Settle UseR 2014-10-13

Copyright 2014, Quantia Analytics, LLC. All rights reserved
Quantia and the Quantia logo are registered trademarks of Quantia Analytics, LLC.
1 Copyright 2014, Quantia Analytics LLC. All rights reserved
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QUANTIA ANALYTICS
Using R on the Azure
Platform
Seattle UseR Group
November 13, 2014
Stephen F Elston, Managing Director Quantia Analytics

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 Bicycle rental data
 Overview of Azure ML Studio
 Running R in Azure ML
• Execute R Script module
• I/O
• Graphics and transformation example
 Building and evaluating models in Azure ML
 Building models in R for Azure ML
• Model and prediction example
• Serialization of R objects
- Evaluating and comparing models
 Publishing models as a web service
Agenda
R code and data available at:
https://github.com/Quantia-Analytics/AzureML-Regression-Example/

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Capital Bicycle Rental Data
 2 years of hourly data
• 17,379 rows
 Forecast hourly demand for rental bikes
• Overestimate is preferred to underestimate
 9 predictor variables
• 5 date-time
• 4 weather
• Compute 5 additional predictors
 Short time series limits modeling
 Citation: Fanaee-T, Hadi, and Gama, Joao, 'Event labeling
combining ensemble detectors and background knowledge',
Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin
Heidelberg

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Why Azure ML?
 Quickly deploy solutions as web service
 Run models in a highly scalable cloud environment
 Code and data in a secure environment
 Variety of efficient built-in algorithms
 Azure ML workflow extensible with R!

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Overview of Azure ML Studio
 Overview of Azure ML Studio
• Modules
• Properties and documentation
• Canvas and workflow
 Starting an Experiment
 Loading data sets
 Reading and writing data

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Execute R Script Module in Azure ML
 R in the Execute R Script
• Execute pure R code
• Each module is an R environment
• > 350 packages available + install script
• Parallel execution using snow and doSNOW packages
 Working between R Studio and Azure ML
• Edit, debug and test in R Studio
• Run R and publish as a web service in Azure ML
 Azure ML debugging tips
• Error messages and printed output available in the output.log file

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5 I/O Ports of Execute R Script Module (input)
 Dataset1, Dataset2
• Input rectangular Azure data tables
• Produce R data frames in Execute R Script module
• Use maml.mapInputPort() function for ports 1 and 2
 Script bundle
• Input zipped R files
• Useful for reference data, utility functions, etc.
• Use familiar source("src/yourfile.R") and load("src/yourData.rdata")
functions

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5 I/O Ports of Execute R Script Module (output)
• Results dataset
• Output an R data frame
• Produces Azure ML rectangular data table
• Use the maml.mapOutputPort() function
• R Device
• R printed and graphics output

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Workflow using Execute R Script
Workflow to visualize bike rental data

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Create and Evaluate an Azure ML Model
Workflow for machine learning
Data Input
Data Transformations
Model Definition
Train
Split
Score
Evaluate
See tutorials and examples at:
http://azure.microsoft.com/en-us/documentation/services/machine-learning/

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Passing R Objects Between Execute R Script Modules
 Azure ML requires output and input to be rectangular tables
• Use R serialize() and unserialize() functions
• Serialize list of R objects as column of a data frame
• Coerce type to integer
• I provide serList() and unserList() functions to get you started
 Provides flexibility
• List can contain most any R object
• E.g. separate model calculation from prediction
• WARNING: limit on size of R vector may be a problem

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Workflow integrating R scripts plus
Workflow for decision
forest model

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Workflow for decision
forest regression
model with outlier
trimming

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Workflow for
experiment with
neural network
regression model
added

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R models in Azure ML
 Use serialization between model computation and prediction
Data Input
Split
Evaluate
Compute Model
Serialize Object
Prediction
Unserialize Object
Execute R Script
Modules

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Workflow for
experiment with R
support vector
machine model
added

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Publishing Azure ML Model as a Web Service
 Publish input and output as web service
• Input to data transformation
• Output from prediction
 Web services run in Azure cloud
• Azure platform for native and R models
• Include data cleaning and transformation modules in flow

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Publishing an Azure ML Model as a Web Service
Trained Model
Results Transformation
Published
Input
Published
Output

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Azure ML web
services workflow
ready for promotion to
production

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 Execute R Script module runs most any pure R code
 R extends Azure ML capabilities
 Clean and transform data with R
 Use R or native Azure ML models
 Integrate R and native modules in Azure ML
• Execute R Script modules in Azure ML work-flow
• Edit, debug and test R code in RStudio
• Execute in Azure ML
• Publish R model in Azure as web service
Summary

Settle UseR 2014-10-13

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Settle UseR 2014-10-13