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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 .
 */

/*
 *    NBTreeClassifierTree.java
 *    Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.j48;

import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 * Class for handling a naive bayes tree structure used for classification.
 * 
 * @author Mark Hall ([email protected])
 * @version $Revision: 10293 $
 */
public class NBTreeClassifierTree extends ClassifierTree {

  /** for serialization */
  private static final long serialVersionUID = -4472639447877404786L;

  public NBTreeClassifierTree(ModelSelection toSelectLocModel) {
    super(toSelectLocModel);
  }

  /**
   * Returns default capabilities of the classifier tree.
   * 
   * @return the capabilities of this classifier tree
   */
  @Override
  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.MISSING_CLASS_VALUES);

    // instances
    result.setMinimumNumberInstances(0);

    return result;
  }

  /**
   * Method for building a naive bayes classifier tree
   * 
   * @exception Exception if something goes wrong
   */
  @Override
  public void buildClassifier(Instances data) throws Exception {
    super.buildClassifier(data);
    cleanup(new Instances(data, 0));
    assignIDs(-1);
  }

  /**
   * Assigns a uniqe id to every node in the tree.
   * 
   * public int assignIDs(int lastID) {
   * 
   * int currLastID = lastID + 1;
   * 
   * m_id = currLastID; if (m_sons != null) { for (int i = 0; i < m_sons.length;
   * i++) { currLastID = m_sons[i].assignIDs(currLastID); } } return currLastID;
   * }
   */

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

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

    return newTree;
  }

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

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

    return newTree;
  }

  /**
   * Print the models at the leaves
   * 
   * @return textual description of the leaf models
   */
  public String printLeafModels() {
    StringBuffer text = new StringBuffer();

    if (m_isLeaf) {
      text.append("\nLeaf number: " + m_id + " ");
      text.append(m_localModel.toString());
      text.append("\n");
    } else {
      for (ClassifierTree m_son : m_sons) {
        text.append(((NBTreeClassifierTree) m_son).printLeafModels());
      }
    }
    return text.toString();
  }

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

    try {
      StringBuffer text = new StringBuffer();

      if (m_isLeaf) {
        text.append(": NB");
        text.append(m_id);
      } else {
        dumpTreeNB(0, text);
      }

      text.append("\n" + printLeafModels());
      text.append("\n\nNumber of Leaves  : \t" + numLeaves() + "\n");
      text.append("\nSize of the tree : \t" + numNodes() + "\n");

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

  /**
   * Help method for printing tree structure.
   * 
   * @exception Exception if something goes wrong
   */
  private void dumpTreeNB(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(": NB ");
        text.append(m_sons[i].m_id);
      } else {
        ((NBTreeClassifierTree) m_sons[i]).dumpTreeNB(depth + 1, text);
      }
    }
  }

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

    StringBuffer text = new StringBuffer();

    text.append("digraph J48Tree {\n");
    if (m_isLeaf) {
      text.append("N" + m_id + " [label=\"" + "NB model" + "\" "
        + "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";
  }

  /**
   * Help method for printing tree structure as a graph.
   * 
   * @exception 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=\"" + "NB Model" + "\" "
          + "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");
        ((NBTreeClassifierTree) m_sons[i]).graphTree(text);
      }
    }
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10293 $");
  }
}