Distance functions: A drop-in replacement for, and a super-set of the scipy.spatial.distance module.
This package contains algorithms for computing distances between data points.
It is a thin Python wrapper around the distances crate, in Rust.
It provides drop-in replacements for the distance functions in scipy.spatial.distance.
pip install abd-distances
import math
import numpy
import abd_distances.simd as distance
a = numpy.array([i for i in range(10_000)], dtype=numpy.float32)
b = a + 1.0
dist = distance.euclidean(a, b)
assert math.fabs(dist - 100.0) < 1e-6
print(dist)
# 100.0
abd_distances.vector.braycurtisabd_distances.vector.canberraabd_distances.vector.chebyshevabd_distances.vector.cosineabd_distances.vector.euclideanabd_distances.vector.manhattan and abd_distances.vector.cityblockabd_distances.vector.minkowskiabd_distances.vector.sqeuclideanabd_distances.vector.cdist and abd_distances.vector.pdistabd_distances.simd.euclideanabd_distances.simd.sqeuclideanabd_distances.simd.cosineabd_distances.simd.cdist and abd_distances.simd.pdistabd_distances.strings.hammingabd_distances.strings.levenshteinabd_distances.strings.needleman_wunschThese benchmarks were run on an Intel Core i7-11700KF CPU @ 4.900GHz, using a single thread. The OS was Arch Linux, with kernel version 6.7.4-arch1-1.
The "Min", "Max", and "Mean" columns show the minimum, maximum, and mean times (in seconds), respectively, taken to compute the pairwise distances using the functions from scipy.spatial.distance.
The "Min (+)", "Max (+)", and "Mean (+)" columns show the speedup of the this package's functions over the scipy functions.
All pairwise distances (cdist and pdist) were computed for 200x200 vectors of 500 dimensions, and the average time was taken over 100 runs.
All individual distances were computed for 20x20 vectors of 500 dimensions, and the average time was taken over 100 runs.
| Benchmark | Min | Max | Mean | Min (+) | Max (+) | Mean (+) |
|---|---|---|---|---|---|---|
| cdist, euclidean, f32 | 2.560 | 2.576 | 2.566 | 0.185 (13.9x) | 0.196 (13.2x) | 0.188 (13.7x) |
| cdist, euclidean, f64 | 2.398 | 2.406 | 2.401 | 0.292 (8.2x) | 0.307 (7.8x) | 0.298 (8.0x) |
| cdist, sqeuclidean, f32 | 2.519 | 2.527 | 2.523 | 0.182 (13.9x) | 0.197 (12.8x) | 0.187 (13.5x) |
| cdist, sqeuclidean, f64 | 2.381 | 2.393 | 2.389 | 0.293 (8.1x) | 0.318 (7.5x) | 0.301 (7.9x) |
| cdist, cosine, f32 | 4.011 | 4.021 | 4.016 | 0.625 (6.4x) | 0.637 (6.3x) | 0.632 (6.4x) |
| cdist, cosine, f64 | 3.978 | 4.009 | 3.992 | 0.626 (6.4x) | 0.666 (6.0x) | 0.638 (6.3x) |
| pdist, euclidean, f32 | 1.235 | 1.249 | 1.241 | 0.252 (4.9x) | 0.263 (4.7x) | 0.257 (4.8x) |
| pdist, euclidean, f64 | 1.216 | 1.262 | 1.234 | 0.302 (4.0x) | 0.312 (4.0x) | 0.308 (4.0x) |
| pdist, sqeuclidean, f32 | 1.229 | 1.250 | 1.237 | 0.251 (4.9x) | 0.303 (4.1x) | 0.265 (4.7x) |
| pdist, sqeuclidean, f64 | 1.209 | 1.213 | 1.211 | 0.306 (3.9x) | 0.313 (3.9x) | 0.310 (3.9x) |
| pdist, cosine, f32 | 2.001 | 2.017 | 2.006 | 0.468 (4.3x) | 0.484 (4.2x) | 0.478 (4.2x) |
| pdist, cosine, f64 | 1.991 | 2.004 | 1.996 | 0.461 (4.3x) | 0.476 (4.2x) | 0.471 (4.2x) |
| euclidean, f32 | 0.644 | 0.670 | 0.654 | 0.076 (8.5x) | 0.080 (8.4x) | 0.078 (8.3x) |
| euclidean, f64 | 0.672 | 0.701 | 0.682 | 0.097 (6.9x) | 0.102 (6.9x) | 0.100 (6.8x) |
| sqeuclidean, f32 | 0.506 | 0.512 | 0.508 | 0.076 (6.6x) | 0.079 (6.5x) | 0.078 (6.5x) |
| sqeuclidean, f64 | 0.515 | 0.519 | 0.518 | 0.100 (5.1x) | 0.104 (5.0x) | 0.103 (5.0x) |
| cosine, f32 | 0.668 | 0.687 | 0.677 | 0.110 (6.1x) | 0.113 (6.1x) | 0.111 (6.1x) |
| cosine, f64 | 0.465 | 0.472 | 0.469 | 0.127 (3.7x) | 0.130 (3.6x) | 0.129 (3.6x) |
| f32 | f64 |
|---|---|
|
|
|
These benchmarks were run on an Intel Core i7-11700KF CPU @ 4.900GHz, using a single thread. The OS was Arch Linux, with kernel version 6.7.4-arch1-1.
