/*
* 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.
*/
/*
* NaiveBayesMultinomial.java
* Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
*/
package weka.classifiers.bayes;
import weka.classifiers.Classifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
/**
<!-- globalinfo-start -->
* Class for building and using a multinomial Naive Bayes classifier. For more information see,<br/>
* <br/>
* Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.<br/>
* <br/>
* The core equation for this classifier:<br/>
* <br/>
* P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)<br/>
* <br/>
* where Ci is class i and D is a document.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Mccallum1998,
* author = {Andrew Mccallum and Kamal Nigam},
* booktitle = {AAAI-98 Workshop on 'Learning for Text Categorization'},
* title = {A Comparison of Event Models for Naive Bayes Text Classification},
* year = {1998}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* @author Andrew Golightly (acg4@cs.waikato.ac.nz)
* @author Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
* @version $Revision: 1.16 $
*/
public class NaiveBayesMultinomial
extends Classifier
implements WeightedInstancesHandler,TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 5932177440181257085L;
/**
* probability that a word (w) exists in a class (H) (i.e. Pr[w|H])
* The matrix is in the this format: probOfWordGivenClass[class][wordAttribute]
* NOTE: the values are actually the log of Pr[w|H]
*/
protected double[][] m_probOfWordGivenClass;
/** the probability of a class (i.e. Pr[H]) */
protected double[] m_probOfClass;
/** number of unique words */
protected int m_numAttributes;
/** number of class values */
protected int m_numClasses;
/** cache lnFactorial computations */
protected double[] m_lnFactorialCache = new double[]{0.0,0.0};
/** copy of header information for use in toString method */
protected Instances m_headerInfo;
/**
* Returns a string describing this classifier
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Class for building and using a multinomial Naive Bayes classifier. "
+ "For more information see,\n\n"
+ getTechnicalInformation().toString() + "\n\n"
+ "The core equation for this classifier:\n\n"
+ "P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)\n\n"
+ "where Ci is class i and D is a document.";
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Andrew Mccallum and Kamal Nigam");
result.setValue(Field.YEAR, "1998");
result.setValue(Field.TITLE, "A Comparison of Event Models for Naive Bayes Text Classification");
result.setValue(Field.BOOKTITLE, "AAAI-98 Workshop on 'Learning for Text Categorization'");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// attributes
result.enable(Capability.NUMERIC_ATTRIBUTES);
// class
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
return result;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception
{
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
m_headerInfo = new Instances(instances, 0);
m_numClasses = instances.numClasses();
m_numAttributes = instances.numAttributes();
m_probOfWordGivenClass = new double[m_numClasses][];
/*
initialising the matrix of word counts
NOTE: Laplace estimator introduced in case a word that does not appear for a class in the
training set does so for the test set
*/
for(int c = 0; c<m_numClasses; c++)
{
m_probOfWordGivenClass[c] = new double[m_numAttributes];
for(int att = 0; att<m_numAttributes; att++)
{
m_probOfWordGivenClass[c][att] = 1;
}
}
//enumerate through the instances
Instance instance;
int classIndex;
double numOccurences;
double[] docsPerClass = new double[m_numClasses];
double[] wordsPerClass = new double[m_numClasses];
java.util.Enumeration enumInsts = instances.enumerateInstances();
while (enumInsts.hasMoreElements())
{
instance = (Instance) enumInsts.nextElement();
classIndex = (int)instance.value(instance.classIndex());
docsPerClass[classIndex] += instance.weight();
for(int a = 0; a<instance.numValues(); a++)
if(instance.index(a) != instance.classIndex())
{
if(!instance.isMissing(a))
{
numOccurences = instance.valueSparse(a) * instance.weight();
if(numOccurences < 0)
throw new Exception("Numeric attribute values must all be greater or equal to zero.");
wordsPerClass[classIndex] += numOccurences;
m_probOfWordGivenClass[classIndex][instance.index(a)] += numOccurences;
}
}
}
/*
normalising probOfWordGivenClass values
and saving each value as the log of each value
*/
for(int c = 0; c<m_numClasses; c++)
for(int v = 0; v<m_numAttributes; v++)
m_probOfWordGivenClass[c][v] = Math.log(m_probOfWordGivenClass[c][v] / (wordsPerClass[c] + m_numAttributes - 1));
/*
calculating Pr(H)
NOTE: Laplace estimator introduced in case a class does not get mentioned in the set of
training instances
*/
final double numDocs = instances.sumOfWeights() + m_numClasses;
m_probOfClass = new double[m_numClasses];
for(int h=0; h<m_numClasses; h++)
m_probOfClass[h] = (double)(docsPerClass[h] + 1)/numDocs;
}
/**
* Calculates the class membership probabilities for the given test
* instance.
