Run PEP8 on content

CATelcoCustomerChurnModeling.py

@@ -16,13 +16,14 @@
 from azureml.logging import get_azureml_logger
 
 # initialize the logger
-run_logger = get_azureml_logger() 
-run_logger.log('amlrealworld.ChurnPrediction.CATelcoCustomerChurnModeling','true')
+run_logger = get_azureml_logger()
+run_logger.log(
+    'amlrealworld.ChurnPrediction.CATelcoCustomerChurnModeling', 'true')
 
 with Package.open_package('CATelcoCustomerChurnTrainingSample.dprep') as pkg:
     df = pkg.dataflows[0].get_dataframe(spark=False)
 
-columns_to_encode = list(df.select_dtypes(include=['category','object']))
+columns_to_encode = list(df.select_dtypes(include=['category', 'object']))
 for column_to_encode in columns_to_encode:
     dummies = pd.get_dummies(df[column_to_encode])
     one_hot_col_names = []
@@ -35,7 +36,7 @@
 model = GaussianNB()
 
 random_seed = 42
-train, test = train_test_split(df, random_state = random_seed, test_size = 0.3)
+train, test = train_test_split(df, random_state=random_seed, test_size=0.3)
 
 target = train['churn'].values
 train = train.drop('churn', 1)
@@ -45,21 +46,22 @@
 expected = test['churn'].values
 test = test.drop('churn', 1)
 predicted = model.predict(test)
-print("Naive Bayes Classification Accuracy", accuracy_score(expected, predicted))
+print("Naive Bayes Classification Accuracy",
+      accuracy_score(expected, predicted))
 # log the Naive Bayes Accuracy
 run_logger.log("Naive Bayes Accuracy", accuracy_score(expected, predicted))
 
 dt = DecisionTreeClassifier(min_samples_split=20, random_state=99)
 dt.fit(train, target)
 predicted = dt.predict(test)
-print("Decision Tree Classification Accuracy", accuracy_score(expected, predicted))
+print("Decision Tree Classification Accuracy",
+      accuracy_score(expected, predicted))
 # log the DTree Accuracy
 run_logger.log("DTree Accuracy", accuracy_score(expected, predicted))
 
 # serialize the model on disk in the special 'outputs' folder
 
-print ("Export the model to model.pkl")
+print("Export the model to model.pkl")
 f = open('./outputs/model.pkl', 'wb')
 pickle.dump(dt, f)
 f.close()
-

CATelcoCustomerChurnModelingWithoutDprep.py

@@ -13,8 +13,9 @@
 from azureml.logging import get_azureml_logger
 
 # initialize the logger
-run_logger = get_azureml_logger() 
-run_logger.log('amlrealworld.ChurnPrediction.CATelcoCustomerChurnModelingWithoutDprep','true') 
+run_logger = get_azureml_logger()
+run_logger.log(
+    'amlrealworld.ChurnPrediction.CATelcoCustomerChurnModelingWithoutDprep', 'true')
 
 # Perform Data Preparation
 df = pd.read_csv('data/CATelcoCustomerChurnTrainingSample.csv')
@@ -24,7 +25,7 @@
 df = df.drop('month', 1)
 
 # One-Hot Encoding
-columns_to_encode = list(df.select_dtypes(include=['category','object']))
+columns_to_encode = list(df.select_dtypes(include=['category', 'object']))
 for column_to_encode in columns_to_encode:
     dummies = pd.get_dummies(df[column_to_encode])
     one_hot_col_names = []
@@ -37,7 +38,7 @@
 model = GaussianNB()
 
 random_seed = 42
-train, test = train_test_split(df, random_state = random_seed, test_size = 0.3)
+train, test = train_test_split(df, random_state=random_seed, test_size=0.3)
 
 target = train['churn'].values
 train = train.drop('churn', 1)
@@ -48,19 +49,21 @@
 expected = test['churn'].values
 test = test.drop('churn', 1)
 predicted = model.predict(test)
-print("Naive Bayes Classification Accuracy", accuracy_score(expected, predicted))
+print("Naive Bayes Classification Accuracy",
+      accuracy_score(expected, predicted))
 # Log the Naive Bayes accuracy
 run_logger.log("Naive Bayes Accuracy", accuracy_score(expected, predicted))
 
 dt = DecisionTreeClassifier(min_samples_split=20, random_state=99)
 dt.fit(train, target)
 predicted = dt.predict(test)
-print("Decision Tree Classification Accuracy", accuracy_score(expected, predicted))
+print("Decision Tree Classification Accuracy",
+      accuracy_score(expected, predicted))
 # log the DTree Accuracy
 run_logger.log("DTree Accuracy", accuracy_score(expected, predicted))
 
 # serialize the model on disk in the special 'outputs' folder
-print ("Export the model to outputs/model.pkl")
+print("Export the model to outputs/model.pkl")
 f = open('./outputs/model.pkl', 'wb')
 pickle.dump(model, f)
 f.close()