testing updates after inactivity (#121)

* update snapshots for new error format

* fix argument name

* update snapshots

* add dev lme4 based on lme4/lme4#758

* snapshot update

* lme4 skips

* rf snapshot update

* update snaps for `test-recipes-nnmf_sparse.R`

* update snaps for `test-sparse-glmnet.R`

* update snap for `test-survival-tune-sa.R`

* account for diff between CRAN and dev version

by skipping the snapshot test on multiple penalty values on the (current) CRAN - will run again in the CRAN GHA once poissonreg is released

* styling of argument in warning changed

* update snap for `test-survival-augment.R`

* more matrix related skips

* use current, not future, version for `Matrix`

* first check for `Matrix` version, then `lme4` version

* use CRAN version of `lme4`

which got released a few days ago

* fix version number

---------

Co-authored-by: Hannah Frick <[email protected]>

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

@@ -1,18 +1,20 @@
-# error traps
-
-    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.
-
----
+# model errors on missing penalty value
 
     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()`
 
+# predict() errors with multiple penalty values
+
+    Code
+      poisson_reg(penalty = 0.1) %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[
+        -(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.
+

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.

tests/testthat/_snaps/parsnip-model-formula.md

@@ -1,4 +1,4 @@
-# error with model formula (workflow, no tune)
+# error without model formula (workflow, no tune)
 
     Code
       gam_fit <- gam_wflow %>% fit(mtcars)
@@ -7,7 +7,7 @@
       ! When working with generalized additive models, please supply the model specification to `workflows::add_model()` along with a `formula` argument.
       i See `?parsnip::model_formula()` to learn more.
 
-# error with model formula (workflow, with tune)
+# error without model formula (workflow, with tune)
 
     Code
       show_notes(gam_res)
@@ -18,7 +18,7 @@
       ! When working with generalized additive models, please supply the model specification to `workflows::add_model()` along with a `formula` argument.
       i See `?parsnip::model_formula()` to learn more.
 
-# error with model formula (no workflow, with tune)
+# error without model formula (no workflow, with tune)
 
     Code
       show_notes(gam_res)

tests/testthat/_snaps/parsnip-survival-censoring-model.md

@@ -9,6 +9,7 @@
 
     Code
       predict(alt_obj, time = 100)
-    Error <rlang_error>
-      Don't know how to predict with a censoring model of type: reverse_km
+    Condition
+      Error in `predict()`:
+      ! Don't know how to predict with a censoring model of type: reverse_km
 

tests/testthat/_snaps/parsnip-survival-censoring-weights.md

@@ -2,44 +2,50 @@
 
     Code
       .censoring_weights_graf("nothing useful")
-    Error <rlang_error>
-      There is no `.censoring_weights_graf()` method for objects with class(es): 'character'
+    Condition
+      Error in `.censoring_weights_graf()`:
+      ! There is no `.censoring_weights_graf()` method for objects with class(es): 'character'
 
 ---
 
     Code
       .censoring_weights_graf(workflows::workflow())
-    Error <rlang_error>
-      The workflow does not have a model fit object.
+    Condition
+      Error in `.censoring_weights_graf()`:
+      ! The workflow does not have a model fit object.
 
 ---
 
     Code
       .censoring_weights_graf(cox_model, lung)
-    Error <rlang_error>
-      There should be a single column of class `Surv`
+    Condition
+      Error:
+      ! There should be a single column of class `Surv`
 
 ---
 
     Code
       lung_left <- lung[1, , drop = FALSE]
       lung_left$surv <- Surv(10, 0, type = "left")
       .censoring_weights_graf(cox_model, lung_left)
-    Error <rlang_error>
-      For this usage, the allowed censoring type is: 'right'
+    Condition
+      Error in `.censoring_weights_graf()`:
+      ! For this usage, the allowed censoring type is: 'right'
 
 ---
 
     Code
       .censoring_weights_graf(cox_model, lung2)
-    Error <rlang_error>
-      The input should have a list column called `.pred`.
+    Condition
+      Error:
+      ! The input should have a list column called `.pred`.
 
 ---
 
     Code
       .censoring_weights_graf(cox_model, preds, cens_predictors = "shouldn't be using this anyway!")
-    Warning <rlang_warning>
+    Condition
+      Warning:
       The 'cens_predictors' argument to the survival weighting function is not currently used.
     Output
       # A tibble: 3 x 2
@@ -53,6 +59,7 @@
 
     Code
       .censoring_weights_graf(wrong_model, mtcars)
-    Error <rlang_error>
-      The model needs to be for mode 'censored regression', not for mode 'regression'.
+    Condition
+      Error in `.check_censor_model()`:
+      ! The model needs to be for mode 'censored regression', not for mode 'regression'.
 

tests/testthat/_snaps/parsnip-survival-standalone.md

@@ -2,20 +2,23 @@
 
     Code
       parsnip:::.is_surv(1)
-    Error <rlang_error>
-      The object does not have class `Surv`.
+    Condition
+      Error:
+      ! The object does not have class `Surv`.
 
 # .check_cens_type()
 
     Code
       parsnip:::.check_cens_type(left_c, type = "right", fail = TRUE)
-    Error <rlang_error>
-      For this usage, the allowed censoring type is: 'right'
+    Condition
+      Error:
+      ! For this usage, the allowed censoring type is: 'right'
 
 ---
 
     Code
       parsnip:::.check_cens_type(left_c, type = c("right", "interval"), fail = TRUE)
-    Error <rlang_error>
-      For this usage, the allowed censoring types are: 'right' and 'interval'
+    Condition
+      Error:
+      ! For this usage, the allowed censoring types are: 'right' and 'interval'
 

tests/testthat/_snaps/randomForest.md

@@ -2,15 +2,19 @@
 
     Code
       f_fit <- spec %>% fit(body_mass_g ~ ., data = penguins)
-    Warning <rlang_warning>
+    Condition
+      Warning:
       1000 columns were requested but there were 6 predictors in the data. 6 will be used.
+      Warning:
       1000 samples were requested but there were 333 rows in the data. 333 will be used.
 
 ---
 
     Code
       xy_fit <- spec %>% fit_xy(x = penguins[, -6], y = penguins$body_mass_g)
-    Warning <rlang_warning>
+    Condition
+      Warning:
       1000 columns were requested but there were 6 predictors in the data. 6 will be used.
+      Warning:
       1000 samples were requested but there were 333 rows in the data. 333 will be used.