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

Example 11 with LargeScaleSimulation

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

the class PerformanceTests method testFci.

public void testFci(int numVars, double edgeFactor, int numCases) {
    double alpha = 0.001;
    int depth = 3;
    init(new File("long.fci." + numVars + ".txt"), "Tests performance of the FCI algorithm");
    long time1 = System.currentTimeMillis();
    System.out.println("Making list of vars");
    List<Node> vars = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new ContinuousVariable("X" + i));
    }
    System.out.println("Finishing list of vars");
    System.out.println("Making graph");
    Graph graph = GraphUtils.randomGraphRandomForwardEdges(vars, 0, (int) (numVars * edgeFactor), 30, 15, 15, false, true);
    System.out.println("Graph done");
    out.println("Graph done");
    System.out.println("Starting simulation");
    LargeScaleSimulation simulator = new LargeScaleSimulation(graph);
    simulator.setOut(out);
    DataSet data = simulator.simulateDataFisher(numCases);
    System.out.println("Finishing simulation");
    long time2 = System.currentTimeMillis();
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    System.out.println("Making covariance matrix");
    // ICovarianceMatrix cov = new CovarianceMatrix2(data);
    ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(data);
    // ICovarianceMatrix cov = new CorreqlationMatrix(new CovarianceMatrix2(data));
    // ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(data, false);
    // ICovarianceMatrix cov = DataUtils.covarianceParanormalDrton(data);
    // ICovarianceMatrix cov = new CovarianceMatrix(DataUtils.covarianceParanormalWasserman(data));
    // System.out.println(cov);
    System.out.println("Covariance matrix done");
    long time3 = System.currentTimeMillis();
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    // out.println(cov);
    IndTestFisherZ independenceTest = new IndTestFisherZ(cov, alpha);
    Fci fci = new Fci(independenceTest);
    fci.setVerbose(false);
    fci.setDepth(depth);
    fci.setMaxPathLength(2);
    // fci.setTrueDag(truePag);
    Graph outGraph = fci.search();
    out.println(outGraph);
    long time4 = System.currentTimeMillis();
    out.println("# Vars = " + numVars);
    out.println("# Edges = " + (int) (numVars * edgeFactor));
    out.println("# Cases = " + numCases);
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    out.println("Elapsed (running FCI) " + (time4 - time3) + " ms");
    out.close();
}

Example 12 with LargeScaleSimulation

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

the class PerformanceTests method testGfci.

public void testGfci(int numVars, double edgeFactor) {
    System.out.println("Seed = " + RandomUtil.getInstance().getSeed());
    // RandomUtil.getInstance().setSeed(1460491316813L);
    double alpha = .1;
    int depth = -1;
    double penaltyDiscount = 4.0;
    int maxPathLength = -1;
    double coefLow = .3;
    double coefHigh = 1.5;
    int numLatentConfounders = 50;
    int numCases = 1000;
    init(new File("long.gfci." + numVars + ".txt"), "Tests performance of the FCI-GES algorithm");
    long time1 = System.currentTimeMillis();
    System.out.println("Making list of vars");
    List<Node> vars = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new ContinuousVariable("X" + i));
    }
    System.out.println("Finishing list of vars");
    System.out.println("Making graph");
    System.out.println("Finishing list of vars");
    Graph dag = GraphUtils.randomGraphRandomForwardEdges(vars, numLatentConfounders, (int) (numVars * edgeFactor), 10, 10, 10, false, false);
    System.out.println("Graph done");
    out.println("Graph done");
    System.out.println("Starting simulation");
    LargeScaleSimulation simulator = new LargeScaleSimulation(dag);
    simulator.setCoefRange(coefLow, coefHigh);
    DataSet data = simulator.simulateDataFisher(numCases);
    data = DataUtils.restrictToMeasured(data);
    System.out.println("Finishing simulation");
    System.out.println("Num measured vars = " + data.getNumColumns());
    long time2 = System.currentTimeMillis();
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    System.out.println("Making covariance matrix");
    ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(data);
    System.out.println("Covariance matrix done");
    long time3 = System.currentTimeMillis();
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    IndependenceTest independenceTest = new IndTestFisherZ(cov, alpha);
    // GFci fci = new GFci(independenceTest);
    SemBicScore score = new SemBicScore(cov);
    score.setPenaltyDiscount(penaltyDiscount);
    GFci fci = new GFci(independenceTest, score);
    fci.setVerbose(false);
    fci.setMaxPathLength(maxPathLength);
    fci.setMaxDegree(depth);
    fci.setFaithfulnessAssumed(false);
    fci.setCompleteRuleSetUsed(true);
    Graph outGraph = fci.search();
    out.println(outGraph);
    System.out.println(MisclassificationUtils.edgeMisclassifications(outGraph, new DagToPag(dag).convert()));
    long time4 = System.currentTimeMillis();
    out.println("# Vars = " + numVars);
    out.println("# Edges = " + (int) (numVars * edgeFactor));
    out.println("# Cases = " + numCases);
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    out.println("Elapsed (running FCI) " + (time4 - time3) + " ms");
    out.close();
}

