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#!/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, update_model_performance
from import model_repository as mr
from import model_management as mm
data = sklearn.datasets.load_boston()
X = pd.DataFrame(, columns=data.feature_names)
y = pd.DataFrame(, 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(), y_train)
# Register the model in SAS Model Manager
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. Target variable must be set before performance
# definitions can be created.
project = mr.get_project(project)
project['targetVariable'] = 'Price'
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 testing data
x = X_test.iloc[0, :]
# Call the published module and score the record
result = module_lm.score(**x)
# Build a second model
dt = DecisionTreeRegressor(), 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)
# Model Manager can track model performance over time if provided with
# historical model observations & predictions. SIMULATE historical data by
# repeatedly sampling from the test set.
perf_df = X_test.copy()
perf_df['var1'] = lm.predict(X_test)
perf_df['Price'] = y
# For each (simulated) historical period, upload model results
for period in ('q12019', 'q22019', 'q32019', 'q42019'):
sample = perf_df.sample(frac=0.2)
update_model_performance(sample, model_name, period)
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