Skip to content

Commit

Permalink
Introduce plot data collection for different confusion matrix stats f…
Browse files Browse the repository at this point in the history 
…rom Anderson Darling Test result.
  • Loading branch information
@vbcwonderland
vbcwonderland committed May 23, 2024
1 parent 7795487 commit de7202a
Show file tree
Hide file tree
Showing 2 changed files with 189 additions and 31 deletions.
174 changes: 167 additions & 7 deletions tetrad-lib/src/main/java/edu/cmu/tetrad/search/MarkovCheck.java
View file
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,9 @@
import org.apache.commons.math3.util.Pair;
import org.jetbrains.annotations.NotNull;

import java.io.BufferedWriter;
import java.io.FileWriter;
import java.io.IOException;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.*;
Expand Down Expand Up @@ -313,24 +316,166 @@ public Double checkAgainstAndersonDarlingTest(List<Double> pValues) {
* @return A list containing two lists: the first list contains the accepted nodes and the second list contains the
* rejected nodes.
*/
public List<List<Node>> getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(IndependenceTest independenceTest, Graph graph, Double threshold) {
public List<List<Node>> getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(IndependenceTest independenceTest, Graph graph, Double threshold, Double shuffleThreshold) {
// When calling, default reject null as <=0.05
List<List<Node>> accepts_rejects = new ArrayList<>();
List<Node> accepts = new ArrayList<>();
List<Node> rejects = new ArrayList<>();
List<Node> allNodes = graph.getNodes();
for (Node x : allNodes) {
List<IndependenceFact> localIndependenceFacts = getLocalIndependenceFacts(x);
List<Double> localPValues = getLocalPValues(independenceTest, localIndependenceFacts);
Double ADTest = checkAgainstAndersonDarlingTest(localPValues);
if (ADTest <= threshold) {
rejects.add(x);
} else {
accepts.add(x);
// All local nodes' p-values for node x
List<List<Double>> shuffledlocalPValues = getLocalPValues(independenceTest, localIndependenceFacts, shuffleThreshold);
for (List<Double> localPValues: shuffledlocalPValues) {
Double ADTest = checkAgainstAndersonDarlingTest(localPValues); // P value obtained from AD test
if (ADTest <= threshold) {
rejects.add(x);
} else {
accepts.add(x);
}
}
}
accepts_rejects.add(accepts);
accepts_rejects.add(rejects);
return accepts_rejects;
}

public List<List<Node>> getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(IndependenceTest independenceTest, Graph estimatedCpdag, Graph trueGraph, Double threshold, Double shuffleThreshold) {
// When calling, default reject null as <=0.05
List<List<Node>> accepts_rejects = new ArrayList<>();
List<Node> accepts = new ArrayList<>();
List<Node> rejects = new ArrayList<>();
List<Node> allNodes = graph.getNodes();

// Confusion stats lists for data processing.
Map<String, String> fileContentMap = new HashMap<>();

List<List<Double>> accepts_AdjP_ADTestP = new ArrayList<>();
List<List<Double>> accepts_AdjR_ADTestP = new ArrayList<>();
List<List<Double>> accepts_AHP_ADTestP = new ArrayList<>();
List<List<Double>> accepts_AHR_ADTestP = new ArrayList<>();
fileContentMap.put("accepts_AdjP_ADTestP_data.csv", "");
fileContentMap.put("accepts_AdjR_ADTestP_data.csv", "");
fileContentMap.put("accepts_AHP_ADTestP_data.csv", "");
fileContentMap.put("accepts_AHR_ADTestP_data.csv", "");

List<List<Double>> rejects_AdjP_ADTestP = new ArrayList<>();
List<List<Double>> rejects_AdjR_ADTestP = new ArrayList<>();
List<List<Double>> rejects_AHP_ADTestP = new ArrayList<>();
List<List<Double>> rejects_AHR_ADTestP = new ArrayList<>();
fileContentMap.put("rejects_AdjP_ADTestP_data.csv", "");
fileContentMap.put("rejects_AdjR_ADTestP_data.csv", "");
fileContentMap.put("rejects_AHP_ADTestP_data.csv", "");
fileContentMap.put("rejects_AHR_ADTestP_data.csv", "");

NumberFormat nf = new DecimalFormat("0.00");
// Classify nodes into accepts and rejects base on ADTest result, and update confusion stats lists accordingly.
for (Node x : allNodes) {
List<IndependenceFact> localIndependenceFacts = getLocalIndependenceFacts(x);
List<Double> ap_ar_ahp_ahr = getPrecisionAndRecallOnMarkovBlanketGraphPlotData(x, estimatedCpdag, trueGraph);
Double ap = ap_ar_ahp_ahr.get(0);
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
for (List<Double> localPValues: shuffledlocalPValues) {
// P value obtained from AD test
Double ADTest = checkAgainstAndersonDarlingTest(localPValues);
// TODO VBC: what should we do for cases when ADTest is NaN and ∞ ?
if (ADTest <= threshold) {
rejects.add(x);
if (!Double.isNaN(ap)) {
rejects_AdjP_ADTestP.add(Arrays.asList(ap, ADTest));
}
if (!Double.isNaN(ar)) {
rejects_AdjR_ADTestP.add(Arrays.asList(ap, ADTest));
}
if (!Double.isNaN(ahp)) {
rejects_AHP_ADTestP.add(Arrays.asList(ap, ADTest));
}
if (!Double.isNaN(ahr)) {
rejects_AHR_ADTestP.add(Arrays.asList(ap, ADTest));
}
} else {
accepts.add(x);
if (!Double.isNaN(ap)) {
accepts_AdjP_ADTestP.add(Arrays.asList(ap, ADTest));
}
if (!Double.isNaN(ar)) {
accepts_AdjR_ADTestP.add(Arrays.asList(ap, ADTest));
}
if (!Double.isNaN(ahp)) {
accepts_AHP_ADTestP.add(Arrays.asList(ap, ADTest));
}
if (!Double.isNaN(ahr)) {
accepts_AHR_ADTestP.add(Arrays.asList(ap, ADTest));
}
}
}
}
accepts_rejects.add(accepts);
accepts_rejects.add(rejects);
// Write into data files.
for (Map.Entry<String, String> entry : fileContentMap.entrySet()) {
try (BufferedWriter writer = new BufferedWriter(new FileWriter(entry.getKey()))) {
writer.write(entry.getValue());
switch (entry.getKey()) {
case "acceptsAdjP_ADTestP_data.csv":
for (List<Double> AdjP_ADTestP_pair : accepts_AdjP_ADTestP) {
writer.write(nf.format(AdjP_ADTestP_pair.get(0)) + "," + nf.format(AdjP_ADTestP_pair.get(1)) + "\n");
}
break;

