#' BPE Tokenization of Character Variables
#'
#' `step_tokenize_bpe()` creates a *specification* of a recipe step that will
#' convert a character predictor into a [`token`][tokenlist()] variable using
#' Byte Pair Encoding.
#'
#' @template args-recipe
#' @template args-dots
#' @template args-role_no-new
#' @template args-trained
#' @template args-columns
#' @param vocabulary_size Integer, indicating the number of tokens in the final
#' vocabulary. Defaults to 1000. Highly encouraged to be tuned.
#' @param options A list of options passed to the tokenizer.
#' @param res The fitted [tokenizers.bpe::bpe()] model tokenizer will be stored
#' here once this preprocessing step has be trained by [prep.recipe()].
#' @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}
#' }
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_tokenize_bpe"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template case-weights-not-supported
#'
#' @seealso [step_untokenize()] to untokenize.
#' @family Steps for Tokenization
#'
#' @examplesIf rlang::is_installed("tokenizers.bpe")
#' library(recipes)
#' library(modeldata)
#' data(tate_text)
#'
#' tate_rec <- recipe(~., data = tate_text) %>%
#' step_tokenize_bpe(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 = 1)
#' tidy(tate_obj, number = 1)
#' @export
step_tokenize_bpe <-
function(recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
vocabulary_size = 1000,
options = list(),
res = NULL,
skip = FALSE,
id = rand_id("tokenize_bpe")) {
recipes::recipes_pkg_check(required_pkgs.step_tokenize_bpe())
add_step(
recipe,
step_tokenize_bpe_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
vocabulary_size = vocabulary_size,
options = options,
res = res,
skip = skip,
id = id
)
)
}
step_tokenize_bpe_new <-
function(terms, role, trained, columns, options, vocabulary_size, res, skip,
id) {
step(
subclass = "tokenize_bpe",
terms = terms,
role = role,
trained = trained,
columns = columns,
vocabulary_size = vocabulary_size,
options = options,
res = res,
skip = skip,
id = id
)
}
#' @export
prep.step_tokenize_bpe <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
training <- factor_to_text(training, col_names)
check_type(training[, col_names], types = c("string", "factor", "ordered"))
tokenizers <- list()
bpe_options <- x$options
if (!is.null(bpe_options$vocab_size)) {
rlang::abort(
"Please supply the vocabulary size using the `vocabulary_size` argument."
)
}
bpe_options$vocab_size <- x$vocabulary_size
for (col_name in col_names) {
text <- training[[col_name]]
check_bpe_vocab_size(text, x$vocabulary_size, col_name)
tokenizers[[col_name]] <- tokenizers_bpe_tokens(text, bpe_options)
}
step_tokenize_bpe_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
vocabulary_size = x$vocabulary_size,
options = x$options,
res = tokenizers,
skip = x$skip,
id = x$id
)
}
check_bpe_vocab_size <- function(text,
vocabulary_size,
column,
call = caller_env()) {
text_count <- strsplit(as.character(text), "")
text_count <- unlist(text_count)
text_count <- unique(text_count)
text_count <- length(text_count)
if (vocabulary_size < text_count) {
rlang::abort(
glue(
"`vocabulary_size` of {vocabulary_size} is too small for column ",
"`{column}` which has a unique character count of {text_count}"
),
call = call
)
}
}
#' @export
bake.step_tokenize_bpe <- function(object, new_data, ...) {
col_names <- object$columns
check_new_data(col_names, object, new_data)
if (is.null(names(object$res))) {
# Backwards compatibility with 1.0.3 (#230)
names(object$res) <- col_names
}
for (col_name in col_names) {
new_data[[col_name]] <- tokenizer_fun(
x = new_data[[col_name]],
options = object$options,
token = object$res[[col_name]]
)
}
new_data
}
#' @export
print.step_tokenize_bpe <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "BPE Tokenization for "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname step_tokenize_bpe
#' @usage NULL
#' @export
tidy.step_tokenize_bpe <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = unname(x$columns)
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names
)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.step
#' @export
required_pkgs.step_tokenize_bpe <- function(x, ...) {
c("tokenizers.bpe", "textrecipes")
}
#' @rdname tunable_textrecipes
#' @export
tunable.step_tokenize_bpe <- function(x, ...) {
tibble::tibble(
name = c("vocabulary_size"),
call_info = list(
list(pkg = "dials", fun = "vocabulary_size", range = c(1000, 32000))
),
source = "recipe",
component = "step_tokenize_bpe",
component_id = x$id
)
}
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