def load_data(cell_line, cross_cell_line, label_rate, k_mer):
    '\n    Load input data from data/cell_line directory.\n\n    | x_20.index   | the indices (IDs) of labeled train instances as list object (for label_rate = 20%) |\n    | ux_20.index  | the indices (IDs) of unlabeled train instances as list object (for label_rate = 20%) |\n    | vx_20.index  | the indices (IDs) of validation instances as list object (for label_rate = 20%) |\n    | tx_20.index  | the indices (IDs) of test instances as list object (for label_rate = 20%) |\n    | features_5mer | the feature vectors of all instances as scipy.sparse.csr.csr_matrix object (for k_mer = 5) |\n    | nodes         | a dict in the format {chromosome_name: ID} as collections.defaultdict object |\n    | labels        | the one-hot labels of all instances as numpy.ndarray object |\n    | graph         | a dict in the format {ID: [IDs_of_neighbor_nodes]} as collections.defaultdict object |\n\n    All objects above must be saved using python pickle module.\n\n    :param cell_line: Name of the cell line to which the datasets belong\n    :return: All data input files loaded (as well the training/test data).\n    '
    if ((cross_cell_line != None) and (cross_cell_line != cell_line)):
        read_dir = 'data/{}_{}/'.format(cell_line, cross_cell_line)
    else:
        read_dir = 'data/{}/'.format(cell_line)
    features_file = open('{}/features_{}mer'.format(read_dir, k_mer), 'rb')
    features = pkl.load(features_file)
    features_file.close()
    labels_file = open('{}/labels'.format(read_dir), 'rb')
    labels = pkl.load(labels_file)
    labels_file.close()
    graph_file = open('{}/graph'.format(read_dir), 'rb')
    graph = pkl.load(graph_file)
    graph_file.close()
    adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
    lr = txt = '{:.2f}'.format(label_rate).split('.')[1]
    idx_x_file = open('{}/x_{}.index'.format(read_dir, lr), 'rb')
    idx_x = pkl.load(idx_x_file)
    idx_x_file.close()
    idx_ux_file = open('{}/ux_{}.index'.format(read_dir, lr), 'rb')
    idx_ux = pkl.load(idx_ux_file)
    idx_ux_file.close()
    idx_vx_file = open('{}/vx_{}.index'.format(read_dir, lr), 'rb')
    idx_vx = pkl.load(idx_vx_file)
    idx_vx_file.close()
    idx_tx_file = open('{}/tx_{}.index'.format(read_dir, lr), 'rb')
    idx_tx = pkl.load(idx_tx_file)
    idx_tx_file.close()
    x = features[idx_x]
    y = labels[idx_x]
    ux = features[idx_ux]
    uy = labels[idx_ux]
    vx = features[idx_vx]
    vy = labels[idx_vx]
    tx = features[idx_tx]
    ty = labels[idx_tx]
    print('x={} ux={} vx={} tx={}'.format(x.shape[0], ux.shape[0], vx.shape[0], tx.shape[0]))
    train_mask = sample_mask(idx_x, labels.shape[0])
    val_mask = sample_mask(idx_vx, labels.shape[0])
    test_mask = sample_mask(idx_tx, labels.shape[0])
    y_train = np.zeros(labels.shape)
    y_val = np.zeros(labels.shape)
    y_test = np.zeros(labels.shape)
    y_train[train_mask, :] = labels[train_mask, :]
    y_val[val_mask, :] = labels[val_mask, :]
    y_test[test_mask, :] = labels[test_mask, :]
    return (adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask)
Load input data from data/cell_line directory.

| x_20.index   | the indices (IDs) of labeled train instances as list object (for label_rate = 20%) |
| ux_20.index  | the indices (IDs) of unlabeled train instances as list object (for label_rate = 20%) |
| vx_20.index  | the indices (IDs) of validation instances as list object (for label_rate = 20%) |
| tx_20.index  | the indices (IDs) of test instances as list object (for label_rate = 20%) |
| features_5mer | the feature vectors of all instances as scipy.sparse.csr.csr_matrix object (for k_mer = 5) |
| nodes         | a dict in the format {chromosome_name: ID} as collections.defaultdict object |
| labels        | the one-hot labels of all instances as numpy.ndarray object |
| graph         | a dict in the format {ID: [IDs_of_neighbor_nodes]} as collections.defaultdict object |

All objects above must be saved using python pickle module.

