ML-008: add statistical baseline model

Closes #19

app/ml/predictor.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import logging
+import math
+from dataclasses import dataclass
 from typing import Sequence
 
 from app.ml.feature_store import FeatureVector
@@ -10,6 +12,15 @@ from app.ml.training import TrainedArtifact, TrainingPipeline
 logger = logging.getLogger(__name__)
 
 
+@dataclass(frozen=True)
+class PredictionResult:
+    artifact_id: str
+    sensor_id: str
+    predictions: dict[str, float]
+    confidence: float
+    model_type: str
+
+
 class Predictor:
     def __init__(
         self,
@@ -24,15 +35,45 @@ class Predictor:
         self._pipeline = pipeline
         self._registry = registry
 
-    def predict(self, artifact_id: str, entity: FeatureVector) -> str:
+    def predict(self, artifact_id: str, entity: FeatureVector) -> PredictionResult:
         artifact = self._get_artifact(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}"
+        sensor_models = artifact.feature_models.get(entity.sensor_id, {})
+        if not sensor_models:
+            raise ValueError(f"Modell '{artifact_id}' enthält keine statistischen Parameter.")
 
-    def predict_batch(self, artifact_id: str, entities: Sequence[FeatureVector]) -> list[str]:
+        feature_names = sorted(set(sensor_models).intersection(entity.values))
+        if not feature_names:
+            raise ValueError(
+                f"Keine Eingabemerkmale werden vom Modell '{artifact_id}' unterstützt."
+            )
+
+        predictions: dict[str, float] = {}
+        confidences: list[float] = []
+        for feature_name in feature_names:
+            model = sensor_models[feature_name]
+            current_value = float(entity.values[feature_name])
+            if not math.isfinite(current_value):
+                raise ValueError("Vorhersagewerte müssen endlich sein.")
+            predictions[feature_name] = model.forecast(current_value)
+            confidences.append(model.confidence)
+
+        return PredictionResult(
+            artifact_id=artifact_id,
+            sensor_id=entity.sensor_id,
+            predictions=predictions,
+            confidence=sum(confidences) / len(confidences),
+            model_type=artifact.model_type,
+        )
+
+    def predict_batch(
+        self,
+        artifact_id: str,
+        entities: Sequence[FeatureVector],
+    ) -> list[PredictionResult]:
         return [self.predict(artifact_id, entity) for entity in entities]
 
     @staticmethod
@@ -47,4 +88,4 @@ class Predictor:
             return self._registry.load_artifact(artifact_id)
         if self._pipeline is not None:
             return self._pipeline.export(artifact_id)
-        raise RuntimeError("Predictor nicht initialisiert.")
+        raise RuntimeError("Predictor nicht initialisiert.")