[EXAMPLE DIFF] (Tree featuresv2) Fork of sklearn that maintains all necessary refactorings to enable downstream functionality #32
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Co-authored-by: Chester Huynh <chester.huynh924@gmail.com> Co-authored-by: Parth Vora <pvora4@jhu.edu>
Co-Authored-By: Thomas Fan <thomasjpfan@gmail.com> Co-Authored-By: Chester Huynh <chester.huynh924@gmail.com> Co-Authored-By: Parth Vora <pvora4@jhu.edu>
tests. The issues still are: - sparse dataset support, which I could possibly split into a follow-up PR? - pickling does not work on roundtrip for some reason - certain issues with max_leaf
Signed-off-by: Adam Li <adam2392@gmail.com>
Signed-off-by: Adam Li <adam2392@gmail.com>
…-learn into tree-featuresv2
Signed-off-by: Adam Li <adam2392@gmail.com>
Signed-off-by: Adam Li <adam2392@gmail.com>
…-learn into tree-featuresv2
Signed-off-by: Adam Li <adam2392@gmail.com>
Signed-off-by: Adam Li <adam2392@gmail.com>
Signed-off-by: Adam Li <adam2392@gmail.com>
Signed-off-by: Adam Li <adam2392@gmail.com>
#### Reference Issues/PRs Closes: #25 Closes: #23 #### What does this implement/fix? Explain your changes. Adds preliminary capability for binning features. The cons is we need to "bin" again during predict time. I've documented how we can get around this in the README, but it will involve some heavy-duty Cython coding. - Remove Oblique tree models and migrated to scikit-tree - Fix up CI so that way unnecessary workflows are not ran on the fork - Updated the documentation with the current limitation of binning --------- Signed-off-by: Adam Li <adam2392@gmail.com>
adam2392
commented
Mar 28, 2023
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Reference Issues/PRs
This is the most up-to-date PR branch to consolidate all proposed refactor changes that work with:
What does this implement/fix? Explain your changes.
Incorporates refactors to:
Internal Cython of scikit-learn's:
Internals of Python in scikit-learns:
Adds the basic implementation of oblique trees. The implementation of oblique trees has been tested on all sklearn's
check_estimatortesting function and has error-checking bounds for the new hyperparameter introduced, which isfeature_combinationsthat defaults tomin(1.5, n_features).TODO:
Any other comments?
Eventually, we want to build wheels in order to make this fork "maintainable".