# common tests that can be grouped together, such as testing the output from fitplp
expect_correct_fitPlp <- function(plpModel, trainData) {
  outcomeId <- 3
  # predictions are same amount as labels
  multiplicativeFactor <- dplyr::n_distinct(plpModel$prediction %>%
    dplyr::pull(.data$evaluationType))
  expect_equal(NROW(trainData$labels) * multiplicativeFactor, NROW(plpModel$prediction))

  # predictions are all between 0 and 1
  expect_true(all((plpModel$prediction$value >= 0) &
    (plpModel$prediction$value <= 1)))

  # model directory exists
  expect_true(dir.exists(plpModel$model))

  expect_equal(plpModel$modelDesign$outcomeId, outcomeId)
  expect_equal(plpModel$modelDesign$targetId, 1)

  # structure of plpModel is correct
  expect_equal(names(plpModel), c(
    "model", "preprocessing", "prediction",
    "modelDesign", "trainDetails", "covariateImportance"
  ))
}

expect_correct_predictions <- function(predictions, testData) {
  # predictions are all between 0 and 1
  expect_true(all((predictions$value >= 0) & (predictions$value <= 1)))
  expect_equal(NROW(testData$labels), NROW(predictions))
}