private static List <Experiment> GetAveragedExperiments(IEnumerable <IGrouping <dynamic, Experiment> > groupedExperimentsCollection)
{
var averagedExperiments = new List <Experiment>();
foreach (var groupedExperiments in groupedExperimentsCollection)
{
var parameters = groupedExperiments.First().ExperimentParameters;
var evolutionStatistics = new EvolutionStatistics
{
BestFitnessScore = groupedExperiments.Average(g => g.Statistics.EvolutionStatistics.BestFitnessScore),
LastFitnessScore = groupedExperiments.Average(g => g.Statistics.EvolutionStatistics.LastFitnessScore),
NumberOfGenerationBestSolutionTakenFrom = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.EvolutionStatistics.NumberOfGenerationBestSolutionTakenFrom))
};
var statistics = new Statistics()
{
Accuracy = groupedExperiments.Average(g => g.Statistics.Accuracy),
EvolutionStatistics = evolutionStatistics,
F1Score = groupedExperiments.Average(g => g.Statistics.F1Score),
FallOut = groupedExperiments.Average(g => g.Statistics.FallOut),
FalseDiscoveryRate = groupedExperiments.Average(g => g.Statistics.FalseDiscoveryRate),
FalseNegatives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.FalseNegatives)),
FalseOmissionRate = groupedExperiments.Average(g => g.Statistics.FalseOmissionRate),
FalsePositives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.FalsePositives)),
JaccardIndex = groupedExperiments.Average(g => g.Statistics.JaccardIndex),
MeanAngle = groupedExperiments.Average(g => g.Statistics.MeanAngle),
MissRate = groupedExperiments.Average(g => g.Statistics.MissRate),
//ModelEvaluationTime = modelEvaluationTime,
NegativePredictiveValue = groupedExperiments.Average(g => g.Statistics.NegativePredictiveValue),
//NegativeTrainingPointsGenerationTime = negativeTrainingPointsGenerationTime,
NumberOfConstraints = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.NumberOfConstraints)),
//PositiveTrainingPointsGenerationTime = positiveTrainingPointsGenerationTime,
Precision = groupedExperiments.Average(g => g.Statistics.Precision),
Recall = groupedExperiments.Average(g => g.Statistics.Recall),
//RedundantConstraintsRemovingTime = redundantConstraintsRemovingTime,
Specificity = groupedExperiments.Average(g => g.Statistics.Specificity),
//TestPointsGenerationTime = testPointsGenerationTime,
//TotalSynthesisTime = totalSynthesisTime,
TrueNegatives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.TrueNegatives)),
TruePositives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.TruePositives)),
};
averagedExperiments.Add(new Experiment
{
ExperimentParameters = parameters,
Statistics = statistics
});
}
return(averagedExperiments);
}
private static CSUSESStatistics GetStatistics(EvolutionStatistics evolutionStatistics)
{
return(new CSUSESStatistics()
{
NumberOfConstraints = GetInt(nameof(CSUSESStatistics.NumberOfConstraints)),
MeanAngle = GetDouble(nameof(CSUSESStatistics.MeanAngle)),
TruePositives = GetInt(nameof(CSUSESStatistics.TruePositives)),
FalsePositives = GetInt(nameof(CSUSESStatistics.FalsePositives)),
TrueNegatives = GetInt(nameof(CSUSESStatistics.TrueNegatives)),
FalseNegatives = GetInt(nameof(CSUSESStatistics.FalseNegatives)),
TotalSynthesisTime = TimeSpan.FromMilliseconds(GetInt(nameof(CSUSESStatistics.TotalSynthesisTime))),
RedundantConstraintsRemovingTime = TimeSpan.FromMilliseconds(GetInt(nameof(CSUSESStatistics.RedundantConstraintsRemovingTime))),
ModelEvaluationTime = TimeSpan.FromMilliseconds(GetInt(nameof(CSUSESStatistics.ModelEvaluationTime))),
PositiveTrainingPointsGenerationTime = TimeSpan.FromMilliseconds(GetInt(nameof(CSUSESStatistics.PositiveTrainingPointsGenerationTime))),
NegativeTrainingPointsGenerationTime = TimeSpan.FromMilliseconds(GetInt(nameof(CSUSESStatistics.NegativeTrainingPointsGenerationTime))),
TestPointsGenerationTime = TimeSpan.FromMilliseconds(GetInt(nameof(CSUSESStatistics.TestPointsGenerationTime))),
EvolutionStatistics = evolutionStatistics
});
}
private static void TuningTable()
{
var groupedExperimentsCollection = Experiments
.GroupBy(e => new
{
e.ExperimentParameters.NumberOfDimensions,
e.ExperimentParameters.UseRedundantConstraintsRemoving,
e.ExperimentParameters.UseDataNormalization,
e.ExperimentParameters.AllowQuadraticTerms,
e.ExperimentParameters.UseSeeding,
e.ExperimentParameters.TypeOfBenchmark,
e.ExperimentParameters.EvolutionParameters.BasePopulationSize,
