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#' Assign missing categories to "unknown"
#'
#' `step_unknown()` creates a *specification* of a recipe step that will assign
#' a missing value in a factor level to `"unknown"`.
#'
#' @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.
#'
#' Note that if the original columns are character, they will be
#' converted to factors by this step.
#'
#' If `new_level` is already in the data given to `prep`, an error
#' is thrown.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `value` , and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{statistic}{character, the factor levels for the new values}
#' \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(Sacramento, package = "modeldata")
#'
#' rec <-
#' recipe(~ city + zip, data = Sacramento) %>%
#' step_unknown(city, new_level = "unknown city") %>%
#' step_unknown(zip, new_level = "unknown zip") %>%
#' prep()
#'
#' table(bake(rec, new_data = NULL) %>% pull(city),
#' Sacramento %>% pull(city),
#' useNA = "always"
#' ) %>%
#' as.data.frame() %>%
#' dplyr::filter(Freq > 0)
#'
#' tidy(rec, number = 1)
step_unknown <-
function(recipe,
...,
role = NA,
trained = FALSE,
new_level = "unknown",
objects = NULL,
skip = FALSE,
id = rand_id("unknown")) {
add_step(
recipe,
step_unknown_new(
terms = enquos(...),
role = role,
trained = trained,
new_level = new_level,
objects = objects,
skip = skip,
id = id
)
)
}
step_unknown_new <-
function(terms, role, trained, new_level, objects, skip, id) {
step(
subclass = "unknown",
terms = terms,
role = role,
trained = trained,
new_level = new_level,
objects = objects,
skip = skip,
id = id
)
}
#' @export
prep.step_unknown <- 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)) {
offenders <- names(level_check)[level_check]
cli::cli_abort(
"Columns already contain the level {.val {x$new_level}}: {offenders}."
)
}
step_unknown_new(
terms = x$terms,
role = x$role,
trained = TRUE,
new_level = x$new_level,
objects = objects,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_unknown <- function(object, new_data, ...) {
col_names <- names(object$objects)
check_new_data(col_names, object, new_data)
for (col_name in col_names) {
new_data[[col_name]] <- ifelse(
is.na(new_data[[col_name]]),
object$new_level,
as.character(new_data[[col_name]])
)
new_levels <- c(object$object[[col_name]], object$new_level)
if (!all(new_data[[col_name]] %in% new_levels)) {
warn_new_levels(
new_data[[col_name]],
new_levels,
column = col_name,
step = "step_unknown",
c("*" = "New levels will be coerced to `NA` by {.fn step_unknown}.")
)
}
new_data[[col_name]] <-
factor(new_data[[col_name]],
levels = new_levels,
ordered = attributes(object$object[[col_name]])$is_ordered
)
}
new_data
}
#' @export
print.step_unknown <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Unknown factor level assignment for "
print_step(names(x$objects), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_unknown <- 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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