#' @title Density Logspline Learner
#' @author RaphaelS1
#' @name mlr_learners_dens.logspline
#'
#' @description
#' Density logspline learner.
#' Calls [logspline::logspline()] from \CRANpkg{logspline}.
#'
#' @template learner
#' @templateVar id dens.logspline
#'
#' @references
#' `r format_bib("kooperberg1992logspline")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerDensLogspline = R6Class("LearnerDensLogspline",
inherit = mlr3proba::LearnerDens,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
lbound = p_dbl(tags = "train"),
ubound = p_dbl(tags = "train"),
maxknots = p_dbl(default = 0, lower = 0, tags = "train"),
knots = p_uty(tags = "train"),
nknots = p_dbl(default = 0, lower = 0, tags = "train"),
penalty = p_uty(tags = "train"),
silent = p_lgl(default = TRUE, tags = "train"),
mind = p_dbl(default = -1, tags = "train"),
error.action = p_int(default = 2, lower = 0, upper = 2, tags = "train")
)
super$initialize(
id = "dens.logspline",
packages = c("mlr3extralearners", "logspline"),
feature_types = c("integer", "numeric"),
predict_types = c("pdf", "cdf"),
param_set = ps,
man = "mlr3extralearners::mlr_learners_dens.logspline",
label = "Logspline Density Estimation"
)
}
),
private = list(
.train = function(task) {
data = task$data()[[1]]
pars = self$param_set$get_values(tags = "train")
fit = invoke(logspline::logspline, x = data, .args = pars)
pdf = function(x) {} # nolint
body(pdf) = substitute({
invoke(logspline::dlogspline, q = x, fit = fit)
})
cdf = function(x) {} # nolint
body(cdf) = substitute({
invoke(logspline::plogspline, q = x, fit = fit)
})
quantile = function(p) {} # nolint
body(quantile) = substitute({
invoke(logspline::qlogspline, p = p, fit = fit)
})
rand = function(n) {} # nolint
body(rand) = substitute({
invoke(logspline::rlogspline, n = n, fit = fit)
})
distr6::Distribution$new(
name = "Logspline Density Estimator",
short_name = "LogsplineDens",
pdf = pdf, cdf = cdf, quantile = quantile, rand = rand, type = set6::Reals$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$cdf(newdata), .args = pars)
}
)
)
.extralrns_dict$add("dens.logspline", LearnerDensLogspline)
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