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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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/*
 *   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 3 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, see .
 */

/*
 *    DecisionStump.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.Sourcable;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.ContingencyTables;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;

/**
 
 * Class for building and using a decision stump. Usually used in conjunction with a boosting algorithm. Does regression (based on mean-squared error) or classification (based on entropy). Missing is treated as a separate value.
 * 

* * Typical usage:

* java weka.classifiers.meta.LogitBoost -I 100 -W weka.classifiers.trees.DecisionStump * -t training_data

* * Valid options are:

* *

 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * @author Eibe Frank ([email protected]) * @version $Revision: 9171 $ */ public class DecisionStump extends AbstractClassifier implements WeightedInstancesHandler, Sourcable { /** for serialization */ static final long serialVersionUID = 1618384535950391L; /** The attribute used for classification. */ protected int m_AttIndex; /** The split point (index respectively). */ protected double m_SplitPoint; /** The distribution of class values or the means in each subset. */ protected double[][] m_Distribution; /** The instances used for training. */ protected Instances m_Instances; /** a ZeroR model in case no model can be built from the data */ protected Classifier m_ZeroR; /** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for building and using a decision stump. Usually used in " + "conjunction with a boosting algorithm. Does regression (based on " + "mean-squared error) or classification (based on entropy). Missing " + "is treated as a separate value."; } /** * 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; } /** * Generates the classifier. * * @param instances set of instances serving as training data * @throws Exception if the classifier has not been generated successfully */ public void buildClassifier(Instances instances) throws Exception { double bestVal = Double.MAX_VALUE, currVal; double bestPoint = -Double.MAX_VALUE; int bestAtt = -1, numClasses; // can classifier handle the data? getCapabilities().testWithFail(instances); // remove instances with missing class instances = new Instances(instances); instances.deleteWithMissingClass(); // only class? -> build ZeroR model if (instances.numAttributes() == 1) { System.err.println( "Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!"); m_ZeroR = new weka.classifiers.rules.ZeroR(); m_ZeroR.buildClassifier(instances); return; } else { m_ZeroR = null; } double[][] bestDist = new double[3][instances.numClasses()]; m_Instances = new Instances(instances); if (m_Instances.classAttribute().isNominal()) { numClasses = m_Instances.numClasses(); } else { numClasses = 1; } // For each attribute boolean first = true; for (int i = 0; i < m_Instances.numAttributes(); i++) { if (i != m_Instances.classIndex()) { // Reserve space for distribution. m_Distribution = new double[3][numClasses]; // Compute value of criterion for best split on attribute if (m_Instances.attribute(i).isNominal()) { currVal = findSplitNominal(i); } else { currVal = findSplitNumeric(i); } if ((first) || (currVal < bestVal)) { bestVal = currVal; bestAtt = i; bestPoint = m_SplitPoint; for (int j = 0; j < 3; j++) { System.arraycopy(m_Distribution[j], 0, bestDist[j], 0, numClasses); } } // First attribute has been investigated first = false; } } // Set attribute, split point and distribution. m_AttIndex = bestAtt; m_SplitPoint = bestPoint; m_Distribution = bestDist; if (m_Instances.classAttribute().isNominal()) { for (int i = 0; i < m_Distribution.length; i++) { double sumCounts = Utils.sum(m_Distribution[i]); if (sumCounts == 0) { // This means