/** Initializes the members. */
protected void initialize() {
super.initialize();
m_PlotShapes = new FastVector();
m_PlotSizes = new FastVector();
m_Classifier = null;
m_ClassIndex = -1;
m_Evaluation = null;
m_SaveForVisualization = true;
m_MinimumPlotSizeNumeric = ExplorerDefaults.getClassifierErrorsMinimumPlotSizeNumeric();
m_MaximumPlotSizeNumeric = ExplorerDefaults.getClassifierErrorsMaximumPlotSizeNumeric();
}
/**
* Accept a classifier to be evaluated.
*
* @param ce a <code>BatchClassifierEvent</code> value
*/
public void acceptClassifier(BatchClassifierEvent ce) {
if (ce.getTestSet() == null || ce.getTestSet().isStructureOnly()) {
return; // can't evaluate empty/non-existent test instances
}
Classifier classifier = ce.getClassifier();
try {
if (ce.getGroupIdentifier() != m_currentBatchIdentifier) {
if (m_setsComplete > 0) {
if (m_logger != null) {
m_logger.statusMessage(
statusMessagePrefix() + "BUSY. Can't accept data " + "at this time.");
m_logger.logMessage(
"[ClassifierPerformanceEvaluator] "
+ statusMessagePrefix()
+ " BUSY. Can't accept data at this time.");
}
return;
}
if (ce.getTrainSet().getDataSet() == null
|| ce.getTrainSet().getDataSet().numInstances() == 0) {
// we have no training set to estimate majority class
// or mean of target from
Evaluation 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(eval);
eval =
adjustForInputMappedClassifier(
eval, ce.getClassifier(), ce.getTestSet().getDataSet(), m_PlotInstances);
eval.useNoPriors();
m_eval = new AggregateableEvaluation(eval);
} else {
// we can set up with the training set here
Evaluation 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(eval);
eval =
adjustForInputMappedClassifier(
eval, ce.getClassifier(), ce.getTrainSet().getDataSet(), m_PlotInstances);
m_eval = new AggregateableEvaluation(eval);
}
m_PlotInstances.setUp();
m_currentBatchIdentifier = ce.getGroupIdentifier();
m_setsComplete = 0;
m_aggregatedPlotInstances = null;
String msg =
"[ClassifierPerformanceEvaluator] "
+ statusMessagePrefix()
+ " starting executor pool ("
+ getExecutionSlots()
+ " slots)...";
// start the execution pool
if (m_executorPool == null) {
startExecutorPool();
}
m_tasks = new ArrayList<EvaluationTask>();
if (m_logger != null) {
m_logger.logMessage(msg);
} else {
System.out.println(msg);
}
}
// if m_tasks == null then we've been stopped
if (m_setsComplete < ce.getMaxSetNumber() && m_tasks != null) {
EvaluationTask newTask =
new EvaluationTask(
classifier,
ce.getTrainSet().getDataSet(),
ce.getTestSet().getDataSet(),
ce.getSetNumber(),
ce.getMaxSetNumber());
String msg =
"[ClassifierPerformanceEvaluator] "
+ statusMessagePrefix()
+ " scheduling "
+ " evaluation of fold "
+ ce.getSetNumber()
+ " for execution...";
if (m_logger != null) {
m_logger.logMessage(msg);
} else {
System.out.println(msg);
}
m_tasks.add(newTask);
m_executorPool.execute(newTask);
}
} catch (Exception ex) {
// stop everything
stop();
}
}
public void execute() {
if (m_stopped) {
return;
}
if (m_logger != null) {
m_logger.statusMessage(statusMessagePrefix() + "Evaluating (" + m_setNum + ")...");
m_visual.setAnimated();
}
try {
ClassifierErrorsPlotInstances plotInstances =
ExplorerDefaults.getClassifierErrorsPlotInstances();
Evaluation eval = null;
if (m_trainData == null || m_trainData.numInstances() == 0) {
eval = new Evaluation(m_testData);
plotInstances.setInstances(m_testData);
plotInstances.setClassifier(m_classifier);
plotInstances.setClassIndex(m_testData.classIndex());
plotInstances.setEvaluation(eval);
eval = adjustForInputMappedClassifier(eval, m_classifier, m_testData, plotInstances);
eval.useNoPriors();
} else {
eval = new Evaluation(m_trainData);
plotInstances.setInstances(m_trainData);
plotInstances.setClassifier(m_classifier);
plotInstances.setClassIndex(m_trainData.classIndex());
plotInstances.setEvaluation(eval);
eval = adjustForInputMappedClassifier(eval, m_classifier, m_trainData, plotInstances);
}
plotInstances.setUp();
for (int i = 0; i < m_testData.numInstances(); i++) {
if (m_stopped) {
break;
}
Instance temp = m_testData.instance(i);
plotInstances.process(temp, m_classifier, eval);
}
if (m_stopped) {
return;
}
aggregateEvalTask(eval, m_classifier, m_testData, plotInstances, m_setNum, m_maxSetNum);
} catch (Exception ex) {
ClassifierPerformanceEvaluator.this.stop(); // stop all processing
if (m_logger != null) {
m_logger.logMessage(
"[ClassifierPerformanceEvaluator] "
+ statusMessagePrefix()
+ " problem evaluating classifier. "
+ ex.getMessage());
}
ex.printStackTrace();
}
}
