add rlang type checkers (#950)

* add type checking files

* remove newly unneeded checking functions

* snapshot updates from tidymodels/recipes#1381

* updates files

* basic replacements

* type checker replacements

* tidymodels/tailor#53

* Update R/checks.R

Co-authored-by: Simon P. Couch <simonpatrickcouch@gmail.com>

* add remote to get proper error messages

* typo

* update remotes?

* only test snapshots with more recent version of R *with* rankdeficient

---------

Co-authored-by: Simon P. Couch <simonpatrickcouch@gmail.com>

Author: topepo

DESCRIPTION

@@ -4,7 +4,7 @@ Version: 1.2.1.9000
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2402-136X")),
-    person(given = "Posit Software, PBC", role = c("cph", "fnd"))
+    person("Posit Software, PBC", role = c("cph", "fnd"))
   )
 Description: The ability to tune models is important. 'tune' contains
     functions and classes to be used in conjunction with other
@@ -27,12 +27,12 @@ Imports:
     ggplot2,
     glue (>= 1.6.2),
     GPfit,
-    hardhat (>= 1.2.0),
+    hardhat (>= 1.4.0.9002),
     lifecycle (>= 1.0.0),
-    parsnip (>= 1.2.0),
+    parsnip (>= 1.2.1.9003),
     purrr (>= 1.0.0),
-    recipes (>= 1.0.4),
-    rlang (>= 1.1.0),
+    recipes (>= 1.1.0.9001),
+    rlang (>= 1.1.4),
     rsample (>= 1.2.1.9000),
     tailor,
     tibble (>= 3.1.0),
@@ -57,8 +57,11 @@ Suggests:
     xgboost,
     xml2
 Remotes:
+    tidymodels/hardhat,
+    tidymodels/parsnip,
+    tidymodels/recipes,
     tidymodels/rsample,
-    tidymodels/tailor, 
+    tidymodels/tailor,
     tidymodels/workflows
 Config/Needs/website: pkgdown, tidymodels, kknn, doParallel, doFuture,
     tidyverse/tidytemplate

NAMESPACE

@@ -244,6 +244,7 @@ export(tune_bayes)
 export(tune_grid)
 export(val_class_and_single)
 export(val_class_or_null)
+import(rlang)
 import(vctrs)
 import(workflows)
 importFrom(GPfit,GP_fit)

R/0_imports.R

@@ -22,7 +22,7 @@
 #' @importFrom cli cli_inform cli_warn cli_abort qty
 #' @importFrom foreach foreach getDoParName %dopar%
 #' @importFrom tibble obj_sum size_sum
-
+#' @import rlang
 
 # ------------------------------------------------------------------------------
 # Only a small number of functions in workflows.

R/acquisition.R

@@ -74,8 +74,8 @@ print.prob_improve <- function(x, ...) {
 #' @export
 predict.prob_improve <-
   function(object, new_data, maximize, iter, best, ...) {
-    check_direction(maximize)
-    check_best(best)
+    check_bool(maximize)
+    check_number_decimal(best, allow_infinite = FALSE)
 
     if (is.function(object$trade_off)) {
       trade_off <- object$trade_off(iter)
@@ -126,8 +126,8 @@ exp_improve <- function(trade_off = 0, eps = .Machine$double.eps) {
 
 #' @export
 predict.exp_improve <- function(object, new_data, maximize, iter, best, ...) {
-  check_direction(maximize)
-  check_best(best)
+  check_bool(maximize)
+  check_number_decimal(best, allow_infinite = FALSE)
 
   if (is.function(object$trade_off)) {
     trade_off <- object$trade_off(iter)
@@ -177,7 +177,7 @@ conf_bound <- function(kappa = 0.1) {
 
 #' @export
 predict.conf_bound <- function(object, new_data, maximize, iter, ...) {
-  check_direction(maximize)
+  check_bool(maximize)
 
   if (is.function(object$kappa)) {
     kappa <- object$kappa(iter)

R/checks.R

@@ -493,26 +493,6 @@ get_objective_name <- function(x, metrics) {
   x
 }
 
-
-# ------------------------------------------------------------------------------
-# acq functions
-
-check_direction <- function(x) {
-  if (!is.logical(x) || length(x) != 1) {
-    rlang::abort("`maximize` should be a single logical.")
-  }
-  invisible(NULL)
-}
-
-
-check_best <- function(x) {
-  if (!is.numeric(x) || length(x) != 1 || is.na(x)) {
-    rlang::abort("`best` should be a single, non-missing numeric.")
-  }
-  invisible(NULL)
-}
-
-
 # ------------------------------------------------------------------------------
 
 check_class_or_null <- function(x, cls = "numeric") {
@@ -537,6 +517,7 @@ val_class_or_null <- function(x, cls = "numeric", where = NULL) {
   }
   invisible(NULL)
 }
+# TODO remove this once finetune is updated
 
 check_class_and_single <- function(x, cls = "numeric") {
   isTRUE(inherits(x, cls) & length(x) == 1)
@@ -558,7 +539,7 @@ val_class_and_single <- function(x, cls = "numeric", where = NULL) {
   }
   invisible(NULL)
 }
-
+# TODO remove this once finetune is updated
 
 # Check the data going into the GP. If there are all missing values, fail. If some
 # are missing, remove them and send a warning. If all metrics are the same, fail.
@@ -644,3 +625,11 @@ check_eval_time <- function(eval_time, metrics) {
   invisible(NULL)
 
