Acquisition Function Expected Improvement

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")

Parameters

• "epsilon" (numeric(1))
  \(\epsilon\) value used to determine the amount of exploration. Higher values result in the importance of improvements predicted by the posterior mean decreasing relative to the importance of potential improvements in regions of high predictive uncertainty. Defaults to 0 (standard Expected Improvement).

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.

See also

Other Acquisition Function: AcqFunction, mlr_acqfunctions, mlr_acqfunctions_aei, mlr_acqfunctions_cb, mlr_acqfunctions_ehvi, mlr_acqfunctions_ehvigh, mlr_acqfunctions_eips, mlr_acqfunctions_mean, mlr_acqfunctions_multi, mlr_acqfunctions_pi, mlr_acqfunctions_sd, mlr_acqfunctions_smsego

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

Public methods

• AcqFunctionEI$new()

• AcqFunctionEI$update()

• AcqFunctionEI$clone()

Inherited methods

• bbotk::Objective$eval()
• bbotk::Objective$format()
• bbotk::Objective$help()
• bbotk::Objective$print()
• mlr3mbo::AcqFunction$eval_dt()
• mlr3mbo::AcqFunction$eval_many()

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Method new()

Creates a new instance of this R6 class.

Usage

AcqFunctionEI$new(surrogate = NULL, epsilon = 0)

Arguments

surrogate

(NULL | SurrogateLearner).

epsilon

(numeric(1)).

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Method update()

Update the acquisition function and set y_best.

Usage

AcqFunctionEI$update()

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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