Java tutorial
/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * REPRandomTree.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ //package weka.classifiers.trees; import weka.classifiers.Classifier; import weka.classifiers.AbstractClassifier; import weka.classifiers.Sourcable; import weka.classifiers.rules.ZeroR; import weka.core.AdditionalMeasureProducer; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.ContingencyTables; import weka.core.Drawable; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.WeightedInstancesHandler; import weka.core.Capabilities.Capability; import java.io.Serializable; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** <!-- globalinfo-start --> * Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5). * <p/> <!-- globalinfo-end --> * <!-- options-start --> * Valid options are: <p/> * * <pre> -M <minimum number of instances> * Set minimum number of instances per leaf (default 2).</pre> * * <pre> -V <minimum variance for split> * Set minimum numeric class variance proportion * of train variance for split (default 1e-3).</pre> * * <pre> -N <number of folds> * Number of folds for reduced error pruning (default 3).</pre> * * <pre> -S <seed> * Seed for random data shuffling (default 1).</pre> * * <pre> -P * No pruning.</pre> * * <pre> -L * Maximum tree depth (default -1, no maximum)</pre> * <!-- options-end --> * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 6954 $ */ public class REPRandomTree extends AbstractClassifier implements OptionHandler, WeightedInstancesHandler, Drawable, AdditionalMeasureProducer, Sourcable { /** for serialization */ static final long serialVersionUID = -9216785998198681299L; /** ZeroR model that is used if no attributes are present. */ protected ZeroR m_zeroR; /** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Fast decision tree learner. Builds a decision/regression tree using " + "information gain/variance and prunes it using reduced-error pruning " + "(with backfitting). Only sorts values for numeric attributes " + "once. Missing values are dealt with by splitting the corresponding " + "instances into pieces (i.e. as in C4.5)."; } /** An inner class for building and storing the tree structure */ protected class Tree implements Serializable, RevisionHandler { /** for serialization */ static final long serialVersionUID = -1635481717888437935L; /** The header information (for printing the tree). */ protected Instances m_Info = null; /** The subtrees of this tree. */ protected Tree[] m_Successors; /** The attribute to split on. */ protected int m_Attribute = -1; /** The split point. */ protected double m_SplitPoint = Double.NaN; /** The proportions of training instances going down each branch. */ protected double[] m_Prop = null; /** Class probabilities from the training data in the nominal case. Holds the mean in the numeric case. */ protected double[] m_ClassProbs = null; /** The (unnormalized) class distribution in the nominal case. Holds the sum of squared errors and the weight in the numeric case. */ protected double[] m_Distribution = null; /** Class distribution of hold-out set at node in the nominal case. Straight sum of weights plus sum of weighted targets in the numeric case (i.e. array has only two elements). */ protected double[] m_HoldOutDist = null; /** The hold-out error of the node. The number of miss-classified instances in the nominal case, the sum of squared errors in the numeric case. */ protected double m_HoldOutError = 0; /** * Computes class distribution of an instance using the tree. * * @param instance the instance to compute the distribution for * @return the distribution * @throws Exception if computation fails */ protected double[] distributionForInstance(Instance instance) throws Exception { double[] returnedDist = null; if (m_Attribute > -1) { // Node is not a leaf if (instance.isMissing(m_Attribute)) { // Value is missing returnedDist = new double[m_Info.numClasses()]; // Split instance up for (int i = 0; i < m_Successors.length; i++) { double[] help = m_Successors[i].distributionForInstance(instance); if (help != null) { for (int j = 0; j < help.length; j++) { returnedDist[j] += m_Prop[i] * help[j]; } } } } else if (m_Info.attribute(m_Attribute).isNominal()) { // For nominal attributes returnedDist = m_Successors[(int) instance.value(m_Attribute)] .distributionForInstance(instance); } else { // For numeric attributes if (instance.value(m_Attribute) < m_SplitPoint) { returnedDist = m_Successors[0].distributionForInstance(instance); } else { returnedDist = m_Successors[1].distributionForInstance(instance); } } } if ((m_Attribute == -1) || (returnedDist == null)) { // Node is a leaf or successor is empty return m_ClassProbs; } else { return returnedDist; } } /** * Returns a string containing java source code equivalent to the test * made at this node. The instance being tested is called "i". This * routine assumes to be called in the order of branching, enabling us to * set the >= condition test (the last one) of a numeric splitpoint * to just "true" (because being there in the flow implies that the * previous less-than test failed). * * @param index index of the value tested * @return a value of type 'String' */ public final String sourceExpression(int index) { StringBuffer expr = null; if (index < 0) { return "i[" + m_Attribute + "] == null"; } if (m_Info.attribute(m_Attribute).isNominal()) { expr = new StringBuffer("i["); expr.append(m_Attribute).append("]"); expr.append(".equals(\"").append(m_Info.attribute(m_Attribute).value(index)).append("\")"); } else { expr = new StringBuffer(""); if (index == 0) { expr.append("((Double)i[").append(m_Attribute).append("]).doubleValue() < ") .append(m_SplitPoint); } else { expr.append("true"); } } return expr.toString(); } /** * Returns source code for the tree as if-then statements. The * class is assigned to variable "p", and assumes the tested * instance is named "i". The results are returned as two stringbuffers: * a section of code for assignment of the class, and a section of * code containing support code (eg: other support methods). * <p/> * TODO: If the outputted source code encounters a missing value * for the evaluated attribute, it stops branching and uses the * class distribution of the current node to decide the return value. * This is unlike the behaviour of distributionForInstance(). * * @param className the classname that this static classifier has * @param parent parent node of the current node * @return an array containing two stringbuffers, the first string containing * assignment code, and the second containing source for support code. * @throws Exception if something goes wrong */ public StringBuffer[] toSource(String className, Tree parent) throws Exception { StringBuffer[] result = new StringBuffer[2]; double[] currentProbs; if (m_ClassProbs == null) currentProbs = parent.m_ClassProbs; else currentProbs = m_ClassProbs; long printID = nextID(); // Is this a leaf? if (m_Attribute == -1) { result[0] = new StringBuffer(" p = "); if (m_Info.classAttribute().isNumeric()) result[0].append(currentProbs[0]); else { result[0].append(Utils.maxIndex(currentProbs)); } result[0].append(";\n"); result[1] = new StringBuffer(""); } else { StringBuffer text = new StringBuffer(""); StringBuffer atEnd = new StringBuffer(""); text.append(" static double N").append(Integer.toHexString(this.hashCode()) + printID) .append("(Object []i) {\n").append(" double p = Double.NaN;\n"); text.append(" /* " + m_Info.attribute(m_Attribute).name() + " */\n"); // Missing attribute? text.append(" if (" + this.sourceExpression(-1) + ") {\n").append(" p = "); if (m_Info.classAttribute().isNumeric()) text.append(currentProbs[0] + ";\n"); else text.append(Utils.maxIndex(currentProbs) + ";\n"); text.append(" } "); // Branching of the tree for (int i = 0; i < m_Successors.length; i++) { text.append("else if (" + this.sourceExpression(i) + ") {\n"); // Is the successor a leaf? if (m_Successors[i].m_Attribute == -1) { double[] successorProbs = m_Successors[i].m_ClassProbs; if (successorProbs == null) successorProbs = m_ClassProbs; text.append(" p = "); if (m_Info.classAttribute().isNumeric()) { text.append(successorProbs[0] + ";\n"); } else { text.append(Utils.maxIndex(successorProbs) + ";\n"); } } else { StringBuffer[] sub = m_Successors[i].toSource(className, this); text.append("" + sub[0]); atEnd.append("" + sub[1]); } text.append(" } "); if (i == m_Successors.length - 1) { text.append("\n"); } } text.append(" return p;\n }\n"); result[0] = new StringBuffer(" p = " + className + ".N"); result[0].append(Integer.toHexString(this.hashCode()) + printID).append("(i);\n"); result[1] = text.append("" + atEnd); } return result; } /** * Outputs one node for graph. * * @param text the buffer to append the output to * @param num the current node id * @param parent the parent of the nodes * @return the next node id * @throws Exception if something goes wrong */ protected int toGraph(StringBuffer text, int num, Tree parent) throws Exception { num++; if (m_Attribute == -1) { text.append("N" + Integer.toHexString(Tree.this.hashCode()) + " [label=\"" + num + leafString(parent) + "\"" + "shape=box]\n"); } else { text.append("N" + Integer.toHexString(Tree.this.hashCode()) + " [label=\"" + num + ": " + m_Info.attribute(m_Attribute).name() + "\"]\n"); for (int i = 0; i < m_Successors.length; i++) { text.append("N" + Integer.toHexString(Tree.this.hashCode()) + "->" + "N" + Integer.toHexString(m_Successors[i].hashCode()) + " [label=\""); if (m_Info.attribute(m_Attribute).isNumeric()) { if (i == 0) { text.append(" < " + Utils.doubleToString(m_SplitPoint, 2)); } else { text.append(" >= " + Utils.doubleToString(m_SplitPoint, 2)); } } else { text.append(" = " + m_Info.attribute(m_Attribute).value(i)); } text.append("\"]\n"); num = m_Successors[i].toGraph(text, num, this); } } return num; } /** * Outputs description of a leaf node. * * @param parent the parent of the node * @return the description of the node * @throws Exception if generation fails */ protected String leafString(Tree parent) throws Exception { if (m_Info.classAttribute().isNumeric()) { double classMean; if (m_ClassProbs == null) { classMean = parent.m_ClassProbs[0]; } else { classMean = m_ClassProbs[0]; } StringBuffer buffer = new StringBuffer(); buffer.append(" : " + Utils.doubleToString(classMean, 2)); double avgError = 0; if (m_Distribution[1] > 0) { avgError = m_Distribution[0] / m_Distribution[1]; } buffer.append(" (" + Utils.doubleToString(m_Distribution[1], 2) + "/" + Utils.doubleToString(avgError, 2) + ")"); avgError = 0; if (m_HoldOutDist[0] > 0) { avgError = m_HoldOutError / m_HoldOutDist[0]; } buffer.append(" [" + Utils.doubleToString(m_HoldOutDist[0], 2) + "/" + Utils.doubleToString(avgError, 2) + "]"); return buffer.toString(); } else { int maxIndex; // start change for class output by Sam Barrett if (m_ClassProbs == null) { maxIndex = Utils.maxIndex(parent.m_ClassProbs); String str = " : "; for (double prob : parent.m_ClassProbs) { str = str + Double.toString(prob) + " "; } return str; } else { maxIndex = Utils.maxIndex(m_ClassProbs); String str = " : "; for (double prob : m_ClassProbs) { str = str + Double.toString(prob) + " "; } return str; } //return " : " + m_Info.classAttribute().value(maxIndex) + //" (" + Utils.doubleToString(Utils.sum(m_Distribution), 2) + //"/" + //Utils.doubleToString((Utils.sum(m_Distribution) - //m_Distribution[maxIndex]), 2) + ")" + //" [" + Utils.doubleToString(Utils.sum(m_HoldOutDist), 2) + "/" + //Utils.doubleToString((Utils.sum(m_HoldOutDist) - //m_HoldOutDist[maxIndex]), 2) + "]"; // end change for class output by Sam Barrett } } /** * Recursively outputs the tree. * * @param level the current level * @param parent the current parent * @return the generated substree */ protected String toString(int level, Tree parent) { try { StringBuffer text = new StringBuffer(); if (m_Attribute == -1) { // Output leaf info return leafString(parent); } else if (m_Info.attribute(m_Attribute).isNominal()) { // For nominal attributes for (int i = 0; i < m_Successors.length; i++) { text.append("\n"); for (int j = 0; j < level; j++) { text.append("| "); } text.append(m_Info.attribute(m_Attribute).name() + " = " + m_Info.attribute(m_Attribute).value(i)); text.append(m_Successors[i].toString(level + 1, this)); } } else { // For numeric attributes text.append("\n"); for (int j = 0; j < level; j++) { text.append("| "); } text.append( m_Info.attribute(m_Attribute).name() + " < " + Utils.doubleToString(m_SplitPoint, 2)); text.append(m_Successors[0].toString(level + 1, this)); text.append("\n"); for (int j = 0; j < level; j++) { text.append("| "); } text.append( m_Info.attribute(m_Attribute).name() + " >= " + Utils.doubleToString(m_SplitPoint, 2)); text.append(m_Successors[1].toString(level + 1, this)); } return text.toString(); } catch (Exception e) { e.printStackTrace(); return "Decision tree: tree can't be printed"; } } /** * Recursively generates a tree. * * @param sortedIndices the sorted indices of the instances * @param weights the weights of the instances * @param data the data to work with * @param totalWeight * @param classProbs the class probabilities * @param header the header of the data * @param minNum the minimum number of instances in a leaf * @param minVariance * @param depth the current depth of the tree * @param maxDepth the maximum allowed depth of the tree * @throws Exception if generation fails */ protected void buildTree(int[][][] sortedIndices, double[][][] weights, Instances data, double totalWeight, double[] classProbs, Instances header, double minNum, double minVariance, int depth, int maxDepth, double featureFrac, Random random) throws Exception { // Store structure of dataset, set minimum number of instances // and make space for potential info from pruning data m_Info = header; if (data.classAttribute().isNumeric()) { m_HoldOutDist = new double[2]; } else { m_HoldOutDist = new double[data.numClasses()]; } // Make leaf if there are no training instances int helpIndex = 0; if (data.classIndex() == 0) { helpIndex = 1; } if (sortedIndices[0][helpIndex].length == 0) { if (data.classAttribute().isNumeric()) { m_Distribution = new double[2]; } else { m_Distribution = new double[data.numClasses()]; } m_ClassProbs = null; sortedIndices[0] = null; weights[0] = null; return; } double priorVar = 0; if (data.classAttribute().isNumeric()) { // Compute prior variance double totalSum = 0, totalSumSquared = 0, totalSumOfWeights = 0; for (int i = 0; i < sortedIndices[0][helpIndex].length; i++) { Instance inst = data.instance(sortedIndices[0][helpIndex][i]); totalSum += inst.classValue() * weights[0][helpIndex][i]; totalSumSquared += inst.classValue() * inst.classValue() * weights[0][helpIndex][i]; totalSumOfWeights += weights[0][helpIndex][i]; } priorVar = singleVariance(totalSum, totalSumSquared, totalSumOfWeights); } // Check if node doesn't contain enough instances, is pure // or the maximum tree depth is reached m_ClassProbs = new double[classProbs.length]; System.arraycopy(classProbs, 0, m_ClassProbs, 0, classProbs.length); if ((totalWeight < (2 * minNum)) || // Nominal case (data.classAttribute().isNominal() && Utils.eq(m_ClassProbs[Utils.maxIndex(m_ClassProbs)], Utils.sum(m_ClassProbs))) || // Numeric case (data.classAttribute().isNumeric() && ((priorVar / totalWeight) < minVariance)) || // Check tree depth ((m_MaxDepth >= 0) && (depth >= maxDepth))) { // Make leaf m_Attribute = -1; if (data.classAttribute().isNominal()) { // Nominal case m_Distribution = new double[m_ClassProbs.length]; for (int i = 0; i < m_ClassProbs.length; i++) { m_Distribution[i] = m_ClassProbs[i]; } doSmoothing(); Utils.normalize(m_ClassProbs); } else { // Numeric case m_Distribution = new double[2]; m_Distribution[0] = priorVar; m_Distribution[1] = totalWeight; } sortedIndices[0] = null; weights[0] = null; return; } // Compute class distributions and value of splitting // criterion for each attribute double[] vals = new double[data.numAttributes()]; double[][][] dists = new double[data.numAttributes()][0][0]; double[][] props = new double[data.numAttributes()][0]; double[][] totalSubsetWeights = new double[data.numAttributes()][0]; double[] splits = new double[data.numAttributes()]; if (data.classAttribute().isNominal()) { // Nominal case for (int i = 0; i < data.numAttributes(); i++) { if (i != data.classIndex()) { splits[i] = distribution(props, dists, i, sortedIndices[0][i], weights[0][i], totalSubsetWeights, data); vals[i] = gain(dists[i], priorVal(dists[i])); } } } else { // Numeric case for (int i = 0; i < data.numAttributes(); i++) { if (i != data.classIndex()) { splits[i] = numericDistribution(props, dists, i, sortedIndices[0][i], weights[0][i], totalSubsetWeights, data, vals); } } } // randomly remove features for (int i = 0; i < data.numAttributes(); i++) { if (i != data.classIndex()) { if (random.nextDouble() > featureFrac) { // remove this feature vals[i] = Double.NEGATIVE_INFINITY; } } } // Find best attribute m_Attribute = Utils.maxIndex(vals); int numAttVals = dists[m_Attribute].length; // Check if there are at least two subsets with // required minimum number of instances int count = 0; for (int i = 0; i < numAttVals; i++) { if (totalSubsetWeights[m_Attribute][i] >= minNum) { count++; } if (count > 1) { break; } } // Any useful split found? if (Utils.gr(vals[m_Attribute], 0) && (count > 1)) { // Set split point, proportions, and temp arrays m_SplitPoint = splits[m_Attribute]; m_Prop = props[m_Attribute]; double[][] attSubsetDists = dists[m_Attribute]; double[] attTotalSubsetWeights = totalSubsetWeights[m_Attribute]; // Release some memory before proceeding further vals = null; dists = null; props = null; totalSubsetWeights = null; splits = null; // Split data int[][][][] subsetIndices = new int[numAttVals][1][data.numAttributes()][0]; double[][][][] subsetWeights = new double[numAttVals][1][data.numAttributes()][0]; splitData(subsetIndices, subsetWeights, m_Attribute, m_SplitPoint, sortedIndices[0], weights[0], data); // Release memory sortedIndices[0] = null; weights[0] = null; // Build successors m_Successors = new Tree[numAttVals]; for (int i = 0; i < numAttVals; i++) { m_Successors[i] = new Tree(); m_Successors[i].buildTree(subsetIndices[i], subsetWeights[i], data, attTotalSubsetWeights[i], attSubsetDists[i], header, minNum, minVariance, depth + 1, maxDepth, featureFrac, random); // Release as much memory as we can attSubsetDists[i] = null; } } else { // Make leaf m_Attribute = -1; sortedIndices[0] = null; weights[0] = null; } // Normalize class counts if (data.classAttribute().isNominal()) { m_Distribution = new double[m_ClassProbs.length]; for (int i = 