Examples with ICovarianceMatrix edu.cmu.tetrad.data.ICovarianceMatrix used on opensource projects

Example 11 with ICovarianceMatrix

use of edu.cmu.tetrad.data.ICovarianceMatrix in project tetrad by cmu-phil.

the class BuildPureClustersParamsEditor method setup.

public void setup() {
    DoubleTextField alphaField = new DoubleTextField(params.getDouble("alpha", 0.001), 4, NumberFormatUtil.getInstance().getNumberFormat());
    alphaField.setFilter(new DoubleTextField.Filter() {

        public double filter(double value, double oldValue) {
            try {
                getParams().set("alpha", 0.001);
                return value;
            } catch (Exception e) {
                return oldValue;
            }
        }
    });
    final TestType[] descriptions = TestType.getTestDescriptions();
    JComboBox testSelector = new JComboBox(descriptions);
    testSelector.setSelectedItem(getParams().get("tetradTestType", TestType.TETRAD_WISHART));
    testSelector.addActionListener(new ActionListener() {

        public void actionPerformed(ActionEvent e) {
            JComboBox combo = (JComboBox) e.getSource();
            TestType testType = (TestType) combo.getSelectedItem();
            getParams().set("tetradTestType", testType);
        }
    });
    final TestType[] purifyDescriptions = TestType.getPurifyTestDescriptions();
    JComboBox purifySelector = new JComboBox(purifyDescriptions);
    purifySelector.setSelectedItem(getParams().get("purifyTestType", TestType.NONE));
    purifySelector.addActionListener(new ActionListener() {

        public void actionPerformed(ActionEvent e) {
            JComboBox combo = (JComboBox) e.getSource();
            TestType testType = (TestType) combo.getSelectedItem();
            getParams().set("purifyTestType", testType);
        }
    });
    // Where is it setting the appropriate knowledge for the search?
    DataModel dataModel = null;
    for (Object parentModel : this.parentModels) {
        if (parentModel instanceof DataWrapper) {
            DataWrapper dataWrapper = (DataWrapper) parentModel;
            dataModel = dataWrapper.getSelectedDataModel();
        }
    }
    if (dataModel == null) {
        throw new IllegalStateException("Null data model.");
    }
    List<String> varNames = new ArrayList<>(dataModel.getVariableNames());
    boolean isDiscreteModel;
    if (dataModel instanceof ICovarianceMatrix) {
        isDiscreteModel = false;
    } else {
        DataSet dataSet = (DataSet) dataModel;
        isDiscreteModel = dataSet.isDiscrete();
    // try {
    // new DataSet((DataSet) dataModel);
    // isDiscreteModel = true;
    // }
    // catch (IllegalArgumentException e) {
    // isDiscreteModel = false;
    // }
    }
    params.set("varNames", varNames);
    alphaField.setValue(params.getDouble("alpha", 0.001));
    Box b = Box.createVerticalBox();
    Box b1 = Box.createHorizontalBox();
    b1.add(new JLabel("Alpha:"));
    b1.add(Box.createHorizontalGlue());
    b1.add(alphaField);
    b.add(b1);
    if (!isDiscreteModel) {
        Box b2 = Box.createHorizontalBox();
        b2.add(new JLabel("Statistical Test:"));
        b2.add(Box.createHorizontalGlue());
        b2.add(testSelector);
        b.add(b2);
        Box b3 = Box.createHorizontalBox();
        b3.add(new JLabel("Purify Test:"));
        b3.add(Box.createHorizontalGlue());
        b3.add(purifySelector);
        b.add(b3);
    } else {
        this.params.set("purifyTestType", TestType.DISCRETE_LRT);
        this.params.set("tetradTestType", TestType.DISCRETE);
    }
    setLayout(new BorderLayout());
    add(b, BorderLayout.CENTER);
}

Example 12 with ICovarianceMatrix

use of edu.cmu.tetrad.data.ICovarianceMatrix in project tetrad by cmu-phil.

the class HsimEvalFromData method main.

