/*
* 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.
*/
/*
* ClassifierPerformanceEvaluator.java
* Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
*
*/
package weka.gui.beans;
import weka.classifiers.Classifier;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Evaluation;
import weka.classifiers.evaluation.ThresholdCurve;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.gui.explorer.ClassifierErrorsPlotInstances;
import weka.gui.explorer.ExplorerDefaults;
import weka.gui.visualize.PlotData2D;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;
/**
* A bean that evaluates the performance of batch trained classifiers
*
* @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
* @version $Revision: 6804 $
*/
public class ClassifierPerformanceEvaluator
extends AbstractEvaluator
implements BatchClassifierListener,
Serializable, UserRequestAcceptor, EventConstraints {
/** for serialization */
private static final long serialVersionUID = -3511801418192148690L;
/**
* Evaluation object used for evaluating a classifier
*/
private transient Evaluation m_eval;
private transient Thread m_evaluateThread = null;
private transient long m_currentBatchIdentifier;
private transient int m_setsComplete;
private Vector m_textListeners = new Vector();
private Vector m_thresholdListeners = new Vector();
private Vector m_visualizableErrorListeners = new Vector();
public ClassifierPerformanceEvaluator() {
m_visual.loadIcons(BeanVisual.ICON_PATH
+"ClassifierPerformanceEvaluator.gif",
BeanVisual.ICON_PATH
+"ClassifierPerformanceEvaluator_animated.gif");
m_visual.setText("ClassifierPerformanceEvaluator");
}
/**
* Set a custom (descriptive) name for this bean
*
* @param name the name to use
*/
public void setCustomName(String name) {
m_visual.setText(name);
}
/**
* Get the custom (descriptive) name for this bean (if one has been set)
*
* @return the custom name (or the default name)
*/
public String getCustomName() {
return m_visual.getText();
}
/**
* Global info for this bean
*
* @return a <code>String</code> value
*/
public String globalInfo() {
return "Evaluate the performance of batch trained classifiers.";
}
// ----- Stuff for ROC curves
private boolean m_rocListenersConnected = false;
/** for generating plottable instance with predictions appended. */
private transient ClassifierErrorsPlotInstances m_PlotInstances = null;
protected static Evaluation adjustForInputMappedClassifier(Evaluation eval,
weka.classifiers.Classifier classifier,
Instances inst, ClassifierErrorsPlotInstances plotInstances) throws Exception {
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
Instances mappedClassifierHeader =
((weka.classifiers.misc.InputMappedClassifier)classifier).
getModelHeader(new Instances(inst, 0));
eval = new Evaluation(new Instances(mappedClassifierHeader, 0));
if (!eval.getHeader().equalHeaders(inst)) {
// When the InputMappedClassifier is loading a model,
// we need to make a new dataset that maps the test instances to
// the structure expected by the mapped classifier - this is only
// to ensure that the ClassifierPlotInstances object is configured
// in accordance with what the embeded classifier was trained with
Instances mappedClassifierDataset =
((weka.classifiers.misc.InputMappedClassifier)classifier).
getModelHeader(new Instances(mappedClassifierHeader, 0));
for (int zz = 0; zz < inst.numInstances(); zz++) {
Instance mapped = ((weka.classifiers.misc.InputMappedClassifier)classifier).
constructMappedInstance(inst.instance(zz));
mappedClassifierDataset.add(mapped);
}
eval.setPriors(mappedClassifierDataset);
plotInstances.setInstances(mappedClassifierDataset);
plotInstances.setClassifier(classifier);
plotInstances.setClassIndex(mappedClassifierDataset.classIndex());
plotInstances.setEvaluation(eval);
}
}
return eval;
}
/**
* Accept a classifier to be evaluated.
