/*
* 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/>.
*/
/*
* Vote.java
* Copyright (C) 2000-2012 University of Waikato
*
*/
package weka.classifiers.meta;
import java.io.BufferedInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.ObjectInputStream;
import java.util.ArrayList;
import java.util.Enumeration;
import java.util.List;
import java.util.Random;
import java.util.Vector;
import weka.classifiers.Classifier;
import weka.classifiers.RandomizableMultipleClassifiersCombiner;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Aggregateable;
import weka.core.Environment;
import weka.core.EnvironmentHandler;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
/**
<!-- globalinfo-start -->
* Class for combining classifiers. Different
* combinations of probability estimates for classification are available.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and
* Algorithms. John Wiley and Sons, Inc..<br/>
* <br/>
* J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining
* classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence.
* 20(3):226-239.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are:
* <p/>
*
* <pre>
* -S <num>
* Random number seed.
* (default 1)
* </pre>
*
* <pre>
* -B <classifier specification>
* Full class name of classifier to include, followed
* by scheme options. May be specified multiple times.
* (default: "weka.classifiers.rules.ZeroR")
* </pre>
*
* <pre>
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
* </pre>
*
* <pre>
* -P <path to serialized classifier>
* Full path to serialized classifier to include.
* May be specified multiple times to include
* multiple serialized classifiers. Note: it does
* not make sense to use pre-built classifiers in
* a cross-validation.
* </pre>
*
* <pre>
* -R <AVG|PROD|MAJ|MIN|MAX|MED>
* The combination rule to use
* (default: AVG)
* </pre>
*
<!-- options-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
*
* <pre>
* @book{Kuncheva2004,
* author = {Ludmila I. Kuncheva},
* publisher = {John Wiley and Sons, Inc.},
* title = {Combining Pattern Classifiers: Methods and Algorithms},
* year = {2004}
* }
*
* @article{Kittler1998,
* author = {J. Kittler and M. Hatef and Robert P.W. Duin and J. Matas},
* journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
* number = {3},
* pages = {226-239},
* title = {On combining classifiers},
* volume = {20},
* year = {1998}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
* @author Alexander K. Seewald (alex@seewald.at)
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Roberto Perdisci (roberto.perdisci@gmail.com)
* @version $Revision: 9785 $
*/
public class Vote extends RandomizableMultipleClassifiersCombiner implements
TechnicalInformationHandler, EnvironmentHandler, Aggregateable<Classifier> {
/** for serialization */
static final long serialVersionUID = -637891196294399624L;
/** combination rule: Average of Probabilities */
public static final int AVERAGE_RULE = 1;
/** combination rule: Product of Probabilities (only nominal classes) */
public static final int PRODUCT_RULE = 2;
/** combination rule: Majority Voting (only nominal classes) */
public static final int MAJORITY_VOTING_RULE = 3;
/** combination rule: Minimum Probability */
public static final int MIN_RULE = 4;
/** combination rule: Maximum Probability */
public static final int MAX_RULE = 5;
/** combination rule: Median Probability (only numeric class) */
public static final int MEDIAN_RULE = 6;
/** combination rules */
public static final Tag[] TAGS_RULES = {
new Tag(AVERAGE_RULE, "AVG", "Average of Probabilities"),
new Tag(PRODUCT_RULE, "PROD", "Product of Probabilities"),
new Tag(MAJORITY_VOTING_RULE, "MAJ", "Majority Voting"),
new Tag(MIN_RULE, "MIN", "Minimum Probability"),
new Tag(MAX_RULE, "MAX", "Maximum Probability"),
new Tag(MEDIAN_RULE, "MED", "Median") };
/** Combination Rule variable */
protected int m_CombinationRule = AVERAGE_RULE;
/**
* the random number generator used for breaking ties in majority voting
*
* @see #distributionForInstanceMajorityVoting(Instance)
*/
protected Random m_Random;
/** List of file paths to serialized models to load */
protected List<String> m_classifiersToLoad = new ArrayList<String>();
/** List of de-serialized pre-built classifiers to include in the ensemble */
protected List<Classifier> m_preBuiltClassifiers = new ArrayList<Classifier>();
/** Environment variables */
protected transient Environment m_env = Environment.getSystemWide();
/** Structure of the training data */
protected Instances m_structure;
/**
* Returns a string describing classifier
*
* @return a description suitable for displaying in the explorer/experimenter
* gui
*/
public String globalInfo() {
return "Class for combining classifiers. Different combinations of "
+ "probability estimates for classification are available.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
@Override
public Enumeration listOptions() {
Enumeration enm;
Vector result;
result = new Vector();
enm = super.listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
result.addElement(new Option(
"\tFull path to serialized classifier to include.\n"
+ "\tMay be specified multiple times to include\n"
+ "\tmultiple serialized classifiers. Note: it does\n"
+ "\tnot make sense to use pre-built classifiers in\n"
+ "\ta cross-validation.", "P", 1, "-P <path to serialized "
+ "classifier>"));
result.addElement(new Option("\tThe combination rule to use\n"
+ "\t(default: AVG)", "R", 1, "-R " + Tag.toOptionList(TAGS_RULES)));
return result.elements();
}
/**
* Gets the current settings of Vote.
