• Home
• nz.ac.waikato.cms.weka
• weka-dev
• 3.9.2
• source code
• HillClimber.java

×

Project download

Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $

You can buy this project and download/modify it how often you want.

weka.classifiers.bayes.net.search.local.HillClimber 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 .
 */

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

package weka.classifiers.bayes.net.search.local;

import java.io.Serializable;
import java.util.Collections;
import java.util.Enumeration;
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.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 *  This Bayes Network learning algorithm uses a hill
 * climbing algorithm adding, deleting and reversing arcs. The search is not
 * restricted by an order on the variables (unlike K2). The difference with B
 * and B2 is that this hill climber also considers arrows part of the naive
 * Bayes structure for deletion.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
 *  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
 * 
 * 
 * 
 * 
 * @author Remco Bouckaert ([email protected])
 * @version $Revision: 10154 $
 */
public class HillClimber extends LocalScoreSearchAlgorithm {

  /** for serialization */
  static final long serialVersionUID = 4322783593818122403L;

  /**
   * the Operation class contains info on operations performed on the current
   * Bayesian network.
   */
  class Operation implements Serializable, RevisionHandler {

    /** for serialization */
    static final long serialVersionUID = -4880888790432547895L;

    // constants indicating the type of an operation
    final static int OPERATION_ADD = 0;
    final static int OPERATION_DEL = 1;
    final static int OPERATION_REVERSE = 2;

    /**
     * c'tor
     */
    public Operation() {
    }

    /**
     * c'tor + initializers
     * 
     * @param nTail
     * @param nHead
     * @param nOperation
     */
    public Operation(int nTail, int nHead, int nOperation) {
      m_nHead = nHead;
      m_nTail = nTail;
      m_nOperation = nOperation;
    }

    /**
     * compare this operation with another
     * 
     * @param other operation to compare with
     * @return true if operation is the same
     */
    public boolean equals(Operation other) {
      if (other == null) {
        return false;
      }
      return ((m_nOperation == other.m_nOperation)
        && (m_nHead == other.m_nHead) && (m_nTail == other.m_nTail));
    } // equals

    /** number of the tail node **/
    public int m_nTail;

    /** number of the head node **/
    public int m_nHead;

    /** type of operation (ADD, DEL, REVERSE) **/
    public int m_nOperation;

    /** change of score due to this operation **/
    public double m_fDeltaScore = -1E100;

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

  /**
   * cache for remembering the change in score for steps in the search space
   */
  class Cache implements RevisionHandler {

    /** change in score due to adding an arc **/
    double[][] m_fDeltaScoreAdd;
    /** change in score due to deleting an arc **/
    double[][] m_fDeltaScoreDel;

    /**
     * c'tor
     * 
     * @param nNrOfNodes number of nodes in network, used to determine memory
     *          size to reserve
     */
    Cache(int nNrOfNodes) {
      m_fDeltaScoreAdd = new double[nNrOfNodes][nNrOfNodes];
      m_fDeltaScoreDel = new double[nNrOfNodes][nNrOfNodes];
    }

    /**
     * set cache entry
     * 
     * @param oOperation operation to perform
     * @param fValue value to put in cache
     */
    public void put(Operation oOperation, double fValue) {
      if (oOperation.m_nOperation == Operation.OPERATION_ADD) {
        m_fDeltaScoreAdd[oOperation.m_nTail][oOperation.m_nHead] = fValue;
      } else {
        m_fDeltaScoreDel[oOperation.m_nTail][oOperation.m_nHead] = fValue;
      }
    } // put

    /**
     * get cache entry
     * 
     * @param oOperation operation to perform
     * @return cache value
     */
    public double get(Operation oOperation) {
      switch (oOperation.m_nOperation) {
      case Operation.OPERATION_ADD:
        return m_fDeltaScoreAdd[oOperation.m_nTail][oOperation.m_nHead];
      case Operation.OPERATION_DEL:
        return m_fDeltaScoreDel[oOperation.m_nTail][oOperation.m_nHead];
      case Operation.OPERATION_REVERSE:
        return m_fDeltaScoreDel[oOperation.m_nTail][oOperation.m_nHead]
          + m_fDeltaScoreAdd[oOperation.m_nHead][oOperation.m_nTail];
      }
      // should never get here
      return 0;
    } // get

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

  /** cache for storing score differences **/
  Cache m_Cache = null;

