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.
   */
  
  /*
   * GlobalScoreSearchAlgorithm.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.classifiers.bayes.net.search.SearchAlgorithm;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.RevisionUtils;
  import weka.core.SelectedTag;
  import weka.core.Tag;
  import weka.core.Utils;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /** 
   <!-- globalinfo-start -->
   * This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <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
   * @version $Revision: 1.10 $
   */
  public class GlobalScoreSearchAlgorithm 
  	extends SearchAlgorithm {
  
    	/** for serialization */
    	static final long serialVersionUID = 7341389867906199781L;
  	
  	/** points to Bayes network for which a structure is searched for **/
  	BayesNet m_BayesNet;
  	
  	/** toggle between scoring using accuracy = 0-1 loss (when false) or class probabilities (when true) **/
  	boolean m_bUseProb = true;
  	
  	/** number of folds for k-fold cross validation **/
  	int m_nNrOfFolds = 10;
  
  	/** constant for score type: LOO-CV */
  	final static int LOOCV = 0;
  	/** constant for score type: k-fold-CV */
  	final static int KFOLDCV = 1;
  	/** constant for score type: Cumulative-CV */
  	final static int CUMCV = 2;
  
  	/** the score types **/
  	public static final Tag[] TAGS_CV_TYPE =
  		{
  			new Tag(LOOCV, "LOO-CV"),
  			new Tag(KFOLDCV, "k-Fold-CV"),
  			new Tag(CUMCV, "Cumulative-CV")
  		};
  	/**
  	 * Holds the cross validation strategy used to measure quality of network
  	 */
  	int m_nCVType = LOOCV;
  
  	/**
  	 * performCV returns the accuracy calculated using cross validation.  
  	 * The dataset used is m_Instances associated with the Bayes Network.
  	 * 
  	 * @param bayesNet : Bayes Network containing structure to evaluate
  	 * @return accuracy (in interval 0..1) measured using cv.
  	 * @throws Exception whn m_nCVType is invalided + exceptions passed on by updateClassifier
  	 */
  	public double calcScore(BayesNet bayesNet) throws Exception {
  		switch (m_nCVType) {
  			case LOOCV: 
  				return leaveOneOutCV(bayesNet);
  			case CUMCV: 
  				return cumulativeCV(bayesNet);
  			case KFOLDCV: 
  				return kFoldCV(bayesNet, m_nNrOfFolds);
  			default:
  				throw new Exception("Unrecognized cross validation type encountered: " + m_nCVType);
  		}
  	} // calcScore
  
  	/**
  	 * Calc Node Score With Added Parent
  	 * 
  	 * @param nNode node for which the score is calculate
  	 * @param nCandidateParent candidate parent to add to the existing parent set
  	 * @return log score
  	 * @throws Exception if something goes wrong
  	 */
  	public double calcScoreWithExtraParent(int nNode, int nCandidateParent) throws Exception {
  		ParentSet oParentSet = m_BayesNet.getParentSet(nNode);
  		Instances instances = m_BayesNet.m_Instances;
  
  		// sanity check: nCandidateParent should not be in parent set already
  		for (int iParent = 0; iParent < oParentSet.getNrOfParents(); iParent++) {
  			if (oParentSet.getParent(iParent) == nCandidateParent) {
  				return -1e100;
  			}
  		}
  
  		// set up candidate parent
  		oParentSet.addParent(nCandidateParent, instances);
  
  		// calculate the score
  		double fAccuracy = calcScore(m_BayesNet);
  
  		// delete temporarily added parent
  		oParentSet.deleteLastParent(instances);
  
  		return fAccuracy;
  	} // calcScoreWithExtraParent
  
  
  	/**
  	 * Calc Node Score With Parent Deleted
  	 * 
  	 * @param nNode node for which the score is calculate
  	 * @param nCandidateParent candidate parent to delete from the existing parent set
  	 * @return log score
  	 * @throws Exception if something goes wrong
  	 */
  	public double calcScoreWithMissingParent(int nNode, int nCandidateParent) throws Exception {
  		ParentSet oParentSet = m_BayesNet.getParentSet(nNode);
  		Instances instances = m_BayesNet.m_Instances;
  
