ObjectiveRFun: Objective interface with custom R function

In bbotk: Black-Box Optimization Toolkit

Objective interface with custom R function

Description

Objective interface where the user can pass a custom R function that expects a list as input. If the return of the function is unnamed, it is named with the ids of the codomain.

Super class

bbotk::Objective -> ObjectiveRFun

Active bindings

fun

(function)
Objective function.

Methods

Public methods

• ObjectiveRFun$new()

• ObjectiveRFun$eval()

• ObjectiveRFun$clone()

Inherited methods

• bbotk::Objective$eval_dt()
• bbotk::Objective$eval_many()
• bbotk::Objective$format()
• bbotk::Objective$help()
• bbotk::Objective$print()

---

Method new()

Creates a new instance of this R6 class.

Usage

ObjectiveRFun$new(
  fun,
  domain,
  codomain = NULL,
  id = "function",
  properties = character(),
  constants = ps(),
  check_values = TRUE
)

Arguments

fun

(function)
R function that encodes objective and expects a list with the input for a single point (e.g. list(x1 = 1, x2 = 2)) and returns the result either as a numeric vector or a list (e.g. list(y = 3)).

domain

(paradox::ParamSet)
Specifies domain of function. The paradox::ParamSet should describe all possible input parameters of the objective function. This includes their id, their types and the possible range.

codomain

(paradox::ParamSet)
Specifies codomain of function. Most importantly the tags of each output "Parameter" define whether it should be minimized or maximized. The default is to minimize each component.

id

(character(1)).

properties

(character()).

constants

(paradox::ParamSet)
Changeable constants or parameters that are not subject to tuning can be stored and accessed here.

check_values

(logical(1))
Should points before the evaluation and the results be checked for validity?

---

Method eval()

Evaluates input value(s) on the objective function. Calls the R function supplied by the user.

Usage

ObjectiveRFun$eval(xs)

Arguments

xs

Input values.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ObjectiveRFun$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# define objective function
fun = function(xs) {
  -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10
}

# set domain
domain = ps(
  x1 = p_dbl(-10, 10),
  x2 = p_dbl(-5, 5)
)

# set codomain
codomain = ps(y = p_dbl(tags = "maximize"))

# create Objective object
obfun = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

bbotk documentation built on June 8, 2025, 11:07 a.m.