public static void main(String[] args) {
    long timestart = System.nanoTime();
    System.out.println("Beginning Evaluation");
    String nl = System.lineSeparator();
    String output = "Simulation edu.cmu.tetrad.study output comparing Fsim and Hsim on predicting graph discovery accuracy" + nl;
    int iterations = 100;
    int vars = 20;
    int cases = 500;
    int edgeratio = 3;
    List<Integer> hsimRepeat = Arrays.asList(40);
    List<Integer> fsimRepeat = Arrays.asList(40);
    List<PRAOerrors>[] fsimErrsByPars = new ArrayList[fsimRepeat.size()];
    int whichFrepeat = 0;
    for (int frepeat : fsimRepeat) {
        fsimErrsByPars[whichFrepeat] = new ArrayList<PRAOerrors>();
        whichFrepeat++;
    }
    List<PRAOerrors>[][] hsimErrsByPars = new ArrayList[1][hsimRepeat.size()];
    // System.out.println(resimSize.size()+" "+hsimRepeat.size());
    int whichHrepeat;
    whichHrepeat = 0;
    for (int hrepeat : hsimRepeat) {
        // System.out.println(whichrsize+" "+whichHrepeat);
        hsimErrsByPars[0][whichHrepeat] = new ArrayList<PRAOerrors>();
        whichHrepeat++;
    }
    // !(*%(@!*^!($%!^ START ITERATING HERE !#$%(*$#@!^(*!$*%(!$#
    try {
        for (int iterate = 0; iterate < iterations; iterate++) {
            System.out.println("iteration " + iterate);
            // @#$%@$%^@$^@$^@%$%@$#^ LOADING THE DATA AND GRAPH @$#%%*#^##*^$#@%$
            DataSet data1;
            Graph graph1 = GraphUtils.loadGraphTxt(new File("graph/graph.1.txt"));
            Dag odag = new Dag(graph1);
            Set<String> eVars = new HashSet<String>();
            eVars.add("MULT");
            Path dataFile = Paths.get("data/data.1.txt");
            TabularDataReader dataReader = new ContinuousTabularDataFileReader(dataFile.toFile(), Delimiter.TAB);
            data1 = (DataSet) DataConvertUtils.toDataModel(dataReader.readInData(eVars));
            vars = data1.getNumColumns();
            cases = data1.getNumRows();
            edgeratio = 3;
            // !#@^$@&%^!#$!&@^ CALCULATING TARGET ERRORS $%$#@^@!%!#^$!%$#%
            ICovarianceMatrix newcov = new CovarianceMatrixOnTheFly(data1);
            SemBicScore oscore = new SemBicScore(newcov);
            Fges ofgs = new Fges(oscore);
            ofgs.setVerbose(false);
            ofgs.setNumPatternsToStore(0);
            // ***********This is the original FGS output on the data
            Graph oFGSGraph = ofgs.search();
            PRAOerrors oErrors = new PRAOerrors(HsimUtils.errorEval(oFGSGraph, odag), "target errors");
            // **then step 1: full resim. iterate through the combinations of estimator parameters (just repeat num)
            for (whichFrepeat = 0; whichFrepeat < fsimRepeat.size(); whichFrepeat++) {
                ArrayList<PRAOerrors> errorsList = new ArrayList<PRAOerrors>();
                for (int r = 0; r < fsimRepeat.get(whichFrepeat); r++) {
                    PatternToDag pickdag = new PatternToDag(oFGSGraph);
                    Graph fgsDag = pickdag.patternToDagMeek();
                    Dag fgsdag2 = new Dag(fgsDag);
                    // then fit an IM to this dag and the data. GeneralizedSemEstimator seems to bug out
                    // GeneralizedSemPm simSemPm = new GeneralizedSemPm(fgsdag2);
                    // GeneralizedSemEstimator gsemEstimator = new GeneralizedSemEstimator();
                    // GeneralizedSemIm fittedIM = gsemEstimator.estimate(simSemPm, oData);
                    SemPm simSemPm = new SemPm(fgsdag2);
                    // BayesPm simBayesPm = new BayesPm(fgsdag2, bayesPm);
                    SemEstimator simSemEstimator = new SemEstimator(data1, simSemPm);
                    SemIm fittedIM = simSemEstimator.estimate();
                    DataSet simData = fittedIM.simulateData(data1.getNumRows(), false);
                    // after making the full resim data (simData), run FGS on that
                    ICovarianceMatrix simcov = new CovarianceMatrixOnTheFly(simData);
                    SemBicScore simscore = new SemBicScore(simcov);
                    Fges simfgs = new Fges(simscore);
                    simfgs.setVerbose(false);
                    simfgs.setNumPatternsToStore(0);
                    Graph simGraphOut = simfgs.search();
                    PRAOerrors simErrors = new PRAOerrors(HsimUtils.errorEval(simGraphOut, fgsdag2), "Fsim errors " + r);
                    errorsList.add(simErrors);
                }
                PRAOerrors avErrors = new PRAOerrors(errorsList, "Average errors for Fsim at repeat=" + fsimRepeat.get(whichFrepeat));
