/**
* Completes a pattern that was modified by an insertion/deletion operator Based on the algorithm
* described on Appendix C of (Chickering, 2002).
*/
private void rebuildPattern(Graph graph) {
SearchGraphUtils.basicPattern(graph, false);
addRequiredEdges(graph);
meekOrient(graph, getKnowledge());
if (TetradLogger.getInstance().isEventActive("rebuiltPatterns")) {
TetradLogger.getInstance().log("rebuiltPatterns", "Rebuilt pattern = " + graph);
}
}
private Graph pickDag(Graph graph) {
SearchGraphUtils.basicPattern(graph, false);
addRequiredEdges(graph);
boolean containsUndirected;
do {
containsUndirected = false;
for (Edge edge : graph.getEdges()) {
if (Edges.isUndirectedEdge(edge)) {
containsUndirected = true;
graph.removeEdge(edge);
Edge _edge = Edges.directedEdge(edge.getNode1(), edge.getNode2());
graph.addEdge(_edge);
}
}
meekOrient(graph, getKnowledge());
} while (containsUndirected);
return graph;
}
public void test1() {
for (int r = 0; r < 1; r++) {
Graph mim = DataGraphUtils.randomSingleFactorModel(5, 5, 6, 0, 0, 0);
Graph mimStructure = structure(mim);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(.5, 1.5);
SemPm pm = new SemPm(mim);
SemIm im = new SemIm(pm, params);
DataSet data = im.simulateData(300, false);
String algorithm = "FOFC";
Graph searchGraph;
List<List<Node>> partition;
if (algorithm.equals("FOFC")) {
FindOneFactorClusters fofc =
new FindOneFactorClusters(data, TestType.TETRAD_WISHART, 0.001);
searchGraph = fofc.search();
partition = fofc.getClusters();
} else if (algorithm.equals("BPC")) {
TestType testType = TestType.TETRAD_WISHART;
TestType purifyType = TestType.TETRAD_BASED2;
BuildPureClusters bpc = new BuildPureClusters(data, 0.001, testType, purifyType);
searchGraph = bpc.search();
partition = MimUtils.convertToClusters2(searchGraph);
} else {
throw new IllegalStateException();
}
List<String> latentVarList = reidentifyVariables(mim, data, partition, 2);
System.out.println(partition);
System.out.println(latentVarList);
System.out.println("True\n" + mimStructure);
Graph mimbuildStructure;
for (int mimbuildMethod : new int[] {3, 4}) {
if (mimbuildMethod == 1) {
System.out.println("Mimbuild 1\n");
Clusters measurements = ClusterUtils.mimClusters(searchGraph);
IndTestMimBuild test = new IndTestMimBuild(data, 0.001, measurements);
MimBuild mimbuild = new MimBuild(test, new Knowledge2());
Graph full = mimbuild.search();
full = changeLatentNames(full, measurements, latentVarList);
mimbuildStructure = structure(full);
System.out.println(
"SHD = "
+ SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
System.out.println("Estimated\n" + mimbuildStructure);
System.out.println();
}
// else if (mimbuildMethod == 2) {
// System.out.println("Mimbuild 2\n");
// Mimbuild2 mimbuild = new Mimbuild2();
// mimbuild.setAlpha(0.001);
// mimbuildStructure = mimbuild.search(partition, latentVarList, data);
// TetradMatrix latentcov = mimbuild.getLatentsCov();
// List<String> latentnames = mimbuild.getLatentNames();
// System.out.println("\nCovariance over the latents");
// System.out.println(MatrixUtils.toStringSquare(latentcov.toArray(),
// latentnames));
// System.out.println("Estimated\n" + mimbuildStructure);
// System.out.println("SHD = " +
// SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
// System.out.println();
// }
else if (mimbuildMethod == 3) {
System.out.println("Mimbuild 3\n");
Mimbuild2 mimbuild = new Mimbuild2();
mimbuild.setAlpha(0.001);
mimbuild.setMinClusterSize(3);
mimbuildStructure = mimbuild.search(partition, latentVarList, new CovarianceMatrix(data));
