use of edu.cmu.tetrad.algcomparison.graph.RandomGraph in project tetrad by cmu-phil.
the class TestFges method main.
public static void main(String... args) {
if (args.length > 0) {
int numMeasures = Integer.parseInt(args[0]);
int avgDegree = Integer.parseInt(args[1]);
Parameters parameters = new Parameters();
parameters.set("numMeasures", numMeasures);
parameters.set("numLatents", 0);
parameters.set("avgDegree", avgDegree);
parameters.set("maxDegree", 20);
parameters.set("maxIndegree", 20);
parameters.set("maxOutdegree", 20);
parameters.set("connected", false);
parameters.set("coefLow", 0.2);
parameters.set("coefHigh", 0.9);
parameters.set("varLow", 1);
parameters.set("varHigh", 3);
parameters.set("verbose", false);
parameters.set("coefSymmetric", true);
parameters.set("numRuns", 1);
parameters.set("percentDiscrete", 0);
parameters.set("numCategories", 3);
parameters.set("differentGraphs", true);
parameters.set("sampleSize", 1000);
parameters.set("intervalBetweenShocks", 10);
parameters.set("intervalBetweenRecordings", 10);
parameters.set("fisherEpsilon", 0.001);
parameters.set("randomizeColumns", true);
RandomGraph graph = new RandomForward();
LinearFisherModel sim = new LinearFisherModel(graph);
sim.createData(parameters);
ScoreWrapper score = new FisherZScore();
Algorithm alg = new edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges(score);
parameters.set("alpha", 1e-8);
for (int i = 0; i < 5; i++) {
Graph out1 = alg.search(sim.getDataModel(0), parameters);
System.out.println(out1);
}
} else {
new TestFges().test9();
}
}
use of edu.cmu.tetrad.algcomparison.graph.RandomGraph in project tetrad by cmu-phil.
the class SimulationEditor method resetPanel.
private void resetPanel(Simulation simulation, String[] graphItems, String[] simulationItems, JTabbedPane tabbedPane) {
RandomGraph randomGraph = (simulation.getSourceGraph() == null) ? new SingleGraph(new EdgeListGraph()) : new SingleGraph(simulation.getSourceGraph());
if (!simulation.isFixedGraph()) {
String graphItem = (String) graphsDropdown.getSelectedItem();
simulation.getParams().set("graphsDropdownPreference", graphItem);
if (graphItem.equals(graphItems[0])) {
randomGraph = new RandomForward();
} else if (graphItem.equals(graphItems[1])) {
randomGraph = new ScaleFree();
} else if (graphItem.equals(graphItems[2])) {
randomGraph = new Cyclic();
} else if (graphItem.equals(graphItems[3])) {
randomGraph = new RandomSingleFactorMim();
} else if (graphItem.equals(graphItems[4])) {
randomGraph = new RandomTwoFactorMim();
} else {
throw new IllegalArgumentException("Unrecognized simulation type: " + graphItem);
}
}
if (!simulation.isFixedSimulation()) {
if (simulation.getSourceGraph() == null) {
String simulationItem = (String) simulationsDropdown.getSelectedItem();
simulation.getParams().set("simulationsDropdownPreference", simulationItem);
simulation.setFixedGraph(false);
if (randomGraph instanceof SingleGraph) {
simulation.setFixedGraph(true);
}
if (simulationItem.equals(simulationItems[0])) {
simulation.setSimulation(new BayesNetSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[1])) {
simulation.setSimulation(new SemSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[2])) {
simulation.setSimulation(new LinearFisherModel(randomGraph, simulation.getInputDataModelList()), simulation.getParams());
} else if (simulationItem.equals(simulationItems[3])) {
simulation.setSimulation(new LeeHastieSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[4])) {
simulation.setSimulation(new ConditionalGaussianSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[5])) {
simulation.setSimulation(new TimeSeriesSemSimulation(randomGraph), simulation.getParams());
} else {
throw new IllegalArgumentException("Unrecognized simulation type: " + simulationItem);
}
} else {
String simulationItem = (String) simulationsDropdown.getSelectedItem();
simulation.getParams().set("simulationsDropdownPreference", simulationItem);
simulation.setFixedGraph(false);
if (randomGraph instanceof SingleGraph) {
simulation.setFixedGraph(true);
}
if (simulationItem.equals(simulationItems[0])) {
simulation.setSimulation(new BayesNetSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[1])) {
simulation.setSimulation(new SemSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[2])) {
simulation.setSimulation(new LinearFisherModel(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[3])) {
simulation.setSimulation(new LeeHastieSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[4])) {
simulation.setSimulation(new ConditionalGaussianSimulation(randomGraph), simulation.getParams());
} else if (simulationItem.equals(simulationItems[5])) {
simulation.setSimulation(new TimeSeriesSemSimulation(randomGraph), simulation.getParams());
} else {
throw new IllegalArgumentException("Unrecognized simulation type: " + simulationItem);
}
}
}
tabbedPane.setComponentAt(0, new PaddingPanel(getParameterPanel(simulation, simulation.getSimulation(), simulation.getParams())));
}
use of edu.cmu.tetrad.algcomparison.graph.RandomGraph in project tetrad by cmu-phil.
the class TestFges method clarkTest.
