TypeError:“（slice（None，None，None），0）”是无效键

Question

我使用Python2.7进行特性选择，数据集加载良好，但当我运行代码时，它会给我这个错误

if __name__ == '__main__':
    def generic_combined_scorer(x1, o1, ii_1, x2, o2, ii_2, y, h):
        s1 = h(x1, y)
        s2 = h(x2, y)
        o1[ii_1] = s1
        o2[ii_2] = s2

    NUM_CV = 3
    RANDOM_SEED = 123
    MAX_ITER = 1000

    # leuk = fetch_mldata('iris', transpose_data=True)

    X = pd.read_csv(r'C:\Users\pc\Desktop\dataset\leukemia.csv')
    y = pd.read_csv(r'C:\Users\pc\Desktop\dataset\leukemia.csv')


    initial_scores = Parallel(n_jobs=n_jobs)(delayed(estimator)(X.iloc[:, 1], y.iloc[:, 1]) for ii in range(num_dim))
    # rank the scores in descending order
    sorted_scores_idxs = np.flipud(np.argsort(initial_scores))

    selected_feature_idxs = np.zeros(num_selected_features, dtype=int)
    remaining_candidate_idxs = range(1, K_MAX_internal)

    # mi_matrix = np.empty((K_MAX_internal,num_selected_features-1))
    # mi_matrix[:] = np.nan

    relevance_vec_fname = os.path.join(tmp_folder, 'relevance_vec')
    feature_redundance_vec_fname = os.path.join(tmp_folder, 'feature_redundance_vec')
    mi_matrix_fname = os.path.join(tmp_folder, 'mi_matrix')
    relevance_vec = np.memmap(relevance_vec_fname, dtype=float,
                              shape=(K_MAX_internal,), mode='w+')
    feature_redundance_vec = np.memmap(feature_redundance_vec_fname, dtype=float,
                                       shape=(K_MAX_internal,), mode='w+')
    mi_matrix = np.memmap(mi_matrix_fname, dtype=float,
                          shape=(K_MAX_internal, num_selected_features - 1), mode='w+')
    mi_matrix[:] = np.nan

    # TODO: investigate whether its worth it to parallelize the nested for-loop?
    with tqdm(total=num_selected_features, desc='Selecting Features ...', disable=(not verbose)) as pbar:
        pbar.update(1)
        for k in range(1, num_selected_features):
            ncand = len(remaining_candidate_idxs)
            last_selected_feature = k - 1

            Parallel(n_jobs=n_jobs)(delayed(generic_combined_scorer)(y, relevance_vec, ii,
                                                                     X_subset[:, selected_feature_idxs[last_selected_feature]],
                                                                     feature_redundance_vec, ii, X_subset.iloc[:, ii],
                                                                     estimator)for ii in remaining_candidate_idxs)

            # copy the redundance into the mi_matrix, which accumulates our redundance as we compute
            mi_matrix[remaining_candidate_idxs, last_selected_feature] = feature_redundance_vec[remaining_candidate_idxs]
            redundance_vec = np.nanmean(mi_matrix[remaining_candidate_idxs, :], axis=1)

            tmp_idx = np.argmax(relevance_vec[remaining_candidate_idxs] - redundance_vec)
            selected_feature_idxs[k] = remaining_candidate_idxs[tmp_idx]
            del remaining_candidate_idxs[tmp_idx]
            pbar.update(1)

^{pr2}$