test basic adjust_equivocal_zone() usage

tests/testthat/test-adjust-equivocal-zone.R

@@ -1,3 +1,56 @@
+test_that("basic adjust_equivocal_zone() usage works", {
+  skip_if_not_installed("modeldata")
+  library(dplyr)
+  library(modeldata)
+
+  # fitting and predicting happens without raising conditions
+  expect_no_condition(
+    tlr <-
+      tailor() %>%
+      adjust_equivocal_zone(value = 1 / 4)
+  )
+
+  expect_no_condition(
+    tlr_fit <- fit(
+      tlr,
+      two_class_example,
+      outcome = c(truth),
+      estimate = c(predicted),
+      probabilities = c(Class1, Class2)
+    )
+  )
+
+  expect_no_condition(
+    tlr_pred <- predict(tlr_fit, two_class_example)
+  )
+
+  # classes are as expected
+  expect_s3_class(tlr, "tailor")
+  expect_s3_class(tlr_fit, "tailor")
+  expect_s3_class(tlr_pred, "tbl_df")
+  expect_s3_class(tlr_pred$predicted, "class_pred")
+
+  # column names are as expected
+  expect_equal(colnames(two_class_example), colnames(tlr_pred))
+
+  # prediction classes are as expected
+  expect_equal(levels(tlr_pred$predicted), levels(two_class_example$predicted))
+  expect_equal(attr(tlr_pred$predicted, "equivocal"), "[EQ]")
+
+  # calculations match those done manually
+  tlr_pred_col <- as.character(tlr_pred$predicted)
+  manual_pred_col <- ifelse(two_class_example$Class1 > .5, "Class1", "Class2")
+  manual_pred_col <-
+    ifelse(
+      two_class_example$Class1 > .25 & two_class_example$Class1 < .75,
+      NA_character_,
+      manual_pred_col
+    )
+  expect_equal(tlr_pred_col, manual_pred_col)
+})
+
+# TODO: test sensitivity to function arguments
+
 test_that("adjustment printing", {
   expect_snapshot(tailor() %>% adjust_equivocal_zone())
   expect_snapshot(tailor() %>% adjust_equivocal_zone(hardhat::tune()))