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

Example 81 with Parameters

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

the class FgesRunner method getParamSettings.

@Override
public Map<String, String> getParamSettings() {
    super.getParamSettings();
    Parameters params = getParams();
    paramSettings.put("Penalty Discount", new DecimalFormat("0.0").format(params.getDouble("penaltyDiscount", 4)));
    return paramSettings;
}

Example 82 with Parameters

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

the class ForbiddenGraphModel method createKnowledge.

private void createKnowledge(Parameters params) {
    IKnowledge knwl = getKnowledge();
    if (knwl == null) {
        return;
    }
    knwl.clear();
    List<String> varNames = getVarNames();
    getKnowledgeBoxInput().getVariableNames().stream().filter(e -> !e.startsWith("E_")).forEach(e -> {
        varNames.add(e);
        knwl.addVariable(e);
    });
    if (resultGraph == null) {
        throw new NullPointerException("I couldn't find a parent graph.");
    }
    List<Node> nodes = resultGraph.getNodes();
    int numOfNodes = nodes.size();
    for (int i = 0; i < numOfNodes; i++) {
        for (int j = i + 1; j < numOfNodes; j++) {
            Node n1 = nodes.get(i);
            Node n2 = nodes.get(j);
            if (n1.getName().startsWith("E_") || n2.getName().startsWith("E_")) {
                continue;
            }
            Edge edge = resultGraph.getEdge(n1, n2);
            if (edge != null && edge.isDirected()) {
                knwl.setForbidden(edge.getNode2().getName(), edge.getNode1().getName());
            }
        }
    }
}

Example 83 with Parameters

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

the class GFciRunner method getIndependenceTest.

public IndependenceTest getIndependenceTest() {
    Object dataModel = getDataModel();
    if (dataModel == null) {
        dataModel = getSourceGraph();
    }
    Parameters params = getParams();
    IndTestType testType;
    if (getParams() instanceof Parameters) {
        Parameters _params = params;
        testType = (IndTestType) _params.get("indTestType", IndTestType.FISHER_Z);
    } else {
        Parameters _params = params;
        testType = (IndTestType) _params.get("indTestType", IndTestType.FISHER_Z);
    }
    return new IndTestChooser().getTest(dataModel, params, testType);
}

