Package org.data2semantics.proppred.kernels.graphkernels

Examples of org.data2semantics.proppred.kernels.graphkernels.WLSubTreeKernel

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        resultsWL.newRow(dataset.getLabel() + " WLSubTreeKernel");
        for (int i = 0; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + "_" + "WL" + fileId + "_" + i + ".txt");
            exp = new LinkPredictionExperiment(new LinkPredictionDataSet(dataset), new WLSubTreeKernel(i), new WLSubTreeKernel(i), 3.0/6.0, 3.0/6.0, seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWL.addResult(exp.getResults().getAccuracy());
            resultsWL.addResult(exp.getResults().getF1());
            resultsWL.addResult(exp.getResults().getrPrecision());
            resultsWL.addResult(exp.getResults().getAveragePrecision());
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        resultsWL.newRow(dataset.getLabel() + " WLSubTreeKernel");
        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new WLSubTreeKernel(i), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWL.addResult(exp.getResults().getAccuracy());
            resultsWL.addResult(exp.getResults().getF1());
          }
        }
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        resultsWL.newRow(dataset.getLabel() + " WLSubTreeKernel");
        for (int i = 0; i < 3; i++) {
          if (experimenter.hasSpace()) { 
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            WLSubTreeKernel kernel = new WLSubTreeKernel(i, true);
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), kernel, seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWL.addResult(exp.getResults().getAccuracy());
            resultsWL.addResult(exp.getResults().getF1());
           
            System.out.println("Running WL, it " + i + " on " + dataset.getLabel());
          }
        }

       
        resultsSTF.newRow(dataset.getLabel() + " IntersectionFullSubTree");
        for (int i = 0; i < 3; i++) {

          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionFullSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionSubTreeKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTF.addResult(exp.getResults().getAccuracy());
            resultsSTF.addResult(exp.getResults().getF1());
           
            System.out.println("Running STF, it " + i + " on " + dataset.getLabel());
          }

        }

        resultsSTP.newRow(dataset.getLabel() + " IntersectionPartialSubTree");
        for (int i = 0; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionPartialSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionPartialSubTreeKernel(i, 0.01), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTP.addResult(exp.getResults().getAccuracy());
            resultsSTP.addResult(exp.getResults().getF1());
           
            System.out.println("Running STP, it " + i + " on " + dataset.getLabel());
          }
        }


       
        resultsIGP.newRow(dataset.getLabel() + " IntersectionGraphPath");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphPath" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphPathKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGP.addResult(exp.getResults().getAccuracy());
            resultsIGP.addResult(exp.getResults().getF1());
           
            System.out.println("Running IGP, it " + i + " on " + dataset.getLabel());
          }
        }       

        resultsIGW.newRow(dataset.getLabel() + " IntersectionGraphWalk");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphWalk" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphWalkKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGW.addResult(exp.getResults().getAccuracy());
            resultsIGW.addResult(exp.getResults().getF1());
           
            System.out.println("Running IGW, it " + i + " on " + dataset.getLabel());
          }
        }
       
      }
     
      //*/
     

      /******
       * ADDITIONAL EXPERIMENTS
       */
      dataSetsParams = new ArrayList<PropertyPredictionDataSetParameters>();
     
     
     
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, false));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, false));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 3, false, false));
      //dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 4, false, false));

     
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, true));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, true));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 3, false, true));
     

     
      for (PropertyPredictionDataSetParameters params : dataSetsParams) {
        dataset = DataSetFactory.createPropertyPredictionDataSet(params);
        dataset.removeSmallClasses(5);
        dataset.removeVertexAndEdgeLabels();

        resultsWLadd.newRow(dataset.getLabel() + " WLSubTreeKernel");
        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) { 
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            WLSubTreeKernel kernel = new WLSubTreeKernel(i, true);
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), kernel, seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWLadd.addResult(exp.getResults().getAccuracy());
            resultsWLadd.addResult(exp.getResults().getF1());
           
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        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new WLSubTreeKernel(i), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWL.addResult(exp.getResults().getAccuracy());
            resultsWL.addResult(exp.getResults().getF1());
           
            System.out.println("Running WL, it " + i + " on " + dataset.getLabel());
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      tic = System.currentTimeMillis();
      PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));
      toc = System.currentTimeMillis();
      double dsComp = toc-tic;
     
      FeatureVectorKernel k = new WLSubTreeKernel(6,true);
     
      System.out.println("WL: " + frac);
      tic = System.currentTimeMillis();
      k.computeFeatureVectors(ds.getGraphs());
      toc = System.currentTimeMillis();
      double[] comp = {(toc-tic) + dsComp};
      Result res = new Result(comp, "comp time");
      resTable.addResult(res);
    }   
    System.out.println(resTable);
   
   
    resTable.newRow("WL Kernel");
    for (double frac : fractionsSlow) {
      createGeoDataSet((int)(1000 * frac), frac, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasUnitClass");   
      tic = System.currentTimeMillis();
      PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));
      toc = System.currentTimeMillis();
      double dsComp = toc-tic;
     
