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Merge pull request #1787 from cmu-phil/vbc-2024-06-25
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Resolve NaN ADTest P value from empty independent facts cases
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@jdramsey
jdramsey authored Jun 25, 2024
2 parents 491d667 + dbe94dd commit d1c4da5
Showing 1 changed file with 60 additions and 58 deletions.
118 changes: 60 additions & 58 deletions tetrad-lib/src/main/java/edu/cmu/tetrad/search/MarkovCheck.java
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Original file line number Diff line number Diff line change
Expand Up @@ -401,40 +401,41 @@ public List<List<Node>> getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlot
Double ar = ap_ar_ahp_ahr.get(1);
Double ahp = ap_ar_ahp_ahr.get(2);
Double ahr = ap_ar_ahp_ahr.get(3);
// All local nodes' p-values for node x.
List<List<Double>> shuffledlocalPValues = getLocalPValues(independenceTest, localIndependenceFacts, shuffleThreshold); // shuffleThreshold default to be 0.5
List<Double> flatList = shuffledlocalPValues.stream()
.flatMap(List::stream)
.collect(Collectors.toList());
Double ADTestPValue = checkAgainstAndersonDarlingTest(flatList);
// TODO VBC: what should we do for cases when ADTest is NaN and ∞ ?
if (ADTestPValue <= threshold) {
rejects.add(x);
if (!Double.isNaN(ap)) {
rejects_AdjP_ADTestP.add(Arrays.asList(ap, ADTestPValue));
}
if (!Double.isNaN(ar)) {
rejects_AdjR_ADTestP.add(Arrays.asList(ar, ADTestPValue));
}
if (!Double.isNaN(ahp)) {
rejects_AHP_ADTestP.add(Arrays.asList(ahp, ADTestPValue));
}
if (!Double.isNaN(ahr)) {
rejects_AHR_ADTestP.add(Arrays.asList(ahr, ADTestPValue));
}
} else {
accepts.add(x);
if (!Double.isNaN(ap)) {
accepts_AdjP_ADTestP.add(Arrays.asList(ap, ADTestPValue));
}
if (!Double.isNaN(ar)) {
accepts_AdjR_ADTestP.add(Arrays.asList(ar, ADTestPValue));
}
if (!Double.isNaN(ahp)) {
accepts_AHP_ADTestP.add(Arrays.asList(ahp, ADTestPValue));
}
if (!Double.isNaN(ahr)) {
accepts_AHR_ADTestP.add(Arrays.asList(ahr, ADTestPValue));
if (!localIndependenceFacts.isEmpty()) {
// All local nodes' p-values for node x.
List<List<Double>> shuffledlocalPValues = getLocalPValues(independenceTest, localIndependenceFacts, shuffleThreshold); // shuffleThreshold default to be 0.5
List<Double> flatList = shuffledlocalPValues.stream()
.flatMap(List::stream)
.collect(Collectors.toList());
Double ADTestPValue = checkAgainstAndersonDarlingTest(flatList);
if (ADTestPValue <= threshold) {
rejects.add(x);
if (!Double.isNaN(ap)) {
rejects_AdjP_ADTestP.add(Arrays.asList(ap, ADTestPValue));
}
if (!Double.isNaN(ar)) {
rejects_AdjR_ADTestP.add(Arrays.asList(ar, ADTestPValue));
}
if (!Double.isNaN(ahp)) {
rejects_AHP_ADTestP.add(Arrays.asList(ahp, ADTestPValue));
}
if (!Double.isNaN(ahr)) {
rejects_AHR_ADTestP.add(Arrays.asList(ahr, ADTestPValue));
}
} else {
accepts.add(x);
if (!Double.isNaN(ap)) {
accepts_AdjP_ADTestP.add(Arrays.asList(ap, ADTestPValue));
}
if (!Double.isNaN(ar)) {
accepts_AdjR_ADTestP.add(Arrays.asList(ar, ADTestPValue));
}
if (!Double.isNaN(ahp)) {
accepts_AHP_ADTestP.add(Arrays.asList(ahp, ADTestPValue));
}
if (!Double.isNaN(ahr)) {
accepts_AHR_ADTestP.add(Arrays.asList(ahr, ADTestPValue));
}
}
}
System.out.println("-----------------------------");
Expand Down Expand Up @@ -515,7 +516,7 @@ public List<List<Node>> getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlot
* @param estimatedCpdag The estimated CPDAG.
* @param trueGraph The true graph.
* @param threshold The threshold value for classifying nodes.
* @param shuffleThreshold The threshold value for shuffling the data.
* @param shuffleThreshold The threshold value for shuffling the data. shuffleThreshold default can set to be 0.5
* @return A list containing two lists: the first list contains the accepted nodes and the second list contains the
*/
public List<List<Node>> getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(IndependenceTest independenceTest, Graph estimatedCpdag, Graph trueGraph, Double threshold, Double shuffleThreshold) {
Expand Down Expand Up @@ -547,29 +548,30 @@ public List<List<Node>> getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlot
List<Double> lgp_lgr = getPrecisionAndRecallOnMarkovBlanketGraphPlotData2(x, estimatedCpdag, trueGraph);
Double lgp = lgp_lgr.get(0);
Double lgr = lgp_lgr.get(1);
// All local nodes' p-values for node x.
List<List<Double>> shuffledlocalPValues = getLocalPValues(independenceTest, localIndependenceFacts, shuffleThreshold); // shuffleThreshold default to be 0.5
List<Double> flatList = shuffledlocalPValues.stream()
.flatMap(List::stream)
.collect(Collectors.toList());
System.out.println("# p values feed into ADTest: " + flatList.size() );
Double ADTestPValue = checkAgainstAndersonDarlingTest(flatList);
// TODO VBC: what should we do for cases when ADTest is NaN and ∞ ?
if (ADTestPValue <= threshold) {
rejects.add(x);
if (!Double.isNaN(lgp)) {
rejects_LGP_ADTestP.add(Arrays.asList(lgp, ADTestPValue));
}
if (!Double.isNaN(lgr)) {
rejects_LGR_ADTestP.add(Arrays.asList(lgr, ADTestPValue));
}
} else {
accepts.add(x);
if (!Double.isNaN(lgp)) {
accepts_LGP_ADTestP.add(Arrays.asList(lgp, ADTestPValue));
}
if (!Double.isNaN(lgr)) {
accepts_LGR_ADTestP.add(Arrays.asList(lgr, ADTestPValue));
if (!localIndependenceFacts.isEmpty()) {
// All local nodes' p-values for node x.
List<List<Double>> shuffledlocalPValues = getLocalPValues(independenceTest, localIndependenceFacts, shuffleThreshold);
List<Double> flatList = shuffledlocalPValues.stream()
.flatMap(List::stream)
.collect(Collectors.toList());
System.out.println("# p values feed into ADTest: " + flatList.size() );
Double ADTestPValue = checkAgainstAndersonDarlingTest(flatList);
if (ADTestPValue <= threshold) {
rejects.add(x);
if (!Double.isNaN(lgp)) {
rejects_LGP_ADTestP.add(Arrays.asList(lgp, ADTestPValue));
}
if (!Double.isNaN(lgr)) {
rejects_LGR_ADTestP.add(Arrays.asList(lgr, ADTestPValue));
}
} else {
accepts.add(x);
if (!Double.isNaN(lgp)) {
accepts_LGP_ADTestP.add(Arrays.asList(lgp, ADTestPValue));
}
if (!Double.isNaN(lgr)) {
accepts_LGR_ADTestP.add(Arrays.asList(lgr, ADTestPValue));
}
}
}
System.out.println("-----------------------------");
Expand Down

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