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[DL-Learner SVN] SF.net SVN: dl-learner: [934] trunk/src
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From: <Jen...@us...> - 2008-06-03 14:29:32
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Revision: 934
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=934&view=rev
Author: JensLehmann
Date: 2008-06-03 07:29:22 -0700 (Tue, 03 Jun 2008)
Log Message:
-----------
intermediate commit (evaluation script)
Modified Paths:
--------------
trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java
trunk/src/dl-learner/org/dllearner/core/ReasonerComponent.java
trunk/src/dl-learner/org/dllearner/core/ReasoningService.java
trunk/src/dl-learner/org/dllearner/core/config/CommonConfigOptions.java
trunk/src/dl-learner/org/dllearner/reasoning/DIGReasoner.java
trunk/src/dl-learner/org/dllearner/reasoning/FastInstanceChecker.java
trunk/src/dl-learner/org/dllearner/reasoning/FastRetrievalReasoner.java
trunk/src/dl-learner/org/dllearner/reasoning/OWLAPIReasoner.java
trunk/src/dl-learner/org/dllearner/refinementoperators/RhoDRDown.java
trunk/src/dl-learner/org/dllearner/scripts/CrossValidation.java
trunk/src/dl-learner/org/dllearner/scripts/PaperStatistics.java
trunk/src/php-examples/LearningSimple.php
Modified: trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -30,6 +30,7 @@
import org.apache.log4j.Logger;
import org.dllearner.core.LearningAlgorithm;
import org.dllearner.core.LearningProblem;
+import org.dllearner.core.ReasoningMethodUnsupportedException;
import org.dllearner.core.ReasoningService;
import org.dllearner.core.Score;
import org.dllearner.core.config.BooleanConfigOption;
@@ -111,6 +112,7 @@
private boolean useCardinalityRestrictions = CommonConfigOptions.useCardinalityRestrictionsDefault;
private boolean useNegation = CommonConfigOptions.useNegationDefault;
private boolean useBooleanDatatypes = CommonConfigOptions.useBooleanDatatypesDefault;
+ private boolean useDoubleDatatypes = CommonConfigOptions.useDoubleDatatypesDefault;
private double noisePercentage = 0.0;
private NamedClass startClass = null;
private boolean usePropernessChecks = false;
@@ -178,6 +180,7 @@
options.add(CommonConfigOptions.useCardinalityRestrictions());
options.add(CommonConfigOptions.useNegation());
options.add(CommonConfigOptions.useBooleanDatatypes());
+ options.add(CommonConfigOptions.useDoubleDatatypes());
options.add(CommonConfigOptions.maxExecutionTimeInSeconds());
options.add(CommonConfigOptions.minExecutionTimeInSeconds());
options.add(CommonConfigOptions.guaranteeXgoodDescriptions());
@@ -244,6 +247,8 @@
noisePercentage = (Double) entry.getValue();
} else if(name.equals("useBooleanDatatypes")) {
useBooleanDatatypes = (Boolean) entry.getValue();
+ } else if(name.equals("useDoubleDatatypes")) {
+ useDoubleDatatypes = (Boolean) entry.getValue();
} else if(name.equals("usePropernessChecks")) {
usePropernessChecks = (Boolean) entry.getValue();
} else if(name.equals("maxPosOnlyExpansion")) {
@@ -321,7 +326,9 @@
if(improveSubsumptionHierarchy)
rs.getSubsumptionHierarchy().improveSubsumptionHierarchy();
rs.prepareRoleHierarchy(usedRoles);
- rs.prepareDatatypePropertyHierarchy();
+ // prepare datatype hierarchy only if necessary
+ if(rs.hasDatatypeSupport())
+ rs.prepareDatatypePropertyHierarchy();
// create a refinement operator and pass all configuration
// variables to it
@@ -334,6 +341,7 @@
useCardinalityRestrictions,
useNegation,
useBooleanDatatypes,
+ useDoubleDatatypes,
startClass
);
