This is the codebase for the NeurIPS 2020 paper:
Learning Compositional Rules via Neural Program Synthsis
Maxwell I. Nye, Armando Solar-Lezama, Joshua B. Tenenbaum, Brenden M. Lake
https://arxiv.org/pdf/2003.05562.pdf
NeurIPS 2020 Proceedings
Much of this code is based on the meta seq2seq code by Brenden Lake.
Python 3.7
PyTorch 1.4.0
PHP intl (installed via sudo apt install php7.0-intl on ubuntu 16.04)
pyro (pip3 install pyro-ppl)
pyprob (installed from source)
Add necessary folders:
mkdir out_models
mkdir results
mkdir logs
mkdir testnums
We use zsh, though bash should also work for running the .sh scripts.
to train synthesis network:
python synthTrain.py --fn_out_model 'miniscan_final.p' --batchsize 128 --episode_type 'rules_gen'
to train meta seq2seq network:
python train_metanet_attn.py --fn_out_model 'metas2s_baseline.p' --episode_type 'rules_gen'
to run evaluation:
zsh miniscan_test.sh
to run evaluation of human tested domain in Figure 2:
zsh human_miniscan.sh
to train synthesis network:
python synthTrain.py --fn_out_model 'scan_final.p' --batchsize 128 --episode_type 'scan_random' --num_pretrain_episodes 1000000
to train meta seq2seq network:
python train_metanet_attn.py --num_episodes 10000000 --fn_out_model 'scan_metas2s_baseline.p' --episode_type 'scan_random'
to replicate baselines in Table 1:
zsh SCAN_baselines.sh
to replicate results of full synthesis model with search (Table 1, top row) and search budget details (Table 2):
zsh scan_search_run.sh
to replicate results of full synthesis model with fixed example sets (Supplement Table 6):
zsh SCAN_fixed_budget.sh
to train synthesis network:
python synthTrain.py --episode_type wordToNumber --type WordToNumber --print_freq 50 --batchsize 128 --save_freq 150 --fn_out_model WordToNum.p
to train meta seq2seq network:
python train_metanet_attn.py --fn_out_model 'MetaNetw2num.p' --episode_type 'wordToNumber'
to run evaluation:
zsh number_test.sh