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。
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