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[DL-Learner SVN] SF.net SVN: dl-learner:[3124] trunk/scripts/src/main/java/org/dllearner/ scripts/evaluation/EnrichmentEvaluation.java
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From: <lor...@us...> - 2011-08-25 15:09:20
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Revision: 3124
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3124&view=rev
Author: lorenz_b
Date: 2011-08-25 15:09:12 +0000 (Thu, 25 Aug 2011)
Log Message:
-----------
Continued eval script.
Modified Paths:
--------------
trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java
Modified: trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java
===================================================================
--- trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-25 12:17:57 UTC (rev 3123)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-25 15:09:12 UTC (rev 3124)
@@ -44,6 +44,8 @@
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
+import java.util.SortedSet;
+import java.util.TreeSet;
import java.util.Map.Entry;
import java.util.Set;
import java.util.prefs.Preferences;
@@ -76,25 +78,42 @@
import org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner;
import org.dllearner.core.AbstractCELA;
+import org.dllearner.core.AbstractReasonerComponent;
import org.dllearner.core.AnnComponentManager;
import org.dllearner.core.AxiomLearningAlgorithm;
import org.dllearner.core.ComponentAnn;
import org.dllearner.core.ComponentInitException;
+import org.dllearner.core.ComponentManager;
import org.dllearner.core.EvaluatedAxiom;
import org.dllearner.core.EvaluatedDescription;
import org.dllearner.core.LearningAlgorithm;
+import org.dllearner.core.LearningProblemUnsupportedException;
+import org.dllearner.core.Score;
import org.dllearner.core.config.ConfigHelper;
+import org.dllearner.core.configurators.CELOEConfigurator;
import org.dllearner.core.owl.Axiom;
import org.dllearner.core.owl.DatatypeProperty;
import org.dllearner.core.owl.DisjointClassesAxiom;
+import org.dllearner.core.owl.EquivalentClassesAxiom;
+import org.dllearner.core.owl.Individual;
import org.dllearner.core.owl.NamedClass;
import org.dllearner.core.owl.ObjectProperty;
+import org.dllearner.core.owl.SubClassAxiom;
import org.dllearner.kb.SparqlEndpointKS;
import org.dllearner.kb.sparql.SPARQLTasks;
import org.dllearner.kb.sparql.SparqlEndpoint;
+import org.dllearner.kb.sparql.SparqlKnowledgeSource;
import org.dllearner.learningproblems.AxiomScore;
+import org.dllearner.learningproblems.ClassLearningProblem;
+import org.dllearner.reasoning.FastInstanceChecker;
+import org.dllearner.reasoning.SPARQLReasoner;
import org.dllearner.utilities.CommonPrefixMap;
import org.dllearner.utilities.Files;
+import org.dllearner.utilities.Helper;
+import org.dllearner.utilities.datastructures.Datastructures;
+import org.dllearner.utilities.datastructures.SetManipulation;
+import org.dllearner.utilities.datastructures.SortedSetTuple;
+import org.dllearner.utilities.examples.AutomaticNegativeExampleFinderSPARQL2;
import org.dllearner.utilities.owl.DLLearnerAxiomConvertVisitor;
import org.dllearner.utilities.owl.OWLAPIConverter;
import org.ini4j.IniPreferences;
@@ -149,9 +168,9 @@
private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();//getEndpointDBpediaLiveAKSW();
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 3;
- private int maxDataProperties = 3;
- private int maxClasses = 3;
+ private int maxObjectProperties = 10;
+ private int maxDataProperties = 10;
+ private int maxClasses = 10;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -288,12 +307,12 @@
}
- private void evaluateObjectProperties(SparqlEndpointKS ks)throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ private void evaluateObjectProperties(SparqlEndpointKS ks)throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<ObjectProperty> properties = new SPARQLTasks(ks.getEndpoint()).getAllObjectProperties();
for (Class<? extends AxiomLearningAlgorithm> algorithmClass : objectPropertyAlgorithms) {
int objectProperties = 0;
-
+ Thread.sleep(10000);
String algName = "";
for (ObjectProperty property : properties) {
@@ -326,8 +345,9 @@
try {
learner.start();
} catch (Exception e) {
+ timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
} else {
e.printStackTrace();
}
@@ -364,12 +384,12 @@
}
}
- private void evaluateDataProperties(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ private void evaluateDataProperties(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<DatatypeProperty> properties = new SPARQLTasks(ks.getEndpoint()).getAllDataProperties();
