Documentation

To build the documentation, run the following command in the project root:

python -m invoke docs.build

Usage: inv[oke] [--core-opts] docs.build [--options] [other tasks here ...]

Docstring:
  Build the documentation using Sphinx.

Options:
  -i, --ignore-warnings

Once built, you can visit the documentation locally in your browser.

Note

If you run into nitpicky errors, you can allow a more permissive documentation build with:

python -m invoke docs.build -i

Continuous integration will still run the strict build, so make sure to fix any errors before making a pull request.

Pull Request Artifact

Reviewers and developers can preview the documentation of a pull request by downloading the documentation artifact, extracting, and then opening it in a browser. You can download the documentation artifact from “Pull Request” workflow.

See also

Downloading Workflow Artifact

Docstrings

CapyMOA uses Sphinx to generate documentation from function, class, and module docstring comments. CapyMOA uses the sphinx/reStructuredText style of docstrings. Rather than having type information in the docstring, we prefer to use Python-type hints. This allows for better type checking and IDE support.

Warning

Some parts of our codebase use the wrong docstring format (e.g. Google style, NumPy style, etc.). These are wrong since they are not parsed correctly by Sphinx and display strangely on the website. We are in the process of fixing these. Please do not use these as examples for your own docstrings.

Here is an example of how to write a docstring for a classifier in CapyMOA:

from capymoa.base import Classifier
from capymoa.stream import Schema


class ExampleClassifier(Classifier):
    """One line docstring.

    You may add a multi-line detailed description of the classifier. You
    should include a citation [#example25]_ to the source paper.

    You may include an example of how to use the classifier. This example is
    serves as both documentation and a test for the classifier. Keep in mind
    that these are run as part of the test suite, so they should be kept
    simple, deterministic, and fast.

    >>> from capymoa.datasets import ElectricityTiny
    >>> from capymoa.classifier import ExampleClassifier
    >>> from capymoa.evaluation import prequential_evaluation
    >>> stream = ElectricityTiny()
    >>> learner = ExampleClassifier(stream.get_schema())
    >>> results = prequential_evaluation(stream, learner, max_instances=1000)
    >>> results["cumulative"].accuracy()
    87.9

    You may include a see also section with links to related classes or
    functions. This is useful for users to find related functionality in the
    library.

    .. seealso::

        :func:`capymoa.evaluation.prequential_evaluation`

    .. [#example25] Example, A., Author, B., & Researcher, C. (2025). Example Classifier.
    """

    class_attr = None
    """One-line docstring for ``class_attr``."""

    def __init__(self, schema: Schema):
        """Construct a new ExampleClassifier.

        :param schema: Describes the structure of the data stream.
        """
        super().__init__(schema)

        #: One-line docstring for ``attr_a``.
        self.attr_a = None

        self.attr_b = None
        """Another syntax for a one-line docstring."""

        self.attr_c = None
        """Multi-line docstring for ``attr_c`` attribute.

        It can include multiple lines and is useful for providing detailed
        information about the attribute's purpose and usage.
        """

For exemplars take a look at the docstrings in the AdaptiveRandomForestClassifier or HoeffdingAdaptiveTree classes.

Important

If you use autodocstring for VSCode, set the docstring format to sphinx-notypes in the settings. (autodocstring)

If you use PyCharm, set the docstring format to reStructuredText in the settings. (PyCharm settings)

If you use an AI tool to generate docstrings please ensure that it actually outputs reStructuredText style docstrings. Also go through the docstring and ensure it is concise and correct. You may have luck setting up a project wide prompt (Copilot docs).

Citations

You should reference sources using the reStructuredText footnotes syntax. We prefer footnotes over citations since they are local to the page and do not require a global bibliography. This makes it easier to read the documentation without having to jump between pages.

For example, to reference a source in the text:

CapyMOA is a Python library for efficient machine learning on data
streams [#gomes25]_.

.. [#gomes25] Gomes, H. M., Lee, A., Gunasekara, N., Sun, Y., Cassales, G. W.,
    Liu, J., Heyden, M., Cerqueira, V., Bahri, M., Koh, Y. S., Pfahringer,
    B., & Bifet, A. (2025). CapyMOA: Efficient machine learning for data
    streams in python. CoRR, abs/2502.07432.
    https://doi.org/10.48550/ARXIV.2502.07432

CapyMOA is a Python library for efficient machine learning on data streams [1].

