mlr_acqfunctions_stochastic_cb {mlr3mbo}R Documentation

Acquisition Function Stochastic Confidence Bound

Description

Lower / Upper Confidence Bound with lambda sampling and decay. The initial \lambda is drawn from an uniform distribution between min_lambda and max_lambda or from an exponential distribution with rate 1 / lambda. \lambda is updated after each update by the formula lambda * exp(-rate * (t %% period)), where t is the number of times the acquisition function has been updated.

While this acquisition function usually would be used within an asynchronous optimizer, e.g., OptimizerAsyncMbo, it can in principle also be used in synchronous optimizers, e.g., OptimizerMbo.

Dictionary

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

mlr_acqfunctions$get("stochastic_cb")
acqf("stochastic_cb")

Parameters

Note

Super classes

bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionStochasticCB

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
AcqFunctionStochasticCB$new(
  surrogate = NULL,
  lambda = 1.96,
  min_lambda = 0.01,
  max_lambda = 10,
  distribution = "uniform",
  rate = 0,
  period = NULL
)
Arguments
surrogate

(NULL | SurrogateLearner).

lambda

(numeric(1)).

min_lambda

(numeric(1)).

max_lambda

(numeric(1)).

distribution

(character(1)).

rate

(numeric(1)).

period

(NULL | integer(1)).


Method update()

Update the acquisition function. Samples and decays lambda.

Usage
AcqFunctionStochasticCB$update()

Method reset()

Reset the acquisition function. Resets the private update counter .t used within the epsilon decay.

Usage
AcqFunctionStochasticCB$reset()

Method clone()

The objects of this class are cloneable with this method.

Usage
AcqFunctionStochasticCB$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

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_eips, mlr_acqfunctions_mean, mlr_acqfunctions_multi, mlr_acqfunctions_pi, mlr_acqfunctions_sd, mlr_acqfunctions_smsego, 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$x ^ 2)
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y = p_dbl(tags = "minimize"))
  objective = ObjectiveRFun$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("stochastic_cb", surrogate = surrogate, lambda = 3)

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

[Package mlr3mbo version 0.2.8 Index]