  /** use the arc reversal operator **/
  boolean m_bUseArcReversal = false;

  /**
   * search determines the network structure/graph of the network with the Taby
   * algorithm.
   * 
   * @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 {
    initCache(bayesNet, instances);

    // go do the search
    Operation oOperation = getOptimalOperation(bayesNet, instances);
    while ((oOperation != null) && (oOperation.m_fDeltaScore > 0)) {
      performOperation(bayesNet, instances, oOperation);
      oOperation = getOptimalOperation(bayesNet, instances);
    }

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

  /**
   * initCache initializes the cache
   * 
   * @param bayesNet Bayes network to be learned
   * @param instances data set to learn from
   * @throws Exception if something goes wrong
   */
  void initCache(BayesNet bayesNet, Instances instances) throws Exception {

    // determine base scores
    double[] fBaseScores = new double[instances.numAttributes()];
    int nNrOfAtts = instances.numAttributes();

    m_Cache = new Cache(nNrOfAtts);

    for (int iAttribute = 0; iAttribute < nNrOfAtts; iAttribute++) {
      updateCache(iAttribute, nNrOfAtts, bayesNet.getParentSet(iAttribute));
    }

    for (int iAttribute = 0; iAttribute < nNrOfAtts; iAttribute++) {
      fBaseScores[iAttribute] = calcNodeScore(iAttribute);
    }

    for (int iAttributeHead = 0; iAttributeHead < nNrOfAtts; iAttributeHead++) {
      for (int iAttributeTail = 0; iAttributeTail < nNrOfAtts; iAttributeTail++) {
        if (iAttributeHead != iAttributeTail) {
          Operation oOperation = new Operation(iAttributeTail, iAttributeHead,
            Operation.OPERATION_ADD);
          m_Cache.put(oOperation,
            calcScoreWithExtraParent(iAttributeHead, iAttributeTail)
              - fBaseScores[iAttributeHead]);
        }
      }
    }

  } // initCache

  /**
   * check whether the operation is not in the forbidden. For base hill climber,
   * there are no restrictions on operations, so we always return true.
   * 
   * @param oOperation operation to be checked
   * @return true if operation is not in the tabu list
   */
  boolean isNotTabu(Operation oOperation) {
    return true;
  } // isNotTabu

  /**
   * getOptimalOperation finds the optimal operation that can be performed on
   * the Bayes network that is not in the tabu list.
   * 
   * @param bayesNet Bayes network to apply operation on
   * @param instances data set to learn from
   * @return optimal operation found
   * @throws Exception if something goes wrong
   */
  Operation getOptimalOperation(BayesNet bayesNet, Instances instances)
    throws Exception {
    Operation oBestOperation = new Operation();

    // Add???
    oBestOperation = findBestArcToAdd(bayesNet, instances, oBestOperation);
    // Delete???
    oBestOperation = findBestArcToDelete(bayesNet, instances, oBestOperation);
    // Reverse???
    if (getUseArcReversal()) {
      oBestOperation = findBestArcToReverse(bayesNet, instances, oBestOperation);
    }

    // did we find something?
    if (oBestOperation.m_fDeltaScore == -1E100) {
      return null;
    }

    return oBestOperation;
  } // getOptimalOperation

  /**
   * performOperation applies an operation on the Bayes network and update the
   * cache.
   * 
   * @param bayesNet Bayes network to apply operation on
   * @param instances data set to learn from
   * @param oOperation operation to perform
   * @throws Exception if something goes wrong
   */
  void performOperation(BayesNet bayesNet, Instances instances,
    Operation oOperation) throws Exception {
    // perform operation
    switch (oOperation.m_nOperation) {
    case Operation.OPERATION_ADD:
      applyArcAddition(bayesNet, oOperation.m_nHead, oOperation.m_nTail,
        instances);
      if (bayesNet.getDebug()) {
        System.out.print("Add " + oOperation.m_nHead + " -> "
          + oOperation.m_nTail);
      }
      break;
    case Operation.OPERATION_DEL:
      applyArcDeletion(bayesNet, oOperation.m_nHead, oOperation.m_nTail,
        instances);
      if (bayesNet.getDebug()) {
        System.out.print("Del " + oOperation.m_nHead + " -> "
          + oOperation.m_nTail);
      }
      break;
    case Operation.OPERATION_REVERSE:
      applyArcDeletion(bayesNet, oOperation.m_nHead, oOperation.m_nTail,
        instances);
      applyArcAddition(bayesNet, oOperation.m_nTail, oOperation.m_nHead,
        instances);
      if (bayesNet.getDebug()) {
        System.out.print("Rev " + oOperation.m_nHead + " -> "
          + oOperation.m_nTail);
      }
      break;
    }
  } // performOperation

