# Rust implementation of the Damerau-Levenshtein distance
[Damerau-Levenshtein](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) implementation in Rust as
Python package.
You should use this package if you need to calculate a distance metric for lists of integers or strings, and you need
high-performance.
If you only need to check the distance between two strings
checkout [editdistance](https://github.com/roy-ht/editdistance) or [jellyfish](https://github.com/jamesturk/jellyfish).
## Install
```shell
pip install pyrsdameraulevenshtein
```
## Use
```python
import pyrsdameraulevenshtein as dl
distance = dl.distance_int([1, 2, 3], [1, 3])
# distance = 1
normalized_distance = dl.normalized_distance_int([1, 2, 3], [1, 3])
# normalized_distance = 0.33
similarity = dl.similarity_int([1, 2, 3], [1, 3])
# similarity = 0.66
distance = dl.distance_str(["A", "B", "C"], ["A", "C"])
# distance = 1
normalized_distance = dl.normalized_distance_str(["A", "B", "C"], ["A", "C"])
# normalized_distance = 0.33
similarity = dl.similarity_str(["A", "B", "C"], ["A", "C"])
# similarity = 0.66
distance = dl.distance_unicode("ABC", "AC")
# distance = 1
normalized_distance = dl.normalized_distance_unicode("ABC", "AC")
# normalized_distance = 0.33
similarity = dl.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
Tests are executed on a Mac Mini with M1 chip with Python 3.10.
Redo these tests in [tests/DamerauLevenshteinTest.py](tests/DamerauLevenshteinTest.py).
#### List comparisons
```python
import random
import time
import pyrsdameraulevenshtein
from fastDamerauLevenshtein import damerauLevenshtein
from pyxdameraulevenshtein import damerau_levenshtein_distance
n = 100000
x = 10
print("Int lists:")
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, THIS implementation")
# 0.0847 seconds, THIS implementation <<< BEST PERFORMANCE
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, pyxdameraulevenshtein")
# 0.3073 seconds, pyxdameraulevenshtein
tic = time.perf_counter()
for a, b in zip(a_lists, b_lists):
result = damerauLevenshtein(a, b, similarity=False)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, fastDamerauLevenshtein")
# 0.1257 seconds, fastDamerauLevenshtein
```
#### String comparisons
```python
import random
import time
import jellyfish
import textdistance
import pyrsdameraulevenshtein
from fastDamerauLevenshtein import damerauLevenshtein
from pyxdameraulevenshtein import damerau_levenshtein_distance
n = 100000
x = 10
print("Strings:")
a_strings = [
"".join(random.sample(list(chr(ord("A") + i) for i in range(x)), k=x, counts=[x for i in range(x)]))
for y in range(n)]
b_strings = [
"".join(random.sample(list(chr(ord("A") + i) for i in range(x)), k=x, counts=[x for i in range(x)]))
for y in range(n)]
tic = time.perf_counter()
for a, b in zip(a_strings, b_strings):
result = pyrsdameraulevenshtein.distance_unicode(a, b)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, THIS implementation")
# 0.0764 seconds, THIS implementation
tic = time.perf_counter()
for a, b in zip(a_strings, b_strings):
result = damerau_levenshtein_distance(a, b)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, pyxdameraulevenshtein")
# 0.3925 seconds, pyxdameraulevenshtein
tic = time.perf_counter()
for a, b in zip(a_strings, b_strings):
result = damerauLevenshtein(a, b, similarity=False)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, fastDamerauLevenshtein")
# 0.1275 seconds, fastDamerauLevenshtein
tic = time.perf_counter()
for a, b in zip(a_strings, b_strings):
result = jellyfish.damerau_levenshtein_distance(a, b)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, jellyfish.damerau_levenshtein_distance")
# 0.0546 seconds, jellyfish.damerau_levenshtein_distance
tic = time.perf_counter()
for a, b in zip(a_strings, b_strings):
result = textdistance.DamerauLevenshtein(a, b)
toc = time.perf_counter()
print(f"{toc - tic:0.4f} seconds, textdistance.DamerauLevenshtein")
# 0.0191 seconds, textdistance.DamerauLevenshtein <<< BEST PERFORMANCE
```
