| Name | mmm-fair-cli JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | CLI-only version of the MMM-Fair boosting classifier |
| upload_time | 2025-07-15 19:07:34 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | Apache License
Version 2.0, January 2004
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| keywords |
fairness
boosting
classification
machine-learning
cli
|
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|
<a href="https://github.com/arjunroyihrpa/MMM_fair">
<img src="https://raw.githubusercontent.com/arjunroyihrpa/MMM_fair/main/images/mmm-fair.png" alt="MMM-Fair Logo" width="200"/>
</a>
# MMM-Fair-CLI
[](https://pypi.org/project/mmm-fair-cli/)
[](https://github.com/arjunroyihrpa/MMM_fair/blob/main/LICENSE)
**MMM-Fair-CLI** is a lightweight, command-line-only version of the [MMM-Fair framework](https://github.com/arjunroyihrpa/MMM_fair) for fairness-aware boosting. It excludes the web UI, LLMs, and chat features.
---
## 🔧 Installation
```bash
pip install mmm-fair-cli
```
Requires Python 3.12+.
Dependencies: numpy, scikit-learn, tqdm, pymoo, pandas, ucimlrepo, skl2onnx, etc.
---
## 🚀 Quick Usage (CLI)
```bash
python -m mmm_fair_cli.train_and_deploy \
--classifier MMM_Fair_GBT \
--dataset mydata.csv \
--target label_col \
--prots prot_1 prot_2 \
--nprotgs npg1 npg2 \
--constraint DP \
--early_stop True \
--n_learners 100 \
--deploy pickle \
--moo_vis True
```
### With Known Dataset from Uciml repo
```bash
python -m mmm_fair_cli.train_and_deploy \
--classifier MMM_Fair_GBT \
--dataset Adult \
--prots race sex \
--nprotgs White Male \
--constraint EO \
--deploy onnx \
--moo_vis True
```
---
### 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)) or gradient-based approach.
4. **Train** with a large number of estimators (n_estimators=300 or max_iter=300).
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.
7. **Use** --moo_vis True to open local multi-objective 3D plots for deeper analysis.
8. **Upload** the .zip file (if exported to onnx) to MAMMOth for bias exploration.
---
#### Note:
1. Setting --moo_vis True triggers an interactive local HTML page for exploring the multi-objective trade-offs in 3D plots (accuracy vs. class-imbalance vs. fairness, etc.).
2. 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.
---
### Additional options
If you want to select the best theta from only the Pareto optimal ensembles set (default is False and selects applies the post-processing to all set of solutions):
--pareto True
If you want to provide test data:
--test 'your_test_file.csv'
Or just test split:
--test 0.3
If you want change style (default is table, choose from {table, console}) of report displayed ([Check FairBench Library for more details](https://fairbench.readthedocs.io/material/visualization/)):
--report_type Console
**When deploying with 'onnx'**, we change the models to ONNX file(s), and store additional parameters in a model_params.npy. This gets zipped into a .zip archive for distribution/analysis.
---
### MAMMOth Toolkit Integration
For the bias exploration using [MAMMOth](https://mammoth-ai.eu) pipeline it is really important to select 'onnx' as the '--deploy' argument. The [ONNX](https://onnxruntime.ai) model accelerator and model_params.npy are used to integrate with the [MAMMOth-toolkit](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.
## 🐍📓 From Notebook
```python
from mmm_fair import MMM_Fair_GradientBoostedClassifier
clf = MMM_Fair_GradientBoostedClassifier(
constraint="EO", # or "DP", "EP"
alpha=0.1, # fairness weight
saIndex=..., # shape (n_samples, n_protected)
saValue=..., # dictionary or None
max_iter=100,
random_state=42,
## any other arguments that the HistGradientBoostingClassifier from sklearn can handle
)
clf.fit(X, y)
preds = clf.predict(X_test)
```
MMM-Fair includes utility functions to seamlessly work with datasets from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets).
