model_selection.py 文件源码

def check_cv(cv=3, y=None, classifier=False):
    """Dask aware version of ``sklearn.model_selection.check_cv``

    Same as the scikit-learn version, but works if ``y`` is a dask object.
    """
    if cv is None:
        cv = 3

    # If ``cv`` is not an integer, the scikit-learn implementation doesn't
    # touch the ``y`` object, so passing on a dask object is fine
    if not is_dask_collection(y) or not isinstance(cv, numbers.Integral):
        return model_selection.check_cv(cv, y, classifier)

    if classifier:
        # ``y`` is a dask object. We need to compute the target type
        target_type = delayed(type_of_target, pure=True)(y).compute()
        if target_type in ('binary', 'multiclass'):
            return StratifiedKFold(cv)
    return KFold(cv)