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context('twoClassSummary testing')
test_that("twoClassSummary is calculating correctly", {
set.seed(1)
tr_dat <- twoClassSim(100)
te_dat <- twoClassSim(100)
mod <- knn3(x = as.matrix(tr_dat[, 1:5]), y = tr_dat$Class)
te_pred <- predict(mod, te_dat[, 1:5], type = "class")
te_prob <- predict(mod, te_dat[, 1:5], type = "prob")
te_prob <- as.data.frame(te_prob, stringsAsFactors = TRUE)
cm <- caret::confusionMatrix(te_pred, te_dat$Class)
skip_if_not_installed("pROC")
library(pROC)
roc_crv <- pROC::roc(te_dat$Class, te_prob$Class1, direction = ">", quiet = TRUE)
roc_auc <- as.numeric(pROC::auc(roc_crv))
te_res <- te_prob
te_res$pred <- te_pred
te_res$obs <- te_dat$Class
tcs_res <- twoClassSummary(te_res, lev = levels(te_pred))
expect_equal(roc_auc, unname(tcs_res["ROC"]))
expect_equal(unname(cm$byClass["Sensitivity"]), unname(tcs_res["Sens"]))
expect_equal(unname(cm$byClass["Specificity"]), unname(tcs_res["Spec"]))
})
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