Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFWLSubTreeKernel$VertexIndexPair

Package org.data2semantics.proppred.kernels.rdfgraphkernels

Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFWLSubTreeKernel$VertexIndexPair

org.data2semantics.proppred.kernels.rdfgraphkernels.RDFWLSubTreeKernel
This class implements a WL kernel directly on an RDF graph. The difference with a normal WL kernel is that subgraphs are not explicitly extracted. However we use the idea of subgraph implicitly by tracking for each vertex/edge the distance from an instance vertex. For one thing, this leads to the fact that 1 black list is applied to the entire RDF graph, instead of 1 (small) blacklist per graph. @author Gerben

            weights[(int) label - 1] = 1 / counts.get(label);
          }
          linParms.setWeightLabels(wLabels);
          linParms.setWeights(weights);


          RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, i, inference, true), seeds2, linParms, dataset, instances, target, blackList, evalFuncs);


          System.out.println("Running WL RDF: " + i + " " + it);
          exp.setDoCV(true);
          exp.run();
          res.add(exp.getResults());

View Full Code Here

    
    ///*
    for (int i : depths) {      
      resTable.newRow("RDF WL forward");
      for (int it : iterations) {
        RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, i, inference, true, forward, false);
        
        //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
        KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);




        System.out.println("Running WL RDF fwd: " + i + " " + it);
        exp.run();


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


    for (int h : hf) {
      for (int i : depths) {      
        resTable.newRow("RDF WL forward Degree " + h);
        for (int it : iterations) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, inference, true, forward);
          k.setHubMap(GraphUtils.createHubMap(degreeHubs, h));


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);




          System.out.println("Running WL RDF fwd Degree: " + i + " " + it + " " + h);
          exp.run();


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


    ///*
    for (int h : hf) {
      for (int i : depths) {      
        resTable.newRow("RDF WL forward SB " + h);
        for (int it : iterations) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, inference, true, forward);
          k.setHubMap(GraphUtils.createHubMap(hubs, h));


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);

View Full Code Here

        pred = LibLINEAR.trainTestSplit(fv, target, linearParms, linearParms.getSplitFraction());
        targetSplit = LibLINEAR.splitTestTarget(target, linearParms.getSplitFraction());
        
        // If we deal with on RDFWLSubTree, then we show the most used features, based on the featuremap created
        if (kernel instanceof RDFWLSubTreeKernel) {
          RDFWLSubTreeKernel k = (RDFWLSubTreeKernel) kernel;
          LibLINEARModel model = LibLINEAR.trainLinearModel(fv, target, linearParms);
          LibLINEARModel.WeightIndexPair[][] fw = model.getFeatureWeights();
          
          Map<String, String> lm = k.getInverseLabelMap();
          
          System.out.println("Map size: " + lm.size() + " fw length: " + fw[0].length + " fv max index: " + fv[0].getLastIndex());
          
          for (LibLINEARModel.WeightIndexPair[] fwc : fw) {
            Arrays.sort(fwc);

View Full Code Here

    


    for (int d : depths) {
      resTable.newRow("WL RDF, depth="+d);
      for (int it : iterations) {
        RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, d, inference, true);
        
        RDFOldKernelExperiment exp = new RDFOldKernelExperiment(k, seeds, svmParms, dataset, instances, labels, blackList);
        
  
        System.out.println("Running WL RDF: " + d + " " + it);
        exp.run();


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




    for (int d : depths) {
      resTable.newRow("WL RDF BoW, depth="+d);
      for (int it : iterations) {
        RDFWLSubTreeWithTextKernel k = new RDFWLSubTreeWithTextKernel(it, d, inference, false);
        k.setDoTFIDFkernel(true);
        
        RDFOldKernelExperiment exp = new RDFOldKernelExperiment(k, seeds, svmParms, dataset, instances, labels, blackList);
    
        System.out.println("Running WL RDF text: " + d + " " + it);
        exp.run();


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


  
    
    
    for (int d : depths) {
      resTable.newRow("ITP, depth="+d);


      RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(d, false, inference, true), seeds, svmParms, dataset, instances, labels, blackList);


      System.out.println("Running Edge Vertex Tree Path: " + d);
      exp.run();


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


    }
    System.out.println(resTable);
    




    for (int d : depths) {
      resTable.newRow("ITP BoW, depth="+d);


      
      RDFIntersectionTreeEdgeVertexPathWithTextKernel k = new RDFIntersectionTreeEdgeVertexPathWithTextKernel(d, false, inference, false);
      k.setDoTFIDFkernel(true);
      
      RDFOldKernelExperiment exp = new RDFOldKernelExperiment(k, seeds, svmParms, dataset, instances, labels, blackList);


      System.out.println("Running Edge Vertex Tree Path with Text: " + d);
      exp.run();

View Full Code Here

  }
  
  @Main
  public SparseVector[] computeFeatureVectors() {
    fv = super.computeFeatureVectors(dataset, instances, blacklist, 
        new RDFWLSubTreeKernel(iterations, depth, inference, normalize, false, reverse), 
        new RDFWLSubTreeKernel(iterations, depth, inference, normalize, false, reverse)
          );
    return fv;
  }

View Full Code Here

    
    
    for (int depth : depths) {
      resTable.newRow("WL RDF forward, depth="+depth);
      for (int it : iterations) {
        RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFWLSubTreeKernel(it, depth, inference, true), seeds, svmParms, dataset, instances, labels, blackList);


