Examples with BayesIm edu.cmu.tetrad.bayes.BayesIm used on opensource projects

Example 26 with BayesIm

use of edu.cmu.tetrad.bayes.BayesIm in project tetrad by cmu-phil.

the class TestFges method explore2.

@Test
public void explore2() {
    RandomUtil.getInstance().setSeed(1457220623122L);
    int numVars = 20;
    double edgeFactor = 1.0;
    int numCases = 1000;
    double structurePrior = 1;
    double samplePrior = 1;
    List<Node> vars = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new ContinuousVariable("X" + i));
    }
    Graph dag = GraphUtils.randomGraphRandomForwardEdges(vars, 0, (int) (numVars * edgeFactor), 30, 15, 15, false, true);
    // printDegreeDistribution(dag, out);
    BayesPm pm = new BayesPm(dag, 2, 3);
    BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
    DataSet data = im.simulateData(numCases, false);
    // out.println("Finishing simulation");
    BDeScore score = new BDeScore(data);
    score.setSamplePrior(samplePrior);
    score.setStructurePrior(structurePrior);
    Fges ges = new Fges(score);
    ges.setVerbose(false);
    ges.setNumPatternsToStore(0);
    ges.setFaithfulnessAssumed(false);
    Graph estPattern = ges.search();
    final Graph truePattern = SearchGraphUtils.patternForDag(dag);
    int[][] counts = SearchGraphUtils.graphComparison(estPattern, truePattern, null);
    int[][] expectedCounts = { { 2, 0, 0, 0, 0, 1 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 }, { 2, 0, 0, 13, 0, 3 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 } };
// for (int i = 0; i < counts.length; i++) {
// assertTrue(Arrays.equals(counts[i], expectedCounts[i]));
// }
// System.out.println(MatrixUtils.toString(expectedCounts));
// System.out.println(MatrixUtils.toString(counts));
// System.out.println(RandomUtil.getInstance().getSeed());
}

Example 27 with BayesIm

use of edu.cmu.tetrad.bayes.BayesIm 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 28 with BayesIm

use of edu.cmu.tetrad.bayes.BayesIm in project tetrad by cmu-phil.

the class TestGeneralBootstrapTest method testFGESd.

@Test
public void testFGESd() {
    double structurePrior = 1, samplePrior = 1;
    boolean faithfulnessAssumed = false;
    int maxDegree = -1;
    int numVars = 20;
    int edgesPerNode = 2;
    int numLatentConfounders = 0;
    int numCases = 50;
    int numBootstrapSamples = 5;
    boolean verbose = true;
    long seed = 123;
    Graph dag = makeDiscreteDAG(numVars, numLatentConfounders, edgesPerNode);
    System.out.println("Truth Graph:");
    System.out.println(dag.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();
    Algorithm algorithm = new Fges(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 Graph:");
    System.out.println(resultGraph.toString());
    // Adjacency Confusion Matrix
    int[][] adjAr = GeneralBootstrapTest.getAdjConfusionMatrix(dag, resultGraph);
    printAdjConfusionMatrix(adjAr);
    // Edge Type Confusion Matrix
    int[][] edgeAr = GeneralBootstrapTest.getEdgeTypeConfusionMatrix(dag, resultGraph);
    printEdgeTypeConfusionMatrix(edgeAr);
}

Example 29 with BayesIm

use of edu.cmu.tetrad.bayes.BayesIm 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);
}

Example 30 with BayesIm

use of edu.cmu.tetrad.bayes.BayesIm in project tetrad by cmu-phil.

the class TestProposition method testUpdate1.

/**
 * Richard's 2-variable example worked by hand.
 */
@Test
public void testUpdate1() {
    BayesIm bayesIm = sampleBayesIm2();
    Proposition prop1 = Proposition.tautology(bayesIm);
    prop1.removeCategory(0, 1);
    prop1.setVariable(1, false);
    Proposition prop2 = new Proposition(bayesIm, prop1);
    assertEquals(prop1, prop2);
    BayesIm bayesIm2 = new MlBayesIm(bayesIm);
    Proposition prop3 = new Proposition(bayesIm2, prop1);
    assertTrue(!prop3.equals(prop1));
}