title: How to print the LDA topics models from gensim? Python
body: <p>Using <code>gensim</code> I was able to extract topics from a set of documents in LSA but how do I access the topics generated from the LDA models?</p>
  
  <p>When printing the <code>lda.print_topics(10)</code> the code gave the following error because <code>print_topics()</code> return a <code>NoneType</code>:</p>
  
  <pre><code>Traceback (most recent call last):
    File "/home/alvas/workspace/XLINGTOP/xlingtop.py", line 93, in &lt;module&gt;
      for top in lda.print_topics(2):
  TypeError: 'NoneType' object is not iterable
  </code></pre>
  
  <p>The code:</p>
  
  <pre><code>from gensim import corpora, models, similarities
  from gensim.models import hdpmodel, ldamodel
  from itertools import izip
  
  documents = ["Human machine interface for lab abc computer applications",
                "A survey of user opinion of computer system response time",
                "The EPS user interface management system",
                "System and human system engineering testing of EPS",
                "Relation of user perceived response time to error measurement",
                "The generation of random binary unordered trees",
                "The intersection graph of paths in trees",
                "Graph minors IV Widths of trees and well quasi ordering",
                "Graph minors A survey"]
  
  # remove common words and tokenize
  stoplist = set('for a of the and to in'.split())
  texts = [[word for word in document.lower().split() if word not in stoplist]
           for document in documents]
  
  # remove words that appear only once
  all_tokens = sum(texts, [])
  tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)
  texts = [[word for word in text if word not in tokens_once]
           for text in texts]
  
  dictionary = corpora.Dictionary(texts)
  corpus = [dictionary.doc2bow(text) for text in texts]
  
  # I can print out the topics for LSA
  lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2)
  corpus_lsi = lsi[corpus]
  
  for l,t in izip(corpus_lsi,corpus):
    print l,"#",t
  print
  for top in lsi.print_topics(2):
    print top
  
  # I can print out the documents and which is the most probable topics for each doc.
  lda = ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=50)
  corpus_lda = lda[corpus]
  
  for l,t in izip(corpus_lda,corpus):
    print l,"#",t
  print
  
  # But I am unable to print out the topics, how should i do it?
  for top in lda.print_topics(10):
    print top
  </code></pre>