skip snapshots with glue() error on R 4.3.3+ (#210)

* skip snapshots with `glue()` error on R 4.3.3+ * update snaps for survival/finetune
tidymodels · Mar 4, 2024 · 0398048 · 0398048
1 parent d18eff2
commit 0398048
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diff --git a/tests/testthat/_snaps/survival-tune_race_anova.md b/tests/testthat/_snaps/survival-tune_race_anova.md
@@ -42,11 +42,11 @@
       # A tibble: 5 x 6
         cost_complexity .metric        .eval_time  mean     n std_err
                   <dbl> <chr>               <dbl> <dbl> <int>   <dbl>
-      1    0.0000000001 brier_survival          1 0.177    30 0.00707
-      2    0.0841       brier_survival          1 0.201    30 0.00415
-      3    0.0891       brier_survival          1 0.201    30 0.00415
-      4    0.0944       brier_survival          1 0.201    30 0.00415
-      5    0.1          brier_survival          1 0.201    30 0.00415
+      1          0.0841 brier_survival          1 0.201    30 0.00415
+      2          0.0891 brier_survival          1 0.201    30 0.00415
+      3          0.0944 brier_survival          1 0.201    30 0.00415
+      4          0.1    brier_survival          1 0.201    30 0.00415
+      5          0.0794 brier_survival          1 0.202    30 0.00418
 
 ---
 
@@ -69,11 +69,11 @@
       # A tibble: 5 x 6
         cost_complexity .metric        .eval_time  mean     n std_err
                   <dbl> <chr>               <dbl> <dbl> <int>   <dbl>
-      1    0.0000000001 brier_survival          1 0.177    30 0.00707
-      2    0.0841       brier_survival          1 0.201    30 0.00415
-      3    0.0891       brier_survival          1 0.201    30 0.00415
-      4    0.0944       brier_survival          1 0.201    30 0.00415
-      5    0.1          brier_survival          1 0.201    30 0.00415
+      1          0.0841 brier_survival          1 0.201    30 0.00415
+      2          0.0891 brier_survival          1 0.201    30 0.00415
+      3          0.0944 brier_survival          1 0.201    30 0.00415
+      4          0.1    brier_survival          1 0.201    30 0.00415
+      5          0.0794 brier_survival          1 0.202    30 0.00418
 
 ---
 
@@ -87,11 +87,11 @@
       # A tibble: 5 x 6
         cost_complexity .metric                   .eval_time  mean     n std_err
                   <dbl> <chr>                          <dbl> <dbl> <int>   <dbl>
-      1    0.0000000001 brier_survival_integrated         NA 0.285    30 0.00426
-      2    0.0794       brier_survival_integrated         NA 0.338    30 0.00487
-      3    0.0841       brier_survival_integrated         NA 0.338    30 0.00480
-      4    0.0891       brier_survival_integrated         NA 0.338    30 0.00480
-      5    0.0944       brier_survival_integrated         NA 0.338    30 0.00480
+      1          0.0794 brier_survival_integrated         NA 0.338    30 0.00487
+      2          0.0841 brier_survival_integrated         NA 0.338    30 0.00480
+      3          0.0891 brier_survival_integrated         NA 0.338    30 0.00480
+      4          0.0944 brier_survival_integrated         NA 0.338    30 0.00480
+      5          0.1    brier_survival_integrated         NA 0.338    30 0.00480
 
 # race tuning (anova) - unneeded eval_time
 

diff --git a/tests/testthat/test-glmnet-linear.R b/tests/testthat/test-glmnet-linear.R
@@ -265,6 +265,7 @@ test_that('multi_predict() with default or single penalty value', {
 
 test_that('error traps', {
   skip_if_not_installed("glmnet")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.0.9001")
 
   expect_snapshot(error = TRUE, {
     linear_reg(penalty = 0.01) %>%
@@ -277,7 +278,6 @@ test_that('error traps', {
       set_engine("glmnet") %>%
       fit(mpg ~ ., data = mtcars[-(1:4), ])
   })
-  skip_if_not_installed("parsnip", minimum_version = "1.0.4.9003")
   expect_snapshot(error = TRUE, {
     linear_reg(penalty = 0.01) %>%
       set_engine("glmnet") %>%

diff --git a/tests/testthat/test-glmnet-logistic.R b/tests/testthat/test-glmnet-logistic.R
@@ -377,6 +377,7 @@ test_that("class predictions are factors with all levels", {
 
 test_that('error traps', {
   skip_if_not_installed("glmnet")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.0.9001")
 
   data("lending_club", package = "modeldata", envir = rlang::current_env())
 
@@ -393,7 +394,6 @@ test_that('error traps', {
       fit(Class ~ log(funded_amnt) + int_rate + term,
           data = lending_club)
   })
-  skip_if_not_installed("parsnip", minimum_version = "1.0.4.9003")
   expect_snapshot(error = TRUE, {
     logistic_reg(penalty = 0.01) %>%
       set_engine("glmnet") %>%

diff --git a/tests/testthat/test-glmnet-multinom.R b/tests/testthat/test-glmnet-multinom.R
@@ -399,6 +399,7 @@ test_that("class predictions are factors with all levels", {
 
 test_that('error traps', {
   skip_if_not_installed("glmnet")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.0.9001")
 
   data("hpc_data", package = "modeldata", envir = rlang::current_env())
 
@@ -413,7 +414,6 @@ test_that('error traps', {
       set_engine("glmnet") %>%
       fit(class ~ ., data = hpc_data)
   })
-  skip_if_not_installed("parsnip", minimum_version = "1.0.4.9003")
   expect_snapshot(error = TRUE, {
     multinom_reg(penalty = 0.01) %>%
       set_engine("glmnet") %>%

diff --git a/tests/testthat/test-glmnet-poisson.R b/tests/testthat/test-glmnet-poisson.R
@@ -134,6 +134,7 @@ test_that("model errors on missing penalty value", {
 
 test_that("predict() errors with multiple penalty values", {
   skip_if_not_installed("glmnet")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.0.9001")
 
   skip_if_not_installed("poissonreg", minimum_version = "1.0.1.9000")
   expect_snapshot(error = TRUE, {