ICovarianceMatrix latentcov = mimbuild.getLatentsCov();
System.out.println("\nCovariance over the latents");
System.out.println(latentcov);
System.out.println("Estimated\n" + mimbuildStructure);
System.out.println(
"SHD = "
+ SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
System.out.println();
} else if (mimbuildMethod == 4) {
System.out.println("Mimbuild Trek\n");
MimbuildTrek mimbuild = new MimbuildTrek();
mimbuild.setAlpha(0.1);
mimbuild.setMinClusterSize(3);
mimbuildStructure = mimbuild.search(partition, latentVarList, new CovarianceMatrix(data));
ICovarianceMatrix latentcov = mimbuild.getLatentsCov();
System.out.println("\nCovariance over the latents");
System.out.println(latentcov);
System.out.println("Estimated\n" + mimbuildStructure);
System.out.println(
"SHD = "
+ SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
System.out.println();
} else {
throw new IllegalStateException();
}
}
}
}
public void rtest4() {
System.out.println("SHD\tP");
// System.out.println("MB1\tMB2\tMB3\tMB4\tMB5\tMB6");
Graph mim = DataGraphUtils.randomSingleFactorModel(5, 5, 8, 0, 0, 0);
Graph mimStructure = structure(mim);
SemPm pm = new SemPm(mim);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(0.5, 1.5);
NumberFormat nf = new DecimalFormat("0.0000");
int totalError = 0;
int errorCount = 0;
int maxScore = 0;
int maxNumMeasures = 0;
double maxP = 0.0;
for (int r = 0; r < 1; r++) {
SemIm im = new SemIm(pm, params);
DataSet data = im.simulateData(1000, false);
mim = GraphUtils.replaceNodes(mim, data.getVariables());
List<List<Node>> trueClusters = MimUtils.convertToClusters2(mim);
CovarianceMatrix _cov = new CovarianceMatrix(data);
ICovarianceMatrix cov = DataUtils.reorderColumns(_cov);
String algorithm = "FOFC";
Graph searchGraph;
List<List<Node>> partition;
if (algorithm.equals("FOFC")) {
FindOneFactorClusters fofc =
new FindOneFactorClusters(cov, TestType.TETRAD_WISHART, 0.001f);
searchGraph = fofc.search();
searchGraph = GraphUtils.replaceNodes(searchGraph, data.getVariables());
partition = MimUtils.convertToClusters2(searchGraph);
} else if (algorithm.equals("BPC")) {
TestType testType = TestType.TETRAD_WISHART;
TestType purifyType = TestType.TETRAD_BASED2;
BuildPureClusters bpc = new BuildPureClusters(data, 0.001, testType, purifyType);
searchGraph = bpc.search();
partition = MimUtils.convertToClusters2(searchGraph);
} else {
throw new IllegalStateException();
}
mimStructure = GraphUtils.replaceNodes(mimStructure, data.getVariables());
List<String> latentVarList = reidentifyVariables(mim, data, partition, 2);
Graph mimbuildStructure;
Mimbuild2 mimbuild = new Mimbuild2();
mimbuild.setAlpha(0.001);
mimbuild.setMinClusterSize(3);
try {
mimbuildStructure = mimbuild.search(partition, latentVarList, cov);
} catch (Exception e) {
e.printStackTrace();
continue;
}
mimbuildStructure = GraphUtils.replaceNodes(mimbuildStructure, data.getVariables());
mimbuildStructure = condense(mimStructure, mimbuildStructure);
// Graph mimSubgraph = new EdgeListGraph(mimStructure);
//
// for (Node node : mimSubgraph.getNodes()) {
// if (!mimStructure.getNodes().contains(node)) {
// mimSubgraph.removeNode(node);
// }
// }
int shd = SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure);
boolean impureCluster = containsImpureCluster(partition, trueClusters);
double pValue = mimbuild.getpValue();
boolean pBelow05 = pValue < 0.05;
boolean numClustersGreaterThan5 = partition.size() != 5;
boolean error = false;
// boolean condition = impureCluster || numClustersGreaterThan5 || pBelow05;
