Settle UseR 2014-10-13

Copyright 2014, Quantia Analytics, LLC. All rights reservedQuantia and the Quantia logo are registered trademarks of Quantia Analytics, LLC.

1 Copyright 2014, Quantia Analytics LLC. All rights reservedQuantia and the Quantia logo are registered trademarks of Quantia Analytics, LLC.

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QUANTIA ANALYTICS

Using R on the Azure PlatformSeattle 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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• 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 learningData Input

Data Transformations

Model Definition

Train

Split

Score

Evaluate

See tutorials and examples at:

http://azure.microsoft.com/en-us/documentation/services/machine-learning/

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


PublishedInput

PublishedOutput


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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

Data & Analytics

Settle UseR 2014-10-13