Method inverse_transform

sklearn/preprocessing/_label.py:146–174

Transform labels back to original encoding. Parameters ---------- y : array-like of shape (n_samples,) Target values. Returns ------- y_original : ndarray of shape (n_samples,) Original encoding.

(self, y)

        return _encode(y, uniques=self.classes_)

    def inverse_transform(self, y):
        """Transform labels back to original encoding.

        Parameters
        ----------
        y : array-like of shape (n_samples,)
            Target values.

        Returns
        -------
        y_original : ndarray of shape (n_samples,)
            Original encoding.
        """
        check_is_fitted(self)
        xp, _ = get_namespace(y)
        y = column_or_1d(y, warn=True)
        # inverse transform of empty array is empty array
        if _num_samples(y) == 0:
            return xp.asarray([])

        diff = xpx.setdiff1d(
            y,
            xp.arange(self.classes_.shape[0], device=device(y)),
            xp=xp,
        )
        if diff.shape[0]:
            raise ValueError("y contains previously unseen labels: %s" % str(diff))
        y = xp.asarray(y)
        return xp.take(self.classes_, y, axis=0)

    def __sklearn_tags__(self):
        tags = super().__sklearn_tags__()