0; i < m_ClassProbs.length; i++) { m_Distribution[i] = m_ClassProbs[i]; } doSmoothing(); Utils.normalize(m_ClassProbs); } else { m_Distribution = new double[2]; m_Distribution[0] = priorVar; m_Distribution[1] = totalWeight; } } /** * Smoothes class probabilities stored at node. */ protected void doSmoothing() { double val = m_InitialCount; if (m_SpreadInitialCount) { val /= (double) m_ClassProbs.length; } for (int i = 0; i < m_ClassProbs.length; i++) { m_ClassProbs[i] += val; } } /** * Computes size of the tree. * * @return the number of nodes */ protected int numNodes() { if (m_Attribute == -1) { return 1; } else { int size = 1; for (int i = 0; i < m_Successors.length; i++) { size += m_Successors[i].numNodes(); } return size; } } /** * Splits instances into subsets. * * @param subsetIndices the sorted indices in the subset * @param subsetWeights the weights of the subset * @param att the attribute index * @param splitPoint the split point for numeric attributes * @param sortedIndices the sorted indices of the whole set * @param weights the weights of the whole set * @param data the data to work with * @throws Exception if something goes wrong */ protected void splitData(int[][][][] subsetIndices, double[][][][] subsetWeights, int att, double splitPoint, int[][] sortedIndices, double[][] weights, Instances data) throws Exception { int j; int[] num; // For each attribute for (int i = 0; i < data.numAttributes(); i++) { if (i != data.classIndex()) { if (data.attribute(att).isNominal()) { // For nominal attributes num = new int[data.attribute(att).numValues()]; for (int k = 0; k < num.length; k++) { subsetIndices[k][0][i] = new int[sortedIndices[i].length]; subsetWeights[k][0][i] = new double[sortedIndices[i].length]; } for (j = 0; j < sortedIndices[i].length; j++) { Instance inst = data.instance(sortedIndices[i][j]); if (inst.isMissing(att)) { // Split instance up for (int k = 0; k < num.length; k++) { if (m_Prop[k] > 0) { subsetIndices[k][0][i][num[k]] = sortedIndices[i][j]; subsetWeights[k][0][i][num[k]] = m_Prop[k] * weights[i][j]; num[k]++; } } } else { int subset = (int) inst.value(att); subsetIndices[subset][0][i][num[subset]] = sortedIndices[i][j]; subsetWeights[subset][0][i][num[subset]] = weights[i][j]; num[subset]++; } } } else { // For numeric attributes num = new int[2]; for (int k = 0; k < 2; k++) { subsetIndices[k][0][i] = new int[sortedIndices[i].length]; subsetWeights[k][0][i] = new double[weights[i].length]; } for (j = 0; j < sortedIndices[i].length; j++) { Instance inst = data.instance(sortedIndices[i][j]); if (inst.isMissing(att)) { // Split instance up for (int k = 0; k < num.length; k++) { if (m_Prop[k] > 0) { subsetIndices[k][0][i][num[k]] = sortedIndices[i][j]; subsetWeights[k][0][i][num[k]] = m_Prop[k] * weights[i][j]; num[k]++; } } } else { int subset = (inst.value(att) < splitPoint) ? 0 : 1; subsetIndices[subset][0][i][num[subset]] = sortedIndices[i][j]; subsetWeights[subset][0][i][num[subset]] = weights[i][j]; num[subset]++; } } } // Trim arrays for (int k = 0; k < num.length; k++) { int[] copy = new int[num[k]]; System.arraycopy(subsetIndices[k][0][i], 0, copy, 0, num[k]); subsetIndices[k][0][i] = copy; double[] copyWeights = new double[num[k]]; System.arraycopy(subsetWeights[k][0][i], 0, copyWeights, 0, num[k]); subsetWeights[k][0][i] = copyWeights; } } } } /** * Computes class distribution for an attribute. * * @param props * @param dists * @param att the attribute index * @param sortedIndices the sorted indices of the instances * @param weights the weights of the instances * @param subsetWeights the weights of the subset * @param data the data to work with * @return the split point * @throws Exception if computation fails */ protected double distribution(double[][] props, double[][][] dists, int att, int[] sortedIndices, double[] weights, double[][] subsetWeights, Instances data) throws Exception { double splitPoint = Double.NaN; Attribute attribute = data.attribute(att); double[][] dist = null; int i; if (attribute.isNominal()) { // For nominal attributes dist = new double[attribute.numValues()][data.numClasses()]; for (i = 0; i < sortedIndices.length; i++) { Instance inst = data.instance(sortedIndices[i]); if (inst.isMissing(att)) { break; } dist[(int) inst.value(att)][(int) inst.classValue()] += weights[i]; } } else { // For numeric attributes double[][] currDist = new double[2][data.numClasses()]; dist = new double[2][data.numClasses()]; // Move all instances into second subset for (int j = 0; j < sortedIndices.length; j++) { Instance inst = data.instance(sortedIndices[j]); if (inst.isMissing(att)) { break; } currDist[1][(int) inst.classValue()] += weights[j]; } double priorVal = priorVal(currDist); System.arraycopy(currDist[1], 0, dist[1], 0, dist[1].length); // Try all possible split points double currSplit = data.instance(sortedIndices[0]).value(att); double currVal, bestVal = -Double.MAX_VALUE; for (i = 0; i < sortedIndices.length; i++) { Instance inst = data.instance(sortedIndices[i]); if (inst.isMissing(att)) { break; } if (inst.value(att) > currSplit) { currVal = gain(currDist, priorVal); if (currVal > bestVal) { bestVal = currVal; splitPoint = (inst.value(att) + currSplit) / 2.0; for (int j = 0; j < currDist.length; j++) { System.arraycopy(currDist[j], 0, dist[j], 0, dist[j].length); } } } currSplit = inst.value(att); currDist[0][(int) inst.classValue()] += weights[i]; currDist[1][(int) inst.classValue()] -= weights[i]; } } // Compute weights props[att] = new double[dist.length]; for (int k = 0; k < props[att].length; k++) { props[att][k] = Utils.sum(dist[k]); } if (!