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

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merged 6 commits into from
Sep 13, 2023
Merged

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

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918a8e4
[python-package] clarify internal handling of eval result tuples
jameslamb Sep 13, 2023
9759f79
more type-hint stuff
jameslamb Sep 13, 2023
352b612
revert list changes
jameslamb Sep 13, 2023
16ab431
revert basic.py changes
jameslamb Sep 13, 2023
77fc927
remove whitespace changes
jameslamb Sep 13, 2023
c5f63ad
remove now-unused typing.Tuple import
jameslamb Sep 13, 2023
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1 change: 1 addition & 0 deletions python-package/lightgbm/basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@
_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[
Expand Down
11 changes: 6 additions & 5 deletions python-package/lightgbm/callback.py
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Expand Up @@ -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
Expand All @@ -20,11 +21,11 @@
_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]
]


Expand Down Expand Up @@ -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:
Expand Down
12 changes: 6 additions & 6 deletions python-package/lightgbm/engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,9 @@

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__ = [
Expand Down Expand Up @@ -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] = {}
Expand All @@ -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(
Expand Down