weka.gui.beans.IncrementalClassifierEvaluator Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of weka-dev Show documentation
Show all versions of weka-dev Show documentation
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 m_listeners = new Vector();
private final Vector m_textListeners = new Vector();
private Vector m_dataLegend = new Vector();
private final 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;
// how often (in milliseconds) to report throughput to the log
private int m_statusFrequency = 2000;
private int m_instanceCount = 0;
// output info retrieval and auc stats for each class (if class is nominal)
private boolean m_outputInfoRetrievalStats = false;
// window size for computing performance metrics - 0 means no window, i.e
// don't "forget" performance on any instances
private int m_windowSize = 0;
private Evaluation m_windowEval;
private LinkedList m_window;
private LinkedList m_windowedPreds;
public IncrementalClassifierEvaluator() {
m_visual.loadIcons(BeanVisual.ICON_PATH
+ "IncrementalClassifierEvaluator.gif", BeanVisual.ICON_PATH
+ "IncrementalClassifierEvaluator_animated.gif");
m_visual.setText("IncrementalClassifierEvaluator");
}
/**
* Set a custom (descriptive) name for this bean
*
* @param name the name to use
*/
@Override
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)
*/
@Override
public String getCustomName() {
return m_visual.getText();
}
/**
* Global info for this bean
*
* @return a String value
*/
public String globalInfo() {
return "Evaluate the performance of incrementally trained classifiers.";
}
protected transient StreamThroughput m_throughput;
/**
* Accepts and processes a classifier encapsulated in an incremental
* classifier event
*
* @param ce an IncrementalClassifierEvent value
*/
@Override
public void acceptClassifier(final IncrementalClassifierEvent ce) {
try {
if (ce.getStatus() == IncrementalClassifierEvent.NEW_BATCH) {
m_throughput = new StreamThroughput(statusMessagePrefix());
m_throughput.setSamplePeriod(m_statusFrequency);
// m_eval = new Evaluation(ce.getCurrentInstance().dataset());
m_eval = new Evaluation(ce.getStructure());
m_eval.useNoPriors();
m_dataLegend = new Vector();
m_reset = true;
m_dataPoint = new double[0];
ce.getStructure();
System.err.println("NEW BATCH");
m_instanceCount = 0;
if (m_windowSize > 0) {
m_window = new LinkedList();
m_windowEval = new Evaluation(ce.getStructure());
m_windowEval.useNoPriors();
m_windowedPreds = new LinkedList();
if (m_logger != null) {
m_logger.logMessage(statusMessagePrefix()
+ "[IncrementalClassifierEvaluator] Chart output using windowed "
+ "evaluation over " + m_windowSize + " instances");
}
}
/*
* if (m_logger != null) { m_logger.statusMessage(statusMessagePrefix()
* + "IncrementalClassifierEvaluator: started processing...");
* m_logger.logMessage(statusMessagePrefix() +
* " [IncrementalClassifierEvaluator]" + statusMessagePrefix() +
* " started processing..."); }
*/
} else {
Instance inst = ce.getCurrentInstance();
if (inst != null) {
m_throughput.updateStart();
m_instanceCount++;
// if (inst.attribute(inst.classIndex()).isNominal()) {
double[] dist = ce.getClassifier().distributionForInstance(inst);
double pred = 0;
if (!inst.isMissing(inst.classIndex())) {
if (m_outputInfoRetrievalStats) {
// store predictions so AUC etc can be output.
m_eval.evaluateModelOnceAndRecordPrediction(dist, inst);
} else {
m_eval.evaluateModelOnce(dist, inst);
}
if (m_windowSize > 0) {
m_windowEval.evaluateModelOnce(dist, inst);
m_window.addFirst(inst);
m_windowedPreds.addFirst(dist);
if (m_instanceCount > m_windowSize) {
// "forget" the oldest prediction
Instance oldest = m_window.removeLast();
double[] oldDist = m_windowedPreds.removeLast();
oldest.setWeight(-oldest.weight());
m_windowEval.evaluateModelOnce(oldDist, oldest);
oldest.setWeight(-oldest.weight());
}
}
} 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 (!inst.isMissing(inst.classIndex())) {
if (m_dataPoint.length < 2) {
m_dataPoint = new double[3];
m_dataLegend.addElement("Accuracy");
m_dataLegend.addElement("RMSE (prob)");
m_dataLegend.addElement("Kappa");
}
// int classV = (int) inst.value(inst.classIndex());
if (m_windowSize > 0) {
m_dataPoint[1] = m_windowEval.rootMeanSquaredError();
m_dataPoint[2] = m_windowEval.kappa();
} else {
m_dataPoint[1] = m_eval.rootMeanSquaredError();
m_dataPoint[2] = m_eval.kappa();
}
// int maxO = Utils.maxIndex(dist);
// if (maxO == classV) {
// dist[classV] = -1;
// maxO = Utils.maxIndex(dist);
// }
// m_dataPoint[1] -= dist[maxO];
} else {
if (m_dataPoint.length < 1) {
m_dataPoint = new double[1];
m_dataLegend.addElement("Confidence");
}
}
double primaryMeasure = 0;
if (!inst.isMissing(inst.classIndex())) {
if (m_windowSize > 0) {
primaryMeasure = 1.0 - m_windowEval.errorRate();
} else {
primaryMeasure = 1.0 - m_eval.errorRate();
}
} else {
// record confidence as the primary measure
// (another possibility would be entropy of
// the distribution, or perhaps average
// confidence)
primaryMeasure = dist[Utils.maxIndex(dist)];
}
// 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 (m_dataPoint.length < 1) {
m_dataPoint = new double[1];
if (inst.isMissing(inst.classIndex())) {
m_dataLegend.addElement("Prediction");
} else {
m_dataLegend.addElement("RMSE");
}
}
if (!inst.isMissing(inst.classIndex())) {
double update;
if (!inst.isMissing(inst.classIndex())) {
if (m_windowSize > 0) {
update = m_windowEval.rootMeanSquaredError();
} else {
update = m_eval.rootMeanSquaredError();
