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

Example 11 with SemBicScore

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

the class ImagesSemBic method getParameters.

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

Example 12 with SemBicScore

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

the class ExampleCompareFromFiles method main.

public static void main(String... args) {
    Parameters parameters = new Parameters();
    parameters.set("numRuns", 1);
    // parameters.set("numMeasures", 20,100,1000);
    parameters.set("numMeasures", 1000);
    parameters.set("numLatents", 200);
    parameters.set("avgDegree", 2);
    parameters.set("sampleSize", 1000);
    parameters.set("penaltyDisount", 2);
    parameters.set("alpha", 1e-4);
    Statistics statistics = new Statistics();
    statistics.add(new ParameterColumn("avgDegree"));
    statistics.add(new ParameterColumn("sampleSize"));
    statistics.add(new AdjacencyPrecision());
    statistics.add(new AdjacencyRecall());
    statistics.add(new ArrowheadPrecision());
    statistics.add(new ArrowheadRecall());
    statistics.add(new MathewsCorrArrow());
    statistics.add(new F1Adj());
    statistics.add(new F1Arrow());
    statistics.add(new SHD());
    statistics.add(new ElapsedTime());
    statistics.setWeight("AP", 1.0);
    statistics.setWeight("AR", 0.5);
    statistics.setWeight("AHP", 1.0);
    statistics.setWeight("AHR", 0.5);
    // statistics.setWeight("TP", 1.0);
    // statistics.setWeight("TR", 0.5);
    Algorithms algorithms = new Algorithms();
    // algorithms.add(new Gfci(new ChiSquare(), new BdeuScore()));
    // algorithms.add(new Fci(new FisherZ()));
    // algorithms.add(new Fci(new ChiSquare()));
    // algorithms.add(new Rfci(new ChiSquare()));
    // algorithms.add(new Rfci(new FisherZ()));
    algorithms.add(new Gfci(new FisherZ(), new SemBicScore()));
    // algorithms.add(new Fges(new BdeuScore(),true));
    // algorithms.add(new Fges(new DiscreteBicScore(),true));
    // algorithms.add(new Fges(new SemBicScore()));
    // algorithms.add(new Gfci(new ChiSquare(), new DiscreteBicScore())));
    // algorithms.add(new Fges(new BdeuScore()));
    // algorithms.add(new Fges(new DiscreteBicScore()));
    // algorithms.add(new PcMax(new FisherZ(), false));
    // algorithms.add(new PcMax(new ChiSquare(),true));
    // algorithms.add(new PcMax(new FisherZ(), false));
    // algorithms.add(new Pc(new FisherZ()));
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(true);
    comparison.setShowSimulationIndices(false);
    comparison.setSortByUtility(true);
    comparison.setShowUtilities(true);
    comparison.setParallelized(false);
    comparison.setSaveGraphs(true);
    // DagToPag p = new DagToPag(graph);
    comparison.compareFromFiles("comparison", algorithms, statistics, parameters);
}

Example 13 with SemBicScore

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

the class ExampleStARS method main.

