Different scenarios of the usage of mlrMBO and visualizations.
#####################################################
###
### optimizing a simple sin(x) with mbo / EI
###
#####################################################
if (FALSE) {
library(ggplot2)
library(mlrMBO)
configureMlr(show.learner.output = FALSE)
set.seed(1)
obj.fun = makeSingleObjectiveFunction(
name = "Sine",
fn = function(x) sin(x),
par.set = makeNumericParamSet(lower = 3, upper = 13, len = 1),
global.opt.value = -1
)
ctrl = makeMBOControl(propose.points = 1)
ctrl = setMBOControlTermination(ctrl, iters = 10L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI(),
opt = "focussearch", opt.focussearch.points = 500L)
lrn = makeMBOLearner(ctrl, obj.fun)
design = generateDesign(6L, getParamSet(obj.fun), fun = lhs::maximinLHS)
run = exampleRun(obj.fun, design = design, learner = lrn,
control = ctrl, points.per.dim = 100, show.info = TRUE)
plotExampleRun(run, densregion = TRUE, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing branin in 2D with mbo / EI #####
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(1)
configureMlr(show.learner.output = FALSE)
obj.fun = makeBraninFunction()
ctrl = makeMBOControl(propose.points = 1L)
ctrl = setMBOControlTermination(ctrl, iters = 10L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI(),
opt = "focussearch", opt.focussearch.points = 2000L)
lrn = makeMBOLearner(ctrl, obj.fun)
design = generateDesign(10L, getParamSet(obj.fun), fun = lhs::maximinLHS)
run = exampleRun(obj.fun, design = design, learner = lrn, control = ctrl,
points.per.dim = 50L, show.info = TRUE)
print(run)
plotExampleRun(run, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing a simple sin(x) with multipoint proposal
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(1)
configureMlr(show.learner.output = FALSE)
obj.fun = makeSingleObjectiveFunction(
name = "Sine",
fn = function(x) sin(x),
par.set = makeNumericParamSet(lower = 3, upper = 13, len = 1L),
global.opt.value = -1
)
ctrl = makeMBOControl(propose.points = 2L)
ctrl = setMBOControlTermination(ctrl, iters = 10L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritMeanResponse())
ctrl = setMBOControlMultiPoint(
ctrl,
method = "moimbo",
moimbo.objective = "ei.dist",
moimbo.dist = "nearest.neighbor",
moimbo.maxit = 200L
)
lrn = makeMBOLearner(ctrl, obj.fun)
design = generateDesign(4L, getParamSet(obj.fun), fun = lhs::maximinLHS)
run = exampleRun(obj.fun, design = design, learner = lrn,
control = ctrl, points.per.dim = 100, show.info = TRUE)
print(run)
plotExampleRun(run, densregion = TRUE, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing branin in 2D with multipoint proposal #####
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(2)
configureMlr(show.learner.output = FALSE)
obj.fun = makeBraninFunction()
ctrl = makeMBOControl(propose.points = 5L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritMeanResponse())
ctrl = setMBOControlTermination(ctrl, iters = 10L)
ctrl = setMBOControlMultiPoint(ctrl,
method = "moimbo",
moimbo.objective = "ei.dist",
moimbo.dist = "nearest.neighbor",
moimbo.maxit = 200L
)
lrn = makeLearner("regr.km", predict.type = "se")
design = generateDesign(10L, getParamSet(obj.fun), fun = lhs::maximinLHS)
run = exampleRun(obj.fun, design = design, learner = lrn, control = ctrl,
points.per.dim = 50L, show.info = TRUE)
print(run)
plotExampleRun(run, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing a simple noisy sin(x) with mbo / EI
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(1)
configureMlr(show.learner.output = FALSE)
# function with noise
obj.fun = makeSingleObjectiveFunction(
name = "Some noisy function",
fn = function(x) sin(x) + rnorm(1, 0, 0.1),
par.set = makeNumericParamSet(lower = 3, upper = 13, len = 1L),
noisy = TRUE,
global.opt.value = -1,
fn.mean = function(x) sin(x)
)
ctrl = makeMBOControl(
propose.points = 1L,
final.method = "best.predicted",
final.evals = 10L
)
ctrl = setMBOControlTermination(ctrl, iters = 5L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI(),
opt = "focussearch", opt.focussearch.points = 500L)
lrn = makeMBOLearner(ctrl, obj.fun)
