ML-004: Training-Feedback und Evaluation-Metriken

app/ml/evaluation.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Sequence
+
+from app.ml.feature_store import FeatureVector
+from app.ml.training import TrainingPipeline, TrainedArtifact
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Metric:
+    name: str
+    value: float
+    threshold: float | None = None
+
+
+@dataclass
+class EvalReport:
+    artifact_id: str
+    sample_size: int
+    metrics: list[Metric]
+
+
+class Evaluator:
+    def __init__(self, pipeline: TrainingPipeline) -> None:
+        self._pipeline = pipeline
+
+    def evaluate(self, artifact_id: str, predictions: Sequence[str]) -> EvalReport:
+        artifacts = list(self._pipeline._artifacts)
+        if not artifacts:
+            raise ValueError("Kein trainiertes Modell für Evaluation vorhanden.")
+
+        supported_sensors = self._pipeline.export(artifact_id).supported_sensors
+        unknown_hits = sum(1 for prediction in predictions if ":" not in prediction)
+        supported_references = sum(1 for sensor in supported_sensors for prediction in predictions if sensor in prediction)
+        sample_size = len(predictions)
+        coverage = supported_references / sample_size if sample_size else 0.0
+        unknown_rate = unknown_hits / sample_size if sample_size else 0.0
+
+        coverage_metric = Metric(name="coverage", value=coverage, threshold=0.8)
+        unknown_metric = Metric(name="unknown_rate", value=unknown_rate, threshold=0.1)
+
+        report = EvalReport(
+            artifact_id=artifact_id,
+            sample_size=sample_size,
+            metrics=[coverage_metric, unknown_metric],
+        )
+        logger.info(
+            "Evaluation %s -> coverage=%.2f, unknown_rate=%.2f",
+            artifact_id,
+            coverage,
+            unknown_rate,
+        )
+        return report