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weka.classifiers.bayes.net.search.global.RepeatedHillClimber 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 .
 */

/*
 * RepeatedHillClimber.java
 * Copyright (C) 2004-2012 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.classifiers.bayes.net.ParentSet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 *  This Bayes Network learning algorithm repeatedly
 * uses hill climbing starting with a randomly generated network structure and
 * return the best structure of the various runs.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -U <integer>
 *  Number of runs
 * 
 * 
 * 
 * -A <seed>
 *  Random number seed
 * 
 * 
 * 
 * -P <nr of parents>
 *  Maximum number of parents
 * 
 * 
 * 
 * -R
 *  Use arc reversal operation.
 *  (default false)
 * 
 * 
 * 
 * -N
 *  Initial structure is empty (instead of Naive Bayes)
 * 
 * 
 * 
 * -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: 10154 $
 */
public class RepeatedHillClimber extends HillClimber {

  /** for serialization */
  static final long serialVersionUID = -7359197180460703069L;

  /** number of runs **/
  int m_nRuns = 10;
  /** random number seed **/
  int m_nSeed = 1;
  /** random number generator **/
  Random m_random;

  /**
   * search determines the network structure/graph of the network with the
   * repeated hill climbing.
   * 
   * @param bayesNet the network to use
   * @param instances the data to use
   * @throws Exception if something goes wrong
   **/
  @Override
  protected void search(BayesNet bayesNet, Instances instances)
    throws Exception {
    m_random = new Random(getSeed());
    // keeps track of score pf best structure found so far
    double fBestScore;
    double fCurrentScore = calcScore(bayesNet);

    // keeps track of best structure found so far
    BayesNet bestBayesNet;

    // initialize bestBayesNet
    fBestScore = fCurrentScore;
    bestBayesNet = new BayesNet();
    bestBayesNet.m_Instances = instances;
    bestBayesNet.initStructure();
    copyParentSets(bestBayesNet, bayesNet);

    // go do the search
    for (int iRun = 0; iRun < m_nRuns; iRun++) {
      // generate random nework
      generateRandomNet(bayesNet, instances);

      // search
      super.search(bayesNet, instances);

      // calculate score
      fCurrentScore = calcScore(bayesNet);

      // keep track of best network seen so far
      if (fCurrentScore > fBestScore) {
        fBestScore = fCurrentScore;
        copyParentSets(bestBayesNet, bayesNet);
      }
    }

    // restore current network to best network
    copyParentSets(bayesNet, bestBayesNet);

    // free up memory
    bestBayesNet = null;
  } // search

  /**
   * 
   * @param bayesNet
   * @param instances
   */
  void generateRandomNet(BayesNet bayesNet, Instances instances) {
    int nNodes = instances.numAttributes();
    // clear network
    for (int iNode = 0; iNode < nNodes; iNode++) {
      ParentSet parentSet = bayesNet.getParentSet(iNode);
      while (parentSet.getNrOfParents() > 0) {
        parentSet.deleteLastParent(instances);
      }
    }

    // initialize as naive Bayes?
    if (getInitAsNaiveBayes()) {
      int iClass = instances.classIndex();
      // initialize parent sets to have arrow from classifier node to
      // each of the other nodes
      for (int iNode = 0; iNode < nNodes; iNode++) {
        if (iNode != iClass) {
          bayesNet.getParentSet(iNode).addParent(iClass, instances);
        }
      }
    }

    // insert random arcs
    int nNrOfAttempts = m_random.nextInt(nNodes * nNodes);
    for (int iAttempt = 0; iAttempt < nNrOfAttempts; iAttempt++) {
      int iTail = m_random.nextInt(nNodes);
      int iHead = m_random.nextInt(nNodes);
      if (bayesNet.getParentSet(iHead).getNrOfParents() < getMaxNrOfParents()
        && addArcMakesSense(bayesNet, instances, iHead, iTail)) {
        bayesNet.getParentSet(iHead).addParent(iTail, instances);
      }
    }
  } // generateRandomNet

  /**
   * copyParentSets copies parent sets of source to dest BayesNet
   * 
   * @param dest destination network
   * @param source source network
   */
  void copyParentSets(BayesNet dest, BayesNet source) {
    int nNodes = source.getNrOfNodes();
    // clear parent set first
    for (int iNode = 0; iNode < nNodes; iNode++) {
      dest.getParentSet(iNode).copy(source.getParentSet(iNode));
    }
  } // CopyParentSets

  /**
   * Returns the number of runs
   * 
   * @return number of runs
   */
  public int getRuns() {
    return m_nRuns;
  } // getRuns

  /**
   * Sets the number of runs
   * 
   * @param nRuns The number of runs to set
   */
  public void setRuns(int nRuns) {
    m_nRuns = nRuns;
  } // setRuns

  /**
   * Returns the random seed
   * 
   * @return random number seed
   */
  public int getSeed() {
    return m_nSeed;
  } // getSeed

  /**
   * Sets the random number seed
   * 
   * @param nSeed The number of the seed to set
   */
  public void setSeed(int nSeed) {
    m_nSeed = nSeed;
  } // setSeed

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration listOptions() {
    Vector newVector = new Vector(4);

    newVector
      .addElement(new Option("\tNumber of runs", "U", 1, "-U "));
    newVector
      .addElement(new Option("\tRandom number seed", "A", 1, "-A "));

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

    return newVector.elements();
  } // listOptions

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

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -U <integer>
   *  Number of runs
   * 
   * 
   * 
   * -A <seed>
   *  Random number seed
   * 
   * 
   * 
   * -P <nr of parents>
   *  Maximum number of parents
   * 
   * 
   * 
   * -R
   *  Use arc reversal operation.
   *  (default false)
   * 
   * 
   * 
   * -N
   *  Initial structure is empty (instead of Naive Bayes)
   * 
   * 
   * 
   * -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 {
    String sRuns = Utils.getOption('U', options);
    if (sRuns.length() != 0) {
      setRuns(Integer.parseInt(sRuns));
    }

    String sSeed = Utils.getOption('A', options);
    if (sSeed.length() != 0) {
      setSeed(Integer.parseInt(sSeed));
    }

    super.setOptions(options);
  } // setOptions

  /**
   * 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("-U");
    options.add("" + getRuns());

    options.add("-A");
    options.add("" + getSeed());

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

    return options.toArray(new String[0]);
  } // getOptions

  /**
   * This will return a string describing the classifier.
   * 
   * @return The string.
   */
  @Override
  public String globalInfo() {
    return "This Bayes Network learning algorithm repeatedly uses hill climbing starting "
      + "with a randomly generated network structure and return the best structure of the "
      + "various runs.";
  } // globalInfo

  /**
   * @return a string to describe the Runs option.
   */
  public String runsTipText() {
    return "Sets the number of times hill climbing is performed.";
  } // runsTipText

  /**
   * @return a string to describe the Seed option.
   */
  public String seedTipText() {
    return "Initialization value for random number generator."
      + " Setting the seed allows replicability of experiments.";
  } // seedTipText

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10154 $");
  }
}