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## BART: Bayesian Additive Regression Trees
## Copyright (C) 2018 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.gbart <- function(
x.train, y.train,
x.test=matrix(0,0,0), type='wbart',
ntype=as.integer(
factor(type,
levels=c('wbart', 'pbart', 'lbart'))),
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL,
xinfo=matrix(0,0,0), usequants=FALSE,
rm.const=TRUE,
sigest=NA, sigdf=3, sigquant=0.90,
k=2, power=2, base=0.95,
##sigmaf=NA,
lambda=NA, tau.num=c(NA, 3, 6)[ntype],
##tau.interval=0.9973,
offset=NULL, w=rep(1, length(y.train)),
ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
ndpost=1000L, nskip=100L,
keepevery=c(1L, 10L, 10L)[ntype],
printevery=100L, transposed=FALSE,
hostname=FALSE,
mc.cores = 2L, nice = 19L, seed = 99L
)
{
if(is.na(ntype))
stop("type argument must be set to either 'wbart', 'pbart' or 'lbart'")
check <- unique(sort(y.train))
if(length(check)==2) {
if(!all(check==0:1))
stop('Binary y.train must be coded as 0 and 1')
if(type=='wbart')
stop("The outcome is binary so set type to 'pbart' or 'lbart'")
}
if(.Platform$OS.type!='unix')
stop('parallel::mcparallel/mccollect do not exist on windows')
RNGkind("L'Ecuyer-CMRG")
set.seed(seed)
parallel::mc.reset.stream()
if(!transposed) {
temp = bartModelMatrix(x.train, numcut, usequants=usequants,
xinfo=xinfo, rm.const=rm.const)
x.train = t(temp$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$rm.const
rm(temp)
}
mc.cores.detected <- detectCores()
if(mc.cores>mc.cores.detected) mc.cores <- mc.cores.detected
mc.ndpost <- ceiling(ndpost/mc.cores)
for(i in 1:mc.cores) {
parallel::mcparallel({psnice(value=nice);
gbart(x.train=x.train, y.train=y.train,
x.test=x.test, type=type, ntype=ntype,
sparse=sparse, theta=theta, omega=omega,
a=a, b=b, augment=augment, rho=rho,
xinfo=xinfo, usequants=usequants,
rm.const=rm.const,
sigest=sigest, sigdf=sigdf, sigquant=sigquant,
k=k, power=power, base=base,
##sigmaf=sigmaf,
lambda=lambda, tau.num=tau.num,
##tau.interval=tau.interval,
offset=offset,
w=w, ntree=ntree, numcut=numcut,
ndpost=mc.ndpost, nskip=nskip,
keepevery=keepevery, printevery=printevery,
transposed=TRUE, hostname=hostname)},
silent=(i!=1))
## to avoid duplication of output
## capture stdout from first posterior only
}
post.list <- parallel::mccollect()
post <- post.list[[1]]
if(mc.cores==1 | attr(post, 'class')!=type) return(post)
else {
if(class(rm.const)[1]!='logical') post$rm.const <- rm.const
post$ndpost <- mc.cores*mc.ndpost
p <- nrow(x.train[post$rm.const, ])
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$trees)
keeptest <- length(x.test)>0
if(type=='wbart') sigma <- post$sigma[-(1:nskip)]
for(i in 2:mc.cores) {
post$hostname[i] <- post.list[[i]]$hostname
post$yhat.train <- rbind(post$yhat.train,
post.list[[i]]$yhat.train)
if(keeptest) post$yhat.test <- rbind(post$yhat.test,
post.list[[i]]$yhat.test)
if(type=='wbart') {
post$sigma <- cbind(post$sigma, post.list[[i]]$sigma)
sigma <- c(sigma, post.list[[i]]$sigma[-(1:nskip)])
}
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)))
post$proc.time['elapsed'] <-
max(post$proc.time['elapsed'],
post.list[[i]]$proc.time['elapsed'])
for(j in 1:5)
if(j!=3)
post$proc.time[j] <-
post$proc.time[j]+post.list[[i]]$proc.time[j]
}
n=length(y.train)
Y=t(matrix(y.train, nrow=n, ncol=post$ndpost))
if(type=='wbart') {
post$yhat.train.mean <- apply(post$yhat.train, 2, mean)
SD=matrix(sigma, nrow=post$ndpost, ncol=n)
##CPO=1/apply(1/dnorm(Y, post$yhat.train, SD), 2, mean)
log.pdf=dnorm(Y, post$yhat.train, SD, TRUE)
post$sigma.mean=mean(SD[ , 1])
if(keeptest)
post$yhat.test.mean <- apply(post$yhat.test, 2, mean)
} else {
post$prob.train.mean <- apply(post$prob.train, 2, mean)
##CPO=1/apply(1/dbinom(Y, 1, post$prob.train), 2, mean)
log.pdf=dbinom(Y, 1, post$prob.train, TRUE)
if(keeptest)
post$prob.test.mean <- apply(post$prob.test, 2, mean)
}
min.log.pdf=t(matrix(apply(log.pdf, 2, min),
nrow=n, ncol=post$ndpost))
log.CPO=log(post$ndpost)+min.log.pdf[1, ]-
log(apply(exp(min.log.pdf-log.pdf), 2, sum))
post$LPML=sum(log.CPO)
##post$LPML=sum(log(CPO))
post$varcount.mean <- apply(post$varcount, 2, mean)
post$varprob.mean <- apply(post$varprob, 2, mean)
attr(post, 'class') <- type
return(post)
}
}
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