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#' @export
makeRLearner.regr.gbm = function() {
makeRLearnerRegr(
cl = "regr.gbm",
package = "gbm",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "distribution", default = "gaussian", values = c("gaussian", "bernoulli", "huberized", "adaboost", "coxph", "pairwise", "laplace", "poisson", "tdist", "quantile")),
makeIntegerLearnerParam(id = "n.trees", default = 100L, lower = 1L),
makeIntegerLearnerParam(id = "cv.folds", default = 0L),
makeIntegerLearnerParam(id = "interaction.depth", default = 1L, lower = 1L),
makeIntegerLearnerParam(id = "n.minobsinnode", default = 10L, lower = 1L),
makeNumericLearnerParam(id = "shrinkage", default = 0.1, lower = 0),
makeNumericLearnerParam(id = "bag.fraction", default = 0.5, lower = 0, upper = 1),
makeNumericLearnerParam(id = "train.fraction", default = 1, lower = 0, upper = 1),
makeNumericLearnerParam(id = "alpha", default = 0.5, lower = 0, upper = 1,
requires = quote(distribution == "quantile")),
makeLogicalLearnerParam(id = "keep.data", default = TRUE, tunable = FALSE),
makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE),
makeIntegerLearnerParam(id = "n.cores", default = 1, tunable = FALSE)
),
par.vals = list(distribution = "gaussian", keep.data = FALSE),
properties = c("missings", "numerics", "factors", "weights", "featimp"),
name = "Gradient Boosting Machine",
short.name = "gbm",
note = paste0(collapse = "", c('`keep.data` is set to FALSE to reduce memory requirements, `distribution` has been set to `"gaussian"` by default.',
"Param 'n.cores' has been to set to '1' by default to suppress parallelization by the package.")),
callees = "gbm"
)
}
#' @export
trainLearner.regr.gbm = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
params = list(...)
if ("alpha" %in% names(params)) {
alpha = params$alpha
params$alpha = NULL
} else {
alpha = 0.5
}
if (params$distribution == "quantile") {
params$distribution = list(name = "quantile", alpha = alpha)
}
params$formula = f
params$data = getTaskData(.task, .subset)
if (!is.null(.weights)) {
params$weights = .weights
}
do.call(gbm::gbm, params)
}
#' @export
predictLearner.regr.gbm = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
gbm::predict.gbm(m, newdata = .newdata, n.trees = length(m$trees), ...)
}
#' @export
getFeatureImportanceLearner.regr.gbm = function(.learner, .model, ...) {
getFeatureImportanceLearner.classif.gbm(.learner, .model, ...)
}
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