scikit-rmt 0.3.1

Random Matrix Theory Python package

scikit-rmt: Random Matrix Theory Python package

Random Matrix Theory, or RMT, is the field of Statistics that analyses matrices that their entries are random variables.

This package offers classes, methods and functions to give support to RMT in Python. Includes a wide range of utils to work with different random matrix ensembles, random matrix spectral laws and estimation of covariance matrices. See documentation or visit the https://github.com/AlejandroSantorum/scikit-rmt of the project for further information on the features included in the package.

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Documentation

The documentation is available at https://scikit-rmt.readthedocs.io/en/latest/, which includes detailed information of the different modules, classes and methods of the package, along with several examples showing different funcionalities.

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Installation

Using a virtual environment is recommended to minimize the chance of conflicts. However, the global installation should work properly as well.

Local installation using venv (recommended)

Navigate to your project directory.

cd MyProject

Create a virtual environment (you can change the name "env").

python3 -m venv env

Activate the environment "env".

source env/bin/activate

Install using pip.

pip install scikit-rmt

You may need to use pip3.

pip3 install scikit-rmt

Global installation

Just install it using pipor pip3.

pip install scikit-rmt

Requirements

scikit-rmt depends on the following packages:

• numpy - The fundamental package for scientific computing with Python
• matplotlib - Plotting with Python
• scipy - Scientific computation in Python

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A brief tutorial

First of all, several random matrix ensembles can be sampled: Gaussian Ensembles, Wishart Ensembles, Manova Ensembles and Circular Ensembles. As an example, the following code shows how to sample a Gaussian Orthogonal Ensemble (GOE) random matrix.

from skrmt.ensemble import GaussianEnsemble
# sampling a GOE (beta=1) matrix of size 3x3
goe = GaussianEnsemble(beta=1, n=3)
print(goe.matrix)

[[ 0.34574696 -0.10802385  0.38245343]
 [-0.10802385 -0.60113963  0.28624612]
 [ 0.38245343  0.28624612 -0.96503739]]

Its spectral density can be easily plotted:

# sampling a GOE matrix of size 1000x1000
goe = GaussianEnsemble(beta=1, n=1000)
# plotting its spectral distribution in the interval (-2,2)
goe.plot_eigval_hist(bins=80, interval=(-2,2), density=True)

If we sample a non-symmetric/non-hermitian random matrix, its eigenvalues do not need to be real, so a 2D complex histogram has been implemented in order to study spectral density of these type of random matrices. It would be the case, for example, of Circular Symplectic Ensemble (CSE).

# sampling a CSE (beta=4) matrix of size 2000x2000
cse = CircularEnsemble(beta=4, n=1000)
cse.plot_eigval_hist(bins=80, interval=(-2.2,2.2))

We can boost histogram representation using the results described by A. Edelman and I. Dumitriu in Matrix Models for Beta Ensembles and by J. Albrecht, C. Chan, and A. Edelman in Sturm Sequences and Random Eigenvalue Distributions (check references). Sampling certain random matrices (Gaussian Ensemble and Wishart Ensemble matrices) in its tridiagonal form we can speed up histogramming procedure. The following graphical simulation using GOE matrices tries to illustrate it.

In addition, several spectral laws can be analyzed using this library, such as Wigner's Semicircle Law, Marchenko-Pastur Law and Tracy-Widom Law.

Plot of Wigner's Semicircle Law, sampling a GOE matrix 5000x5000:

from skrmt.ensemble import wigner_semicircular_law

wigner_semicircular_law(ensemble='goe', n_size=5000, bins=80, density=True)

Plot of Marchenko-Pastur Law, sampling a WRE matrix 5000x5000:

from skrmt.ensemble import marchenko_pastur_law

marchenko_pastur_law(ensemble='wre', p_size=5000, n_size=15000, bins=80, density=True)

Plot of Tracy-Widom Law, sampling 20000 GOE matrices of size 100x100:

from skrmt.ensemble import tracy_widom_law

tracy_widom_law(ensemble='goe', n_size=100, times=20000, bins=80, density=True)

The other module of this library implements several covariance matrix estimators:

• Sample estimator.
• Finite-sample optimal estimator (FSOpt estimator).
• Non-linear shrinkage analytical estimator (Ledoit & Wolf, 2020).
• Linear shrinkage estimator (Ledoit & Wolf, 2004).
• Empirical Bayesian estimator (Haff, 1980).
• Minimax estimator (Stain, 1982).

For certain problems, sample covariance matrix is not the best estimation for the population covariance matrix.

The following code illustrates the usage of the estimators.

from skrmt.covariance import analytical_shrinkage_estimator

# load dataset with your own/favorite function (such as pandas.read_csv)
X = load_dataset('dataset_file.data')

# get estimation
Sigma = analytical_shrinkage_estimator(X)

# ... Do something with Sigma. For example, PCA.

For more information or insight about the usage of the library, you can visit the official documentation https://scikit-rmt.readthedocs.io/en/latest/ or the directory notebooks, that contains several Python notebooks with tutorials and plenty of examples.

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License

The package is licensed under the BSD 3-Clause License. A copy of the license can be found along with the code.

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Main references

• James Albrecht, Cy Chan, and Alan Edelman, "Sturm Sequences and Random Eigenvalue Distributions", Foundations of Computational Mathematics, vol. 9 iss. 4 (2009), pp 461-483. [pdf] [doi]

• Ioana Dumitriu and Alan Edelman, "Matrix Models for Beta Ensembles", Journal of Mathematical Physics, vol. 43 no. 11 (2002), pp. 5830-5547 arXiv:math-ph/0206043

• Rowan Killip and Rostyslav Kozhan, "Matrix Models and Eigenvalue Statistics for Truncations of Classical Ensembles of Random Unitary Matrices", Communications in Mathematical Physics, vol. 349 (2017) pp. 991-1027. arxiv.org/pdf/1501.05160.pdf

• Olivier Ledoit and Michael Wolf, "Analytical Nonlinear Shrinkage of Large-dimensional Covariance Matrices", Annals of Statistics, vol. 48, no. 5 (2020) pp. 3043–3065. [pdf]

• Olivier Ledoit and Michael Wolf, "A Well-conditioned Estimator for Large-dimensional Covariance Matrices", Journal of Multivariate Analysis, vol. 88 (2004) pp. 365–411. [pdf]

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Attribution

This project has been developed by Alejandro Santorum Varela (2021) as part of the final degree project in Computer Science (Autonomous University of Madrid), supervised by Alberto Suárez González.

If you happen to use scikit-rmt in your work or research, please cite its GitHub repository:

A. Santorum, "scikit-rmt", https://github.com/AlejandroSantorum/scikit-rmt, 2021. GitHub repository.

The corresponding BibTex entry is

@misc{Santorum2021,
  author = {A. Santorum},
  title = {scikit-rmt},
  year = {2021},
  howpublished = {\url{https://github.com/AlejandroSantorum/scikit-rmt}},
  note = {GitHub repository}
}