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[DL-Learner SVN] SF.net SVN: dl-learner:[3091] trunk/scripts/src/main/java/org/dllearner/ scripts/evaluation/EnrichmentEvaluation.java
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From: <lor...@us...> - 2011-08-22 13:54:48
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Revision: 3091
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3091&view=rev
Author: lorenz_b
Date: 2011-08-22 13:54:38 +0000 (Mon, 22 Aug 2011)
Log Message:
-----------
Added column in evaluation database table, which declares whether the learned axiom was entailed by the knowledge base before.
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 2011-08-22 13:21:34 UTC (rev 3090)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-22 13:54:38 UTC (rev 3091)
@@ -35,6 +35,7 @@
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Statement;
+import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
@@ -72,6 +73,7 @@
import org.dllearner.core.AxiomLearningAlgorithm;
import org.dllearner.core.ComponentInitException;
import org.dllearner.core.EvaluatedAxiom;
+import org.dllearner.core.EvaluatedDescription;
import org.dllearner.core.LearningAlgorithm;
import org.dllearner.core.config.ConfigHelper;
import org.dllearner.core.owl.DatatypeProperty;
@@ -80,6 +82,7 @@
import org.dllearner.kb.SparqlEndpointKS;
import org.dllearner.kb.sparql.SPARQLTasks;
import org.dllearner.kb.sparql.SparqlEndpoint;
+import org.dllearner.learningproblems.AxiomScore;
import org.dllearner.utilities.CommonPrefixMap;
import org.dllearner.utilities.Files;
import org.dllearner.utilities.owl.OWLAPIConverter;
@@ -181,10 +184,10 @@
s.executeUpdate("DROP TABLE IF EXISTS evaluation");
s.executeUpdate("CREATE TABLE evaluation ("
+ "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
- + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20))");
+ + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20), entailed BOOLEAN)");
s.close();
ps = conn.prepareStatement("INSERT INTO evaluation ("
- + "entity, algorithm, axiom, score, runtime_ms ) " + "VALUES(?,?,?,?,?)");
+ + "entity, algorithm, axiom, score, runtime_ms, entailed ) " + "VALUES(?,?,?,?,?,?)");
} catch (ClassNotFoundException e) {
e.printStackTrace();
} catch (SQLException e) {
@@ -198,13 +201,14 @@
}
}
- private void writeToDB(String entity, String algorithm, String axiom, double score, long runTime) {
+ private void writeToDB(String entity, String algorithm, String axiom, double score, long runTime, boolean entailed) {
try {
ps.setString(1, entity);
ps.setString(2, algorithm);
ps.setString(3, axiom);
ps.setDouble(4, score);
ps.setLong(5, runTime);
+ ps.setBoolean(6, entailed);
ps.executeUpdate();
} catch (SQLException e) {
@@ -270,9 +274,9 @@
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
if(timeout){
- writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
} else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
- writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime);
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
} else {
for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
double score = learnedAxiom.getScore().getAccuracy();
@@ -280,7 +284,7 @@
score = -1;
}
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
- score, runTime);
+ score, runTime, isEntailed(learnedAxiom));
}
}
objectProperties++;
@@ -330,9 +334,9 @@
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
if(timeout){
- writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
} else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
- writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime);
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
} else {
for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
double score = learnedAxiom.getScore().getAccuracy();
@@ -340,7 +344,7 @@
score = -1;
}
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
- score, runTime);
+ score, runTime, isEntailed(learnedAxiom));
}
}
dataProperties++;
@@ -384,25 +388,31 @@
}
}
long runTime = System.currentTimeMillis() - startTime;
+ List<EvaluatedAxiom> learnedAxioms = null;
if(learner instanceof AxiomLearningAlgorithm){
- List<EvaluatedAxiom> learnedAxioms = ((AxiomLearningAlgorithm)learner)
+ learnedAxioms = ((AxiomLearningAlgorithm)learner)
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
- if(timeout){
- writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
- } else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
- writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime);
- } else {
- for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
- double score = learnedAxiom.getScore().getAccuracy();
- if (Double.isNaN(score)) {
- score = -1;
- }System.out.println(learnedAxiom.getAxiom().toManchesterSyntaxString(algName, prefixes) + ":" + isEntailed(learnedAxiom));
- writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
- score, runTime);
+ } 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())));
+ }
+ }
+ if(timeout){
+ writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
+ } else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
+ writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
+ } else {
+ for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
+ double score = learnedAxiom.getScore().getAccuracy();
+ if (Double.isNaN(score)) {
+ score = -1;
}
+ writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
+ score, runTime, isEntailed(learnedAxiom));
}
- } else if(learner instanceof AbstractCELA){
-
}
classesCnt++;
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