there were only missing attribute values System.arraycopy(m_Distribution[2], 0, m_Distribution[i], 0, m_Distribution[2].length); Utils.normalize(m_Distribution[i]); } else { Utils.normalize(m_Distribution[i], sumCounts); } } } // Save memory m_Instances = new Instances(m_Instances, 0); } /** * Calculates the class membership probabilities for the given test instance. * * @param instance the instance to be classified * @return predicted class probability distribution * @throws Exception if distribution can't be computed */ public double[] distributionForInstance(Instance instance) throws Exception { // default model? if (m_ZeroR != null) { return m_ZeroR.distributionForInstance(instance); } return m_Distribution[whichSubset(instance)]; } /** * Returns the decision tree as Java source code. * * @param className the classname of the generated code * @return the tree as Java source code * @throws Exception if something goes wrong */ public String toSource(String className) throws Exception { StringBuffer text = new StringBuffer("class "); Attribute c = m_Instances.classAttribute(); text.append(className) .append(" {\n" +" public static double classify(Object[] i) {\n"); text.append(" /* " + m_Instances.attribute(m_AttIndex).name() + " */\n"); text.append(" if (i[").append(m_AttIndex); text.append("] == null) { return "); text.append(sourceClass(c, m_Distribution[2])).append(";"); if (m_Instances.attribute(m_AttIndex).isNominal()) { text.append(" } else if (((String)i[").append(m_AttIndex); text.append("]).equals(\""); text.append(m_Instances.attribute(m_AttIndex).value((int)m_SplitPoint)); text.append("\")"); } else { text.append(" } else if (((Double)i[").append(m_AttIndex); text.append("]).doubleValue() <= ").append(m_SplitPoint); } text.append(") { return "); text.append(sourceClass(c, m_Distribution[0])).append(";"); text.append(" } else { return "); text.append(sourceClass(c, m_Distribution[1])).append(";"); text.append(" }\n }\n}\n"); return text.toString(); } /** * Returns the value as string out of the given distribution * * @param c the attribute to get the value for * @param dist the distribution to extract the value * @return the value */ protected String sourceClass(Attribute c, double []dist) { if (c.isNominal()) { return Integer.toString(Utils.maxIndex(dist)); } else { return Double.toString(dist[0]); } } /** * Returns a description of the classifier. * * @return a description of the classifier as a string. */ public String toString(){ // only ZeroR model? if (m_ZeroR != null) { StringBuffer buf = new StringBuffer(); buf.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n"); buf.append(this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n"); buf.append("Warning: No model could be built, hence ZeroR model is used:\n\n"); buf.append(m_ZeroR.toString()); return buf.toString(); } if (m_Instances == null) { return "Decision Stump: No model built yet."; } try { StringBuffer text = new StringBuffer(); text.append("Decision Stump\n\n"); text.append("Classifications\n\n"); Attribute att = m_Instances.attribute(m_AttIndex); if (att.isNominal()) { text.append(att.name() + " = " + att.value((int)m_SplitPoint) + " : "); text.append(printClass(m_Distribution[0])); text.append(att.name() + " != " + att.value((int)m_SplitPoint) + " : "); text.append(printClass(m_Distribution[1])); } else { text.append(att.name() + " <= " + m_SplitPoint + " : "); text.append(printClass(m_Distribution[0])); text.append(att.name() + " > " + m_SplitPoint + " : "); text.append(printClass(m_Distribution[1])); } text.append(att.name() + " is missing : "); text.append(printClass(m_Distribution[2])); if (m_Instances.classAttribute().isNominal()) { text.append("\nClass distributions\n\n"); if (att.isNominal()) { text.append(att.name() + " = " + att.value((int)m_SplitPoint) + "\n"); text.append(printDist(m_Distribution[0])); text.append(att.name() + " != " + att.value((int)m_SplitPoint) + "\n"); text.append(printDist(m_Distribution[1])); } else { text.append(att.name() + " <= " + m_SplitPoint + "\n"); text.append(printDist(m_Distribution[0])); text.append(att.name() + " > " + m_SplitPoint + "\n"); text.append(printDist(m_Distribution[1])); } text.append(att.name() + " is missing\n"); text.append(printDist(m_Distribution[2])); } return text.toString(); } catch (Exception e) { return "Can't print decision stump classifier!"; } } /** * Prints a class distribution. * * @param dist the class distribution to print * @return the distribution as a string * @throws Exception if distribution can't be printed */ protected String printDist(double[] dist) throws Exception { StringBuffer text = new StringBuffer(); if (m_Instances.classAttribute().isNominal()) { for (int i = 0; i < m_Instances.numClasses(); i++) { text.append(m_Instances.classAttribute().value(i) + "\t"); } text.append("\n"); for (int i = 0; i < m_Instances.numClasses(); i++) { text.append(dist[i] + "\t"); } text.append("\n"); } return text.toString(); } /** * Prints a classification. * * @param dist the class distribution * @return the classificationn as a string * @throws Exception if the classification can't be printed */ protected String printClass(double[] dist) throws Exception { StringBuffer text = new StringBuffer(); if (m_Instances.classAttribute().isNominal()) { text.append(m_Instances.classAttribute().value(Utils.maxIndex(dist))); } else { text.append(dist[0]); } return text.toString() + "\n"; } /** * Finds best split for nominal attribute and returns value. * * @param index attribute index * @return value of criterion for the best split * @throws Exception if something goes wrong */ protected double findSplitNominal(int index) throws Exception { if (m_Instances.classAttribute().isNominal()) { return findSplitNominalNominal(index); } else { return findSplitNominalNumeric(index); } } /** * Finds best split for nominal attribute and nominal class * and returns value. * * @param index attribute index * @return value of criterion for the best split * @throws Exception if something goes wrong */ protected double findSplitNominalNominal(int index) throws Exception { double bestVal = Double.MAX_VALUE, currVal; double[][] counts = new double[m_Instances.attribute(index).numValues() + 1][m_Instances.numClasses()]; double[] sumCounts = new double[m_Instances.numClasses()]; double[][] bestDist = new double[3][m_Instances.numClasses()]; int numMissing = 0; // Compute counts for all the values for (int i = 0; i < m_Instances.numInstances(); i++) { Instance inst = m_Instances.instance(i); if (inst.isMissing(index)) { numMissing++; counts[m_Instances.attribute(index).numValues()] [(int)inst.classValue()] += inst.weight(); } else { counts[(int)inst.value(index)][(int)inst.classValue()] += inst .weight(); } } // Compute sum of counts for (int i = 0; i < m_Instances.attribute(index).numValues(); i++) { for (int j = 0; j < m_Instances.numClasses(); j++) { sumCounts[j] += counts[i][j]; } } // Make split counts for each possible split and evaluate System.arraycopy(counts[m_Instances.attribute(index).numValues()], 0, m_Distribution[2], 0, m_Instances.numClasses()); for (int i = 0; i < m_Instances.attribute(index).numValues(); i++) { for (int j = 0; j < m_Instances.numClasses(); j++) { m_Distribution[0][j] = counts[i][j]; m_Distribution[1][j] = sumCounts[j] - counts[i][j]; } currVal = ContingencyTables.entropyConditionedOnRows(m_Distribution); if (currVal < bestVal) { bestVal = currVal; m_SplitPoint = (double)i; for (int j = 0; j < 3; j++) { System.arraycopy(m_Distribution[j], 0, bestDist[j], 0, m_Instances.numClasses()); } } } // No missing values in training data. if (numMissing == 0) { System.arraycopy(sumCounts, 0, bestDist[2], 0, m_Instances.numClasses()); } m_Distribution = bestDist; return bestVal; } /** * Finds best split for nominal attribute and numeric class * and returns value. * * @param index attribute index * @return value of criterion for the best split * @throws