def test_predict_topic(self): lda = CustomLda(self.text) lda.train(2) docs = [['good', 'one'], ['bad', 'one']] topics = lda.predict_topic(docs) self.assertEqual(type(topics), list) self.assertEqual(len(topics), 2)
def test_lda_init(self): lda = CustomLda(self.text) self.assertEqual(type(lda.data), list) self.assertEqual(type(lda.corpus), list) self.assertGreater(len(lda.dictionary), 1) self.assertGreater(len(lda.data), 1) self.assertGreater(len(lda.corpus), 1)
def test_pickle_unpickle(self): lda = CustomLda(self.text) lda.train(2) filename = os.path.join(self.test_dir, 'model.pickle') lda.pickle(filename) lda2 = CustomLda().unpickle(filename) self.assertEqual(lda.corpus, lda2.corpus) self.assertEqual(lda.data, lda2.data) self.assertEqual(lda.dictionary, lda2.dictionary)
def test_save_lda(self): lda = CustomLda(self.text) lda.train(2) filename = os.path.join(self.test_dir, 'model.lda') lda.save_lda(filename) self.assertTrue(os.path.isfile(filename))
def test_save_ldavis(self): lda = CustomLda(self.text) lda.train(2) filename = os.path.join(self.test_dir, 'test_ldavis.html') lda.save_ldavis(filename) self.assertTrue(os.path.isfile(filename))
def test_get_perplexity(self): lda = CustomLda(self.text) lda.train(2) perp = lda.get_preplexity() self.assertEqual(type(perp), np.float64)
def test_get_topic_terms(self): lda = CustomLda(self.text) lda.train(2) topics = lda.get_topic_terms(1) self.assertEqual(type(topics), list) self.assertGreater(len(topics), 1)
def test_get_coherence(self): lda = CustomLda(self.text) lda.train(2) coherence = lda.get_coherence() self.assertEqual(type(coherence), np.float64)
def test_train_multicore(self): lda = CustomLda(self.text) lda.train(2, workers=2) self.assertEqual(lda.num_topics, 2)
def test_train(self): lda = CustomLda(self.text) lda.train(2) self.assertEqual(lda.num_topics, 2)