )
             if payload_ranks:
                 delete_query = delete_query.where(
@@ -63,27 +65,31 @@ def replace_results(
                 result = conn.execute(
                     self.table.update()
                     .where(self.table.c.rank_no == row["rank_no"])
+                    .where(func.date(self.table.c.detected_at) == func.current_date())
                     .values(**update_values),
                 )
                 if not result.rowcount:
                     conn.execute(self.table.insert(), row)
 
     def prune_results_after_rank(self, max_rank: int) -> None:
+        from sqlalchemy import func
         with self.engine.begin() as conn:
             conn.execute(
-                self.table.delete().where(self.table.c.rank_no > max_rank),
+                self.table.delete()
+                .where(self.table.c.rank_no > max_rank)
+                .where(func.date(self.table.c.detected_at) == func.current_date())
             )
 
     def list_results(self, *, cursor: int, size: int) -> list[dict[str, Any]]:
-        from sqlalchemy import select
+        from sqlalchemy import select, func
 
-        start_rank = cursor * size + 1
-        end_rank = start_rank + size - 1
+        offset = cursor * size
 
         query = (
             select(self.table)
-            .where(self.table.c.rank_no.between(start_rank, end_rank))
+            .where(func.date(self.table.c.detected_at) == func.current_date())
             .order_by(self.table.c.rank_no.asc(), self.table.c.id.asc())
+            .offset(offset)
             .limit(size)
         )
 
@@ -93,12 +99,16 @@ def list_results(self, *, cursor: int, size: int) -> list[dict[str, Any]]:
     def count_results(self) -> int:
         from sqlalchemy import func, select
 
-        query = select(func.count()).select_from(self.table)
+        query = (
+            select(func.count())
+            .select_from(self.table)
+            .where(func.date(self.table.c.detected_at) == func.current_date())
+        )
         with self.engine.connect() as conn:
             return int(conn.execute(query).scalar_one())
 
     def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
-        from sqlalchemy import select
+        from sqlalchemy import select, func
 
         query = (
             select(
@@ -110,6 +120,7 @@ def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
                 manufacturing_event_json.c.event_json,
             )
             .where(manufacturing_event_json.c.is_sent.is_(True))
+            .where(func.date(manufacturing_event_json.c.created_at) == func.current_date())
             .order_by(manufacturing_event_json.c.id.asc())
         )
         with self.event_engine.connect() as conn:
diff --git a/app/repository/defect_transfer_prediction_repository.py b/app/repository/defect_transfer_prediction_repository.py
index 58af761..33a1be6 100644
--- a/app/repository/defect_transfer_prediction_repository.py
+++ b/app/repository/defect_transfer_prediction_repository.py
@@ -73,11 +73,12 @@ def replace_prediction_result(
                 },
             )
 
+        from sqlalchemy import func
         with self.engine.begin() as conn:
             conn.execute(
-                self.table.delete().where(
-                    self.table.c.car_master_id == car_master_id,
-                ),
+                self.table.delete()
+                .where(self.table.c.car_master_id == car_master_id)
+                .where(func.date(self.table.c.predicted_at) == func.current_date())
             )
             if self.engine.dialect.name != "mysql":
                 from sqlalchemy import func, select
@@ -243,9 +244,9 @@ def _all_prediction_rows(
         *,
         car_master_id: int | None = None,
     ) -> list[dict[str, Any]]:
-        from sqlalchemy import select
+        from sqlalchemy import select, func
 
-        query = select(self.table)
+        query = select(self.table).where(func.date(self.table.c.predicted_at) == func.current_date())
         if car_master_id is not None:
             query = query.where(self.table.c.car_master_id == car_master_id)
         query = query.order_by(

From 5387e56a6c62897e394e4333cc13740c63c74d79 Mon Sep 17 00:00:00 2001
From: haseokyung6 <haseokyung@naver.com>
Date: Sat, 4 Jul 2026 15:44:27 +0900
Subject: [PATCH 102/148] fix: newTag image update, secret update

---
 .github/workflows/deploy-ai-service.yml | 93 ++++++++++++++++++++++---
 1 file changed, 83 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/deploy-ai-service.yml b/.github/workflows/deploy-ai-service.yml
index b7aa4df..79a54aa 100644
--- a/.github/workflows/deploy-ai-service.yml
+++ b/.github/workflows/deploy-ai-service.yml
@@ -29,7 +29,9 @@ env:
   NAMESPACE: ai-services
 
   INFRA_REPOSITORY: SK-Rookies-AIMS/infra
-  INFRA_MANIFEST_PATH: k8s/ai-service/ai-service.yaml
+
+  # 기존 ai-service.yaml에서 kustomization.yaml로 변경
+  INFRA_MANIFEST_PATH: k8s/ai-service/kustomization.yaml
 
 jobs:
   deploy:
@@ -66,7 +68,10 @@ jobs:
 
       - name: Build and push Docker image
         id: build-image
+        shell: bash
         run: |
+          set -euo pipefail
+
           IMAGE_TAG="${GITHUB_REF_NAME}-${GITHUB_SHA::12}"
           IMAGE_URI="${{ steps.login-ecr.outputs.registry }}/${ECR_REPOSITORY}:${IMAGE_TAG}"
           BRANCH_IMAGE_URI="${{ steps.login-ecr.outputs.registry }}/${ECR_REPOSITORY}:${GITHUB_REF_NAME}"
@@ -92,7 +97,10 @@ jobs:
 
       - name: Update kubeconfig and ensure namespace
         if: github.ref_name == 'dev'
+        shell: bash
         run: |
+          set -euo pipefail
+
           aws eks update-kubeconfig \
             --region "$AWS_REGION" \
             --name "$EKS_CLUSTER_NAME"
@@ -101,9 +109,13 @@ jobs:
             --dry-run=client \
             -o yaml | kubectl apply -f -
 
+      # 변경: DB 이름과 Redis 정보를 추가로 조회
       - name: Load AI service parameters from SSM
         if: github.ref_name == 'dev'
+        shell: bash
         run: |
+          set -euo pipefail
+
           RDS_SECRET_ARN=$(aws ssm get-parameter \
             --name "/aims/dev/rds/secret-arn" \
             --region "$AWS_REGION" \
@@ -122,6 +134,30 @@ jobs:
             --query "Parameter.Value" \
             --output text)
 
+          MAIN_DB_NAME=$(aws ssm get-parameter \
+            --name "/aims/dev/backend/main-db-name" \
+            --region "$AWS_REGION" \
+            --query "Parameter.Value" \
+            --output text)
+
+          SAMPLE_DB_NAME=$(aws ssm get-parameter \
+            --name "/aims/dev/backend/sample-db-name" \
+            --region "$AWS_REGION" \
+            --query "Parameter.Value" \
+            --output text)
+
+          REDIS_HOST=$(aws ssm get-parameter \
+            --name "/aims/dev/redis/host" \
+            --region "$AWS_REGION" \
+            --query "Parameter.Value" \
+            --output text)
+
+          REDIS_PORT=$(aws ssm get-parameter \
+            --name "/aims/dev/redis/port" \
+            --region "$AWS_REGION" \
+            --query "Parameter.Value" \
+            --output text)
+
           OPENAI_API_KEY=$(aws ssm get-parameter \
             --name "/aims/dev/ai-service/openai-api-key" \
             --region "$AWS_REGION" \
@@ -133,6 +169,10 @@ jobs:
             "$RDS_SECRET_ARN" \
             "$RDS_HOST" \
             "$RDS_PORT" \
+            "$MAIN_DB_NAME" \
+            "$SAMPLE_DB_NAME" \
+            "$REDIS_HOST" \
+            "$REDIS_PORT" \
             "$OPENAI_API_KEY"
           do
             if [ -z "$VALUE" ] || [ "$VALUE" = "None" ]; then
@@ -144,14 +184,24 @@ jobs:
           echo "::add-mask::$RDS_SECRET_ARN"
           echo "::add-mask::$OPENAI_API_KEY"
 
-          echo "RDS_SECRET_ARN=$RDS_SECRET_ARN" >> "$GITHUB_ENV"
-          echo "RDS_HOST=$RDS_HOST" >> "$GITHUB_ENV"
-          echo "RDS_PORT=$RDS_PORT" >> "$GITHUB_ENV"
-          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> "$GITHUB_ENV"
-
+          {
+            echo "RDS_SECRET_ARN=$RDS_SECRET_ARN"
+            echo "RDS_HOST=$RDS_HOST"
+            echo "RDS_PORT=$RDS_PORT"
+            echo "MAIN_DB_NAME=$MAIN_DB_NAME"
+            echo "SAMPLE_DB_NAME=$SAMPLE_DB_NAME"
+            echo "REDIS_HOST=$REDIS_HOST"
+            echo "REDIS_PORT=$REDIS_PORT"
+            echo "OPENAI_API_KEY=$OPENAI_API_KEY"
+          } >> "$GITHUB_ENV"
+
+      # 변경: DB_HOST, DB_PORT, DB 이름, URL, Redis URL 추가
       - name: Create or update AI service Secret
         if: github.ref_name == 'dev'
+        shell: bash
         run: |
+          set -euo pipefail
+
           SECRET_JSON=$(aws secretsmanager get-secret-value \
             --secret-id "$RDS_SECRET_ARN" \
             --region "$AWS_REGION" \
@@ -174,27 +224,50 @@ jobs:
           echo "::add-mask::$DB_USER"
           echo "::add-mask::$DB_PASSWORD"
 
+          DB_USER_ENCODED=$(printf '%s' "$DB_USER" | jq -sRr @uri)
+          DB_PASSWORD_ENCODED=$(printf '%s' "$DB_PASSWORD" | jq -sRr @uri)
+
+          MAIN_DATABASE_URL="mysql+pymysql://${DB_USER_ENCODED}:${DB_PASSWORD_ENCODED}@${RDS_HOST}:${RDS_PORT}/${MAIN_DB_NAME}?charset=utf8mb4"
+          REDIS_URL="redis://${REDIS_HOST}:${REDIS_PORT}/0"
+
+          echo "::add-mask::$MAIN_DATABASE_URL"
+
           kubectl create secret generic ai-service-secret \
             --namespace "$NAMESPACE" \
             --from-literal=OPENAI_API_KEY="$OPENAI_API_KEY" \
+            --from-literal=MAIN_DATABASE_URL="$MAIN_DATABASE_URL" \
             --from-literal=DB_USER="$DB_USER" \
             --from-literal=DB_PASSWORD="$DB_PASSWORD" \
+            --from-literal=DB_HOST="$RDS_HOST" \
+            --from-literal=DB_PORT="$RDS_PORT" \
+            --from-literal=MAIN_DB_NAME="$MAIN_DB_NAME" \
+            --from-literal=SAMPLE_DB_NAME="$SAMPLE_DB_NAME" \
+            --from-literal=REDIS_URL="$REDIS_URL" \
             --dry-run=client \
             -o yaml | kubectl apply -f -
 
-      - name: Update AI service image in GitOps repository
+      # 변경: image: 전체 URI가 아니라 newTag:만 변경
+      - name: Update AI service image tag in GitOps repository
         env:
           IMAGE_URI: ${{ steps.build-image.outputs.image_uri }}
+        shell: bash
         run: |
+          set -euo pipefail
+
           MANIFEST="infra/${INFRA_MANIFEST_PATH}"
 
-          test -f "$MANIFEST"
+          if [ ! -f "$MANIFEST" ]; then
+            echo "Manifest not found: $MANIFEST"
+            exit 1
+          fi
+
+          IMAGE_TAG="${IMAGE_URI##*:}"
 
           sed -i -E \
-            "s|^([[:space:]]*)image:.*aims/ai-service.*$|\1image: ${IMAGE_URI}|g" \
+            "s|^([[:space:]]*)newTag:.*$|\1newTag: ${IMAGE_TAG}|" \
             "$MANIFEST"
 
-          grep -n "image:" "$MANIFEST"
+          grep -n "newTag:" "$MANIFEST"
 
           cd infra
 

From ceb0475a903399860a383e2df12cca4ecaf6d954 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Sat, 4 Jul 2026 15:48:45 +0900
Subject: [PATCH 103/148] fix : ai-manual update

---
 app/ai_manual/prompt/prompt_template.py       |   6 +-
 .../repository/alert_event_repository.py      |  77 ++--------
 app/ai_manual/service/manual_service.py       | 136 +++++++++---------
 app/api/router.py                             |   4 +-
 app/api/routers/events.py                     |  20 ---
 app/main.py                                   |   9 +-
 6 files changed, 85 insertions(+), 167 deletions(-)
 delete mode 100644 app/api/routers/events.py

diff --git a/app/ai_manual/prompt/prompt_template.py b/app/ai_manual/prompt/prompt_template.py
index d5eda02..30e37f9 100644
--- a/app/ai_manual/prompt/prompt_template.py
+++ b/app/ai_manual/prompt/prompt_template.py
@@ -74,7 +74,7 @@
 
 ## 반드시 반환해야 하는 JSON 형식
 
-{
+{{
     "title": "...",
     "summary": "...",
     "difficulty": "...",
@@ -84,7 +84,7 @@
     "completion_check": [],
     "escalation": "...",
     "prevention": []
-}
+}}
 
 JSON 외의 어떠한 문장도 출력하지 마십시오.
 """
@@ -93,7 +93,7 @@
 HUMAN_PROMPT = """
 # 현재 Critical Event
 
-{Critical_event}
+{critical_event}
 
 ------------------------------------------------
 
diff --git a/app/ai_manual/repository/alert_event_repository.py b/app/ai_manual/repository/alert_event_repository.py
index 022f0ca..4089e8a 100644
--- a/app/ai_manual/repository/alert_event_repository.py
+++ b/app/ai_manual/repository/alert_event_repository.py
@@ -1,48 +1,10 @@
-# ai_manual/repository/alert_event_repository.py
-
-import os
-from decimal import Decimal
-from urllib.parse import quote_plus
-
-from dotenv import load_dotenv
-from sqlalchemy import create_engine, text
+from sqlalchemy import text
+from app.db import main_engine
 
 
 class AlertEventRepository:
 
-    def __init__(self):
-        load_dotenv()
-
-        DB_USER = os.getenv("DB_USER")
-        DB_PASSWORD = quote_plus(
-            os.getenv("DB_PASSWORD")
-        )
-        DB_HOST = os.getenv("DB_HOST")
-        DB_PORT = os.getenv("DB_PORT")
-        MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-
-        MAIN_DATABASE_URL = (
-            f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-            f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-        )
-
-        self.engine = create_engine(
-            MAIN_DATABASE_URL,
-            pool_pre_ping=True
-        )
-
-    def get_highest_priority_event(self):
-        """
-        현재 처리해야 하는 가장 높은 우선순위의
-        Critical Event를 조회한다.
-
-        조건
-        - action_status = PENDING
-        - resolved_at IS NULL
-        - priority_score DESC
-        - created_at ASC
-        """
-
+    def get_highest_risk_event(self):
         query = text("""
             SELECT
                 log_no,
@@ -65,38 +27,17 @@ def get_highest_priority_event(self):
                 created_at,
                 resolved_at
             FROM alert_event
-            WHERE action_status = 'PENDING'
+            WHERE severity = 'DANGER'
+              AND action_status = 'PENDING'
               AND resolved_at IS NULL
-            ORDER BY priority_score DESC,
-                     created_at ASC
+            ORDER BY risk_score DESC
             LIMIT 1
         """)
 
-        with self.engine.connect() as conn:
+        with main_engine.connect() as conn:
             row = conn.execute(query).mappings().first()
 
-        if row is None:
+        if not row:
             return None
 
-        return self._convert(row)
-
-    def _convert(self, row):
-        """
-        SQLAlchemy RowMapping -> dict
-        Decimal과 datetime을 JSON 직렬화 가능한 형태로 변환
-        """
-
-        result = {}
-
-        for key, value in row.items():
-
-            if isinstance(value, Decimal):
-                result[key] = float(value)
-
-            elif hasattr(value, "isoformat"):
-                result[key] = value.isoformat()
-
-            else:
-                result[key] = value
-
-        return result
\ No newline at end of file
+        return dict(row)
\ No newline at end of file
diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index 939e597..c0adf40 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -1,16 +1,10 @@
-# ai_manual/service/manual_service.py
-
 import os
-
 from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
 
 from app.ai_manual.prompt.prompt_template import manual_prompt
 from app.ai_manual.rag.vector_store import VectorStore
-
-from app.ai_manual.repository.alert_event_repository import (
-    AlertEventRepository
-)
+from app.ai_manual.repository.alert_event_repository import AlertEventRepository
 
 from app.ai_manual.schema.request import (
     CriticalEvent,
@@ -31,125 +25,123 @@ def __init__(self):
         load_dotenv()
 
         self.repository = AlertEventRepository()
-
         self.vector_store = VectorStore()
 
         self.llm = ChatOpenAI(
             api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4.1",
+            model="gpt-5-mini",
             temperature=0.2
-        ).with_structured_output(
-            ManualResponse
-        )
+        ).with_structured_output(ManualResponse)
 
+    # ======================================================
+    # MAIN ENTRY
+    # ======================================================
     def generate_manual(
         self,
         operator_grade: str = "Junior"
     ) -> ManualResponse | None:
 
-        event = self.repository.get_highest_priority_event()
+        # 1. EVENT FETCH
+        event = self.repository.get_highest_risk_event()
 
         if event is None:
             return None
 
-        request = self._build_request(
-            event,
-            operator_grade
-        )
+        # 2. BUILD STRONG REQUEST OBJECT
+        request = self._build_request(event, operator_grade)
 
-        prompt = manual_prompt.invoke(
-            {
-                "critical_event":
-                    request.critical_event.model_dump_json(
-                        indent=2,
-                        ensure_ascii=False
-                    ),
-
-                "operator":
-                    request.operator.model_dump_json(
-                        indent=2,
-                        ensure_ascii=False
-                    ),
-
-                "factory_context":
-                    request.factory_context.model_dump_json(
-                        indent=2,
-                        ensure_ascii=False
-                    ),
-
-                "rag_context":
-                    "\n".join(
-                        request.rag_context.documents
-                    )
-            }
-        )
+        # 3. PROMPT BUILD
+        prompt = manual_prompt.invoke({
+            "critical_event": self._format_json(request.critical_event),
+            "operator": self._format_json(request.operator),
+            "factory_context": self._format_json(request.factory_context),
+            "rag_context": self._format_rag(request.rag_context)
+        })
 
+        # 4. LLM CALL
         response = self.llm.invoke(prompt)
 
         return response
 
+    # ======================================================
+    # REQUEST BUILDER
+    # ======================================================
     def _build_request(
         self,
         event: dict,
         operator_grade: str
     ) -> ManualRequest:
 
+        # --------------------------
+        # 1. Equipment mapping (safe)
+        # --------------------------
         equipment = None
 
-        if event["equipment_id"] is not None:
-
+        if event.get("equipment_id") is not None:
             equipment = EquipmentInfo(
                 id=event["equipment_id"],
-                name=f"Equipment-{event['equipment_id']}",
+                name=f"EQ-{event['equipment_id']}",  # fallback only
                 type="Industrial Equipment"
             )
 
+        # --------------------------
+        # 2. Critical Event DTO
+        # --------------------------
         critical_event = CriticalEvent(
             event_id=event["event_id"],
-            priority_score=event["priority_score"],
-            risk_score=event["risk_score"],
+            priority_score=float(event.get("priority_score") or 0),
+            risk_score=float(event.get("risk_score") or 0),
             severity=event["severity"],
             alert_type=event["alert_type"],
             process_code=event["process_code"],
             equipment=equipment,
             title=event["title"],
             description=event["contents"],
-            occurred_at=event["created_at"]
+            occurred_at=str(event["created_at"])
         )
 
-        rag_documents = self.vector_store.search(
-            critical_event
-        )
+        # --------------------------
+        # 3. RAG SEARCH
+        # --------------------------
+        rag_documents = self.vector_store.search(critical_event)
 
+        # --------------------------
+        # 4. FINAL REQUEST
+        # --------------------------
         return ManualRequest(
-
             critical_event=critical_event,
-
             operator=OperatorInfo(
                 grade=operator_grade
             ),
-
             factory_context=FactoryContext(
                 system_name="AIMS Smart Factory",
-                description="""
-자동차 제조 스마트팩토리입니다.
-
-PRESS
-BODY
-PAINT
-ASSEMBLY
-
-MES
-Kafka
-AI-Service
-Main-Service
-PLC
-Robot
-Vision Inspection
-"""
+                description=(
+                    "자동차 제조 스마트팩토리 시스템\n\n"
+                    "공정: PRESS / BODY / PAINT / ASSEMBLY\n"
+                    "시스템: Kafka / AI-Service / Main-Service / PLC / Robot / Vision"
+                )
             ),
-
             rag_context=RagContext(
                 documents=rag_documents
             )
+        )
+
+    # ======================================================
+    # HELPERS
+    # ======================================================
+    def _format_json(self, obj) -> str:
+        """
+        LLM readability 개선용 JSON formatter
+        """
+        return obj.model_dump_json(
+            indent=2,
+            ensure_ascii=False
+        )
+
+    def _format_rag(self, rag: RagContext) -> str:
+        """
+        RAG 문서 readable string 변환
+        """
+        return "\n".join(
+            f"- {doc}" for doc in rag.documents
         )
\ No newline at end of file
diff --git a/app/api/router.py b/app/api/router.py
index b24307a..7a72451 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -9,7 +9,6 @@
     process_analysis_ws,
     root,
     manual,
-    events,
 )
 
 api_router = APIRouter()
@@ -20,5 +19,4 @@
 api_router.include_router(process_analysis_ws.router)
 api_router.include_router(manufacturing_event.router)
 api_router.include_router(ml_dataset.router)
-api_router.include_router(manual.router)
-api_router.include_router(events.router)
+api_router.include_router(manual.router)
\ No newline at end of file
diff --git a/app/api/routers/events.py b/app/api/routers/events.py
deleted file mode 100644
index 91b147c..0000000
--- a/app/api/routers/events.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# app/api/routers/events.py
-
-from fastapi import APIRouter
-
-router = APIRouter(prefix="/events", tags=["events"])
-
-@router.get("")
-def get_events():
-    return [
-        {
-            "id": 1,
-            "title": "Robot Collision",
-            "severity": "위험",
-            "area": "BODY",
-            "subArea": "Robot 2",
-            "equipmentNo": "RB-201",
-            "aiScore": 95,
-            "status": "조치 필요"
-        }
-    ]
\ No newline at end of file
diff --git a/app/main.py b/app/main.py
index ffbe284..e8c54c1 100644
--- a/app/main.py
+++ b/app/main.py
@@ -6,6 +6,7 @@
 from app.core.config import settings
 from app.core.exceptions import register_exception_handlers
 from app.core.logging import configure_logging
+from fastapi.middleware.cors import CORSMiddleware
 from app.kafka.raw_event_consumer import (
     start_raw_event_consumer,
     stop_raw_event_consumer,
@@ -19,7 +20,6 @@
     resume_incomplete_generation_jobs,
 )
 
-
 logger = logging.getLogger(__name__)
 
 OPENAPI_TAGS = [
@@ -50,6 +50,13 @@ def create_app() -> FastAPI:
 
     app.include_router(api_router)
     register_exception_handlers(app)
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["http://localhost:5173"],  # 프론트 주소
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
 
     @app.on_event("startup")
     def initialize_sampledb_schema() -> None:

From 8747ddc24d0a177a25f65d56ab67f930ffe3e63b Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Sat, 4 Jul 2026 16:21:30 +0900
Subject: [PATCH 104/148] fix : branch push

---
 app/ai_manual/service/manual_service.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index c0adf40..2c9dab0 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -61,7 +61,17 @@ def generate_manual(
         # 4. LLM CALL
         response = self.llm.invoke(prompt)
 
-        return response
+        return {
+            "event": {
+                "eventId": event["event_id"],
+                "title": event["title"],
+                "severity": event["severity"],
+                "process": event["process_code"],
+                "equipmentId": event["equipment_id"],
+                "riskScore": event["risk_score"],
+            },
+            "manual": response.model_dump()
+        }
 
     # ======================================================
     # REQUEST BUILDER

From c1e93de15a958c24ad26f482b073ab99278dc2bf Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 7 Jul 2026 12:25:49 +0900
Subject: [PATCH 105/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9&=EB=B6=88?=
 =?UTF-8?q?=EB=9F=89=20=ED=83=90=EC=A7=80=20analysis=20=ED=86=A0=ED=94=BD?=
 =?UTF-8?q?=20=EC=A4=91=EB=B3=B5=20=EB=B0=9C=ED=96=89=20=EB=B0=A9=EC=A7=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- raw 이벤트 분석 플래그(bottleneck_analysis_done, defect_transfer_analysis_done) 기준으로 1회만 처리하도록 수정

- 분석 완료 후 플래그를 갱신해 동일 이벤트의 중복 발행을 방지

- 병목/불량 전이 결과 저장과 analysis 토픽 발행 흐름을 분리해 재처리 가능성 정리

- sampledb 기준 raw 이벤트 조회로 분석 대상 일관성 확보
---
 app/api/routers/defect_transfer.py            |  61 ++---
 .../defect_transfer_prediction_backfill.py    |   1 +
 .../manufacturing_event_json_builder.py       |   2 +
 app/dto/response/analysis_common.py           |  12 +
 app/dto/response/bottleneck_response.py       | 114 ++-------
 app/dto/response/defect_transfer_response.py  |  12 +
 app/kafka/raw_event_consumer.py               | 140 ++++++-----
 .../bottleneck_analysis_repository.py         |  90 ++++++-
 .../defect_transfer_prediction_repository.py  | 179 +++++++++----
 .../manufacturing_event_repository.py         | 109 +++++++-
 app/repository/sampledb_schema.py             |  24 ++
 app/repository/sampledb_schema_manager.py     |  31 +++
 app/service/analysis/bottleneck_service.py    | 236 +++++++-----------
 .../analysis/defect_transfer_service.py       | 134 ++++++----
 .../manufacturing_event_json_service.py       |   2 +
 app/utils/analysis_time_window.py             |  58 +++++
 16 files changed, 759 insertions(+), 446 deletions(-)
 create mode 100644 app/dto/response/analysis_common.py
 create mode 100644 app/utils/analysis_time_window.py

diff --git a/app/api/routers/defect_transfer.py b/app/api/routers/defect_transfer.py
index dbf4647..ed88e16 100644
--- a/app/api/routers/defect_transfer.py
+++ b/app/api/routers/defect_transfer.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import Any
 
 from fastapi import APIRouter, Depends, Query
@@ -24,45 +26,33 @@
 @router.get(
     "/predictions",
     response_model=DefectTransferPredictionResponse,
-    summary="불량 전이 예측 목록 조회",
+    summary="불량 전이 목록 조회",
 )
 def get_defect_transfer_predictions(
-    cursor: int | None = Query(
-        default=None,
-        ge=0,
-        description="페이지 번호입니다. 생략하면 0으로 처리합니다.",
-        examples=[0],
-    ),
-    size: int = Query(
-        default=5,
-        ge=1,
-        le=100,
-        description="페이지당 반환할 예측 결과 수입니다.",
-        examples=[5],
-    ),
-    service: DefectTransferAnalysisService = Depends(
-        get_defect_transfer_analysis_service,
-    ),
+    cursor: int | None = Query(default=None, ge=0, examples=[0]),
+    size: int = Query(default=5, ge=1, le=100, examples=[5]),
+    service: DefectTransferAnalysisService = Depends(get_defect_transfer_analysis_service),
 ) -> DefectTransferPredictionResponse:
     return success_response(
-        data=service.get_cached_predictions(cursor=cursor, size=size),
-        message="불량 전이 예측 목록 조회가 완료되었습니다.",
+        data=service.get_cached_predictions(
+            cursor=cursor,
+            size=size,
+        ),
+        message="불량 전이 목록 조회가 완료되었습니다.",
     )
 
 
 @router.get(
     "/diagnostics",
     response_model=DefectTransferDiagnosticsResponse,
-    summary="불량 전이 예측 데이터 저장/조회 상태 진단",
+    summary="불량 전이 데이터 진단",
 )
 def get_defect_transfer_diagnostics(
-    service: DefectTransferAnalysisService = Depends(
-        get_defect_transfer_analysis_service,
-    ),
+    service: DefectTransferAnalysisService = Depends(get_defect_transfer_analysis_service),
 ) -> DefectTransferDiagnosticsResponse:
     return success_response(
         data=service.get_diagnostics(),
-        message="불량 전이 예측 데이터 진단이 완료되었습니다.",
+        message="불량 전이 데이터 진단이 완료되었습니다.",
     )
 
 
@@ -75,26 +65,11 @@ def get_defect_transfer_causes(
     vehicle_id: str | None = Query(
         default=None,
         alias="vehicleId",
-        description=(
-            "car_master.vehicle_id입니다. 생략하면 예측 불량 확률이 가장 높은 차량을 선택합니다."
-        ),
-    ),
-    cursor: int | None = Query(
-        default=None,
-        ge=0,
-        description="원인 목록 페이지 번호입니다. 생략하면 0으로 처리합니다.",
-        examples=[0],
-    ),
-    size: int = Query(
-        default=5,
-        ge=1,
-        le=100,
-        description="페이지당 반환할 원인 수입니다.",
-        examples=[5],
-    ),
-    service: DefectTransferAnalysisService = Depends(
-        get_defect_transfer_analysis_service,
+        description="car_master.vehicle_id. If omitted, the latest vehicle is used.",
     ),
+    cursor: int | None = Query(default=None, ge=0, examples=[0]),
+    size: int = Query(default=5, ge=1, le=100, examples=[5]),
+    service: DefectTransferAnalysisService = Depends(get_defect_transfer_analysis_service),
 ) -> DefectTransferCauseResponse:
     return success_response(
         data=service.get_cached_cause_analysis(
diff --git a/app/batch/defect_transfer_prediction_backfill.py b/app/batch/defect_transfer_prediction_backfill.py
index b1bc11c..01a0088 100644
--- a/app/batch/defect_transfer_prediction_backfill.py
+++ b/app/batch/defect_transfer_prediction_backfill.py
@@ -44,6 +44,7 @@ def backfill_defect_transfer_predictions(*, limit: int | None = None) -> dict[st
             manufacturing_event_json.c.process_code,
             manufacturing_event_json.c.event_json,
         )
+        .where(manufacturing_event_json.c.dispatch_status == "SENT")
         .where(manufacturing_event_json.c.is_sent.is_(True))
         .order_by(manufacturing_event_json.c.id.asc())
     )
diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index 54265c5..6691267 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -305,6 +305,8 @@ def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
                     # 정상 분석 결과를 받은 뒤 Consumer가 READY로 전환한다.
                     "dispatch_status": initial_dispatch_status(process_code),
                     "analysis_status": "NOT_ANALYZED",
+                    "bottleneck_analysis_done": False,
+                    "defect_transfer_analysis_done": False,
                     "retry_count": 0,
                     "error_message": None,
                 }
diff --git a/app/dto/response/analysis_common.py b/app/dto/response/analysis_common.py
new file mode 100644
index 0000000..da511d2
--- /dev/null
+++ b/app/dto/response/analysis_common.py
@@ -0,0 +1,12 @@
+from __future__ import annotations
+
+from datetime import date as DateType
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class AnalysisDateOption(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    date: DateType
+    sample_event_id: str | None = Field(alias="sampleEventId")
diff --git a/app/dto/response/bottleneck_response.py b/app/dto/response/bottleneck_response.py
index 7ab66dd..7a5fa8f 100644
--- a/app/dto/response/bottleneck_response.py
+++ b/app/dto/response/bottleneck_response.py
@@ -1,105 +1,25 @@
-from pydantic import BaseModel, ConfigDict, Field
+from __future__ import annotations
+
+from datetime import datetime
 
+from pydantic import BaseModel, ConfigDict, Field
 
 class BottleneckAnalysisItem(BaseModel):
-    model_config = ConfigDict(
-        populate_by_name=True,
-        json_schema_extra={
-            "example": {
-                "rankNo": 1,
-                "processCode": "도장 (L3)",
-                "delayTime": 12.4,
-                "affectedVehicleCount": 128,
-                "riskScore": 5.0,
-                "riskLevel": "위험",
-            },
-        },
-    )
+    model_config = ConfigDict(populate_by_name=True)
 
-    rank_no: int = Field(
-        alias="rankNo",
-        description="병목 위험 순위입니다. 1이 가장 위험도가 높은 결과입니다.",
-        examples=[1],
-    )
-    process_code: str = Field(
-        alias="processCode",
-        description=(
-            "공정 표시명입니다. 공정명과 설비 번호를 조합해 "
-            "`도장 (L3)`, `프레스 (P4)` 형식으로 반환합니다."
-        ),
-        examples=["도장 (L3)"],
-    )
-    delay_time: float = Field(
-        alias="delayTime",
-        description=(
-            "평균 지연 시간(초)입니다. DB에서는 원본 double 값으로 저장하고 "
-            "API 응답에서는 소수 둘째 자리까지 반환합니다."
-        ),
-        examples=[12.4],
-    )
-    affected_vehicle_count: int = Field(
-        alias="affectedVehicleCount",
-        description="해당 병목 후보에 영향을 받은 차량 수입니다.",
-        examples=[128],
-    )
-    risk_score: float = Field(
-        alias="riskScore",
-        description=(
-            "Rule Engine과 Isolation Forest 결과를 결합해 산출한 병목 위험도입니다. "
-            "값이 클수록 위험도가 높습니다."
-        ),
-        examples=[5.0],
-    )
-    risk_level: str = Field(
-        alias="riskLevel",
-        description="병목 위험도 요약입니다. 보통 또는 위험으로 반환됩니다.",
-        examples=["위험"],
-    )
+    rank_no: int = Field(alias="rankNo")
+    process_code: str = Field(alias="processCode")
+    delay_time: float = Field(alias="delayTime")
+    affected_vehicle_count: int = Field(alias="affectedVehicleCount")
+    risk_score: float = Field(alias="riskScore")
+    risk_level: str = Field(alias="riskLevel")
 
 
 class BottleneckAnalysisPage(BaseModel):
-    model_config = ConfigDict(
-        populate_by_name=True,
-        json_schema_extra={
-            "example": {
-                "mostBottleneckProcess": "도장",
-                "mostBottleneckRiskLevel": "위험",
-                "content": [
-                    {
-                        "rankNo": 1,
-                        "processCode": "도장 (L3)",
-                        "delayTime": 12.4,
-                        "affectedVehicleCount": 128,
-                        "riskScore": 5.0,
-                        "riskLevel": "위험",
-                    },
-                ],
-                "hasNext": True,
-                "nextCursor": 1,
-            },
-        },
-    )
+    model_config = ConfigDict(populate_by_name=True)
 
-    most_bottleneck_process: str | None = Field(
-        alias="mostBottleneckProcess",
-        description="가장 병목인 공정명입니다.",
-        examples=["도장"],
-    )
-    most_bottleneck_risk_level: str | None = Field(
-        alias="mostBottleneckRiskLevel",
-        description="가장 병목인 공정의 위험도 요약입니다. 보통 또는 위험으로 반환됩니다.",
-        examples=["위험"],
-    )
-    content: list[BottleneckAnalysisItem] = Field(
-        description="병목 분석 결과 목록입니다. rankNo 오름차순으로 정렬됩니다.",
-    )
-    has_next: bool = Field(
-        alias="hasNext",
-        description="다음 페이지 존재 여부입니다. false이면 다음 무한 스크롤을 호출하지 않아야 합니다.",
-        examples=[True],
-    )
-    next_cursor: int | None = Field(
-        alias="nextCursor",
-        description="다음 페이지 cursor입니다. hasNext가 false이면 null입니다.",
-        examples=[1],
-    )
+    most_bottleneck_process: str | None = Field(alias="mostBottleneckProcess")
+    most_bottleneck_risk_level: str | None = Field(alias="mostBottleneckRiskLevel")
+    content: list[BottleneckAnalysisItem]
+    has_next: bool = Field(alias="hasNext")
+    next_cursor: int | None = Field(alias="nextCursor")
diff --git a/app/dto/response/defect_transfer_response.py b/app/dto/response/defect_transfer_response.py
index 0f90baf..2aed4ae 100644
--- a/app/dto/response/defect_transfer_response.py
+++ b/app/dto/response/defect_transfer_response.py
@@ -1,3 +1,8 @@
+from __future__ import annotations
+
+from datetime import date as DateType, datetime
+from typing import Any
+
 from pydantic import BaseModel, ConfigDict, Field
 
 
@@ -17,6 +22,9 @@ class DefectTransferPredictionPage(BaseModel):
     model_config = ConfigDict(populate_by_name=True)
 
     content: list[DefectTransferPredictionItem]
+    date: DateType | None = None
+    from_: datetime | None = Field(default=None, alias="from")
+    to: datetime | None = None
     has_next: bool = Field(alias="hasNext")
     next_cursor: int | None = Field(alias="nextCursor")
 
@@ -30,6 +38,7 @@ class DefectTransferCauseItem(BaseModel):
     value: str
     impact: float
     message: str
+    main_causes: list[dict[str, Any]] = Field(default_factory=list, alias="mainCauses")
 
 
 class DefectTransferCausePage(BaseModel):
@@ -43,5 +52,8 @@ class DefectTransferCausePage(BaseModel):
     predicted_defect_process: str | None = Field(alias="predictedDefectProcess")
     transfer_probability: int | None = Field(alias="transferProbability")
     content: list[DefectTransferCauseItem]
+    date: DateType | None = None
+    from_: datetime | None = Field(default=None, alias="from")
+    to: datetime | None = None
     has_next: bool = Field(alias="hasNext")
     next_cursor: int | None = Field(alias="nextCursor")
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 1324b63..497094e 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -1,5 +1,5 @@
+# -*- coding: utf-8 -*-
 from __future__ import annotations
-
 import asyncio
 import json
 import logging
@@ -8,9 +8,7 @@
 from threading import Event, Lock
 from typing import Any
 from uuid import uuid4
-
 from fastapi import FastAPI
-
 from app.core.config import settings
 from app.kafka.iam_provider import MSKTokenProvider
 from app.ml.inference.defect_transfer_detector import (
@@ -25,12 +23,9 @@
 from app.service.analysis.bottleneck_service import BottleneckAnalysisService
 from app.utils.datetime_utils import seoul_now_iso
 from app.websocket.analysis_manager import analysis_websocket_manager
-
-
 logger = logging.getLogger(__name__)
 PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
-
-# assembly-service의 app.kafka.topics.raw.name과 동일한 실제 raw 토픽명.
+# assembly-service의 app.kafka.topics.raw.name과 동일한 raw 토픽.
 RAW_TOPIC = "factory.manufacturing.raw"
 ANALYSIS_TOPIC = "factory.manufacturing.analysis"
 RAW_CONSUMER_GROUP_ID = "ai-analysis-consumer-group"
@@ -40,7 +35,7 @@
 
 
 def start_raw_event_consumer(app: FastAPI) -> None:
-    """FastAPI startup 시 제조 raw Kafka consumer를 백그라운드 thread로 시작한다."""
+    """FastAPI startup 시 raw Kafka consumer를 백그라운드 thread로 실행한다."""
     bootstrap_servers = _bootstrap_servers()
     if not bootstrap_servers:
         logger.info("Kafka bootstrap servers are not set. Raw Kafka consumer is disabled.")
@@ -48,7 +43,6 @@ def start_raw_event_consumer(app: FastAPI) -> None:
     if not settings.sample_database_connection_url:
         logger.info("SAMPLE_DB_NAME is not set. Raw Kafka consumer is disabled.")
         return
-
     stop_event = Event()
     tasks = [
         asyncio.create_task(
@@ -61,13 +55,13 @@ def start_raw_event_consumer(app: FastAPI) -> None:
         )
         for consumer_index in range(1, RAW_CONSUMER_CONCURRENCY + 1)
     ]
-    # shutdown 이벤트에서 thread consumer loop를 안전하게 종료하기 위한 공유 신호.
+    # shutdown 시 백그라운드 thread consumer loop를 종료하기 위한 상태를 저장한다.
     app.state.raw_event_consumer_stop_event = stop_event
     app.state.raw_event_consumer_tasks = tasks
 
 
 async def stop_raw_event_consumer(app: FastAPI) -> None:
-    """FastAPI shutdown 시 consumer loop 종료 신호를 보내고 thread 종료를 기다린다."""
+    """FastAPI shutdown 시 consumer loop 종료 신호를 보내고 thread 작업을 정리한다."""
     tasks = getattr(app.state, "raw_event_consumer_tasks", None)
     if not tasks:
         return
@@ -87,8 +81,7 @@ def _run_raw_event_consumer(
     except ModuleNotFoundError:
         logger.exception("kafka-python is required to consume manufacturing raw events.")
         return
-
-    # 기존 품질 Kafka 연동과 동일하게 MSK IAM 인증(SASL_SSL/OAUTHBEARER)을 사용한다.
+    # 운영 Kafka 연결에서는 MSK IAM 인증(SASL_SSL/OAUTHBEARER)을 사용한다.
     consumer = KafkaConsumer(
         RAW_TOPIC,
         ssl_context=ssl.create_default_context(),
@@ -110,7 +103,6 @@ def _run_raw_event_consumer(
         KafkaProducer,
         bootstrap_servers,
     )
-
     logger.info(
         "Raw Kafka consumer started: topic=%s group=%s concurrency=%s/%s bootstrap=%s auth=SASL_SSL/OAUTHBEARER",
         RAW_TOPIC,
@@ -141,7 +133,7 @@ def _run_raw_event_consumer(
                         record.offset,
                     )
                 finally:
-                    # DB 저장 시도 후 offset을 커밋해 재기동 시 같은 메시지의 무한 재처리를 막는다.
+                    # DB 저장/분석 실패 여부와 관계없이 offset을 커밋해 같은 메시지의 무한 재처리를 막는다.
                     consumer.commit()
     except Exception:
         logger.exception("Raw Kafka consumer failed.")
@@ -173,17 +165,59 @@ def _consume_record(
         [row],
         update_existing=True,
     )
-    bottleneck_summaries = _refresh_bottleneck_analysis(
-        analysis_service,
-        record,
-        row,
-    )
-    defect_prediction = _predict_defect_transfer(defect_detector, row)
-    defect_rows_saved = _save_defect_transfer_prediction(
-        defect_result_repository,
-        row,
-        defect_prediction,
-    )
+    if not _should_analyze_row(row):
+        logger.info(
+            "Kafka raw event skipped because dispatch_status/is_sent do not match analysis condition: "
+            "topic=%s partition=%s offset=%s key=%s event_id=%s dispatch_status=%s is_sent=%s",
+            record.topic,
+            record.partition,
+            record.offset,
+            raw_event.get("_kafka_key"),
+            row["event_id"],
+            row.get("dispatch_status"),
+            row.get("is_sent"),
+        )
+        return
+    bottleneck_summaries: list[dict[str, Any]] = []
+    defect_prediction: DefectTransferPrediction | None = None
+    defect_rows_saved = 0
+    analysis_ran = False
+    if not repository.events.is_bottleneck_analysis_done(row["event_id"]):
+        bottleneck_summaries = _refresh_bottleneck_analysis(
+            analysis_service,
+            record,
+            row,
+        )
+        if bottleneck_summaries is not None:
+            repository.events.mark_bottleneck_analysis_done(row["event_id"])
+            analysis_ran = True
+    if not repository.events.is_defect_transfer_analysis_done(row["event_id"]):
+        if (
+            defect_result_repository is not None
+            and defect_result_repository.has_prediction_for_event(row["event_id"])
+        ):
+            repository.events.mark_defect_transfer_analysis_done(row["event_id"])
+        else:
+            defect_prediction = _predict_defect_transfer(defect_detector, row)
+            defect_rows_saved = _save_defect_transfer_prediction(
+                defect_result_repository,
+                row,
+                defect_prediction,
+            )
+            if defect_rows_saved > 0:
+                repository.events.mark_defect_transfer_analysis_done(row["event_id"])
+                analysis_ran = True
+    if not analysis_ran:
+        logger.info(
+            "Kafka raw event skipped because it was already analyzed: "
+            "topic=%s partition=%s offset=%s key=%s event_id=%s",
+            record.topic,
+            record.partition,
+            record.offset,
+            raw_event.get("_kafka_key"),
+            row["event_id"],
+        )
+        return
     analysis_published = _publish_bottleneck_analysis_event(
         analysis_producer,
         raw_event,
@@ -191,7 +225,7 @@ def _consume_record(
         bottleneck_summaries,
         defect_prediction,
     )
-    # 새 raw 이벤트와 자동 분석 결과가 반영되면 Redis 캐시를 비운다.
+    # raw 이벤트 연동 분석 결과가 반영되면 Redis 캐시를 비운다.
     _clear_bottleneck_cache()
     _clear_defect_transfer_cache()
     _broadcast_process_analysis_updates(
@@ -242,7 +276,6 @@ def _broadcast_process_analysis_updates(
             "resultCount": len(bottleneck_summaries),
         },
     )
-
     analysis_websocket_manager.broadcast_from_thread(
         {
             **base_message,
@@ -281,7 +314,6 @@ def _vehicle_id_for_car_master_id(
     car_master_id: int,
 ) -> str | None:
     from sqlalchemy import select
-
     try:
         query = select(car_master.c.vehicle_id).where(car_master.c.id == car_master_id)
         with repository.engine.connect() as conn:
@@ -296,15 +328,13 @@ def _vehicle_id_for_car_master_id(
 
 
 def _parse_raw_event(record: Any) -> dict[str, Any]:
-    """Kafka record의 value JSON과 key(carId)를 분석하기 쉬운 dict로 정규화한다."""
+    """Kafka record의 value JSON과 key(carId)를 파싱해 dict로 반환한다."""
     payload = json.loads(record.value.decode("utf-8"))
     if not isinstance(payload, dict):
         raise ValueError("Manufacturing raw event must be a JSON object.")
-
     event_json = _parse_event_json(payload.get("eventJson") or payload.get("event_json"))
     if event_json is None:
         raise ValueError("Manufacturing raw event must include eventJson object.")
-
     key_text = record.key.decode("utf-8") if record.key else None
     payload["eventJson"] = event_json
     payload["_kafka_key"] = key_text
@@ -312,7 +342,7 @@ def _parse_raw_event(record: Any) -> dict[str, Any]:
 
 
 def _raw_event_to_row(raw_event: dict[str, Any]) -> dict[str, Any]:
-    """assembly-service raw envelope를 manufacturing_event_json 테이블 컬럼으로 매핑한다."""
+    """assembly-service raw envelope를 manufacturing_event_json 저장 형식으로 변환한다."""
     event_json = raw_event["eventJson"]
     process_code = _normalize_process_code(
         _first_present(raw_event, "processCode", "process_code", "PROCESS_CODE"),
@@ -324,7 +354,6 @@ def _raw_event_to_row(raw_event: dict[str, Any]) -> dict[str, Any]:
     )
     if not event_id:
         raise ValueError("Manufacturing raw event requires eventId.")
-
     car_master_id = _nullable_int(
         _first_present(raw_event, "carMasterId", "car_master_id", "carId", "car_id"),
     )
@@ -335,7 +364,6 @@ def _raw_event_to_row(raw_event: dict[str, Any]) -> dict[str, Any]:
         car_master_id = _nullable_int(raw_event.get("_kafka_key"))
     if car_master_id is None:
         raise ValueError(f"Manufacturing raw event requires carId key or carMasterId. event_id={event_id}")
-
     equipment_id = _nullable_int(
         _first_present(raw_event, "equipmentId", "equipment_id"),
     ) or 0
@@ -355,7 +383,7 @@ def _raw_event_to_row(raw_event: dict[str, Any]) -> dict[str, Any]:
 
 
 def _normalize_process_code(value: Any, event_json: dict[str, Any]) -> str:
-    """PRESS/BODY/PAINT/ASSEMBLY 중 하나로 공정 코드를 표준화한다."""
+    """PRESS/BODY/PAINT/ASSEMBLY 중 하나의 공정 코드로 정규화한다."""
     process_code = str(value or "").strip().upper()
     if not process_code:
         process_data = event_json.get("processData")
@@ -492,7 +520,6 @@ def _create_analysis_producer(
             "Bottleneck results will still be stored in DB.",
         )
         return None
-
     logger.info(
         "Kafka analysis producer started: topic=%s bootstrap=%s auth=SASL_SSL/OAUTHBEARER",
         ANALYSIS_TOPIC,
@@ -508,7 +535,6 @@ def _refresh_bottleneck_analysis(
 ) -> list[dict[str, Any]]:
     if analysis_service is None:
         return []
-
     with _bottleneck_analysis_lock:
         try:
             summaries = analysis_service.refresh_results_from_events()
@@ -523,7 +549,6 @@ def _refresh_bottleneck_analysis(
                 row.get("process_code"),
             )
             return []
-
     logger.info(
         "Bottleneck analysis refreshed after raw Kafka event: "
         "topic=%s partition=%s offset=%s event_id=%s process_code=%s result_count=%s",
@@ -534,7 +559,7 @@ def _refresh_bottleneck_analysis(
         row.get("process_code"),
         len(summaries),
     )
-    return summaries
+    return summaries or []
 
 
 def _publish_bottleneck_analysis_event(
@@ -546,7 +571,6 @@ def _publish_bottleneck_analysis_event(
 ) -> bool:
     if producer is None:
         return False
-
     event = _build_bottleneck_analysis_event(
         raw_event,
         row,
@@ -564,7 +588,6 @@ def _publish_bottleneck_analysis_event(
             row.get("event_id"),
         )
         return False
-
     logger.info(
         "Bottleneck analysis event published: topic=%s partition=%s offset=%s "
         "key=%s event_id=%s analysis_id=%s analysis_type=%s",
@@ -652,7 +675,6 @@ def _build_bottleneck_analysis_event(
         )
         > 0
     )
-
     return {
         "analysisId": f"ANL-{uuid4()}",
         "eventId": row["event_id"],
@@ -831,7 +853,6 @@ def _format_predicted_defect_process(
         equipment_code_for_car_process,
         format_process_with_line,
     )
-
     source_code = str(row.get("process_code") or "").strip().upper()
     normalized = str(process_code).strip().upper()
     if normalized == source_code:
@@ -844,6 +865,20 @@ def _format_predicted_defect_process(
     return format_process_with_line(normalized, equipment_code)
 
 
+def _should_analyze_row(row: dict[str, Any]) -> bool:
+    return str(row.get("dispatch_status") or "").upper() == "SENT" and _analysis_flag_is_true(
+        row.get("is_sent"),
+    )
+
+
+def _analysis_flag_is_true(value: Any) -> bool:
+    if isinstance(value, bool):
+        return value
+    if value is None:
+        return False
+    return str(value).strip().lower() == "true"
+
+
 def _source_equipment_code(row: dict[str, Any]) -> str | None:
     event_json = row.get("event_json") or {}
     equipment = event_json.get("equipment", {}) if isinstance(event_json, dict) else {}
@@ -857,7 +892,7 @@ def _matching_bottleneck_summary(
     process_code: str,
     equipment_code: str,
 ) -> dict[str, Any] | None:
-    for summary in summaries:
+    for summary in summaries or []:
         if (
             str(summary.get("process_code")) == process_code
             and str(summary.get("equipment_code")) == equipment_code
@@ -894,7 +929,6 @@ def _defect_probability(event_json: dict[str, Any], process_code: str) -> float:
     vibration = sensor.get("vibration", {})
     robot = sensor.get("robotArmVibration", {})
     thermal = sensor.get("thermal", {})
-
     if process_code == "PAINT":
         paint = process_data.get("paint", {})
         defect_score = _safe_float(paint.get("defectScore"), default=0.0)
@@ -905,7 +939,6 @@ def _defect_probability(event_json: dict[str, Any], process_code: str) -> float:
             default=100.0,
         )
         return round(max(defect_score, max(0.0, 100.0 - surface_quality) / 100.0), 4)
-
     if process_code == "ASSEMBLY":
         assembly = process_data.get("assembly", {})
         error_count = (
@@ -915,10 +948,9 @@ def _defect_probability(event_json: dict[str, Any], process_code: str) -> float:
         )
         if error_count == 0:
             return 0.0
-        # 에러 건수당 불량 확률을 현실적으로 조정 (너무 쉽게 1.0(100%)이 되지 않도록 함)
-        # 에러 1건당 약 8%씩 추가되며, 기본 50%의 위험도를 갖게 하여 최대 98% 내외로 산출
+        # 오류 건수에 따라 불량 확률을 현실적으로 조정한다.
+        # 오류 1건당 약 8%를 추가하고, 기본 50% 위험도를 부여해 최대 98% 내외로 산출한다.
         return round(min(error_count * 0.08 + 0.50, 0.98), 4)
-
     vibration_score = max(
         _safe_float(vibration.get("vibrationScore"), default=0.0),
         _safe_float(robot.get("vibrationScore"), default=0.0),
@@ -954,9 +986,9 @@ def _bottleneck_reason(
     if is_equipment_fault:
         return "설비 상태값에서 고장 또는 정지 위험이 감지되었습니다."
     if is_sequence_error:
-        return "의장 공정의 작업 순서 오류가 감지되었습니다."
+        return "의장 공정에서 작업 순서 오류가 감지되었습니다."
     if risk_score >= 3:
-        return "Rule Engine과 Isolation Forest 기준 병목 위험이 감지되었습니다."
+        return "Rule Engine과 Isolation Forest 기준으로 병목 위험이 감지되었습니다."
     if delay_time > 0:
         return "공정 지연 시간이 감지되었지만 위험도는 낮습니다."
     return "주요 병목 지표가 정상 범위입니다."
@@ -1004,10 +1036,8 @@ def _json_default(value: Any) -> str:
 def _clear_bottleneck_cache() -> None:
     if not settings.redis_url:
         return
-
     try:
         from redis import Redis
-
         redis_client = Redis.from_url(settings.redis_connection_url, decode_responses=True)
         pattern = f"{settings.redis_key_prefix}:process:bottleneck:*"
         keys = list(redis_client.scan_iter(match=pattern))
@@ -1020,10 +1050,8 @@ def _clear_bottleneck_cache() -> None:
 def _clear_defect_transfer_cache() -> None:
     if not settings.redis_url:
         return
-
     try:
         from redis import Redis
-
         redis_client = Redis.from_url(settings.redis_connection_url, decode_responses=True)
         pattern = f"{settings.redis_key_prefix}:process:defect-transfer:*"
         keys = list(redis_client.scan_iter(match=pattern))
diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index ece585d..89107b3 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -80,29 +80,45 @@ def prune_results_after_rank(self, max_rank: int) -> None:
                 .where(func.date(self.table.c.detected_at) == func.current_date())
             )
 
-    def list_results(self, *, cursor: int, size: int) -> list[dict[str, Any]]:
-        from sqlalchemy import select, func
+    def list_results(
+        self,
+        *,
+        cursor: int,
+        size: int,
+    ) -> list[dict[str, Any]]:
+        event_ids = self._event_ids_for_today()
+        if not event_ids:
+            return []
 
-        offset = cursor * size
+        from sqlalchemy import select
 
         query = (
             select(self.table)
-            .where(func.date(self.table.c.detected_at) == func.current_date())
-            .order_by(self.table.c.rank_no.asc(), self.table.c.id.asc())
-            .offset(offset)
+            .where(self.table.c.manufacturing_event_id.in_(event_ids))
+            .order_by(
+                self.table.c.rank_no.asc(),
+                self.table.c.id.asc(),
+            )
+            .offset(cursor * size)
             .limit(size)
         )
 
         with self.engine.connect() as conn:
             return [dict(row) for row in conn.execute(query).mappings()]
 
-    def count_results(self) -> int:
+    def count_results(
+        self,
+    ) -> int:
+        event_ids = self._event_ids_for_today()
+        if not event_ids:
+            return 0
+
         from sqlalchemy import func, select
 
         query = (
             select(func.count())
             .select_from(self.table)
-            .where(func.date(self.table.c.detected_at) == func.current_date())
+            .where(self.table.c.manufacturing_event_id.in_(event_ids))
         )
         with self.engine.connect() as conn:
             return int(conn.execute(query).scalar_one())
@@ -119,8 +135,34 @@ def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
                 manufacturing_event_json.c.event_time,
                 manufacturing_event_json.c.event_json,
             )
+            .where(manufacturing_event_json.c.dispatch_status == "SENT")
+            .where(manufacturing_event_json.c.is_sent.is_(True))
+            .where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
+            .order_by(manufacturing_event_json.c.id.asc())
+        )
+        with self.event_engine.connect() as conn:
+            rows = conn.execute(query).mappings()
+            return [
+                self._to_bottleneck_history(row)
+                for row in rows
+            ]
+
+    def list_pending_manufacturing_event_histories(self) -> list[dict[str, Any]]:
+        from sqlalchemy import select, func
+
+        query = (
+            select(
+                manufacturing_event_json.c.id,
+                manufacturing_event_json.c.car_master_id,
+                manufacturing_event_json.c.process_code,
+                manufacturing_event_json.c.equipment_id,
+                manufacturing_event_json.c.event_time,
+                manufacturing_event_json.c.event_json,
+            )
+            .where(manufacturing_event_json.c.dispatch_status == "SENT")
             .where(manufacturing_event_json.c.is_sent.is_(True))
-            .where(func.date(manufacturing_event_json.c.created_at) == func.current_date())
+            .where(manufacturing_event_json.c.bottleneck_analysis_done.is_(False))
+            .where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
             .order_by(manufacturing_event_json.c.id.asc())
         )
         with self.event_engine.connect() as conn:
@@ -130,6 +172,36 @@ def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
                 for row in rows
             ]
 
+    def _event_ids_for_today(self) -> list[int]:
+        from sqlalchemy import func, select
+
+        query = (
+            select(manufacturing_event_json.c.id)
+            .where(manufacturing_event_json.c.dispatch_status == "SENT")
+            .where(manufacturing_event_json.c.is_sent.is_(True))
+            .where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
+        )
+        with self.event_engine.connect() as conn:
+            return [int(row[0]) for row in conn.execute(query).all()]
+
+    def mark_bottleneck_analysis_done(self, event_ids: Iterable[int]) -> int:
+        event_ids = [int(event_id) for event_id in event_ids]
+        if not event_ids:
+            return 0
+
+        from sqlalchemy import func
+
+        with self.event_engine.begin() as conn:
+            result = conn.execute(
+                manufacturing_event_json.update()
+                .where(manufacturing_event_json.c.id.in_(event_ids))
+                .values(
+                    bottleneck_analysis_done=True,
+                    updated_at=func.current_timestamp(),
+                ),
+            )
+        return int(result.rowcount or 0)
+
     def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
         event_json = self._event_json_dict(row["event_json"])
         equipment = event_json.get("equipment", {})
diff --git a/app/repository/defect_transfer_prediction_repository.py b/app/repository/defect_transfer_prediction_repository.py
index 33a1be6..366dfb0 100644
--- a/app/repository/defect_transfer_prediction_repository.py
+++ b/app/repository/defect_transfer_prediction_repository.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import json
-from datetime import datetime
+from datetime import date, datetime
 from typing import Any
 
 from app.repository.sampledb_schema import car_master, equipment, manufacturing_event_json
@@ -48,54 +48,65 @@ def replace_prediction_result(
         predicted_at: datetime,
     ) -> int:
         manufacturing_event_id = self._manufacturing_event_id(event_id)
-        rows = []
+        main_causes = self._normalize_main_causes(causes)
         cause_rows = causes or [
             {
-                "message": "모델 기반 주요 원인이 산출되지 않았습니다.",
+                "message": "no main cause available",
                 "impact": 0.0,
             },
         ]
-        for cause in cause_rows[:5]:
-            rows.append(
-                {
-                    "manufacturing_event_id": manufacturing_event_id,
-                    "car_master_id": car_master_id,
-                    "source_process_code": source_process_code,
-                    "target_process_code": target_process_code,
-                    "current_defect_probability": current_defect_probability,
-                    "target_defect_probability": target_defect_probability,
-                    "predicted_defect_process": predicted_defect_process,
-                    "expected_occurrence_step": expected_occurrence_step,
-                    "risk_grade": risk_grade,
-                    "main_cause": str(cause.get("message") or cause.get("label") or "")[:200],
-                    "influence_score": float(cause.get("impact") or 0.0),
-                    "predicted_at": predicted_at,
-                },
-            )
+        top_cause = cause_rows[0] if cause_rows else {}
+        row = {
+            "manufacturing_event_id": manufacturing_event_id,
+            "car_master_id": car_master_id,
+            "source_process_code": source_process_code,
+            "target_process_code": target_process_code,
+            "current_defect_probability": current_defect_probability,
+            "target_defect_probability": target_defect_probability,
+            "predicted_defect_process": predicted_defect_process,
+            "expected_occurrence_step": expected_occurrence_step,
+            "risk_grade": risk_grade,
+            "main_causes": main_causes,
+            "influence_score": float(top_cause.get("impact") or 0.0),
+            "predicted_at": predicted_at,
+        }
 
-        from sqlalchemy import func
         with self.engine.begin() as conn:
             conn.execute(
-                self.table.delete()
-                .where(self.table.c.car_master_id == car_master_id)
-                .where(func.date(self.table.c.predicted_at) == func.current_date())
+                self.table.delete().where(
+                    self.table.c.manufacturing_event_id == manufacturing_event_id,
+                ),
             )
             if self.engine.dialect.name != "mysql":
                 from sqlalchemy import func, select
 
-                next_id = int(
-                    conn.execute(select(func.max(self.table.c.id))).scalar() or 0,
-                )
-                rows = [
-                    {**row, "id": next_id + index}
-                    for index, row in enumerate(rows, 1)
-                ]
-            conn.execute(self.table.insert(), rows)
-        return len(rows)
-
-    def list_prediction_page(self, *, cursor: int, size: int) -> tuple[list[dict[str, Any]], bool]:
-        from sqlalchemy import select
+                next_id = int(conn.execute(select(func.max(self.table.c.id))).scalar() or 0)
+                row = {**row, "id": next_id + 1}
+            conn.execute(self.table.insert(), [row])
+        return 1
+
+    def has_prediction_for_event(self, event_id: str) -> bool:
+        manufacturing_event_id = self._manufacturing_event_id(event_id)
+        if manufacturing_event_id is None:
+            return False
+
+        from sqlalchemy import func, select
+
+        query = (
+            select(func.count())
+            .select_from(self.table)
+            .where(self.table.c.manufacturing_event_id == manufacturing_event_id)
+            .where(func.date(self.table.c.predicted_at) == func.current_date())
+        )
+        with self.engine.connect() as conn:
+            return int(conn.execute(query).scalar_one()) > 0
 
+    def list_prediction_page(
+        self,
+        *,
+        cursor: int,
+        size: int,
+    ) -> tuple[list[dict[str, Any]], bool]:
         rows = self._all_prediction_rows()
         latest_by_car: dict[int, dict[str, Any]] = {}
         for row in rows:
@@ -132,7 +143,9 @@ def list_cause_page(
         size: int,
     ) -> tuple[dict[str, Any] | None, list[dict[str, Any]], bool]:
         car_master_id = self._car_master_id(vehicle_id) if vehicle_id else None
-        rows = self._all_prediction_rows(car_master_id=car_master_id)
+        rows = self._all_prediction_rows(
+            car_master_id=car_master_id,
+        )
         if not rows:
             return None, [], False
 
@@ -156,6 +169,27 @@ def list_cause_page(
         page = latest_rows[offset : offset + size]
         return latest_rows[0], page, len(latest_rows) > offset + size
 
+    def list_date_options(self, *, vehicle_id: str | None = None) -> list[dict[str, Any]]:
+        from sqlalchemy import func, select
+
+        car_master_id = self._car_master_id(vehicle_id) if vehicle_id else None
+        event_ids = self._prediction_event_ids(car_master_id=car_master_id)
+        if not event_ids:
+            return []
+
+        query = (
+            select(
+                func.date(manufacturing_event_json.c.event_time).label("date"),
+                func.min(manufacturing_event_json.c.event_id).label("sample_event_id"),
+            )
+            .where(manufacturing_event_json.c.id.in_(event_ids))
+            .where(manufacturing_event_json.c.event_time.is_not(None))
+            .group_by(func.date(manufacturing_event_json.c.event_time))
+            .order_by(func.date(manufacturing_event_json.c.event_time).desc())
+        )
+        with self.event_engine.connect() as conn:
+            return [dict(row) for row in conn.execute(query).mappings()]
+
     def diagnostics(self) -> dict[str, Any]:
         from sqlalchemy import distinct, func, select
 
@@ -168,6 +202,7 @@ def diagnostics(self) -> dict[str, Any]:
                 conn.execute(
                     select(func.count())
                     .select_from(manufacturing_event_json)
+                    .where(manufacturing_event_json.c.dispatch_status == "SENT")
                     .where(manufacturing_event_json.c.is_sent.is_(True)),
                 ).scalar()
                 or 0,
@@ -244,9 +279,15 @@ def _all_prediction_rows(
         *,
         car_master_id: int | None = None,
     ) -> list[dict[str, Any]]:
-        from sqlalchemy import select, func
+        from sqlalchemy import select
 
-        query = select(self.table).where(func.date(self.table.c.predicted_at) == func.current_date())
+        event_ids = self._prediction_event_ids(car_master_id=car_master_id)
+        if not event_ids:
+            return []
+
+        query = select(self.table).where(
+            self.table.c.manufacturing_event_id.in_(event_ids),
+        )
         if car_master_id is not None:
             query = query.where(self.table.c.car_master_id == car_master_id)
         query = query.order_by(
@@ -285,6 +326,20 @@ def _car_master_id(self, vehicle_id: str | None) -> int | None:
             value = conn.execute(query).scalar()
         return int(value) if value is not None else None
 
+    def _prediction_event_ids(self, *, car_master_id: int | None = None) -> list[int]:
+        from sqlalchemy import func, select
+
+        query = (
+            select(manufacturing_event_json.c.id)
+            .where(manufacturing_event_json.c.dispatch_status == "SENT")
+            .where(manufacturing_event_json.c.is_sent.is_(True))
+            .where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
+        )
+        if car_master_id is not None:
+            query = query.where(manufacturing_event_json.c.car_master_id == car_master_id)
+        with self.event_engine.connect() as conn:
+            return [int(row[0]) for row in conn.execute(query).all()]
+
     @staticmethod
     def _result_probability_percent(row: dict[str, Any]) -> int:
         value = row.get("target_defect_probability")
@@ -421,7 +476,7 @@ def _attach_vehicle_ids(self, rows: list[dict[str, Any]]) -> None:
 
     def _init_sqlalchemy(self) -> None:
         try:
-            from sqlalchemy import BigInteger, Column, DateTime, Double, Enum
+            from sqlalchemy import BigInteger, Column, DateTime, Double, Enum, JSON
             from sqlalchemy import Index, Integer, MetaData, String, Table, func
             from sqlalchemy import create_engine
         except ModuleNotFoundError as exc:
@@ -444,7 +499,7 @@ def _init_sqlalchemy(self) -> None:
             Column("predicted_defect_process", String(50)),
             Column("expected_occurrence_step", Integer),
             Column("risk_grade", String(20)),
-            Column("main_cause", String(200)),
+            Column("main_causes", JSON),
             Column("influence_score", Double),
             Column("predicted_at", DateTime, nullable=False),
             Column("created_at", DateTime, nullable=False, server_default=func.current_timestamp()),
@@ -493,7 +548,7 @@ def _align_result_schema(self) -> None:
             "predicted_defect_process": "VARCHAR(50) NULL",
             "expected_occurrence_step": "INT NULL",
             "risk_grade": "VARCHAR(20) NULL",
-            "main_cause": "VARCHAR(200) NULL",
+            "main_causes": "JSON NULL",
             "influence_score": "DOUBLE NULL",
             "predicted_at": "DATETIME NOT NULL",
             "created_at": "DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP",
@@ -502,7 +557,19 @@ def _align_result_schema(self) -> None:
                 "ON UPDATE CURRENT_TIMESTAMP"
             ),
         }
+        removed_columns = ("main_cause",)
         with self.engine.begin() as conn:
+            for column_name in removed_columns:
+                if column_name not in columns:
+                    continue
+                conn.execute(
+                    text(
+                        f"ALTER TABLE {table_name} "
+                        f"DROP COLUMN {column_name}",
+                    ),
+                )
+                columns.remove(column_name)
+
             for column_name, definition in expected_columns.items():
                 if column_name not in columns:
                     conn.execute(
@@ -526,3 +593,31 @@ def _align_result_schema(self) -> None:
                         f"MODIFY COLUMN {column_name} {definition}",
                     ),
                 )
+
+    @staticmethod
+    def _normalize_main_causes(causes: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        normalized: list[dict[str, Any]] = []
+        for cause in causes[:5]:
+            message = str(cause.get("message") or cause.get("label") or "").strip()
+            if not message:
+                continue
+            try:
+                impact = float(cause.get("impact") or 0.0)
+            except (TypeError, ValueError):
+                impact = 0.0
+            normalized.append(
+                {
+                    "message": message,
+                    "impact": impact,
+                },
+            )
+
+        if normalized:
+            return normalized
+
+        return [
+            {
+                "message": "no main cause available",
+                "impact": 0.0,
+            },
+        ]
diff --git a/app/repository/manufacturing_event_repository.py b/app/repository/manufacturing_event_repository.py
index e30604e..fbcf645 100644
--- a/app/repository/manufacturing_event_repository.py
+++ b/app/repository/manufacturing_event_repository.py
@@ -59,6 +59,12 @@ def insert_rows(
                         str(row["process_code"]),
                     ),
                     "analysis_status": row.get("analysis_status", "NOT_ANALYZED"),
+                    "bottleneck_analysis_done": _analysis_flag_value(
+                        row.get("bottleneck_analysis_done", False),
+                    ),
+                    "defect_transfer_analysis_done": _analysis_flag_value(
+                        row.get("defect_transfer_analysis_done", False),
+                    ),
                     "is_sent": row.get("is_sent", False),
                     "retry_count": row.get("retry_count", 0),
                     "error_message": row.get("error_message"),
@@ -94,7 +100,13 @@ def insert_raw_rows(
                     "equipment_id": row["equipment_id"],
                     "event_json": event_json,
                     "dispatch_status": "SENT",
-                    "is_sent": True,
+                    "bottleneck_analysis_done": _analysis_flag_value(
+                        row.get("bottleneck_analysis_done", False),
+                    ),
+                    "defect_transfer_analysis_done": _analysis_flag_value(
+                        row.get("defect_transfer_analysis_done", False),
+                    ),
+                    "is_sent": _is_true_flag(row.get("is_sent", True)),
                     "retry_count": row.get("retry_count", 0),
                     "error_message": row.get("error_message"),
                     "updated_at": now,
@@ -161,12 +173,37 @@ def _insert_payload_once(
                     ),
                 ).mappings()
             }
+            existing_rows = {
+                row["event_id"]: row
+                for row in conn.execute(
+                    select(manufacturing_event_json).where(
+                        manufacturing_event_json.c.event_id.in_(event_ids),
+                    ),
+                ).mappings()
+            }
 
             updated_rows = 0
             if update_existing:
                 for row in payload:
                     if row["event_id"] not in existing_ids:
                         continue
+                    existing_row = existing_rows.get(row["event_id"])
+                    if existing_row is not None:
+                        row = {
+                            **row,
+                            "analysis_status": existing_row.get(
+                                "analysis_status",
+                                row.get("analysis_status"),
+                            ),
+                            "bottleneck_analysis_done": existing_row.get(
+                                "bottleneck_analysis_done",
+                                row.get("bottleneck_analysis_done"),
+                            ),
+                            "defect_transfer_analysis_done": existing_row.get(
+                                "defect_transfer_analysis_done",
+                                row.get("defect_transfer_analysis_done"),
+                            ),
+                        }
                     result = conn.execute(
                         manufacturing_event_json.update()
                         .where(
@@ -230,6 +267,76 @@ def list_rows(
         with self.engine.connect() as conn:
             return [dict(row) for row in conn.execute(query).mappings()]
 
