/*
* 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 <http://www.gnu.org/licenses/>.
*/
/*
* MultiNomialBMAEstimator.java
* Copyright (C) 2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.estimate;
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.FastVector;
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;
/**
<!-- globalinfo-start -->
* Multinomial BMA Estimator.
* <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 -->
*
* @version $Revision: 8034 $
* @author Remco Bouckaert (rrb@xm.co.nz)
*/
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
*/
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)
*/
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);
while (instances.numInstances() > 0) {
instances.delete(0);
}
for (int iAttribute = instances.numAttributes() - 1; iAttribute >= 0; iAttribute--) {
if (iAttribute != instances.classIndex()) {
FastVector values = new FastVector();
values.addElement("0");
values.addElement("1");
Attribute a = new Attribute(instances.attribute(iAttribute).name(), (FastVector) values);
instances.deleteAttributeAt(iAttribute);
instances.insertAttributeAt(a,iAttribute);
}
}
for (int iInstance = 0; iInstance < bayesNet.m_Instances.numInstances(); iInstance++) {
Instance instanceOrig = bayesNet.m_Instances.instance(iInstance);
Instance instance = new DenseInstance(instances.numAttributes());
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
if (iAttribute != instances.classIndex()) {
if (instanceOrig.value(iAttribute) > 0) {
instance.setValue(iAttribute, 1);
}
} else {
instance.setValue(iAttribute, instanceOrig.value(iAttribute));
}
}
}
// 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.
*/
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
*/
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
*/
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
*/
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: 8034 $");
}
} // class MultiNomialBMAEstimator