computeUI

This repository provides code to compute the information decomposition defined in Quantifying Unique Information. The code implements the admUI algorithm proposed by Banerjee, et al. in Computing the Unique Information.

The repository contains two implementations:

• an implementation in Python that works together with the Python package dit.
• an implementation in Matlab.

Python

The Python implementation requires to have numpy installed. It can be used standalone, plus there are wrapper functions that allow to work with probability distributions generated using dit (version 1.0.0.dev6). The standalone version was tested with Python versions 3.4.2 and 2.7.9.

Installation

The package can install via pip:

pip3 install https://github.com/infodeco/computeUI/archive/master.zip

Files

This package contains two important files, namely:

• admUI_numpy.py: This file contains the function computeQUI_numpy that implements the alternating divergence minimization algorithm for computing the unique information (admUI).
• admUI.py: This file contains the wrapper function computeQUI that allows to work with probability distributions generated using dit.

The following files are tests and examples:

• test_dit_simple.py: testcase comparing the admUI algorithm with the Frank-Wolfe implementation in the dit package for some small examples. On small examples, both algorithms perform well, and the dit implementation often beats admUI. The comparison demonstrates that admUI achieves the specified error rate (unless one of the loops reaches the maximum number of iterations).
• test_cvxUI_dataPs.py: testcases for generating datapoints to compare the admUI with an implementation cvxopt_solve using the python interior-point solver CVXOPT. To run the test, CVXOPT needs to be installed, as well as cvxopt_solve. Make sure that the script finds all its dependencies; e.g. use something like:

$ PYTHONPATH='.:../../BROJA-Bivariate-Partial_Information_Decomposition/Python' python3 tests/test_cvxUI_dataPs.py

• test_dit_dataPs.py: testcases for generating datapoints to compare the admUI with an implementation based on the Frank-Wolfe algorithm from the dit package.

Example: The AND distribution S = AND(X,Y) using dit

The following code prepares the joint distribution of the AND example, using dit:

import admUI
import dit
d = dit.Distribution(['000', '001', '010', '111'], [1. / 4] * 4) 
d.set_rv_names('SXY')
print(d)

The following code computes the unique information of X using the admUI algorithm:

Q = admUI.computeQUI(distSXY = d)
print(Q)
dit.shannon.conditional_entropy(Q, 'S', 'Y') + dit.shannon.conditional_entropy(Q, 'X', 'Y') - dit.shannon.conditional_entropy(Q, 'SX', 'Y')

Alternatively, the dit package can be used to compute the same quantity:

dit_pid = dit.pid.PID_BROJA(d, ['X', 'Y'], 'S')
print(dit_pid)

Example: The AND distribution with numpy

The function computeQUI_numpy in admUI_numpy.py expects three inputs: the conditional distributions of X given S and Y given S and the marginal distribution of S. The inputs have to be numpy arrays.

import admUI_numpy
import numpy
PXgS = numpy.array([[ 2./3,  0.],
                    [ 1./3,  1.]])
PYgS = PXgS
PS = numpy.array([[ 0.75], [ 0.25]])
Q = admUI_numpy.computeQUI_numpy(PXgS, PYgS, PS)

The output is a threedimensional numpy array of the joint distribution.

Matlab

The Matlab version was tested with Matlab 2017a, but should also work with older versions of Matlab. However, Matlab 2017a is needed to make use of the MEX-feature (see below).

An easy example: The AND distribution

ns = 2; ny = 2; nz = 2; 
P  = [1 1 1 0 0 0 0 1]; P = P/sum(P);
Pzys = reshape(P,nz,ny,ns); Psy = squeeze(sum(Pzys,1))'; Psz = squeeze(sum(Pzys,2))';
[UI, Q] = admUIg(Psy, Psz)

Files

These functions perform the main computations:

• admUIg.m: the alternating divergence minimization algorithm for computing the unique information (admUI). Supports two different stopping criterias: a heuristic and a rigorous one.

• fn_UI_fmincon: fmincon implementation of the optimization problem with options for including the gradient and Hessian.

• test_dataPs.m, test_dataPy.m, test_dataPz.m: testcases comparing the admUI algorithm with fmincon from the Matlab Optimization Toolbox (algorithm: interior-point) with options for including the gradient and Hessian.

• copytest.m: testcase comparing the admUI algorithm and fmincon for the Copy example, vis-a-vis two different stopping criteria: a heuristic and a rigorous one.

• test_admUI_accn_eps.m: testcase comparing the convergence of an accelerated version of the admUI algorithm with the original, for a given accuracy (currently tested only for binary-valued S, Y, Z).

MEX

The Matlab MEX (Matlab executable) feature allows to precompile code, which greatly increases the speed. The MEX files are generated and tested in Matlab 2017a.

To generate the MEX function admUI_mex from the Matlab function admUI, do the following:

• Open the included MATLAB Coder project file admUI.prj in computeUI/matlab/MEX/ to specify the various code generation parameters like the sizes and data types of inputs.
• Select the script testAdmUI.m that exercises the target function admUI.
• Select Build Type: MEX to generate the MEX function admUI_mex which is used in the testcases in test_dataPs, test_dataPy, test_dataPz.

Repeat the same process to generate the MEX function admUIg_mex from the Matlab function admUIg which supports a more rigorous stopping criteria and is used in the copytest wrapping function.