+    def get_analysis_flags(self, event_id: str) -> dict[str, bool] | None:
+        query = select(
+            manufacturing_event_json.c.event_id,
+            manufacturing_event_json.c.bottleneck_analysis_done,
+            manufacturing_event_json.c.defect_transfer_analysis_done,
+        ).where(manufacturing_event_json.c.event_id == event_id)
+        with self.engine.connect() as conn:
+            row = conn.execute(query).mappings().first()
+        if row is None:
+            return None
+        return {
+            "bottleneck_analysis_done": _is_true_flag(
+                row["bottleneck_analysis_done"],
+            ),
+            "defect_transfer_analysis_done": _is_true_flag(
+                row["defect_transfer_analysis_done"],
+            ),
+        }
+
+    def is_bottleneck_analysis_done(self, event_id: str) -> bool:
+        query = select(manufacturing_event_json.c.bottleneck_analysis_done).where(
+            manufacturing_event_json.c.event_id == event_id,
+        )
+        with self.engine.connect() as conn:
+            row = conn.execute(query).mappings().first()
+        return False if row is None else _is_true_flag(row["bottleneck_analysis_done"])
+
+    def is_defect_transfer_analysis_done(self, event_id: str) -> bool:
+        query = select(manufacturing_event_json.c.defect_transfer_analysis_done).where(
+            manufacturing_event_json.c.event_id == event_id,
+        )
+        with self.engine.connect() as conn:
+            row = conn.execute(query).mappings().first()
+        return False if row is None else _is_true_flag(row["defect_transfer_analysis_done"])
+
+    def mark_bottleneck_analysis_done(self, event_id: str) -> int:
+        return self._mark_analysis_done(
+            event_id,
+            bottleneck_analysis_done=True,
+        )
+
+    def mark_defect_transfer_analysis_done(self, event_id: str) -> int:
+        return self._mark_analysis_done(
+            event_id,
+            defect_transfer_analysis_done=True,
+        )
+
+    def _mark_analysis_done(self, event_id: str, **values: Any) -> int:
+        from sqlalchemy import func
+
+        with self.engine.begin() as conn:
+            result = conn.execute(
+                manufacturing_event_json.update()
+                .where(manufacturing_event_json.c.event_id == event_id)
+                .values(**values, updated_at=func.current_timestamp()),
+            )
+        return int(result.rowcount or 0)
+
+
+def _analysis_flag_value(value: Any) -> bool:
+    return _is_true_flag(value)
+
+
+def _is_true_flag(value: Any) -> bool:
+    if isinstance(value, bool):
+        return value
+    if value is None:
+        return False
+    return str(value).strip().lower() == "true"
+
 
 def _mysql_error_code(exc: OperationalError) -> int | None:
     original = getattr(exc, "orig", None)
diff --git a/app/repository/sampledb_schema.py b/app/repository/sampledb_schema.py
index 69309a1..86324ad 100644
--- a/app/repository/sampledb_schema.py
+++ b/app/repository/sampledb_schema.py
@@ -96,6 +96,18 @@
         nullable=False,
         server_default="NOT_ANALYZED",
     ),
+    Column(
+        "bottleneck_analysis_done",
+        Boolean,
+        nullable=False,
+        server_default="0",
+    ),
+    Column(
+        "defect_transfer_analysis_done",
+        Boolean,
+        nullable=False,
+        server_default="0",
+    ),
     Column("is_sent", Boolean, nullable=False, server_default="0"),
     Column("retry_count", Integer, nullable=False, server_default="0"),
     Column("error_message", Text),
@@ -109,6 +121,18 @@
     ),
     # Scheduler의 READY/미전송 조회와 차량별 공정 진행 갱신을 위한 핵심 인덱스.
     Index("idx_dispatch", "dispatch_status", "is_sent", "id"),
+    Index(
+        "idx_bottleneck_analysis_pending",
+        "is_sent",
+        "bottleneck_analysis_done",
+        "id",
+    ),
+    Index(
+        "idx_defect_transfer_analysis_pending",
+        "is_sent",
+        "defect_transfer_analysis_done",
+        "id",
+    ),
     Index("idx_car_process", "car_master_id", "process_code"),
     Index("idx_process_status", "process_code", "dispatch_status"),
     Index("idx_equipment_status", "equipment_id", "dispatch_status"),
diff --git a/app/repository/sampledb_schema_manager.py b/app/repository/sampledb_schema_manager.py
index 4954171..e846593 100644
--- a/app/repository/sampledb_schema_manager.py
+++ b/app/repository/sampledb_schema_manager.py
@@ -75,7 +75,23 @@ def _align_equipment_mysql_types(self) -> None:
     def _align_manufacturing_event_json_mysql(self) -> None:
         if self.engine.dialect.name != "mysql":
             return
+        existing_columns = {
+            column["name"]
+            for column in inspect(self.engine).get_columns("manufacturing_event_json")
+        }
         with self.engine.begin() as conn:
+            for column_name, definition in {
+                "bottleneck_analysis_done": "TINYINT(1) NOT NULL DEFAULT 0",
+                "defect_transfer_analysis_done": "TINYINT(1) NOT NULL DEFAULT 0",
+            }.items():
+                if column_name in existing_columns:
+                    continue
+                conn.execute(
+                    text(
+                        "ALTER TABLE manufacturing_event_json "
+                        f"ADD COLUMN {column_name} {definition}",
+                    ),
+                )
             conn.execute(
                 text(
                     "ALTER TABLE manufacturing_event_json "
@@ -86,10 +102,25 @@ def _align_manufacturing_event_json_mysql(self) -> None:
                     "MODIFY COLUMN analysis_status "
                     "ENUM('NOT_ANALYZED','NORMAL','ABNORMAL') "
                     "NOT NULL DEFAULT 'NOT_ANALYZED', "
+                    "MODIFY COLUMN bottleneck_analysis_done "
+                    "TINYINT(1) NOT NULL DEFAULT 0, "
+                    "MODIFY COLUMN defect_transfer_analysis_done "
+                    "TINYINT(1) NOT NULL DEFAULT 0, "
                     "MODIFY COLUMN updated_at DATETIME NOT NULL "
                     "DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP",
                 ),
             )
+            conn.execute(
+                text(
+                    "UPDATE manufacturing_event_json "
+                    "SET bottleneck_analysis_done = "
+                    "CASE WHEN LOWER(CAST(bottleneck_analysis_done AS CHAR)) IN "
+                    "('1', 'true') THEN 1 ELSE 0 END, "
+                    "defect_transfer_analysis_done = "
+                    "CASE WHEN LOWER(CAST(defect_transfer_analysis_done AS CHAR)) IN "
+                    "('1', 'true') THEN 1 ELSE 0 END",
+                ),
+            )
 
     def _add_missing_columns(
         self,
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 8f84db3..7e67913 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -1,6 +1,9 @@
+from __future__ import annotations
+
 import logging
 import re
 from collections import Counter
+from datetime import datetime
 from pathlib import Path
 from typing import Any
 
@@ -16,13 +19,13 @@
 
 
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
-BOTTLENECK_CACHE_VERSION = "v10"
+BOTTLENECK_CACHE_VERSION = "v12"
 PROCESS_CODE_LABELS = {
-    "PRESS": "프레스",
-    "BODY": "차체",
-    "PAINT": "도장",
-    "ASSEMBLY": "의장",
-    "INSPECTION": "검사",
+    "PRESS": "PRESS",
+    "BODY": "BODY",
+    "PAINT": "PAINT",
+    "ASSEMBLY": "ASSEMBLY",
+    "INSPECTION": "INSPECTION",
 }
 PROCESS_EQUIPMENT_PREFIXES = {
     "PRESS": "P",
@@ -35,15 +38,12 @@
 
 
 class BottleneckAnalysisService:
-    """공정 이력 데이터를 분석해 병목 순위 결과를 제공하는 서비스"""
-
     def __init__(
         self,
         *,
         model_path: str | Path | None = None,
         database_url: str | None = None,
     ) -> None:
-        """병목 분석에 필요한 저장소, 모델, Redis 클라이언트 상태를 초기화"""
         self.model_path = Path(model_path or DEFAULT_BOTTLENECK_MODEL_PATH)
         self.repository = BottleneckAnalysisRepository(
             database_url or settings.bottleneck_database_url,
@@ -58,12 +58,12 @@ def get_realtime_bottlenecks(
         cursor: int | None,
         size: int,
     ) -> BottleneckAnalysisPage:
-        """현재 공정 이력을 분석하고 순위가 매겨진 결과 페이지를 반환"""
         size = max(1, min(size, 100))
         page = max(cursor or 0, 0)
-
-        # 저장소에서 요청 페이지 범위만 조회
-        rows = self.repository.list_results(cursor=page, size=size)
+        rows = self.repository.list_results(
+            cursor=page,
+            size=size,
+        )
         if not rows:
             return BottleneckAnalysisPage(
                 mostBottleneckProcess=None,
@@ -76,13 +76,10 @@ def get_realtime_bottlenecks(
         top_row = rows[0]
         total_count = self.repository.count_results()
         has_next = total_count > (page + 1) * size
-        next_cursor = page + 1 if has_next else None
 
         return BottleneckAnalysisPage(
             mostBottleneckProcess=self._format_process_label(top_row["process_code"]),
-            mostBottleneckRiskLevel=self._risk_level_label(
-                float(top_row["risk_score"]),
-            ),
+            mostBottleneckRiskLevel=self._risk_level_label(float(top_row["risk_score"])),
             content=[
                 BottleneckAnalysisItem(
                     rankNo=int(row["rank_no"]),
@@ -98,7 +95,7 @@ def get_realtime_bottlenecks(
                 for row in rows
             ],
             hasNext=has_next,
-            nextCursor=next_cursor,
+            nextCursor=page + 1 if has_next else None,
         )
 
     def get_cached_realtime_bottlenecks(
@@ -107,32 +104,27 @@ def get_cached_realtime_bottlenecks(
         cursor: int | None,
         size: int,
     ) -> BottleneckAnalysisPage:
-        """Redis에 결과가 있으면 반환하고, 없으면 분석 후 캐시에 저장"""
-        cache_key = self._bottleneck_cache_key(cursor=cursor, size=size)
+        cache_key = self._bottleneck_cache_key(
+            cursor=cursor,
+            size=size,
+        )
         try:
             redis_client = self._redis()
-            # cursor와 size를 key에 포함해 페이지별 캐시를 분리
             cached_value = redis_client.get(cache_key)
             if cached_value:
-                logger.info(
-                    "Bottleneck analysis cache hit: key=%s source=redis",
-                    cache_key,
-                )
                 return BottleneckAnalysisPage.model_validate(from_json(cached_value))
         except Exception as exc:
             self._redis_client = None
             raise AppException(
-                "Redis 캐시 조회에 실패했습니다.",
+                "Redis cache lookup failed.",
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             ) from exc
 
-        logger.info(
-            "Bottleneck analysis cache miss: key=%s source=kafka_raw_db",
-            cache_key,
+        page = self.get_realtime_bottlenecks(
+            cursor=cursor,
+            size=size,
         )
-        page = self.get_realtime_bottlenecks(cursor=cursor, size=size)
         try:
-            # 오래된 결과가 과도하게 남지 않도록 설정된 TTL로 저장
             self._redis().setex(
                 cache_key,
                 settings.redis_cache_ttl_seconds,
@@ -141,146 +133,54 @@ def get_cached_realtime_bottlenecks(
         except Exception as exc:
             self._redis_client = None
             raise AppException(
-                "Redis 캐시 저장에 실패했습니다.",
+                "Redis cache write failed.",
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             ) from exc
         return page
 
     def run_analysis_and_save(self, *, cursor: int, size: int) -> tuple[int, bool]:
-        """전체 공정 이력을 분석하고 요청한 순위 페이지 결과만 저장"""
-        histories = self.repository.list_manufacturing_event_histories()
-        if not histories:
-            logger.info(
-                "Bottleneck analysis skipped: source=kafka_raw_db "
-                "table=manufacturing_event_json filter=is_sent:true total=0 "
-                "cursor=%s size=%s",
-                cursor,
-                size,
-            )
-            self.repository.prune_results_after_rank(0)
-            return 0, False
-
-        process_counts = Counter(str(row.get("process_code")) for row in histories)
-        event_ids = [
-            int(row["manufacturing_event_id"])
-            for row in histories
-            if row.get("manufacturing_event_id") is not None
-        ]
-        delay_times = [
-            float(row.get("delay_time") or 0.0)
-            for row in histories
-        ]
-        logger.info(
-            "Bottleneck analysis input loaded: source=kafka_raw_db "
-            "table=manufacturing_event_json filter=is_sent:true total=%s "
-            "process_counts=%s delay_time_range=%.3f..%.3f "
-            "manufacturing_event_id_range=%s..%s cursor=%s size=%s",
-            len(histories),
-            dict(sorted(process_counts.items())),
-            min(delay_times) if delay_times else 0.0,
-            max(delay_times) if delay_times else 0.0,
-            min(event_ids) if event_ids else None,
-            max(event_ids) if event_ids else None,
-            cursor,
-            size,
-        )
-
-        if not self.model_path.exists():
-            raise AppException(
-                f"병목 탐지 모델을 찾을 수 없습니다: {self.model_path}",
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            )
-
-        summaries = self.detector.summarize_manufacturing_event_histories(histories)
+        merged_summaries = self.refresh_results_from_events()
         offset = cursor * size
-        page_summaries = summaries[offset : offset + size]
-        has_next = len(summaries) > offset + size
-        start_rank = offset + 1
-        end_rank = offset + size
-
-        self.repository.replace_results(
-            page_summaries,
-            detected_at=seoul_now().replace(tzinfo=None),
-            start_rank=start_rank,
-            end_rank=end_rank,
-        )
-        if not has_next:
-            self.repository.prune_results_after_rank(len(summaries))
-
-        logger.info(
-            "Bottleneck analysis results saved: source=kafka_raw_db "
-            "result_count=%s rank_range=%s..%s has_next=%s",
-            len(page_summaries),
-            start_rank,
-            end_rank,
-            has_next,
-        )
+        page_summaries = merged_summaries[offset : offset + size]
+        has_next = len(merged_summaries) > offset + size
         return len(page_summaries), has_next
 
     def refresh_results_from_events(self) -> list[dict[str, Any]]:
-        """Kafka raw 이벤트 수신 후 전체 병목 분석 결과를 즉시 갱신한다."""
-        histories = self.repository.list_manufacturing_event_histories()
+        histories = self.repository.list_pending_manufacturing_event_histories()
         if not histories:
-            logger.info(
-                "Bottleneck analysis skipped: source=kafka_raw_db "
-                "table=manufacturing_event_json filter=is_sent:true total=0 "
-                "trigger=kafka_raw_event",
-            )
-            self.repository.prune_results_after_rank(0)
-            return []
-
-        process_counts = Counter(str(row.get("process_code")) for row in histories)
-        event_ids = [
-            int(row["manufacturing_event_id"])
-            for row in histories
-            if row.get("manufacturing_event_id") is not None
-        ]
-        delay_times = [
-            float(row.get("delay_time") or 0.0)
-            for row in histories
-        ]
-        logger.info(
-            "Bottleneck analysis input loaded: source=kafka_raw_db "
-            "table=manufacturing_event_json filter=is_sent:true total=%s "
-            "process_counts=%s delay_time_range=%.3f..%.3f "
-            "manufacturing_event_id_range=%s..%s trigger=kafka_raw_event",
-            len(histories),
-            dict(sorted(process_counts.items())),
-            min(delay_times) if delay_times else 0.0,
-            max(delay_times) if delay_times else 0.0,
-            min(event_ids) if event_ids else None,
-            max(event_ids) if event_ids else None,
-        )
+            return self._current_bottleneck_results()
 
         if not self.model_path.exists():
             raise AppException(
-                f"병목 탐지 모델을 찾을 수 없습니다: {self.model_path}",
+                f"Bottleneck model not found: {self.model_path}",
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             )
 
-        summaries = self.detector.summarize_manufacturing_event_histories(histories)
+        new_summaries = self.detector.summarize_manufacturing_event_histories(histories)
+        merged_summaries = self._rank_bottleneck_summaries(
+            self._current_bottleneck_results() + new_summaries,
+        )
         self.repository.replace_results(
-            summaries,
+            merged_summaries,
             detected_at=seoul_now().replace(tzinfo=None),
             start_rank=1,
-            end_rank=max(len(summaries), 1),
+            end_rank=max(len(merged_summaries), 1),
         )
-        self.repository.prune_results_after_rank(len(summaries))
-        logger.info(
-            "Bottleneck analysis results saved: source=kafka_raw_db "
-            "trigger=kafka_raw_event result_count=%s rank_range=1..%s",
-            len(summaries),
-            len(summaries),
+        self.repository.mark_bottleneck_analysis_done(
+            [
+                int(row["manufacturing_event_id"])
+                for row in histories
+                if row.get("manufacturing_event_id") is not None
+            ],
         )
-        return summaries
+        return merged_summaries
 
     def _redis(self) -> Any:
-        """병목 캐시 작업에 사용할 Redis 클라이언트를 생성하고 재사용"""
         if self._redis_client is None:
             try:
                 from redis import Redis
             except ModuleNotFoundError as exc:
-                raise RuntimeError("병목 캐시를 사용하려면 redis 패키지가 필요합니다.") from exc
+                raise RuntimeError("redis package is required for bottleneck cache.") from exc
 
             self._redis_client = Redis.from_url(
                 settings.redis_connection_url,
@@ -288,8 +188,12 @@ def _redis(self) -> Any:
             )
         return self._redis_client
 
-    def _bottleneck_cache_key(self, *, cursor: int | None, size: int) -> str:
-        """요청한 병목 결과 페이지에 대한 Redis key를 생성"""
+    def _bottleneck_cache_key(
+        self,
+        *,
+        cursor: int | None,
+        size: int,
+    ) -> str:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
         return (
@@ -297,6 +201,11 @@ def _bottleneck_cache_key(self, *, cursor: int | None, size: int) -> str:
             f"{BOTTLENECK_CACHE_VERSION}:{page}:{safe_size}"
         )
 
+    @staticmethod
+    def _safe_cache_part(value: str | None) -> str:
+        text = str(value or "__default__").strip()
+        return text.replace(":", "_").replace("/", "_").replace("\\", "_")
+
     @staticmethod
     def _format_process_code(
         process_code: Any,
@@ -305,9 +214,7 @@ def _format_process_code(
         normalized = str(process_code or "").strip().upper()
         label = PROCESS_CODE_LABELS.get(normalized, normalized)
         prefix = PROCESS_EQUIPMENT_PREFIXES.get(normalized)
-        equipment_no = BottleneckAnalysisService._equipment_number(
-            equipment_code,
-        )
+        equipment_no = BottleneckAnalysisService._equipment_number(equipment_code)
         if prefix and equipment_no is not None:
             return f"{label} ({prefix}{equipment_no})"
         return label
@@ -319,18 +226,43 @@ def _format_process_label(process_code: Any) -> str:
 
     @staticmethod
     def _risk_level_label(risk_score: float) -> str:
-        return "위험" if risk_score >= 3.0 else "보통"
+        return "HIGH" if risk_score >= 3.0 else "NORMAL"
+
+    def _current_bottleneck_results(self) -> list[dict[str, Any]]:
+        total_count = self.repository.count_results()
+        if total_count <= 0:
+            return []
+        return self.repository.list_results(
+            cursor=0,
+            size=total_count,
+        )
+
+    @staticmethod
+    def _rank_bottleneck_summaries(
+        summaries: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        ranked = sorted(
+            summaries,
+            key=lambda row: (
+                float(row.get("risk_score") or 0.0),
+                float(row.get("avg_delay_time") or 0.0),
+                int(row.get("affected_vehicle_count") or 0),
+                int(row.get("manufacturing_event_id") or 0),
+                int(row.get("car_master_id") or 0),
+            ),
+            reverse=True,
+        )
+        return [{**row, "rank_no": index + 1} for index, row in enumerate(ranked)]
 
     @staticmethod
     def _equipment_number(equipment_code: Any | None) -> int | None:
         if equipment_code is None:
             return None
-
         match = re.search(r"(\d+)$", str(equipment_code).strip())
         if not match:
             return None
         return int(match.group(1))
 
+
 def get_bottleneck_analysis_service() -> BottleneckAnalysisService:
-    """FastAPI 의존성 주입에 사용할 병목 분석 서비스를 생성"""
     return BottleneckAnalysisService()
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index 643ec4d..ee71792 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -20,7 +20,8 @@
 from app.utils.json_utils import from_json, to_json
 from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
 
-DEFECT_TRANSFER_CACHE_VERSION = "v7"
+
+DEFECT_TRANSFER_CACHE_VERSION = "v8"
 
 
 class DefectTransferAnalysisService:
@@ -50,16 +51,20 @@ def get_cached_predictions(
         cursor: int | None,
         size: int,
     ) -> DefectTransferPredictionPage:
-        cache_key = self._prediction_cache_key(cursor=cursor, size=size)
+        cache_key = self._prediction_cache_key(
+            cursor=cursor,
+            size=size,
+        )
         cached_value = self._cache_get(cache_key)
         if cached_value:
-            cached_page = DefectTransferPredictionPage.model_validate(
-                from_json(cached_value),
-            )
+            cached_page = DefectTransferPredictionPage.model_validate(from_json(cached_value))
             if cached_page.content:
                 return cached_page
 
-        page = self.get_predictions(cursor=cursor, size=size)
+        page = self.get_predictions(
+            cursor=cursor,
+            size=size,
+        )
         self._cache_set(cache_key, page.model_dump(by_alias=False))
         return page
 
@@ -101,7 +106,7 @@ def clear_cache(self) -> None:
         except Exception as exc:
             self._redis_client = None
             raise AppException(
-                "불량 전이 예측 Redis 캐시 삭제에 실패했습니다.",
+                "Defect transfer Redis cache clear failed.",
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             ) from exc
 
@@ -135,19 +140,19 @@ def get_diagnostics(self) -> dict[str, Any]:
 
         if diagnostics["sourceEventCount"] == 0:
             status_text = "NO_SOURCE_EVENTS"
-            message = "sampledb.manufacturing_event_json에 원천 이벤트가 없습니다."
+            message = "sampledb.manufacturing_event_json source events not found."
         elif diagnostics["sentSourceEventCount"] == 0:
             status_text = "NO_SENT_SOURCE_EVENTS"
-            message = "원천 이벤트는 있지만 is_sent=true 이벤트가 없습니다."
+            message = "No sent source events were found."
         elif diagnostics["predictionResultRowCount"] == 0:
             status_text = "NO_PREDICTION_RESULTS"
-            message = "원천 이벤트는 있지만 예측 결과 테이블에 저장된 행이 없습니다."
+            message = "No defect transfer prediction results were found."
         elif diagnostics["visiblePredictionCarCount"] == 0:
             status_text = "NO_VISIBLE_PREDICTIONS"
-            message = "예측 결과는 저장됐지만 화면 목록 필터를 통과하는 차량이 없습니다."
+            message = "Prediction rows exist, but none are visible in the list."
         else:
             status_text = "OK"
-            message = "화면에 표시 가능한 불량 전이 예측 데이터가 있습니다."
+            message = "Defect transfer analysis data is available."
 
         return {
             **diagnostics,
@@ -187,11 +192,7 @@ def get_cause_analysis(
                 nextCursor=None,
             )
 
-        display_rows = [
-            row
-            for row in rows
-            if self._is_displayable_cause(row)
-        ]
+        display_rows = [row for row in rows if self._is_displayable_cause(row)]
 
         return DefectTransferCausePage(
             vehicleId=str(selected.get("vehicle_id")),
@@ -205,13 +206,9 @@ def get_cause_analysis(
             predictedDefectProcess=self._resolve_predicted_defect_process(selected),
             transferProbability=self._percent(selected.get("target_defect_probability")),
             content=[
-                DefectTransferCauseItem(
+                self._to_cause_item(
+                    row,
                     rank=index,
-                    feature="main_cause",
-                    label=str(row.get("main_cause") or ""),
-                    value="",
-                    impact=float(row.get("influence_score") or 0.0),
-                    message=str(row.get("main_cause") or ""),
                 )
                 for index, row in enumerate(display_rows, page * safe_size + 1)
             ],
@@ -223,9 +220,8 @@ def _to_prediction_item(
         self,
         row: dict[str, Any],
     ) -> DefectTransferPredictionItem:
-        vehicle_id = str(row.get("vehicle_id"))
         return DefectTransferPredictionItem(
-            vehicleId=vehicle_id,
+            vehicleId=str(row.get("vehicle_id")),
             carMasterId=int(row["car_master_id"]),
             currentProcess=format_process_with_line(
                 row.get("source_process_code"),
@@ -259,7 +255,6 @@ def _resolve_predicted_defect_process(cls, row: dict[str, Any]) -> str | None:
         if cls._result_probability(row) <= 0:
             return None
 
-        # Return the database column value if available
         db_val = row.get("predicted_defect_process")
         if db_val:
             return db_val
@@ -284,7 +279,7 @@ def _display_value(value: Any) -> str:
 
     @classmethod
     def _is_displayable_cause(cls, row: dict[str, Any]) -> bool:
-        message = str(row.get("main_cause") or "").strip()
+        message = cls._primary_cause_message(row).strip()
         if not message:
             return False
 
@@ -293,23 +288,67 @@ def _is_displayable_cause(cls, row: dict[str, Any]) -> bool:
             return True
 
         thresholds = {
-            "도막 두께 편차": 0.0,
-            "도장 온도 편차": 4.0,
-            "공정 지연": 12.0,
-            "Cycle Time 증가": 55.0,
-            "대기열 증가": 8.0,
-            "WIP 증가": 24.0,
-            "전류 RMS 편차": 2.2,
-            "진동 Score 상승": 0.45,
-            "로봇 진동 Score 상승": 0.45,
-            "열화상 Score 상승": 55.0,
-            "최고 온도 상승": 58.0,
+            "pressure": 0.0,
+            "temperature": 4.0,
+            "vibration": 0.45,
+            "cycle time": 55.0,
+            "wip": 24.0,
+            "rms": 2.2,
         }
+        lower_message = message.lower()
         for prefix, threshold in thresholds.items():
-            if message.startswith(prefix):
+            if prefix in lower_message:
                 return value > threshold
         return value > 0.0
 
+    @staticmethod
+    def _primary_cause_message(row: dict[str, Any]) -> str:
+        main_causes = row.get("main_causes")
+        if isinstance(main_causes, list) and main_causes:
+            first = main_causes[0]
+            if isinstance(first, dict):
+                message = first.get("message") or first.get("label") or ""
+                return str(message).strip()
+
+        return ""
+
+    @classmethod
+    def _to_cause_item(
+        cls,
+        row: dict[str, Any],
+        *,
+        rank: int,
+    ) -> DefectTransferCauseItem:
+        main_causes = row.get("main_causes")
+        normalized_main_causes: list[dict[str, Any]] = []
+        if isinstance(main_causes, list):
+            for cause in main_causes:
+                if not isinstance(cause, dict):
+                    continue
+                message = str(cause.get("message") or cause.get("label") or "").strip()
+                if not message:
+                    continue
+                try:
+                    impact = float(cause.get("impact") or 0.0)
+                except (TypeError, ValueError):
+                    impact = 0.0
+                normalized_main_causes.append(
+                    {
+                        "message": message,
+                        "impact": impact,
+                    },
+                )
+
+        return DefectTransferCauseItem(
+            rank=rank,
+            feature="main_causes",
+            label=cls._primary_cause_message(row),
+            value="",
+            impact=float(row.get("influence_score") or 0.0),
+            message=cls._primary_cause_message(row),
+            main_causes=normalized_main_causes,
+        )
+
     @staticmethod
     def _first_number(text: str) -> float | None:
         import re
@@ -320,8 +359,7 @@ def _first_number(text: str) -> float | None:
     @staticmethod
     def _db_exception() -> AppException:
         return AppException(
-            "불량 전이 예측 결과 데이터베이스 조회에 실패했습니다. "
-            "MAIN_DB_NAME/SAMPLE_DB_NAME/DB 계정 권한을 확인해주세요.",
+            "Defect transfer analysis query failed. Please check DB settings and permissions.",
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
         )
 
@@ -333,7 +371,7 @@ def _cache_get(self, cache_key: str) -> str | None:
         except Exception as exc:
             self._redis_client = None
             raise AppException(
-                "불량 전이 예측 Redis 캐시 조회에 실패했습니다.",
+                "Defect transfer Redis cache lookup failed.",
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             ) from exc
 
@@ -349,7 +387,7 @@ def _cache_set(self, cache_key: str, data: dict[str, Any]) -> None:
         except Exception as exc:
             self._redis_client = None
             raise AppException(
-                "불량 전이 예측 Redis 캐시 저장에 실패했습니다.",
+                "Defect transfer Redis cache write failed.",
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             ) from exc
 
@@ -371,7 +409,12 @@ def _safe_cache_part(value: Any) -> str:
         text = str(value or "__default__").strip()
         return text.replace(":", "_").replace("/", "_").replace("\\", "_")
 
-    def _prediction_cache_key(self, *, cursor: int | None, size: int) -> str:
+    def _prediction_cache_key(
+        self,
+        *,
+        cursor: int | None,
+        size: int,
+    ) -> str:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
         return (
@@ -388,10 +431,9 @@ def _cause_cache_key(
     ) -> str:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
-        safe_vehicle_id = self._safe_cache_part(vehicle_id)
         return (
             f"{settings.redis_key_prefix}:process:defect-transfer:"
-            f"causes:{DEFECT_TRANSFER_CACHE_VERSION}:{safe_vehicle_id}:{page}:{safe_size}"
+            f"causes:{DEFECT_TRANSFER_CACHE_VERSION}:{self._safe_cache_part(vehicle_id)}:{page}:{safe_size}"
         )
 
 
diff --git a/app/service/manufacturing/manufacturing_event_json_service.py b/app/service/manufacturing/manufacturing_event_json_service.py
index c7753ef..28c6a63 100644
--- a/app/service/manufacturing/manufacturing_event_json_service.py
+++ b/app/service/manufacturing/manufacturing_event_json_service.py
@@ -940,6 +940,8 @@ def _materialize_template_row(
         # 재생된 이벤트도 최초 공정 제어 규칙을 그대로 따른다.
         "dispatch_status": initial_dispatch_status(str(row["process_code"])),
         "analysis_status": "NOT_ANALYZED",
+        "bottleneck_analysis_done": False,
+        "defect_transfer_analysis_done": False,
         "retry_count": 0,
         "error_message": None,
     }
diff --git a/app/utils/analysis_time_window.py b/app/utils/analysis_time_window.py
new file mode 100644
index 0000000..bfcfa11
--- /dev/null
+++ b/app/utils/analysis_time_window.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import date, datetime, time, timedelta
+
+from app.utils.datetime_utils import seoul_now
+
+
+@dataclass(frozen=True)
+class AnalysisTimeWindow:
+    selected_date: date | None
+    start_at: datetime | None
+    end_at: datetime | None
+
+
+def build_analysis_time_window(
+    *,
+    date_value: date | None = None,
+    from_at: datetime | None = None,
+    to_at: datetime | None = None,
+    end_at: datetime | None = None,
+    default_to_today: bool = True,
+) -> AnalysisTimeWindow:
+    if date_value is not None:
+        start_at = datetime.combine(date_value, time.min)
+        return AnalysisTimeWindow(
+            selected_date=date_value,
+            start_at=start_at,
+            end_at=start_at + timedelta(days=1),
+        )
+
+    normalized_from = _normalize_datetime(from_at)
+    normalized_to = _normalize_datetime(to_at)
+    normalized_end = _normalize_datetime(end_at)
+    final_end = normalized_to or normalized_end
+
+    if normalized_from is None and final_end is None and default_to_today:
+        today = seoul_now().date()
+        start_at = datetime.combine(today, time.min)
+        return AnalysisTimeWindow(
+            selected_date=today,
+            start_at=start_at,
+            end_at=start_at + timedelta(days=1),
+        )
+
+    return AnalysisTimeWindow(
+        selected_date=None,
+        start_at=normalized_from,
+        end_at=final_end,
+    )
+
+
+def _normalize_datetime(value: datetime | None) -> datetime | None:
+    if value is None:
+        return None
+    if value.tzinfo is not None:
+        return value.replace(tzinfo=None)
+    return value

From 272ac0e525b8d706a8bacfb65a89ecc4f18bc971 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 7 Jul 2026 13:09:56 +0900
Subject: [PATCH 106/148] feat : users table ID get

---
 .../repository/alert_event_repository.py      | 41 ++++++++++++++++++-
 app/ai_manual/service/manual_service.py       |  6 ++-
 app/api/router.py                             |  4 +-
 app/api/routers/events.py                     | 16 ++++++++
 app/api/routers/manual.py                     |  9 ++--
 5 files changed, 69 insertions(+), 7 deletions(-)
 create mode 100644 app/api/routers/events.py

diff --git a/app/ai_manual/repository/alert_event_repository.py b/app/ai_manual/repository/alert_event_repository.py
index 4089e8a..fb464c5 100644
--- a/app/ai_manual/repository/alert_event_repository.py
+++ b/app/ai_manual/repository/alert_event_repository.py
@@ -40,4 +40,43 @@ def get_highest_risk_event(self):
         if not row:
             return None
 
-        return dict(row)
\ No newline at end of file
+        return dict(row)
+    
+    def get_events(self):
+
+        sql = """
+        SELECT
+            event_id,
+            title,
+            contents,
+            severity,
+            priority_score,
+            risk_score,
+            process_code,
+            equipment_id,
+            created_at
+        FROM alert_event
+        ORDER BY priority_score DESC
+        """
+
+        with main_engine.connect() as conn:
+
+            result = conn.execute(text(sql))
+
+            return [dict(row._mapping) for row in result]
+        
+    def get_user_role(self, user_id: int):
+
+        sql = text("""
+            SELECT role
+            FROM users
+            WHERE id = :user_id
+        """)
+
+        with main_engine.connect() as conn:
+            role = conn.execute(
+                sql,
+                {"user_id": user_id}
+            ).scalar()
+
+        return role or "Junior"
\ No newline at end of file
diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index 2c9dab0..aa13f42 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -38,7 +38,7 @@ def __init__(self):
     # ======================================================
     def generate_manual(
         self,
-        operator_grade: str = "Junior"
+        user_id: int
     ) -> ManualResponse | None:
 
         # 1. EVENT FETCH
@@ -48,6 +48,10 @@ def generate_manual(
             return None
 
         # 2. BUILD STRONG REQUEST OBJECT
+        operator_grade = self.repository.get_user_role(user_id)
+
+        if operator_grade is None:
+            operator_grade = "Junior"
         request = self._build_request(event, operator_grade)
 
         # 3. PROMPT BUILD
diff --git a/app/api/router.py b/app/api/router.py
index 7a72451..91950c0 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -9,6 +9,7 @@
     process_analysis_ws,
     root,
     manual,
+    events
 )
 
 api_router = APIRouter()
@@ -19,4 +20,5 @@
 api_router.include_router(process_analysis_ws.router)
 api_router.include_router(manufacturing_event.router)
 api_router.include_router(ml_dataset.router)
-api_router.include_router(manual.router)
\ No newline at end of file
+api_router.include_router(manual.router)
+api_router.include_router(events.router)
\ No newline at end of file
diff --git a/app/api/routers/events.py b/app/api/routers/events.py
new file mode 100644
index 0000000..176f130
--- /dev/null
+++ b/app/api/routers/events.py
@@ -0,0 +1,16 @@
+from fastapi import APIRouter
+
+from app.ai_manual.repository.alert_event_repository import AlertEventRepository
+
+router = APIRouter(
+    prefix="/events",
+    tags=["Events"]
+)
+
+repository = AlertEventRepository()
+
+
+@router.get("")
+def get_events():
+
+    return repository.get_events()
\ No newline at end of file
diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 494c503..2e42609 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -12,11 +12,11 @@
 service = ManualService()
 
 
-@router.get("")
-def generate_manual():
+@router.get("/{user_id}")
+def generate_manual(user_id: int):
 
     result = service.generate_manual(
-        operator_grade="Junior"
+        user_id=user_id
     )
 
     if result is None:
@@ -25,4 +25,5 @@ def generate_manual():
             detail="처리할 이벤트가 없습니다."
         )
 
-    return result
\ No newline at end of file
+    return result
+

From 52919c144e99b71b04b94c2984c5d81e3410f1f2 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 7 Jul 2026 13:33:03 +0900
Subject: [PATCH 107/148] =?UTF-8?q?fix:=20raw=20Kafka=20consumer=20offset?=
 =?UTF-8?q?=20commit=20=EC=8B=A4=ED=8C=A8=20=EC=B2=98=EB=A6=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- CommitFailedError 발생 시 consumer가 비정상 종료되지 않도록 예외 처리 추가

- consumer group rebalance로 active group에서 제외된 경우 commit을 건너뛰도록 수정

- max_poll_records 및 max_poll_interval_ms 설정을 조정해 장시간 분석 중 group 이탈 가능성 완화
---
 app/core/config.py                         | 48 ++++++++++++++++++++++
 app/kafka/config.py                        | 11 ++++-
 app/kafka/consumer.py                      | 31 +++++---------
 app/kafka/producer.py                      | 28 ++++++++-----
 app/kafka/raw_event_consumer.py            | 34 +++++++++++----
 app/service/analysis/bottleneck_service.py | 10 ++---
 6 files changed, 115 insertions(+), 47 deletions(-)

diff --git a/app/core/config.py b/app/core/config.py
index 5d4b1b0..1aaa318 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -54,6 +54,54 @@ class Settings(BaseSettings):
 
     broker_url_1: str | None = Field(default=None, alias="BROKER_URL_1")
     broker_url_2: str | None = Field(default=None, alias="BROKER_URL_2")
+    kafka_raw_topic: str = Field(
+        default="factory.manufacturing.raw",
+        alias="KAFKA_RAW_TOPIC",
+    )
+    kafka_analysis_topic: str = Field(
+        default="factory.manufacturing.analysis",
+        alias="KAFKA_ANALYSIS_TOPIC",
+    )
+    kafka_raw_consumer_group_id: str = Field(
+        default="ai-analysis-consumer-group",
+        alias="KAFKA_RAW_CONSUMER_GROUP_ID",
+    )
+    kafka_raw_auto_offset_reset: str = Field(
+        default="earliest",
+        alias="KAFKA_RAW_AUTO_OFFSET_RESET",
+    )
+    kafka_raw_consumer_concurrency: int = Field(
+        default=2,
+        alias="KAFKA_RAW_CONSUMER_CONCURRENCY",
+    )
+    kafka_raw_consumer_max_poll_interval_ms: int = Field(
+        default=900_000,
+        alias="KAFKA_RAW_CONSUMER_MAX_POLL_INTERVAL_MS",
+    )
+    kafka_raw_consumer_session_timeout_ms: int = Field(
+        default=30_000,
+        alias="KAFKA_RAW_CONSUMER_SESSION_TIMEOUT_MS",
+    )
+    kafka_raw_consumer_heartbeat_interval_ms: int = Field(
+        default=10_000,
+        alias="KAFKA_RAW_CONSUMER_HEARTBEAT_INTERVAL_MS",
+    )
+    kafka_raw_consumer_max_poll_records: int = Field(
+        default=1,
+        alias="KAFKA_RAW_CONSUMER_MAX_POLL_RECORDS",
+    )
+    kafka_raw_consumer_timeout_ms: int = Field(
+        default=1_000,
+        alias="KAFKA_RAW_CONSUMER_TIMEOUT_MS",
+    )
+    kafka_analysis_producer_retries: int = Field(
+        default=3,
+        alias="KAFKA_ANALYSIS_PRODUCER_RETRIES",
+    )
+    kafka_analysis_producer_linger_ms: int = Field(
+        default=10,
+        alias="KAFKA_ANALYSIS_PRODUCER_LINGER_MS",
+    )
 
     redis_url: str | None = Field(default=None, alias="REDIS_URL")
     redis_key_prefix: str = Field(default="aims:ai-service", alias="REDIS_KEY_PREFIX")
diff --git a/app/kafka/config.py b/app/kafka/config.py
index 1077bf0..8f4f033 100644
--- a/app/kafka/config.py
+++ b/app/kafka/config.py
@@ -1,12 +1,19 @@
 # kafka/config.py
+RAW_TOPIC = "factory.manufacturing.raw"
+ANALYSIS_TOPIC = "factory.manufacturing.analysis"
+RAW_CONSUMER_GROUP_ID = "ai-analysis-consumer-group"
+RAW_CONSUMER_CONCURRENCY = 2
 AUTO_OFFSET_RESET = "earliest"
 
 ENABLE_AUTO_COMMIT = False
+CONSUMER_TIMEOUT_MS = 1000
 
 SESSION_TIMEOUT_MS = 30000
 
 HEARTBEAT_INTERVAL_MS = 10000
 
-MAX_POLL_RECORDS = 100
+MAX_POLL_RECORDS = 1
 
-MAX_POLL_INTERVAL_MS = 300000
\ No newline at end of file
+MAX_POLL_INTERVAL_MS = 900000
+PRODUCER_RETRIES = 3
+PRODUCER_LINGER_MS = 10
diff --git a/app/kafka/consumer.py b/app/kafka/consumer.py
index 170b90e..1a00d90 100644
--- a/app/kafka/consumer.py
+++ b/app/kafka/consumer.py
@@ -1,37 +1,28 @@
-# app/kafka/consumer.py
-
 import json
 import ssl
+
 from kafka import KafkaConsumer
+
+from app.core.config import settings
 from app.kafka.iam_provider import MSKTokenProvider
-from dotenv import load_dotenv
-import os
 
-load_dotenv()
-def create_consumer(topic, group_id):
 
+def _bootstrap_servers() -> list[str]:
+    servers = [settings.broker_url_1 or "", settings.broker_url_2 or ""]
+    return [server.strip() for server in servers if server.strip()]
+
+
+def create_consumer(topic: str, group_id: str) -> KafkaConsumer:
     consumer = KafkaConsumer(
         topic,
         ssl_context=ssl.create_default_context(),
-
-        bootstrap_servers=[
-        os.getenv("BROKER_URL_1"),
-        os.getenv("BROKER_URL_2")
-        ],
-
+        bootstrap_servers=_bootstrap_servers(),
         group_id=group_id,
-
         auto_offset_reset="earliest",
-
         enable_auto_commit=True,
-
         security_protocol="SASL_SSL",
-
         sasl_mechanism="OAUTHBEARER",
-
         sasl_oauth_token_provider=MSKTokenProvider(),
-
-        value_deserializer=lambda x: json.loads(x.decode("utf-8"))
+        value_deserializer=lambda x: json.loads(x.decode("utf-8")),
     )
-
     return consumer
diff --git a/app/kafka/producer.py b/app/kafka/producer.py
index ba63f45..c488fc2 100644
--- a/app/kafka/producer.py
+++ b/app/kafka/producer.py
@@ -1,17 +1,23 @@
-from kafka import KafkaProducer
-from dotenv import load_dotenv
-import os
 import json
+import ssl
 
+from kafka import KafkaProducer
 
-def create_producer():
+from app.core.config import settings
+from app.kafka.iam_provider import MSKTokenProvider
 
-    load_dotenv()
 
+def _bootstrap_servers() -> list[str]:
+    servers = [settings.broker_url_1 or "", settings.broker_url_2 or ""]
+    return [server.strip() for server in servers if server.strip()]
+
+
+def create_producer():
     return KafkaProducer(
-        bootstrap_servers=[
-            os.getenv("BROKER_URL_1"),
-            os.getenv("BROKER_URL_2")
-        ],
-        value_serializer=lambda x: json.dumps(x, default=str).encode("utf-8")
-    )
\ No newline at end of file
+        ssl_context=ssl.create_default_context(),
+        bootstrap_servers=_bootstrap_servers(),
+        security_protocol="SASL_SSL",
+        sasl_mechanism="OAUTHBEARER",
+        sasl_oauth_token_provider=MSKTokenProvider(),
+        value_serializer=lambda x: json.dumps(x, default=str).encode("utf-8"),
+    )
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 497094e..f97ae98 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -10,6 +10,7 @@
 from uuid import uuid4
 from fastapi import FastAPI
 from app.core.config import settings
+from app.kafka import config as kafka_config
 from app.kafka.iam_provider import MSKTokenProvider
 from app.ml.inference.defect_transfer_detector import (
     DefectTransferDetector,
@@ -26,11 +27,11 @@
 logger = logging.getLogger(__name__)
 PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
 # assembly-service의 app.kafka.topics.raw.name과 동일한 raw 토픽.
-RAW_TOPIC = "factory.manufacturing.raw"
-ANALYSIS_TOPIC = "factory.manufacturing.analysis"
-RAW_CONSUMER_GROUP_ID = "ai-analysis-consumer-group"
-RAW_AUTO_OFFSET_RESET = "earliest"
-RAW_CONSUMER_CONCURRENCY = 2
+RAW_TOPIC = kafka_config.RAW_TOPIC
+ANALYSIS_TOPIC = kafka_config.ANALYSIS_TOPIC
+RAW_CONSUMER_GROUP_ID = kafka_config.RAW_CONSUMER_GROUP_ID
+RAW_AUTO_OFFSET_RESET = kafka_config.AUTO_OFFSET_RESET
+RAW_CONSUMER_CONCURRENCY = kafka_config.RAW_CONSUMER_CONCURRENCY
 _bottleneck_analysis_lock = Lock()
 
 
@@ -78,6 +79,7 @@ def _run_raw_event_consumer(
 ) -> None:
     try:
         from kafka import KafkaConsumer, KafkaProducer
+        from kafka.errors import CommitFailedError
     except ModuleNotFoundError:
         logger.exception("kafka-python is required to consume manufacturing raw events.")
         return
@@ -89,10 +91,14 @@ def _run_raw_event_consumer(
         group_id=RAW_CONSUMER_GROUP_ID,
         auto_offset_reset=RAW_AUTO_OFFSET_RESET,
         enable_auto_commit=False,
+        max_poll_interval_ms=kafka_config.MAX_POLL_INTERVAL_MS,
+        session_timeout_ms=kafka_config.SESSION_TIMEOUT_MS,
+        heartbeat_interval_ms=kafka_config.HEARTBEAT_INTERVAL_MS,
+        max_poll_records=kafka_config.MAX_POLL_RECORDS,
         security_protocol="SASL_SSL",
         sasl_mechanism="OAUTHBEARER",
         sasl_oauth_token_provider=MSKTokenProvider(),
-        consumer_timeout_ms=1000,
+        consumer_timeout_ms=kafka_config.CONSUMER_TIMEOUT_MS,
     )
     repository = SampleDbRepository(settings.sample_database_connection_url)
     repository.schema.ensure_schema()
@@ -134,7 +140,17 @@ def _run_raw_event_consumer(
                     )
                 finally:
                     # DB 저장/분석 실패 여부와 관계없이 offset을 커밋해 같은 메시지의 무한 재처리를 막는다.
-                    consumer.commit()
+                    try:
+                        consumer.commit()
+                    except CommitFailedError:
+                        logger.warning(
+                            "Offset commit skipped because the consumer group was rebalanced: "
+                            "topic=%s partition=%s offset=%s consumer_index=%s",
+                            record.topic,
+                            record.partition,
+                            record.offset,
+                            consumer_index,
+                        )
     except Exception:
         logger.exception("Raw Kafka consumer failed.")
     finally:
@@ -511,8 +527,8 @@ def _create_analysis_producer(
                 ensure_ascii=False,
                 default=_json_default,
             ).encode("utf-8"),
-            retries=3,
-            linger_ms=10,
+            retries=kafka_config.PRODUCER_RETRIES,
+            linger_ms=kafka_config.PRODUCER_LINGER_MS,
         )
     except Exception:
         logger.exception(
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 7e67913..76caddb 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -21,11 +21,11 @@
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
 BOTTLENECK_CACHE_VERSION = "v12"
 PROCESS_CODE_LABELS = {
-    "PRESS": "PRESS",
-    "BODY": "BODY",
-    "PAINT": "PAINT",
-    "ASSEMBLY": "ASSEMBLY",
-    "INSPECTION": "INSPECTION",
+    "PRESS": "프레스",
+    "BODY": "차체",
+    "PAINT": "도장",
+    "ASSEMBLY": "의장",
+    "INSPECTION": "검사",
 }
 PROCESS_EQUIPMENT_PREFIXES = {
     "PRESS": "P",

From 9f18531289f246698e928f5bc695c029a659a708 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 7 Jul 2026 14:38:19 +0900
Subject: [PATCH 108/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9=EB=B6=84?=
 =?UTF-8?q?=EC=84=9D=20DB=20=EB=9E=AD=ED=82=B9=EA=B4=80=EB=A0=A8=20PK=20?=
 =?UTF-8?q?=EC=98=A4=EB=A5=98=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../bottleneck_analysis_repository.py         | 75 ++++++++++---------
 1 file changed, 40 insertions(+), 35 deletions(-)

diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 89107b3..7242f49 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -40,36 +40,19 @@ def replace_results(
         start_rank: int,
         end_rank: int,
     ) -> None:
-        payload = [{**row, "detected_at": detected_at} for row in rows]
-        payload_ranks = {int(row["rank_no"]) for row in payload}
-
-        from sqlalchemy import func
+        payload = [
+            {
+                key: value
+                for key, value in {**row, "detected_at": detected_at}.items()
+                if key != "id"
+            }
+            for row in rows
+        ]
+        if not payload:
+            return
 
         with self.engine.begin() as conn:
-            delete_query = (
-                self.table.delete()
-                .where(self.table.c.rank_no.between(start_rank, end_rank))
-                .where(func.date(self.table.c.detected_at) == func.current_date())
-            )
-            if payload_ranks:
-                delete_query = delete_query.where(
-                    self.table.c.rank_no.not_in(payload_ranks),
-                )
-            conn.execute(delete_query)
-
-            for row in payload:
-                update_values = {
-                    **row,
-                    "updated_at": func.current_timestamp(),
-                }
-                result = conn.execute(
-                    self.table.update()
-                    .where(self.table.c.rank_no == row["rank_no"])
-                    .where(func.date(self.table.c.detected_at) == func.current_date())
-                    .values(**update_values),
-                )
-                if not result.rowcount:
-                    conn.execute(self.table.insert(), row)
+            conn.execute(self.table.insert(), payload)
 
     def prune_results_after_rank(self, max_rank: int) -> None:
         from sqlalchemy import func
@@ -86,15 +69,26 @@ def list_results(
         cursor: int,
         size: int,
     ) -> list[dict[str, Any]]:
-        event_ids = self._event_ids_for_today()
-        if not event_ids:
+        snapshot_detected_at = self._latest_snapshot_detected_at()
+        if snapshot_detected_at is None:
             return []
 
         from sqlalchemy import select
 
         query = (
-            select(self.table)
-            .where(self.table.c.manufacturing_event_id.in_(event_ids))
+            select(
+                self.table.c.id,
+                self.table.c.manufacturing_event_id,
+                self.table.c.car_master_id,
+                self.table.c.process_code,
+                self.table.c.equipment_code,
+                self.table.c.rank_no,
+                self.table.c.avg_delay_time,
+                self.table.c.affected_vehicle_count,
+                self.table.c.risk_score,
+                self.table.c.detected_at,
+            )
+            .where(self.table.c.detected_at == snapshot_detected_at)
             .order_by(
                 self.table.c.rank_no.asc(),
                 self.table.c.id.asc(),
@@ -109,8 +103,8 @@ def list_results(
     def count_results(
         self,
     ) -> int:
-        event_ids = self._event_ids_for_today()
-        if not event_ids:
+        snapshot_detected_at = self._latest_snapshot_detected_at()
+        if snapshot_detected_at is None:
             return 0
 
         from sqlalchemy import func, select
@@ -118,7 +112,7 @@ def count_results(
         query = (
             select(func.count())
             .select_from(self.table)
-            .where(self.table.c.manufacturing_event_id.in_(event_ids))
+            .where(self.table.c.detected_at == snapshot_detected_at)
         )
         with self.engine.connect() as conn:
             return int(conn.execute(query).scalar_one())
@@ -184,6 +178,17 @@ def _event_ids_for_today(self) -> list[int]:
         with self.event_engine.connect() as conn:
             return [int(row[0]) for row in conn.execute(query).all()]
 
+    def _latest_snapshot_detected_at(self) -> datetime | None:
+        from sqlalchemy import func, select
+
+        query = (
+            select(func.max(self.table.c.detected_at))
+            .where(func.date(self.table.c.detected_at) == func.current_date())
+        )
+        with self.engine.connect() as conn:
+            value = conn.execute(query).scalar_one()
+        return value
+
     def mark_bottleneck_analysis_done(self, event_ids: Iterable[int]) -> int:
         event_ids = [int(event_id) for event_id in event_ids]
         if not event_ids:

From 488f8a3b45174a82b47a61fe4e662b56fcd0595c Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 7 Jul 2026 15:05:02 +0900
Subject: [PATCH 109/148] feat : ai-manual router update

---
 app/ai_manual/repository/alert_event_repository.py | 2 +-
 app/api/routers/manual.py                          | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/app/ai_manual/repository/alert_event_repository.py b/app/ai_manual/repository/alert_event_repository.py
index fb464c5..9bda0d3 100644
--- a/app/ai_manual/repository/alert_event_repository.py
+++ b/app/ai_manual/repository/alert_event_repository.py
@@ -28,7 +28,7 @@ def get_highest_risk_event(self):
                 resolved_at
             FROM alert_event
             WHERE severity = 'DANGER'
-              AND action_status = 'PENDING'
+              AND action_status = 'INCOMPLETE'
               AND resolved_at IS NULL
             ORDER BY risk_score DESC
             LIMIT 1
diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 2e42609..1b52ae5 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -14,7 +14,6 @@
 
 @router.get("/{user_id}")
 def generate_manual(user_id: int):
-
     result = service.generate_manual(
         user_id=user_id
     )

From 4d6e0c4df2da6291377410da4b9bd55c37713bb8 Mon Sep 17 00:00:00 2001
From: haseokyung6 <haseokyung@naver.com>
Date: Tue, 7 Jul 2026 15:33:28 +0900
Subject: [PATCH 110/148] =?UTF-8?q?refactor:=20namespace,=20branch=20?=
 =?UTF-8?q?=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .github/workflows/deploy-ai-service.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/deploy-ai-service.yml b/.github/workflows/deploy-ai-service.yml
index 79a54aa..c46926d 100644
--- a/.github/workflows/deploy-ai-service.yml
+++ b/.github/workflows/deploy-ai-service.yml
@@ -26,11 +26,10 @@ env:
   AWS_REGION: ap-northeast-2
   EKS_CLUSTER_NAME: aims-dev-eks
   ECR_REPOSITORY: aims/ai-service
-  NAMESPACE: ai-services
+  NAMESPACE: aims-project
 
   INFRA_REPOSITORY: SK-Rookies-AIMS/infra
-
-  # 기존 ai-service.yaml에서 kustomization.yaml로 변경
+  INFRA_BRANCH: dev
   INFRA_MANIFEST_PATH: k8s/ai-service/kustomization.yaml
 
 jobs:

From 2c3c2c921582c8e047101ac4fa4b88371711ed5c Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 7 Jul 2026 17:02:10 +0900
Subject: [PATCH 111/148] feat : front token value update

---
 app/ai_manual/prompt/prompt_template.py | 11 ++++++++++-
 app/api/routers/manual.py               | 25 +++++++++++++++++--------
 requirements.txt                        |  1 +
 3 files changed, 28 insertions(+), 9 deletions(-)

diff --git a/app/ai_manual/prompt/prompt_template.py b/app/ai_manual/prompt/prompt_template.py
index 30e37f9..14202a4 100644
--- a/app/ai_manual/prompt/prompt_template.py
+++ b/app/ai_manual/prompt/prompt_template.py
@@ -40,7 +40,7 @@
 Error Code를 임의 생성하지 마십시오.
 
 6.
-반드시 단계별 조치 방법을 작성하십시오.
+반드시 단계별 조치 최소 3단계, 최대 5단계로 방법을 작성하십시오.
 
 7.
 반드시 안전 관련 주의사항을 포함하십시오.
@@ -48,6 +48,15 @@
 8.
 항상 JSON 형식으로만 응답하십시오.
 
+9.
+summary는 반드시 2~3문장으로 작성하시오.
+
+10.
+summary는 최대 100자 내외로 작성하시오.
+
+11.
+말풍선(UI)에 표시되므로 긴 설명은 금지하십시오.
+
 --------------------------------------------
 
 ## Junior 담당자
diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 1b52ae5..102b37b 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -1,8 +1,8 @@
 # app/api/routers/manual.py
-
-from fastapi import APIRouter, HTTPException
-
 from app.ai_manual.service.manual_service import ManualService
+from fastapi import APIRouter, HTTPException, Header
+from jose import jwt
+import os
 
 router = APIRouter(
     prefix="/manual",
@@ -11,13 +11,23 @@
 
 service = ManualService()
 
+@router.get("")
+def generate_manual(
+    authorization: str = Header(...)
+):
+    token = authorization.replace("Bearer ", "")
 
-@router.get("/{user_id}")
-def generate_manual(user_id: int):
-    result = service.generate_manual(
-        user_id=user_id
+    payload = jwt.decode(
+        token,
+        os.getenv("JWT_SECRET_KEY"),
+        algorithms=["HS384"]
     )
 
+    user_id = int(payload["sub"])
+    print(payload)
+
+    result = service.generate_manual(user_id)
+
     if result is None:
         raise HTTPException(
             status_code=404,
@@ -25,4 +35,3 @@ def generate_manual(user_id: int):
         )
 
     return result
-
diff --git a/requirements.txt b/requirements.txt
index 4f77b9e..ea6fd7b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -34,6 +34,7 @@ redis>=5.2.0,<6.0.0
 pandas>=2.2.0
 numpy>=1.26.0
 scipy>=1.13.0
+python-jose>=3.5.0
 
 # Machine Learning
 joblib>=1.4.0

From f0a019d1b0abe6e32f86507060c7e62072f20b91 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Tue, 7 Jul 2026 17:05:01 +0900
Subject: [PATCH 112/148] fix : stomp lib del

---
 app/scheduler/quality/stomp_client.py | 38 ---------------------------
 1 file changed, 38 deletions(-)
 delete mode 100644 app/scheduler/quality/stomp_client.py

diff --git a/app/scheduler/quality/stomp_client.py b/app/scheduler/quality/stomp_client.py
deleted file mode 100644
index b676b87..0000000
--- a/app/scheduler/quality/stomp_client.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import stomp
-import json
-from dotenv import load_dotenv
-import os
-
-def run():
-    load_dotenv()
-    QUALITY_URL = os.getenv("QUALITY_URL")
-
-    #class AIListener(stomp.ConnectionListener):
-
-        #def on_message(self, frame):
-            #data = json.loads(frame.body)
-            #print("📩 수신 데이터:", data)
-
-            # 👉 여기서 AI 로직 실행
-            # risk_score 계산, anomaly detection 등
-
-
-    conn = stomp.Connection([(QUALITY_URL, 8083)])
-
-    #conn.set_listener('', AIListener())
-
-    conn.connect(wait=True)
-
-    # Spring topic 구독
-    conn.subscribe(destination='/topic/summary', id=1, ack='auto')
-    conn.subscribe(destination='/topic/process', id=2, ack='auto')
-    conn.subscribe(destination='/topic/drive-detail', id=3, ack='auto')
-    conn.subscribe(destination='/topic/status-detail', id=4, ack='auto')
-
-    print("🚀 AI-service WebSocket listening...")
-
-    while True:
-        pass
-
-if __name__ == "__main__":
-    run()
\ No newline at end of file

From d551f4a968bf36ed05597aee6db6581a8fe9706a Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 9 Jul 2026 09:40:30 +0900
Subject: [PATCH 113/148] fix : dev branch update

---
 app/api/routers/manual.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 102b37b..702df72 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -1,6 +1,6 @@
 # app/api/routers/manual.py
 from app.ai_manual.service.manual_service import ManualService
-from fastapi import APIRouter, HTTPException, Header
+from fastapi import APIRouter, HTTPException, Header, Request
 from jose import jwt
 import os
 
@@ -12,20 +12,30 @@
 service = ManualService()
 
 @router.get("")
-def generate_manual(
-    authorization: str = Header(...)
-):
+def generate_manual(request: Request):
+
+    authorization = request.headers.get("authorization")
+
+    if not authorization:
+        raise HTTPException(
+            status_code=401,
+            detail="Authorization header missing"
+        )
+
     token = authorization.replace("Bearer ", "")
 
+    print("TOKEN:", token)
+
     payload = jwt.decode(
         token,
         os.getenv("JWT_SECRET_KEY"),
         algorithms=["HS384"]
     )
 
-    user_id = int(payload["sub"])
     print(payload)
 
+    user_id = int(payload["id"])
+
     result = service.generate_manual(user_id)
 
     if result is None:
@@ -34,4 +44,4 @@ def generate_manual(
             detail="처리할 이벤트가 없습니다."
         )
 
-    return result
+    return result
\ No newline at end of file

From 09e8440da800b9d0e1dd9a70fb65840a1d934ac7 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 9 Jul 2026 10:14:26 +0900
Subject: [PATCH 114/148] feat : prompt_template.py AI manual text lange update

---
 app/ai_manual/prompt/prompt_template.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/ai_manual/prompt/prompt_template.py b/app/ai_manual/prompt/prompt_template.py
index 14202a4..d21e1d3 100644
--- a/app/ai_manual/prompt/prompt_template.py
+++ b/app/ai_manual/prompt/prompt_template.py
@@ -52,7 +52,7 @@
 summary는 반드시 2~3문장으로 작성하시오.
 
 10.
-summary는 최대 100자 내외로 작성하시오.
+summary는 최대 50자 내외로 작성하시오.
 
 11.
 말풍선(UI)에 표시되므로 긴 설명은 금지하십시오.

From 97c09fd4a63cbdae6dafb062ee0345e97c89856e Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Thu, 9 Jul 2026 12:34:51 +0900
Subject: [PATCH 115/148] =?UTF-8?q?fix:=20=EB=B6=88=EB=9F=89=EC=A0=84?=
 =?UTF-8?q?=EC=9D=B4=20=ED=8C=90=EB=8B=A8=20=EC=9B=90=EC=9D=B8=20=EC=B6=94?=
 =?UTF-8?q?=EC=B6=9C=EB=90=9C=20=EA=B2=B0=EA=B3=BC=EB=A7=8C=20=EB=B0=98?=
 =?UTF-8?q?=ED=99=98=ED=95=98=EB=8F=84=EB=A1=9D=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/batch/defect_transfer_prediction_backfill.py | 13 ++++++++++++-
 app/kafka/raw_event_consumer.py                  | 16 ++++++++++++++++
 app/ml/inference/defect_transfer_detector.py     | 11 ++++++++++-
 3 files changed, 38 insertions(+), 2 deletions(-)

diff --git a/app/batch/defect_transfer_prediction_backfill.py b/app/batch/defect_transfer_prediction_backfill.py
index 01a0088..1357d71 100644
--- a/app/batch/defect_transfer_prediction_backfill.py
+++ b/app/batch/defect_transfer_prediction_backfill.py
@@ -8,7 +8,10 @@
 from sqlalchemy import create_engine, select
 
 from app.core.config import settings
-from app.ml.inference.defect_transfer_detector import DefectTransferDetector
+from app.ml.inference.defect_transfer_detector import (
+    DefectTransferDetector,
+    has_only_model_probability_cause,
+)
 from app.repository.defect_transfer_prediction_repository import (
     DefectTransferPredictionRepository,
 )
@@ -63,6 +66,14 @@ def backfill_defect_transfer_predictions(*, limit: int | None = None) -> dict[st
                 _event_json(row["event_json"]),
                 str(row["process_code"]),
             )
+            if has_only_model_probability_cause(prediction.causes):
+                logger.info(
+                    "Skipped defect transfer backfill because only fallback model probability cause was produced: "
+                    "event_id=%s process_code=%s",
+                    row.get("event_id"),
+                    row.get("process_code"),
+                )
+                continue
             saved_rows += result_repository.replace_prediction_result(
                 event_id=str(row["event_id"]),
                 car_master_id=int(row["car_master_id"]),
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index f97ae98..0660c5f 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -15,6 +15,7 @@
 from app.ml.inference.defect_transfer_detector import (
     DefectTransferDetector,
     DefectTransferPrediction,
+    has_only_model_probability_cause,
 )
 from app.repository.defect_transfer_prediction_repository import (
     DefectTransferPredictionRepository,
@@ -215,6 +216,21 @@ def _consume_record(
             repository.events.mark_defect_transfer_analysis_done(row["event_id"])
         else:
             defect_prediction = _predict_defect_transfer(defect_detector, row)
+            if (
+                defect_prediction is not None
+                and has_only_model_probability_cause(defect_prediction.causes)
+            ):
+                logger.info(
+                    "Skipped defect transfer storage and analysis publish because only fallback model probability cause was produced: "
+                    "topic=%s partition=%s offset=%s key=%s event_id=%s process_code=%s",
+                    record.topic,
+                    record.partition,
+                    record.offset,
+                    raw_event.get("_kafka_key"),
+                    row["event_id"],
+                    row["process_code"],
+                )
+                return
             defect_rows_saved = _save_defect_transfer_prediction(
                 defect_result_repository,
                 row,
diff --git a/app/ml/inference/defect_transfer_detector.py b/app/ml/inference/defect_transfer_detector.py
index e6c2ffc..07ebddd 100644
--- a/app/ml/inference/defect_transfer_detector.py
+++ b/app/ml/inference/defect_transfer_detector.py
@@ -46,6 +46,15 @@ class DefectTransferPrediction:
     feature_values: dict[str, Any]
 
 
+def has_only_model_probability_cause(causes: list[DefectCause]) -> bool:
+    """Return True when the detector produced only the fallback model-probability cause."""
+    return (
+        len(causes) == 1
+        and causes[0].feature == "model_probability"
+        and causes[0].impact > 0
+    )
+
+
 class DefectTransferDetector:
     """Run event-level defect detection and adjacent-process transfer prediction."""
 
