binsdpy - binary similarity and distance measures

Python implementation of binary similarity (see [1]) and distance measures (see [2]). The bitsets (immutable ordered set data type) and numpy.ndarray are suported as feature vectors.

Example

Example based on bitsets:

from bitsets import bitset
from binsdpy.similarity import jaccard
from binsdpy.distance import euclid

Colors = bitset("Colors", ("red", "blue", "green", "yellow"))

a = Colors.frommembers(["red", "blue"])
b = Colors.frommembers(["red", "yellow"])

jaccard(a, b)
# > 0.3333333333333333
euclid(a, b)
# > 1.4142135623730951

Example based on np.ndarray:

import numpy as np
from binsdpy.similarity import jaccard
from binsdpy.distance import euclid

a = np.array([1, 1, 0, 0], dtype=bool)
b = np.array([1, 0, 0, 1], dtype=bool)

jaccard(a, b)
# > 0.3333333333333333
euclid(a, b)
# > 1.4142135623730951

Installation

Package is avaliable in alpha version via pip.

$ pip install binsdpy

Dependencies

binsdpy requires:

• Python (>= 3.6)
• bitset
• numpy

Reference

[1] Brusco, M., Cradit, J. D., & Steinley, D. (2021). A comparison of 71 binary similarity coefficients: The effect of base rates. Plos one, 16(4), e0247751. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247751

[2] Choi, S. S., Cha, S. H., & Tappert, C. C. (2010). A survey of binary similarity and distance measures. Journal of systemics, cybernetics and informatics, 8(1), 43-48. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.352.6123&rep=rep1&type=pdf