(y, indicate_one_vs_all=False)
return (y, type_y == "multilabel-indicator") if indicate_one_vs_all else y
Check the target types to be conform to the current samplers.

    The current samplers should be compatible with ``'binary'``,
    ``'multilabel-indicator'`` and ``'multiclass'`` targets only.

    Parameters
    ----------
    y : ndarray
        The array containing the target.

    indicate_one_vs_all : bool, default=False
        Either to indicate if the targets are encoded in a one-vs-all fashion.

    Returns
    -------
    y : ndarray
        The returned target.

    is_one_vs_all : bool, optional
        Indicate if the target was originally encoded in a one-vs-all fashion.
        Only returned if ``indicate_multilabel=True``.
Check the target types to be conform to the current samplers.
Check the target types to be conform to the current samplers .
def check_target_type(y, indicate_one_vs_all=False):
    """Check the target types to be conform to the current samplers.

    The current samplers should be compatible with ``'binary'``,
    ``'multilabel-indicator'`` and ``'multiclass'`` targets only.

    Parameters
    ----------
    y : ndarray
        The array containing the target.

    indicate_one_vs_all : bool, default=False
        Either to indicate if the targets are encoded in a one-vs-all fashion.

    Returns
    -------
    y : ndarray
        The returned target.

    is_one_vs_all : bool, optional
        Indicate if the target was originally encoded in a one-vs-all fashion.
        Only returned if ``indicate_multilabel=True``.
    """
    type_y = type_of_target(y)
    if type_y == "multilabel-indicator":
        if np.any(y.sum(axis=1) > 1):
            raise ValueError(
                "Imbalanced-learn currently supports binary, multiclass and "
                "binarized encoded multiclasss targets. Multilabel and "
                "multioutput targets are not supported."
            )
        y = y.argmax(axis=1)
    else:
        y = column_or_1d(y)

    return (y, type_y == "multilabel-indicator") if indicate_one_vs_all else y
def check_target_type ( y , indicate_one_vs_all = False ) : type_y = type_of_target ( y ) if type_y == "multilabel-indicator" : if np . any ( y . sum ( axis = 1 ) > 1 ) : raise ValueError ( "Imbalanced-learn currently supports binary, multiclass and " "binarized encoded multiclasss targets. Multilabel and " "multioutput targets are not supported." ) y = y . argmax ( axis = 1 ) else : y = column_or_1d ( y ) return ( y , type_y == "multilabel-indicator" ) if indicate_one_vs_all else y