protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits) {
var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel(tree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
var classificationProblemData = (IClassificationProblemData)problemData;
var rows = classificationProblemData.TrainingIndices;
model.RecalculateModelParameters(classificationProblemData, rows);
return model;
}
public static ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, SymbolicRegressionSolutionImpactValuesCalculator impactValuesCalculator, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IRegressionProblemData problemData, DoubleLimit estimationLimits, IEnumerable<int> rows, double nodeImpactThreshold = 0.0, bool pruneOnlyZeroImpactNodes = false) {
var clonedTree = (ISymbolicExpressionTree)tree.Clone();
var model = new SymbolicRegressionModel(problemData.TargetVariable, clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
var nodes = clonedTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList(); // skip the nodes corresponding to the ProgramRootSymbol and the StartSymbol
double qualityForImpactsCalculation = double.NaN; // pass a NaN value initially so the impact calculator will calculate the quality
for (int i = 0; i < nodes.Count; ++i) {
var node = nodes[i];
if (node is ConstantTreeNode) continue;
double impactValue, replacementValue;
double newQualityForImpactsCalculation;
impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, out newQualityForImpactsCalculation, qualityForImpactsCalculation);
if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0)) continue;
if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold) continue;
var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
constantNode.Value = replacementValue;
ReplaceWithConstant(node, constantNode);
i += node.GetLength() - 1; // skip subtrees under the node that was folded
qualityForImpactsCalculation = newQualityForImpactsCalculation;
}
return model.SymbolicExpressionTree;
}
public DensityTrackbar(string name, DoubleLimit limit, IList<double> trainingValues) {
InitializeComponent();
this.trainingValues = trainingValues;
doubleLimitView.Content = limit;
doubleLimitView.Content.ValueChanged += doubleLimit_ValueChanged;
textBox.Text = Value.ToString(CultureInfo.InvariantCulture);
radioButton.Text = name;
UpdateDensityChart();
}
public DensityTrackbar(string name, DoubleLimit limit, IList <double> trainingValues)
{
InitializeComponent();
this.trainingValues = trainingValues;
doubleLimitView.Content = limit;
doubleLimitView.Content.ValueChanged += doubleLimit_ValueChanged;
textBox.Text = Value.ToString(CultureInfo.InvariantCulture);
radioButton.Text = name;
UpdateDensityChart();
}
protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits)
{
var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel(tree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
var classificationProblemData = (IClassificationProblemData)problemData;
var rows = classificationProblemData.TrainingIndices;
model.RecalculateModelParameters(classificationProblemData, rows);
return(model);
}
public static ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, ISymbolicClassificationModelCreator modelCreator,
SymbolicClassificationSolutionImpactValuesCalculator impactValuesCalculator, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
IClassificationProblemData problemData, DoubleLimit estimationLimits, IEnumerable <int> rows,
double nodeImpactThreshold = 0.0, bool pruneOnlyZeroImpactNodes = false)
{
var clonedTree = (ISymbolicExpressionTree)tree.Clone();
var model = modelCreator.CreateSymbolicClassificationModel(clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
var nodes = clonedTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList();
double qualityForImpactsCalculation = double.NaN;
for (int i = 0; i < nodes.Count; ++i)
{
var node = nodes[i];
if (node is ConstantTreeNode)
{
continue;
}
double impactValue, replacementValue, newQualityForImpactsCalculation;
impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, out newQualityForImpactsCalculation, qualityForImpactsCalculation);
if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0))
{
continue;
}
if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold)
{
continue;
}
var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
constantNode.Value = replacementValue;
ReplaceWithConstant(node, constantNode);
i += node.GetLength() - 1; // skip subtrees under the node that was folded
qualityForImpactsCalculation = newQualityForImpactsCalculation;
}
return(model.SymbolicExpressionTree);
}
protected abstract ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits);
protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits) {
var regressionProblemData = (IRegressionProblemData)problemData;
return new SymbolicRegressionModel(regressionProblemData.TargetVariable, tree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
}
protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits)
{
var regressionProblemData = (IRegressionProblemData)problemData;
return(new SymbolicRegressionModel(regressionProblemData.TargetVariable, tree, interpreter, estimationLimits.Lower, estimationLimits.Upper));
}
protected abstract ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits);
protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits)
{
return(new SymbolicRegressionModel(tree, interpreter, estimationLimits.Lower, estimationLimits.Upper));
}
protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits) {
return new SymbolicRegressionModel(tree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
}
