[MNT] Fix bugs in pfs example

examples/example_image/main.py

@@ -167,13 +167,11 @@ def test_search(gamma=0.1, load_market=True):
             acc_list.append(acc)
             logger.info("search rank: %d, score: %.3f, learnware_id: %s, acc: %.3f" % (idx, score, learnware.id, acc))
         # test reuse
-        """
         reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list)
         reuse_predict = reuse_baseline.predict(user_data=user_data)
         reuse_score = eval_prediction(reuse_predict, user_label)
         job_selector_score_list.append(reuse_score)
         print(f"mixture reuse loss: {reuse_score}\n")
-        """
 
         reuse_ensemble = EnsembleReuser(learnware_list=mixture_learnware_list, mode="vote")
         ensemble_predict_y = reuse_ensemble.predict(user_data=user_data)
@@ -188,7 +186,7 @@ def test_search(gamma=0.1, load_market=True):
         % (np.mean(select_list), np.std(select_list), np.mean(avg_list), np.std(avg_list))
     )
     logger.info("Average performance improvement: %.3f" % (np.mean(improve_list)))
-    # logger.info("Average Job Selector Reuse Performance: %.3f +/- %.3f"%(np.mean(job_selector_score_list), np.std(job_selector_score_list)))
+    logger.info("Average Job Selector Reuse Performance: %.3f +/- %.3f"%(np.mean(job_selector_score_list), np.std(job_selector_score_list)))
     logger.info(
         "Ensemble Reuse Performance: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list))
     )

examples/example_pfs/main.py

@@ -141,7 +141,7 @@ class PFSDatasetWorkflow:
                 single_learnware_list,
                 mixture_score,
                 mixture_learnware_list,
-            ) = easy_market.search_learnware(user_info, search_method = "auto")
+            ) = easy_market.search_learnware(user_info)
 
             print(f"search result of user{idx}:")
             print(

learnware/market/easy.py

@@ -378,7 +378,7 @@ class EasyMarket(BaseMarket):
         if len(mixture_list) <= 1:
             mixture_list = [learnware_list[sort_by_weight_idx_list[0]]]
             mixture_weight = [1]
-            mmd_dist = user_rkme.dist(mixture_list[0])
+            mmd_dist = user_rkme.dist(mixture_list[0].specification.get_stat_spec_by_name("RKMEStatSpecification"))
         else:
             if len(mixture_list) > max_search_num:
                 mixture_list = mixture_list[:max_search_num]