This function is used to read 3 kinds of data into list, 3 kinds of data are stored in files given by parameter
    :param test_file: path string, the testing set used for SVm rank
    :param predict_file_lst: filename lst, all prediction file output by SVM rank
    :param nonfeature_file: path string, contain all the score data not used as feature (aligned with test_file)
    :return: None

def load_dataset_items(test_file, predict_file_lst, nonfeature_file):
    """
    This function is used to read 3 kinds of data into list, 3 kinds of data are stored in files given by parameter
    :param test_file: path string, the testing set used for SVm rank
    :param predict_file_lst: filename lst, all prediction file output by SVM rank
    :param nonfeature_file: path string, contain all the score data not used as feature (aligned with test_file)
    :return: None
    """
    print 'Reading baseline feature & bleu...'
    with open(test_file, 'r') as reader:
        for line in reader:
            items = line.split(' ')
            label = float(items[0])

            id_list.append(items[1])
            bleu_list.append(label)
            word_count_list.append(float(items[2].split(':')[1]))
            attri_count_list.append(float(items[10].split(':')[1]))

    print 'Reading svm rankscore...'
    global prediction_dict
    for predict_file in predict_file_lst:
        mark = predict_file.replace('predictions', '')
        prediction_dict[mark] = []
        with open(result_file_path + predict_file, 'r') as reader:
            for line in reader:
                rankscore = float(line)
                prediction_dict[mark].append(rankscore)

    print 'Reading NonFeature score...'
    with open(nonfeature_file, 'r') as reader:
        for line in reader:
            nonfeature_items = line.split()
            w_score = float(nonfeature_items[2].split(':')[1])
            m_score = float(nonfeature_items[3].split(':')[1])
            weighted_attri_list.append(w_score)
            meteor_score_list.append(m_score)