public static BayesianNetworkClassifier CreateABCMinerPlusIClassifier(int seed, int iterations, int colonySize, int localColonySize, int convergence, int maxParents, Dataset trainingSet, DataMining.ClassificationMeasures.IClassificationQualityMeasure measure, bool performLocalSearch, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
VariableTypeAssigmentInvalidator invalidator1 = new VariableTypeAssigmentInvalidator();
BayesianClassificationQualityEvaluator evaluator = new BayesianClassificationQualityEvaluator(measure);
SimpleVariableTypeAssignmentLocalSearch localSearch1 = new SimpleVariableTypeAssignmentLocalSearch(evaluator);
DefaultHeuristicCalculator <VariableTypeAssignment> calculator1 = new DefaultHeuristicCalculator <VariableTypeAssignment>();
Problem <VariableTypeAssignment> problem1 = new Problem <VariableTypeAssignment>(invalidator1, calculator1, evaluator, localSearch1);
CyclicRelationInvalidator invalidator2 = new CyclicRelationInvalidator();
BackwardRemovalLocalSearch localSearch2 = new BackwardRemovalLocalSearch(evaluator);
CMICalculator calculator = new CMICalculator();
Problem <Edge> problem2 = new Problem <Edge>(invalidator2, calculator, evaluator, localSearch2);
ABCMinerPlusI abcMinerPlusI = new ABCMinerPlusI(iterations, colonySize, localColonySize, convergence, problem2, problem1, maxParents, trainingSet, performLocalSearch);
if (fireEvents)
{
abcMinerPlusI.ABCAlgorithm.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
abcMinerPlusI.ABCAlgorithm.OnPostColonyIteration += new EventHandler(abclassifier_OnPostColonyIteration);
abcMinerPlusI.ABCMinerAlgorithm.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
abcMinerPlusI.ABCMinerAlgorithm.OnPostColonyIteration += new EventHandler(abclassifier_OnPostColonyIteration);
}
BayesianNetworks.Model.BayesianNetworkClassifier bnclassifier = abcMinerPlusI.CreateClassifier() as BayesianNetworkClassifier;
return(bnclassifier);
}
public iFourmi.DataMining.Model.IClassifier CreateClassifier()
{
Solution <Edge> solution = new Solution <Edge>();
CyclicRelationInvalidator invalidator = new CyclicRelationInvalidator();
invalidator.MaxDependencies = 1;
ConstructionGraph <Edge> constructionGraph = ConstructionGraphBuilder.BuildBNConstructionGraph(this._trainingset.Metadata);
CMICalculator cmiCalculator = new CMICalculator();
cmiCalculator.Dataset = this._trainingset;
constructionGraph.SetHeuristicValues(cmiCalculator, false);
List <DecisionComponent <Edge> > components = constructionGraph.Components.OrderByDescending(e => e.Heuristic).ToList();
while (components.Count != 0)
{
DecisionComponent <Edge> component = components[0];
solution.Components.Add(component);
invalidator.Invalidate(component, solution, constructionGraph);
components = constructionGraph.Components.Where(e => e.IsValid).OrderByDescending(e => e.Heuristic).ToList();
}
BayesianNetworkClassifier BNClassifier = new BayesianNetworks.Model.BayesianNetworkClassifier(this._trainingset.Metadata, solution.ToList());
BNClassifier.LearnParameters(this._trainingset);
return(BNClassifier);
}
public static void TestABCMiner()
{
Console.WriteLine("Start");
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure1 = new DataMining.ClassificationMeasures.MicroAccuracyMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure2 = new DataMining.ClassificationMeasures.ProbabilityAccuracyMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure3 = new DataMining.ClassificationMeasures.ReducedErrorMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure measure4 = new DataMining.ClassificationMeasures.ProbabilityReducedErrorMeasure();
IHeuristicValueCalculator <Edge> calculator = new CMICalculator();
int seed = (int)DateTime.Now.Ticks;
BayesianNetworkClassifier abclassifier = SingleTest.CreateABCMinerClassifier(seed, 100, 10, 10, 3, trainingSet, measure1, calculator, false, true);
//double quality1 = SingleTest.TestClassifier(abclassifier, testingSet, measure1);
//quality1 = Math.Round(quality1 * 100, 2);
//double quality2 = SingleTest.TestClassifier(abclassifier, testingSet, measure2);
//quality2 = Math.Round(quality2 * 100, 2);
//double quality3 = SingleTest.TestClassifier(abclassifier, testingSet, measure3);
//quality3 = Math.Round(quality3 * 100, 2);
double quality4 = SingleTest.TestClassifier(abclassifier, testingSet, measure4);
quality4 = Math.Round(quality4 * 100, 2);
//Console.WriteLine("ABC Quality1: " + quality1.ToString());
//Console.WriteLine("ABC Quality2: " + quality2.ToString());
//Console.WriteLine("ABC Quality3: " + quality3.ToString());
Console.WriteLine("ABC Quality4: " + quality4.ToString());
Console.WriteLine("End");
string xml = BayesianNetworks.Utilities.GraphExporter.ExportToGaphSharpXml(abclassifier);
System.IO.File.WriteAllText(@"C:\0 - Khalid\Academics\" + datasetName + "1.xml", xml);
Console.ReadLine();
}
