#' @export
makeRLearner.classif.obliqueRF = function() {
makeRLearnerClassif(
cl = "classif.obliqueRF",
package = "!obliqueRF",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ntree", default = 100L, lower = 1L),
makeIntegerLearnerParam(id = "mtry", lower = 1L),
makeDiscreteLearnerParam(id = "training_method", default = "ridge",
values = c("ridge", "ridge_slow", "pls", "svm", "log", "rnd"))
),
properties = c("twoclass", "numerics", "factors", "ordered", "prob"),
name = "Oblique Random Forest",
short.name = "obliqueRF",
note = ""
)
}
#' @export
trainLearner.classif.obliqueRF = function(.learner, .task, .subset, .weights = NULL, ...) {
df = getTaskData(.task, .subset, target.extra = TRUE)
features = as.matrix(df$data)
target = ifelse(df$target == .task$task.desc$positive, 1, 0)
obliqueRF::obliqueRF(x = features, y = target, bImportance = FALSE,
bProximity = FALSE, verbose = FALSE, ...)
}
#' @export
predictLearner.classif.obliqueRF = function(.learner, .model, .newdata, ...) {
features = .newdata[, .model$features]
features = data.frame(features)
p = predict(.model$learner.model, newdata = features, type = .learner$predict.type, proximity = FALSE, ...)
if(.learner$predict.type == "prob"){
colnames(p) = c(.model$task.desc$negative, .model$task.desc$positive)
}else{
p = as.factor(p)
p = as.factor(ifelse(p == 1L, .model$task.desc$positive, .model$task.desc$negative))
}
return(p)
}
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