/**
* @param predictions the predictions to use
* @param classIndex the class index
* @return the probabilities
*/
private double[] getProbabilities(FastVector predictions, int classIndex) {
// sort by predicted probability of the desired class.
double[] probs = new double[predictions.size()];
for (int i = 0; i < probs.length; i++) {
NominalPrediction pred = (NominalPrediction) predictions.elementAt(i);
probs[i] = pred.distribution()[classIndex];
}
return probs;
}
/**
* Calculates the performance stats for the desired class and return results as a set of
* Instances.
*
* @param predictions the predictions to base the curve on
* @param classIndex index of the class of interest.
* @return datapoints as a set of instances.
*/
public Instances getCurve(FastVector predictions, int classIndex) {
if ((predictions.size() == 0)
|| (((NominalPrediction) predictions.elementAt(0)).distribution().length <= classIndex)) {
System.out.println(
"Foooobared "
+ predictions.size()
+ " "
+ ((NominalPrediction) predictions.elementAt(0)).distribution().length
+ " "
+ classIndex);
return null;
}
double totPos = 0, totNeg = 0;
double[] probs = getProbabilities(predictions, classIndex);
// Get distribution of positive/negatives
for (int i = 0; i < probs.length; i++) {
NominalPrediction pred = (NominalPrediction) predictions.elementAt(i);
if (pred.actual() == Prediction.MISSING_VALUE) {
System.err.println(getClass().getName() + " Skipping prediction with missing class value");
continue;
}
if (pred.weight() < 0) {
System.err.println(getClass().getName() + " Skipping prediction with negative weight");
continue;
}
if (pred.actual() == classIndex) {
totPos += pred.weight();
} else {
totNeg += pred.weight();
}
}
Instances insts = makeHeader();
int[] sorted = Utils.sort(probs);
TwoClassStats tc = new TwoClassStats(totPos, totNeg, 0, 0);
double threshold = 0;
double cumulativePos = 0;
double cumulativeNeg = 0;
for (int i = 0; i < sorted.length; i++) {
if ((i == 0) || (probs[sorted[i]] > threshold)) {
tc.setTruePositive(tc.getTruePositive() - cumulativePos);
tc.setFalseNegative(tc.getFalseNegative() + cumulativePos);
tc.setFalsePositive(tc.getFalsePositive() - cumulativeNeg);
tc.setTrueNegative(tc.getTrueNegative() + cumulativeNeg);
threshold = probs[sorted[i]];
insts.add(makeInstance(tc, threshold));
cumulativePos = 0;
cumulativeNeg = 0;
if (i == sorted.length - 1) {
break;
}
}
NominalPrediction pred = (NominalPrediction) predictions.elementAt(sorted[i]);
if (pred.actual() == Prediction.MISSING_VALUE) {
System.err.println(getClass().getName() + " Skipping prediction with missing class value");
continue;
}
if (pred.weight() < 0) {
System.err.println(getClass().getName() + " Skipping prediction with negative weight");
continue;
}
if (pred.actual() == classIndex) {
cumulativePos += pred.weight();
} else {
cumulativeNeg += pred.weight();
}
/*
System.out.println(tc + " " + probs[sorted[i]]
+ " " + (pred.actual() == classIndex));
*/
/*if ((i != (sorted.length - 1)) &&
((i == 0) ||
(probs[sorted[i]] != probs[sorted[i - 1]]))) {
insts.add(makeInstance(tc, probs[sorted[i]]));
}*/
}
// make sure a zero point gets into the curve
if (tc.getFalseNegative() != totPos || tc.getTrueNegative() != totNeg) {
tc = new TwoClassStats(0, 0, totNeg, totPos);
threshold = probs[sorted[sorted.length - 1]] + 10e-6;
insts.add(makeInstance(tc, threshold));
}
return insts;
}