  		// sanity check: nCandidateParent should be in parent set already
  		if (!oParentSet.contains( nCandidateParent)) {
  				return -1e100;
  		}
  
  		// set up candidate parent
  		int iParent = oParentSet.deleteParent(nCandidateParent, instances);
  
  		// calculate the score
  		double fAccuracy = calcScore(m_BayesNet);
  
  		// reinsert temporarily deleted parent
  		oParentSet.addParent(nCandidateParent, iParent, instances);
  
  		return fAccuracy;
  	} // calcScoreWithMissingParent
  
  	/**
  	 * Calc Node Score With Arrow reversed
  	 * 
  	 * @param nNode node for which the score is calculate
  	 * @param nCandidateParent candidate parent to delete from the existing parent set
  	 * @return log score
  	 * @throws Exception if something goes wrong
  	 */
  	public double calcScoreWithReversedParent(int nNode, int nCandidateParent) throws Exception {
  		ParentSet oParentSet = m_BayesNet.getParentSet(nNode);
  		ParentSet oParentSet2 = m_BayesNet.getParentSet(nCandidateParent);
  		Instances instances = m_BayesNet.m_Instances;
  
  		// sanity check: nCandidateParent should be in parent set already
  		if (!oParentSet.contains( nCandidateParent)) {
  				return -1e100;
  		}
  
  		// set up candidate parent
  		int iParent = oParentSet.deleteParent(nCandidateParent, instances);
  		oParentSet2.addParent(nNode, instances);
  
  		// calculate the score
  		double fAccuracy = calcScore(m_BayesNet);
  
  		// restate temporarily reversed arrow
  		oParentSet2.deleteLastParent(instances);
  		oParentSet.addParent(nCandidateParent, iParent, instances);
  
  		return fAccuracy;
  	} // calcScoreWithReversedParent
  
  	/**
  	 * LeaveOneOutCV returns the accuracy calculated using Leave One Out
  	 * cross validation. The dataset used is m_Instances associated with
  	 * the Bayes Network.
  	 * @param bayesNet : Bayes Network containing structure to evaluate
  	 * @return accuracy (in interval 0..1) measured using leave one out cv.
  	 * @throws Exception passed on by updateClassifier
  	 */
  	public double leaveOneOutCV(BayesNet bayesNet) throws Exception {
  		m_BayesNet = bayesNet;
  		double fAccuracy = 0.0;
  		double fWeight = 0.0;
  		Instances instances = bayesNet.m_Instances;
  		bayesNet.estimateCPTs();
  		for (int iInstance = 0; iInstance < instances.numInstances(); iInstance++) {
  			Instance instance = instances.instance(iInstance);
  			instance.setWeight(-instance.weight());
  			bayesNet.updateClassifier(instance);
  			fAccuracy += accuracyIncrease(instance);
  			fWeight += instance.weight();
  			instance.setWeight(-instance.weight());
  			bayesNet.updateClassifier(instance);
  		}
  		return fAccuracy / fWeight;
  	} // LeaveOneOutCV
  
  	/**
  	 * CumulativeCV returns the accuracy calculated using cumulative
  	 * cross validation. The idea is to run through the data set and
  	 * try to classify each of the instances based on the previously
  	 * seen data.
  	 * The data set used is m_Instances associated with the Bayes Network.
  	 * @param bayesNet : Bayes Network containing structure to evaluate
  	 * @return accuracy (in interval 0..1) measured using leave one out cv.
  	 * @throws Exception passed on by updateClassifier
  	 */
  	public double cumulativeCV(BayesNet bayesNet) throws Exception {
  		m_BayesNet = bayesNet;
  		double fAccuracy = 0.0;
  		double fWeight = 0.0;
  		Instances instances = bayesNet.m_Instances;
  		bayesNet.initCPTs();
  		for (int iInstance = 0; iInstance < instances.numInstances(); iInstance++) {
  			Instance instance = instances.instance(iInstance);
  			fAccuracy += accuracyIncrease(instance);
  			bayesNet.updateClassifier(instance);
  			fWeight += instance.weight();
  		}
  		return fAccuracy / fWeight;
  	} // LeaveOneOutCV
  	