The "Min", "Max", and "Mean" columns show the minimum, maximum, and mean times (in seconds), respectively, taken to compute the pairwise distances using the functions from scipy.spatial.distance.
The "Min (+)", "Max (+)", and "Mean (+)" columns show the speedup of the this package's functions over the scipy functions.
All pairwise distances (cdist and pdist) were computed for 200x200 vectors of 500 dimensions, and the average time was taken over 100 runs.
All individual distances were computed for 20x20 vectors of 500 dimensions, and the average time was taken over 100 runs.
These benchmarks were run using the richbench package.
| Benchmark | Min | Max | Mean | Min (+) | Max (+) | Mean (+) |
|---|---|---|---|---|---|---|
| braycurtis, f32 | 1.103 | 1.134 | 1.114 | 0.323 (3.4x) | 0.324 (3.5x) | 0.323 (3.4x) |
| braycurtis, f64 | 0.834 | 0.843 | 0.838 | 0.170 (4.9x) | 0.173 (4.9x) | 0.171 (4.9x) |
| canberra, f32 | 2.524 | 2.529 | 2.526 | 0.153 (16.5x) | 0.155 (16.3x) | 0.154 (16.4x) |
| canberra, f64 | 2.216 | 2.260 | 2.235 | 0.168 (13.2x) | 0.170 (13.3x) | 0.169 (13.2x) |
| chebyshev, f32 | 2.738 | 2.774 | 2.753 | 0.149 (18.3x) | 0.151 (18.4x) | 0.150 (18.4x) |
| chebyshev, f64 | 2.777 | 2.784 | 2.781 | 0.165 (16.8x) | 0.166 (16.8x) | 0.165 (16.8x) |
| euclidean, f32 | 0.641 | 0.641 | 0.641 | 0.150 (4.3x) | 0.150 (4.3x) | 0.150 (4.3x) |
| euclidean, f64 | 0.657 | 0.662 | 0.660 | 0.167 (3.9x) | 0.168 (3.9x) | 0.168 (3.9x) |
| sqeuclidean, f32 | 0.506 | 0.509 | 0.507 | 0.149 (3.4x) | 0.149 (3.4x) | 0.149 (3.4x) |
| sqeuclidean, f64 | 0.514 | 0.518 | 0.516 | 0.165 (3.1x) | 0.173 (3.0x) | 0.170 (3.0x) |
| cityblock, f32 | 0.437 | 0.443 | 0.440 | 0.150 (2.9x) | 0.150 (2.9x) | 0.150 (2.9x) |
| cityblock, f64 | 0.444 | 0.451 | 0.447 | 0.167 (2.7x) | 0.168 (2.7x) | 0.168 (2.7x) |
| cosine, f32 | 0.659 | 0.668 | 0.664 | 0.314 (2.1x) | 0.315 (2.1x) | 0.314 (2.1x) |
| cosine, f64 | 0.459 | 0.471 | 0.465 | 0.321 (1.4x) | 0.325 (1.4x) | 0.324 (1.4x) |
| cdist, braycurtis, f32 | 4.902 | 4.906 | 4.904 | 1.802 (2.7x) | 1.875 (2.6x) | 1.833 (2.7x) |
| cdist, braycurtis, f64 | 4.765 | 4.775 | 4.768 | 0.710 (6.7x) | 0.735 (6.5x) | 0.725 (6.6x) |
| cdist, canberra, f32 | 6.914 | 6.943 | 6.930 | 1.356 (5.1x) | 1.385 (5.0x) | 1.367 (5.1x) |
| cdist, canberra, f64 | 6.782 | 6.813 | 6.797 | 0.684 (9.9x) | 0.701 (9.7x) | 0.692 (9.8x) |
| cdist, chebyshev, f32 | 2.763 | 2.768 | 2.765 | 0.640 (4.3x) | 0.663 (4.2x) | 0.649 (4.3x) |
| cdist, chebyshev, f64 | 2.659 | 2.677 | 2.664 | 0.647 (4.1x) | 0.662 (4.0x) | 0.655 (4.1x) |
| cdist, euclidean, f32 | 2.563 | 2.570 | 2.564 | 0.644 (4.0x) | 0.658 (3.9x) | 0.653 (3.9x) |
| cdist, euclidean, f64 | 2.378 | 2.400 | 2.388 | 0.630 (3.8x) | 0.649 (3.7x) | 0.640 (3.7x) |
