/*
* 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/>.
*/
/*
* K2.java
* Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.search.global;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.classifiers.bayes.BayesNet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
/**
<!-- globalinfo-start -->
* This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* G.F. Cooper, E. Herskovits (1990). A Bayesian method for constructing Bayesian belief networks from databases.<br/>
* <br/>
* G. Cooper, E. Herskovits (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 9(4):309-347.<br/>
* <br/>
* Works with nominal variables and no missing values only.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @proceedings{Cooper1990,
* author = {G.F. Cooper and E. Herskovits},
* booktitle = {Proceedings of the Conference on Uncertainty in AI},
* pages = {86-94},
* title = {A Bayesian method for constructing Bayesian belief networks from databases},
* year = {1990}
* }
*
* @article{Cooper1992,
* author = {G. Cooper and E. Herskovits},
* journal = {Machine Learning},
* number = {4},
* pages = {309-347},
* title = {A Bayesian method for the induction of probabilistic networks from data},
* volume = {9},
* year = {1992}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N
* Initial structure is empty (instead of Naive Bayes)</pre>
*
* <pre> -P <nr of parents>
* Maximum number of parents</pre>
*
* <pre> -R
* Random order.
* (default false)</pre>
*
* <pre> -mbc
* Applies a Markov Blanket correction to the network structure,
* after a network structure is learned. This ensures that all
* nodes in the network are part of the Markov blanket of the
* classifier node.</pre>
*
* <pre> -S [LOO-CV|k-Fold-CV|Cumulative-CV]
* Score type (LOO-CV,k-Fold-CV,Cumulative-CV)</pre>
*
* <pre> -Q
* Use probabilistic or 0/1 scoring.
* (default probabilistic scoring)</pre>
*
<!-- options-end -->
*
* @author Remco Bouckaert (rrb@xm.co.nz)
* @version $Revision: 8034 $
*/
public class K2
extends GlobalScoreSearchAlgorithm
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -6626871067466338256L;
/** Holds flag to indicate ordering should be random **/
boolean m_bRandomOrder = false;
/**
* 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;
TechnicalInformation additional;
result = new TechnicalInformation(Type.PROCEEDINGS);
result.setValue(Field.AUTHOR, "G.F. Cooper and E. Herskovits");
result.setValue(Field.YEAR, "1990");
result.setValue(Field.TITLE, "A Bayesian method for constructing Bayesian belief networks from databases");
result.setValue(Field.BOOKTITLE, "Proceedings of the Conference on Uncertainty in AI");
result.setValue(Field.PAGES, "86-94");
additional = result.add(Type.ARTICLE);
additional.setValue(Field.AUTHOR, "G. Cooper and E. Herskovits");
additional.setValue(Field.YEAR, "1992");
additional.setValue(Field.TITLE, "A Bayesian method for the induction of probabilistic networks from data");
additional.setValue(Field.JOURNAL, "Machine Learning");
additional.setValue(Field.VOLUME, "9");
additional.setValue(Field.NUMBER, "4");
additional.setValue(Field.PAGES, "309-347");
return result;
}
/**
* search determines the network structure/graph of the network
* with the K2 algorithm, restricted by its initial structure (which can
* be an empty graph, or a Naive Bayes graph.
*
* @param bayesNet the network
* @param instances the data to work with
* @throws Exception if something goes wrong
*/
public void search (BayesNet bayesNet, Instances instances) throws Exception {
int nOrder[] = new int [instances.numAttributes()];
nOrder[0] = instances.classIndex();
int nAttribute = 0;
for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
if (nAttribute == instances.classIndex()) {
nAttribute++;
}
nOrder[iOrder] = nAttribute++;
}
if (m_bRandomOrder) {
// generate random ordering (if required)
Random random = new Random();
int iClass;
if (getInitAsNaiveBayes()) {
iClass = 0;
} else {
iClass = -1;
}
for (int iOrder = 0; iOrder < instances.numAttributes(); iOrder++) {
int iOrder2 = Math.abs(random.nextInt()) % instances.numAttributes();
if (iOrder != iClass && iOrder2 != iClass) {
int nTmp = nOrder[iOrder];
nOrder[iOrder] = nOrder[iOrder2];
nOrder[iOrder2] = nTmp;
}
}
}
// determine base scores
double fBaseScore = calcScore(bayesNet);
// K2 algorithm: greedy search restricted by ordering
for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
int iAttribute = nOrder[iOrder];
double fBestScore = fBaseScore;
boolean bProgress = (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents());
while (bProgress && (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents())) {
int nBestAttribute = -1;
for (int iOrder2 = 0; iOrder2 < iOrder; iOrder2++) {
int iAttribute2 = nOrder[iOrder2];
double fScore = calcScoreWithExtraParent(iAttribute, iAttribute2);
if (fScore > fBestScore) {
fBestScore = fScore;
nBestAttribute = iAttribute2;
}
}
if (nBestAttribute != -1) {
bayesNet.getParentSet(iAttribute).addParent(nBestAttribute, instances);
fBaseScore = fBestScore;
bProgress = true;
} else {
bProgress = false;
}
}
}
} // search
/**
* Sets the max number of parents
*
* @param nMaxNrOfParents the max number of parents
*/
public void setMaxNrOfParents(int nMaxNrOfParents) {
m_nMaxNrOfParents = nMaxNrOfParents;
}
/**
* Gets the max number of parents.
