dice_count_similarity

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

Dice similarity for vectors of count values.

Computes the Dice similarity [1] [2] [3] for count data between two input arrays or sparse matrices using the formula:

\[sim(vec_a, vec_b) = \frac{2 \cdot vec_a \cdot vec_b}{\|vec_a\|^2 + \|vec_b\|^2}\]

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 count input array or sparse matrix.

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

Returns:

similarity – Dice similarity between vec_a and vec_b.

Return type:

float

References

[1]

Dice, Lee R. “Measures of the amount of ecologic association between species.” Ecology 26.3 (1945): 297-302. <https://esajournals.onlinelibrary.wiley.com/doi/abs/10.2307/1932409>

[2]

Brusco M., Cradit J. D., Steinley D. “A comparison of 71 binary similarity coefficients: The effect of base rates.” PloS one 16.4 (2021): e0247751. <https://doi.org/10.1371/journal.pone.0247751>

[3]

Todeschini R., Consonni V., Xiang H., Holliday J., Buscema M., Willett P. “Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets.” Journal of Chemical Information and Modeling 52.11 (2012): 2884-2901. <https://doi.org/10.1021/ci300261r>

Examples

>>> from skfp.distances import dice_count_similarity
>>> import numpy as np
>>> vec_a = np.array([7, 1, 1])
>>> vec_b = np.array([7, 1, 2])
>>> sim = dice_count_similarity(vec_a, vec_b)
>>> sim
0.9904761904761905

>>> from skfp.distances import dice_count_similarity
>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[7, 1, 1]])
>>> vec_b = csr_array([[7, 1, 2]])
>>> sim = dice_count_similarity(vec_a, vec_b)
>>> sim
0.9904761904761905