fix lint errors

BayesWitnesses · Nov 12, 2019 · a9d6fd7 · a9d6fd7
1 parent 01e5fda
commit a9d6fd7
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Showing 2 changed files with 24 additions and 11 deletions.
diff --git a/tests/e2e/test_e2e.py b/tests/e2e/test_e2e.py
@@ -48,6 +48,7 @@ def classification_binary(model):
         CLASSIFICATION,
     )
 
+
 def regression_random(model):
     return (
         model,
@@ -71,6 +72,7 @@ def classification_binary_random(model):
         CLASSIFICATION,
     )
 
+
 # Absolute tolerance. Used in np.isclose to compare 2 values.
 # We compare 6 decimal digits.
 ATOL = 1.e-6
@@ -84,9 +86,10 @@ def classification_binary_random(model):
 SVC_PARAMS = dict(random_state=RANDOM_SEED, decision_function_shape="ovo")
 
 XGBOOST_PARAMS_LARGE = dict(base_score=0.6, n_estimators=100, max_depth=12,
-                      random_state=RANDOM_SEED)
+                            random_state=RANDOM_SEED)
 LIGHT_GBM_PARAMS_LARGE = dict(n_estimators=100, num_leaves=100, max_depth=64,
-                    random_state=RANDOM_SEED)
+                              random_state=RANDOM_SEED)
+
 
 @utils.cartesian_e2e_params(
     # These are the languages which support all models specified in the
@@ -108,19 +111,25 @@ def classification_binary_random(model):
         classification_binary(lightgbm.LGBMClassifier(**LIGHT_GBM_PARAMS)),
 
         # LightGBM (Large Trees)
-        regression_random(lightgbm.LGBMRegressor(**LIGHT_GBM_PARAMS_LARGE)),
-        classification_random(lightgbm.LGBMClassifier(**LIGHT_GBM_PARAMS_LARGE)),
-        classification_binary_random(lightgbm.LGBMClassifier(**LIGHT_GBM_PARAMS_LARGE)),
+        regression_random(
+            lightgbm.LGBMRegressor(**LIGHT_GBM_PARAMS_LARGE)),
+        classification_random(
+            lightgbm.LGBMClassifier(**LIGHT_GBM_PARAMS_LARGE)),
+        classification_binary_random(
+            lightgbm.LGBMClassifier(**LIGHT_GBM_PARAMS_LARGE)),
 
         # XGBoost
         regression(xgboost.XGBRegressor(**XGBOOST_PARAMS)),
         classification(xgboost.XGBClassifier(**XGBOOST_PARAMS)),
         classification_binary(xgboost.XGBClassifier(**XGBOOST_PARAMS)),
 
         # XGBoost (Large Trees)
-        regression_random(xgboost.XGBRegressor(**XGBOOST_PARAMS_LARGE)),
-        classification_random(xgboost.XGBClassifier(**XGBOOST_PARAMS_LARGE)),
-        classification_binary_random(xgboost.XGBClassifier(**XGBOOST_PARAMS_LARGE)),
+        regression_random(
+            xgboost.XGBRegressor(**XGBOOST_PARAMS_LARGE)),
+        classification_random(
+            xgboost.XGBClassifier(**XGBOOST_PARAMS_LARGE)),
+        classification_binary_random(
+            xgboost.XGBClassifier(**XGBOOST_PARAMS_LARGE)),
 
         # Linear SVM
         regression(svm.LinearSVR(random_state=RANDOM_SEED)),

diff --git a/tests/utils.py b/tests/utils.py
@@ -74,6 +74,7 @@ def train_model_classification_binary(estimator, test_fraction=0.1):
     return _train_model(estimator, datasets.load_breast_cancer(),
                         test_fraction)
 
+
 def train_model_regression_random_data(estimator, test_fraction=0.01):
     np.random.seed(seed=7)
     N = 1000
@@ -93,7 +94,8 @@ def train_model_classification_random_data(estimator, test_fraction=0.01):
     return _train_model(estimator, (data, target), test_fraction)
 
 
-def train_model_classification_binary_random_data(estimator, test_fraction=0.01):
+def train_model_classification_binary_random_data(estimator,
+                                                  test_fraction=0.01):
     np.random.seed(seed=7)
     N = 1000
 
@@ -151,10 +153,12 @@ def verify_python_model_is_expected(model_code, input, expected_output):
 
 
 def predict_from_commandline(exec_args):
-    result = subprocess.Popen(exec_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    result = subprocess.Popen(exec_args, stdout=subprocess.PIPE,
+                              stderr=subprocess.PIPE)
     stdout, stderr = result.communicate()
     if result.returncode is not 0:
-        raise Exception("bad exit code ({}) stderr: {}".format(result.returncode, stderr.decode("utf-8")))
+        raise Exception("bad exit code ({}) stderr: {}".format(
+                        result.returncode, stderr.decode("utf-8")))
 
     items = stdout.decode("utf-8").strip().split(" ")