Installation

The package is registered in the General registry and so can be installed at the REPL with ] add StringDistances.

Supported Distances

Distances are defined for AbstractStrings, and any iterator that define length() (e.g. graphemes, AbstractVector...)

The available distances are:

• Edit Distances
  • Hamming Distance Hamming()
  • Jaro and Jaro-Winkler Distance Jaro() JaroWinkler()
  • Levenshtein Distance Levenshtein()
  • Damerau-Levenshtein Distance DamerauLevenshtein()
  • RatcliffObershelp Distance RatcliffObershelp()
• Q-gram distances compare the set of all substrings of length q in each string.
  • QGram Distance Qgram(q::Int)
  • Cosine Distance Cosine(q::Int)
  • Jaccard Distance Jaccard(q::Int)
  • Overlap Distance Overlap(q::Int)
  • Sorensen-Dice Distance SorensenDice(q::Int)
  • MorisitaOverlap Distance MorisitaOverlap(q::Int)
  • Normalized Multiset Distance NMD(q::Int)

The package also defines Distance "modifiers" that can be applied to any distance.

• Partial returns the minimum of the distance between the shorter string and substrings of the longer string.
• TokenSort adjusts for differences in word orders by returning the distance of the two strings, after re-ordering words alphabetically.
• TokenSet adjusts for differences in word orders and word numbers by returning the distance between the intersection of two strings with each string.
• TokenMax normalizes the distance, and combine the Partial, TokenSort and TokenSet modifiers, with penalty terms depending on string lengths. This is a good distance to match strings composed of multiple words, like addresses. TokenMax(Levenshtein()) corresponds to the distance defined in fuzzywuzzy

Basic Use

evaluate

You can always compute a certain distance between two strings using the following syntax:

evaluate(dist, s1, s2)
dist(s1, s2)

For instance, with the Levenshtein distance,

evaluate(Levenshtein(), "martha", "marhta")
Levenshtein()("martha", "marhta")

pairwise

pairwise returns the matrix of distance between two AbstractVectors of AbstractStrings

pairwise(Jaccard(3), ["martha", "kitten"], ["marhta", "sitting"])

It is particularly fast for QGram-distances (each element is processed once).

similarly scores

• The function compare returns the similarity score, defined as 1 minus the normalized distance between two strings. It always returns a Float64. A value of 0.0 means completely different and a value of 1.0 means completely similar.

   Levenshtein()("martha", "martha")
   #> 0.0
   compare("martha", "martha", Levenshtein())
   #> 1.0

• findnearest returns the value and index of the element in itr with the highest similarity score with s. Its syntax is:

   findnearest(s, itr, dist::StringDistance)

• findall returns the indices of all elements in itr with a similarity score with s higher than a minimum value (default to 0.8). Its syntax is:

   findall(s, itr, dist::StringDistance; min_score = 0.8)

The functions findnearest and findall are particularly optimized for Levenshtein, DamerauLevenshtein distances (as well as their modifications via Partial, TokenSort, TokenSet, or TokenMax).

References

• The stringdist Package for Approximate String Matching Mark P.J. van der Loo
• fuzzywuzzy