[MNT] Black

examples/example_image/get_data.py

@@ -192,7 +192,7 @@ def get_zca_matrix(X, reg_coef=0.1):
 
 def layernorm_data(X):
     X_processed = X - torch.mean(X, [1, 2, 3], keepdim=True)
-    X_processed = X_processed / torch.sqrt(torch.sum(X_processed ** 2, [1, 2, 3], keepdim=True))
+    X_processed = X_processed / torch.sqrt(torch.sum(X_processed**2, [1, 2, 3], keepdim=True))
 
     return X_processed
 
@@ -240,7 +240,10 @@ def augment(images, dc_aug_param, device):
         def scalefun(i):
             h = int((np.random.uniform(1 - scale, 1 + scale)) * shape[2])
             w = int((np.random.uniform(1 - scale, 1 + scale)) * shape[2])
-            tmp = F.interpolate(images[i : i + 1], [h, w],)[0]
+            tmp = F.interpolate(
+                images[i : i + 1],
+                [h, w],
+            )[0]
             mhw = max(h, w, shape[2], shape[3])
             im_ = torch.zeros(shape[1], mhw, mhw, dtype=torch.float, device=device)
             r = int((mhw - h) / 2)

examples/example_image/main.py

@@ -38,7 +38,10 @@ os.makedirs(model_save_root, exist_ok=True)
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -46,7 +49,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -54,7 +60,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -64,7 +73,10 @@ semantic_specs = [
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {"Values": ["Classification"], "Type": "Class",},
+    "Task": {
+        "Values": ["Classification"],
+        "Type": "Class",
+    },
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "Description"},

examples/example_m5/m5/utils.py

@@ -70,7 +70,7 @@ def measure_aux_algo(idx, test_sample, model):
 
 # Simple "Memory profilers" to see memory usage
 def get_memory_usage():
-    return np.round(psutil.Process(os.getpid()).memory_info()[0] / 2.0 ** 30, 2)
+    return np.round(psutil.Process(os.getpid()).memory_info()[0] / 2.0**30, 2)
 
 
 def sizeof_fmt(num, suffix="B"):
@@ -84,7 +84,7 @@ def sizeof_fmt(num, suffix="B"):
 # Memory Reducer
 def reduce_mem_usage(df, float16_flag=True, verbose=True):
     numerics = ["int16", "int32", "int64", "float16", "float32", "float64"]
-    start_mem = df.memory_usage().sum() / 1024 ** 2
+    start_mem = df.memory_usage().sum() / 1024**2
     for col in df.columns:
         col_type = df[col].dtypes
         if col_type in numerics:
@@ -106,7 +106,7 @@ def reduce_mem_usage(df, float16_flag=True, verbose=True):
                     df[col] = df[col].astype(np.float32)
                 else:
                     df[col] = df[col].astype(np.float64)
-    end_mem = df.memory_usage().sum() / 1024 ** 2
+    end_mem = df.memory_usage().sum() / 1024**2
     if verbose:
         print(
             "Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)".format(

examples/example_m5/main.py

@@ -15,7 +15,10 @@ from m5 import DataLoader
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -23,7 +26,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -31,7 +37,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -41,7 +50,10 @@ semantic_specs = [
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {"Values": ["Classification"], "Type": "Class",},
+    "Task": {
+        "Values": ["Classification"],
+        "Type": "Class",
+    },
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "Description"},

examples/example_market_db/example_db.py

@@ -40,7 +40,10 @@ semantic_specs = [
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {"Values": ["Classification"], "Type": "Class",},
+    "Task": {
+        "Values": ["Classification"],
+        "Type": "Class",
+    },
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "Description"},

examples/example_pfs/main.py

@@ -15,7 +15,10 @@ from pfs import Dataloader
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -23,7 +26,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -31,7 +37,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -41,7 +50,10 @@ semantic_specs = [
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {"Values": ["Classification"], "Type": "Class",},
+    "Task": {
+        "Values": ["Classification"],
+        "Type": "Class",
+    },
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "Description"},

examples/example_pfs/pfs/pfs_cross_transfer.py

@@ -67,7 +67,7 @@ def get_split_errs(algo):
             for tmp in range(len(proportion_list)):
                 model = lgb.LGBMModel(
                     boosting_type="gbdt",
-                    num_leaves=2 ** 7 - 1,
+                    num_leaves=2**7 - 1,
                     learning_rate=0.01,
                     objective="rmse",
                     metric="rmse",
@@ -119,7 +119,7 @@ def get_errors(algo):
         if algo == "lgb":
             model = lgb.LGBMModel(
                 boosting_type="gbdt",
-                num_leaves=2 ** 7 - 1,
+                num_leaves=2**7 - 1,
                 learning_rate=0.01,
                 objective="rmse",
                 metric="rmse",

