FuzzyWuzzy

Tests Coverage

This is a Dart port of the popular FuzzyWuzzy python package. This algorithm uses Levenshtein distance to calculate similarity between strings.

I personally needed to use this for my own search algorithms, and there weren't any packages as good as my experience with FuzzyWuzzy was, so here we are. Enjoy!

  • Just one dependency (collection).
  • Pure Dart implementation of the superfast python-Levenshtein.
  • Simple to use.
  • Lightweight.
  • Massive props to the folks over at seatgeek for coming up with the algorithm.

Get started

Add dependency

dependencies:
  fuzzywuzzy: 0.1.6 # latest version

Usage

First, import the package

import 'package:fuzzywuzzy/fuzzywuzzy.dart'

Simple ratio

ratio("mysmilarstring", "myawfullysimilarstirng") // 72
ratio("mysmilarstring", "mysimilarstring")        // 97

Partial ratio

partialRatio("similar", "somewhresimlrbetweenthisstring") // 71

Token sort ratio

tokenSortPartialRatio("order words out of", "words out of order") // 100
tokenSortRatio("order words out of","  words out of order")       // 100

Token set ratio

tokenSetRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")         // 100
tokenSetPartialRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")  // 100

Weighted ratio

weightedRatio("The quick brown fox jimps ofver the small lazy dog", "the quick brown fox jumps over the small lazy dog") // 97

Extract

extractOne(
        query: 'cowboys',
        choices: [
          'Atlanta Falcons',
          'New York Jets',
          'New York Giants',
          'Dallas Cowboys'
        ],
        cutoff: 10,
      ) // (string Dallas Cowboys, score: 90, index: 3)
extractTop(
        query: 'goolge',
        choices: [
          'google',
          'bing',
          'facebook',
          'linkedin',
          'twitter',
          'googleplus',
          'bingnews',
          'plexoogl'
        ],
        limit: 4,
        cutoff: 50,
      ) // [(string google, score: 83, index: 0), (string googleplus, score: 75, index: 5)]
extractAllSorted(
        query: 'goolge',
        choices: [
          'google',
          'bing',
          'facebook',
          'linkedin',
          'twitter',
          'googleplus',
          'bingnews',
          'plexoogl'
        ],
        cutoff: 10,
      ) // [(string google, score: 83, index: 0), (string googleplus, score: 75, index: 5), (string plexoogl, score: 43, index: 7), (string bingnews, score: 29, index: 6), (string linkedin, score: 29, index: 3), (string facebook, score: 29, index: 2), (string bing, score: 23, index: 1), (string twitter, score: 15, index: 4)]
extractAll(
        query: 'goolge',
        choices: [
          'google',
          'bing',
          'facebook',
          'linkedin',
          'twitter',
          'googleplus',
          'bingnews',
          'plexoogl'
        ],
        cutoff: 10,
      ) // [(string google, score: 83, index: 0), (string bing, score: 23, index: 1), (string facebook, score: 29, index: 2), (string linkedin, score: 29, index: 3), (string twitter, score: 15, index: 4), (string googleplus, score: 75, index: 5), (string bingnews, score: 29, index: 6), (string plexoogl, score: 43, index: 7)]

Extract using any a list of any type

All extract methods can receive List<T> and return List<ExtractedResult<T>>

class TestContainer {
  final String innerVal;
  TestContainer(this.innerVal);
}

extractOne<TestContainer>(
        query: 'cowboys',
        choices: [
          'Atlanta Falcons',
          'New York Jets',
          'New York Giants',
          'Dallas Cowboys'
        ].map((e) => TestContainer(e)).toList(),
        cutoff: 10,
        getter: (x) => x.innerVal
      ).toString(); // (string Dallas Cowboys, score: 90, index: 3)

Libraries

applicable
diff_utils
edit_op
edit_type
extracted_result
extractor
fuzzywuzzy
matching_block
op_code
partial_ratio
simple_ratio
to_string_function
token_set
token_sort
weighted_ratio