• Home
• nz.ac.waikato.cms.weka
• weka-dev
• 3.9.5
• source code
• LAGDHillClimber.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.LAGDHillClimber 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 .
 */

/*
 * LAGDHillClimber.java
 * Copyright (C) 2005-2012 Manuel Neubach
 * 
 */

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

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

import weka.classifiers.bayes.BayesNet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 *  This Bayes Network learning algorithm uses a Look
 * Ahead Hill Climbing algorithm called LAGD Hill Climbing. Unlike Greedy Hill
 * Climbing it doesn't calculate a best greedy operation (adding, deleting or
 * reversing an arc) but a sequence of nrOfLookAheadSteps operations, which
 * leads to a network structure whose score is most likely higher in comparison
 * to the network obtained by performing a sequence of nrOfLookAheadSteps greedy
 * operations. 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:
 * 

 * 
 * 
 * -L <nr of look ahead steps>
 *  Look Ahead Depth
 * 
 * 
 * 
 * -G <nr of good operations>
 *  Nr of Good Operations
 * 
 * 
 * 
 * -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 Manuel Neubach
 * @version $Revision: 10154 $
 */
public class LAGDHillClimber extends HillClimber {

  /** for serialization */
  static final long serialVersionUID = 7217437499439184344L;

  /** Number of Look Ahead Steps **/
  int m_nNrOfLookAheadSteps = 2;

  /** Number of Good Operations per Step **/
  int m_nNrOfGoodOperations = 5;

  /**
   * search determines the network structure/graph of the network
   * 
   * @param bayesNet the network
   * @param instances the data to use
   * @throws Exception if something goes wrong
   */
  @Override
  protected void search(BayesNet bayesNet, Instances instances)
    throws Exception {
    int k = m_nNrOfLookAheadSteps; // Number of Look Ahead Steps
    int l = m_nNrOfGoodOperations; // Number of Good Operations per step
    lookAheadInGoodDirectionsSearch(bayesNet, instances, k, l);
  } // search

  /**
   * lookAheadInGoodDirectionsSearch determines the network structure/graph of
   * the network with best score according to LAGD Hill Climbing
   * 
   * @param bayesNet the network
   * @param instances the data to use
   * @param nrOfLookAheadSteps
   * @param nrOfGoodOperations
   * @throws Exception if something goes wrong
   */
  protected void lookAheadInGoodDirectionsSearch(BayesNet bayesNet,
    Instances instances, int nrOfLookAheadSteps, int nrOfGoodOperations)
    throws Exception {
    System.out.println("Initializing Cache");
    initCache(bayesNet, instances);

    while (nrOfLookAheadSteps > 1) {
      System.out.println("Look Ahead Depth: " + nrOfLookAheadSteps);
      boolean legalSequence = true;
      double sequenceDeltaScore = 0;
      Operation[] bestOperation = new Operation[nrOfLookAheadSteps];

      bestOperation = getOptimalOperations(bayesNet, instances,
        nrOfLookAheadSteps, nrOfGoodOperations);
      for (int i = 0; i < nrOfLookAheadSteps; i++) {
        if (bestOperation[i] == null) {
          legalSequence = false;
        } else {
          sequenceDeltaScore += bestOperation[i].m_fDeltaScore;
        }
      }
      while (legalSequence && sequenceDeltaScore > 0) {
        System.out.println("Next Iteration..........................");
        for (int i = 0; i < nrOfLookAheadSteps; i++) {
          performOperation(bayesNet, instances, bestOperation[i]);
        }
        bestOperation = getOptimalOperations(bayesNet, instances,
          nrOfLookAheadSteps, nrOfGoodOperations);
        sequenceDeltaScore = 0;
        for (int i = 0; i < nrOfLookAheadSteps; i++) {
          if (bestOperation[i] != null) {
            System.out.println(bestOperation[i].m_nOperation + " "
              + bestOperation[i].m_nHead + " " + bestOperation[i].m_nTail);
            sequenceDeltaScore += bestOperation[i].m_fDeltaScore;
          } else {
            legalSequence = false;

          }
          System.out.println("DeltaScore: " + sequenceDeltaScore);
        }
      }
      --nrOfLookAheadSteps;
    }

    /** last steps with greedy HC **/
    Operation oOperation = getOptimalOperation(bayesNet, instances);
    while ((oOperation != null) && (oOperation.m_fDeltaScore > 0)) {
      performOperation(bayesNet, instances, oOperation);
      System.out.println("Performing last greedy steps");
      oOperation = getOptimalOperation(bayesNet, instances);
    }
    // free up memory
    m_Cache = null;
  } // lookAheadInGoodDirectionsSearch

