public static void TestEnsembleH()
{
string folderPath = @"C:\0 - Khalid\Academics\Datasets\ageing\test";
//string folderPath = @"C:\0 - Khalid\Academics\Datasets\ageing";
iFourmi.DataMining.Algorithms.IClassificationAlgorithm algorithm = new NaiveBayesAlgorithm();
iFourmi.DataMining.ClassificationMeasures.IClassificationQualityMeasure evaluator = new iFourmi.DataMining.ClassificationMeasures.AccuracyMeasure();
List <DataMining.Data.Dataset> dataRepresentations = new List <Dataset>();
foreach (string filePath in System.IO.Directory.GetFiles(folderPath))
{
dataRepresentations.Add(ArffHelper.LoadHierarchicalDatasetFromTxt(filePath, true));
}
Console.WriteLine("Begin");
iFourmi.DataMining.EnsembleStrategy.IEnsembleClassificationStrategy ensembleStrategy = new iFourmi.DataMining.EnsembleStrategy.EnsembleBestClassificationStrategy();
iFourmi.DataMining.Model.Hierarchical.IHierarchicalClassifier hClassifier = SingleTest.CreateLocalPerNodeHierarchicalClassifier(algorithm, dataRepresentations, ensembleStrategy, evaluator, true, true);
double quality = SingleTest.TestClassifier(hClassifier, dataRepresentations, evaluator);
quality = Math.Round(quality * 100, 2);
Console.WriteLine("Quality: " + quality.ToString());
Console.WriteLine("End");
}
public static void TestKmeanClusteringBMN()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
double avgQualiy = 0;
int k = 8;
for (int i = 0; i < 10; i++)
{
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
//DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure accuracy = new DataMining.ClassificationMeasures.AccuracyMeasure();
DataMining.Algorithms.IClassificationAlgorithm naive = new NaiveBayesAlgorithm();
DataMining.Algorithms.IClusteringAlgorithm kmeans = new DataMining.Algorithms.KMeans(trainingSet, k, similarityMeasure, 100, true);
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateClusteringBMNClassifier(seed, k, trainingSet, similarityMeasure, accuracy, kmeans, naive, false);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
avgQualiy += quality;
}
Console.WriteLine(avgQualiy / 10);
Console.WriteLine("End");
}
public static void TestKMeans()
{
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset dataset = ArffHelper.LoadDatasetFromArff(datasetFile);
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ProximityMeasures.IClusteringQualityMeasure cohesionEvaluator = new DataMining.ProximityMeasures.CohesionClusteringMeasure();
double maxQuality = 0;
double avgQuality = 0;
for (int i = 0; i < 1; i++)
{
int seed = (int)DateTime.Now.Ticks;
DataMining.Model.ClusteringSolution solution = SingleTest.CreateKMeansClusters(seed, dataset, 5, similarityMeasure, 1000, true);
double currentQuality = cohesionEvaluator.CalculateQuality(solution);
Console.WriteLine("Iteration " + i.ToString() + ":" + currentQuality.ToString());
avgQuality += currentQuality;
if (currentQuality > maxQuality)
{
maxQuality = currentQuality;
}
}
// avgQuality /= 10;
// Console.WriteLine("Maximum:" + maxQuality.ToString());
// Console.WriteLine("Average:" + avgQuality.ToString());
}
public static void TestLBMNABC()
{
Console.WriteLine("Start");
string dsname = "car";
string trainingSetPath = @"C:\0 - Khalid\Academics\Datasets\" + dsname + @"\TR0_" + dsname + ".arff";
string testingSetPath = @"C:\0 - Khalid\Academics\Datasets\" + dsname + @"\TS0_" + dsname + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(trainingSetPath);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(testingSetPath);
DataMining.ClassificationMeasures.AccuracyMeasure qualityEvaluator = new DataMining.ClassificationMeasures.AccuracyMeasure();
ISolutionQualityEvaluator <Edge> trainingQualityEvaluator = new likelihoodQualityEvaluator(trainingSet);
IHeuristicValueCalculator <Edge> calculator = new MICalculator();
int seed = (int)DateTime.Now.Ticks;
BayesianMultinetClassifier lmnabclassifier = SingleTest.CreateLMNAntBayesianClassification(seed, 10, 5, 10, 3, trainingSet, trainingQualityEvaluator, calculator, true);
double quality = SingleTest.TestClassifier(lmnabclassifier, testingSet, qualityEvaluator);
quality = Math.Round(quality * 100, 2);
Console.WriteLine("LMNABC Quality: " + quality.ToString());
Console.WriteLine("End");
//Console.ReadLine();
}
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();
}
public static void TestACOCluster_MB()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
DataMining.ProximityMeasures.IClusteringQualityMeasure cohesionEvaluator = new DataMining.ProximityMeasures.CohesionClusteringMeasure();
DataMining.Model.ClusteringSolution solution = SingleTest.CreateACOClusters_MB(seed, trainingSet, 3, similarityMeasure, 1000, 10, 10, true, true);
double quality = cohesionEvaluator.CalculateQuality(solution);
Console.WriteLine("Final Quality:" + quality.ToString());
Console.WriteLine("End");
}
public static void TestANTClustBMN_IB()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
//DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure accuracy = new DataMining.ClassificationMeasures.AccuracyMeasure();
DataMining.Algorithms.IClassificationAlgorithm naive = new NaiveBayesAlgorithm();
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateAntClustBMNClassifier_IB(seed, trainingSet, 1, 1, 10, 3, similarityMeasure, accuracy, naive, true);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
}
public static void TestACOCluster_IBThenBMN()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
double avgQualiy = 0;
for (int i = 0; i < 1; i++)
{
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
//DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure accuracy = new DataMining.ClassificationMeasures.AccuracyMeasure();
DataMining.Algorithms.IClassificationAlgorithm naive = new NaiveBayesAlgorithm();
DefaultHeuristicCalculator <ClusterExampleAssignment> calculator = new DefaultHeuristicCalculator <ClusterExampleAssignment>();
ClusteringIBInvalidator invalidator = new ClusteringIBInvalidator();
DataMining.ProximityMeasures.IClusteringQualityMeasure measure = new CohesionClusteringMeasure();
ClusteringQualityEvaluator cohesionEvaluator = new ClusteringQualityEvaluator(measure);
KMeansLocalSearch localSearch = new KMeansLocalSearch(trainingSet, 1, similarityMeasure, cohesionEvaluator);
ACO.ProblemSpecifics.ISolutionQualityEvaluator <DataMining.Model.ClusterExampleAssignment> evaluator = new ClusteringQualityEvaluator(measure);
Problem <DataMining.Model.ClusterExampleAssignment> problem = new Problem <DataMining.Model.ClusterExampleAssignment>(invalidator, calculator, evaluator, localSearch);
DataMining.Algorithms.IClusteringAlgorithm AntClustering = new ACOClustering_IB(1000, 10, 10, problem, 10, similarityMeasure, true);
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateClusteringBMNClassifier(seed, 10, trainingSet, similarityMeasure, accuracy, AntClustering, naive, true);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
avgQualiy += quality;
}
Console.WriteLine(avgQualiy / 10);
Console.WriteLine("End");
}