[MRG+2]: Fix Ridge GCV with centered data #6178
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| @@ -854,7 +859,11 @@ def _errors_and_values_helper(self, alpha, y, v, Q, QT_y): | |||
| ----- | |||
| We don't construct matrix G, instead compute action on y & diagonal. | |||
| """ | |||
| w = 1.0 / (v + alpha) | |||
| w = 1. / (v + alpha) | |||
| dummy_column = np.var(Q, 0) < 1.e-12 | |||
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I think that I would call this "constant_column" rather than "dummy_column", no?
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A few minor comments that I would like addressed :) |
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| centered_kernel = True | ||
| if sparse.issparse(X) or not self.fit_intercept: | ||
| centered_kernel = False |
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This could be written:
centered_kernel = not sparse.issparse(X) and self.fit_intercept|
LGTM as well. @mblondel do you want to have a look? Otherwise we can merge. |
…ble with sparse matrices
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@ogrisel, addressed your comments and rebased on master. |
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Thanks for the fix! Feel free to merge without my review. |
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flake8 is complaining ;) |
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Fixed. |
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Merged. I will add a whats new to master directly. |
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# Conflicts: # doc/whats_new.rst
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Addresses #1807
The following code solves it correctly as far as I can see. I've tried to deal cleanly with sample weight and sparse matrices, as well as the direct/kernel fomulations.
Comments welcome !