Require isort on all Python files. (#8420)

demo/CLI/binary_classification/mknfold.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
-import sys
 import random
+import sys
 
 if len(sys.argv) < 2:
     print ('Usage:<filename> <k> [nfold = 5]')

demo/CLI/regression/mknfold.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
-import sys
 import random
+import sys
 
 if len(sys.argv) < 2:
     print('Usage:<filename> <k> [nfold = 5]')

demo/CLI/yearpredMSD/csv2libsvm.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import sys
+
 fo = open(sys.argv[2], 'w')
 
 for l in open(sys.argv[1]):

demo/aft_survival/aft_survival_demo.py

@@ -6,9 +6,11 @@ Demo for survival analysis (regression). using Accelerated Failure Time (AFT) mo
 """
 
 import os
-from sklearn.model_selection import ShuffleSplit
-import pandas as pd
+
 import numpy as np
+import pandas as pd
+from sklearn.model_selection import ShuffleSplit
+
 import xgboost as xgb
 
 # The Veterans' Administration Lung Cancer Trial

demo/aft_survival/aft_survival_demo_with_optuna.py

@@ -6,11 +6,12 @@ Demo for survival analysis (regression) using Accelerated Failure Time (AFT) mod
 using Optuna to tune hyperparameters
 
 """
-from sklearn.model_selection import ShuffleSplit
-import pandas as pd
 import numpy as np
-import xgboost as xgb
 import optuna
+import pandas as pd
+from sklearn.model_selection import ShuffleSplit
+
+import xgboost as xgb
 
 # The Veterans' Administration Lung Cancer Trial
 # The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)

demo/aft_survival/aft_survival_viz_demo.py

@@ -6,9 +6,10 @@ This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The
 model starts out as a flat line and evolves into a step function in order to account for
 all ranged labels.
 """
-import numpy as np
-import xgboost as xgb
 import matplotlib.pyplot as plt
+import numpy as np
+
+import xgboost as xgb
 
 plt.rcParams.update({'font.size': 13})
 

demo/dask/cpu_survival.py

@@ -4,12 +4,14 @@ Example of training survival model with Dask on CPU
 
 """
 
-import xgboost as xgb
 import os
-from xgboost.dask import DaskDMatrix
+
 import dask.dataframe as dd
-from dask.distributed import Client
-from dask.distributed import LocalCluster
+from dask.distributed import Client, LocalCluster
+from xgboost.dask import DaskDMatrix
+
+import xgboost as xgb
+
 
 def main(client):
     # Load an example survival data from CSV into a Dask data frame.

demo/dask/cpu_training.py

@@ -3,11 +3,11 @@ Example of training with Dask on CPU
 ====================================
 
 """
-import xgboost as xgb
-from xgboost.dask import DaskDMatrix
-from dask.distributed import Client
-from dask.distributed import LocalCluster
 from dask import array as da
+from dask.distributed import Client, LocalCluster
+from xgboost.dask import DaskDMatrix
+
+import xgboost as xgb
 
 
 def main(client):

demo/dask/dask_callbacks.py

@@ -3,12 +3,12 @@ Example of using callbacks with Dask
 ====================================
 """
 import numpy as np
-import xgboost as xgb
-from xgboost.dask import DaskDMatrix
-from dask.distributed import Client
-from dask.distributed import LocalCluster
+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
 
 
 def probability_for_going_backward(epoch):

demo/dask/gpu_training.py

@@ -2,14 +2,15 @@
 Example of training with Dask on GPU
 ====================================
 """
-from dask_cuda import LocalCUDACluster
 import dask_cudf
-from dask.distributed import Client
 from dask import array as da
 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/dask/sklearn_cpu_training.py

@@ -2,9 +2,9 @@
 Use scikit-learn regressor interface with CPU histogram tree method
 ===================================================================
 """
-from dask.distributed import Client
-from dask.distributed import LocalCluster
 from dask import array as da
+from dask.distributed import Client, LocalCluster
+
 import xgboost
 
 

demo/dask/sklearn_gpu_training.py

@@ -3,10 +3,12 @@ Use scikit-learn regressor interface with GPU histogram tree method
 ===================================================================
 """
 
