xdrop/fuzzywuzzy

Java fuzzy string matching implementation of the well known Python's fuzzywuzzy algorithm. Fuzzy search for Java

Download

Step 1. Add the JitPack repository to your build file

Add it in your root settings.gradle at the end of repositories:

	dependencyResolutionManagement {
		repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
		repositories {
			mavenCentral()
			maven { url 'https://jitpack.io' }
		}
	}

Add it in your settings.gradle.kts at the end of repositories:

	dependencyResolutionManagement {
		repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
		repositories {
			mavenCentral()
			maven { url = uri("https://jitpack.io") }
		}
	}

Add to pom.xml

	<repositories>
		<repository>
		    <id>jitpack.io</id>
		    <url>https://jitpack.io</url>
		</repository>
	</repositories>

Add it in your build.sbt at the end of resolvers:

 
    resolvers += "jitpack" at "https://jitpack.io"

Add it in your project.clj at the end of repositories:

 
    :repositories [["jitpack" "https://jitpack.io"]]

Step 2. Add the dependency

	dependencies {
		implementation 'com.github.xdrop:fuzzywuzzy:1.3.0'
	}
	dependencies {
		implementation("com.github.xdrop:fuzzywuzzy:1.3.0")
	}
	<dependency>
	    <groupId>com.github.xdrop</groupId>
	    <artifactId>fuzzywuzzy</artifactId>
	    <version>1.3.0</version>
	</dependency>

                            
    libraryDependencies += "com.github.xdrop" % "fuzzywuzzy" % "1.3.0"
        
        

                            
    :dependencies [[com.github.xdrop/fuzzywuzzy "1.3.0"]]

Readme

Maven Central

JavaWuzzy

FuzzyWuzzy Java Implementation

Fuzzy string matching for java based on the FuzzyWuzzy Python algorithm. The algorithm uses Levenshtein distance to calculate similarity between strings.

I've personally needed to use this but all of the other Java implementations out there either had a crazy amount of dependencies, or simply did not output the correct results as the python one, so I've decided to properly re-implement this in Java. Enjoy!

  • No dependencies!
  • Includes implementation of the super-fast python-Levenshtein in Java!
  • Simple to use!
  • Lightweight!
  • Credits to the great folks at seatgeek for coming up with the algorithm (More here)

Installation

In Maven and Gradle examples, remember to replace "VERSION" with the latest release of this library.

Maven Central

<dependency>
    <groupId>me.xdrop</groupId>
    <artifactId>fuzzywuzzy</artifactId>
    <version>VERSION</version>
</dependency>

Gradle

repositories {
    mavenCentral()
}

dependencies {
    implementation 'me.xdrop:fuzzywuzzy:VERSION'
}

JPMS

If you use Java 9 or newer, and use the Java Platform Module System (JPMS), you will need to add the following declarations to your module-info.java file:

module my.modulename.here {
    requires java.base;
    requires me.xdrop.fuzzywuzzy;
}

Jar release

Download the latest release here and add to your classpath.

Usage

Simple Ratio

FuzzySearch.ratio("mysmilarstring","myawfullysimilarstirng")
72

FuzzySearch.ratio("mysmilarstring","mysimilarstring")
97

Partial Ratio

FuzzySearch.partialRatio("similar", "somewhresimlrbetweenthisstring")
71

Token Sort Ratio

FuzzySearch.tokenSortPartialRatio("order words out of","  words out of order")
100
FuzzySearch.tokenSortRatio("order words out of","  words out of order")
100

Token Set Ratio

FuzzySearch.tokenSetRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100
FuzzySearch.tokenSetPartialRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100

Weighted Ratio

FuzzySearch.weightedRatio("The quick brown fox jimps ofver the small lazy dog", "the quick brown fox jumps over the small lazy dog")
97

Extract

// groovy

FuzzySearch.extractOne("cowboys", ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"])
(string: Dallas Cowboys, score: 90, index: 3)

FuzzySearch.extractTop("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 3)
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index:5), (string: plexoogl, score: 43, index: 7)]

FuzzySearch.extractAll("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"]);
[(string: google, score: 83, index: 0), (string: bing, score: 20, index: 1), (string: facebook, score: 29, index: 2), (string: linkedin, score: 29, index: 3), (string: twitter, score: 15, index: 4), (string: googleplus, score: 63, index: 5), (string: bingnews, score: 29, index: 6), (string: plexoogl, score: 43, index: 7)]
// score cutoff
FuzzySearch.extractAll("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 40) 
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index: 5), (string: plexoogl, score: 43, index: 7)]

FuzzySearch.extractSorted("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"]);
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index: 5), (string: plexoogl, score: 43, index: 7), (string: facebook, score: 29, index: 2), (string: linkedin, score: 29, index: 3), (string: bingnews, score: 29, index: 6), (string: bing, score: 20, index: 1), (string: twitter, score: 15, index: 4)]
// score cutoff
FuzzySearch.extractSorted("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 3);
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index: 5), (string: plexoogl, score: 43, index: 7)]

Extract using any object

extractOne and related methods can receive Collection<T> and produce BoundExtractedResult<T>

List<Foo> foo = ...;
BoundExtractedResult<Foo> match = FuzzySearch.extractOne("cowboys", foo, x -> x.toString());
Foo matchFoo = match.getReferent();

Credits

  • seatgeek
  • Adam Cohen
  • David Necas (python-Levenshtein)
  • Mikko Ohtamaa (python-Levenshtein)
  • Antti Haapala (python-Levenshtein)
  • Tobias Burdow (burdoto)