A look-ahead multi-entity Transformer for modeling coordinated agents.

Last update: Dec 16, 2022

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Overview

`baller2vec++`

This is the repository for the paper:

Michael A. Alcorn and Anh Nguyen. baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents. arXiv. 2021.


To learn statistically dependent agent trajectories, `baller2vec++` uses a specially designed self-attention mask to simultaneously process three different sets of features vectors in a single Transformer. The three sets of feature vectors consist of location feature vectors like those found in `baller2vec`, look-ahead trajectory feature vectors, and starting location feature vectors. This design allows the model to integrate information about concurrent agent trajectories through multiple Transformer layers without seeing the future (in contrast to `baller2vec`).

To learn statistically dependent agent trajectories, baller2vec++ uses a specially designed self-attention mask to simultaneously process three different sets of features vectors in a single Transformer. The three sets of feature vectors consist of location feature vectors like those found in baller2vec, look-ahead trajectory feature vectors, and starting location feature vectors. This design allows the model to integrate information about concurrent agent trajectories through multiple Transformer layers without seeing the future (in contrast to baller2vec).


Training sample	`baller2vec`	`baller2vec++`

When trained on a dataset of perfectly coordinated agent trajectories, the trajectories generated by baller2vec are completely uncoordinated while the trajectories generated by baller2vec++ are perfectly coordinated.


Ground truth	`baller2vec`	`baller2vec`	`baller2vec`

Ground truth	`baller2vec++`	`baller2vec++`	`baller2vec++`

While baller2vec occasionally generates realistic trajectories for the red defender, it also makes egregious errors. In contrast, the trajectories generated by baller2vec++ often seem plausible. The red player was placed last in the player order when generating his trajectory with baller2vec++.

Citation

If you use this code for your own research, please cite:

@article{alcorn2021baller2vec,
   title={\texttt{baller2vec++}: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents},
   author={Alcorn, Michael A. and Nguyen, Anh},
   journal={arXiv preprint arXiv:2104.11980},
   year={2021}
}

Training `baller2vec++`

Setting up `.basketball_profile`

After you've cloned the repository to your desired location, create a file called .basketball_profile in your home directory:

nano ~/.basketball_profile

and copy and paste in the contents of .basketball_profile, replacing each of the variable values with paths relevant to your environment. Next, add the following line to the end of your ~/.bashrc:

source ~/.basketball_profile

and either log out and log back in again or run:

source ~/.bashrc

You should now be able to copy and paste all of the commands in the various instructions sections. For example:

echo ${PROJECT_DIR}

should print the path you set for PROJECT_DIR in .basketball_profile.

Installing the necessary Python packages

cd ${PROJECT_DIR}
pip3 install --upgrade -r requirements.txt

Organizing the play-by-play and tracking data

Copy events.zip (which I acquired from here [mirror here] using https://downgit.github.io) to the DATA_DIR directory and unzip it:

mkdir -p ${DATA_DIR}
cp ${PROJECT_DIR}/events.zip ${DATA_DIR}
cd ${DATA_DIR}
unzip -q events.zip
rm events.zip

Descriptions for the various EVENTMSGTYPEs can be found here (mirror here).

Clone the tracking data from here (mirror here) to the DATA_DIR directory:

cd ${DATA_DIR}
git clone [email protected]:linouk23/NBA-Player-Movements.git

A description of the tracking data can be found here.

Generating the training data

cd ${PROJECT_DIR}
nohup python3 generate_game_numpy_arrays.py > data.log &

You can monitor its progress with:

top

or:

ls -U ${GAMES_DIR} | wc -l

There should be 1,262 NumPy arrays (corresponding to 631 X/y pairs) when finished.

Running the training script

Run (or copy and paste) the following script, editing the variables as appropriate.

