/*
* 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 <alpha>
* 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 <alpha>
* 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