ViktorAJStein / Regularized_f_Divergence_Particle_Flows

Discretized Wasserstein Particle Flows of a MMD-regularized f-divergence functional.

MMD-regularized $f$-divergence Wasserstein-2 particle flows

A python script to evaluate and plot the discretized Wasserstein-2 gradient flow starting at an empirical measure with respect to an Maximum-Mean-Discrepancy-regularized f-divergence functional, whose target is an empirical measure as well.

Overview

This repository provides the method

MMD_reg_f_div_flow (from the file MMD_reg_fDiv_ParticleFlows.py)

used to produce the numerical experiments for the paper

Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces by Sebastian Neumayer, Viktor Stein, Gabriele Steidl and Nikolaj Rux.

If you use this code please cite this preprint, preferably like this:

@unpublished{NSSR24,
 author = {Neumayer, Sebastian and Stein, Viktor and Steidl, Gabriele and Rux, Nicolaj},
 title = {Wasserstein Gradient Flows for {M}oreau Envelopes of $f$-Divergences in Reproducing Kernel {H}ilbert Spaces},
 note = {ArXiv preprint},
 volume = {arXiv:2402.04613},
 year = {2024},
 month = {Feb},
 url = {https://arxiv.org/abs/2402.04613},
 doi = {10.48550/arXiv.2402.04613}
 }

The other python files contain auxillary functions. Scripts to exactly reproduce the figures in the preprint are soon to come. An example file is AlphaComparison.py.

Feedback / Contact

This code is written and maintained by Viktor Stein. Any comments, feedback, questions and bug reports are welcome! Alternatively you can use the GitHub issue tracker.

Contents

1. Required packages
2. Supported kernels
3. Supported $f$-divergences / entropy functions
4. Supported targets

Required packages

This script requires the following Python packages. We tested the code with Python 3.11.7 and the following package versions:

• torch 2.1.2
• scipy 1.12.0
• numpy 1.26.3
• pillow 10.2.0 (if you want to generate a gif of the evolution of the flow)
• matplotlib 3.8.2
• pot 0.9.3 (if you want to evaluate the exact Wasserstein-2 loss along the flow)
• sklearn.datasets 1.4.1.post1 (for more targets)

Usually code is also compatible with some later or earlier versions of those packages.

Supported kernels

The following kernels all are radial and twice-differentiable, hence fulfilling all assumptions in the paper. We denote the reLU by $(x)_+ := \max(x, 0)$ and the Euclidean norm by $| \cdot |$ and $s > 0$ the shape parameter.

Kernel	Name	Expression $K(x, y) =$
inverse multiquadric	imq	$(s + | x - y |^2)^{-\frac{1}{2}}$
Gauss	gauss	$\exp\left(- \frac{1}{2 s} | x - y |^2\right)$
Matérn-$\frac{3}{2}$	matern	$\left(1 + \frac{\sqrt{3} | x - y |}{s}\right) \exp\left(- \frac{\sqrt{3} | x - y |}{s}\right)$
Matérn-$\frac{5}{2}$	matern2	$\left(1 + \frac{\sqrt{5} | x - y |}{s} + \frac{5 | x - y |^2}{3 s^2} \right) \exp\left(- \frac{\sqrt{5} | x - y |}{s}\right)$
$B_{2\ell+1}$-Spline	compact	$(1 - | x - y |)_{+}^{s + 2}$
Another Spline	compact2	$(1 - | x - y |)_{+}^{s + 3} \left( (s + 3) | x - y | + 1 \right)$
inverse log	inv_log	$\left(s + \ln(1 + | x - y |^2)\right)^{-\frac{1}{2}}$
inverse quadric	inv_quad	$(1 + s | x - y |^2)^{-1}$
student t	student	$\frac{\Gamma\left(\frac{s + 1}{2}\right)}{\sqrt{s \pi} \Gamma\left(\frac{s}{2}\right)} \left(1 + \frac{1}{s} | x - y |^2\right)^{- \frac{s + 1}{2}}$

We also implemented the following two "$W_2$-metrizing kernels", which metrize the Wasserstein-2 distance on $\mathcal P_2(\mathbb R^d)$, detailed in Example 4 of Modeste, Dombry: "Characterization of translation invariant MMD on R d and connections with Wasserstein distances". However, they are unbounded, not translation invariant (and one is not differentiable on the diagonal), so they do not fulfill our assumptions.

Kernel	Name	Expression $K(x, y) =$
$W_2$-metrizing I	W2_1	$\exp\left(- \frac{1}{2 s} | x - y |^2\right) + \sum_{k = 1}^d | x_k y_k |$
$W_2$-metrizing II	W2_2	$\exp\left(- \frac{1}{2 s} | x - y |^2\right) + \sum_{k = 1}^d x_k^2 y_k^2$

Supported f-divergences / entropy functions

The following entropy functions each have an infinite recession constant if $\alpha > 1$.

Entropy	Name	Expression $f(x)$ for $x \ge 0$
Kullback-Leibler	tsallis, $\alpha = 1$	$x \ln(x) - x + 1$ for $x > 0$.
Tsallis-$\alpha$	tsallis	$\frac{1}{\alpha - 1} \left( x^{\alpha} - \alpha x + \alpha - 1 \right)$
Jeffreys	jeffreys	$(x - 1) \ln(x)$ for $x > 0$
$\chi^{\alpha}$	chi	$| x - 1 |^{\alpha}$

Below we list some other implemented entropy functions with finite recession constant. For even more entropy functions we refer to table 1 in the above mentioned preprint.

Entropy	Name	Expression $f(x)$ for $x \ge 0$
Burg	reverse_kl	$x - 1 - \ln(x)$ for $x > 0$
Jensen-Shannon	jensen_shannon	$\log(x) - (x + 1) \ln\left(\frac{x+1}{2}\right)$ for $x > 0$
total variation	tv	$| x - 1 |$
Matusita	matusita	$|1 - x^{\alpha} |^{\frac{1}{\alpha}}$
Kafka	kafka	$|1 - x |^{\frac{1}{\alpha}} (1 + x)^{\frac{\alpha - 1}{\alpha}}$
Marton	marton	$\max(1 - x, 0)^2$
perimeter	perimeter	$\frac{\text{sign}(\alpha)}{1 - \alpha}\left( (x^{\frac{1}{\alpha}} + 1)^{\alpha} - 2^{\alpha - 1}(x + 1)\right)$

Supported targets

• two_lines: the two parabolas in the gif at the top
• circles: three circles

• cross: four versions of Neals funnel arranged in a cross shape

• GMM: two exactly equal Gaussians which have a symmetry axis at $y = - x$

• four_wells: a sum of four Gaussians, which don't have a symmetry axis. The initial measure is initiated at one of the Gaussians.

• swiss_role_2d:

We also include some target measures from sklearn.data: moons, annulus

     

and the three-dimensional data sets swiss_role_3d and s_curve.

     

Speed

I am still working on improving the speed of this script, the bottleneck being the L-BFGS-B on the CPU. Currently, running the simulation for 50000 steps (exact parameters: tsallis-divergence, alpha=3, lambd=1.0, tau=0.001, kernel = IMQ, sigma = 0.5, N = 900, target_name = bananas) takes less than 12 minutes on a CUDA 7.5 GPU with 12 GB of RAM.