Specify src path for isort.

demo/dask/cpu_survival.py

@@ -8,9 +8,9 @@
 
 import dask.dataframe as dd
 from dask.distributed import Client, LocalCluster
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
+from xgboost.dask import DaskDMatrix
 
 
 def main(client):

demo/dask/cpu_training.py

@@ -5,9 +5,9 @@
 """
 from dask import array as da
 from dask.distributed import Client, LocalCluster
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
+from xgboost.dask import DaskDMatrix
 
 
 def main(client):

demo/dask/dask_callbacks.py

@@ -6,9 +6,9 @@
 from dask.distributed import Client, LocalCluster
 from dask_ml.datasets import make_regression
 from dask_ml.model_selection import train_test_split
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
+from xgboost.dask import DaskDMatrix
 
 
 def probability_for_going_backward(epoch):

demo/dask/gpu_training.py

@@ -7,10 +7,10 @@
 from dask import dataframe as dd
 from dask.distributed import Client
 from dask_cuda import LocalCUDACluster
-from xgboost.dask import DaskDMatrix
 
 import xgboost as xgb
 from xgboost import dask as dxgb
+from xgboost.dask import DaskDMatrix
 
 
 def using_dask_matrix(client: Client, X, y):

demo/guide-python/spark_estimator_examples.py

@@ -10,6 +10,7 @@
 from pyspark.sql import SparkSession
 from pyspark.sql.functions import rand
 from sklearn.model_selection import train_test_split
+
 from xgboost.spark import SparkXGBClassifier, SparkXGBRegressor
 
 spark = SparkSession.builder.master("local[*]").getOrCreate()

demo/nvflare/custom/controller.py

@@ -4,14 +4,15 @@
 """
 import multiprocessing
 
-import xgboost.federated
 from nvflare.apis.client import Client
 from nvflare.apis.fl_context import FLContext
 from nvflare.apis.impl.controller import Controller, Task
 from nvflare.apis.shareable import Shareable
 from nvflare.apis.signal import Signal
 from trainer import SupportedTasks
 
+import xgboost.federated
+
 
 class XGBoostController(Controller):
     def __init__(self, port: int, world_size: int, server_key_path: str,

python-package/xgboost/spark/core.py

@@ -34,12 +34,12 @@
     ShortType,
 )
 from scipy.special import expit, softmax  # pylint: disable=no-name-in-module
-from xgboost.compat import is_cudf_available
-from xgboost.core import Booster
-from xgboost.training import train as worker_train
 
 import xgboost
 from xgboost import XGBClassifier, XGBRanker, XGBRegressor
+from xgboost.compat import is_cudf_available
+from xgboost.core import Booster
+from xgboost.training import train as worker_train
 
 from .data import (
     _read_csr_matrix_from_unwrapped_spark_vec,

python-package/xgboost/spark/data.py

@@ -6,9 +6,9 @@
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
-from xgboost.compat import concat
 
 from xgboost import DataIter, DMatrix, QuantileDMatrix, XGBModel
+from xgboost.compat import concat
 
 from .._typing import ArrayLike
 from ..core import _convert_ntree_limit

python-package/xgboost/spark/model.py

@@ -8,6 +8,7 @@
 from pyspark import SparkFiles, cloudpickle
 from pyspark.ml.util import DefaultParamsReader, DefaultParamsWriter, MLReader, MLWriter
 from pyspark.sql import SparkSession
+
 from xgboost.core import Booster
 
 from .utils import get_class_name, get_logger

python-package/xgboost/spark/utils.py

@@ -8,9 +8,9 @@
 import pyspark
 from pyspark import BarrierTaskContext, SparkContext
 from pyspark.sql.session import SparkSession
-from xgboost.tracker import RabitTracker
 
 from xgboost import collective
+from xgboost.tracker import RabitTracker
 
 
 def get_class_name(cls: Type) -> str:

python-package/xgboost/testing/__init__.py

@@ -34,10 +34,10 @@
 import numpy as np
 import pytest
 from scipy import sparse
-from xgboost.core import ArrayLike
-from xgboost.sklearn import SklObjective
 
