alphaCSC: Convolution sparse coding for time-series#
This is a library to perform shift-invariant sparse dictionary learning, also known as convolutional sparse coding (CSC), on time-series data. It includes a number of different models:
multivariate CSC with a rank-1 constraint 1
univariate CSC with an alpha-stable distribution 2
A mathematical descriptions of these models is available in the documentation.
To install this package, the easiest way is using
pip. It will install this
package and its dependencies. The
setup.py depends on
cython for the installation so it is advised to install them beforehand. To
install this package, please run one of the two commands:
(Latest stable version)
pip install alphacsc
pip install git+https://github.com/alphacsc/alphacsc.git#egg=alphacsc
pip install numpy cython pip install alphacsc[dicodile]
To use dicodile backend, do not forget to set
If you do not have admin privileges on the computer, use the
pip. To upgrade, use the
--upgrade flag provided by
To check if everything worked fine, you can run:
python -c 'import alphacsc'
and it should not give any error messages.
Here is an example to present briefly the API:
import numpy as np import matplotlib.pyplot as plt from alphacsc import BatchCDL # Define the different dimensions of the problem n_atoms = 10 n_times_atom = 50 n_channels = 5 n_trials = 10 n_times = 1000 # Generate a random set of signals X = np.random.randn(n_trials, n_channels, n_times) # Learn a dictionary with batch algorithm and rank1 constraints. cdl = BatchCDL(n_atoms, n_times_atom, rank1=True) cdl.fit(X) # Display the learned atoms fig, axes = plt.subplots(n_atoms, 2, num="Dictionary") for k in range(n_atoms): axes[k, 0].plot(cdl.u_hat_[k]) axes[k, 1].plot(cdl.v_hat_[k]) axes[0, 0].set_title("Spatial map") axes[0, 1].set_title("Temporal map") for ax in axes.ravel(): ax.set_xticklabels() ax.set_yticklabels() plt.show()
AlphaCSC can use a dicodile-based backend to perform sparse encoding in parallel.
To install dicodile, run
pip install alphacsc[dicodile].
Known OpenMPI issues#
When self-installing OpenMPI (for instance to run dicodile on a single machine, or for continuous integration), running the dicodile solver might end up causing a deadlock (no output for a long time). It is often due to communication issue between the workers. This issue can often be solved by disabling Docker-related virtual NICs, for instance by running
Cite our work#
If you use this code in your project, please consider citing our work:
Dupré La Tour, T., Moreau, T., Jas, M., & Gramfort, A. (2018). Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. Advances in Neural Information Processing Systems (NIPS).
Jas, M., Dupré La Tour, T., Şimşekli, U., & Gramfort, A. (2017). Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding. Advances in Neural Information Processing Systems (NIPS), pages 1099–1108.
- Model descriptions
- Examples Gallery
- API Documentation