[python-package] fix type annotations for eval result tracking (#5793)

python-package/lightgbm/callback.py

@@ -15,6 +15,10 @@ __all__ = [
 
 _EvalResultDict = Dict[str, Dict[str, List[Any]]]
 _EvalResultTuple = Union[
+    _LGBM_BoosterEvalMethodResultType,
+    Tuple[str, str, float, bool, float]
+]
+_ListOfEvalResultTuples = Union[
     List[_LGBM_BoosterEvalMethodResultType],
     List[Tuple[str, str, float, bool, float]]
 ]
@@ -23,7 +27,7 @@ _EvalResultTuple = Union[
 class EarlyStopException(Exception):
     """Exception of early stopping."""
 
-    def __init__(self, best_iteration: int, best_score: _EvalResultTuple) -> None:
+    def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) -> None:
         """Create early stopping exception.
 
         Parameters
@@ -55,7 +59,7 @@ def _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str:
         return f"{value[0]}'s {value[1]}: {value[2]:g}"
     elif len(value) == 5:
         if show_stdv:
-            return f"{value[0]}'s {value[1]}: {value[2]:g} + {value[4]:g}"
+            return f"{value[0]}'s {value[1]}: {value[2]:g} + {value[4]:g}"  # type: ignore[misc]
         else:
             return f"{value[0]}'s {value[1]}: {value[2]:g}"
     else:
@@ -256,7 +260,7 @@ class _EarlyStoppingCallback:
     def _reset_storages(self) -> None:
         self.best_score: List[float] = []
         self.best_iter: List[int] = []
-        self.best_score_list: List[Union[_EvalResultTuple, None]] = []
+        self.best_score_list: List[_ListOfEvalResultTuples] = []
         self.cmp_op: List[Callable[[float, float], bool]] = []
         self.first_metric = ''
 
@@ -327,7 +331,6 @@ class _EarlyStoppingCallback:
         self.first_metric = env.evaluation_result_list[0][1].split(" ")[-1]
         for eval_ret, delta in zip(env.evaluation_result_list, deltas):
             self.best_iter.append(0)
-            self.best_score_list.append(None)
             if eval_ret[3]:  # greater is better
                 self.best_score.append(float('-inf'))
                 self.cmp_op.append(partial(self._gt_delta, delta=delta))
@@ -350,11 +353,16 @@ class _EarlyStoppingCallback:
             self._init(env)
         if not self.enabled:
             return
+        # self.best_score_list is initialized to an empty list
+        first_time_updating_best_score_list = (self.best_score_list == [])
         for i in range(len(env.evaluation_result_list)):
             score = env.evaluation_result_list[i][2]
-            if self.best_score_list[i] is None or self.cmp_op[i](score, self.best_score[i]):
+            if first_time_updating_best_score_list or self.cmp_op[i](score, self.best_score[i]):
                 self.best_score[i] = score
                 self.best_iter[i] = env.iteration
+                if first_time_updating_best_score_list:
+                    self.best_score_list.append(env.evaluation_result_list)
+                else:
                     self.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(" ")

python-package/lightgbm/engine.py

@@ -11,8 +11,9 @@ import numpy as np
 
 from . import callback
 from .basic import (Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _InnerPredictor,
-                    _LGBM_CategoricalFeatureConfiguration, _LGBM_CustomObjectiveFunction, _LGBM_EvalFunctionResultType,
-                    _LGBM_FeatureNameConfiguration, _log_warning)
+                    _LGBM_BoosterEvalMethodResultType, _LGBM_CategoricalFeatureConfiguration,
+                    _LGBM_CustomObjectiveFunction, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration,
+                    _log_warning)
 from .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold
 
 __all__ = [
@@ -257,7 +258,7 @@ def train(
 
         booster.update(fobj=fobj)
 
-        evaluation_result_list = []
+        evaluation_result_list: List[_LGBM_BoosterEvalMethodResultType] = []
         # check evaluation result.
         if valid_sets is not None:
             if is_valid_contain_train: