Make tests PEP8 compliant (#829)

* Make tests PEP8 compliant

* bug fix

* Implement PR review comments

* Implement PR review sugestions

* fix small bug

Author: gui-miotto

ci_scripts/flake8_full.sh

@@ -6,3 +6,4 @@ flake8 --max-line-length=100 --show-source autosklearn/data
 flake8 --max-line-length=100 --show-source autosklearn/metrics
 flake8 --max-line-length=100 --show-source autosklearn/metalearning
 flake8 --max-line-length=100 --show-source autosklearn/pipeline
+flake8 --max-line-length=100 --show-source test/

test/test_automl/base.py

@@ -15,7 +15,7 @@ def setUp(self):
         self.test_dir = os.path.dirname(__file__)
 
         try:
-            travis = os.environ['TRAVIS']
+            os.environ['TRAVIS']
             self.travis = True
         except Exception:
             self.travis = False
@@ -55,4 +55,4 @@ def _tearDown(self, dir):
                     shutil.rmtree(dir)
                     break
                 except OSError:
-                    time.sleep(1)
+                    time.sleep(1)

test/test_automl/test_automl.py

@@ -12,7 +12,7 @@
 from smac.scenario.scenario import Scenario
 from smac.facade.roar_facade import ROAR
 
-from autosklearn.util.backend import Backend, BackendContext
+from autosklearn.util.backend import Backend
 from autosklearn.automl import AutoML
 import autosklearn.automl
 from autosklearn.metrics import accuracy
@@ -23,7 +23,7 @@
 from smac.tae.execute_ta_run import StatusType
 
 sys.path.append(os.path.dirname(__file__))
-from base import Base
+from base import Base  # noqa
 
 
 class AutoMLStub(AutoML):
@@ -391,5 +391,5 @@ def test_fail_if_dummy_prediction_fails(self, ta_run_mock):
         self._tearDown(backend_api.output_directory)
 
 
-if __name__=="__main__":
+if __name__ == "__main__":
     unittest.main()

test/test_data/test_competition_c_functions.py

@@ -35,5 +35,6 @@ def test_read_dense(self):
             filename, 670, 20, 0.01)
         self.assertEqual(data.shape, (131, 20))
 
+
 if __name__ == "__main__":
     unittest.main()

test/test_data/test_competition_data_manager.py

@@ -1,8 +1,6 @@
 import os
 import unittest
 
-from autosklearn.data.competition_data_manager import CompetitionDataManager
-
 
 class CompetitionDataManagerTest(unittest.TestCase):
     _multiprocess_can_split_ = True

test/test_data/test_data_manager.py

@@ -1,4 +1,3 @@
-import os
 import unittest
 
 import numpy as np
@@ -90,7 +89,7 @@ def test_perform1HotEncoding_categorical_data(self):
 
     def test_perform1HotEncoding_binary_data_with_missing_values(self):
         # self.D.feat_type = [N, N, N, N, B, B]
-        #self.D.info = {'is_sparse': 0, 'has_missing': 1}
-        #self.D.perform1HotEncoding()
-        #self.assertEqual((3, 8), self.D.data['X_train'].shape)
+        # self.D.info = {'is_sparse': 0, 'has_missing': 1}
+        # self.D.perform1HotEncoding()
+        # self.assertEqual((3, 8), self.D.data['X_train'].shape)
         pass

test/test_ensemble_builder/__init__.py

@@ -1,4 +1,2 @@
 # -*- encoding: utf-8 -*-
 __author__ = 'mlindauer'
-
-

test/test_ensemble_builder/test_ensemble.py

@@ -50,7 +50,7 @@ def testRead(self):
         ensbuilder = EnsembleBuilder(
             backend=self.backend,
             dataset_name="TEST",
-            task_type=1,  #Binary Classification
+            task_type=1,  # Binary Classification
             metric=roc_auc,
             limit=-1,  # not used,
             seed=0,  # important to find the test files

test/test_evaluation/__init__.py

@@ -1,4 +1,2 @@
 # -*- encoding: utf-8 -*-
 __author__ = 'feurerm'
-
-

test/test_evaluation/evaluation_util.py

@@ -1,14 +1,14 @@
 import functools
 import os
-import sys
 import traceback
 import unittest
 
 import numpy as np
 from numpy.linalg import LinAlgError
 import sklearn.datasets
 
-from autosklearn.constants import *
+from autosklearn.constants import \
+    MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, BINARY_CLASSIFICATION, REGRESSION
 from autosklearn.util.data import convert_to_bin
 from autosklearn.data.competition_data_manager import CompetitionDataManager
 from autosklearn.pipeline.util import get_dataset
@@ -123,13 +123,13 @@ def get_multilabel_classification_datamanager():
     Y_train = Y_train[indices]
 
     Y_train = np.array(convert_to_bin(Y_train, 3))
-    #for i in range(Y_train_.shape[0]):
+    # for i in range(Y_train_.shape[0]):
     #    Y_train_[:, Y_train[i]] = 1
-    #Y_train = Y_train_
+    # Y_train = Y_train_
     Y_test = np.array(convert_to_bin(Y_test, 3))
-    #for i in range(Y_test_.shape[0]):
+    # for i in range(Y_test_.shape[0]):
     #    Y_test_[:, Y_test[i]] = 1
-    #Y_test = Y_test_
+    # Y_test = Y_test_
 
     X_valid = X_test[:25, ]
     Y_valid = Y_test[:25, ]