Nothing
library(parsnip)
library(recipes)
library(hardhat)
data(two_class_dat, package = "modeldata")
xgb <- boost_tree(trees = 3) %>% set_mode("classification")
rec <-
recipe(Class ~ A + B, two_class_dat) %>%
step_normalize(A) %>%
step_normalize(B)
sparse_bp <- default_recipe_blueprint(composition = "dgCMatrix")
test_that('update model', {
expect_error(
new_set <- update_workflow_model(two_class_res, "none_cart", spec = xgb),
regexp = NA
)
expect_true(
inherits(extract_spec_parsnip(new_set, id = "none_cart"),
"boost_tree")
)
expect_equal(new_set$result[[1]], list())
expect_error(
new_new_set <-
update_workflow_model(new_set,
"none_glm",
spec = xgb,
formula = Class ~ log(A) + B),
regexp = NA
)
new_wflow <- extract_workflow(new_new_set, "none_glm")
expect_equal(
new_wflow$fit$actions$model$formula,
Class ~ log(A) + B
)
})
test_that('update recipe', {
expect_error(
new_set <- update_workflow_recipe(two_class_res, "yj_trans_cart", recipe = rec),
regexp = NA
)
new_rec <- extract_recipe(new_set, id = "yj_trans_cart", estimated = FALSE)
expect_true(all(tidy(new_rec)$type == "normalize"))
expect_equal(new_set$result[[4]], list())
expect_error(
new_new_set <-
update_workflow_recipe(
new_set,
"yj_trans_cart",
recipe = rec,
blueprint = sparse_bp
),
regexp = NA
)
new_wflow <- extract_workflow(new_new_set, "yj_trans_cart")
expect_equal(new_wflow$pre$actions$recipe$blueprint, sparse_bp)
})
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