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fairlib-0.1.0
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مشاهده بیشترتوضیحات
Unified framework for assessing and improving fairness.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fairlib-0.1.0 |
| نام | fairlib |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Xudong Han |
| ایمیل نویسنده | xudongh1@student.unimelb.edu.au |
| آدرس صفحه اصلی | https://github.com/HanXudong/fairlib |
| آدرس اینترنتی | https://pypi.org/project/fairlib/ |
| مجوز | - |
# *fairlib*
[*fairlib*](https://github.com/HanXudong/fairlib) is a Python framework for assessing and improving fairness. Built-in algorithms can be applied to text inputs, structured inputs, and image inputs.
The *fairlib* package includes metrics for fairness evaluation, [algorithms for bias mitigation](https://hanxudong.github.io/fairlib/supported_bias_mitigation_algorithms.html), and functions for analysis.
For those who want to start with *fairlib* now, you can try our [Colab Tutorial](https://colab.research.google.com/github/HanXudong/fairlib/blob/main/tutorial/fairlib_demo.ipynb), which provides a gentle introduction to the concepts and capabilities.
[The tutorials and other notebooks](https://hanxudong.github.io/fairlib/tutorial_interactive_demos.html) offer a deeper introduction. The [complete API](https://hanxudong.github.io/fairlib) is also available.
## Installation
*fairlib* currently requires Python3.7+ and [Pytorch](https://pytorch.org) 1.10 or higher.
Dependencies of the core modules are listed in [`requirements.txt`](https://github.com/HanXudong/fairlib/blob/main/requirements.txt).
We *strongly* recommend using a [venv](https://docs.python.org/3/library/venv.html) or [conda](https://www.anaconda.com/) environment for installation.
**Standard Installation**
If you do not need further modifications, you can install it with:
```bash
# Start a new virtual environment:
conda create -n fairlib python=3.7
conda activate fairlib
pip install fairlib
```
**Development Installation**
To set up a development environment, run the following commands to clone the repository and install
*fairlib*:
```bash
git clone https://github.com/HanXudong/fairlib.git ~/fairlib
cd ~/fairlib
python setup.py develop
```
**Benchmark Datasets**
Please refer to [data/README.md](https://github.com/HanXudong/fairlib/blob/main/data/README.md) for a list of fairness benchmark datasets.
## Usage
The full description of *fairlib* usages can be found in [*fairlib* cheat sheet](https://hanxudong.github.io/fairlib/tutorial_usage.html) and API reference. Here are the most basic examples.
- *fairlib* can be run from the command line:
```bash
python fairlib --exp_id EXP_NAME
```
- *fairlib* can be imported as a package
```python
from fairlib.base_options import options
from src import networks
config_file = 'opt.yaml'
# Get options
state = options.get_state(conf_file=config_file)
# Init the model
model = networks.get_main_model(state)
# Training with debiasing
model.train_self()
```
## Model Selection and Fairness Evaluation
Besides the classical loss- and performance-based model selection, we provide performance-fairness trade-off based model selection (see the paper below).
Please see [this tutorial](https://hanxudong.github.io/fairlib/tutorial_notebooks/tutorial_Moji_demo.html) for an example of loading training history, performing model selections based on different strategies, and creating basic plots.
Moreover, [interactive plots](https://hanxudong.github.io/fairlib/tutorial_notebooks/tutorial_interactive_plots.html) are also supported, which can be used for analysis.
## Known issues and limitations
None are known at this time.
## Getting help
If you have any problem with our code or have some suggestions, including the future feature, feel free to contact
- Xudong Han (xudongh1@student.unimelb.edu.au)
or describe it in Issues.
## Paper
[fairlib: A Unified Framework for Assessing and Improving Classification Fairness](https://arxiv.org/abs/2205.01876)
Cite Us
```
@article{han2022fairlib,
title={fairlib: A Unified Framework for Assessing and Improving Classification Fairness},
author={Han, Xudong and Shen, Aili and Li, Yitong and Frermann, Lea and Baldwin, Timothy and Cohn, Trevor},
journal={arXiv preprint arXiv:2205.01876},
year={2022}
}
```
## Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
## License
This project is distributed under the terms of the [APACHE LICENSE, VERSION 2.0](https://www.apache.org/licenses/LICENSE-2.0). The license applies to all files in the [GitHub repository](http://github.com/HanXudong/fairlib) hosting this file.
## Acknowledgments
* https://github.com/HanXudong/Decoupling_Adversarial_Training_for_Fair_NLP
* https://github.com/HanXudong/Diverse_Adversaries_for_Mitigating_Bias_in_Training
* https://github.com/SsnL/dataset-distillation
* https://github.com/huggingface/torchMoji
* https://github.com/mhucka/readmine
* https://github.com/yanaiela/demog-text-removal
* https://github.com/lrank/Robust_and_Privacy_preserving_Text_Representations
* https://github.com/yuji-roh/fairbatch
* https://github.com/shauli-ravfogel/nullspace_projection
* https://github.com/AiliAili/contrastive_learning_fair_representations
* https://github.com/AiliAili/Difference_Mean_Fair_Models
نیازمندی
| مقدار | نام |
|---|---|
| - | tqdm |
| - | numpy |
| - | docopt |
| - | pandas |
| - | scikit-learn |
| - | torch |
| - | PyYAML |
| - | seaborn |
| - | matplotlib |
| - | pickle5 |
| - | transformers |
| - | sacremoses |
| - | sentencepiece |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fairlib-0.1.0:
pip install fairlib-0.1.0.whl
نصب پکیج tar.gz fairlib-0.1.0:
pip install fairlib-0.1.0.tar.gz