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Python_LDA实现方法详解

更新时间:2020-05-09 17:42:01 作者:startmvc
LDA(LatentDirichletallocation)模型是一种常用而用途广泛地概率主题模型。其实现一般通过Variation

LDA(Latent Dirichlet allocation)模型是一种常用而用途广泛地概率主题模型。其实现一般通过Variational inference和Gibbs Samping实现。作者在提出LDA模型时给出了其变分推理的C源码(后续贴出C++改编的类),这里贴出基于Python的第三方模块改写的LDA类及实现。


#coding:utf-8
import numpy as np
import lda
import lda.datasets
import jieba
import codecs
class LDA_v20161130():
 def __init__(self, topics=2):
 self.n_topic = topics
 self.corpus = None
 self.vocab = None
 self.ppCountMatrix = None
 self.stop_words = [u',', u'。', u'、', u'(', u')', u'·', u'!', u' ', u':', u'“', u'”', u'\n']
 self.model = None
 def loadCorpusFromFile(self, fn):
 # 中文分词
 f = open(fn, 'r')
 text = f.readlines()
 text = r' '.join(text)
 seg_generator = jieba.cut(text)
 seg_list = [i for i in seg_generator if i not in self.stop_words]
 seg_list = r' '.join(seg_list)
 # 切割统计所有出现的词纳入词典
 seglist = seg_list.split(" ")
 self.vocab = []
 for word in seglist:
 if (word != u' ' and word not in self.vocab):
 self.vocab.append(word)
 CountMatrix = []
 f.seek(0, 0)
 # 统计每个文档中出现的词频
 for line in f:
 # 置零
 count = np.zeros(len(self.vocab),dtype=np.int)
 text = line.strip()
 # 但还是要先分词
 seg_generator = jieba.cut(text)
 seg_list = [i for i in seg_generator if i not in self.stop_words]
 seg_list = r' '.join(seg_list)
 seglist = seg_list.split(" ")
 # 查询词典中的词出现的词频
 for word in seglist:
 if word in self.vocab:
 count[self.vocab.index(word)] += 1
 CountMatrix.append(count)
 f.close()
 #self.ppCountMatrix = (len(CountMatrix), len(self.vocab))
 self.ppCountMatrix = np.array(CountMatrix)
 print "load corpus from %s success!"%fn
 def setStopWords(self, word_list):
 self.stop_words = word_list
 def fitModel(self, n_iter = 1500, _alpha = 0.1, _eta = 0.01):
 self.model = lda.LDA(n_topics=self.n_topic, n_iter=n_iter, alpha=_alpha, eta= _eta, random_state= 1)
 self.model.fit(self.ppCountMatrix)
 def printTopic_Word(self, n_top_word = 8):
 for i, topic_dist in enumerate(self.model.topic_word_):
 topic_words = np.array(self.vocab)[np.argsort(topic_dist)][:-(n_top_word + 1):-1]
 print "Topic:",i,"\t",
 for word in topic_words:
 print word,
 print
 def printDoc_Topic(self):
 for i in range(len(self.ppCountMatrix)):
 print ("Doc %d:((top topic:%s) topic distribution:%s)"%(i, self.model.doc_topic_[i].argmax(),self.model.doc_topic_[i]))
 def printVocabulary(self):
 print "vocabulary:"
 for word in self.vocab:
 print word,
 print
 def saveVocabulary(self, fn):
 f = codecs.open(fn, 'w', 'utf-8')
 for word in self.vocab:
 f.write("%s\n"%word)
 f.close()
 def saveTopic_Words(self, fn, n_top_word = -1):
 if n_top_word==-1:
 n_top_word = len(self.vocab)
 f = codecs.open(fn, 'w', 'utf-8')
 for i, topic_dist in enumerate(self.model.topic_word_):
 topic_words = np.array(self.vocab)[np.argsort(topic_dist)][:-(n_top_word + 1):-1]
 f.write( "Topic:%d\t"%i)
 for word in topic_words:
 f.write("%s "%word)
 f.write("\n")
 f.close()
 def saveDoc_Topic(self, fn):
 f = codecs.open(fn, 'w', 'utf-8')
 for i in range(len(self.ppCountMatrix)):
 f.write("Doc %d:((top topic:%s) topic distribution:%s)\n" % (i, self.model.doc_topic_[i].argmax(), self.model.doc_topic_[i]))
 f.close()

