This repository contains the code and text of the suspended paper
"Predicting Satisfiablity of Benchmark Instances"
This research project was discontinued
Before running the scripts to reproduce the experiments, you should
- Set up an environment (optional, but recommended).
- Install all necessary dependencies.
Our code is implemented in Python (version 3.8; other versions, including lower ones, might work as well).
If you use conda
, you can directly install the correct Python version into a new conda
environment
and activate the environment as follows:
conda create --name <conda-env-name> python=3.8
conda activate <conda-env-name>
Choose <conda-env-name>
as you like.
To leave the environment, run
conda deactivate
We used virtualenv
(version 20.4.7; other versions might work as well) to create an environment for our experiments.
First, you need to install the correct Python version yourself.
Let's assume the Python executable is located at <path/to/python>
.
Next, you install virtualenv
with
python -m pip install virtualenv==20.4.7
To set up an environment with virtualenv
, run
python -m virtualenv -p <path/to/python> <path/to/env/destination>
Choose <path/to/env/destination>
as you like.
Activate the environment in Linux with
source <path/to/env/destination>/bin/activate
Activate the environment in Windows (note the back-slashes) with
<path\to\env\destination>\Scripts\activate
To leave the environment, run
deactivate
After activating the environment, you can use python
and pip
as usual.
To install all necessary dependencies for this repo, switch to the directory code/
and run
python -m pip install -r requirements.txt
If you make changes to the environment and you want to persist them, run
python -m pip freeze > requirements.txt
After setting up and activating an environment, you are ready to run the code.
From the directory code/
, run
python -m prepare_datasets
to download and pre-process the input data for the experiments from the GBD website. Next, start the experimental pipeline with
python -m run_experiments
Depending on your hardware, this might take some time. To print statistics and create the plots for the paper, run
python -m run_evaluation
All scripts have a few command-line options, which you can see by running the scripts like
python -m prepare_datasets --help