Surrogate model containing a single mlr3::LearnerRegr.
Parameters
assert_insample_perf
logical(1)
Should the insample performance of the mlr3::LearnerRegr be asserted after updating the surrogate? If the assertion fails (i.e., the insample performance based on theperf_measure
does not meet theperf_threshold
), an error is thrown. Default isFALSE
.perf_measure
mlr3::MeasureRegr
Performance measure which should be use to assert the insample performance of the mlr3::LearnerRegr. Only relevant ifassert_insample_perf = TRUE
. Default is mlr3::mlr_measures_regr.rsq.perf_threshold
numeric(1)
Threshold the insample performance of the mlr3::LearnerRegr should be asserted against. Only relevant ifassert_insample_perf = TRUE
. Default is0
.catch_errors
logical(1)
Should errors during updating the surrogate be caught and propagated to theloop_function
which can then handle the failed acquisition function optimization (as a result of the failed surrogate) appropriately by, e.g., proposing a randomly sampled point for evaluation? Default isTRUE
.
Super class
mlr3mbo::Surrogate
-> SurrogateLearner
Active bindings
print_id
(
character
)
Id used when printing.n_learner
(
integer(1)
)
Returns the number of surrogate models.assert_insample_perf
(
numeric()
)
Asserts whether the current insample performance meets the performance threshold.packages
(
character()
)
Set of required packages. A warning is signaled if at least one of the packages is not installed, but loaded (not attached) later on-demand viarequireNamespace()
.feature_types
(
character()
)
Stores the feature types the surrogate can handle, e.g."logical"
,"numeric"
, or"factor"
. A complete list of candidate feature types, grouped by task type, is stored inmlr_reflections$task_feature_types
.properties
(
character()
)
Stores a set of properties/capabilities the surrogate has. A complete list of candidate properties, grouped by task type, is stored inmlr_reflections$learner_properties
.predict_type
(
character(1)
)
Retrieves the currently active predict type, e.g."response"
.
Methods
Method new()
Creates a new instance of this R6 class.
Usage
SurrogateLearner$new(learner, archive = NULL, cols_x = NULL, col_y = NULL)
Arguments
learner
archive
(bbotk::Archive |
NULL
)
bbotk::Archive of the bbotk::OptimInstance.cols_x
(
character()
|NULL
)
Column id's of variables that should be used as features. By default, automatically inferred based on the archive.col_y
(
character(1)
|NULL
)
Column id of variable that should be used as a target. By default, automatically inferred based on the archive.
Method predict()
Predict mean response and standard error.
Arguments
xdt
(
data.table::data.table()
)
New data. One row per observation.
Returns
data.table::data.table()
with the columns mean
and se
.
Examples
if (requireNamespace("mlr3learners") &
requireNamespace("DiceKriging") &
requireNamespace("rgenoud")) {
library(bbotk)
library(paradox)
library(mlr3learners)
fun = function(xs) {
list(y = xs$x ^ 2)
}
domain = ps(x = p_dbl(lower = -10, upper = 10))
codomain = ps(y = p_dbl(tags = "minimize"))
objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
instance = OptimInstanceBatchSingleCrit$new(
objective = objective,
terminator = trm("evals", n_evals = 5))
xdt = generate_design_random(instance$search_space, n = 4)$data
instance$eval_batch(xdt)
learner = default_gp()
surrogate = srlrn(learner, archive = instance$archive)
surrogate$update()
surrogate$learner$model
}
#>
#> Call:
#> DiceKriging::km(design = data, response = task$truth(), covtype = "matern5_2",
#> nugget = 2.83305750865222e-07, optim.method = "gen", control = pv$control)
#>
#> Trend coeff.:
#> Estimate
#> (Intercept) 5.8590
#>
#> Covar. type : matern5_2
#> Covar. coeff.:
#> Estimate
#> theta(x) 1.1710
#>
#> Variance estimate: 21.95183
#>
#> Nugget effect : 2.833058e-07
#>