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

Example 46 with SemIm

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

the class TimeSeriesSemSimulation method createData.

@Override
public void createData(Parameters parameters) {
    dataSets = new ArrayList<>();
    graphs = new ArrayList<>();
    Graph graph = randomGraph.createGraph(parameters);
    graph = TimeSeriesUtils.graphToLagGraph(graph, parameters.getInt("numLags"));
    topToBottomLayout((TimeLagGraph) graph);
    this.knowledge = TimeSeriesUtils.getKnowledge(graph);
    for (int i = 0; i < parameters.getInt("numRuns"); i++) {
        if (parameters.getBoolean("differentGraphs") && i > 0) {
            graph = randomGraph.createGraph(parameters);
            graph = TimeSeriesUtils.graphToLagGraph(graph, 2);
            topToBottomLayout((TimeLagGraph) graph);
        }
        graphs.add(graph);
        SemPm pm = new SemPm(graph);
        SemIm im = new SemIm(pm, parameters);
        final int sampleSize = parameters.getInt("sampleSize");
        boolean saveLatentVars = parameters.getBoolean("saveLatentVars");
        DataSet dataSet = im.simulateData(sampleSize, saveLatentVars);
        int numLags = ((TimeLagGraph) graph).getMaxLag();
        dataSet = TimeSeriesUtils.createLagData(dataSet, numLags);
        dataSet.setName("" + (i + 1));
        dataSet.setKnowledge(knowledge.copy());
        dataSets.add(dataSet);
    // LargeScaleSimulation sim = new LargeScaleSimulation(graph);
    // if (parameters.getDouble("coefHigh") > 0.80) {
    // System.out.println("Coefficients have been set (perhaps by default) too " +
    // "high for stationary time series.");
    // System.out.println("Setting coefficient range to [0.20,0.60].");
    // sim.setCoefRange(0.20, 0.60);
    // } else sim.setCoefRange(parameters.getDouble("coefLow"), parameters.getDouble("coefHigh"));
    // boolean isStableTetradMatrix;
    // int attempt = 1;
    // int tierSize = parameters.getInt("numMeasures") + parameters.getInt("numLatents"); //params.getNumVars();
    // int[] sub = new int[tierSize];
    // int[] sub2 = new int[tierSize];
    // for (int j = 0; j < tierSize; j++) {
    // sub[j] = j;
    // sub2[j] = tierSize + j;
    // }
    // DataSet dataSet;
    // do {
    // dataSet = sim.simulateDataFisher(parameters.getInt("sampleSize")); //params.getSampleSize());
    // 
    // 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);
    // 
    // isStableTetradMatrix = TimeSeriesUtils.allEigenvaluesAreSmallerThanOneInModulus(A1);
    // 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(parameters.getInt("sampleSize"));//params.getSampleSize());
    // } //else System.out.println("Coefficient matrix is stable.");
    // dataSet.setName("" + (i + 1));
    // dataSet.setKnowledge(knowledge.copy());
    // dataSets.add(dataSet);
    }
}

Example 47 with SemIm

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

the class PerformanceTestsDan method testIdaOutputForDan.