 import xgboost as xgb
+from xgboost.core import ArrayLike
+from xgboost.sklearn import SklObjective
 
 hypothesis = pytest.importorskip("hypothesis")
 

python-package/xgboost/testing/dask.py

@@ -2,9 +2,9 @@
 import numpy as np
 from dask import array as da
 from distributed import Client
-from xgboost.testing.updater import get_basescore
 
 import xgboost as xgb
+from xgboost.testing.updater import get_basescore
 
 
 def check_init_estimation_clf(tree_method: str, client: Client) -> None:

python-package/xgboost/testing/data.py

@@ -2,6 +2,7 @@
 from typing import Any, Generator, Tuple, Union
 
 import numpy as np
+
 from xgboost.data import pandas_pyarrow_mapper
 
 

python-package/xgboost/testing/shared.py

@@ -8,9 +8,9 @@
 from typing import Any, Callable, Dict, Type
 
 import numpy as np
-from xgboost._typing import ArrayLike
 
 import xgboost as xgb
+from xgboost._typing import ArrayLike
 
 
 def validate_leaf_output(leaf: np.ndarray, num_parallel_tree: int) -> None:

python-package/xgboost/testing/updater.py

@@ -4,9 +4,9 @@
 from typing import Dict
 
 import numpy as np
-import xgboost.testing as tm
 
 import xgboost as xgb
+import xgboost.testing as tm
 
 
 def get_basescore(model: xgb.XGBModel) -> float:
@@ -78,6 +78,7 @@ def check_quantile_loss(tree_method: str, weighted: bool) -> None:
     """Test for quantile loss."""
     from sklearn.datasets import make_regression
     from sklearn.metrics import mean_pinball_loss
+
     from xgboost.sklearn import _metric_decorator
 
     n_samples = 4096

tests/ci_build/lint_python.py

@@ -3,12 +3,15 @@
 import subprocess
 import sys
 from multiprocessing import Pool, cpu_count
-from typing import Dict, Tuple
+from typing import Dict, Optional, Tuple
 
 from pylint import epylint
 from test_utils import PY_PACKAGE, ROOT, cd, print_time, record_time
 
 CURDIR = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
+SRCPATH = os.path.normpath(
+    os.path.join(CURDIR, os.path.pardir, os.path.pardir, "python-package")
+)
 
 
 @record_time
@@ -29,7 +32,7 @@ def run_black(rel_path: str) -> bool:
 
 @record_time
 def run_isort(rel_path: str) -> bool:
-    cmd = ["isort", "--check", "--profile=black", rel_path]
+    cmd = ["isort", f"--src={SRCPATH}", "--profile=black", rel_path]
     ret = subprocess.run(cmd).returncode
     if ret != 0:
         subprocess.run(["isort", "--version"])

tests/python-gpu/test_gpu_data_iterator.py

@@ -2,6 +2,7 @@
 
 import pytest
 from hypothesis import given, settings, strategies
+
 from xgboost.testing import no_cupy
 
 sys.path.append("tests/python")

tests/python-gpu/test_gpu_eval_metrics.py

@@ -1,10 +1,10 @@
 import sys
 
 import pytest
-from xgboost.testing.metrics import check_quantile_error
 
 import xgboost
 from xgboost import testing as tm
+from xgboost.testing.metrics import check_quantile_error
 
 sys.path.append("tests/python")
 import test_eval_metrics as test_em  # noqa

tests/python-gpu/test_gpu_prediction.py

@@ -3,10 +3,10 @@
 import numpy as np
 import pytest
 from hypothesis import assume, given, settings, strategies
-from xgboost.compat import PANDAS_INSTALLED
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.compat import PANDAS_INSTALLED
 
 if PANDAS_INSTALLED:
     from hypothesis.extra.pandas import column, data_frames, range_indexes

tests/python-gpu/test_gpu_updaters.py

@@ -4,11 +4,11 @@
 import numpy as np
 import pytest
 from hypothesis import assume, given, note, settings, strategies
-from xgboost.testing.params import cat_parameter_strategy, hist_parameter_strategy
-from xgboost.testing.updater import check_init_estimation, check_quantile_loss
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.params import cat_parameter_strategy, hist_parameter_strategy
+from xgboost.testing.updater import check_init_estimation, check_quantile_loss
 