@@ -482,4 +491,4 @@ def _station_code(equipment_code: str) -> str:
 def _cause_message(label: str, value: Any, unit: str) -> str:
     if isinstance(value, (int, float, np.number)):
         return f"{label} {float(value):.2f}{unit}"
-    return f"{label} {value}"
\ No newline at end of file
+    return f"{label} {value}"

From a6112d24c0c40dd92304422bd8fa192b9f4e8d22 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 9 Jul 2026 13:44:00 +0900
Subject: [PATCH 116/148] fix : process_producer update

---
 app/scheduler/quality/process_producer.py | 92 +++++++++--------------
 1 file changed, 37 insertions(+), 55 deletions(-)

diff --git a/app/scheduler/quality/process_producer.py b/app/scheduler/quality/process_producer.py
index 72883ed..c11c7ea 100644
--- a/app/scheduler/quality/process_producer.py
+++ b/app/scheduler/quality/process_producer.py
@@ -18,18 +18,13 @@ def run(stop_event):
             os.getenv("BROKER_URL_1"),
             os.getenv("BROKER_URL_2")
         ],
-
         security_protocol="SASL_SSL",
-
         sasl_mechanism="OAUTHBEARER",
-
         sasl_oauth_token_provider=MSKTokenProvider(),
-
-        value_serializer=lambda x:
-            json.dumps(
-                x,
-                default=str
-            ).encode("utf-8")
+        value_serializer=lambda x: json.dumps(
+            x,
+            default=str
+        ).encode("utf-8")
     )
 
     last_id = 0
@@ -64,9 +59,25 @@ def run(stop_event):
                 if stop_event.is_set():
                     break
 
-                current_count = car["id"]
-
-                # 생산이 모두 끝난 경우
+                # ============================
+                # 해당 날짜 생산 차량 개수 계산
+                # ============================
+                with main_engine.connect() as conn:
+
+                    current_count = conn.execute(
+                        text("""
+                            SELECT COUNT(*)
+                            FROM inspection_master
+                            WHERE DATE(created_at) = DATE(:created_at)
+                              AND id <= :current_id
+                        """),
+                        {
+                            "created_at": car["created_at"],
+                            "current_id": car["id"]
+                        }
+                    ).scalar()
+
+                # 생산 완료
                 if current_count >= TOTAL_TARGET:
 
                     process_list = [
@@ -82,51 +93,30 @@ def run(stop_event):
 
                         (
                             "VISUAL",
-                            min(
-                                current_count,
-                                TOTAL_TARGET
-                            )
+                            min(current_count, TOTAL_TARGET)
                         ),
 
                         (
                             "FUNCTION",
-                            min(
-                                max(
-                                    current_count - 1,
-                                    0
-                                ),
-                                TOTAL_TARGET
-                            )
+                            min(max(current_count - 1, 0), TOTAL_TARGET)
                         ),
 
                         (
                             "DRIVE",
-                            min(
-                                max(
-                                    current_count - 2,
-                                    0
-                                ),
-                                TOTAL_TARGET
-                            )
+                            min(max(current_count - 2, 0), TOTAL_TARGET)
                         ),
 
                         (
                             "FINAL",
-                            min(
-                                max(
-                                    current_count - 3,
-                                    0
-                                ),
-                                TOTAL_TARGET
-                            )
+                            min(max(current_count - 3, 0), TOTAL_TARGET)
                         )
                     ]
 
                 for process_name, completed in process_list:
 
                     waiting = max(
-                        0,
-                        TOTAL_TARGET - completed
+                        TOTAL_TARGET - completed,
+                        0
                     )
 
                     progress_rate = round(
@@ -136,35 +126,27 @@ def run(stop_event):
 
                     if progress_rate >= 100:
                         process_status = "COMPLETE"
-
                     elif progress_rate == 0:
                         process_status = "WAIT"
-
                     else:
                         process_status = "RUNNING"
 
                     message = {
 
-                        "process_name":
-                            process_name,
+                        "process_name": process_name,
+
+                        "total_vehicle_count": TOTAL_TARGET,
 
-                        "total_vehicle_count":
-                            TOTAL_TARGET,
+                        "completed_count": completed,
 
-                        "completed_count":
-                            completed,
+                        "waiting_count": waiting,
 
-                        "waiting_count":
-                            waiting,
+                        "progress_rate": progress_rate,
 
-                        "progress_rate":
-                            progress_rate,
+                        "process_status": process_status,
 
-                        "process_status":
-                            process_status,
+                        "created_at": car["created_at"]
 
-                        "created_at":
-                            car["created_at"]
                     }
 
                     producer.send(

From dad76cb929e859d3c6d3de14a7240fa22c8c0d2f Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Thu, 9 Jul 2026 14:11:54 +0900
Subject: [PATCH 117/148] fix summary_reposiotory update

---
 app/repository/quality_summary_repository.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/app/repository/quality_summary_repository.py b/app/repository/quality_summary_repository.py
index 8fb5d84..03c36b1 100644
--- a/app/repository/quality_summary_repository.py
+++ b/app/repository/quality_summary_repository.py
@@ -38,6 +38,11 @@ def run(stop_event):
                             MAX(created_at) AS created_at
 
                         FROM inspection_drive_detail
+
+                        WHERE DATE(created_at) = (
+                            SELECT DATE(MAX(created_at))
+                            FROM inspection_drive_detail
+                        )
                     """)
                 ).mappings().first()
 
@@ -159,7 +164,6 @@ def run(stop_event):
 
 
 if __name__ == "__main__":
-
     import threading
 
     run(threading.Event())
\ No newline at end of file

From a7fb07dbc506d6fa4389f7cf33de81e047c51d16 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 10 Jul 2026 10:04:15 +0900
Subject: [PATCH 118/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9=EC=88=9C?=
 =?UTF-8?q?=EC=9C=84=20=EC=A4=91=EB=B3=B5=20=EB=B0=98=ED=99=98=20=EC=98=A4?=
 =?UTF-8?q?=EB=A5=98=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../bottleneck_analysis_repository.py         |  54 ++-
 app/service/analysis/bottleneck_service.py    |  19 +-
 docs/elasticsearch-migration-guideline.md     | 455 ++++++++++++++++++
 3 files changed, 522 insertions(+), 6 deletions(-)
 create mode 100644 docs/elasticsearch-migration-guideline.md

diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 7242f49..8bd1cd0 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -93,12 +93,14 @@ def list_results(
                 self.table.c.rank_no.asc(),
                 self.table.c.id.asc(),
             )
-            .offset(cursor * size)
-            .limit(size)
         )
 
         with self.engine.connect() as conn:
-            return [dict(row) for row in conn.execute(query).mappings()]
+            rows = [dict(row) for row in conn.execute(query).mappings()]
+
+        deduped = self._dedupe_rows(rows)
+        offset = cursor * size
+        return deduped[offset : offset + size]
 
     def count_results(
         self,
@@ -115,7 +117,12 @@ def count_results(
             .where(self.table.c.detected_at == snapshot_detected_at)
         )
         with self.engine.connect() as conn:
-            return int(conn.execute(query).scalar_one())
+            raw_count = int(conn.execute(query).scalar_one())
+
+        if raw_count <= 0:
+            return 0
+
+        return len(self.list_results(cursor=0, size=raw_count))
 
     def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
         from sqlalchemy import select, func
@@ -261,6 +268,45 @@ def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
             "wip_count": self._safe_float(metrics.get("wipCount")),
         }
 
+    @staticmethod
+    def _dedupe_rows(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        if not rows:
+            return []
+
+        deduped: list[dict[str, Any]] = []
+        seen_keys: set[tuple[str, str]] = set()
+        for row in sorted(
+            rows,
+            key=lambda item: (
+                float(item.get("risk_score") or 0.0),
+                float(item.get("avg_delay_time") or 0.0),
+                int(item.get("affected_vehicle_count") or 0),
+                int(item.get("manufacturing_event_id") or 0),
+                int(item.get("car_master_id") or 0),
+                str(item.get("process_code") or "").strip().upper(),
+                str(item.get("equipment_code") or "").strip().upper(),
+                int(item.get("rank_no") or 0),
+                int(item.get("id") or 0),
+            ),
+            reverse=True,
+        ):
+            key = (
+                str(row.get("process_code") or "").strip().upper(),
+                str(row.get("equipment_code") or "").strip().upper(),
+            )
+            if key in seen_keys:
+                continue
+            seen_keys.add(key)
+            deduped.append(row)
+
+        deduped.sort(
+            key=lambda item: (
+                int(item.get("rank_no") or 0),
+                int(item.get("id") or 0),
+            ),
+        )
+        return deduped
+
     @staticmethod
     def _event_json_dict(value: Any) -> dict[str, Any]:
         if isinstance(value, dict):
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 76caddb..5ef4547 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -19,7 +19,7 @@
 
 
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
-BOTTLENECK_CACHE_VERSION = "v12"
+BOTTLENECK_CACHE_VERSION = "v13"
 PROCESS_CODE_LABELS = {
     "PRESS": "프레스",
     "BODY": "차체",
@@ -249,10 +249,25 @@ def _rank_bottleneck_summaries(
                 int(row.get("affected_vehicle_count") or 0),
                 int(row.get("manufacturing_event_id") or 0),
                 int(row.get("car_master_id") or 0),
+                str(row.get("process_code") or "").strip().upper(),
+                str(row.get("equipment_code") or "").strip().upper(),
             ),
             reverse=True,
         )
-        return [{**row, "rank_no": index + 1} for index, row in enumerate(ranked)]
+        deduped: list[dict[str, Any]] = []
+        seen_keys: set[tuple[str, str]] = set()
+
+        for row in ranked:
+            key = (
+                str(row.get("process_code") or "").strip().upper(),
+                str(row.get("equipment_code") or "").strip().upper(),
+            )
+            if key in seen_keys:
+                continue
+            seen_keys.add(key)
+            deduped.append(row)
+
+        return [{**row, "rank_no": index + 1} for index, row in enumerate(deduped)]
 
     @staticmethod
     def _equipment_number(equipment_code: Any | None) -> int | None:
diff --git a/docs/elasticsearch-migration-guideline.md b/docs/elasticsearch-migration-guideline.md
new file mode 100644
index 0000000..bce824a
--- /dev/null
+++ b/docs/elasticsearch-migration-guideline.md
@@ -0,0 +1,455 @@
+# 병목 및 불량전이 조회의 Elasticsearch 전환 가이드
+
+## 1. 목적
+
+현재 병목 조회와 불량전이 조회는 MySQL 결과 테이블과 Redis 캐시를 중심으로 동작한다.
+이 문서는 조회 계층을 Elasticsearch(OpenSearch 포함) 기반으로 전환할 때의 설계 원칙, 마이그레이션 순서, 운영 체크포인트를 정리한 가이드다.
+
+핵심 목표는 다음과 같다.
+
+- 조회 성능을 안정화한다.
+- 최신 결과를 빠르게 검색 가능하게 만든다.
+- 기존 API 응답 형식은 유지한다.
+- DB는 원장(source of truth), ES는 조회용 read model로 분리한다.
+
+## 2. 현재 구조 요약
+
+### 병목 조회
+
+현재 병목 조회는 다음 흐름이다.
+
+- Kafka raw 이벤트 수신
+- 병목 분석 수행
+- `bottleneck_analysis_result` 테이블에 저장
+- Redis 캐시로 페이지 응답 캐싱
+- API는 Redis 캐시를 우선 조회하고, 없으면 DB에서 조회
+
+관련 코드:
+
+- [`app/api/routers/process.py`](../app/api/routers/process.py)
+- [`app/service/analysis/bottleneck_service.py`](../app/service/analysis/bottleneck_service.py)
+- [`app/repository/bottleneck_analysis_repository.py`](../app/repository/bottleneck_analysis_repository.py)
+
+### 불량전이 조회
+
+현재 불량전이 조회는 다음 흐름이다.
+
+- raw 이벤트 또는 배치에서 불량전이 예측 수행
+- `defect_transfer_prediction_result` 테이블에 저장
+- Redis 캐시로 predictions / causes 페이지 캐싱
+- API는 캐시를 우선 조회하고, 없으면 DB와 이벤트 DB를 조합해 조회
+
+관련 코드:
+
+- [`app/api/routers/defect_transfer.py`](../app/api/routers/defect_transfer.py)
+- [`app/service/analysis/defect_transfer_service.py`](../app/service/analysis/defect_transfer_service.py)
+- [`app/repository/defect_transfer_prediction_repository.py`](../app/repository/defect_transfer_prediction_repository.py)
+
+## 3. 전환 원칙
+
+### 3.1 유지할 것
+
+- API 경로와 응답 DTO
+- 분석/예측의 계산 로직
+- MySQL 결과 테이블의 저장 책임
+- Redis 캐시의 선택적 사용
+
+### 3.2 바꿀 것
+
+- 조회 대상의 1차 소스를 MySQL에서 ES로 이동
+- 페이지 조회 방식에 맞는 ES 문서 구조 설계
+- 인덱싱/재색인/동기화 파이프라인 추가
+
+### 3.3 절대 분리할 것
+
+- MySQL은 결과 저장과 정합성 기준으로 유지
+- ES는 검색과 리스트 응답 전용으로 사용
+- ES 장애 시에는 DB fallback 또는 캐시 fallback 전략을 둔다
+
+## 4. 목표 아키텍처
+
+권장 구조는 아래와 같다.
+
+```text
+Kafka/raw event -> analysis service -> MySQL result table
+                               \-> outbox/event topic -> ES indexer -> Elasticsearch
+
+API -> Redis cache -> Elasticsearch -> fallback MySQL
+```
+
+권장 역할 분리:
+
+- MySQL
+  - 분석 결과의 원장 저장
+  - 재처리/검증 기준
+- Elasticsearch
+  - 조회용 read model
+  - 최신 결과 검색, 정렬, 필터, collapse, highlight
+- Redis
+  - 짧은 TTL 응답 캐시
+  - 동일 페이지 반복 요청 완화
+- Kafka 또는 outbox
+  - ES 동기화 이벤트 전달
+
+## 5. 인덱스 설계
+
+## 5.1 병목 인덱스
+
+권장 인덱스 예시:
+
+- `ai-bottleneck-result-v1`
+
+문서 단위는 다음 둘 중 하나를 추천한다.
+
+1. `rank` 기준 스냅샷 문서
+2. `manufacturing_event_id` 기준 원천 이벤트 문서
+
+현재 API가 “병목 순위 페이지”를 반환하므로, 실무적으로는 스냅샷 단위 문서가 단순하다.
+
+예시 필드:
+
+- `snapshotId`
+- `detectedAt`
+- `rankNo`
+- `processCode`
+- `equipmentCode`
+- `avgDelayTime`
+- `affectedVehicleCount`
+- `riskScore`
+- `riskLevel`
+- `mostBottleneckProcess`
+- `mostBottleneckRiskLevel`
+
+권장 정렬:
+
+- `rankNo asc`
+- 필요 시 `riskScore desc`
+- 필요 시 `detectedAt desc`
+
+### 병목 문서 ID
+
+권장 ID는 다음 중 하나다.
+
+- `detectedAt + rankNo`
+- `snapshotId + rankNo`
+
+이렇게 하면 재색인 시 중복 문서를 쉽게 방지할 수 있다.
+
+## 5.2 불량전이 인덱스
+
+권장 인덱스 예시:
+
+- `ai-defect-transfer-result-v1`
+
+불량전이는 조회 유형이 두 가지다.
+
+1. predictions: 차량/이벤트 단위 목록
+2. causes: 특정 차량의 최신 이벤트에 대한 원인 목록
+
+권장 문서 구조:
+
+- `eventId`
+- `manufacturingEventId`
+- `carMasterId`
+- `vehicleId`
+- `sourceProcessCode`
+- `sourceEquipmentCode`
+- `targetProcessCode`
+- `targetEquipmentCode`
+- `currentDefectProbability`
+- `targetDefectProbability`
+- `predictedDefectProcess`
+- `expectedOccurrenceStep`
+- `riskGrade`
+- `predictedAt`
+- `influenceScore`
+- `mainCauses` as nested or object array
+
+### 불량전이 문서 ID
+
+권장 ID는 `manufacturingEventId` 또는 `eventId`다.
+
+이유:
+
+- 같은 이벤트에 대해 예측 결과는 최신 값 1개가 적합하다.
+- upsert가 쉽다.
+- 중복 적재를 피하기 쉽다.
+
+## 6. 매핑 가이드
+
+### 6.1 병목 매핑 포인트
+
+현재 `bottleneck_analysis_result` 테이블의 컬럼을 ES 필드로 1:1 매핑한다.
+
+중요한 점:
+
+- `riskScore`, `avgDelayTime`는 numeric type으로 저장
+- `rankNo`는 integer
+- `detectedAt`는 date
+- `processCode`는 keyword
+- `equipmentCode`는 keyword
+
+### 6.2 불량전이 매핑 포인트
+
+불량전이는 검색 조건이 다양하므로 다음처럼 나누는 것이 좋다.
+
+- 식별자: `keyword`
+- 수치: `double` or `integer`
+- 시간: `date`
+- 원인 목록: `nested` 권장
+
+`mainCauses`가 단순 배열이 아니라 원인별 필터링/정렬의 대상이 된다면 `nested`로 설계하는 편이 안전하다.
+
+## 7. 조회 로직 변경 방식
+
+## 7.1 병목 조회
+
+현재 로직은 DB에서 최신 스냅샷을 읽고 페이지를 자른다.
+ES 전환 후에는 다음과 같이 바꾼다.
+
+### 추천 방식
+
+- 최신 스냅샷을 ES에서 찾는다.
+- `detectedAt`이 가장 최신인 문서 집합만 조회한다.
+- `rankNo asc`로 페이지를 자른다.
+
+### 구현 포인트
+
+- `cursor`가 페이지 번호이므로 우선 `from + size` 방식으로 시작할 수 있다.
+- 데이터량이 많아지면 `search_after`로 바꾸는 것이 좋다.
+- 응답의 `mostBottleneckProcess`와 `mostBottleneckRiskLevel`은 첫 문서 또는 별도 summary 문서에서 가져온다.
+
+### 서비스 변경 방향
+
+- `BottleneckAnalysisService.get_realtime_bottlenecks()`가 ES repository를 호출하도록 변경
+- Redis 캐시는 유지 가능
+- DB fallback은 장애 대응용으로 둔다
+
+## 7.2 불량전이 predictions
+
+현재는 결과 테이블의 전체 rows를 읽고 차량별 최신 결과를 뽑는다.
+ES에서는 다음 둘 중 하나를 추천한다.
+
+### 옵션 A: 최신 결과만 저장
+
+- 차량 또는 이벤트별 최신 결과 1건만 ES에 저장
+- `collapse` 없이 바로 정렬 가능
+
+장점:
+
+- 조회가 단순하다
+- 응답 속도가 좋다
+
+단점:
+
+- 이력 조회가 어려워진다
+
+### 옵션 B: 모든 결과 저장 후 최신 문서 선별
+
+- `carMasterId`로 collapse
+- `predictedAt desc`로 최신값 선택
+
+장점:
+
+- 이력 보존이 쉽다
+
+단점:
+
+- 쿼리가 다소 복잡하다
+
+실무적으로는 predictions는 옵션 A, 이력/감사 목적은 MySQL 원장 유지가 가장 무난하다.
+
+## 7.3 불량전이 causes
+
+현재는 특정 vehicleId의 최신 이벤트를 기준으로 cause list를 만든다.
+ES 전환 후에는 다음과 같이 설계한다.
+
+- `vehicleId` 또는 `carMasterId`로 필터
+- `predictedAt desc`로 최신 이벤트 선택
+- `mainCauses`는 nested query 또는 source filtering으로 조회
+
+`get_cached_cause_analysis()`는 ES 문서를 읽어 DTO로 변환하는 역할만 하도록 단순화한다.
+
+## 8. 동기화 전략
+
+ES 전환의 핵심은 “어떻게 최신성을 보장할 것인가”다.
+
+### 8.1 권장 순서
+
+1. MySQL에 결과 저장
+2. 저장 완료 후 outbox 또는 Kafka 이벤트 발행
+3. ES indexer가 이벤트 소비
+4. ES에 upsert
+
+### 8.2 동기화 방식 선택
+
+#### 방식 1. 애플리케이션 dual write
+
+- 저장 서비스에서 MySQL 저장 후 ES도 직접 저장
+
+장점:
+
+- 구현이 빠르다
+
+단점:
+
+- 부분 실패와 재시도 처리 복잡
+- 정합성 이슈 가능
+
+#### 방식 2. Kafka/outbox 기반 비동기 동기화
+
+- 저장과 발행을 분리
+- ES indexer가 별도 소비자 역할 수행
+
+장점:
+
+- 운영 안정성이 높다
+- 재처리와 재색인이 쉽다
+
+단점:
+
+- 구성 요소가 하나 더 필요하다
+
+권장:
+
+- 장기적으로는 방식 2
+- 단기 POC는 방식 1도 가능
+
+## 9. 마이그레이션 단계
+
+### 1단계. ES 설정 추가
+
+- ES/OpenSearch URL
+- 인증 정보
+- 인덱스 이름
+- timeouts / retry / bulk size
+
+### 2단계. Read repository 추가
+
+- `BottleneckSearchRepository`
+- `DefectTransferSearchRepository`
+
+### 3단계. Indexer 추가
+
+- 병목 결과 upsert
+- 불량전이 결과 upsert
+- bulk indexing 지원
+
+### 4단계. 백필 작업 추가
+
+- 기존 MySQL 결과를 ES에 한 번 채운다
+- 문서 ID 기준으로 재실행 가능해야 한다
+
+### 5단계. API read path 전환
+
+- Redis -> ES -> DB fallback
+- 또는 Redis -> ES만 먼저 적용
+
+### 6단계. 정합성 검증
+
+- DB와 ES 결과 수 비교
+- 상위 N개 순위 비교
+- 차량별 최신 예측 비교
+
+### 7단계. 캐시 버전 업
+
+- ES 전환 시 캐시 키 버전을 올려 구 응답과 섞이지 않게 한다
+
+## 10. 코드 변경 포인트
+
+### 10.1 설정
+
+`app/core/config.py`에 ES 관련 설정을 추가한다.
+
+예시:
+
+- `ELASTICSEARCH_URL`
+- `ELASTICSEARCH_USERNAME`
+- `ELASTICSEARCH_PASSWORD`
+- `ELASTICSEARCH_BOTTLENECK_INDEX`
+- `ELASTICSEARCH_DEFECT_TRANSFER_INDEX`
+
+### 10.2 서비스
+
+다음 서비스가 핵심 변경 지점이다.
+
+- [`app/service/analysis/bottleneck_service.py`](../app/service/analysis/bottleneck_service.py)
+- [`app/service/analysis/defect_transfer_service.py`](../app/service/analysis/defect_transfer_service.py)
+
+현재는 repository를 통해 DB를 읽는데, 이후에는 search repository를 주입받도록 바꾼다.
+
+### 10.3 저장 로직
+
+다음 저장 지점에서 ES 이벤트를 함께 발행하거나 outbox를 남긴다.
+
+- 병목 분석 저장
+- 불량전이 예측 저장
+
+### 10.4 캐시 무효화
+
+현재 Redis 캐시 무효화는 raw event 수신 시점에 수행된다.
+ES 전환 후에는 다음 중 하나를 선택한다.
+
+- 캐시 유지 + ES 반영 시점에 키 무효화
+- 캐시 축소 + 짧은 TTL만 유지
+
+## 11. 성능 가이드
+
+### 권장 기준
+
+- page size는 100 이하 유지
+- bulk size는 500~2000 사이에서 조정
+- index refresh interval은 write 빈도에 맞춘다
+- shard 수는 초기에는 최소화한다
+
+### 검색 팁
+
+- keyword 필드는 `term` / `terms`
+- 날짜 범위는 `range`
+- 최신 1건은 `sort + size=1`
+- 차량별 최신 결과는 `collapse` 또는 미리 최신 문서만 저장
+
+## 12. 장애 및 롤백 전략
+
+### 장애 대응
+
+- ES 조회 실패 시 DB fallback
+- ES indexer 실패 시 재시도 큐에 적재
+- bulk 실패는 item 단위 실패 로그를 남긴다
+
+### 롤백
+
+- read path를 다시 DB로 되돌릴 수 있어야 한다
+- ES를 단순 보조 인프라로 취급한다
+- 인덱스 버전은 `v1`, `v2`처럼 명시한다
+
+## 13. 검증 항목
+
+전환 후 아래를 반드시 비교한다.
+
+- 병목 상위 5개 순위가 DB와 동일한가
+- 병목 `mostBottleneckProcess`가 동일한가
+- 불량전이 predictions 결과 건수가 동일한가
+- vehicleId별 cause 페이지가 동일한가
+- 최신성 지연이 허용 범위 이내인가
+
+## 14. 추천 구현 순서
+
+1. ES 설정과 client 추가
+2. search repository 작성
+3. 기존 DB 결과를 ES로 백필
+4. 병목 조회부터 ES read path로 전환
+5. 불량전이 predictions 전환
+6. 불량전이 causes 전환
+7. 캐시와 fallback 정리
+8. DB-only 조회 코드 정리
+
+## 15. 결론
+
+가장 중요한 원칙은 두 가지다.
+
+1. 저장의 기준은 MySQL로 유지한다.
+2. 조회의 기준은 Elasticsearch로 옮긴다.
+
+이렇게 하면 정합성은 유지하면서도, 병목/불량전이 같은 조회성 API를 훨씬 유연하게 확장할 수 있다.
+

From 14088942e65b299a27f7dde1bbf88bdc2d24a0fa Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 10:07:02 +0900
Subject: [PATCH 119/148] feat : logger update

---
 app/worker_main.py | 35 +++++++++++++++++++++++++----------
 1 file changed, 25 insertions(+), 10 deletions(-)

diff --git a/app/worker_main.py b/app/worker_main.py
index 8fec748..15bc8d5 100644
--- a/app/worker_main.py
+++ b/app/worker_main.py
@@ -4,6 +4,8 @@
 import time
 import signal
 import sys
+import logging
+import os
 
 from app.db import (
     main_dispose_engine,
@@ -24,6 +26,19 @@
 from app.repository.quality_process_repository import run as process_repository
 from app.repository.quality_summary_repository import run as summary_repository
 
+os.makedirs("logs", exist_ok=True)
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] [%(threadName)s] %(message)s",
+    handlers=[
+        logging.FileHandler("logs/worker.log", encoding="utf-8"),
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+
+logger = logging.getLogger(__name__)
+
 # =========================
 # STOP FLAG (핵심)
 # =========================
@@ -36,7 +51,7 @@
 # THREAD WRAPPER
 # =========================
 def start_thread(name, target):
-    print(f"[START] {name}")
+    logger.info(f"[START] {name}")
 
     thread = threading.Thread(
         target=lambda: target(stop_event),
@@ -59,13 +74,13 @@ def cleanup():
 
     cleanup_done = True
 
-    print("\n🧹 Graceful Shutdown 시작...")
+    logger.info("\n🧹 Graceful Shutdown 시작...")
 
     # 1. stop signal 전달
     stop_event.set()
 
     # 2. thread 종료 대기
-    print("⏳ threads join 중...")
+    logger.info("⏳ threads join 중...")
     for t in threads:
         try:
             t.join(timeout=5)
@@ -77,16 +92,16 @@ def cleanup():
         main_dispose_engine()
         sample_dispose_engine()
     except Exception as e:
-        print("DB dispose error:", e)
+        logger.info("DB dispose error:", e)
 
-    print("✅ Shutdown 완료")
+    logger.info("✅ Shutdown 완료")
 
 
 # =========================
 # SIGNAL HANDLER
 # =========================
 def handle_exit(signum, frame):
-    print("\n⚠️ 종료 신호 감지 (Ctrl+C)")
+    logger.info("\n⚠️ 종료 신호 감지 (Ctrl+C)")
     cleanup()
     sys.exit(0)
 
@@ -100,9 +115,9 @@ def handle_exit(signum, frame):
 # =========================
 if __name__ == "__main__":
 
-    print("=" * 60)
-    print("🚀 AI-Service 시작")
-    print("=" * 60)
+    logger.info("=" * 60)
+    logger.info("🚀 AI-Service 시작")
+    logger.info("=" * 60)
 
     # =====================
     # PRODUCERS
@@ -128,7 +143,7 @@ def handle_exit(signum, frame):
     # =====================
     #threads.append(start_thread("STOMP Client", stomp_client))
 
-    print("\n📡 서비스 실행 중... (Ctrl+C로 종료)")
+    logger.info("\n📡 서비스 실행 중... (Ctrl+C로 종료)")
 
     # =====================
     # BLOCKING LOOP (STOP EVENT 기반)

From 4c9b9d0bc3f14a96af12c65e6156ae13f2945a62 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Fri, 10 Jul 2026 11:08:34 +0900
Subject: [PATCH 120/148] =?UTF-8?q?feat:=20Elasticsearch=20=EA=B8=B0?=
 =?UTF-8?q?=EB=B0=98=20=EB=B3=91=EB=AA=A9=C2=B7=EB=B6=88=EB=9F=89=EC=A0=84?=
 =?UTF-8?q?=EC=9D=B4=20=EC=A1=B0=ED=9A=8C=20=EC=97=B0=EB=8F=99?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

1. 병목 분석 결과와 불량전이 예측 결과를 ES 인덱스에 저장하고 조회하도록 연동

2. 병목은 최신 snapshot 기준으로 조회하고, 불량전이는 carMasterId collapse와 predictedAt 정렬로 최신 문서를 선별

3. Kafka analysis topic을 재사용해 ES sync 이벤트를 발행/소비하도록 구성하고, ES 미설정 시 DB fallback 처리 추가
---
 .env.example                                  |   8 +
 app/core/config.py                            |  22 +
 app/kafka/analysis_sync_consumer.py           | 100 ++++
 app/kafka/config.py                           |   3 +
 app/kafka/consumer.py                         |  19 +-
 app/kafka/raw_event_consumer.py               | 190 ++++++-
 app/main.py                                   |   6 +
 app/search/process_analysis_search.py         | 530 ++++++++++++++++++
 app/service/analysis/bottleneck_service.py    |  41 +-
 .../analysis/defect_transfer_service.py       |  90 ++-
 docs/elasticsearch-migration-guideline.md     |  19 +-
 11 files changed, 998 insertions(+), 30 deletions(-)
 create mode 100644 app/kafka/analysis_sync_consumer.py
 create mode 100644 app/search/process_analysis_search.py

diff --git a/.env.example b/.env.example
index 2afc65f..db99b5e 100644
--- a/.env.example
+++ b/.env.example
@@ -18,6 +18,14 @@ REDIS_URL=redis://localhost:6379/0
 REDIS_KEY_PREFIX=aims:ai-service
 REDIS_CACHE_TTL_SECONDS=60
 
+# Elasticsearch / OpenSearch
+ELASTICSEARCH_URL=http://localhost:9200
+ELASTICSEARCH_USERNAME=
+ELASTICSEARCH_PASSWORD=
+ELASTICSEARCH_VERIFY_CERTS=true
+ELASTICSEARCH_BOTTLENECK_INDEX=aims-bottleneck-analysis-v1
+ELASTICSEARCH_DEFECT_TRANSFER_INDEX=aims-defect-transfer-analysis-v1
+
 # MySQL DB
 # MAIN_DATABASE_URL contains the shared driver/user/password/host/port.
 # MAIN_DB_NAME and SAMPLE_DB_NAME select the actual schema/database.
diff --git a/app/core/config.py b/app/core/config.py
index 1aaa318..4192d04 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -107,6 +107,28 @@ class Settings(BaseSettings):
     redis_key_prefix: str = Field(default="aims:ai-service", alias="REDIS_KEY_PREFIX")
     redis_cache_ttl_seconds: int = Field(default=60, alias="REDIS_CACHE_TTL_SECONDS")
 
+    elasticsearch_url: str | None = Field(default=None, alias="ELASTICSEARCH_URL")
+    elasticsearch_username: str | None = Field(
+        default=None,
+        alias="ELASTICSEARCH_USERNAME",
+    )
+    elasticsearch_password: str | None = Field(
+        default=None,
+        alias="ELASTICSEARCH_PASSWORD",
+    )
+    elasticsearch_verify_certs: bool = Field(
+        default=True,
+        alias="ELASTICSEARCH_VERIFY_CERTS",
+    )
+    elasticsearch_bottleneck_index: str = Field(
+        default="aims-bottleneck-analysis-v1",
+        alias="ELASTICSEARCH_BOTTLENECK_INDEX",
+    )
+    elasticsearch_defect_transfer_index: str = Field(
+        default="aims-defect-transfer-analysis-v1",
+        alias="ELASTICSEARCH_DEFECT_TRANSFER_INDEX",
+    )
+
     main_database_url: str | None = Field(default=None, alias="MAIN_DATABASE_URL")
     main_db_name: str | None = Field(default=None, alias="MAIN_DB_NAME")
     sample_db_name: str | None = Field(default=None, alias="SAMPLE_DB_NAME")
diff --git a/app/kafka/analysis_sync_consumer.py b/app/kafka/analysis_sync_consumer.py
new file mode 100644
index 0000000..9a69e8d
--- /dev/null
+++ b/app/kafka/analysis_sync_consumer.py
@@ -0,0 +1,100 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from threading import Event
+from typing import Any
+
+from fastapi import FastAPI
+
+from app.core.config import settings
+from app.kafka import config as kafka_config
+from app.kafka.consumer import create_consumer
+from app.search.process_analysis_search import ProcessAnalysisSearchRepository
+
+logger = logging.getLogger(__name__)
+
+
+def start_analysis_sync_consumer(app: FastAPI) -> None:
+    if not settings.elasticsearch_url:
+        logger.info("Elasticsearch is not configured. Analysis sync consumer is disabled.")
+        return
+    if not (settings.broker_url_1 or settings.broker_url_2):
+        logger.info("Kafka bootstrap servers are not set. Analysis sync consumer is disabled.")
+        return
+
+    stop_event = Event()
+    tasks = [
+        asyncio.create_task(
+            asyncio.to_thread(
+                _run_analysis_sync_consumer,
+                stop_event,
+                1,
+            ),
+        )
+    ]
+    app.state.analysis_sync_consumer_stop_event = stop_event
+    app.state.analysis_sync_consumer_tasks = tasks
+
+
+async def stop_analysis_sync_consumer(app: FastAPI) -> None:
+    tasks = getattr(app.state, "analysis_sync_consumer_tasks", None)
+    if not tasks:
+        return
+    stop_event = getattr(app.state, "analysis_sync_consumer_stop_event", None)
+    if stop_event is not None:
+        stop_event.set()
+    await asyncio.gather(*tasks, return_exceptions=True)
+
+
+def _run_analysis_sync_consumer(
+    stop_event: Event,
+    consumer_index: int,
+) -> None:
+    consumer = create_consumer(
+        kafka_config.ANALYSIS_SYNC_TOPIC,
+        kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
+        enable_auto_commit=False,
+        consumer_timeout_ms=kafka_config.CONSUMER_TIMEOUT_MS,
+    )
+    search_repository = ProcessAnalysisSearchRepository()
+    try:
+        search_repository.ensure_indices()
+    except Exception:
+        logger.exception("Failed to ensure Elasticsearch indices for analysis sync.")
+        consumer.close()
+        return
+
+    logger.info(
+        "Analysis sync consumer started: topic=%s group=%s consumer_index=%s",
+        kafka_config.ANALYSIS_SYNC_TOPIC,
+        kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
+        consumer_index,
+    )
+    try:
+        while not stop_event.is_set():
+            for message in consumer:
+                if stop_event.is_set():
+                    break
+                try:
+                    payload = message.value
+                    if not isinstance(payload, dict):
+                        logger.warning("Skipping non-dict analysis sync payload.")
+                        continue
+                    search_repository.index_sync_event(payload)
+                    consumer.commit()
+                except Exception:
+                    logger.exception(
+                        "Failed to index analysis sync event: topic=%s partition=%s offset=%s",
+                        message.topic,
+                        message.partition,
+                        message.offset,
+                    )
+    finally:
+        consumer.close()
+        logger.info(
+            "Analysis sync consumer stopped: topic=%s group=%s consumer_index=%s",
+            kafka_config.ANALYSIS_SYNC_TOPIC,
+            kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
+            consumer_index,
+        )
diff --git a/app/kafka/config.py b/app/kafka/config.py
index 8f4f033..45cd722 100644
--- a/app/kafka/config.py
+++ b/app/kafka/config.py
@@ -1,7 +1,10 @@
 # kafka/config.py
 RAW_TOPIC = "factory.manufacturing.raw"
 ANALYSIS_TOPIC = "factory.manufacturing.analysis"
+# ES sync events reuse the existing analysis topic to avoid requiring an extra Kafka topic.
+ANALYSIS_SYNC_TOPIC = ANALYSIS_TOPIC
 RAW_CONSUMER_GROUP_ID = "ai-analysis-consumer-group"
+ANALYSIS_SYNC_CONSUMER_GROUP_ID = "ai-analysis-sync-consumer-group"
 RAW_CONSUMER_CONCURRENCY = 2
 AUTO_OFFSET_RESET = "earliest"
 
diff --git a/app/kafka/consumer.py b/app/kafka/consumer.py
index 1a00d90..bb36209 100644
--- a/app/kafka/consumer.py
+++ b/app/kafka/consumer.py
@@ -12,17 +12,28 @@ def _bootstrap_servers() -> list[str]:
     return [server.strip() for server in servers if server.strip()]
 
 
-def create_consumer(topic: str, group_id: str) -> KafkaConsumer:
-    consumer = KafkaConsumer(
-        topic,
+def create_consumer(
+    topic: str,
+    group_id: str,
+    *,
+    enable_auto_commit: bool = True,
+    consumer_timeout_ms: int | None = None,
+) -> KafkaConsumer:
+    consumer_kwargs = dict(
         ssl_context=ssl.create_default_context(),
         bootstrap_servers=_bootstrap_servers(),
         group_id=group_id,
         auto_offset_reset="earliest",
-        enable_auto_commit=True,
+        enable_auto_commit=enable_auto_commit,
         security_protocol="SASL_SSL",
         sasl_mechanism="OAUTHBEARER",
         sasl_oauth_token_provider=MSKTokenProvider(),
         value_deserializer=lambda x: json.loads(x.decode("utf-8")),
     )
+    if consumer_timeout_ms is not None:
+        consumer_kwargs["consumer_timeout_ms"] = consumer_timeout_ms
+    consumer = KafkaConsumer(
+        topic,
+        **consumer_kwargs,
+    )
     return consumer
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 0660c5f..8e1cf5f 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -30,6 +30,7 @@
 # assembly-service의 app.kafka.topics.raw.name과 동일한 raw 토픽.
 RAW_TOPIC = kafka_config.RAW_TOPIC
 ANALYSIS_TOPIC = kafka_config.ANALYSIS_TOPIC
+ANALYSIS_SYNC_TOPIC = kafka_config.ANALYSIS_SYNC_TOPIC
 RAW_CONSUMER_GROUP_ID = kafka_config.RAW_CONSUMER_GROUP_ID
 RAW_AUTO_OFFSET_RESET = kafka_config.AUTO_OFFSET_RESET
 RAW_CONSUMER_CONCURRENCY = kafka_config.RAW_CONSUMER_CONCURRENCY
@@ -257,6 +258,15 @@ def _consume_record(
         bottleneck_summaries,
         defect_prediction,
     )
+    sync_published = _publish_process_analysis_sync_events(
+        analysis_producer,
+        repository,
+        raw_event,
+        row,
+        bottleneck_summaries,
+        defect_prediction,
+        defect_rows_saved,
+    )
     # raw 이벤트 연동 분석 결과가 반영되면 Redis 캐시를 비운다.
     _clear_bottleneck_cache()
     _clear_defect_transfer_cache()
@@ -271,7 +281,8 @@ def _consume_record(
         "Kafka raw event consumed and stored: topic=%s partition=%s offset=%s "
         "key=%s event_id=%s manufacturing_event_id_source=manufacturing_event_json.id "
         "car_master_id=%s process_code=%s affected_rows=%s "
-        "bottleneck_result_count=%s defect_result_saved=%s analysis_topic_published=%s",
+        "bottleneck_result_count=%s defect_result_saved=%s analysis_topic_published=%s "
+        "analysis_sync_published=%s",
         record.topic,
         record.partition,
         record.offset,
@@ -283,6 +294,7 @@ def _consume_record(
         len(bottleneck_summaries),
         defect_rows_saved,
         analysis_published,
+        sync_published,
     )
 
 
@@ -634,6 +646,173 @@ def _publish_bottleneck_analysis_event(
     return True
 
 
+def _publish_process_analysis_sync_events(
+    producer: Any | None,
+    repository: SampleDbRepository,
+    raw_event: dict[str, Any],
+    row: dict[str, Any],
+    bottleneck_summaries: list[dict[str, Any]],
+    defect_prediction: DefectTransferPrediction | None,
+    defect_rows_saved: int,
+) -> bool:
+    if producer is None:
+        return False
+
+    published = False
+    if bottleneck_summaries:
+        bottleneck_event = _build_bottleneck_sync_event(
+            repository=repository,
+            raw_event=raw_event,
+            row=row,
+            summaries=bottleneck_summaries,
+        )
+        try:
+            producer.send(
+                ANALYSIS_SYNC_TOPIC,
+                key=f"bottleneck:{row['event_id']}",
+                value=bottleneck_event,
+            ).get(timeout=10)
+            published = True
+        except Exception:
+            logger.exception(
+                "Failed to publish bottleneck sync event: topic=%s event_id=%s",
+                ANALYSIS_SYNC_TOPIC,
+                row.get("event_id"),
+            )
+
+    if defect_prediction is not None and defect_rows_saved > 0:
+        defect_event = _build_defect_transfer_sync_event(
+            repository=repository,
+            raw_event=raw_event,
+            row=row,
+            prediction=defect_prediction,
+        )
+        try:
+            producer.send(
+                ANALYSIS_SYNC_TOPIC,
+                key=f"defect:{row['event_id']}",
+                value=defect_event,
+            ).get(timeout=10)
+            published = True
+        except Exception:
+            logger.exception(
+                "Failed to publish defect transfer sync event: topic=%s event_id=%s",
+                ANALYSIS_SYNC_TOPIC,
+                row.get("event_id"),
+            )
+
+    return published
+
+
+def _build_bottleneck_sync_event(
+    *,
+    repository: SampleDbRepository,
+    raw_event: dict[str, Any],
+    row: dict[str, Any],
+    summaries: list[dict[str, Any]],
+) -> dict[str, Any]:
+    sync_id = f"SNAP-{uuid4()}"
+    detected_at = seoul_now_iso()
+    first_summary = summaries[0] if summaries else {}
+    return {
+        "syncId": sync_id,
+        "analysisType": "BOTTLENECK_ANALYSIS_SYNC",
+        "sourceService": "AI_SERVICE",
+        "detectedAt": detected_at,
+        "analyzedAt": detected_at,
+        "eventId": row["event_id"],
+        "carMasterId": row["car_master_id"],
+        "mostBottleneckProcess": first_summary.get("process_code"),
+        "mostBottleneckRiskLevel": _risk_level(float(first_summary.get("risk_score") or 0.0)),
+        "items": [
+            {
+                "manufacturingEventId": summary.get("manufacturing_event_id"),
+                "carMasterId": summary.get("car_master_id") or row["car_master_id"],
+                "processCode": summary.get("process_code"),
+                "equipmentCode": summary.get("equipment_code"),
+                "rankNo": summary.get("rank_no"),
+                "avgDelayTime": summary.get("avg_delay_time"),
+                "affectedVehicleCount": summary.get("affected_vehicle_count"),
+                "riskScore": summary.get("risk_score"),
+                "riskLevel": _risk_level(float(summary.get("risk_score") or 0.0)),
+            }
+            for summary in summaries
+        ],
+        "rawEvent": {
+            "eventId": row["event_id"],
+            "carMasterId": row["car_master_id"],
+            "processCode": row["process_code"],
+            "equipmentId": row.get("equipment_id"),
+            "vehicleId": _vehicle_id_for_car_master_id(repository, int(row["car_master_id"])),
+        },
+    }
+
+
+def _build_defect_transfer_sync_event(
+    *,
+    repository: SampleDbRepository,
+    raw_event: dict[str, Any],
+    row: dict[str, Any],
+    prediction: DefectTransferPrediction,
+) -> dict[str, Any]:
+    sync_id = f"SYNC-{uuid4()}"
+    predicted_at = seoul_now_iso()
+    vehicle_id = _vehicle_id_for_car_master_id(repository, int(row["car_master_id"]))
+    source_equipment_code = _source_equipment_code(row)
+    current_process = _format_current_process(row, source_equipment_code)
+    predicted_process = _format_predicted_defect_process(
+        prediction.predicted_process_code,
+        row,
+    )
+    causes = [
+        {
+            "rank": cause.rank,
+            "feature": cause.feature,
+            "label": cause.label,
+            "value": cause.value,
+            "impact": cause.impact,
+            "message": cause.message,
+        }
+        for cause in prediction.causes
+    ]
+    return {
+        "syncId": sync_id,
+        "analysisType": "DEFECT_TRANSFER_ANALYSIS_SYNC",
+        "sourceService": "AI_SERVICE",
+        "eventId": row["event_id"],
+        "carMasterId": row["car_master_id"],
+        "vehicleId": vehicle_id,
+        "currentProcessCode": prediction.current_process_code,
+        "currentProcess": current_process,
+        "sourceEquipmentCode": source_equipment_code,
+        "predictedDefectProcess": predicted_process,
+        "defectProbability": round(prediction.defect_probability * 100.0),
+        "currentDefectProbability": prediction.defect_probability,
+        "transferProbability": prediction.transfer_probability,
+        "defectThreshold": prediction.defect_threshold,
+        "transferThreshold": prediction.transfer_threshold,
+        "expectedStepsAfter": prediction.expected_steps_after,
+        "expectedTime": (
+            f"{prediction.expected_steps_after}단계 후"
+            if prediction.expected_steps_after is not None
+            else None
+        ),
+        "riskLevel": prediction.risk_level,
+        "predictedAt": predicted_at,
+        "createdAt": predicted_at,
+        "mainCauses": causes,
+        "causes": causes,
+        "featureValues": prediction.feature_values,
+        "rawEvent": {
+            "eventId": row["event_id"],
+            "carMasterId": row["car_master_id"],
+            "processCode": row["process_code"],
+            "equipmentId": row.get("equipment_id"),
+            "vehicleId": vehicle_id,
+        },
+    }
+
+
 def _build_bottleneck_analysis_event(
     raw_event: dict[str, Any],
     row: dict[str, Any],
@@ -897,6 +1076,15 @@ def _format_predicted_defect_process(
     return format_process_with_line(normalized, equipment_code)
 
 
+def _format_current_process(
+    row: dict[str, Any],
+    equipment_code: str | None,
+) -> str:
+    from app.utils.process_label_utils import format_process_with_line
+
+    return format_process_with_line(row.get("process_code"), equipment_code)
+
+
 def _should_analyze_row(row: dict[str, Any]) -> bool:
     return str(row.get("dispatch_status") or "").upper() == "SENT" and _analysis_flag_is_true(
         row.get("is_sent"),
diff --git a/app/main.py b/app/main.py
index e8c54c1..7fd8e1f 100644
--- a/app/main.py
+++ b/app/main.py
@@ -11,6 +11,10 @@
     start_raw_event_consumer,
     stop_raw_event_consumer,
 )
+from app.kafka.analysis_sync_consumer import (
+    start_analysis_sync_consumer,
+    stop_analysis_sync_consumer,
+)
 from app.repository.sampledb_repository import initialize_sampledb
 from app.scheduler.manufacturing import (
     start_manufacturing_event_scheduler,
@@ -78,9 +82,11 @@ async def start_background_schedulers() -> None:
                 logger.exception("미완료 제조 이벤트 생성 job 복구에 실패했습니다.")
         start_manufacturing_event_scheduler(app)
         start_raw_event_consumer(app)
+        start_analysis_sync_consumer(app)
 
     @app.on_event("shutdown")
     async def stop_background_schedulers() -> None:
+        await stop_analysis_sync_consumer(app)
         await stop_raw_event_consumer(app)
         await stop_manufacturing_event_scheduler(app)
 
diff --git a/app/search/process_analysis_search.py b/app/search/process_analysis_search.py
new file mode 100644
index 0000000..d037e45
--- /dev/null
+++ b/app/search/process_analysis_search.py
@@ -0,0 +1,530 @@
+from __future__ import annotations
+
+import logging
+from datetime import datetime
+from typing import Any
+from urllib.parse import urlparse
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+class ProcessAnalysisSearchRepository:
+    def __init__(self) -> None:
+        self._client: Any | None = None
+
+    @property
+    def enabled(self) -> bool:
+        return bool(settings.elasticsearch_url)
+
+    @property
+    def client(self) -> Any:
+        if self._client is None:
+            self._client = self._create_client()
+        return self._client
+
+    def ensure_indices(self) -> None:
+        if not self.enabled:
+            return
+
+        client = self.client
+        for index_name, body in self._index_definitions().items():
+            try:
+                if client.indices.exists(index=index_name):
+                    continue
+                client.indices.create(index=index_name, body=body)
+                logger.info("Elasticsearch index created: %s", index_name)
+            except Exception:
+                logger.exception("Failed to ensure Elasticsearch index: %s", index_name)
+                raise
+
+    def index_sync_event(self, event: dict[str, Any]) -> None:
+        analysis_type = str(event.get("analysisType") or "").strip().upper()
+        if analysis_type == "BOTTLENECK_ANALYSIS_SYNC":
+            self.index_bottleneck_snapshot(event)
+            return
+        if analysis_type == "DEFECT_TRANSFER_ANALYSIS_SYNC":
+            self.index_defect_transfer_prediction(event)
+            return
+        # The sync consumer reuses the existing analysis topic, so non-sync
+        # analysis events can arrive here as well. They are intentionally ignored.
+        return
+
+    def index_bottleneck_snapshot(self, event: dict[str, Any]) -> None:
+        if not self.enabled:
+            return
+
+        snapshot_id = str(event.get("snapshotId") or event.get("syncId") or "").strip()
+        items = event.get("items") or []
+        if not snapshot_id or not isinstance(items, list) or not items:
+            return
+
+        detected_at = self._normalize_datetime(event.get("detectedAt"))
+        analyzed_at = self._normalize_datetime(event.get("analyzedAt"))
+        base_doc = {
+            "analysisType": "BOTTLENECK_ANALYSIS",
+            "syncId": str(event.get("syncId") or snapshot_id),
+            "snapshotId": snapshot_id,
+            "detectedAt": detected_at,
+            "analyzedAt": analyzed_at,
+            "eventId": event.get("eventId"),
+            "carMasterId": event.get("carMasterId"),
+            "mostBottleneckProcess": event.get("mostBottleneckProcess"),
+            "mostBottleneckRiskLevel": event.get("mostBottleneckRiskLevel"),
+            "sourceService": event.get("sourceService") or "AI_SERVICE",
+        }
+        actions: list[dict[str, Any]] = []
+        for item in items:
+            if not isinstance(item, dict):
+                continue
+            doc = {
+                **base_doc,
+                "rankNo": self._safe_int(item.get("rankNo")),
+                "manufacturingEventId": item.get("manufacturingEventId"),
+                "carMasterId": item.get("carMasterId") or event.get("carMasterId"),
+                "processCode": item.get("processCode"),
+                "equipmentCode": item.get("equipmentCode"),
+                "avgDelayTime": self._safe_float(item.get("avgDelayTime")),
+                "affectedVehicleCount": self._safe_int(item.get("affectedVehicleCount")),
+                "riskScore": self._safe_float(item.get("riskScore")),
+                "riskLevel": item.get("riskLevel"),
+            }
+            doc_id = self._bottleneck_doc_id(snapshot_id, doc)
+            actions.append(
+                {
+                    "_index": settings.elasticsearch_bottleneck_index,
+                    "_id": doc_id,
+                    "_source": doc,
+                },
+            )
+        self._bulk_index(actions)
+
+    def index_defect_transfer_prediction(self, event: dict[str, Any]) -> None:
+        if not self.enabled:
+            return
+
+        sync_id = str(event.get("syncId") or "").strip()
+        if not sync_id:
+            return
+
+        doc = {
+            "analysisType": "DEFECT_TRANSFER_ANALYSIS",
+            "syncId": sync_id,
+            "eventId": event.get("eventId"),
+            "carMasterId": event.get("carMasterId"),
+            "vehicleId": event.get("vehicleId"),
+            "currentProcess": event.get("currentProcess"),
+            "currentProcessCode": event.get("currentProcessCode"),
+            "sourceProcessCode": event.get("sourceProcessCode"),
+            "sourceEquipmentCode": event.get("sourceEquipmentCode"),
+            "predictedDefectProcess": event.get("predictedDefectProcess"),
+            "currentDefectProbability": self._safe_float(event.get("currentDefectProbability")),
+            "transferProbability": self._safe_float(event.get("transferProbability")),
+            "defectThreshold": self._safe_float(event.get("defectThreshold")),
+            "transferThreshold": self._safe_float(event.get("transferThreshold")),
+            "defectProbability": self._safe_int(event.get("defectProbability")),
+            "expectedTime": event.get("expectedTime"),
+            "expectedStepsAfter": self._safe_int(event.get("expectedStepsAfter")),
+            "riskLevel": event.get("riskLevel"),
+            "predictedAt": self._normalize_datetime(event.get("predictedAt")),
+            "createdAt": self._normalize_datetime(event.get("createdAt") or event.get("predictedAt")),
+            "mainCauses": self._normalize_main_causes(event.get("mainCauses") or event.get("causes")),
+            "featureValues": event.get("featureValues") or {},
+            "sourceService": event.get("sourceService") or "AI_SERVICE",
+        }
+        doc_id = f"{sync_id}:{doc.get('eventId') or doc.get('carMasterId')}"
+        self._bulk_index(
+            [
+                {
+                    "_index": settings.elasticsearch_defect_transfer_index,
+                    "_id": doc_id,
+                    "_source": doc,
+                },
+            ],
+        )
+
+    def list_bottleneck_page(
+        self,
+        *,
+        cursor: int,
+        size: int,
+    ) -> tuple[list[dict[str, Any]], bool]:
+        latest_snapshot_id = self._latest_bottleneck_snapshot_id()
+        if not latest_snapshot_id:
+            return [], False
+
+        page = max(cursor, 0)
+        safe_size = max(1, min(size, 100))
+        query = {
+            "query": {
+                "term": {
+                    "snapshotId": latest_snapshot_id,
+                },
+            },
+            "sort": [
+                {"rankNo": {"order": "asc"}},
+                {"processCode": {"order": "asc"}},
+                {"equipmentCode": {"order": "asc"}},
+            ],
+            "from": page * safe_size,
+            "size": safe_size + 1,
+            "track_total_hits": True,
+        }
+        response = self.client.search(
+            index=settings.elasticsearch_bottleneck_index,
+            body=query,
+        )
+        rows = [
+            self._map_bottleneck_source(hit.get("_source") or {})
+            for hit in response.get("hits", {}).get("hits", [])
+        ]
+        has_next = len(rows) > safe_size
+        return rows[:safe_size], has_next
+
+    def count_bottleneck_page(self) -> int:
+        latest_snapshot_id = self._latest_bottleneck_snapshot_id()
+        if not latest_snapshot_id:
+            return 0
+
+        query = {
+            "query": {
+                "term": {
+                    "snapshotId": latest_snapshot_id,
+                },
+            },
+            "size": 0,
+            "track_total_hits": True,
+        }
+        response = self.client.search(
+            index=settings.elasticsearch_bottleneck_index,
+            body=query,
+        )
+        total = response.get("hits", {}).get("total", 0)
+        if isinstance(total, dict):
+            return int(total.get("value") or 0)
+        return int(total or 0)
+
+    def list_defect_prediction_page(
+        self,
+        *,
+        cursor: int,
+        size: int,
+    ) -> tuple[list[dict[str, Any]], bool]:
+        page = max(cursor, 0)
+        safe_size = max(1, min(size, 100))
+        query = {
+            "query": {"match_all": {}},
+            "collapse": {"field": "carMasterId"},
+            "sort": [
+                {"transferProbability": {"order": "desc", "missing": "_last"}},
+                {"currentDefectProbability": {"order": "desc", "missing": "_last"}},
+                {"predictedAt": {"order": "desc"}},
+                {"syncId": {"order": "desc"}},
+            ],
+            "from": page * safe_size,
+            "size": safe_size + 1,
+            "track_total_hits": True,
+        }
+        response = self.client.search(
+            index=settings.elasticsearch_defect_transfer_index,
+            body=query,
+        )
+        rows = [
+            self._map_defect_source(hit.get("_source") or {})
+            for hit in response.get("hits", {}).get("hits", [])
+        ]
+        has_next = len(rows) > safe_size
+        return rows[:safe_size], has_next
+
+    def get_latest_defect_cause_document(
+        self,
+        *,
+        vehicle_id: str | None,
+    ) -> dict[str, Any] | None:
+        query: dict[str, Any] = {
+            "size": 1,
+            "sort": [
+                {"predictedAt": {"order": "desc"}},
+                {"syncId": {"order": "desc"}},
+            ],
+        }
+        if vehicle_id:
+            query["query"] = {"term": {"vehicleId": vehicle_id}}
+        else:
+            query["query"] = {"match_all": {}}
+
+        response = self.client.search(
+            index=settings.elasticsearch_defect_transfer_index,
+            body=query,
+        )
+        hits = response.get("hits", {}).get("hits", [])
+        if not hits:
+            return None
+        return self._map_defect_source(hits[0].get("_source") or {})
+
+    def _latest_bottleneck_snapshot_id(self) -> str | None:
+        query = {
+            "size": 1,
+            "query": {"match_all": {}},
+            "sort": [
+                {"detectedAt": {"order": "desc"}},
+                {"rankNo": {"order": "asc"}},
+                {"syncId": {"order": "desc"}},
+            ],
+        }
+        response = self.client.search(
+            index=settings.elasticsearch_bottleneck_index,
+            body=query,
+        )
+        hits = response.get("hits", {}).get("hits", [])
+        if not hits:
+            return None
+        source = hits[0].get("_source") or {}
+        snapshot_id = source.get("snapshotId")
+        return str(snapshot_id) if snapshot_id is not None else None
+
+    def _bulk_index(self, actions: list[dict[str, Any]]) -> None:
+        if not actions:
+            return
+        from opensearchpy import helpers
+
+        helpers.bulk(self.client, actions, raise_on_error=True)
+
+    def _create_client(self) -> Any:
+        if not settings.elasticsearch_url:
+            raise RuntimeError("Elasticsearch URL is not configured.")
+
+        try:
+            from opensearchpy import OpenSearch
+        except ModuleNotFoundError as exc:
+            raise RuntimeError("opensearch-py is required for Elasticsearch integration.") from exc
+
+        parsed = urlparse(settings.elasticsearch_url)
+        if not parsed.scheme or not parsed.hostname:
+            raise ValueError(f"Invalid Elasticsearch URL: {settings.elasticsearch_url}")
+
+        hosts = [
+            {
+                "host": parsed.hostname,
+                "port": parsed.port or (443 if parsed.scheme == "https" else 80),
+                "scheme": parsed.scheme,
+            },
+        ]
+        kwargs: dict[str, Any] = {
+            "hosts": hosts,
+            "use_ssl": parsed.scheme == "https",
+            "verify_certs": settings.elasticsearch_verify_certs,
+            "ssl_show_warn": False,
+            "request_timeout": 30,
+            "retry_on_timeout": True,
+            "max_retries": 3,
+        }
+        if settings.elasticsearch_username:
+            kwargs["http_auth"] = (
+                settings.elasticsearch_username,
+                settings.elasticsearch_password or "",
+            )
+        return OpenSearch(**kwargs)
+
+    def _index_definitions(self) -> dict[str, dict[str, Any]]:
+        return {
+            settings.elasticsearch_bottleneck_index: {
+                "settings": {
+                    "index": {
+                        "number_of_shards": 1,
+                        "number_of_replicas": 0,
+                    },
+                },
+                "mappings": {
+                    "dynamic": True,
+                    "properties": {
+                        "analysisType": {"type": "keyword"},
+                        "syncId": {"type": "keyword"},
+                        "snapshotId": {"type": "keyword"},
+                        "detectedAt": {"type": "date"},
+                        "analyzedAt": {"type": "date"},
+                        "eventId": {"type": "keyword"},
+                        "carMasterId": {"type": "long"},
+                        "mostBottleneckProcess": {"type": "keyword"},
+                        "mostBottleneckRiskLevel": {"type": "keyword"},
+                        "rankNo": {"type": "integer"},
+                        "manufacturingEventId": {"type": "long"},
+                        "processCode": {"type": "keyword"},
+                        "equipmentCode": {"type": "keyword"},
+                        "avgDelayTime": {"type": "double"},
+                        "affectedVehicleCount": {"type": "integer"},
+                        "riskScore": {"type": "double"},
+                        "riskLevel": {"type": "keyword"},
+                        "sourceService": {"type": "keyword"},
+                    },
+                },
+            },
+            settings.elasticsearch_defect_transfer_index: {
+                "settings": {
+                    "index": {
+                        "number_of_shards": 1,
+                        "number_of_replicas": 0,
+                    },
+                },
+                "mappings": {
+                    "dynamic": True,
+                    "properties": {
+                        "analysisType": {"type": "keyword"},
+                        "syncId": {"type": "keyword"},
+                        "eventId": {"type": "keyword"},
+                        "carMasterId": {"type": "long"},
+                        "vehicleId": {"type": "keyword"},
+                        "currentProcess": {"type": "keyword"},
+                        "currentProcessCode": {"type": "keyword"},
+                        "sourceProcessCode": {"type": "keyword"},
+                        "sourceEquipmentCode": {"type": "keyword"},
+                        "predictedDefectProcess": {"type": "keyword"},
+                        "currentDefectProbability": {"type": "double"},
+                        "transferProbability": {"type": "double"},
+                        "defectThreshold": {"type": "double"},
+                        "transferThreshold": {"type": "double"},
+                        "defectProbability": {"type": "integer"},
+                        "expectedTime": {"type": "keyword"},
+                        "expectedStepsAfter": {"type": "integer"},
+                        "riskLevel": {"type": "keyword"},
+                        "predictedAt": {"type": "date"},
+                        "createdAt": {"type": "date"},
+                        "sourceService": {"type": "keyword"},
+                        "mainCauses": {
+                            "type": "nested",
+                            "properties": {
+                                "rank": {"type": "integer"},
+                                "feature": {"type": "keyword"},
+                                "label": {"type": "text"},
+                                "value": {"type": "keyword"},
+                                "impact": {"type": "double"},
+                                "message": {"type": "text"},
+                            },
+                        },
+                    },
+                },
+            },
+        }
+
+    @staticmethod
+    def _normalize_datetime(value: Any) -> str | None:
+        if value is None:
+            return None
+        if isinstance(value, datetime):
+            return value.isoformat()
+        text = str(value).strip()
+        return text or None
+
+    @staticmethod
+    def _safe_int(value: Any) -> int | None:
+        if value is None:
+            return None
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return None
+
+    @staticmethod
+    def _safe_float(value: Any) -> float | None:
+        if value is None:
+            return None
+        try:
+            return float(value)
+        except (TypeError, ValueError):
+            return None
+
+    @staticmethod
+    def _normalize_main_causes(value: Any) -> list[dict[str, Any]]:
+        if not isinstance(value, list):
+            return []
+        normalized: list[dict[str, Any]] = []
+        for cause in value:
+            if not isinstance(cause, dict):
+                continue
+            message = str(cause.get("message") or cause.get("label") or "").strip()
+            if not message:
+                continue
+            normalized.append(
+                {
+                    "rank": ProcessAnalysisSearchRepository._safe_int(cause.get("rank")) or 0,
+                    "feature": str(cause.get("feature") or ""),
+                    "label": str(cause.get("label") or message),
+                    "value": str(cause.get("value") or ""),
+                    "impact": ProcessAnalysisSearchRepository._safe_float(cause.get("impact")) or 0.0,
+                    "message": message,
+                },
+            )
+        return normalized
+
+    @staticmethod
+    def _bottleneck_doc_id(snapshot_id: str, doc: dict[str, Any]) -> str:
+        rank_no = doc.get("rankNo")
+        process_code = str(doc.get("processCode") or "").strip().upper() or "UNKNOWN"
+        equipment_code = str(doc.get("equipmentCode") or "").strip().upper() or "UNKNOWN"
+        return f"{snapshot_id}:{rank_no}:{process_code}:{equipment_code}"
+
+    @staticmethod
+    def _map_bottleneck_source(source: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "analysis_type": source.get("analysisType"),
+            "sync_id": source.get("syncId"),
+            "snapshot_id": source.get("snapshotId"),
+            "detected_at": source.get("detectedAt"),
+            "analyzed_at": source.get("analyzedAt"),
+            "event_id": source.get("eventId"),
+            "car_master_id": source.get("carMasterId"),
+            "most_bottleneck_process": source.get("mostBottleneckProcess"),
+            "most_bottleneck_risk_level": source.get("mostBottleneckRiskLevel"),
+            "manufacturing_event_id": source.get("manufacturingEventId"),
+            "process_code": source.get("processCode"),
+            "equipment_code": source.get("equipmentCode"),
+            "rank_no": source.get("rankNo"),
+            "avg_delay_time": source.get("avgDelayTime"),
+            "affected_vehicle_count": source.get("affectedVehicleCount"),
+            "risk_score": source.get("riskScore"),
+            "risk_level": source.get("riskLevel"),
+            "source_service": source.get("sourceService"),
+        }
+
+    @staticmethod
+    def _map_defect_source(source: dict[str, Any]) -> dict[str, Any]:
+        current_defect_probability = source.get("currentDefectProbability")
+        transfer_probability = source.get("transferProbability")
+        defect_probability = source.get("defectProbability")
+        if defect_probability is None and current_defect_probability is not None:
+            defect_probability = round(float(current_defect_probability) * 100)
+        return {
+            "analysis_type": source.get("analysisType"),
+            "sync_id": source.get("syncId"),
+            "event_id": source.get("eventId"),
+            "vehicle_id": source.get("vehicleId"),
+            "car_master_id": source.get("carMasterId"),
+            "current_process": source.get("currentProcess"),
+            "current_process_code": source.get("currentProcessCode"),
+            "source_process_code": source.get("sourceProcessCode") or source.get("currentProcessCode"),
+            "source_equipment_code": source.get("sourceEquipmentCode"),
+            "predicted_defect_process": source.get("predictedDefectProcess"),
+            "target_defect_probability": transfer_probability,
+            "defect_probability": defect_probability,
+            "current_defect_probability": current_defect_probability,
+            "transfer_probability": transfer_probability,
+            "defect_threshold": source.get("defectThreshold"),
+            "transfer_threshold": source.get("transferThreshold"),
+            "expected_time": source.get("expectedTime"),
+            "expected_steps_after": source.get("expectedStepsAfter"),
+            "expected_occurrence_step": source.get("expectedStepsAfter"),
+            "risk_level": source.get("riskLevel"),
+            "risk_grade": source.get("riskLevel"),
+            "predicted_at": source.get("predictedAt"),
+            "created_at": source.get("createdAt"),
+            "main_causes": source.get("mainCauses") or source.get("causes") or [],
+            "causes": source.get("causes") or source.get("mainCauses") or [],
+            "feature_values": source.get("featureValues") or {},
+            "source_service": source.get("sourceService"),
+            "influence_score": (
+                float((source.get("mainCauses") or [{}])[0].get("impact") or 0.0)
+                if isinstance(source.get("mainCauses"), list) and source.get("mainCauses")
+                else 0.0
+            ),
+        }
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 5ef4547..5a168a5 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -14,6 +14,7 @@
 from app.dto.response import BottleneckAnalysisItem, BottleneckAnalysisPage
 from app.ml.inference.bottleneck_detector import BottleneckDetector
 from app.repository.bottleneck_analysis_repository import BottleneckAnalysisRepository
+from app.search.process_analysis_search import ProcessAnalysisSearchRepository
 from app.utils.datetime_utils import seoul_now
 from app.utils.json_utils import from_json, to_json
 
@@ -49,6 +50,7 @@ def __init__(
             database_url or settings.bottleneck_database_url,
             event_database_url=settings.sample_database_connection_url,
         )
+        self.search_repository = self._create_search_repository()
         self.detector = BottleneckDetector(self.model_path)
         self._redis_client: Any | None = None
 
@@ -60,10 +62,27 @@ def get_realtime_bottlenecks(
     ) -> BottleneckAnalysisPage:
         size = max(1, min(size, 100))
         page = max(cursor or 0, 0)
-        rows = self.repository.list_results(
-            cursor=page,
-            size=size,
-        )
+        rows: list[dict[str, Any]]
+        has_next = False
+        if self.search_repository is not None:
+            try:
+                rows, has_next = self.search_repository.list_bottleneck_page(
+                    cursor=page,
+                    size=size,
+                )
+            except Exception:
+                logger.exception("Elasticsearch bottleneck query failed. Falling back to DB.")
+                rows = self.repository.list_results(
+                    cursor=page,
+                    size=size,
+                )
+                has_next = self.repository.count_results() > (page + 1) * size
+        else:
+            rows = self.repository.list_results(
+                cursor=page,
+                size=size,
+            )
+            has_next = self.repository.count_results() > (page + 1) * size
         if not rows:
             return BottleneckAnalysisPage(
                 mostBottleneckProcess=None,
@@ -74,8 +93,6 @@ def get_realtime_bottlenecks(
             )
 
         top_row = rows[0]
-        total_count = self.repository.count_results()
-        has_next = total_count > (page + 1) * size
 
         return BottleneckAnalysisPage(
             mostBottleneckProcess=self._format_process_label(top_row["process_code"]),
@@ -237,6 +254,18 @@ def _current_bottleneck_results(self) -> list[dict[str, Any]]:
             size=total_count,
         )
 
+    @staticmethod
+    def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
+        if not settings.elasticsearch_url:
+            return None
+        try:
+            repository = ProcessAnalysisSearchRepository()
+            repository.ensure_indices()
+            return repository
+        except Exception:
+            logger.exception("Elasticsearch bottleneck repository is unavailable.")
+            return None
+
     @staticmethod
     def _rank_bottleneck_summaries(
         summaries: list[dict[str, Any]],
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index ee71792..546d676 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 from functools import lru_cache
 from typing import Any
 
@@ -17,11 +18,13 @@
 from app.repository.defect_transfer_prediction_repository import (
     DefectTransferPredictionRepository,
 )
+from app.search.process_analysis_search import ProcessAnalysisSearchRepository
 from app.utils.json_utils import from_json, to_json
 from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
 
 
 DEFECT_TRANSFER_CACHE_VERSION = "v8"
+logger = logging.getLogger(__name__)
 
 
 class DefectTransferAnalysisService:
@@ -43,6 +46,7 @@ def __init__(
             main_database_url,
             event_database_url=sample_database_url,
         )
+        self.search_repository = self._create_search_repository()
         self._redis_client: Any | None = None
 
     def get_cached_predictions(
@@ -118,13 +122,31 @@ def get_predictions(
     ) -> DefectTransferPredictionPage:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
-        try:
-            rows, has_next = self.repository.list_prediction_page(
-                cursor=page,
-                size=safe_size,
-            )
-        except SQLAlchemyError as exc:
-            raise self._db_exception() from exc
+        rows: list[dict[str, Any]]
+        has_next = False
+        if self.search_repository is not None:
+            try:
+                rows, has_next = self.search_repository.list_defect_prediction_page(
+                    cursor=page,
+                    size=safe_size,
+                )
+            except Exception:
+                logger.exception("Elasticsearch defect prediction query failed. Falling back to DB.")
+                try:
+                    rows, has_next = self.repository.list_prediction_page(
+                        cursor=page,
+                        size=safe_size,
+                    )
+                except SQLAlchemyError as exc:
+                    raise self._db_exception() from exc
+        else:
+            try:
+                rows, has_next = self.repository.list_prediction_page(
+                    cursor=page,
+                    size=safe_size,
+                )
+            except SQLAlchemyError as exc:
+                raise self._db_exception() from exc
 
         return DefectTransferPredictionPage(
             content=[self._to_prediction_item(row) for row in rows],
@@ -169,14 +191,34 @@ def get_cause_analysis(
     ) -> DefectTransferCausePage:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
-        try:
-            selected, rows, has_next = self.repository.list_cause_page(
-                vehicle_id=vehicle_id,
-                cursor=page,
-                size=safe_size,
-            )
-        except SQLAlchemyError as exc:
-            raise self._db_exception() from exc
+        selected: dict[str, Any] | None
+        rows: list[dict[str, Any]]
+        has_next = False
+        if self.search_repository is not None:
+            try:
+                selected = self.search_repository.get_latest_defect_cause_document(
+                    vehicle_id=vehicle_id,
+                )
+                rows = [selected] if selected is not None else []
+            except Exception:
+                logger.exception("Elasticsearch defect cause query failed. Falling back to DB.")
+                try:
+                    selected, rows, has_next = self.repository.list_cause_page(
+                        vehicle_id=vehicle_id,
+                        cursor=page,
+                        size=safe_size,
+                    )
+                except SQLAlchemyError as exc:
+                    raise self._db_exception() from exc
+        else:
+            try:
+                selected, rows, has_next = self.repository.list_cause_page(
+                    vehicle_id=vehicle_id,
+                    cursor=page,
+                    size=safe_size,
+                )
+            except SQLAlchemyError as exc:
+                raise self._db_exception() from exc
 
         if selected is None:
             return DefectTransferCausePage(
@@ -192,7 +234,11 @@ def get_cause_analysis(
                 nextCursor=None,
             )
 
-        display_rows = [row for row in rows if self._is_displayable_cause(row)]
+        offset = page * safe_size
+        paged_rows = rows[offset : offset + safe_size]
+        display_rows = [row for row in paged_rows if self._is_displayable_cause(row)]
+        if has_next is False:
+            has_next = len(rows) > offset + safe_size
 
         return DefectTransferCausePage(
             vehicleId=str(selected.get("vehicle_id")),
@@ -363,6 +409,18 @@ def _db_exception() -> AppException:
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
         )
 
+    @staticmethod
+    def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
+        if not settings.elasticsearch_url:
+            return None
+        try:
+            repository = ProcessAnalysisSearchRepository()
+            repository.ensure_indices()
+            return repository
+        except Exception:
+            logger.exception("Elasticsearch defect transfer repository is unavailable.")
+            return None
+
     def _cache_get(self, cache_key: str) -> str | None:
         if not settings.redis_url:
             return None
diff --git a/docs/elasticsearch-migration-guideline.md b/docs/elasticsearch-migration-guideline.md
index bce824a..9cbf3f4 100644
--- a/docs/elasticsearch-migration-guideline.md
+++ b/docs/elasticsearch-migration-guideline.md
@@ -5,6 +5,9 @@
 현재 병목 조회와 불량전이 조회는 MySQL 결과 테이블과 Redis 캐시를 중심으로 동작한다.
 이 문서는 조회 계층을 Elasticsearch(OpenSearch 포함) 기반으로 전환할 때의 설계 원칙, 마이그레이션 순서, 운영 체크포인트를 정리한 가이드다.
 
+이 저장소 기준의 연동 주체는 **제조 서비스가 아니라 현재 AI 서비스**다.
+즉, 제조 서비스는 원천 이벤트 생성과 전달까지만 책임지고, 병목/불량전이의 ES 인덱싱과 조회는 이 서비스에서 처리하는 구성이 맞다.
+
 핵심 목표는 다음과 같다.
 
 - 조회 성능을 안정화한다.
@@ -47,6 +50,17 @@
 
 ## 3. 전환 원칙
 
+### 3.0 연동 위치
+
+- 제조 서비스
+  - 원천 제조 이벤트 생성
+  - 분석 요청 또는 raw event 발행
+  - ES 직접 연동은 하지 않음
+- 현재 AI 서비스
+  - 병목/불량전이 분석 결과 생성
+  - ES 인덱싱
+  - 조회 API 제공
+
 ### 3.1 유지할 것
 
 - API 경로와 응답 DTO
@@ -244,7 +258,7 @@ ES에서는 다음 둘 중 하나를 추천한다.
 
 - 이력 조회가 어려워진다
 
-### 옵션 B: 모든 결과 저장 후 최신 문서 선별
+### 옵션 B: 모든 결과 저장 후 최신 문서 선별 [선택]
 
 - `carMasterId`로 collapse
 - `predictedAt desc`로 최신값 선택
@@ -296,7 +310,7 @@ ES 전환의 핵심은 “어떻게 최신성을 보장할 것인가”다.
 - 부분 실패와 재시도 처리 복잡
 - 정합성 이슈 가능
 
-#### 방식 2. Kafka/outbox 기반 비동기 동기화
+#### 방식 2. Kafka/outbox 기반 비동기 동기화 [선택]
 
 - 저장과 발행을 분리
 - ES indexer가 별도 소비자 역할 수행
@@ -452,4 +466,3 @@ ES 전환 후에는 다음 중 하나를 선택한다.
 2. 조회의 기준은 Elasticsearch로 옮긴다.
 