*
* @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(Instance instance) throws Exception
{
double[] probOfClassGivenDoc = new double[m_numClasses];
//calculate the array of log(Pr[D|C])
double[] logDocGivenClass = new double[m_numClasses];
for(int h = 0; h<m_numClasses; h++)
logDocGivenClass[h] = probOfDocGivenClass(instance, h);
double max = logDocGivenClass[Utils.maxIndex(logDocGivenClass)];
double probOfDoc = 0.0;
for(int i = 0; i<m_numClasses; i++)
{
probOfClassGivenDoc[i] = Math.exp(logDocGivenClass[i] - max) * m_probOfClass[i];
probOfDoc += probOfClassGivenDoc[i];
}
Utils.normalize(probOfClassGivenDoc,probOfDoc);
return probOfClassGivenDoc;
}
/**
* log(N!) + (for all the words)(log(Pi^ni) - log(ni!))
*
* where
* N is the total number of words
* Pi is the probability of obtaining word i
* ni is the number of times the word at index i occurs in the document
*
* @param inst The instance to be classified
* @param classIndex The index of the class we are calculating the probability with respect to
*
* @return The log of the probability of the document occuring given the class
*/
private double probOfDocGivenClass(Instance inst, int classIndex)
{
double answer = 0;
//double totalWords = 0; //no need as we are not calculating the factorial at all.
double freqOfWordInDoc; //should be double
for(int i = 0; i<inst.numValues(); i++)
if(inst.index(i) != inst.classIndex())
{
freqOfWordInDoc = inst.valueSparse(i);
//totalWords += freqOfWordInDoc;
answer += (freqOfWordInDoc * m_probOfWordGivenClass[classIndex][inst.index(i)]
); //- lnFactorial(freqOfWordInDoc));
}
//answer += lnFactorial(totalWords);//The factorial terms don't make
//any difference to the classifier's
//accuracy, so not needed.
return answer;
}
/**
* Fast computation of ln(n!) for non-negative ints
*
* negative ints are passed on to the general gamma-function
* based version in weka.core.SpecialFunctions
*
* if the current n value is higher than any previous one,
* the cache is extended and filled to cover it
*
* the common case is reduced to a simple array lookup
*
* @param n the integer
* @return ln(n!)
*/
public double lnFactorial(int n)
{
if (n < 0) return weka.core.SpecialFunctions.lnFactorial(n);
if (m_lnFactorialCache.length <= n) {
double[] tmp = new double[n+1];
System.arraycopy(m_lnFactorialCache,0,tmp,0,m_lnFactorialCache.length);
for(int i = m_lnFactorialCache.length; i < tmp.length; i++)
tmp[i] = tmp[i-1] + Math.log(i);
m_lnFactorialCache = tmp;
}
return m_lnFactorialCache[n];
}
/**
* Returns a string representation of the classifier.
*
* @return a string representation of the classifier
*/
public String toString()
{
StringBuffer result = new StringBuffer("The independent probability of a class\n--------------------------------------\n");
for(int c = 0; c<m_numClasses; c++)
result.append(m_headerInfo.classAttribute().value(c)).append("\t").append(Double.toString(m_probOfClass[c])).append("\n");
result.append("\nThe probability of a word given the class\n-----------------------------------------\n\t");
for(int c = 0; c<m_numClasses; c++)
result.append(m_headerInfo.classAttribute().value(c)).append("\t");
result.append("\n");
for(int w = 0; w<m_numAttributes; w++)
{
result.append(m_headerInfo.attribute(w).name()).append("\t");
for(int c = 0; c<m_numClasses; c++)
result.append(Double.toString(Math.exp(m_probOfWordGivenClass[c][w]))).append("\t");
result.append("\n");
}
return result.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.16 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new NaiveBayesMultinomial(), argv);
}
}