Fixed KeyError in coherence model

CHANGELOG.md

@@ -9,6 +9,7 @@ Changes
 * [#3136](https://github.com/RaRe-Technologies/gensim/pull/3136): Fix indexing error in word2vec_inner.pyx, by [@bluekura](https://github.com/bluekura)
 * [#3174](https://github.com/RaRe-Technologies/gensim/pull/3174): Fix a bug when upgrading phraser from gensim 3.x to 4.0, by [@emgucv](https://github.com/emgucv)
 * [#3176](https://github.com/RaRe-Technologies/gensim/pull/3176): Eliminate obsolete step parameter from doc2vec infer_vector and similarity_unseen_docs, by [@rock420](https://github.com/rock420)
+* [#2830](https://github.com/RaRe-Technologies/gensim/pull/2830): Fixed KeyError in coherence model, by [@pietrotrope](https://github.com/pietrotrope)
 
 ### :+1: Improvements
 

gensim/models/coherencemodel.py

@@ -444,11 +444,14 @@ def topics(self, topics):
         self._topics = new_topics
 
     def _ensure_elements_are_ids(self, topic):
-        try:
-            return np.array([self.dictionary.token2id[token] for token in topic])
-        except KeyError:  # might be a list of token ids already, but let's verify all in dict
-            topic = (self.dictionary.id2token[_id] for _id in topic)
-            return np.array([self.dictionary.token2id[token] for token in topic])
+        ids_from_tokens = [self.dictionary.token2id[t] for t in topic if t in self.dictionary.token2id]
+        ids_from_ids = [i for i in topic if i in self.dictionary]
+        if len(ids_from_tokens) > len(ids_from_ids):
+            return np.array(ids_from_tokens)
+        elif len(ids_from_ids) > len(ids_from_tokens):
+            return np.array(ids_from_ids)
+        else:
+            raise ValueError('unable to interpret topic as either a list of tokens or a list of ids')
 
     def _update_accumulator(self, new_topics):
         if self._relevant_ids_will_differ(new_topics):