*
* @return an array of strings suitable for passing to setOptions()
*/
@Override
public String[] getOptions() {
int i;
Vector result;
String[] options;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
result.add("-R");
result.add("" + getCombinationRule());
for (i = 0; i < m_classifiersToLoad.size(); i++) {
result.add("-P");
result.add(m_classifiersToLoad.get(i));
}
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Parses a given list of options.
* <p/>
*
<!-- options-start -->
* Valid options are:
* <p/>
*
* <pre>
* -S <num>
* Random number seed.
* (default 1)
* </pre>
*
* <pre>
* -B <classifier specification>
* Full class name of classifier to include, followed
* by scheme options. May be specified multiple times.
* (default: "weka.classifiers.rules.ZeroR")
* </pre>
*
* <pre>
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
* </pre>
*
* <pre>
* -P <path to serialized classifier>
* Full path to serialized classifier to include.
* May be specified multiple times to include
* multiple serialized classifiers. Note: it does
* not make sense to use pre-built classifiers in
* a cross-validation.
* </pre>
*
* <pre>
* -R <AVG|PROD|MAJ|MIN|MAX|MED>
* The combination rule to use
* (default: AVG)
* </pre>
*
<!-- options-end -->
*
* @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 {
String tmpStr;
tmpStr = Utils.getOption('R', options);
if (tmpStr.length() != 0)
setCombinationRule(new SelectedTag(tmpStr, TAGS_RULES));
else
setCombinationRule(new SelectedTag(AVERAGE_RULE, TAGS_RULES));
m_classifiersToLoad.clear();
while (true) {
String loadString = Utils.getOption('P', options);
if (loadString.length() == 0) {
break;
}
m_classifiersToLoad.add(loadString);
}
super.setOptions(options);
}
/**
* 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
*/
@Override
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
TechnicalInformation additional;
result = new TechnicalInformation(Type.BOOK);
result.setValue(Field.AUTHOR, "Ludmila I. Kuncheva");
result.setValue(Field.TITLE,
"Combining Pattern Classifiers: Methods and Algorithms");
result.setValue(Field.YEAR, "2004");
result.setValue(Field.PUBLISHER, "John Wiley and Sons, Inc.");
additional = result.add(Type.ARTICLE);
additional.setValue(Field.AUTHOR,
"J. Kittler and M. Hatef and Robert P.W. Duin and J. Matas");
additional.setValue(Field.YEAR, "1998");
additional.setValue(Field.TITLE, "On combining classifiers");
additional.setValue(Field.JOURNAL,
"IEEE Transactions on Pattern Analysis and Machine Intelligence");
additional.setValue(Field.VOLUME, "20");
additional.setValue(Field.NUMBER, "3");
additional.setValue(Field.PAGES, "226-239");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
@Override
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
if (m_preBuiltClassifiers.size() == 0 && m_classifiersToLoad.size() > 0) {
try {
loadClassifiers(null);
} catch (Exception e) {
e.printStackTrace();
}
}
if (m_preBuiltClassifiers.size() > 0) {
if (m_Classifiers.length == 0) {
result = (Capabilities) m_preBuiltClassifiers.get(0).getCapabilities()
.clone();
}
for (int i = 1; i < m_preBuiltClassifiers.size(); i++) {
result.and(m_preBuiltClassifiers.get(i).getCapabilities());
}
for (Capability cap : Capability.values()) {
result.enableDependency(cap);
}
}
// class
if ((m_CombinationRule == PRODUCT_RULE)
|| (m_CombinationRule == MAJORITY_VOTING_RULE)) {
result.disableAllClasses();
result.disableAllClassDependencies();
result.enable(Capability.NOMINAL_CLASS);
result.enableDependency(Capability.NOMINAL_CLASS);
} else if (m_CombinationRule == MEDIAN_RULE) {
result.disableAllClasses();
result.disableAllClassDependencies();
result.enable(Capability.NUMERIC_CLASS);
result.enableDependency(Capability.NUMERIC_CLASS);
}
return result;
}
/**
* Buildclassifier selects a classifier from the set of classifiers by
* minimising error on the training data.
