Package org.data2semantics.proppred.kernels.rdfgraphkernels

Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFFeatureVectorKernel.computeFeatureVectors()

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        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);
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        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);
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        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);
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        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);
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    // 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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