liquidSVM for Java

Welcome to the Java bindings for liquidSVM.

Summary:

Both liquidSVM and these bindings are provided under the AGPL 3.0 license.

API Usage Example

The API can be investigated in the javadoc But to give you a heads up consider the File liquidSVM_java/Example.java:

import de.uni_stuttgart.isa.liquidsvm.Config;
import de.uni_stuttgart.isa.liquidsvm.ResultAndErrors;
import de.uni_stuttgart.isa.liquidsvm.SVM;
import de.uni_stuttgart.isa.liquidsvm.SVM.LS;
import de.uni_stuttgart.isa.liquidsvm.LiquidData;

public class Example {

    public static void main(String[] args) throws java.io.IOException {
    
        String filePrefix = (args.length==0) ? "reg-1d" : args[0];
        
        // read comma separated training and testing data
        LiquidData data = new LiquidData(filePrefix);

        // Now train a least squares SVM on a 10by10 hyperparameter grid
        // and select the best parameters. The configuration displays
        // some progress information and specifies to only use two threads.
        SVM s = new LS(data.train, new Config().display(1).threads(2));

        // evaluate the selected SVM on the test features  
        double[] predictions = s.predict(data.testX);
        // or (since we have labels) do this and calculate the error
        ResultAndErrors result = s.test(data.test);
        
        System.out.println("Test error: " + result.errors[0][0]);
        for(int i=0; i<Math.min(result.result.length, 5); i++)
            System.out.println(predictions[i] + "==" + result.result[i][0]);

    }
}

The reg-1d data set is a artificial dataset provided by us.

Compile and run this:

javac -classpath liquidSVM.jar Example.java
java -Djava.library.path=. -cp .:liquidSVM.jar Example reg-1d

Using

Native Library Compilation

liquidSVM is implemented in C++ therefore a native library needs to be compiled and included in the Java process. Binaries for MacOS and Windows are included, however if it is possible for you, we recommend you compile it for every machine to get full performance. Two prerequisites have to be fulfilled:

  1. the environment Variable JAVA_HOME has to be set
  2. a Unix-type toolchain is available including make and a compiler like gcc or clang.

Then on the command line you can use different options:

make native
usually the fastest, but the resulting library is usually not portable to other machines.
make generic
should be portable to most machines, yet slower (factor 2 to 4?)
make debug
compiles with debugging activated (can be debugged e.g. with gdb)
make empty
No special compilation options activated.

To fulfill the prerequisites here follow some hints depending on your OS.

Linux

If echo $JAVA_HOME gives nothing, in many cases it suffices to issue

export JAVA_HOME=/usr/lib/jvm/default-java

Which can be put e.g. into ~/.bashrc.

MacOS

The toolchain can be installed if Xcode is installed and then the optional command line tools are installed from therein.

Usually JAVA_HOME is given under

export JAVA_HOME=/Library/Java/JavaVirtualMachines/*/Contents/Home

Windows

To have JAVA_HOME correct use something like

set JAVA_HOME=C:\Program Files\Java\jdk1.8.0_92

An easy possibility to install a Unix-type toolchain are the Rtools:

https://cran.r-project.org/bin/windows/Rtools/Rtools33.exe

They should be usable without installing R. We assume here:

path=%RTOOLS%\bin;%RTOOLS%\gcc-4.6.3\bin;%path%

where %RTOOLS% is the location where they were installed (e.g. C:\Rtools).