| Name | pygrinder JSON |
| Version |
0.6.4
JSON |
| download |
| home_page | None |
| Summary | A Python toolkit for introducing missing values into datasets |
| upload_time | 2024-09-12 01:29:11 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | Copyright (c) 2023-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 corruption
incomplete data
data mining
pypots
missingness
partially observed
irregular sampled
partially-observed time series
incomplete time series
missing data
missing values
simulation
pypots
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<a href='https://github.com/WenjieDu/PyGrinder'><img src='https://pypots.com/figs/pypots_logos/PyGrinder/logo_FFBG.svg' width='200' align='right' /></a>
<h3 align="center">Welcome to PyGrinder</h3>
*<p align='center'>a Python toolkit for grinding data beans into the incomplete</p>*
<p align='center'>
<a href='https://github.com/WenjieDu/PyGrinder'>
<img alt='Python version' src='https://img.shields.io/badge/python-v3-E97040?logo=python&logoColor=white'>
</a>
<a href="https://github.com/WenjieDu/PyGrinder/releases">
<img alt="the latest release version" src="https://img.shields.io/github/v/release/wenjiedu/PyGrinder?color=EE781F&include_prereleases&label=Release&logo=github&logoColor=white">
</a>
<a href="https://github.com/WenjieDu/PyGrinder/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/blob/main/README.md#-community">
<img alt="Community" src="https://img.shields.io/badge/join_us-community!-C8A062">
</a>
<a href="https://github.com/WenjieDu/PyGrinder/graphs/contributors">
<img alt="GitHub contributors" src="https://img.shields.io/github/contributors/wenjiedu/pygrinder?color=D8E699&label=Contributors&logo=GitHub">
</a>
<a href="https://star-history.com/#wenjiedu/pygrinder">
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/wenjiedu/pygrinder?logo=None&color=6BB392&label=%E2%98%85%20Stars">
</a>
<a href="https://github.com/WenjieDu/PyGrinder/network/members">
<img alt="GitHub Repo forks" src="https://img.shields.io/github/forks/wenjiedu/pygrinder?logo=forgejo&logoColor=black&label=Forks">
</a>
<a href="https://codeclimate.com/github/WenjieDu/PyGrinder">
<img alt="Code Climate maintainability" src="https://img.shields.io/codeclimate/maintainability-percentage/WenjieDu/PyGrinder?color=3C7699&label=Maintainability&logo=codeclimate">
</a>
<a href='https://coveralls.io/github/WenjieDu/PyGrinder'>
<img alt='Coveralls report' src='https://img.shields.io/coverallsCoverage/github/WenjieDu/PyGrinder?branch=main&logo=coveralls&color=75C1C4&label=Coverage'>
</a>
<a href='https://github.com/WenjieDu/PyGrinder/actions/workflows/testing_ci.yml'>
<img alt='GitHub Testing' src='https://img.shields.io/github/actions/workflow/status/wenjiedu/PyGrinder/testing_ci.yml?logo=github&color=C8D8E1&label=CI'>
</a>
<a href="https://arxiv.org/abs/2305.18811">
<img alt="arXiv DOI" src="https://img.shields.io/badge/DOI-10.48550/arXiv.2305.18811-F8F7F0">
</a>
<a href="https://anaconda.org/conda-forge/PyGrinder">
<img alt="Conda downloads" src="https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/conda_pygrinder_downloads.json">
</a>
<a href='https://pepy.tech/project/PyGrinder'>
<img alt='PyPI downloads' src='https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/pypi_pygrinder_downloads.json'>
</a>
</p>
<a href='https://github.com/WenjieDu/PyPOTS'><img src='https://pypots.com/figs/pypots_logos/PyPOTS/logo_FFBG.svg' width='160' align='left' /></a>
PyGrinder is a part of
<a href="https://github.com/WenjieDu/PyPOTS">
PyPOTS <img align="center" src="https://img.shields.io/github/stars/WenjieDu/PyPOTS?style=social">
</a>
(a Python toolbox for data mining on
Partially-Observed Time Series), was called PyCorruptor and separated from PyPOTS for decoupling missingness-creating functionalities from
learning algorithms.
In data analysis and modeling, sometimes we may need to corrupt the original data to achieve our goal, for instance,
evaluating models' ability to reconstruct corrupted data or assessing the model's performance on only partially-observed
data. PyGrinder is such a tool to help you corrupt your data, which provides several patterns to create missing values
in the given data.
