Require isort on all Python files. (#8420)

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})