benchpots


Namebenchpots JSON
Version 0.2.2 PyPI version JSON
download
home_pageNone
SummaryA Python Toolbox for Benchmarking Machine Learning on Partially-Observed Time Series
upload_time2024-08-14 17:38:01
maintainerNone
docs_urlNone
authorNone
requires_python>=3.8
licenseCopyright (c) 2024-present, Wenjie Du All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
keywords data mining benchmark neural networks machine learning deep learning artificial intelligence time-series analysis time series imputation classification clustering forecasting partially observed irregular sampled partially-observed time series incomplete time series missing data missing values
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            <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">
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    <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.


## ❖ 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>❗️
> 
> 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

```python
import benchpots

# Load PhysioNet2012 all three subsets and apply MCAR with 0.1 rate 
benchpots.datasets.preprocess_physionet2012(subset="all", rate="0.1")

```

## ❖ Citing BenchPOTS/PyPOTS
The paper introducing PyPOTS is available [on arXiv](https://arxiv.org/abs/2305.18811),
A short version of it is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series ([MiLeTS'23](https://kdd-milets.github.io/milets2023/))).
**Additionally**, PyPOTS has been included as a [PyTorch Ecosystem](https://pytorch.org/ecosystem/) project.
We are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for
[Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). If you use PyPOTS in your work,
please cite it as below and 🌟star this repository to make others notice this library. 🤗

There are scientific research projects using PyPOTS and referencing in their papers.
Here is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2022&q=%E2%80%9CPyPOTS%E2%80%9D&hl=en).

<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>

``` bibtex
@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
> Wenjie Du.
> PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
> arXiv, abs/2305.18811, 2023.



<details>
<summary>🏠 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>

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href=\"https://codeclimate.com/github/WenjieDu/BenchPOTS\">\n        <img alt=\"Code Climate maintainability\" src=\"https://img.shields.io/codeclimate/maintainability-percentage/WenjieDu/BenchPOTS?color=3C7699&label=Maintainability&logo=codeclimate\">\n    </a>\n    <a href=\"https://coveralls.io/github/WenjieDu/BenchPOTS\">\n        <img alt=\"Coveralls coverage\" src=\"https://img.shields.io/coverallsCoverage/github/WenjieDu/BenchPOTS?branch=main&logo=coveralls&color=75C1C4&label=Coverage\">\n    </a>\n    <a href=\"https://github.com/WenjieDu/BenchPOTS/actions/workflows/testing_ci.yml\">\n        <img alt=\"GitHub Testing\" src=\"https://img.shields.io/github/actions/workflow/status/wenjiedu/benchpots/testing_ci.yml?logo=circleci&color=C8D8E1&label=CI\">\n    </a>\n    <a href=\"https://docs.pypots.com/en/latest/benchpots.html\">\n        <img alt=\"Docs building\" src=\"https://img.shields.io/readthedocs/pypots?logo=readthedocs&label=Docs&logoColor=white&color=395260\">\n    </a>\n    <a href=\"https://anaconda.org/conda-forge/benchpots\">\n        <img alt=\"Conda downloads\" src=\"https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/conda_benchpots_downloads.json\">\n    </a>\n    <a href=\"https://pepy.tech/project/benchpots\">\n        <img alt=\"PyPI downloads\" src=\"https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/pypi_benchpots_downloads.json\">\n    </a>\n</p>\n\nTo evaluate the performance of algorithms on POTS datasets, a benchmarking toolkit is necessary, hence the ecosystem library BenchPOTS is developed.\nBenchPOTS provides the standard and unified preprocessing pipelines of a variety of POTS datasets.\nIt supports a variety of evaluation tasks to help users understand the performance of different algorithms.\n\n\n## \u2756 Usage Examples\n> [!IMPORTANT]\n> 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> \n> 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>\u2757\ufe0f\n> \n> Install via pip:\n> > pip install benchpots\n> \n> or install from source code:\n> > pip install `https://github.com/WenjieDu/BenchPOTS/archive/main.zip`\n>\n> or install via conda:\n> > conda install benchpots -c conda-forge\n\n```python\nimport benchpots\n\n# Load PhysioNet2012 all three subsets and apply MCAR with 0.1 rate \nbenchpots.datasets.preprocess_physionet2012(subset=\"all\", rate=\"0.1\")\n\n```\n\n## \u2756 Citing BenchPOTS/PyPOTS\nThe paper introducing PyPOTS is available [on arXiv](https://arxiv.org/abs/2305.18811),\nA short version of it is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series ([MiLeTS'23](https://kdd-milets.github.io/milets2023/))).\n**Additionally**, PyPOTS has been included as a [PyTorch Ecosystem](https://pytorch.org/ecosystem/) project.\nWe are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for\n[Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). 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