public Set<Edge> getNonadjacencies() {
Graph complete = GraphUtils.completeGraph(graph);
Set<Edge> nonAdjacencies = complete.getEdges();
Graph undirected = GraphUtils.undirectedGraph(graph);
nonAdjacencies.removeAll(undirected.getEdges());
return new HashSet<Edge>(nonAdjacencies);
}
// TODO Fix this.
private List<ScoredGraph> arrangeGraphs() {
IGesRunner runner = (IGesRunner) getAlgorithmRunner();
Graph resultGraph = runner.getResultGraph();
List<ScoredGraph> topGraphs = runner.getTopGraphs();
if (topGraphs == null) topGraphs = new ArrayList<ScoredGraph>();
Graph latestWorkbenchGraph = runner.getParams().getSourceGraph();
Graph sourceGraph = runner.getSourceGraph();
boolean arrangedAll = false;
for (ScoredGraph topGraph1 : topGraphs) {
arrangedAll = GraphUtils.arrangeBySourceGraph(topGraph1.getGraph(), latestWorkbenchGraph);
}
if (!arrangedAll) {
arrangedAll = GraphUtils.arrangeBySourceGraph(resultGraph, sourceGraph);
}
if (!arrangedAll) {
for (ScoredGraph topGraph : topGraphs) {
GraphUtils.circleLayout(topGraph.getGraph(), 200, 200, 150);
GraphUtils.circleLayout(resultGraph, 200, 200, 150);
}
}
return topGraphs;
}
/**
* Return the longest suffix of bases shared among all provided vertices
*
* <p>For example, if the vertices have sequences AC, CC, and ATC, this would return a single C.
* However, for ACC and TCC this would return CC. And for AC and TG this would return null;
*
* @param middleVertices a non-empty set of vertices
* @return a single vertex that contains the common suffix of all middle vertices
*/
@Requires("!middleVertices.isEmpty()")
protected static SeqVertex commonSuffix(final Collection<SeqVertex> middleVertices) {
final List<byte[]> kmers = GraphUtils.getKmers(middleVertices);
final int min = GraphUtils.minKmerLength(kmers);
final int suffixLen = GraphUtils.compSuffixLen(kmers, min);
final byte[] kmer = kmers.get(0);
final byte[] suffix = Arrays.copyOfRange(kmer, kmer.length - suffixLen, kmer.length);
return new SeqVertex(suffix);
}
protected void doDefaultArrangement(Graph resultGraph) {
if (getLatestWorkbenchGraph() != null) { // (alreadyLaidOut) {
GraphUtils.arrangeBySourceGraph(resultGraph, getLatestWorkbenchGraph());
} else if (getKnowledge().isDefaultToKnowledgeLayout()) {
SearchGraphUtils.arrangeByKnowledgeTiers(resultGraph, getKnowledge());
// alreadyLaidOut = true;
} else {
GraphUtils.circleLayout(resultGraph, 200, 200, 150);
// alreadyLaidOut = true;
}
}
@Test
public void test7() {
RandomUtil.getInstance().setSeed(29999483L);
List<Node> nodes = new ArrayList<>();
int numVars = 10;
for (int i = 0; i < numVars; i++) nodes.add(new ContinuousVariable("X" + (i + 1)));
Graph graph =
GraphUtils.randomGraphRandomForwardEdges(nodes, 0, numVars, 30, 15, 15, false, true);
GeneralizedSemPm pm = new GeneralizedSemPm(graph);
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print(im);
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.67, aSquaredStar, 0.01);
}
private void calculateArrowsForward(Node x, Node y, Graph graph) {
clearArrow(x, y);
if (!knowledgeEmpty()) {
if (getKnowledge().isForbidden(x.getName(), y.getName())) {
return;
}
}
List<Node> naYX = getNaYX(x, y, graph);
List<Node> t = getTNeighbors(x, y, graph);
