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weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator 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 .
 */

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

package weka.classifiers.bayes.net.estimate;

import java.util.ArrayList;
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.Attribute;
import weka.core.DenseInstance;
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;

/**
 *  Multinomial BMA Estimator.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -k2
 *  Whether to use K2 prior.
 * 
 * 
 * 
 * -A <alpha>
 *  Initial count (alpha)
 * 
 * 
 * 
 * 
 * @version $Revision: 12470 $
 * @author Remco Bouckaert ([email protected])
 */
public class MultiNomialBMAEstimator extends BayesNetEstimator {

  /** for serialization */
  static final long serialVersionUID = 8330705772601586313L;

  /** whether to use K2 prior */
  protected boolean m_bUseK2Prior = true;

  /**
   * 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 "Multinomial BMA Estimator.";
  }

  /**
   * estimateCPTs estimates the conditional probability tables for the Bayes Net
   * using the network structure.
   * 
   * @param bayesNet the bayes net to use
   * @throws Exception if number of parents doesn't fit (more than 1)
   */
  @Override
  public void estimateCPTs(BayesNet bayesNet) throws Exception {
    initCPTs(bayesNet);

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

    // filter data to binary
    Instances instances = new Instances(bayesNet.m_Instances);
    for (int iAttribute = instances.numAttributes() - 1; iAttribute >= 0; iAttribute--) {
      if (instances.attribute(iAttribute).numValues() != 2) {
        throw new Exception("MultiNomialBMAEstimator can only handle binary nominal attributes!");
      }
    }

    // ok, now all data is binary, except the class attribute
    // now learn the empty and tree network

    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));
        // normalize weigths
        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 doesn't apply
   */
  @Override
  public void initCPTs(BayesNet bayesNet) throws Exception {
    // Reserve sufficient memory
    bayesNet.m_Distributions = new Estimator[bayesNet.m_Instances
      .numAttributes()][2];
  } // initCPTs

  /**
   * @return boolean
   */
  public boolean isUseK2Prior() {
    return m_bUseK2Prior;
  }

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

  /**
   * Calculates the class membership probabilities for the given test instance.
   * 
   * @param bayesNet the bayes net to use
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @throws Exception if there is a problem generating the prediction
   */
  @Override
  public double[] distributionForInstance(BayesNet bayesNet, Instance instance)
    throws Exception {
    Instances instances = bayesNet.m_Instances;
    int nNumClasses = instances.numClasses();
    double[] fProbs = new double[nNumClasses];

    for (int iClass = 0; iClass < nNumClasses; iClass++) {
      fProbs[iClass] = 1.0;
    }

    for (int iClass = 0; iClass < nNumClasses; iClass++) {
      double logfP = 0;

      for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
        double iCPT = 0;

        for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute)
          .getNrOfParents(); iParent++) {
          int nParent = bayesNet.getParentSet(iAttribute).getParent(iParent);

          if (nParent == instances.classIndex()) {
            iCPT = iCPT * nNumClasses + iClass;
          } else {
            iCPT = iCPT * instances.attribute(nParent).numValues()
              + instance.value(nParent);
          }
        }

        if (iAttribute == instances.classIndex()) {
          logfP += Math.log(bayesNet.m_Distributions[iAttribute][(int) iCPT]
            .getProbability(iClass));
        } else {
          logfP += instance.value(iAttribute)
            * Math.log(bayesNet.m_Distributions[iAttribute][(int) iCPT]
              .getProbability(instance.value(1)));
        }
      }

      fProbs[iClass] += logfP;
    }

    // Find maximum
    double fMax = fProbs[0];
    for (int iClass = 0; iClass < nNumClasses; iClass++) {
      if (fProbs[iClass] > fMax) {
        fMax = fProbs[iClass];
      }
    }
    // transform from log-space to normal-space
    for (int iClass = 0; iClass < nNumClasses; iClass++) {
      fProbs[iClass] = Math.exp(fProbs[iClass] - fMax);
    }

    // Display probabilities
    Utils.normalize(fProbs);

    return fProbs;
  } // distributionForInstance

  /**
   * 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");
    }

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

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 12470 $");
  }
} // class MultiNomialBMAEstimator