This Julia package computes various distances between strings (ASCII)

Syntax

The function compare returns a similarity score between two strings. The function always returns a score between 0 and 1, with a value of 0 being completely different and a value of 1 being completely similar.

using StringDistances
compare(Hamming(), "martha", "martha")
#> 1.0
compare(Hamming(), "martha", "marhta")
#> 0.6666666666666667

Distances

Edit Distances

• Damerau-Levenshtein Distance DamerauLevenshtein()
• Hamming Distance Hamming()
• Jaro Distance Jaro()
• Levenshtein Distance Levenshtein()

Q-Grams Distances

Q-gram distances compare the set of all substrings of length q in each string.

• QGram Distance Qgram(q)
• Cosine Distance Cosine(q)
• Jaccard Distance Jaccard(q)
• Overlap Distance Overlap(q)
• Sorensen-Dice Distance SorensenDice(q)

Others

• RatcliffObershelp Distance RatcliffObershelp()

Distance Modifiers

The package includes distance "modifiers", that can be applied to any distance.

• Winkler boosts the similary score of strings with common prefixes. The Winkler adjustment was originally defined for the Jaro similarity score but this package defines it for any string distance.

  compare(Jaro(), "martha", "marhta")
  #> 0.9444444444444445
  compare(Winkler(Jaro()), "martha", "marhta")
  #> 0.9611111111111111
  
  compare(QGram(2), "william", "williams")
  #> 0.9230769230769231
  compare(Winkler(QGram(2)), "william", "williams")
  #> 0.9538461538461539
  

• Modifiers from the Python library fuzzywuzzy .

  • Partial returns the maximal similarity score between the shorter string and substrings of the longer string.

    compare(Levenshtein(), "New York Yankees", "Yankees")
    #> 0.4375
    compare(Partial(Levenshtein()), "New York Yankees", "Yankees")
    #> 1.0
    

  • TokenSort adjusts for differences in word orders by reording words alphabetically.

    compare(RatcliffObershelp(), "mariners vs angels", "angels vs mariners")
    #> 0.44444
    compare(TokenSort(RatcliffObershelp()),"mariners vs angels", "angels vs mariners")
    #> 1.0
    

  • TokenSet adjusts for differences in word orders and word numbers by comparing the intersection of two strings with each string.

    compare(Jaro(),"mariners vs angels", "los angeles angels at seattle mariners")
    #> 0.559904
    compare(TokenSet(Jaro()),"mariners vs angels", "los angeles angels at seattle mariners")
    #> 0.944444
    

  • TokenMax combines scores using the base distance, the Partial, TokenSort and TokenSet modifiers, with penalty terms depending on string lengths. This is the default distance in fuzzywuzzy .

    compare(TokenMax(RatcliffObershelp()),"mariners vs angels", "los angeles angels at seattle mariners")
    #> 0.855
    

Compare vs Evaluate

The function compare returns a similarity score: a value of 0 means completely different and a value of 1 means completely similar. In contrast, the function evaluate returns the litteral distance between two strings, with a value of 0 being completely similar. some distances are between 0 and 1. Others are unbouded.

compare(Levenshtein(), "New York", "New York")
#> 1.0
evaluate(Levenshtein(), "New York", "New York")
#> 0

Which distance should I use?

As a rule of thumb,

• Standardize strings before comparing them (cases, whitespaces, accents, abbreviations...)
• Don't use one of the Edit distances if word order do not matter.
• The distance Tokenmax(RatcliffObershelp()) is a good choice to link names or adresses across datasets.

References

• The stringdist Package for Approximate String Matching Mark P.J. van der Loo
• fuzzywuzzy