'Transform labels back to original encoding.
Parameters
y : numpy array of shape [n_samples]
Target values.
Returns
y : numpy array of shape [n_samples]'

def inverse_transform(self, y):

    check_is_fitted(self, 'classes_')
    diff = np.setdiff1d(y, np.arange(len(self.classes_)))
    if diff:
        raise ValueError(('y    contains    new    labels:    %s' % str(diff)))
    y = np.asarray(y)
    return self.classes_[y]
