Weka is a commonly used Machine Learning suite of algorithms for Data Mining with Java. We’ve developed a Singularity container so that your Weka environment and data can now be moved cross-system on-demand, with all the benefits of the Singularity Image Format (SIF).
BootStrap: docker From: ubuntu:16.04 %post apt-get -y update apt-get -y install curl apt-get -y install unzip apt-get install -y openjdk-8-jre curl -sSL "https://prdownloads.sourceforge.net/weka/weka-3-8-3.zip" > weka.zip unzip weka.zip -d / && rm -f weka.zip* echo 'export CLASSPATH=/weka-3-8-3/weka.jar' >> /environment apt-get clean
To build the Weka container, we run:
$ sudo singularity build weka.sif weka.def
Weka builds without any setup required and its basic usage is:
$ singularity exec weka.sif java weka.classifiers.object
Toy datasets are included with this install, let’s test them out with a command:
$ singularity exec weka.sif java weka.classifiers.functions.MultilayerPerceptron \ -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a -t /weka-3-8-3/data/breast-cancer.arff
It also comes with a multitude of other functions, try the BayesNet function:
$ singularity exec weka.sif java weka.classifiers.bayes.BayesNet -t /weka-3-8-3/data/iris.arff -D \ -Q weka.classifiers.bayes.net.search.local.K2 -- -P 2 -S ENTROPY \ -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 1.0
Of course, when you run Weka you’ll want to use real data by adding the -B flag to bind your data directory into the container:
$ singularity exec -B path/to/data:/weka-3-8-3/data weka.sif java weka.classifiers.functions.[function here] \ -t /weka-3-8-3/data/yourdataset/file.arff [args]
For more information about Weka visit their home page.
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