Examples with DagToPag edu.cmu.tetrad.search.DagToPag used on opensource projects

Example 1 with DagToPag

use of edu.cmu.tetrad.search.DagToPag 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 2 with DagToPag

use of edu.cmu.tetrad.search.DagToPag in project tetrad by cmu-phil.

the class TimeoutComparison method doRun.

private void doRun(List<AlgorithmSimulationWrapper> algorithmSimulationWrappers, List<AlgorithmWrapper> algorithmWrappers, List<SimulationWrapper> simulationWrappers, Statistics statistics, int numGraphTypes, double[][][][] allStats, Run run) {
    System.out.println();
    System.out.println("Run " + (run.getRunIndex() + 1));
    System.out.println();
    AlgorithmSimulationWrapper algorithmSimulationWrapper = algorithmSimulationWrappers.get(run.getAlgSimIndex());
    AlgorithmWrapper algorithmWrapper = algorithmSimulationWrapper.getAlgorithmWrapper();
    SimulationWrapper simulationWrapper = algorithmSimulationWrapper.getSimulationWrapper();
    DataModel data = simulationWrapper.getDataModel(run.getRunIndex());
    Graph trueGraph = simulationWrapper.getTrueGraph(run.getRunIndex());
    System.out.println((run.getAlgSimIndex() + 1) + ". " + algorithmWrapper.getDescription() + " simulationWrapper: " + simulationWrapper.getDescription());
    long start = System.currentTimeMillis();
    Graph out;
    try {
        Algorithm algorithm = algorithmWrapper.getAlgorithm();
        Simulation simulation = simulationWrapper.getSimulation();
        if (algorithm instanceof HasKnowledge && simulation instanceof HasKnowledge) {
            ((HasKnowledge) algorithm).setKnowledge(((HasKnowledge) simulation).getKnowledge());
        }
        if (algorithmWrapper.getAlgorithm() instanceof ExternalAlgorithm) {
            ExternalAlgorithm external = (ExternalAlgorithm) algorithmWrapper.getAlgorithm();
            external.setSimulation(simulationWrapper.getSimulation());
            external.setPath(resultsPath);
            external.setSimIndex(simulationWrappers.indexOf(simulationWrapper));
        }
        if (algorithm instanceof MultiDataSetAlgorithm) {
            List<Integer> indices = new ArrayList<>();
            int numDataModels = simulationWrapper.getSimulation().getNumDataModels();
            for (int i = 0; i < numDataModels; i++) {
                indices.add(i);
            }
            Collections.shuffle(indices);
            List<DataModel> dataModels = new ArrayList<>();
            int randomSelectionSize = algorithmWrapper.getAlgorithmSpecificParameters().getInt("randomSelectionSize");
            for (int i = 0; i < Math.min(numDataModels, randomSelectionSize); i++) {
                dataModels.add(simulationWrapper.getSimulation().getDataModel(indices.get(i)));
            }
            Parameters _params = algorithmWrapper.getAlgorithmSpecificParameters();
            out = ((MultiDataSetAlgorithm) algorithm).search(dataModels, _params);
        } else {
            DataModel dataModel = copyData ? data.copy() : data;
            Parameters _params = algorithmWrapper.getAlgorithmSpecificParameters();
            out = algorithm.search(dataModel, _params);
        }