+from dask import array as da
 from dask.distributed import Client
+
 # It's recommended to use dask_cuda for GPU assignment
 from dask_cuda import LocalCUDACluster
-from dask import array as da
+
 import xgboost
 
 

demo/gpu_acceleration/cover_type.py

@@ -1,7 +1,9 @@
-import xgboost as xgb
+import time
+
 from sklearn.datasets import fetch_covtype
 from sklearn.model_selection import train_test_split
-import time
+
+import xgboost as xgb
 
 # Fetch dataset using sklearn
 cov = fetch_covtype()

demo/guide-python/basic_walkthrough.py

@@ -9,13 +9,14 @@ interfaces in the Python package like scikit-learn interface and Dask interface.
 See :doc:`/python/python_intro` and :doc:`/tutorials/index` for other references.
 
 """
-import numpy as np
-import pickle
-import xgboost as xgb
 import os
+import pickle
 
+import numpy as np
 from sklearn.datasets import load_svmlight_file
 
+import xgboost as xgb
+
 # Make sure the demo knows where to load the data.
 CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
 XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))

demo/guide-python/boost_from_prediction.py

@@ -3,8 +3,8 @@ Demo for boosting from prediction
 =================================
 """
 import os
-import xgboost as xgb
 
+import xgboost as xgb
 
 CURRENT_DIR = os.path.dirname(__file__)
 dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))

demo/guide-python/callbacks.py

@@ -4,14 +4,16 @@ Demo for using and defining callback functions
 
     .. versionadded:: 1.3.0
 '''
-import xgboost as xgb
-import tempfile
+import argparse
 import os
+import tempfile
+
 import numpy as np
+from matplotlib import pyplot as plt
 from sklearn.datasets import load_breast_cancer
 from sklearn.model_selection import train_test_split
-from matplotlib import pyplot as plt
-import argparse
+
+import xgboost as xgb
 
 
 class Plotting(xgb.callback.TrainingCallback):

demo/guide-python/continuation.py

@@ -3,11 +3,13 @@ Demo for training continuation
 ==============================
 """
 
-from sklearn.datasets import load_breast_cancer
-import xgboost
+import os
 import pickle
 import tempfile
-import os
+
+from sklearn.datasets import load_breast_cancer
+
+import xgboost
 
 
 def training_continuation(tmpdir: str, use_pickle: bool) -> None:

demo/guide-python/cross_validation.py

@@ -3,7 +3,9 @@ Demo for using cross validation
 ===============================
 """
 import os
+
 import numpy as np
+
 import xgboost as xgb
 
 # load data in do training

demo/guide-python/custom_rmsle.py

@@ -14,14 +14,16 @@ The `SLE` objective reduces impact of outliers in training dataset, hence here w
 compare its performance with standard squared error.
 
 """
-import numpy as np
-import xgboost as xgb
-from typing import Tuple, Dict, List
-from time import time
 import argparse
+from time import time
+from typing import Dict, List, Tuple
+
 import matplotlib
+import numpy as np
 from matplotlib import pyplot as plt
 
+import xgboost as xgb
+
 # shape of generated data.
 kRows = 4096
 kCols = 16

demo/guide-python/custom_softmax.py

@@ -10,11 +10,13 @@ See :doc:`/tutorials/custom_metric_obj` for detailed tutorial and notes.
 
 '''
 
-import numpy as np
-import xgboost as xgb
-from matplotlib import pyplot as plt
 import argparse
 
+import numpy as np
+from matplotlib import pyplot as plt
+
+import xgboost as xgb
+
 np.random.seed(1994)
 
 kRows = 100

demo/guide-python/evals_result.py

@@ -3,6 +3,7 @@ This script demonstrate how to access the eval metrics
 ======================================================
 """
 import os
+
 import xgboost as xgb
 
 CURRENT_DIR = os.path.dirname(__file__)

demo/guide-python/external_memory.py

@@ -12,11 +12,13 @@ See :doc:`the tutorial </tutorials/external_memory>` for more details.
 
