enable model_spec tunable() tests

tests/testthat/_snaps/tunable.md

@@ -5,3 +5,382 @@
     Output
       <simpleError: The `parsnip` model database doesn't know about the arguments for model `potato`. Was it registered?>
 
+# test tunable parameter values
+
+    Code
+      boost_tree(trees = tune(), min_n = tune(), sample_size = tune()) %>% set_engine(
+        "C5.0") %>% print_parameters()
+    Output
+      $trees
+      $trees$pkg
+      [1] "dials"
+
+      $trees$fun
+      [1] "trees"
+
+      $trees$range
+      [1]   1 100
+
+
+      $min_n
+      $min_n$pkg
+      [1] "dials"
+
+      $min_n$fun
+      [1] "min_n"
+
+
+      $sample_size
+      $sample_size$pkg
+      [1] "dials"
+
+      $sample_size$fun
+      [1] "sample_prop"
+
+
+
+---
+
+    Code
+      C5_rules(trees = tune(), min_n = tune()) %>% set_engine("C5.0") %>%
+        print_parameters()
+    Output
+      $trees
+      $trees$pkg
+      [1] "dials"
+
+      $trees$fun
+      [1] "trees"
+
+      $trees$range
+      [1]   1 100
+
+
+      $min_n
+      $min_n$pkg
+      [1] "dials"
+
+      $min_n$fun
+      [1] "min_n"
+
+      $min_n$range
+      [1]  2 40
+
+
+
+---
+
+    Code
+      decision_tree(min_n = tune()) %>% set_engine("C5.0") %>% print_parameters()
+    Output
+      $min_n
+      $min_n$pkg
+      [1] "dials"
+
+      $min_n$fun
+      [1] "min_n"
+
+
+
+---
+
+    Code
+      logistic_reg(penalty = tune()) %>% set_engine("brulee") %>% print_parameters()
+    Output
+      $penalty
+      $penalty$pkg
+      [1] "dials"
+
+      $penalty$fun
+      [1] "penalty"
+
+
+      $mixture
+      $mixture$pkg
+      [1] "dials"
+
+      $mixture$fun
+      [1] "mixture"
+
+
+
+---
+
+    Code
+      mars(prod_degree = tune()) %>% set_engine("earth") %>% set_mode(
+        "classification") %>% print_parameters()
+    Output
+      $num_terms
+      $num_terms$pkg
+      [1] "dials"
+
+      $num_terms$fun
+      [1] "num_terms"
+
+      $num_terms$range
+      [1] 2 5
+
+
+      $prod_degree
+      $prod_degree$pkg
+      [1] "dials"
+
+      $prod_degree$fun
+      [1] "prod_degree"
+
+
+      $prune_method
+      $prune_method$pkg
+      [1] "dials"
+
+      $prune_method$fun
+      [1] "prune_method"
+
+
+
+---
+
+    Code
+      multinom_reg(penalty = tune()) %>% set_engine("brulee") %>% print_parameters()
+    Output
+      $penalty
+      $penalty$pkg
+      [1] "dials"
+
+      $penalty$fun
+      [1] "penalty"
+
+
+      $mixture
+      $mixture$pkg
+      [1] "dials"
+
+      $mixture$fun
+      [1] "mixture"
+
+
+
+---
+
+    Code
+      rand_forest(mtry = tune(), min_n = tune()) %>% set_engine("randomForest") %>%
+        set_mode("classification") %>% print_parameters()
+    Output
+      $mtry
+      $mtry$pkg
+      [1] "dials"
+
+      $mtry$fun
+      [1] "mtry"
+
+
+      $trees
+      $trees$pkg
+      [1] "dials"
+
+      $trees$fun
+      [1] "trees"
+
+
+      $min_n
+      $min_n$pkg
+      [1] "dials"
+
+      $min_n$fun
+      [1] "min_n"
+