DepthChoiceGenerator gen = new DepthChoiceGenerator(t.size(), t.size());
int[] choice;
while ((choice = gen.next()) != null) {
List<Node> s = GraphUtils.asList(choice, t);
if (!knowledgeEmpty()) {
if (!validSetByKnowledge(y, s)) {
continue;
}
}
double bump = insertEval(x, y, s, naYX, graph);
if (bump > 0.0) {
Arrow arrow = new Arrow(bump, x, y, s, naYX);
sortedArrows.add(arrow);
addLookupArrow(x, y, arrow);
}
}
}
@Test(dataProvider = "PrefixSuffixData")
public void testPrefixSuffix(
final List<String> strings, int expectedPrefixLen, int expectedSuffixLen) {
final List<byte[]> bytes = new ArrayList<>();
int min = Integer.MAX_VALUE;
for (final String s : strings) {
bytes.add(s.getBytes());
min = Math.min(min, s.length());
}
final int actualPrefixLen = GraphUtils.compPrefixLen(bytes, min);
Assert.assertEquals(actualPrefixLen, expectedPrefixLen, "Failed prefix test");
final int actualSuffixLen = GraphUtils.compSuffixLen(bytes, min - actualPrefixLen);
Assert.assertEquals(actualSuffixLen, expectedSuffixLen, "Failed suffix test");
}
public Graph orient() {
Graph skeleton = GraphUtils.undirectedGraph(getPattern());
Graph graph = new EdgeListGraph(skeleton.getNodes());
List<Node> nodes = skeleton.getNodes();
// Collections.shuffle(nodes);
if (isR1Done()) {
ruleR1(skeleton, graph, nodes);
}
for (Edge edge : skeleton.getEdges()) {
if (!graph.isAdjacentTo(edge.getNode1(), edge.getNode2())) {
graph.addUndirectedEdge(edge.getNode1(), edge.getNode2());
}
}
if (isR2Done()) {
ruleR2(skeleton, graph);
}
if (isMeekDone()) {
new MeekRules().orientImplied(graph);
}
return graph;
}
/**
* Transforms a maximally directed pattern (PDAG) represented in graph <code>g</code> into an
* arbitrary DAG by modifying <code>g</code> itself. Based on the algorithm described in
* Chickering (2002) "Optimal structure identification with greedy search" Journal of Machine
* Learning Research. R. Silva, June 2004
*/
public static void pdagToDag(Graph g) {
Graph p = new EdgeListGraph(g);
List<Edge> undirectedEdges = new ArrayList<Edge>();
for (Edge edge : g.getEdges()) {
if (edge.getEndpoint1() == Endpoint.TAIL
&& edge.getEndpoint2() == Endpoint.TAIL
&& !undirectedEdges.contains(edge)) {
undirectedEdges.add(edge);
}
}
g.removeEdges(undirectedEdges);
List<Node> pNodes = p.getNodes();
do {
Node x = null;
for (Node pNode : pNodes) {
x = pNode;
if (p.getChildren(x).size() > 0) {
continue;
}
Set<Node> neighbors = new HashSet<Node>();
for (Edge edge : p.getEdges()) {
if (edge.getNode1() == x || edge.getNode2() == x) {
if (edge.getEndpoint1() == Endpoint.TAIL && edge.getEndpoint2() == Endpoint.TAIL) {
if (edge.getNode1() == x) {
neighbors.add(edge.getNode2());
} else {
neighbors.add(edge.getNode1());
}
}
}
}
if (neighbors.size() > 0) {
Collection<Node> parents = p.getParents(x);
Set<Node> all = new HashSet<Node>(neighbors);
all.addAll(parents);
if (!GraphUtils.isClique(all, p)) {
continue;
}
}
for (Node neighbor : neighbors) {
Node node1 = g.getNode(neighbor.getName());
Node node2 = g.getNode(x.getName());
g.addDirectedEdge(node1, node2);
}
p.removeNode(x);
break;
}
pNodes.remove(x);
} while (pNodes.size() > 0);
}
// Cannot be done if the graph changes.