public static void main(String... args) {
    Parameters parameters = new Parameters();
    // parameters.set("numMeasures", 100);
    // parameters.set("avgDegree", 2, 4);
    // parameters.set("sampleSize", 100, 500);
    // parameters.set("numRuns", 5);
    parameters.set("numMeasures", 200);
    parameters.set("avgDegree", 2, 4, 6);
    parameters.set("sampleSize", 100, 500);
    parameters.set("numRuns", 2);
    parameters.set("differentGraphs", true);
    parameters.set("numLatents", 0);
    parameters.set("maxDegree", 100);
    parameters.set("maxIndegree", 100);
    parameters.set("maxOutdegree", 100);
    parameters.set("connected", false);
    parameters.set("coefLow", 0.2);
    parameters.set("coefHigh", 0.9);
    parameters.set("varLow", 1);
    parameters.set("varHigh", 3);
    parameters.set("verbose", false);
    parameters.set("coefSymmetric", true);
    parameters.set("percentDiscrete", 0);
    parameters.set("numCategories", 3);
    parameters.set("differentGraphs", true);
    parameters.set("intervalBetweenShocks", 10);
    parameters.set("intervalBetweenRecordings", 10);
    parameters.set("fisherEpsilon", 0.001);
    parameters.set("randomizeColumns", true);
    parameters.set("alpha", 0.01);
    parameters.set("depth", -1);
    parameters.set("penaltyDiscount", 2);
    parameters.set("useMaxPOrientationHeuristic", false);
    parameters.set("maxPOrientationMaxPathLength", 3);
    parameters.set("verbose", false);
    parameters.set("scaleFreeAlpha", 0.00001);
    parameters.set("scaleFreeBeta", 0.4);
    parameters.set("scaleFreeDeltaIn", .1);
    parameters.set("scaleFreeDeltaOut", 3);
    parameters.set("symmetricFirstStep", false);
    parameters.set("faithfulnessAssumed", true);
    parameters.set("maxDegree", 100);
    parameters.set("StARS.tolerance", .5);
    parameters.set("StARS.cutoff", .05);
    parameters.set("numSubsamples", 7);
    parameters.set("percentSubsampleSize", .5);
    parameters.set("percentStability", .5);
    Statistics statistics = new Statistics();
    statistics.add(new ParameterColumn("numMeasures"));
    statistics.add(new ParameterColumn("avgDegree"));
    statistics.add(new ParameterColumn("sampleSize"));
    statistics.add(new AdjacencyPrecision());
    statistics.add(new AdjacencyRecall());
    statistics.add(new ArrowheadPrecision());
    statistics.add(new ArrowheadRecall());
    statistics.add(new ElapsedTime());
    statistics.setWeight("AP", 0.25);
    statistics.setWeight("AR", 0.25);
    statistics.setWeight("AHP", 0.25);
    statistics.setWeight("AHR", 0.25);
    Algorithms algorithms = new Algorithms();
    parameters.set("logScale", false);
    algorithms.add(new StabilitySelection(new Fges(new SemBicScore())));
    algorithms.add(new StARS(new Fges(new SemBicScore()), "penaltyDiscount", 1, 5));
    algorithms.add(new FirstInflection(new Fges(new SemBicScore()), "penaltyDiscount", 1, 5, .1));
    // parameters.set("penaltyDiscount", 5, 11, 15);
    // algorithms.add(new Fges(new SemBicScore()));
    // parameters.set("logScale", true);
    // Algorithm fges = new Fges(new FisherZScore());
    // algorithms.add(new StARS(fges, "alpha", -10, -2, -8));
    // algorithms.add(new FirstInflection(fges, "alpha", -10, -2, -3));
    Simulations simulations = new Simulations();
    simulations.add(new LinearFisherModel(new RandomForward()));
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(true);
    comparison.setShowSimulationIndices(true);
    comparison.setSortByUtility(false);
    comparison.setShowUtilities(false);
    comparison.setParallelized(true);
    comparison.setComparisonGraph(Comparison.ComparisonGraph.Pattern_of_the_true_DAG);
    comparison.compareFromSimulations("first.inflection", simulations, algorithms, statistics, parameters);
}

Example 14 with SemBicScore

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

the class TestGeneralBootstrapTest method testFGESc.

@Test
public void testFGESc() {
    int penaltyDiscount = 2;
    boolean faithfulnessAssumed = false;
    int maxDegree = -1;
    int numVars = 20;
    int edgesPerNode = 2;
    int numLatentConfounders = 0;
    int numCases = 50;
    int numBootstrapSamples = 5;
    boolean verbose = true;
    Graph dag = makeContinuousDAG(numVars, numLatentConfounders, edgesPerNode);
    System.out.println("Truth Graph:");
    System.out.println(dag.toString());
    int[] causalOrdering = new int[numVars];
    for (int i = 0; i < numVars; i++) {
        causalOrdering[i] = i;
    }
    LargeScaleSimulation simulator = new LargeScaleSimulation(dag, dag.getNodes(), causalOrdering);
    DataSet data = simulator.simulateDataFisher(numCases);
    Parameters parameters = new Parameters();
    parameters.set("penaltyDiscount", penaltyDiscount);
    parameters.set("faithfulnessAssumed", faithfulnessAssumed);
    parameters.set("maxDegree", maxDegree);
    parameters.set("numPatternsToStore", 0);
    parameters.set("verbose", verbose);
    ScoreWrapper score = new SemBicScore();
    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 15 with SemBicScore

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

the class TestGeneralBootstrapTest method testGFCIc.

@Test
public void testGFCIc() {
    int penaltyDiscount = 2;
    boolean faithfulnessAssumed = false;
    int maxDegree = -1;
    int numVars = 20;
    int edgesPerNode = 2;
    int numLatentConfounders = 2;
    int numCases = 50;
    int numBootstrapSamples = 5;
    boolean verbose = true;
    Graph dag = makeContinuousDAG(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());
    int[] causalOrdering = new int[numVars];
    for (int i = 0; i < numVars; i++) {
        causalOrdering[i] = i;
    }
    LargeScaleSimulation simulator = new LargeScaleSimulation(dag, dag.getNodes(), causalOrdering);
    DataSet data = simulator.simulateDataFisher(numCases);
    Parameters parameters = new Parameters();
    parameters.set("penaltyDiscount", penaltyDiscount);
    parameters.set("faithfulnessAssumed", faithfulnessAssumed);
    parameters.set("maxDegree", maxDegree);
    parameters.set("numPatternsToStore", 0);
    parameters.set("verbose", verbose);
    ScoreWrapper score = new SemBicScore();
    IndependenceWrapper test = new FisherZ();
    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 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);
}