public static BayesianNetworkClassifier CreateTANClassifier(Dataset trainingSet)
{
TAN tan = new TAN();
tan.Dataset = trainingSet;
BayesianNetworkClassifier bnClassifier = tan.CreateClassifier() as BayesianNetworkClassifier;
return(bnClassifier);
}
public static BayesianNetworkClassifier CreateNaiveBayesianClassifier(Dataset trainingSet)
{
NaiveBayesAlgorithm nb = new NaiveBayesAlgorithm();
nb.Dataset = trainingSet;
BayesianNetworkClassifier bnClassifier = nb.CreateClassifier() as BayesianNetworkClassifier;
return(bnClassifier);
}
public static void TestABCMinerPlus()
{
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();
BayesianNetworkClassifier abcBNClassifier = null;
int seed = (int)DateTime.Now.Ticks;
BayesianNetworkClassifier abcMinerPlusclassifier = SingleTest.CreateABCMinerPlusClassifier(seed, 100, 10, 10, 3, trainingSet, measure2, false, true, out abcBNClassifier);
//double quality11 = SingleTest.TestClassifier(abcBNClassifier, testingSet, measure1);
//double quality21 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure1);
//double quality12 = SingleTest.TestClassifier(abcBNClassifier, testingSet, measure2);
//double quality22 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure2);
//double quality13 = SingleTest.TestClassifier(abcBNClassifier, testingSet, measure3);
//double quality23 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure3);
//double quality14 = SingleTest.TestClassifier(abcBNClassifier, testingSet, measure4);
double quality24 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure4);
//Console.WriteLine("ABC Quality: " + quality11.ToString());
//Console.WriteLine("ABCMinerPlus Quality: " + quality21.ToString());
//Console.WriteLine("ABC Quality: " + quality12.ToString());
//Console.WriteLine("ABCMinerPlus Quality: " + quality22.ToString());
//Console.WriteLine("ABC Quality: " + quality13.ToString());
//Console.WriteLine("ABCMinerPlus Quality: " + quality23.ToString());
//Console.WriteLine("ABC Quality: " + quality14.ToString());
Console.WriteLine("ABCMinerPlus Quality: " + quality24.ToString());
string xml = BayesianNetworks.Utilities.GraphExporter.ExportToGaphSharpXml(abcMinerPlusclassifier);
System.IO.File.WriteAllText(@"C:\0 - Khalid\Academics\" + datasetName + "2.xml", xml);
//Console.WriteLine("ABC Size: " + abcBNClassifier.Edges.Length);
//Console.WriteLine("ABCMinerPlus Size: " + abcMinerPlusclassifier.Edges.Length);
Console.WriteLine("End");
Console.ReadLine();
}
public static void TestNaive()
{
Console.WriteLine("Start");
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFilePath);
DataMining.ClassificationMeasures.IClassificationQualityMeasure qualityEvaluator = new DataMining.ClassificationMeasures.AccuracyMeasure();
BayesianNetworkClassifier naive = SingleTest.CreateNaiveBayesianClassifier(trainingSet);
double quality = SingleTest.TestClassifier(naive, testingSet, qualityEvaluator);
quality = Math.Round(quality * 100, 2);
Console.WriteLine("Naive Quality: " + quality.ToString());
Console.WriteLine("End");
}
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();
}
public static void TestABCMinerPlusI()
{
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();
int seed = (int)DateTime.Now.Ticks;
BayesianNetworkClassifier abcMinerPlusclassifier = SingleTest.CreateABCMinerPlusIClassifier(seed, 10, 1, 5, 10, 2, trainingSet, measure1, false, true);
double quality1 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure1);
quality1 = Math.Round(quality1 * 100, 2);
double quality2 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure2);
quality2 = Math.Round(quality2 * 100, 2);
double quality3 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure3);
quality3 = Math.Round(quality3 * 100, 2);
double quality4 = SingleTest.TestClassifier(abcMinerPlusclassifier, testingSet, measure4);
quality4 = Math.Round(quality4 * 100, 2);
Console.WriteLine("ABCMinerPlusI Quality1: " + quality1.ToString());
Console.WriteLine("ABCMinerPlusI Quality2: " + quality2.ToString());
Console.WriteLine("ABCMinerPlusI Quality3: " + quality3.ToString());
Console.WriteLine("ABCMinerPlusI Quality4: " + quality4.ToString());
Console.WriteLine("End");
Console.WriteLine("End");
Console.ReadLine();
}
static void abcMinerPlus_OnVariableTypeAssignmentCompleted(object sender, EventArgs e)
{
BayesianNetworkClassifier abcBNC = sender as BayesianNetworkClassifier;
abcBNClassfier = abcBNC;
}
public static BayesianNetworkClassifier CreateABCMinerPlusClassifier(int seed, int iterations, int colonySize, int convergence, int maxParents, Dataset trainingSet, DataMining.ClassificationMeasures.IClassificationQualityMeasure measure, bool performLocalSearch, bool fireEvents, out BayesianNetworkClassifier abcBNClassifier)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
VariableTypeAssigmentInvalidator invalidator1 = new VariableTypeAssigmentInvalidator();
BayesianClassificationQualityEvaluator evaluator = new BayesianClassificationQualityEvaluator(measure);
VariableTypeAssignmentLocalSearch localSearch1 = new VariableTypeAssignmentLocalSearch(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);
ABCMinerPlus abcMinerPlus = new ABCMinerPlus(iterations, colonySize, convergence, problem2, problem1, maxParents, trainingSet, performLocalSearch);
if (fireEvents)
{
abcMinerPlus.ABCAlgorithm.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
abcMinerPlus.ABCAlgorithm.OnPostColonyIteration += new EventHandler(abclassifier_OnPostColonyIteration);
abcMinerPlus.ABCMinerAlgorithm.OnPostAntSolutionContruction += new EventHandler(abclassifier_OnPostAntSolutionContruction);
abcMinerPlus.ABCMinerAlgorithm.OnPostColonyIteration += new EventHandler(abclassifier_OnPostColonyIteration);
abcMinerPlus.OnVariableTypeAssignmentCompleted += new EventHandler(abcMinerPlus_OnVariableTypeAssignmentCompleted);
}
BayesianNetworks.Model.BayesianNetworkClassifier bnclassifier = abcMinerPlus.CreateClassifier() as BayesianNetworkClassifier;
abcBNClassifier = abcBNClassfier;
return(bnclassifier);
}
