#' Missing value column filter
#'
#' `step_filter_missing()` creates a *specification* of a recipe step that will
#' potentially remove variables that have too many missing values.
#'
#' @inheritParams step_center
#' @param threshold A value for the threshold of missing values in column. The
#' step will remove the columns where the proportion of missing values exceeds
#' the threshold.
#' @param removals A character string that contains the names of columns that
#' should be removed. These values are not determined until [prep()] is
#' called.
#' @template step-return
#' @template filter-steps
#' @family variable filter steps
#' @export
#'
#' @details
#'
#' This step will remove variables if the proportion of missing values exceeds
#' the `threshold`.
#'
#' All variables with missing values will be removed for `threshold = 0`.
#'
#' # 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_filter_missing"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template sparse-preserve
#'
#' @template case-weights-unsupervised
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(credit_data, package = "modeldata")
#'
#' rec <- recipe(Status ~ ., data = credit_data) %>%
#' step_filter_missing(all_predictors(), threshold = 0)
#'
#' filter_obj <- prep(rec)
#'
#' filtered_te <- bake(filter_obj, new_data = NULL)
#'
#' tidy(rec, number = 1)
#' tidy(filter_obj, number = 1)
step_filter_missing <- function(
recipe,
...,
role = NA,
trained = FALSE,
threshold = 0.1,
removals = NULL,
skip = FALSE,
id = rand_id("filter_missing")
) {
add_step(
recipe,
step_filter_missing_new(
terms = enquos(...),
role = role,
trained = trained,
threshold = threshold,
removals = removals,
skip = skip,
id = id,
case_weights = NULL
)
)
}
step_filter_missing_new <-
function(terms, role, trained, threshold, removals, skip, id, case_weights) {
step(
subclass = "filter_missing",
terms = terms,
role = role,
trained = trained,
threshold = threshold,
removals = removals,
skip = skip,
id = id,
case_weights = case_weights
)
}
#' @export
prep.step_filter_missing <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_number_decimal(x$threshold, min = 0, max = 1, arg = "threshold")
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
if (isFALSE(were_weights_used)) {
wts <- NULL
}
if (length(col_names) > 1) {
filter <- filter_missing_fun(
x = training[, col_names],
threshold = x$threshold,
wts = wts
)
} else {
filter <- character(0)
}
step_filter_missing_new(
terms = x$terms,
role = x$role,
trained = TRUE,
threshold = x$threshold,
removals = filter,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
#' @export
bake.step_filter_missing <- function(object, new_data, ...) {
new_data <- recipes_remove_cols(new_data, object)
new_data
}
#' @export
print.step_filter_missing <-
function(x, width = max(20, options()$width - 36), ...) {
if (x$trained) {
title <- "Missing value column filter removed "
} else {
title <- "Missing value column filter on "
}
print_step(
x$removals,
x$terms,
x$trained,
title,
width,
case_weights = x$case_weights
)
invisible(x)
}
filter_missing_fun <- function(x, threshold, wts) {
n <- NCOL(x)
removal_ind <- logical(n)
for (i in seq_len(n)) {
values <- x[[i]]
if (sparsevctrs::is_sparse_vector(values)) {
nas <- sparsevctrs::sparse_is_na(values, type = "integer")
missing <- sparsevctrs::sparse_mean(nas, wts = wts)
} else {
nas <- is.na(values)
missing <- averages(data.frame(nas), wts = wts)
}
if (missing > threshold) {
removal_ind[[i]] <- TRUE
}
}
names(x)[removal_ind]
}
#' @rdname tidy.recipe
#' @export
tidy.step_filter_missing <- tidy_filter
#' @export
tunable.step_filter_missing <- function(x, ...) {
tibble::tibble(
name = "threshold",
call_info = list(
list(pkg = "dials", fun = "threshold", range = c(0.05, 1.00))
),
source = "recipe",
component = "step_filter_missing",
component_id = x$id
)
}
#' @export
.recipes_preserve_sparsity.step_filter_missing <- function(x, ...) {
TRUE
}
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