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
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
#' @title Result of multi-criteria tuning.
#'
#' @description
#' Container for results of hyperparameter tuning.
#' Contains the obtained pareto set and front
#' and the optimization path which lead there.
#'
#' Object members:
#' \describe{
#' \item{learner ([Learner])}{Learner that was optimized.}
#' \item{control ([TuneControl])}{Control object from tuning.}
#' \item{x ([list])}{List of lists of non-dominated hyperparameter settings in pareto set.
#' Note that when you have trafos on some of your params, `x` will always be
#' on the TRANSFORMED scale so you directly use it.}
#' \item{y ([matrix])}{Pareto front for `x`.}
#' \item{threshold}{Currently `NULL`.}
#' \item{opt.path ([ParamHelpers::OptPath])}{Optimization path which lead to `x`.
#' Note that when you have trafos on some of your params, the opt.path always contains the
#' UNTRANSFORMED values on the original scale. You can simply call `trafoOptPath(opt.path)` to
#' transform them, or, \code{as.data.frame{trafoOptPath(opt.path)}}}
#' \item{ind (`integer(n)`)}{Indices of Pareto optimal params in `opt.path`.}
#' \item{measures [(list of) [Measure])}{Performance measures.}
#' }
#' @name TuneMultiCritResult
#' @rdname TuneMultiCritResult
NULL
makeTuneMultiCritResult = function(learner, ind, x, y, resampling, control, opt.path, measures, ...) {
# set threshold to NULL, we can not currently tune for it in an MCO way
or = makeOptResult(learner, control, x, y, resampling, NULL, opt.path, "TuneMultiCritResult", ...)
or$ind = ind
or$measures = measures
return(or)
}
makeTuneMultiCritResultFromOptPath = function(learner, par.set, measures, resampling, control, opt.path) {
j = getOptPathParetoFront(opt.path, index = TRUE)
els = lapply(j, getOptPathEl, op = opt.path)
xs = extractSubList(els, "x", simplify = FALSE)
xs = lapply(xs, trafoValue, par = par.set)
xs = lapply(xs, removeMissingValues)
ys = extractSubList(els, "y", simplify = "rows")
colnames(ys) = opt.path$y.names
makeTuneMultiCritResult(learner, j, xs, ys, resampling, control, opt.path, measures)
}
#' @export
print.TuneMultiCritResult = function(x, ...) {
catf("Tune multicrit result:")
catf("Points on front: %i", length(x$x))
}
|
