codeaudit / rulesynthesis

Code for "Learning Compositional Rules via Neural Program Synthesis"

PyTorch implementation of "Learning Compositional Rules via Neural Program Synthesis"

This is the codebase for the following 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

Much of this code is based on the meta seq2seq code by Brenden Lake.

Requirements

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.

MiniSCAN experiments

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

SCAN experiments

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

Number word experments

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