[doc] Display survival demos in sphinx doc. [skip ci] (#8328)

demo/aft_survival/aft_survival_demo_with_optuna.py

@@ -1,6 +1,10 @@
 """
-Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model, using Optuna
-to tune hyperparameters
+Demo for survival analysis (regression) with Optuna.
+====================================================
+
+Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model,
+using Optuna to tune hyperparameters
+
 """
 from sklearn.model_selection import ShuffleSplit
 import pandas as pd
@@ -45,7 +49,7 @@ def objective(trial):
     params.update(base_params)
     pruning_callback = optuna.integration.XGBoostPruningCallback(trial, 'valid-aft-nloglik')
     bst = xgb.train(params, dtrain, num_boost_round=10000,
-                    evals=[(dtrain, 'train'), (dvalid, 'valid')], 
+                    evals=[(dtrain, 'train'), (dvalid, 'valid')],
                     early_stopping_rounds=50, verbose_eval=False, callbacks=[pruning_callback])
     if bst.best_iteration >= 25:
         return bst.best_score
@@ -63,7 +67,7 @@ params.update(study.best_trial.params)
 # Re-run training with the best hyperparameter combination
 print('Re-running the best trial... params = {}'.format(params))
 bst = xgb.train(params, dtrain, num_boost_round=10000,
-                evals=[(dtrain, 'train'), (dvalid, 'valid')], 
+                evals=[(dtrain, 'train'), (dvalid, 'valid')],
                 early_stopping_rounds=50)
 
 # Run prediction on the validation set