-
Notifications
You must be signed in to change notification settings - Fork 4
/
PreProcess.py
352 lines (299 loc) · 17.5 KB
/
PreProcess.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
import re
import os
import pickle
import random
import statistics
import numpy as np
import pandas as pd
import datetime
import matplotlib.pyplot as plt
from collections import Counter
from tensorflow.contrib import learn
# from sklearn.model_selection import train_test_split
# from sklearn.feature_extraction.text import TfidfVectorizer
from extral_features import *
path = os.getcwd()
columns = ['index', 'question1', 'question2', 'edit_distance',
'sentiment1', 'sentiment2', 'sentiment3', 'sentiment4', 'sentiment5', 'sentiment6',
'ratio1', 'ratio2', 'ratio3', 'ratio4','ratio5', 'ratio6', 'lcs',
'length_difference1', 'length_difference2',
'tf_idf_word_match',
'cosine', 'euclidean', 'manhattan', 'wordmoversdistance',
'is_duplicate']
columns_extra_feature = ['edit_distance',
'sentiment1', 'sentiment2', 'sentiment3', 'sentiment4', 'sentiment5', 'sentiment6',
'ratio1', 'ratio2', 'ratio3', 'ratio4', 'ratio5', 'ratio6', 'lcs',
'length_difference1', 'length_difference2', 'tf_idf_word_match',
'cosine',
'euclidean',
'manhattan'
# 'wordmoversdistance'
]
def remove_stop_words(sentence, stop_words_set):
ans = []
for word in sentence.split():
if word.lower() not in stop_words_set:
ans.append(word)
return " ".join(ans)
def preprocess_tocsv(file_path):
df_old = pd.read_csv(file_path).fillna("")
query_a = [data.text_to_wordlist(text) for text in df_old['question1'].values][0:500000]
query_b = [data.text_to_wordlist(text) for text in df_old['question2'].values][0:500000]
query, words = [], []
for text in np.append(query_a, query_b):
query.append(text)
if len(text) < 5:
print(text)
length = []
for text in query:
length.append(len(text))
for word in text.split(" "):
words.append(word)
print("长度均值 :", statistics.mean(length))
print("长度中位数 :", statistics.median(length))
print("长度方差 :", statistics.stdev(length))
print("长度最小值:{}, 长度最大值:{}".format(min(length), max(length)))
length = sorted(Counter(length).items(), key=lambda val: val[0], reverse=False)
dict_sentence = sorted(Counter(query).items(), key=lambda val: val[1], reverse=True)
dict_words = sorted(Counter(words).items(), key=lambda val: val[1], reverse=True)
_, value_sentence = zip(*dict_sentence)
_, value_word = zip(*dict_words)
len_keys, value_len = zip(*length)
plt.plot(range(1000), value_sentence[0:1000], 'r')
plt.xlabel("words serial")
plt.ylabel("number of words")
plt.savefig("word_counter.png")
plt.close()
plt.plot(range(300), value_word[0:300], 'r')
plt.xlabel("sentences serial")
plt.ylabel("number of sentence")
plt.savefig("sentence_counter.png")
plt.close()
plt.plot(len_keys[0:100], value_len[0:100], "r")
plt.xlabel("句子长度")
plt.ylabel("相同长度句子个数")
plt.savefig("句子长度个数.png")
plt.close()
df_new = pd.DataFrame(data={"question1": query_a, "question2": query_b},
columns=[['question1', 'question2']])
df_new.to_csv('test_new.csv', index=False)
def pre_split_train(out_feature, train_distance):
df = pd.read_csv("./data/csv/train.csv").dropna()
data = df[['question1', 'question2', 'is_duplicate']].values
# 获取句子的extra feature
outer_feature = np.array(pickle.load(open(out_feature, "rb")))
train_distance = np.array(pickle.load(open(train_distance, "rb")))
print(outer_feature[0].shape)
print(outer_feature[1].shape)
outer_feature = np.concatenate((outer_feature[0], outer_feature[1]), axis=0)
print(outer_feature.shape)
np.random.shuffle(data)
data_x = data[0:len(data), 0:2]
data_y = data[0:len(data), 2:3]
test_x, train_x = data_x[0:5000, :], data_x[5000:len(data_x), :]
test_y, train_y = data_y[0:5000, :], data_y[5000:len(data_y), :]
test_cosine, train_cosine = train_distance[0:5000, 0:1], train_distance[5000:len(train_distance), 0:1]
