Search in sources :

Example 6 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class Comparison2 method compare.

/**
 * Simulates data from model parameterizing the given DAG, and runs the
 * algorithm on that data, printing out error statistics.
 */
public static ComparisonResult compare(ComparisonParameters params) {
    DataSet dataSet = null;
    Graph trueDag = null;
    IndependenceTest test = null;
    Score score = null;
    ComparisonResult result = new ComparisonResult(params);
    if (params.isDataFromFile()) {
        /**
         * Set path to the data directory *
         */
        String path = "/Users/dmalinsky/Documents/research/data/danexamples";
        File dir = new File(path);
        File[] files = dir.listFiles();
        if (files == null) {
            throw new NullPointerException("No files in " + path);
        }
        for (File file : files) {
            if (file.getName().startsWith("graph") && file.getName().contains(String.valueOf(params.getGraphNum())) && file.getName().endsWith(".g.txt")) {
                params.setGraphFile(file.getName());
                trueDag = GraphUtils.loadGraphTxt(file);
                break;
            }
        }
        String trialGraph = String.valueOf(params.getGraphNum()).concat("-").concat(String.valueOf(params.getTrial())).concat(".dat.txt");
        for (File file : files) {
            if (file.getName().startsWith("graph") && file.getName().endsWith(trialGraph)) {
                Path dataFile = Paths.get(path.concat("/").concat(file.getName()));
                Delimiter delimiter = Delimiter.TAB;
                if (params.getDataType() == ComparisonParameters.DataType.Continuous) {
                    try {
                        TabularDataReader dataReader = new ContinuousTabularDataFileReader(dataFile.toFile(), delimiter);
                        dataSet = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData());
                    } catch (IOException e) {
                        e.printStackTrace();
                    }
                    params.setDataFile(file.getName());
                    break;
                } else {
                    try {
                        TabularDataReader dataReader = new VerticalDiscreteTabularDataReader(dataFile.toFile(), delimiter);
                        dataSet = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData());
                    } catch (IOException e) {
                        e.printStackTrace();
                    }
                    params.setDataFile(file.getName());
                    break;
                }
            }
        }
        System.out.println("current graph file = " + params.getGraphFile());
        System.out.println("current data set file = " + params.getDataFile());
    }
    if (params.isNoData()) {
        List<Node> nodes = new ArrayList<>();
        for (int i = 0; i < params.getNumVars(); i++) {
            nodes.add(new ContinuousVariable("X" + (i + 1)));
        }
        trueDag = GraphUtils.randomGraphRandomForwardEdges(nodes, 0, params.getNumEdges(), 10, 10, 10, false, true);
        /**
         * added 5.25.16 for tsFCI *
         */
        if (params.getAlgorithm() == ComparisonParameters.Algorithm.TsFCI) {
            trueDag = GraphUtils.randomGraphRandomForwardEdges(nodes, 0, params.getNumEdges(), 10, 10, 10, false, true);
            trueDag = TimeSeriesUtils.graphToLagGraph(trueDag, 2);
            System.out.println("Creating Time Lag Graph : " + trueDag);
        }
        /**
         * ************************
         */
        test = new IndTestDSep(trueDag);
        score = new GraphScore(trueDag);
        if (params.getAlgorithm() == null) {
            throw new IllegalArgumentException("Algorithm not set.");
        }
        long time1 = System.currentTimeMillis();
        if (params.getAlgorithm() == ComparisonParameters.Algorithm.PC) {
            if (test == null) {
                throw new IllegalArgumentException("Test not set.");
            }
            Pc search = new Pc(test);
            result.setResultGraph(search.search());
            result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
        } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.CPC) {
            if (test == null) {
                throw new IllegalArgumentException("Test not set.");
            }
            Cpc search = new Cpc(test);
            result.setResultGraph(search.search());
            result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
        } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.PCLocal) {
            if (test == null) {
                throw new IllegalArgumentException("Test not set.");
            }
            PcLocal search = new PcLocal(test);
            result.setResultGraph(search.search());
            result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
        } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.PCStableMax) {
            if (test == null) {
                throw new IllegalArgumentException("Test not set.");
            }
            PcStableMax search = new PcStableMax(test);
            result.setResultGraph(search.search());
