@Override
protected pikater.ontology.messages.Evaluation evaluateCA() {
Evaluation eval = test();
pikater.ontology.messages.Evaluation result = new pikater.ontology.messages.Evaluation();
result.setError_rate((float) eval.errorRate());
try {
result.setKappa_statistic((float) eval.kappa());
} catch (Exception e) {
result.setKappa_statistic(-1);
}
result.setMean_absolute_error((float) eval.meanAbsoluteError());
try {
result.setRelative_absolute_error((float) eval.relativeAbsoluteError());
} catch (Exception e) {
result.setRelative_absolute_error(-1);
}
result.setRoot_mean_squared_error((float) eval.rootMeanSquaredError());
result.setRoot_relative_squared_error((float) eval.rootRelativeSquaredError());
return result;
}
/**
* 用分类器测试
*
* @param trainFileName
* @param testFileName
*/
public static void classify(String trainFileName, String testFileName) {
try {
File inputFile = new File(fileName + trainFileName); // 训练语料文件
ArffLoader atf = new ArffLoader();
atf.setFile(inputFile);
Instances instancesTrain = atf.getDataSet(); // 读入训练文件
// 设置类标签类
inputFile = new File(fileName + testFileName); // 测试语料文件
atf.setFile(inputFile);
Instances instancesTest = atf.getDataSet(); // 读入测试文件
instancesTest.setClassIndex(instancesTest.numAttributes() - 1);
instancesTrain.setClassIndex(instancesTrain.numAttributes() - 1);
classifier = (Classifier) Class.forName(CLASSIFIERNAME).newInstance();
classifier.buildClassifier(instancesTrain);
Evaluation eval = new Evaluation(instancesTrain);
// 第一个为一个训练过的分类器，第二个参数是在某个数据集上评价的数据集
eval.evaluateModel(classifier, instancesTest);
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString());
System.out.println(eval.toMatrixString());
System.out.println("precision is :" + (1 - eval.errorRate()));
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* Finds the best parameter combination. (recursive for each parameter being optimised).
*
* @param depth the index of the parameter to be optimised at this level
* @param trainData the data the search is based on
* @param random a random number generator
* @throws Exception if an error occurs
*/
protected void findParamsByCrossValidation(int depth, Instances trainData, Random random)
throws Exception {
if (depth < m_CVParams.size()) {
CVParameter cvParam = (CVParameter) m_CVParams.elementAt(depth);
double upper;
switch ((int) (cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
case 1:
upper = m_NumAttributes;
break;
case 2:
upper = m_TrainFoldSize;
break;
default:
upper = cvParam.m_Upper;
break;
}
double increment = (upper - cvParam.m_Lower) / (cvParam.m_Steps - 1);
for (cvParam.m_ParamValue = cvParam.m_Lower;
cvParam.m_ParamValue <= upper;
cvParam.m_ParamValue += increment) {
findParamsByCrossValidation(depth + 1, trainData, random);
}
} else {
Evaluation evaluation = new Evaluation(trainData);
// Set the classifier options
String[] options = createOptions();
if (m_Debug) {
System.err.print("Setting options for " + m_Classifier.getClass().getName() + ":");
for (int i = 0; i < options.length; i++) {
System.err.print(" " + options[i]);
}
System.err.println("");
}
((OptionHandler) m_Classifier).setOptions(options);
for (int j = 0; j < m_NumFolds; j++) {
// We want to randomize the data the same way for every
// learning scheme.
Instances train = trainData.trainCV(m_NumFolds, j, new Random(1));
Instances test = trainData.testCV(m_NumFolds, j);
m_Classifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(m_Classifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println("Cross-validated error rate: " + Utils.doubleToString(error, 6, 4));
}
if ((m_BestPerformance == -99) || (error < m_BestPerformance)) {
m_BestPerformance = error;
m_BestClassifierOptions = createOptions();
}
}
}
/**
* Buildclassifier selects a classifier from the set of classifiers by minimising error on the
* training data.
*
* @param data the training data to be used for generating the boosted classifier.
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifiers.length == 0) {
throw new Exception("No base classifiers have been set!");
}
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
newData.randomize(new Random(m_Seed));
if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1))
newData.stratify(m_NumXValFolds);
Instances train = newData; // train on all data by default
Instances test = newData; // test on training data by default
Classifier bestClassifier = null;
int bestIndex = -1;
double bestPerformance = Double.NaN;
int numClassifiers = m_Classifiers.length;
for (int i = 0; i < numClassifiers; i++) {
Classifier currentClassifier = getClassifier(i);
Evaluation evaluation;
if (m_NumXValFolds > 1) {
evaluation = new Evaluation(newData);
for (int j = 0; j < m_NumXValFolds; j++) {
train = newData.trainCV(m_NumXValFolds, j);
test = newData.testCV(m_NumXValFolds, j);
currentClassifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(currentClassifier, test);
}
} else {
currentClassifier.buildClassifier(train);
evaluation = new Evaluation(train);
evaluation.evaluateModel(currentClassifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println(
"Error rate: "
+ Utils.doubleToString(error, 6, 4)
+ " for classifier "
+ currentClassifier.getClass().getName());
}
if ((i == 0) || (error < bestPerformance)) {
bestClassifier = currentClassifier;
bestPerformance = error;
bestIndex = i;
}
}
m_ClassifierIndex = bestIndex;
m_Classifier = bestClassifier;
if (m_NumXValFolds > 1) {
m_Classifier.buildClassifier(newData);
}
}
/**
* Predict the accuracy of the prototypes based on the learning set. It uses cross validation to
* draw the prediction.
*
* @param nbFolds the number of folds for the x-validation
* @return the predicted accuracy
*/
public double predictAccuracyXVal(int nbFolds) throws Exception {
Evaluation eval = new Evaluation(trainingData);
eval.crossValidateModel(this, trainingData, nbFolds, new Random(), new Object[] {});
return eval.errorRate();
}
/**
* Accepts and processes a classifier encapsulated in an incremental classifier event
*
* @param ce an <code>IncrementalClassifierEvent</code> 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];
Instances inst = ce.getStructure();
System.err.println("NEW BATCH");
m_instanceCount = 0;
if (m_windowSize > 0) {
m_window = new LinkedList<Instance>();
m_windowEval = new Evaluation(ce.getStructure());
m_windowEval.useNoPriors();
m_windowedPreds = new LinkedList<double[]>();
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();
}
}