#' @export
makeRLearner.surv.cv.CoxBoost = function() {
makeRLearnerSurv(
cl = "surv.cv.CoxBoost",
package = "!CoxBoost",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "maxstepno", default = 100L, lower = 0L),
makeIntegerLearnerParam(id = "K", default = 10L, lower = 1L),
makeDiscreteLearnerParam(id = "type", default = "verweij", values = c("verweij", "naive")),
makeLogicalLearnerParam(id = "parallel", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "upload.x", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "multicore", default = FALSE, tunable = FALSE),
makeIntegerVectorLearnerParam(id = "unpen.index"),
makeLogicalLearnerParam(id = "standardize", default = TRUE),
makeNumericLearnerParam(id = "penalty", lower = 0),
makeDiscreteLearnerParam(id = "criterion", default = "pscore", values = c("pscore", "score", "hpscore", "hscore")),
makeNumericLearnerParam(id = "stepsize.factor", default = 1, lower = 0),
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE)
),
properties = c("numerics", "factors", "weights"),
name = "Cox Proportional Hazards Model with Componentwise Likelihood based Boosting, tuned for the optimal number of boosting steps",
short.name = "cv.CoxBoost",
note = "Factors automatically get converted to dummy columns, ordered factors to integer.",
callees = c("cv.CoxBoost", "CoxBoost")
)
}
#' @export
trainLearner.surv.cv.CoxBoost = function(.learner, .task, .subset, .weights = NULL, penalty = NULL, unpen.index = NULL, ...) {
data = getTaskData(.task, subset = .subset, target.extra = TRUE, recode.target = "surv")
info = getFixDataInfo(data$data, factors.to.dummies = TRUE, ordered.to.int = TRUE)
if (is.null(penalty))
penalty = 9 * sum(data$target[, 2L])
pars = c(list(
time = data$target[, 1L],
status = data$target[, 2L],
x = as.matrix(fixDataForLearner(data$data, info)),
penalty = penalty,
weights = .weights
), list(...))
rm(data)
res = do.call(CoxBoost::cv.CoxBoost, pars)
res$optimal.step
if (res$optimal.step == 0L)
warning("Could not determine the optimal step number in cv.CoxBoost")
pars = insert(pars, list(stepno = res$optimal.step))
pars$maxstepno = NULL
attachTrainingInfo(do.call(CoxBoost::CoxBoost, pars), info)
}
#' @export
predictLearner.surv.cv.CoxBoost = function(.learner, .model, .newdata, ...) {
info = getTrainingInfo(.model)
.newdata = as.matrix(fixDataForLearner(.newdata, info))
as.numeric(predict(.model$learner.model, newdata = .newdata, type = "lp"))
}
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