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[FEA] Handle sparse input for SVM #2197
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This PR adds sparse input support (CSR) for GramMatrix kernel computation. This is a requirement to enable SVM support for sparse input in [cuML issue 2197](rapidsai/cuml#2197). It also adds row norm computation for CSR which is utilized for expanded L2 norm computation within RBF kernels. Although this branch introduces a new API it is still backwards compatible with the old GramMatrix API (which is marked as deprecated). CC @cjnolet @tfeher Authors: - Malte Förster (https://github.com/mfoerste4) - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Tamas Bela Feher (https://github.com/tfeher) - Corey J. Nolet (https://github.com/cjnolet) URL: #1296
This PR adds sparse input support (CSR) for GramMatrix kernel computation. This is a requirement to enable SVM support for sparse input in [cuML issue 2197](rapidsai/cuml#2197). It also adds row norm computation for CSR which is utilized for expanded L2 norm computation within RBF kernels. Although this branch introduces a new API it is still backwards compatible with the old GramMatrix API (which is marked as deprecated). CC @cjnolet @tfeher Authors: - Malte Förster (https://github.com/mfoerste4) - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Tamas Bela Feher (https://github.com/tfeher) - Corey J. Nolet (https://github.com/cjnolet) URL: rapidsai#1296
This PR adds support for sparse input to SVR and SVC. 'fit' as well as 'predict' can be called with sparse data compatible/convertible to SparseCumlArray. Support vectors in the model might also be stored as sparse data and can be retrieved as such. This PR requires rapidsai/raft#1296 to provide sparse kernel computations. Corresponding issue: #2197 Authors: - Malte Förster (https://github.com/mfoerste4) - Tamas Bela Feher (https://github.com/tfeher) Approvers: - Tamas Bela Feher (https://github.com/tfeher) - Corey J. Nolet (https://github.com/cjnolet) URL: #5273
Is your feature request related to a problem? Please describe.
cuML's SVM implementation currently uses dense arrays. It works reasonable well even if the number of nonzeros are only 20% of the input data, but it is not ideal for very sparse problems.
Describe the solution you'd like
Allow sparse format for the feature matrix X.
Additional context
The feature matrix enter only during the kernel function calculation K(x_i, x_j), therefore the required changes should be reasonable well confined.
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