expect_error -> expect_no_error (#953)

tests/testthat/_snaps/checks.md

@@ -291,22 +291,22 @@
 # check parameter finalization
 
     Code
-      expect_error(p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = character(
-        0)), regex = NA)
+      expect_no_error(p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = character(
+        0)))
     Message
       i Creating pre-processing data to finalize unknown parameter: mtry
 
 ---
 
     Code
-      expect_error(p2 <- tune:::check_parameters(w2, data = mtcars), regex = NA)
+      expect_no_error(p2 <- tune:::check_parameters(w2, data = mtcars))
     Message
       i Creating pre-processing data to finalize unknown parameter: mtry
 
 ---
 
     Code
-      expect_error(p3_a <- tune:::check_parameters(w3, data = mtcars), regex = NA)
+      expect_no_error(p3_a <- tune:::check_parameters(w3, data = mtcars))
     Message
       i Creating pre-processing data to finalize unknown parameter: mtry
 

tests/testthat/test-autoplot.R

@@ -206,7 +206,7 @@ test_that("coord_obs_pred", {
 
   p2 <- p + coord_obs_pred()
 
-  expect_error(print(p2), regexp = NA)
+  expect_no_error(print(p2))
 
   expect_true(inherits(p2$coordinates, "CoordObsPred"))
   expect_equal(p2$coordinates$limits$x, rng)

tests/testthat/test-bayes.R

@@ -57,7 +57,7 @@ test_that("tune recipe only", {
   expect_null(.get_tune_eval_time_target(res))
 
   set.seed(1)
-  expect_error(
+  expect_no_error(
     suppressMessages(
       tune_bayes(
         wflow,
@@ -67,8 +67,7 @@ test_that("tune recipe only", {
         iter = iter2,
         corr = list(type = "matern", nu = 3 / 2)
       )
-    ),
-    regexp = NA
+    )
   )
 
 

tests/testthat/test-checks.R

@@ -3,7 +3,7 @@ test_that("rsample objects", {
   obj_loo <- rsample::loo_cv(mtcars)
   obj_nst <- rsample::nested_cv(mtcars, obj_cv, inside = rsample::bootstraps())
   obj_permut <- rsample::permutations(mtcars, hp)
-  expect_error(tune:::check_rset(obj_cv), regexp = NA)
+  expect_no_error(tune:::check_rset(obj_cv))
   expect_snapshot(error = TRUE, tune:::check_rset(obj_loo))
   expect_snapshot(error = TRUE, tune:::check_rset(obj_nst))
   expect_snapshot(error = TRUE, tune:::check_rset(obj_permut))
@@ -69,7 +69,7 @@ test_that("grid objects", {
     add_model(svm_mod) %>%
     add_recipe(bare_rec)
 
-  expect_error(grid_2 <- tune:::check_grid(6, wflow_1), NA)
+  expect_no_error(grid_2 <- tune:::check_grid(6, wflow_1))
   expect_equal(nrow(grid_2), 6)
   expect_true(inherits(grid_2, "data.frame"))
 
@@ -226,17 +226,15 @@ test_that("workflow objects (will not tune, tidymodels/tune#548)", {
   lr_glmnet_2 <- lr_lm_2 %>% parsnip::set_engine("glmnet")
 
   # don't error when supplied tune args make sense given engine / steps
-  expect_error_na <- function(x) {testthat::expect_error(x, regexp = NA)}
+  expect_no_error(check_workflow(workflow(rec_bare, lr_lm_0)))
+  expect_no_error(check_workflow(workflow(rec_bare, lr_glmnet_0)))
+  expect_no_error(check_workflow(workflow(rec_bare, lr_glmnet_1)))
+  expect_no_error(check_workflow(workflow(rec_bare, lr_glmnet_2)))
 