*
* @param data the training data to be used for generating the boosted
* classifier.
* @throws Exception if the classifier could not be built successfully
*/
@Override
public void buildClassifier(Instances data) throws Exception {
// remove instances with missing class
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
m_structure = new Instances(newData, 0);
m_Random = new Random(getSeed());
if (m_classifiersToLoad.size() > 0) {
m_preBuiltClassifiers.clear();
loadClassifiers(data);
int index = 0;
if (m_Classifiers.length == 1
&& m_Classifiers[0] instanceof weka.classifiers.rules.ZeroR) {
// remove the single ZeroR
m_Classifiers = new Classifier[0];
}
}
// can classifier handle the data?
getCapabilities().testWithFail(data);
for (int i = 0; i < m_Classifiers.length; i++) {
getClassifier(i).buildClassifier(newData);
}
}
/**
* Load serialized models to include in the ensemble
*
* @param data training instances (used in a header compatibility check with
* each of the loaded models)
*
* @throws Exception if there is a problem de-serializing a model
*/
private void loadClassifiers(Instances data) throws Exception {
for (String path : m_classifiersToLoad) {
if (Environment.containsEnvVariables(path)) {
try {
path = m_env.substitute(path);
} catch (Exception ex) {
}
}
File toLoad = new File(path);
if (!toLoad.isFile()) {
throw new Exception("\"" + path
+ "\" does not seem to be a valid file!");
}
ObjectInputStream is = new ObjectInputStream(new BufferedInputStream(
new FileInputStream(toLoad)));
Object c = is.readObject();
if (!(c instanceof Classifier)) {
throw new Exception("\"" + path + "\" does not contain a classifier!");
}
Object header = null;
header = is.readObject();
if (header instanceof Instances) {
if (data != null && !data.equalHeaders((Instances) header)) {
throw new Exception("\"" + path + "\" was trained with data that is "
+ "of a differnet structure than the incoming training data");
}
}
if (header == null) {
System.out.println("[Vote] warning: no header instances for \"" + path
+ "\"");
}
addPreBuiltClassifier((Classifier) c);
}
}
/**
* Add a prebuilt classifier to the list for use in the ensemble
*
* @param c a prebuilt Classifier to add.
*/
public void addPreBuiltClassifier(Classifier c) {
m_preBuiltClassifiers.add(c);
}
/**
* Remove a prebuilt classifier from the list to use in the ensemble
*
* @param c the classifier to remove
*/
public void removePreBuiltClassifier(Classifier c) {
m_preBuiltClassifiers.remove(c);
}
/**
* Classifies the given test instance.
