[FIX] fix bugs in LearnwareClient

learnware/client/container.py

@@ -31,7 +31,7 @@ class ModelEnvContainer(BaseModel):
 
             with open(model_path, "wb") as model_fp:
                 pickle.dump(self.model_config, model_fp)
-
+            
             system_execute(
                 [
                     "conda",

learnware/client/learnware_client.py

@@ -337,11 +337,8 @@ class LearnwareClient:
 
             if load_model:
                 learnware_obj.instantiate_model()
-                pass
-
+            
             return learnware_obj
-            pass
-        pass
 
     def system(self, command):
         retcd = os.system(command)

tests/test_client/test_download.py

@@ -1,12 +1,55 @@
 import os
+import zipfile
 import numpy as np
 
 import learnware
+from learnware.learnware import get_learnware_from_dirpath
 from learnware.client import LearnwareClient
 from learnware.client.container import ModelEnvContainer, LearnwaresContainer
 from learnware.learnware.reuse import AveragingReuser
 
 
+def test_container(zip_paths):
+    semantic_specification = dict()
+    semantic_specification["Data"] = {"Type": "Class", "Values": ["Text"]}
+    semantic_specification["Task"] = {"Type": "Class", "Values": ["Ranking"]}
+    semantic_specification["Library"] = {"Type": "Class", "Values": ["Scikit-learn"]}
+    semantic_specification["Scenario"] = {"Type": "Tag", "Values": "Financial"}
+    semantic_specification["Name"] = {"Type": "String", "Values": "test"}
+    semantic_specification["Description"] = {"Type": "String", "Values": "test"}
+    
+    learnware_list = []
+    for id, zip_path in enumerate(zip_paths):
+        dir_path = zip_path[:-4]
+        with zipfile.ZipFile(zip_path, "r") as z_file:
+            z_file.extractall(dir_path)
+
+        learnware = get_learnware_from_dirpath(f"test_id{id}", semantic_specification, dir_path)
+        learnware_list.append(learnware)
+    
+    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
+        learnware_list = env_container.get_learnware_list_with_container()
+        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+        input_array = np.random.random(size=(20, 13))
+        print(reuser.predict(input_array))
+        
+        for idx, learnware in enumerate(learnware_list):
+            print(f"learnware_{idx}", learnware.predict(input_array))
+            
+
+def test_load(zip_paths):
+    learnware_list = [client.load_learnware(file, load_model=False) for file in zip_paths]
+        
+    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
+        learnware_list = env_container.get_learnware_list_with_container()
+        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+        input_array = np.random.random(size=(20, 13))
+        print(reuser.predict(input_array))
+        
+        for idx, learnware in enumerate(learnware_list):
+            print(f"learnware_{idx}", learnware.predict(input_array))
+    
+
 if __name__ == "__main__":
     email = "liujd@lamda.nju.edu.cn"
     token = "f7e647146a314c6e8b4e2e1079c4bca4"
@@ -21,13 +64,5 @@ if __name__ == "__main__":
         zip_paths[i] = os.path.join(root, zip_paths[i])
         client.download_learnware(learnware_ids[i], zip_paths[i])
     
-    learnware_list = [client.load_learnware(file, load_model=False) for file in zip_paths]
-
-    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
-        learnware_list = env_container.get_learnware_list_with_container()
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-        
-        for idx, learnware in enumerate(learnware_list):
-            print(f"learnware_{idx}", reuser.predict(learnware))
+    test_container(zip_paths)
+    # test_load(zip_paths)