  /**
   * getAntiOperation determines the Operation, which is needed to cancel
   * oOperation
   * 
   * @param oOperation Operation to cancel
   * @return antiOperation to oOperation
   * @throws Exception if something goes wrong
   */
  protected Operation getAntiOperation(Operation oOperation) throws Exception {
    if (oOperation.m_nOperation == Operation.OPERATION_ADD) {
      return (new Operation(oOperation.m_nTail, oOperation.m_nHead,
        Operation.OPERATION_DEL));
    } else {
      if (oOperation.m_nOperation == Operation.OPERATION_DEL) {
        return (new Operation(oOperation.m_nTail, oOperation.m_nHead,
          Operation.OPERATION_ADD));
      } else {
        return (new Operation(oOperation.m_nHead, oOperation.m_nTail,
          Operation.OPERATION_REVERSE));
      }
    }
  } // getAntiOperation

  /**
   * getGoodOperations determines the nrOfGoodOperations best Operations, which
   * are considered for the calculation of an optimal operationsequence
   * 
   * @param bayesNet Bayes network to apply operation on
   * @param instances data set to learn from
   * @param nrOfGoodOperations number of good operations to consider
   * @return good operations to consider
   * @throws Exception if something goes wrong
   **/
  protected Operation[] getGoodOperations(BayesNet bayesNet,
    Instances instances, int nrOfGoodOperations) throws Exception {
    Operation[] goodOperations = new Operation[nrOfGoodOperations];
    for (int i = 0; i < nrOfGoodOperations; i++) {
      goodOperations[i] = getOptimalOperation(bayesNet, instances);
      if (goodOperations[i] != null) {
        m_Cache.put(goodOperations[i], -1E100);
      } else {
        i = nrOfGoodOperations;
      }
    }
    for (int i = 0; i < nrOfGoodOperations; i++) {
      if (goodOperations[i] != null) {
        if (goodOperations[i].m_nOperation != Operation.OPERATION_REVERSE) {
          m_Cache.put(goodOperations[i], goodOperations[i].m_fDeltaScore);
        } else {
          m_Cache
            .put(
              goodOperations[i],
              goodOperations[i].m_fDeltaScore
                - m_Cache.m_fDeltaScoreAdd[goodOperations[i].m_nHead][goodOperations[i].m_nTail]);
        }
      } else {
        i = nrOfGoodOperations;
      }
    }
    return goodOperations;
  } // getGoodOperations

  /**
   * getOptimalOperations determines an optimal operationsequence in respect of
   * the parameters nrOfLookAheadSteps and nrOfGoodOperations
   * 
   * @param bayesNet Bayes network to apply operation on
   * @param instances data set to learn from
   * @param nrOfLookAheadSteps number of lood ahead steps to use
   * @param nrOfGoodOperations number of good operations to consider
   * @return optimal sequence of operations in respect to nrOfLookAheadSteps and
   *         nrOfGoodOperations
   * @throws Exception if something goes wrong
   **/
  protected Operation[] getOptimalOperations(BayesNet bayesNet,
    Instances instances, int nrOfLookAheadSteps, int nrOfGoodOperations)
    throws Exception {
    if (nrOfLookAheadSteps == 1) { // Abbruch der Rekursion
      Operation[] bestOperation = new Operation[1];
      bestOperation[0] = getOptimalOperation(bayesNet, instances);
      return (bestOperation); // Abbruch der Rekursion
    } else {
      double bestDeltaScore = 0;
      double currentDeltaScore = 0;
      Operation[] bestOperation = new Operation[nrOfLookAheadSteps];
      Operation[] goodOperations = new Operation[nrOfGoodOperations];
      Operation[] tempOperation = new Operation[nrOfLookAheadSteps - 1];
      goodOperations = getGoodOperations(bayesNet, instances,
        nrOfGoodOperations);
      for (int i = 0; i < nrOfGoodOperations; i++) {
        if (goodOperations[i] != null) {
          performOperation(bayesNet, instances, goodOperations[i]);
          tempOperation = getOptimalOperations(bayesNet, instances,
            nrOfLookAheadSteps - 1, nrOfGoodOperations); // rekursiver Abstieg
          currentDeltaScore = goodOperations[i].m_fDeltaScore;
          for (int j = 0; j < nrOfLookAheadSteps - 1; j++) {
            if (tempOperation[j] != null) {
              currentDeltaScore += tempOperation[j].m_fDeltaScore;
            }
          }
          performOperation(bayesNet, instances,
            getAntiOperation(goodOperations[i]));
          if (currentDeltaScore > bestDeltaScore) {
            bestDeltaScore = currentDeltaScore;
            bestOperation[0] = goodOperations[i];
            for (int j = 1; j < nrOfLookAheadSteps; j++) {
              bestOperation[j] = tempOperation[j - 1];
            }
          }
        } else {
          i = nrOfGoodOperations;
        }
      }
      return (bestOperation);
    }
  } // getOptimalOperations