    } catch (Exception e) {
        System.out.println("Could not run " + algorithmWrapper.getDescription());
        e.printStackTrace();
        return;
    }
    int simIndex = simulationWrappers.indexOf(simulationWrapper) + 1;
    int algIndex = algorithmWrappers.indexOf(algorithmWrapper) + 1;
    long stop = System.currentTimeMillis();
    long elapsed = stop - start;
    saveGraph(resultsPath, out, run.getRunIndex(), simIndex, algIndex, algorithmWrapper, elapsed);
    if (trueGraph != null) {
        out = GraphUtils.replaceNodes(out, trueGraph.getNodes());
    }
    if (algorithmWrapper.getAlgorithm() instanceof ExternalAlgorithm) {
        ExternalAlgorithm extAlg = (ExternalAlgorithm) algorithmWrapper.getAlgorithm();
        extAlg.setSimIndex(simulationWrappers.indexOf(simulationWrapper));
        extAlg.setSimulation(simulationWrapper.getSimulation());
        extAlg.setPath(resultsPath);
        elapsed = extAlg.getElapsedTime(data, simulationWrapper.getSimulationSpecificParameters());
    }
    Graph[] est = new Graph[numGraphTypes];
    Graph comparisonGraph;
    if (this.comparisonGraph == ComparisonGraph.true_DAG) {
        comparisonGraph = new EdgeListGraph(trueGraph);
    } else if (this.comparisonGraph == ComparisonGraph.Pattern_of_the_true_DAG) {
        comparisonGraph = SearchGraphUtils.patternForDag(new EdgeListGraph(trueGraph));
    } else if (this.comparisonGraph == ComparisonGraph.PAG_of_the_true_DAG) {
        comparisonGraph = new DagToPag(new EdgeListGraph(trueGraph)).convert();
    } else {
        throw new IllegalArgumentException("Unrecognized graph type.");
    }
    // Graph comparisonGraph = trueGraph == null ? null : algorithmSimulationWrapper.getComparisonGraph(trueGraph);
    est[0] = out;
    graphTypeUsed[0] = true;
    if (data.isMixed()) {
        est[1] = getSubgraph(out, true, true, data);
        est[2] = getSubgraph(out, true, false, data);
        est[3] = getSubgraph(out, false, false, data);
        graphTypeUsed[1] = true;
        graphTypeUsed[2] = true;
        graphTypeUsed[3] = true;
    }
    Graph[] truth = new Graph[numGraphTypes];
    truth[0] = comparisonGraph;
    if (data.isMixed() && comparisonGraph != null) {
        truth[1] = getSubgraph(comparisonGraph, true, true, data);
        truth[2] = getSubgraph(comparisonGraph, true, false, data);
        truth[3] = getSubgraph(comparisonGraph, false, false, data);
    }
    if (comparisonGraph != null) {
        for (int u = 0; u < numGraphTypes; u++) {
            if (!graphTypeUsed[u]) {
                continue;
            }
            int statIndex = -1;
            for (Statistic _stat : statistics.getStatistics()) {
                statIndex++;
                if (_stat instanceof ParameterColumn) {
                    continue;
                }
                double stat;
                if (_stat instanceof ElapsedTime) {
                    stat = elapsed / 1000.0;
                } else {
                    stat = _stat.getValue(truth[u], est[u]);
                }
                allStats[u][run.getAlgSimIndex()][statIndex][run.getRunIndex()] = stat;
            }
        }
    }
}

