Examples with Fges edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges used on opensource projects

Example 1 with Fges

use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.

the class TestConditionalGaussianSimulation method testBryan.

public void testBryan(String... args) {
    Parameters parameters = new Parameters();
    parameters.set("numCategoriesToDiscretize", 5);
    parameters.set("numRuns", 10);
    parameters.set("numMeasures", 100);
    parameters.set("avgDegree", 2);
    parameters.set("sampleSize", 10000);
    parameters.set("minCategories", 2);
    parameters.set("maxCategories", 5);
    parameters.set("percentDiscrete", 50);
    parameters.set("differentGraphs", true);
    parameters.set("maxDegree", 5);
    parameters.set("maxIndegree", 5);
    parameters.set("maxOutdegree", 5);
    parameters.set("structurePrior", 1);
    parameters.set("fDegree", -1);
    parameters.set("discretize", true);
    // parameters.set("discretize", true, false);
    Statistics statistics = new Statistics();
    statistics.add(new AdjacencyPrecision());
    statistics.add(new AdjacencyRecall());
    statistics.add(new ArrowheadPrecision());
    statistics.add(new ArrowheadRecall());
    statistics.add(new ElapsedTime());
    statistics.setWeight("AHP", 1.0);
    statistics.setWeight("AHR", 1.0);
    Algorithms algorithms = new Algorithms();
    algorithms.add(new Fges(new ConditionalGaussianBicScore()));
    Simulations simulations = new Simulations();
    simulations.add(new ConditionalGaussianSimulation(new RandomForward()));
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(true);
    comparison.setShowSimulationIndices(true);
    comparison.setSortByUtility(true);
    comparison.setShowUtilities(true);
    comparison.setParallelized(false);
    comparison.compareFromSimulations("comparison", simulations, algorithms, statistics, parameters);
}

Example 2 with Fges

use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges 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 Fges

use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.

the class TsImagesSemBic method getParameters.

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

Example 4 with Fges

use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.

the class Condition1 method generateTetradResults.

public void generateTetradResults() {
    Parameters parameters = new Parameters();
    parameters.set("alpha", 0.001);
    parameters.set("numRuns", 10);
    // parameters.set("penaltyDiscount", 4);
    parameters.set("useMaxPOrientationHeuristic", true);
    parameters.set("maxPOrientationMaxPathLength", 3);
    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.add(new PercentBidirectedEdges());
    // statistics.add(new NumAmbiguousTriples());
    statistics.setWeight("AP", 1.0);
    statistics.setWeight("AR", 0.5);
    statistics.setWeight("AHP", 1.0);
    statistics.setWeight("AHR", 0.5);
    Algorithms algorithms = new Algorithms();
    // algorithms.add(new Pc(new FisherZ()));
    // algorithms.add(new PcStable(new FisherZ()));
    // algorithms.add(new PcStableMax(new FisherZ(), false));
    // algorithms.add(new Cpc(new FisherZ()));
    // algorithms.add(new CpcStable(new FisherZ()));
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(true);
    comparison.setShowSimulationIndices(true);
    comparison.setSortByUtility(false);
    comparison.setShowUtilities(true);
    comparison.setSaveGraphs(true);
    // comparison.compareFromFiles("/Users/user/comparison-data/condition_1",
    // "/Users/user/causal-comparisons/condition_1",
    // algorithms, statistics, parameters);
    // 
    // algorithms = new Algorithms();
    // 
    // parameters.set("alpha", 0.001, 0.0001, 1e-8);
    // parameters.set("faithfulnessAssumed", true);
    // //
    // algorithms.add(new Fges(new FisherZScore()));
    // 
    // comparison.compareFromFiles("/Users/user/comparison-data/condition_1",
    // "/Users/user/causal-comparisons/condition_1",
    // algorithms, statistics, parameters);
    // 
    // algorithms = new Algorithms();
    parameters.set("penaltyDiscount", 4);
    // 
    algorithms.add(new Fges(new SemBicScore()));
    comparison.compareFromFiles("/Users/user/comparison-data/condition_1", "/Users/user/causal-comparisons/condition_1", algorithms, statistics, parameters);
}

Example 5 with Fges

use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.

the class ExampleFirstInflection method main.

public static void main(String... args) {
    Parameters parameters = new Parameters();
    parameters.set("numMeasures", 40, 100);
    parameters.set("avgDegree", 2);
    parameters.set("sampleSize", 400, 800);
    parameters.set("numRuns", 10);
    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", 1e-8);
    parameters.set("depth", -1);
    parameters.set("penaltyDiscount", 4);
    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("logScale", true);
    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();
    Algorithm fges = new Fges(new SemBicScore());
    // algorithms.add(new FirstInflection(fges, "alpha", -7, -2, -.5));
    algorithms.add(new FirstInflection(fges, "penaltyDiscount", 0.7, 5, 1));
    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);
}