LDA主题模型原理解析与python实现_lda python csdn

本文转自：LDA主题模型原理解析与python实现_wind_blast的博客-CSDN博客 

 python实现：

#-*- 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
#把整个文档及真的单词构成vocabulary（不允许重复）
class DataPreProcessing(object):
    def __init__(self):
        self.docs_count = 0
        self.words_count = 0
        #保存每个文档d的信息(单词序列，以及length)
        self.docs = []
        #建立vocabulary表，照片文档的单词
        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)
        # nw,词word在主题topic上的分布
        self.nw = np.zeros((self.dpre.words_count,self.K),dtype="int")
        # nwsum,每各topic的词的总数
        self.nwsum = np.zeros(self.K,dtype="int")
        # nd,每个doc中各个topic的词的总数
        self.nd = np.zeros((self.dpre.docs_count,self.K),dtype="int")
        # ndsum,每各doc中词的总数
        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，为每个文档中的各个单词分配主题
        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)#随机取一个主题
                self.Z[x][y] = topic#文档中词的主题分布
                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):
        #换主题
        topic = self.Z[i][j]
        #只是单词的编号，都是从0开始word就是等于j
        word = self.dpre.docs[i].words[j]
        #if word==j:
        #    print 'true'
        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)
 
        #随机更新主题的吗
        # 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
 
        #按这个更新主题更好理解，这个效果还不错
        p = np.squeeze(np.asarray(self.p/np.sum(self.p)))
        topic = np.argmax(np.random.multinomial(1, p))
 
        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):#遍历文档的个数词
            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。")
# 数据预处理，即：生成d（）单词序列，以及词汇表
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...")
    # 大的文档集
    dpre = DataPreProcessing()
    items_idx = 0
    for line in docs:
        if line != "":
            tmp = line.strip().split()
            # 生成一个文档对象：包含单词序列（w1,w2,w3,,,,,wn）可以重复的
            doc = Document()
            for item in tmp:
                if dpre.word2id.has_key(item):# 已有的话，只是当前文档追加
                    doc.words.append(dpre.word2id[item])
                else:  # 没有的话，要更新vocabulary中的单词词典及wordidmap
                    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) # 文档数
    dpre.words_count = len(dpre.word2id) # 词汇数
    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():
    # 处理文档集，及计算文档数，以及vocabulary词的总个数，以及每个文档的单词序列
    dpre = preprocessing()
    lda = LDAModel(dpre)
    lda.est()
if __name__ == '__main__':
    run()