*
* @param ce a <code>BatchClassifierEvent</code> value
*/
public void acceptClassifier(final BatchClassifierEvent ce) {
if (ce.getTestSet() == null || ce.getTestSet().isStructureOnly()) {
return; // cant evaluate empty/non-existent test instances
}
try {
if (m_evaluateThread == null) {
m_evaluateThread = new Thread() {
public void run() {
boolean errorOccurred = false;
// final String oldText = m_visual.getText();
Classifier classifier = ce.getClassifier();
try {
// if (ce.getSetNumber() == 1) {
if (ce.getGroupIdentifier() != m_currentBatchIdentifier) {
if (ce.getTrainSet().getDataSet() == null ||
ce.getTrainSet().getDataSet().numInstances() == 0) {
// we have no training set to estimate majority class
// or mean of target from
m_eval = new Evaluation(ce.getTestSet().getDataSet());
m_PlotInstances = ExplorerDefaults.getClassifierErrorsPlotInstances();
m_PlotInstances.setInstances(ce.getTestSet().getDataSet());
m_PlotInstances.setClassifier(ce.getClassifier());
m_PlotInstances.setClassIndex(ce.getTestSet().getDataSet().classIndex());
m_PlotInstances.setEvaluation(m_eval);
m_eval = adjustForInputMappedClassifier(m_eval, ce.getClassifier(),
ce.getTestSet().getDataSet(), m_PlotInstances);
m_eval.useNoPriors();
} else {
// we can set up with the training set here
m_eval = new Evaluation(ce.getTrainSet().getDataSet());
m_PlotInstances = ExplorerDefaults.getClassifierErrorsPlotInstances();
m_PlotInstances.setInstances(ce.getTrainSet().getDataSet());
m_PlotInstances.setClassifier(ce.getClassifier());
m_PlotInstances.setClassIndex(ce.getTestSet().getDataSet().classIndex());
m_PlotInstances.setEvaluation(m_eval);
m_eval = adjustForInputMappedClassifier(m_eval, ce.getClassifier(),
ce.getTrainSet().getDataSet(), m_PlotInstances);
}
// m_classifier = ce.getClassifier();
m_PlotInstances.setUp();
m_currentBatchIdentifier = ce.getGroupIdentifier();
m_setsComplete = 0;
}
// if (ce.getSetNumber() <= ce.getMaxSetNumber()) {
if (m_setsComplete < ce.getMaxSetNumber()) {
/*if (ce.getTrainSet().getDataSet() != null &&
ce.getTrainSet().getDataSet().numInstances() > 0) {
// set the priors
m_eval.setPriors(ce.getTrainSet().getDataSet());
} */
// m_visual.setText("Evaluating ("+ce.getSetNumber()+")...");
if (m_logger != null) {
m_logger.statusMessage(statusMessagePrefix()
+"Evaluating ("+ce.getSetNumber()
+")...");
}
m_visual.setAnimated();
/*
m_eval.evaluateModel(ce.getClassifier(),
ce.getTestSet().getDataSet()); */
for (int i = 0; i < ce.getTestSet().getDataSet().numInstances(); i++) {
Instance temp = ce.getTestSet().getDataSet().instance(i);
m_PlotInstances.process(temp, ce.getClassifier(), m_eval);
}
m_setsComplete++;
}
// if (ce.getSetNumber() == ce.getMaxSetNumber()) {
if (m_setsComplete == ce.getMaxSetNumber()) {
// System.err.println(m_eval.toSummaryString());
// m_resultsString.append(m_eval.toSummaryString());
// m_outText.setText(m_resultsString.toString());
String textTitle = classifier.getClass().getName();
String textOptions = "";
if (classifier instanceof OptionHandler) {
textOptions =
Utils.joinOptions(((OptionHandler)classifier).getOptions());
}
textTitle =
textTitle.substring(textTitle.lastIndexOf('.')+1,
textTitle.length());
String resultT = "=== Evaluation result ===\n\n"
+ "Scheme: " + textTitle + "\n"
+ ((textOptions.length() > 0) ? "Options: " + textOptions + "\n": "")
+ "Relation: " + ce.getTestSet().getDataSet().relationName()
+ "\n\n" + m_eval.toSummaryString();
if (ce.getTestSet().getDataSet().
classAttribute().isNominal()) {
resultT += "\n" + m_eval.toClassDetailsString()
+ "\n" + m_eval.toMatrixString();
}
TextEvent te =
new TextEvent(ClassifierPerformanceEvaluator.this,
resultT,
textTitle);
notifyTextListeners(te);
// set up visualizable errors
if (m_visualizableErrorListeners.size() > 0) {
PlotData2D errorD = m_PlotInstances.getPlotData(
textTitle + " " + textOptions);
VisualizableErrorEvent vel =
new VisualizableErrorEvent(ClassifierPerformanceEvaluator.this, errorD);
notifyVisualizableErrorListeners(vel);
m_PlotInstances.cleanUp();
}
if (ce.getTestSet().getDataSet().classAttribute().isNominal() &&
m_thresholdListeners.size() > 0) {
ThresholdCurve tc = new ThresholdCurve();
Instances result = tc.getCurve(m_eval.predictions(), 0);
result.
