Merge pull request #112 from mottodora/v2

Apply autopep8 and flake8 to obey PEP8.

chainer_chemistry/models/schnet.py

@@ -92,15 +92,15 @@ def __init__(self, out_dim):
         super(AtomwiseLinear, self).__init__()
         with self.init_scope():
             self.out_dim = out_dim
-            self.l = links.Linear(self.out_dim)
+            self.linear = links.Linear(self.out_dim)
 
     def __call__(self, embeded_atom_features):
         # s0 is the minibatch axis
         # s1 is the atom axis
         # s2 is the channel (feature) axis
         s0, s1, s2 = embeded_atom_features.shape
         x = functions.reshape(embeded_atom_features, (s0 * s1, s2))
-        x = self.l(x)
+        x = self.linear(x)
         x = functions.reshape(x, (s0, s1, self.out_dim))
         return x
 

chainer_chemistry/training/extensions/roc_auc_evaluator.py

@@ -12,10 +12,10 @@
 
 def _get_1d_numpy_array(v):
     """Convert array or Variable to 1d numpy array
-    
+
     Args:
         v (numpy.ndarray or cupy.ndarray or chainer.Variable): array to be
-            converted to 1d numpy array 
+            converted to 1d numpy array
 
     Returns (numpy.ndarray): Raveled 1d numpy array
 
@@ -27,7 +27,7 @@ def _get_1d_numpy_array(v):
 
 def _to_list(a):
     """convert value `a` to list
-    
+
     Args:
         a: value to be convert to `list`
 
@@ -49,7 +49,7 @@ class ROCAUCEvaluator(Evaluator):
 
     Args:
         iterator: Dataset iterator for the dataset to calculate ROC AUC score.
-            It can also be a dictionary of iterators. If this is just an 
+            It can also be a dictionary of iterators. If this is just an
             iterator, the iterator is registered by the name ``'main'``.
         target: Link object or a dictionary of links to evaluate. If this is
             just a link object, the link is registered by the name ``'main'``.
@@ -65,8 +65,8 @@ class ROCAUCEvaluator(Evaluator):
             object is passed at each call.
         eval_func: Evaluation function called at each iteration. The target
             link to evaluate as a callable is used by default.
-        name (str): name of this extension. When `name` is None, 
-            `default_name='validation'` which is defined in super class 
+        name (str): name of this extension. When `name` is None,
+            `default_name='validation'` which is defined in super class
             `Evaluator` is used as extension name. This name affects to the
             reported key name.
         pos_labels (int or list): labels of the positive class, other classes

docs/source/conf.py

@@ -17,6 +17,8 @@
 import os
 import pkg_resources
 import sys
+
+import sphinx_rtd_theme
 # sys.path.insert(0, os.path.abspath('.'))
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
@@ -34,15 +36,15 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = ['sphinx.ext.autodoc',
-    'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.coverage',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.ifconfig',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.napoleon']
+              'sphinx.ext.doctest',
+              'sphinx.ext.intersphinx',
+              'sphinx.ext.todo',
+              'sphinx.ext.coverage',
+              'sphinx.ext.mathjax',
+              'sphinx.ext.ifconfig',
+              'sphinx.ext.viewcode',
+              'sphinx.ext.autosummary',
+              'sphinx.ext.napoleon']
 
 autosummary_generate = True
 
@@ -96,7 +98,6 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-import sphinx_rtd_theme
 
 html_theme = "sphinx_rtd_theme"
 
@@ -183,7 +184,5 @@
 ]
 
 
-
-
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {'https://docs.python.org/': None}

examples/qm9/train_qm9.py

@@ -34,9 +34,9 @@ class GraphConvPredictor(chainer.Chain):
 
     def __init__(self, graph_conv, mlp=None):
         """
-        
+
         Args:
-            graph_conv: graph convolution network to obtain molecule feature 
+            graph_conv: graph convolution network to obtain molecule feature
                         representation
             mlp: multi layer perceptron, used as final connected layer.
                 It can be `None` if no operation is necessary after
@@ -215,5 +215,6 @@ def scaled_abs_error(x0, x1):
     trainer.extend(E.ProgressBar())
     trainer.run()
 
+
 if __name__ == '__main__':
     main()

examples/tox21/predictor.py

@@ -145,7 +145,8 @@ def inference(self, X):
                 If the predictor is a graph convolution model
                 (e.g. :class:`chainer_chemistry.models.NFP`),
                 we can use the output of corresponding preprocessor
-                (e.g. :class:`chainer_chemistry.dataset.preprocessors.NFPPreprocessor`).
+                (e.g. :class:`chainer_chemistry.dataset.preprocessors\
+                .NFPPreprocessor`).
 
