changes for tidymodels/parsnip#1214 (#226)

Co-authored-by: ‘topepo’ <‘mxkuhn@gmail.com’>

Author: topepo

inst/WORDLIST

@@ -1,4 +1,8 @@
-Lifecycle
-tidymodels
-cron
 GH
+GHA
+PRs
+cron
+dev
+repo
+repos
+tidymodels

tests/testthat/_snaps/glmnet-linear.md

@@ -4,7 +4,7 @@
       linear_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[
         -(1:4), ]) %>% predict(mtcars[-(1:4), ], penalty = 0:1)
     Condition
-      Error in `.check_glmnet_penalty_predict()`:
+      Error in `predict()`:
       ! `penalty` should be a single numeric value.
       i `multi_predict()` can be used to get multiple predictions per row of data.
 
@@ -13,7 +13,7 @@
     Code
       linear_reg() %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[-(1:4), ])
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       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.

tests/testthat/_snaps/glmnet-logistic.md

@@ -13,7 +13,7 @@
         funded_amnt) + int_rate + term, data = lending_club) %>% predict(lending_club,
         penalty = 0:1)
     Condition
-      Error in `.check_glmnet_penalty_predict()`:
+      Error in `predict()`:
       ! `penalty` should be a single numeric value.
       i `multi_predict()` can be used to get multiple predictions per row of data.
 
@@ -23,7 +23,7 @@
       logistic_reg() %>% set_engine("glmnet") %>% fit(Class ~ log(funded_amnt) +
         int_rate + term, data = lending_club)
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       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.

tests/testthat/_snaps/glmnet-multinom.md

@@ -12,7 +12,7 @@
       multinom_reg(penalty = 0.01) %>% set_engine("glmnet") %>% fit(class ~ ., data = hpc_data) %>%
         predict(hpc_data, penalty = 0:1)
     Condition
-      Error in `.check_glmnet_penalty_predict()`:
+      Error in `predict()`:
       ! `penalty` should be a single numeric value.
       i `multi_predict()` can be used to get multiple predictions per row of data.
 
@@ -21,7 +21,7 @@
     Code
       multinom_reg() %>% set_engine("glmnet") %>% fit(class ~ ., data = hpc_data)
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       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.

tests/testthat/_snaps/glmnet-poisson.md

@@ -3,7 +3,7 @@
     Code
       poisson_reg() %>% set_engine("glmnet") %>% fit(mpg ~ ., data = mtcars[-(1:4), ])
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       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.
@@ -15,7 +15,7 @@
       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()`:
+      Error in `predict()`:
       ! `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 = "1.2.1.9002")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9003")
 
   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 = "1.2.1.9002")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9003")
 
   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 = "1.2.1.9002")
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9003")
 
   data("hpc_data", package = "modeldata", envir = rlang::current_env())
 

tests/testthat/test-glmnet-poisson.R

@@ -2,7 +2,6 @@ library(poissonreg)
 
 test_that("glmnet model object", {
   skip_if_not_installed("glmnet")
-
   data(seniors, package = "poissonreg", envir = rlang::current_env())
   seniors_x <- model.matrix(~ ., data = seniors[, -4])[, -1]
   seniors_y <- seniors$count
@@ -122,9 +121,9 @@ test_that("formula interface can deal with missing values", {
 })
 
 test_that("model errors on missing penalty value", {
+  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9003")
   skip_if_not_installed("glmnet")
 
-  skip_if_not_installed("parsnip", minimum_version = "1.2.1.9001")
   expect_snapshot(error = TRUE, {
     poisson_reg() %>%
       set_engine("glmnet") %>%