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#' Orthogonal Polynomial Basis Functions
#'
#' `step_poly` creates a *specification* of a recipe
#' step that will create new columns that are basis expansions of
#' variables using orthogonal polynomials.
#'
#' @inheritParams step_center
#' @param ... One or more selector functions to choose which
#' variables are affected by the step. 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?. By default, the function assumes
#' that the new columns created from the original variables will be
#' used as predictors in a model.
#' @param objects A list of [stats::poly()] objects
#' created once the step has been trained.
#' @param degree The polynomial degree (an integer).
#' @param options A list of options for [stats::poly()]
#' which should not include `x`, `degree`, or `simple`. Note that
#' the option `raw = TRUE` will produce the regular polynomial
#' values (not orthogonalized).
#' @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` (the
#' columns that will be affected) and `degree`.
#' @keywords datagen
#' @concept preprocessing
#' @concept basis_expansion
#' @export
#' @details `step_poly` can new features from a single
#' variable that enable fitting routines to model this variable in
#' a nonlinear manner. The extent of the possible nonlinearity is
#' determined by the `degree` argument of
#' [stats::poly()]. The original variables are removed
#' from the data and new columns are added. The naming convention
#' for the new variables is `varname_poly_1` and so on.
#' @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)
#'
#' quadratic <- rec %>%
#' step_poly(carbon, hydrogen)
#' quadratic <- prep(quadratic, training = biomass_tr)
#'
#' expanded <- bake(quadratic, biomass_te)
#' expanded
#'
#' tidy(quadratic, number = 1)
#' @seealso [step_ns()] [recipe()]
#' [prep.recipe()] [bake.recipe()]
step_poly <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
objects = NULL,
degree = 2,
options = list(),
skip = FALSE,
id = rand_id("poly")) {
if (!is_tune(degree) & !is_varying(degree)) {
degree <- as.integer(degree)
}
if (any(names(options) == "degree")) {
degree <- options$degree
message(
paste(
"The `degree` argument is now a main argument instead of being",
"within `options`."
)
)
}
add_step(
recipe,
step_poly_new(
terms = ellipse_check(...),
trained = trained,
role = role,
objects = objects,
degree = degree,
options = options,
skip = skip,
id = id
)
)
}
step_poly_new <-
function(terms, role, trained, objects, degree, options, skip, id) {
step(
subclass = "poly",
terms = terms,
role = role,
trained = trained,
objects = objects,
degree = degree,
options = options,
skip = skip,
id = id
)
}
poly_wrapper <- function(x, args) {
args$x <- x
args$simple <- FALSE
poly_obj <- do.call("poly", args)
## don't need to save the original data so keep 1 row
out <- matrix(NA, ncol = ncol(poly_obj), nrow = 1)
class(out) <- c("poly", "basis", "matrix")
attr(out, "degree") <- attr(poly_obj, "degree")
attr(out, "coefs") <- attr(poly_obj, "coefs")
out
}
#' @export
prep.step_poly <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
opts <- x$options
opts$degree <- x$degree
obj <- lapply(training[, col_names], poly_wrapper, opts)
for (i in seq(along.with = col_names)) {
attr(obj[[i]], "var") <- col_names[i]
}
step_poly_new(
terms = x$terms,
role = x$role,
trained = TRUE,
objects = obj,
degree = x$degree,
options = x$options,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_poly <- function(object, new_data, ...) {
col_names <- names(object$objects)
new_names <- purrr::map(object$objects, ~ paste(attr(.x, "var"), "poly", 1:ncol(.x), sep = "_"))
poly_values <-
purrr::map2(new_data[, col_names], object$objects, ~ predict(.y, .x)) %>%
purrr::map(as_tibble) %>%
purrr::map2_dfc(new_names, ~ setNames(.x, .y))
new_data <- dplyr::bind_cols(new_data, poly_values)
new_data <- dplyr::select(new_data, -col_names)
new_data
}
print.step_poly <-
function(x, width = max(20, options()$width - 35), ...) {
cat("Orthogonal polynomials on ")
printer(names(x$objects), x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_poly
#' @param x A `step_poly` object.
#' @export
tidy.step_poly <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = names(x$objects), degree = x$degree)
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names, degree = x$degree)
}
res$id <- x$id
res
}
#' @rdname tunable.step
#' @export
tunable.step_poly <- function(x, ...) {
tibble::tibble(
name = c("degree"),
call_info = list(
list(pkg = "dials", fun = "degree_int")
),
source = "recipe",
component = "step_poly",
component_id = x$id
)
}
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