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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
rs.pbart <- function(
x.train, y.train, x.test = matrix(0.0, 0L, 0L),
C = floor(length(y.train)/2000),
k = 2.0, ## BEWARE: do NOT use k for other purposes below
power = 2.0, base = 0.95,
binaryOffset = 0,
ntree=50L, numcut=100L,
ndpost=1000L, nskip=100L,
keepevery=1L, printevery=100L,
keeptrainfits=FALSE,
transposed=FALSE,
#treesaslists=FALSE,
mc.cores = 2L, nice = 19L,
seed = 99L
)
{
if(C<=1) stop('The number of shards must be >1')
if(.Platform$OS.type!='unix')
stop('parallel::mcparallel/mccollect do not exist on windows')
RNGkind("L'Ecuyer-CMRG")
set.seed(seed)
parallel::mc.reset.stream()
if(!transposed) {
temp = bartModelMatrix(x.train, numcut)
x.train = t(temp$X)
numcut = temp$numcut
xinfo = temp$xinfo
rm(temp)
##x.test <- t(x.test)
}
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
p <- nrow(x.train)
##xinfo <- makexinfo(x.train, numcut=numcut, transposed=TRUE)
## xinfo <- matrix(nrow=p, ncol=numcut)
## for(i in 1:p)
## xinfo[i, ] <- seq(min(x.train[i, ]), max(x.train[i, ]),
## length.out=numcut+2)[2:(numcut+1)]
rs <- stratrs(y.train, C)
post.list <- as.list(1:C)
shard <- as.list(1:C)
for(h in 1:C) {
shard[[h]] <- rs==h
for(i in 1:mc.cores) {
parallel::mcparallel({psnice(value=nice);
pbart(x.train=x.train[ , shard[[h]] ], y.train=y.train[shard[[h]] ],
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)},
silent=(i!=1))
## to avoid duplication of output
## capture stdout from first posterior only
}
post.list[[h]] <- parallel::mccollect()
if(h==1) {
post <- post.list[[1]][[1]]
if(attr(post, 'class')!='pbart') return(post)
## for(i in 1:p)
## if(!all(xinfo[i, ]==post$treedraws$cutpoints[[i]])) {
## stop('Cutpoints must be identical for all samples')
## ##print(c(xinfo=xinfo[[i]]))
## ##print(c(cutpoints=post$treedraws$cutpoints[[i]]))
## }
post$yhat.shard <- matrix(nrow=mc.ndpost*mc.cores, ncol=length(y.train))
post$yhat.shard[1:mc.ndpost, shard[[1]] ] <- post$yhat.train
post$yhat.train <- NULL
post$yhat.train.mean <- NULL
}
}
old.text <- paste0(as.character(mc.ndpost), ' ', as.character(ntree), ' ', as.character(p))
##old.text <- paste0(as.character(mc.nkeep), ' ', as.character(ntree), ' ', as.character(p))
old.stop <- nchar(old.text)
post$treedraws$trees <- sub(old.text,
paste0(as.character(C*mc.cores*mc.ndpost), ' ',
as.character(ntree), ' ',
##paste0(as.character(mc.cores*mc.nkeep), ' ', as.character(ntree), ' ',
as.character(p)),
post$treedraws$trees)
##keeptestfits <- length(x.test)>0
for(h in 1:C)
for(i in 1:mc.cores)
if(!(h==1 && i==1)) {
##if(keeptrainfits) post$yhat.train <- rbind(post$yhat.train, post.list[[h]][[i]]$yhat.train)
##if(keeptestfits) post$yhat.test <- rbind(post$yhat.test, post.list[[h]][[i]]$yhat.test)
post$yhat.shard[(i-1)*mc.ndpost+1:mc.ndpost, shard[[h]] ] <- post.list[[h]][[i]]$yhat.train
post$varcount <- rbind(post$varcount, post.list[[h]][[i]]$varcount)
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)
}
## 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)
attr(post, 'class') <- 'pbart'
if(keeptrainfits) {
post$x.train <- t(x.train)
post$yhat.train <- predict(post, newdata=post$x.train, mc.cores=mc.cores)
}
if(length(x.test)>0) {
post$x.test <- bartModelMatrix(x.test)
post$yhat.test <- predict(post, newdata=post$x.test, mc.cores=mc.cores)
}
return(post)
}
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