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#' Radial Basis Function Kernel PCA Signal Extraction
#'
#' `step_kpca_rbf` creates a *specification* of a recipe step that
#' will convert numeric data into one or more principal components
#' using a radial basis function kernel basis expansion.
#'
#' @inheritParams step_pca
#' @inheritParams step_center
#' @param sigma A numeric value for the radial basis function parameter.
#' @param res An S4 [kernlab::kpca()] object is stored
#' here once this preprocessing step has be trained by
#' [prep()].
#' @param columns A character string of variable names that will
#' be populated elsewhere.
#' @template step-return
#' @family multivariate transformation steps
#' @export
#' @template kpca-info
#'
#' @template case-weights-not-supported
#'
#' @examplesIf rlang::is_installed(c("modeldata", "ggplot2", "kernlab"))
#' library(ggplot2)
#' 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
#' )
#'
#' kpca_trans <- rec %>%
#' step_YeoJohnson(all_numeric_predictors()) %>%
#' step_normalize(all_numeric_predictors()) %>%
#' step_kpca_rbf(all_numeric_predictors())
#'
#' kpca_estimates <- prep(kpca_trans, training = biomass_tr)
#'
#' kpca_te <- bake(kpca_estimates, biomass_te)
#'
#' ggplot(kpca_te, aes(x = kPC1, y = kPC2)) +
#' geom_point() +
#' coord_equal()
#'
#' tidy(kpca_trans, number = 3)
#' tidy(kpca_estimates, number = 3)
step_kpca_rbf <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
num_comp = 5,
res = NULL,
columns = NULL,
sigma = 0.2,
prefix = "kPC",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("kpca_rbf")) {
recipes_pkg_check(required_pkgs.step_kpca_rbf())
add_step(
recipe,
step_kpca_rbf_new(
terms = enquos(...),
role = role,
trained = trained,
num_comp = num_comp,
res = res,
columns = columns,
sigma = sigma,
prefix = prefix,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
)
}
step_kpca_rbf_new <-
function(terms, role, trained, num_comp, res, columns, sigma, prefix,
keep_original_cols, skip, id) {
step(
subclass = "kpca_rbf",
terms = terms,
role = role,
trained = trained,
num_comp = num_comp,
res = res,
columns = columns,
sigma = sigma,
prefix = prefix,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
}
#' @export
prep.step_kpca_rbf <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
if (x$num_comp > 0 && length(col_names) > 0) {
cl <-
rlang::call2(
"kpca",
.ns = "kernlab",
x = rlang::expr(as.matrix(training[, col_names])),
features = x$num_comp,
kernel = "rbfdot",
kpar = list(sigma = x$sigma)
)
kprc <- try(rlang::eval_tidy(cl), silent = TRUE)
if (inherits(kprc, "try-error")) {
rlang::abort(paste0("`step_kpca_rbf` failed with error:\n", as.character(kprc)))
}
} else {
kprc <- NULL
}
step_kpca_rbf_new(
terms = x$terms,
role = x$role,
trained = TRUE,
num_comp = x$num_comp,
sigma = x$sigma,
res = kprc,
columns = col_names,
prefix = x$prefix,
keep_original_cols = get_keep_original_cols(x),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_kpca_rbf <- function(object, new_data, ...) {
uses_dim_red(object)
if (object$num_comp > 0 && length(object$columns) > 0) {
check_new_data(object$columns, object, new_data)
cl <-
rlang::call2(
"predict",
.ns = "kernlab",
object = object$res,
rlang::expr(as.matrix(new_data[, object$columns]))
)
comps <- rlang::eval_tidy(cl)
comps <- comps[, 1:object$num_comp, drop = FALSE]
colnames(comps) <- names0(ncol(comps), object$prefix)
comps <- check_name(comps, new_data, object)
new_data <- bind_cols(new_data, as_tibble(comps))
keep_original_cols <- get_keep_original_cols(object)
if (!keep_original_cols) {
new_data <- new_data[, !(colnames(new_data) %in% object$columns), drop = FALSE]
}
}
new_data
}
print.step_kpca_rbf <- function(x, width = max(20, options()$width - 40), ...) {
title <- "RBF kernel PCA extraction with "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_kpca_rbf <- function(x, ...) {
uses_dim_red(x)
if (is_trained(x)) {
res <- tibble(terms = unname(x$columns))
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names)
}
res$id <- x$id
res
}
#' @export
tunable.step_kpca_rbf <- function(x, ...) {
tibble::tibble(
name = c("num_comp", "sigma"),
call_info = list(
list(pkg = "dials", fun = "num_comp", range = c(1L, 4L)),
list(pkg = "dials", fun = "rbf_sigma")
),
source = "recipe",
component = "step_kpca_rbf",
component_id = x$id
)
}
#' @rdname required_pkgs.recipe
#' @export
required_pkgs.step_kpca_rbf <- function(x, ...) {
c("kernlab")
}
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