meka.classifiers.multilabel.Evaluation Java Examples

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);
}
 

/**
 * Main - do some tests.
 */
public static void main(String args[]) throws Exception {
	Instances D = Evaluation.loadDataset(args);
	MLUtils.prepareData(D);
	int L = D.classIndex();

	double CD[][] = null;

	if (args[2].equals("L")) {
		String I = "I";
		if (args.length >= 3) 
			I = args[3];
		CD = StatUtils.LEAD(D, new SMO(), new Random(), I);
	}
	else {
		CD = StatUtils.margDepMatrix(D,args[2]);
	}
	System.out.println(MatrixUtils.toString(CD, "M" + args[2]));
}
 

public static void main(String[] args) throws Exception {
  if (args.length != 1)
    throw new IllegalArgumentException("Required arguments: <dataset>");

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

  System.out.println("Cross-validate BR classifier");
  BR classifier = new BR();
  // further configuration of classifier
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.cvModel(classifier, data, 10, top, vop);

  JFrame frame = new JFrame("Micro curve");
  frame.setDefaultCloseOperation(JDialog.EXIT_ON_CLOSE);
  frame.getContentPane().setLayout(new BorderLayout());
  Instances performance = (Instances) result.getMeasurement(CURVE_DATA_MICRO);
  try {
    VisualizePanel panel = createPanel(performance);
    frame.getContentPane().add(panel, BorderLayout.CENTER);
  }
  catch (Exception ex) {
    System.err.println("Failed to create plot!");
    ex.printStackTrace();
  }
  frame.setSize(800, 600);
  frame.setLocationRelativeTo(null);
  frame.setVisible(true);
}
 

public static void main(String[] args) throws Exception {
  if (args.length != 1)
    throw new IllegalArgumentException("Required arguments: <dataset>");

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

  int numFolds = 10;
  System.out.println("Cross-validate BR classifier using " + numFolds + " folds");
  BR classifier = new BR();
  // further configuration of classifier
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.cvModel(classifier, data, numFolds, top, vop);

  System.out.println(result);
}
 

@Test
public void testFitAndPredictWithHoldoutSplitter() throws Exception {
	BR br = new BR();
	br.buildClassifier(splitterSplit.get(0).getInstances());
	Result res = Evaluation.testClassifier(br, splitterSplit.get(1).getInstances());
	double[][] mekaPredictions = res.allPredictions();

	MekaClassifier classifier = new MekaClassifier(new BR());
	classifier.fit(splitterSplit.get(0));
	IMultiLabelClassificationPredictionBatch pred = classifier.predict(splitterSplit.get(1));

	assertEquals("Number of predictions is not consistent.", splitterSplit.get(1).size(), pred.getNumPredictions());

	double[][] jaicorePredictions = pred.getPredictionMatrix();
	assertEquals("Length of prediction matrices is not consistent.", mekaPredictions.length, jaicorePredictions.length);
	assertEquals("Width of prediction matrices is not consistent.", mekaPredictions[0].length, jaicorePredictions[0].length);

	for (int i = 0; i < mekaPredictions.length; i++) {
		for (int j = 0; j < mekaPredictions[i].length; j++) {
			assertEquals("The prediction for instance " + i + " and label " + j + " is not consistent.", mekaPredictions[i][j], jaicorePredictions[i][j], 1E-8);
		}
	}
}
 

public static void main(String [] argv) {

    try {
    	Evaluation.runExperiment(((MultiLabelClassifier) new ARAMNetwork()), argv);
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
 

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);
}
 

/**
 * 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);
}
 

public static void main(String [] argv) {

    try {
    	Evaluation.runExperiment((MultiLabelClassifier) new WARAM(), argv);
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
 

public static void main(String [] argv) {

    try {
    	Evaluation.runExperiment(((MultiLabelClassifier) new WvARAM()), argv);
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
 

public static void main(String [] argv) {

    try {
    	Evaluation.runExperiment(new WvARAM(), argv);
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
 

public static void main(String [] argv) {

    try {
    	Evaluation.runExperiment(((MultiLabelClassifier) new WvARAM()), argv);
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
 

public static void main(String [] argv) {

    try {
    	Evaluation.runExperiment(((MultiLabelClassifier) new WvARAM()), argv);
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
 

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);
}
 

/**
 * Main method for testing this class.
 *
 * @param argv the options
 */
public static void main(String [] argv) {

  try {
  	Evaluation.runExperiment(new WvARAM(), argv);
  } catch (Exception e) {
    e.printStackTrace();
    System.err.println(e.getMessage());
  }
  System.out.println("Done");
}
 

public static void main(String [] argv) {

    try {
    	Evaluation.runExperiment(new WvARAM(), argv);
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
 