 """
 import os
-import xgboost
-from typing import Callable, List, Tuple
-from sklearn.datasets import make_regression
 import tempfile
+from typing import Callable, List, Tuple
+
 import numpy as np
+from sklearn.datasets import make_regression
+
+import xgboost
 
 
 def make_batches(

demo/guide-python/feature_weights.py

@@ -5,11 +5,13 @@ Demo for using feature weight to change column sampling
     .. versionadded:: 1.3.0
 '''
 
-import numpy as np
-import xgboost
-from matplotlib import pyplot as plt
 import argparse
 
+import numpy as np
+from matplotlib import pyplot as plt
+
+import xgboost
+
 
 def main(args):
     rng = np.random.RandomState(1994)

demo/guide-python/gamma_regression.py

@@ -2,9 +2,10 @@
 Demo for gamma regression
 =========================
 """
-import xgboost as xgb
 import numpy as np
 
+import xgboost as xgb
+
 #  this script demonstrates how to fit gamma regression model (with log link function)
 #  in xgboost, before running the demo you need to generate the autoclaims dataset
 #  by running gen_autoclaims.R located in xgboost/demo/data.

demo/guide-python/generalized_linear_model.py

@@ -3,7 +3,9 @@ Demo for GLM
 ============
 """
 import os
+
 import xgboost as xgb
+
 ##
 #  this script demonstrate how to fit generalized linear model in xgboost
 #  basically, we are using linear model, instead of tree for our boosters

demo/guide-python/multioutput_regression.py

@@ -10,10 +10,11 @@ See :doc:`/tutorials/multioutput` for more information.
 """
 
 import argparse
-from typing import Dict, Tuple, List
+from typing import Dict, List, Tuple
 
 import numpy as np
 from matplotlib import pyplot as plt
+
 import xgboost as xgb
 
 

demo/guide-python/predict_first_ntree.py

@@ -3,10 +3,12 @@ Demo for prediction using number of trees
 =========================================
 """
 import os
+
 import numpy as np
-import xgboost as xgb
 from sklearn.datasets import load_svmlight_file
 
+import xgboost as xgb
+
 CURRENT_DIR = os.path.dirname(__file__)
 train = os.path.join(CURRENT_DIR, "../data/agaricus.txt.train")
 test = os.path.join(CURRENT_DIR, "../data/agaricus.txt.test")

demo/guide-python/predict_leaf_indices.py

@@ -3,6 +3,7 @@ Demo for obtaining leaf index
 =============================
 """
 import os
+
 import xgboost as xgb
 
 # load data in do training

demo/guide-python/quantile_data_iterator.py

@@ -17,10 +17,11 @@ using `itertools.tee` might incur significant memory usage according to:
 
 '''
 
-import xgboost
 import cupy
 import numpy
 
+import xgboost
+
 COLS = 64
 ROWS_PER_BATCH = 1000            # data is splited by rows
 BATCHES = 32

demo/guide-python/sklearn_evals_result.py

@@ -3,10 +3,11 @@ Demo for accessing the xgboost eval metrics by using sklearn interface
 ======================================================================
 """
 
-import xgboost as xgb
 import numpy as np
 from sklearn.datasets import make_hastie_10_2
 
+import xgboost as xgb
+
 X, y = make_hastie_10_2(n_samples=2000, random_state=42)
 
 # Map labels from {-1, 1} to {0, 1}

demo/guide-python/sklearn_examples.py

@@ -7,12 +7,13 @@ Created on 1 Apr 2015
 @author: Jamie Hall
 '''
 import pickle
-import xgboost as xgb
 
 import numpy as np
-from sklearn.model_selection import KFold, train_test_split, GridSearchCV
+from sklearn.datasets import fetch_california_housing, load_digits, load_iris
 from sklearn.metrics import confusion_matrix, mean_squared_error
-from sklearn.datasets import load_iris, load_digits, fetch_california_housing
+from sklearn.model_selection import GridSearchCV, KFold, train_test_split
+
+import xgboost as xgb
 
 rng = np.random.RandomState(31337)
 

demo/guide-python/sklearn_parallel.py

@@ -2,11 +2,13 @@
 Demo for using xgboost with sklearn
 ===================================
 """
-from sklearn.model_selection import GridSearchCV
-from sklearn.datasets import fetch_california_housing
-import xgboost as xgb
 import multiprocessing
 
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import GridSearchCV
+
+import xgboost as xgb
+
 if __name__ == "__main__":
     print("Parallel Parameter optimization")
     X, y = fetch_california_housing(return_X_y=True)

demo/guide-python/update_process.py

@@ -7,9 +7,10 @@ experiment.
 
