p(\beta_{1:K},\theta_{1:D},z_{1:D},w_{1:D})
= \prod_{i=i}^k p(\beta_i) \prod_{d=1}^D p(\theta_d) (\prod_{n=1}^N p(z_{d,n}|\theta_d)p(w_{d,n}|\beta_{1:K},z_{d,n}))

p(\beta_{1:K},\theta_{1:D},z_{1:D}|w_{1:D})
= p(\beta_{1:K},\theta_{1:D},z_{1:D}, w_{1:D}) / p(w_{1:D})

#-*- 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()