submit/lephare

.github/workflows/submit_lephare.yaml

@@ -0,0 +1,38 @@
+---
+# This workflow will install Python dependencies and run tests
+
+name: Unit test and code coverage
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.13']
+        submission: ['lephare']
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          sudo apt-get update
+          sudo apt install libbz2-dev
+          python -m pip install --upgrade pip
+          pip install wheel
+          pip install .
+          pip install .[dev]
+          if [ -f requirements_${{ matrix.submission }}.txt ]; then pip install -r requirements_${{ matrix.submission }}.txt; fi
+      - name: Run unit tests with pytest
+        run: |
+          python -m pytest tests/test_${{ matrix.submission }}.py

requirements_lephare.txt

@@ -0,0 +1,2 @@
+pz-rail-base
+pz-rail-lephare

tests/conftest.py

@@ -12,7 +12,9 @@ def setup_public_area(request: pytest.FixtureRequest) -> int:
     """
     A pytest fixture to download the public data
     """
-
+    #
+    submit_utils._DOWNLOAD_TIMEOUT = 120
+    submit_utils._DOWNLOAD_RETRY_DELAY = 30
     if not os.path.exists("tests/public"):
         # Note that the tar file has "public" as top level directory
         # so we if we extract to "tests" the files actually end

tests/test_lephare.py

@@ -0,0 +1,337 @@
+import os
+from pathlib import Path
+import pytest
+
+import numpy as np
+
+from rail.core.data import TableHandle
+from rail.estimation.algos.lephare import (
+    LephareInformer,
+    LephareEstimator,
+    lsst_default_config,
+)
+from rail.utils import catalog_utils
+
+import lephare as lp
+
+from pz_data_challenge.taskset_1 import run_taskset_1
+from pz_data_challenge.taskset_2 import run_taskset_2
+
+from pz_data_challenge import submit_utils
+
+# We seem to need more for LePHARE at the current address
+submit_utils._DOWNLOAD_TIMEOUT = 120
+submit_utils._DOWNLOAD_RETRY_DELAY = 30
+
+SUBMISSION_NAME: str = "lephare"
+# SUBMISSION_URL: str = "https://www.dropbox.com/scl/fi/fq1mvmt0d4cgb0qs0zbe2/submit_lephare.tgz?rlkey=3kmk5yiu6pqkx4g3vxbtkz41x&st=yrvmegii&e=1&dl=1"
+SUBMISSION_URL: str = "https://www.raphaelshirley.co.uk/data/submit_lephare.tgz"
+
+# don't change these
+SUBMIT_DIR: str = f"submissions/{SUBMISSION_NAME}"
+PUBLIC_AREA: str = "tests/public"
+
+# LePHARE specific globals
+flux_cols = [f"mag_{b}_lsst" for b in "ugrizy"]
+flux_cols += [f"mag_{b}_roman" for b in "YJH"]
+flux_err_cols = [f"mag_{b}_lsst_err" for b in "ugrizy"]
+flux_err_cols += [f"mag_{b}_roman_err" for b in "YJH"]
+
+config = lsst_default_config.copy()
+updates = {
+    "MAG_REF": "2",
+    "ERR_SCALE": "0.02",
+    "FILTER_CALIB": "0",
+    "FILTER_LIST": lsst_default_config["FILTER_LIST"]
+    + ",roman/Roman_WFI.F106.dat,roman/Roman_WFI.F129.dat,roman/Roman_WFI.F158.dat",
+    "GLB_CONTEXT": "0",
+    "MABS_CONTEXT": "0",
+    # Remove AGN for speed
+    "ZPHOTLIB": "LSST_STAR_MAG,LSST_GAL_MAG",
+    "Z_STEP": "0.02,0.,3.",
+    "EM_DISPERSION": "0.5,1.,1.5",
+    "ERR_FACTOR": "1.",
+}
+config.update(updates)
+params = {f"lephare.{k}": v for k, v in updates.items()}
+params["gal.MOD_EXTINC"] = ("16,24,24,31,24,31,24,31",)
+
+
+@pytest.fixture(name="setup_submit_area", scope="module")
+def setup_submit_area(request: pytest.FixtureRequest) -> int:
+    if not os.path.exists(SUBMIT_DIR):
+        submit_utils.download_and_extract_tar(SUBMISSION_URL, SUBMIT_DIR)
+
+    def teardown_submit_area() -> None:
+        if not os.environ.get("NO_TEARDOWN"):
+            os.system(f"\\rm -rf {SUBMIT_DIR}")
+
+    try:
+        os.makedirs(os.path.join(SUBMIT_DIR, "outputs_2"))
+    except Exception:
+        pass
+
+    try:
+        os.makedirs(os.path.join(SUBMIT_DIR, "outputs_3"))
+    except Exception:
+        pass
+
+    request.addfinalizer(teardown_submit_area)
+
+    catalog_utils.load_yaml("tests/catalogs.yaml")
+    catalog_utils.apply("cardinal_roman_rubin")
+
+    lp.data_retrieval.get_auxiliary_data(
+        keymap=config, additional_files=["examples/output.para"]
+    )
+
+    return 0
+
+
+def run_taskset_1_estimation_only(
+    model_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run estimation for task set 1
+
+    This function should use a model stored in model_file, which
+    is downloaded as part of the submission tar file.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    model_file:
+        Path to the model.  This should be part of the submission
+        tar file.
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    test_data = TableHandle("test", path=test_file)
+    estimator = LephareEstimator.make_stage(
+        name="estimate_lephare",
+        model=model_file,
+        output_mode="return",
+        run_dir=SUBMIT_DIR,