         Returns:
             numpy.ndarray: Prediction results

examples/tox21/train_tox21.py

@@ -14,7 +14,7 @@
 
 import argparse
 import chainer
-from chainer import functions as F, reporter, cuda
+from chainer import functions as F
 from chainer import iterators as I
 from chainer import links as L
 from chainer import optimizers as O

setup.py

@@ -16,11 +16,13 @@
 
 here = os.path.abspath(os.path.dirname(__file__))
 __version__ = imp.load_source(
-    '_version', os.path.join(here, 'chainer_chemistry', '_version.py')).__version__
+    '_version', os.path.join(here,
+                             'chainer_chemistry', '_version.py')).__version__
 
 setup(name='chainer-chemistry',
       version=__version__,
-      description='Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry',
+      description='Chainer Chemistry: A Library for Deep Learning in Biology\
+      and Chemistry',
       author='Kosuke Nakago',
       author_email='nakago@preferred.jp',
       packages=find_packages(),

tests/dataset_tests/parsers_tests/test_csv_file_parser.py

@@ -60,7 +60,7 @@ def test_csv_file_parser_not_return_smiles(csv_file, mols):
     dataset = result['dataset']
     smiles = result['smiles']
     assert len(dataset) == 2
-    assert smiles == None
+    assert smiles is None
 
     # As we want test CSVFileParser, we assume
     # NFPPreprocessor works as documented.

tests/dataset_tests/parsers_tests/test_sdf_file_parser.py

@@ -38,7 +38,7 @@ def test_sdf_file_parser_not_return_smiles(sdf_file, mols):
     dataset = result['dataset']
     smiles = result['smiles']
     assert len(dataset) == 2
-    assert smiles == None
+    assert smiles is None
 
     # As we want test SDFFileParser, we assume
     # NFPPreprocessor works as documented.

tests/dataset_tests/preprocessor_tests/test_common.py

@@ -11,7 +11,7 @@ def sample_molecule():
 
 
 class TestGetAtomicNumbers(object):
-    
+
     def test_normal(self, sample_molecule):
         actual = common.construct_atomic_number_array(sample_molecule)
 
@@ -29,7 +29,7 @@ def test_padding(self, sample_molecule):
     def test_normal_truncated(self, sample_molecule):
         with pytest.raises(ValueError):
             adj = common.construct_atomic_number_array(sample_molecule, 3)
-        
+
 
 @pytest.fixture
 def sample_molecule_2():
@@ -89,5 +89,6 @@ def test_normal_truncated(self, sample_molecule_2):
         with pytest.raises(ValueError):
             adj = common.construct_adj_matrix(sample_molecule_2, 6)
 
+
 if __name__ == '__main__':
     pytest.main([__file__, '-v', '-s'])

tests/dataset_tests/preprocessors_tests/test_atomic_number_preprocessor.py

@@ -39,7 +39,7 @@ def test_atomic_number_preprocessor_with_tox21():
 
     # labels=None as default, and label information is not returned.
     dataset = SDFFileParser(preprocessor)\
-            .parse(get_tox21_filepath('train'))['dataset']
+        .parse(get_tox21_filepath('train'))['dataset']
     index = numpy.random.choice(len(dataset), None)
     atoms, = dataset[index]
 

tests/dataset_tests/preprocessors_tests/test_ggnn_preprocessor.py

@@ -1,4 +1,3 @@
-import os
 import numpy
 import pytest
 
@@ -33,5 +32,6 @@ def test_nfp_preprocessor_assert_raises():
     with pytest.raises(ValueError):
         pp = GGNNPreprocessor(max_atoms=3, out_size=2)
 