gensim/test/test_coherencemodel.py

@@ -0,0 +1,300 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2010 Radim Rehurek <radimrehurek@seznam.cz>
+# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
+
+"""
+Automated tests for checking transformation algorithms (the models package).
+"""
+
+import logging
+import unittest
+import multiprocessing as mp
+from functools import partial
+
+import numpy as np
+from gensim.matutils import argsort
+from gensim.models.coherencemodel import CoherenceModel, BOOLEAN_DOCUMENT_BASED
+from gensim.models.ldamodel import LdaModel
+from gensim.test.utils import get_tmpfile, common_texts, common_dictionary, common_corpus
+
+
+class TestCoherenceModel(unittest.TestCase):
+
+    # set up vars used in testing ("Deerwester" from the web tutorial)
+    texts = common_texts
+    dictionary = common_dictionary
+    corpus = common_corpus
+
+    def setUp(self):
+        # Suppose given below are the topics which two different LdaModels come up with.
+        # `topics1` is clearly better as it has a clear distinction between system-human
+        # interaction and graphs. Hence both the coherence measures for `topics1` should be
+        # greater.
+        self.topics1 = [
+            ['human', 'computer', 'system', 'interface'],
+            ['graph', 'minors', 'trees', 'eps']
+        ]
+        self.topics2 = [
+            ['user', 'graph', 'minors', 'system'],
+            ['time', 'graph', 'survey', 'minors']
+        ]
+        self.topics3 = [
+            ['token', 'computer', 'system', 'interface'],
+            ['graph', 'minors', 'trees', 'eps']
+        ]
+        # using this list the model should be unable to interpret topic
+        # as either a list of tokens or a list of ids
+        self.topics4 = [
+            ['not a token', 'not an id', 'tests using', "this list"],
+            ['should raise', 'an error', 'to pass', 'correctly']
+        ]
+        self.topicIds1 = []
+        for topic in self.topics1:
+            self.topicIds1.append([self.dictionary.token2id[token] for token in topic])
+
+        self.ldamodel = LdaModel(
+            corpus=self.corpus, id2word=self.dictionary, num_topics=2,
+            passes=0, iterations=0
+        )
+
+    def check_coherence_measure(self, coherence):
+        """Check provided topic coherence algorithm on given topics"""
+        if coherence in BOOLEAN_DOCUMENT_BASED:
+            kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence=coherence)
+        else:
+            kwargs = dict(texts=self.texts, dictionary=self.dictionary, coherence=coherence)
+
+        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
+        cm2 = CoherenceModel(topics=self.topics2, **kwargs)
+        cm3 = CoherenceModel(topics=self.topics3, **kwargs)
+        cm4 = CoherenceModel(topics=self.topicIds1, **kwargs)
+        self.assertRaises(ValueError, lambda: CoherenceModel(topics=self.topics4, **kwargs))
+        self.assertEqual(cm1.get_coherence(), cm4.get_coherence())
+        self.assertIsInstance(cm3.get_coherence(), np.double)
+        self.assertGreater(cm1.get_coherence(), cm2.get_coherence())
+
+    def testUMass(self):
+        """Test U_Mass topic coherence algorithm on given topics"""
+        self.check_coherence_measure('u_mass')
+
+    def testCv(self):
+        """Test C_v topic coherence algorithm on given topics"""
+        self.check_coherence_measure('c_v')
+
+    def testCuci(self):
+        """Test C_uci topic coherence algorithm on given topics"""
+        self.check_coherence_measure('c_uci')
+
+    def testCnpmi(self):
+        """Test C_npmi topic coherence algorithm on given topics"""
+        self.check_coherence_measure('c_npmi')
+
+    def testUMassLdaModel(self):
+        """Perform sanity check to see if u_mass coherence works with LDA Model"""
+        # Note that this is just a sanity check because LDA does not guarantee a better coherence
+        # value on the topics if iterations are increased. This can be seen here:
+        # https://gist.github.com/dsquareindia/60fd9ab65b673711c3fa00509287ddde
+        CoherenceModel(model=self.ldamodel, corpus=self.corpus, coherence='u_mass')
+
+    def testCvLdaModel(self):
+        """Perform sanity check to see if c_v coherence works with LDA Model"""
+        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_v')
+
+    def testCw2vLdaModel(self):
+        """Perform sanity check to see if c_w2v coherence works with LDAModel."""
+        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_w2v')
+
+    def testCuciLdaModel(self):
+        """Perform sanity check to see if c_uci coherence works with LDA Model"""
+        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_uci')
+
+    def testCnpmiLdaModel(self):
+        """Perform sanity check to see if c_npmi coherence works with LDA Model"""
+        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_npmi')
+
+    def testErrors(self):
+        """Test if errors are raised on bad input"""
+        # not providing dictionary
+        self.assertRaises(
+            ValueError, CoherenceModel, topics=self.topics1, corpus=self.corpus,
+            coherence='u_mass'
+        )
+        # not providing texts for c_v and instead providing corpus
+        self.assertRaises(
+            ValueError, CoherenceModel, topics=self.topics1, corpus=self.corpus,
+            dictionary=self.dictionary, coherence='c_v'
+        )
+        # not providing corpus or texts for u_mass
+        self.assertRaises(
