ENH: Add Learning Fair Representations (LFR) preprocessing algorithm #8 - #14
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ENH: Add Learning Fair Representations (LFR) preprocessing algorithm #8#14divyanshbhai wants to merge 10 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
This was referenced Oct 15, 2025
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Summary
Implements the Learning Fair Representations (LFR) preprocessing algorithm by Zemel et al. (2013) to address issue #8.
Changes Made
LearningFairRepresentationsclass infairlearn.preprocessingtest/unit/preprocessing/Key Features
Validation
Files Changed
fairlearn/preprocessing/_learning_fair_representations.py(new)fairlearn/preprocessing/__init__.py(updated exports)test/unit/preprocessing/test_learning_fair_representations.py(new)test/unit/preprocessing/test_learning_fair_representations_predict.py(new)test/unit/preprocessing/test_preprocessing_fairness.py(updated)docs/user_guide/mitigation/preprocessing.rst(updated)docs/user_guide/mitigation/index.rst(updated)examples/plot_learning_fair_representations.py(new)Closes #8