public void setInitialGraph(Graph initialGraph) {
initialGraph = GraphUtils.replaceNodes(initialGraph, variables);
out.println("Initial graph variables: " + initialGraph.getNodes());
out.println("Data set variables: " + variables);
if (!new HashSet<Node>(initialGraph.getNodes()).equals(new HashSet<Node>(variables))) {
throw new IllegalArgumentException("Variables aren't the same.");
}
this.initialGraph = initialGraph;
}
public static Graph bestGuessCycleOrientation(Graph graph, IndependenceTest test) {
while (true) {
List<Node> cycle = GraphUtils.directedCycle(graph);
if (cycle == null) {
break;
}
LinkedList<Node> _cycle = new LinkedList<Node>(cycle);
Node first = _cycle.getFirst();
Node last = _cycle.getLast();
_cycle.addFirst(last);
_cycle.addLast(first);
int _j = -1;
double minP = Double.POSITIVE_INFINITY;
for (int j = 1; j < _cycle.size() - 1; j++) {
int i = j - 1;
int k = j + 1;
Node x = test.getVariable(_cycle.get(i).getName());
Node y = test.getVariable(_cycle.get(j).getName());
Node z = test.getVariable(_cycle.get(k).getName());
test.isIndependent(x, z, Collections.singletonList(y));
System.out.println("Testing " + x + " _||_ " + z + " | " + y);
double p = test.getPValue();
System.out.println("p = " + p);
if (p < minP) {
_j = j;
minP = p;
}
}
Node x = _cycle.get(_j - 1);
Node y = _cycle.get(_j);
Node z = _cycle.get(_j + 1);
graph.removeEdge(x, y);
graph.removeEdge(z, y);
graph.addDirectedEdge(x, y);
graph.addDirectedEdge(z, y);
}
return graph;
}
private void ruleR1(Graph skeleton, Graph graph, List<Node> nodes) {
for (Node node : nodes) {
SortedMap<Double, String> scoreReports = new TreeMap<Double, String>();
List<Node> adj = skeleton.getAdjacentNodes(node);
DepthChoiceGenerator gen = new DepthChoiceGenerator(adj.size(), adj.size());
int[] choice;
double maxScore = Double.NEGATIVE_INFINITY;
List<Node> parents = null;
while ((choice = gen.next()) != null) {
List<Node> _parents = GraphUtils.asList(choice, adj);
double score = score(node, _parents);
scoreReports.put(-score, _parents.toString());
if (score > maxScore) {
maxScore = score;
parents = _parents;
}
}
for (double score : scoreReports.keySet()) {
TetradLogger.getInstance()
.log(
"score",
"For " + node + " parents = " + scoreReports.get(score) + " score = " + -score);
}
TetradLogger.getInstance().log("score", "");
if (parents == null) {
continue;
}
if (normal(node, parents)) continue;
for (Node _node : adj) {
if (parents.contains(_node)) {
Edge parentEdge = Edges.directedEdge(_node, node);
if (!graph.containsEdge(parentEdge)) {
graph.addEdge(parentEdge);
}
}
}
}
}
/////////////////////////////////////////////////////////////////////////////
// set the sepSet of x and y to the minimal such subset of the given sepSet
// and remove the edge <x, y> if background knowledge allows
/////////////////////////////////////////////////////////////////////////////
private void setMinSepSet(List<Node> sepSet, Node x, Node y) {
// It is assumed that BK has been considered before calling this method
// (for example, setting independent1 and independent2 in ruleR0_RFCI)
/*
// background knowledge requires this edge
if (knowledge.noEdgeRequired(x.getNode(), y.getNode()))
{
return;
}
*/
List<Node> empty = Collections.emptyList();
boolean indep;
try {
indep = independenceTest.isIndependent(x, y, empty);
