Examples with Parameters edu.cmu.tetrad.util.Parameters used on opensource projects

Example 16 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class ExampleCompareFromFiles 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("alpha", 1e-4);
    parameters.set("numRuns", 10);
    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 MathewsCorrAdj());
    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);
    Algorithms algorithms = new Algorithms();
    algorithms.add(new Pc(new FisherZ()));
    algorithms.add(new Cpc(new FisherZ()));
    algorithms.add(new PcStable(new FisherZ()));
    algorithms.add(new CpcStable(new FisherZ()));
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(false);
    comparison.setShowSimulationIndices(false);
    comparison.setSortByUtility(true);
    comparison.setShowUtilities(true);
    comparison.setParallelized(true);
    comparison.compareFromFiles("comparison", algorithms, statistics, parameters);
}

Example 17 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class ExampleCompareSimulationTimeSeries method main.

public static void main(String... args) {
    Parameters parameters = new Parameters();
    parameters.set("numRuns", 10);
    parameters.set("numMeasures", 10);
    parameters.set("avgDegree", 4);
    parameters.set("sampleSize", 500);
    parameters.set("alpha", 1e-4, 1e-3, 1e-2);
    Statistics statistics = new Statistics();
    statistics.add(new AdjacencyPrecision());
    statistics.add(new AdjacencyRecall());
    statistics.add(new ArrowheadPrecision());
    statistics.add(new ArrowheadRecall());
    statistics.add(new MathewsCorrAdj());
    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);
    Algorithms algorithms = new Algorithms();
    algorithms.add(new TsFci(new FisherZ()));
    algorithms.add(new TsGfci(new FisherZ(), new SemBicScore()));
    algorithms.add(new TsImages(new SemBicScore()));
    Simulations simulations = new Simulations();
    simulations.add(new TimeSeriesSemSimulation(new RandomForward()));
    Comparison comparison = new Comparison();
    comparison.setSortByUtility(true);
    comparison.setShowUtilities(true);
    comparison.compareFromSimulations("comparison", simulations, algorithms, statistics, parameters);
}

Example 18 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class ExampleSave method main.

public static void main(String... args) {
    Parameters parameters = new Parameters();
    parameters.set("numRuns", 10);
    parameters.set("numMeasures", 100);
    parameters.set("avgDegree", 4);
    parameters.set("sampleSize", 100, 500, 1000);
    Simulation simulation = new SemSimulation(new RandomForward());
    Comparison comparison = new Comparison();
    comparison.setShowAlgorithmIndices(true);
    comparison.saveToFiles("comparison", simulation, parameters);
}

Example 19 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class FgesMbRunner method execute.

