tabusearch.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 * 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 Remco Bouckaert
 * 
 */
 
package weka.classifiers.bayes.net.search.local;

import weka.classifiers.bayes.BayesNet;
import weka.core.*;
import java.util.*;

/** TabuSearch implements tabu search for learning Bayesian network
 * structures. For details, see for example 
 * 
 * R.R. Bouckaert. 
 * Bayesian Belief Networks: from Construction to Inference. 
 * Ph.D. thesis, 
 * University of Utrecht, 
 * 1995
 * 
 * @author Remco Bouckaert ([e-mail])
 * Version: $Revision$
 */
public class TabuSearch extends HillClimber {

    /** number of runs **/
    int m_nRuns = 10;
	    	
	/** size of tabu list **/
	int m_nTabuList = 5;

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

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

		// keeps track of score pf best structure found so far 
		double fBestScore;	
		double fCurrentScore = 0.0;
		for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
			fCurrentScore += calcNodeScore(iAttribute);
		}

		// 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_fDeltaScore;
			// 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;
		m_Cache = 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\n", "L", 1, "-L <integer>"));
		newVector.addElement(new Option("\tNumber of runs\n", "U", 1, "-U <integer>"));
		newVector.addElement(new Option("\tMaximum number of parents\n", "P", 1, "-P <nr of parents>"));
		newVector.addElement(new Option("\tUse arc reversal operation.\n\t(default false)", "R", 0, "-R"));

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

	/**
	 * Parses a given list of options. Valid options are:<p>
	 *
	 * For other options see search algorithm.
	 *
	 * @param options the list of options as an array of strings
	 * @exception 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.";
	} // 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

} // TabuSearch