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#' @title Use nested stacking 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 nested stacking multilabel learner.
#' Nested stacking trains a binary classifier for each label following a given order. In training phase,
#' the feature space of each classifier is extended with predicted label information (by cross validation)
#' of all previous labels in the chain.
#' During the prediction phase, predicted labels are obtained by the classifiers, which have been learned on
#' all training data.
#'
#' Models can easily be accessed via [getLearnerModel].
#'
#' @template arg_learner
#' @template arg_multilabel_order
#' @template arg_multilabel_cvfolds
#' @template ret_learner
#' @references
#' Montanes, E. et al. (2013),
#' *Dependent binary relevance models for multi-label classification*
#' Artificial Intelligence Center, University of Oviedo at Gijon, Spain.
#' @family wrapper
#' @family multilabel
#' @export
#' @example inst/examples/MultilabelWrapper.R
makeMultilabelNestedStackingWrapper = function(learner, order = NULL, cv.folds = 2) {
learner = checkLearner(learner, type = "classif", props = "twoclass")
id = stri_paste("multilabel.nestedStacking", getLearnerId(learner), sep = ".")
packs = getLearnerPackages(learner)
type = getLearnerType(learner)
x = makeHomogeneousEnsemble(id, type, learner, packs,
learner.subclass = "MultilabelNestedStackingWrapper",
model.subclass = "MultilabelNestedStackingModel")
x$type = "multilabel"
x$order = order
x$cv.folds = cv.folds
return(x)
}
#' @export
trainLearner.MultilabelNestedStackingWrapper = function(.learner, .task, .subset = NULL, .weights = NULL, ...) {
if (is.null(.learner$order)) {
order = sample(getTaskTargetNames(.task)) # random order
} else {
order = .learner$order
}
assertSetEqual(order, getTaskTargetNames(.task))
targets = getTaskTargetNames(.task)
.task = subsetTask(.task, subset = .subset)
data = getTaskData(.task)
models = namedList(order)
data.nst = dropNamed(data, targets)
chained.targets = targets
for (i in seq_along(targets)) {
tn = order[i]
if (i >= 2) {
tnprevious = order[i - 1]
data2 = data.frame(data.nst, data[tnprevious]) # for inner resampling to produce predicted labels
innertask = makeClassifTask(id = tnprevious, data = data2, target = tnprevious)
rdesc = makeResampleDesc("CV", iters = .learner$cv.folds)
r = resample(.learner$next.learner, innertask, rdesc, weights = .weights, show.info = FALSE)
predlabel = as.numeric(as.logical(r$pred$data[order(r$pred$data$id), ]$response)) # did not use getPredictionResponse, because of ordering
data2 = data.frame(data.nst, data[tn])
data2[[tnprevious]] = predlabel
data.nst[[tnprevious]] = predlabel
} else {
data2 = dropNamed(data, setdiff(chained.targets, tn))
}
ctask = makeClassifTask(id = tn, data = data2, target = tn)
models[[tn]] = train(.learner$next.learner, ctask, weights = .weights)
}
makeHomChainModel(.learner, models)
}
#' @export
predictLearner.MultilabelNestedStackingWrapper = function(.learner, .model, .newdata, .subset = NULL, ...) {
models = getLearnerModel(.model, more.unwrap = FALSE)
predmatrix = matrix(ncol = length(models), nrow = nrow(.newdata), dimnames = list(NULL, names(models)))
if (.learner$predict.type == "response") {
for (tn in names(models)) {
predmatrix[, tn] = as.logical(getPredictionResponse(predict(models[[tn]], newdata = .newdata, subset = .subset, ...)))
.newdata[tn] = as.numeric(predmatrix[, tn])
}
} else {
for (tn in names(models)) {
predmatrix[, tn] = getPredictionProbabilities(predict(models[[tn]], newdata = .newdata, subset = .subset, ...), cl = "TRUE")
.newdata[tn] = predmatrix[, tn]
}
}
predmatrix[, .model$task.desc$class.levels] # bring labels back in original order
}
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