Gensim: How to save LDA model's produced topics to a readable format (csv,txt,etc)?

Question

last parts of the code:

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

bash output:

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>

So I'm wondering i'm able to save the resulting topics that it generated, to a readable format. I've tried the .save() methods, but it always outputs something unreadable.

Answer 1

Here is how to save a model for gensim LDA:

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')

To print topics, here are a few ways:

# 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)])