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#' Impute numeric data using a rolling window statistic
#'
#' `step_impute_roll()` creates a *specification* of a recipe step that will
#' substitute missing values of numeric variables by the measure of location
#' (e.g. median) within a moving window.
#'
#' @inheritParams step_center
#' @inheritParams step_pca
#' @param ... One or more selector functions to choose variables to be imputed;
#' these columns must be non-integer numerics (i.e., double precision).
#' See [selections()] for more details.
#' @param window The size of the window around a point to be imputed. Should be
#' an odd integer greater than one. See Details below for a discussion of
#' points at the ends of the series.
#' @param statistic A function with a single argument for the data to compute
#' the imputed value. Only complete values will be passed to the function and
#' it should return a double precision value.
#' @template step-return
#' @family imputation steps
#' @family row operation steps
#' @export
#' @details On the tails, the window is shifted towards the ends.
#' For example, for a 5-point window, the windows for the first
#' four points are `1:5`, `1:5`, `1:5`, and then `2:6`.
#'
#' When missing data are in the window, they are not passed to the
#' function. If all of the data in the window are missing, a
#' missing value is returned.
#'
#' The statistics are calculated on the training set values
#' _before_ imputation. This means that if previous data within the
#' window are missing, their imputed values are not included in the
#' window data used for imputation. In other words, each imputation
#' does not know anything about previous imputations in the series
#' prior to the current point.
#'
#' As of `recipes` 0.1.16, this function name changed from `step_rollimpute()`
#' to `step_impute_roll()`.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the selectors or variables selected) and `window`
#' (the window size) is returned.
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_impute_roll"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template case-weights-not-supported
#'
#' @examples
#' library(lubridate)
#'
#' set.seed(145)
#' example_data <-
#' data.frame(
#' day = ymd("2012-06-07") + days(1:12),
#' x1 = round(runif(12), 2),
#' x2 = round(runif(12), 2),
#' x3 = round(runif(12), 2)
#' )
#' example_data$x1[c(1, 5, 6)] <- NA
#' example_data$x2[c(1:4, 10)] <- NA
#'
#' library(recipes)
#' seven_pt <- recipe(~., data = example_data) %>%
#' update_role(day, new_role = "time_index") %>%
#' step_impute_roll(all_numeric_predictors(), window = 7) %>%
#' prep(training = example_data)
#'
#' # The training set:
#' bake(seven_pt, new_data = NULL)
step_impute_roll <-
function(recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
statistic = median,
window = 5,
skip = FALSE,
id = rand_id("impute_roll")) {
if (!is_tune(window)) {
if (window < 3 | window %% 2 != 1) {
rlang::abort("`window` should be an odd integer >= 3")
}
window <- as.integer(floor(window))
}
add_step(
recipe,
step_impute_roll_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
statistic = statistic,
window = window,
skip = skip,
id = id
)
)
}
#' @rdname step_impute_roll
#' @export
step_rollimpute <-
function(recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
statistic = median,
window = 5,
skip = FALSE,
id = rand_id("impute_roll")) {
lifecycle::deprecate_stop(
when = "0.1.16",
what = "recipes::step_rollimpute()",
with = "recipes::step_impute_roll()"
)
step_impute_roll(
recipe,
...,
role = role,
trained = trained,
columns = columns,
statistic = statistic,
window = window,
skip = skip,
id = id
)
}
step_impute_roll_new <-
function(terms, role, trained, columns, statistic, window, skip, id) {
step(
subclass = "impute_roll",
terms = terms,
role = role,
trained = trained,
columns = columns,
statistic = statistic,
window = window,
skip = skip,
id = id
)
}
#' @export
prep.step_impute_roll <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = "double")
step_impute_roll_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
statistic = x$statistic,
window = x$window,
skip = x$skip,
id = x$id
)
}
#' @export
#' @keywords internal
prep.step_rollimpute <- prep.step_impute_roll
get_window_ind <- function(i, n, k) {
sides <- (k - 1) / 2
if (i - sides >= 1 & i + sides <= n) {
return((i - sides):(i + sides))
}
if (i - sides < 1) {
return(1:k)
}
if (i + sides > n) {
return((n - k + 1):n)
}
}
get_rolling_ind <- function(inds, n, k) {
map(inds, get_window_ind, n = n, k = k)
}
window_est <- function(inds, x, statfun) {
x <- x[inds]
x <- x[!is.na(x)]
out <- if (length(x) == 0) {
na_dbl
} else {
statfun(x)
}
if (!is.double(out)) {
out <- as.double(out)
}
out
}
impute_rolling <- function(inds, x, statfun) {
map_dbl(inds, window_est, x = x, statfun = statfun)
}
#' @export
bake.step_impute_roll <- function(object, new_data, ...) {
col_names <- names(object$columns)
check_new_data(col_names, object, new_data)
n <- nrow(new_data)
missing_ind <- lapply(
new_data[, col_names],
function(x) which(is.na(x))
)
has_missing <- map_lgl(missing_ind, function(x) length(x) > 0)
missing_ind <- missing_ind[has_missing]
roll_ind <- lapply(missing_ind, get_rolling_ind, n = n, k = object$window)
for (col_name in col_names) {
estimates <- impute_rolling(
roll_ind[[col_name]],
new_data[[col_name]],
object$statistic
)
new_data[missing_ind[[col_name]], col_name] <- estimates
}
new_data
}
#' @export
#' @keywords internal
bake.step_rollimpute <- bake.step_impute_roll
#' @export
print.step_impute_roll <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Rolling imputation for "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @export
#' @keywords internal
print.step_rollimpute <- print.step_impute_roll
#' @rdname tidy.recipe
#' @export
tidy.step_impute_roll <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = unname(x$columns), window = unname(x$window))
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names, window = unname(x$window))
}
res$id <- x$id
res
}
#' @export
#' @keywords internal
tidy.step_rollimpute <- tidy.step_impute_roll
#' @export
tunable.step_impute_roll <- function(x, ...) {
tibble::tibble(
name = c("statistic", "window"),
call_info = list(
list(pkg = "dials", fun = "summary_stat"),
list(pkg = "dials", fun = "window_size")
),
source = "recipe",
component = "step_impute_roll",
component_id = x$id
)
}
#' @export
#' @keywords internal
tunable.step_rollimpute <- tunable.step_impute_roll
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