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#' @title Feature Selection with Design Points
#'
#' @include mlr_fselectors.R
#' @name mlr_fselectors_design_points
#'
#' @description
#' Feature selection using user-defined feature sets.
#'
#' @details
#' The feature sets are evaluated in order as given.
#'
#' The feature selection terminates itself when all feature sets are evaluated.
#' It is not necessary to set a termination criterion.
#'
#' @templateVar id design_points
#' @template section_dictionary_fselectors
#'
#' @inheritSection bbotk::OptimizerBatchDesignPoints Parameters
#'
#' @family FSelector
#' @export
#' @examples
#' # Feature Selection
#' \donttest{
#'
#' # retrieve task and load learner
#' task = tsk("pima")
#' learner = lrn("classif.rpart")
#'
#' # create design
#' design = mlr3misc::rowwise_table(
#' ~age, ~glucose, ~insulin, ~mass, ~pedigree, ~pregnant, ~pressure, ~triceps,
#' TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE,
#' TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
#' TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE,
#' TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE
#' )
#'
#' # run feature selection on the Pima Indians diabetes data set
#' instance = fselect(
#' fselector = fs("design_points", design = design),
#' task = task,
#' learner = learner,
#' resampling = rsmp("holdout"),
#' measure = msr("classif.ce")
#' )
#'
#' # best performing feature set
#' instance$result
#'
#' # all evaluated feature sets
#' as.data.table(instance$archive)
#'
#' # subset the task and fit the final model
#' task$select(instance$result_feature_set)
#' learner$train(task)
#' }
FSelectorBatchDesignPoints = R6Class("FSelectorBatchDesignPoints",
inherit = FSelectorBatchFromOptimizerBatch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
super$initialize(
optimizer = OptimizerBatchDesignPoints$new(),
man = "mlr3fselect::mlr_fselectors_design_points"
)
}
)
)
mlr_fselectors$add("design_points", FSelectorBatchDesignPoints)
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