From 132abc965910626ad3cfe6b602a1451cf7b944a4 Mon Sep 17 00:00:00 2001
From: simonpcouch <simonpatrickcouch@gmail.com>
Date: Fri, 5 Apr 2024 10:26:04 -0500
Subject: [PATCH 1/3] test tunable output

---
 tests/testthat/_snaps/tunable.md | 410 +++++++++++++++++++++++++++++++
 tests/testthat/test-tunable.R    | 105 ++++++++
 2 files changed, 515 insertions(+)
 create mode 100644 tests/testthat/_snaps/tunable.md
 create mode 100644 tests/testthat/test-tunable.R

diff --git a/tests/testthat/_snaps/tunable.md b/tests/testthat/_snaps/tunable.md
new file mode 100644
index 000000000..e75281f08
--- /dev/null
+++ b/tests/testthat/_snaps/tunable.md
@@ -0,0 +1,410 @@
+# tunable.linear_reg()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      tunable(spec %>% set_engine("lm"))
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      tunable(spec %>% set_engine("glmnet"))
+    Output
+      # A tibble: 2 x 5
+        name    call_info        source     component  component_id
+        <chr>   <list>           <chr>      <chr>      <chr>       
+      1 penalty <named list [2]> model_spec linear_reg main        
+      2 mixture <named list [3]> model_spec linear_reg main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("brulee"))
+    Output
+      # A tibble: 2 x 5
+        name    call_info        source     component  component_id
+        <chr>   <list>           <chr>      <chr>      <chr>       
+      1 penalty <named list [2]> model_spec linear_reg main        
+      2 mixture <named list [2]> model_spec linear_reg main        
+
+# tunable.logistic_reg()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      tunable(spec %>% set_engine("glm"))
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      tunable(spec %>% set_engine("glmnet"))
+    Output
+      # A tibble: 2 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec logistic_reg main        
+      2 mixture <named list [3]> model_spec logistic_reg main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("brulee"))
+    Output
+      # A tibble: 2 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec logistic_reg main        
+      2 mixture <named list [2]> model_spec logistic_reg main        
+
+# tunable.multinom_reg()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 1 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec multinom_reg main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("glmnet"))
+    Output
+      # A tibble: 2 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec multinom_reg main        
+      2 mixture <named list [2]> model_spec multinom_reg main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("spark"))
+    Output
+      # A tibble: 2 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec multinom_reg main        
+      2 mixture <named list [2]> model_spec multinom_reg main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("keras"))
+    Output
+      # A tibble: 1 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec multinom_reg main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("nnet"))
+    Output
+      # A tibble: 1 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec multinom_reg main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("brulee"))
+    Output
+      # A tibble: 2 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec multinom_reg main        
+      2 mixture <named list [2]> model_spec multinom_reg main        
+
+# tunable.boost_tree()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 8 x 5
+        name           call_info        source     component  component_id
+        <chr>          <list>           <chr>      <chr>      <chr>       
+      1 tree_depth     <named list [2]> model_spec boost_tree main        
+      2 trees          <named list [2]> model_spec boost_tree main        
+      3 learn_rate     <named list [3]> model_spec boost_tree main        
+      4 mtry           <named list [2]> model_spec boost_tree main        
+      5 min_n          <named list [2]> model_spec boost_tree main        
+      6 loss_reduction <named list [2]> model_spec boost_tree main        
+      7 sample_size    <named list [2]> model_spec boost_tree main        
+      8 stop_iter      <named list [2]> model_spec boost_tree main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("xgboost"))
+    Output
+      # A tibble: 8 x 5
+        name           call_info        source     component  component_id
+        <chr>          <list>           <chr>      <chr>      <chr>       
+      1 tree_depth     <named list [2]> model_spec boost_tree main        
+      2 trees          <named list [2]> model_spec boost_tree main        
+      3 learn_rate     <named list [3]> model_spec boost_tree main        
+      4 mtry           <named list [2]> model_spec boost_tree main        
+      5 min_n          <named list [2]> model_spec boost_tree main        
+      6 loss_reduction <named list [2]> model_spec boost_tree main        
+      7 sample_size    <named list [2]> model_spec boost_tree main        
+      8 stop_iter      <named list [2]> model_spec boost_tree main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("C5.0"))
+    Output
+      # A tibble: 3 x 5
+        name        call_info        source     component  component_id
+        <chr>       <list>           <chr>      <chr>      <chr>       
+      1 trees       <named list [3]> model_spec boost_tree main        
+      2 min_n       <named list [2]> model_spec boost_tree main        