*
* @return the max number of parents
*/
public int getMaxNrOfParents() {
return m_nMaxNrOfParents;
}
/**
* Sets whether to init as naive bayes
*
* @param bInitAsNaiveBayes whether to init as naive bayes
*/
public void setInitAsNaiveBayes(boolean bInitAsNaiveBayes) {
m_bInitAsNaiveBayes = bInitAsNaiveBayes;
}
/**
* Gets whether to init as naive bayes
*
* @return whether to init as naive bayes
*/
public boolean getInitAsNaiveBayes() {
return m_bInitAsNaiveBayes;
}
/**
* Set random order flag
*
* @param bRandomOrder the random order flag
*/
public void setRandomOrder(boolean bRandomOrder) {
m_bRandomOrder = bRandomOrder;
} // SetRandomOrder
/**
* Get random order flag
*
* @return the random order flag
*/
public boolean getRandomOrder() {
return m_bRandomOrder;
} // getRandomOrder
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(0);
newVector.addElement(new Option("\tInitial structure is empty (instead of Naive Bayes)",
"N", 0, "-N"));
newVector.addElement(new Option("\tMaximum number of parents", "P", 1,
"-P <nr of parents>"));
newVector.addElement(new Option(
"\tRandom order.\n"
+ "\t(default false)",
"R", 0, "-R"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N
* Initial structure is empty (instead of Naive Bayes)</pre>
*
* <pre> -P <nr of parents>
* Maximum number of parents</pre>
*
* <pre> -R
* Random order.
* (default false)</pre>
*
* <pre> -mbc
* Applies a Markov Blanket correction to the network structure,
* after a network structure is learned. This ensures that all
* nodes in the network are part of the Markov blanket of the
* classifier node.</pre>
*
* <pre> -S [LOO-CV|k-Fold-CV|Cumulative-CV]
* Score type (LOO-CV,k-Fold-CV,Cumulative-CV)</pre>
*
* <pre> -Q
* Use probabilistic or 0/1 scoring.
* (default probabilistic scoring)</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 {
setRandomOrder(Utils.getFlag('R', options));
m_bInitAsNaiveBayes = !(Utils.getFlag('N', options));
String sMaxNrOfParents = Utils.getOption('P', options);
if (sMaxNrOfParents.length() != 0) {
setMaxNrOfParents(Integer.parseInt(sMaxNrOfParents));
} else {
setMaxNrOfParents(100000);
}
super.setOptions(options);
}
/**
* Gets the current settings of the search algorithm.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String[] superOptions = super.getOptions();
String[] options = new String[4 + superOptions.length];
int current = 0;
options[current++] = "-P";
options[current++] = "" + m_nMaxNrOfParents;
if (!m_bInitAsNaiveBayes) {
options[current++] = "-N";
}
if (getRandomOrder()) {
options[current++] = "-R";
}
// 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++] = "";
}
// Fill up rest with empty strings, not nulls!
return options;
}
/**
* @return a string to describe the RandomOrder option.
*/
public String randomOrderTipText() {
return "When set to true, the order of the nodes in the network is random." +
" Default random order is false and the order" +
" of the nodes in the dataset is used." +
" In any case, when the network was initialized as Naive Bayes Network, the" +
" class variable is first in the ordering though.";
} // randomOrderTipText
/**
* This will return a string describing the search algorithm.
* @return The string.
*/
public String globalInfo() {
return
"This Bayes Network learning algorithm uses a hill climbing algorithm "
+ "restricted by an order on the variables.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString() + "\n\n"
+ "Works with nominal variables and no missing values only.";
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}