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Acquisition Function Optimizer

Source: R/AcqOptimizer.R
AcqOptimizer.Rd

Optimizer for AcqFunctions which performs the acquisition function optimization. Wraps an bbotk::Optimizer and bbotk::Terminator.

Parameters

n_candidates

integer(1)
Number of candidate points to propose. Note that this does not affect how the acquisition function itself is calculated (e.g., setting n_candidates > 1 will not result in computing the q- or multi-Expected Improvement) but rather the top n-candidates are selected from the bbotk::Archive of the acquisition function bbotk::OptimInstance. Note that setting n_candidates > 1 is usually not a sensible idea but it is still supported for experimental reasons. Default is 1.

logging_level

character(1)
Logging level during the acquisition function optimization. Can be "fatal", "error", "warn", "info", "debug" or "trace". Default is "warn", i.e., only warnings are logged.

warmstart

logical(1)
Should the acquisition function optimization be warm-started by evaluating the best point(s) present in the bbotk::Archive of the actual bbotk::OptimInstance? This is sensible when using a population based acquisition function optimizer, e.g., local search or mutation. Default is FALSE.

warmstart_size

integer(1) | "all"
Number of best points selected from the bbotk::Archive that are to be used for warm starting. Can also be "all" to use all available points. Only relevant if warmstart = TRUE. Default is 1.

skip_already_evaluated

logical(1)
It can happen that the candidate resulting of the acquisition function optimization was already evaluated in a previous iteration. Should this candidate proposal be ignored and the next best point be selected as a candidate? Default is TRUE.

catch_errors

logical(1)
Should errors during the acquisition function optimization be caught and propagated to the loop_function which can then handle the failed acquisition function optimization appropriately by, e.g., proposing a randomly sampled point for evaluation? Default is TRUE.

Public fields

optimizer

(bbotk::Optimizer).

terminator

(bbotk::Terminator).

acq_function

(AcqFunction).

Active bindings

print_id

(character)
Id used when printing.

param_set

(paradox::ParamSet)
Set of hyperparameters.

Methods

Public methods

Method new()

Creates a new instance of this R6 class.

Usage

AcqOptimizer$new(optimizer, terminator, acq_function = NULL)

Arguments

optimizer

(bbotk::Optimizer).

terminator

(bbotk::Terminator).

acq_function

(NULL | AcqFunction).

Method format()

Helper for print outputs.

Usage

AcqOptimizer$format()

Method print()

Print method.

Usage

AcqOptimizer$print()

Returns

(character()).

Method optimize()

Optimize the acquisition function.

Usage

AcqOptimizer$optimize()

Returns

data.table::data.table() with 1 row per optimum and x as columns.

Method clone()

The objects of this class are cloneable with this method.

Usage

AcqOptimizer$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 = OptimInstanceSingleCrit$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_optimizer = acqo(
    optimizer = opt("random_search", batch_size = 1000),
    terminator = trm("evals", n_evals = 1000),
    acq_function = acq_function)

  acq_optimizer$optimize()
}
#> Loading required namespace: DiceKriging
#> Loading required namespace: rgenoud
#> Loading required package: paradox
#> Loading required package: mlr3
#>           x  x_domain   acq_ei .already_evaluated
#>       <num>    <list>    <num>             <lgcl>
#> 1: 1.187665 <list[1]> 5.305171              FALSE