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

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

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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

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

pkuseg多领域中文分词工具; The pkuseg toolkit for multi-domain Chinese word segmentation

使用Mask LM预训练任务来预训练Bert模型。训练垂直领域语料的模型表征，提升下游任务的表现。

Conditional probing: measuring usable information beyond a baseline

Edge-Augmented Graph Transformer

Natural Language Processing with transformers

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

The code from the whylogs workshop in DataTalks.Club on 29 March 2022

Turn clang-tidy warnings and fixes to comments in your pull request

InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

Original implementation of the pooling method introduced in "Speaker embeddings by modeling channel-wise correlations"

A library for Multilingual Unsupervised or Supervised word Embeddings

A method for cleaning and classifying text using transformers.

TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech

Sentiment Analysis Project using Count Vectorizer and TF-IDF Vectorizer

[NeurIPS 2021] Code for Learning Signal-Agnostic Manifolds of Neural Fields

Just Another Telegram Ai Chat Bot Written In Python With Pyrogram.

Code repository for "It's About Time: Analog clock Reading in the Wild"

LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search

ConferencingSpeech2022; Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge

`baller2vec++`

Training `baller2vec++`

Setting up `.basketball_profile`