README.md

In wtcooper/glmToJava: Simple R method to Export a Java GLM Model

glmToJava

R package: takes a coefficient table and converts it to a Java (POJO) file

Overview

A simple converter to take a GLM coefficient table (gaussian, binomial, poisson, and multinomial) and convert to a pure Java (POJO) file with no dependencies.

Note: using R's extensive PMML support in conjunction with Java PMML (https://github.com/jpmml) provides much more flexibility, but this simple converter was designed to work on any GLM output (R, SAS, H2O) to ease migration to production -- all you need is a coefficient table in the correct format (see example usage in R file below).

This follows the approach in H2O and uses some of the utility code from their exported Java classes, and was first created since H2O didn't provide Java output for their GLM model at the time. While manually typing in GLM coefficients into a Java class is trivial, this eases the translation for large numbers of predictors (e.g., with an lasso/ridge/elastic net model) and will automatically set up the correct link functions.

Installation

library(devtools) 
devtools::install_github("wtcooper/glmToJava")

Use

### Fit Gaussian model ###  
x = as.matrix(iris[,2:4])  
y = iris[,1]  
normFit=cv.glmnet(x,y, family="gaussian")  
normCoefs = as.data.frame(t(as.matrix(coef(normFit, s="lambda.min"))))  

buildGLMJavaClass(modCoefs=normCoefs, modType="gaussian", filePath="./NormalMod.java", package="glm2java", addMainMeth=TRUE)  


### Fit Poisson model ###  
poisFit=cv.glmnet(x,y, family="poisson")  
poisCoefs = as.data.frame(t(as.matrix(coef(poisFit, s="lambda.min"))))  

# With the default naive threshold (0.5)  
buildGLMJavaClass(modCoefs=poisCoefs, modType="poisson",  filePath="./PoissonMod.java", package="glm2java", addMainMeth=TRUE)  


### Fit binomial model ###  
x = as.matrix(iris[,1:4])  
y = iris[,5]  
y = as.character(y)  
y[y=="virginica"]="versicolor"  
y=as.factor(y)  

binFit=cv.glmnet(x,y, family="binomial")  
binCoefs = as.data.frame(t(as.matrix(coef(binFit, s="lambda.min"))))  

# With the default naive threshold (0.5)  
buildGLMJavaClass(modCoefs=binCoefs, modType="binomial", filePath="./BinaryMod.java", package="glm2java", addMainMeth=TRUE)  


# With a user provided threshold (0.2)  
buildGLMJavaClass(modCoefs=binCoefs, modType="binomial", filePath="./BinaryMod_2.java", package="glm2java", threshold=0.2, addMainMeth=TRUE)  



### Fit multinomial model ###  
x = as.matrix(iris[,1:4])  
y = iris[,5]  
multiFit = cv.glmnet(x,y, family="multinomial" )  
multiCoefs = lapply(coef(multiFit, s="lambda.min"),   
  FUN=function (x) as.data.frame(t(as.matrix(x))))  

buildGLMJavaClass(modCoefs=multiCoefs, modType="multinomial", filePath="./MultinomialMod.java", package="glm2java", addMainMeth=TRUE)  

wtcooper/glmToJava documentation built on May 4, 2019, 11:59 a.m.