Fix "GaussianProcessRegressor doesn't work with multidemensional output when normalize_y=True"#18300
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MathieuBertin wants to merge 2 commits intoscikit-learn:mainfrom
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Fix "GaussianProcessRegressor doesn't work with multidemensional output when normalize_y=True"#18300MathieuBertin wants to merge 2 commits intoscikit-learn:mainfrom
MathieuBertin wants to merge 2 commits intoscikit-learn:mainfrom
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…ut when normalize_y=True" Added a dimension to y_std and y_cov if model fitted with multidimensional target
Corrected: comment line too long
Contributor
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Interesting, does this mean that we don't have a test for a multi-dimensional output with normalisze_y=True? |
Author
Yes this is correct. I added the test in my code (file test_gpr.py, function test_y_normalized_multioutput) |
Member
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This has been addressed in another PR. |
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Reference Issues/PRs
Fixes #18065
What does this implement/fix? Explain your changes.
The current GaussianProcessRegressor doesn't work with multidimensional output when normalize_y=True, and either y_std or y_cov is queried.
y_std depends on the std of the target if the target is normalized.
Hence it was necessary to add a dimension to y_std if the target is multidimensional, to allow a different set of values for each of the dimensions of the target.
The same problem arose for y_cov, and was fixed similarly by adding a dimension when the target is multidimensional.
Any other comments?
My first contribution here. Sorry in advance if I misunderstood any of the guidelines.
I am not sure of what should happen when the target is multidimensional but not normalized (normalize_y=False)
In the hereby submitted code, a dimension is added to y_std and y_cov if the target is multidimensional, regardless of the value of normalize_y.
The drawback is that if normalize_y=False, values of y_std and y_cov will be identical for all values of this new dimension, which seems redundant.