(Utils.sum(props[att]) > 0)) { for (int k = 0; k < props[att].length; k++) { props[att][k] = 1.0 / (double) props[att].length; } } else { Utils.normalize(props[att]); } // Distribute counts while (i < sortedIndices.length) { Instance inst = data.instance(sortedIndices[i]); for (int j = 0; j < dist.length; j++) { dist[j][(int) inst.classValue()] += props[att][j] * weights[i]; } i++; } // Compute subset weights subsetWeights[att] = new double[dist.length]; for (int j = 0; j < dist.length; j++) { subsetWeights[att][j] += Utils.sum(dist[j]); } // Return distribution and split point dists[att] = dist; return splitPoint; } /** * Computes class distribution for an attribute. * * @param props * @param dists * @param att the attribute index * @param sortedIndices the sorted indices of the instances * @param weights the weights of the instances * @param subsetWeights the weights of the subset * @param data the data to work with * @param vals * @return the split point * @throws Exception if computation fails */ protected double numericDistribution(double[][] props, double[][][] dists, int att, int[] sortedIndices, double[] weights, double[][] subsetWeights, Instances data, double[] vals) throws Exception { double splitPoint = Double.NaN; Attribute attribute = data.attribute(att); double[][] dist = null; double[] sums = null; double[] sumSquared = null; double[] sumOfWeights = null; double totalSum = 0, totalSumSquared = 0, totalSumOfWeights = 0; int i; if (attribute.isNominal()) { // For nominal attributes sums = new double[attribute.numValues()]; sumSquared = new double[attribute.numValues()]; sumOfWeights = new double[attribute.numValues()]; int attVal; for (i = 0; i < sortedIndices.length; i++) { Instance inst = data.instance(sortedIndices[i]); if (inst.isMissing(att)) { break; } attVal = (int) inst.value(att); sums[attVal] += inst.classValue() * weights[i]; sumSquared[attVal] += inst.classValue() * inst.classValue() * weights[i]; sumOfWeights[attVal] += weights[i]; } totalSum = Utils.sum(sums); totalSumSquared = Utils.sum(sumSquared); totalSumOfWeights = Utils.sum(sumOfWeights); } else { // For numeric attributes sums = new double[2]; sumSquared = new double[2]; sumOfWeights = new double[2]; double[] currSums = new double[2]; double[] currSumSquared = new double[2]; double[] currSumOfWeights = new double[2]; // Move all instances into second subset for (int j = 0; j < sortedIndices.length; j++) { Instance inst = data.instance(sortedIndices[j]); if (inst.isMissing(att)) { break; } currSums[1] += inst.classValue() * weights[j]; currSumSquared[1] += inst.classValue() * inst.classValue() * weights[j]; currSumOfWeights[1] += weights[j]; } totalSum = currSums[1]; totalSumSquared = currSumSquared[1]; totalSumOfWeights = currSumOfWeights[1]; sums[1] = currSums[1]; sumSquared[1] = currSumSquared[1]; sumOfWeights[1] = currSumOfWeights[1]; // Try all possible split points double currSplit = data.instance(sortedIndices[0]).value(att); double currVal, bestVal = Double.MAX_VALUE; for (i = 0; i < sortedIndices.length; i++) { Instance inst = data.instance(sortedIndices[i]); if (inst.isMissing(att)) { break; } if (inst.value(att) > currSplit) { currVal = variance(currSums, currSumSquared, currSumOfWeights); if (currVal < bestVal) { bestVal = currVal; splitPoint = (inst.value(att) + currSplit) / 2.0; for (int j = 0; j < 2; j++) { sums[j] = currSums[j]; sumSquared[j] = currSumSquared[j]; sumOfWeights[j] = currSumOfWeights[j]; } } } currSplit = inst.value(att); double classVal = inst.classValue() * weights[i]; double classValSquared = inst.classValue() * classVal; currSums[0] += classVal; currSumSquared[0] += classValSquared; currSumOfWeights[0] += weights[i]; currSums[1] -= classVal; currSumSquared[1] -= classValSquared; currSumOfWeights[1] -= weights[i]; } } // Compute weights props[att] = new double[sums.length]; for (int k = 0; k < props[att].length; k++) { props[att][k] = sumOfWeights[k]; } if (!(Utils.sum(props[att]) > 0)) { for (int k = 0; k < props[att].length; k++) { props[att][k] = 1.0 / (double) props[att].length; } } else { Utils.normalize(props[att]); } // Distribute counts for missing values while (i < sortedIndices.length) { Instance inst = data.instance(sortedIndices[i]); for (int j = 0; j < sums.length; j++) { sums[j] += props[att][j] * inst.classValue() * weights[i]; sumSquared[j] += props[att][j] * inst.classValue() * inst.classValue() * weights[i]; sumOfWeights[j] += props[att][j] * weights[i]; } totalSum += inst.classValue() * weights[i]; totalSumSquared += inst.classValue() * inst.classValue() * weights[i]; totalSumOfWeights += weights[i]; i++; } // Compute final distribution dist = new double[sums.length][data.numClasses()]; for (int j = 0; j < sums.length; j++) { if (sumOfWeights[j] > 0) { dist[j][0] = sums[j] / sumOfWeights[j]; } else { dist[j][0] = totalSum / totalSumOfWeights; } } // Compute variance gain double priorVar = singleVariance(totalSum, totalSumSquared, totalSumOfWeights); double var = variance(sums, sumSquared, sumOfWeights); double gain = priorVar - var; // Return distribution and split point subsetWeights[att] = sumOfWeights; dists[att] = dist; vals[att] = gain; return splitPoint; } /** * Computes variance for subsets. * * @param s * @param sS * @param sumOfWeights * @return the variance */ protected double variance(double[] s, double[] sS, double[] sumOfWeights) { double var = 0; for (int i = 0; i < s.length; i++) { if (sumOfWeights[i] > 0) { var += singleVariance(s[i], sS[i], sumOfWeights[i]); } } return var; } /** * Computes the variance for a single set * * @param s * @param sS * @param weight the weight * @return the variance */ protected double singleVariance(double s, double sS, double weight) { return sS - ((s * s) / weight); } /** * Computes value of splitting criterion before split. * * @param dist * @return the splitting criterion */ protected double priorVal(double[][] dist) { return ContingencyTables.entropyOverColumns(dist); } /** * Computes value of splitting criterion after split. * * @param dist * @param priorVal the splitting criterion * @return the gain after splitting */ protected double gain(double[][] dist, double priorVal) { return priorVal - ContingencyTables.entropyConditionedOnRows(dist); } /** * Prunes the tree using the hold-out data (bottom-up). * * @return the error * @throws Exception if pruning fails for some reason */ protected double reducedErrorPrune() throws Exception { // Is node leaf ? if (m_Attribute == -1) { return m_HoldOutError; } // Prune all sub trees double errorTree = 0; for (int i = 0; i < m_Successors.length; i++) { errorTree += m_Successors[i].reducedErrorPrune(); } // Replace sub tree with leaf if error doesn't get worse if (errorTree >= m_HoldOutError) { m_Attribute = -1; m_Successors = null; return m_HoldOutError; } else { return errorTree; } } /** * Inserts hold-out set