OptimInstance {bbotk} | R Documentation |
Optimization Instance with budget and archive
Description
Abstract base class.
Technical details
The Optimizer writes the final result to the .result
field by using
the $assign_result()
method. .result
stores a data.table::data.table
consisting of x values in the search space, (transformed) x values in the
domain space and y values in the codomain space of the Objective. The
user can access the results with active bindings (see below).
Public fields
objective
(Objective).
search_space
terminator
(Terminator).
archive
(Archive).
progressor
(
progressor()
)
Storesprogressor
function.objective_multiplicator
(
integer()
).callbacks
(List of CallbackOptimizations).
Active bindings
result
(data.table::data.table)
Get resultresult_x_search_space
(data.table::data.table)
x part of the result in the search space.result_x_domain
(
list()
)
(transformed) x part of the result in the domain space of the objective.result_y
(
numeric()
)
Optimal outcome.is_terminated
(
logical(1)
).
Methods
Public methods
Method new()
Creates a new instance of this R6 class.
Usage
OptimInstance$new( objective, search_space = NULL, terminator, keep_evals = "all", check_values = TRUE, callbacks = list() )
Arguments
objective
(Objective).
search_space
(paradox::ParamSet)
Specifies the search space for the Optimizer. The paradox::ParamSet describes either a subset of thedomain
of the Objective or it describes a set of parameters together with atrafo
function that transforms values from the search space to values of the domain. Depending on the context, this value defaults to the domain of the objective.terminator
(Terminator).
keep_evals
(
character(1)
)
Keepall
or onlybest
evaluations in archive?check_values
(
logical(1)
)
Should x-values that are added to the archive be checked for validity? Search space that is logged into archive.callbacks
(list of mlr3misc::Callback)
List of callbacks.
Method format()
Helper for print outputs.
Usage
OptimInstance$format(...)
Arguments
...
(ignored).
Method print()
Printer.
Usage
OptimInstance$print(...)
Arguments
...
(ignored).
Method eval_batch()
Evaluates all input values in xdt
by calling
the Objective. Applies possible transformations to the input values
and writes the results to the Archive.
Before each batch-evaluation, the Terminator is checked, and if it
is positive, an exception of class terminated_error
is raised. This
function should be internally called by the Optimizer.
Usage
OptimInstance$eval_batch(xdt)
Arguments
xdt
(
data.table::data.table()
)
x values asdata.table()
with one point per row. Contains the value in the search space of the OptimInstance object. Can contain additional columns for extra information.
Method assign_result()
The Optimizer object writes the best found point and estimated performance value here. For internal use.
Usage
OptimInstance$assign_result(xdt, y)
Arguments
xdt
(
data.table::data.table()
)
x values asdata.table::data.table()
with one row. Contains the value in the search space of the OptimInstance object. Can contain additional columns for extra information.y
(
numeric(1)
)
Optimal outcome.
Method objective_function()
Evaluates (untransformed) points of only numeric values. Returns a
numeric scalar for single-crit or a numeric vector for multi-crit. The
return value(s) are negated if the measure is maximized. Internally,
$eval_batch()
is called with a single row. This function serves as a
objective function for optimizers of numeric spaces - which should always
be minimized.
Usage
OptimInstance$objective_function(x)
Arguments
x
(
numeric()
)
Untransformed points.
Returns
Objective value as numeric(1)
, negated for maximization problems.
Method clear()
Reset terminator and clear all evaluation results from archive and results.
Usage
OptimInstance$clear()
Method clone()
The objects of this class are cloneable with this method.
Usage
OptimInstance$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.