  /**
   * 
   * @param bayesNet
   * @param iHead
   * @param iTail
   * @param instances
   */
  void applyArcAddition(BayesNet bayesNet, int iHead, int iTail,
    Instances instances) {
    ParentSet bestParentSet = bayesNet.getParentSet(iHead);
    bestParentSet.addParent(iTail, instances);
    updateCache(iHead, instances.numAttributes(), bestParentSet);
  } // applyArcAddition

  /**
   * 
   * @param bayesNet
   * @param iHead
   * @param iTail
   * @param instances
   */
  void applyArcDeletion(BayesNet bayesNet, int iHead, int iTail,
    Instances instances) {
    ParentSet bestParentSet = bayesNet.getParentSet(iHead);
    bestParentSet.deleteParent(iTail, instances);
    updateCache(iHead, instances.numAttributes(), bestParentSet);
  } // applyArcAddition

  /**
   * find best (or least bad) arc addition operation
   * 
   * @param bayesNet Bayes network to add arc to
   * @param instances data set
   * @param oBestOperation
   * @return Operation containing best arc to add, or null if no arc addition is
   *         allowed (this can happen if any arc addition introduces a cycle, or
   *         all parent sets are filled up to the maximum nr of parents).
   */
  Operation findBestArcToAdd(BayesNet bayesNet, Instances instances,
    Operation oBestOperation) {
    int nNrOfAtts = instances.numAttributes();
    // find best arc to add
    for (int iAttributeHead = 0; iAttributeHead < nNrOfAtts; iAttributeHead++) {
      if (bayesNet.getParentSet(iAttributeHead).getNrOfParents() < m_nMaxNrOfParents) {
        for (int iAttributeTail = 0; iAttributeTail < nNrOfAtts; iAttributeTail++) {
          if (addArcMakesSense(bayesNet, instances, iAttributeHead,
            iAttributeTail)) {
            Operation oOperation = new Operation(iAttributeTail,
              iAttributeHead, Operation.OPERATION_ADD);
            if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
              if (isNotTabu(oOperation)) {
                oBestOperation = oOperation;
                oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
              }
            }
          }
        }
      }
    }
    return oBestOperation;
  } // findBestArcToAdd

  /**
   * find best (or least bad) arc deletion operation
   * 
   * @param bayesNet Bayes network to delete arc from
   * @param instances data set
   * @param oBestOperation
   * @return Operation containing best arc to delete, or null if no deletion can
   *         be made (happens when there is no arc in the network yet).
   */
  Operation findBestArcToDelete(BayesNet bayesNet, Instances instances,
    Operation oBestOperation) {
    int nNrOfAtts = instances.numAttributes();
    // find best arc to delete
    for (int iNode = 0; iNode < nNrOfAtts; iNode++) {
      ParentSet parentSet = bayesNet.getParentSet(iNode);
      for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
        Operation oOperation = new Operation(parentSet.getParent(iParent),
          iNode, Operation.OPERATION_DEL);
        if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
          if (isNotTabu(oOperation)) {
            oBestOperation = oOperation;
            oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
          }
        }
      }
    }
    return oBestOperation;
  } // findBestArcToDelete

  /**
   * find best (or least bad) arc reversal operation
   * 
   * @param bayesNet Bayes network to reverse arc in
   * @param instances data set
   * @param oBestOperation
   * @return Operation containing best arc to reverse, or null if no reversal is
   *         allowed (happens if there is no arc in the network yet, or when any
   *         such reversal introduces a cycle).
   */
  Operation findBestArcToReverse(BayesNet bayesNet, Instances instances,
    Operation oBestOperation) {
    int nNrOfAtts = instances.numAttributes();
    // find best arc to reverse
    for (int iNode = 0; iNode < nNrOfAtts; iNode++) {
      ParentSet parentSet = bayesNet.getParentSet(iNode);
      for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
        int iTail = parentSet.getParent(iParent);
        // is reversal allowed?
        if (reverseArcMakesSense(bayesNet, instances, iNode, iTail)
          && bayesNet.getParentSet(iTail).getNrOfParents() < m_nMaxNrOfParents) {
          // go check if reversal results in the best step forward
          Operation oOperation = new Operation(parentSet.getParent(iParent),
            iNode, Operation.OPERATION_REVERSE);
          if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
            if (isNotTabu(oOperation)) {
              oBestOperation = oOperation;
              oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
            }
          }
        }
      }
    }
    return oBestOperation;
  } // findBestArcToReverse