 sys.path.append("tests/python")
 import test_updaters as test_up

tests/python-gpu/test_gpu_with_sklearn.py

@@ -5,10 +5,10 @@
 
 import numpy as np
 import pytest
-from xgboost.testing.ranking import run_ranking_qid_df
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.ranking import run_ranking_qid_df
 
 sys.path.append("tests/python")
 import test_with_sklearn as twskl  # noqa

tests/python/test_data_iterator.py

@@ -4,11 +4,11 @@
 import pytest
 from hypothesis import given, settings, strategies
 from scipy.sparse import csr_matrix
-from xgboost.data import SingleBatchInternalIter as SingleBatch
-from xgboost.testing import IteratorForTest, make_batches, non_increasing
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.data import SingleBatchInternalIter as SingleBatch
+from xgboost.testing import IteratorForTest, make_batches, non_increasing
 
 pytestmark = tm.timeout(30)
 

tests/python/test_dmatrix.py

@@ -6,10 +6,10 @@
 import scipy.sparse
 from hypothesis import given, settings, strategies
 from scipy.sparse import csr_matrix, rand
-from xgboost.testing.data import np_dtypes
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.data import np_dtypes
 
 rng = np.random.RandomState(1)
 

tests/python/test_early_stopping.py

@@ -1,9 +1,9 @@
 import numpy as np
 import pytest
-from xgboost.testing.updater import get_basescore
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.updater import get_basescore
 
 rng = np.random.RandomState(1994)
 

tests/python/test_eval_metrics.py

@@ -1,9 +1,9 @@
 import numpy as np
 import pytest
-from xgboost.testing.metrics import check_quantile_error
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.metrics import check_quantile_error
 
 rng = np.random.RandomState(1337)
 

tests/python/test_predict.py

@@ -5,11 +5,11 @@
 import pandas as pd
 import pytest
 from scipy import sparse
-from xgboost.testing.data import np_dtypes, pd_dtypes
-from xgboost.testing.shared import validate_leaf_output
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.data import np_dtypes, pd_dtypes
+from xgboost.testing.shared import validate_leaf_output
 
 
 def run_threaded_predict(X, rows, predict_func):

tests/python/test_quantile_dmatrix.py

@@ -4,6 +4,8 @@
 import pytest
 from hypothesis import given, settings, strategies
 from scipy import sparse
+
+import xgboost as xgb
 from xgboost.testing import (
     IteratorForTest,
     make_batches,
@@ -15,8 +17,6 @@
 )
 from xgboost.testing.data import np_dtypes
 
-import xgboost as xgb
-
 
 class TestQuantileDMatrix:
     def test_basic(self) -> None:

tests/python/test_ranking.py

@@ -6,10 +6,10 @@
 import pytest
 from hypothesis import given, note, settings
 from scipy.sparse import csr_matrix
-from xgboost.testing.params import lambdarank_parameter_strategy
 
 import xgboost
 from xgboost import testing as tm
+from xgboost.testing.params import lambdarank_parameter_strategy
 
 
 def test_ranking_with_unweighted_data():

tests/python/test_updaters.py

@@ -5,16 +5,16 @@
 import numpy as np
 import pytest
 from hypothesis import given, note, settings, strategies
+
+import xgboost as xgb
+from xgboost import testing as tm
 from xgboost.testing.params import (
     cat_parameter_strategy,
     exact_parameter_strategy,
     hist_parameter_strategy,
 )
 from xgboost.testing.updater import check_init_estimation, check_quantile_loss
 
-import xgboost as xgb
-from xgboost import testing as tm
-
 
 def train_result(param, dmat, num_rounds):
     result = {}

tests/python/test_with_pandas.py

@@ -3,10 +3,10 @@
 import numpy as np
 import pytest
 from test_dmatrix import set_base_margin_info
-from xgboost.testing.data import pd_arrow_dtypes, pd_dtypes
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.data import pd_arrow_dtypes, pd_dtypes
 
 try:
     import pandas as pd