// =================PUBLIC METHODS OVERRIDING ABSTRACT=================//
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
public void execute() {
    IKnowledge knowledge = (IKnowledge) getParams().get("knowledge", new Knowledge2());
    String targetName = getParams().getString("targetName", null);
    Object model = getDataModel();
    if (model == null && getSourceGraph() != null) {
        model = getSourceGraph();
    }
    if (model == null) {
        throw new RuntimeException("Data source is unspecified. You may need to double click all your data boxes, \n" + "then click Save, and then right click on them and select Propagate Downstream. \n" + "The issue is that we use a seed to simulate from IM's, so your data is not saved to \n" + "file when you save the session. It can, however, be recreated from the saved seed.");
    }
    Parameters params = getParams();
    Node target = null;
    if (model instanceof Graph) {
        GraphScore gesScore = new GraphScore((Graph) model);
        target = gesScore.getVariable(targetName);
        fges = new FgesMb(gesScore);
        fges.setKnowledge((IKnowledge) getParams().get("knowledge", new Knowledge2()));
        fges.setNumPatternsToStore(params.getInt("numPatternsToSave", 1));
        fges.setVerbose(true);
    } else if (model instanceof DataSet) {
        DataSet dataSet = (DataSet) model;
        if (dataSet.isContinuous()) {
            SemBicScore score = new SemBicScore(new CovarianceMatrixOnTheFly((DataSet) model));
            target = score.getVariable(targetName);
            score.setPenaltyDiscount(params.getDouble("penaltyDiscount", 4));
            fges = new FgesMb(score);
        } else if (dataSet.isDiscrete()) {
            // ((Parameters) getParameters()).getSamplePrior();
            double samplePrior = 1;
            // ((Parameters) getParameters()).getStructurePrior();
            double structurePrior = 1;
            BDeuScore score = new BDeuScore(dataSet);
            score.setSamplePrior(samplePrior);
            score.setStructurePrior(structurePrior);
            target = score.getVariable(targetName);
            fges = new FgesMb(score);
        } else {
            throw new IllegalStateException("Data set must either be continuous or discrete.");
        }
    } else if (model instanceof ICovarianceMatrix) {
        SemBicScore gesScore = new SemBicScore((ICovarianceMatrix) model);
        gesScore.setPenaltyDiscount(params.getDouble("alpha", 0.001));
        gesScore.setPenaltyDiscount(params.getDouble("penaltyDiscount", 4));
        target = gesScore.getVariable(targetName);
        fges = new FgesMb(gesScore);
    } else if (model instanceof DataModelList) {
        DataModelList list = (DataModelList) model;
        for (DataModel dataModel : list) {
            if (!(dataModel instanceof DataSet || dataModel instanceof ICovarianceMatrix)) {
                throw new IllegalArgumentException("Need a combination of all continuous data sets or " + "covariance matrices, or else all discrete data sets, or else a single initialGraph.");
            }
        }
        if (allContinuous(list)) {
            double penalty = getParams().getDouble("penaltyDiscount", 4);
            if (params.getBoolean("firstNontriangular", false)) {
                SemBicScoreImages fgesScore = new SemBicScoreImages(list);
                fgesScore.setPenaltyDiscount(penalty);
                target = fgesScore.getVariable(targetName);
                fges = new FgesMb(fgesScore);
            } else {
                SemBicScoreImages fgesScore = new SemBicScoreImages(list);
                fgesScore.setPenaltyDiscount(penalty);
                target = fgesScore.getVariable(targetName);
                fges = new FgesMb(fgesScore);
            }
        } else if (allDiscrete(list)) {
            double structurePrior = getParams().getDouble("structurePrior", 1);
            double samplePrior = getParams().getDouble("samplePrior", 1);
            BdeuScoreImages fgesScore = new BdeuScoreImages(list);
            fgesScore.setSamplePrior(samplePrior);
            fgesScore.setStructurePrior(structurePrior);
            target = fgesScore.getVariable(targetName);
            if (params.getBoolean("firstNontriangular", false)) {
                fges = new FgesMb(fgesScore);
            } else {
                fges = new FgesMb(fgesScore);
            }
        } else {
            throw new IllegalArgumentException("Data must be either all discrete or all continuous.");
        }
    } else {
        System.out.println("No viable input.");
    }
    // Graph searchGraph;
    // 
    // if (true) {
    // DataModel dataModel = getDataModelList().getSelectedModel();
    // ICovarianceMatrix cov;
    // Node target;
    // FgesMb fges;
    // 
    // if (dataModel instanceof DataSet) {
    // DataSet dataSet = (DataSet) dataModel;
    // target = dataSet.getVariable(targetName);
    // 
    // if (dataSet.isContinuous()) {
    // SemBicScore gesScore = new SemBicScore(new CovarianceMatrixOnTheFly((DataSet) dataModel),
    // getParameters().getDepErrorsAlpha());
    // fges = new FgesMb(gesScore, target);
    // } else if (dataSet.isDiscrete()) {
    // double structurePrior = 1;
    // double samplePrior = getParameters().getDepErrorsAlpha();
    // BDeuScore score = new BDeuScore(dataSet);
    // score.setSamplePrior(samplePrior);
    // score.setStructurePrior(structurePrior);
    // fges = new FgesMb(score, target);
    // } else {
    // throw new IllegalStateException("Data set must either be continuous or discrete.");
    // }
    // } else if (dataModel instanceof ICovarianceMatrix) {
    // cov = (ICovarianceMatrix) dataModel;
    // SemBicScore score = new SemBicScore(cov,
    // getParameters().getDepErrorsAlpha());
    // target = cov.getVariable(targetName);
    // fges = new FgesMb(score, target);
    // } else {
    // throw new IllegalArgumentException("Expecting a data set or a covariance matrix.");
    // }
    // 
    // fges.setVerbose(true);
    // fges.setHeuristicSpeedup(true);
    // searchGraph = fges.search();
    // } else {
    // Node target = getIndependenceTest().getVariable(targetName);
    // System.out.println("Target = " + target);
    // 
    // int depth = getParameters().getMaxDegree();
    // 
    // ScoredIndTest fgesScore = new ScoredIndTest(getIndependenceTest());
    // fgesScore.setParameter1(getParameters().getDepErrorsAlpha());
    // FgesMb search = new FgesMb(fgesScore, target);
    // search.setKnowledge(knowledge);
    // search.setMaxDegree(depth);
    // search.setVerbose(true);
    // search.setHeuristicSpeedup(true);
    // searchGraph = search.search();
    // }
    // if (getSourceGraph() != null) {
    // GraphUtils.arrangeBySourceGraph(searchGraph, getSourceGraph());
    // } else if (knowledge.isDefaultToKnowledgeLayout()) {
    // SearchGraphUtils.arrangeByKnowledgeTiers(searchGraph, knowledge);
    // } else {
    // GraphUtils.circleLayout(searchGraph, 200, 200, 150);
    // }
    // fges.setInitialGraph(initialGraph);
    fges.setKnowledge((IKnowledge) getParams().get("knowledge", new Knowledge2()));
    fges.setNumPatternsToStore(params.getInt("numPatternsToSave", 1));
    fges.setVerbose(true);
    // fges.setHeuristicSpeedup(((Parameters) params.getIndTestParams()).isFaithfulnessAssumed());
    fges.setMaxIndegree(params.getInt("depth", -1));
    Graph graph = fges.search(target);
    if (getSourceGraph() != null) {
        GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
    } else if (((IKnowledge) getParams().get("knowledge", new Knowledge2())).isDefaultToKnowledgeLayout()) {
        SearchGraphUtils.arrangeByKnowledgeTiers(graph, (IKnowledge) getParams().get("knowledge", new Knowledge2()));
    } else {
        GraphUtils.circleLayout(graph, 200, 200, 150);
    }
    this.topGraphs = new ArrayList<>(fges.getTopGraphs());
    if (topGraphs.isEmpty()) {
        topGraphs.add(new ScoredGraph(getResultGraph(), Double.NaN));
    }
    setIndex(topGraphs.size() - 1);
    setResultGraph(graph);
}