examples/workflow_by_code/main.py

@@ -18,7 +18,10 @@ curr_root = os.path.dirname(os.path.abspath(__file__))
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -26,7 +29,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -34,7 +40,10 @@ semantic_specs = [
     },
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class",},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "Description"},
@@ -44,7 +53,10 @@ semantic_specs = [
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {"Values": ["Classification"], "Type": "Class",},
+    "Task": {
+        "Values": ["Classification"],
+        "Type": "Class",
+    },
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "Description"},

learnware/config.py

@@ -66,7 +66,10 @@ os.makedirs(LEARNWARE_FOLDER_POOL_PATH, exist_ok=True)
 os.makedirs(DATABASE_PATH, exist_ok=True)
 
 semantic_config = {
-    "Data": {"Values": ["Tabular", "Image", "Video", "Text", "Audio"], "Type": "Class",},  # Choose only one class
+    "Data": {
+        "Values": ["Tabular", "Image", "Video", "Text", "Audio"],
+        "Type": "Class",
+    },  # Choose only one class
     "Task": {
         "Values": [
             "Classification",
@@ -79,7 +82,10 @@ semantic_config = {
         ],
         "Type": "Class",  # Choose only one class
     },
-    "Device": {"Values": ["CPU", "GPU"], "Type": "Tag",},  # Choose one or more tags
+    "Device": {
+        "Values": ["CPU", "GPU"],
+        "Type": "Tag",
+    },  # Choose one or more tags
     "Scenario": {
         "Values": [
             "Business",
@@ -99,8 +105,14 @@ semantic_config = {
         ],
         "Type": "Tag",  # Choose one or more tags
     },
-    "Description": {"Values": None, "Type": "Description",},
-    "Name": {"Values": None, "Type": "Name",},
+    "Description": {
+        "Values": None,
+        "Type": "Description",
+    },
+    "Name": {
+        "Values": None,
+        "Type": "Name",
+    },
 }
 
 _DEFAULT_CONFIG = {
@@ -111,7 +123,10 @@ _DEFAULT_CONFIG = {
     "learnware_pool_path": LEARNWARE_POOL_PATH,
     "learnware_zip_pool_path": LEARNWARE_ZIP_POOL_PATH,
     "learnware_folder_pool_path": LEARNWARE_FOLDER_POOL_PATH,
-    "learnware_folder_config": {"yaml_file": "learnware.yaml", "module_file": "__init__.py",},
+    "learnware_folder_config": {
+        "yaml_file": "learnware.yaml",
+        "module_file": "__init__.py",
+    },
     "database_path": DATABASE_PATH,
     "max_reduced_set_size": 1000000,
 }

learnware/learnware/__init__.py

@@ -31,7 +31,10 @@ def get_learnware_from_dirpath(id: str, semantic_spec: dict, learnware_dirpath:
         The contructed learnware object, return None if build failed
     """
     learnware_config = {
-        "model": {"class_name": "Model", "kwargs": {},},
+        "model": {
+            "class_name": "Model",
+            "kwargs": {},
+        },
         "stat_specifications": [
             {
                 "module_path": "learnware.specification",

learnware/market/easy.py

@@ -144,7 +144,10 @@ class EasyMarket(BaseMarket):
             self.learnware_folder_list[id] = target_folder_dir
             self.count += 1
             add_learnware_to_db(
-                id, semantic_spec=semantic_spec, zip_path=target_zip_dir, folder_path=target_folder_dir,
+                id,
+                semantic_spec=semantic_spec,
+                zip_path=target_zip_dir,
+                folder_path=target_folder_dir,
             )
             return id, True
 

learnware/specification/rkme.py

@@ -354,7 +354,9 @@ class RKMEStatSpecification(BaseStatSpecification):
         rkme_to_save["beta"] = rkme_to_save["beta"].tolist()
         rkme_to_save["device"] = "gpu" if rkme_to_save["cuda_idx"] != -1 else "cpu"
         json.dump(
-            rkme_to_save, codecs.open(save_path, "w", encoding="utf-8"), separators=(",", ":"),
+            rkme_to_save,
+            codecs.open(save_path, "w", encoding="utf-8"),
+            separators=(",", ":"),
         )
 
     def load(self, filepath: str) -> bool:
@@ -442,7 +444,7 @@ def torch_rbf_kernel(x1, x2, gamma) -> torch.Tensor:
     """
     x1 = x1.double()
     x2 = x2.double()
-    X12norm = torch.sum(x1 ** 2, 1, keepdim=True) - 2 * x1 @ x2.T + torch.sum(x2 ** 2, 1, keepdim=True).T
+    X12norm = torch.sum(x1**2, 1, keepdim=True) - 2 * x1 @ x2.T + torch.sum(x2**2, 1, keepdim=True).T
     return torch.exp(-X12norm * gamma)