Examples with BootstrapEdgeEnsemble edu.pitt.dbmi.algo.bootstrap.BootstrapEdgeEnsemble used on opensource projects

Example 31 with BootstrapEdgeEnsemble

use of edu.pitt.dbmi.algo.bootstrap.BootstrapEdgeEnsemble in project tetrad by cmu-phil.

the class Skew method search.

@Override
public Graph search(DataModel dataSet, Parameters parameters) {
    if (parameters.getInt("bootstrapSampleSize") < 1) {
        Graph graph = algorithm.search(dataSet, parameters);
        if (graph != null) {
            initialGraph = graph;
        } else {
            throw new IllegalArgumentException("This Skew algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
        }
        List<DataSet> dataSets = new ArrayList<>();
        dataSets.add(DataUtils.getContinuousDataSet(dataSet));
        Lofs2 lofs = new Lofs2(initialGraph, dataSets);
        lofs.setRule(Lofs2.Rule.Skew);
        return lofs.orient();
    } else {
        Skew skew = new Skew(algorithm);
        if (initialGraph != null) {
            skew.setInitialGraph(initialGraph);
        }
        DataSet data = (DataSet) dataSet;
        GeneralBootstrapTest search = new GeneralBootstrapTest(data, skew, parameters.getInt("bootstrapSampleSize"));
        BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
        switch(parameters.getInt("bootstrapEnsemble", 1)) {
            case 0:
                edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
                break;
            case 1:
                edgeEnsemble = BootstrapEdgeEnsemble.Highest;
                break;
            case 2:
                edgeEnsemble = BootstrapEdgeEnsemble.Majority;
        }
        search.setEdgeEnsemble(edgeEnsemble);
        search.setParameters(parameters);
        search.setVerbose(parameters.getBoolean("verbose"));
        return search.search();
    }
}

Example 32 with BootstrapEdgeEnsemble

use of edu.pitt.dbmi.algo.bootstrap.BootstrapEdgeEnsemble in project tetrad by cmu-phil.

the class Tanh method search.

@Override
public Graph search(DataModel dataSet, Parameters parameters) {
    if (parameters.getInt("bootstrapSampleSize") < 1) {
        Graph graph = algorithm.search(dataSet, parameters);
        if (graph != null) {
            initialGraph = graph;
        } else {
            throw new IllegalArgumentException("This Tanh algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
        }
        List<DataSet> dataSets = new ArrayList<>();
        dataSets.add(DataUtils.getContinuousDataSet(dataSet));
        Lofs2 lofs = new Lofs2(initialGraph, dataSets);
        lofs.setRule(Lofs2.Rule.Tanh);
        return lofs.orient();
    } else {
        Tanh tanh = new Tanh(algorithm);
        if (initialGraph != null) {
            tanh.setInitialGraph(initialGraph);
        }
        DataSet data = (DataSet) dataSet;
        GeneralBootstrapTest search = new GeneralBootstrapTest(data, tanh, parameters.getInt("bootstrapSampleSize"));
        BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
        switch(parameters.getInt("bootstrapEnsemble", 1)) {
            case 0:
                edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
                break;
            case 1:
                edgeEnsemble = BootstrapEdgeEnsemble.Highest;
                break;
            case 2:
                edgeEnsemble = BootstrapEdgeEnsemble.Majority;
        }
        search.setEdgeEnsemble(edgeEnsemble);
        search.setParameters(parameters);
        search.setVerbose(parameters.getBoolean("verbose"));
        return search.search();
    }
}

Example 33 with BootstrapEdgeEnsemble

use of edu.pitt.dbmi.algo.bootstrap.BootstrapEdgeEnsemble in project tetrad by cmu-phil.

the class MBFS method search.

@Override
public Graph search(DataModel dataSet, Parameters parameters) {
    if (parameters.getInt("bootstrapSampleSize") < 1) {
        IndependenceTest test = this.test.getTest(dataSet, parameters);
        edu.cmu.tetrad.search.Mbfs search = new edu.cmu.tetrad.search.Mbfs(test, parameters.getInt("depth"));
        search.setDepth(parameters.getInt("depth"));
        search.setKnowledge(knowledge);
        this.targetName = parameters.getString("targetName");
        if (targetName.isEmpty()) {
            throw new IllegalArgumentException("Target variable name needs to be provided.");
        }
        if (test.getVariable(targetName) == null) {
            throw new IllegalArgumentException("Target variable name '" + targetName + "' not found in dataset.");
        }
        Node target = test.getVariable(targetName);
        return search.search(target.getName());
    } else {
        MBFS algorithm = new MBFS(test);
        // algorithm.setKnowledge(knowledge);
        DataSet data = (DataSet) dataSet;
        GeneralBootstrapTest search = new GeneralBootstrapTest(data, algorithm, parameters.getInt("bootstrapSampleSize"));
        search.setKnowledge(knowledge);
        BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
        switch(parameters.getInt("bootstrapEnsemble", 1)) {
            case 0:
                edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
                break;
            case 1:
                edgeEnsemble = BootstrapEdgeEnsemble.Highest;
                break;
            case 2:
                edgeEnsemble = BootstrapEdgeEnsemble.Majority;
        }
        search.setEdgeEnsemble(edgeEnsemble);
        search.setParameters(parameters);
        search.setVerbose(parameters.getBoolean("verbose"));
        return search.search();
    }
}

