#' @title Regression Conditional Inference Tree Learner
#' @author sumny
#' @name mlr_learners_regr.ctree
#'
#' @description
#' Regression Partition Tree where a significance test is used to determine the univariate splits.
#' Calls [partykit::ctree()] from \CRANpkg{partykit}.
#'
#'
#' @template learner
#' @templateVar id regr.ctree
#'
#' @references
#' `r format_bib("hothorn_2015", "hothorn_2006")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerRegrCTree = R6Class("LearnerRegrCTree",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
teststat = p_fct(levels = c("quadratic", "maximum"),
default = "quadratic", tags = "train"),
splitstat = p_fct(levels = c("quadratic", "maximum"),
default = "quadratic", tags = "train"),
splittest = p_lgl(default = FALSE, tags = "train"),
testtype = p_fct(levels = c("Bonferroni", "MonteCarlo",
"Univariate", "Teststatistic"), default = "Bonferroni",
tags = "train"),
nmax = p_uty(tags = "train"),
alpha = p_dbl(lower = 0, upper = 1, default = 0.05,
tags = "train"),
mincriterion = p_dbl(lower = 0, upper = 1, default = 0.95,
tags = "train"),
logmincriterion = p_dbl(tags = "train"),
minsplit = p_int(lower = 1L, default = 20L, tags = "train"),
minbucket = p_int(lower = 1L, default = 7L, tags = "train"),
minprob = p_dbl(lower = 0, default = 0.01, tags = "train"),
stump = p_lgl(default = FALSE, tags = "train"),
lookahead = p_lgl(default = FALSE, tags = "train"),
MIA = p_lgl(default = FALSE, tags = "train"),
maxvar = p_int(lower = 1L, tags = "train"),
nresample = p_int(lower = 1L, default = 9999L,
tags = "train"),
tol = p_dbl(lower = 0, tags = "train"),
maxsurrogate = p_int(lower = 0L, default = 0L,
tags = "train"),
numsurrogate = p_lgl(default = FALSE, tags = "train"),
mtry = p_int(lower = 0L, special_vals = list(Inf),
default = Inf, tags = "train"),
maxdepth = p_int(lower = 0L, special_vals = list(Inf),
default = Inf, tags = "train"),
multiway = p_lgl(default = FALSE, tags = "train"),
splittry = p_int(lower = 0L, default = 2L, tags = "train"),
intersplit = p_lgl(default = FALSE, tags = "train"),
majority = p_lgl(default = FALSE, tags = "train"),
caseweights = p_lgl(default = FALSE, tags = "train"),
applyfun = p_uty(tags = "train"),
cores = p_int(special_vals = list(NULL), default = NULL,
tags = c("train", "threads")),
saveinfo = p_lgl(default = TRUE, tags = "train"),
update = p_lgl(default = FALSE, tags = "train"),
splitflavour = p_fct(default = "ctree", # goes into control
levels = c("ctree", "exhaustive"), tags = "train"),
offset = p_uty(tags = "train"),
cluster = p_uty(tags = "train"),
scores = p_uty(tags = "train"),
doFit = p_lgl(default = TRUE, tags = "train"),
maxpts = p_int(default = 25000L, tags = "train"),
abseps = p_dbl(default = 0.001, lower = 0, tags = "train"),
releps = p_dbl(default = 0, lower = 0, tags = "train")
)
ps$add_dep("nresample", "testtype", CondEqual$new("MonteCarlo"))
super$initialize(
id = "regr.ctree",
packages = c("mlr3extralearners", "partykit", "sandwich", "coin"),
feature_types = c("integer", "numeric", "factor", "ordered"),
predict_types = "response",
param_set = ps,
properties = "weights",
man = "mlr3extralearners::mlr_learners_regr.ctree",
label = "Conditional Inference Tree"
)
}
),
private = list(
.train = function(task) {
pars = self$param_set$get_values(tags = "train")
pars_pargs = pars[names(pars) %in% formalArgs(mvtnorm::GenzBretz)]
pars = pars[names(pars) %nin% formalArgs(mvtnorm::GenzBretz)]
if ("weights" %in% task$properties) {
pars$weights = task$weights$weight
}
pars$pargs = invoke(mvtnorm::GenzBretz, pars_pargs)
invoke(partykit::ctree, formula = task$formula(),
data = task$data(), .args = pars)
},
.predict = function(task) {
newdata = ordered_features(task, self)
pars = self$param_set$get_values(tags = "predict")
p = invoke(predict, self$model, newdata = newdata, .args = pars)
list(response = p)
}
)
)
.extralrns_dict$add("regr.ctree", LearnerRegrCTree)
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