require optional arguments to be named

NEWS.md

@@ -43,10 +43,11 @@
 
 * For iterative optimization routines, `autoplot()` will use integer breaks when `type = "performance"` or `type = "parameters"`.
 
-## Breaking Change
+## Breaking Changes
 
-* Several functions gain an `eval_time` argument for the evaluation time of dynamic metrics for censored regression. The placement of the argument breaks passing-by-position for one or more other arguments to `fit_best.tune_results()`, `show_best.tune_results()`, and the developer-focused `check_initial()` (#857).
+* Several functions gained an `eval_time` argument for the evaluation time of dynamic metrics for censored regression. The placement of the argument breaks passing-by-position for one or more other arguments to `autoplot.tune_results()` and the developer-focused `check_initial()` (#857).
 
+* Ellipses (...) are now used consistently in the package to require optional arguments to be named. For functions that previously had ellipses at the end of the function signature, they have been moved to follow the last argument without a default value: this applies to `augment.tune_results()`, `collect_predictions.tune_results()`, `collect_metrics.tune_results()`, and the developer-focused `estimate_tune_results()`, `load_pkgs()`, and `encode_set()`. Several other functions that previously did not have ellipses in their signatures gained them: this applies to `conf_mat_resampled()` and the developer-focused `check_workflow()`. Optional arguments previously passed by position will now error informatively prompting them to be named. These changes don't apply in cases when the ellipses are currently in use to forward arguments to other functions (#863).
 
 # tune 1.1.2
 

R/augment.R

@@ -7,12 +7,12 @@
 #' @param x An object resulting from one of the `tune_*()` functions,
 #'  `fit_resamples()`, or `last_fit()`. The control specifications for these
 #'  objects should have used the option `save_pred = TRUE`.
+#' @param ... Not currently used.
 #' @param parameters A data frame with a single row that indicates what
 #' tuning parameters should be used to generate the predictions (for `tune_*()`
-#' objects only). If `NULL`, `select_best(x)` will be used with the first 
-#' metric and, if applicable, the first evaluation time point, used to 
+#' objects only). If `NULL`, `select_best(x)` will be used with the first
+#' metric and, if applicable, the first evaluation time point, used to
 #' create `x`.
-#' @param ... Not currently used.
 #' @return A data frame with one or more additional columns for model
 #' predictions.
 #'
@@ -34,24 +34,15 @@
 #' results.
 #'
 #' @export
-augment.tune_results <- function(x, parameters = NULL, ...) {
-  dots <- rlang::list2(...)
-  if (length(dots) > 0) {
-    rlang::abort(
-      paste(
-        "The only two arguments for `augment.tune_results()` are",
-        "'x' and 'parameters'. Others were passed:",
-        paste0("'", names(dots), "'", collapse = ", ")
-      )
-    )
-  }
+augment.tune_results <- function(x, ..., parameters = NULL) {
+  rlang::check_dots_empty()
 
   # check/determine best settings
   if (is.null(parameters)) {
     obj_fun <- .get_tune_metric_names(x)[1]
     obj_eval_time <- choose_eval_time(
-      x, 
-      metric = obj_fun, 
+      x,
+      metric = obj_fun,
       eval_time = NULL,
       quietly = TRUE
     )
@@ -70,16 +61,7 @@ augment.tune_results <- function(x, parameters = NULL, ...) {
 #' @rdname augment.tune_results
 #' @export
 augment.resample_results <- function(x, ...) {
-  dots <- rlang::list2(...)
-  if (length(dots) > 0) {
-    rlang::abort(
-      paste(
-        "The only argument for `augment.fit_resamples()` is",
-        "'x'. Others were passed:",
-        paste0("'", names(dots), "'", collapse = ", ")
-      )
-    )
-  }
+  rlang::check_dots_empty()
 
   pred <- collect_predictions(x, summarize = TRUE)
   y_nm <- .get_tune_outcome_names(x)
@@ -91,16 +73,7 @@ augment.resample_results <- function(x, ...) {
 #' @rdname augment.tune_results
 #' @export
 augment.last_fit <- function(x, ...) {
-  dots <- rlang::list2(...)
-  if (length(dots) > 0) {
-    rlang::abort(
-      paste(
-        "The only argument for `augment.last_fit()` is",
-        "'x'. Others were passed:",
-        paste0("'", names(dots), "'", collapse = ", ")
-      )
-    )
-  }
+  rlang::check_dots_empty()
 
   pred <- collect_predictions(x, summarize = TRUE)
   pred$.row <- 1:nrow(pred)

