content: /*
   * 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
  