deprecate step_feature_hash()

NAMESPACE

@@ -5,7 +5,6 @@ S3method(bake,step_collapse_stringdist)
 S3method(bake,step_discretize_cart)
 S3method(bake,step_discretize_xgb)
 S3method(bake,step_embed)
-S3method(bake,step_feature_hash)
 S3method(bake,step_lencode_bayes)
 S3method(bake,step_lencode_glm)
 S3method(bake,step_lencode_mixed)
@@ -19,7 +18,6 @@ S3method(prep,step_collapse_stringdist)
 S3method(prep,step_discretize_cart)
 S3method(prep,step_discretize_xgb)
 S3method(prep,step_embed)
-S3method(prep,step_feature_hash)
 S3method(prep,step_lencode_bayes)
 S3method(prep,step_lencode_glm)
 S3method(prep,step_lencode_mixed)
@@ -33,7 +31,6 @@ S3method(print,step_collapse_stringdist)
 S3method(print,step_discretize_cart)
 S3method(print,step_discretize_xgb)
 S3method(print,step_embed)
-S3method(print,step_feature_hash)
 S3method(print,step_lencode_bayes)
 S3method(print,step_lencode_glm)
 S3method(print,step_lencode_mixed)
@@ -47,7 +44,6 @@ S3method(required_pkgs,step_collapse_stringdist)
 S3method(required_pkgs,step_discretize_cart)
 S3method(required_pkgs,step_discretize_xgb)
 S3method(required_pkgs,step_embed)
-S3method(required_pkgs,step_feature_hash)
 S3method(required_pkgs,step_lencode_bayes)
 S3method(required_pkgs,step_lencode_glm)
 S3method(required_pkgs,step_lencode_mixed)
@@ -61,7 +57,6 @@ S3method(tidy,step_collapse_stringdist)
 S3method(tidy,step_discretize_cart)
 S3method(tidy,step_discretize_xgb)
 S3method(tidy,step_embed)
-S3method(tidy,step_feature_hash)
 S3method(tidy,step_lencode_bayes)
 S3method(tidy,step_lencode_glm)
 S3method(tidy,step_lencode_mixed)

NEWS.md

@@ -1,5 +1,7 @@
 # embed (development version)
 
+* `step_feature_hash()` has been fully deprecated in favor of `textrecipes::step_dummy_hash()`. (#253)
+
 # embed 1.1.5
 
 ## Improvements

R/feature_hash.R

@@ -1,262 +1,27 @@
 #' Dummy Variables Creation via Feature Hashing
 #'
-#' @description `r lifecycle::badge("soft-deprecated")`
+#' @description 
+#' `r lifecycle::badge("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()`][recipes::tidy.recipe] this step, a tibble is returned with
-#' columns `terms` and `id`:
+#' Please use [textrecipes::step_dummy_hash()] instead.
 #' 
-#' \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()
+#' @keywords internal
 #' @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$terms, training, info)
-
-  check_number_whole(x$num_hash, min = 0, arg = "num_hash")
-
-  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
+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_stop(
+    "0.2.0",
+    "embed::step_feature_hash()",
+    "textrecipes::step_dummy_hash()"
   )
-
-  new_cols <- recipes::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")
 }