[FIX] fix bug in _selector_grid_search: Unknown parameter: booster

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
 
@@ -240,10 +240,7 @@ 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

@@ -38,10 +38,7 @@ 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"},
@@ -49,10 +46,7 @@ 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"},
@@ -60,10 +54,7 @@ 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"},
@@ -73,10 +64,7 @@ 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

@@ -15,10 +15,7 @@ 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"},
@@ -26,10 +23,7 @@ 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"},
@@ -37,10 +31,7 @@ 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"},
@@ -50,10 +41,7 @@ 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

@@ -40,10 +40,7 @@ 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

@@ -15,10 +15,7 @@ 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"},
@@ -26,10 +23,7 @@ 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"},
@@ -37,10 +31,7 @@ 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"},
@@ -50,10 +41,7 @@ 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

@@ -18,10 +18,7 @@ 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"},
@@ -29,10 +26,7 @@ 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"},
@@ -40,10 +34,7 @@ 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"},
@@ -53,10 +44,7 @@ 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

@@ -66,10 +66,7 @@ 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",
@@ -82,10 +79,7 @@ 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",
@@ -105,14 +99,8 @@ 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 = {
@@ -123,10 +111,7 @@ _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

@@ -30,10 +30,7 @@ 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/learnware/reuse.py

@@ -187,7 +187,7 @@ class ReuseBaseline:
                     n_estimators=2000,
                     # objective="multiclass",
                     # num_class=num_class,
-                    booster="gbtree",
+                    boosting_type="gbdt",
                     seed=0,
                 )
                 train_y = train_y.astype(np.int)
@@ -205,7 +205,7 @@ class ReuseBaseline:
             n_estimators=2000,
             # objective="multiclass",
             # num_class=num_class,
-            booster="gbtree",
+            boosting_type="gbdt",
             seed=0,
         )
         model.fit(

learnware/market/easy.py

@@ -141,10 +141,7 @@ 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

@@ -354,9 +354,7 @@ 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:
@@ -444,7 +442,7 @@ 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)