Opda

======================
.. rubric:: Additions

* Add the hyperparameter tuning scaling results from tuning ResNet18
for ImageNet via random search.

* Add the data itself: ``data/resnet/resnet18_scaling.results.jsonl``.
* Add a license for the data: ``data/resnet/LICENSE``.
* Add a README for the data: ``data/resnet/README.md``.

* Add support for ``scipy == 1.14`` and ``numpy == 2.1``.
* Add the ``C_MIN`` and ``C_MAX`` class attributes to
``opda.parametric.QuadraticDistribution`` and
``opda.parametric.NoisyQuadraticDistribution``.
* Add ``opda.utils.normal_ppf``, a fast function for computing the
standard normal's PPF (quantile function).
* Check that ``c`` is finite when validating the arguments to
``opda.parametric.QuadraticDistribution`` and
``opda.parametric.NoisyQuadraticDistribution``.
* Add ``fit`` methods to the parametric distributions:
``opda.parametric.QuadraticDistribution.fit`` and
``opda.parametric.NoisyQuadraticDistribution.fit``.
* Add support for Python 3.13.

.. rubric:: Changes

* Simplify the implementation of
``opda.parametric.NoisyQuadraticDistribution`` by making ``scale``
the standard deviation of the corresponding normal random variable.
* Upgrade packaging tools to ``build == 1.2.1`` and ``twine == 5.1.1``.
* Skip some tests for ``opda.utils.beta_equal_tailed_interval``,
``opda.utils.beta_highest_density_interval``, and
``opda.utils.beta_equal_tailed_coverage`` when they fail due to
`an issue <https://github.com/scipy/scipy/issues/21303>`_ in
``scipy == 1.14.0`` on Linux that causes spurious NaN values.
* Use the point mass, noiseless, and normal approximations to speed up
the ``ppf`` method of ``opda.parametric.NoisyQuadraticDistribution``.
* Disable lint rules:

* ``B023``: "Function definition does not bind loop variable
{name}".
* ``PLW2901``: "Outer {outer_kind} variable {name} overwritten by
inner {inner_kind} target".
* ``SIM102``: "Use a single ``if`` statement instead of nested
``if`` statements".

* Restrict the range of values for ``c`` supported by
``opda.parametric.QuadraticDistribution`` to ``1`` to ``10``.

.. rubric:: Removals

* Drop support for ``numpy == 1.21``, ``numpy == 1.22``,
``scipy == 1.8``, and ``scipy == 1.9``.
* Remove the ``estimate_initial_parameters_and_bounds`` method from
``opda.parametric.QuadraticDistribution``.

.. rubric:: Fixes

* Update tests for the ``__repr__`` methods of
``opda.nonparametric.EmpiricalDistribution``,
``opda.parametric.QuadraticDistribution``, and
``opda.parametric.NoisyQuadraticDistribution`` so that they're
compatible with ``numpy == 2.0``.
* Fix flakiness in the test:
``NoisyQuadraticDistributionTestCase.test_ppf_is_inverse_of_cdf``.
* Fix the ``quantile_tuning_curve`` methods of
``opda.parametric.QuadraticDistribution`` and
``opda.parametric.NoisyQuadratricDistribution`` which gave incorrect
output when ``q`` wasn't equal to ``0.5`` and either ``minimize`` was
``True`` or ``minimize`` was ``None`` and ``convex`` was ``True``.

.. rubric:: Documentation

* Correct the quantile function's definition in the docstrings for the
``ppf`` methods of ``opda.parametric.QuadraticDistribution`` and
``opda.parametric.NoisyQuadraticDistribution``.
* Make docstring formatting more consistent:

* Always use "Returns" and never "Return" for the docstring section
header.
* Always document optional types with "or None" coming at the *end* of
the type description.
* Always document each returned value with a separate subheading when
a function or method returns multiple values.

* Remove default values from parameters' type descriptions.

======================
.. rubric:: Additions

* Add support for ``numpy == 2.0`` and ``scipy == 1.13``.

.. rubric:: Changes

* Disable lint rule ``PLR1714``: "Consider merging multiple
comparisons".

.. rubric:: Documentation

* Split the Jupyter notebook *Evaluating DeBERTaV3 with the
Nonparametric Analysis* into several smaller more focused notebooks:

* *Evaluating DeBERTaV3 with the Nonparametric Analysis*
* *Choosing a Sample Size for the Nonparametric Analysis*
* *Demonstrating the Exact Coverage of the Nonparametric Analysis*
* *Studying Ablations of the Nonparametric Analysis*

* Add a section about how to analyze a hyperparameter (the number of
epochs) to the Jupyter notebook *Evaluating DeBERTaV3 with the
Nonparametric Analysis*.
* In the *Examples* doc (``docs/tutorial/examples.rst``), improve the
code, wording, and title of the model comparison example (previously
titled *Compare Models' Tuning Curves*, now titled *Compare Models*).
* In the *Examples* doc (``docs/tutorial/examples.rst``), expand the
*Compare Models* example with discussion on how to compare models with
different training costs.
* Fix incorrect markup in the *Examples* doc
(``docs/tutorial/examples.rst``).
* Add a new example showing how to analyze a hyperparameter in the
*Examples* doc (``docs/tutorial/examples.rst``).
* Add plots of DeBERTa and DeBERTaV3's tuning curves with confidence
bands using large sample sizes (1,024) to the Jupyter notebook
*Evaluating DeBERTaV3 with the Nonparametric Analysis*.