算法实现demo:

例如,抓取BBC川普当选的新闻作为语料,输入以下代码:


if __name__=="__main__":
 _lda = LDA_v20161130(topics=20)
 stop = [u'!', u'@', u'#', u',',u'.',u'/',u';',u' ',u'[',u']',u'$',u'%',u'^',u'&',u'*',u'(',u')',
 u'"',u':',u'<',u'>',u'?',u'{',u'}',u'=',u'+',u'_',u'-',u'''''']
 _lda.setStopWords(stop)
 _lda.loadCorpusFromFile(u'C:\\Users\Administrator\Desktop\\BBC.txt')
 _lda.fitModel(n_iter=1500)
 _lda.printTopic_Word(n_top_word=10)
 _lda.printDoc_Topic()
 _lda.saveVocabulary(u'C:\\Users\Administrator\Desktop\\vocab.txt')
 _lda.saveTopic_Words(u'C:\\Users\Administrator\Desktop\\topic_word.txt')
 _lda.saveDoc_Topic(u'C:\\Users\Administrator\Desktop\\doc_topic.txt')

因为语料全部为英文,因此这里的stop_words全部设置为英文符号,主题设置20个,迭代1500次。结果显示,文档148篇,词典1347词,总词数4174,在i3的电脑上运行17s。 Topic_words部分输出如下:

Topic: 0 to will and of he be trumps the what policy Topic: 1 he would in said not no with mr this but Topic: 2 for or can some whether have change health obamacare insurance Topic: 3 the to that president as of us also first all Topic: 4 trump to when with now were republican mr office presidential Topic: 5 the his trump from uk who president to american house Topic: 6 a to that was it by issue vote while marriage Topic: 7 the to of an are they which by could from Topic: 8 of the states one votes planned won two new clinton Topic: 9 in us a use for obama law entry new interview Topic: 10 and on immigration has that there website vetting action given

Doc_Topic部分输出如下:

Doc 0:((top topic:4) topic distribution:[ 0.02972973 0.0027027 0.0027027 0.16486486 0.32702703 0.19189189 0.0027027 0.0027027 0.02972973 0.0027027 0.02972973 0.0027027 0.0027027 0.0027027 0.02972973 0.0027027 0.02972973 0.0027027 0.13783784 0.0027027 ]) Doc 1:((top topic:18) topic distribution:[ 0.21 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.11 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.31 0.21]) Doc 2:((top topic:18) topic distribution:[ 0.02075472 0.00188679 0.03962264 0.00188679 0.00188679 0.00188679 0.00188679 0.15283019 0.00188679 0.02075472 0.00188679 0.24716981 0.00188679 0.07735849 0.00188679 0.00188679 0.00188679 0.00188679 0.41698113 0.00188679])

当然,对于英文语料,需要排除大部分的虚词以及常用无意义词,例如it, this, there, that...在实际操作中,需要合理地设置参数。

换中文语料尝试,采用习大大就卡斯特罗逝世发表的吊唁文章和朴槿惠辞职的新闻。

Topic: 0 的 同志 和 人民 卡斯特罗 菲德尔 古巴 他 了 我 Topic: 1 在 朴槿惠 向 表示 总统 对 将 的 月 国民 Doc 0:((top topic:0) topic distribution:[ 0.91714123 0.08285877]) Doc 1:((top topic:1) topic distribution:[ 0.09200666 0.90799334])

还是存在一些虚词,例如“的”,“和”,“了”,“对”等词的干扰,但是大致来说,两则新闻的主题分布很明显,效果还不赖。

总结

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