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#' @export
makeRLearner.regr.penalized = function() {
makeRLearnerRegr(
cl = "regr.penalized",
package = "!penalized",
par.set = makeParamSet(
makeNumericLearnerParam(id = "lambda1", default = 0, lower = 0),
makeNumericLearnerParam(id = "lambda2", default = 0, lower = 0),
makeLogicalLearnerParam(id = "fusedl", default = FALSE),
makeUntypedLearnerParam(id = "unpenalized", tunable = FALSE),
makeLogicalVectorLearnerParam(id = "positive", default = FALSE),
makeDiscreteLearnerParam(id = "model", default = "linear",
values = c("linear", "poisson")),
makeNumericVectorLearnerParam(id = "startbeta"),
makeNumericVectorLearnerParam(id = "startgamma"),
makeNumericLearnerParam(id = "epsilon", lower = 0, default = 1e-10),
# untyped here because one can also pass "Park" to steps
makeUntypedLearnerParam(id = "steps", default = 1L, tunable = FALSE),
# FIXME: Parameter dependent default for maxiter:
# default is 25 if lambda1 = 0, lambda2 > 0, infinite otherwise
makeIntegerLearnerParam(id = "maxiter", lower = 1L),
makeLogicalLearnerParam(id = "standardize", default = FALSE),
makeLogicalLearnerParam(id = "trace", default = TRUE, tunable = FALSE)
),
par.vals = list(),
properties = c("numerics", "factors"),
name = "Penalized Regression",
short.name = "penalized",
note = "trace=FALSE was set by default to disable logging output.",
callees = "penalized"
)
}
#' @export
trainLearner.regr.penalized = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
penalized::penalized(f, data = getTaskData(.task, .subset), ...)
}
#' @export
predictLearner.regr.penalized = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
# FIXME: should be removed, reported in issue 840
m@formula$unpenalized[[2L]] = as.symbol(.model$task.desc$target)
.newdata[, .model$task.desc$target] = 0
penalized::predict(m, data = .newdata, ...)[, "mu"]
}
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