R/checks.R

@@ -275,7 +275,7 @@ check_param_objects <- function(pset) {
 #' @keywords internal
 #' @rdname empty_ellipses
 #' @param check_dials A logical for check for a NULL parameter object.
-check_workflow <- function(x, pset = NULL, check_dials = FALSE, call = caller_env()) {
+check_workflow <- function(x, ..., pset = NULL, check_dials = FALSE, call = caller_env()) {
   if (!inherits(x, "workflow")) {
     rlang::abort("The `object` argument should be a 'workflow' object.")
   }
@@ -288,6 +288,8 @@ check_workflow <- function(x, pset = NULL, check_dials = FALSE, call = caller_en
     rlang::abort("A parsnip model is required.")
   }
 
+  rlang::check_dots_empty(call = call)
+
   if (check_dials) {
     if (is.null(pset)) {
       pset <- hardhat::extract_parameter_set_dials(x)

R/collect.R

@@ -3,6 +3,7 @@
 #' @param x The results of [tune_grid()], [tune_bayes()], [fit_resamples()],
 #'  or [last_fit()]. For [collect_predictions()], the control option `save_pred
 #'  = TRUE` should have been used.
+#' @param ... Not currently used.
 #' @param summarize A logical; should metrics be summarized over resamples
 #' (`TRUE`) or return the values for each individual resample. Note that, if `x`
 #' is created by [last_fit()], `summarize` has no effect. For the other object
@@ -17,7 +18,6 @@
 #'  each metric has its own column and the `n` and `std_err` columns are removed,
 #'  if they exist.
 #'
-#' @param ... Not currently used.
 #' @return A tibble. The column names depend on the results and the mode of the
 #' model.
 #'
@@ -120,7 +120,11 @@
 #'
 #' collect_predictions(resampled) %>% arrange(.row)
 #' collect_predictions(resampled, summarize = TRUE) %>% arrange(.row)
-#' collect_predictions(resampled, summarize = TRUE, grid[1, ]) %>% arrange(.row)
+#' collect_predictions(
+#'   resampled,
+#'   summarize = TRUE,
+#'   parameters = grid[1, ]
+#' ) %>% arrange(.row)
 #'
 #' collect_extracts(resampled)
 #'
@@ -139,7 +143,9 @@ collect_predictions.default <- function(x, ...) {
 
 #' @export
 #' @rdname collect_predictions
-collect_predictions.tune_results <- function(x, summarize = FALSE, parameters = NULL, ...) {
+collect_predictions.tune_results <- function(x, ..., summarize = FALSE, parameters = NULL) {
+  rlang::check_dots_empty()
+
   names <- colnames(x)
   coll_col <- ".predictions"
 
@@ -454,7 +460,8 @@ collect_metrics.default <- function(x, ...) {
 
 #' @export
 #' @rdname collect_predictions
-collect_metrics.tune_results <- function(x, summarize = TRUE, type = c("long", "wide"), ...) {
+collect_metrics.tune_results <- function(x, ..., summarize = TRUE, type = c("long", "wide")) {
+  rlang::check_dots_empty()
   rlang::arg_match0(type, values = c("long", "wide"))
 
   if (inherits(x, "last_fit")) {
@@ -551,7 +558,8 @@ collector <- function(x, coll_col = ".predictions") {
 #' @export
 #' @keywords internal
 #' @rdname empty_ellipses
-estimate_tune_results <- function(x, col_name = ".metrics", ...) {
+estimate_tune_results <- function(x, ..., col_name = ".metrics") {
+  rlang::check_dots_empty()
   param_names <- .get_tune_parameter_names(x)
   id_names <- grep("^id", names(x), value = TRUE)
   group_cols <- .get_extra_col_names(x)

R/compute_metrics.R

@@ -83,9 +83,10 @@ compute_metrics.default <- function(x,
 #' @rdname compute_metrics
 compute_metrics.tune_results <- function(x,
                                          metrics,
+                                         ...,
                                          summarize = TRUE,
-                                         event_level = "first",
-                                         ...) {
+                                         event_level = "first") {
+  rlang::check_dots_empty()
   if (!".predictions" %in% names(x)) {
     rlang::abort(paste0(
       "`x` must have been generated with the ",

R/conf_mat_resampled.R

@@ -5,6 +5,7 @@
 #'
 #' @param x An object with class `tune_results` that was used with a
 #' classification model that was run with `control_*(save_pred = TRUE)`.
+#' @param ... Currently unused, must be empty.
 #' @param parameters A tibble with a single tuning parameter combination. Only
 #' one tuning parameter combination (if any were used) is allowed here.
 #' @param tidy Should the results come back in a tibble (`TRUE`) or a `conf_mat`
@@ -30,7 +31,8 @@
 #' conf_mat_resampled(res)
 #' conf_mat_resampled(res, tidy = FALSE)
 #' @export
-conf_mat_resampled <- function(x, parameters = NULL, tidy = TRUE) {
+conf_mat_resampled <- function(x, ..., parameters = NULL, tidy = TRUE) {
+  rlang::check_dots_empty()
   if (!inherits(x, "tune_results")) {
     rlang::abort(
       "The first argument needs to be an object with class 'tune_results'."

