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@@ -100,11 +100,6 @@
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           subclass = c("vfold_cv", "rset"))
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}
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# Get the indices of the analysis set from the assessment set
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vfold_complement <- function(ind, n) {
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  list(analysis = setdiff(1:n, ind),
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       assessment = ind)
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}
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vfold_splits <- function(data, v = 10, strata = NULL, breaks = 4) {
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  if (!is.numeric(v) || length(v) != 1)
@@ -125,7 +120,7 @@
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    indices <- split_unnamed(stratas$idx, stratas$folds)
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  }
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  indices <- lapply(indices, vfold_complement, n = n)
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  indices <- lapply(indices, default_complement, n = n)
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  split_objs <- purrr::map(indices, make_splits, data = data, class = "vfold_split")
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  tibble::tibble(splits = split_objs,

@@ -1,10 +1,18 @@
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#' Determine the Assessment Samples
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#'
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#' Given an `rsplit` object, `complement` will determine which
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#' This method and function help find which data belong in the analysis and
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#' assessment sets.
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#'
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#' Given an `rsplit` object, `complement()` will determine which
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#'   of the data rows are contained in the assessment set. To save space,
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#'   many of the `rset` objects will not contain indices for the
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#'   assessment split.
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#'
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#' `rsplit_complement()` handles the determination for sets for most resampling
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#' methods. Unless the row indices for the assessment set are already
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#' determined, this function selects all of the rows that are not in the
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#' analysis set and returns those as the assessment set.
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#'
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#' @param x An `rsplit` object
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#' @param ... Not currently used
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#' @return A integer vector.
@@ -20,37 +28,29 @@
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  UseMethod("complement")
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#' @export
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complement.vfold_split <- function(x, ...) {
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#' @rdname complement
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rsplit_complement <- function(x, ...) {
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  if (!is_missing_out_id(x)) {
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    return(x$out_id)
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  } else {
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    setdiff(1:nrow(x$data), x$in_id)
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    (1:nrow(x$data))[-unique(x$in_id)]
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  }
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}
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#' @export
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complement.mc_split  <- complement.vfold_split
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complement.vfold_split <- rsplit_complement
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#' @export
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complement.val_split <- complement.vfold_split
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complement.mc_split  <- rsplit_complement
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#' @export
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complement.loo_split <- complement.vfold_split
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complement.val_split <- rsplit_complement
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#' @export
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complement.group_vfold_split <- complement.vfold_split
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complement.loo_split <- rsplit_complement
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#' @export
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complement.boot_split <- function(x, ...) {
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  if (!is_missing_out_id(x)) {
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    return(x$out_id)
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  } else {
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    (1:nrow(x$data))[-unique(x$in_id)]
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  }
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}
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complement.group_vfold_split <- rsplit_complement
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#' @export
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complement.perm_split <- function(x, ...) {
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  if (!is_missing_out_id(x)) {
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    return(x$out_id)
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  } else {
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    (1:nrow(x$data))[-unique(x$in_id)]
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  }
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}
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complement.boot_split <- rsplit_complement
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#' @export
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complement.perm_split <- rsplit_complement
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#' @export
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complement.rof_split <- function(x, ...) {
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  get_stored_out_id(x)
@@ -91,6 +91,18 @@
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  }
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}
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#' Get the indices of the analysis set from the assessment set
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#' @param ind A vector of integers for which rows of data belong in the
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#' assessment set.
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#' @param n A single integer for the total number of rows in the data set.
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#' @return A named list of integer vectors.
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#' @export
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#' @keywords internal
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default_complement <- function(ind, n) {
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  list(analysis = setdiff(1:n, ind),
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       assessment = unique(ind))
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}
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#' Add Assessment Indices
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#'

@@ -99,7 +99,7 @@
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    full_join(keys, by = "..group") %>%
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    arrange(..index)
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  indices <- split_unnamed(data_ind$..index, data_ind$..folds)
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  indices <- lapply(indices, vfold_complement, n = nrow(data))
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  indices <- lapply(indices, default_complement, n = nrow(data))
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  split_objs <-
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    purrr::map(indices,
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               make_splits,
Files Coverage
R 82.33%
Project Totals (28 files) 82.33%
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comment: false
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coverage:
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  status:
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    project:
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      default:
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        target: auto
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        threshold: 1%
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        informational: true
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    patch:
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      default:
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        target: auto
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        threshold: 1%
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        informational: true
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