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#' Spatial Sign Preprocessing
#'
#' `step_spatialsign` is a *specification* of a recipe
#' step that will convert numeric data into a projection on to a
#' unit sphere.
#'
#' @inheritParams step_center
#' @inherit step_center return
#' @param ... One or more selector functions to choose which
#' variables will be used for the normalization. See
#' [selections()] for more details. For the `tidy`
#' method, these are not currently used.
#' @param role For model terms created by this step, what analysis
#' role should they be assigned?
#' @param na_rm A logical: should missing data be removed from the
#' norm computation?
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @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` which
#' is the columns that will be affected.
#' @keywords datagen
#' @concept preprocessing
#' @concept projection_methods
#' @export
#' @details The spatial sign transformation projects the variables
#' onto a unit sphere and is related to global contrast
#' normalization. The spatial sign of a vector `w` is
#' `w/norm(w)`.
#'
#' The variables should be centered and scaled prior to the
#' computations.
#'
#' @references Serneels, S., De Nolf, E., and Van Espen, P.
#' (2006). Spatial sign preprocessing: a simple way to impart
#' moderate robustness to multivariate estimators. *Journal of
#' Chemical Information and Modeling*, 46(3), 1402-1409.
#' @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)
#'
#' ss_trans <- rec %>%
#' step_center(carbon, hydrogen) %>%
#' step_scale(carbon, hydrogen) %>%
#' step_spatialsign(carbon, hydrogen)
#'
#' ss_obj <- prep(ss_trans, training = biomass_tr)
#'
#' transformed_te <- bake(ss_obj, biomass_te)
#'
#' plot(biomass_te$carbon, biomass_te$hydrogen)
#'
#' plot(transformed_te$carbon, transformed_te$hydrogen)
#'
#' tidy(ss_trans, number = 3)
#' tidy(ss_obj, number = 3)
step_spatialsign <-
function(recipe,
...,
role = "predictor",
na_rm = TRUE,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("spatialsign")) {
add_step(recipe,
step_spatialsign_new(
terms = ellipse_check(...),
role = role,
na_rm = na_rm,
trained = trained,
columns = columns,
skip = skip,
id = id
))
}
step_spatialsign_new <-
function(terms, role, na_rm, trained, columns, skip, id) {
step(
subclass = "spatialsign",
terms = terms,
role = role,
na_rm = na_rm,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_spatialsign <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
step_spatialsign_new(
terms = x$terms,
role = x$role,
na_rm = x$na_rm,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_spatialsign <- function(object, new_data, ...) {
col_names <- object$columns
ss <- function(x, na_rm) {
x / sqrt(sum(x ^ 2, na.rm = na_rm))
}
res <- t(apply(as.matrix(new_data[, col_names]), 1, ss, na_rm = object$na_rm))
res <- tibble::as_tibble(res)
new_data[, col_names] <- res
tibble::as_tibble(new_data)
}
print.step_spatialsign <-
function(x, width = max(20, options()$width - 26), ...) {
cat("Spatial sign on ", sep = "")
printer(x$columns, x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_spatialsign
#' @param x A `step_spatialsign` object.
#' @export
tidy.step_spatialsign <- function(x, ...) {
res <-simple_terms(x, ...)
res$id <- x$id
res
}
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