source: tags/MetisMQIDemo/src/main/java/weka/classifiers/bayes/net/estimate/BMAEstimator.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.
 */

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

import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.search.local.K2;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Statistics;
import weka.core.Utils;
import weka.estimators.Estimator;

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

/** 
 <!-- globalinfo-start -->
 * BMAEstimator estimates conditional probability tables of a Bayes network using Bayes Model Averaging (BMA).
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -k2
 *  Whether to use K2 prior.
 * </pre>
 * 
 * <pre> -A &lt;alpha&gt;
 *  Initial count (alpha)
 * </pre>
 * 
 <!-- options-end -->
 * 
 * @author Remco Bouckaert (rrb@xm.co.nz)
 * @version $Revision: 1.8 $
 */
public class BMAEstimator 
    extends SimpleEstimator {

    /** for serialization */
    static final long serialVersionUID = -1846028304233257309L;
  
    /** whether to use K2 prior */
    protected boolean m_bUseK2Prior = false;
    
    /**
     * Returns a string describing this object
     * @return a description of the classifier suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return 
          "BMAEstimator estimates conditional probability tables of a Bayes "
        + "network using Bayes Model Averaging (BMA).";
    }

    /**
     * estimateCPTs estimates the conditional probability tables for the Bayes
     * Net using the network structure.
     * 
     * @param bayesNet the bayes net to use
     * @throws Exception if an error occurs
     */
    public void estimateCPTs(BayesNet bayesNet) throws Exception {
        initCPTs(bayesNet);

        Instances instances = bayesNet.m_Instances;
        // sanity check to see if nodes have not more than one parent
        for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
            if (bayesNet.getParentSet(iAttribute).getNrOfParents() > 1) {
                throw new Exception("Cannot handle networks with nodes with more than 1 parent (yet).");
            }
        }

        BayesNet EmptyNet = new BayesNet();
        K2 oSearchAlgorithm = new K2();
        oSearchAlgorithm.setInitAsNaiveBayes(false);
        oSearchAlgorithm.setMaxNrOfParents(0);
        EmptyNet.setSearchAlgorithm(oSearchAlgorithm);
        EmptyNet.buildClassifier(instances);

        BayesNet NBNet = new BayesNet();
        oSearchAlgorithm.setInitAsNaiveBayes(true);
        oSearchAlgorithm.setMaxNrOfParents(1);
        NBNet.setSearchAlgorithm(oSearchAlgorithm);
        NBNet.buildClassifier(instances);

        // estimate CPTs
        for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
            if (iAttribute != instances.classIndex()) {
                  double w1 = 0.0, w2 = 0.0;
                  int nAttValues = instances.attribute(iAttribute).numValues();
                  if (m_bUseK2Prior == true) {
                      // use Cooper and Herskovitz's metric
                      for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) {
                        w1 += Statistics.lnGamma(1 + ((DiscreteEstimatorBayes)EmptyNet.m_Distributions[iAttribute][0]).getCount(iAttValue))
                              - Statistics.lnGamma(1);
                      }
                      w1 += Statistics.lnGamma(nAttValues) - Statistics.lnGamma(nAttValues + instances.numInstances());

                      for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getCardinalityOfParents(); iParent++) {
                        int nTotal = 0;
                          for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) {
                            double nCount = ((DiscreteEstimatorBayes)NBNet.m_Distributions[iAttribute][iParent]).getCount(iAttValue);
                            w2 += Statistics.lnGamma(1 + nCount)
                                  - Statistics.lnGamma(1);
                            nTotal += nCount;
                          }
                        w2 += Statistics.lnGamma(nAttValues) - Statistics.lnGamma(nAttValues + nTotal);
                      }
                  } else {
                      // use BDe metric
                      for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) {
                        w1 += Statistics.lnGamma(1.0/nAttValues + ((DiscreteEstimatorBayes)EmptyNet.m_Distributions[iAttribute][0]).getCount(iAttValue))
                              - Statistics.lnGamma(1.0/nAttValues);
                      }
                      w1 += Statistics.lnGamma(1) - Statistics.lnGamma(1 + instances.numInstances());

                      int nParentValues = bayesNet.getParentSet(iAttribute).getCardinalityOfParents();
                      for (int iParent = 0; iParent < nParentValues; iParent++) {
                        int nTotal = 0;
                          for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) {
                            double nCount = ((DiscreteEstimatorBayes)NBNet.m_Distributions[iAttribute][iParent]).getCount(iAttValue);
                            w2 += Statistics.lnGamma(1.0/(nAttValues * nParentValues) + nCount)
                                  - Statistics.lnGamma(1.0/(nAttValues * nParentValues));
                            nTotal += nCount;
                          }
                        w2 += Statistics.lnGamma(1) - Statistics.lnGamma(1 + nTotal);
                      }
                  }
                
