pyrsdameraulevenshtein 1.0.1

Damererau-Levenshtein implementation with Rust for fast performance.

Rust implementation of the Damerau-Levenshtein distance

Damerau-Levenshtein implementation in Rust as Python package. You can use this package if you need to calculate a distance metric for lists of integers or strings, and you need high-performance.

This package is based on the C implementation pyxDamerauLevenshtein.

Install

pip install pyrsdameraulevenshtein

Use

import pyrsdameraulevenshtein

distance = pyrsdameraulevenshtein.distance_int([1, 2, 3], [1, 3])
# distance = 1
normalized_distance = pyrsdameraulevenshtein.normalized_distance_int([1, 2, 3], [1, 3])
# normalized_distance = 0.33
similarity = pyrsdameraulevenshtein.similarity_int([1, 2, 3], [1, 3])
# similarity = 0.66
distance = pyrsdameraulevenshtein.distance_str(["A", "B", "C"], ["A", "C"])
# distance = 1
normalized_distance = pyrsdameraulevenshtein.normalized_distance_str(["A", "B", "C"], ["A", "C"])
# normalized_distance = 0.33
similarity = pyrsdameraulevenshtein.similarity_str(["A", "B", "C"], ["A", "C"])
# similarity = 0.66
distance = pyrsdameraulevenshtein.distance_unicode("ABC", "AC")
# distance = 1
normalized_distance = pyrsdameraulevenshtein.normalized_distance_unicode("ABC", "AC")
# normalized_distance = 0.33
similarity = pyrsdameraulevenshtein.similarity_unicode("ABC", "AC")
# similarity = 0.66

Get started

1. First, create a virtual python environment.
2. Install packages pip install -r requirements.txt
3. Create the Rust binary
   1. Full performance: maturin build --release and pip install target/wheels/*.whl
   2. Develop version: maturin develop
4. Run the tests python tests/DamerauLevenshteinTest.py

Performance

Speed comparison with the C implementation pyxDamerauLevenshtein results in 4 times faster performance.

import random
import time
import pyrsdameraulevenshtein
from pyxdameraulevenshtein import damerau_levenshtein_distance

n = 100000
x = 10
a_lists = [random.sample(list(range(x)), k=x, counts=[x for i in range(x)]) for i in range(n)]
b_lists = [random.sample(list(range(x)), k=x, counts=[x for i in range(x)]) for i in range(n)]

tic = time.perf_counter()
for a, b in zip(a_lists, b_lists):
    result = pyrsdameraulevenshtein.distance_int(a, b)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, RUST implementation")
# 0.0864 seconds, RUST implementation

tic = time.perf_counter()
for a, b in zip(a_lists, b_lists):
    result = damerau_levenshtein_distance(a, b)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, Gold standard - pyxdameraulevenshtein implementation")
# 0.3195 seconds, Gold standard - pyxdameraulevenshtein implementation