FairEMOL - Mitigating Unfairness via Evolutionary Multiobjective Optimisation Learning

This is the code for the paper "Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning" in IEEE Transactions on Evolutionary Computation, in which we proposed a framework based on evolutionary multi-objective optimisation learning. The PDF file is available at the IEEE website.

Personal Use Only. No Commercial Use

Please consider citing this work if you use this repository. The bibtex is as followes:

@ARTICLE{9902997,
  author={Zhang, Qingquan and Liu, Jialin and Zhang, Zeqi and Wen, Junyi and Mao, Bifei and Yao, Xin},
  journal={IEEE Transactions on Evolutionary Computation}, 
  title={Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning}, 
  year={2022},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TEVC.2022.3209544}}

Main environments that have been tested

• python 3.7.13
• numpy 1.21.5
• scipy 1.7.3
• matplotlib 3.5.2
• torch 1.12.1
• pandas 1.3.5
• sklearn 1.0.2

How to use

Overview

There are two parts for this work, model training and ensemble learning.

1. Model training: this part is implemented using python, which is mainly based on geatpy. Further, we modified it so that the code supports individuals (solutions) in a population being neural networks during optimisation process.

2. Ensemble learning: model selection strategies are implemented using matlab mainly based on PlatEMO. Analysis codes are also provided.

Set running environment

In FairEMOL/__init__.py, if your system is linux, please choose

lib_path = __file__[:-11] + 'core/Linux/lib64/v3.7/'

If your syetem is windows, please choose

lib_path = __file__[:-11] + 'core/Windows/lib64/v3.7/'

Load/Add dataset

1. Data pre-processing is based on the approach.
   1. Put the raw data in FairEMOL/EvolutionaryCodes/data/raw
   2. Write the code to process raw data in FairEMOL/EvolutionaryCodes/data/objects, e.g., German.py
   3. Run the code FairEMOL/EvolutionaryCodes/preprocess.py
   4. Obtain the processed data in FairEMOL/EvolutionaryCodes/data/preprocessed, e.g., german_numerical-for-NN.csv
   5. Restore processed data in the folder FairEMOL/EvolutionaryCodes/data, e.g., German
2. Use FairEMOL/EvolutionaryCodes/load_data.py to splite the data into training data, validation data, ensemble data (if you need) and test data. You can also set save_csv=1 to manually store them.
3. Add the dataset's name in FairEMOL/EvolutionaryCodes/data/objects/list.py

Run algorithm

1. Set algorithmic parameters in Fairness_main.py
2. Run Fairness_main.py

Obtain results

• Results will be stored in the folder Result/time_of_starting_the_run, e.g., 2022-10-25-16-13-29.
  • Parameters.txt: record all the paremeters
  • allobjs/ALL_Objs_{valid, test, ensemble, test}_genxxx_sofar.csv: three or four csv file to record the objectives of all the individuals during optimisation process
  • detect/: record (1) the details of the dataset and (2) the objectives of all the individuals during optimisation process every logMetric (set in Fairness_main.py) generation
  • nets/: the neural network files of all the individuals during optimisation process every logMetric (set in Fairness_main.py) generation

Ensemble Learning

Based on the trained ML models, ensemble outputs considering some ML models can be calculated. This part is implemented using Matlab and the codes are in the folder EnsembleCodes.

1. compute_info.m: Process the results of each run in the folder Result/time_of_starting_the_run/detect/
   1. Load data stored in the folder Result/time_of_starting_the_run/detect/
   2. Apply ensemble strategies and calculate the ensemble outputs
   3. Store the results of each run in the file time_of_starting_the_run.mat
2. merge_info.m: merge all the ensemble results over all the runs for a dataset into one .mat file
3. analyse_info.m: based on the results, process analysis