            result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
        } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.FGES) {
            if (score == null) {
                throw new IllegalArgumentException("Score not set.");
            }
            Fges search = new Fges(score);
            // search.setFaithfulnessAssumed(params.isOneEdgeFaithfulnessAssumed());
            result.setResultGraph(search.search());
            result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
        } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.FCI) {
            if (test == null) {
                throw new IllegalArgumentException("Test not set.");
            }
            Fci search = new Fci(test);
            result.setResultGraph(search.search());
            result.setCorrectResult(new DagToPag(trueDag).convert());
        } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.GFCI) {
            if (test == null) {
                throw new IllegalArgumentException("Test not set.");
            }
            GFci search = new GFci(test, score);
            result.setResultGraph(search.search());
            result.setCorrectResult(new DagToPag(trueDag).convert());
        } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.TsFCI) {
            if (test == null) {
                throw new IllegalArgumentException("Test not set.");
            }
            TsFci search = new TsFci(test);
            IKnowledge knowledge = getKnowledge(trueDag);
            search.setKnowledge(knowledge);
            result.setResultGraph(search.search());
            result.setCorrectResult(new TsDagToPag(trueDag).convert());
            System.out.println("Correct result for trial = " + result.getCorrectResult());
            System.out.println("Search result for trial = " + result.getResultGraph());
        } else {
            throw new IllegalArgumentException("Unrecognized algorithm.");
        }
        long time2 = System.currentTimeMillis();
        long elapsed = time2 - time1;
        result.setElapsed(elapsed);
        result.setTrueDag(trueDag);
        return result;
    } else if (params.getDataFile() != null) {
        // dataSet = loadDataFile(params.getDataFile());
        System.out.println("Using data from file... ");
        if (params.getGraphFile() == null) {
            throw new IllegalArgumentException("True graph file not set.");
        } else {
            System.out.println("Using graph from file... ");
        // trueDag = GraphUtils.loadGraph(File params.getGraphFile());
        }
    } else {
        if (params.getNumVars() == -1) {
            throw new IllegalArgumentException("Number of variables not set.");
        }
        if (params.getNumEdges() == -1) {
            throw new IllegalArgumentException("Number of edges not set.");
        }
        if (params.getDataType() == ComparisonParameters.DataType.Continuous) {
            List<Node> nodes = new ArrayList<>();
            for (int i = 0; i < params.getNumVars(); i++) {
                nodes.add(new ContinuousVariable("X" + (i + 1)));
            }
            trueDag = GraphUtils.randomGraphRandomForwardEdges(nodes, 0, params.getNumEdges(), 10, 10, 10, false, true);
            /**
             * added 6.08.16 for tsFCI *
             */
            if (params.getAlgorithm() == ComparisonParameters.Algorithm.TsFCI) {
                trueDag = GraphUtils.randomGraphRandomForwardEdges(nodes, 0, params.getNumEdges(), 10, 10, 10, false, true);
                trueDag = TimeSeriesUtils.graphToLagGraph(trueDag, 2);
                System.out.println("Creating Time Lag Graph : " + trueDag);
            }
            if (params.getDataType() == null) {
                throw new IllegalArgumentException("Data type not set or inferred.");
            }
            if (params.getSampleSize() == -1) {
                throw new IllegalArgumentException("Sample size not set.");
            }
            LargeScaleSimulation sim = new LargeScaleSimulation(trueDag);
            /**
             * added 6.08.16 for tsFCI *
             */
            if (params.getAlgorithm() == ComparisonParameters.Algorithm.TsFCI) {
                sim.setCoefRange(0.20, 0.50);
            }
            /**
             * added 6.08.16 for tsFCI *
             */
            if (params.getAlgorithm() == ComparisonParameters.Algorithm.TsFCI) {
                // //                    System.out.println("Coefs matrix : " + sim.getCoefs());
                // System.out.println(MatrixUtils.toString(sim.getCoefficientMatrix()));
                // //                    System.out.println("dim = " + sim.getCoefs()[1][1]);
                // boolean isStableTetradMatrix = allEigenvaluesAreSmallerThanOneInModulus(new TetradMatrix(sim.getCoefficientMatrix()));
                // //this TetradMatrix needs to be the matrix of coefficients from the SEM!
                // if (!isStableTetradMatrix) {
                // System.out.println("%%%%%%%%%% WARNING %%%%%%%%% not a stable set of eigenvalues for data generation");
                // System.out.println("Skipping this attempt!");
                // sim.setCoefRange(0.2, 0.5);
                // dataSet = sim.simulateDataAcyclic(params.getSampleSize());
