Merge pull request #532 from PriKalra/cliErrorRssample514

There are snapshot test errors for validation_set and vfold.

Author: hfrick

NAMESPACE

@@ -416,6 +416,7 @@ export(validation_time_split)
 export(vfold_cv)
 import(vctrs)
 importFrom(cli,cli_abort)
+importFrom(cli,cli_warn)
 importFrom(dplyr,"%>%")
 importFrom(dplyr,arrange)
 importFrom(dplyr,arrange_)

NEWS.md

@@ -2,7 +2,7 @@
 
 * The new `inner_split()` function and its methods for various resamples is for usage in tune to create a inner resample of the analysis set to fit the preprocessor and model on one part and the post-processor on the other part (#483, #488, #489).
 
-* Started moving error messages to cli (#499, #502). With contributions from @PriKalra (#523, #526, #528, #530, #531) and @JamesHWade (#518).
+* Started moving error messages to cli (#499, #502). With contributions from @PriKalra (#523, #526, #528, #530, #531, #532) and @JamesHWade (#518).
 
 * Fixed example for `nested_cv()` (@seb09, #520).
 

R/rsample-package.R

@@ -3,7 +3,7 @@
 
 ## usethis namespace: start
 #' @importFrom lifecycle deprecated
-#' @importFrom cli cli_abort
+#' @importFrom cli cli_abort cli_warn
 ## usethis namespace: end
 NULL
 

R/tidy.R

@@ -119,7 +119,7 @@ tidy.nested_cv <- function(x, unique_ind = TRUE, ...) {
 
   inner_id <- grep("^id", names(inner_tidy))
   if (length(inner_id) != length(id_cols)) {
-    rlang::abort("Cannot merge tidy data sets")
+    cli_abort("Cannot merge tidy data sets.")
   }
   names(inner_tidy)[inner_id] <- id_cols
   full_join(outer_tidy, inner_tidy, by = id_cols)

R/validation_set.R

@@ -91,8 +91,8 @@ validation.val_split <- function(x, ...) {
 #' @rdname validation_set
 #' @export
 testing.val_split <- function(x, ...) {
-  rlang::abort(
-    "The testing data is not part of the validation set object.",
-    i = "It is part of the result of the initial 3-way split, e.g., with `initial_validation_split()`."
-  )
+ cli_abort(c(
+  "The testing data is not part of the validation set object.",
+  "i" = "It is part of the result of the initial 3-way split, e.g., with {.fun initial_validation_split}."
+))
 }

R/vfold.R

@@ -81,8 +81,8 @@ vfold_cv <- function(data, v = 10, repeats = 1,
     )
   } else {
     if (v == nrow(data)) {
-      rlang::abort(
-        glue::glue("Repeated resampling when `v` is {v} would create identical resamples")
+      cli_abort(
+        "Repeated resampling when {.arg v} is {v} would create identical resamples."
       )
     }
     for (i in 1:repeats) {
@@ -225,14 +225,13 @@ group_vfold_cv <- function(data, group = NULL, v = NULL, repeats = 1, balance =
     split_objs <- group_vfold_splits(data = data, group = group, v = v, balance = balance, strata = strata, pool = pool)
   } else {
     if (is.null(v)) {
-      rlang::abort(
-        "Repeated resampling when `v` is `NULL` would create identical resamples"
+     cli_abort(
+        "Repeated resampling when {.arg v} is {.val NULL} would create identical resamples."
       )
     }
     if (v == length(unique(getElement(data, group)))) {
-      rlang::abort(
-        glue::glue("Repeated resampling when `v` is {v} would create identical resamples")
-      )
+      cli_abort("Repeated resampling when {.arg v} is {.val {v}} would create identical resamples.")
+
     }
     for (i in 1:repeats) {
       tmp <- group_vfold_splits(data = data, group = group, v = v, balance = balance, strata = strata, pool = pool)
@@ -288,21 +287,20 @@ group_vfold_splits <- function(data, group, v = NULL, balance, strata = NULL, po
         )$count
       )
       message <- c(
-        "Leaving `v = NULL` while using stratification will set `v` to the number of groups present in the least common stratum."
+        "Leaving {.code v = NULL} while using stratification will set {.arg v} to the number of groups present in the least common stratum."
       )
 
       if (max_v < 5) {
-        rlang::abort(c(
+        cli_abort(c(
           message,
-          x = glue::glue("The least common stratum only had {max_v} groups, which may not be enough for cross-validation."),
-          i = "Set `v` explicitly to override this error."
-        ),
-        call = rlang::caller_env())
+          "*" = "The least common stratum only had {.val {max_v}} groups, which may not be enough for cross-validation.",
+          "i" = "Set {.arg v} explicitly to override this error."
+        ), call = rlang::caller_env())
       }
 