-  expect_error_na(check_workflow(workflow(rec_bare, lr_lm_0)))
-  expect_error_na(check_workflow(workflow(rec_bare, lr_glmnet_0)))
-  expect_error_na(check_workflow(workflow(rec_bare, lr_glmnet_1)))
-  expect_error_na(check_workflow(workflow(rec_bare, lr_glmnet_2)))
-
-  expect_error_na(check_workflow(workflow(rec_tune, lr_lm_0)))
-  expect_error_na(check_workflow(workflow(rec_tune, lr_glmnet_0)))
-  expect_error_na(check_workflow(workflow(rec_tune, lr_glmnet_1)))
-  expect_error_na(check_workflow(workflow(rec_tune, lr_glmnet_2)))
+  expect_no_error(check_workflow(workflow(rec_tune, lr_lm_0)))
+  expect_no_error(check_workflow(workflow(rec_tune, lr_glmnet_0)))
+  expect_no_error(check_workflow(workflow(rec_tune, lr_glmnet_1)))
+  expect_no_error(check_workflow(workflow(rec_tune, lr_glmnet_2)))
 
   # error when supplied tune args don't make sense given engine / steps
   expect_error_nt <- function(x) {testthat::expect_error(x, class = "not_tunable_error")}
@@ -321,7 +319,7 @@ test_that("metrics must match the parsnip engine", {
 # ------------------------------------------------------------------------------
 
 test_that("grid control objects", {
-  expect_error(control_grid(), NA)
+  expect_no_error(control_grid())
   expect_snapshot(error = TRUE, control_grid(tomato = 1))
   expect_snapshot(error = TRUE, control_grid(verbose = 1))
   expect_snapshot(error = TRUE, control_grid(verbose = rep(TRUE, 2)))
@@ -330,18 +328,18 @@ test_that("grid control objects", {
   expect_snapshot(error = TRUE, control_grid(extract = Inf))
   expect_snapshot(error = TRUE, control_grid(pkgs = Inf))
 
-  expect_error(control_grid(verbose = TRUE), NA)
-  expect_error(control_grid(allow_par = FALSE), NA)
-  expect_error(control_grid(save_pred = TRUE), NA)
-  expect_error(control_grid(extract = NULL), NA)
-  expect_error(control_grid(extract = I), NA)
-  expect_error(control_grid(pkgs = NULL), NA)
-  expect_error(control_grid(pkgs = letters), NA)
+  expect_no_error(control_grid(verbose = TRUE))
+  expect_no_error(control_grid(allow_par = FALSE))
+  expect_no_error(control_grid(save_pred = TRUE))
+  expect_no_error(control_grid(extract = NULL))
+  expect_no_error(control_grid(extract = I))
+  expect_no_error(control_grid(pkgs = NULL))
+  expect_no_error(control_grid(pkgs = letters))
   expect_s3_class(control_grid(), c("control_grid", "control_resamples"))
 })
 
 test_that("Bayes control objects", {
-  expect_error(control_bayes(), NA)
+  expect_no_error(control_bayes())
   expect_snapshot(error = TRUE, control_bayes(tomato = 1))
   expect_snapshot(error = TRUE, control_bayes(verbose = 1))
   expect_snapshot(error = TRUE, control_bayes(verbose = rep(TRUE, 2)))
@@ -357,15 +355,15 @@ test_that("Bayes control objects", {
     tmp <- control_bayes(no_improve = 2, uncertain = 5)
   )
 
-  expect_error(control_bayes(verbose = TRUE), NA)
-  expect_error(control_bayes(no_improve = 2), NA)
-  expect_error(control_bayes(uncertain = 2), NA)
-  expect_error(control_bayes(save_pred = TRUE), NA)
-  expect_error(control_bayes(extract = NULL), NA)
-  expect_error(control_bayes(extract = I), NA)
-  expect_error(control_bayes(pkgs = NULL), NA)
-  expect_error(control_bayes(pkgs = letters), NA)
-  expect_error(control_bayes(time_limit = 2), NA)
+  expect_no_error(control_bayes(verbose = TRUE))
+  expect_no_error(control_bayes(no_improve = 2))
+  expect_no_error(control_bayes(uncertain = 2))
+  expect_no_error(control_bayes(save_pred = TRUE))
+  expect_no_error(control_bayes(extract = NULL))
+  expect_no_error(control_bayes(extract = I))
+  expect_no_error(control_bayes(pkgs = NULL))
+  expect_no_error(control_bayes(pkgs = letters))
+  expect_no_error(control_bayes(time_limit = 2))
   expect_s3_class(control_bayes(), "control_bayes")
 })
 
