FEA Add examples recommender system

continuous_integration/install.sh

@@ -12,7 +12,7 @@ if [ "$DISTRIB" == "mamba" ]; then
     if [ "$PLATFORM" != "Linux" ]; then
         conda remove -y memory_profiler
     fi
-    PIP_DEPENDENCIES="jupyterlite-sphinx>=0.8.0,<0.9.0 jupyterlite-pyodide-kernel<0.1.0 libarchive-c"
+    PIP_DEPENDENCIES="jupyterlite-sphinx>=0.8.0,<0.9.0 jupyterlite-pyodide-kernel<0.1.0 libarchive-c numpy"
 elif [ "$DISTRIB" == "minimal" ]; then
     PIP_DEPENDENCIES=""
 elif [ "$DISTRIB" == "pip" ]; then

doc/configuration.rst

@@ -45,6 +45,7 @@ file:
 - ``reset_modules_order`` (:ref:`reset_modules_order`)
 - ``abort_on_example_error`` (:ref:`abort_on_first`)
 - ``only_warn_on_example_error`` (:ref:`warning_on_error`)
+- ``recommender`` (:ref:`recommend_examples`)
 - ``expected_failing_examples`` (:ref:`dont_fail_exit`)
 - ``min_reported_time`` (:ref:`min_reported_time`)
 - ``show_memory`` (:ref:`show_memory`)
@@ -1785,6 +1786,57 @@ flag is passed to ``sphinx-build``. This can be enabled by setting::
     }
 
 
+.. _recommend_examples:
+
+Enabling the example recommender system
+=======================================
+
+Sphinx-Gallery can be configured to generate content-based recommendations for
+an example gallery. A list of related examples is automatically generated by
+computing the closest examples in the `TF-IDF
+<https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_ space of their text contents.
+Only examples within a single gallery (including it's sub-galleries) are used to
+compute the closest examples. The most similar content is then displayed at the bottom
+of each example as a set of thumbnails.
+
+The recommender system can be enabled by setting ``enable`` to ``True``. To
+configure it, pass a dictionary to the ``sphinx_gallery_conf``, e.g.::
+
+    sphinx_gallery_conf = {
+        ...
+        "recommender": {"enable": True, "n_examples": 5, "min_df": 3, "max_df": 0.9},
+    }
+
+The only necessary parameter is ``enable``. If any other parameters is not
+specified, the default value is used. Below is a more complete explanation of
+each field:
+
+enable (type: bool, default: False)
+  Whether to generate recommendations inside the example gallery. Enabling this
+  feature requires adding `numpy` to the dependencies.
+n_examples (type: int, default: 5)
+  Number of most relevant examples to display.
+min_df (type: float in range [0.0, 1.0] | int, default: 3)
+  When building the vocabulary ignore terms that have a document frequency
+  strictly lower than the given threshold. If float, the parameter represents a
+  proportion of documents, integer represents absolute counts. This value is
+  also called cut-off in the literature.
+max_df (type: float in range [0.0, 1.0] | int, default: 0.9)
+  When building the vocabulary ignore terms that have a document frequency
+  strictly higher than the given threshold. If float, the parameter represents a
+  proportion of documents, integer represents absolute counts.
+rubric_header (type: str, default: "Related examples")
+  Customizable rubric header. It can be edited to more descriptive text or to
+  add external links, e.g. to the API doc of the recommender system on the
+  sphinx-gallery documentation.
+
+The parameters ``min_df`` and ``max_df`` can be customized by the user to trim
+the very rare/very common words. This may improve the recommendations quality,
+but more importantly, it spares some computation resources that would be wasted
+on non-informative tokens.
+
+Currently example recommendations are only computed for ``.py`` files.
+
 .. _setting_thumbnail_size:
 
 Setting gallery thumbnail size

setup.py

@@ -30,6 +30,8 @@
 with open("requirements.txt") as fid:
     install_requires = [line.strip() for line in fid if line.strip()]
 
