Require isort on all Python files. (#8420)

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():