Java Code Examples for meka.classifiers.multilabel.Evaluation#evaluateModel()

The following examples show how to use meka.classifiers.multilabel.Evaluation#evaluateModel() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: TrainTestSplit.java    From meka with GNU General Public License v3.0 6 votes vote down vote up 
public static void main(String[] args) throws Exception {
  if (args.length != 2)
    throw new IllegalArgumentException("Required arguments: <dataset> <percentage>");

  System.out.println("Loading data: " + args[0]);
  Instances data = DataSource.read(args[0]);
  MLUtils.prepareData(data);

  double percentage = Double.parseDouble(args[1]);
  int trainSize = (int) (data.numInstances() * percentage / 100.0);
  Instances train = new Instances(data, 0, trainSize);
  Instances test = new Instances(data, trainSize, data.numInstances() - trainSize);

  System.out.println("Build BR classifier on " + percentage + "%");
  BR classifier = new BR();
  // further configuration of classifier
  classifier.buildClassifier(train);

  System.out.println("Evaluate BR classifier on " + (100.0 - percentage) + "%");
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.evaluateModel(classifier, train, test, top, vop);

  System.out.println(result);
}
Example 2
Source File: ExportPredictionsOnTestSet.java    From meka with GNU General Public License v3.0 5 votes vote down vote up 
public static void main(String[] args) throws Exception {
  if (args.length != 3)
    throw new IllegalArgumentException("Required arguments: <train> <test> <output>");

  System.out.println("Loading train: " + args[0]);
  Instances train = DataSource.read(args[0]);
  MLUtils.prepareData(train);

  System.out.println("Loading test: " + args[1]);
  Instances test = DataSource.read(args[1]);
  MLUtils.prepareData(test);

  // compatible?
  String msg = train.equalHeadersMsg(test);
  if (msg != null)
    throw new IllegalStateException(msg);

  System.out.println("Build BR classifier on " + args[0]);
  BR classifier = new BR();
  // further configuration of classifier
  classifier.buildClassifier(train);

  System.out.println("Evaluate BR classifier on " + args[1]);
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.evaluateModel(classifier, train, test, top, vop);

  System.out.println(result);

  System.out.println("Saving predictions test set to " + args[2]);
  Instances performance = Result.getPredictionsAsInstances(result);
  DataSink.write(args[2], performance);
}
Example 3
Source File: TrainTestSet.java    From meka with GNU General Public License v3.0 5 votes vote down vote up 
public static void main(String[] args) throws Exception {
  if (args.length != 2)
    throw new IllegalArgumentException("Required arguments: <train> <test>");

  System.out.println("Loading train: " + args[0]);
  Instances train = DataSource.read(args[0]);
  MLUtils.prepareData(train);

  System.out.println("Loading test: " + args[1]);
  Instances test = DataSource.read(args[1]);
  MLUtils.prepareData(test);

  // compatible?
  String msg = train.equalHeadersMsg(test);
  if (msg != null)
    throw new IllegalStateException(msg);

  System.out.println("Build BR classifier on " + args[0]);
  BR classifier = new BR();
  // further configuration of classifier
  classifier.buildClassifier(train);

  System.out.println("Evaluate BR classifier on " + args[1]);
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.evaluateModel(classifier, train, test, top, vop);

  System.out.println(result);
}
Example 4
Source File: StatUtils.java    From meka with GNU General Public License v3.0 5 votes vote down vote up 
/**
 * LEAD - Performs LEAD on dataset 'D', using BR with base classifier 'h', under random seed 'r'.
 * <br>
 * WARNING: changing this method will affect the perfomance of e.g., BCC -- on the other hand the original BCC paper did not use LEAD, so don't worry.
 */
public static double[][] LEAD(Instances D, Classifier h, Random r)  throws Exception {
	Instances D_r = new Instances(D);
	D_r.randomize(r);
	Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100);
	Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances());
	BR br = new BR();
	br.setClassifier(h);
	Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1");
	return LEAD2(D_test,result);
}
Example 5
Source File: StatUtils.java    From meka with GNU General Public License v3.0 5 votes vote down vote up 
public static double[][] LEAD(Instances D, Classifier h, Random r, String MDType)  throws Exception {
	Instances D_r = new Instances(D);
	D_r.randomize(r);
	Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100);
	Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances());
	BR br = new BR();
	br.setClassifier(h);
	Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1");

	return LEAD(D_test, result, MDType);
}