Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
weka.classifiers.meta.Vote Maven / Gradle / Ivy
The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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
*
*
* Valid options are:
*
*
*
* -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.
*
*
*
* -R <AVG|PROD|MAJ|MIN|MAX|MED>
* The combination rule to use
* (default: AVG)
*
*
*
* -print
* Print the individual models in the output
*
*
*
* -S <num>
* Random number seed.
* (default 1)
*
*
*
* -B <classifier specification>
* Full class name of classifier to include, followed
* by scheme options. May be specified multiple times.
* (default: "weka.classifiers.rules.ZeroR")
*
*
*
* -output-debug-info
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
*
* -do-not-check-capabilities
* If set, classifier capabilities are not checked before classifier is built
* (use with caution).
*
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
*
* -output-debug-info
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
*
* -do-not-check-capabilities
* If set, classifier capabilities are not checked before classifier is built
* (use with caution).
*
*
*
*
* BibTeX:
*
*
* @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}
* }
*
*
*
*
* @author Alexander K. Seewald ([email protected] )
* @author Eibe Frank ([email protected] )
* @author Roberto Perdisci ([email protected] )
* @version $Revision: 15479 $
*/
public class Vote extends RandomizableMultipleClassifiersCombiner implements
TechnicalInformationHandler, EnvironmentHandler, Aggregateable, WeightedInstancesHandler {
/** 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;
/** List of file paths to serialized models to load */
protected List m_classifiersToLoad = new ArrayList();
/** List of de-serialized pre-built classifiers to include in the ensemble */
protected List m_preBuiltClassifiers =
new ArrayList();
/** Environment variables */
protected transient Environment m_env = Environment.getSystemWide();
/** Structure of the training data */
protected Instances m_structure;
/** Print the individual models in the output */
protected boolean m_dontPrintModels;
/**
* 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() + "\n\n"
+ "If a base classifier cannot handle instance weights, and the instance weights are not uniform, "
+ "the data will be resampled with replacement based on the weights before being passed "
+ "to that base classifier.";
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
@Override
public Enumeration listOptions() {
Vector result = new Vector ();
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 "));
result.addElement(new Option("\tThe combination rule to use\n"
+ "\t(default: AVG)", "R", 1, "-R " + Tag.toOptionList(TAGS_RULES)));
result.addElement(new Option(
"\tSuppress the printing of the individual models in the output",
"do-not-print", 0, "-do-not-print"));
result.addAll(Collections.list(super.listOptions()));
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 = new Vector();
String[] options;
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));
}
if (m_dontPrintModels) {
result.add("-do-not-print");
}
return result.toArray(new String[result.size()]);
}
/**
* Parses a given list of options.
*
*
* Valid options are:
*
*
*
* -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.
*
*
*
* -R <AVG|PROD|MAJ|MIN|MAX|MED>
* The combination rule to use
* (default: AVG)
*
*
*
* -print
* Print the individual models in the output
*
*
*
* -S <num>
* Random number seed.
* (default 1)
*
*
*
* -B <classifier specification>
* Full class name of classifier to include, followed
* by scheme options. May be specified multiple times.
* (default: "weka.classifiers.rules.ZeroR")
*
*
*
* -output-debug-info
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
*
* -do-not-check-capabilities
* If set, classifier capabilities are not checked before classifier is built
* (use with caution).
*
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
*
* -output-debug-info
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
*
* -do-not-check-capabilities
* If set, classifier capabilities are not checked before classifier is built
* (use with caution).
*
*
*
*
* @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);
}
setDoNotPrintModels(Utils.getFlag("do-not-print", options));
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;
}
/**
* Builds all classifiers in the ensemble
*
* @param data the training data to be used for generating the ensemble.
