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#' Simple Value Assignments for Novel Factor Levels
#'
#' `step_novel` creates a *specification* of a recipe
#' step that will assign a previously unseen factor level to a
#' new value.
#'
#' @inheritParams step_center
#' @param new_level A single character value that will be assigned
#' to new factor levels.
#' @param objects A list of objects that contain the information
#' on factor levels that will be determined by [prep()].
#' @template step-return
#' @family dummy variable and encoding steps
#' @seealso [dummy_names()]
#' @export
#' @details The selected variables are adjusted to have a new
#' level (given by `new_level`) that is placed in the last
#' position. During preparation there will be no data points
#' associated with this new level since all of the data have been
#' seen.
#'
#' Note that if the original columns are character, they will be
#' converted to factors by this step.
#'
#' Missing values will remain missing.
#'
#' If `new_level` is already in the data given to `prep`, an error
#' is thrown.
#'
#' When fitting a model that can deal with new factor levels, consider using
#' [workflows::add_recipe()] with `allow_novel_levels = TRUE` set in
#' [hardhat::default_recipe_blueprint()]. This will allow your model to handle
#' new levels at prediction time, instead of throwing warnings or errors.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the columns that will be affected) and `value` (the factor
#' levels that is used for the new value) is returned.
#'
#' @template case-weights-not-supported
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(Sacramento, package = "modeldata")
#'
#' sacr_tr <- Sacramento[1:800, ]
#' sacr_te <- Sacramento[801:806, ]
#' sacr_te$city[3] <- "beeptown"
#' sacr_te$city[4] <- "boopville"
#'
#' rec <- recipe(~ city + zip, data = sacr_tr)
#'
#' rec <- rec %>%
#' step_novel(city, zip)
#' rec <- prep(rec, training = sacr_tr)
#'
#' processed <- bake(rec, sacr_te)
#' tibble(old = sacr_te$city, new = processed$city)
#'
#' tidy(rec, number = 1)
step_novel <-
function(recipe,
...,
role = NA,
trained = FALSE,
new_level = "new",
objects = NULL,
skip = FALSE,
id = rand_id("novel")) {
add_step(
recipe,
step_novel_new(
terms = enquos(...),
role = role,
trained = trained,
new_level = new_level,
objects = objects,
skip = skip,
id = id
)
)
}
step_novel_new <-
function(terms, role, trained, new_level, objects, skip, id) {
step(
subclass = "novel",
terms = terms,
role = role,
trained = trained,
new_level = new_level,
objects = objects,
skip = skip,
id = id
)
}
get_existing_values <- function(x) {
if (is.character(x)) {
out <- unique(x)
attr(out, "is_ordered") <- FALSE
} else {
if (is.factor(x)) {
out <- levels(x)
attr(out, "is_ordered") <- is.ordered(x)
} else {
rlang::abort("Data should be either character or factor")
}
}
out
}
#' @export
prep.step_novel <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("string", "factor", "ordered"))
# Get existing levels and their factor type (i.e. ordered)
objects <- lapply(training[, col_names], get_existing_values)
# Check to make sure that there are not duplicate levels
level_check <-
map_lgl(objects, function(x, y) y %in% x, y = x$new_level)
if (any(level_check)) {
rlang::abort(
paste0(
"Columns already contain the new level: ",
paste0(names(level_check)[level_check], collapse = ", ")
)
)
}
step_novel_new(
terms = x$terms,
role = x$role,
trained = TRUE,
new_level = x$new_level,
objects = objects,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_novel <- function(object, new_data, ...) {
check_new_data(names(object$objects), object, new_data)
for (i in names(object$objects)) {
new_data[[i]] <- ifelse(
# Preserve NA values by adding them to the list of existing
# possible values
!(new_data[[i]] %in% c(object$object[[i]], NA)),
object$new_level,
as.character(new_data[[i]])
)
new_data[[i]] <-
factor(new_data[[i]],
levels = c(object$object[[i]], object$new_level),
ordered = attributes(object$object[[i]])$is_ordered
)
}
new_data
}
print.step_novel <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Novel factor level assignment for "
print_step(names(x$objects), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_novel <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = names(x$objects),
value = rep(x$new_level, length(x$objects))
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
value = rep(x$new_level, length(term_names))
)
}
res$id <- x$id
res
}
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