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#' Natural splines
#'
#' `step_spline_natural()` creates a *specification* of a recipe step that
#' creates natural spline features.
#'
#' @inheritParams step_spline_b
#' @param deg_free The degrees of freedom for the natural spline. As the
#' degrees of freedom for a natural spline increase, more flexible and
#' complex curves can be generated. This step requires at least two degrees of
#' freedom.
#' @param options A list of options for [splines2::naturalSpline()]
#' which should not include `x`, `df`, or `intercept`.
#' @return An object with classes `"step_spline_natural"` and `"step"`.
#' @export
#' @details
#'
#' Spline transformations take a numeric column and create multiple features
#' that, when used in a model, can estimate nonlinear trends between the column
#' and some outcome. The degrees of freedom determines how many new features
#' are added to the data.
#'
#' This spline is a piece-wise cubic polynomial function.
#'
#' If the spline expansion fails for a selected column, the step will
#' remove that column's results (but will retain the original data). Use the
#' `tidy()` method to determine which columns were used.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms` and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{id}{character, id of this step}
#' }
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_spline_natural"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @examplesIf rlang::is_installed(c("modeldata", "ggplot2"))
#' library(tidyr)
#' library(dplyr)
#'
#' library(ggplot2)
#' data(ames, package = "modeldata")
#'
#' spline_rec <- recipe(Sale_Price ~ Longitude, data = ames) %>%
#' step_spline_natural(Longitude, deg_free = 6, keep_original_cols = TRUE) %>%
#' prep()
#'
#' tidy(spline_rec, number = 1)
#'
#' # Show where each feature is active
#' spline_rec %>%
#' bake(new_data = NULL,-Sale_Price) %>%
#' pivot_longer(c(starts_with("Longitude_")), names_to = "feature", values_to = "value") %>%
#' mutate(feature = gsub("Longitude_", "feature ", feature)) %>%
#' filter(value > 0) %>%
#' ggplot(aes(x = Longitude, y = value)) +
#' geom_line() +
#' facet_wrap(~ feature)
#' @template case-weights-not-supported
#' @seealso [splines2::naturalSpline()]
step_spline_natural <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
deg_free = 10,
complete_set = FALSE,
options = NULL,
keep_original_cols = FALSE,
results = NULL,
skip = FALSE,
id = rand_id("spline_natural")) {
recipes_pkg_check(required_pkgs.step_spline_natural())
add_step(
recipe,
step_spline_natural_new(
terms = enquos(...),
trained = trained,
role = role,
deg_free = deg_free,
complete_set = complete_set,
options = options,
keep_original_cols = keep_original_cols,
results = results,
skip = skip,
id = id
)
)
}
step_spline_natural_new <-
function(terms, trained, role, deg_free, complete_set, options,
keep_original_cols, results, na_rm, skip, id) {
step(
subclass = "spline_natural",
terms = terms,
role = role,
trained = trained,
deg_free = deg_free,
complete_set = complete_set,
options = options,
keep_original_cols = keep_original_cols,
results = results,
skip = skip,
id = id
)
}
#' @export
prep.step_spline_natural <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
res <- list()
for (col_name in col_names) {
res[[col_name]] <- spline2_create(
training[[col_name]],
nm = col_name,
.fn = "naturalSpline",
df = max(x$deg_free, 2),
complete_set = x$complete_set,
degree = x$degree,
fn_opts = x$options
)
}
# check for errors
bas_res <- purrr::map_lgl(res, is.null)
res <- res[!bas_res]
col_names <- col_names[!bas_res]
names(res) <- col_names
step_spline_natural_new(
terms = x$terms,
role = x$role,
trained = TRUE,
deg_free = x$deg_free,
complete_set = x$complete_set,
options = x$options,
keep_original_cols = x$keep_original_cols,
results = res,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_spline_natural <- function(object, new_data, ...) {
col_names <- names(object$results)
check_new_data(col_names, object, new_data)
if (length(col_names) == 0) {
return(new_data)
}
new_cols <- list()
for (col_name in col_names) {
new_cols[[col_name]] <- spline2_apply(
object$results[[col_name]],
new_data[[col_name]]
)
}
new_cols <- purrr::list_cbind(unname(new_cols))
new_cols <- check_name(new_cols, new_data, object, names(new_cols))
new_data <- vec_cbind(new_data, new_cols)
new_data <- remove_original_cols(new_data, object, col_names)
new_data
}
# ------------------------------------------------------------------------------
#' @export
print.step_spline_natural <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Natural spline expansion "
cols_used <- names(x$results)
if (length(cols_used) == 0) {
cols_used <- "<none>"
}
print_step(cols_used, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_spline_natural <- function(x, ...) {
if (is_trained(x)) {
terms <- names(x$results)
} else {
terms <- sel2char(x$terms)
}
tibble(terms = terms, id = x$id)
}
# ------------------------------------------------------------------------------
#' @rdname required_pkgs.recipe
#' @export
required_pkgs.step_spline_natural <- function(x, ...) {
c("splines2")
}
# ------------------------------------------------------------------------------
#' @export
tunable.step_spline_natural <- function(x, ...) {
tibble::tibble(
name = c("deg_free"),
call_info = list(
list(pkg = "dials", fun = "spline_degree", range = c(2L, 15L))
),
source = "recipe",
component = "step_spline_natural",
component_id = x$id
)
}
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