[1]

Gomes, H. M., Lee, A., Gunasekara, N., Sun, Y., Cassales, G. W., Liu, J., Heyden, M., Cerqueira, V., Bahri, M., Koh, Y. S., Pfahringer, B., & Bifet, A. (2025). CapyMOA: Efficient machine learning for data streams in python. CoRR, abs/2502.07432. https://doi.org/10.48550/ARXIV.2502.07432

Cross Reference

You can link to the documentation of a module, class, method, function, attribute, or other programming constructs using the sphinx cross-reference syntax.

Module	:mod:`capymoa.stream capymoa.stream
Class	:class:`capymoa.stream.Stream capymoa.stream.Stream
Method	:meth:`capymoa.stream.Stream.next_instance capymoa.stream.Stream.next_instance()
Function	:func:`capymoa.stream.stream_from_file` capymoa.stream.stream_from_file()
Attribute	:attr:`capymoa.stream.Schema.dataset_name capymoa.stream.Schema.dataset_name

Add the prefix ~ to the name to display the name without the prefixing path:

:meth:`~capymoa.stream.Stream.next_instance`

This will display as next_instance().

See also

Sphinx Cross-referencing

Sphinx Cross-referencing Python Objects

See Also

It can be handy to link to related documentation pages or external resources without explicitly referencing them in the text. This can be done using the sphinx seealso directive.

..  seealso::

    `See Also <https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#directive-seealso>`_
        Documents Sphinx seealso directive.

    `Definition List  <https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html#definition-lists>`_
        Documents reStructuredText definition lists.

See also

See Also

Documents Sphinx seealso directive.

Definition List

Documents reStructuredText definition lists.

Math

You can include LaTex mathematical equations directly with the math directive.

Block equation:

..  math::

    E = mc^2

Inline equation: :math:`E = mc^2`.

Block equation:

\[E = mc^2\]

Inline equation: \(E = mc^2\).

Notebooks

CapyMOA documentation includes Jupyter Notebooks for tutorials, and narrative style documentation. These notebooks are run as tests to ensure they are kept up-to-date. This document explains how to run, render and test notebooks.

• Added to the /notebooks directory.

• Rendered to HTML and included in the documentation of the website using

• To add a notebook to the documentation, add the notebook to the /notebooks directory and add the filename to the toctree in notebooks/index.rst.

• Please check the notebooks are being converted and included in the documentation by building the documentation locally. See Documentation.

• The parser for markdown used by Jupiter Notebooks is different from the one used by nbsphinx. This can lead to markdown rendering unexpectedly you might need to adjust the markdown in the notebooks to render correctly on the website.

  • Bullet points should have a newline after the bullet point.

    * Bullet point 1
    
    * Bullet point 2
    

Slow Notebooks

Some notebooks may take a long time to run. Heres how we handle slow notebooks:

• The NB_FAST environment variable is set to Tue when the notebooks should be run quickly.

• Add hidden cells that check NB_FAST and speed up the notebook by using smaller datasets or fewer iterations.

• For example, you can add the following cell to the top of a notebook to replace some large datasets with smaller ones. You should ensure the cell is hidden on the website (See Hide Cells).

  # This cell is hidden on capymoa.org. See docs/contributing/docs.rst
  from util.nbmock import mock_datasets, is_nb_fast
  if is_nb_fast():
      mock_datasets()
  

Hide Cells

You can remove a cell from being rendered on the website by adding the following to the cell’s metadata:

"metadata": {
    "nbsphinx": "hidden"
}

Testing or Overwriting Notebook Output

The tasks.py defines aliases for running the notebooks as tests or for overwriting the outputs of the notebooks. To run the notebooks as tests:

invoke test.nb # add --help for options

Usage: inv[oke] [--core-opts] test.nb [--options] [other tasks here ...]

Docstring:
  Run the notebooks and check for errors.

  Uses nbmake https://github.com/treebeardtech/nbmake to execute the notebooks
  and check for errors.

  The `--overwrite` flag can be used to overwrite the notebooks with the
  executed output.

Options:
  -k STRING, --k-pattern=STRING   Run only the notebooks that match the
                                  pattern. Same as `pytest -k`
  -n, --no-skip                   Do not skip any notebooks.
  -o, --overwrite                 Overwrite the notebooks with the executed
                                  output. Requires ``--slow``.
  -p, --parallel                  Run the notebooks in parallel.
  -s, --slow                      Run the notebooks in slow mode by setting the
                                  environment variable `NB_FAST` to `false`.

Manual Documentation

Manually written documentation in the /docs directory. These can be written in reStructuredText or Markdown. To add a new page to the documentation, add a new file to the /docs directory and add the filename to the toctree in index.rst or the appropriate location in the documentation.