test_euclidean, train_euclidean = train_distance[0:5000, 1:2], train_distance[5000:len(train_distance), 1:2]
test_manhattan, train_manhattan = train_distance[0:5000, 2:3], train_distance[5000:len(train_distance), 2:3]
test_wordmoversdistance, train_wordmoversdistanqce = train_distance[0:5000, 3:4], train_distance[5000:len(train_distance), 3:4]
test_df = pd.DataFrame(data={'question1': np.squeeze(test_x[:, 0:1], axis=1),
'question2': np.squeeze(test_x[:, 1:2], axis=1),
'edit_distance': np.squeeze(outer_feature[0:5000, 0:1], axis=1),
'sentiment1': np.squeeze(outer_feature[0:5000, 1:2], axis=1),
'sentiment2': np.squeeze(outer_feature[0:5000, 2:3], axis=1),
'sentiment3': np.squeeze(outer_feature[0:5000, 3:4], axis=1),
'sentiment4': np.squeeze(outer_feature[0:5000, 4:5], axis=1),
'sentiment5': np.squeeze(outer_feature[0:5000, 5:6], axis=1),
'sentiment6': np.squeeze(outer_feature[0:5000, 6:7], axis=1),
'ratio1': np.squeeze(outer_feature[0:5000, 7:8], axis=1),
'ratio2': np.squeeze(outer_feature[0:5000, 8:9], axis=1),
'ratio3': np.squeeze(outer_feature[0:5000, 9:10], axis=1),
'ratio4': np.squeeze(outer_feature[0:5000, 10:11], axis=1),
'ratio5': np.squeeze(outer_feature[0:5000, 11:12], axis=1),
'ratio6': np.squeeze(outer_feature[0:5000, 12:13], axis=1),
'lcs': np.squeeze(outer_feature[0:5000, 13:14], axis=1),
'length_difference1': np.squeeze(outer_feature[0:5000, 14:15], axis=1),
'length_difference2': np.squeeze(outer_feature[0:5000, 15:16], axis=1),
'tf_idf_word_match': np.squeeze(outer_feature[0:5000, 16:17], axis=1),
'cosine': np.squeeze(test_cosine, axis=1),
'euclidean': np.squeeze(test_euclidean, axis=1),
'manhattan': np.squeeze(test_manhattan, axis=1),
'wordmoversdistance': np.squeeze(test_wordmoversdistance, axis=1),
'is_duplicate': np.squeeze(test_y, axis=1)},
columns=[columns])
train_df = pd.DataFrame(data={'question1': np.squeeze(train_x[:, 0:1], axis=1),
'question2': np.squeeze(train_x[:, 1:2], axis=1),
'edit_distance': np.squeeze(outer_feature[5000:len(outer_feature), 0:1], axis=1),
'sentiment1': np.squeeze(outer_feature[5000:len(outer_feature), 1:2], axis=1),
'sentiment2': np.squeeze(outer_feature[5000:len(outer_feature), 2:3], axis=1),
'sentiment3': np.squeeze(outer_feature[5000:len(outer_feature), 3:4], axis=1),
'sentiment4': np.squeeze(outer_feature[5000:len(outer_feature), 4:5], axis=1),
'sentiment5': np.squeeze(outer_feature[5000:len(outer_feature), 5:6], axis=1),
'sentiment6': np.squeeze(outer_feature[5000:len(outer_feature), 6:7], axis=1),
'ratio1': np.squeeze(outer_feature[5000:len(outer_feature), 7:8], axis=1),
'ratio2': np.squeeze(outer_feature[5000:len(outer_feature), 8:9], axis=1),
'ratio3': np.squeeze(outer_feature[5000:len(outer_feature), 9:10], axis=1),
'ratio4': np.squeeze(outer_feature[5000:len(outer_feature), 10:11], axis=1),
'ratio5': np.squeeze(outer_feature[5000:len(outer_feature), 11:12], axis=1),
'ratio6': np.squeeze(outer_feature[5000:len(outer_feature), 12:13], axis=1),
'lcs': np.squeeze(outer_feature[5000:len(outer_feature), 13:14], axis=1),
'length_difference1': np.squeeze(outer_feature[5000:len(outer_feature), 14:15], axis=1),
'length_difference2': np.squeeze(outer_feature[5000:len(outer_feature), 15:16], axis=1),
'tf_idf_word_match': np.squeeze(outer_feature[5000:len(outer_feature), 16:17], axis=1),
'cosine': np.squeeze(train_cosine, axis=1),
'euclidean': np.squeeze(train_euclidean, axis=1),
'manhattan': np.squeeze(train_manhattan, axis=1),
'wordmoversdistance': np.squeeze(train_wordmoversdistanqce, axis=1),
'is_duplicate': np.squeeze(train_y, axis=1)},
columns=[columns])
print("写入csv数据。。。")
test_df.to_csv("./data/csv/train_test.csv", columns=columns)
train_df.to_csv("./data/csv/train_train.csv", columns=columns)
print("写入csv数据成功!!!")