Exception if something goes wrong */ protected double findSplitNominalNumeric(int index) throws Exception { double bestVal = Double.MAX_VALUE, currVal; double[] sumsSquaresPerValue = new double[m_Instances.attribute(index).numValues()], sumsPerValue = new double[m_Instances.attribute(index).numValues()], weightsPerValue = new double[m_Instances.attribute(index).numValues()]; double totalSumSquaresW = 0, totalSumW = 0, totalSumOfWeightsW = 0, totalSumOfWeights = 0, totalSum = 0; double[] sumsSquares = new double[3], sumOfWeights = new double[3]; double[][] bestDist = new double[3][1]; // Compute counts for all the values for (int i = 0; i < m_Instances.numInstances(); i++) { Instance inst = m_Instances.instance(i); if (inst.isMissing(index)) { m_Distribution[2][0] += inst.classValue() * inst.weight(); sumsSquares[2] += inst.classValue() * inst.classValue() * inst.weight(); sumOfWeights[2] += inst.weight(); } else { weightsPerValue[(int)inst.value(index)] += inst.weight(); sumsPerValue[(int)inst.value(index)] += inst.classValue() * inst.weight(); sumsSquaresPerValue[(int)inst.value(index)] += inst.classValue() * inst.classValue() * inst.weight(); } totalSumOfWeights += inst.weight(); totalSum += inst.classValue() * inst.weight(); } // Check if the total weight is zero if (totalSumOfWeights <= 0) { return bestVal; } // Compute sum of counts without missing ones for (int i = 0; i < m_Instances.attribute(index).numValues(); i++) { totalSumOfWeightsW += weightsPerValue[i]; totalSumSquaresW += sumsSquaresPerValue[i]; totalSumW += sumsPerValue[i]; } // Make split counts for each possible split and evaluate for (int i = 0; i < m_Instances.attribute(index).numValues(); i++) { m_Distribution[0][0] = sumsPerValue[i]; sumsSquares[0] = sumsSquaresPerValue[i]; sumOfWeights[0] = weightsPerValue[i]; m_Distribution[1][0] = totalSumW - sumsPerValue[i]; sumsSquares[1] = totalSumSquaresW - sumsSquaresPerValue[i]; sumOfWeights[1] = totalSumOfWeightsW - weightsPerValue[i]; currVal = variance(m_Distribution, sumsSquares, sumOfWeights); if (currVal < bestVal) { bestVal = currVal; m_SplitPoint = (double)i; for (int j = 0; j < 3; j++) { if (sumOfWeights[j] > 0) { bestDist[j][0] = m_Distribution[j][0] / sumOfWeights[j]; } else { bestDist[j][0] = totalSum / totalSumOfWeights; } } } } m_Distribution = bestDist; return bestVal; } /** * Finds best split for numeric attribute and returns value. * * @param index attribute index * @return value of criterion for the best split * @throws Exception if something goes wrong */ protected double findSplitNumeric(int index) throws Exception { if (m_Instances.classAttribute().isNominal()) { return findSplitNumericNominal(index); } else { return findSplitNumericNumeric(index); } } /** * Finds best split for numeric attribute and nominal class * and returns value. * * @param index attribute index * @return value of criterion for the best split * @throws Exception if something goes wrong */ protected double findSplitNumericNominal(int index) throws Exception { double bestVal = Double.MAX_VALUE, currVal, currCutPoint; int numMissing = 0; double[] sum = new double[m_Instances.numClasses()]; double[][] bestDist = new double[3][m_Instances.numClasses()]; // Compute counts for all the values for (int i = 0; i < m_Instances.numInstances(); i++) { Instance inst = m_Instances.instance(i); if (!inst.isMissing(index)) { m_Distribution[1][(int)inst.classValue()] += inst.weight(); } else { m_Distribution[2][(int)inst.classValue()] += inst.weight(); numMissing++; } } System.arraycopy(m_Distribution[1], 0, sum, 0, m_Instances.numClasses()); // Save current distribution as best distribution for (int j = 0; j < 3; j++) { System.arraycopy(m_Distribution[j], 0, bestDist[j], 0, m_Instances.numClasses()); } // Sort instances m_Instances.sort(index); // Make split counts for each possible split and evaluate for (int i = 0; i < m_Instances.numInstances() - (numMissing + 1); i++) { Instance inst = m_Instances.instance(i); Instance