@Test
public void clarkTest() {
RandomGraph randomGraph = new RandomForward();
Simulation simulation = new LinearFisherModel(randomGraph);
Parameters parameters = new Parameters();
parameters.set("numMeasures", 100);
parameters.set("numLatents", 0);
parameters.set("coefLow", 0.2);
parameters.set("coefHigh", 0.8);
parameters.set("avgDegree", 2);
parameters.set("maxDegree", 100);
parameters.set("maxIndegree", 100);
parameters.set("maxOutdegree", 100);
parameters.set("connected", false);
parameters.set("numRuns", 1);
parameters.set("differentGraphs", false);
parameters.set("sampleSize", 1000);
parameters.set("faithfulnessAssumed", false);
parameters.set("maxDegree", -1);
parameters.set("verbose", false);
parameters.set("alpha", 0.01);
simulation.createData(parameters);
DataSet dataSet = (DataSet) simulation.getDataModel(0);
Graph trueGraph = simulation.getTrueGraph(0);
// trueGraph = SearchGraphUtils.patternForDag(trueGraph);
ScoreWrapper score = new edu.cmu.tetrad.algcomparison.score.SemBicScore();
IndependenceWrapper test = new FisherZ();
Algorithm fges = new edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges(score, false);
Graph fgesGraph = fges.search(dataSet, parameters);
clarkTestForAlpha(0.05, parameters, dataSet, trueGraph, fgesGraph, test);
clarkTestForAlpha(0.01, parameters, dataSet, trueGraph, fgesGraph, test);
}
use of edu.cmu.tetrad.algcomparison.graph.RandomGraph in project tetrad by cmu-phil.
the class TestFges method test9.
public void test9() {
Parameters parameters = new Parameters();
parameters.set("numMeasures", 50);
parameters.set("numLatents", 0);
parameters.set("avgDegree", 2);
parameters.set("maxDegree", 20);
parameters.set("maxIndegree", 20);
parameters.set("maxOutdegree", 20);
parameters.set("connected", false);
parameters.set("coefLow", 0.2);
parameters.set("coefHigh", 0.9);
parameters.set("varLow", 1);
parameters.set("varHigh", 3);
parameters.set("verbose", false);
parameters.set("coefSymmetric", true);
parameters.set("numRuns", 1);
parameters.set("percentDiscrete", 0);
parameters.set("numCategories", 3);
parameters.set("differentGraphs", true);
parameters.set("sampleSize", 500);
parameters.set("intervalBetweenShocks", 10);
parameters.set("intervalBetweenRecordings", 10);
parameters.set("fisherEpsilon", 0.001);
parameters.set("randomizeColumns", true);
RandomGraph graph = new RandomForward();
LinearFisherModel sim = new LinearFisherModel(graph);
sim.createData(parameters);
Graph previous = null;
int prevDiff = Integer.MAX_VALUE;
// for (int l = 7; l >= 1; l--) {
for (int i = 2; i <= 20; i++) {
parameters.set("penaltyDiscount", i / (double) 10);
// parameters.set("alpha", Double.parseDouble("1E-" + l));
// ScoreWrapper score = new edu.cmu.tetrad.algcomparison.score.SemBicScore();
// Algorithm alg = new edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges(score);
IndependenceWrapper test = new SemBicTest();
// IndependenceWrapper test = new FisherZ();
Algorithm alg = new edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Cpc(test);
Graph out = alg.search(sim.getDataModel(0), parameters);
// Graph out = GraphUtils.undirectedGraph(alg.search(sim.getDataModel(0), parameters));
Set<Edge> edges1 = out.getEdges();
int numEdges = edges1.size();
if (previous != null) {
Set<Edge> edges2 = previous.getEdges();
edges2.removeAll(edges1);
int diff = edges2.size();
//
System.out.println("Penalty discount =" + parameters.getDouble("penaltyDiscount") + " # edges = " + numEdges + " # additional = " + diff);
previous = out;
if (diff > prevDiff)
break;
prevDiff = diff;
} else {
previous = out;
}
}
Graph estGraph = previous;
Graph trueGraph = sim.getTrueGraph(0);
estGraph = GraphUtils.replaceNodes(estGraph, trueGraph.getNodes());
Statistic ap = new AdjacencyPrecision();
Statistic ar = new AdjacencyRecall();
Statistic ahp = new ArrowheadPrecision();
Statistic ahr = new ArrowheadRecall();
System.out.println("AP = " + ap.getValue(trueGraph, estGraph));
System.out.println("AR = " + ar.getValue(trueGraph, estGraph));
System.out.println("AHP = " + ahp.getValue(trueGraph, estGraph));
System.out.println("AHR = " + ahr.getValue(trueGraph, estGraph));
}
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