Example 13 with LargeScaleSimulation

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

the class PerformanceTests method testCpcStable.

public void testCpcStable(int numVars, double edgeFactor, int numCases, double alpha) {
    int depth = 3;
    init(new File("long.cpcstable." + numVars + ".txt"), "Tests performance of the CPC algorithm");
    long time1 = System.currentTimeMillis();
    System.out.println("Making list of vars");
    System.out.println("Finishing list of vars");
    System.out.println("Making graph");
    // Graph graph = DataGraphUtils.randomGraphRandomForwardEdges(vars, 0, (int) (numVars * edgeFactor));
    // Graph graph = DataGraphUtils.randomGraphUniform(vars, 0, (int) (numVars * edgeFactor), 5, 5, 5, false);
    Graph graph = makeDag(numVars, edgeFactor);
    System.out.println("Graph done");
    out.println("Graph done");
    System.out.println("Starting simulation");
    LargeScaleSimulation simulator = new LargeScaleSimulation(graph);
    simulator.setOut(out);
    DataSet data = simulator.simulateDataFisher(numCases);
    System.out.println("Finishing simulation");
    long time2 = System.currentTimeMillis();
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    System.out.println("Making covariance matrix");
    // ICovarianceMatrix cov = new CovarianceMatrix2(data);
    ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(data);
    // ICovarianceMatrix cov = new CorrelationMatrix(new CovarianceMatrix2(data));
    // ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(data, false);
    // ICovarianceMatrix cov = DataUtils.covarianceParanormalDrton(data);
    // ICovarianceMatrix cov = new CovarianceMatrix(DataUtils.covarianceParanormalWasserman(data));
    // System.out.println(cov);
    System.out.println("Covariance matrix done");
    long time3 = System.currentTimeMillis();
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    // out.println(cov);
    IndTestFisherZ test = new IndTestFisherZ(cov, alpha);
    CpcStable cpcStable = new CpcStable(test);
    cpcStable.setVerbose(false);
    cpcStable.setDepth(depth);
    cpcStable.setOut(out);
    Graph outGraph = cpcStable.search();
    // out.println(outGraph);
    long time4 = System.currentTimeMillis();
    // out.println("# Vars = " + numVars);
    // out.println("# Edges = " + (int) (numVars * edgeFactor));
    out.println("# Cases = " + numCases);
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    out.println("Elapsed (running CPC-Stable) " + (time4 - time3) + " ms");
    out.println("Total elapsed (cov + CPC-Stable) " + (time4 - time2) + " ms");
    final Graph truePattern = SearchGraphUtils.patternForDag(graph);
    SearchGraphUtils.graphComparison(outGraph, truePattern, out);
    out.println("# ambiguous triples = " + outGraph.getAmbiguousTriples().size());
    out.close();
}

Example 14 with LargeScaleSimulation

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

the class PerformanceTests method testFgesMb.

