ai-content-maker/.venv/Lib/site-packages/sklearn/model_selection/__init__.py

89 lines
2.3 KiB
Python

import typing
from ._plot import LearningCurveDisplay, ValidationCurveDisplay
from ._search import GridSearchCV, ParameterGrid, ParameterSampler, RandomizedSearchCV
from ._split import (
BaseCrossValidator,
BaseShuffleSplit,
GroupKFold,
GroupShuffleSplit,
KFold,
LeaveOneGroupOut,
LeaveOneOut,
LeavePGroupsOut,
LeavePOut,
PredefinedSplit,
RepeatedKFold,
RepeatedStratifiedKFold,
ShuffleSplit,
StratifiedGroupKFold,
StratifiedKFold,
StratifiedShuffleSplit,
TimeSeriesSplit,
check_cv,
train_test_split,
)
from ._validation import (
cross_val_predict,
cross_val_score,
cross_validate,
learning_curve,
permutation_test_score,
validation_curve,
)
if typing.TYPE_CHECKING:
# Avoid errors in type checkers (e.g. mypy) for experimental estimators.
# TODO: remove this check once the estimator is no longer experimental.
from ._search_successive_halving import ( # noqa
HalvingGridSearchCV,
HalvingRandomSearchCV,
)
__all__ = [
"BaseCrossValidator",
"BaseShuffleSplit",
"GridSearchCV",
"TimeSeriesSplit",
"KFold",
"GroupKFold",
"GroupShuffleSplit",
"LeaveOneGroupOut",
"LeaveOneOut",
"LeavePGroupsOut",
"LeavePOut",
"RepeatedKFold",
"RepeatedStratifiedKFold",
"ParameterGrid",
"ParameterSampler",
"PredefinedSplit",
"RandomizedSearchCV",
"ShuffleSplit",
"StratifiedKFold",
"StratifiedGroupKFold",
"StratifiedShuffleSplit",
"check_cv",
"cross_val_predict",
"cross_val_score",
"cross_validate",
"learning_curve",
"LearningCurveDisplay",
"permutation_test_score",
"train_test_split",
"validation_curve",
"ValidationCurveDisplay",
]
# TODO: remove this check once the estimator is no longer experimental.
def __getattr__(name):
if name in {"HalvingGridSearchCV", "HalvingRandomSearchCV"}:
raise ImportError(
f"{name} is experimental and the API might change without any "
"deprecation cycle. To use it, you need to explicitly import "
"enable_halving_search_cv:\n"
"from sklearn.experimental import enable_halving_search_cv"
)
raise AttributeError(f"module {__name__} has no attribute {name}")