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[python] add type hints for custom objective and metric functions in scikit-learn interface #4547

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Nov 15, 2021
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[python] add type hints for custom objective and metric functions in scikit-learn interface #4547

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a8af94e
[python] add type hints for custom objective and metric functions in …
jameslamb Aug 23, 2021
9d00c4c
merge master
jameslamb Nov 8, 2021
ea1aada
update type hints
jameslamb Nov 8, 2021
f469ccb
remote unnecessary input
jameslamb Nov 8, 2021
07d2170
Merge branch 'master' into function-hints
jameslamb Nov 14, 2021
d39c040
Update python-package/lightgbm/sklearn.py
jameslamb Nov 15, 2021
a2ff6ee
Merge branch 'master' into function-hints
jameslamb Nov 15, 2021
ccca965
remove type hint on objective being callable
jameslamb Nov 15, 2021
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23 changes: 12 additions & 11 deletions python-package/lightgbm/dask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from copy import deepcopy
from enum import Enum, auto
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union
from urllib.parse import urlparse

import numpy as np
Expand All @@ -21,8 +21,9 @@
from .compat import (DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED, Client, LGBMNotFittedError, concat,
dask_Array, dask_array_from_delayed, dask_bag_from_delayed, dask_DataFrame, dask_Series,
default_client, delayed, pd_DataFrame, pd_Series, wait)
from .sklearn import (LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor, _lgbmmodel_doc_custom_eval_note,
_lgbmmodel_doc_fit, _lgbmmodel_doc_predict)
from .sklearn import (LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor, _LGBM_ScikitCustomEvalFunction,
_LGBM_ScikitCustomObjectiveFunction, _lgbmmodel_doc_custom_eval_note, _lgbmmodel_doc_fit,
_lgbmmodel_doc_predict)

_DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series]
_DaskMatrixLike = Union[dask_Array, dask_DataFrame]
Expand Down Expand Up @@ -400,7 +401,7 @@ def _train(
eval_class_weight: Optional[List[Union[dict, str]]] = None,
eval_init_score: Optional[List[_DaskCollection]] = None,
eval_group: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[Union[Callable, str, List[Union[Callable, str]]]] = None,
eval_metric: Optional[Union[_LGBM_ScikitCustomEvalFunction, str, List[Union[_LGBM_ScikitCustomEvalFunction, str]]]] = None,
eval_at: Optional[Iterable[int]] = None,
**kwargs: Any
) -> LGBMModel:
Expand Down Expand Up @@ -1029,7 +1030,7 @@ def _lgb_dask_fit(
eval_class_weight: Optional[List[Union[dict, str]]] = None,
eval_init_score: Optional[List[_DaskCollection]] = None,
eval_group: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[Union[Callable, str, List[Union[Callable, str]]]] = None,
eval_metric: Optional[Union[_LGBM_ScikitCustomEvalFunction, str, List[Union[_LGBM_ScikitCustomEvalFunction, str]]]] = None,
eval_at: Optional[Iterable[int]] = None,
early_stopping_rounds: Optional[int] = None,
**kwargs: Any
Expand Down Expand Up @@ -1096,7 +1097,7 @@ def __init__(
learning_rate: float = 0.1,
n_estimators: int = 100,
subsample_for_bin: int = 200000,
objective: Optional[Union[Callable, str]] = None,
objective: Optional[Union[_LGBM_ScikitCustomObjectiveFunction, str]] = None,
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class_weight: Optional[Union[dict, str]] = None,
min_split_gain: float = 0.,
min_child_weight: float = 1e-3,
Expand Down Expand Up @@ -1165,7 +1166,7 @@ def fit(
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_class_weight: Optional[List[Union[dict, str]]] = None,
eval_init_score: Optional[List[_DaskCollection]] = None,
eval_metric: Optional[Union[Callable, str, List[Union[Callable, str]]]] = None,
eval_metric: Optional[Union[_LGBM_ScikitCustomEvalFunction, str, List[Union[_LGBM_ScikitCustomEvalFunction, str]]]] = None,
early_stopping_rounds: Optional[int] = None,
**kwargs: Any
) -> "DaskLGBMClassifier":
Expand Down Expand Up @@ -1281,7 +1282,7 @@ def __init__(
learning_rate: float = 0.1,
n_estimators: int = 100,
subsample_for_bin: int = 200000,
objective: Optional[Union[Callable, str]] = None,
objective: Optional[Union[_LGBM_ScikitCustomObjectiveFunction, str]] = None,
class_weight: Optional[Union[dict, str]] = None,
min_split_gain: float = 0.,
min_child_weight: float = 1e-3,
Expand Down Expand Up @@ -1348,7 +1349,7 @@ def fit(
eval_names: Optional[List[str]] = None,
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_init_score: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[Union[Callable, str, List[Union[Callable, str]]]] = None,
eval_metric: Optional[Union[_LGBM_ScikitCustomEvalFunction, str, List[Union[_LGBM_ScikitCustomEvalFunction, str]]]] = None,
early_stopping_rounds: Optional[int] = None,
**kwargs: Any
) -> "DaskLGBMRegressor":
Expand Down Expand Up @@ -1446,7 +1447,7 @@ def __init__(
learning_rate: float = 0.1,
n_estimators: int = 100,
subsample_for_bin: int = 200000,
objective: Optional[Union[Callable, str]] = None,
objective: Optional[Union[_LGBM_ScikitCustomObjectiveFunction, str]] = None,
class_weight: Optional[Union[dict, str]] = None,
min_split_gain: float = 0.,
min_child_weight: float = 1e-3,
Expand Down Expand Up @@ -1516,7 +1517,7 @@ def fit(
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_init_score: Optional[List[_DaskVectorLike]] = None,
eval_group: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[Union[Callable, str, List[Union[Callable, str]]]] = None,
eval_metric: Optional[Union[_LGBM_ScikitCustomEvalFunction, str, List[Union[_LGBM_ScikitCustomEvalFunction, str]]]] = None,
eval_at: Iterable[int] = (1, 2, 3, 4, 5),
early_stopping_rounds: Optional[int] = None,
**kwargs: Any
Expand Down
39 changes: 34 additions & 5 deletions python-package/lightgbm/sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,23 +2,52 @@
"""Scikit-learn wrapper interface for LightGBM."""
import copy
from inspect import signature
from typing import Callable, Dict, Optional, Union
from typing import Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import scipy.sparse as ss