+      3 sample_size <named list [2]> model_spec boost_tree main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("spark"))
+    Output
+      # A tibble: 7 x 5
+        name           call_info        source     component  component_id
+        <chr>          <list>           <chr>      <chr>      <chr>       
+      1 tree_depth     <named list [2]> model_spec boost_tree main        
+      2 trees          <named list [2]> model_spec boost_tree main        
+      3 learn_rate     <named list [2]> model_spec boost_tree main        
+      4 mtry           <named list [2]> model_spec boost_tree main        
+      5 min_n          <named list [2]> model_spec boost_tree main        
+      6 loss_reduction <named list [2]> model_spec boost_tree main        
+      7 sample_size    <named list [2]> model_spec boost_tree main        
+
+# tunable.rand_forest()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 3 x 5
+        name  call_info        source     component   component_id
+        <chr> <list>           <chr>      <chr>       <chr>       
+      1 mtry  <named list [2]> model_spec rand_forest main        
+      2 trees <named list [2]> model_spec rand_forest main        
+      3 min_n <named list [2]> model_spec rand_forest main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("ranger"))
+    Output
+      # A tibble: 3 x 5
+        name  call_info        source     component   component_id
+        <chr> <list>           <chr>      <chr>       <chr>       
+      1 mtry  <named list [2]> model_spec rand_forest main        
+      2 trees <named list [2]> model_spec rand_forest main        
+      3 min_n <named list [2]> model_spec rand_forest main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("randomForest"))
+    Output
+      # A tibble: 3 x 5
+        name  call_info        source     component   component_id
+        <chr> <list>           <chr>      <chr>       <chr>       
+      1 mtry  <named list [2]> model_spec rand_forest main        
+      2 trees <named list [2]> model_spec rand_forest main        
+      3 min_n <named list [2]> model_spec rand_forest main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("spark"))
+    Output
+      # A tibble: 3 x 5
+        name  call_info        source     component   component_id
+        <chr> <list>           <chr>      <chr>       <chr>       
+      1 mtry  <named list [2]> model_spec rand_forest main        
+      2 trees <named list [2]> model_spec rand_forest main        
+      3 min_n <named list [2]> model_spec rand_forest main        
+
+# tunable.mars()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 3 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 num_terms    <named list [3]> model_spec mars      main        
+      2 prod_degree  <named list [2]> model_spec mars      main        
+      3 prune_method <named list [2]> model_spec mars      main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("earth"))
+    Output
+      # A tibble: 3 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 num_terms    <named list [3]> model_spec mars      main        
+      2 prod_degree  <named list [2]> model_spec mars      main        
+      3 prune_method <named list [2]> model_spec mars      main        
+
+# tunable.decision_tree()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 3 x 5
+        name            call_info        source     component     component_id
+        <chr>           <list>           <chr>      <chr>         <chr>       
+      1 tree_depth      <named list [2]> model_spec decision_tree main        
+      2 min_n           <named list [2]> model_spec decision_tree main        
+      3 cost_complexity <named list [2]> model_spec decision_tree main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("rpart"))
+    Output
+      # A tibble: 3 x 5
+        name            call_info        source     component     component_id
+        <chr>           <list>           <chr>      <chr>         <chr>       
+      1 tree_depth      <named list [2]> model_spec decision_tree main        
+      2 min_n           <named list [2]> model_spec decision_tree main        
+      3 cost_complexity <named list [2]> model_spec decision_tree main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("C5.0"))
+    Output
+      # A tibble: 1 x 5
+        name  call_info        source     component     component_id
+        <chr> <list>           <chr>      <chr>         <chr>       
+      1 min_n <named list [2]> model_spec decision_tree main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("spark"))
+    Output
+      # A tibble: 2 x 5
+        name       call_info        source     component     component_id
+        <chr>      <list>           <chr>      <chr>         <chr>       
+      1 tree_depth <named list [2]> model_spec decision_tree main        
+      2 min_n      <named list [2]> model_spec decision_tree main        
+
+# tunable.svm_poly()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 4 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 cost         <named list [3]> model_spec svm_poly  main        
+      2 degree       <named list [3]> model_spec svm_poly  main        
+      3 scale_factor <named list [2]> model_spec svm_poly  main        
+      4 margin       <named list [2]> model_spec svm_poly  main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("kernlab"))
+    Output
+      # A tibble: 4 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 cost         <named list [3]> model_spec svm_poly  main        
