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[DL-Learner SVN] SF.net SVN: dl-learner:[1913] trunk/src/dl-learner/org/dllearner
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From: <jen...@us...> - 2009-11-16 22:10:01
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Revision: 1913
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=1913&view=rev
Author: jenslehmann
Date: 2009-11-16 22:09:55 +0000 (Mon, 16 Nov 2009)
Log Message:
-----------
implemented CELOE single suggestion mode for learning problems with >1000 examples (i.e. all evaluations should be approximated for performance reasons)
Modified Paths:
--------------
trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java
trunk/src/dl-learner/org/dllearner/core/configurators/CELOEConfigurator.java
trunk/src/dl-learner/org/dllearner/core/configurators/PelletReasonerConfigurator.java
trunk/src/dl-learner/org/dllearner/examples/MonogenicDiseases.java
Modified: trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java 2009-11-15 13:55:34 UTC (rev 1912)
+++ trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java 2009-11-16 22:09:55 UTC (rev 1913)
@@ -21,6 +21,7 @@
import java.text.DecimalFormat;
import java.util.Collection;
+import java.util.Date;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
@@ -36,6 +37,7 @@
import org.dllearner.core.LearningProblem;
import org.dllearner.core.ReasonerComponent;
import org.dllearner.core.configurators.CELOEConfigurator;
+import org.dllearner.core.options.BooleanConfigOption;
import org.dllearner.core.options.CommonConfigOptions;
import org.dllearner.core.options.ConfigOption;
import org.dllearner.core.owl.ClassHierarchy;
@@ -95,6 +97,12 @@
private EvaluatedDescriptionSet bestEvaluatedDescriptions;
+ // if true, then each solution is evaluated exactly instead of approximately
+ // private boolean exactBestDescriptionEvaluation = false;
+ private boolean singleSuggestionMode;
+ private Description bestDescription;
+ private double bestAccuracy = Double.MIN_VALUE;
+
private NamedClass classToDescribe;
// examples are either 1.) instances of the class to describe 2.) positive examples
// 3.) union of pos.+neg. examples depending on the learning problem at hand
@@ -154,6 +162,7 @@
options.add(CommonConfigOptions.getNoisePercentage());
options.add(CommonConfigOptions.getMaxDepth(7));
options.add(CommonConfigOptions.maxNrOfResults(10));
+ options.add(new BooleanConfigOption("singleSuggestionMode", "Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.", false));
return options;
}
@@ -172,6 +181,8 @@
startClass = Thing.instance;
+ singleSuggestionMode = configurator.getSingleSuggestionMode();
+
// create refinement operator
operator = new RhoDRDown(reasoner, classHierarchy, startClass, configurator);
baseURI = reasoner.getBaseURI();
@@ -246,7 +257,7 @@
int loop = 0;
while (!terminationCriteriaSatisfied()) {
- if(bestEvaluatedDescriptions.getBest().getAccuracy() > highestAccuracy) {
+ if(!singleSuggestionMode && bestEvaluatedDescriptions.getBest().getAccuracy() > highestAccuracy) {
highestAccuracy = bestEvaluatedDescriptions.getBest().getAccuracy();
logger.info("more accurate (" + dfPercent.format(highestAccuracy) + ") class expression found: " + descriptionToString(bestEvaluatedDescriptions.getBest().getDescription()));
}
@@ -260,6 +271,11 @@
TreeSet<Description> refinements = refineNode(nextNode);
mon.stop();
+// System.out.println("next node: " + nextNode);
+// for(Description refinement : refinements) {
+// System.out.println("refinement: " + refinement);
+// }
+
while(refinements.size() != 0) {
// pick element from set
Description refinement = refinements.pollFirst();
@@ -269,24 +285,30 @@
// (this also avoids duplicate node children)
if(length > horizExp && refinement.getDepth() <= maxDepth) {
+// System.out.println("potentially adding " + refinement + " to search tree as child of " + nextNode + " " + new Date());
Monitor mon2 = MonitorFactory.start("addNode");
addNode(refinement, nextNode);
mon2.stop();
-
+// System.out.println("addNode finished" + " " + new Date());
}
}
updateMinMaxHorizExp(nextNode);
+// System.out.println(loop);
loop++;
}
-
+
if (stop) {
logger.info("Algorithm stopped ("+expressionTests+" descriptions tested).\n");
} else {
logger.info("Algorithm terminated successfully ("+expressionTests+" descriptions tested).\n");
}
+
+ if(singleSuggestionMode) {
+ bestEvaluatedDescriptions.add(bestDescription, bestAccuracy, learningProblem);
+ }
// print solution(s)
logger.info("solutions:\n" + getSolutionString());
@@ -343,8 +365,10 @@
return false;
}
+// System.out.println("Test " + new Date());
// quality of description (return if too weak)
double accuracy = learningProblem.getAccuracyOrTooWeak(description, noise);
+// System.out.println("Test2 " + new Date());
expressionTests++;
// System.out.println(description + " " + accuracy);
if(accuracy == -1) {
@@ -361,7 +385,19 @@
}
nodes.add(node);
+// System.out.println("Test3 " + new Date());
+ // in some cases (e.g. mutation) fully evaluating even a single description is too expensive
