Run pre-commit hooks

python-package/lightgbm/__init__.py

@@ -6,7 +6,7 @@
 
 from pathlib import Path
 
-from .basic import Booster, Dataset, Sequence, ObjectiveFunction, register_logger
+from .basic import Booster, Dataset, ObjectiveFunction, Sequence, register_logger
 from .callback import EarlyStopException, early_stopping, log_evaluation, record_evaluation, reset_parameter
 from .engine import CVBooster, cv, train
 

python-package/lightgbm/basic.py

@@ -5311,7 +5311,7 @@ def __init__(self, name: str, params: Dict[str, Any]):
         self.num_class = params.get("num_class", 1)
 
         if "objective" in params and params["objective"] != self.name:
-            raise ValueError("The name should be consistent with the params[\"objective\"] field.")
+            raise ValueError('The name should be consistent with the params["objective"] field.')
 
         self.__create()
 
@@ -5383,7 +5383,6 @@ def __init_from_dataset(self, dataset: Dataset) -> "ObjectiveFunction":
         if self._handle is None:
             raise ValueError("Dealocated ObjectiveFunction cannot be initialized")
 
-        ref_dataset = dataset._handle
         tmp_num_data = ctypes.c_int(0)
         _safe_call(
             _LIB.LGBM_ObjectiveFunctionInit(

tests/python_package_test/test_engine.py

@@ -32,8 +32,8 @@
     logistic_sigmoid,
     make_synthetic_regression,
     mse_obj,
-    pickle_and_unpickle_object,
     multiclass_custom_objective,
+    pickle_and_unpickle_object,
     softmax,
 )
 
@@ -4393,25 +4393,30 @@ def test_quantized_training():
     quant_rmse = np.sqrt(np.mean((quant_bst.predict(X) - y) ** 2))
     assert quant_rmse < rmse + 6.0
 
+
 @pytest.mark.parametrize("use_weight", [False, True])
-@pytest.mark.parametrize("test_data", [
-    {
-        "custom_objective": mse_obj,
-        "objective_name": "regression",
-        "df": make_synthetic_regression(),
-        "num_class": 1
-    },
-    {
-        "custom_objective": multiclass_custom_objective,
-        "objective_name": "multiclass",
-        "df": make_blobs(n_samples=100, centers=[[-4, -4], [4, 4], [-4, 4]], random_state=42),
-        "num_class": 3
-    },
-])
+@pytest.mark.parametrize(
+    "test_data",
+    [
+        {
+            "custom_objective": mse_obj,
+            "objective_name": "regression",
+            "df": make_synthetic_regression(),
+            "num_class": 1,
+        },
+        {
+            "custom_objective": multiclass_custom_objective,
+            "objective_name": "multiclass",
+            "df": make_blobs(n_samples=100, centers=[[-4, -4], [4, 4], [-4, 4]], random_state=42),
+            "num_class": 3,
+        },
+    ],
+)
 @pytest.mark.parametrize("num_boost_round", [5, 15])
 def test_objective_function_class(use_weight, test_data, num_boost_round):
     X, y = test_data["df"]
-    weight = np.random.choice([1, 2], y.shape) if use_weight else None
+    rng = np.random.default_rng()
+    weight = rng.choice([1, 2], y.shape) if use_weight else None
     lgb_train = lgb.Dataset(X, y, weight=weight, init_score=np.zeros((len(y), test_data["num_class"])))
 
     params = {"verbose": -1, "objective": test_data["objective_name"], "num_class": test_data["num_class"]}

tests/python_package_test/utils.py

@@ -169,8 +169,8 @@ def multiclass_custom_objective(y_pred, ds):
 
 
 def builtin_objective(name, params):
-    """Mimics the builtin objective functions to mock training.
-    """
+    """Mimics the builtin objective functions to mock training."""
+
     def wrapper(y_pred, dtrain):
         fobj = lgb.ObjectiveFunction(name, params)
         fobj.init(dtrain)
@@ -181,6 +181,7 @@ def wrapper(y_pred, dtrain):
             hess = hess.reshape((fobj.num_class, -1)).transpose()
         print(grad, hess)
         return (grad, hess)
+
     return wrapper