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#' @export
makeRLearner.classif.cforest = function() {
makeRLearnerClassif(
cl = "classif.cforest",
package = "party",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ntree", lower = 1L, default = 500L),
makeIntegerLearnerParam(id = "mtry", lower = 1L, default = 5L),
makeLogicalLearnerParam(id = "replace", default = FALSE),
makeNumericLearnerParam(id = "fraction", lower = 0, upper = 1, default = 0.632,
requires = quote(replace == FALSE)),
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE),
makeDiscreteLearnerParam(id = "teststat", values = c("quad", "max"), default = "quad"),
makeDiscreteLearnerParam(id = "testtype",
values = c("Bonferroni", "MonteCarlo", "Univariate", "Teststatistic"),
default = "Univariate"),
makeNumericLearnerParam(id = "mincriterion", lower = 0, default = 0),
makeIntegerLearnerParam(id = "minsplit", lower = 1L, default = 20L),
makeIntegerLearnerParam(id = "minbucket", lower = 1L, default = 7L),
makeLogicalLearnerParam(id = "stump", default = FALSE),
makeIntegerLearnerParam(id = "nresample", lower = 1L, default = 9999L),
makeIntegerLearnerParam(id = "maxsurrogate", lower = 0L, default = 0L),
makeIntegerLearnerParam(id = "maxdepth", lower = 0L, default = 0L),
makeLogicalLearnerParam(id = "savesplitstats", default = FALSE, tunable = FALSE)
),
properties = c("twoclass", "multiclass", "prob", "factors", "numerics", "ordered", "weights", "missings", "featimp"),
par.vals = list(),
name = "Random forest based on conditional inference trees",
short.name = "cforest",
note = "See `?ctree_control` for possible breakage for nominal features with missingness.",
callees = c("cforest", "cforest_control", "ctree_control")
)
}
#' @export
trainLearner.classif.cforest = function(.learner, .task, .subset,
.weights = NULL, ntree, mtry, replace, fraction, trace, teststat,
testtype, mincriterion, minsplit, minbucket, stump,
nresample, maxsurrogate, maxdepth, savesplitstats, ...) {
f = getTaskFormula(.task)
d = getTaskData(.task, .subset)
# default handling necessary because the default of controls is `cforest_unbiased()` which does not allow all parameters (e.g. replace)
defaults = getDefaults(getParamSet(.learner))
if (missing(teststat)) teststat = defaults$teststat
if (missing(testtype)) testtype = defaults$testtype
if (missing(mincriterion)) mincriterion = defaults$mincriterion
if (missing(replace)) replace = defaults$replace
if (missing(fraction)) fraction = defaults$fraction
ctrl = learnerArgsToControl(party::cforest_control, ntree, mtry, replace,
fraction, trace, teststat, testtype, mincriterion,
minsplit, minbucket, stump, nresample, maxsurrogate,
maxdepth, savesplitstats)
party::cforest(f, data = d, controls = ctrl, weights = .weights, ...)
}
#' @export
predictLearner.classif.cforest = function(.learner, .model, .newdata, ...) {
if (.learner$predict.type == "prob") {
p = predict(.model$learner.model, newdata = .newdata, type = "prob", ...)
# FIXME: this will break for nrow(.newdata) == 1? do not use sapply!
p = t(sapply(p, "["))
colnames(p) = .model$task.desc$class.levels
} else {
p = predict(.model$learner.model, newdata = .newdata, ...)
}
p
}
#' @export
getFeatureImportanceLearner.classif.cforest = function(.learner, .model, auc = FALSE, ...) {
mod = getLearnerModel(.model, more.unwrap = TRUE)
if (auc) {
party::varimpAUC(mod, ...)
} else {
party::varimp(mod, ...)
}
}
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