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#' Convert values to predefined integers
#'
#' `step_integer` creates a *specification* of a recipe
#' step that will convert new data into a set of integers based
#' on the original data values.
#'
#' @inheritParams step_pca
#' @inheritParams step_center
#' @param key A list that contains the information needed to
#' create integer variables for each variable contained in
#' `terms`. This is `NULL` until the step is trained by
#' [prep()].
#' @param strict A logical for whether the values should be returned as
#' integers (as opposed to double).
#' @param zero_based A logical for whether the integers should start at zero and
#' new values be appended as the largest integer.
#' @template step-return
#' @family dummy variable and encoding steps
#' @export
#' @details `step_integer` will determine the unique values of
#' each variable from the training set (excluding missing values),
#' order them, and then assign integers to each value. When baked,
#' each data point is translated to its corresponding integer or a
#' value of zero for yet unseen data (although see the `zero_based`
#' argument above). Missing values propagate.
#'
#' Factor inputs are ordered by their levels. All others are
#' ordered by `sort`.
#'
#' Despite the name, the new values are returned as numeric unless
#' `strict = TRUE`, which will coerce the results to integers.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the selectors or variables selected) and `value`
#' (a _list column_ with the conversion key) is returned.
#'
#' @template case-weights-not-supported
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(Sacramento, package = "modeldata")
#'
#' sacr_tr <- Sacramento[1:100, ]
#' sacr_tr$sqft[1] <- NA
#'
#' sacr_te <- Sacramento[101:105, ]
#' sacr_te$sqft[1] <- NA
#' sacr_te$city[1] <- "whoville"
#' sacr_te$city[2] <- NA
#'
#' rec <- recipe(type ~ ., data = sacr_tr) %>%
#' step_integer(all_predictors()) %>%
#' prep(training = sacr_tr)
#'
#' bake(rec, sacr_te, all_predictors())
#' tidy(rec, number = 1)
step_integer <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
strict = TRUE,
zero_based = FALSE,
key = NULL,
skip = FALSE,
id = rand_id("integer")) {
add_step(
recipe,
step_integer_new(
terms = enquos(...),
role = role,
trained = trained,
strict = strict,
zero_based = zero_based,
key = key,
skip = skip,
id = id
)
)
}
step_integer_new <-
function(terms, role, trained, strict, zero_based, key, skip, id) {
step(
subclass = "integer",
terms = terms,
role = role,
trained = trained,
strict = strict,
zero_based = zero_based,
key = key,
skip = skip,
id = id
)
}
get_unique_values <- function(x, zero = FALSE) {
if (is.factor(x)) {
res <- levels(x)
} else {
res <- sort(unique(x))
}
res <- res[!is.na(res)]
ints <- seq_along(res)
if (zero) {
ints <- ints - 1
}
tibble(value = res, integer = ints)
}
#' @export
prep.step_integer <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(
training[, col_names],
types = c("string", "factor", "ordered", "integer", "double", "logical",
"date", "datetime")
)
step_integer_new(
terms = x$terms,
role = x$role,
trained = TRUE,
strict = x$strict,
zero_based = x$zero_based,
key = map(training[, col_names], get_unique_values, zero = x$zero_based),
skip = x$skip,
id = x$id
)
}
map_key_to_int <- function(dat, key, strict = FALSE, zero = FALSE) {
if (is.factor(dat)) {
dat <- as.character(dat)
}
res <- full_join(tibble(value = dat, .row = seq_along(dat)), key, by = "value")
res <- dplyr::filter(res, !is.na(.row))
res <- arrange(res, .row)
if (zero) {
res$integer[is.na(res$integer) & !is.na(res$value)] <-
max(key$integer, na.rm = TRUE) + 1
} else {
res$integer[is.na(res$integer) & !is.na(res$value)] <- 0
}
if (strict) {
res$integer <- as.integer(res$integer)
}
res[["integer"]]
}
#' @export
bake.step_integer <- function(object, new_data, ...) {
check_new_data(names(object$key), object, new_data)
for (i in names(object$key)) {
new_data[[i]] <-
map_key_to_int(new_data[[i]], object$key[[i]], object$strict, object$zero_based)
}
new_data
}
print.step_integer <-
function(x, width = max(20, options()$width - 20), ...) {
title <- "Integer encoding for "
print_step(names(x$key), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_integer <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = names(x$key), value = unname(x$key))
} else {
res <- tibble(terms = sel2char(x$terms), value = list(NULL))
}
res$id <- x$id
res
}
|
