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Include specify file for test check nodewise markov #1799

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Jul 17, 2024
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Include specify file for test check nodewise markov #1799

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c3547d7
Include specify file for test check nodewise markov
vbcwonderland Jul 16, 2024
829ed76
nit
vbcwonderland Jul 16, 2024
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49 changes: 49 additions & 0 deletions testTrueGraphForCheckNodewiseMarkov.txt
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Original file line number Diff line number Diff line change
@@ -0,0 +1,49 @@
Graph Nodes:
X1;X2;X3;X4;X5;X6;X7;X8;X9;X10

Graph Edges:
1. X1 --> X2
2. X1 --> X3
3. X1 --> X5
4. X1 --> X6
5. X1 --> X7
6. X1 --> X8
7. X1 --> X9
8. X1 --> X10
9. X2 --> X3
10. X2 --> X4
11. X2 --> X5
12. X2 --> X8
13. X2 --> X9
14. X2 --> X10
15. X3 --> X4
16. X3 --> X5
17. X3 --> X6
18. X3 --> X7
19. X3 --> X8
20. X3 --> X9
21. X3 --> X10
22. X4 --> X5
23. X4 --> X6
24. X4 --> X9
25. X4 --> X10
26. X5 --> X6
27. X5 --> X7
28. X5 --> X8
29. X5 --> X9
30. X5 --> X10
31. X6 --> X7
32. X6 --> X8
33. X6 --> X9
34. X6 --> X10
35. X7 --> X8
36. X7 --> X9
37. X7 --> X10
38. X8 --> X9
39. X8 --> X10
40. X9 --> X10


Test True Graph size: 10
Test Estimated CPDAG Graph: Graph Nodes:
X1;X2;X3;X4;X5;X6;X7;X8;X9;X10
44 changes: 43 additions & 1 deletion tetrad-lib/src/test/java/edu/cmu/tetrad/test/TestCheckNodewiseMarkov.java
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Expand Up @@ -12,14 +12,56 @@
import edu.cmu.tetrad.util.Params;
import org.junit.Test;

import java.io.File;
import java.util.List;



public class TestCheckNodewiseMarkov {

public static void main(String... args) {
testGaussianDAGPrecisionRecallForLocalOnMarkovBlanket(10, 40, 40, 0.5, 1.0, 0.8);
// testGaussianDAGPrecisionRecallForLocalOnMarkovBlanket(10, 40, 40, 0.5, 1.0, 0.8);
String filePath = "testTrueGraphForCheckNodewiseMarkov.txt";
File file = new File(filePath);
if (file.exists()) {
System.out.println("Loading true graph file: " + filePath);
testGaussianDAGPrecisionRecallForLocalOnMarkovBlanket(file, 0.5, 1.0, 0.8);
} else {
System.out.println("File does not exist at the specified path.");
}
}

public static void testGaussianDAGPrecisionRecallForLocalOnMarkovBlanket(File txtFile, double threshold, double shuffleThreshold, double lowRecallBound) {
Graph trueGraph = GraphSaveLoadUtils.loadGraphTxt(txtFile);
System.out.println("Test True Graph: " + trueGraph);
System.out.println("Test True Graph size: " + trueGraph.getNodes().size());

SemPm pm = new SemPm(trueGraph);
// Parameters without additional setting default tobe Gaussian
SemIm im = new SemIm(pm, new Parameters());
DataSet data = im.simulateData(10000, false);
SemBicScore score = new SemBicScore(data, false);
score.setPenaltyDiscount(2);
Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
// TODO VBC: Next check different search algo to generate estimated graph. e.g. PC
System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
testGaussianDAGPrecisionRecallForLocalOnMarkovBlanketUsingAdjAHConfusionMatrix(data, trueGraph, estimatedCpdag, threshold, shuffleThreshold, lowRecallBound);
testGaussianDAGPrecisionRecallForLocalOnMarkovBlanketUsingLGConfusionMatrix(data, trueGraph, estimatedCpdag, threshold, shuffleThreshold, lowRecallBound);
System.out.println("~~~~~~~~~~~~~Full Graph~~~~~~~~~~~~~~~");
estimatedCpdag = GraphUtils.replaceNodes(estimatedCpdag, trueGraph.getNodes());
double whole_ap = new AdjacencyPrecision().getValue(trueGraph, estimatedCpdag, null);
double whole_ar = new AdjacencyRecall().getValue(trueGraph, estimatedCpdag, null);
double whole_ahp = new ArrowheadPrecision().getValue(trueGraph, estimatedCpdag, null);
double whole_ahr = new ArrowheadRecall().getValue(trueGraph, estimatedCpdag, null);
double whole_lgp = new LocalGraphPrecision().getValue(trueGraph, estimatedCpdag, null);
double whole_lgr = new LocalGraphRecall().getValue(trueGraph, estimatedCpdag, null);
System.out.println("whole_ap: " + whole_ap);
System.out.println("whole_ar: " + whole_ar );
System.out.println("whole_ahp: " + whole_ahp);
System.out.println("whole_ahr: " + whole_ahr);
System.out.println("whole_lgp: " + whole_lgp);
System.out.println("whole_lgr: " + whole_lgr);
}

public static void testGaussianDAGPrecisionRecallForLocalOnMarkovBlanket(int numNodes, int maxNumEdges, int maxDegree, double threshold, double shuffleThreshold, double lowRecallBound) {
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