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#' Extending rsample with new rset subclasses
#'
#' `rset_reconstruct()` encapsulates the logic for allowing new rset
#' subclasses to work properly with vctrs (through `vctrs::vec_restore()`) and
#' dplyr (through `dplyr::dplyr_reconstruct()`). It is intended to be a
#' developer tool, and is not required for normal usage of rsample.
#'
#' rset objects are considered "reconstructable" after a vctrs/dplyr operation
#' if:
#'
#' - `x` and `to` both have an identical column named `"splits"` (column
#' and row order do not matter).
#'
#' - `x` and `to` both have identical columns prefixed with `"id"` (column
#' and row order do not matter).
#'
#' @param x A data frame to restore to an rset subclass.
#' @param to An rset subclass to restore to.
#'
#' @return `x` restored to the rset subclass of `to`.
#'
#' @export
#' @examples
#' to <- bootstraps(mtcars, times = 25)
#'
#' # Imitate a vctrs/dplyr operation,
#' # where the class might be lost along the way
#' x <- tibble::as_tibble(to)
#'
#' # Say we added a new column to `x`. Here we mock a `mutate()`.
#' x$foo <- "bar"
#'
#' # This is still reconstructable to `to`
#' rset_reconstruct(x, to)
#'
#' # Say we lose the first row
#' x <- x[-1, ]
#'
#' # This is no longer reconstructable to `to`, as `x` is no longer an rset
#' # bootstraps object with 25 bootstraps if one is lost!
#' rset_reconstruct(x, to)
rset_reconstruct <- function(x, to) {
if (rset_reconstructable(x, to)) {
df_reconstruct(x, to)
} else {
tib_upcast(x)
}
}
# ------------------------------------------------------------------------------
# Two data frames are considered identical by `rset_reconstructable()` if the rset
# sub-data-frames are identical. This means that if we select out the rset
# specific columns, they should be exactly the same (modulo reordering).
# It is expected that `to` is an rset object already, but `x` can be a
# bare data frame, or even a named list.
rset_reconstructable <- function(x, to) {
x_names <- names(x)
to_names <- names(to)
x_rset_indicator <- col_equals_splits(x_names) | col_starts_with_id(x_names)
to_rset_indicator <- col_equals_splits(to_names) | col_starts_with_id(to_names)
# Special casing of `nested_cv` to also look for `inner_resamples`
if (inherits(to, "nested_cv")) {
x_rset_indicator <- x_rset_indicator | col_equals_inner_resamples(x_names)
to_rset_indicator <- to_rset_indicator | col_equals_inner_resamples(to_names)
}
x_rset_names <- x_names[x_rset_indicator]
to_rset_names <- to_names[to_rset_indicator]
# Ignore ordering
x_rset_names <- sort(x_rset_names)
to_rset_names <- sort(to_rset_names)
# Early return if names aren't identical
if (!identical(x_rset_names, to_rset_names)) {
return(FALSE)
}
# Avoid all non-bare-data-frame S3 dispatch and
# don't compare outer data frame attributes.
# Only look at column names and actual column data.
x <- new_data_frame(x)
to <- new_data_frame(to)
# Early return if number of rows doesn't match
if (!identical(vec_size(x), vec_size(to))) {
return(FALSE)
}
x_rset_cols <- x[x_rset_names]
to_rset_cols <- to[to_rset_names]
# Row order doesn't matter
x_rset_cols <- vec_sort(x_rset_cols)
to_rset_cols <- vec_sort(to_rset_cols)
# Check identical structures
identical(x_rset_cols, to_rset_cols)
}
# ------------------------------------------------------------------------------
test_data <- function() {
data.frame(
x = 1:50,
y = rep(seq.int(10), each = 5),
index = as.Date(0:49, origin = "1970-01-01")
)
}
# Keep this list up to date with known rset subclasses for testing.
# Delay assignment because we are creating this directly in the R script
# and not all of the required helpers might have been sourced yet.
delayedAssign("rset_subclasses", {
if (rlang::is_installed("withr")) {
withr::with_seed(
123,
list(
bootstraps = bootstraps(test_data()),
group_bootstraps = group_bootstraps(test_data(), y),
vfold_cv = vfold_cv(test_data(), v = 10, repeats = 2),
group_vfold_cv = group_vfold_cv(test_data(), y),
loo_cv = loo_cv(test_data()),
mc_cv = mc_cv(test_data()),
group_mc_cv = group_mc_cv(test_data(), y),
nested_cv = nested_cv(test_data(), outside = vfold_cv(v = 3), inside = bootstraps(times = 5)),
validation_split = validation_split(test_data()),
validation_time_split = validation_time_split(test_data()),
group_validation_split = group_validation_split(test_data(), y),
rolling_origin = rolling_origin(test_data()),
sliding_window = sliding_window(test_data()),
sliding_index = sliding_index(test_data(), index),
sliding_period = sliding_period(test_data(), index, "week"),
manual_rset = manual_rset(list(initial_time_split(test_data()), initial_time_split(test_data())), c("ID1", "ID2")),
apparent = apparent(test_data()),
permutations = permutations(test_data(), y),
clustering_cv = clustering_cv(test_data(), y, repeats = 2)
)
)
} else {
NULL
}
})
# ------------------------------------------------------------------------------
col_equals_splits <- function(x) {
vec_equal(x, "splits")
}
col_starts_with_id <- function(x) {
grepl("(^id$)|(^id[1-9]$)", x)
}
col_equals_inner_resamples <- function(x) {
vec_equal(x, "inner_resamples")
}
# ------------------------------------------------------------------------------
# Maybe this should live in vctrs?
# Fallback to a tibble from the current data frame subclass.
# Removes subclass specific attributes and additional ones added by the user.
tib_upcast <- function(x) {
size <- df_size(x)
# Strip all attributes except names to construct
# a bare list to build the tibble back up from.
attributes(x) <- list(names = names(x))
tibble::new_tibble(x, nrow = size)
}
df_size <- function(x) {
if (!is.list(x)) {
rlang::abort("Cannot get the df size of a non-list.")
}
if (length(x) == 0L) {
return(0L)
}
col <- x[[1L]]
vec_size(col)
}
# ------------------------------------------------------------------------------
# Maybe this should live in vctrs?
df_reconstruct <- function(x, to) {
attrs <- attributes(to)
attrs$names <- names(x)
attrs$row.names <- .row_names_info(x, type = 0L)
attributes(x) <- attrs
x
}
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