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#' @export
makeRLearner.classif.kknn = function() {
makeRLearnerClassif(
cl = "classif.kknn",
# FIXME: kknn set its own contr.dummy function, if we requireNamespace,
# this is not found, see issue 226
package = "!kknn",
# FIXME: find out what ykernel and contrasts really do
par.set = makeParamSet(
makeIntegerLearnerParam(id = "k", default = 7L, lower = 1L),
makeNumericLearnerParam(id = "distance", default = 2, lower = 0),
makeDiscreteLearnerParam(id = "kernel", default = "optimal",
values = list("rectangular", "triangular", "epanechnikov", "biweight",
"triweight", "cos", "inv", "gaussian", "optimal")),
makeLogicalLearnerParam(id = "scale", default = TRUE)
),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob"),
name = "k-Nearest Neighbor",
short.name = "kknn",
callees = "kknn"
)
}
#' @export
trainLearner.classif.kknn = function(.learner, .task, .subset, .weights = NULL, ...) {
list(td = getTaskDesc(.task), data = getTaskData(.task, .subset), parset = list(...))
}
#' @export
predictLearner.classif.kknn = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
f = getTaskFormula(.model$task.desc)
pars = list(formula = f, train = m$data, test = .newdata)
pars = c(pars, m$parset, list(...))
m = do.call(kknn::kknn, pars)
if (.learner$predict.type == "response") {
return(m$fitted.values)
} else {
return(m$prob)
}
}
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