Nothing
cv.multinom.enetLTS <- function(index=NULL, xx, yy, alphas, lambdas,
nfold, repl, ncores, plot=TRUE){
RTMSPE <- RMSPE <- TMNLL <- MNLL <- NULL
# return only smallest fraction 1-trim of values:
uptrim <- function(x,trim=0.1) {
return(sort(x)[1:(length(x)*(1-trim))])
}
family <- "multinomial"
k <- dim(yy)[2]
n <- dim(xx)[1]
wh <- (alphas<0 | alphas>1)
if (sum(wh)>0) stop("alphas can take the values only between 0 and 1")
if (missing(alphas)) stop("provide an alphas sequence")
if (missing(lambdas)) stop("provide an lambdas sequence")
combis_ind <- expand.grid(1:length(alphas), 1:length(lambdas))
indcombi <- 1:nrow(combis_ind)
calc_evalCrit <- function(rowind, combis_ind, alphas, lambdas,
index, xx, yy, nfold, repl){
i <- combis_ind[rowind, 1] # alphas
j <- combis_ind[rowind, 2] # lambdas
alpha <- alphas[i]
lambda <- lambdas[j]
if (is.null(index)) {
x <- xx
y <- yy } else {
x <- xx[index[[i]][,j],]
y <- yy[index[[i]][,j],]
}
evalCritl <- rep(NA,repl)
for (l in 1:repl){ # repeate CV
folds_k <- lapply(1:k, function(c,y,nfold){cvTools::cvFolds(length(y[,c][y[,c]==1]),K=nfold,R=1,type="random")},y,nfold)
# print(folds_k)
loss <- lapply(1:k, function(c,y){rep(NA,length(y[,c][y[,c]==1]))},y)
for (f in 1:nfold) {
xtrain_k <- lapply(1:k, function(c,x,y,f) {
x[which(y[,c]==1),][folds_k[[c]]$subsets[folds_k[[c]]$which != f,1], ]
},x,y,f)
xtrain <- do.call(rbind,xtrain_k)
xtest_k <- lapply(1:k, function(c,x,y,f) {
x[which(y[,c]==1),][folds_k[[c]]$subsets[folds_k[[c]]$which == f,1], ]
},x,y,f)
xtest <- do.call(rbind,xtest_k)
ytrain_k <- lapply(1:k, function(c,y,f) {
y[which(y[,c]==1),][folds_k[[c]]$subsets[folds_k[[c]]$which != f,1], ]
},y,f)
ytrain <- do.call(rbind,ytrain_k)
ytest_k <- lapply(1:k, function(c,y,f) {
y[which(y[,c]==1),][folds_k[[c]]$subsets[folds_k[[c]]$which == f,1], ]
},y,f)
ytest <- do.call(rbind,ytest_k)
res <- tryCatch({
trainmod <- glmnet(xtrain,ytrain,family,alpha=alpha,lambda=lambda,
standardize=FALSE,intercept=FALSE)},error=function(err){
error <- TRUE
return(error)
})
if (is.logical(res)){
print(paste("CV broke off for alpha=",alpha ,"and lambda=", lambda))
} else {
trainmod <- res
loss_k <- lapply(1:k, function(c,xtest_k,ytest_k,trainmod,f){
(-apply(ytest_k[[c]]*log(drop(predict(trainmod,newx=xtest_k[[c]],type="response"))),1,sum))
},xtest_k,ytest_k,trainmod,f)
for (ii in 1:k){
loss[[ii]][folds_k[[ii]]$which == f ] <- loss_k[[ii]]
}
}
}
evalgroups <- vector("list",k)
for (i in 1:k){
evalgroups[[i]] <- uptrim(loss[[i]])
}
evalCritl[l] <- mean(unlist(evalgroups),na.rm=TRUE) # that works if we have repl more than 1, now only mean
}
evalCrit <- mean(evalCritl,na.rm=TRUE)
return(evalCrit = evalCrit)
}
temp_result <- mclapply(indcombi,
FUN = calc_evalCrit,
combis_ind = combis_ind,
alphas = alphas,
lambdas = lambdas,
index = index,
xx = xx,
yy = yy,
nfold = nfold,
repl = repl,
mc.cores = ncores,
mc.allow.recursive = FALSE)
evalCrit <- matrix(unlist(temp_result), ncol = length(lambdas) , byrow = FALSE) # row alphas, col lambdas
dimnames(evalCrit) <- list(paste("alpha", alphas), paste("lambda", lambdas))
optind <- which(evalCrit == min(evalCrit, na.rm = TRUE), arr.ind = TRUE)[1, ]
alpha_optind <- unlist(optind[1]) # row
lambda_optind <- unlist(optind[2]) # col
minevalCrit <- evalCrit[alpha_optind,lambda_optind]
indexbest <- index[[alpha_optind]][,lambda_optind]
alphas <- round(alphas,4)
lambdas <- round(lambdas,4)
alpha <- alphas[alpha_optind]
lambda <- lambdas[lambda_optind]
if (plot==TRUE){
print(paste("optimal model: lambda =", lambda, "alpha =", alpha))
lenCol <- length(alphas)*length(lambdas)
mycol.b <- colorRampPalette(c("black","blue2", "purple", "orange", "yellow"))(lenCol)
ggmspe <- evalCrit
rownames(ggmspe) <- alphas
colnames(ggmspe) <- lambdas
ggmspe <- melt(ggmspe)
if (is.null(index)){
names(ggmspe) <- c("lambda","alpha","TMNLL")
mspeplot <- ggplot(ggmspe,aes(x=as.factor(alpha),y=as.factor(lambda),fill=TMNLL)) +
geom_tile() + scale_fill_gradientn(colours=mycol.b) + theme(axis.text.x=element_text(angle=-90))
mspeplot <- mspeplot + ggtitle(paste0("TMNLL (optimal at lambda=",lambda,",alpha=",alpha,", ",family,")"))
} else {
names(ggmspe) <- c("lambda","alpha","MNLL")
mspeplot <- ggplot(ggmspe,aes(x=as.factor(alpha),y=as.factor(lambda),fill=MNLL)) +
geom_tile() + scale_fill_gradientn(colours=mycol.b) + theme(axis.text.x=element_text(angle=-90))
mspeplot <- mspeplot + ggtitle(paste0("MNLL (optimal at lambda=",lambda,",alpha=",alpha,",",family,")"))
}
mspeplot <- mspeplot + xlab("lambda") + ylab("alpha")
grid.newpage()
pushViewport(viewport(layout=grid.layout(1,1)))
print(mspeplot, vp=viewport(layout.pos.row=1, layout.pos.col=1))
}
return(list(indexbest=indexbest,evalCrit=evalCrit,minevalCrit=minevalCrit,lambdaopt=lambda,alphaopt=alpha))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.