intel
feat: Add OASEstimator class with oneDAL support and corresponding tests
- Implemented OASEstimator class in oas_estimator.py inheriting from EmpiricalCovariance.
- Added methods _sklearn_fit, _onedal_fit, score, and _onedal_score to support both scikit-learn and oneDAL backends.
- Integrated dispatch mechanism to switch between scikit-learn and oneDAL implementations.
- Added tests for OASEstimator in est_oas_estimator.py to verify fit and score functionalities.
Description
Add a comprehensive description of proposed changes
List associated issue number(s) if exist(s): #6 (for example)
Documentation PR (if needed): #1340 (for example)
Benchmarks PR (if needed): https://github.com/IntelPython/scikit-learn_bench/pull/155 (for example)
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It looks like the sklearn equivalent is named just 'OAS': https://scikit-learn.org/stable/modules/generated/sklearn.covariance.OAS.html
It is also exposed as a function that returns arrays: https://scikit-learn.org/stable/modules/generated/oas-function.html
Would be ideal to follow those same interfaces so that patching could be used. As a reference, here's the other sklearn function that gets 'patched' by this library: https://github.com/uxlfoundation/scikit-learn-intelex/blob/main/sklearnex/model_selection/split.py
ref https://github.com/uxlfoundation/scikit-learn-intelex/issues/2305
Hello @marcialouis! Thank you so much for your contribution. Just to add to the great suggestions by @david-cortes-intel , you can find what additional code is necessary here: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/covariance/_shrunk_covariance.py#L46 . Essentially, a small amount of additional python commands can be used on top of our implementation of EmpericialCovariance.