Modified: trunk/src/dl-learner/org/dllearner/core/ReasonerComponent.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/ReasonerComponent.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/core/ReasonerComponent.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -45,6 +45,8 @@
*/
public abstract class ReasonerComponent extends Component implements Reasoner {
+ public abstract boolean hasDatatypeSupport();
+
public boolean subsumes(Description superConcept, Description subConcept)
throws ReasoningMethodUnsupportedException {
throw new ReasoningMethodUnsupportedException();
Modified: trunk/src/dl-learner/org/dllearner/core/ReasoningService.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/ReasoningService.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/core/ReasoningService.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -80,7 +80,7 @@
// private SortedSet<Concept> retrievalsSet = new TreeSet<Concept>(new ConceptComparator());
- private Reasoner reasoner;
+ private ReasonerComponent reasoner;
/**
* Constructs a reasoning service object. Note that you must not
@@ -459,6 +459,10 @@
return result;
}
+ public boolean hasDatatypeSupport() {
+ return reasoner.hasDatatypeSupport();
+ }
+
public Map<Individual, SortedSet<Double>> getDoubleDatatypeMembers(DatatypeProperty datatypeProperty) {
try {
return reasoner.getDoubleDatatypeMembers(datatypeProperty);
Modified: trunk/src/dl-learner/org/dllearner/core/config/CommonConfigOptions.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/config/CommonConfigOptions.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/core/config/CommonConfigOptions.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -29,6 +29,8 @@
*/
public final class CommonConfigOptions {
+ // some default values
+
//public static boolean applyAllFilterDefault = true;
//public static boolean applyExistsFilterDefault = true;
//public static boolean useTooWeakListDefault = true;
@@ -40,14 +42,13 @@
public static boolean useCardinalityRestrictionsDefault = true;
public static boolean useNegationDefault = true;
public static boolean useBooleanDatatypesDefault = true;
+ public static boolean useDoubleDatatypesDefault = true;
public static int maxExecutionTimeInSecondsDefault = 0;
public static int minExecutionTimeInSecondsDefault = 0;
public static int guaranteeXgoodDescriptionsDefault = 1;
public static String logLevelDefault = "DEBUG";
//public static double noisePercentageDefault = 0.0;
-
-
public static StringConfigOption getVerbosityOption() {
StringConfigOption verbosityOption = new StringConfigOption("verbosity", "control verbosity of output for this component", "warning");
String[] allowedValues = new String[] {"quiet", "error", "warning", "notice", "info", "debug"};
@@ -110,6 +111,10 @@
return new BooleanConfigOption("useBooleanDatatypes", "specifies whether boolean datatypes are used in the learning algorothm",useBooleanDatatypesDefault);
}
+ public static BooleanConfigOption useDoubleDatatypes() {
+ return new BooleanConfigOption("useBooleanDatatypes", "specifies whether boolean datatypes are used in the learning algorothm",useDoubleDatatypesDefault);
+ }
+
public static IntegerConfigOption maxExecutionTimeInSeconds() {
return new IntegerConfigOption("maxExecutionTimeInSeconds", "algorithm will stop after specified seconds",maxExecutionTimeInSecondsDefault);
}
Modified: trunk/src/dl-learner/org/dllearner/reasoning/DIGReasoner.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/reasoning/DIGReasoner.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/reasoning/DIGReasoner.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -795,4 +795,9 @@
