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

Example 6 with GeneralizedSemPm

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

the class TestSimulatedFmri method testClark.

// @Test
public void testClark() {
    double f = .1;
    int N = 512;
    double alpha = 1.0;
    double penaltyDiscount = 1.0;
    for (int i = 0; i < 100; i++) {
        {
            Node x = new ContinuousVariable("X");
            Node y = new ContinuousVariable("Y");
            Node z = new ContinuousVariable("Z");
            Graph g = new EdgeListGraph();
            g.addNode(x);
            g.addNode(y);
            g.addNode(z);
            g.addDirectedEdge(x, y);
            g.addDirectedEdge(z, x);
            g.addDirectedEdge(z, y);
            GeneralizedSemPm pm = new GeneralizedSemPm(g);
            try {
                pm.setNodeExpression(g.getNode("X"), "0.5 * Z + E_X");
                pm.setNodeExpression(g.getNode("Y"), "0.5 * X + 0.5 * Z + E_Y");
                pm.setNodeExpression(g.getNode("Z"), "E_Z");
                String error = "pow(Uniform(0, 1), " + f + ")";
                pm.setNodeExpression(pm.getErrorNode(g.getNode("X")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Y")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Z")), error);
            } catch (ParseException e) {
                System.out.println(e);
            }
            GeneralizedSemIm im = new GeneralizedSemIm(pm);
            DataSet data = im.simulateData(N, false);
            edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
            score.setPenaltyDiscount(penaltyDiscount);
            Fask fask = new Fask(data, score);
            fask.setPenaltyDiscount(penaltyDiscount);
            fask.setAlpha(alpha);
            Graph out = fask.search();
            System.out.println(out);
        }
        {
            Node x = new ContinuousVariable("X");
            Node y = new ContinuousVariable("Y");
            Node z = new ContinuousVariable("Z");
            Graph g = new EdgeListGraph();
            g.addNode(x);
            g.addNode(y);
            g.addNode(z);
            g.addDirectedEdge(x, y);
            g.addDirectedEdge(x, z);
            g.addDirectedEdge(y, z);
            GeneralizedSemPm pm = new GeneralizedSemPm(g);
            try {
                pm.setNodeExpression(g.getNode("X"), "E_X");
                pm.setNodeExpression(g.getNode("Y"), "0.4 * X + E_Y");
                pm.setNodeExpression(g.getNode("Z"), "0.4 * X + 0.4 * Y + E_Z");
                String error = "pow(Uniform(0, 1), " + f + ")";
                pm.setNodeExpression(pm.getErrorNode(g.getNode("X")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Y")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Z")), error);
            } catch (ParseException e) {
                System.out.println(e);
            }
            GeneralizedSemIm im = new GeneralizedSemIm(pm);
            DataSet data = im.simulateData(N, false);
            edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
            score.setPenaltyDiscount(penaltyDiscount);
            Fask fask = new Fask(data, score);
            fask.setPenaltyDiscount(penaltyDiscount);
            fask.setAlpha(alpha);
            Graph out = fask.search();
            System.out.println(out);
        }
    }
}

Example 7 with GeneralizedSemPm

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

the class GraphWrapper method getStrongestInfluenceGraph.

