title: Print document wise topics in Gensim
body: <p>I am trying to learn topic modelling using Gensim python library.
  I have tried so many different tutorials including official one.</p>
  
  <p>Question:
  How do I get document wise topic distribution using Gensim.</p>
  
  <p>My current output is list of topics with its keywords and probability as shown below.</p>
  
  <pre><code>(0, u'0.086*good + 0.086*brocolli + 0.086*health + 0.061*eat)
  (1, u'0.068*mother + 0.068*brother + 0.068*drive + 0.041*pressur)
  </code></pre>
  
  <p>I would like to know if it is possible to have a list of say each document and top topics for that particular document?</p>
  
  <p>My code is as following:</p>
  
  <pre><code>tokenizer = RegexpTokenizer(r'\w+')
  
  # create English stop words list
  en_stop = get_stop_words('en')
  
  # Create p_stemmer of class PorterStemmer
  p_stemmer = PorterStemmer()
  
  # create sample documents
  doc_a = "Brocolli is good to eat. My brother likes to eat good brocolli, but not my mother."
  doc_b = "My mother spends a lot of time driving my brother around to baseball practice."
  doc_c = "Some health experts suggest that driving may cause increased tension and blood pressure."
  doc_d = "I often feel pressure to perform well at school, but my mother never seems to drive my brother to do better."
  doc_e = "Health professionals say that brocolli is good for your health." 
  
  # compile sample documents into a list
  doc_set = [doc_a, doc_b, doc_c, doc_d, doc_e]
  num_topics=2;
  
  # list for tokenized documents in loop
  texts = []
  
  # loop through document list
  for i in doc_set:
  
      # clean and tokenize document string
      raw = i.lower()
      tokens = tokenizer.tokenize(raw)
  
      # remove stop words from tokens
      stopped_tokens = [i for i in tokens if not i in en_stop]
      #print(stopped_tokens)
  
      # stem tokens
      stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
      #print("printing stemmed tokens")
      #print(stemmed_tokens)
  
      # add tokens to list
      texts.append(stemmed_tokens)
  
  # turn our tokenized documents into a id &lt;-&gt; term dictionary
  dictionary = corpora.Dictionary(texts)
  print("printing each token/words along with unique integer id..")
  print(dictionary.token2id)
  
  # convert tokenized documents into a document-term matrix
  corpus = [dictionary.doc2bow(text) for text in texts]
  print("printing sample bag of words")
  print(corpus[0])
  
  # generate LDA model
  ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=2, id2word = dictionary, passes=20)
  #print(ldamodel.print_topics(num_topics=3, num_words=3))
  #print ldamodel.top_topics(corpus,2)
  print(ldamodel.show_topics(num_topics=2, num_words=3, log=False, formatted=True))
  print(ldamodel.show_topics())
  print("from for loop.")
  for i in ldamodel.show_topics(len(dictionary)):
      print i
  </code></pre>