+        bands=flux_cols,
+        err_bands=flux_err_cols,
+        hdf5_groupname="",
+        lephare_config_from_model=False,
+        **params,
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_1_training_and_estimation(
+    train_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run training and estimation for task set 1
+
+    This function should train a model and use it.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    train_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be trained
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    train_data = TableHandle("train", path=train_file)
+    test_data = TableHandle("test", path=test_file)
+
+    informer = LephareInformer.make_stage(
+        name="inform_lephare",
+        nondetect_val=np.nan,
+        model="lephare.pkl",
+        hdf5_groupname="",
+        **params,
+        bands=flux_cols,
+        err_bands=flux_err_cols,
+        ref_band="mag_g_lsst",
+        zmin=float(config["Z_STEP"].split(",")[1]),
+        zmax=float(config["Z_STEP"].split(",")[2]),
+        nzbins=1
+        + float(config["Z_STEP"].split(",")[2]) / float(config["Z_STEP"].split(",")[0]),
+    )
+    model = informer.inform(train_data)
+
+    estimator = LephareEstimator.make_stage(
+        name="estimate_lephare",
+        # nondetect_val=np.nan,
+        model="lephare.pkl",
+        hdf5_groupname="",
+        # aliases=dict(input="test_data", output="lephare_estim"),
+        # use_inform_offsets=False,
+        bands=flux_cols,
+        err_bands=flux_err_cols,
+        output_mode="return",
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_2_estimation_only(
+    model_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run estimation for task set 1
+
+    This function should use a model stored in model_file, which
+    is downloaded as part of the submission tar file.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    model_file:
+        Path to the model.  This should be part of the submission
+        tar file.
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    test_data = TableHandle("test", path=test_file)
+    estimator = LephareEstimator.make_stage(
+        name="estimate_lephare",
+        model=model_file,
+        output_mode="return",
+        run_dir=SUBMIT_DIR,
+        bands=flux_cols,
+        err_bands=flux_err_cols,
+        hdf5_groupname="",
+        lephare_config_from_model=False,
+        **params,
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_2_training_and_estimation(
+    train_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run training and estimation for task set 1
+
+    This function should train a model and use it.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    train_data = TableHandle("train", path=train_file)
+    test_data = TableHandle("test", path=test_file)
+
+    informer = LephareInformer.make_stage(
+        name="inform_lephare",
+        nondetect_val=np.nan,
+        model="lephare.pkl",
+        hdf5_groupname="",
+        **params,
+        bands=flux_cols,
+        err_bands=flux_err_cols,
+        ref_band="mag_g_lsst",
+        zmin=float(config["Z_STEP"].split(",")[1]),
+        zmax=float(config["Z_STEP"].split(",")[2]),
+        nzbins=1
+        + float(config["Z_STEP"].split(",")[2]) / float(config["Z_STEP"].split(",")[0]),
+    )
+    model = informer.inform(train_data)
+
+    estimator = LephareEstimator.make_stage(
+        name="estimate_lephare",
+        # nondetect_val=np.nan,
+        model="lephare.pkl",
+        hdf5_groupname="",
+        # aliases=dict(input="test_data", output="lephare_estim"),
+        # use_inform_offsets=False,
+        bands=flux_cols,
+        err_bands=flux_err_cols,
+        output_mode="return",
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def test_example_taskset_1(
+    setup_public_area: int,
+    setup_submit_area: int,
+) -> None:
+    """
+    Test fuction to validate a submisson for Taskset 1
+
+    You should not need to change this function
+    """
+
+    assert setup_public_area == 0
+    assert setup_submit_area == 0
+
+    run_taskset_1(
+        PUBLIC_AREA,
+        SUBMISSION_NAME,
+        run_taskset_1_estimation_only,
+        run_taskset_1_training_and_estimation,
+    )
+
+
+def test_example_taskset_2(
+    setup_public_area: int,
+    setup_submit_area: int,
+) -> None:
+    """
+    Test fuction to validate a submisson for Taskset 1
+
+    You should not need to change this function
+    """
+
+    assert setup_public_area == 0
+    assert setup_submit_area == 0
+
+    run_taskset_2(
+        PUBLIC_AREA,
+        SUBMISSION_NAME,
+        run_taskset_2_estimation_only,
+        run_taskset_2_training_and_estimation,
+    )