@@ -448,16 +446,14 @@ test_that("check parameter finalization", {
     add_model(rf1)
 
   expect_snapshot(
-    expect_error(
-      p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = character(0)),
-      regex = NA
+    expect_no_error(
+      p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = character(0))
     )
   )
   expect_false(any(dials::has_unknowns(p1$object)))
 
-  expect_error(
-    p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = "mtry"),
-    regex = NA
+  expect_no_error(
+    p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = "mtry")
   )
 
   w2 <-
@@ -466,9 +462,8 @@ test_that("check parameter finalization", {
     add_model(rf1)
 
   expect_snapshot(
-    expect_error(
-      p2 <- tune:::check_parameters(w2, data = mtcars),
-      regex = NA
+    expect_no_error(
+      p2 <- tune:::check_parameters(w2, data = mtcars)
     )
   )
   expect_false(any(dials::has_unknowns(p2$object)))
@@ -480,9 +475,8 @@ test_that("check parameter finalization", {
   p3 <- extract_parameter_set_dials(w3)
 
   expect_snapshot(
-    expect_error(
-      p3_a <- tune:::check_parameters(w3, data = mtcars),
-      regex = NA
+    expect_no_error(
+      p3_a <- tune:::check_parameters(w3, data = mtcars)
     )
   )
   expect_false(any(dials::has_unknowns(p3_a$object)))
@@ -500,9 +494,8 @@ test_that("check parameter finalization", {
     extract_parameter_set_dials(w4) %>%
     update(mtry = dials::mtry(c(1, 10)))
 
-  expect_error(
-    p4_b <- tune:::check_parameters(w4, p4_a, data = mtcars),
-    regex = NA
+  expect_no_error(
+    p4_b <- tune:::check_parameters(w4, p4_a, data = mtcars)
   )
   expect_true(inherits(p4_b, "parameters"))
 
@@ -511,9 +504,8 @@ test_that("check parameter finalization", {
     add_recipe(rec_tune) %>%
     add_model(lm1)
 
-  expect_error(
-    p5 <- tune:::check_parameters(w5, data = mtcars),
-    regex = NA
+  expect_no_error(
+    p5 <- tune:::check_parameters(w5, data = mtcars)
   )
   expect_true(inherits(p5, "parameters"))
 })

tests/testthat/test-conf-mat-resampled.R

@@ -1,15 +1,19 @@
 test_that("appropriate return values", {
   svm_results <- readRDS(test_path("data", "svm_results.rds"))
 
-  expect_error(
-    cm_1 <- conf_mat_resampled(svm_results, parameters = select_best(svm_results, metric = "accuracy")),
-    regex = NA
+  expect_no_error(
+    cm_1 <-
+      conf_mat_resampled(svm_results, parameters = select_best(svm_results, metric = "accuracy"))
   )
   expect_true(tibble::is_tibble(cm_1))
 
-  expect_error(
-    cm_2 <- conf_mat_resampled(svm_results, parameters = select_best(svm_results, metric = "accuracy"), tidy = FALSE),
-    regex = NA
+  expect_no_error(
+    cm_2 <-
+      conf_mat_resampled(
+        svm_results,
+        parameters = select_best(svm_results, metric = "accuracy"),
+        tidy = FALSE
+      )
   )
   expect_equal(class(cm_2), "conf_mat")
 

tests/testthat/test-engine-parameters.R

@@ -35,27 +35,23 @@ test_that("tuning with engine parameters with dials objects", {
   rs <- rsample::vfold_cv(mtcars)
 
   set.seed(19828)
-  expect_error(
+  expect_no_error(
     suppressMessages(
       rf_tune <- rf_mod %>% tune_grid(mpg ~ ., resamples = rs, grid = 3)
-    ),
-    regex = NA
+    )
   )
-  expect_error(
-    p <- autoplot(rf_tune),
-    regex = NA
+  expect_no_error(
+    p <- autoplot(rf_tune)
   )
 
   set.seed(283)
-  expect_error(
+  expect_no_error(
     suppressMessages(
       rf_search <- rf_mod %>% tune_bayes(mpg ~ ., resamples = rs, initial = 3, iter = 2)
-    ),
-    regex = NA
+    )
   )
-  expect_error(
-    p <- autoplot(rf_search),
-    regex = NA
+  expect_no_error(
+    p <- autoplot(rf_search)
   )
 })
 
