本文整理汇总了Python中gensim.models.ldamodel.LdaModel.bound方法的典型用法代码示例。如果您正苦于以下问题：Python LdaModel.bound方法的具体用法？Python LdaModel.bound怎么用？Python LdaModel.bound使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类gensim.models.ldamodel.LdaModel的用法示例。
在下文中一共展示了LdaModel.bound方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train_model
# 需要导入模块: from gensim.models.ldamodel import LdaModel [as 别名]
# 或者: from gensim.models.ldamodel.LdaModel import bound [as 别名]
def train_model(texts, **kwargs):
# parse args
filter_stopwords = kwargs.get('filter_stopwords', True)
normalizer = kwargs.get('normalizer', 'porter')
tfidf = kwargs.get('tfidf', True)
num_topics = kwargs.get('num_topics', 20)
min_freq = kwargs.get('min_freq', 2)
use_pickle = kwargs.get('use_pickle', True)
update_pickle = kwargs.get('update_pickle', True)
report = kwargs.get('report', True)
distributed = kwargs.get('distributed', False)
# build corpus or read it in from pickle
if use_pickle:
print "INFO: loading pickled corpus and word hash"
corpus = pickle.load( open( "pickles/corpus.p", "rb" ) )
id2word = pickle.load( open( "pickles/id2word.p", "rb" ) )
else:
print "INFO: processing text and building corpus..."
corpus, id2word = process_texts(
texts = texts,
filter_stopwords = filter_stopwords,
normalizer = normalizer,
min_freq = min_freq
)
if update_pickle:
# pickle files
print "INFO: updating pickled coprus and word hash"
pickle.dump(corpus, open( "pickles/corpus.p", "wb" ) )
pickle.dump(id2word, open( "pickles/id2word.p", "wb" ) )
# optional tfidf transformation
if tfidf:
print "INFO: applying tfidf transformation..."
tfidf = TfidfModel(corpus)
corpus = tfidf[corpus]
# fit model
print "INFO: fitting model..."
lda = LdaModel(
corpus = corpus,
id2word = id2word,
num_topics = num_topics,
distributed = distributed
)
# report
if report:
perplexity = lda.bound(corpus)
print "RESULTS:"
print "\nperplexity: ", perplexity, "\n"
topics = lda.show_topics(num_topics)
for i, t in enumerate(topics):
print "topic %d:" % i
print t
return lda, corpus, id2word