for (Class<? extends AxiomLearningAlgorithm> algorithmClass : dataPropertyAlgorithms) {
int dataProperties = 0;
-
+ Thread.sleep(10000);
String algName = "";
for (DatatypeProperty property : properties) {
@@ -402,8 +422,9 @@
try {
learner.start();
} catch (Exception e) {
+ timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
} else {
e.printStackTrace();
}
@@ -439,11 +460,12 @@
}
}
- private void evaluateClasses(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ private void evaluateClasses(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<NamedClass> classes = new SPARQLTasks(ks.getEndpoint()).getAllClasses();
for (Class<? extends LearningAlgorithm> algorithmClass : classAlgorithms) {
int classesCnt = 0;
+ Thread.sleep(10000);
for (NamedClass cls : classes) {
try{
@@ -475,8 +497,9 @@
try {
learner.start();
} catch (Exception e) {
+ timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
} else {
e.printStackTrace();
}
@@ -489,12 +512,7 @@
learnedAxioms = ((AxiomLearningAlgorithm)learner)
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
} else if(learner instanceof AbstractCELA){
- learnedAxioms = new ArrayList<EvaluatedAxiom>();
- List<? extends EvaluatedDescription> learnedDescriptions = ((AbstractCELA)learner).getCurrentlyBestEvaluatedDescriptions(nrOfAxiomsToLearn);
- for(EvaluatedDescription learnedDescription : learnedDescriptions){
- //TODO create axioms and differ between equivalent and subclass mode in CELOE
- learnedAxioms.add(new EvaluatedAxiom(null, new AxiomScore(learnedDescription.getAccuracy())));
- }
+ learnedAxioms = applyCELOE(ks, cls, false);
}
if(timeout && learnedAxioms.isEmpty()){
writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
@@ -522,7 +540,88 @@
}
}
}
+
+ private List<EvaluatedAxiom> applyCELOE(SparqlEndpointKS ks, NamedClass nc, boolean equivalence) throws ComponentInitException {
+ // get instances of class as positive examples
+ SPARQLReasoner sr = new SPARQLReasoner(ks);
+ SortedSet<Individual> posExamples = sr.getIndividuals(nc, 20);
+ SortedSet<String> posExStr = Helper.getStringSet(posExamples);
+
+ // get negative examples via various strategies
+ System.out.print("finding negatives ... ");
+ AutomaticNegativeExampleFinderSPARQL2 finder = new AutomaticNegativeExampleFinderSPARQL2(ks.getEndpoint());
+ SortedSet<String> negExStr = finder.getNegativeExamples(nc.getName(), posExStr);
+ negExStr = SetManipulation.fuzzyShrink(negExStr, 20);
+ SortedSet<Individual> negExamples = Helper.getIndividualSet(negExStr);
+ SortedSetTuple<Individual> examples = new SortedSetTuple<Individual>(posExamples, negExamples);
+
+ System.out.println("done (" + negExStr.size()+ ")");
+
+ ComponentManager cm = ComponentManager.getInstance();
+ SparqlKnowledgeSource ks2 = cm.knowledgeSource(SparqlKnowledgeSource.class);
+ ks2.getConfigurator().setInstances(Datastructures.individualSetToStringSet(examples.getCompleteSet()));
+ ks2.getConfigurator().setUrl(ks.getEndpoint().getURL());
+ ks2.getConfigurator().setDefaultGraphURIs(new TreeSet<String>(ks.getEndpoint().getDefaultGraphURIs()));
+ ks2.getConfigurator().setUseLits(false);
+ ks2.getConfigurator().setUseCacheDatabase(true);
+ ks2.getConfigurator().setRecursionDepth(2);
+ ks2.getConfigurator().setCloseAfterRecursion(true);
+// ks2.getConfigurator().setSaveExtractedFragment(true);
+ System.out.println("getting fragment ... ");
+ ks2.init();
+ System.out.println("done");
+
+ AbstractReasonerComponent rc = cm.reasoner(FastInstanceChecker.class, ks2);
+ rc.init();
+
+ // TODO: super class learning
+ ClassLearningProblem lp = cm.learningProblem(ClassLearningProblem.class, rc);
+// lp.setPositiveExamples(posExamples);
+// lp.setNegativeExamples(negExamples);
+ try {
+ lp.getConfigurator().setClassToDescribe(nc.getURI().toURL());
+ } catch (MalformedURLException e1) {
+ e1.printStackTrace();
+ }
+ lp.getConfigurator().setType("equivalence");
+ lp.getConfigurator().setAccuracyMethod("fmeasure");
+ lp.getConfigurator().setUseApproximations(false);
+ lp.getConfigurator().setMaxExecutionTimeInSeconds(10);
+ lp.init();
+
+ CELOE la = null;
+ try {
+ la = cm.learningAlgorithm(CELOE.class, lp, rc);
+ } catch (LearningProblemUnsupportedException e) {
+ e.printStackTrace();
+ }
+ CELOEConfigurator cc = la.getConfigurator();
+ cc.setMaxExecutionTimeInSeconds(10);
+ cc.setNoisePercentage(25);
+ la.init();
+ System.out.print("running CELOE ... ");
+ la.start();
+ System.out.println("done");
+