-      rlang::warn(c(
+      cli_warn(c(
         message,
-        i = "Set `v` explicitly to override this warning."
+        i = "Set {.arg v} explicitly to override this warning."
       ),
       call = rlang::caller_env())
     }
@@ -334,10 +332,11 @@ add_vfolds <- function(x, v) {
 
 check_v <- function(v, max_v, rows = "rows", prevent_loo = TRUE, call = rlang::caller_env()) {
   if (!is.numeric(v) || length(v) != 1 || v < 2) {
-    rlang::abort("`v` must be a single positive integer greater than 1", call = call)
+    cli_abort("{.arg v} must be a single positive integer greater than 1.", call = call)
   } else if (v > max_v) {
-    rlang::abort(
-      glue::glue("The number of {rows} is less than `v = {v}`"), call = call
+    cli_abort(
+      "The number of {rows} is less than {.arg v} = {.val {v}}.",
+      call = call
     )
   } else if (prevent_loo && isTRUE(v == max_v)) {
     cli_abort(c(
@@ -364,14 +363,14 @@ check_grouped_strata <- function(group, strata, pool, data) {
 
   if (nrow(vctrs::vec_unique(grouped_table)) !=
       nrow(vctrs::vec_unique(grouped_table["group"]))) {
-    rlang::abort("`strata` must be constant across all members of each `group`.")
+    cli_abort("{.arg strata} must be constant across all members of each {.arg group}.")
   }
 
   strata
 }
 
 check_repeats <- function(repeats, call = rlang::caller_env()) {
   if (!is.numeric(repeats) || length(repeats) != 1 || repeats < 1) {
-    rlang::abort("`repeats` must be a single positive integer", call = call)
+    cli_abort("{.arg repeats} must be a single positive integer.", call = call)
   }
 }

tests/testthat/_snaps/clustering.md

@@ -4,11 +4,11 @@
 
 ---
 
-    `v` must be a single positive integer greater than 1
+    `v` must be a single positive integer greater than 1.
 
 ---
 
-    The number of rows is less than `v = 500`
+    The number of rows is less than `v` = 500.
 
 ---
 
@@ -20,15 +20,15 @@
       clustering_cv(Orange, v = 1, vars = "Tree")
     Condition
       Error in `clustering_cv()`:
-      ! `v` must be a single positive integer greater than 1
+      ! `v` must be a single positive integer greater than 1.
 
 ---
 
-    `repeats` must be a single positive integer
+    `repeats` must be a single positive integer.
 
 ---
 
-    `repeats` must be a single positive integer
+    `repeats` must be a single positive integer.
 
 ---
 

tests/testthat/_snaps/validation_set.md

@@ -5,4 +5,5 @@
     Condition
       Error in `testing()`:
       ! The testing data is not part of the validation set object.
+      i It is part of the result of the initial 3-way split, e.g., with `initial_validation_split()`.
 

tests/testthat/_snaps/vfold.md

@@ -9,31 +9,31 @@
 
 # bad args
 
-    `v` must be a single positive integer greater than 1
+    `v` must be a single positive integer greater than 1.
 
 ---
 
-    `v` must be a single positive integer greater than 1
+    `v` must be a single positive integer greater than 1.
 
 ---
 
-    `v` must be a single positive integer greater than 1
+    `v` must be a single positive integer greater than 1.
 
 ---
 
-    The number of rows is less than `v = 500`
+    The number of rows is less than `v` = 500.
 
 ---
 
-    Repeated resampling when `v` is 150 would create identical resamples
+    Repeated resampling when `v` is 150 would create identical resamples.
 
 ---
 
-    `repeats` must be a single positive integer
+    `repeats` must be a single positive integer.
 
 ---
 
-    `repeats` must be a single positive integer
+    `repeats` must be a single positive integer.
 
 ---
 
@@ -67,19 +67,19 @@
 
 # grouping -- bad args
 
-    Repeated resampling when `v` is 4 would create identical resamples
+    Repeated resampling when `v` is 4 would create identical resamples.
 
 ---
 
-    Repeated resampling when `v` is `NULL` would create identical resamples
+    Repeated resampling when `v` is "NULL" would create identical resamples.
 
 ---
 
     Code
       group_vfold_cv(Orange, v = 1, group = "Tree")
     Condition
       Error in `group_vfold_cv()`:
-      ! `v` must be a single positive integer greater than 1
+      ! `v` must be a single positive integer greater than 1.
 
 # grouping -- other balance methods