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#' Sample rows using dplyr
#'
#' `step_sample` creates a *specification* of a recipe step
#' that will sample rows using [dplyr::sample_n()] or
#' [dplyr::sample_frac()].
#'
#' @template row-ops
#' @inheritParams step_center
#' @param ... Argument ignored; included for consistency with other step
#' specification functions.
#' @param size An integer or fraction. If the value is within (0, 1),
#' [dplyr::sample_frac()] is applied to the data. If an integer
#' value of 1 or greater is used, [dplyr::sample_n()] is applied.
#' The default of `NULL` uses [dplyr::sample_n()] with the size
#' of the training set (or smaller for smaller `new_data`).
#' @param replace Sample with or without replacement?
#' @template step-return
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `size`, `replace`, and `id` is returned.
#'
#' @template case-weights-unsupervised
#'
#' @family row operation steps
#' @family dplyr steps
#' @export
#' @examples
#'
#' # Uses `sample_n`
#' recipe(~., data = mtcars) %>%
#' step_sample(size = 1) %>%
#' prep(training = mtcars) %>%
#' bake(new_data = NULL) %>%
#' nrow()
#'
#' # Uses `sample_frac`
#' recipe(~., data = mtcars) %>%
#' step_sample(size = 0.9999) %>%
#' prep(training = mtcars) %>%
#' bake(new_data = NULL) %>%
#' nrow()
#'
#' # Uses `sample_n` and returns _at maximum_ 20 samples.
#' smaller_cars <-
#' recipe(~., data = mtcars) %>%
#' step_sample() %>%
#' prep(training = mtcars %>% slice(1:20))
#'
#' bake(smaller_cars, new_data = NULL) %>% nrow()
#' bake(smaller_cars, new_data = mtcars %>% slice(21:32)) %>% nrow()
step_sample <- function(recipe, ...,
role = NA,
trained = FALSE,
size = NULL,
replace = FALSE,
skip = TRUE,
id = rand_id("sample")) {
if (length(list(...)) > 0) {
rlang::warn("Selectors are not used for this step.")
}
if (!is_tune(size) & !is_varying(size)) {
if (!is.null(size) & (!is.numeric(size) || size < 0)) {
rlang::abort("`size` should be a positive number or NULL.")
}
}
if (!is_tune(replace) & !is_varying(replace)) {
if (!is.logical(replace)) {
rlang::abort("`replace` should be a single logical.")
}
}
add_step(
recipe,
step_sample_new(
terms = terms,
trained = trained,
role = role,
size = size,
replace = replace,
skip = skip,
id = id,
case_weights = NULL
)
)
}
step_sample_new <-
function(terms, role, trained, size, replace, skip, id, case_weights) {
step(
subclass = "sample",
terms = terms,
role = role,
trained = trained,
size = size,
replace = replace,
skip = skip,
id = id,
case_weights = case_weights
)
}
#' @export
prep.step_sample <- function(x, training, info = NULL, ...) {
if (is.null(x$size)) {
x$size <- nrow(training)
}
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
if (isFALSE(were_weights_used)) {
wts <- NULL
}
step_sample_new(
terms = x$terms,
trained = TRUE,
role = x$role,
size = x$size,
replace = x$replace,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
#' @export
bake.step_sample <- function(object, new_data, ...) {
if (isTRUE(object$case_weights)) {
wts_col <- purrr::map_lgl(new_data, hardhat::is_case_weights)
wts <- getElement(new_data, names(which(wts_col)))
wts <- as.double(wts)
} else {
wts <- NULL
}
if (object$size >= 1) {
n <- min(object$size, nrow(new_data))
new_data <-
dplyr::sample_n(
new_data, size = floor(n), replace = object$replace, weight = wts
)
} else {
new_data <-
dplyr::sample_frac(
new_data, size = object$size, replace = object$replace, weight = wts
)
}
new_data
}
print.step_sample <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Row sampling "
if (x$replace) {
title <- paste(title, "with replacement ")
}
print_step(NULL, NULL, x$trained, title, width,
case_weights = x$case_weights)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_sample <- function(x, ...) {
tibble(
size = x$size,
replace = x$replace,
id = x$inputs
)
}
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