multi-label soft metrics (#754)

Summary:
Pull Request resolved: #754

compute multi-label soft metrics

Reviewed By: liaimi

Differential Revision: D16114819

fbshipit-source-id: 93a26cab8a3ea09fd2e54dde3aae495054fc5779

Author: Hao Wu

pytext/metrics/__init__.py

@@ -629,11 +629,8 @@ def compute_multi_label_soft_metrics(
         y_true = []
         y_score = []
         for label_scores, _, expected in predictions:
-            for expected_label in expected:
-                if expected_label < 0:
-                    break
-                y_true.append(expected_label == i)
-                y_score.append(label_scores[i])
+            y_true.append(i in expected)
+            y_score.append(label_scores[i])
         y_true_sorted, y_score_sorted = sort_by_score(y_true, y_score)
         ap = average_precision_score(y_true_sorted, y_score_sorted)
         recall_at_precision_dict = recall_at_precision(

pytext/workflow.py

@@ -55,7 +55,7 @@ def _set_fp16(use_fp16: bool) -> None:
 
 
 def _set_distributed(
-    rank: int, world_size: int, dist_init_url: str, device_id: int,
+    rank: int, world_size: int, dist_init_url: str, device_id: int
 ) -> None:
     if dist_init_url and world_size > 1:
         distributed.dist_init(rank, world_size, dist_init_url, device_id)