Default Surrogate

This is a helper function that constructs a default Surrogate based on properties of the bbotk::OptimInstance.

For purely numeric (including integers) parameter spaces without any dependencies a Gaussian Process is constricted via default_gp(). For mixed numeric-categorical parameter spaces, or spaces with conditional parameters a random forest is constructed via default_rf().

In any case, learners are encapsulated using "evaluate", and a fallback learner is set, in cases where the surrogate learner errors. Currently, the following learner is used as a fallback: lrn("regr.ranger", num.trees = 10L, keep.inbag = TRUE, se.method = "jack").

If additionally dependencies are present in the parameter space, inactive conditional parameters are represented by missing NA values in the training design data. We simply handle those with the internal NA handling method na.action = "na_learn of ranger.

If n_learner is 1, the learner is wrapped as a SurrogateLearner. Otherwise, if n_learner is larger than 1, multiple deep clones of the learner are wrapped as a SurrogateLearnerCollection.

Usage

default_surrogate(
  instance,
  learner = NULL,
  n_learner = NULL,
  force_random_forest = FALSE
)

Arguments

instance

(bbotk::OptimInstance)
An object that inherits from bbotk::OptimInstance.

learner

(NULL | mlr3::Learner). If specified, this learner will be used instead of the defaults described above.

n_learner

(NULL | integer(1)). Number of learners to be considered in the construction of the Surrogate. If not specified will be based on the number of objectives as stated by the instance.

force_random_forest

(logical(1)). If TRUE, a random forest is constructed even if the parameter space is purely numeric.

Value

Surrogate

See also

Other mbo_defaults: default_acqfunction(), default_acqoptimizer(), default_gp(), default_loop_function(), default_result_assigner(), default_rf(), mbo_defaults