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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 .
 */

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

package weka.classifiers.trees.j48;

import java.io.Serializable;
import java.util.LinkedList;
import java.util.Queue;

import weka.core.Capabilities;
import weka.core.CapabilitiesHandler;
import weka.core.Drawable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 * Class for handling a tree structure used for classification.
 * 
 * @author Eibe Frank ([email protected])
 * @version $Revision: 11269 $
 */
public class ClassifierTree implements Drawable, Serializable,
  CapabilitiesHandler, RevisionHandler {

  /** for serialization */
  static final long serialVersionUID = -8722249377542734193L;

  /** The model selection method. */
  protected ModelSelection m_toSelectModel;

  /** Local model at node. */
  protected ClassifierSplitModel m_localModel;

  /** References to sons. */
  protected ClassifierTree[] m_sons;

  /** True if node is leaf. */
  protected boolean m_isLeaf;

  /** True if node is empty. */
  protected boolean m_isEmpty;

  /** The training instances. */
  protected Instances m_train;

  /** The pruning instances. */
  protected Distribution m_test;

  /** The id for the node. */
  protected int m_id;

  /**
   * For getting a unique ID when outputting the tree (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 unique node ID counter (e.g. between repeated separate print
   * types)
   */
  protected static void resetID() {

    PRINTED_NODES = 0;
  }

  /**
   * Constructor.
   */
  public ClassifierTree(ModelSelection toSelectLocModel) {

    m_toSelectModel = toSelectLocModel;
  }

  /**
   * Returns default capabilities of the classifier tree.
   * 
   * @return the capabilities of this classifier tree
   */
  @Override
  public Capabilities getCapabilities() {
    Capabilities result = new Capabilities(this);
    result.enableAll();

    return result;
  }

  /**
   * Method for building a classifier tree.
   * 
   * @param data the data to build the tree from
   * @throws Exception if something goes wrong
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier tree handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();

    buildTree(data, false);
  }

  /**
   * Builds the tree structure.
   * 
   * @param data the data for which the tree structure is to be generated.
   * @param keepData is training data to be kept?
   * @throws Exception if something goes wrong
   */
  public void buildTree(Instances data, boolean keepData) throws Exception {

    Instances[] localInstances;

    if (keepData) {
      m_train = data;
    }
    m_test = null;
    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    m_localModel = m_toSelectModel.selectModel(data);
    if (m_localModel.numSubsets() > 1) {
      localInstances = m_localModel.split(data);
      data = null;
      m_sons = new ClassifierTree[m_localModel.numSubsets()];
      for (int i = 0; i < m_sons.length; i++) {
        m_sons[i] = getNewTree(localInstances[i]);
        localInstances[i] = null;
      }
    } else {
      m_isLeaf = true;
      if (Utils.eq(data.sumOfWeights(), 0)) {
        m_isEmpty = true;
      }
      data = null;
    }
  }

  /**
   * Builds the tree structure with hold out set
   * 
   * @param train the data for which the tree structure is to be generated.
   * @param test the test data for potential pruning
   * @param keepData is training Data to be kept?
   * @throws Exception if something goes wrong
   */
  public void buildTree(Instances train, Instances test, boolean keepData)
    throws Exception {

    Instances[] localTrain, localTest;
    int i;

    if (keepData) {
      m_train = train;
    }
    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    m_localModel = m_toSelectModel.selectModel(train, test);
    m_test = new Distribution(test, m_localModel);
    if (m_localModel.numSubsets() > 1) {
      localTrain = m_localModel.split(train);
      localTest = m_localModel.split(test);
      train = null;
      test = null;
      m_sons = new ClassifierTree[m_localModel.numSubsets()];
      for (i = 0; i < m_sons.length; i++) {
        m_sons[i] = getNewTree(localTrain[i], localTest[i]);
        localTrain[i] = null;
        localTest[i] = null;
      }
    } else {
      m_isLeaf = true;
      if (Utils.eq(train.sumOfWeights(), 0)) {
        m_isEmpty = true;
      }
      train = null;
      test = null;
    }
  }

  /**
   * Classifies an instance.
   * 
   * @param instance the instance to classify
   * @return the classification
   * @throws Exception if something goes wrong
   */
  public double classifyInstance(Instance instance) throws Exception {

    double maxProb = -1;
    double currentProb;
    int maxIndex = 0;
    int j;

    for (j = 0; j < instance.numClasses(); j++) {
      currentProb = getProbs(j, instance, 1);
      if (Utils.gr(currentProb, maxProb)) {
        maxIndex = j;
        maxProb = currentProb;
      }
    }

    return maxIndex;
  }

  /**
   * Cleanup in order to save memory.
   * 
   * @param justHeaderInfo
   */
  public final void cleanup(Instances justHeaderInfo) {

    m_train = justHeaderInfo;
    m_test = null;
    if (!m_isLeaf) {
      for (ClassifierTree m_son : m_sons) {
        m_son.cleanup(justHeaderInfo);
      }
    }
  }

