Make negative ns_exponent work correctly

gensim/models/word2vec.py

@@ -388,7 +388,7 @@ def __init__(
 
         self.hs = int(hs)
         self.negative = int(negative)
-        self.ns_exponent = ns_exponent
+        self.ns_exponent = float(ns_exponent)
         self.cbow_mean = int(cbow_mean)
         self.compute_loss = bool(compute_loss)
         self.running_training_loss = 0
@@ -1947,6 +1947,7 @@ def _load_specials(self, *args, **kwargs):
         # for backward compatibility, add/rearrange properties from prior versions
         if not hasattr(self, 'ns_exponent'):
             self.ns_exponent = 0.75
+        self.ns_exponent = float(self.ns_exponent)
         if self.negative and hasattr(self.wv, 'index_to_key'):
             self.make_cum_table()  # rebuild cum_table from vocabulary
         if not hasattr(self, 'corpus_count'):

gensim/test/test_doc2vec.py

@@ -720,6 +720,15 @@ def test_train_warning(self, loglines):
     def test_load_on_class_error(self):
         """Test if exception is raised when loading doc2vec model on instance"""
         self.assertRaises(AttributeError, load_on_instance)
+
+    def test_negative_ns_exp(self):
+        # We expect that model should train, save, load and continue training without any exceptions
+        model = doc2vec.Doc2Vec(sentences, ns_exponent=-1, min_count=1, workers=1)
+        tmpf = get_tmpfile('d2v_negative_exp.tst')
+        model.save(tmpf)
+        loaded_model = doc2vec.Doc2Vec.load(tmpf)
+        loaded_model.train(sentences, total_examples=model.corpus_count, epochs=1)
+        assert loaded_model.ns_exponent == -1, loaded_model.ns_exponent
 # endclass TestDoc2VecModel
 
 

gensim/test/test_fasttext.py

@@ -762,6 +762,15 @@ def test_vectors_for_all_without_inference(self):
         predicted = vectors_for_all['responding']
         assert np.allclose(expected, predicted)
 
+    def test_negative_ns_exp(self):
+        # We expect that model should train, save, load and continue training without any exceptions
+        model = FT_gensim(sentences, ns_exponent=-1, min_count=1, workers=1)
+        tmpf = get_tmpfile('fasttext_negative_exp.tst')
+        model.save(tmpf)
+        loaded_model = FT_gensim.load(tmpf)
+        loaded_model.train(sentences, total_examples=model.corpus_count, epochs=1)
+        assert loaded_model.ns_exponent == -1, loaded_model.ns_exponent
+
 
 @pytest.mark.parametrize('shrink_windows', [True, False])
 def test_cbow_hs_training(shrink_windows):

gensim/test/test_word2vec.py

@@ -1054,6 +1054,15 @@ def test_compute_training_loss(self):
         training_loss_val = model.get_latest_training_loss()
         self.assertTrue(training_loss_val > 0.0)
 
+    def test_negative_ns_exp(self):
+        # We expect that model should train, save, load and continue training without any exceptions
+        model = word2vec.Word2Vec(sentences, ns_exponent=-1, min_count=1, workers=1)
+        tmpf = get_tmpfile('w2v_negative_exp.tst')
+        model.save(tmpf)
+        loaded_model = word2vec.Word2Vec.load(tmpf)
+        loaded_model.train(sentences, total_examples=model.corpus_count, epochs=1)
+        assert loaded_model.ns_exponent == -1, loaded_model.ns_exponent
+
 
 # endclass TestWord2VecModel