 """
 
-import xgboost as xgb
-from sklearn.datasets import fetch_california_housing
 import numpy as np
+from sklearn.datasets import fetch_california_housing
+
+import xgboost as xgb
 
 
 def main():

demo/kaggle-higgs/higgs-cv.py

@@ -1,5 +1,6 @@
 #!/usr/bin/python
 import numpy as np
+
 import xgboost as xgb
 
 ### load data in do training

demo/kaggle-higgs/higgs-pred.py

@@ -1,6 +1,7 @@
 #!/usr/bin/python
 # make prediction
 import numpy as np
+
 import xgboost as xgb
 
 # path to where the data lies

demo/kaggle-higgs/speedtest.py

@@ -1,9 +1,12 @@
 #!/usr/bin/python
 # this is the example script to use xgboost to train
-import numpy as np
-import xgboost as xgb
-from sklearn.ensemble import GradientBoostingClassifier
 import time
+
+import numpy as np
+from sklearn.ensemble import GradientBoostingClassifier
+
+import xgboost as xgb
+
 test_size = 550000
 
 # path to where the data lies

demo/multiclass_classification/train.py

@@ -3,6 +3,7 @@
 from __future__ import division
 
 import numpy as np
+
 import xgboost as xgb
 
 # label need to be 0 to num_class -1

demo/nvflare/custom/controller.py

@@ -10,7 +10,6 @@ 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
 
 

demo/nvflare/custom/trainer.py

@@ -1,7 +1,7 @@
 import os
 
 from nvflare.apis.executor import Executor
-from nvflare.apis.fl_constant import ReturnCode, FLContextKey
+from nvflare.apis.fl_constant import FLContextKey, ReturnCode
 from nvflare.apis.fl_context import FLContext
 from nvflare.apis.shareable import Shareable, make_reply
 from nvflare.apis.signal import Signal

demo/rank/rank.py

@@ -1,8 +1,8 @@
 #!/usr/bin/python
-import xgboost as xgb
-from xgboost import DMatrix
 from sklearn.datasets import load_svmlight_file
 
+import xgboost as xgb
+from xgboost import DMatrix
 
 #  This script demonstrate how to do ranking with xgboost.train
 x_train, y_train = load_svmlight_file("mq2008.train")

demo/rank/rank_sklearn.py

@@ -1,7 +1,8 @@
 #!/usr/bin/python
-import xgboost as xgb
 from sklearn.datasets import load_svmlight_file
 
+import xgboost as xgb
+
 #  This script demonstrate how to do ranking with XGBRanker
 x_train, y_train = load_svmlight_file("mq2008.train")
 x_valid, y_valid = load_svmlight_file("mq2008.vali")

demo/rank/trans_data.py

@@ -1,5 +1,6 @@
 import sys
 
+
 def save_data(group_data,output_feature,output_group):
     if len(group_data) == 0:
         return

demo/rmm_plugin/rmm_mgpu_with_dask.py

@@ -1,8 +1,9 @@
-import xgboost as xgb
-from sklearn.datasets import make_classification
 import dask
 from dask.distributed import Client
 from dask_cuda import LocalCUDACluster
+from sklearn.datasets import make_classification
+
+import xgboost as xgb
 
 
 def main(client):

demo/rmm_plugin/rmm_singlegpu.py

@@ -1,7 +1,8 @@
-import xgboost as xgb
 import rmm
 from sklearn.datasets import make_classification
 
+import xgboost as xgb
+
 # Initialize RMM pool allocator
 rmm.reinitialize(pool_allocator=True)
 # Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md

dev/prepare_jvm_release.py

@@ -1,16 +1,17 @@
-import re
-import os
-import sys
-import platform
-import errno
 import argparse
-import subprocess
+import errno
 import glob
+import os
+import platform
+import re
 import shutil
+import subprocess
+import sys
 import tempfile
 import zipfile
-from urllib.request import urlretrieve
 from contextlib import contextmanager
+from urllib.request import urlretrieve
+
 
 def normpath(path):
     """Normalize UNIX path to a native path."""

dev/query_contributors.py

@@ -1,10 +1,11 @@
 """Query list of all contributors and reviewers in a release"""
 