private void testIdaOutputForDan() {
    int numRuns = 100;
    for (int run = 0; run < numRuns; run++) {
        double alphaGFci = 0.01;
        double alphaPc = 0.01;
        int penaltyDiscount = 1;
        int depth = 3;
        int maxPathLength = 3;
        final int numVars = 15;
        final double edgesPerNode = 1.0;
        final int numCases = 1000;
        // final int numLatents = RandomUtil.getInstance().nextInt(3) + 1;
        final int numLatents = 6;
        // writeToFile = false;
        PrintStream out1;
        PrintStream out2;
        PrintStream out3;
        PrintStream out4;
        PrintStream out5;
        PrintStream out6;
        PrintStream out7;
        PrintStream out8;
        PrintStream out9;
        PrintStream out10;
        PrintStream out11;
        PrintStream out12;
        File dir0 = new File("gfci.output");
        dir0.mkdirs();
        File dir = new File(dir0, "" + (run + 1));
        dir.mkdir();
        try {
            out1 = new PrintStream(new File(dir, "hyperparameters.txt"));
            out2 = new PrintStream(new File(dir, "variables.txt"));
            out3 = new PrintStream(new File(dir, "dag.long.txt"));
            out4 = new PrintStream(new File(dir, "dag.matrix.txt"));
            out5 = new PrintStream(new File(dir, "coef.matrix.txt"));
            out6 = new PrintStream(new File(dir, "pag.long.txt"));
            out7 = new PrintStream(new File(dir, "pag.matrix.txt"));
            out8 = new PrintStream(new File(dir, "pattern.long.txt"));
            out9 = new PrintStream(new File(dir, "pattern.matrix.txt"));
            out10 = new PrintStream(new File(dir, "data.txt"));
            out11 = new PrintStream(new File(dir, "true.pag.long.txt"));
            out12 = new PrintStream(new File(dir, "true.pag.matrix.txt"));
        } catch (FileNotFoundException e) {
            e.printStackTrace();
            throw new RuntimeException(e);
        }
        out1.println("Num _vars = " + numVars);
        out1.println("Num edges = " + (int) (numVars * edgesPerNode));
        out1.println("Num cases = " + numCases);
        out1.println("Alpha for PC = " + alphaPc);
        out1.println("Alpha for FFCI = " + alphaGFci);
        out1.println("Penalty discount = " + penaltyDiscount);
        out1.println("Depth = " + depth);
        out1.println("Maximum reachable path length for dsep search and discriminating undirectedPaths = " + maxPathLength);
        List<Node> vars = new ArrayList<>();
        for (int i = 0; i < numVars; i++) vars.add(new GraphNode("X" + (i + 1)));
        // Graph dag = DataGraphUtils.randomDagQuick2(varsWithLatents, 0, (int) (varsWithLatents.size() * edgesPerNode));
        Graph dag = GraphUtils.randomGraph(vars, 0, (int) (vars.size() * edgesPerNode), 5, 5, 5, false);
        GraphUtils.fixLatents1(numLatents, dag);
        // List<Node> varsWithLatents = new ArrayList<Node>();
        // 
        // Graph dag = getLatentGraph(_vars, varsWithLatents, edgesPerNode, numLatents);
        out3.println(dag);
        printDanMatrix(vars, dag, out4);
        SemPm pm = new SemPm(dag);
        SemIm im = new SemIm(pm);
        NumberFormat nf = new DecimalFormat("0.0000");
        for (int i = 0; i < vars.size(); i++) {
            for (Node var : vars) {
                if (im.existsEdgeCoef(var, vars.get(i))) {
                    double coef = im.getEdgeCoef(var, vars.get(i));
                    out5.print(nf.format(coef) + "\t");
                } else {
                    out5.print(nf.format(0) + "\t");
                }
            }
            out5.println();
        }
        out5.println();
        String vars_temp = vars.toString();
        vars_temp = vars_temp.replace("[", "");
        vars_temp = vars_temp.replace("]", "");
        vars_temp = vars_temp.replace("X", "");
        out2.println(vars_temp);
        List<Node> _vars = new ArrayList<>();
        for (Node node : vars) {
            if (node.getNodeType() == NodeType.MEASURED) {
                _vars.add(node);
            }
        }
        String _vars_temp = _vars.toString();
        _vars_temp = _vars_temp.replace("[", "");
        _vars_temp = _vars_temp.replace("]", "");
        _vars_temp = _vars_temp.replace("X", "");
        out2.println(_vars_temp);
        DataSet fullData = im.simulateData(numCases, false);
        DataSet data = DataUtils.restrictToMeasured(fullData);
        ICovarianceMatrix cov = new CovarianceMatrix(data);
        final IndTestFisherZ independenceTestGFci = new IndTestFisherZ(cov, alphaGFci);
        final edu.cmu.tetrad.search.SemBicScore scoreGfci = new edu.cmu.tetrad.search.SemBicScore(cov);
        out6.println("GFCI.PAG_of_the_true_DAG");
        GFci gFci = new GFci(independenceTestGFci, scoreGfci);
        gFci.setVerbose(false);
        gFci.setMaxDegree(depth);
        gFci.setMaxPathLength(maxPathLength);
        // gFci.setPossibleDsepSearchDone(true);
        gFci.setCompleteRuleSetUsed(true);
        Graph pag = gFci.search();
        out6.println(pag);
        printDanMatrix(_vars, pag, out7);
        out8.println("Pattern_of_the_true_DAG OVER MEASURED VARIABLES");
        final IndTestFisherZ independencePc = new IndTestFisherZ(cov, alphaPc);
        Pc pc = new Pc(independencePc);
        pc.setVerbose(false);
        pc.setDepth(depth);
        Graph pattern = pc.search();
        out8.println(pattern);
        printDanMatrix(_vars, pattern, out9);
        out10.println(data);
        out11.println("True PAG_of_the_true_DAG");
        final Graph truePag = new DagToPag(dag).convert();
        out11.println(truePag);
        printDanMatrix(_vars, truePag, out12);
        out1.close();
        out2.close();
        out3.close();
        out4.close();
        out5.close();
        out6.close();
        out7.close();
        out8.close();
        out9.close();
        out10.close();
        out11.close();
        out12.close();
    }
}