                // }
                // 
                // /***************************/
                boolean isStableTetradMatrix;
                int attempt = 1;
                int tierSize = params.getNumVars();
                int[] sub = new int[tierSize];
                int[] sub2 = new int[tierSize];
                for (int i = 0; i < tierSize; i++) {
                    sub[i] = i;
                    sub2[i] = tierSize + i;
                }
                do {
                    dataSet = sim.simulateDataFisher(params.getSampleSize());
                    // System.out.println("Variable Nodes : " + sim.getVariableNodes());
                    // System.out.println(MatrixUtils.toString(sim.getCoefficientMatrix()));
                    TetradMatrix coefMat = new TetradMatrix(sim.getCoefficientMatrix());
                    TetradMatrix B = coefMat.getSelection(sub, sub);
                    TetradMatrix Gamma1 = coefMat.getSelection(sub2, sub);
                    TetradMatrix Gamma0 = TetradMatrix.identity(tierSize).minus(B);
                    TetradMatrix A1 = Gamma0.inverse().times(Gamma1);
                    // TetradMatrix B2 = coefMat.getSelection(sub2, sub2);
                    // System.out.println("B matrix : " + B);
                    // System.out.println("B2 matrix : " + B2);
                    // System.out.println("Gamma1 matrix : " + Gamma1);
                    // isStableTetradMatrix = allEigenvaluesAreSmallerThanOneInModulus(new TetradMatrix(sim.getCoefficientMatrix()));
                    isStableTetradMatrix = TimeSeriesUtils.allEigenvaluesAreSmallerThanOneInModulus(A1);
                    System.out.println("isStableTetradMatrix? : " + isStableTetradMatrix);
                    attempt++;
                } while ((!isStableTetradMatrix) && attempt <= 5);
                if (!isStableTetradMatrix) {
                    System.out.println("%%%%%%%%%% WARNING %%%%%%%% not a stable coefficient matrix, forcing coefs to [0.15,0.3]");
                    System.out.println("Made " + (attempt - 1) + " attempts to get stable matrix.");
                    sim.setCoefRange(0.15, 0.3);
                    dataSet = sim.simulateDataFisher(params.getSampleSize());
                } else {
                    System.out.println("Coefficient matrix is stable.");
                }
            }
        } else if (params.getDataType() == ComparisonParameters.DataType.Discrete) {
            List<Node> nodes = new ArrayList<>();
            for (int i = 0; i < params.getNumVars(); i++) {
                nodes.add(new DiscreteVariable("X" + (i + 1), 3));
            }
            trueDag = GraphUtils.randomGraphRandomForwardEdges(nodes, 0, params.getNumEdges(), 10, 10, 10, false, true);
            if (params.getDataType() == null) {
                throw new IllegalArgumentException("Data type not set or inferred.");
            }
            if (params.getSampleSize() == -1) {
                throw new IllegalArgumentException("Sample size not set.");
            }
            int[] tiers = new int[nodes.size()];
            for (int i = 0; i < nodes.size(); i++) {
                tiers[i] = i;
            }
            BayesPm pm = new BayesPm(trueDag, 3, 3);
            MlBayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
            dataSet = im.simulateData(params.getSampleSize(), false, tiers);
        } else {
            throw new IllegalArgumentException("Unrecognized data type.");
        }
        if (dataSet == null) {
            throw new IllegalArgumentException("No data set.");
        }
    }
    if (params.getIndependenceTest() == ComparisonParameters.IndependenceTestType.FisherZ) {
        if (params.getDataType() != null && params.getDataType() != ComparisonParameters.DataType.Continuous) {
            throw new IllegalArgumentException("Data type previously set to something other than continuous.");
        }
        if (Double.isNaN(params.getAlpha())) {
            throw new IllegalArgumentException("Alpha not set.");
        }
        test = new IndTestFisherZ(dataSet, params.getAlpha());
        params.setDataType(ComparisonParameters.DataType.Continuous);
    } else if (params.getIndependenceTest() == ComparisonParameters.IndependenceTestType.ChiSquare) {
        if (params.getDataType() != null && params.getDataType() != ComparisonParameters.DataType.Discrete) {
            throw new IllegalArgumentException("Data type previously set to something other than discrete.");
        }
        if (Double.isNaN(params.getAlpha())) {
            throw new IllegalArgumentException("Alpha not set.");
        }
        test = new IndTestChiSquare(dataSet, params.getAlpha());
        params.setDataType(ComparisonParameters.DataType.Discrete);
    }
    if (params.getScore() == ScoreType.SemBic) {
        if (params.getDataType() != null && params.getDataType() != ComparisonParameters.DataType.Continuous) {
            throw new IllegalArgumentException("Data type previously set to something other than continuous.");
        }
        if (Double.isNaN(params.getPenaltyDiscount())) {
            throw new IllegalArgumentException("Penalty discount not set.");
        }
