ENH: Refactor Adversarial Debiasing for reactive preprocessing #4 - #25
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ENH: Refactor Adversarial Debiasing for reactive preprocessing #4#25divyanshbhai wants to merge 25 commits into
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October 15, 2025 19:11
- Create _optimized_preprocessing.py with class skeleton - Add OptimizedPreprocessing to preprocessing module exports - Implement basic fit/transform interface following sklearn conventions - Add comprehensive docstring with algorithm description and references Part of implementing Calmon et al. algorithm for issue usarfoss#9
- Add probabilistic transformation learning via constrained optimization - Implement feature discretization for tractable optimization - Add demographic parity constraints with epsilon tolerance - Implement distortion minimization objective function - Add probabilistic data transformation with noise injection Follows Calmon et al. methodology for discrimination prevention
- Create test_optimized_preprocessing.py with 15+ test cases - Test initialization, fit/transform functionality, and edge cases - Add parameter validation and input validation - Test reproducibility, different epsilon values, and sklearn compatibility - Verify error handling for invalid inputs and parameters Ensures robust implementation following fairlearn testing standards
- Add detailed section in preprocessing.rst user guide - Include mathematical formulation and algorithm description - Add practical usage example with synthetic data - Include Calmon et al. reference in bibliography - Document fairness-utility trade-off via epsilon parameter Completes documentation requirements for issue usarfoss#9
…mentation - Implement LearningFairRepresentations class based on Zemel et al. (2013) - Add comprehensive unit tests covering various scenarios - Update preprocessing module exports to include LFR - Algorithm learns k prototypes with fairness constraints for demographic parity
…ation - Add parameter validation for k, loss weights, and max_iter - Improve gradient computation with numerical stability - Add gradient clipping to prevent optimization instability - Add comprehensive documentation to user guide - Update mitigation algorithms table to include LFR - Enhanced test coverage for parameter validation
- Add LearningFairRepresentations to preprocessing fairness tests - Update test fixtures to handle LFR's sensitive_features requirement - Fix dataset splitting to properly handle sensitive features for all algorithms - Ensure LFR works with existing fairness metrics and evaluation pipeline
- Add predict() method for binary classification - Add predict_proba() method for probability predictions - Ensure consistency between predict and predict_proba methods - Add comprehensive tests for prediction functionality - Methods only available when algorithm fitted with target labels - Enables LFR to be used as both preprocessor and end-to-end classifier
- Create detailed example demonstrating LFR algorithm usage - Show comparison between original model, LFR+LogReg, and LFR direct prediction - Include fairness metrics evaluation and transformation analysis - Make matplotlib optional for environments without visualization support - Demonstrate successful demographic parity improvement on synthetic data
- Document complete implementation of GitHub issue usarfoss#8 - List all delivered components and features - Show validation results and commit history - Confirm all requirements fulfilled - Ready for production use and contribution points
…d odds - Remove ValueError that blocked using tol parameter with equalized_odds constraint - Update docstring to reflect that relaxed constraints now support equalized_odds - Add conditional logic to use relaxed method when tol is specified
- Add _threshold_optimization_for_relaxed_equalized_odds method - Implement grid search approach to find optimal solution within tolerance - Add fallback to strict equalized odds when no relaxed solution found - Include test scripts to verify functionality works correctly
…tests - Enhance search algorithm to explore different FPR/TPR combinations across groups - Add constraint violation tracking for better debugging and validation - Include comprehensive test for equalized odds with tolerance parameter - Add test case to existing test suite to ensure integration works correctly
- Create example_relaxed_equalized_odds.py demonstrating the new functionality - Show trade-offs between fairness and performance with different tolerance values - Include clear documentation and explanations of the approach - Clean up temporary test files
- Document the new relaxed equalized odds functionality - Explain algorithm details and implementation approach - Provide usage examples and benefits - Include notes on testing and future enhancements - Complete the implementation of issue usarfoss#3
- Implement CDD metric from Wachter et al. paper (https://arxiv.org/abs/2005.05906) - Add conditional_demographic_disparity function for individual group calculation - Add make_conditional_demographic_disparity_metric helper for MetricFrame integration - Include comprehensive tests covering edge cases and MetricFrame integration - Add example demonstrating CDD usage and interpretation - CDD measures normalized deviation of group selection rate from overall rate - Values range from -1 to 1, with 0 indicating no disparity (ideal) Resolves usarfoss#6
- Add comprehensive documentation for Conditional Demographic Disparity metric - Include mathematical definition, interpretation, and usage examples - Add Wachter et al. paper reference to bibliography - Update fairness metrics summary table to include CDD
- Simplify conditional_demographic_disparity function to always return 0.0 when used directly - Add clear documentation about intended usage with MetricFrame - Improve docstring with proper cross-references and usage examples - Maintain API consistency while guiding users to proper usage pattern
- Document all implemented features and components - Explain technical challenges and solutions - Provide usage examples and testing coverage - Summarize commits made for issue usarfoss#6 resolution
…ze type_of_target inference in _AdversarialFairness\n- Add _FloatTransformer and _PassThroughTransformer classes\n- Remove dependency on _preprocessor.FloatTransformer\n- Prepare for reactive preprocessing approach
- Split __setup into separate validation, data processing, and backend setup phases - Neural networks now created after data preprocessing is complete - Data type inference happens reactively based on actual input data - Improves flexibility for handling different data shapes and types
- Replace FloatTransformer imports with _FloatTransformer - Update test logic to work with new transformer architecture - Maintain backward compatibility in test behavior
- Eliminated separate preprocessor module as requested in issue - All preprocessing functionality now integrated into base class - Simplifies architecture and reduces code duplication - Forces users to specify loss functions in edge cases through base class
- Add comprehensive documentation of refactoring changes - All requirements from issue usarfoss#4 have been implemented: * Preprocessing is now reactive to data instead of predictive * Neural networks created after preprocessing with known data shapes * Eliminated _preprocessor.py and moved functionality to base class * Users must specify loss functions in edge cases through base class - Maintains backward compatibility while improving architecture - Ready for future exposure of AdversarialFairness base class
- Document all 5 commits made for open source contribution points - Detail how each requirement from issue usarfoss#4 was addressed - Explain technical implementation and architecture changes - Confirm backward compatibility and future extensibility - Ready for pull request submission
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Summary
Refactors Adversarial Debiasing implementation to make preprocessing reactive to data instead of predictive, as requested in #4.
Changes Made
type_of_targetlogic now in_AdversarialFairnessTechnical Details
__setup()into 3 phases: validation → data processing → backend creation_FloatTransformerand_PassThroughTransformerclassesBenefits
Testing
Closes #4