if (!familiar:::test_data_package_installed("binomial")) testthat::skip()

# Create test dataset.
data <- familiar:::test_create_good_data(outcome_type = "binomial")

weights <- familiar:::create_instance_weights(
  data = data,
  method = "inverse_number_of_samples")

# Check that weights sum to the number of classes (2)
testthat::test_that("Weights sum to the number of classes.", {
  testthat::expect_equal(sum(weights), 2)
})

weights <- familiar:::create_instance_weights(
  data = data,
  method = "inverse_number_of_samples",
  normalisation = "average_one")

# Check that the mean weight is 1.0.
testthat::test_that("Mean weight is 1.0.", {
  testthat::expect_equal(mean(weights), 1.0)
})

weights <- familiar:::create_instance_weights(
  data = data,
  method = "inverse_number_of_samples",
  normalisation = "sum_one")

# Check that weights sum to the number of classes (2)
testthat::test_that("Weights sum to 1.0.", {
  testthat::expect_equal(sum(weights), 1.0)
})

# Check that with beta equal to 0.9, weights between classes are similar.
weights <- familiar:::create_instance_weights(
  data = data,
  method = "effective_number_of_samples",
  beta = 0.9)

testthat::test_that("Weights between classes are almost the same.", {
  testthat::expect_equal(abs(diff(unique(weights))) < 10^-4, TRUE)
})

# Check that with beta equal to 0.99999, weights between classes are similar to
# inverse number of samples.
inv_weights <- familiar:::create_instance_weights(
  data = data,
  method = "inverse_number_of_samples")

weights <- familiar:::create_instance_weights(
  data = data,
  method = "effective_number_of_samples",
  beta = 0.99999)

testthat::test_that("Weights are similar to inverse number of samples.", {
  testthat::expect_equal(all(abs(inv_weights - weights) < 10^-4), TRUE)
})

weights <- familiar:::create_instance_weights(
  data = data,
  method = "effective_number_of_samples",
  beta = 0.9,
  normalisation = "average_one")

# Check that the mean weight is 1.0.
testthat::test_that("Mean weight is 1.0.", {
  testthat::expect_equal(mean(weights), 1.0)
})

weights <- familiar:::create_instance_weights(
  data = data,
  method = "effective_number_of_samples",
  beta = 0.9,
  normalisation = "sum_one")

# Check that weights sum to the number of classes (2)
testthat::test_that("Weights sum to 1.0.", {
  testthat::expect_equal(sum(weights), 1.0)
})


weights <- familiar:::create_instance_weights(
  data = data,
  method = "effective_number_of_samples",
  beta = 0.9)

comp_weights <- familiar:::create_instance_weights(
  data = data,
  method = "effective_number_of_samples",
  beta = familiar:::..compute_effective_number_of_samples_beta(-1))

testthat::test_that("Weights are the same.", {
  testthat::expect_equal(all(weights - comp_weights == 0.0), TRUE)
})

testthat::skip("Skip hyperparameter optimisation, unless manual.")

# Check that variable importance functions correctly.
model <- familiar::train_familiar(
  data = data,
  fs_method = "mrmr",
  learner = "glm_logistic",
  outcome_type = "binomial",
  outcome_column = "cell_malignancy",
  sample_id_column = "id",
  class_levels = c("benign", "malignant"),
  verbose = FALSE
)

# Check that sample weighting is inverse_number_of_samples (default).
testthat::test_that("Default method is inverse_number_of_samples.", {
  testthat::expect_equal(
    as.character(model@hyperparameters$sample_weighting),
    "inverse_number_of_samples")
})

# Check that effective_number_of_samples functions.
model <- familiar::train_familiar(
  data = data,
  fs_method = "mrmr",
  learner = "glm_logistic",
  hyperparameter = list("sample_weighting" = "effective_number_of_samples"),
  outcome_type = "binomial",
  outcome_column = "cell_malignancy",
  sample_id_column = "id",
  class_levels = c("benign", "malignant"),
  verbose = FALSE
)