        SemBicScore semBicScore = new SemBicScore(new CovarianceMatrixOnTheFly(dataSet));
        semBicScore.setPenaltyDiscount(params.getPenaltyDiscount());
        score = semBicScore;
        params.setDataType(ComparisonParameters.DataType.Continuous);
    } else if (params.getScore() == ScoreType.BDeu) {
        if (params.getDataType() != null && params.getDataType() != ComparisonParameters.DataType.Discrete) {
            throw new IllegalArgumentException("Data type previously set to something other than discrete.");
        }
        if (Double.isNaN(params.getSamplePrior())) {
            throw new IllegalArgumentException("Sample prior not set.");
        }
        if (Double.isNaN(params.getStructurePrior())) {
            throw new IllegalArgumentException("Structure prior not set.");
        }
        score = new BDeuScore(dataSet);
        ((BDeuScore) score).setSamplePrior(params.getSamplePrior());
        ((BDeuScore) score).setStructurePrior(params.getStructurePrior());
        params.setDataType(ComparisonParameters.DataType.Discrete);
        params.setDataType(ComparisonParameters.DataType.Discrete);
    }
    if (params.getAlgorithm() == null) {
        throw new IllegalArgumentException("Algorithm not set.");
    }
    long time1 = System.currentTimeMillis();
    if (params.getAlgorithm() == ComparisonParameters.Algorithm.PC) {
        if (test == null) {
            throw new IllegalArgumentException("Test not set.");
        }
        Pc search = new Pc(test);
        result.setResultGraph(search.search());
        result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
    } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.CPC) {
        if (test == null) {
            throw new IllegalArgumentException("Test not set.");
        }
        Cpc search = new Cpc(test);
        result.setResultGraph(search.search());
        result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
    } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.PCLocal) {
        if (test == null) {
            throw new IllegalArgumentException("Test not set.");
        }
        PcLocal search = new PcLocal(test);
        result.setResultGraph(search.search());
        result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
    } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.PCStableMax) {
        if (test == null) {
            throw new IllegalArgumentException("Test not set.");
        }
        PcStableMax search = new PcStableMax(test);
        result.setResultGraph(search.search());
        result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
    } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.FGES) {
        if (score == null) {
            throw new IllegalArgumentException("Score not set.");
        }
        Fges search = new Fges(score);
        // search.setFaithfulnessAssumed(params.isOneEdgeFaithfulnessAssumed());
        result.setResultGraph(search.search());
        result.setCorrectResult(SearchGraphUtils.patternForDag(new EdgeListGraph(trueDag)));
    } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.FCI) {
        if (test == null) {
            throw new IllegalArgumentException("Test not set.");
        }
        Fci search = new Fci(test);
        result.setResultGraph(search.search());
        result.setCorrectResult(new DagToPag(trueDag).convert());
    } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.GFCI) {
        if (test == null) {
            throw new IllegalArgumentException("Test not set.");
        }
        GFci search = new GFci(test, score);
        result.setResultGraph(search.search());
        result.setCorrectResult(new DagToPag(trueDag).convert());
    } else if (params.getAlgorithm() == ComparisonParameters.Algorithm.TsFCI) {
        if (test == null) {
            throw new IllegalArgumentException("Test not set.");
        }
        TsFci search = new TsFci(test);
        IKnowledge knowledge = getKnowledge(trueDag);
        search.setKnowledge(knowledge);
        result.setResultGraph(search.search());
        result.setCorrectResult(new TsDagToPag(trueDag).convert());
    } else {
        throw new IllegalArgumentException("Unrecognized algorithm.");
    }
    long time2 = System.currentTimeMillis();
    long elapsed = time2 - time1;
    result.setElapsed(elapsed);
    result.setTrueDag(trueDag);
    return result;
}
Also used : MlBayesIm(edu.cmu.tetrad.bayes.MlBayesIm) Node(edu.cmu.tetrad.graph.Node) ArrayList(java.util.ArrayList) ArrayList(java.util.ArrayList) List(java.util.List) EdgeListGraph(edu.cmu.tetrad.graph.EdgeListGraph) EdgeListGraph(edu.cmu.tetrad.graph.EdgeListGraph) Graph(edu.cmu.tetrad.graph.Graph) TabularDataReader(edu.pitt.dbmi.data.reader.tabular.TabularDataReader) VerticalDiscreteTabularDataReader(edu.pitt.dbmi.data.reader.tabular.VerticalDiscreteTabularDataReader) LargeScaleSimulation(edu.cmu.tetrad.sem.LargeScaleSimulation) VerticalDiscreteTabularDataReader(edu.pitt.dbmi.data.reader.tabular.VerticalDiscreteTabularDataReader) Path(java.nio.file.Path) Delimiter(edu.pitt.dbmi.data.Delimiter) ContinuousTabularDataFileReader(edu.pitt.dbmi.data.reader.tabular.ContinuousTabularDataFileReader) TetradMatrix(edu.cmu.tetrad.util.TetradMatrix) BayesPm(edu.cmu.tetrad.bayes.BayesPm)

