Machine Learning on Boolean Formulas
Companion code of the paper "Understanding Boolean Function Learnability on Deep Neural Networks" (https://arxiv.org/abs/2009.05908)
Tested on Ubuntu Linux 18.04.
You need python 3.8 and the following libraries (commands to install assume a conda environment):
- scikit-learn & pandas (
conda install -c anaconda scikit-learn pandas) - fire (
conda install -c conda-forge fire) - pysat (
pip install python-sat[pblib,aiger])
Then you just need to clone this repository:
git clone https://github.com/andertavares/mlbf.git and enter the new directory mlsat to be able to execute.
- Replicating Section 4 experiments:
python mlbf/main.py *.cnf --output=out.csv
This will generate a dataset, run 5-fold cross validation of a 2-hidden layer MLP (200 and 100 neurons, respectively) for each .cnf file, writing the statistics on out.csv. If the dataset was already generated, it will be used. Run python mlbf/main.py -- --help for additional options.
SATLIB formulas are on instances/satlib_mis.tar.gz and large formulas from the model sampling benchmark are on instances/tacas15.tar.gz. Our kclique instances are at https://drive.google.com/file/d/1R4PhugDBrIuznHlTGsjopT2sar-b1Q-r/view?usp=sharing.
- Replicating Section 5 experiments:
python mlbf/mlpsize.py mlpsize *.cnf --output=out.csv
This will generate a dataset and test how many neurons in a single-hidden-layer MLP are required for perfect accuracy on 5-fold CV for each .cnf file, writing the statistics on out.csv. If the dataset was already generated, it will be used. Run python mlbf/mlpsize.py -- --help for additional options. The random 3-CNF instances, together with the respective datasets are at https://drive.google.com/file/d/18ubvvZTGsmS6_2tiqbG07LJWyjaxvuWk/view?usp=sharing.