mlrMBO_examples: mlrMBO examples

In mlrMBO: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions

mlrMBO examples

Description

Different scenarios of the usage of mlrMBO and visualizations.

Examples

#####################################################
###
### optimizing a simple sin(x) with mbo / EI
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing branin in 2D with mbo / EI #####
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing a simple sin(x) with multipoint proposal
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing branin in 2D with multipoint proposal #####
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing a simple noisy sin(x) with mbo / EI
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing 1D fun with 3 categorical level and
### noisy outout with random forest
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing mixed space function
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing multi-objective function
###
#####################################################
## Not run: 
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()))

## End(Not run)
#####################################################
###
### optimizing multi objective function and plots
###
#####################################################
## Not run: 
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)

## End(Not run)

mlrMBO documentation built on July 4, 2022, 9:05 a.m.