test_that('model from dense design matrix has expected fields', {
  dataset <- make_mtcars_test()
  depth <- 2
  trees <- 3
  m_xrf <- xrf(
    am ~ mpg + as.factor(cyl) + disp,
    dataset,
    family = 'binomial',
    xgb_control = list(nrounds = trees, max_depth = depth),
    sparse = FALSE
  )

  test_expected_fields(m_xrf, depth, trees)
})

test_that('model from sparse design matrix has expected fields', {
  dataset <- make_mtcars_test()

  depth <- 2
  trees <- 3
  m_xrf <- xrf(
    am ~ mpg + cyl + disp,
    dataset,
    family = 'binomial',
    xgb_control = list(nrounds = 3, max_depth = 2)
  )

  test_expected_fields(m_xrf, depth, trees)
})

test_that('model predicts binary outcome', {
  dataset <- make_mtcars_test()

  m_xrf <- xrf(
    am ~ mpg + cyl + disp + hp + drat + wt + qsec,
    dataset,
    family = 'binomial',
    xgb_control = list(nrounds = 3, max_depth = 2),
    glm_control = list(type.measure = 'deviance', nfolds = 10)
  )
  preds_response_dense <- predict(
    m_xrf,
    dataset,
    type = 'response',
    sparse = FALSE
  )
  preds_response_sparse <- predict(
    m_xrf,
    dataset,
    type = 'response',
    sparse = TRUE
  )

  preds_link <- predict(m_xrf, dataset, type = 'link')

  expect_equal(preds_response_dense, preds_response_sparse)
  expect_true(
    all(preds_response_dense < 1 & preds_response_dense > 0)
  )
  expect_true(
    all(preds_response_sparse < 1 & preds_response_sparse > 0),
  )
  # since we are using deviance on the LASSO fit, the model should be calibrated
  expect_equal(mean(preds_response_dense), mean(dataset$am == '1'))

  expect_true(
    any(preds_link < 0 | preds_link > 1)
  )
})

test_that('model predicts continuous outcome', {
  dataset <- make_mtcars_test()

  m_xrf <- xrf(
    mpg ~ .,
    dataset,
    family = 'gaussian',
    xgb_control = list(nrounds = 3, max_depth = 2),
    glm_control = list(type.measure = 'deviance', nfolds = 10)
  )

  preds <- predict(m_xrf, dataset, type = 'response', sparse = FALSE)

  expect_equal(mean(preds), mean(dataset$mpg))
  mae <- mean(abs(preds - dataset$mpg))
  expect_lt(mae, 5) # since this should be highly parameterized / overfit
})


test_that('model predicts without outcome column', {
  # issue #9
  mod <- xrf(
    mpg ~ .,
    data = mtcars[-(1:5), ],
    xgb_control = list(nrounds = 5, max_depth = 2),
    family = "gaussian"
  )
  expect_equal(
    predict(mod, mtcars[1:5, ])[, 1],
    predict(mod, mtcars[1:5, -1])[, 1]
  )

  mod_nsp <- xrf(
    mpg ~ .,
    data = mtcars[-(1:5), ],
    xgb_control = list(nrounds = 5, max_depth = 2),
    family = "gaussian",
    sparse = FALSE
  )
  expect_equal(
    predict(mod_nsp, mtcars[1:5, ], sparse = FALSE)[, 1],
    predict(mod_nsp, mtcars[1:5, -1], sparse = FALSE)[, 1]
  )
})

test_that('call scrubbed', {
  mod_nsp <- xrf(
    mpg ~ .,
    data = mtcars[-(1:5), ],
    xgb_control = list(nrounds = 5, max_depth = 2),
    family = "gaussian",
    sparse = FALSE
  )

  # in previous version:
  # > object.size(mod_nsp$glm$model$call)
  # 211544 bytes
  expect_true(object.size(mod_nsp$glm$model$call) < 211544)
})


test_that('single feature model', {
  set.seed(55414)
  x1 <- rbinom(100, 1, .7)
  y <- rnorm(100, 0, .5) + x1
  dat <- data.frame(y, x1)
  mod <- xrf(
    y ~ x1,
    data = dat,
    xgb_control = list(nrounds = 5, max_depth = 2),
    family = "gaussian",
    sparse = FALSE
  )
  expect_gt(nrow(mod$rules), 0)
})