add first sparsity test

tests/testthat/_snaps/sparsity.md

@@ -0,0 +1,8 @@
+# id: 1, recipe sparsity: yes, sparsity: high, model support: yes, arg: auto
+
+    Code
+      fit(wf_spec, ames)
+    Condition
+      Error in `parsnip::xgb_train()`:
+      ! correct: x is sparse matrix
+

tests/testthat/test-sparsity.R

@@ -0,0 +1,64 @@
+# recipe sparsity: yes
+# step_dummy(all_nominal_predictors())
+# recipe sparsity: no
+# step_integer(all_nominal_predictors())
+#
+# sparsity: high
+# no change
+# sparsity: low
+# ames <- dplyr::select(ames, Street, where(is.numeric))
+#
+# model support: yes
+# mod_spec <- boost_tree("regression", "xgboost")
+# model support: no
+# mod_spec <- linear_reg("regression", "lm")
+#
+# arg: auto
+# step_dummy(all_nominal_predictors())
+# arg: no
+# step_dummy(all_nominal_predictors(), sparse = "no")
+# arg: yes
+# step_dummy(all_nominal_predictors(), sparse = "yes")
+
+test_that("id: 1, recipe sparsity: yes, sparsity: high, model support: yes, arg: auto", {
+  skip_if_not_installed("xgboost")
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("hardhat", "1.4.0.9002")
+  skip_if_not_installed("recipes", "1.1.0.9001")
+  skip_if_not_installed("parsnip", "1.2.1.9004")
+  skip_if_not_installed("workflows", "1.1.4.9001")
+
+  data("ames", package = "modeldata")
+
+  rec_spec <- recipe(Sale_Price ~ ., data = ames) |>
+    step_dummy(all_nominal_predictors())
+
+  mod_spec <- boost_tree("regression", "xgboost")
+
+  wf_spec <- workflow(rec_spec, mod_spec)
+
+  expect_no_error(
+    wf_fit <- fit(wf_spec, ames)
+  )
+
+  expect_identical(
+    extract_preprocessor(wf_fit)$steps[[1]]$sparse,
+    "yes"
+  )
+
+  local_mocked_bindings(
+    xgb_train = function(x, ...) {
+      if (methods::is(x, "sparseMatrix")) {
+        cli::cli_abort("correct: x is sparse matrix")
+      } else {
+        cli::cli_abort("wrong: x is dense matrix")
+      }
+    }, 
+    .package = "parsnip"
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    fit(wf_spec, ames)
+  )
+})