The Objective class describes a black-box objective function that maps an arbitrary domain to a numerical codomain.
Details
Objective objects can have the following properties: "noisy", "deterministic", "single-crit" and "multi-crit".
Public fields
callbacks(list of mlr3misc::Callback)
Callbacks applied during the optimization.context(ContextBatch)
Stores the context for the callbacks.id(
character(1))).properties(
character()).domain(paradox::ParamSet)
Specifies domain of function, hence its input parameters, their types and ranges.codomain(paradox::ParamSet)
Specifies codomain of function, hence its feasible values.constants(paradox::ParamSet).
Changeable constants or parameters that are not subject to tuning can be stored and accessed here. Set constant values are passed to$.eval()and$.eval_many()as named arguments.check_values(
logical(1))
Active bindings
label(
character(1))
Label for this object. Can be used in tables, plot and text output instead of the ID.man(
character(1))
String in the format[pkg]::[topic]pointing to a manual page for this object. The referenced help package can be opened via method$help().xdim(
integer(1))
Dimension of domain.ydim(
integer(1))
Dimension of codomain.
Methods
Method new()
Creates a new instance of this R6 class.
Arguments
id(
character(1)).properties(
character()).domain(paradox::ParamSet)
Specifies domain of function. The paradox::ParamSet should describe all possible input parameters of the objective function. This includes theirid, their types and the possible range.codomain(paradox::ParamSet)
Specifies codomain of function. Most importantly the tags of each output "Parameter" define whether it should be minimized or maximized. The default is to minimize each component.constants(paradox::ParamSet)
Changeable constants or parameters that are not subject to tuning can be stored and accessed here.check_values(
logical(1))
Should points before the evaluation and the results be checked for validity?label(
character(1))
Label for this object. Can be used in tables, plot and text output instead of the ID.man(
character(1))
String in the format[pkg]::[topic]pointing to a manual page for this object. The referenced help package can be opened via method$help().
Method eval()
Evaluates a single input value on the objective function. If
check_values = TRUE, the validity of the point as well as the validity
of the result is checked.
Arguments
xs(
list())
A list that contains a single x value, e.g.list(x1 = 1, x2 = 2).
Returns
list() that contains the result of the evaluation, e.g. list(y = 1).
The list can also contain additional named entries that will be stored in the
archive if called through the OptimInstance.
These extra entries are referred to as extras.
Method eval_many()
Evaluates multiple input values on the objective function. If
check_values = TRUE, the validity of the points as well as the validity
of the results are checked. bbotk does not take care of
parallelization. If the function should make use of parallel computing,
it has to be implemented by deriving from this class and overwriting this
function.
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 eval_dt()
Evaluates multiple input values on the objective function
Arguments
xdt(
data.table::data.table())
Set of untransformed points / points from the search space. One point per row, e.g.data.table(x1 = c(1, 3), x2 = c(2, 4)). Column names have to match ids of thesearch_space. However,xdtcan contain additional columns.