Example 84 with Parameters

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

the class GFciRunner 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 = getParameters().getKnowledge();
// Parameters searchParams = getParameters();
// 
// Parameters params = (Parameters) searchParams;
// 
// Graph graph;
// 
// if (getIndependenceTest() instanceof IndTestDSep) {
// GFci gfci = new GFci(getIndependenceTest());
// graph = gfci.search();
// } else {
// GFci fci = new GFci(getIndependenceTest());
// fci.setKnowledge(knowledge);
// fci.setCompleteRuleSetUsed(params.isCompleteRuleSetUsed());
// fci.setMaxPathLength(params.getMaxReachablePathLength());
// fci.setMaxIndegree(params.getMaxIndegree());
// double penaltyDiscount = params.getPenaltyDiscount();
// 
// fci.setCorrErrorsAlpha(penaltyDiscount);
// fci.setSamplePrior(params.getSamplePrior());
// fci.setStructurePrior(params.getStructurePrior());
// fci.setCompleteRuleSetUsed(false);
// fci.setHeuristicSpeedup(params.isFaithfulnessAssumed());
// graph = fci.search();
// }
// 
// if (getSourceGraph() != null) {
// GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
// } else if (knowledge.isDefaultToKnowledgeLayout()) {
// SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
// } else {
// GraphUtils.circleLayout(graph, 200, 200, 150);
// }
// 
// setResultGraph(graph);
// }
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
public void execute() {
    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();
    double penaltyDiscount = params.getDouble("penaltyDiscount", 4);
    if (model instanceof Graph) {
        IndependenceTest test = new IndTestDSep((Graph) model);
        GraphScore gesScore = new GraphScore((Graph) model);
        gfci = new GFci(test, gesScore);
        gfci.setKnowledge((IKnowledge) getParams().get("knowledge", new Knowledge2()));
        gfci.setVerbose(true);
    } else {
        if (model instanceof DataSet) {
            DataSet dataSet = (DataSet) model;
            if (dataSet.isContinuous()) {
                IndependenceTest test = new IndTestFisherZ(new CovarianceMatrixOnTheFly((DataSet) model), 0.001);
                SemBicScore gesScore = new SemBicScore(new CovarianceMatrixOnTheFly((DataSet) model));
                gesScore.setPenaltyDiscount(penaltyDiscount);
                // SemBicScore2 gesScore = new SemBicScore2(new CovarianceMatrixOnTheFly((DataSet) model));
                // SemGpScore gesScore = new SemGpScore(new CovarianceMatrixOnTheFly((DataSet) model));
                // SvrScore gesScore = new SvrScore((DataSet) model);
                gesScore.setPenaltyDiscount(penaltyDiscount);
                System.out.println("Score done");
                gfci = new GFci(test, gesScore);
            } else // else if (dataSet.isDiscrete()) {
            // double samplePrior = ((Parameters) getParameters()).getSamplePrior();
            // double structurePrior = ((Parameters) getParameters()).getStructurePrior();
            // BDeuScore score = new BDeuScore(dataSet);
            // score.setSamplePrior(samplePrior);
            // score.setStructurePrior(structurePrior);
            // gfci = new GFci(score);
            // }
            {
                throw new IllegalStateException("Data set must either be continuous or discrete.");
            }
        } else if (model instanceof ICovarianceMatrix) {
            IndependenceTest test = new IndTestFisherZ((ICovarianceMatrix) model, 0.001);
            SemBicScore gesScore = new SemBicScore((ICovarianceMatrix) model);
            gesScore.setPenaltyDiscount(penaltyDiscount);
            gesScore.setPenaltyDiscount(penaltyDiscount);
            gfci = new GFci(test, 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 (list.size() != 1) {
                throw new IllegalArgumentException("FGES takes exactly one data set, covariance matrix, or initialGraph " + "as input. For multiple data sets as input, use IMaGES.");
            }
            if (allContinuous(list)) {
                double penalty = params.getDouble("penaltyDiscount", 4);
                SemBicScoreImages fgesScore = new SemBicScoreImages(list);
                IndTestScore test = new IndTestScore(fgesScore);
                fgesScore.setPenaltyDiscount(penalty);
                gfci = new GFci(test, fgesScore);
            } else // else if (allDiscrete(list)) {
            // double structurePrior = ((Parameters) getParameters()).getStructurePrior();
            // double samplePrior = ((Parameters) getParameters()).getSamplePrior();
            // 
            // BdeuScoreImages fgesScore = new BdeuScoreImages(list);
            // fgesScore.setSamplePrior(samplePrior);
            // fgesScore.setStructurePrior(structurePrior);
            // 
            // gfci = new GFci(fgesScore);
            // }
            {
                throw new IllegalArgumentException("Data must be either all discrete or all continuous.");
            }
        } else {
            System.out.println("No viable input.");
        }
    }
    // gfci.setInitialGraph(initialGraph);
    // gfci.setKnowledge(getParameters().getKnowledge());
    // gfci.setNumPatternsToStore(params.getNumPatternsToSave());
    gfci.setVerbose(true);
    // gfci.setHeuristicSpeedup(true);
    // gfci.setMaxIndegree(3);
    gfci.setFaithfulnessAssumed(params.getBoolean("faithfulnessAssumed", true));
    Graph graph = gfci.search();
    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);
    }
    setResultGraph(graph);
// this.topGraphs = new ArrayList<>(gfci.getTopGraphs());
// 
// if (topGraphs.isEmpty()) {
// 
// topGraphs.add(new ScoredGraph(getResultGraph(), Double.NaN));
// }
// 
// setIndex(topGraphs.size() - 1);
}

Example 85 with Parameters

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

the class TsGFciRunner method getIndependenceTest.

public IndependenceTest getIndependenceTest() {
    Object dataModel = getDataModel();
    if (dataModel == null) {
        dataModel = getSourceGraph();
    }
    Parameters params = getParams();
    IndTestType testType;
    if (getParams() instanceof Parameters) {
        Parameters _params = params;
        testType = (IndTestType) _params.get("indTestType", IndTestType.FISHER_Z);
    } else {
        Parameters _params = params;
        testType = (IndTestType) _params.get("indTestType", IndTestType.FISHER_Z);
    }
    return new IndTestChooser().getTest(dataModel, params, testType);
}