<div align="center">
<img src="static/rpy_banner_light.png#gh-light-mode-only">
<img src="static/rpy_banner_dark.png#gh-dark-mode-only">
**Simple and flexible library for Reservoir Computing architectures like Echo State Networks (ESN).**
[](https://badge.fury.io/py/reservoirpy)
[](https://inria.hal.science/hal-02595026)
<br/>
[](https://pepy.tech/project/reservoirpy)
[](https://reservoirpy.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/reservoirpy/reservoirpy/actions/workflows/test.yml)
[](https://codecov.io/gh/reservoirpy/reservoirpy)
</div>
---
<p> <img src="static/googlecolab.svg" alt="Google Colab icon" width=32 height=32 align="left"><b>Tutorials:</b> <a href="https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/1-Getting_Started.ipynb">Open in Colab</a> </p>
<!--<p><img src="static/changelog.svg" alt="2" width =32 height=32 align="left"><b>Changelog:</b> https://github.com/reservoirpy/reservoirpy/releases</p>-->
<p> <img src="static/documentation.svg" alt="Open book icon" width=32 height=32 align="left"><b>Documentation:</b> <a href="https://reservoirpy.readthedocs.io/">https://reservoirpy.readthedocs.io/</a></p>
<!--<p> <img src="static/user_guide.svg" width=32 height=32 align="left"><b>User Guide:</b> https://reservoirpy.readthedocs.io/en/latest/user_guide/</a></p>-->
---
**Feature overview:**
- easy creation of [complex architectures](https://reservoirpy.readthedocs.io/en/latest/user_guide/model.html) with multiple reservoirs (e.g. *deep reservoirs*),
readouts
- [feedback loops](https://reservoirpy.readthedocs.io/en/latest/user_guide/advanced_demo.html#Feedback-connections)
- [offline and online training](https://reservoirpy.readthedocs.io/en/latest/user_guide/learning_rules.html)
- [parallel implementation](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.ESN.html)
- sparse matrix computation
- advanced learning rules (e.g. [*Intrinsic Plasticity*](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.IPReservoir.html), [*Local Plasticity*](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.LocalPlasticityReservoir.html) or [*NVAR* (Next-Generation RC)](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.NVAR.html))
- interfacing with [scikit-learn](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.ScikitLearnNode.html) models
- and many more!
Moreover, graphical tools are included to **easily explore hyperparameters**
with the help of the *hyperopt* library.
## Quick try ⚡
### Installation
```bash
pip install reservoirpy
```
### An example on chaotic timeseries prediction (Mackey-Glass)
For a general introduction to reservoir computing and ReservoirPy features, take
a look at the [tutorials](#tutorials)
```python
from reservoirpy.datasets import mackey_glass, to_forecasting
from reservoirpy.nodes import Reservoir, Ridge
from reservoirpy.observables import rmse, rsquare
### Step 1: Load the dataset
X = mackey_glass(n_timesteps=2000) # (2000, 1)-shaped array
# create y by shifting X, and train/test split
x_train, x_test, y_train, y_test = to_forecasting(X, test_size=0.2)
### Step 2: Create an Echo State Network
# 100 neurons reservoir, spectral radius = 1.25, leak rate = 0.3
reservoir = Reservoir(units=100, sr=1.25, lr=0.3)
# feed-forward layer of neurons, trained with L2-regularization
readout = Ridge(ridge=1e-5)
# connect the two nodes
esn = reservoir >> readout
### Step 3: Fit, run and evaluate the ESN
esn.fit(x_train, y_train, warmup=100)
predictions = esn.run(x_test)
print(f"RMSE: {rmse(y_test, predictions)}; R^2 score: {rsquare(y_test, predictions)}")
# RMSE: 0.0020282; R^2 score: 0.99992
```
## More examples and tutorials 🎓
### Tutorials
- [**1 - Getting started with ReservoirPy**](./tutorials/1-Getting_Started.ipynb)
[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/1-Getting_Started.ipynb)
- [**2 - Advanced features**](./tutorials/2-Advanced_Features.ipynb)
[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/2-Advanced_Features.ipynb)
- [**3 - General introduction to Reservoir Computing**](./tutorials/3-General_Introduction_to_Reservoir_Computing.ipynb)
[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/3-General_Introduction_to_Reservoir_Computing.ipynb)
- [**4 - Understand and optimise hyperparameters**](./tutorials/4-Understand_and_optimize_hyperparameters.ipynb)
[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/4-Understand_and_optimize_hyperparameters.ipynb)
- [**5 - Classification with reservoir computing**](./tutorials/5-Classification-with-RC.ipynb)
[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/5-Classification-with-RC.ipynb)
- [**6 - Interfacing ReservoirPy with scikit-learn**](./tutorials/6-Interfacing_with_scikit-learn.ipynb)
[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/6-Interfacing_with_scikit-learn.ipynb)
### Examples
For advanced users, we also showcase partial reproduction of papers on reservoir computing to demonstrate some features of the library.
