# Determine the effect of subsets for subsetN = 50
rm(list=ls())
library(SPOT)
library(babsim.hospital)
n <- 29
reps <- 10
funEvals <- 10*n
size <- 2*n
subsetN <- 50
nCores <- 8
progSpot <- matrix(NA, nrow = reps, ncol = 2*funEvals)
#x0 <- getStartParameter()
x0 <- matrix(as.numeric(babsim.hospital::getParaSet(5374)[1,-1]),1,)
bounds <- getBounds()
a <- bounds$lower
b <- bounds$upper
g <- function(x) {
return(rbind(a[1] - x[1], x[1] - b[1], a[2] - x[2], x[2] - b[2],
a[3] - x[3], x[3] - b[3], a[4] - x[4], x[4] - b[4],
a[5] - x[5], x[5] - b[5], a[6] - x[6], x[6] - b[6],
a[7] - x[7], x[7] - b[7], a[8] - x[8], x[8] - b[8],
a[9] - x[9], x[9] - b[9], a[10] - x[10], x[10] - b[10],
a[11] - x[11], x[11] - b[11], a[12] - x[12], x[12] - b[12],
a[13] - x[13], x[13] - b[13], a[14] - x[14], x[14] - b[14],
a[15] - x[15], x[15] - b[15], a[16] - x[16], x[16] - b[16],
a[17] - x[17], x[17] - b[17], a[18] - x[18], x[18] - b[18],
a[19] - x[19], x[19] - b[19], a[20] - x[20], x[20] - b[20],
a[21] - x[21], x[21] - b[21], a[22] - x[22], x[22] - b[22],
a[23] - x[23], x[23] - b[23], a[24] - x[24], x[24] - b[24],
a[25] - x[25], x[25] - b[25], a[26] - x[26], x[26] - b[26],
a[27] - x[27], x[27] - b[27], x[15] + x[16] - 1,
x[17] + x[18] + x[19] - 1, x[20] + x[21] - 1, x[23] + x[29] - 1)
)
}
for(r in 1:reps){
set.seed(r)
print(r)
sol <- spot(x = x0,
fun = funBaBSimHospital,
lower = a,
upper = b,
verbosity = 0,
nCores = nCores,
control = list(funEvals = 2*funEvals,
noise = TRUE,
designControl = list(
# inequalityConstraint = g,
size = size,
retries = 1000),
optimizer = optimNLOPTR,
optimizerControl = list(
opts = list(algorithm = "NLOPT_GN_ISRES"),
eval_g_ineq = g),
model = buildKriging,
plots = FALSE,
progress = TRUE,
directOpt = optimNLOPTR,
directOptControl = list(
funEvals = 0),
eval_g_ineq = g,
subsetSelect = selectN,
subsetControl = list(N = subsetN))
)
progSpot[r, ] <- prepareBestObjectiveVal(sol$y, 2*funEvals)
}
matplot(t(progSpot), type="l", col="red", lty=1, xlab="n: blackbox evaluations", ylab="best objective value", log="y")
abline(v=size, lty=2)
save(progSpot, file="experiment9_50.RData")# Determine the effect of subsetN
rm(list=ls())
library(SPOT)
library(babsim.hospital)
n <- 29
reps <- 10
funEvals <- 10*n
size <- 2*n
nCores <- 8
subsetN <- 100
FILENAME = "experiment9_100.RData"
progSpot <- matrix(NA, nrow = reps, ncol = 2*funEvals)
#x0 <- getStartParameter()
x0 <- matrix(as.numeric(babsim.hospital::getParaSet(5374)[1,-1]),1,)
bounds <- getBounds()
a <- bounds$lower
b <- bounds$upper
g <- function(x) {
return(rbind(a[1] - x[1], x[1] - b[1], a[2] - x[2], x[2] - b[2],
a[3] - x[3], x[3] - b[3], a[4] - x[4], x[4] - b[4],
a[5] - x[5], x[5] - b[5], a[6] - x[6], x[6] - b[6],
a[7] - x[7], x[7] - b[7], a[8] - x[8], x[8] - b[8],
a[9] - x[9], x[9] - b[9], a[10] - x[10], x[10] - b[10],