from .basic import Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _log_warning
from .callback import log_evaluation, record_evaluation
from .compat import (SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckArray,
_LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMCheckXY, _LGBMClassifierBase,
_LGBMComputeSampleWeight, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase, dt_DataTable,
pd_DataFrame)
pd_DataFrame, pd_Series)
from .engine import train

_ArrayLike = Union[List, np.ndarray, pd_Series]
_EvalResultType = Tuple[str, float, bool]

_LGBM_ScikitCustomObjectiveFunction = Union[
Callable[
[_ArrayLike, np.ndarray],
Tuple[np.ndarray, np.ndarray]
],
Callable[
[_ArrayLike, np.ndarray, np.ndarray],
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Tuple[np.ndarray, np.ndarray]
],
]
_LGBM_ScikitCustomEvalFunction = Union[
Callable[
[_ArrayLike, np.ndarray],
Union[_EvalResultType, List[_EvalResultType]]
],
Callable[
[_ArrayLike, np.ndarray, np.ndarray],
Union[_EvalResultType, List[_EvalResultType]]
],
Callable[
[_ArrayLike, np.ndarray, np.ndarray, np.ndarray],
Union[_EvalResultType, List[_EvalResultType]]
],
]


class _ObjectiveFunctionWrapper:
"""Proxy class for objective function."""

def __init__(self, func):
def __init__(self, func: _LGBM_ScikitCustomObjectiveFunction):
"""Construct a proxy class.

This class transforms objective function to match objective function with signature ``new_func(preds, dataset)``
Expand Down Expand Up @@ -107,7 +136,7 @@ def __call__(self, preds, dataset):
class _EvalFunctionWrapper:
"""Proxy class for evaluation function."""

def __init__(self, func):
def __init__(self, func: _LGBM_ScikitCustomEvalFunction):
"""Construct a proxy class.

This class transforms evaluation function to match evaluation function with signature ``new_func(preds, dataset)``
Expand Down Expand Up @@ -358,7 +387,7 @@ def __init__(
learning_rate: float = 0.1,
n_estimators: int = 100,
subsample_for_bin: int = 200000,
objective: Optional[Union[str, Callable]] = None,
objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,
class_weight: Optional[Union[Dict, str]] = None,
min_split_gain: float = 0.,
min_child_weight: float = 1e-3,
Expand Down