| Name | mmm-fair JSON |
| Version |
0.2.0
JSON |
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| Summary | A multi-objective multi-fairness boosting classifier |
| upload_time | 2025-02-17 17:01:29 |
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| requires_python | >=3.11 |
| license | Apache License
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### 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](https://archive.ics.uci.edu)
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](https://github.com/mammoth-eu/mammoth-toolkit-releases) or the demonstrator app from the [mammoth-commons](https://github.com/mammoth-eu/mammoth-toolkit-releases) 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](https://link.springer.com/chapter/10.1007/978-3-031-18840-4_21),” 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 [arjun.roy@unibw.de](mailto:arjun.roy@unibw.de)
Check out the source code at: [GITHUB](https://github.com/arjunroyihrpa/MMM_fair).
Raw data
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"description": "### MMM-Fair is a multi-objective, fairness-aware boosting classifier originally inspired by the paper: \"Multi-fairness Under Class-Imbalance\"\nhttps://link.springer.com/chapter/10.1007/978-3-031-18840-4_21\n#\n\nThe original algorithm targeted Equalized Odds (a.k.a. Disparate Mistreatment). This MMM-Fair implementation generalizes to multiple fairness objectives:\n\t\u2022\tDemographic Parity (DP)\n\t\u2022\tEqual Opportunity (EP)\n\t\u2022\tEqualized Odds (EO)\n\nWe further improve the approach by:\n\t1.\tFlexible Base Learners: Any scikit-learn estimator (e.g. DecisionTreeClassifier, LogisticRegression, MLP) can be used as the base learner.\n\t2.\tFairness-Weighted Alpha: The boosting weight (alpha) accounts for fairness metrics alongside classification error.\n\t3.\tDynamic Handling of Over-Boosted Samples: Reduces excessive emphasis on specific samples once fairness goals are partially met.\n\n\n## Installation\n\n[pip install mmm-fair](##pip install mmm-fair)\n\nRequires Python 3.11+.\n\nDependencies: numpy, scikit-learn, tqdm, pymoo, pandas, ucimlrepo, skl2onnx, etc.\n\n## Usage Overview\n\nYou can import and use MMM-Fair directly:\n\n[from mmm_fair import MMM_Fair](##from mmm_fair import MMM_Fair)\n[from sklearn.tree import DecisionTreeClassifier](##from sklearn.tree import DecisionTreeClassifier)\n\n# Suppose you have X (features), y (labels)\n### \nmmm = MMM_Fair(\n estimator=DecisionTreeClassifier(max_depth=5),\n constraints=\"EO\", # or \"DP\", \"EP\"\n n_estimators=1000,\n random_state=42,\n # other parameters, e.g. gamma, saIndex, saValue...\n)\nmmm.fit(X, y)\npreds = mmm.predict(X_test)\n\nFairness Constraints\n\t\u2022\tconstraints=\"DP\" \u2192 Demographic Parity\n\t\u2022\tconstraints=\"EP\" \u2192 Equal Opportunity\n\t\u2022\tconstraints=\"EO\" \u2192 Equalized Odds\n\nPass 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.\n\nTrain & Deploy Script\n\nThis package provides a script, train_and_deploy.py, which:\n\t1.\tLoads data (from a known UCI dataset or a local CSV).\n\t2.\tSpecifies fairness constraints, protected attributes, and base learner.\n\t3.\tTrains MMM-Fair with your chosen hyperparameters.\n\t4.\tDeploys the model in ONNX or pickle format.\n\n### Example command:\n\n[using UCI library](https://archive.ics.uci.edu)\n\npython -m mmm_fair.train_and_deploy \\\n --dataset Adult \\\n --prots race sex\\\n --nprotgs White Male \\\n --constraint EO \\\n --base_learner Logistic\\\n --deploy onnx\n\n[using local \"csv\" data]\n\npython -m mmm_fair.train_and_deploy \\\n --dataset mydata.csv \\\n --target label_col \\\n --prots prot_1 prot_2 prot_3 \\\n --nprotgs npg1 npg2 npg3 \\\n --constraint EO \\\n --base_learner tree \\\n --deploy onnx\n\n#### Currently the fairness intervention only implemented for categorical groups. \n#### 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. \n\n\n\t\u2022\tResult: Multiple ONNX files (one per boosting round) plus a model_params.npy inside a directory. It\u2019s then zipped into a .zip archive for distribution or analysis.\n\nMAMMOth Toolkit Integration\n\nThe ONNX output and model_params.npy are designed to integrate with the [MAMMOth](https://github.com/mammoth-eu/mammoth-toolkit-releases) or the demonstrator app from the [mammoth-commons](https://github.com/mammoth-eu/mammoth-toolkit-releases) project.\n\nBy providing the .zip archive, you can:\n\t\u2022\tUpload it to MAMMOth,\n\t\u2022\tExamine bias and performance metrics across subgroups,\n\t\u2022\tCompare fairness trade-offs with a user-friendly interface.\n\nExample Workflow\n\t1.\tChoose Fairness Constraint: e.g., DP, EO, or EP.\n\t2.\tDefine sensitive attributes in saIndex and the protected-group condition in saValue.\n\t3.\tPick base learner (e.g., DecisionTreeClassifier(max_depth=5)).\n\t4.\tTrain with a large number of estimators (n_estimators=300 or 1000) for best performance.\n\t5.\tOptionally do partial ensemble selection with update_theta(criteria=\"all\") or update_theta(criteria=\"fairness\") .\n\t6.\tExport to ONNX or pickle for downstream usage.\n\nReferences\n\t\u2022\tOriginal Paper:\n\u201c[Multi-Fairness Under Class-Imbalance](https://link.springer.com/chapter/10.1007/978-3-031-18840-4_21),\u201d Roy, Arjun, Vasileios Iosifidis, and Eirini Ntoutsi. International Conference on Discovery Science. Cham: Springer Nature Switzerland, 2022.\n\n\nLicense & Contributing\n\nThis project is released under [Apache License Version 2.0].\nContributions are welcome\u2014please open an issue or pull request on GitHub.\n\nContact\n\nFor questions or collaborations, please contact [arjun.roy@unibw.de](mailto:arjun.roy@unibw.de) \nCheck out the source code at: [GITHUB](https://github.com/arjunroyihrpa/MMM_fair).\n",
"bugtrack_url": null,
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