def _inverse_binarize_multiclass(y, classes):
    """Inverse label binarization transformation for multiclass.

    Multiclass uses the maximal score instead of a threshold.
    """
    classes = np.asarray(classes)

    if sp.issparse(y):
        # Find the argmax for each row in y where y is a CSR matrix

        y = y.tocsr()
        n_samples, n_outputs = y.shape
        outputs = np.arange(n_outputs)
        row_max = sparse_min_max(y, 1)[1]
        row_nnz = np.diff(y.indptr)

        y_data_repeated_max = np.repeat(row_max, row_nnz)
        # picks out all indices obtaining the maximum per row
        y_i_all_argmax = np.flatnonzero(y_data_repeated_max == y.data)

        # For corner case where last row has a max of 0
        if row_max[-1] == 0:
            y_i_all_argmax = np.append(y_i_all_argmax, [len(y.data)])

        # Gets the index of the first argmax in each row from y_i_all_argmax
        index_first_argmax = np.searchsorted(y_i_all_argmax, y.indptr[:-1])
        # first argmax of each row
        y_ind_ext = np.append(y.indices, [0])
        y_i_argmax = y_ind_ext[y_i_all_argmax[index_first_argmax]]
        # Handle rows of all 0
        y_i_argmax[np.where(row_nnz == 0)[0]] = 0

        # Handles rows with max of 0 that contain negative numbers
        samples = np.arange(n_samples)[(row_nnz > 0) &
                                       (row_max.ravel() == 0)]
        for i in samples:
            ind = y.indices[y.indptr[i]:y.indptr[i + 1]]
            y_i_argmax[i] = classes[np.setdiff1d(outputs, ind)][0]

        return classes[y_i_argmax]
    else:
        return classes.take(y.argmax(axis=1), mode="clip")
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/preprocessing/label.py/_inverse_binarize_multiclass/original