Data Table Storage

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

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.

Returns

data.table::data.table()

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.

Returns

data.table::data.table()

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

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)
#>          x1        x2  x_domain        y
#>       <num>     <num>    <list>    <num>
#> 1: 4.706392 -3.040433 <list[2]> 2.673808

# all evaluated configuration
instance$archive
#> 
#> ── <ArchiveBatch> - Data Table Storage ─────────────────────────────────────────
#>        x1    x2     y           timestamp batch_nr x_domain_x1 x_domain_x2
#>     <num> <num> <num>              <POSc>    <int>       <num>       <num>
#>  1:  -2.2  4.76   -68 2025-10-24 13:34:06        1        -2.2        4.76
#>  2:  -4.2  1.78   -51 2025-10-24 13:34:06        2        -4.2        1.78
#>  3:   4.7 -3.04     3 2025-10-24 13:34:06        3         4.7       -3.04
#>  4:   9.6  2.42   -77 2025-10-24 13:34:06        4         9.6        2.42
#>  5:  -9.0  0.30  -121 2025-10-24 13:34:06        5        -9.0        0.30
#>  6:   3.9  1.89   -18 2025-10-24 13:34:06        6         3.9        1.89
#>  7:  -9.4 -2.74  -119 2025-10-24 13:34:06        7        -9.4       -2.74
#>  8:  -4.0  1.36   -45 2025-10-24 13:34:06        8        -4.0        1.36
#>  9:  -0.4 -0.68    -1 2025-10-24 13:34:06        9        -0.4       -0.68
#> 10:   4.1  4.49   -51 2025-10-24 13:34:06       10         4.1        4.49
#> 11:  -6.4 -2.83   -60 2025-10-24 13:34:06       11        -6.4       -2.83
#> 12:   3.6 -0.01    -2 2025-10-24 13:34:06       12         3.6       -0.01
#> 13:   2.8  1.60   -12 2025-10-24 13:34:06       13         2.8        1.60
#> 14:  -8.1  2.66  -124 2025-10-24 13:34:06       14        -8.1        2.66
#> 15:   5.4  4.91   -64 2025-10-24 13:34:06       15         5.4        4.91
#> 16:   9.4 -1.11   -48 2025-10-24 13:34:06       16         9.4       -1.11
#> 17:  -0.8 -1.85     1 2025-10-24 13:34:06       17        -0.8       -1.85
#> 18:  -6.5  0.32   -73 2025-10-24 13:34:06       18        -6.5        0.32
#> 19:  -0.1  2.79   -28 2025-10-24 13:34:06       19        -0.1        2.79
#> 20:  -5.9  2.13   -79 2025-10-24 13:34:06       20        -5.9        2.13
#>        x1    x2     y           timestamp batch_nr x_domain_x1 x_domain_x2

# best performing configuration
instance$archive$best()
#>          x1        x2        y  x_domain           timestamp batch_nr
#>       <num>     <num>    <num>    <list>              <POSc>    <int>
#> 1: 4.706392 -3.040433 2.673808 <list[2]> 2025-10-24 13:34:06        3

# covert to data.table
as.data.table(instance$archive)
#>             x1          x2            y           timestamp batch_nr
#>          <num>       <num>        <num>              <POSc>    <int>
#>  1: -2.2259737  4.75547835  -68.0062985 2025-10-24 13:34:06        1
#>  2: -4.2021541  1.78380427  -51.3514987 2025-10-24 13:34:06        2
#>  3:  4.7063920 -3.04043267    2.6738077 2025-10-24 13:34:06        3
#>  4:  9.6107935  2.41521529  -77.2487342 2025-10-24 13:34:06        4
#>  5: -8.9710745  0.30212464 -121.2685022 2025-10-24 13:34:06        5
#>  6:  3.9164776  1.88556003  -17.5415832 2025-10-24 13:34:06        6
#>  7: -9.3753935 -2.74437465 -119.4649214 2025-10-24 13:34:06        7
#>  8: -3.9833839  1.36465615  -44.8511059 2025-10-24 13:34:06        8
#>  9: -0.4195090 -0.67828742   -1.2443731 2025-10-24 13:34:06        9
#> 10:  4.1286768  4.48576576  -50.5679538 2025-10-24 13:34:06       10
#> 11: -6.3932246 -2.83100124  -60.4747804 2025-10-24 13:34:06       11
#> 12:  3.6032584 -0.01154389   -1.5013072 2025-10-24 13:34:06       12
#> 13:  2.8335870  1.60284349  -11.8810354 2025-10-24 13:34:06       13
#> 14: -8.0795168  2.65600164 -123.5870142 2025-10-24 13:34:06       14
#> 15:  5.3934961  4.90712312  -64.0384118 2025-10-24 13:34:06       15
#> 16:  9.4104181 -1.10817239  -48.4933075 2025-10-24 13:34:06       16
#> 17: -0.7762707 -1.84758248    0.9642548 2025-10-24 13:34:06       17
#> 18: -6.5064821  0.31573541  -73.3543394 2025-10-24 13:34:06       18
#> 19: -0.1272597  2.79308626  -28.0850822 2025-10-24 13:34:06       19
#> 20: -5.9164331  2.13397279  -79.0275903 2025-10-24 13:34:06       20
#>             x1          x2            y           timestamp batch_nr
#>     x_domain_x1 x_domain_x2
#>           <num>       <num>
#>  1:  -2.2259737  4.75547835
#>  2:  -4.2021541  1.78380427
#>  3:   4.7063920 -3.04043267
#>  4:   9.6107935  2.41521529
#>  5:  -8.9710745  0.30212464
#>  6:   3.9164776  1.88556003
#>  7:  -9.3753935 -2.74437465
#>  8:  -3.9833839  1.36465615
#>  9:  -0.4195090 -0.67828742
#> 10:   4.1286768  4.48576576
#> 11:  -6.3932246 -2.83100124
#> 12:   3.6032584 -0.01154389
#> 13:   2.8335870  1.60284349
#> 14:  -8.0795168  2.65600164
#> 15:   5.3934961  4.90712312
#> 16:   9.4104181 -1.10817239
#> 17:  -0.7762707 -1.84758248
#> 18:  -6.5064821  0.31573541
#> 19:  -0.1272597  2.79308626
#> 20:  -5.9164331  2.13397279
#>     x_domain_x1 x_domain_x2

S3 Methods

Super class

Public fields

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Method add_evals()

Usage

Arguments

Method best()

Usage

Arguments

Returns

Method nds_selection()

Usage

Arguments

Returns

Method clear()

Usage

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `add_evals()`

Method `best()`

Method `nds_selection()`

Method `clear()`

Method `clone()`