+ // convert the result to axioms (to make it compatible with the other algorithms)
+ List<? extends EvaluatedDescription> learnedDescriptions = la.getCurrentlyBestEvaluatedDescriptions(threshold);
+ List<EvaluatedAxiom> evaluatedAxioms = new LinkedList<EvaluatedAxiom>();
+ for(EvaluatedDescription learnedDescription : learnedDescriptions) {
+ Axiom axiom;
+ if(equivalence) {
+ axiom = new EquivalentClassesAxiom(nc, learnedDescription.getDescription());
+ } else {
+ axiom = new SubClassAxiom(nc, learnedDescription.getDescription());
+ }
+ Score score = lp.computeScore(learnedDescription.getDescription());
+ evaluatedAxioms.add(new EvaluatedAxiom(axiom, score));
+ }
+
+ cm.freeAllComponents();
+ return evaluatedAxioms;
+ }
+
public void printResultsPlain() {
}
@@ -540,7 +639,7 @@
ps.execute();
StringBuilder table1 = new StringBuilder();
- table1.append("\\begin{tabulary}{\\textwidth}{LCCCCC}\\toprule\n");
+ table1.append("\\begin{tabulary}{\\textwidth}{LRRRRR}\\toprule\n");
table1.append(" algorithm & Avg. \\#suggestions & Avg. runtime in ms & timeout in \\% & Avg. score & Avg. maximum score\\\\\\midrule\n");
for(Class<? extends LearningAlgorithm> algo : algorithms){
@@ -615,7 +714,7 @@
System.out.println("Avg. max. score: " + avgMaxScore);
table1.
- append(algoName.replace("learner", "").trim()).append(" & ").
+ append(algoName.replace("axiom learner", "").trim()).append(" & ").
append(avgSuggestionsAboveThreshold).append(" & ").
append(avgRuntimeInMilliseconds).append(" & ").
append(timeoutRatio).append(" & ").
@@ -624,33 +723,19 @@
append("\\\\\n");
}
-
table1.append("\\bottomrule\n\\end{tabulary}");
System.out.println(table1.toString());
- //get all axiomtypes and corresponding algorithm
- Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> axiomType2Algorithm = new HashMap<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>>();
- axiomType2Algorithm.put(AxiomType.SUBCLASS_OF, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SimpleSubclassLearner.class, CELOE.class}));
- axiomType2Algorithm.put(AxiomType.EQUIVALENT_CLASSES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{CELOE.class}));
- axiomType2Algorithm.put(AxiomType.DISJOINT_CLASSES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DisjointClassesLearner.class}));
+ //second part of evaluation
- axiomType2Algorithm.put(AxiomType.SUB_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SubObjectPropertyOfAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.EQUIVALENT_OBJECT_PROPERTIES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{EquivalentObjectPropertyAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.OBJECT_PROPERTY_DOMAIN, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ObjectPropertyDomainAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.OBJECT_PROPERTY_RANGE, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ObjectPropertyRangeAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.TRANSITIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{TransitiveObjectPropertyAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalDataPropertyAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.INVERSE_FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{InverseFunctionalObjectPropertyAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.SYMMETRIC_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SymmetricObjectPropertyAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.REFLEXIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ReflexiveObjectPropertyAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.IRREFLEXIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{IrreflexiveObjectPropertyAxiomLearner.class}));
+ StringBuilder table2 = new StringBuilder();
+ table2.append("\\begin{tabulary}{\\textwidth}{LCCCCC}\\toprule\n");
+ table2.append("& & & \\multicolumn{3}{c}{Estimated precision} \\\\\n");
+ table2.append(" axiom type & recall & additional axioms & no & maybe & yes \\\\\\midrule\n");
- axiomType2Algorithm.put(AxiomType.SUB_DATA_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SubDataPropertyOfAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.EQUIVALENT_DATA_PROPERTIES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{EquivalentDataPropertyAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.DATA_PROPERTY_DOMAIN, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DataPropertyDomainAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.DATA_PROPERTY_RANGE, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DataPropertyRangeAxiomLearner.class}));
