[MRG] MNT use check_scalar to validate scalar in SpectralClustering#21881
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jjerphan merged 7 commits intoscikit-learn:mainfrom Jan 7, 2022
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ogrisel
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Dec 7, 2021
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Hello @ogrisel ! Thank you for your review, I just updated the changelog. |
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It looks like there's a bug causing CI to fail (may be fixed in #22049) |
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
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Thank you for your review @jjerphan ! I just updated the code with your suggestions. |
jjerphan
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Jan 7, 2022
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| @pytest.mark.parametrize( | ||
| "input, params, err_type, err_msg", | ||
| [ | ||
| (X, {"n_clusters": -1}, ValueError, "n_clusters == -1, must be >= 1"), | ||
| (X, {"n_clusters": 0}, ValueError, "n_clusters == 0, must be >= 1"), | ||
| ( | ||
| X, | ||
| {"n_clusters": 1.5}, | ||
| TypeError, | ||
| "n_clusters must be an instance of <class 'numbers.Integral'>," | ||
| " not <class 'float'>", | ||
| ), | ||
| (X, {"n_init": -1}, ValueError, "n_init == -1, must be >= 1"), | ||
| (X, {"n_init": 0}, ValueError, "n_init == 0, must be >= 1"), | ||
| ( | ||
| X, | ||
| {"n_init": 1.5}, | ||
| TypeError, | ||
| "n_init must be an instance of <class 'numbers.Integral'>," | ||
| " not <class 'float'>", | ||
| ), | ||
| (X, {"gamma": -1}, ValueError, "gamma == -1, must be >= 1"), | ||
| (X, {"gamma": 0}, ValueError, "gamma == 0, must be >= 1"), | ||
| (X, {"n_neighbors": -1}, ValueError, "n_neighbors == -1, must be >= 1"), | ||
| (X, {"n_neighbors": 0}, ValueError, "n_neighbors == 0, must be >= 1"), | ||
| ( | ||
| X, | ||
| {"eigen_tol": -1, "eigen_solver": "arpack"}, | ||
| ValueError, | ||
| "eigen_tol == -1, must be >= 0", | ||
| ), | ||
| (X, {"degree": -1}, ValueError, "degree == -1, must be >= 1"), | ||
| (X, {"degree": 0}, ValueError, "degree == 0, must be >= 1"), | ||
| ], | ||
| ) |
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Reference Issues/PRs
Addresses #20724
#DataUmbrella
What does this implement/fix? Explain your changes.
Summary of changes to SpectralClustering:
Add tests to ensure appropriate behavior when invalid arguments are passed in.
Use the helper function check_scalar from sklearn.utils to validate the scalar parameters.
Progress:
References
Any other comments?
This is my very first contibution to an open source project, please do not hesitate to give me a feedback. Thank you!