Syntactic Sugar Surrogate Construction

This function allows to construct a SurrogateLearner or SurrogateLearnerCollection in the spirit of mlr_sugar from mlr3.

If the archive references more than one target variable or cols_y contains more than one target variable but only a single learner is specified, this learner is replicated as many times as needed to build the SurrogateLearnerCollection.

Usage

srlrn(learner, archive = NULL, cols_x = NULL, cols_y = NULL, ...)

Arguments

learner: (mlr3::LearnerRegr | List of mlr3::LearnerRegr)
mlr3::LearnerRegr that is to be used within the SurrogateLearner or a list of mlr3::LearnerRegr that are to be used within the SurrogateLearnerCollection.
archive: (NULL | bbotk::Archive)
bbotk::Archive of the bbotk::OptimInstance used. Can also be NULL.
cols_x: (NULL | character())
Column ids in the bbotk::Archive that should be used as features. Can also be NULL in which case this is automatically inferred based on the archive.
cols_y: (NULL | character())
Column id(s) in the bbotk::Archive that should be used as a target. If a list of mlr3::LearnerRegr is provided as the learner argument and cols_y is specified as well, as many column names as learners must be provided. Can also be NULL in which case this is automatically inferred based on the archive.
...: (named list())
Named arguments passed to the constructor, to be set as parameters in the paradox::ParamSet.

Value

SurrogateLearner | SurrogateLearnerCollection

Examples

library(mlr3)
srlrn(lrn("regr.featureless"), catch_errors = FALSE)
#> <SurrogateLearner>: LearnerRegrFeatureless
#> * Parameters: assert_insample_perf=FALSE, catch_errors=FALSE,
#>   impute_method=random
srlrn(list(lrn("regr.featureless"), lrn("regr.featureless")))
#> <SurrogateLearnerCollection>: (LearnerRegrFeatureless | LearnerRegrFeatureless)
#> * Parameters: assert_insample_perf=FALSE, catch_errors=TRUE,
#>   impute_method=random