protected override void OnIndividualEvaluationFinished(Individual individual, FitnessResult fitness, object[] results)
{
MDLFitnessResult fitnessResult = fitness as MDLFitnessResult;
if (SubTreePool != null)
{
int usedAttributes = fitnessResult.UsedAttributes;
int classificationError = fitnessResult.ClassificationError;
if (usedAttributes > 0)
{
double utilityMeasure = CalculateUtilityMeasure(usedAttributes, classificationError);
foreach (var id in fitnessResult.Dataset.Features)
{
// utility measure is maximized between 0 and 1 -> fitness is minimized
SubTreePool.Add(individual.SyntaxTree.FindNodeById(id), 1 - utilityMeasure);
}
SubTreePool.TrimToMaxSize();
}
}
}
public FitnessResult CalculateFitness(Individual individual, TestSuite testSuite, object[] results)
{
double fitness = StandardFitnessCalculator.CalculateFitness(individual, testSuite, results).Fitness; // standard fitness f0
MLDataset dataset = MLDataset.FromExecutionTraces(individual, Singleton <ExecutionRecord> .Instance.Traces);
LogDatasetFeatures(dataset);
var fitnessResult = new MDLFitnessResult(fitness, dataset);
if (dataset.Features.Count() > 0)
{
// Variant 1 (results of all nodes):
//var input = dataset.ToRawInputDataset();
// Variant 2 (results and operation types of first n and last n operations in chronological order):
//var input = dataset.ToRawFirstNLastNInputDataset(5, 5);
// Veriant 3 (results only consider values of bool and int expressions and operations)
int startContinuousFeatures;
var input = MLDataset.ConvertTracesToTypedSteps(individual, Singleton <ExecutionRecord> .Instance.Traces,
Steps, true, out startContinuousFeatures);
if (input.Length > 0 && input[0].Length > 0)
{
var expected = GetExpectedOutputDataset(testSuite);
LogDataset(input, expected);
var decisionTree = CreateDecisionTree(input, expected, startContinuousFeatures);
if (decisionTree != null)
{
int error = GetClassificationError(decisionTree, input, expected);
int treeSize = GetTreeSize(decisionTree);
double mdlFitness = CalculateMDLFitness(error, treeSize, results.Length);
double stdFitness = fitness;
fitness *= mdlFitness;
var rules = decisionTree.ToRules();
LogResult(fitness, error, treeSize, mdlFitness, rules);
int usedAttributes = GetUsedAttributes(decisionTree).Count;
fitnessResult.Fitness = fitness;
fitnessResult.ClassificationError = error;
fitnessResult.TreeSize = treeSize;
fitnessResult.StandardFitness = stdFitness;
fitnessResult.UsedAttributes = usedAttributes;
// log MDL result details
if (fitness == 0)
{
Logger.WriteLine(1, "Std fitness: " + stdFitness);
var predicted = decisionTree.Decide(input);
var loss = Math.Round(new ZeroOneLoss(expected).Loss(predicted) * expected.Length);
var classificationErrorFactor = (((double)error + 1) / (results.Length + 1));
Logger.WriteLine(1, "Program error: " + error + "; loss: " + loss + "; factor: " + classificationErrorFactor);
var treeSizeFactor = Math.Log(treeSize + 1, 2);
Logger.WriteLine(1, "Tree size: " + treeSize + "; factor: " + treeSizeFactor);
Logger.WriteLine(1, "MDL fitness: " + mdlFitness);
}
}
}
}
else if (fitness == 0)
{
Logger.WriteLine(1, "Standard fitness: 0");
}
return(fitnessResult);
}
