// BackwardsCompatibility3.4
#region Backwards compatible code, remove with 3.5
// for compatibility with old API
public static SupportVectorRegressionSolution CreateSupportVectorRegressionSolution(
IRegressionProblemData problemData, IEnumerable <string> allowedInputVariables,
string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, int degree,
out double trainingR2, out double testR2, out int nSv)
{
ISupportVectorMachineModel model;
Run(problemData, allowedInputVariables, svmType, kernelType, cost, nu, gamma, epsilon, degree, out model, out nSv);
var solution = new SupportVectorRegressionSolution((SupportVectorMachineModel)model, (IRegressionProblemData)problemData.Clone());
trainingR2 = solution.TrainingRSquared;
testR2 = solution.TestRSquared;
return(solution);
}
private SupportVectorRegressionSolution(SupportVectorRegressionSolution original, Cloner cloner)
: base(original, cloner)
{
}
protected override void Run()
{
IRegressionProblemData problemData = Problem.ProblemData;
IEnumerable <string> selectedInputVariables = problemData.AllowedInputVariables;
int nSv;
ISupportVectorMachineModel model;
Run(problemData, selectedInputVariables, SvmType.Value, KernelType.Value, Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value, Degree.Value, out model, out nSv);
if (CreateSolution)
{
var solution = new SupportVectorRegressionSolution((SupportVectorMachineModel)model, (IRegressionProblemData)problemData.Clone());
Results.Add(new Result("Support vector regression solution", "The support vector regression solution.", solution));
}
Results.Add(new Result("Number of support vectors", "The number of support vectors of the SVR solution.", new IntValue(nSv)));
{
// calculate regression model metrics
var ds = problemData.Dataset;
var trainRows = problemData.TrainingIndices;
var testRows = problemData.TestIndices;
var yTrain = ds.GetDoubleValues(problemData.TargetVariable, trainRows);
var yTest = ds.GetDoubleValues(problemData.TargetVariable, testRows);
var yPredTrain = model.GetEstimatedValues(ds, trainRows).ToArray();
var yPredTest = model.GetEstimatedValues(ds, testRows).ToArray();
OnlineCalculatorError error;
var trainMse = OnlineMeanSquaredErrorCalculator.Calculate(yPredTrain, yTrain, out error);
if (error != OnlineCalculatorError.None)
{
trainMse = double.MaxValue;
}
var testMse = OnlineMeanSquaredErrorCalculator.Calculate(yPredTest, yTest, out error);
if (error != OnlineCalculatorError.None)
{
testMse = double.MaxValue;
}
Results.Add(new Result("Mean squared error (training)", "The mean of squared errors of the SVR solution on the training partition.", new DoubleValue(trainMse)));
Results.Add(new Result("Mean squared error (test)", "The mean of squared errors of the SVR solution on the test partition.", new DoubleValue(testMse)));
var trainMae = OnlineMeanAbsoluteErrorCalculator.Calculate(yPredTrain, yTrain, out error);
if (error != OnlineCalculatorError.None)
{
trainMae = double.MaxValue;
}
var testMae = OnlineMeanAbsoluteErrorCalculator.Calculate(yPredTest, yTest, out error);
if (error != OnlineCalculatorError.None)
{
testMae = double.MaxValue;
}
Results.Add(new Result("Mean absolute error (training)", "The mean of absolute errors of the SVR solution on the training partition.", new DoubleValue(trainMae)));
Results.Add(new Result("Mean absolute error (test)", "The mean of absolute errors of the SVR solution on the test partition.", new DoubleValue(testMae)));
var trainRelErr = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(yPredTrain, yTrain, out error);
if (error != OnlineCalculatorError.None)
{
trainRelErr = double.MaxValue;
}
var testRelErr = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(yPredTest, yTest, out error);
if (error != OnlineCalculatorError.None)
{
testRelErr = double.MaxValue;
}
Results.Add(new Result("Average relative error (training)", "The mean of relative errors of the SVR solution on the training partition.", new DoubleValue(trainRelErr)));
Results.Add(new Result("Average relative error (test)", "The mean of relative errors of the SVR solution on the test partition.", new DoubleValue(testRelErr)));
}
}
private SupportVectorRegressionSolution(SupportVectorRegressionSolution original, Cloner cloner)
: base(original, cloner) {
}
