0dcba7771fe19ca8910e364f7897ea5f8c52a57b,run_classifier.py,,main,#,14

Before Change

    parser = argparse.ArgumentParser()
    parser.add_argument("doc_codes", type=str, help="path to the doc codes file")
    parser.add_argument("doc_labels", type=str, help="path to the doc labels file")
    parser.add_argument("-ns", "--n_splits", type=int, default=10, help="num of folds")
    parser.add_argument("-ne", "--n_epoch", type=int, default=100, help="num of epoches (default 100)")
    parser.add_argument("-bs", "--batch_size", type=int, default=100, help="batch size (default 100)")
    args = parser.parse_args()

After Change

    train_doc_codes = load_json(args.train_doc_codes)
    train_doc_labels = load_json(args.train_doc_labels)
    test_doc_codes = load_json(args.test_doc_codes)
    test_doc_labels = load_json(args.test_doc_labels)
    X_train = np.r_[train_doc_codes.values()]
    Y_train = [train_doc_labels[i] for i in train_doc_codes.keys()]
    X_test = np.r_[test_doc_codes.values()]
    Y_test = [test_doc_labels[i] for i in test_doc_codes.keys()]


    // // DBN
    // X_train = np.array(load_marshal(args.train_doc_codes))

Instances