Enable config setting sparse_interface to control sparray and spmatrix creation
#31177
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Another note: We can work around it for now with e.g. The recent features for sparse by version are:
This info might help us decide when to support which versions. I think the construction functions are all that is currently needed. If/when we start using indexing code for both sparse and dense, we will likely want 1.15. If/when we want nD sparse we will need v1.16, and broadcasting binary operations in v1.17. But for now, 1.8 leaves out only construction functions from current code. |
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It sounds like the community has decided to convert to SciPy sparray using the config parameter. This PR is a start toward that. I've removed the "Draft status" from the PR. I am ready to implement this approach in other parts of the library. I have two questions:
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This PR sets up a config parameter
sparse_interfaceto indicate "sparray" or "spmatrix" outputs, as suggested in #26418.The first commit sets everything up and implements the system for a few modules. Please take a look and provide feedback for whether this is the way to proceed. The next commit(s) will implement these same style of changes throughout the library. If you would prefer they be in separate PRs let me know. (I'll keep Draft status until there is feedback and the full library is convered.)
More specifically, this PR does the following:
sparse_interfaceto the config parameters. (I think this name is better than sparse_format because "format" means csr/coo/lil, etc in the sparse world.) The values it can hold are "sparray" or "spmatrix". Update config tests accordingly.utils._sparse.pywith (private) helper functions. The difference is how much checking is done. Tests added too._as_sparse(x_sparse), raises unless sparse input. converts to interface chosen by config._select_interface_if_sparse(x), allows dense input with no action, sparse input uses_as_sparse._convert_from_spmatrix_to_sparray(x)and_convert_from_sparray_to_spmatrix(x)sklearn/feature_selection/text.pyand adapts tests.sklearn/linear_model/_coordinate_descent.pyno tests change needed.sklearn/manifold/_locally_linear.pyno tests change needed.@thomasjpfan can you see if this does what you had in mind? I tried to pick modules that cover returning sparse, setting estimators to hold sparse, and transforming to sparse, so you can see how this would work.
Let me know if you think
_as_sparseshould be a public function, and if my approach aligns with how you want it. The next steps for this PR are to repeat these type of changes throughout the library.