+      2 degree       <named list [3]> model_spec svm_poly  main        
+      3 scale_factor <named list [2]> model_spec svm_poly  main        
+      4 margin       <named list [2]> model_spec svm_poly  main        
+
+# tunable.mlp()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 3 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 hidden_units <named list [2]> model_spec mlp       main        
+      2 penalty      <named list [2]> model_spec mlp       main        
+      3 epochs       <named list [2]> model_spec mlp       main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("keras"))
+    Output
+      # A tibble: 5 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 hidden_units <named list [2]> model_spec mlp       main        
+      2 penalty      <named list [2]> model_spec mlp       main        
+      3 dropout      <named list [2]> model_spec mlp       main        
+      4 epochs       <named list [2]> model_spec mlp       main        
+      5 activation   <named list [2]> model_spec mlp       main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("nnet"))
+    Output
+      # A tibble: 3 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 hidden_units <named list [2]> model_spec mlp       main        
+      2 penalty      <named list [2]> model_spec mlp       main        
+      3 epochs       <named list [2]> model_spec mlp       main        
+
+---
+
+    Code
+      tunable(spec %>% set_engine("brulee"))
+    Output
+      # A tibble: 6 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 hidden_units <named list [2]> model_spec mlp       main        
+      2 penalty      <named list [2]> model_spec mlp       main        
+      3 epochs       <named list [3]> model_spec mlp       main        
+      4 dropout      <named list [2]> model_spec mlp       main        
+      5 learn_rate   <named list [3]> model_spec mlp       main        
+      6 activation   <named list [3]> model_spec mlp       main        
+
+# tunable.survival_reg()
+
+    Code
+      tunable(spec)
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
diff --git a/tests/testthat/test-tunable.R b/tests/testthat/test-tunable.R
new file mode 100644
index 000000000..8f799594c
--- /dev/null
+++ b/tests/testthat/test-tunable.R
@@ -0,0 +1,105 @@
+# general pattern, for each tunable method:
+# define `spec`, run `show_engines()` with only parsnip loaded,
+# snapshot test `tunable()` output for each unique engine.
+#
+# note that, as implemented, parsnip can return `tunable()` information
+# for engines that it cannot fit without first loading an extension package.
+#
+# the specific contents of call_info are just hard-coded tibbles in the
+# source, so snapshot testing only for their presence rather than contents.
+
+test_that("tunable.linear_reg()", {
+  spec <- linear_reg()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("lm")))
+  expect_snapshot(tunable(spec %>% set_engine("glmnet")))
+  expect_snapshot(tunable(spec %>% set_engine("brulee")))
+
+  # tests for call_info in tidymodels/extratests
+})
+
+test_that("tunable.logistic_reg()", {
+  spec <- logistic_reg()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("glm")))
+  expect_snapshot(tunable(spec %>% set_engine("glmnet")))
+  expect_snapshot(tunable(spec %>% set_engine("brulee")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+test_that("tunable.multinom_reg()", {
+  spec <- multinom_reg()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("glmnet")))
+  expect_snapshot(tunable(spec %>% set_engine("spark")))
+  expect_snapshot(tunable(spec %>% set_engine("keras")))
+  expect_snapshot(tunable(spec %>% set_engine("nnet")))
+  expect_snapshot(tunable(spec %>% set_engine("brulee")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+test_that("tunable.boost_tree()", {
+  spec <- boost_tree()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("xgboost")))
+  expect_snapshot(tunable(spec %>% set_engine("C5.0")))
+  expect_snapshot(tunable(spec %>% set_engine("spark")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+test_that("tunable.rand_forest()", {
+  spec <- rand_forest()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("ranger")))
+  expect_snapshot(tunable(spec %>% set_engine("randomForest")))
+  expect_snapshot(tunable(spec %>% set_engine("spark")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+test_that("tunable.mars()", {
+  spec <- mars()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("earth")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+test_that("tunable.decision_tree()", {
+  spec <- decision_tree()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("rpart")))
+  expect_snapshot(tunable(spec %>% set_engine("C5.0")))
+  expect_snapshot(tunable(spec %>% set_engine("spark")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+test_that("tunable.svm_poly()", {
+  spec <- svm_poly()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("kernlab")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+test_that("tunable.mlp()", {
+  spec <- mlp()
+  expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("keras")))
+  expect_snapshot(tunable(spec %>% set_engine("nnet")))
+  expect_snapshot(tunable(spec %>% set_engine("brulee")))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})
+
+
+test_that("tunable.survival_reg()", {
+  spec <- survival_reg()
+  expect_snapshot(tunable(spec))
+
+  # tests for call_info and additional engines in tidymodels/extratests
+})