setRelationName(ce.getTestSet().getDataSet().relationName());
PlotData2D pd = new PlotData2D(result);
String htmlTitle = "<html><font size=-2>"
+ textTitle;
String newOptions = "";
if (classifier instanceof OptionHandler) {
String[] options =
((OptionHandler) classifier).getOptions();
if (options.length > 0) {
for (int ii = 0; ii < options.length; ii++) {
if (options[ii].length() == 0) {
continue;
}
if (options[ii].charAt(0) == '-' &&
!(options[ii].charAt(1) >= '0' &&
options[ii].charAt(1)<= '9')) {
newOptions += "<br>";
}
newOptions += options[ii];
}
}
}
htmlTitle += " " + newOptions + "<br>"
+ " (class: "
+ce.getTestSet().getDataSet().
classAttribute().value(0) + ")"
+ "</font></html>";
pd.setPlotName(textTitle + " (class: "
+ce.getTestSet().getDataSet().
classAttribute().value(0) + ")");
pd.setPlotNameHTML(htmlTitle);
boolean [] connectPoints =
new boolean [result.numInstances()];
for (int jj = 1; jj < connectPoints.length; jj++) {
connectPoints[jj] = true;
}
pd.setConnectPoints(connectPoints);
ThresholdDataEvent rde =
new ThresholdDataEvent(ClassifierPerformanceEvaluator.this,
pd, ce.getTestSet().getDataSet().classAttribute());
notifyThresholdListeners(rde);
/*te = new TextEvent(ClassifierPerformanceEvaluator.this,
result.toString(),
"ThresholdCurveInst");
notifyTextListeners(te); */
}
if (m_logger != null) {
m_logger.statusMessage(statusMessagePrefix() + "Finished.");
}
// save memory
m_PlotInstances = null;
}
} catch (Exception ex) {
errorOccurred = true;
ClassifierPerformanceEvaluator.this.stop(); // stop all processing
if (m_logger != null) {
m_logger.logMessage("[ClassifierPerformanceEvaluator] "
+ statusMessagePrefix()
+ " problem evaluating classifier. "
+ ex.getMessage());
}
ex.printStackTrace();
} finally {
// m_visual.setText(oldText);
m_visual.setStatic();
m_evaluateThread = null;
if (m_logger != null) {
if (errorOccurred) {
m_logger.statusMessage(statusMessagePrefix()
+ "ERROR (See log for details)");
} else if (isInterrupted()) {
m_logger.logMessage("[" + getCustomName() +"] Evaluation interrupted!");
m_logger.statusMessage(statusMessagePrefix()
+ "INTERRUPTED");
}
}
block(false);
}
}
};
m_evaluateThread.setPriority(Thread.MIN_PRIORITY);
m_evaluateThread.start();
// make sure the thread is still running before we block
// if (m_evaluateThread.isAlive()) {
block(true);
// }
m_evaluateThread = null;
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Returns true if. at this time, the bean is busy with some
* (i.e. perhaps a worker thread is performing some calculation).
*
* @return true if the bean is busy.
*/
public boolean isBusy() {
return (m_evaluateThread != null);
}
/**
* Try and stop any action
*/
public void stop() {
// tell the listenee (upstream bean) to stop
if (m_listenee instanceof BeanCommon) {
// System.err.println("Listener is BeanCommon");
((BeanCommon)m_listenee).stop();
}
// stop the evaluate thread
if (m_evaluateThread != null) {
m_evaluateThread.interrupt();
m_evaluateThread.stop();
m_evaluateThread = null;
m_visual.setStatic();
}
}
/**
* Function used to stop code that calls acceptClassifier. This is
* needed as classifier evaluation is performed inside a separate
* thread of execution.
