fitash/jlibsvm

Efficient training of Support Vector Machines in Java

jLibSVM

Efficient training of Support Vector Machines in Java

• Heavily refactored Java port of the venerable LIBSVM (version 2.88).
• Provides idiomatic Java class structure and APIs (unlike the Java version provided by LIBSVM, which is transliterated C code).
• Easy to add new kernels, in addition to the five standard ones provided by LIBSVM.
• On the mathematical side, jlibsvm performs exactly the same computations as LIBSVM, including shrinking and all the fancy stuff described in the LIBSVM implementation docs.
• Optimized kernel implementations run faster, particularly when input vectors are sparse. For instance, on the mushrooms dataset, jlibsvm trained ~25% faster than LIBSVM (java version) with an RBF kernel and ~40% faster with a linear kernel. (The C version of LIBSVM is still faster, though).
• Multithreaded training to take advantage of modern multi-core machines (using Conja).
• Integrated scaling and normalization so you don't have to explicitly preprocess your data.
• Integrated grid search for optimal kernel parameters.
• Drop-in replacement if you use the command-line tools (e.g. svm-train, etc.), but not if you use LIBSVM programmatically.
• Uses Java generics throughout, including for classification labels, so you can specify that the "label" of a class be of whatever Java type you like. In an email-filtering application, for example, you could use objects of type Mailbox as the labels. That would allow you to write something like mySvmSolutionModel.predict(incomingEmail).addMessage(incomingEmail). The predict() method returns a classification label, which in this case is an object of class Mailbox, which has an addMessage() method.

Status

This is beta code. While LIBSVM is stable, it's possible that I broke something in the process of refactoring it. I've done ad-hoc testing primarily with the C_SVC machine and an RBF kernel, and got results that were identical to LIBSVM as far as I could tell. There are not (yet?) any unit tests. I'm running some automated verifications that jlibsvm behaves identically to LIBSVM for a number of input datasets and parameter choices; results will be available here soon. Please let me know if you find a situation in which the two packages give different results.

Documentation

• API docs

Sorry, I haven't really had a chance to write any docs. Have a look at the sources for the command-line programs in the legacyexec package to see how jlibsvm gets called. Very briefly, you'll need to:

1. instantiate the KernelFunction that you want
2. set up some parameters in a new SvmParameter object
3. instantiate a concrete subclass of SvmProblem (binary, multiclass, or regression), and populate it with training data
4. instantiate a concrete subclass of SVM, choosing a type appropriate for your problem
5. Call SVM.train(problem) to yield a SolutionModel, which can be used to make predictions

Download

Maven is by far the easiest way to make use of jlibsvm. Just add these to your pom.xml:

<repositories>
	<repository>
		<id>dev.davidsoergel.com releases</id>
		<url>http://dev.davidsoergel.com/nexus/content/repositories/releases</url>
		<snapshots>
			<enabled>false</enabled>
		</snapshots>
	</repository>
	<repository>
		<id>dev.davidsoergel.com snapshots</id>
		<url>http://dev.davidsoergel.com/nexus/content/repositories/snapshots</url>
		<releases>
			<enabled>false</enabled>
		</releases>
	</repository>
</repositories>

<dependencies>
	<dependency>
		<groupId>edu.berkeley.compbio</groupId>
		<artifactId>jlibsvm</artifactId>
		<version>0.911</version>
	</dependency>
</dependencies>

If you really want just the jar, you can get the latest release from the Maven repo; or get the latest stable build from the build server.