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#' @export
makeRLearner.regr.mob = function() {
makeRLearnerRegr(
cl = "regr.mob",
package = c("party", "modeltools"),
par.set = makeParamSet(
makeNumericLearnerParam(id = "alpha", default = 0.05, lower = 0, upper = 1),
makeLogicalLearnerParam(id = "bonferroni", default = TRUE),
makeIntegerLearnerParam(id = "minsplit", default = 20L, lower = 1L),
makeNumericLearnerParam(id = "trim", default = 0.1, lower = 0, upper = 1),
makeLogicalLearnerParam(id = "breakties", default = FALSE),
makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE),
makeDiscreteLearnerParam(id = "model", default = "glinearModel", values = list("glinearModel", "linearModel")),
makeUntypedLearnerParam(id = "part.feats"),
makeUntypedLearnerParam(id = "term.feats")
),
par.vals = list(),
properties = c("numerics", "factors", "weights"),
name = "Model-based Recursive Partitioning Yielding a Tree with Fitted Models Associated with each Terminal Node",
short.name = "mob",
callees = c("mob", "mob_control")
)
}
#' @export
trainLearner.regr.mob = function(.learner, .task, .subset, .weights = NULL, alpha, bonferroni, minsplit,
trim, breakties, verbose, part.feats, term.feats, model, ...) {
cntrl = learnerArgsToControl(party::mob_control, alpha, bonferroni, minsplit, trim, breakties, verbose)
feats = getTaskFeatureNames(.task)
# FIXME: document stuff
# FIXME: think about these defaults, also ask julia
if (missing(part.feats)) {
part.feats = feats
}
if (missing(term.feats)) {
term.feats = feats
}
target = getTaskTargetNames(.task)
f = as.formula(stri_paste(target, "~", collapse(term.feats, sep = " + "), "|", collapse(part.feats, sep = " + "), sep = " "))
args = list(f, data = getTaskData(.task, .subset), control = cntrl, ...)
if (!is.null(.weights)) {
args$weights = .weights
}
if (!missing(model)) {
if (is.character(model)) {
args$model = getFromNamespace(model, "mda")
} else {
args$model = model # this allows to set the model if on.par.out.of.bounds is set to "warn" or "quiet"
}
}
model = do.call(party::mob, args)
# sometimes mob fails to fit a model but does not signal an exception.
if (anyMissing(coef(model))) {
stop("Failed to fit party::mob. Some coefficients are estimated as NA")
}
model
}
#' @export
predictLearner.regr.mob = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, ...)
}
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