Package org.data2semantics.exp

Source Code of org.data2semantics.exp.FullClassExperiment

package org.data2semantics.exp;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Random;

import org.data2semantics.exp.utils.KernelExperiment;
import org.data2semantics.exp.utils.RDFLinearKernelExperiment;
import org.data2semantics.exp.utils.RDFOldKernelExperiment;
import org.data2semantics.exp.utils.Result;
import org.data2semantics.exp.utils.ResultsTable;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFFeatureVectorKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFGraphKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionSubTreeKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFWLSubTreeKernel;
import org.data2semantics.proppred.learners.evaluation.Accuracy;
import org.data2semantics.proppred.learners.evaluation.EvaluationFunction;
import org.data2semantics.proppred.learners.evaluation.EvaluationUtils;
import org.data2semantics.proppred.learners.evaluation.F1;
import org.data2semantics.proppred.learners.liblinear.LibLINEARParameters;
import org.data2semantics.proppred.learners.libsvm.LibSVM;
import org.data2semantics.proppred.learners.libsvm.LibSVMParameters;
import org.data2semantics.tools.rdf.RDFFileDataSet;
import org.openrdf.model.Resource;
import org.openrdf.model.Statement;
import org.openrdf.model.Value;
import org.openrdf.rio.RDFFormat;

public class FullClassExperiment extends RDFMLExperiment {

  /**
   * @param args
   */
  public static void main(String[] args) {
    //long[] seeds = {11,21,31,41,51,61,71,81,91,101};
    long[] seeds = {11,31,51,71,91};
    double[] cs = { 1, 10, 100, 1000, 10000}
    // 0.001, 0.01, 0.1,

    //int[] depths = {1, 2, 3};
    //int[] iterations = {0, 2, 4, 6};
    int[] depths = {1,2,3};
    int[] iterations = {0,2,4,6};

    boolean inference = true;

    dataset = new RDFFileDataSet("C:\\Users\\Gerben\\Dropbox\\data_bgs_ac_uk_ALL", RDFFormat.NTRIPLES);

    createGeoDataSet(10, 0.1, 1, "http://data.bgs.ac.uk/ref/Lexicon/hasUnitClass");
    List<Double> target = EvaluationUtils.createTarget(labels);


    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

    ResultsTable resTable = new ResultsTable();
    resTable.setManWU(0.05);




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

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

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

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

    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()) {
        resTable.addResult(res);
     
    }
    System.out.println(resTable);
   
    for (int i : depths) {     
      resTable.newRow("");

      LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
   
      KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(new RDFIntersectionSubTreeKernel(i, 1, inference, true), seeds, svmParms, dataset, instances, labels, blackList);

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

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


    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);
  }



  protected static void createGeoDataSet(int minSize, double frac, long seed, String property) {
    List<Statement> stmts = dataset.getStatementsFromStrings(null, "http://www.w3.org/2000/01/rdf-schema#isDefinedBy", "http://data.bgs.ac.uk/ref/Lexicon/NamedRockUnit");
    instances = new ArrayList<Resource>();
    labels = new ArrayList<Value>();
    blackList = new ArrayList<Statement>();

    Random rand = new Random(seed);

    // http://data.bgs.ac.uk/ref/Lexicon/hasRockUnitRank
    // http://data.bgs.ac.uk/ref/Lexicon/hasTheme

    for(Statement stmt: stmts) {
      List<Statement> stmts2 = dataset.getStatementsFromStrings(stmt.getSubject().toString(), property, null);

      if (stmts2.size() > 1) {
        System.out.println("more than 1 Class");
      }

      for (Statement stmt2 : stmts2) {

        if (rand.nextDouble() < frac) {
          instances.add(stmt2.getSubject());
          labels.add(stmt2.getObject());
        }
      }
    }

    removeSmallClasses(minSize);
    createBlackList();

    Map<Value, Integer> labelMap = new HashMap<Value, Integer>();

    System.out.println(LibSVM.computeClassCounts(LibSVM.createTargets(labels, labelMap)));
  }

}
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