tests/testBinaryClass.R

In forestFloor: Visualizes Random Forests with Feature Contributions

if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
library(randomForest)
library(forestFloor)
require(utils)

data(iris)
X = iris[1:100,!names(iris) %in% "Species"]
Y = iris[1:100,"Species"]
Y = Y[,drop=T]
Y
#test randomForest binary classification, treated as binary classification
rf.test42 = randomForest(X,Y,keep.forest=T,replace=F,keep.inbag=T,samp=66,ntree=500)
ff.test42 = forestFloor(rf.test42,X,binary_reg = F)
nLevels = length(levels(Y))
pred = sapply(1:nLevels,function(i) apply(ff.test42$FCarray[,,i],1,sum))+1/nLevels
rfPred = predict(rf.test42,type="vote",norm.votes=T)
rfPred[is.nan(rfPred)] = 1/nLevels
if(cor(as.vector(rfPred),as.vector(pred))^2<0.99) stop("fail accuracy binaryMultiClass")
attributes(ff.test42)
args(forestFloor:::plot.forestFloor_multiClass)
plot(ff.test42,plot_GOF=T,cex=.7,
     colLists=list(c("#FF0000A5"),
                   c("#00FF0050"),
                   c("#0000FF35")))
show3d(ff.test42,1:2,3:4)


##test randomForest binary classification treated as, regression
rf.test42 = randomForest(X,Y,keep.forest=T,replace=T,keep.inbag=T,samp=100,ntree=500)
ff.test42 = forestFloor(rf.test42,X,binary_reg = T,calc_np = F)
ff.test42 = forestFloor(rf.test42,X,binary_reg = T,calc_np = T)
plot(ff.test42,col=Y)
show3d(ff.test42,col=as.numeric(Y))

#make test set
set.seed(1)
Xtest = X
Xtest[] = lapply(X,sample,size=100,replace=T)
##test test set
ff.test43 = forestFloor(rf.test42,X,Xtest,binary_reg = T,calc_np = F)
Col = fcol(ff.test43,1:2)
plot(ff.test43,col=Col)
show3d(ff.test43,col=Col)