private static Graph getStrongestInfluenceGraph(GeneralizedSemIm im) {
    GeneralizedSemPm pm = im.getGeneralizedSemPm();
    Graph imGraph = im.getGeneralizedSemPm().getGraph();
    List<Node> nodes = new ArrayList<>();
    for (Node node : imGraph.getNodes()) {
        if (!(node.getNodeType() == NodeType.ERROR)) {
            nodes.add(node);
        }
    }
    Graph graph2 = new EdgeListGraph(nodes);
    for (Edge edge : imGraph.getEdges()) {
        Node node1 = edge.getNode1();
        Node node2 = edge.getNode2();
        if (!graph2.containsNode(node1))
            continue;
        if (!graph2.containsNode(node2))
            continue;
        if (graph2.isAdjacentTo(node1, node2)) {
            continue;
        }
        List<Edge> edges = imGraph.getEdges(node1, node2);
        if (edges.size() == 1) {
            graph2.addEdge(edges.get(0));
        } else {
            Expression expression1 = pm.getNodeExpression(node1);
            Expression expression2 = pm.getNodeExpression(node2);
            String param1 = findParameter(expression1, node2.getName());
            String param2 = findParameter(expression2, node1.getName());
            if (param1 == null || param2 == null) {
                continue;
            }
            double value1 = im.getParameterValue(param1);
            double value2 = im.getParameterValue(param2);
            if (value2 > value1) {
                graph2.addDirectedEdge(node1, node2);
            } else if (value1 > value2) {
                graph2.addDirectedEdge(node2, node1);
            }
        }
    }
    return graph2;
}

Example 8 with GeneralizedSemPm

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

the class LeeHastieSimulation method simulate.

private DataSet simulate(Graph dag, Parameters parameters) {
    HashMap<String, Integer> nd = new HashMap<>();
    List<Node> nodes = dag.getNodes();
    Collections.shuffle(nodes);
    if (this.shuffledOrder == null) {
        List<Node> shuffledNodes = new ArrayList<>(nodes);
        Collections.shuffle(shuffledNodes);
        this.shuffledOrder = shuffledNodes;
    }
    for (int i = 0; i < nodes.size(); i++) {
        if (i < nodes.size() * parameters.getDouble("percentDiscrete") * 0.01) {
            final int minNumCategories = parameters.getInt("numCategories");
            final int maxNumCategories = parameters.getInt("numCategories");
            final int value = pickNumCategories(minNumCategories, maxNumCategories);
            nd.put(shuffledOrder.get(i).getName(), value);
        } else {
            nd.put(shuffledOrder.get(i).getName(), 0);
        }
    }
    Graph graph = MixedUtils.makeMixedGraph(dag, nd);
    GeneralizedSemPm pm = MixedUtils.GaussianCategoricalPm(graph, "Split(-1.5,-.5,.5,1.5)");
    GeneralizedSemIm im = MixedUtils.GaussianCategoricalIm(pm);
    boolean saveLatentVars = parameters.getBoolean("saveLatentVars");
    DataSet ds = im.simulateDataAvoidInfinity(parameters.getInt("sampleSize"), saveLatentVars);
    return MixedUtils.makeMixedData(ds, nd);
}

Example 9 with GeneralizedSemPm

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

the class MixedUtils method main.