+
 if __name__ == '__main__':
     pytest.main([__file__, '-v', '-s'])

tests/dataset_tests/preprocessors_tests/test_schnet_preprocessor.py

@@ -39,6 +39,7 @@ def test_schnet_preprocessor(mol, pp):
 def test_schnet_preprocessor_with_tox21():
     preprocessor = SchNetPreprocessor()
 
+
     dataset = SDFFileParser(preprocessor, postprocess_label=None
                             ).parse(get_tox21_filepath('train'))['dataset']
 

tests/dataset_tests/test_numpy_tuple_feature_indexer.py

@@ -25,7 +25,7 @@ class TestNumpyTupleDatasetFeatureIndexer(object):
 
     def test_feature_length(self, indexer):
         assert indexer.features_length() == 3
-        
+
     @pytest.mark.parametrize('slice_index', [0, 1, slice(0, 2, None)])
     @pytest.mark.parametrize('j', [0, 1])
     def test_extract_feature_by_slice(self, indexer, data, slice_index, j):

tests/datasets_tests/test_tox21.py

@@ -1,7 +1,7 @@
 import os
 import numpy
 import pytest
-from chainer_chemistry.dataset.preprocessors.atomic_number_preprocessor import \
+from chainer_chemistry.dataset.preprocessors.atomic_number_preprocessor import\
     AtomicNumberPreprocessor
 
 from chainer_chemistry.datasets import tox21

tests/links_tests/test_embed_atom_id.py

@@ -10,6 +10,7 @@
 out_size = 4
 batch_size = 2
 
+
 @pytest.fixture
 def model():
     l = links.EmbedAtomID(in_size=in_size, out_size=out_size)

tests/models_tests/test_ggnn.py

@@ -67,5 +67,6 @@ def test_backward_gpu(model, data):
     gradient_check.check_backward(model, (atom_data, adj_data), y_grad,
                                   atol=1e-3, rtol=1e-3)
 
+
 if __name__ == '__main__':
     pytest.main([__file__, '-v'])

tests/models_tests/test_schnet.py

@@ -62,5 +62,6 @@ def test_backward_gpu(model, data):
     gradient_check.check_backward(model, (atom_data, adj_data), y_grad,
                                   atol=1e-1, rtol=1e-1)
 
+
 if __name__ == '__main__':
     pytest.main([__file__, '-v'])

tests/models_tests/test_weavenet.py

@@ -74,8 +74,9 @@ def test_backward_cpu(model, model_processed, data):
     atom_data_processed, atom_data, adj_data, y_grad = data
     gradient_check.check_backward(model, (atom_data, adj_data), y_grad,
                                   atol=1e-1, rtol=1e-1)
-    gradient_check.check_backward(model_processed, (atom_data_processed, adj_data),
-                                  y_grad, atol=1e-1, rtol=1e-1)
+    gradient_check.check_backward(model_processed, (atom_data_processed,
+                                                    adj_data), y_grad,
+                                  atol=1e-1, rtol=1e-1)
 
 
 @pytest.mark.gpu
@@ -86,8 +87,10 @@ def test_backward_gpu(model, model_processed, data):
     model_processed.to_gpu()
     gradient_check.check_backward(model, (atom_data, adj_data), y_grad,
                                   atol=1e-1, rtol=1e-1)
-    gradient_check.check_backward(model_processed, (atom_data_processed, adj_data),
-                                  y_grad, atol=1e-1, rtol=1e-1)
+    gradient_check.check_backward(model_processed, (atom_data_processed,
+                                                    adj_data), y_grad,
+                                  atol=1e-1, rtol=1e-1)
+
 
 if __name__ == '__main__':
     pytest.main([__file__, '-v'])

tests/training_tests/extensions_tests/test_roc_auc_evaluator.py

@@ -1,6 +1,7 @@
 """
 ROCAUCEvaluator uses `sklearn.metrics.roc_auc_score` internally.
-Refer: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html
+Refer: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.\
+roc_auc_score.html
 """
 import numpy
 import pytest