/*
* 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.
*/
/*
* IncrementalClassifierEvaluator.java
* Copyright (C) 2002 Mark Hall
*
*/
package weka.gui.beans;
import weka.classifiers.Classifier;
import weka.classifiers.DistributionClassifier;
import weka.classifiers.Evaluation;
import weka.core.Instances;
import weka.core.Instance;
import weka.core.Utils;
import weka.gui.Logger;
import java.io.Serializable;
import java.util.Vector;
import java.util.Enumeration;
import javax.swing.JPanel;
import javax.swing.JLabel;
import javax.swing.ImageIcon;
import javax.swing.SwingConstants;
import javax.swing.BorderFactory;
import java.awt.*;
/**
* Bean that evaluates incremental classifiers
*
* @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
* @version $Revision: 1.1.1.1 $
*/
public class IncrementalClassifierEvaluator
extends AbstractEvaluator
implements IncrementalClassifierListener,
EventConstraints {
private transient Evaluation m_eval;
private transient Classifier m_classifier;
private Vector m_listeners = new Vector();
private Vector m_textListeners = new Vector();
private Vector m_dataLegend = new Vector();
private ChartEvent m_ce = new ChartEvent(this);
private double [] m_dataPoint = new double[1];
private boolean m_reset = false;
private double m_min = Double.MAX_VALUE;
private double m_max = Double.MIN_VALUE;
public IncrementalClassifierEvaluator() {
super();
m_visual.setText("IncrementalClassifierEvaluator");
}
/**
* Accepts and processes a classifier encapsulated in an incremental
* classifier event
*
* @param ce an <code>IncrementalClassifierEvent</code> value
*/
public void acceptClassifier(final IncrementalClassifierEvent ce) {
try {
if (ce.getStatus() == IncrementalClassifierEvent.NEW_BATCH) {
m_eval = new Evaluation(ce.getCurrentInstance().dataset());
m_dataLegend = new Vector();
m_reset = true;
m_dataPoint = new double[1];
Instance inst = ce.getCurrentInstance();
if (inst.classIndex() >= 0) {
if (inst.attribute(inst.classIndex()).isNominal()) {
if (inst.isMissing(inst.classIndex())) {
m_dataLegend.addElement("Confidence");
} else {
m_dataLegend.addElement("Accuracy");
}
} else {
if (inst.isMissing(inst.classIndex())) {
m_dataLegend.addElement("Prediction");
} else {
m_dataLegend.addElement("RRSE");
}
}
}
} else {
Instance inst = ce.getCurrentInstance();
// if (inst.attribute(inst.classIndex()).isNominal()) {
double [] dist = null;
double pred = 0;
if (ce.getClassifier() instanceof DistributionClassifier) {
dist = ((DistributionClassifier)ce.getClassifier())
.distributionForInstance(inst);
}
if (dist == null) {
if (!inst.isMissing(inst.classIndex())) {
m_eval.evaluateModelOnce(ce.getClassifier(), inst);
} else {
pred = ce.getClassifier().classifyInstance(inst);
}
} else {
if (!inst.isMissing(inst.classIndex())) {
m_eval.evaluateModelOnce(dist, inst);
} else {
pred = ce.getClassifier().classifyInstance(inst);
}
}
if (inst.classIndex() >= 0) {
// need to check that the class is not missing
if (inst.attribute(inst.classIndex()).isNominal()) {
if (dist != null && !inst.isMissing(inst.classIndex())) {
if (m_dataPoint.length < 2) {
m_dataPoint = new double[2];
m_dataLegend.addElement("RMSE (prob)");
}
// int classV = (int) inst.value(inst.classIndex());
m_dataPoint[1] = m_eval.rootMeanSquaredError();
// int maxO = Utils.maxIndex(dist);
// if (maxO == classV) {
// dist[classV] = -1;
// maxO = Utils.maxIndex(dist);
// }
// m_dataPoint[1] -= dist[maxO];
}
double primaryMeasure = 0;
if (!inst.isMissing(inst.classIndex())) {
primaryMeasure = 1.0 - m_eval.errorRate();
} else if (dist != null) {
// record confidence as the primary measure
// (another possibility would be entropy of
// the distribution, or perhaps average
// confidence)
primaryMeasure = dist[Utils.maxIndex(dist)];
} else {
// need something for non distribution classifiers when the
// actual class is missing!
}
// double [] dataPoint = new double[1];
m_dataPoint[0] = primaryMeasure;
// double min = 0; double max = 100;
/* ChartEvent e =
new ChartEvent(IncrementalClassifierEvaluator.this,
m_dataLegend, min, max, dataPoint); */
m_ce.setLegendText(m_dataLegend);
m_ce.setMin(0); m_ce.setMax(1);
m_ce.setDataPoint(m_dataPoint);
m_ce.setReset(m_reset);
m_reset = false;
} else {
// numeric class
if (dist != null && !inst.isMissing(inst.classIndex())) {
double update;
if (!inst.isMissing(inst.classIndex())) {
update = m_eval.rootRelativeSquaredError();
} else {
update = pred;
}
m_dataPoint[0] = update;
if (update > m_max) {
m_max = update;
}
if (update < m_min) {
m_min = update;
}
}
m_ce.setLegendText(m_dataLegend);
m_ce.setMin((inst.isMissing(inst.classIndex())
? m_min
: 0));
m_ce.setMax(m_max);
m_ce.setDataPoint(m_dataPoint);
m_ce.setReset(m_reset);
m_reset = false;
}
notifyChartListeners(m_ce);
if (ce.getStatus() == IncrementalClassifierEvent.BATCH_FINISHED) {
if (m_textListeners.size() > 0) {
String textTitle = ce.getClassifier().getClass().getName();
textTitle =
textTitle.substring(textTitle.lastIndexOf('.')+1,
textTitle.length());
TextEvent te =
new TextEvent(this,
m_eval.toSummaryString(),
textTitle);
notifyTextListeners(te);
}
}
}
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* 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("incrementalClassifier")) {
return false;
}
}
return true;
}
/**
* Stop all action
*/
public void stop() {
// nothing to do
}
private void notifyChartListeners(ChartEvent ce) {
Vector l;
synchronized (this) {
l = (Vector)m_listeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
((ChartListener)l.elementAt(i)).acceptDataPoint(ce);
}
}
}
/**
* 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);
}
}
}
/**
* Add a chart listener
*
* @param cl a <code>ChartListener</code> value
*/
public synchronized void addChartListener(ChartListener cl) {
m_listeners.addElement(cl);
}
/**
* Remove a chart listener
*
* @param cl a <code>ChartListener</code> value
*/
public synchronized void removeChartListener(ChartListener cl) {
m_listeners.remove(cl);
}
/**
* 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);
}
}