piskvorky#1380: Make topn a property so setting it to higher values…

… will uncache the accumulator and the topics will be shrunk/expanded accordingly.

gensim/models/coherencemodel.py

@@ -24,8 +24,7 @@
 
 import numpy as np
 
-from gensim import interfaces
-from gensim.matutils import argsort
+from gensim import interfaces, matutils
 from gensim.topic_coherence import (segmentation, probability_estimation,
                                     direct_confirmation_measure, indirect_confirmation_measure,
                                     aggregation)
@@ -209,7 +208,7 @@ def __init__(self, model=None, topics=None, texts=None, corpus=None, dictionary=
         else:
             raise ValueError("%s coherence is not currently supported.", coherence)
 
-        self.topn = topn
+        self._topn = topn
         self._model = model
         self._accumulator = None
         self._topics = None
@@ -232,13 +231,34 @@ def model(self, model):
             self._update_accumulator(new_topics)
             self._topics = new_topics
 
+    @property
+    def topn(self):
+        return self._topn
+
+    @topn.setter
+    def topn(self, topn):
+        current_topic_length = len(self._topics[0])
+        requires_expansion = current_topic_length < topn
+
+        if self.model is not None:
+            self._topn = topn
+            if requires_expansion:
+                self.model = self._model  # trigger topic expansion from model
+        else:
+            if requires_expansion:
+                raise ValueError("Model unavailable and topic sizes are less than topn=%d" % topn)
+            self._topn = topn  # topics will be truncated in getter
+
     @property
     def measure(self):
         return COHERENCE_MEASURES[self.coherence]
 
     @property
     def topics(self):
-        return self._topics
+        if len(self._topics[0]) > self._topn:
+            return [topic[:self._topn] for topic in self._topics]
+        else:
+            return self._topics
 
     @topics.setter
     def topics(self, topics):
@@ -279,7 +299,7 @@ def _get_topics(self):
         """Internal helper function to return topics from a trained topic model."""
         try:
             return [
-                argsort(topic, topn=self.topn, reverse=True) for topic in
+                matutils.argsort(topic, topn=self.topn, reverse=True) for topic in
                 self.model.get_topics()
             ]
         except AttributeError:

gensim/test/test_coherencemodel.py

@@ -276,6 +276,45 @@ def testAccumulatorCachingWithModelSetting(self):
         self.assertTrue(np.array_equal(topics, cm1.topics))
         self.assertIsNone(cm1._accumulator)
 
+    def testAccumulatorCachingWithTopnSettingGivenTopics(self):
+        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, topn=5, coherence='u_mass')
+        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
+        cm1.estimate_probabilities()
+        self.assertIsNotNone(cm1._accumulator)
+
+        accumulator = cm1._accumulator
+        topics_before = cm1._topics
+        cm1.topn = 3
+        self.assertEqual(accumulator, cm1._accumulator)
+        self.assertEqual(3, len(cm1.topics[0]))
+        self.assertEqual(topics_before, cm1._topics)
+
+        # Topics should not have been truncated, so topn settings below 5 should work
+        cm1.topn = 4
+        self.assertEqual(accumulator, cm1._accumulator)
+        self.assertEqual(4, len(cm1.topics[0]))
+        self.assertEqual(topics_before, cm1._topics)
+
+        with self.assertRaises(ValueError):
+            cm1.topn = 6  # can't expand topics any further without model
+
+    def testAccumulatorCachingWithTopnSettingGivenModel(self):
+        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, topn=5, coherence='u_mass')
+        cm1 = CoherenceModel(model=self.ldamodel, **kwargs)
+        cm1.estimate_probabilities()
+        self.assertIsNotNone(cm1._accumulator)
+
+        accumulator = cm1._accumulator
+        topics_before = cm1._topics
+        cm1.topn = 3
+        self.assertEqual(accumulator, cm1._accumulator)
+        self.assertEqual(3, len(cm1.topics[0]))
+        self.assertEqual(topics_before, cm1._topics)
+
+        cm1.topn = 6  # should be able to expand given the model
+        self.assertIsNone(cm1._accumulator)  # should uncache due to missing terms in accumulator
+        self.assertEqual(6, len(cm1.topics[0]))
+
 
 if __name__ == '__main__':
     logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)