mlr_acqfunctions_cb {mlr3mbo}

Acquisition Function Confidence Bound

Description

Lower / Upper Confidence Bound.

Dictionary

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

mlr_acqfunctions$get("cb")
acqf("cb")

Parameters

• "lambda" (numeric(1))
  \lambda value used for the confidence bound. Defaults to 2.

Super classes

bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionCB

Methods

Public methods

• AcqFunctionCB$new()

• AcqFunctionCB$clone()

Inherited methods

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

Method new()

Creates a new instance of this R6 class.

Usage

AcqFunctionCB$new(surrogate = NULL, lambda = 2)

Arguments

surrogate

(NULL | SurrogateLearner).

lambda

(numeric(1)).

Method clone()

The objects of this class are cloneable with this method.

Usage

AcqFunctionCB$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

• Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization of Machine Learning Algorithms.” In Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances in Neural Information Processing Systems, volume 25, 2951–2959.

See Also

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

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("cb", surrogate = surrogate, lambda = 3)

  acq_function$surrogate$update()
  acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}