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Fix a bug in CRLI, switch to BSD-3 license
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WenjieDu authored Nov 6, 2023
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<img alt="the latest release version" src="https://img.shields.io/github/v/release/wenjiedu/pypots?color=EE781F&include_prereleases&label=Release&logo=github&logoColor=white">
</a>
<a href="https://github.com/WenjieDu/PyPOTS/blob/main/LICENSE">
<img alt="GPL-v3 license" src="https://img.shields.io/badge/License-GPL--v3-E9BB41?logo=opensourceinitiative&logoColor=white">
<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">
Expand Down Expand Up @@ -79,37 +79,39 @@ The rest of this readme file is organized as follows:


## ❖ PyPOTS Ecosystem
At PyPOTS, time series datasets are taken as coffee beans, and POTS datasets are incomplete coffee beans with missing parts that have their own meanings.
At PyPOTS, things are related to coffee, which we're familiar with. Yes, this is a coffee universe!
As you can see, there is a coffee pot in the PyPOTS logo.
And what else? Please read on ;-)

<a href="https://github.com/WenjieDu/TSDB">
<img src="https://pypots.com/figs/pypots_logos/TSDB_logo_FFBG.svg" align="left" width="130" alt="TSDB logo"/>
<img src="https://pypots.com/figs/pypots_logos/TSDB_logo_FFBG.svg" align="left" width="140" alt="TSDB logo"/>
</a>

👈 To make various open-source time-series datasets readily available to our users,
PyPOTS gets supported by its ecosystem library <i>Time Series Data Beans (TSDB)</i>, a toolbox making loading time-series datasets super easy!
👈 Time series datasets are taken as coffee beans at PyPOTS, and POTS datasets are incomplete coffee beans with missing parts that have their own meanings.
To make various public time-series datasets readily available to users,
<i>Time Series Data Beans (TSDB)</i> is created to make loading time-series datasets super easy!
Visit [TSDB](https://github.com/WenjieDu/TSDB) right now to know more about this handy tool 🛠, and it now supports a total of 168 open-source datasets!

<a href="https://github.com/WenjieDu/PyGrinder">
<img src="https://pypots.com/figs/pypots_logos/PyGrinder_logo_FFBG.svg" align="right" width="130" alt="PyGrinder logo"/>
<img src="https://pypots.com/figs/pypots_logos/PyGrinder_logo_FFBG.svg" align="right" width="140" alt="PyGrinder logo"/>
</a>

👉 To simulate the real-world data beans with missingness, the ecosystem library [PyGrinder](https://github.com/WenjieDu/PyGrinder),
a toolkit helping grind your coffee beans into incomplete ones, is created. Missing patterns fall into three categories according to Robin's theory[^13]:
MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random).
PyGrinder supports all of them and additional functionalities related to missingness.
👉 To simulate the real-world data beans with missingness, the ecosystem library [PyGrinder](https://github.com/WenjieDu/PyGrinder),
a toolkit helping grind your coffee beans into incomplete ones, is created. Missing patterns fall into three categories according to Robin's theory[^13]:
MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random).
PyGrinder supports all of them and additional functionalities related to missingness.
With PyGrinder, you can introduce synthetic missing values into your datasets with a single line of code.

<a href="https://github.com/WenjieDu/BrewPOTS">
<img src="https://pypots.com/figs/pypots_logos/BrewPOTS_logo_FFBG.svg" align="left" width="130" alt="BrewPOTS logo"/>
<img src="https://pypots.com/figs/pypots_logos/BrewPOTS_logo_FFBG.svg" align="left" width="140" alt="BrewPOTS logo"/>
</a>

👈 Now we have the beans, the grinder, and the pot, how to brew us a cup of coffee? Tutorials are necessary!
Considering the future workload, PyPOTS tutorials is released in a single repo,
Considering the future workload, PyPOTS tutorials are released in a single repo,
and you can find them in [BrewPOTS](https://github.com/WenjieDu/BrewPOTS).
Take a look at it now, and learn how to brew your POTS datasets!

☕️ Enjoy it and have fun!
☕️ Welcome to the universe of PyPOTS. Enjoy it and have fun!


## ❖ Installation
Expand All @@ -131,8 +133,9 @@ conda update -c conda-forge pypots # update pypots to the latest version
Alternatively, you can install from the latest source code with the latest features but may be not officially released yet:
> pip install https://github.com/WenjieDu/PyPOTS/archive/main.zip