}
} 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);
}
m_throughput.updateEnd(m_logger);
}
if (ce.getStatus() == IncrementalClassifierEvent.BATCH_FINISHED
|| inst == null) {
if (m_logger != null) {
m_logger.logMessage("[IncrementalClassifierEvaluator]"
+ statusMessagePrefix() + " Finished processing.");
}
m_throughput.finished(m_logger);
// save memory if using windowed evaluation for charting
m_windowEval = null;
m_window = null;
m_windowedPreds = null;
if (m_textListeners.size() > 0) {
String textTitle = ce.getClassifier().getClass().getName();
textTitle = textTitle.substring(textTitle.lastIndexOf('.') + 1,
textTitle.length());
String results = "=== Performance information ===\n\n"
+ "Scheme: " + textTitle + "\n" + "Relation: "
+ m_eval.getHeader().relationName() + "\n\n"
+ m_eval.toSummaryString();
if (m_eval.getHeader().classIndex() >= 0
&& m_eval.getHeader().classAttribute().isNominal()
&& (m_outputInfoRetrievalStats)) {
results += "\n" + m_eval.toClassDetailsString();
}
if (m_eval.getHeader().classIndex() >= 0
&& m_eval.getHeader().classAttribute().isNominal()) {
results += "\n" + m_eval.toMatrixString();
}
textTitle = "Results: " + textTitle;
TextEvent te = new TextEvent(this, results, textTitle);
notifyTextListeners(te);
}
}
}
} catch (Exception ex) {
if (m_logger != null) {
m_logger.logMessage("[IncrementalClassifierEvaluator]"
+ statusMessagePrefix() + " Error processing prediction "
+ ex.getMessage());
m_logger.statusMessage(statusMessagePrefix()
+ "ERROR: problem processing prediction (see log for details)");
}
ex.printStackTrace();
stop();
}
}
/**
* 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
*/
@Override
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
*/
@Override
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();
}
}
/**
* 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.
*/
@Override
public boolean isBusy() {
return false;
}
@SuppressWarnings("unchecked")
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++) {
l.elementAt(i).acceptDataPoint(ce);
}
}
}
/**
* Notify all text listeners of a TextEvent
*
* @param te a TextEvent value
*/
@SuppressWarnings("unchecked")
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)");
l.elementAt(i).acceptText(te);
}
}
}
/**
* Set how often progress is reported to the status bar.
*
* @param s report progress every s instances
*/
public void setStatusFrequency(int s) {
m_statusFrequency = s;
}
/**
* Get how often progress is reported to the status bar.
*
* @return after how many instances, progress is reported to the status bar
*/
public int getStatusFrequency() {
return m_statusFrequency;
}
/**
* Return a tip text string for this property
*
* @return a string for the tip text
*/
public String statusFrequencyTipText() {
return "How often to report progress to the status bar.";
}
/**
* Set whether to output per-class information retrieval statistics (nominal
* class only).
*
* @param i true if info retrieval stats are to be output
*/
public void setOutputPerClassInfoRetrievalStats(boolean i) {
m_outputInfoRetrievalStats = i;
}
/**
* Get whether per-class information retrieval stats are to be output.
*
* @return true if info retrieval stats are to be output
*/
public boolean getOutputPerClassInfoRetrievalStats() {
return m_outputInfoRetrievalStats;
}
/**
* Return a tip text string for this property
*
* @return a string for the tip text
*/
public String outputPerClassInfoRetrievalStatsTipText() {
return "Output per-class info retrieval stats. If set to true, predictions get "
+ "stored so that stats such as AUC can be computed. Note: this consumes some memory.";
}
/**
* Set whether to compute evaluation for charting over a fixed sized window of
* the most recent instances (rather than the whole stream).
*
* @param windowSize the size of the window to use for computing the
* evaluation metrics used for charting. Setting a value of zero or
* less specifies that no windowing is to be used.
*/
public void setChartingEvalWindowSize(int windowSize) {
m_windowSize = windowSize;
}
/**
* Get whether to compute evaluation for charting over a fixed sized window of
* the most recent instances (rather than the whole stream).
*
* @return the size of the window to use for computing the evaluation metrics
* used for charting. Setting a value of zero or less specifies that
* no windowing is to be used.
*/
public int getChartingEvalWindowSize() {
return m_windowSize;
}
/**
* Return a tip text string for this property
*
* @return a string for the tip text
*/
public String chartingEvalWindowSizeTipText() {
return "For charting only, specify a sliding window size over which to compute "
+ "performance stats. <= 0 means eval on whole stream";
}
/**
* Add a chart listener
*
* @param cl a ChartListener value
*/
public synchronized void addChartListener(ChartListener cl) {
m_listeners.addElement(cl);
}
/**
* Remove a chart listener
*
* @param cl a ChartListener value
*/
public synchronized void removeChartListener(ChartListener cl) {
m_listeners.remove(cl);
}
/**
* Add a text listener
*
* @param cl a TextListener value
*/
public synchronized void addTextListener(TextListener cl) {
m_textListeners.addElement(cl);
}
/**
* Remove a text listener
*
* @param cl a TextListener value
*/
public synchronized void removeTextListener(TextListener cl) {
m_textListeners.remove(cl);
}
private String statusMessagePrefix() {
return getCustomName() + "$" + hashCode() + "|";
}
}
© 2015 - 2024 Weber Informatics LLC | Privacy Policy