Update levenshtein_distance.py (#11171)

* Update levenshtein_distance.py * Update levenshtein_distance.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update levenshtein_distance.py * Update levenshtein_distance.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update levenshtein_distance.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update levenshtein_distance.py * Update levenshtein_distance.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com>

strings/levenshtein_distance.py

@@ -1,20 +1,9 @@
-"""
-This is a Python implementation of the levenshtein distance.
-Levenshtein distance is a string metric for measuring the
-difference between two sequences.
-
-For doctests run following command:
-python -m doctest -v levenshtein-distance.py
-or
-python3 -m doctest -v levenshtein-distance.py
-
-For manual testing run:
-python levenshtein-distance.py
-"""
+from collections.abc import Callable
 
 
 def levenshtein_distance(first_word: str, second_word: str) -> int:
- """Implementation of the levenshtein distance in Python.
+ """
+ Implementation of the Levenshtein distance in Python.
  :param first_word: the first word to measure the difference.
  :param second_word: the second word to measure the difference.
  :return: the levenshtein distance between the two words.
@@ -47,7 +36,7 @@ def levenshtein_distance(first_word: str, second_word: str) -> int:
  current_row = [i + 1]
 
  for j, c2 in enumerate(second_word):
- # Calculate insertions, deletions and substitutions
+ # Calculate insertions, deletions, and substitutions
  insertions = previous_row[j + 1] + 1
  deletions = current_row[j] + 1
  substitutions = previous_row[j] + (c1 != c2)
@@ -62,9 +51,75 @@ def levenshtein_distance(first_word: str, second_word: str) -> int:
  return previous_row[-1]
 
 
+def levenshtein_distance_optimized(first_word: str, second_word: str) -> int:
+ """
+ Compute the Levenshtein distance between two words (strings).
+ The function is optimized for efficiency by modifying rows in place.
+ :param first_word: the first word to measure the difference.
+ :param second_word: the second word to measure the difference.
+ :return: the Levenshtein distance between the two words.
+ Examples:
+ >>> levenshtein_distance_optimized("planet", "planetary")
+ 3
+ >>> levenshtein_distance_optimized("", "test")
+ 4
+ >>> levenshtein_distance_optimized("book", "back")
+ 2
+ >>> levenshtein_distance_optimized("book", "book")
+ 0
+ >>> levenshtein_distance_optimized("test", "")
+ 4
+ >>> levenshtein_distance_optimized("", "")
+ 0
+ >>> levenshtein_distance_optimized("orchestration", "container")
+ 10
+ """
+ if len(first_word) < len(second_word):
+ return levenshtein_distance_optimized(second_word, first_word)
+
+ if len(second_word) == 0:
+ return len(first_word)
+
+ previous_row = list(range(len(second_word) + 1))
+
+ for i, c1 in enumerate(first_word):
+ current_row = [i + 1] + [0] * len(second_word)
+
+ for j, c2 in enumerate(second_word):
+ insertions = previous_row[j + 1] + 1
+ deletions = current_row[j] + 1
+ substitutions = previous_row[j] + (c1 != c2)
+ current_row[j + 1] = min(insertions, deletions, substitutions)
+
+ previous_row = current_row
+
+ return previous_row[-1]
+
+
+def benchmark_levenshtein_distance(func: Callable) -> None:
+ """
+ Benchmark the Levenshtein distance function.
+ :param str: The name of the function being benchmarked.
+ :param func: The function to be benchmarked.
+ """
+ from timeit import timeit
+
+ stmt = f"{func.__name__}('sitting', 'kitten')"
+ setup = f"from __main__ import {func.__name__}"
+ number = 25_000
+ result = timeit(stmt=stmt, setup=setup, number=number)
+ print(f"{func.__name__:<30} finished {number:,} runs in {result:.5f} seconds")
+
+
 if __name__ == "__main__":
- first_word = input("Enter the first word:\n").strip()
- second_word = input("Enter the second word:\n").strip()
+ # Get user input for words
+ first_word = input("Enter the first word for Levenshtein distance:\n").strip()
+ second_word = input("Enter the second word for Levenshtein distance:\n").strip()
+
+ # Calculate and print Levenshtein distances
+ print(f"{levenshtein_distance(first_word, second_word) = }")
+ print(f"{levenshtein_distance_optimized(first_word, second_word) = }")
 
- result = levenshtein_distance(first_word, second_word)
- print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
+ # Benchmark the Levenshtein distance functions
+ benchmark_levenshtein_distance(levenshtein_distance)
+ benchmark_levenshtein_distance(levenshtein_distance_optimized)