- axiomType2Algorithm.put(AxiomType.FUNCTIONAL_DATA_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalDataPropertyAxiomLearner.class}));
+ //get all axiomtypes and corresponding algorithm
+ Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> axiomType2Algorithm = getAxiomTypesWithLearningAlgorithms();
// get all entities in database because we compute recall only for axioms of entities which we have tested
// we use only entities for which triples in the endpoint are contained
@@ -668,7 +753,7 @@
}
}
- //compute for each axiom type the recall
+ //compute recall for each axiom type
for(Entry<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> entry : axiomType2Algorithm.entrySet()){
AxiomType<? extends OWLAxiom> type = entry.getKey();
algorithms = entry.getValue();
@@ -685,10 +770,41 @@
}
}
}
+ table2.
+ append(type.getName()).append(" & ").
+ append(found + "/" + all ).append(" & & & & \\\\\n");
System.out.println(type.getName() + ": " + found + "/" + all);
}
+
+ table2.append("\\end{tabulary}");
+ System.out.println(table2.toString());
}
+ private Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> getAxiomTypesWithLearningAlgorithms(){
+ Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> axiomType2Algorithm = new HashMap<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>>();
+ axiomType2Algorithm.put(AxiomType.SUBCLASS_OF, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SimpleSubclassLearner.class, CELOE.class}));
+ axiomType2Algorithm.put(AxiomType.EQUIVALENT_CLASSES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{CELOE.class}));
+ axiomType2Algorithm.put(AxiomType.DISJOINT_CLASSES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DisjointClassesLearner.class}));
+
+ axiomType2Algorithm.put(AxiomType.SUB_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SubObjectPropertyOfAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.EQUIVALENT_OBJECT_PROPERTIES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{EquivalentObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.OBJECT_PROPERTY_DOMAIN, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ObjectPropertyDomainAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.OBJECT_PROPERTY_RANGE, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ObjectPropertyRangeAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.TRANSITIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{TransitiveObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalDataPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.INVERSE_FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{InverseFunctionalObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.SYMMETRIC_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SymmetricObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.REFLEXIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ReflexiveObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.IRREFLEXIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{IrreflexiveObjectPropertyAxiomLearner.class}));
+
+ axiomType2Algorithm.put(AxiomType.SUB_DATA_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SubDataPropertyOfAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.EQUIVALENT_DATA_PROPERTIES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{EquivalentDataPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.DATA_PROPERTY_DOMAIN, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DataPropertyDomainAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.DATA_PROPERTY_RANGE, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DataPropertyRangeAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.FUNCTIONAL_DATA_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalDataPropertyAxiomLearner.class}));
+ return axiomType2Algorithm;
+ }
+
private boolean isRelevantAxiom(OWLAxiom axiom, Set<OWLEntity> entities){
if(axiom instanceof OWLObjectPropertyAxiom){
return containsOneOf(axiom.getObjectPropertiesInSignature(), entities);
@@ -731,7 +847,7 @@
}
private double round(double value){
- return Math.round( value * 100. ) / 100.;
+ return Math.round( value * 10. ) / 10.;
}
public String printHTMLTable() throws SQLException {
@@ -793,9 +909,11 @@
}
}
- public static void main(String[] args) throws Exception {
+ public static void main(String[] args) throws Exception
+
+ {
EnrichmentEvaluation ee = new EnrichmentEvaluation();
- ee.start();
+// ee.start();
// ee.printResultsPlain();
ee.printResultsLaTeX();
Files.createFile(new File("enrichment_eval.html"), ee.printHTMLTable());
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