Spatial-KWD 0.4.0

Spatial KWD for Large Spatial Maps

Spatial-KWD: Kantorovich-Wasserstein Distances for Large Spatial Maps

The Spatial-KWD package contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances [1], which are customized for large spatial maps. All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm [1,2]. Each implemented algorithm builds a different network, exploiting the special structure of spatial maps.

Details

This library contains three helper functions and two main classes.

The three helper functions are compareOneToOne, compareOneToMany, and compareAll. All the functions take in input the data and an options list. Using the options is possible to configure the Kantorivich-Wasserstein solver, so that it uses different algorithms with different parameters.

The helper functions are built on top of two main classes: Histogram2D and Solver.

Note that in case of non-convex maps, the algorithm builds the convex-hull of the input bins and pads the weights with zeros.

Prerequisities

You only need the following two standard python libraries:

• cython
• numpy

In the case you want to compile the source files, you need the python-dev, which on linux can be installed by>

apt install python3-dev  # Ubuntu

Installation

To install Spatial-KWD you can run the following command:

pip3 install Spatial-KWD

This will compile the C++ code and build the python wrapper.

Testing

For testing the library you can run the following command:

python3 test_matrix.py

The test program is the following

import numpy as np

from KWD import compareOneToOne, compareOneToMany, compareAll


np.random.seed(13)

N = 32*32
M = 3

# Random data
Coordinates = np.random.randint(0, 32, size=(N, 2), dtype=np.int32)
Weights = np.random.uniform(0, 100, size=(N, 2))

# Testing the first helper function
print('-----------------------------\nTest one2one approx:')
Options = {'Verbosity': 'debug'}
sol = compareOneToOne(Coordinates, Weights, Options)
for k in sol:
    print(k, sol[k])

References

[1] Bassetti, F., Gualandi, S. and Veneroni, M., 2020. On the Computation of Kantorovich--Wasserstein Distances Between Two-Dimensional Histograms by Uncapacitated Minimum Cost Flows. SIAM Journal on Optimization, 30(3), pp.2441-2469.

[2] Cunningham, W.H., 1976. A Network Simplex method. Mathematical Programming, 11(1), pp.105-116.

[3] https://github.com/eurostat/Spatial-KWD

Authors and maintainers

Stefano Gualandi, [e-mail].

Maintainer: Stefano Gualandi [e-mail]