        
        System.out.println("Running WL RDF Fwd: " + depth + " " + it);
        exp.run();


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


    for (int depth : depths) {
      resTable.newRow("WL RDF reverse, depth="+depth);
      for (int it : iterations) {
        RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFWLSubTreeKernel(it, depth, inference, true, true, false), seeds, svmParms, dataset, instances, labels, blackList);


        
        System.out.println("Running WL RDF Rev: " + depth + " " + it);
        exp.run();

View Full Code Here


      double[] 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");    


        RDFFeatureVectorKernel k = new RDFWLSubTreeKernel(6,3,false, true);


        System.out.println("RDF WL FV: " + frac);
        tic = System.currentTimeMillis();
        k.computeFeatureVectors(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      Result res = new Result(comp, "RDF WL FV");
      resTable.addResult(res);
  
      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");    


        RDFGraphKernel k = new RDFWLSubTreeKernel(6,3,false, true);


        System.out.println("RDF WL Kernel: " + frac);
        tic = System.currentTimeMillis();
        k.compute(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "RDF WL Kernel");
      resTable.addResult(res);
  
      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");    


        RDFFeatureVectorKernel k = new RDFIntersectionTreeEdgeVertexPathKernel(3,false, false, true);


        System.out.println("RDF ITP FV: " + frac);
        tic = System.currentTimeMillis();
        k.computeFeatureVectors(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "RDF ITP FV");
      resTable.addResult(res);
  
      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");    


        RDFGraphKernel k = new RDFIntersectionTreeEdgeVertexPathKernel(3,false, false, true);


        System.out.println("RDF ITP Kernel: " + frac);
        tic = System.currentTimeMillis();
        k.compute(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "RDF ITP Kernel");
      resTable.addResult(res);
    
      
      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");    
        RDFGraphKernel k = new RDFIntersectionSubTreeKernel(3,1, false, true);




        System.out.println("RDF IST: " + frac);
        tic = System.currentTimeMillis();
        k.compute(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "RDF IST");
      resTable.addResult(res);

View Full Code Here

    
    ///*
    for (int i : depths) {      
      resTable.newRow("RDF WL forward");
      for (int it : iterations) {
        RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, i, inference, true, forward, false);
        
        //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
        KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);




        System.out.println("Running WL RDF fwd: " + i + " " + it);
        exp.run();


        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }  
      }
    }
    resTable.addCompResults(resTable.getBestResults());
    //resTable.addCompResults(table2.getBestResults());
    System.out.println(resTable);
    
    
    //*/


    for (int h : hf) {
      for (int i : depths) {      
        resTable.newRow("RDF WL forward Degree " + h);
        for (int it : iterations) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, inference, true, forward);
          k.setHubMap(GraphUtils.createHubMap(degreeHubs, h));


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);




          System.out.println("Running WL RDF fwd Degree: " + i + " " + it + " " + h);
          exp.run();


          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          }  
        }
      }
      resTable.addCompResults(resTable.getBestResults());
      //resTable.addCompResults(table2.getBestResults());
      System.out.println(resTable);
    }
    
    


    ///*
    for (int h : hf) {
      for (int i : depths) {      
        resTable.newRow("RDF WL forward SB " + h);
        for (int it : iterations) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, inference, true, forward);
          k.setHubMap(GraphUtils.createHubMap(hubs, h));


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);

View Full Code Here

    resTable.setDigits(2);


    for (int depth : depths) {
      resTable.newRow("WL RDF, depth="+depth);
      for (int it : iterations) {
        RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFWLSubTreeKernel(it, depth, inference, true), seeds, svmParms, dataset, instances, labels, blackList);


        
        System.out.println("Running WL RDF: " + depth + " " + it);
        exp.run();

View Full Code Here

    
    for (int d = 1; d < 4; d++) {
      for (int it : iterations2) {    
        resTable.newRow("WL RDF, " + d + ", " + it);
        
        RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, d, false, true, true, false);
        
        RDFGraphKernelExperiment exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);


        System.out.println("Running WL RDF, it: " + it);
        exp.run();


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




    for (int d = 1; d < 4; d++) {
      resTable.newRow("WL RDF type, d " + d);
      for (int it : iterations2) {    


        RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, d, false, true, true);
        k.setHubMap(GraphUtils.createRDFTypeHubMap(ts, false));


        RDFGraphKernelExperiment exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);


        System.out.println("Running WL RDF, type it: " + it);
        exp.run();


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


    int[] hf = {0,1,2,3,4,5,6,7};


    for (int h : hf) {
      for (int i : depths) {      
        resTable.newRow("RDF WL NonSig " + h);
        for (int it : iterations2) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, false, true, true);
          k.setHubMap(GraphUtils.createNonSigHubMap(nonSigDegreeHubs, h));


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);




          System.out.println("Running WL RDF NonSig: " + i + " " + it + " " + h);
          exp.run();


          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          }  
        }
      }
      System.out.println(resTable);
    }
    
    for (int h : hf) {
      for (int i : depths) {      
        resTable.newRow("RDF WL SB " + h);
        for (int it : iterations2) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, false, true, true);
          k.setHubMap(GraphUtils.createHubMap(hubs, h));


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);

View Full Code Here

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