+
+
+---
+
+    Code
+      rand_forest(mtry = tune(), min_n = tune()) %>% set_engine("ranger") %>%
+        set_mode("classification") %>% print_parameters()
+    Output
+      $mtry
+      $mtry$pkg
+      [1] "dials"
+
+      $mtry$fun
+      [1] "mtry"
+
+
+      $trees
+      $trees$pkg
+      [1] "dials"
+
+      $trees$fun
+      [1] "trees"
+
+
+      $min_n
+      $min_n$pkg
+      [1] "dials"
+
+      $min_n$fun
+      [1] "min_n"
+
+
+
+---
+
+    Code
+      linear_reg(penalty = tune()) %>% set_engine("brulee") %>% print_parameters()
+    Output
+      $penalty
+      $penalty$pkg
+      [1] "dials"
+
+      $penalty$fun
+      [1] "penalty"
+
+
+      $mixture
+      $mixture$pkg
+      [1] "dials"
+
+      $mixture$fun
+      [1] "mixture"
+
+
+
+---
+
+    Code
+      boost_tree(tree_depth = tune(), trees = tune(), learn_rate = tune(), min_n = tune(),
+      loss_reduction = tune(), sample_size = tune(), stop_iter = tune()) %>%
+        set_engine("xgboost") %>% set_mode("classification") %>% print_parameters()
+    Output
+      $tree_depth
+      $tree_depth$pkg
+      [1] "dials"
+
+      $tree_depth$fun
+      [1] "tree_depth"
+
+
+      $trees
+      $trees$pkg
+      [1] "dials"
+
+      $trees$fun
+      [1] "trees"
+
+
+      $learn_rate
+      $learn_rate$pkg
+      [1] "dials"
+
+      $learn_rate$fun
+      [1] "learn_rate"
+
+      $learn_rate$range
+      [1] -3.0 -0.5
+
+
+      $mtry
+      $mtry$pkg
+      [1] "dials"
+
+      $mtry$fun
+      [1] "mtry"
+
+
+      $min_n
+      $min_n$pkg
+      [1] "dials"
+
+      $min_n$fun
+      [1] "min_n"
+
+
+      $loss_reduction
+      $loss_reduction$pkg
+      [1] "dials"
+
+      $loss_reduction$fun
+      [1] "loss_reduction"
+
+
+      $sample_size
+      $sample_size$pkg
+      [1] "dials"
+
+      $sample_size$fun
+      [1] "sample_prop"
+
+
+      $stop_iter
+      $stop_iter$pkg
+      [1] "dials"
+
+      $stop_iter$fun
+      [1] "stop_iter"
+
+
+
+---
+
+    Code
+      mlp(hidden_units = tune(), penalty = tune(), dropout = tune(), epochs = tune(),
+      activation = tune()) %>% set_engine("brulee") %>% set_mode("classification") %>%
+        print_parameters()
+    Output
+      $hidden_units
+      $hidden_units$pkg
+      [1] "dials"
+
+      $hidden_units$fun
+      [1] "hidden_units"
+
+
+      $penalty
+      $penalty$pkg
+      [1] "dials"
+
+      $penalty$fun
+      [1] "penalty"
+
+
+      $epochs
+      $epochs$pkg
+      [1] "dials"
+
+      $epochs$fun
+      [1] "epochs"
+
+      $epochs$range
+      [1]   5 500
+
+
+      $dropout
+      $dropout$pkg
+      [1] "dials"
+
+      $dropout$fun
+      [1] "dropout"
+
+
+      $learn_rate
+      $learn_rate$pkg
+      [1] "dials"
+
+      $learn_rate$fun
+      [1] "learn_rate"
+
+      $learn_rate$range
+      [1] -3.0 -0.5
+
+
+      $activation
+      $activation$pkg
+      [1] "dials"
+
+      $activation$fun
+      [1] "activation"
+
+      $activation$values
+      [1] "relu" "elu"  "tanh"
+
+
+