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#' @title Retrieve binary classification measures for multilabel classification predictions.
#'
#' @description
#' Measures the quality of each binary label prediction w.r.t. some binary classification
#' performance measure.
#'
#' @param pred ([Prediction])\cr
#' Multilabel Prediction object.
#' @param measures ([Measure] | list of [Measure])\cr
#' Performance measure(s) to evaluate, must be applicable to binary classification performance.
#' Default is `mmce`.
#' @return (named `matrix`). Performance value(s), column names are measure(s), row names are labels.
#' @export
#' @family multilabel
#' @examples
#' # see makeMultilabelBinaryRelevanceWrapper
getMultilabelBinaryPerformances = function(pred, measures) {
checkPrediction(pred, task.type = "multilabel")
measures = checkMeasures(measures, "classif")
p = matrix(, length(pred$task.desc$class.levels), length(measures))
colnames(p) = vcapply(measures, measureAggrName)
rownames(p) = pred$task.desc$class.levels
truths = getPredictionTruth(pred)
responses = getPredictionResponse(pred)
if (pred$predict.type == "prob") {
probs = getPredictionProbabilities(pred)
}
for (measure in measures) {
predi = pred
predi$task.desc$type = "classif"
predi$task.desc$class.levels = c("TRUE", "FALSE")
predi$task.desc$positive = "TRUE"
predi$task.desc$negative = "FALSE"
measurename = measureAggrName(measure)
for (label in pred$task.desc$class.levels) {
predi$data = data.frame(truth = truths[, label], response = responses[, label])
if (pred$predict.type == "prob") {
predi$data$prob.TRUE = probs[, label]
}
p[label, measurename] = performance(predi, measure)
}
}
return(p)
}
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