Example 7 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class GdistanceRandomApply method main.

public static void main(String... args) {
    // thresholds are the barriers between histogram buckets.
    double[] thresholds;
    thresholds = new double[5];
    thresholds[0] = 0;
    thresholds[1] = 2;
    thresholds[2] = 4;
    thresholds[3] = 6;
    thresholds[4] = 8;
    // load the location map
    String workingDirectory = System.getProperty("user.dir");
    System.out.println(workingDirectory);
    Path mapPath = Paths.get("erich_coordinates.txt");
    System.out.println(mapPath);
    TabularDataReader dataReaderMap = new ContinuousTabularDataFileReader(mapPath.toFile(), Delimiter.COMMA);
    try {
        DataSet locationMap = (DataSet) DataConvertUtils.toDataModel(dataReaderMap.readInData());
        System.out.println("locationMap loaded");
        GdistanceRandom simRandGdistances = new GdistanceRandom(locationMap);
        System.out.println("GdistanceRandom constructed");
        simRandGdistances.setNumEdges1(300);
        simRandGdistances.setNumEdges2(300);
        simRandGdistances.setVerbose(false);
        System.out.println("Edge parameters set, starting simulations");
        List<List<Double>> GdistanceLists = simRandGdistances.randomSimulation(2);
        System.out.println("Simulations done, calculating histograms");
        for (List<Double> gdist : GdistanceLists) {
            double[] histogram = GdistanceUtils.histogram(gdist, thresholds);
            // making the string to print out histogram values
            String histString = " ";
            for (int i = 0; i < Array.getLength(histogram); i++) {
                histString = histString + " " + histogram[i];
            }
            System.out.println(histString);
        }
    } catch (Exception IOException) {
        IOException.printStackTrace();
    }
}
Also used : Path(java.nio.file.Path) TabularDataReader(edu.pitt.dbmi.data.reader.tabular.TabularDataReader) DataSet(edu.cmu.tetrad.data.DataSet) ContinuousTabularDataFileReader(edu.pitt.dbmi.data.reader.tabular.ContinuousTabularDataFileReader) List(java.util.List)

