https://github.com/berndbischl/mlr
Tip revision: c10008ef66e9d822126f9136ce98fc83c8494e8b authored by pat-s on 30 September 2020, 20:10:13 UTC
http -> https
http -> https
Tip revision: c10008e
RLearner_surv_cforest.R
#' @export
makeRLearner.surv.cforest = function() {
makeRLearnerSurv(
cl = "surv.cforest",
package = c("party", "survival"),
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ntree", lower = 1L, default = 500L),
makeIntegerLearnerParam(id = "mtry", lower = 1L, default = 5L),
makeLogicalLearnerParam(id = "replace", default = FALSE),
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE),
makeNumericLearnerParam(id = "fraction", lower = 0, upper = 1, default = 0.632,
requires = quote(replace == FALSE)),
makeDiscreteLearnerParam(id = "teststat", values = c("quad", "max"), default = "quad"),
makeDiscreteLearnerParam(id = "testtype", default = "Univariate",
values = c("Bonferroni", "MonteCarlo", "Univariate", "Teststatistic")),
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("factors", "numerics", "ordered", "weights", "missings", "featimp"),
par.vals = list(),
name = "Random Forest based on Conditional Inference Trees",
short.name = "crf",
note = "See `?ctree_control` for possible breakage for nominal features with missingness.",
callees = c("cforest", "cforest_control", "ctree_control")
)
}
#' @export
trainLearner.surv.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)
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$fraction
ctrl = learnerArgsToControl(party::cforest_control, ntree, mtry, replace,
fraction, trace, teststat, testtype, mincriterion,
minsplit, minbucket, stump, nresample, maxsurrogate,
maxdepth, savesplitstats)
party::cforest(f, data = d, controls = ctrl, weights = .weights, ...)
}
#' @export
predictLearner.surv.cforest = function(.learner, .model, .newdata, ...) {
# cforest returns median survival times; multiply by -1 so that high values correspond to high risk
-1 * predict(.model$learner.model, newdata = .newdata, type = "response", ...)
}
#' @export
getFeatureImportanceLearner.surv.cforest = function(.learner, .model, ...) {
getFeatureImportanceLearner.classif.cforest(.learner, .model, ...)
}