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faim-0.0.3
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FAIM (FAir Interpolation Method), described in "Beyond Incompatibility: Interpolation between Mutually
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | faim-0.0.3 |
| نام | faim |
| نسخه کتابخانه | 0.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Meike Zehlike, Alex Loosley |
| ایمیل نویسنده | meike.zehlike@zalando.de, alex.loosley@zalando.de |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/faim/ |
| مجوز | Apache License Version 2.0 |
[](https://github.com/MilkaLichtblau/faim/actions/workflows/python-build-test.yaml)
[](https://opensource.org/licenses/Apache-2.0)
# FAIM
FAIM (FAir Interpolation Method), described in
[Beyond Incompatibility: Interpolation between Mutually
Exclusive Fairness Criteria in Classification Problems](https://arxiv.org/abs/2212.00469),
is a post-processing algorithm for achieving a combination of group-fairness criteria
(equalized false positive rates, equalized false negative rates, group calibration).
**This README.md is under construction!**
## Installation
### Environment
Ensure you have a environment with Python>=3.7 and pip>=2.21, preferably by creating a virtual environment.
One way to do this is using [miniconda](https://docs.conda.io/en/latest/miniconda.html). Install miniconda following
the instructions on [this page](https://docs.conda.io/en/latest/miniconda.html)
and create a python 3.10 environment:
```bash
conda create -n faim python=3.10
```
Activate the environment
```bash
conda activate faim
```
Check that versions of python are >=3.7 and >=2.21, respectively:
```bash
python --version
pip --version
```
### Python Package
If you intend to develop the package and/or contribute, follow the install instructions in the
[Development Environment](#development-environment) section below instead. Otherwise, follow these instructions.
The package and experiment CLI can be installed with pip:
```bash
pip install "faim[experiment]"
```
Note the `[experiment]` notation is required for now since, for the moment, the algorithm can only be run in experiment
mode for recreating experimental results in the [paper](https://arxiv.org/abs/2212.00469).
In the future, `faim` will be made available for post-processing classifier scores
(given ground truth and group information), going beyond reproducing paper experiments.
### Removal
From the environment where you installed the package, run
```bash
pip uninstall faim
```
## Usage
Installing faim also (currently) installs one command line interface (CLI) tool, `faim-experiment` which can be
used to reproduce the work in the paper.
[A general API will be added soon]
### Experiments
This section contains information for reproducing experiments in our [paper](https://arxiv.org/abs/2212.00469).
Ensure the package has been installed with `[experiment]` extra requirements before continuing
(see [Installation | Python Package](#python-package))!
#### Prepare Data
The CLI can be used to prepare any of the three datasets used in the [paper](https://arxiv.org/abs/2212.00469):
```bash
faim-experiment --prepare-data DATASET
```
where `DATASET` is one of:
* `synthetic-from-paper`
* `compas`
* `zalando` [waiting for permission to release, contact us for more information]
The dataset will be downloaded, and prepared to a folder called `prepared-data`.
The following sections include info about each dataset:
###### Synthetic data
The raw dataset in the GitHub repo corresponds to synthetic prediction and ground truth scores for two groups,
for each group sampling from a corresponding binormal distribution.
###### COMPAS data
The raw data was obtained from [ProPublica's COMPAS Analysis repository](https://github.com/propublica/compas-analysis).
###### Zalando data
**Under construction, more information to follow!**
#### Run Experiment
Having prepared data following the instruction above, you are ready to run a FAIM experiment:
```bash
faim-experiment --run PREPARED-DATASET LOW_SCORE_VAL,HIGH_SCORE_VAL THETAS PREPARED_DATA_FILEPATH
```
`PREPARED-DATASET` is now one of the following options (depending on what has been prepared):
* `syntheticTwoGroups` (prepared using `--prepare-data synthetic`)
* `compasGender` (prepared using `--prepare-data compas`)
* `compasRace` (prepared using `--prepare-data compas`)
* `compasAge` (prepared using `--prepare-data compas`)
* `zalando` (prepared using `--prepare-data zalando`) [waiting for permission to release, contact us for more information]
`LOW_SCORE_VAL,HIGH_SCORE_VAL` are two numbers that define the score range.
`THETAS` correspond to the fairness compromise you want. There are three thetas per group corresponding to the
desired amount of the three fairness criteria that the system should achieve:
1. group calibration
2. equalized false negative rates
3. equalized false positive rates
Note, as discussed in the paper, thetas = 1,1,1 does not indicate that the system will simultaneously achieve all
three (mutually incompatible) fairness criteria, but rather the result will be a compromise between all three.
See the [paper](https://arxiv.org/abs/2212.00469) for more details.
Finally, `PREPARED_DATA_FILEPATH` corresponds to the filepath of the prepared data.
###### Examples
Run all of the following from the same folder where `faim-experiment --prepare-data` was run.
In each example, a FAIM post-processor is trained and evaluated with results saved under the `results` folder:
* Train FAIM model on synthetic dataset with callibration as fairness correction
```bash
faim-experiment --run syntheticTwoGroups 0.1 1,0,0,1,0,0 prepared-data/synthetic/2groups/2022-01-12/dataset.csv
```
* Train FAIM model on synthetic dataset to achieve a combination of all three fairness criteria.
```bash
faim-experiment --run syntheticTwoGroups 0.1 1,1,1,1,1,1 prepared-data/synthetic/2groups/2022-01-12/dataset.csv
```
#### Visualize Results
**Needs documentation!**
### Development Environment
To develop and/or contribute, clone the repository
```bash
git clone <this repo URL>
```
From the root directory of the git repository, install the package with pip in editable mode (`-e`)
with extra requirements for experiments (experiment) and development (dev):
```bash
pip install -e ".[experiment,dev]"
```
Don't confuse the `[]` to mean optional. The `".[experiment, dev]"` notation tells pip to install extra
"experiment" and "dev" requirements including things like `pytest` and `pre-commit`.
When contributing, be sure to install (and use) our [pre-commit](https://pre-commit.com/) hooks:
```bash
pre-commit install -t pre-commit -t pre-push
```
نیازمندی
| مقدار | نام |
|---|---|
| >=0.8.0 | pot |
| >=1.0.0 | pandas |
| >=2.20.0 | pre-commit |
| >=7.0.0 | pytest |
| >=3.0.0 | pytest-cov |
| >=1.0.0 | jupyter |
| >=4.9.1 | lxml |
| >=3.5.0 | matplotlib |
| >=1.1.0 | scikit-learn |
| >=0.13.5 | statsmodels |
| >=5.11.0 | plotly |
| >=1.6.0 | pooch |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl faim-0.0.3:
pip install faim-0.0.3.whl
نصب پکیج tar.gz faim-0.0.3:
pip install faim-0.0.3.tar.gz