R/FSelectInstanceSingleCrit.R

In mlr3fselect: Feature Selection for 'mlr3'

#' @title Class for Single Criterion Feature Selection
#'
#' @include ArchiveFSelect.R
#'
#' @description
#' The [FSelectInstanceSingleCrit] specifies a feature selection problem for [FSelectors][FSelector].
#' The function [fsi()] creates a [FSelectInstanceSingleCrit] and the function [fselect()] creates an instance internally.
#'
#' @description
#' The instance contains an [ObjectiveFSelect] object that encodes the black box objective function a [FSelector] has to optimize.
#' The instance allows the basic operations of querying the objective at design points (`$eval_batch()`).
#' This operation is usually done by the [FSelector].
#' Evaluations of feature subsets are performed in batches by calling [mlr3::benchmark()] internally.
#' The evaluated feature subsets are stored in the [Archive][ArchiveFSelect] (`$archive`).
#' Before a batch is evaluated, the [bbotk::Terminator] is queried for the remaining budget.
#' If the available budget is exhausted, an exception is raised, and no further evaluations can be performed from this point on.
#' The [FSelector] is also supposed to store its final result, consisting of a selected feature subset and associated estimated performance values, by calling the method `instance$assign_result()`.
#'
#' @section Default Measures:
#' If no measure is passed, the default measure is used.
#' The default measure depends on the task type.
#'
#' | Task           | Default Measure     | Package               |
#' |----------------|---------------------|-----------------------|
#' | `"classif"`    | `"classif.ce"`      | \CRANpkg{mlr3}        |
#' | `"regr"`       | `"regr.mse"`        | \CRANpkg{mlr3}        |
#' | `"surv"`       | `"surv.cindex"`     | \CRANpkg{mlr3proba}   |
#' | `"dens"`       | `"dens.logloss"`    | \CRANpkg{mlr3proba}   |
#' | `"classif_st"` | `"classif.ce"`      | \CRANpkg{mlr3spatial} |
#' | `"regr_st"`    | `"regr.mse"`        | \CRANpkg{mlr3spatial} |
#' | `"clust"`      | `"clust.dunn"`      | \CRANpkg{mlr3cluster} |
#'
#' @inheritSection ArchiveFSelect Analysis
#'
#' @section Resources:
#' There are several sections about feature selection in the [mlr3book](https://mlr3book.mlr-org.com).
#'
#' * Getting started with [wrapper feature selection](https://mlr3book.mlr-org.com/feature-selection.html#fs-wrapper).
#'
#' The [gallery](https://mlr-org.com/gallery.html) features a collection of case studies and demos about optimization.
#'
#' * Utilize the built-in feature importance of models with [Recursive Feature Elimination](https://mlr-org.com/gallery/optimization/2023-02-07-recursive-feature-elimination/).
#' * Run a feature selection with [Shadow Variable Search](https://mlr-org.com/gallery/optimization/2023-02-01-shadow-variable-search/).
#' * [Feature Selection](https://mlr-org.com/gallery/optimization/2020-09-14-mlr3fselect-basic/) on the Titanic data set.
#'
#' @template param_task
#' @template param_learner
#' @template param_resampling
#' @template param_measure
#' @template param_terminator
#' @template param_store_models
#' @template param_check_values
#' @template param_store_benchmark_result
#' @template param_callbacks
#' @template param_xdt
#'
#' @export
#' @examples
#' # Feature selection on Palmer Penguins data set
#' \donttest{
#'
#' task = tsk("penguins")
#' learner = lrn("classif.rpart")
#'
#' # Construct feature selection instance
#' instance = fsi(
#'   task = task,
#'   learner = learner,
#'   resampling = rsmp("cv", folds = 3),
#'   measures = msr("classif.ce"),
#'   terminator = trm("evals", n_evals = 4)
#' )
#'
#' # Choose optimization algorithm
#' fselector = fs("random_search", batch_size = 2)
#'
#' # Run feature selection
#' fselector$optimize(instance)
#'
#' # Subset task to optimal feature set
#' task$select(instance$result_feature_set)
#'
#' # Train the learner with optimal feature set on the full data set
#' learner$train(task)
#'
#' # Inspect all evaluated sets
#' as.data.table(instance$archive)
#' }
FSelectInstanceSingleCrit = R6Class("FSelectInstanceSingleCrit",
  inherit = OptimInstanceSingleCrit,
  public = list(

    #' @description
    #' Creates a new instance of this [R6][R6::R6Class] class.
    initialize = function(task, learner, resampling, measure, terminator, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
      # initialized specialized fselect archive and objective
      archive = ArchiveFSelect$new(
        search_space = task_to_domain(assert_task(task)),
        codomain = measures_to_codomain(assert_measure(measure)),
        check_values = check_values)

      objective = ObjectiveFSelect$new(
        task = task,
        learner = learner,
        resampling = resampling,
        measures = measure,
        store_benchmark_result = store_benchmark_result,
        store_models = store_models,
        check_values = check_values,
        archive = archive,
        callbacks = callbacks)

      super$initialize(objective, objective$domain, terminator, callbacks = callbacks)

      # super class of instance initializes default archive, overwrite with fselect archive
      self$archive = archive

      private$.objective_function = objective_function
    },

    #' @description
    #' The [FSelector] writes the best found feature subset and estimated performance value here.
    #' For internal use.
    #'
    #' @param y (`numeric(1)`)\cr
    #'   Optimal outcome.
    assign_result = function(xdt, y) {
      # Add feature names to result for easy task subsetting
      features = list(self$objective$task$feature_names[as.logical(xdt)])
      xdt[, features := list(features)]
      assert_data_table(xdt, nrows = 1L)
      assert_names(names(xdt), must.include = self$search_space$ids())
      assert_number(y)
      assert_names(names(y), permutation.of = self$objective$codomain$ids())
      private$.result = cbind(xdt, t(y)) # t(y) so the name of y stays
    },

    #' @description
    #' Printer.
    #'
    #' @param ... (ignored).
    print = function(...) {
      catf(format(self))
      catf(str_indent("* State: ", if (is.null(private$.result)) "Not optimized" else "Optimized"))
      catf(str_indent("* Objective:", format(self$objective)))
      catf(str_indent("* Terminator:", format(self$terminator)))
      if (!is.null(private$.result)) {
        catf("* Result:")
        print(self$result[, c(self$archive$cols_x, self$archive$cols_y), with = FALSE])
        catf("* Archive:")
        print(as.data.table(self$archive)[, c(self$archive$cols_x, self$archive$cols_y), with = FALSE])
      }
    }
  ),

  active = list(
    #' @field result_feature_set (`character()`)\cr
    #' Feature set for task subsetting.
    result_feature_set = function() {
      unlist(self$result$features)
    }
  )
)

objective_function = function(x, inst, multiplicator) {
  xs = set_names(as.list(as.logical(x)), inst$search_space$ids())
  inst$search_space$assert(xs)
  xdt = as.data.table(xs)
  res = inst$eval_batch(xdt)
  y = as.numeric(res[, inst$archive$cols_y, with = FALSE])
  y * multiplicator
}