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library(testthat)
library(recipes)
library(tibble)

lmh <- c("Low", "Med", "High")

examples <- data.frame(
  X1 = factor(rep(letters[1:4], each = 3)),
  X2 = ordered(rep(lmh, each = 4),
    levels = lmh
  )
)
rec <- recipe(~ X1 + X2, data = examples)

test_that("correct var", {
  rec1 <- rec %>% step_unorder(X2)

  rec1_trained <- prep(rec1, training = examples, verbose = FALSE)
  rec1_trans <- bake(rec1_trained, new_data = examples)

  expect_true(is.factor(rec1_trans$X2))
  expect_true(!is.ordered(rec1_trans$X2))

  expect_equal(levels(rec1_trans$X2), levels(examples$X2))
  expect_equal(as.character(rec1_trans$X2), as.character(examples$X2))
})

test_that("wrong vars", {
  rec2 <- rec %>% step_unorder(X1, X2)
  expect_snapshot(prep(rec2, training = examples, verbose = FALSE))
  rec3 <- rec %>% step_unorder(X1)
  expect_snapshot(prep(rec3, training = examples, verbose = FALSE))
})

test_that("printing", {
  rec4 <- rec %>% step_unorder(X2)
  expect_snapshot(print(rec4))
  expect_snapshot(prep(rec4))
})

test_that("empty selection prep/bake is a no-op", {
  rec1 <- recipe(mpg ~ ., mtcars)
  rec2 <- step_unorder(rec1)

  rec1 <- prep(rec1, mtcars)
  rec2 <- prep(rec2, mtcars)

  baked1 <- bake(rec1, mtcars)
  baked2 <- bake(rec2, mtcars)

  expect_identical(baked1, baked2)
})

test_that("empty selection tidy method works", {
  rec <- recipe(mpg ~ ., mtcars)
  rec <- step_unorder(rec)

  expect <- tibble(terms = character(), id = character())

  expect_identical(tidy(rec, number = 1), expect)

  rec <- prep(rec, mtcars)

  expect_identical(tidy(rec, number = 1), expect)
})

test_that("empty printing", {
  skip_if(packageVersion("rlang") < "1.0.0")
  rec <- recipe(mpg ~ ., mtcars)
  rec <- step_unorder(rec)

  expect_snapshot(rec)

  rec <- prep(rec, mtcars)

  expect_snapshot(rec)
})

test_that("bake method errors when needed non-standard role columns are missing", {
  rec1 <- rec %>% step_unorder(X2) %>%
    update_role(X2, new_role = "potato") %>%
    update_role_requirements(role = "potato", bake = FALSE)

  rec1_trained <- prep(rec1, training = examples, verbose = FALSE)

  expect_error(bake(rec1_trained, new_data = examples[, 1, drop = FALSE]),
               class = "new_data_missing_column")
})

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