5.3 KiB
This Julia package computes various distances between strings (UTF-8 encoding)
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. One difference with this Python library is that modifiers are defined for any distance, not just the levenshtein one.
-
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
andTokenSet
modifiers, with penalty terms depending on string lengths.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...)
- Only consider using one of the Edit distances if word order matters.
- 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