+            ValueError, CoherenceModel, topics=self.topics1, dictionary=self.dictionary,
+            coherence='u_mass'
+        )
+
+    def testProcesses(self):
+        get_model = partial(CoherenceModel,
+            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass'
+        )
+
+        model, used_cpus = get_model(), mp.cpu_count() - 1
+        self.assertEqual(model.processes, used_cpus)
+        for p in range(-2, 1):
+            self.assertEqual(get_model(processes=p).processes, used_cpus)
+
+        for p in range(1, 4):
+            self.assertEqual(get_model(processes=p).processes, p)
+
+    def testPersistence(self):
+        fname = get_tmpfile('gensim_models_coherence.tst')
+        model = CoherenceModel(
+            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass'
+        )
+        model.save(fname)
+        model2 = CoherenceModel.load(fname)
+        self.assertTrue(model.get_coherence() == model2.get_coherence())
+
+    def testPersistenceCompressed(self):
+        fname = get_tmpfile('gensim_models_coherence.tst.gz')
+        model = CoherenceModel(
+            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass'
+        )
+        model.save(fname)
+        model2 = CoherenceModel.load(fname)
+        self.assertTrue(model.get_coherence() == model2.get_coherence())
+
+    def testPersistenceAfterProbabilityEstimationUsingCorpus(self):
+        fname = get_tmpfile('gensim_similarities.tst.pkl')
+        model = CoherenceModel(
+            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass'
+        )
+        model.estimate_probabilities()
+        model.save(fname)
+        model2 = CoherenceModel.load(fname)
+        self.assertIsNotNone(model2._accumulator)
+        self.assertTrue(model.get_coherence() == model2.get_coherence())
+
+    def testPersistenceAfterProbabilityEstimationUsingTexts(self):
+        fname = get_tmpfile('gensim_similarities.tst.pkl')
+        model = CoherenceModel(
+            topics=self.topics1, texts=self.texts, dictionary=self.dictionary, coherence='c_v'
+        )
+        model.estimate_probabilities()
+        model.save(fname)
+        model2 = CoherenceModel.load(fname)
+        self.assertIsNotNone(model2._accumulator)
+        self.assertTrue(model.get_coherence() == model2.get_coherence())
+
+    def testAccumulatorCachingSameSizeTopics(self):
+        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
+        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
+        cm1.estimate_probabilities()
+        accumulator = cm1._accumulator
+        self.assertIsNotNone(accumulator)
+        cm1.topics = self.topics1
+        self.assertEqual(accumulator, cm1._accumulator)
+        cm1.topics = self.topics2
+        self.assertEqual(None, cm1._accumulator)
+
+    def testAccumulatorCachingTopicSubsets(self):
+        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
+        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
+        cm1.estimate_probabilities()
+        accumulator = cm1._accumulator
+        self.assertIsNotNone(accumulator)
+        cm1.topics = [t[:2] for t in self.topics1]
+        self.assertEqual(accumulator, cm1._accumulator)
+        cm1.topics = self.topics1
+        self.assertEqual(accumulator, cm1._accumulator)
+
+    def testAccumulatorCachingWithModelSetting(self):
+        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
+        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
+        cm1.estimate_probabilities()
+        self.assertIsNotNone(cm1._accumulator)
+        cm1.model = self.ldamodel
+        topics = []
+        for topic in self.ldamodel.state.get_lambda():
+            bestn = argsort(topic, topn=cm1.topn, reverse=True)
+            topics.append(bestn)
+        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.assertEqual(6, len(cm1.topics[0]))
+
+    def testCompareCoherenceForTopics(self):
+        topics = [self.topics1, self.topics2]
+        cm = CoherenceModel.for_topics(
+            topics, dictionary=self.dictionary, texts=self.texts, coherence='c_v')
+        self.assertIsNotNone(cm._accumulator)
+
+        # Accumulator should have all relevant IDs.
+        for topic_list in topics:
+            cm.topics = topic_list
+            self.assertIsNotNone(cm._accumulator)
+
+        (coherence_topics1, coherence1), (coherence_topics2, coherence2) = \
+            cm.compare_model_topics(topics)
+
+        self.assertAlmostEqual(np.mean(coherence_topics1), coherence1, 4)
+        self.assertAlmostEqual(np.mean(coherence_topics2), coherence2, 4)
+        self.assertGreater(coherence1, coherence2)
+
+    def testCompareCoherenceForModels(self):
+        models = [self.ldamodel, self.ldamodel]
+        cm = CoherenceModel.for_models(
+            models, dictionary=self.dictionary, texts=self.texts, coherence='c_v')
+        self.assertIsNotNone(cm._accumulator)
+
+        # Accumulator should have all relevant IDs.
+        for model in models:
+            cm.model = model
+            self.assertIsNotNone(cm._accumulator)
+
+        (coherence_topics1, coherence1), (coherence_topics2, coherence2) = \
+            cm.compare_models(models)
+
+        self.assertAlmostEqual(np.mean(coherence_topics1), coherence1, 4)
+        self.assertAlmostEqual(np.mean(coherence_topics2), coherence2, 4)
+        self.assertAlmostEqual(coherence1, coherence2, places=4)
+
+
+if __name__ == '__main__':
+    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
+    unittest.main()