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## 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.recur.bart <- function(
x.train = matrix(0,0,0),
y.train=NULL, times=NULL, delta=NULL,
x.test = matrix(0,0,0),
x.test.nogrid = FALSE, ## you may not need the whole grid
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL,
xinfo=matrix(0,0,0), usequants=FALSE,
##cont=FALSE,
rm.const=TRUE, type='pbart',
ntype=as.integer(
factor(type, levels=c('wbart', 'pbart', 'lbart'))),
k = 2, ## BEWARE: do NOT use k for other purposes below
power = 2, base = 0.95,
offset = NULL, tau.num=c(NA, 3, 6)[ntype],
##binaryOffset = NULL,
ntree = 50L, numcut = 100L,
ndpost = 1000L, nskip = 250L,
keepevery = 10L,
##nkeeptrain=ndpost, nkeeptest=ndpost,
##nkeeptestmean=ndpost,
##nkeeptreedraws=ndpost,
printevery=100L,
##treesaslists=FALSE,
keeptrainfits=TRUE,
seed = 99L, ## only used by mc.recur.bart
mc.cores = 2L, ## ditto
nice=19L ## ditto
)
{
if(.Platform$OS.type!='unix')
stop('parallel::mcparallel/mccollect do not exist on windows')
RNGkind("L'Ecuyer-CMRG")
set.seed(seed)
parallel::mc.reset.stream()
if(is.na(ntype) || ntype==1)
stop("type argument must be set to either 'pbart' or 'lbart'")
x.train <- bartModelMatrix(x.train)
##x.test <- bartModelMatrix(x.test)
if(length(y.train)==0) {
recur <- recur.pre.bart(times, delta, x.train)
##recur <- recur.pre.bart(times, delta, x.train, x.test)
y.train <- recur$y.train
x.train <- recur$tx.train
x.test <- recur$tx.test
}
## if(length(binaryOffset)==0) {
## lambda <- sum(delta, na.rm=TRUE)/
## sum(apply(times, 1, max, na.rm=TRUE))
## ##lambda <- sum(delta)/sum(times[ , ncol(times)])
## binaryOffset <- qnorm(1-exp(-lambda))
## }
## }
## else if(length(binaryOffset)==0) binaryOffset <- 0
H <- 1
Mx <- 2^31-1
Nx <- max(nrow(x.train), nrow(x.test))
if(Nx>Mx%/%ndpost) {
H <- ceiling(ndpost / (Mx %/% Nx))
ndpost <- ndpost %/% H
##nrow*ndpost>2Gi!
##due to the 2Gi limit in sendMaster, breaking run into H parts
##this bug/feature might be addressed in R-devel post 3.3.2
##i.e., the fix is NOT in R version 3.3.2
##will revisit once there is a fix in an official R release
##New Features entry for R-devel post 3.3.2
## The unexported low-level functions in package parallel for passing
## serialized R objects to and from forked children now support long
## vectors on 64-bit platforms. This removes some limits on
## higher-level functions such as mclapply()
}
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
post.list <- list()
for(h in 1:H) {
for(i in 1:mc.cores) {
parallel::mcparallel({psnice(value=nice);
recur.bart(x.train=x.train, y.train=y.train,
x.test=x.test, x.test.nogrid=x.test.nogrid,
sparse=sparse, theta=theta, omega=omega,
a=a, b=b, augment=augment, rho=rho,
xinfo=xinfo, usequants=usequants,
##cont=cont,
rm.const=rm.const, type=type,
k=k, power=power, base=base,
offset=offset, tau.num=tau.num,
##binaryOffset=binaryOffset,
ntree=ntree, numcut=numcut,
ndpost=mc.ndpost, nskip=nskip, keepevery=keepevery,
##nkeeptrain=mc.ndpost, nkeeptest=mc.ndpost,
##nkeeptestmean=mc.ndpost,
##nkeeptreedraws=mc.ndpost,
printevery=printevery, ##treesaslists=treesaslists,
keeptrainfits=keeptrainfits)},
silent=(i!=1))
## to avoid duplication of output
## capture stdout from first posterior only
}
post.list[[h]] <- parallel::mccollect()
}
if((H==1 & mc.cores==1) | attr(post.list[[1]][[1]], 'class')!='recurbart') return(post.list[[1]][[1]])
else {
for(h in 1:H) for(i in mc.cores:1) {
if(h==1 & i==mc.cores) {
post <- post.list[[1]][[mc.cores]]
post$ndpost <- H*mc.cores*mc.ndpost
p <- ncol(x.train[ , post$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$trees)
}
else {
if(keeptrainfits) {
post$yhat.train <- rbind(post$yhat.train,
post.list[[h]][[i]]$yhat.train)
post$prob.train <- rbind(post$prob.train,
post.list[[h]][[i]]$prob.train)
post$haz.train <- rbind(post$haz.train,
post.list[[h]][[i]]$haz.train)
post$cum.train <- rbind(post$cum.train,
post.list[[h]][[i]]$cum.train)
}
if(length(x.test)>0) {
post$yhat.test <- rbind(post$yhat.test, post.list[[h]][[i]]$yhat.test)
post$prob.test <- rbind(post$prob.test, post.list[[h]][[i]]$prob.test)
post$haz.test <- rbind(post$haz.test, post.list[[h]][[i]]$haz.test)
if(!x.test.nogrid) post$cum.test <- rbind(post$cum.test, post.list[[h]][[i]]$cum.test)
}
## if(length(post$sigma)>0)
## post$sigma <- c(post$sigma, post.list[[h]][[i]]$sigma)
post$varcount <- rbind(post$varcount, post.list[[h]][[i]]$varcount)
post$varprob <- rbind(post$varprob, post.list[[h]][[i]]$varprob)
post$treedraws$trees <- paste0(post$treedraws$trees,
substr(post.list[[h]][[i]]$treedraws$trees, old.stop+2,
nchar(post.list[[h]][[i]]$treedraws$trees)))
## if(treesaslists) post$treedraws$lists <-
## c(post$treedraws$lists, post.list[[h]][[i]]$treedraws$lists)
}
post.list[[h]][[i]] <- NULL
}
##post$yhat.train.mean <- apply(post$yhat.train, 2, mean)
if(keeptrainfits) {
post$prob.train.mean <- apply(post$prob.train, 2, mean)
post$haz.train.mean <- apply(post$haz.train, 2, mean)
post$cum.train.mean <- apply(post$cum.train, 2, mean)
}
if(length(x.test)>0) {
post$prob.test.mean <- apply(post$prob.test, 2, mean)
post$haz.test.mean <- apply(post$haz.test, 2, mean)
if(!x.test.nogrid) post$cum.test.mean <- apply(post$cum.test, 2, mean)
}
post$varcount.mean <- apply(post$varcount, 2, mean)
post$varprob.mean <- apply(post$varprob, 2, mean)
attr(post, 'class') <- 'recurbart'
return(post)
}
}
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