#' @export
makeRLearner.regr.gamboost = function() {
makeRLearnerRegr(
cl = "regr.gamboost",
package = "mboost",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "baselearner", values = c("bbs", "bols", "btree")),
makeIntegerLearnerParam(id = "dfbase", default = 4L),
makeNumericLearnerParam(id = "offset"),
makeDiscreteLearnerParam(id = "family", default = "Gaussian", values = c("Gaussian", "Laplace",
"Huber", "Poisson", "GammaReg", "NBinomial", "Hurdle", "custom.family")),
# families 'Poisson', 'NBinomial' and 'Hurdle' are for count data
makeUntypedLearnerParam(id = "custom.family.definition", requires = quote(family == "custom.family")),
makeNumericVectorLearnerParam(id = "nuirange", default = c(0, 100),
requires = quote(family %in% c("GammaReg", "NBinomial", "Hurdle"))),
makeNumericLearnerParam(id = "d", requires = quote(family == "Huber")),
makeIntegerLearnerParam(id = "mstop", default = 100L, lower = 1L),
makeNumericLearnerParam(id = "nu", default = 0.1, lower = 0, upper = 1),
makeDiscreteLearnerParam(id = "risk", values = c("inbag", "oobag", "none")),
makeLogicalLearnerParam(id = "stopintern", default = FALSE),
# 'risk' and 'stopintern' will be kept for completeness sake
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE)
),
par.vals = list(),
properties = c("numerics", "factors", "weights"),
name = "Gradient Boosting with Smooth Components",
short.name = "gamboost",
callees = c("gamboost", "mboost_fit", "boost_control", "Gaussian", "Laplace",
"Huber", "Poisson", "GammaReg", "NBinomial", "Hurdle")
)
}
#' @export
trainLearner.regr.gamboost = function(.learner, .task, .subset, .weights = NULL, family = "Gaussian", nuirange = c(0, 100), d = NULL, custom.family.definition, mstop, nu, risk, trace, stopintern, ...) {
requirePackages("mboost", why = "argument 'baselearner' requires package", suppress.warnings = TRUE)
ctrl = learnerArgsToControl(mboost::boost_control, mstop, nu, risk, trace, stopintern)
data = getTaskData(.task, .subset)
f = getTaskFormula(.task)
family = switch(family,
Gaussian = mboost::Gaussian(),
Laplace = mboost::Laplace(),
Huber = mboost::Huber(d),
Poisson = mboost::Poisson(),
GammaReg = mboost::GammaReg(nuirange = nuirange),
NBinomial = mboost::NBinomial(nuirange = nuirange),
Hurdle = mboost::Hurdle(nuirange = nuirange),
custom.family = custom.family.definition
)
if (is.null(.weights)) {
model = mboost::gamboost(f, data = data, control = ctrl, family = family, ...)
} else {
model = mboost::gamboost(f, data = data, control = ctrl, weights = .weights, family = family, ...)
}
model
}
#' @export
predictLearner.regr.gamboost = function(.learner, .model, .newdata, ...) {
p = predict(.model$learner.model, newdata = .newdata, ...)
return(as.vector(p))
}
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