@article{alcorn2021baller2vec,
   title={baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling},
   author={Alcorn, Michael A. and Nguyen, Anh},
   journal={arXiv preprint arXiv:1609.03675},
   year={2021}
}

nano ~/.basketball_profile

source ~/.basketball_profile

source ~/.bashrc

echo ${PROJECT_DIR}

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

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

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

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

top

ls -U ${GAMES_DIR} | wc -l

# From your local machine.
mkdir -p ~/scratch
cd ~/scratch

username=michael
server=gpu3.cse.eng.auburn.edu
data_dir=/home/michael/baller2vec_data
scp ${username}@${server}:${data_dir}/baller2vec_config.pydict .

games_dir=${data_dir}/games
gameid=0021500622

scp ${username}@${server}:${games_dir}/\{${gameid}_X.npy,${gameid}_y.npy\} .

import os

from animator import Game
from settings import DATA_DIR, GAMES_DIR

gameid = "0021500622"
try:
    game = Game(DATA_DIR, GAMES_DIR, gameid)
except FileNotFoundError:
    home_dir = os.path.expanduser("~")
    DATA_DIR = f"{home_dir}/scratch"
    GAMES_DIR = f"{home_dir}/scratch"
    game = Game(DATA_DIR, GAMES_DIR, gameid)

# https://youtu.be/FRrh_WkyXko?t=109
start_period = 3
start_time = "1:55"
stop_period = 3
stop_time = "1:51"
game.show_seq(start_period, start_time, stop_period, stop_time)

#!/usr/bin/env bash

# Experiment identifier. Output will be saved to ${EXPERIMENTS_DIR}/${JOB}.
JOB=$(date +%Y%m%d%H%M%S)

# Training options.
echo "train:" >> ${JOB}.yaml
task=ball_traj  # ball_traj, ball_loc, event, player_traj, score, or seq2seq.
echo "  task: ${task}" >> ${JOB}.yaml
echo "  min_playing_time: 0" >> ${JOB}.yaml  # 0/13314/39917/1.0e+6 --> 100%/75%/50%/0%.
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" >> ${JOB}.yaml
if [[ ("$task" = "event") || ("$task" = "score") ]]
then
    prev_model=False
    echo "  prev_model: ${prev_model}" >> ${JOB}.yaml
    if [[ "$prev_model" != "False" ]]
    then
        echo "  patience: 5" >> ${JOB}.yaml
    fi
fi

# Dataset options.
echo "dataset:" >> ${JOB}.yaml
echo "  hz: 5" >> ${JOB}.yaml
echo "  secs: 4" >> ${JOB}.yaml
echo "  player_traj_n: 11" >> ${JOB}.yaml
echo "  max_player_move: 4.5" >> ${JOB}.yaml
echo "  ball_traj_n: 19" >> ${JOB}.yaml
echo "  max_ball_move: 8.5" >> ${JOB}.yaml
echo "  n_players: 10" >> ${JOB}.yaml
echo "  next_score_change_time_max: 35" >> ${JOB}.yaml
echo "  n_time_to_next_score_change: 36" >> ${JOB}.yaml
echo "  n_ball_loc_x: 95" >> ${JOB}.yaml
echo "  n_ball_loc_y: 51" >> ${JOB}.yaml
echo "  ball_future_secs: 2" >> ${JOB}.yaml

# Model options.
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" >> ${JOB}.yaml
if [[ "$task" != "seq2seq" ]]
then
    echo "  use_cls: False" >> ${JOB}.yaml
    echo "  embed_before_mlp: True" >> ${JOB}.yaml
fi

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

# Start training the model.
gpu=0
cd ${PROJECT_DIR}
nohup python3 train_baller2vec.py ${JOB} ${gpu} > ${EXPERIMENTS_DIR}/${JOB}/train.log &