#' @title Density Penalized Learner
#' @author RaphaelS1
#' @name mlr_learners_dens.pen
#'
#' @description
#' Density estimation using penalized B-splines with automatic selection of smoothing parameter.
#' Calls [pendensity::pendensity()] from \CRANpkg{pendensity}.
#'
#' @template learner
#' @templateVar id dens.pen
#'
#' @references
#' `r format_bib("schellhase2012density")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerDensPenalized = R6Class("LearnerDensPenalized",
inherit = mlr3proba::LearnerDens,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
base = p_fct(default = "bspline",
levels = c("bspline", "gaussian"), tags = "train"),
no.base = p_dbl(default = 41, tags = "train"),
max.iter = p_dbl(default = 20, tags = "train"),
lambda0 = p_dbl(default = 500, tags = "train"),
q = p_dbl(default = 3, tags = "train"),
sort = p_lgl(default = TRUE, tags = "train"),
with.border = p_uty(tags = "train"),
m = p_dbl(default = 3, tags = "train"),
eps = p_dbl(default = 0.01, tags = "train")
)
super$initialize(
id = "dens.pen",
packages = c("mlr3extralearners", "pendensity"),
feature_types = c("integer", "numeric"),
predict_types = c("pdf", "cdf"),
param_set = ps,
man = "mlr3extralearners::mlr_learners_dens.pen",
label = "Penalized Density Estimation"
)
}
),
private = list(
.train = function(task) {
pars = self$param_set$get_values(tags = "train")
fit = invoke(pendensity::pendensity,
form = task$data()[[1]] ~ 1,
.args = pars)
pdf = function(x) {} # nolint
body(pdf) = substitute({
invoke(pendensity::dpendensity,
x = fit,
val = x)
})
cdf = function(x) {} # nolint
body(cdf) = substitute({
invoke(pendensity::ppendensity,
x = fit,
val = x)
})
base = if (is.null(pars$base)) {
"gaussian"
} else {
pars$base
}
distr6::Distribution$new(
name = paste("Penalized Density", base),
short_name = paste0("PenDens_", base),
pdf = pdf, cdf = cdf, type = set6::Reals$new())
},
.predict = function(task) {
newdata = task$data()[[1]]
pars = self$param_set$get_values(tags = "predict")
invoke(list, pdf = self$model$pdf(newdata), cdf = self$model$pdf(newdata), .args = pars)
}
)
)
.extralrns_dict$add("dens.pen", LearnerDensPenalized)
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