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#' @title Cross-Validation Resampling
#'
#' @name mlr_resamplings_cv
#' @include Resampling.R
#'
#' @description
#' Splits data using a `folds`-folds (default: 10 folds) cross-validation.
#'
#' @templateVar id cv
#' @template resampling
#'
#' @section Parameters:
#' * `folds` (`integer(1)`)\cr
#' Number of folds.
#'
#' @references
#' `r format_bib("bischl_2012")`
#'
#' @template seealso_resampling
#' @export
#' @examples
#' # Create a task with 10 observations
#' task = tsk("penguins")
#' task$filter(1:10)
#'
#' # Instantiate Resampling
#' cv = rsmp("cv", folds = 3)
#' cv$instantiate(task)
#'
#' # Individual sets:
#' cv$train_set(1)
#' cv$test_set(1)
#'
#' # Disjunct sets:
#' intersect(cv$train_set(1), cv$test_set(1))
#'
#' # Internal storage:
#' cv$instance # table
ResamplingCV = R6Class("ResamplingCV", inherit = Resampling,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
folds = p_int(2L, tags = "required")
)
ps$values = list(folds = 10L)
super$initialize(id = "cv", param_set = ps,
label = "Cross-Validation", man = "mlr3::mlr_resamplings_cv")
}
),
active = list(
#' @template field_iters
iters = function(rhs) {
assert_ro_binding(rhs)
as.integer(self$param_set$values$folds)
}
),
private = list(
.sample = function(ids, ...) {
data.table(
row_id = ids,
fold = shuffle(seq_along0(ids) %% as.integer(self$param_set$values$folds) + 1L),
key = "fold"
)
},
.get_train = function(i) {
self$instance[!list(i), "row_id", on = "fold"][[1L]]
},
.get_test = function(i) {
self$instance[list(i), "row_id", on = "fold"][[1L]]
},
.combine = function(instances) {
rbindlist(instances, use.names = TRUE)
},
deep_clone = function(name, value) {
switch(name,
"instance" = copy(value),
"param_set" = value$clone(deep = TRUE),
value
)
}
)
)
#' @include mlr_resamplings.R
mlr_resamplings$add("cv", function() ResamplingCV$new())
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