/**
* 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);
}
}
public void configurationActived(TetradLoggerEvent evt) {
TetradLoggerConfig config = evt.getTetradLoggerConfig();
// if logging is actually turned on, then open display.
if (TetradLogger.getInstance().isLogging()
&& config.isActive()
&& TetradLogger.getInstance().isDisplayLogEnabled()) {
// if the log display isn't already up, open it.
if (!isDisplayLogging() && allowAutomaticLogPopup()) {
setDisplayLogging(true);
}
}
}
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);
}
}
}
}
}
private void bes(Graph graph) {
TetradLogger.getInstance().log("info", "** BACKWARD EQUIVALENCE SEARCH");
initializeArrowsBackward(graph);
while (!sortedArrows.isEmpty()) {
Arrow arrow = sortedArrows.first();
sortedArrows.remove(arrow);
Node x = arrow.getX();
Node y = arrow.getY();
clearArrow(x, y);
if (!validDelete(arrow.getHOrT(), arrow.getNaYX(), graph)) {
continue;
}
List<Node> h = arrow.getHOrT();
double bump = arrow.getBump();
delete(x, y, h, graph, bump);
score += bump;
rebuildPattern(graph);
storeGraph(graph);
initializeArrowsBackward(
graph); // Rebuilds Arrows from scratch each time. Fast enough for backwards.
}
}
/**
* States whether the log display should be automatically displayed, if there is no value in the
* user's prefs then it will display a prompt asking the user whether they would like to disable
* automatic popups.
*/
private boolean allowAutomaticLogPopup() {
Boolean allowed = TetradLogger.getInstance().isAutomaticLogDisplayEnabled();
// ask the user whether they way the feature etc.
if (allowed == null) {
String message =
"<html>Whenever Tetrad's logging features are active any generated log <br>"
+ "output will be automatically display in Tetrad's log display. Would you like Tetrad<br>"
+ "to continue to automatically open the log display window whenever there is logging output?</html>";
int option =
JOptionPane.showConfirmDialog(
this, message, "Automatic Logging", JOptionPane.YES_NO_OPTION);
if (option == JOptionPane.NO_OPTION) {
JOptionPane.showMessageDialog(
this, "This feature can be enabled later by going to Logging>Setup Logging.");
}
TetradLogger.getInstance().setAutomaticLogDisplayEnabled(option == JOptionPane.YES_OPTION);
// return true, so that opens this time, in the future the user's pref will be used.
return true;
}
return allowed;
}
/**
* Sets whether the display log output should be displayed or not. If true then a text area
* roughly 20% of the screen size will appear on the bottom and will display any log output,
* otherwise just the standard tetrad workbend is shown.
*
* @param displayLogging
*/
public void setDisplayLogging(boolean displayLogging) {
if (displayLogging) {
this.logArea = new TetradLogArea(this);
} else {
if (this.logArea != null) {
TetradLogger.getInstance().removeOutputStream(this.logArea.getOutputStream());
}
this.logArea = null;
}
setupDesktop();
revalidate();
repaint();
}
/** Constructs a new desktop. */
public TetradDesktop() {
setBackground(new Color(204, 204, 204));
sessionNodeKeys = new ArrayList();
// Create the desktop pane.
this.desktopPane = new JDesktopPane();
// Do layout.
setLayout(new BorderLayout());
desktopPane.setDesktopManager(new DefaultDesktopManager());
desktopPane.setBorder(new BevelBorder(BevelBorder.LOWERED));
desktopPane.addPropertyChangeListener(this);
this.setupDesktop();
TetradLogger.getInstance().addTetradLoggerListener(new LoggerListener());
}
/**
* Forward equivalence search.
*
* @param graph The graph in the state prior to the forward equivalence search.