into tree. * * @param data the data to insert * @throws Exception if something goes wrong */ protected void insertHoldOutSet(Instances data) throws Exception { for (int i = 0; i < data.numInstances(); i++) { insertHoldOutInstance(data.instance(i), data.instance(i).weight(), this); } } /** * Inserts an instance from the hold-out set into the tree. * * @param inst the instance to insert * @param weight the weight of the instance * @param parent the parent of the node * @throws Exception if insertion fails */ protected void insertHoldOutInstance(Instance inst, double weight, Tree parent) throws Exception { // Insert instance into hold-out class distribution if (inst.classAttribute().isNominal()) { // Nominal case m_HoldOutDist[(int) inst.classValue()] += weight; int predictedClass = 0; if (m_ClassProbs == null) { predictedClass = Utils.maxIndex(parent.m_ClassProbs); } else { predictedClass = Utils.maxIndex(m_ClassProbs); } if (predictedClass != (int) inst.classValue()) { m_HoldOutError += weight; } } else { // Numeric case m_HoldOutDist[0] += weight; m_HoldOutDist[1] += weight * inst.classValue(); double diff = 0; if (m_ClassProbs == null) { diff = parent.m_ClassProbs[0] - inst.classValue(); } else { diff = m_ClassProbs[0] - inst.classValue(); } m_HoldOutError += diff * diff * weight; } // The process is recursive if (m_Attribute != -1) { // If node is not a leaf if (inst.isMissing(m_Attribute)) { // Distribute instance for (int i = 0; i < m_Successors.length; i++) { if (m_Prop[i] > 0) { m_Successors[i].insertHoldOutInstance(inst, weight * m_Prop[i], this); } } } else { if (m_Info.attribute(m_Attribute).isNominal()) { // Treat nominal attributes m_Successors[(int) inst.value(m_Attribute)].insertHoldOutInstance(inst, weight, this); } else { // Treat numeric attributes if (inst.value(m_Attribute) < m_SplitPoint) { m_Successors[0].insertHoldOutInstance(inst, weight, this); } else { m_Successors[1].insertHoldOutInstance(inst, weight, this); } } } } } /** * Backfits data from holdout set. * * @throws Exception if insertion fails */ protected void backfitHoldOutSet() throws Exception { // Insert instance into hold-out class distribution if (m_Info.classAttribute().isNominal()) { // Nominal case if (m_ClassProbs == null) { m_ClassProbs = new double[m_Info.numClasses()]; } System.arraycopy(m_Distribution, 0, m_ClassProbs, 0, m_Info.numClasses()); for (int i = 0; i < m_HoldOutDist.length; i++) { m_ClassProbs[i] += m_HoldOutDist[i]; } if (Utils.sum(m_ClassProbs) > 0) { doSmoothing(); Utils.normalize(m_ClassProbs); } else { m_ClassProbs = null; } } else { // Numeric case double sumOfWeightsTrainAndHoldout = m_Distribution[1] + m_HoldOutDist[0]; if (sumOfWeightsTrainAndHoldout <= 0) { return; } if (m_ClassProbs == null) { m_ClassProbs = new double[1]; } else { m_ClassProbs[0] *= m_Distribution[1]; } m_ClassProbs[0] += m_HoldOutDist[1]; m_ClassProbs[0] /= sumOfWeightsTrainAndHoldout; } // The process is recursive if (m_Attribute != -1) { for (int i = 0; i < m_Successors.length; i++) { m_Successors[i].backfitHoldOutSet(); } } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 6954 $"); } } /** The Tree object */ protected Tree m_Tree = null; /** Number of folds for reduced error pruning. */ protected int m_NumFolds = 3; /** Seed for random data shuffling. */ protected int m_Seed = 1; /** Don't prune */ protected boolean m_NoPruning = false; /** The minimum number of instances per leaf. */ protected double m_MinNum = 2; /** The minimum proportion of the total variance (over all the data) required for split. */ protected double m_MinVarianceProp = 1e-3; /** Upper bound on the tree depth */ protected int m_MaxDepth = -1; /** The initial class count */ protected double m_InitialCount = 0; /** Whether to spread initial count across all values */ protected boolean m_SpreadInitialCount = false; /** Fraction of time to consider a feature for splitting */ protected double m_FeatureFrac = 1.0; /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String noPruningTipText() { return "Whether pruning is performed."; } /** * Get the value of NoPruning. * * @return Value of NoPruning. */ public boolean getNoPruning() { return m_NoPruning; } /** * Set the value of NoPruning. * * @param newNoPruning Value to assign to NoPruning. */ public void setNoPruning(boolean newNoPruning) { m_NoPruning = newNoPruning; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String minNumTipText() { return "The minimum total weight of the instances in a leaf."; } /** * Get the value of MinNum. * * @return Value of MinNum. */ public double getMinNum() { return m_MinNum; } /** * Set the value of MinNum. * * @param newMinNum Value to assign to MinNum. */ public void setMinNum(double newMinNum) { m_MinNum = newMinNum; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String minVariancePropTipText() { return "The minimum proportion of the variance on all the data " + "that needs to be present at a node in order for splitting to " + "be performed in regression trees."; } /** * Get the value of MinVarianceProp. * * @return Value of MinVarianceProp. */ public double getMinVarianceProp() { return m_MinVarianceProp; } /** * Set the value of MinVarianceProp. * * @param newMinVarianceProp Value to assign to MinVarianceProp. */ public void setMinVarianceProp(double newMinVarianceProp) { m_MinVarianceProp = newMinVarianceProp; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String seedTipText() { return "The seed used for randomizing the data."; } /** * Get the value of Seed. * * @return Value of Seed. */ public int getSeed() { return m_Seed; } /** * Set the value of Seed. * * @param newSeed Value to assign to Seed. */ public void setSeed(int newSeed) { m_Seed = newSeed; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numFoldsTipText() { return "Determines the amount of data used for pruning. One fold is used for " + "pruning, the rest for growing the rules."; } /** * Get the value of NumFolds. * * @return Value of NumFolds. */ public int getNumFolds() { return m_NumFolds; } /** * Set the value of NumFolds. * * @param newNumFolds Value to assign to NumFolds. */ public void setNumFolds(int newNumFolds) { m_NumFolds = newNumFolds; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String