mmm-fair 0.2.1

A multi-objective multi-fairness boosting classifier

MMM-Fair is a multi-objective, fairness-aware boosting classifier originally inspired by the paper: "Multi-fairness Under Class-Imbalance"

https://link.springer.com/chapter/10.1007/978-3-031-18840-4_21

The original algorithm targeted Equalized Odds (a.k.a. Disparate Mistreatment). This MMM-Fair implementation generalizes to multiple fairness objectives: • Demographic Parity (DP) • Equal Opportunity (EP) • Equalized Odds (EO)

We further improve the approach by: 1. Flexible Base Learners: Any scikit-learn estimator (e.g. DecisionTreeClassifier, LogisticRegression, MLP) can be used as the base learner. 2. Fairness-Weighted Alpha: The boosting weight (alpha) accounts for fairness metrics alongside classification error. 3. Dynamic Handling of Over-Boosted Samples: Reduces excessive emphasis on specific samples once fairness goals are partially met.

Installation

[pip install mmm-fair](##pip install mmm-fair)

Requires Python 3.11+.

Dependencies: numpy, scikit-learn, tqdm, pymoo, pandas, ucimlrepo, skl2onnx, etc.

Usage Overview

You can import and use MMM-Fair directly:

[from mmm_fair import MMM_Fair](##from mmm_fair import MMM_Fair) [from sklearn.tree import DecisionTreeClassifier](##from sklearn.tree import DecisionTreeClassifier)

Suppose you have X (features), y (labels)

mmm = MMM_Fair( estimator=DecisionTreeClassifier(max_depth=5), constraints="EO", # or "DP", "EP" n_estimators=1000, random_state=42, # other parameters, e.g. gamma, saIndex, saValue... ) mmm.fit(X, y) preds = mmm.predict(X_test)

Fairness Constraints • constraints="DP" → Demographic Parity • constraints="EP" → Equal Opportunity • constraints="EO" → Equalized Odds

Pass the relevant saIndex (sensitive attribute array) and saValue (dictionary of protected vs. non-protected group mappings) to MMM-Fair if you want it to track fairness properly for subgroups.

Train & Deploy Script

This package provides a script, train_and_deploy.py, which: 1. Loads data (from a known UCI dataset or a local CSV). 2. Specifies fairness constraints, protected attributes, and base learner. 3. Trains MMM-Fair with your chosen hyperparameters. 4. Deploys the model in ONNX or pickle format.

Example command:

using UCI library

python -m mmm_fair.train_and_deploy
--dataset Adult
--prots race sex
--nprotgs White Male
--constraint EO
--base_learner Logistic
--deploy onnx

[using local "csv" data]

python -m mmm_fair.train_and_deploy
--dataset mydata.csv
--target label_col
--prots prot_1 prot_2 prot_3
--nprotgs npg1 npg2 npg3
--constraint EO
--base_learner tree
--deploy onnx

Currently the fairness intervention only implemented for categorical groups.

So if protected attribute is numerical e.g. "age" then for non-protected value i.e. --nprotgs provide a range like 30_60 as argument.

•	Result: Multiple ONNX files (one per boosting round) plus a model_params.npy inside a directory. It’s then zipped into a .zip archive for distribution or analysis.

MAMMOth Toolkit Integration

The ONNX output and model_params.npy are designed to integrate with the MAMMOth or the demonstrator app from the mammoth-commons project.

By providing the .zip archive, you can: • Upload it to MAMMOth, • Examine bias and performance metrics across subgroups, • Compare fairness trade-offs with a user-friendly interface.

Example Workflow 1. Choose Fairness Constraint: e.g., DP, EO, or EP. 2. Define sensitive attributes in saIndex and the protected-group condition in saValue. 3. Pick base learner (e.g., DecisionTreeClassifier(max_depth=5)). 4. Train with a large number of estimators (n_estimators=300 or 1000) for best performance. 5. Optionally do partial ensemble selection with update_theta(criteria="all") or update_theta(criteria="fairness") . 6. Export to ONNX or pickle for downstream usage.

References • Original Paper: “Multi-Fairness Under Class-Imbalance,” Roy, Arjun, Vasileios Iosifidis, and Eirini Ntoutsi. International Conference on Discovery Science. Cham: Springer Nature Switzerland, 2022.

License & Contributing

This project is released under [Apache License Version 2.0]. Contributions are welcome—please open an issue or pull request on GitHub.

Contact

For questions or collaborations, please contact [e-mail] Check out the source code at: GITHUB.