struct-bias 1.2.1

BIAS toolbox: Structural bias detection for continuous optimization algorithms

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, with the following packages installed:

• PoweR
• AutoSEARCH
• nortest
• data.table
• goftest
• ddst

Then install via pip:

pip install struct-bias

Detailed setup using virtual env

1. Download and install R from https://cran.r-project.org/
2. Download this repository (clone or as zip)
3. Create a python virtual env python -m venv env
4. Activate the env (in powershell for example: env/Scripts/Activate.ps1 )
5. Install dependencies pip install -r requirements.txt
6. 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.