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demonstrate 1104
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‎tests/testthat/_snaps/tunable.md

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@@ -38,6 +38,18 @@
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1 penalty <named list [2]> model_spec linear_reg main
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2 mixture <named list [2]> model_spec linear_reg main
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---
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Code
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tunable(spec %>% set_engine("glmnet", dfmax = tune()))
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Output
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# A tibble: 3 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 penalty <named list [2]> model_spec linear_reg main
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2 mixture <named list [3]> model_spec linear_reg main
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3 dfmax <NULL> model_spec linear_reg engine
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# tunable.logistic_reg()
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Code
@@ -78,6 +90,18 @@
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1 penalty <named list [2]> model_spec logistic_reg main
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2 mixture <named list [2]> model_spec logistic_reg main
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---
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Code
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tunable(spec %>% set_engine("glmnet", dfmax = tune()))
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Output
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# A tibble: 3 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 penalty <named list [2]> model_spec logistic_reg main
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2 mixture <named list [3]> model_spec logistic_reg main
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3 dfmax <NULL> model_spec logistic_reg engine
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# tunable.multinom_reg()
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Code
@@ -141,6 +165,18 @@
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1 penalty <named list [2]> model_spec multinom_reg main
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2 mixture <named list [2]> model_spec multinom_reg main
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---
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Code
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tunable(spec %>% set_engine("glmnet", dfmax = tune()))
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Output
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# A tibble: 3 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 penalty <named list [2]> model_spec multinom_reg main
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2 mixture <named list [2]> model_spec multinom_reg main
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3 dfmax <NULL> model_spec multinom_reg engine
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# tunable.boost_tree()
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Code
@@ -203,6 +239,24 @@
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6 loss_reduction <named list [2]> model_spec boost_tree main
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7 sample_size <named list [2]> model_spec boost_tree main
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---
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Code
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tunable(spec %>% set_engine("xgboost", feval = tune()))
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Output
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# A tibble: 9 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 tree_depth <named list [2]> model_spec boost_tree main
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2 trees <named list [2]> model_spec boost_tree main
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3 learn_rate <named list [3]> model_spec boost_tree main
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4 mtry <named list [2]> model_spec boost_tree main
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5 min_n <named list [2]> model_spec boost_tree main
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6 loss_reduction <named list [2]> model_spec boost_tree main
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7 sample_size <named list [2]> model_spec boost_tree main
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8 stop_iter <named list [2]> model_spec boost_tree main
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9 feval <NULL> model_spec boost_tree engine
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# tunable.rand_forest()
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Code
@@ -251,6 +305,19 @@
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2 trees <named list [2]> model_spec rand_forest main
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3 min_n <named list [2]> model_spec rand_forest main
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---
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Code
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tunable(spec %>% set_engine("ranger", min.bucket = tune()))
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Output
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# A tibble: 4 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 mtry <named list [2]> model_spec rand_forest main
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2 trees <named list [2]> model_spec rand_forest main
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3 min_n <named list [2]> model_spec rand_forest main
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4 min.bucket <NULL> model_spec rand_forest engine
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# tunable.mars()
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Code
@@ -275,6 +342,19 @@
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2 prod_degree <named list [2]> model_spec mars main
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3 prune_method <named list [2]> model_spec mars main
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---
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Code
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tunable(spec %>% set_engine("earth", minspan = tune()))
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Output
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# A tibble: 4 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 num_terms <named list [3]> model_spec mars main
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2 prod_degree <named list [2]> model_spec mars main
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3 prune_method <named list [2]> model_spec mars main
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4 minspan <NULL> model_spec mars engine
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# tunable.decision_tree()
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@@ -320,6 +400,19 @@
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1 tree_depth <named list [2]> model_spec decision_tree main
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2 min_n <named list [2]> model_spec decision_tree main
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---
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Code
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tunable(spec %>% set_engine("rpart", parms = tune()))
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Output
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# A tibble: 4 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 tree_depth <named list [2]> model_spec decision_tree main
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2 min_n <named list [2]> model_spec decision_tree main
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3 cost_complexity <named list [2]> model_spec decision_tree main
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4 parms <NULL> model_spec decision_tree engine
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# tunable.svm_poly()
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Code
@@ -346,6 +439,20 @@
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3 scale_factor <named list [2]> model_spec svm_poly main
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4 margin <named list [2]> model_spec svm_poly main
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---
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Code
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tunable(spec %>% set_engine("kernlab", tol = tune()))
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Output
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# A tibble: 5 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 cost <named list [3]> model_spec svm_poly main
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2 degree <named list [3]> model_spec svm_poly main
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3 scale_factor <named list [2]> model_spec svm_poly main
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4 margin <named list [2]> model_spec svm_poly main
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5 tol <NULL> model_spec svm_poly engine
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# tunable.mlp()
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@@ -399,6 +506,21 @@
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5 learn_rate <named list [3]> model_spec mlp main
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6 activation <named list [3]> model_spec mlp main
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---
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Code
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tunable(spec %>% set_engine("keras", ragged = tune()))
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Output
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# A tibble: 6 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 hidden_units <named list [2]> model_spec mlp main
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2 penalty <named list [2]> model_spec mlp main
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3 dropout <named list [2]> model_spec mlp main
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4 epochs <named list [2]> model_spec mlp main
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5 activation <named list [2]> model_spec mlp main
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6 ragged <NULL> model_spec mlp engine
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# tunable.survival_reg()
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Code
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# i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
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# component_id <chr>
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---
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Code
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tunable(spec %>% set_engine("survival"))
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Output
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# A tibble: 0 x 5
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# i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
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# component_id <chr>
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---
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Code
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tunable(spec %>% set_engine("survival", parms = tune()))
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Output
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# A tibble: 1 x 5
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name call_info source component component_id
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<chr> <list> <chr> <chr> <chr>
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1 parms <NULL> model_spec survival_reg engine
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‎tests/testthat/test-tunable.R

