Container around a data.table::data.table which stores all performed function calls of the Objective.
as.data.table(archive)
Archive -> data.table::data.table()
Returns a tabular view of all performed function calls of the
Objective. The x_domain column is unnested to separate columns.
search_space(paradox::ParamSet) Search space of objective.
codomain(Codomain) Codomain of objective function.
start_time(POSIXct) Time stamp of when the optimization started. The time is set by the Optimizer.
check_values(logical(1))
Determines if points and results are checked for validity.
data(data.table::data.table) Contains all performed Objective function calls.
n_evals(integer(1))
Number of evaluations stored in the archive.
n_batch(integer(1))
Number of batches stored in the archive.
cols_x(character())
Column names of search space parameters.
cols_y(character())
Column names of codomain target parameters.
new()Creates a new instance of this R6 class.
Archive$new(search_space, codomain, check_values = TRUE)
search_space(paradox::ParamSet)
Specifies the search space for the Optimizer. The paradox::ParamSet
describes either a subset of the domain of the Objective or it describes
a set of parameters together with a trafo function that transforms values
from the search space to values of the domain. Depending on the context, this
value defaults to the domain of the objective.
codomain(paradox::ParamSet) Specifies codomain of function. Most importantly the tags of each output "Parameter" define whether it should be minimized or maximized. The default is to minimize each component.
check_values(logical(1))
Should x-values that are added to the archive be checked for validity?
Search space that is logged into archive.
add_evals()Adds function evaluations to the archive table.
Archive$add_evals(xdt, xss_trafoed = NULL, ydt)
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 the search_space.
However, xdt can contain additional columns.
xss_trafoed(list())
Transformed point(s) in the domain space.
ydt(data.table::data.table())
Optimal outcome.
best()Returns the best scoring evaluation(s). For single-crit optimization, the solution that minimizes / maximizes the objective function. For multi-crit optimization, the Pareto set / front.
Archive$best(batch = NULL, n_select = 1)
batch(integer())
The batch number(s) to limit the best results to. Default is
all batches.
n_select(integer(1L))
Amount of points to select. Ignored for multi-crit optimization.
nds_selection()Calculate best points w.r.t. non dominated sorting with hypervolume contribution.
Archive$nds_selection(batch = NULL, n_select = 1, ref_point = NULL)
batch(integer())
The batch number(s) to limit the best points to. Default is
all batches.
n_select(integer(1L))
Amount of points to select.
ref_point(numeric())
Reference point for hypervolume.
format()Helper for print outputs.
Archive$format()
print()Printer.
Archive$print()
...(ignored).
clear()Clear all evaluation results from archive.
Archive$clear()
clone()The objects of this class are cloneable with this method.
Archive$clone(deep = FALSE)
deepWhether to make a deep clone.