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or
* Utils.missingValue() if no prediction is made
* @throws Exception if an error occurred during the prediction
*/
@Override
public double classifyInstance(Instance instance) throws Exception {
double result;
double[] dist;
int index;
switch (m_CombinationRule) {
case AVERAGE_RULE:
case PRODUCT_RULE:
case MAJORITY_VOTING_RULE:
case MIN_RULE:
case MAX_RULE:
dist = distributionForInstance(instance);
if (instance.classAttribute().isNominal()) {
index = Utils.maxIndex(dist);
if (dist[index] == 0)
result = Utils.missingValue();
else
result = index;
} else if (instance.classAttribute().isNumeric()) {
result = dist[0];
} else {
result = Utils.missingValue();
}
break;
case MEDIAN_RULE:
result = classifyInstanceMedian(instance);
break;
default:
throw new IllegalStateException("Unknown combination rule '"
+ m_CombinationRule + "'!");
}
return result;
}
/**
* Classifies the given test instance, returning the median from all
* classifiers.
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or
* Utils.missingValue() if no prediction is made
* @throws Exception if an error occurred during the prediction
*/
protected double classifyInstanceMedian(Instance instance) throws Exception {
double[] results = new double[m_Classifiers.length
+ m_preBuiltClassifiers.size()];
double result;
for (int i = 0; i < m_Classifiers.length; i++)
results[i] = m_Classifiers[i].classifyInstance(instance);
for (int i = 0; i < m_preBuiltClassifiers.size(); i++) {
results[i + m_Classifiers.length] = m_preBuiltClassifiers.get(i)
.classifyInstance(instance);
}
if (results.length == 0)
result = 0;
else if (results.length == 1)
result = results[0];
else
result = Utils.kthSmallestValue(results, results.length / 2);
return result;
}
/**
* Classifies a given instance using the selected combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified successfully
*/
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
double[] result = new double[instance.numClasses()];
switch (m_CombinationRule) {
case AVERAGE_RULE:
result = distributionForInstanceAverage(instance);
break;
case PRODUCT_RULE:
result = distributionForInstanceProduct(instance);
break;
case MAJORITY_VOTING_RULE:
result = distributionForInstanceMajorityVoting(instance);
break;
case MIN_RULE:
result = distributionForInstanceMin(instance);
break;
case MAX_RULE:
result = distributionForInstanceMax(instance);
break;
case MEDIAN_RULE:
result[0] = classifyInstance(instance);
break;
default:
throw new IllegalStateException("Unknown combination rule '"
+ m_CombinationRule + "'!");
}
if (!instance.classAttribute().isNumeric() && (Utils.sum(result) > 0))
Utils.normalize(result);
return result;
}
/**
* Classifies a given instance using the Average of Probabilities combination
* rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified successfully
*/
protected double[] distributionForInstanceAverage(Instance instance)
throws Exception {
double[] probs = (m_Classifiers.length > 0) ? getClassifier(0)
.distributionForInstance(instance) : m_preBuiltClassifiers.get(0)
.distributionForInstance(instance);
probs = probs.clone();
for (int i = 1; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
for (int j = 0; j < dist.length; j++) {
probs[j] += dist[j];
}
}
int index = (m_Classifiers.length > 0) ? 0 : 1;
for (int i = index; i < m_preBuiltClassifiers.size(); i++) {
double[] dist = m_preBuiltClassifiers.get(i).distributionForInstance(
instance);
for (int j = 0; j < dist.length; j++) {
probs[j] += dist[j];
}
}
for (int j = 0; j < probs.length; j++) {
probs[j] /= (m_Classifiers.length + m_preBuiltClassifiers.size());
}
return probs;
}
/**
* Classifies a given instance using the Product of Probabilities combination
* rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified successfully
*/
protected double[] distributionForInstanceProduct(Instance instance)
throws Exception {
double[] probs = (m_Classifiers.length > 0) ? getClassifier(0)
.distributionForInstance(instance) : m_preBuiltClassifiers.get(0)
.distributionForInstance(instance);
probs = probs.clone();
