#' Step earth
#'
#' @param recipe recipe
#' @param ... selector
#' @param role role
#' @param trained trained
#' @param drop drop
#' @param outcome outcome
#' @param options options
#' @param res res
#' @param prefix prefix
#' @param skip skip
#' @param id id
#'
#' @export
#'
#' @importFrom recipes bake
#' @importFrom recipes prep
#'
#' @examples
step_earth <- function(recipe,
...,
role = "predictor",
trained = FALSE,
# num_terms = 10,
# prune_method = 'backward',
drop = TRUE,
outcome = NULL,
options = list(),
res = NULL,
prefix = "EARTH",
skip = FALSE,
id = recipes::rand_id("earth")) {
# ADD RANGE HANDLING TO num_terms AND prune_terms
# if (!is_tune(threshold) & !is_varying(threshold)) {
# if (!is.na(threshold) && (threshold > 1 | threshold <= 0)) {
# rlang::abort("`threshold` should be on (0, 1].")
# }
# }
if (is.null(outcome)) {
rlang::abort("`outcome` should select at least one column.")
}
recipes::add_step(
recipe,
step_earth_new(
terms = recipes::ellipse_check(...),
role = role,
trained = trained,
# num_terms = num_terms,
# prune_method = prune_method,
drop = drop,
outcome = outcome,
options = options,
res = res,
prefix = prefix,
skip = skip,
id = id
)
)
}
step_earth_new <-
function(terms, role, trained,
# num_terms, prune_method,
drop, outcome,
options, res,
prefix, skip, id) {
recipes::step(
subclass = "earth",
terms = terms,
role = role,
trained = trained,
# num_terms = num_terms,
# prune_method = prune_method,'
drop = drop,
outcome = outcome,
options = options,
res = res,
prefix = prefix,
skip = skip,
id = id
)
}
#' @export
prep.step_earth <- function(x, training, info = NULL, ...) {
x_names <- recipes::terms_select(x$terms, info = info)
y_names <- recipes::terms_select(x$outcome, info = info)
recipes::check_type(training[, x_names])
earth_call <- rlang::expr(earth::earth())
if (length(x$options) > 0)
earth_call <- recipes:::mod_call_args(earth_call, args = x$options)
earth_call$x <- expr(training[, x_names, drop = FALSE])
earth_call$y <- expr(training[, y_names, drop = FALSE])
earth_obj <- eval(earth_call)
step_earth_new(
terms = x$terms,
role = x$role,
trained = TRUE,
# num_tune = x$num_tune,
# prune_method = x$prune_method,
drop = x$drop,
outcome = x$outcome,
options = x$options,
res = earth_obj,
prefix = x$prefix,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_earth <- function(object, new_data, ...) {
if (!is.null(object$res$cuts)) {
which.terms <- c(1, grep('^h\\(', rownames(object$res$cuts)))
if(length(which.terms) > 1){
x <- earth:::get.earth.x(object$res, data = new_data, parent.frame())
x <- earth:::get.bx(x, which.terms, object$res$dirs, object$res$cuts)
x <- as.data.frame(x)
x_new <- x[,-1]
colnames(x_new) <- sprintf('%s_%s', object$prefix, colnames(x_new))
new_data <- dplyr::bind_cols(new_data, tibble::as_tibble(x_new))
if(object$drop) {
new_data <-
new_data[, !(colnames(new_data) %in% object$res$namesx), drop = FALSE]
}
}
}
tibble::as_tibble(new_data)
}
print.step_earth <-
function(x, width = max(20, options()$width - 29), ...) {
if (is.null(x$res$cuts)) {
cat("No EARTH splines were extracted.\n")
} else {
cat("EARTH extraction with ")
recipes::printer(rownames(x$res$namesx), x$terms, x$trained, width = width)
}
invisible(x)
}
#' pca_coefs <- function(x) {
#' rot <- as.data.frame(x$res$rotation)
#' vars <- rownames(rot)
#' if (x$num_comp > 0) {
#' npc <- ncol(rot)
#' res <- utils::stack(rot)
#' colnames(res) <- c("value", "component")
#' res$component <- as.character(res$component)
#' res$terms <- rep(vars, npc)
#' res <- as_tibble(res)[, c("terms", "value", "component")]
#' } else {
#' res <- tibble::tibble(terms = vars, value = rlang::na_dbl,
#' component = rlang::na_chr)
#' }
#' res
#' }
#'
#' pca_variances <- function(x) {
#' rot <- as.data.frame(x$res$rotation)
#' vars <- rownames(rot)
#' if (x$num_comp > 0) {
#' variances <- x$res$sdev ^ 2
#' p <- length(variances)
#' tot <- sum(variances)
#' y <- c(variances,
#' cumsum(variances),
#' variances / tot * 100,
#' cumsum(variances) / tot * 100)
#' x <-
#' rep(
#' c(
#' "variance",
#' "cumulative variance",
#' "percent variance",
#' "cumulative percent variance"
#' ),
#' each = p
#' )
#'
#' res <- tibble::tibble(terms = x,
#' value = y,
#' component = rep(1:p, 4))
#' } else {
#' res <- tibble::tibble(
#' terms = vars,
#' value = rep(rlang::na_dbl, length(vars)),
#' component = rep(rlang::na_chr, length(vars))
#' )
#' }
#' res
#' }
#'
#'
#'
#' #' @rdname step_pca
#' #' @param x A `step_pca` object.
#' #' @export
#' tidy.step_pca <- function(x, type = "coef", ...) {
#' if (!is_trained(x)) {
#' term_names <- sel2char(x$terms)
#' res <- tibble(terms = term_names,
#' value = na_dbl,
#' component = na_chr)
#' } else {
#' type <- match.arg(type, c("coef", "variance"))
#' if (type == "coef") {
#' res <- pca_coefs(x)
#' } else {
#' res <- pca_variances(x)
#' }
#' }
#' res$id <- x$id
#' res
#' }
#'
#'
#'
#' #' @rdname tunable.step
#' #' @export
#' tunable.step_pca <- function(x, ...) {
#' tibble::tibble(
#' name = "num_comp",
#' call_info = list(list(pkg = "dials", fun = "num_comp", range = c(1L, 4L))),
#' source = "recipe",
#' component = "step_pca",
#' component_id = x$id
#' )
#' }
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