code: def load_dataset_items(test_file, predict_file_lst, nonfeature_file):
      
      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)
docs: 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