test_that("Calculations are correct - two class", {
  # BrierScore(two_class_example |>
  #   dplyr::select(Class1, Class2) |>
  #   as.matrix, two_class_example$truth)

  expect_equal(
    brier_class_vec(two_class_example$truth, two_class_example$Class1),
    0.10561859,
    tolerance = 0.01
  )
})

test_that("Calculations are correct - multi class", {
  # With the mclust pakcage, BrierScore(hpc_cv |> select(VF:L) |> as.matrix, hpc_cv$obs)
  hpc_exp <- 0.21083946

  expect_equal(
    brier_class(hpc_cv, obs, VF:L)[[".estimate"]],
    hpc_exp,
    tolerance = 0.01
  )
})

test_that("Calculations handles NAs", {
  hpc_cv$VF[1:10] <- NA

  expect_equal(
    brier_class(hpc_cv, obs, VF:L)[[".estimate"]],
    0.21143119
  )

  expect_equal(
    brier_class(hpc_cv, obs, VF:L, na_rm = FALSE)[[".estimate"]],
    NA_real_
  )
})

test_that("Case weights calculations are correct", {
  wts <- rep(1, nrow(hpc_cv))
  wts[1] <- 5
  hpc_wts <- hpc_cv[c(rep(1, 4), seq_len(nrow(hpc_cv))), ]

  expect_equal(
    brier_class(hpc_cv, obs, VF:L),
    brier_class(hpc_wts, obs, VF:L),
    tolerance = 0.01
  )
})

test_that("works with hardhat case weights", {
  df <- two_class_example

  imp_wgt <- hardhat::importance_weights(seq_len(nrow(df)))
  freq_wgt <- hardhat::frequency_weights(seq_len(nrow(df)))

  expect_no_error(
    brier_class_vec(df$truth, df$Class1, case_weights = imp_wgt)
  )

  expect_no_error(
    brier_class_vec(df$truth, df$Class1, case_weights = freq_wgt)
  )
})

test_that("errors with class_pred input", {
  skip_if_not_installed("probably")

  cp_truth <- probably::as_class_pred(two_class_example$truth, which = 1)
  fct_truth <- two_class_example$truth
  fct_truth[1] <- NA

  estimate <- two_class_example$Class1

  expect_snapshot(
    error = TRUE,
    brier_class_vec(cp_truth, estimate)
  )
})

test_that("na_rm argument check", {
  expect_snapshot(
    error = TRUE,
    brier_class_vec(1, 1, na_rm = "yes")
  )
})

test_that("range values are correct", {
  direction <- metric_direction(brier_class)
  range <- metric_range(brier_class)
  perfect <- ifelse(direction == "minimize", range[1], range[2])
  worst <- ifelse(direction == "minimize", range[2], range[1])

  df <- tibble::tibble(
    truth = factor(c("a", "a", "a", "b", "b"), levels = c("a", "b")),
    perfect = c(1, 1, 1, 0, 0),
    off = c(0.5, 0.5, 0.5, 0.5, 0.5)
  )

  expect_equal(brier_class_vec(df$truth, df$perfect), perfect)

  if (direction == "minimize") {
    expect_gt(brier_class_vec(df$truth, df$off), perfect)
    expect_lte(brier_class_vec(df$truth, df$off), worst)
  }
  if (direction == "maximize") {
    expect_lt(brier_class_vec(df$truth, df$off), perfect)
    expect_gte(brier_class_vec(df$truth, df$off), worst)
  }
})

test_that("doesn't produce NaN with high valued case weights (#614)", {
  df <- two_class_example

  wts <- rep(1, (nrow(df)))
  wts_high <- wts * 5000

  expect_identical(
    brier_class_vec(df$truth, df$Class1, case_weights = wts),
    brier_class_vec(df$truth, df$Class1, case_weights = wts_high)
  )
})

test_that("`event_level = 'second'` works", {
  df <- two_class_example

  df_rev <- df
  df_rev$truth <- stats::relevel(df_rev$truth, "Class2")

  expect_equal(
    brier_class_vec(df$truth, df$Class1),
    brier_class_vec(df_rev$truth, df_rev$Class1, event_level = "second")
  )

  expect_true(
    brier_class_vec(df_rev$truth, df_rev$Class1, event_level = "first") !=
      brier_class_vec(df_rev$truth, df_rev$Class1, event_level = "second")
  )
})