a[11] - x[11], x[11] - b[11], a[12] - x[12], x[12] - b[12],
a[13] - x[13], x[13] - b[13], a[14] - x[14], x[14] - b[14],
a[15] - x[15], x[15] - b[15], a[16] - x[16], x[16] - b[16],
a[17] - x[17], x[17] - b[17], a[18] - x[18], x[18] - b[18],
a[19] - x[19], x[19] - b[19], a[20] - x[20], x[20] - b[20],
a[21] - x[21], x[21] - b[21], a[22] - x[22], x[22] - b[22],
a[23] - x[23], x[23] - b[23], a[24] - x[24], x[24] - b[24],
a[25] - x[25], x[25] - b[25], a[26] - x[26], x[26] - b[26],
a[27] - x[27], x[27] - b[27], x[15] + x[16] - 1,
x[17] + x[18] + x[19] - 1, x[20] + x[21] - 1, x[23] + x[29] - 1)
)
}
for(r in 1:reps){
set.seed(r)
print(r)
sol <- spot(x = x0,
fun = funBaBSimHospital,
lower = a,
upper = b,
verbosity = 0,
nCores = nCores,
control = list(funEvals = 2*funEvals,
noise = TRUE,
designControl = list(
# inequalityConstraint = g,
size = size,
retries = 1000),
optimizer = optimNLOPTR,
optimizerControl = list(
opts = list(algorithm = "NLOPT_GN_ISRES"),
eval_g_ineq = g),
model = buildKriging,
plots = FALSE,
progress = TRUE,
directOpt = optimNLOPTR,
directOptControl = list(
funEvals = 0),
eval_g_ineq = g,
subsetSelect = selectN,
subsetControl = list(N = subsetN))
)
progSpot[r, ] <- prepareBestObjectiveVal(sol$y, 2*funEvals)
}
matplot(t(progSpot), type="l", col="red", lty=1, xlab="n: blackbox evaluations", ylab="best objective value", log="y")
abline(v=size, lty=2)
save(progSpot, file=FILENAME)#pdf("experiment1.pdf")
load("~/workspace/SPOT/vignettes/experiment9_50.RData")
res50 <- progSpot
load("~/workspace/SPOT/vignettes/experiment9_100.RData")
res100 <- progSpot
load("~/workspace/SPOT/vignettes/experiment9_150.RData")
res150 <- progSpot
{
matplot(t(res150), type="l", col="red", lty=1, xlab="n: blackbox evaluations", ylab="best objective value", log="y")
matlines(t(res100), type="l", col="blue", lty=1)
matlines(t(res50), type="l", col="gray", lty=1)
legend("topright", c("res100", "res50"), col=c("red", "grey"), lty=1, bty="n")
}
#dev.off()load("~/workspace/SPOT/vignettes/experiment9_50.RData")
res50 <- progSpot
load("~/workspace/SPOT/vignettes/experiment9_100.RData")
res100 <- progSpot
load("~/workspace/SPOT/vignettes/experiment9_150.RData")
res150 <- progSpot
ySpot50 <- res50[,ncol(res50)]
ySpot100 <- res100[,ncol(res100)]
ySpot150 <- res150[,ncol(res150)]
boxplot(ySpot50, ySpot100, ySpot150)yMeanSpot50 <-apply(res50, 2,mean)
yMeanSpot100<-apply(res100, 2,mean)
yMeanSpot150<-apply(res150, 2,mean)
x<-1:length(yMeanSpot50)
{
plot(x, yMeanSpot150, type ="l")
lines(x, yMeanSpot100, type ="l", col = "red")
lines(x, yMeanSpot50, type ="l", col = "blue")
}
# library("Hmisc")
#
# ySdSpot<-apply(progSpot, 2, sd)
# ySdSpotHyb<-apply(progSpotHyb, 2, sd)
# errbar(x, yMeanSpot, yMeanSpot - ySdSpot, yMeanSpot + ySdSpot )
#
# errbar(x, yMeanSpotHyb, yMeanSpotHyb - ySdSpotHyb, yMeanSpotHyb + ySdSpotHyb )