private void testFgesMb(int numVars, double edgeFactor, int numCases, int numRuns, boolean continuous) {
    double penaltyDiscount = 4.0;
    int structurePrior = 10;
    int samplePrior = 10;
    int maxIndegree = -1;
    // boolean faithfulness = false;
    List<int[][]> allCounts = new ArrayList<>();
    List<double[]> comparisons = new ArrayList<>();
    List<Double> degrees = new ArrayList<>();
    List<Long> elapsedTimes = new ArrayList<>();
    System.out.println("Making dag");
    Graph dag = makeDag(numVars, edgeFactor);
    System.out.println(new Date());
    System.out.println("Calculating pattern for DAG");
    Graph pattern = SearchGraphUtils.patternForDag(dag);
    int[] tiers = new int[dag.getNumNodes()];
    for (int i = 0; i < dag.getNumNodes(); i++) {
        tiers[i] = i;
    }
    System.out.println("Graph done");
    long time1 = System.currentTimeMillis();
    out.println("Graph done");
    System.out.println(new Date());
    System.out.println("Starting simulation");
    Graph estPattern;
    long elapsed;
    FgesMb fges;
    List<Node> vars;
    if (continuous) {
        init(new File("FgesMb.comparison.continuous" + numVars + "." + (int) (edgeFactor * numVars) + "." + numCases + "." + numRuns + ".txt"), "Num runs = " + numRuns);
        out.println("Num vars = " + numVars);
        out.println("Num edges = " + (int) (numVars * edgeFactor));
        out.println("Num cases = " + numCases);
        out.println("Penalty discount = " + penaltyDiscount);
        out.println("Depth = " + maxIndegree);
        out.println();
        out.println(new Date());
        vars = dag.getNodes();
        LargeScaleSimulation simulator = new LargeScaleSimulation(dag, vars, tiers);
        simulator.setVerbose(false);
        simulator.setOut(out);
        DataSet data = simulator.simulateDataFisher(numCases);
        System.out.println("Finishing simulation");
        System.out.println(new Date());
        long time2 = System.currentTimeMillis();
        out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
        System.out.println(new Date());
        System.out.println("Making covariance matrix");
        long time3 = System.currentTimeMillis();
        ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(data, true);
        System.out.println("Covariance matrix done");
        out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms\n");
        SemBicScore score = new SemBicScore(cov);
        score.setPenaltyDiscount(penaltyDiscount);
        System.out.println(new Date());
        System.out.println("\nStarting FGES-MB");
        fges = new FgesMb(score);
        fges.setVerbose(false);
        fges.setNumPatternsToStore(0);
        fges.setOut(System.out);
        // fges.setHeuristicSpeedup(faithfulness);
        fges.setMaxIndegree(maxIndegree);
        fges.setCycleBound(-1);
    } else {
        init(new File("FgesMb.comparison.discrete" + numVars + "." + (int) (edgeFactor * numVars) + "." + numCases + "." + numRuns + ".txt"), "Num runs = " + numRuns);
        out.println("Num vars = " + numVars);
        out.println("Num edges = " + (int) (numVars * edgeFactor));
        out.println("Num cases = " + numCases);
        out.println("Sample prior = " + samplePrior);
        out.println("Structure prior = " + structurePrior);
        out.println("Depth = " + maxIndegree);
        out.println();
        out.println(new Date());
        BayesPm pm = new BayesPm(dag, 3, 3);
        MlBayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
        DataSet data = im.simulateData(numCases, false, tiers);
        vars = data.getVariables();
        pattern = GraphUtils.replaceNodes(pattern, vars);
        System.out.println("Finishing simulation");
        long time2 = System.currentTimeMillis();
        out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
        long time3 = System.currentTimeMillis();
        BDeuScore score = new BDeuScore(data);
        score.setStructurePrior(structurePrior);
        score.setSamplePrior(samplePrior);
        System.out.println(new Date());
        System.out.println("\nStarting FGES");
        long time4 = System.currentTimeMillis();
        fges = new FgesMb(score);
        fges.setVerbose(false);
        fges.setNumPatternsToStore(0);
        fges.setOut(System.out);
        // fges.setHeuristicSpeedup(faithfulness);
        fges.setMaxIndegree(maxIndegree);
        fges.setCycleBound(-1);
        long timeb = System.currentTimeMillis();
        out.println("Time consructing BDeu score " + (time4 - time3) + " ms");
        out.println("Time for FGES-MB constructor " + (timeb - time4) + " ms");
        out.println();
    }
    int numSkipped = 0;
    for (int run = 0; run < numRuns; run++) {
        out.println("\n\n\n******************************** RUN " + (run + 1) + " ********************************\n\n");
        Node target = vars.get(RandomUtil.getInstance().nextInt(vars.size()));
        System.out.println("Target = " + target);
        long timea = System.currentTimeMillis();
        estPattern = fges.search(target);
        long timed = System.currentTimeMillis();
        elapsed = timed - timea;
        Set<Node> mb = new HashSet<>();
        mb.add(target);
        mb.addAll(pattern.getAdjacentNodes(target));
        for (Node child : pattern.getChildren(target)) {
            mb.addAll(pattern.getParents(child));
        }
        Graph trueMbGraph = pattern.subgraph(new ArrayList<>(mb));
        long timec = System.currentTimeMillis();
        out.println("Time for FGES-MB search " + (timec - timea) + " ms");
        out.println();
        System.out.println("Done with FGES");
        System.out.println(new Date());
        double[] comparison = new double[4];
        System.out.println("Counting misclassifications.");
        int[][] counts = GraphUtils.edgeMisclassificationCounts(trueMbGraph, estPattern, false);
        allCounts.add(counts);
        System.out.println(new Date());
        int sumRow = counts[4][0] + counts[4][3] + counts[4][5];
        int sumCol = counts[0][3] + counts[4][3] + counts[5][3] + counts[7][3];
        int trueArrow = counts[4][3];
        int sumTrueAdjacencies = 0;
        for (int i = 0; i < 7; i++) {
            for (int j = 0; j < 5; j++) {
                sumTrueAdjacencies += counts[i][j];
            }
        }
        int falsePositiveAdjacencies = 0;
        for (int j = 0; j < 5; j++) {
            falsePositiveAdjacencies += counts[7][j];
        }
        int falseNegativeAdjacencies = 0;
        for (int i = 0; i < 5; i++) {
            falseNegativeAdjacencies += counts[i][5];
        }
        comparison[0] = sumTrueAdjacencies / (double) (sumTrueAdjacencies + falsePositiveAdjacencies);
        comparison[1] = sumTrueAdjacencies / (double) (sumTrueAdjacencies + falseNegativeAdjacencies);
        comparison[2] = trueArrow / (double) sumCol;
        comparison[3] = trueArrow / (double) sumRow;
        // if (Double.isNaN(comparison[0]) || Double.isNaN(comparison[1]) || Double.isNaN(comparison[2]) ||
        // Double.isNaN(comparison[3])) {
        // run--;
        // numSkipped++;
        // continue;
        // }
        comparisons.add(comparison);
        out.println(GraphUtils.edgeMisclassifications(counts));
        out.println(precisionRecall(comparison));
        // printAverageConfusion("Average", allCounts);
        elapsedTimes.add(elapsed);
        out.println("\nElapsed: " + elapsed + " ms");
    }
    printAverageConfusion("Average", allCounts, new DecimalFormat("0.0"));
    printAveragePrecisionRecall(comparisons);
    out.println("Number of runs skipped because of undefined accuracies: " + numSkipped);
    printAverageStatistics(elapsedTimes, degrees);
    out.close();
}

