harris_lahey_binary_distance

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

Harris-Lahey distance for vectors of binary values.

Computes the Harris-Lahey distance [1] [2] [3] for binary data between two input arrays or sparse matrices by subtracting the similarity from 1, using the formula:

\[dist(a, b) = 1 - sim(a, b)\]

See also harris_lahey_binary_similarity(). It uses the normalized similarity, scaled to range [0, 1]. The calculated distance falls within the range \([0, 1]\). Passing all-zero or all-ones vectors to this function results in a distance of 0.

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:

distance – Harris-Lahey distance between vec_a and vec_b.

Return type:

float

References

[1]

Francis C. Harris, Benjamin B. Lahey “A method for combining occurrence and nonoccurrence interobserver agreement scores” J Appl Behav Anal. 1978 Winter;11(4):523-7.

[2]

Brusco M, Cradit JD, Steinley D. “A comparison of 71 binary similarity coefficients: The effect of base rates” PLoS One. 2021 Apr 7;16(4):e0247751.

[3]

R. Todeschini, V. Consonni, H. Xiang, J. Holliday, M. Buscema, P. Willett “Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets” J. Chem. Inf. Model. 2012, 52, 11, 2884–2901

Examples

>>> from skfp.distances import harris_lahey_binary_distance
>>> import numpy as np
>>> vec_a = np.array([1, 0, 1])
>>> vec_b = np.array([1, 0, 1])
>>> dist = harris_lahey_binary_distance(vec_a, vec_b)
>>> dist
0.0

>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[1, 0, 1]])
>>> vec_b = csr_array([[1, 0, 1]])
>>> dist = harris_lahey_binary_distance(vec_a, vec_b)
>>> dist
0.0