 이렇게 하면 정합성은 유지하면서도, 병목/불량전이 같은 조회성 API를 훨씬 유연하게 확장할 수 있다.
-

From aef499cf3a97715c001c02d9222d485641954b20 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 13:10:53 +0900
Subject: [PATCH 121/148] fix : infra ai-manual path update

---
 app/api/routers/manual.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 702df72..34af027 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -5,7 +5,7 @@
 import os
 
 router = APIRouter(
-    prefix="/manual",
+    prefix="/api/ai/manual",
     tags=["AI Manual"]
 )
 

From 0a1a3145cd3a75d926eb73c329b2770f25fb6c95 Mon Sep 17 00:00:00 2001
From: haseokyung6 <haseokyung@naver.com>
Date: Fri, 10 Jul 2026 13:29:03 +0900
Subject: [PATCH 122/148] =?UTF-8?q?fix:=20=EC=88=9C=EC=B0=A8=20=EB=B0=B0?=
 =?UTF-8?q?=ED=8F=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .github/workflows/deploy-ai-service.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-ai-service.yml b/.github/workflows/deploy-ai-service.yml
index c46926d..c2d7370 100644
--- a/.github/workflows/deploy-ai-service.yml
+++ b/.github/workflows/deploy-ai-service.yml
@@ -20,7 +20,7 @@ on:
 
 concurrency:
   group: ai-service-${{ github.ref_name }}
-  cancel-in-progress: true
+  cancel-in-progress: false
 
 env:
   AWS_REGION: ap-northeast-2

From 56e707a274737799f66f4dd068fc76af9785e193 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 13:36:43 +0900
Subject: [PATCH 123/148] feat : log nomber add

---
 app/ai_manual/service/manual_service.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index aa13f42..7344dec 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -73,6 +73,7 @@ def generate_manual(
                 "process": event["process_code"],
                 "equipmentId": event["equipment_id"],
                 "riskScore": event["risk_score"],
+                "logNo": event["log_no"],
             },
             "manual": response.model_dump()
         }

From d8e2d804914651113fd76c72a3dd3bfac9789f4f Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 14:37:25 +0900
Subject: [PATCH 124/148] Revert "feat : log nomber add"

This reverts commit 56e707a274737799f66f4dd068fc76af9785e193.
---
 app/ai_manual/service/manual_service.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index 7344dec..aa13f42 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -73,7 +73,6 @@ def generate_manual(
                 "process": event["process_code"],
                 "equipmentId": event["equipment_id"],
                 "riskScore": event["risk_score"],
-                "logNo": event["log_no"],
             },
             "manual": response.model_dump()
         }

From b1c43510437ff38cfeec8031451f60c6621be31b Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 16:03:31 +0900
Subject: [PATCH 125/148] fix : alg update

---
 app/api/routers/manual.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 34af027..c98dbb1 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -29,7 +29,7 @@ def generate_manual(request: Request):
     payload = jwt.decode(
         token,
         os.getenv("JWT_SECRET_KEY"),
-        algorithms=["HS384"]
+        algorithms=["HS512"]
     )
 
     print(payload)

From de0b4ab64f964c3fcd792493e2b300bc9448e6f5 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 16:34:45 +0900
Subject: [PATCH 126/148] fix : JWT alg update

---
 app/api/routers/manual.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index c98dbb1..34af027 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -29,7 +29,7 @@ def generate_manual(request: Request):
     payload = jwt.decode(
         token,
         os.getenv("JWT_SECRET_KEY"),
-        algorithms=["HS512"]
+        algorithms=["HS384"]
     )
 
     print(payload)

From d58af024202f2172a8e16b3f0bcdeb64ecb2ae81 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 17:13:40 +0900
Subject: [PATCH 127/148] fix : env del

---
 app/ai_manual/service/manual_service.py | 12 ++++---
 app/core/config.py                      | 10 ++++++
 app/db.py                               | 44 ++-----------------------
 3 files changed, 20 insertions(+), 46 deletions(-)

diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index aa13f42..8eb6fd5 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -1,10 +1,9 @@
-import os
-from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
 
 from app.ai_manual.prompt.prompt_template import manual_prompt
 from app.ai_manual.rag.vector_store import VectorStore
 from app.ai_manual.repository.alert_event_repository import AlertEventRepository
+from app.core.config import settings
 
 from app.ai_manual.schema.request import (
     CriticalEvent,
@@ -22,13 +21,16 @@ class ManualService:
 
     def __init__(self):
 
-        load_dotenv()
-
         self.repository = AlertEventRepository()
         self.vector_store = VectorStore()
 
+        if not settings.openai_api_key:
+            raise ValueError(
+                "OPENAI_API_KEY is not configured"
+            )
+
         self.llm = ChatOpenAI(
-            api_key=os.getenv("OPENAI_API_KEY"),
+            api_key=settings.openai_api_key,
             model="gpt-5-mini",
             temperature=0.2
         ).with_structured_output(ManualResponse)
diff --git a/app/core/config.py b/app/core/config.py
index 4192d04..5b00707 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -103,6 +103,16 @@ class Settings(BaseSettings):
         alias="KAFKA_ANALYSIS_PRODUCER_LINGER_MS",
     )
 
+    jwt_secret_key: str | None = Field(
+        default=None,
+        alias="JWT_SECRET_KEY",
+    )
+
+    jwt_algorithm: str = Field(
+        default="HS512",
+        alias="JWT_ALGORITHM",
+    )
+
     redis_url: str | None = Field(default=None, alias="REDIS_URL")
     redis_key_prefix: str = Field(default="aims:ai-service", alias="REDIS_KEY_PREFIX")
     redis_cache_ttl_seconds: int = Field(default=60, alias="REDIS_CACHE_TTL_SECONDS")
diff --git a/app/db.py b/app/db.py
index e410437..20a498b 100644
--- a/app/db.py
+++ b/app/db.py
@@ -1,48 +1,11 @@
 # app/db.py
 
-import os
 from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker, scoped_session
-from dotenv import load_dotenv
-from urllib.parse import quote_plus
+from app.core.config import settings
 
-load_dotenv()
-
-# =========================
-# DATABASE URL
-# =========================
-DB_USER = os.getenv("DB_USER")
-DB_PASSWORD = quote_plus(
-    os.getenv("DB_PASSWORD")
-)
-DB_HOST = os.getenv("DB_HOST")
-DB_PORT = os.getenv("DB_PORT")
-MAIN_DB_NAME = os.getenv("MAIN_DB_NAME")
-SAMPLE_DB_NAME = os.getenv("SAMPLE_DB_NAME")
-
-MAIN_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{MAIN_DB_NAME}"
-)
-
-SAMPLE_DATABASE_URL = (
-    f"mysql+pymysql://{DB_USER}:{DB_PASSWORD}"
-    f"@{DB_HOST}:{DB_PORT}/{SAMPLE_DB_NAME}"
-)
-
-if not MAIN_DATABASE_URL:
-    raise Exception("MAIN_DATABASE_URL is not set")
-
-if not SAMPLE_DATABASE_URL:
-    raise Exception("SAMPLE_DATABASE_URL is not set")
-
-# =========================
-# ENGINE (SINGLETON)
-# =========================
 main_engine = create_engine(
-    MAIN_DATABASE_URL,
-    echo=False,
-
+    settings.main_database_connection_url,
     # ===== connection pool =====
     pool_size=10,            # 기본 유지 커넥션
     max_overflow=20,         # 추가 커넥션 허용
@@ -55,7 +18,7 @@
 )
 
 sample_engine = create_engine(
-    SAMPLE_DATABASE_URL,
+    settings.sample_database_connection_url,
     echo=False,
 
     # ===== connection pool =====
@@ -69,7 +32,6 @@
     # connect_args={"check_same_thread": False}  # SQLite일 때만
 )
 
-
 # =========================
 # SESSION FACTORY
 # =========================

From 11500e7d63861b50b68c119bc93cdb8a4775874f Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Fri, 10 Jul 2026 18:10:19 +0900
Subject: [PATCH 128/148] fix : env update

---
 app/ai_manual/service/manual_service.py | 1 +
 app/api/routers/manual.py               | 6 +++---
 app/core/config.py                      | 8 ++++----
 3 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/app/ai_manual/service/manual_service.py b/app/ai_manual/service/manual_service.py
index 8eb6fd5..27a2c82 100644
--- a/app/ai_manual/service/manual_service.py
+++ b/app/ai_manual/service/manual_service.py
@@ -35,6 +35,7 @@ def __init__(self):
             temperature=0.2
         ).with_structured_output(ManualResponse)
 
+
     # ======================================================
     # MAIN ENTRY
     # ======================================================
diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 34af027..1a5e6ea 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -2,7 +2,7 @@
 from app.ai_manual.service.manual_service import ManualService
 from fastapi import APIRouter, HTTPException, Header, Request
 from jose import jwt
-import os
+from app.core.config import settings
 
 router = APIRouter(
     prefix="/api/ai/manual",
@@ -28,8 +28,8 @@ def generate_manual(request: Request):
 
     payload = jwt.decode(
         token,
-        os.getenv("JWT_SECRET_KEY"),
-        algorithms=["HS384"]
+        settings.jwt_secret_key,
+        algorithms=[settings.jwt_algorithm]
     )
 
     print(payload)
diff --git a/app/core/config.py b/app/core/config.py
index 5b00707..3bc456d 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -103,13 +103,13 @@ class Settings(BaseSettings):
         alias="KAFKA_ANALYSIS_PRODUCER_LINGER_MS",
     )
 
-    jwt_secret_key: str | None = Field(
-        default=None,
+    jwt_secret_key: str = Field(
+        ...,
         alias="JWT_SECRET_KEY",
     )
 
     jwt_algorithm: str = Field(
-        default="HS512",
+        default="HS384",
         alias="JWT_ALGORITHM",
     )
 
@@ -217,4 +217,4 @@ def get_settings() -> Settings:
     return Settings()
 
 
-settings = get_settings()
+settings = get_settings()
\ No newline at end of file

From fa18934796114c7a1f9acfac2a56bfab2026cbdc Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Mon, 13 Jul 2026 10:41:18 +0900
Subject: [PATCH 129/148] feat : template update

---
 app/ai_manual/prompt/prompt_template.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/ai_manual/prompt/prompt_template.py b/app/ai_manual/prompt/prompt_template.py
index d21e1d3..3008ad3 100644
--- a/app/ai_manual/prompt/prompt_template.py
+++ b/app/ai_manual/prompt/prompt_template.py
@@ -40,7 +40,7 @@
 Error Code를 임의 생성하지 마십시오.
 
 6.
-반드시 단계별 조치 최소 3단계, 최대 5단계로 방법을 작성하십시오.
+반드시 단계별 조치 최소 3단계, 최대 5단계로 방법을 작성하고 각 단계별 글자 수는 30자로 제한하시오.
 
 7.
 반드시 안전 관련 주의사항을 포함하십시오.

From d1f7e712e347d75ca8c143fceca0a9ebe2ec1f95 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 13 Jul 2026 11:09:59 +0900
Subject: [PATCH 130/148] =?UTF-8?q?fix:=20AI=20=EB=B6=84=EC=84=9D=20?=
 =?UTF-8?q?=EC=A1=B0=ED=9A=8C=20ES-first=20=EB=B0=8F=20=EB=82=A0=EC=A7=9C?=
 =?UTF-8?q?=20=EC=98=B5=EC=85=98=20=EA=B0=9C=EC=84=A0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 병목 분석, 불량 전이 예측, SHAP 원인 분석 조회를 ES 우선 / DB fallback 구조로 정리

- dateOptions를 병목은 detected_at, 불량 전이는 predicted_at 기준으로 제공하도록 수정

- manufacturing_event_json 조인은 sampledb 연결을 사용하도록 변경하고 Redis는 ES 장애 시 응급 캐시로만 사용
---
 app/api/routers/defect_transfer.py            |  11 +
 app/api/routers/process.py                    |   6 +
 app/docs/__init__.py                          |   2 -
 app/dto/response/__init__.py                  |   2 +
 app/dto/response/bottleneck_response.py       |   6 +-
 app/dto/response/defect_transfer_response.py  |   4 +
 .../bottleneck_analysis_repository.py         |  88 ++++++-
 .../defect_transfer_prediction_repository.py  |  81 +++++--
 app/search/process_analysis_search.py         |  37 ++-
 app/service/analysis/bottleneck_service.py    | 123 +++++++---
 .../analysis/defect_transfer_service.py       | 224 ++++++++++++++----
 11 files changed, 477 insertions(+), 107 deletions(-)
 delete mode 100644 app/docs/__init__.py

diff --git a/app/api/routers/defect_transfer.py b/app/api/routers/defect_transfer.py
index ed88e16..81bf9d7 100644
--- a/app/api/routers/defect_transfer.py
+++ b/app/api/routers/defect_transfer.py
@@ -3,6 +3,7 @@
 from typing import Any
 
 from fastapi import APIRouter, Depends, Query
+from datetime import date as DateType
 
 from app.dto.response import CommonResponse
 from app.dto.response.defect_transfer_response import (
@@ -29,6 +30,10 @@
     summary="불량 전이 목록 조회",
 )
 def get_defect_transfer_predictions(
+    date: DateType | None = Query(
+        default=None,
+        description="조회할 날짜입니다. 미지정 시 최신 가능한 날짜를 사용합니다.",
+    ),
     cursor: int | None = Query(default=None, ge=0, examples=[0]),
     size: int = Query(default=5, ge=1, le=100, examples=[5]),
     service: DefectTransferAnalysisService = Depends(get_defect_transfer_analysis_service),
@@ -37,6 +42,7 @@ def get_defect_transfer_predictions(
         data=service.get_cached_predictions(
             cursor=cursor,
             size=size,
+            date=date,
         ),
         message="불량 전이 목록 조회가 완료되었습니다.",
     )
@@ -67,6 +73,10 @@ def get_defect_transfer_causes(
         alias="vehicleId",
         description="car_master.vehicle_id. If omitted, the latest vehicle is used.",
     ),
+    date: DateType | None = Query(
+        default=None,
+        description="조회할 날짜입니다. 미지정 시 최신 가능한 날짜를 사용합니다.",
+    ),
     cursor: int | None = Query(default=None, ge=0, examples=[0]),
     size: int = Query(default=5, ge=1, le=100, examples=[5]),
     service: DefectTransferAnalysisService = Depends(get_defect_transfer_analysis_service),
@@ -76,6 +86,7 @@ def get_defect_transfer_causes(
             vehicle_id=vehicle_id,
             cursor=cursor,
             size=size,
+            date=date,
         ),
         message="SHAP 기반 AI 원인 분석 조회가 완료되었습니다.",
     )
diff --git a/app/api/routers/process.py b/app/api/routers/process.py
index a220bc8..1d61990 100644
--- a/app/api/routers/process.py
+++ b/app/api/routers/process.py
@@ -1,4 +1,5 @@
 from fastapi import APIRouter, Depends, Query
+from datetime import date as DateType
 
 from app.dto.response import BottleneckAnalysisPage, CommonResponse
 from app.service.analysis.bottleneck_service import (
@@ -94,6 +95,10 @@
     },
 )
 def get_bottleneck_analysis(
+    date: DateType | None = Query(
+        default=None,
+        description="조회할 날짜입니다. 미지정 시 최신 날짜를 사용합니다.",
+    ),
     cursor: int | None = Query(
         default=None,
         ge=0,
@@ -116,6 +121,7 @@ def get_bottleneck_analysis(
     page: BottleneckAnalysisPage = service.get_cached_realtime_bottlenecks(
         cursor=cursor,
         size=size,
+        date=date,
     )
     return success_response(
         data=page,
diff --git a/app/docs/__init__.py b/app/docs/__init__.py
deleted file mode 100644
index 9597a6f..0000000
--- a/app/docs/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Docs package."""
-
diff --git a/app/dto/response/__init__.py b/app/dto/response/__init__.py
index fccf3b6..d7211fb 100644
--- a/app/dto/response/__init__.py
+++ b/app/dto/response/__init__.py
@@ -1,5 +1,6 @@
 """Response DTO package."""
 
+from app.dto.response.analysis_common import AnalysisDateOption
 from app.dto.response.bottleneck_response import (
     BottleneckAnalysisItem,
     BottleneckAnalysisPage,
@@ -13,6 +14,7 @@
 )
 
 __all__ = [
+    "AnalysisDateOption",
     "BottleneckAnalysisItem",
     "BottleneckAnalysisPage",
     "CommonResponse",
diff --git a/app/dto/response/bottleneck_response.py b/app/dto/response/bottleneck_response.py
index 7a5fa8f..c9c4b01 100644
--- a/app/dto/response/bottleneck_response.py
+++ b/app/dto/response/bottleneck_response.py
@@ -1,9 +1,11 @@
 from __future__ import annotations
 
-from datetime import datetime
+from datetime import date as DateType
 
 from pydantic import BaseModel, ConfigDict, Field
 
+from app.dto.response.analysis_common import AnalysisDateOption
+
 class BottleneckAnalysisItem(BaseModel):
     model_config = ConfigDict(populate_by_name=True)
 
@@ -20,6 +22,8 @@ class BottleneckAnalysisPage(BaseModel):
 
     most_bottleneck_process: str | None = Field(alias="mostBottleneckProcess")
     most_bottleneck_risk_level: str | None = Field(alias="mostBottleneckRiskLevel")
+    date: DateType | None = None
+    date_options: list[AnalysisDateOption] = Field(default_factory=list, alias="dateOptions")
     content: list[BottleneckAnalysisItem]
     has_next: bool = Field(alias="hasNext")
     next_cursor: int | None = Field(alias="nextCursor")
diff --git a/app/dto/response/defect_transfer_response.py b/app/dto/response/defect_transfer_response.py
index 2aed4ae..daa327f 100644
--- a/app/dto/response/defect_transfer_response.py
+++ b/app/dto/response/defect_transfer_response.py
@@ -5,6 +5,8 @@
 
 from pydantic import BaseModel, ConfigDict, Field
 
+from app.dto.response.analysis_common import AnalysisDateOption
+
 
 class DefectTransferPredictionItem(BaseModel):
     model_config = ConfigDict(populate_by_name=True)
@@ -25,6 +27,7 @@ class DefectTransferPredictionPage(BaseModel):
     date: DateType | None = None
     from_: datetime | None = Field(default=None, alias="from")
     to: datetime | None = None
+    date_options: list[AnalysisDateOption] = Field(default_factory=list, alias="dateOptions")
     has_next: bool = Field(alias="hasNext")
     next_cursor: int | None = Field(alias="nextCursor")
 
@@ -55,5 +58,6 @@ class DefectTransferCausePage(BaseModel):
     date: DateType | None = None
     from_: datetime | None = Field(default=None, alias="from")
     to: datetime | None = None
+    date_options: list[AnalysisDateOption] = Field(default_factory=list, alias="dateOptions")
     has_next: bool = Field(alias="hasNext")
     next_cursor: int | None = Field(alias="nextCursor")
diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 8bd1cd0..708d793 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -2,6 +2,7 @@
 
 import json
 from collections.abc import Iterable
+from datetime import date as DateType
 from datetime import datetime
 from typing import Any
 
@@ -68,8 +69,9 @@ def list_results(
         *,
         cursor: int,
         size: int,
+        analysis_date: DateType | None = None,
     ) -> list[dict[str, Any]]:
-        snapshot_detected_at = self._latest_snapshot_detected_at()
+        snapshot_detected_at = self._latest_snapshot_detected_at(analysis_date)
         if snapshot_detected_at is None:
             return []
 
@@ -104,8 +106,10 @@ def list_results(
 
     def count_results(
         self,
+        *,
+        analysis_date: DateType | None = None,
     ) -> int:
-        snapshot_detected_at = self._latest_snapshot_detected_at()
+        snapshot_detected_at = self._latest_snapshot_detected_at(analysis_date)
         if snapshot_detected_at is None:
             return 0
 
@@ -124,7 +128,11 @@ def count_results(
 
         return len(self.list_results(cursor=0, size=raw_count))
 
-    def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
+    def list_manufacturing_event_histories(
+        self,
+        *,
+        analysis_date: DateType | None = None,
+    ) -> list[dict[str, Any]]:
         from sqlalchemy import select, func
 
         query = (
@@ -138,7 +146,10 @@ def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
             )
             .where(manufacturing_event_json.c.dispatch_status == "SENT")
             .where(manufacturing_event_json.c.is_sent.is_(True))
-            .where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
+            .where(
+                func.date(manufacturing_event_json.c.event_time)
+                == (analysis_date or func.current_date())
+            )
             .order_by(manufacturing_event_json.c.id.asc())
         )
         with self.event_engine.connect() as conn:
@@ -148,7 +159,11 @@ def list_manufacturing_event_histories(self) -> list[dict[str, Any]]:
                 for row in rows
             ]
 
-    def list_pending_manufacturing_event_histories(self) -> list[dict[str, Any]]:
+    def list_pending_manufacturing_event_histories(
+        self,
+        *,
+        analysis_date: DateType | None = None,
+    ) -> list[dict[str, Any]]:
         from sqlalchemy import select, func
 
         query = (
@@ -163,7 +178,10 @@ def list_pending_manufacturing_event_histories(self) -> list[dict[str, Any]]:
             .where(manufacturing_event_json.c.dispatch_status == "SENT")
             .where(manufacturing_event_json.c.is_sent.is_(True))
             .where(manufacturing_event_json.c.bottleneck_analysis_done.is_(False))
-            .where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
+            .where(
+                func.date(manufacturing_event_json.c.event_time)
+                == (analysis_date or func.current_date())
+            )
             .order_by(manufacturing_event_json.c.id.asc())
         )
         with self.event_engine.connect() as conn:
@@ -173,6 +191,52 @@ def list_pending_manufacturing_event_histories(self) -> list[dict[str, Any]]:
                 for row in rows
             ]
 
+    def list_date_options(self) -> list[dict[str, Any]]:
+        from sqlalchemy import func, select
+
+        query = (
+            select(
+                func.date(self.table.c.detected_at).label("date"),
+                func.min(self.table.c.manufacturing_event_id).label(
+                    "sample_manufacturing_event_id",
+                ),
+            )
+            .where(self.table.c.detected_at.is_not(None))
+            .group_by(func.date(self.table.c.detected_at))
+            .order_by(func.date(self.table.c.detected_at).desc())
+        )
+        with self.engine.connect() as conn:
+            date_rows = [dict(row) for row in conn.execute(query).mappings()]
+
+        sample_event_ids = sorted(
+            {
+                int(row["sample_manufacturing_event_id"])
+                for row in date_rows
+                if row.get("sample_manufacturing_event_id") is not None
+            },
+        )
+        event_id_by_id: dict[int, str] = {}
+        if sample_event_ids:
+            event_query = select(
+                manufacturing_event_json.c.id,
+                manufacturing_event_json.c.event_id,
+            ).where(manufacturing_event_json.c.id.in_(sample_event_ids))
+            with self.event_engine.connect() as conn:
+                for event_row in conn.execute(event_query).mappings():
+                    event_id_by_id[int(event_row["id"])] = str(event_row["event_id"])
+
+        return [
+            {
+                "date": row["date"],
+                "sample_event_id": (
+                    event_id_by_id.get(int(row["sample_manufacturing_event_id"]))
+                    if row.get("sample_manufacturing_event_id") is not None
+                    else None
+                ),
+            }
+            for row in date_rows
+        ]
+
     def _event_ids_for_today(self) -> list[int]:
         from sqlalchemy import func, select
 
@@ -185,13 +249,15 @@ def _event_ids_for_today(self) -> list[int]:
         with self.event_engine.connect() as conn:
             return [int(row[0]) for row in conn.execute(query).all()]
 
-    def _latest_snapshot_detected_at(self) -> datetime | None:
+    def _latest_snapshot_detected_at(
+        self,
+        analysis_date: DateType | None = None,
+    ) -> datetime | None:
         from sqlalchemy import func, select
 
-        query = (
-            select(func.max(self.table.c.detected_at))
-            .where(func.date(self.table.c.detected_at) == func.current_date())
-        )
+        query = select(func.max(self.table.c.detected_at))
+        if analysis_date is not None:
+            query = query.where(func.date(self.table.c.detected_at) == analysis_date)
         with self.engine.connect() as conn:
             value = conn.execute(query).scalar_one()
         return value
diff --git a/app/repository/defect_transfer_prediction_repository.py b/app/repository/defect_transfer_prediction_repository.py
index 366dfb0..2aeb95f 100644
--- a/app/repository/defect_transfer_prediction_repository.py
+++ b/app/repository/defect_transfer_prediction_repository.py
@@ -106,8 +106,9 @@ def list_prediction_page(
         *,
         cursor: int,
         size: int,
+        analysis_date: date | None = None,
     ) -> tuple[list[dict[str, Any]], bool]:
-        rows = self._all_prediction_rows()
+        rows = self._all_prediction_rows(analysis_date=analysis_date)
         latest_by_car: dict[int, dict[str, Any]] = {}
         for row in rows:
             car_id = int(row["car_master_id"])
@@ -141,10 +142,12 @@ def list_cause_page(
         vehicle_id: str | None,
         cursor: int,
         size: int,
+        analysis_date: date | None = None,
     ) -> tuple[dict[str, Any] | None, list[dict[str, Any]], bool]:
         car_master_id = self._car_master_id(vehicle_id) if vehicle_id else None
         rows = self._all_prediction_rows(
             car_master_id=car_master_id,
+            analysis_date=analysis_date,
         )
         if not rows:
             return None, [], False
@@ -173,22 +176,51 @@ def list_date_options(self, *, vehicle_id: str | None = None) -> list[dict[str,
         from sqlalchemy import func, select
 
         car_master_id = self._car_master_id(vehicle_id) if vehicle_id else None
-        event_ids = self._prediction_event_ids(car_master_id=car_master_id)
-        if not event_ids:
-            return []
 
         query = (
             select(
-                func.date(manufacturing_event_json.c.event_time).label("date"),
-                func.min(manufacturing_event_json.c.event_id).label("sample_event_id"),
+                func.date(self.table.c.predicted_at).label("date"),
+                func.min(self.table.c.manufacturing_event_id).label(
+                    "sample_manufacturing_event_id",
+                ),
             )
-            .where(manufacturing_event_json.c.id.in_(event_ids))
-            .where(manufacturing_event_json.c.event_time.is_not(None))
-            .group_by(func.date(manufacturing_event_json.c.event_time))
-            .order_by(func.date(manufacturing_event_json.c.event_time).desc())
+            .where(self.table.c.predicted_at.is_not(None))
+            .group_by(func.date(self.table.c.predicted_at))
+            .order_by(func.date(self.table.c.predicted_at).desc())
         )
-        with self.event_engine.connect() as conn:
-            return [dict(row) for row in conn.execute(query).mappings()]
+        if car_master_id is not None:
+            query = query.where(self.table.c.car_master_id == car_master_id)
+        with self.engine.connect() as conn:
+            date_rows = [dict(row) for row in conn.execute(query).mappings()]
+
+        sample_event_ids = sorted(
+            {
+                int(row["sample_manufacturing_event_id"])
+                for row in date_rows
+                if row.get("sample_manufacturing_event_id") is not None
+            },
+        )
+        event_id_by_id: dict[int, str] = {}
+        if sample_event_ids:
+            event_query = select(
+                manufacturing_event_json.c.id,
+                manufacturing_event_json.c.event_id,
+            ).where(manufacturing_event_json.c.id.in_(sample_event_ids))
+            with self.event_engine.connect() as conn:
+                for event_row in conn.execute(event_query).mappings():
+                    event_id_by_id[int(event_row["id"])] = str(event_row["event_id"])
+
+        return [
+            {
+                "date": row["date"],
+                "sample_event_id": (
+                    event_id_by_id.get(int(row["sample_manufacturing_event_id"]))
+                    if row.get("sample_manufacturing_event_id") is not None
+                    else None
+                ),
+            }
+            for row in date_rows
+        ]
 
     def diagnostics(self) -> dict[str, Any]:
         from sqlalchemy import distinct, func, select
@@ -278,18 +310,15 @@ def _all_prediction_rows(
         self,
         *,
         car_master_id: int | None = None,
+        analysis_date: date | None = None,
     ) -> list[dict[str, Any]]:
-        from sqlalchemy import select
-
-        event_ids = self._prediction_event_ids(car_master_id=car_master_id)
-        if not event_ids:
-            return []
+        from sqlalchemy import func, select
 
-        query = select(self.table).where(
-            self.table.c.manufacturing_event_id.in_(event_ids),
-        )
+        query = select(self.table)
         if car_master_id is not None:
             query = query.where(self.table.c.car_master_id == car_master_id)
+        if analysis_date is not None:
+            query = query.where(func.date(self.table.c.predicted_at) == analysis_date)
         query = query.order_by(
             self.table.c.predicted_at.desc(),
             self.table.c.id.desc(),
@@ -326,15 +355,23 @@ def _car_master_id(self, vehicle_id: str | None) -> int | None:
             value = conn.execute(query).scalar()
         return int(value) if value is not None else None
 
-    def _prediction_event_ids(self, *, car_master_id: int | None = None) -> list[int]:
+    def _prediction_event_ids(
+        self,
+        *,
+        car_master_id: int | None = None,
+        analysis_date: date | None = None,
+    ) -> list[int]:
         from sqlalchemy import func, select
 
         query = (
             select(manufacturing_event_json.c.id)
             .where(manufacturing_event_json.c.dispatch_status == "SENT")
             .where(manufacturing_event_json.c.is_sent.is_(True))
-            .where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
         )
+        if analysis_date is None:
+            query = query.where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
+        else:
+            query = query.where(func.date(manufacturing_event_json.c.event_time) == analysis_date)
         if car_master_id is not None:
             query = query.where(manufacturing_event_json.c.car_master_id == car_master_id)
         with self.event_engine.connect() as conn:
diff --git a/app/search/process_analysis_search.py b/app/search/process_analysis_search.py
index d037e45..14fce45 100644
--- a/app/search/process_analysis_search.py
+++ b/app/search/process_analysis_search.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import logging
+from datetime import date as DateType
 from datetime import datetime
 from typing import Any
 from urllib.parse import urlparse
@@ -149,8 +150,11 @@ def list_bottleneck_page(
         *,
         cursor: int,
         size: int,
+        analysis_date: DateType | None = None,
     ) -> tuple[list[dict[str, Any]], bool]:
-        latest_snapshot_id = self._latest_bottleneck_snapshot_id()
+        latest_snapshot_id = self._latest_bottleneck_snapshot_id(
+            analysis_date=analysis_date,
+        )
         if not latest_snapshot_id:
             return [], False
 
@@ -210,6 +214,7 @@ def list_defect_prediction_page(
         *,
         cursor: int,
         size: int,
+        analysis_date: DateType | None = None,
     ) -> tuple[list[dict[str, Any]], bool]:
         page = max(cursor, 0)
         safe_size = max(1, min(size, 100))
@@ -226,6 +231,8 @@ def list_defect_prediction_page(
             "size": safe_size + 1,
             "track_total_hits": True,
         }
+        if analysis_date is not None:
+            query["query"] = self._date_range_query("predictedAt", analysis_date)
         response = self.client.search(
             index=settings.elasticsearch_defect_transfer_index,
             body=query,
@@ -241,6 +248,7 @@ def get_latest_defect_cause_document(
         self,
         *,
         vehicle_id: str | None,
+        analysis_date: DateType | None = None,
     ) -> dict[str, Any] | None:
         query: dict[str, Any] = {
             "size": 1,
@@ -253,6 +261,13 @@ def get_latest_defect_cause_document(
             query["query"] = {"term": {"vehicleId": vehicle_id}}
         else:
             query["query"] = {"match_all": {}}
+        if analysis_date is not None:
+            query["query"] = {
+                "bool": {
+                    "must": [query["query"]],
+                    "filter": [self._date_range_query("predictedAt", analysis_date)],
+                },
+            }
 
         response = self.client.search(
             index=settings.elasticsearch_defect_transfer_index,
@@ -263,7 +278,11 @@ def get_latest_defect_cause_document(
             return None
         return self._map_defect_source(hits[0].get("_source") or {})
 
-    def _latest_bottleneck_snapshot_id(self) -> str | None:
+    def _latest_bottleneck_snapshot_id(
+        self,
+        *,
+        analysis_date: DateType | None = None,
+    ) -> str | None:
         query = {
             "size": 1,
             "query": {"match_all": {}},
@@ -273,6 +292,8 @@ def _latest_bottleneck_snapshot_id(self) -> str | None:
                 {"syncId": {"order": "desc"}},
             ],
         }
+        if analysis_date is not None:
+            query["query"] = self._date_range_query("detectedAt", analysis_date)
         response = self.client.search(
             index=settings.elasticsearch_bottleneck_index,
             body=query,
@@ -284,6 +305,18 @@ def _latest_bottleneck_snapshot_id(self) -> str | None:
         snapshot_id = source.get("snapshotId")
         return str(snapshot_id) if snapshot_id is not None else None
 
+    @staticmethod
+    def _date_range_query(field: str, analysis_date: DateType) -> dict[str, Any]:
+        next_date = analysis_date.fromordinal(analysis_date.toordinal() + 1)
+        return {
+            "range": {
+                field: {
+                    "gte": analysis_date.isoformat(),
+                    "lt": next_date.isoformat(),
+                },
+            },
+        }
+
     def _bulk_index(self, actions: list[dict[str, Any]]) -> None:
         if not actions:
             return
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 5a168a5..40a6120 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -3,6 +3,7 @@
 import logging
 import re
 from collections import Counter
+from datetime import date as DateType
 from datetime import datetime
 from pathlib import Path
 from typing import Any
@@ -11,7 +12,11 @@
 
 from app.core.config import settings
 from app.core.exceptions import AppException
-from app.dto.response import BottleneckAnalysisItem, BottleneckAnalysisPage
+from app.dto.response import (
+    AnalysisDateOption,
+    BottleneckAnalysisItem,
+    BottleneckAnalysisPage,
+)
 from app.ml.inference.bottleneck_detector import BottleneckDetector
 from app.repository.bottleneck_analysis_repository import BottleneckAnalysisRepository
 from app.search.process_analysis_search import ProcessAnalysisSearchRepository
@@ -20,7 +25,7 @@
 
 
 DEFAULT_BOTTLENECK_MODEL_PATH = Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl")
-BOTTLENECK_CACHE_VERSION = "v13"
+BOTTLENECK_CACHE_VERSION = "v14"
 PROCESS_CODE_LABELS = {
     "PRESS": "프레스",
     "BODY": "차체",
@@ -59,9 +64,17 @@ def get_realtime_bottlenecks(
         *,
         cursor: int | None,
         size: int,
+        date: DateType | None = None,
     ) -> BottleneckAnalysisPage:
         size = max(1, min(size, 100))
         page = max(cursor or 0, 0)
+        cache_key = self._bottleneck_cache_key(
+            cursor=cursor,
+            size=size,
+            date=date,
+        )
+        date_options = self._safe_get_date_options()
+        selected_date = self._resolve_date(date, date_options)
         rows: list[dict[str, Any]]
         has_next = False
         if self.search_repository is not None:
@@ -69,24 +82,39 @@ def get_realtime_bottlenecks(
                 rows, has_next = self.search_repository.list_bottleneck_page(
                     cursor=page,
                     size=size,
+                    analysis_date=selected_date,
                 )
             except Exception:
                 logger.exception("Elasticsearch bottleneck query failed. Falling back to DB.")
+                cached_value = self._cache_get(cache_key)
+                if cached_value:
+                    return BottleneckAnalysisPage.model_validate(from_json(cached_value))
                 rows = self.repository.list_results(
                     cursor=page,
                     size=size,
+                    analysis_date=selected_date,
                 )
-                has_next = self.repository.count_results() > (page + 1) * size
+                has_next = self.repository.count_results(
+                    analysis_date=selected_date,
+                ) > (page + 1) * size
         else:
+            cached_value = self._cache_get(cache_key)
+            if cached_value:
+                return BottleneckAnalysisPage.model_validate(from_json(cached_value))
             rows = self.repository.list_results(
                 cursor=page,
                 size=size,
+                analysis_date=selected_date,
             )
-            has_next = self.repository.count_results() > (page + 1) * size
+            has_next = self.repository.count_results(
+                analysis_date=selected_date,
+            ) > (page + 1) * size
         if not rows:
             return BottleneckAnalysisPage(
                 mostBottleneckProcess=None,
                 mostBottleneckRiskLevel=None,
+                date=selected_date,
+                dateOptions=date_options,
                 content=[],
                 hasNext=False,
                 nextCursor=None,
@@ -97,6 +125,8 @@ def get_realtime_bottlenecks(
         return BottleneckAnalysisPage(
             mostBottleneckProcess=self._format_process_label(top_row["process_code"]),
             mostBottleneckRiskLevel=self._risk_level_label(float(top_row["risk_score"])),
+            date=selected_date,
+            dateOptions=date_options,
             content=[
                 BottleneckAnalysisItem(
                     rankNo=int(row["rank_no"]),
@@ -120,39 +150,19 @@ def get_cached_realtime_bottlenecks(
         *,
         cursor: int | None,
         size: int,
+        date: DateType | None = None,
     ) -> BottleneckAnalysisPage:
         cache_key = self._bottleneck_cache_key(
             cursor=cursor,
             size=size,
+            date=date,
         )
-        try:
-            redis_client = self._redis()
-            cached_value = redis_client.get(cache_key)
-            if cached_value:
-                return BottleneckAnalysisPage.model_validate(from_json(cached_value))
-        except Exception as exc:
-            self._redis_client = None
-            raise AppException(
-                "Redis cache lookup failed.",
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            ) from exc
-
         page = self.get_realtime_bottlenecks(
             cursor=cursor,
             size=size,
+            date=date,
         )
-        try:
-            self._redis().setex(
-                cache_key,
-                settings.redis_cache_ttl_seconds,
-                to_json(page.model_dump(by_alias=False)),
-            )
-        except Exception as exc:
-            self._redis_client = None
-            raise AppException(
-                "Redis cache write failed.",
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            ) from exc
+        self._cache_set(cache_key, page.model_dump(by_alias=False))
         return page
 
     def run_analysis_and_save(self, *, cursor: int, size: int) -> tuple[int, bool]:
@@ -205,17 +215,41 @@ def _redis(self) -> Any:
             )
         return self._redis_client
 
+    def _cache_get(self, cache_key: str) -> str | None:
+        if not settings.redis_url:
+            return None
+        try:
+            return self._redis().get(cache_key)
+        except Exception:
+            self._redis_client = None
+            logger.exception("Bottleneck Redis cache lookup failed.")
+            return None
+
+    def _cache_set(self, cache_key: str, data: dict[str, Any]) -> None:
+        if not settings.redis_url:
+            return
+        try:
+            self._redis().setex(
+                cache_key,
+                settings.redis_cache_ttl_seconds,
+                to_json(data),
+            )
+        except Exception:
+            self._redis_client = None
+            logger.exception("Bottleneck Redis cache write failed.")
+
     def _bottleneck_cache_key(
         self,
         *,
         cursor: int | None,
         size: int,
+        date: DateType | None,
     ) -> str:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
         return (
             f"{settings.redis_key_prefix}:process:bottleneck:"
-            f"{BOTTLENECK_CACHE_VERSION}:{page}:{safe_size}"
+            f"{BOTTLENECK_CACHE_VERSION}:{self._safe_cache_part(date.isoformat() if date else None)}:{page}:{safe_size}"
         )
 
     @staticmethod
@@ -254,6 +288,37 @@ def _current_bottleneck_results(self) -> list[dict[str, Any]]:
             size=total_count,
         )
 
+    def _get_date_options(self) -> list[AnalysisDateOption]:
+        options = self.repository.list_date_options()
+        return [
+            AnalysisDateOption.model_validate(
+                {
+                    "date": row["date"],
+                    "sampleEventId": row.get("sample_event_id"),
+                },
+            )
+            for row in options
+            if row.get("date") is not None
+        ]
+
+    def _safe_get_date_options(self) -> list[AnalysisDateOption]:
+        try:
+            return self._get_date_options()
+        except Exception:
+            logger.exception("Failed to load bottleneck date options.")
+            return []
+
+    @staticmethod
+    def _resolve_date(
+        requested_date: DateType | None,
+        date_options: list[AnalysisDateOption],
+    ) -> DateType | None:
+        if requested_date is not None:
+            return requested_date
+        if date_options:
+            return date_options[0].date
+        return None
+
     @staticmethod
     def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
         if not settings.elasticsearch_url:
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index 546d676..fdc915b 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -2,6 +2,7 @@
 
 import logging
 from functools import lru_cache
+from datetime import date as DateType
 from typing import Any
 
 from fastapi import status
@@ -9,6 +10,7 @@
 
 from app.core.config import settings
 from app.core.exceptions import AppException
+from app.dto.response import AnalysisDateOption
 from app.dto.response.defect_transfer_response import (
     DefectTransferCauseItem,
     DefectTransferCausePage,
@@ -23,7 +25,7 @@
 from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
 
 
-DEFECT_TRANSFER_CACHE_VERSION = "v8"
+DEFECT_TRANSFER_CACHE_VERSION = "v10"
 logger = logging.getLogger(__name__)
 
 
@@ -54,20 +56,17 @@ def get_cached_predictions(
         *,
         cursor: int | None,
         size: int,
+        date: DateType | None = None,
     ) -> DefectTransferPredictionPage:
         cache_key = self._prediction_cache_key(
             cursor=cursor,
             size=size,
+            date=date,
         )
-        cached_value = self._cache_get(cache_key)
-        if cached_value:
-            cached_page = DefectTransferPredictionPage.model_validate(from_json(cached_value))
-            if cached_page.content:
-                return cached_page
-
         page = self.get_predictions(
             cursor=cursor,
             size=size,
+            date=date,
         )
         self._cache_set(cache_key, page.model_dump(by_alias=False))
         return page
@@ -78,22 +77,19 @@ def get_cached_cause_analysis(
         vehicle_id: str | None,
         cursor: int | None,
         size: int,
+        date: DateType | None = None,
     ) -> DefectTransferCausePage:
         cache_key = self._cause_cache_key(
             vehicle_id=vehicle_id,
             cursor=cursor,
             size=size,
+            date=date,
         )
-        cached_value = self._cache_get(cache_key)
-        if cached_value:
-            cached_page = DefectTransferCausePage.model_validate(from_json(cached_value))
-            if cached_page.content:
-                return cached_page
-
         page = self.get_cause_analysis(
             vehicle_id=vehicle_id,
             cursor=cursor,
             size=size,
+            date=date,
         )
         self._cache_set(cache_key, page.model_dump(by_alias=False))
         return page
@@ -119,9 +115,17 @@ def get_predictions(
         *,
         cursor: int | None,
         size: int,
+        date: DateType | None = None,
     ) -> DefectTransferPredictionPage:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
+        cache_key = self._prediction_cache_key(
+            cursor=cursor,
+            size=size,
+            date=date,
+        )
+        date_options = self._safe_get_date_options()
+        selected_date = self._resolve_date(date, date_options)
         rows: list[dict[str, Any]]
         has_next = False
         if self.search_repository is not None:
@@ -129,27 +133,42 @@ def get_predictions(
                 rows, has_next = self.search_repository.list_defect_prediction_page(
                     cursor=page,
                     size=safe_size,
+                    analysis_date=selected_date,
                 )
             except Exception:
                 logger.exception("Elasticsearch defect prediction query failed. Falling back to DB.")
+                cached_value = self._cache_get(cache_key)
+                if cached_value:
+                    cached_page = DefectTransferPredictionPage.model_validate(from_json(cached_value))
+                    if cached_page.content:
+                        return cached_page
                 try:
                     rows, has_next = self.repository.list_prediction_page(
                         cursor=page,
                         size=safe_size,
+                        analysis_date=selected_date,
                     )
                 except SQLAlchemyError as exc:
                     raise self._db_exception() from exc
         else:
+            cached_value = self._cache_get(cache_key)
+            if cached_value:
+                cached_page = DefectTransferPredictionPage.model_validate(from_json(cached_value))
+                if cached_page.content:
+                    return cached_page
             try:
                 rows, has_next = self.repository.list_prediction_page(
                     cursor=page,
                     size=safe_size,
+                    analysis_date=selected_date,
                 )
             except SQLAlchemyError as exc:
                 raise self._db_exception() from exc
 
         return DefectTransferPredictionPage(
             content=[self._to_prediction_item(row) for row in rows],
+            date=selected_date,
+            dateOptions=date_options,
             hasNext=has_next,
             nextCursor=page + 1 if has_next else None,
         )
@@ -188,34 +207,53 @@ def get_cause_analysis(
         vehicle_id: str | None,
         cursor: int | None,
         size: int,
+        date: DateType | None = None,
     ) -> DefectTransferCausePage:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
+        cache_key = self._cause_cache_key(
+            vehicle_id=vehicle_id,
+            cursor=cursor,
+            size=size,
+            date=date,
+        )
+        date_options = self._safe_get_date_options(vehicle_id=vehicle_id)
+        selected_date = self._resolve_date(date, date_options)
         selected: dict[str, Any] | None
-        rows: list[dict[str, Any]]
-        has_next = False
         if self.search_repository is not None:
             try:
                 selected = self.search_repository.get_latest_defect_cause_document(
                     vehicle_id=vehicle_id,
+                    analysis_date=selected_date,
                 )
-                rows = [selected] if selected is not None else []
             except Exception:
                 logger.exception("Elasticsearch defect cause query failed. Falling back to DB.")
+                cached_value = self._cache_get(cache_key)
+                if cached_value:
+                    cached_page = DefectTransferCausePage.model_validate(from_json(cached_value))
+                    if cached_page.content:
+                        return cached_page
                 try:
-                    selected, rows, has_next = self.repository.list_cause_page(
+                    selected, _rows, _has_next = self.repository.list_cause_page(
                         vehicle_id=vehicle_id,
                         cursor=page,
                         size=safe_size,
+                        analysis_date=selected_date,
                     )
                 except SQLAlchemyError as exc:
                     raise self._db_exception() from exc
         else:
+            cached_value = self._cache_get(cache_key)
+            if cached_value:
+                cached_page = DefectTransferCausePage.model_validate(from_json(cached_value))
+                if cached_page.content:
+                    return cached_page
             try:
-                selected, rows, has_next = self.repository.list_cause_page(
+                selected, _rows, _has_next = self.repository.list_cause_page(
                     vehicle_id=vehicle_id,
                     cursor=page,
                     size=safe_size,
+                    analysis_date=selected_date,
                 )
             except SQLAlchemyError as exc:
                 raise self._db_exception() from exc
@@ -229,16 +267,18 @@ def get_cause_analysis(
                 currentProcess=None,
                 predictedDefectProcess=None,
                 transferProbability=None,
+                date=selected_date,
+                dateOptions=date_options,
                 content=[],
                 hasNext=False,
                 nextCursor=None,
             )
 
+        cause_rows = self._build_cause_rows(selected)
+
         offset = page * safe_size
-        paged_rows = rows[offset : offset + safe_size]
-        display_rows = [row for row in paged_rows if self._is_displayable_cause(row)]
-        if has_next is False:
-            has_next = len(rows) > offset + safe_size
+        display_rows = cause_rows[offset : offset + safe_size]
+        has_next = len(cause_rows) > offset + safe_size
 
         return DefectTransferCausePage(
             vehicleId=str(selected.get("vehicle_id")),
@@ -251,6 +291,8 @@ def get_cause_analysis(
             ),
             predictedDefectProcess=self._resolve_predicted_defect_process(selected),
             transferProbability=self._percent(selected.get("target_defect_probability")),
+            date=selected_date,
+            dateOptions=date_options,
             content=[
                 self._to_cause_item(
                     row,
@@ -358,6 +400,66 @@ def _primary_cause_message(row: dict[str, Any]) -> str:
 
         return ""
 
+    @classmethod
+    def _build_cause_rows(cls, row: dict[str, Any]) -> list[dict[str, Any]]:
+        main_causes = cls._normalize_main_causes(row.get("main_causes") or row.get("causes"))
+        if main_causes:
+            return [
+                {
+                    "rank": cause.get("rank") or index,
+                    "feature": cause.get("feature") or "main_causes",
+                    "label": cause.get("label") or cause.get("message") or "",
+                    "value": cause.get("value") or "",
+                    "impact": cause.get("impact") or 0.0,
+                    "message": cause.get("message") or cause.get("label") or "",
+                    "main_causes": main_causes,
+                }
+                for index, cause in enumerate(main_causes, start=1)
+            ]
+
+        summary_message = cls._primary_cause_message(row)
+        if not summary_message:
+            summary_message = "no main cause available"
+        return [
+            {
+                "rank": 1,
+                "feature": "main_causes",
+                "label": summary_message,
+                "value": "",
+                "impact": float(row.get("influence_score") or 0.0),
+                "message": summary_message,
+                "main_causes": [],
+            },
+        ]
+
+    @staticmethod
+    def _normalize_main_causes(causes: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        normalized: list[dict[str, Any]] = []
+        for cause in causes[:5]:
+            message = str(cause.get("message") or cause.get("label") or "").strip()
+            if not message:
+                continue
+            try:
+                impact = float(cause.get("impact") or 0.0)
+            except (TypeError, ValueError):
+                impact = 0.0
+            normalized.append(
+                {
+                    "message": message,
+                    "impact": impact,
+                },
+            )
+
+        if normalized:
+            return normalized
+
+        return [
+            {
+                "message": "no main cause available",
+                "impact": 0.0,
+            },
+        ]
+
     @classmethod
     def _to_cause_item(
         cls,
@@ -385,13 +487,19 @@ def _to_cause_item(
                     },
                 )
 
+        message = str(row.get("message") or row.get("label") or "").strip()
+        if not message:
+            message = cls._primary_cause_message(row)
+        if not message:
+            message = "no main cause available"
+
         return DefectTransferCauseItem(
-            rank=rank,
-            feature="main_causes",
-            label=cls._primary_cause_message(row),
-            value="",
-            impact=float(row.get("influence_score") or 0.0),
-            message=cls._primary_cause_message(row),
+            rank=int(row.get("rank") or rank),
+            feature=str(row.get("feature") or "main_causes"),
+            label=str(row.get("label") or message),
+            value=str(row.get("value") or ""),
+            impact=float(row.get("impact") or 0.0),
+            message=message,
             main_causes=normalized_main_causes,
         )
 
@@ -426,12 +534,10 @@ def _cache_get(self, cache_key: str) -> str | None:
             return None
         try:
             return self._redis().get(cache_key)
-        except Exception as exc:
+        except Exception:
             self._redis_client = None
-            raise AppException(
-                "Defect transfer Redis cache lookup failed.",
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            ) from exc
+            logger.exception("Defect transfer Redis cache lookup failed.")
+            return None
 
     def _cache_set(self, cache_key: str, data: dict[str, Any]) -> None:
         if not settings.redis_url:
@@ -442,12 +548,9 @@ def _cache_set(self, cache_key: str, data: dict[str, Any]) -> None:
                 settings.redis_cache_ttl_seconds,
                 to_json(data),
             )
-        except Exception as exc:
+        except Exception:
             self._redis_client = None
-            raise AppException(
-                "Defect transfer Redis cache write failed.",
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            ) from exc
+            logger.exception("Defect transfer Redis cache write failed.")
 
     def _redis(self) -> Any:
         if self._redis_client is None:
@@ -472,12 +575,13 @@ def _prediction_cache_key(
         *,
         cursor: int | None,
         size: int,
+        date: DateType | None,
     ) -> str:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
         return (
             f"{settings.redis_key_prefix}:process:defect-transfer:"
-            f"predictions:{DEFECT_TRANSFER_CACHE_VERSION}:{page}:{safe_size}"
+            f"predictions:{DEFECT_TRANSFER_CACHE_VERSION}:{self._safe_cache_part(date.isoformat() if date else None)}:{page}:{safe_size}"
         )
 
     def _cause_cache_key(
@@ -486,14 +590,54 @@ def _cause_cache_key(
         vehicle_id: str | None,
         cursor: int | None,
         size: int,
+        date: DateType | None,
     ) -> str:
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
         return (
             f"{settings.redis_key_prefix}:process:defect-transfer:"
-            f"causes:{DEFECT_TRANSFER_CACHE_VERSION}:{self._safe_cache_part(vehicle_id)}:{page}:{safe_size}"
+            f"causes:{DEFECT_TRANSFER_CACHE_VERSION}:{self._safe_cache_part(vehicle_id)}:{self._safe_cache_part(date.isoformat() if date else None)}:{page}:{safe_size}"
         )
 
+    def _get_date_options(
+        self,
+        *,
+        vehicle_id: str | None = None,
+    ) -> list[AnalysisDateOption]:
+        options = self.repository.list_date_options(vehicle_id=vehicle_id)
+        return [
+            AnalysisDateOption.model_validate(
+                {
+                    "date": row["date"],
+                    "sampleEventId": row.get("sample_event_id"),
+                },
+            )
+            for row in options
+            if row.get("date") is not None
+        ]
+
+    def _safe_get_date_options(
+        self,
+        *,
+        vehicle_id: str | None = None,
+    ) -> list[AnalysisDateOption]:
+        try:
+            return self._get_date_options(vehicle_id=vehicle_id)
+        except Exception:
+            logger.exception("Failed to load defect transfer date options.")
+            return []
+
+    @staticmethod
+    def _resolve_date(
+        requested_date: DateType | None,
+        date_options: list[AnalysisDateOption],
+    ) -> DateType | None:
+        if requested_date is not None:
+            return requested_date
+        if date_options:
+            return date_options[0].date
+        return None
+
 
 @lru_cache(maxsize=1)
 def get_defect_transfer_analysis_service() -> DefectTransferAnalysisService:

From 40c80d3392e42c9bbd92e7f374acee6fda2cd989 Mon Sep 17 00:00:00 2001
From: haseokyung6 <haseokyung@naver.com>
Date: Mon, 13 Jul 2026 12:27:03 +0900
Subject: [PATCH 131/148] fix: update secret key

---
 .github/workflows/deploy-ai-service.yml | 74 +++++++++++++++++--------
 1 file changed, 50 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/deploy-ai-service.yml b/.github/workflows/deploy-ai-service.yml
index c2d7370..09fa6a2 100644
--- a/.github/workflows/deploy-ai-service.yml
+++ b/.github/workflows/deploy-ai-service.yml
@@ -56,14 +56,19 @@ jobs:
 
       - name: Ensure ECR repository exists
         run: |
-          aws ecr describe-repositories \
+          set -euo pipefail
+
+          if ! aws ecr describe-repositories \
             --repository-names "$ECR_REPOSITORY" \
-            --region "$AWS_REGION" >/dev/null 2>&1 \
-          || aws ecr create-repository \
-            --repository-name "$ECR_REPOSITORY" \
             --region "$AWS_REGION" \
-            --image-scanning-configuration scanOnPush=true \
-            --image-tag-mutability MUTABLE
+            > /dev/null 2>&1; then
+
+            aws ecr create-repository \
+              --repository-name "$ECR_REPOSITORY" \
+              --region "$AWS_REGION" \
+              --image-scanning-configuration scanOnPush=true \
+              --image-tag-mutability MUTABLE
+          fi
 
       - name: Build and push Docker image
         id: build-image
@@ -75,6 +80,8 @@ jobs:
           IMAGE_URI="${{ steps.login-ecr.outputs.registry }}/${ECR_REPOSITORY}:${IMAGE_TAG}"
           BRANCH_IMAGE_URI="${{ steps.login-ecr.outputs.registry }}/${ECR_REPOSITORY}:${GITHUB_REF_NAME}"
 
+          echo "Building image: $IMAGE_URI"
+
           docker build \
             -t "$IMAGE_URI" \
             -t "$BRANCH_IMAGE_URI" \
@@ -84,12 +91,13 @@ jobs:
           docker push "$BRANCH_IMAGE_URI"
 
           echo "image_uri=$IMAGE_URI" >> "$GITHUB_OUTPUT"
+          echo "image_tag=$IMAGE_TAG" >> "$GITHUB_OUTPUT"
 
       - name: Checkout infra repository
         uses: actions/checkout@v4
         with:
           repository: ${{ env.INFRA_REPOSITORY }}
-          ref: ${{ github.ref_name }}
+          ref: ${{ env.INFRA_BRANCH }}
           token: ${{ secrets.INFRA_REPO_TOKEN }}
           path: infra
           fetch-depth: 0
@@ -106,10 +114,10 @@ jobs:
 
           kubectl create namespace "$NAMESPACE" \
             --dry-run=client \
-            -o yaml | kubectl apply -f -
+            -o yaml |
+          kubectl apply -f -
 
-      # 변경: DB 이름과 Redis 정보를 추가로 조회
-      - name: Load AI service parameters from SSM
+      - name: Load AI service runtime parameters from SSM
         if: github.ref_name == 'dev'
         shell: bash
         run: |
@@ -157,6 +165,13 @@ jobs:
             --query "Parameter.Value" \
             --output text)
 
+          JWT_SECRET_KEY=$(aws ssm get-parameter \
+            --name "/aims/dev/backend/jwt-secret-key" \
+            --region "$AWS_REGION" \
+            --with-decryption \
+            --query "Parameter.Value" \
+            --output text)
+
           OPENAI_API_KEY=$(aws ssm get-parameter \
             --name "/aims/dev/ai-service/openai-api-key" \
             --region "$AWS_REGION" \
@@ -172,15 +187,17 @@ jobs:
             "$SAMPLE_DB_NAME" \
             "$REDIS_HOST" \
             "$REDIS_PORT" \
-            "$OPENAI_API_KEY"
-          do
+            "$JWT_SECRET_KEY" \
+            "$OPENAI_API_KEY"; do
+
             if [ -z "$VALUE" ] || [ "$VALUE" = "None" ]; then
-              echo "Required AI service parameter is empty"
+              echo "Required AI service SSM parameter is empty"
               exit 1
             fi
           done
 
           echo "::add-mask::$RDS_SECRET_ARN"
+          echo "::add-mask::$JWT_SECRET_KEY"
           echo "::add-mask::$OPENAI_API_KEY"
 
           {
@@ -191,10 +208,10 @@ jobs:
             echo "SAMPLE_DB_NAME=$SAMPLE_DB_NAME"
             echo "REDIS_HOST=$REDIS_HOST"
             echo "REDIS_PORT=$REDIS_PORT"
+            echo "JWT_SECRET_KEY=$JWT_SECRET_KEY"
             echo "OPENAI_API_KEY=$OPENAI_API_KEY"
           } >> "$GITHUB_ENV"
 
-      # 변경: DB_HOST, DB_PORT, DB 이름, URL, Redis URL 추가
       - name: Create or update AI service Secret
         if: github.ref_name == 'dev'
         shell: bash
@@ -220,20 +237,23 @@ jobs:
             exit 1
           fi
 
-          echo "::add-mask::$DB_USER"
-          echo "::add-mask::$DB_PASSWORD"
-
           DB_USER_ENCODED=$(printf '%s' "$DB_USER" | jq -sRr @uri)
           DB_PASSWORD_ENCODED=$(printf '%s' "$DB_PASSWORD" | jq -sRr @uri)
 
           MAIN_DATABASE_URL="mysql+pymysql://${DB_USER_ENCODED}:${DB_PASSWORD_ENCODED}@${RDS_HOST}:${RDS_PORT}/${MAIN_DB_NAME}?charset=utf8mb4"
+
           REDIS_URL="redis://${REDIS_HOST}:${REDIS_PORT}/0"
 
+          echo "::add-mask::$DB_USER"
+          echo "::add-mask::$DB_PASSWORD"
           echo "::add-mask::$MAIN_DATABASE_URL"
+          echo "::add-mask::$REDIS_URL"
+          echo "::add-mask::$JWT_SECRET_KEY"
 
           kubectl create secret generic ai-service-secret \
             --namespace "$NAMESPACE" \
             --from-literal=OPENAI_API_KEY="$OPENAI_API_KEY" \
+            --from-literal=JWT_SECRET_KEY="$JWT_SECRET_KEY" \
             --from-literal=MAIN_DATABASE_URL="$MAIN_DATABASE_URL" \
             --from-literal=DB_USER="$DB_USER" \
             --from-literal=DB_PASSWORD="$DB_PASSWORD" \
@@ -243,9 +263,9 @@ jobs:
             --from-literal=SAMPLE_DB_NAME="$SAMPLE_DB_NAME" \
             --from-literal=REDIS_URL="$REDIS_URL" \
             --dry-run=client \
-            -o yaml | kubectl apply -f -
+            -o yaml |
+          kubectl apply -f -
 
-      # 변경: image: 전체 URI가 아니라 newTag:만 변경
       - name: Update AI service image tag in GitOps repository
         env:
           IMAGE_URI: ${{ steps.build-image.outputs.image_uri }}
@@ -266,6 +286,7 @@ jobs:
             "s|^([[:space:]]*)newTag:.*$|\1newTag: ${IMAGE_TAG}|" \
             "$MANIFEST"
 
+          echo "Updated AI service image tag:"
           grep -n "newTag:" "$MANIFEST"
 
           cd infra
@@ -280,13 +301,18 @@ jobs:
             exit 0
           fi
 
-          git commit -m "chore(ai-service): deploy ${GITHUB_REF_NAME}-${GITHUB_SHA::12}"
+          git diff --cached
+
+          git commit \
+            -m "chore(ai-service): deploy ${GITHUB_REF_NAME}-${GITHUB_SHA::12}"
+
+          for ATTEMPT in 1 2 3; do
+            echo "GitOps push attempt: $ATTEMPT"
 
-          for ATTEMPT in 1 2 3
-          do
-            git pull --rebase origin "$GITHUB_REF_NAME"
+            git pull --rebase origin "$INFRA_BRANCH"
 
-            if git push origin "HEAD:$GITHUB_REF_NAME"; then
+            if git push origin "HEAD:$INFRA_BRANCH"; then
+              echo "GitOps image update pushed successfully"
               exit 0
             fi
 

From 32f6bc6007a46f0dd1e1c4f59523e5a943cf1f22 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 13 Jul 2026 13:38:17 +0900
Subject: [PATCH 132/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9/=EB=B6=88?=
 =?UTF-8?q?=EB=9F=89=20=EC=A0=84=EC=9D=B4/SHAP=20=EB=B6=84=EC=84=9D=20?=
 =?UTF-8?q?=EC=A1=B0=ED=9A=8C=20=EB=B0=8F=20ES=20=EC=9E=AC=EC=83=89?=
 =?UTF-8?q?=EC=9D=B8=20=EC=A0=95=ED=95=A9=EC=84=B1=20=EA=B0=9C=EC=84=A0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 병목 분석은 detected_at, 불량 전이/SHAP는 predicted_at 기준으로 조회 및 dateOptions 정리

- SHAP 원인 분석에서 ES 미존재 시 DB fallback 추가 및 vehicleId 누락 케이스 보완

- 불량 전이 predictedDefectProcess를 도장(L1)처럼 라인 포함 형식으로 저장/반환

- 불량 확률 응답을 0~1 기준으로 정규화하고 date 의미를 dateOperation 기준으로 문서화
---
 app/api/router.py                             |   4 +-
 app/api/routers/analysis_maintenance.py       |  99 ++
 .../defect_transfer_prediction_backfill.py    |   3 +-
 app/dto/request/__init__.py                   |   5 +
 .../request/analysis_maintenance_request.py   |  22 +
 app/dto/response/__init__.py                  |   6 +
 .../response/analysis_maintenance_response.py |  37 +
 app/dto/response/defect_transfer_response.py  |   9 +-
 app/kafka/raw_event_consumer.py               |  30 +-
 .../bottleneck_analysis_repository.py         |  27 +
 .../defect_transfer_prediction_repository.py  |  98 +-
 app/search/process_analysis_search.py         |  78 +-
 .../analysis/analysis_maintenance_service.py  | 842 ++++++++++++++++++
 app/service/analysis/bottleneck_service.py    |  14 +-
 .../analysis/defect_transfer_service.py       | 149 ++--
 15 files changed, 1329 insertions(+), 94 deletions(-)
 create mode 100644 app/api/routers/analysis_maintenance.py
 create mode 100644 app/dto/request/analysis_maintenance_request.py
 create mode 100644 app/dto/response/analysis_maintenance_response.py
 create mode 100644 app/service/analysis/analysis_maintenance_service.py

diff --git a/app/api/router.py b/app/api/router.py
index 91950c0..8195205 100644
--- a/app/api/router.py
+++ b/app/api/router.py
@@ -1,6 +1,7 @@
 from fastapi import APIRouter
 
 from app.api.routers import (
+    analysis_maintenance,
     defect_transfer,
     health,
     manufacturing_event,
@@ -17,8 +18,9 @@
 api_router.include_router(health.router)
 api_router.include_router(process.router)
 api_router.include_router(defect_transfer.router)
+api_router.include_router(analysis_maintenance.router)
 api_router.include_router(process_analysis_ws.router)
 api_router.include_router(manufacturing_event.router)
 api_router.include_router(ml_dataset.router)
 api_router.include_router(manual.router)
-api_router.include_router(events.router)
\ No newline at end of file
+api_router.include_router(events.router)
diff --git a/app/api/routers/analysis_maintenance.py b/app/api/routers/analysis_maintenance.py
new file mode 100644
index 0000000..b2ab36d
--- /dev/null
+++ b/app/api/routers/analysis_maintenance.py
@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+from datetime import date as DateType
+
+from fastapi import APIRouter, Depends
+from app.dto.request import AnalysisMaintenanceRequest
+from app.dto.response import AnalysisMaintenanceResponse, CommonResponse
+from app.service.analysis.analysis_maintenance_service import AnalysisMaintenanceService
+from app.utils.datetime_utils import seoul_now
+from app.utils.response_utils import success_response
+
+
+router = APIRouter(prefix="/api/ai/admin/analysis", tags=["admin-analysis"])
+
+
+def _resolve_dates(payload: AnalysisMaintenanceRequest) -> tuple[DateType, DateType]:
+    start = payload.from_date or payload.to_date or seoul_now().date()
+    end = payload.to_date or payload.from_date or start
+    return start, end
+
+
+def _service() -> AnalysisMaintenanceService:
+    return AnalysisMaintenanceService()
+
+
+@router.post(
+    "/backfill/bottleneck",
+    response_model=CommonResponse[AnalysisMaintenanceResponse],
+    summary="병목 백필 및 ES 재반영",
+)
+def backfill_bottleneck(
+    payload: AnalysisMaintenanceRequest,
+    service: AnalysisMaintenanceService = Depends(_service),
+) -> CommonResponse[AnalysisMaintenanceResponse]:
+    from_date, to_date = _resolve_dates(payload)
+    response = service.backfill_bottleneck(
+        from_date=from_date,
+        to_date=to_date,
+        reindex_es=payload.reindex_es,
+        reset_flags=payload.reset_flags,
+        dry_run=payload.dry_run,
+    )
+    return success_response(data=response, message="병목 백필이 완료되었습니다.")
+
+
+@router.post(
+    "/backfill/defect-transfer",
+    response_model=CommonResponse[AnalysisMaintenanceResponse],
+    summary="불량 전이/SHAP 백필 및 ES 재반영",
+)
+def backfill_defect_transfer(
+    payload: AnalysisMaintenanceRequest,
+    service: AnalysisMaintenanceService = Depends(_service),
+) -> CommonResponse[AnalysisMaintenanceResponse]:
+    from_date, to_date = _resolve_dates(payload)
+    response = service.backfill_defect_transfer(
+        from_date=from_date,
+        to_date=to_date,
+        reindex_es=payload.reindex_es,
+        reset_flags=payload.reset_flags,
+        dry_run=payload.dry_run,
+    )
+    return success_response(data=response, message="불량 전이 백필이 완료되었습니다.")
+
+
+@router.post(
+    "/reindex/bottleneck",
+    response_model=CommonResponse[AnalysisMaintenanceResponse],
+    summary="병목 ES 재색인",
+)
+def reindex_bottleneck(
+    payload: AnalysisMaintenanceRequest,
+    service: AnalysisMaintenanceService = Depends(_service),
+) -> CommonResponse[AnalysisMaintenanceResponse]:
+    from_date, to_date = _resolve_dates(payload)
+    response = service.reindex_bottleneck(
+        from_date=from_date,
+        to_date=to_date,
+        dry_run=payload.dry_run,
+    )
+    return success_response(data=response, message="병목 ES 재색인이 완료되었습니다.")
+
+
+@router.post(
+    "/reindex/defect-transfer",
+    response_model=CommonResponse[AnalysisMaintenanceResponse],
+    summary="불량 전이/SHAP ES 재색인",
+)
+def reindex_defect_transfer(
+    payload: AnalysisMaintenanceRequest,
+    service: AnalysisMaintenanceService = Depends(_service),
+) -> CommonResponse[AnalysisMaintenanceResponse]:
+    from_date, to_date = _resolve_dates(payload)
+    response = service.reindex_defect_transfer(
+        from_date=from_date,
+        to_date=to_date,
+        dry_run=payload.dry_run,
+    )
+    return success_response(data=response, message="불량 전이 ES 재색인이 완료되었습니다.")
diff --git a/app/batch/defect_transfer_prediction_backfill.py b/app/batch/defect_transfer_prediction_backfill.py
index 1357d71..bd2abfc 100644
--- a/app/batch/defect_transfer_prediction_backfill.py
+++ b/app/batch/defect_transfer_prediction_backfill.py
@@ -62,6 +62,7 @@ def backfill_defect_transfer_predictions(*, limit: int | None = None) -> dict[st
     failed = 0
     for row in rows:
         try:
+            predicted_at = row.get("event_time")
             prediction = detector.predict_event(
                 _event_json(row["event_json"]),
                 str(row["process_code"]),
@@ -95,7 +96,7 @@ def backfill_defect_transfer_predictions(*, limit: int | None = None) -> dict[st
                     }
                     for cause in prediction.causes
                 ],
-                predicted_at=datetime.now(),
+                predicted_at=predicted_at if isinstance(predicted_at, datetime) else datetime.now(),
             )
             processed += 1
         except Exception:
diff --git a/app/dto/request/__init__.py b/app/dto/request/__init__.py
index cca5fbb..257550e 100644
--- a/app/dto/request/__init__.py
+++ b/app/dto/request/__init__.py
@@ -1,2 +1,7 @@
 """Request DTO package."""
 
