R/PipelineMCBoost.R

In mcboost: Multi-Calibration Boosting

#' Multi-calibration pipeline
#'
#' Wraps MCBoost in a Pipeline to be used with `mlr3pipelines`.
#' For now this assumes training on the same dataset that is later used
#' for multi-calibration.
#' @param learner (mlr3)[`mlr3::Learner`]\cr
#'   Initial learner. Internally wrapped into a `PipeOpLearnerCV`
#'   with `resampling.method = "insample"` as a default.
#'   All parameters can be adjusted through the resulting Graph's `param_set`.
#'   Defaults to `lrn("classif.featureless")`.
#'   Note: An initial predictor can also be supplied via the `init_predictor` parameter.
#' @param param_vals `list` \cr
#'   List of parameter values passed on to `MCBoost$new`.
#' @return (mlr3pipelines) [`Graph`][mlr3pipelines::Graph]
#' @examples
#'   \dontrun{
#'   library("mlr3pipelines")
#'   gr = ppl_mcboost()
#'   }
#' @export
ppl_mcboost = function(learner = lrn("classif.featureless"), param_vals = list()) {
  mlr3misc::require_namespaces("mlr3pipelines")
  po_lrn = mlr3pipelines::po("learner_cv", learner = learner, resampling.method = "insample")
  gr = mlr3pipelines::`%>>%`(
    mlr3pipelines::gunion(list(
      "data" = mlr3pipelines::po("nop"),
      "prediction" = po_lrn
    )),
    PipeOpMCBoost$new(param_vals = param_vals)
  )
}