alles kaput!!

de.uniwue.ls6.rulelearning/InstanceLoading/src/de/uniwue/ls6/rulelearning/instanceloading/featuregenerator/EACL_UIMA_FeatureGen.java

+package de.uniwue.ls6.rulelearning.instanceloading.featuregenerator;
+
+import org.apache.uima.cas.Type;
+import org.apache.uima.cas.text.AnnotationFS;
+
+public class EACL_UIMA_FeatureGen extends AFeatureGenerator {
+
+	int prefixLen;
+	String typeS;
+	private String feature;
+
+	public EACL_UIMA_FeatureGen(String typeS,String feature, String labelId) {
+		super(labelId);
+		this.typeS = typeS;
+		this.feature = feature;
+	}
+
+	@Override
+	public String[] generateFeatures(AnnotationFS token) {
+		Type type= token.getCAS().getTypeSystem().getType(typeS);
+		for(AnnotationFS anno : token.getCAS().getAnnotationIndex(type)){
+			String name = type.getName();
+			name = anno.getFeatureValueAsString(type.getFeatureByBaseName(feature));
+			if(anno.getBegin()==token.getBegin() && anno.getEnd()==token.getEnd()){
+				return new String[] { super.featureIdentifier + "=" + "B-"+name };
+			} else if(token.getBegin()>anno.getBegin() && token.getEnd()<=anno.getEnd()){
+				return new String[] { super.featureIdentifier + "=" + "I-"+name };
+			}
+		}
+		return new String[] { super.featureIdentifier + "=" + "O" };
+	}
+
+}

de.uniwue.ls6.rulelearning/InstanceLoading/src/de/uniwue/ls6/rulelearning/instanceloading/featuregenerator/EACL_UIMA_FeatureGenB_E.java

+package de.uniwue.ls6.rulelearning.instanceloading.featuregenerator;
+
+import org.apache.uima.cas.Type;
+import org.apache.uima.cas.text.AnnotationFS;
+
+import de.uniwue.ls6.datastructure.LabelAlphabet;
+
+public class EACL_UIMA_FeatureGenB_E extends AFeatureGenerator {
+
+	int prefixLen;
+	String typeS;
+	private String feature;
+
+	public EACL_UIMA_FeatureGenB_E(String typeS,String feature, String labelId) {
+		super(labelId);
+		this.typeS = typeS;
+		this.feature = feature;
+	}
+
+	@Override
+	public String[] generateFeatures(AnnotationFS token) {
+		Type type= token.getCAS().getTypeSystem().getType(typeS);
+		for(AnnotationFS anno : token.getCAS().getAnnotationIndex(type)){
+			String name = type.getName();
+			name = anno.getFeatureValueAsString(type.getFeatureByBaseName(feature));
+			if(anno.getBegin()==token.getBegin() && anno.getEnd()==token.getEnd()){
+				return new String[] { super.featureIdentifier + "=" + "B-"+name };
+			} else if(token.getEnd()==anno.getEnd() ){
+				return new String[] { super.featureIdentifier + "=" + "E-"+name };
+			}
+		}
+		return new String[] { LabelAlphabet.getFeatureToId(0) };
+	}
+
+}

de.uniwue.ls6.rulelearning/InstanceLoading/src/de/uniwue/ls6/rulelearning/instanceloading/featuregenerator/WordCategorization.java

+package de.uniwue.ls6.rulelearning.instanceloading.featuregenerator;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+import org.apache.uima.cas.text.AnnotationFS;
+
+public class WordCategorization extends AFeatureGenerator {
+
+	public WordCategorization() {
+		super("WordBuilding");
+	}
+
+	@Override
+	public String[] generateFeatures(AnnotationFS token) {
+		String text = unifyString(token.getCoveredText());
+		int len = text.length();
+		List<String> ngrams = new ArrayList<String>();
+		for (int beg = 0; beg < len; beg++) {
+			for (int end = beg + 1; end <= len; end++) {
+				String ngram = text.substring(beg, end);
+				if (beg > 0)
+					ngram = "*" + ngram;
+				if (end < len)
+					ngram += "*";
+
+				ngrams.add(super.featureIdentifier + "=" + ngram);
+			}
+		}
+		return ngrams.toArray(new String[0]);
+	}
+
+	private String unifyString(String coveredText) {
+
+		String refinedString = "";
+		for (Character c : coveredText.toCharArray()) {
+
+			if (c.toString().matches("[a-z]")) {
+				refinedString += "x";
+			} else if (c.toString().matches("[A-Z]")) {
+				refinedString += "X";
+			} else if (c.toString().matches("[0-9]")) {
+				refinedString += "D";
+			} else {
+				refinedString += c.toString();
+			}
+		}
+		return refinedString;
+	}
+
+}

de.uniwue.ls6.rulelearning/RuleLearning/src/de/uniwue/ls6/rulelearning/algorithm/impl/BinaryRepresentationRuleLearningAlgorithm.java