* @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 = newData.stringFreeStructure();
if (m_classifiersToLoad.size() > 0) {
m_preBuiltClassifiers.clear();
loadClassifiers(data);
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(newData);
boolean uniformWeights = newData.allInstanceWeightsIdentical();
for (int i = 0; i < m_Classifiers.length; i++) {
if (!uniformWeights && !(getClassifier(i) instanceof WeightedInstancesHandler)) {
Random r = (newData.numInstances() > 0) ? newData.getRandomNumberGenerator(getSeed()) : new Random(getSeed());
getClassifier(i).buildClassifier(newData.resampleWithWeights(r));
} else {
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)) {
is.close();
throw new Exception("\"" + path + "\" does not contain a classifier!");
}
Object header = null;
header = is.readObject();
if ((header instanceof Instances) && !(c instanceof InputMappedClassifier)) {
if (data != null && !data.equalHeaders((Instances) header)) {
is.close();
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
+ "\"");
}
is.close();
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. Can assume that class is numeric.
*
* @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()];
int numResults = 0;
for (Classifier m_Classifier : m_Classifiers) {
double pred = m_Classifier.classifyInstance(instance);
if (!Utils.isMissingValue(pred)) {
results[numResults++] = pred;
}
}
for (int i = 0; i < m_preBuiltClassifiers.size(); i++) {
double pred = m_preBuiltClassifiers.get(i).classifyInstance(instance);
if (!Utils.isMissingValue(pred)) {
results[numResults++] = pred;
}
}
if (numResults == 0) {
return Utils.missingValue();
} else if (numResults == 1) {
return results[0];
} else {
double[] actualResults = new double[numResults];
System.arraycopy(results, 0, actualResults, 0, numResults);
return Utils.kthSmallestValue(actualResults, actualResults.length / 2);
}
}
/**
* 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 = new double[instance.numClasses()];
double numPredictions = 0;
for (int i = 0; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
if (!instance.classAttribute().isNumeric()
|| !Utils.isMissingValue(dist[0])) {
for (int j = 0; j < dist.length; j++) {
probs[j] += dist[j];
}
numPredictions++;
}
}
for (int i = 0; i < m_preBuiltClassifiers.size(); i++) {
double[] dist =
m_preBuiltClassifiers.get(i).distributionForInstance(instance);
if (!instance.classAttribute().isNumeric()
|| !Utils.isMissingValue(dist[0])) {
for (int j = 0; j < dist.length; j++) {
probs[j] += dist[j];
}
numPredictions++;
}
}
if (instance.classAttribute().isNumeric()) {
if (numPredictions == 0) {
probs[0] = Utils.missingValue();
} else {
for (int j = 0; j < probs.length; j++) {
probs[j] /= numPredictions;
}
}
} else {
// Should normalize "probability" distribution
if (Utils.sum(probs) > 0) {
Utils.normalize(probs);
}
}
return probs;
}
/**
* Classifies a given instance using the Product of Probabilities combination
* rule. Can assume that class is nominal.
*
* @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 = new double[instance.numClasses()];
for (int i = 0; i < probs.length; i++) {
probs[i] = 1.0;
}
int numPredictions = 0;
for (int i = 0; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
if (Utils.sum(dist) > 0) {
for (int j = 0; j < dist.length; j++) {
probs[j] *= dist[j];
}
numPredictions++;
}
}
for (int i = 0; i < m_preBuiltClassifiers.size(); i++) {
double[] dist =
m_preBuiltClassifiers.get(i).distributionForInstance(instance);
if (Utils.sum(dist) > 0) {
for (int j = 0; j < dist.length; j++) {
probs[j] *= dist[j];
}
numPredictions++;
}
}
// No predictions?
if (numPredictions == 0) {
return new double[instance.numClasses()];
}
// Should normalize to get "probabilities"
if (Utils.sum(probs) > 0) {
Utils.normalize(probs);
}
return probs;
}
/**
* Classifies a given instance using the Majority Voting combination rule. Can
* assume that class is nominal.