*/
private void fes(Graph graph, List<Node> nodes) {
TetradLogger.getInstance().log("info", "** FORWARD EQUIVALENCE SEARCH");
lookupArrows = new HashMap<OrderedPair, Set<Arrow>>();
initializeArrowsForward(nodes);
while (!sortedArrows.isEmpty()) {
Arrow arrow = sortedArrows.first();
sortedArrows.remove(arrow);
Node x = arrow.getX();
Node y = arrow.getY();
clearArrow(x, y);
if (graph.isAdjacentTo(x, y)) {
continue;
}
if (!validInsert(x, y, arrow.getHOrT(), arrow.getNaYX(), graph)) {
continue;
}
List<Node> t = arrow.getHOrT();
double bump = arrow.getBump();
Set<Edge> edges = graph.getEdges();
insert(x, y, t, graph, bump);
score += bump;
rebuildPattern(graph);
// Try to avoid duplicating scoring calls. First clear out all of the edges that need to be
// changed,
// then change them, checking to see if they're already been changed. I know, roundabout, but
// there's
// a performance boost.
for (Edge edge : graph.getEdges()) {
if (!edges.contains(edge)) {
reevaluateForward(graph, nodes, edge.getNode1(), edge.getNode2());
}
}
storeGraph(graph);
}
}
/**
* Determines whether variable x is independent of variable y given a list of conditioning
* variables z.
*
* @param x the one variable being compared.
* @param y the second variable being compared.
* @param z the list of conditioning variables.
* @return true iff x _||_ y | z.
* @throws RuntimeException if a matrix singularity is encountered.
*/
public boolean isIndependent(Node x, Node y, List<Node> z) {
TetradMatrix submatrix = subMatrix(x, y, z);
double r = 0;
try {
r = StatUtils.partialCorrelation(submatrix);
if (Double.isNaN((r)) || r < -1. || r > 1.) throw new RuntimeException();
} catch (Exception e) {
DepthChoiceGenerator gen = new DepthChoiceGenerator(z.size(), z.size());
int[] choice;
while ((choice = gen.next()) != null) {
try {
List<Node> z2 = new ArrayList<Node>(z);
z2.removeAll(GraphUtils.asList(choice, z));
submatrix = subMatrix(x, y, z2);
r = StatUtils.partialCorrelation(submatrix);
} catch (Exception e2) {
continue;
}
// if (Double.isNaN(r)) continue;
//
// if (r > 1.) r = 1.;
// if (r < -1.) r = -1.;
if (Double.isNaN(r) || r < -1. || r > 1.) continue;
break;
}
}
// Either dividing by a zero standard deviation (in which case it's dependent) or doing a
// regression
// (effectively) with a multicolliarity
if (Double.isNaN(r)) {
int[] _z = new int[z.size()];
// for (int i = 0; i < _z.length; i++) _z[i] = i + 2;
//
//// double varx = StatUtils.partialVariance(submatrix, 0, _z); // submatrix.get(0,
// 0);
//// double vary = StatUtils.partialVariance(submatrix, 1, _z); //submatrix.get(1,
// 1);
//
// double varx = submatrix.get(0, 0);
// double vary = submatrix.get(1, 1);
//
// if (varx * vary == 0) {
return true;
// }
}
if (r > 1.) r = 1.;
if (r < -1.) r = -1.;
this.fisherZ =
Math.sqrt(sampleSize() - z.size() - 3.0) * 0.5 * (Math.log(1.0 + r) - Math.log(1.0 - r));
if (Double.isNaN(this.fisherZ)) {
throw new IllegalArgumentException(
"The Fisher's Z "
+ "score for independence fact "
+ x
+ " _||_ "
+ y
+ " | "
+ z
+ " is undefined. r = "
+ r);
}
boolean independent = getPValue() > alpha;
if (independent) {
TetradLogger.getInstance()
.log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, getPValue()));
} else {
TetradLogger.getInstance()
.log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, getPValue()));
}
return independent;
}
private void addRequiredEdges(Graph graph) {
if (true) return;
if (knowledgeEmpty()) return;
for (Iterator<KnowledgeEdge> it = getKnowledge().requiredEdgesIterator(); it.hasNext(); ) {
KnowledgeEdge next = it.next();
Node nodeA = graph.getNode(next.getFrom());
Node nodeB = graph.getNode(next.getTo());
if (!graph.isAncestorOf(nodeB, nodeA)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeA, nodeB);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeA, nodeB));
}
}
for (Edge edge : graph.getEdges()) {
final String A = edge.getNode1().getName();
final String B = edge.getNode2().getName();
if (knowledge.isForbidden(A, B)) {
Node nodeA = edge.getNode1();
Node nodeB = edge.getNode2();