## ❖ Usage
Besides [BrewPOTS](https://github.com/WenjieDu/BrewPOTS), you can also find a simple and quick-start tutorial notebook
Besides [BrewPOTS](https://github.com/WenjieDu/BrewPOTS), you can also find a simple and quick-start tutorial notebook
on Google Colab with [this link](https://colab.research.google.com/drive/1HEFjylEy05-r47jRy0H9jiS_WhD0UWmQ?usp=sharing).
If you have further questions, please refer to PyPOTS documentation [docs.pypots.com](https://docs.pypots.com).
You can also [raise an issue](https://github.com/WenjieDu/PyPOTS/issues) or [ask in our community](#-community).
Expand Down Expand Up @@ -162,7 +165,8 @@ dataset = {"X": X}
print(dataset["X"].shape) # (11988, 48, 37), 11988 samples, 48 time steps, 37 features
# Model training. This is PyPOTS showtime.
saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_heads=4, d_k=64, d_v=64, dropout=0.1, epochs=10)
saits.fit(dataset) # train the model. Here I use the whole dataset as the training set, because ground truth is not visible to the model.
# Here I use the whole dataset as the training set because ground truth is not visible to the model, you can also split it into train/val/test sets
saits.fit(dataset)
imputation = saits.impute(dataset) # impute the originally-missing values and artificially-missing values
mae = cal_mae(imputation, X_intact, indicating_mask) # calculate mean absolute error on the ground truth (artificially-missing values)
```
Expand All @@ -174,12 +178,12 @@ PyPOTS supports imputation, classification, clustering, and forecasting tasks on

| ***`Imputation`*** | 🚥 | 🚥 | 🚥 |
|:----------------------:|:-----------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| **Type** | **Abbr.** | **Full name of the algorithm/model** | **Year** |
| Neural Net | SAITS | Self-Attention-based Imputation for Time Series [^1] | 2023 |
| Neural Net | Transformer | Attention is All you Need [^2];<br>Self-Attention-based Imputation for Time Series [^1];<br><sub>Note: proposed in [^2], and re-implemented as an imputation model in [^1].</sub> | 2017 |
| Neural Net | CSDI | Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [^12] | 2021 |
| Neural Net | US-GAN | Generative Semi-supervised Learning for Multivariate Time Series Imputation [^10] | 2021 |
| Neural Net | GP-VAE | GP-VAE: Deep Probabilistic Time Series Imputation [^11] | 2020 |
| Neural Net | US-GAN | Unsupervised GAN for Multivariate Time Series Imputation [^10] | 2021 |
| Neural Net | GP-VAE | Gaussian Process Variational Autoencoder [^11] | 2020 |
| Neural Net | BRITS | Bidirectional Recurrent Imputation for Time Series [^3] | 2018 |
| Neural Net | M-RNN | Multi-directional Recurrent Neural Network [^9] | 2019 |
| Naive | LOCF | Last Observation Carried Forward | - |
Expand Down Expand Up @@ -212,7 +216,7 @@ Here is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2
``` bibtex
@article{du2023PyPOTS,
title={{PyPOTS: a Python toolbox for machine learning on Partially-Observed Time Series}},
title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
author={Wenjie Du},
year={2023},
eprint={2305.18811},
Expand All @@ -224,14 +228,14 @@ doi={10.48550/arXiv.2305.18811},
```
> Wenjie Du. (2023).
> PyPOTS: a Python toolbox for machine learning on Partially-Observed Time Series.
> PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
> arXiv, abs/2305.18811.https://arxiv.org/abs/2305.18811
or
``` bibtex
@inproceedings{du2023PyPOTS,
title={{PyPOTS: a Python toolbox for machine learning on Partially-Observed Time Series}},
title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
booktitle={9th SIGKDD workshop on Mining and Learning from Time Series (MiLeTS'23)},
author={Wenjie Du},
year={2023},
Expand All @@ -240,7 +244,7 @@ url={https://arxiv.org/abs/2305.18811},
```
> Wenjie Du. (2023).
> PyPOTS: a Python toolbox for machine learning on Partially-Observed Time Series.
> PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
> In *9th SIGKDD workshop on Mining and Learning from Time Series (MiLeTS'23)*. https://arxiv.org/abs/2305.18811
Expand Down Expand Up @@ -268,16 +272,17 @@ Your star is your recognition to PyPOTS, and it matters!
</i></b>
</summary>
<a href="https://github.com/WenjieDu/PyPOTS/stargazers">
<img alt="PyPOTS stargazers" src="https://reporoster.com/stars/dark/WenjieDu/PyPOTS">
<img alt="PyPOTS stargazers" src="http://reporoster.com/stars/dark/WenjieDu/PyPOTS">
</a>
<br>
<a href="https://github.com/WenjieDu/PyPOTS/network/members">
<img alt="PyPOTS forkers" src="https://reporoster.com/forks/dark/WenjieDu/PyPOTS">
<img alt="PyPOTS forkers" src="http://reporoster.com/forks/dark/WenjieDu/PyPOTS">
</a>
</details>
👀 Check out a full list of our users' affiliations [on PyPOTS website here](https://pypots.com/users/)!
## ❖ Community
We care about the feedback from our users, so we're building PyPOTS community on
Expand All @@ -289,6 +294,7 @@ We care about the feedback from our users, so we're building PyPOTS community on
If you have any suggestions or want to contribute ideas or share time-series related papers, join us and tell.
PyPOTS community is open, transparent, and surely friendly. Let's work together to build and improve PyPOTS!
[//]: # (Use APA reference style below)
[^1]: Du, W., Cote, D., & Liu, Y. (2023). [SAITS: Self-Attention-based Imputation for Time Series](https://doi.org/10.1016/j.eswa.2023.119619). *Expert systems with applications*.
[^2]: Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). [Attention is All you Need](https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html). *NeurIPS 2017*.
Expand All @@ -302,7 +308,7 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
[^10]: Miao, X., Wu, Y., Wang, J., Gao, Y., Mao, X., & Yin, J. (2021). [Generative Semi-supervised Learning for Multivariate Time Series Imputation](https://ojs.aaai.org/index.php/AAAI/article/view/17086). *AAAI 2021*.
[^11]: Fortuin, V., Baranchuk, D., Raetsch, G. & Mandt, S. (2020). [GP-VAE: Deep Probabilistic Time Series Imputation](https://proceedings.mlr.press/v108/fortuin20a.html). *AISTATS 2020*.
[^12]: Tashiro, Y., Song, J., Song, Y., & Ermon, S. (2021). [CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation](https://proceedings.neurips.cc/paper/2021/hash/cfe8504bda37b575c70ee1a8276f3486-Abstract.html). *NeurIPS 2021*.
[^13]: Rubin, D. B. (1976). [Inference and missing data](https://academic.oup.com/biomet/article-abstract/63/3/581/270932). *Biometrika*, 63(3), 581-592.
[^13]: Rubin, D. B. (1976). [Inference and missing data](https://academic.oup.com/biomet/article-abstract/63/3/581/270932). *Biometrika*.
<details>
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