[MRG] Implement fitting intercept with sparse_cg solver in Ridge regression
#13336
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glemaitre
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Feb 28, 2019
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agramfort
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Mar 1, 2019
agramfort
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agramfort
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Mar 1, 2019
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@btel you have pep8 errors cf. circle lint |
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glemaitre
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Mar 1, 2019
GaelVaroquaux
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Mar 1, 2019
@GaelVaroquaux I am happy to discuss/implement prettier solutions for propagating |
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one solution would be to change the current design so Ridge just calls
ridge_regression and all prepare_data and set_intercept code is done in
ridge_regression not any more in the object.
… |
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@agramfort sounds good, but it will require a good deal of refactoring. |
xhluca
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…gression (scikit-learn#13336) * add skeleton for fit_intercept with sparse_cg * fix sparse_cg solver with fit_intercept=True * fix test * linting * add what's new entry * remove X_scale and X_offset from public interface of ridge_regression * reformat if clause * fixed linting issues * add comments on about the conditions of different code branches * update warning * remove whitespace * add extra checks in the test of ridge with fit_intercept * remove unused argument
xhluca
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Apr 28, 2019
…Ridge regression (scikit-learn#13336)" This reverts commit 3ed0547.
xhluca
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…Ridge regression (scikit-learn#13336)" This reverts commit 3ed0547.
koenvandevelde
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…gression (scikit-learn#13336) * add skeleton for fit_intercept with sparse_cg * fix sparse_cg solver with fit_intercept=True * fix test * linting * add what's new entry * remove X_scale and X_offset from public interface of ridge_regression * reformat if clause * fixed linting issues * add comments on about the conditions of different code branches * update warning * remove whitespace * add extra checks in the test of ridge with fit_intercept * remove unused argument
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Reference Issues/PRs
See also #470
It follows the same trick as introduced in PR #13279 by @agramfort
What does this implement/fix? Explain your changes.
This implements fitting intercept with
sparse_cgsolver in Ridge regression (i.e. whenfit_intercept==True) for sparse inputs. It also means that both sparse and dense cases give the same result.Any other comments?
Important: This PR changes the auto-selected solver (
solver='auto') fromsagtosparse_cgwhenfit_intercept==Trueand input is sparse.There are still problems with the code:
'auto' mode in ridge_regression function may trigger wrong solvers (for example, when inputs is sparse and sample_weight is passed)
Warning message about changing the solver is not fully informative/correct. For example, user might choose the
sparse_cgsolver instead of going thesagway.The estimator object is not informed about the fact that the solver was changed (or which solver was selected by
auto)They are not related to this PR and will be fixed in another PR.