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

Example 21 with MlBayesIm

use of edu.cmu.tetrad.bayes.MlBayesIm 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 22 with MlBayesIm

use of edu.cmu.tetrad.bayes.MlBayesIm 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 23 with MlBayesIm

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

Example 24 with MlBayesIm

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

the class TestHistogram method testHistogram.

@Test
public void testHistogram() {
    RandomUtil.getInstance().setSeed(4829384L);
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Dag trueGraph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 30, 15, 15, false));
    int sampleSize = 1000;
    // Continuous
    SemPm semPm = new SemPm(trueGraph);
    SemIm semIm = new SemIm(semPm);
    DataSet data = semIm.simulateData(sampleSize, false);
    Histogram histogram = new Histogram(data);
    histogram.setTarget("X1");
    histogram.setNumBins(20);
    assertEquals(3.76, histogram.getMax(), 0.01);
    assertEquals(-3.83, histogram.getMin(), 0.01);
    assertEquals(1000, histogram.getN());
    histogram.setTarget("X1");
    histogram.setNumBins(10);
    histogram.addConditioningVariable("X3", 0, 1);
    histogram.addConditioningVariable("X4", 0, 1);
    histogram.removeConditioningVariable("X3");
    assertEquals(3.76, histogram.getMax(), 0.01);
    assertEquals(-3.83, histogram.getMin(), 0.01);
    assertEquals(188, histogram.getN());
    double[] arr = histogram.getContinuousData("X2");
    histogram.addConditioningVariable("X2", StatUtils.min(arr), StatUtils.mean(arr));
    // Discrete
    BayesPm bayesPm = new BayesPm(trueGraph);
    BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
    DataSet data2 = bayesIm.simulateData(sampleSize, false);
    // For some reason these are giving different
    // values when all of the unit tests are run are
    // once. TODO They produce stable values when
    // this particular test is run repeatedly.
    Histogram histogram2 = new Histogram(data2);
    histogram2.setTarget("X1");
    int[] frequencies1 = histogram2.getFrequencies();
    // assertEquals(928, frequencies1[0]);
    // assertEquals(72, frequencies1[1]);
    histogram2.setTarget("X1");
    histogram2.addConditioningVariable("X2", 0);
    histogram2.addConditioningVariable("X3", 1);
    int[] frequencies = histogram2.getFrequencies();
// assertEquals(377, frequencies[0]);
// assertEquals(28, frequencies[1]);
}

Example 25 with MlBayesIm

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

the class TestBayesIm method testAddRemoveValues.

/**
 * Tests whether the BayesIm does the right thing in a very simple case
 * where values of a nodes are added or removed from the BayesPm. Start with
 * graph a -> b <- c, construct and fill in probability tables in BayesIm.
 * Then add edge c -> b "manually." This should create a table of values for
 * c that is unspecified, and it should double up the rows from b. Then
 * remove the node c. Now the table for b should be completely unspecified.
 */
@Test
public void testAddRemoveValues() {
    Node a = new GraphNode("a");
    Node b = new GraphNode("b");
    Node c = new GraphNode("c");
    Dag dag = new Dag();
    dag.addNode(a);
    dag.addNode(b);
    dag.addNode(c);
    dag.addDirectedEdge(a, b);
    dag.addDirectedEdge(c, b);
    assertTrue(Edges.isDirectedEdge(dag.getEdge(a, b)));
    BayesPm bayesPm = new BayesPm(dag, 3, 3);
    BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
    bayesPm.setNumCategories(a, 4);
    bayesPm.setNumCategories(c, 4);
    BayesIm bayesIm2 = new MlBayesIm(bayesPm, bayesIm, MlBayesIm.MANUAL);
    bayesPm.setNumCategories(a, 2);
    BayesIm bayesIm3 = new MlBayesIm(bayesPm, bayesIm2, MlBayesIm.MANUAL);
    bayesPm.setNumCategories(b, 2);
    BayesIm bayesIm4 = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
    for (int node = 0; node < bayesIm4.getNumNodes(); node++) {
        for (int row = 0; row < bayesIm4.getNumRows(node); row++) {
            for (int col = 0; col < bayesIm4.getNumColumns(node); col++) {
                bayesIm4.setProbability(node, row, col, Double.NaN);
            }
        }
    }
    double[][] aTable = { { .2, .8 } };
    double[][] bTable = { { .1, .9 }, { .7, .3 }, { .3, .7 }, { .5, .5 }, { .09, .91 }, { .6, .4 }, { .2, .8 }, { .8, .2 } };
    double[][] cTable = { { .1, .2, .3, .4 } };
    int _a = bayesIm.getNodeIndex(a);
    for (int row = 0; row < bayesIm4.getNumRows(_a); row++) {
        for (int col = 0; col < bayesIm4.getNumColumns(_a); col++) {
            bayesIm4.setProbability(_a, row, col, aTable[row][col]);
        }
    }
    int _b = bayesIm.getNodeIndex(b);
    for (int row = 0; row < bayesIm4.getNumRows(_b); row++) {
        for (int col = 0; col < bayesIm4.getNumColumns(_b); col++) {
            bayesIm4.setProbability(_b, row, col, bTable[row][col]);
        }
    }
    int _c = bayesIm.getNodeIndex(c);
    for (int row = 0; row < bayesIm4.getNumRows(_c); row++) {
        for (int col = 0; col < bayesIm4.getNumColumns(_c); col++) {
            bayesIm4.setProbability(_c, row, col, cTable[row][col]);
        }
    }
}