#' Dummy Variables Creation via Feature Hashing
#'
#' @description `r lifecycle::badge("soft-deprecated")`
#'
#' `step_feature_hash()` is being deprecated in favor of
#' [textrecipes::step_dummy_hash()]. This function creates a *specification*
#' of a recipe step that will convert nominal data (e.g. character or factors)
#' into one or more numeric binary columns using the levels of the original
#' data.
#'
#' @inheritParams recipes::step_pca
#' @param num_hash The number of resulting dummy variable columns.
#' @param preserve Use `keep_original_cols` instead to specify whether the
#' selected column(s) should be retained in addition to the new dummy
#' variables.
#' @param columns A character vector for the selected columns. This is `NULL`
#' until the step is trained by [recipes::prep()].
#' @template step-return
#' @details
#'
#' `step_feature_hash()` will create a set of binary dummy variables from a
#' factor or character variable. The values themselves are used to determine
#' which row that the dummy variable should be assigned (as opposed to having a
#' specific column that the value will map to).
#'
#' Since this method does not rely on a pre-determined assignment of levels to
#' columns, new factor levels can be added to the selected columns without
#' issue. Missing values result in missing values for all of the hashed columns.
#'
#' Note that the assignment of the levels to the hashing columns does not try to
#' maximize the allocation. It is likely that multiple levels of the column will
#' map to the same hashed columns (even with small data sets). Similarly, it is
#' likely that some columns will have all zeros. A zero-variance filter (via
#' [recipes::step_zv()]) is recommended for any recipe that uses hashed columns.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is retruned with
#' columns `terms` and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @references
#'
#' Weinberger, K, A Dasgupta, J Langford, A Smola, and J Attenberg. 2009.
#' "Feature Hashing for Large Scale Multitask Learning." In Proceedings of the
#' 26th Annual International Conference on Machine Learning, 1113–20. ACM.
#'
#' Kuhn and Johnson (2020) _Feature Engineering and Selection: A Practical
#' Approach for Predictive Models_. CRC/Chapman Hall
#' \url{https://bookdown.org/max/FES/encoding-predictors-with-many-categories.html}
#' @seealso [recipes::step_dummy()], [recipes::step_zv()]
#' @examplesIf !embed:::is_cran_check() && rlang::is_installed(c("modeldata", "keras"))
#' data(grants, package = "modeldata")
#' rec <-
#' recipe(class ~ sponsor_code, data = grants_other) %>%
#' step_feature_hash(
#' sponsor_code,
#' num_hash = 2^6, keep_original_cols = TRUE
#' ) %>%
#' prep()
#'
#' # How many of the 298 locations ended up in each hash column?
#' results <-
#' bake(rec, new_data = NULL, starts_with("sponsor_code")) %>%
#' distinct()
#'
#' apply(results %>% select(-sponsor_code), 2, sum) %>% table()
#' @export
step_feature_hash <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
num_hash = 2^6,
preserve = deprecated(),
columns = NULL,
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("feature_hash")) {
lifecycle::deprecate_soft(
"0.2.0",
"embed::step_feature_hash()",
"textrecipes::step_dummy_hash()"
)
if (lifecycle::is_present(preserve)) {
lifecycle::deprecate_soft(
"0.1.5",
"step_feature_hash(preserve = )",
"step_feature_hash(keep_original_cols = )"
)
keep_original_cols <- preserve
}
# warm start for tf to avoid a bug in tensorflow
is_tf_available()
add_step(
recipe,
step_feature_hash_new(
terms = enquos(...),
role = role,
trained = trained,
num_hash = num_hash,
preserve = keep_original_cols,
columns = columns,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
)
}
step_feature_hash_new <-
function(terms, role, trained, num_hash, preserve, columns,
keep_original_cols, skip, id) {
step(
subclass = "feature_hash",
terms = terms,
role = role,
trained = trained,
num_hash = num_hash,
preserve = preserve,
columns = columns,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
}
#' @export
prep.step_feature_hash <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
if (length(col_names) > 0) {
check_type(training[, col_names], types = c("string", "factor", "ordered"))
}
step_feature_hash_new(
terms = x$terms,
role = x$role,
trained = TRUE,
num_hash = x$num_hash,
preserve = x$preserve,
columns = col_names,
keep_original_cols = get_keep_original_cols(x),
skip = x$skip,
id = x$id
)
}
make_hash_vars <- function(x, prefix, num_hash = 2^8) {
if (!is.character(x)) {
x <- as.character(x)
}
tmp <- tibble(data = x, ..order = seq_along(x))
uni_x <- unique(x)
rlang::check_installed("keras")
column_int <-
purrr::map_int(
uni_x,
keras::text_hashing_trick,
n = num_hash,
filters = "",
split = "dont split characters",
lower = FALSE
)
column_int[is.na(uni_x)] <- NA
nms <- names0(num_hash, prefix)
make_hash_tbl(column_int, nms) %>%
dplyr::mutate(data = uni_x) %>%
dplyr::left_join(tmp, by = "data", multiple = "all") %>%
dplyr::arrange(..order) %>%
dplyr::select(-data, -..order)
}
make_row <- function(ind, p) {
if (!is.na(ind)) {
x <- rep(0, p)
x[ind] <- 1
} else {
x <- rep(NA_real_, p)
}
x
}
make_hash_tbl <- function(ind, nms) {
p <- length(nms)
x <- purrr::map(ind, make_row, p = p)
x <- do.call("rbind", x)
colnames(x) <- nms
tibble::as_tibble(x)
}
#' @export
bake.step_feature_hash <- function(object, new_data, ...) {
col_names <- names(object$columns)
check_new_data(col_names, object, new_data)
# If no terms were selected
if (length(col_names) == 0) {
return(new_data)
}
new_names <- paste0(col_names, "_hash_")
new_cols <- purrr::map2_dfc(
new_data[, col_names],
new_names, make_hash_vars,
num_hash =
object$num_hash
)
new_cols <- check_name(new_cols, new_data, object, names(new_cols))
new_data <- vec_cbind(new_data, new_cols)
new_data <- remove_original_cols(new_data, object, col_names)
new_data
}
#' @export
print.step_feature_hash <-
function(x, width = max(20, options()$width - 31), ...) {
title <- "Feature hashed dummy variables for "
print_step(names(x$mapping), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname step_feature_hash
#' @usage NULL
#' @export
tidy.step_feature_hash <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = unname(x$columns))
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.embed
#' @export
required_pkgs.step_feature_hash <- function(x, ...) {
c("keras", "embed")
}
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