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#' @export
makeRLearner.surv.CoxBoost = function() {
makeRLearnerSurv(
cl = "surv.CoxBoost",
package = "!CoxBoost",
par.set = makeParamSet(
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),
makeIntegerLearnerParam(id = "stepno", default = 100L, lower = 1),
makeLogicalLearnerParam(id = "return.score", default = TRUE, tunable = FALSE),
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE)
),
par.vals = list(return.score = FALSE),
properties = c("numerics", "factors", "ordered", "weights"),
name = "Cox Proportional Hazards Model with Componentwise Likelihood based Boosting",
short.name = "coxboost",
note = "Factors automatically get converted to dummy columns, ordered factors to integer.",
callees = "CoxBoost"
)
}
#' @export
trainLearner.surv.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$data, factors.to.dummies = TRUE, ordered.to.int = TRUE)
data$data = as.matrix(fixDataForLearner(data$data, info))
if (is.null(penalty))
penalty = 9 * sum(data$target[, 2L])
attachTrainingInfo(CoxBoost::CoxBoost(
time = data$target[, 1L],
status = data$target[, 2L],
x = data$data,
weights = .weights,
penalty = penalty,
...
), info)
}
#' @export
predictLearner.surv.CoxBoost = function(.learner, .model, .newdata, ...) {
info = getTrainingInfo(.model)
.newdata = as.matrix(fixDataForLearner(.newdata, info))
as.numeric(predict(.model$learner.model, newdata = .newdata, type = "lp"))
}
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