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Expected Improvement.

Dictionary

This AcqFunction can be instantiated via the dictionary mlr_acqfunctions or with the associated sugar function acqf():

mlr_acqfunctions$get("ei")
acqf("ei")

References

  • Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization of Expensive Black-Box Functions.” Journal of Global optimization, 13(4), 455--492.

Super classes

bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEI

Public fields

y_best

(numeric(1))
Best objective function value observed so far. In the case of maximization, this already includes the necessary change of sign.

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage

AcqFunctionEI$new(surrogate = NULL)

Arguments

surrogate

(NULL | SurrogateLearner).


Method update()

Updates acquisition function and sets y_best.

Usage

AcqFunctionEI$update()


Method clone()

The objects of this class are cloneable with this method.

Usage

AcqFunctionEI$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("mlr3learners") &
    requireNamespace("DiceKriging") &
    requireNamespace("rgenoud")) {
  library(bbotk)
  library(paradox)
  library(mlr3learners)
  library(data.table)

  fun = function(xs) {
    list(y = xs$x ^ 2)
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y = p_dbl(tags = "minimize"))
  objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)

  instance = OptimInstanceBatchSingleCrit$new(
    objective = objective,
    terminator = trm("evals", n_evals = 5))

  instance$eval_batch(data.table(x = c(-6, -5, 3, 9)))

  learner = default_gp()

  surrogate = srlrn(learner, archive = instance$archive)

  acq_function = acqf("ei", surrogate = surrogate)

  acq_function$surrogate$update()
  acq_function$update()
  acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}
#>      acq_ei
#>       <num>
#> 1: 4.092188
#> 2: 4.549039
#> 3: 5.037109