-from sh.contrib import git
-import sys
-import re
-import requests
 import json
+import re
+import sys
+
+import requests
+from sh.contrib import git
 
 if len(sys.argv) != 5:
     print(f'Usage: {sys.argv[0]} [starting commit/tag] [ending commit/tag] [GitHub username] ' +

dev/release-py-r.py

@@ -2,14 +2,15 @@
 
 tqdm, sh are required to run this script.
 """
-from urllib.request import urlretrieve
 import argparse
-from typing import List, Optional
-from sh.contrib import git
-from packaging import version
-import subprocess
-import tqdm
 import os
+import subprocess
+from typing import List, Optional
+from urllib.request import urlretrieve
+
+import tqdm
+from packaging import version
+from sh.contrib import git
 
 # The package building is managed by Jenkins CI.
 PREFIX = "https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds/release_"

doc/conf.py

@@ -11,14 +11,15 @@
 #
 # All configuration values have a default; values that are commented out
 # serve to show the default.
-from subprocess import call
-from sh.contrib import git
-import urllib.request
-from urllib.error import HTTPError
-import sys
-import re
 import os
+import re
 import subprocess
+import sys
+import urllib.request
+from subprocess import call
+from urllib.error import HTTPError
+
+from sh.contrib import git
 
 git_branch = os.getenv('SPHINX_GIT_BRANCH', default=None)
 if not git_branch:

doc/sphinx_util.py

@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 """Helper utility function for customization."""
-import sys
 import os
 import subprocess
+import sys
 
 READTHEDOCS_BUILD = (os.environ.get('READTHEDOCS', None) is not None)
 

python-package/setup.py

@@ -1,13 +1,14 @@
 """Setup xgboost package."""
+import logging
 import os
 import shutil
 import subprocess
-import logging
-from typing import Optional, List
 import sys
 from platform import system
-from setuptools import setup, find_packages, Extension
-from setuptools.command import build_ext, sdist, install_lib, install
+from typing import List, Optional
+
+from setuptools import Extension, find_packages, setup
+from setuptools.command import build_ext, install, install_lib, sdist
 
 # You can't use `pip install .` as pip copies setup.py to a temporary
 # directory, parent directory is no longer reachable (isolated build) .

python-package/xgboost/callback.py

@@ -6,17 +6,28 @@
 
 """
 
-from abc import ABC
 import collections
 import os
 import pickle
-from typing import Callable, List, Optional, Union, Dict, Tuple, TypeVar, cast, Sequence, Any
+from abc import ABC
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    TypeVar,
+    Union,
+    cast,
+)
+
 import numpy
 
 from . import collective
 from .core import Booster, DMatrix, XGBoostError, _get_booster_layer_trees
 
-
 __all__ = [
     "TrainingCallback",
     "LearningRateScheduler",

python-package/xgboost/collective.py

@@ -4,12 +4,12 @@ import json
 import logging
 import pickle
 from enum import IntEnum, unique
-from typing import Any, List, Dict
+from typing import Any, Dict, List
 
 import numpy as np
 
 from ._typing import _T
-from .core import _LIB, _check_call, c_str, py_str, from_pystr_to_cstr
+from .core import _LIB, _check_call, c_str, from_pystr_to_cstr, py_str
 
 LOGGER = logging.getLogger("[xgboost.collective]")
 

python-package/xgboost/core.py

@@ -282,7 +282,7 @@ def _has_categorical(booster: "Booster", data: DataType) -> bool:
     """Check whether the booster and input data for prediction contain categorical data.
 
     """
-    from .data import _is_pandas_df, _is_cudf_df
+    from .data import _is_cudf_df, _is_pandas_df
     if _is_pandas_df(data) or _is_cudf_df(data):
         ft = booster.feature_types
         if ft is None:
@@ -355,8 +355,7 @@ def ctypes2cupy(cptr: CNumericPtr, length: int, dtype: Type[np.number]) -> CupyT
     """Convert a ctypes pointer array to a cupy array."""
     # pylint: disable=import-error
     import cupy
-    from cupy.cuda.memory import MemoryPointer
-    from cupy.cuda.memory import UnownedMemory
+    from cupy.cuda.memory import MemoryPointer, UnownedMemory
 
