[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

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

doc/conf.py

@@ -93,9 +93,9 @@ extensions = [
 
 sphinx_gallery_conf = {
     # path to your example scripts
-    "examples_dirs": ["../demo/guide-python", "../demo/dask"],
+    "examples_dirs": ["../demo/guide-python", "../demo/dask", "../demo/aft_survival"],
     # path to where to save gallery generated output
-    "gallery_dirs": ["python/examples", "python/dask-examples"],
+    "gallery_dirs": ["python/examples", "python/dask-examples", "python/survival-examples"],
     "matplotlib_animations": True,
 }
 

doc/python/.gitignore

@@ -1,2 +1,3 @@
 examples
 dask-examples
+survival-examples

doc/python/index.rst

@@ -15,3 +15,4 @@ Contents
   model
   examples/index
   dask-examples/index
+  survival-examples/index

doc/tutorials/aft_survival_analysis.rst

@@ -165,4 +165,4 @@ Currently, you can choose from three probability distributions for ``aft_loss_di
 ``extreme``               :math:`e^z e^{-\exp{z}}`
 ========================= ===========================================
 
-Note that it is not yet possible to set the ranged label using the scikit-learn interface (e.g. :class:`xgboost.XGBRegressor`). For now, you should use :class:`xgboost.train` with :class:`xgboost.DMatrix`.
+Note that it is not yet possible to set the ranged label using the scikit-learn interface (e.g. :class:`xgboost.XGBRegressor`). For now, you should use :class:`xgboost.train` with :class:`xgboost.DMatrix`. For a collection of Python examples, see :doc:`/python/survival-examples/index`