Examples of weka.experiment.RandomSplitResultProducer

weka.experiment.RandomSplitResultProducer

Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.

Valid options are:

 -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 split evaluator. @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 6255 $

      
      m_Exp.setResultProducer(cvrp);
      m_Exp.setPropertyPath(propertyPath);


    } else {
      RandomSplitResultProducer rsrp = new RandomSplitResultProducer();
      rsrp.setRandomizeData(m_ExperimentTypeCBox.getSelectedItem() == TYPE_RANDOMSPLIT_TEXT);
      rsrp.setTrainPercent(m_trainPercent);
      rsrp.setSplitEvaluator(se);


      PropertyNode[] propertyPath = new PropertyNode[2];
      try {
  propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator",
                      RandomSplitResultProducer.class),

View Full Code Here

      }


    } else {
      
      if (m_Exp.getResultProducer() instanceof RandomSplitResultProducer) {
  RandomSplitResultProducer rsrp = (RandomSplitResultProducer) m_Exp.getResultProducer();
  rsrp.setRandomizeData(m_ExperimentTypeCBox.getSelectedItem() == TYPE_RANDOMSPLIT_TEXT);
  rsrp.setTrainPercent(m_trainPercent);
      } else {
  //System.err.println("not rsrp");
  return;
      }
    }

View Full Code Here

    return false;
  }
      }
      m_ExperimentTypeCBox.setSelectedItem(TYPE_CROSSVALIDATION_TEXT);
    } else if (exp.getResultProducer() instanceof RandomSplitResultProducer) {
      RandomSplitResultProducer rsrp = (RandomSplitResultProducer) exp.getResultProducer();
      if (rsrp.getRandomizeData()) {
  m_ExperimentTypeCBox.setSelectedItem(TYPE_RANDOMSPLIT_TEXT);
      } else {
  m_ExperimentTypeCBox.setSelectedItem(TYPE_FIXEDSPLIT_TEXT);
      }
      if (rsrp.getSplitEvaluator() instanceof ClassifierSplitEvaluator) {
  m_ExpClassificationRBut.setSelected(true);
  m_ExpRegressionRBut.setSelected(false);
      } else if (rsrp.getSplitEvaluator() instanceof RegressionSplitEvaluator) {
  m_ExpClassificationRBut.setSelected(false);
  m_ExpRegressionRBut.setSelected(true);
      } else {
  // unknown split evaluator
  System.out.println(Messages.getInstance().getString("SimpleSetupPanel_SetExperiment_UnknownSplitEvaluator_Text_First"));
  if (userWantsToConvert()) {
    m_ExpClassificationRBut.setSelected(true);
    m_ExpRegressionRBut.setSelected(false);
  } else {
    return false;
  }
      }
      m_trainPercent = rsrp.getTrainPercent();
      m_ExperimentParameterTField.setText("" + m_trainPercent);
      
    } else {
      // unknown experiment type
      System.out.println(Messages.getInstance().getString("SimpleSetupPanel_SetExperiment_UnknownPropertyIteration_Text"));

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    return false;
  }
      }
      m_ExperimentTypeCBox.setSelectedItem(TYPE_CROSSVALIDATION_TEXT);
    } else if (exp.getResultProducer() instanceof RandomSplitResultProducer) {
      RandomSplitResultProducer rsrp = (RandomSplitResultProducer) exp.getResultProducer();
      if (rsrp.getRandomizeData()) {
  m_ExperimentTypeCBox.setSelectedItem(TYPE_RANDOMSPLIT_TEXT);
      } else {
  m_ExperimentTypeCBox.setSelectedItem(TYPE_FIXEDSPLIT_TEXT);
      }
      if (rsrp.getSplitEvaluator() instanceof ClassifierSplitEvaluator) {
  m_ExpClassificationRBut.setSelected(true);
  m_ExpRegressionRBut.setSelected(false);
      } else if (rsrp.getSplitEvaluator() instanceof RegressionSplitEvaluator) {
  m_ExpClassificationRBut.setSelected(false);
  m_ExpRegressionRBut.setSelected(true);
      } else {
  // unknown split evaluator
  System.out.println("SimpleSetup incompatibility: unrecognised split evaluator");
  if (userWantsToConvert()) {
    m_ExpClassificationRBut.setSelected(true);
    m_ExpRegressionRBut.setSelected(false);
  } else {
    return false;
  }
      }
      m_trainPercent = rsrp.getTrainPercent();
      m_ExperimentParameterTField.setText("" + m_trainPercent);
      
