Require isort on all Python files. (#8420)

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"]