Use check_outcome for all fit paths (#625)

* Use `check_outcome` for all fit paths

* Update NEWS

NEWS.md

@@ -12,6 +12,8 @@
 
 * The list column produced when creating survival probability predictions is now always called `.pred` (with `.pred_survival` being used inside of the list column). 
 
+* Fixed outcome type checking affecting a subset of regression models (#625).
+
 ## Other Changes
 
 * When the xy interface is used and the underlying model expects to use a matrix, a better warning is issued when predictors contain non-numeric columns (including dates). 

R/fit_helpers.R

@@ -6,17 +6,10 @@
 form_form <-
   function(object, control, env, ...) {
 
+    check_outcome(eval_tidy(env$formula[[2]], env$data), object)
+
     # prob rewrite this as simple subset/levels
     y_levels <- levels_from_formula(env$formula, env$data)
-
-    if (object$mode == "classification") {
-      if (!inherits(env$data, "tbl_spark") && is.null(y_levels))
-        rlang::abort("For a classification model, the outcome should be a factor.")
-    } else if (object$mode == "regression") {
-      if (!inherits(env$data, "tbl_spark") && !is.null(y_levels))
-        rlang::abort("For a regression model, the outcome should be numeric.")
-    }
-
     object <- check_mode(object, y_levels)
 
     # if descriptors are needed, update descr_env with the calculated values
@@ -150,14 +143,7 @@ form_xy <- function(object, control, env,
   env$x <- data_obj$x
   env$y <- data_obj$y
 
-  res <- list(lvl = levels_from_formula(env$formula, env$data), spec = object)
-  if (object$mode == "classification") {
-    if (is.null(res$lvl))
-      rlang::abort("For a classification model, the outcome should be a factor.")
-  } else if (object$mode == "regression") {
-    if (!is.null(res$lvl))
-      rlang::abort("For a regression model, the outcome should be numeric.")
-  }
+  check_outcome(env$y, object)
 
   res <- xy_xy(
     object = object,

tests/testthat/test_linear_reg.R

@@ -247,6 +247,16 @@ test_that('lm execution', {
     regexp = "For a regression model"
   )
 
+  expect_error(
+    res <- fit_xy(
+      hpc_basic,
+      x = hpc[, num_pred],
+      y = as.character(hpc$class),
+      control = ctrl
+    ),
+    regexp = "For a regression model"
+  )
+
   expect_error(
     res <- fit(
       hpc_basic,