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

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

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;

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

/** 
 <!-- globalinfo-start -->
 * 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.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -U &lt;integer&gt;
 *  Number of runs</pre>
 * 
 * <pre> -A &lt;seed&gt;
 *  Random number seed</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: 1.6 $
 */
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
        **/
        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.
         */
        public Enumeration listOptions() {
                Vector newVector = new Vector(4);

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

                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> -U &lt;integer&gt;
         *  Number of runs</pre>
         * 
         * <pre> -A &lt;seed&gt;
         *  Random number seed</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 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
         */
        public String[] getOptions() {
                String[] superOptions = super.getOptions();
                String[] options = new String[7 + superOptions.length];
                int current = 0;

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

                options[current++] = "-A";
                options[current++] = "" + getSeed();

                // 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 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
         */
        public String getRevision() {
          return RevisionUtils.extract("$Revision: 1.6 $");
        }
}