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StringDistances.jl
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matthieugomez 2019-12-11 16:12:24 -05:00
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2
.travis.yml
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@ -7,7 +7,7 @@ matrix:
- julia: nightly
after_success:
- julia -e 'using Pkg; Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
notifications:
notifications:
email: false
on_success: never
on_failure: change

22
README.md
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@ -3,10 +3,12 @@
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())
@ -15,8 +17,6 @@ compare("martha", "marhta", Hamming())
#> 0.6666666666666667
```
## Distances
#### Edit Distances
@ -89,7 +89,6 @@ The package includes distance "modifiers", that can be applied to any distance.
#> 0.855
```
## Find
`find_best` returns the element of an iterator with the highest similarity score
```julia
@ -107,16 +106,9 @@ find_all("New York", ["NewYork", "Newark", "San Francisco"], Levenshtein(); min_
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`)
## 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 names or adresses across datasets.
## 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.
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())
@ -125,6 +117,12 @@ 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.
## 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/)