From 6e8106ec9443c95c06e727037d4d4bbc14745611 Mon Sep 17 00:00:00 2001
From: simonpcouch <simonpatrickcouch@gmail.com>
Date: Fri, 5 Apr 2024 10:42:08 -0500
Subject: [PATCH 2/3] demonstrate 1104

---
 tests/testthat/_snaps/tunable.md | 141 +++++++++++++++++++++++++++++++
 tests/testthat/test-tunable.R    |  31 ++++---
 2 files changed, 162 insertions(+), 10 deletions(-)

diff --git a/tests/testthat/_snaps/tunable.md b/tests/testthat/_snaps/tunable.md
index e75281f08..e2ec2c208 100644
--- a/tests/testthat/_snaps/tunable.md
+++ b/tests/testthat/_snaps/tunable.md
@@ -38,6 +38,18 @@
       1 penalty <named list [2]> model_spec linear_reg main        
       2 mixture <named list [2]> model_spec linear_reg main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("glmnet", dfmax = tune()))
+    Output
+      # A tibble: 3 x 5
+        name    call_info        source     component  component_id
+        <chr>   <list>           <chr>      <chr>      <chr>       
+      1 penalty <named list [2]> model_spec linear_reg main        
+      2 mixture <named list [3]> model_spec linear_reg main        
+      3 dfmax   <NULL>           model_spec linear_reg engine      
+
 # tunable.logistic_reg()
 
     Code
@@ -78,6 +90,18 @@
       1 penalty <named list [2]> model_spec logistic_reg main        
       2 mixture <named list [2]> model_spec logistic_reg main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("glmnet", dfmax = tune()))
+    Output
+      # A tibble: 3 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec logistic_reg main        
+      2 mixture <named list [3]> model_spec logistic_reg main        
+      3 dfmax   <NULL>           model_spec logistic_reg engine      
+
 # tunable.multinom_reg()
 
     Code
@@ -141,6 +165,18 @@
       1 penalty <named list [2]> model_spec multinom_reg main        
       2 mixture <named list [2]> model_spec multinom_reg main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("glmnet", dfmax = tune()))
+    Output
+      # A tibble: 3 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec multinom_reg main        
+      2 mixture <named list [2]> model_spec multinom_reg main        
+      3 dfmax   <NULL>           model_spec multinom_reg engine      
+
 # tunable.boost_tree()
 
     Code
@@ -203,6 +239,24 @@
       6 loss_reduction <named list [2]> model_spec boost_tree main        
       7 sample_size    <named list [2]> model_spec boost_tree main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("xgboost", feval = tune()))
+    Output
+      # A tibble: 9 x 5
+        name           call_info        source     component  component_id
+        <chr>          <list>           <chr>      <chr>      <chr>       
+      1 tree_depth     <named list [2]> model_spec boost_tree main        
+      2 trees          <named list [2]> model_spec boost_tree main        
+      3 learn_rate     <named list [3]> model_spec boost_tree main        
+      4 mtry           <named list [2]> model_spec boost_tree main        
+      5 min_n          <named list [2]> model_spec boost_tree main        
+      6 loss_reduction <named list [2]> model_spec boost_tree main        
+      7 sample_size    <named list [2]> model_spec boost_tree main        
+      8 stop_iter      <named list [2]> model_spec boost_tree main        
+      9 feval          <NULL>           model_spec boost_tree engine      
+
 # tunable.rand_forest()
 
