added full example.

increment version.

Author: jlwalke2

CHANGELOG.md

@@ -1,6 +1,10 @@
 
 Unreleased
 ----------
+- 
+
+v1.0.1 (2019-07-31)
+-------------------
 **Changed**
  - Exceptions moved from `sasctl.core` to `sasctl.exceptions`
  - `SWATCASActionError` raised if ASTORE cannot be saved during model registration.

examples/full_lifecycle.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+# encoding: utf-8
+#
+# Copyright © 2019, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import pandas as pd
+import sklearn.datasets
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+
+from sasctl import Session
+from sasctl.tasks import register_model, publish_model
+from sasctl.services import model_repository as mr
+from sasctl.services import model_management as mm
+
+
+data = sklearn.datasets.load_boston()
+X = pd.DataFrame(data.data, columns=data.feature_names)
+y = pd.DataFrame(data.target, columns=['Price'])
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+
+# Establish a session with SAS Viya
+Session('hostname', 'username', 'password')
+
+project = 'Boston Housing'
+model_name = 'Boston Regression'
+
+# Fit a linear regression model using sci-kit learn
+lm = LinearRegression()
+lm.fit(X_train, y_train)
+
+# Register the model in SAS Model Manager
+register_model(lm,
+               model_name,
+               input=X_train,       # Use X to determine model inputs
+               project=project,     # Register in "Iris" project
+               force=True)          # Create project if it doesn't exist
+
+# Update project properties
+project = mr.get_project(project)
+project['function'] = 'prediction'
+project['targetLevel'] = 'interval'
+project['targetVariable'] = 'Price'
+project['predictionVariable'] = 'var1'
+project = mr.update_project(project)
+
+# Instruct the project to look for tables in the "Public" CAS library with
+# names starting with "boston_" and use these tables to track model
+# performance over time.
+mm.create_performance_definition(model_name, 'Public', 'boston')
+
+# Publish the model to the real-time scoring engine
+module_lm = publish_model(model_name, 'maslocal')
+
+# Select the first row of training data
+x = X.iloc[0, :]
+
+# Call the published module and score the record
+result = module_lm.score(**x)
+print(result)
+
+# Build a second model
+dt = DecisionTreeRegressor()
+dt.fit(X_train, y_train)
+
+# Register the second model in Model Manager
+model_dt = register_model(dt, 'Decision Tree', project, input=X)
+
+# Publish from Model Manager -> MAS
+module_dt = publish_model(model_dt, 'maslocal')
+
+# Use MAS to score some new data
+result = module_dt.score(**x)
+print(result)
+
+
+

src/sasctl/__init__.py

@@ -4,7 +4,7 @@
 # Copyright © 2019, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = '1.0.0'
+__version__ = '1.0.1'
 __author__ = 'SAS'
 __credits__ = ['Yi Jian Ching, Lucas De Paula, Peter Tobac, Chris Toth, Jon '
                'Walker']