| cdist, sqeuclidean, f32 | 2.516 | 2.523 | 2.519 | 0.648 (3.9x) | 0.660 (3.8x) | 0.652 (3.9x) |
| cdist, sqeuclidean, f64 | 2.412 | 2.423 | 2.417 | 0.631 (3.8x) | 0.645 (3.8x) | 0.638 (3.8x) |
| cdist, cityblock, f32 | 4.545 | 4.552 | 4.548 | 0.647 (7.0x) | 0.671 (6.8x) | 0.658 (6.9x) |
| cdist, cityblock, f64 | 4.406 | 4.407 | 4.407 | 0.633 (7.0x) | 0.657 (6.7x) | 0.647 (6.8x) |
| cdist, cosine, f32 | 4.010 | 4.020 | 4.013 | 2.254 (1.8x) | 2.292 (1.8x) | 2.270 (1.8x) |
| cdist, cosine, f64 | 3.987 | 3.992 | 3.990 | 2.241 (1.8x) | 2.288 (1.7x) | 2.258 (1.8x) |
| pdist, braycurtis, f32 | 2.382 | 2.387 | 2.385 | 1.062 (2.2x) | 1.074 (2.2x) | 1.069 (2.2x) |
| pdist, braycurtis, f64 | 2.368 | 2.378 | 2.371 | 0.510 (4.6x) | 0.523 (4.5x) | 0.516 (4.6x) |
| pdist, canberra, f32 | 3.374 | 3.411 | 3.389 | 0.831 (4.1x) | 0.841 (4.1x) | 0.835 (4.1x) |
| pdist, canberra, f64 | 3.369 | 3.411 | 3.396 | 0.504 (6.7x) | 0.515 (6.6x) | 0.509 (6.7x) |
| pdist, chebyshev, f32 | 1.362 | 1.364 | 1.363 | 0.478 (2.9x) | 0.488 (2.8x) | 0.484 (2.8x) |
| pdist, chebyshev, f64 | 1.338 | 1.343 | 1.341 | 0.476 (2.8x) | 0.485 (2.8x) | 0.481 (2.8x) |
| pdist, euclidean, f32 | 1.241 | 1.250 | 1.246 | 0.482 (2.6x) | 0.487 (2.6x) | 0.484 (2.6x) |
| pdist, euclidean, f64 | 1.222 | 1.228 | 1.225 | 0.474 (2.6x) | 0.503 (2.4x) | 0.482 (2.5x) |
| pdist, sqeuclidean, f32 | 1.224 | 1.247 | 1.233 | 0.477 (2.6x) | 0.490 (2.5x) | 0.481 (2.6x) |
| pdist, sqeuclidean, f64 | 1.211 | 1.214 | 1.213 | 0.470 (2.6x) | 0.478 (2.5x) | 0.475 (2.6x) |
| pdist, cityblock, f32 | 2.204 | 2.207 | 2.205 | 0.483 (4.6x) | 0.491 (4.5x) | 0.486 (4.5x) |
| pdist, cityblock, f64 | 2.189 | 2.198 | 2.192 | 0.476 (4.6x) | 0.483 (4.5x) | 0.481 (4.6x) |
| pdist, cosine, f32 | 2.000 | 2.004 | 2.002 | 1.292 (1.5x) | 1.302 (1.5x) | 1.296 (1.5x) |
| pdist, cosine, f64 | 1.988 | 1.992 | 1.990 | 1.288 (1.5x) | 1.296 (1.5x) | 1.292 (1.5x) |
| F32 | F64 |
|---|---|
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| u32 | u64 |
|---|---|
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These benchmarks were run on an Intel Core i7-11700KF CPU @ 4.900GHz, using a single thread. The OS was Arch Linux, with kernel version 6.7.4-arch1-1.
All string distances were computed 100 times each, among different pairs of strings, and the average time was taken.
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This package is licensed under the MIT license.
FAQs
Distance functions: A drop-in replacement for, and a super-set of the scipy.spatial.distance module.
We found that abd-distances demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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