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step2process_data.py
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#!usr/bin/env python
#-*- coding:utf-8 -*-
#按拼音随机构造训练数据
import random
import pandas as pd
from pypinyin import lazy_pinyin
from step1data_loader import load_dataset, save_data
from sklearn.model_selection import train_test_split
from tqdm import tqdm
def is_china_char(ch):
if u'\u4e00' <= ch <= u'\u9fff':
return True
return False
def gen_char_dict(dataset):
"""
统计字频，字:字频率
"""
char_dict = {}
for line in dataset:
line = line.strip()
for char in line:
if len(char) != 0 and is_china_char(char):
char_dict[char] = char_dict.get(char, 0) + 1
return char_dict
def gen_pinyin_dict(dataset, char_dict,filename):
"""
统计拼音频率，拼音:频率
"""
char_pinyin_dict = {}
for i, line in tqdm(enumerate(dataset)):
# print(i)
line = line.strip()
for c in line:
if is_china_char(c):
word_pinyin = ''.join(lazy_pinyin(c))
if len(word_pinyin) != 0:
if word_pinyin not in char_pinyin_dict:
char_pinyin_dict[word_pinyin] = c + '_' + str(char_dict.get(c, 0))
else:
char_pinyin_dict[word_pinyin] += ';' + c + '_' + str(char_dict.get(c, 0)) #多音字直接加法加上
data = {}
for pinyin, words in char_pinyin_dict.items():
tmp = {}
for word in words.split(';'):
# print(word)
if len(word) != 0:
word_word = word.split('_')[0]
word_count = int(word.split('_')[1])
tmp[word_word] = word_count
data[pinyin] = tmp
#生成拼音字典，每个拼音下还有对应的字对应的字频的字典
f = open(f'data/{filename}.model', 'w',encoding='utf-8')
f.write(str(data))
f.close()
def load_pinyin_dict(file_path):
f = open(file_path, 'r',encoding='utf-8')
a = f.read()
char_pinyin_dict = eval(a)
f.close()
return char_pinyin_dict
#构造数据集：随机拼音，随机mask
def random_word(sentence, char_pinyin_dict, char_dict, confusion_dict=None):
tokens = [x for x in sentence]
out = []
for i, token in enumerate(sentence):
if not is_china_char(token):
out.append(str(0))
continue
prob = random.random()
if prob < 0.15:
prob /= 0.15
# 谐音 80%
if prob < 0.80:
candiation = char_pinyin_dict.get(''.join(lazy_pinyin(token)), {token:''})
#拼音对应字频字典，按字频排序
candiation = sorted(candiation.items(), key=lambda x:x[1], reverse=True)
candiation = candiation[:int(len(candiation)/2+0.5)]
candiation = [x[0] for x in candiation if x[0] != token]
if len(candiation) == 0:
out.append(str(0))
continue
tokens[i] = random.choice(candiation)
# 随机 20%
else:
candiation = sorted(char_dict.items(), key=lambda x:x[1], reverse=True)
candiation = candiation[:int(len(candiation)/2+0.5)]
candiation = [x[0] for x in candiation if x[0] != token]
if len(candiation) == 0:
out.append(str(0))
continue
tokens[i] = random.choice(candiation)
out.append(str(1))
# # 删除 5%
# elif prob < 0.95:
# tokens[i] = ''
# # 添加 5%
# else:
# candiation = sorted(char_dict.items(), key=lambda x:x[1], reverse=True)
# candiation = candiation[:int(len(candiation)/2+0.5)]
# candiation = [x[0] for x in candiation]
# tokens.insert(i+1, random.choice(candiation))
else:
out.append(str(0))
return ''.join(tokens), ' '.join(out)
def random_dataset(dataset, char_pinyin_dict, char_dict):
text = []
out = []
for ids, line in tqdm(enumerate(dataset)):
# print(ids)
line, label = random_word(line, char_pinyin_dict, char_dict)
text.append(line)
out.append(label)
return text, out
if __name__ == '__main__':
################头条语料
