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feat: Add relaxed fairness constraint fulfillment for equalized odds (Issue #3) - #17

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usarfoss:mainfrom
divyanshbhai:fix-issue-3
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feat: Add relaxed fairness constraint fulfillment for equalized odds (Issue #3)#17
divyanshbhai wants to merge 15 commits into
usarfoss:mainfrom
divyanshbhai:fix-issue-3

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@divyanshbhai divyanshbhai commented Oct 15, 2025

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Fix #3

Summary

Implements relaxed fairness constraint fulfillment via postprocessing for equalized odds, addressing issue #3.

Changes Made

  • ✅ Remove restriction preventing tol parameter usage with equalized_odds constraint
  • ✅ Add _threshold_optimization_for_relaxed_equalized_odds() method with comprehensive search algorithm
  • ✅ Enable controlled trade-off between fairness and performance via tolerance parameter
  • ✅ Maintain backward compatibility with existing ThresholdOptimizer functionality
  • ✅ Add comprehensive tests and example demonstrating the feature

Key Features

  • Relaxed Constraints: Allow TPR/FPR differences up to specified tolerance between groups
  • Optimization: Maximize objective while respecting fairness constraints within tolerance
  • Fallback: Automatically fall back to strict equalized odds when no relaxed solution exists
  • Integration: Seamlessly integrates with existing postprocessing framework

Usage Example

optimizer = ThresholdOptimizer(
    estimator=classifier,
    constraints="equalized_odds",
    objective="accuracy_score", 
    tol=0.1,  # Allow up to 0.1 difference in TPR/FPR between groups
    prefit=True
)

Divyansh added 15 commits October 15, 2025 19:11
Divyansh Divyansh
Add basic OptimizedPreprocessing class structure
aec5844 
- 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
Divyansh Divyansh
Implement core optimization algorithm for OptimizedPreprocessing
468a7d9 
- 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
Divyansh Divyansh
Add comprehensive unit tests for OptimizedPreprocessing
e195f4c 
- 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
Divyansh Divyansh
Add comprehensive documentation for OptimizedPreprocessing
69489c8 
- 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
Divyansh Divyansh
feat: Add initial Learning Fair Representations (LFR) algorithm imple…
6eb3c80 
…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
Divyansh Divyansh
feat: Improve LFR algorithm with better optimization and add document…
f56b792 
…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
Divyansh Divyansh
feat: Integrate LFR with fairness testing framework
c8c4941 
- 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
Divyansh Divyansh
feat: Add prediction capabilities to LFR algorithm
9d4f8ee 
- 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
Divyansh Divyansh
feat: Add comprehensive example for Learning Fair Representations
d0d77e5 
- 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
Divyansh Divyansh
docs: Add comprehensive implementation summary for LFR
161dc03 
- 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
Divyansh Divyansh
feat: remove restriction preventing tolerance parameter with equalize…
0c46cdf 
…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
Divyansh Divyansh
feat: implement basic relaxed equalized odds optimization
9688930 
- 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
Divyansh Divyansh
feat: improve relaxed equalized odds algorithm and add comprehensive …
43e2674 
…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
Divyansh Divyansh
feat: add comprehensive example for relaxed equalized odds
d56e5be 
- 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
Divyansh Divyansh
docs: add comprehensive documentation for relaxed equalized odds
ca45bc0 
- 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
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ENH relaxed fairness constraint fulfillment via postprocessing (currently only supports strict fulfillment)

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