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rsplit <- function(data, in_id, out_id) {
if (!is.data.frame(data) & !is.matrix(data)) {
rlang::abort("`data` must be a data frame.")
}
if (!is.integer(in_id) | any(in_id < 1)) {
rlang::abort("`in_id` must be a positive integer vector.")
}
if (!all(is.na(out_id))) {
if (!is.integer(out_id) | any(out_id < 1)) {
rlang::abort("`out_id` must be a positive integer vector.")
}
}
if (length(in_id) == 0) {
rlang::abort("At least one row should be selected for the analysis set.")
}
structure(
list(
data = data,
in_id = in_id,
out_id = out_id
),
class = "rsplit"
)
}
#' @export
print.rsplit <- function(x, ...) {
out_char <-
if (is_missing_out_id(x)) {
paste(length(complement(x)))
} else {
paste(length(x$out_id))
}
cat("<Analysis/Assess/Total>\n")
cat("<",
length(x$in_id), "/",
out_char, "/",
nrow(x$data), ">\n",
sep = ""
)
}
#' @export
as.integer.rsplit <-
function(x, data = c("analysis", "assessment"), ...) {
data <- match.arg(data)
if (data == "analysis") {
out <- x$in_id
} else {
out <- if (is_missing_out_id(x)) {
complement(x)
} else {
x$out_id
}
}
out
}
#' Convert an `rsplit` object to a data frame
#'
#' The analysis or assessment code can be returned as a data
#' frame (as dictated by the `data` argument) using
#' `as.data.frame.rsplit`. `analysis` and
#' `assessment` are shortcuts.
#' @param x An `rsplit` object.
#' @param row.names `NULL` or a character vector giving the row names for the data frame. Missing values are not allowed.
#' @param optional A logical: should the column names of the data be checked for legality?
#' @param data Either "analysis" or "assessment" to specify which data are returned.
#' @param ... Additional arguments to be passed to or from methods. Not currently used.
#' @examples
#' library(dplyr)
#' set.seed(104)
#' folds <- vfold_cv(mtcars)
#'
#' model_data_1 <- folds$splits[[1]] %>% analysis()
#' holdout_data_1 <- folds$splits[[1]] %>% assessment()
#' @export
as.data.frame.rsplit <-
function(x,
row.names = NULL,
optional = FALSE,
data = "analysis",
...) {
if (!is.null(row.names)) {
rlang::warn(paste0(
"`row.names` is kept for consistency with the underlying class but ",
"non-NULL values will be ignored."
))
}
if (optional) {
rlang::warn(paste0(
"`optional` is kept for consistency with the underlying class but ",
"TRUE values will be ignored."
))
}
if (!is.null(x$col_id)) {
if (identical(data, "assessment")) {
rsplit_class <- class(x)[[1]]
msg <- paste0(
"There is no assessment data set for an `rsplit` object",
" with class `", rsplit_class, "`."
)
rlang::abort(msg)
}
permuted_col <-
x$data[as.integer(x, data = data, ...), x$col_id, drop = FALSE]
x$data[, x$col_id] <- permuted_col
return(x$data)
}
x$data[as.integer(x, data = data, ...), , drop = FALSE]
}
#' @rdname as.data.frame.rsplit
#' @export
analysis <- function(x, ...) {
if (!inherits(x, "rsplit")) {
rlang::abort("`x` should be an `rsplit` object")
}
as.data.frame(x, data = "analysis", ...)
}
#' @rdname as.data.frame.rsplit
#' @export
assessment <- function(x, ...) {
if (!inherits(x, "rsplit")) {
rlang::abort("`x` should be an `rsplit` object")
}
as.data.frame(x, data = "assessment", ...)
}
#' @export
dim.rsplit <- function(x, ...) {
c(
analysis = length(x$in_id),
assessment = length(complement(x)),
n = nrow(x$data),
p = ncol(x$data)
)
}
#' @method obj_sum rsplit
#' @export
obj_sum.rsplit <- function(x, ...) {
out_char <-
if (is_missing_out_id(x)) {
paste(length(complement(x)))
} else {
paste(length(x$out_id))
}
paste0(
"split [",
length(x$in_id), "/",
out_char, "]"
)
}
#' @method type_sum rsplit
#' @export
type_sum.rsplit <- function(x, ...) {
out_char <-
if (is_missing_out_id(x)) {
format_n(length(complement(x)))
} else {
format_n(length(x$out_id))
}
paste0(
"split [",
format_n(length(x$in_id)), "/",
out_char, "]"
)
}
format_n <- function(x, digits = 1) {
case_when(
log10(x) < 3 ~ paste(x),
log10(x) >= 3 & log10(x) < 6 ~ paste0(round(x / 1000, digits = digits), "K"),
TRUE ~ paste0(round(x / 1000000, digits = digits), "M"),
)
}
is_rsplit <- function(x) {
inherits(x, "rsplit")
}
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