:param cell_line: Name of the cell line to which the datasets belong
:return: All data input files loaded (as well the training/test data).
def load_data(cell_line, cross_cell_line, label_rate, k_mer):
    '\n    Load input data from data/cell_line directory.\n\n    | x_20.index   | the indices (IDs) of labeled train instances as list object (for label_rate = 20%) |\n    | ux_20.index  | the indices (IDs) of unlabeled train instances as list object (for label_rate = 20%) |\n    | vx_20.index  | the indices (IDs) of validation instances as list object (for label_rate = 20%) |\n    | tx_20.index  | the indices (IDs) of test instances as list object (for label_rate = 20%) |\n    | features_5mer | the feature vectors of all instances as scipy.sparse.csr.csr_matrix object (for k_mer = 5) |\n    | nodes         | a dict in the format {chromosome_name: ID} as collections.defaultdict object |\n    | labels        | the one-hot labels of all instances as numpy.ndarray object |\n    | graph         | a dict in the format {ID: [IDs_of_neighbor_nodes]} as collections.defaultdict object |\n\n    All objects above must be saved using python pickle module.\n\n    :param cell_line: Name of the cell line to which the datasets belong\n    :return: All data input files loaded (as well the training/test data).\n    '
    if ((cross_cell_line != None) and (cross_cell_line != cell_line)):
        read_dir = 'data/{}_{}/'.format(cell_line, cross_cell_line)
    else:
        read_dir = 'data/{}/'.format(cell_line)
    features_file = open('{}/features_{}mer'.format(read_dir, k_mer), 'rb')
    features = pkl.load(features_file)
    features_file.close()
    labels_file = open('{}/labels'.format(read_dir), 'rb')
    labels = pkl.load(labels_file)
    labels_file.close()
    graph_file = open('{}/graph'.format(read_dir), 'rb')
    graph = pkl.load(graph_file)
    graph_file.close()
    adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
    lr = txt = '{:.2f}'.format(label_rate).split('.')[1]
    idx_x_file = open('{}/x_{}.index'.format(read_dir, lr), 'rb')
    idx_x = pkl.load(idx_x_file)
    idx_x_file.close()
    idx_ux_file = open('{}/ux_{}.index'.format(read_dir, lr), 'rb')
    idx_ux = pkl.load(idx_ux_file)
    idx_ux_file.close()
    idx_vx_file = open('{}/vx_{}.index'.format(read_dir, lr), 'rb')
    idx_vx = pkl.load(idx_vx_file)
    idx_vx_file.close()
    idx_tx_file = open('{}/tx_{}.index'.format(read_dir, lr), 'rb')
    idx_tx = pkl.load(idx_tx_file)
    idx_tx_file.close()
    x = features[idx_x]
    y = labels[idx_x]
    ux = features[idx_ux]
    uy = labels[idx_ux]
    vx = features[idx_vx]
    vy = labels[idx_vx]
    tx = features[idx_tx]
    ty = labels[idx_tx]
    print('x={} ux={} vx={} tx={}'.format(x.shape[0], ux.shape[0], vx.shape[0], tx.shape[0]))
    train_mask = sample_mask(idx_x, labels.shape[0])
    val_mask = sample_mask(idx_vx, labels.shape[0])
    test_mask = sample_mask(idx_tx, labels.shape[0])
    y_train = np.zeros(labels.shape)
    y_val = np.zeros(labels.shape)
    y_test = np.zeros(labels.shape)
    y_train[train_mask, :] = labels[train_mask, :]
    y_val[val_mask, :] = labels[val_mask, :]
    y_test[test_mask, :] = labels[test_mask, :]
    return (adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask)