test program modification

src/test/TestA2CNESIterative.java

 package test;
 
+import java.io.BufferedWriter;
 import java.io.File;
 import java.io.FileNotFoundException;
+import java.io.FileWriter;
 import java.io.IOException;
+import java.io.PrintWriter;
 import java.nio.file.DirectoryStream;
 import java.nio.file.Files;
 import java.nio.file.Path;
@@ -20,7 +23,16 @@ import jcl.clustering.constraints.CannotLinkConstraint;
 import jcl.clustering.constraints.Constraint;
 import jcl.clustering.constraints.MustLinkConstraint;
 import jcl.data.Data;
+import jcl.data.Model;
+import jcl.data.SimpleData;
 import jcl.data.attribute.AttributeMultiDimSequence;
+import jcl.data.distance.Distance;
+import jcl.data.distance.DistanceParameter;
+import jcl.data.distance.EmptyDistanceParameter;
+import jcl.data.distance.MetaDistance;
+import jcl.data.distance.MetaDistanceEuclidean;
+import jcl.data.distance.NumericalEuclideanDistance;
+import jcl.data.distance.sequential.ParameterDTW;
 import jcl.data.mask.IntArrayMask;
 import jcl.data.mask.Mask;
 import jcl.io.results.CSVResultWriter;
@@ -38,14 +50,21 @@ public class TestA2CNESIterative {
 	public static void main(String[] args) {
 		HybridClassification classification = new HybridClassification();
 
-		final String datasetName = "ElectricDevices";
-		final String datasetPath = "ElectricDevices";
-		final String dataPath = "/home/baptiste/A2CNES/";
-		final String resultPath = "/home/baptiste/A2CNES/results_iter/";
-//		String testResultPath = "/home/baptiste/A2CNES/Train_results/";
+		final String datasetName = "FacesUCR";
+		final String datasetPath = "FacesUCR";
+		final String dataPath = "~/A2CNES/";
+		final String resultPath = "~/A2CNES/results_iter/";
+		final int nInf = 12;
+		final int nSup = 17;
+		final int ag1_seeds = 16;
+		final int ag2_seeds = 20;
+		final int ag3_seeds = 24;
+		final int nb_iter = 15; 
+		
+//		String testResultPath = "~/A2CNES/Train_results/";
 		
 //		Data dataTrain = TestA2CNES.getDataFromFile(dataPath+datasetPath+"/train/"+datasetName+".data", '\t', "train", null);
-		Data dataTest = TestA2CNES.getDataFromFile(dataPath+datasetPath+"/test/"+datasetName+".data", '\t', "test", null);
+		final Data dataTest = TestA2CNES.getDataFromFile(dataPath+datasetPath+"/test/"+datasetName+".data", '\t', "test", null);
 		
 
 //		AttributeMultiDimSequence.setMode(AttributeMultiDimSequence.EUCLIDIEN);
@@ -53,8 +72,6 @@ public class TestA2CNESIterative {
 		
 		final DateTime startTime = DateTime.now();
 		
-		int nInf = 6;
-		int nSup = 9;
 
 		// parametre pour la precision des conflits
 		final double minC = 0.9;
@@ -65,7 +82,7 @@ public class TestA2CNESIterative {
 		// parametre qualite/similitude
 		final double ps = 0.2;
 		final double pq = 1.0 - ps;
-		final double constraintsWgt = 80;
+		final double constraintsWgt = 0;
 		final double valueKExtern = 60;
 		final double kExtern = valueKExtern * ((100-constraintsWgt)/100);
 		final double kIntern = (100 - valueKExtern) * ((100-constraintsWgt)/100);
@@ -181,19 +198,19 @@ public class TestA2CNESIterative {
 	    			e.printStackTrace();
 	    		}
 	            
-	    		final int subsetSize = (int) Math.ceil(constraints.size() * 0.05);
+	    		final int subsetSize = (int) Math.ceil(constraints.size() * 0.04);
 	            
 	            for(int i = 0 ; i < 1; i++) {
 		        	final HybridClassification classif = (HybridClassification) classification.clone();
 		        	
 					Data currentData = (Data) dataTest.clone();
 					final Vector<Constraint> subset = new Vector<Constraint>();
-					extractAndAddConstraints(subset, constraints, subsetSize, null);
-					currentData.updateAndSetConstraintsToSample(subset);
+//					extractAndAddConstraints(subset, constraints, subsetSize, null);
+//					currentData.updateAndSetConstraintsToSample(subset);
 
