Examples of WLSubTreeKernel

org.data2semantics.exp.molecules.WLSubTreeKernel
Class implementing the Weisfeiler-Lehman graph kernel for Multigraphs with a root node, which occurs in the RDF use case. The current implementation can be made more efficient, since the compute function for test examples recomputes the label dictionary, instead of reusing the one created during training. This makes the applicability of the implementation slightly more general. TODO include a boolean for saving the labelDict to speed up computation of the kernel in the test phase. @author Gerben
org.data2semantics.proppred.kernels.graphkernels.WLSubTreeKernel
Class implementing the Weisfeiler-Lehman graph kernel for Multigraphs with a root node, which occurs in the RDF use case. The current implementation can be made more efficient, since the compute function for test examples recomputes the label dictionary, instead of reusing the one created during training. This makes the applicability of the implementation slightly more general. TODO include a boolean for saving the labelDict to speed up computation of the kernel in the test phase. @author Gerben

Examples of org.data2semantics.exp.molecules.WLSubTreeKernel

        ///*
        //List<DTNode<String,String>> newIN = new ArrayList<DTNode<String,String>>(instanceNodes3);
        //DTGraph<String,String> newG = GraphUtils.simplifyGraph(graph3, GraphUtils.createHubMap(hubList, th), newIN, false, true);
        //System.out.println("New #links: "+ newG.numLinks() + ", old #links: " + graph3.numLinks());


        exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), 
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(0), newIN.get(0), depth), target, evalFuncs);


        System.out.println("running, remove hubs, th: " + th);
        exp.run();


        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }


        //newIN = new ArrayList<DTNode<String,String>>(instanceNodes3);
        //newG = GraphUtils.simplifyGraph(graph3, GraphUtils.createHubMap(hubList, th), newIN, true, false);
        //System.out.println("New #links: "+ newG.numLinks() + ", old #links: " + graph3.numLinks());


        exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), 
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(1), newIN.get(1), depth), target, evalFuncs);


        System.out.println("running, relabel hubs, th: " + th);
        exp.run();


        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }


        //newIN = new ArrayList<DTNode<String,String>>(instanceNodes3);
        //newG = GraphUtils.simplifyGraph(graph3, GraphUtils.createHubMap(hubList, th), newIN, true, true);
        //System.out.println("New #links: "+ newG.numLinks() + ", old #links: " + graph3.numLinks());


        exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), 
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(2), newIN.get(2), depth), target, evalFuncs);


        System.out.println("running, relabel+remove hubs, th: " + th);
        exp.run();


        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
        //*/


      }
      System.out.println(resTable);
    }


    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);    
    System.out.println(resTable.allScoresToString());


    saveResults(resTable.toString(), "results_simp_" + System.currentTimeMillis() + ".txt");
    saveResults(resTable.allScoresToString(), "results_full_simp_" + System.currentTimeMillis() + ".txt");


/*
 * INSTANCE EXTRACTION!!!!! ah yeah ;)
 * 
 */
    
    // Discover average size
    List<DTGraph<String,String>> sg = GraphUtils.getSubGraphs(graph3, new ArrayList<DTNode<String,String>>(instanceNodes3), 3);
    double avg = 0;
    for (DTGraph<String,String> sgp : sg) {
      avg += sgp.size();
    }
    avg /= sg.size();
    System.out.println("Average Number of nodes: " + avg);
    
    // Results Table
    ResultsTable resTable2 = new ResultsTable();
    resTable2.setDigits(3);


    double[] fracs = {0.25, 0.5, 0.75, 1.0, 1.5, 2.0};


    for (double frac : fracs) {
      resTable2.newRow("Fraction: " + frac);
      
      List<DTGraph<String,String>> ihDepth = InstanceHelper.getInstances(graph4, instanceNodes4, target, InstanceHelper.Method.DEPTH, (int) Math.round(frac*avg), 4, true);  
      
      exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), seeds, svmParms, ihDepth, target, evalFuncs);
      
      System.out.println("running, Depth: " + frac);
      exp.run();


      for (Result res : exp.getResults()) {
        resTable2.addResult(res);
      }
      
      List<DTGraph<String,String>> ihUnInformed = InstanceHelper.getInstances(graph4, instanceNodes4, target, InstanceHelper.Method.UNINFORMED, (int) Math.round(frac*avg), 4, true);
      
      exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), seeds, svmParms, ihUnInformed, target, evalFuncs);
      
      System.out.println("running, UnInformed: " + frac);
      exp.run();


      for (Result res : exp.getResults()) {
        resTable2.addResult(res);
      }
      
      List<DTGraph<String,String>> ihInformed = InstanceHelper.getInstances(graph4, instanceNodes4, target, InstanceHelper.Method.INFORMED, (int) Math.round(frac*avg), 4, true);


      exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), seeds, svmParms, ihInformed, target, evalFuncs);
      
      System.out.println("running, Informed: " + frac);
      exp.run();


      for (Result res : exp.getResults()) {

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Examples of org.data2semantics.exp.molecules.WLSubTreeKernel

        
        // 1
        List<WLSubTreeKernel> kernelsWL = new ArrayList<WLSubTreeKernel>();
        
        for (int iti : it) {
          kernelsWL.add(new WLSubTreeKernel(iti, true, forward));
        }    
        
        expWL = new MoleculeListMultiGraphExperiment<DTGraph<String,String>>(kernelsWL,  seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(0), newIN.get(0), depth), target, evalFuncs);


        System.out.println("WL running, remove hubs, th: " + th);
        expWL.run();


        for (Result res : expWL.getResults()) {
          resTableWL.addResult(res);
        }


        // 2
        kernelsWL = new ArrayList<WLSubTreeKernel>();
        for (int iti : it) {
          kernelsWL.add(new WLSubTreeKernel(iti, true, forward));
        }
  
        expWL = new MoleculeListMultiGraphExperiment<DTGraph<String,String>>(kernelsWL, 
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(1), newIN.get(1), depth), target, evalFuncs);


        System.out.println("WL running, relabel hubs, th: " + th);
        expWL.run();


        for (Result res : expWL.getResults()) {
          resTableWL.addResult(res);
        }
        
        


        // 3
        kernelsWL = new ArrayList<WLSubTreeKernel>();
        for (int iti : it) {
          kernelsWL.add(new WLSubTreeKernel(iti, true, forward));
        }
        
        expWL = new MoleculeListMultiGraphExperiment<DTGraph<String,String>>(kernelsWL, 
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(2), newIN.get(2), depth), target, evalFuncs);

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

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

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

        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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Examples of org.data2semantics.proppred.kernels.graphkernels.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());
            
            System.out.println("Running WL, it " + i + " on " + dataset.getLabel());

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

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

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

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

          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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