  /**
   * Returns class probabilities for a weighted instance.
   * 
   * @param instance the instance to get the distribution for
   * @param useLaplace whether to use laplace or not
   * @return the distribution
   * @throws Exception if something goes wrong
   */
  public final double[] distributionForInstance(Instance instance,
    boolean useLaplace) throws Exception {

    double[] doubles = new double[instance.numClasses()];

    for (int i = 0; i < doubles.length; i++) {
      if (!useLaplace) {
        doubles[i] = getProbs(i, instance, 1);
      } else {
        doubles[i] = getProbsLaplace(i, instance, 1);
      }
    }

    return doubles;
  }

  /**
   * Assigns a uniqe id to every node in the tree.
   * 
   * @param lastID the last ID that was assign
   * @return the new current ID
   */
  public int assignIDs(int lastID) {

    int currLastID = lastID + 1;

    m_id = currLastID;
    if (m_sons != null) {
      for (ClassifierTree m_son : m_sons) {
        currLastID = m_son.assignIDs(currLastID);
      }
    }
    return currLastID;
  }

  /**
   * Returns the type of graph this classifier represents.
   * 
   * @return Drawable.TREE
   */
  @Override
  public int graphType() {
    return Drawable.TREE;
  }

  /**
   * Returns graph describing the tree.
   * 
   * @throws Exception if something goes wrong
   * @return the tree as graph
   */
  @Override
  public String graph() throws Exception {

    StringBuffer text = new StringBuffer();

    assignIDs(-1);
    text.append("digraph J48Tree {\n");
    if (m_isLeaf) {
      text.append("N" + m_id + " [label=\""
        + Utils.backQuoteChars(m_localModel.dumpLabel(0, m_train)) + "\" "
        + "shape=box style=filled ");
      if (m_train != null && m_train.numInstances() > 0) {
        text.append("data =\n" + m_train + "\n");
        text.append(",\n");

      }
      text.append("]\n");
    } else {
      text.append("N" + m_id + " [label=\""
        + Utils.backQuoteChars(m_localModel.leftSide(m_train)) + "\" ");
      if (m_train != null && m_train.numInstances() > 0) {
        text.append("data =\n" + m_train + "\n");
        text.append(",\n");
      }
      text.append("]\n");
      graphTree(text);
    }

    return text.toString() + "}\n";
  }

  /**
   * Returns tree in prefix order.
   * 
   * @throws Exception if something goes wrong
   * @return the prefix order
   */
  public String prefix() throws Exception {

    StringBuffer text;

    text = new StringBuffer();
    if (m_isLeaf) {
      text.append("[" + m_localModel.dumpLabel(0, m_train) + "]");
    } else {
      prefixTree(text);
    }

    return text.toString();
  }

  /**
   * Returns source code for the tree as an if-then statement. 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).
   * 
   * @param className the classname that this static classifier has
   * @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) throws Exception {

    StringBuffer[] result = new StringBuffer[2];
    if (m_isLeaf) {
      result[0] = new StringBuffer("    p = "
        + m_localModel.distribution().maxClass(0) + ";\n");
      result[1] = new StringBuffer("");
    } else {
      StringBuffer text = new StringBuffer();
      StringBuffer atEnd = new StringBuffer();

      long printID = ClassifierTree.nextID();

      text.append("  static double N")
        .append(Integer.toHexString(m_localModel.hashCode()) + printID)
        .append("(Object []i) {\n").append("    double p = Double.NaN;\n");

      text.append("    if (")
        .append(m_localModel.sourceExpression(-1, m_train)).append(") {\n");
      text.append("      p = ").append(m_localModel.distribution().maxClass(0))
        .append(";\n");
      text.append("    } ");
      for (int i = 0; i < m_sons.length; i++) {
        text.append("else if (" + m_localModel.sourceExpression(i, m_train)
          + ") {\n");
        if (m_sons[i].m_isLeaf) {
          text.append("      p = " + m_localModel.distribution().maxClass(i)
            + ";\n");
        } else {
          StringBuffer[] sub = m_sons[i].toSource(className);
          text.append(sub[0]);
          atEnd.append(sub[1]);
        }
        text.append("    } ");
        if (i == m_sons.length - 1) {
          text.append('\n');
        }
      }

      text.append("    return p;\n  }\n");

      result[0] = new StringBuffer("    p = " + className + ".N");
      result[0].append(Integer.toHexString(m_localModel.hashCode()) + printID)
        .append("(i);\n");
      result[1] = text.append(atEnd);
    }
    return result;
  }

