public void GetRealWorldInstanceTest()
{
var target = new RegressionRealWorldInstanceProvider();
StringBuilder erroneousInstances = new StringBuilder();
int count = 0;
foreach (var id in target.GetDataDescriptors())
{
try {
target.LoadData(id);
} catch (Exception ex) {
erroneousInstances.AppendLine(id.Name + ": " + ex.Message);
}
count++;
}
Assert.IsTrue(count > 0, "No problem instances were found.");
Assert.IsTrue(erroneousInstances.Length == 0, "Some instances could not be parsed: " + Environment.NewLine + erroneousInstances.ToString());
}
public void GetRealWorldInstanceTest() {
var target = new RegressionRealWorldInstanceProvider();
StringBuilder erroneousInstances = new StringBuilder();
int count = 0;
foreach (var id in target.GetDataDescriptors()) {
try {
target.LoadData(id);
} catch (Exception ex) {
erroneousInstances.AppendLine(id.Name + ": " + ex.Message);
}
count++;
}
Assert.IsTrue(count > 0, "No problem instances were found.");
Assert.IsTrue(erroneousInstances.Length == 0, "Some instances could not be parsed: " + Environment.NewLine + erroneousInstances.ToString());
}
private GeneticAlgorithm CreateGpSymbolicRegressionSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
symbRegProblem.Name = "Tower Symbolic Regression Problem";
symbRegProblem.Description = "Tower Dataset (downloaded from: http://www.symbolicregression.com/?q=towerProblem)";
RegressionRealWorldInstanceProvider provider = new RegressionRealWorldInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Tower")).Single();
var towerProblemData = (RegressionProblemData)provider.LoadData(instance);
towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
.First(v => v.Value == "towerResponse");
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
towerProblemData.InputVariables.SetItemCheckedState(
towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
towerProblemData.TrainingPartition.Start = 0;
towerProblemData.TrainingPartition.End = 3136;
towerProblemData.TestPartition.Start = 3136;
towerProblemData.TestPartition.End = 4999;
towerProblemData.Name = "Data imported from towerData.txt";
towerProblemData.Description = "Chemical concentration at top of distillation tower, dataset downloaded from: http://vanillamodeling.com/realproblems.html, best R² achieved with nu-SVR = 0.97";
symbRegProblem.ProblemData = towerProblemData;
// configure grammar
var grammar = new TypeCoherentExpressionGrammar();
grammar.ConfigureAsDefaultRegressionGrammar();
grammar.Symbols.OfType <VariableCondition>().Single().InitialFrequency = 0.0;
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;
symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
// configure remaining problem parameters
symbRegProblem.BestKnownQuality.Value = 0.97;
symbRegProblem.FitnessCalculationPartition.Start = 0;
symbRegProblem.FitnessCalculationPartition.End = 2300;
symbRegProblem.ValidationPartition.Start = 2300;
symbRegProblem.ValidationPartition.End = 3136;
symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
symbRegProblem.MaximumFunctionDefinitions.Value = 0;
symbRegProblem.MaximumFunctionArguments.Value = 0;
symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
#endregion
#region Algorithm Configuration
ga.Problem = symbRegProblem;
ga.Name = "Genetic Programming - Symbolic Regression";
ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
SamplesUtils.ConfigureGeneticAlgorithmParameters <TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
ga, 1000, 1, 50, 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 <SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
.Single(), false);
ga.Analyzer.Operators.SetItemCheckedState(
ga.Analyzer.Operators
.OfType <SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
.First(), false);
#endregion
return(ga);
}