-
Notifications
You must be signed in to change notification settings - Fork 31
/
preprocess.py
165 lines (136 loc) · 7.36 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
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import os
import sys
import pickle
import logging
import jieba
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tqdm import tqdm
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s -- %(name)s -- %(levelname)s -- %(message)s')
logger = logging.getLogger(__name__)
class NNfeature(object):
def __init__(self, data_path='./data/context'):
logger.info('NNfeature loading corpus ...')
# 枚举所有的文件
jieba.enable_parallel(4)
self.context, self.label = [], []
for file in tqdm(os.listdir(path=data_path)):
try:
label = file.split('_')[0]
filePath = os.path.join(data_path, file)
with open(filePath, 'r', encoding='utf-8') as fd:
context = fd.read()
self.context.append(context)
self.label.append(label)
except:
logger.warning('file %s have some problem ...' % file)
self.label_list = ['Military', 'Economy', 'Culture', 'Sports', 'Auto', 'Medicine']
self.label = [self.label_list.index(label) for label in self.label]
self.one_hot_label = np.zeros(shape=(len(self.label), 6))
self.one_hot_label[np.arange(0, len(self.label)), self.label] = 1
logger.debug('one hot label shape: (%d, %d)' % (self.one_hot_label.shape))
self.context = [' '.join(list(jieba.cut(context))) for context in tqdm(self.context)]
self.tokenizer = tf.keras.preprocessing.text.Tokenizer(
num_words=20000, # 词表去20000,词表的提取根据TF的计算结果排序
filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~ ',
lower=True,
split=' ',
char_level=False,
oov_token=None,
document_count=0)
self.tokenizer.fit_on_texts(self.context)
self.context = np.array(self.tokenizer.texts_to_sequences(self.context))
self.context = pad_sequences(self.context, maxlen=50, padding='post')
logger.debug('context idx shape: (%d, %d)' % (self.context.shape))
self.train_x, self.test_x, self.train_y, self.test_y = \
train_test_split(self.context, self.one_hot_label, test_size=0.05)
logger.debug('self.train_x shape: (%d, %d)' % (self.train_x.shape))
logger.debug('self.test_x shape: (%d, %d)' % (self.test_x.shape))
logger.debug('self.train_y shape: (%d, %d)' % (self.train_y.shape))
logger.debug('self.test_y shape: (%d, %d)' % (self.test_y.shape))
pickle.dump((self.test_x, self.test_y), open("./data/pkl/test.pkl", "wb"))
pickle.dump((self.train_x, self.train_y), open("./data/pkl/train.pkl", "wb"))
class fastTextfeature(object):
def __init__(self, data_path='./data/context'):
logger.info('fastTextfeature loading corpus ...')
self.label_list = ['Military', 'Economy', 'Culture', 'Sports', 'Auto', 'Medicine']
# 枚举所有的文件
jieba.enable_parallel(8)
self.context, self.label = [], []
for file in tqdm(os.listdir(path=data_path)):
try:
label = file.split('_')[0]
filePath = os.path.join(data_path, file)
with open(filePath, 'r', encoding='utf-8') as fd:
context = fd.read().replace('\n', ' ').replace('\r', ' ').replace('\t', ' ')
self.context.append(context)
self.label.append(self.label_list.index(label))
except:
logger.warning('file %s have some problem ...' % file)
self.context = [' '.join(list(jieba.cut(context))) for context in tqdm(self.context)]
self.train_context, self.test_context, self.train_label, self.test_label =\
train_test_split(self.context, self.label, test_size=0.05)
train_data_fd = open('./data/fastTextData/train_data', 'w+')
for label, context in zip(self.train_label, self.train_context):
train_data_fd.write("__label__" + str(label) + '\t' + context + '\n')
train_data_fd.close()
valid_data_fd = open('./data/fastTextData/valid_data', 'w+')
for label, context in zip(self.test_label, self.test_context):
valid_data_fd.write("__label__" + str(label) + '\t' + context + '\n')
valid_data_fd.close()
logger.debug('self.train_context shape: %d' % len(self.train_context))
logger.debug('self.test_context shape: %d' % len(self.test_context))
logger.debug('self.train_label shape: %d' % len(self.train_label))
logger.debug('self.test_label shape: %d' % len(self.test_label))
class DynamicRnnfeature(object):
def __init__(self, data_path='./data/context'):
logger.info('fastTextfeature loading corpus ...')
self.label_list = ['Military', 'Economy', 'Culture', 'Sports', 'Auto', 'Medicine']
# 枚举所有的文件
jieba.enable_parallel(8)
self.context, self.label = [], []
for file in tqdm(os.listdir(path=data_path)):
try:
label = file.split('_')[0]
filePath = os.path.join(data_path, file)
with open(filePath, 'r', encoding='utf-8') as fd:
context = fd.read().replace('\n', ' ').replace('\r', ' ').replace('\t', ' ')
self.context.append(context)
self.label.append(self.label_list.index(label))
except:
logger.warning('file %s have some problem ...' % file)
self.context = [' '.join(list(jieba.cut(context))) for context in tqdm(self.context)]
self.one_hot_label = np.zeros(shape=(len(self.label), 6))
self.one_hot_label[np.arange(0, len(self.label)), self.label] = 1
logger.debug('one hot label shape: (%d, %d)' % (self.one_hot_label.shape))
self.tokenizer = tf.keras.preprocessing.text.Tokenizer(
num_words=20000, # 词表去20000,词表的提取根据TF的计算结果排序
filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~ ',
lower=True,
split=' ',
char_level=False,
oov_token=None,
document_count=0)
self.tokenizer.fit_on_texts(self.context)
self.context = np.array(self.tokenizer.texts_to_sequences(self.context))
self.sequence_length = np.array([len(sequence) for sequence in self.context])
self.context = pad_sequences(self.context, maxlen=50, padding='post')
logger.debug('context idx shape: (%d, %d)' % self.context.shape)
self.train_x, self.test_x, self.train_y, self.test_y, self.seq_len_train, self.seq_len_test = \
train_test_split(self.context, self.one_hot_label, self.sequence_length, test_size=0.05)
logger.debug('self.train_x shape: (%d, %d)' % (self.train_x.shape))
logger.debug('self.test_x shape: (%d, %d)' % (self.test_x.shape))
logger.debug('self.train_y shape: (%d, %d)' % (self.train_y.shape))
logger.debug('self.test_y shape: (%d, %d)' % (self.test_y.shape))
logger.debug('self.seq_len_train shape: (%d)' % (self.seq_len_train.shape))
logger.debug('self.seq_len_test shape: (%d)' % (self.seq_len_test.shape))
if __name__ == '__main__':
# f = NNfeature()
# f = fastTextfeature()
f = DynamicRnnfeature()