[python-package] reorganize early stopping callback

python-package/lightgbm/callback.py

@@ -229,7 +229,12 @@ def __call__(self, env: CallbackEnv) -> None:
             if new_param != env.params.get(key, None):
                 new_parameters[key] = new_param
         if new_parameters:
-            env.model.reset_parameter(new_parameters)
+            if isinstance(env.model, Booster):
+                env.model.reset_parameter(new_parameters)
+            else:
+                # CVBooster holds a list of Booster objects, each needs to be updated
+                for i in range(len(env.model.boosters)):
+                    env.model.boosters[i].reset_parameter(new_parameters)
             env.params.update(new_parameters)
 
 
@@ -291,32 +296,49 @@ def _gt_delta(self, curr_score: float, best_score: float, delta: float) -> bool:
     def _lt_delta(self, curr_score: float, best_score: float, delta: float) -> bool:
         return curr_score < best_score - delta
 
-    def _is_train_set(self, ds_name: str, eval_name: str, train_name: str) -> bool:
-        return (ds_name == "cv_agg" and eval_name == "train") or ds_name == train_name
+    def _is_train_set(self, ds_name: str, eval_name: str, env: CallbackEnv) -> bool:
+        """Check, by name, if a given Dataset is the training data"""
+        # for lgb.cv() with eval_train_metric=True, evaluation is also done on the training set
+        # and those metrics are considered for early stopping
+        if ds_name == "cv_agg" and eval_name == "train":
+            return True
+
+        # for lgb.train(), it's possible to pass the training data via valid_sets with any eval_name
+        if isinstance(env.model, Booster):
+            if ds_name == env.model._train_data_name:
+                return True
+
+        return False
 
     def _init(self, env: CallbackEnv) -> None:
         if env.evaluation_result_list is None or env.evaluation_result_list == []:
             raise ValueError(
                 "For early stopping, at least one dataset and eval metric is required for evaluation"
             )
+
+        if self.stopping_rounds <= 0:
+            raise ValueError(f"stopping_rounds should be greater than zero. got: {self.stopping_rounds}")
+
         is_dart = any(env.params.get(alias, "") == 'dart' for alias in _ConfigAliases.get("boosting"))
-        only_train_set = (
-            len(env.evaluation_result_list) == 1
-            and self._is_train_set(
-                ds_name=env.evaluation_result_list[0][0],
-                eval_name=env.evaluation_result_list[0][1].split(" ")[0],
-                train_name=env.model._train_data_name)
-        )
-        self.enabled = not is_dart and not only_train_set
-        if not self.enabled:
-            if is_dart:
-                _log_warning('Early stopping is not available in dart mode')
-            elif only_train_set:
-                _log_warning('Only training set found, disabling early stopping.')
+        if is_dart:
+            self.enabled = False
+            _log_warning('Early stopping is not available in dart mode')
             return
 
-        if self.stopping_rounds <= 0:
-            raise ValueError("stopping_rounds should be greater than zero.")
+        # validation sets are guaranteed to not be identical to the training data in cv()
+        if isinstance(env.model, Booster):
+            only_train_set = (
+                len(env.evaluation_result_list) == 1
+                and self._is_train_set(
+                    ds_name=env.evaluation_result_list[0][0],
+                    eval_name=env.evaluation_result_list[0][1].split(" ")[0],
+                    env=env
+                )
+            )
+            if only_train_set:
+                self.enabled = False
+                _log_warning('Only training set found, disabling early stopping.')
+                return
 
         if self.verbose:
             _log_info(f"Training until validation scores don't improve for {self.stopping_rounds} rounds")
@@ -395,7 +417,11 @@ def __call__(self, env: CallbackEnv) -> None:
             eval_name_splitted = env.evaluation_result_list[i][1].split(" ")
             if self.first_metric_only and self.first_metric != eval_name_splitted[-1]:
                 continue  # use only the first metric for early stopping
-            if self._is_train_set(env.evaluation_result_list[i][0], eval_name_splitted[0], env.model._train_data_name):
+            if self._is_train_set(
+                ds_name=env.evaluation_result_list[i][0],
+                eval_name=eval_name_splitted[0],
+                env=env
+            ):
                 continue  # train data for lgb.cv or sklearn wrapper (underlying lgb.train)
             elif env.iteration - self.best_iter[i] >= self.stopping_rounds:
                 if self.verbose: