slice 16g: LightGBM objective=mape for sap_score + peui_ucl

Per ADR-0008: the v15 baseline reports MAPE but optimises MSE, which
under-weights tail rows. Switching to objective='mape' applies gradient
proportional to 1/|y| and lets the model focus where MAPE penalises.

Targets co2_emissions, space_heating_kwh, hot_water_kwh, and peui_raw
retain the default 'regression' objective (some rows have ~zero CO2 from
heavy PV; MAPE objective destabilises near zero).

Sample weights deferred to slice 16i if slice 16h's per-decile residuals
still show tail bias after the objective switch.

services/ml_training_data/src/ml_training_data/train_baseline.py

@@ -26,6 +26,15 @@ from ml_training_data.storage import Storage
 
 _CERT_NUM_COLUMN = "certificate_number"
 
+# Per-target LightGBM objective overrides (ADR-0008, slice 16g).  Defaults to
+# 'regression' (MSE); we use 'mape' for sap_score and peui_ucl because the
+# default MSE under-weights tail rows relative to the MAPE we report.
+# co2_emissions cannot use 'mape' safely (some rows are ~0 from heavy PV).
+_OBJECTIVE_OVERRIDES: dict[str, str] = {
+    "sap_score": "mape",
+    "peui_ucl": "mape",
+}
+
 
 def train_baseline(
     df: pd.DataFrame,
@@ -58,7 +67,10 @@ def train_baseline(
         )
         x_train, x_test, y_train, y_test = split
 
-        model: Any = lgb.LGBMRegressor(n_estimators=n_estimators, random_state=seed, verbose=-1)
+        objective = _OBJECTIVE_OVERRIDES.get(target, "regression")
+        model: Any = lgb.LGBMRegressor(
+            n_estimators=n_estimators, random_state=seed, verbose=-1, objective=objective,
+        )
         model.fit(x_train, y_train)
         preds: np.ndarray[Any, Any] = np.asarray(model.predict(x_test))
 

services/ml_training_data/tests/unit/test_train_baseline.py

@@ -122,6 +122,34 @@ def test_train_baseline_writes_per_decile_residuals_per_target(tmp_path: Path) -
     assert true_mins == sorted(true_mins)
 
 
+def test_train_baseline_uses_mape_objective_for_sap_score_and_peui_ucl(tmp_path: Path) -> None:
+    # Arrange — sap_score + peui_ucl should use objective="mape" per ADR-0008.
+    # We can't directly inspect LGBMRegressor.objective post-fit reliably, so
+    # instead we verify the per-target override map is wired and that training
+    # completes (LightGBM raises if the objective name is unknown).
+    storage = LocalStorage(root=tmp_path)
+    df = _synthetic_dataset(n=300)
+    df["peui_ucl"] = df["sap_score"].astype(float) + 5.0
+
+    # Act
+    metrics = train_baseline(
+        df=df,
+        targets=["sap_score", "peui_ucl"],
+        storage=storage,
+        run_key="runs/2026-05-16/",
+        seed=42,
+    )
+
+    # Assert — both targets fit successfully under the mape objective.
+    assert "sap_score" in metrics
+    assert "peui_ucl" in metrics
+    # Verify the override map is present and contains both targets.
+    from ml_training_data.train_baseline import _OBJECTIVE_OVERRIDES  # noqa: PLC0415
+    assert _OBJECTIVE_OVERRIDES.get("sap_score") == "mape"
+    assert _OBJECTIVE_OVERRIDES.get("peui_ucl") == "mape"
+    assert _OBJECTIVE_OVERRIDES.get("co2_emissions") is None
+
+
 def test_train_baseline_residuals_emitted_per_target_independently(tmp_path: Path) -> None:
     # Arrange
     storage = LocalStorage(root=tmp_path)