用python计算lda语言模型的困惑度并作图

 1 # -*- coding: UTF-8-*-
 2 import numpy
 3 import math
 4 import string
 5 import matplotlib.pyplot as plt
 6 import re
 7 
 8 def dictionary_found(wordlist):               #对模型训练出来的词转换成一个词为KEY,概率为值的字典。
 9     word_dictionary1={}
10     for i in xrange(len(wordlist)):
11         if i%2==0:
12             if word_dictionary1.has_key(wordlist[i])==True:
13                 word_probability=word_dictionary1.get(wordlist[i])
14                 word_probability=float(word_probability)+float(wordlist[i+1])
15                 word_dictionary1.update({wordlist[i]:word_probability})
16             else:
17                 word_dictionary1.update({wordlist[i]:wordlist[i+1]})
18         else:
19             pass
20     return word_dictionary1
21 
22 def look_into_dic(dictionary,testset):          #对于测试集的每一个词，在字典中查找其概率。
23     ‘‘‘Calculates the TF-list for perplexity‘‘‘    
24     frequency=[]
25     letter_list=[]
26     a=0.0
27     for letter in testset.split():
28         if letter not in letter_list:
29             letter_list.append(letter)
30             letter_frequency=(dictionary.get(letter))
31             frequency.append(letter_frequency)
32         else:
33             pass
34     for each in frequency:
35         if each!=None:
36             a+=float(each)
37         else:
38             pass
39     return a
40 
41 
42 def f_testset_word_count(testset):                                     #测试集的词数统计
43     ‘‘‘reture the sum of words in testset which is the denominator of the formula of Perplexity‘‘‘
44     testset_clean=testset.split()
45     return (len(testset_clean)-testset.count("\n"))
46 
47 def f_perplexity(word_frequency,word_count):             #计算困惑度
48     ‘‘‘Search the probability of each word in dictionary
49     Calculates the perplexity of the LDA model for every parameter T‘‘‘
50     duishu=-math.log(word_frequency)
51     kuohaoli=duishu/word_count
52     perplexity=math.exp(kuohaoli)
53     return perplexity
54 
55 def graph_draw(topic,perplexity):             #做主题数与困惑度的折线图
56     x=topic
57     y=perplexity
58     plt.plot(x,y,color="red",linewidth=2)
59     plt.xlabel("Number of Topic")
60     plt.ylabel("Perplexity")
61     plt.show()
62 
63 
64 topic=[]
65 perplexity_list=[]
66 f1=open(‘/home/alber/lda/GibbsLDA/jd/test.txt‘,‘r‘)
67 testset=f1.read()
68 testset_word_count=f_testset_word_count(testset)         #call the function to count the sum-words in testset
69 for i in xrange(14):
70     dictionary={}
71     topic.append(5*(3i+1))
72     trace="/home/alber/lda/GibbsLDA/jd/stats/model-final-"+str(5*(i+1))+".txt"
73     f=open(trace,‘r‘)
74     text=f.readlines()
75     word_list=[]
76     for line in text:
77         if "Topic" not in line:
78             line_clean=line.split()
79             word_list.extend(line_clean)    
80         else:
81             pass
82     word_dictionary=dictionary_found(word_list)
83     frequency=look_into_dic(word_dictionary,testset)      
84     perplexity=f_perplexity(frequency,testset_word_count)       
85     perplexity_list.append(perplexity)        
86 graph_draw(topic,perplexity_list)