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#' Percentile Transformation
#'
#' `step_percentile` creates a *specification* of a recipe step that
#' replaces the value of a variable with its percentile from the training set.
#'
#' @inheritParams step_pca
#' @inheritParams step_center
#' @param ref_dist The computed percentiles is stored here once this
#' preprocessing step has be trained by [prep()].
#' @param options A named list of options to pass to [stats::quantile()].
#' See Details for more information.
#' @template step-return
#' @family individual transformation steps
#' @export
#' @rdname step_percentile
#'
#' @template case-weights-unsupervised
#'
#' @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_percentile(carbon)
#'
#' prepped_rec <- prep(rec)
#'
#' prepped_rec %>%
#' bake(biomass_te)
#'
#' tidy(rec, 1)
#' tidy(prepped_rec, 1)
step_percentile <-
function(recipe,
...,
role = NA,
trained = FALSE,
ref_dist = NULL,
options = list(probs = (0:100) / 100),
skip = FALSE,
id = rand_id("percentile")) {
add_step(
recipe,
step_percentile_new(
terms = enquos(...),
trained = trained,
role = role,
ref_dist = ref_dist,
options = options,
skip = skip,
id = id,
case_weights = NULL
)
)
}
step_percentile_new <-
function(terms, role, trained, ref_dist, options, skip, id, case_weights) {
step(
subclass = "percentile",
terms = terms,
role = role,
trained = trained,
ref_dist = ref_dist,
options = options,
skip = skip,
id = id,
case_weights = case_weights
)
}
#' @export
prep.step_percentile <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
if (isFALSE(were_weights_used)) {
wts <- NULL
}
## We'll use the names later so make sure they are available
x$options$names <- TRUE
if (!any(names(x$options) == "probs")) {
x$options$probs <- (0:100) / 100
} else {
x$options$probs <- sort(unique(x$options$probs))
}
ref_dist <- purrr::map(
training[, col_names],
get_train_pctl,
wts = wts,
args = x$options
)
step_percentile_new(
terms = x$terms,
trained = TRUE,
role = x$role,
ref_dist = ref_dist,
options = x$options,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
get_train_pctl <- function(x, wts, args = NULL) {
if (is.null(wts)) {
res <- rlang::exec("quantile", x = x, !!!args)
} else {
wts <- as.double(wts)
res <- rlang::exec("wrighted_quantile", x = x, wts = wts, !!!args)
}
# Remove duplicate percentile values
res[!duplicated(res)]
}
wrighted_quantile <- function(x, wts, probs, ...) {
order_x <- order(x)
x <- x[order_x]
wts <- wts[order_x]
wts_norm <- cumsum(wts) / sum(wts)
res <- purrr::map_dbl(probs, ~x[min(which(wts_norm >= .x))])
names(res) <- paste0(probs * 100, "%")
res
}
#' @export
bake.step_percentile <- function(object, new_data, ...) {
vars <- names(object$ref_dist)
check_new_data(vars, object, new_data)
new_data[, vars] <-
purrr::map2_dfc(new_data[, vars], object$ref_dist, pctl_by_approx)
new_data
}
pctl_by_approx <- function(x, ref) {
# In case duplicates were removed, get the percentiles from
# the names of the reference object
grid <- as.numeric(gsub("%$", "", names(ref)))
stats::approx(x = ref, y = grid, xout = x)$y / 100
}
print.step_percentile <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Percentile transformation on "
print_step(names(x$ref_dist), x$terms, x$trained, title, width,
case_weights = x$case_weights)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_percentile <- function(x, ...) {
if (is_trained(x)) {
if (length(x$ref_dist) == 0) {
res <- tibble(
terms = character(),
value = numeric(),
percentile = numeric()
)
} else {
res <- map_dfr(x$ref_dist, format_pctl, .id = "term")
}
} else {
term_names <- sel2char(x$terms)
res <-
tibble(
terms = term_names,
value = rlang::na_dbl,
percentile = rlang::na_dbl
)
}
res$id <- x$id
res
}
format_pctl <- function(x) {
tibble::tibble(
value = unname(x),
percentile = as.numeric(gsub("%$", "", names(x)))
)
}
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