     Code
@@ -251,6 +305,19 @@
       2 trees <named list [2]> model_spec rand_forest main        
       3 min_n <named list [2]> model_spec rand_forest main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("ranger", min.bucket = tune()))
+    Output
+      # A tibble: 4 x 5
+        name       call_info        source     component   component_id
+        <chr>      <list>           <chr>      <chr>       <chr>       
+      1 mtry       <named list [2]> model_spec rand_forest main        
+      2 trees      <named list [2]> model_spec rand_forest main        
+      3 min_n      <named list [2]> model_spec rand_forest main        
+      4 min.bucket <NULL>           model_spec rand_forest engine      
+
 # tunable.mars()
 
     Code
@@ -275,6 +342,19 @@
       2 prod_degree  <named list [2]> model_spec mars      main        
       3 prune_method <named list [2]> model_spec mars      main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("earth", minspan = tune()))
+    Output
+      # A tibble: 4 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 num_terms    <named list [3]> model_spec mars      main        
+      2 prod_degree  <named list [2]> model_spec mars      main        
+      3 prune_method <named list [2]> model_spec mars      main        
+      4 minspan      <NULL>           model_spec mars      engine      
+
 # tunable.decision_tree()
 
     Code
@@ -320,6 +400,19 @@
       1 tree_depth <named list [2]> model_spec decision_tree main        
       2 min_n      <named list [2]> model_spec decision_tree main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("rpart", parms = tune()))
+    Output
+      # A tibble: 4 x 5
+        name            call_info        source     component     component_id
+        <chr>           <list>           <chr>      <chr>         <chr>       
+      1 tree_depth      <named list [2]> model_spec decision_tree main        
+      2 min_n           <named list [2]> model_spec decision_tree main        
+      3 cost_complexity <named list [2]> model_spec decision_tree main        
+      4 parms           <NULL>           model_spec decision_tree engine      
+
 # tunable.svm_poly()
 
     Code
@@ -346,6 +439,20 @@
       3 scale_factor <named list [2]> model_spec svm_poly  main        
       4 margin       <named list [2]> model_spec svm_poly  main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("kernlab", tol = tune()))
+    Output
+      # A tibble: 5 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 cost         <named list [3]> model_spec svm_poly  main        
+      2 degree       <named list [3]> model_spec svm_poly  main        
+      3 scale_factor <named list [2]> model_spec svm_poly  main        
+      4 margin       <named list [2]> model_spec svm_poly  main        
+      5 tol          <NULL>           model_spec svm_poly  engine      
+
 # tunable.mlp()
 
     Code
@@ -399,6 +506,21 @@
       5 learn_rate   <named list [3]> model_spec mlp       main        
       6 activation   <named list [3]> model_spec mlp       main        
 
+---
+
+    Code
+      tunable(spec %>% set_engine("keras", ragged = tune()))
+    Output
+      # A tibble: 6 x 5
+        name         call_info        source     component component_id
+        <chr>        <list>           <chr>      <chr>     <chr>       
+      1 hidden_units <named list [2]> model_spec mlp       main        
+      2 penalty      <named list [2]> model_spec mlp       main        
+      3 dropout      <named list [2]> model_spec mlp       main        
+      4 epochs       <named list [2]> model_spec mlp       main        
+      5 activation   <named list [2]> model_spec mlp       main        
+      6 ragged       <NULL>           model_spec mlp       engine      
+
 # tunable.survival_reg()
 
     Code
@@ -408,3 +530,22 @@
       # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
       #   component_id <chr>
 
