squic 1.0.3

SQUIC is a second-order, L1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation. This repository contains the source code for the Python interface of SQUIC.

SQUIC for Python

SQUIC is a second-order, L1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation. This repository contains the source code for the Python(v3) interface of SQUIC.

For an interactive session using SQUIC for Python, see Google Colab:

Installation

Step 1:

Download the shared library libSQUIC from www.gitlab.ci.inf.usi.ch/SQUIC/libSQUIC, and follow its README instructions. The default and recommended location for libSQUIC is the home directory, i.e., ~/. Note that precompiled versions are available.

Step 2:

Run the following command to install the library:

pip3 install squic

Note: The environment variable SQUIC_LIB_PATH defines the location of libSQUIC - this is by default set to the home directory of the user. If this is not the location of libSQUIC, it can be changed via terminal bash> export SQUIC_LIB_PATH=/path/to/squic/

Example

In this example, we will use SQUIC to estimate the precision matrix of a synthetically generated dataset with correlated random variables, where the true precision matrix is tridiagonal.

import squic
import numpy as np

# generate sample from tridiagonal precision matrix
p = 1024
n = 100
l = .4

# generate a tridiagonal matrix
np.random.seed(1)
a = -0.5 * np.ones(p-1)
b = 1.25 * np.ones(p)
iC_star = np.diag(a,-1) + np.diag(b,0) + np.diag(a,1)

# generate the data
L = np.linalg.cholesky(iC_star)
Y = np.linalg.solve(L.T,np.random.randn(p,n))

[X,W,info_times,info_objective,info_logdetX,info_trSX] = squic.run(Y,l)

For further details type help(SQUIC).

Publications & References

Please cite our publications if it helps your research:

@article{X,
 author = {X},
 title = {X}
 journal = {X}
 volume = {X}
 number = {X}
 pages = {X}
 year = {X}
 doi = {X}
}

For related research on SQUIC please see:

• Bollhöfer, M., Eftekhari, A., Scheidegger, S. and Schenk, O., 2019. Large-Scale Sparse Inverse Covariance Matrix Estimation. SIAM Journal on Scientific Computing, 41(1), pp.A380-A401

• Eftekhari, A., Bollhöfer, M. and Schenk, O., 2018, November. Distributed Memory Sparse Inverse Covariance Matrix Estimation on High-performance Computing Architectures. In SC18: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 253-264). IEEE

• Eftekhari, A., Pasadakis, D., Bollhöfer, M., Scheidegger, S. and Schenk, O., 2021. Block-Enhanced Precision Matrix Estimation for Large-Scale Datasets. Journal of Computational Science, p. 101389