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

demo/aft_survival/README.rst

@@ -0,0 +1,5 @@
+Survival Analysis Walkthrough
+=============================
+
+This is a collection of examples for using the XGBoost Python package for training
+survival models. For an introduction, see :doc:`/tutorials/aft_survival_analysis`

demo/aft_survival/aft_survival_demo.py

@@ -1,6 +1,10 @@
 """
-Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model
+Demo for survival analysis (regression).
+========================================
+
+Demo for survival analysis (regression). using Accelerated Failure Time (AFT) model.
 """
+
 import os
 from sklearn.model_selection import ShuffleSplit
 import pandas as pd

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

demo/aft_survival/aft_survival_viz_demo.py

@@ -1,9 +1,10 @@
 """
 Visual demo for survival analysis (regression) with Accelerated Failure Time (AFT) model.
+=========================================================================================
 
-This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble model
-starts out as a flat line and evolves into a step function in order to account for all ranged
-labels.
+This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble
+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
@@ -57,7 +58,7 @@ def plot_intermediate_model_callback(env):
     #              the corresponding predicted label (y_pred)
     acc = np.sum(np.logical_and(y_pred >= y_lower, y_pred <= y_upper)/len(X) * 100)
     accuracy_history.append(acc)
-    
+
     # Plot ranged labels as well as predictions by the model
     plt.subplot(5, 3, env.iteration + 1)
     plot_censored_labels(X, y_lower, y_upper)