instPlusOne = m_Instances.instance(i + 1); m_Distribution[0][(int)inst.classValue()] += inst.weight(); m_Distribution[1][(int)inst.classValue()] -= inst.weight(); if (inst.value(index) < instPlusOne.value(index)) { currCutPoint = (inst.value(index) + instPlusOne.value(index)) / 2.0; currVal = ContingencyTables.entropyConditionedOnRows(m_Distribution); if (currVal < bestVal) { m_SplitPoint = currCutPoint; bestVal = currVal; for (int j = 0; j < 3; j++) { System.arraycopy(m_Distribution[j], 0, bestDist[j], 0, m_Instances.numClasses()); } } } } // No missing values in training data. if (numMissing == 0) { System.arraycopy(sum, 0, bestDist[2], 0, m_Instances.numClasses()); } m_Distribution = bestDist; return bestVal; } /** * Finds best split for numeric attribute and numeric class * and returns value. * * @param index attribute index * @return value of criterion for the best split * @throws Exception if something goes wrong */ protected double findSplitNumericNumeric(int index) throws Exception { double bestVal = Double.MAX_VALUE, currVal, currCutPoint; int numMissing = 0; double[] sumsSquares = new double[3], sumOfWeights = new double[3]; double[][] bestDist = new double[3][1]; double totalSum = 0, totalSumOfWeights = 0; // Compute counts for all the values for (int i = 0; i < m_Instances.numInstances(); i++) { Instance inst = m_Instances.instance(i); if (!inst.isMissing(index)) { m_Distribution[1][0] += inst.classValue() * inst.weight(); sumsSquares[1] += inst.classValue() * inst.classValue() * inst.weight(); sumOfWeights[1] += inst.weight(); } else { m_Distribution[2][0] += inst.classValue() * inst.weight(); sumsSquares[2] += inst.classValue() * inst.classValue() * inst.weight(); sumOfWeights[2] += inst.weight(); numMissing++; } totalSumOfWeights += inst.weight(); totalSum += inst.classValue() * inst.weight(); } // Check if the total weight is zero if (totalSumOfWeights <= 0) { return bestVal; } // Sort instances m_Instances.sort(index); // Make split counts for each possible split and evaluate for (int i = 0; i < m_Instances.numInstances() - (numMissing + 1); i++) { Instance inst = m_Instances.instance(i); Instance instPlusOne = m_Instances.instance(i + 1); m_Distribution[0][0] += inst.classValue() * inst.weight(); sumsSquares[0] += inst.classValue() * inst.classValue() * inst.weight(); sumOfWeights[0] += inst.weight(); m_Distribution[1][0] -= inst.classValue() * inst.weight(); sumsSquares[1] -= inst.classValue() * inst.classValue() * inst.weight(); sumOfWeights[1] -= inst.weight(); if (inst.value(index) < instPlusOne.value(index)) { currCutPoint = (inst.value(index) + instPlusOne.value(index)) / 2.0; currVal = variance(m_Distribution, sumsSquares, sumOfWeights); if (currVal < bestVal) { m_SplitPoint = currCutPoint; bestVal = currVal; for (int j = 0; j < 3; j++) { if (sumOfWeights[j] > 0) { bestDist[j][0] = m_Distribution[j][0] / sumOfWeights[j]; } else { bestDist[j][0] = totalSum / totalSumOfWeights; } } } } } m_Distribution = bestDist; return bestVal; } /** * 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 += sS[i] - ((s[i][0] * s[i][0]) / (double) sumOfWeights[i]); } } return var; } /** * Returns the subset an instance falls into. * * @param instance the instance to check * @return the subset the instance falls into * @throws Exception if something goes wrong */ protected int whichSubset(Instance instance) throws Exception { if (instance.isMissing(m_AttIndex)) { return 2; } else if (instance.attribute(m_AttIndex).isNominal()) { if ((int)instance.value(m_AttIndex) == m_SplitPoint) { return 0; } else { return 1; } } else { if (instance.value(m_AttIndex) <= m_SplitPoint) { return 0; } else { return 1; } } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 9171 $"); } /** * Main method for testing this class. * * @param argv the options */ public static void main(String [] argv) { runClassifier(new DecisionStump(), argv); } }




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