@@ -217,7 +217,7 @@ public class BinaryRepresentationRuleLearningAlgorithm implements IRepresentatio
 
 			// assert that the maximum is growing
 			assert (maximumScore <= iterationMatrix.getMaximumScore()) : "Maximum decreased within iteration!";
-			if (!betterRuleCanBeLearned(mappings.get(mappings.size() - 2), mappings.get(mappings.size() - 1))) {
+			if (!betterRuleCanBeLearned(maximumScore,iterationMatrix)) {
 				mappings.remove(mappingForMaximum);
 				break;
 			}
@@ -242,7 +242,13 @@ public class BinaryRepresentationRuleLearningAlgorithm implements IRepresentatio
 				maximumScore, (maxEntryLocation.getTp() / (maxEntryLocation.getFp() + maxEntryLocation.getTp())));
 	}
 
-	
+	private boolean betterRuleCanBeLearned(List<MatrixMapping> mappings) {
+
+		if (mappings.size() < 2)
+			return true;
+		return betterRuleCanBeLearned(mappings.get(mappings.size() - 2), mappings.get(mappings.size() - 1));
+	}
+
 	private boolean betterRuleCanBeLearned(MatrixMapping lastMapping, MatrixMapping newMapping) {
 		Set<Set<Point>> lastFeatures = new HashSet<Set<Point>>(lastMapping.getDenseIndexToFeaturesMapping().values());
 		Set<Set<Point>> newFeatures = new HashSet<Set<Point>>(newMapping.getDenseIndexToFeaturesMapping().values());
@@ -251,8 +257,9 @@ public class BinaryRepresentationRuleLearningAlgorithm implements IRepresentatio
 			return false;
 		}
 		return true;
-		
+
 	}
+
 	private boolean betterRuleCanBeLearned(int maximumScore, MatrixMcMatrixFace matrixInFocus) {
 
 		if (matrixInFocus.getMaximumScore() > maximumScore) {
@@ -282,8 +289,8 @@ public class BinaryRepresentationRuleLearningAlgorithm implements IRepresentatio
 			RepresentationRule rule = pass.apply(instanceToClassify);
 			if (rule != null) {
 				predictedLabel = pass.getLabel();
-				score+=rule.getUniquenessScore();
-				//score = rule.getPrecision();
+				//score += rule.getUniquenessScore();
+				 score = rule.getPrecision();
 			} else {
 				break;
 			}

de.uniwue.ls6.rulelearning/RuleLearning/src/de/uniwue/ls6/rulelearning/algorithm/impl/MultiClassRepresentationRuleAlgorithm.java

@@ -84,7 +84,10 @@ public class MultiClassRepresentationRuleAlgorithm implements IRepresentationRul
 		Set<Integer> labelSet = new HashSet<>();
 
 		for (Instance i : instances) {
-			labelSet.add(i.getLabel());
+			// never create a classifier for the DEFAULT label
+			if (i.getLabel() != 0) {
+				labelSet.add(i.getLabel());
+			}
 			instanceToLabelMapping.put(i, i.getLabel());
 		}
 		return labelSet;
@@ -105,7 +108,7 @@ public class MultiClassRepresentationRuleAlgorithm implements IRepresentationRul
 		double maxScore = 0;
 		int label = 0;
 		for (ALabelling labelling : binaryLabellings) {
-			//we sum only non default classifications!
+			// we sum only non default classifications!
 			if (labelling.getLabel() == 0)
 				continue;
 			fullScore += labelling.getScore();

de.uniwue.ls6.rulelearning/RuleLearning/src/test/FirstTestEACL.java

+package test;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+import org.apache.uima.fit.factory.TypeSystemDescriptionFactory;
+import org.apache.uima.resource.metadata.TypeSystemDescription;
+
+import de.uniwue.ls6.datastructure.ALabelling;
+import de.uniwue.ls6.datastructure.Instance;
+import de.uniwue.ls6.datastructure.SimpleLabelling;
+import de.uniwue.ls6.rulelearning.algorithm.impl.MultiClassRepresentationRuleAlgorithm;
+import de.uniwue.ls6.rulelearning.evaluation.eval.LabelAccuracyEvaluation;
+import de.uniwue.ls6.rulelearning.evaluation.fold.FoldUtil;
+import de.uniwue.ls6.rulelearning.evaluation.fold.UnstructuredFold;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.BIO_UIMA_FeatureGen;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.EACL_UIMA_FeatureGen;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.EACL_UIMA_FeatureGenB_E;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.IsUppercaseFeatureGenerator;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.NGramGenerator;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.POSTagFeatureGenerator;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.PrefixNGenerator;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.SuffixNGenerator;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.WordCategorization;
+import de.uniwue.ls6.rulelearning.instanceloading.featuregenerator.WordFeaturegenerator;
+import de.uniwue.ls6.rulelearning.instanceloading.io.InstanceCreationFactory;
+
+public class FirstTestEACL {
+
+	public static void main(String[] args) throws Exception {
+
+		File korpusFOlder = new File("C:\\Users\\mkrug\\owncloud_neu\\paper_RegelLernen\\xmi_gold_preprocessed");
+		String uriDkPro = new File("C:\\marian_eclipse\\workspace_mars\\dkprocoreExample\\TypeSystemDK.xml").toPath()
+				.toUri().toString();
+
+
+		TypeSystemDescription tsd = TypeSystemDescriptionFactory.createTypeSystemDescriptionFromPath(uriDkPro);
+		MultiClassRepresentationRuleAlgorithm algorithm = new MultiClassRepresentationRuleAlgorithm(10);
+
+		List<Instance> instances = new ArrayList<Instance>();
+		for (File f : korpusFOlder.listFiles()) {
+			if (!f.getName().endsWith(".xmi"))
+				continue;
+			instances.addAll(InstanceCreationFactory.createWindowedInstancesFromUIMA(f, instances.size(), 2, 2,
+					"de.tudarmstadt.ukp.dkpro.core.api.lexmorph.type.pos.POS", tsd,
+					new EACL_UIMA_FeatureGenB_E("de.tudarmstadt.ukp.dkpro.core.api.lexmorph.type.morph.Morpheme",
+							"morphTag", "Entity"),
+					new WordFeaturegenerator(), new SuffixNGenerator(4), new SuffixNGenerator(3),
+					new SuffixNGenerator(2), new SuffixNGenerator(1), new PrefixNGenerator(1),
+					new IsUppercaseFeatureGenerator(), new NGramGenerator(),new WordCategorization(),
+					new BIO_UIMA_FeatureGen("de.tudarmstadt.ukp.dkpro.core.api.syntax.type.chunk.Chunk", "Chunk"),
+					new EACL_UIMA_FeatureGen("de.tudarmstadt.ukp.dkpro.core.api.ner.type.NamedEntity", "value","NE"),
+					new POSTagFeatureGenerator("PosValue")));
+			System.out.println("instances: " + instances.size());
+			// if(instances.size()>20000)break;
+		}
+
+		int id = 0;
+		for (Instance i : instances) {
+			i.setId(id);
+			id++;
+		}
+
+		// create 5 folds
+		List<UnstructuredFold> folds = FoldUtil.readInstancesToFold(instances, new Random(13374211), 5);
+
+		for (UnstructuredFold fold : folds) {
+
+			algorithm.learn(fold.getTrainingset().toArray(new Instance[0]));
+
+			// evaluate
+
+			List<ALabelling> goldLabels = new ArrayList<>();
+			List<ALabelling> systemLabels = new ArrayList<>();
+
+			for (Instance i : fold.getTestSet()) {
+				goldLabels.add(new SimpleLabelling(i.getLabel(), 0));
+				systemLabels.add(algorithm.apply(i));
+			}
+
+			// evaluate this fold
+
+			String evaluateToString = new LabelAccuracyEvaluation()
+					.evaluateToString(goldLabels.toArray(new ALabelling[0]), systemLabels.toArray(new ALabelling[0]));
+			System.out.println(evaluateToString);
+			break;
+		}
+
+	}
+
+}