     CUPY_TO_CTYPES_MAPPING: Dict[Type[np.number], Type[CNumeric]] = {
         cupy.float32: ctypes.c_float,
@@ -512,8 +511,7 @@ class DataIter(ABC):  # pylint: disable=too-many-instance-attributes
             feature_types: Optional[FeatureTypes] = None,
             **kwargs: Any,
         ) -> None:
-            from .data import dispatch_proxy_set_data
-            from .data import _proxy_transform
+            from .data import _proxy_transform, dispatch_proxy_set_data
 
             new, cat_codes, feature_names, feature_types = _proxy_transform(
                 data,
@@ -732,7 +730,7 @@ class DMatrix:  # pylint: disable=too-many-instance-attributes,too-many-public-m
             self.handle: Optional[ctypes.c_void_p] = None
             return
 
-        from .data import dispatch_data_backend, _is_iter
+        from .data import _is_iter, dispatch_data_backend
 
         if _is_iter(data):
             self._init_from_iter(data, enable_categorical)
@@ -1406,10 +1404,10 @@ class QuantileDMatrix(DMatrix):
         **meta: Any,
     ) -> None:
         from .data import (
-            _is_dlpack,
-            _transform_dlpack,
-            _is_iter,
             SingleBatchInternalIter,
+            _is_dlpack,
+            _is_iter,
+            _transform_dlpack,
         )
 
         if _is_dlpack(data):

python-package/xgboost/data.py

@@ -278,10 +278,7 @@ def _pandas_feature_info(
     enable_categorical: bool,
 ) -> Tuple[Optional[FeatureNames], Optional[FeatureTypes]]:
     import pandas as pd
-    from pandas.api.types import (
-        is_sparse,
-        is_categorical_dtype,
-    )
+    from pandas.api.types import is_categorical_dtype, is_sparse
 
     # handle feature names
     if feature_names is None and meta is None:
@@ -308,10 +305,10 @@ def _pandas_feature_info(
 def is_nullable_dtype(dtype: PandasDType) -> bool:
     """Wether dtype is a pandas nullable type."""
     from pandas.api.types import (
-        is_integer_dtype,
         is_bool_dtype,
-        is_float_dtype,
         is_categorical_dtype,
+        is_float_dtype,
+        is_integer_dtype,
     )
 
     # dtype: pd.core.arrays.numeric.NumericDtype
@@ -325,6 +322,7 @@ def is_nullable_dtype(dtype: PandasDType) -> bool:
 
 def _pandas_cat_null(data: DataFrame) -> DataFrame:
     from pandas.api.types import is_categorical_dtype
+
     # handle category codes and nullable.
     cat_columns = [
         col
@@ -363,10 +361,7 @@ def _transform_pandas_df(
     meta: Optional[str] = None,
     meta_type: Optional[NumpyDType] = None,
 ) -> Tuple[np.ndarray, Optional[FeatureNames], Optional[FeatureTypes]]:
-    from pandas.api.types import (
-        is_sparse,
-        is_categorical_dtype,
-    )
+    from pandas.api.types import is_categorical_dtype, is_sparse
 
     if not all(
         dtype.name in _pandas_dtype_mapper
@@ -533,8 +528,9 @@ def _from_dt_df(
             ptrs[icol] = ctypes.c_void_p(ptr)
     else:
         # datatable<=0.8.0
-        from datatable.internal import \
-            frame_column_data_r  # pylint: disable=no-name-in-module
+        from datatable.internal import (
+            frame_column_data_r,  # pylint: disable=no-name-in-module
+        )
         for icol in range(data.ncols):
             ptrs[icol] = frame_column_data_r(data, icol)
 

python-package/xgboost/libpath.py

@@ -3,8 +3,8 @@
 
 import os
 import platform
-from typing import List
 import sys
+from typing import List
 
 
 class XGBoostLibraryNotFound(Exception):

python-package/xgboost/plotting.py

@@ -2,9 +2,9 @@
 # pylint: disable=too-many-branches
 # coding: utf-8
 """Plotting Library."""
-from io import BytesIO
 import json
-from typing import Optional, Any
+from io import BytesIO
+from typing import Any, Optional
 