Example 8 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class TestGFci method testDiscreteData.

@Test
public void testDiscreteData() throws IOException {
    double alpha = 0.05;
    char delimiter = '\t';
    Path dataFile = Paths.get("./src/test/resources/sim_discrete_data_20vars_100cases.txt");
    TabularDataReader dataReader = new VerticalDiscreteTabularDataReader(dataFile.toFile(), DelimiterUtils.toDelimiter(delimiter));
    DataSet dataSet = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData());
    IndependenceTest indTest = new IndTestChiSquare(dataSet, alpha);
    BDeuScore score = new BDeuScore(dataSet);
    score.setStructurePrior(1.0);
    score.setSamplePrior(1.0);
    GFci gFci = new GFci(indTest, score);
    gFci.setFaithfulnessAssumed(true);
    gFci.setMaxDegree(-1);
    gFci.setMaxPathLength(-1);
    gFci.setCompleteRuleSetUsed(false);
    gFci.setVerbose(true);
    long start = System.currentTimeMillis();
    gFci.search();
    long stop = System.currentTimeMillis();
    System.out.println("Elapsed " + (stop - start) + " ms");
}
Also used : Path(java.nio.file.Path) TabularDataReader(edu.pitt.dbmi.data.reader.tabular.TabularDataReader) VerticalDiscreteTabularDataReader(edu.pitt.dbmi.data.reader.tabular.VerticalDiscreteTabularDataReader) VerticalDiscreteTabularDataReader(edu.pitt.dbmi.data.reader.tabular.VerticalDiscreteTabularDataReader) Test(org.junit.Test)

Example 9 with TabularDataReader

use of edu.pitt.dbmi.data.reader.tabular.TabularDataReader in project tetrad by cmu-phil.

the class TestDM method readAndSearchData.

// Reads in data and runs search. Note: Assumes variable names are of the form X0, X1, etc.
// Both input and output integer arrays are the indexes of their respective variables.
public DMSearch readAndSearchData(String fileLocation, int[] inputs, int[] outputs, boolean useGES, int[] trueInputs) {
    File file = new File(fileLocation);
    DataSet data = null;
    try {
        TabularDataReader dataReader = new ContinuousTabularDataFileReader(file, Delimiter.SPACE);
        data = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData());
    } catch (IOException e) {
        print("Failed to read in data.");
        e.printStackTrace();
    }
    print("Read Data");
    DMSearch search = new DMSearch();
    search.setInputs(inputs);
    search.setOutputs(outputs);
    if (useGES == false) {
        search.setAlphaPC(.05);
        search.setUseFges(useGES);
        search.setData(data);
        search.setTrueInputs(trueInputs);
        search.search();
    } else {
        search.setData(data);
        search.setTrueInputs(trueInputs);
        search.search();
    // search.search(data, trueInputs);
    }
    return (search);
}
Also used : TabularDataReader(edu.pitt.dbmi.data.reader.tabular.TabularDataReader) DataSet(edu.cmu.tetrad.data.DataSet) ContinuousTabularDataFileReader(edu.pitt.dbmi.data.reader.tabular.ContinuousTabularDataFileReader) IOException(java.io.IOException) File(java.io.File) DMSearch(edu.cmu.tetrad.search.DMSearch)

Aggregations

TabularDataReader (edu.pitt.dbmi.data.reader.tabular.TabularDataReader)9 ContinuousTabularDataFileReader (edu.pitt.dbmi.data.reader.tabular.ContinuousTabularDataFileReader)7 Path (java.nio.file.Path)7 DataSet (edu.cmu.tetrad.data.DataSet)5 Graph (edu.cmu.tetrad.graph.Graph)4 VerticalDiscreteTabularDataReader (edu.pitt.dbmi.data.reader.tabular.VerticalDiscreteTabularDataReader)4 File (java.io.File)3 PrintWriter (java.io.PrintWriter)3 Node (edu.cmu.tetrad.graph.Node)2 Delimiter (edu.pitt.dbmi.data.Delimiter)2 ArrayList (java.util.ArrayList)2 HashSet (java.util.HashSet)2 List (java.util.List)2 BayesPm (edu.cmu.tetrad.bayes.BayesPm)1 MlBayesIm (edu.cmu.tetrad.bayes.MlBayesIm)1 ContinuousVariable (edu.cmu.tetrad.data.ContinuousVariable)1 CovarianceMatrixOnTheFly (edu.cmu.tetrad.data.CovarianceMatrixOnTheFly)1 DataModel (edu.cmu.tetrad.data.DataModel)1 ICovarianceMatrix (edu.cmu.tetrad.data.ICovarianceMatrix)1 Dag (edu.cmu.tetrad.graph.Dag)1