source: tags/MetisMQIDemo/src/main/java/weka/classifiers/bayes/net/search/global/TabuSearch.java

/*
 * 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;

/** 
 <!-- globalinfo-start -->
 * 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.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * R.R. Bouckaert (1995). Bayesian Belief Networks: from Construction to Inference. Utrecht, Netherlands.
 * <p/>
 <!-- globalinfo-end -->
 * 
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;phdthesis{Bouckaert1995,
 *    address = {Utrecht, Netherlands},
 *    author = {R.R. Bouckaert},
 *    institution = {University of Utrecht},
 *    title = {Bayesian Belief Networks: from Construction to Inference},
 *    year = {1995}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 * 
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -L &lt;integer&gt;
 *  Tabu list length</pre>
 * 
 * <pre> -U &lt;integer&gt;
 *  Number of runs</pre>
 * 
 * <pre> -P &lt;nr of parents&gt;
 *  Maximum number of parents</pre>
 * 
 * <pre> -R
 *  Use arc reversal operation.
 *  (default false)</pre>
 * 
 * <pre> -P &lt;nr of parents&gt;
 *  Maximum number of parents</pre>
 * 
 * <pre> -R
 *  Use arc reversal operation.
 *  (default false)</pre>
 * 
 * <pre> -N
 *  Initial structure is empty (instead of Naive Bayes)</pre>
 * 
 * <pre> -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.</pre>
 * 
 * <pre> -S [LOO-CV|k-Fold-CV|Cumulative-CV]
 *  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)</pre>
 * 
 * <pre> -Q
 *  Use probabilistic or 0/1 scoring.
 *  (default probabilistic scoring)</pre>
 * 
 <!-- options-end -->
 *
 * @author Remco Bouckaert (rrb@xm.co.nz)
 * @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 <integer>"));
                newVector.addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
                newVector.addElement(new Option("\tMaximum number of parents", "P", 1, "-P <nr of parents>"));
                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. <p/>
         *
         <!-- options-start -->
         * Valid options are: <p/>
         * 
         * <pre> -L &lt;integer&gt;
         *  Tabu list length</pre>
         * 
         * <pre> -U &lt;integer&gt;
         *  Number of runs</pre>
         * 
         * <pre> -P &lt;nr of parents&gt;
         *  Maximum number of parents</pre>
         * 
         * <pre> -R
         *  Use arc reversal operation.
         *  (default false)</pre>
         * 
         * <pre> -P &lt;nr of parents&gt;
         *  Maximum number of parents</pre>
         * 
         * <pre> -R
         *  Use arc reversal operation.
         *  (default false)</pre>
         * 
         * <pre> -N
         *  Initial structure is empty (instead of Naive Bayes)</pre>
         * 
         * <pre> -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.</pre>
         * 
         * <pre> -S [LOO-CV|k-Fold-CV|Cumulative-CV]
         *  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)</pre>
         * 
         * <pre> -Q
         *  Use probabilistic or 0/1 scoring.
         *  (default probabilistic scoring)</pre>
         * 
         <!-- options-end -->
         *
         * @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