return null;
}
+ @Override
+ public boolean hasDatatypeSupport() {
+ return false;
+ }
+
}
Modified: trunk/src/dl-learner/org/dllearner/reasoning/FastInstanceChecker.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/reasoning/FastInstanceChecker.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/reasoning/FastInstanceChecker.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -630,5 +630,11 @@
public void setReasonerType(String type){
reasonerType=type;
}
+
+
+ @Override
+ public boolean hasDatatypeSupport() {
+ return true;
+ }
}
Modified: trunk/src/dl-learner/org/dllearner/reasoning/FastRetrievalReasoner.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/reasoning/FastRetrievalReasoner.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/reasoning/FastRetrievalReasoner.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -193,4 +193,9 @@
public void releaseKB() {
rc.releaseKB();
}
+
+ @Override
+ public boolean hasDatatypeSupport() {
+ return true;
+ }
}
Modified: trunk/src/dl-learner/org/dllearner/reasoning/OWLAPIReasoner.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/reasoning/OWLAPIReasoner.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/reasoning/OWLAPIReasoner.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -904,4 +904,9 @@
reasonerType=type;
}
+ @Override
+ public boolean hasDatatypeSupport() {
+ return true;
+ }
+
}
Modified: trunk/src/dl-learner/org/dllearner/refinementoperators/RhoDRDown.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/refinementoperators/RhoDRDown.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/refinementoperators/RhoDRDown.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -167,11 +167,11 @@
// private Map<NamedClass,Map<NamedClass,Boolean>> notABMeaningful = new TreeMap<NamedClass,Map<NamedClass,Boolean>>();
public RhoDRDown(ReasoningService reasoningService) {
- this(reasoningService, true, true, true, true, true, true, true, null);
+ this(reasoningService, true, true, true, true, true, true, true, true, null);
}
public RhoDRDown(ReasoningService reasoningService, boolean applyAllFilter, boolean applyExistsFilter, boolean useAllConstructor,
- boolean useExistsConstructor,boolean useCardinalityRestrictions,boolean useNegation, boolean useBooleanDatatypes, NamedClass startClass) {
+ boolean useExistsConstructor,boolean useCardinalityRestrictions,boolean useNegation, boolean useBooleanDatatypes, boolean useDoubleDatatypes, NamedClass startClass) {
this.rs = reasoningService;
this.applyAllFilter = applyAllFilter;
this.applyExistsFilter = applyExistsFilter;
@@ -180,6 +180,7 @@
this.useCardinalityRestrictions = useCardinalityRestrictions;
this.useNegation = useNegation;
this.useBooleanDatatypes = useBooleanDatatypes;
+ this.useDoubleDatatypes = useDoubleDatatypes;
subHierarchy = rs.getSubsumptionHierarchy();
Modified: trunk/src/dl-learner/org/dllearner/scripts/CrossValidation.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/scripts/CrossValidation.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/scripts/CrossValidation.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -57,6 +57,11 @@
private static Logger logger = Logger.getRootLogger();
+ // statistical values
+ private Stat runtime = new Stat();
+ private Stat accuracy = new Stat();
+ private Stat length = new Stat();
+
public static void main(String[] args) {
File file = new File(args[0]);
@@ -90,7 +95,11 @@
}
public CrossValidation(File file, int folds, boolean leaveOneOut) {