  /**
   * Returns number of leaves in tree structure.
   * 
   * @return the number of leaves
   */
  public int numLeaves() {

    int num = 0;
    int i;

    if (m_isLeaf) {
      return 1;
    } else {
      for (i = 0; i < m_sons.length; i++) {
        num = num + m_sons[i].numLeaves();
      }
    }

    return num;
  }

  /**
   * Returns number of nodes in tree structure.
   * 
   * @return the number of nodes
   */
  public int numNodes() {

    int no = 1;
    int i;

    if (!m_isLeaf) {
      for (i = 0; i < m_sons.length; i++) {
        no = no + m_sons[i].numNodes();
      }
    }

    return no;
  }

  /**
   * Prints tree structure.
   * 
   * @return the tree structure
   */
  @Override
  public String toString() {

    try {
      StringBuffer text = new StringBuffer();

      if (m_isLeaf) {
        text.append(": ");
        text.append(m_localModel.dumpLabel(0, m_train));
      } else {
        dumpTree(0, text);
      }
      text.append("\n\nNumber of Leaves  : \t" + numLeaves() + "\n");
      text.append("\nSize of the tree : \t" + numNodes() + "\n");

      return text.toString();
    } catch (Exception e) {
      return "Can't print classification tree.";
    }
  }

  /**
   * Returns a newly created tree.
   * 
   * @param data the training data
   * @return the generated tree
   * @throws Exception if something goes wrong
   */
  protected ClassifierTree getNewTree(Instances data) throws Exception {

    ClassifierTree newTree = new ClassifierTree(m_toSelectModel);
    newTree.buildTree(data, false);

    return newTree;
  }

  /**
   * Returns a newly created tree.
   * 
   * @param train the training data
   * @param test the pruning data.
   * @return the generated tree
   * @throws Exception if something goes wrong
   */
  protected ClassifierTree getNewTree(Instances train, Instances test)
    throws Exception {

    ClassifierTree newTree = new ClassifierTree(m_toSelectModel);
    newTree.buildTree(train, test, false);

    return newTree;
  }

  /**
   * Help method for printing tree structure.
   * 
   * @param depth the current depth
   * @param text for outputting the structure
   * @throws Exception if something goes wrong
   */
  private void dumpTree(int depth, StringBuffer text) throws Exception {

    int i, j;

    for (i = 0; i < m_sons.length; i++) {
      text.append("\n");
      ;
      for (j = 0; j < depth; j++) {
        text.append("|   ");
      }
      text.append(m_localModel.leftSide(m_train));
      text.append(m_localModel.rightSide(i, m_train));
      if (m_sons[i].m_isLeaf) {
        text.append(": ");
        text.append(m_localModel.dumpLabel(i, m_train));
      } else {
        m_sons[i].dumpTree(depth + 1, text);
      }
    }
  }

  /**
   * Help method for printing tree structure as a graph.
   * 
   * @param text for outputting the tree
   * @throws Exception if something goes wrong
   */
  private void graphTree(StringBuffer text) throws Exception {

    for (int i = 0; i < m_sons.length; i++) {
      text.append("N" + m_id + "->" + "N" + m_sons[i].m_id + " [label=\""
        + Utils.backQuoteChars(m_localModel.rightSide(i, m_train).trim())
        + "\"]\n");
      if (m_sons[i].m_isLeaf) {
        text.append("N" + m_sons[i].m_id + " [label=\""
          + Utils.backQuoteChars(m_localModel.dumpLabel(i, m_train)) + "\" "
          + "shape=box style=filled ");
        if (m_train != null && m_train.numInstances() > 0) {
          text.append("data =\n" + m_sons[i].m_train + "\n");
          text.append(",\n");
        }
        text.append("]\n");
      } else {
        text.append("N" + m_sons[i].m_id + " [label=\""
          + Utils.backQuoteChars(m_sons[i].m_localModel.leftSide(m_train))
          + "\" ");
        if (m_train != null && m_train.numInstances() > 0) {
          text.append("data =\n" + m_sons[i].m_train + "\n");
          text.append(",\n");
        }
        text.append("]\n");
        m_sons[i].graphTree(text);
      }
    }
  }

