Switch if/else conditions order in fit

microsoft · Dec 18, 2024 · 9a32376 · 9a32376
1 parent 38edc42
commit 9a32376
Showing 1 changed file with 50 additions and 50 deletions.
diff --git a/python-package/lightgbm/sklearn.py b/python-package/lightgbm/sklearn.py
@@ -981,7 +981,56 @@ def fit(
             params=params,
         )
 
-        if self.early_stopping is True and eval_set is None:
+        valid_sets: List[Dataset] = []
+        if eval_set is not None:
+            if isinstance(eval_set, tuple):
+                eval_set = [eval_set]
+            for i, valid_data in enumerate(eval_set):
+                # reduce cost for prediction training data
+                if valid_data[0] is X and valid_data[1] is y:
+                    valid_set = train_set
+                else:
+                    valid_weight = _extract_evaluation_meta_data(
+                        collection=eval_sample_weight,
+                        name="eval_sample_weight",
+                        i=i,
+                    )
+                    valid_class_weight = _extract_evaluation_meta_data(
+                        collection=eval_class_weight,
+                        name="eval_class_weight",
+                        i=i,
+                    )
+                    if valid_class_weight is not None:
+                        if isinstance(valid_class_weight, dict) and self._class_map is not None:
+                            valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()}
+                        valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1])
+                        if valid_weight is None or len(valid_weight) == 0:
+                            valid_weight = valid_class_sample_weight
+                        else:
+                            valid_weight = np.multiply(valid_weight, valid_class_sample_weight)
+                    valid_init_score = _extract_evaluation_meta_data(
+                        collection=eval_init_score,
+                        name="eval_init_score",
+                        i=i,
+                    )
+                    valid_group = _extract_evaluation_meta_data(
+                        collection=eval_group,
+                        name="eval_group",
+                        i=i,
+                    )
+                    valid_set = Dataset(
+                        data=valid_data[0],
+                        label=valid_data[1],
+                        weight=valid_weight,
+                        group=valid_group,
+                        init_score=valid_init_score,
+                        categorical_feature="auto",
+                        params=params,
+                    )
+
+                valid_sets.append(valid_set)
+
+        elif self.early_stopping is True:
             if self.validation_fraction is not None:
                 n_splits = max(int(np.ceil(1 / self.validation_fraction)), 2)
                 stratified = isinstance(self, LGBMClassifier)
@@ -1001,55 +1050,6 @@ def fit(
                 valid_set = train_set
             valid_set = valid_set.construct()
             valid_sets = [valid_set]
-        else:
-            valid_sets: List[Dataset] = []
-            if eval_set is not None:
-                if isinstance(eval_set, tuple):
-                    eval_set = [eval_set]
-                for i, valid_data in enumerate(eval_set):
-                    # reduce cost for prediction training data
-                    if valid_data[0] is X and valid_data[1] is y:
-                        valid_set = train_set
-                    else:
-                        valid_weight = _extract_evaluation_meta_data(
-                            collection=eval_sample_weight,
-                            name="eval_sample_weight",
-                            i=i,
-                        )
-                        valid_class_weight = _extract_evaluation_meta_data(
-                            collection=eval_class_weight,
-                            name="eval_class_weight",
-                            i=i,
-                        )
-                        if valid_class_weight is not None:
-                            if isinstance(valid_class_weight, dict) and self._class_map is not None:
-                                valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()}
-                            valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1])
-                            if valid_weight is None or len(valid_weight) == 0:
-                                valid_weight = valid_class_sample_weight
-                            else:
-                                valid_weight = np.multiply(valid_weight, valid_class_sample_weight)
-                        valid_init_score = _extract_evaluation_meta_data(
-                            collection=eval_init_score,
-                            name="eval_init_score",
-                            i=i,
-                        )
-                        valid_group = _extract_evaluation_meta_data(
-                            collection=eval_group,
-                            name="eval_group",
-                            i=i,
-                        )
-                        valid_set = Dataset(
-                            data=valid_data[0],
-                            label=valid_data[1],
-                            weight=valid_weight,
-                            group=valid_group,
-                            init_score=valid_init_score,
-                            categorical_feature="auto",
-                            params=params,
-                        )
-
-                    valid_sets.append(valid_set)
 
         if isinstance(init_model, LGBMModel):
             init_model = init_model.booster_