Package org.data2semantics.exp.utils

Examples of org.data2semantics.exp.utils.ResultsTable.newRow()

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    //*/

    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);
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    ///*
    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);
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        //*/


       
        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, maxClassSize, new FileOutputStream(file));
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    evalFuncs.add(new F1());

   
   
    for (int i : depths) { 
      resTable.newRow("WL RDF, depth="+i); 
      for (int it : iterations) {

        List<List<Result>> res = new ArrayList<List<Result>>();
        for (long seed : seeds) {
          long[] seeds2 = {seed};
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    }
    System.out.println(resTable);


    for (int i : depths) { 
      resTable.newRow("WL RDF BoW, depth="+i); 
      for (int it : iterations) {

        List<List<Result>> res = new ArrayList<List<Result>>();
        for (long seed : seeds) {
          long[] seeds2 = {seed};
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    boolean inference = true;

    LibSVMParameters parms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    ResultsTable resTable = new ResultsTable();

    resTable.newRow("WL RDF");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {
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        res.addResult(exp.getResults().get(0));
      }
      resTable.addResult(res);
    }

    resTable.newRow("IST");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {
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    long tic, toc;



    resTable.newRow("WL");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {
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      resTable.addResult(exp.getResults().get(exp.getResults().size()-1));
    }*/


    resTable.newRow("IGW");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {
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    ResultsTable resTable = new ResultsTable();
    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);
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