Examples with Comparison edu.cmu.tetrad.algcomparison.Comparison used on opensource projects

Example 21 with Comparison

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

the class CompareFromFiles method main.

public static void main(String... args) {
    Parameters parameters = new Parameters();
    // Can leave the simulation parameters out since
    // we're loading from file here.
    parameters.set("numRuns", 3);
    parameters.set("maxDistinctValuesDiscrete", 5);
    parameters.set("structurePrior", -0.5);
    parameters.set("fDegree", -1);
    parameters.set("discretize", 0);
    parameters.set("alpha", 1e-3, 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 ElapsedTime());
    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 Fges(new ConditionalGaussianBicScore()));
    // algorithms.add(new Fges(new ConditionalGaussianOtherBicScore()));
    algorithms.add(new Fges(new MVPBicScore()));
    // algorithms.add(new Fges(new MNLRBicScore()));
    // algorithms.add(new Fges(new DiscreteMixedBicScore()));
    // algorithms.add(new Cpc(new ConditionalGaussianLRT()));
    // algorithms.add(new Cpc(new MVPLRT()));
    // algorithms.add(new Cpc(new MNLRLRT(), new Mgm()));
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(true);
    comparison.setShowSimulationIndices(true);
    comparison.setSortByUtility(false);
    comparison.setShowUtilities(false);
    comparison.setComparisonGraph(Comparison.ComparisonGraph.true_DAG);
    comparison.compareFromFiles("comparison", algorithms, statistics, parameters);
}

Example 22 with Comparison

use of edu.cmu.tetrad.algcomparison.Comparison 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 23 with Comparison

use of edu.cmu.tetrad.algcomparison.Comparison 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 24 with Comparison

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

the class Condition1 method compileTable.

