andrehe02 / go-probe

Code repository for Understanding Game-Playing Agents with Natural Language Annotations

go-probe

This is the code repository for Understanding Game-Playing Agents with Natural Language Annotations

Getting Started

You can also use environment.yml to setup your conda environment

conda create -n go python=3.6
conda activate go
conda install pytorch==1.7.1 cudatoolkit=10.1 -c pytorch
conda install tqdm
pip install -U scikit-learn

cd ~
git clone https://github.com/AndreHe02/go-probe.git
export PYTHONPATH=$PYTHONPATH:~/go-probe/
git clone https://github.com/AndreHe02/deep_learning_and_the_game_of_go.git
export PYTHONPATH=$PYTHONPATH:~/deep_learning_and_the_game_of_go/code/

Downloading the Dataset

Convert raw sgf files into a list of board-comment pairs

git clone https://github.com/AndreHe02/go.git
cd go/src/
python board2comments.py -d ../data/annotations/ -o b2c/
mv b2c/annotations.pkl ~/go-probe/

Preproccessing

Sort keywords by frequency and pick control words as described in the paper. Generate input representations for the imitation learning model and ELF OpenGo and store them on disk. This will write about 38 GB of data into dataset/

cd ~/go-probe/
python go_probe/datasets/filter_annotations.py
python go_probe/datasets/generate_word_sets.py -f annotations_filtered.pkl -g go_dict.txt
mkdir dataset
python go_probe/datasets/generate_dataset.py -f annotations_filtered.pkl -d dataset/

Get ELF Weights

Download ELF weights from https://github.com/pytorch/ELF/releases/download/pretrained-go-19x19-v2/pretrained-go-19x19-v2.bin and place under go-probe/

Running Experiments

By default, this runs probes for both keyword-based and pattern-based features and stores results in numpy arrays with shape (# cross validation folds, # probed layers, # features classifiers). The experiment was run on a Quadro RTX 6000.

python go_probe/experiments/go_experiment.py -d dataset/ -n 10 -w go_model_weights.tar
python go_probe/experiments/elf_experiment.py -d dataset/ -n 10 -w pretrained-go-19x19-v2.bin