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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.
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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 2 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, write to the Free Software
 * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 * TabuSearch.java
 * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
 * 
 */
 
package weka.classifiers.bayes.net.search.global;

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.Type;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;

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

/** 
 
 * This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure. Tabu search is hill climbing till an optimum is reached. The following step is the least worst possible step. The last X steps are kept in a list and none of the steps in this so called tabu list is considered in taking the next step. The best network found in this traversal is returned.

 * 

 * For more information see:

 * 

 * R.R. Bouckaert (1995). Bayesian Belief Networks: from Construction to Inference. Utrecht, Netherlands.
 * 
 
 * 
 
 * BibTeX:
 * 
 * @phdthesis{Bouckaert1995,
 *    address = {Utrecht, Netherlands},
 *    author = {R.R. Bouckaert},
 *    institution = {University of Utrecht},
 *    title = {Bayesian Belief Networks: from Construction to Inference},
 *    year = {1995}
 * }
 * 
 * 
 
 * 
 
 * Valid options are: 

 * 
 * 
 -L <integer>
 *  Tabu list length
 * 
 *  -U <integer>
 *  Number of runs
 * 
 *  -P <nr of parents>
 *  Maximum number of parents
 * 
 *  -R
 *  Use arc reversal operation.
 *  (default false)
 * 
 *  -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: 1.5 $
 */
public class TabuSearch 
    extends HillClimber
    implements TechnicalInformationHandler {

    /** for serialization */
    static final long serialVersionUID = 1176705618756672292L;
  
    /** number of runs **/
    int m_nRuns = 10;
	    	
	/** size of tabu list **/
	int m_nTabuList = 5;

	/** the actual tabu list **/
	Operation[] m_oTabuList = null;

	/**
	 * 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
	 */
	public TechnicalInformation getTechnicalInformation() {
	  TechnicalInformation 	result;
	  
	  result = new TechnicalInformation(Type.PHDTHESIS);
	  result.setValue(Field.AUTHOR, "R.R. Bouckaert");
	  result.setValue(Field.YEAR, "1995");
	  result.setValue(Field.TITLE, "Bayesian Belief Networks: from Construction to Inference");
	  result.setValue(Field.INSTITUTION, "University of Utrecht");
	  result.setValue(Field.ADDRESS, "Utrecht, Netherlands");
	  
	  return result;
	}

	/**
	 * search determines the network structure/graph of the network
	 * with the Tabu search algorithm.
	 * 
	 * @param bayesNet the network to use
	 * @param instances the instances to use
	 * @throws Exception if something goes wrong
	 */
	protected void search(BayesNet bayesNet, Instances instances) throws Exception {
        m_oTabuList = new Operation[m_nTabuList];
        int iCurrentTabuList = 0;

		// 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++) {
            Operation oOperation = getOptimalOperation(bayesNet, instances);
			performOperation(bayesNet, instances, oOperation);
            // sanity check
            if (oOperation  == null) {
				throw new Exception("Panic: could not find any step to make. Tabu list too long?");
            }
            // update tabu list
            m_oTabuList[iCurrentTabuList] = oOperation;
            iCurrentTabuList = (iCurrentTabuList + 1) % m_nTabuList;

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

			if (bayesNet.getDebug()) {
				printTabuList();
			}
        }
        
        // restore current network to best network
		copyParentSets(bayesNet, bestBayesNet);
		
		// free up memory
		bestBayesNet = null;
    } // search


	/** 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

	/** check whether the operation is not in the tabu list
	 * @param oOperation operation to be checked
	 * @return true if operation is not in the tabu list
	 */
	boolean isNotTabu(Operation oOperation) {
		for (int iTabu = 0; iTabu < m_nTabuList; iTabu++) {
			if (oOperation.equals(m_oTabuList[iTabu])) {
					return false;
				}
		}
		return true;
	} // isNotTabu

	/** print tabu list for debugging purposes.
	 */
	void printTabuList() {
		for (int i = 0; i < m_nTabuList; i++) {
			Operation o = m_oTabuList[i];
			if (o != null) {
				if (o.m_nOperation == 0) {System.out.print(" +(");} else {System.out.print(" -(");}
				System.out.print(o.m_nTail + "->" + o.m_nHead + ")");
			}
		}
		System.out.println();
	} // printTabuList

    /**
    * @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

    /**
     * @return the Tabu List length
     */
    public int getTabuList() {
        return m_nTabuList;
    } // getTabuList

    /**
     * Sets the Tabu List length.
     * @param nTabuList The nTabuList to set
     */
    public void setTabuList(int nTabuList) {
        m_nTabuList = nTabuList;
    } // setTabuList

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

		newVector.addElement(new Option("\tTabu list length", "L", 1, "-L "));
		newVector.addElement(new Option("\tNumber of runs", "U", 1, "-U "));
		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"));

		Enumeration enu = super.listOptions();
		while (enu.hasMoreElements()) {
			newVector.addElement(enu.nextElement());
		}
		return newVector.elements();
	} // listOptions

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

	 * 
	 * 
 -L <integer>
	 *  Tabu list length
	 * 
	 *  -U <integer>
	 *  Number of runs
	 * 
	 *  -P <nr of parents>
	 *  Maximum number of parents
	 * 
	 *  -R
	 *  Use arc reversal operation.
	 *  (default false)
	 * 
	 *  -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
	 */
	public void setOptions(String[] options) throws Exception {
		String sTabuList = Utils.getOption('L', options);
		if (sTabuList.length() != 0) {
			setTabuList(Integer.parseInt(sTabuList));
		}
		String sRuns = Utils.getOption('U', options);
		if (sRuns.length() != 0) {
			setRuns(Integer.parseInt(sRuns));
		}
		
		super.setOptions(options);
	} // setOptions

	/**
	 * Gets the current settings of the search algorithm.
	 *
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {
		String[] superOptions = super.getOptions();
		String[] options = new String[7 + superOptions.length];
		int current = 0;
		
		options[current++] = "-L";
		options[current++] = "" + getTabuList();

		options[current++] = "-U";
		options[current++] = "" + getRuns();

		// insert options from parent class
		for (int iOption = 0; iOption < superOptions.length; iOption++) {
			options[current++] = superOptions[iOption];
		}

		// Fill up rest with empty strings, not nulls!
		while (current < options.length) {
			options[current++] = "";
		}
		return options;
	} // getOptions

	/**
	 * This will return a string describing the classifier.
	 * @return The string.
	 */
	public String globalInfo() {
		return "This Bayes Network learning algorithm uses tabu search for finding a well scoring " +
		"Bayes network structure. Tabu search is hill climbing till an optimum is reached. The " +
		"following step is the least worst possible step. The last X steps are kept in a list and " +
		"none of the steps in this so called tabu list is considered in taking the next step. " +
		"The best network found in this traversal is returned.\n\n"
		+ "For more information see:\n\n"
		+ getTechnicalInformation().toString();
	} // globalInfo
	
	/**
	 * @return a string to describe the Runs option.
	 */
	public String runsTipText() {
	  return "Sets the number of steps to be performed.";
	} // runsTipText

	/**
	 * @return a string to describe the TabuList option.
	 */
	public String tabuListTipText() {
	  return "Sets the length of the tabu list.";
	} // tabuListTipText

	/**
	 * Returns the revision string.
	 * 
	 * @return		the revision
	 */
	public String getRevision() {
	  return RevisionUtils.extract("$Revision: 1.5 $");
	}

} // TabuSearch