======================
.. rubric:: Additions

* Add ``opda.approximation``, a module for approximation-theoretic
operations, with the following functions:

* ``opda.approximation.lagrange_interpolate``: Interpolate points with
a polynomial.
* ``opda.approximation.remez``: Identify the reference corresponding
to the minimax polynomial approximation of a function.
* ``opda.approximation.minimax_polynomial_approximation``: Evaluate
the minimax polynomial approximation of a function.
* ``opda.approximation.minimax_polynomial_coefficients``: Compute the
coefficients of the minimax polynomial approximation of a function.
* ``opda.approximation.piecewise_polynomial_knots``: Find the knots
for the minimax piecewise polynomial approximation of a function.

* Add ``opda.exceptions.NumericalError``, an exception for numerical
issues.
* Add more tests for ``opda.parametric.QuadraticDistribution``.
* Add ``mean`` and ``variance`` attributes to
``opda.nonparametric.EmpiricalDistribution``.
* Add ``mean`` and ``variance`` attributes to
``opda.parametric.QuadraticDistribution``.
* Add ``opda.utils.normal_pdf``, a fast function for computing the
standard normal's PDF.
* Add ``opda.utils.normal_cdf``, a fast function for computing the
standard normal's CDF.
* Add ``opda.exceptions.IntegrationError``, an exception for
integration issues.
* Add ``opda.parametric.NoisyQuadraticDistribution``, a probability
distribution representing a quadratic random variable plus normal
noise.
* Increase argument validation in ``opda.utils.dkw_epsilon``.
* Add more test cases for ``opda.utils.dkw_epsilon``.
* Validate that ``lightness`` is between 0 and 1 (inclusive) in
``experiments.visualization.color_with_lightness``.
* Validate that arguments are *finite* floats where appropriate.

.. rubric:: Changes

* Reparametrize ``parametric.QuadraticDistribution`` so ``c`` is the
effective number of hyperparameters instead of half the number.
* Completely disable the eradicate (``ERA``) lint rules.
* Enable ``"py"`` as the primary domain in the documentation.
* Always use numpy's numeric types for scalar class attributes,
instead of Python's native numeric types.
* Improve tests for ``parametric.QuadraticDistribution``, making them
more thorough, robust, and avoiding re-running redundant test cases.
* Update the tests for ``parametric.QuadraticDistribution`` to cover
the case when ``a == b``.
* Move the source repository from ``github.com/nalourie/opda`` to
``github.com/nicholaslourie/opda``, and move the docs from
``nalourie.github.io/opda`` to
``nicholaslourie.github.io/opda``. Update the project URLs in
``pyproject.toml`` and all the links throughout the repository to
reflect these changes.
* Require ``fraction`` is greater than 0 in
``opda.parametric.QuadraticDistribution.estimate_initial_parameters_and_bounds``.
* Throw an error if ``fraction`` is too small and thus causes
``opda.parametric.QuadraticDistribution.estimate_initial_parameters_and_bounds`` to
try and form an estimate from an empty list.

.. rubric:: Fixes

* Fix ``parametric.QuadraticDistribution`` (the ``.pdf``, ``.cdf``,
and ``.estimate_initial_parameters_and_bounds`` methods) for the
case when ``a == b``, in which case the distribution is an atom
(point mass).
* Fix
``opda.parametric.QuadraticDistribution.estimate_initial_parameters_and_bounds``
when ``convex`` is ``False`` and ``fraction`` is small enough so
that the estimate should be based on an empty list. In this case,
the method incorrectly uses all of ``ys``. Instead, throw an error
saying that fraction is too small (as it produces an empty list).
* Avoid throwing an unnecessary warning in ``opda.utils.dkw_epsilon``
when ``confidence`` is 1.

.. rubric:: Documentation

* Improve the docstring for
``experiments.analytic.get_approximation_parameters``.
* Use inline math markup in docstrings.
* Fix the equation in the docstring for
``opda.parametric.QuadraticDistribution.ppf``. The infimum that
defines the quantile function has as its domain the interval from
``a`` to ``b``, not the entire real line.
* Add "See Also" and "Notes" sections to the docstring for
``opda.parametric.QuadraticDistribution``, matching the newly added
docstring for ``opda.parametric.NoisyQuadraticDistribution``.
* Update all links to use ``github.com/nicholaslourie`` and
``nicholaslourie.github.io/opda`` in place of
``github.com/nalourie`` and ``nalourie.github.io/opda``.
* Document stricter dependent type constraints (e.g., non-negativity,
finiteness) for function and method inputs and outputs.
* Document range constraints for inputs and outputs more precisely and
consistently (e.g., ``q`` is a float from 0 to 1 inclusive).
* Improve the docstring for ``experiments.analytic.ellipse_volume``.
* Fix docstrings across the code base in order to consistently
document when a value can take on either scalar (e.g., float) or
array (e.g., array of floats) values.

