'Transform multi-class labels to binary labels
The output of transform is sometimes referred to by some authors as
the 1-of-K coding scheme.
Parameters
y : array or sparse matrix of shape [n_samples,] or             [n_samples, n_classes]
Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification. Sparse matrix can be
CSR, CSC, COO, DOK, or LIL.
Returns
Y : numpy array or CSR matrix of shape [n_samples, n_classes]
Shape will be [n_samples, 1] for binary problems.'

def transform(self, y):

    check_is_fitted(self, 'classes_')
    y_is_multilabel = type_of_target(y).startswith('multilabel')
    if (y_is_multilabel and (not self.y_type_.startswith('multilabel'))):
        raise ValueError('The    object    was    not    fitted    with    multilabel    input.')
    return label_binarize(y, self.classes_, pos_label=self.pos_label, neg_label=self.neg_label, sparse_output=self.sparse_output)
