113 lines
5.0 KiB
Markdown
113 lines
5.0 KiB
Markdown
[](https://travis-ci.org/matthieugomez/StringDistances.jl)
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[](https://coveralls.io/r/matthieugomez/StringDistances.jl?branch=master)
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This Julia package computes various distances between `AbstractString`s
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## Syntax
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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.
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```julia
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using StringDistances
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compare("martha", "martha", Hamming())
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#> 1.0
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compare("martha", "marhta", Hamming())
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#> 0.6666666666666667
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```
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## Distances
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#### Edit Distances
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- [Hamming Distance](https://en.wikipedia.org/wiki/Hamming_distance) `Hamming()`
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- [Jaro Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) `Jaro()`
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- [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance) `Levenshtein()`
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- [Damerau-Levenshtein Distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) `DamerauLevenshtein()`
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- [RatcliffObershelp Distance](https://xlinux.nist.gov/dads/HTML/ratcliffObershelp.html) `RatcliffObershelp()`
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#### Q-Grams Distances
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Q-gram distances compare the set of all substrings of length `q` in each string.
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- QGram Distance `Qgram(q)`
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- [Cosine Distance](https://en.wikipedia.org/wiki/Cosine_similarity) `Cosine(q)`
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- [Jaccard Distance](https://en.wikipedia.org/wiki/Jaccard_index) `Jaccard(q)`
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- [Overlap Distance](https://en.wikipedia.org/wiki/Overlap_coefficient) `Overlap(q)`
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- [Sorensen-Dice Distance](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) `SorensenDice(q)`
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## Distance Modifiers
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The package includes distance "modifiers", that can be applied to any distance.
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- [Winkler](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) 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.
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```julia
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compare("martha", "marhta", Jaro())
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#> 0.9444444444444445
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compare("martha", "marhta", Winkler(Jaro()))
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#> 0.9611111111111111
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compare("william", "williams", QGram(2))
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#> 0.9230769230769231
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compare("william", "williams", Winkler(QGram(2)))
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#> 0.9538461538461539
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```
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- Modifiers from the Python library [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/), that can be applied to any distance.
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- [Partial](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) returns the maximal similarity score between the shorter string and substrings of the longer string.
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```julia
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compare("New York Yankees", "Yankees", Levenshtein())
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#> 0.4375
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compare("New York Yankees", "Yankees", Partial(Levenshtein()))
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#> 1.0
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```
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- [TokenSort](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders by reording words alphabetically.
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```julia
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compare("mariners vs angels", "angels vs mariners", RatcliffObershelp())
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#> 0.44444
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compare("mariners vs angels", "angels vs mariners", TokenSort(RatcliffObershelp())
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#> 1.0
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```
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- [TokenSet](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders and word numbers by comparing the intersection of two strings with each string.
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```julia
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compare("mariners vs angels", "los angeles angels at seattle mariners", Jaro())
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#> 0.559904
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compare("mariners vs angels", "los angeles angels at seattle mariners", TokenSet(Jaro()))
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#> 0.944444
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```
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- [TokenMax](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) combines scores using the base distance, the `Partial`, `TokenSort` and `TokenSet` modifiers, with penalty terms depending on string lengths.
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```julia
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compare("mariners vs angels", "los angeles angels at seattle mariners", TokenMax(RatcliffObershelp()))
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#> 0.855
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```
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## Compare vs Evaluate
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The function `compare` returns a similarity score: a value of 0 means completely different and a value of 1 means completely similar.
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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.
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```julia
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compare("New York", "New York", Levenshtein())
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#> 1.0
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evaluate(Levenshtein(), "New York", "New York")
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#> 0
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```
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## Which distance should I use?
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As a rule of thumb,
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- Standardize strings before comparing them (cases, whitespaces, accents, abbreviations...)
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- The distance `Tokenmax(Levenshtein())` is a good choice to link names or adresses across datasets.
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## References
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- [The stringdist Package for Approximate String Matching](https://journal.r-project.org/archive/2014-1/loo.pdf) Mark P.J. van der Loo
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- [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/)
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