Example 3 with DagToPag

use of edu.cmu.tetrad.search.DagToPag in project tetrad by cmu-phil.

the class Comparison method doRun.

private void doRun(List<AlgorithmSimulationWrapper> algorithmSimulationWrappers, List<AlgorithmWrapper> algorithmWrappers, List<SimulationWrapper> simulationWrappers, Statistics statistics, int numGraphTypes, double[][][][] allStats, Run run) {
    System.out.println();
    System.out.println("Run " + (run.getRunIndex() + 1));
    System.out.println();
    AlgorithmSimulationWrapper algorithmSimulationWrapper = algorithmSimulationWrappers.get(run.getAlgSimIndex());
    AlgorithmWrapper algorithmWrapper = algorithmSimulationWrapper.getAlgorithmWrapper();
    SimulationWrapper simulationWrapper = algorithmSimulationWrapper.getSimulationWrapper();
    DataModel data = simulationWrapper.getDataModel(run.getRunIndex());
    Graph trueGraph = simulationWrapper.getTrueGraph(run.getRunIndex());
    System.out.println((run.getAlgSimIndex() + 1) + ". " + algorithmWrapper.getDescription() + " simulationWrapper: " + simulationWrapper.getDescription());
    long start = System.currentTimeMillis();
    Graph out;
    try {
        Algorithm algorithm = algorithmWrapper.getAlgorithm();
        Simulation simulation = simulationWrapper.getSimulation();
        if (algorithm instanceof HasKnowledge && simulation instanceof HasKnowledge) {
            ((HasKnowledge) algorithm).setKnowledge(((HasKnowledge) simulation).getKnowledge());
        }
        if (algorithmWrapper.getAlgorithm() instanceof ExternalAlgorithm) {
            ExternalAlgorithm external = (ExternalAlgorithm) algorithmWrapper.getAlgorithm();
            external.setSimulation(simulationWrapper.getSimulation());
            external.setPath(resultsPath);
            external.setSimIndex(simulationWrappers.indexOf(simulationWrapper));
        }
        if (algorithm instanceof MultiDataSetAlgorithm) {
            List<Integer> indices = new ArrayList<>();
            int numDataModels = simulationWrapper.getSimulation().getNumDataModels();
            for (int i = 0; i < numDataModels; i++) indices.add(i);
            Collections.shuffle(indices);
            List<DataModel> dataModels = new ArrayList<>();
            int randomSelectionSize = algorithmWrapper.getAlgorithmSpecificParameters().getInt("randomSelectionSize");
            for (int i = 0; i < Math.min(numDataModels, randomSelectionSize); i++) {
                dataModels.add(simulationWrapper.getSimulation().getDataModel(indices.get(i)));
            }
            Parameters _params = algorithmWrapper.getAlgorithmSpecificParameters();
            out = ((MultiDataSetAlgorithm) algorithm).search(dataModels, _params);
        } else {
            DataModel dataModel = copyData ? data.copy() : data;
            Parameters _params = algorithmWrapper.getAlgorithmSpecificParameters();
            out = algorithm.search(dataModel, _params);
        }
    } catch (Exception e) {
        System.out.println("Could not run " + algorithmWrapper.getDescription());
        e.printStackTrace();
        return;
    }
    int simIndex = simulationWrappers.indexOf(simulationWrapper) + 1;
    int algIndex = algorithmWrappers.indexOf(algorithmWrapper) + 1;
    long stop = System.currentTimeMillis();
    long elapsed = stop - start;
    saveGraph(resultsPath, out, run.getRunIndex(), simIndex, algIndex, algorithmWrapper, elapsed);
    if (trueGraph != null) {
        out = GraphUtils.replaceNodes(out, trueGraph.getNodes());
    }
    if (algorithmWrapper.getAlgorithm() instanceof ExternalAlgorithm) {
        ExternalAlgorithm extAlg = (ExternalAlgorithm) algorithmWrapper.getAlgorithm();
        extAlg.setSimIndex(simulationWrappers.indexOf(simulationWrapper));
        extAlg.setSimulation(simulationWrapper.getSimulation());
        extAlg.setPath(resultsPath);
        elapsed = extAlg.getElapsedTime(data, simulationWrapper.getSimulationSpecificParameters());
    }
    Graph[] est = new Graph[numGraphTypes];
    Graph comparisonGraph;
    if (this.comparisonGraph == ComparisonGraph.true_DAG) {
        comparisonGraph = new EdgeListGraph(trueGraph);
    } else if (this.comparisonGraph == ComparisonGraph.Pattern_of_the_true_DAG) {
        comparisonGraph = SearchGraphUtils.patternForDag(new EdgeListGraph(trueGraph));
    } else if (this.comparisonGraph == ComparisonGraph.PAG_of_the_true_DAG) {
        comparisonGraph = new DagToPag(new EdgeListGraph(trueGraph)).convert();
    } else {
        throw new IllegalArgumentException("Unrecognized graph type.");
    }
    // Graph comparisonGraph = trueGraph == null ? null : algorithmSimulationWrapper.getComparisonGraph(trueGraph);
    est[0] = out;
    graphTypeUsed[0] = true;
    if (data.isMixed()) {
        est[1] = getSubgraph(out, true, true, data);
        est[2] = getSubgraph(out, true, false, data);
        est[3] = getSubgraph(out, false, false, data);
        graphTypeUsed[1] = true;
        graphTypeUsed[2] = true;
        graphTypeUsed[3] = true;
    }
    Graph[] truth = new Graph[numGraphTypes];
    truth[0] = comparisonGraph;
    if (data.isMixed() && comparisonGraph != null) {
        truth[1] = getSubgraph(comparisonGraph, true, true, data);
        truth[2] = getSubgraph(comparisonGraph, true, false, data);
        truth[3] = getSubgraph(comparisonGraph, false, false, data);
    }
    if (comparisonGraph != null) {
        for (int u = 0; u < numGraphTypes; u++) {
            if (!graphTypeUsed[u])
                continue;
            int statIndex = -1;
            for (Statistic _stat : statistics.getStatistics()) {
                statIndex++;
                if (_stat instanceof ParameterColumn)
                    continue;
                double stat;
                if (_stat instanceof ElapsedTime) {
                    stat = elapsed / 1000.0;
                } else {
                    stat = _stat.getValue(truth[u], est[u]);
                }
                allStats[u][run.getAlgSimIndex()][statIndex][run.getRunIndex()] = stat;
            }
        }
    }
}

Example 4 with DagToPag

use of edu.cmu.tetrad.search.DagToPag in project tetrad by cmu-phil.

the class TestGeneralBootstrapTest method testFCId.

