test_that("basic adjust_probability_threshold() usage works", {
  skip_if_not_installed("modeldata")
  library(modeldata)

  # fitting and predicting happens without raising conditions
  expect_no_condition(
    tlr <-
      tailor() |>
      adjust_probability_threshold(.1)
  )

  expect_no_condition(
    tlr_fit <- fit(
      tlr,
      two_class_example,
      outcome = c(truth),
      estimate = c(predicted),
      probabilities = c(Class1, Class2)
    )
  )

  expect_no_condition(
    tlr_pred <- predict(tlr_fit, two_class_example)
  )

  # classes are as expected
  expect_s3_class(tlr, "tailor")
  expect_s3_class(tlr_fit, "tailor")
  expect_s3_class(tlr_pred, "tbl_df")

  # column names are as expected
  expect_equal(colnames(two_class_example), colnames(tlr_pred))

  # calculations match those done manually
  manual_pred <- factor(ifelse(
    two_class_example$Class1 > .1,
    "Class1",
    "Class2"
  ))
  expect_equal(tlr_pred$predicted, manual_pred)
})

test_that("adjustment printing", {
  expect_snapshot(tailor() |> adjust_probability_threshold())
  expect_snapshot(tailor() |> adjust_probability_threshold(hardhat::tune()))

  skip_if_not_installed("modeldata")
  data("two_class_example", package = "modeldata")

  expect_snapshot(
    tailor() |>
      adjust_probability_threshold() |>
      fit(
        two_class_example,
        outcome = c(truth),
        estimate = c(predicted),
        probabilities = c(Class1, Class2)
      )
  )
})

test_that("tunable", {
  tlr <-
    tailor() |>
    adjust_probability_threshold(.1)
  adj_param <- tunable(tlr$adjustments[[1]])
  expect_equal(adj_param$name, "threshold")
  expect_true(all(adj_param$source == "tailor"))
  expect_true(is.list(adj_param$call_info))
  expect_equal(nrow(adj_param), 1)
  expect_equal(
    names(adj_param),
    c("name", "call_info", "source", "component", "component_id")
  )
})

test_that("required packages", {
  tlr <-
    tailor() |>
    adjust_probability_threshold(.1)

  expect_equal(required_pkgs(tlr), "tailor")
})