Python dictionary.Dictionary方法代碼示例

# 需要導入模塊: import dictionary [as 別名]
# 或者: from dictionary import Dictionary [as 別名]
def load_rel_separated_dictionary(filename):
    rel1_dict = Dictionary()
    rel1_dict.add_unk_token()
    rel1_dict.add_pad_token()
    rel2_dict = Dictionary()
    rel2_dict.add_unk_token()
    rel2_dict.add_pad_token()
    with open(filename) as f:
        for line in f:
            if not line:break
            line = line.strip()
            if not line:continue
            line = line.split('.')
            rel1 = '.'.join(line[:-1])
            rel2 = line[-1]
            rel1_dict.add(rel1)
            rel2_dict.add(rel2)
    return rel1_dict, rel2_dict 

# 需要導入模塊: import dictionary [as 別名]
# 或者: from dictionary import Dictionary [as 別名]
def creat_word_rel_dict(r_file, *q_files):
    word_dict = Dictionary()
    word_dict.add_unk_token()
    word_dict.add_pad_token()
    word_dict.add_start_token()

    for q_file in q_files:
        qa_data = pickle.load(open(q_file, 'rb'))
        for data in qa_data:
            q = data.question
            tokens = q.split(' ')
            for token in tokens:
                word_dict.add(token)
    print(len(word_dict))

    rels = pickle.load(open(r_file, 'rb'))
    for rel in rels:
        rel_word = []
        w = rel[3:].split('.')
        for i in w:
            rel_word.extend(i.split('_'))
        for word in rel_word:
            word_dict.add(word)
    print(len(word_dict))
    return word_dict 

# 需要導入模塊: import dictionary [as 別名]
# 或者: from dictionary import Dictionary [as 別名]
def get_postag_data(config, train_path, dev_path, vocab_path=None, label_path=None):
  use_se_marker = config.use_se_marker
  raw_train_sents = get_sentences(train_path, use_se_marker)
  raw_dev_sents = get_sentences(dev_path, use_se_marker)
  word_to_embeddings = get_pretrained_embeddings(WORD_EMBEDDINGS[config.word_embedding])
  
  # Prepare word dictionary.
  word_dict = Dictionary(unknown_token=UNKNOWN_TOKEN)
  if use_se_marker:
    word_dict.add_all([START_MARKER, END_MARKER])
  if vocab_path != None:
    with open(vocab_path, 'r') as f_vocab:
      for line in f_vocab:
        word_dict.add(line.strip())
      f_vocab.close()
    word_dict.accept_new = False
    print 'Load {} words. Dictionary freezed.'.format(word_dict.size())
  
  # Parpare label dictionary.
  label_dict = Dictionary() 
  if label_path != None:
    with open(label_path, 'r') as f_labels:
      for line in f_labels:
        label_dict.add(line.strip())
      f_labels.close()
    label_dict.set_unknown_token(UNKNOWN_LABEL)
    label_dict.accept_new = False
    print 'Load {} labels. Dictionary freezed.'.format(label_dict.size())

  train_sents = [(string_sequence_to_ids(sent[0], word_dict, True, word_to_embeddings),
          string_sequence_to_ids(sent[1], label_dict)) for sent in raw_train_sents]
  dev_sents = [(string_sequence_to_ids(sent[0], word_dict, True, word_to_embeddings),
          string_sequence_to_ids(sent[1], label_dict)) for sent in raw_dev_sents]

  print("Extracted {} words and {} tags".format(word_dict.size(), label_dict.size()))
  print("Max training sentence length: {}".format(max([len(s[0]) for s in train_sents])))
  print("Max development sentence length: {}".format(max([len(s[0]) for s in dev_sents])))
  word_embedding = [word_to_embeddings[w] for w in word_dict.idx2str]
  word_embedding_shape = [len(word_embedding), len(word_embedding[0])]
  return (train_sents, dev_sents, word_dict, label_dict, [word_embedding], [word_embedding_shape]) 

# 需要導入模塊: import dictionary [as 別名]
# 或者: from dictionary import Dictionary [as 別名]
def load_word_dictionary(filename, word_dict=None):
    if word_dict is None:
        word_dict = Dictionary()
        word_dict.add_unk_token()
        word_dict.add_pad_token()
    with open(filename) as f:
        for line in f:
            if not line:break
            line = line.strip()
            if not line:continue
            word_dict.add(line)
    return word_dict