} catch (Exception e) {
indep = false;
}
if (indep) {
getSepsets().set(x, y, empty);
return;
}
int sepSetSize = sepSet.size();
for (int i = 1; i <= sepSetSize; i++) {
ChoiceGenerator cg = new ChoiceGenerator(sepSetSize, i);
int[] combination;
while ((combination = cg.next()) != null) {
List<Node> condSet = GraphUtils.asList(combination, sepSet);
try {
indep = independenceTest.isIndependent(x, y, condSet);
} catch (Exception e) {
indep = false;
}
if (indep) {
getSepsets().set(x, y, condSet);
return;
}
}
}
}
public static List<Dag> getAllDagsInUndirectedGraph(Graph graph) {
Graph undirected = GraphUtils.undirectedGraph(graph);
DagIterator iterator = new DagIterator(undirected);
List<Dag> dags = new ArrayList<Dag>();
while (iterator.hasNext()) {
Graph _graph = iterator.next();
try {
Dag dag = new Dag(_graph);
dags.add(dag);
} catch (IllegalArgumentException e) {
//
}
}
return dags;
}
/** Tests to see if d separation facts are symmetric. */
public void testDSeparation2() {
EdgeListGraphSingleConnections graph =
new EdgeListGraphSingleConnections(
new Dag(GraphUtils.randomGraph(7, 0, 14, 30, 15, 15, true)));
List<Node> nodes = graph.getNodes();
int depth = -1;
for (int i = 0; i < nodes.size(); i++) {
for (int j = i; j < nodes.size(); j++) {
Node x = nodes.get(i);
Node y = nodes.get(j);
List<Node> theRest = new ArrayList<Node>(nodes);
// theRest.remove(x);
// theRest.remove(y);
DepthChoiceGenerator gen = new DepthChoiceGenerator(theRest.size(), depth);
int[] choice;
while ((choice = gen.next()) != null) {
List<Node> z = new LinkedList<Node>();
for (int k = 0; k < choice.length; k++) {
z.add(theRest.get(choice[k]));
}
boolean dConnectedTo = graph.isDConnectedTo(x, y, z);
boolean dConnectedTo1 = graph.isDConnectedTo(y, x, z);
if (dConnectedTo != dConnectedTo1) {
System.out.println(x + " d connected to " + y + " given " + z);
System.out.println(graph);
System.out.println("dconnectedto = " + dConnectedTo);
System.out.println("dconnecteto1 = " + dConnectedTo1);
fail();
}
}
}
}
}
// Invalid if then nodes or graph changes.
private void calculateArrowsBackward(Node x, Node y, Graph graph) {
if (x == y) {
return;
}
if (!graph.isAdjacentTo(x, y)) {
return;
}
if (!knowledgeEmpty()) {
if (!getKnowledge().noEdgeRequired(x.getName(), y.getName())) {
return;
}
}
List<Node> naYX = getNaYX(x, y, graph);
clearArrow(x, y);
List<Node> _naYX = new ArrayList<Node>(naYX);
DepthChoiceGenerator gen = new DepthChoiceGenerator(_naYX.size(), _naYX.size());
int[] choice;
while ((choice = gen.next()) != null) {
List<Node> H = GraphUtils.asList(choice, _naYX);
if (!knowledgeEmpty()) {
if (!validSetByKnowledge(y, H)) {
continue;
}
}
double bump = deleteEval(x, y, H, naYX, graph);
if (bump > 0.0) {
Arrow arrow = new Arrow(bump, x, y, H, naYX);
sortedArrows.add(arrow);
addLookupArrow(x, y, arrow);
}
}
}
/** Tests to see if d separation facts are symmetric. */
public void testDSeparation() {
EdgeListGraphSingleConnections graph =
new EdgeListGraphSingleConnections(
new Dag(GraphUtils.randomGraph(7, 0, 7, 30, 15, 15, true)));
System.out.println(graph);
List<Node> nodes = graph.getNodes();
int depth = -1;
for (int i = 0; i < nodes.size(); i++) {
for (int j = i + 1; j < nodes.size(); j++) {
Node x = nodes.get(i);
Node y = nodes.get(j);
List<Node> theRest = new ArrayList<Node>(nodes);
theRest.remove(x);