def remove_sample_shorter_than_ten(filePath="./data/csv/train_train.csv"):
data = pd.read_csv(filePath).values
number = 0
length, range_index = len(data), []
for index in range(length):
if len(data[index][1].split()) < 4 and len(data[index][2].split()) < 4:
range_index.append(index)
number += 1
data = np.delete(data, range_index, axis=0)
print(number, len(data))
pd.DataFrame(data=data, columns=columns).to_csv(filePath)
class data(object):
def __init__(self, train_file_path, test_file_path, stop_words_file):
self.path = os.path.dirname(__file__)
self.stop_words = set(open(stop_words_file, "r").read().split())
# 获取训练数据,数据来源于 train_file_path
self.df = pd.read_csv(train_file_path).dropna()
self.data = self.df[['question1', 'question2']].values
self.label = self.df[['is_duplicate']].values
self.train_feature = self.df[columns_extra_feature].values
# 获取测试数据,数据来源于 train_file_path
self.test_df = pd.read_csv(test_file_path).dropna()
self.test_data = self.test_df[['question1', 'question2']].values
self.test_label = self.test_df[['is_duplicate']].values
self.test_feature = self.test_df[columns_extra_feature].values
print(datetime.datetime.now().isoformat())
print("当前文件路径 :", self.path)
print("self.data.shape :", self.data.shape)
print("self.label.shape :", self.label.shape)
print("self.train_feature.shape :", self.train_feature.shape)
print("self.test_data.shape :", self.test_data.shape)
print("self.test_label.shape :", self.test_label.shape)
print("self.test_feature.shape :", self.test_feature.shape)
@staticmethod
def text_to_wordlist(text):
text = re.sub(r"[^A-Za-z0-9]", " ", text)
text = re.sub(r"what's", "", text)
text = re.sub(r"What's", "", text)
text = re.sub(r"\'s", " ", text)
text = re.sub(r"\'ve", " have ", text)
text = re.sub(r"can't", "cannot ", text)
text = re.sub(r"n't", " not ", text)
text = re.sub(r"I'm", "I am", text)
text = re.sub(r" m ", " am ", text)
text = re.sub(r"\'re", " are ", text)
text = re.sub(r"\'d", " would ", text)
text = re.sub(r"\'ll", " will ", text)
text = re.sub(r"60k", " 60000 ", text)
text = re.sub(r" e g ", " eg ", text)
text = re.sub(r" b g ", " bg ", text)
text = re.sub(r"\0s", "0", text)
text = re.sub(r" 9 11 ", "911", text)
text = re.sub(r"e-mail", "email", text)
text = re.sub(r"\s{2,}", " ", text)
text = re.sub(r"quikly", "quickly", text)
text = re.sub(r" usa ", " America ", text)
text = re.sub(r" USA ", " America ", text)
text = re.sub(r" u s ", " America ", text)
text = re.sub(r" uk ", " England ", text)
text = re.sub(r" UK ", " England ", text)
text = re.sub(r"india", "India", text)
text = re.sub(r"switzerland", "Switzerland", text)
text = re.sub(r"china", "China", text)
text = re.sub(r"chinese", "Chinese", text)
text = re.sub(r"imrovement", "improvement", text)
text = re.sub(r"intially", "initially", text)
text = re.sub(r"quora", "Quora", text)
text = re.sub(r" dms ", "direct messages ", text)
text = re.sub(r"demonitization", "demonetization", text)
text = re.sub(r"actived", "active", text)
text = re.sub(r"kms", " kilometers ", text)
text = re.sub(r"KMs", " kilometers ", text)
text = re.sub(r" cs ", " computer science ", text)
text = re.sub(r" upvotes ", " up votes ", text)
text = re.sub(r" iPhone ", " phone ", text)
text = re.sub(r"\0rs ", " rs ", text)
text = re.sub(r"calender", "calendar", text)
text = re.sub(r"ios", "operating system", text)
text = re.sub(r"gps", "GPS", text)
