Examples with BdeuScore edu.cmu.tetrad.algcomparison.score.BdeuScore used on opensource projects

Example 1 with BdeuScore

use of edu.cmu.tetrad.algcomparison.score.BdeuScore in project tetrad by cmu-phil.

the class TsImages method search.

@Override
public Graph search(List<DataModel> dataSets, Parameters parameters) {
    List<DataModel> dataModels = new ArrayList<>();
    for (DataModel dataSet : dataSets) {
        dataModels.add(dataSet);
    }
    TsGFci search;
    if (score instanceof SemBicScore) {
        SemBicScoreImages gesScore = new SemBicScoreImages(dataModels);
        gesScore.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
        IndependenceTest test = new IndTestScore(gesScore);
        search = new TsGFci(test, gesScore);
    } else if (score instanceof BdeuScore) {
        double samplePrior = parameters.getDouble("samplePrior", 1);
        double structurePrior = parameters.getDouble("structurePrior", 1);
        BdeuScoreImages score = new BdeuScoreImages(dataModels);
        score.setSamplePrior(samplePrior);
        score.setStructurePrior(structurePrior);
        IndependenceTest test = new IndTestScore(score);
        search = new TsGFci(test, score);
    } else {
        throw new IllegalStateException("Sorry, data must either be all continuous or all discrete.");
    }
    IKnowledge knowledge = dataModels.get(0).getKnowledge();
    search.setKnowledge(knowledge);
    return search.search();
}

Example 2 with BdeuScore

use of edu.cmu.tetrad.algcomparison.score.BdeuScore in project tetrad by cmu-phil.

the class ImagesBDeu method getParameters.

@Override
public List<String> getParameters() {
    List<String> parameters = new Fges(new BdeuScore(), false).getParameters();
    parameters.add("numRuns");
    parameters.add("randomSelectionSize");
    // Bootstrapping
    parameters.add("bootstrapSampleSize");
    parameters.add("bootstrapEnsemble");
    parameters.add("verbose");
    return parameters;
}

Example 3 with BdeuScore

use of edu.cmu.tetrad.algcomparison.score.BdeuScore in project tetrad by cmu-phil.

the class TestGeneralBootstrapTest method testFGESd.

@Test
public void testFGESd() {
    double structurePrior = 1, samplePrior = 1;
    boolean faithfulnessAssumed = false;
    int maxDegree = -1;
    int numVars = 20;
    int edgesPerNode = 2;
    int numLatentConfounders = 0;
    int numCases = 50;
    int numBootstrapSamples = 5;
    boolean verbose = true;
    long seed = 123;
    Graph dag = makeDiscreteDAG(numVars, numLatentConfounders, edgesPerNode);
    System.out.println("Truth Graph:");
    System.out.println(dag.toString());
    BayesPm pm = new BayesPm(dag, 2, 3);
    BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
    DataSet data = im.simulateData(numCases, seed, false);
    Parameters parameters = new Parameters();
    parameters.set("structurePrior", structurePrior);
    parameters.set("samplePrior", samplePrior);
    parameters.set("faithfulnessAssumed", faithfulnessAssumed);
    parameters.set("maxDegree", maxDegree);
    parameters.set("numPatternsToStore", 0);
    parameters.set("verbose", verbose);
    ScoreWrapper score = new BdeuScore();
    Algorithm algorithm = new Fges(score);
    GeneralBootstrapTest bootstrapTest = new GeneralBootstrapTest(data, algorithm, numBootstrapSamples);
    bootstrapTest.setVerbose(verbose);
    bootstrapTest.setParameters(parameters);
    bootstrapTest.setEdgeEnsemble(BootstrapEdgeEnsemble.Highest);
    Graph resultGraph = bootstrapTest.search();
    System.out.println("Estimated Graph:");
    System.out.println(resultGraph.toString());
    // Adjacency Confusion Matrix
    int[][] adjAr = GeneralBootstrapTest.getAdjConfusionMatrix(dag, resultGraph);
    printAdjConfusionMatrix(adjAr);
    // Edge Type Confusion Matrix
    int[][] edgeAr = GeneralBootstrapTest.getEdgeTypeConfusionMatrix(dag, resultGraph);
    printEdgeTypeConfusionMatrix(edgeAr);
}

Example 4 with BdeuScore

use of edu.cmu.tetrad.algcomparison.score.BdeuScore in project tetrad by cmu-phil.

the class TestGeneralBootstrapTest method testGFCId.

@Test
public void testGFCId() {
    double structurePrior = 1, samplePrior = 1;
    boolean faithfulnessAssumed = false;
    int maxDegree = -1;
    int numVars = 20;
    int edgesPerNode = 2;
    int numLatentConfounders = 4;
    int numCases = 50;
    int numBootstrapSamples = 5;
    boolean verbose = true;
    long seed = 123;
    Graph dag = makeDiscreteDAG(numVars, numLatentConfounders, edgesPerNode);
    DagToPag dagToPag = new DagToPag(dag);
    Graph truePag = dagToPag.convert();
    System.out.println("Truth PAG_of_the_true_DAG Graph:");
    System.out.println(truePag.toString());
    BayesPm pm = new BayesPm(dag, 2, 3);
    BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
    DataSet data = im.simulateData(numCases, seed, false);
    Parameters parameters = new Parameters();
    parameters.set("structurePrior", structurePrior);
    parameters.set("samplePrior", samplePrior);
    parameters.set("faithfulnessAssumed", faithfulnessAssumed);
    parameters.set("maxDegree", maxDegree);
    parameters.set("numPatternsToStore", 0);
    parameters.set("verbose", verbose);
    ScoreWrapper score = new BdeuScore();
    IndependenceWrapper test = new ChiSquare();
    Algorithm algorithm = new Gfci(test, score);
    GeneralBootstrapTest bootstrapTest = new GeneralBootstrapTest(data, algorithm, numBootstrapSamples);
    bootstrapTest.setVerbose(verbose);
    bootstrapTest.setParameters(parameters);
    bootstrapTest.setEdgeEnsemble(BootstrapEdgeEnsemble.Highest);
    Graph resultGraph = bootstrapTest.search();
    System.out.println("Estimated Bootstrapped PAG_of_the_true_DAG Graph:");
    System.out.println(resultGraph.toString());
    // Adjacency Confusion Matrix
    int[][] adjAr = GeneralBootstrapTest.getAdjConfusionMatrix(truePag, resultGraph);
    printAdjConfusionMatrix(adjAr);
    // Edge Type Confusion Matrix
    int[][] edgeAr = GeneralBootstrapTest.getEdgeTypeConfusionMatrix(truePag, resultGraph);
    printEdgeTypeConfusionMatrix(edgeAr);
}