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

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ReservoirPy (v0.3.0-beta2) 🌀 🧠

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

from reservoirpy.nodes import Reservoir, Ridge, Input

data      = Input(input_dim=1)
reservoir = Reservoir(100, lr=0.3, sr=1.1)
readout   = Ridge(1, ridge=1e-6)

esn = data >> reservoir >> readout

forecast =, y).run(timeseries)

ReservoirPy is a simple user-friendly library based on Python scientific modules. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Advanced features of ReservoirPy allow to improve computation time efficiency on a simple laptop compared to basic Python implementation. Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, etc. Moreover, graphical tools are included to easily explore hyperparameters with the help of the hyperopt library.

This library works for Python 3.8 and higher.

Offcial documentation 📖

See the official ReservoirPy's documentation to learn more about the main features of ReservoirPy, its API and the installation process.


You can install ReservoirPy using pip:

pip install reservoirpy

⚠️ The version currently displayed in the master branch is a pre-release of ReservoirPy ⚠️

To install it, change the command to:

pip install --pre reservoirpy


pip install reservoirpy==0.3.0b2

Quick try ⚡

An example on Chaotic timeseries prediction (MackeyGlass)

Step 1: Load the dataset

ReservoirPy comes with some handy data generator able to create synthetic timeseries for well-known tasks such as Mackey-Glass timeseries forecasting.

from reservoirpy.datasets import mackey_glass

X = mackey_glass(n_timesteps=2000)

Step 2: Create an Echo State Network...

...or any kind of model you wish to use to solve your task. In this simple use case, we will try out Echo State Networks (ESNs), one of the most minimal architecture of Reservoir Computing machines.

An ESN is made of a reservoir, a random recurrent network used to encode our inputs in a "close-to-chaos" high dimensional space, and a readout, a simple feed-forward layer of neurons in charge with reading-out the desired output from the activations of the reservoir.

from reservoirpy.nodes import Reservoir, Ridge

reservoir = Reservoir(units=100, lr=0.3, sr=1.25)
readout   = Ridge(output_dim=1, ridge=1e-5)

We here obtain a reservoir with 100 neurons, a spectral radius of 1.25 and a leak rate of 0.3 (you can learn more about these hyperparameters going through the tutorial Introduction to Reservoir Computing). Our readout is just a layer of one single neuron, that we will next connect to the reservoir neurons. Note that only the readout layer connections are trained! This is one of the cornerstone of all Reservoir Computing techniques. In our case, we will train these connections using linear regression, with a regularization coefficient of 10-5.

Now, let's connect everything using the >> operator.

esn = reservoir >> readout

That's it! Next step: fit the readout weights to perform the task we want. We will train the ESN to make one-step-ahead forecasts of our timeseries.

Step 3: Fit and run the ESN

predictions =[:500], X[1:501]).run(X[501:-1])

Our ESN is now trained and ready to use. Let's evaluate its performances:

Step 4: Evaluate the ESN

from reservoirpy.observables import rmse, rsquare
print("RMSE:", rmse(X[502:], predictions), 
      "R^2 score:", rsquare(X[502:], predictions))

Run and analyse these two files to see how to make timeseries prediction with Echo State Networks:

  • (using the ESN class)

  • (without the ESN class)


Examples and tutorials 🎓

Go to the tutorial folder for tutorials in Jupyter Notebooks.

Go to the examples folder for examples and papers with codes, also in Jupyter Notebooks.

Paper with tutorials

Tutorial on ReservoirPy can be found in this Paper (Trouvain et al. 2020).

Explore Hyper-Parameters with Hyperopt

A quick tutorial on how to explore hyperparameters with ReservoirPy and Hyperopt can be found in this paper (Trouvain et al. 2020).

Take a look at our advices and general method to explore hyperparameters for reservoirs in our recent paper: (Hinaut et al 2021) HTML HAL

Turorial and Jupyter Notebook for hyper-parameter exploration

More info on hyperopt: Official website

Papers and projects using ReservoirPy

  • Trouvain & Hinaut (2021) Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs. ICANN 2021 HTML HAL PDF
  • Pagliarini et al. (2021) Canary Vocal Sensorimotor Model with RNN Decoder and Low-dimensional GAN Generator. ICDL 2021. HTML
  • Pagliarini et al. (2021) What does the Canary Say? Low-Dimensional GAN Applied to Birdsong. HAL preprint. HAL PDF
  • Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters. ICANN 2021 HTML HAL PDF


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

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