# objective: Test that Zero-One Loss # implemented in {SLmetrics} is aligned with # target functions. testthat::test_that( desc = "Test `zerooneloss()`-function", code = { testthat::skip_on_cran() # 0) construct zerooneloss # wrapper wrapped_zerooneloss <- function( actual, predicted, w = NULL) { if (is.null(w)) { zerooneloss( actual = actual, predicted = predicted ) } else { weighted.zerooneloss( actual = actual, predicted = predicted, w = w ) } } for (balanced in c(FALSE, TRUE)) { # 1) generate class # values actual <- create_factor(balanced = balanced) predicted <- create_factor(balanced = balanced) w <- runif(n = length(actual)) for (weighted in c(TRUE, FALSE)) { # 2) test that the are # equal to target values # 2.1) generate sensible # label information info <- paste( "Balanced = ", balanced, "Weighted = ", weighted ) # 2.2) generate score # from {slmetrics} score <- wrapped_zerooneloss( actual = actual, predicted = predicted, w = if (weighted) w else NULL ) # 2.3) test that the values # are sensible the values # can be NA testthat::expect_true(is.numeric(score), info = info) testthat::expect_true(length(score) == 1, info = info) # 2.4) test that the values # are equal to target value # 2.4.1) calculate py_score py_score <- py_zerooneloss( actual = actual, predicted = predicted, w = if (weighted) w else NULL ) # 2.4.2) test for equality testthat::expect_true( object = set_equal( current = as.numeric(score), target = as.numeric(py_score) ), info = info ) } } } )