test_that("Calculations are correct - two class", {
  # Doesn't work on binary case
  # Here for completeness
  expect_true(TRUE)
})

test_that("Calculations are correct - multi class", {
  soybeans <- data_soybean()

  # Code to generate this value and `data_soybean()` is in `helper-data.R`
  measures_mlr <- 0.964025841424236

  expect_equal(
    roc_aunp(soybeans, truth, `2-4-d-injury`:`rhizoctonia-root-rot`)[[
      ".estimate"
    ]],
    measures_mlr
  )
})

test_that("Calculations handles NAs", {
  soybeans <- data_soybean()
  soybeans[["2-4-d-injury"]][1:10] <- NA

  # Code to generate this value and `data_soybean()` is in `helper-data.R`
  measures_mlr <- 0.95982361

  expect_equal(
    roc_aunp(soybeans, truth, `2-4-d-injury`:`rhizoctonia-root-rot`)[[
      ".estimate"
    ]],
    measures_mlr
  )
  expect_equal(
    roc_aunp(
      soybeans,
      truth,
      `2-4-d-injury`:`rhizoctonia-root-rot`,
      na_rm = FALSE
    )[[
      ".estimate"
    ]],
    NA_real_
  )
})

test_that("Case weights calculations are correct", {
  hpc_cv$weight <- read_weights_hpc_cv()

  expect_equal(
    roc_auc(
      hpc_cv,
      obs,
      VF:L,
      estimator = "macro_weighted",
      case_weights = weight
    )[[".estimate"]],
    roc_aunp(hpc_cv, obs, VF:L, case_weights = weight)[[".estimate"]]
  )
})

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(
    roc_aunp_vec(
      df$truth,
      as.matrix(df[c("Class1", "Class2")]),
      case_weights = imp_wgt
    )
  )

  expect_no_error(
    roc_aunp_vec(
      df$truth,
      as.matrix(df[c("Class1", "Class2")]),
      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 <- as.matrix(two_class_example[c("Class1", "Class2")])

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

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

test_that("`options` is defunct", {
  expect_snapshot(
    error = TRUE,
    roc_aunp(two_class_example, truth, Class1, Class2, options = 1)
  )

  expect_snapshot(
    error = TRUE,
    roc_aunp_vec(
      truth = two_class_example$truth,
      estimate = as.matrix(two_class_example[c("Class1", "Class2")]),
      options = 1
    )
  )
})

test_that("AUNP is equivalent to macro_weighted estimator", {
  hpc_f1 <- data_hpc_fold1()

  expect_equal(
    roc_auc(hpc_f1, obs, VF:L, estimator = "macro_weighted")[[".estimate"]],
    roc_aunp(hpc_f1, obs, VF:L)[[".estimate"]]
  )
})

test_that("errors on binary case", {
  expect_snapshot(
    error = TRUE,
    roc_aunp(two_class_example, truth, Class1)
  )
})

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

  hpc_f1 <- data_hpc_fold1()

  result <- roc_aunp(hpc_f1, obs, VF:L)[[".estimate"]]

  if (direction == "minimize") {
    expect_gte(result, perfect)
    expect_lte(result, worst)
  }
  if (direction == "maximize") {
    expect_gte(result, worst)
    expect_lte(result, perfect)
  }
})