+ this(file, folds, leaveOneOut, null);
+ }
+ public CrossValidation(File file, int folds, boolean leaveOneOut, LearningAlgorithm la) {
+
DecimalFormat df = new DecimalFormat();
ComponentManager cm = ComponentManager.getInstance();
@@ -178,11 +187,6 @@
System.exit(0);
}
- // statistical values
- Stat runtime = new Stat();
- Stat accuracy = new Stat();
- Stat length = new Stat();
-
// run the algorithm
for(int currFold=0; currFold<folds; currFold++) {
// we always perform a full initialisation to make sure that
@@ -204,7 +208,8 @@
// es fehlt init zwischendurch
- LearningAlgorithm la = start.getLearningAlgorithm();
+ if(la == null)
+ la = start.getLearningAlgorithm();
// init again, because examples have changed
try {
la.init();
@@ -315,4 +320,16 @@
return str;
}
+ public Stat getAccuracy() {
+ return accuracy;
+ }
+
+ public Stat getLength() {
+ return length;
+ }
+
+ public Stat getRuntime() {
+ return runtime;
+ }
+
}
Modified: trunk/src/dl-learner/org/dllearner/scripts/PaperStatistics.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/scripts/PaperStatistics.java 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/dl-learner/org/dllearner/scripts/PaperStatistics.java 2008-06-03 14:29:22 UTC (rev 934)
@@ -25,21 +25,16 @@
import java.util.HashMap;
import java.util.List;
import java.util.Map;
-import java.util.SortedSet;
import org.dllearner.algorithms.gp.GP;
import org.dllearner.core.ComponentManager;
import org.dllearner.core.KnowledgeSource;
import org.dllearner.core.LearningAlgorithm;
-import org.dllearner.core.LearningProblem;
-import org.dllearner.core.LearningProblemUnsupportedException;
import org.dllearner.core.OntologyFormat;
import org.dllearner.core.ReasonerComponent;
import org.dllearner.core.ReasoningService;
-import org.dllearner.core.Score;
import org.dllearner.kb.OWLFile;
import org.dllearner.learningproblems.PosNegDefinitionLP;
-import org.dllearner.parser.ConfParser;
import org.dllearner.reasoning.DIGReasoner;
import org.dllearner.utilities.Files;
import org.dllearner.utilities.Helper;
@@ -69,6 +64,7 @@
// experimental setup:
+ // 5 fold cross validation
// algorithms: refinement, GP, hybrid GP (YinYang)
// settings GP:
// - average over 10 runs
@@ -95,18 +91,18 @@
// - uncle (FORTE)
// - more?
- String exampleBaseDir = "examples/";
+ String exampleBaseDir = "examples/cross-benchmark/";
String gnuplotBaseDir = "log/gnuplot/";
String statBaseDir = "log/stat/";
- File[] confFiles = new File[7];
- confFiles[0] = new File(exampleBaseDir + "trains", "trains_owl.conf");
- confFiles[1] = new File(exampleBaseDir + "arch", "arch_owl.conf");
- confFiles[2] = new File(exampleBaseDir + "moral_reasoner", "moral_43examples_owl.conf");
- confFiles[3] = new File(exampleBaseDir + "moral_reasoner", "moral_43examples_complex_owl.conf");
- confFiles[4] = new File(exampleBaseDir + "poker", "pair_owl.conf");
- confFiles[5] = new File(exampleBaseDir + "poker", "straight_owl.conf");
- confFiles[6] = new File(exampleBaseDir + "forte", "forte_uncle_owl.conf");
+ File[] confFiles = new File[1];
+// confFiles[0] = new File(exampleBaseDir + "trains", "trains_owl");
+ confFiles[0] = new File(exampleBaseDir + "arch", "arch");
+// confFiles[2] = new File(exampleBaseDir + "moral_reasoner", "moral_43examples_owl");
+// confFiles[3] = new File(exampleBaseDir + "moral_reasoner", "moral_43examples_complex_owl");
+// confFiles[4] = new File(exampleBaseDir + "poker", "pair_owl");
+// confFiles[5] = new File(exampleBaseDir + "poker", "straight_owl");
+// confFiles[6] = new File(exampleBaseDir + "forte", "forte_uncle_owl");
