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discretize.R
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discretize.R
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#' Discretize Numeric Variables
#'
#' `discretize()` converts a numeric vector into a factor with
#' bins having approximately the same number of data points (based
#' on a training set).
#'
#' @export
#' @param x A numeric vector
discretize <- function(x, ...) {
UseMethod("discretize")
}
#' @export
#' @rdname discretize
discretize.default <- function(x, ...) {
cli::cli_abort(c(
x = "Only numeric {.arg x} is accepted.",
i = "The {.arg x} was passed {.obj_type_friendly {x}}."
))
}
#' @rdname discretize
#' @param cuts An integer defining how many cuts to make of the
#' data.
#' @param labels A character vector defining the factor levels
#' that will be in the new factor (from smallest to largest). This
#' should have length `cuts+1` and should not include a level
#' for missing (see `keep_na` below).
#' @param prefix A single parameter value to be used as a prefix
#' for the factor levels (e.g. `bin1`, `bin2`, ...). If
#' the string is not a valid R name, it is coerced to one.
#' If `prefix = NULL` then the factor levels will be labelled
#' according to the output of `cut()`.
#' @param keep_na A logical for whether a factor level should be
#' created to identify missing values in `x`. If `keep_na` is
#' set to `TRUE` then `na.rm = TRUE` is used when calling
#' [stats::quantile()].
#' @param infs A logical indicating whether the smallest and
#' largest cut point should be infinite.
#' @param min_unique An integer defining a sample size line of
#' dignity for the binning. If (the number of unique
#' values)`/(cuts+1)` is less than `min_unique`, no
#' discretization takes place.
#' @param ... Options to pass to
#' [stats::quantile()] that should not include `x`
#' or `probs`.
#' @return `discretize` returns an object of class
#' `discretize` and `predict.discretize` returns a factor
#' vector.
#' @export
#' @details `discretize` estimates the cut points from
#' `x` using percentiles. For example, if `cuts = 3`, the
#' function estimates the quartiles of `x` and uses these as
#' the cut points. If `cuts = 2`, the bins are defined as
#' being above or below the median of `x`.
#'
#' The `predict` method can then be used to turn numeric
#' vectors into factor vectors.
#'
#' If `keep_na = TRUE`, a suffix of "_missing" is used as a
#' factor level (see the examples below).
#'
#' If `infs = FALSE` and a new value is greater than the
#' largest value of `x`, a missing value will result.
#' @examplesIf rlang::is_installed("modeldata")
#' data(biomass, package = "modeldata")
#'
#' biomass_tr <- biomass[biomass$dataset == "Training", ]
#' biomass_te <- biomass[biomass$dataset == "Testing", ]
#'
#' median(biomass_tr$carbon)
#' discretize(biomass_tr$carbon, cuts = 2)
#' discretize(biomass_tr$carbon, cuts = 2, infs = FALSE)
#' discretize(biomass_tr$carbon, cuts = 2, infs = FALSE, keep_na = FALSE)
#' discretize(biomass_tr$carbon, cuts = 2, prefix = "maybe a bad idea to bin")
#'
#' carbon_binned <- discretize(biomass_tr$carbon)
#' table(predict(carbon_binned, biomass_tr$carbon))
#'
#' carbon_no_infs <- discretize(biomass_tr$carbon, infs = FALSE)
#' predict(carbon_no_infs, c(50, 100))
discretize.numeric <-
function(x,
cuts = 4,
labels = NULL,
prefix = "bin",
keep_na = TRUE,
infs = TRUE,
min_unique = 10,
...) {
unique_vals <- length(unique(x))
missing_lab <- "_missing"
if (cuts < 2) {
cli::cli_abort(
"There should be at least 2 {.arg cuts} but {.val {cuts}} was supplied."
)
}
dots <- list(...)
if (keep_na) {
dots$na.rm <- TRUE
}
if (unique_vals / (cuts + 1) >= min_unique) {
cl <- rlang::call2(
"quantile",
.ns = "stats",
x = x,
probs = seq(0, 1, length = cuts + 1)
)
cl <- rlang::call_modify(cl, !!!dots)
breaks <- rlang::eval_tidy(cl)
num_breaks <- length(breaks)
breaks <- unique(breaks)
if (num_breaks > length(breaks)) {
cli::cli_warn(
"Not enough data for {cuts} breaks. \\
Only {length(breaks)} breaks were used."
)
}
if (infs) {
breaks[1] <- -Inf
breaks[length(breaks)] <- Inf
}
breaks <- unique(breaks)
if (is.null(labels)) {
prefix <- prefix[1]
if (make.names(prefix) != prefix && !is.null(prefix)) {
cli::cli_warn(
"The prefix {.val {prefix}} is not a valid R name. \\
It has been changed to {.val {make.names(prefix)}}."
)
prefix <- make.names(prefix)
}
labels <- names0(length(breaks) - 1, "")
}
out <- list(
breaks = breaks,
bins = length(breaks) - 1,
prefix = prefix,
labels = if (keep_na) {
labels <- c(missing_lab, labels)
} else {
labels
},
keep_na = keep_na
)
} else {
out <- list(bins = 0)
cli::cli_warn(
"Data not binned; too few unique values per bin. \\
Adjust {.arg min_unique} as needed."
)
}
class(out) <- "discretize"
out
}
#' @rdname discretize
#' @param object An object of class `discretize`.
#' @param new_data A new numeric object to be binned.
