#' @export
makeRLearner.classif.rotationForest = function() {
makeRLearnerClassif(
cl = "classif.rotationForest",
package = "rotationForest",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "K", default = 3L, lower = 1L),
makeIntegerLearnerParam(id = "L", default = 10L, lower = 1L)
),
properties = c("twoclass", "numerics", "factors", "ordered", "prob"),
name = "Rotation Forest",
short.name = "rotationForest",
callees = "rotationForest"
)
}
#' @export
trainLearner.classif.rotationForest = function(.learner, .task, .subset, .weights = NULL, ...) {
df = getTaskData(.task, .subset, target.extra = TRUE)
features = df$data
#rotationForest needs 0-1 coding
target = as.factor(ifelse(df$target == .task$task.desc$positive, 1L, 0L))
rotationForest::rotationForest(x = features, y = target, ...)
}
#' @export
predictLearner.classif.rotationForest = function(.learner, .model, .newdata, ...) {
features = .newdata[, names(.newdata) == .model$features]
p = predict(.model$learner.model, newdata = features, all = FALSE, ...)
if (.learner$predict.type == "prob"){
levs = c(.model$task.desc$positive, .model$task.desc$negative)
propVectorToMatrix(1 - p, levs)
}else{
as.factor(ifelse(p > 0.5, .model$task.desc$positive, .model$task.desc$negative))
}
}
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