  /**
   * Prints the tree in prefix form
   * 
   * @param text the buffer to output the prefix form to
   * @throws Exception if something goes wrong
   */
  private void prefixTree(StringBuffer text) throws Exception {

    text.append("[");
    text.append(m_localModel.leftSide(m_train) + ":");
    for (int i = 0; i < m_sons.length; i++) {
      if (i > 0) {
        text.append(",\n");
      }
      text.append(m_localModel.rightSide(i, m_train));
    }
    for (int i = 0; i < m_sons.length; i++) {
      if (m_sons[i].m_isLeaf) {
        text.append("[");
        text.append(m_localModel.dumpLabel(i, m_train));
        text.append("]");
      } else {
        m_sons[i].prefixTree(text);
      }
    }
    text.append("]");
  }

  /**
   * Help method for computing class probabilities of a given instance.
   * 
   * @param classIndex the class index
   * @param instance the instance to compute the probabilities for
   * @param weight the weight to use
   * @return the laplace probs
   * @throws Exception if something goes wrong
   */
  private double getProbsLaplace(int classIndex, Instance instance,
    double weight) throws Exception {

    double prob = 0;

    if (m_isLeaf) {
      return weight * localModel().classProbLaplace(classIndex, instance, -1);
    } else {
      int treeIndex = localModel().whichSubset(instance);
      if (treeIndex == -1) {
        double[] weights = localModel().weights(instance);
        for (int i = 0; i < m_sons.length; i++) {
          if (!son(i).m_isEmpty) {
            prob += son(i).getProbsLaplace(classIndex, instance,
              weights[i] * weight);
          }
        }
        return prob;
      } else {
        if (son(treeIndex).m_isEmpty) {
          return weight
            * localModel().classProbLaplace(classIndex, instance, treeIndex);
        } else {
          return son(treeIndex).getProbsLaplace(classIndex, instance, weight);
        }
      }
    }
  }

  /**
   * Help method for computing class probabilities of a given instance.
   * 
   * @param classIndex the class index
   * @param instance the instance to compute the probabilities for
   * @param weight the weight to use
   * @return the probs
   * @throws Exception if something goes wrong
   */
  private double getProbs(int classIndex, Instance instance, double weight)
    throws Exception {

    double prob = 0;

    if (m_isLeaf) {
      return weight * localModel().classProb(classIndex, instance, -1);
    } else {
      int treeIndex = localModel().whichSubset(instance);
      if (treeIndex == -1) {
        double[] weights = localModel().weights(instance);
        for (int i = 0; i < m_sons.length; i++) {
          if (!son(i).m_isEmpty) {
            prob += son(i).getProbs(classIndex, instance, weights[i] * weight);
          }
        }
        return prob;
      } else {
        if (son(treeIndex).m_isEmpty) {
          return weight
            * localModel().classProb(classIndex, instance, treeIndex);
        } else {
          return son(treeIndex).getProbs(classIndex, instance, weight);
        }
      }
    }
  }

  /**
   * Method just exists to make program easier to read.
   */
  private ClassifierSplitModel localModel() {

    return m_localModel;
  }

  /**
   * Method just exists to make program easier to read.
   */
  private ClassifierTree son(int index) {

    return m_sons[index];
  }

  /**
   * Computes a list that indicates node membership
   */
  public double[] getMembershipValues(Instance instance) throws Exception {

    // Set up array for membership values
    double[] a = new double[numNodes()];

    // Initialize queues
    Queue queueOfWeights = new LinkedList();
    Queue queueOfNodes = new LinkedList();
    queueOfWeights.add(instance.weight());
    queueOfNodes.add(this);
    int index = 0;

    // While the queue is not empty
    while (!queueOfNodes.isEmpty()) {

      a[index++] = queueOfWeights.poll();
      ClassifierTree node = queueOfNodes.poll();

      // Is node a leaf?
      if (node.m_isLeaf) {
        continue;
      }

      // Which subset?
      int treeIndex = node.localModel().whichSubset(instance);

      // Space for weight distribution
      double[] weights = new double[node.m_sons.length];

      // Check for missing value
      if (treeIndex == -1) {
        weights = node.localModel().weights(instance);
      } else {
        weights[treeIndex] = 1.0;
      }
      for (int i = 0; i < node.m_sons.length; i++) {
        queueOfNodes.add(node.son(i));
        queueOfWeights.add(a[index - 1] * weights[i]);
      }
    }
    return a;
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 11269 $");
  }
}