text = re.sub(r"gst", "GST", text)
text = re.sub(r"programing", "programming", text)
text = re.sub(r"bestfriend", "best friend", text)
text = re.sub(r"dna", "DNA", text)
text = re.sub(r"III", "3", text)
text = re.sub(r"the US", "America", text)
text = re.sub(r"Astrology", "astrology", text)
text = re.sub(r"Method", "method", text)
text = re.sub(r"Find", "find", text)
text = re.sub(r"banglore", "Banglore", text)
text = re.sub(r" J K ", " JK ", text)
return text
def get_one_hot(self):
if not os.path.exists(os.path.join(self.path, "data/pkl/test.pkl")):
x_text = np.append(self.data, self.test_data).reshape(2 * len(self.data) + 2 * len(self.test_data)) # 所有的文本数据
# reshape 数据
self.data = self.data.reshape(2 * len(self.data))
self.test_data = self.test_data.reshape(2 * len(self.test_data))
# 清洗数据
self.data = [self.text_to_wordlist(line) for line in self.data]
self.test_data = [self.text_to_wordlist(line) for line in self.test_data]
# 转化成数据,将词汇进行编号
vocab_processor = learn.preprocessing.VocabularyProcessor(70, min_frequency=5)
vocab_processor = vocab_processor.fit(x_text)
print("vocab_processor 训练结束")
# 训练数据和测试数据进行编号
self.vec_train = list(vocab_processor.transform(self.data))
self.vec_test = list(vocab_processor.transform(self.test_data))
# 编号
self.vec_train = [(self.vec_train[index], self.vec_train[index + 1]) for index in range(0, len(self.vec_train), 2)]
self.vec_test = [(self.vec_test[index], self.vec_test[index + 1]) for index in range(0, len(self.vec_test), 2)]
print("vocab_processor 转化结束")
context_ids = [list(range(len(vocab_processor.vocabulary_)))]
print("number of words :", len(vocab_processor.vocabulary_))
# print(vocab_processor.reverse(context_ids))
# for article in vocab_processor.reverse(context_ids):
# for word in article.split():
# print(word)
pickle.dump((vocab_processor), open(os.path.join(self.path, "data/vocab.model"), "wb"))
pickle.dump((self.vec_train, self.label), open(os.path.join(self.path, "data/pkl/train.pkl"), "wb"))
pickle.dump((self.vec_test, self.test_label), open(os.path.join(self.path, "data/pkl/test.pkl"), "wb"))
pickle.dump(list(vocab_processor.reverse(context_ids)), open(os.path.join(self.path, "data/pkl/bag.pkl"), "wb"))
pickle.dump((self.test_feature, self.train_feature), open(os.path.join(self.path, "data/pkl/extra_feature.pkl"), "wb"))
print("number of words :", len(vocab_processor.vocabulary_))
print("dump 结束")
else:
self.vec_train, self.label = pickle.load(open(os.path.join(self.path, "data/pkl/train.pkl"), "rb"))
self.vec_test, self.test_label = pickle.load(open(os.path.join(self.path, "data/pkl/test.pkl"), "rb"))
self.test_feature, self.train_feature = pickle.load(open(os.path.join(self.path, "data/pkl/extra_feature.pkl"), "rb"))
return self
@staticmethod
def get_batch(epoches, batch_size, data, out_feature, label):
data = list(zip(data, out_feature, label))
for epoch in range(epoches):
random.shuffle(data)
for batch in range(0, len(data), batch_size):
if batch + batch_size >= len(data):
yield data[batch: len(data)]
else:
yield data[batch: (batch + batch_size)]
if __name__ == '__main__':
# pre_split_train()
data_file = "./data/csv/train.csv"
train_file = "./data/csv/train_train.csv"
test_file = "./data/csv/train_test.csv"
stop_words_file = "./data/stop_words_eng.txt"
# pre_split_train("./data/pkl/extra_feature.pkl", "./data/pkl/train_distance.pkl")
remove_sample_shorter_than_ten()