#' @title Use binary relevance method to create a multilabel learner.
#'
#' @description
#' Every learner which is implemented in mlr and which supports binary
#' classification can be converted to a wrapped binary relevance multilabel learner.
#' The multilabel classification problem is converted into simple binary classifications
#' for each label/target on which the binary learner is applied.
#'
#' Models can easily be accessed via \code{\link{getLearnerModel}}.
#'
#' Note that it does not make sense to set a threshold in the used base \code{learner}
#' when you predict probabilities.
#' On the other hand, it can make a lot of sense, to call \code{\link{setThreshold}}
#' on the \code{MultilabelBinaryRelevanceWrapper} for each label indvidually;
#' Or to tune these thresholds with \code{\link{tuneThreshold}}; especially when you face very
#' unabalanced class distributions for each binary label.
#'
#' @template arg_learner
#' @template ret_learner
#' @references
#' Tsoumakas, G., & Katakis, I. (2006)
#' \emph{Multi-label classification: An overview.}
#' Dept. of Informatics, Aristotle University of Thessaloniki, Greece.
#' @family wrapper
#' @family multilabel
#' @export
#' @example inst/examples/MultilabelWrapper.R
makeMultilabelBinaryRelevanceWrapper = function(learner) {
learner = checkLearner(learner, type = "classif")
id = stri_paste("multilabel.binaryRelevance", getLearnerId(learner), sep = ".")
packs = getLearnerPackages(learner)
type = getLearnerType(learner)
x = makeHomogeneousEnsemble(id, type, learner, packs,
learner.subclass = "MultilabelBinaryRelevanceWrapper", model.subclass = "MultilabelBinaryRelevanceModel")
x$type = "multilabel"
return(x)
}
#' @export
trainLearner.MultilabelBinaryRelevanceWrapper = function(.learner, .task, .subset = NULL, .weights = NULL, ...) {
targets = getTaskTargetNames(.task)
.task = subsetTask(.task, subset = .subset)
parallelLibrary("mlr", master = FALSE, level = "mlr.ensemble", show.info = FALSE)
exportMlrOptions(level = "mlr.ensemble")
models = parallelMap(
doMultilabelBinaryRelevanceTrainIteration, tn = targets,
more.args = list(weights = .weights, learner = .learner$next.learner, task = .task),
level = "mlr.ensemble")
names(models) = targets
makeHomChainModel(.learner, models)
}
doMultilabelBinaryRelevanceTrainIteration = function(tn, learner, task, weights) {
setSlaveOptions()
data = getTaskData(task)
task = makeClassifTask(id = tn, data = dropNamed(data, setdiff(getTaskTargetNames(task), tn)), target = tn)
train(learner, task, weights = weights)
}
#' @export
predictLearner.MultilabelBinaryRelevanceWrapper = function(.learner, .model, .newdata, .subset = NULL, ...) {
models = getLearnerModel(.model, more.unwrap = FALSE)
f = if (.learner$predict.type == "response")
function(m) as.logical(getPredictionResponse(predict(m, newdata = .newdata, subset = .subset, ...)))
else
function(m) getPredictionProbabilities(predict(m, newdata = .newdata, subset = .subset, ...), cl = "TRUE")
asMatrixCols(lapply(models, f))
}
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