+ // due to the high number of examples -- so we just stick to the approximate accuracy
+ if(singleSuggestionMode) {
+ if(accuracy > bestAccuracy) {
+ bestAccuracy = accuracy;
+ bestDescription = description;
+ logger.info("more accurate (" + dfPercent.format(bestAccuracy) + ") class expression found: " + descriptionToString(bestDescription));
+ }
+ return true;
+ }
+
// maybe add to best descriptions (method keeps set size fixed);
// we need to make sure that this does not get called more often than
// necessary since rewriting is expensive
@@ -374,6 +410,7 @@
(accuracy >= accThreshold && description.getLength() < worst.getDescriptionLength()));
}
+// System.out.println("Test4 " + new Date());
if(isCandidate) {
Description niceDescription = rewriteNode(node);
ConceptTransformation.transformToOrderedForm(niceDescription, descriptionComparator);
@@ -383,18 +420,20 @@
// a subdescription of this one unless accuracy is different
boolean shorterDescriptionExists = false;
for(EvaluatedDescription ed : bestEvaluatedDescriptions.getSet()) {
- if(ed.getAccuracy()==accuracy && ConceptTransformation.isSubdescription(niceDescription, ed.getDescription())) {
+ if(Math.abs(ed.getAccuracy()-accuracy) <= 0.00001 && ConceptTransformation.isSubdescription(niceDescription, ed.getDescription())) {
shorterDescriptionExists = true;
break;
}
}
if(!shorterDescriptionExists) {
- bestEvaluatedDescriptions.add(niceDescription, accuracy, learningProblem);
+ bestEvaluatedDescriptions.add(niceDescription, accuracy, learningProblem);
}
}
+// System.out.println("Test5 " + new Date());
+// System.out.println("best evaluated descriptions size: " + bestEvaluatedDescriptions.size() + " worst: " + bestEvaluatedDescriptions.getWorst());
return true;
}
Modified: trunk/src/dl-learner/org/dllearner/core/configurators/CELOEConfigurator.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/configurators/CELOEConfigurator.java 2009-11-15 13:55:34 UTC (rev 1912)
+++ trunk/src/dl-learner/org/dllearner/core/configurators/CELOEConfigurator.java 2009-11-16 22:09:55 UTC (rev 1913)
@@ -174,6 +174,15 @@
public int getMaxNrOfResults() {
return (Integer) ComponentManager.getInstance().getConfigOptionValue(cELOE, "maxNrOfResults") ;
}
+/**
+* singleSuggestionMode Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples..
+* mandatory: false| reinit necessary: true
+* default value: false
+* @return boolean
+**/
+public boolean getSingleSuggestionMode() {
+return (Boolean) ComponentManager.getInstance().getConfigOptionValue(cELOE, "singleSuggestionMode") ;
+}
/**
* @param useAllConstructor specifies whether the universal concept constructor is used in the learning algorithm.
@@ -292,6 +301,15 @@
ComponentManager.getInstance().applyConfigEntry(cELOE, "maxNrOfResults", maxNrOfResults);
reinitNecessary = true;
}
+/**
+* @param singleSuggestionMode Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples..
+* mandatory: false| reinit necessary: true
+* default value: false
+**/
+public void setSingleSuggestionMode(boolean singleSuggestionMode) {
+ComponentManager.getInstance().applyConfigEntry(cELOE, "singleSuggestionMode", singleSuggestionMode);
+reinitNecessary = true;
+}
/**
* true, if this component needs reinitializsation.
Modified: trunk/src/dl-learner/org/dllearner/core/configurators/PelletReasonerConfigurator.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/configurators/PelletReasonerConfigurator.java 2009-11-15 13:55:34 UTC (rev 1912)
+++ trunk/src/dl-learner/org/dllearner/core/configurators/PelletReasonerConfigurator.java 2009-11-16 22:09:55 UTC (rev 1913)
@@ -21,7 +21,6 @@
package org.dllearner.core.configurators;
import java.util.Set;
-
import org.dllearner.core.ComponentManager;
import org.dllearner.core.KnowledgeSource;
import org.dllearner.reasoning.PelletReasoner;
@@ -73,7 +72,6 @@
reinitNecessary = true;
}
-
/**
* true, if this component needs reinitializsation.
* @return boolean
Modified: trunk/src/dl-learner/org/dllearner/examples/MonogenicDiseases.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/examples/MonogenicDiseases.java 2009-11-15 13:55:34 UTC (rev 1912)
+++ trunk/src/dl-learner/org/dllearner/examples/MonogenicDiseases.java 2009-11-16 22:09:55 UTC (rev 1913)
@@ -63,6 +63,9 @@
private static boolean generatePosExampleClass = true;
// set to true if accessing PostreSQL and false for MySQL
private static boolean pgSQL = true;
+ // generate fragment => limits the number of individuals in the ontology
+ // to speed up learning
+// private static boolean onlyFragment = true;
public static void main(String[] args) throws ClassNotFoundException, BackingStoreException, SQLException {
@@ -233,6 +236,10 @@
kb.addAxiom(new DatatypePropertyDomainAxiom(reliabilityDeltagProp, mutationClass));
kb.addAxiom(new DatatypePropertyRangeAxiom(reliabilityDeltagProp, Datatype.DOUBLE));
+ if(generatePosExampleClass) {
+ kb.addAxiom(new SubClassAxiom(deleteriousMutationClass, mutationClass));
+ }
+
// select data (restricted to pos/neg examples for efficiency)
Statement stmt = conn.createStatement();
ResultSet rs = null;
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