theRest.remove(y);
DepthChoiceGenerator gen = new DepthChoiceGenerator(theRest.size(), depth);
int[] choice;
while ((choice = gen.next()) != null) {
List<Node> z = new LinkedList<Node>();
for (int k = 0; k < choice.length; k++) {
z.add(theRest.get(choice[k]));
}
if (graph.isDSeparatedFrom(x, y, z) != graph.isDSeparatedFrom(y, x, z)) {
fail(
SearchLogUtils.independenceFact(x, y, z)
+ " should have same d-sep result as "
+ SearchLogUtils.independenceFact(y, x, z));
}
}
}
}
}
public Graph search(List<Node> nodes) {
long startTime = System.currentTimeMillis();
localScoreCache.clear();
if (!dataSet().getVariables().containsAll(nodes)) {
throw new IllegalArgumentException("All of the nodes must be in " + "the supplied data set.");
}
Graph graph;
if (initialGraph == null) {
graph = new EdgeListGraphSingleConnections(nodes);
} else {
initialGraph = GraphUtils.replaceNodes(initialGraph, variables);
graph = new EdgeListGraphSingleConnections(initialGraph);
}
topGraphs.clear();
buildIndexing(graph);
addRequiredEdges(graph);
score = 0.0;
// Do forward search.
fes(graph, nodes);
// Do backward search.
bes(graph);
long endTime = System.currentTimeMillis();
this.elapsedTime = endTime - startTime;
this.logger.log("graph", "\nReturning this graph: " + graph);
this.logger.log("info", "Elapsed time = " + (elapsedTime) / 1000. + " s");
this.logger.flush();
return graph;
}
public void rtestDSeparation4() {
Graph graph = new Dag(GraphUtils.randomGraph(100, 20, 100, 5, 5, 5, false));
long start, stop;
int depth = -1;
IndependenceTest test = new IndTestDSep(graph);
Rfci fci = new Rfci(test);
Fas fas = new Fas(test);
start = System.currentTimeMillis();
fci.setDepth(depth);
fci.setVerbose(true);
fci.search(fas, fas.getNodes());
stop = System.currentTimeMillis();
System.out.println("DSEP RFCI");
System.out.println("# dsep checks = " + fas.getNumIndependenceTests());
System.out.println("Elapsed " + (stop - start));
System.out.println("Per " + fas.getNumIndependenceTests() / (double) (stop - start));
SemPm pm = new SemPm(graph);
SemIm im = new SemIm(pm);
DataSet data = im.simulateData(1000, false);
IndependenceTest test2 = new IndTestFisherZ(data, 0.001);
Rfci fci3 = new Rfci(test2);
Fas fas2 = new Fas(test2);
start = System.currentTimeMillis();
fci3.setDepth(depth);
fci3.search(fas2, fas2.getNodes());
stop = System.currentTimeMillis();
System.out.println("FISHER Z RFCI");
System.out.println("# indep checks = " + fas.getNumIndependenceTests());
System.out.println("Elapsed " + (stop - start));
System.out.println("Per " + fas.getNumIndependenceTests() / (double) (stop - start));
}
@Test
public void test6() {
RandomUtil.getInstance().setSeed(29999483L);
int numVars = 5;
List<Node> nodes = new ArrayList<>();
for (int i = 0; i < numVars; i++) nodes.add(new ContinuousVariable("X" + (i + 1)));
Graph graph =
GraphUtils.randomGraphRandomForwardEdges(nodes, 0, numVars, 30, 15, 15, false, true);
SemPm spm = new SemPm(graph);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(0.5, 1.5);
params.setVarRange(1, 3);
SemIm sim = new SemIm(spm, params);
GeneralizedSemPm pm = new GeneralizedSemPm(spm);
GeneralizedSemIm im = new GeneralizedSemIm(pm, sim);
DataSet data = im.simulateData(1000, false);
print(im);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.59, aSquaredStar, 0.01);
}
@Test
