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#' Untokenization of Token Variables
#'
#' `step_untokenize()` creates a *specification* of a recipe step that will
#' convert a [`token`][tokenlist()] variable into a character predictor.
#'
#' @template args-recipe
#' @template args-dots
#' @template args-role_no-new
#' @template args-trained
#' @template args-columns
#' @param sep a character to determine how the tokens should be separated when
#' pasted together. Defaults to `" "`.
#' @template args-skip
#' @template args-id
#'
#' @template returns
#'
#' @details
#'
#' This steps will turn a [`token`][tokenlist()] vector back into a character
#' vector. This step is calling `paste` internally to put the tokens back
#' together to a character.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns `terms`
#' (the selectors or variables selected) and `value` (seperator used for
#' collapsing).
#'
#' @template case-weights-not-supported
#'
#' @seealso [step_tokenize()] to turn characters into [`tokens`][tokenlist()]
#' @family Steps for Un-Tokenization
#'
#' @examples
#' library(recipes)
#' library(modeldata)
#' data(tate_text)
#'
#' tate_rec <- recipe(~., data = tate_text) %>%
#' step_tokenize(medium) %>%
#' step_untokenize(medium)
#'
#' tate_obj <- tate_rec %>%
#' prep()
#'
#' bake(tate_obj, new_data = NULL, medium) %>%
#' slice(1:2)
#'
#' bake(tate_obj, new_data = NULL) %>%
#' slice(2) %>%
#' pull(medium)
#'
#' tidy(tate_rec, number = 2)
#' tidy(tate_obj, number = 2)
#' @export
step_untokenize <-
function(recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
sep = " ",
skip = FALSE,
id = rand_id("untokenize")) {
add_step(
recipe,
step_untokenize_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
sep = sep,
skip = skip,
id = id
)
)
}
step_untokenize_new <-
function(terms, role, trained, columns, sep, skip, id) {
step(
subclass = "untokenize",
terms = terms,
role = role,
trained = trained,
columns = columns,
sep = sep,
skip = skip,
id = id
)
}
#' @export
prep.step_untokenize <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = "tokenlist")
step_untokenize_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
sep = x$sep,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_untokenize <- function(object, new_data, ...) {
col_names <- object$columns
check_new_data(col_names, object, new_data)
for (col_name in col_names) {
tokens <- get_tokens(new_data[[col_name]])
new_data[[col_name]] <- map_chr(tokens, paste, collapse = object$sep)
new_data[[col_name]] <- factor(new_data[[col_name]])
}
new_data
}
#' @export
print.step_untokenize <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Untokenization for "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @param x A `step_untokenize` object.
#' @export
tidy.step_untokenize <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = unname(x$columns),
value = x$sep
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
value = na_chr
)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.step
#' @export
required_pkgs.step_untokenize <- function(x, ...) {
c("textrecipes")
}
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