Merge pull request #94 from mlverse/bugfix/93_parsnip

fix #93

Author: cregouby

DESCRIPTION

@@ -31,6 +31,7 @@ Suggests:
     testthat (>= 3.0.0),
     modeldata,
     recipes,
+    rsample,
     parsnip,
     dials,
     withr,
@@ -43,6 +44,7 @@ Suggests:
     tidyr,
     purrr,
     tune,
-    workflows
+    workflows,
+    yardstick
 VignetteBuilder: knitr
 Config/testthat/edition: 3

R/hardhat.R

@@ -329,7 +329,9 @@ tabnet_bridge <- function(processed, config = tabnet_config(), tabnet_model, fro
 
   }
   if (task == "supervised") {
-    stopifnot("Error: found missing values in the response vector" = sum(is.na(outcomes))==0)
+    if (sum(is.na(outcomes))>0) {
+      rlang::abort("Error: found missing values in the response vector")
+    }
     if (is.null(tabnet_model)) {
       # new supervised model needs network initialization
       tabnet_model_lst <- tabnet_initialize(predictors, outcomes, config = config)

R/parsnip.R

@@ -307,7 +307,7 @@ update.tabnet <- function(object, parameters = NULL, epochs = NULL, penalty = NU
                           num_steps = NULL, feature_reusage = NULL, virtual_batch_size = NULL,
                           num_independent = NULL, num_shared = NULL, momentum = NULL, ...) {
   rlang::check_installed("parsnip")
-  eng_args <- parsnip::update_engine_parameters(object$eng_args, ...)
+  eng_args <- parsnip::update_engine_parameters(object$eng_args, fresh=TRUE, ...)
   args <- list(
     epochs = rlang::enquo(epochs),
     penalty = rlang::enquo(penalty),

tests/testthat/test-parsnip.R

@@ -62,3 +62,36 @@ test_that("Check we can finalize a workflow", {
   expect_equal(rlang::eval_tidy(wf$fit$actions$model$spec$args$penalty), 0.01)
   expect_equal(rlang::eval_tidy(wf$fit$actions$model$spec$args$epochs), 1)
 })
+
+test_that("Check we can finalize a workflow from a tune_grid", {
+
+  library(parsnip)
+  data("ames", package = "modeldata")
+
+  model <- tabnet(epochs = tune()) %>%
+    set_mode("regression") %>%
+    set_engine("torch")
+
+  wf <- workflows::workflow() %>%
+    workflows::add_model(model) %>%
+    workflows::add_formula(Sale_Price ~ .)
+
+  custom_grid <- tidyr::crossing(epochs = c(1,2,3))
+  cv_folds <- ames %>%
+    rsample::vfold_cv(v = 2, repeats = 1)
+
+  at <- tune::tune_grid(
+    object = wf,
+    resamples = cv_folds,
+    grid = custom_grid,
+    metrics = yardstick::metric_set(yardstick::rmse),
+    control = tune::control_grid(verbose = F)
+  )
+
+  best_rmse <- tune::select_best(at, "rmse")
+
+  expect_error(
+    final_wf <- tune::finalize_workflow(wf, best_rmse),
+    regexp = NA
+  )
+})