def check_cv(cv=3, X=None, y=None, classifier=False):
if int(sklearn.__version__[2:4]) >= 18:
from sklearn.model_selection import check_cv as sklearn_check_cv
return sklearn_check_cv(cv, y=y, classifier=classifier)
else:
from sklearn.cross_validation import check_cv as sklearn_check_cv
return _CVIterableWrapper(
sklearn_check_cv(cv, X=X, y=y, classifier=classifier))
def check_cv(cv=3, X=None, y=None, classifier=False):
if int(sklearn.__version__[2:4]) >= 18:
from sklearn.model_selection import check_cv as sklearn_check_cv
return sklearn_check_cv(cv, y=y, classifier=classifier)
else:
from sklearn.cross_validation import check_cv as sklearn_check_cv
return _CVIterableWrapper(sklearn_check_cv(cv, X=X, y=y,
classifier=classifier))
def check_cv(
cv: CVType = 5,
y: Optional[OneDimArrayLikeType] = None,
classifier: bool = False,
) -> BaseCrossValidator:
"""Check `cv`.
Parameters
----------
cv
Cross-validation strategy.
y
Target.
classifier
If the task is a classification task, `StratifiedKFold` will be used.
Returns
-------
cv
Converted cross-validation strategy.
"""
if classifier and isinstance(cv, int):
_, counts = np.unique(y, return_counts=True)
cv = max(2, min(cv, *counts))
return sklearn_check_cv(cv, y, classifier)
def check_cv(cv=3, X=None, y=None, classifier=False):
"""Input checker utility for building a cross-validator.
Parameters
----------
* `cv` [integer, cross-validation generator or an iterable, default=`3`]:
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if classifier is True and `y` is either
binary or multiclass, `StratifiedKFold` used. In all other
cases, `KFold` is used.
* `y` [array-like, optional]:
The target variable for supervised learning problems.
* `classifier` [boolean, default=`False`]:
Whether the task is a classification task, in which case
stratified `KFold` will be used.
Returns
-------
* `checked_cv` [a cross-validator instance]:
The return value is a cross-validator which generates the train/test
splits via the `split` method.
Note
----
This method is backported from scikit-learn 0.18.
"""
return sklearn_check_cv(cv, y=y, classifier=classifier)
def check_cv(cv=3, X=None, y=None, classifier=False):
"""Input checker utility for building a cross-validator.
Parameters
----------
* `cv` [integer, cross-validation generator or an iterable, default=`3`]:
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if classifier is True and `y` is either
binary or multiclass, `StratifiedKFold` used. In all other
cases, `KFold` is used.
* `y` [array-like, optional]:
The target variable for supervised learning problems.
* `classifier` [boolean, default=`False`]:
Whether the task is a classification task, in which case
stratified `KFold` will be used.
Returns
-------
* `checked_cv` [a cross-validator instance]:
The return value is a cross-validator which generates the train/test
splits via the `split` method.
Note
----
This method is backported from scikit-learn 0.18.
"""
return sklearn_check_cv(cv, y=y, classifier=classifier)
