tests/testthat/test-misc.R

In tidymodels/tune: Tidy Tuning Tools

test_that("determine foreach operator", {
  skip_if(foreach::getDoParWorkers() > 1 || future::nbrOfWorkers() > 1)
  skip_if_not_installed("modeldata")
  skip_if_not_installed("splines2")

  data("Chicago", package = "modeldata")
  spline_rec <-
    recipes::recipe(ridership ~ ., data = head(Chicago)) %>%
    recipes::step_date(date) %>%
    recipes::step_holiday(date) %>%
    recipes::step_rm(date, dplyr::ends_with("away")) %>%
    recipes::step_impute_knn(recipes::all_predictors(), neighbors = tune("imputation")) %>%
    recipes::step_other(recipes::all_nominal(), threshold = tune()) %>%
    recipes::step_dummy(recipes::all_nominal()) %>%
    recipes::step_normalize(recipes::all_numeric_predictors()) %>%
    recipes::step_spline_b(recipes::all_predictors(), deg_free = tune(), degree = tune())
  glmn <- parsnip::linear_reg(penalty = tune(), mixture = tune()) %>%
    parsnip::set_engine("glmnet")
  chi_wflow <-
    workflows::workflow() %>%
    workflows::add_recipe(spline_rec) %>%
    workflows::add_model(glmn)

  expect_equal(tune:::get_operator(object = chi_wflow)[[1]], foreach::`%do%`)
  expect_equal(tune:::get_operator(FALSE, chi_wflow)[[1]], foreach::`%do%`)
})

# ------------------------------------------------------------------------------

test_that("exponential decay", {
  expect_equal(
    expo_decay(1, start_val = 0, limit_val = 1, slope = 1 / 5), 0
  )
  expect_equal(
    expo_decay(1000, start_val = 0, limit_val = 1, slope = 1 / 5), 1
  )
  expect_equal(
    expo_decay(10, start_val = 0, limit_val = 50, slope = 1), (1 - exp(-9)) * 50
  )
})

# ------------------------------------------------------------------------------

test_that("in-line formulas on outcome", {

  # see issues 121
  w1 <-
    workflow() %>%
    add_formula(log(mpg) ~ .) %>%
    add_model(parsnip::linear_reg() %>% parsnip::set_engine("lm"))

  expect_no_error(
    f1 <- fit_resamples(w1, resamples = rsample::vfold_cv(mtcars))
  )
  expect_true(inherits(f1, "resample_results"))

  w2 <-
    workflow() %>%
    add_recipe(recipes::recipe(mpg ~ ., data = mtcars) %>% recipes::step_log(mpg)) %>%
    add_model(parsnip::linear_reg() %>% parsnip::set_engine("lm"))

  expect_no_error(
    f2 <- fit_resamples(w2, resamples = rsample::vfold_cv(mtcars))
  )
  expect_true(inherits(f2, "resample_results"))
})

# ------------------------------------------------------------------------------

test_that("empty ellipses", {
  expect_no_error(tune:::empty_ellipses())
  expect_snapshot(tune:::empty_ellipses(a = 1))
})

# ------------------------------------------------------------------------------

test_that("accessor functions", {
  load(test_path("data", "test_objects.RData"))

  expect_equal(.get_tune_parameter_names(mt_spln_knn_bo), attributes(mt_spln_knn_bo)$parameters$id)
  attr(mt_spln_knn_bo, "parameters") <- NULL
  expect_equal(.get_tune_parameter_names(mt_spln_knn_bo), character(0))

  expect_equal(.get_tune_metrics(mt_knn_bo), attributes(mt_knn_bo)$metrics)
  attr(mt_knn_bo, "metrics") <- NULL
  expect_null(.get_tune_metrics(mt_knn_bo))

  expect_equal(
    .get_tune_metric_names(mt_spln_knn_bo),
    names(attributes(attributes(mt_spln_knn_bo)$metrics)$metrics)
  )
  attr(mt_spln_knn_bo, "metrics") <- NULL
  expect_equal(.get_tune_metric_names(mt_spln_knn_bo), character(0))

  expect_equal(.get_tune_outcome_names(mt_spln_knn_bo), attributes(mt_spln_knn_bo)$outcomes)
  attr(mt_spln_knn_bo, "outcomes") <- NULL
  expect_equal(.get_tune_outcome_names(mt_spln_knn_bo), character(0))
})

test_that("accessor functions", {
  skip_if(utils::packageVersion("dials") <= "0.0.7")

  load(test_path("data", "test_objects.RData"))

  expect_equal(
    tibble::as_tibble(.get_tune_parameters(mt_knn_bo)),
    tibble::as_tibble(attributes(mt_knn_bo)$parameters)
  )
  attr(mt_knn_bo, "parameters") <- NULL
  expect_equal(.get_tune_parameters(mt_knn_bo), tibble::tibble())
})

# ------------------------------------------------------------------------------

test_that("rsample fingerprinting", {
  expect_equal(.get_fingerprint(ames_grid_search), "bfb2d02564c955d27ed78316b820e8ff")
  expect_equal(.get_fingerprint(ames_iter_search), "bfb2d02564c955d27ed78316b820e8ff")
})