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Replace editdistance by rapidfuzz for Levenshtein distance #434

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Replace editdistance by rapidfuzz for Levenshtein distance #434

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2 changes: 1 addition & 1 deletion requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -2,4 +2,4 @@ amazon-textract-caller>=0.2.4,<1
Pillow
tabulate>=0.9,<0.10
XlsxWriter>=3.0,<4
editdistance>=0.6.2,<0.9
rapidfuzz>=3.9.6
22 changes: 2 additions & 20 deletions textractor/utils/search_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,7 @@
# The latter has numpy as dependency.
pass

import math
import editdistance
from rapidfuzz.distance import Levenshtein
from textractor.data.constants import SimilarityMetric
from textractor.exceptions import MissingDependencyException

Expand Down Expand Up @@ -59,7 +58,7 @@ def get_word_similarity(
cls.util = util

if similarity_metric == SimilarityMetric.LEVENSHTEIN:
return normalized_edit_distance(word_1.lower(), word_2.lower())
return Levenshtein.normalized_similarity(word_1.lower(), word_2.lower())
elif similarity_metric == SimilarityMetric.EUCLIDEAN:
ref_word_emb = cls.model.encode([word_1])
word_emb = cls.model.encode([word_2])
Expand Down Expand Up @@ -110,20 +109,3 @@ def get_metadata_attr_name(cell_atr):
return cell_map[cell_atr]
except:
return ""


def normalized_edit_distance(s1: str, s2: str):
"""
Returns the normalized edit distance

:param s1: First string
:type s1: str
:param s2: Second string
:type s2: str
"""

dist = editdistance.eval(s1, s2)
max_length = max(len(s1), len(s2))
if max_length - dist == 0:
return 0.0
return (max_length - dist) / max_length