-					classif.addAgent(new ParametersKmeans(7, 15, weights), currentData);
-					classif.addAgent(new ParametersKmeans(11, 15, weights), currentData);
-					classif.addAgent(new ParametersKmeans(14, 15, weights), currentData);
+					classif.addAgent(new ParametersKmeans(ag1_seeds, nb_iter, weights), currentData);
+					classif.addAgent(new ParametersKmeans(ag2_seeds, nb_iter, weights), currentData);
+					classif.addAgent(new ParametersKmeans(ag3_seeds, nb_iter, weights), currentData);
 					
 					final String path_to_add = resultPath + datasetName + "/";
 					classif.setName(ImageHelper.stripExtension(filename)+"-"+i+ 
@@ -214,6 +231,25 @@ public class TestA2CNESIterative {
 								e.printStackTrace();
 							}
 							System.out.println("First classification completed");
+							
+							FileWriter fw = null;
+							try {
+								fw = new FileWriter("log_cst"+classif.getName()+".log", true);
+							} catch (IOException e) {
+								e.printStackTrace();
+							}
+						    BufferedWriter bw = new BufferedWriter(fw);
+						    PrintWriter out = new PrintWriter(bw);
+						    
+						    Distance[] distances = new Distance[1]; // a distance is set for every attribute
+							distances[0] = jcl.data.distance.sequential.DistanceDTW.getInstance(); // second attribute (sequential) compared with the DTW distance 
+							MetaDistance metaDistance = MetaDistanceEuclidean.getInstance(); // defines the way the two scores are combined (possibility to weight) 
+							Model model = new Model(distances, metaDistance);
+
+							int seqLength = ((AttributeMultiDimSequence) dataTest.getOneDataObject().getAttribute(0)).sequence.length;
+							DistanceParameter[] distanceParameters = new DistanceParameter[1];
+							distanceParameters[0] = new ParameterDTW(new double[seqLength][seqLength]); //but yes for DTW (requires a matrix to work in)
+						    
 							for (int i = 0 ; i < 5 ; i++) {
 								int[] clustMap = classif.getClusteringResult().getClusterMap();
 								int[] satisifiedMap = new int[constraints.size()];
@@ -230,10 +266,36 @@ public class TestA2CNESIterative {
 											satisifiedMap[j] = 1;
 									}
 								}
+								
 								Mask satisfactionMask = new IntArrayMask(satisifiedMap, 1, true);
 								extractAndAddConstraints(subset, constraints, subsetSize, satisfactionMask);
+								out.println("------- new iter : "+ (i+1) + " --------");
+								for (Constraint c : constraints) {
+									if (c instanceof MustLinkConstraint) {
+										MustLinkConstraint ml = (MustLinkConstraint) c;
+										out.println(c.toString()+";"+
+												Constraint.marginalSilhouetteScore(
+														ml.getFirstIndex(), classif.getClusteringResult(), 
+														model, distanceParameters)+";"+
+												Constraint.marginalSilhouetteScore(
+														ml.getSecondIndex(), classif.getClusteringResult(), 
+														model, distanceParameters)
+												);	
+									} else {
+
+										CannotLinkConstraint cl = (CannotLinkConstraint) c;
+										out.println(c.toString()+";"+
+												Constraint.marginalSilhouetteScore(
+														cl.getFirstIndex(), classif.getClusteringResult(), 
+														model, distanceParameters)+";"+
+												Constraint.marginalSilhouetteScore(
+														cl.getSecondIndex(), classif.getClusteringResult(), 
+														model, distanceParameters)
+												);	
+									}
+								}
 								classif.setAdvancedParameters(degradation, classRatio, solutionType, kIntern, 
-										kExtern, unificationType, criterion, 90);
+										kExtern, unificationType, criterion, 95);
 								classif.newIteration(subset);
 
 								try {
@@ -243,6 +305,13 @@ public class TestA2CNESIterative {
 								}
 								System.out.println("Iteration "+(i+1)+" completed");
 							}
+							try {
+								out.close();
+								bw.close();
+								fw.close();
+							} catch (IOException e) {
+								e.printStackTrace();
+							}
 						}
 					};
 					t.start();