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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_pca
#' @inheritParams step_center
#' @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.
#' @template step-return
#' @family multivariate transformation steps
#' @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.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with column
#' `terms` (the columns that will be affected) is returned.
#'
#' @section Case weights:
#'
#' This step performs an unsupervised operation that can utilize case weights.
#' As a result, only frequency weights are allowed. For more
#' information, see the documentation in [case_weights] and the examples on
#' `tidymodels.org`.
#'
#' Unlike most, this step requires the case weights to be available when new
#' samples are processed (e.g., when `bake()` is used or `predict()` with a
#' workflow). To tell recipes that the case weights are required at bake time,
#' use
#' `recipe %>% update_role_requirements(role = "case_weights", bake = TRUE)`.
#' See [update_role_requirements()] for more information.
#'
#' @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.
#'
#' @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
#' )
#'
#' 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 = enquos(...),
role = role,
na_rm = na_rm,
trained = trained,
columns = columns,
skip = skip,
id = id,
case_weights = NULL
)
)
}
step_spatialsign_new <-
function(terms, role, na_rm, trained, columns, skip, id, case_weights) {
step(
subclass = "spatialsign",
terms = terms,
role = role,
na_rm = na_rm,
trained = trained,
columns = columns,
skip = skip,
id = id,
case_weights = case_weights
)
}
#' @export
prep.step_spatialsign <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
if (isFALSE(were_weights_used)) {
wts <- NULL
}
step_spatialsign_new(
terms = x$terms,
role = x$role,
na_rm = x$na_rm,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
#' @export
bake.step_spatialsign <- function(object, new_data, ...) {
check_new_data(names(object$columns), object, new_data)
col_names <- object$columns
if (isTRUE(object$case_weights)) {
wts_col <- purrr::map_lgl(new_data, hardhat::is_case_weights)
wts <- getElement(new_data, names(which(wts_col)))
wts <- as.double(wts)
} else {
wts <- 1
}
res <- as.matrix(new_data[, col_names])
res <- res / sqrt(rowSums((sqrt(1/wts) * res)^2, na.rm = object$na_rm))
res <- tibble::as_tibble(res)
new_data[, col_names] <- res
new_data
}
print.step_spatialsign <-
function(x, width = max(20, options()$width - 26), ...) {
title <- "Spatial sign on "
print_step(x$columns, x$terms, x$trained, title, width,
case_weights = x$case_weights)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_spatialsign <- function(x, ...) {
res <- simple_terms(x, ...)
res$id <- x$id
res
}
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