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#' Scaling Numeric Data to a Specific Range
#'
#' `step_range` creates a *specification* of a recipe
#' step that will normalize numeric data to be within a pre-defined
#' range of values.
#'
#' @inheritParams step_center
#' @param min A single numeric value for the smallest value in the
#' range.
#' @param max A single numeric value for the largest value in the
#' range.
#' @param clipping A single logical value for determining whether
#' application of transformation onto new data should be forced
#' to be inside `min` and `max`. Defaults to TRUE.
#' @param ranges A character vector of variables that will be
#' normalized. Note that this is ignored until the values are
#' determined by [prep()]. Setting this value will
#' be ineffective.
#' @template step-return
#' @family normalization steps
#' @export
#' @details When a new data point is outside of the ranges seen in
#' the training set, the new values are truncated at `min` or
#' `max`.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the selectors or variables selected), `min`, and `max` is
#' returned.
#'
#' @template case-weights-not-supported
#'
#' @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
#' )
#'
#' ranged_trans <- rec %>%
#' step_range(carbon, hydrogen)
#'
#' ranged_obj <- prep(ranged_trans, training = biomass_tr)
#'
#' transformed_te <- bake(ranged_obj, biomass_te)
#'
#' biomass_te[1:10, names(transformed_te)]
#' transformed_te
#'
#' tidy(ranged_trans, number = 1)
#' tidy(ranged_obj, number = 1)
step_range <-
function(recipe,
...,
role = NA,
trained = FALSE,
min = 0,
max = 1,
clipping = TRUE,
ranges = NULL,
skip = FALSE,
id = rand_id("range")) {
add_step(
recipe,
step_range_new(
terms = enquos(...),
role = role,
trained = trained,
min = min,
max = max,
clipping = clipping,
ranges = ranges,
skip = skip,
id = id
)
)
}
step_range_new <-
function(terms, role, trained, min, max, clipping, ranges, skip, id) {
step(
subclass = "range",
terms = terms,
role = role,
trained = trained,
min = min,
max = max,
clipping = clipping,
ranges = ranges,
skip = skip,
id = id
)
}
#' @export
prep.step_range <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
mins <-
vapply(training[, col_names], min, c(min = 0), na.rm = TRUE)
maxs <-
vapply(training[, col_names], max, c(max = 0), na.rm = TRUE)
step_range_new(
terms = x$terms,
role = x$role,
trained = TRUE,
min = x$min,
max = x$max,
clipping = x$clipping,
ranges = rbind(mins, maxs),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_range <- function(object, new_data, ...) {
check_new_data(colnames(object$ranges), object, new_data)
for (column in colnames(object$ranges)) {
min <- object$ranges["mins", column]
max <- object$ranges["maxs", column]
new_data[[column]] <- (new_data[[column]] - min) *
(object$max - object$min) / (max - min) + object$min
if (object$clipping) {
new_data[[column]] <- pmax(new_data[[column]], object$min)
new_data[[column]] <- pmin(new_data[[column]], object$max)
}
}
new_data
}
print.step_range <-
function(x, width = max(20, options()$width - 30), ...) {
title <- glue::glue("Range scaling to [{x$min},{x$max}] for ")
print_step(colnames(x$ranges), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_range <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = colnames(x$ranges) %||% character(),
min = unname(x$ranges["mins", ]),
max = unname(x$ranges["maxs", ])
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
min = na_dbl,
max = na_dbl
)
}
res$id <- x$id
res
}
|
