use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.
the class TestKunMeasurementError method TestCycles_Data_fMRI_FASK.
public void TestCycles_Data_fMRI_FASK() {
Parameters parameters = new Parameters();
parameters.set("numRuns", 20);
parameters.set("penaltyDiscount", 1);
parameters.set("depth", -1);
parameters.set("determinismThreshold", .1);
parameters.set("verbose", true);
parameters.set("symmetricFirstStep", false);
parameters.set("faithfulnessAssumed", false);
parameters.set("maxDegree", 100);
Statistics statistics = new Statistics();
// statistics.add(new ParameterColumn("determinismThreshold"));
statistics.add(new AdjacencyPrecision());
statistics.add(new AdjacencyRecall());
statistics.add(new ArrowheadPrecision());
statistics.add(new ArrowheadRecall());
Simulations simulations = new Simulations();
// String dir = "/Users/user/Downloads/Simul1_T500";
String dir = "/Users/user/Downloads/Simul1_T2000";
// String dir = "/Users/user/Downloads/Simul2_T500";
// String dir = "/Users/user/Downloads/Simul2_T2000";
simulations.add(new LoadContinuousDataAndSingleGraphKun(dir, "Cov_X"));
simulations.add(new LoadContinuousDataAndSingleGraphKun(dir, "Cov_tilde"));
simulations.add(new LoadContinuousDataAndSingleGraphKun(dir, "Cov_tilde_hat"));
//
Algorithms algorithms = new Algorithms();
IndependenceWrapper test = new SemBicTest();
ScoreWrapper score = new SemBicScore();
algorithms.add(new Pc(test));
algorithms.add(new Fges(score));
algorithms.add(new Pcd());
algorithms.add(new FgesD());
Comparison comparison = new Comparison();
comparison.setShowAlgorithmIndices(true);
comparison.setShowSimulationIndices(true);
comparison.setSortByUtility(false);
comparison.setShowUtilities(false);
comparison.setParallelized(false);
comparison.setSaveGraphs(false);
comparison.setTabDelimitedTables(false);
comparison.compareFromSimulations("comparison", simulations, algorithms, statistics, parameters);
}
use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.
the class ImagesSemBic method getParameters.
@Override
public List<String> getParameters() {
List<String> parameters = new Fges(new SemBicScore(), false).getParameters();
parameters.add("numRuns");
parameters.add("randomSelectionSize");
// Bootstrapping
parameters.add("bootstrapSampleSize");
parameters.add("bootstrapEnsemble");
parameters.add("verbose");
return parameters;
}
use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.
the class ExampleStARS method main.
public static void main(String... args) {
Parameters parameters = new Parameters();
// parameters.set("numMeasures", 100);
// parameters.set("avgDegree", 2, 4);
// parameters.set("sampleSize", 100, 500);
// parameters.set("numRuns", 5);
parameters.set("numMeasures", 200);
parameters.set("avgDegree", 2, 4, 6);
parameters.set("sampleSize", 100, 500);
parameters.set("numRuns", 2);
parameters.set("differentGraphs", true);
parameters.set("numLatents", 0);
parameters.set("maxDegree", 100);
parameters.set("maxIndegree", 100);
parameters.set("maxOutdegree", 100);
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("percentDiscrete", 0);
parameters.set("numCategories", 3);
parameters.set("differentGraphs", true);
parameters.set("intervalBetweenShocks", 10);
parameters.set("intervalBetweenRecordings", 10);
parameters.set("fisherEpsilon", 0.001);
parameters.set("randomizeColumns", true);
parameters.set("alpha", 0.01);
parameters.set("depth", -1);
parameters.set("penaltyDiscount", 2);
parameters.set("useMaxPOrientationHeuristic", false);
parameters.set("maxPOrientationMaxPathLength", 3);
parameters.set("verbose", false);
parameters.set("scaleFreeAlpha", 0.00001);
parameters.set("scaleFreeBeta", 0.4);
parameters.set("scaleFreeDeltaIn", .1);
parameters.set("scaleFreeDeltaOut", 3);
parameters.set("symmetricFirstStep", false);
parameters.set("faithfulnessAssumed", true);
parameters.set("maxDegree", 100);
parameters.set("StARS.tolerance", .5);
parameters.set("StARS.cutoff", .05);
parameters.set("numSubsamples", 7);
parameters.set("percentSubsampleSize", .5);
parameters.set("percentStability", .5);
Statistics statistics = new Statistics();
statistics.add(new ParameterColumn("numMeasures"));
statistics.add(new ParameterColumn("avgDegree"));
statistics.add(new ParameterColumn("sampleSize"));
statistics.add(new AdjacencyPrecision());
statistics.add(new AdjacencyRecall());
statistics.add(new ArrowheadPrecision());
statistics.add(new ArrowheadRecall());
statistics.add(new ElapsedTime());
statistics.setWeight("AP", 0.25);
statistics.setWeight("AR", 0.25);
statistics.setWeight("AHP", 0.25);
statistics.setWeight("AHR", 0.25);
Algorithms algorithms = new Algorithms();
parameters.set("logScale", false);
algorithms.add(new StabilitySelection(new Fges(new SemBicScore())));
algorithms.add(new StARS(new Fges(new SemBicScore()), "penaltyDiscount", 1, 5));
algorithms.add(new FirstInflection(new Fges(new SemBicScore()), "penaltyDiscount", 1, 5, .1));
// parameters.set("penaltyDiscount", 5, 11, 15);
// algorithms.add(new Fges(new SemBicScore()));
// parameters.set("logScale", true);
// Algorithm fges = new Fges(new FisherZScore());
// algorithms.add(new StARS(fges, "alpha", -10, -2, -8));
// algorithms.add(new FirstInflection(fges, "alpha", -10, -2, -3));
Simulations simulations = new Simulations();
simulations.add(new LinearFisherModel(new RandomForward()));
Comparison comparison = new Comparison();
comparison.setShowAlgorithmIndices(true);
comparison.setShowSimulationIndices(true);
comparison.setSortByUtility(false);
comparison.setShowUtilities(false);
comparison.setParallelized(true);
comparison.setComparisonGraph(Comparison.ComparisonGraph.Pattern_of_the_true_DAG);
comparison.compareFromSimulations("first.inflection", simulations, algorithms, statistics, parameters);
}
use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges in project tetrad by cmu-phil.
the class TestGeneralBootstrapTest method testFGESc.
@Test
public void testFGESc() {
int penaltyDiscount = 2;
boolean faithfulnessAssumed = false;
int maxDegree = -1;
int numVars = 20;
int edgesPerNode = 2;
int numLatentConfounders = 0;
int numCases = 50;
int numBootstrapSamples = 5;
boolean verbose = true;
Graph dag = makeContinuousDAG(numVars, numLatentConfounders, edgesPerNode);
System.out.println("Truth Graph:");
System.out.println(dag.toString());
int[] causalOrdering = new int[numVars];
for (int i = 0; i < numVars; i++) {
causalOrdering[i] = i;
}
LargeScaleSimulation simulator = new LargeScaleSimulation(dag, dag.getNodes(), causalOrdering);
DataSet data = simulator.simulateDataFisher(numCases);
Parameters parameters = new Parameters();
parameters.set("penaltyDiscount", penaltyDiscount);
parameters.set("faithfulnessAssumed", faithfulnessAssumed);
parameters.set("maxDegree", maxDegree);
parameters.set("numPatternsToStore", 0);
parameters.set("verbose", verbose);
ScoreWrapper score = new SemBicScore();
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);
}
use of edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges 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);
}
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