public double CalculateMeasure(Model.IClassifier classifier, Data.Dataset dataset)
{
double measure = 0;
switch (this._mode)
{
case AUCMode.Pooled:
{
measure = this.CalculatePooledAUC(classifier, dataset);
break;
}
case AUCMode.Averaged:
{
measure = this.CalculateAverageAUC(classifier, dataset);
break;
}
case AUCMode.Wegithed:
{
measure = this.CalculateWeightedAUC(classifier, dataset);
break;
}
}
return(measure);
}
public static ConfusionMatrix[] ComputeConfusionMatrixes(Model.IClassifier classifier, Data.Dataset testset)
{
ConfusionMatrix[] list = new ConfusionMatrix[testset.Metadata.Target.Values.Length];
Instance instance = null;
for (int i = 0; i < testset.Size; i++)
{
instance = testset[i];
bool correct = false;
int actual = instance.Label;
Prediction prediction = classifier.Classify(instance);
int predicted = prediction.Label;
double probability = prediction.Probabilities[prediction.Label];
if (predicted == actual)
{
correct = true;
}
for (int classIndex = 0; classIndex < list.Length; classIndex++)
{
if (correct)
{
if (classIndex == actual)
{
list[classIndex].TP++;
list[classIndex].TP_Prob += probability;
}
else
{
list[classIndex].TN++;
list[classIndex].TN_Prob += probability;
}
}
else
{
if (classIndex == actual)
{
list[classIndex].FN++;
list[classIndex].FN_Prob += probability;
}
else if (classIndex == predicted)
{
list[classIndex].FP++;
list[classIndex].FP_Prob += probability;
}
else
{
list[classIndex].TN++;
list[classIndex].TN_Prob += probability;
}
}
}
}
return(list);
}
public static ConfusionMatrix[] GetConfusionMatrixes(Model.IClassifier classifier, Data.Dataset testset)
{
ConfusionMatrix[] list = new ConfusionMatrix[testset.Metadata.Target.Values.Length];
foreach (Data.Example example in testset)
{
bool correct = false;
int actual = example.Label;
Prediction prediction = classifier.Classify(example);
int predicted = prediction.Label;
double probability = prediction.Probability;
if (predicted == actual)
{
correct = true;
}
for (int classIndex = 0; classIndex < list.Length; classIndex++)
{
if (correct)
{
if (classIndex == actual)
{
list[classIndex].TP++;
list[classIndex].TP_Prob += probability;
}
else
{
list[classIndex].TN++;
list[classIndex].TN_Prob += probability;
}
}
else
{
if (classIndex == actual)
{
list[classIndex].FN++;
list[classIndex].FN_Prob += probability;
}
else if (classIndex == predicted)
{
list[classIndex].FP++;
list[classIndex].FP_Prob += probability;
}
else
{
list[classIndex].TN++;
list[classIndex].TN_Prob += probability;
}
}
}
}
return(list);
}