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

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

  /**
   * Sets the number of look-ahead steps
   * 
   * @param nNrOfLookAheadSteps the number of look-ahead steps
   */
  public void setNrOfLookAheadSteps(int nNrOfLookAheadSteps) {
    m_nNrOfLookAheadSteps = nNrOfLookAheadSteps;
  }

  /**
   * Gets the number of look-ahead steps
   * 
   * @return the number of look-ahead step
   */
  public int getNrOfLookAheadSteps() {
    return m_nNrOfLookAheadSteps;
  }

  /**
   * Sets the number of "good operations"
   * 
   * @param nNrOfGoodOperations the number of "good operations"
   */
  public void setNrOfGoodOperations(int nNrOfGoodOperations) {
    m_nNrOfGoodOperations = nNrOfGoodOperations;
  }

  /**
   * Gets the number of "good operations"
   * 
   * @return the number of "good operations"
   */
  public int getNrOfGoodOperations() {
    return m_nNrOfGoodOperations;
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration listOptions() {
    Vector newVector = new Vector(2);

    newVector.addElement(new Option("\tLook Ahead Depth", "L", 2,
      "-L "));
    newVector.addElement(new Option("\tNr of Good Operations", "G", 5,
      "-G "));

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

    return newVector.elements();
  } // listOptions

  /**
   * Parses a given list of options. Valid options are:
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -L <nr of look ahead steps>
   *  Look Ahead Depth
   * 
   * 
   * 
   * -G <nr of good operations>
   *  Nr of Good Operations
   * 
   * 
   * 
   * -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 {
    String sNrOfLookAheadSteps = Utils.getOption('L', options);
    if (sNrOfLookAheadSteps.length() != 0) {
      setNrOfLookAheadSteps(Integer.parseInt(sNrOfLookAheadSteps));
    } else {
      setNrOfLookAheadSteps(2);
    }

    String sNrOfGoodOperations = Utils.getOption('G', options);
    if (sNrOfGoodOperations.length() != 0) {
      setNrOfGoodOperations(Integer.parseInt(sNrOfGoodOperations));
    } else {
      setNrOfGoodOperations(5);
    }

    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("-L");
    options.add("" + m_nNrOfLookAheadSteps);

    options.add("-G");
    options.add("" + m_nNrOfGoodOperations);

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

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

  /**
   * This will return a string describing the search algorithm.
   * 
   * @return The string.
   */
  @Override
  public String globalInfo() {
    return "This Bayes Network learning algorithm uses a Look Ahead Hill Climbing algorithm called LAGD Hill Climbing."
      + " Unlike Greedy Hill Climbing it doesn't calculate a best greedy operation (adding, deleting or reversing an arc) "
      + "but a sequence of nrOfLookAheadSteps operations, which leads to a network structure whose score is most likely "
      + "higher in comparison to the network obtained by performing a sequence of nrOfLookAheadSteps greedy operations. "
      + "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 Number of Look Ahead Steps option.
   */
  public String nrOfLookAheadStepsTipText() {
    return "Sets the Number of Look Ahead Steps. 'nrOfLookAheadSteps = 2' means that all network structures in a "
      + "distance of 2 (from the current network structure) are taken into account for the decision which arcs to add, "
      + "remove or reverse. 'nrOfLookAheadSteps = 1' results in Greedy Hill Climbing.";
  } // nrOfLookAheadStepsTipText

  /**
   * @return a string to describe the Number of Good Operations option.
   */
  public String nrOfGoodOperationsTipText() {
    return "Sets the Number of Good Operations per Look Ahead Step. 'nrOfGoodOperations = 5' means that for the next "
      + "Look Ahead Step only the 5 best Operations (adding, deleting or reversing an arc) are taken into account for the "
      + "calculation of the best sequence consisting of nrOfLookAheadSteps operations.";
  } // nrOfGoodOperationsTipText

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

} // LAGDHillClimber