A simple and flexible code for Reservoir Computing architectures like Echo State Networks.
Project description
Tutorials: Open in Colab
Documentation: https://reservoirpy.readthedocs.io/
[!TIP] 🎉 Exciting News! We just launched a new beta tool based on a Large Language Model! 🚀 You can chat with ReservoirChat and ask anything about Reservoir Computing and ReservoirPy! 🤖💡 Don’t miss out, it’s available for a limited time! ⏳
Feature overview:
- easy creation of complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts
- feedback loops
- offline and online training
- parallel implementation
- sparse matrix computation
- advanced learning rules (e.g. Intrinsic Plasticity, Local Plasticity or NVAR (Next-Generation RC))
- interfacing with scikit-learn models
- and many more!
Moreover, graphical tools are included to easily explore hyperparameters
with the help of the hyperopt library.
Quick try ⚡
Installation
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
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
- 2 - Advanced features
- 3 - General introduction to Reservoir Computing
- 4 - Understand and optimise hyperparameters
- 5 - Classification with reservoir computing
- 6 - Interfacing ReservoirPy with scikit-learn
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)
- Interactive reservoir computing for chunking information streams (Asabuki et al., 2018)
- Next-Generation reservoir computing (Gauthier et al., 2021)
- Edge of stability Echo State Network (Ceni et al., 2023)
Papers and projects using ReservoirPy
If you want your paper to appear here, please contact us (see contact link below).
- ( HAL | PDF | Code ) Leger et al. (2024) Evolving Reservoirs for Meta Reinforcement Learning. EvoAPPS 2024
- ( arXiv | PDF ) Chaix-Eichel et al. (2022) From implicit learning to explicit representations. arXiv preprint arXiv:2204.02484.
- ( HTML | HAL | PDF ) Trouvain & Hinaut (2021) Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs. ICANN 2021
- ( HTML ) Pagliarini et al. (2021) Canary Vocal Sensorimotor Model with RNN Decoder and Low-dimensional GAN Generator. ICDL 2021.
- ( HAL | PDF ) Pagliarini et al. (2021) What does the Canary Say? Low-Dimensional GAN Applied to Birdsong. HAL preprint.
- ( HTML | HAL | PDF ) 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 | HAL | PDF )
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
This package is developed and supported by Inria at Bordeaux, France in Mnemosyne group. Inria is a French Research Institute in Digital Sciences (Computer Science, Mathematics, Robotics, ...).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file reservoirpy-0.3.13.post1.tar.gz
.
File metadata
- Download URL: reservoirpy-0.3.13.post1.tar.gz
- Upload date:
- Size: 169.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.11.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
2a71fd26a169f6e1aa152adeb4712d0449da4903a41861c99084c4ad3efd4235
|
|
MD5 |
b75a6ce6679ca0e848958b345feb706e
|
|
BLAKE2b-256 |
430ffbdeddb8f5050912edd8b360394dd22f2048b75006696346b8fb0ed7a447
|
File details
Details for the file reservoirpy-0.3.13.post1-py3-none-any.whl
.
File metadata
- Download URL: reservoirpy-0.3.13.post1-py3-none-any.whl
- Upload date:
- Size: 209.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.11.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
4d469f229d8eb6812eb1ca47b4b64d195a28fa0c7d068e6564023311d876e996
|
|
MD5 |
d63083e23bd2beb40ca6531da6a80dc1
|
|
BLAKE2b-256 |
df58764c5367274978a3a638b58e4a5c9bb7a2c155931d4ec2c6f7e3ba63817e
|