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#' @title Wraps a regression learner for use in cost-sensitive learning.
#'
#' @description
#' Creates a wrapper, which can be used like any other learner object.
#' Models can easily be accessed via [getLearnerModel].
#'
#' For each class in the task, an individual regression model is fitted for the costs of that class.
#' During prediction, the class with the lowest predicted costs is selected.
#'
#' @template arg_learner_regr
#' @template ret_learner
#' @export
#' @family costsens
#' @family wrapper
#' @aliases CostSensRegrWrapper CostSensRegrModel
makeCostSensRegrWrapper = function(learner) {
learner = checkLearner(learner, "regr")
# we cannot make use of 'se' here
learner = setPredictType(learner, "response")
id = stri_paste("costsens", learner$id, sep = ".")
makeHomogeneousEnsemble(id, type = "costsens", learner, package = learner$package,
learner.subclass = "CostSensRegrWrapper", model.subclass = "CostSensRegrModel")
}
#' @export
trainLearner.CostSensRegrWrapper = function(.learner, .task, .subset = NULL, ...) {
# note that no hyperpars can be in ..., they would refer to the wrapper
.task = subsetTask(.task, subset = .subset)
d = getTaskData(.task)
parallelLibrary("mlr", master = FALSE, level = "mlr.ensemble", show.info = FALSE)
exportMlrOptions(level = "mlr.ensemble")
models = parallelMap(doCostSensRegrTrainIteration,
class.levels = getTaskDesc(.task)$class.levels, more.args = list("d" = d,
"costs" = getTaskCosts(.task), "learner" = .learner), level = "mlr.ensemble")
makeHomChainModel(.learner, models)
}
doCostSensRegrTrainIteration = function(learner, class.levels, costs, d) {
setSlaveOptions()
data = cbind(d, ..y.. = costs[, class.levels])
task = makeRegrTask(id = class.levels, data = data, target = "..y..", check.data = FALSE, fixup.data = "quiet")
train(learner$next.learner, task)
}
#' @export
predictLearner.CostSensRegrWrapper = function(.learner, .model, .newdata, ...) {
p = predictHomogeneousEnsemble(.learner, .model, .newdata, ...)
# get class per row with minimal estimated costs
p = apply(p, 1L, getMinIndex)
classes = .model$task.desc$class.levels
factor(classes[p], levels = classes)
}
#' @export
getLearnerProperties.CostSensRegrWrapper = function(learner) {
setdiff(getLearnerProperties(learner$next.learner), c("weights", "prob"))
}
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