TST Extend tests for scipy.sparse.*array in sklearn/utils/tests/test_sparsefuncs.py#27242
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OmarManzoor merged 2 commits intoscikit-learn:mainfrom Sep 13, 2023
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| sp.hstack( | ||
| [ | ||
| csr_container(np.full((13, 1), fill_value=np.nan)), | ||
| sp.random(13, 1, density=0.8, random_state=42), | ||
| ], | ||
| format="csr", | ||
| ), | ||
| ) | ||
| for csr_container in CSR_CONTAINERS |
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Due to the call to sp.hstack, we whill always return a sparse matrix.
The random in the previous tuple is also only testing sparse matrices.
One potential solution is to create sparse matrices, and parametrize with CSR_CONTAINER and intenally cast X1 and X2:
X1 = csr_container(X1)
X2 = csr_container(X2)
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Otherwise, LGTM for the rest. |
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@glemaitre I removed csr_container in first part of parameterisation and left just np.full if it becomes sparse matrix anyway |
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LGTM. Thanks @Tialo.
Another ready @OmarManzoor
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…st_sparsefuncs.py` (scikit-learn#27242)
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Reference Issues/PRs
Towards #27090.
What does this implement/fix? Explain your changes.
Any other comments?
In tests like
test_mean_variance_axis0I checked if*arraywill work with*matrix. If you think that we should check matrices only with matrices, same for arrays, I can reformat parameterisations usingzip