for (int i = 1; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
for (int j = 0; j < dist.length; j++) {
probs[j] *= dist[j];
}
}
int index = (m_Classifiers.length > 0) ? 0 : 1;
for (int i = index; i < m_preBuiltClassifiers.size(); i++) {
double[] dist = m_preBuiltClassifiers.get(i).distributionForInstance(
instance);
for (int j = 0; j < dist.length; j++) {
probs[j] *= dist[j];
}
}
return probs;
}
/**
* Classifies a given instance using the Majority Voting combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified successfully
*/
protected double[] distributionForInstanceMajorityVoting(Instance instance)
throws Exception {
double[] probs = new double[instance.classAttribute().numValues()];
double[] votes = new double[probs.length];
for (int i = 0; i < m_Classifiers.length; i++) {
probs = getClassifier(i).distributionForInstance(instance);
int maxIndex = 0;
for (int j = 0; j < probs.length; j++) {
if (probs[j] > probs[maxIndex])
maxIndex = j;
}
// Consider the cases when multiple classes happen to have the same
// probability
for (int j = 0; j < probs.length; j++) {
if (probs[j] == probs[maxIndex])
votes[j]++;
}
}
for (int i = 0; i < m_preBuiltClassifiers.size(); i++) {
probs = m_preBuiltClassifiers.get(i).distributionForInstance(instance);
int maxIndex = 0;
for (int j = 0; j < probs.length; j++) {
if (probs[j] > probs[maxIndex])
maxIndex = j;
}
// Consider the cases when multiple classes happen to have the same
// probability
for (int j = 0; j < probs.length; j++) {
if (probs[j] == probs[maxIndex])
votes[j]++;
}
}
int tmpMajorityIndex = 0;
for (int k = 1; k < votes.length; k++) {
if (votes[k] > votes[tmpMajorityIndex])
tmpMajorityIndex = k;
}
// Consider the cases when multiple classes receive the same amount of votes
Vector<Integer> majorityIndexes = new Vector<Integer>();
for (int k = 0; k < votes.length; k++) {
if (votes[k] == votes[tmpMajorityIndex])
majorityIndexes.add(k);
}
// Resolve the ties according to a uniform random distribution
int majorityIndex = majorityIndexes.get(m_Random.nextInt(majorityIndexes
.size()));
// set probs to 0
probs = new double[probs.length];
probs[majorityIndex] = 1; // the class that have been voted the most
// receives 1
return probs;
}
/**
* Classifies a given instance using the Maximum Probability combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified successfully
*/
protected double[] distributionForInstanceMax(Instance instance)
throws Exception {
double[] max = (m_Classifiers.length > 0) ? getClassifier(0)
.distributionForInstance(instance) : m_preBuiltClassifiers.get(0)
.distributionForInstance(instance);
max = max.clone();
for (int i = 1; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
for (int j = 0; j < dist.length; j++) {
if (max[j] < dist[j])
max[j] = dist[j];
}
}
int index = (m_Classifiers.length > 0) ? 0 : 1;
for (int i = index; i < m_preBuiltClassifiers.size(); i++) {
double[] dist = m_preBuiltClassifiers.get(i).distributionForInstance(
instance);
for (int j = 0; j < dist.length; j++) {
if (max[j] < dist[j])
max[j] = dist[j];
}
}
return max;
}
/**
* Classifies a given instance using the Minimum Probability combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified successfully
*/
protected double[] distributionForInstanceMin(Instance instance)
throws Exception {
double[] min = (m_Classifiers.length > 0) ? getClassifier(0)
.distributionForInstance(instance) : m_preBuiltClassifiers.get(0)
.distributionForInstance(instance);
min = min.clone();
for (int i = 1; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
for (int j = 0; j < dist.length; j++) {
if (dist[j] < min[j])
min[j] = dist[j];
}
}
int index = (m_Classifiers.length > 0) ? 0 : 1;
for (int i = index; i < m_preBuiltClassifiers.size(); i++) {
double[] dist = m_preBuiltClassifiers.get(i).distributionForInstance(
instance);
for (int j = 0; j < dist.length; j++) {
if (dist[j] < min[j])
min[j] = dist[j];
}
}
return min;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String combinationRuleTipText() {
return "The combination rule used.";
}
/**
* Gets the combination rule used
*
* @return the combination rule used
*/
