## test-autoML.R

test_that("nrm_benchmark returns metrics data frame", {
  data(nrm_example, package = "NRMstatsML")
  n     <- nrow(nrm_example)
  train <- nrm_example[seq_len(floor(0.75 * n)), ]
  test  <- nrm_example[seq(floor(0.75 * n) + 1L, n), ]

  m_ols  <- lm(crop_yield ~ N + P + K, data = train)
  m_full <- lm(crop_yield ~ N + P + K + rainfall, data = train)

  bm <- nrm_benchmark(
    models       = list(ols = m_ols, full = m_full),
    test_data    = test,
    response_var = "crop_yield"
  )

  expect_s3_class(bm, "nrm_benchmark")
  expect_true(is.data.frame(bm$metrics))
  expect_true(all(c("model", "RMSE", "MAE", "Rsq") %in% names(bm$metrics)))
  expect_equal(nrow(bm$metrics), 2L)
  # Results should be sorted by RMSE ascending
  expect_true(bm$metrics$RMSE[1] <= bm$metrics$RMSE[2])
})

test_that("nrm_benchmark errors on unnamed model list", {
  data(nrm_example, package = "NRMstatsML")
  m <- lm(crop_yield ~ N, data = nrm_example)
  expect_error(
    nrm_benchmark(list(m), test_data = nrm_example,
                  response_var = "crop_yield"),
    "named list"
  )
})

test_that("nrm_benchmark errors when response_var is missing", {
  data(nrm_example, package = "NRMstatsML")
  m <- lm(crop_yield ~ N, data = nrm_example)
  expect_error(
    nrm_benchmark(list(m1 = m), test_data = nrm_example,
                  response_var = "nonexistent"),
    "Column\\(s\\) not found|not found"
  )
})

test_that("nrm_data_check catches missing values", {
  data(nrm_example, package = "NRMstatsML")
  d_miss        <- nrm_example
  d_miss$N[3]   <- NA
  result <- nrm_data_check(d_miss, verbose = FALSE)
  expect_true(length(result$warnings) > 0)
  expect_true(any(grepl("N", result$warnings)))
})

test_that("nrm_data_check warns on missing time column", {
  data(nrm_example, package = "NRMstatsML")
  result <- nrm_data_check(nrm_example, time_var = "date", verbose = FALSE)
  expect_true(any(grepl("date", result$warnings)))
})

test_that("nrm_summary dispatches without error for nrm_multivariate", {
  data(nrm_example, package = "NRMstatsML")
  mv <- nrm_multivariate(nrm_example,
          formula = crop_yield ~ N + P + K)
  expect_invisible(nrm_summary(mv))
})