protected OnlineAccuracyCalculator(OnlineAccuracyCalculator original, Cloner cloner)
: base(original, cloner)
{
correctlyClassified = original.correctlyClassified;
n = original.n;
errorState = original.errorState;
}
public static double Calculate(IEnumerable <double> originalValues, IEnumerable <double> estimatedValues, out OnlineCalculatorError errorState)
{
IEnumerator <double> originalEnumerator = originalValues.GetEnumerator();
IEnumerator <double> estimatedEnumerator = estimatedValues.GetEnumerator();
OnlineAccuracyCalculator accuracyCalculator = new OnlineAccuracyCalculator();
// always move forward both enumerators (do not use short-circuit evaluation!)
while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext())
{
double original = originalEnumerator.Current;
double estimated = estimatedEnumerator.Current;
accuracyCalculator.Add(original, estimated);
if (accuracyCalculator.ErrorState != OnlineCalculatorError.None)
{
break;
}
}
// check if both enumerators are at the end to make sure both enumerations have the same length
if (accuracyCalculator.ErrorState == OnlineCalculatorError.None &&
(estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()))
{
throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
}
else
{
errorState = accuracyCalculator.ErrorState;
return(accuracyCalculator.Accuracy);
}
}
public static double CalculateQuality(IEnumerable <double> targetValues, IEnumerable <double> estimatedClassValues)
{
OnlineCalculatorError errorState;
var ret = OnlineAccuracyCalculator.Calculate(targetValues, estimatedClassValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
throw new InvalidOperationException("Error during calculation with replaced inputs.");
}
return(ret);
}
public static double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
OnlineAccuracyCalculator accuracyCalculator = new OnlineAccuracyCalculator();
// always move forward both enumerators (do not use short-circuit evaluation!)
while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
double original = originalEnumerator.Current;
double estimated = estimatedEnumerator.Current;
accuracyCalculator.Add(original, estimated);
if (accuracyCalculator.ErrorState != OnlineCalculatorError.None) break;
}
// check if both enumerators are at the end to make sure both enumerations have the same length
if (accuracyCalculator.ErrorState == OnlineCalculatorError.None &&
(estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())) {
throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
} else {
errorState = accuracyCalculator.ErrorState;
return accuracyCalculator.Accuracy;
}
}
protected void CalculateClassificationResults()
{
double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
double[] originalTrainingClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values
double[] originalTestClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray();
var positiveClassName = ProblemData.PositiveClass;
double positiveClassValue = ProblemData.GetClassValue(positiveClassName);
ClassificationPerformanceMeasuresCalculator trainingPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue);
ClassificationPerformanceMeasuresCalculator testPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue);
OnlineCalculatorError errorState;
double trainingAccuracy = OnlineAccuracyCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
trainingAccuracy = double.NaN;
}
double testAccuracy = OnlineAccuracyCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
testAccuracy = double.NaN;
}
TrainingAccuracy = trainingAccuracy;
TestAccuracy = testAccuracy;
double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
trainingNormalizedGini = double.NaN;
}
double testNormalizedGini = NormalizedGiniCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
testNormalizedGini = double.NaN;
}
TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
TestNormalizedGiniCoefficient = testNormalizedGini;
ClassificationPerformanceMeasures.Reset();
trainingPerformanceCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues);
if (trainingPerformanceCalculator.ErrorState == OnlineCalculatorError.None)
{
ClassificationPerformanceMeasures.SetTrainingResults(trainingPerformanceCalculator);
}
testPerformanceCalculator.Calculate(originalTestClassValues, estimatedTestClassValues);
if (testPerformanceCalculator.ErrorState == OnlineCalculatorError.None)
{
ClassificationPerformanceMeasures.SetTestResults(testPerformanceCalculator);
}
if (ProblemData.Classes == 2)
{
var f1Training = FOneScoreCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState);
if (errorState == OnlineCalculatorError.None)
{
ClassificationPerformanceMeasures.TrainingF1Score = f1Training;
}
var f1Test = FOneScoreCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState);
if (errorState == OnlineCalculatorError.None)
{
ClassificationPerformanceMeasures.TestF1Score = f1Test;
}
}
var mccTraining = MatthewsCorrelationCoefficientCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState);
if (errorState == OnlineCalculatorError.None)
{
ClassificationPerformanceMeasures.TrainingMatthewsCorrelation = mccTraining;
}
var mccTest = MatthewsCorrelationCoefficientCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState);
if (errorState == OnlineCalculatorError.None)
{
ClassificationPerformanceMeasures.TestMatthewsCorrelation = mccTest;
}
}