Transform labels to normalized encoding.

        Parameters
        ----------
        y : array-like of shape [n_samples]
            Target values.

        Returns
        -------
        y : array-like of shape [n_samples]

def transform(self, y):
        """Transform labels to normalized encoding.

        Parameters
        ----------
        y : array-like of shape [n_samples]
            Target values.

        Returns
        -------
        y : array-like of shape [n_samples]
        """
        check_is_fitted(self, 'classes_')
        y = column_or_1d(y, warn=True)

        classes = np.unique(y)
        _check_numpy_unicode_bug(classes)
        if len(np.intersect1d(classes, self.classes_)) < len(classes):
            diff = np.setdiff1d(classes, self.classes_)
            raise ValueError("y contains new labels: %s" % str(diff))
        return np.searchsorted(self.classes_, y)