*
* @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
if (probs[maxIndex] > 0) {
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
if (probs[maxIndex] > 0) {
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;
}
}
// No votes received
if (votes[tmpMajorityIndex] == 0) {
return new double[instance.numClasses()];
}
// Consider the cases when multiple classes receive the same amount of votes
Vector majorityIndexes = new Vector();
for (int k = 0; k < votes.length; k++) {
if (votes[k] == votes[tmpMajorityIndex]) {
majorityIndexes.add(k);
}
}
int majorityIndex = tmpMajorityIndex;
if (majorityIndexes.size() > 1) {
// resolve ties by looking at the predicted distribution
double[] distPreds = distributionForInstanceAverage(instance);
majorityIndex = Utils.maxIndex(distPreds);
// Resolve the ties according to a uniform random distribution
// 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[] probs = new double[instance.numClasses()];
double numPredictions = 0;
for (int i = 0; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
if (!instance.classAttribute().isNumeric()
|| !Utils.isMissingValue(dist[0])) {
for (int j = 0; j < dist.length; j++) {
if ((probs[j] < dist[j]) || (numPredictions == 0)) {
probs[j] = dist[j];
}
}
numPredictions++;
}
}
for (int i = 0; i < m_preBuiltClassifiers.size(); i++) {
double[] dist =
m_preBuiltClassifiers.get(i).distributionForInstance(instance);
if (!instance.classAttribute().isNumeric()
|| !Utils.isMissingValue(dist[0])) {
for (int j = 0; j < dist.length; j++) {
if ((probs[j] < dist[j]) || (numPredictions == 0)) {
probs[j] = dist[j];
}
}
numPredictions++;
}
}
if (instance.classAttribute().isNumeric()) {
if (numPredictions == 0) {
probs[0] = Utils.missingValue();
}
} else {
// Should normalize "probability" distribution
if (Utils.sum(probs) > 0) {
Utils.normalize(probs);
}
}
return probs;
}
/**
* 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[] probs = new double[instance.numClasses()];
double numPredictions = 0;
for (int i = 0; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
if (!instance.classAttribute().isNumeric()
|| !Utils.isMissingValue(dist[0])) {
for (int j = 0; j < dist.length; j++) {
if ((probs[j] > dist[j]) || (numPredictions == 0)) {
probs[j] = dist[j];
}
}
numPredictions++;
}
}
for (int i = 0; i < m_preBuiltClassifiers.size(); i++) {
double[] dist =
m_preBuiltClassifiers.get(i).distributionForInstance(instance);
if (!instance.classAttribute().isNumeric()
|| !Utils.isMissingValue(dist[0])) {
for (int j = 0; j < dist.length; j++) {
if ((probs[j] > dist[j]) || (numPredictions == 0)) {
probs[j] = dist[j];
}
}
numPredictions++;
}
}
if (instance.classAttribute().isNumeric()) {
if (numPredictions == 0) {
probs[0] = Utils.missingValue();
}
} else {
// Should normalize "probability" distribution
if (Utils.sum(probs) > 0) {
Utils.normalize(probs);
}
}
return probs;
}
/**
* 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 (File element : preBuilt) {
String path = element.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;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String doNotPrintModelsTipText() {
return "Do not print the individual trees in the output";
}
/**
* Set whether to print the individual ensemble models in the output
*
* @param print true if the individual models are to be printed
*/
public void setDoNotPrintModels(boolean print) {
m_dontPrintModels = print;
}
/**
* Get whether to print the individual ensemble models in the output
*
* @return true if the individual models are to be printed
*/
public boolean getDoNotPrintModels() {
return m_dontPrintModels;
}
/**
* 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";
break;
case PRODUCT_RULE:
result += "Product";
break;
case MAJORITY_VOTING_RULE:
result += "Majority Voting";
break;
case MIN_RULE:
result += "Minimum";
break;
case MAX_RULE:
result += "Maximum";
break;
case MEDIAN_RULE:
result += "Median";
break;
default:
throw new IllegalStateException("Unknown combination rule '"
+ m_CombinationRule + "'!");
}
result += "' combination rule \n";
StringBuilder resultBuilder = null;
if (!m_dontPrintModels) {
resultBuilder = new StringBuilder();
resultBuilder.append(result).append("\nAll the models:\n\n");
for (Classifier c : m_Classifiers) {
resultBuilder.append(c).append("\n");
}
for (Classifier c : m_preBuiltClassifiers) {
resultBuilder.append(c).append("\n");
}
}
return resultBuilder == null ? result : resultBuilder.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 15479 $");
}
/**
* 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);
}
}
