Merge branch 'dev' of git.nju.edu.cn:learnware/learnware-market into dev

examples/example_pfs/main.py

@@ -122,10 +122,13 @@ class PFSDatasetWorkflow:
 
         pfs = Dataloader()
         idx_list = pfs.get_idx_list()
+        os.makedirs("./user_spec", exist_ok=True)
 
         for idx in idx_list:
             train_x, train_y, test_x, test_y = pfs.get_idx_data(idx)
             user_spec = specification.utils.generate_rkme_spec(X=test_x, gamma=0.1, cuda_idx=0)
+            user_spec_path = f"./user_spec/user_{idx}.json"
+            user_spec.save(user_spec_path)
 
             user_info = BaseUserInfo(
                 id=f"user_{idx}", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec}

learnware/market/easy.py

@@ -79,6 +79,7 @@ class EasyMarket(BaseMarket):
             learnware.instantiate_model()
         except Exception as e:
             logger.warning(f"The learnware [{learnware.id}] is instantiated failed! Due to {repr(e)}")
+            raise
             return cls.INVALID_LEARNWARE
 
         try:
@@ -340,7 +341,7 @@ class EasyMarket(BaseMarket):
         learnware_list: List[Learnware],
         user_rkme: RKMEStatSpecification,
         max_search_num: int,
-        weight_cutoff: float = 0.95,
+        weight_cutoff: float = 0.98,
     ) -> Tuple[List[float], List[Learnware]]:
         """Select learnwares based on a total mixture ratio, then recalculate their mixture weights
 
@@ -456,7 +457,7 @@ class EasyMarket(BaseMarket):
         learnware_list: List[Learnware],
         user_rkme: RKMEStatSpecification,
         max_search_num: int,
-        score_cutoff: float = 0.01,
+        score_cutoff: float = 0.001,
     ) -> Tuple[List[float], List[Learnware]]:
         """Greedily match learnwares such that their mixture become more and more closer to user's rkme
 
@@ -588,6 +589,7 @@ class EasyMarket(BaseMarket):
             user_semantic_spec = user_info.get_semantic_spec()
             if match_semantic_spec(learnware_semantic_spec, user_semantic_spec):
                 match_learnwares.append(learnware)
+        logger.info("semantic_spec search: choose %d from %d learnwares" % (len(match_learnwares), len(learnware_list)))
         return match_learnwares
 
     def search_learnware(