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