  /**
   * update the cache due to change of parent set of a node
   * 
   * @param iAttributeHead node that has its parent set changed
   * @param nNrOfAtts number of nodes/attributes in data set
   * @param parentSet new parents set of node iAttributeHead
   */
  void updateCache(int iAttributeHead, int nNrOfAtts, ParentSet parentSet) {
    // update cache entries for arrows heading towards iAttributeHead
    double fBaseScore = calcNodeScore(iAttributeHead);
    int nNrOfParents = parentSet.getNrOfParents();
    for (int iAttributeTail = 0; iAttributeTail < nNrOfAtts; iAttributeTail++) {
      if (iAttributeTail != iAttributeHead) {
        if (!parentSet.contains(iAttributeTail)) {
          // add entries to cache for adding arcs
          if (nNrOfParents < m_nMaxNrOfParents) {
            Operation oOperation = new Operation(iAttributeTail,
              iAttributeHead, Operation.OPERATION_ADD);
            m_Cache.put(oOperation,
              calcScoreWithExtraParent(iAttributeHead, iAttributeTail)
                - fBaseScore);
          }
        } else {
          // add entries to cache for deleting arcs
          Operation oOperation = new Operation(iAttributeTail, iAttributeHead,
            Operation.OPERATION_DEL);
          m_Cache.put(oOperation,
            calcScoreWithMissingParent(iAttributeHead, iAttributeTail)
              - fBaseScore);
        }
      }
    }
  } // updateCache

  /**
   * 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;
  }

  /**
   * 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("\tMaximum number of parents", "P", 1,
      "-P "));
    newVector.addElement(new Option(
      "\tUse arc reversal operation.\n\t(default false)", "R", 0, "-R"));
    newVector.addElement(new Option(
      "\tInitial structure is empty (instead of Naive Bayes)", "N", 0, "-N"));
    newVector.addElement(new Option(
      "\tInitial structure specified in XML BIF file", "X", 1, "-X"));

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

    return newVector.elements();
  } // listOptions

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

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
   *  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
   * 
   * 
   * 
   * 
   * @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 {
    setUseArcReversal(Utils.getFlag('R', options));

    setInitAsNaiveBayes(!(Utils.getFlag('N', options)));

    m_sInitalBIFFile = Utils.getOption('X', options);

    String sMaxNrOfParents = Utils.getOption('P', options);
    if (sMaxNrOfParents.length() != 0) {
      setMaxNrOfParents(Integer.parseInt(sMaxNrOfParents));
    } else {
      setMaxNrOfParents(100000);
    }

    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();

    if (getUseArcReversal()) {
      options.add("-R");
    }

    if (!getInitAsNaiveBayes()) {
      options.add("-N");
    }
    if (m_sInitalBIFFile != null && !m_sInitalBIFFile.equals("")) {
      options.add("-X");
      options.add(m_sInitalBIFFile);
    }

    options.add("-P");
    options.add("" + m_nMaxNrOfParents);

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

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

  /**
   * 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;
  }

  /**
   * get use the arc reversal operation
   * 
   * @return whether the arc reversal operation should be used
   */
  public boolean getUseArcReversal() {
    return m_bUseArcReversal;
  } // getUseArcReversal

  /**
   * set use the arc reversal operation
   * 
   * @param bUseArcReversal whether the arc reversal operation should be used
   */
  public void setUseArcReversal(boolean bUseArcReversal) {
    m_bUseArcReversal = bUseArcReversal;
  } // setUseArcReversal

  /**
   * 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 "
      + "adding, deleting and reversing arcs. The search is not restricted by an order "
      + "on the variables (unlike K2). The difference with B and B2 is that this hill "
      + "climber also considers arrows part of the naive Bayes structure for deletion.";
  } // globalInfo

  /**
   * @return a string to describe the Use Arc Reversal option.
   */
  public String useArcReversalTipText() {
    return "When set to true, the arc reversal operation is used in the search.";
  } // useArcReversalTipText

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

} // HillClimber