 }
+
+check_time_limit_arg <- function(x, call = rlang::caller_env()) {
+  if (!inherits(x, c("logical", "numeric")) || length(x) != 1L) {
+    cli::cli_abort("{.arg time_limit} should be either a single numeric or
+                    logical value.", call = call)
+  }
+  invisible(NULL)
+}

R/compute_metrics.R

@@ -87,6 +87,7 @@ compute_metrics.tune_results <- function(x,
                                          summarize = TRUE,
                                          event_level = "first") {
   rlang::check_dots_empty()
+  check_bool(summarize)
   if (!".predictions" %in% names(x)) {
     rlang::abort(paste0(
       "`x` must have been generated with the ",
@@ -114,10 +115,6 @@ compute_metrics.tune_results <- function(x,
     ))
   }
 
-  if (!inherits(summarize, "logical") || length(summarize) != 1L) {
-    rlang::abort("The `summarize` argument must be a single logical value.")
-  }
-
   param_names <- .get_tune_parameter_names(x)
   outcome_name <- .get_tune_outcome_names(x)
 

R/control.R

@@ -38,15 +38,15 @@ control_grid <- function(verbose = FALSE, allow_par = TRUE,
   # Any added arguments should also be added in superset control functions
   # in other packages
 
-  # add options for  seeds per resample
+  # add options for seeds per resample
+  check_bool(verbose)
+  check_bool(allow_par)
+  check_bool(save_pred)
+  check_bool(save_workflow)
+  check_string(event_level)
+  check_character(pkgs, allow_null = TRUE)
+  check_function(extract, allow_null = TRUE)
 
-  val_class_and_single(verbose, "logical", "control_grid()")
-  val_class_and_single(allow_par, "logical", "control_grid()")
-  val_class_and_single(save_pred, "logical", "control_grid()")
-  val_class_and_single(save_workflow, "logical", "control_grid()")
-  val_class_and_single(event_level, "character", "control_grid()")
-  val_class_or_null(pkgs, "character", "control_grid()")
-  val_class_or_null(extract, "function", "control_grid()")
   val_parallel_over(parallel_over, "control_grid()")
 
 
@@ -241,26 +241,27 @@ control_bayes <-
     # in other packages
 
     # add options for seeds per resample
+    check_bool(verbose)
+    check_bool(verbose_iter)
+    check_bool(allow_par)
+    check_bool(save_pred)
+    check_bool(save_workflow)
+    check_bool(save_gp_scoring)
+    check_character(pkgs, allow_null = TRUE)
+    check_function(extract, allow_null = TRUE)
+    check_number_whole(no_improve, min = 0, allow_infinite = TRUE)
+    check_number_whole(uncertain, min = 0, allow_infinite = TRUE)
+    check_number_whole(seed)
+
+    check_time_limit_arg(time_limit)
 
-    val_class_and_single(verbose, "logical", "control_bayes()")
-    val_class_and_single(verbose_iter, "logical", "control_bayes()")
-    val_class_and_single(save_pred, "logical", "control_bayes()")
-    val_class_and_single(save_gp_scoring, "logical", "control_bayes()")
-    val_class_and_single(save_workflow, "logical", "control_bayes()")
-    val_class_and_single(no_improve, c("numeric", "integer"), "control_bayes()")
-    val_class_and_single(uncertain, c("numeric", "integer"), "control_bayes()")
-    val_class_and_single(seed, c("numeric", "integer"), "control_bayes()")
-    val_class_or_null(extract, "function", "control_bayes()")
-    val_class_and_single(time_limit, c("logical", "numeric"), "control_bayes()")
-    val_class_or_null(pkgs, "character", "control_bayes()")
-    val_class_and_single(event_level, "character", "control_bayes()")
     val_parallel_over(parallel_over, "control_bayes()")
-    val_class_and_single(allow_par, "logical", "control_bayes()")
 
 
     if (!is.infinite(uncertain) && uncertain > no_improve) {
-      cli::cli_alert_warning(
-        "Uncertainty sample scheduled after {uncertain} poor iterations but the search will stop after {no_improve}."
+      cli::cli_warn(
+        "Uncertainty sample scheduled after {uncertain} poor iterations but the
+         search will stop after {no_improve}."
       )
     }
 
@@ -296,13 +297,11 @@ print.control_bayes <- function(x, ...) {
 # ------------------------------------------------------------------------------
 
 val_parallel_over <- function(parallel_over, where) {
-  if (is.null(parallel_over)) {
-    return(invisible(NULL))
+  check_string(parallel_over, allow_null = TRUE)
+  if (!is.null(parallel_over)) {
+    rlang::arg_match0(parallel_over, c("resamples", "everything"), "parallel_over")
   }
 
-  val_class_and_single(parallel_over, "character", where)
-  rlang::arg_match0(parallel_over, c("resamples", "everything"), "parallel_over")
-
   invisible(NULL)
 }
 

R/extract.R

@@ -109,9 +109,7 @@ extract_spec_parsnip.tune_results <- function(x, ...) {
 #' @rdname extract-tune
 extract_recipe.tune_results <- function(x, ..., estimated = TRUE) {
   check_empty_dots(...)
-  if (!rlang::is_bool(estimated)) {
-    rlang::abort("`estimated` must be a single `TRUE` or `FALSE`.")
-  }
+  check_bool(estimated)
   extract_recipe(extract_workflow(x), estimated = estimated)
 }
 check_empty_dots <- function(...) {