Metadata-Version: 2.1
Name: struct-bias
Version: 1.2.2
Summary: BIAS toolbox: Structural bias detection for continuous optimization algorithms
Author: Diederick Vermetten, Niki van Stein
Author-email: d.l.vermetten@liacs.leidenuniv.nl
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: numpy
Requires-Dist: tensorflow
Requires-Dist: shap
Requires-Dist: rpy2
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: sklearn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: statsmodels
Requires-Dist: regex

# 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

```py
#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.