+from app.dto.request.analysis_maintenance_request import AnalysisMaintenanceRequest
+
+__all__ = [
+    "AnalysisMaintenanceRequest",
+]
diff --git a/app/dto/request/analysis_maintenance_request.py b/app/dto/request/analysis_maintenance_request.py
new file mode 100644
index 0000000..ab746b7
--- /dev/null
+++ b/app/dto/request/analysis_maintenance_request.py
@@ -0,0 +1,22 @@
+from __future__ import annotations
+
+from datetime import date
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+
+class AnalysisMaintenanceRequest(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    from_date: date | None = Field(default=None, alias="fromDate")
+    to_date: date | None = Field(default=None, alias="toDate")
+    limit: int | None = Field(default=None, ge=1, le=100_000)
+    reindex_es: bool = Field(default=True, alias="reindexEs")
+    reset_flags: bool = Field(default=True, alias="resetFlags")
+    dry_run: bool = Field(default=False, alias="dryRun")
+
+    @model_validator(mode="after")
+    def validate_date_range(self) -> "AnalysisMaintenanceRequest":
+        if self.from_date and self.to_date and self.from_date > self.to_date:
+            raise ValueError("fromDate must be less than or equal to toDate.")
+        return self
diff --git a/app/dto/response/__init__.py b/app/dto/response/__init__.py
index d7211fb..019fd92 100644
--- a/app/dto/response/__init__.py
+++ b/app/dto/response/__init__.py
@@ -6,6 +6,10 @@
     BottleneckAnalysisPage,
 )
 from app.dto.response.common_response import CommonResponse
+from app.dto.response.analysis_maintenance_response import (
+    AnalysisMaintenanceResponse,
+    AnalysisMaintenanceSummary,
+)
 from app.dto.response.defect_transfer_response import (
     DefectTransferCauseItem,
     DefectTransferCausePage,
@@ -15,6 +19,8 @@
 
 __all__ = [
     "AnalysisDateOption",
+    "AnalysisMaintenanceResponse",
+    "AnalysisMaintenanceSummary",
     "BottleneckAnalysisItem",
     "BottleneckAnalysisPage",
     "CommonResponse",
diff --git a/app/dto/response/analysis_maintenance_response.py b/app/dto/response/analysis_maintenance_response.py
new file mode 100644
index 0000000..71679e8
--- /dev/null
+++ b/app/dto/response/analysis_maintenance_response.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from datetime import date
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class AnalysisMaintenanceSummary(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    analysis_name: str = Field(alias="analysisName")
+    source_from: date | None = Field(default=None, alias="sourceFrom")
+    source_to: date | None = Field(default=None, alias="sourceTo")
+    source_count: int = Field(alias="sourceCount")
+    processed_count: int = Field(alias="processedCount")
+    saved_count: int = Field(alias="savedCount")
+    skipped_count: int = Field(alias="skippedCount")
+    failed_count: int = Field(alias="failedCount")
+    deleted_count: int = Field(alias="deletedCount")
+    es_reindexed_count: int = Field(alias="esReindexedCount")
+    notes: list[str] = Field(default_factory=list)
+
+
+class AnalysisMaintenanceResponse(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
+    mode: str
+    items: list[AnalysisMaintenanceSummary]
+    total_source_count: int = Field(alias="totalSourceCount")
+    total_processed_count: int = Field(alias="totalProcessedCount")
+    total_saved_count: int = Field(alias="totalSavedCount")
+    total_skipped_count: int = Field(alias="totalSkippedCount")
+    total_failed_count: int = Field(alias="totalFailedCount")
+    total_deleted_count: int = Field(alias="totalDeletedCount")
+    total_es_reindexed_count: int = Field(alias="totalEsReindexedCount")
+    extra: dict[str, Any] = Field(default_factory=dict)
diff --git a/app/dto/response/defect_transfer_response.py b/app/dto/response/defect_transfer_response.py
index daa327f..a677fc9 100644
--- a/app/dto/response/defect_transfer_response.py
+++ b/app/dto/response/defect_transfer_response.py
@@ -15,7 +15,7 @@ class DefectTransferPredictionItem(BaseModel):
     car_master_id: int = Field(alias="carMasterId")
     current_process: str = Field(alias="currentProcess")
     predicted_defect_process: str | None = Field(alias="predictedDefectProcess")
-    defect_probability: int = Field(alias="defectProbability")
+    defect_probability: float = Field(alias="defectProbability")
     expected_time: str | None = Field(alias="expectedTime")
     risk_level: str = Field(alias="riskLevel")
 
@@ -41,7 +41,6 @@ class DefectTransferCauseItem(BaseModel):
     value: str
     impact: float
     message: str
-    main_causes: list[dict[str, Any]] = Field(default_factory=list, alias="mainCauses")
 
 
 class DefectTransferCausePage(BaseModel):
@@ -49,12 +48,14 @@ class DefectTransferCausePage(BaseModel):
 
     vehicle_id: str | None = Field(alias="vehicleId")
     car_master_id: int | None = Field(alias="carMasterId")
-    predicted_defect_probability: int | None = Field(alias="predictedDefectProbability")
+    predicted_defect_probability: float | None = Field(alias="predictedDefectProbability")
     risk_level: str | None = Field(alias="riskLevel")
     current_process: str | None = Field(alias="currentProcess")
     predicted_defect_process: str | None = Field(alias="predictedDefectProcess")
-    transfer_probability: int | None = Field(alias="transferProbability")
+    transfer_probability: float | None = Field(alias="transferProbability")
     content: list[DefectTransferCauseItem]
+    representative_cause: DefectTransferCauseItem | None = Field(default=None, alias="representativeCause")
+    detail_causes: list[DefectTransferCauseItem] = Field(default_factory=list, alias="detailCauses")
     date: DateType | None = None
     from_: datetime | None = Field(default=None, alias="from")
     to: datetime | None = None
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 8e1cf5f..0b9ebf4 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -325,12 +325,12 @@ def _broadcast_process_analysis_updates(
             **base_message,
             "type": "DEFECT_TRANSFER_UPDATED",
             "defectProbability": (
-                round(defect_prediction.defect_probability * 100.0, 1)
+                round(defect_prediction.defect_probability, 4)
                 if defect_prediction is not None
                 else None
             ),
             "transferProbability": (
-                round(defect_prediction.transfer_probability * 100.0, 1)
+                round(defect_prediction.transfer_probability, 4)
                 if defect_prediction is not None
                 and defect_prediction.transfer_probability is not None
                 else None
@@ -712,7 +712,7 @@ def _build_bottleneck_sync_event(
     summaries: list[dict[str, Any]],
 ) -> dict[str, Any]:
     sync_id = f"SNAP-{uuid4()}"
-    detected_at = seoul_now_iso()
+    detected_at = _iso_or_none(row.get("event_time")) or seoul_now_iso()
     first_summary = summaries[0] if summaries else {}
     return {
         "syncId": sync_id,
@@ -756,7 +756,7 @@ def _build_defect_transfer_sync_event(
     prediction: DefectTransferPrediction,
 ) -> dict[str, Any]:
     sync_id = f"SYNC-{uuid4()}"
-    predicted_at = seoul_now_iso()
+    predicted_at = _iso_or_none(row.get("event_time")) or seoul_now_iso()
     vehicle_id = _vehicle_id_for_car_master_id(repository, int(row["car_master_id"]))
     source_equipment_code = _source_equipment_code(row)
     current_process = _format_current_process(row, source_equipment_code)
@@ -764,6 +764,7 @@ def _build_defect_transfer_sync_event(
         prediction.predicted_process_code,
         row,
     )
+    target_equipment_code = _target_equipment_code(prediction.predicted_process_code, row)
     causes = [
         {
             "rank": cause.rank,
@@ -785,8 +786,9 @@ def _build_defect_transfer_sync_event(
         "currentProcessCode": prediction.current_process_code,
         "currentProcess": current_process,
         "sourceEquipmentCode": source_equipment_code,
+        "targetEquipmentCode": target_equipment_code,
         "predictedDefectProcess": predicted_process,
-        "defectProbability": round(prediction.defect_probability * 100.0),
+        "defectProbability": round(prediction.defect_probability, 4),
         "currentDefectProbability": prediction.defect_probability,
         "transferProbability": prediction.transfer_probability,
         "defectThreshold": prediction.defect_threshold,
@@ -1076,6 +1078,24 @@ def _format_predicted_defect_process(
     return format_process_with_line(normalized, equipment_code)
 
 
+def _target_equipment_code(
+    process_code: str | None,
+    row: dict[str, Any],
+) -> str | None:
+    if process_code is None:
+        return None
+    from app.utils.process_label_utils import equipment_code_for_car_process
+
+    normalized = str(process_code).strip().upper()
+    source_code = str(row.get("process_code") or "").strip().upper()
+    if normalized == source_code:
+        return _source_equipment_code(row)
+    return equipment_code_for_car_process(
+        car_master_id=int(row["car_master_id"]),
+        process_code=normalized,
+    )
+
+
 def _format_current_process(
     row: dict[str, Any],
     equipment_code: str | None,
diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index 708d793..a673b37 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -128,6 +128,15 @@ def count_results(
 
         return len(self.list_results(cursor=0, size=raw_count))
 
+    def delete_results_by_date(self, analysis_date: DateType) -> int:
+        from sqlalchemy import func
+
+        with self.engine.begin() as conn:
+            result = conn.execute(
+                self.table.delete().where(func.date(self.table.c.detected_at) == analysis_date),
+            )
+        return int(result.rowcount or 0)
+
     def list_manufacturing_event_histories(
         self,
         *,
@@ -280,6 +289,24 @@ def mark_bottleneck_analysis_done(self, event_ids: Iterable[int]) -> int:
             )
         return int(result.rowcount or 0)
 
+    def reset_bottleneck_analysis_done(self, event_ids: Iterable[int]) -> int:
+        event_ids = [int(event_id) for event_id in event_ids]
+        if not event_ids:
+            return 0
+
+        from sqlalchemy import func
+
+        with self.event_engine.begin() as conn:
+            result = conn.execute(
+                manufacturing_event_json.update()
+                .where(manufacturing_event_json.c.id.in_(event_ids))
+                .values(
+                    bottleneck_analysis_done=False,
+                    updated_at=func.current_timestamp(),
+                ),
+            )
+        return int(result.rowcount or 0)
+
     def _to_bottleneck_history(self, row: dict[str, Any]) -> dict[str, Any]:
         event_json = self._event_json_dict(row["event_json"])
         equipment = event_json.get("equipment", {})
diff --git a/app/repository/defect_transfer_prediction_repository.py b/app/repository/defect_transfer_prediction_repository.py
index 2aeb95f..2f35137 100644
--- a/app/repository/defect_transfer_prediction_repository.py
+++ b/app/repository/defect_transfer_prediction_repository.py
@@ -96,7 +96,6 @@ def has_prediction_for_event(self, event_id: str) -> bool:
             select(func.count())
             .select_from(self.table)
             .where(self.table.c.manufacturing_event_id == manufacturing_event_id)
-            .where(func.date(self.table.c.predicted_at) == func.current_date())
         )
         with self.engine.connect() as conn:
             return int(conn.execute(query).scalar_one()) > 0
@@ -118,7 +117,7 @@ def list_prediction_page(
         items = [
             row
             for row in latest_by_car.values()
-            if self._result_probability_percent(row) > 0
+            if self._result_probability(row) > 0
         ]
         items.sort(
             key=lambda row: (
@@ -136,6 +135,17 @@ def list_prediction_page(
         page = items[offset : offset + size]
         return page, len(items) > offset + size
 
+    def list_prediction_rows(
+        self,
+        *,
+        analysis_date: date | None = None,
+        car_master_id: int | None = None,
+    ) -> list[dict[str, Any]]:
+        return self._all_prediction_rows(
+            car_master_id=car_master_id,
+            analysis_date=analysis_date,
+        )
+
     def list_cause_page(
         self,
         *,
@@ -260,7 +270,7 @@ def diagnostics(self) -> dict[str, Any]:
         visible_latest_rows = [
             row
             for row in latest_by_car.values()
-            if self._result_probability_percent(row) > 0
+            if self._result_probability(row) > 0
         ]
 
         with self.engine.connect() as conn:
@@ -306,6 +316,15 @@ def diagnostics(self) -> dict[str, Any]:
             "latestPredictionResult": self._json_ready_row(latest_prediction),
         }
 
+    def delete_predictions_by_date(self, analysis_date: date) -> int:
+        from sqlalchemy import func
+
+        with self.engine.begin() as conn:
+            result = conn.execute(
+                self.table.delete().where(func.date(self.table.c.predicted_at) == analysis_date),
+            )
+        return int(result.rowcount or 0)
+
     def _all_prediction_rows(
         self,
         *,
@@ -326,6 +345,58 @@ def _all_prediction_rows(
         with self.engine.connect() as conn:
             return [dict(row) for row in conn.execute(query).mappings()]
 
+    def list_prediction_source_events(
+        self,
+        *,
+        analysis_date: date | None = None,
+        car_master_id: int | None = None,
+    ) -> list[dict[str, Any]]:
+        from sqlalchemy import func, select
+
+        query = (
+            select(
+                manufacturing_event_json.c.id,
+                manufacturing_event_json.c.event_id,
+                manufacturing_event_json.c.event_time,
+                manufacturing_event_json.c.car_master_id,
+                manufacturing_event_json.c.process_code,
+                manufacturing_event_json.c.event_json,
+            )
+            .where(manufacturing_event_json.c.dispatch_status == "SENT")
+            .where(manufacturing_event_json.c.is_sent.is_(True))
+        )
+        if analysis_date is None:
+            query = query.where(func.date(manufacturing_event_json.c.event_time) == func.current_date())
+        else:
+            query = query.where(func.date(manufacturing_event_json.c.event_time) == analysis_date)
+        if car_master_id is not None:
+            query = query.where(manufacturing_event_json.c.car_master_id == car_master_id)
+        query = query.order_by(manufacturing_event_json.c.id.asc())
+        with self.event_engine.connect() as conn:
+            return [dict(row) for row in conn.execute(query).mappings()]
+
+    def delete_source_analysis_done_flags(
+        self,
+        *,
+        event_ids: list[int],
+        column_name: str,
+    ) -> int:
+        if not event_ids:
+            return 0
+
+        from sqlalchemy import func
+
+        if column_name not in {"bottleneck_analysis_done", "defect_transfer_analysis_done"}:
+            raise ValueError(f"Unsupported analysis flag column: {column_name}")
+
+        with self.event_engine.begin() as conn:
+            result = conn.execute(
+                manufacturing_event_json.update()
+                .where(manufacturing_event_json.c.id.in_(event_ids))
+                .values(**{column_name: False, "updated_at": func.current_timestamp()}),
+            )
+        return int(result.rowcount or 0)
+
     @staticmethod
     def _json_ready_row(row: Any | None) -> dict[str, Any] | None:
         if row is None:
@@ -378,13 +449,24 @@ def _prediction_event_ids(
             return [int(row[0]) for row in conn.execute(query).all()]
 
     @staticmethod
-    def _result_probability_percent(row: dict[str, Any]) -> int:
-        value = row.get("target_defect_probability")
+    def _normalize_probability(value: Any) -> float:
         if value is None:
-            value = row.get("current_defect_probability")
+            return 0.0
+        normalized = float(value)
+        if abs(normalized) > 1.0:
+            normalized /= 100.0
+        return round(normalized, 4)
+
+    @classmethod
+    def _result_probability(cls, row: dict[str, Any]) -> float:
+        value = row.get("current_defect_probability")
         if value is None:
-            return 0
-        return round(float(value) * 100)
+            value = row.get("target_defect_probability")
+        if value is None:
+            value = row.get("defect_probability")
+        if value is None:
+            return 0.0
+        return cls._normalize_probability(value)
 
     def _attach_process_equipment_codes(self, rows: list[dict[str, Any]]) -> None:
         if not rows:
diff --git a/app/search/process_analysis_search.py b/app/search/process_analysis_search.py
index 14fce45..73d26d6 100644
--- a/app/search/process_analysis_search.py
+++ b/app/search/process_analysis_search.py
@@ -119,12 +119,13 @@ def index_defect_transfer_prediction(self, event: dict[str, Any]) -> None:
             "currentProcessCode": event.get("currentProcessCode"),
             "sourceProcessCode": event.get("sourceProcessCode"),
             "sourceEquipmentCode": event.get("sourceEquipmentCode"),
+            "targetEquipmentCode": event.get("targetEquipmentCode"),
             "predictedDefectProcess": event.get("predictedDefectProcess"),
             "currentDefectProbability": self._safe_float(event.get("currentDefectProbability")),
             "transferProbability": self._safe_float(event.get("transferProbability")),
             "defectThreshold": self._safe_float(event.get("defectThreshold")),
             "transferThreshold": self._safe_float(event.get("transferThreshold")),
-            "defectProbability": self._safe_int(event.get("defectProbability")),
+            "defectProbability": self._safe_float(event.get("defectProbability")),
             "expectedTime": event.get("expectedTime"),
             "expectedStepsAfter": self._safe_int(event.get("expectedStepsAfter")),
             "riskLevel": event.get("riskLevel"),
@@ -145,6 +146,51 @@ def index_defect_transfer_prediction(self, event: dict[str, Any]) -> None:
             ],
         )
 
+    def delete_bottleneck_documents_by_date(self, analysis_date: DateType) -> int:
+        if not self.enabled:
+            return 0
+        response = self.client.delete_by_query(
+            index=settings.elasticsearch_bottleneck_index,
+            body={"query": self._date_range_query("detectedAt", analysis_date)},
+            refresh=True,
+            conflicts="proceed",
+        )
+        return self._safe_int(response.get("deleted")) or 0
+
+    def delete_defect_transfer_documents_by_date(self, analysis_date: DateType) -> int:
+        if not self.enabled:
+            return 0
+        response = self.client.delete_by_query(
+            index=settings.elasticsearch_defect_transfer_index,
+            body={"query": self._date_range_query("predictedAt", analysis_date)},
+            refresh=True,
+            conflicts="proceed",
+        )
+        return self._safe_int(response.get("deleted")) or 0
+
+    def delete_defect_transfer_documents_by_vehicle_and_date(
+        self,
+        *,
+        vehicle_id: str | None,
+        analysis_date: DateType,
+    ) -> int:
+        if not self.enabled:
+            return 0
+        query: dict[str, Any] = {
+            "bool": {
+                "filter": [self._date_range_query("predictedAt", analysis_date)],
+            },
+        }
+        if vehicle_id:
+            query["bool"]["must"] = [{"term": {"vehicleId": vehicle_id}}]
+        response = self.client.delete_by_query(
+            index=settings.elasticsearch_defect_transfer_index,
+            body={"query": query},
+            refresh=True,
+            conflicts="proceed",
+        )
+        return self._safe_int(response.get("deleted")) or 0
+
     def list_bottleneck_page(
         self,
         *,
@@ -252,7 +298,10 @@ def get_latest_defect_cause_document(
     ) -> dict[str, Any] | None:
         query: dict[str, Any] = {
             "size": 1,
+            "collapse": {"field": "carMasterId"},
             "sort": [
+                {"transferProbability": {"order": "desc", "missing": "_last"}},
+                {"currentDefectProbability": {"order": "desc", "missing": "_last"}},
                 {"predictedAt": {"order": "desc"}},
                 {"syncId": {"order": "desc"}},
             ],
@@ -412,12 +461,13 @@ def _index_definitions(self) -> dict[str, dict[str, Any]]:
                         "currentProcessCode": {"type": "keyword"},
                         "sourceProcessCode": {"type": "keyword"},
                         "sourceEquipmentCode": {"type": "keyword"},
+                        "targetEquipmentCode": {"type": "keyword"},
                         "predictedDefectProcess": {"type": "keyword"},
                         "currentDefectProbability": {"type": "double"},
                         "transferProbability": {"type": "double"},
                         "defectThreshold": {"type": "double"},
                         "transferThreshold": {"type": "double"},
-                        "defectProbability": {"type": "integer"},
+                        "defectProbability": {"type": "double"},
                         "expectedTime": {"type": "keyword"},
                         "expectedStepsAfter": {"type": "integer"},
                         "riskLevel": {"type": "keyword"},
@@ -522,11 +572,17 @@ def _map_bottleneck_source(source: dict[str, Any]) -> dict[str, Any]:
 
     @staticmethod
     def _map_defect_source(source: dict[str, Any]) -> dict[str, Any]:
-        current_defect_probability = source.get("currentDefectProbability")
-        transfer_probability = source.get("transferProbability")
-        defect_probability = source.get("defectProbability")
+        current_defect_probability = ProcessAnalysisSearchRepository._normalize_probability(
+            source.get("currentDefectProbability"),
+        )
+        transfer_probability = ProcessAnalysisSearchRepository._normalize_probability(
+            source.get("transferProbability"),
+        )
+        defect_probability = ProcessAnalysisSearchRepository._normalize_probability(
+            source.get("defectProbability"),
+        )
         if defect_probability is None and current_defect_probability is not None:
-            defect_probability = round(float(current_defect_probability) * 100)
+            defect_probability = current_defect_probability
         return {
             "analysis_type": source.get("analysisType"),
             "sync_id": source.get("syncId"),
@@ -537,6 +593,7 @@ def _map_defect_source(source: dict[str, Any]) -> dict[str, Any]:
             "current_process_code": source.get("currentProcessCode"),
             "source_process_code": source.get("sourceProcessCode") or source.get("currentProcessCode"),
             "source_equipment_code": source.get("sourceEquipmentCode"),
+            "target_equipment_code": source.get("targetEquipmentCode"),
             "predicted_defect_process": source.get("predictedDefectProcess"),
             "target_defect_probability": transfer_probability,
             "defect_probability": defect_probability,
@@ -561,3 +618,12 @@ def _map_defect_source(source: dict[str, Any]) -> dict[str, Any]:
                 else 0.0
             ),
         }
+
+    @staticmethod
+    def _normalize_probability(value: Any) -> float | None:
+        if value is None:
+            return None
+        normalized = float(value)
+        if abs(normalized) > 1.0:
+            normalized /= 100.0
+        return round(normalized, 4)
diff --git a/app/service/analysis/analysis_maintenance_service.py b/app/service/analysis/analysis_maintenance_service.py
new file mode 100644
index 0000000..687a6c7
--- /dev/null
+++ b/app/service/analysis/analysis_maintenance_service.py
@@ -0,0 +1,842 @@
+from __future__ import annotations
+
+import logging
+from datetime import date as DateType
+from datetime import datetime
+from pathlib import Path
+from uuid import uuid4
+from typing import Any
+
+from sqlalchemy import select
+
+from app.core.config import settings
+from app.dto.response.analysis_maintenance_response import (
+    AnalysisMaintenanceResponse,
+    AnalysisMaintenanceSummary,
+)
+from app.ml.inference.bottleneck_detector import BottleneckDetector
+from app.ml.inference.defect_transfer_detector import (
+    DefectTransferDetector,
+    has_only_model_probability_cause,
+)
+from app.repository.bottleneck_analysis_repository import BottleneckAnalysisRepository
+from app.repository.defect_transfer_prediction_repository import (
+    DefectTransferPredictionRepository,
+)
+from app.repository.sampledb_schema import car_master
+from app.search.process_analysis_search import ProcessAnalysisSearchRepository
+from app.utils.database_utils import mysql_connect_args_for_seoul
+from app.utils.datetime_utils import seoul_now
+from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
+from app.utils.process_label_utils import equipment_code_for_car_process
+
+
+logger = logging.getLogger(__name__)
+
+
+class AnalysisMaintenanceService:
+    def __init__(self) -> None:
+        self.bottleneck_repository = BottleneckAnalysisRepository(
+            settings.bottleneck_database_url,
+            event_database_url=settings.sample_database_connection_url,
+        )
+        self.defect_repository = DefectTransferPredictionRepository(
+            settings.main_database_connection_url,
+            event_database_url=settings.sample_database_connection_url or settings.main_database_connection_url,
+        )
+        self.search_repository = self._create_search_repository()
+        self.bottleneck_detector = BottleneckDetector(
+            Path("app/ml/artifacts/bottleneck/bottleneck_iforest_model.pkl"),
+        )
+        self.defect_detector = DefectTransferDetector()
+
+    def backfill_bottleneck(
+        self,
+        *,
+        from_date: DateType,
+        to_date: DateType,
+        reindex_es: bool = True,
+        reset_flags: bool = True,
+        dry_run: bool = False,
+    ) -> AnalysisMaintenanceResponse:
+        items: list[AnalysisMaintenanceSummary] = []
+        totals = self._empty_totals()
+
+        for current_date in self._date_range(from_date, to_date):
+            histories = self.bottleneck_repository.list_manufacturing_event_histories(
+                analysis_date=current_date,
+            )
+            source_count = len(histories)
+            if source_count == 0:
+                items.append(
+                    self._summary(
+                        "bottleneck",
+                        current_date,
+                        source_count=0,
+                        processed=0,
+                        saved=0,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["no source events"],
+                    ),
+                )
+                continue
+
+            if reset_flags and not dry_run:
+                self.bottleneck_repository.reset_bottleneck_analysis_done(
+                    [
+                        int(row["manufacturing_event_id"])
+                        for row in histories
+                        if row.get("manufacturing_event_id") is not None
+                    ],
+                )
+
+            if dry_run:
+                items.append(
+                    self._summary(
+                        "bottleneck",
+                        current_date,
+                        source_count=source_count,
+                        processed=source_count,
+                        saved=source_count,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["dry-run"],
+                    ),
+                )
+                totals["source_count"] += source_count
+                totals["processed_count"] += source_count
+                totals["saved_count"] += source_count
+                continue
+
+            deleted_count = self.bottleneck_repository.delete_results_by_date(current_date)
+            new_summaries = self.bottleneck_detector.summarize_manufacturing_event_histories(histories)
+            ranked_summaries = self._rank_bottleneck_summaries(new_summaries)
+            detected_at = self._analysis_detected_at(histories)
+            self.bottleneck_repository.replace_results(
+                ranked_summaries,
+                detected_at=detected_at,
+                start_rank=1,
+                end_rank=max(len(ranked_summaries), 1),
+            )
+
+            es_reindexed_count = 0
+            if reindex_es:
+                es_reindexed_count = self._reindex_bottleneck_day(
+                    current_date=current_date,
+                    histories=histories,
+                    summaries=ranked_summaries,
+                    detected_at=detected_at,
+                )
+
+            self.bottleneck_repository.mark_bottleneck_analysis_done(
+                [
+                    int(row["manufacturing_event_id"])
+                    for row in histories
+                    if row.get("manufacturing_event_id") is not None
+                ],
+            )
+
+            items.append(
+                self._summary(
+                    "bottleneck",
+                    current_date,
+                    source_count=source_count,
+                    processed=source_count,
+                    saved=len(ranked_summaries),
+                    skipped=0,
+                    failed=0,
+                    deleted=deleted_count,
+                    reindexed=es_reindexed_count,
+                    notes=[],
+                ),
+            )
+            totals["source_count"] += source_count
+            totals["processed_count"] += source_count
+            totals["saved_count"] += len(ranked_summaries)
+            totals["deleted_count"] += deleted_count
+            totals["es_reindexed_count"] += es_reindexed_count
+            self._clear_analysis_caches()
+
+        return self._response("backfill", items, totals)
+
+    def backfill_defect_transfer(
+        self,
+        *,
+        from_date: DateType,
+        to_date: DateType,
+        reindex_es: bool = True,
+        reset_flags: bool = True,
+        dry_run: bool = False,
+    ) -> AnalysisMaintenanceResponse:
+        items: list[AnalysisMaintenanceSummary] = []
+        totals = self._empty_totals()
+
+        for current_date in self._date_range(from_date, to_date):
+            source_rows = self.defect_repository.list_prediction_source_events(
+                analysis_date=current_date,
+            )
+            source_count = len(source_rows)
+            if source_count == 0:
+                items.append(
+                    self._summary(
+                        "defect-transfer",
+                        current_date,
+                        source_count=0,
+                        processed=0,
+                        saved=0,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["no source events"],
+                    ),
+                )
+                continue
+
+            if reset_flags and not dry_run:
+                self.defect_repository.delete_source_analysis_done_flags(
+                    event_ids=[int(row["id"]) for row in source_rows],
+                    column_name="defect_transfer_analysis_done",
+                )
+
+            if dry_run:
+                items.append(
+                    self._summary(
+                        "defect-transfer",
+                        current_date,
+                        source_count=source_count,
+                        processed=source_count,
+                        saved=source_count,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["dry-run"],
+                    ),
+                )
+                totals["source_count"] += source_count
+                totals["processed_count"] += source_count
+                totals["saved_count"] += source_count
+                continue
+
+            deleted_count = self.defect_repository.delete_predictions_by_date(current_date)
+            processed = 0
+            saved = 0
+            skipped = 0
+            failed = 0
+            for row in source_rows:
+                try:
+                    prediction = self.defect_detector.predict_event(
+                        self._event_json(row.get("event_json")),
+                        str(row.get("process_code") or ""),
+                    )
+                    if has_only_model_probability_cause(prediction.causes):
+                        skipped += 1
+                        continue
+                    predicted_at = self._predict_at(row, current_date)
+                    saved += self.defect_repository.replace_prediction_result(
+                        event_id=str(row["event_id"]),
+                        car_master_id=int(row["car_master_id"]),
+                        source_process_code=prediction.current_process_code,
+                        target_process_code=prediction.predicted_process_code,
+                        current_defect_probability=prediction.defect_probability,
+                        target_defect_probability=prediction.transfer_probability,
+                        predicted_defect_process=self._format_predicted_defect_process(
+                            prediction.predicted_process_code,
+                            row,
+                        ),
+                        expected_occurrence_step=prediction.expected_steps_after,
+                        risk_grade=prediction.risk_level,
+                        causes=[
+                            {
+                                "message": cause.message,
+                                "label": cause.label,
+                                "impact": cause.impact,
+                            }
+                            for cause in prediction.causes
+                        ],
+                        predicted_at=predicted_at,
+                    )
+                    processed += 1
+                    self.defect_repository.mark_defect_transfer_analysis_done(str(row["event_id"]))
+                except Exception:
+                    failed += 1
+                    logger.exception(
+                        "Failed to backfill defect transfer prediction: event_id=%s",
+                        row.get("event_id"),
+                    )
+
+            es_reindexed_count = 0
+            if reindex_es:
+                es_reindexed_count = self._reindex_defect_transfer_day(
+                    current_date=current_date,
+                    source_rows=source_rows,
+                )
+
+            items.append(
+                self._summary(
+                    "defect-transfer",
+                    current_date,
+                    source_count=source_count,
+                    processed=processed,
+                    saved=saved,
+                    skipped=skipped,
+                    failed=failed,
+                    deleted=deleted_count,
+                    reindexed=es_reindexed_count,
+                    notes=[],
+                ),
+            )
+            totals["source_count"] += source_count
+            totals["processed_count"] += processed
+            totals["saved_count"] += saved
+            totals["skipped_count"] += skipped
+            totals["failed_count"] += failed
+            totals["deleted_count"] += deleted_count
+            totals["es_reindexed_count"] += es_reindexed_count
+            self._clear_analysis_caches()
+
+        return self._response("backfill", items, totals)
+
+    def reindex_bottleneck(
+        self,
+        *,
+        from_date: DateType,
+        to_date: DateType,
+        dry_run: bool = False,
+    ) -> AnalysisMaintenanceResponse:
+        items: list[AnalysisMaintenanceSummary] = []
+        totals = self._empty_totals()
+
+        for current_date in self._date_range(from_date, to_date):
+            histories = self.bottleneck_repository.list_manufacturing_event_histories(
+                analysis_date=current_date,
+            )
+            source_count = len(histories)
+            if source_count == 0:
+                items.append(
+                    self._summary(
+                        "bottleneck",
+                        current_date,
+                        source_count=0,
+                        processed=0,
+                        saved=0,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["no source events"],
+                    ),
+                )
+                continue
+
+            if dry_run:
+                items.append(
+                    self._summary(
+                        "bottleneck",
+                        current_date,
+                        source_count=source_count,
+                        processed=source_count,
+                        saved=source_count,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["dry-run"],
+                    ),
+                )
+                totals["source_count"] += source_count
+                totals["processed_count"] += source_count
+                totals["saved_count"] += source_count
+                continue
+
+            summaries = self._rank_bottleneck_summaries(
+                self.bottleneck_detector.summarize_manufacturing_event_histories(histories),
+            )
+            detected_at = self._analysis_detected_at(histories)
+            es_reindexed_count = self._reindex_bottleneck_day(
+                current_date=current_date,
+                histories=histories,
+                summaries=summaries,
+                detected_at=detected_at,
+            )
+
+            items.append(
+                self._summary(
+                    "bottleneck",
+                    current_date,
+                    source_count=source_count,
+                    processed=source_count,
+                    saved=len(summaries),
+                    skipped=0,
+                    failed=0,
+                    deleted=0,
+                    reindexed=es_reindexed_count,
+                    notes=[],
+                ),
+            )
+            totals["source_count"] += source_count
+            totals["processed_count"] += source_count
+            totals["saved_count"] += len(summaries)
+            totals["es_reindexed_count"] += es_reindexed_count
+            self._clear_analysis_caches()
+
+        return self._response("reindex", items, totals)
+
+    def reindex_defect_transfer(
+        self,
+        *,
+        from_date: DateType,
+        to_date: DateType,
+        dry_run: bool = False,
+    ) -> AnalysisMaintenanceResponse:
+        items: list[AnalysisMaintenanceSummary] = []
+        totals = self._empty_totals()
+
+        for current_date in self._date_range(from_date, to_date):
+            source_rows = self.defect_repository.list_prediction_source_events(
+                analysis_date=current_date,
+            )
+            source_count = len(source_rows)
+            if source_count == 0:
+                items.append(
+                    self._summary(
+                        "defect-transfer",
+                        current_date,
+                        source_count=0,
+                        processed=0,
+                        saved=0,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["no source events"],
+                    ),
+                )
+                continue
+
+            if dry_run:
+                items.append(
+                    self._summary(
+                        "defect-transfer",
+                        current_date,
+                        source_count=source_count,
+                        processed=source_count,
+                        saved=source_count,
+                        skipped=0,
+                        failed=0,
+                        deleted=0,
+                        reindexed=0,
+                        notes=["dry-run"],
+                    ),
+                )
+                totals["source_count"] += source_count
+                totals["processed_count"] += source_count
+                totals["saved_count"] += source_count
+                continue
+
+            es_reindexed_count = self._reindex_defect_transfer_day(
+                current_date=current_date,
+                source_rows=source_rows,
+            )
+            items.append(
+                self._summary(
+                    "defect-transfer",
+                    current_date,
+                    source_count=source_count,
+                    processed=source_count,
+                    saved=0,
+                    skipped=0,
+                    failed=0,
+                    deleted=0,
+                    reindexed=es_reindexed_count,
+                    notes=[],
+                ),
+            )
+            totals["source_count"] += source_count
+            totals["processed_count"] += source_count
+            totals["es_reindexed_count"] += es_reindexed_count
+            self._clear_analysis_caches()
+
+        return self._response("reindex", items, totals)
+
+    def _reindex_bottleneck_day(
+        self,
+        *,
+        current_date: DateType,
+        histories: list[dict[str, Any]],
+        summaries: list[dict[str, Any]],
+        detected_at: datetime,
+    ) -> int:
+        if self.search_repository is None:
+            return 0
+        self.search_repository.delete_bottleneck_documents_by_date(current_date)
+        sync_event = self._build_bottleneck_sync_event(
+            histories=histories,
+            summaries=summaries,
+            detected_at=detected_at,
+        )
+        self.search_repository.index_bottleneck_snapshot(sync_event)
+        return len(summaries)
+
+    def _reindex_defect_transfer_day(
+        self,
+        *,
+        current_date: DateType,
+        source_rows: list[dict[str, Any]],
+    ) -> int:
+        if self.search_repository is None:
+            return 0
+        self.search_repository.delete_defect_transfer_documents_by_date(current_date)
+        indexed = 0
+        for row in source_rows:
+            try:
+                prediction = self.defect_detector.predict_event(
+                    self._event_json(row.get("event_json")),
+                    str(row.get("process_code") or ""),
+                )
+                if has_only_model_probability_cause(prediction.causes):
+                    continue
+                sync_event = self._build_defect_transfer_sync_event(
+                    row=row,
+                    prediction=prediction,
+                )
+                self.search_repository.index_defect_transfer_prediction(sync_event)
+                indexed += 1
+            except Exception:
+                logger.exception(
+                    "Failed to reindex defect transfer document: event_id=%s",
+                    row.get("event_id"),
+                )
+        return indexed
+
+    def _build_bottleneck_sync_event(
+        self,
+        *,
+        histories: list[dict[str, Any]],
+        summaries: list[dict[str, Any]],
+        detected_at: datetime,
+    ) -> dict[str, Any]:
+        sync_id = f"SNAP-{uuid4()}"
+        first_history = histories[0]
+        first_summary = summaries[0] if summaries else {}
+        return {
+            "syncId": sync_id,
+            "analysisType": "BOTTLENECK_ANALYSIS_SYNC",
+            "sourceService": "AI_SERVICE",
+            "detectedAt": detected_at.isoformat(),
+            "analyzedAt": detected_at.isoformat(),
+            "eventId": first_history.get("event_id"),
+            "carMasterId": first_history.get("car_master_id"),
+            "mostBottleneckProcess": first_summary.get("process_code"),
+            "mostBottleneckRiskLevel": self._risk_level(float(first_summary.get("risk_score") or 0.0)),
+            "items": [
+                {
+                    "manufacturingEventId": summary.get("manufacturing_event_id"),
+                    "carMasterId": summary.get("car_master_id") or first_history.get("car_master_id"),
+                    "processCode": summary.get("process_code"),
+                    "equipmentCode": summary.get("equipment_code"),
+                    "rankNo": summary.get("rank_no"),
+                    "avgDelayTime": summary.get("avg_delay_time"),
+                    "affectedVehicleCount": summary.get("affected_vehicle_count"),
+                    "riskScore": summary.get("risk_score"),
+                    "riskLevel": self._risk_level(float(summary.get("risk_score") or 0.0)),
+                }
+                for summary in summaries
+            ],
+        }
+
+    def _build_defect_transfer_sync_event(
+        self,
+        *,
+        row: dict[str, Any],
+        prediction: Any,
+    ) -> dict[str, Any]:
+        sync_id = f"SYNC-{uuid4()}"
+        event_json = self._event_json(row.get("event_json"))
+        vehicle_id = self._vehicle_id_for_car_master_id(int(row["car_master_id"]))
+        source_equipment_code = self._source_equipment_code(row, event_json)
+        current_process = format_process_with_line(
+            str(row.get("process_code") or ""),
+            source_equipment_code,
+        )
+        predicted_process = self._format_predicted_defect_process(
+            prediction.predicted_process_code,
+            row,
+        )
+        target_equipment_code = self._target_equipment_code(
+            prediction.predicted_process_code,
+            row,
+            event_json,
+        )
+        fallback_date = self._analysis_date(row)
+        predicted_at = self._predict_at(row, fallback_date)
+        causes = [
+            {
+                "rank": cause.rank,
+                "feature": cause.feature,
+                "label": cause.label,
+                "value": cause.value,
+                "impact": cause.impact,
+                "message": cause.message,
+            }
+            for cause in prediction.causes
+        ]
+        predicted_at_iso = predicted_at.isoformat()
+        return {
+            "syncId": sync_id,
+            "analysisType": "DEFECT_TRANSFER_ANALYSIS_SYNC",
+            "sourceService": "AI_SERVICE",
+            "eventId": row["event_id"],
+            "carMasterId": row["car_master_id"],
+            "vehicleId": vehicle_id,
+            "currentProcessCode": prediction.current_process_code,
+            "currentProcess": current_process,
+            "sourceEquipmentCode": source_equipment_code,
+            "targetEquipmentCode": target_equipment_code,
+            "predictedDefectProcess": predicted_process,
+            "defectProbability": round(prediction.defect_probability, 4),
+            "currentDefectProbability": prediction.defect_probability,
+            "transferProbability": prediction.transfer_probability,
+            "defectThreshold": prediction.defect_threshold,
+            "transferThreshold": prediction.transfer_threshold,
+            "expectedStepsAfter": prediction.expected_steps_after,
+            "expectedTime": (
+                f"{prediction.expected_steps_after} steps later"
+                if prediction.expected_steps_after is not None
+                else None
+            ),
+            "riskLevel": prediction.risk_level,
+            "predictedAt": predicted_at_iso,
+            "createdAt": predicted_at_iso,
+            "mainCauses": causes,
+            "causes": causes,
+            "featureValues": prediction.feature_values,
+        }
+
+    @staticmethod
+    def _source_equipment_code(row: dict[str, Any], event_json: dict[str, Any]) -> str | None:
+        equipment = event_json.get("equipment", {}) if isinstance(event_json, dict) else {}
+        code = equipment.get("equipmentCode") if isinstance(equipment, dict) else None
+        if code:
+            return str(code)
+        equipment_id = row.get("equipment_id")
+        return str(equipment_id) if equipment_id is not None else None
+
+    def _vehicle_id_for_car_master_id(self, car_master_id: int) -> str | None:
+        with self.defect_repository.event_engine.connect() as conn:
+            value = conn.execute(
+                select(car_master.c.vehicle_id).where(car_master.c.id == car_master_id),
+            ).scalar()
+        return str(value) if value is not None else None
+
+    @staticmethod
+    def _analysis_date(row: dict[str, Any]) -> DateType:
+        value = row.get("event_time")
+        if isinstance(value, datetime):
+            return value.date()
+        return seoul_now().date()
+
+    @staticmethod
+    def _predict_at(row: dict[str, Any], fallback_date: DateType) -> datetime:
+        value = row.get("event_time")
+        if isinstance(value, datetime):
+            return value if value.tzinfo is None else value.replace(tzinfo=None)
+        return datetime.combine(fallback_date, datetime.min.time())
+
+    @staticmethod
+    def _format_predicted_defect_process(
+        process_code: str | None,
+        row: dict[str, Any],
+    ) -> str | None:
+        if process_code is None:
+            return None
+        normalized = str(process_code).strip().upper()
+        source_code = str(row.get("process_code") or "").strip().upper()
+        if normalized == source_code:
+            event_json = row.get("event_json")
+            if isinstance(event_json, dict):
+                equipment = event_json.get("equipment", {})
+                equipment_code = str(equipment.get("equipmentCode") or "")
+            else:
+                equipment_code = ""
+        else:
+            equipment_code = equipment_code_for_car_process(
+                car_master_id=int(row["car_master_id"]),
+                process_code=normalized,
+            )
+        return format_process_with_line(normalized, equipment_code)
+
+    @staticmethod
+    def _target_equipment_code(
+        process_code: str | None,
+        row: dict[str, Any],
+        event_json: dict[str, Any],
+    ) -> str | None:
+        if process_code is None:
+            return None
+        normalized = str(process_code).strip().upper()
+        source_code = str(row.get("process_code") or "").strip().upper()
+        if normalized == source_code:
+            return AnalysisMaintenanceService._source_equipment_code(row, event_json)
+        return equipment_code_for_car_process(
+            car_master_id=int(row["car_master_id"]),
+            process_code=normalized,
+        )
+
+    @staticmethod
+    def _date_range(from_date: DateType, to_date: DateType):
+        current = from_date
+        while current <= to_date:
+            yield current
+            current = current.fromordinal(current.toordinal() + 1)
+
+    @staticmethod
+    def _risk_level(risk_score: float) -> str:
+        return "HIGH" if risk_score >= 3.0 else "NORMAL"
+
+    @staticmethod
+    def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
+        candidates = [
+            value.replace(tzinfo=None) if isinstance(value, datetime) and value.tzinfo else value
+            for value in (row.get("event_time") for row in histories)
+            if isinstance(value, datetime)
+        ]
+        if candidates:
+            return max(candidates)
+        return seoul_now().replace(tzinfo=None)
+
+    @staticmethod
+    def _rank_bottleneck_summaries(
+        summaries: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        ranked = sorted(
+            summaries,
+            key=lambda row: (
+                float(row.get("risk_score") or 0.0),
+                float(row.get("avg_delay_time") or 0.0),
+                int(row.get("affected_vehicle_count") or 0),
+                int(row.get("manufacturing_event_id") or 0),
+                int(row.get("car_master_id") or 0),
+                str(row.get("process_code") or "").strip().upper(),
+                str(row.get("equipment_code") or "").strip().upper(),
+            ),
+            reverse=True,
+        )
+        deduped: list[dict[str, Any]] = []
+        seen_keys: set[tuple[str, str]] = set()
+        for row in ranked:
+            key = (
+                str(row.get("process_code") or "").strip().upper(),
+                str(row.get("equipment_code") or "").strip().upper(),
+            )
+            if key in seen_keys:
+                continue
+            seen_keys.add(key)
+            deduped.append(row)
+        return [{**row, "rank_no": index + 1} for index, row in enumerate(deduped)]
+
+    @staticmethod
+    def _event_json(value: Any) -> dict[str, Any]:
+        if isinstance(value, dict):
+            return value
+        return {}
+
+    @staticmethod
+    def _empty_totals() -> dict[str, int]:
+        return {
+            "source_count": 0,
+            "processed_count": 0,
+            "saved_count": 0,
+            "skipped_count": 0,
+            "failed_count": 0,
+            "deleted_count": 0,
+            "es_reindexed_count": 0,
+        }
+
+    @staticmethod
+    def _summary(
+        analysis_name: str,
+        source_from: DateType,
+        *,
+        source_count: int,
+        processed: int,
+        saved: int,
+        skipped: int,
+        failed: int,
+        deleted: int,
+        reindexed: int,
+        notes: list[str],
+    ) -> AnalysisMaintenanceSummary:
+        return AnalysisMaintenanceSummary(
+            analysisName=analysis_name,
+            sourceFrom=source_from,
+            sourceTo=source_from,
+            sourceCount=source_count,
+            processedCount=processed,
+            savedCount=saved,
+            skippedCount=skipped,
+            failedCount=failed,
+            deletedCount=deleted,
+            esReindexedCount=reindexed,
+            notes=notes,
+        )
+
+    @staticmethod
+    def _response(
+        mode: str,
+        items: list[AnalysisMaintenanceSummary],
+        totals: dict[str, int],
+    ) -> AnalysisMaintenanceResponse:
+        return AnalysisMaintenanceResponse(
+            mode=mode,
+            items=items,
+            totalSourceCount=totals["source_count"],
+            totalProcessedCount=totals["processed_count"],
+            totalSavedCount=totals["saved_count"],
+            totalSkippedCount=totals["skipped_count"],
+            totalFailedCount=totals["failed_count"],
+            totalDeletedCount=totals["deleted_count"],
+            totalEsReindexedCount=totals["es_reindexed_count"],
+            extra={},
+        )
+
+    @staticmethod
+    def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
+        if not settings.elasticsearch_url:
+            return None
+        try:
+            repository = ProcessAnalysisSearchRepository()
+            repository.ensure_indices()
+            return repository
+        except Exception:
+            logger.exception("Elasticsearch maintenance repository is unavailable.")
+            return None
+
+    @staticmethod
+    def _clear_analysis_caches() -> None:
+        if not settings.redis_url:
+            return
+        try:
+            from redis import Redis
+
+            redis_client = Redis.from_url(
+                settings.redis_connection_url,
+                decode_responses=True,
+            )
+            patterns = [
+                f"{settings.redis_key_prefix}:process:bottleneck:*",
+                f"{settings.redis_key_prefix}:process:defect-transfer:*",
+            ]
+            keys: list[str] = []
+            for pattern in patterns:
+                keys.extend(list(redis_client.scan_iter(match=pattern)))
+            if keys:
+                redis_client.delete(*keys)
+        except Exception:
+            logger.exception("Failed to clear analysis caches after maintenance.")
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 40a6120..40007f7 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -187,9 +187,10 @@ def refresh_results_from_events(self) -> list[dict[str, Any]]:
         merged_summaries = self._rank_bottleneck_summaries(
             self._current_bottleneck_results() + new_summaries,
         )
+        detected_at = self._analysis_detected_at(histories)
         self.repository.replace_results(
             merged_summaries,
-            detected_at=seoul_now().replace(tzinfo=None),
+            detected_at=detected_at,
             start_rank=1,
             end_rank=max(len(merged_summaries), 1),
         )
@@ -319,6 +320,17 @@ def _resolve_date(
             return date_options[0].date
         return None
 
+    @staticmethod
+    def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
+        candidates = [
+            value.replace(tzinfo=None) if isinstance(value, datetime) and value.tzinfo else value
+            for value in (row.get("event_time") for row in histories)
+            if isinstance(value, datetime)
+        ]
+        if candidates:
+            return max(candidates)
+        return seoul_now().replace(tzinfo=None)
+
     @staticmethod
     def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
         if not settings.elasticsearch_url:
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index fdc915b..b520f3d 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -22,10 +22,14 @@
 )
 from app.search.process_analysis_search import ProcessAnalysisSearchRepository
 from app.utils.json_utils import from_json, to_json
-from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
+from app.utils.process_label_utils import (
+    NEXT_PROCESS,
+    equipment_code_for_car_process,
+    format_process_with_line,
+)
 
 
-DEFECT_TRANSFER_CACHE_VERSION = "v10"
+DEFECT_TRANSFER_CACHE_VERSION = "v11"
 logger = logging.getLogger(__name__)
 
 
@@ -226,6 +230,13 @@ def get_cause_analysis(
                     vehicle_id=vehicle_id,
                     analysis_date=selected_date,
                 )
+                if selected is None:
+                    selected, _rows, _has_next = self.repository.list_cause_page(
+                        vehicle_id=vehicle_id,
+                        cursor=page,
+                        size=safe_size,
+                        analysis_date=selected_date,
+                    )
             except Exception:
                 logger.exception("Elasticsearch defect cause query failed. Falling back to DB.")
                 cached_value = self._cache_get(cache_key)
@@ -274,32 +285,29 @@ def get_cause_analysis(
                 nextCursor=None,
             )
 
-        cause_rows = self._build_cause_rows(selected)
-
+        representative_cause, detail_causes = self._build_cause_sections(selected)
         offset = page * safe_size
-        display_rows = cause_rows[offset : offset + safe_size]
-        has_next = len(cause_rows) > offset + safe_size
+        display_detail_causes = detail_causes[offset : offset + safe_size]
+        has_next = len(detail_causes) > offset + safe_size
 
         return DefectTransferCausePage(
             vehicleId=str(selected.get("vehicle_id")),
             carMasterId=int(selected["car_master_id"]),
-            predictedDefectProbability=self._result_probability(selected),
+            predictedDefectProbability=self._normalize_probability(
+                selected.get("target_defect_probability"),
+            ),
             riskLevel=selected.get("risk_grade"),
             currentProcess=format_process_with_line(
                 selected.get("source_process_code"),
                 selected.get("source_equipment_code"),
             ),
             predictedDefectProcess=self._resolve_predicted_defect_process(selected),
-            transferProbability=self._percent(selected.get("target_defect_probability")),
+            transferProbability=self._normalize_probability(selected.get("target_defect_probability")),
             date=selected_date,
             dateOptions=date_options,
-            content=[
-                self._to_cause_item(
-                    row,
-                    rank=index,
-                )
-                for index, row in enumerate(display_rows, page * safe_size + 1)
-            ],
+            content=[representative_cause],
+            representativeCause=representative_cause,
+            detailCauses=display_detail_causes,
             hasNext=has_next,
             nextCursor=page + 1 if has_next else None,
         )
@@ -326,37 +334,45 @@ def _to_prediction_item(
         )
 
     @staticmethod
-    def _percent(value: Any) -> int:
+    def _normalize_probability(value: Any) -> float:
         if value is None:
-            return 0
-        return round(float(value) * 100)
+            return 0.0
+        normalized = float(value)
+        if abs(normalized) > 1.0:
+            normalized /= 100.0
+        return round(normalized, 4)
 
     @classmethod
-    def _result_probability(cls, row: dict[str, Any]) -> int:
-        value = row.get("target_defect_probability")
+    def _result_probability(cls, row: dict[str, Any]) -> float:
+        value = row.get("current_defect_probability")
+        if value is None:
+            value = row.get("defect_probability")
         if value is None:
-            value = row.get("current_defect_probability")
-        return cls._percent(value)
+            value = row.get("target_defect_probability")
+        return cls._normalize_probability(value)
 
     @classmethod
     def _resolve_predicted_defect_process(cls, row: dict[str, Any]) -> str | None:
         if cls._result_probability(row) <= 0:
             return None
 
-        db_val = row.get("predicted_defect_process")
-        if db_val:
-            return db_val
-
         source_code = str(row.get("source_process_code") or "").strip().upper()
-        if row.get("target_defect_probability") is not None:
-            process_code = row.get("target_process_code") or NEXT_PROCESS.get(source_code)
-            equipment_code = row.get("target_equipment_code")
-        else:
-            process_code = row.get("source_process_code")
-            equipment_code = row.get("source_equipment_code")
-
+        process_code = row.get("target_process_code") or NEXT_PROCESS.get(source_code)
         if not process_code:
             return None
+        process_code = str(process_code).strip().upper()
+        if process_code == source_code:
+            equipment_code = row.get("source_equipment_code")
+        else:
+            equipment_code = row.get("target_equipment_code") or equipment_code_for_car_process(
+                car_master_id=int(row["car_master_id"]),
+                process_code=process_code,
+            )
+
+        db_val = row.get("predicted_defect_process")
+        if db_val and "(" in str(db_val) and ")" in str(db_val):
+            return str(db_val)
+
         return format_process_with_line(process_code, equipment_code)
 
     @staticmethod
@@ -401,26 +417,43 @@ def _primary_cause_message(row: dict[str, Any]) -> str:
         return ""
 
     @classmethod
-    def _build_cause_rows(cls, row: dict[str, Any]) -> list[dict[str, Any]]:
+    def _build_cause_sections(
+        cls,
+        row: dict[str, Any],
+    ) -> tuple[DefectTransferCauseItem, list[DefectTransferCauseItem]]:
         main_causes = cls._normalize_main_causes(row.get("main_causes") or row.get("causes"))
         if main_causes:
-            return [
+            representative_cause = cls._to_cause_item(
                 {
-                    "rank": cause.get("rank") or index,
-                    "feature": cause.get("feature") or "main_causes",
-                    "label": cause.get("label") or cause.get("message") or "",
-                    "value": cause.get("value") or "",
-                    "impact": cause.get("impact") or 0.0,
-                    "message": cause.get("message") or cause.get("label") or "",
-                    "main_causes": main_causes,
-                }
-                for index, cause in enumerate(main_causes, start=1)
+                    "rank": main_causes[0].get("rank") or 1,
+                    "feature": main_causes[0].get("feature") or "main_causes",
+                    "label": main_causes[0].get("label") or main_causes[0].get("message") or "",
+                    "value": main_causes[0].get("value") or "",
+                    "impact": main_causes[0].get("impact") or 0.0,
+                    "message": main_causes[0].get("message") or main_causes[0].get("label") or "",
+                },
+                rank=1,
+            )
+            detail_causes = [
+                cls._to_cause_item(
+                    {
+                        "rank": cause.get("rank") or index,
+                        "feature": cause.get("feature") or "main_causes",
+                        "label": cause.get("label") or cause.get("message") or "",
+                        "value": cause.get("value") or "",
+                        "impact": cause.get("impact") or 0.0,
+                        "message": cause.get("message") or cause.get("label") or "",
+                    },
+                    rank=index,
+                )
+                for index, cause in enumerate(main_causes[1:], start=2)
             ]
+            return representative_cause, detail_causes
 
         summary_message = cls._primary_cause_message(row)
         if not summary_message:
             summary_message = "no main cause available"
-        return [
+        representative_cause = cls._to_cause_item(
             {
                 "rank": 1,
                 "feature": "main_causes",
@@ -428,9 +461,10 @@ def _build_cause_rows(cls, row: dict[str, Any]) -> list[dict[str, Any]]:
                 "value": "",
                 "impact": float(row.get("influence_score") or 0.0),
                 "message": summary_message,
-                "main_causes": [],
             },
-        ]
+            rank=1,
+        )
+        return representative_cause, []
 
     @staticmethod
     def _normalize_main_causes(causes: list[dict[str, Any]]) -> list[dict[str, Any]]:
@@ -467,26 +501,6 @@ def _to_cause_item(
         *,
         rank: int,
     ) -> DefectTransferCauseItem:
-        main_causes = row.get("main_causes")
-        normalized_main_causes: list[dict[str, Any]] = []
-        if isinstance(main_causes, list):
-            for cause in main_causes:
-                if not isinstance(cause, dict):
-                    continue
-                message = str(cause.get("message") or cause.get("label") or "").strip()
-                if not message:
-                    continue
-                try:
-                    impact = float(cause.get("impact") or 0.0)
-                except (TypeError, ValueError):
-                    impact = 0.0
-                normalized_main_causes.append(
-                    {
-                        "message": message,
-                        "impact": impact,
-                    },
-                )
-
         message = str(row.get("message") or row.get("label") or "").strip()
         if not message:
             message = cls._primary_cause_message(row)
@@ -500,7 +514,6 @@ def _to_cause_item(
             value=str(row.get("value") or ""),
             impact=float(row.get("impact") or 0.0),
             message=message,
-            main_causes=normalized_main_causes,
         )
 
     @staticmethod

From 95d2ecaee23ade1856c1a0bb8800327ab2e8782f Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 13 Jul 2026 14:32:40 +0900
Subject: [PATCH 133/148] =?UTF-8?q?fix:=20=EC=A0=9C=EC=A1=B0=20=ED=94=84?=
 =?UTF-8?q?=EB=A0=88=EC=8A=A4&=EC=B0=A8=EC=B2=B4=20=EC=9D=B4=EC=83=81?=
 =?UTF-8?q?=ED=83=90=EC=A7=80=20=EB=A0=88=ED=8D=BC=EB=9F=B0=20=EB=A7=9E?=
 =?UTF-8?q?=EA=B2=8C=20manufacutring=5Fevent=5Fjson=20=EB=8D=B0=EC=9D=B4?=
 =?UTF-8?q?=ED=84=B0=20=EC=83=9D=EC=84=B1=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../manufacturing_event_json_builder.py       | 366 ++++++++++++++----
 .../manufacturing_event_json_service.py       |  34 +-
 docs/MANUFACUTRING_REFERENCE.md               | 357 +++++++++++++++++
 test_script.py                                |  19 -
 4 files changed, 670 insertions(+), 106 deletions(-)
 create mode 100644 docs/MANUFACUTRING_REFERENCE.md
 delete mode 100644 test_script.py

diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index 6691267..46e25b7 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -1,4 +1,4 @@
-from __future__ import annotations
+from __future__ import annotations
 
 import hashlib
 import json
@@ -13,7 +13,7 @@
 import pandas as pd
 
 
-# PRD의 차량 생산 순서다. 한 차량마다 아래 4개 공정 이벤트를 정확히 한 건씩 만든다.
+# PRD??李⑤웾 ?앹궛 ?쒖꽌?? ??李⑤웾留덈떎 ?꾨옒 4媛?怨듭젙 ?대깽?몃? ?뺥솗????嫄댁뵫 留뚮뱺??
 PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
 PROCESS_ROUTE_PREFIX = {
     "PRESS": "P",
@@ -22,7 +22,7 @@
     "ASSEMBLY": "A",
 }
 
-# PRD 기준 공정별 설비 수와 목표 이상 데이터 비율이다.
+# PRD 湲곗? 怨듭젙蹂??ㅻ퉬 ?섏? 紐⑺몴 ?댁긽 ?곗씠??鍮꾩쑉?대떎.
 LINE_STATION_COUNT = 5
 ABNORMAL_RATIO = 0.30
 PROCESS_DATA_REQUIRED_FIELDS: dict[str, frozenset[str]] = {
@@ -70,21 +70,48 @@
 
 
 def initial_dispatch_status(process_code: str) -> str:
-    """원천 이벤트 최초 발행 상태를 반환한다.
+    """?먯쿇 ?대깽??理쒖큹 諛쒗뻾 ?곹깭瑜?諛섑솚?쒕떎.
 
-    차량 생산 흐름은 PRESS부터 시작하므로 PRESS만 READY이며, BODY/PAINT/
-    ASSEMBLY는 이전 공정의 정상 분석 결과가 오기 전까지 PENDING이다.
+    李⑤웾 ?앹궛 ?먮쫫?€ PRESS遺€???쒖옉?섎?濡?PRESS留?READY?대ʼn, BODY/PAINT/
+    ASSEMBLY???댁쟾 怨듭젙???뺤긽 遺꾩꽍 寃곌낵媛€ ?ㅺ린 ?꾧퉴吏€ PENDING?대떎.
     """
     return "READY" if process_code == "PRESS" else "PENDING"
 
 
 def is_abnormal_operation_status(operation_status: Any) -> bool:
-    """이벤트 JSON의 운전 상태가 이상 상태인지 반환한다."""
+    """?대깽??JSON???댁쟾 ?곹깭媛€ ?댁긽 ?곹깭?몄? 諛섑솚?쒕떎."""
     return operation_status in {"FAULT", "STOPPED"}
 
 
+def _press_count_increase_flag(
+    *,
+    station_delay_sec: float,
+    equipment_idle_time_sec: float,
+) -> bool | None:
+    """?꾨젅???앹궛 移댁슫??利앷? ?щ?瑜??뺤긽/寃쎄퀬/?꾪뿕 湲곗??쇰줈 遺꾨━?쒕떎."""
+    if station_delay_sec <= 2.0 and equipment_idle_time_sec < 6.0:
+        return True
+    if station_delay_sec <= 3.0:
+        return None
+    return False
+
+
+def _body_robot_motion_status(vibration_score: float) -> str:
+    """李⑥껜 濡쒕큸 吏꾨룞 ?먯닔瑜?諛뷀깢?쇰줈 ?곹깭瑜??먯젙?쒕떎."""
+    if vibration_score >= 0.45:
+        return "ABNORMAL"
+    if vibration_score >= 0.40:
+        return "WARNING"
+    return "NORMAL"
+
+
+def _body_robot_operation_mode(vibration_score: float) -> str:
+    """李⑥껜 濡쒕큸 ?댁쟾 紐⑤뱶瑜?吏꾨룞 ?먯닔 湲곗??쇰줈 援ъ꽦?쒕떎."""
+    return "STOPPED" if vibration_score >= 0.45 else "AUTO"
+
+
 def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
-    """제조 이벤트 JSON을 PRD에 정의된 필드만 남긴 구조로 정규화한다."""
+    """?쒖“ ?대깽??JSON??PRD???뺤쓽???꾨뱶留??④릿 援ъ“濡??뺢퇋?뷀븳??"""
     event = event_json.get("event", {})
     equipment = event_json.get("equipment", {})
     equipment_status = event_json.get("equipmentStatus", {})
@@ -185,13 +212,13 @@ def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
     "PAINT": {
         "equipmentType": "CAMERA",
         "eventType": "QUALITY_CHECK",
-        "eventName": "도장 공정 품질 관제 이벤트",
+        "eventName": "도장 공정 비전 관제 이벤트",
         "targetCycleTimeSec": 64.0,
     },
     "ASSEMBLY": {
         "equipmentType": "CONVEYOR",
         "eventType": "PROCESS_STATUS",
-        "eventName": "의장 공정 조립 관제 이벤트",
+        "eventName": "조립 공정 통합 관제 이벤트",
         "targetCycleTimeSec": 58.0,
     },
 }
@@ -210,19 +237,19 @@ def normalize_event_json(event_json: dict[str, Any]) -> dict[str, Any]:
 
 @dataclass(frozen=True)
 class EventBuildRequest:
-    """한 번의 제조 이벤트 생성 작업에 필요한 범위와 마스터 데이터."""
+    """??踰덉쓽 ?쒖“ ?대깽???앹꽦 ?묒뾽???꾩슂??踰붿쐞?€ 留덉뒪???곗씠??"""
 
     start_date: date
     end_date: date
-    # 기존 API 이름을 유지하지만 현재 의미는 기간 전체 이벤트 수다.
+    # 湲곗〈 API ?대쫫???좎??섏?留??꾩옱 ?섎???湲곌컙 ?꾩껜 ?대깽???섎떎.
     events_per_day: int
-    # dict 삽입 순서가 car_master.id 오름차순을 보존한다.
+    # dict ?쎌엯 ?쒖꽌媛€ car_master.id ?ㅻ쫫李⑥닚??蹂댁〈?쒕떎.
     car_id_map: dict[str, int]
     equipment_map: dict[str, dict[str, Any]]
 
 
 class ManufacturingEventJsonBuilder:
-    """CSV 원천 데이터를 PRD의 통합 제조 이벤트 JSON으로 변환한다."""
+    """CSV ?먯쿇 ?곗씠?곕? PRD???듯빀 ?쒖“ ?대깽??JSON?쇰줈 蹂€?섑븳??"""
 
     def __init__(self, dataset_root: Path = DATASET_ROOT) -> None:
         self.dataset_root = dataset_root
@@ -233,9 +260,9 @@ def build_rows(self, request: EventBuildRequest) -> list[dict[str, Any]]:
         return list(self.iter_rows(request))
 
     def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
-        """차량 단위로 4공정 row를 순차 생성한다."""
-        # Repository가 car_master.id 오름차순으로 만든 순서를 그대로 사용해야
-        # PRD의 "id 1번 차량부터 생성" 조건을 지킬 수 있다.
+        """李⑤웾 ?⑥쐞濡?4怨듭젙 row瑜??쒖감 ?앹꽦?쒕떎."""
+        # Repository媛€ car_master.id ?ㅻ쫫李⑥닚?쇰줈 留뚮뱺 ?쒖꽌瑜?洹몃?濡??ъ슜?댁빞
+        # PRD??"id 1踰?李⑤웾遺€???앹꽦" 議곌굔??吏€?????덈떎.
         car_ids = list(request.car_id_map)
         if not car_ids:
             return
@@ -244,24 +271,24 @@ def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
         if days <= 0:
             return
 
-        # 차량마다 4건이므로 이벤트 수와 차량 수를 독립적으로 받을 수 없다.
-        # 여기서 다시 검증해 서비스 외부에서 Builder를 직접 호출해도 구조가 깨지지 않게 한다.
+        # 李⑤웾留덈떎 4嫄댁씠誘€濡??대깽???섏? 李⑤웾 ?섎? ?낅┰?곸쑝濡?諛쏆쓣 ???녿떎.
+        # ?ш린???ㅼ떆 寃€利앺빐 ?쒕퉬???몃??먯꽌 Builder瑜?吏곸젒 ?몄텧?대룄 援ъ“媛€ 源⑥?吏€ ?딄쾶 ?쒕떎.
         total_events = len(car_ids) * len(PROCESS_SEQUENCE)
         if request.events_per_day != total_events:
             raise ValueError(
-                "event_count는 car_pool_size * 4와 같아야 합니다: "
+                "event_count??car_pool_size * 4?€ 媛숈븘???⑸땲?? "
                 f"event_count={request.events_per_day}, expected={total_events}",
             )
 
-        # 날짜별 실제 차량 수를 기준으로 정상 70% / 폐기(이상) 30%를 배치한다.
-        # 이상 차량도 4공정을 모두 가지므로 공정별 이상 건수가 자동으로 동일해진다.
+        # ?좎쭨蹂??ㅼ젣 李⑤웾 ?섎? 湲곗??쇰줈 ?뺤긽 70% / ?먭린(?댁긽) 30%瑜?諛곗튂?쒕떎.
+        # ?댁긽 李⑤웾??4怨듭젙??紐⑤몢 媛€吏€誘€濡?怨듭젙蹂??댁긽 嫄댁닔媛€ ?먮룞?쇰줈 ?숈씪?댁쭊??
         abnormal_vehicle_count = round(len(car_ids) * ABNORMAL_RATIO)
         normal_vehicle_count = len(car_ids) - abnormal_vehicle_count
 
         for production_sequence_index, car_id in enumerate(car_ids):
             car_master_id = request.car_id_map[car_id]
             is_abnormal_vehicle = production_sequence_index >= normal_vehicle_count
-            # 같은 차량의 이벤트는 PRESS -> BODY -> PAINT -> ASSEMBLY 순서를 유지한다.
+            # 媛숈? 李⑤웾???대깽?몃뒗 PRESS -> BODY -> PAINT -> ASSEMBLY ?쒖꽌瑜??좎??쒕떎.
             for process_index, process_code in enumerate(PROCESS_SEQUENCE):
                 global_index = (
                     production_sequence_index * len(PROCESS_SEQUENCE) + process_index
@@ -301,8 +328,8 @@ def iter_rows(self, request: EventBuildRequest) -> Iterator[dict[str, Any]]:
                     "equipment_status": event["equipmentStatus"]["operationStatus"],
                     "event_type": event["event"]["eventType"],
                     "event_json": _json_safe(event),
-                    # 최초에는 PRESS만 발행할 수 있다. 후속 공정은 이전 공정의
-                    # 정상 분석 결과를 받은 뒤 Consumer가 READY로 전환한다.
+                    # 理쒖큹?먮뒗 PRESS留?諛쒗뻾?????덈떎. ?꾩냽 怨듭젙?€ ?댁쟾 怨듭젙??
+                    # ?뺤긽 遺꾩꽍 寃곌낵瑜?諛쏆? ??Consumer媛€ READY濡??꾪솚?쒕떎.
                     "dispatch_status": initial_dispatch_status(process_code),
                     "analysis_status": "NOT_ANALYZED",
                     "bottleneck_analysis_done": False,
@@ -324,20 +351,20 @@ def _build_event(
         is_abnormal: bool,
         equipment_map: dict[str, dict[str, Any]],
     ) -> dict[str, Any]:
-        # 사용자의 요청에 따라 PRESS는 이상 빈도를 낮추고 PAINT, ASSEMBLY는 높인다.
-        # global_index를 활용해 결정론적(deterministic) 난수를 생성한다.
+        # ?ъ슜?먯쓽 ?붿껌???곕씪 PRESS???댁긽 鍮덈룄瑜???텛怨?PAINT, ASSEMBLY???믪씤??
+        # global_index瑜??쒖슜??寃곗젙濡좎쟻(deterministic) ?쒖닔瑜??앹꽦?쒕떎.
         if is_abnormal:
             if process_code == "PRESS":
-                # PRESS는 원래 이상의 20%만 유지
+                # PRESS???먮옒 ?댁긽??20%留??좎?
                 is_abnormal = (global_index % 10) < 2
             elif process_code in {"PAINT", "ASSEMBLY"}:
-                # PAINT, ASSEMBLY는 원래 이상의 80%를 유지 (빈도 높임)
+                # PAINT, ASSEMBLY???먮옒 ?댁긽??80%瑜??좎? (鍮덈룄 ?믪엫)
                 is_abnormal = (global_index % 10) < 8
 
         meta = PROCESS_META[process_code]
-        # 같은 차량도 공정마다 서로 다른 설비를 사용할 수 있도록 차량 PK와
-        # 공정 코드를 함께 사용해 1~5호기를 독립적으로 배정한다.
-        # 해시 기반이라 분포는 랜덤하지만 재생성·템플릿 replay 결과는 동일하다.
+        # 媛숈? 李⑤웾??怨듭젙留덈떎 ?쒕줈 ?ㅻⅨ ?ㅻ퉬瑜??ъ슜?????덈룄濡?李⑤웾 PK?€
+        # 怨듭젙 肄붾뱶瑜??④퍡 ?ъ슜??1~5?멸린瑜??낅┰?곸쑝濡?諛곗젙?쒕떎.
+        # ?댁떆 湲곕컲?대씪 遺꾪룷???쒕뜡?섏?留??ъ깮?굿룻뀥?뚮┸ replay 寃곌낵???숈씪?섎떎.
         line_station_no = _equipment_no_for_process(
             car_master_id=car_master_id,
             process_code=process_code,
@@ -345,7 +372,7 @@ def _build_event(
         equipment_code = f"EQ_{process_code}_{line_station_no:03d}"
         equipment_row = equipment_map[equipment_code]
 
-        # 공개 데이터셋의 실제 행을 읽어 원천 추적 정보와 기본 센서 특성을 만든다.
+        # 怨듦컻 ?곗씠?곗뀑???ㅼ젣 ?됱쓣 ?쎌뼱 ?먯쿇 異붿쟻 ?뺣낫?€ 湲곕낯 ?쇱꽌 ?뱀꽦??留뚮뱺??
         forming = self._forming_row(production_sequence_index)
         current = self._current_features(process_code, global_index)
         ford = self._ford_features(global_index)
@@ -359,8 +386,8 @@ def _build_event(
             vision=vision,
             bosch=bosch,
         )
-        # 원천 데이터의 실제 클래스 비율은 데이터셋마다 다르므로, PRD가 요구한
-        # 7:3 비율과 Java 판정 임계값을 안정적으로 만족하도록 프로필을 적용한다.
+        # ?먯쿇 ?곗씠?곗쓽 ?ㅼ젣 ?대옒??鍮꾩쑉?€ ?곗씠?곗뀑留덈떎 ?ㅻⅤ誘€濡? PRD媛€ ?붽뎄??
+        # 7:3 鍮꾩쑉怨?Java ?먯젙 ?꾧퀎媛믪쓣 ?덉젙?곸쑝濡?留뚯”?섎룄濡??꾨줈?꾩쓣 ?곸슜?쒕떎.
         self._apply_detection_profile(
             process_code=process_code,
             is_abnormal=is_abnormal,
@@ -392,7 +419,7 @@ def _build_event(
         return {
             "event": {
                 "eventId": event_id,
-                # 원천 이벤트 생성 시점에는 실제 발생 시각이 확정되지 않았으므로
+                # ?먯쿇 ?대깽???앹꽦 ?쒖젏?먮뒗 ?ㅼ젣 諛쒖깮 ?쒓컖???뺤젙?섏? ?딆븯?쇰?濡?
                 "eventTime": event_time.isoformat(),
                 "eventType": meta["eventType"],
                 "eventName": meta["eventName"],
@@ -403,7 +430,7 @@ def _build_event(
                 "equipmentType": equipment_row["equipment_type"],
             },
             "equipmentStatus": {
-                # Java enum EquipmentOperationStatus 값만 사용한다.
+                # Java enum EquipmentOperationStatus 媛믩쭔 ?ъ슜?쒕떎.
                 "operationStatus": operation_status,
                 "lastNormalTime": (
                     (event_time - timedelta(seconds=31)).isoformat()
@@ -426,8 +453,8 @@ def _build_event(
                 "machineVisionRowId": vision["rowId"],
                 "boschId": bosch["id"],
             },
-            # process_code에 해당하는 블록 하나만 포함한다.
-            # PRESS면 press, BODY면 body, PAINT면 paint, ASSEMBLY면 assembly만 존재한다.
+            # process_code???대떦?섎뒗 釉붾줉 ?섎굹留??ы븿?쒕떎.
+            # PRESS硫?press, BODY硫?body, PAINT硫?paint, ASSEMBLY硫?assembly留?議댁옱?쒕떎.
             "processData": process_data,
         }
 
