ENH Use matutils.corpus2dense instead of looping

luispedro · luispedro · commit 30a1512cd0c1 · 2015-03-20T13:07:43.000+01:00
diff --git a/ch04/blei_lda.py b/ch04/blei_lda.py
@@ -8,7 +8,7 @@
 from __future__ import print_function
 from wordcloud import create_cloud
 try:
- from gensim import corpora, models
+ from gensim import corpora, models, matutils
 except:
 print("import gensim failed.")
 print()
@@ -44,15 +44,11 @@
 # We first identify the most discussed topic, i.e., the one with the
 # highest total weight
 
-# First, we need to sum up the weights across all the documents
-weight = np.zeros(model.num_topics)
-for doc in corpus:
- for col, val in model[doc]:
- weight[col] += val
- # As a reasonable alternative, we could have used the log of val:
- # weight[col] += np.log(val)
+topics = matutils.corpus2dense(model[corpus], num_terms=model.num_topics)
+weight = topics.sum(1)
 max_topic = weight.argmax()
 
+
 # Get the top 64 words for this topic
 # Without the argument, show_topic would return only 10 words
 words = model.show_topic(max_topic, 64)

-Original file line number
+Diff line change
 from __future__ importprint_function
 fromwordcloudimportcreate_cloud
 try:
 -fromgensimimportcorpora, models
 +fromgensimimportcorpora, models, matutils
 except:
 print("import gensim failed.")
 print()
 # We first identify the most discussed topic, i.e., the one with the
 # highest total weight
 -# First, we need to sum up the weights across all the documents
 -weight=np.zeros(model.num_topics)
 -fordocincorpus:
 -forcol, valinmodel[doc]:
 -weight[col] +=val
 -# As a reasonable alternative, we could have used the log of val:
 -# weight[col] += np.log(val)
 +topics=matutils.corpus2dense(model[corpus], num_terms=model.num_topics)
 +weight=topics.sum(1)
 max_topic=weight.argmax()
++
 # Get the top 64 words for this topic
 # Without the argument, show_topic would return only 10 words
 words=model.show_topic(max_topic, 64)