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'"color: #008080">总结
以上就是本文关于Python_LDA实现方法详解的全部内容，希望对大家有所帮助。感兴趣的朋友可以继续参阅本站：python+mongodb数据抓取详细介绍、Python探索之创建二叉树、Python探索之修改Python搜索路径等，有什么问题可以随时留言，欢迎大家一起交流讨论。感谢朋友们对本站的支持！