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