[MNT] format code

examples/dataset_pfs_workflow/pfs/pfs_cross_transfer.py

@@ -85,7 +85,9 @@ def get_split_errs(algo):
                 split = train_xs.shape[0] - proportion_list[tmp]
 
                 model.fit(
-                    train_xs[split:,],
+                    train_xs[
+                        split:,
+                    ],
                     train_ys[split:],
                     eval_set=[(val_xs, val_ys)],
                     early_stopping_rounds=50,

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

@@ -337,7 +337,7 @@ class LearnwareClient:
 
             if load_model:
                 learnware_obj.instantiate_model()
-            
+
             return learnware_obj
 
     def system(self, command):

learnware/client/utils.py

@@ -82,4 +82,17 @@ def install_environment(zip_path, conda_env):
                 raise Exception("Environment.yaml or requirements.txt not found in the learnware zip file.")
 
     logger.info(f"install learnware package for conda env [{conda_env}]")
-    system_execute(args=["conda", "run", "-n", f"{conda_env}", "--no-capture-output", "python3", "-m", "pip", "install", "learnware"])
+    system_execute(
+        args=[
+            "conda",
+            "run",
+            "-n",
+            f"{conda_env}",
+            "--no-capture-output",
+            "python3",
+            "-m",
+            "pip",
+            "install",
+            "learnware",
+        ]
+    )

tests/test_client/test_download.py

@@ -17,7 +17,7 @@ def test_container(zip_paths):
     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]
@@ -26,43 +26,43 @@ def test_container(zip_paths):
 
         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"
-    
+
     client = LearnwareClient()
     client.login(email, token)
-    
+
     learnware_ids = ["00000084", "00000154", "00000155"]
     zip_paths = ["1.zip", "2.zip", "3.zip"]
     root = os.path.dirname(__file__)
     for i in range(len(learnware_ids)):
         zip_paths[i] = os.path.join(root, zip_paths[i])
         client.download_learnware(learnware_ids[i], zip_paths[i])
-    
+
     test_container(zip_paths)
-    # test_load(zip_paths)
+    # test_load(zip_paths)