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#' @template surv_learner
#' @templateVar title Rpart Survival Trees
#' @templateVar fullname LearnerSurvRpart
#' @templateVar caller [rpart::rpart()]
#' @templateVar crank using [rpart::predict.rpart()]
#'
#' @description
#' Parameter `xval` is set to 0 in order to save some computation time.
#' Parameter `model` has been renamed to `keep_model`.
#'
#' @references
#' `r format_bib("breiman_1984")`
#'
#' @export
LearnerSurvRpart = R6Class("LearnerSurvRpart",
inherit = LearnerSurv,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
parms = p_dbl(default = 1, tags = "train"),
minbucket = p_int(lower = 1L, tags = "train"),
minsplit = p_int(default = 20L, lower = 1L, tags = "train"),
cp = p_dbl(default = 0.01, lower = 0, upper = 1, tags = "train"),
maxcompete = p_int(default = 4L, lower = 0L, tags = "train"),
maxsurrogate = p_int(default = 5L, lower = 0L, tags = "train"),
maxdepth = p_int(default = 30L, lower = 1L, upper = 30L, tags = "train"),
usesurrogate = p_int(default = 2L, lower = 0L, upper = 2L, tags = "train"),
surrogatestyle = p_int(default = 0L, lower = 0L, upper = 1L, tags = "train"),
xval = p_int(default = 10L, lower = 0L, tags = "train"),
cost = p_uty(tags = "train"),
keep_model = p_lgl(default = FALSE, tags = "train")
)
ps$values = list(xval = 0L)
super$initialize(
id = "surv.rpart",
param_set = ps,
predict_types = c("crank", "distr"),
feature_types = c("logical", "integer", "numeric", "character", "factor", "ordered"),
properties = c("weights", "missings", "importance", "selected_features"),
packages = c("rpart", "distr6", "survival"),
label = "Survival Tree",
man = "mlr3proba::mlr_learners_surv.rpart"
)
},
#' @description
#' The importance scores are extracted from the model slot `variable.importance`.
#' @return Named `numeric()`.
importance = function() {
if (is.null(self$model)) {
stopf("No model stored")
}
# importance is only present if there is at least on split
sort(self$model$variable.importance %??% set_names(numeric()), decreasing = TRUE)
},
#' @description
#' Selected features are extracted from the model slot `frame$var`.
#' @return `character()`.
selected_features = function() {
if (is.null(self$model)) {
stopf("No model stored")
}
unique(setdiff(self$model$frame$var, "<leaf>"))
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
names(pv) = replace(names(pv), names(pv) == "keep_model", "model")
if ("weights" %in% task$properties) {
pv = insert_named(pv, list(weights = task$weights$weight))
}
invoke(rpart::rpart,
formula = task$formula(), data = task$data(),
method = "exp", .args = pv)
},
.predict = function(task) {
preds = invoke(predict, object = self$model, newdata = task$data(cols = task$feature_names))
list(crank = preds)
}
)
)
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