#' Combine Multiple Token Variables Into One
#'
#' `step_tokenmerge()` creates a *specification* of a recipe step that will take
#' multiple [`token`][tokenlist()] variables and combine them into one
#' [`token`][tokenlist()] variable.
#'
#' @template args-recipe
#' @template args-dots
#' @template args-role_predictors
#' @template args-trained
#' @template args-columns
#' @param prefix A prefix for generated column names, defaults to "tokenmerge".
#' @template args-keep_original_cols
#' @template args-skip
#' @template args-id
#'
#' @template returns
#'
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms` and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @seealso [step_tokenize()] to turn characters into [`tokens`][tokenlist()]
#' @family Steps for Token Modification
#'
#' @examples
#' library(recipes)
#' library(modeldata)
#' data(tate_text)
#'
#' tate_rec <- recipe(~., data = tate_text) %>%
#' step_tokenize(medium, artist) %>%
#' step_tokenmerge(medium, artist)
#'
#' tate_obj <- tate_rec %>%
#' prep()
#'
#' bake(tate_obj, new_data = NULL)
#'
#' tidy(tate_rec, number = 2)
#' tidy(tate_obj, number = 2)
#' @export
step_tokenmerge <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
columns = NULL,
prefix = "tokenmerge",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("tokenmerge")) {
add_step(
recipe,
step_tokenmerge_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
prefix = prefix,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
)
}
step_tokenmerge_new <-
function(terms, role, trained, columns, prefix, keep_original_cols,
skip, id) {
step(
subclass = "tokenmerge",
terms = terms,
role = role,
trained = trained,
columns = columns,
prefix = prefix,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
}
#' @export
prep.step_tokenmerge <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = "tokenlist")
step_tokenmerge_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
prefix = x$prefix,
keep_original_cols = get_keep_original_cols(x),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_tokenmerge <- function(object, new_data, ...) {
if (length(object$column) == 0L) {
# Empty selection
return(new_data)
}
col_names <- object$columns
check_new_data(col_names, object, new_data)
new_col <- as.list(unname(as.data.frame(new_data[, col_names, drop = FALSE]))) %>%
map(get_tokens) %>%
pmap(c)
new_col <- tibble(tokenlist(new_col))
names(new_col) <- object$prefix
new_data <- remove_original_cols(new_data, object, col_names)
new_col <- check_name(new_col, new_data, object, names(new_col))
new_data <- vec_cbind(new_data, new_col)
new_data
}
#' @export
print.step_tokenmerge <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Merging tokens for "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname step_tokenmerge
#' @usage NULL
#' @export
tidy.step_tokenmerge <- function(x, ...) {
if (is_trained(x)) {
term_names <- unname(x$columns)
res <- tibble(terms = term_names)
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.step
#' @export
required_pkgs.step_tokenmerge <- function(x, ...) {
"textrecipes"
}
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