// main for testing
public static void main(String[] args) {
    // Graph g = GraphConverter.convert("X1-->X2,X2-->X3,X3-->X4");
    Graph g = GraphConverter.convert("X1-->X2,X2-->X3,X3-->X4, X5-->X4");
    // simple graph pm im gen example
    HashMap<String, Integer> nd = new HashMap<>();
    nd.put("X1", 0);
    nd.put("X2", 0);
    nd.put("X3", 4);
    nd.put("X4", 4);
    nd.put("X5", 0);
    g = makeMixedGraph(g, nd);
    /*Graph g = new EdgeListGraph();
        g.addNode(new ContinuousVariable("X1"));
        g.addNode(new ContinuousVariable("X2"));
        g.addNode(new DiscreteVariable("X3", 4));
        g.addNode(new DiscreteVariable("X4", 4));
        g.addNode(new ContinuousVariable("X5"));
        g.addDirectedEdge(g.getNode("X1"), g.getNode("X2"));
        g.addDirectedEdge(g.getNode("X2"), g.getNode("X3"));
        g.addDirectedEdge(g.getNode("X3"), g.getNode("X4"));
        g.addDirectedEdge(g.getNode("X4"), g.getNode("X5"));
        */
    GeneralizedSemPm pm = GaussianCategoricalPm(g, "Split(-1.5,-1,1,1.5)");
    System.out.println(pm);
    System.out.println("STARTS WITH");
    System.out.println(pm.getStartsWithParameterTemplate("C"));
    try {
        MixedUtils.setStartsWith("C", "Split(-.9,-.5,.5,.9)", pm);
    } catch (Throwable t) {
        t.printStackTrace();
    }
    System.out.println("STARTS WITH");
    System.out.println(pm.getStartsWithParameterTemplate("C"));
    System.out.println(pm);
    GeneralizedSemIm im = GaussianCategoricalIm(pm);
    System.out.println(im);
    int samps = 15;
    DataSet ds = im.simulateDataFisher(samps);
    System.out.println(ds);
    System.out.println("num cats " + ((DiscreteVariable) g.getNode("X4")).getNumCategories());
/*Graph trueGraph = DataGraphUtils.loadGraphTxt(new File(MixedUtils.class.getResource("test_data").getPath(), "DAG_0_graph.txt"));
        HashMap<String, Integer> nd = new HashMap<>();
        List<Node> nodes = trueGraph.getNodes();
        for(int i = 0; i < nodes.size(); i++){
            int coin = RandomUtil.getInstance().nextInt(2);
            int dist = (coin==0) ? 0 : 3; //continuous if coin == 0
            nd.put(nodes.get(i).getNode(), dist);
        }
        //System.out.println(getEdgeParams(g.getNode("X3"), g.getNode("X2"), pm).toString());
        //System.out.println(getEdgeParams(g.getNode("X4"), g.getNode("X3"), pm).toString());
        //System.out.println(getEdgeParams(g.getNode("X5"), g.getNode("X4"), pm).toString());
        //System.out.println("num cats " + ((DiscreteVariable) g.getNode("X4")).getNumCategories());
        /*
        HashMap<String, String> nd2 = new HashMap<>();
        nd2.put("X1", "Norm");
        nd2.put("X2", "Norm");
        nd2.put("X3", "Disc");
        nd2.put("X4", "Disc");
        nd2.put("X5", "Disc");
        GeneralizedSemPm pm2 = GaussianTrinaryPm(g, nd2, 10, "Split(-1.5,-.5,.5,1.5)");
        System.out.println("OLD pm:");
        System.out.print(pm2);
        */
}

Example 10 with GeneralizedSemPm

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

the class TestSimulatedFmri method testClark2.

// @Test
public void testClark2() {
    Node x = new ContinuousVariable("X");
    Node y = new ContinuousVariable("Y");
    Node z = new ContinuousVariable("Z");
    Graph g = new EdgeListGraph();
    g.addNode(x);
    g.addNode(y);
    g.addNode(z);
    g.addDirectedEdge(x, y);
    g.addDirectedEdge(x, z);
    g.addDirectedEdge(y, z);
    GeneralizedSemPm pm = new GeneralizedSemPm(g);
    try {
        pm.setNodeExpression(g.getNode("X"), "E_X");
        pm.setNodeExpression(g.getNode("Y"), "0.4 * X + E_Y");
        pm.setNodeExpression(g.getNode("Z"), "0.4 * X + 0.4 * Y + E_Z");
        String error = "pow(Uniform(0, 1), 1.5)";
        pm.setNodeExpression(pm.getErrorNode(g.getNode("X")), error);
        pm.setNodeExpression(pm.getErrorNode(g.getNode("Y")), error);
        pm.setNodeExpression(pm.getErrorNode(g.getNode("Z")), error);
    } catch (ParseException e) {
        System.out.println(e);
    }
    GeneralizedSemIm im = new GeneralizedSemIm(pm);
    DataSet data = im.simulateData(1000, false);
    edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
    Fask fask = new Fask(data, score);
    fask.setPenaltyDiscount(1);
    fask.setAlpha(0.5);
    Graph out = fask.search();
    System.out.println(out);
}