Merge pull request #156 from hugovk/add-3.7

Add support for Python 3.7

.travis.yml

@@ -1,7 +1,5 @@
 language: python
 
-sudo: false
-
 cache:
   directories:
   - $HOME/env
@@ -15,6 +13,11 @@ python:
     - "3.5"
     - "3.6"
 
+matrix:
+  include:
+    - python: 3.7
+      dist: xenial
+
 before_install:
     - bash .travis_dependencies.sh
     - export PATH="$HOME/env/miniconda$TRAVIS_PYTHON_VERSION/bin:$PATH";

examples/mux/mux_files_example.py

@@ -110,4 +110,4 @@ def npz_generator(npz_path):
 # At this point, you can use the Mux as a streamer normally:
 
 for data in mux(max_iter=10):
-    print(dict((k, v.shape) for k, v in data.items()))
+    print({k: v.shape for k, v in data.items()})

setup.py

@@ -10,10 +10,11 @@
     description='Multiplex generators for incremental learning',
     author='Pescador developers',
     author_email='[email protected]',
-    url='http://github.com/pescadores/pescador',
-    download_url='http://github.com/pescadores/pescador/releases',
+    url='https://github.com/pescadores/pescador',
+    download_url='https://github.com/pescadores/pescador/releases',
     packages=find_packages(),
     long_description='Multiplex generators for incremental learning',
+    python_requires=">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*",
     classifiers=[
         "License :: OSI Approved :: ISC License (ISCL)",
         "Programming Language :: Python",
@@ -26,6 +27,7 @@
         "Programming Language :: Python :: 3.4",
         "Programming Language :: Python :: 3.5",
         "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3.7",
     ],
     keywords='machine learning',
     license='ISC',

tests/test_core.py

@@ -131,7 +131,7 @@ def test_streamer_cycle(generate):
         gen = streamer(cycle=True)
 
     for i, x in enumerate(gen):
-        data_results.append((isinstance(x, dict) and 'X' in x))
+        data_results.append(isinstance(x, dict) and 'X' in x)
         if (i + 1) >= count_max:
             break
     assert (len(data_results) == count_max and all(data_results))

tests/test_mux.py

@@ -158,8 +158,8 @@ def test_mux_of_mux():
 
     train_result = list(train_mux.iterate(100))
     sample_counts = collections.Counter(train_result)
-    assert set(sample_counts.keys()) == set([
-        'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
+    assert set(sample_counts.keys()) == {
+        'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'}
 
 
 @pytest.mark.parametrize('mux_class', [
@@ -647,7 +647,7 @@ def test_shuffled_mux_with_empty_streams(self):
         assert len(samples) == 30
 
         counter = collections.Counter(samples)
-        assert set(counter.keys()) == set(['a', 'b', 'c'])
+        assert set(counter.keys()) == {'a', 'b', 'c'}
         for key in ['a', 'b', 'c']:
             assert counter[key] > 0
 
@@ -664,7 +664,7 @@ def test_shuffled_mux_weights(self):
         counter = collections.Counter(samples)
 
         # Test that there is [a, b, c] in the set
-        assert set(counter.keys()) == set(['a', 'b', 'c'])
+        assert set(counter.keys()) == {'a', 'b', 'c'}
 
         # Test the statistics on the counts.
         # Does the sampling approximately match the weights?