mlr3mbo (version 0.2.9)

mlr_acqfunctions_eips: Acquisition Function Expected Improvement Per Second

Description

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")

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.

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

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)))
}