protected OnlineMeanSquaredErrorCalculator(OnlineMeanSquaredErrorCalculator original, Cloner cloner)
: base(original, cloner)
{
sse = original.sse;
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();
OnlineMeanSquaredErrorCalculator mseCalculator = new OnlineMeanSquaredErrorCalculator();
// always move forward both enumerators (do not use short-circuit evaluation!)
while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext())
{
double original = originalEnumerator.Current;
double estimated = estimatedEnumerator.Current;
mseCalculator.Add(original, estimated);
if (mseCalculator.ErrorState != OnlineCalculatorError.None)
{
break;
}
}
// check if both enumerators are at the end to make sure both enumerations have the same length
if (mseCalculator.ErrorState == OnlineCalculatorError.None &&
(estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()))
{
throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
}
else
{
errorState = mseCalculator.ErrorState;
return(mseCalculator.MeanSquaredError);
}
}
protected void CalculateRegressionResults()
{
IEnumerable <double> estimatedTrainingValues = EstimatedTrainingValues; // cache values
IEnumerable <double> originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices);
IEnumerable <double> estimatedTestValues = EstimatedTestValues; // cache values
IEnumerable <double> originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices);
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 trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : 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 trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
TrainingRootMeanSquaredError = Math.Sqrt(TrainingMeanSquaredError);
TestRootMeanSquaredError = Math.Sqrt(TestMeanSquaredError);
// BackwardsCompatibility3.3
#region Backwards compatible code, remove with 3.5
if (ContainsKey(TrainingMeanErrorResultName))
{
double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
}
if (ContainsKey(TestMeanErrorResultName))
{
double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
}
#endregion
}
private void CalculateTrainingPrognosisResults()
{
OnlineCalculatorError errorState;
var problemData = Solution.ProblemData;
if (!problemData.TrainingIndices.Any())
{
return;
}
var model = Solution.Model;
//mean model
double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
var meanModel = new ConstantModel(trainingMean);
//AR1 model
double alpha, beta;
IEnumerable <double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
var trainingHorizions = problemData.TrainingIndices.Select(r => Math.Min(trainingHorizon, problemData.TrainingPartition.End - r)).ToList();
IEnumerable <IEnumerable <double> > trainingTargetValues = problemData.TrainingIndices.Zip(trainingHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
IEnumerable <IEnumerable <double> > trainingEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
IEnumerable <IEnumerable <double> > trainingMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
IEnumerable <IEnumerable <double> > trainingAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
IEnumerable <double> originalTrainingValues = trainingTargetValues.SelectMany(x => x).ToList();
IEnumerable <double> estimatedTrainingValues = trainingEstimatedValues.SelectMany(x => x).ToList();
double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
PrognosisTrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
PrognosisTrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingDirectionalSymmetry : 0.0;
PrognosisTrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
PrognosisTrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingWeightedDirectionalSymmetry : 0.0;
PrognosisTrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
PrognosisTrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticAR1 : double.PositiveInfinity;
PrognosisTrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
PrognosisTrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticMean : double.PositiveInfinity;
}
private void AfterDeserialization()
{
if (string.IsNullOrEmpty(Model.TargetVariable))
{
Model.TargetVariable = this.ProblemData.TargetVariable;
}
// BackwardsCompatibility3.4
#region Backwards compatible code, remove with 3.5
if (!ContainsKey(TrainingMeanAbsoluteErrorResultName))
{
OnlineCalculatorError errorState;
Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
}
if (!ContainsKey(TestMeanAbsoluteErrorResultName))
{
OnlineCalculatorError errorState;
Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
}
if (!ContainsKey(TrainingRootMeanSquaredErrorResultName))
{
OnlineCalculatorError errorState;
Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
TrainingRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(trainingMSE) : double.NaN;
}
if (!ContainsKey(TestRootMeanSquaredErrorResultName))
{
OnlineCalculatorError errorState;
Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
TestRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(testMSE) : double.NaN;
}
#endregion
}
public static double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
OnlineMeanSquaredErrorCalculator mseCalculator = new OnlineMeanSquaredErrorCalculator();
// always move forward both enumerators (do not use short-circuit evaluation!)
while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
double original = originalEnumerator.Current;
double estimated = estimatedEnumerator.Current;
mseCalculator.Add(original, estimated);
if (mseCalculator.ErrorState != OnlineCalculatorError.None) break;
}
// check if both enumerators are at the end to make sure both enumerations have the same length
if (mseCalculator.ErrorState == OnlineCalculatorError.None &&
(estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())) {
throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
} else {
errorState = mseCalculator.ErrorState;
return mseCalculator.MeanSquaredError;
}
}
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;
}
