# test_that("fit_resample accepts rsample splits", {
#   skip_if_not_installed("rsample")
#   df <- make_class_df(12)
#   rs <- rsample::vfold_cv(df, v = 2)
#   attr(rs, "bioLeak_perm_mode") <- "subject_grouped"
#   fit <- fit_resample_quiet(df, outcome = "outcome", splits = rs,
#                             learner = "glm", custom_learners = make_custom_learners(),
#                             metrics = "accuracy", refit = FALSE)
#   expect_true(nrow(fit@metrics) > 0)
# })

# test_that("rsample grouped splits drop grouping columns", {
#   skip_if_not_installed("rsample")
#   df <- make_class_df(12)
#   rs <- rsample::group_vfold_cv(df, group = subject, v = 2)
#   fit <- fit_resample_quiet(df, outcome = "outcome", splits = rs,
#                             learner = "glm", custom_learners = make_custom_learners(),
#                             metrics = "accuracy", refit = FALSE)
#   expect_true(length(fit@feature_names) > 0)
#   expect_false("subject" %in% fit@feature_names)
# })

# test_that("rsample split_cols override drops identifiers", {
#   skip_if_not_installed("rsample")
#   df <- make_class_df(12)
#   rs <- rsample::vfold_cv(df, v = 2)
#   attr(rs, "bioLeak_perm_mode") <- "subject_grouped"
#   fit <- fit_resample_quiet(df, outcome = "outcome", splits = rs,
#                             split_cols = list(group = "subject", batch = "batch"),
#                             learner = "glm", custom_learners = make_custom_learners(),
#                             metrics = "accuracy", refit = FALSE)
#   expect_false("subject" %in% fit@feature_names)
#   expect_false("batch" %in% fit@feature_names)
# })

# test_that("rsample auto split_cols drops common identifiers", {
#   skip_if_not_installed("rsample")
#   df <- make_class_df(12)
#   rs <- rsample::vfold_cv(df, v = 2)
#   attr(rs, "bioLeak_perm_mode") <- "subject_grouped"
#   fit <- fit_resample_quiet(df, outcome = "outcome", splits = rs,
#                             learner = "glm", custom_learners = make_custom_learners(),
#                             metrics = "accuracy", refit = FALSE)
#   expect_false("subject" %in% fit@feature_names)
#   expect_false("batch" %in% fit@feature_names)
#   expect_false("time" %in% fit@feature_names)
# })

test_that("as_rsample converts LeakSplits", {
  skip_if_not_installed("rsample")
  df <- make_class_df(12)
  splits <- make_split_plan_quiet(df, outcome = "outcome",
                              mode = "subject_grouped", group = "subject", v = 3, seed = 1)
  rs <- as_rsample(splits, data = df)
  expect_true(inherits(rs, "rset"))
  expect_equal(nrow(rs), length(splits@indices))
  expect_equal(attr(rs, "group"), "subject")
  expect_equal(attr(rs, "bioLeak_mode"), "subject_grouped")
  back <- bioLeak:::.bio_as_leaksplits_from_rsample(rs, n = nrow(df), coldata = df, split_cols = "auto")
  expect_equal(back@mode, "subject_grouped")
  expect_equal(back@info$perm_mode, "subject_grouped")
})

# test_that("rsample group splits enable restricted permutations", {
#   skip_if_not_installed("rsample")
#   df <- make_class_df(12)
#   rs <- rsample::group_vfold_cv(df, group = subject, v = 2)
#   splits <- bioLeak:::.bio_as_leaksplits_from_rsample(rs, n = nrow(df), coldata = df, split_cols = "auto")
#   expect_equal(splits@info$perm_mode, "subject_grouped")
#   expect_equal(bioLeak:::.bio_perm_mode(splits), "subject_grouped")
# })

# test_that("fit_resample accepts recipe preprocessing", {
#   skip_if_not_installed("recipes")
#   df <- make_class_df(12)
#   splits <- make_split_plan_quiet(df, outcome = "outcome",
#                               mode = "subject_grouped", group = "subject", v = 3, seed = 1)
#   rec <- recipes::recipe(outcome ~ ., data = df) |>
#     recipes::step_normalize(recipes::all_numeric_predictors())
#   fit <- fit_resample_quiet(df, outcome = "outcome", splits = splits,
#                             preprocess = rec,
#                             learner = "glm", custom_learners = make_custom_learners(),
#                             metrics = "accuracy", refit = FALSE)
#   expect_true(nrow(fit@metrics) > 0)
# })

test_that("fit_resample accepts workflow learners", {
  skip_if_not_installed("workflows")
  skip_if_not_installed("parsnip")
  df <- make_class_df(12)
  splits <- make_split_plan_quiet(df, outcome = "outcome",
                              mode = "subject_grouped", group = "subject", v = 3, seed = 1)
  spec <- parsnip::logistic_reg(mode = "classification") |>
    parsnip::set_engine("glm")
  wf <- workflows::workflow() |>
    workflows::add_model(spec) |>
    workflows::add_formula(outcome ~ .)
  fit <- fit_resample_quiet(df, outcome = "outcome", splits = splits,
                            learner = wf, metrics = "accuracy", refit = FALSE)
  expect_true(nrow(fit@metrics) > 0)
})

test_that("fit_resample accepts yardstick metric sets", {
  skip_if_not_installed("yardstick")
  df <- make_class_df(12)
  splits <- make_split_plan_quiet(df, outcome = "outcome",
                              mode = "subject_grouped", group = "subject", v = 3, seed = 1)
  ys <- yardstick::metric_set(yardstick::accuracy, yardstick::roc_auc)
  fit <- fit_resample_quiet(df, outcome = "outcome", splits = splits,
                            learner = "glm", custom_learners = make_custom_learners(),
                            metrics = ys, refit = FALSE)
  expect_true(all(c("accuracy", "roc_auc") %in% colnames(fit@metrics)))
})