Result Assigner Based on a Surrogate Mean Prediction
Source:R/ResultAssignerSurrogate.R
mlr_result_assigners_surrogate.Rd
Result assigner that chooses the final point(s) based on a surrogate mean prediction of all evaluated points in the bbotk::Archive. This is especially useful in the case of noisy objective functions.
In the case of operating on an bbotk::OptimInstanceBatchMultiCrit the SurrogateLearnerCollection must use as many learners as there are objective functions.
See also
Other Result Assigner:
ResultAssigner
,
mlr_result_assigners
,
mlr_result_assigners_archive
Super class
mlr3mbo::ResultAssigner
-> ResultAssignerSurrogate
Active bindings
surrogate
(Surrogate |
NULL
)
The surrogate.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()
.
Methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
ResultAssignerSurrogate$new(surrogate = NULL)
Arguments
surrogate
(Surrogate |
NULL
)
The surrogate that is used to predict the mean of all evaluated points.
Method assign_result()
Assigns the result, i.e., the final point(s) to the instance.
If $surrogate
is NULL
, default_surrogate(instance)
is used and also assigned to $surrogate
.
Arguments
instance
(bbotk::OptimInstanceBatchSingleCrit | bbotk::OptimInstanceBatchMultiCrit)
The bbotk::OptimInstance the final result should be assigned to.
Examples
result_assigner = ras("surrogate")