+---
+
+    Code
+      tunable(spec %>% set_engine("survival"))
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      tunable(spec %>% set_engine("survival", parms = tune()))
+    Output
+      # A tibble: 1 x 5
+        name  call_info source     component    component_id
+        <chr> <list>    <chr>      <chr>        <chr>       
+      1 parms <NULL>    model_spec survival_reg engine      
+
diff --git a/tests/testthat/test-tunable.R b/tests/testthat/test-tunable.R
index 8f799594c..8e8dd6ab7 100644
--- a/tests/testthat/test-tunable.R
+++ b/tests/testthat/test-tunable.R
@@ -15,7 +15,8 @@ test_that("tunable.linear_reg()", {
   expect_snapshot(tunable(spec %>% set_engine("glmnet")))
   expect_snapshot(tunable(spec %>% set_engine("brulee")))
 
-  # tests for call_info in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("glmnet", dfmax = tune())))
 })
 
 test_that("tunable.logistic_reg()", {
@@ -25,7 +26,8 @@ test_that("tunable.logistic_reg()", {
   expect_snapshot(tunable(spec %>% set_engine("glmnet")))
   expect_snapshot(tunable(spec %>% set_engine("brulee")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("glmnet", dfmax = tune())))
 })
 
 test_that("tunable.multinom_reg()", {
@@ -37,7 +39,8 @@ test_that("tunable.multinom_reg()", {
   expect_snapshot(tunable(spec %>% set_engine("nnet")))
   expect_snapshot(tunable(spec %>% set_engine("brulee")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("glmnet", dfmax = tune())))
 })
 
 test_that("tunable.boost_tree()", {
@@ -47,7 +50,8 @@ test_that("tunable.boost_tree()", {
   expect_snapshot(tunable(spec %>% set_engine("C5.0")))
   expect_snapshot(tunable(spec %>% set_engine("spark")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("xgboost", feval = tune())))
 })
 
 test_that("tunable.rand_forest()", {
@@ -57,7 +61,8 @@ test_that("tunable.rand_forest()", {
   expect_snapshot(tunable(spec %>% set_engine("randomForest")))
   expect_snapshot(tunable(spec %>% set_engine("spark")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("ranger", min.bucket = tune())))
 })
 
 test_that("tunable.mars()", {
@@ -65,7 +70,8 @@ test_that("tunable.mars()", {
   expect_snapshot(tunable(spec))
   expect_snapshot(tunable(spec %>% set_engine("earth")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("earth", minspan = tune())))
 })
 
 test_that("tunable.decision_tree()", {
@@ -75,7 +81,8 @@ test_that("tunable.decision_tree()", {
   expect_snapshot(tunable(spec %>% set_engine("C5.0")))
   expect_snapshot(tunable(spec %>% set_engine("spark")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("rpart", parms = tune())))
 })
 
 test_that("tunable.svm_poly()", {
@@ -83,7 +90,8 @@ test_that("tunable.svm_poly()", {
   expect_snapshot(tunable(spec))
   expect_snapshot(tunable(spec %>% set_engine("kernlab")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("kernlab", tol = tune())))
 })
 
 test_that("tunable.mlp()", {
@@ -93,13 +101,16 @@ test_that("tunable.mlp()", {
   expect_snapshot(tunable(spec %>% set_engine("nnet")))
   expect_snapshot(tunable(spec %>% set_engine("brulee")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("keras", ragged = tune())))
 })
 
 
 test_that("tunable.survival_reg()", {
   spec <- survival_reg()
   expect_snapshot(tunable(spec))
+  expect_snapshot(tunable(spec %>% set_engine("survival")))
 
-  # tests for call_info and additional engines in tidymodels/extratests
+  # don't include rows for non-tunable args marked with tune() (#1104)
+  expect_snapshot(tunable(spec %>% set_engine("survival", parms = tune())))
 })

