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#' Apply (Smoothed) Rectified Linear Transformation
#'
#' `step_relu` creates a *specification* of a recipe step that
#' will apply the rectified linear or softplus transformations to numeric
#' data. The transformed data is added as new columns to the data matrix.
#'
#' @inheritParams step_center
#' @param recipe A recipe object. The step will be added to the sequence of
#' operations for this recipe.
#' @param ... One or more selector functions to choose which variables are
#' affected by the step. See [selections()] for more details.
#' @param role Defaults to "predictor".
#' @param trained A logical to indicate if the quantities for preprocessing
#' have been estimated.
#' @param shift A numeric value dictating a translation to apply to the data.
#' @param reverse A logical to indicate if the left hinge should be used as
#' opposed to the right hinge.
#' @param smooth A logical indicating if the softplus function, a smooth
#' approximation to the rectified linear transformation, should be used.
#' @param prefix A prefix for generated column names, default to "right_relu_"
#' when right hinge transformation and "left_relu_" for reversed/left hinge
#' transformations.
#' @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).
#' @export
#' @rdname step_relu
#'
#' @details The rectified linear transformation is calculated as
#' \deqn{max(0, x - c)} and is also known as the ReLu or right hinge function.
#' If `reverse` is true, then the transformation is reflected about the
#' y-axis, like so: \deqn{max(0, c - x)} Setting the `smooth` option
#' to true will instead calculate a smooth approximation to ReLu
#' according to \deqn{ln(1 + e^(x - c)} The `reverse` argument may
#' also be applied to this transformation.
#'
#' @section Connection to MARS:
#'
#' The rectified linear transformation is used in Multivariate Adaptive
#' Regression Splines as a basis function to fit piecewise linear functions to
#' data in a strategy similar to that employed in tree based models. The
#' transformation is a popular choice as an activation function in many
#' neural networks, which could then be seen as a stacked generalization of
#' MARS when making use of ReLu activations. The hinge function also appears
#' in the loss function of Support Vector Machines, where it penalizes
#' residuals only if they are within a certain margin of the decision boundary.
#'
#' @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)
#'
#' transformed_te <- rec %>%
#' step_relu(carbon, shift = 40) %>%
#' prep(biomass_tr) %>%
#' bake(biomass_te)
#'
#' transformed_te
#'
#' @seealso [recipe()] [prep.recipe()]
#' [bake.recipe()]
step_relu <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
shift = 0,
reverse = FALSE,
smooth = FALSE,
prefix = "right_relu_",
columns = NULL,
skip = FALSE,
id = rand_id("relu")) {
if (!is_tune(shift) & !is_varying(shift)) {
if (!is.numeric(shift)) {
rlang::abort("Shift argument must be a numeric value.")
}
}
if (!is_tune(reverse) & !is_varying(reverse)) {
if (!is.logical(reverse)) {
rlang::abort("Reverse argument must be a logical value.")
}
}
if (!is_tune(smooth) & !is_varying(smooth)) {
if (!is.logical(smooth)) {
rlang::abort("Smooth argument must be logical value.")
}
}
if (reverse & prefix == "right_relu_") {
prefix <- "left_relu_"
}
add_step(
recipe,
step_relu_new(
terms = ellipse_check(...),
role = role,
trained = trained,
shift = shift,
reverse = reverse,
smooth = smooth,
prefix = prefix,
columns = columns,
skip = skip,
id = id
)
)
}
step_relu_new <-
function(terms, role, trained, shift, reverse, smooth, prefix, columns, skip, id) {
step(
subclass = "relu",
terms = terms,
role = role,
trained = trained,
shift = shift,
reverse = reverse,
smooth = smooth,
prefix = prefix,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_relu <- function(x, training, info = NULL, ...) {
columns <- eval_select_recipes(x$terms, training, info)
check_type(training[, columns])
step_relu_new(
terms = x$terms,
role = x$role,
trained = TRUE,
shift = x$shift,
reverse = x$reverse,
smooth = x$smooth,
prefix = x$prefix,
columns = columns,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_relu <- function(object, new_data, ...) {
make_relu_call <- function(col) {
call2("relu", sym(col), object$shift, object$reverse, object$smooth)
}
exprs <- purrr::map(object$columns, make_relu_call)
newname <- paste0(object$prefix, object$columns)
exprs <- check_name(exprs, new_data, object, newname, TRUE)
dplyr::mutate(new_data, !!!exprs)
}
print.step_relu <-
function(x, width = max(20, options()$width - 30), ...) {
cat("Adding relu transform for ", sep = "")
cat(format_selectors(x$terms, width = width))
if (x$trained)
cat(" [trained]\n")
else
cat("\n")
invisible(x)
}
relu <- function(x, shift = 0, reverse = FALSE, smooth = FALSE) {
if (!is.numeric(x))
rlang::abort("step_relu can only be applied to numeric data.")
if (reverse) {
shifted <- shift - x
} else {
shifted <- x - shift
}
if (smooth) {
out <- log1p(exp(shifted)) # use log1p for numerical accuracy
} else {
out <- pmax(shifted, rep(0, length(shifted)))
}
out
}
#' @rdname step_relu
#' @param x A `step_relu` object.
#' @export
tidy.step_relu <- function(x, ...) {
out <- simple_terms(x, ...)
out$shift <- x$shift
out$reverse <- x$reverse
out$id <- x$id
out
}
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