public void test5() {
RandomUtil.getInstance().setSeed(29999483L);
List<Node> nodes = new ArrayList<>();
for (int i1 = 0; i1 < 5; i1++) {
nodes.add(new ContinuousVariable("X" + (i1 + 1)));
}
Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 30, 15, 15, false));
SemPm semPm = new SemPm(graph);
SemIm semIm = new SemIm(semPm);
semIm.simulateDataReducedForm(1000, false);
GeneralizedSemPm pm = new GeneralizedSemPm(semPm);
GeneralizedSemIm im = new GeneralizedSemIm(pm, semIm);
TetradVector e = new TetradVector(5);
for (int i = 0; i < e.size(); i++) {
e.set(i, RandomUtil.getInstance().nextNormal(0, 1));
}
TetradVector record1 = semIm.simulateOneRecord(e);
TetradVector record2 = im.simulateOneRecord(e);
print("XXX1" + e);
print("XXX2" + record1);
print("XXX3" + record2);
for (int i = 0; i < record1.size(); i++) {
assertEquals(record1.get(i), record2.get(i), 1e-10);
}
}
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);
}
/**
* Calculates the error variance for the given error node, given all of the coefficient values in
* the model.
*
* @param error An error term in the model--i.e. a variable with NodeType.ERROR.
* @return The value of the error variance, or Double.NaN is the value is undefined.
*/
private double calculateErrorVarianceFromParams(Node error) {
error = semGraph.getNode(error.getName());
Node child = semGraph.getChildren(error).get(0);
List<Node> parents = semGraph.getParents(child);
double otherVariance = 0;
for (Node parent : parents) {
if (parent == error) continue;
double coef = getEdgeCoefficient(parent, child);
otherVariance += coef * coef;
}
if (parents.size() >= 2) {
ChoiceGenerator gen = new ChoiceGenerator(parents.size(), 2);
int[] indices;
while ((indices = gen.next()) != null) {
Node node1 = parents.get(indices[0]);
Node node2 = parents.get(indices[1]);
double coef1, coef2;
if (node1.getNodeType() != NodeType.ERROR) {
coef1 = getEdgeCoefficient(node1, child);
} else {
coef1 = 1;
}
if (node2.getNodeType() != NodeType.ERROR) {
coef2 = getEdgeCoefficient(node2, child);
} else {
coef2 = 1;
}
List<List<Node>> treks = GraphUtils.treksIncludingBidirected(semGraph, node1, node2);
double cov = 0.0;
for (List<Node> trek : treks) {
double product = 1.0;
for (int i = 1; i < trek.size(); i++) {
Node _node1 = trek.get(i - 1);
Node _node2 = trek.get(i);
Edge edge = semGraph.getEdge(_node1, _node2);
double factor;
if (Edges.isBidirectedEdge(edge)) {
factor = edgeParameters.get(edge);
} else if (!edgeParameters.containsKey(edge)) {
factor = 1;
} else if (semGraph.isParentOf(_node1, _node2)) {
factor = getEdgeCoefficient(_node1, _node2);
} else {
factor = getEdgeCoefficient(_node2, _node1);
}
product *= factor;
}
cov += product;
}
otherVariance += 2 * coef1 * coef2 * cov;
}
}
return 1.0 - otherVariance <= 0 ? Double.NaN : 1.0 - otherVariance;
}
public static CpcTripleType getCpcTripleType(
Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph) {
// System.out.println("getCpcTripleType 1");
boolean existsSepsetContainingY = false;
boolean existsSepsetNotContainingY = false;
Set<Node> __nodes = new HashSet<Node>(graph.getAdjacentNodes(x));
__nodes.remove(z);
// System.out.println("getCpcTripleType 2");
List<Node> _nodes = new LinkedList<Node>(__nodes);
TetradLogger.getInstance()
.log("adjacencies", "Adjacents for " + x + "--" + y + "--" + z + " = " + _nodes);
