Examples of RDFFeatureVectorKernel

org.data2semantics.proppred.kernels.rdfgraphkernels.RDFFeatureVectorKernel
Compute SparseVector feature vectors, instead of a kernel matrix on an RDFDataSet. TODO, add a method for computation on a test set @author Gerben

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

    }
    linParms.setWeightLabels(wLabels);
    linParms.setWeights(weights);


    
    RDFFeatureVectorKernel kernel = new RDFWLSubTreeWithTextKernel(4, 2, inference, false);
    
    List<Resource> allInstances = new ArrayList<Resource>(instances);
    allInstances.addAll(testInstances);
    
    System.out.println("Computing kernel....");
    SparseVector[] fv = kernel.computeFeatureVectors(dataset, allInstances, blackList);
    System.out.println("Computing TFIDF....");
    fv = TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
    fv = KernelUtils.normalize(fv);
    
    SparseVector[] trainFV = Arrays.copyOfRange(fv, 0, instances.size());

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

    }
    linParms.setWeightLabels(wLabels);
    linParms.setWeights(weights);


    
    RDFFeatureVectorKernel kernel = new RDFIntersectionTreeEdgeVertexPathWithTextKernel(2, false, inference, false);
    
    List<Resource> allInstances = new ArrayList<Resource>(instances);
    allInstances.addAll(testInstances);
    
    System.out.println("Computing kernel....");
    SparseVector[] fv = kernel.computeFeatureVectors(dataset, allInstances, blackList);
    System.out.println("Computing TFIDF....");
    fv = TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
    fv = KernelUtils.normalize(fv);
    
    SparseVector[] trainFV = Arrays.copyOfRange(fv, 0, instances.size());

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


      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);

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




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


      RDFFeatureVectorKernel kernel = new RDFIntersectionTreeEdgeVertexPathWithTextKernel(d, false, inference, false);


      System.out.println("Running PathWithText kernel: " + d );


      Map<EvaluationFunction, double[]> resultMap = new HashMap<EvaluationFunction,double[]>();
      Map<EvaluationFunction, double[]> resultMap2 = new HashMap<EvaluationFunction,double[]>();


      List<Result> results = new ArrayList<Result>();


      for (EvaluationFunction evalFunc : evalFuncs1) {
        Result res = new Result();
        double[] resA = new double[seeds.length];
        res.setLabel(evalFunc.getLabel());
        res.setScores(resA);
        res.setHigherIsBetter(evalFunc.isHigherIsBetter());
        results.add(res);
        resultMap.put(evalFunc, resA);
      }


      for (EvaluationFunction evalFunc : evalFuncs2) {
        Result res = new Result();
        double[] resA = new double[seeds.length];
        res.setLabel(evalFunc.getLabel());
        res.setScores(resA);
        res.setHigherIsBetter(evalFunc.isHigherIsBetter());
        results.add(res);
        resultMap2.put(evalFunc, resA);
      }


      Result compR = new Result();
      results.add(compR);




      long tic, toc;


      List<Double> tempLabels = new ArrayList<Double>();
      List<Double> tempLabelsBins = new ArrayList<Double>();
      tempLabels.addAll(target);
      tempLabelsBins.addAll(targetBins);


      tic = System.currentTimeMillis();
      SparseVector[] fv = kernel.computeFeatureVectors(dataset, instances, blackList);
      toc = System.currentTimeMillis();


      fv = TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
      fv = KernelUtils.normalize(fv);




      List<SparseVector> fvList = Arrays.asList(fv);




      compR.setLabel("kernel comp time");


      for (int j = 0; j < seeds.length; j++) {
        Collections.shuffle(fvList, new Random(seeds[j]));
        Collections.shuffle(tempLabels, new Random(seeds[j]));
        Collections.shuffle(tempLabelsBins, new Random(seeds[j]));  


        fv = fvList.toArray(new SparseVector[1]);
        double[] targetA = new double[tempLabels.size()];
        double[] targetABins = new double[tempLabelsBins.size()];
        for (int i = 0; i < targetA.length; i++) {
          targetA[i] = tempLabels.get(i);
          targetABins[i] = tempLabelsBins.get(i);
        }


        Prediction[] pred = LibLINEAR.trainTestSplit(fv, targetABins, linParms, linParms.getSplitFraction());      
        Prediction[] pred2 = LibLINEAR.trainTestSplit(fv, targetA, linParms2, linParms2.getSplitFraction());        


        double[] targetSplit = LibLINEAR.splitTestTarget(targetA, linParms.getSplitFraction());


        for (EvaluationFunction ef : evalFuncs1) {
          resultMap.get(ef)[j] = ef.computeScore(targetSplit, pred);  
        }        
        for (EvaluationFunction ef : evalFuncs2) {
          resultMap2.get(ef)[j] = ef.computeScore(targetSplit, pred2);  
        }
      } 
      double[] comp = {toc - tic};
      compR.setScores(comp);


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




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


        /*
        List<RDFFeatureVectorKernel> kernels = new ArrayList<RDFFeatureVectorKernel>();
        kernels.add(new RDFWLSubTreeKernel(it,d, inference, false));
        kernels.add(new RDFSimpleTextKernel(d, inference, false));
        RDFFeatureVectorKernel kernel = new RDFCombinedKernel(kernels, true);
        */
        
        RDFFeatureVectorKernel kernel = new RDFWLSubTreeWithTextKernel(it, d, inference, false);
        
