[python-package] fix mypy errors related to eval result tuples (#6097)

python-package/lightgbm/basic.py

@@ -54,6 +54,7 @@ _ctypes_float_array = Union[
 _LGBM_EvalFunctionResultType = Tuple[str, float, bool]
 _LGBM_BoosterBestScoreType = Dict[str, Dict[str, float]]
 _LGBM_BoosterEvalMethodResultType = Tuple[str, str, float, bool]
+_LGBM_BoosterEvalMethodResultWithStandardDeviationType = Tuple[str, str, float, bool, float]
 _LGBM_CategoricalFeatureConfiguration = Union[List[str], List[int], "Literal['auto']"]
 _LGBM_FeatureNameConfiguration = Union[List[str], "Literal['auto']"]
 _LGBM_GroupType = Union[

python-package/lightgbm/callback.py

@@ -3,9 +3,10 @@
 from collections import OrderedDict
 from dataclasses import dataclass
 from functools import partial
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
 
-from .basic import Booster, _ConfigAliases, _LGBM_BoosterEvalMethodResultType, _log_info, _log_warning
+from .basic import (Booster, _ConfigAliases, _LGBM_BoosterEvalMethodResultType,
+                    _LGBM_BoosterEvalMethodResultWithStandardDeviationType, _log_info, _log_warning)
 
 if TYPE_CHECKING:
     from .engine import CVBooster
@@ -20,11 +21,11 @@ __all__ = [
 _EvalResultDict = Dict[str, Dict[str, List[Any]]]
 _EvalResultTuple = Union[
     _LGBM_BoosterEvalMethodResultType,
-    Tuple[str, str, float, bool, float]
+    _LGBM_BoosterEvalMethodResultWithStandardDeviationType
 ]
 _ListOfEvalResultTuples = Union[
     List[_LGBM_BoosterEvalMethodResultType],
-    List[Tuple[str, str, float, bool, float]]
+    List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]
 ]
 
 
@@ -54,7 +55,7 @@ class CallbackEnv:
     iteration: int
     begin_iteration: int
     end_iteration: int
-    evaluation_result_list: Optional[List[_LGBM_BoosterEvalMethodResultType]]
+    evaluation_result_list: Optional[_ListOfEvalResultTuples]
 
 
 def _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str:

python-package/lightgbm/engine.py

@@ -11,9 +11,9 @@ import numpy as np
 
 from . import callback
 from .basic import (Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _InnerPredictor,
-                    _LGBM_BoosterEvalMethodResultType, _LGBM_CategoricalFeatureConfiguration,
-                    _LGBM_CustomObjectiveFunction, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration,
-                    _log_warning)
+                    _LGBM_BoosterEvalMethodResultType, _LGBM_BoosterEvalMethodResultWithStandardDeviationType,
+                    _LGBM_CategoricalFeatureConfiguration, _LGBM_CustomObjectiveFunction, _LGBM_EvalFunctionResultType,
+                    _LGBM_FeatureNameConfiguration, _log_warning)
 from .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold
 
 __all__ = [
@@ -519,8 +519,8 @@ def _make_n_folds(
 
 
 def _agg_cv_result(
-    raw_results: List[List[Tuple[str, str, float, bool]]]
-) -> List[Tuple[str, str, float, bool, float]]:
+    raw_results: List[List[_LGBM_BoosterEvalMethodResultType]]
+) -> List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]:
     """Aggregate cross-validation results."""
     cvmap: Dict[str, List[float]] = OrderedDict()
     metric_type: Dict[str, bool] = {}
@@ -530,7 +530,7 @@ def _agg_cv_result(
             metric_type[key] = one_line[3]
             cvmap.setdefault(key, [])
             cvmap[key].append(one_line[2])
-    return [('cv_agg', k, np.mean(v), metric_type[k], np.std(v)) for k, v in cvmap.items()]
+    return [('cv_agg', k, float(np.mean(v)), metric_type[k], float(np.std(v))) for k, v in cvmap.items()]
 
 
 def cv(