# Tests for weighting()
# Run with: devtools::test() or testthat::test_file("tests/testthat/test-weighting.R")

# Helper: create a small minority data frame with known outliers
make_minority <- function(n_normal = 30, n_outlier = 3, seed = 42) {
  set.seed(seed)
  normal <- data.frame(
    x1    = rnorm(n_normal, mean = 0, sd = 1),
    x2    = rnorm(n_normal, mean = 0, sd = 1),
    class = "positive"
  )
  outliers <- data.frame(
    x1    = rnorm(n_outlier, mean = 10, sd = 0.1),
    x2    = rnorm(n_outlier, mean = 10, sd = 0.1),
    class = "positive"
  )
  rbind(normal, outliers)
}

# ── Output structure ───────────────────────────────────────────────────────

test_that("weighting returns a data frame", {
  df <- make_minority()
  result <- weighting(df, threshold = 0.01, weight_func = 1)
  expect_s3_class(result, "data.frame")
})

test_that("weighting adds MD, weights, and prob columns", {
  df <- make_minority()
  result <- weighting(df, threshold = 0.01, weight_func = 1)
  expect_true(all(c("MD", "weights", "prob") %in% colnames(result)))
})

test_that("number of rows is unchanged after weighting", {
  df <- make_minority()
  result <- weighting(df, threshold = 0.01, weight_func = 1)
  expect_equal(nrow(result), nrow(df))
})

# ── Probability distribution ───────────────────────────────────────────────

test_that("prob column sums to 1", {
  df <- make_minority()
  result <- weighting(df, threshold = 0.01, weight_func = 1)
  expect_equal(sum(result$prob), 1, tolerance = 1e-10)
})

test_that("prob values are all non-negative", {
  df <- make_minority()
  result <- weighting(df, threshold = 0.01, weight_func = 1)
  expect_true(all(result$prob >= 0))
})

# ── Weighting functions ────────────────────────────────────────────────────

test_that("weight_func = 1: outliers receive weight 0", {
  df <- make_minority(n_outlier = 3)
  result <- weighting(df, threshold = 0.01, weight_func = 1)
  # Outlier observations (last 3 rows) should have weight 0
  outlier_rows <- tail(result, 3)
  expect_true(all(outlier_rows$weights == 0))
})

test_that("weight_func = 1: non-outliers receive weight 1", {
  df <- make_minority(n_outlier = 3)
  result <- weighting(df, threshold = 0.01, weight_func = 1)
  normal_rows <- head(result, 30)
  expect_true(sum(normal_rows$weights > 0) >= 24)
})

test_that("weight_func = 2: outliers receive weight < 1", {
  df <- make_minority(n_outlier = 3)
  result <- weighting(df, threshold = 0.01, weight_func = 2)
  outlier_rows <- tail(result, 3)
  expect_true(all(outlier_rows$weights < 1))
  expect_true(all(outlier_rows$weights > 0))  # not zero
})

test_that("weight_func = 3: outliers receive weight < 1", {
  df <- make_minority(n_outlier = 3)
  result <- weighting(df, threshold = 0.01, weight_func = 3)
  outlier_rows <- tail(result, 3)
  expect_true(all(outlier_rows$weights < 1))
  expect_true(all(outlier_rows$weights > 0))  # not zero
})

test_that("weight ordering: omega_A <= omega_B <= omega_C for outliers", {
  df <- make_minority(n_outlier = 3)
  r1 <- weighting(df, threshold = 0.01, weight_func = 1)
  r2 <- weighting(df, threshold = 0.01, weight_func = 2)
  r3 <- weighting(df, threshold = 0.01, weight_func = 3)

  # For each outlier: w_A <= w_B <= w_C
  outlier_idx <- tail(seq_len(nrow(df)), 3)
  expect_true(all(r1$weights[outlier_idx] <= r2$weights[outlier_idx]))
  expect_true(all(r2$weights[outlier_idx] <= r3$weights[outlier_idx]))
})

# ── Input validation ───────────────────────────────────────────────────────

test_that("missing 'class' column raises error", {
  df <- data.frame(x1 = rnorm(20), x2 = rnorm(20))
  expect_error(weighting(df), regexp = "class")
})

test_that("invalid weight_func raises error", {
  df <- make_minority()
  expect_error(weighting(df, weight_func = 5), regexp = "weight_func")
})

test_that("threshold outside (0,1) raises error", {
  df <- make_minority()
  expect_error(weighting(df, threshold = 1.5), regexp = "threshold")
  expect_error(weighting(df, threshold = 0),   regexp = "threshold")
})

test_that("non-data-frame input raises error", {
  expect_error(weighting(list(x = 1:10, class = "positive")),
               regexp = "data frame")
})

# ── All estimators ─────────────────────────────────────────────────────────

test_that("all cov_method options work in weighting", {
  df <- make_minority(n_normal = 50)
  methods <- c("mcd", "mve", "mest", "mmest", "sde", "sest", "ogk")
  for (m in methods) {
    expect_no_error(weighting(df, cov_method = m))
  }
})