test_that("tune_blackboost() returns a minimal grid with metrics and refit_best yields a fitted model", {
  skip_on_cran()
  skip_if_not_installed("mboost")
  skip_if_not_installed("partykit")
  skip_if_not_installed("survival")

  df <- survival::veteran
  set.seed(777)

  Surv <- survival::Surv

  grid <- expand.grid(
    mstop = c(25, 50),
    nu = 0.1,
    maxdepth = 2,
    KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE
  )

  res <- tune_blackboost(
    formula = Surv(time, status) ~ age + karno + celltype,   
    data = df,
    times = c(30, 90, 150),
    param_grid = grid,
    metrics = c("cindex", "ibs"),
    folds = 2,
    seed = 777,
    refit_best = FALSE
  )

  expect_s3_class(res, "tuned_surv")
  expect_true(all(c("mstop", "nu", "maxdepth") %in% names(res)))
  expect_true(all(c("cindex", "ibs") %in% names(res)))

  best_mod <- tune_blackboost(
    formula = Surv(time, status) ~ age + karno + celltype,  
    data = df,
    times = c(30, 90, 150),
    param_grid = grid,
    metrics = c("cindex", "ibs"),
    folds = 2,
    seed = 777,
    refit_best = TRUE
  )

  expect_s3_class(best_mod, "mlsurv_model")
  expect_identical(best_mod$learner, "blackboost")
  expect_true(inherits(best_mod$model, "mboost"))
})