Metadata-Version: 2.2
Name: benchpots
Version: 0.3.2
Summary: A Python Toolbox for Benchmarking Machine Learning on Partially-Observed Time Series
Author-email: Wenjie Du <wenjay.du@gmail.com>
License: Copyright (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.
        
Project-URL: Source, https://github.com/WenjieDu/BenchPOTS
Project-URL: Homepage, https://pypots.com
Project-URL: Documentation, https://docs.pypots.com
Project-URL: Bug Tracker, https://github.com/WenjieDu/BenchPOTS/issues
Project-URL: Download, https://github.com/WenjieDu/BenchPOTS/archive/main.zip
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
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: h5py
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: torch>=1.10
Requires-Dist: tsdb>=0.6.1
Requires-Dist: pygrinder>=0.6.2

<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.


## ❖ 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>