// boolean condition = numClustersGreaterThan5 || pBelow05;
boolean condition = numClustered(partition) == 40;
if (!condition && (shd > 5)) {
error = true;
}
if (!condition) {
totalError += shd;
errorCount++;
}
// if (numClustered(partition) > maxNumMeasures) {
// maxNumMeasures = numClustered(partition);
// maxP = pValue;
// maxScore = shd;
// System.out.println("maxNumMeasures = " + maxNumMeasures);
// System.out.println("maxScore = " + maxScore);
// System.out.println("maxP = " + maxP);
// System.out.println("clusters = " + clusterSizes(partition, trueClusters));
// }
// else
if (pValue > maxP) {
maxScore = shd;
maxP = mimbuild.getpValue();
maxNumMeasures = numClustered(partition);
System.out.println("maxNumMeasures = " + maxNumMeasures);
System.out.println("maxScore = " + maxScore);
System.out.println("maxP = " + maxP);
System.out.println("clusters = " + clusterSizes(partition, trueClusters));
}
System.out.print(
shd
+ "\t"
+ nf.format(pValue)
+ " "
// + (error ? 1 : 0) + " "
// + (pBelow05 ? "P < 0.05 " : "")
// + (impureCluster ? "Impure cluster " : "")
// + (numClustersGreaterThan5 ? "# Clusters != 5 " : "")
// + clusterSizes(partition, trueClusters)
+ numClustered(partition));
System.out.println();
}
System.out.println("\nAvg SHD for not-flagged cases = " + (totalError / (double) errorCount));
System.out.println("maxNumMeasures = " + maxNumMeasures);
System.out.println("maxScore = " + maxScore);
System.out.println("maxP = " + maxP);
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
Object source = dataWrapper.getSelectedDataModel();
DataModel dataModel = (DataModel) source;
IKnowledge knowledge = params2.getKnowledge();
if (initialGraph == null) {
initialGraph = new EdgeListGraph(dataModel.getVariables());
}
Graph graph2 = new EdgeListGraph(initialGraph);
graph2 = GraphUtils.replaceNodes(graph2, dataModel.getVariables());
Bff search;
if (dataModel instanceof DataSet) {
DataSet dataSet = (DataSet) dataModel;
if (getAlgorithmType() == AlgorithmType.BEAM) {
search = new BffBeam(graph2, dataSet, knowledge);
} else if (getAlgorithmType() == AlgorithmType.GES) {
search = new BffGes(graph2, dataSet);
search.setKnowledge(knowledge);
} else {
throw new IllegalStateException();
}
} else if (dataModel instanceof CovarianceMatrix) {
CovarianceMatrix covarianceMatrix = (CovarianceMatrix) dataModel;
if (getAlgorithmType() == AlgorithmType.BEAM) {
search = new BffBeam(graph2, covarianceMatrix, knowledge);
} else if (getAlgorithmType() == AlgorithmType.GES) {
throw new IllegalArgumentException(
"GES method requires a dataset; a covariance matrix was provided.");
// search = new BffGes(graph2, covarianceMatrix);
// search.setKnowledge(knowledge);
} else {
throw new IllegalStateException();
}
} else {
throw new IllegalStateException();
}
PcIndTestParams indTestParams = (PcIndTestParams) getParams().getIndTestParams();
search.setAlpha(indTestParams.getAlpha());
search.setBeamWidth(indTestParams.getBeamWidth());
search.setHighPValueAlpha(indTestParams.getZeroEdgeP());
this.graph = search.search();
// this.graph = search.getNewSemIm().getSemPm().getGraph();
setOriginalSemIm(search.getOriginalSemIm());
this.newSemIm = search.getNewSemIm();
fireGraphChange(graph);
if (getSourceGraph() != null) {
GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
} else if (knowledge.isDefaultToKnowledgeLayout()) {
SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
} else {
GraphUtils.circleLayout(graph, 200, 200, 150);
}
setResultGraph(SearchGraphUtils.patternForDag(graph, knowledge));
}