Hellinger distance for discrete probability distributions in Python

hellinger.py

"""
Three ways of computing the Hellinger distance between two discrete
probability distributions using NumPy and SciPy.
"""

import numpy as np
from scipy.linalg import norm
from scipy.spatial.distance import euclidean


_SQRT2 = np.sqrt(2)     # sqrt(2) with default precision np.float64


def hellinger1(p, q):
    return norm(np.sqrt(p) - np.sqrt(q)) / _SQRT2


def hellinger2(p, q):
    return euclidean(np.sqrt(p), np.sqrt(q)) / _SQRT2


def hellinger3(p, q):
    return np.sqrt(np.sum((np.sqrt(p) - np.sqrt(q)) ** 2)) / _SQRT2

To anyone that finds this gist at a later date and you're getting the exception ValueError: array must not contain infs or NaNs

Make sure that the distributions given to these functions only contain positive values. Otherwise, sqrt is going to cause you pain. Throw them through np.absolute() first if you need to.

@cscorley Probability distributions aren't supposed to ever contain negative numbers. These functions don't do input validation for speed reasons.

In case anyone is wondering, I believe hellinger2 and hellinger3 are faster than hellinger1. (I had been using hellinger1 in one of my projects until some profiling determined it was a rate-limiting step.) Here is some timing code:

"""
Three ways of computing the Hellinger distance between two discrete
probability distributions using NumPy and SciPy.
"""
import time
import numpy as np
from scipy.linalg import norm
from scipy.spatial.distance import euclidean

_SQRT2 = np.sqrt(2)     # sqrt(2) with default precision np.float64

def hellinger1(p, q):
    return norm(np.sqrt(p) - np.sqrt(q)) / _SQRT2

def hellinger2(p, q):
    return euclidean(np.sqrt(p), np.sqrt(q)) / _SQRT2

def hellinger3(p, q):
    return np.sqrt(np.sum((np.sqrt(p) - np.sqrt(q)) ** 2)) / _SQRT2

repeat = 1000000
p = np.array([.05, .05, .1, .1, .2, .2, .3] * repeat)
q = np.array([  0,   0,  0, .1, .3, .3 ,.3] * repeat)

p /= p.sum()
q /= q.sum()

for hellingerFunction in [hellinger1, hellinger2, hellinger3]:
    start = time.time()
    hellingerFunction(p=p, q=q)
    duration = time.time() - start
    print("{} took {} long".format(hellingerFunction.__name__, duration))

Should get something like:

hellinger1 took 0.15966796875 long
hellinger2 took 0.10175800323486328 long
hellinger3 took 0.09865593910217285 long

The difference shrinks for shorter arrays p and q, but even if repeat=1 so that p and q are of length 7, hellinger3 is still faster.

Hope this is right.

In case anyone is interested, I've implemented Hellinger Distance in Cython as a split criterion for sklearn DecisionTreeClassifier and RandomForestClassifier.
It performs great in my use cases of imbalanced data classification, beats RandomForestClassifier with gini and XGBClassifier.
You are welcome to check it out on https://github.com/EvgeniDubov/hellinger-distance-criterion