#' Impute via bagged trees
#'
#' `step_impute_bag()` creates a *specification* of a recipe step that will
#' create bagged tree models to impute missing data.
#'
#' @inheritParams step_center
#' @param ... One or more selector functions to choose variables to be imputed.
#' When used with `imp_vars`, these dots indicate which variables are used to
#' predict the missing data in each variable. See [selections()] for more
#' details.
#' @param impute_with A call to `imp_vars` to specify which variables are used
#' to impute the variables that can include specific variable names separated
#' by commas or different selectors (see [selections()]). If a column is
#' included in both lists to be imputed and to be an imputation predictor, it
#' will be removed from the latter and not used to impute itself.
#' @param trees An integer for the number of bagged trees to use in each model.
#' @param options A list of options to [ipred::ipredbagg()]. Defaults are set
#' for the arguments `nbagg` and `keepX` but others can be passed in. **Note**
#' that the arguments `X` and `y` should not be passed here.
#' @param seed_val An integer used to create reproducible models. The same seed
#' is used across all imputation models.
#' @param models The [ipred::ipredbagg()] objects are stored here once this
#' bagged trees have be trained by [prep()].
#' @template step-return
#' @family imputation steps
#' @export
#' @details For each variable requiring imputation, a bagged tree is created
#' where the outcome is the variable of interest and the predictors are any
#' other variables listed in the `impute_with` formula. One advantage to the
#' bagged tree is that is can accept predictors that have missing values
#' themselves. This imputation method can be used when the variable of interest
#' (and predictors) are numeric or categorical. Imputed categorical variables
#' will remain categorical. Also, integers will be imputed to integer too.
#'
#' Note that if a variable that is to be imputed is also in `impute_with`,
#' this variable will be ignored.
#'
#' It is possible that missing values will still occur after imputation if a
#' large majority (or all) of the imputing variables are also missing.
#'
#' As of `recipes` 0.1.16, this function name changed from `step_bagimpute()`
#' to `step_impute_bag()`.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the selectors or variables selected) and `model`
#' (the bagged tree object) is returned.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `model` , and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{model}{list, the bagged tree object}
#' \item{id}{character, id of this step}
#' }
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_impute_bag"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template case-weights-not-supported
#'
#' @references Kuhn, M. and Johnson, K. (2013). *Applied Predictive Modeling*.
#' Springer Verlag.
#' @examplesIf rlang::is_installed("modeldata")
#' data("credit_data", package = "modeldata")
#'
#' ## missing data per column
#' vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))
#'
#' set.seed(342)
#' in_training <- sample(1:nrow(credit_data), 2000)
#'
#' credit_tr <- credit_data[in_training, ]
#' credit_te <- credit_data[-in_training, ]
#' missing_examples <- c(14, 394, 565)
#'
#' rec <- recipe(Price ~ ., data = credit_tr)
#' \dontrun{
#' impute_rec <- rec %>%
#' step_impute_bag(Status, Home, Marital, Job, Income, Assets, Debt)
#'
#' imp_models <- prep(impute_rec, training = credit_tr)
#'
#' imputed_te <- bake(imp_models, new_data = credit_te)
#'
#' credit_te[missing_examples, ]
#' imputed_te[missing_examples, names(credit_te)]
#'
#' tidy(impute_rec, number = 1)
#' tidy(imp_models, number = 1)
#'
#' ## Specifying which variables to imputate with
#'
#' impute_rec <- rec %>%
#' step_impute_bag(Status, Home, Marital, Job, Income, Assets, Debt,
#' impute_with = imp_vars(Time, Age, Expenses),
#' # for quick execution, nbagg lowered
#' options = list(nbagg = 5, keepX = FALSE)
#' )
#'
#' imp_models <- prep(impute_rec, training = credit_tr)
#'
#' imputed_te <- bake(imp_models, new_data = credit_te)
#'
#' credit_te[missing_examples, ]
#' imputed_te[missing_examples, names(credit_te)]
#'
#' tidy(impute_rec, number = 1)
#' tidy(imp_models, number = 1)
#' }
step_impute_bag <-
function(
recipe,
...,
role = NA,
trained = FALSE,
impute_with = imp_vars(all_predictors()),
trees = 25,
models = NULL,
options = list(keepX = FALSE),
seed_val = sample.int(10^4, 1),
skip = FALSE,
id = rand_id("impute_bag")
) {
if (is.null(impute_with)) {
cli::cli_abort("{.arg impute_with} must not be empty.")
