#' @title Ranger Survival Learner
#' @author be-marc
#' @name mlr_learners_surv.ranger
#'
#' @description
#' Random survival forest.
#' Calls [ranger::ranger()] from package \CRANpkg{ranger}.
#'
#' @section Custom mlr3 parameters:
#' - `mtry`:
#' - This hyperparameter can alternatively be set via our hyperparameter `mtry.ratio`
#' as `mtry = max(ceiling(mtry.ratio * n_features), 1)`.
#' Note that `mtry` and `mtry.ratio` are mutually exclusive.
#'
#' @section Initial parameter values:
#' - `num.threads` is initialized to 1 to avoid conflicts with parallelization via \CRANpkg{future}.
#'
#' @templateVar id surv.ranger
#' @template learner
#'
#' @references
#' `r format_bib("wright_2017", "breiman_2001")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerSurvRanger = R6Class("LearnerSurvRanger",
inherit = mlr3proba::LearnerSurv,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
alpha = p_dbl(default = 0.5, tags = "train"),
always.split.variables = p_uty(tags = "train"),
holdout = p_lgl(default = FALSE, tags = "train"), # FIXME: do we need this?
importance = p_fct(c("none", "impurity", "impurity_corrected", "permutation"), tags = "train"),
keep.inbag = p_lgl(default = FALSE, tags = "train"),
max.depth = p_int(default = NULL, lower = 0L, special_vals = list(NULL), tags = "train"),
min.node.size = p_int(1L, default = 5L, tags = "train"),
minprop = p_dbl(default = 0.1, tags = "train"),
mtry = p_int(lower = 1L, tags = "train"),
mtry.ratio = p_dbl(lower = 0, upper = 1, tags = "train"),
num.random.splits = p_int(1L, default = 1L, tags = "train"), # requires = quote(splitrule == "extratrees")
num.threads = p_int(1L, default = 1L, tags = c("train", "predict", "threads")),
num.trees = p_int(1L, default = 500L, tags = c("train", "predict")),
oob.error = p_lgl(default = TRUE, tags = "train"),
regularization.factor = p_uty(default = 1, tags = "train"),
regularization.usedepth = p_lgl(default = FALSE, tags = "train"),
replace = p_lgl(default = TRUE, tags = "train"),
respect.unordered.factors = p_fct(c("ignore", "order", "partition"), default = "ignore", tags = "train"), # for splitrule == "extratrees", def = partition
sample.fraction = p_dbl(0L, 1L, tags = "train"), # for replace == FALSE, def = 0.632
save.memory = p_lgl(default = FALSE, tags = "train"),
scale.permutation.importance = p_lgl(default = FALSE, tags = "train"), # requires = quote(importance == "permutation")
seed = p_int(default = NULL, special_vals = list(NULL), tags = c("train", "predict")),
split.select.weights = p_dbl(0, 1, tags = "train"),
splitrule = p_fct(c("logrank", "extratrees", "C", "maxstat"), default = "logrank", tags = "train"),
verbose = p_lgl(default = TRUE, tags = c("train", "predict")),
write.forest = p_lgl(default = TRUE, tags = "train"),
min.bucket = p_int(default = 3, tags = "train"),
time.interest = p_int(default = NULL, lower = 1L, special_vals = list(NULL), tags = "train"),
node.stats = p_lgl(default = FALSE, tags = "train")
)
ps$values = list(num.threads = 1L)
super$initialize(
id = "surv.ranger",
param_set = ps,
predict_types = c("distr", "crank"),
feature_types = c("logical", "integer", "numeric", "character", "factor", "ordered"),
properties = c("weights", "importance", "oob_error"),
packages = c("mlr3extralearners", "ranger"),
man = "mlr3extralearners::mlr_learners_surv.ranger",
label = "Random Forest"
)
},
#' @description
#' The importance scores are extracted from the model slot `variable.importance`.
#' @return Named `numeric()`.
importance = function() {
if (is.null(self$model)) {
stopf("No model stored")
}
if (self$model$importance.mode == "none") {
stopf("No importance stored")
}
sort(self$model$variable.importance, decreasing = TRUE)
},
#' @description
#' The out-of-bag error is extracted from the model slot `prediction.error`.
#' @return `numeric(1)`.
oob_error = function() {
self$model$prediction.error
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
pv = convert_ratio(pv, "mtry", "mtry.ratio", length(task$feature_names))
targets = task$target_names
invoke(ranger::ranger,
formula = NULL,
dependent.variable.name = targets[1L],
status.variable.name = targets[2L],
data = task$data(),
case.weights = task$weights$weight,
.args = pv
)
},
.predict = function(task) {
pv = self$param_set$get_values(tags = "predict")
newdata = ordered_features(task, self)
prediction = invoke(predict, object = self$model, data = newdata, .args = pv)
mlr3proba::.surv_return(times = prediction$unique.death.times, surv = prediction$survival)
}
)
)
.extralrns_dict$add("surv.ranger", LearnerSurvRanger)
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