More robust eval

haystack/indexing/utils.py

@@ -45,19 +45,31 @@ def eval_data_from_file(filename: str) -> Tuple[List[Document], List[Label]]:
 
                 # Get Labels
                 for qa in paragraph["qas"]:
-                    for answer in qa["answers"]:
+                    if len(qa["answers"]) > 0:
+                        for answer in qa["answers"]:
+                            label = Label(
+                                question=qa["question"],
+                                answer=answer["text"],
+                                is_correct_answer=True,
+                                is_correct_document=True,
+                                document_id=cur_doc.id,
+                                offset_start_in_doc=answer["answer_start"],
+                                no_answer=qa["is_impossible"],
+                                origin="gold_label",
+                                )
+                            labels.append(label)
+                    else:
                         label = Label(
                             question=qa["question"],
-                            answer=answer["text"],
+                            answer="",
                             is_correct_answer=True,
                             is_correct_document=True,
                             document_id=cur_doc.id,
-                            offset_start_in_doc=answer["answer_start"],
+                            offset_start_in_doc=0,
                             no_answer=qa["is_impossible"],
                             origin="gold_label",
-                            )
+                        )
                         labels.append(label)
-
         return docs, labels
 
 

haystack/reader/farm.py

@@ -394,6 +394,11 @@ class FARMReader(BaseReader):
         :param doc_index: Index/Table name where documents that are used for evaluation are stored
         """
 
+        if self.top_k_per_candidate != 4:
+            logger.warning(f"Performing Evaluation using top_k_per_candidate = {self.top_k_per_candidate} \n"
+                           f"and consequently, QuestionAnsweringPredictionHead.n_best = {self.top_k_per_candidate + 1}. \n"
+                           f"This deviates from FARM's default where QuestionAnsweringPredictionHead.n_best = 5")
+
         # extract all questions for evaluation
         filters = {"origin": [label_origin]}
 
@@ -409,7 +414,8 @@ class FARMReader(BaseReader):
 
         # Create squad style dicts
         d: Dict[str, Any] = {}
-        for doc_id in aggregated_per_doc.keys():
+        all_doc_ids = [x.id for x in document_store.get_all_documents(doc_index)]
+        for doc_id in all_doc_ids:
             doc = document_store.get_document_by_id(doc_id, index=doc_index)
             if not doc:
                 logger.error(f"Document with the ID '{doc_id}' is not present in the document store.")
@@ -419,21 +425,25 @@ class FARMReader(BaseReader):
             }
             # get all questions / answers
             aggregated_per_question: Dict[str, Any] = defaultdict(list)
-            for label in aggregated_per_doc[doc_id]:
-                # add to existing answers
-                if label.question in aggregated_per_question.keys():
-                    aggregated_per_question[label.question]["answers"].append({
-                                "text": label.answer,
-                                "answer_start": label.offset_start_in_doc})
-                # create new one
-                else:
-                    aggregated_per_question[label.question] = {
-                        "id": str(hash(str(doc_id)+label.question)),
-                        "question": label.question,
-                        "answers": [{
-                                "text": label.answer,
-                                "answer_start": label.offset_start_in_doc}]
-                    }
+            if doc_id in aggregated_per_doc:
+                for label in aggregated_per_doc[doc_id]:
+                    # add to existing answers
+                    if label.question in aggregated_per_question.keys():
+                        # Hack to fix problem where duplicate questions are merged by doc_store processing creating a QA example with 8 annotations > 6 annotation max
+                        if len(aggregated_per_question[label.question]["answers"]) >= 6:
+                            continue
+                        aggregated_per_question[label.question]["answers"].append({
+                                    "text": label.answer,
+                                    "answer_start": label.offset_start_in_doc})
+                    # create new one
+                    else:
+                        aggregated_per_question[label.question] = {
+                            "id": str(hash(str(doc_id)+label.question)),
+                            "question": label.question,
+                            "answers": [{
+                                    "text": label.answer,
+                                    "answer_start": label.offset_start_in_doc}]
+                        }
             # Get rid of the question key again (after we aggregated we don't need it anymore)
             d[str(doc_id)]["qas"] = [v for v in aggregated_per_question.values()]
 

test/test_eval.py

@@ -11,7 +11,7 @@ def test_add_eval_data(document_store):
     document_store.add_eval_data(filename="samples/squad/small.json", doc_index="test_eval_document", label_index="test_feedback")
 
     assert document_store.get_document_count(index="test_eval_document") == 87
-    assert document_store.get_label_count(index="test_feedback") == 881
+    assert document_store.get_label_count(index="test_feedback") == 1214
 
     # test documents
     docs = document_store.get_all_documents(index="test_eval_document")