@@ -89,11 +85,10 @@ test_that("tuning with engine parameters without dials objects", {
 
   ## ---------------------------------------------------------------------------
 
-  expect_error(
+  expect_no_error(
     suppressMessages(
       rf_tune <- rf_mod %>% tune_grid(mpg ~ ., resamples = rs, grid = grid)
-    ),
-    regex = NA
+    )
   )
   expect_snapshot(error = TRUE, {
     p <- autoplot(rf_tune)

tests/testthat/test-extract.R

@@ -10,17 +10,16 @@ test_that("tune recipe only", {
     extract_recipe(x) %>% tidy(number = 2)
   }
   before_kind <- RNGkind()[[1]]
-  expect_error(
+  expect_no_error(
     res_1_1 <-
       workflow() %>%
       add_recipe(helper_objects$rec_tune_1) %>%
       add_model(helper_objects$lm_mod) %>%
-      tune_grid(resamples = mt_folds, control = control_grid(extract = extr_1_1)),
-    NA
+      tune_grid(resamples = mt_folds, control = control_grid(extract = extr_1_1))
   )
   after_kind <- RNGkind()[[1]]
   expect_equal(before_kind, after_kind)
-  expect_error(extract_1_1 <- dplyr::bind_rows(res_1_1$.extracts), NA)
+  expect_no_error(extract_1_1 <- dplyr::bind_rows(res_1_1$.extracts))
 
   expect_true(all(names(extract_1_1) == c("num_comp", ".extracts", ".config")))
   expect_true(
@@ -42,7 +41,7 @@ test_that("tune model only", {
     tibble(index = mod@alphaindex[[1]], estimate = mod@coef[[1]])
   }
 
-  expect_error(
+  expect_no_error(
     res_2_1 <-
       workflow() %>%
       add_recipe(helper_objects$rec_no_tune_1) %>%
@@ -51,10 +50,9 @@ test_that("tune model only", {
         resamples = mt_folds,
         grid = 2,
         control = control_grid(extract = extr_2_1)
-      ),
-    NA
+      )
   )
-  expect_error(extract_2_1 <- dplyr::bind_rows(res_2_1$.extracts), NA)
+  expect_no_error(extract_2_1 <- dplyr::bind_rows(res_2_1$.extracts))
 
   expect_true(all(names(extract_2_1) == c("cost", ".extracts", ".config")))
   expect_true(
@@ -69,7 +67,7 @@ test_that("tune model only", {
   }
 
   # should not fail:
-  expect_error(
+  expect_no_error(
     res_2_2 <-
       workflow() %>%
       add_recipe(helper_objects$rec_tune_1) %>%
@@ -78,15 +76,13 @@ test_that("tune model only", {
         resamples = mt_folds,
         grid = 2,
         control = control_grid(extract = extr_2_2)
-      ),
-    NA
+      )
   )
 
-  expect_error(
+  expect_no_error(
     extract_2_2 <-
       dplyr::bind_rows(res_2_2$.extracts) %>%
-      tidyr::unnest(cols = c(.extracts)),
-    NA
+      tidyr::unnest(cols = c(.extracts))
   )
   expect_true(all(!extract_2_2$is_null_rec))
 })
@@ -178,16 +174,15 @@ test_that("tune model and recipe", {
     update(num_comp = dials::num_comp(c(2, 5))) %>%
     dials::grid_space_filling(size = 4)
 
-  expect_error(
+  expect_no_error(
     res_3_1 <- tune_grid(
       wflow_3,
       resamples = mt_folds,
       grid = grid_3,
       control = control_grid(extract = extr_3_1)
-    ),
-    NA
+    )
   )
-  expect_error(extract_3_1 <- dplyr::bind_rows(res_3_1$.extracts), NA)
+  expect_no_error(extract_3_1 <- dplyr::bind_rows(res_3_1$.extracts))
 
   expect_true(all(names(extract_3_1) == c("num_comp", "cost", ".extracts", ".config")))
   expect_true(