R/fit_best.R

@@ -6,6 +6,7 @@
 #' @param x The results of class `tune_results` (coming from functions such as
 #' [tune_grid()], [tune_bayes()], etc). The control option
 #' [`save_workflow = TRUE`][tune::control_grid] should have been used.
+#' @param ... Not currently used, must be empty.
 #' @param metric A character string (or `NULL`) for which metric to optimize. If
 #' `NULL`, the first metric is used.
 #' @param parameters An optional 1-row tibble of tuning parameter settings, with
@@ -22,7 +23,6 @@
 #' `NULL`, the validation set is not used for resamples originating from
 #' [rsample::validation_set()] while it is used for resamples originating
 #' from [rsample::validation_split()].
-#' @param ... Not currently used.
 #' @inheritParams select_best
 #' @details
 #' This function is a shortcut for the manual steps of:
@@ -88,15 +88,13 @@ fit_best.default <- function(x, ...) {
 #' @export
 #' @rdname fit_best
 fit_best.tune_results <- function(x,
+                                  ...,
                                   metric = NULL,
                                   eval_time = NULL,
                                   parameters = NULL,
                                   verbose = FALSE,
-                                  add_validation_set = NULL,
-                                  ...) {
-  if (length(list(...))) {
-    cli::cli_abort(c("x" = "The `...` are not used by this function."))
-  }
+                                  add_validation_set = NULL) {
+  rlang::check_dots_empty()
   wflow <- .get_tune_workflow(x)
   if (is.null(wflow)) {
     cli::cli_abort(c("x" = "The control option `save_workflow = TRUE` should be used when tuning."))

R/load_ns.R

@@ -8,17 +8,19 @@
 #' @return An invisible NULL.
 #' @keywords internal
 #' @export
-load_pkgs <- function(x, infra = TRUE, ...) {
+load_pkgs <- function(x, ..., infra = TRUE) {
   UseMethod("load_pkgs")
 }
 
 #' @export
 load_pkgs.character <- function(x, ...) {
+  rlang::check_dots_empty()
   withr::with_preserve_seed(.load_namespace(x))
 }
 
 #' @export
-load_pkgs.model_spec <- function(x, infra = TRUE, ...) {
+load_pkgs.model_spec <- function(x, ..., infra = TRUE) {
+  rlang::check_dots_empty()
   pkgs <- required_pkgs(x)
   if (infra) {
     pkgs <- c(infra_pkgs, pkgs)
@@ -27,7 +29,8 @@ load_pkgs.model_spec <- function(x, infra = TRUE, ...) {
 }
 
 #' @export
-load_pkgs.workflow <- function(x, infra = TRUE, ...) {
+load_pkgs.workflow <- function(x, ..., infra = TRUE) {
+  rlang::check_dots_empty()
   load_pkgs.model_spec(extract_spec_parsnip(x), infra = infra)
 }
 

R/metric-selection.R

@@ -68,7 +68,8 @@ check_mult_metrics <- function(metric, ..., call = rlang::caller_env()) {
 
 #' @rdname choose_metric
 #' @export
-check_metric_in_tune_results <- function(mtr_info, metric, call = rlang::caller_env()) {
+check_metric_in_tune_results <- function(mtr_info, metric, ..., call = rlang::caller_env()) {
+  rlang::check_dots_empty(call = call)
   if (!any(mtr_info$metric == metric)) {
     cli::cli_abort("{.val {metric}} was not in the metric set. Please choose
                     from: {.val {mtr_info$metric}}.", call = call)
@@ -97,8 +98,8 @@ contains_survival_metric <- function(mtr_info) {
 # choose_eval_time() is called by show_best(), select_best(), and augment()
 #' @rdname choose_metric
 #' @export
-choose_eval_time <- function(x, metric, eval_time = NULL, quietly = FALSE, call = rlang::caller_env()) {
-
+choose_eval_time <- function(x, metric, ..., eval_time = NULL, quietly = FALSE, call = rlang::caller_env()) {
+  rlang::check_dots_empty(call = call)
   mtr_set <- .get_tune_metrics(x)
   mtr_info <- tibble::as_tibble(mtr_set)
 
@@ -183,7 +184,7 @@ first_metric <- function(mtr_set) {
 # such as tune_bayes().
 #' @rdname choose_metric
 #' @export
-first_eval_time <- function(mtr_set, metric = NULL, eval_time = NULL, ..., quietly = FALSE, call = rlang::caller_env()) {
+first_eval_time <- function(mtr_set, ..., metric = NULL, eval_time = NULL, quietly = FALSE, call = rlang::caller_env()) {
   rlang::check_dots_empty()
 
   num_times <- length(eval_time)
@@ -253,7 +254,8 @@ first_eval_time <- function(mtr_set, metric = NULL, eval_time = NULL, ..., quiet
 
