Nothing
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017 Robert McCulloch and Rodney Sparapani
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
## You should have received a copy of the GNU General Public License
## along with this program; if not, a copy is available at
## https://www.R-project.org/Licenses/GPL-2
mc.pbart <- function(
x.train, y.train, x.test = matrix(0.0, 0L, 0L),
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL,
xinfo=matrix(0.0,0,0), usequants=FALSE,
cont=FALSE, rm.const=TRUE,
k = 2.0, ## BEWARE: do NOT use k for other purposes below
power = 2.0, base = 0.95,
binaryOffset = NULL,
ntree=50L, numcut=100L,
ndpost=1000L, nskip=100L,
keepevery=1L, printevery=100L,
keeptrainfits=TRUE, transposed=FALSE,
## treesaslists=FALSE,
mc.cores = 2L, nice = 19L,
seed = 99L
)
{
if(.Platform$OS.type!='unix')
stop('parallel::mcparallel/mccollect do not exist on windows')
RNGkind("L'Ecuyer-CMRG")
set.seed(seed)
parallel::mc.reset.stream()
if(!transposed) {
temp = bartModelMatrix(x.train, numcut, usequants=usequants,
cont=cont, xinfo=xinfo, rm.const=rm.const)
x.train = t(temp$X)
numcut = temp$numcut
xinfo = temp$xinfo
## if(length(x.test)>0)
## x.test = t(bartModelMatrix(x.test[ , temp$rm.const]))
if(length(x.test)>0) {
x.test = bartModelMatrix(x.test)
x.test = t(x.test[ , temp$rm.const])
}
rm.const <- temp$rm.const
rm(temp)
}
mc.cores.detected <- detectCores()
if(mc.cores>mc.cores.detected) mc.cores <- mc.cores.detected
## warning(paste0('The number of cores requested, mc.cores=', mc.cores,
## ',\n exceeds the number of cores detected via detectCores() ',
## 'which yields ', mc.cores.detected, ' .'))
mc.ndpost <- ceiling(ndpost/mc.cores)
## mc.ndpost <- ((ndpost %/% mc.cores) %/% keepevery)*keepevery
## while(mc.ndpost*mc.cores<ndpost) mc.ndpost <- mc.ndpost+keepevery
## mc.nkeep <- mc.ndpost %/% keepevery
for(i in 1:mc.cores) {
parallel::mcparallel({psnice(value=nice);
pbart(x.train=x.train, y.train=y.train, x.test=x.test,
sparse=sparse, theta=theta, omega=omega,
a=a, b=b, augment=augment, rho=rho,
xinfo=xinfo,
k=k, power=power, base=base,
binaryOffset=binaryOffset,
ntree=ntree, numcut=numcut,
ndpost=mc.ndpost, nskip=nskip, keepevery=keepevery,
## nkeeptrain=mc.nkeep, nkeeptest=mc.nkeep,
## nkeeptestmean=mc.nkeep, nkeeptreedraws=mc.nkeep,
printevery=printevery, transposed=TRUE)},
##treesaslists=treesaslists)},
silent=(i!=1))
## to avoid duplication of output
## capture stdout from first posterior only
}
post.list <- parallel::mccollect()
post <- post.list[[1]]
if(mc.cores==1 | attr(post, 'class')!='pbart') return(post)
else {
if(class(rm.const)[1]!='logical') post$rm.const <- rm.const
post$ndpost <- mc.cores*mc.ndpost
p <- nrow(x.train[post$rm.const, ])
##p <- nrow(x.train[ , post$rm.const])
## if(length(rm.const)==0) rm.const <- 1:p
## post$rm.const <- rm.const
old.text <- paste0(as.character(mc.ndpost), ' ', as.character(ntree),
' ', as.character(p))
old.stop <- nchar(old.text)
post$treedraws$trees <- sub(old.text,
paste0(as.character(post$ndpost), ' ',
as.character(ntree), ' ',
as.character(p)),
post$treedraws$trees)
keeptestfits <- length(x.test)>0
for(i in 2:mc.cores) {
if(keeptrainfits) {
post$yhat.train <- rbind(post$yhat.train,
post.list[[i]]$yhat.train)
post$prob.train <- rbind(post$prob.train,
post.list[[i]]$prob.train)
}
if(keeptestfits) {
post$yhat.test <- rbind(post$yhat.test,
post.list[[i]]$yhat.test)
post$prob.test <- rbind(post$prob.test,
post.list[[i]]$prob.test)
}
post$varcount <- rbind(post$varcount, post.list[[i]]$varcount)
post$varprob <- rbind(post$varprob, post.list[[i]]$varprob)
post$treedraws$trees <- paste0(post$treedraws$trees,
substr(post.list[[i]]$treedraws$trees, old.stop+2,
nchar(post.list[[i]]$treedraws$trees)))
## if(treesaslists) post$treedraws$lists <-
## c(post$treedraws$lists, post.list[[i]]$treedraws$lists)
}
## if(length(post$yhat.train.mean)>0)
## post$yhat.train.mean <- apply(post$yhat.train, 2, mean)
## if(length(post$yhat.test.mean)>0)
## post$yhat.test.mean <- apply(post$yhat.test, 2, mean)
if(length(post$prob.train.mean)>0)
post$prob.train.mean <- apply(post$prob.train, 2, mean)
if(length(post$prob.test.mean)>0)
post$prob.test.mean <- apply(post$prob.test, 2, mean)
post$varcount.mean <- apply(post$varcount, 2, mean)
post$varprob.mean <- apply(post$varprob, 2, mean)
attr(post, 'class') <- 'pbart'
return(post)
}
}
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.