      GraphKernel k = new WLSubTreeKernel(6,true);
     
      System.out.println("WL: " + frac);
      tic = System.currentTimeMillis();
      k.compute(ds.getGraphs());
      toc = System.currentTimeMillis();
      double[] comp = {(toc-tic) + dsComp};
      Result res = new Result(comp, "comp time");
      resTable.addResult(res);
    }   
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        tic = System.currentTimeMillis();
        PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));
        toc = System.currentTimeMillis();
        double dsComp = toc-tic;

        FeatureVectorKernel k = new WLSubTreeKernel(6,true);

        System.out.println("WL: " + frac);
        tic = System.currentTimeMillis();
        k.computeFeatureVectors(ds.getGraphs());
        toc = System.currentTimeMillis();
        comp[i] = (toc-tic) + dsComp;
      }
      res = new Result(comp, "comp time");
      resTable.addResult(res);
    //}   
    //System.out.println(resTable);


    //resTable.newRow("WL Kernel");
    //for (double frac : fractionsSlow) {
      comp = new double[seeds.length];
      for (int i = 0; i < seeds.length; i++) {
        createGeoDataSet((int)(1000 * frac), frac, seeds[i], "http://data.bgs.ac.uk/ref/Lexicon/hasTheme");   
        tic = System.currentTimeMillis();
        PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));
        toc = System.currentTimeMillis();
        double dsComp = toc-tic;

        GraphKernel k = new WLSubTreeKernel(6,true);

        System.out.println("WL: " + frac);
        tic = System.currentTimeMillis();
        k.compute(ds.getGraphs());
        toc = System.currentTimeMillis();
        comp[i] = (toc-tic) + dsComp;
      }
      res = new Result(comp, "comp time");
      resTable.addResult(res);
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  /**
   * Construct the default SVMPropertyPredictor. A C-SVC support vector machine is used, with a WLSubtreeKernel and extraction depth 2. This setting is good to start with for a new classification task.
   *
   */
  public SVMPropertyPredictor() {
    this(new WLSubTreeKernel(2,true), 2);
    this.setDefaultLibSVMParams();
  }
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          File file = new File("D:\\workspaces\\datasets\\aifb\\" + fileId + "_" + "IGP" + "_" + i + ".txt");
          //file.mkdirs();
         
          try {
            //exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionSubTreeKernel(i, 1), seeds, cs);
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new WLSubTreeKernel(i), seeds, cs);
           
            //exp = new ClassificationExperiment(new GraphClassificationDataSet(dataset), new IntersectionGraphPathKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            results.add(exp.getResults());
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    LinkPredictionDataSet set = DataSetFactory.createLinkPredictonDataSet(testSet, "http://swrc.ontoware.org/ontology#Person", "http://swrc.ontoware.org/ontology#ResearchGroup", "http://swrc.ontoware.org/ontology#affiliation", bl, 2, false, false);
   
   
   
   
    new LinkPredictionExperiment(set, new WLSubTreeKernel(2), new WLSubTreeKernel(2), 1, 0, seeds, cs).run();
    new LinkPredictionExperiment(set, new WLSubTreeKernel(2), new WLSubTreeKernel(2), 0.75, 0.25, seeds, cs).run();
    new LinkPredictionExperiment(set, new WLSubTreeKernel(2), new WLSubTreeKernel(2), 0.5, 0.5, seeds, cs).run();
    new LinkPredictionExperiment(set, new WLSubTreeKernel(2), new WLSubTreeKernel(2), 0.25, 0.75, seeds, cs).run();
    new LinkPredictionExperiment(set, new WLSubTreeKernel(2), new WLSubTreeKernel(2), 0, 1, seeds, cs).run();
   
   
   
    /*
   
 
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Related Classes of org.data2semantics.proppred.kernels.graphkernels.WLSubTreeKernel

  • org.data2semantics.exp.experiments.ClassificationExperimentTest
  • org.data2semantics.exp.experiments.LinkPredictionExperimentTest
  • org.data2semantics.exp.FullClassRunningTimeExperiment
  • org.data2semantics.exp.FullThemeRunningTimeExperiment
  • org.data2semantics.exp.old.AffiliationLinkPredictionExperiment
  • org.data2semantics.exp.old.AffiliationPredictionExperiment
  • org.data2semantics.exp.old.BibleReligionPredictionExperiment
  • org.data2semantics.exp.old.CommitteeMemberPredictionExperiment
  • org.data2semantics.proppred.learners.SparseVector
  • org.data2semantics.proppred.predictors.SVMPropertyPredictor
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