[python] remove workaround for `UnboundLocalError` in early stopping callback for Python 2 (#4855)

python-package/lightgbm/callback.py

@@ -220,13 +220,19 @@ def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbos
     best_iter = []
     best_score_list: list = []
     cmp_op = []
-    enabled = [True]
-    first_metric = ['']
+    enabled = True
+    first_metric = ''
 
     def _init(env: CallbackEnv) -> None:
-        enabled[0] = not any(env.params.get(boost_alias, "") == 'dart' for boost_alias
-                             in _ConfigAliases.get("boosting"))
-        if not enabled[0]:
+        nonlocal best_score
+        nonlocal best_iter
+        nonlocal best_score_list
+        nonlocal cmp_op
+        nonlocal enabled
+        nonlocal first_metric
+        enabled = not any(env.params.get(boost_alias, "") == 'dart' for boost_alias
+                          in _ConfigAliases.get("boosting"))
+        if not enabled:
             _log_warning('Early stopping is not available in dart mode')
             return
         if not env.evaluation_result_list:
@@ -263,7 +269,7 @@ def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbos
             deltas = [min_delta] * n_datasets * n_metrics
 
         # split is needed for "<dataset type> <metric>" case (e.g. "train l1")
-        first_metric[0] = env.evaluation_result_list[0][1].split(" ")[-1]
+        first_metric = env.evaluation_result_list[0][1].split(" ")[-1]
         for eval_ret, delta in zip(env.evaluation_result_list, deltas):
             best_iter.append(0)
             best_score_list.append(None)
@@ -275,6 +281,8 @@ def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbos
                 cmp_op.append(partial(_lt_delta, delta=delta))
 
     def _final_iteration_check(env: CallbackEnv, eval_name_splitted: List[str], i: int) -> None:
+        nonlocal best_iter
+        nonlocal best_score_list
         if env.iteration == env.end_iteration - 1:
             if verbose:
                 best_score_str = '\t'.join([_format_eval_result(x) for x in best_score_list[i]])
@@ -285,9 +293,15 @@ def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbos
             raise EarlyStopException(best_iter[i], best_score_list[i])
 
     def _callback(env: CallbackEnv) -> None:
+        nonlocal best_score
+        nonlocal best_iter
+        nonlocal best_score_list
+        nonlocal cmp_op
+        nonlocal enabled
+        nonlocal first_metric
         if not cmp_op:
             _init(env)
-        if not enabled[0]:
+        if not enabled:
             return
         for i in range(len(env.evaluation_result_list)):
             score = env.evaluation_result_list[i][2]
@@ -297,7 +311,7 @@ def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbos
                 best_score_list[i] = env.evaluation_result_list
             # split is needed for "<dataset type> <metric>" case (e.g. "train l1")
             eval_name_splitted = env.evaluation_result_list[i][1].split(" ")
-            if first_metric_only and first_metric[0] != eval_name_splitted[-1]:
+            if first_metric_only and first_metric != eval_name_splitted[-1]:
                 continue  # use only the first metric for early stopping
             if ((env.evaluation_result_list[i][0] == "cv_agg" and eval_name_splitted[0] == "train"
                  or env.evaluation_result_list[i][0] == env.model._train_data_name)):