#' @title AUC Filter
#'
#' @name mlr_filters_auc
#'
#' @description
#' Area under the (ROC) Curve filter, analogously to [mlr3measures::auc()] from
#' \CRANpkg{mlr3measures}. Missing values of the features are removed before
#' calculating the AUC. If the AUC is undefined for the input, it is set to 0.5
#' (random classifier). The absolute value of the difference between the AUC and
#' 0.5 is used as final filter value.
#'
#' @references
#' For a benchmark of filter methods:
#'
#' `r format_bib("bommert_2020")`
#'
#' @family Filter
#' @include Filter.R
#' @template seealso_filter
#' @export
#' @examples
#' task = mlr3::tsk("sonar")
#' filter = flt("auc")
#' filter$calculate(task)
#' head(as.data.table(filter), 3)
#'
#' if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
#' library("mlr3pipelines")
#' task = mlr3::tsk("spam")
#'
#' # Note: `filter.frac` is selected randomly and should be tuned.
#'
#' graph = po("filter", filter = flt("auc"), filter.frac = 0.5) %>>%
#' po("learner", mlr3::lrn("classif.rpart"))
#'
#' graph$train(task)
#' }
FilterAUC = R6Class("FilterAUC",
inherit = Filter,
public = list(
#' @description Create a FilterAUC object.
initialize = function() {
super$initialize(
id = "auc",
task_types = "classif",
task_properties = "twoclass",
feature_types = c("integer", "numeric"),
packages = "mlr3measures",
label = "Area Under the ROC Curve Score",
man = "mlr3filters::mlr_filters_auc"
)
}
),
private = list(
.calculate = function(task, nfeat) {
y = task$truth() == task$positive
x = task$data(cols = task$feature_names)
score = map_dbl(x, function(x) {
keep = !is.na(x)
auc(y[keep], x[keep])
})
abs(0.5 - score)
}
)
)
#' @include mlr_filters.R
mlr_filters$add("auc", FilterAUC)
auc = function(truth, prob) {
n_pos = sum(truth)
n_neg = length(truth) - n_pos
if (n_pos == 0L || n_neg == 0L) {
return(0.5) # nocov
}
r = rank(prob, ties.method = "average")
(sum(r[truth]) - n_pos * (n_pos + 1L) / 2L) / (n_pos * n_neg)
}
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