@@ -443,25 +470,196 @@ def _apply_detection_profile(
         bosch: dict[str, Any],
         process_metrics: dict[str, Any],
     ) -> None:
-        """PRD의 Java 판정식에서 정상/이상이 명확히 갈리도록 입력값을 보정한다.
-
-        이상 프로필은 전류·진동·열화상·지연·조립 오류를 함께 높여 모든 공정의
-        전용 위험도가 임계값을 넘게 한다. 정상 프로필은 반대로 충분한 여유를 둬
-        날짜별 작은 변동이 적용되어도 정상 범위를 벗어나지 않게 한다.
-        """
         target = float(PROCESS_META[process_code]["targetCycleTimeSec"])
+        current_variation = global_index % 11
+        vibration_variation = global_index % 13
+        paint_variation = global_index % 10
+        metric_variation = global_index % 8
+
+        if process_code == "PRESS":
+            if is_abnormal:
+                rms_ampere = 3.05 + current_variation * 0.16
+                acceleration_g = 0.022 + current_variation * 0.0015
+                vibration_score = min(0.99, 0.44 + vibration_variation * 0.015)
+                vibration_rms = 1.65 + vibration_variation * 0.06
+                vibration_peak = 2.45 + vibration_variation * 0.08
+                cycle_time = target + 4.5 + metric_variation * 0.95
+                station_delay = cycle_time - target
+
+                current.update(
+                    rmsAmpere=round(rms_ampere, 3),
+                    maxAmpere=round(rms_ampere + 0.38 + current_variation * 0.02, 3),
+                    minAmpere=round(max(0.0, rms_ampere - 0.28), 3),
+                    accelerationG=round(acceleration_g, 4),
+                )
+                ford.update(
+                    label=1,
+                    vibrationScore=round(vibration_score, 4),
+                    vibrationRms=round(vibration_rms, 3),
+                    vibrationPeak=round(vibration_peak, 3),
+                )
+                vision.update(
+                    label=0,
+                    avgTemperature=round(38.2 + paint_variation * 0.12, 3),
+                    maxTemperature=round(40.2 + paint_variation * 0.15, 3),
+                    minTemperature=round(36.8 + paint_variation * 0.09, 3),
+                    thermalStdTemp=round(0.62 + paint_variation * 0.03, 3),
+                    defectScore=round(0.05 + paint_variation * 0.006, 4),
+                    thicknessValue=round(113.5 + paint_variation * 0.35, 3),
+                    surfaceQualityScore=round(98.0 - paint_variation * 0.25, 3),
+                )
+                bosch["response"] = 0
+                process_metrics.update(
+                    cycleTimeSec=round(cycle_time, 3),
+                    waitingTimeSec=round(5.5 + metric_variation * 0.45, 3),
+                    processingTimeSec=round(max(1.0, target - 1.5 + metric_variation * 0.25), 3),
+                    stationDelaySec=round(station_delay, 3),
+                    throughputPerMin=round(60 / cycle_time, 3),
+                    queueLength=6 + metric_variation,
+                    wipCount=18 + metric_variation * 2,
+                    equipmentIdleTimeSec=round(7.0 + metric_variation * 0.9, 3),
+                )
+                return
+
+            rms_ampere = 1.68 + current_variation * 0.04
+            acceleration_g = 0.004 + current_variation * 0.0006
+            vibration_score = 0.14 + vibration_variation * 0.014
+            vibration_rms = 0.28 + vibration_variation * 0.025
+            vibration_peak = 0.48 + vibration_variation * 0.035
+            cycle_time = target + metric_variation * 0.28
+            station_delay = max(0.0, cycle_time - target)
+
+            current.update(
+                rmsAmpere=round(rms_ampere, 3),
+                maxAmpere=round(rms_ampere + 0.17 + current_variation * 0.01, 3),
+                minAmpere=round(max(0.0, rms_ampere - 0.15), 3),
+                accelerationG=round(acceleration_g, 5),
+            )
+            ford.update(
+                label=1,
+                vibrationScore=round(vibration_score, 4),
+                vibrationRms=round(vibration_rms, 3),
+                vibrationPeak=round(vibration_peak, 3),
+            )
+            vision.update(
+                label=0,
+                avgTemperature=round(37.8 + paint_variation * 0.18, 3),
+                maxTemperature=round(39.8 + paint_variation * 0.2, 3),
+                minTemperature=round(36.5 + paint_variation * 0.12, 3),
+                thermalStdTemp=round(0.55 + paint_variation * 0.05, 3),
+                defectScore=round(0.03 + paint_variation * 0.006, 4),
+                thicknessValue=round(113.0 + paint_variation * 0.4, 3),
+                surfaceQualityScore=round(98.6 - paint_variation * 0.2, 3),
+            )
+            bosch["response"] = 0
+            process_metrics.update(
+                cycleTimeSec=round(cycle_time, 3),
+                waitingTimeSec=round(1.4 + metric_variation * 0.2, 3),
+                processingTimeSec=round(max(1.0, target - 2.0 + metric_variation * 0.12), 3),
+                stationDelaySec=round(station_delay, 3),
+                throughputPerMin=round(60 / cycle_time, 3),
+                queueLength=1 + metric_variation % 3,
+                wipCount=3 + metric_variation,
+                equipmentIdleTimeSec=round(metric_variation * 0.18, 3),
+            )
+            return
+
+        if process_code == "BODY":
+            if is_abnormal:
+                rms_ampere = 1.95 + current_variation * 0.03
+                acceleration_g = 0.006 + current_variation * 0.0004
+                vibration_score = min(0.99, 0.46 + vibration_variation * 0.012)
+                vibration_rms = 1.55 + vibration_variation * 0.055
+                vibration_peak = 2.15 + vibration_variation * 0.075
+                cycle_time = target + 6.0 + metric_variation * 0.85
+                station_delay = cycle_time - target
+
+                current.update(
+                    rmsAmpere=round(rms_ampere, 3),
+                    maxAmpere=round(rms_ampere + 0.16 + current_variation * 0.008, 3),
+                    minAmpere=round(max(0.0, rms_ampere - 0.13), 3),
+                    accelerationG=round(acceleration_g, 5),
+                )
+                ford.update(
+                    label=-1,
+                    vibrationScore=round(vibration_score, 4),
+                    vibrationRms=round(vibration_rms, 3),
+                    vibrationPeak=round(vibration_peak, 3),
+                )
+                vision.update(
+                    label=0,
+                    avgTemperature=round(38.0 + paint_variation * 0.1, 3),
+                    maxTemperature=round(40.0 + paint_variation * 0.12, 3),
+                    minTemperature=round(36.8 + paint_variation * 0.08, 3),
+                    thermalStdTemp=round(0.58 + paint_variation * 0.02, 3),
+                    defectScore=round(0.04 + paint_variation * 0.005, 4),
+                    thicknessValue=round(113.8 + paint_variation * 0.3, 3),
+                    surfaceQualityScore=round(98.2 - paint_variation * 0.18, 3),
+                )
+                bosch["response"] = 0
+                process_metrics.update(
+                    cycleTimeSec=round(cycle_time, 3),
+                    waitingTimeSec=round(7.5 + metric_variation * 0.5, 3),
+                    processingTimeSec=round(max(1.0, target - 2.0 + metric_variation * 0.2), 3),
+                    stationDelaySec=round(station_delay, 3),
+                    throughputPerMin=round(60 / cycle_time, 3),
+                    queueLength=7 + metric_variation,
+                    wipCount=20 + metric_variation * 2,
+                    equipmentIdleTimeSec=round(8.5 + metric_variation * 0.95, 3),
+                )
+                return
+
+            rms_ampere = 1.72 + current_variation * 0.035
+            acceleration_g = 0.005 + current_variation * 0.00045
+            vibration_score = 0.24 + vibration_variation * 0.013
+            vibration_rms = 0.36 + vibration_variation * 0.03
+            vibration_peak = 0.62 + vibration_variation * 0.04
+            cycle_time = target + metric_variation * 0.38
+            station_delay = max(0.0, cycle_time - target)
+
+            current.update(
+                rmsAmpere=round(rms_ampere, 3),
+                maxAmpere=round(rms_ampere + 0.14 + current_variation * 0.009, 3),
+                minAmpere=round(max(0.0, rms_ampere - 0.13), 3),
+                accelerationG=round(acceleration_g, 5),
+            )
+            ford.update(
+                label=1,
+                vibrationScore=round(vibration_score, 4),
+                vibrationRms=round(vibration_rms, 3),
+                vibrationPeak=round(vibration_peak, 3),
+            )
+            vision.update(
+                label=0,
+                avgTemperature=round(37.9 + paint_variation * 0.14, 3),
+                maxTemperature=round(39.9 + paint_variation * 0.16, 3),
+                minTemperature=round(36.6 + paint_variation * 0.1, 3),
+                thermalStdTemp=round(0.56 + paint_variation * 0.04, 3),
+                defectScore=round(0.03 + paint_variation * 0.005, 4),
+                thicknessValue=round(113.2 + paint_variation * 0.32, 3),
+                surfaceQualityScore=round(98.4 - paint_variation * 0.16, 3),
+            )
+            bosch["response"] = 0
+            process_metrics.update(
+                cycleTimeSec=round(cycle_time, 3),
+                waitingTimeSec=round(2.1 + metric_variation * 0.22, 3),
+                processingTimeSec=round(max(1.0, target - 1.8 + metric_variation * 0.15), 3),
+                stationDelaySec=round(station_delay, 3),
+                throughputPerMin=round(60 / cycle_time, 3),
+                queueLength=2 + metric_variation % 4,
+                wipCount=5 + metric_variation,
+                equipmentIdleTimeSec=round(metric_variation * 0.22, 3),
+            )
+            return
+
         if is_abnormal:
-            # 설비 위험 및 불량 전이 위험을 높이는 공통 센서 프로필.
-            # 기존에는 고정값만 넣어 상세 테이블 값이 반복 저장될 수 있었으므로,
-            # global_index 기반 deterministic variation을 더해 재생성 결과는 유지하면서
-            # 이벤트별 수치는 다양하게 만든다.
             current_variation = global_index % 11
             vibration_variation = global_index % 13
             paint_variation = global_index % 10
             metric_variation = global_index % 8
 
-            rms_ampere = 3.8 + current_variation * 0.13
-            acceleration_g = 0.065 + current_variation * 0.004
+            rms_ampere = 3.2 + current_variation * 0.12
+            acceleration_g = 0.055 + current_variation * 0.003
             vibration_score = min(0.99, 0.72 + vibration_variation * 0.018)
             vibration_rms = 2.35 + vibration_variation * 0.075
             vibration_peak = 3.55 + vibration_variation * 0.095
@@ -491,7 +689,6 @@ def _apply_detection_profile(
                 surfaceQualityScore=round(max(0.0, 72.0 - paint_variation * 1.8), 3),
             )
             bosch["response"] = 1
-            # 병목 위험도도 함께 높아지도록 지연, WIP, 유휴 시간을 보정한다.
             process_metrics.update(
                 cycleTimeSec=round(cycle_time, 3),
                 waitingTimeSec=round(14.0 + metric_variation * 1.1, 3),
@@ -504,8 +701,6 @@ def _apply_detection_profile(
             )
             return
 
-        # 정상 프로필은 PRD 기준 cycle time과 낮은 센서 위험도를 사용한다.
-        # 정상 데이터도 완전 고정값이 아니라 작은 범위에서 흔들리게 한다.
         current_variation = global_index % 9
         vibration_variation = global_index % 7
         paint_variation = global_index % 8
@@ -547,7 +742,6 @@ def _apply_detection_profile(
             wipCount=3 + metric_variation,
             equipmentIdleTimeSec=round(metric_variation * 0.2, 3),
         )
-
     def _forming_row(self, index: int) -> dict[str, Any]:
         df = self._load_forming()
         row = df.iloc[index % len(df)]
@@ -758,18 +952,23 @@ def _process_data(
         process_metrics: dict[str, Any],
     ) -> dict[str, Any]:
         if process_code == "PRESS":
+            count_increase = _press_count_increase_flag(
+                station_delay_sec=float(process_metrics["stationDelaySec"]),
+                equipment_idle_time_sec=float(process_metrics["equipmentIdleTimeSec"]),
+            )
             return {
                 "press": {
-                    "countIncreaseYn": process_metrics["equipmentIdleTimeSec"] < 18,
+                    "countIncreaseYn": count_increase,
                     "targetCycleTimeSec": PROCESS_META["PRESS"]["targetCycleTimeSec"],
                     "timestampDelaySec": process_metrics["stationDelaySec"],
                 },
             }
         if process_code == "BODY":
+            robot_score = float(ford["vibrationScore"])
             return {
                 "body": {
-                    "robotMotionStatus": "WARNING" if ford["label"] < 0 else "NORMAL",
-                    "robotOperationMode": "AUTO",
+                    "robotMotionStatus": _body_robot_motion_status(robot_score),
+                    "robotOperationMode": _body_robot_operation_mode(robot_score),
                     "frequencyPeakBand": ford["frequencyPeakBand"],
                     "frequencyBands": ford["frequencyBands"],
                 },
@@ -791,7 +990,7 @@ def _process_data(
             has_sequence_error = bool(bosch["response"])
             expected_sequence = _equipment_route_for_car(car_master_id)
             expected_steps = expected_sequence.split(">")
-            # 이상 데이터는 BODY와 PAINT 통과 순서를 바꿔 순서 오류를 표현한다.
+            # ?댁긽 ?곗씠?곕뒗 BODY?€ PAINT ?듦낵 ?쒖꽌瑜?諛붽퓭 ?쒖꽌 ?ㅻ쪟瑜??쒗쁽?쒕떎.
             abnormal_steps = [
                 expected_steps[0],
                 expected_steps[2],
@@ -816,12 +1015,12 @@ def _process_data(
                     "sequenceErrorCount": sequence_error_count,
                 },
             }
-        raise ValueError(f"지원하지 않는 process_code입니다: {process_code}")
+        raise ValueError(f"吏€?먰븯吏€ ?딅뒗 process_code?낅땲?? {process_code}")
 
     def _load_forming(self) -> pd.DataFrame:
         return self._cached_csv(
             "forming",
-            self.dataset_root / "소성가공 자원최적화 AI 데이터셋" / "공정_데이터_2022년_8월.csv",
+            self.dataset_root / "?뚯꽦媛€怨??먯썝理쒖쟻??AI ?곗씠?곗뀑" / "怨듭젙_?곗씠??2022??8??csv",
             nrows=4096,
         )
 
@@ -830,8 +1029,8 @@ def _load_press_current(self, index: int) -> pd.DataFrame:
         return self._cached_csv(
             f"press_current_{file_index}",
             self.dataset_root
-            / "소성가공 자원최적화 AI 데이터셋"
-            / f"프레스_{file_index}호-유압모터_전류데이터.csv",
+            / "?뚯꽦媛€怨??먯썝理쒖쟻??AI ?곗씠?곗뀑"
+            / f"?꾨젅??{file_index}???좎븬紐⑦꽣_?꾨쪟?곗씠??csv",
             nrows=4096,
         )
 
@@ -840,15 +1039,15 @@ def _load_robot_current(self, index: int) -> pd.DataFrame:
         return self._cached_csv(
             f"robot_current_{file_index}",
             self.dataset_root
-            / "소성가공 자원최적화 AI 데이터셋"
-            / f"로봇_{file_index}호-전류_데이터.csv",
+            / "?뚯꽦媛€怨??먯썝理쒖쟻??AI ?곗씠?곗뀑"
+            / f"濡쒕큸_{file_index}???꾨쪟_?곗씠??csv",
             nrows=4096,
         )
 
     def _load_vision(self, side: str) -> tuple[pd.DataFrame, list[float]]:
         cache_key = f"vision_{side}"
         if cache_key not in self._cache:
-            base = self.dataset_root / "머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)"
+            base = self.dataset_root / "癒몄떊鍮꾩쟾 AI ?곗씠?곗뀑 (?댄솕??湲곕컲 ?덉쭏 寃€???곗씠??"
             df = pd.read_csv(base / f"2nd_process_{side}_data.csv", nrows=4096)
             with (base / f"2nd_process_{side}_label.json").open(
                 encoding="utf-8",
@@ -866,7 +1065,7 @@ def _load_bosch_numeric(self) -> pd.DataFrame:
 
     def _load_ford_rows(self) -> list[list[float]]:
         if "ford_train" not in self._cache:
-            base = self.dataset_root / "Ford 엔진 진동 데이터셋"
+            base = self.dataset_root / "Ford ?붿쭊 吏꾨룞 ?곗씠?곗뀑"
             path = base / "FordA_TRAIN.txt"
             rows: list[list[float]] = []
             with path.open(encoding="utf-8", errors="ignore") as file:
@@ -960,9 +1159,9 @@ def _event_time_for_range_slot(
     slot: int,
     total_events: int,
 ) -> datetime:
-    """전체 이벤트를 날짜 범위에 균등 분할한 뒤 일별 생산 밀도를 적용한다."""
+    """?꾩껜 ?대깽?몃? ?좎쭨 踰붿쐞??洹좊벑 遺꾪븷?????쇰퀎 ?앹궛 諛€?꾨? ?곸슜?쒕떎."""
     days = (end_date - start_date).days + 1
-    # 나머지는 앞 날짜부터 한 건씩 배분해 전체 건수가 정확히 유지되게 한다.
+    # ?섎㉧吏€?????좎쭨遺€????嫄댁뵫 諛곕텇???꾩껜 嫄댁닔媛€ ?뺥솗???좎??섍쾶 ?쒕떎.
     base_count, remainder = divmod(total_events, days)
     day_offset = 0
     day_start_slot = 0
@@ -981,7 +1180,7 @@ def _event_time_for_range_slot(
 
 
 def _weighted_event_offset_us(slot: int, events_per_day: int) -> int:
-    """일별 slot을 시간대별 생산 밀도에 맞는 microsecond offset으로 변환한다."""
+    """?쇰퀎 slot???쒓컙?€蹂??앹궛 諛€?꾩뿉 留욌뒗 microsecond offset?쇰줈 蹂€?섑븳??"""
     windows = _weighted_event_windows(events_per_day)
     remaining_slot = slot
     for start_us, end_us, count in windows:
@@ -1047,18 +1246,18 @@ def _largest_fraction_window_index(
 
 
 def _deterministic_jitter_us(slot: int, interval_us: int) -> int:
-    """재실행 결과는 같게 유지하면서 이벤트 시각의 기계적인 등간격을 완화한다."""
+    """?ъ떎??寃곌낵??媛숆쾶 ?좎??섎㈃???대깽???쒓컖??湲곌퀎?곸씤 ?깃컙寃⑹쓣 ?꾪솕?쒕떎."""
     jitter_window = max(1, interval_us // 5)
     pseudo_random = (slot * 1103515245 + 12345) & 0x7FFFFFFF
     return pseudo_random % (2 * jitter_window + 1) - jitter_window
 
 
 def _equipment_no_for_process(*, car_master_id: int, process_code: str) -> int:
-    """차량·공정 조합별로 독립적인 설비 번호(1~5)를 결정한다.
+    """李⑤웾쨌怨듭젙 議고빀蹂꾨줈 ?낅┰?곸씤 ?ㅻ퉬 踰덊샇(1~5)瑜?寃곗젙?쒕떎.
 
-    일반 random 모듈을 사용하면 배치를 다시 실행할 때 설비가 달라질 수 있다.
-    이벤트 재생성과 upsert가 안정적으로 동작하도록 같은 입력에는 항상 같은
-    번호가 나오는 해시 기반 결정적 랜덤 방식을 사용한다.
+    ?쇰컲 random 紐⑤뱢???ъ슜?섎㈃ 諛곗튂瑜??ㅼ떆 ?ㅽ뻾?????ㅻ퉬媛€ ?щ씪吏????덈떎.
+    ?대깽???ъ깮?깃낵 upsert媛€ ?덉젙?곸쑝濡??숈옉?섎룄濡?媛숈? ?낅젰?먮뒗 ??긽 媛숈?
+    踰덊샇媛€ ?섏삤???댁떆 湲곕컲 寃곗젙???쒕뜡 諛⑹떇???ъ슜?쒕떎.
     """
     digest = hashlib.blake2b(
         f"{car_master_id}:{process_code}:equipment".encode("utf-8"),
@@ -1068,9 +1267,9 @@ def _equipment_no_for_process(*, car_master_id: int, process_code: str) -> int:
 
 
 def _equipment_route_for_car(car_master_id: int) -> str:
-    """차량이 통과할 4개 공정의 실제 설비 경로를 문자열로 만든다.
+    """李⑤웾???듦낵??4媛?怨듭젙???ㅼ젣 ?ㅻ퉬 寃쎈줈瑜?臾몄옄?대줈 留뚮뱺??
 
-    예: PRESS 2호, BODY 3호, PAINT 1호, ASSEMBLY 4호
+    ?? PRESS 2?? BODY 3?? PAINT 1?? ASSEMBLY 4??
     -> P02>B03>PA01>A04
     """
     return ">".join(
@@ -1103,7 +1302,7 @@ def _equipment_status_for_event(
     process_code: str,
     is_abnormal: bool,
 ) -> dict[str, str]:
-    """Java enum 기준 운전 상태를 조정하여 장비 이상(STOPPED/FAULT)을 줄인다."""
+    """Java enum 湲곗? ?댁쟾 ?곹깭瑜?議곗젙?섏뿬 ?λ퉬 ?댁긽(STOPPED/FAULT)??以꾩씤??"""
     _ = is_abnormal
     digest = hashlib.blake2b(
         f"{car_master_id}:{process_code}:status".encode("utf-8"),
@@ -1127,28 +1326,28 @@ def validate_process_data(
     process_code: str,
     process_data: dict[str, Any],
 ) -> None:
-    """processData가 공정별 전용 JSON 구조를 정확히 따르는지 검증한다."""
+    """processData媛€ 怨듭젙蹂??꾩슜 JSON 援ъ“瑜??뺥솗???곕Ⅴ?붿? 寃€利앺븳??"""
     expected_key = PROCESS_DATA_KEY.get(process_code)
     required_fields = PROCESS_DATA_REQUIRED_FIELDS.get(process_code)
     if expected_key is None or required_fields is None:
-        raise ValueError(f"지원하지 않는 process_code입니다: {process_code}")
+        raise ValueError(f"吏€?먰븯吏€ ?딅뒗 process_code?낅땲?? {process_code}")
 
     if set(process_data) != {expected_key}:
         raise ValueError(
-            f"{process_code} processData는 {expected_key} 블록만 포함해야 합니다: "
+            f"{process_code} processData??{expected_key} 釉붾줉留??ы븿?댁빞 ?⑸땲?? "
             f"actual={sorted(process_data)}",
         )
 
     process_payload = process_data.get(expected_key)
     if not isinstance(process_payload, dict):
         raise ValueError(
-            f"{process_code} processData.{expected_key}는 JSON object여야 합니다.",
+            f"{process_code} processData.{expected_key}??JSON object?ъ빞 ?⑸땲??",
         )
 
     missing_fields = required_fields - set(process_payload)
     if missing_fields:
         raise ValueError(
-            f"{process_code} processData 필수 필드가 누락되었습니다: "
+            f"{process_code} processData ?꾩닔 ?꾨뱶媛€ ?꾨씫?섏뿀?듬땲?? "
             f"{sorted(missing_fields)}",
         )
 
@@ -1187,3 +1386,4 @@ def _json_safe(value: Any) -> Any:
             return None
         return value
     return value
+
diff --git a/app/service/manufacturing/manufacturing_event_json_service.py b/app/service/manufacturing/manufacturing_event_json_service.py
index 28c6a63..600e3f9 100644
--- a/app/service/manufacturing/manufacturing_event_json_service.py
+++ b/app/service/manufacturing/manufacturing_event_json_service.py
@@ -1147,6 +1147,30 @@ def _vary_process_metrics(metrics: dict[str, Any], seed_key: str) -> None:
     metrics["equipmentIdleTimeSec"] = idle_time
 
 
+def _press_count_increase_flag(
+    *,
+    station_delay_sec: float,
+    equipment_idle_time_sec: float,
+) -> bool | None:
+    if station_delay_sec <= 2.0 and equipment_idle_time_sec < 6.0:
+        return True
+    if station_delay_sec <= 3.0:
+        return None
+    return False
+
+
+def _body_robot_motion_status(vibration_score: float) -> str:
+    if vibration_score >= 0.45:
+        return "ABNORMAL"
+    if vibration_score >= 0.40:
+        return "WARNING"
+    return "NORMAL"
+
+
+def _body_robot_operation_mode(vibration_score: float) -> str:
+    return "STOPPED" if vibration_score >= 0.45 else "AUTO"
+
+
 def _sync_process_data(
     process_data: dict[str, Any],
     sensor: dict[str, Any],
@@ -1160,16 +1184,18 @@ def _sync_process_data(
     press = process_data.get("press")
     if isinstance(press, dict):
         press["timestampDelaySec"] = metrics.get("stationDelaySec", 0.0)
-        press["countIncreaseYn"] = _as_float(
-            metrics.get("equipmentIdleTimeSec"),
-        ) < 18.0
+        press["countIncreaseYn"] = _press_count_increase_flag(
+            station_delay_sec=_as_float(metrics.get("stationDelaySec")),
+            equipment_idle_time_sec=_as_float(metrics.get("equipmentIdleTimeSec")),
+        )
 
     body = process_data.get("body")
     if isinstance(body, dict):
         robot_score = _as_float(
             sensor.get("robotArmVibration", {}).get("vibrationScore"),
         )
-        body["robotMotionStatus"] = "WARNING" if robot_score >= 0.68 else "NORMAL"
+        body["robotMotionStatus"] = _body_robot_motion_status(robot_score)
+        body["robotOperationMode"] = _body_robot_operation_mode(robot_score)
 
     paint = process_data.get("paint")
     if isinstance(paint, dict):
diff --git a/docs/MANUFACUTRING_REFERENCE.md b/docs/MANUFACUTRING_REFERENCE.md
new file mode 100644
index 0000000..1e2205a
--- /dev/null
+++ b/docs/MANUFACUTRING_REFERENCE.md
@@ -0,0 +1,357 @@
+# 프레스·차체 이상탐지 기준값 레퍼런스 정리
+
+현재 프레스와 차체 공정의 기준값은 일부가 코드에 고정 숫자로 들어가 있지만, 그 숫자들이 ISO 문서에 직접 명시된 공식 기준값은 아닙니다.
+
+예를 들어 현재 코드의 다음 값들은 내부 하드코딩 값입니다.
+
+```
+프레스 전류 RMS: 1.5A
+차체 진동 점수: 0.75 / 1.25
+차체 주파수 피크: 0.015 / 0.03
+```
+
+따라서 이 값을 다른 고정 숫자로 단순 교체하기보다는, ISO 문서의 측정·관리 원칙을 근거로 **설비별 정상 데이터를 이용해 NORMAL, WARNING, DANGER 기준을 만드는 방식**이 적절합니다.
+
+## 핵심 개념
+
+ISO 문서에서 모든 프레스와 로봇에 공통으로 적용되는 다음과 같은 숫자를 제공하는 것은 아닙니다.
+
+```
+프레스 전류 10A 이상 위험
+로봇 진동 0.5 이상 위험
+사이클 시간 40초 이상 위험
+```
+
+설비 종류, 모터 정격, 차종, 금형, 로봇 프로그램, 작업 속도에 따라 정상값이 달라지기 때문입니다.
+
+그래서 다음 방식으로 기준을 만듭니다.
+
+```
+정상 데이터의 평균값 = μ
+정상 데이터가 평소 흔들리는 정도 = σ
+```
+
+이를 기준으로:
+
+| 상태 | 기준 |
+| --- | --- |
+| 정상 | 평균에서 2σ 이내 |
+| 경고 | 평균에서 2σ 이상 3σ 미만 벗어남 |
+| 위험 | 평균에서 3σ 이상 벗어남 |
+
+여기서 `σ`는 시그마라고 읽으며, 정상 데이터의 변동 정도를 뜻합니다.
+
+예를 들어 정상 사이클 시간이 평균 40초이고 표준편차가 1초라면:
+
+| 상태 | 기준 |
+| --- | --- |
+| 정상 | 38~42초 |
+| 경고 | 37~38초 또는 42~43초 |
+| 위험 | 37초 미만 또는 43초 초과 |
+
+즉, ISO 기반 기준이라는 것은 ISO에 적힌 절대 숫자를 그대로 쓰는 것이 아니라, **ISO의 관리도 원칙을 우리 설비의 정상 데이터에 적용해 기준값을 만드는 방식**입니다.
+
+---
+
+# 1. 프레스 공정
+
+현재 프레스 이상탐지에는 다음 값이 사용됩니다.
+
+| 지표 | 의미 |
+| --- | --- |
+| 사이클 시간 | 제품 한 개를 처리하는 데 걸린 시간 |
+| 지연시간 | 공정이 평소보다 지연된 시간 |
+| 전류 RMS | 모터나 설비가 사용하는 전류의 평균적인 크기 |
+| 생산 카운트 | 생산 수량이 정상적으로 증가했는지 여부 |
+| 설비 상태 | RUNNING, WARNING, STOPPED, FAULT 등의 상태 |
+
+현재 프레스에서는 압력, 하중, 금형 상태, 제품 치수 등은 이상탐지에 직접 사용하지 않습니다.
+
+## 프레스 기준값에 사용할 레퍼런스
+
+### ISO 22400
+
+제조 공정의 KPI, 사이클 시간, 생산성 등의 정의와 관리 방식을 다루는 표준입니다.
+
+이 문서는 “프레스 사이클은 40초가 정상”이라는 숫자를 정해주는 것이 아니라, 사이클 시간을 어떤 의미로 관리해야 하는지를 제공합니다.
+
+따라서 사이클 시간과 지연시간을 관리하는 기본 근거로 사용할 수 있습니다.
+
+### ISO 7870
+
+통계적 공정관리와 관리도에 대한 표준입니다.
+
+정상 데이터의 평균과 표준편차를 이용해 정상, 경고, 위험 구간을 만드는 근거로 사용할 수 있습니다.
+
+즉:
+
+```
+2σ 이내 = 정상
+2σ~3σ = 경고
+3σ 초과 = 위험
+```
+
+이라는 운영 기준을 만들 때 사용하는 핵심 문서입니다.
+
+### ISO 20958
+
+전동기의 전류 신호를 이용한 상태감시 방법을 다루는 표준입니다.
+
+현재 코드의 `1.5A`처럼 모든 설비에 같은 전류 기준을 쓰는 대신, 모터와 운전 조건별 전류 패턴을 분석해야 한다는 근거로 사용할 수 있습니다.
+
+이 표준이 “1.5A 이상 위험”이라는 값을 제공하는 것은 아닙니다.
+
+## 프레스 추천 기준
+
+| 지표 | 추천 기준 |
+| --- | --- |
+| 사이클 시간 | 차종·금형·작업별 정상 평균 ±2σ는 정상, 2~3σ는 경고, 3σ 이상은 위험 |
+| 지연시간 | 정상 평균+2σ 이하는 정상, 2~3σ는 경고, 3σ 초과는 위험 |
+| 전류 RMS | 동일 모터·동작 단계의 정상 평균 ±2σ는 정상, 2~3σ는 경고, 3σ 이상 또는 제조사 과부하 한계 초과는 위험 |
+| 생산 카운트 | `true` 정상, `null` 데이터 누락 경고, `false` 위험 |
+| 설비 상태 | `RUNNING` 정상, `WARNING` 경고, `STOPPED/FAULT` 위험 |
+
+### 사이클 시간
+
+사이클 시간은 모든 설비에 같은 기준을 쓰면 안 됩니다.
+
+다음 조건별로 정상 기준을 따로 만들어야 합니다.
+
+```
+프레스 설비
+차종
+금형
+작업 코드
+```
+
+예를 들어:
+
+```
+프레스 1호기 + 차종 A + 금형 D01
+평균 사이클 시간: 40초
+표준편차: 1초
+```
+
+이라면:
+
+```
+정상: 38~42초
+경고: 37~38초 또는 42~43초
+위험: 37초 미만 또는 43초 초과
+```
+
+처럼 기준을 만들 수 있습니다.
+
+### 지연시간
+
+지연시간은 낮은 값이 문제가 되지 않으므로 상한만 확인하면 됩니다.
+
+예를 들어:
+
+```
+평균 지연시간: 3초
+표준편차: 1초
+```
+
+라면:
+
+```
+정상: 5초 이하
+경고: 5초 초과~6초 이하
+위험: 6초 초과
+```
+
+가 됩니다.
+
+### 전류 RMS
+
+현재 코드의 `1.5A` 기준은 설비 정격이나 운전 조건과 무관하기 때문에 근거가 부족합니다.
+
+전류 기준을 만들기 전에 다음 정보가 필요합니다.
+
+```
+어떤 모터의 전류인지
+단상인지 3상 RMS인지
+타격·복귀·대기 중 언제 측정한 값인지
+모터 정격전류가 얼마인지
+```
+
+그다음 동작 단계별 정상 데이터를 모아 평균과 표준편차를 계산해야 합니다.
+
+예를 들어 타격 단계 전류가:
+
+```
+평균 12A
+표준편차 0.8A
+```
+
+라면:
+
+```
+정상: 10.4~13.6A
+경고: 9.6~10.4A 또는 13.6~14.4A
+위험: 9.6A 미만 또는 14.4A 초과
+```
+
+로 설정할 수 있습니다.
+
+또한 모터 제조사에서 정한 과부하 한계가 있다면 통계 기준보다 제조사 한계를 우선 적용해야 합니다.
+
+---
+
+# 2. 차체 공정
+
+현재 차체 이상탐지에서는 용접 전류나 용접 품질값을 사용하지 않습니다.
+
+실제 사용되는 값은 다음과 같습니다.
+
+| 지표 | 의미 |
+| --- | --- |
+| 로봇 진동 점수 | 로봇이 평소보다 얼마나 심하게 진동하는지 나타내는 내부 점수 |
+| 주파수 피크 | 특정 주파수에서 진동이 얼마나 크게 발생하는지 |
+| 주파수 대역값 | 저주파·중주파·고주파 등 각 구간의 진동 크기 |
+| 로봇 동작 상태 | NORMAL, ABNORMAL, COLLISION_RISK 등의 상태 |
+| 로봇 운전 모드 | AUTO, MANUAL, STOPPED 등의 상태 |
+| 설비 상태 | RUNNING, WARNING, STOPPED, FAULT 등의 상태 |
+
+현재 코드의 `0.75`, `1.25`, `0.015`, `0.03`은 ISO 공식 진동 기준이 아니라 내부 점수식에서 사용하기 위해 설정된 값입니다.
+
+## 차체 기준값에 사용할 레퍼런스
+
+### ISO 13373
+
+진동 센서의 설치 위치, 측정 조건, 데이터 수집, 시간영역 분석, 주파수영역 분석 방법을 다루는 표준입니다.
+
+차체 로봇 진동과 주파수 데이터를 사용할 때 다음을 명확히 해야 한다는 근거가 됩니다.
+
+```
+센서 위치
+측정 단위
+샘플링 주파수
+FFT 계산 방식
+RMS인지 Peak인지
+동일한 로봇 동작 조건인지
+```
+
+### ISO 20816
+
+기계 진동의 크기와 변화량을 이용해 상태를 평가하는 원칙을 제공합니다.
+
+다만 현재의 `robotVibrationScore`처럼 단위가 불명확한 내부 점수에 ISO 20816의 수치를 직접 적용할 수는 없습니다.
+
+먼저 진동값이 다음 중 무엇인지 확인해야 합니다.
+
+```
+속도 RMS: mm/s
+가속도 RMS: m/s² 또는 g
+Peak
+Peak-to-Peak
+FFT amplitude
+PSD
+```
+
+### ISO 7870
+
+프레스와 마찬가지로 정상 데이터의 평균과 표준편차를 이용해 정상, 경고, 위험 구간을 만드는 근거로 사용합니다.
+
+## 차체 추천 기준
+
+| 지표 | 추천 기준 |
+| --- | --- |
+| 로봇 진동 점수 | 같은 로봇·프로그램·속도·적재량 조건의 평균+2σ 이하 정상, 2~3σ 경고, 3σ 초과 위험 |
+| 주파수 피크 | 각 주파수 대역의 정상 평균+2σ 이하 정상, 2~3σ 경고, 3σ 초과 위험 |
+| 로봇 상태 | `NORMAL` 정상, `WARNING/UNKNOWN` 경고, `ABNORMAL/COLLISION_RISK` 위험 |
+| 운전 모드 | 생산 중 `AUTO` 정상, 계획정비 중 `MANUAL/STOPPED` 제외, 생산 중 예상하지 않은 `MANUAL/STOPPED` 위험 |
+| 설비 상태 | `RUNNING` 정상, `WARNING` 경고, `STOPPED/FAULT` 위험 |
+
+### 로봇 진동 점수
+
+로봇 진동은 다음 조건에 따라 정상값이 달라질 수 있습니다.
+
+```
+로봇 번호
+작업 프로그램
+작업 속도
+payload
+로봇 자세
+작업 구간
+```
+
+따라서 다음 데이터는 하나의 기준으로 섞으면 안 됩니다.
+
+```
+R01 + 프로그램 P100 + 속도 80% + payload 20kg
+R01 + 프로그램 P200 + 속도 60% + payload 10kg
+R02 + 프로그램 P100 + 속도 80% + payload 20kg
+```
+
+각 조건별로 정상 평균과 표준편차를 따로 계산해야 합니다.
+
+예를 들어:
+
+```
+조건: R01 / P100 / 속도 80% / payload 20kg
+평균 진동 점수: 0.30
+표준편차: 0.05
+```
+
+라면:
+
+```
+정상: 0.40 이하
+경고: 0.40 초과~0.45 이하
+위험: 0.45 초과
+```
+
+로 기준을 만들 수 있습니다.
+
+### 주파수 피크와 주파수 대역
+
+현재처럼 모든 주파수 대역에 동일한 `0.015`, `0.03`을 적용하는 것은 적절하지 않습니다.
+
+각 주파수 구간은 원래 진동 크기가 다를 수 있기 때문에 대역별 기준이 필요합니다.
+
+예:
+
+```
+0~10Hz
+10~50Hz
+50~100Hz
+```
+
+각 대역에서 정상 데이터를 별도로 모아 평균과 표준편차를 계산합니다.
+
+예를 들어:
+
+| 주파수 대역 | 정상 평균 | 표준편차 | 정상 상한 | 경고 상한 | 위험 |
+| --- | --- | --- | --- | --- | --- |
+| 0~10Hz | 0.006 | 0.001 | 0.008 | 0.009 | 0.009 초과 |
+| 10~50Hz | 0.015 | 0.003 | 0.021 | 0.024 | 0.024 초과 |
+| 50~100Hz | 0.004 | 0.0005 | 0.005 | 0.0055 | 0.0055 초과 |
+
+즉, “대역별 기준”이란 주파수 구간마다 서로 다른 정상·경고·위험 값을 사용하는 것을 의미합니다.
+
+---
+
+# 최종 정리
+
+## 프레스
+
+| 지표 | 레퍼런스 | 기준 설정 방법 |
+| --- | --- | --- |
+| 사이클 시간 | ISO 22400 + ISO 7870 | 차종·금형·작업별 정상 평균과 표준편차로 2σ·3σ 기준 생성 |
+| 지연시간 | ISO 7870 | 작업별 정상 지연시간의 평균+2σ, 평균+3σ 사용 |
+| 전류 RMS | ISO 20958 + ISO 7870 | 모터·운전 단계별 정상 전류 기준 생성, 제조사 과부하 한계 병행 |
+| 생산 카운트 | 공정 논리 기준 | `true` 정상, `null` 경고, `false` 위험 |
+| 설비 상태 | 설비 상태 코드 | `RUNNING` 정상, `WARNING` 경고, `STOPPED/FAULT` 위험 |
+
+## 차체
+
+| 지표 | 레퍼런스 | 기준 설정 방법 |
+| --- | --- | --- |
+| 로봇 진동 점수 | ISO 13373 + ISO 7870 | 로봇·프로그램·속도·payload별 정상 평균과 2σ·3σ 기준 생성 |
+| 주파수 피크 | ISO 13373 + ISO 7870 | 각 주파수 대역별 정상 평균과 2σ·3σ 기준 생성 |
+| 진동 물리량 | ISO 20816 | 단위와 센서 위치가 확인된 경우 상태평가 참고 |
+| 로봇 상태 | 컨트롤러 상태 코드 | `NORMAL`, `WARNING`, `ABNORMAL/COLLISION_RISK`로 구분 |
+| 운전 모드 | 생산 운영 기준 | 생산 중 `AUTO` 정상, 비정상 `MANUAL/STOPPED` 위험 |
+| 설비 상태 | 설비 상태 코드 | `RUNNING` 정상, `WARNING` 경고, `STOPPED/FAULT` 위험 |
\ No newline at end of file
diff --git a/test_script.py b/test_script.py
deleted file mode 100644
index 3b5e9bf..0000000
--- a/test_script.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from app.kafka.raw_event_consumer import _defect_probability, _create_defect_transfer_detector, _predict_defect_transfer
-from app.data_generation.manufacturing_event_json_builder import ManufacturingEventJsonBuilder
-
-class Req:
-    car_id_map = {1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8, 9:9, 10:10}
-builder = ManufacturingEventJsonBuilder()
-events = list(builder.iter_rows(Req()))
-assembly_events = [e for e in events if e['process_code'] == 'ASSEMBLY']
-
-detector = _create_defect_transfer_detector()
-
-for row in assembly_events[:10]:
-    e = row['event_json']
-    fallback_prob = _defect_probability(e, 'ASSEMBLY')
-    
-    pred = _predict_defect_transfer(detector, row)
-    ml_prob = pred.defect_probability if pred else None
-    
-    print(f"Fallback: {fallback_prob}, ML: {ml_prob}")

From c176553425f6e1cfc6d26d75fc4454531bdb28ee Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 13 Jul 2026 15:08:08 +0900
Subject: [PATCH 134/148] =?UTF-8?q?docs:=20=EB=B3=91=EB=AA=A9/=EB=B6=88?=
 =?UTF-8?q?=EB=9F=89=20=EC=A0=84=EC=9D=B4=20=EB=B6=84=EC=84=9D=20=EC=BD=94?=
 =?UTF-8?q?=EB=93=9C=20=EC=A3=BC=EC=84=9D=20=EB=B3=B4=EA=B0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 병목/불량 전이 조회 흐름과 ES-first, DB fallback 동작을 설명하는 주석 추가

- 주요 함수 위 docstring을 정리해 코드 가독성 개선
---
 .../bottleneck_analysis_repository.py         | 19 +++++++++++++-
 .../defect_transfer_prediction_repository.py  | 25 ++++++++++++++++++-
 app/service/analysis/bottleneck_service.py    | 14 +++++++++++
 .../analysis/defect_transfer_service.py       | 15 +++++++++++
 4 files changed, 71 insertions(+), 2 deletions(-)

diff --git a/app/repository/bottleneck_analysis_repository.py b/app/repository/bottleneck_analysis_repository.py
index a673b37..20143d2 100644
--- a/app/repository/bottleneck_analysis_repository.py
+++ b/app/repository/bottleneck_analysis_repository.py
@@ -11,7 +11,7 @@
 
 
 class BottleneckAnalysisRepository:
-    """Store bottleneck results and read source events from manufacturing_event_json."""
+    """병목 분석 결과를 저장하고 sampledb.manufacturing_event_json 원천 이벤트를 읽는다."""
 
     def __init__(
         self,
@@ -30,6 +30,8 @@ def __init__(
         self.ensure_schema()
 
     def ensure_schema(self) -> None:
+        """병목 결과 테이블이 없거나 구조가 다르면 DB 스키마를 맞춘다."""
+        # 병목 결과 테이블이 없거나 컬럼이 다르면 여기서 맞춘다.
         self.metadata.create_all(self.engine)
         self._align_result_schema()
 
@@ -41,6 +43,7 @@ def replace_results(
         start_rank: int,
         end_rank: int,
     ) -> None:
+        """계산된 병목 순위 결과를 detected_at 스냅샷으로 저장한다."""
         payload = [
             {
                 key: value
@@ -52,10 +55,12 @@ def replace_results(
         if not payload:
             return
 
+        # 새 분석 결과를 한 번에 저장한다.
         with self.engine.begin() as conn:
             conn.execute(self.table.insert(), payload)
 
     def prune_results_after_rank(self, max_rank: int) -> None:
+        """현재 날짜의 병목 결과 중 상위 순위만 남기고 나머지를 삭제한다."""
         from sqlalchemy import func
         with self.engine.begin() as conn:
             conn.execute(
@@ -71,6 +76,8 @@ def list_results(
         size: int,
         analysis_date: DateType | None = None,
     ) -> list[dict[str, Any]]:
+        """지정한 날짜의 최신 병목 스냅샷을 기준으로 결과 목록을 반환한다."""
+        # detected_at 스냅샷 기준으로 같은 날짜의 병목 결과만 읽는다.
         snapshot_detected_at = self._latest_snapshot_detected_at(analysis_date)
         if snapshot_detected_at is None:
             return []
@@ -109,6 +116,8 @@ def count_results(
         *,
         analysis_date: DateType | None = None,
     ) -> int:
+        """지정한 날짜의 최신 병목 결과 건수를 반환한다."""
+        # 조회 기준 날짜의 결과 건수를 세어 페이지네이션 여부를 판단한다.
         snapshot_detected_at = self._latest_snapshot_detected_at(analysis_date)
         if snapshot_detected_at is None:
             return 0
@@ -129,6 +138,7 @@ def count_results(
         return len(self.list_results(cursor=0, size=raw_count))
 
     def delete_results_by_date(self, analysis_date: DateType) -> int:
+        """병목 결과 테이블에서 특정 날짜 스냅샷을 제거한다."""
         from sqlalchemy import func
 
         with self.engine.begin() as conn:
@@ -142,6 +152,8 @@ def list_manufacturing_event_histories(
         *,
         analysis_date: DateType | None = None,
     ) -> list[dict[str, Any]]:
+        """병목 분석용 원천 이벤트를 sampledb에서 읽어온다."""
+        # 병목 분석 대상 원천 이벤트를 sampledb에서 읽는다.
         from sqlalchemy import select, func
 
         query = (
@@ -173,6 +185,8 @@ def list_pending_manufacturing_event_histories(
         *,
         analysis_date: DateType | None = None,
     ) -> list[dict[str, Any]]:
+        """아직 병목 분석이 끝나지 않은 원천 이벤트만 가져온다."""
+        # 아직 병목 분석이 끝나지 않은 원천 이벤트만 다시 가져온다.
         from sqlalchemy import select, func
 
         query = (
@@ -201,6 +215,8 @@ def list_pending_manufacturing_event_histories(
             ]
 
     def list_date_options(self) -> list[dict[str, Any]]:
+        """dateOptions 표시용으로 detected_at 날짜와 대표 event_id를 묶어 반환한다."""
+        # 날짜 선택 UI용으로 detected_at 기준 옵션을 만든다.
         from sqlalchemy import func, select
 
         query = (
@@ -247,6 +263,7 @@ def list_date_options(self) -> list[dict[str, Any]]:
         ]
 
     def _event_ids_for_today(self) -> list[int]:
+        """오늘 날짜로 들어온 병목 대상 이벤트 id 목록을 조회한다."""
         from sqlalchemy import func, select
 
         query = (
diff --git a/app/repository/defect_transfer_prediction_repository.py b/app/repository/defect_transfer_prediction_repository.py
index 2f35137..245dac4 100644
--- a/app/repository/defect_transfer_prediction_repository.py
+++ b/app/repository/defect_transfer_prediction_repository.py
@@ -10,7 +10,7 @@
 
 
 class DefectTransferPredictionRepository:
-    """Store and read defect-transfer prediction results in main_db."""
+    """불량 전이 예측 결과를 main_db에 저장하고 sampledb 원천 이벤트를 읽는다."""
 
     def __init__(
         self,
@@ -29,6 +29,7 @@ def __init__(
         self.ensure_schema()
 
     def ensure_schema(self) -> None:
+        """불량 전이 결과 테이블의 스키마를 DB에 맞게 생성하거나 보정한다."""
         self.metadata.create_all(self.engine)
         self._align_result_schema()
 
@@ -47,6 +48,9 @@ def replace_prediction_result(
         causes: list[dict[str, Any]],
         predicted_at: datetime,
     ) -> int:
+        """하나의 제조 이벤트에 대한 예측 결과를 최신 값으로 다시 저장한다."""
+        """하나의 제조 이벤트에 대한 예측 결과를 최신 값으로 다시 저장한다."""
+        # 하나의 제조 이벤트에 대해 예측 결과 1건만 유지한다.
         manufacturing_event_id = self._manufacturing_event_id(event_id)
         main_causes = self._normalize_main_causes(causes)
         cause_rows = causes or [
@@ -86,6 +90,7 @@ def replace_prediction_result(
         return 1
 
     def has_prediction_for_event(self, event_id: str) -> bool:
+        """이미 해당 이벤트의 예측 결과가 저장되어 있는지 확인한다."""
         manufacturing_event_id = self._manufacturing_event_id(event_id)
         if manufacturing_event_id is None:
             return False
@@ -107,6 +112,8 @@ def list_prediction_page(
         size: int,
         analysis_date: date | None = None,
     ) -> tuple[list[dict[str, Any]], bool]:
+        """차량별 최신 예측 결과를 모아 목록 페이지를 만든다."""
+        # 차량별 최신 예측만 추려서 목록 페이지를 만든다.
         rows = self._all_prediction_rows(analysis_date=analysis_date)
         latest_by_car: dict[int, dict[str, Any]] = {}
         for row in rows:
@@ -141,6 +148,7 @@ def list_prediction_rows(
         analysis_date: date | None = None,
         car_master_id: int | None = None,
     ) -> list[dict[str, Any]]:
+        """조건에 맞는 불량 전이 결과 원본 row를 모두 반환한다."""
         return self._all_prediction_rows(
             car_master_id=car_master_id,
             analysis_date=analysis_date,
@@ -154,6 +162,8 @@ def list_cause_page(
         size: int,
         analysis_date: date | None = None,
     ) -> tuple[dict[str, Any] | None, list[dict[str, Any]], bool]:
+        """대표 원인 1개와 상세 원인 리스트를 함께 반환한다."""
+        # 대표 원인 1개와 상세 원인 목록을 같은 이벤트 묶음으로 반환한다.
         car_master_id = self._car_master_id(vehicle_id) if vehicle_id else None
         rows = self._all_prediction_rows(
             car_master_id=car_master_id,
@@ -183,6 +193,8 @@ def list_cause_page(
         return latest_rows[0], page, len(latest_rows) > offset + size
 
     def list_date_options(self, *, vehicle_id: str | None = None) -> list[dict[str, Any]]:
+        """predicted_at 기준으로 날짜 선택 옵션을 생성한다."""
+        # 날짜 선택용 옵션은 predicted_at 기준으로 만든다.
         from sqlalchemy import func, select
 
         car_master_id = self._car_master_id(vehicle_id) if vehicle_id else None
@@ -233,6 +245,8 @@ def list_date_options(self, *, vehicle_id: str | None = None) -> list[dict[str,
         ]
 
     def diagnostics(self) -> dict[str, Any]:
+        """원천 이벤트와 예측 결과의 현재 적재 상태를 점검한다."""
+        # 원천 이벤트, 예측 결과, 최신 상태를 한 번에 점검한다.
         from sqlalchemy import distinct, func, select
 
         with self.event_engine.connect() as conn:
@@ -331,6 +345,7 @@ def _all_prediction_rows(
         car_master_id: int | None = None,
         analysis_date: date | None = None,
     ) -> list[dict[str, Any]]:
+        """필요한 조건에 맞는 예측 결과 원본 row를 조회한다."""
         from sqlalchemy import func, select
 
         query = select(self.table)
@@ -432,6 +447,7 @@ def _prediction_event_ids(
         car_master_id: int | None = None,
         analysis_date: date | None = None,
     ) -> list[int]:
+        """분석 대상 manufacturing_event_json id를 필터링해서 반환한다."""
         from sqlalchemy import func, select
 
         query = (
@@ -450,6 +466,7 @@ def _prediction_event_ids(
 
     @staticmethod
     def _normalize_probability(value: Any) -> float:
+        """확률값이 0~1 또는 0~100 형태여도 화면용 소수로 맞춘다."""
         if value is None:
             return 0.0
         normalized = float(value)
@@ -459,6 +476,7 @@ def _normalize_probability(value: Any) -> float:
 
     @classmethod
     def _result_probability(cls, row: dict[str, Any]) -> float:
+        """현재 화면에서 사용할 대표 확률값을 선택한다."""
         value = row.get("current_defect_probability")
         if value is None:
             value = row.get("target_defect_probability")
@@ -469,6 +487,7 @@ def _result_probability(cls, row: dict[str, Any]) -> float:
         return cls._normalize_probability(value)
 
     def _attach_process_equipment_codes(self, rows: list[dict[str, Any]]) -> None:
+        """화면 표시에 필요한 공정명과 설비 코드를 보강한다."""
         if not rows:
             return
         from sqlalchemy import select
@@ -574,6 +593,7 @@ def _target_process_code(row: dict[str, Any]) -> str | None:
         return NEXT_PROCESS.get(source)
 
     def _attach_vehicle_ids(self, rows: list[dict[str, Any]]) -> None:
+        """manufacturing_event_id를 기반으로 vehicle_id를 붙인다."""
         if not rows:
             return
         from sqlalchemy import select
@@ -594,6 +614,7 @@ def _attach_vehicle_ids(self, rows: list[dict[str, Any]]) -> None:
             )
 
     def _init_sqlalchemy(self) -> None:
+        """예측 결과 저장소의 SQLAlchemy 테이블과 엔진을 준비한다."""
         try:
             from sqlalchemy import BigInteger, Column, DateTime, Double, Enum, JSON
             from sqlalchemy import Index, Integer, MetaData, String, Table, func
@@ -646,6 +667,7 @@ def _init_sqlalchemy(self) -> None:
         )
 
     def _align_result_schema(self) -> None:
+        """기존 결과 테이블과 현재 코드의 컬럼 구조를 맞춘다."""
         if self.engine.dialect.name != "mysql":
             return
 
@@ -715,6 +737,7 @@ def _align_result_schema(self) -> None:
 
     @staticmethod
     def _normalize_main_causes(causes: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        """상위 원인 목록을 저장용 간단한 구조로 정리한다."""
         normalized: list[dict[str, Any]] = []
         for cause in causes[:5]:
             message = str(cause.get("message") or cause.get("label") or "").strip()
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 40007f7..1532b74 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -44,6 +44,7 @@
 
 
 class BottleneckAnalysisService:
+    """병목 분석 결과를 조회하고 필요 시 원천 이벤트를 다시 분석한다."""
     def __init__(
         self,
         *,
@@ -66,6 +67,8 @@ def get_realtime_bottlenecks(
         size: int,
         date: DateType | None = None,
     ) -> BottleneckAnalysisPage:
+        """병목 결과를 ES 우선으로 읽고, 실패 시 캐시와 DB로 내려준다."""
+        # ES를 우선 조회하고, 실패하면 Redis와 DB 순으로 안전하게 내려간다.
         size = max(1, min(size, 100))
         page = max(cursor or 0, 0)
         cache_key = self._bottleneck_cache_key(
@@ -152,6 +155,7 @@ def get_cached_realtime_bottlenecks(
         size: int,
         date: DateType | None = None,
     ) -> BottleneckAnalysisPage:
+        """조회한 병목 결과를 Redis 캐시에 저장한 뒤 반환한다."""
         cache_key = self._bottleneck_cache_key(
             cursor=cursor,
             size=size,
@@ -166,6 +170,7 @@ def get_cached_realtime_bottlenecks(
         return page
 
     def run_analysis_and_save(self, *, cursor: int, size: int) -> tuple[int, bool]:
+        """원천 이벤트를 다시 분석해 병목 결과를 갱신한다."""
         merged_summaries = self.refresh_results_from_events()
         offset = cursor * size
         page_summaries = merged_summaries[offset : offset + size]
@@ -173,6 +178,8 @@ def run_analysis_and_save(self, *, cursor: int, size: int) -> tuple[int, bool]:
         return len(page_summaries), has_next
 
     def refresh_results_from_events(self) -> list[dict[str, Any]]:
+        """아직 분석되지 않은 원천 이벤트만 읽어 병목 순위를 재계산한다."""
+        # 아직 분석되지 않은 원천 이벤트를 다시 읽어서 병목 순위를 갱신한다.
         histories = self.repository.list_pending_manufacturing_event_histories()
         if not histories:
             return self._current_bottleneck_results()
@@ -290,6 +297,8 @@ def _current_bottleneck_results(self) -> list[dict[str, Any]]:
         )
 
     def _get_date_options(self) -> list[AnalysisDateOption]:
+        """화면용 날짜 옵션을 detected_at 스냅샷 기준으로 만든다."""
+        # 화면의 날짜 선택 옵션은 detected_at 스냅샷 기준으로 구성한다.
         options = self.repository.list_date_options()
         return [
             AnalysisDateOption.model_validate(
@@ -303,6 +312,8 @@ def _get_date_options(self) -> list[AnalysisDateOption]:
         ]
 
     def _safe_get_date_options(self) -> list[AnalysisDateOption]:
+        """날짜 옵션 조회 실패 시 빈 목록으로 안전하게 처리한다."""
+        # 날짜 옵션 조회 실패로 전체 API가 깨지지 않도록 방어한다.
         try:
             return self._get_date_options()
         except Exception:
@@ -322,6 +333,7 @@ def _resolve_date(
 
     @staticmethod
     def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
+        """분석된 이벤트 묶음의 기준 detected_at 시각을 계산한다."""
         candidates = [
             value.replace(tzinfo=None) if isinstance(value, datetime) and value.tzinfo else value
             for value in (row.get("event_time") for row in histories)
@@ -333,6 +345,7 @@ def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
 
     @staticmethod
     def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
+        """ES 설정이 있으면 검색 저장소를 만들고, 없으면 사용하지 않는다."""
         if not settings.elasticsearch_url:
             return None
         try:
@@ -347,6 +360,7 @@ def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
     def _rank_bottleneck_summaries(
         summaries: list[dict[str, Any]],
     ) -> list[dict[str, Any]]:
+        """병목 점수와 지연 시간을 기준으로 결과를 정렬한다."""
         ranked = sorted(
             summaries,
             key=lambda row: (
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index b520f3d..0f821f6 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -34,6 +34,7 @@
 
 
 class DefectTransferAnalysisService:
+    """불량 전이 예측 결과와 원인 분석을 ES 우선으로 조회한다."""
     def __init__(
         self,
         *,
@@ -99,6 +100,7 @@ def get_cached_cause_analysis(
         return page
 
     def clear_cache(self) -> None:
+        """불량 전이 조회 캐시를 모두 삭제한다."""
         if not settings.redis_url:
             return
         try:
@@ -121,6 +123,9 @@ def get_predictions(
         size: int,
         date: DateType | None = None,
     ) -> DefectTransferPredictionPage:
+        """불량 전이 목록을 ES 우선으로 조회하고, 실패 시 DB로 내려간다."""
+        """조회한 불량 전이 목록을 Redis 캐시에 저장한다."""
+        # ES를 먼저 보고, 실패하면 Redis 캐시와 DB 결과로 이어서 반환한다.
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
         cache_key = self._prediction_cache_key(
@@ -213,6 +218,9 @@ def get_cause_analysis(
         size: int,
         date: DateType | None = None,
     ) -> DefectTransferCausePage:
+        """대표 원인과 상세 원인을 함께 반환하는 불량 전이 원인 분석을 조회한다."""
+        """조회한 불량 전이 원인 분석을 Redis 캐시에 저장한다."""
+        # 원인 분석은 대표 원인과 상세 원인을 분리해서 화면에 맞게 구성한다.
         page = max(cursor or 0, 0)
         safe_size = max(1, min(size, 100))
         cache_key = self._cause_cache_key(
@@ -316,6 +324,8 @@ def _to_prediction_item(
         self,
         row: dict[str, Any],
     ) -> DefectTransferPredictionItem:
+        """DB row를 불량 전이 목록 응답 DTO로 변환한다."""
+        # 저장된 결과 row를 목록 카드용 DTO로 변환한다.
         return DefectTransferPredictionItem(
             vehicleId=str(row.get("vehicle_id")),
             carMasterId=int(row["car_master_id"]),
@@ -335,6 +345,7 @@ def _to_prediction_item(
 
     @staticmethod
     def _normalize_probability(value: Any) -> float:
+        # 99.24 또는 0.9924처럼 들어와도 화면에서는 동일한 확률로 맞춘다.
         if value is None:
             return 0.0
         normalized = float(value)
@@ -344,6 +355,7 @@ def _normalize_probability(value: Any) -> float:
 
     @classmethod
     def _result_probability(cls, row: dict[str, Any]) -> float:
+        # 현재 화면 기준 확률은 current -> defect -> target 순으로 확인한다.
         value = row.get("current_defect_probability")
         if value is None:
             value = row.get("defect_probability")
@@ -353,6 +365,7 @@ def _result_probability(cls, row: dict[str, Any]) -> float:
 
     @classmethod
     def _resolve_predicted_defect_process(cls, row: dict[str, Any]) -> str | None:
+        # 목표 공정이 있으면 우선 사용하고, 없으면 다음 공정을 계산한다.
         if cls._result_probability(row) <= 0:
             return None
 
@@ -421,6 +434,8 @@ def _build_cause_sections(
         cls,
         row: dict[str, Any],
     ) -> tuple[DefectTransferCauseItem, list[DefectTransferCauseItem]]:
+        """대표 원인 1개와 상세 원인 리스트를 분리해 구성한다."""
+        # 대표 원인 1개와 보조 원인 리스트를 분리해 응답 구조를 만든다.
         main_causes = cls._normalize_main_causes(row.get("main_causes") or row.get("causes"))
         if main_causes:
             representative_cause = cls._to_cause_item(

From c1a2ac52f5db0fac684d9575c76eb1689a40a3a2 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 13 Jul 2026 15:29:44 +0900
Subject: [PATCH 135/148] =?UTF-8?q?fix:=20=EB=8F=84=EC=9E=A5=20=EB=A0=88?=
 =?UTF-8?q?=ED=8D=BC=EB=9F=B0=EC=8A=A4=20=EA=B8=B0=EB=B0=98=20manufacturin?=
 =?UTF-8?q?g=5Fevent=5Fjson=20=EC=83=9D=EC=84=B1=20=EA=B0=9C=EC=84=A0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 도장 공정의 surfaceQualityScore, thicknessValue, defectScore, thermalStdTemp 기준 반영

- 날짜 재적용 시 도장 값이 동일 품질 구간을 유지하도록 동기화 로직 수정

- 도장 레퍼런스 문서 갱신 내용 반영
---
 .../manufacturing_event_json_builder.py       | 153 ++++++++++++++++++
 .../manufacturing_event_json_service.py       |  64 ++++++--
 docs/MANUFACUTRING_REFERENCE.md               |  14 +-
 3 files changed, 221 insertions(+), 10 deletions(-)

diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index 46e25b7..aea71e2 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -652,6 +652,159 @@ def _apply_detection_profile(
             )
             return
 
+        if process_code == "PAINT":
+            paint_is_warning = is_abnormal and (global_index % 3 == 0)
+            low_thickness_band = (global_index % 2) == 0
+
+            if is_abnormal and paint_is_warning:
+                rms_ampere = 1.82 + current_variation * 0.045
+                acceleration_g = 0.0065 + current_variation * 0.00045
+                vibration_score = min(0.55, 0.42 + vibration_variation * 0.012)
+                vibration_rms = 0.55 + vibration_variation * 0.028
+                vibration_peak = 0.82 + vibration_variation * 0.035
+                thermal_std_temp = round(2.25 + paint_variation * 0.18, 3)
+                defect_score = round(min(0.59, 0.42 + paint_variation * 0.018), 4)
+                thickness_value = round(
+                    85.0 + paint_variation * 0.45 if low_thickness_band else 121.0 + paint_variation * 0.85,
+                    3,
+                )
+                surface_quality_score = round(max(60.0, 78.0 - paint_variation * 1.05), 3)
+                cycle_time = target + 2.8 + metric_variation * 0.42
+                station_delay = cycle_time - target
+
+                current.update(
+                    rmsAmpere=round(rms_ampere, 3),
+                    maxAmpere=round(rms_ampere + 0.19 + current_variation * 0.012, 3),
+                    minAmpere=round(max(0.0, rms_ampere - 0.14), 3),
+                    accelerationG=round(acceleration_g, 5),
+                )
+                ford.update(
+                    label=1,
+                    vibrationScore=round(vibration_score, 4),
+                    vibrationRms=round(vibration_rms, 3),
+                    vibrationPeak=round(vibration_peak, 3),
+                )
+                vision.update(
+                    label=1,
+                    avgTemperature=round(41.5 + paint_variation * 0.42, 3),
+                    maxTemperature=round(46.0 + paint_variation * 0.48, 3),
+                    minTemperature=round(37.5 + paint_variation * 0.26, 3),
+                    thermalStdTemp=thermal_std_temp,
+                    defectScore=defect_score,
+                    thicknessValue=thickness_value,
+                    surfaceQualityScore=surface_quality_score,
+                )
+                bosch["response"] = 1
+                process_metrics.update(
+                    cycleTimeSec=round(cycle_time, 3),
+                    waitingTimeSec=round(4.2 + metric_variation * 0.32, 3),
+                    processingTimeSec=round(max(1.0, target - 1.2 + metric_variation * 0.16), 3),
+                    stationDelaySec=round(station_delay, 3),
+                    throughputPerMin=round(60 / cycle_time, 3),
+                    queueLength=4 + metric_variation,
+                    wipCount=12 + metric_variation * 2,
+                    equipmentIdleTimeSec=round(3.0 + metric_variation * 0.55, 3),
+                )
+                return
+
+            if is_abnormal:
+                rms_ampere = 2.08 + current_variation * 0.075
+                acceleration_g = 0.012 + current_variation * 0.0008
+                vibration_score = min(0.83, 0.65 + vibration_variation * 0.015)
+                vibration_rms = 1.1 + vibration_variation * 0.04
+                vibration_peak = 1.65 + vibration_variation * 0.05
+                thermal_std_temp = round(5.2 + paint_variation * 0.28, 3)
+                defect_score = round(min(0.99, 0.66 + paint_variation * 0.022), 4)
+                thickness_value = round(
+                    78.0 - paint_variation * 0.45 if low_thickness_band else 131.5 + paint_variation * 1.05,
+                    3,
+                )
+                surface_quality_score = round(max(0.0, 58.0 - paint_variation * 1.45), 3)
+                cycle_time = target + 6.8 + metric_variation * 0.92
+                station_delay = cycle_time - target
+
+                current.update(
+                    rmsAmpere=round(rms_ampere, 3),
+                    maxAmpere=round(rms_ampere + 0.29 + current_variation * 0.018, 3),
+                    minAmpere=round(max(0.0, rms_ampere - 0.22), 3),
+                    accelerationG=round(acceleration_g, 5),
+                )
+                ford.update(
+                    label=1,
+                    vibrationScore=round(vibration_score, 4),
+                    vibrationRms=round(vibration_rms, 3),
+                    vibrationPeak=round(vibration_peak, 3),
+                )
+                vision.update(
+                    label=1,
+                    avgTemperature=round(49.5 + paint_variation * 0.72, 3),
+                    maxTemperature=round(57.5 + paint_variation * 0.82, 3),
+                    minTemperature=round(42.0 + paint_variation * 0.48, 3),
+                    thermalStdTemp=thermal_std_temp,
+                    defectScore=defect_score,
+                    thicknessValue=thickness_value,
+                    surfaceQualityScore=surface_quality_score,
+                )
+                bosch["response"] = 1
+                process_metrics.update(
+                    cycleTimeSec=round(cycle_time, 3),
+                    waitingTimeSec=round(10.5 + metric_variation * 0.95, 3),
+                    processingTimeSec=round(max(1.0, target - 2.8 + metric_variation * 0.35), 3),
+                    stationDelaySec=round(station_delay, 3),
+                    throughputPerMin=round(60 / cycle_time, 3),
+                    queueLength=8 + metric_variation,
+                    wipCount=22 + metric_variation * 2,
+                    equipmentIdleTimeSec=round(10.0 + metric_variation * 1.1, 3),
+                )
+                return
+
+            rms_ampere = 1.62 + current_variation * 0.028
+            acceleration_g = 0.0045 + current_variation * 0.0003
+            vibration_score = 0.11 + vibration_variation * 0.008
+            vibration_rms = 0.24 + vibration_variation * 0.018
+            vibration_peak = 0.41 + vibration_variation * 0.022
+            thermal_std_temp = round(0.68 + paint_variation * 0.12, 3)
+            defect_score = round(min(0.39, 0.10 + paint_variation * 0.027), 4)
+            thickness_value = round(99.0 + paint_variation * 1.5, 3)
+            surface_quality_score = round(max(80.0, 92.0 - paint_variation * 1.25), 3)
+            cycle_time = target + 0.35 + metric_variation * 0.14
+            station_delay = max(0.0, cycle_time - target)
+
+            current.update(
+                rmsAmpere=round(rms_ampere, 3),
+                maxAmpere=round(rms_ampere + 0.13 + current_variation * 0.008, 3),
+                minAmpere=round(max(0.0, rms_ampere - 0.11), 3),
+                accelerationG=round(acceleration_g, 5),
+            )
+            ford.update(
+                label=0,
+                vibrationScore=round(vibration_score, 4),
+                vibrationRms=round(vibration_rms, 3),
+                vibrationPeak=round(vibration_peak, 3),
+            )
+            vision.update(
+                label=0,
+                avgTemperature=round(38.6 + paint_variation * 0.22, 3),
+                maxTemperature=round(40.8 + paint_variation * 0.2, 3),
+                minTemperature=round(36.9 + paint_variation * 0.12, 3),
+                thermalStdTemp=thermal_std_temp,
+                defectScore=defect_score,
+                thicknessValue=thickness_value,
+                surfaceQualityScore=surface_quality_score,
+            )
+            bosch["response"] = 0
+            process_metrics.update(
+                cycleTimeSec=round(cycle_time, 3),
+                waitingTimeSec=round(1.8 + metric_variation * 0.12, 3),
+                processingTimeSec=round(max(1.0, target - 2.2 + metric_variation * 0.1), 3),
+                stationDelaySec=round(station_delay, 3),
+                throughputPerMin=round(60 / cycle_time, 3),
+                queueLength=2 + metric_variation % 3,
+                wipCount=5 + metric_variation,
+                equipmentIdleTimeSec=round(metric_variation * 0.18, 3),
+            )
+            return
+
         if is_abnormal:
             current_variation = global_index % 11
             vibration_variation = global_index % 13
diff --git a/app/service/manufacturing/manufacturing_event_json_service.py b/app/service/manufacturing/manufacturing_event_json_service.py
index 600e3f9..0e00b36 100644
--- a/app/service/manufacturing/manufacturing_event_json_service.py
+++ b/app/service/manufacturing/manufacturing_event_json_service.py
@@ -1199,36 +1199,82 @@ def _sync_process_data(
 
     paint = process_data.get("paint")
     if isinstance(paint, dict):
-        paint["thermalStdTemp"] = _jitter_numeric(
+        thermal_std_temp = _jitter_numeric(
             _as_float(paint.get("thermalStdTemp")),
             seed_key,
             "paint.thermalStdTemp",
-            pct=0.04,
-            absolute=0.12,
+            pct=0.05,
+            absolute=0.18,
             precision=3,
             min_value=0.0,
         )
-        paint["thicknessValue"] = _jitter_numeric(
+        original_thermal_std_temp = _as_float(paint.get("thermalStdTemp"))
+        if original_thermal_std_temp < 2.0:
+            thermal_std_temp = min(1.99, thermal_std_temp)
+        elif original_thermal_std_temp < 5.0:
+            thermal_std_temp = min(4.99, max(2.0, thermal_std_temp))
+        else:
+            thermal_std_temp = max(5.0, thermal_std_temp)
+        paint["thermalStdTemp"] = _round(thermal_std_temp, 3)
+
+        thickness_value = _jitter_numeric(
             _as_float(paint.get("thicknessValue")),
             seed_key,
             "paint.thicknessValue",
-            pct=0.004,
-            absolute=0.45,
+            pct=0.01,
+            absolute=0.9,
             precision=3,
             min_value=0.0,
         )
+        original_thickness_value = _as_float(paint.get("thicknessValue"))
+        if original_thickness_value < 80.0:
+            thickness_value = min(79.9, thickness_value)
+        elif original_thickness_value < 90.0:
+            thickness_value = min(89.9, max(80.0, thickness_value))
+        elif original_thickness_value <= 120.0:
+            thickness_value = min(120.0, max(90.0, thickness_value))
+        elif original_thickness_value <= 130.0:
+            thickness_value = min(130.0, max(120.0, thickness_value))
+        else:
+            thickness_value = max(130.1, thickness_value)
+        paint["thicknessValue"] = _round(thickness_value, 3)
+
+        original_defect_score = _as_float(paint.get("defectScore"))
         defect_score = _round(
             _clamp(
-                _as_float(paint.get("defectScore"))
+                original_defect_score
                 + _noise(seed_key, "paint.defectScore", -0.025, 0.025),
                 0.0,
                 0.99,
             ),
             4,
         )
+        if original_defect_score < 0.4:
+            defect_score = min(0.39, defect_score)
+        elif original_defect_score < 0.6:
+            defect_score = min(0.59, max(0.4, defect_score))
+        else:
+            defect_score = max(0.6, defect_score)
         paint["defectScore"] = defect_score
-        paint["surfaceQualityScore"] = _round(max(0.0, 100.0 - defect_score * 32), 3)
-        paint["visionLabel"] = "DEFECT" if defect_score >= 0.45 else "NORMAL"
+
+        surface_quality_score = _jitter_numeric(
+            _as_float(paint.get("surfaceQualityScore")),
+            seed_key,
+            "paint.surfaceQualityScore",
+            pct=0.02,
+            absolute=1.5,
+            precision=3,
+            min_value=0.0,
+        )
+        original_surface_quality_score = _as_float(paint.get("surfaceQualityScore"))
+        if original_surface_quality_score >= 80.0:
+            surface_quality_score = max(80.0, surface_quality_score)
+        elif original_surface_quality_score >= 60.0:
+            surface_quality_score = min(79.9, max(60.0, surface_quality_score))
+        else:
+            surface_quality_score = min(59.9, surface_quality_score)
+        paint["surfaceQualityScore"] = _round(surface_quality_score, 3)
+        paint["visionLabel"] = "DEFECT" if defect_score >= 0.4 else "NORMAL"
 
     assembly = process_data.get("assembly")
     if isinstance(assembly, dict):
diff --git a/docs/MANUFACUTRING_REFERENCE.md b/docs/MANUFACUTRING_REFERENCE.md
index 1e2205a..507e6ac 100644
--- a/docs/MANUFACUTRING_REFERENCE.md
+++ b/docs/MANUFACUTRING_REFERENCE.md
@@ -354,4 +354,16 @@ R02 + 프로그램 P100 + 속도 80% + payload 20kg
 | 진동 물리량 | ISO 20816 | 단위와 센서 위치가 확인된 경우 상태평가 참고 |
 | 로봇 상태 | 컨트롤러 상태 코드 | `NORMAL`, `WARNING`, `ABNORMAL/COLLISION_RISK`로 구분 |
 | 운전 모드 | 생산 운영 기준 | 생산 중 `AUTO` 정상, 비정상 `MANUAL/STOPPED` 위험 |
-| 설비 상태 | 설비 상태 코드 | `RUNNING` 정상, `WARNING` 경고, `STOPPED/FAULT` 위험 |
\ No newline at end of file
+| 설비 상태 | 설비 상태 코드 | `RUNNING` 정상, `WARNING` 경고, `STOPPED/FAULT` 위험 |
+
+
+# 도장 레퍼런스
+
+도장 기준점 & 레퍼런스 정리
+
+| 지표 | 추천 기준점 | 레퍼런스 / 설명 |
+| --- | --- | --- |
+| 표면 품질 점수 `surfaceQualityScore` | 정상: 80점 이상 / 경고: 60~80점 / 위험: 60점 미만 | ISO 4628-1은 도막 결함의 수량·크기·강도 및 외관 변화를 0~5 등급으로 평가하는 체계를 제시함. 이를 내부 100점 점수로 환산해 ISO 1 수준 이하를 정상, ISO 2 수준을 경고, ISO 3 이상을 위험으로 매핑. |
+| 도막 두께 `thicknessValue` | 목표: 115μm / 정상: 90~120μm / 경고: 80~90 또는 120~130μm / 위험: 80μm 미만 또는 130μm 초과 | PPG Refinish 자료에서 OEM 도장 마감 두께는 약 90~120μm라고 설명함. ISO 2808은 도료·바니시 도막 두께 측정 방법을 다루는 표준. 80~130μm는 90~120μm 정상 범위에 ±10μm guard band를 둔 운영 기준. |
+| 불량 점수 `defectScore` | 정상: 0.4 미만 / 경고: 0.4~0.6 / 위험: 0.6 이상 | ISO 4628-1의 0~5 결함 등급을 0~1 내부 점수로 정규화. ISO 2/5 = 0.4를 경고, ISO 3/5 = 0.6을 위험 기준으로 매핑. 단, `visionLabel != NORMAL`이면 최소 WARNING 유지. |
+| 온도 편차 `thermalStdTemp` | 정상: 2℃ 미만 / 경고: 2~5℃ / 위험: 5℃ 이상 | Eurotherm의 건조·경화 오븐 자료에서 표준 오븐 온도 범위는 ±5℃, 중요 적용 분야는 ±2℃까지 좁힐 수 있다고 설명함. 이를 온도 균일도 관리 기준으로 사용. |
\ No newline at end of file

