NER example: fix divisions by zero

example/named_entity_recognition/src/metrics.py

@@ -50,15 +50,20 @@ def classifer_metrics(label, pred):
     correct_entitites = np.sum(corr_pred[pred_is_entity])
 
     #precision: when we predict entity, how often are we right?
-    precision = correct_entitites/entity_preds
     if entity_preds == 0:
         precision = np.nan
+    else:
+        precision = correct_entitites/entity_preds
 
     #recall: of the things that were an entity, how many did we catch?
     recall = correct_entitites / num_entities
     if num_entities == 0:
         recall = np.nan
-    f1 = 2 * precision * recall / (precision + recall)
+    # To prevent dozens of warning: RuntimeWarning: divide by zero encountered in long_scalars
+    if precision + recall == 0:
+        f1 = 0
+    else:
+        f1 = 2 * precision * recall / (precision + recall)
     return precision, recall, f1
 
 def entity_precision(label, pred):