Regularize whitening #2744
Regularize whitening #2744
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@samuelgarcia @alejoe91 It would be great if this could be merged before the 'rc' release :) |
alejoe91
added
the
hackathon-24
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| n_jobs = int(n_jobs * os.cpu_count()) | ||
| regularize_kwargs["n_jobs"] = n_jobs | ||
| estimator = sklearn.covariance.GraphicalLassoCV(**regularize_kwargs) |
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maybe we could have a regularize method that can use lasso but maybe others no ?
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We could, but I would say let's start with one...
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Yes but we could have already the regularized_method in the dict no ?
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I've tried something... We can pass method: "GraphicalLassoCV" or something else, and the appropriate node is created
Co-authored-by: Alessio Buccino <[email protected]>
Co-authored-by: Alessio Buccino <[email protected]>
Co-authored-by: Alessio Buccino <[email protected]>
Co-authored-by: Alessio Buccino <[email protected]>
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| @@ -41,6 +41,14 @@ def test_whiten(create_cache_folder): | |||
| assert rec4.get_dtype() == "int16" | |||
| assert rec4._kwargs["M"] is None | |||
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| # test regularization | |||
| with pytest.raises(AssertionError): | |||
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I do not see in the code wich assert is raised for this.
We should have better error than assert in this no ?
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I've used what was already done, open to suggestions
| W, M = compute_whitening_matrix( | ||
| rec, "local", random_chunk_kwargs, apply_mean=False, radius_um=None, regularize=True | ||
| ) | ||
| # W must be sparse |
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which W are you testing here ?
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I'm just computing W with the default regularization method
…regularize_whitening
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Merci. |
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Add an option to use regularized whitening