Official repository for the paper "DeepDFA: Automata Learning through Neural Probabilistic Relaxations" accepted by the 27th European Conference on Artificial Intelligence (ECAI 2024).
- Pythomata
- Pytorch
To reproduce the experiments in the paper execute the file do_experiments.py.
Run do_experiments.py --help for a complete list of the parameters used by the script (below).
USAGE: do_experiments.py [flags]
flags:
do_experiments.py:
--dataset_dir: path to the datasets
(default: 'datasets_Tomita/')
--error_rate: portion of errated labels in the training dataset
(default: '0.0')
(a number)
--experiment: which experiment to execute, choose one in the list ['tomita', 'random_dfa']
(default: 'tomita')
--ground_truth_dfa_dir: path to save ground truth automatons
(default: 'DFA_ground_truth/')
--plot_dir: path to save the plots
(default: 'Plots/')
--predicted_dfa_dir: path to save predicted automatons
(default: 'DFA_predicted_nesy/')
--results_dir: path to save the results
(default: 'Results/')
Examples:
To learn the Tomita languages from data execute the script with default parameters
python do_experiments.py
To learn the random DFAs in the directory random_DFAs run
python do_experiments.py --dataset_dir datasets_random_dfa/ --experiment random_dfa
To repeat the expermints on a corrupted version of the datasets, with 1% of flipped labels, add to the commands above --error_rate 0.01