iSOUP-Tree integration files (#253)

moa/src/main/java/moa/classifiers/multilabel/core/splitcriteria/ICVarianceReduction.java

@@ -33,14 +33,14 @@ public double[] getBranchSplitVarianceOutput(DoubleVector[] postSplitDists) {
 
 	}
 
-	private double computeVariance(double n, double sum, double squares) {
+	protected double computeVariance(double n, double sum, double squares) {
 		if (n > 1) {
 			return (squares - sum * sum / n) / (n - 1);
 		}
 		return 0;
 	}
 
-	private double computeVariance(DoubleVector v) {
+	protected double computeVariance(DoubleVector v) {
 		return computeVariance(v.getValue(0), v.getValue(1), v.getValue(2));
 	}
 

moa/src/main/java/moa/classifiers/multilabel/core/splitcriteria/PCTWeightedICVarianceReduction.java

@@ -0,0 +1,91 @@
+package moa.classifiers.multilabel.core.splitcriteria;
+
+import moa.core.DoubleVector;
+
+public class PCTWeightedICVarianceReduction extends WeightedICVarianceReduction {
+
+	private static final long serialVersionUID = 1L;
+
+	public DoubleVector inputWeights;
+
+	public double targetInputTradeoff; // \in [0, 1]
+
+	public PCTWeightedICVarianceReduction(DoubleVector targetWeights, DoubleVector inputWeights,
+			double targetInputTradeoff) {
+		super(targetWeights);
+		this.inputWeights = inputWeights;
+		this.targetInputTradeoff = targetInputTradeoff;
+	}
+
+	public double getMeritOfSplit(DoubleVector[] preSplitTargetDist, DoubleVector[][] postSplitTargetDists,
+			DoubleVector[] preSplitInputDist, DoubleVector[][] postSplitInputDists) {
+		double targetError = 0;
+		double inputError = 0;
+		int numTargets = preSplitTargetDist.length;
+		int numInputs = preSplitInputDist.length;
+		for (int i = 0; i < numTargets; i++) {
+			if (postSplitTargetDists[i][0].getValue(0) == 0 || postSplitTargetDists[i][1].getValue(0) == 0)
+				// If the split would result in a leaf without labeled instances, discard it
+				return Double.NaN;
+			targetError += weights.getValue(i)
+					* getMeritOfSplitForAttribute(preSplitTargetDist[i], postSplitTargetDists[i]);
+		}
+		for (int i = 0; i < numInputs; i++)
+			inputError += inputWeights.getValue(i)
+					* getMeritOfSplitForAttribute(preSplitInputDist[i], postSplitInputDists[i]);
+
+		return targetInputTradeoff * targetError / weights.sumOfValues()
+				+ (1 - targetInputTradeoff) * inputError / inputWeights.sumOfValues();
+	}
+
+	//@Override
+	protected double getMeritOfSplitForAttribute(DoubleVector preSplitDist, DoubleVector[] postSplitDists) {
+		double merit = 0;
+		// count number of branches with weightSeen higher than threshold
+		int count = 0;
+		for (int i = 0; i < postSplitDists.length; i++)
+			if (postSplitDists[i].getValue(0) >= 0.05 * preSplitDist.getValue(0))
+				count = count + 1;
+		// Consider split if all branches have required weight seen
+		if (count == postSplitDists.length) {
+			double varPreSplit = computeVariance(preSplitDist);
+			double sumVarPostSplit = 0;
+			double weightTotal = 0;
+			for (int i = 0; i < postSplitDists.length; i++) {
+				weightTotal += postSplitDists[i].getValue(0);
+			}
+			double[] variances = getBranchSplitVarianceOutput(postSplitDists);
+			for (int i = 0; i < variances.length; i++)
+				if (postSplitDists[i].getValue(0) > 0)
+					sumVarPostSplit += (postSplitDists[i].getValue(0) / weightTotal * variances[i]); // weight variance
+			if (varPreSplit > 0.0)
+				merit = 1 - sumVarPostSplit / varPreSplit;
+			else
+				merit = 0.0;
+		}
+		/*
+		 * if(merit<0 || merit>1) System.out.println("out of range");
+		 */
+		return merit;
+	}
+
+//	private double computeVariance(double n, double sum, double squares) {
+//		if (n > 1) {
+//			return (squares - sum * sum / n) / (n - 1);
+//		}
+//		return 0;
+//	}
+
+//	private double computeVariance(DoubleVector v) {
+//		return computeVariance(v.getValue(0), v.getValue(1), v.getValue(2));
+//	}
+
+	public double getMeritOfSplit(DoubleVector[] preSplitTargetDist, DoubleVector[] preSplitInputDist,
+			DoubleVector[][] postSplitTargetDists, DoubleVector[][] postSplitInputDists) {
+		double error = 0;
+		int numOutputs = preSplitTargetDist.length;
+		for (int i = 0; i < numOutputs; i++)
+			error += weights.getValue(i) * getMeritOfSplitForOutput(preSplitTargetDist, postSplitTargetDists, i);
+		return error / weights.sumOfValues();
+	}
+}

moa/src/main/java/moa/classifiers/multilabel/core/splitcriteria/WeightedICVarianceReduction.java

@@ -0,0 +1,49 @@
+package moa.classifiers.multilabel.core.splitcriteria;
+
+import moa.core.DoubleVector;
+import moa.core.ObjectRepository;
+import moa.tasks.TaskMonitor;
+
+/**
+ * Weighted intra cluster variance reduction split criterion
+ * 
+ * @author Aljaž Osojnik <[email protected]>
+ *
+ */
+
+public class WeightedICVarianceReduction extends ICVarianceReduction {
+
+	/**
+	 *
+	 */
+	private static final long serialVersionUID = 1L;
+
+	public DoubleVector weights;
+
+	public WeightedICVarianceReduction(DoubleVector targetWeights) {
+		super();
+		this.weights = targetWeights;
+	}
+
+	@Override
+	public double getMeritOfSplit(DoubleVector[] preSplitDist, DoubleVector[][] postSplitDists) {
+		double error = 0;
+		int numOutputs = preSplitDist.length;
+		for (int i = 0; i < numOutputs; i++)
+			// TEMPORARY
+			// error += (1 / weights.getValue(i)) *
+			// getMeritOfSplitForOutput(preSplitDist,postSplitDists,i);
+			error += weights.getValue(i) * getMeritOfSplitForOutput(preSplitDist, postSplitDists, i);
+		return error / weights.sumOfValues();
+	}
+
+	@Override
+	public void getDescription(StringBuilder sb, int indent) {
+
+	}
+
+	@Override
+	protected void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) {
+	}
+
+}