Hosting and Using “R” with Azure ML

November 18, 2014

Meetup Azure Machine Learning

http://www.meetup.com/New-York-Azure-Machine-Learning-Meetup/

Why host “R” in Azure ML

• R has great depth and breadth in many areas

• Very high value but not always easy to transition to a pluggable solution callable by other processes or services

• Can be combined with existing Azure ML modules (mix and match)

• ORSimply host a working R solution as a web service

Common challenge

Focus on right hand side…

One Azure ML module to learn and use

More input ports than usual

Output ports for “data” and R device output

Adding additional “R” packages/scripts…

From script to running web service

Using the web service…

references

• https://marketbasket.cloudapp.net/

• https://datamarket.azure.com/dataset/amla/mba

• https://azureinfo.microsoft.com/CO-Azure-WBNR-FY15-11Nov-OperationalizingRasaWebService-ThankYou.html?aliId=8371905

• http://azure.microsoft.com/en-us/documentation/articles/machine-learning-r-csharp-web-service-examples/

Next meetup – will be mid-January 2015

• Will post slides and script from tonight to

• http://www.meetup.com/New-York-Azure-Machine-Learning-Meetup/files/

• Remaining slides have R script and illustrations.

R scripts used last night

• Loading and referencing an external R package• Note: make sure follow steps on slide 8

• #this is trivial and just used to show package load and testinstall.packages("src/slam_0.1-32.zip",lib=".",repos=NULL,verbose=TRUE)install.packages("src/clue_0.3-48.zip",lib=".",repos=NULL,verbose=TRUE)install.packages("src/skmeans_0.2-6.zip",lib=".",repos=NULL,verbose=TRUE)library(skmeans,lib.loc=".",verbose=TRUE)#our package and libraries should be loaded up#stuff <- packages.installed(skmeans)#dataset1 <- maml.mapInputPort(1) # class: data.frame#dataset2 <- maml.mapInputPort(2) # class: data.framesamp <-matrix(sample.int(1000,size=20*50,replace=TRUE),nrow=20,ncol=500,dimnames=list(1:20,1:500))fit <-skmeans(samp,5)result <- data.frame(list(rownames(samp),fit$cluster),row.names=NULL)colnames(result) <- c("sample row","cluster")print(result) #R console output

Simple kmeans cluster

mydata <- maml.mapInputPort(1) # get our data from the R script input module instead of inline – this is the web service input signature

# parse and structure the input data to become a dataframe for the clustering

data.split <- strsplit(mydata[1,1], ",")[[1]]

data.split <- sapply(data.split, strsplit, ";", simplify = TRUE)

data.split <- sapply(data.split, strsplit, ";", simplify = TRUE)

data.split <- as.data.frame(t(data.split))

data.split <- data.matrix(data.split)

data.split <- data.frame(data.split)

# K-Means Cluster Analysis

fit <- kmeans(data.split, mydata$k) # k-cluster solution

# get cluster means

aggregate(data.split,by=list(fit$cluster),FUN=mean)

# append cluster assignment

mydatafinal <- data.frame(t(fit$cluster))

n_col=ncol(mydatafinal)

colnames(mydatafinal) <- paste("V",1:n_col,sep="")

mydatafinal

maml.mapOutPortPort(mydatafinal) # this will become the web service publishing port – i.e. what is returned – output must be a dataframe…

Input schema and sample data for kmeansthis is hosted in the “R” script module

mydata <- data.frame(value = "1; 3; 5; 6; 7; 7, 5; 5; 6; 7; 2; 1, 3; 7; 2; 9; 56; 6, 1; 4; 5; 26; 4; 23, 15; 35; 6; 7; 12; 1, 32; 51; 62; 7; 21; 1", k=5, stringsAsFactors=FALSE)

maml.mapOutputPort("data"); # this is the key as it wires the sample schema above to the downstream receiver (see the next illustration)

A model that is ready to be published as a web service.Note the publishing icons on the lower modules input and output ports.

Input Schema

Simple kmeans cluster script