Get the resampling indices from a tuning or feature selection wrapper..

Source: R/getResamplingIndices.R

After you resampled a tuning or feature selection wrapper (see makeTuneWrapper) with resample(..., extract = getTuneResult) or resample(..., extract = getFeatSelResult) this helper returns a list with the resampling indices used for the respective method.

getResamplingIndices(object, inner = FALSE)

Arguments

object	(ResampleResult) The result of resampling of a tuning or feature selection wrapper.
inner	(logical) If TRUE, returns the inner indices of a nested resampling setting.

Value

(list). One list for each outer resampling fold.

See also

Other tune: TuneControl, getNestedTuneResultsOptPathDf, getNestedTuneResultsX, getTuneResult, makeModelMultiplexerParamSet, makeModelMultiplexer, makeTuneControlCMAES, makeTuneControlDesign, makeTuneControlGenSA, makeTuneControlGrid, makeTuneControlIrace, makeTuneControlMBO, makeTuneControlRandom, makeTuneWrapper, tuneParams, tuneThreshold

Examples

task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.rpart")
# stupid mini grid
ps = makeParamSet(
  makeDiscreteParam("cp", values = c(0.05, 0.1)),
  makeDiscreteParam("minsplit", values = c(10, 20))
 )
ctrl = makeTuneControlGrid()
inner = makeResampleDesc("Holdout")
outer = makeResampleDesc("CV", iters = 2)
lrn = makeTuneWrapper(lrn, resampling = inner, par.set = ps, control = ctrl)
# nested resampling for evaluation
# we also extract tuned hyper pars in each iteration and by that the resampling indices
r = resample(lrn, task, outer, extract = getTuneResult)
# get tuning indices
getResamplingIndices(r, inner = TRUE)
#> [[1]]
#> [[1]]$train.inds
#> [[1]]$train.inds[[1]]
#>  [1] 22 59 30 64 67  4 37 61 69 31 63 73 43 36  7 54 15  3 19 60 49 38 34 52 53
#> [26] 62 27 29 14 68 44 40 72 51  2 20 65  9 12 56  5 74 47 71 35 66 46 33  8 23
#> 
#> 
#> [[1]]$test.inds
#> [[1]]$test.inds[[1]]
#>  [1] 25 18 50 48 42  1 17  6 11 58 39 55 41 10 57 13 70 21 45 32 28 24 26 16 75
#> 
#> 
#> 
#> [[2]]
#> [[2]]$train.inds
#> [[2]]$train.inds[[1]]
#>  [1] 22 59 30 64 67  4 37 61 69 31 63 73 43 36  7 54 15  3 19 60 49 38 34 52 53
#> [26] 62 27 29 14 68 44 40 72 51  2 20 65  9 12 56  5 74 47 71 35 66 46 33  8 23
#> 
#> 
#> [[2]]$test.inds
#> [[2]]$test.inds[[1]]
#>  [1] 25 18 50 48 42  1 17  6 11 58 39 55 41 10 57 13 70 21 45 32 28 24 26 16 75
#> 
#> 
#>