BIAS toolbox: Structural bias detection for continuous optimization algorithms
Project description
Deep-BIAS: Bias In Algorithms, Structural
A toolbox for detecting structural bias in continuous optimization heuristics.
With a deep-learning extension to better evaluate the type of bias and gain insights using explainable AI
Setup
This package requires an R-installation to be present.
The R packages can be installed with the install_r_packages
command from the BIAS toolbox.
Install the BIAS toolbox using pip:
pip install struct-bias
Then install the required R packages
from BIAS import install_r_packages
#run first time to install required R packages
install_r_packages()
This installs the following R packages:
- PoweR
- AutoSEARCH
- nortest
- data.table
- goftest
- ddst
Detailed setup using virtual env
- Download and install R from https://cran.r-project.org/
- Download this repository (clone or as zip)
- Create a python virtual env
python -m venv env
- Activate the env (in powershell for example:
env/Scripts/Activate.ps1
) - Install dependencies
pip install -r requirements.txt
- Checkout the
example.py
to start using the BIAS toolbox.
Example
#example of using the BIAS toolbox to test a DE algorithm
from scipy.optimize import differential_evolution
import numpy as np
from BIAS import BIAS, f0, install_r_packages
#run first time to install required R packages
install_r_packages()
bounds = [(0,1), (0, 1), (0, 1), (0, 1), (0, 1)]
#do 30 independent runs (5 dimensions)
samples = []
print("Performing optimization method 30 times of f0.")
for i in np.arange(30):
result = differential_evolution(f0, bounds, maxiter=100)
samples.append(result.x)
samples = np.array(samples)
test = BIAS()
print(test.predict(samples, show_figure=True))
y, preds = test.predict_deep(samples)
test.explain(samples, preds, filename="explanation.png")
Additional files
Note: The code for generating the RF used to predict the type of bias is included, but the full RF is not. These can be found on zenodo: https://doi.org/10.6084/m9.figshare.16546041. The RF models will be downloaded automatically the first time the predict function requires them.
Citation
If you use the BIAS toolbox in a scientific publication, we would appreciate using the following citations:
@ARTICLE{9828803,
author={Vermetten, Diederick and van Stein, Bas and Caraffini, Fabio and Minku, Leandro L. and Kononova, Anna V.},
journal={IEEE Transactions on Evolutionary Computation},
title={BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain},
year={2022},
volume={26},
number={6},
pages={1380-1393},
doi={10.1109/TEVC.2022.3189848}
}
@software{niki_van_stein_2023_7803623,
author = {Niki van Stein and
Diederick Vermetten},
title = {Basvanstein/BIAS: v1.1 Deep-BIAS Toolbox},
month = apr,
year = 2023,
publisher = {Zenodo},
version = {v1.1},
doi = {10.5281/zenodo.7803623},
url = {https://doi.org/10.5281/zenodo.7803623}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for struct_bias-1.2.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8cdacb73755ac38a8088ab493167733d8e1e7fa5454fc79dbea8bf1a2019d3ba |
|
MD5 | 5221e4dec9414120ef7b604f8ce317c8 |
|
BLAKE2b-256 | 5958b629e40fa21a913d9b00628bd1dda804992cb1b7ce4482bc195839d27c32 |