R/LearnerRegrLiblineaRL2L1SVR.R

In mlr3learners/mlr3learners.liblinear: Learners from LiblineaR package for mlr3

#' @title L2-Regularized L1-Loss Support Vector Regression Learner
#'
#' @name mlr_learners_regr.liblinearl2l1svr
#'
#' @description
#' L2-Regularized L1-Loss support vector regression learner. Calls
#' [LiblineaR::LiblineaR()] (`type = 13`) from package \CRANpkg{LiblineaR}.
#'
#' @section Custom mlr3 defaults:
#' - `svr_eps`:
#'   - Actual default: `NULL`
#'   - Adjusted default: 0.001
#'   - Reason for change: `svr_eps` is type dependent and the "type" is handled
#'   by the mlr3learner. The default value is set to th default of the respective
#'   "type".
#' - `epsilon`:
#'   - Actual default: 0.01
#'   - Adjusted default: Removed
#'   - Reason for change: For regr SVR learners paramter `svr_eps` overwrites
#'   param `epsilon`.
#'
#' @templateVar id regr.liblinearl2l1svr
#' @template section_dictionary_learner
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerRegrLiblineaRL2L1SVR = R6Class("LearnerRegrLiblineaRL2L1SVR",
  inherit = LearnerRegr,
  public = list(

    #' @description
    #' Creates a new instance of this [R6][R6::R6Class] class.
    initialize = function() {

      ps = ParamSet$new(
        params = list(
          ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),
          ParamDbl$new(id = "bias", default = 1, tags = "train"),
          ParamDbl$new(
            id = "svr_eps", default = NULL, special_vals = list(NULL),
            lower = 0, tags = "train"),
          ParamInt$new(id = "cross", default = 0L, lower = 0L, tags = "train"),
          ParamLgl$new(id = "verbose", default = FALSE, tags = "train"),
          ParamLgl$new(id = "findC", default = FALSE, tags = "train"),
          ParamLgl$new(id = "useInitC", default = TRUE, tags = "train")
        )
      )

      # 50 is an arbitrary choice here
      ps$add_dep("findC", "cross", CondAnyOf$new(seq(2:50)))
      ps$add_dep("useInitC", "findC", CondEqual$new(TRUE))

      # custom defaults
      ps$values = list(
        # Package default is NULL but for regr SVR learners takes precedence over epsilon
        svr_eps = 0.1
      )

      super$initialize(
        id = "regr.liblinearl2l1svr",
        packages = "LiblineaR",
        feature_types = c("integer", "numeric"),
        predict_types = "response",
        param_set = ps,
        man = "mlr3learners.liblinear::mlr_learners_regr.liblinearl2l1svr"
      )
    }
  ),
  private = list(
    .train = function(task) {
      pars = self$param_set$get_values(tags = "train")
      data = task$data()
      train = data[, task$feature_names, with = FALSE]
      target = data[, task$target_names, with = FALSE]

      invoke(LiblineaR::LiblineaR, data = train, target = target, type = 13L, .args = pars)
    },

    .predict = function(task) {
      newdata = task$data(cols = task$feature_names)

      p = invoke(predict, self$model, newx = newdata)
      PredictionRegr$new(task = task, response = p$predictions)
    }
  )
)