 import numpy as np
 
@@ -269,8 +269,8 @@ def plot_tree(
 
     """
     try:
-        from matplotlib import pyplot as plt
         from matplotlib import image
+        from matplotlib import pyplot as plt
     except ImportError as e:
         raise ImportError('You must install matplotlib to plot tree') from e
 

python-package/xgboost/rabit.py

@@ -2,7 +2,7 @@
 import logging
 import warnings
 from enum import IntEnum, unique
-from typing import Any, TypeVar, Callable, Optional, List
+from typing import Any, Callable, List, Optional, TypeVar
 
 import numpy as np
 

python-package/xgboost/testing/__init__.py

@@ -10,7 +10,6 @@ import os
 import platform
 import socket
 import sys
-import urllib
 import zipfile
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import contextmanager
@@ -29,6 +28,7 @@ from typing import (
     TypedDict,
     Union,
 )
+from urllib import request
 
 import numpy as np
 import pytest
@@ -439,7 +439,7 @@ def get_mq2008(
     src = "https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip"
     target = dpath + "/MQ2008.zip"
     if not os.path.exists(target):
-        urllib.request.urlretrieve(url=src, filename=target)
+        request.urlretrieve(url=src, filename=target)
 
     with zipfile.ZipFile(target, "r") as f:
         f.extractall(path=dpath)

python-package/xgboost/tracker.py

@@ -3,14 +3,13 @@
 This script is a variant of dmlc-core/dmlc_tracker/tracker.py,
 which is a specialized version for xgboost tasks.
 """
+import argparse
+import logging
 import socket
 import struct
-import logging
-from threading import Thread
-import argparse
 import sys
-
-from typing import Dict, List, Tuple, Union, Optional, Set
+from threading import Thread
+from typing import Dict, List, Optional, Set, Tuple, Union
 
 _RingMap = Dict[int, Tuple[int, int]]
 _TreeMap = Dict[int, List[int]]

python-package/xgboost/training.py

@@ -5,15 +5,26 @@
 import copy
 import os
 import warnings
-from typing import Optional, Dict, Any, Union, Tuple, Sequence, List, cast, Iterable
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union, cast
 
 import numpy as np
 
-from .callback import TrainingCallback, CallbackContainer, EvaluationMonitor, EarlyStopping
-from .core import Booster, DMatrix, XGBoostError, _deprecate_positional_args
-from .core import Metric, Objective
-from .compat import SKLEARN_INSTALLED, XGBStratifiedKFold, DataFrame
-from ._typing import Callable, FPreProcCallable, BoosterParam
+from ._typing import BoosterParam, Callable, FPreProcCallable
+from .callback import (
+    CallbackContainer,
+    EarlyStopping,
+    EvaluationMonitor,
+    TrainingCallback,
+)
+from .compat import SKLEARN_INSTALLED, DataFrame, XGBStratifiedKFold
+from .core import (
+    Booster,
+    DMatrix,
+    Metric,
+    Objective,
+    XGBoostError,
+    _deprecate_positional_args,
+)
 
 _CVFolds = Sequence["CVPack"]
 

tests/ci_build/insert_vcomp140.py

@@ -1,7 +1,7 @@
-import sys
-import re
-import zipfile
 import glob
+import re
+import sys
+import zipfile
 
 if len(sys.argv) != 2:
     print('Usage: {} [wheel]'.format(sys.argv[0]))

tests/ci_build/lint_python.py

@@ -12,16 +12,31 @@ CURDIR = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
 PROJECT_ROOT = os.path.normpath(os.path.join(CURDIR, os.path.pardir, os.path.pardir))
 
 
-def run_formatter(rel_path: str) -> bool:
-    path = os.path.join(PROJECT_ROOT, rel_path)
-    isort_ret = subprocess.run(["isort", "--check", "--profile=black", path]).returncode
-    black_ret = subprocess.run(["black", "--check", rel_path]).returncode
-    if isort_ret != 0 or black_ret != 0:
-        msg = (
-            "Please run the following command on your machine to address the format"
-            f" errors:\n isort --profile=black {rel_path}\n black {rel_path}\n"
-        )
-        print(msg, file=sys.stdout)
+def run_black(rel_path: str) -> bool:
+    cmd = ["black", "-q", "--check", rel_path]
+    ret = subprocess.run(cmd).returncode
+    if ret != 0:
+        subprocess.run(["black", "--version"])
+        msg = """
+Please run the following command on your machine to address the formatting error:
+
+        """
+        msg += " ".join(cmd)
+        print(msg, file=sys.stderr)
+        return False
+    return True
+
+
+def run_isort(rel_path: str) -> bool:
+    cmd = ["isort", "--check", "--profile=black", rel_path]
+    ret = subprocess.run(cmd).returncode
+    if ret != 0:
+        msg = """
+Please run the following command on your machine to address the formatting error:
+
+        """
+        msg += " ".join(cmd)
+        print(msg, file=sys.stderr)
         return False
     return True
 
