# objective: Test that BalancedAccuracy # implemented in {SLmetrics} is aligned with # target functions. testthat::test_that( desc = "Test `baccuracy()`-function", code = { testthat::skip_on_cran() # 0) construct Balanced Accuracy # wrapper wrapped_baccuracy <- function( actual, predicted, adjust, w = NULL) { if (is.null(w)) { baccuracy( actual = actual, predicted = predicted, adjust = adjust ) } else { weighted.baccuracy( actual = actual, predicted = predicted, w = w, adjust = adjust ) } } 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 for (adjust in c(TRUE, FALSE)) { # 2.1) generate sensible # label information info <- paste( "Balanced = ", balanced, "Adjusted = ", adjust, "Weighted = ", weighted ) # 2.2) generate score # from {slmetrics} score <- wrapped_baccuracy( actual = actual, predicted = predicted, w = if (weighted) w else NULL, adjust = adjust ) # 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_baccuracy( actual = actual, predicted = predicted, adjust = adjust, 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 ) } } } } )