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import string
import jieba
from tqdm import tqdm
import json
import numpy as np
stop_words = ['！', '……', '？', '的', '了', '嗯', '哦', '啊', '我', '你',
'她', '他', '它', '在', '和', '吗', '呢', '可以', '，', '。',
'：', '；']
def cut_sentence_by_word(sentence, stopwords):
"""
按照单个字进行分词，需要处理单个英文字
:param sentence:
:param stopwords:
:return:word_list
"""
continue_words = string.ascii_lowercase + string.digits
temp = ""
result = []
for word in sentence:
if word in continue_words:
temp += word
continue
if len(temp) > 0:
result.append(temp)
temp = ""
result.append(word)
if len(temp) > 0:
result.append(temp)
return [word for word in result if word not in stopwords]
def cut_sentence(sentence, stopwords):
"""
按照词语进行分词
:param sentence:
:param stopwords:
:return: words_list
"""
return [word for word in jieba.lcut(sentence) if word not in stopwords]
def transfer_file_to_list(file_path):
"""
:param file_path:
:return:
"""
with open(file_path, 'r', encoding='utf-8') as f:
lines = [line.rstrip().lower() for line in f]
return lines
class TxtToIndex(object):
def __init__(self, train_txt_path, test_txt_path, cuf_fn):
"""
初始化
:param train_txt_path: 原始训练数据地址
:param test_txt_path: 原始测试数据地址
:param cuf_fn: 分割函数，可以按字或者词分割
"""
self.cut_fn = cuf_fn
self._origin_train_data = transfer_file_to_list(train_txt_path)
self._origin_test_data = transfer_file_to_list(test_txt_path)
self._labels = []
self._vocab = {} # {"汀": 0, "哟": 1}
self._create_vocab_and_labels()
def _create_vocab_and_labels(self):
vocab_length = 0
label_set = set() # 去重
for i in tqdm(range(0, len(self._origin_train_data)), desc='Initializing'):
sentence, label = self._origin_train_data[i].rsplit(sep=' ', maxsplit=1)
label_set.add(label)
word_list = self.cut_fn(sentence, stop_words)
for word in word_list:
if not self._vocab.get(word):
self._vocab[word] = vocab_length
vocab_length += 1
self._labels = list(label_set)
# 异常保护，如果原文中带<unk>或<pad>则用原文中的值
self._vocab['<unk>'] = vocab_length if not self._vocab.get('<unk>', None) else self._vocab.get('<unk>')
self._vocab['<pad>'] = vocab_length + 1 if not self._vocab.get('<unk>', None) else self._vocab.get('<unk>')
def save_labels(self, label_path):
"""
包括所有标签
:param label_path: 保存地址
"""
with open(label_path, 'w', encoding='utf-8') as f:
for label in self._labels:
f.writelines(label + '\n')
def save_vocabulary(self, vocab_path):
"""
包括train和dev的字典
:param vocab_path: 保存地址
:return:
"""
if not vocab_path.endswith('.json'):
print('vocabulary should be a json file')
return
with open(vocab_path, 'w', encoding='utf-8') as f:
json.dump(self._vocab, f, ensure_ascii=False)
def _save_labeled_data(self, save_to, description, txt_data):
if len(txt_data) == 0:
return
f = open(save_to, 'w', encoding='utf-8')
for i in tqdm(range(0, len(txt_data)), desc=description):
sentence, label = txt_data[i].rsplit(sep=' ', maxsplit=1)
word_list = self.cut_fn(sentence, stop_words)
label_idx = self._labels.index(label)
sentence_idx = [str(self._vocab.get(word, self._vocab['<unk>'])) for word in word_list]
f.writelines(' '.join(sentence_idx) + '\t' + str(label_idx) + '\n')
f.close()
def _save_non_labeled_data(self, save_to, description, txt_data):
f = open(save_to, 'w', encoding='utf-8')
for i in tqdm(range(0, len(txt_data)), desc=description):
word_list = self.cut_fn(txt_data[i], stop_words)
sentence_idx = list(map(str, [self._vocab.get(word, self._vocab['<unk>']) for word in word_list]))
f.writelines(' '.join(sentence_idx) + '\n')
f.close()
def split_and_save(self, train_idx_path, dev_idx_path=None, frac=0.4):
"""
将训练数据按比例分为训练集和数据集，并保存为索引
:param train_idx_path: 训练集索引保存地址
:param dev_idx_path: 验证集索引保存地址
:param frac: 训练集和验证集比例
:return:
"""
if frac <= 0 or frac > 1:
print('分割比例必须大于0 且小于等于1')
return
if frac < 1 and not dev_idx_path:
print('分割比例小于1时，必须指定测试集路径')
return
if frac == 1 and dev_idx_path:
print('分割比例等于1时全部数据均用作训练集,测试集路径{}无效'.format(dev_idx_path))
np.random.shuffle(self._origin_train_data) # 打乱数据，使每次保存的验证集和测试集均不同
split_point = int(len(self._origin_train_data) * frac)
self._save_labeled_data(train_idx_path, 'Saving Train Index', self._origin_train_data[:split_point])
self._save_labeled_data(dev_idx_path, 'Saving Dev Index', self._origin_train_data[split_point:])
def save_test_set(self, test_idx_path):
"""
将测试数据保存为索引
:param test_idx_path:
"""
self._save_non_labeled_data(test_idx_path, 'Saving Test Index', self._origin_test_data)
if __name__ == '__main__':
txt_to_idx = TxtToIndex(
train_txt_path='data/myTrain.txt',
test_txt_path='data/myTest.txt',
cuf_fn=cut_sentence_by_word,
)
txt_to_idx.save_test_set('datatest/test_idx.txt')
txt_to_idx.save_labels('datatest/labels.txt')
txt_to_idx.save_vocabulary('datatest/vocab.json')
txt_to_idx.split_and_save('datatest/train_idx.txt', 'datatest/dev_idx.txt', frac=0.6)
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