Empirical Information Bottleneck

## Project description

# EMBO - Empirical Bottleneck

A Python package for working with the Information Bottleneck [Tishby, Pereira, Bialek 2001] and the Deterministic (and Generalized) Information Bottleneck [Strouse and Schwab 2016]. Embo is especially geared towards the analysis of concrete, finite-size data sets.

## Requirements

`embo`

requires Python 3, `numpy>=1.7`

and `scipy`

.

## Installation

To install the latest release, run:

pip install embo

(depending on your system, you may need to use `pip3`

instead of `pip`

in the command above).

### Testing

(requires `setuptools`

). If `embo`

is already installed on your
system, look for the copy of the `test_embo.py`

script installed
alongside the rest of the `embo`

files and execute it. For example:

python /usr/lib/python3.X/site-packages/embo/test/test_embo.py

## Usage

### The Information Bottleneck

We refer to [Tishby, Pereira, Bialek 2001] and [Strouse and Schwab 2016] for a general introduction to the Information Bottleneck. Briefly, if X and Y are two random variables, we are interested in finding another random variable M (called the "bottleneck" variable) that solves the following optimisation problem:

min_{p(m|x)} = H(M) - α H(M|X) - β I(M:Y)

for any β>0 and 0≤α≤1, and where M is constrained to be independent on Y conditional on X:

p(x,m,y) = p(x)p(m|x)p(y|x)

Intuitively, we want to find the stochastic mapping p(M|X) that
extracts from X as much information about Y as possible while
forgetting all irrelevant information. β is a free parameter that sets
the relative importance of forgetting irrelevant information versus
remembering useful information. α determines what notion of
"forgetting" we use: α=1 ("vanilla" bottleneck or IB) implies that we
want to minimise the mutual information I(M:X), α=0 (deterministic
bottleneck or DIB) that we want to make M a good *compression* of X by
minimising its entropy H(M), and intermediate values interpolate
between these two conditions.

Typically, one is interested in the curve described by I(M:Y) as a function of I(M:X) or H(M) at the solution of the bottleneck problem for a range of values of β. This curve gives the optimal tradeoff of compression and prediction, telling us what is the minimum amount of information one needs to know about X (or minimum amount of entropy one needs to retain) to be able to predict Y to a certain accuracy, or vice versa, what is the maximum accuracy one can have in predicting Y given a certain amount of information about X.

### Using `embo`

Embo can solve the information bottleneck problem for discrete random
variables starting from a set of joint empirical observations. The
main point of entry to the package is the `InformationBottleneck`

class. In its constructor, `InformationBottleneck`

takes as arguments an
array of observations for X and an (equally long) array of
observations for Y, together with other optional parameters (see the
docstring for details). In the most basic use case, users can call the
`get_bottleneck`

method of an `InformationBottleneck`

object, which will
assume α=1 and return a set of β values and the optimal values of
I(M:X), I(M:Y) and H(M) corresponding to those β. The optimal tradeoff
can then be visualised by plotting I(M:Y) vs I(M:Y).

For instance:

import numpy as np from matplotlib import pyplot as plt from embo import InformationBottleneck # data sequences x = np.array([0,0,0,1,0,1,0,1,0,1]) y = np.array([0,1,0,1,0,1,0,1,0,1]) # compute the IB bound from the data (vanilla IB; Tishby et al 2001) I_x,I_y,H_m,β = InformationBottleneck(x,y).get_bottleneck() # plot the IB bound plt.plot(I_x,I_y)

Embo can also operate starting from a joint (X,Y) probability distribution, encoded as a 2D array containing the probability of each combination of states for X and Y.

# define joint probability mass function for a 2x2 joint pmf pxy = np.array([[0.1, 0.4],[0.35, 0.15]]), # compute IB I_x,I_y,H_m,β = InformationBottleneck(pxy=pxy).get_bottleneck() # plot I(M:Y) vs I(M:X) plt.plot(I_x,I_y)

The deterministic and generalised bottleneck can be computed by
setting appropriately the parameter `alpha`

:

# compute Deterministic Information Bottleneck (Strouse 2016) I_x,I_y,H_m,β = InformationBottleneck(pxy=pxy, alpha=0).get_bottleneck() # plot I(M:Y) vs H(M) plt.plot(H_m,I_y)

### More examples

The `embo/examples`

directory contains some Jupyter notebook that
should exemplify most of the package's functionality.

- Basic-Example.ipynb: basics; how to compute and plot an IB bound.
- Markov-Chains.ipynb: using embo
for
*past-future bottleneck*type analyses on data from Markov chains. - Deterministic-Bottleneck.ipynb: Deterministic and Generalized Information Bottleneck. Here we reproduce a key figure from the Deterministic Bottleneck paper, and we explore the algorithm's behaviour as α changes from 0 to 1.
- Compare-embo-dit.ipynb: here we compare embo with dit [James et al 2018]. We compare the solutions found by the two packages on a set of simple IB problems (including a problem taken from dit's documentation), and we show that embo is orders of magnitude faster than dit.

### Further details

For more details, please consult the docstrings in
`InformationBottleneck`

.

## Changelog

See the CHANGELOG.md file for a list of changes from older versions.

## Authors

`embo`

is maintained by Eugenio Piasini, Alexandre Filipowicz and
Jonathan Levine.

## Project details

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