3.5 Resampling

3.5.1 Settings

In this example we use the iris task and a simple classification tree (package rpart).

task = mlr_tasks$get("iris")
learner = mlr_learners$get("classif.rpart")

When performing resampling with a dataset, we first need to define which approach should be used. The resampling strategies of mlr3 can be queried using the .$keys() method of the mlr_resamplings dictionary.

mlr_resamplings
## <DictionaryResampling> with 7 stored values
## Keys: bootstrap, custom, cv, cv3, holdout, repeated_cv, subsampling

Additional resampling methods for special use cases will be available via extension packages, such as mlr3survival for survival analysis or mlr3spatiotemporal for spatial data (still in development).

The model fit conducted in the train/predict/score chapter is equivalent to a “holdout”, so let’s consider this one first.

resampling = mlr_resamplings$get("holdout")
print(resampling)
## <ResamplingHoldout> with 1 iterations
## Instantiated: FALSE
## Parameters: ratio=0.6667

Note that the Instantianated field is set to FALSE. This means we did not actually apply the strategy on a dataset yet but just performed a dry-run. Applying the strategy on a dataset is done in section next Instantation.

By default we get a .66/.33 split of the data. There are two ways how the ratio can be changed:

1. Overwriting the slot in .$param_set$values using a named list.

resampling$param_set$values = list(ratio = 0.8)

2. Specifying the resampling parameters directly during construction using the param_vals argument:

mlr_resamplings$get("holdout", param_vals = list(ratio = 0.8))
## <ResamplingHoldout> with 1 iterations
## Instantiated: FALSE
## Parameters: ratio=0.8

3.5.2 Instantiation

So far we just set the stage and selected the resampling strategy. To actually perform the splitting, we need to apply the settings on a dataset. This can be done in two ways:

1. Manually by calling the method .$instantiate() on a Task

resampling = mlr_resamplings$get("cv", param_vals = list(folds = 3L))
resampling$instantiate(task)
resampling$iters
## [1] 3
resampling$train_set(1)
##   [1]   3   5  10  11  14  16  17  21  25  28  32  37  42  44  45  46  54  58
##  [19]  61  63  64  67  73  76  79  80  82  89  90  94  95  97  98 102 107 108
##  [37] 109 114 115 117 122 126 127 133 139 142 144 147 148 150   1   2   4   6
##  [55]   8   9  15  20  24  26  27  29  31  34  39  40  47  49  50  52  55  56
##  [73]  59  60  66  68  69  70  72  78  86  88  91 100 101 112 113 118 123 124
##  [91] 125 128 129 130 131 138 140 143 145 149

2. Automatically by passing the resampling object to resample(). Here, the splitting is done within the resample() call based on the supplied Task.

learner1 = mlr_learners$get("classif.rpart") # simple classification tree
learner2 = mlr_learners$get("classif.featureless") # featureless learner, prediction majority class
rr1 = resample(task, learner1, resampling)
rr2 = resample(task, learner2, resampling)

setequal(rr1$resampling$train_set(1), rr2$resampling$train_set(1))
## [1] TRUE

If you want to compare multiple learners, you should use the same resampling per task to reduce the variance of the performance estimation (method 1). If you use method 2 (and do not instantiate manually before), the resampling splits will differ between both runs.

If you aim is to compare different Task, Learner or Resampling, you are better off using the benchmark() function. It is basically a wrapper around resample() simplifying the handling of multiple settings.

If you discover this only after you’ve run multiple resample() calls, don’t worry - you can transform multiple single ResampleResult objects into a BenchmarkResult (explained later) using the .$combine() method.

3.5.3 Execution

With a Task, a Learner and Resampling object we can call resample() and create a ResampleResult object.

rr = resample(task, learner, resampling)
print(rr)
## <ResampleResult> of 3 iterations
## Task: iris
## Learner: classif.rpart

Before we go into more detail, let’s change the resampling to a “3-fold cross-validation” to better illustrate what operations are possible with a ResampleResult. Additionally, we tell resample() to keep the fitted models via a control object (see mlr_control()):

resampling = mlr_resamplings$get("cv", param_vals = list(folds = 3L))
ctrl = mlr_control(store_models = TRUE)
rr = resample(task, learner, resampling, ctrl = ctrl)
print(rr)
## <ResampleResult> of 3 iterations
## Task: iris
## Learner: classif.rpart

The following operations are supported with ResampleResult objects:

• Extract the performance for the individual resampling iterations:

rr$performance("classif.ce")
##             task task_id               learner    learner_id     resampling
## 1: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
## 2: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
## 3: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
##    resampling_id iteration          prediction classif.ce
## 1:            cv         1 <PredictionClassif>       0.10
## 2:            cv         2 <PredictionClassif>       0.02
## 3:            cv         3 <PredictionClassif>       0.06

• Extract and inspect the resampling splits:

rr$resampling
## <ResamplingCV> with 3 iterations
## Instantiated: TRUE
## Parameters: folds=3
rr$resampling$iters
## [1] 3
rr$resampling$test_set(1)
##  [1]   2   9  11  14  17  19  25  27  30  36  37  38  42  43  46  48  51  52  54
## [20]  68  71  73  79  81  84  89  91  92  95  98 100 102 103 104 105 106 107 113
## [39] 120 122 127 129 135 138 139 140 142 143 146 148
rr$resampling$train_set(3)
##   [1]   2   9  11  14  17  19  25  27  30  36  37  38  42  43  46  48  51  52
##  [19]  54  68  71  73  79  81  84  89  91  92  95  98 100 102 103 104 105 106
##  [37] 107 113 120 122 127 129 135 138 139 140 142 143 146 148   1   6  12  13
##  [55]  18  20  24  26  28  29  31  32  33  34  44  45  47  49  53  55  58  59
##  [73]  65  69  72  74  76  82  83  85  86  90  94  96 101 109 110 111 112 116
##  [91] 117 118 119 121 125 128 133 136 137 141

• Retrieve the learner of a specific iteration and inspect it:

lrn = rr$learners[[1]]
lrn$model
## n= 100 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 100 65 versicolor (0.34000 0.35000 0.31000)  
##   2) Petal.Length< 2.45 34  0 setosa (1.00000 0.00000 0.00000) *
##   3) Petal.Length>=2.45 66 31 versicolor (0.00000 0.53030 0.46970)  
##     6) Petal.Length< 4.85 33  0 versicolor (0.00000 1.00000 0.00000) *
##     7) Petal.Length>=4.85 33  2 virginica (0.00000 0.06061 0.93939) *

3.5.4 Custom resampling

Sometimes it is necessary to perform resampling with custom splits. If you want to do that because you are coming from a specific modeling field, take a look first at the mlr3 extension packages to make sure your custom resampling method hasn’t been implemented already.

If your custom resampling method is widely used in your field, feel welcome to integrate it into one of the existing mlr3 extension packages or create your own one.

A manual resampling instance can be created using the "custom" template from the mlr_resamplings dictionary.

resampling = mlr_resamplings$get("custom")
resampling$instantiate(task,
  list(c(1:10, 51:60, 101:110)),
  list(c(11:20, 61:70, 111:120))
)
resampling$iters
## [1] 1
resampling$train_set(1)
##  [1]   1   2   3   4   5   6   7   8   9  10  51  52  53  54  55  56  57  58  59
## [20]  60 101 102 103 104 105 106 107 108 109 110
resampling$test_set(1)
##  [1]  11  12  13  14  15  16  17  18  19  20  61  62  63  64  65  66  67  68  69
## [20]  70 111 112 113 114 115 116 117 118 119 120