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Add flake8-docstrings linting rules for documentation using ruff PR #227
Introduce flake8-docstrings linting rules and ruff for automatic resolving in the formatting process.
2 parents a5e815e + 972c23d commit 9462c0b

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‎.gitignore

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build/
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dist/
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docs/src/examples
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sg_execution_times.rst

‎docs/src/contributing.rst

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pip install -e .
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This install the package in development mode, making it importable globally and allowing
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you to edit the code and directly use the updated version.
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you to edit the code and directly use the updated version. To see a list of all
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supported tox environments please use
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.. code-block:: bash
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tox -av
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Running the tests
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-----------------

‎examples/reconstruction/PlotGFRE.py

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"""
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Global Feature Reconstruction Error (GFRE) and Distortion (GFRD)
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================================================================
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Example for the usage of the :class:`skmatter.metrics.global_reconstruction_error`
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as global feature reconstruction error (GFRE) and
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Example for the usage of the :class:`skmatter.metrics.global_reconstruction_error` as
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global feature reconstruction error (GFRE) and
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:class:`skmatter.metrics.global_reconstruction_distortion` global feature reconstruction
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distortion (GFRD). We apply the global reconstruction measures on the degenerate
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CH4 manifold dataset. This dataset was specifically constructed to be
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representable by a 4-body features (bispectrum) but not by a 3-body features
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(power spectrum). In other words the dataset contains environments which are
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different, but have the same 3-body features. For more details about the dataset
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please refer to `Pozdnyakov 2020 <https://doi.org/10.1103/PhysRevLett.125.166001>`_.
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distortion (GFRD). We apply the global reconstruction measures on the degenerate CH4
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manifold dataset. This dataset was specifically constructed to be representable by a
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4-body features (bispectrum) but not by a 3-body features (power spectrum). In other
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words the dataset contains environments which are different, but have the same 3-body
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features. For more details about the dataset please refer to `Pozdnyakov 2020
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<https://doi.org/10.1103/PhysRevLett.125.166001>`_.
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The ``skmatter`` dataset already contains the 3 and 4-body features computed with
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`librascal <https://github.com/lab-cosmo/librascal>`_ so we can load it and
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compare it with the GFRE/GFRD.
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`librascal <https://github.com/lab-cosmo/librascal>`_ so we can load it and compare it
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with the GFRE/GFRD.
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"""
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# %%
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#

‎examples/reconstruction/PlotLFRE.py

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"""
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Pointwise Local Reconstruction Error
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====================================
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Example for the usage of the
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:class:`skmatter.metrics.pointwise_local_reconstruction_error` as pointwise local
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reconstruction error (LFRE) on the degenerate CH4 manifold. We apply the local
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dataset please refer to `Pozdnyakov 2020
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<https://doi.org/10.1103/PhysRevLett.125.166001>`_.
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The skmatter dataset already contains the 3 and 4-body features computed with
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`librascal <https://github.com/lab-cosmo/librascal>`_ so we can load it and compare it
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with the LFRE.
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The skmatter dataset already contains the 3 and 4-body features computed with `librascal
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<https://github.com/lab-cosmo/librascal>`_ so we can load it and compare it with the
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LFRE.
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"""
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# %%
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#

‎examples/reconstruction/PlotPointwiseGFRE.py

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"""
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Pointwise GFRE applied on RKHS features
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================================================================
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=======================================
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Example for the usage of the
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:class:`skmatter.metrics.pointwise_global_reconstruction_error` as the pointwise global
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feature reconstruction error (pointwise GFRE). We apply the pointwise global feature

‎examples/regression/OrthogonalRegressionNonAnalytic.py

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r"""
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Regression with orthogonal projector/matrices
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=============================================
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In this example, we explain how when using
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:class:`skmatter.linear_model.OrthogonalRegression` the option
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``use_orthogonal_projector`` can result in non-analytic behavior. In

‎examples/regression/Ridge2FoldCVRegularization.py

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# %%
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r"""
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Ridge2FoldCV for data with low effective rank
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=======================================================
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In this notebook we explain in more detail how
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:class:`skmatter.linear_model.Ridge2FoldCV` speeds up the
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cross-validation optimizing the regularitzation parameter :param alpha: and
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compare it with existing solution for that in scikit-learn
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:class:`slearn.linear_model.RidgeCV`.
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:class:`skmatter.linear_model.Ridge2FoldCV` was designed to predict
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efficiently feature matrices, but it can be also useful for the prediction
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single targets.
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Ridge2FoldCV for data with low effective rank
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=============================================
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In this notebook we explain in more detail how
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:class:`skmatter.linear_model.Ridge2FoldCV` speeds up the cross-validation optimizing
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the regularitzation parameter :param alpha: and compare it with existing solution for
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that in scikit-learn :class:`slearn.linear_model.RidgeCV`.
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:class:`skmatter.linear_model.Ridge2FoldCV` was designed to predict efficiently feature
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matrices, but it can be also useful for the prediction single targets.
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"""
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# %%
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#
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def micro_bench(ridge):
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"""A small benchmark function."""
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global N_REPEAT_MICRO_BENCH, X, y
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timings = []
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train_mse = []
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def get_train_test_error(estimator):
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"""The train tets error based on the estimator."""
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global X_train, y_train, X_test, y_test
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estimator = estimator.fit(X_train, y_train)
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return (

‎pyproject.toml

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include_trailing_comma = true
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lines_after_imports = 2
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known_first_party = "skmatter"
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[tool.ruff]
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exclude = ["docs/src/examples/"]
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lint.ignore = [
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"F401",
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"E203",
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"D100",
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"D101",
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"D102",
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"D205",
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"D400",
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"D401",
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]
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line-length = 88
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lint.select = [
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"D",
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"E",
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"F",
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"W",
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]
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[tool.ruff.lint.pydocstyle]
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convention = "numpy"
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[tool.ruff.lint.per-file-ignores]
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"examples/**" = [
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"D205",
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"D400",
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]

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