pytorch-lifestream
or ptls a library built upon PyTorch for building embeddings on discrete event sequences using self-supervision. It can process terabyte-size volumes of raw events like game history events, clickstream data, purchase history or card transactions.
It supports various methods of self-supervised training, adapted for event sequences:
It supports several types of encoders, including Transformer and RNN. It also supports many types of self-supervised losses.
The following variants of the contrastive losses are supported:
pip install pytorch-lifestream
# Ubuntu 20.04
sudo apt install python3.8 python3-venv
pip3 install pipenv
pipenv sync --dev # install packages exactly as specified in Pipfile.lock
pipenv shell
pytest
Library description index
pytorch-lifestream
usage experiments on several public event datasets are available in the separate repo
ptls_tests
.pytest
: Example.We have a paper you can cite it:
@inproceedings{
Babaev_2022, series={SIGMOD/PODS ’22},
title={CoLES: Contrastive Learning for Event Sequences with Self-Supervision},
url={http://dx.doi.org/10.1145/3514221.3526129},
DOI={10.1145/3514221.3526129},
booktitle={Proceedings of the 2022 International Conference on Management of Data},
publisher={ACM},
author={Babaev, Dmitrii and Ovsov, Nikita and Kireev, Ivan and Ivanova, Maria and Gusev, Gleb and Nazarov, Ivan and Tuzhilin, Alexander},
year={2022},
month=jun, collection={SIGMOD/PODS ’22}
}