ML-009: add explainable predictions

Closes #20

app/ml/predictor.py

@@ -5,6 +5,7 @@ import math
 from dataclasses import dataclass
 from typing import Sequence
 
+from app.ml.explanation import FeatureExplanation, explain_feature
 from app.ml.feature_store import FeatureVector
 from app.ml.registry.model_registry import ModelRegistry
 from app.ml.training import TrainedArtifact, TrainingPipeline
@@ -19,6 +20,7 @@ class PredictionResult:
     predictions: dict[str, float]
     confidence: float
     model_type: str
+    explanations: dict[str, FeatureExplanation]
 
 
 class Predictor:
@@ -52,13 +54,21 @@ class Predictor:
             )
 
         predictions: dict[str, float] = {}
+        explanations: dict[str, FeatureExplanation] = {}
         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)
+            predicted_value = model.forecast(current_value)
+            predictions[feature_name] = predicted_value
+            explanations[feature_name] = explain_feature(
+                feature_name,
+                current_value,
+                predicted_value,
+                model,
+            )
             confidences.append(model.confidence)
 
         return PredictionResult(
@@ -67,6 +77,7 @@ class Predictor:
             predictions=predictions,
             confidence=sum(confidences) / len(confidences),
             model_type=artifact.model_type,
+            explanations=explanations,
         )
 
     def predict_batch(