}
add_step(
recipe,
step_impute_bag_new(
terms = enquos(...),
role = role,
trained = trained,
impute_with = impute_with,
trees = trees,
models = models,
options = options,
seed_val = seed_val,
skip = skip,
id = id
)
)
}
step_impute_bag_new <-
function(
terms,
role,
trained,
models,
options,
impute_with,
trees,
seed_val,
skip,
id
) {
step(
subclass = "impute_bag",
terms = terms,
role = role,
trained = trained,
impute_with = impute_with,
trees = trees,
models = models,
options = options,
seed_val = seed_val,
skip = skip,
id = id
)
}
bag_wrap <- function(vars, dat, opt, seed_val) {
seed_val <- seed_val[1]
dat <- as.data.frame(dat[, c(vars$y, vars$x)])
if (is.character(dat[[vars$y]])) {
dat[[vars$y]] <- factor(dat[[vars$y]])
}
if (!is.null(seed_val) && !is.na(seed_val)) {
set.seed(seed_val)
}
out <- do.call(
"ipredbagg",
c(
list(
y = dat[, vars$y],
X = dat[, vars$x, drop = FALSE]
),
opt
)
)
out$..imp_vars <- vars$x
out
}
## This figures out which data should be used to predict each variable
## scheduled for imputation
impute_var_lists <- function(to_impute, impute_using, training, info) {
to_impute <- recipes_eval_select(to_impute, training, info)
impute_using <- recipes_eval_select(impute_using, training, info)
var_lists <- vector(mode = "list", length = length(to_impute))
for (i in seq_along(var_lists)) {
var_lists[[i]] <- list(
y = to_impute[i],
x = impute_using[!(impute_using %in% to_impute[i])]
)
}
var_lists
}
#' @export
prep.step_impute_bag <- function(x, training, info = NULL, ...) {
check_number_whole(x$trees, arg = "trees", min = 1)
check_number_whole(x$seed_val, arg = "seed_val")
var_lists <-
impute_var_lists(
to_impute = x$terms,
impute_using = x$impute_with,
training = training,
info = info
)
opt <- x$options
opt$nbagg <- x$trees
x$models <- lapply(
var_lists,
bag_wrap,
dat = training,
opt = opt,
seed_val = x$seed_val
)
names(x$models) <- vapply(var_lists, function(x) x$y, c(""))
step_impute_bag_new(
terms = x$terms,
role = x$role,
trained = TRUE,
models = x$models,
options = x$options,
impute_with = x$impute_with,
trees = x$trees,
seed_val = x$seed_val,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_impute_bag <- function(object, new_data, ...) {
col_names <- names(object$models)
check_new_data(col_names, object, new_data)
missing_rows <- !vec_detect_complete(new_data)
if (!any(missing_rows)) {
return(new_data)
}
old_data <- new_data
for (col_name in col_names) {
missing_rows <- !vec_detect_complete(new_data[[col_name]])
if (!any(missing_rows)) {
next
}
preds <- object$models[[col_name]]$..imp_vars
pred_data <- old_data[missing_rows, preds, drop = FALSE]
## do a better job of checking this:
if (all(is.na(pred_data))) {
cli::cli_warn("All predictors are missing; cannot impute.")
} else {
pred_vals <- predict(object$models[[col_name]], pred_data)
# For an ipred bug reported on 2021-09-14:
pred_vals <- cast(pred_vals, object$models[[col_name]]$y)
new_data[missing_rows, col_name] <- pred_vals
}
}
new_data
}
#' @export
print.step_impute_bag <-
function(x, width = max(20, options()$width - 31), ...) {
title <- "Bagged tree imputation for "
print_step(names(x$models), x$terms, x$trained, title, width)
invisible(x)
}
#' @export
#' @rdname step_impute_bag
imp_vars <- function(...) quos(...)
#' @rdname tidy.recipe
#' @export
tidy.step_impute_bag <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = names(x$models),
model = unname(x$models)
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names, model = list(NULL))
}
res$id <- x$id
res
}
# ------------------------------------------------------------------------------
#' @export
tunable.step_impute_bag <- function(x, ...) {
tibble::tibble(
name = "trees",
call_info = list(list(pkg = "dials", fun = "trees", range = c(5L, 25L))),
source = "recipe",
component = "step_impute_bag",
component_id = x$id
)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.