Adaptive lasso#10
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DataboyUsen merged 4 commits intomainfrom Apr 27, 2026
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Add support for adaptive lasso in plqERM_ElasticNet by introducing the omega parameter, which provides feature-specific weight coefficients. This modifies the regularization to apply weighted penalties, enhancing flexibility for feature selection. variable type change: rho parameter in ReHLine_solver and rehline_internal changed from scalar to array to accommodate adaptive lasso weights.
- perf(core): optimize adaptive LASSO conditional logic in C++ layer * Reduce redundant computations when omega is not provided - feat(validation): add parameter validation for omega * Validate length matches number of features * Ensure all omega values are positive * Add informative error messages - test(ci): add Adaptive ElasticNet CI tests * Test adaptive weighting with synthetic data - docs(examples): add Adaptive ElasticNet example * Compare adaptive vs standard ElasticNet
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Description
Add support for adaptive lasso in
plqERM_ElasticNetby introducing theomegaparameter, which provides feature-specific weight coefficients.Adaptive Lasso
rhoparameter changed from scalar to arrayrehline.hplqERM_ElasticNetAdd corresponding CI tests
omegaAdd Adaptive ElasticNet example
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Type of Change
Checklist
pytest tests/ -vAdditional Notes