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"description": "# Rust implementation of the Damerau-Levenshtein distance\n\n[Damerau-Levenshtein](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) implementation in Rust as\nPython package.\nYou should use this package if you need to calculate a distance metric for lists of integers or strings, and you need\nhigh-performance.\nIf you only need to check the distance between two strings\ncheckout [editdistance](https://github.com/roy-ht/editdistance) or [jellyfish](https://github.com/jamesturk/jellyfish).\n\n## Install\n\n```shell\npip install pyrsdameraulevenshtein\n```\n\n## Use\n\n```python\nimport pyrsdameraulevenshtein as dl\n\ndistance = dl.distance_int([1, 2, 3], [1, 3])\n# distance = 1\nnormalized_distance = dl.normalized_distance_int([1, 2, 3], [1, 3])\n# normalized_distance = 0.33\nsimilarity = dl.similarity_int([1, 2, 3], [1, 3])\n# similarity = 0.66\ndistance = dl.distance_str([\"A\", \"B\", \"C\"], [\"A\", \"C\"])\n# distance = 1\nnormalized_distance = dl.normalized_distance_str([\"A\", \"B\", \"C\"], [\"A\", \"C\"])\n# normalized_distance = 0.33\nsimilarity = dl.similarity_str([\"A\", \"B\", \"C\"], [\"A\", \"C\"])\n# similarity = 0.66\ndistance = dl.distance_unicode(\"ABC\", \"AC\")\n# distance = 1\nnormalized_distance = dl.normalized_distance_unicode(\"ABC\", \"AC\")\n# normalized_distance = 0.33\nsimilarity = dl.similarity_unicode(\"ABC\", \"AC\")\n# similarity = 0.66\n```\n\n## Get started\n\n1. First, create a virtual python environment.\n2. Install packages `pip install -r requirements.txt`\n3. Create the Rust binary\n 1. Full performance: `maturin build --release` and `pip install target/wheels/*.whl`\n 2. Develop version: `maturin develop`\n4. Run the tests `python tests/DamerauLevenshteinTest.py`\n\n## Performance\n\nTests are executed on a Mac Mini with M1 chip with Python 3.10.\nRedo these tests in [tests/DamerauLevenshteinTest.py](tests/DamerauLevenshteinTest.py).\n\n#### List comparisons\n\n```python\nimport random\nimport time\nimport pyrsdameraulevenshtein\nfrom fastDamerauLevenshtein import damerauLevenshtein\nfrom pyxdameraulevenshtein import damerau_levenshtein_distance\n\nn = 100000\nx = 10\n\nprint(\"Int lists:\")\na_lists = [random.sample(list(range(x)), k=x, counts=[x for i in range(x)]) for i in range(n)]\nb_lists = [random.sample(list(range(x)), k=x, counts=[x for i in range(x)]) for i in range(n)]\ntic = time.perf_counter()\nfor a, b in zip(a_lists, b_lists):\n result = pyrsdameraulevenshtein.distance_int(a, b)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, THIS implementation\")\n# 0.0847 seconds, THIS implementation <<< BEST PERFORMANCE\ntic = time.perf_counter()\nfor a, b in zip(a_lists, b_lists):\n result = damerau_levenshtein_distance(a, b)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, pyxdameraulevenshtein\")\n# 0.3073 seconds, pyxdameraulevenshtein\ntic = time.perf_counter()\nfor a, b in zip(a_lists, b_lists):\n result = damerauLevenshtein(a, b, similarity=False)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, fastDamerauLevenshtein\")\n# 0.1257 seconds, fastDamerauLevenshtein\n```\n\n#### String comparisons\n\n```python\nimport random\nimport time\nimport jellyfish\nimport textdistance\nimport pyrsdameraulevenshtein\nfrom fastDamerauLevenshtein import damerauLevenshtein\nfrom pyxdameraulevenshtein import damerau_levenshtein_distance\n\nn = 100000\nx = 10\n\nprint(\"Strings:\")\na_strings = [\n \"\".join(random.sample(list(chr(ord(\"A\") + i) for i in range(x)), k=x, counts=[x for i in range(x)]))\n for y in range(n)]\nb_strings = [\n \"\".join(random.sample(list(chr(ord(\"A\") + i) for i in range(x)), k=x, counts=[x for i in range(x)]))\n for y in range(n)]\ntic = time.perf_counter()\nfor a, b in zip(a_strings, b_strings):\n result = pyrsdameraulevenshtein.distance_unicode(a, b)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, THIS implementation\")\n# 0.0764 seconds, THIS implementation\ntic = time.perf_counter()\nfor a, b in zip(a_strings, b_strings):\n result = damerau_levenshtein_distance(a, b)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, pyxdameraulevenshtein\")\n# 0.3925 seconds, pyxdameraulevenshtein\ntic = time.perf_counter()\nfor a, b in zip(a_strings, b_strings):\n result = damerauLevenshtein(a, b, similarity=False)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, fastDamerauLevenshtein\")\n# 0.1275 seconds, fastDamerauLevenshtein\ntic = time.perf_counter()\nfor a, b in zip(a_strings, b_strings):\n result = jellyfish.damerau_levenshtein_distance(a, b)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, jellyfish.damerau_levenshtein_distance\")\n# 0.0546 seconds, jellyfish.damerau_levenshtein_distance\ntic = time.perf_counter()\nfor a, b in zip(a_strings, b_strings):\n result = textdistance.DamerauLevenshtein(a, b)\ntoc = time.perf_counter()\nprint(f\"{toc - tic:0.4f} seconds, textdistance.DamerauLevenshtein\")\n# 0.0191 seconds, textdistance.DamerauLevenshtein <<< BEST PERFORMANCE\n```\n\n",
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