        
        
        System.out.println("Running RDFWL + text kernel: " + d + " " + it);


        Map<EvaluationFunction, double[]> resultMap = new HashMap<EvaluationFunction,double[]>();
        Map<EvaluationFunction, double[]> resultMap2 = new HashMap<EvaluationFunction,double[]>();


        List<Result> results = new ArrayList<Result>();


        for (EvaluationFunction evalFunc : evalFuncs1) {
          Result res = new Result();
          double[] resA = new double[seeds.length];
          res.setLabel(evalFunc.getLabel());
          res.setScores(resA);
          res.setHigherIsBetter(evalFunc.isHigherIsBetter());
          results.add(res);
          resultMap.put(evalFunc, resA);
        }


        for (EvaluationFunction evalFunc : evalFuncs2) {
          Result res = new Result();
          double[] resA = new double[seeds.length];
          res.setLabel(evalFunc.getLabel());
          res.setScores(resA);
          res.setHigherIsBetter(evalFunc.isHigherIsBetter());
          results.add(res);
          resultMap2.put(evalFunc, resA);
        }


        Result compR = new Result();
        results.add(compR);




        long tic, toc;


        List<Double> tempLabels = new ArrayList<Double>();
        List<Double> tempLabelsBins = new ArrayList<Double>();
        tempLabels.addAll(target);
        tempLabelsBins.addAll(targetBins);


        tic = System.currentTimeMillis();
        SparseVector[] fv = kernel.computeFeatureVectors(dataset, instances, blackList);
        toc = System.currentTimeMillis();


        fv = TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
        fv = KernelUtils.normalize(fv);

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

    
    resTable.newRow("WLRDF FV");
    for (double frac : fractions) {
      createGeoDataSet((int)(1000 * frac), frac, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasUnitClass");    


      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();
      double[] comp = {toc-tic};
      Result res = new Result(comp, "comp time");
      resTable.addResult(res);
    }  
    System.out.println(resTable);
    
    resTable.newRow("WLRDF Kernel");
    for (double frac : fractions) {
      createGeoDataSet((int)(1000 * frac), frac, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasUnitClass");    


      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();
      double[] comp = {toc-tic};
      Result res = new Result(comp, "comp time");
      resTable.addResult(res);
    }  
    System.out.println(resTable);
    
    resTable.newRow("WLRDF String FV");
    for (double frac : fractions) {
      createGeoDataSet((int)(1000 * frac), frac, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasUnitClass");    
      RDFFeatureVectorKernel k = new RDFWLSubTreeKernelString(6,3, false, true);
  
      
      System.out.println("RDF WL String FV: " + frac);
      tic = System.currentTimeMillis();
      k.computeFeatureVectors(dataset, instances, blackList);
      toc = System.currentTimeMillis();
      double[] comp = {toc-tic};
      Result res = new Result(comp, "comp time");
      resTable.addResult(res);
    }
    System.out.println(resTable);
    
    resTable.newRow("WLRDF String Kernel");
    for (double frac : fractions) {
      createGeoDataSet((int)(1000 * frac), frac, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasUnitClass");    
      RDFGraphKernel k = new RDFWLSubTreeKernelString(6,3, false, true);
  
      
      System.out.println("RDF WL String: " + frac);
      tic = System.currentTimeMillis();
      k.compute(dataset, instances, blackList);
      toc = System.currentTimeMillis();
      double[] comp = {toc-tic};
      Result res = new Result(comp, "comp time");
      resTable.addResult(res);
    }
    System.out.println(resTable);
  
    
    
    resTable.newRow("RDF IST");
    for (double frac : fractions) {
      createGeoDataSet((int)(1000 * frac), frac, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasUnitClass");    
      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();
      double[] comp = {toc-tic};
      Result res = new Result(comp, "comp time");
      resTable.addResult(res);
    }
    System.out.println(resTable);
    
    
    
    
    resTable.newRow("WL FV");
    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;
      
      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.rdfgraphkernels.RDFFeatureVectorKernel

        RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, depth, inference, normalize);
        
        kernels.add(k);
        kernels.add(new RDFSimpleTextKernel(depth, inference, normalize));


        RDFFeatureVectorKernel kernel = new RDFCombinedKernel(kernels, normalize);


        
        RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(kernel, seeds, linParms, dataset, instances, targets, blackList, evalFuncs);


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

View Full Code Here

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

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


      //resTable.newRow("WLRDF 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 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, "comp time");
      resTable.addResult(res);
      //}  
      //System.out.println(resTable);


      //resTable.newRow("WLRDF 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 RDFWLSubTreeWithTextKernel(6,3,false, false);


        System.out.println("RDF WL 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("EVP 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 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.RDFFeatureVectorKernel

    
    // We remove classes with fewer than 5 instances
    EvaluationUtils.removeSmallClasses(instances, labels, 5);
    
    // Create the RDFFeatureVectorKernel that we are going to use
    RDFFeatureVectorKernel kernel = new RDFWLSubTreeKernel(6,3,true,true);
    
    // Compute feature vectors
    SparseVector[] featureVectors = kernel.computeFeatureVectors(dataset, instances, blackList);
    
    // Create a list of doubles as target, with our labelMap, so that we can use it later on (i.e. get the reverseMap)
    Map<Value, Double> labelMap = new HashMap<Value, Double>();
    List<Double> target = EvaluationUtils.createTarget(labels, labelMap);

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