From e5c42dff39b3ddee76bea4e04855be81f9a50db7 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Mon, 13 Jul 2026 17:26:42 +0900
Subject: [PATCH 136/148] =?UTF-8?q?fix:=20=EC=A0=9C=EC=A1=B0=20=EC=9D=B4?=
 =?UTF-8?q?=EB=B2=A4=ED=8A=B8=20=EC=83=9D=EC=84=B1=20=EB=B0=8F=20=EB=B6=84?=
 =?UTF-8?q?=EC=84=9D=20ES=20=EC=83=89=EC=9D=B8=20=EC=95=88=EC=A0=95?=
 =?UTF-8?q?=ED=99=94?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 프레스/차체/도장 레퍼런스에 맞춰 manufacturing_event_json 생성 로직 보강

- 분석 sync consumer가 ES 장애 시 종료되지 않고 재시도하도록 수정

- raw Kafka 처리 단계에서 병목/불량 전이 결과를 ES에 직접 색인하도록 보강
---
 .../manufacturing_event_json_builder.py       | 14 ++--
 app/kafka/analysis_sync_consumer.py           | 82 ++++++++++++-------
 app/kafka/raw_event_consumer.py               | 53 ++++++++++++
 3 files changed, 111 insertions(+), 38 deletions(-)

diff --git a/app/data_generation/manufacturing_event_json_builder.py b/app/data_generation/manufacturing_event_json_builder.py
index aea71e2..ccc29b3 100644
--- a/app/data_generation/manufacturing_event_json_builder.py
+++ b/app/data_generation/manufacturing_event_json_builder.py
@@ -1173,7 +1173,7 @@ def _process_data(
     def _load_forming(self) -> pd.DataFrame:
         return self._cached_csv(
             "forming",
-            self.dataset_root / "?뚯꽦媛€怨??먯썝理쒖쟻??AI ?곗씠?곗뀑" / "怨듭젙_?곗씠??2022??8??csv",
+            self.dataset_root / "소성가공 자원최적화 AI 데이터셋" / "공정_데이터_2022년_8월.csv",
             nrows=4096,
         )
 
@@ -1182,8 +1182,8 @@ def _load_press_current(self, index: int) -> pd.DataFrame:
         return self._cached_csv(
             f"press_current_{file_index}",
             self.dataset_root
-            / "?뚯꽦媛€怨??먯썝理쒖쟻??AI ?곗씠?곗뀑"
-            / f"?꾨젅??{file_index}???좎븬紐⑦꽣_?꾨쪟?곗씠??csv",
+            / "소성가공 자원최적화 AI 데이터셋"
+            / f"프레스_{file_index}호-유압모터_전류데이터.csv",
             nrows=4096,
         )
 
@@ -1192,15 +1192,15 @@ def _load_robot_current(self, index: int) -> pd.DataFrame:
         return self._cached_csv(
             f"robot_current_{file_index}",
             self.dataset_root
-            / "?뚯꽦媛€怨??먯썝理쒖쟻??AI ?곗씠?곗뀑"
-            / f"濡쒕큸_{file_index}???꾨쪟_?곗씠??csv",
+            / "소성가공 자원최적화 AI 데이터셋"
+            / f"로봇_{file_index}호-전류_데이터.csv",
             nrows=4096,
         )
 
     def _load_vision(self, side: str) -> tuple[pd.DataFrame, list[float]]:
         cache_key = f"vision_{side}"
         if cache_key not in self._cache:
-            base = self.dataset_root / "癒몄떊鍮꾩쟾 AI ?곗씠?곗뀑 (?댄솕??湲곕컲 ?덉쭏 寃€???곗씠??"
+            base = self.dataset_root / "머신비전 AI 데이터셋 (열화상 기반 품질 검사 데이터)"
             df = pd.read_csv(base / f"2nd_process_{side}_data.csv", nrows=4096)
             with (base / f"2nd_process_{side}_label.json").open(
                 encoding="utf-8",
@@ -1218,7 +1218,7 @@ def _load_bosch_numeric(self) -> pd.DataFrame:
 
     def _load_ford_rows(self) -> list[list[float]]:
         if "ford_train" not in self._cache:
-            base = self.dataset_root / "Ford ?붿쭊 吏꾨룞 ?곗씠?곗뀑"
+            base = self.dataset_root / "Ford 엔진 진동 데이터셋"
             path = base / "FordA_TRAIN.txt"
             rows: list[list[float]] = []
             with path.open(encoding="utf-8", errors="ignore") as file:
diff --git a/app/kafka/analysis_sync_consumer.py b/app/kafka/analysis_sync_consumer.py
index 9a69e8d..ed9b5bf 100644
--- a/app/kafka/analysis_sync_consumer.py
+++ b/app/kafka/analysis_sync_consumer.py
@@ -2,6 +2,7 @@
 
 import asyncio
 import logging
+import time
 from threading import Event
 from typing import Any
 
@@ -51,47 +52,66 @@ def _run_analysis_sync_consumer(
     stop_event: Event,
     consumer_index: int,
 ) -> None:
-    consumer = create_consumer(
-        kafka_config.ANALYSIS_SYNC_TOPIC,
-        kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
-        enable_auto_commit=False,
-        consumer_timeout_ms=kafka_config.CONSUMER_TIMEOUT_MS,
-    )
     search_repository = ProcessAnalysisSearchRepository()
-    try:
-        search_repository.ensure_indices()
-    except Exception:
-        logger.exception("Failed to ensure Elasticsearch indices for analysis sync.")
-        consumer.close()
-        return
-
+    reconnect_delay_sec = 5.0
+    consumer: Any | None = None
     logger.info(
-        "Analysis sync consumer started: topic=%s group=%s consumer_index=%s",
+        "Analysis sync consumer starting: topic=%s group=%s consumer_index=%s",
         kafka_config.ANALYSIS_SYNC_TOPIC,
         kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
         consumer_index,
     )
     try:
         while not stop_event.is_set():
-            for message in consumer:
-                if stop_event.is_set():
+            try:
+                consumer = create_consumer(
+                    kafka_config.ANALYSIS_SYNC_TOPIC,
+                    kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
+                    enable_auto_commit=False,
+                    consumer_timeout_ms=kafka_config.CONSUMER_TIMEOUT_MS,
+                )
+                search_repository.ensure_indices()
+                logger.info(
+                    "Analysis sync consumer connected: topic=%s group=%s consumer_index=%s",
+                    kafka_config.ANALYSIS_SYNC_TOPIC,
+                    kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
+                    consumer_index,
+                )
+                while not stop_event.is_set():
+                    for message in consumer:
+                        if stop_event.is_set():
+                            break
+                        try:
+                            payload = message.value
+                            if not isinstance(payload, dict):
+                                logger.warning("Skipping non-dict analysis sync payload.")
+                                continue
+                            search_repository.index_sync_event(payload)
+                            consumer.commit()
+                        except Exception:
+                            logger.exception(
+                                "Failed to index analysis sync event: topic=%s partition=%s offset=%s",
+                                message.topic,
+                                message.partition,
+                                message.offset,
+                            )
+                            raise
+            except Exception:
+                logger.exception(
+                    "Analysis sync consumer will retry after ES/Kafka failure: topic=%s group=%s consumer_index=%s",
+                    kafka_config.ANALYSIS_SYNC_TOPIC,
+                    kafka_config.ANALYSIS_SYNC_CONSUMER_GROUP_ID,
+                    consumer_index,
+                )
+                if stop_event.wait(reconnect_delay_sec):
                     break
-                try:
-                    payload = message.value
-                    if not isinstance(payload, dict):
-                        logger.warning("Skipping non-dict analysis sync payload.")
-                        continue
-                    search_repository.index_sync_event(payload)
-                    consumer.commit()
-                except Exception:
-                    logger.exception(
-                        "Failed to index analysis sync event: topic=%s partition=%s offset=%s",
-                        message.topic,
-                        message.partition,
-                        message.offset,
-                    )
+            finally:
+                if consumer is not None:
+                    consumer.close()
+                    consumer = None
     finally:
-        consumer.close()
+        if consumer is not None:
+            consumer.close()
         logger.info(
             "Analysis sync consumer stopped: topic=%s group=%s consumer_index=%s",
             kafka_config.ANALYSIS_SYNC_TOPIC,
diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 0b9ebf4..6d96b33 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -22,6 +22,7 @@
 )
 from app.repository.sampledb_schema import car_master
 from app.repository.sampledb_repository import SampleDbRepository
+from app.search.process_analysis_search import ProcessAnalysisSearchRepository
 from app.service.analysis.bottleneck_service import BottleneckAnalysisService
 from app.utils.datetime_utils import seoul_now_iso
 from app.websocket.analysis_manager import analysis_websocket_manager
@@ -111,6 +112,7 @@ def _run_raw_event_consumer(
         KafkaProducer,
         bootstrap_servers,
     )
+    analysis_search_repository = _create_analysis_search_repository()
     logger.info(
         "Raw Kafka consumer started: topic=%s group=%s concurrency=%s/%s bootstrap=%s auth=SASL_SSL/OAUTHBEARER",
         RAW_TOPIC,
@@ -131,6 +133,7 @@ def _run_raw_event_consumer(
                         defect_detector,
                         defect_result_repository,
                         analysis_producer,
+                        analysis_search_repository,
                         record,
                     )
                 except Exception:
@@ -174,6 +177,7 @@ def _consume_record(
     defect_detector: DefectTransferDetector | None,
     defect_result_repository: DefectTransferPredictionRepository | None,
     analysis_producer: Any | None,
+    analysis_search_repository: ProcessAnalysisSearchRepository | None,
     record: Any,
 ) -> None:
     """Kafka raw 메시지를 manufacturing_event_json row로 변환해 sampledb에 upsert한다."""
@@ -260,6 +264,7 @@ def _consume_record(
     )
     sync_published = _publish_process_analysis_sync_events(
         analysis_producer,
+        analysis_search_repository,
         repository,
         raw_event,
         row,
@@ -538,6 +543,21 @@ def _create_defect_transfer_result_repository() -> DefectTransferPredictionRepos
         return None
 
 
+def _create_analysis_search_repository() -> ProcessAnalysisSearchRepository | None:
+    if not settings.elasticsearch_url:
+        return None
+    try:
+        repository = ProcessAnalysisSearchRepository()
+        repository.ensure_indices()
+        return repository
+    except Exception:
+        logger.exception(
+            "Analysis search repository is unavailable. "
+            "Raw Kafka events will still be stored.",
+        )
+        return None
+
+
 def _create_analysis_producer(
     producer_cls: Any,
     bootstrap_servers: list[str],
@@ -648,6 +668,7 @@ def _publish_bottleneck_analysis_event(
 
 def _publish_process_analysis_sync_events(
     producer: Any | None,
+    search_repository: ProcessAnalysisSearchRepository | None,
     repository: SampleDbRepository,
     raw_event: dict[str, Any],
     row: dict[str, Any],
@@ -673,6 +694,7 @@ def _publish_process_analysis_sync_events(
                 value=bottleneck_event,
             ).get(timeout=10)
             published = True
+            _index_bottleneck_sync_event(search_repository, bottleneck_event)
         except Exception:
             logger.exception(
                 "Failed to publish bottleneck sync event: topic=%s event_id=%s",
@@ -694,6 +716,7 @@ def _publish_process_analysis_sync_events(
                 value=defect_event,
             ).get(timeout=10)
             published = True
+            _index_defect_sync_event(search_repository, defect_event)
         except Exception:
             logger.exception(
                 "Failed to publish defect transfer sync event: topic=%s event_id=%s",
@@ -704,6 +727,36 @@ def _publish_process_analysis_sync_events(
     return published
 
 
+def _index_bottleneck_sync_event(
+    search_repository: ProcessAnalysisSearchRepository | None,
+    bottleneck_event: dict[str, Any],
+) -> None:
+    if search_repository is None:
+        return
+    try:
+        search_repository.index_sync_event(bottleneck_event)
+    except Exception:
+        logger.exception(
+            "Failed to index bottleneck sync event directly: event_id=%s",
+            bottleneck_event.get("eventId"),
+        )
+
+
+def _index_defect_sync_event(
+    search_repository: ProcessAnalysisSearchRepository | None,
+    defect_event: dict[str, Any],
+) -> None:
+    if search_repository is None:
+        return
+    try:
+        search_repository.index_sync_event(defect_event)
+    except Exception:
+        logger.exception(
+            "Failed to index defect transfer sync event directly: event_id=%s",
+            defect_event.get("eventId"),
+        )
+
+
 def _build_bottleneck_sync_event(
     *,
     repository: SampleDbRepository,

From 68a1acf59105dd40ca02c4cc635fcfe09e01f30d Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 15 Jul 2026 10:14:04 +0900
Subject: [PATCH 137/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9&=EB=B6=88?=
 =?UTF-8?q?=EB=9F=89=20=EC=A0=84=EC=9D=B4=20=EC=A1=B0=ED=9A=8C=20dateOptio?=
 =?UTF-8?q?ns=20=EB=B0=98=ED=99=98=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md                                     | 685 +++++++++---------
 app/search/process_analysis_search.py         |  75 ++
 app/service/analysis/bottleneck_service.py    |   9 +-
 .../analysis/defect_transfer_service.py       |  11 +-
 4 files changed, 448 insertions(+), 332 deletions(-)

diff --git a/README.md b/README.md
index 936790d..9c9aed5 100644
--- a/README.md
+++ b/README.md
@@ -1,405 +1,430 @@
 # AI Service
 
-FastAPI 기반 AI 서비스 API Gateway / Orchestrator입니다.
+**서비스명: AIMS (Auto Intelligence Manufacturing System)**
 
-`ai-services` Namespace 내에서 Pod 형태로 배포되며, ChatGPT API, AI 매뉴얼 API, colleague-skill(dot-skill) 등 외부 AI/API 및 내부 지식 서비스를 통합 연계합니다.
+AIMS는 자동차 스마트팩토리의 제조 데이터를 중심으로 병목 분석, 불량 전이 예측, SHAP 기반 원인 분석, AI 메뉴얼 생성을 제공하는
+AI 기반 제조 관제 시스템입니다.
 
-## 기술 스택
+이 서비스는 단순히 결과를 조회하는 API가 아니라, 제조 이벤트를 수집하고 분석한 뒤
+결과를 DB와 Elasticsearch에 함께 저장하고, 운영 중 데이터가 어긋나면 다시 맞추는 백엔드 역할까지 담당합니다.
 
-- Python
-- FastAPI
-- Uvicorn
-- Kubernetes Pod 배포
+핵심적으로 AIMS가 하는 일은 다음과 같습니다.
 
-## 프로젝트 구조
+- 생산 공정에서 어느 구간이 병목인지 계산합니다.
+- 어떤 차량이 다음 공정으로 불량이 전이될 가능성이 높은지 예측합니다.
+- 예측 결과의 근거를 SHAP 기반으로 설명합니다.
+- 사용자 요청에 따라 AI 메뉴얼을 생성합니다.
+- Kafka로 들어오는 제조 이벤트를 받아 분석 파이프라인에 연결합니다.
+- Elasticsearch를 조회용 검색 인덱스로 사용하고, 필요 시 다시 재색인합니다.
 
-```text
-ai-service/
-├─ app/
-│  ├─ core/
-│  │  ├─ config.py
-│  │  ├─ exceptions.py
-│  │  └─ logging.py
-│  ├─ api/
-│  │  ├─ router.py
-│  │  └─ routers/
-│  │     ├─ health.py
-│  │     └─ root.py
-│  ├─ service/
-│  │  ├─ orchestrator/
-│  │  ├─ llm/
-│  │  └─ analysis/
-│  ├─ ml/
-│  │  ├─ datasets/
-│  │  ├─ preprocessing/
-│  │  ├─ features/
-│  │  ├─ training/
-│  │  ├─ evaluation/
-│  │  ├─ inference/
-│  │  ├─ registry/
-│  │  └─ artifacts/
-│  ├─ kafka/
-│  ├─ dto/
-│  │  ├─ request/
-│  │  └─ response/
-│  │     └─ common_response.py
-│  ├─ utils/
-│  │  ├─ datetime_utils.py
-│  │  ├─ json_utils.py
-│  │  └─ response_utils.py
-│  └─ main.py
-├─ main.py
-├─ requirements.txt
-└─ README.md
-```
+---
 
-### 패키지 역할
-
-- `app/core/config.py`: `.env` 기반 애플리케이션 설정 관리
-- `app/core/logging.py`: 공통 로깅 설정
-- `app/core/exceptions.py`: 공통 예외 클래스 및 FastAPI 예외 핸들러 등록
-- `app/api/router.py`: 전체 API 라우터 집계
-- `app/api/routers`: 기능별 FastAPI 라우터 모듈
-- `app/api/routers/root.py`: 루트 상태 응답 라우터
-- `app/api/routers/health.py`: 헬스 체크 라우터
-- `app/service/orchestrator`: ChatGPT API, AI 매뉴얼 API, colleague-skill 연계 흐름 제어
-- `app/service/llm`: LLM API 연동 및 프롬프트 처리
-- `app/service/analysis`: 요청 분석, 응답 후처리, 분석 로직
-- `app/ml/datasets`: 학습 및 평가 데이터셋 관리
-- `app/ml/preprocessing`: 데이터 전처리 로직
-- `app/ml/features`: 피처 생성 및 변환 로직
-- `app/ml/training`: 모델 학습 로직
-- `app/ml/evaluation`: 모델 평가 로직
-- `app/ml/inference`: 모델 추론 로직
-- `app/ml/registry`: 모델 버전 및 메타데이터 관리
-- `app/ml/artifacts`: 모델 산출물 및 관련 파일 관리
-- `app/kafka`: Kafka 메시지 발행 및 구독 연동
-- `app/dto/request`: 요청 DTO 정의
-- `app/dto/response/common_response.py`: 공통 API 응답 DTO 정의
-- `app/utils/datetime_utils.py`: UTC 날짜/시간 공통 함수
-- `app/utils/json_utils.py`: JSON 직렬화 및 역직렬화 공통 함수
-- `app/utils/response_utils.py`: 공통 성공/실패 응답 생성 함수
-
-## 공통 응답 형식
-
-모든 API 응답은 아래 구조를 기본 형식으로 사용합니다.
-
-```json
-{
-  "success": true,
-  "data": {},
-  "message": "로그인 성공",
-  "timestamp": "2026-06-08T16:00:00"
-}
-```
+## 핵심 기능
 
-필드 설명:
+### 1. 병목 분석
 
-- `success`: 요청 성공 여부
-- `data`: 응답 데이터
-- `message`: 응답 메시지
-- `timestamp`: 응답 생성 시간
+- `sampledb.manufacturing_event_json`의 제조 이벤트를 읽어 공정별 병목을 계산합니다.
+- 결과는 공정 순위, 지연 시간, 영향 차량 수, 위험도 형태로 정리됩니다.
+- 결과는 `bottleneck_analysis_result`에 저장됩니다.
+- 조회 시에는 ES의 `detectedAt` 날짜를 기준으로 날짜 옵션을 만듭니다.
+- Elasticsearch가 살아 있으면 ES 우선으로 조회하고, 실패하면 DB에서 동일한 결과를 다시 읽습니다.
+- 같은 날짜 구간을 다시 계산하는 백필 / 재색인 기능도 함께 제공합니다.
 
-관련 코드:
+#### 구현 로직
 
-- `app/dto/response/common_response.py`: `CommonResponse` DTO
-- `app/utils/response_utils.py`: `success_response`, `error_response` 헬퍼
+1. `BottleneckAnalysisService.get_realtime_bottlenecks()`가 먼저 날짜 옵션을 읽고, 요청 날짜가 없으면 최신 날짜를 선택합니다.
+2. `ProcessAnalysisSearchRepository.list_bottleneck_date_options()`가 ES의 `detectedAt` 날짜를 집계해 날짜 옵션을 만듭니다.
+3. `BottleneckAnalysisService`는 ES에서 `list_bottleneck_page()`를 먼저 호출해 최신 병목 row를 가져옵니다.
+4. ES 조회가 실패하면 Redis 캐시를 확인하고, 캐시도 없으면 DB 조회로 fallback합니다.
+5. 백필이나 재색인이 실행되면 해당 날짜의 기존 결과를 지우고 다시 계산해서 저장합니다.
+6. 따라서 병목 화면은 최신 분석 결과를 기본으로 보여주되, 날짜 선택 시 과거 분석도 다시 볼 수 있습니다.
 
+### 2. 불량 전이 예측
 
-## 가상환경 생성 및 실행
+- 차량 단위로 불량이 다음 공정으로 전이될 가능성을 예측합니다.
+- 현재 공정, 예측 공정, 전이 확률, 예상 발생 시점, 위험도를 함께 계산합니다.
+- 결과는 `defect_transfer_prediction_result`에 저장됩니다.
+- 조회 시에는 ES의 `predictedAt` 날짜를 기준으로 날짜 옵션을 만듭니다.
+- 목록 조회는 차량별 최신 1건만 보여줘서, 차량의 현재 상태를 빠르게 확인할 수 있습니다.
+- 같은 결과 테이블을 원인 분석에도 사용합니다.
 
-PowerShell 기준:
+#### 구현 로직
 
-```powershell
-python -m venv venv
-.\venv\Scripts\Activate.ps1
-```
+1. `DefectTransferAnalysisService.get_predictions()`가 날짜 옵션을 읽고, 요청 날짜가 없으면 최신 날짜를 선택합니다.
+2. `ProcessAnalysisSearchRepository.list_defect_transfer_date_options()`가 ES의 `predictedAt` 날짜를 집계합니다.
+3. ES가 가능하면 `list_defect_prediction_page()`에서 차량별 최신 1건을 가져옵니다.
+4. ES가 실패하면 Redis 캐시를 먼저 보고, 없으면 DB의 `list_prediction_page()`로 fallback합니다.
+5. 저장 단계에서는 각 제조 이벤트마다 모델 예측을 수행한 뒤 `replace_prediction_result()`로 결과를 저장합니다.
+6. 저장할 때는 같은 `manufacturing_event_id`가 있으면 기존 row를 지우고 새 row를 넣어서 이벤트 기준 중복을 막습니다.
+7. 재색인 시에는 해당 날짜의 문서를 ES에서 먼저 삭제한 뒤, 소스 이벤트를 다시 예측해서 넣습니다.
 
-가상환경이 정상적으로 활성화되면 프롬프트 앞에 `(venv)`가 표시됩니다.
+### 3. SHAP 기반 원인 분석
 
-```powershell
-(venv) PS C:\rookies\aims\ai-service>
-```
+- 특정 차량의 최신 불량 전이 결과를 기준으로 원인을 설명합니다.
+- 대표 원인 1개와 상세 원인 목록을 분리해 반환합니다.
+- 각 원인은 영향도, 레이블, 설명 메시지를 함께 포함합니다.
+- 불량 전이 예측과 같은 ES 날짜 옵션을 사용해 같은 시점의 데이터를 일관되게 조회합니다.
 
-의존성 설치:
+#### 구현 로직
 
-```powershell
-pip install -r requirements.txt
-```
+1. `DefectTransferAnalysisService.get_cause_analysis()`가 차량 ID와 날짜 옵션을 기준으로 조회 대상을 결정합니다.
+2. ES가 있으면 `get_latest_defect_cause_document()`가 차량별 최신 문서를 하나 가져옵니다.
+3. 조회된 문서는 대표 원인과 상세 원인으로 분리됩니다.
+4. 대표 원인은 화면의 summary 영역에 쓰고, 상세 원인은 리스트 형태로 보여줍니다.
+5. 차량 ID가 없으면 최신 차량 기준으로 조회할 수 있습니다.
 
-`requirements.txt`에는 FastAPI, LLM/API 연동, Kafka, 데이터 처리, ML 관련 의존성을 모두 포함합니다.
+### 4. AI 메뉴얼
 
-Windows에서 Python 3.14를 사용하는 경우 일부 패키지의 사전 빌드 wheel이 없으면 `pydantic-core`, `orjson`, `pandas`, `scipy`, `scikit-learn` 등이 소스 빌드를 시도할 수 있습니다. 이 경우 Visual Studio Build Tools가 필요할 수 있으므로, 설치 문제가 반복되면 Python 3.12 또는 3.13 사용을 권장합니다.
+- JWT를 읽어서 로그인한 사용자 기준의 AI 메뉴얼을 생성합니다.
+- `Authorization: Bearer ...` 헤더가 필요합니다.
+- 메뉴얼 생성은 `app/ai_manual` 아래에서 별도로 관리합니다.
+- 일반 분석 API와 분리된 독립 기능이라, 운영 정책이나 권한 조건을 따로 둘 수 있습니다.
 
-개발 서버 실행:
+#### 구현 로직
 
-```powershell
-uvicorn app.main:app --reload
-```
+1. `ManualService.generate_manual(user_id)`가 요청의 시작점입니다.
+2. 가장 위험도가 높은 알림 이벤트를 `AlertEventRepository`에서 먼저 가져옵니다.
+3. 사용자 ID로 사용자 등급을 읽고, 없으면 `Junior`로 기본 처리합니다.
+4. `CriticalEvent`, `OperatorInfo`, `FactoryContext`, `RagContext`를 묶어서 LLM 입력용 요청 객체를 만듭니다.
+5. `VectorStore.search()`로 관련 문서를 검색해 RAG 컨텍스트를 구성합니다.
+6. `manual_prompt`에 컨텍스트를 넣고 `ChatOpenAI`로 응답을 생성합니다.
+7. 최종적으로 이벤트 정보와 생성된 메뉴얼을 함께 반환합니다.
 
-서버 실행 후 아래 주소에서 확인할 수 있습니다.
+### 5. 관리 기능
 
-- API Root: `http://127.0.0.1:8000/`
-- Health Check: `http://127.0.0.1:8000/api/health`
-- Swagger UI: `http://127.0.0.1:8000/docs`
-- OpenAPI Schema: `http://127.0.0.1:8000/openapi.json`
+- 병목 백필 / 재색인
+- 불량 전이 백필 / 재색인
+- 날짜 단위 결과 삭제 후 재생성
+- DB와 Elasticsearch 정합성 재구성
 
-가상환경 비활성화:
+---
 
-```powershell
-deactivate
-```
+## Kafka / Elasticsearch 아키텍처 상세
 
-## FastAPI 역할
+### Kafka 기반 비동기 데이터 파이프라인
 
-FastAPI는 AI 서비스의 API Gateway 및 Orchestrator 역할을 수행합니다.
+Kafka는 AIMS 아키텍처에서 제조 데이터의 실시간 수집 및 분석 파이프라인의 핵심 백본(Backbone) 역할을 수행합니다.
 
-주요 역할:
+- **원천 데이터 수집 (Raw Topic)**:
+  - `factory.manufacturing.raw` 토픽은 제조 현장(MES, PLC 등)으로부터 발생하는 모든 원천 제조 이벤트를 수집하는 진입점입니다.
+  - 이벤트 스트림의 처리 순서 보장과 분산 처리를 위해 `vehicle_id` 또는 `equipment_id`를 파티션 키(Partition Key)로 활용할 수 있는 다중 파티션 구조를 가집니다.
+  - `ai-analysis-consumer-group` 컨슈머 그룹에 속한 `app/kafka/raw_event_consumer.py`가 메시지를 소비하여 `sampledb.manufacturing_event_json`에 원천 데이터를 영구 저장합니다.
 
-- ChatGPT API 연동
-- AI 매뉴얼 API 연동
-- colleague-skill(dot-skill) 연동
-- API Orchestration 및 서비스 연계
-- 사용자 요청의 중앙 집중 처리
-- 외부 AI 서비스와 내부 지식 서비스의 응답 조합
+- **분석 결과 발행 및 동기화 (Analysis Topic)**:
+  - 원천 이벤트를 기반으로 병목 분석 및 불량 전이 추론(AI 모델 수행)이 완료되면, 그 결과 데이터는 `factory.manufacturing.analysis` 토픽으로 발행(Publish)됩니다.
+  - 발행된 분석 결과는 `ai-analysis-sync-consumer-group`에 속한 `analysis_sync_consumer`가 비동기적으로 소비하여 Elasticsearch에 동기화(Indexing)합니다.
+  - 이를 통해 무거운 **AI 모델 추론**과 I/O 바운드 작업인 **검색엔진 색인**을 완전히 디커플링(Decoupling)하여, 시스템의 안정성과 확장성을 극대화합니다.
 
-배포 형태:
+### Elasticsearch 기반 실시간 검색 및 집계
 
-- Kubernetes `ai-services` Namespace 내 Pod 형태로 배포
+Elasticsearch는 대용량 AI 분석 결과를 지연 없이 빠르게 조회하고, 다차원 집계(Aggregation)를 수행하기 위한 메인 검색 및 분석 엔진입니다.
 
-## ai-services Namespace 구성
+- **인덱스 설계 및 전략 (Index Strategy)**:
+  - **병목 인덱스** (`settings.elasticsearch_bottleneck_index`, 기본값 `ai-bottleneck-result-v1` 계열): 공정별 지연 상태, 위험도 데이터를 저장합니다. `detectedAt` 필드를 기준으로 시계열로 관리됩니다.
+  - **불량 전이 인덱스** (`settings.elasticsearch_defect_transfer_index`, 기본값 `ai-defect-transfer-result-v1` 계열): 차량별 불량 전이 확률, 발생 시점, SHAP 원인 분석 데이터를 저장합니다. `predictedAt` 필드를 기준으로 관리됩니다.
+  - 시계열 데이터의 특성을 살려 인덱스 롤오버(Rollover) 및 ILM(Index Lifecycle Management) 정책을 적용하기 용이한 구조를 취합니다.
 
-### 1. FastAPI
+- **특화된 검색 및 집계 쿼리 활용 (Advanced Query)**:
+  - **차량별 최신 상태 추출 (`collapse`)**: 동일 차량에 대해 공정 진행에 따라 여러 분석 결과가 누적될 수 있습니다. 응답 속도 최적화를 위해 ES의 `collapse` 파라미터를 사용하여 최신 타임스탬프(`predictedAt`) 기준 1건의 문서만 빠르게 추출합니다.
+  - **날짜별 동적 옵션 생성 (`date_histogram`)**: 화면에서 제공되는 '분석 날짜 옵션'은 ES의 `date_histogram` 집계(Aggregation)를 활용해, 실제 분석 데이터가 존재하는 날짜 리스트만 빠르고 정확하게 동적 생성하여 제공합니다.
 
-FastAPI는 API Gateway / Orchestrator 역할을 담당합니다.
+- **장애 대응 및 정합성 보장 (Fallback & Reindex)**:
+  - **고가용성**: ES 클러스터에 일시적 장애가 발생해도 서비스는 중단되지 않습니다. 조회 API는 ES 실패를 감지하면 자동으로 Redis 캐시와 DB 폴백(Fallback) 조회를 수행합니다.
+  - **데이터 재구성**: 백필(Backfill)이나 재색인(Reindex) 요청 시, 대상 날짜의 기존 ES 문서를 일괄 삭제(Delete By Query)하고 소스 이벤트로부터 다시 계산/색인하여 DB와 ES 간의 데이터 정합성을 일관되게 맞춥니다.
 
-주요 역할:
+### Kafka -> ES 색인 흐름
 
-- 사용자 요청 수신
-- ChatGPT API 호출
-- AI 매뉴얼 API 호출
-- colleague-skill(dot-skill) 연계
-- 각 서비스 응답 조합 및 최종 응답 반환
+1. raw topic의 제조 이벤트가 `raw_event_consumer`로 들어옵니다.
+2. 원천 이벤트가 `manufacturing_event_json`에 저장됩니다.
+3. 병목 / 불량 전이 분석이 수행됩니다.
+4. 분석 결과가 `factory.manufacturing.analysis` topic으로 발행됩니다.
+5. `analysis_sync_consumer`가 해당 메시지를 읽습니다.
+6. `ProcessAnalysisSearchRepository`가 병목 / 불량 전이 결과를 Elasticsearch에 색인합니다.
+7. 조회 API는 ES를 먼저 보고, ES가 없을 때만 DB / Redis를 사용합니다.
 
-배포 형태:
+### 주요 Kafka / ES 엔터티
 
-- Pod 형태로 배포
+| 구분 | 이름 | 역할 |
+| --- | --- | --- |
+| Raw Topic | `factory.manufacturing.raw` | 제조 원천 이벤트 입력 |
+| Analysis Topic | `factory.manufacturing.analysis` | 분석 결과 발행 및 ES 동기화 입력 |
+| Raw Consumer Group | `ai-analysis-consumer-group` | 원천 이벤트 소비 |
+| Sync Consumer Group | `ai-analysis-sync-consumer-group` | 분석 결과 ES 동기화 |
+| Bottleneck Index | `settings.elasticsearch_bottleneck_index` | 병목 결과 검색 |
+| Defect Transfer Index | `settings.elasticsearch_defect_transfer_index` | 불량 전이 / 원인 검색 |
 
-### 2. 외부 AI/API 연동 영역
+---
 
-#### ChatGPT API
+## API 요약
 
-- 자연어 질의 처리
-- AI 응답 생성
-- FastAPI와 연동
+### 분석 조회
 
-#### AI 매뉴얼 API
+- `GET /api/ai/process/bottleneck`
+- `GET /api/ai/process/defect-transfer/predictions`
+- `GET /api/ai/process/defect-transfer/causes`
 
-- 매뉴얼 및 문서 기반 질의응답 제공
-- ChatGPT API와 연계하여 결과 생성
+### 관리 API
 
-#### colleague-skill(dot-skill)
+- `POST /api/ai/admin/analysis/backfill/bottleneck`
+- `POST /api/ai/admin/analysis/backfill/defect-transfer`
+- `POST /api/ai/admin/analysis/reindex/bottleneck`
+- `POST /api/ai/admin/analysis/reindex/defect-transfer`
 
-- 사내 업무 지식 및 스킬셋 제공
-- AI 매뉴얼 API와 연동
+### AI 메뉴얼
 
-### 3. 데이터 흐름
+- `GET /api/ai/manual`
 
-```text
-사용자 요청
-    ↓
-FastAPI
-(API Gateway / Orchestrator)
-    ↓
-┌─────────────────────┐
-│     ChatGPT API     │
-└─────────────────────┘
-           ↕
-┌─────────────────────┐
-│    AI 매뉴얼 API     │
-└─────────────────────┘
-           ↕
-┌─────────────────────┐
-│ colleague-skill     │
-│    (dot-skill)      │
-└─────────────────────┘
-           ↓
-FastAPI
-           ↓
-응답 반환
-```
+---
 
-### 4. 아키텍처 요약
-
-- FastAPI가 AI 서비스의 API Gateway 및 Orchestrator 역할을 수행합니다.
-- FastAPI는 ChatGPT API, AI 매뉴얼 API, colleague-skill 서비스를 통합 관리합니다.
-- 외부 AI 서비스와 내부 지식 서비스를 조합하여 응답을 생성합니다.
-- 모든 요청 흐름은 FastAPI를 통해 중앙 집중적으로 처리됩니다.
-- Kubernetes 환경에서는 `ai-services` Namespace 내 Pod로 배포됩니다.
-- 향후 AI 서비스 추가 시 FastAPI에서 Orchestration만 확장하면 되므로 확장성이 높습니다.
-
-## 제조 이벤트 데이터 생성
-
-제조 이벤트 생성 기능은 기존 `car_master`와 `equipment` 마스터를 참조하여
-`manufacturing_event_json`에 원천 이벤트를 저장합니다. 차량 마스터를 생성하거나
-수정하지 않으며, `car_master.id=1`부터 요청한 차량 수만큼 순서대로 매핑합니다.
-
-### 기본 생성 규칙
-
-- 차량 수: `vehicle_id`의 생산일자가 요청 날짜와 일치하는 `car_master` 전체
-- 차량당 이벤트 수: 4건
-- 전체 이벤트 수: 해당 날짜 차량 수 × 4
-- 공정 순서: `PRESS -> BODY -> PAINT -> ASSEMBLY`
-- 공정별 이벤트 수: 해당 날짜 차량 수와 동일
-- 정상/이상 비율: 약 7:3
-- 공정별 이상 이벤트: 각 공정에 균등 분배
-- 설비: 차량의 공정마다 1~5호기 중 독립적으로 결정
-- 최초 발행 상태: `PRESS=READY`, 나머지 공정은 `PENDING`
-
-예를 들어 `AVANTE-20260601-10474`처럼 `vehicle_id`에 `20260601`이 포함된
-차량이 10,700대라면 정상 7,490대, 폐기(이상) 3,210대로 구성하고 총
-42,800건의 이벤트를 생성합니다.
-
-`event_count`와 `car_pool_size`의 기본값은 `null`입니다. 값을 생략하면 해당
-생산일자의 차량 전체를 사용합니다. 제한값을 지정한다면 `event_count`는
-`car_pool_size * 4`와 같아야 합니다.
-
-### 이벤트 JSON 구조
-
-`manufacturing_event_json.event_json`에는 아래 최상위 필드만 저장합니다.
-아래 JSON은 필드 위치를 보여주는 축약 예시이며, 실제 생성 시 `sensor`,
-`processMetrics`, `sourceTrace`, `processData`의 공정별 상세 값이 채워집니다.
-
-```json
-{
-  "event": {
-    "eventId": "EVT-20260601-000001",
-    "eventTime": null,
-    "eventType": "PROCESS_STATUS",
-    "eventName": "프레스 공정 통합 관제 이벤트"
-  },
-  "equipment": {
-    "equipmentCode": "EQ_PRESS_001",
-    "equipmentName": "프레스 유압모터 1호",
-    "equipmentType": "HYDRAULIC_PRESS"
-  },
-  "equipmentStatus": {
-    "operationStatus": "RUNNING",
-    "lastNormalTime": null,
-    "statusChangedTime": null
-  },
-  "product": {
-    "carMasterId": 1
-  },
-  "sensor": {},
-  "processMetrics": {},
-  "sourceTrace": {},
-  "processData": {
-    "press": {
-      "countIncreaseYn": true,
-      "targetCycleTimeSec": 40.0,
-      "timestampDelaySec": 5.7
-    }
-  }
-}
-```
+## 데이터 흐름
 
-- `product.carMasterId`는 기존 `car_master.id`입니다.
-- DB 컬럼 `event_time`과 JSON의 `event.eventTime`은 최초 생성 시 `NULL`입니다.
-- `lastNormalTime`, `statusChangedTime`도 최초 생성 시 `NULL`입니다.
-- `operationStatus`는 정상 데이터의 경우 `RUNNING`/`IDLE`, 이상 데이터의 경우
-  `FAULT`/`STOPPED`/`MAINTENANCE` 중에서 결정됩니다.
-- 같은 차량과 공정은 재생성해도 동일한 상태를 갖도록 결정적 난수를 사용합니다.
-- `processData`에는 현재 공정에 해당하는 `press`, `body`, `paint`, `assembly`
-  블록 중 하나만 포함됩니다.
+### Kafka 수집
 
-### 생성 API
+1. 외부 시스템이 제조 원천 이벤트를 Kafka raw topic으로 보냅니다.
+2. `raw_event_consumer`가 메시지를 읽습니다.
+3. 원천 이벤트를 `manufacturing_event_json`에 저장합니다.
+4. 필요 시 병목 / 불량 전이 분석 이벤트를 `factory.manufacturing.analysis` topic으로 발행합니다.
+5. `analysis_sync_consumer`가 분석 결과를 읽고 Elasticsearch에 색인합니다.
 
-모든 제조 이벤트 API prefix는 `/api/manufacturing/events`입니다.
+### 병목
 
-| Method | Endpoint | 설명 |
-| --- | --- | --- |
-| `POST` | `/templates/generate` | 날짜 재생용 템플릿 생성 |
-| `GET` | `/templates/{template_name}` | 저장된 템플릿 조회 |
-| `GET` | `/templates/{template_name}/replay` | DB 저장 없이 특정 날짜 기준 replay 조회 |
-| `POST` | `/generate` | 지정 날짜의 실제 이벤트 생성 및 적재 |
-| `POST` | `/generate/tomorrow` | 템플릿을 이용해 다음날 이벤트 적재 |
-| `GET` | `/generate/jobs/{job_id}` | 비동기 생성 job 상태 조회 |
-| `GET` | `/` | 저장된 이벤트 조회 |
+1. `sampledb.manufacturing_event_json`에서 이벤트를 읽습니다.
+2. 병목 모델이 공정별 병목을 계산합니다.
+3. `bottleneck_analysis_result`에 저장합니다.
+4. Elasticsearch에 재색인합니다.
+5. 조회 API는 ES 우선으로 응답합니다.
 
-예를 들어 2026년 6월 1일 차량 전체의 이벤트는 Swagger에서 `POST /generate`를
-아래처럼 호출하여 생성할 수 있습니다.
+### 불량 전이 / 원인
 
-```text
-start_date=2026-06-01
-end_date=2026-06-01
-event_count=null
-car_pool_size=null
-insert_chunk_size=1000
-```
+1. `sampledb.manufacturing_event_json`에서 SENT 이벤트를 읽습니다.
+2. 불량 전이 모델이 예측을 수행합니다.
+3. `defect_transfer_prediction_result`에 저장합니다.
+4. Elasticsearch에 재색인합니다.
+5. 조회 API는 차량별 최신 1건을 반환합니다.
+6. 원인 분석은 최신 불량 전이 결과의 SHAP 원인을 보여줍니다.
 
-API는 즉시 `jobId`를 반환합니다. 이후
-`GET /api/manufacturing/events/generate/jobs/{jobId}`에서 `SUCCEEDED` 여부를
-확인합니다. 실패한 job을 다시 실행할 때는 `/generate`를 새로 호출하면 되며,
-동일한 `event_id`는 중복 생성하지 않고 갱신합니다.
+### AI 메뉴얼
 
-템플릿 생성도 `event_count`와 `car_pool_size`의 기본값은 `null`입니다.
-`production_date`와 `vehicle_id` 생산일자가 일치하는 차량 전체를 사용합니다.
+1. 요청 헤더에서 JWT를 읽습니다.
+2. 사용자 ID를 추출합니다.
+3. 메뉴얼 서비스를 호출해 응답을 만듭니다.
 
-### 관련 테이블
+### Kafka / ES 전체 흐름
 
-| 테이블 | 용도 |
-| --- | --- |
-| `car_master` | 기존 차량 마스터. 생성 로직은 조회만 수행 |
-| `equipment` | 공정별 설비 마스터 |
-| `manufacturing_event_json` | 실제 원천 이벤트 JSON |
-| `manufacturing_event_template` | 날짜별 재생에 사용하는 이벤트 템플릿 |
-| `manufacturing_event_generation_job` | 비동기 생성 작업 상태와 진행률 |
+```mermaid
+flowchart LR
+    A["외부 제조 시스템"] --> B["Kafka Raw Topic<br/>factory.manufacturing.raw"]
+    B --> C["raw_event_consumer"]
+    C --> D["sampledb.manufacturing_event_json"]
+    D --> E["병목 / 불량 전이 분석"]
+    E --> F["Kafka Analysis Topic<br/>factory.manufacturing.analysis"]
+    F --> G["analysis_sync_consumer"]
+    G --> H1["Elasticsearch Bottleneck Index"]
+    G --> H2["Elasticsearch Defect Transfer Index"]
+    H1 --> I1["병목 조회 API"]
+    H2 --> I2["불량 전이 / 원인 조회 API"]
+```
 
-### Repository 구조
+### ES 조회 우선순위
+
+```mermaid
+sequenceDiagram
+    participant API as API
+    participant ES as Elasticsearch
+    participant Cache as Redis
+    participant DB as DB
+
+    API->>ES: dateOptions / content 조회
+    alt ES success
+        ES-->>API: 최신 결과
+    else ES fail
+        API->>Cache: cached response lookup
+        alt Cache hit
+            Cache-->>API: cached page
+        else Cache miss
+            API->>DB: fallback query
+            DB-->>API: db rows
+        end
+    end
+```
 
-`SampleDbRepository`는 DB 연결과 아래 repository를 묶는 얇은 facade입니다.
-테이블별 SQL과 상태 관리 로직은 각각의 repository에 위치합니다.
+---
+
+## 프로젝트 구조
 
 ```text
-app/repository/
-├── sampledb_repository.py
-├── sampledb_schema.py
-├── sampledb_schema_manager.py
-├── car_master_repository.py
-├── equipment_repository.py
-├── manufacturing_event_repository.py
-├── manufacturing_event_template_repository.py
-└── manufacturing_generation_job_repository.py
+app/
+├─ api/                     # FastAPI 라우터
+│  ├─ router.py             # API 라우터 통합
+│  └─ routers/              # 기능별 엔드포인트
+│     ├─ process.py         # 병목 조회
+│     ├─ defect_transfer.py # 불량 전이 / 원인 조회
+│     ├─ analysis_maintenance.py # 병목 / 불량 전이 백필, 재색인
+│     ├─ manual.py          # AI 메뉴얼
+│     ├─ manufacturing_event.py  # 제조 이벤트 생성/조회
+│     └─ health.py          # 헬스 체크
+├─ service/
+│  ├─ analysis/              # 병목, 불량 전이, 재색인, 조회 로직
+│  ├─ manufacturing/         # 제조 이벤트 생성 및 처리
+│  └─ llm/                   # LLM 연동 계층
+├─ repository/               # DB 읽기 / 쓰기
+│  ├─ bottleneck_analysis_repository.py
+│  ├─ defect_transfer_prediction_repository.py
+│  ├─ manufacturing_event_repository.py
+│  ├─ manufacturing_event_template_repository.py
+│  ├─ equipment_repository.py
+│  └─ sampledb_schema.py
+├─ search/                   # Elasticsearch 저장 / 조회
+├─ ml/                       # 모델 추론, 학습 산출물, feature 처리
+│  ├─ inference/             # 추론기
+│  ├─ training/              # 학습 산출물 / 실험 노트북
+│  ├─ features/              # feature 생성
+│  └─ artifacts/             # 배포용 모델 파일
+├─ ai_manual/                # AI 메뉴얼 생성
+│  ├─ repository/            # 알림 이벤트, 사용자 정보 조회
+│  ├─ rag/                   # 벡터 검색
+│  ├─ prompt/                # 프롬프트 템플릿
+│  └─ schema/                # 메뉴얼 요청 / 응답 모델
+├─ kafka/                    # 제조 이벤트 수집 / 발행
+├─ dto/                      # 요청 / 응답 모델
+├─ utils/                    # 공통 유틸
+├─ scheduler/                # 주기 실행 품질/제조 작업
+└─ batch/                    # 백필 / 배치 작업
 ```
 
-- `CarMasterRepository`: 기존 차량 조회 및 ID 매핑
-- `EquipmentRepository`: 기본 설비 초기화 및 조회
-- `ManufacturingEventRepository`: 이벤트 저장·갱신·조회 및 데드락 재시도
-- `ManufacturingEventTemplateRepository`: 템플릿 저장·조회·삭제
-- `ManufacturingGenerationJobRepository`: job 상태·진행률 및 DB 전역 실행 잠금
-- `SampleDbSchemaManager`: 테이블 생성과 점진적 스키마 마이그레이션
-
-MySQL 오류 `1205` 또는 `1213`이 발생하면 이벤트 저장 청크를 자동으로
-재시도합니다. 다중 프로세스나 `uvicorn --reload` 환경에서는 DB advisory lock으로
-생성 job이 동시에 실행되지 않도록 직렬화합니다.
+### 역할 요약
+
+- `app/api`: 외부 요청을 받는 진입점
+- `app/service/analysis`: 병목, 불량 전이, 재색인, 조회 로직
+- `app/repository`: DB 읽기 / 쓰기
+- `app/search`: Elasticsearch 저장 / 조회
+- `app/ai_manual`: AI 메뉴얼 생성
+- `app/ml`: 모델 추론과 산출물 관리
+- `app/kafka`: 제조 이벤트 수집과 후속 처리
+- `app/scheduler`: 주기적으로 실행되는 품질/제조 작업
+- `app/batch`: 수동 실행용 백필 / 배치 작업
+
+---
+
+## 시스템 다이어그램
+
+### 전체 분석 흐름
+
+```mermaid
+flowchart TD
+    A["Manufacturing Event Source"] --> B["Kafka Raw Topic"]
+    B --> C["raw_event_consumer"]
+    C --> D["DB: manufacturing_event_json"]
+    D --> E["Analysis Service"]
+    E --> F1["Bottleneck Detector"]
+    E --> F2["Defect Transfer Detector"]
+    F1 --> G1["DB: bottleneck_analysis_result"]
+    F2 --> G2["DB: defect_transfer_prediction_result"]
+    G1 --> H1["Kafka Analysis Topic"]
+    G2 --> H1
+    H1 --> I["analysis_sync_consumer"]
+    I --> J1["Elasticsearch Bottleneck Index"]
+    I --> J2["Elasticsearch Defect Transfer Index"]
+    J1 --> K1["/api/ai/process/bottleneck"]
+    J2 --> K2["/api/ai/process/defect-transfer/predictions"]
+    J2 --> K3["/api/ai/process/defect-transfer/causes"]
+```
 
-### 환경변수
+### 병목 조회 흐름
+
+```mermaid
+sequenceDiagram
+    participant U as User
+    participant API as Bottleneck API
+    participant S as Bottleneck Service
+    participant ES as Elasticsearch
+    participant DB as DB
+
+    U->>API: GET /api/ai/process/bottleneck
+    API->>S: get_cached_realtime_bottlenecks()
+    S->>ES: list_bottleneck_page()
+    alt ES success
+        ES-->>S: latest bottleneck rows
+    else ES fail
+        S->>DB: list_results()
+        DB-->>S: fallback rows
+    end
+    S-->>API: BottleneckAnalysisPage
+    API-->>U: JSON response
+```
 
-아래 값은 코드 기본값이 있으므로 `.env`에 없더라도 동일하게 동작합니다.
-운영 환경에서 값을 변경할 때만 설정하면 됩니다.
+### 불량 전이 / 원인 흐름
+
+```mermaid
+sequenceDiagram
+    participant U as User
+    participant API as Defect API
+    participant S as Defect Service
+    participant ES as Elasticsearch
+    participant DB as DB
+
+    U->>API: GET /api/ai/process/defect-transfer/predictions
+    API->>S: get_cached_predictions()
+    S->>ES: list_defect_prediction_page()
+    alt ES success
+        ES-->>S: vehicle latest rows
+    else ES fail
+        S->>DB: list_prediction_page()
+        DB-->>S: fallback rows
+    end
+    S-->>API: DefectTransferPredictionPage
+    API-->>U: JSON response
+
+    U->>API: GET /api/ai/process/defect-transfer/causes
+    API->>S: get_cached_cause_analysis()
+    S->>ES: get_latest_defect_cause_document()
+    ES-->>S: latest vehicle cause docs
+    S-->>API: DefectTransferCausePage
+    API-->>U: JSON response
+```
 
-```dotenv
-MANUFACTURING_EVENT_SCHEDULER_ENABLED=true
-MANUFACTURING_EVENT_INSERT_CHUNK_SIZE=1000
+### AI 메뉴얼 흐름
+
+```mermaid
+sequenceDiagram
+    participant U as User
+    participant API as Manual API
+    participant M as Manual Service
+    participant R as RAG / Manual Data
+
+    U->>API: GET /api/ai/manual
+    API->>API: Read JWT from Authorization header
+    API->>M: generate_manual(user_id)
+    M->>R: load relevant manual context
+    R-->>M: manual content
+    M-->>API: generated manual
+    API-->>U: JSON response
 ```
 
-`MANUFACTURING_EVENT_SCHEDULER_EVENTS_PER_DAY`과
-`MANUFACTURING_EVENT_CAR_POOL_SIZE`를 생략하면 대상 날짜의 차량 전체를
-사용합니다.
+---
 
-스케줄러를 사용하지 않으려면 다음과 같이 설정합니다.
+## 실행
 
-```dotenv
-MANUFACTURING_EVENT_SCHEDULER_ENABLED=false
+```powershell
+python -m venv venv
+.\venv\Scripts\Activate.ps1
+pip install -r requirements.txt
+uvicorn app.main:app --reload
 ```
+
+주요 주소:
+
+- API Root: `http://127.0.0.1:8000/`
+- Health: `http://127.0.0.1:8000/api/health`
+- Swagger: `http://127.0.0.1:8000/docs`
+
+---
+
+## 참고 포인트
+
+- 병목의 날짜 기준 필드는 `detected_at`입니다.
+- 불량 전이와 SHAP의 날짜 기준 필드는 `predicted_at`입니다.
+- 병목은 공정 단위, 불량 전이는 차량 단위로 조회합니다.
+- `dateOptions`는 ES 기준으로 만들고, ES 실패 시 DB로 내려갑니다.
+- 병목 / 불량 전이 / SHAP / 메뉴얼은 서로 다른 책임을 가지지만, 모두 제조 이벤트를 중심으로 연결됩니다.
+- DB는 정본 데이터, Elasticsearch는 빠른 조회용 검색 인덱스로 이해하면 전체 구조를 잡기 쉽습니다.
diff --git a/app/search/process_analysis_search.py b/app/search/process_analysis_search.py
index 73d26d6..20a8127 100644
--- a/app/search/process_analysis_search.py
+++ b/app/search/process_analysis_search.py
@@ -232,6 +232,14 @@ def list_bottleneck_page(
         has_next = len(rows) > safe_size
         return rows[:safe_size], has_next
 
+    def list_bottleneck_date_options(self) -> list[dict[str, Any]]:
+        if not self.enabled:
+            return []
+        return self._list_date_options(
+            index=settings.elasticsearch_bottleneck_index,
+            date_field="detectedAt",
+        )
+
     def count_bottleneck_page(self) -> int:
         latest_snapshot_id = self._latest_bottleneck_snapshot_id()
         if not latest_snapshot_id:
@@ -290,6 +298,22 @@ def list_defect_prediction_page(
         has_next = len(rows) > safe_size
         return rows[:safe_size], has_next
 
+    def list_defect_transfer_date_options(
+        self,
+        *,
+        vehicle_id: str | None = None,
+    ) -> list[dict[str, Any]]:
+        if not self.enabled:
+            return []
+        query: dict[str, Any] = {"match_all": {}}
+        if vehicle_id:
+            query = {"term": {"vehicleId": vehicle_id}}
+        return self._list_date_options(
+            index=settings.elasticsearch_defect_transfer_index,
+            date_field="predictedAt",
+            query=query,
+        )
+
     def get_latest_defect_cause_document(
         self,
         *,
@@ -366,6 +390,57 @@ def _date_range_query(field: str, analysis_date: DateType) -> dict[str, Any]:
             },
         }
 
+    def _list_date_options(
+        self,
+        *,
+        index: str,
+        date_field: str,
+        query: dict[str, Any] | None = None,
+    ) -> list[dict[str, Any]]:
+        body: dict[str, Any] = {
+            "size": 0,
+            "track_total_hits": False,
+            "query": query or {"match_all": {}},
+            "aggs": {
+                "by_date": {
+                    "date_histogram": {
+                        "field": date_field,
+                        "calendar_interval": "day",
+                        "time_zone": "+09:00",
+                        "order": {"_key": "desc"},
+                        "min_doc_count": 1,
+                    },
+                    "aggs": {
+                        "sample_doc": {
+                            "top_hits": {
+                                "size": 1,
+                                "_source": ["eventId"],
+                            },
+                        },
+                    },
+                },
+            },
+        }
+        response = self.client.search(index=index, body=body)
+        buckets = response.get("aggregations", {}).get("by_date", {}).get("buckets", [])
+        options: list[dict[str, Any]] = []
+        for bucket in buckets:
+            date_value = bucket.get("key_as_string")
+            if not date_value:
+                continue
+            sample_event_id = None
+            sample_hits = bucket.get("sample_doc", {}).get("hits", {}).get("hits", [])
+            if sample_hits:
+                sample_source = sample_hits[0].get("_source") or {}
+                sample_event_id = sample_source.get("eventId")
+            options.append(
+                {
+                    "date": str(date_value)[:10],
+                    "sample_event_id": sample_event_id,
+                },
+            )
+        return options
+
     def _bulk_index(self, actions: list[dict[str, Any]]) -> None:
         if not actions:
             return
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index 1532b74..a7a8ff0 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -299,7 +299,14 @@ def _current_bottleneck_results(self) -> list[dict[str, Any]]:
     def _get_date_options(self) -> list[AnalysisDateOption]:
         """화면용 날짜 옵션을 detected_at 스냅샷 기준으로 만든다."""
         # 화면의 날짜 선택 옵션은 detected_at 스냅샷 기준으로 구성한다.
-        options = self.repository.list_date_options()
+        if self.search_repository is not None:
+            try:
+                options = self.search_repository.list_bottleneck_date_options()
+            except Exception:
+                logger.exception("Failed to load bottleneck date options from Elasticsearch.")
+                options = self.repository.list_date_options()
+        else:
+            options = self.repository.list_date_options()
         return [
             AnalysisDateOption.model_validate(
                 {
diff --git a/app/service/analysis/defect_transfer_service.py b/app/service/analysis/defect_transfer_service.py
index 0f821f6..b313049 100644
--- a/app/service/analysis/defect_transfer_service.py
+++ b/app/service/analysis/defect_transfer_service.py
@@ -632,7 +632,16 @@ def _get_date_options(
         *,
         vehicle_id: str | None = None,
     ) -> list[AnalysisDateOption]:
-        options = self.repository.list_date_options(vehicle_id=vehicle_id)
+        if self.search_repository is not None:
+            try:
+                options = self.search_repository.list_defect_transfer_date_options(
+                    vehicle_id=vehicle_id,
+                )
+            except Exception:
+                logger.exception("Failed to load defect transfer date options from Elasticsearch.")
+                options = self.repository.list_date_options(vehicle_id=vehicle_id)
+        else:
+            options = self.repository.list_date_options(vehicle_id=vehicle_id)
         return [
             AnalysisDateOption.model_validate(
                 {

From fa1de8b577191a6d9fb5e985fd607e032bdcb6fe Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 15 Jul 2026 13:09:25 +0900
Subject: [PATCH 138/148] fix : 404 error update

---
 .../repository/alert_event_repository.py        |  3 +++
 app/api/routers/manual.py                       | 17 +++++++++++------
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/app/ai_manual/repository/alert_event_repository.py b/app/ai_manual/repository/alert_event_repository.py
index 9bda0d3..08cb841 100644
--- a/app/ai_manual/repository/alert_event_repository.py
+++ b/app/ai_manual/repository/alert_event_repository.py
@@ -37,6 +37,9 @@ def get_highest_risk_event(self):
         with main_engine.connect() as conn:
             row = conn.execute(query).mappings().first()
 
+            print("조회 결과:", row)
+            print("타입:", type(row))
+
         if not row:
             return None
 
diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 1a5e6ea..600f2df 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -39,9 +39,14 @@ def generate_manual(request: Request):
     result = service.generate_manual(user_id)
 
     if result is None:
-        raise HTTPException(
-            status_code=404,
-            detail="처리할 이벤트가 없습니다."
-        )
-
-    return result
\ No newline at end of file
+        return {
+            "success": True,
+            "message": "처리할 이벤트가 없습니다.",
+            "data": None
+        }
+
+    return {
+        "success": True,
+        "message": "매뉴얼 생성 완료",
+        "data": result
+    }
\ No newline at end of file

From 92c03f5eeced2a2bbcc8a510041e8ca87a43a906 Mon Sep 17 00:00:00 2001
From: kimgeon <kimgeon0802@gmail.com>
Date: Wed, 15 Jul 2026 15:01:09 +0900
Subject: [PATCH 139/148] manual.py return value update

---
 app/api/routers/manual.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 600f2df..6d4b37c 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -45,8 +45,4 @@ def generate_manual(request: Request):
             "data": None
         }
 
-    return {
-        "success": True,
-        "message": "매뉴얼 생성 완료",
-        "data": result
-    }
\ No newline at end of file
+    return result

From 6b921b9102c10fd6bd3e6cc24b3d92fa21fbf4bc Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 15 Jul 2026 16:36:07 +0900
Subject: [PATCH 140/148] =?UTF-8?q?docs:=20README=20=EB=AC=B8=EC=84=9C=20?=
 =?UTF-8?q?=EC=A0=95=EB=A6=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 504 ++++++++++++++++++++++--------------------------------
 1 file changed, 205 insertions(+), 299 deletions(-)

diff --git a/README.md b/README.md
index 9c9aed5..678f3a4 100644
--- a/README.md
+++ b/README.md
@@ -1,146 +1,200 @@
-# AI Service
+# AIMS - AI Service
 
-**서비스명: AIMS (Auto Intelligence Manufacturing System)**
+### AIMS (Auto Intelligence Manufacturing System) - AI 기반 자동차 스마트팩토리 관제 시스템
 
-AIMS는 자동차 스마트팩토리의 제조 데이터를 중심으로 병목 분석, 불량 전이 예측, SHAP 기반 원인 분석, AI 메뉴얼 생성을 제공하는
-AI 기반 제조 관제 시스템입니다.
+`ai-service`는 SK 쉴더스 루키즈 개발 5기 **AI 기반 자동차 스마트팩토리 관제 시스템 AIMS**에서 발생하는 제조 이벤트를 기반으로 병목 분석, 불량 전이 예측, SHAP 기반 원인 분석, AI 메뉴얼 생성을 제공하는 FastAPI 기반 AI 서비스입니다.
 
-이 서비스는 단순히 결과를 조회하는 API가 아니라, 제조 이벤트를 수집하고 분석한 뒤
-결과를 DB와 Elasticsearch에 함께 저장하고, 운영 중 데이터가 어긋나면 다시 맞추는 백엔드 역할까지 담당합니다.
+제조 이벤트를 수집하고 분석 결과를 DB와 Elasticsearch에 함께 반영한 뒤, 운영 화면이 빠르게 최신 상태를 볼 수 있도록 돕는 역할을 합니다.
 
-핵심적으로 AIMS가 하는 일은 다음과 같습니다.
+핵심적으로는 다음을 수행합니다.
 
-- 생산 공정에서 어느 구간이 병목인지 계산합니다.
-- 어떤 차량이 다음 공정으로 불량이 전이될 가능성이 높은지 예측합니다.
-- 예측 결과의 근거를 SHAP 기반으로 설명합니다.
-- 사용자 요청에 따라 AI 메뉴얼을 생성합니다.
-- Kafka로 들어오는 제조 이벤트를 받아 분석 파이프라인에 연결합니다.
-- Elasticsearch를 조회용 검색 인덱스로 사용하고, 필요 시 다시 재색인합니다.
+- 공정별 병목을 계산합니다.
+- 어떤 차량이 다음 공정에서 불량으로 이어질 가능성이 있는지 예측합니다.
+- SHAP으로 불량 전이 원인을 설명합니다.
+- 운영자가 바로 읽을 수 있는 AI 메뉴얼을 생성합니다.
+- Kafka로 입력과 분석을 분리하고, Elasticsearch로 조회 성능을 확보합니다.
 
----
 
-## 핵심 기능
+## ✨ 핵심 기능
 
-### 1. 병목 분석
+### 1. 불량 탐지 및 전이 예측
+
+<img width="984" height="1263" alt="혼합불량" src="https://github.com/user-attachments/assets/386bac8e-5f96-4dbe-a413-1cdc48c0ef93" />
+
+- 차량 단위로 다음 공정 불량 가능성을 예측하고 전이 경로를 함께 봅니다.
+- 현재 공정, 다음 공정, 설비 신호, 사이클 타임, 대기 시간, 재공 수량, 진동/온도/도막 두께를 함께 봅니다.
+- 결과는 `defect_transfer_prediction_result`에 저장됩니다.
+- 조회 시에는 ES의 `predictedAt` 날짜를 기준으로 날짜 옵션을 만듭니다.
+- 목록은 차량별 최신 1건을 보여줍니다.
+
+#### 구현 로직
+
+1. `DefectTransferAnalysisService.get_predictions()`가 날짜 옵션을 읽고, 요청 날짜가 없으면 최신 날짜를 선택합니다.
+2. `ProcessAnalysisSearchRepository.list_defect_transfer_date_options()`가 ES의 `predictedAt` 날짜를 집계합니다.
+3. ES가 가능하면 `list_defect_prediction_page()`에서 차량별 최신 1건을 가져옵니다.
+4. ES가 실패하면 Redis 캐시를 먼저 보고, 없으면 DB의 `list_prediction_page()`로 fallback합니다.
+5. 저장 단계에서 같은 이벤트가 다시 들어오면 기존 row를 덮어써 중복 예측을 줄입니다.
+6. 재색인 시 해당 날짜의 문서를 다시 읽어 ES에 최신 상태를 맞춥니다.
+
+#### ML 모델링
+
+- `ColumnTransformer`로 수치형과 범주형 feature를 분리 처리합니다.
+- 범주형은 `OneHotEncoder`로 변환하고, 희귀 범주는 `min_frequency`를 활용해 묶습니다.
+- 후보 모델은 `LightGBM`, `XGBoost`, `CatBoost`, `Logistic Regression` 계열을 비교합니다.
+- 평가 지표는 `accuracy`, `precision`, `recall`, `f1`, `PR-AUC`, `ROC-AUC`를 함께 봅니다.
+- 최종 결과는 차량별 `predictedDefectProcess`, `transferProbability`, `riskLevel` 형태로 내려갑니다.
+- SHAP으로 주요 원인을 계산하고, API에서는 `main_causes`, `detailCauses`로 제공합니다.
+- 예측 결과는 Kafka 분석 이벤트로 이어져 ES와 화면이 동기화됩니다.
+
+불량 예측 및 전이 예측에서 함께 보는 맥락은 아래와 같습니다.
+
+- 현재 공정
+- 다음 공정으로의 전이 가능성
+- 설비 신호
+- 사이클 타임
+- 대기 시간
+- 재공 수량
+- 진동, 온도, 도막 두께 같은 공정 특성
+
+즉, 차량 단위 예측이지만 실제 판단은 제조 이벤트 feature 전체를 보는 구조입니다.
+
+#### 흐름
+
+1. `sampledb.manufacturing_event_json`에서 SENT 이벤트를 읽습니다.
+2. 모델이 차량별 전이 확률을 계산합니다.
+3. 예측 결과를 DB와 ES에 저장합니다.
+4. ES의 `predictedAt`을 기준으로 최신 1건을 보여줍니다.
+
+#### SHAP 원인 분석
+<img width="884" height="684" alt="main 원인" src="https://github.com/user-attachments/assets/2165f137-0bee-4cb5-a5d1-d1ea7373e464" />
+
+- 원인 분석은 불량 탐지 및 전이 예측 결과를 해석하는 단계입니다.
+- SHAP 값을 이용해 주요 원인 1개와 상세 원인 여러 개를 분리합니다.
+- `main_causes`는 대표 원인, `detailCauses`는 보조 원인입니다.
+
+##### 구현 로직
+
+1. `DefectTransferAnalysisService.get_cause_analysis()`가 차량 ID와 날짜 옵션을 기준으로 조회 대상을 결정합니다.
+2. ES가 있으면 `get_latest_defect_cause_document()`에서 차량별 최신 문서를 가져옵니다.
+3. 조회된 문서의 대표 원인과 상세 원인을 분리합니다.
+4. 대표 원인은 화면의 summary 영역으로, 상세 원인은 리스트 형태로 보여줍니다.
+5. 차량 ID가 없으면 최신 차량 기준으로 조회할 수 있습니다.
+
+##### 흐름
+
+1. 최신 불량 탐지 및 전이 예측 문서를 찾습니다.
+2. SHAP 기반 원인을 정리합니다.
+3. 대표 원인과 상세 원인을 나눠 반환합니다.
+
+### 2. 병목 분석
+
+<img width="868" height="556" alt="다운로드 (1)" src="https://github.com/user-attachments/assets/0f681bf1-4859-4ecc-abe3-c193a68efa81" />
 
 - `sampledb.manufacturing_event_json`의 제조 이벤트를 읽어 공정별 병목을 계산합니다.
 - 결과는 공정 순위, 지연 시간, 영향 차량 수, 위험도 형태로 정리됩니다.
 - 결과는 `bottleneck_analysis_result`에 저장됩니다.
 - 조회 시에는 ES의 `detectedAt` 날짜를 기준으로 날짜 옵션을 만듭니다.
-- Elasticsearch가 살아 있으면 ES 우선으로 조회하고, 실패하면 DB에서 동일한 결과를 다시 읽습니다.
+- Elasticsearch가 살아 있으면 ES 우선으로 조회하고, 실패하면 DB와 Redis에서 다시 읽습니다.
 - 같은 날짜 구간을 다시 계산하는 백필 / 재색인 기능도 함께 제공합니다.
 
 #### 구현 로직
 
-1. `BottleneckAnalysisService.get_realtime_bottlenecks()`가 먼저 날짜 옵션을 읽고, 요청 날짜가 없으면 최신 날짜를 선택합니다.
+1. `BottleneckAnalysisService.get_realtime_bottlenecks()`가 날짜 옵션을 읽고, 요청 날짜가 없으면 최신 날짜를 선택합니다.
 2. `ProcessAnalysisSearchRepository.list_bottleneck_date_options()`가 ES의 `detectedAt` 날짜를 집계해 날짜 옵션을 만듭니다.
 3. `BottleneckAnalysisService`는 ES에서 `list_bottleneck_page()`를 먼저 호출해 최신 병목 row를 가져옵니다.
 4. ES 조회가 실패하면 Redis 캐시를 확인하고, 캐시도 없으면 DB 조회로 fallback합니다.
 5. 백필이나 재색인이 실행되면 해당 날짜의 기존 결과를 지우고 다시 계산해서 저장합니다.
 6. 따라서 병목 화면은 최신 분석 결과를 기본으로 보여주되, 날짜 선택 시 과거 분석도 다시 볼 수 있습니다.
 
-### 2. 불량 전이 예측
+#### 모델링
 
-- 차량 단위로 불량이 다음 공정으로 전이될 가능성을 예측합니다.
-- 현재 공정, 예측 공정, 전이 확률, 예상 발생 시점, 위험도를 함께 계산합니다.
-- 결과는 `defect_transfer_prediction_result`에 저장됩니다.
-- 조회 시에는 ES의 `predictedAt` 날짜를 기준으로 날짜 옵션을 만듭니다.
-- 목록 조회는 차량별 최신 1건만 보여줘서, 차량의 현재 상태를 빠르게 확인할 수 있습니다.
-- 같은 결과 테이블을 원인 분석에도 사용합니다.
+- 기본 모델은 `IsolationForest`입니다.
+- 연속형 공정 지표는 `StandardScaler`로 정규화한 뒤 학습합니다.
+- 공정별 지연 특성을 반영한 규칙 기반 feature를 함께 사용합니다.
+- `bottleneck_station`처럼 병목이 발생한 공정을 설명 가능한 형태로 정리합니다.
+- 결과는 공정 단위로 집계하고, station 요약과 KPI 요약도 함께 생성합니다.
+- SHAP은 `IsolationForest`의 `decision_function`이 어떤 feature에 반응했는지를 설명하는 용도로 사용됩니다.
 
-#### 구현 로직
+병목에서 보는 핵심 feature는 아래와 같습니다.
 
-1. `DefectTransferAnalysisService.get_predictions()`가 날짜 옵션을 읽고, 요청 날짜가 없으면 최신 날짜를 선택합니다.
-2. `ProcessAnalysisSearchRepository.list_defect_transfer_date_options()`가 ES의 `predictedAt` 날짜를 집계합니다.
-3. ES가 가능하면 `list_defect_prediction_page()`에서 차량별 최신 1건을 가져옵니다.
-4. ES가 실패하면 Redis 캐시를 먼저 보고, 없으면 DB의 `list_prediction_page()`로 fallback합니다.
-5. 저장 단계에서는 각 제조 이벤트마다 모델 예측을 수행한 뒤 `replace_prediction_result()`로 결과를 저장합니다.
-6. 저장할 때는 같은 `manufacturing_event_id`가 있으면 기존 row를 지우고 새 row를 넣어서 이벤트 기준 중복을 막습니다.
-7. 재색인 시에는 해당 날짜의 문서를 ES에서 먼저 삭제한 뒤, 소스 이벤트를 다시 예측해서 넣습니다.
+- 공정 체류 시간
+- 최대 station span
+- 활성 공정 수
+- rule risk score
+- iforest risk score
 
-### 3. SHAP 기반 원인 분석
+병목은 단일 점수만 보는 게 아니라, `어느 공정이 막혔는지`와 `왜 그렇게 판단했는지`를 같이 보여주는 구조입니다.
 
