What (general)?

  • In statistics, the earth mover’s distance (EMD) is a measure of the distance between two probability distributions over a region D.[ref]

  • In stats or computer science, it’s “Earth mover’s distance”.
  • In maths, it’s “Wasserstein metric
  • The Wasserstein distance is the minimum cost of transporting mass in converting the data distribution q to the data distribution p.

What (math way)?

The idea borrowed from this. The first Wasserstein distance between the distributions uu and vv is:

l1(u,v)=infπΓ(u,v)R×Rxydπ(x,y) l_1 (u, v) = \inf_{\pi \in \Gamma (u, v)} \int_{\mathbb{R} \times \mathbb{R}} |x-y| \mathrm{d} \pi (x, y)

where Γ(u,v)\Gamma(u,v) is the set of (probability) distributions on R×R\mathbb{R}\times \mathbb{R} whose marginals are and on the first and second factors respectively.

If UU and VV are the respective CDFs of uu and vv, this distance also equals to:

l1(u,v)=+UV l_1(u, v) = \int_{-\infty}^{+\infty} |U-V|

Example of metric

Suppose we wanna move the blocks on the left to dotted-blocks on the right, we wanna find the “energy” (or metric) to do that.

Energy = Σ\Sigma weight of block x distance to move that block.

Suppose that weight of each block is 1. All below figures are copied from this.

EMD example

There are 2 ways to do that,

EMD example 2 ways of moving blocks from left to right.

Above example gives the same energies (4242) but there are usually different as below example,

EMD example

Coding

from scipy.stats import wasserstein_distance
arr1 = [1,2,3,4,5,6]
arr2 = [1,2,3,4,5,6]
wasserstein_distance(arr1, arr2)
0.0
# they are exactly the same!
arr1 = [1,2,3]
arr2 = [4,5,6]
wasserstein_distance(arr1, arr2)
# 3.0000000000000004

import seaborn as sns
sns.distplot(arr1, kde=False, hist_kws={"histtype": "step", "linewidth": 3, "alpha": 1, "color": "b"})
sns.distplot(arr2, kde=False, hist_kws={"histtype": "step", "linewidth": 3, "alpha": 1, "color": "r"})

EMD example

References

Notes with this notation aren't good enough. They are being updated.