fix(STEF-2854): handle empty input in combiner predict

_predict_quantile now returns empty Series when input data is empty
after inner join, preventing lightgbm from crashing with
'Input data must be 2 dimensional and non empty.'

Signed-off-by: Egor Dmitriev <[email protected]>
Signed-off-by: Egor Dmitriev <[email protected]>

Author: egordm

packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py

@@ -585,14 +585,14 @@ def _fit_combiner(
 
         if val_ensemble_dataset is not None:
             prediction_val = self._predict_combiner(val_ensemble_dataset, features=features_val)
-            metrics_val = self._calculate_score(prediction=prediction_val)
+            metrics_val = self._calculate_score(prediction=prediction_val) if not prediction_val.data.empty else None
         else:
             prediction_val = None
             metrics_val = None
 
         if test_ensemble_dataset is not None:
             prediction_test = self._predict_combiner(test_ensemble_dataset, features=features_test)
-            metrics_test = self._calculate_score(prediction=prediction_test)
+            metrics_test = self._calculate_score(prediction=prediction_test) if not prediction_test.data.empty else None
         else:
             prediction_test = None
             metrics_test = None

packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py

@@ -286,6 +286,8 @@ def _predict_quantile(
         quantile: Quantile,
     ) -> pd.Series:
         input_data = self._prepare_input_data(dataset=dataset, additional_features=additional_features)
+        if input_data.empty:
+            return pd.Series(dtype=float, index=dataset.index)
         weights = self._predict_weights(base_predictions=input_data, quantile=quantile)
 
         if self.hard_selection: