reservoirpy 0.4.2

A simple and flexible code for Reservoir Computing architectures like Echo State Networks.

Simple and flexible library for Reservoir Computing architectures like Echo State Networks (ESN).

Tutorials: Open in Colab

Documentation: https://reservoirpy.readthedocs.io/

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Feature overview:

• easy creation of complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts
• feedback loops
• offline and online training
• parallelization across sequences
• 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

For more complete installation (including hyperparameter search, ...), look at the complete installation guide on ReadTheDocs.

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 import ESN

from reservoirpy.datasets import mackey_glass, to_forecasting
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, sr = spectral radius, lr = leak rate
esn = ESN(units=100, sr=1.25, lr=0.3, ridge=1e-5)

### 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
• 7 - Introduction to the Jax backend

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, ...).