Example 34 with BootstrapEdgeEnsemble

use of edu.pitt.dbmi.algo.bootstrap.BootstrapEdgeEnsemble in project tetrad by cmu-phil.

the class FactorAnalysis method search.

public Graph search(DataModel ds, Parameters parameters) {
    if (parameters.getInt("bootstrapSampleSize") < 1) {
        DataSet selectedModel = (DataSet) ds;
        if (selectedModel == null) {
            throw new NullPointerException("Data not specified.");
        }
        edu.cmu.tetrad.search.FactorAnalysis analysis = new edu.cmu.tetrad.search.FactorAnalysis(selectedModel);
        analysis.setThreshold(parameters.getDouble("convergenceThreshold"));
        analysis.setNumFactors(parameters.getInt("numFactors"));
        double threshold = parameters.getDouble("fa_threshold");
        TetradMatrix unrotatedSolution = analysis.successiveResidual();
        TetradMatrix rotatedSolution = analysis.successiveFactorVarimax(unrotatedSolution);
        SemGraph graph = new SemGraph();
        Vector<Node> observedVariables = new Vector<>();
        for (Node a : selectedModel.getVariables()) {
            graph.addNode(a);
            observedVariables.add(a);
        }
        Vector<Node> factors = new Vector<>();
        if (parameters.getBoolean("useVarimax")) {
            for (int i = 0; i < rotatedSolution.columns(); i++) {
                ContinuousVariable factor = new ContinuousVariable("Factor" + (i + 1));
                factor.setNodeType(NodeType.LATENT);
                graph.addNode(factor);
                factors.add(factor);
            }
            for (int i = 0; i < rotatedSolution.rows(); i++) {
                for (int j = 0; j < rotatedSolution.columns(); j++) {
                    if (Math.abs(rotatedSolution.get(i, j)) > threshold) {
                        graph.addDirectedEdge(factors.get(j), observedVariables.get(i));
                    }
                }
            }
        } else {
            for (int i = 0; i < unrotatedSolution.columns(); i++) {
                ContinuousVariable factor = new ContinuousVariable("Factor" + (i + 1));
                factor.setNodeType(NodeType.LATENT);
                graph.addNode(factor);
                factors.add(factor);
            }
            for (int i = 0; i < unrotatedSolution.rows(); i++) {
                for (int j = 0; j < unrotatedSolution.columns(); j++) {
                    if (Math.abs(unrotatedSolution.get(i, j)) > threshold) {
                        graph.addDirectedEdge(factors.get(j), observedVariables.get(i));
                    }
                }
            }
        }
        if (parameters.getBoolean("verbose")) {
            NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
            String output = "Unrotated Factor Loading Matrix:\n";
            output += tableString(unrotatedSolution, nf, Double.POSITIVE_INFINITY);
            if (unrotatedSolution.columns() != 1) {
                output += "\n\nRotated Matrix (using sequential varimax):\n";
                output += tableString(rotatedSolution, nf, threshold);
            }
            System.out.println(output);
            TetradLogger.getInstance().forceLogMessage(output);
        }
        return graph;
    } else {
        FactorAnalysis algorithm = new FactorAnalysis();
        DataSet data = (DataSet) ds;
        GeneralBootstrapTest search = new GeneralBootstrapTest(data, algorithm, parameters.getInt("bootstrapSampleSize"));
        BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
        switch(parameters.getInt("bootstrapEnsemble", 1)) {
            case 0:
                edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
                break;
            case 1:
                edgeEnsemble = BootstrapEdgeEnsemble.Highest;
                break;
            case 2:
                edgeEnsemble = BootstrapEdgeEnsemble.Majority;
        }
        search.setEdgeEnsemble(edgeEnsemble);
        search.setParameters(parameters);
        search.setVerbose(parameters.getBoolean("verbose"));
        return search.search();
    }
}

Example 35 with BootstrapEdgeEnsemble

use of edu.pitt.dbmi.algo.bootstrap.BootstrapEdgeEnsemble in project tetrad by cmu-phil.

the class R2 method search.

@Override
public Graph search(DataModel dataSet, Parameters parameters) {
    if (parameters.getInt("bootstrapSampleSize") < 1) {
        Graph graph = algorithm.search(dataSet, parameters);
        if (graph != null) {
            initialGraph = graph;
        } else {
            throw new IllegalArgumentException("This R2 algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
        }
        List<DataSet> dataSets = new ArrayList<>();
        dataSets.add(DataUtils.getContinuousDataSet(dataSet));
        Lofs2 lofs = new Lofs2(initialGraph, dataSets);
        lofs.setRule(Lofs2.Rule.R2);
        return lofs.orient();
    } else {
        R2 r2 = new R2(algorithm);
        if (initialGraph != null) {
            r2.setInitialGraph(initialGraph);
        }
        DataSet data = (DataSet) dataSet;
        GeneralBootstrapTest search = new GeneralBootstrapTest(data, r2, parameters.getInt("bootstrapSampleSize"));
        BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
        switch(parameters.getInt("bootstrapEnsemble", 1)) {
            case 0:
                edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
                break;
            case 1:
                edgeEnsemble = BootstrapEdgeEnsemble.Highest;
                break;
            case 2:
                edgeEnsemble = BootstrapEdgeEnsemble.Majority;
        }
        search.setEdgeEnsemble(edgeEnsemble);
        search.setParameters(parameters);
        search.setVerbose(parameters.getBoolean("verbose"));
        return search.search();
    }
}