Changed verbosity switch in SVMParameter objects

mustard-experiments/src/main/java/org/data2semantics/mustard/experiments/cluster/LocalExecutor.java

@@ -7,11 +7,11 @@
 
 public class LocalExecutor {
 	// parameter settings file 
-	private static final String parmsFile = "../src/main/java/org/data2semantics/mustard/experiments/JWS2015/parms.txt";
+	private static final String parmsFile = "../../src/main/java/org/data2semantics/mustard/experiments/JWS2015/parms.txt";
 	//private static final String parmsFile = "../src/main/java/org/data2semantics/mustard/experiments/JWS2015/parms_minfreq.txt";
 
 	// regular affiliation prediction
-	private static final String prefix = "-dataset AIFB -file ../datasets/aifb-fixed_complete.n3";
+	private static final String prefix = "-dataset AIFB -file ../../datasets/aifb-fixed_complete.n3";
 
 	// regular affiliation prediction with minfreq
 	//private static final String prefix = "-leaveRootLabel true -dataset AIFB -file ../datasets/aifb-fixed_complete.n3";

mustard-kernels/src/main/java/org/data2semantics/mustard/kernels/KernelUtils.java

@@ -16,6 +16,48 @@
 public class KernelUtils {
 	public static final String ROOTID = "ROOT1337"; // Special root label used in some kernels
 
+	/**
+	 * extract a subset train (square) kernel matrix from a larger (square) kernel matrix
+	 * 
+	 * @param kernel
+	 * @param startIdx, begin index inclusive
+	 * @param endIdx, end index exclusive
+	 * @return train kernel matrix of size: (endIdx-startIdx) x (endIdx-startIdx)
+	 */
+	public static double[][] trainSubset(double[][] kernel, int startIdx, int endIdx) {
+		double[][] ss = new double[endIdx - startIdx][endIdx - startIdx];
+
+		for (int i = startIdx; i < endIdx; i++) {
+			for (int j = startIdx; j < endIdx; j++) {
+				ss[i][j] = kernel[i][j];
+			}
+		}	
+		return ss;
+	}
+
+	/**
+	 * extract a subset test (rectangle) kernel matrix from a larger (square) kernel matrix
+	 * Each row of this matrix represents of test instance, each column a train instance.
+	 * 
+	 * @param kernel
+	 * @param startIdx, begin index of the test instances, inclusive
+	 * @param endIdx, end index of the test instances, exclusive
+	 * @return, test kernel matrix of size: (endIdx - startIdx) x (kernel.length - (endIdx-startIdx))
+	 */
+	public static double[][] testSubset(double[][] kernel, int startIdx, int endIdx) {		
+		double[][] ss = new double[endIdx - startIdx][kernel.length - (endIdx-startIdx)];
+
+		for (int i = startIdx; i < endIdx; i++) {
+			for (int j = 0; j < startIdx; j++) {
+				ss[i][j] = kernel[i][j];
+			}
+			for (int j = endIdx; j < kernel.length; j++) {
+				ss[i][j] = kernel[i][j];
+			}	
+		}
+		return ss;
+	}
+
 	/**
 	 * returns a copy of the kernel matrix.
 	 * 
@@ -52,24 +94,6 @@ public static double[][] shuffle(double[][] kernel, long seed) {
 		return convert2DoublePrimitives(kernelDouble);
 	}
 
-	/**
-	 * Convert an array of SparseVectors to a kernel matrix, by computing the dot product of all the SparseVectors
-	 * 
-	 * @param featureVectors, an array of SparseVectors
-	 * @return a 2d array of doubles, the kernel matrix
-	 */
-	public static double[][] featureVectors2Kernel(SparseVector[] featureVectors) {
-		double[][] kernel = initMatrix(featureVectors.length, featureVectors.length);
-
-		for (int i = 0; i < featureVectors.length; i++) {
-			for (int j = i; j < featureVectors.length; j++) {
-				kernel[i][j] = featureVectors[i].dot(featureVectors[j]);
-				kernel[j][i] = kernel[i][j];
-			}
-		}
-		return kernel;
-	}
-
 	/**
 	 * Convert an array of SparseVectors to binary SparseVectors, i.e. only 0 or 1 values.
 	 * 

mustard-learners/src/main/java/org/data2semantics/mustard/learners/liblinear/LibLINEAR.java

@@ -58,7 +58,7 @@ private static LibLINEARModel trainLinearModel(Problem prob, LibLINEARParameters
 	}
 
 	private static LibLINEARModel trainLinearModel(Map<Kernel, Problem> probs, LibLINEARParameters params) {
-		if (!params.isVerbose()) {
+		if (params.getVerbosity() != LibLINEARParameters.VERBOSITY_FULL) {
 			Linear.disableDebugOutput();
 		}
 
@@ -133,15 +133,19 @@ public int compare(Kernel o1, Kernel o2) {
 			label = bestSetting.getLabel();
 		} 		
 
