python类load_data()的实例源码

def ge_cmd_learn():
    args = parse_arg_learn()

    # prepare input to GE_learn
    data = GE_data()
    data.dat = util.load_data(args.data)
    data.labeled_features = util.load_labeled_features(args.labeled_features)
    init_model = GE_model()
    param = GE_param()
    if args.l2:
        param.l2_regularization = args.l2
    final_model_path = args.model

    # print data

    final_model = GE_learn(data, init_model, param)
    util.save_model(final_model, final_model_path)
    return

# parse arguments, get data and model, output prediction

def describe(name):
    """
    Describe the dataset
    """
    df = load_data(name)
    s = df.groupby(level=[0, 1]).size()
    print('Dataset               :', name)
    print('Users                 :', len(s.groupby(level=0)))
    print('Sessions/user         :', s.groupby(level=0).size().mean())
    print('Sample size           :', s.mean(), '+/-', s.std())
    print('Mean pp interval (ms) :',
          df.groupby(level=[0, 1]).apply(lambda x: x['timepress'].diff().dropna().mean()).mean())
    print('Mean duration (ms)    :',
          df.groupby(level=[0, 1]).apply(lambda x: (x['timerelease'] - x['timepress']).mean()).mean())

    for target in TARGETS[1:]:
        s = df.reset_index().groupby([target, 'session']).size().groupby(level=0).size()
        print(target)
        print(s / s.sum())
    return

def obfuscate_keystrokes(name, strategy, param):
    """

    """
    df = load_data(name)
    df = df.groupby(level=[0, 1]).apply(keystrokes2events).reset_index(level=[2, 3], drop=True)

    if strategy == 'delay':
        df = df.groupby(level=[0, 1]).apply(lambda x: delay_mix(x, param))
    elif strategy == 'interval':
        df = df.groupby(level=[0, 1]).apply(lambda x: interval_mix(x, param))
    else:
        raise Exception('Unknown masking strategy')

    df = df.groupby(level=[0, 1]).apply(events2keystrokes).reset_index(level=[2, 3], drop=True)
    save_data(df, name, masking=(strategy, param))
    return

def main():
    # img_width, img_height = 48, 48
    img_width, img_height = 200, 60
    img_channels = 1 
    # batch_size = 1024
    batch_size = 32
    nb_epoch = 1000
    post_correction = False

    save_dir = 'save_model/' + str(datetime.now()).split('.')[0].split()[0] + '/' # model is saved corresponding to the datetime
    train_data_dir = 'train_data/ip_train/'
    # train_data_dir = 'train_data/single_1000000/'
    val_data_dir = 'train_data/ip_val/'
    test_data_dir = 'test_data//'
    weights_file_path = 'save_model/2016-10-27/weights.11-1.58.hdf5'
    char_set, char2idx = get_char_set(train_data_dir)
    nb_classes = len(char_set)
    max_nb_char = get_maxnb_char(train_data_dir)
    label_set = get_label_set(train_data_dir)
    # val 'char_set:', char_set
    print 'nb_classes:', nb_classes
    print 'max_nb_char:', max_nb_char
    print 'size_label_set:', len(label_set)
    model = build_shallow(img_channels, img_width, img_height, max_nb_char, nb_classes) # build CNN architecture
    # model.load_weights(weights_file_path) # load trained model

    val_data = load_data(val_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
    # val_data = None 
    train_data = load_data(train_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx) 
    train(model, batch_size, nb_epoch, save_dir, train_data, val_data, char_set)

    # train_data = load_data(train_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
    # test(model, train_data, char_set, label_set, post_correction)
    # val_data = load_data(val_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
    # test(model, val_data, char_set, label_set, post_correction)
    # test_data = load_data(test_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
    # test(model, test_data, char_set, label_set, post_correction)

def ge_cmd_predict():
    args = parse_arg_predict()

    # prepare input to GE_learn
    data = util.load_data(args.data)
    model = util.load_model(args.model)
    pred_path = args.output

    pred = GE_predict(data, model)
    util.write_prediction(pred, pred_path)
    return

def load_data(x_path, y_path, shuffle=True):
    xs, ys = load_dataset_csv(x_path, y_path)

    n = len(xs)
    shuffle_indices = range(n)
    np.random.shuffle(shuffle_indices)

    return xs, ys, n

def preprocess_villani(in_file, out_file, long_fixed_out_file):
    """
    Preprocess the raw Villani dataset and extend the long fixed dataset
    """
    df = pd.read_csv(in_file, index_col=[0, 1])

    # Make age a binary target, <30 and >=30
    df['age'] = df['agegroup'].map({
        'under20': '<30',
        '20-29': '<30',
        '30-39': '>=30',
        '40-49': '>=30',
        '50-59': '>=30',
        'over60': '>=30'}
    )

    # Ignore missing data
    df = df.dropna()
    df = remove_repeated_keys(df)

    # combine the villani fixed text with citefa dataset fixed text
    long_fixed = load_data('long_fixed')
    slf = long_fixed.groupby(level=[0, 1]).size()

    villani_fixed = df[df['inputtype'] == 'fixed']
    villani_fixed = villani_fixed.groupby(level=[0, 1]).apply(lambda x: make_sessions(x, slf.mean(), slf.std()))
    villani_fixed = villani_fixed.reset_index(level=[0, 1], drop=True)
    villani_fixed = reduce_dataset(villani_fixed, min_samples=10, max_samples=10)

    long_fixed = pd.concat([long_fixed, villani_fixed])
    long_fixed = long_fixed[COLS]
    long_fixed.to_csv(long_fixed_out_file)

    # Free-text input only
    villani_free = df[df['inputtype'] == 'free']
    villani_free = villani_free.groupby(level=[0, 1]).apply(lambda x: make_sessions(x, slf.mean(), slf.std()))
    villani_free = villani_free.reset_index(level=[0, 1], drop=True)

    villani_free = reduce_dataset(villani_free, min_samples=10, max_samples=10)
    villani_free = villani_free[COLS]
    villani_free.to_csv(out_file)
    return