ML-002: Trainingspipeline mit Tainted-Data-Check

tests/ml/test_training.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+import pytest
+
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.training import TrainingPipeline, TrainedArtifact
+
+
+def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
+
+
+def store_with_data() -> TrainingPipeline:
+    store = FeatureStore()
+    store.add_batch(
+        [
+            _vector("sensor.kitchen", 19.0),
+            _vector("sensor.kitchen", 20.0),
+            _vector("sensor.bedroom", 18.5),
+        ]
+    )
+    return TrainingPipeline(store)
+
+
+def test_run_returns_trained_artifact() -> None:
+    pipeline = store_with_data()
+    artifact = pipeline.run("artifact_v1")
+    assert artifact.artifact_id == "artifact_v1"
+    assert artifact.supported_sensors == ("sensor.bedroom", "sensor.kitchen")
+
+
+def test_run_without_data_raises_value_error() -> None:
+    pipeline = TrainingPipeline(FeatureStore())
+    with pytest.raises(ValueError):
+        pipeline.run("artifact_v1")
+
+
+def test_export_returns_registered_artifact() -> None:
+    pipeline = store_with_data()
+    pipeline.run("artifact_v1")
+    exported = pipeline.export("artifact_v1")
+    assert exported == pipeline.export("artifact_v1")
+
+
+def test_export_missing_artifact_raises_key_error() -> None:
+    pipeline = store_with_data()
+    with pytest.raises(KeyError):
+        pipeline.export("artifact_v1")