MLWizard can be used via the command line, as well. It will be started using the provided jar-file:
java -cp /path/to/rapidminer.jar:/path/to/MLWizard.jar de.dfki.madm.mlwizard.cli.CommandLineInterface <dataset> <task> [outfile]
The first argument is the path to the dataset in XRFF-format that will be used as input.
The second argument is the task that should be performed and can be one of the following:
The third argument is optional and defines where the results are written to. The result depends on the task and might be meta-features, a parameter set or a RapidMiner pipeline. If no file is supplied, the results are written to stdout.
A sample session might be:
java -cp /path/to/rapidminer.jar:/path/to/MLWizard.jar de.dfki.madm.mlwizard.cli.CommandLineInterface iris.xrff wizard result.xml
[ 0] k-NN 0.87
[ 1] Rule Induction 0.87
[ 2] Neural Net 0.87
[ 3] SVM 0.86
[ 4] Decision Tree 0.84
[ 5] Naive Bayes 0.79
Enter comma separated numbers of classifiers you want to be evaluated:
0,1,2
[ 0] k-NN 0.97
k-NN.weighted_vote true
[ 1] Rule Induction 0.96
Rule Induction.criterion information_gain
[ 2] Neural Net 0.98
Neural Net.decay false
[ 3] SVM <not evaluated>
[ 4] Decision Tree <not evaluated>
[ 5] Naive Bayes <not evaluated>
Enter number of classifier you want a system for:
2
The created RapidMiner pipeline will be stored in result.xml and can be imported into Rapidminer.