  	/**
  	 * kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes
  	 * network classifier.
  	 * @param bayesNet : Bayes Network containing structure to evaluate
  	 * @param nNrOfFolds : the number of folds k to perform k-fold cv
  	 * @return accuracy (in interval 0..1) measured using leave one out cv.
  	 * @throws Exception passed on by updateClassifier
  	 */
  	public double kFoldCV(BayesNet bayesNet, int nNrOfFolds) throws Exception {
  		m_BayesNet = bayesNet;
  		double fAccuracy = 0.0;
  		double fWeight = 0.0;
  		Instances instances = bayesNet.m_Instances;
  		// estimate CPTs based on complete data set
  		bayesNet.estimateCPTs();
  		int nFoldStart = 0;
  		int nFoldEnd = instances.numInstances() / nNrOfFolds;
  		int iFold = 1;
  		while (nFoldStart < instances.numInstances()) {
  			// remove influence of fold iFold from the probability distribution
  			for (int iInstance = nFoldStart; iInstance < nFoldEnd; iInstance++) {
  				Instance instance = instances.instance(iInstance);
  				instance.setWeight(-instance.weight());
  				bayesNet.updateClassifier(instance);
  			}
  			
  			// measure accuracy on fold iFold
  			for (int iInstance = nFoldStart; iInstance < nFoldEnd; iInstance++) {
  				Instance instance = instances.instance(iInstance);
  				instance.setWeight(-instance.weight());
  				fAccuracy += accuracyIncrease(instance);
  				instance.setWeight(-instance.weight());
  				fWeight += instance.weight();
  			}
  
  			// restore influence of fold iFold from the probability distribution
  			for (int iInstance = nFoldStart; iInstance < nFoldEnd; iInstance++) {
  				Instance instance = instances.instance(iInstance);
  				instance.setWeight(-instance.weight());
  				bayesNet.updateClassifier(instance);
  			}
  
  			// go to next fold
  			nFoldStart = nFoldEnd;
  			iFold++;
  			nFoldEnd = iFold * instances.numInstances() / nNrOfFolds;
  		}
  		return fAccuracy / fWeight;
  	} // kFoldCV
  	
  	/** accuracyIncrease determines how much the accuracy estimate should
  	 * be increased due to the contribution of a single given instance. 
  	 * 
  	 * @param instance : instance for which to calculate the accuracy increase.
  	 * @return increase in accuracy due to given instance. 
  	 * @throws Exception passed on by distributionForInstance and classifyInstance
  	 */
  	double accuracyIncrease(Instance instance) throws Exception {
  		if (m_bUseProb) {
  			double [] fProb = m_BayesNet.distributionForInstance(instance);
  			return fProb[(int) instance.classValue()] * instance.weight();
  		} else {
  			if (m_BayesNet.classifyInstance(instance) == instance.classValue()) {
  				return instance.weight();
  			}
  		}
  		return 0;
  	} // accuracyIncrease
  
  	/**
  	 * @return use probabilities or not in accuracy estimate
  	 */
  	public boolean getUseProb() {
  		return m_bUseProb;
  	} // getUseProb
  
  	/**
  	 * @param useProb : use probabilities or not in accuracy estimate
  	 */
  	public void setUseProb(boolean useProb) {
  		m_bUseProb = useProb;
  	} // setUseProb
  	
  	/**
  	 * set cross validation strategy to be used in searching for networks.
  	 * @param newCVType : cross validation strategy 
  	 */
  	public void setCVType(SelectedTag newCVType) {
  		if (newCVType.getTags() == TAGS_CV_TYPE) {
  			m_nCVType = newCVType.getSelectedTag().getID();
  		}
  	} // setCVType
  
  	/**
  	 * get cross validation strategy to be used in searching for networks.
  	 * @return cross validation strategy 
  	 */
  	public SelectedTag getCVType() {
  		return new SelectedTag(m_nCVType, TAGS_CV_TYPE);
  	} // getCVType
  
