# FIXME: interface was changed, read page, pars, maybe rename
#' @export
makeRLearner.classif.boosting = function() {
makeRLearnerClassif(
cl = "classif.boosting",
package = c("adabag", "rpart"),
par.set = makeParamSet(
makeLogicalLearnerParam(id = "boos", default = TRUE),
makeIntegerLearnerParam(id = "mfinal", default = 100L, lower = 1L),
makeDiscreteLearnerParam(id = "coeflearn", default = "Breiman", values = c("Breiman", "Freund", "Zhu")),
# rpart.control arguments
makeIntegerLearnerParam(id = "minsplit", default = 20L, lower = 1L),
makeIntegerLearnerParam(id = "minbucket", lower = 1L),
makeNumericLearnerParam(id = "cp", default = 0.01, lower = 0, upper = 1),
makeIntegerLearnerParam(id = "maxcompete", default = 4L, lower = 0L),
makeIntegerLearnerParam(id = "maxsurrogate", default = 5L, lower = 0L),
makeDiscreteLearnerParam(id = "usesurrogate", default = 2L, values = 0:2),
makeDiscreteLearnerParam(id = "surrogatestyle", default = 0L, values = 0:1),
# we use 30 as upper limit, see docs of rpart.control
makeIntegerLearnerParam(id = "maxdepth", default = 30L, lower = 1L, upper = 30L),
makeIntegerLearnerParam(id = "xval", default = 10L, lower = 0L, tunable = FALSE)
),
par.vals = list(xval = 0L),
properties = c("twoclass", "multiclass", "missings", "numerics", "factors", "prob", "featimp"),
name = "Adabag Boosting",
short.name = "adabag",
note = "`xval` has been set to `0` by default for speed.",
callees = c("boosting", "rpart.control")
)
}
#' @export
trainLearner.classif.boosting = function(.learner, .task, .subset, .weights = NULL, minsplit, minbucket, cp, maxcompete, maxsurrogate, usesurrogate, surrogatestyle, maxdepth, xval, ...) {
f = getTaskFormula(.task)
ctrl = learnerArgsToControl(rpart::rpart.control, minsplit, minbucket, cp, maxcompete, maxsurrogate, usesurrogate, surrogatestyle, maxdepth, xval)
adabag::boosting(f, data = getTaskData(.task, .subset), control = ctrl, ...)
}
#' @export
predictLearner.classif.boosting = function(.learner, .model, .newdata, ...) {
levs = .model$task.desc$class.levels
# stupid adaboost
.newdata[, .model$task.desc$target] = factor(rep(1, nrow(.newdata)), levels = levs)
p = predict(.model$learner.model, newdata = .newdata, ...)
if (.learner$predict.type == "prob") {
return(setColNames(p$prob, levs))
} else {
return(as.factor(p$class))
}
}
#' @export
getFeatureImportanceLearner.classif.boosting = function(.learner, .model, ...) {
mod = getLearnerModel(.model, more.unwrap = TRUE)
mod$importance[.model$features]
}
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