@@ -114,8 +129,8 @@ if __name__ == "__main__":
     parser.add_argument("--pylint", type=int, choices=[0, 1], default=1)
     args = parser.parse_args()
     if args.format == 1:
-        if not all(
-            run_formatter(path)
+        black_results = [
+            run_black(path)
             for path in [
                 # core
                 "python-package/xgboost/__init__.py",
@@ -141,7 +156,28 @@ if __name__ == "__main__":
                 "demo/guide-python/categorical.py",
                 "demo/guide-python/spark_estimator_examples.py",
             ]
-        ):
+        ]
+        if not all(black_results):
+            sys.exit(-1)
+
+        isort_results = [
+            run_isort(path)
+            for path in [
+                # core
+                "python-package/",
+                # tests
+                "tests/test_distributed/",
+                "tests/python/",
+                "tests/python-gpu/",
+                "tests/ci_build/",
+                # demo
+                "demo/",
+                # misc
+                "dev/",
+                "doc/",
+            ]
+        ]
+        if not all(black_results):
             sys.exit(-1)
 
     if args.type_check == 1:

tests/ci_build/rename_whl.py

@@ -1,5 +1,5 @@
-import sys
 import os
+import sys
 from contextlib import contextmanager
 
 

tests/ci_build/tidy.py

@@ -1,15 +1,16 @@
 #!/usr/bin/env python
-import subprocess
-import yaml
-import json
-from multiprocessing import Pool, cpu_count
-import shutil
-import os
-import sys
-import re
 import argparse
+import json
+import os
+import re
+import shutil
+import subprocess
+import sys
+from multiprocessing import Pool, cpu_count
 from time import time
 
+import yaml
+
 
 def call(args):
     '''Subprocess run wrapper.'''

tests/python-gpu/test_gpu_updaters.py

@@ -4,13 +4,10 @@ from typing import Any, Dict
 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
 
 import xgboost as xgb
 from xgboost import testing as tm
-from xgboost.testing.params import (
-    hist_parameter_strategy,
-    cat_parameter_strategy,
-)
 
 sys.path.append("tests/python")
 import test_updaters as test_up

tests/python/test_demos.py

@@ -1,7 +1,7 @@
 import os
 import subprocess
-import tempfile
 import sys
+import tempfile
 
 import pytest
 

tests/python/test_tracker.py

@@ -5,9 +5,8 @@ import numpy as np
 import pytest
 
 import xgboost as xgb
-from xgboost import RabitTracker
+from xgboost import RabitTracker, collective
 from xgboost import testing as tm
-from xgboost import collective
 
 if sys.platform.startswith("win"):
     pytest.skip("Skipping dask tests on Windows", allow_module_level=True)

tests/python/test_updaters.py

@@ -5,14 +5,14 @@ from typing import Any, Dict
 import numpy as np
 import pytest
 from hypothesis import given, note, settings, strategies
+from xgboost.testing.params import (
+    cat_parameter_strategy,
+    exact_parameter_strategy,
+    hist_parameter_strategy,
+)
 
 import xgboost as xgb
 from xgboost import testing as tm
-from xgboost.testing.params import (
-    exact_parameter_strategy,
-    hist_parameter_strategy,
-    cat_parameter_strategy,
-)
 
 
 def train_result(param, dmat, num_rounds):

tests/test_distributed/test_with_dask/test_with_dask.py

@@ -12,7 +12,7 @@ from itertools import starmap
 from math import ceil
 from operator import attrgetter, getitem
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Type, Union, Generator
+from typing import Any, Dict, Generator, Optional, Tuple, Type, Union
 
 import hypothesis
 import numpy as np