Fix nothing return

relation_extraction.py

@@ -87,7 +87,7 @@ class relation_extraction(evaluate.Metric):
         # TODO: Download external resources if needed
         pass
 
-    def _compute(self, pred_relations, gt_relations, mode="strict", relation_types=[]):
+    def _compute(self, predictions, references, mode="strict", relation_types=[]):
         """Returns the scores"""
         # TODO: Compute the different scores of the module
 
@@ -95,7 +95,7 @@ class relation_extraction(evaluate.Metric):
 
         # construct relation_types from ground truth if not given
         if len(relation_types) == 0:
-            for triplets in gt_relations:
+            for triplets in references:
                 for triplet in triplets:
                     relation = triplet["type"]
                     if relation not in relation_types:
@@ -104,12 +104,12 @@ class relation_extraction(evaluate.Metric):
         scores = {rel: {"tp": 0, "fp": 0, "fn": 0} for rel in relation_types + ["ALL"]}
 
         # Count GT relations and Predicted relations
-        n_sents = len(gt_relations)
-        n_rels = sum([len([rel for rel in sent]) for sent in gt_relations])
-        n_found = sum([len([rel for rel in sent]) for sent in pred_relations])
+        n_sents = len(references)
+        n_rels = sum([len([rel for rel in sent]) for sent in references])
+        n_found = sum([len([rel for rel in sent]) for sent in predictions])
 
         # Count TP, FP and FN per type
-        for pred_sent, gt_sent in zip(pred_relations, gt_relations):
+        for pred_sent, gt_sent in zip(predictions, references):
             for rel_type in relation_types:
                 # strict mode takes argument types into account
                 if mode == "strict":
@@ -164,4 +164,6 @@ class relation_extraction(evaluate.Metric):
         # Compute Macro F1 Scores
         scores["ALL"]["Macro_f1"] = np.mean([scores[ent_type]["f1"] for ent_type in relation_types])
         scores["ALL"]["Macro_p"] = np.mean([scores[ent_type]["p"] for ent_type in relation_types])
-        scores["ALL"]["Macro_r"] = np.mean([scores[ent_type]["r"] for ent_type in relation_types])
+        scores["ALL"]["Macro_r"] = np.mean([scores[ent_type]["r"] for ent_type in relation_types])
+
+        return scores