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[ci] [python-package] tell mypy 'auto' has a special meaning for feature_name and categorical_feature #5874

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merged 5 commits into from
May 10, 2023
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[ci] [python-package] tell mypy 'auto' has a special meaning for feature_name and categorical_feature #5874

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4108c1c
[ci] [python-package] tell mypy 'auto' has a special meaning for feat…
jameslamb May 8, 2023
e62f8d8
isort
jameslamb May 8, 2023
7922149
Merge branch 'master' into ci/mypy-literals
jameslamb May 9, 2023
51ab03a
put branch back
jameslamb May 9, 2023
ceef1f6
with type ignore
jameslamb May 9, 2023
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11 changes: 7 additions & 4 deletions python-package/lightgbm/basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,17 @@
from os.path import getsize
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union

import numpy as np
import scipy.sparse

from .compat import PANDAS_INSTALLED, concat, dt_DataTable, pd_CategoricalDtype, pd_DataFrame, pd_Series
from .libpath import find_lib_path

if TYPE_CHECKING:
from typing import Literal

__all__ = [
'Booster',
'Dataset',
Expand Down Expand Up @@ -49,8 +52,8 @@
_LGBM_EvalFunctionResultType = Tuple[str, float, bool]
_LGBM_BoosterBestScoreType = Dict[str, Dict[str, float]]
_LGBM_BoosterEvalMethodResultType = Tuple[str, str, float, bool]
_LGBM_CategoricalFeatureConfiguration = Union[List[str], List[int], str]
_LGBM_FeatureNameConfiguration = Union[List[str], str]
_LGBM_CategoricalFeatureConfiguration = Union[List[str], List[int], "Literal['auto']"]
_LGBM_FeatureNameConfiguration = Union[List[str], "Literal['auto']"]
_LGBM_GroupType = Union[
List[float],
List[int],
Expand Down Expand Up @@ -688,7 +691,7 @@ def _data_from_pandas(
if categorical_feature == 'auto': # use cat cols from DataFrame
categorical_feature = cat_cols_not_ordered
else: # use cat cols specified by user
categorical_feature = list(categorical_feature)
categorical_feature = list(categorical_feature) # type: ignore[assignment]
if feature_name == 'auto':
feature_name = list(data.columns)
_check_for_bad_pandas_dtypes(data.dtypes)
Expand Down