String[] examples = new String[7];
examples[0] = "trains";
@@ -118,192 +114,31 @@
examples[6] = "uncle (FORTE data set)";
int startExampleNr = 0;
- String[] algorithms = new String[3];
- algorithms[0] = "refinement";
- algorithms[1] = "gp";
- algorithms[2] = "hybrid";
-
- int[] algorithmRuns = {1,10,10};
+ // for any example, we create conf files for each configuration to be tested
+ String[] algorithmPostfix = new String[4];
+ algorithmPostfix[0] = "_refexamples";
+ algorithmPostfix[1] = "_refexamples_fast";
+ algorithmPostfix[2] = "_gp";
+ algorithmPostfix[3] = "_hybrid";
int startAlgorithmNr = 0;
- // Config.GP.maxConceptLength = 30;
- // Config.writeDIGProtocol = true;
- // Config.digProtocolFile = new File(statBaseDir, "dig.log");
-
- // do not plot anything
- // File[][][] gnuplotFiles = new File[examples.length][algorithms.length][3];
- // for(int i=0; i<examples.length; i++) {
- // for(int j=0; j<algorithms.length; j++) {
- // gnuplotFiles[i][j][0] = new File(gnuplotBaseDir, examples[i] + "_classification_" + algorithms[j] + ".data");
- // gnuplotFiles[i][j][1] = new File(gnuplotBaseDir, examples[i] + "_length_" + algorithms[j] + ".data");
- // gnuplotFiles[i][j][2] = new File(gnuplotBaseDir, examples[i] + "_runtime_" + algorithms[j] + ".data");
- // }
- //}
-
File statFile = new File(statBaseDir, "statistics.txt");
- File statDetailsFile = new File(statBaseDir, "statistics_details.txt");
String statString = "**automatically generated statistics**\n\n";
- String statDetailsString = statString;
- ComponentManager cm = ComponentManager.getInstance();
-
- // just set default options
-// ConfigurationManager confMgr = new ConfigurationManager();
-// confMgr.applyOptions();
-
for(int exampleNr=startExampleNr; exampleNr < examples.length; exampleNr++) {
- // parse current conf file
- ConfParser learner = ConfParser.parseFile(confFiles[exampleNr]);
-
- String baseDir = confFiles[exampleNr].getParent();
-
- // read which files were imported (internal KB is ignored) and initialise reasoner
- Map<URL, OntologyFormat> imports = getImports(learner.getFunctionCalls(), confFiles[exampleNr]);
- //Map<URL, Class<? extends KnowledgeSource>> imports = Start.getImportedFiles(learner, baseDir);
-
- // detect specified positive and negative examples
- SortedSet<String> positiveExamples = learner.getPositiveExamples();
- SortedSet<String> negativeExamples = learner.getNegativeExamples();
- int nrOfExamples = positiveExamples.size() + negativeExamples.size();
-
- statString += "example: " + examples[exampleNr] + "\n\n";
-
- for(int algorithmNr=startAlgorithmNr; algorithmNr < algorithms.length; algorithmNr++) {
+ for(int algorithmNr=startAlgorithmNr; algorithmNr < algorithmPostfix.length; algorithmNr++) {
// reset algorithm number (next example starts with first algorithm)
startAlgorithmNr = 0;
+ File confFile = new File(confFiles[exampleNr] + algorithmPostfix[algorithmNr] + ".conf");
+
+ CrossValidation cv = new CrossValidation(confFile, 5, false);
Stat classification = new Stat();
Stat length = new Stat();
Stat runtime = new Stat();
- for(int runNr=0; runNr < algorithmRuns[algorithmNr]; runNr++) {
-
- // create reasoner (this has to be done in this inner loop to
- // ensure that none of the algorithm benefits from e.g. caching
- // of previous reasoning requests
- // Reasoner reasoner = Main.createReasoner(new KB(), imports);
- // TODO: needs fixing
- KnowledgeSource ks = cm.knowledgeSource(OWLFile.class);
- ReasonerComponent reasoner = cm.reasoner(DIGReasoner.class, ks);
