feat(ml): enregistrer le modèle dans le MLflow Model Registry

ml/enervision_ml/train.py

@@ -181,7 +181,11 @@ def _log_to_mlflow(
         )
         mlflow.log_metrics({f"model_{cle}": valeur for cle, valeur in model_metrics.items()})
         mlflow.log_metrics({f"baseline_{cle}": valeur for cle, valeur in baseline_metrics.items()})
-        mlflow.lightgbm.log_model(booster, name="model")
+        mlflow.lightgbm.log_model(
+            booster,
+            name="model",
+            registered_model_name="consumption-forecast-lightgbm",
+        )
         mlflow.log_artifact(str(model_output))
 
 

ml/tests/test_train.py

@@ -3,6 +3,7 @@ from pathlib import Path
 
 import numpy as np
 import pandas as pd
+import pytest
 
 from enervision_ml.features import TARGET_COLUMN, build_features, feature_columns
 from enervision_ml.train import chronological_split, prepare_dataset, train
@@ -74,3 +75,18 @@ def test_train_runs_end_to_end_on_synthetic_data_and_beats_a_dummy_baseline(
     assert model_metrics["n_observations"] > 0
     assert model_metrics["mae"] >= 0
     assert baseline_metrics["n_observations"] == model_metrics["n_observations"]
+    assert model_metrics["mae"] < baseline_metrics["mae"]
+
+def test_train_raises_when_the_validation_window_is_empty(tmp_path: Path) -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    frame = make_frame("site-a", heures=50, depart=depart)  # trop court pour un lag de 168h
+    csv_path = tmp_path / "trop_court.csv"
+    frame.to_csv(csv_path, index=False)
+
+    with pytest.raises(ValueError, match="Fenetre d'entrainement ou de validation vide"):
+        train(
+            csv_path=csv_path,
+            model_output=tmp_path / "model.txt",
+            test_fraction=0.2,
+            tracking_uri=f"sqlite:///{tmp_path / 'mlflow.db'}",
+        )