FIX add n_targets allowing consistent shape prediction before calling fit in GPR - #23099
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Hi @glemaitre, could you help take a look at this PR, thank you |
glemaitre
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It looks good. Could you add a what's new entry in 1.2.
Note that this is a bug fix rather than a feature.
n_targets for GPRn_targets allowing consistent shape prediction before calling fit in GPR
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
thomasjpfan
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Jan 18, 2023
jeremiedbb
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Thanks for the PR @MaxwellLZH. I directly pushed the remaining requested change. LGTM.
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@thomasjpfan are you happy with the latest updates ? |
glemaitre
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May 31, 2023
| n_targets_seen = y.shape[1] if y.ndim > 1 else 1 | ||
| if self.n_targets is not None and n_targets_seen != self.n_targets: | ||
| raise ValueError( | ||
| "Number of targets seen != n_targets. " |
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I would favor a more literal error:
The number of targets seen in `y` is different from the parameter `n_targets`.
Got ...
glemaitre
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Still my +1 on this one. Just a nitpick regarding the error message raised.
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LGTM. Merging since the changes requested by @thomasjpfan have been implemented. |
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…ng `fit` in GPR (scikit-learn#23099) Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]>
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Fixes #22430
Reference Issues/PRs
This PR adds a new argument
n_targetsforGaussianProcessRegressoras suggested in issue #22430 , which will be used to determine the output shape of bothpredictandsample_y, so the shape will be consistent before and after callingfitmethod.Any other comments?
I'm not sure if we should raise error during the fit process if the dimension of
ydoes not matchn_targets, since once we fit the model the output shape will be determined by the shape of y.