if (nodeA != null
&& nodeB != null
&& graph.isAdjacentTo(nodeA, nodeB)
&& !graph.isChildOf(nodeA, nodeB)) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
if (!graph.isChildOf(nodeA, nodeB)
&& getKnowledge().isForbidden(nodeA.getName(), nodeB.getName())) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
} else if (knowledge.isForbidden(B, A)) {
Node nodeA = edge.getNode2();
Node nodeB = edge.getNode1();
if (nodeA != null
&& nodeB != null
&& graph.isAdjacentTo(nodeA, nodeB)
&& !graph.isChildOf(nodeA, nodeB)) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
if (!graph.isChildOf(nodeA, nodeB)
&& getKnowledge().isForbidden(nodeA.getName(), nodeB.getName())) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
}
}
}
/** Do an actual deletion (Definition 13 from Chickering, 2002). */
private void delete(Node x, Node y, List<Node> subset, Graph graph, double bump) {
Edge trueEdge = null;
if (trueGraph != null) {
Node _x = trueGraph.getNode(x.getName());
Node _y = trueGraph.getNode(y.getName());
trueEdge = trueGraph.getEdge(_x, _y);
}
if (log && verbose) {
Edge oldEdge = graph.getEdge(x, y);
String label = trueGraph != null && trueEdge != null ? "*" : "";
TetradLogger.getInstance()
.log(
"deletedEdges",
(graph.getNumEdges() - 1)
+ ". DELETE "
+ oldEdge
+ " "
+ subset
+ " ("
+ bump
+ ") "
+ label);
out.println(
(graph.getNumEdges() - 1)
+ ". DELETE "
+ oldEdge
+ " "
+ subset
+ " ("
+ bump
+ ") "
+ label);
} else {
int numEdges = graph.getNumEdges() - 1;
if (numEdges % 50 == 0) out.println(numEdges);
}
graph.removeEdge(x, y);
for (Node h : subset) {
Edge oldEdge = graph.getEdge(y, h);
graph.removeEdge(y, h);
graph.addDirectedEdge(y, h);
if (log) {
TetradLogger.getInstance()
.log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(y, h));
}
if (verbose) {
out.println("--- Directing " + oldEdge + " to " + graph.getEdge(y, h));
}
if (Edges.isUndirectedEdge(graph.getEdge(x, h))) {
if (!graph.isAdjacentTo(x, h))
throw new IllegalArgumentException("Not adjacent: " + x + ", " + h);
oldEdge = graph.getEdge(x, h);
graph.removeEdge(x, h);
graph.addDirectedEdge(x, h);
if (log) {
TetradLogger.getInstance()
.log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(x, h));
}
if (verbose) {
out.println("--- Directing " + oldEdge + " to " + graph.getEdge(x, h));
}
}
}
}
// serial.
private void insert(Node x, Node y, List<Node> t, Graph graph, double bump) {
if (graph.isAdjacentTo(x, y)) {
return; // The initial graph may already have put this edge in the graph.
// throw new IllegalArgumentException(x + " and " + y + " are already adjacent in
// the graph.");
}
Edge trueEdge = null;
if (trueGraph != null) {
Node _x = trueGraph.getNode(x.getName());
Node _y = trueGraph.getNode(y.getName());
trueEdge = trueGraph.getEdge(_x, _y);
}
graph.addDirectedEdge(x, y);
if (log) {
String label = trueGraph != null && trueEdge != null ? "*" : "";
TetradLogger.getInstance()
.log(
"insertedEdges",
graph.getNumEdges()
+ ". INSERT "
+ graph.getEdge(x, y)
+ " "
+ t
+ " "
+ bump
+ " "
+ label);
} else {
int numEdges = graph.getNumEdges() - 1;
if (verbose) {
if (numEdges % 50 == 0) out.println(numEdges);
}
}
if (verbose) {
String label = trueGraph != null && trueEdge != null ? "*" : "";
out.println(
graph.getNumEdges()
+ ". INSERT "
+ graph.getEdge(x, y)
+ " "
+ t
+ " "
+ bump
+ " "
+ label);
} else {
int numEdges = graph.getNumEdges() - 1;
if (verbose) {
if (numEdges % 50 == 0) out.println(numEdges);
}
}
for (Node _t : t) {
Edge oldEdge = graph.getEdge(_t, y);
if (oldEdge == null) throw new IllegalArgumentException("Not adjacent: " + _t + ", " + y);
graph.removeEdge(_t, y);
graph.addDirectedEdge(_t, y);
if (log && verbose) {
TetradLogger.getInstance()
.log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(_t, y));
out.println("--- Directing " + oldEdge + " to " + graph.getEdge(_t, y));
}
}
}
/**
* GesSearch is an implentation of the GES algorithm, as specified in Chickering (2002) "Optimal
* structure identification with greedy search" Journal of Machine Learning Research. It works for
* both BayesNets and SEMs.