+extras_require = {"recommender": ["numpy"]}
+
 setup(
     name="sphinx-gallery",
     description=description,  # noqa: E501, analysis:ignore
@@ -55,6 +57,7 @@
     author="Óscar Nájera",
     author_email="najera.oscar@gmail.com",
     install_requires=install_requires,
+    extras_require=extras_require,
     python_requires=">=3.8",
     license="3-clause BSD",
     classifiers=[

sphinx_gallery/gen_gallery.py

@@ -18,6 +18,7 @@
 import os
 import pathlib
 from xml.sax.saxutils import quoteattr, escape
+from itertools import chain
 
 from sphinx.errors import ConfigError, ExtensionError
 import sphinx.util
@@ -39,6 +40,7 @@
 from .interactive_example import post_configure_jupyterlite_sphinx
 from .interactive_example import create_jupyterlite_contents
 from .directives import MiniGallery, ImageSg, imagesg_addnode
+from .recommender import ExampleRecommender, _write_recommendations
 
 _KNOWN_CSS = (
     "sg_gallery",
@@ -85,6 +87,7 @@ def __call__(self, gallery_conf, script_vars):
     "download_all_examples": True,
     "abort_on_example_error": False,
     "only_warn_on_example_error": False,
+    "recommender": {"enable": False},
     "failing_examples": {},
     "passing_examples": [],
     "stale_examples": [],  # ones that did not need to be run due to md5sum
@@ -663,6 +666,42 @@ def generate_gallery_rst(app):
             costs += subsection_costs
             write_computation_times(gallery_conf, target_dir, subsection_costs)
 
+        # Build recommendation system
+        if gallery_conf["recommender"]["enable"]:
+            try:
+                import numpy as np  # noqa: F401
+            except ImportError:
+                raise ConfigError("gallery_conf['recommender'] requires numpy")
+
+            recommender_params = copy.deepcopy(gallery_conf["recommender"])
+            recommender_params.pop("enable")
+            recommender_params.pop("rubric_header", None)
+            recommender = ExampleRecommender(**recommender_params)
+
+            gallery_py_files = []
+            # root and subsection directories containing python examples
+            gallery_directories = [gallery_dir_abs_path] + subsecs
+            for current_dir in gallery_directories:
+                src_dir = os.path.join(gallery_dir_abs_path, current_dir)
+                # sort python files to have a deterministic input across call
+                py_files = sorted(
+                    [
+                        fname
+                        for fname in Path(src_dir).iterdir()
+                        if fname.suffix == ".py"
+                    ],
+                    key=gallery_conf["within_subsection_order"](src_dir),
+                )
+                gallery_py_files.append(
+                    [os.path.join(src_dir, fname) for fname in py_files]
+                )
+            # flatten the list of list
+            gallery_py_files = list(chain.from_iterable(gallery_py_files))
+
+            recommender.fit(gallery_py_files)
+            for fname in gallery_py_files:
+                _write_recommendations(recommender, fname, gallery_conf)
+
         # generate toctree with subsections
         if gallery_conf["nested_sections"] is True:
             subsections_toctree = _format_toctree(

sphinx_gallery/gen_rst.py

@@ -226,6 +226,10 @@ def __exit__(self, type_, value, tb):
       :download:`Download Jupyter notebook: {0} <{0}>`
 """
 
+RECOMMENDATIONS_INCLUDE = """\n
+.. include:: {0}.recommendations
+"""
+
 
 def codestr2rst(codestr, lang="python", lineno=None):
     """Return reStructuredText code block from code string."""
@@ -1467,6 +1471,11 @@ def save_rst_example(
 
     example_rst += CODE_DOWNLOAD.format(example_file.name, language)
 
+    if gallery_conf["recommender"]["enable"]:
+        # extract the filename without the extension
+        recommend_fname = Path(example_fname).stem
+        example_rst += RECOMMENDATIONS_INCLUDE.format(recommend_fname)
+
     if gallery_conf["show_signature"]:
         example_rst += SPHX_GLR_SIG