### 🔧 Load a UCI dataset (e.g. Adult dataset)
```python
from mmm_fair import data_uci
from mmm_fair import build_sensitives
# Load dataset with target column
data = data_uci(dataset_name="Adult", target="income")
```
### 🛡️ Define Sensitive Attributes
```python
saIndex, saValue = build_sensitives(
data.data,
protected_cols=["race", "sex"],
non_protected_vals=["White", "Male"]
)
```
---
## 🤖 Need a Web UI or LLM Explanation?
👉 Use the full version:
🔗 [https://pypi.org/project/mmm-fair/](https://pypi.org/project/mmm-fair/)
#### Maintainer: Arjun Roy (arjunroyihrpa@gmail.com)
#### Contributors: Swati Swati (swati17293@gmail.com), Emmanoui Panagiotou (panagiotouemm@gmail.com)
### 🏛️ Funding
MMM-Fair is a research-driven project supported by several public funding initiatives. We gratefully acknowledge the generous support of:
<p align="center">
<img src="https://bias-project.org/wp-content/themes/wp-bootstrap-starter/images/Bias_Logo.svg" alt="bias-logo" width="120" />
<img src="https://mammoth-ai.eu/wp-content/uploads/2022/09/mammoth.svg" alt="mammoth-logo" width="150" style="margin: 0 20px"/>
<img src="https://stelar-project.eu/wp-content/uploads/2022/08/cropped-stelar-sq.png" alt="stelar-logo" width="100" />
</p>
<p align="center">
<a href="https://bias-project.org"><strong>Volkswagen Foundation – BIAS</strong></a>
<a href="https://mammoth-ai.eu"><strong>EU Horizon – MAMMOth</strong></a>
<a href="https://stelar-project.eu"><strong>EU Horizon – STELAR</strong></a>
</p>
### 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": "<a href=\"https://github.com/arjunroyihrpa/MMM_fair\">\n <img src=\"https://raw.githubusercontent.com/arjunroyihrpa/MMM_fair/main/images/mmm-fair.png\" alt=\"MMM-Fair Logo\" width=\"200\"/>\n</a>\n\n# MMM-Fair-CLI\n\n[](https://pypi.org/project/mmm-fair-cli/)\n[](https://github.com/arjunroyihrpa/MMM_fair/blob/main/LICENSE)\n\n\n\n**MMM-Fair-CLI** is a lightweight, command-line-only version of the [MMM-Fair framework](https://github.com/arjunroyihrpa/MMM_fair) for fairness-aware boosting. It excludes the web UI, LLMs, and chat features.\n\n---\n\n## \ud83d\udd27 Installation\n\n```bash\npip install mmm-fair-cli\n```\n\nRequires Python 3.12+.\n\nDependencies: numpy, scikit-learn, tqdm, pymoo, pandas, ucimlrepo, skl2onnx, etc.\n\n---\n## \ud83d\ude80 Quick Usage (CLI)\n\n```bash\npython -m mmm_fair_cli.train_and_deploy \\\n --classifier MMM_Fair_GBT \\\n --dataset mydata.csv \\\n --target label_col \\\n --prots prot_1 prot_2 \\\n --nprotgs npg1 npg2 \\\n --constraint DP \\\n --early_stop True \\\n --n_learners 100 \\\n --deploy pickle \\\n --moo_vis True\n```\n### With Known Dataset from Uciml repo\n\n```bash\npython -m mmm_fair_cli.train_and_deploy \\\n --classifier MMM_Fair_GBT \\\n --dataset Adult \\\n --prots race sex \\\n --nprotgs White Male \\\n --constraint EO \\\n --deploy onnx \\\n --moo_vis True\n```\n---\n\n### Example Workflow\n1.\t**Choose** Fairness Constraint: e.g., DP, EO, or EP.\n2.\t**Define** sensitive attributes in saIndex and the protected-group condition in saValue.\n3.\t**Pick** base learner (e.g., DecisionTreeClassifier(max_depth=5)) or gradient-based approach.\n4.\t**Train** with a large number of estimators (n_estimators=300 or max_iter=300).\n5.\t**Optionally** do partial ensemble selection with update_theta(criteria=\"all\") or update_theta(criteria=\"fairness\") .\n6.\t**Export** to ONNX or pickle for downstream usage.\n7. **Use** --moo_vis True to open local multi-objective 3D plots for deeper analysis.\n8. **Upload** the .zip file (if exported to onnx) to MAMMOth for bias exploration.\n\n---\n\n#### Note: \n1. Setting --moo_vis True triggers an interactive local HTML page for exploring the multi-objective trade-offs in 3D plots (accuracy vs. class-imbalance vs. fairness, etc.).