[![Build Status](https://travis-ci.org/matthieugomez/StringDistances.jl.svg?branch=master)](https://travis-ci.org/matthieugomez/StringDistances.jl) [![Coverage Status](https://coveralls.io/repos/matthieugomez/StringDistances.jl/badge.svg?branch=master)](https://coveralls.io/r/matthieugomez/StringDistances.jl?branch=master) [![StringDistances](http://pkg.julialang.org/badges/StringDistances_0.4.svg)](http://pkg.julialang.org/?pkg=StringDistances) This Julia package computes various distances between strings. ## Distances #### Edit Distances - [Hamming Distance](https://en.wikipedia.org/wiki/Hamming_distance) - [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance) - [Damerau-Levenshtein Distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) #### Q-Grams Distances Q-gram distances compare the set of all substrings of length `q` in each string. - QGram Distance - [Cosine Distance](https://en.wikipedia.org/wiki/Cosine_similarity) - [Jaccard Distance](https://en.wikipedia.org/wiki/Jaccard_index) - [Overlap Distance](https://en.wikipedia.org/wiki/Overlap_coefficient) - [Sorensen-Dice Distance](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) #### Others - [Jaro Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) - [RatcliffObershelp Distance](https://xlinux.nist.gov/dads/HTML/ratcliffObershelp.html) is based on the length of matching subsequences. It is used in the Python library [difflib](https://docs.python.org/2/library/difflib.html). ## Syntax #### evaluate The function `evaluate` returns the litteral *distance* between two strings (a value of 0 being identical). While some distances are bounded by 1, other distances like `Hamming`, `Levenshtein`, `Damerau-Levenshtein`, `Jaccard` can be higher than 1. ```julia using StringDistances evaluate(Hamming(), "martha", "marhta") #> 2 evaluate(QGram(2), "martha", "marhta") #> 6 ``` #### compare The higher level function `compare` returns *a similarity score* between two strings, based on the inverse of the distance between two strings. The similarity score is always between 0 and 1. A value of 0 being completely different and a value of 1 being completely similar. ```julia using StringDistances compare(Hamming(), "martha", "marhta") #> 0.6666666666666667 compare(QGram(2), "martha", "marhta") #> 0.4 ``` ## Modifiers The package defines a number of ways to modify string metrics: - [Winkler](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) boosts the similary score of strings with common prefixes ```julia compare(Jaro(), "martha", "marhta") #> 0.9444444444444445 compare(Winkler(Jaro()), "martha", "marhta") #> 0.9611111111111111 ``` The Winkler adjustment was originally defined for the Jaro distance but this package defines it for any string distance. ```julia compare(QGram(2), "william", "williams") #> 0.9230769230769231 compare(Winkler(QGram(2)), "william", "williams") #> 0.9538461538461539 ``` - The Python library [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) defines a few modifiers for the `RatcliffObershelp` distance. This package defines them for any string distance: - [Partial](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in string lengths. The function returns the maximal similarity score between the shorter string and all substrings of the longer string. ```julia compare(Levenshtein(), "New York Yankees", "Yankees") #> 0.4375 compare(Partial(Levenshtein()), "New York Yankees", "Yankees") #> 1.0 ``` - [TokenSort](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders by reording words alphabetically. ```julia compare(RatcliffObershelp(), "mariners vs angels", "angels vs mariners") #> 0.44444 compare(TokenSort(RatcliffObershelp()),"mariners vs angels", "angels vs mariners") #> 1.0 ``` - [TokenSet](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders and word numbers. ```julia 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 ``` - You can compose multiple modifiers: ```julia compare(Winkler(Partial(Jaro())),"mariners vs angels", "los angeles angels at seattle mariners") #> 0.7378917378917379 compare(TokenSet(Partial(RatcliffObershel())),"mariners vs angels", "los angeles angels at seattle mariners") #> 1.0 ``` ## Tips - Each distance is tailored to a specific problem. Edit distances works well with local spelling errors, the Ratcliff-Obsershelp distance works well with edited texts, the Jaro Winkler distance was invented for short strings such as person names, the QGrams distances works well with strings composed of multiple words and fluctuating orderings. - Most distances perform poorly when comparing company or individual names, where each string is composed of multiple words. - While word ordering is mostly irrelevant in this situation, edit distances heavily penalize different orderings. Instead, use either a distance robust to word order (like QGram distances), or compose a distance with `TokenSort`, which reorders the words alphabetically. ```julia compare(RatcliffObershelp(), "mariners vs angels", "angels vs mariners") #> 0.44444 compare(TokenSort(RatcliffObershelp()),"mariners vs angels", "angels vs mariners") #> 1.0 compare(Cosine(3), "mariners vs angels", "angels vs mariners") #> 0.8125 ``` - General words (like "bank", "company") may appear in one string but no the other. One solution is to abbreviate these common names to diminish their importance (ie "bk", "co"). Another solution is to use the `Overlap` distance, which compares common qgrams to the length of the shorter strings. Another solution is to use the `Partial` modifier or `TokenSet` modifiers. - Standardize strings before comparing them (lowercase, punctuation, whitespaces, accents, abbreviations...) ## References - [The stringdist Package for Approximate String Matching](https://journal.r-project.org/archive/2014-1/loo.pdf) Mark P.J. van der Loo - [fuzzywuzzy blog post](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/)