/**
 * Test classifier h, on dataset D, under super-class partition 'partition'.
 * <br>
 * TODO should be able to use something out of meka.classifiers.Evaluation instead of all this ...
 */
public Result testClassifier(Classifier h, Instances D_train, Instances D_test, int partition[][]) throws Exception {

	trainClassifier(m_Classifier,D_train,partition);

	Result result = Evaluation.testClassifier((ProblemTransformationMethod)h, D_test);

	if (h instanceof MultiTargetClassifier || Evaluation.isMT(D_test)) {
		result.setInfo("Type","MT");
	}
	else if (h instanceof ProblemTransformationMethod) {
		result.setInfo("Threshold", MLEvalUtils.getThreshold(result.predictions, D_train, "PCut1"));
		result.setInfo("Type","ML");
	}

	result.setValue("N_train",D_train.numInstances());
	result.setValue("N_test",D_test.numInstances());
	result.setValue("LCard_train",MLUtils.labelCardinality(D_train));
	result.setValue("LCard_test",MLUtils.labelCardinality(D_test));

	//result.setValue("Build_time",(after - before)/1000.0);
	//result.setValue("Test_time",(after_test - before_test)/1000.0);
	//result.setValue("Total_time",(after_test - before)/1000.0);

	result.setInfo("Classifier_name",h.getClass().getName());
	//result.setInfo("Classifier_ops", Arrays.toString(h.getOptions()));
	result.setInfo("Classifier_info",h.toString());
	result.setInfo("Dataset_name",MLUtils.getDatasetName(D_test));

	result.output = Result.getStats(result,"1");
	return result;
}
 

public static void main(String[] args) throws Exception {
  if (args.length != 1)
    throw new IllegalArgumentException("Required arguments: <dataset>");

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

  System.out.println("Cross-validate BR classifier");
  BR classifier = new BR();
  // further configuration of classifier
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.cvModel(classifier, data, 10, top, vop);

  JFrame frame = new JFrame("Precision-recall");
  frame.setDefaultCloseOperation(JDialog.EXIT_ON_CLOSE);
  frame.getContentPane().setLayout(new BorderLayout());
  JTabbedPane tabbed = new JTabbedPane();
  frame.getContentPane().add(tabbed, BorderLayout.CENTER);
  Instances[] curves = (Instances[]) result.getMeasurement(CURVE_DATA);
  for (int i = 0; i < curves.length; i++) {
    try {
      ThresholdVisualizePanel panel = createPanel(curves[i], "Label " + i);
      tabbed.addTab("" + i, panel);
    }
    catch (Exception ex) {
      System.err.println("Failed to create plot for label " + i);
      ex.printStackTrace();
    }
  }
  frame.setSize(800, 600);
  frame.setLocationRelativeTo(null);
  frame.setVisible(true);
}
 

public static void main(String[] args) throws Exception {
  if (args.length != 1)
    throw new IllegalArgumentException("Required arguments: <dataset>");

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

  System.out.println("Cross-validate BR classifier");
  BR classifier = new BR();
  // further configuration of classifier
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.cvModel(classifier, data, 10, top, vop);

  JFrame frame = new JFrame("Macro curve");
  frame.setDefaultCloseOperation(JDialog.EXIT_ON_CLOSE);
  frame.getContentPane().setLayout(new BorderLayout());
  Instances performance = (Instances) result.getMeasurement(CURVE_DATA_MACRO);
  try {
    VisualizePanel panel = createPanel(performance);
    frame.getContentPane().add(panel, BorderLayout.CENTER);
  }
  catch (Exception ex) {
    System.err.println("Failed to create plot!");
    ex.printStackTrace();
  }
  frame.setSize(800, 600);
  frame.setLocationRelativeTo(null);
  frame.setVisible(true);
}
 

public static void main(String[] args) throws Exception {
  if (args.length != 1)
    throw new IllegalArgumentException("Required arguments: <dataset>");

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

  System.out.println("Cross-validate BR classifier");
  BR classifier = new BR();
  // further configuration of classifier
  String top = "PCut1";
  String vop = "3";
  Result result = Evaluation.cvModel(classifier, data, 10, top, vop);

  JFrame frame = new JFrame("ROC");
  frame.setDefaultCloseOperation(JDialog.EXIT_ON_CLOSE);
  frame.getContentPane().setLayout(new BorderLayout());
  JTabbedPane tabbed = new JTabbedPane();
  frame.getContentPane().add(tabbed, BorderLayout.CENTER);
  Instances[] curves = (Instances[]) result.getMeasurement(CURVE_DATA);
  for (int i = 0; i < curves.length; i++) {
    try {
      ThresholdVisualizePanel panel = createPanel(curves[i], "Label " + i);
      tabbed.addTab("" + i, panel);
    }
    catch (Exception ex) {
      System.err.println("Failed to create plot for label " + i);
      ex.printStackTrace();
    }
  }
  frame.setSize(800, 600);
  frame.setLocationRelativeTo(null);
  frame.setVisible(true);
}
 

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);
}