(
        self,
        favorable_labels,
        protected_attributes,
        unfavorable_labels=None,
        n_splits=5,
        shuffle=False,
        random_state=None,
    )
Parameters
        ----------
        favorable_labels : array

          Label values which are considered favorable (i.e. "positive").

        protected_attributes : array

          Features for which fairness is desired.

        unfavorable_labels : array or None, default None

          Label values which are considered unfavorable (i.e. "negative").

        n_splits : integer, optional, default 5

          Number of folds. Must be at least 2.

        shuffle : boolean, optional, default False

          Whether to shuffle each class's samples before splitting into batches.

        random_state : union type, not for optimizer, default None

          When shuffle is True, random_state affects the ordering of the indices.

          - None

              RandomState used by np.random

          - numpy.random.RandomState

              Use the provided random state, only affecting other users of that same random state instance.

          - integer

              Explicit seed.
Parameters
        ----------
        favorable_labels : array
Parameters ---------- favorable_labels : array
def __init__(
        self,
        favorable_labels,
        protected_attributes,
        unfavorable_labels=None,
        n_splits=5,
        shuffle=False,
        random_state=None,
    ):
        """
        Parameters
        ----------
        favorable_labels : array

          Label values which are considered favorable (i.e. "positive").

        protected_attributes : array

          Features for which fairness is desired.

        unfavorable_labels : array or None, default None

          Label values which are considered unfavorable (i.e. "negative").

        n_splits : integer, optional, default 5

          Number of folds. Must be at least 2.

        shuffle : boolean, optional, default False

          Whether to shuffle each class's samples before splitting into batches.

        random_state : union type, not for optimizer, default None

          When shuffle is True, random_state affects the ordering of the indices.

          - None

              RandomState used by np.random

          - numpy.random.RandomState

              Use the provided random state, only affecting other users of that same random state instance.

          - integer

              Explicit seed.
        """
        _validate_fairness_info(
            favorable_labels, protected_attributes, unfavorable_labels, True
        )
        self._fairness_info = {
            "favorable_labels": favorable_labels,
            "protected_attributes": protected_attributes,
            "unfavorable_labels": unfavorable_labels,
        }
        self._stratified_k_fold = sklearn.model_selection.StratifiedKFold(
            n_splits=n_splits, shuffle=shuffle, random_state=random_state
        )
def __init__ ( self , favorable_labels , protected_attributes , unfavorable_labels = None , n_splits = 5 , shuffle = False , random_state = None , ) : _validate_fairness_info ( favorable_labels , protected_attributes , unfavorable_labels , True ) self . _fairness_info = { "favorable_labels" : favorable_labels , "protected_attributes" : protected_attributes , "unfavorable_labels" : unfavorable_labels , } self . _stratified_k_fold = sklearn . model_selection . StratifiedKFold ( n_splits = n_splits , shuffle = shuffle , random_state = random_state )