                // if (verbosity>3) System.out.println(avErrors.allToString());
                // ****calculate the squared errors of prediction, store all these errors in a list
                double FsimAR2 = (avErrors.getAdjRecall() - oErrors.getAdjRecall()) * (avErrors.getAdjRecall() - oErrors.getAdjRecall());
                double FsimAP2 = (avErrors.getAdjPrecision() - oErrors.getAdjPrecision()) * (avErrors.getAdjPrecision() - oErrors.getAdjPrecision());
                double FsimOR2 = (avErrors.getOrientRecall() - oErrors.getOrientRecall()) * (avErrors.getOrientRecall() - oErrors.getOrientRecall());
                double FsimOP2 = (avErrors.getOrientPrecision() - oErrors.getOrientPrecision()) * (avErrors.getOrientPrecision() - oErrors.getOrientPrecision());
                PRAOerrors Fsim2 = new PRAOerrors(new double[] { FsimAR2, FsimAP2, FsimOR2, FsimOP2 }, "squared errors for Fsim at repeat=" + fsimRepeat.get(whichFrepeat));
                // add the fsim squared errors to the appropriate list
                fsimErrsByPars[whichFrepeat].add(Fsim2);
            }
            // **then step 2: hybrid sim. iterate through combos of params (repeat num, resimsize)
            for (whichHrepeat = 0; whichHrepeat < hsimRepeat.size(); whichHrepeat++) {
                HsimRepeatAC study = new HsimRepeatAC(data1);
                PRAOerrors HsimErrors = new PRAOerrors(study.run(1, hsimRepeat.get(whichHrepeat)), "Hsim errors" + "at rsize=" + 1 + " repeat=" + hsimRepeat.get(whichHrepeat));
                // ****calculate the squared errors of prediction
                double HsimAR2 = (HsimErrors.getAdjRecall() - oErrors.getAdjRecall()) * (HsimErrors.getAdjRecall() - oErrors.getAdjRecall());
                double HsimAP2 = (HsimErrors.getAdjPrecision() - oErrors.getAdjPrecision()) * (HsimErrors.getAdjPrecision() - oErrors.getAdjPrecision());
                double HsimOR2 = (HsimErrors.getOrientRecall() - oErrors.getOrientRecall()) * (HsimErrors.getOrientRecall() - oErrors.getOrientRecall());
                double HsimOP2 = (HsimErrors.getOrientPrecision() - oErrors.getOrientPrecision()) * (HsimErrors.getOrientPrecision() - oErrors.getOrientPrecision());
                PRAOerrors Hsim2 = new PRAOerrors(new double[] { HsimAR2, HsimAP2, HsimOR2, HsimOP2 }, "squared errors for Hsim, rsize=" + 1 + " repeat=" + hsimRepeat.get(whichHrepeat));
                hsimErrsByPars[0][whichHrepeat].add(Hsim2);
            }
        }
        // Average the squared errors for each set of fsim/hsim params across all iterations
        PRAOerrors[] fMSE = new PRAOerrors[fsimRepeat.size()];
        PRAOerrors[][] hMSE = new PRAOerrors[1][hsimRepeat.size()];
        String[][] latexTableArray = new String[1 * hsimRepeat.size() + fsimRepeat.size()][5];
        for (int j = 0; j < fMSE.length; j++) {
            fMSE[j] = new PRAOerrors(fsimErrsByPars[j], "MSE for Fsim at vars=" + vars + " edgeratio=" + edgeratio + " cases=" + cases + " frepeat=" + fsimRepeat.get(j) + " iterations=" + iterations);
            // if(verbosity>0){System.out.println(fMSE[j].allToString());}
            output = output + fMSE[j].allToString() + nl;
            latexTableArray[j] = prelimToPRAOtable(fMSE[j]);
        }
        for (int j = 0; j < hMSE.length; j++) {
            for (int k = 0; k < hMSE[j].length; k++) {
                hMSE[j][k] = new PRAOerrors(hsimErrsByPars[j][k], "MSE for Hsim at vars=" + vars + " edgeratio=" + edgeratio + " cases=" + cases + " rsize=" + 1 + " repeat=" + hsimRepeat.get(k) + " iterations=" + iterations);
                // if(verbosity>0){System.out.println(hMSE[j][k].allToString());}
                output = output + hMSE[j][k].allToString() + nl;
                latexTableArray[fsimRepeat.size() + j * hMSE[j].length + k] = prelimToPRAOtable(hMSE[j][k]);
            }
        }
        // record all the params, the base error values, and the fsim/hsim mean squared errors
        String latexTable = HsimUtils.makeLatexTable(latexTableArray);
        PrintWriter writer = new PrintWriter("latexTable.txt", "UTF-8");
        writer.println(latexTable);
        writer.close();
        PrintWriter writer2 = new PrintWriter("HvsF-SimulationEvaluation.txt", "UTF-8");
        writer2.println(output);
        writer2.close();
        long timestop = System.nanoTime();
        System.out.println("Evaluation Concluded. Duration: " + (timestop - timestart) / 1000000000 + "s");
    } catch (Exception IOException) {
        IOException.printStackTrace();
    }
}

