[FIX] add license in semantic spec

examples/dataset_image_workflow/main.py

@@ -50,6 +50,7 @@ semantic_specs = [
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
         "Output": {"Dimension": 10},
+        "License": {"Values": ["MIT"], "Type": "Class"},
     }
 ]
 
@@ -60,6 +61,7 @@ user_semantic = {
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
+    "License": {"Values": ["MIT"], "Type": "Class"},
 }
 
 
@@ -175,7 +177,10 @@ def test_search(gamma=0.1, load_market=True):
             pred_y = single_item.learnware.predict(user_data)
             acc = eval_prediction(pred_y, user_label)
             acc_list.append(acc)
-            logger.info("Search rank: %d, score: %.3f, learnware_id: %s, acc: %.3f" % (idx, single_item.score, single_item.learnware.id, acc))
+            logger.info(
+                "Search rank: %d, score: %.3f, learnware_id: %s, acc: %.3f"
+                % (idx, single_item.score, single_item.learnware.id, acc)
+            )
 
         # test reuse (job selector)
         # reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list, herding_num=100)

examples/dataset_m5_workflow/main.py

@@ -36,6 +36,7 @@ semantic_specs = [
         "Name": {"Values": "learnware_1", "Type": "String"},
         "Input": input_description,
         "Output": output_description,
+        "License": {"Values": ["MIT"], "Type": "Class"},
     }
 ]
 
@@ -48,6 +49,7 @@ user_semantic = {
     "Name": {"Values": "", "Type": "String"},
     "Input": input_description,
     "Output": output_description,
+    "License": {"Values": ["MIT"], "Type": "Class"},
 }
 
 
@@ -158,7 +160,7 @@ class M5DatasetWorkflow:
             search_result = easy_market.search_learnware(user_info)
             single_result = search_result.get_single_results()
             multiple_result = search_result.get_multiple_results()
-            
+
             print(f"search result of user{idx}:")
             print(
                 f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}"

examples/dataset_m5_workflow/upload.py

@@ -66,6 +66,7 @@ def main():
             "Scenario": {"Values": ["Business"], "Type": "Tag"},
             "Description": {"Values": "A sales-forecasting model from Walmart store", "Type": "String"},
             "Name": {"Values": name, "Type": "String"},
+            "License": {"Values": ["MIT"], "Type": "Class"},
         }
         res = session.post(
             submit_url,

examples/dataset_pfs_workflow/main.py

@@ -35,6 +35,7 @@ semantic_specs = [
         "Name": {"Values": "learnware_1", "Type": "String"},
         "Input": input_description,
         "Output": output_description,
+        "License": {"Values": ["MIT"], "Type": "Class"},
     }
 ]
 
@@ -47,6 +48,7 @@ user_semantic = {
     "Name": {"Values": "", "Type": "String"},
     "Input": input_description,
     "Output": output_description,
+    "License": {"Values": ["MIT"], "Type": "Class"},
 }
 
 
@@ -155,7 +157,7 @@ class PFSDatasetWorkflow:
             search_result = easy_market.search_learnware(user_info)
             single_result = search_result.get_single_results()
             multiple_result = search_result.get_multiple_results()
-            
+
             print(f"search result of user{idx}:")
             print(
                 f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}"

examples/dataset_pfs_workflow/upload.py

@@ -69,6 +69,7 @@ def main():
                 "Type": "String",
             },
             "Name": {"Values": name, "Type": "String"},
+            "License": {"Values": ["MIT"], "Type": "Class"},
         }
         res = session.post(
             submit_url,

examples/dataset_text_workflow/main.py

@@ -48,6 +48,7 @@ semantic_specs = [
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
         "Output": output_description,
+        "License": {"Values": ["MIT"], "Type": "Class"},
     }
 ]
 
@@ -59,6 +60,7 @@ user_semantic = {
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
     "Output": output_description,
+    "License": {"Values": ["MIT"], "Type": "Class"},
 }
 
 
@@ -73,7 +75,6 @@ class TextDatasetWorkflow:
         generate_uploader(X_train, y_train, n_uploaders=n_uploaders, data_save_root=uploader_save_root)
         generate_user(X_test, y_test, n_users=n_users, data_save_root=user_save_root)
 