// System.out.println("getCpcTripleType 3");
int _depth = depth;
if (_depth == -1) {
_depth = 1000;
}
_depth = Math.min(_depth, _nodes.size());
// System.out.println("getCpcTripleType 4");
for (int d = 0; d <= _depth; d++) {
// System.out.println("getCpcTripleType 5");
ChoiceGenerator cg = new ChoiceGenerator(_nodes.size(), d);
int[] choice;
while ((choice = cg.next()) != null) {
// System.out.println("getCpcTripleType 6");
List<Node> condSet = GraphUtils.asList(choice, _nodes);
// System.out.println("getCpcTripleType 7");
if (test.isIndependent(x, z, condSet)) {
if (condSet.contains(y)) {
existsSepsetContainingY = true;
} else {
existsSepsetNotContainingY = true;
}
}
}
}
// System.out.println("getCpcTripleType 8");
__nodes = new HashSet<Node>(graph.getAdjacentNodes(z));
__nodes.remove(x);
_nodes = new LinkedList<Node>(__nodes);
TetradLogger.getInstance()
.log("adjacencies", "Adjacents for " + x + "--" + y + "--" + z + " = " + _nodes);
// System.out.println("getCpcTripleType 9");
_depth = depth;
if (_depth == -1) {
_depth = 1000;
}
_depth = Math.min(_depth, _nodes.size());
// System.out.println("getCpcTripleType 10");
for (int d = 0; d <= _depth; d++) {
// System.out.println("getCpcTripleType 11");
ChoiceGenerator cg = new ChoiceGenerator(_nodes.size(), d);
int[] choice;
while ((choice = cg.next()) != null) {
List<Node> condSet = GraphUtils.asList(choice, _nodes);
if (test.isIndependent(x, z, condSet)) {
if (condSet.contains(y)) {
existsSepsetContainingY = true;
} else {
existsSepsetNotContainingY = true;
}
}
}
}
// System.out.println("getCpcTripleType 12");
if (existsSepsetContainingY == existsSepsetNotContainingY) {
return CpcTripleType.AMBIGUOUS;
} else if (!existsSepsetNotContainingY) {
return CpcTripleType.NONCOLLIDER;
} else {
return CpcTripleType.COLLIDER;
}
}
private void resolveOneEdgeMax(Graph graph, Node x, Node y, boolean strong, Graph oldGraph) {
if (RandomUtil.getInstance().nextDouble() > 0.5) {
Node temp = x;
x = y;
y = temp;
}
TetradLogger.getInstance().log("info", "\nEDGE " + x + " --- " + y);
SortedMap<Double, String> scoreReports = new TreeMap<Double, String>();
List<Node> neighborsx = graph.getAdjacentNodes(x);
neighborsx.remove(y);
double max = Double.NEGATIVE_INFINITY;
boolean left = false;
boolean right = false;
DepthChoiceGenerator genx = new DepthChoiceGenerator(neighborsx.size(), neighborsx.size());
int[] choicex;
while ((choicex = genx.next()) != null) {
List<Node> condxMinus = GraphUtils.asList(choicex, neighborsx);
List<Node> condxPlus = new ArrayList<Node>(condxMinus);
condxPlus.add(y);
double xPlus = score(x, condxPlus);
double xMinus = score(x, condxMinus);
List<Node> neighborsy = graph.getAdjacentNodes(y);
neighborsy.remove(x);
DepthChoiceGenerator geny = new DepthChoiceGenerator(neighborsy.size(), neighborsy.size());
int[] choicey;
while ((choicey = geny.next()) != null) {
List<Node> condyMinus = GraphUtils.asList(choicey, neighborsy);
// List<Node> parentsY = oldGraph.getParents(y);
// parentsY.remove(x);
// if (!condyMinus.containsAll(parentsY)) {
// continue;
// }
List<Node> condyPlus = new ArrayList<Node>(condyMinus);
condyPlus.add(x);
double yPlus = score(y, condyPlus);
double yMinus = score(y, condyMinus);
// Checking them all at once is expensive but avoids lexical ordering problems in the
// algorithm.