Example 48 with SemIm

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

the class EdgeWeightComparison method getDisplayString.

public String getDisplayString() {
    String displayString = "";
    SemIm ref = reference;
    TetradMatrix referenceMatrix = ref.getEdgeCoef();
    TetradMatrix targetMatrix = target.getEdgeCoef();
    if (targetMatrix.columns() != referenceMatrix.columns() || targetMatrix.rows() != referenceMatrix.rows())
        return "The SEM IM's you selected don't have the same number of variables!  No comparison is possible here.";
    double score = 0;
    for (int i = 0; i < ref.getEdgeCoef().rows(); i++) {
        for (int j = 0; j < ref.getEdgeCoef().columns(); j++) {
            score += (targetMatrix.get(i, j) - referenceMatrix.get(i, j)) * (targetMatrix.get(i, j) - referenceMatrix.get(i, j));
        }
    }
    displayString += "Scheines Score: " + score + "\n\n";
    displayString += "(Calculated by summing the squared differences\n of each corresponding edge weight.)";
    return displayString;
}

Example 49 with SemIm

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

the class SemEstimatorEditor method compileReport.

private String compileReport() {
    StringBuilder builder = new StringBuilder();
    builder.append("Datset\tFrom\tTo\tType\tValue\tSE\tT\tP");
    java.util.List<SemEstimator> estimators = wrapper.getMultipleResultList();
    for (int i = 0; i < estimators.size(); i++) {
        SemEstimator estimator = estimators.get(i);
        SemIm estSem = estimator.getEstimatedSem();
        String dataName = estimator.getDataSet().getName();
        estSem.getFreeParameters().forEach(parameter -> {
            builder.append("\n");
            builder.append(dataName).append("\t");
            builder.append(parameter.getNodeA()).append("\t");
            builder.append(parameter.getNodeB()).append("\t");
            builder.append(typeString(parameter)).append("\t");
            builder.append(asString(paramValue(estSem, parameter))).append("\t");
            /**
             * Maximum number of free parameters for which statistics will
             * be calculated. (Calculating standard errors is high
             * complexity.) Set this to zero to turn off statistics
             * calculations (which can be problematic sometimes).
             */
            int maxFreeParamsForStatistics = 200;
            builder.append(asString(estSem.getStandardError(parameter, maxFreeParamsForStatistics))).append("\t");
            builder.append(asString(estSem.getTValue(parameter, maxFreeParamsForStatistics))).append("\t");
            builder.append(asString(estSem.getPValue(parameter, maxFreeParamsForStatistics))).append("\t");
        });
        List<Node> nodes = estSem.getVariableNodes();
        for (int j = 0; j < nodes.size(); j++) {
            Node node = nodes.get(j);
            int n = estSem.getSampleSize();
            int df = n - 1;
            double mean = estSem.getMean(node);
            double stdDev = estSem.getMeanStdDev(node);
            double stdErr = stdDev / Math.sqrt(n);
            double tValue = mean / stdErr;
            double p = 2.0 * (1.0 - ProbUtils.tCdf(Math.abs(tValue), df));
            builder.append("\n");
            builder.append(dataName).append("\t");
            builder.append(nodes.get(j)).append("\t");
            builder.append(nodes.get(j)).append("\t");
            builder.append("Mean").append("\t");
            builder.append(asString(mean)).append("\t");
            builder.append(asString(stdErr)).append("\t");
            builder.append(asString(tValue)).append("\t");
            builder.append(asString(p)).append("\t");
        }
    }
    return builder.toString();
}

Example 50 with SemIm

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

the class Washdown method gof.

/**
 * @return the p value of the given model.
 */
private double gof(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();
    return est.getBicScore();
}