-		System.out.println("Trained Linear SVM for " + label + ", with C: " + bestC + " and P: " + bestP);
+		if (params.getVerbosity() == LibLINEARParameters.VERBOSITY_FULL || params.getVerbosity() == LibLINEARParameters.VERBOSITY_DEFAULT) {
+			System.out.println("Trained Linear SVM for " + label + ", with C: " + bestC + " and P: " + bestP);
+		}
 
 		double avg = 0;
 		for (Feature[] v : probs.get(bestSetting).x) {
 			avg += v.length;
 		}
 		avg /= probs.get(bestSetting).x.length;
 
-		System.out.println("#instances:" + probs.get(bestSetting).l + ", #features: " + probs.get(bestSetting).n + ", #avg-non-zero: " + avg);
+		if (params.getVerbosity() == LibLINEARParameters.VERBOSITY_FULL || params.getVerbosity() == LibLINEARParameters.VERBOSITY_DEFAULT) {
+			System.out.println("#instances:" + probs.get(bestSetting).l + ", #features: " + probs.get(bestSetting).n + ", #avg-non-zero: " + avg);
+		}
 
 		return model;
 	}

mustard-learners/src/main/java/org/data2semantics/mustard/learners/liblinear/LibLINEARParameters.java

@@ -14,14 +14,18 @@ public class LibLINEARParameters {
 	public static final int SVR_PRIMAL = 4;
 	public static final int LR_DUAL = 5;
 	public static final int LR_PRIMAL = 6;	
+
+	public static final int VERBOSITY_NONE = 0;
+	public static final int VERBOSITY_DEFAULT = 1;
+	public static final int VERBOSITY_FULL = 2;
 
 	private int[] weightLabels;
 	private double[] weights;
 	private int algorithm;
 	private Parameter params;	
 	private double[] cs;
 	private double[] ps;
-	private boolean verbose;
+	private int verbose;
 	private double bias;
 
 	private boolean doCrossValidation;
@@ -67,7 +71,7 @@ public LibLINEARParameters(int algorithm) {
 		break;
 		}
 
-		verbose = false;
+		verbose = VERBOSITY_DEFAULT;
 		bias = -1;
 		doCrossValidation = true;
 		doWeightLabels = false;
@@ -94,8 +98,8 @@ public void setCs(double[] itParams) {
 		this.cs = itParams;
 	}
 
-	public void setVerbose(boolean verbose) {
-		this.verbose = verbose;
+	public void setVerbosity(int verbosity) {
+		this.verbose = verbosity;
 	}
 
 	public void setBias(double bias) {
@@ -141,7 +145,7 @@ public double getEps() {
 		return eps;
 	}
 
-	public boolean isVerbose() {
+	public int getVerbosity() {
 		return verbose;
 	}
 

mustard-learners/src/main/java/org/data2semantics/mustard/learners/libsvm/LibSVM.java

@@ -111,7 +111,7 @@ public static LibSVMModel trainSVMModel(double[][] kernel, double[] target, LibS
 	}
 
 	private static LibSVMModel trainSVMModel(Map<Kernel, svm_problem> svmProbs, double[] target, LibSVMParameters params) {	
-		if (!params.isVerbose()) {
+		if (params.getVerbosity() != LibSVMParameters.VERBOSITY_FULL) {
 			setNoOutput();
 		}
 
@@ -173,7 +173,9 @@ public int compare(Kernel o1, Kernel o2) {
 			label = bestSetting.getLabel();
 		} 
 
-		System.out.println("Trained SVM for " + label + ", with C: " + bestC + " and P: " + bestP);
+		if (params.getVerbosity() == LibSVMParameters.VERBOSITY_DEFAULT || params.getVerbosity() == LibSVMParameters.VERBOSITY_FULL) {
+			System.out.println("Trained SVM for " + label + ", with C: " + bestC + " and P: " + bestP);
+		}
 		return model;
 	}
 

mustard-learners/src/main/java/org/data2semantics/mustard/learners/libsvm/LibSVMParameters.java

@@ -22,11 +22,15 @@ public class LibSVMParameters implements Serializable {
 	public static final int EPSILON_SVR = svm_parameter.EPSILON_SVR;
 	public static final int NU_SVR = svm_parameter.NU_SVR;	
 
+	public static final int VERBOSITY_NONE = 0;
+	public static final int VERBOSITY_DEFAULT = 1;
+	public static final int VERBOSITY_FULL = 2;
+
 
 	private svm_parameter params;
 	private double[] itParams;
 	private double[] ps;
-	private boolean verbose;
+	private int verbose;
 	private int numFolds;
 
 	private EvaluationFunction evalFunction;
@@ -62,7 +66,7 @@ public LibSVMParameters(int algorithm) {
 		// Weights, SVR epsilon, verbosity, evalFunction can be changed afterwards via setters
 		params.nr_weight = 0;
 		params.p = 0.1;
-		verbose = false;
+		verbose = VERBOSITY_DEFAULT;
 		ps = new double[1];
 		ps[0] = 0.1;
 
@@ -123,11 +127,11 @@ public double[] getItParams() {
 		return itParams;
 	}
 
-	public void setVerbose(boolean verbose) {
-		this.verbose = verbose;
+	public void setVerbosity(int verbosity) {
+		this.verbose = verbosity;
 	}
 
-	public boolean isVerbose() {
+	public int getVerbosity() {
 		return verbose;
 	}