CI doctest after make doc

Author: MatthewSZhang

.github/workflows/ci.yml

@@ -40,6 +40,7 @@ jobs:
     - name: Test with doctest
       shell: bash
       run: |
+        pixi run doc
         CMD=doctest pixi run doc
     - name: Test coverage
       shell: bash
@@ -69,7 +70,7 @@ jobs:
     steps:
     - uses: actions/checkout@v4
     - name: Build wheels
-      uses: pypa/[email protected].0
+      uses: pypa/[email protected].3
       env:
           CIBW_BUILD: cp3*-*
           CIBW_SKIP: pp* *i686* *musllinux* *-macosx_universal2 *-manylinux_ppc64le *-manylinux_s390x

doc/auto_examples/auto_examples_jupyter.zip

@@ -0,0 +1,100 @@
+:orphan:
+
+.. _examples:
+
+Examples
+========
+
+Below is a gallery of examples.
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will compare the robustness of the three feature selection methods on affine transformed features.">
+
+.. only:: html
+
+  .. image:: /auto_examples/images/thumb/sphx_glr_plot_affinity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_plot_affinity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Affine Invariance</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will compare the computational speed of three different feature selection methods: h-correlation based FastCan, eta-cosine based FastCan, and baseline model based on sklearn.cross_decomposition.CCA.">
+
+.. only:: html
+
+  .. image:: /auto_examples/images/thumb/sphx_glr_plot_speed_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_plot_speed.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Computational speed comparison</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will compare the performance of feature selectors on the datasets, which contain redundant features. Here four types of features should be distinguished:">
+
+.. only:: html
+
+  .. image:: /auto_examples/images/thumb/sphx_glr_plot_redundancy_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_plot_redundancy.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Feature selection performance on redundant features</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/plot_affinity
+   /auto_examples/plot_speed
+   /auto_examples/plot_redundancy
+
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-gallery
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_