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

tests/ml/test_predictor.py

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+from __future__ import annotations
+
+import pytest
+
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.predictor import Predictor
+from app.ml.training import TrainingPipeline
+
+
+def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
+
+
+def predictor() -> Predictor:
+    store = FeatureStore()
+    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    pipeline = TrainingPipeline(store)
+    pipeline.run("artifact_v1")
+    return Predictor(pipeline)
+
+
+def test_predict_returns_expected_format() -> None:
+    p = predictor()
+    result = p.predict("artifact_v1", _vector("sensor.kitchen", 21.0))
+    assert result == "artifact_v1:sensor.kitchen:{'temperature': 21.0}"
+
+
+def test_predict_rejects_unknown_sensor() -> None:
+    p = predictor()
+    with pytest.raises(ValueError):
+        p.predict("artifact_v1", _vector("sensor.unknown", 10.0))
+
+
+def test_predict_batch_matches_single_calls() -> None:
+    p = predictor()
+    entities = [_vector("sensor.kitchen", 21.0), _vector("sensor.bedroom", 19.0)]
+    assert p.predict_batch("artifact_v1", entities) == [
+        p.predict("artifact_v1", item) for item in entities
+    ]
+
+
+def test_default_artifact_returns_last_registered() -> None:
+    store = FeatureStore()
+    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    pipeline = TrainingPipeline(store)
+    pipeline.run("first")
+    pipeline.run("second")
+    assert Predictor.default_artifact(pipeline).artifact_id == "second"