-
Notifications
You must be signed in to change notification settings - Fork 121
/
lda.py
247 lines (219 loc) · 9.28 KB
/
lda.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
#-*- coding:utf-8 -*-
import logging
import logging.config
import ConfigParser
import numpy as np
import random
import codecs
import os
from collections import OrderedDict
#获取当前路径
path = os.getcwd()
#导入日志配置文件
logging.config.fileConfig("logging.conf")
#创建日志对象
logger = logging.getLogger()
# loggerInfo = logging.getLogger("TimeInfoLogger")
# Consolelogger = logging.getLogger("ConsoleLogger")
#导入配置文件
conf = ConfigParser.ConfigParser()
conf.read("setting.conf")
#文件路径
trainfile = os.path.join(path,os.path.normpath(conf.get("filepath", "trainfile")))
wordidmapfile = os.path.join(path,os.path.normpath(conf.get("filepath","wordidmapfile")))
thetafile = os.path.join(path,os.path.normpath(conf.get("filepath","thetafile")))
phifile = os.path.join(path,os.path.normpath(conf.get("filepath","phifile")))
paramfile = os.path.join(path,os.path.normpath(conf.get("filepath","paramfile")))
topNfile = os.path.join(path,os.path.normpath(conf.get("filepath","topNfile")))
tassginfile = os.path.join(path,os.path.normpath(conf.get("filepath","tassginfile")))
#模型初始参数
K = int(conf.get("model_args","K"))
alpha = float(conf.get("model_args","alpha"))
beta = float(conf.get("model_args","beta"))
iter_times = int(conf.get("model_args","iter_times"))
top_words_num = int(conf.get("model_args","top_words_num"))
class Document(object):
def __init__(self):
self.words = []
self.length = 0
class DataPreProcessing(object):
def __init__(self):
self.docs_count = 0
self.words_count = 0
self.docs = []
self.word2id = OrderedDict()
def cachewordidmap(self):
with codecs.open(wordidmapfile, 'w','utf-8') as f:
for word,id in self.word2id.items():
f.write(word +"\t"+str(id)+"\n")
class LDAModel(object):
def __init__(self,dpre):
self.dpre = dpre #获取预处理参数
#
#模型参数
#聚类个数K，迭代次数iter_times,每个类特征词个数top_words_num,超参数α（alpha） β(beta)
#
self.K = K
self.beta = beta
self.alpha = alpha
self.iter_times = iter_times
self.top_words_num = top_words_num
#
#文件变量
#分好词的文件trainfile
#词对应id文件wordidmapfile
#文章-主题分布文件thetafile
#词-主题分布文件phifile
#每个主题topN词文件topNfile
#最后分派结果文件tassginfile
#模型训练选择的参数文件paramfile
#
self.wordidmapfile = wordidmapfile
self.trainfile = trainfile
self.thetafile = thetafile
self.phifile = phifile
self.topNfile = topNfile
self.tassginfile = tassginfile
self.paramfile = paramfile
# p,概率向量 double类型，存储采样的临时变量
# nw,词word在主题topic上的分布
# nwsum,每各topic的词的总数
# nd,每个doc中各个topic的词的总数
# ndsum,每各doc中词的总数
self.p = np.zeros(self.K)
self.nw = np.zeros((self.dpre.words_count,self.K),dtype="int")
self.nwsum = np.zeros(self.K,dtype="int")
self.nd = np.zeros((self.dpre.docs_count,self.K),dtype="int")
self.ndsum = np.zeros(dpre.docs_count,dtype="int")
self.Z = np.array([ [0 for y in xrange(dpre.docs[x].length)] for x in xrange(dpre.docs_count)]) # M*doc.size()，文档中词的主题分布
#随机先分配类型
for x in xrange(len(self.Z)):
self.ndsum[x] = self.dpre.docs[x].length
for y in xrange(self.dpre.docs[x].length):
topic = random.randint(0,self.K-1)
self.Z[x][y] = topic
self.nw[self.dpre.docs[x].words[y]][topic] += 1
self.nd[x][topic] += 1
self.nwsum[topic] += 1
self.theta = np.array([ [0.0 for y in xrange(self.K)] for x in xrange(self.dpre.docs_count) ])
self.phi = np.array([ [ 0.0 for y in xrange(self.dpre.words_count) ] for x in xrange(self.K)])
def sampling(self,i,j):
topic = self.Z[i][j]
word = self.dpre.docs[i].words[j]
self.nw[word][topic] -= 1
self.nd[i][topic] -= 1
self.nwsum[topic] -= 1
