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closestmatch 📃

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closestmatch is a simple and fast Go library for fuzzy matching an input string to a list of target strings. closestmatch is useful for handling input from a user where the input (which could be mispelled or out of order) needs to match a key in a database. closestmatch uses a bag-of-words approach to precompute character n-grams to represent each possible target string. The closest matches have highest overlap between the sets of n-grams. The precomputation scales well and is much faster and more accurate than Levenshtein for long strings.

Getting Started

Install

go get -u -v github.com/schollz/closestmatch

Use

Create a closestmatch object from a list words

// Take a slice of keys, say band names that are similar
// http://www.tonedeaf.com.au/412720/38-bands-annoyingly-similar-names.htm
wordsToTest := []string{"King Gizzard", "The Lizard Wizard", "Lizzard Wizzard"}

// Choose a set of bag sizes, more is more accurate but slower
bagSizes := []int{2}

// Create a closestmatch object
cm := closestmatch.New(wordsToTest, bagSizes)

Find the closest match, or find the N closest matches

fmt.Println(cm.Closest("kind gizard"))
// returns 'King Gizzard'

fmt.Println(cm.ClosestN("kind gizard",3))
// returns [King Gizzard Lizzard Wizzard The Lizard Wizard]

Calculate the accuracy

// Calculate accuracy
fmt.Println(cm.AccuracyMutatingWords())
// ~ 66 % (still way better than Levenshtein which hits 0% with this particular set)

// Improve accuracy by adding more bags
bagSizes = []int{2, 3, 4}
cm = closestmatch.New(wordsToTest, bagSizes)
fmt.Println(cm.AccuracyMutatingWords())
// accuracy improves to ~ 76 %

Save/Load

// Save your current calculated bags
cm.Save("closestmatches.gob")

// Open it again
cm2, _ := closestmatch.Load("closestmatches.gob")
fmt.Println(cm2.Closest("lizard wizard"))
// prints "The Lizard Wizard"

Advantages

closestmatch is more accurate than Levenshtein for long strings (like in the test corpus).

closestmatch is ~20x faster than a fast implementation of Levenshtein. Try it yourself with the benchmarks:

cd $GOPATH/src/github.com/schollz/closestmatch && go test -run=None -bench=. > closestmatch.bench
cd $GOPATH/src/github.com/schollz/closestmatch/levenshtein && go test -run=None -bench=. > levenshtein.bench
benchcmp levenshtein.bench ../closestmatch.bench

which gives the following benchmark (on Intel i7-3770 CPU @ 3.40GHz w/ 8 processors):

benchmark                 old ns/op     new ns/op     delta
BenchmarkNew-8            1.47          1933870       +131555682.31%
BenchmarkClosestOne-8     104603530     4855916       -95.36%

The New() function in closestmatch is so slower than levenshtein because there is precomputation needed.

Disadvantages

closestmatch does worse for matching lists of single words, like a dictionary. For comparison:

$ cd $GOPATH/src/github.com/schollz/closestmatch && go test
Accuracy with mutating words in book list:      90.0%
Accuracy with mutating letters in book list:    100.0%
Accuracy with mutating letters in dictionary:   38.9%

while levenshtein performs slightly better for a single-word dictionary (but worse for longer names, like book titles):

$ cd $GOPATH/src/github.com/schollz/closestmatch/levenshtein && go test
Accuracy with mutating words in book list:      40.0%
Accuracy with mutating letters in book list:    100.0%
Accuracy with mutating letters in dictionary:   64.8%

License

MIT