data_fetch.py 文件源码

def fetch_from_config(cfg):
    data_set_name = cfg['fetch']['name']
    if cfg['fetch'].getboolean('sklearn'):
        if data_set_name == 'OLIVETTI':
            data_set = skd.fetch_olivetti_faces(shuffle=True)
        else:
            data_set = skd.fetch_mldata(data_set_name)
        X, y = data_set.data, data_set.target
        if data_set_name == 'MNIST original':
            if cfg['pre_process'].getboolean('normalize'):
                X = X / 255.
    else:
        if data_set_name == 'LETTERS':
            X, y = fetch_load_letters()
        elif data_set_name == 'ISOLET':
            x_tr, x_te, y_tr, y_te = fetch_load_isolet()
        elif data_set_name == 'SHREC14':
            X, y = load_shrec14(real=cfg['fetch']['real'], desc=cfg['fetch']['desc'])
            X = prep.normalize(X, norm=cfg['pre_process']['norm'])
        else:
            raise NameError('No data set {} found!'.format(data_set_name))

    # Separate training and testing set
    if data_set_name == 'MNIST original':
        x_tr, x_te, y_tr, y_te = X[:60000], X[60000:], y[:60000], y[60000:]
    elif data_set_name != 'ISOLET':
        test_size = cfg['train_test'].getfloat('test_size')
        x_tr, x_te, y_tr, y_te = train_test_split(X, y, test_size=test_size, stratify=y)

    return x_tr, x_te, y_tr, y_te