(self, X, y, groups=None)
return result
Generate indices to split data into training and test set.

        X : array **of** items : array **of** items : Any

            Training data, including columns with the protected attributes.

        y : union type

            Target class labels; the array is over samples.

            - array **of** items : float

            - array **of** items : string

        groups : Any

            Always ignored, exists for compatibility.

        Yields
        ------
        result : tuple

            - train

                The training set indices for that split.

            - test

                The testing set indices for that split.
Generate indices to split data into training and test set.
Generate indices to split data into training and test set .
def split(self, X, y, groups=None):
        """
        Generate indices to split data into training and test set.

        X : array **of** items : array **of** items : Any

            Training data, including columns with the protected attributes.

        y : union type

            Target class labels; the array is over samples.

            - array **of** items : float

            - array **of** items : string

        groups : Any

            Always ignored, exists for compatibility.

        Yields
        ------
        result : tuple

            - train

                The training set indices for that split.

            - test

                The testing set indices for that split.
        """
        stratify = _column_for_stratification(X, y, **self._fairness_info)
        result = self._stratified_k_fold.split(X, stratify, groups)
        return result
def split ( self , X , y , groups = None ) : stratify = _column_for_stratification ( X , y , * * self . _fairness_info ) result = self . _stratified_k_fold . split ( X , stratify , groups ) return result