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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_center
#' @inherit step_center return
#' @param ... One or more selector functions to choose which
#' variables will be used to create the integer variables. See
#' [selections()] for more details. For the `tidy` method, these
#' are not currently used.
#' @param role For model terms created by this step, what analysis
#' role should they be assigned?. By default, the function assumes
#' that the new columns created by the original variables will be
#' used as predictors in a model.
#' @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.recipe()].
#' @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.
#' @return An updated version of `recipe` with the new step added
#' to the sequence of existing steps (if any). For the `tidy`
#' method, a tibble with columns `terms` (the selectors or
#' variables selected) and `value` is a _list column_ with the
#' conversion key.
#' @keywords datagen
#' @concept preprocessing
#' @concept variable_encodings
#' @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.
#'
#'
#' @seealso [step_factor2string()], [step_string2factor()],
#' [step_regex()], [step_count()],
#' [step_ordinalscore()], [step_unorder()], [step_other()]
#' [step_novel()], [step_dummy()]
#' @examples
#' library(modeldata)
#' data(okc)
#'
#' okc$location <- factor(okc$location)
#'
#' okc_tr <- okc[1:100, ]
#' okc_tr$age[1] <- NA
#'
#' okc_te <- okc[101:105, ]
#' okc_te$age[1] <- NA
#' okc_te$diet[1] <- "fast food"
#' okc_te$diet[2] <- NA
#'
#' rec <- recipe(Class ~ ., data = okc_tr) %>%
#' step_integer(all_predictors()) %>%
#' prep(training = okc_tr)
#'
#' bake(rec, okc_te, all_predictors())
#' tidy(rec, number = 1)
step_integer <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
strict = FALSE,
zero_based = FALSE,
key = NULL,
skip = FALSE,
id = rand_id("integer")) {
add_step(
recipe,
step_integer_new(
terms = ellipse_check(...),
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 <- eval_select_recipes(x$terms, training, info)
step_integer_new(
terms = x$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$integer)
res[["integer"]]
}
#' @export
bake.step_integer <- function(object, new_data, ...) {
for (i in names(object$key)) {
new_data[[i]] <-
map_key_to_int(new_data[[i]], object$key[[i]], object$strict, object$zero_based)
}
if (!is_tibble(new_data))
new_data <- as_tibble(new_data)
new_data
}
print.step_integer <-
function(x, width = max(20, options()$width - 20), ...) {
if (x$trained) {
cat("Integer encoding for ")
cat(format_ch_vec(names(x$key), width = width))
} else {
cat("Integer encoding for ", sep = "")
cat(format_selectors(x$terms, width = width))
}
if (x$trained)
cat(" [trained]\n")
else
cat("\n")
invisible(x)
}
#' @rdname step_integer
#' @param x A `step_integer` object.
#' @export
tidy.step_integer <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = names(x$key), value = x$key)
} else {
res <- tibble(terms = sel2char(x$terms))
res$value = NA
}
res$id <- x$id
res
}
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