- [**Improving reservoir using Intrinsic Plasticity** (Schrauwen et al., 2008)](/examples/Improving%20reservoirs%20using%20Intrinsic%20Plasticity/Intrinsic_Plasiticity_Schrauwen_et_al_2008.ipynb)
- [**Interactive reservoir computing for chunking information streams** (Asabuki et al., 2018)](/examples/Interactive%20reservoir%20computing%20for%20chunking%20information%20streams/Chunking_Asabuki_et_al_2018.ipynb)
- [**Next-Generation reservoir computing** (Gauthier et al., 2021)](/examples/Next%20Generation%20Reservoir%20Computing/NG-RC_Gauthier_et_al_2021.ipynb)
- [**Edge of stability Echo State Network** (Ceni et al., 2023)](/examples/Edge%20of%20Stability%20Echo%20State%20Network/Edge_of_stability_Ceni_Gallicchio_2023.ipynb)
## Papers and projects using ReservoirPy
*If you want your paper to appear here, please contact us (see contact link below).*
- ( [HAL](https://inria.hal.science/hal-04354303) | [PDF](https://arxiv.org/pdf/2312.06695) | [Code](https://github.com/corentinlger/ER-MRL) ) Leger et al. (2024) *Evolving Reservoirs for Meta Reinforcement Learning.* EvoAPPS 2024
- ( [arXiv](https://arxiv.org/abs/2204.02484) | [PDF](https://arxiv.org/pdf/2204.02484) ) Chaix-Eichel et al. (2022) *From implicit learning to explicit representations.* arXiv preprint arXiv:2204.02484.
- ( [HTML](https://link.springer.com/chapter/10.1007/978-3-030-86383-8_6) | [HAL](https://hal.inria.fr/hal-03203374) | [PDF](https://hal.inria.fr/hal-03203374/document) ) Trouvain & Hinaut (2021) *Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs.* ICANN 2021
- ( [HTML](https://ieeexplore.ieee.org/abstract/document/9515607) ) Pagliarini et al. (2021) *Canary Vocal Sensorimotor Model with RNN Decoder and Low-dimensional GAN Generator.* ICDL 2021.
- ( [HAL](https://hal.inria.fr/hal-03244723/) | [PDF](https://hal.inria.fr/hal-03244723/document) ) Pagliarini et al. (2021) *What does the Canary Say? Low-Dimensional GAN Applied to Birdsong.* HAL preprint.
- ( [HTML](https://link.springer.com/chapter/10.1007/978-3-030-86383-8_7) | [HAL](https://hal.inria.fr/hal-03203318) | [PDF](https://hal.inria.fr/hal-03203318) ) Hinaut & Trouvain (2021) *Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters.* ICANN 2021
## Awesome Reservoir Computing
We also provide a curated list of tutorials, papers, projects and tools for Reservoir Computing (not necessarily related to ReservoirPy) here!:
**https://github.com/reservoirpy/awesome-reservoir-computing**
## Contact
If you have a question regarding the library, please open an issue.
If you have more general question or feedback you can contact us by email to **xavier dot hinaut the-famous-home-symbol inria dot fr**.
## Citing ReservoirPy
Trouvain, N., Pedrelli, L., Dinh, T. T., Hinaut, X. (2020) *ReservoirPy: an efficient and user-friendly library to design echo state networks. In International Conference on Artificial Neural Networks* (pp. 494-505). Springer, Cham. ( [HTML](https://link.springer.com/chapter/10.1007/978-3-030-61616-8_40) | [HAL](https://hal.inria.fr/hal-02595026) | [PDF](https://hal.inria.fr/hal-02595026/document) )
If you're using ReservoirPy in your work, please cite our package using the following bibtex entry:
```
@incollection{Trouvain2020,
doi = {10.1007/978-3-030-61616-8_40},
url = {https://doi.org/10.1007/978-3-030-61616-8_40},
year = {2020},
publisher = {Springer International Publishing},
pages = {494--505},
author = {Nathan Trouvain and Luca Pedrelli and Thanh Trung Dinh and Xavier Hinaut},
title = {{ReservoirPy}: An Efficient and User-Friendly Library to Design Echo State Networks},
booktitle = {Artificial Neural Networks and Machine Learning {\textendash} {ICANN} 2020}
}
```
## Acknowledgement
<div align="left">
<img src="./static/inria_red.svg" width=300><br>
</div>
This package is developed and supported by Inria at Bordeaux, France in [Mnemosyne](https://team.inria.fr/mnemosyne/) group. [Inria](https://www.inria.fr/en) is a French Research Institute in Digital Sciences (Computer Science, Mathematics, Robotics, ...).
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"description": "<div align=\"center\">\n <img src=\"static/rpy_banner_light.png#gh-light-mode-only\">\n <img src=\"static/rpy_banner_dark.png#gh-dark-mode-only\">\n\n **Simple and flexible library for Reservoir Computing architectures like Echo State Networks (ESN).**\n\n [](https://badge.fury.io/py/reservoirpy)\n [](https://inria.hal.science/hal-02595026)\n \n <br/>\n [](https://pepy.tech/project/reservoirpy)\n [](https://reservoirpy.readthedocs.io/en/latest/?badge=latest)\n [](https://github.com/reservoirpy/reservoirpy/actions/workflows/test.yml)\n [](https://codecov.io/gh/reservoirpy/reservoirpy)\n</div>\n\n\n\n---\n\n<p> <img src=\"static/googlecolab.svg\" alt=\"Google Colab icon\" width=32 height=32 align=\"left\"><b>Tutorials:</b> <a href=\"https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/1-Getting_Started.ipynb\">Open in Colab</a> </p>\n<!--<p><img src=\"static/changelog.svg\" alt=\"2\" width =32 height=32 align=\"left\"><b>Changelog:</b> https://github.com/reservoirpy/reservoirpy/releases</p>-->\n<p> <img src=\"static/documentation.svg\" alt=\"Open book icon\" width=32 height=32 align=\"left\"><b>Documentation:</b> <a href=\"https://reservoirpy.readthedocs.io/\">https://reservoirpy.readthedocs.io/</a></p>\n<!--<p> <img src=\"static/user_guide.svg\" width=32 height=32 align=\"left\"><b>User Guide:</b> https://reservoirpy.readthedocs.io/en/latest/user_guide/</a></p>-->\n\n---\n\n**Feature overview:**\n- easy creation of [complex architectures](https://reservoirpy.readthedocs.io/en/latest/user_guide/model.html) with multiple reservoirs (e.g. *deep reservoirs*),\nreadouts\n- [feedback loops](https://reservoirpy.readthedocs.io/en/latest/user_guide/advanced_demo.html#Feedback-connections)\n- [offline and online training](https://reservoirpy.readthedocs.io/en/latest/user_guide/learning_rules.html)\n- [parallel implementation](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.ESN.html)\n- sparse matrix computation\n- advanced learning rules (e.g. [*Intrinsic Plasticity*](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.IPReservoir.html), [*Local Plasticity*](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.LocalPlasticityReservoir.html) or [*NVAR* (Next-Generation RC)](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.NVAR.html))\n- interfacing with [scikit-learn](https://reservoirpy.readthedocs.io/en/latest/api/generated/reservoirpy.nodes.ScikitLearnNode.html) models\n- and many more!\n\nMoreover, graphical tools are included to **easily explore hyperparameters**\nwith the help of the *hyperopt* library.\n\n## Quick try \u26a1\n\n### Installation\n\n```bash\npip install reservoirpy\n```\n\n### An example on chaotic timeseries prediction (Mackey-Glass)\n\nFor a general introduction to reservoir computing and ReservoirPy features, take\na look at the [tutorials](#tutorials)\n\n```python\nfrom reservoirpy.datasets import mackey_glass, to_forecasting\nfrom reservoirpy.nodes import Reservoir, Ridge\nfrom reservoirpy.observables import rmse, rsquare\n\n### Step 1: Load the dataset\n\nX = mackey_glass(n_timesteps=2000) # (2000, 1)-shaped array\n# create y by shifting X, and train/test split\nx_train, x_test, y_train, y_test = to_forecasting(X, test_size=0.2)\n\n### Step 2: Create an Echo State Network\n\n# 100 neurons reservoir, spectral radius = 1.25, leak rate = 0.3\nreservoir = Reservoir(units=100, sr=1.25, lr=0.3)\n# feed-forward layer of neurons, trained with L2-regularization\nreadout = Ridge(ridge=1e-5)\n# connect the two nodes\nesn = reservoir >> readout\n\n### Step 3: Fit, run and evaluate the ESN\n\nesn.fit(x_train, y_train, warmup=100)\npredictions = esn.run(x_test)\n\nprint(f\"RMSE: {rmse(y_test, predictions)}; R^2 score: {rsquare(y_test, predictions)}\")\n# RMSE: 0.0020282; R^2 score: 0.99992\n```\n\n\n## More examples and tutorials \ud83c\udf93\n\n### Tutorials\n\n- [**1 - Getting started with ReservoirPy**](./tutorials/1-Getting_Started.ipynb)\n[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/1-Getting_Started.ipynb)\n- [**2 - Advanced features**](./tutorials/2-Advanced_Features.ipynb)\n[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/2-Advanced_Features.ipynb)\n- [**3 - General introduction to Reservoir Computing**](./tutorials/3-General_Introduction_to_Reservoir_Computing.ipynb)\n[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/3-General_Introduction_to_Reservoir_Computing.ipynb)\n- [**4 - Understand and optimise hyperparameters**](./tutorials/4-Understand_and_optimize_hyperparameters.ipynb)\n[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/4-Understand_and_optimize_hyperparameters.ipynb)\n- [**5 - Classification with reservoir computing**](./tutorials/5-Classification-with-RC.ipynb)\n[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/5-Classification-with-RC.ipynb)\n- [**6 - Interfacing ReservoirPy with scikit-learn**](./tutorials/6-Interfacing_with_scikit-learn.ipynb)\n[](https://colab.research.google.com/github/reservoirpy/reservoirpy/blob/master/tutorials/6-Interfacing_with_scikit-learn.ipynb)\n\n### Examples\n\nFor advanced users, we also showcase partial reproduction of papers on reservoir computing to demonstrate some features of the library.\n\n- [**Improving reservoir using Intrinsic Plasticity** (Schrauwen et al., 2008)](/examples/Improving%20reservoirs%20using%20Intrinsic%20Plasticity/Intrinsic_Plasiticity_Schrauwen_et_al_2008.ipynb)\n- [**Interactive reservoir computing for chunking information streams** (Asabuki et al., 2018)](/examples/Interactive%20reservoir%20computing%20for%20chunking%20information%20streams/Chunking_Asabuki_et_al_2018.ipynb)\n- [**Next-Generation reservoir computing** (Gauthier et al., 2021)](/examples/Next%20Generation%20Reservoir%20Computing/NG-RC_Gauthier_et_al_2021.ipynb)\n- [**Edge of stability Echo State Network** (Ceni et al., 2023)](/examples/Edge%20of%20Stability%20Echo%20State%20Network/Edge_of_stability_Ceni_Gallicchio_2023.ipynb)\n\n\n## Papers and projects using ReservoirPy\n\n*If you want your paper to appear here, please contact us (see contact link below).*\n\n- ( [HAL](https://inria.hal.science/hal-04354303) | [PDF](https://arxiv.org/pdf/2312.06695) | [Code](https://github.com/corentinlger/ER-MRL) ) Leger et al. (2024) *Evolving Reservoirs for Meta Reinforcement Learning.* EvoAPPS 2024\n- ( [arXiv](https://arxiv.org/abs/2204.02484) | [PDF](https://arxiv.org/pdf/2204.02484) ) Chaix-Eichel et al. (2022) *From implicit learning to explicit representations.* arXiv preprint arXiv:2204.02484.\n- ( [HTML](https://link.springer.com/chapter/10.1007/978-3-030-86383-8_6) | [HAL](https://hal.inria.fr/hal-03203374) | [PDF](https://hal.inria.fr/hal-03203374/document) ) Trouvain & Hinaut (2021) *Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs.* ICANN 2021\n- ( [HTML](https://ieeexplore.ieee.org/abstract/document/9515607) ) Pagliarini et al. (2021) *Canary Vocal Sensorimotor Model with RNN Decoder and Low-dimensional GAN Generator.* ICDL 2021.\n- ( [HAL](https://hal.inria.fr/hal-03244723/) | [PDF](https://hal.inria.fr/hal-03244723/document) ) Pagliarini et al. (2021) *What does the Canary Say? Low-Dimensional GAN Applied to Birdsong.* HAL preprint.\n- ( [HTML](https://link.springer.com/chapter/10.1007/978-3-030-86383-8_7) | [HAL](https://hal.inria.fr/hal-03203318) | [PDF](https://hal.inria.fr/hal-03203318) ) Hinaut & Trouvain (2021) *Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters.* ICANN 2021\n\n## Awesome Reservoir Computing\n\nWe also provide a curated list of tutorials, papers, projects and tools for Reservoir Computing (not necessarily related to ReservoirPy) here!:\n\n**https://github.com/reservoirpy/awesome-reservoir-computing**\n\n## Contact\nIf you have a question regarding the library, please open an issue.\n\nIf you have more general question or feedback you can contact us by email to **xavier dot hinaut the-famous-home-symbol inria dot fr**.\n\n## Citing ReservoirPy\n\nTrouvain, N., Pedrelli, L., Dinh, T. T., Hinaut, X. (2020) *ReservoirPy: an efficient and user-friendly library to design echo state networks. In International Conference on Artificial Neural Networks* (pp. 494-505). Springer, Cham. 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[Inria](https://www.inria.fr/en) is a French Research Institute in Digital Sciences (Computer Science, Mathematics, Robotics, ...).\n",
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