Example 13 with ICovarianceMatrix

use of edu.cmu.tetrad.data.ICovarianceMatrix in project tetrad by cmu-phil.

the class TestRegression method testCovariance.

/**
 * Same problem, using the covariance matrix.
 */
@Test
public void testCovariance() {
    setUp();
    RandomUtil.getInstance().setSeed(3848283L);
    ICovarianceMatrix cov = new CovarianceMatrix(data);
    List<Node> nodes = cov.getVariables();
    Node target = nodes.get(0);
    List<Node> regressors = new ArrayList<>();
    for (int i = 1; i < nodes.size(); i++) {
        regressors.add(nodes.get(i));
    }
    Regression regression = new RegressionCovariance(cov);
    RegressionResult result = regression.regress(target, regressors);
    double[] coeffs = result.getCoef();
    assertEquals(0.00, coeffs[0], 0.01);
    assertEquals(-.053, coeffs[1], 0.01);
    assertEquals(0.036, coeffs[2], 0.01);
    assertEquals(.019, coeffs[3], 0.01);
    assertEquals(.007, coeffs[4], 0.01);
}

Example 14 with ICovarianceMatrix

use of edu.cmu.tetrad.data.ICovarianceMatrix in project tetrad by cmu-phil.

the class TestSem method testSet2.

@Test
public void testSet2() {
    Graph graph = constructGraph2();
    SemPm semPm = new SemPm(graph);
    ICovarianceMatrix covMatrix = constructCovMatrix2();
    SemIm sem = new SemIm(semPm, covMatrix);
}

Example 15 with ICovarianceMatrix

use of edu.cmu.tetrad.data.ICovarianceMatrix in project tetrad by cmu-phil.

the class Mimbuild2 method search.

// =================================== PUBLIC METHODS =========================================//
public Graph search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov) {
    List<String> _latentNames = new ArrayList<>(latentNames);
    List<String> allVarNames = new ArrayList<>();
    for (List<Node> cluster : clustering) {
        for (Node node : cluster) allVarNames.add(node.getName());
    }
    measuresCov = measuresCov.getSubmatrix(allVarNames);
    List<List<Node>> _clustering = new ArrayList<>();
    for (List<Node> cluster : clustering) {
        List<Node> _cluster = new ArrayList<>();
        for (Node node : cluster) {
            _cluster.add(measuresCov.getVariable(node.getName()));
        }
        _clustering.add(_cluster);
    }
    List<Node> latents = defineLatents(_latentNames);
    this.latents = latents;
    // This removes the small clusters and their names.
    removeSmallClusters(latents, _clustering, getMinClusterSize());
    this.clustering = _clustering;
    Node[][] indicators = new Node[latents.size()][];
    for (int i = 0; i < latents.size(); i++) {
        indicators[i] = new Node[_clustering.get(i).size()];
        for (int j = 0; j < _clustering.get(i).size(); j++) {
            indicators[i][j] = _clustering.get(i).get(j);
        }
    }
    TetradMatrix cov = getCov(measuresCov, latents, indicators);
    CovarianceMatrix latentscov = new CovarianceMatrix(latents, cov, measuresCov.getSampleSize());
    this.latentsCov = latentscov;
    Graph graph;
    Cpc search = new Cpc(new IndTestFisherZ(latentscov, alpha));
    search.setKnowledge(knowledge);
    graph = search.search();
    // try {
    // Ges search = new Ges(latentscov);
    // search.setDepErrorsAlpha(penaltyDiscount);
    // search.setKnowledge(knowledge);
    // graph = search.search();
    // } catch (Exception e) {
    // //            e.printStackTrace();
    // CPC search = new CPC(new IndTestFisherZ(latentscov, alpha));
    // search.setKnowledge(knowledge);
    // graph = search.search();
    // }
    this.structureGraph = new EdgeListGraph(graph);
    GraphUtils.fruchtermanReingoldLayout(this.structureGraph);
    return this.structureGraph;
}