package org.data2semantics.exp.dmold;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Random;
import org.data2semantics.exp.RDFMLExperiment;
import org.data2semantics.exp.utils.RDFOldKernelExperiment;
import org.data2semantics.exp.utils.Result;
import org.data2semantics.exp.utils.ResultsTable;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionSubTreeKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFIntersectionTreeEdgeVertexPathWithTextKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFWLSubTreeKernel;
import org.data2semantics.proppred.kernels.rdfgraphkernels.RDFWLSubTreeWithTextKernel;
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.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.model.vocabulary.RDF;
import org.openrdf.rio.RDFFormat;
public class DMoLDTask2Experiment extends RDFMLExperiment {
private static String dataFile = "C:\\Users\\Gerben\\Dropbox\\D2S\\Task2\\LDMC_Task2_train.ttl";
public static void main(String[] args) {
for (int i = 0; i < args.length; i++) {
if (args[i].equals("-file")) {
i++;
dataFile = args[i];
}
}
createTask2DataSet(1,11);
long[] seeds = {11,21,31,41,51,61,71,81,91,101};
double[] cs = {0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000};
int[] depths = {1,2,3};
int[] iterations = {0,2,4,6};
List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
evalFuncs.add(new Accuracy());
evalFuncs.add(new F1());
List<Double> targets = EvaluationUtils.createTarget(labels);
LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
linParms.setEvalFunction(new Accuracy());
linParms.setDoCrossValidation(true);
linParms.setNumFolds(10);
Map<Double, Double> counts = EvaluationUtils.computeClassCounts(targets);
int[] wLabels = new int[counts.size()];
double[] weights = new double[counts.size()];
for (double label : counts.keySet()) {
wLabels[(int) label - 1] = (int) label;
weights[(int) label - 1] = 1 / counts.get(label);
}
linParms.setWeightLabels(wLabels);
linParms.setWeights(weights);
LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
svmParms.setNumFolds(10);
ResultsTable resTable = new ResultsTable();
resTable.setManWU(0.05);
resTable.setDigits(2);
boolean inference = true;
for (int d : depths) {
resTable.newRow("WL RDF, depth="+d);
for (int it : iterations) {
RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, d, inference, true);
RDFOldKernelExperiment exp = new RDFOldKernelExperiment(k, seeds, svmParms, dataset, instances, labels, blackList);
System.out.println("Running WL RDF: " + d + " " + it);
exp.run();
for (Result res : exp.getResults()) {
resTable.addResult(res);
}
}
}
System.out.println(resTable);
for (int d : depths) {
resTable.newRow("WL RDF BoW, depth="+d);
for (int it : iterations) {
RDFWLSubTreeWithTextKernel k = new RDFWLSubTreeWithTextKernel(it, d, inference, false);
k.setDoTFIDFkernel(true);
RDFOldKernelExperiment exp = new RDFOldKernelExperiment(k, seeds, svmParms, dataset, instances, labels, blackList);
System.out.println("Running WL RDF text: " + d + " " + it);
exp.run();
for (Result res : exp.getResults()) {
resTable.addResult(res);
}
}
}
System.out.println(resTable);
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()) {
resTable.addResult(res);
}
}
System.out.println(resTable);
for (int d : depths) {
resTable.newRow("ITP BoW, depth="+d);
RDFIntersectionTreeEdgeVertexPathWithTextKernel k = new RDFIntersectionTreeEdgeVertexPathWithTextKernel(d, false, inference, false);
k.setDoTFIDFkernel(true);
RDFOldKernelExperiment exp = new RDFOldKernelExperiment(k, seeds, svmParms, dataset, instances, labels, blackList);
System.out.println("Running Edge Vertex Tree Path with Text: " + d);
exp.run();
for (Result res : exp.getResults()) {
resTable.addResult(res);
}
}
System.out.println(resTable);
for (int d : depths) {
resTable.newRow("IST, depth="+d);
RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFIntersectionSubTreeKernel(d, 1, inference, true), seeds, svmParms, dataset, instances, labels, blackList);
System.out.println("Running IST: " + d);
exp.run();
for (Result res : exp.getResults()) {
resTable.addResult(res);
}
}
System.out.println(resTable);
resTable.addCompResults(resTable.getBestResults());
System.out.println(resTable);
}
private static void createTask2DataSet(double fraction, long seed) {
RDFFileDataSet d = new RDFFileDataSet(dataFile, RDFFormat.TURTLE);
dataset = d;
Random rand = new Random(seed);
List<Statement> stmts = dataset.getStatementsFromStrings(null, RDF.TYPE.toString(), "http://purl.org/procurement/public-contracts#Contract");
instances = new ArrayList<Resource>();
labels = new ArrayList<Value>();
blackList = new ArrayList<Statement>();
for(Statement stmt: stmts) {
List<Statement> stmts2 = dataset.getStatementsFromStrings(stmt.getSubject().toString(), "http://example.com/multicontract", null);
for (Statement stmt2 : stmts2) {
if (rand.nextDouble() < fraction) {
instances.add(stmt2.getSubject());
labels.add(stmt2.getObject());
}
}
}
removeSmallClasses(5);
createBlackList();
System.out.println(EvaluationUtils.computeClassCounts(EvaluationUtils.createTarget(labels)));
}
}