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#' Scaling Numeric Data
#'
#' `step_scale` creates a *specification* of a recipe
#' step that will normalize numeric data to have a standard
#' deviation of one.
#'
#' @inheritParams step_center
#' @param ... One or more selector functions to choose which
#' variables are affected by the step. 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 sds A named numeric vector of standard deviations. This
#' is `NULL` until computed by [prep.recipe()].
#' @param factor A numeric value of either 1 or 2 that scales the
#' numeric inputs by one or two standard deviations. By dividing
#' by two standard deviations, the coefficients attached to
#' continuous predictors can be interpreted the same way as with
#' binary inputs. Defaults to `1`. More in reference below.
#' @param na_rm A logical value indicating whether `NA`
#' values should be removed when computing the standard deviation.
#' @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) and `value` (the
#' standard deviations).
#' @keywords datagen
#' @concept preprocessing
#' @concept normalization_methods
#' @export
#' @details Scaling data means that the standard deviation of a
#' variable is divided out of the data. `step_scale` estimates
#' the variable standard deviations from the data used in the
#' `training` argument of `prep.recipe`.
#' `bake.recipe` then applies the scaling to new data sets
#' using these standard deviations.
#' @references Gelman, A. (2007) "Scaling regression inputs by
#' dividing by two standard deviations." Unpublished. Source:
#' \url{http://www.stat.columbia.edu/~gelman/research/unpublished/standardizing.pdf}.
#' @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)
#'
#' scaled_trans <- rec %>%
#' step_scale(carbon, hydrogen)
#'
#' scaled_obj <- prep(scaled_trans, training = biomass_tr)
#'
#' transformed_te <- bake(scaled_obj, biomass_te)
#'
#' biomass_te[1:10, names(transformed_te)]
#' transformed_te
#' tidy(scaled_trans, number = 1)
#' tidy(scaled_obj, number = 1)
#'
step_scale <-
function(recipe,
...,
role = NA,
trained = FALSE,
sds = NULL,
factor = 1,
na_rm = TRUE,
skip = FALSE,
id = rand_id("scale")) {
add_step(
recipe,
step_scale_new(
terms = ellipse_check(...),
role = role,
trained = trained,
sds = sds,
factor = factor,
na_rm = na_rm,
skip = skip,
id = id
)
)
}
step_scale_new <-
function(terms, role, trained, sds, factor, na_rm, skip, id) {
step(
subclass = "scale",
terms = terms,
role = role,
trained = trained,
sds = sds,
factor = factor,
na_rm = na_rm,
skip = skip,
id = id
)
}
#' @export
prep.step_scale <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
if (x$factor != 1 & x$factor != 2) {
rlang::warn("Scaling `factor` should take either a value of 1 or 2")
}
sds <-
vapply(training[, col_names], sd, c(sd = 0), na.rm = x$na_rm)
sds <- sds * x$factor
step_scale_new(
terms = x$terms,
role = x$role,
trained = TRUE,
sds,
factor = x$factor,
na_rm = x$na_rm,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_scale <- function(object, new_data, ...) {
res <-
sweep(as.matrix(new_data[, names(object$sds)]), 2, object$sds, "/")
res <- tibble::as_tibble(res)
new_data[, names(object$sds)] <- res
as_tibble(new_data)
}
print.step_scale <-
function(x, width = max(20, options()$width - 30), ...) {
cat("Scaling for ", sep = "")
printer(names(x$sds), x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_scale
#' @param x A `step_scale` object.
#' @export
tidy.step_scale <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = names(x$sds),
value = x$sds)
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names,
value = na_dbl)
}
res$id <- x$id
res
}
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