public void compileTable() {
    Parameters parameters = new Parameters();
    parameters.set("numRuns", 10);
    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 F1Adj());
    statistics.add(new F1Arrow());
    statistics.add(new F1All());
    statistics.add(new ElapsedTime());
    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 ExternalAlgorithmTetrad("PC_(\"Peter_and_Clark\"),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmTetrad("PC-Stable_(\"Peter_and_Clark\"_Stable),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmTetrad("PC-Stable-Max_(\"Peter_and_Clark\"),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmTetrad("CPC_(Conservative_\"Peter_and_Clark\"),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmTetrad("CPC-Stable_(Conservative_\"Peter_and_Clark\"_Stable),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001"));
    // 
    // algorithms.add(new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Fisher_Z_Score,_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Fisher_Z_Score,_alpha_=_1.0E-4"));
    // algorithms.add(new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Fisher_Z_Score,_alpha_=_1.0E-8"));
    algorithms.add(new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Sem_BIC_Score,_penaltyDiscount_=_1"));
    algorithms.add(new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Sem_BIC_Score,_penaltyDiscount_=_2"));
    algorithms.add(new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Sem_BIC_Score,_penaltyDiscount_=_4"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("MMPC_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("GrowShrink_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("IAMB_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("Fast.IAMB_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("Inter.IAMB_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("si.hiton.pc_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("MMHC_alpha_=_0.001"));
    // 
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=cor"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=mc-cor"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=mc-mi-g"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=mc-zf"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=mi-g"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=mi-g-sh"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=smc-cor"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=smc-mi-g"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=smc-zf"));
    // algorithms.add(new ExternalAlgorithmBnlearnMmhc("iamb_alpha_=_0.001.test=zf"));
    // 
    // algorithms.add(new ExternalAlgorithmPcalgPc("PC_pcalg_defaults_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmPcalgPc("PC_pcalg_defaults_alpha_=_0.001.parallize.dynamic"));
    // 
    // algorithms.add(new ExternalAlgorithmPcalgPc("PC-Stable_pcalg_defaults_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmPcalgPc("PC-Stable_pcalg_ncores=4_alpha_=_0.001"));
    // //        algorithms.add(new ExternalAlgorithmPcalgPc("CPC_pcalg_defaults_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmPcalgPc("CPC_pcalg_majority.rule_defaults_alpha_=_0.001"));
    algorithms.add(new ExternalAlgorithmPcalgGes("GES_pcalg_defaults_1.0*log(nrow(data)"));
    algorithms.add(new ExternalAlgorithmPcalgGes("GES_pcalg_defaults_2.0*log(nrow(data)"));
    // algorithms.add(new ExternalAlgorithmBNTPc("learn_struct_pdag_pc_alpha_=_0.001"));
    // algorithms.add(new ExternalAlgorithmIntersection("Intersection (Tetrad) CPC, PC_Stable-Max, CPC-Stable",
    // new ExternalAlgorithmTetrad("CPC_(Conservative_\"Peter_and_Clark\"),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001"),
    // new ExternalAlgorithmTetrad("PC-Stable-Max_(\"Peter_and_Clark\"),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001"),
    // new ExternalAlgorithmTetrad("CPC-Stable_(Conservative_\"Peter_and_Clark\"_Stable),_Priority_Rule,_using_Fisher_Z_test,_alpha_=_0.001")
    // 
    // ));
    // 
    // algorithms.add(new ExternalAlgorithmIntersection("Intersection FGES alpha .001, 1e-8, penalty 2",
    // new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Fisher_Z_Score,_alpha_=_0.001"),
    // new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Fisher_Z_Score,_alpha_=_1.0E-8"),
    // new ExternalAlgorithmTetrad("FGES_(Fast_Greedy_Equivalence_Search)_using_Sem_BIC_Score,_penalty_discount_=_2.0")
    // ));
    // 
    // algorithms.add(new ExternalAlgorithmIntersection("Intersection PC, CPC, CPC majority, pcalg",
    // new ExternalAlgorithmPcalgPc("PC_pcalg_defaults_alpha_=_0.001"),
    // new ExternalAlgorithmPcalgPc("CPC_pcalg_defaults_alpha_=_0.001"),
    // new ExternalAlgorithmPcalgPc("CPC_majority_pcalg_defaults_alpha_=_0.001")
    // ));
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(true);
    comparison.setShowSimulationIndices(true);
    comparison.setSortByUtility(false);
    comparison.setShowUtilities(false);
    comparison.setSaveGraphs(true);
    comparison.setComparisonGraph(Comparison.ComparisonGraph.Pattern_of_the_true_DAG);
    comparison.generateReportFromExternalAlgorithms("/Users/user/comparison-data/condition_1", "/Users/user/causal-comparisons/condition_1", algorithms, statistics, parameters);
}

Example 25 with Comparison

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

the class Condition2 method generateTetradResults.

public void generateTetradResults() {
    Parameters parameters = new Parameters();
    parameters.set("alpha", 0.01);
    parameters.set("numRuns", 10);
    // parameters.set("penaltyDiscount", 4);
    parameters.set("useMaxPOrientationHeuristic", false);
    // 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 ParameterColumn("faithfulnessAssumed"));
    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_2",
    // "/Users/user/causal-comparisons/condition_2",
    // algorithms, statistics, parameters);
    // 
    // algorithms = new Algorithms();
    // 
    // parameters.set("penaltyDiscount", 2, 4);
    // parameters.set("alpha", 0.001, 0.0001, 1e-8);
    // parameters.set("faithfulnessAssumed", true, false);
    // 
    // algorithms.add(new Fges(new FisherZScore()));
    // 
    // comparison.compareFromFiles("/Users/user/comparison-data/condition_2",
    // "/Users/user/causal-comparisons/condition_2",
    // algorithms, statistics, parameters);
    // 
    // algorithms = new Algorithms();
    // algorithms.add(new Fges(new SemBicScore()));
    comparison.compareFromFiles("/Users/user/comparison-data/condition_2", "/Users/user/causal-comparisons/condition_2", algorithms, statistics, parameters);
}