diff --git a/gensim/models/wrappers/ldamallet.py b/gensim/models/wrappers/ldamallet.py
index ab4cdc4adf..c93af78a1a 100644
--- a/gensim/models/wrappers/ldamallet.py
+++ b/gensim/models/wrappers/ldamallet.py
@@ -183,7 +183,7 @@ def __getitem__(self, bow, iterations=100):
 
     def load_word_topics(self):
         logger.info("loading assigned topics from %s", self.fstate())
-        word_topics = numpy.zeros((self.num_topics, self.num_terms), dtype=numpy.float32)
+        word_topics = numpy.zeros((self.num_topics, self.num_terms), dtype=numpy.float64)
         if hasattr(self.id2word, 'token2id'):
             word2id = self.id2word.token2id
         else:
diff --git a/gensim/test/test_sklearn_api.py b/gensim/test/test_sklearn_api.py
index d990cc403e..997b3d9fb1 100644
--- a/gensim/test/test_sklearn_api.py
+++ b/gensim/test/test_sklearn_api.py
@@ -846,7 +846,7 @@ def testModelNotFitted(self):
 class TestHdpTransformer(unittest.TestCase):
     def setUp(self):
         numpy.random.seed(0)
-        self.model = HdpTransformer(id2word=dictionary)
+        self.model = HdpTransformer(id2word=dictionary, random_state=42)
         self.corpus = mmcorpus.MmCorpus(datapath('testcorpus.mm'))
         self.model.fit(self.corpus)
 
@@ -855,23 +855,23 @@ def testTransform(self):
         doc = self.corpus[0]
         transformed_doc = self.model.transform(doc)
         expected_doc = [[0.81043386270128193, 0.049357139518070477, 0.035840906753517532, 0.026542006926698079, 0.019925705902962578, 0.014776690981729117, 0.011068909979528148]]
-        self.assertTrue(numpy.allclose(transformed_doc, expected_doc))
+        self.assertTrue(numpy.allclose(transformed_doc, expected_doc, atol=1e-2))
 
         # tranform multiple documents
         docs = [self.corpus[0], self.corpus[1]]
         transformed_docs = self.model.transform(docs)
         expected_docs = [[0.81043386270128193, 0.049357139518070477, 0.035840906753517532, 0.026542006926698079, 0.019925705902962578, 0.014776690981729117, 0.011068909979528148],
-            [0.0368655605, 0.709055041, 0.194436428, 0.0151706795, 0.0113863652, 1.00000000e-12, 1.00000000e-12]]
-        self.assertTrue(numpy.allclose(transformed_docs[0], expected_docs[0]))
-        self.assertTrue(numpy.allclose(transformed_docs[1], expected_docs[1]))
+            [0.03795908, 0.39542609, 0.50650585, 0.0151082, 0.01132749, 0., 0.]]
+        self.assertTrue(numpy.allclose(transformed_docs[0], expected_docs[0], atol=1e-2))
+        self.assertTrue(numpy.allclose(transformed_docs[1], expected_docs[1], atol=1e-2))
 
     def testPartialFit(self):
-        for i in range(10):
+        for i in range(5):
             self.model.partial_fit(X=self.corpus)  # fit against the model again
-            doc = list(self.corpus)[0]  # transform only the first document
-            transformed = self.model.transform(doc)
-        expected = numpy.array([0.76777752, 0.01757334, 0.01600339, 0.01374061, 0.01275931, 0.01126313, 0.01058131, 0.01167185])
-        passed = numpy.allclose(sorted(transformed[0]), sorted(expected), atol=1e-1)
+
+        transformed = self.model.transform(list(self.corpus)[0])
+        expected = numpy.array([0.77901173, 0.0232508, 0.02054655, 0.01769651, 0.01600487, 0.01478038, 0.01237056, 0.01194372, 0.01070444])
+        passed = numpy.allclose(transformed[0], expected, atol=1e-2)
         self.assertTrue(passed)
 
     def testSetGetParams(self):