def LSAmodel(words, num_topics=5, num_words=5):
dictionary = corpora.Dictionary(words)
# Term Document Frequency
corpus = [dictionary.doc2bow(word) for word in words]
# save it!
pickle.dump(corpus, open('corpus.pkl', 'wb'))
dictionary.save('dictionary.gensim')
# Train model
lsimodel = LsiModel(corpus=corpus, num_topics=num_topics, id2word=dictionary)
# print_topics(num_topics=20, num_words=10)
topics = lsimodel.print_topics(num_topics=num_topics, num_words=num_words)
# Validation
# A measure of how good the model is. lower the better.
val_perplexity = lsimodel.log_perplexity(corpus)
# cohherent score
coherence_lsimodel = CoherenceModel(model=lsimodel, texts=words, dictionary=dictionary, coherence='c_v')
val_coherence = coherence_lsimodel.get_coherence()
return topics, val_perplexity, val_coherence
