Examples of RDFIntersectionTreeEdgeVertexPathKernel

org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel
Implementation of the Intersection Tree Path kernel, as described in the DMoLD paper. This is the variant in the paper, with path's described by edge and vertex labels. With the probabilities boolean in the constructor set to true, the values in the featureVectors will be computed as transition probabilities. The Intersection Tree Path kernel has performance comparable to the IntersectionSubTree kernel but is much, much faster to compute. @author Gerben

Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel

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


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


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

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Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel

    
    
    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()) {

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Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel

  
      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);

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Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel

    
    for (int depth : depths) {
      resTable.newRow("ITP, depth="+depth);
      table2.newRow("");
      
      RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(depth, false, inference, true), seeds, svmParms, dataset, instances, labels, blackList);


      System.out.println("Running ITP: " + depth);
      exp.run();


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

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Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel

      //for (double frac : fractions) {
      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 EVP FV: " + frac);
        tic = System.currentTimeMillis();
        k.computeFeatureVectors(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "comp time");
      resTable.addResult(res);
      //}
      //System.out.println(resTable);


      //resTable.newRow("EVP Kernel");
      //for (double frac : fractions) {
      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 EVP Kernel: " + frac);
        tic = System.currentTimeMillis();
        k.compute(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "comp time");
      resTable.addResult(res);
      //}
      //System.out.println(resTable);


      //resTable.newRow("EVP text FV");
      //for (double frac : fractions) {
      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 RDFIntersectionTreeEdgeVertexPathWithTextKernel(3,false, false, false);


        System.out.println("EVP text FV: " + frac);
        tic = System.currentTimeMillis();
        TextUtils.computeTFIDF(Arrays.asList(k.computeFeatureVectors(dataset, instances, blackList)));        
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "comp time");
      resTable.addResult(res);
      //}
      //System.out.println(resTable);








      //resTable.newRow("RDF IST");
      //for (double frac : fractions) {
      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, "comp time");
      resTable.addResult(res);
      //}
      //System.out.println(resTable);






      
    //resTable.newRow("WL FV");
    //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;


        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.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel

        //for (int it : iterations) {
          resTable.newRow("");  


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, i, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);


          KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(i, false, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);


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

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Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel


    for (int i : depths) {      
      resTable.newRow("");


      LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
      KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(i, false, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);


      System.out.println("Running EVP: " + i);
      exp.run();


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

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