======================
This version is the first uploaded to PyPI and available via ``pip``!

.. rubric:: Additions

* Add a continuous integration job to ensure every pull request
updates the changelog.
* In the continuous integration job for building the packages, add a
step to list the packages' contents.
* Add the "release" nox session for making new releases to PyPI.

.. rubric:: Changes

* Upgrade the development dependencies.
* Upgrade the ``Development Status`` PyPI classifier for opda from
``3 - Alpha`` to ``4 - Beta``.

.. rubric:: Fixes

* Fix flakiness in the test:
``EmpiricalDistributionTestCase.test_average_tuning_curve``.

.. rubric:: Documentation

* Pin links to the source on GitHub to the commit that builds the
documentation.
* Move development documentation into the "Contributing" section of
the sidebar and URL tree.
* Omit from the documentation's sidebar any project URLs that link to
the documentation.
* Add an announcement banner to the documentation when it's built for
an unreleased version.
* Add a changelog (``CHANGELOG.rst``).
* Document the project's various conventions in the development docs.
* Add the "Release" doc describing the release process.
* Update the docs to suggest installing opda from PyPI rather than the
source for regular usage.

======================
.. rubric:: Additions

* Add the ``package`` optional dependencies.
* Add a build for "distribution" as opposed to "local" use. The
distribution package contains only the ``opda`` library and not
``experiments``.

* Add a ``nox`` session for building the distribution package.
* Add a continuous integration job to build the package and store it
as an artifact on each pull request.
* Add a continuous integration job to test the distribution package
against all combinations of supported versions of major
dependencies.

.. rubric:: Changes

* Increase retention for documentation build artifacts from 60 to 90
days in continuous integration.
* Prune each set of optional dependencies.
* Rename the ``tests`` optional dependencies to ``test``.
* Split the ``test`` session in ``nox`` into ``test``, for testing the
local project, and ``testpackage``, for testing distribution packages.
* In continuous integration, only test the local build against *default*
versions of major dependencies, since we now build and test the
distribution package against *all* combinations of supported versions.

.. rubric:: Documentation

* Document how to build and test the distribution package.

======================
.. rubric:: Additions

* Extend ``nonparametric.QuadraticDistribution.sample`` and
``nonparametric.EmpiricalDistribution.sample`` to return a scalar when
``size=None``, and make it the default argument.
* Add documentation builds via Sphinx:

* Create a Sphinx setup for building the documentation.
* Add tutorial-style documentation for users.
* Add development documentation.
* Automatically generate API reference documentation.

* Add a GitHub Actions workflow for building and publishing the
documentation to GitHub Pages.
* Make tests backwards compatible with ``numpy >= 1.21``.
* Adjust package dependency requirements to allow ``numpy >= 1.21`` and
``scipy >= 1.8``.
* Add ``ci`` optional dependencies for continuous integration.
* Add ``nox`` for automating development tasks, testing against all
supported major dependencies, and continuous integration.
* Add a GitHub Actions workflow for continuous integration. Run it on
each pull request as well as every calendar quarter. Use the
workflow to:

* Check ``opda``'s major dependency versions are up-to-date.
* Lint the project.
* Build and test the documentation.
* Test the project against all combinations of supported versions of
major dependencies.

.. rubric:: Changes

* Always return scalars rather than 0 dimensional arrays from methods
(``nonparametric.EmpiricalDistribution.pmf`` and
``parametric.QuadraticDistribution.pdf``).
* Explicitly test that all methods of
``nonparametric.EmpiricalDistribution`` and
``parametric.QuadraticDistribution`` return scalars rather than 0D
arrays.
* Configure ``pytest`` to always use a non-interactive backend for
``matplotlib``.
* Update the project URLs in packaging.
* Split out the ``experiments`` package's dependencies as optional
dependencies.

.. rubric:: Fixes

* Include ``src/experiments/default.mplstyle`` in the package data for
the experiments package so the style can be used from non-editable
installs.
* Make tests more robust to changes in rounding errors across
environments by replacing some equality checks with near equality.

.. rubric:: Documentation

* Remove broken references to the sections of numpy-style
docstrings. Standard tooling doesn't make these sections linkable.
* Fix errors in the docstrings' markup.
* Use cross-references in the docs wherever possible and appropriate.
* Use proper markup for citations.
* Change the language from ``bash`` to ``console`` in code blocks.
* Improve the modules' docstrings.
* Rewrite ``README.rst``, adding a "Quickstart" section and moving much
of the old content into new tutorial-style documentation built with
Sphinx.
* Document how to build and test the documentation.
* Document how to setup and use ``nox`` for common development tasks.