#!/usr/bin/env bash

JOB=$(date +%Y%m%d%H%M%S)

echo "train:" >> ${JOB}.yaml
task=basketball  # "basketball" or "toy".
echo "  task: ${task}" >> ${JOB}.yaml
if [[ "$task" = "basketball" ]]
then

    echo "  train_valid_prop: 0.95" >> ${JOB}.yaml
    echo "  train_prop: 0.95" >> ${JOB}.yaml
    echo "  train_samples_per_epoch: 20000" >> ${JOB}.yaml
    echo "  valid_samples: 1000" >> ${JOB}.yaml
    echo "  workers: 10" >> ${JOB}.yaml
    echo "  learning_rate: 1.0e-5" >> ${JOB}.yaml
    echo "  patience: 20" >> ${JOB}.yaml

    echo "dataset:" >> ${JOB}.yaml
    echo "  hz: 5" >> ${JOB}.yaml
    echo "  secs: 4.2" >> ${JOB}.yaml
    echo "  player_traj_n: 11" >> ${JOB}.yaml
    echo "  max_player_move: 4.5" >> ${JOB}.yaml

    echo "model:" >> ${JOB}.yaml
    echo "  embedding_dim: 20" >> ${JOB}.yaml
    echo "  sigmoid: none" >> ${JOB}.yaml
    echo "  mlp_layers: [128, 256, 512]" >> ${JOB}.yaml
    echo "  nhead: 8" >> ${JOB}.yaml
    echo "  dim_feedforward: 2048" >> ${JOB}.yaml
    echo "  num_layers: 6" >> ${JOB}.yaml
    echo "  dropout: 0.0" >> ${JOB}.yaml
    echo "  b2v: False" >> ${JOB}.yaml

else

    echo "  workers: 10" >> ${JOB}.yaml
    echo "  learning_rate: 1.0e-4" >> ${JOB}.yaml

    echo "model:" >> ${JOB}.yaml
    echo "  embedding_dim: 20" >> ${JOB}.yaml
    echo "  sigmoid: none" >> ${JOB}.yaml
    echo "  mlp_layers: [64, 128]" >> ${JOB}.yaml
    echo "  nhead: 4" >> ${JOB}.yaml
    echo "  dim_feedforward: 512" >> ${JOB}.yaml
    echo "  num_layers: 2" >> ${JOB}.yaml
    echo "  dropout: 0.0" >> ${JOB}.yaml
    echo "  b2v: True" >> ${JOB}.yaml

fi

# Save experiment settings.
mkdir -p ${EXPERIMENTS_DIR}/${JOB}
mv ${JOB}.yaml ${EXPERIMENTS_DIR}/${JOB}/

gpu=0
cd ${PROJECT_DIR}
nohup python3 train_baller2vecplusplus.py ${JOB} ${gpu} > ${EXPERIMENTS_DIR}/${JOB}/train.log &

A look-ahead multi-entity Transformer for modeling coordinated agents.

Related tags

Overview

`baller2vec++`

Citation

Training `baller2vec++`

Setting up `.basketball_profile`

Installing the necessary Python packages

Organizing the play-by-play and tracking data

Generating the training data

Running the training script

Owner

Michael A. Alcorn

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A look-ahead multi-entity Transformer for modeling coordinated agents.

Related tags

Overview

baller2vec++

Citation

Training baller2vec++

Setting up .basketball_profile

Installing the necessary Python packages

Organizing the play-by-play and tracking data

Generating the training data

Running the training script

Owner

Michael A. Alcorn

Amazon Multilingual Counterfactual Dataset (AMCD)

Converts python code into c++ by using OpenAI CODEX.

STonKGs is a Sophisticated Transformer that can be jointly trained on biomedical text and knowledge graphs

Python library for parsing resumes using natural language processing and machine learning

GVT is a generic translation tool for parts of text on the PC screen with Text to Speak functionality.

Multilingual Emotion classification using BERT (fine-tuning). Published at the WASSA workshop (ACL2022).

Linking data between GBIF, Biodiverse, and Open Tree of Life

Associated Repository for "Translation between Molecules and Natural Language"

Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体

The ibet-Prime security token management system for ibet network.

An easy-to-use framework for BERT models, with trainers, various NLP tasks and detailed annonations

Anuvada: Interpretable Models for NLP using PyTorch

This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

Korean Simple Contrastive Learning of Sentence Embeddings using SKT KoBERT and kakaobrain KorNLU dataset

ProtFeat is protein feature extraction tool that utilizes POSSUM and iFeature.

Example code for "Real-World Natural Language Processing"

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ReCoin - Restoring our environment and businesses in parallel

This repo is to provide a list of literature regarding Deep Learning on Graphs for NLP

`baller2vec++`

Training `baller2vec++`

Setting up `.basketball_profile`