tanimoto_binary_similarity

skfp.distances.tanimoto_binary_similarity(vec_a: ndarray | csr_array, vec_b: ndarray | csr_array) → float

Tanimoto similarity for vectors of binary values.

Computes the Tanimoto similarity [1] for binary data between two input arrays or sparse matrices using the Jaccard index using formula:

\[sim(vec_a, vec_b) = \frac{|vec_a \cap vec_b|}{|vec_a| + |vec_b| - |vec_a \cap vec_b|}\]

The calculated similarity falls within the range [0, 1]. Passing all-zero vectors to this function results in a similarity of 1.

Parameters:

• vec_a ({ndarray, sparse matrix}) – First binary input array or sparse matrix.

• vec_b ({ndarray, sparse matrix}) – Second binary input array or sparse matrix.

Returns:

similarity – Tanimoto similarity between vec_a and vec_b.

Return type:

float

References

[1]

Bajusz, D., Rácz, A. & Héberger, K. “Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?” J Cheminform, 7, 20 (2015).

Examples

>>> from skfp.distances import tanimoto_binary_similarity
>>> import numpy as np
>>> vec_a = np.array([1, 0, 1])
>>> vec_b = np.array([1, 0, 1])
>>> sim = tanimoto_binary_similarity(vec_a, vec_b)
>>> sim
1.0

>>> from skfp.distances import tanimoto_binary_similarity
>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[1, 0, 1]])
>>> vec_b = csr_array([[1, 0, 1]])
>>> sim = tanimoto_binary_similarity(vec_a, vec_b)
>>> sim
1.0