if (normal(y, condyPlus)
|| normal(x, condxMinus)
|| normal(x, condxPlus)
|| normal(y, condyMinus)) {
continue;
}
double delta = 0.0;
if (strong) {
if (yPlus <= xPlus + delta && xMinus <= yMinus + delta) {
double score = combinedScore(xPlus, yMinus);
if (yPlus <= yMinus + delta && xMinus <= xPlus + delta) {
StringBuilder builder = new StringBuilder();
builder.append("\nStrong " + y + "->" + x + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
if (score > max) {
max = score;
left = true;
right = false;
}
} else {
StringBuilder builder = new StringBuilder();
builder.append("\nNo directed edge " + x + "--" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
}
} else if (xPlus <= yPlus + delta && yMinus <= xMinus + delta) {
double score = combinedScore(yPlus, xMinus);
if (yMinus <= yPlus + delta && xPlus <= xMinus + delta) {
StringBuilder builder = new StringBuilder();
builder.append("\nStrong " + x + "->" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
if (score > max) {
max = score;
left = false;
right = true;
}
} else {
StringBuilder builder = new StringBuilder();
builder.append("\nNo directed edge " + x + "--" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
}
} else if (yPlus <= xPlus + delta && yMinus <= xMinus + delta) {
double score = combinedScore(yPlus, xMinus);
StringBuilder builder = new StringBuilder();
builder.append("\nNo directed edge " + x + "--" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
} else if (xPlus <= yPlus + delta && xMinus <= yMinus + delta) {
double score = combinedScore(yPlus, xMinus);
StringBuilder builder = new StringBuilder();
builder.append("\nNo directed edge " + x + "--" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
}
} else {
if (yPlus <= xPlus + delta && xMinus <= yMinus + delta) {
double score = combinedScore(xPlus, yMinus);
StringBuilder builder = new StringBuilder();
builder.append("\nWeak " + y + "->" + x + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
if (score > max) {
max = score;
left = true;
right = false;
}
} else if (xPlus <= yPlus + delta && yMinus <= xMinus + delta) {
double score = combinedScore(yPlus, xMinus);
StringBuilder builder = new StringBuilder();
builder.append("\nWeak " + x + "->" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
if (score > max) {
max = score;
left = false;
right = true;
}
} else if (yPlus <= xPlus + delta && yMinus <= xMinus + delta) {
double score = combinedScore(yPlus, xMinus);
StringBuilder builder = new StringBuilder();
builder.append("\nNo directed edge " + x + "--" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
} else if (xPlus <= yPlus + delta && xMinus <= yMinus + delta) {
double score = combinedScore(yPlus, xMinus);
StringBuilder builder = new StringBuilder();
builder.append("\nNo directed edge " + x + "--" + y + " " + score);
builder.append("\n Parents(" + x + ") = " + condxMinus);
builder.append("\n Parents(" + y + ") = " + condyMinus);
scoreReports.put(-score, builder.toString());
}
}
}
}
for (double score : scoreReports.keySet()) {
TetradLogger.getInstance().log("info", scoreReports.get(score));
}
graph.removeEdges(x, y);
if (left) {
graph.addDirectedEdge(y, x);
}
if (right) {
graph.addDirectedEdge(x, y);
}
if (!graph.isAdjacentTo(x, y)) {
graph.addUndirectedEdge(x, y);
}
}