test_that("Isotonic estimates work - data.frame", {
  skip_if_not_installed("modeldata")

  set.seed(100)
  sl_isotonic <- cal_estimate_isotonic(segment_logistic, Class)
  expect_cal_type(sl_isotonic, "binary")
  expect_cal_method(sl_isotonic, "Isotonic regression calibration")
  expect_cal_rows(sl_isotonic)
  expect_snapshot(print(sl_isotonic))

  set.seed(100)
  sl_isotonic_group <- segment_logistic |>
    dplyr::mutate(group = .pred_poor > 0.5) |>
    cal_estimate_isotonic(Class, .by = group)

  expect_cal_type(sl_isotonic_group, "binary")
  expect_cal_method(sl_isotonic_group, "Isotonic regression calibration")
  expect_cal_rows(sl_isotonic_group)
  expect_snapshot(print(sl_isotonic_group))

  set.seed(100)
  expect_snapshot_error(
    segment_logistic |>
      dplyr::mutate(group1 = 1, group2 = 2) |>
      cal_estimate_isotonic(Class, .by = c(group1, group2))
  )
})

test_that("Isotonic estimates work - tune_results", {
  skip_if_not_installed("modeldata")

  set.seed(100)
  tl_isotonic <- cal_estimate_isotonic(testthat_cal_binary())
  expect_cal_type(tl_isotonic, "binary")
  expect_cal_method(tl_isotonic, "Isotonic regression calibration")
  expect_snapshot(print(tl_isotonic))

  expect_equal(
    testthat_cal_binary_count(),
    nrow(cal_apply(testthat_cal_binary(), tl_isotonic))
  )

  # ------------------------------------------------------------------------------
  # multinomial outcomes

  set.seed(100)
  mtnl_isotonic <- cal_estimate_isotonic(testthat_cal_multiclass())
  expect_cal_type(mtnl_isotonic, "one_vs_all")
  expect_cal_method(mtnl_isotonic, "Isotonic regression calibration")
  expect_snapshot(print(mtnl_isotonic))

  expect_equal(
    testthat_cal_multiclass_count(),
    nrow(cal_apply(testthat_cal_multiclass(), mtnl_isotonic))
  )
})

test_that("Isotonic estimates errors - grouped_df", {
  expect_snapshot_error(
    cal_estimate_isotonic(dplyr::group_by(mtcars, vs))
  )
})

test_that("Isotonic linear estimates work - data.frame", {
  skip_if_not_installed("modeldata")

  set.seed(2983)
  sl_logistic <- cal_estimate_isotonic(
    boosting_predictions_oob,
    outcome,
    estimate = .pred
  )
  expect_cal_type(sl_logistic, "regression")
  expect_cal_method(sl_logistic, "Isotonic regression calibration")
  expect_cal_rows(sl_logistic, 2000)
  expect_snapshot(print(sl_logistic))

  set.seed(38)
  sl_logistic_group <- boosting_predictions_oob |>
    cal_estimate_isotonic(outcome, estimate = .pred, .by = id)

  expect_cal_type(sl_logistic_group, "regression")
  expect_cal_method(sl_logistic_group, "Isotonic regression calibration")
  expect_cal_rows(sl_logistic_group, 2000)
  expect_snapshot(print(sl_logistic_group))

  expect_snapshot_error(
    boosting_predictions_oob |>
      dplyr::mutate(group1 = 1, group2 = 2) |>
      cal_estimate_isotonic(outcome, estimate = .pred, .by = c(group1, group2))
  )
})

# -------------------------- Isotonic Bootstrapped -----------------------------
test_that("Isotonic Bootstrapped estimates work - data.frame", {
  skip_if_not_installed("modeldata")

  set.seed(1)
  sl_boot <- cal_estimate_isotonic_boot(segment_logistic, Class)
  expect_cal_type(sl_boot, "binary")
  expect_cal_method(sl_boot, "Bootstrapped isotonic regression calibration")
  expect_snapshot(print(sl_boot))

  sl_boot_group <- segment_logistic |>
    dplyr::mutate(group = .pred_poor > 0.5) |>
    cal_estimate_isotonic_boot(Class, .by = group)

  expect_cal_type(sl_boot_group, "binary")
  expect_cal_method(
    sl_boot_group,
    "Bootstrapped isotonic regression calibration"
  )
  expect_snapshot(print(sl_boot_group))

  expect_snapshot_error(
    segment_logistic |>
      dplyr::mutate(group1 = 1, group2 = 2) |>
      cal_estimate_isotonic_boot(Class, .by = c(group1, group2))
  )
})

test_that("Isotonic Bootstrapped estimates work - tune_results", {
  skip_if_not_installed("modeldata")

  set.seed(100)
  tl_isotonic <- cal_estimate_isotonic_boot(testthat_cal_binary())
  expect_cal_type(tl_isotonic, "binary")
  expect_cal_method(tl_isotonic, "Bootstrapped isotonic regression calibration")
  expect_snapshot(print(tl_isotonic))

  expect_equal(
    testthat_cal_binary_count(),
    nrow(cal_apply(testthat_cal_binary(), tl_isotonic))
  )

  # ------------------------------------------------------------------------------
  # multinomial outcomes

  set.seed(100)
  mtnl_isotonic <- cal_estimate_isotonic_boot(testthat_cal_multiclass())
  expect_cal_type(mtnl_isotonic, "one_vs_all")
  expect_cal_method(
    mtnl_isotonic,
    "Bootstrapped isotonic regression calibration"
  )
  expect_snapshot(print(mtnl_isotonic))

  expect_equal(
    testthat_cal_multiclass_count(),
    nrow(cal_apply(testthat_cal_multiclass(), mtnl_isotonic))
  )
})

# ----------------------------------- Other ------------------------------------
test_that("Isotonic Bootstrapped estimates errors - grouped_df", {
  expect_snapshot_error(
    cal_estimate_isotonic_boot(dplyr::group_by(mtcars, vs))
  )
})

test_that("Non-default names used for estimate columns", {
  skip_if_not_installed("modeldata")

  new_segment <- segment_logistic
  colnames(new_segment) <- c("poor", "good", "Class")

  set.seed(100)
  expect_snapshot(
    cal_estimate_isotonic(new_segment, Class, c(good, poor))
  )
})

test_that("Test exceptions", {
  expect_error(
    cal_estimate_isotonic(segment_logistic, Class, dplyr::starts_with("bad"))
  )
})

test_that("non-standard column names", {
  library(dplyr)
  # issue 145
  seg <- segment_logistic |>
    rename_with(~ paste0(.x, "-1"), matches(".pred")) |>
    mutate(
      Class = paste0(Class, "-1"),
      Class = factor(Class),
      .pred_class = ifelse(`.pred_poor-1` >= 0.5, "poor-1", "good-1")
    )
  calib <- cal_estimate_isotonic(seg, Class)
  new_pred <- cal_apply(seg, calib, pred_class = .pred_class)
  expect_named(
    new_pred,
    c(".pred_poor-1", ".pred_good-1", "Class", ".pred_class")
  )
})