TSP

Data-driven Mutual Information Estimator based on regularized tree-structured partitions.

Installation

This package now counts with uv, this makes installation of the dependencies very easy. If you have uv already installed, you just need to run:

uv sync

This command will automatically generate a virtual environment with all that is needed for correctly executing the TSP module

Usage

• Python package TSP.py example of use.

import numpy as np

# Import the package
from TSP import TSP

# Initialize NumPy arrays of shape (n_samples, dim) with random 
# variables samples in Fortran-contiguous memory order
X = np.array([...], order='F').reshape(-1, 1)
Y = np.array([...], order='F').reshape(-1, 1)

# Initialize the TSP with its parameters
l_bn = 0.167
w_bn = 5e-2
reg_factor = 2.5e-5
tsp = TSP(l_bn, w_bn, reg_factor)

# Set observations to the estimator
tsp.grow(X, Y)

# Get TSP size and estimated mutual information
size = tsp.size()
emi = tsp.emi()

# Regularize the TSP mutual information estimator
tsp.regularize()

# Get the regularized TSP size and estimated mutual information
reg_size = tsp.reg_size()
reg_emi = tsp.reg_emi()

# Visualize the space partitions in a 2D scenario
tsp.visualize(X, Y)

Reference

[1] Silva, J. F., & Narayanan, S. (2012). Complexity-Regularized Tree-Structured Partition for Mutual Information Estimation. IEEE Transactions on Information Theory, 58(3), 1940-1952.