1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
|
#' @export
makeRLearner.classif.knn = function() {
makeRLearnerClassif(
cl = "classif.knn",
package = "class",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "k", default = 1L, lower = 1L),
makeNumericLearnerParam(id = "l", default = 0L, lower = 0L),
makeLogicalLearnerParam(id = "prob", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "use.all", default = TRUE)
),
# knn cannot really return probs, only for the winning class (yeah well done BR)
# knn also cannot handle factors in features apparantly
properties = c("twoclass", "multiclass", "numerics"),
name = "k-Nearest Neighbor",
short.name = "knn",
callees = "knn"
)
}
#' @export
trainLearner.classif.knn = function(.learner, .task, .subset, .weights = NULL, ...) {
z = getTaskData(.task, .subset, target.extra = TRUE)
c(list(train = z$data, cl = z$target), list(...))
}
#' @export
predictLearner.classif.knn = function(.learner, .model, .newdata, ...) {
args = .model$learner.model
args$test = .newdata
do.call(class::knn, args)
}
|
