-
Notifications
You must be signed in to change notification settings - Fork 9
/
make_data_files.py
170 lines (143 loc) · 6.64 KB
/
make_data_files.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
import os
import shutil
import collections
from tqdm import tqdm
from tensorflow.core.example import example_pb2
import struct
import random
import re
import jieba
import pandas as pd
finished_path = "data/finished"
unfinished_path = "data/unfinished"
chunk_path = "data/chunked"
vocab_path = "data/vocab"
VOCAB_SIZE = 50000
CHUNK_SIZE = 100 # num examples per chunk, for the chunked data
train_bin_path = os.path.join(finished_path, "train.bin")
valid_bin_path = os.path.join(finished_path, "valid.bin")
def make_folder(folder_path):
if not os.path.exists(folder_path):
os.makedirs(folder_path)
def delete_folder(folder_path):
if os.path.exists(folder_path):
shutil.rmtree(folder_path)
def shuffle_text_data(unshuffled_art, unshuffled_abs, shuffled_art,
shuffled_abs):
article_itr = open(os.path.join(unfinished_path, unshuffled_art), "r", encoding='utf-8')
abstract_itr = open(os.path.join(unfinished_path, unshuffled_abs), "r", encoding='utf-8')
list_of_pairs = []
for article in article_itr:
article = article.strip()
abstract = next(abstract_itr).strip()
list_of_pairs.append((article, abstract))
article_itr.close()
abstract_itr.close()
random.shuffle(list_of_pairs)
article_itr = open(os.path.join(unfinished_path, shuffled_art), "w", encoding='utf-8')
abstract_itr = open(os.path.join(unfinished_path, shuffled_abs), "w", encoding='utf-8')
for pair in list_of_pairs:
article_itr.write(pair[0] + "\n")
abstract_itr.write(pair[1] + "\n")
article_itr.close()
abstract_itr.close()
def write_to_bin(article_path, abstract_path, out_file, vocab_counter=None):
with open(out_file, 'wb') as writer:
article_itr = open(article_path, 'r', encoding='utf-8')
abstract_itr = open(abstract_path, 'r', encoding='utf-8')
for article in tqdm(article_itr):
article = article.strip()
abstract = next(abstract_itr).strip()
tf_example = example_pb2.Example()
tf_example.features.feature['article'].bytes_list.value.extend(
[bytes(article, encoding='utf8')])
tf_example.features.feature['abstract'].bytes_list.value.extend(
[bytes(abstract, encoding='utf8')])
tf_example_str = tf_example.SerializeToString()
str_len = len(tf_example_str)
writer.write(struct.pack('q', str_len))
writer.write(struct.pack('%ds' % str_len, tf_example_str))
if vocab_counter is not None:
art_tokens = article.split(' ')
abs_tokens = abstract.split(' ')
tokens = art_tokens + abs_tokens
tokens = [t.strip() for t in tokens] # strip
tokens = [t for t in tokens if t != ""] # remove empty
vocab_counter.update(tokens)
if vocab_counter is not None:
with open(vocab_path, 'w',encoding='utf-8') as writer:
# 这块是取5000条词表，可以全部存进去。
for word, count in vocab_counter.most_common(VOCAB_SIZE):
writer.write(word + ' ' + str(count) + '\n')
def creating_finished_data():
make_folder(finished_path)
vocab_counter = collections.Counter()
write_to_bin(os.path.join(unfinished_path, "train.art.shuf.txt"),
os.path.join(unfinished_path, "train.abs.shuf.txt"),
train_bin_path, vocab_counter)
write_to_bin(os.path.join(unfinished_path, "valid.art.shuf.txt"),
os.path.join(unfinished_path, "valid.abs.shuf.txt"),
valid_bin_path)
def chunk_file(set_name, chunks_dir, bin_file):
make_folder(chunks_dir)
reader = open(bin_file, "rb")
chunk = 0
finished = False
while not finished:
chunk_fname = os.path.join(chunks_dir, '%s_%04d.bin' %
(set_name, chunk)) # new chunk
with open(chunk_fname, 'wb') as writer:
for _ in range(CHUNK_SIZE):
len_bytes = reader.read(8)
if not len_bytes:
finished = True
break
str_len = struct.unpack('q', len_bytes)[0]
example_str = struct.unpack('%ds' % str_len,
reader.read(str_len))[0]
writer.write(struct.pack('q', str_len))
writer.write(struct.pack('%ds' % str_len, example_str))
chunk += 1
def preprocess(x):
x = str(x).replace('\r', '').replace('\n',
'').replace('\t',
'').replace(' ', '')
regex = re.compile(r"[()，+-.、。；！：《》（）:——“”？_【】\/]")
x = regex.sub('', x)
mytext = jieba.cut(x, cut_all=False)
return ' '.join(mytext)
if __name__ == "__main__":
for file in ['eval', 'test', 'train']:
df = pd.read_csv('./Pre LCSTS/%s.csv' % file, header=None)
if file == 'eval':
file = 'valid'
with open('./data/unfinished/%s.title.txt' % file, 'w', encoding='utf-8') as f:
for data in tqdm(df[0].tolist()):
f.write(preprocess(data))
f.write('\n')
with open('./data/unfinished/%s.article.txt' % file, 'w', encoding='utf-8') as f:
for data in tqdm(df[1].tolist()):
f.write(preprocess(data))
f.write('\n')
shuffle_text_data("train.article.txt", "train.title.txt",
"train.art.shuf.txt", "train.abs.shuf.txt")
shuffle_text_data("valid.article.txt", "valid.title.txt",
"valid.art.shuf.txt", "valid.abs.shuf.txt")
print("Completed shuffling train & valid text files")
delete_folder(finished_path)
creating_finished_data()
print("Completed creating bin file for train & valid")
delete_folder(chunk_path)
chunk_file("train", os.path.join(chunk_path, "train"), train_bin_path)
chunk_file("valid", os.path.join(chunk_path, "main_valid"), valid_bin_path)
print("Completed chunking main bin files into smaller ones")
make_folder(os.path.join(chunk_path, "valid"))
make_folder(os.path.join(chunk_path, "test"))
bin_chunks = os.listdir(os.path.join(chunk_path, "main_valid"))
bin_chunks.sort()
samples = random.sample(set(bin_chunks[:-1]), 2)
valid_chunk, test_chunk = samples[0], samples[1]
shutil.copyfile(os.path.join(chunk_path, "main_valid", valid_chunk),
os.path.join(chunk_path, "valid", "valid_00.bin"))
shutil.copyfile(os.path.join(chunk_path, "main_valid", test_chunk),
os.path.join(chunk_path, "test", "test_00.bin"))