public static ResultObject EvaluateACOMinerDR_WekaClassifier(string algorithm, string trainingSetPath, string testSetPath, Dataset trainingSet, bool useAttributes, bool useInstances)
{
Classifier classifier = WekaNETBridge.WekaClassification.GetWekaClassifier(algorithm, trainingSetPath);
//DefaultDRHeuristicCalculator calculator = new DefaultDRHeuristicCalculator();
ADRHeuristicCalculator calculator = new ADRHeuristicCalculator();
DRComponentInvalidator invalidator = new DRComponentInvalidator();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
WekaClassificationQualityEvaluator evaluator = new WekaClassificationQualityEvaluator(wekaClassification, !useAttributes);
DRLocalSearch localSearch = new DRLocalSearch(evaluator);
Problem <DRComponent> problem = new Problem <DRComponent>(invalidator, calculator, evaluator, localSearch);
ADRMiner acominer = new ADRMiner(maxIterations, colonySize, convergenceIterations, problem, useAttributes, useInstances, false, trainingSet);
acominer.OnPostAntSolutionContruction += OnPostAntSolutionContruction;
acominer.OnPostColonyIteration += OnPostColonyIteration;
//acominer.Initialize();
//acominer.Work();
//weka.classifiers.Classifier final = evaluator.CreateClassifier(acominer.BestAnt.Solution);
WekaNETBridge.WekaClassifier wekcaClassifier = acominer.CreateWekaClassifier();
double quality = WekaNETBridge.WekaClassification.EvaluateClassifier(wekcaClassifier, testSetPath);
double attribueReduction = acominer.BestAnt.Solution.FeatureCount() / (double)trainingSet.Metadata.Attributes.Length;
double instanceReduction = acominer.BestAnt.Solution.InstanceCount() / (double)trainingSet.Size;
ResultObject result = new ResultObject()
{
Quality = quality, AttributeReduction = attribueReduction, InstanceReduciton = instanceReduction
};
return(result);
}
public static ResultObject EvaluateACOMinerDR2_WekaClassifier(string algorithm, string trainingSetPath, string testSetPath, Dataset trainingSet, bool attributeFirst)
{
Classifier classifier = WekaNETBridge.WekaClassification.GetWekaClassifier(algorithm, trainingSetPath);
ADRHeuristicCalculator calculator = new ADRHeuristicCalculator();
DRComponentInvalidator invalidator = new DRComponentInvalidator();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
WekaClassificationQualityEvaluator evaluator = new WekaClassificationQualityEvaluator(wekaClassification, false);
DRLocalSearch localSearch = new DRLocalSearch(evaluator);
Problem <DRComponent> problem = new Problem <DRComponent>(invalidator, calculator, evaluator, localSearch);
ADRMiner2 acominer = new ADRMiner2(maxIterations, colonySize, convergenceIterations, problem, attributeFirst, false, trainingSet);
acominer.ACO1.OnPostAntSolutionContruction += OnPostAntSolutionContruction;
acominer.ACO1.OnPostColonyIteration += OnPostColonyIteration;
acominer.ACO2.OnPostAntSolutionContruction += OnPostAntSolutionContruction;
acominer.ACO2.OnPostColonyIteration += OnPostColonyIteration;
WekaNETBridge.WekaClassifier wekcaClassifier = acominer.CreateWekaClassifier();
double quality = WekaNETBridge.WekaClassification.EvaluateClassifier(wekcaClassifier, testSetPath);
double attribueReduction = acominer.BestSolution.AttributesToRemove().Length;
double instanceReduction = acominer.BestSolution.InstanceCount() / (double)trainingSet.Size;
ResultObject result = new ResultObject()
{
Quality = quality, AttributeReduction = attribueReduction, InstanceReduciton = instanceReduction
};
return(result);
}
