private void saveConfusionMaxtrixWithWriter(BufferedWriter writer, ConfusionMatrixObject cmo) {
try {
writer.write(
String.format(
fmt,
cmo.getTp(),
cmo.getFp(),
cmo.getFn(),
cmo.getTn(),
cmo.getWeightedTp(),
cmo.getWeightedFp(),
cmo.getWeightedFn(),
cmo.getWeightedTn(),
cmo.getScore()));
} catch (IOException e) {
try {
writer.close();
} catch (IOException e1) {
log.error("Could not close the writer while write into confusion matrix");
}
log.error("Could not write into confusion matrix");
}
}
public List<ConfusionMatrixObject> calculate() {
List<ConfusionMatrixObject> cmoList = new ArrayList<ConfusionMatrixObject>();
// Calculate the sum
Double sumPos = 0.0, sumNeg = 0.0, sumWeightedPos = 0.0, sumWeightedNeg = 0.0;
for (ModelResultObject mo : moList) {
if (posTags.contains(mo.getTag())) {
// Positive
sumPos += posScaleFactor;
sumWeightedPos += mo.getWeight() * posScaleFactor;
} else {
// Negative
sumNeg += negScaleFactor;
sumWeightedNeg += mo.getWeight() * negScaleFactor;
}
}
// init ConfusionMatrix
ConfusionMatrixObject initCmo = new ConfusionMatrixObject();
initCmo.setTp(0.0);
initCmo.setFp(0.0);
initCmo.setFn(sumPos);
initCmo.setTn(sumNeg);
initCmo.setWeightedTp(0.0);
initCmo.setWeightedFp(0.0);
initCmo.setWeightedFn(sumWeightedPos);
initCmo.setWeightedTn(sumWeightedNeg);
initCmo.setScore(moList.get(0).getScore());
cmoList.add(initCmo);
// Calculate the rest
ConfusionMatrixObject prevCmo = initCmo;
for (ModelResultObject mo : moList) {
ConfusionMatrixObject cmo = new ConfusionMatrixObject(prevCmo);
if (posTags.contains(mo.getTag())) {
// Positive Instance
cmo.setTp(cmo.getTp() + posScaleFactor);
cmo.setFn(cmo.getFn() - posScaleFactor);
cmo.setWeightedTp(cmo.getWeightedTp() + mo.getWeight() * posScaleFactor);
cmo.setWeightedFn(cmo.getWeightedFn() - mo.getWeight() * posScaleFactor);
} else {
// Negative Instance
cmo.setFp(cmo.getFp() + negScaleFactor);
cmo.setTn(cmo.getTn() - negScaleFactor);
cmo.setWeightedFp(cmo.getWeightedFp() + mo.getWeight() * negScaleFactor);
cmo.setWeightedTn(cmo.getWeightedTn() - mo.getWeight() * negScaleFactor);
}
cmo.setScore(mo.getScore());
cmoList.add(cmo);
prevCmo = cmo;
}
return cmoList;
}
public void calculate(BufferedWriter writer) {
Double sumPos = 0.0, sumNeg = 0.0, sumWeightedPos = 0.0, sumWeightedNeg = 0.0;
for (ModelResultObject mo : moList) {
if (posTags.contains(mo.getTag())) {
// Positive
sumPos += posScaleFactor;
sumWeightedPos += mo.getWeight() * posScaleFactor;
} else {
// Negative
sumNeg += negScaleFactor;
sumWeightedNeg += mo.getWeight() * negScaleFactor;
}
}
ConfusionMatrixObject prevCmo = new ConfusionMatrixObject();
prevCmo.setTp(0.0);
prevCmo.setFp(0.0);
prevCmo.setFn(sumPos);
prevCmo.setTn(sumNeg);
prevCmo.setWeightedTp(0.0);
prevCmo.setWeightedFp(0.0);
prevCmo.setWeightedFn(sumWeightedPos);
prevCmo.setWeightedTn(sumWeightedNeg);
prevCmo.setScore(1000);
saveConfusionMaxtrixWithWriter(writer, prevCmo);
for (ModelResultObject mo : moList) {
ConfusionMatrixObject cmo = new ConfusionMatrixObject(prevCmo);
if (posTags.contains(mo.getTag())) {
// Positive Instance
cmo.setTp(cmo.getTp() + posScaleFactor);
cmo.setFn(cmo.getFn() - posScaleFactor);
cmo.setWeightedTp(cmo.getWeightedTp() + mo.getWeight() * posScaleFactor);
cmo.setWeightedFn(cmo.getWeightedFn() - mo.getWeight() * posScaleFactor);
} else {
// Negative Instance
cmo.setFp(cmo.getFp() + negScaleFactor);
cmo.setTn(cmo.getTn() - negScaleFactor);
cmo.setWeightedFp(cmo.getWeightedFp() + mo.getWeight() * negScaleFactor);
cmo.setWeightedTn(cmo.getWeightedTn() - mo.getWeight() * negScaleFactor);
}
cmo.setScore(mo.getScore());
saveConfusionMaxtrixWithWriter(writer, cmo);
prevCmo = cmo;
}
}