public SelectedTag getCombinationRule() {
return new SelectedTag(m_CombinationRule, TAGS_RULES);
}
/**
* Sets the combination rule to use. Values other than
*
* @param newRule the combination rule method to use
*/
public void setCombinationRule(SelectedTag newRule) {
if (newRule.getTags() == TAGS_RULES)
m_CombinationRule = newRule.getSelectedTag().getID();
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String preBuiltClassifiersTipText() {
return "The pre-built serialized classifiers to include. Multiple "
+ "serialized classifiers can be included alongside those "
+ "that are built from scratch when this classifier runs. "
+ "Note that it does not make sense to include pre-built "
+ "classifiers in a cross-validation since they are static "
+ "and their models do not change from fold to fold.";
}
/**
* Set the paths to pre-built serialized classifiers to load and include in
* the ensemble
*
* @param preBuilt an array of File paths to serialized models
*/
public void setPreBuiltClassifiers(File[] preBuilt) {
m_classifiersToLoad.clear();
if (preBuilt != null && preBuilt.length > 0) {
for (int i = 0; i < preBuilt.length; i++) {
String path = preBuilt[i].toString();
m_classifiersToLoad.add(path);
}
}
}
/**
* Get the paths to pre-built serialized classifiers to load and include in
* the ensemble
*
* @return an array of File paths to serialized models
*/
public File[] getPreBuiltClassifiers() {
File[] result = new File[m_classifiersToLoad.size()];
for (int i = 0; i < m_classifiersToLoad.size(); i++) {
result[i] = new File(m_classifiersToLoad.get(i));
}
return result;
}
/**
* Output a representation of this classifier
*
* @return a string representation of the classifier
*/
@Override
public String toString() {
if (m_Classifiers == null) {
return "Vote: No model built yet.";
}
String result = "Vote combines";
result += " the probability distributions of these base learners:\n";
for (int i = 0; i < m_Classifiers.length; i++) {
result += '\t' + getClassifierSpec(i) + '\n';
}
for (Classifier c : m_preBuiltClassifiers) {
result += "\t" + c.getClass().getName()
+ Utils.joinOptions(((OptionHandler) c).getOptions()) + "\n";
}
result += "using the '";
switch (m_CombinationRule) {
case AVERAGE_RULE:
result += "Average of Probabilities";
break;
case PRODUCT_RULE:
result += "Product of Probabilities";
break;
case MAJORITY_VOTING_RULE:
result += "Majority Voting";
break;
case MIN_RULE:
result += "Minimum Probability";
break;
case MAX_RULE:
result += "Maximum Probability";
break;
case MEDIAN_RULE:
result += "Median Probability";
break;
default:
throw new IllegalStateException("Unknown combination rule '"
+ m_CombinationRule + "'!");
}
result += "' combination rule \n";
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 9785 $");
}
/**
* Set environment variable values to substitute in the paths of serialized
* models to load
*
* @param env the environment variables to use
*/
@Override
public void setEnvironment(Environment env) {
m_env = env;
}
/**
* Aggregate an object with this one
*
* @param toAggregate the object to aggregate
* @return the result of aggregation
* @throws Exception if the supplied object can't be aggregated for some
* reason
*/
@Override
public Classifier aggregate(Classifier toAggregate) throws Exception {
if (m_structure == null && m_Classifiers.length == 1 &&
(m_Classifiers[0] instanceof weka.classifiers.rules.ZeroR)) {
// remove the single untrained ZeroR
setClassifiers(new Classifier[0]);
}
// Can't do any training data compatibility checks unfortunately
addPreBuiltClassifier(toAggregate);
return this;
}
/**
* Call to complete the aggregation process. Allows implementers to do any
* final processing based on how many objects were aggregated.
*
* @throws Exception if the aggregation can't be finalized for some reason
*/
@Override
public void finalizeAggregation() throws Exception {
// nothing to do
}
/**
* Main method for testing this class.
*
* @param argv should contain the following arguments: -t training file [-T
* test file] [-c class index]
*/
public static void main(String[] argv) {
runClassifier(new Vote(), argv);
}
}