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This Julia package computes various distances between `AbstractString`s

## Installation
The package is registered in the [`General`](https://github.com/JuliaRegistries/General) registry and so can be installed at the REPL with `] add StringDistances`.

## 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.

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

## Distances

#### Edit Distances
- [Hamming Distance](https://en.wikipedia.org/wiki/Hamming_distance) `Hamming()`
- [Jaro Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) `Jaro()`
- [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance) `Levenshtein()`
- [Damerau-Levenshtein Distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) `DamerauLevenshtein()`
- [RatcliffObershelp Distance](https://xlinux.nist.gov/dads/HTML/ratcliffObershelp.html) `RatcliffObershelp()`


#### Q-Grams Distances
Q-gram distances compare the set of all substrings of length `q` in each string.
- QGram Distance `Qgram(q)`
- [Cosine Distance](https://en.wikipedia.org/wiki/Cosine_similarity) `Cosine(q)`
- [Jaccard Distance](https://en.wikipedia.org/wiki/Jaccard_index) `Jaccard(q)`
- [Overlap Distance](https://en.wikipedia.org/wiki/Overlap_coefficient) `Overlap(q)`
- [Sorensen-Dice Distance](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) `SorensenDice(q)`

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

- [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.

	```julia
	compare("martha", "marhta", Jaro())
	#> 0.9444444444444445
	compare("martha", "marhta", Winkler(Jaro()))
	#> 0.9611111111111111

	compare("william", "williams", QGram(2))
	#> 0.9230769230769231
	compare("william", "williams", Winkler(QGram(2)))
	#> 0.9538461538461539
	```

- Modifiers from the Python library [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/), that can be applied to any distance.

	- [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.

		```julia
		compare("New York Yankees", "Yankees", Levenshtein())
		#> 0.4375
		compare("New York Yankees", "Yankees", Partial(Levenshtein()))
		#> 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("mariners vs angels", "angels vs mariners", RatcliffObershelp())
		#> 0.44444
		compare("mariners vs angels", "angels vs mariners", TokenSort(RatcliffObershelp())
		#> 1.0
		```

	- [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.

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


	- [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.

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

## Find
`find_best` returns the element of an iterator with the highest similarity score
```julia
find_best("New York", ["NewYork", "Newark", "San Francisco"], Levenshtein())
#> "NewYork"
```

`find_all` returns all the elements of an iterator with a similarity score higher than a minimum value (default to 0.8)

```julia
find_all("New York", ["NewYork", "Newark", "San Francisco"], Levenshtein(); min_score = 0.8)
#> 1-element Array{String,1}:
#> "NewYork"
```

While these functions are defined for any distance, they are particularly optimized for `Levenshtein` and `DamerauLevenshtein` distances (as well as their modifications via `Partial`, `TokenSort`, `TokenSet`, or `TokenMax`)

## 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, while others are unbouded.

```julia
compare("New York", "New York", Levenshtein())
#> 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...)
- The distance `Tokenmax(Levenshtein())` is a good choice to link sequence of words (adresses, names) across datasets (see [`fuzzywuzzy`](https://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/))

## 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](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/)