Examples of weka.experiment.Experiment

weka.experiment.Experiment

Holds all the necessary configuration information for a standard type experiment. This object is able to be serialized for storage on disk. Valid options are:

 -L <num> The lower run number to start the experiment from. (default 1)

 -U <num> The upper run number to end the experiment at (inclusive). (default 10)

 -T <arff file> The dataset to run the experiment on. (required, may be specified multiple times)

 -P <class name> The full class name of a ResultProducer (required). eg: weka.experiment.RandomSplitResultProducer

 -D <class name> The full class name of a ResultListener (required). eg: weka.experiment.CSVResultListener

 -N <string> A string containing any notes about the experiment. (default none)

  Options specific to result producer weka.experiment.RandomSplitResultProducer:

 -P <percent> The percentage of instances to use for training. (default 66)

 -D Save raw split evaluator output.

 -O <file/directory name/path> The filename where raw output will be stored. If a directory name is specified then then individual outputs will be gzipped, otherwise all output will be zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)

 -W <class name> The full class name of a SplitEvaluator. eg: weka.experiment.ClassifierSplitEvaluator

 -R Set when data is not to be randomized and the data sets' size. Is not to be determined via probabilistic rounding.

  Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:

 -W <class name> The full class name of the classifier. eg: weka.classifiers.bayes.NaiveBayes

 -C <index> The index of the class for which IR statistics are to be output. (default 1)

 -I <index> The index of an attribute to output in the results. This attribute should identify an instance in order to know which instances are in the test set of a cross validation. if 0 no output (default 0).

 -P Add target and prediction columns to the result for each fold.

  Options specific to classifier weka.classifiers.rules.ZeroR:

 -D If set, classifier is run in debug mode and may output additional info to the console

All options after -- will be passed to the result producer.

@author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 5401 $


    // create action listeners
    m_NewBut.setMnemonic('N');
    m_NewBut.addActionListener(new ActionListener() {
  public void actionPerformed(ActionEvent e) {
    Experiment newExp = new Experiment();
    CrossValidationResultProducer cvrp = new CrossValidationResultProducer();
    cvrp.setNumFolds(10);
    cvrp.setSplitEvaluator(new ClassifierSplitEvaluator());
    newExp.setResultProducer(cvrp);
    newExp.setPropertyArray(new Classifier[0]);
    newExp.setUsePropertyIterator(true);
    setExperiment(newExp);


          // defaults
          if (ExperimenterDefaults.getUseClassification())
            m_ExpClassificationRBut.setSelected(true);

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      if (!expFile.getName().toLowerCase().endsWith(".xml"))
        expFile = new File(expFile.getParent(), expFile.getName() + ".xml");
    }
    
    try {
      Experiment exp = Experiment.read(expFile.getAbsolutePath());
      if (!setExperiment(exp)) {
  if (m_modePanel != null) m_modePanel.switchToAdvanced(exp);
      }
      System.err.println("Opened experiment:\n" + exp);
    } catch (Exception ex) {

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    m_TabbedPane.setSelectedIndex(0);
    m_TabbedPane.setEnabledAt(1, false);
    m_SetupPanel.addPropertyChangeListener(new PropertyChangeListener() {
      public void propertyChange(PropertyChangeEvent e) {
  //System.err.println("Updated experiment");
  Experiment exp = m_SetupPanel.getExperiment();
  exp.classFirst(m_ClassFirst);
  m_RunPanel.setExperiment(exp);
  //m_ResultsPanel.setExperiment(exp);
  m_TabbedPane.setEnabledAt(1, true);
      }
    });

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