/// <summary> Calculates the performance stats for the desired class and return
/// results as a set of Instances.
///
/// </summary>
/// <param name="classIndex">index of the class of interest.
/// </param>
/// <returns> datapoints as a set of instances.
/// </returns>
public virtual Instances getCurve(FastVector predictions, int classIndex)
{
if ((predictions.size() == 0) || (((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() == weka.classifiers.evaluation.Prediction_Fields.MISSING_VALUE)
{
System.Console.Error.WriteLine(GetType().FullName + " Skipping prediction with missing class value");
continue;
}
if (pred.weight() < 0)
{
System.Console.Error.WriteLine(GetType().FullName + " 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.TruePositive = tc.TruePositive - cumulativePos;
tc.FalseNegative = tc.FalseNegative + cumulativePos;
tc.FalsePositive = tc.FalsePositive - cumulativeNeg;
tc.TrueNegative = tc.TrueNegative + 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() == weka.classifiers.evaluation.Prediction_Fields.MISSING_VALUE)
{
System.Console.Error.WriteLine(GetType().FullName + " Skipping prediction with missing class value");
continue;
}
if (pred.weight() < 0)
{
System.Console.Error.WriteLine(GetType().FullName + " 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]]));
}*/
}
return insts;
}
private Instance makeInstance(TwoClassStats tc, double prob)
{
int count = 0;
double[] vals = new double[11];
vals[count++] = tc.TruePositive;
vals[count++] = tc.FalseNegative;
vals[count++] = tc.FalsePositive;
vals[count++] = tc.TrueNegative;
vals[count++] = tc.FalsePositiveRate;
vals[count++] = tc.TruePositiveRate;
vals[count++] = tc.Precision;
vals[count++] = tc.Recall;
vals[count++] = tc.Fallout;
vals[count++] = tc.FMeasure;
vals[count++] = prob;
return new Instance(1.0, vals);
}
