Require isort on all Python files. (#8420)

Author: trivialfis

Diff for: 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]')

Diff for: 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]')

Diff for: 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]):

Diff for: demo/aft_survival/aft_survival_demo.py

@@ -6,9 +6,11 @@
 """
 
 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

Diff for: demo/aft_survival/aft_survival_demo_with_optuna.py

@@ -6,11 +6,12 @@
 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)

Diff for: demo/aft_survival/aft_survival_viz_demo.py

@@ -6,9 +6,10 @@
 model starts out as a flat line and evolves into a step function in order to account for
 all ranged labels.
 """
+import matplotlib.pyplot as plt
 import numpy as np
+
 import xgboost as xgb
-import matplotlib.pyplot as plt
 
 plt.rcParams.update({'font.size': 13})
 

Diff for: demo/dask/cpu_survival.py

@@ -4,12 +4,14 @@
 
 """
 
-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.

Diff for: demo/dask/cpu_training.py

@@ -3,11 +3,11 @@
 ====================================
 
 """
-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):

Diff for: demo/dask/dask_callbacks.py

@@ -3,12 +3,12 @@
 ====================================
 """
 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):

Diff for: 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):

Diff for: 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
 
 

Diff for: demo/dask/sklearn_gpu_training.py

@@ -3,10 +3,12 @@
 ===================================================================
 """
 
+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
 
 

Diff for: 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()

Diff for: demo/guide-python/basic_walkthrough.py

@@ -9,13 +9,14 @@
 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))

Diff for: demo/guide-python/boost_from_prediction.py

@@ -3,8 +3,8 @@
 =================================
 """
 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'))

Diff for: demo/guide-python/callbacks.py

@@ -4,14 +4,16 @@
 
     .. 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):

Diff for: demo/guide-python/continuation.py

@@ -3,11 +3,13 @@
 ==============================
 """
 
-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:

Diff for: demo/guide-python/cross_validation.py

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

Diff for: demo/guide-python/custom_rmsle.py

@@ -14,14 +14,16 @@
 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

Diff for: demo/guide-python/custom_softmax.py

@@ -10,10 +10,12 @@
 
 '''
 
+import argparse
+
 import numpy as np
-import xgboost as xgb
 from matplotlib import pyplot as plt
-import argparse
+
+import xgboost as xgb
 
 np.random.seed(1994)
 

Diff for: demo/guide-python/evals_result.py

@@ -3,6 +3,7 @@
 ======================================================
 """
 import os
+
 import xgboost as xgb
 
 CURRENT_DIR = os.path.dirname(__file__)

Diff for: demo/guide-python/external_memory.py

@@ -12,11 +12,13 @@
 
 """
 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(

Diff for: demo/guide-python/feature_weights.py

@@ -5,10 +5,12 @@
     .. versionadded:: 1.3.0
 '''
 
+import argparse
+
 import numpy as np
-import xgboost
 from matplotlib import pyplot as plt
-import argparse
+
+import xgboost
 
 
 def main(args):

Diff for: 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.

Diff for: demo/guide-python/generalized_linear_model.py

@@ -3,7 +3,9 @@
 ============
 """
 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

Diff for: demo/guide-python/multioutput_regression.py

@@ -10,10 +10,11 @@
 """
 
 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
 
 

Diff for: demo/guide-python/predict_first_ntree.py

@@ -3,10 +3,12 @@
 =========================================
 """
 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")

Diff for: demo/guide-python/predict_leaf_indices.py

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

Diff for: demo/guide-python/quantile_data_iterator.py

@@ -17,10 +17,11 @@
 
 '''
 
-import xgboost
 import cupy
 import numpy
 
+import xgboost
+
 COLS = 64
 ROWS_PER_BATCH = 1000            # data is splited by rows
 BATCHES = 32