public override IOperation Apply()
{
if (ModelParameter.ActualValue != null && CreateSolutionParameter.ActualValue.Value == true)
{
var m = (IGaussianProcessModel)ModelParameter.ActualValue.Clone();
m.FixParameters();
var data = (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone();
var s = new GaussianProcessRegressionSolution(m, data);
SolutionParameter.ActualValue = s;
var results = ResultsParameter.ActualValue;
if (!results.ContainsKey(SolutionParameterName))
{
results.Add(new Result(SolutionParameterName, "The Gaussian process regression solution", s));
results.Add(new Result(TrainingRSquaredResultName,
"The Pearson's R² of the Gaussian process solution on the training partition.",
new DoubleValue(s.TrainingRSquared)));
results.Add(new Result(TestRSquaredResultName,
"The Pearson's R² of the Gaussian process solution on the test partition.",
new DoubleValue(s.TestRSquared)));
results.Add(new Result(NegLogPseudoLikelihood,
"The negative log pseudo-likelihood (from leave-one-out-cross-validation).",
new DoubleValue(m.LooCvNegativeLogPseudoLikelihood)));
}
else
{
results[SolutionParameterName].Value = s;
results[TrainingRSquaredResultName].Value = new DoubleValue(s.TrainingRSquared);
results[TestRSquaredResultName].Value = new DoubleValue(s.TestRSquared);
results[NegLogPseudoLikelihood].Value = new DoubleValue(m.LooCvNegativeLogPseudoLikelihood);
}
}
return(base.Apply());
}
public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int numberOfParameters)
{
if (pd.Dataset.Rows < MinLeafSize(pd))
{
throw new ArgumentException("The number of training instances is too small to create a Gaussian process model");
}
Regression.Problem = new RegressionProblem {
ProblemData = pd
};
var cvscore = double.MaxValue;
GaussianProcessRegressionSolution sol = null;
for (var i = 0; i < Tries; i++)
{
var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
var t = res.Select(x => x.Value).OfType <GaussianProcessRegressionSolution>().FirstOrDefault();
var score = ((DoubleValue)res["Negative log pseudo-likelihood (LOO-CV)"].Value).Value;
if (score >= cvscore || t == null || double.IsNaN(t.TrainingRSquared))
{
continue;
}
cvscore = score;
sol = t;
}
Regression.Runs.Clear();
if (sol == null)
{
throw new ArgumentException("Could not create Gaussian process model");
}
numberOfParameters = pd.Dataset.Rows + 1
+ Regression.CovarianceFunction.GetNumberOfParameters(pd.AllowedInputVariables.Count())
+ Regression.MeanFunction.GetNumberOfParameters(pd.AllowedInputVariables.Count());
return(sol.Model);
}
private GaussianProcessRegressionSolution(GaussianProcessRegressionSolution original, Cloner cloner)
: base(original, cloner)
{
}
public override IOperation Apply() {
if (ModelParameter.ActualValue != null && CreateSolutionParameter.ActualValue.Value == true) {
var m = (IGaussianProcessModel)ModelParameter.ActualValue.Clone();
m.FixParameters();
var data = (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone();
var s = new GaussianProcessRegressionSolution(m, data);
SolutionParameter.ActualValue = s;
var results = ResultsParameter.ActualValue;
if (!results.ContainsKey(SolutionParameterName)) {
results.Add(new Result(SolutionParameterName, "The Gaussian process regression solution", s));
results.Add(new Result(TrainingRSquaredResultName,
"The Pearson's R² of the Gaussian process solution on the training partition.",
new DoubleValue(s.TrainingRSquared)));
results.Add(new Result(TestRSquaredResultName,
"The Pearson's R² of the Gaussian process solution on the test partition.",
new DoubleValue(s.TestRSquared)));
} else {
results[SolutionParameterName].Value = s;
results[TrainingRSquaredResultName].Value = new DoubleValue(s.TrainingRSquared);
results[TestRSquaredResultName].Value = new DoubleValue(s.TestRSquared);
}
}
return base.Apply();
}
private GaussianProcessRegressionSolution(GaussianProcessRegressionSolution original, Cloner cloner)
: base(original, cloner) {
evaluationCache = new Dictionary<int, double>(original.evaluationCache);
}
private GaussianProcessRegressionSolution(GaussianProcessRegressionSolution original, Cloner cloner)
: base(original, cloner)
{
evaluationCache = new Dictionary <int, double>(original.evaluationCache);
}
private GaussianProcessRegressionSolution(GaussianProcessRegressionSolution original, Cloner cloner)
: base(original, cloner) { }