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#' @title Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
#'
#' @description
#' Creates a learner object, which can be
#' used like any other learner object.
#' Internally uses [oversample] or [undersample] before every model fit.
#'
#' Note that observation weights do not influence the sampling and are simply passed
#' down to the next learner.
#'
#' @template arg_learner
#' @param usw.rate (`numeric(1)`)\cr
#' Factor to downsample a class. Must be between 0 and 1,
#' where 1 means no downsampling, 0.5 implies reduction to 50 percent
#' and 0 would imply reduction to 0 observations.
#' Default is 1.
#' @param osw.rate (`numeric(1)`)\cr
#' Factor to oversample a class. Must be between 1 and `Inf`,
#' where 1 means no oversampling and 2 would mean doubling the class size.
#' Default is 1.
#' @param usw.cl (`character(1)`)\cr
#' Class that should be undersampled.
#' Default is `NULL`, which means the larger one.
#' @param osw.cl (`character(1)`)\cr
#' Class that should be oversampled.
#' Default is `NULL`, which means the smaller one.
#' @template ret_learner
#' @family imbalancy
#' @family wrapper
#' @export
makeUndersampleWrapper = function(learner, usw.rate = 1, usw.cl = NULL) {
learner = checkLearner(learner, "classif")
pv = list()
if (!missing(usw.rate)) {
assertNumber(usw.rate, lower = 0, upper = 1)
pv$usw.rate = usw.rate
}
if (!is.null(usw.cl)) {
assertString(usw.cl)
pv$usw.cl = usw.cl
}
id = stri_paste(learner$id, "undersampled", sep = ".")
ps = makeParamSet(
makeNumericLearnerParam(id = "usw.rate", lower = 0, upper = 1),
makeUntypedLearnerParam(id = "usw.cl", default = NULL, tunable = FALSE)
)
makeBaseWrapper(id, "classif", learner, package = "mlr", par.set = ps, par.vals = pv,
learner.subclass = "UndersampleWrapper", model.subclass = "UndersampleModel")
}
#' @rdname makeUndersampleWrapper
#' @export
makeOversampleWrapper = function(learner, osw.rate = 1, osw.cl = NULL) {
learner = checkLearner(learner, "classif")
pv = list()
if (!missing(osw.rate)) {
assertNumber(osw.rate, lower = 1)
pv$osw.rate = osw.rate
}
if (!is.null(osw.cl)) {
assertString(osw.cl)
pv$osw.cl = osw.cl
}
id = stri_paste(learner$id, "oversampled", sep = ".")
ps = makeParamSet(
makeNumericLearnerParam(id = "osw.rate", lower = 1),
makeUntypedLearnerParam(id = "osw.cl", default = NULL, tunable = FALSE)
)
makeBaseWrapper(id, "classif", learner, package = "mlr", par.set = ps, par.vals = pv,
learner.subclass = "OversampleWrapper", model.subclass = "OversampleModel")
}
#' @export
trainLearner.UndersampleWrapper = function(.learner, .task, .subset = NULL, .weights = NULL, usw.rate = 1, usw.cl = NULL, ...) {
# If weights vector length fits to task size, set weights before subsetting (Issue #838)
if (length(.weights) == getTaskSize(.task)) {
.task$weights = .weights
.task = subsetTask(.task, .subset)
# otherwise subset first and then set weights
} else {
.task = subsetTask(.task, .subset)
.task$weights = .weights
}
.task = undersample(.task, rate = usw.rate, cl = usw.cl)
m = train(.learner$next.learner, .task, weights = .task$.weights)
m$train.task = .task
makeChainModel(next.model = m, cl = "UndersampleModel")
}
#' @export
trainLearner.OversampleWrapper = function(.learner, .task, .subset = NULL, .weights = NULL, osw.rate = 1, osw.cl = NULL, ...) {
# If weights vector length fits to task size, set weights before subsetting (Issue #838)
if (length(.weights) == getTaskSize(.task)) {
.task$weights = .weights
.task = subsetTask(.task, .subset)
# otherwise subset first and then set weights
} else {
.task = subsetTask(.task, .subset)
.task$weights = .weights
}
.task = oversample(.task, rate = osw.rate, cl = osw.cl)
m = train(.learner$next.learner, .task, weights = .task$.weights)
m$train.task = .task
makeChainModel(next.model = m, cl = "OversampleModel")
}
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