Container around a data.table::data.table which stores all performed function calls of the Objective.
The data is stored in a private
.data field that contains a
data.table::data.table which logs all performed function calls of the Objective.
This data.table::data.table is accessed with the public
$data() method. New
values can be added with the
$add_evals() method. This however is usually
done through the evaluation of the OptimInstance by the Optimizer.
Creates a new instance of this R6 class.
Archive$new(search_space, codomain, check_values = TRUE)
Specifies the search space for the Optimizer. The paradox::ParamSet describes either a subset of the
domain of the Objective or it describes a set of parameters together with a
trafo 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.
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.
Should x-values that are added to the archive be checked for validity? Search space that is logged into archive.
Adds function evaluations to the archive table.
Archive$add_evals(xdt, xss_trafoed, ydt)
Set of untransformed points / points from the search space. One point per row, e.g.
data.table(x1 = c(1, 3), x2 = c(2, 4)).
Column names have to match ids of the
xdt can contain additional columns.
Transformed point(s) in the domain space.
Returns the best scoring evaluation. For single-crit optimization, the solution that minimizes / maximizes the objective function. For multi-crit optimization, the Pareto set / front.
Archive$best(m = NULL)
Take only batches
m into account. Default is all batches.
Archive$data(unnest = NULL)
Helper for print outputs.
Clear all evaluation results from archive.
The objects of this class are cloneable with this method.
Archive$clone(deep = FALSE)
Whether to make a deep clone.