self.ndsum[i] -= 1
Vbeta = self.dpre.words_count * self.beta
Kalpha = self.K * self.alpha
self.p = (self.nw[word] + self.beta)/(self.nwsum + Vbeta) * \
(self.nd[i] + self.alpha) / (self.ndsum[i] + Kalpha)
for k in xrange(1,self.K):
self.p[k] += self.p[k-1]
u = random.uniform(0,self.p[self.K-1])
for topic in xrange(self.K):
if self.p[topic]>u:
break
self.nw[word][topic] +=1
self.nwsum[topic] +=1
self.nd[i][topic] +=1
self.ndsum[i] +=1
return topic
def est(self):
# Consolelogger.info(u"迭代次数为%s 次" % self.iter_times)
for x in xrange(self.iter_times):
for i in xrange(self.dpre.docs_count):
for j in xrange(self.dpre.docs[i].length):
topic = self.sampling(i,j)
self.Z[i][j] = topic
logger.info(u"迭代完成。")
logger.debug(u"计算文章-主题分布")
self._theta()
logger.debug(u"计算词-主题分布")
self._phi()
logger.debug(u"保存模型")
self.save()
def _theta(self):
for i in xrange(self.dpre.docs_count):
self.theta[i] = (self.nd[i]+self.alpha)/(self.ndsum[i]+self.K * self.alpha)
def _phi(self):
for i in xrange(self.K):
self.phi[i] = (self.nw.T[i] + self.beta)/(self.nwsum[i]+self.dpre.words_count * self.beta)
def save(self):
#保存theta文章-主题分布
logger.info(u"文章-主题分布已保存到%s" % self.thetafile)
with codecs.open(self.thetafile,'w') as f:
for x in xrange(self.dpre.docs_count):
for y in xrange(self.K):
f.write(str(self.theta[x][y]) + '\t')
f.write('\n')
#保存phi词-主题分布
logger.info(u"词-主题分布已保存到%s" % self.phifile)
with codecs.open(self.phifile,'w') as f:
for x in xrange(self.K):
for y in xrange(self.dpre.words_count):
f.write(str(self.phi[x][y]) + '\t')
f.write('\n')
#保存参数设置
logger.info(u"参数设置已保存到%s" % self.paramfile)
with codecs.open(self.paramfile,'w','utf-8') as f:
f.write('K=' + str(self.K) + '\n')
f.write('alpha=' + str(self.alpha) + '\n')
f.write('beta=' + str(self.beta) + '\n')
f.write(u'迭代次数 iter_times=' + str(self.iter_times) + '\n')
f.write(u'每个类的高频词显示个数 top_words_num=' + str(self.top_words_num) + '\n')
#保存每个主题topic的词
logger.info(u"主题topN词已保存到%s" % self.topNfile)
with codecs.open(self.topNfile,'w','utf-8') as f:
self.top_words_num = min(self.top_words_num,self.dpre.words_count)
for x in xrange(self.K):
f.write(u'第' + str(x) + u'类：' + '\n')
twords = []
twords = [(n,self.phi[x][n]) for n in xrange(self.dpre.words_count)]
twords.sort(key = lambda i:i[1], reverse= True)
for y in xrange(self.top_words_num):
word = OrderedDict({value:key for key, value in self.dpre.word2id.items()})[twords[y][0]]
f.write('\t'*2+ word +'\t' + str(twords[y][1])+ '\n')
#保存最后退出时，文章的词分派的主题的结果
logger.info(u"文章-词-主题分派结果已保存到%s" % self.tassginfile)
with codecs.open(self.tassginfile,'w') as f:
for x in xrange(self.dpre.docs_count):
for y in xrange(self.dpre.docs[x].length):
f.write(str(self.dpre.docs[x].words[y])+':'+str(self.Z[x][y])+ '\t')
f.write('\n')
logger.info(u"模型训练完成。")
def preprocessing():
logger.info(u'载入数据......')
with codecs.open(trainfile, 'r','utf-8') as f:
docs = f.readlines()
logger.debug(u"载入完成,准备生成字典对象和统计文本数据...")
dpre = DataPreProcessing()
items_idx = 0
for line in docs:
if line != "":
tmp = line.strip().split()
#生成一个文档对象
doc = Document()
for item in tmp:
if dpre.word2id.has_key(item):
doc.words.append(dpre.word2id[item])
else:
dpre.word2id[item] = items_idx
doc.words.append(items_idx)
items_idx += 1
doc.length = len(tmp)
dpre.docs.append(doc)
else:
pass
dpre.docs_count = len(dpre.docs)
dpre.words_count = len(dpre.word2id)
logger.info(u"共有%s个文档" % dpre.docs_count)
dpre.cachewordidmap()
logger.info(u"词与序号对应关系已保存到%s" % wordidmapfile)
return dpre
def run():
dpre = preprocessing()
lda = LDAModel(dpre)
lda.est()
if __name__ == '__main__':
run()