test_that("Constructor validates params", {
  expect_error(new_temporal_detector(time_col = 123))
  expect_error(new_temporal_detector(time_col = "time", lookahead_window = 0))
  expect_error(new_temporal_detector(time_col = "time", lookahead_window = -1))
  det <- new_temporal_detector(time_col = "time", lookahead_window = 2)
  expect_s3_class(det, c("temporal_detector", "detector"))
})

test_that("Errors on invalid time_col", {
  det <- new_temporal_detector(time_col = "time", lookahead_window = 1)
  df <- data.frame(date = Sys.Date() + 1:10, value = 1:10)
  split <- c(rep("train", 5), rep("test", 5))
  
  expect_error(new_temporal_detector(time_col = NULL), "time_col must be")
  expect_error(new_temporal_detector(time_col = 123), "time_col must be")
  
  det_missing <- new_temporal_detector(time_col = "nonexistent")
  expect_error(run_detector(det_missing, df, split = split), "time_col not found")
  
  df2 <- data.frame(time = as.character(1:10), value = 1:10)
  det2 <- new_temporal_detector(time_col = "time", lookahead_window = 1)
  expect_error(run_detector(det2, df2, split = split))
})

test_that("Detects temporal leakage with correct severity", {
  # Use lookahead_window = 1 (not 0)
  det <- new_temporal_detector(time_col = "date", lookahead_window = 1)
  
  # Low: no overlap
  df_low <- data.frame(
    date = seq.Date(as.Date("2020-01-01"), by = "day", length.out = 20),
    value = 1:20
  )
  split_low <- c(rep("train", 10), rep("test", 10))
  res_low <- run_detector(det, df_low, split = split_low)
  expect_equal(res_low$issues$severity, "low")
  
  # Medium: 5-19 test points within training period
  df_med <- data.frame(
    date = c(seq.Date(as.Date("2020-01-01"), by = "day", length.out = 10),
             seq.Date(as.Date("2020-01-05"), by = "day", length.out = 10)),
    value = 1:20
  )
  split_med <- c(rep("train", 10), rep("test", 10))
  res_med <- run_detector(det, df_med, split = split_med)
  expect_true(res_med$issues$severity %in% c("medium", "high"))
  
  # High: 20+ test points within training period
  df_high <- data.frame(
    date = c(seq.Date(as.Date("2020-01-01"), by = "day", length.out = 50),
             seq.Date(as.Date("2020-01-01"), by = "day", length.out = 50)),
    value = 1:100
  )
  split_high <- c(rep("train", 50), rep("test", 50))
  res_high <- run_detector(det, df_high, split = split_high)
  expect_equal(res_high$issues$severity, "high")
})

test_that("Handles edge cases", {
  det <- new_temporal_detector(time_col = "date", lookahead_window = 1)
  
  empty_df <- data.frame(date = as.Date(character(0)))
  result <- run_detector(det, empty_df, split = character(0))
  expect_equal(result$issues$severity, "low")
  
  df_all_test <- data.frame(date = as.Date("2025-01-01") + 0:2)
  res <- run_detector(det, df_all_test, split = rep("test", 3))
  expect_equal(res$issues$severity, "low")
})

test_that("Config argument accepted", {
  det <- new_temporal_detector(time_col = "date", lookahead_window = 1)
  df <- data.frame(date = Sys.Date() + 0:4)
  split <- c("train", "train", "test", "test", "test")
  res <- run_detector(det, df, split = split, config = list(debug = TRUE))
  expect_true("issues" %in% names(res))
})