*
* @param tf a <code>boolean</code> value
*/
private synchronized void block(boolean tf) {
if (tf) {
try {
// only block if thread is still doing something useful!
if (m_evaluateThread != null && m_evaluateThread.isAlive()) {
wait();
}
} catch (InterruptedException ex) {
}
} else {
notifyAll();
}
}
/**
* Return an enumeration of user activated requests for this bean
*
* @return an <code>Enumeration</code> value
*/
public Enumeration enumerateRequests() {
Vector newVector = new Vector(0);
if (m_evaluateThread != null) {
newVector.addElement("Stop");
}
return newVector.elements();
}
/**
* Perform the named request
*
* @param request the request to perform
* @exception IllegalArgumentException if an error occurs
*/
public void performRequest(String request) {
if (request.compareTo("Stop") == 0) {
stop();
} else {
throw new
IllegalArgumentException(request
+ " not supported (ClassifierPerformanceEvaluator)");
}
}
/**
* Add a text listener
*
* @param cl a <code>TextListener</code> value
*/
public synchronized void addTextListener(TextListener cl) {
m_textListeners.addElement(cl);
}
/**
* Remove a text listener
*
* @param cl a <code>TextListener</code> value
*/
public synchronized void removeTextListener(TextListener cl) {
m_textListeners.remove(cl);
}
/**
* Add a threshold data listener
*
* @param cl a <code>ThresholdDataListener</code> value
*/
public synchronized void addThresholdDataListener(ThresholdDataListener cl) {
m_thresholdListeners.addElement(cl);
}
/**
* Remove a Threshold data listener
*
* @param cl a <code>ThresholdDataListener</code> value
*/
public synchronized void removeThresholdDataListener(ThresholdDataListener cl) {
m_thresholdListeners.remove(cl);
}
/**
* Add a visualizable error listener
*
* @param vel a <code>VisualizableErrorListener</code> value
*/
public synchronized void addVisualizableErrorListener(VisualizableErrorListener vel) {
m_visualizableErrorListeners.add(vel);
}
/**
* Remove a visualizable error listener
*
* @param vel a <code>VisualizableErrorListener</code> value
*/
public synchronized void removeVisualizableErrorListener(VisualizableErrorListener vel) {
m_visualizableErrorListeners.remove(vel);
}
/**
* Notify all text listeners of a TextEvent
*
* @param te a <code>TextEvent</code> value
*/
private void notifyTextListeners(TextEvent te) {
Vector l;
synchronized (this) {
l = (Vector)m_textListeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
// System.err.println("Notifying text listeners "
// +"(ClassifierPerformanceEvaluator)");
((TextListener)l.elementAt(i)).acceptText(te);
}
}
}
/**
* Notify all ThresholdDataListeners of a ThresholdDataEvent
*
* @param te a <code>ThresholdDataEvent</code> value
*/
private void notifyThresholdListeners(ThresholdDataEvent re) {
Vector l;
synchronized (this) {
l = (Vector)m_thresholdListeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
// System.err.println("Notifying text listeners "
// +"(ClassifierPerformanceEvaluator)");
((ThresholdDataListener)l.elementAt(i)).acceptDataSet(re);
}
}
}
/**
* Notify all VisualizableErrorListeners of a VisualizableErrorEvent
*
* @param te a <code>VisualizableErrorEvent</code> value
*/
private void notifyVisualizableErrorListeners(VisualizableErrorEvent re) {
Vector l;
synchronized (this) {
l = (Vector)m_visualizableErrorListeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
// System.err.println("Notifying text listeners "
// +"(ClassifierPerformanceEvaluator)");
((VisualizableErrorListener)l.elementAt(i)).acceptDataSet(re);
}
}
}
/**
* Returns true, if at the current time, the named event could
* be generated. Assumes that supplied event names are names of
* events that could be generated by this bean.
*
* @param eventName the name of the event in question
* @return true if the named event could be generated at this point in
* time
*/
public boolean eventGeneratable(String eventName) {
if (m_listenee == null) {
return false;
}
if (m_listenee instanceof EventConstraints) {
if (!((EventConstraints)m_listenee).
eventGeneratable("batchClassifier")) {
return false;
}
}
return true;
}
private String statusMessagePrefix() {
return getCustomName() + "$" + hashCode() + "|";
}
}