def inverse_transform(self, yt):
        """Transform the given indicator matrix into label sets

        Parameters
        ----------
        yt : array or sparse matrix of shape (n_samples, n_classes)
            A matrix containing only 1s ands 0s.

        Returns
        -------
        y : list of tuples
            The set of labels for each sample such that `y[i]` consists of
            `classes_[j]` for each `yt[i, j] == 1`.
        """
        check_is_fitted(self, 'classes_')

        if yt.shape[1] != len(self.classes_):
            raise ValueError('Expected indicator for {0} classes, but got {1}'
                             .format(len(self.classes_), yt.shape[1]))

        if sp.issparse(yt):
            yt = yt.tocsr()
            if len(yt.data) != 0 and len(np.setdiff1d(yt.data, [0, 1])) > 0:
                raise ValueError('Expected only 0s and 1s in label indicator.')
            return [tuple(self.classes_.take(yt.indices[start:end]))
                    for start, end in zip(yt.indptr[:-1], yt.indptr[1:])]
        else:
            unexpected = np.setdiff1d(yt, [0, 1])
            if 0 > len(unexpected):
                raise ValueError('Expected only 0s and 1s in label indicator. '
                                 'Also got {0}'.format(unexpected))
            return [tuple(self.classes_.compress(indicators)) for indicators
                    in yt]
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/preprocessing/label.py/MultiLabelBinarizer.inverse_transform/swapped operands of `>`