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weka.classifiers.bayes.net.search.global.K2 Maven / Gradle / Ivy

/*
 *   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 .
 */

/*
 * K2.java
 * Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 * 
 */
package weka.classifiers.bayes.net.search.global;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

import weka.classifiers.bayes.BayesNet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;

/**
 *  This Bayes Network learning algorithm uses a hill
 * climbing algorithm restricted by an order on the variables.

 * 

 * For more information see:

 * 

 * G.F. Cooper, E. Herskovits (1990). A Bayesian method for constructing
 * Bayesian belief networks from databases.

 * 

 * G. Cooper, E. Herskovits (1992). A Bayesian method for the induction of
 * probabilistic networks from data. Machine Learning. 9(4):309-347.

 * 

 * Works with nominal variables and no missing values only.
 * 

 * 
 * 
 *  BibTeX:
 * 
 * 
 * @proceedings{Cooper1990,
 *    author = {G.F. Cooper and E. Herskovits},
 *    booktitle = {Proceedings of the Conference on Uncertainty in AI},
 *    pages = {86-94},
 *    title = {A Bayesian method for constructing Bayesian belief networks from databases},
 *    year = {1990}
 * }
 * 
 * @article{Cooper1992,
 *    author = {G. Cooper and E. Herskovits},
 *    journal = {Machine Learning},
 *    number = {4},
 *    pages = {309-347},
 *    title = {A Bayesian method for the induction of probabilistic networks from data},
 *    volume = {9},
 *    year = {1992}
 * }
 * 
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -N
 *  Initial structure is empty (instead of Naive Bayes)
 * 
 * 
 * 
 * -P <nr of parents>
 *  Maximum number of parents
 * 
 * 
 * 
 * -R
 *  Random order.
 *  (default false)
 * 
 * 
 * 
 * -mbc
 *  Applies a Markov Blanket correction to the network structure, 
 *  after a network structure is learned. This ensures that all 
 *  nodes in the network are part of the Markov blanket of the 
 *  classifier node.
 * 
 * 
 * 
 * -S [LOO-CV|k-Fold-CV|Cumulative-CV]
 *  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
 * 
 * 
 * 
 * -Q
 *  Use probabilistic or 0/1 scoring.
 *  (default probabilistic scoring)
 * 
 * 
 * 
 * 
 * @author Remco Bouckaert ([email protected])
 * @version $Revision: 11247 $
 */
public class K2 extends GlobalScoreSearchAlgorithm implements
  TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = -6626871067466338256L;

  /** Holds flag to indicate ordering should be random **/
  boolean m_bRandomOrder = false;

  /**
   * Returns an instance of a TechnicalInformation object, containing detailed
   * information about the technical background of this class, e.g., paper
   * reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  @Override
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation result;
    TechnicalInformation additional;

    result = new TechnicalInformation(Type.PROCEEDINGS);
    result.setValue(Field.AUTHOR, "G.F. Cooper and E. Herskovits");
    result.setValue(Field.YEAR, "1990");
    result
      .setValue(Field.TITLE,
        "A Bayesian method for constructing Bayesian belief networks from databases");
    result.setValue(Field.BOOKTITLE,
      "Proceedings of the Conference on Uncertainty in AI");
    result.setValue(Field.PAGES, "86-94");

    additional = result.add(Type.ARTICLE);
    additional.setValue(Field.AUTHOR, "G. Cooper and E. Herskovits");
    additional.setValue(Field.YEAR, "1992");
    additional
      .setValue(Field.TITLE,
        "A Bayesian method for the induction of probabilistic networks from data");
    additional.setValue(Field.JOURNAL, "Machine Learning");
    additional.setValue(Field.VOLUME, "9");
    additional.setValue(Field.NUMBER, "4");
    additional.setValue(Field.PAGES, "309-347");

    return result;
  }

  /**
   * search determines the network structure/graph of the network with the K2
   * algorithm, restricted by its initial structure (which can be an empty
   * graph, or a Naive Bayes graph.
   * 
   * @param bayesNet the network
   * @param instances the data to work with
   * @throws Exception if something goes wrong
   */
  @Override
  public void search(BayesNet bayesNet, Instances instances) throws Exception {
    int nOrder[] = new int[instances.numAttributes()];
    nOrder[0] = instances.classIndex();

    int nAttribute = 0;

    for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
      if (nAttribute == instances.classIndex()) {
        nAttribute++;
      }
      nOrder[iOrder] = nAttribute++;
    }

    if (m_bRandomOrder) {
      // generate random ordering (if required)
      Random random = new Random();
      int iClass;
      if (getInitAsNaiveBayes()) {
        iClass = 0;
      } else {
        iClass = -1;
      }
      for (int iOrder = 0; iOrder < instances.numAttributes(); iOrder++) {
        int iOrder2 = random.nextInt(instances.numAttributes());
        if (iOrder != iClass && iOrder2 != iClass) {
          int nTmp = nOrder[iOrder];
          nOrder[iOrder] = nOrder[iOrder2];
          nOrder[iOrder2] = nTmp;
        }
      }
    }

    // determine base scores
    double fBaseScore = calcScore(bayesNet);

