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For those of you who don't know MOA. MOA stands for Massive On-line Analysis and is an open-source framework that allows to build and run experiments of machine learning or data mining on evolving data streams. The website of MOA (http://moa.cms.waikato.ac.nz) indicates it contains machine learning algorithms for classification, regression, clustering, outlier detection and recommendation engines.

 

For R users who work with a lot of data or encounter RAM issues when building models on large datasets, MOA and in general data streams have some nice features. Namely:

Unfortunately it is written in Java and not easily accessible for R users to use. For users mostly interested in clustering, the stream package already facilites this (this blog item gave an example when using ff alongside the stream package). In our day-to-day use cases, classification is a more common request. The stream package only allows to do clustering. So hence the decision to make the classification algorithms of MOA easily available to R users as well. For this the RMOA package was created and is available on github (https://github.com/jwijffels/RMOA).

 

The current features of RMOA are:

 

Feel free to use it and we welcome any feedback in your day-to-day RMOA usage experiences at https://github.com/jwijffels/RMOA in order to improve the package. For more documentation on MOA for R users: see http://jwijffels.github.io/RMOA/

 

An example of R code which constructs a HoeffdingTree and a boosted set of HoeffdingTrees is shown below.

 

## 
## Installation from github
## 
library(devtools)
install.packages("ff")
install.packages("rJava")
install_github("jwijffels/RMOA", subdir="RMOAjars/pkg")
install_github("jwijffels/RMOA", subdir="RMOA/pkg")

## 
## HoeffdingTree example
## 
require(RMOA)
hdt <- HoeffdingTree(numericEstimator = "GaussianNumericAttributeClassObserver")
hdt
## Define a stream - e.g. a stream based on a data.frame
data(iris)
iris <- factorise(iris)
irisdatastream <- datastream_dataframe(data=iris)
  
## Train the HoeffdingTree on the iris dataset
mymodel <- trainMOA(model = hdt, 
  formula = Species ~ Sepal.Length + Sepal.Width + Petal.Length, 
  data = irisdatastream)
## Predict using the HoeffdingTree on the iris dataset
scores <- predict(mymodel, newdata=iris, type="response")
table(scores, iris$Species)
scores <- predict(mymodel, newdata=iris, type="votes")
head(scores)

## 
## Boosted set of HoeffdingTrees
## 
irisdatastream <- datastream_dataframe(data=iris)
mymodel <- OzaBoost(baseLearner = "trees.HoeffdingTree", ensembleSize = 30)
mymodel <- trainMOA(model = mymodel, 
  formula = Species ~ Sepal.Length + Sepal.Width + Petal.Length, 
  data = irisdatastream)
  
## Predict 
scores <- predict(mymodel, newdata=iris, type="response")
table(scores, iris$Species)
scores <- predict(mymodel, newdata=iris, type="votes")
head(scores)

 

 

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