squic 1.0.4

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 library $\mathtt{libSQUIC}$ from here, and follow its README instructions. The default and recommended location for $\mathtt{libSQUIC}$ 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

Use export SQUIC_LIB_PATH=/path/to/squic/, replacing /path/to/squic/ with the path of where $\mathtt{libSQUIC}$ can be found. If this variable is not set, the default location of $\mathtt{libSQUIC}$ is $HOME. This should be done before calling Python.

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) or check the User Manual.