Lines changed: 21 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -15,7 +15,8 @@ test_that("tunable.linear_reg()", {
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expect_snapshot(tunable(spec %>% set_engine("glmnet")))
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expect_snapshot(tunable(spec %>% set_engine("brulee")))
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18-
# tests for call_info in tidymodels/extratests
18+
# don't include rows for non-tunable args marked with tune() (#1104)
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expect_snapshot(tunable(spec %>% set_engine("glmnet", dfmax = tune())))
1920
})
2021

2122
test_that("tunable.logistic_reg()", {
@@ -25,7 +26,8 @@ test_that("tunable.logistic_reg()", {
2526
expect_snapshot(tunable(spec %>% set_engine("glmnet")))
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expect_snapshot(tunable(spec %>% set_engine("brulee")))
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28-
# tests for call_info and additional engines in tidymodels/extratests
29+
# don't include rows for non-tunable args marked with tune() (#1104)
30+
expect_snapshot(tunable(spec %>% set_engine("glmnet", dfmax = tune())))
2931
})
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3133
test_that("tunable.multinom_reg()", {
@@ -37,7 +39,8 @@ test_that("tunable.multinom_reg()", {
3739
expect_snapshot(tunable(spec %>% set_engine("nnet")))
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expect_snapshot(tunable(spec %>% set_engine("brulee")))
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40-
# tests for call_info and additional engines in tidymodels/extratests
42+
# don't include rows for non-tunable args marked with tune() (#1104)
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expect_snapshot(tunable(spec %>% set_engine("glmnet", dfmax = tune())))
4144
})
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4346
test_that("tunable.boost_tree()", {
@@ -47,7 +50,8 @@ test_that("tunable.boost_tree()", {
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expect_snapshot(tunable(spec %>% set_engine("C5.0")))
4851
expect_snapshot(tunable(spec %>% set_engine("spark")))
4952

50-
# tests for call_info and additional engines in tidymodels/extratests
53+
# don't include rows for non-tunable args marked with tune() (#1104)
54+
expect_snapshot(tunable(spec %>% set_engine("xgboost", feval = tune())))
5155
})
5256

5357
test_that("tunable.rand_forest()", {
@@ -57,15 +61,17 @@ test_that("tunable.rand_forest()", {
5761
expect_snapshot(tunable(spec %>% set_engine("randomForest")))
5862
expect_snapshot(tunable(spec %>% set_engine("spark")))
5963

60-
# tests for call_info and additional engines in tidymodels/extratests
64+
# don't include rows for non-tunable args marked with tune() (#1104)
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expect_snapshot(tunable(spec %>% set_engine("ranger", min.bucket = tune())))
6166
})
6267

6368
test_that("tunable.mars()", {
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spec <- mars()
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expect_snapshot(tunable(spec))
6671
expect_snapshot(tunable(spec %>% set_engine("earth")))
6772

68-
# tests for call_info and additional engines in tidymodels/extratests
73+
# don't include rows for non-tunable args marked with tune() (#1104)
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expect_snapshot(tunable(spec %>% set_engine("earth", minspan = tune())))
6975
})
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7177
test_that("tunable.decision_tree()", {
@@ -75,15 +81,17 @@ test_that("tunable.decision_tree()", {
7581
expect_snapshot(tunable(spec %>% set_engine("C5.0")))
7682
expect_snapshot(tunable(spec %>% set_engine("spark")))
7783

78-
# tests for call_info and additional engines in tidymodels/extratests
84+
# don't include rows for non-tunable args marked with tune() (#1104)
85+
expect_snapshot(tunable(spec %>% set_engine("rpart", parms = tune())))
7986
})
8087

8188
test_that("tunable.svm_poly()", {
8289
spec <- svm_poly()
8390
expect_snapshot(tunable(spec))
8491
expect_snapshot(tunable(spec %>% set_engine("kernlab")))
8592

86-
# tests for call_info and additional engines in tidymodels/extratests
93+
# don't include rows for non-tunable args marked with tune() (#1104)
94+
expect_snapshot(tunable(spec %>% set_engine("kernlab", tol = tune())))
8795
})
8896

8997
test_that("tunable.mlp()", {
@@ -93,13 +101,16 @@ test_that("tunable.mlp()", {
93101
expect_snapshot(tunable(spec %>% set_engine("nnet")))
94102
expect_snapshot(tunable(spec %>% set_engine("brulee")))
95103

96-
# tests for call_info and additional engines in tidymodels/extratests
104+
# don't include rows for non-tunable args marked with tune() (#1104)
105+
expect_snapshot(tunable(spec %>% set_engine("keras", ragged = tune())))
97106
})
98107

99108

100109
test_that("tunable.survival_reg()", {
101110
spec <- survival_reg()
102111
expect_snapshot(tunable(spec))
112+
expect_snapshot(tunable(spec %>% set_engine("survival")))
103113

104-
# tests for call_info and additional engines in tidymodels/extratests
114+
# don't include rows for non-tunable args marked with tune() (#1104)
115+
expect_snapshot(tunable(spec %>% set_engine("survival", parms = tune())))
105116
})

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