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] 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
+})