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#' Discretize numeric variables with CART
#'
#' `step_discretize_cart()` creates a *specification* of a recipe step that will
#' discretize numeric data (e.g. integers or doubles) into bins in a supervised
#' way using a CART model.
#'
#' @param recipe A recipe object. The step will be added to the sequence of
#' operations for this recipe.
#' @param ... One or more selector functions to choose which variables are
#' affected by the step. See [selections()] for more details.
#' @param role Defaults to `"predictor"`.
#' @param trained A logical to indicate if the quantities for preprocessing have
#' been estimated.
#' @param outcome A call to `vars` to specify which variable is used as the
#' outcome to train CART models in order to discretize explanatory variables.
#' @param cost_complexity The regularization parameter. Any split that does not
#' decrease the overall lack of fit by a factor of `cost_complexity` is not
#' attempted. Corresponds to `cp` in [rpart::rpart()]. Defaults to 0.01.
#' @param tree_depth The _maximum_ depth in the final tree. Corresponds to
#' `maxdepth` in [rpart::rpart()]. Defaults to 10.
#' @param min_n The number of data points in a node required to continue
#' splitting. Corresponds to `minsplit` in [rpart::rpart()]. Defaults to 20.
#' @param rules The splitting rules of the best CART tree to retain for each
#' variable. If length zero, splitting could not be used on that column.
#' @param id A character string that is unique to this step to identify it.
#' @param skip A logical. Should the step be skipped when the recipe is baked by
#' [recipes::bake()]? While all operations are baked when [recipes::prep()] is
#' run, some operations may not be able to be conducted on new data (e.g.
#' processing the outcome variable(s)). Care should be taken when using `skip
#' = TRUE` as it may affect the computations for subsequent operations
#' @template step-return
#' @details
#'
#' `step_discretize_cart()` creates non-uniform bins from numerical variables by
#' utilizing the information about the outcome variable and applying a CART
#' model.
#'
#' The best selection of buckets for each variable is selected using the
#' standard cost-complexity pruning of CART, which makes this discretization
#' method resistant to overfitting.
#'
#' This step requires the \pkg{rpart} package. If not installed, the step will
#' stop with a note about installing the package.
#'
#' Note that the original data will be replaced with the new bins.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is retruned with
#' columns `terms`, `value`, and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{value}{numeric, location of the splits}
#' \item{id}{character, id of this step}
#' }
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_discretize_cart"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template case-weights-supervised
#'
#' @examplesIf rlang::is_installed("modeldata")
#' library(modeldata)
#' data(ad_data)
#' library(rsample)
#'
#' split <- initial_split(ad_data, strata = "Class")
#'
#' ad_data_tr <- training(split)
#' ad_data_te <- testing(split)
#'
#' cart_rec <-
#' recipe(Class ~ ., data = ad_data_tr) %>%
#' step_discretize_cart(
#' tau, age, p_tau, Ab_42,
#' outcome = "Class", id = "cart splits"
#' )
#'
#' cart_rec <- prep(cart_rec, training = ad_data_tr)
#'
#' # The splits:
#' tidy(cart_rec, id = "cart splits")
#'
#' bake(cart_rec, ad_data_te, tau)
#' @seealso [embed::step_discretize_xgb()], [recipes::recipe()],
#' [recipes::prep()], [recipes::bake()]
#' @export
step_discretize_cart <-
function(recipe,
...,
role = NA,
trained = FALSE,
outcome = NULL,
cost_complexity = 0.01,
tree_depth = 10,
min_n = 20,
rules = NULL,
skip = FALSE,
id = rand_id("discretize_cart")) {
recipes_pkg_check(required_pkgs.step_discretize_cart())
if (is.null(outcome)) {
rlang::abort("`outcome` should select at least one column.")
