Fix additional warnings from the CI test suite

gensim/matutils.py

@@ -191,9 +191,9 @@ def pad(mat, padrow, padcol):
     if padcol < 0:
         padcol = 0
     rows, cols = mat.shape
-    return np.bmat([
-        [mat, np.matrix(np.zeros((rows, padcol)))],
-        [np.matrix(np.zeros((padrow, cols + padcol)))],
+    return np.block([
+        [mat, np.zeros((rows, padcol))],
+        [np.zeros((padrow, cols + padcol))],
     ])
 
 

gensim/models/keyedvectors.py

@@ -1475,7 +1475,7 @@ def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None,
                 row = self[key]
                 if binary:
                     row = row.astype(REAL)
-                    fout.write(utils.to_utf8(prefix + str(key)) + b" " + row.tostring())
+                    fout.write(utils.to_utf8(prefix + str(key)) + b" " + row.tobytes())
                 else:
                     fout.write(utils.to_utf8("%s%s %s\n" % (prefix, str(key), ' '.join(repr(val) for val in row))))
 

gensim/models/wrappers/wordrank.py

@@ -254,8 +254,7 @@ def load_wordrank_model(cls, model_file, vocab_file=None, context_file=None, sor
             If 1 - use ensemble of word and context vectors.
 
         """
-        glove2word2vec(model_file, model_file + '.w2vformat')
-        model = cls.load_word2vec_format('%s.w2vformat' % model_file)
+        model = cls.load_word2vec_format(model_file, binary=False, no_header=True)
         if ensemble and context_file:
             model.ensemble_embedding(model_file, context_file)
         if sorted_vocab and vocab_file:

gensim/test/test_fasttext.py

@@ -1375,7 +1375,8 @@ def test_in_vocab(self):
 
     def test_out_of_vocab(self):
         model = train_gensim(bucket=0)
-        self.assertRaises(KeyError, model.wv.word_vec, 'streamtrain')
+        with self.assertRaises(KeyError):
+            model.wv.get_vector('streamtrain')
 
     def test_cbow_neg(self):
         """See `gensim.test.test_word2vec.TestWord2VecModel.test_cbow_neg`."""

gensim/test/test_lsimodel.py

@@ -66,7 +66,7 @@ def testTransformFloat32(self):
     def testCorpusTransform(self):
         """Test lsi[corpus] transformation."""
         model = self.model
-        got = np.vstack(matutils.sparse2full(doc, 2) for doc in model[self.corpus])
+        got = np.vstack([matutils.sparse2full(doc, 2) for doc in model[self.corpus]])
         expected = np.array([
             [0.65946639, 0.14211544],
             [2.02454305, -0.42088759],

gensim/test/test_phrases.py

@@ -236,7 +236,7 @@ def testBigramConstructionFromArray(self):
         bigram1_seen = False
         bigram2_seen = False
 
-        for s in self.bigram[np.array(self.sentences)]:
+        for s in self.bigram[np.array(self.sentences, dtype=object)]:
             if not bigram1_seen and self.bigram1 in s:
                 bigram1_seen = True
             if not bigram2_seen and self.bigram2 in s:

gensim/test/test_wordrank_wrapper.py

@@ -39,15 +39,13 @@ def testLoadWordrankFormat(self):
         vocab_size, dim = 76, 50
         self.assertEqual(model.vectors.shape, (vocab_size, dim))
         self.assertEqual(len(model), vocab_size)
-        os.remove(self.wr_file + '.w2vformat')
 
     def testEnsemble(self):
         """Test ensemble of two embeddings"""
         if not self.wr_path:
             return
         new_emb = self.test_model.ensemble_embedding(self.wr_file, self.wr_file)
         self.assertEqual(new_emb.shape, (76, 50))
-        os.remove(self.wr_file + '.w2vformat')
 
     def testPersistence(self):
         """Test storing/loading the entire model"""