private GeneticAlgorithm CreateGpSymbolicClassificationSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
symbClassProblem.Name = "Mammography Classification Problem";
symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
UCIInstanceProvider provider = new UCIInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Mammography, M. Elter, 2007")).Single();
var mammoData = (ClassificationProblemData)provider.LoadData(instance);
mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
.First(v => v.Value == "Severity");
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Age"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Density"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
mammoData.TrainingPartition.Start = 0;
mammoData.TrainingPartition.End = 800;
mammoData.TestPartition.Start = 800;
mammoData.TestPartition.End = 961;
mammoData.Name = "Data imported from mammographic_masses.csv";
mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
symbClassProblem.ProblemData = mammoData;
// configure grammar
var grammar = new TypeCoherentExpressionGrammar();
grammar.ConfigureAsDefaultClassificationGrammar();
grammar.Symbols.OfType<VariableCondition>().Single().Enabled = false;
var varSymbol = grammar.Symbols.OfType<Variable>().Where(x => !(x is LaggedVariable)).Single();
varSymbol.WeightMu = 1.0;
varSymbol.WeightSigma = 1.0;
varSymbol.WeightManipulatorMu = 0.0;
varSymbol.WeightManipulatorSigma = 0.05;
varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
var constSymbol = grammar.Symbols.OfType<Constant>().Single();
constSymbol.MaxValue = 20;
constSymbol.MinValue = -20;
constSymbol.ManipulatorMu = 0.0;
constSymbol.ManipulatorSigma = 1;
constSymbol.MultiplicativeManipulatorSigma = 0.03;
symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
// configure remaining problem parameters
symbClassProblem.BestKnownQuality.Value = 0.0;
symbClassProblem.FitnessCalculationPartition.Start = 0;
symbClassProblem.FitnessCalculationPartition.End = 400;
symbClassProblem.ValidationPartition.Start = 400;
symbClassProblem.ValidationPartition.End = 800;
symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
symbClassProblem.MaximumFunctionDefinitions.Value = 0;
symbClassProblem.MaximumFunctionArguments.Value = 0;
symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
#endregion
#region Algorithm Configuration
ga.Problem = symbClassProblem;
ga.Name = "Genetic Programming - Symbolic Classification";
ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
ga, 1000, 1, 100, 0.15, 5
);
var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType<SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
.Single(), false);
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
.First(), false);
#endregion
return ga;
}
private GeneticAlgorithm CreateGpSymbolicClassificationSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
symbClassProblem.Name = "Mammography Classification Problem";
symbClassProblem.Description = "Mammography dataset imported from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)";
UCIInstanceProvider provider = new UCIInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Mammography, M. Elter, 2007")).Single();
var mammoData = (ClassificationProblemData)provider.LoadData(instance);
mammoData.TargetVariableParameter.Value = mammoData.TargetVariableParameter.ValidValues
.First(v => v.Value == "Severity");
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "BI-RADS"), false);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Age"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Shape"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Margin"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Density"), true);
mammoData.InputVariables.SetItemCheckedState(
mammoData.InputVariables.Single(x => x.Value == "Severity"), false);
mammoData.TrainingPartition.Start = 0;
mammoData.TrainingPartition.End = 800;
mammoData.TestPartition.Start = 800;
mammoData.TestPartition.End = 961;
mammoData.Name = "Data imported from mammographic_masses.csv";
mammoData.Description = "Original dataset: http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, missing values have been replaced with median values.";
symbClassProblem.ProblemData = mammoData;
// configure grammar
var grammar = new TypeCoherentExpressionGrammar();
grammar.ConfigureAsDefaultClassificationGrammar();
grammar.Symbols.OfType <VariableCondition>().Single().Enabled = false;
foreach (var varSy in grammar.Symbols.OfType <VariableBase>())
{
varSy.VariableChangeProbability = 1.0; // for backwards compatibilty
}
var varSymbol = grammar.Symbols.OfType <Variable>().Single();
varSymbol.WeightMu = 1.0;
varSymbol.WeightSigma = 1.0;
varSymbol.WeightManipulatorMu = 0.0;
varSymbol.WeightManipulatorSigma = 0.05;
varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
var constSymbol = grammar.Symbols.OfType <Constant>().Single();
constSymbol.MaxValue = 20;
constSymbol.MinValue = -20;
constSymbol.ManipulatorMu = 0.0;
constSymbol.ManipulatorSigma = 1;
constSymbol.MultiplicativeManipulatorSigma = 0.03;
symbClassProblem.SymbolicExpressionTreeGrammar = grammar;
// configure remaining problem parameters
symbClassProblem.BestKnownQuality.Value = 0.0;
symbClassProblem.FitnessCalculationPartition.Start = 0;
symbClassProblem.FitnessCalculationPartition.End = 400;
symbClassProblem.ValidationPartition.Start = 400;
symbClassProblem.ValidationPartition.End = 800;
symbClassProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
symbClassProblem.MaximumSymbolicExpressionTreeLength.Value = 100;
symbClassProblem.MaximumSymbolicExpressionTreeDepth.Value = 10;
symbClassProblem.MaximumFunctionDefinitions.Value = 0;
symbClassProblem.MaximumFunctionArguments.Value = 0;
symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
#endregion
#region Algorithm Configuration
ga.Problem = symbClassProblem;
ga.Name = "Genetic Programming - Symbolic Classification";
ga.Description = "A standard genetic programming algorithm to solve a classification problem (Mammographic+Mass dataset)";
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
ga, 1000, 1, 100, 0.15, 5
);
var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
mutator.Operators.OfType <FullTreeShaker>().Single().ShakingFactor = 0.1;
mutator.Operators.OfType <OnePointShaker>().Single().ShakingFactor = 1.0;
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType <SymbolicClassificationSingleObjectiveOverfittingAnalyzer>()
.Single(), false);
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType <SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
.First(), false);
#endregion
return(ga);
}