    } else {
      // unknown experiment type
      System.out.println("SimpleSetup incompatibility: unrecognised resultProducer");

View Full Code Here

      
      m_Exp.setResultProducer(cvrp);
      m_Exp.setPropertyPath(propertyPath);


    } else {
      RandomSplitResultProducer rsrp = new RandomSplitResultProducer();
      rsrp.setRandomizeData(m_ExperimentTypeCBox.getSelectedItem() == TYPE_RANDOMSPLIT_TEXT);
      rsrp.setTrainPercent(m_trainPercent);
      rsrp.setSplitEvaluator(se);


      PropertyNode[] propertyPath = new PropertyNode[2];
      try {
  propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator",
                      RandomSplitResultProducer.class),

View Full Code Here

      }


    } else {
      
      if (m_Exp.getResultProducer() instanceof RandomSplitResultProducer) {
  RandomSplitResultProducer rsrp = (RandomSplitResultProducer) m_Exp.getResultProducer();
  rsrp.setRandomizeData(m_ExperimentTypeCBox.getSelectedItem() == TYPE_RANDOMSPLIT_TEXT);
  rsrp.setTrainPercent(m_trainPercent);
      } else {
  //System.err.println("not rsrp");
  return;
      }
    }

View Full Code Here

    return false;
  }
      }
      m_ExperimentTypeCBox.setSelectedItem(TYPE_CROSSVALIDATION_TEXT);
    } else if (exp.getResultProducer() instanceof RandomSplitResultProducer) {
      RandomSplitResultProducer rsrp = (RandomSplitResultProducer) exp.getResultProducer();
      if (rsrp.getRandomizeData()) {
  m_ExperimentTypeCBox.setSelectedItem(TYPE_RANDOMSPLIT_TEXT);
      } else {
  m_ExperimentTypeCBox.setSelectedItem(TYPE_FIXEDSPLIT_TEXT);
      }
      if (rsrp.getSplitEvaluator() instanceof ClassifierSplitEvaluator) {
  m_ExpClassificationRBut.setSelected(true);
  m_ExpRegressionRBut.setSelected(false);
      } else if (rsrp.getSplitEvaluator() instanceof RegressionSplitEvaluator) {
  m_ExpClassificationRBut.setSelected(false);
  m_ExpRegressionRBut.setSelected(true);
      } else {
  // unknown split evaluator
  System.out.println("SimpleSetup incompatibility: unrecognised split evaluator");
  if (userWantsToConvert()) {
    m_ExpClassificationRBut.setSelected(true);
    m_ExpRegressionRBut.setSelected(false);
  } else {
    return false;
  }
      }
      m_trainPercent = rsrp.getTrainPercent();
      m_ExperimentParameterTField.setText("" + m_trainPercent);
      
    } else {
      // unknown experiment type
      System.out.println("SimpleSetup incompatibility: unrecognised resultProducer");

View Full Code Here

      
      m_Exp.setResultProducer(cvrp);
      m_Exp.setPropertyPath(propertyPath);


    } else {
      RandomSplitResultProducer rsrp = new RandomSplitResultProducer();
      rsrp.setRandomizeData(m_ExperimentTypeCBox.getSelectedItem() == TYPE_RANDOMSPLIT_TEXT);
      rsrp.setTrainPercent(m_trainPercent);
      rsrp.setSplitEvaluator(se);


      PropertyNode[] propertyPath = new PropertyNode[2];
      try {
  propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator",
                      RandomSplitResultProducer.class),

View Full Code Here

      }


    } else {
      
      if (m_Exp.getResultProducer() instanceof RandomSplitResultProducer) {
  RandomSplitResultProducer rsrp = (RandomSplitResultProducer) m_Exp.getResultProducer();
  rsrp.setRandomizeData(m_ExperimentTypeCBox.getSelectedItem() == TYPE_RANDOMSPLIT_TEXT);
  rsrp.setTrainPercent(m_trainPercent);
      } else {
  //System.err.println("not rsrp");
  return;
      }
    }

View Full Code Here

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Related Classes of weka.experiment.RandomSplitResultProducer

weka.core.Instance

weka.core.Instances

weka.core.Option

weka.gui.experiment.SimpleSetupPanel

java.util.Enumeration

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