Fisher Information Matrix in Python

The Fisher Information Matrix plays a crucial role in various aspects of machine learning and statistics. Its primary significance lies in providing a measure of the amount of information that an observable random variable carries about an unknown parameter upon which the probability depends.

Introduction
Information Geometry
       Key elements
       Use cases
Fisher information matrix
Implementation
       Normal distribution
       Beta distribution
References
Appendix        

What you will learn: How to estimate and visualize the Fisher information matrix for Normal and Beta distributions on a hypersphere.

Notes: 

• Environments: Python  3.10.10, Geomstats 2.7.0
• This article assumes that the reader is somewhat familiar with differential and tensor calculus [ref 1]. Please refer to our previous articles related to geometric learning listed on Appendix.
• Source code is available at  Github.com/patnicolas/Data_Exploration/Information Geometry
• To enhance the readability of the algorithm implementations, we have omitted non-essential code elements like error checking, comments, exceptions, validation of class and method arguments, scoping qualifiers, and import statements.
• Please subscribe to Hands-on Geometric Deep Learning for in-depth topics on Geometric learning, reviews and exercises.

Introduction

This article is the 10th installments of our ongoing series focused on geometric learning. It introduces some basic elements of information geometry as an extension of differential geometry. As with previous articles, we utilize the Geomstats Python library [ref. 2] to implement concepts associated with geometric learning. 

Note: Summaries of my earlier articles on this topic can be found in the Appendix

As a reminder, the primary goal of learning Riemannian geometry is to understand and analyze the properties of curved spaces that cannot be described adequately using Euclidean geometry alone. 

Here is a synapsis of this article

1 Brief introduction to information geometry

2 Overview and mathematical formulation of the Fisher information matrix

3 Computation of the Fisher metric to Normal and Beta distributions

4. Implementation in Python using the Geomstats library

Information geometry

Information geometry applies the principles and methods of differential geometry to problems in probability theory and statistics [ref 3]. It studies the manifold of probability distributions and provides a natural framework for understanding and analyzing statistical models. 

Key elements

• Statistical manifolds: Families of probability distributions are considered as a manifold, with each distribution representing a point on this manifold.
• Riemannian metrics: The Fisher information metric is commonly used to define a Riemannian metric on the statistical manifold. This metric measures the amount of information that an observable random variable carries about an unknown parameter.
• Divergence measures: Divergence measures like the Kullback-Leibler (KL) divergence, which quantify the difference between two probability distributions.
• Connections and curvature: Differential geometry concepts such as affine connections and curvature are used to describe the geometric properties of statistical models (i.e. α-connection family).
• Dualistic inference: Exponential and mixture connections provide a rich structure for statistical inference.

Use cases

Here is a non-exclusive list of application of information geometry

• Statistical Inference: Parameter estimation, hypothesis testing, and model selection (i.e. Bayesian posterior distributions and in the development of efficient sampling algorithms like Hamiltonian Monte Carlo)
• Optimization: Natural gradient descent method uses the Fisher information matrix to adjust the learning rate dynamically, leading to faster convergence compared to traditional gradient descent.
• Finance: Modeling uncertainties and analyzing statistical properties of financial models.
• Machine Learning: Optimization of learning algorithms (i.e. Understanding the EM algorithm used in statistical estimation for latent variable model)
• Neuroscience: Neural coding and information processing in the brain by modeling neural responses as probability distributions.
• Robotics: Development of probabilistic robotics, where uncertainty and sensor noise are modeled using probability distributions.
• Information Theory: Concepts for encoding, compression, and transmission of information.

Fisher information matrix

The Fisher information matrix is a type of Riemannian metric that can be applied to a smooth statistical manifold [ref 4]. It serves to quantify the informational difference between measurements. The points on this manifold represent probability measures defined within a Euclidean probability space, such as the Normal distribution. Mathematically, it is represented by the Hessian of the Kullback-Leibler divergence.'

Let's consider a statistical manifold with coordinates (or parameters) θ and its probability density function over an interval X as follow:

The Fisher metric is a Riemann metric tensor defined as the expectation of the partial derivative of the negative log likelihood over two coordinates θ.

The Fisher information or Fisher-Rao metric quantifies the amount of information in the data regarding a parameter θ. The Fisher-Rao metric, an intrinsic measure, enables the analysis of a finite, n-dimensional statistical manifold M.

The Fisher metric for the normal distribution θ = {μ, σ} is computed as:

The Fisher metric for the beta distribution  θ = {α, β} is computed as:

Implementation

We leverage the following classes defined in the previous articles:

• ManifoldPoint in Riemann Metric & Connection for Geometric Learning - Setup
• HypersphereSpace in Differentiable Manifolds for Geometric Learning - Hypersphere

Let's first define a base class for all distributions to be defined on a hypersphere [ref 5].

The purpose of the method show_points is to display the various data point with optional tangent vector on the hypersphere. The argument num_pts specifies the number of random points to be defined in the hypersphere. The tangent vector is displayed if the argument tgt_vector not defined as the origin (_ZERO_TGT_VEC).

Normal distribution

The class NormalHypersphere encapsulates the display of the normal distribution on the hypersphere. The constructor initialized the normal distribution implemented in the Geomstats library.

The method show_distribution display num_pdfs probability density function over a set of num_manifold_pts, manifold points on the hypersphere. This specific implementation uses only two points. The Fisher-Rao metric is computed using the metric.geodesic the Geomstats method.

The metric is applied to 100 points along the geodesic between the two points A and B. Finally, the density functions, pdfs are computed by converting the metric values to the NormalDistribution.point_to_pdf Geomstats method.

 Let's plot 2 randomly sampled data points associated with a tangent_vector on Hypersphere (1) then visualize 40 normalized normal probability density distributions (2).

Beta distribution

Let's wrap the evaluation of the Beta distribution on a hypersphere into the class BetaHypersphere that inherits GeometriDistribution. It leverages the BetaDistributions class in Geomstats. 

The method show_distribution generates random points on the Hypersphere (1)  and compute the beta density function at these points using the Geomstats method BetaDistributions.point_to_pdf (2). The values generated by the pdfs are normalized then plotted (3) 

Let's plot 10 randomly sampled data points on Hypersphere (1) then visualize 200  normalized beta probability density distributions (2).

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References

[1] Tensor Calculus - Eigenchris

[2] Introduction to Geometric Learning in Python with Geomstats

[3] An elementary introduction to information geometry - F. Nielsen

[4] Information Geometry - Near Randomness and Near Independence  K. Arwini, CTJ Dobson - Springer-Verlag 2008

[5] Differentiable Manifolds for Geometric Learning: Hypersphere

Patrick Nicolas has over 25 years of experience in software and data engineering, architecture design and end-to-end deployment and support with extensive knowledge in machine learning.  He has been director of data engineering at Aideo Technologies since 2017 and he is the author of "Scala for Machine Learning", Packt Publishing ISBN 978-1-78712-238-3 and Hands-on Geometric Deep Learning newsletter

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