wnet 1.1.1

Tools for calculation of Wasserstein metric between distributions based on Network Flow algorithm

wnet

Wasserstein Network (wnet) is a Python/C++ library for working with Wasserstein distances. It uses the Min Cost Flow algorithm as implemented by the LEMON library, exposed to Python via the pylmcf module, enabling efficient computation and manipulation of Wasserstein distances between multidimensional distributions.

Features

• Wasserstein and Truncated Wasserstein distance between multidimensional distributions (dimensions 1–20)
• Three distance metrics: L1, L2, L∞
• Order-p (Lp) Wasserstein for any real p ≥ 1 (per-pair cost = ground_distance**p, result is the p-th root; fractional p via automatic cost scaling)
• Derivatives with respect to peak intensities and spectrum mixture proportions
• Position gradients (∂cost/∂position) with warm-restart re-solving after peak position updates
• Support for distribution mixtures and efficient recalculation with changed mixture proportions
• Picklable Distribution objects

Installation

You can install the Python package using pip:

pip install wnet

Usage

Basic distance

import numpy as np
from wnet import WassersteinDistance, Distribution
from wnet.distances import DistanceMetric

positions1 = np.array([[0, 1, 5, 10], [0, 0, 0, 3]])
intensities1 = np.array([10, 5, 5, 5])

positions2 = np.array([[1, 10], [0, 0]])
intensities2 = np.array([20, 5])

S1 = Distribution(positions1, intensities1)
S2 = Distribution(positions2, intensities2)

print(WassersteinDistance(S1, S2, DistanceMetric.L1))
# 45

Order-p (Lp) Wasserstein

By default the distance is the 1-Wasserstein distance. Pass p to use the order-p Wasserstein distance, where each unit of mass moved a ground distance d costs d**p and the returned value is the p-th root of the optimal transport cost:

# Quadratic (W2) Wasserstein distance with a Euclidean ground metric
print(WassersteinDistance(S1, S2, DistanceMetric.L2, p=2))
print(TruncatedWassersteinDistance(S1, S2, DistanceMetric.L2, max_distance=3.0, p=2))

# Fractional orders work too (e.g. p = 1.5)
print(WassersteinDistance(S1, S2, DistanceMetric.L2, p=1.5))

p can be any real number ≥ 1. The ground metric (L1/L2/L∞) is chosen independently of p. For p != 1 the dense transport network is always used — the fast 1D chain solver is only valid for p == 1, since exponentiated step costs are not additive along a chain.

p == 1 is bit-exact with the classic 1-Wasserstein distance. For p != 1 the cost d**p is fractional, so the integer min-cost-flow solver works in automatically scaled units (round(scale_factor() * d**p)); the public results divide that scale back out, so no tuning is needed.

At the WassersteinNetwork level, total_cost() and all derivatives are in W_p**p units (the sum of d**p · flow); take the p-th root for the literal W_p distance, as WassersteinDistance() does.

Truncated Wasserstein

Mass that cannot be matched within max_distance is discarded at a fixed cost rather than transported arbitrarily far:

from wnet import TruncatedWassersteinDistance

print(TruncatedWassersteinDistance(S1, S2, DistanceMetric.L2, max_distance=3.0))

Derivatives w.r.t. peak intensities

signal_part_derivatives() returns the marginal cost of increasing each theoretical peak's intensity by 1 — useful for scoring how well each peak is explained:

from wnet import WassersteinNetwork

W = WassersteinNetwork(S1, [S2], DistanceMetric.L2, max_distance=10.0)
W.build()
W.solve()

derivs = W.signal_part_derivatives()   # np.ndarray, one value per peak in S1

Optimising peak positions

After an initial solve, positions can be updated and re-solved cheaply via a warm restart. update_positions_and_get_gradient() returns ∂cost/∂position for all peaks so you can feed them directly into a gradient-based optimiser:

W = WassersteinNetwork(S1, [S2], DistanceMetric.L2, max_distance=10.0)
W.build()
W.solve()

for _ in range(100):
    grad_empirical, grad_theoretical = W.update_positions_and_get_gradient(new_positions)
    new_positions -= 0.01 * grad_empirical

Licence

MIT Licence

Citation

If you use this software, please cite:

Król J, Bochenek M, Jopa S, Kazimierczuk K, Gambin A, Startek MP (2026). WNetAlign: fast and accurate spectra alignment using truncated Wasserstein distance and network simplex. Briefings in Bioinformatics, 27(3), bbag247. https://doi.org/10.1093/bib/bbag247

@article{krol2026wnetalign,
  title   = {WNetAlign: fast and accurate spectra alignment using truncated Wasserstein distance and network simplex},
  author  = {Kr{\'o}l, Justyna and Bochenek, Maria and Jopa, Sylwia and Kazimierczuk, Krzysztof and Gambin, Anna and Startek, Micha{\l} Piotr},
  journal = {Briefings in Bioinformatics},
  volume  = {27},
  number  = {3},
  pages   = {bbag247},
  year    = {2026},
  doi     = {10.1093/bib/bbag247}
}

Related Projects

• pylmcf - Python bindings for Min Cost Flow algorithms from LEMON library.
• wnetalign - Alignment of MS/NMR spectra using Truncated Wasserstein Distance