maxDepthTipText() { return "The maximum tree depth (-1 for no restriction)."; } /** * Get the value of MaxDepth. * * @return Value of MaxDepth. */ public int getMaxDepth() { return m_MaxDepth; } /** * Set the value of MaxDepth. * * @param newMaxDepth Value to assign to MaxDepth. */ public void setMaxDepth(int newMaxDepth) { m_MaxDepth = newMaxDepth; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String initialCountTipText() { return "Initial class value count."; } /** * Get the value of InitialCount. * * @return Value of InitialCount. */ public double getInitialCount() { return m_InitialCount; } /** * Set the value of InitialCount. * * @param newInitialCount Value to assign to InitialCount. */ public void setInitialCount(double newInitialCount) { m_InitialCount = newInitialCount; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String spreadInitialCountTipText() { return "Spread initial count across all values instead of using the count per value."; } /** * Get the value of SpreadInitialCount. * * @return Value of SpreadInitialCount. */ public boolean getSpreadInitialCount() { return m_SpreadInitialCount; } /** * Set the value of SpreadInitialCount. * * @param newSpreadInitialCount Value to assign to SpreadInitialCount. */ public void setSpreadInitialCount(boolean newSpreadInitialCount) { m_SpreadInitialCount = newSpreadInitialCount; } public double getFeatureFrac() { return m_FeatureFrac; } public void setFeatureFrac(double frac) { m_FeatureFrac = frac; } /** * Lists the command-line options for this classifier. * * @return an enumeration over all commandline options */ public Enumeration listOptions() { Vector newVector = new Vector(9); newVector.addElement(new Option("\tSet minimum number of instances per leaf " + "(default 2).", "M", 1, "-M <minimum number of instances>")); newVector.addElement(new Option( "\tSet minimum numeric class variance proportion\n" + "\tof train variance for split (default 1e-3).", "V", 1, "-V <minimum variance for split>")); newVector.addElement(new Option("\tNumber of folds for reduced error pruning " + "(default 3).", "N", 1, "-N <number of folds>")); newVector.addElement(new Option("\tSeed for random data shuffling (default 1).", "S", 1, "-S <seed>")); newVector.addElement(new Option("\tNo pruning.", "P", 0, "-P")); newVector.addElement(new Option("\tMaximum tree depth (default -1, no maximum)", "L", 1, "-L")); newVector.addElement(new Option("\tInitial class value count (default 0)", "I", 1, "-I")); newVector.addElement(new Option( "\tSpread initial count over all class values (i.e." + " don't use 1 per value)", "R", 0, "-R")); newVector.addElement(new Option("\tFraction of features to consider for splitting", "F", 1, "-F")); return newVector.elements(); } /** * Gets options from this classifier. * * @return the options for the current setup */ public String[] getOptions() { String[] options = new String[15]; int current = 0; options[current++] = "-M"; options[current++] = "" + (int) getMinNum(); options[current++] = "-V"; options[current++] = "" + getMinVarianceProp(); options[current++] = "-N"; options[current++] = "" + getNumFolds(); options[current++] = "-S"; options[current++] = "" + getSeed(); options[current++] = "-L"; options[current++] = "" + getMaxDepth(); if (getNoPruning()) { options[current++] = "-P"; } options[current++] = "-I"; options[current++] = "" + getInitialCount(); if (getSpreadInitialCount()) { options[current++] = "-R"; } options[current++] = "-F" + getFeatureFrac(); while (current < options.length) { options[current++] = ""; } return options; } /** * Parses a given list of options. <p/> * <!-- options-start --> * Valid options are: <p/> * * <pre> -M <minimum number of instances> * Set minimum number of instances per leaf (default 2).</pre> * * <pre> -V <minimum variance for split> * Set minimum numeric class variance proportion * of train variance for split (default 1e-3).</pre> * * <pre> -N <number of folds> * Number of folds for reduced error pruning (default 3).</pre> * * <pre> -S <seed> * Seed for random data shuffling (default 1).</pre> * * <pre> -P * No pruning.</pre> * * <pre> -L * Maximum tree depth (default -1, no maximum)</pre> * <!-- options-end --> * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String minNumString = Utils.getOption('M', options); if (minNumString.length() != 0) { m_MinNum = (double) Integer.parseInt(minNumString); } else { m_MinNum = 2; } String minVarString = Utils.getOption('V', options); if (minVarString.length() != 0) { m_MinVarianceProp = Double.parseDouble(minVarString); } else { m_MinVarianceProp = 1e-3; } String numFoldsString = Utils.getOption('N', options); if (numFoldsString.length() != 0) { m_NumFolds = Integer.parseInt(numFoldsString); } else { m_NumFolds = 3; } String seedString = Utils.getOption('S', options); if (seedString.length() != 0) { m_Seed = Integer.parseInt(seedString); } else { m_Seed = 1; } m_NoPruning = Utils.getFlag('P', options); String depthString = Utils.getOption('L', options); if (depthString.length() != 0) { m_MaxDepth = Integer.parseInt(depthString); } else { m_MaxDepth = -1; } String initialCountString = Utils.getOption('I', options); if (initialCountString.length() != 0) { m_InitialCount = Double.parseDouble(initialCountString); } else { m_InitialCount = 0; } m_SpreadInitialCount = Utils.getFlag('R', options); String featureFracString = Utils.getOption('F', options); if (featureFracString.length() != 0) { m_FeatureFrac = Double.parseDouble(featureFracString); } else { m_FeatureFrac = 1.0; } Utils.checkForRemainingOptions(options); } /** * Computes size of the tree. * * @return the number of nodes */ public int numNodes() { return m_Tree.numNodes(); } /** * Returns an enumeration of the additional measure names. * * @return an enumeration of the measure names */ public Enumeration enumerateMeasures() { Vector newVector = new Vector(1); newVector.addElement("measureTreeSize"); return newVector.elements(); } /** * Returns the value of the named measure. * * @param additionalMeasureName the name of the measure to query for its value * @return the value of the named measure * @throws IllegalArgumentException if the named measure is not supported */ public double getMeasure(String