import logging, gensim logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) # load id->word mapping (the dictionary), one of the results of step 2 above id2word = gensim.corpora.Dictionary.load_from_text('wiki_cs/wiki_cs_wordids.txt') # load corpus iterator mm = gensim.corpora.MmCorpus('wiki_cs/wiki_cs_tfidf.mm') # extract 10 LSA topics; use the default one-pass algorithm lsa = gensim.models.lsimodel.LsiModel(corpus=mm, id2word=id2word, num_topics=10) # extract 10 LDA topics, using 1 pass and updating once every 1 chunk (5,000 documents) lda = gensim.models.ldamodel.LdaModel(corpus=mm, id2word=id2word, num_topics=10, update_every=1, chunksize=5000, passes=1)