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weka.classifiers.bayes.net.estimate.BMAEstimator Maven / Gradle / Ivy

/*
 *   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 3 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, see .
 */

/*
 * BayesNet.java
 * Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 * 
 */

package weka.classifiers.bayes.net.estimate;

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

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;

/**
 *  BMAEstimator estimates conditional probability
 * tables of a Bayes network using Bayes Model Averaging (BMA).
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -k2
 *  Whether to use K2 prior.
 * 
 * 
 * 
 * -A <alpha>
 *  Initial count (alpha)
 * 
 * 
 * 
 * 
 * @author Remco Bouckaert ([email protected])
 * @version $Revision: 10154 $
 */
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
   */
  @Override
  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
   */
  @Override
  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.
   */
  @Override
  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
   */
  @Override
  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
   */
  @Override
  public Enumeration listOptions() {
    Vector newVector = new Vector(1);

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

    newVector.addAll(Collections.list(super.listOptions()));

    return newVector.elements();
  } // listOptions

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -k2
   *  Whether to use K2 prior.
   * 
   * 
   * 
   * -A <alpha>
   *  Initial count (alpha)
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  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
   */
  @Override
  public String[] getOptions() {

    Vector options = new Vector();

    if (isUseK2Prior()) {
      options.add("-k2");
    }

    Collections.addAll(options, super.getOptions());

    return options.toArray(new String[0]);
  } // getOptions

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10154 $");
  }
} // class BMAEstimator