## ❖ Usage Examples
PyGrinder now is available on <a alt='Anaconda' href='https://anaconda.org/conda-forge/tsdb'><img align='center' src='https://img.shields.io/badge/Anaconda--lightgreen?style=social&logo=anaconda'></a>❗️
Install it with `conda install pygrinder`, you may need to specify the channel with option `-c conda-forge`
or install via PyPI:
> pip install pygrinder
or install from source code:
> pip install `https://github.com/WenjieDu/PyGrinder/archive/main.zip`
```python
import numpy as np
from pygrinder import (
mcar,
mar_logistic,
mnar_x,
mnar_t,
rdo,
seq_missing,
block_missing,
calc_missing_rate
)
# given a time-series dataset with 128 samples, each sample with 10 time steps and 36 data features
ts_dataset = np.random.randn(128, 10, 36)
# grind the dataset with MCAR pattern, 10% missing probability, and using 0 to fill missing values
X_with_mcar_data = mcar(ts_dataset, p=0.1)
# grind the dataset with MAR pattern
X_with_mar_data = mar_logistic(ts_dataset[:, 0, :], obs_rate=0.1, missing_rate=0.1)
# grind the dataset with MNAR pattern
X_with_mnar_x_data = mnar_x(ts_dataset, offset=0.1)
X_with_mnar_t_data = mnar_t(ts_dataset, cycle=20, pos=10, scale=3)
# grind the dataset with RDO pattern
X_with_rdo_data = rdo(ts_dataset, p=0.1)
# grind the dataset with Sequence-Missing pattern
X_with_seq_missing_data = seq_missing(ts_dataset, p=0.1, seq_len=5)
# grind the dataset with Block-Missing pattern
X_with_block_missing_data = block_missing(ts_dataset, factor=0.1, block_width=3, block_len=3)
# calculate the missing rate of the dataset
missing_rate = calc_missing_rate(X_with_mcar_data)
```
## ❖ Citing PyGrinder/PyPOTS
<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>
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).
``` 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>
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</details>
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PyGrinder is such a tool to help you corrupt your data, which provides several patterns to create missing values\nin the given data.\n\n\n## \u2756 Usage Examples\nPyGrinder now is available on <a alt='Anaconda' href='https://anaconda.org/conda-forge/tsdb'><img align='center' src='https://img.shields.io/badge/Anaconda--lightgreen?style=social&logo=anaconda'></a>\u2757\ufe0f\n\nInstall it with `conda install pygrinder`, you may need to specify the channel with option `-c conda-forge`\n\nor install via PyPI:\n> pip install pygrinder\n\nor install from source code:\n> pip install `https://github.com/WenjieDu/PyGrinder/archive/main.zip`\n\n```python\nimport numpy as np\n\nfrom pygrinder import (\n mcar,\n mar_logistic,\n mnar_x,\n mnar_t,\n rdo,\n seq_missing,\n block_missing,\n calc_missing_rate\n)\n\n# given a time-series dataset with 128 samples, each sample with 10 time steps and 36 data features\nts_dataset = np.random.randn(128, 10, 36)\n\n# grind the dataset with MCAR pattern, 10% missing probability, and using 0 to fill missing values\nX_with_mcar_data = mcar(ts_dataset, p=0.1)\n\n# grind the dataset with MAR pattern\nX_with_mar_data = mar_logistic(ts_dataset[:, 0, :], obs_rate=0.1, missing_rate=0.1)\n\n# grind the dataset with MNAR pattern\nX_with_mnar_x_data = mnar_x(ts_dataset, offset=0.1)\nX_with_mnar_t_data = mnar_t(ts_dataset, cycle=20, pos=10, scale=3)\n\n# grind the dataset with RDO pattern\nX_with_rdo_data = rdo(ts_dataset, p=0.1)\n\n# grind the dataset with Sequence-Missing pattern\nX_with_seq_missing_data = seq_missing(ts_dataset, p=0.1, seq_len=5)\n\n# grind the dataset with Block-Missing pattern\nX_with_block_missing_data = block_missing(ts_dataset, factor=0.1, block_width=3, block_len=3)\n\n# calculate the missing rate of the dataset\nmissing_rate = calc_missing_rate(X_with_mcar_data)\n```\n\n\n## \u2756 Citing PyGrinder/PyPOTS\n<p align=\"center\">\n<a href=\"https://github.com/WenjieDu/PyPOTS\">\n <img src=\"https://pypots.com/figs/pypots_logos/Ecosystem/PyPOTS_Ecosystem_Pipeline.png\" width=\"95%\"/>\n</a>\n</p>\n\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/)). If you use PyPOTS in your work,\nplease cite it as below and \ud83c\udf1fstar this repository to make others notice this library. \ud83e\udd17\n\nThere are scientific research projects using PyPOTS and referencing in their papers.\nHere is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2022&q=%E2%80%9CPyPOTS%E2%80%9D&hl=en).\n\n``` bibtex\n@article{du2023pypots,\ntitle={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},\nauthor={Wenjie Du},\njournal={arXiv preprint arXiv:2305.18811},\nyear={2023},\n}\n```\nor\n> Wenjie Du.\n> PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.\n> arXiv, abs/2305.18811, 2023.\n\n\n<details>\n<summary>\ud83c\udfe0 Visits</summary>\n<img align='left' src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FWenjieDu%2FPyCorruptor&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Visits+since+May+2022&edge_flat=false'>\n</details>\n",
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