update snaps with parsnip cli error formatting (#219)

* update snaps with parsnip cli error formatting
* bump minimum parsnip version

tests/testthat/_snaps/glmnet-linear.md

@@ -5,18 +5,19 @@
         -(1:4), ]) %>% predict(mtcars[-(1:4), ], penalty = 0:1)
     Condition
       Error in `.check_glmnet_penalty_predict()`:
-      ! `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+      ! `penalty` should be a single numeric value.
+      i `multi_predict()` can be used to get multiple predictions per row of data.
 
 ---
 
     Code
       linear_reg() %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[-(1:4), ])
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 

tests/testthat/_snaps/glmnet-logistic.md

@@ -14,7 +14,8 @@
         penalty = 0:1)
     Condition
       Error in `.check_glmnet_penalty_predict()`:
-      ! `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+      ! `penalty` should be a single numeric value.
+      i `multi_predict()` can be used to get multiple predictions per row of data.
 
 ---
 
@@ -23,10 +24,10 @@
         int_rate + term, data = lending_club)
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 

tests/testthat/_snaps/glmnet-multinom.md

@@ -13,18 +13,19 @@
         predict(hpc_data, penalty = 0:1)
     Condition
       Error in `.check_glmnet_penalty_predict()`:
-      ! `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+      ! `penalty` should be a single numeric value.
+      i `multi_predict()` can be used to get multiple predictions per row of data.
 
 ---
 
     Code
       multinom_reg() %>% set_engine("glmnet") %>% fit(class ~ ., data = hpc_data)
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 ---
 

tests/testthat/_snaps/glmnet-poisson.md

@@ -4,10 +4,10 @@
       poisson_reg() %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[-(1:4), ])
     Condition
       Error in `.check_glmnet_penalty_fit()`:
-      ! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
-      * There are 0 values for `penalty`.
-      * To try multiple values for total regularization, use the tune package.
-      * To predict multiple penalties, use `multi_predict()`
+      x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+      ! There are 0 values for `penalty`.
+      i To try multiple values for total regularization, use the tune package.
+      i To predict multiple penalties, use `multi_predict()`.
 
 # predict() errors with multiple penalty values
 
@@ -16,5 +16,6 @@
         -(1:4), ]) %>% predict(mtcars[-(1:4), ], penalty = 0:1)
     Condition
       Error in `.check_glmnet_penalty_predict()`:
-      ! `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+      ! `penalty` should be a single numeric value.
+      i `multi_predict()` can be used to get multiple predictions per row of data.
 

tests/testthat/test-glmnet-linear.R

@@ -265,7 +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")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9001")
 
   expect_snapshot(error = TRUE, {
     linear_reg(penalty = 0.01) %>%

tests/testthat/test-glmnet-logistic.R

@@ -377,7 +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")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9001")
 
   data("lending_club", package = "modeldata", envir = rlang::current_env())
 

tests/testthat/test-glmnet-multinom.R

@@ -399,7 +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")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9001")
 
   data("hpc_data", package = "modeldata", envir = rlang::current_env())
 

tests/testthat/test-glmnet-poisson.R

@@ -124,7 +124,7 @@ test_that("formula interface can deal with missing values", {
 test_that("model errors on missing penalty value", {
   skip_if_not_installed("glmnet")
 
-  skip_if_not_installed("parsnip", minimum_version = "1.0.3.9000")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9001")
   expect_snapshot(error = TRUE, {
     poisson_reg() %>%
       set_engine("glmnet") %>%
@@ -134,7 +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("parsnip", minimum_version = "1.2.1.9001")
 
   skip_if_not_installed("poissonreg", minimum_version = "1.0.1.9000")
   expect_snapshot(error = TRUE, {