public static int[,] ComputeConfusionMatrix(this ISupervisedClassifier classifier, IEnumerable <Instance> testingDataset, Feature classFeature)
{
int errors = 0;
var instanceList = testingDataset.ToList();
int[] trueLabel = new int[instanceList.Count];
int[] predicted = new int[instanceList.Count];
for (int i = 0; i < instanceList.Count; i++)
{
Instance instance = testingDataset.ElementAt <Instance>(i);
double[] classification_result = classifier.Classify(instance);
trueLabel[i] = (int)instance[classFeature];
predicted[i] = classification_result.ArgMax <double>();
if (trueLabel[i] != predicted[i])
{
errors++;
}
}
// Computing confusion matrix
int classCount = classFeature.ClassValues().Length;
int[,] confusionMatrix = new int[classCount, classCount];
for (int i = 0; i < trueLabel.Length; i++)
{
confusionMatrix[trueLabel[i], predicted[i]]++;
}
return(confusionMatrix);
}
public static Tuple <double, double, double, double, double> TestClassifier(this ISupervisedClassifier classifier, IEnumerable <Instance> testingDataset, Feature classFeature)
{
int errors = 0;
var instanceList = testingDataset.ToList();
int[] trueLabel = new int[instanceList.Count];
int[] predicted = new int[instanceList.Count];
for (int i = 0; i < instanceList.Count; i++)
{
Instance instance = testingDataset.ElementAt <Instance>(i);
double[] classification_result = classifier.Classify(instance);
trueLabel[i] = (int)instance[classFeature];
predicted[i] = classification_result.ArgMax <double>();
if (trueLabel[i] != predicted[i])
{
errors++;
}
}
// Computing confusion matrix
int classCount = classFeature.ClassValues().Length;
int[,] confusionMatrix = new int[classCount, classCount];
for (int i = 0; i < trueLabel.Length; i++)
{
confusionMatrix[trueLabel[i], predicted[i]]++;
}
double acc = 100.0 * (instanceList.Count - errors) / instanceList.Count;
double auc = ComputeMultiClassAUC(confusionMatrix);
double precision = ComputeMultiClassPrecision(confusionMatrix);
double recall = ComputeMultiClassRecall(confusionMatrix);
double f1 = ComputeMultiClassF1(confusionMatrix);
return(new Tuple <double, double, double, double, double>(acc, auc, precision, recall, f1));
}
