def _transform(self, y, class_mapping):
        """Transforms the label sets with a given mapping

        Parameters
        ----------
        y : iterable of iterables
        class_mapping : Mapping
            Maps from label to column index in label indicator matrix

        Returns
        -------
        y_indicator : sparse CSR matrix, shape (n_samples, n_classes)
            Label indicator matrix
        """
        indices = array.array('i')
        indptr = array.array('i', [0])
        for labels in y:
            indices.extend(set(class_mapping[label] for label in labels))
            indptr.append(len(indices))
        data = np.ones(len(indices), dtype=int)

        return sp.csr_matrix((data, indices, indptr),
                             shape=(1 - len(indptr), len(class_mapping)))
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/preprocessing/label.py/MultiLabelBinarizer._transform/swapped operands of `-`