Resampling Class

This is the abstract base class for resampling objects like ResamplingCV and ResamplingBootstrap.

The objects of this class define how a task is partitioned for resampling (e.g., in resample() or benchmark()), using a set of hyperparameters such as the number of folds in cross-validation.

Resampling objects can be instantiated on a Task, which applies the strategy on the task and manifests in a fixed partition of row_ids of the Task.

Predefined resamplings are stored in the dictionary mlr_resamplings, e.g. cv or bootstrap.

Stratification

All derived classes support stratified sampling. The stratification variables are assumed to be discrete and must be stored in the Task with column role "stratum". In case of multiple stratification variables, each combination of the values of the stratification variables forms a strata.

First, the observations are divided into subpopulations based one or multiple stratification variables (assumed to be discrete), c.f. task$strata.

Second, the sampling is performed in each of the k subpopulations separately. Each subgroup is divided into iter training sets and iter test sets by the derived Resampling. These sets are merged based on their iteration number: all training sets from all subpopulations with iteration 1 are combined, then all training sets with iteration 2, and so on. Same is done for all test sets. The merged sets can be accessed via $train_set(i) and $test_set(i), respectively.

Grouping / Blocking

All derived classes support grouping of observations. The grouping variable is assumed to be discrete and must be stored in the Task with column role "group".

Observations in the same group are treated like a "block" of observations which must be kept together. These observations either all go together into the training set or together into the test set.

The sampling is performed by the derived Resampling on the grouping variable. Next, the grouping information is replaced with the respective row ids to generate training and test sets. The sets can be accessed via $train_set(i) and $test_set(i), respectively.

See also

Dictionary of Resamplings: mlr_resamplings

as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.

Other Resampling: mlr_resamplings_bootstrap, mlr_resamplings_custom, mlr_resamplings_cv, mlr_resamplings_holdout, mlr_resamplings_insample, mlr_resamplings_loo, mlr_resamplings_repeated_cv, mlr_resamplings_subsampling, mlr_resamplings

Other Resampling: mlr_resamplings_bootstrap, mlr_resamplings_custom, mlr_resamplings_cv, mlr_resamplings_holdout, mlr_resamplings_insample, mlr_resamplings_loo, mlr_resamplings_repeated_cv, mlr_resamplings_subsampling, mlr_resamplings

Public fields

id

(character(1))
Identifier of the object. Used in tables, plot and text output.

param_set

(paradox::ParamSet)
Set of hyperparameters.

instance

(any)
During instantiate(), the instance is stored in this slot in an arbitrary format. Note that if a grouping variable is present in the Task, a Resampling may operate on the group ids internally instead of the row ids (which may lead to confusion).It is advised to not work directly with the instance, but instead only use the getters $train_set() and $test_set().

task_hash

(character(1))
The hash of the Task which was passed to r$instantiate().

task_nrow

(integer(1))
The number of observations of the Task which was passed to r$instantiate().

duplicated_ids

(logical(1))
If TRUE, duplicated rows can occur within a single training set or within a single test set. E.g., this is TRUE for Bootstrap, and FALSE for cross validation. Only used internally.

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. Defaults to NA, but can be set by child classes.

Active bindings

is_instantiated

(logical(1))
Is TRUE if the resampling has been instantiated.

hash

(character(1))
Hash (unique identifier) for this object.

Methods

Public methods

• Resampling$new()

• Resampling$format()

• Resampling$print()

• Resampling$help()

• Resampling$instantiate()

• Resampling$train_set()

• Resampling$test_set()

• Resampling$clone()

---

Method new()

Creates a new instance of this R6 class.

Usage

Resampling$new(
  id,
  param_set = ParamSet$new(),
  duplicated_ids = FALSE,
  man = NA_character_
)

Arguments

id

(character(1))
Identifier for the new instance.

param_set

(paradox::ParamSet)
Set of hyperparameters.

duplicated_ids

(logical(1))
Set to TRUE if this resampling strategy may have duplicated row ids in a single training set or test set.Note that this object is typically constructed via a derived classes, e.g. ResamplingCV or ResamplingHoldout.

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().

---

Method format()

Helper for print outputs.

Usage

Resampling$format()

---

Method print()

Printer.

Usage

Resampling$print(...)

Arguments

...

(ignored).

---

Method help()

Opens the corresponding help page referenced by field $man.

Usage

Resampling$help()

---

Method instantiate()

Materializes fixed training and test splits for a given task and stores them in r$instance in an arbitrary format.

Usage

Resampling$instantiate(task)

Arguments

task

(Task)
Task used for instantiation.

Returns

Returns the object itself, but modified by reference. You need to explicitly $clone() the object beforehand if you want to keeps the object in its previous state.

---

Method train_set()

Returns the row ids of the i-th training set.

Usage

Resampling$train_set(i)

Arguments

i

(integer(1))
Iteration.

Returns

(integer()) of row ids.

---

Method test_set()

Returns the row ids of the i-th test set.

Usage

Resampling$test_set(i)

Arguments

i

(integer(1))
Iteration.

Returns

(integer()) of row ids.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Resampling$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

r = rsmp("subsampling")

# Default parametrization
r$param_set$values
#> $repeats
#> [1] 30
#> 
#> $ratio
#> [1] 0.6666667
#> 

# Do only 3 repeats on 10% of the data
r$param_set$values = list(ratio = 0.1, repeats = 3)
r$param_set$values
#> $ratio
#> [1] 0.1
#> 
#> $repeats
#> [1] 3
#> 

# Instantiate on iris task
task = tsk("iris")
r$instantiate(task)

# Extract train/test sets
train_set = r$train_set(1)
print(train_set)
#>  [1]  88 150 122  40 133  89 118 112 119  13  94  83 103  77  14
intersect(train_set, r$test_set(1))
#> integer(0)

# Another example: 10-fold CV
r = rsmp("cv")$instantiate(task)
r$train_set(1)
#>   [1]  11  24  40  41  61  62  73  75  76  82  91 109 119 131 142  18  19  64
#>  [19]  71  79  84  95 105 116 124 126 127 130 146 148   6   9  10  12  33  38
#>  [37]  55  59  87  89 115 123 134 136 144  23  47  52  56  66  80  86  96 100
#>  [55] 106 112 122 129 133 150   3   8  17  34  44  45  63  65  70 102 110 135
#>  [73] 138 139 145   4  15  25  26  31  50  57  58  85  94 101 111 114 125 128
#>  [91]  28  37  42  51  72  77  81  92 104 107 117 118 120 137 143   1  14  16
#> [109]  30  36  39  43  53  54  67  69  90  97 141 147   7  13  21  27  35  46
#> [127]  49  74  83 108 113 121 132 140 149

# Stratification
task = tsk("pima")
prop.table(table(task$truth())) # moderately unbalanced
#> 
#>       pos       neg 
#> 0.3489583 0.6510417 
task$col_roles$stratum = task$target_names

r = rsmp("subsampling")
r$instantiate(task)
prop.table(table(task$truth(r$train_set(1)))) # roughly same proportion
#> 
#>       pos       neg 
#> 0.3496094 0.6503906