Revert "Update sas regression example to use Session.as_swat()"

This reverts commit da592a6.

Author: smlindauer

examples/register_sas_regression_model.py

@@ -4,39 +4,37 @@
 # Copyright © 2019, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
 
+import swat
 from sasctl import Session
 from sasctl.tasks import register_model, publish_model
 
-hostname = ''
-username = ''
-password = ''
 
-# Use sasctl to connect to SAS
-sess = Session('hostname', 'username', 'password', protocol='http')
-cas = sess.as_swat()
+with swat.CAS('hostname', 5570, 'username', 'password') as cas:
+    # Load the regression actions in CAS
+    cas.loadactionset('regression')
 
-# Load the regression actions in CAS
-cas.loadactionset('regression')
+    # Upload the local CSV file to a CAS table
+    tbl = cas.upload('data/boston_house_prices.csv').casTable
 
-# Upload the local CSV file to a CAS table
-tbl = cas.upload('data/boston_house_prices.csv').casTable
+    # Model input features are everything except the target
+    features = tbl.columns[tbl.columns != 'medv']
 
-# Model input features are everything except the target
-features = tbl.columns[tbl.columns != 'medv']
+    # Fit a linear regression model in CAS and output an ASTORE
+    tbl.glm(target='medv', inputs=list(features), savestate='model_table')
 
-# Fit a linear regression model in CAS and output an ASTORE
-tbl.glm(target='medv', inputs=list(features), savestate='model_table')
+    astore = cas.CASTable('model_table')
 
-astore = cas.CASTable('model_table')
+    # Use sasctl to connect to SAS
+    Session('hostname', 'username', 'password')
 
-# Register the model in SAS Model Manager, creating the "Boston Housing"
-# project if it doesn't already exist
-model = register_model(astore, 'Linear Regression', 'Boston Housing', force=True)
+    # Register the model in SAS Model Manager, creating the "Boston Housing"
+    # project if it doesn't already exist
+    model = register_model(astore, 'Linear Regression', 'Boston Housing', force=True)
 
-# Publish the model to a real-time scoring engine
-module = publish_model(model, 'maslocal')
+    # Publish the model to a real-time scoring engine
+    module = publish_model(model, 'maslocal')
 
-# Pass a row of data to MAS and receive the predicted result.
-first_row = tbl.head(1)
-result = module.score(first_row)
-print(result)
+    # Pass a row of data to MAS and receive the predicted result.
+    first_row = tbl.head(1)
+    result = module.score(first_row)
+    print(result)