Java Code Examples for weka.classifiers.functions.SMO#setBuildCalibrationModels()

public void testDeepML() {
	System.out.println("Test Stacked Boltzmann Machines with an off-the-shelf multi-label classifier");
	DeepML dbn = new DeepML();

	MCC h = new MCC();
	SMO smo = new SMO();
	smo.setBuildCalibrationModels(true);
	h.setClassifier(smo);

	dbn.setClassifier(h);
	dbn.setE(100);
	dbn.setH(30);

	Result r = EvaluationTests.cvEvaluateClassifier(dbn);
	System.out.println("DeepML + MCC" + r.getMeasurement("Accuracy"));
	String s = (String)r.getMeasurement("Accuracy");
	assertTrue("DeepML+MCC Accuracy Correct", s.startsWith("0.53")); // Good enough 
}
 

/**
 * Initializes the CAWPE ensemble model consisting of five classifiers (SMO,
 * KNN, J48, Logistic and MLP) using a majority voting strategy. The ensemble
 * uses Weka classifiers. It refers to "Heterogeneous ensemble of standard
 * classification algorithms" (HESCA) as described in Lines, Jason & Taylor,
 * Sarah & Bagnall, Anthony. (2018). Time Series Classification with HIVE-COTE:
 * The Hierarchical Vote Collective of Transformation-Based Ensembles. ACM
 * Transactions on Knowledge Discovery from Data. 12. 1-35. 10.1145/3182382.
 *
 * @param seed
 *            Seed used within the classifiers and the majority confidence
 *            voting scheme
 * @param numFolds
 *            Number of folds used within the determination of the classifier
 *            weights for the {@link MajorityConfidenceVote}
 * @return Returns an initialized (but untrained) ensemble model.
 * @throws Exception
 *             Thrown when the initialization has failed
 */
public static Classifier provideCAWPEEnsembleModel(final int seed, final int numFolds) throws Exception {
	Classifier[] classifiers = new Classifier[5];

	Vote voter = new MajorityConfidenceVote(numFolds, seed);

	SMO smo = new SMO();
	smo.turnChecksOff();
	smo.setBuildCalibrationModels(true);
	PolyKernel kl = new PolyKernel();
	kl.setExponent(1);
	smo.setKernel(kl);
	smo.setRandomSeed(seed);
	classifiers[0] = smo;

	IBk k = new IBk(100);
	k.setCrossValidate(true);
	EuclideanDistance ed = new EuclideanDistance();
	ed.setDontNormalize(true);
	k.getNearestNeighbourSearchAlgorithm().setDistanceFunction(ed);
	classifiers[1] = k;

	J48 c45 = new J48();
	c45.setSeed(seed);
	classifiers[2] = c45;

	classifiers[3] = new Logistic();

	classifiers[4] = new MultilayerPerceptron();

	voter.setClassifiers(classifiers);
	return voter;
}
 

public void testMCC() {
	// Test MCC (with SMO -- -M)
	System.out.println("Test MCC");
	MCC h = new MCC();
	SMO smo = new SMO();
	smo.setBuildCalibrationModels(true);
	h.setClassifier(smo);
	Result r = EvaluationTests.cvEvaluateClassifier(h);
	assertEquals("MCC Accuracy Correct", "0.561 +/- 0.035", r.getMeasurement("Accuracy"));
}
 

public void testPMCC() {
	// Test MCC (with SMO -- -M)
	System.out.println("Test PMCC");
	PMCC h = new PMCC();
	h.setM(10);
	h.setChainIterations(50);
	h.setInferenceIterations(20);
	SMO smo = new SMO();
	smo.setBuildCalibrationModels(true);
	h.setClassifier(smo);
	Result r = EvaluationTests.cvEvaluateClassifier(h);
	assertEquals("PMCC Accuracy Correct", "0.594 +/- 0.029", r.getMeasurement("Accuracy"));
}
 

public void testPCC() {
	// Test PCC (with SMO -- -M)
	System.out.println("Test PCC");
	PCC h = new PCC();
	SMO smo = new SMO();
	smo.setBuildCalibrationModels(true);
	h.setClassifier(smo);
	Result r = EvaluationTests.cvEvaluateClassifier(h);
	assertEquals("PCC Accuracy Correct", "0.565 +/- 0.032", r.getMeasurement("Accuracy"));
}
 

public void testCT() {
	// Test CT (with SMO -- -M)
	System.out.println("Test CT");
	CT h = new CT();
	SMO smo = new SMO();
	smo.setBuildCalibrationModels(true);
	h.setClassifier(smo);
	h.setInferenceIterations(10);
	h.setChainIterations(10);
	Result r = EvaluationTests.cvEvaluateClassifier(h);
	//System.out.println("CT ACC: "+r.getMeasurement("Accuracy"));
	assertEquals("CT Accuracy Correct", "0.56  +/- 0.034", r.getMeasurement("Accuracy"));
}
 

public void testCDT() {
	// Test CDT (with SMO -- -M)
	System.out.println("Test CDT");
	CDT h = new CDT();
	SMO smo = new SMO();
	smo.setBuildCalibrationModels(true);
	h.setClassifier(smo);
	Result r = EvaluationTests.cvEvaluateClassifier(h);
	//System.out.println("CDT ACC: "+r.getMeasurement("Accuracy"));
	assertEquals("CDT Accuracy Correct", "0.519 +/- 0.039", r.getMeasurement("Accuracy") );
}
 

/**
 * Initializes the HIVE COTE ensemble consisting of 7 classifiers using a
 * majority voting strategy as described in J. Lines, S. Taylor and A. Bagnall,
 * "HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based
 * Ensembles for Time Series Classification," 2016 IEEE 16th International
 * Conference on Data Mining (ICDM), Barcelona, 2016, pp. 1041-1046. doi:
 * 10.1109/ICDM.2016.0133.
 *
 * @param seed
 *            Seed used within the classifiers and the majority confidence
 *            voting scheme
 * @param numFolds
 *            Number of folds used within the determination of the classifier
 *            weights for the {@link MajorityConfidenceVote}
 * @return Returns the initialized (but untrained) HIVE COTE ensemble model.
 */
public static Classifier provideHIVECOTEEnsembleModel(final long seed) {
	Classifier[] classifier = new Classifier[7];

	Vote voter = new MajorityConfidenceVote(5, seed);

	// SMO poly2
	SMO smop = new SMO();
	smop.turnChecksOff();
	smop.setBuildCalibrationModels(true);
	PolyKernel kernel = new PolyKernel();
	kernel.setExponent(2);
	smop.setKernel(kernel);
	smop.setRandomSeed((int)seed);
	classifier[0] = smop;

	// Random Forest
	RandomForest rf = new RandomForest();
	rf.setSeed((int)seed);
	rf.setNumIterations(500);
	classifier[1] = rf;

	// Rotation forest
	RotationForest rotF = new RotationForest();
	rotF.setSeed((int)seed);
	rotF.setNumIterations(100);
	classifier[2] = rotF;

	// NN
	IBk nn = new IBk();
	classifier[3] = nn;

	// Naive Bayes
	NaiveBayes nb = new NaiveBayes();
	classifier[4] = nb;

	// C45
	J48 c45 = new J48();
	c45.setSeed((int)seed);
	classifier[5] = c45;

	// SMO linear
	SMO smol = new SMO();
	smol.turnChecksOff();
	smol.setBuildCalibrationModels(true);
	PolyKernel linearKernel = new PolyKernel();
	linearKernel.setExponent(1);
	smol.setKernel(linearKernel);
	classifier[6] = smol;

	voter.setClassifiers(classifier);
	return voter;
}