Create virtual environment. E.g
python3 -m venv ~/.venvs/lipi_ae_gecco_24
Activate virtual environment. E.g
source ~/.venvs/lipi_ae_gecco_24/bin/activate
Install dependencies
pip install -r requirements.txt
Create Binary Clustering problems
PYTHONPATH=src python src/aes_lipi/datasets/data_loader.py --n_dim 1000 --n_clusters 10
Test binary clustering problem and autoencoder
PYTHONPATH=src python src/aes_lipi/environments/binary_clustering.py --method=Autoencoder --dataset_name=binary_clustering_10_100_1000
Run Lipi-Ae on binary clustering problem
PYTHONPATH=src python src/aes_lipi/lipi_ae.py --configuration_file=tests/gecco_2024/configurations/binary_clustering/test_bc/binary_clustering_epoch_node_demo_lipi_ae.json
Run experiments
time PYTHONPATH=src python src/aes_lipi/utilities/gecco_experiments.py --configuration_directory tests/gecco_2024/configurations/binary_clustering/test_bc --sensitivity tests/gecco_2024/configurations/binary_clustering/test_bc/sensitivity_values.json
Update dataset in sensitivity_values.json
key "dataset_name"
by adding the new dataset to the list
Analyze data from --root_dir
based on --param_dir
parameters.
time PYTHONPATH=src python src/aes_lipi/utilities/analyse_data.py --root_dir out_binary_clustering --param_dir out_binary_clustering
Save the parameters at every iteration
Run Lipi-Ae with solution concept best_case
PYTHONPATH=src python src/aes_lipi/lipi_ae.py --dataset_name binary_clustering_10_100_1000 --environment AutoencoderBinaryClustering --epochs 3 --batch_size 400 --population_size 2 --ae_quality_measures L1 --solution_concept best_case --checkpoint_interval 1 --do_not_overwrite_checkpoint
@inproceedings{hemberg2024ae,
title={Cooperative Spatial Topologies for Autoencoder Training},
author={Hemberg, Erik and Toutouh, Jamal and O'Reilly, Una-May},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
year={2024}
}