additionalMeasureName) { if (additionalMeasureName.equalsIgnoreCase("measureTreeSize")) { return (double) numNodes(); } else { throw new IllegalArgumentException(additionalMeasureName + " not supported (REPRandomTree)"); } } /** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); result.disableAll(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.NUMERIC_CLASS); result.enable(Capability.DATE_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Builds classifier. * * @param data the data to train with * @throws Exception if building fails */ public void buildClassifier(Instances data) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(data); // remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); Random random = new Random(m_Seed); m_zeroR = null; if (data.numAttributes() == 1) { m_zeroR = new ZeroR(); m_zeroR.buildClassifier(data); return; } // Randomize and stratify data.randomize(random); if (data.classAttribute().isNominal()) { data.stratify(m_NumFolds); } // Split data into training and pruning set Instances train = null; Instances prune = null; if (!m_NoPruning) { train = data.trainCV(m_NumFolds, 0, random); prune = data.testCV(m_NumFolds, 0); } else { train = data; } // Create array of sorted indices and weights int[][][] sortedIndices = new int[1][train.numAttributes()][0]; double[][][] weights = new double[1][train.numAttributes()][0]; double[] vals = new double[train.numInstances()]; for (int j = 0; j < train.numAttributes(); j++) { if (j != train.classIndex()) { weights[0][j] = new double[train.numInstances()]; if (train.attribute(j).isNominal()) { // Handling nominal attributes. Putting indices of // instances with missing values at the end. sortedIndices[0][j] = new int[train.numInstances()]; int count = 0; for (int i = 0; i < train.numInstances(); i++) { Instance inst = train.instance(i); if (!inst.isMissing(j)) { sortedIndices[0][j][count] = i; weights[0][j][count] = inst.weight(); count++; } } for (int i = 0; i < train.numInstances(); i++) { Instance inst = train.instance(i); if (inst.isMissing(j)) { sortedIndices[0][j][count] = i; weights[0][j][count] = inst.weight(); count++; } } } else { // Sorted indices are computed for numeric attributes for (int i = 0; i < train.numInstances(); i++) { Instance inst = train.instance(i); vals[i] = inst.value(j); } sortedIndices[0][j] = Utils.sort(vals); for (int i = 0; i < train.numInstances(); i++) { weights[0][j][i] = train.instance(sortedIndices[0][j][i]).weight(); } } } } // Compute initial class counts double[] classProbs = new double[train.numClasses()]; double totalWeight = 0, totalSumSquared = 0; for (int i = 0; i < train.numInstances(); i++) { Instance inst = train.instance(i); if (data.classAttribute().isNominal()) { classProbs[(int) inst.classValue()] += inst.weight(); totalWeight += inst.weight(); } else { classProbs[0] += inst.classValue() * inst.weight(); totalSumSquared += inst.classValue() * inst.classValue() * inst.weight(); totalWeight += inst.weight(); } } m_Tree = new Tree(); double trainVariance = 0; if (data.classAttribute().isNumeric()) { trainVariance = m_Tree.singleVariance(classProbs[0], totalSumSquared, totalWeight) / totalWeight; classProbs[0] /= totalWeight; } // Build tree m_Tree.buildTree(sortedIndices, weights, train, totalWeight, classProbs, new Instances(train, 0), m_MinNum, m_MinVarianceProp * trainVariance, 0, m_MaxDepth, m_FeatureFrac, random); // Insert pruning data and perform reduced error pruning if (!m_NoPruning) { m_Tree.insertHoldOutSet(prune); m_Tree.reducedErrorPrune(); m_Tree.backfitHoldOutSet(); } } /** * Computes class distribution of an instance using the tree. * * @param instance the instance to compute the distribution for * @return the computed class probabilities * @throws Exception if computation fails */ public double[] distributionForInstance(Instance instance) throws Exception { if (m_zeroR != null) { return m_zeroR.distributionForInstance(instance); } else { return m_Tree.distributionForInstance(instance); } } /** * For getting a unique ID when outputting the tree source * (hashcode isn't guaranteed unique) */ private static long PRINTED_NODES = 0; /** * Gets the next unique node ID. * * @return the next unique node ID. */ protected static long nextID() { return PRINTED_NODES++; } /** * resets the counter for the nodes */ protected static void resetID() { PRINTED_NODES = 0; } /** * Returns the tree as if-then statements. * * @param className the name for the generated class * @return the tree as a Java if-then type statement * @throws Exception if something goes wrong */ public String toSource(String className) throws Exception { if (m_Tree == null) { throw new Exception("REPRandomTree: No model built yet."); } StringBuffer[] source = m_Tree.toSource(className, m_Tree); return "class " + className + " {\n\n" + " public static double classify(Object [] i)\n" + " throws Exception {\n\n" + " double p = Double.NaN;\n" + source[0] // Assignment code + " return p;\n" + " }\n" + source[1] // Support code + "}\n"; } /** * Returns the type of graph this classifier * represents. * @return Drawable.TREE */ public int graphType() { return Drawable.TREE; } /** * Outputs the decision tree as a graph * * @return the tree as a graph * @throws Exception if generation fails */ public String graph() throws Exception { if (m_Tree == null) { throw new Exception("REPRandomTree: No model built yet."); } StringBuffer resultBuff = new StringBuffer(); m_Tree.toGraph(resultBuff, 0, null); String result = "digraph Tree {\n" + "edge [style=bold]\n" + resultBuff.toString() + "\n}\n"; return result; } /** * Outputs the decision tree. * * @return a string representation of the classifier */ public String toString() { if (m_zeroR != null) { return "No attributes other than class. Using ZeroR.\n\n" + m_zeroR.toString(); } if ((m_Tree == null)) { return "REPRandomTree: No model built yet."; } return "\nREPRandomTree\n============\n" + m_Tree.toString(0, null) + "\n" + "\nSize of the tree : " + numNodes(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 6954 $"); } /** * Main method for this class. * * @param argv the commandline options */ public static void main(String[] argv) { runClassifier(new REPRandomTree(), argv); } }