//    System.out.println(w1 + " " + w2 + " " + (w2 - w1));
                  if (w1 < w2) {
                    w2 = w2 - w1;
                    w1 = 0;
                    w1 = 1 / (1 + Math.exp(w2));
                    w2 = Math.exp(w2) / (1 + Math.exp(w2));
                  } else {
                    w1 = w1 - w2;
                    w2 = 0;
                    w2 = 1 / (1 + Math.exp(w1));
                    w1 = Math.exp(w1) / (1 + Math.exp(w1));
                  }
                
                  for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getCardinalityOfParents(); iParent++) {
                      bayesNet.m_Distributions[iAttribute][iParent] = 
                      new DiscreteEstimatorFullBayes(
                        instances.attribute(iAttribute).numValues(), 
                        w1, w2,
                        (DiscreteEstimatorBayes) EmptyNet.m_Distributions[iAttribute][0],
                        (DiscreteEstimatorBayes) NBNet.m_Distributions[iAttribute][iParent],
                        m_fAlpha
                       );
                  } 
            }
        }
        int iAttribute = instances.classIndex();
        bayesNet.m_Distributions[iAttribute][0] = EmptyNet.m_Distributions[iAttribute][0];
    } // estimateCPTs

    /**
     * Updates the classifier with the given instance.
     * 
     * @param bayesNet the bayes net to use
     * @param instance the new training instance to include in the model
     * @throws Exception if the instance could not be incorporated in
     * the model.
     */
    public void updateClassifier(BayesNet bayesNet, Instance instance) throws Exception {
        throw new Exception("updateClassifier does not apply to BMA estimator");
    } // updateClassifier

    /** 
     * initCPTs reserves space for CPTs and set all counts to zero
     * 
     * @param bayesNet the bayes net to use
     * @throws Exception if something goes wrong
     */
    public void initCPTs(BayesNet bayesNet) throws Exception {
        // Reserve space for CPTs
        int nMaxParentCardinality = 1;

        for (int iAttribute = 0; iAttribute < bayesNet.m_Instances.numAttributes(); iAttribute++) {
            if (bayesNet.getParentSet(iAttribute).getCardinalityOfParents() > nMaxParentCardinality) {
                nMaxParentCardinality = bayesNet.getParentSet(iAttribute).getCardinalityOfParents();
            }
        }

        // Reserve plenty of memory
        bayesNet.m_Distributions = new Estimator[bayesNet.m_Instances.numAttributes()][nMaxParentCardinality];
    } // initCPTs


    /**
     * Returns whether K2 prior is used
     * 
     * @return true if K2 prior is used
     */
    public boolean isUseK2Prior() {
        return m_bUseK2Prior;
    }

    /**
     * Sets the UseK2Prior.
     * 
     * @param bUseK2Prior The bUseK2Prior to set
     */
    public void setUseK2Prior(boolean bUseK2Prior) {
        m_bUseK2Prior = bUseK2Prior;
    }

    /**
     * Returns an enumeration describing the available options
     * 
     * @return an enumeration of all the available options
     */
    public Enumeration listOptions() {
        Vector newVector = new Vector(1);

        newVector.addElement(new Option(
            "\tWhether to use K2 prior.\n", 
            "k2", 0, "-k2"));

        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> -k2
     *  Whether to use K2 prior.
     * </pre>
     * 
     * <pre> -A &lt;alpha&gt;
     *  Initial count (alpha)
     * </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 {
        setUseK2Prior(Utils.getFlag("k2", options));

        super.setOptions(options);
    } // setOptions

    /**
     * Gets the current settings of the classifier.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {
        String[] superOptions = super.getOptions();
        String[] options = new String[1 + superOptions.length];
        int current = 0;

        if (isUseK2Prior())
          options[current++] = "-k2";

        // 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
    
    /**
     * Returns the revision string.
     * 
     * @return          the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.8 $");
    }
} // class BMAEstimator