Distances and divergences between distributions implemented in python.

## Project description

Distances and divergences between discrete distributions described as dictionaries implemented in python.

These are meant as fast solutions to compute distances and divergences between discrete distributions, expecially when the two distributions contains a significant amount of events with nill probability which are not described in the dictionaries.

## How do I install this package?

pip install dictances

## Available metrics

A number of distances and divergences are available:

Distances

Methods

Bhattacharyya distance

bhattacharyya

Bhattacharyya coefficient

bhattacharyya_coefficient

Canberra distance

canberra

Chebyshev distance

chebyshev

Chi Square distance

chi_square

Cosine Distance

cosine

Euclidean distance

euclidean

Hamming distance

hamming

Jensen-Shannon divergence

jensen_shannon

Kullback-Leibler divergence

kullback_leibler

Mean absolute error

mae

Taxicab geometry

manhattan, cityblock, total_variation

Minkowski distance

minkowsky

Mean squared error

mse

Pearson’s distance

pearson

Squared deviations from the mean

squared_variation

## Usage example with points

Suppose you have a point described by my_first_dictionary and another one described by my_second_dictionary:

from dictances import cosine

my_first_dictionary = {
"a": 56,
"b": 34,
"c": 89
}

my_second_dictionary = {
"a": 21,
"d": 51,
"e": 74
}

cosine(my_first_dictionary, my_second_dictionary)
#>>> 0.8847005261889619

## Usage example with distributions

Suppose you have a point described by my_first_dictionary and another one described by my_second_dictionary:

from dictances import bhattacharyya, bhattacharyya_coefficient

a = {
"event_1": 0.4,
"event_2": 0.1,
"event_3": 0.2,
"event_4": 0.3,
}
b = {
"event_1": 0.1,
"event_2": 0.2,
"event_5": 0.2,
"event_9": 0.5,
}

bhattacharyya_coefficient(a, b)
#>>> 0.3414213562373095
bhattacharyya(a, b)
#>>> 1.07463791569453

## Handling nested dictionaries

If you need to compute the distance between two nested dictionaries you can use deflate_dict as follows:

from dictances import cosine
from deflate_dict import deflate

my_first_dictionary = {
"a": 8,
"b": {
"c": 3,
"d": 6
}
}

my_second_dictionary = {
"b": {
"c": 8,
"d": 1
},
"y": 3,

}

cosine(deflate(my_first_dictionary), deflate(my_second_dictionary))

## Project details

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