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.plsdaCaret = function() {
makeRLearnerClassif(cl = "classif.plsdaCaret",
package = c("caret", "pls"),
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ncomp", default = 2, lower = 1),
makeDiscreteLearnerParam(id = "probMethod", values = c("softmax", "Bayes"), default = "softmax"),
makeDiscreteLearnerParam(id = "method", default = "kernelpls",
values = c("kernelpls", "widekernelpls", "simpls", "oscorespls"))
),
properties = c("numerics", "prob", "twoclass", "multiclass"),
name = "Partial Least Squares (PLS) Discriminant Analysis",
short.name = "plsdacaret",
callees = c("plsda", "plsr")
)
}
#' @export
trainLearner.classif.plsdaCaret = function(.learner, .task, .subset, .weights, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
caret::plsda(d$data, d$target, ...)
}
#' @export
predictLearner.classif.plsdaCaret = function(.learner, .model, .newdata, ...) {
type = ifelse(.learner$predict.type == "response", "class", "prob")
p = predict(.model$learner.model, newdata = .newdata, type = type, ...)
if (type == "prob") {
p = array(c(p), dim(p)[-3], dimnames = dimnames(p)[1:2])
}
return(p)
}
|
