Combine and expose SVC's support vectors when fitting multi-class data #4454
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wphicks
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dantegd
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Stylistic changes LGTM. I think we're good to merge as soon as we know what's up with CI. |
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@gpucibot merge |
cjnolet
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…lass data (rapidsai#4454)" This reverts commit abae602.
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rapidsai#4454) The purpose of this PR is to resolve issue rapidsai#4206; filling SVC's `support_` attribute by combining the support_ attribute from each of the estimators used in a multi-class one-versus-one SVC fit. This is a new PR, now updated to be current with branch-21.12; this replaces [this PR](rapidsai#4308), [this PR](rapidsai#4218) and [this PR](rapidsai#4305), all of which have now been closed. This change will allow libraries that rely on sklearn's SVC attribute `support_`, [like imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn/blob/56eefdf3d92afca77bc16fc13d315db5287df2fa/imblearn/over_sampling/_smote/filter.py#L366), to utilize cuML's SVC in place of sklearn's SVC. In order to properly fill the `support_` indices, we must first extract the `support_` indices from each estimator in the multi-class wrapper. Then, these indices must be aligned with the full multi-class dataset, as each estimator only receives a binary (ovo) dataset that has certain classes removed by the multi-class wrapper. [Here is a gist](https://gist.github.com/NV-jpt/48c324cd2cf3b972af32c2913f6c1b35) that displays and compares the behavior of cuml with these changes to that of sklearn (prior to the changes in this PR, `clf_cuml.support_` simply returned `None`). Authors: - https://github.com/NV-jpt - Dante Gama Dessavre (https://github.com/dantegd) Approvers: - William Hicks (https://github.com/wphicks) URL: rapidsai#4454
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The purpose of this PR is to resolve issue #4206; filling SVC's
support_attribute by combining the support_ attribute from each of the estimators used in a multi-class one-versus-one SVC fit.This is a new PR, now updated to be current with branch-21.12; this replaces this PR, this PR and this PR, all of which have now been closed.
This change will allow libraries that rely on sklearn's SVC attribute
support_, like imbalanced-learn, to utilize cuML's SVC in place of sklearn's SVC.In order to properly fill the
support_indices, we must first extract thesupport_indices from each estimator in the multi-class wrapper. Then, these indices must be aligned with the full multi-class dataset, as each estimator only receives a binary (ovo) dataset that has certain classes removed by the multi-class wrapper.Here is a gist that displays and compares the behavior of cuml with these changes to that of sklearn (prior to the changes in this PR,
clf_cuml.support_simply returnedNone).