fssqawj / classification_task

STST for NLPCC 2017 Task2

./stst/data_tools

1. define the sent object

• get_sent
• get_label **required in model **
• get_instance_string **required in model **

2. load the data

• load data
• load parse data

./features/features_unigram.py

1. Some Modification

• extract idf during train, and load the dict during dev and test
• extract can return str object (for sparse features), but must return the dimension in the first info list Next is a example of the Unigram Feature.

 class TFFeature(Feature):

   def __init__(self, type='word', convey='count', **kwargs):
       super(TFFeature, self).__init__(**kwargs)
       self.type = type
       self.convey = convey
       self.feature_name = '{}-{}#{}'.format(self.feature_name, type, self.convey)

   def extract_information(self, train_instances):
       if self.is_training:
           sents = []
           for train_instance in train_instances:
               sent = train_instance.get_sent(self.type)
               sents.append(sent)
           idf_dict = utils.idf_calculator(sents)
           with utils.create_write_file(config.DICTIONARY_DIR + '/{}_idf_dict.txt'.format(self.type)) as fw:
               idf_dict_tuple = sorted(idf_dict.items(), key=lambda x: x[1], reverse=True)
               for key, value in idf_dict_tuple:
                   print('{}\t{}'.format(key, value), file=fw)
       else:
           with utils.create_read_file(config.DICTIONARY_DIR + '/{}_idf_dict.txt'.format(self.type)) as fr:
               idf_dict = {}
               for line in fr:
                   line = line.strip().split('\t')
                   idf_dict[line[0]] = float(line[1])
       self.unigram_dict = idf_dict
       word_keys = sorted(idf_dict.keys(), reverse=True)
       self.word2index = {word: i for i, word in enumerate(word_keys)}

   def extract(self, train_instance):
       sent = train_instance.get_sent(self.type)
       feats = utils.sparse_vectorize(sent, self.unigram_dict, self.word2index, self.convey)
       infos = [len(self.unigram_dict), 'tf']
       feats = Feature._feat_dict_to_string(feats)
       return feats, infos

main_lr.py

main_stack.py

• Idea:

  1. 将数据分成5折，四份做train，一份做test
  2. 将得到的每一份test进行合并，得到数据集大小的用于stack的训练数据stack_train
  3. 训练stack的分类器

• Steps: 1和2. => model.cross_validation, 写入model.output_file 3. => model.train() 训练一个分类器

  在测试阶段，将用5份数据训练好的1级分类器测试得到第一部分结果，再通过二级分类器得到最终结果, model.test会自动完成:

  elif isinstance(feature_class, Model):
       if dev:
           feature_class.test(train_instances, train_file)
           feature_string = feature_class.load_model_score(train_file)
       else:
           ''' seperate to train for speed up '''
           # feature_class.train(train_instances, train_file)
           feature_string = feature_class.load_model_score(train_file)
  

Other tools

• ./eval.py gold_file predict_file
• ./error_analysis.py gold_file predict_file output_file 按csv格式保存到output_file
• ./statistic.py 用于数据统计
• ./test.py 将生成的prob变成json格式
• ./test_super.py 将最后的结果进行ensemble，并输出

./data

数据集为NLPCC 2017 Shared Task2 官方数据: https://github.com/FudanNLP/nlpcc2017_news_headline_categorization 我们整理的数据: 百度云盘(176MB) http://pan.baidu.com/s/1nu8hUhb