*
* <p>Some code optimization could be done for the scoring part of the graph for discrete models
* (method scoreGraphChange). Some of Andrew Moore's approaches for caching sufficient statistics,
* for instance.
*
* @author Ricardo Silva, Summer 2003
* @author Joseph Ramsey, Revisions 10/2005
*/
public final class GesConcurrent implements GraphSearch, GraphScorer {
/** The data set, various variable subsets of which are to be scored. */
private DataSet dataSet;
/** The covariance matrix for the data set. */
private ICovarianceMatrix covariances;
/** Sample size, either from the data set or from the variances. */
private int sampleSize;
/** Specification of forbidden and required edges. */
private IKnowledge knowledge = new Knowledge2();
/** Map from variables to their column indices in the data set. */
private HashMap<Node, Integer> hashIndices;
/** List of variables in the data set, in order. */
private List<Node> variables;
/** True iff the data set is discrete. */
private boolean discrete;
/**
* The true graph, if known. If this is provided, asterisks will be printed out next to false
* positive added edges (that is, edges added that aren't adjacencies in the true graph).
*/
private Graph trueGraph;
/** An initial graph to start from. */
private Graph initialGraph;
/** Caches scores for discrete search. */
private final LocalScoreCache localScoreCache = new LocalScoreCache();
/** Elapsed time of the most recent search. */
private long elapsedTime;
/**
* True if cycles are to be aggressively prevented. May be expensive for large graphs (but also
* useful for large graphs).
*/
private boolean aggressivelyPreventCycles = false;
/** Listeners for graph change events. */
private transient List<PropertyChangeListener> listeners;
/** Penalty discount--the BIC penalty is multiplied by this (for continuous variables). */
private double penaltyDiscount = 1.0;
/** The score for discrete searches. */
private LocalDiscreteScore discreteScore;
/** The logger for this class. The config needs to be set. */
private TetradLogger logger = TetradLogger.getInstance();
/** The top n graphs found by the algorithm, where n is numPatternsToStore. */
private SortedSet<ScoredGraph> topGraphs = new TreeSet<ScoredGraph>();
/** The number of top patterns to store. */
private int numPatternsToStore = 0;
// Potential arrows sorted by bump high to low. The first one is a candidate for adding to the
// graph.
private SortedSet<Arrow> sortedArrows = new ConcurrentSkipListSet<Arrow>();
// Arrows added to sortedArrows for each <i, j>.
private Map<OrderedPair, Set<Arrow>> lookupArrows;
/** True if graphs should be stored. */
private boolean log = true;
private boolean verbose = false;
private int NTHREADS = Runtime.getRuntime().availableProcessors() * 5;
// private boolean checkedKnowledgeEmpty = false;
// private boolean knowledgeEmpty = false;
private ForkJoinPool pool = ForkJoinPoolInstance.getInstance().getPool();
private double score;
private PrintStream out;
// ===========================CONSTRUCTORS=============================//
public GesConcurrent(DataSet dataSet) {
setDataSet(dataSet);
if (dataSet.isDiscrete()) {
BDeuScore score = new BDeuScore(dataSet);
score.setSamplePrior(10);
score.setStructurePrior(0.001);
}
setStructurePrior(0.001);
setSamplePrior(10.);
}
public GesConcurrent(ICovarianceMatrix covMatrix) {
setCovMatrix(covMatrix);
setStructurePrior(0.001);
setSamplePrior(10.);
}
// ==========================PUBLIC METHODS==========================//
public boolean isAggressivelyPreventCycles() {
return this.aggressivelyPreventCycles;
}
public void setAggressivelyPreventCycles(boolean aggressivelyPreventCycles) {
this.aggressivelyPreventCycles = aggressivelyPreventCycles;
}
/**
* Greedy equivalence search: Start from the empty graph, add edges till model is significant.
* Then start deleting edges till a minimum is achieved.
*
* @return the resulting Pattern.