@Test
public void testFCId() {
    double structurePrior = 1, samplePrior = 1;
    int depth = -1;
    int maxPathLength = -1;
    int numVars = 20;
    int edgesPerNode = 2;
    int numLatentConfounders = 4;
    int numCases = 50;
    int numBootstrapSamples = 5;
    boolean verbose = true;
    long seed = 123;
    Graph dag = makeDiscreteDAG(numVars, numLatentConfounders, edgesPerNode);
    DagToPag dagToPag = new DagToPag(dag);
    Graph truePag = dagToPag.convert();
    System.out.println("Truth PAG_of_the_true_DAG Graph:");
    System.out.println(truePag.toString());
    BayesPm pm = new BayesPm(dag, 2, 3);
    BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
    DataSet data = im.simulateData(numCases, seed, false);
    Parameters parameters = new Parameters();
    parameters.set("structurePrior", structurePrior);
    parameters.set("samplePrior", samplePrior);
    parameters.set("depth", depth);
    parameters.set("maxPathLength", maxPathLength);
    parameters.set("numPatternsToStore", 0);
    parameters.set("verbose", verbose);
    IndependenceWrapper test = new ChiSquare();
    Algorithm algorithm = new Fci(test);
    GeneralBootstrapTest bootstrapTest = new GeneralBootstrapTest(data, algorithm, numBootstrapSamples);
    bootstrapTest.setVerbose(verbose);
    bootstrapTest.setParameters(parameters);
    bootstrapTest.setEdgeEnsemble(BootstrapEdgeEnsemble.Highest);
    Graph resultGraph = bootstrapTest.search();
    System.out.println("Estimated Bootstrapped PAG_of_the_true_DAG Graph:");
    System.out.println(resultGraph.toString());
    // Adjacency Confusion Matrix
    int[][] adjAr = GeneralBootstrapTest.getAdjConfusionMatrix(truePag, resultGraph);
    printAdjConfusionMatrix(adjAr);
    // Edge Type Confusion Matrix
    int[][] edgeAr = GeneralBootstrapTest.getEdgeTypeConfusionMatrix(truePag, resultGraph);
    printEdgeTypeConfusionMatrix(edgeAr);
}

Example 5 with DagToPag

use of edu.cmu.tetrad.search.DagToPag in project tetrad by cmu-phil.

the class TestGeneralBootstrapTest method testGFCId.

@Test
public void testGFCId() {
    double structurePrior = 1, samplePrior = 1;
    boolean faithfulnessAssumed = false;
    int maxDegree = -1;
    int numVars = 20;
    int edgesPerNode = 2;
    int numLatentConfounders = 4;
    int numCases = 50;
    int numBootstrapSamples = 5;
    boolean verbose = true;
    long seed = 123;
    Graph dag = makeDiscreteDAG(numVars, numLatentConfounders, edgesPerNode);
    DagToPag dagToPag = new DagToPag(dag);
    Graph truePag = dagToPag.convert();
    System.out.println("Truth PAG_of_the_true_DAG Graph:");
    System.out.println(truePag.toString());
    BayesPm pm = new BayesPm(dag, 2, 3);
    BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
    DataSet data = im.simulateData(numCases, seed, false);
    Parameters parameters = new Parameters();
    parameters.set("structurePrior", structurePrior);
    parameters.set("samplePrior", samplePrior);
    parameters.set("faithfulnessAssumed", faithfulnessAssumed);
    parameters.set("maxDegree", maxDegree);
    parameters.set("numPatternsToStore", 0);
    parameters.set("verbose", verbose);
    ScoreWrapper score = new BdeuScore();
    IndependenceWrapper test = new ChiSquare();
    Algorithm algorithm = new Gfci(test, score);
    GeneralBootstrapTest bootstrapTest = new GeneralBootstrapTest(data, algorithm, numBootstrapSamples);
    bootstrapTest.setVerbose(verbose);
    bootstrapTest.setParameters(parameters);
    bootstrapTest.setEdgeEnsemble(BootstrapEdgeEnsemble.Highest);
    Graph resultGraph = bootstrapTest.search();
    System.out.println("Estimated Bootstrapped PAG_of_the_true_DAG Graph:");
    System.out.println(resultGraph.toString());
    // Adjacency Confusion Matrix
    int[][] adjAr = GeneralBootstrapTest.getAdjConfusionMatrix(truePag, resultGraph);
    printAdjConfusionMatrix(adjAr);
    // Edge Type Confusion Matrix
    int[][] edgeAr = GeneralBootstrapTest.getEdgeTypeConfusionMatrix(truePag, resultGraph);
    printEdgeTypeConfusionMatrix(edgeAr);
}