⚡ Bolt: ML予測のエンドポイントにLRUキャッシュを導入し、NumPyのオーバーヘッドを削減

.jules/bolt.md

@@ -9,3 +9,7 @@
 ## 2026-06-10 - [Lazy Loading Scikit-learn & NumPy]
 **Learning:** Top-level imports of `numpy` and `joblib` plus loading a serialized model file (`.joblib`) during FastAPI startup adds significant overhead (e.g., ~3 seconds). Refactoring this into a service with `cached_property` and local imports deferred the cost until the first request.
 **Action:** Move all heavy ML model loading and their dependencies into lazy-loaded properties to ensure near-instant application startup.
+
+## 2026-06-11 - [ML Prediction Caching & NumPy Overhead]
+**Learning:** Standard `lru_cache` on instance methods can lead to memory leaks and issues with unhashable `self`. Using a `cached_property` that returns a decorated inner function allows for per-instance caching while keeping the cache key simple and avoiding unhashable `self` issues. Additionally, passing a list of tuples instead of a `numpy` array for single-sample predictions in Scikit-learn models reduces overhead by eliminating `numpy` imports and array allocations in the hot path.
+**Action:** Implement per-instance caching for ML predictions using `cached_property` and closures, and favor plain Python structures over `numpy` for single-sample inference in high-frequency paths.

ml_service.py

@@ -3,6 +3,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 class MLService:
     @cached_property
     def model(self):
@@ -16,11 +17,24 @@ def model(self):
             return None
 
     def predict(self, features_list):
-        """Perform prediction using the lazy-loaded model."""
+        """Perform prediction using the lazy-loaded model with caching."""
         if self.model is None:
             raise RuntimeError("ML model could not be loaded")
 
-        import numpy as np
-        features = np.array(features_list).reshape(1, -1)
-        prediction = self.model.predict(features)
-        return int(prediction[0])
+        # Convert list to tuple for caching (tuples are hashable)
+        features_tuple = tuple(features_list)
+        prediction = self._predict_with_cache(features_tuple)
+        return int(prediction)
+
+    @cached_property
+    def _predict_with_cache(self):
+        """Lazy-loaded LRU cache for predictions."""
+        from functools import lru_cache
+
+        @lru_cache(maxsize=128)
+        def _get_prediction(features_tuple):
+            # The model is accessed from the outer scope (the MLService instance)
+            # This avoids including the unhashable model object in the cache key.
+            return self.model.predict([features_tuple])[0]
+
+        return _get_prediction