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bbotk (version 1.7.1)

ArchiveBatch: Data Table Storage

Description

The ArchiveBatch stores all evaluated points and performance scores in a data.table::data.table().

Arguments

S3 Methods

  • as.data.table(archive)
    ArchiveBatch -> 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.

Super class

bbotk::Archive -> ArchiveBatch

Public fields

data

(data.table::data.table)
Contains all performed Objective function calls.

data_extra

(named list)
Data created by specific Optimizers that does not relate to any individual function evaluation and can therefore not be held in $data. Every optimizer should create and refer to its own entry in this list, named by its class().

Active bindings

n_evals

(integer(1))
Number of evaluations stored in the archive.

n_batch

(integer(1))
Number of batches stored in the archive.

Methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

ArchiveBatch$new(search_space, codomain, check_values = FALSE)

Arguments

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.

Method add_evals()

Adds function evaluations to the archive table.

Usage

ArchiveBatch$add_evals(xdt, xss_trafoed = NULL, ydt)

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 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.

Method 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.

Usage

ArchiveBatch$best(batch = NULL, n_select = 1L, ties_method = "first")

Arguments

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.

ties_method

(character(1L))
Method to break ties when multiple points have the same score. Either "first" (default) or "random". Ignored for multi-crit optimization. If n_select > 1L, the tie method is ignored and the first point is returned.

Method nds_selection()

Calculate best points w.r.t. non dominated sorting with hypervolume contribution.

Usage

ArchiveBatch$nds_selection(batch = NULL, n_select = 1, ref_point = NULL)

Arguments

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.

Method clear()

Clear all evaluation results from archive.

Usage

ArchiveBatch$clear()

Method clone()

The objects of this class are cloneable with this method.

Usage

ArchiveBatch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

Run this code
fun = function(xs) {
  list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}

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

# initialize instance
instance = oi(
  objective = objective,
  terminator = trm("evals", n_evals = 20)
)

# load optimizer
optimizer = opt("random_search")

# trigger optimization
optimizer$optimize(instance)

# all evaluated configuration
instance$archive

# best performing configuration
instance$archive$best()

# covert to data.table
as.data.table(instance$archive)

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