#' @export
makeRLearner.classif.LiblineaRL2LogReg = function() {
makeRLearnerClassif(
cl = "classif.LiblineaRL2LogReg",
package = "LiblineaR",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "type", default = 0L, values = c(0L, 7L)),
makeNumericLearnerParam(id = "cost", default = 1, lower = 0),
# FIXME: Add default value when parameter dependent defaults are implemented:
## if type = 0: eps default = 0.01, if type = 7: eps default = 0.1
makeNumericLearnerParam(id = "epsilon", lower = 0),
makeLogicalLearnerParam(id = "bias", default = TRUE),
makeNumericVectorLearnerParam(id = "wi", len = NA_integer_),
makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE),
makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)
),
par.vals = list(type = 0L),
# FIXME default in LiblieaR() for type is 0, par.vals is redundant here.
properties = c("twoclass", "multiclass", "numerics", "class.weights", "prob"),
class.weights.param = "wi",
name = "L2-Regularized Logistic Regression",
short.name = "liblinl2logreg",
note = "`type = 0` (the default) is primal and `type = 7` is dual problem.",
callees = "LiblineaR"
)
}
#' @export
trainLearner.classif.LiblineaRL2LogReg = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
LiblineaR::LiblineaR(data = d$data, target = d$target, ...)
}
#' @export
predictLearner.classif.LiblineaRL2LogReg = function(.learner, .model, .newdata, ...) {
if (.learner$predict.type == "response") {
as.factor(predict(.model$learner.model, newx = .newdata, ...)$predictions)
} else {
predict(.model$learner.model, newx = .newdata, proba = TRUE, ...)$probabilities
}
}
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