#' @export
predict.discretize <- function(object, new_data, ...) {
if (is.matrix(new_data) |
is.data.frame(new_data)) {
new_data <- new_data[, 1]
}
object$labels <- if (is.null(object$prefix)) {
object$prefix
} else {
paste0(object$prefix, object$labels)
}
if (object$bins >= 1) {
labs <- if (object$keep_na) {
object$labels[-1]
} else {
object$labels
}
out <-
cut(new_data,
object$breaks,
labels = labs,
include.lowest = TRUE
)
if (object$keep_na) {
out_levels <- levels(out)
out <- as.character(out)
if (any(is.na(new_data))) {
missing_label <- object$labels[1] %||% "[missing]"
out[is.na(new_data)] <- missing_label
out_levels <- c(missing_label, out_levels)
}
out <- factor(out, levels = out_levels)
}
} else {
out <- new_data
}
out
}
#' @export
print.discretize <-
function(x, digits = max(3L, getOption("digits") - 3L), ...) {
if (length(x$breaks) > 0) {
cat("Bins:", length(x$labels))
if (any(grepl("_missing", x$labels))) {
cat(" (includes missing category)")
}
cat("\n")
if (length(x$breaks) <= 6) {
cat(
"Breaks:",
paste(signif(x$breaks, digits = digits), collapse = ", ")
)
}
} else {
if (x$bins == 0) {
cat("Too few unique data points. No binning was used.")
} else {
cat("Non-numeric data. No binning was used.")
}
}
}
#' Discretize Numeric Variables
#'
#' `step_discretize()` creates a *specification* of a recipe step that will
#' convert numeric data into a factor with bins having approximately the same
#' number of data points (based on a training set).
#'
#' @inheritParams step_center
#' @param num_breaks An integer defining how many cuts to make of the
#' data.
#' @param min_unique An integer defining a sample size line of
#' dignity for the binning. If (the number of unique
#' values)`/(cuts+1)` is less than `min_unique`, no
#' discretization takes place.
#' @param objects The [discretize()] objects are stored
#' here once the recipe has be trained by
#' [prep()].
#' @param options A list of options to [discretize()]. A
#' default is set for the argument `x`. Note that using
#' the options `prefix` and `labels` when more than one
#' variable is being transformed might be problematic as all
#' variables inherit those values.
#' @template step-return
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `value` , and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{value}{numeric, the breaks}
#' \item{id}{character, id of this step}
#' }
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_discretize"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template case-weights-not-supported
#'
#' @family discretization steps
#' @export
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(biomass, package = "modeldata")
#'
#' biomass_tr <- biomass[biomass$dataset == "Training", ]
#' biomass_te <- biomass[biomass$dataset == "Testing", ]
#'
#' rec <- recipe(
#' HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
#' data = biomass_tr
#' ) %>%
#' step_discretize(carbon, hydrogen)
#'
#' rec <- prep(rec, biomass_tr)
#' binned_te <- bake(rec, biomass_te)
#' table(binned_te$carbon)
#'
#' tidy(rec, 1)
step_discretize <- function(recipe,
...,
role = NA,
trained = FALSE,
num_breaks = 4,
min_unique = 10,
objects = NULL,
options = list(prefix = "bin"),
skip = FALSE,
id = rand_id("discretize")) {
if (any(names(options) %in% c("cuts", "min_unique"))) {
num_breaks <- options$cuts
min_unique <- options$min_unique
}
add_step(
recipe,
step_discretize_new(
terms = enquos(...),
trained = trained,
role = role,
num_breaks = num_breaks,
min_unique = min_unique,
objects = objects,
options = options,
skip = skip,
id = id
)
)
}
step_discretize_new <-
function(terms, role, trained, objects, num_breaks, min_unique, options, skip, id) {
step(
subclass = "discretize",
terms = terms,
role = role,
trained = trained,
num_breaks = num_breaks,
min_unique = min_unique,
objects = objects,
options = options,
skip = skip,
id = id
)
}
bin_wrapper <- function(x, args) {
cl <- rlang::call2("discretize", .ns = "recipes", x = x)
cl <- rlang::call_modify(cl, !!!args)
rlang::eval_tidy(cl)
}
#' @export
prep.step_discretize <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
if (length(col_names) > 1 & any(names(x$options) %in% c("prefix", "labels"))) {
cli::cli_warn(
"Note that the options {.arg prefix} and {.arg labels} will be applied \\
to all variables."
)
}
x$options$cuts <- x$num_breaks
x$options$min_unique <- x$min_unique
obj <- lapply(training[, col_names], bin_wrapper, x$options)
step_discretize_new(
terms = x$terms,
role = x$role,
trained = TRUE,
objects = obj,
num_breaks = x$num_breaks,
min_unique = x$min_unique,
options = x$options,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_discretize <- function(object, new_data, ...) {
col_names <- names(object$objects)
check_new_data(col_names, object, new_data)
for (col_name in col_names) {
new_data[[col_name]] <- predict(
object$objects[[col_name]],
new_data[[col_name]]
)
}
new_data
}
#' @export
print.step_discretize <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Discretize numeric variables from "
print_step(names(x$objects), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_discretize <- function(x, ...) {
if (is_trained(x)) {
brks <- lapply(x$objects, function(x) x$breaks)
num_brks <- vapply(brks, length, c(1L))
brk_vars <- rep(names(num_brks), num_brks)
brks <- unname(brks)
brks <- lapply(brks, unname)
values <- vctrs::list_unchop(brks, ptype = double())
res <- tibble(terms = brk_vars, value = values)
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names, value = na_dbl)
}
res$id <- x$id
res
}
#' @export
tunable.step_discretize <- function(x, ...) {
tibble::tibble(
name = c("min_unique", "num_breaks"),
call_info = list(
list(pkg = "dials", fun = "min_unique"),
list(pkg = "dials", fun = "num_breaks")
),
source = "recipe",
component = "step_discretize",
component_id = x$id
)
}