public override IOperation Apply()
{
IEnumerable <int> rows = GenerateRowsToEvaluate();
if (!rows.Any())
{
return(base.Apply());
}
var results = ResultCollection;
// create empty parameter and result values
if (ValidationBestSolutions == null)
{
ValidationBestSolutions = new ItemList <S>();
ValidationBestSolutionQualities = new ItemList <DoubleArray>();
results.Add(new Result(ValidationBestSolutionQualitiesParameter.Name, ValidationBestSolutionQualitiesParameter.Description, ValidationBestSolutionQualities));
results.Add(new Result(ValidationBestSolutionsParameter.Name, ValidationBestSolutionsParameter.Description, ValidationBestSolutions));
}
//if the pareto front of best solutions shall be updated regardless of the quality, the list initialized empty to discard old solutions
IList <double[]> trainingBestQualities;
if (UpdateAlways.Value)
{
trainingBestQualities = new List <double[]>();
}
else
{
trainingBestQualities = ValidationBestSolutionQualities.Select(x => x.ToArray()).ToList();
}
#region find best trees
IList <int> nonDominatedIndexes = new List <int>();
ISymbolicExpressionTree[] tree = SymbolicExpressionTree.ToArray();
bool[] maximization = Maximization.ToArray();
List <double[]> newNonDominatedQualities = new List <double[]>();
var evaluator = EvaluatorParameter.ActualValue;
var problemData = ProblemDataParameter.ActualValue;
IExecutionContext childContext = (IExecutionContext)ExecutionContext.CreateChildOperation(evaluator);
var qualities = tree
.Select(t => evaluator.Evaluate(childContext, t, problemData, rows))
.ToArray();
for (int i = 0; i < tree.Length; i++)
{
if (IsNonDominated(qualities[i], trainingBestQualities, maximization) &&
IsNonDominated(qualities[i], qualities, maximization))
{
if (!newNonDominatedQualities.Contains(qualities[i], new DoubleArrayComparer()))
{
newNonDominatedQualities.Add(qualities[i]);
nonDominatedIndexes.Add(i);
}
}
}
#endregion
#region update Pareto-optimal solution archive
if (nonDominatedIndexes.Count > 0)
{
ItemList <DoubleArray> nonDominatedQualities = new ItemList <DoubleArray>();
ItemList <S> nonDominatedSolutions = new ItemList <S>();
// add all new non-dominated solutions to the archive
foreach (var index in nonDominatedIndexes)
{
S solution = CreateSolution(tree[index], qualities[index]);
nonDominatedSolutions.Add(solution);
nonDominatedQualities.Add(new DoubleArray(qualities[index]));
}
// add old non-dominated solutions only if they are not dominated by one of the new solutions
for (int i = 0; i < trainingBestQualities.Count; i++)
{
if (IsNonDominated(trainingBestQualities[i], newNonDominatedQualities, maximization))
{
if (!newNonDominatedQualities.Contains(trainingBestQualities[i], new DoubleArrayComparer()))
{
nonDominatedSolutions.Add(ValidationBestSolutions[i]);
nonDominatedQualities.Add(ValidationBestSolutionQualities[i]);
}
}
}
results[ValidationBestSolutionsParameter.Name].Value = nonDominatedSolutions;
results[ValidationBestSolutionQualitiesParameter.Name].Value = nonDominatedQualities;
}
#endregion
return(base.Apply());
}
public override IOperation Apply()
{
var results = ResultCollection;
// create empty parameter and result values
if (TrainingBestSolutions == null)
{
TrainingBestSolutions = new ItemList <T>();
TrainingBestSolutionQualities = new ItemList <DoubleArray>();
results.Add(new Result(TrainingBestSolutionQualitiesParameter.Name, TrainingBestSolutionQualitiesParameter.Description, TrainingBestSolutionQualities));
results.Add(new Result(TrainingBestSolutionsParameter.Name, TrainingBestSolutionsParameter.Description, TrainingBestSolutions));
}
if (!results.ContainsKey(TrainingBestSolutionParameterName))
{
results.Add(new Result(TrainingBestSolutionParameterName, "", typeof(ISymbolicDataAnalysisSolution)));
}
//if the pareto front of best solutions shall be updated regardless of the quality, the list initialized empty to discard old solutions
List <double[]> trainingBestQualities;
if (UpdateAlways)
{
trainingBestQualities = new List <double[]>();
}
else
{
trainingBestQualities = TrainingBestSolutionQualities.Select(x => x.ToArray()).ToList();
}
ISymbolicExpressionTree[] trees = SymbolicExpressionTree.ToArray();
List <double[]> qualities = Qualities.Select(x => x.ToArray()).ToList();
bool[] maximization = Maximization.ToArray();
var nonDominatedIndividuals = new[] { new { Tree = default(ISymbolicExpressionTree), Qualities = default(double[]) } }.ToList();
nonDominatedIndividuals.Clear();
// build list of new non-dominated solutions
for (int i = 0; i < trees.Length; i++)
{
if (IsNonDominated(qualities[i], nonDominatedIndividuals.Select(ind => ind.Qualities), maximization) &&
IsNonDominated(qualities[i], trainingBestQualities, maximization))
{
for (int j = nonDominatedIndividuals.Count - 1; j >= 0; j--)
{
if (IsBetterOrEqual(qualities[i], nonDominatedIndividuals[j].Qualities, maximization))
{
nonDominatedIndividuals.RemoveAt(j);
}
}
nonDominatedIndividuals.Add(new { Tree = trees[i], Qualities = qualities[i] });
}
}
var nonDominatedSolutions = nonDominatedIndividuals.Select(x => new { Solution = CreateSolution(x.Tree, x.Qualities), Qualities = x.Qualities }).ToList();
nonDominatedSolutions.ForEach(s => s.Solution.Name = string.Join(",", s.Qualities.Select(q => q.ToString())));
#region update Pareto-optimal solution archive
if (nonDominatedSolutions.Count > 0)
{
//add old non-dominated solutions only if they are not dominated by one of the new solutions
for (int i = 0; i < trainingBestQualities.Count; i++)
{
if (IsNonDominated(trainingBestQualities[i], nonDominatedSolutions.Select(x => x.Qualities), maximization))
{
nonDominatedSolutions.Add(new { Solution = TrainingBestSolutions[i], Qualities = TrainingBestSolutionQualities[i].ToArray() });
}
}
//assumes the the first objective is always the accuracy
var sortedNonDominatedSolutions = maximization[0]
? nonDominatedSolutions.OrderByDescending(x => x.Qualities[0])
: nonDominatedSolutions.OrderBy(x => x.Qualities[0]);
var trainingBestSolution = sortedNonDominatedSolutions.Select(s => s.Solution).First();
results[TrainingBestSolutionParameterName].Value = trainingBestSolution;
TrainingBestSolutions = new ItemList <T>(sortedNonDominatedSolutions.Select(x => x.Solution));
results[TrainingBestSolutionsParameter.Name].Value = TrainingBestSolutions;
TrainingBestSolutionQualities = new ItemList <DoubleArray>(sortedNonDominatedSolutions.Select(x => new DoubleArray(x.Qualities)));
results[TrainingBestSolutionQualitiesParameter.Name].Value = TrainingBestSolutionQualities;
}
#endregion
return(base.Apply());
}
