README.md

Set Similarity Search in Go

This is a mirror implementation of the Python SetSimilaritySearch library in Go, with better performance.

Benchmarks

Run AllPairs algorithm on 3.5 GHz Intel Core i7, using similarity function jaccard and similarity threshold 0.5.

Dataset	Input Sets	Avg. Size	go-set-similarity-search Runtime	SetSimilaritySearch Runtime
Pokec social network (relationships): from-nodes are set IDs; to-nodes are elements	1432693	27.31	1m25s	10m49s
LiveJournal: from-nodes are set IDs; to-nodes are elements	4308452	16.01	4m11s	28m51s

Library Usage

For All-Pairs, it takes an input of a list of sets, and output pairs that meet the similarity threshold.

import (
    "fmt"
    "go-set-similarity-search"
)


func main() {
    // Each raw set must be a slice of unique string tokens.
    rawSets := [][]string{
        []string{"a"},
        []string{"a", "b"},
        []string{"a", "b", "c"},
        []string{"a", "b", "c", "d"},
        []string{"a", "b", "c", "d", "e"},
    }
    // Use frequency order transformation to replace the string tokens
    // with integers.
    sets, _ := SetSimilaritySearch.FrequencyOrderTransform(rawSets)
    // Run all-pairs algorithm, get a channel of pairs.
    pairs, _ := SetSimilaritySearch.AllPairs(sets,    
        /*similarityFunctionName=*/"jaccard", 
        /*similarityThreshold=*/0.1)
    for pair := range pairs {
        // X and Y are indexes to the original rawSets and sets slices.
        fmt.Println(pair.X, pair.Y, pair.Similarity)
    }
}

For Query, it takes an input of a list of sets, and builds a search index that can compute any number of queries. Currently the search index only supports a static collection of sets with no updates.

import (
    "fmt"
    "go-set-similarity-search"
)

func main() {
    // Each raw set must be a slice of unique string tokens.
    rawSets := [][]string{
        []string{"a"},
        []string{"a", "b"},
        []string{"a", "b", "c"},
        []string{"a", "b", "c", "d"},
        []string{"a", "b", "c", "d", "e"},
    }
    // Use frequency order transformation to replace the string tokens
    // with integers.
    sets, dict := SetSimilaritySearch.FrequencyOrderTransform(rawSets)
    // Build a search index.
    searchIndex, err := SetSimilaritySearch.NewSearchIndex(sets,
        /*similarityFunctionName=*/"jaccard", 
        /*similarityThreshold=*/0.1)
    // Use dictionary to transform a query set.
    querySet := dict.Transform([]string{"a", "c", "d"})
    // Query the search index.
    searchResults := searchIndex.Query(querySet)
    for _, result := range searchResults {
        // X is the index to the original rawSets and sets slices.
        fmt.Println(result.X, result.Similarity)
    }
}

Supported similarity functions (more to come):

• Jaccard: intersection size divided by union size; set similarityFunctionName="jaccard".
• Cosine: intersection size divided by square root of the product of sizes; set similarityFunctionName="cosine".
• Containment: intersection size divided by the size of the first set (or query set); set similarityFunctionName="containment".