Merge pull request #271 from cmu-delphi/resid-hotfix

simplify grab_residuals

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.6
+Version: 0.0.7
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),

NEWS.md

@@ -1,5 +1,9 @@
 # epipredict (development)
 
+# epipredict 0.0.7
+
+* simplify `layer_residual_quantiles()` to avoid timesuck in `utils::methods()`
+
 # epipredict 0.0.6
 
 * rename the `dist_quantiles()` to be more descriptive, breaking change)

R/layer_residual_quantiles.R

@@ -133,12 +133,10 @@ slather.layer_residual_quantiles <-
 
 grab_residuals <- function(the_fit, components) {
   if (the_fit$spec$mode != "regression") {
-    rlang::abort("For meaningful residuals, the predictor should be a regression model.")
+    cli::cli_abort("For meaningful residuals, the predictor should be a regression model.")
   }
-  r_generic <- attr(utils::methods(class = class(the_fit$fit)[1]), "info")$generic
-  if ("residuals" %in% r_generic) { # Try to use the available method.
-    cl <- class(the_fit$fit)[1]
-    r <- residuals(the_fit$fit)
+  r <- stats::residuals(the_fit$fit)
+  if (!is.null(r)) { # Got something from the method
     if (inherits(r, "data.frame")) {
       if (".resid" %in% names(r)) { # success
         return(r)

tests/testthat/test-layer_residual_quantiles.R

@@ -30,3 +30,22 @@ test_that("Returns expected number or rows and columns", {
   expect_equal(nrow(unnested), 9L)
   expect_equal(unique(unnested$quantile_levels), c(.0275, .8, .95))
 })
+
+
+test_that("Errors when used with a classifier", {
+  tib <- tibble(
+    y = factor(rep(c("a", "b"), length.out = 100)),
+    x1 = rnorm(100),
+    x2 = rnorm(100),
+    time_value = 1:100,
+    geo_value = "ak"
+  ) %>% as_epi_df()
+
+  r <- epi_recipe(y ~ x1 + x2, data = tib)
+  wf <- epi_workflow(r, parsnip::logistic_reg()) %>% fit(tib)
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_residual_quantiles()
+  wf <- wf %>% add_frosting(f)
+  expect_error(predict(wf, tib))
+})