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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 ... One or more selector functions to choose which
#' variables will be scaled. See [selections()] for more
#' details. For the `tidy` method, these are not currently
#' used.
#' @param role Not used by this step since no new variables are
#' created.
#' @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 ranges A character vector of variables that will be
#' normalized. Note that this is ignored until the values are
#' determined by [prep.recipe()]. Setting this value will
#' be ineffective.
#' @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), `min`, and `max`.
#' @keywords datagen
#' @concept preprocessing
#' @concept normalization_methods
#' @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`.
#' @examples
#' library(modeldata)
#' data(biomass)
#'
#' 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,
ranges = NULL,
skip = FALSE,
id = rand_id("range")) {
add_step(
recipe,
step_range_new(
terms = ellipse_check(...),
role = role,
trained = trained,
min = min,
max = max,
ranges = ranges,
skip = skip,
id = id
)
)
}
step_range_new <-
function(terms, role, trained, min, max, ranges, skip, id) {
step(
subclass = "range",
terms = terms,
role = role,
trained = trained,
min = min,
max = max,
ranges = ranges,
skip = skip,
id = id
)
}
#' @export
prep.step_range <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
mins <-
vapply(training[, col_names], min, c(min = 0), na.rm = TRUE)
maxs <-
vapply(training[, col_names], max, c(max = 0), na.rm = TRUE)
step_range_new(
terms = x$terms,
role = x$role,
trained = TRUE,
min = x$min,
max = x$max,
ranges = rbind(mins, maxs),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_range <- function(object, new_data, ...) {
tmp <- as.matrix(new_data[, colnames(object$ranges)])
tmp <- sweep(tmp, 2, object$ranges[1, ], "-")
tmp <- tmp * (object$max - object$min)
tmp <- sweep(tmp, 2, object$ranges[2, ] - object$ranges[1, ], "/")
tmp <- tmp + object$min
tmp[tmp < object$min] <- object$min
tmp[tmp > object$max] <- object$max
tmp <- tibble::as_tibble(tmp)
new_data[, colnames(object$ranges)] <- tmp
as_tibble(new_data)
}
print.step_range <-
function(x, width = max(20, options()$width - 30), ...) {
cat("Range scaling to [", x$min, ",", x$max, "] for ", sep = "")
printer(colnames(x$ranges), x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_range
#' @param x A `step_range` object.
#' @export
tidy.step_range <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = colnames(x$ranges),
min = x$ranges["mins",],
max = x$ranges["maxs",])
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names,
min = na_dbl,
max = na_dbl)
}
res$id <- x$id
res
}
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