Getting Started

This page is a user guide for the developers to quickly be able to use FairSeldonian Python library in their own project or, extending/enhancing the codebase to enhance the framework.

Pre-requisites

Library pre-requisites

• sklearn - machine learning model implementations like logistic regression.

• matplotlib - visualizations of the plots.

• numpy - handling data.

• pandas - handling data in the form of dataframes.

• ray - for parallelisation of the algorithm.

• torch - for tensors used in the codebase.

Quick start

The complete code resides in root folder. To run the experiment, you need to amend configurations in main.py.

Go to terminal or any IDE and run that file using the following command:

python main.py <mode>

The default mode is set as base. To understand other modes present in the code, refer to the Variants section of this documentation.

Configuration

The user must setup the following things to make full use of this framework:

• Configuration of the experiment structure : In the main.py, the user must configure the values for number of threads, number of fractions, number of trials per thread, test:train ratio of the dataset, etc.

• Configure logistic regression functions : The user must provide the values of delta (significance level), inequality type (currently supports Hoeffding inequality and t-test), fairness constraint (currently, it supports base variables as real number, True positive, true negative, false positive and false negative; binary operators: +, -, *, / and unary operator: absolute) and candidate-to-safety ratio in this file. In addition to this, the user is expected to replace simple_logistic with their model, and provide predict function for their model.

Collaboration

Please feel free to contribute to this code base. For any queries, contact parul100495@gmail.com