rebased

gensim/models/basemodel.py

@@ -0,0 +1,10 @@
+class BaseTopicModel():
+    def print_topic(self, topicno, topn=10):
+        """
+        Return a single topic as a formatted string. See `show_topic()` for parameters.
+
+        >>> lsimodel.print_topic(10, topn=5)
+        '-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + -0.174 * "functor" + -0.168 * "operator"'
+
+        """
+        return ' + '.join(['%.3f*"%s"' % (v, k) for k, v in self.show_topic(topicno, topn)])

gensim/models/ldamodel.py

@@ -36,6 +36,8 @@
 import numbers
 
 from gensim import interfaces, utils, matutils
+from gensim.models import basemodel
+
 from itertools import chain
 from scipy.special import gammaln, psi  # gamma function utils
 from scipy.special import polygamma
@@ -193,7 +195,7 @@ def get_Elogbeta(self):
 # endclass LdaState
 
 
-class LdaModel(interfaces.TransformationABC):
+class LdaModel(interfaces.TransformationABC,basemodel.BaseTopicModel):
     """
     The constructor estimates Latent Dirichlet Allocation model parameters based
     on a training corpus:
@@ -833,10 +835,6 @@ def get_topic_terms(self, topicid, topn=10):
         bestn = matutils.argsort(topic, topn, reverse=True)
         return [(id, topic[id]) for id in bestn]
 
-    def print_topic(self, topicid, topn=10):
-        """Return the result of `show_topic`, but formatted as a single string."""
-        return ' + '.join(['%.3f*%s' % (v, k) for k, v in self.show_topic(topicid, topn)])
-
     def top_topics(self, corpus, num_words=20):
         """
         Calculate the Umass topic coherence for each topic. Algorithm from

gensim/models/lsimodel.py

@@ -59,6 +59,8 @@
 from scipy.sparse import sparsetools
 
 from gensim import interfaces, matutils, utils
+from gensim.models import basemodel
+
 from six import iterkeys
 from six.moves import xrange
 
@@ -221,7 +223,7 @@ def merge(self, other, decay=1.0):
 #endclass Projection
 
 
-class LsiModel(interfaces.TransformationABC):
+class LsiModel(interfaces.TransformationABC,basemodel.BaseTopicModel):
     """
     Objects of this class allow building and maintaining a model for Latent
     Semantic Indexing (also known as Latent Semantic Analysis).
@@ -490,16 +492,6 @@ def show_topic(self, topicno, topn=10):
         most = matutils.argsort(numpy.abs(c), topn, reverse=True)
         return [(self.id2word[val], 1.0 * c[val] / norm) for val in most]
 
-    def print_topic(self, topicno, topn=10):
-        """
-        Return a single topic as a formatted string. See `show_topic()` for parameters.
-
-        >>> lsimodel.print_topic(10, topn=5)
-        '-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + -0.174 * "functor" + -0.168 * "operator"'
-
-        """
-        return ' + '.join(['%.3f*"%s"' % (v, k) for k, v in self.show_topic(topicno, topn)])
-
     def show_topics(self, num_topics=-1, num_words=10, log=False, formatted=True):
         """
         Return `num_topics` most significant topics (return all by default).