https://github.com/berndbischl/mlr
Tip revision: 3d7e0aa91936e82cf108aff3c46b19e3f953eefd authored by pat-s on 10 January 2020, 22:23:02 UTC
Bump version to 2.17.0.9000
Bump version to 2.17.0.9000
Tip revision: 3d7e0aa
RLearner_regr_rknn.R
#' @export
makeRLearner.regr.rknn = function() {
makeRLearnerRegr(
cl = "regr.rknn",
package = "rknn",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "k", default = 1L, lower = 1L),
makeIntegerLearnerParam(id = "r", default = 500L, lower = 1L),
makeIntegerLearnerParam(id = "mtry", lower = 1L),
makeIntegerLearnerParam(id = "seed", default = 2015L, lower = 1L),
makeUntypedLearnerParam(id = "cluster", default = NULL)
),
# rknn can't handle factors or return probs
properties = c("numerics", "ordered"),
name = "Random k-Nearest-Neighbors",
short.name = "rknn",
note = "",
callees = "rknnReg"
)
}
#' @export
trainLearner.regr.rknn = function(.learner, .task, .subset, .weights = NULL, ...) {
z = getTaskData(.task, .subset, target.extra = TRUE)
c(list(data = z$data, y = z$target), list(...))
}
#' @export
predictLearner.regr.rknn = function(.learner, .model, .newdata, ...) {
args = .model$learner.model
args$newdata = .newdata
do.call(rknn::rknnReg, args)$pred
}