public override void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node,
IDataAnalysisProblemData problemData, IEnumerable<int> rows, out double impactValue, out double replacementValue, out double newQualityForImpactsCalculation,
double qualityForImpactsCalculation = Double.NaN) {
var classificationModel = (ISymbolicClassificationModel)model;
var classificationProblemData = (IClassificationProblemData)problemData;
if (double.IsNaN(qualityForImpactsCalculation))
qualityForImpactsCalculation = CalculateQualityForImpacts(classificationModel, classificationProblemData, rows);
replacementValue = CalculateReplacementValue(classificationModel, node, classificationProblemData, rows);
var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue };
var cloner = new Cloner();
var tempModel = cloner.Clone(classificationModel);
var tempModelNode = (ISymbolicExpressionTreeNode)cloner.GetClone(node);
var tempModelParentNode = tempModelNode.Parent;
int i = tempModelParentNode.IndexOfSubtree(tempModelNode);
tempModelParentNode.RemoveSubtree(i);
tempModelParentNode.InsertSubtree(i, constantNode);
OnlineCalculatorError errorState;
var dataset = classificationProblemData.Dataset;
var targetClassValues = dataset.GetDoubleValues(classificationProblemData.TargetVariable, rows);
var estimatedClassValues = tempModel.GetEstimatedClassValues(dataset, rows);
newQualityForImpactsCalculation = OnlineAccuracyCalculator.Calculate(targetClassValues, estimatedClassValues, out errorState);
if (errorState != OnlineCalculatorError.None) newQualityForImpactsCalculation = 0.0;
impactValue = qualityForImpactsCalculation - newQualityForImpactsCalculation;
}
public virtual void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable <int> rows,
out double impactValue, out double replacementValue, out double newQualityForImpactsCalculation,
double qualityForImpactsCalculation = double.NaN)
{
if (double.IsNaN(qualityForImpactsCalculation))
{
qualityForImpactsCalculation = CalculateQualityForImpacts(model, problemData, rows);
}
var cloner = new Cloner();
var tempModel = cloner.Clone(model);
var tempModelNode = (ISymbolicExpressionTreeNode)cloner.GetClone(node);
var tempModelParentNode = tempModelNode.Parent;
int i = tempModelParentNode.IndexOfSubtree(tempModelNode);
double bestReplacementValue = 0.0;
double bestImpactValue = double.PositiveInfinity;
newQualityForImpactsCalculation = qualityForImpactsCalculation; // initialize
// try the potentially reasonable replacement values and use the best one
foreach (var repValue in CalculateReplacementValues(node, model.SymbolicExpressionTree, model.Interpreter, problemData.Dataset, problemData.TrainingIndices))
{
tempModelParentNode.RemoveSubtree(i);
var constantNode = new ConstantTreeNode(new Constant())
{
Value = repValue
};
tempModelParentNode.InsertSubtree(i, constantNode);
newQualityForImpactsCalculation = CalculateQualityForImpacts(tempModel, problemData, rows);
impactValue = qualityForImpactsCalculation - newQualityForImpactsCalculation;
if (impactValue < bestImpactValue)
{
bestImpactValue = impactValue;
bestReplacementValue = repValue;
}
}
replacementValue = bestReplacementValue;
impactValue = bestImpactValue;
}
public override void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node,
IDataAnalysisProblemData problemData, IEnumerable <int> rows, out double impactValue, out double replacementValue, out double newQualityForImpactsCalculation,
double qualityForImpactsCalculation = Double.NaN)
{
var regressionModel = (ISymbolicRegressionModel)model;
var regressionProblemData = (IRegressionProblemData)problemData;
var dataset = regressionProblemData.Dataset;
var targetValues = dataset.GetDoubleValues(regressionProblemData.TargetVariable, rows);
OnlineCalculatorError errorState;
if (double.IsNaN(qualityForImpactsCalculation))
{
qualityForImpactsCalculation = CalculateQualityForImpacts(regressionModel, regressionProblemData, rows);
}
replacementValue = CalculateReplacementValue(regressionModel, node, regressionProblemData, rows);
var constantNode = new ConstantTreeNode(new Constant())
{
Value = replacementValue
};
var cloner = new Cloner();
var tempModel = cloner.Clone(regressionModel);
var tempModelNode = (ISymbolicExpressionTreeNode)cloner.GetClone(node);
var tempModelParentNode = tempModelNode.Parent;
int i = tempModelParentNode.IndexOfSubtree(tempModelNode);
tempModelParentNode.RemoveSubtree(i);
tempModelParentNode.InsertSubtree(i, constantNode);
var estimatedValues = tempModel.GetEstimatedValues(dataset, rows);
double r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
r = 0.0;
}
newQualityForImpactsCalculation = r * r;
impactValue = qualityForImpactsCalculation - newQualityForImpactsCalculation;
}