    // K2 algorithm: greedy search restricted by ordering
    for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
      int iAttribute = nOrder[iOrder];
      double fBestScore = fBaseScore;

      boolean bProgress = (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents());
      while (bProgress
        && (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents())) {
        int nBestAttribute = -1;
        for (int iOrder2 = 0; iOrder2 < iOrder; iOrder2++) {
          int iAttribute2 = nOrder[iOrder2];
          double fScore = calcScoreWithExtraParent(iAttribute, iAttribute2);
          if (fScore > fBestScore) {
            fBestScore = fScore;
            nBestAttribute = iAttribute2;
          }
        }
        if (nBestAttribute != -1) {
          bayesNet.getParentSet(iAttribute)
            .addParent(nBestAttribute, instances);
          fBaseScore = fBestScore;
          bProgress = true;
        } else {
          bProgress = false;
        }
      }
    }
  } // search

  /**
   * Sets the max number of parents
   * 
   * @param nMaxNrOfParents the max number of parents
   */
  public void setMaxNrOfParents(int nMaxNrOfParents) {
    m_nMaxNrOfParents = nMaxNrOfParents;
  }

  /**
   * Gets the max number of parents.
   * 
   * @return the max number of parents
   */
  public int getMaxNrOfParents() {
    return m_nMaxNrOfParents;
  }

  /**
   * Sets whether to init as naive bayes
   * 
   * @param bInitAsNaiveBayes whether to init as naive bayes
   */
  public void setInitAsNaiveBayes(boolean bInitAsNaiveBayes) {
    m_bInitAsNaiveBayes = bInitAsNaiveBayes;
  }

  /**
   * Gets whether to init as naive bayes
   * 
   * @return whether to init as naive bayes
   */
  public boolean getInitAsNaiveBayes() {
    return m_bInitAsNaiveBayes;
  }

  /**
   * Set random order flag
   * 
   * @param bRandomOrder the random order flag
   */
  public void setRandomOrder(boolean bRandomOrder) {
    m_bRandomOrder = bRandomOrder;
  } // SetRandomOrder

  /**
   * Get random order flag
   * 
   * @return the random order flag
   */
  public boolean getRandomOrder() {
    return m_bRandomOrder;
  } // getRandomOrder

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration listOptions() {
    Vector newVector = new Vector(0);

    newVector.addElement(new Option(
      "\tInitial structure is empty (instead of Naive Bayes)", "N", 0, "-N"));

    newVector.addElement(new Option("\tMaximum number of parents", "P", 1,
      "-P "));

    newVector.addElement(new Option("\tRandom order.\n" + "\t(default false)",
      "R", 0, "-R"));

    newVector.addAll(Collections.list(super.listOptions()));

    return newVector.elements();
  }

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -N
   *  Initial structure is empty (instead of Naive Bayes)
   * 
   * 
   * 
   * -P <nr of parents>
   *  Maximum number of parents
   * 
   * 
   * 
   * -R
   *  Random order.
   *  (default false)
   * 
   * 
   * 
   * -mbc
   *  Applies a Markov Blanket correction to the network structure, 
   *  after a network structure is learned. This ensures that all 
   *  nodes in the network are part of the Markov blanket of the 
   *  classifier node.
   * 
   * 
   * 
   * -S [LOO-CV|k-Fold-CV|Cumulative-CV]
   *  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
   * 
   * 
   * 
   * -Q
   *  Use probabilistic or 0/1 scoring.
   *  (default probabilistic scoring)
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {

    setRandomOrder(Utils.getFlag('R', options));

    m_bInitAsNaiveBayes = !(Utils.getFlag('N', options));

    String sMaxNrOfParents = Utils.getOption('P', options);

    if (sMaxNrOfParents.length() != 0) {
      setMaxNrOfParents(Integer.parseInt(sMaxNrOfParents));
    } else {
      setMaxNrOfParents(100000);
    }
    super.setOptions(options);
  }

  /**
   * Gets the current settings of the search algorithm.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String[] getOptions() {

    Vector options = new Vector();

    options.add("-P");
    options.add("" + m_nMaxNrOfParents);
    if (!m_bInitAsNaiveBayes) {
      options.add("-N");
    }
    if (getRandomOrder()) {
      options.add("-R");
    }

    Collections.addAll(options, super.getOptions());

    // Fill up rest with empty strings, not nulls!
    return options.toArray(new String[0]);
  }

  /**
   * @return a string to describe the RandomOrder option.
   */
  public String randomOrderTipText() {
    return "When set to true, the order of the nodes in the network is random."
      + " Default random order is false and the order"
      + " of the nodes in the dataset is used."
      + " In any case, when the network was initialized as Naive Bayes Network, the"
      + " class variable is first in the ordering though.";
  } // randomOrderTipText

  /**
   * This will return a string describing the search algorithm.
   * 
   * @return The string.
   */
  @Override
  public String globalInfo() {
    return "This Bayes Network learning algorithm uses a hill climbing algorithm "
      + "restricted by an order on the variables.\n\n"
      + "For more information see:\n\n"
      + getTechnicalInformation().toString()
      + "\n\n" + "Works with nominal variables and no missing values only.";
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 11247 $");
  }
}