e.ExperimentParameters.EvolutionParameters.OffspringPopulationSize,
e.ExperimentParameters.EvolutionParameters.NumberOfGenerations,
//e.ExperimentParameters.MaximumNumberOfConstraints,
//e.ExperimentParameters.NumberOfConstraintsCoefficients,
//e.ExperimentParameters.TrackEvolutionSteps,
//e.ExperimentParameters.BallnBoundaryValue,
//e.ExperimentParameters.CubenBoundaryValue,
//e.ExperimentParameters.SimplexnBoundaryValue,
//e.ExperimentParameters.NumberOfDomainSamples,
//e.ExperimentParameters.NumberOfTestPoints,
//e.ExperimentParameters.NumberOfPositivePoints,
//e.ExperimentParameters.NumberOfNegativePoints,
//e.ExperimentParameters.DefaultDomainLowerLimit,
//e.ExperimentParameters.DefaultDomainUpperLimit,
//e.ExperimentParameters.MaxNumberOfPointsInSingleArray,
//e.ExperimentParameters.EvolutionParameters.ObjectVectorSize,
//e.ExperimentParameters.EvolutionParameters.OneFifthRuleCheckInterval,
//e.ExperimentParameters.EvolutionParameters.OneFifthRuleScalingFactor,
//e.ExperimentParameters.EvolutionParameters.NumberOfParentsSolutionsToSelect,
//e.ExperimentParameters.EvolutionParameters.TypeOfParentsSelection,
//e.ExperimentParameters.EvolutionParameters.TypeOfSurvivorsSelection,
//e.ExperimentParameters.EvolutionParameters.GlobalLearningRate,
//e.ExperimentParameters.EvolutionParameters.IndividualLearningRate,
//e.ExperimentParameters.EvolutionParameters.StepThreshold,
//e.ExperimentParameters.EvolutionParameters.RotationAngle,
//e.ExperimentParameters.EvolutionParameters.TypeOfMutation,
//e.ExperimentParameters.EvolutionParameters.UseRecombination,
//e.ExperimentParameters.EvolutionParameters.TypeOfObjectsRecombination,
//e.ExperimentParameters.EvolutionParameters.TypeOfStdDeviationsRecombination,
//e.ExperimentParameters.EvolutionParameters.TypeOfRotationsRecombination,
});
var averagedExperiments = new List <Experiment>();
foreach (var groupedExperiments in groupedExperimentsCollection)
{
var parameters = groupedExperiments.First().ExperimentParameters;
var evolutionStatistics = new EvolutionStatistics
{
BestFitnessScore = groupedExperiments.Average(g => g.Statistics.EvolutionStatistics.BestFitnessScore),
LastFitnessScore = groupedExperiments.Average(g => g.Statistics.EvolutionStatistics.LastFitnessScore),
NumberOfGenerationBestSolutionTakenFrom = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.EvolutionStatistics.NumberOfGenerationBestSolutionTakenFrom))
};
var statistics = new Statistics()
{
Accuracy = groupedExperiments.Average(g => g.Statistics.Accuracy),
EvolutionStatistics = evolutionStatistics,
F1Score = groupedExperiments.Average(g => g.Statistics.F1Score),
FallOut = groupedExperiments.Average(g => g.Statistics.FallOut),
FalseDiscoveryRate = groupedExperiments.Average(g => g.Statistics.FalseDiscoveryRate),
FalseNegatives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.FalseNegatives)),
FalseOmissionRate = groupedExperiments.Average(g => g.Statistics.FalseOmissionRate),
FalsePositives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.FalsePositives)),
JaccardIndex = groupedExperiments.Average(g => g.Statistics.JaccardIndex),
MeanAngle = groupedExperiments.Average(g => g.Statistics.MeanAngle),
MissRate = groupedExperiments.Average(g => g.Statistics.MissRate),
//ModelEvaluationTime = modelEvaluationTime,
NegativePredictiveValue = groupedExperiments.Average(g => g.Statistics.NegativePredictiveValue),
//NegativeTrainingPointsGenerationTime = negativeTrainingPointsGenerationTime,
NumberOfConstraints = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.NumberOfConstraints)),
//PositiveTrainingPointsGenerationTime = positiveTrainingPointsGenerationTime,
Precision = groupedExperiments.Average(g => g.Statistics.Precision),
Recall = groupedExperiments.Average(g => g.Statistics.Recall),
//RedundantConstraintsRemovingTime = redundantConstraintsRemovingTime,
Specificity = groupedExperiments.Average(g => g.Statistics.Specificity),
//TestPointsGenerationTime = testPointsGenerationTime,
//TotalSynthesisTime = totalSynthesisTime,
TrueNegatives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.TrueNegatives)),
TruePositives = (int)Math.Round(groupedExperiments.Average(g => g.Statistics.TruePositives)),
};
averagedExperiments.Add(new Experiment
{
ExperimentParameters = parameters,
Statistics = statistics
});
}
SaveToFile("tuningTable.tex", CreateTuningTable(averagedExperiments));
}
