#' @title Regression Kernlab Support Vector Machine
#' @author mboecker
#' @name mlr_learners_regr.ksvm
#'
#' @description
#' Support Vector Regression.
#' Calls [kernlab::ksvm()] from \CRANpkg{kernlab}.
#'
#' @template learner
#' @templateVar id regr.ksvm
#'
#' @references
#' `r format_bib("karatzoglou2004kernlab")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerRegrKSVM = R6Class("LearnerRegrKSVM",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
# the default for kpar is "automatic", which uses "sigest" in case the kernel is "rbfdot"
# (also the default). Only when the sigma, ... are set, the "automatic" value is overwritten
# Note that this is not explicitly checked with dependencies (maybe add at some point)
ps = ps(
scaled = p_lgl(default = TRUE, tags = "train"),
type = p_fct(default = "eps-svr",
levels = c("eps-svr", "nu-svr", "eps-bsvr"), tags = "train"),
kernel = p_fct(default = "rbfdot",
levels = c(
"rbfdot", "polydot", "vanilladot",
"laplacedot", "besseldot", "anovadot"),
tags = "train"),
C = p_dbl(default = 1, tags = "train"),
nu = p_dbl(default = 0.2, lower = 0, tags = "train"),
epsilon = p_dbl(default = 0.1, tags = "train"),
cache = p_int(default = 40, lower = 1L, tags = "train"),
tol = p_dbl(default = 0.001, lower = 0, tags = "train"),
shrinking = p_lgl(default = TRUE, tags = "train"),
sigma = p_dbl(default = NO_DEF, lower = 0, tags = "train"),
degree = p_int(default = NO_DEF, lower = 1L,
tags = "train"),
scale = p_dbl(default = NO_DEF, lower = 0, tags = "train"),
order = p_int(default = NO_DEF, tags = "train"),
offset = p_dbl(default = NO_DEF, tags = "train"),
na.action = p_uty(default = na.omit, tags = "train"),
fit = p_lgl(default = TRUE, tags = "train")
)
ps$add_dep(
"sigma", "kernel",
CondAnyOf$new(c("rbfdot", "laplacedot", "besseldot", "anovadot")))
ps$add_dep(
"degree", "kernel",
CondAnyOf$new(c("polydot", "besseldot", "anovadot")))
ps$add_dep("scale", "kernel", CondAnyOf$new("polydot"))
ps$add_dep("order", "kernel", CondAnyOf$new("besseldot"))
ps$add_dep("offset", "kernel", CondAnyOf$new("polydot"))
super$initialize(
id = "regr.ksvm",
packages = c("mlr3extralearners", "kernlab"),
feature_types = c(
"logical", "integer", "numeric",
"character", "factor", "ordered"),
predict_types = "response",
param_set = ps,
properties = "weights",
man = "mlr3extralearners::mlr_learners_regr.ksvm",
label = "Support Vector Machine"
)
}
),
private = list(
.train = function(task) {
pars = self$param_set$get_values(tags = "train")
kpar = intersect(
c("sigma", "degree", "scale", "order", "offset"),
names(pars))
if ("weights" %in% task$properties) {
pars$class.weights = task$weights$weight
}
if (length(kpar) > 0) {
pars$kpar = pars[kpar]
pars[kpar] = NULL
}
f = task$formula()
data = task$data()
invoke(kernlab::ksvm, x = f, data = data, .args = pars)
},
.predict = function(task) {
newdata = ordered_features(task, self)
pars = self$param_set$get_values(tags = "predict")
p = invoke(kernlab::predict, self$model,
newdata = newdata,
type = "response",
.args = pars
)
list(response = p)
}
)
)
.extralrns_dict$add("regr.ksvm", LearnerRegrKSVM)
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