Awesome
<a href="https://github.com/WenjieDu/BenchPOTS"> <img src="https://pypots.com/figs/pypots_logos/BenchPOTS/logo_FFBG.svg" width="200" align="right"> </a> <h3 align="center">Welcome to BenchPOTS</h3> <p align="center"><i>a Python toolbox for benchmarking ML on POTS (Partially-Observed Time Series)</i></p> <p align="center"> <a href="https://docs.pypots.com/en/latest/install.html#reasons-of-version-limitations-on-dependencies"> <img alt="Python version" src="https://img.shields.io/badge/Python-v3.8+-E97040?logo=python&logoColor=white"> </a> <a href="https://github.com/WenjieDu/BenchPOTS/releases"> <img alt="the latest release version" src="https://img.shields.io/github/v/release/wenjiedu/benchpots?color=EE781F&include_prereleases&label=Release&logo=github&logoColor=white"> </a> <a href="https://github.com/WenjieDu/BenchPOTS/blob/main/LICENSE"> <img alt="BSD-3 license" src="https://img.shields.io/badge/License-BSD--3-E9BB41?logo=opensourceinitiative&logoColor=white"> </a> <a href="https://github.com/WenjieDu/PyPOTS#-community"> <img alt="Community" src="https://img.shields.io/badge/join_us-community!-C8A062"> </a> <a href="https://github.com/WenjieDu/BenchPOTS/graphs/contributors"> <img alt="GitHub contributors" src="https://img.shields.io/github/contributors/wenjiedu/benchpots?color=D8E699&label=Contributors&logo=GitHub"> </a> <a href="https://star-history.com/#wenjiedu/benchpots"> <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/wenjiedu/benchpots?logo=None&color=6BB392&label=%E2%98%85%20Stars"> </a> <a href="https://github.com/WenjieDu/BenchPOTS/network/members"> <img alt="GitHub Repo forks" src="https://img.shields.io/github/forks/wenjiedu/benchpots?logo=forgejo&logoColor=black&label=Forks"> </a> <a href="https://codeclimate.com/github/WenjieDu/BenchPOTS"> <img alt="Code Climate maintainability" src="https://img.shields.io/codeclimate/maintainability-percentage/WenjieDu/BenchPOTS?color=3C7699&label=Maintainability&logo=codeclimate"> </a> <a href="https://coveralls.io/github/WenjieDu/BenchPOTS"> <img alt="Coveralls coverage" src="https://img.shields.io/coverallsCoverage/github/WenjieDu/BenchPOTS?branch=main&logo=coveralls&color=75C1C4&label=Coverage"> </a> <a href="https://github.com/WenjieDu/BenchPOTS/actions/workflows/testing_ci.yml"> <img alt="GitHub Testing" src="https://img.shields.io/github/actions/workflow/status/wenjiedu/benchpots/testing_ci.yml?logo=circleci&color=C8D8E1&label=CI"> </a> <a href="https://docs.pypots.com/en/latest/benchpots.html"> <img alt="Docs building" src="https://img.shields.io/readthedocs/pypots?logo=readthedocs&label=Docs&logoColor=white&color=395260"> </a> <a href="https://anaconda.org/conda-forge/benchpots"> <img alt="Conda downloads" src="https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/conda_benchpots_downloads.json"> </a> <a href="https://pepy.tech/project/benchpots"> <img alt="PyPI downloads" src="https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/pypi_benchpots_downloads.json"> </a> </p>To evaluate the performance of algorithms on POTS datasets, a benchmarking toolkit is necessary, hence the ecosystem library BenchPOTS is developed. BenchPOTS provides the standard and unified preprocessing pipelines of a variety of POTS datasets. It supports a variety of evaluation tasks to help users understand the performance of different algorithms.
β Usage Examples
[!IMPORTANT] BenchPOTS is available on both <a alt='PyPI' href='https://pypi.python.org/pypi/benchpots'><img align='center' src='https://img.shields.io/badge/PyPI--lightgreen?style=social&logo=pypi'></a> and <a alt='Anaconda' href='https://anaconda.org/conda-forge/benchpots'><img align='center' src='https://img.shields.io/badge/Anaconda--lightgreen?style=social&logo=anaconda'></a>βοΈ
Install via pip:
pip install benchpots
or install from source code:
pip install
https://github.com/WenjieDu/BenchPOTS/archive/main.zip
or install via conda:
conda install benchpots -c conda-forge
import benchpots
# Load PhysioNet2012 all three subsets and apply MCAR with 0.1 rate
benchpots.datasets.preprocess_physionet2012(subset="all", rate="0.1")
β Citing BenchPOTS/PyPOTS
The paper introducing PyPOTS is available on arXiv, A short version of it is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series (MiLeTS'23)). Additionally, PyPOTS has been included as a PyTorch Ecosystem project. We are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for Machine Learning Open Source Software). If you use PyPOTS in your work, please cite it as below and πstar this repository to make others notice this library. π€
There are scientific research projects using PyPOTS and referencing in their papers. Here is an incomplete list of them.
<p align="center"> <a href="https://github.com/WenjieDu/PyPOTS"> <img src="https://pypots.com/figs/pypots_logos/Ecosystem/PyPOTS_Ecosystem_Pipeline.png" width="95%"/> </a> </p>@article{du2023pypots,
title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
author={Wenjie Du},
journal={arXiv preprint arXiv:2305.18811},
year={2023},
}
or
<details> <summary>π Visits</summary> <a href="https://github.com/WenjieDu/BenchPOTS"> <img alt="BenchPOTS visits" align="left" src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FWenjieDu%2FBenchPOTS&count_bg=%23009A0A&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Visits%20since%20June%202024&edge_flat=false"> </a> </details> <br>Wenjie Du. PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series. arXiv, abs/2305.18811, 2023.