  	/**
  	 * 
  	 * @param bMarkovBlanketClassifier
  	 */
  	public void setMarkovBlanketClassifier(boolean bMarkovBlanketClassifier) {
  	  super.setMarkovBlanketClassifier(bMarkovBlanketClassifier);
  	}
  
  	/**
  	 * 
  	 * @return
  	 */
  	public boolean getMarkovBlanketClassifier() {
  	  return super.getMarkovBlanketClassifier();
  	}
  
  	/**
  	 * Returns an enumeration describing the available options
  	 * 
  	 * @return an enumeration of all the available options
  	 */
  	public Enumeration listOptions() {
  		Vector newVector = new Vector();
  
  		newVector.addElement(new Option(
  		    "\tApplies a Markov Blanket correction to the network structure, \n"
  		    + "\tafter a network structure is learned. This ensures that all \n"
  		    + "\tnodes in the network are part of the Markov blanket of the \n"
  		    + "\tclassifier node.",
  		    "mbc", 0, "-mbc"));
        
  		newVector.addElement(
  			new Option(
  				"\tScore type (LOO-CV,k-Fold-CV,Cumulative-CV)",
  				"S",
  				1,
  				"-S [LOO-CV|k-Fold-CV|Cumulative-CV]"));
  
  		newVector.addElement(new Option("\tUse probabilistic or 0/1 scoring.\n\t(default probabilistic scoring)", "Q", 0, "-Q"));
  
  		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> -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 {
  
  	  	setMarkovBlanketClassifier(Utils.getFlag("mbc", options));
  
  		String sScore = Utils.getOption('S', options);
  
  		if (sScore.compareTo("LOO-CV") == 0) {
  			setCVType(new SelectedTag(LOOCV, TAGS_CV_TYPE));
  		}
  		if (sScore.compareTo("k-Fold-CV") == 0) {
  			setCVType(new SelectedTag(KFOLDCV, TAGS_CV_TYPE));
  		}
  		if (sScore.compareTo("Cumulative-CV") == 0) {
  			setCVType(new SelectedTag(CUMCV, TAGS_CV_TYPE));
  		}
  		setUseProb(!Utils.getFlag('Q', options));		
  		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[4 + superOptions.length];
  		int current = 0;
  
  		if (getMarkovBlanketClassifier())
  		  options[current++] = "-mbc";
  
  		options[current++] = "-S";
  
  		switch (m_nCVType) {
  			case (LOOCV) :
  				options[current++] = "LOO-CV";
  				break;
  			case (KFOLDCV) :
  				options[current++] = "k-Fold-CV";
  				break;
  			case (CUMCV) :
  				options[current++] = "Cumulative-CV";
  				break;
  		}
  		
  		if (!getUseProb()) {
  		  options[current++] = "-Q";
  		}
  
                  // 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
  
  	/**
  	 * @return a string to describe the CVType option.
  	 */
  	public String CVTypeTipText() {
  	  return "Select cross validation strategy to be used in searching for networks." +
  	  "LOO-CV = Leave one out cross validation\n" +
  	  "k-Fold-CV = k fold cross validation\n" +
  	  "Cumulative-CV = cumulative cross validation."
  	  ;
  	} // CVTypeTipText
  
  	/**
  	 * @return a string to describe the UseProb option.
  	 */
  	public String useProbTipText() {
  	  return "If set to true, the probability of the class if returned in the estimate of the "+
  	  "accuracy. If set to false, the accuracy estimate is only increased if the classifier returns " +
  	  "exactly the correct class.";
  	} // useProbTipText
  
  	/**
  	 * This will return a string describing the search algorithm.
  	 * @return The string.
  	 */
  	public String globalInfo() {
  	  return "This Bayes Network learning algorithm uses cross validation to estimate " +
  	  "classification accuracy.";
  	} // globalInfo
  	
  	/**
  	 * @return a string to describe the MarkovBlanketClassifier option.
  	 */
  	public String markovBlanketClassifierTipText() {
  	  return super.markovBlanketClassifierTipText();
  	}
  
  	/**
  	 * Returns the revision string.
  	 * 
  	 * @return		the revision
  	 */
  	public String getRevision() {
  	  return RevisionUtils.extract("$Revision: 1.10 $");
  	}
  }
  