- ReasoningService rs = new ReasoningService(reasoner);
-
- // System.out.println(positiveExamples);
- // System.out.println(negativeExamples);
- // System.exit(0);
-
- // create learning problem
- // LearningProblem learningProblem = new LearningProblem(rs, positiveExamples, negativeExamples);
- LearningProblem learningProblem = cm.learningProblem(PosNegDefinitionLP.class, rs);
-
- // prepare reasoner for using subsumption and role hierarchy
- // TODO: currently, it is a small unfairness that each algorithm
- // uses the same reasoning object (e.g. the second algorithm may
- // have a small advantage if the reasoner cached reasoning requests
- // of the first algorithm)
-// Helper.autoDetectConceptsAndRoles(rs);
-// try {
-// reasoner.prepareSubsumptionHierarchy();
-// reasoner.prepareRoleHierarchy();
-// // improving the subsumption hierarchy makes only sense
-// // for the refinement based algorithm
-// if(algorithmNr==0)
-// reasoner.getSubsumptionHierarchy().improveSubsumptionHierarchy();
-// } catch (ReasoningMethodUnsupportedException e) {
-// e.printStackTrace();
-// }
-
- LearningAlgorithm learningAlgorithm = null;
- if(algorithmNr==0) {
- // Config.algorithm = Algorithm.REFINEMENT;
- // Config.Refinement.heuristic = Config.Refinement.Heuristic.FLEXIBLE;
-// Config.Refinement.horizontalExpansionFactor = 0.6;
-// Config.Refinement.quiet = true;
- // Config.percentPerLengthUnit = 0.05;
- // learningAlgorithm = new ROLearner(learningProblem);
- // learningAlgorithm = cm.learningAlgorithm(ROLearner.class, learningProblem);
- } else if(algorithmNr==1) {
- // Config.algorithm = Algorithm.GP;
-// Config.GP.algorithmType = GP.AlgorithmType.GENERATIONAL;
-//// Config.GP.selectionType = GP.SelectionType.RANK_SELECTION;
-// Config.GP.generations = 50;
-// Config.GP.useFixedNumberOfGenerations = true;
-// Config.GP.numberOfIndividuals = 201;
- // if(exampleNr == 3 || exampleNr == 4)
- // Config.GP.numberOfIndividuals = 51;
-// Config.GP.refinementProbability = 0;
-// Config.GP.mutationProbability = 0.02;
-// Config.GP.crossoverProbability = 0.8;
-// Config.GP.hillClimbingProbability = 0;
- // Config.percentPerLengthUnit = 0.005;
- // give GP a chance to find the long solution of the
- // uncle problem
- // if(exampleNr==3 || exampleNr==5 || exampleNr == 6)
- // Config.percentPerLengthUnit = 0.002;
- // learningAlgorithm = new GP(learningProblem);
- try {
- learningAlgorithm = cm.learningAlgorithm(GP.class, learningProblem, rs);
- } catch (LearningProblemUnsupportedException e) {
- // TODO Auto-generated catch block
- e.printStackTrace();
- }
- } else if(algorithmNr==2) {
- // Config.algorithm = Algorithm.HYBRID_GP;
-// Config.GP.algorithmType = GP.AlgorithmType.GENERATIONAL;
-// Config.GP.selectionType = GP.SelectionType.RANK_SELECTION;
-// Config.GP.generations = 50;
-// Config.GP.useFixedNumberOfGenerations = true;
-// Config.GP.numberOfIndividuals = 201;
- //if(exampleNr == 3 || exampleNr == 4)
- // Config.GP.numberOfIndividuals = 51;
-// Config.GP.refinementProbability = 0.65;
-// Config.GP.mutationProbability = 0.02;
-// Config.GP.crossoverProbability = 0.2;
-// Config.GP.hillClimbingProbability = 0;
- // Config.percentPerLengthUnit = 0.005;
- // if(exampleNr == 3 || exampleNr==5 || exampleNr==6)
-// Config.percentPerLengthUnit = 0.002;
- // learningAlgorithm = new GP(learningProblem);
- try {
- learningAlgorithm = cm.learningAlgorithm(GP.class, learningProblem, rs);
- } catch (LearningProblemUnsupportedException e) {
- // TODO Auto-generated catch block
