[MNT] Update setup

examples/example_image/main.py

@@ -153,9 +153,7 @@ def test_search(gamma=0.1, load_market=True):
         user_data = np.load(user_data_path)
         user_label = np.load(user_label_path)
         user_stat_spec = specification.utils.generate_rkme_spec(X=user_data, gamma=gamma, cuda_idx=0)
-        user_info = BaseUserInfo(
-            semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_stat_spec}
-        )
+        user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_stat_spec})
         logger.info("Searching Market for user: %d" % (i))
         sorted_score_list, single_learnware_list, mixture_score, mixture_learnware_list = image_market.search_learnware(
             user_info

examples/example_m5/main.py

@@ -135,9 +135,7 @@ class M5DatasetWorkflow:
             user_spec_path = f"./user_spec/user_{idx}.json"
             user_spec.save(user_spec_path)
 
-            user_info = BaseUserInfo(
-                semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
-            )
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,

examples/example_market_db/example_db.py

@@ -150,9 +150,7 @@ def test_stat_search():
 
         user_spec = specification.rkme.RKMEStatSpecification()
         user_spec.load(os.path.join(unzip_dir, "svm.json"))
-        user_info = BaseUserInfo(
-            semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec}
-        )
+        user_info = BaseUserInfo(semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec})
         sorted_score_list, single_learnware_list, mixture_learnware_list = easy_market.search_learnware(user_info)
 
         print(f"search result of user{idx}:")

examples/example_pfs/main.py

@@ -133,9 +133,7 @@ class PFSDatasetWorkflow:
             user_spec_path = f"./user_spec/user_{idx}.json"
             user_spec.save(user_spec_path)
 
-            user_info = BaseUserInfo(
-                semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
-            )
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,

examples/workflow_by_code/main.py

@@ -158,9 +158,7 @@ class LearnwareMarketWorkflow:
 
             user_spec = specification.rkme.RKMEStatSpecification()
             user_spec.load(os.path.join(unzip_dir, "svm.json"))
-            user_info = BaseUserInfo(
-                semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
-            )
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,

learnware/learnware/reuse.py

@@ -221,13 +221,13 @@ class JobSelectorReuser(BaseReuser):
         learning_rate = [0.01]
         max_depth = [66]
         params = (0, 0)
-        
+
         lgb_params = {
             "boosting_type": "gbdt",
             "n_estimators": 2000,
             "boost_from_average": False,
         }
-        
+
         if num_class == 2:
             lgb_params["objective"] = "binary"
             lgb_params["metric"] = "binary_logloss"
@@ -252,7 +252,9 @@ class JobSelectorReuser(BaseReuser):
         lgb_params["learning_rate"] = params[0]
         lgb_params["max_depth"] = params[1]
         model = LGBMClassifier(**lgb_params)
-        model.fit(org_train_x, org_train_y, eval_set=[(org_train_x, org_train_y)], early_stopping_rounds=300, verbose=False)
+        model.fit(
+            org_train_x, org_train_y, eval_set=[(org_train_x, org_train_y)], early_stopping_rounds=300, verbose=False
+        )
 
         return model
 

learnware/market/evolve.py

@@ -39,4 +39,4 @@ class EvolvedMarket(BaseMarket):
         id_list : List[str]
             Id list for learnwares
         """
-        pass
+        pass

setup.py

@@ -51,7 +51,7 @@ def get_platform():
 # What packages are required for this module to be executed?
 # `estimator` may depend on other packages. In order to reduce dependencies, it is not written here.
 REQUIRED = [
-    "numpy>=1.20.0",
+    "numpy>=1.22.0,<1.24.0",
     "pandas>=0.25.1",
     "scipy>=1.0.0",
     "matplotlib>=3.1.3",