Examples with SemEstimator edu.cmu.tetrad.sem.SemEstimator used on opensource projects

Example 1 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class SemEstimatorEditor method reestimate.

private void reestimate() {
    SemOptimizer optimizer;
    String type = wrapper.getSemOptimizerType();
    switch(type) {
        case "Regression":
            optimizer = new SemOptimizerRegression();
            break;
        case "EM":
            optimizer = new SemOptimizerEm();
            break;
        case "Powell":
            optimizer = new SemOptimizerPowell();
            break;
        case "Random Search":
            optimizer = new SemOptimizerScattershot();
            break;
        case "RICF":
            optimizer = new SemOptimizerRicf();
            break;
        default:
            throw new IllegalArgumentException("Unexpected optimizer type: " + type);
    }
    int numRestarts = wrapper.getNumRestarts();
    optimizer.setNumRestarts(numRestarts);
    java.util.List<SemEstimator> estimators = wrapper.getMultipleResultList();
    java.util.List<SemEstimator> newEstimators = new ArrayList<>();
    estimators.forEach(estimator -> {
        SemPm semPm = estimator.getSemPm();
        DataSet dataSet = estimator.getDataSet();
        ICovarianceMatrix covMatrix = estimator.getCovMatrix();
        SemEstimator newEstimator;
        if (dataSet != null) {
            newEstimator = new SemEstimator(dataSet, semPm, optimizer);
            newEstimator.setNumRestarts(numRestarts);
            newEstimator.setScoreType(wrapper.getScoreType());
        } else if (covMatrix != null) {
            newEstimator = new SemEstimator(covMatrix, semPm, optimizer);
            newEstimator.setNumRestarts(numRestarts);
            newEstimator.setScoreType(wrapper.getScoreType());
        } else {
            throw new IllegalStateException("Only continuous rectangular" + " data sets and covariance matrices can be processed.");
        }
        newEstimator.estimate();
        newEstimators.add(newEstimator);
    });
    wrapper.setSemEstimator(newEstimators.get(0));
    wrapper.setMultipleResultList(newEstimators);
    resetSemImEditor();
}

Example 2 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class Washdown method pValue.

private double pValue(List<List<Node>> clusters) {
    clusters = removeEmpty(clusters);
    Graph graph = pureMeasurementModel(clusters);
    SemPm pm = new SemPm(graph);
    SemEstimator estimator;
    if (cov != null) {
        estimator = new SemEstimator(cov, pm);
    } else {
        estimator = new SemEstimator(dataSet, pm);
    }
    SemIm est = estimator.estimate();
    double pValue = est.getPValue();
    return pValue;
}

Example 3 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator 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 4 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class TestSemVarMeans method testMeansCholesky.

@Test
public void testMeansCholesky() {
    Graph graph = constructGraph1();
    SemPm semPm1 = new SemPm(graph);
    List<Parameter> parameters = semPm1.getParameters();
    for (Parameter p : parameters) {
        p.setInitializedRandomly(false);
    }
    SemIm semIm1 = new SemIm(semPm1);
    double[] means = { 5.0, 4.0, 3.0, 2.0, 1.0 };
    RandomUtil.getInstance().setSeed(-379467L);
    for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
        Node node = semIm1.getVariableNodes().get(i);
        semIm1.setMean(node, means[i]);
    }
    DataSet dataSet = semIm1.simulateDataCholesky(1000, false);
    SemEstimator semEst = new SemEstimator(dataSet, semPm1);
    semEst.estimate();
    SemIm estSemIm = semEst.getEstimatedSem();
    List<Node> nodes = semPm1.getVariableNodes();
    for (Node node : nodes) {
        double mean = semIm1.getMean(node);
        assertEquals(mean, estSemIm.getMean(node), 0.6);
    }
}

Example 5 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class TestSemVarMeans method testMeansReducedForm.

@Test
public void testMeansReducedForm() {
    Graph graph = constructGraph1();
    SemPm semPm1 = new SemPm(graph);
    List<Parameter> parameters = semPm1.getParameters();
    for (Parameter p : parameters) {
        p.setInitializedRandomly(false);
    }
    SemIm semIm1 = new SemIm(semPm1);
    double[] means = { 5.0, 4.0, 3.0, 2.0, 1.0 };
    RandomUtil.getInstance().setSeed(-379467L);
    for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
        Node node = semIm1.getVariableNodes().get(i);
        semIm1.setMean(node, means[i]);
    }
    DataSet dataSet = semIm1.simulateDataReducedForm(1000, false);
    SemEstimator semEst = new SemEstimator(dataSet, semPm1);
    semEst.estimate();
    SemIm estSemIm = semEst.getEstimatedSem();
    List<Node> nodes = semPm1.getVariableNodes();
    for (Node node : nodes) {
        double mean = semIm1.getMean(node);
        assertEquals(mean, estSemIm.getMean(node), 0.5);
    }
}