Objective Interface with Custom R Function

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

See also

ObjectiveRFun, ObjectiveRFunDt

Super class

bbotk::Objective -> ObjectiveRFunMany

Active bindings

fun

(function)
Objective function.

Methods

Public methods

• ObjectiveRFunMany$new()

• ObjectiveRFunMany$eval_many()

• ObjectiveRFunMany$clone()

Inherited methods

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

---

Method new()

Creates a new instance of this R6 class.

Usage

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

Arguments

fun

(function)
R function that encodes objective and expects a list of lists that contains multiple x values, e.g. list(list(x1 = 1, x2 = 2), list(x1 = 3, x2 = 4)). The function must return a data.table::data.table() that contains one y-column for single-criteria functions and multiple y-columns for multi-criteria functions, e.g. data.table(y = 1:2) or data.table(y1 = 1:2, y2 = 3:4).

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.

packages

(character())
Set of required packages to run the objective function.

check_values

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

---

Method eval_many()

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

Usage

ObjectiveRFunMany$eval_many(xss)

Arguments

xss

(list())
A list of lists that contains multiple x values, e.g. list(list(x1 = 1, x2 = 2), list(x1 = 3, x2 = 4)).

Returns

data.table::data.table() that contains one y-column for single-criteria functions and multiple y-columns for multi-criteria functions, e.g. data.table(y = 1:2) or data.table(y1 = 1:2, y2 = 3:4). It may also contain additional columns that will be stored in the archive if called through the OptimInstance. These extra columns are referred to as extras.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ObjectiveRFunMany$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# define objective function
fun = function(xss) {
  res = lapply(xss, function(xs) -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
  data.table::data.table(y = as.numeric(res))
}

# 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
objective = ObjectiveRFunMany$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

# evaluate objective function
objective$eval(list(x1 = 1, x2 = 2))
#> $y
#> [1] -16
#> 

# evaluate multiple input values
objective$eval_many(list(list(x1 = 1, x2 = 2), list(x1 = 3, x2 = 4)))
#>        y
#>    <num>
#> 1:   -16
#> 2:   -40

# evaluate multiple input values as data.table
objective$eval_dt(data.table::data.table(x1 = 1:2, x2 = 3:4))
#>        y
#>    <num>
#> 1:   -27
#> 2:   -39