\n2. 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. \n\n---\n\n### Additional options\n\nIf you want to select the best theta from only the Pareto optimal ensembles set (default is False and selects applies the post-processing to all set of solutions): \n\n --pareto True\n\nIf you want to provide test data: \n\n --test 'your_test_file.csv'\n \nOr just test split: \n\n --test 0.3\n \nIf you want change style (default is table, choose from {table, console}) of report displayed ([Check FairBench Library for more details](https://fairbench.readthedocs.io/material/visualization/)):\n\n --report_type Console\n\n \n**When deploying with 'onnx'**, we change the models to ONNX file(s), and store additional parameters in a model_params.npy. This gets zipped into a .zip archive for distribution/analysis.\n\n---\n\n### MAMMOth Toolkit Integration\n\nFor the bias exploration using [MAMMOth](https://mammoth-ai.eu) pipeline it is really important to select 'onnx' as the '--deploy' argument. The [ONNX](https://onnxruntime.ai) model accelerator and model_params.npy are used to integrate with the [MAMMOth-toolkit](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 \n## \ud83d\udc0d\ud83d\udcd3 From Notebook\n\n\n```python\nfrom mmm_fair import MMM_Fair_GradientBoostedClassifier\n\nclf = MMM_Fair_GradientBoostedClassifier(\n constraint=\"EO\", # or \"DP\", \"EP\"\n alpha=0.1, # fairness weight\n saIndex=..., # shape (n_samples, n_protected)\n saValue=..., # dictionary or None\n max_iter=100,\n random_state=42,\n ## any other arguments that the HistGradientBoostingClassifier from sklearn can handle\n)\nclf.fit(X, y)\npreds = clf.predict(X_test)\n```\n\nMMM-Fair includes utility functions to seamlessly work with datasets from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets).\n\n### \ud83d\udd27 Load a UCI dataset (e.g. Adult dataset)\n```python\nfrom mmm_fair import data_uci\nfrom mmm_fair import build_sensitives\n\n# Load dataset with target column\ndata = data_uci(dataset_name=\"Adult\", target=\"income\")\n```\n### \ud83d\udee1\ufe0f Define Sensitive Attributes\n```python\nsaIndex, saValue = build_sensitives(\n data.data,\n protected_cols=[\"race\", \"sex\"],\n non_protected_vals=[\"White\", \"Male\"]\n)\n```\n\n---\n\n## \ud83e\udd16 Need a Web UI or LLM Explanation?\n\n\ud83d\udc49 Use the full version:\n\ud83d\udd17 [https://pypi.org/project/mmm-fair/](https://pypi.org/project/mmm-fair/)\n\n\n\n\n\n\n\n\n#### Maintainer: Arjun Roy (arjunroyihrpa@gmail.com)\n\n#### Contributors: Swati Swati (swati17293@gmail.com), Emmanoui Panagiotou (panagiotouemm@gmail.com)\n\n### \ud83c\udfdb\ufe0f Funding\n\nMMM-Fair is a research-driven project supported by several public funding initiatives. We gratefully acknowledge the generous support of:\n\n<p align=\"center\">\n <img src=\"https://bias-project.org/wp-content/themes/wp-bootstrap-starter/images/Bias_Logo.svg\" alt=\"bias-logo\" width=\"120\" /> \n <img src=\"https://mammoth-ai.eu/wp-content/uploads/2022/09/mammoth.svg\" alt=\"mammoth-logo\" width=\"150\" style=\"margin: 0 20px\"/>\n <img src=\"https://stelar-project.eu/wp-content/uploads/2022/08/cropped-stelar-sq.png\" alt=\"stelar-logo\" width=\"100\" />\n</p>\n\n<p align=\"center\">\n <a href=\"https://bias-project.org\"><strong>Volkswagen Foundation \u2013 BIAS</strong></a> \n <a href=\"https://mammoth-ai.eu\"><strong>EU Horizon \u2013 MAMMOth</strong></a> \n <a href=\"https://stelar-project.eu\"><strong>EU Horizon \u2013 STELAR</strong></a>\n</p>\n\n\n### License & 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\n### Contact\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,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. 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