Examples with Node edu.cmu.tetrad.graph.Node used on opensource projects

Example 81 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class IndTestRegression method getVariableNames.

/**
 * @return the list of variable varNames.
 */
public List<String> getVariableNames() {
    List<Node> variables = getVariables();
    List<String> variableNames = new ArrayList<>();
    for (Node variable : variables) {
        variableNames.add(variable.getName());
    }
    return variableNames;
}

Example 82 with Node

use of edu.cmu.tetrad.graph.Node 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 83 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class FofcRunner 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() {
    Graph searchGraph;
    FindOneFactorClusters fofc;
    Object source = getData();
    TestType tetradTestType = (TestType) getParams().get("tetradTestType", TestType.TETRAD_WISHART);
    if (tetradTestType == null || (!(tetradTestType == TestType.TETRAD_DELTA || tetradTestType == TestType.TETRAD_WISHART))) {
        tetradTestType = TestType.TETRAD_DELTA;
        getParams().set("tetradTestType", tetradTestType);
    }
    FindOneFactorClusters.Algorithm algorithm = (FindOneFactorClusters.Algorithm) getParams().get("fofcAlgorithm", FindOneFactorClusters.Algorithm.GAP);
    if (source instanceof DataSet) {
        fofc = new FindOneFactorClusters((DataSet) source, tetradTestType, algorithm, getParams().getDouble("alpha", 0.001));
        searchGraph = fofc.search();
    } else if (source instanceof CovarianceMatrix) {
        fofc = new FindOneFactorClusters((CovarianceMatrix) source, tetradTestType, algorithm, getParams().getDouble("alpha", 0.001));
        searchGraph = fofc.search();
    } else {
        throw new IllegalArgumentException("Unrecognized data type.");
    }
    if (semIm != null) {
        List<List<Node>> partition = MimUtils.convertToClusters2(searchGraph);
        List<String> variableNames = ReidentifyVariables.reidentifyVariables2(partition, trueGraph, (DataSet) getData());
        rename(searchGraph, partition, variableNames);
    // searchGraph = reidentifyVariables2(searchGraph, semIm);
    } else if (trueGraph != null) {
        List<List<Node>> partition = MimUtils.convertToClusters2(searchGraph);
        List<String> variableNames = ReidentifyVariables.reidentifyVariables1(partition, trueGraph);
        rename(searchGraph, partition, variableNames);
    // searchGraph = reidentifyVariables(searchGraph, trueGraph);
    }
    System.out.println("Search Graph " + searchGraph);
    try {
        Graph graph = new MarshalledObject<>(searchGraph).get();
        GraphUtils.circleLayout(graph, 200, 200, 150);
        GraphUtils.fruchtermanReingoldLayout(graph);
        setResultGraph(graph);
        setClusters(MimUtils.convertToClusters(graph, getData().getVariables()));
    } catch (Exception e) {
        e.printStackTrace();
        throw new RuntimeException(e);
    }
}

Example 84 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class FofcRunner method getVariables.

public List<Node> getVariables() {
    List<Node> latents = new ArrayList<>();
    for (String name : getVariableNames()) {
        Node node = new ContinuousVariable(name);
        node.setNodeType(NodeType.LATENT);
        latents.add(node);
    }
    return latents;
}

Example 85 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class FtfcRunner 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() {
    Graph searchGraph;
    FindTwoFactorClusters ftfc;
    Object source = getData();
    TestType tetradTestType = (TestType) getParams().get("tetradTestType", TestType.TETRAD_WISHART);
    if (tetradTestType == null || (!(tetradTestType == TestType.TETRAD_DELTA || tetradTestType == TestType.TETRAD_WISHART))) {
        tetradTestType = TestType.TETRAD_DELTA;
        getParams().set("tetradTestType", tetradTestType);
    }
    FindTwoFactorClusters.Algorithm algorithm = (FindTwoFactorClusters.Algorithm) getParams().get("ftfcAlgorithm", FindTwoFactorClusters.Algorithm.GAP);
    if (source instanceof DataSet) {
        ftfc = new FindTwoFactorClusters((DataSet) source, algorithm, getParams().getDouble("alpha", 0.001));
        ftfc.setVerbose(true);
        searchGraph = ftfc.search();
    } else if (source instanceof CovarianceMatrix) {
        ftfc = new FindTwoFactorClusters((CovarianceMatrix) source, algorithm, getParams().getDouble("alpha", 0.001));
        ftfc.setVerbose(true);
        searchGraph = ftfc.search();
    } else {
        throw new IllegalArgumentException("Unrecognized data type.");
    }
    if (semIm != null) {
        List<List<Node>> partition = MimUtils.convertToClusters2(searchGraph);
        List<String> variableNames = ReidentifyVariables.reidentifyVariables2(partition, trueGraph, (DataSet) getData());
        rename(searchGraph, partition, variableNames);
    // searchGraph = reidentifyVariables2(searchGraph, semIm);
    } else if (trueGraph != null) {
        List<List<Node>> partition = MimUtils.convertToClusters2(searchGraph);
        List<String> variableNames = ReidentifyVariables.reidentifyVariables1(partition, trueGraph);
        rename(searchGraph, partition, variableNames);
    // searchGraph = reidentifyVariables(searchGraph, trueGraph);
    }
    System.out.println("Search Graph " + searchGraph);
    try {
        Graph graph = new MarshalledObject<>(searchGraph).get();
        GraphUtils.circleLayout(graph, 200, 200, 150);
        GraphUtils.fruchtermanReingoldLayout(graph);
        setResultGraph(graph);
        setClusters(MimUtils.convertToClusters(graph, getData().getVariables()));
    } catch (Exception e) {
        e.printStackTrace();
        throw new RuntimeException(e);
    }
}