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:   4.7  2.73   -30 2026-03-20 06:19:04        1         4.7        2.73
#>  2:   7.5 -3.25   -20 2026-03-20 06:19:04        2         7.5       -3.25
#>  3:  -9.3 -1.80  -119 2026-03-20 06:19:04        3        -9.3       -1.80
#>  4:  -2.0 -3.04    -6 2026-03-20 06:19:04        4        -2.0       -3.04
#>  5:  -1.9 -4.36    -7 2026-03-20 06:19:04        5        -1.9       -4.36
#>  6:  -2.2  4.76   -68 2026-03-20 06:19:04        6        -2.2        4.76
#>  7:  -4.2  1.78   -51 2026-03-20 06:19:04        7        -4.2        1.78
#>  8:   4.7 -3.04     3 2026-03-20 06:19:04        8         4.7       -3.04
#>  9:   9.6  2.42   -77 2026-03-20 06:19:04        9         9.6        2.42
#> 10:  -9.0  0.30  -121 2026-03-20 06:19:04       10        -9.0        0.30
#> 11:   3.9  1.89   -18 2026-03-20 06:19:04       11         3.9        1.89
#> 12:  -9.4 -2.74  -119 2026-03-20 06:19:04       12        -9.4       -2.74
#> 13:  -4.0  1.36   -45 2026-03-20 06:19:04       13        -4.0        1.36
#> 14:  -0.4 -0.68    -1 2026-03-20 06:19:04       14        -0.4       -0.68
#> 15:   4.1  4.49   -51 2026-03-20 06:19:04       15         4.1        4.49
#> 16:  -6.4 -2.83   -60 2026-03-20 06:19:04       16        -6.4       -2.83
#> 17:   3.6 -0.01    -2 2026-03-20 06:19:05       17         3.6       -0.01
#> 18:   2.8  1.60   -12 2026-03-20 06:19:05       18         2.8        1.60
#> 19:  -8.1  2.66  -124 2026-03-20 06:19:05       19        -8.1        2.66
#> 20:   5.4  4.91   -64 2026-03-20 06:19:05       20         5.4        4.91
#>        x1    x2     y           timestamp batch_nr x_domain_x1 x_domain_x2
#>     <num> <num> <num>              <POSc>    <int>       <num>       <num>

# 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]> 2026-03-20 06:19:04        8

# covert to data.table
as.data.table(instance$archive)
#>            x1          x2           y           timestamp batch_nr x_domain_x1
#>         <num>       <num>       <num>              <POSc>    <int>       <num>
#>  1:  4.657640  2.72521511  -29.841137 2026-03-20 06:19:04        1    4.657640
#>  2:  7.492013 -3.25059373  -20.225006 2026-03-20 06:19:04        2    7.492013
#>  3: -9.315173 -1.79614269 -119.482420 2026-03-20 06:19:04        3   -9.315173
#>  4: -1.953435 -3.04330165   -5.631525 2026-03-20 06:19:04        4   -1.953435
#>  5: -1.929238 -4.36338543   -7.297728 2026-03-20 06:19:04        5   -1.929238
#>  6: -2.225974  4.75547835  -68.006298 2026-03-20 06:19:04        6   -2.225974
#>  7: -4.202154  1.78380427  -51.351499 2026-03-20 06:19:04        7   -4.202154
#>  8:  4.706392 -3.04043267    2.673808 2026-03-20 06:19:04        8    4.706392
#>  9:  9.610793  2.41521529  -77.248734 2026-03-20 06:19:04        9    9.610793
#> 10: -8.971074  0.30212464 -121.268502 2026-03-20 06:19:04       10   -8.971074
#> 11:  3.916478  1.88556003  -17.541583 2026-03-20 06:19:04       11    3.916478
#> 12: -9.375393 -2.74437465 -119.464921 2026-03-20 06:19:04       12   -9.375393
#> 13: -3.983384  1.36465615  -44.851106 2026-03-20 06:19:04       13   -3.983384
#> 14: -0.419509 -0.67828742   -1.244373 2026-03-20 06:19:04       14   -0.419509
#> 15:  4.128677  4.48576576  -50.567954 2026-03-20 06:19:04       15    4.128677
#> 16: -6.393225 -2.83100124  -60.474780 2026-03-20 06:19:04       16   -6.393225
#> 17:  3.603258 -0.01154389   -1.501307 2026-03-20 06:19:05       17    3.603258
#> 18:  2.833587  1.60284349  -11.881035 2026-03-20 06:19:05       18    2.833587
#> 19: -8.079517  2.65600164 -123.587014 2026-03-20 06:19:05       19   -8.079517
#> 20:  5.393496  4.90712312  -64.038412 2026-03-20 06:19:05       20    5.393496
#>            x1          x2           y           timestamp batch_nr x_domain_x1
#>         <num>       <num>       <num>              <POSc>    <int>       <num>
#>     x_domain_x2
#>           <num>
#>  1:  2.72521511
#>  2: -3.25059373
#>  3: -1.79614269
#>  4: -3.04330165
#>  5: -4.36338543
#>  6:  4.75547835
#>  7:  1.78380427
#>  8: -3.04043267
#>  9:  2.41521529
#> 10:  0.30212464
#> 11:  1.88556003
#> 12: -2.74437465
#> 13:  1.36465615
#> 14: -0.67828742
#> 15:  4.48576576
#> 16: -2.83100124
#> 17: -0.01154389
#> 18:  1.60284349
#> 19:  2.65600164
#> 20:  4.90712312
#>     x_domain_x2
#>           <num>

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()`