# dataset = load_dataset('data/processed_data/all_same_765376/all_data_765376.txt')
# char_dict = gen_char_dict(dataset)
# gen_pinyin_dict(dataset, char_dict,'pinyin2char')
# char_pinyin_dict = load_pinyin_dict('data/pinyin2char.model')
# process_dataset, process_label = random_dataset(dataset, char_pinyin_dict, char_dict)
# save_data(process_dataset, 'data/processed_data/process_data_765376.txt')
# df = pd.DataFrame(columns=['origin_text','random_text','label'])
# df['origin_text'] = dataset
# df['random_text'] = process_dataset
# df['label'] = process_label
# df.to_csv('data/processed_data/processed_765376.csv', index=False)
# # df = pd.read_csv('data/processed_data/all_same_765376/processed_765376.csv')
#
# dataset = df.values
# train, test = train_test_split(dataset, test_size=0.1)
# df = pd.DataFrame(columns=['origin_text','random_text','label'], data=train)
# df.to_csv('data/processed_data/all_same_765376/train.csv', index=False)
# df = pd.DataFrame(columns=['origin_text','random_text','label'], data=test)
# df.to_csv('data/processed_data/all_same_765376/test.csv', index=False)
##########################我的语料
# for windows in [1,2,3,4,5]:
# dataset = load_dataset(f'data/processed_data/my/{windows}windows_utterence.txt')
# char_dict = gen_char_dict(dataset)
# gen_pinyin_dict(dataset, char_dict,'mypinyin2char')
# char_pinyin_dict = load_pinyin_dict('data/mypinyin2char.model')
#
# process_dataset, process_label = random_dataset(dataset, char_pinyin_dict, char_dict)
# save_data(process_dataset, 'data/processed_data/my_process_data.txt')
# df = pd.DataFrame(columns=['origin_text','random_text','label'])
# df['origin_text'] = dataset
# df['random_text'] = process_dataset
# df['label'] = process_label
# df.to_csv('data/processed_data/my_processed.csv', index=False)
#
# # df = pd.read_csv('data/processed_data/all_same_765376/processed_765376.csv')
#
# dataset = df.values
# df = pd.DataFrame(columns=['origin_text','random_text','label'], data=dataset)
# df.to_csv(f'data/processed_data/my/windwos{windows}_dataset.csv', index=False)
####################对话语料
# dataset = load_dataset(f'data/processed_data/my/LCCC.txt')
# char_dict = gen_char_dict(dataset)
# gen_pinyin_dict(dataset, char_dict, 'LCCCpinyin2char')
# char_pinyin_dict = load_pinyin_dict('data/LCCCpinyin2char.model')
#
# process_dataset, process_label = random_dataset(dataset, char_pinyin_dict, char_dict)
# save_data(process_dataset, 'data/processed_data/LCCC_process_data.txt')
# df = pd.DataFrame(columns=['origin_text', 'random_text', 'label'])
# df['origin_text'] = dataset
# df['random_text'] = process_dataset
# df['label'] = process_label
# df.to_csv('data/processed_data/LCCC_processed.csv', index=False)
#
# # df = pd.read_csv('data/processed_data/all_same_765376/processed_765376.csv')
#
# dataset = df.values
# df = pd.DataFrame(columns=['origin_text', 'random_text', 'label'], data=dataset)
# df.to_csv(f'data/processed_data/my/LCCC_dataset.csv', index=False)
#################垂域数据
dataset = load_dataset(f'data/processed_data/垂域数据/data.txt')
char_dict = gen_char_dict(dataset)
# gen_pinyin_dict(dataset, char_dict, 'mypinyin2char')
char_pinyin_dict = load_pinyin_dict('data/mypinyin2char.model')
process_dataset, process_label = random_dataset(dataset, char_pinyin_dict, char_dict)
save_data(process_dataset, 'data/processed_data/垂域数据/data_processed.txt')
df = pd.DataFrame(columns=['origin_text', 'random_text', 'label'])
df['origin_text'] = dataset
df['random_text'] = process_dataset
df['label'] = process_label
df.to_csv('data/processed_data/垂域数据/data_processed.csv', index=False)
# df = pd.read_csv('data/processed_data/all_same_765376/processed_765376.csv')
dataset = df.values
df = pd.DataFrame(columns=['origin_text', 'random_text', 'label'], data=dataset)
df.to_csv(f'data/processed_data/垂域数据/data_processed.csv', index=False)