Picard

This is a library to run the Preconditioned ICA for Real Data (PICARD) algorithm [1] and its orthogonal version (PICARD-O) [2]. These algorithms show fast convergence even on real data for which sources independence do not perfectly hold.

Installation

We recommend the Anaconda Python distribution.

conda

Picard can be installed with conda-forge. You need to add conda-forge to your conda channels, and then do:

$ conda install python-picard

pip

Otherwise, to install picard, you first need to install its dependencies:

$ pip install numpy matplotlib numexpr scipy

Then install Picard with pip:

$ pip install python-picard

If you do not have admin privileges on the computer, use the --user flag with pip. To upgrade, use the --upgrade flag provided by pip.

check

To check if everything worked fine, you can do:

$ python -c 'import picard'

and it should not give any error message.

Quickstart

The easiest way to get started is to copy the following lines of code in your script:

>>> import numpy as np
>>> from picard import picard
>>> N, T = 3, 1000
>>> S = np.random.laplace(size=(N, T))
>>> A = np.random.randn(N, N)
>>> X = np.dot(A, S)
>>> K, W, Y = picard(X)  

Picard outputs the whitening matrix, K, the estimated unmixing matrix, W, and the estimated sources Y. It means that:

\[Y = W K X\]

NEW in 0.6: scikit-learn compatible API

Introducing picard.Picard, which mimics sklearn.decomposition.FastICA behavior:

>>> from sklearn.datasets import load_digits
>>> from picard import Picard
>>> X, _ = load_digits(return_X_y=True)
>>> transformer = Picard(n_components=7)
>>> X_transformed = transformer.fit_transform(X)
>>> X_transformed.shape

Dependencies

These are the dependencies to use Picard:

  • numpy (>=1.8)

  • matplotlib (>=1.3)

  • numexpr (>= 2.0)

  • scipy (>=0.19)

  • scikit-learn (>=0.23)

These are the dependencies to run the EEG example:

  • mne (>=0.14)

Cite

If you use this code in your project, please cite:

Pierre Ablin, Jean-Francois Cardoso, Alexandre Gramfort
Faster independent component analysis by preconditioning with Hessian approximations
IEEE Transactions on Signal Processing, 2018
https://arxiv.org/abs/1706.08171

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Faster ICA under orthogonal constraint
ICASSP, 2018
https://arxiv.org/abs/1711.10873

Bug reports

Use the github issue tracker to report bugs.

API

Fork me on GitHub