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#' Check for New Values
#'
#' `check_new_values` creates a *specification* of a recipe
#' operation that will check if variables contain new values.
#'
#' @inheritParams check_missing
#' @param ignore_NA A logical that indicates if we should consider missing
#' values as value or not. Defaults to `TRUE`.
#' @param values A named list with the allowed values.
#' This is `NULL` until computed by prep.recipe().
#' @template check-return
#' @family checks
#' @export
#' @details This check will break the `bake` function if any of the checked
#' columns does contain values it did not contain when `prep` was called
#' on the recipe. If the check passes, nothing is changed to the data.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this check, a tibble with columns
#' `terms` (the selectors or variables selected) is returned.
#'
#' @template case-weights-not-supported
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(credit_data, package = "modeldata")
#'
#' # If the test passes, `new_data` is returned unaltered
#' recipe(credit_data) %>%
#' check_new_values(Home) %>%
#' prep() %>%
#' bake(new_data = credit_data)
#'
#' # If `new_data` contains values not in `x` at the [prep()] function,
#' # the [bake()] function will break.
#' \dontrun{
#' recipe(credit_data %>% dplyr::filter(Home != "rent")) %>%
#' check_new_values(Home) %>%
#' prep() %>%
#' bake(new_data = credit_data)
#' }
#'
#' # By default missing values are ignored, so this passes.
#' recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
#' check_new_values(Home) %>%
#' prep() %>%
#' bake(credit_data)
#'
#' # Use `ignore_NA = FALSE` if you consider missing values as a value,
#' # that should not occur when not observed in the train set.
#' \dontrun{
#' recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
#' check_new_values(Home, ignore_NA = FALSE) %>%
#' prep() %>%
#' bake(credit_data)
#' }
check_new_values <-
function(recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
ignore_NA = TRUE,
values = NULL,
skip = FALSE,
id = rand_id("new_values")) {
add_check(
recipe,
check_new_values_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
ignore_NA = ignore_NA,
values = values,
skip = skip,
id = id
)
)
}
check_new_values_new <-
function(terms, role, trained, columns, skip, id, values, ignore_NA) {
check(
subclass = "new_values",
prefix = "check_",
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id,
values = values,
ignore_NA = ignore_NA
)
}
new_values_func <- function(x,
allowed_values,
colname = "x",
ignore_NA = TRUE) {
new_vals <- setdiff(as.character(x), as.character(allowed_values))
if (length(new_vals) == 0) {
return()
}
if (all(is.na(new_vals)) && ignore_NA) {
return()
}
if (ignore_NA) new_vals <- new_vals[!is.na(new_vals)]
rlang::abort(paste0(
colname,
" contains the new value(s): ",
paste(new_vals, collapse = ",")
))
}
prep.check_new_values <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
values <- lapply(training[, col_names], unique)
check_new_values_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id,
values = values,
ignore_NA = x$ignore_NA
)
}
bake.check_new_values <- function(object,
new_data,
...) {
col_names <- names(object$values)
for (i in seq_along(col_names)) {
colname <- col_names[i]
new_values_func(new_data[[colname]],
object$values[[colname]],
colname,
ignore_NA = object$ignore_NA
)
}
new_data
}
print.check_new_values <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Checking no new_values for "
print_step(names(x$values), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.check_new_values <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = unname(x$columns))
} else {
res <- tibble(terms = sel2char(x$terms))
}
res$id <- x$id
res
}
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