- e.printStackTrace();
- }
- }
-
- // rs.resetStatistics();
-
- long algorithmStartTime = System.nanoTime();
- learningAlgorithm.start();
- long algorithmTime = System.nanoTime() - algorithmStartTime;
- // long algorithmTimeSeconds = algorithmTime / 1000000000;
-
- int conceptLength = learningAlgorithm.getBestSolution().getLength();
- Score bestScore = learningAlgorithm.getSolutionScore();
- int misClassifications = bestScore.getCoveredNegatives().size()
- + bestScore.getNotCoveredPositives().size();
- double classificationRatePercent = 100 * ((nrOfExamples - misClassifications) / (double) nrOfExamples);
-
- classification.addNumber(classificationRatePercent);
- length.addNumber(conceptLength);
- runtime.addNumber(algorithmTime);
-
- // free knowledge base to avoid memory leaks
- ((DIGReasoner) reasoner).releaseKB();
-
- statDetailsString += "example: " + examples[exampleNr] + "\n";
- statDetailsString += "algorithm: " + algorithms[algorithmNr] + "\n";
- statDetailsString += "learned concept: " + learningAlgorithm.getBestSolution() + "\n";
- statDetailsString += "classification: " + classificationRatePercent + "%\n";
- statDetailsString += "concept length: " + conceptLength + "\n";
- statDetailsString += "runtime: " + Helper.prettyPrintNanoSeconds(algorithmTime) + "\n\n";
-
- Files.createFile(statDetailsFile, statDetailsString);
-
- } // end run loop
-
- statString += "algorithm: " + algorithms[algorithmNr] + " (runs: " + algorithmRuns[algorithmNr] + ")\n";
+ statString += "conf file: " + confFile + "\n";
statString += "classification: " + classification.getMean() + "% (standard deviation: " + classification.getStandardDeviation() + "%)\n";
statString += "concept length: " + length.getMean() + " (standard deviation: " + length.getStandardDeviation() + ")\n";
statString += "runtime: " + Helper.prettyPrintNanoSeconds(Math.round(runtime.getMean())) + " (standard deviation: " + Helper.prettyPrintNanoSeconds(Math.round(runtime.getStandardDeviation())) + ")\n\n";
@@ -316,6 +151,7 @@
}
+ @SuppressWarnings({"unused"})
private static Map<URL, OntologyFormat> getImports(Map<String,List<List<String>>> functionCalls, File confFile) {
Map<URL, OntologyFormat> importedFiles = new HashMap<URL, OntologyFormat>();
@@ -358,9 +194,6 @@
return importedFiles;
}
- // erzeugt Statistiken für MLDM-Paper zur Verarbeitung mit GnuPlot
- // Vorsicht: Laufzeit von mehreren Stunden
-
/**
* Has been used to create the statistics for the MLDM 2007 paper.
* Warning: this method runs for several hours
Modified: trunk/src/php-examples/LearningSimple.php
===================================================================
--- trunk/src/php-examples/LearningSimple.php 2008-06-02 12:36:06 UTC (rev 933)
+++ trunk/src/php-examples/LearningSimple.php 2008-06-03 14:29:22 UTC (rev 934)
@@ -30,7 +30,7 @@
// load WSDL files (has to be done due to a Java web service bug)
ini_set("soap.wsdl_cache_enabled","0");
$wsdluri="http://localhost:8181/services?wsdl";
-// Utilities::loadWSDLfiles($wsdluri);
+Utilities::loadWSDLfiles($wsdluri);
// specifiy ontology
$ontology = 'file:'.realpath("../../examples/family/father.owl");
@@ -42,7 +42,7 @@
// load owl file in DIG reasoner (you need a running DIG reasoner)
$id = $client->generateID();
$ksID = $client->addKnowledgeSource($id, "owlfile", $ontology);
-$rID = $client->setReasoner($id, "dig");
+$rID = $client->setReasoner($id, "owlapi");
// create a learning problem
$posExamples = array('http://example.com/father#stefan',
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