-- 특정 차량의 최신 불량 전이 결과를 기준으로 원인을 설명합니다.
-- 대표 원인 1개와 상세 원인 목록을 분리해 반환합니다.
-- 각 원인은 영향도, 레이블, 설명 메시지를 함께 포함합니다.
-- 불량 전이 예측과 같은 ES 날짜 옵션을 사용해 같은 시점의 데이터를 일관되게 조회합니다.
+#### 흐름
 
-#### 구현 로직
+1. `sampledb.manufacturing_event_json`에서 제조 이벤트를 읽습니다.
+2. 공정별 지연 feature를 계산합니다.
+3. `IsolationForest`와 규칙 기반 점수로 병목 순위를 산출합니다.
+4. 결과를 DB와 ES에 저장합니다.
+5. ES의 `detectedAt`을 기준으로 날짜 옵션과 목록을 만듭니다.
 
-1. `DefectTransferAnalysisService.get_cause_analysis()`가 차량 ID와 날짜 옵션을 기준으로 조회 대상을 결정합니다.
-2. ES가 있으면 `get_latest_defect_cause_document()`가 차량별 최신 문서를 하나 가져옵니다.
-3. 조회된 문서는 대표 원인과 상세 원인으로 분리됩니다.
-4. 대표 원인은 화면의 summary 영역에 쓰고, 상세 원인은 리스트 형태로 보여줍니다.
-5. 차량 ID가 없으면 최신 차량 기준으로 조회할 수 있습니다.
-
-### 4. AI 메뉴얼
+### 3. 🤖 AI 메뉴얼
+| 이상 이벤트 발생 | 주니어 | 시니어 |
+|---|---|---|
+| <img width="582" height="506" alt="이상 이벤트 발생" src="https://github.com/user-attachments/assets/faac7e24-4756-4ee7-be91-bf9e813e6a2c" /> | <img width="582" height="506" alt="주니어" src="https://github.com/user-attachments/assets/9e0900e0-50ef-4b2f-aafd-7d18428d8a58" /> | <img width="565" height="500" alt="시니어" src="https://github.com/user-attachments/assets/6122f166-a6af-4e34-9474-0b7ba454f977" /> |
 
-- JWT를 읽어서 로그인한 사용자 기준의 AI 메뉴얼을 생성합니다.
-- `Authorization: Bearer ...` 헤더가 필요합니다.
-- 메뉴얼 생성은 `app/ai_manual` 아래에서 별도로 관리합니다.
-- 일반 분석 API와 분리된 독립 기능이라, 운영 정책이나 권한 조건을 따로 둘 수 있습니다.
+- JWT 인증을 통과한 사용자만 메뉴얼을 생성합니다.
+- 이벤트, 설비 맥락, 사내 지침, 검색된 문서를 함께 사용합니다.
+- 메뉴얼은 현장 조치용 설명서로 반환됩니다.
+- 사용자는 `Junior` / `Senior`로 구분되며, `Junior`는 실행 절차 중심, `Senior`는 원인·판단 근거와 운영 관점까지 포함한 메뉴얼을 받습니다.
 
 #### 구현 로직
 
 1. `ManualService.generate_manual(user_id)`가 요청의 시작점입니다.
-2. 가장 위험도가 높은 알림 이벤트를 `AlertEventRepository`에서 먼저 가져옵니다.
-3. 사용자 ID로 사용자 등급을 읽고, 없으면 `Junior`로 기본 처리합니다.
-4. `CriticalEvent`, `OperatorInfo`, `FactoryContext`, `RagContext`를 묶어서 LLM 입력용 요청 객체를 만듭니다.
+2. 현재 위험도가 높은 알람 이벤트를 `AlertEventRepository`에서 먼저 가져옵니다.
+3. 사용자의 ID로 사용자의 권한을 읽고, 없으면 `Junior`로 기본 처리합니다.
+4. `CriticalEvent`, `OperatorInfo`, `FactoryContext`, `RagContext`를 묶어 LLM 입력 객체를 만듭니다.
 5. `VectorStore.search()`로 관련 문서를 검색해 RAG 컨텍스트를 구성합니다.
-6. `manual_prompt`에 컨텍스트를 넣고 `ChatOpenAI`로 응답을 생성합니다.
-7. 최종적으로 이벤트 정보와 생성된 메뉴얼을 함께 반환합니다.
+6. 권한이 `Junior`면 즉시 수행할 점검 항목과 순서를 강조하고, `Senior`면 원인 해석과 판단 근거를 더 자세히 포함하도록 프롬프트를 구성합니다.
+7. `manual_prompt`에 컨텍스트를 넣고 `ChatOpenAI`로 응답을 생성합니다.
+8. 최종적으로 이벤트 정보와 권한별로 다른 깊이의 메뉴얼을 함께 반환합니다.
 
-### 5. 관리 기능
+#### 흐름
 
-- 병목 백필 / 재색인
-- 불량 전이 백필 / 재색인
-- 날짜 단위 결과 삭제 후 재생성
-- DB와 Elasticsearch 정합성 재구성
+1. JWT와 권한을 확인합니다.
+2. 현재 알람과 관련 이벤트를 가져옵니다.
+3. 운영 문서를 검색해 RAG 컨텍스트를 구성합니다.
+4. 권한에 따라 프롬프트의 상세 수준을 다르게 구성합니다.
+5. LLM이 역할별 메뉴얼을 생성합니다.
+6. 운영자 조치 가이드로 반환합니다.
 
----
-
-## Kafka / Elasticsearch 아키텍처 상세
+## 🔄 Kafka / Elasticsearch 아키텍처 상세
 
 ### Kafka 기반 비동기 데이터 파이프라인
 
-Kafka는 AIMS 아키텍처에서 제조 데이터의 실시간 수집 및 분석 파이프라인의 핵심 백본(Backbone) 역할을 수행합니다.
-
-- **원천 데이터 수집 (Raw Topic)**:
-  - `factory.manufacturing.raw` 토픽은 제조 현장(MES, PLC 등)으로부터 발생하는 모든 원천 제조 이벤트를 수집하는 진입점입니다.
-  - 이벤트 스트림의 처리 순서 보장과 분산 처리를 위해 `vehicle_id` 또는 `equipment_id`를 파티션 키(Partition Key)로 활용할 수 있는 다중 파티션 구조를 가집니다.
-  - `ai-analysis-consumer-group` 컨슈머 그룹에 속한 `app/kafka/raw_event_consumer.py`가 메시지를 소비하여 `sampledb.manufacturing_event_json`에 원천 데이터를 영구 저장합니다.
-
-- **분석 결과 발행 및 동기화 (Analysis Topic)**:
-  - 원천 이벤트를 기반으로 병목 분석 및 불량 전이 추론(AI 모델 수행)이 완료되면, 그 결과 데이터는 `factory.manufacturing.analysis` 토픽으로 발행(Publish)됩니다.
-  - 발행된 분석 결과는 `ai-analysis-sync-consumer-group`에 속한 `analysis_sync_consumer`가 비동기적으로 소비하여 Elasticsearch에 동기화(Indexing)합니다.
-  - 이를 통해 무거운 **AI 모델 추론**과 I/O 바운드 작업인 **검색엔진 색인**을 완전히 디커플링(Decoupling)하여, 시스템의 안정성과 확장성을 극대화합니다.
+- Kafka raw topic(`factory.manufacturing.raw`)은 외부 제조 시스템의 원천 이벤트 진입점입니다.
+- `raw_event_consumer`는 raw topic을 읽어 `sampledb.manufacturing_event_json`에 저장합니다.
+- 분석 대상 이벤트는 병목 / 불량 전이 추론을 수행하고, 결과를 `factory.manufacturing.analysis` topic으로 발행합니다.
+- `analysis_sync_consumer`는 `factory.manufacturing.analysis` topic을 다시 받아 Elasticsearch에 색인합니다.
 
 ### Elasticsearch 기반 실시간 검색 및 집계
 
-Elasticsearch는 대용량 AI 분석 결과를 지연 없이 빠르게 조회하고, 다차원 집계(Aggregation)를 수행하기 위한 메인 검색 및 분석 엔진입니다.
-
-- **인덱스 설계 및 전략 (Index Strategy)**:
-  - **병목 인덱스** (`settings.elasticsearch_bottleneck_index`, 기본값 `ai-bottleneck-result-v1` 계열): 공정별 지연 상태, 위험도 데이터를 저장합니다. `detectedAt` 필드를 기준으로 시계열로 관리됩니다.
-  - **불량 전이 인덱스** (`settings.elasticsearch_defect_transfer_index`, 기본값 `ai-defect-transfer-result-v1` 계열): 차량별 불량 전이 확률, 발생 시점, SHAP 원인 분석 데이터를 저장합니다. `predictedAt` 필드를 기준으로 관리됩니다.
-  - 시계열 데이터의 특성을 살려 인덱스 롤오버(Rollover) 및 ILM(Index Lifecycle Management) 정책을 적용하기 용이한 구조를 취합니다.
-
-- **특화된 검색 및 집계 쿼리 활용 (Advanced Query)**:
-  - **차량별 최신 상태 추출 (`collapse`)**: 동일 차량에 대해 공정 진행에 따라 여러 분석 결과가 누적될 수 있습니다. 응답 속도 최적화를 위해 ES의 `collapse` 파라미터를 사용하여 최신 타임스탬프(`predictedAt`) 기준 1건의 문서만 빠르게 추출합니다.
-  - **날짜별 동적 옵션 생성 (`date_histogram`)**: 화면에서 제공되는 '분석 날짜 옵션'은 ES의 `date_histogram` 집계(Aggregation)를 활용해, 실제 분석 데이터가 존재하는 날짜 리스트만 빠르고 정확하게 동적 생성하여 제공합니다.
-
-- **장애 대응 및 정합성 보장 (Fallback & Reindex)**:
-  - **고가용성**: ES 클러스터에 일시적 장애가 발생해도 서비스는 중단되지 않습니다. 조회 API는 ES 실패를 감지하면 자동으로 Redis 캐시와 DB 폴백(Fallback) 조회를 수행합니다.
-  - **데이터 재구성**: 백필(Backfill)이나 재색인(Reindex) 요청 시, 대상 날짜의 기존 ES 문서를 일괄 삭제(Delete By Query)하고 소스 이벤트로부터 다시 계산/색인하여 DB와 ES 간의 데이터 정합성을 일관되게 맞춥니다.
+- Elasticsearch는 분석 결과를 빠르게 조회하기 위한 검색 인덱스입니다.
+- 병목 인덱스는 `detectedAt` 기준으로 날짜 옵션과 목록 조회에 사용됩니다.
+- 불량 전이 인덱스는 `predictedAt` 기준으로 날짜 옵션, 목록 조회, 원인 조회에 사용됩니다.
+- 조회 API는 ES 우선으로 응답하고, ES가 실패하면 DB/Redis로 fallback합니다.
+- ES에서는 날짜 집계를 `date_histogram`으로 처리하고, 차량별 최신 결과는 대표 문서 1건만 보여줍니다.
 
 ### Kafka -> ES 색인 흐름
 
-1. raw topic의 제조 이벤트가 `raw_event_consumer`로 들어옵니다.
-2. 원천 이벤트가 `manufacturing_event_json`에 저장됩니다.
-3. 병목 / 불량 전이 분석이 수행됩니다.
-4. 분석 결과가 `factory.manufacturing.analysis` topic으로 발행됩니다.
-5. `analysis_sync_consumer`가 해당 메시지를 읽습니다.
-6. `ProcessAnalysisSearchRepository`가 병목 / 불량 전이 결과를 Elasticsearch에 색인합니다.
-7. 조회 API는 ES를 먼저 보고, ES가 없을 때만 DB / Redis를 사용합니다.
+```mermaid
+flowchart TD
+    A["외부 제조 시스템"] --> B["Kafka Raw Topic\nfactory.manufacturing.raw"]
+    B --> C["raw_event_consumer"]
+    C --> D["sampledb.manufacturing_event_json"]
+    D --> E["병목 / 불량 전이 추론"]
+    E --> F["Kafka Analysis Topic\nfactory.manufacturing.analysis"]
+    F --> G["analysis_sync_consumer"]
+    G --> H1["Elasticsearch Bottleneck Index"]
+    G --> H2["Elasticsearch Defect Transfer Index"]
+    H1 --> I1["병목 조회 API"]
+    H2 --> I2["불량 전이 / 원인 조회 API"]
+```
 
 ### 주요 Kafka / ES 엔터티
 
@@ -153,9 +207,8 @@ Elasticsearch는 대용량 AI 분석 결과를 지연 없이 빠르게 조회하
 | Bottleneck Index | `settings.elasticsearch_bottleneck_index` | 병목 결과 검색 |
 | Defect Transfer Index | `settings.elasticsearch_defect_transfer_index` | 불량 전이 / 원인 검색 |
 
----
 
-## API 요약
+## 📡 API 요약
 
 ### 분석 조회
 
@@ -174,55 +227,57 @@ Elasticsearch는 대용량 AI 분석 결과를 지연 없이 빠르게 조회하
 
 - `GET /api/ai/manual`
 
----
-
-## 데이터 흐름
-
-### Kafka 수집
-
-1. 외부 시스템이 제조 원천 이벤트를 Kafka raw topic으로 보냅니다.
-2. `raw_event_consumer`가 메시지를 읽습니다.
-3. 원천 이벤트를 `manufacturing_event_json`에 저장합니다.
-4. 필요 시 병목 / 불량 전이 분석 이벤트를 `factory.manufacturing.analysis` topic으로 발행합니다.
-5. `analysis_sync_consumer`가 분석 결과를 읽고 Elasticsearch에 색인합니다.
 
-### 병목
+## 🏗️ 프로젝트 구조
 
-1. `sampledb.manufacturing_event_json`에서 이벤트를 읽습니다.
-2. 병목 모델이 공정별 병목을 계산합니다.
-3. `bottleneck_analysis_result`에 저장합니다.
-4. Elasticsearch에 재색인합니다.
-5. 조회 API는 ES 우선으로 응답합니다.
+```text
+app/
+├─ api/                     # FastAPI router 계층
+│  ├─ routers/
+│  │  ├─ process.py         # 병목 / 불량 전이 조회
+│  │  ├─ defect_transfer.py # 불량 전이 / 원인 조회
+│  │  ├─ analysis_maintenance.py # 병목 / 불량 전이 백필·재색인
+│  │  ├─ manual.py          # AI 메뉴얼
+│  │  └─ health.py          # 헬스 체크
+├─ service/
+│  ├─ analysis/             # 분석 조회 / 백필 / 재색인
+│  ├─ manufacturing/        # 제조 이벤트 처리
+│  └─ llm/                  # LLM 연동
+├─ repository/              # DB 접근 계층
+├─ search/                  # Elasticsearch 저장 / 조회
+├─ kafka/                   # Kafka 소비 / 발행
+├─ ml/                      # 모델 학습 / 추론 / SHAP
+├─ ai_manual/               # AI 메뉴얼 생성
+├─ dto/                     # 요청 / 응답 스키마
+├─ batch/                   # 배치 / 백필 작업
+└─ scheduler/               # 주기 실행 작업
+```
 
-### 불량 전이 / 원인
+### 역할 요약
 
-1. `sampledb.manufacturing_event_json`에서 SENT 이벤트를 읽습니다.
-2. 불량 전이 모델이 예측을 수행합니다.
-3. `defect_transfer_prediction_result`에 저장합니다.
-4. Elasticsearch에 재색인합니다.
-5. 조회 API는 차량별 최신 1건을 반환합니다.
-6. 원인 분석은 최신 불량 전이 결과의 SHAP 원인을 보여줍니다.
+- `app/service/analysis`: 분석 결과 조회와 관리 작업을 담당합니다.
+- `app/search`: Elasticsearch 인덱싱과 조회를 담당합니다.
+- `app/kafka`: 제조 이벤트 수집과 분석 결과 동기화를 담당합니다.
+- `app/ml`: 병목 탐지와 불량 전이 모델을 담당합니다.
+- `app/ai_manual`: 메뉴얼 생성 로직을 담당합니다.
 
-### AI 메뉴얼
+## 🛠 전체 데이터 기능 흐름
+<img width="10217" height="5316" alt="데이터 기능 흐름도" src="https://github.com/user-attachments/assets/02488ac3-03af-4d68-ae0a-96fbdced0e4a" />
 
-1. 요청 헤더에서 JWT를 읽습니다.
-2. 사용자 ID를 추출합니다.
-3. 메뉴얼 서비스를 호출해 응답을 만듭니다.
+## 🧭 시스템 다이어그램
 
-### Kafka / ES 전체 흐름
+### AI 서비스 분석 흐름
 
 ```mermaid
 flowchart LR
-    A["외부 제조 시스템"] --> B["Kafka Raw Topic<br/>factory.manufacturing.raw"]
+    A["외부 제조 시스템"] --> B["Kafka Raw Topic"]
     B --> C["raw_event_consumer"]
-    C --> D["sampledb.manufacturing_event_json"]
-    D --> E["병목 / 불량 전이 분석"]
-    E --> F["Kafka Analysis Topic<br/>factory.manufacturing.analysis"]
+    C --> D["DB / 원천 저장"]
+    D --> E["병목 / 불량 전이 추론"]
+    E --> F["Kafka Analysis Topic"]
     F --> G["analysis_sync_consumer"]
-    G --> H1["Elasticsearch Bottleneck Index"]
-    G --> H2["Elasticsearch Defect Transfer Index"]
-    H1 --> I1["병목 조회 API"]
-    H2 --> I2["불량 전이 / 원인 조회 API"]
+    G --> H["Elasticsearch"]
+    H --> I["조회 API"]
 ```
 
 ### ES 조회 우선순위
@@ -238,7 +293,7 @@ sequenceDiagram
     alt ES success
         ES-->>API: 최신 결과
     else ES fail
-        API->>Cache: cached response lookup
+        API->>Cache: 캐시 조회
         alt Cache hit
             Cache-->>API: cached page
         else Cache miss
@@ -248,162 +303,7 @@ sequenceDiagram
     end
 ```
 
----
-
-## 프로젝트 구조
-
-```text
-app/
-├─ api/                     # FastAPI 라우터
-│  ├─ router.py             # API 라우터 통합
-│  └─ routers/              # 기능별 엔드포인트
-│     ├─ process.py         # 병목 조회
-│     ├─ defect_transfer.py # 불량 전이 / 원인 조회
-│     ├─ analysis_maintenance.py # 병목 / 불량 전이 백필, 재색인
-│     ├─ manual.py          # AI 메뉴얼
-│     ├─ manufacturing_event.py  # 제조 이벤트 생성/조회
-│     └─ health.py          # 헬스 체크
-├─ service/
-│  ├─ analysis/              # 병목, 불량 전이, 재색인, 조회 로직
-│  ├─ manufacturing/         # 제조 이벤트 생성 및 처리
-│  └─ llm/                   # LLM 연동 계층
-├─ repository/               # DB 읽기 / 쓰기
-│  ├─ bottleneck_analysis_repository.py
-│  ├─ defect_transfer_prediction_repository.py
-│  ├─ manufacturing_event_repository.py
-│  ├─ manufacturing_event_template_repository.py
-│  ├─ equipment_repository.py
-│  └─ sampledb_schema.py
-├─ search/                   # Elasticsearch 저장 / 조회
-├─ ml/                       # 모델 추론, 학습 산출물, feature 처리
-│  ├─ inference/             # 추론기
-│  ├─ training/              # 학습 산출물 / 실험 노트북
-│  ├─ features/              # feature 생성
-│  └─ artifacts/             # 배포용 모델 파일
-├─ ai_manual/                # AI 메뉴얼 생성
-│  ├─ repository/            # 알림 이벤트, 사용자 정보 조회
-│  ├─ rag/                   # 벡터 검색
-│  ├─ prompt/                # 프롬프트 템플릿
-│  └─ schema/                # 메뉴얼 요청 / 응답 모델
-├─ kafka/                    # 제조 이벤트 수집 / 발행
-├─ dto/                      # 요청 / 응답 모델
-├─ utils/                    # 공통 유틸
-├─ scheduler/                # 주기 실행 품질/제조 작업
-└─ batch/                    # 백필 / 배치 작업
-```
-
-### 역할 요약
-
-- `app/api`: 외부 요청을 받는 진입점
-- `app/service/analysis`: 병목, 불량 전이, 재색인, 조회 로직
-- `app/repository`: DB 읽기 / 쓰기
-- `app/search`: Elasticsearch 저장 / 조회
-- `app/ai_manual`: AI 메뉴얼 생성
-- `app/ml`: 모델 추론과 산출물 관리
-- `app/kafka`: 제조 이벤트 수집과 후속 처리
-- `app/scheduler`: 주기적으로 실행되는 품질/제조 작업
-- `app/batch`: 수동 실행용 백필 / 배치 작업
-
----
-
-## 시스템 다이어그램
-
-### 전체 분석 흐름
-
-```mermaid
-flowchart TD
-    A["Manufacturing Event Source"] --> B["Kafka Raw Topic"]
-    B --> C["raw_event_consumer"]
-    C --> D["DB: manufacturing_event_json"]
-    D --> E["Analysis Service"]
-    E --> F1["Bottleneck Detector"]
-    E --> F2["Defect Transfer Detector"]
-    F1 --> G1["DB: bottleneck_analysis_result"]
-    F2 --> G2["DB: defect_transfer_prediction_result"]
-    G1 --> H1["Kafka Analysis Topic"]
-    G2 --> H1
-    H1 --> I["analysis_sync_consumer"]
-    I --> J1["Elasticsearch Bottleneck Index"]
-    I --> J2["Elasticsearch Defect Transfer Index"]
-    J1 --> K1["/api/ai/process/bottleneck"]
-    J2 --> K2["/api/ai/process/defect-transfer/predictions"]
-    J2 --> K3["/api/ai/process/defect-transfer/causes"]
-```
-
-### 병목 조회 흐름
-
-```mermaid
-sequenceDiagram
-    participant U as User
-    participant API as Bottleneck API
-    participant S as Bottleneck Service
-    participant ES as Elasticsearch
-    participant DB as DB
-
-    U->>API: GET /api/ai/process/bottleneck
-    API->>S: get_cached_realtime_bottlenecks()
-    S->>ES: list_bottleneck_page()
-    alt ES success
-        ES-->>S: latest bottleneck rows
-    else ES fail
-        S->>DB: list_results()
-        DB-->>S: fallback rows
-    end
-    S-->>API: BottleneckAnalysisPage
-    API-->>U: JSON response
-```
-
-### 불량 전이 / 원인 흐름
-
-```mermaid
-sequenceDiagram
-    participant U as User
-    participant API as Defect API
-    participant S as Defect Service
-    participant ES as Elasticsearch
-    participant DB as DB
-
-    U->>API: GET /api/ai/process/defect-transfer/predictions
-    API->>S: get_cached_predictions()
-    S->>ES: list_defect_prediction_page()
-    alt ES success
-        ES-->>S: vehicle latest rows
-    else ES fail
-        S->>DB: list_prediction_page()
-        DB-->>S: fallback rows
-    end
-    S-->>API: DefectTransferPredictionPage
-    API-->>U: JSON response
-
-    U->>API: GET /api/ai/process/defect-transfer/causes
-    API->>S: get_cached_cause_analysis()
-    S->>ES: get_latest_defect_cause_document()
-    ES-->>S: latest vehicle cause docs
-    S-->>API: DefectTransferCausePage
-    API-->>U: JSON response
-```
-
-### AI 메뉴얼 흐름
-
-```mermaid
-sequenceDiagram
-    participant U as User
-    participant API as Manual API
-    participant M as Manual Service
-    participant R as RAG / Manual Data
-
-    U->>API: GET /api/ai/manual
-    API->>API: Read JWT from Authorization header
-    API->>M: generate_manual(user_id)
-    M->>R: load relevant manual context
-    R-->>M: manual content
-    M-->>API: generated manual
-    API-->>U: JSON response
-```
-
----
-
-## 실행
+## ⚙️ 실행
 
 ```powershell
 python -m venv venv
@@ -418,13 +318,19 @@ uvicorn app.main:app --reload
 - Health: `http://127.0.0.1:8000/api/health`
 - Swagger: `http://127.0.0.1:8000/docs`
 
----
 
-## 참고 포인트
 
-- 병목의 날짜 기준 필드는 `detected_at`입니다.
-- 불량 전이와 SHAP의 날짜 기준 필드는 `predicted_at`입니다.
-- 병목은 공정 단위, 불량 전이는 차량 단위로 조회합니다.
-- `dateOptions`는 ES 기준으로 만들고, ES 실패 시 DB로 내려갑니다.
-- 병목 / 불량 전이 / SHAP / 메뉴얼은 서로 다른 책임을 가지지만, 모두 제조 이벤트를 중심으로 연결됩니다.
-- DB는 정본 데이터, Elasticsearch는 빠른 조회용 검색 인덱스로 이해하면 전체 구조를 잡기 쉽습니다.
+## 🔧 기술 스택
+![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi&logoColor=white)
+![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)
+![Kafka](https://img.shields.io/badge/Apache%20Kafka-231F20?style=for-the-badge&logo=apachekafka&logoColor=white)
+![Elasticsearch](https://img.shields.io/badge/Elasticsearch-005571?style=for-the-badge&logo=elasticsearch&logoColor=white)
+![MySQL](https://img.shields.io/badge/MySQL-4479A1?style=for-the-badge&logo=mysql&logoColor=white)
+![Redis](https://img.shields.io/badge/Redis-DC382D?style=for-the-badge&logo=redis&logoColor=white)
+![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=for-the-badge&logo=scikitlearn&logoColor=white)
+![LightGBM](https://img.shields.io/badge/LightGBM-00A86B?style=for-the-badge&logo=lightgbm&logoColor=white)
+![XGBoost](https://img.shields.io/badge/XGBoost-1E1E1E?style=for-the-badge&logo=xgboost&logoColor=white)
+![CatBoost](https://img.shields.io/badge/CatBoost-FF9D00?style=for-the-badge&logo=catboost&logoColor=white)
+![LightGBM](https://img.shields.io/badge/LightGBM-00A86B?style=for-the-badge&logo=lightgbm&logoColor=white)
+![SHAP](https://img.shields.io/badge/SHAP-4B5563?style=for-the-badge&logo=shap&logoColor=white)
+![OpenAI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge&logo=openai&logoColor=white)

From b3b57c61ce13c0232cbc70a30a340c238913b81f Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 15 Jul 2026 16:37:55 +0900
Subject: [PATCH 141/148] =?UTF-8?q?docs:=20README=20=EB=AC=B8=EC=84=9C=20?=
 =?UTF-8?q?=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 34 ++++++++++++++++------------------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 678f3a4..34b2e67 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@
 - Kafka로 입력과 분석을 분리하고, Elasticsearch로 조회 성능을 확보합니다.
 
 
-## ✨ 핵심 기능
+## ✨ 주요 기능
 
 ### 1. 불량 탐지 및 전이 예측
 
@@ -303,23 +303,6 @@ sequenceDiagram
     end
 ```
 
-## ⚙️ 실행
-
-```powershell
-python -m venv venv
-.\venv\Scripts\Activate.ps1
-pip install -r requirements.txt
-uvicorn app.main:app --reload
-```
-
-주요 주소:
-
-- API Root: `http://127.0.0.1:8000/`
-- Health: `http://127.0.0.1:8000/api/health`
-- Swagger: `http://127.0.0.1:8000/docs`
-
-
-
 ## 🔧 기술 스택
 ![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi&logoColor=white)
 ![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)
@@ -334,3 +317,18 @@ uvicorn app.main:app --reload
 ![LightGBM](https://img.shields.io/badge/LightGBM-00A86B?style=for-the-badge&logo=lightgbm&logoColor=white)
 ![SHAP](https://img.shields.io/badge/SHAP-4B5563?style=for-the-badge&logo=shap&logoColor=white)
 ![OpenAI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge&logo=openai&logoColor=white)
+
+## ⚙️ 실행
+
+```powershell
+python -m venv venv
+.\venv\Scripts\Activate.ps1
+pip install -r requirements.txt
+uvicorn app.main:app --reload
+```
+
+주요 주소:
+
+- API Root: `http://127.0.0.1:8000/`
+- Health: `http://127.0.0.1:8000/api/health`
+- Swagger: `http://127.0.0.1:8000/docs`

From f608871b418bdac3af33b08f15acc476dad05ace Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 15 Jul 2026 17:34:29 +0900
Subject: [PATCH 142/148] =?UTF-8?q?docs:=20README=20=EB=AC=B8=EC=84=9C=20?=
 =?UTF-8?q?=EC=A0=95=EB=A6=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 78 +++++++++++++++++++++++++++++++------------------------
 1 file changed, 44 insertions(+), 34 deletions(-)

diff --git a/README.md b/README.md
index 34b2e67..0370dcb 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 - 운영자가 바로 읽을 수 있는 AI 메뉴얼을 생성합니다.
 - Kafka로 입력과 분석을 분리하고, Elasticsearch로 조회 성능을 확보합니다.
 
-
+&nbsp;
 ## ✨ 주요 기능
 
 ### 1. 불량 탐지 및 전이 예측
@@ -86,6 +86,8 @@
 2. SHAP 기반 원인을 정리합니다.
 3. 대표 원인과 상세 원인을 나눠 반환합니다.
 
+
+&nbsp;
 ### 2. 병목 분석
 
 <img width="868" height="556" alt="다운로드 (1)" src="https://github.com/user-attachments/assets/0f681bf1-4859-4ecc-abe3-c193a68efa81" />
@@ -133,6 +135,7 @@
 4. 결과를 DB와 ES에 저장합니다.
 5. ES의 `detectedAt`을 기준으로 날짜 옵션과 목록을 만듭니다.
 
+&nbsp;
 ### 3. 🤖 AI 메뉴얼
 | 이상 이벤트 발생 | 주니어 | 시니어 |
 |---|---|---|
@@ -163,6 +166,7 @@
 5. LLM이 역할별 메뉴얼을 생성합니다.
 6. 운영자 조치 가이드로 반환합니다.
 
+&nbsp;
 ## 🔄 Kafka / Elasticsearch 아키텍처 상세
 
 ### Kafka 기반 비동기 데이터 파이프라인
@@ -208,6 +212,7 @@ flowchart TD
 | Defect Transfer Index | `settings.elasticsearch_defect_transfer_index` | 불량 전이 / 원인 검색 |
 
 
+&nbsp;
 ## 📡 API 요약
 
 ### 분석 조회
@@ -228,39 +233,7 @@ flowchart TD
 - `GET /api/ai/manual`
 
 
-## 🏗️ 프로젝트 구조
-
-```text
-app/
-├─ api/                     # FastAPI router 계층
-│  ├─ routers/
-│  │  ├─ process.py         # 병목 / 불량 전이 조회
-│  │  ├─ defect_transfer.py # 불량 전이 / 원인 조회
-│  │  ├─ analysis_maintenance.py # 병목 / 불량 전이 백필·재색인
-│  │  ├─ manual.py          # AI 메뉴얼
-│  │  └─ health.py          # 헬스 체크
-├─ service/
-│  ├─ analysis/             # 분석 조회 / 백필 / 재색인
-│  ├─ manufacturing/        # 제조 이벤트 처리
-│  └─ llm/                  # LLM 연동
-├─ repository/              # DB 접근 계층
-├─ search/                  # Elasticsearch 저장 / 조회
-├─ kafka/                   # Kafka 소비 / 발행
-├─ ml/                      # 모델 학습 / 추론 / SHAP
-├─ ai_manual/               # AI 메뉴얼 생성
-├─ dto/                     # 요청 / 응답 스키마
-├─ batch/                   # 배치 / 백필 작업
-└─ scheduler/               # 주기 실행 작업
-```
-
-### 역할 요약
-
-- `app/service/analysis`: 분석 결과 조회와 관리 작업을 담당합니다.
-- `app/search`: Elasticsearch 인덱싱과 조회를 담당합니다.
-- `app/kafka`: 제조 이벤트 수집과 분석 결과 동기화를 담당합니다.
-- `app/ml`: 병목 탐지와 불량 전이 모델을 담당합니다.
-- `app/ai_manual`: 메뉴얼 생성 로직을 담당합니다.
-
+&nbsp;
 ## 🛠 전체 데이터 기능 흐름
 <img width="10217" height="5316" alt="데이터 기능 흐름도" src="https://github.com/user-attachments/assets/02488ac3-03af-4d68-ae0a-96fbdced0e4a" />
 
@@ -303,6 +276,41 @@ sequenceDiagram
     end
 ```
 
+&nbsp;
+## 🏗️ 프로젝트 구조
+
+```text
+app/
+├─ api/                     # FastAPI router 계층
+│  ├─ routers/
+│  │  ├─ process.py         # 병목 / 불량 전이 조회
+│  │  ├─ defect_transfer.py # 불량 전이 / 원인 조회
+│  │  ├─ analysis_maintenance.py # 병목 / 불량 전이 백필·재색인
+│  │  ├─ manual.py          # AI 메뉴얼
+│  │  └─ health.py          # 헬스 체크
+├─ service/
+│  ├─ analysis/             # 분석 조회 / 백필 / 재색인
+│  ├─ manufacturing/        # 제조 이벤트 처리
+│  └─ llm/                  # LLM 연동
+├─ repository/              # DB 접근 계층
+├─ search/                  # Elasticsearch 저장 / 조회
+├─ kafka/                   # Kafka 소비 / 발행
+├─ ml/                      # 모델 학습 / 추론 / SHAP
+├─ ai_manual/               # AI 메뉴얼 생성
+├─ dto/                     # 요청 / 응답 스키마
+├─ batch/                   # 배치 / 백필 작업
+└─ scheduler/               # 주기 실행 작업
+```
+
+### 역할 요약
+
+- `app/service/analysis`: 분석 결과 조회와 관리 작업을 담당합니다.
+- `app/search`: Elasticsearch 인덱싱과 조회를 담당합니다.
+- `app/kafka`: 제조 이벤트 수집과 분석 결과 동기화를 담당합니다.
+- `app/ml`: 병목 탐지와 불량 전이 모델을 담당합니다.
+- `app/ai_manual`: 메뉴얼 생성 로직을 담당합니다.
+
+&nbsp;
 ## 🔧 기술 스택
 ![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi&logoColor=white)
 ![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)
@@ -318,6 +326,8 @@ sequenceDiagram
 ![SHAP](https://img.shields.io/badge/SHAP-4B5563?style=for-the-badge&logo=shap&logoColor=white)
 ![OpenAI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge&logo=openai&logoColor=white)
 
+
+&nbsp;
 ## ⚙️ 실행
 
 ```powershell

From 1b6016da288675038cf362c593963ccbe36d9e45 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 15 Jul 2026 17:35:43 +0900
Subject: [PATCH 143/148] =?UTF-8?q?docs:=20README=20=EC=88=9C=EC=84=9C=20?=
 =?UTF-8?q?=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 42 +++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index 0370dcb..1d3fcc4 100644
--- a/README.md
+++ b/README.md
@@ -212,27 +212,6 @@ flowchart TD
 | Defect Transfer Index | `settings.elasticsearch_defect_transfer_index` | 불량 전이 / 원인 검색 |
 
 
-&nbsp;
-## 📡 API 요약
-
-### 분석 조회
-
-- `GET /api/ai/process/bottleneck`
-- `GET /api/ai/process/defect-transfer/predictions`
-- `GET /api/ai/process/defect-transfer/causes`
-
-### 관리 API
-
-- `POST /api/ai/admin/analysis/backfill/bottleneck`
-- `POST /api/ai/admin/analysis/backfill/defect-transfer`
-- `POST /api/ai/admin/analysis/reindex/bottleneck`
-- `POST /api/ai/admin/analysis/reindex/defect-transfer`
-
-### AI 메뉴얼
-
-- `GET /api/ai/manual`
-
-
 &nbsp;
 ## 🛠 전체 데이터 기능 흐름
 <img width="10217" height="5316" alt="데이터 기능 흐름도" src="https://github.com/user-attachments/assets/02488ac3-03af-4d68-ae0a-96fbdced0e4a" />
@@ -310,6 +289,27 @@ app/
 - `app/ml`: 병목 탐지와 불량 전이 모델을 담당합니다.
 - `app/ai_manual`: 메뉴얼 생성 로직을 담당합니다.
 
+&nbsp;
+## 📡 API 요약
+
+### 분석 조회
+
+- `GET /api/ai/process/bottleneck`
+- `GET /api/ai/process/defect-transfer/predictions`
+- `GET /api/ai/process/defect-transfer/causes`
+
+### 관리 API
+
+- `POST /api/ai/admin/analysis/backfill/bottleneck`
+- `POST /api/ai/admin/analysis/backfill/defect-transfer`
+- `POST /api/ai/admin/analysis/reindex/bottleneck`
+- `POST /api/ai/admin/analysis/reindex/defect-transfer`
+
+### AI 메뉴얼
+
+- `GET /api/ai/manual`
+
+
 &nbsp;
 ## 🔧 기술 스택
 ![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=for-the-badge&logo=fastapi&logoColor=white)

From 69301c52d41d8582dd7f1cada8016bbdf31a7157 Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Wed, 15 Jul 2026 17:43:31 +0900
Subject: [PATCH 144/148] chore: git action trigger

---
 app/main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/main.py b/app/main.py
index 7fd8e1f..6575513 100644
--- a/app/main.py
+++ b/app/main.py
@@ -71,7 +71,7 @@ def initialize_sampledb_schema() -> None:
         try:
             initialize_sampledb(settings.sample_database_connection_url)
         except Exception:
-            logger.exception("sampledb 스키마 초기화에 실패했습니다.")
+            logger.exception("sampledb 스키마 초기화에 실패했습니다.") 
 
     @app.on_event("startup")
     async def start_background_schedulers() -> None:

From 29429fe5b5ffcbfc12305f00dc2a36497456e87c Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Thu, 16 Jul 2026 09:00:48 +0900
Subject: [PATCH 145/148] =?UTF-8?q?chore:=20swagger=20=EC=84=A4=EB=AA=85?=
 =?UTF-8?q?=20=EB=B3=B4=EA=B0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md                               | 82 ++++++++++++++++++++++++-
 app/api/routers/analysis_maintenance.py | 14 +++--
 app/api/routers/defect_transfer.py      | 68 +++++++++++++++++---
 app/api/routers/manual.py               |  6 +-
 app/api/routers/process.py              | 79 ++++++++++++------------
 5 files changed, 189 insertions(+), 60 deletions(-)

diff --git a/README.md b/README.md
index 1d3fcc4..674d7a8 100644
--- a/README.md
+++ b/README.md
@@ -298,6 +298,51 @@ app/
 - `GET /api/ai/process/defect-transfer/predictions`
 - `GET /api/ai/process/defect-transfer/causes`
 
+#### 병목 조회 API
+
+- 병목 목록과 날짜 옵션을 함께 조회합니다.
+- 기본적으로 ES의 `detectedAt` 기준 최신 날짜를 우선 보여줍니다.
+- 응답에는 공정 순위, 지연 시간, 영향 차량 수, 위험도, 다음 페이지 여부가 포함됩니다.
+- 날짜를 지정하면 해당 일자의 병목 결과만 다시 조회합니다.
+
+예시 응답 필드:
+
+- `mostBottleneckProcess`
+- `mostBottleneckRiskLevel`
+- `date`
+- `dateOptions`
+- `content`
+- `hasNext`
+- `nextCursor`
+
+#### 불량 예측 및 전이 예측 API
+
+- 차량별 불량 예측 결과와 전이 경로를 조회합니다.
+- 기본적으로 ES의 `predictedAt` 기준 최신 날짜를 우선 보여줍니다.
+- 응답에는 차량 ID, 현재 공정, 예측 공정, 전이 확률, 위험도, 다음 페이지 여부가 포함됩니다.
+- 날짜를 지정하면 해당 일자의 예측 결과만 다시 조회합니다.
+
+예시 응답 필드:
+
+- `date`
+- `dateOptions`
+- `content`
+- `vehicleId`
+- `carMasterId`
+- `currentProcess`
+- `predictedDefectProcess`
+- `defectProbability`
+- `riskLevel`
+- `hasNext`
+- `nextCursor`
+
+#### 원인 분석 API
+
+- 특정 차량의 최신 불량 예측 결과를 기반으로 원인을 조회합니다.
+- `main_causes`는 대표 원인, `detailCauses`는 상세 원인입니다.
+- 차량 ID가 없으면 최신 차량 기준으로 조회할 수 있습니다.
+- 응답은 `대표 원인`과 `상세 원인`을 분리해 화면에 바로 뿌릴 수 있는 형태입니다.
+
 ### 관리 API
 
 - `POST /api/ai/admin/analysis/backfill/bottleneck`
@@ -328,15 +373,50 @@ app/
 
 
 &nbsp;
-## ⚙️ 실행
+## ⚙️ 가상환경 생성 및 실행
+
+PowerShell 기준:
 
 ```powershell
 python -m venv venv
 .\venv\Scripts\Activate.ps1
+```
+
+가상환경이 정상적으로 활성화되면 프롬프트 앞에 `(venv)`가 표시됩니다.
+
+```powershell
+(venv) PS C:\rookies\aims\ai-service>
+```
+
+의존성 설치:
+
+```powershell
 pip install -r requirements.txt
+```
+
+`requirements.txt`에는 FastAPI, LLM/API 연동, Kafka, 데이터 처리, ML 관련 의존성을 모두 포함합니다.
+
+Windows에서 Python 3.14를 사용하는 경우 일부 패키지의 사전 빌드 wheel이 없으면 `pydantic-core`, `orjson`, `pandas`, `scipy`, `scikit-learn` 등이 소스 빌드를 시도할 수 있습니다. 이 경우 Visual Studio Build Tools가 필요할 수 있으므로, 설치 문제가 반복되면 Python 3.12 또는 3.13 사용을 권장합니다.
+
+개발 서버 실행:
+
+```powershell
 uvicorn app.main:app --reload
 ```
 
+서버 실행 후 아래 주소에서 확인할 수 있습니다.
+
+- API Root: `http://127.0.0.1:8000/`
+- Health Check: `http://127.0.0.1:8000/api/health`
+- Swagger UI: `http://127.0.0.1:8000/docs`
+- OpenAPI Schema: `http://127.0.0.1:8000/openapi.json`
+
+가상환경 비활성화:
+
+```powershell
+deactivate
+```
+
 주요 주소:
 
 - API Root: `http://127.0.0.1:8000/`
diff --git a/app/api/routers/analysis_maintenance.py b/app/api/routers/analysis_maintenance.py
index b2ab36d..1be2ae2 100644
--- a/app/api/routers/analysis_maintenance.py
+++ b/app/api/routers/analysis_maintenance.py
@@ -3,13 +3,13 @@
 from datetime import date as DateType
 
 from fastapi import APIRouter, Depends
+
 from app.dto.request import AnalysisMaintenanceRequest
 from app.dto.response import AnalysisMaintenanceResponse, CommonResponse
 from app.service.analysis.analysis_maintenance_service import AnalysisMaintenanceService
 from app.utils.datetime_utils import seoul_now
 from app.utils.response_utils import success_response
 
-
 router = APIRouter(prefix="/api/ai/admin/analysis", tags=["admin-analysis"])
 
 
@@ -27,6 +27,7 @@ def _service() -> AnalysisMaintenanceService:
     "/backfill/bottleneck",
     response_model=CommonResponse[AnalysisMaintenanceResponse],
     summary="병목 백필 및 ES 재반영",
+    description="지정한 날짜 구간의 병목 결과를 다시 계산하고 Elasticsearch에 재반영합니다.",
 )
 def backfill_bottleneck(
     payload: AnalysisMaintenanceRequest,
@@ -46,7 +47,8 @@ def backfill_bottleneck(
 @router.post(
     "/backfill/defect-transfer",
     response_model=CommonResponse[AnalysisMaintenanceResponse],
-    summary="불량 전이/SHAP 백필 및 ES 재반영",
+    summary="불량 예측 및 전이 백필과 ES 재반영",
+    description="지정한 날짜 구간의 불량 예측, 전이 예측, SHAP 원인 분석 결과를 다시 계산하고 Elasticsearch에 재반영합니다.",
 )
 def backfill_defect_transfer(
     payload: AnalysisMaintenanceRequest,
@@ -60,13 +62,14 @@ def backfill_defect_transfer(
         reset_flags=payload.reset_flags,
         dry_run=payload.dry_run,
     )
-    return success_response(data=response, message="불량 전이 백필이 완료되었습니다.")
+    return success_response(data=response, message="불량 예측 및 전이 백필이 완료되었습니다.")
 
 
 @router.post(
     "/reindex/bottleneck",
     response_model=CommonResponse[AnalysisMaintenanceResponse],
     summary="병목 ES 재색인",
+    description="기존 병목 결과를 삭제한 뒤 원천 데이터를 다시 읽어 Elasticsearch 인덱스를 재생성합니다.",
 )
 def reindex_bottleneck(
     payload: AnalysisMaintenanceRequest,
@@ -84,7 +87,8 @@ def reindex_bottleneck(
 @router.post(
     "/reindex/defect-transfer",
     response_model=CommonResponse[AnalysisMaintenanceResponse],
-    summary="불량 전이/SHAP ES 재색인",
+    summary="불량 예측 및 전이 ES 재색인",
+    description="기존 불량 예측, 전이 예측, SHAP 원인 분석 결과를 삭제한 뒤 원천 데이터를 다시 읽어 Elasticsearch 인덱스를 재생성합니다.",
 )
 def reindex_defect_transfer(
     payload: AnalysisMaintenanceRequest,
@@ -96,4 +100,4 @@ def reindex_defect_transfer(
         to_date=to_date,
         dry_run=payload.dry_run,
     )
-    return success_response(data=response, message="불량 전이 ES 재색인이 완료되었습니다.")
+    return success_response(data=response, message="불량 예측 및 전이 ES 재색인이 완료되었습니다.")
diff --git a/app/api/routers/defect_transfer.py b/app/api/routers/defect_transfer.py
index 81bf9d7..6ec5d7e 100644
--- a/app/api/routers/defect_transfer.py
+++ b/app/api/routers/defect_transfer.py
@@ -1,9 +1,9 @@
 from __future__ import annotations
 
+from datetime import date as DateType
 from typing import Any
 
 from fastapi import APIRouter, Depends, Query
-from datetime import date as DateType
 
 from app.dto.response import CommonResponse
 from app.dto.response.defect_transfer_response import (
@@ -16,18 +16,66 @@
 )
 from app.utils.response_utils import success_response
 
-
 router = APIRouter(prefix="/api/ai/process/defect-transfer", tags=["process"])
 
 DefectTransferPredictionResponse = CommonResponse[DefectTransferPredictionPage]
 DefectTransferCauseResponse = CommonResponse[DefectTransferCausePage]
 DefectTransferDiagnosticsResponse = CommonResponse[dict[str, Any]]
 
+PREDICTION_DESCRIPTION = """
+불량 예측 및 전이 예측 조회 API입니다.
+
+무엇을 반환하나요
+- 차량별 불량 예측 결과
+- 현재 공정과 예측 공정
+- 전이 확률과 위험도
+- 날짜 옵션과 페이지 정보
+
+어떤 데이터를 사용하나요
+- `sampledb.manufacturing_event_json`의 SENT 이벤트
+- ES 인덱스의 `predictedAt` 기준 날짜 옵션
+- 불량 예측 결과 저장 테이블 `defect_transfer_prediction_result`
+
+조회 방식
+- 기본적으로 ES를 우선 조회합니다.
+- ES가 비어 있거나 실패하면 Redis 캐시를 확인하고, 캐시가 없으면 DB로 fallback합니다.
+- 날짜를 주지 않으면 최신 가능한 날짜를 자동 선택합니다.
+- 목록은 차량별 최신 1건만 보여줍니다.
+"""
+
+CAUSE_DESCRIPTION = """
+원인 분석 조회 API입니다.
+
+무엇을 반환하나요
+- 불량 예측 결과에 대한 대표 원인
+- SHAP 기반 상세 원인
+- 차량별 최신 원인 분석 결과
+
+조회 방식
+- 차량 ID가 있으면 해당 차량을 우선 조회합니다.
+- 차량 ID가 없으면 최신 차량 기준으로 조회합니다.
+- ES의 `predictedAt` 기준 날짜를 사용합니다.
+
+응답 의미
+- `main_causes`: 화면에 먼저 보여줄 대표 원인
+- `detailCauses`: 추가로 확인할 상세 원인
+"""
+
+DIAGNOSTICS_DESCRIPTION = """
+불량 예측 조회 상태를 점검하는 진단 API입니다.
+
+무엇을 확인하나요
+- 현재 ES 연결 상태
+- 캐시 및 조회 가능 여부
+- 예측 데이터의 간단한 상태 정보
+"""
+
 
 @router.get(
     "/predictions",
     response_model=DefectTransferPredictionResponse,
-    summary="불량 전이 목록 조회",
+    summary="불량 예측 및 전이 예측 목록 조회",
+    description=PREDICTION_DESCRIPTION,
 )
 def get_defect_transfer_predictions(
     date: DateType | None = Query(
@@ -44,34 +92,36 @@ def get_defect_transfer_predictions(
             size=size,
             date=date,
         ),
-        message="불량 전이 목록 조회가 완료되었습니다.",
+        message="불량 예측 및 전이 예측 목록 조회가 완료되었습니다.",
     )
 
 
 @router.get(
     "/diagnostics",
     response_model=DefectTransferDiagnosticsResponse,
-    summary="불량 전이 데이터 진단",
+    summary="불량 예측 진단 정보 조회",
+    description=DIAGNOSTICS_DESCRIPTION,
 )
 def get_defect_transfer_diagnostics(
     service: DefectTransferAnalysisService = Depends(get_defect_transfer_analysis_service),
 ) -> DefectTransferDiagnosticsResponse:
     return success_response(
         data=service.get_diagnostics(),
-        message="불량 전이 데이터 진단이 완료되었습니다.",
+        message="불량 예측 진단 정보 조회가 완료되었습니다.",
     )
 
 
 @router.get(
     "/causes",
     response_model=DefectTransferCauseResponse,
-    summary="SHAP 기반 AI 원인 분석 조회",
+    summary="불량 예측 원인 분석 조회",
+    description=CAUSE_DESCRIPTION,
 )
 def get_defect_transfer_causes(
     vehicle_id: str | None = Query(
         default=None,
         alias="vehicleId",
-        description="car_master.vehicle_id. If omitted, the latest vehicle is used.",
+        description="차량 ID입니다. 미지정 시 최신 차량을 사용합니다.",
     ),
     date: DateType | None = Query(
         default=None,
@@ -88,5 +138,5 @@ def get_defect_transfer_causes(
             size=size,
             date=date,
         ),
-        message="SHAP 기반 AI 원인 분석 조회가 완료되었습니다.",
+        message="불량 예측 원인 분석 조회가 완료되었습니다.",
     )
diff --git a/app/api/routers/manual.py b/app/api/routers/manual.py
index 600f2df..1638cec 100644
--- a/app/api/routers/manual.py
+++ b/app/api/routers/manual.py
@@ -45,8 +45,4 @@ def generate_manual(request: Request):
             "data": None
         }
 
-    return {
-        "success": True,
-        "message": "매뉴얼 생성 완료",
-        "data": result
-    }
\ No newline at end of file
+    return result
\ No newline at end of file
diff --git a/app/api/routers/process.py b/app/api/routers/process.py
index 1d61990..1ef844d 100644
--- a/app/api/routers/process.py
+++ b/app/api/routers/process.py
@@ -1,6 +1,9 @@
-from fastapi import APIRouter, Depends, Query
+from __future__ import annotations
+
 from datetime import date as DateType
 
+from fastapi import APIRouter, Depends, Query
+
 from app.dto.response import BottleneckAnalysisPage, CommonResponse
 from app.service.analysis.bottleneck_service import (
     BottleneckAnalysisService,
@@ -13,63 +16,59 @@
 BottleneckAnalysisResponse = CommonResponse[BottleneckAnalysisPage]
 
 BottleneckAnalysisDescription = """
-Kafka raw 제조 이벤트를 기반으로 현재 제조 공정의 병목 순위를 조회합니다.
+병목 분석 조회 API입니다.
 
-분석 입력 데이터:
-- `sample_db.manufacturing_event_json` 테이블의 `is_sent = true` 이벤트만 사용합니다.
-- Kafka consumer가 `factory.manufacturing.raw` 토픽에서 받은 record value의 `eventJson`을 저장한 데이터입니다.
-- `manufacturing_event_id`는 `manufacturing_event_json.id`를 의미합니다.
-- `analysis_status`는 병목 분석 상태가 아니라 다른 이상 탐지 로직용 값이므로 병목 분석 필터로 사용하지 않습니다.
+무엇을 반환하나요
+- 공정별 병목 순위
+- 지연 시간
+- 영향 차량 수
+- 위험도와 위험 점수
+- 다음 페이지 여부와 다음 커서
 
-분석 방식:
-- PRESS, BODY, PAINT, ASSEMBLY 이벤트를 Kafka에서 계속 수신해 DB에 적재합니다.
-- Kafka raw 이벤트가 저장되면 Rule Engine + Isolation Forest 모델로 병목 결과를 자동 갱신합니다.
-- AI 병목 분석 결과는 `factory.manufacturing.analysis` 토픽으로도 발행합니다.
-- 분석 결과 토픽의 Message Key는 raw 토픽과 동일하게 `carId`입니다.
-- 발행 payload에는 `manufacturingAnalysisData`와 `aiAnalysisData`를 포함합니다.
-- 병목 조회 시에도 저장된 raw 이벤트 기준으로 최신 결과를 다시 확인합니다.
-- 결과는 `bottleneck_analysis_result` 테이블에 저장됩니다.
-- `delayTime`은 DB에는 계산된 원본 double 값으로 저장하고, API 응답에서는 소수점 둘째 자리까지 반환합니다.
-- `processCode`는 장비 코드 기준으로 `도장 (L1)`, `프레스 (P4)` 형식으로 반환합니다.
+어떤 데이터를 사용하나요
+- `sampledb.manufacturing_event_json`의 `is_sent = true` 이벤트
+- ES 인덱스의 `detectedAt` 기준 날짜 옵션
+- 병목 결과 저장 테이블 `bottleneck_analysis_result`
 
-페이지네이션:
-- `cursor`는 페이지 번호입니다. 생략하면 `0`으로 처리됩니다.
-- `size=5`, `cursor=0`이면 1~5위, `cursor=1`이면 6~10위를 반환합니다.
-- `hasNext=false`이면 다음 페이지를 호출하지 않아야 합니다.
-- 분석 가능한 이벤트가 없거나 cursor가 마지막 페이지를 넘으면 404가 아니라 빈 `content`와 `hasNext=false`를 반환합니다.
+조회 방식
+- 기본적으로 ES를 우선 조회합니다.
+- ES가 비어 있거나 실패하면 Redis 캐시를 확인하고, 캐시가 없으면 DB로 fallback합니다.
+- 날짜를 주지 않으면 최신 가능한 날짜를 자동 선택합니다.
+- `cursor`와 `size`로 페이지를 제어합니다.
 
-캐시:
-- Redis에 cursor/size별 결과를 캐시합니다.
-- Kafka raw 이벤트가 새로 수신되면 병목 캐시를 무효화합니다.
+주의 사항
+- 병목은 공정 단위로 집계됩니다.
+- 날짜 옵션은 ES 기준으로 생성됩니다.
+- 재색인 / 백필 시 해당 날짜의 기존 결과를 다시 계산합니다.
 """
 
 BottleneckAnalysisExample = {
     "success": True,
     "data": {
-        "mostBottleneckProcess": "도장",
-        "mostBottleneckRiskLevel": "위험",
+        "mostBottleneckProcess": "차체",
+        "mostBottleneckRiskLevel": "HIGH",
         "content": [
             {
                 "rankNo": 1,
-                "processCode": "도장 (L3)",
+                "processCode": "차체 (L3)",
                 "delayTime": 12.4,
                 "affectedVehicleCount": 128,
                 "riskScore": 5.0,
-                "riskLevel": "위험",
+                "riskLevel": "HIGH",
             },
             {
                 "rankNo": 2,
-                "processCode": "차체 (S12)",
+                "processCode": "의장 (S12)",
                 "delayTime": 9.8,
                 "affectedVehicleCount": 92,
                 "riskScore": 4.0,
-                "riskLevel": "위험",
+                "riskLevel": "HIGH",
             },
         ],
         "hasNext": True,
         "nextCursor": 1,
     },
-    "message": "병목 분석이 완료되었습니다.",
+    "message": "병목 분석 조회가 완료되었습니다.",
     "timestamp": "2026-06-30T11:10:00+09:00",
 }
 
@@ -77,9 +76,9 @@
 @router.get(
     "/bottleneck",
     response_model=BottleneckAnalysisResponse,
-    summary="제조 공정 병목 분석 결과 조회",
+    summary="병목 분석 결과 조회",
     description=BottleneckAnalysisDescription,
-    response_description="제조 공정 병목 순위 페이지",
+    response_description="병목 순위 페이지",
     responses={
         200: {
             "description": "병목 분석 결과 조회 성공",
@@ -90,21 +89,21 @@
             },
         },
         500: {
-            "description": "Redis 캐시, 모델 파일, DB 처리 중 오류가 발생한 경우",
+            "description": "Redis 캐시, ES 인덱스, DB 처리 중 오류가 발생한 경우",
         },
     },
 )
 def get_bottleneck_analysis(
     date: DateType | None = Query(
         default=None,
-        description="조회할 날짜입니다. 미지정 시 최신 날짜를 사용합니다.",
+        description="조회할 날짜입니다. 미지정 시 최신 가능한 날짜를 사용합니다.",
     ),
     cursor: int | None = Query(
         default=None,
         ge=0,
         description=(
-            "조회할 페이지 번호입니다. 생략하면 0으로 처리합니다. "
-            "cursor=0,size=5는 1~5위, cursor=1,size=5는 6~10위를 반환합니다."
+            "조회할 페이지 번호입니다. 미지정 시 0으로 처리합니다. "
+            "cursor=0,size=5는 1~5건, cursor=1,size=5는 6~10건을 의미합니다."
         ),
         examples=[0],
     ),
@@ -117,7 +116,7 @@ def get_bottleneck_analysis(
     ),
     service: BottleneckAnalysisService = Depends(get_bottleneck_analysis_service),
 ) -> BottleneckAnalysisResponse:
-    """Redis 캐시를 통해 병목 분석 결과 페이지를 반환합니다."""
+    """Redis 캐시를 우선 사용해 병목 분석 페이지를 반환합니다."""
     page: BottleneckAnalysisPage = service.get_cached_realtime_bottlenecks(
         cursor=cursor,
         size=size,
@@ -125,5 +124,5 @@ def get_bottleneck_analysis(
     )
     return success_response(
         data=page,
-        message="병목 분석이 완료되었습니다.",
+        message="병목 분석 조회가 완료되었습니다.",
     )

From 26a202642230e472d26bc15936ebf76a5ba790aa Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 21 Jul 2026 13:51:11 +0900
Subject: [PATCH 146/148] =?UTF-8?q?docs:=20README=20=EC=82=AC=EC=A7=84=20?=
 =?UTF-8?q?=EA=B9=A8=EC=A7=90=20=EC=88=98=EC=A0=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 674d7a8..955ad91 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,8 @@
 
 ### 1. 불량 탐지 및 전이 예측
 
-<img width="984" height="1263" alt="혼합불량" src="https://github.com/user-attachments/assets/386bac8e-5f96-4dbe-a413-1cdc48c0ef93" />
+<img width="1784" height="455" alt="image" src="https://github.com/user-attachments/assets/cddde118-b7f0-4ec5-9987-f6f3ec648414" />
+<img width="1784" height="455" alt="image" src="https://github.com/user-attachments/assets/8cdcc7e2-0104-4370-b755-d0d3ada78545" />
 
 - 차량 단위로 다음 공정 불량 가능성을 예측하고 전이 경로를 함께 봅니다.
 - 현재 공정, 다음 공정, 설비 신호, 사이클 타임, 대기 시간, 재공 수량, 진동/온도/도막 두께를 함께 봅니다.
@@ -66,7 +67,7 @@
 4. ES의 `predictedAt`을 기준으로 최신 1건을 보여줍니다.
 
 #### SHAP 원인 분석
-<img width="884" height="684" alt="main 원인" src="https://github.com/user-attachments/assets/2165f137-0bee-4cb5-a5d1-d1ea7373e464" />
+<img width="884" height="684" alt="image" src="https://github.com/user-attachments/assets/d35a34ac-7cc0-4a24-b07e-229312bd9c8e" />
 
 - 원인 분석은 불량 탐지 및 전이 예측 결과를 해석하는 단계입니다.
 - SHAP 값을 이용해 주요 원인 1개와 상세 원인 여러 개를 분리합니다.
@@ -90,7 +91,7 @@
 &nbsp;
 ### 2. 병목 분석
 
-<img width="868" height="556" alt="다운로드 (1)" src="https://github.com/user-attachments/assets/0f681bf1-4859-4ecc-abe3-c193a68efa81" />
+<img width="868" height="556" alt="image" src="https://github.com/user-attachments/assets/fe4bcab4-d22c-4958-87e1-242e399bf981" />
 
 - `sampledb.manufacturing_event_json`의 제조 이벤트를 읽어 공정별 병목을 계산합니다.
 - 결과는 공정 순위, 지연 시간, 영향 차량 수, 위험도 형태로 정리됩니다.
@@ -139,7 +140,7 @@
 ### 3. 🤖 AI 메뉴얼
 | 이상 이벤트 발생 | 주니어 | 시니어 |
 |---|---|---|
-| <img width="582" height="506" alt="이상 이벤트 발생" src="https://github.com/user-attachments/assets/faac7e24-4756-4ee7-be91-bf9e813e6a2c" /> | <img width="582" height="506" alt="주니어" src="https://github.com/user-attachments/assets/9e0900e0-50ef-4b2f-aafd-7d18428d8a58" /> | <img width="565" height="500" alt="시니어" src="https://github.com/user-attachments/assets/6122f166-a6af-4e34-9474-0b7ba454f977" /> |
+| <img width="582" height="506" alt="이상 이벤트 발생" src="https://github.com/user-attachments/assets/faac7e24-4756-4ee7-be91-bf9e813e6a2c" /> | <img width="582" height="506" alt="주니어" src="https://github.com/user-attachments/assets/9e0900e0-50ef-4b2f-aafd-7d18428d8a58" /> | <img width="565" height="500" alt="시니어" src="https://github.com/user-attachments/assets/b8444157-0aad-4c70-b03f-dfe28db7b259" /> |
 
 - JWT 인증을 통과한 사용자만 메뉴얼을 생성합니다.
 - 이벤트, 설비 맥락, 사내 지침, 검색된 문서를 함께 사용합니다.

From 078d9a9ebded299036468c6603466f3862ab3c7e Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 21 Jul 2026 13:53:58 +0900
Subject: [PATCH 147/148] chore: git action trigger

---
 app/dto/response/common_response.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/app/dto/response/common_response.py b/app/dto/response/common_response.py
index b67c9e7..59bf739 100644
--- a/app/dto/response/common_response.py
+++ b/app/dto/response/common_response.py
@@ -4,7 +4,6 @@
 
 DataT = TypeVar("DataT")
 
-
 class CommonResponse(BaseModel, Generic[DataT]):
     success: bool = Field(description="요청 성공 여부")
     data: DataT | None = Field(default=None, description="응답 데이터")

From b185bbabae27e54d5aa963220ed955a67b26d4aa Mon Sep 17 00:00:00 2001
From: mmije0ng <victon777@naver.com>
Date: Tue, 21 Jul 2026 15:44:30 +0900
Subject: [PATCH 148/148] =?UTF-8?q?fix:=20=EB=B3=91=EB=AA=A9&=EB=B6=88?=
 =?UTF-8?q?=EB=9F=89=EC=A0=84=EC=9D=B4=20ES=20KST=EB=A1=9C=20=EB=B3=80?=
 =?UTF-8?q?=EA=B2=BD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/kafka/raw_event_consumer.py               | 13 ++++++---
 app/search/process_analysis_search.py         | 17 ++++++++++--
 .../analysis/analysis_maintenance_service.py  | 27 ++++++++++++++-----
 app/service/analysis/bottleneck_service.py    | 10 +++++--
 4 files changed, 52 insertions(+), 15 deletions(-)

diff --git a/app/kafka/raw_event_consumer.py b/app/kafka/raw_event_consumer.py
index 6d96b33..6570b59 100644
--- a/app/kafka/raw_event_consumer.py
+++ b/app/kafka/raw_event_consumer.py
@@ -24,7 +24,7 @@
 from app.repository.sampledb_repository import SampleDbRepository
 from app.search.process_analysis_search import ProcessAnalysisSearchRepository
 from app.service.analysis.bottleneck_service import BottleneckAnalysisService
-from app.utils.datetime_utils import seoul_now_iso
+from app.utils.datetime_utils import SEOUL_TZ, seoul_now, seoul_now_iso
 from app.websocket.analysis_manager import analysis_websocket_manager
 logger = logging.getLogger(__name__)
 PROCESS_SEQUENCE = ("PRESS", "BODY", "PAINT", "ASSEMBLY")
@@ -470,7 +470,10 @@ def _parse_datetime(value: Any) -> datetime | None:
     if not text:
         return None
     try:
-        return datetime.fromisoformat(text.replace("Z", "+00:00")).replace(tzinfo=None)
+        parsed = datetime.fromisoformat(text.replace("Z", "+00:00"))
+        if parsed.tzinfo is not None:
+            return parsed.astimezone(SEOUL_TZ).replace(tzinfo=None)
+        return parsed
     except ValueError:
         logger.warning("Cannot parse raw event time: %s", text)
         return None
@@ -1098,7 +1101,7 @@ def _save_defect_transfer_prediction(
             expected_occurrence_step=prediction.expected_steps_after,
             risk_grade=prediction.risk_level,
             causes=causes,
-            predicted_at=datetime.now(),
+            predicted_at=seoul_now().replace(tzinfo=None),
         )
     except Exception:
         logger.exception(
@@ -1316,7 +1319,9 @@ def _safe_float(value: Any, *, default: float) -> float:
 
 def _iso_or_none(value: Any) -> str | None:
     if isinstance(value, datetime):
-        return value.isoformat()
+        if value.tzinfo is None:
+            return value.replace(tzinfo=SEOUL_TZ).isoformat()
+        return value.astimezone(SEOUL_TZ).isoformat()
     return str(value) if value is not None else None
 
 
diff --git a/app/search/process_analysis_search.py b/app/search/process_analysis_search.py
index 20a8127..b6d899d 100644
--- a/app/search/process_analysis_search.py
+++ b/app/search/process_analysis_search.py
@@ -7,6 +7,7 @@
 from urllib.parse import urlparse
 
 from app.core.config import settings
+from app.utils.datetime_utils import SEOUL_TZ
 
 logger = logging.getLogger(__name__)
 
@@ -570,9 +571,21 @@ def _normalize_datetime(value: Any) -> str | None:
         if value is None:
             return None
         if isinstance(value, datetime):
-            return value.isoformat()
+            if value.tzinfo is None:
+                return value.replace(tzinfo=SEOUL_TZ).isoformat()
+            return value.astimezone(SEOUL_TZ).isoformat()
         text = str(value).strip()
-        return text or None
+        if not text:
+            return None
+        try:
+            parsed = datetime.fromisoformat(text.replace("Z", "+00:00"))
+        except ValueError:
+            return text
+        if parsed.tzinfo is None:
+            parsed = parsed.replace(tzinfo=SEOUL_TZ)
+        else:
+            parsed = parsed.astimezone(SEOUL_TZ)
+        return parsed.isoformat()
 
     @staticmethod
     def _safe_int(value: Any) -> int | None:
diff --git a/app/service/analysis/analysis_maintenance_service.py b/app/service/analysis/analysis_maintenance_service.py
index 687a6c7..5c2b371 100644
--- a/app/service/analysis/analysis_maintenance_service.py
+++ b/app/service/analysis/analysis_maintenance_service.py
@@ -26,7 +26,7 @@
 from app.repository.sampledb_schema import car_master
 from app.search.process_analysis_search import ProcessAnalysisSearchRepository
 from app.utils.database_utils import mysql_connect_args_for_seoul
-from app.utils.datetime_utils import seoul_now
+from app.utils.datetime_utils import SEOUL_TZ, seoul_now
 from app.utils.process_label_utils import NEXT_PROCESS, format_process_with_line
 from app.utils.process_label_utils import equipment_code_for_car_process
 
@@ -525,12 +525,13 @@ def _build_bottleneck_sync_event(
         sync_id = f"SNAP-{uuid4()}"
         first_history = histories[0]
         first_summary = summaries[0] if summaries else {}
+        detected_at_iso = self._to_seoul_iso(detected_at)
         return {
             "syncId": sync_id,
             "analysisType": "BOTTLENECK_ANALYSIS_SYNC",
             "sourceService": "AI_SERVICE",
-            "detectedAt": detected_at.isoformat(),
-            "analyzedAt": detected_at.isoformat(),
+            "detectedAt": detected_at_iso,
+            "analyzedAt": detected_at_iso,
             "eventId": first_history.get("event_id"),
             "carMasterId": first_history.get("car_master_id"),
             "mostBottleneckProcess": first_summary.get("process_code"),
@@ -576,6 +577,7 @@ def _build_defect_transfer_sync_event(
         )
         fallback_date = self._analysis_date(row)
         predicted_at = self._predict_at(row, fallback_date)
+        predicted_at_iso = self._to_seoul_iso(predicted_at)
         causes = [
             {
                 "rank": cause.rank,
@@ -587,7 +589,6 @@ def _build_defect_transfer_sync_event(
             }
             for cause in prediction.causes
         ]
-        predicted_at_iso = predicted_at.isoformat()
         return {
             "syncId": sync_id,
             "analysisType": "DEFECT_TRANSFER_ANALYSIS_SYNC",
@@ -639,14 +640,14 @@ def _vehicle_id_for_car_master_id(self, car_master_id: int) -> str | None:
     def _analysis_date(row: dict[str, Any]) -> DateType:
         value = row.get("event_time")
         if isinstance(value, datetime):
-            return value.date()
+            return AnalysisMaintenanceService._to_seoul_naive(value).date()
         return seoul_now().date()
 
     @staticmethod
     def _predict_at(row: dict[str, Any], fallback_date: DateType) -> datetime:
         value = row.get("event_time")
         if isinstance(value, datetime):
-            return value if value.tzinfo is None else value.replace(tzinfo=None)
+            return AnalysisMaintenanceService._to_seoul_naive(value)
         return datetime.combine(fallback_date, datetime.min.time())
 
     @staticmethod
@@ -703,7 +704,7 @@ def _risk_level(risk_score: float) -> str:
     @staticmethod
     def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
         candidates = [
-            value.replace(tzinfo=None) if isinstance(value, datetime) and value.tzinfo else value
+            AnalysisMaintenanceService._to_seoul_naive(value)
             for value in (row.get("event_time") for row in histories)
             if isinstance(value, datetime)
         ]
@@ -711,6 +712,18 @@ def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
             return max(candidates)
         return seoul_now().replace(tzinfo=None)
 
+    @staticmethod
+    def _to_seoul_naive(value: datetime) -> datetime:
+        if value.tzinfo is None:
+            return value
+        return value.astimezone(SEOUL_TZ).replace(tzinfo=None)
+
+    @staticmethod
+    def _to_seoul_iso(value: datetime) -> str:
+        if value.tzinfo is None:
+            return value.replace(tzinfo=SEOUL_TZ).isoformat()
+        return value.astimezone(SEOUL_TZ).isoformat()
+
     @staticmethod
     def _rank_bottleneck_summaries(
         summaries: list[dict[str, Any]],
diff --git a/app/service/analysis/bottleneck_service.py b/app/service/analysis/bottleneck_service.py
index a7a8ff0..79da595 100644
--- a/app/service/analysis/bottleneck_service.py
+++ b/app/service/analysis/bottleneck_service.py
@@ -20,7 +20,7 @@
 from app.ml.inference.bottleneck_detector import BottleneckDetector
 from app.repository.bottleneck_analysis_repository import BottleneckAnalysisRepository
 from app.search.process_analysis_search import ProcessAnalysisSearchRepository
-from app.utils.datetime_utils import seoul_now
+from app.utils.datetime_utils import SEOUL_TZ, seoul_now
 from app.utils.json_utils import from_json, to_json
 
 
@@ -342,7 +342,7 @@ def _resolve_date(
     def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
         """분석된 이벤트 묶음의 기준 detected_at 시각을 계산한다."""
         candidates = [
-            value.replace(tzinfo=None) if isinstance(value, datetime) and value.tzinfo else value
+            BottleneckAnalysisService._to_seoul_naive(value)
             for value in (row.get("event_time") for row in histories)
             if isinstance(value, datetime)
         ]
@@ -350,6 +350,12 @@ def _analysis_detected_at(histories: list[dict[str, Any]]) -> datetime:
             return max(candidates)
         return seoul_now().replace(tzinfo=None)
 
+    @staticmethod
+    def _to_seoul_naive(value: datetime) -> datetime:
+        if value.tzinfo is None:
+            return value
+        return value.astimezone(SEOUL_TZ).replace(tzinfo=None)
+
     @staticmethod
     def _create_search_repository() -> ProcessAnalysisSearchRepository | None:
         """ES 설정이 있으면 검색 저장소를 만들고, 없으면 사용하지 않는다."""