update to testthat 3e + consolidate translate() tests

DESCRIPTION

@@ -60,7 +60,7 @@ Suggests:
     rpart,
     sparklyr (>= 1.0.0),
     survival,
-    testthat,
+    testthat (>= 3.0.0),
     xgboost (>= 1.5.0.1)
 VignetteBuilder: 
     knitr
@@ -89,4 +89,5 @@ Config/rcmdcheck/ignore-inconsequential-notes: true
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
+Config/testthat/edition: 3
 RoxygenNote: 7.1.2.9000

tests/testthat/_snaps/boost_tree.md

@@ -0,0 +1,92 @@
+# updating
+
+    Code
+      expr1 %>% update(trees = 10)
+    Output
+      Boosted Tree Model Specification (unknown)
+
+      Main Arguments:
+        trees = 10
+
+      Engine-Specific Arguments:
+        verbose = 0
+
+      Computational engine: xgboost 
+
+
+---
+
+    Code
+      expr1 %>% update(param_tibb)
+    Output
+      Boosted Tree Model Specification (unknown)
+
+      Main Arguments:
+        mtry = 1
+        trees = 7
+
+      Engine-Specific Arguments:
+        verbose = 0
+
+      Computational engine: xgboost 
+
+
+---
+
+    Code
+      expr1 %>% update(param_list)
+    Output
+      Boosted Tree Model Specification (unknown)
+
+      Main Arguments:
+        mtry = 1
+        trees = 7
+
+      Engine-Specific Arguments:
+        verbose = 0
+
+      Computational engine: xgboost 
+
+
+---
+
+    Code
+      expr2 %>% update(bands = 10)
+    Output
+      Boosted Tree Model Specification (unknown)
+
+      Main Arguments:
+        trees = tune()
+
+      Engine-Specific Arguments:
+        bands = 10
+
+      Computational engine: C5.0 
+
+
+---
+
+    Code
+      expr3 %>% update(trees = 1, fresh = TRUE)
+    Output
+      Boosted Tree Model Specification (unknown)
+
+      Main Arguments:
+        trees = 1
+
+      Computational engine: xgboost 
+
+
+---
+
+    Code
+      expr4 %>% update(noGlobalPruning = TRUE)
+    Output
+      Boosted Tree Model Specification (unknown)
+
+      Engine-Specific Arguments:
+        noGlobalPruning = TRUE
+
+      Computational engine: C5.0 
+
+

tests/testthat/_snaps/decision_tree.md

@@ -0,0 +1,79 @@
+# updating
+
+    Code
+      expr1 %>% update(cost_complexity = 0.1)
+    Output
+      Decision Tree Model Specification (unknown)
+
+      Main Arguments:
+        cost_complexity = 0.1
+
+      Engine-Specific Arguments:
+        model = FALSE
+
+      Computational engine: rpart 
+
+
+---
+
+    Code
+      expr1 %>% update(param_tibb)
+    Output
+      Decision Tree Model Specification (unknown)
+
+      Main Arguments:
+        cost_complexity = 0.1
+        min_n = 1
+
+      Engine-Specific Arguments:
+        model = FALSE
+
+      Computational engine: rpart 
+
+
+---
+
+    Code
+      expr1 %>% update(param_list)
+    Output
+      Decision Tree Model Specification (unknown)
+
+      Main Arguments:
+        cost_complexity = 0.1
+        min_n = 1
+
+      Engine-Specific Arguments:
+        model = FALSE
+
+      Computational engine: rpart 
+
+
+---
+
+    Code
+      expr2 %>% update(model = FALSE)
+    Output
+      Decision Tree Model Specification (unknown)
+
+      Main Arguments:
+        cost_complexity = tune()
+
+      Engine-Specific Arguments:
+        model = FALSE
+
+      Computational engine: rpart 
+
+
+---
+
+    Code
+      expr3 %>% update(cost_complexity = 1, fresh = TRUE)
+    Output
+      Decision Tree Model Specification (unknown)
+
+      Main Arguments:
+        cost_complexity = 1
+
+      Computational engine: rpart 
+
+

tests/testthat/_snaps/linear_reg.md

@@ -0,0 +1,109 @@
+# updating
+
+    Code
+      expr1 %>% update(mixture = 0)
+    Output
+      Linear Regression Model Specification (regression)
+
+      Main Arguments:
+        mixture = 0
+
+      Engine-Specific Arguments:
+        model = FALSE
+
+      Computational engine: lm 
+
+
+---
+
+    Code
+      expr2 %>% update(nlambda = 10)
+    Output
+      Linear Regression Model Specification (regression)
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr3 %>% update(mixture = 1, fresh = TRUE, nlambda = 10)
+    Output
+      Linear Regression Model Specification (regression)
+
+      Main Arguments:
+        mixture = 1
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr3 %>% update(nlambda = 10)
+    Output
+      Linear Regression Model Specification (regression)
+
+      Main Arguments:
+        penalty = tune()
+        mixture = 0
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr4 %>% update(param_tibb)
+    Output
+      Linear Regression Model Specification (regression)
+
+      Main Arguments:
+        penalty = 1
+        mixture = 0.333333333333333
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr4 %>% update(param_list)
+    Output
+      Linear Regression Model Specification (regression)
+
+      Main Arguments:
+        penalty = 1
+        mixture = 0.333333333333333
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr5 %>% update(family = "poisson")
+    Output
+      Linear Regression Model Specification (regression)
+
+      Engine-Specific Arguments:
+        family = poisson
+
+      Computational engine: glm 
+
+

tests/testthat/_snaps/logistic_reg.md

@@ -0,0 +1,82 @@
+# updating
+
+    Code
+      expr1 %>% update(mixture = 0)
+    Output
+      Logistic Regression Model Specification (classification)
+
+      Main Arguments:
+        mixture = 0
+
+      Engine-Specific Arguments:
+        family = expr(binomial(link = "probit"))
+
+      Computational engine: glm 
+
+
+---
+
+    Code
+      expr2 %>% update(nlambda = 10)
+    Output
+      Logistic Regression Model Specification (classification)
+
+      Main Arguments:
+        mixture = tune()
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr3 %>% update(mixture = 1, fresh = TRUE, nlambda = 10)
+    Output
+      Logistic Regression Model Specification (classification)
+
+      Main Arguments:
+        mixture = 1
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr4 %>% update(param_tibb)
+    Output
+      Logistic Regression Model Specification (classification)
+
+      Main Arguments:
+        penalty = 1
+        mixture = 0.333333333333333
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+
+---
+
+    Code
+      expr4 %>% update(param_list)
+    Output
+      Logistic Regression Model Specification (classification)
+
+      Main Arguments:
+        penalty = 1
+        mixture = 0.333333333333333
+
+      Engine-Specific Arguments:
+        nlambda = 10
+
+      Computational engine: glmnet 
+
+