-
     def _prepare_model(self):
         dataloader = TextDataLoader(data_save_root, train=True)
         for i in range(n_uploaders):
@@ -92,9 +93,8 @@ class TextDatasetWorkflow:
 
             logger.info("Model saved to '%s' and '%s'" % (modelv_save_path, modell_save_path))
 
-
-    def _prepare_learnware(self, 
-        data_path, modelv_path, modell_path, init_file_path, yaml_path, env_file_path, save_root, zip_name
+    def _prepare_learnware(
+        self, data_path, modelv_path, modell_path, init_file_path, yaml_path, env_file_path, save_root, zip_name
     ):
         os.makedirs(save_root, exist_ok=True)
         tmp_spec_path = os.path.join(save_root, "rkme.json")
@@ -139,7 +139,6 @@ class TextDatasetWorkflow:
         logger.info("New Learnware Saved to %s" % (zip_file_name))
         return zip_file_name
 
-
     def prepare_market(self, regenerate_flag=False):
         if regenerate_flag:
             self._init_text_dataset()
@@ -175,7 +174,6 @@ class TextDatasetWorkflow:
 
         logger.info("Total Item: %d" % (len(text_market)))
 
-
     def test(self, regenerate_flag=False):
         self.prepare_market(regenerate_flag)
         text_market = instantiate_learnware_market(market_id="ae")
@@ -199,11 +197,11 @@ class TextDatasetWorkflow:
             user_stat_spec.generate_stat_spec_from_data(X=user_data)
             user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETextSpecification": user_stat_spec})
             logger.info("Searching Market for user: %d" % (i))
-            
+
             search_result = text_market.search_learnware(user_info)
             single_result = search_result.get_single_results()
             multiple_result = search_result.get_multiple_results()
-            
+
             print(f"search result of user{i}:")
             print(
                 f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}"
@@ -220,7 +218,7 @@ class TextDatasetWorkflow:
             print(
                 f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, acc: {acc_list[0]}"
             )
-            
+
             if len(multiple_result) > 0:
                 mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares])
                 print(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}")

tests/test_hetero_market/test_hetero.py

@@ -35,6 +35,7 @@ user_semantic = {
     "Scenario": {"Values": ["Education"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
+    "License": {"Values": ["MIT"], "Type": "Class"},
 }
 
 
@@ -259,15 +260,15 @@ class TestMarket(unittest.TestCase):
                 str(key): semantic_spec["Input"]["Description"][str(key)] for key in range(user_dim)
             }
             user_info = BaseUserInfo(semantic_spec=semantic_spec, stat_info={"RKMETableSpecification": user_spec})
-            
+
             search_result = hetero_market.search_learnware(user_info)
             single_result = search_result.get_single_results()
             multiple_result = search_result.get_multiple_results()
-            
+
             print(f"search result of user{idx}:")
             for single_item in single_result:
                 print(f"score: {single_item.score}, learnware_id: {single_item.learnware.id}")
-                
+
             for multiple_item in multiple_result:
                 print(
                     f"mixture_score: {multiple_item.score}, mixture_learnware_ids: {[item.id for item in multiple_item.learnwares]}"
@@ -335,7 +336,7 @@ class TestMarket(unittest.TestCase):
             print(f"search result of user{idx}:")
             for single_item in single_result:
                 print(f"score: {single_item.score}, learnware_id: {single_item.learnware.id}")
-            
+
             for multiple_item in multiple_result:
                 print(f"mixture_score: {multiple_item.score}\n")
                 mixture_id = " ".join([learnware.id for learnware in multiple_item.learnwares])
@@ -363,9 +364,11 @@ class TestMarket(unittest.TestCase):
         # print search results
         for single_item in single_result:
             print(f"score: {single_item.score}, learnware_id: {single_item.learnware.id}")
-        
+
         for multiple_item in multiple_result:
-            print(f"mixture_score: {multiple_item.score}, mixture_learnware_ids: {[item.id for item in multiple_item.learnwares]}")
+            print(
+                f"mixture_score: {multiple_item.score}, mixture_learnware_ids: {[item.id for item in multiple_item.learnwares]}"
+            )
 
         # single model reuse
         hetero_learnware = HeteroMapAlignLearnware(single_result[0].learnware, mode="regression")