refactor(supervision/labeler): raise error if gold contains ABSTAIN

If a user calls get_gold_labels before loading gold labels, they will
get a label matrix with all values being -1 (ABSTAIN)

    L_gold_train = labeler.get_gold_labels(train_cands, annotator='gold')
    print(L_gold_train[0].reshape(-1))
    [-1. -1. -1. ... -1. -1. -1.]

Without any indicator that that is incorrect, if they do an LFAnalysis
they will have 0 empirical accuracy.

To be more friendly to the user, we throw an error to indicate that this
is an error.

Closes #403.

src/fonduer/supervision/labeler.py

@@ -404,17 +404,29 @@ def get_gold_labels(
         :param annotator: A specific annotator key to get labels for. Default
             None.
         :type annotator: str
+        :raises ValueError: If get_gold_labels is called before gold labels are
+            loaded, the result will contain ABSTAIN values. We raise a
+            ValueError to help indicate this potential mistake to the user.
         :return: A list of MxN dense matrix where M are the candidates and N is the
             annotators. If annotator is provided, return a list of Mx1 matrix.
         :rtype: list[np.ndarray]
         """
-        return [
+        gold_labels = [
             unshift_label_matrix(m)
             for m in get_sparse_matrix(
                 self.session, GoldLabelKey, cand_lists, key=annotator
             )
         ]
 
+        for cand_labels in gold_labels:
+            if ABSTAIN in cand_labels:
+                raise ValueError(
+                    "Gold labels contain ABSTAIN labels. "
+                    "Did you load gold labels beforehand?"
+                )
+
+        return gold_labels
+
     def get_label_matrices(self, cand_lists: List[List[Candidate]]) -> List[np.ndarray]:
         """Load dense matrix of Labels for each candidate_class.
 

tests/e2e/test_e2e.py

@@ -337,6 +337,10 @@ def test_e2e():
 
     labeler = Labeler(session, [PartTemp, PartVolt])
 
+    # This should raise an error, since gold labels are not yet loaded.
+    with pytest.raises(ValueError):
+        _ = labeler.get_gold_labels(train_cands, annotator="gold")
+
     labeler.apply(
         docs=last_docs,
         lfs=[[gold], [gold]],