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# ExpOut
This project is an extension of LimeOut[1]. It aims at tackle process fairness for classification, while keeping the accuracy level (or improving). 
More precisely, ExpOut incorporates different explainers.

Classifiers available: 
* Multilayer Perceptron
* Logistic Regression
* Random Forest
* Bagging
* AdaBoost
* Gaussian Mixture
* Gradiente Boosting


Explainers
* LIME
* Anchors

# Example
`python runner.py --data german.data --trainsize 0.8 --algo mlp --max_features 10 --cat_features 0 2 3 5 6 8 9 11 13 14 16 18 19 --drop 8 18 19 --exp anchors`

# References 
[1] Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli. LimeOut: An Ensemble Approach To Improve Process Fairness. 2020. ⟨hal-02864059v2⟩


## Dependencies
* Python >= 3.7
* Scikit-learn >= 0.20.3
* numpy                              1.16.4
* pandas                             0.24.2
* scipy                              1.3.0
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* seaborn                            0.9.0