#' @title Regression Gradient Boosting Machine Learner
#'
#' @name mlr_learners_regr.gbm
#'
#' @description
#' Regression gradient boosting machine models.
#' Calls [gbm::gbm()] from package \CRANpkg{gbm}.
#'
#' @section Custom mlr3 defaults:
#' - `keep_data`:
#' - Actual default: TRUE
#' - Adjusted default: FALSE
#' - Reason for change: `keep_data = FALSE` saves memory during model fitting.
#' - `n.cores`:
#' - Actual default: NULL
#' - Adjusted default: 1
#' - Reason for change: Suppressing the automatic internal parallelization if
#' `cv.folds` > 0.
#' @templateVar id regr.gbm
#' @template section_dictionary_learner
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerRegrGBM = R6Class("LearnerRegrGBM",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ParamSet$new(
params = list(
ParamFct$new(
id = "distribution", default = "gaussian",
levels = c("gaussian", "laplace", "poisson", "tdist", "quantile"),
tags = "train"),
ParamInt$new(
id = "n.trees", default = 100L, lower = 1L,
tags = c("train", "predict", "importance")),
ParamInt$new(
id = "interaction.depth", default = 1L, lower = 1L,
tags = "train"),
ParamInt$new(
id = "n.minobsinnode", default = 10L, lower = 1L,
tags = "train"),
ParamDbl$new(
id = "shrinkage", default = 0.001, lower = 0,
tags = "train"),
ParamDbl$new(
id = "bag.fraction", default = 0.5, lower = 0, upper = 1,
tags = "train"),
ParamDbl$new(
id = "train.fraction", default = 1, lower = 0, upper = 1,
tags = "train"),
ParamInt$new(id = "cv.folds", default = 0L, tags = "train"),
ParamDbl$new(
id = "alpha", default = 0.5, lower = 0, upper = 1,
tags = "train"),
# Set to FALSE to reduce memory requirements
ParamLgl$new(id = "keep.data", default = FALSE, tags = "train"),
ParamLgl$new(id = "verbose", default = FALSE, tags = "train"),
# Set to 1 to suppress parallelization by the package
ParamInt$new(id = "n.cores", default = 1, tags = "train"),
ParamUty$new(id = "var.monotone", tags = "train")
)
)
ps$values = list(keep.data = FALSE, n.cores = 1)
ps$add_dep("alpha", "distribution", CondEqual$new("quantile"))
super$initialize(
id = "regr.gbm",
packages = "gbm",
feature_types = c("integer", "numeric", "factor", "ordered"),
predict_types = "response",
param_set = ps,
properties = c("weights", "importance", "missings"),
man = "mlr3learners.gbm::mlr_learners_regr.gbm"
)
},
#' @description
#' The importance scores are extracted by `gbm::relative.influence()` from
#' the model.
#'
#' @return Named `numeric()`.
importance = function() {
if (is.null(self$model)) {
stop("No model stored")
}
pars = self$param_set$get_values(tags = "importance")
# n.trees is required for prediction. If not set by the user, we take the
# default (100)
if (is.null(self$param_set$values$n.trees)) {
pars$n.trees = self$param_set$default$n.trees # nolint
}
imp = mlr3misc::invoke(gbm::relative.influence, self$model, .args = pars)
sort(imp, decreasing = TRUE)
}
),
private = list(
.train = function(task) {
pars = self$param_set$get_values(tags = "train")
f = task$formula()
data = task$data()
if (!is.null(pars$distribution)) {
if (pars$distribution == "quantile") {
alpha = ifelse(is.null(pars$alpha), 0.5, pars$alpha)
pars$distribution = list(name = "quantile", alpha = alpha)
}
}
if ("weights" %in% task$properties) {
pars = insert_named(pars, list(weights = task$weights$weight))
}
mlr3misc::invoke(gbm::gbm, formula = f, data = data, .args = pars)
},
.predict = function(task) {
pars = self$param_set$get_values(tags = "predict")
# n.trees is required for prediction. If not set by the user, we take the
# default (100)
if (is.null(self$param_set$values$n.trees)) {
pars$n.trees = self$param_set$default$n.trees # nolint
}
newdata = task$data(cols = task$feature_names)
p = mlr3misc::invoke(predict, self$model, newdata = newdata, .args = pars)
PredictionRegr$new(task = task, response = p)
}
)
)
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