Update several files

abl/abducer/abducer_base.py

@@ -68,8 +68,8 @@ class AbducerBase(abc.ABC):
                 if len(candidates) > 0:
                     score += np.min(self._get_cost_list(pred_res[idx], pred_res_prob[idx], candidates))
                 else:
-                    score += len(pred_res)
-            return -self._zoopt_score_multiple(pred_res, key, sol.get_x())
+                    score += len(pred_res[idx])
+            return score
         
     def _constrain_address_num(self, solution, max_address_num):
         x = solution.get_x()

abl/framework_hed.py

@@ -291,7 +291,7 @@ def train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len
                 
                 INFO('consist_rule_acc is %f, %f\n' %(true_consist_rule_acc, false_consist_rule_acc))
                 # decide next course or restart
-                if true_consist_rule_acc > 0.9 and false_consist_rule_acc < 0.1:
+                if true_consist_rule_acc > 0.95 and false_consist_rule_acc < 0.1:
                     torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len)
                     break
                 else:

abl/utils/utils.py

@@ -19,7 +19,8 @@ def reform_idx(flatten_pred_res, save_pred_res):
     return re
 
 def hamming_dist(A, B):
-    B = np.array(B)
+    A = np.array(A, dtype='<U')
+    B = np.array(B, dtype='<U')
     A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B)))
     return np.sum(A != B, axis = 1)