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#' Natural Spline Basis Functions
#'
#' `step_ns` creates a *specification* of a recipe step
#' that will create new columns that are basis expansions of
#' variables using natural splines.
#'
#' @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 deg_free The degrees of freedom.
#' @param objects A list of [splines::ns()] objects
#' created once the step has been trained.
#' @param options A list of options for [splines::ns()]
#' which should not include `x` or `df`.
#' @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` which is
#' the columns that will be affected and `holiday`.
#' @keywords datagen
#' @concept preprocessing
#' @concept basis_expansion
#' @export
#' @details `step_ns` can create 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 `df` or `knot` arguments of
#' [splines::ns()]. The original variables are removed
#' from the data and new columns are added. The naming convention
#' for the new variables is `varname_ns_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)
#'
#' with_splines <- rec %>%
#' step_ns(carbon, hydrogen)
#' with_splines <- prep(with_splines, training = biomass_tr)
#'
#' expanded <- bake(with_splines, biomass_te)
#' expanded
#' @seealso [step_poly()] [recipe()]
#' [prep.recipe()] [bake.recipe()]
step_ns <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
objects = NULL,
deg_free = 2,
options = list(),
skip = FALSE,
id = rand_id("ns")) {
add_step(
recipe,
step_ns_new(
terms = ellipse_check(...),
trained = trained,
deg_free = deg_free,
role = role,
objects = objects,
options = options,
skip = skip,
id = id
)
)
}
step_ns_new <-
function(terms, role, trained, deg_free, objects, options, skip, id) {
step(
subclass = "ns",
terms = terms,
role = role,
trained = trained,
deg_free = deg_free,
objects = objects,
options = options,
skip = skip,
id = id
)
}
ns_wrapper <- function(x, args) {
if (!("Boundary.knots" %in% names(args)))
args$Boundary.knots <- range(x)
args$x <- x
ns_obj <- do.call("ns", args)
## don't need to save the original data so keep 1 row
out <- matrix(NA, ncol = ncol(ns_obj), nrow = 1)
class(out) <- c("ns", "basis", "matrix")
attr(out, "knots") <- attr(ns_obj, "knots")[]
attr(out, "Boundary.knots") <- attr(ns_obj, "Boundary.knots")
attr(out, "intercept") <- attr(ns_obj, "intercept")
out
}
#' @export
prep.step_ns <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
opt <- x$options
opt$df <- x$deg_free
obj <- lapply(training[, col_names], ns_wrapper, opt)
for (i in seq(along.with = col_names))
attr(obj[[i]], "var") <- col_names[i]
step_ns_new(
terms = x$terms,
role = x$role,
trained = TRUE,
deg_free = x$deg_free,
objects = obj,
options = x$options,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_ns <- function(object, new_data, ...) {
## pre-allocate a matrix for the basis functions.
new_cols <- vapply(object$objects, ncol, c(int = 1L))
ns_values <-
matrix(NA, nrow = nrow(new_data), ncol = sum(new_cols))
colnames(ns_values) <- rep("", sum(new_cols))
strt <- 1
for (i in names(object$objects)) {
cols <- (strt):(strt + new_cols[i] - 1)
orig_var <- attr(object$objects[[i]], "var")
ns_values[, cols] <-
predict(object$objects[[i]], getElement(new_data, i))
new_names <-
paste(orig_var, "ns", names0(new_cols[i], ""), sep = "_")
colnames(ns_values)[cols] <- new_names
strt <- max(cols) + 1
new_data[, orig_var] <- NULL
}
new_data <- bind_cols(new_data, as_tibble(ns_values))
if (!is_tibble(new_data))
new_data <- as_tibble(new_data)
new_data
}
print.step_ns <-
function(x, width = max(20, options()$width - 28), ...) {
cat("Natural Splines on ")
printer(names(x$objects), x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_ns
#' @param x A `step_ns` object.
#' @export
tidy.step_ns <- function(x, ...) {
if (is_trained(x)) {
cols <- tibble(terms = names(x$objects))
} else {
cols <- sel2char(x$terms)
}
res <- expand.grid(terms = cols, stringsAsFactors = FALSE)
res$id <- x$id
as_tibble(res)
}
#' @rdname tunable.step
#' @export
tunable.step_ns <- function(x, ...) {
tibble::tibble(
name = c("deg_free"),
call_info = list(
list(pkg = "dials", fun = "spline_degree", range = c(1L, 15L))
),
source = "recipe",
component = "step_ns",
component_id = x$id
)
}
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