🌟 New! ABLkit released: A toolkit for Abductive Learning with high flexibility, user-friendly interface, and optimized performance. Welcome to try it out!🚀
Update: This repository is NO longer actively developed. It has been (mostly) superseded by ABLkit. The code for ABL-HED with ABLkit is in this link. For the latest advancements and updates, we encourage you to visit the new repository.
This is the code repository of the abductive learning framework for handwritten equation decipherment experiments in Bridging Machine Learning and Logical Reasoning by Abductive Learning in NeurIPS 2019.
This code is only tested in Linux environment.
- Swi-Prolog
- Python3 with Numpy, Tensorflow and Keras
- ZOOpt (as a submodule)
http://www.swi-prolog.org/build/unix.html
https://wiki.python.org/moin/BeginnersGuide/Download
#install numpy tensorflow keras
pip3 install numpy
pip3 install tensorflow
pip3 install keras
pip3 install zoopt
Set environment variables(Should change file path according to your situation)
# cd to ABL-HED
git submodule update --init --recursive
export ABL_HOME=$PWD
cp /usr/local/lib/swipl/lib/x86_64-linux/libswipl.so $ABL_HOME/src/logic/lib/
export LD_LIBRARY_PATH=$ABL_HOME/src/logic/lib
export SWI_HOME_DIR=/usr/local/lib/swipl/
# for GPU user
export LD_LIBRARY_PATH=$ABL_HOME/src/logic/lib:/usr/local/cuda:$LD_LIBRARY_PATH
First change the swipl_include_dir
and swipl_lib_dir
in setup.py
to your own SWI-Prolog path.
cd src/logic/prolog
python3 setup.py install
Change directory to ABL-HED
, and run equaiton generator to get the training data
cd src/
python3 equation_generator.py
Run abductive learning code
cd src/
python3 main.py
or
python3 main.py --help
To test the RBA example, please specify the src_data_name
and src_data_file
together, e.g.,
python main.py --src_data_name random_images --src_data_file random_equation_data_train_len_26_test_len_26_sys_2_.pk
- Wang-Zhou Dai (Imperial College London)
- Yu-Xuan Huang (Nanjing University)
- Le-Wen Cai (Nanjing University)