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#' @export
makeRLearner.classif.fgam = function() {
makeRLearnerClassif(
cl = "classif.fgam",
package = "refund",
par.set = fgam.ps,
par.vals = fgam.par.vals,
properties = c("functionals", "single.functional", "twoclass", "prob"),
name = "functional general additive model",
short.name = "FGAM"
)
}
#' @export
trainLearner.classif.fgam = function(.learner, .task, .subset, .weights = NULL, ...) {
parlist = list(...)
tn = getTaskTargetNames(.task)
dd = getTaskData(.task, target.extra = FALSE, functionals.as = "matrix")
# tranform target to 0, 1
dd[[tn]] = as.integer(dd[[tn]]) - 1
formmat = getFGAMFormulaMat(mdata = dd, targetname = tn, fns = getTaskFeatureNames(.task), parlist)
pfr = refund::pfr # Weird hack but required since refund behaves weird when it is not loaded.
mod = pfr(formula = formmat$form, data = formmat$mat.list, family = binomial())
mod$uvt = unique(getTaskTargets(.task))
return(mod)
}
#' @export
predictLearner.classif.fgam = function(.learner, .model, .newdata, ...) {
assert(hasFunctionalFeatures(.newdata))
nl = as.list(.newdata)
pred = predict(.model$learner.model, newdata = nl, type = "response")
if (.learner$predict.type == "prob") {
return(propVectorToMatrix(pred, c(.model$task.desc$negative, .model$task.desc$positive)))
} else {
return(factor(round(pred), labels = .model$learner.model$uvt))
}
}
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