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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. Note that this procedure can lead to set sizes that are slightly different from those without stratification.

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

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

Public fields

label

(character(1))
Label for this object. Can be used in tables, plot and text output instead of the ID.

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

id

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

is_instantiated

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

hash

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

Methods


Method new()

Creates a new instance of this R6 class.

Usage

Resampling$new(
  id,
  param_set = ps(),
  duplicated_ids = FALSE,
  label = NA_character_,
  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.

label

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

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(...)

Arguments

...

(ignored).


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
#> $ratio
#> [1] 0.6666667
#> 
#> $repeats
#> [1] 30
#> 

# 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 penguins task
task = tsk("penguins")
r$instantiate(task)

# Extract train/test sets
train_set = r$train_set(1)
print(train_set)
#>  [1]  42  75  33 106  32 249  85 300 190  96  40  79 224 199 270 304  89 259 279
#> [20] 343 317 123 318 195 255 188  49 284 205 337 233 307  31 230
intersect(train_set, r$test_set(1))
#> integer(0)

# Another example: 10-fold CV
r = rsmp("cv")$instantiate(task)
r$train_set(1)
#>   [1]  10  12  15  22  79  97 104 126 133 135 136 137 139 141 159 166 172 183
#>  [19] 184 186 188 225 228 237 250 253 264 271 275 279 298 312 326 329 340   5
#>  [37]   6  20  28  42  47  58  72  80  81  82 107 117 119 123 130 164 168 169
#>  [55] 185 191 197 209 215 227 234 244 249 266 270 276 280 300 311 316   4   7
#>  [73]  16  33  48  51  52  54  59  67  70  88 103 121 138 155 173 176 179 196
#>  [91] 201 204 239 261 267 284 290 299 304 305 308 328 330 337 339  19  34  35
#> [109]  44  77  84  85  90  92  93 109 112 116 124 145 158 162 200 203 206 207
#> [127] 212 213 221 226 241 245 274 277 287 289 301 310 322   2  23  24  32  41
#> [145]  61  71  74  83 101 105 106 128 143 144 149 153 154 193 195 218 220 230
#> [163] 247 248 257 268 272 286 303 313 314 324 332  18  25  31  40  50  62  65
#> [181]  69  96 114 122 125 131 140 156 165 174 175 194 217 251 258 263 269 281
#> [199] 285 288 294 297 327 331 333 334 343  17  27  29  56  57  89  94  98 100
#> [217] 102 108 120 146 147 170 181 182 205 211 216 219 222 223 231 256 265 282
#> [235] 291 295 309 319 341 342 344  14  21  30  38  39  45  53  60  66  73  75
#> [253]  95 111 132 163 171 199 202 232 235 236 242 252 254 262 283 293 296 302
#> [271] 307 318 321 323 325   3   8   9  36  46  49  63  64  76  78 110 113 129
#> [289] 134 142 148 157 160 161 180 187 189 192 224 229 240 243 255 278 306 317
#> [307] 320 336 338

# 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