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

Example 11 with SemPm

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

the class SemSimulation method simulate.

private DataSet simulate(Graph graph, Parameters parameters) {
    boolean saveLatentVars = parameters.getBoolean("saveLatentVars");
    SemIm im = this.im;
    if (im == null) {
        SemPm pm = this.pm;
        if (pm == null) {
            pm = new SemPm(graph);
            im = new SemIm(pm, parameters);
            ims.add(im);
            return im.simulateData(parameters.getInt("sampleSize"), saveLatentVars);
        } else {
            im = new SemIm(pm, parameters);
            ims.add(im);
            return im.simulateData(parameters.getInt("sampleSize"), saveLatentVars);
        }
    } else {
        ims.add(im);
        return im.simulateData(parameters.getInt("sampleSize"), saveLatentVars);
    }
}

Example 12 with SemPm

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

the class StandardizedSemSimulation method simulate.

private DataSet simulate(Graph graph, Parameters parameters) {
    if (standardizedIm == null) {
        SemPm pm = this.pm;
        if (pm == null) {
            pm = new SemPm(graph);
        }
        standardizedIm = new StandardizedSemIm(new SemIm(pm));
    }
    return standardizedIm.simulateData(parameters.getInt("sampleSize"), false);
}

Example 13 with SemPm

use of edu.cmu.tetrad.sem.SemPm 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 14 with SemPm

use of edu.cmu.tetrad.sem.SemPm 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 15 with SemPm

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

the class TestLogisticRegression method test1.

@Test
public void test1() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 3, 3, 3, false));
    System.out.println(graph);
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateDataRecursive(1000, false);
    Node x1 = data.getVariable("X1");
    Node x2 = data.getVariable("X2");
    Node x3 = data.getVariable("X3");
    Node x4 = data.getVariable("X4");
    Node x5 = data.getVariable("X5");
    Discretizer discretizer = new Discretizer(data);
    discretizer.equalCounts(x1, 2);
    DataSet d2 = discretizer.discretize();
    LogisticRegression regression = new LogisticRegression(d2);
    List<Node> regressors = new ArrayList<>();
    regressors.add(x2);
    regressors.add(x3);
    regressors.add(x4);
    regressors.add(x5);
    DiscreteVariable x1b = (DiscreteVariable) d2.getVariable("X1");
    regression.regress(x1b, regressors);
    System.out.println(regression);
}