ML-003: Predictor mit Sensor-Validierung und Batch-Interface

app/ml/predictor.py

@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+import logging
+from functools import lru_cache
+from typing import Sequence
+
+from app.ml.feature_store import FeatureVector
+from app.ml.training import TrainingPipeline, TrainedArtifact
+
+logger = logging.getLogger(__name__)
+
+
+class Predictor:
+    def __init__(self, pipeline: TrainingPipeline) -> None:
+        self._pipeline = pipeline
+
+    def predict(self, artifact_id: str, entity: FeatureVector) -> str:
+        artifact = self._pipeline.export(artifact_id)
+        if entity.sensor_id not in artifact.supported_sensors:
+            raise ValueError(
+                f"Sensor '{entity.sensor_id}' wird vom Modell '{artifact_id}' nicht unterstützt."
+            )
+        return f"{artifact_id}:{entity.sensor_id}:{entity.values}"
+
+    def predict_batch(self, artifact_id: str, entities: Sequence[FeatureVector]) -> list[str]:
+        return [self.predict(artifact_id, entity) for entity in entities]
+
+    @staticmethod
+    def default_artifact(pipeline: TrainingPipeline) -> TrainedArtifact:
+        artifacts = list(pipeline._artifacts)
+        if not artifacts:
+            raise ValueError("Kein trainiertes Modell gefunden.")
+        return pipeline.export(artifacts[-1])