design = generateDesign(6L, getParamSet(obj.fun), fun = lhs::maximinLHS)
run = exampleRun(obj.fun, design = design, learner = lrn,
control = ctrl, points.per.dim = 200L, noisy.evals = 50L,
show.info = TRUE)
print(run)
plotExampleRun(run, densregion = TRUE, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing 1D fun with 3 categorical level and
### noisy outout with random forest
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(1)
configureMlr(show.learner.output = FALSE)
obj.fun = makeSingleObjectiveFunction(
name = "Mixed decision space function",
fn = function(x) {
if (x$foo == "a") {
return(5 + x$bar^2 + rnorm(1))
} else if (x$foo == "b") {
return(4 + x$bar^2 + rnorm(1, sd = 0.5))
} else {
return(3 + x$bar^2 + rnorm(1, sd = 1))
}
},
par.set = makeParamSet(
makeDiscreteParam("foo", values = letters[1:3]),
makeNumericParam("bar", lower = -5, upper = 5)
),
has.simple.signature = FALSE, # function expects a named list of parameter values
noisy = TRUE
)
ctrl = makeMBOControl()
ctrl = setMBOControlTermination(ctrl, iters = 10L)
# we can basically do an exhaustive search in 3 values
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI(),
opt.restarts = 1L, opt.focussearch.points = 3L, opt.focussearch.maxit = 1L)
design = generateDesign(20L, getParamSet(obj.fun), fun = lhs::maximinLHS)
lrn = makeMBOLearner(ctrl, obj.fun)
run = exampleRun(obj.fun, design = design, learner = lrn, control = ctrl,
points.per.dim = 50L, show.info = TRUE)
print(run)
plotExampleRun(run, densregion = TRUE, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing mixed space function
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(1)
configureMlr(show.learner.output = FALSE)
obj.fun = makeSingleObjectiveFunction(
name = "Mixed functions",
fn = function(x) {
if (x$cat == "a")
x$num^2
else
x$num^2 + 3
},
par.set = makeParamSet(
makeDiscreteParam("cat", values = c("a", "b")),
makeNumericParam("num", lower = -5, upper = 5)
),
has.simple.signature = FALSE,
global.opt.value = -1
)
ctrl = makeMBOControl(propose.points = 1L)
ctrl = setMBOControlTermination(ctrl, iters = 10L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI(),
opt = "focussearch", opt.focussearch.points = 500L)
lrn = makeMBOLearner(ctrl, obj.fun)
design = generateDesign(4L, getParamSet(obj.fun), fun = lhs::maximinLHS)
run = exampleRun(obj.fun, design = design, learner = lrn,
control = ctrl, points.per.dim = 100L, show.info = TRUE)
print(run)
plotExampleRun(run, densregion = TRUE, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing multi-objective function
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(1)
configureMlr(show.learner.output = FALSE)
obj.fun = makeZDT1Function(dimensions = 2L)
ctrl = makeMBOControl(n.objectives = 2L, propose.points = 2L, save.on.disk.at = integer(0L))
ctrl = setMBOControlTermination(ctrl, iters = 5L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritDIB(),
opt.focussearch.points = 10000L)
ctrl = setMBOControlMultiObj(ctrl, parego.s = 100)
learner = makeMBOLearner(ctrl, obj.fun)
design = generateDesign(5L, getParamSet(obj.fun), fun = lhs::maximinLHS)
run = exampleRunMultiObj(obj.fun, design = design, learner = learner, ctrl, points.per.dim = 50L,
show.info = TRUE, nsga2.args = list())
plotExampleRun(run, gg.objects = list(theme_bw()))
}
#####################################################
###
### optimizing multi objective function and plots
###
#####################################################
if (FALSE) {
library(mlrMBO)
library(ggplot2)
set.seed(1)
configureMlr(show.learner.output = FALSE)
obj.fun = makeDTLZ1Function(dimensions = 5L, n.objectives = 2L)
ctrl = makeMBOControl(n.objectives = 2L,
propose.points = 2L)
ctrl = setMBOControlTermination(ctrl, iters = 10L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI(), opt.focussearch.points = 1000L,
opt.focussearch.maxit = 3L)
ctrl = setMBOControlMultiObj(ctrl, method = "parego")
lrn = makeMBOLearner(ctrl, obj.fun)
design = generateDesign(8L, getParamSet(obj.fun), fun = lhs::maximinLHS)
res = mbo(obj.fun, design = design, learner = lrn, control = ctrl, show.info = TRUE)
plot(res)
}