case "accepts_AdjR_ADTestP_data.csv":
for (List<Double> AdjR_ADTestP_pair : accepts_AdjR_ADTestP) {
writer.write(nf.format(AdjR_ADTestP_pair.get(0)) + "," + nf.format(AdjR_ADTestP_pair.get(1)) + "\n");
}
break;

case "accepts_AHP_ADTestP_data.csv":
for (List<Double> AHP_ADTestP_pair : accepts_AHP_ADTestP) {
writer.write(nf.format(AHP_ADTestP_pair.get(0)) + "," + nf.format(AHP_ADTestP_pair.get(1)) + "\n");
}
break;

case "accepts_AHR_ADTestP_data.csv":
for (List<Double> AHR_ADTestP_pair : accepts_AHR_ADTestP) {
writer.write(nf.format(AHR_ADTestP_pair.get(0)) + "," + nf.format(AHR_ADTestP_pair.get(1)) + "\n");
}
break;

case "rejects_AdjP_ADTestP_data.csv":
for (List<Double> AdjP_ADTestP_pair : rejects_AdjP_ADTestP) {
writer.write(nf.format(AdjP_ADTestP_pair.get(0)) + "," + nf.format(AdjP_ADTestP_pair.get(1)) + "\n");
}
break;

case "rejects_AdjR_ADTestP_data.csv":
for (List<Double> AdjR_ADTestP_pair : rejects_AdjR_ADTestP) {
writer.write(nf.format(AdjR_ADTestP_pair.get(0)) + "," + nf.format(AdjR_ADTestP_pair.get(1)) + "\n");
}
break;

case "rejects_AHP_ADTestP_data.csv":
for (List<Double> AHP_ADTestP_pair : rejects_AHP_ADTestP) {
writer.write(nf.format(AHP_ADTestP_pair.get(0)) + "," + nf.format(AHP_ADTestP_pair.get(1)) + "\n");
}
break;

case "rejects_AHR_ADTestP_data.csv":
for (List<Double> AHR_ADTestP_pair : rejects_AHR_ADTestP) {
writer.write(nf.format(AHR_ADTestP_pair.get(0)) + "," + nf.format(AHR_ADTestP_pair.get(1)) + "\n");
}
break;
default:
break;
}
System.out.println("Successfully written to " + entry.getKey());
} catch (IOException e) {
e.printStackTrace();
}
}
return accepts_rejects;
}

Expand Down Expand Up @@ -362,6 +507,21 @@ public void getPrecisionAndRecallOnMarkovBlanketGraph(Node x, Graph estimatedGra
" ArrowHeadPrecision = " + nf.format(ahp) + " ArrowHeadRecall = " + nf.format(ahr));
}

public List<Double> getPrecisionAndRecallOnMarkovBlanketGraphPlotData(Node x, Graph estimatedGraph, Graph trueGraph) {
// Lookup graph is the same structure as trueGraph's structure but node objects replaced by estimated graph nodes.
Graph lookupGraph = GraphUtils.replaceNodes(trueGraph, estimatedGraph.getNodes());
Graph xMBLookupGraph = GraphUtils.getMarkovBlanketSubgraphWithTargetNode(lookupGraph, x);
System.out.println("xMBLookupGraph:" + xMBLookupGraph);
Graph xMBEstimatedGraph = GraphUtils.getMarkovBlanketSubgraphWithTargetNode(estimatedGraph, x);
System.out.println("xMBEstimatedGraph:" + xMBEstimatedGraph);

double ap = new AdjacencyPrecision().getValue(xMBLookupGraph, xMBEstimatedGraph, null);
double ar = new AdjacencyRecall().getValue(xMBLookupGraph, xMBEstimatedGraph, null);
double ahp = new ArrowheadPrecision().getValue(xMBLookupGraph, xMBEstimatedGraph, null);
double ahr = new ArrowheadRecall().getValue(xMBLookupGraph, xMBEstimatedGraph, null);
return Arrays.asList(ap, ar, ahp, ahr);
}

/**
* Calculates the precision and recall using LocalGraphConfusion
* (which calculates the combination of Adjacency and ArrowHead) on the Markov Blanket graph for a given node.
Expand Down
Loading

0 comments on commit de7202a

Please sign in to comment.