}
add_step(
recipe,
step_discretize_cart_new(
terms = enquos(...),
role = role,
trained = trained,
outcome = outcome,
cost_complexity = cost_complexity,
tree_depth = tree_depth,
min_n = min_n,
rules = rules,
skip = skip,
id = id,
case_weights = NULL
)
)
}
step_discretize_cart_new <-
function(terms, role, trained, outcome, cost_complexity, tree_depth,
min_n, rules, skip, id, case_weights) {
step(
subclass = "discretize_cart",
terms = terms,
role = role,
trained = trained,
outcome = outcome,
cost_complexity = cost_complexity,
tree_depth = tree_depth,
min_n = min_n,
rules = rules,
skip = skip,
id = id,
case_weights = case_weights
)
}
cart_binning <- function(predictor, term, outcome, cost_complexity, tree_depth,
min_n, wts = NULL) {
df <- data.frame(y = outcome, x = predictor)
if (is.null(wts)) {
wts <- rep(1, nrow(df))
}
cart_mdl <-
try(
rpart::rpart(
y ~ x,
data = df,
weights = as.double(wts),
cp = cost_complexity,
minsplit = min_n,
maxdepth = tree_depth,
maxcompete = 0,
maxsurrogate = 0
),
silent = TRUE
)
if (inherits(cart_mdl, "try-error")) {
err <- conditionMessage(attr(cart_mdl, "condition"))
msg <-
glue(
"`step_discretize_cart()` failed to create a tree with error for ",
"predictor '{term}', which will not be binned. The error: {err}"
)
rlang::warn(msg)
return(numeric(0))
}
if (any(names(cart_mdl) == "splits")) {
cart_split <- sort(unique(cart_mdl$splits[, "index"]))
} else {
msg <-
glue(
"`step_discretize_cart()` failed to find any meaningful splits for ",
"predictor '{term}', which will not be binned."
)
rlang::warn(msg)
cart_split <- numeric(0)
}
cart_split
}
#' @export
prep.step_discretize_cart <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts)
if (isFALSE(were_weights_used)) {
wts <- rep(1, nrow(training))
}
if (length(col_names) > 0) {
check_type(training[, col_names], types = c("double", "integer"))
y_name <- recipes_eval_select(x$outcome, training, info)
col_names <- col_names[col_names != y_name]
rules <-
purrr::map2(
training[, col_names],
col_names,
cart_binning,
outcome = training[[y_name]],
cost_complexity = x$cost_complexity,
tree_depth = x$tree_depth,
min_n = x$min_n,
wts = wts
)
has_splits <- purrr::map_lgl(rules, ~ length(.x) > 0)
rules <- rules[has_splits]
col_names <- col_names[has_splits]
if (length(col_names) > 0) {
names(rules) <- col_names
}
} else {
rules <- list()
}
step_discretize_cart_new(
terms = x$terms,
role = x$role,
trained = TRUE,
outcome = x$outcome,
cost_complexity = x$cost_complexity,
tree_depth = x$tree_depth,
min_n = x$min_n,
rules = rules,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
#' @export
bake.step_discretize_cart <- function(object, new_data, ...) {
vars <- object$rules
check_new_data(names(vars), object, new_data)
for (i in seq_along(vars)) {
if (length(vars[[i]]) > 0) {
var <- names(vars)[[i]]
binned_data <- new_data
binned_data[[var]] <- cut(
new_data[[var]],
breaks = c(-Inf, object$rules[[i]], Inf),
include.lowest = TRUE,
right = FALSE,
dig.lab = 4
)
check_name(binned_data, new_data, object)
new_data <- binned_data
}
}
new_data
}
#' @export
print.step_discretize_cart <- function(x, width = max(20, options()$width - 30),
...) {
title <- "Discretizing variables using CART "
print_step(
names(x$rules), x$terms, x$trained, title, width,
case_weights = x$case_weights
)
invisible(x)
}
#' @rdname step_discretize_cart
#' @usage NULL
#' @export
tidy.step_discretize_cart <- function(x, ...) {
if (is_trained(x)) {
num_splits <- purrr::map_int(x$rules, length)
if (length(num_splits) > 0) {
res <- tibble(
terms = rep(names(x$rules), num_splits),
value = unlist(x$rules, use.names = FALSE)
)
} else {
res <- tibble(
terms = character(),
value = double()
)
}
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
value = NA_real_
)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.embed
#' @export
required_pkgs.step_discretize_cart <- function(x, ...) {
c("rpart", "embed")
}
#' @export
#' @rdname tunable_embed
tunable.step_discretize_cart <- function(x, ...) {
tibble::tibble(
name = c("cost_complexity", "tree_depth", "min_n"),
call_info = list(
list(pkg = "dials", fun = "cost_complexity"),
list(pkg = "dials", fun = "tree_depth"),
list(pkg = "dials", fun = "min_n")
),
source = "recipe",
component = "step_discretize_cart",
component_id = x$id
)
}
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