From 21a56c85da2fff272d3f594eaa6e39acabe6e99f Mon Sep 17 00:00:00 2001
From: simonpcouch <simonpatrickcouch@gmail.com>
Date: Fri, 5 Apr 2024 10:48:16 -0500
Subject: [PATCH 3/3] exclude non-tunable engine arguments in `tunable()`

closes #1104
---
 R/tunable.R                      | 20 ++++++++--------
 tests/testthat/_snaps/tunable.md | 41 +++++++++++++-------------------
 2 files changed, 26 insertions(+), 35 deletions(-)

diff --git a/R/tunable.R b/R/tunable.R
index 85c8bff29..271c470e2 100644
--- a/R/tunable.R
+++ b/R/tunable.R
@@ -248,7 +248,7 @@ tunable.linear_reg <- function(x, ...) {
   } else if (x$engine == "brulee") {
     res <- add_engine_parameters(res, brulee_linear_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -260,7 +260,7 @@ tunable.logistic_reg <- function(x, ...) {
   } else if (x$engine == "brulee") {
     res <- add_engine_parameters(res, brulee_logistic_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -272,7 +272,7 @@ tunable.multinomial_reg <- function(x, ...) {
   } else if (x$engine == "brulee") {
     res <- add_engine_parameters(res, brulee_multinomial_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -295,7 +295,7 @@ tunable.boost_tree <- function(x, ...) {
     res$call_info[res$name == "sample_size"] <-
       list(list(pkg = "dials", fun = "sample_prop"))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -310,7 +310,7 @@ tunable.rand_forest <- function(x, ...) {
   } else if (x$engine == "aorsf") {
     res <- add_engine_parameters(res, aorsf_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -319,7 +319,7 @@ tunable.mars <- function(x, ...) {
   if (x$engine == "earth") {
     res <- add_engine_parameters(res, earth_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -333,7 +333,7 @@ tunable.decision_tree <- function(x, ...) {
                             partykit_engine_args %>%
                               dplyr::mutate(component = "decision_tree"))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -343,7 +343,7 @@ tunable.svm_poly <- function(x, ...) {
     res$call_info[res$name == "degree"] <-
       list(list(pkg = "dials", fun = "prod_degree", range = c(1L, 3L)))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 
@@ -357,7 +357,7 @@ tunable.mlp <- function(x, ...) {
     res$call_info[res$name == "epochs"] <-
       list(list(pkg = "dials", fun = "epochs", range = c(5L, 500L)))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -366,7 +366,7 @@ tunable.survival_reg <- function(x, ...) {
   if (x$engine == "flexsurvspline") {
     res <- add_engine_parameters(res, flexsurvspline_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 # nocov end
diff --git a/tests/testthat/_snaps/tunable.md b/tests/testthat/_snaps/tunable.md
index e2ec2c208..9f8b6ba3b 100644
--- a/tests/testthat/_snaps/tunable.md
+++ b/tests/testthat/_snaps/tunable.md
@@ -43,12 +43,11 @@
     Code
       tunable(spec %>% set_engine("glmnet", dfmax = tune()))
     Output
-      # A tibble: 3 x 5
+      # A tibble: 2 x 5
         name    call_info        source     component  component_id
         <chr>   <list>           <chr>      <chr>      <chr>       
       1 penalty <named list [2]> model_spec linear_reg main        
       2 mixture <named list [3]> model_spec linear_reg main        
-      3 dfmax   <NULL>           model_spec linear_reg engine      
 
 # tunable.logistic_reg()
 
@@ -95,12 +94,11 @@
     Code
       tunable(spec %>% set_engine("glmnet", dfmax = tune()))
     Output
-      # A tibble: 3 x 5
+      # A tibble: 2 x 5
         name    call_info        source     component    component_id
         <chr>   <list>           <chr>      <chr>        <chr>       
       1 penalty <named list [2]> model_spec logistic_reg main        
       2 mixture <named list [3]> model_spec logistic_reg main        
-      3 dfmax   <NULL>           model_spec logistic_reg engine      
 
 # tunable.multinom_reg()
 
@@ -244,7 +242,7 @@
     Code
       tunable(spec %>% set_engine("xgboost", feval = tune()))
     Output
-      # A tibble: 9 x 5
+      # A tibble: 8 x 5
         name           call_info        source     component  component_id
         <chr>          <list>           <chr>      <chr>      <chr>       
       1 tree_depth     <named list [2]> model_spec boost_tree main        
@@ -255,7 +253,6 @@
       6 loss_reduction <named list [2]> model_spec boost_tree main        
       7 sample_size    <named list [2]> model_spec boost_tree main        
       8 stop_iter      <named list [2]> model_spec boost_tree main        
-      9 feval          <NULL>           model_spec boost_tree engine      
 
