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Non Gaussian DAG/CPDAG for Local Markov Blanket test case, confusion stats using LocalGraphConfusion #1780

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May 24, 2024
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Non Gaussian DAG/CPDAG for Local Markov Blanket test case, confusion stats using LocalGraphConfusion #1780

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Non Gaussian DAG for Local Markov Blanket test case, confusion stats …
vbcwonderland May 24, 2024
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Non Gaussian CPDAG for Local Markov Blanket test case, confusion stat…
vbcwonderland May 24, 2024
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65 changes: 65 additions & 0 deletions tetrad-lib/src/test/java/edu/cmu/tetrad/test/TestCheckMarkov.java
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Original file line number Diff line number Diff line change
Expand Up @@ -489,4 +489,69 @@ public void testGaussianCPDAGPrecisionRecallForLocalOnMarkovBlanket2() {
System.out.println("Rejects size: " + rejects.size());
}

@Test
public void testNonGaussianDAGPrecisionRecallForLocalOnMarkovBlanket2() {
Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
System.out.println("Test True Graph: " + trueGraph);
System.out.println("Test True Graph size: " + trueGraph.getNodes().size());

SemPm pm = new SemPm(trueGraph);

Parameters params = new Parameters();
// Manually set non-Gaussian
params.set(Params.SIMULATION_ERROR_TYPE, 3);
params.set(Params.SIMULATION_PARAM1, 1);

SemIm im = new SemIm(pm, params);
DataSet data = im.simulateData(1000, false);
edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, false);
score.setPenaltyDiscount(2);
Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");

IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
// ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
// List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes2(fisherZTest, estimatedCpdag, 0.05, 0.3);
List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3);
List<Node> accepts = accepts_rejects.get(0);
List<Node> rejects = accepts_rejects.get(1);
System.out.println("Accepts size: " + accepts.size());
System.out.println("Rejects size: " + rejects.size());
}

@Test
public void testNonGaussianCPDAGPrecisionRecallForLocalOnMarkovBlanket2() {
Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
// The completed partially directed acyclic graph (CPDAG) for the given DAG.
Graph trueGraphCPDAG = GraphTransforms.dagToCpdag(trueGraph);
System.out.println("Test True Graph: " + trueGraph);
System.out.println("Test True Graph CPDAG: " + trueGraphCPDAG);

SemPm pm = new SemPm(trueGraph);

Parameters params = new Parameters();
// Manually set non-Gaussian
params.set(Params.SIMULATION_ERROR_TYPE, 3);
params.set(Params.SIMULATION_PARAM1, 1);

SemIm im = new SemIm(pm, params);
DataSet data = im.simulateData(1000, false);
edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, false);
score.setPenaltyDiscount(2);
Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");

IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
// ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
// List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes2(fisherZTest, estimatedCpdag, 0.05, 0.5);
List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3);
List<Node> accepts = accepts_rejects.get(0);
List<Node> rejects = accepts_rejects.get(1);
System.out.println("Accepts size: " + accepts.size());
System.out.println("Rejects size: " + rejects.size());
}
}