update snapshots for new error format

tests/testthat/_snaps/glmnet-linear.md

@@ -3,15 +3,17 @@
     Code
       linear_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[
         -(1:4), ]) %>% predict(mtcars[-(1:4), ], penalty = 0:1)
-    Error <rlang_error>
-      `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+    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.
 
 ---
 
     Code
       linear_reg() %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[-(1:4), ])
-    Error <rlang_error>
-      For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+    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()`
@@ -21,6 +23,7 @@
     Code
       linear_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars) %>%
         multi_predict(mtcars, type = "class")
-    Error <rlang_error>
-      For class predictions, the object should be a classification model.
+    Condition
+      Error in `check_pred_type()`:
+      ! For class predictions, the object should be a classification model.
 

tests/testthat/_snaps/glmnet-logistic.md

@@ -2,25 +2,28 @@
 
     Code
       multi_predict(class_fit, newdata = wa_churn[1:4, vars], type = "prob")
-    Error <rlang_error>
-      Please use `new_data` instead of `newdata`.
+    Condition
+      Error in `multi_predict()`:
+      ! Please use `new_data` instead of `newdata`.
 
 # error traps
 
     Code
       logistic_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(Class ~ log(
         funded_amnt) + int_rate + term, data = lending_club) %>% predict(lending_club,
         penalty = 0:1)
-    Error <rlang_error>
-      `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+    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.
 
 ---
 
     Code
       logistic_reg() %>% set_engine("glmnet") %>% fit(Class ~ log(funded_amnt) +
         int_rate + term, data = lending_club)
-    Error <rlang_error>
-      For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+    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()`
@@ -31,6 +34,7 @@
       logistic_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(Class ~ log(
         funded_amnt) + int_rate + term, data = lending_club) %>% multi_predict(
         lending_club, type = "time")
-    Error <rlang_error>
-      For event time predictions, the object should be a censored regression.
+    Condition
+      Error in `check_pred_type()`:
+      ! For event time predictions, the object should be a censored regression.
 

tests/testthat/_snaps/glmnet-multinom.md

@@ -2,23 +2,26 @@
 
     Code
       multi_predict(xy_fit, newdata = hpc[rows, 1:4], penalty = c(0.1, 0.5))
-    Error <rlang_error>
-      Please use `new_data` instead of `newdata`.
+    Condition
+      Error in `multi_predict()`:
+      ! Please use `new_data` instead of `newdata`.
 
 # error traps
 
     Code
       multinom_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(class ~ ., data = hpc_data) %>%
         predict(hpc_data, penalty = 0:1)
-    Error <rlang_error>
-      `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+    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.
 
 ---
 
     Code
       multinom_reg() %>% set_engine("glmnet") %>% fit(class ~ ., data = hpc_data)
-    Error <rlang_error>
-      For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+    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()`
@@ -28,6 +31,7 @@
     Code
       multinom_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(class ~ ., data = hpc_data) %>%
         multi_predict(hpc_data, type = "numeric")
-    Error <rlang_error>
-      For numeric predictions, the object should be a regression model.
+    Condition
+      Error in `check_pred_type()`:
+      ! For numeric predictions, the object should be a regression model.
 

tests/testthat/_snaps/glmnet-poisson.md

@@ -3,15 +3,17 @@
     Code
       poisson_reg(penalty = 0.1) %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[
         -(1:4), ]) %>% predict(mtcars[-(1:4), ], penalty = 0:1)
-    Error <rlang_error>
-      `penalty` should be a single numeric value. `multi_predict()` can be used to get multiple predictions per row of data.
+    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.
 
 ---
 
     Code
       poisson_reg() %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[-(1:4), ])
-    Error <rlang_error>
-      For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
+    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()`

tests/testthat/_snaps/parsnip-extension-messaging.md

@@ -2,7 +2,7 @@
 
     Code
       bag_tree() %>% set_engine("rpart") %>% set_mode("regression")
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `bag_tree` regression model specifications using the `rpart` engine.
       i The parsnip extension package baguette implements support for this specification.
       i Please install (if needed) and load to continue.
@@ -20,7 +20,7 @@
 
     Code
       bag_tree() %>% set_mode("censored regression")
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `bag_tree` censored regression model specifications.
       i The parsnip extension package censored implements support for this specification.
       i Please install (if needed) and load to continue.
@@ -38,7 +38,7 @@
 
     Code
       bag_tree()
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `bag_tree` model specifications.
       i The parsnip extension packages censored and baguette implement support for this specification.
       i Please install (if needed) and load to continue.
@@ -56,7 +56,7 @@
 
     Code
       bag_tree() %>% set_engine("rpart")
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `bag_tree` model specifications using the `rpart` engine.
       i The parsnip extension packages censored and baguette implement support for this specification.
       i Please install (if needed) and load to continue.
@@ -102,7 +102,7 @@
 
     Code
       bag_tree() %>% set_mode("regression") %>% set_engine("rpart")
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `bag_tree` regression model specifications using the `rpart` engine.
       i The parsnip extension package baguette implements support for this specification.
       i Please install (if needed) and load to continue.
@@ -120,7 +120,7 @@
 
     Code
       bag_tree() %>% set_mode("classification") %>% set_engine("C5.0")
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `bag_tree` classification model specifications using the `C5.0` engine.
       i The parsnip extension package baguette implements support for this specification.
       i Please install (if needed) and load to continue.
@@ -182,7 +182,7 @@
 
     Code
       decision_tree() %>% set_engine("partykit") %>% set_mode("regression")
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `decision_tree` regression model specifications using the `partykit` engine.
       i The parsnip extension package bonsai implements support for this specification.
       i Please install (if needed) and load to continue.
@@ -216,7 +216,7 @@
 
     Code
       decision_tree() %>% set_mode("regression") %>% set_engine("partykit")
-    Message <rlang_message>
+    Message
       ! parsnip could not locate an implementation for `decision_tree` regression model specifications using the `partykit` engine.
       i The parsnip extension package bonsai implements support for this specification.
       i Please install (if needed) and load to continue.