 # tunable.rand_forest()
 
@@ -310,13 +307,12 @@
     Code
       tunable(spec %>% set_engine("ranger", min.bucket = tune()))
     Output
-      # A tibble: 4 x 5
-        name       call_info        source     component   component_id
-        <chr>      <list>           <chr>      <chr>       <chr>       
-      1 mtry       <named list [2]> model_spec rand_forest main        
-      2 trees      <named list [2]> model_spec rand_forest main        
-      3 min_n      <named list [2]> model_spec rand_forest main        
-      4 min.bucket <NULL>           model_spec rand_forest engine      
+      # A tibble: 3 x 5
+        name  call_info        source     component   component_id
+        <chr> <list>           <chr>      <chr>       <chr>       
+      1 mtry  <named list [2]> model_spec rand_forest main        
+      2 trees <named list [2]> model_spec rand_forest main        
+      3 min_n <named list [2]> model_spec rand_forest main        
 
 # tunable.mars()
 
@@ -347,13 +343,12 @@
     Code
       tunable(spec %>% set_engine("earth", minspan = tune()))
     Output
-      # A tibble: 4 x 5
+      # A tibble: 3 x 5
         name         call_info        source     component component_id
         <chr>        <list>           <chr>      <chr>     <chr>       
       1 num_terms    <named list [3]> model_spec mars      main        
       2 prod_degree  <named list [2]> model_spec mars      main        
       3 prune_method <named list [2]> model_spec mars      main        
-      4 minspan      <NULL>           model_spec mars      engine      
 
 # tunable.decision_tree()
 
@@ -405,13 +400,12 @@
     Code
       tunable(spec %>% set_engine("rpart", parms = tune()))
     Output
-      # A tibble: 4 x 5
+      # A tibble: 3 x 5
         name            call_info        source     component     component_id
         <chr>           <list>           <chr>      <chr>         <chr>       
       1 tree_depth      <named list [2]> model_spec decision_tree main        
       2 min_n           <named list [2]> model_spec decision_tree main        
       3 cost_complexity <named list [2]> model_spec decision_tree main        
-      4 parms           <NULL>           model_spec decision_tree engine      
 
 # tunable.svm_poly()
 
@@ -444,14 +438,13 @@
     Code
       tunable(spec %>% set_engine("kernlab", tol = tune()))
     Output
-      # A tibble: 5 x 5
+      # A tibble: 4 x 5
         name         call_info        source     component component_id
         <chr>        <list>           <chr>      <chr>     <chr>       
       1 cost         <named list [3]> model_spec svm_poly  main        
       2 degree       <named list [3]> model_spec svm_poly  main        
       3 scale_factor <named list [2]> model_spec svm_poly  main        
       4 margin       <named list [2]> model_spec svm_poly  main        
-      5 tol          <NULL>           model_spec svm_poly  engine      
 
 # tunable.mlp()
 
@@ -511,7 +504,7 @@
     Code
       tunable(spec %>% set_engine("keras", ragged = tune()))
     Output
-      # A tibble: 6 x 5
+      # A tibble: 5 x 5
         name         call_info        source     component component_id
         <chr>        <list>           <chr>      <chr>     <chr>       
       1 hidden_units <named list [2]> model_spec mlp       main        
@@ -519,7 +512,6 @@
       3 dropout      <named list [2]> model_spec mlp       main        
       4 epochs       <named list [2]> model_spec mlp       main        
       5 activation   <named list [2]> model_spec mlp       main        
-      6 ragged       <NULL>           model_spec mlp       engine      
 
 # tunable.survival_reg()
 
@@ -544,8 +536,7 @@
     Code
       tunable(spec %>% set_engine("survival", parms = tune()))
     Output
-      # A tibble: 1 x 5
-        name  call_info source     component    component_id
-        <chr> <list>    <chr>      <chr>        <chr>       
-      1 parms <NULL>    model_spec survival_reg engine      
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>