public void printPerformance(InstanceList instanceList, String description) {
System.out.println(description);
System.out.println("Accuracy: " + classifier.getAccuracy(instanceList));
LabelAlphabet labelAlphabet = classifier.getLabelAlphabet();
Iterator iterator = labelAlphabet.iterator();
while (iterator.hasNext()) {
Object label = iterator.next();
double p = classifier.getPrecision(instanceList, label);
double r = classifier.getRecall(instanceList, label);
double f1 = classifier.getF1(instanceList, label);
System.out.println("Precision[" + label + "] = " + p);
System.out.println("Recall[" + label + "] = " + r);
System.out.println("F1[" + label + "] = " + f1);
System.out.println("");
}
ExtendedTrial trial = new ExtendedTrial(classifier, instanceList);
System.out.println("Overall performance\n=====");
System.out.println("Precision: " + trial.getPrecision());
System.out.println("Recall: " + trial.getRecall());
System.out.println("F1: " + trial.getF1());
System.out.println("Fall-out: " + trial.getFallOut());
trial.showErrors();
}
public static ArrayList<Double> getAverageCrossValidationScore(
InstanceList ilist, int i, ClassifierTrainer trainer) {
double crossValidAccSum = 0;
double crossValidPrcSum = 0;
double crossValidRecSum = 0;
double crossValidF1Sum = 0;
int count = 0;
// get gross validation folds
CrossValidationIterator cvIlists = ilist.crossValidationIterator(i);
while (cvIlists.hasNext()) {
System.out.println("#############Performing " + count + " iteration###########");
InstanceList[] ilists = cvIlists.next();
System.out.println("The train set size is " + ilists[0].size());
System.out.println("The test set size is " + ilists[1].size());
Classifier classifier = trainer.train(ilists[0]);
System.out.println("The training accuracy is " + classifier.getAccuracy(ilists[0]));
System.out.println("The testing accuracy is " + classifier.getAccuracy(ilists[1]));
System.out.println("The testing precision is " + classifier.getPrecision(ilists[1], 1));
System.out.println("The testing recall is " + classifier.getRecall(ilists[1], 1));
System.out.println("The testing f1score is " + classifier.getF1(ilists[1], 1));
crossValidAccSum += classifier.getAccuracy(ilists[1]);
crossValidPrcSum += classifier.getPrecision(ilists[1], 1);
crossValidRecSum += classifier.getRecall(ilists[1], 1);
crossValidF1Sum += classifier.getF1(ilists[1], 1);
count++;
// additional calculations
ArrayList<Classification> outClassifications = classifier.classify(ilists[1]);
int p1l1 = 0;
int p1l0 = 0;
int p0l1 = 0;
int p0l0 = 0;
int countCorrect = 0;
int countIncorrect = 0;
System.out.println("Outclassification size " + outClassifications.size());
for (int k = 0; k < outClassifications.size(); k++) {
// System.out.println("Data "+outClassifications.get(k).getInstance().getName());
// System.out.println("Labeling "+outClassifications.get(k).getLabeling()); uncomment to get
// score
double predictedLabel = outClassifications.get(k).getLabeling().getBestIndex();
// System.out.println("Predicted label "+ predictedLabel);
double targetLabel =
Double.valueOf(outClassifications.get(k).getInstance().getTarget().toString());
// System.out.println("Target "+ targetLabel);
boolean bestlabelIsCorrect = outClassifications.get(k).bestLabelIsCorrect();
// System.out.println("Prediction "+bestlabelIsCorrect);
if (bestlabelIsCorrect) countCorrect++;
else countIncorrect++;
if ((predictedLabel == 1.0) && (targetLabel == 1.0)) p1l1++;
else if ((predictedLabel == 1.0) && (targetLabel == 0.0)) p1l0++;
else if ((predictedLabel == 0.0) && (targetLabel == 1.0)) p0l1++;
else if ((predictedLabel == 0.0) && (targetLabel == 0.0)) p0l0++;
}
System.out.println("Count Correct " + countCorrect);
System.out.println("Count Incorrect " + countIncorrect);
System.out.println("p1l1 " + p1l1);
System.out.println("p1l0 " + p1l0);
System.out.println("p0l1 " + p0l1);
System.out.println("p0l0 " + p0l0);
}
ArrayList<Double> results = new ArrayList<Double>();
double crossValidAccAvg = crossValidAccSum / count;
double crossValidPrcAvg = crossValidPrcSum / count;
double crossValidRecAvg = crossValidRecSum / count;
double crossValidF1Avg = crossValidF1Sum / count;
results.add(crossValidAccAvg);
results.add(crossValidPrcAvg);
results.add(crossValidRecAvg);
results.add(crossValidF1Avg);
return results;
}