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#' @title Fuse learner with an extractFDAFeatures method.
#'
#' @description
#' Fuses a base learner with an extractFDAFeatures method. Creates a learner object, which can be
#' used like any other learner object.
#' Internally uses [extractFDAFeatures] before training the learner and
#' [reextractFDAFeatures] before predicting.
#'
#' @template arg_learner
#' @inheritParams extractFDAFeatures
#' @export
#' @family fda
#' @family wrapper
#' @template ret_learner
makeExtractFDAFeatsWrapper = function(learner, feat.methods = list()) {
# FIXME:
# This is stupid, we can not handle multiple tasks for a single wrapper this way.
# (Impute cant do this neither if using cols = list("X1" = ...)).
# One solution would be to be able to specify "all" features (regexp would be overkill?).
learner = checkLearner(learner)
args = list(feat.methods = feat.methods)
rm(list = names(args))
trainfun = function(data, target, args) {
l = do.call(extractFDAFeatures, c(list(obj = data, target = target), args))
names(l) = c("data", "control")
l
}
predictfun = function(data, target, args, control) {
reextractFDAFeatures(data, control)
}
lrn = makePreprocWrapper(learner, trainfun, predictfun, par.vals = args)
lrn$id = stri_replace(lrn$id, replacement = ".extracted", regex = "[.]preproc$")
addClasses(lrn, "extractFDAFeatsWrapper")
}
#' @export
getLearnerProperties.extractFDAFeatsWrapper = function(learner) {
union(getLearnerProperties(learner$next.learner), c("functionals", "single.functional"))
}
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