Example 20 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class GlassoRunner method execute.

// ===================PUBLIC METHODS OVERRIDING ABSTRACT================//
public void execute() {
    Object dataModel = getDataModel();
    Parameters params = getParams();
    if (dataModel instanceof DataSet) {
        DataSet dataSet = (DataSet) dataModel;
        DoubleMatrix2D cov = new DenseDoubleMatrix2D(dataSet.getCovarianceMatrix().toArray());
        Glasso glasso = new Glasso(cov);
        glasso.setMaxit((int) params.get("maxit", 10000));
        glasso.setIa(params.getBoolean("ia", false));
        glasso.setIs(params.getBoolean("is", false));
        glasso.setItr(params.getBoolean("itr", false));
        glasso.setIpen(params.getBoolean("ipen", false));
        glasso.setThr(params.getDouble("thr", 1e-4));
        glasso.setRhoAllEqual(1.0);
        Glasso.Result result = glasso.search();
        TetradMatrix wwi = new TetradMatrix(result.getWwi().toArray());
        List<Node> variables = dataSet.getVariables();
        Graph resultGraph = new EdgeListGraph(variables);
        for (int i = 0; i < variables.size(); i++) {
            for (int j = i + 1; j < variables.size(); j++) {
                if (wwi.get(i, j) != 0.0 && wwi.get(i, j) != 0.0) {
                    resultGraph.addUndirectedEdge(variables.get(i), variables.get(j));
                }
            }
        }
        setResultGraph(resultGraph);
    }
}