protected void CalculateRegressionResults()
{
double[] estimatedTrainingValues = EstimatedTrainingValues.ToArray(); // cache values
double[] originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
double[] estimatedTestValues = EstimatedTestValues.ToArray(); // cache values
double[] originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray();
OnlineCalculatorError errorState;
double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
trainingNormalizedGini = double.NaN;
}
double testNormalizedGini = NormalizedGiniCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
testNormalizedGini = double.NaN;
}
TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
TestNormalizedGiniCoefficient = testNormalizedGini;
}
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;
}
}
