Examples with Discretizer edu.cmu.tetrad.data.Discretizer used on opensource projects

Example 1 with Discretizer

use of edu.cmu.tetrad.data.Discretizer in project tetrad by cmu-phil.

the class TestLogisticRegression method test1.

@Test
public void test1() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 3, 3, 3, false));
    System.out.println(graph);
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateDataRecursive(1000, false);
    Node x1 = data.getVariable("X1");
    Node x2 = data.getVariable("X2");
    Node x3 = data.getVariable("X3");
    Node x4 = data.getVariable("X4");
    Node x5 = data.getVariable("X5");
    Discretizer discretizer = new Discretizer(data);
    discretizer.equalCounts(x1, 2);
    DataSet d2 = discretizer.discretize();
    LogisticRegression regression = new LogisticRegression(d2);
    List<Node> regressors = new ArrayList<>();
    regressors.add(x2);
    regressors.add(x3);
    regressors.add(x4);
    regressors.add(x5);
    DiscreteVariable x1b = (DiscreteVariable) d2.getVariable("X1");
    regression.regress(x1b, regressors);
    System.out.println(regression);
}

Example 2 with Discretizer

use of edu.cmu.tetrad.data.Discretizer in project tetrad by cmu-phil.

the class TestIndTestWaldLR method testIsIndependent.

@Test
public void testIsIndependent() {
    RandomUtil.getInstance().setSeed(1450705713157L);
    int numPassed = 0;
    for (int i = 0; i < 10; i++) {
        List<Node> nodes = new ArrayList<>();
        for (int i1 = 0; i1 < 10; i1++) {
            nodes.add(new ContinuousVariable("X" + (i1 + 1)));
        }
        Graph graph = GraphUtils.randomGraph(nodes, 0, 10, 3, 3, 3, false);
        SemPm pm = new SemPm(graph);
        SemIm im = new SemIm(pm);
        DataSet data = im.simulateData(1000, false);
        Discretizer discretizer = new Discretizer(data);
        discretizer.setVariablesCopied(true);
        discretizer.equalCounts(data.getVariable(0), 2);
        discretizer.equalCounts(data.getVariable(3), 2);
        data = discretizer.discretize();
        Node x1 = data.getVariable("X1");
        Node x2 = data.getVariable("X2");
        Node x3 = data.getVariable("X3");
        Node x4 = data.getVariable("X4");
        Node x5 = data.getVariable("X5");
        List<Node> cond = new ArrayList<>();
        cond.add(x3);
        cond.add(x4);
        cond.add(x5);
        Node x1Graph = graph.getNode(x1.getName());
        Node x2Graph = graph.getNode(x2.getName());
        List<Node> condGraph = new ArrayList<>();
        for (Node node : cond) {
            condGraph.add(graph.getNode(node.getName()));
        }
        // Using the Wald LR test since it's most up to date.
        IndependenceTest test = new IndTestMultinomialLogisticRegressionWald(data, 0.05, false);
        IndTestDSep dsep = new IndTestDSep(graph);
        boolean correct = test.isIndependent(x2, x1, cond) == dsep.isIndependent(x2Graph, x1Graph, condGraph);
        if (correct) {
            numPassed++;
        }
    }
    // System.out.println(RandomUtil.getInstance().getSeed());
    // Do not always get all 10.
    assertEquals(10, numPassed);
}

Example 3 with Discretizer

use of edu.cmu.tetrad.data.Discretizer in project tetrad by cmu-phil.

the class SemThenDiscretize method simulate.

private DataSet simulate(Graph graph, Parameters parameters) {
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet continuousData = im.simulateData(parameters.getInt("sampleSize"), false);
    if (this.shuffledOrder == null) {
        List<Node> shuffledNodes = new ArrayList<>(continuousData.getVariables());
        Collections.shuffle(shuffledNodes);
        this.shuffledOrder = shuffledNodes;
    }
    Discretizer discretizer = new Discretizer(continuousData);
    for (int i = 0; i < shuffledOrder.size() * parameters.getDouble("percentDiscrete") * 0.01; i++) {
        discretizer.equalIntervals(continuousData.getVariable(shuffledOrder.get(i).getName()), parameters.getInt("numCategories"));
    }
    return discretizer.discretize();
}