Acquisition Function Expected Improvement Per Second

Expected Improvement per Second.

It is assumed that calculations are performed on an bbotk::OptimInstanceBatchSingleCrit. Additionally to target values of the codomain that should be minimized or maximized, the bbotk::Objective of the bbotk::OptimInstanceBatchSingleCrit should return time values. The column names of the target variable and time variable must be passed as cols_y in the order (target, time) when constructing the SurrogateLearnerCollection that is being used as a surrogate.

Dictionary

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

mlr_acqfunctions$get("eips")
acqf("eips")

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_cb, mlr_acqfunctions_ehvi, mlr_acqfunctions_ehvigh, mlr_acqfunctions_ei, mlr_acqfunctions_mean, mlr_acqfunctions_multi, mlr_acqfunctions_pi, mlr_acqfunctions_sd, mlr_acqfunctions_smsego, mlr_acqfunctions_stochastic_cb, mlr_acqfunctions_stochastic_ei

Super classes

bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEIPS

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.

Active bindings

col_y

(character(1)).

col_time

(character(1)).

Methods

Public methods

• AcqFunctionEIPS$new()

• AcqFunctionEIPS$update()

• AcqFunctionEIPS$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$reset()

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Method new()

Creates a new instance of this R6 class.

Usage

AcqFunctionEIPS$new(surrogate = NULL)

Arguments

surrogate

(NULL | SurrogateLearnerCollection).

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Method update()

Update the acquisition function and set y_best.

Usage

AcqFunctionEIPS$update()

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Method clone()

The objects of this class are cloneable with this method.

Usage

AcqFunctionEIPS$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$x ^ 2, time = abs(xs$x))
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y = p_dbl(tags = "minimize"), time = p_dbl(tags = "time"))
  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(list(learner, learner$clone(deep = TRUE)), archive = instance$archive)
  surrogate$cols_y = c("y", "time")

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

  acq_function$surrogate$update()
  acq_function$update()
  acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}
#>    acq_eips
#>       <num>
#> 1: 4.401246
#> 2: 4.864655
#> 3: 5.297142