 #' @rdname choose_metric
 #' @export
-check_metrics_arg <- function(mtr_set, wflow, call = rlang::caller_env()) {
+check_metrics_arg <- function(mtr_set, wflow, ..., call = rlang::caller_env()) {
+  rlang::check_dots_empty(call = call)
   mode <- extract_spec_parsnip(wflow)$mode
 
   if (is.null(mtr_set)) {
@@ -308,7 +310,8 @@ check_metrics_arg <- function(mtr_set, wflow, call = rlang::caller_env()) {
 
 #' @rdname choose_metric
 #' @export
-check_eval_time_arg <- function(eval_time, mtr_set, call = rlang::caller_env()) {
+check_eval_time_arg <- function(eval_time, mtr_set, ..., call = rlang::caller_env()) {
+  rlang::check_dots_empty(call = call)
   mtr_info <- tibble::as_tibble(mtr_set)
 
   # Not a survival metric

R/select_best.R

@@ -15,6 +15,12 @@
 #'  performance is within some acceptable limit.
 #'
 #' @param x The results of [tune_grid()] or [tune_bayes()].
+#' @param ... For [select_by_one_std_err()] and [select_by_pct_loss()], this
+#' argument is passed directly to [dplyr::arrange()] so that the user can sort
+#' the models from *most simple to most complex*. That is, for a parameter `p`,
+#' pass the unquoted expression `p` if smaller values of `p` indicate a simpler
+#' model, or `desc(p)` if larger values indicate a simpler model. At
+#' least one term is required for these two functions. See the examples below.
 #' @param metric A character value for the metric that will be used to sort
 #'  the models. (See
 #'  \url{https://yardstick.tidymodels.org/articles/metric-types.html} for
@@ -24,12 +30,6 @@
 #' @param n An integer for the number of top results/rows to return.
 #' @param limit The limit of loss of performance that is acceptable (in percent
 #' units). See details below.
-#' @param ... For [select_by_one_std_err()] and [select_by_pct_loss()], this
-#' argument is passed directly to [dplyr::arrange()] so that the user can sort
-#' the models from *most simple to most complex*. That is, for a parameter `p`,
-#' pass the unquoted expression `p` if smaller values of `p` indicate a simpler
-#' model, or `desc(p)` if larger values indicate a simpler model. At
-#' least one term is required for these two functions. See the examples below.
 #' @param eval_time A single numeric time point where dynamic event time
 #' metrics should be chosen (e.g., the time-dependent ROC curve, etc). The
 #' values should be consistent with the values used to create `x`. The `NULL` 
@@ -78,10 +78,10 @@ show_best.default <- function(x, ...) {
 #' @export
 #' @rdname show_best
 show_best.tune_results <- function(x, 
+                                   ...,
                                    metric = NULL, 
                                    eval_time = NULL,
                                    n = 5, 
-                                   ..., 
                                    call = rlang::current_env()) {
   rlang::check_dots_empty()
 
@@ -119,7 +119,7 @@ select_best.default <- function(x, ...) {
 
 #' @export
 #' @rdname show_best
-select_best.tune_results <- function(x, metric = NULL, eval_time = NULL, ...) {
+select_best.tune_results <- function(x, ..., metric = NULL, eval_time = NULL) {
   rlang::check_dots_empty()
 
   metric_info <- choose_metric(x, metric)

R/tune_bayes.R

@@ -296,7 +296,7 @@ tune_bayes_workflow <- function(object,
     maximize <- opt_metric$direction == "maximize"
 
     eval_time <- check_eval_time_arg(eval_time, metrics, call = call)
-    opt_metric_time <- first_eval_time(metrics, opt_metric_name, eval_time, call = call)
+    opt_metric_time <- first_eval_time(metrics, metric = opt_metric_name, eval_time = eval_time, call = call)
 
     if (is.null(param_info)) {
       param_info <- hardhat::extract_parameter_set_dials(object)
@@ -547,7 +547,8 @@ check_iter <- function(iter, call) {
 #' @rdname empty_ellipses
 #' @param pset A `parameters` object.
 #' @param as_matrix A logical for the return type.
-encode_set <- function(x, pset, as_matrix = FALSE, ...) {
+encode_set <- function(x, pset, ..., as_matrix = FALSE) {
+  rlang::check_dots_empty()
   # change the numeric variables to the transformed scale (if any)
   has_trans <- purrr::map_lgl(pset$object, ~ !is.null(.x$trans))
   if (any(has_trans)) {