9

代码的最后部分： 

lda = LdaModel(corpus=corpus,id2word=dictionary, num_topics=2)
print lda

bash 输出：

INFO : adding document #0 to Dictionary(0 unique tokens)
INFO : built Dictionary(18 unique tokens) from 5 documents (total  20 corpus positions)
INFO : using serial LDA version on this node
INFO : running online LDA training, 2 topics, 1 passes over the supplied corpus of 5 documents, updating model once every 5 documents
WARNING : too few updates, training might not converge; consider increasing the number of passes to improve accuracy
INFO : PROGRESS: iteration 0, at document #5/5
INFO : 2/5 documents converged within 50 iterations
INFO : topic #0: 0.079*cute + 0.076*broccoli + 0.070*adopted + 0.069*yesterday + 0.069*eat + 0.069*sister + 0.068*kitten + 0.068*kittens + 0.067*bananas + 0.067*chinchillas
INFO : topic #1: 0.082*broccoli + 0.079*cute + 0.071*piece + 0.070*munching + 0.069*spinach + 0.068*hamster + 0.068*ate + 0.067*banana + 0.066*breakfast + 0.066*smoothie
INFO : topic diff=0.470477, rho=1.000000
<gensim.models.ldamodel.LdaModel object at 0x10f1f4050>

所以我想知道我是否能够将它生成的结果主题保存为可读格式。我已经尝试过这些.save()方法，但它总是输出一些不可读的东西。

4

4 回答 4

29

以下是如何为 gensim LDA 保存模型：

from gensim import corpora, models, similarities

# create corpus and dictionary
corpus = ...
dictionary = ...

# train model, this might takes time
model = models.LdaModel.LdaModel(corpus=corpus,id2word=dictionary, num_topics=200,passes=5, alpha='auto')
# save model to disk (no need to use pickle module)
model.save('lda.model')

要打印主题，有以下几种方法：

# later on, load trained model from file
model =  models.LdaModel.load('lda.model')

# print all topics
model.show_topics(topics=200, topn=20)

# print topic 28
model.print_topic(109, topn=20)

# another way
for i in range(0, model.num_topics-1):
    print model.print_topic(i)

# and another way, only prints top words
for t in range(0, model.num_topics-1):
    print 'topic {}: '.format(t) + ', '.join([v[1] for v in model.show_topic(t, 20)])
于 2014-02-26T07:05:16.360 回答
3

您只需要使用lda.show_topics(topics=-1)或任意数量的主题（主题=10，主题=15，主题=1000 ....）。我通常只做：

logfile = open('.../yourfile.txt', 'a')
print>>logfile, lda.show_topics(topics=-1, topn=10)

所有这些参数和其他参数都可以在 gensim文档中找到。

于 2013-06-28T02:53:47.250 回答
3

您可以使用pickle模块。

import pickle
# your code
pickle.dump(lda,open(filename,'w'))
# you may load it back again
lda_copy = pickle.load(file(filename))
于 2013-06-27T22:44:41.637 回答
0

.save()给你模型本身，而不是主题（因此，不可读的输出）。

使用：

with open('topic_file', 'w') as topic_file:
    topics=lda_model.top_topics(corpus)
    topic_file.write('\n'.join('%s %s' %topic for topic in topics))

您将获得所有集群主题的可读文件及其相关概率。

于 2018-08-02T19:00:42.210 回答