Example 15 with LargeScaleSimulation

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

the class PerformanceTests method testPcStableMax.

public void testPcStableMax(int numVars, double edgeFactor, int numCases, double alpha) {
    int depth = -1;
    init(new File("long.pcstablemax." + numVars + "." + edgeFactor + "." + alpha + ".txt"), "Tests performance of the PC Max algorithm");
    long time1 = System.currentTimeMillis();
    Graph dag = makeDag(numVars, edgeFactor);
    System.out.println("Graph done");
    out.println("Graph done");
    System.out.println("Starting simulation");
    LargeScaleSimulation simulator = new LargeScaleSimulation(dag);
    simulator.setOut(out);
    DataSet data = simulator.simulateDataFisher(numCases);
    System.out.println("Finishing simulation");
    long time2 = System.currentTimeMillis();
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    System.out.println("Making covariance matrix");
    // ICovarianceMatrix cov = new CovarianceMatrix(data);
    ICovarianceMatrix cov = new CovarianceMatrixOnTheFly(data);
    // ICovarianceMatrix cov = new CorrelationMatrix(new CovarianceMatrix(data));
    // ICovarianceMatrix cov = DataUtils.covarianceParanormalDrton(data);
    // ICovarianceMatrix cov = new CovarianceMatrix(DataUtils.covarianceParanormalWasserman(data));
    // System.out.println(cov);
    System.out.println("Covariance matrix done");
    long time3 = System.currentTimeMillis();
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    // out.println(cov);
    IndTestFisherZ test = new IndTestFisherZ(cov, alpha);
    PcStableMax pcStable = new PcStableMax(test);
    // pcStable.setVerbose(false);
    // pcStable.setDepth(depth);
    // pcStable.setOut(out);
    Graph estPattern = pcStable.search();
    // out.println(estPattern);
    long time4 = System.currentTimeMillis();
    // out.println("# Vars = " + numVars);
    // out.println("# Edges = " + (int) (numVars * edgeFactor));
    out.println("# Cases = " + numCases);
    out.println("alpha = " + alpha);
    out.println("depth = " + depth);
    out.println("Elapsed (simulating the data): " + (time2 - time1) + " ms");
    out.println("Elapsed (calculating cov): " + (time3 - time2) + " ms");
    out.println("Elapsed (running PC-Max) " + (time4 - time3) + " ms");
    out.println("Total elapsed (cov + PC-Max) " + (time4 - time2) + " ms");
    final Graph truePattern = SearchGraphUtils.patternForDag(dag);
    System.out.println("# edges in true pattern = " + truePattern.getNumEdges());
    System.out.println("# edges in est pattern = " + estPattern.getNumEdges());
    SearchGraphUtils.graphComparison(estPattern, truePattern, out);
    out.println("seed = " + RandomUtil.getInstance().getSeed() + "L");
    out.close();
}