*/
public Graph search() {
Graph graph;
if (initialGraph == null) {
graph = new EdgeListGraphSingleConnections(getVariables());
} else {
graph = new EdgeListGraphSingleConnections(initialGraph);
}
fireGraphChange(graph);
buildIndexing(graph);
addRequiredEdges(graph);
topGraphs.clear();
storeGraph(graph);
List<Node> nodes = graph.getNodes();
long start = System.currentTimeMillis();
score = 0.0;
// Do forward search.
fes(graph, nodes);
// Do backward search.
bes(graph);
long endTime = System.currentTimeMillis();
this.elapsedTime = endTime - start;
this.logger.log("graph", "\nReturning this graph: " + graph);
this.logger.log("info", "Elapsed time = " + (elapsedTime) / 1000. + " s");
this.logger.flush();
return graph;
}
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 IKnowledge getKnowledge() {
return knowledge;
}
/**
* Sets the background knowledge.
*
* @param knowledge the knowledge object, specifying forbidden and required edges.
*/
public void setKnowledge(IKnowledge knowledge) {
if (knowledge == null) throw new NullPointerException();
this.knowledge = knowledge;
}
public void setStructurePrior(double structurePrior) {
if (getDiscreteScore() != null) {
getDiscreteScore().setStructurePrior(structurePrior);
}
}
public void setSamplePrior(double samplePrior) {
if (getDiscreteScore() != null) {
getDiscreteScore().setSamplePrior(samplePrior);
}
}
public long getElapsedTime() {
return elapsedTime;
}
public void addPropertyChangeListener(PropertyChangeListener l) {
getListeners().add(l);
}
public double getPenaltyDiscount() {
return penaltyDiscount;
}
public void setPenaltyDiscount(double penaltyDiscount) {
if (penaltyDiscount < 0) {
throw new IllegalArgumentException("Penalty discount must be >= 0: " + penaltyDiscount);
}
this.penaltyDiscount = penaltyDiscount;
}
public void setTrueGraph(Graph trueGraph) {
this.trueGraph = trueGraph;
}
public double getScore(Graph dag) {
return scoreDag(dag);
}
public SortedSet<ScoredGraph> getTopGraphs() {
return topGraphs;
}
public int getNumPatternsToStore() {
return numPatternsToStore;
}
public void setNumPatternsToStore(int numPatternsToStore) {
if (numPatternsToStore < 0) {
throw new IllegalArgumentException(
"# graphs to store must at least 0: " + numPatternsToStore);
}
this.numPatternsToStore = numPatternsToStore;
}
public LocalDiscreteScore getDiscreteScore() {
return discreteScore;
}
public void setDiscreteScore(LocalDiscreteScore discreteScore) {
if (discreteScore.getDataSet() != dataSet) {
throw new IllegalArgumentException("Must use the same data set.");
}
this.discreteScore = discreteScore;
}
// ===========================PRIVATE METHODS========================//
/**
* Forward equivalence search.
*
* @param graph The graph in the state prior to the forward equivalence search.
*/
private void fes(Graph graph, List<Node> nodes) {
TetradLogger.getInstance().log("info", "** FORWARD EQUIVALENCE SEARCH");
lookupArrows = new HashMap<OrderedPair, Set<Arrow>>();
initializeArrowsForward(nodes);
while (!sortedArrows.isEmpty()) {
Arrow arrow = sortedArrows.first();
sortedArrows.remove(arrow);
Node x = arrow.getX();
Node y = arrow.getY();
clearArrow(x, y);
if (graph.isAdjacentTo(x, y)) {
continue;
}
if (!validInsert(x, y, arrow.getHOrT(), arrow.getNaYX(), graph)) {
continue;
}
List<Node> t = arrow.getHOrT();
double bump = arrow.getBump();
Set<Edge> edges = graph.getEdges();
insert(x, y, t, graph, bump);
score += bump;
rebuildPattern(graph);
// Try to avoid duplicating scoring calls. First clear out all of the edges that need to be
// changed,
// then change them, checking to see if they're already been changed. I know, roundabout, but
// there's
// a performance boost.
for (Edge edge : graph.getEdges()) {
if (!edges.contains(edge)) {
reevaluateForward(graph, nodes, edge.getNode1(), edge.getNode2());
}
}
storeGraph(graph);
}
}
private void bes(Graph graph) {
TetradLogger.getInstance().log("info", "** BACKWARD EQUIVALENCE SEARCH");
initializeArrowsBackward(graph);
while (!sortedArrows.isEmpty()) {
Arrow arrow = sortedArrows.first();
sortedArrows.remove(arrow);
Node x = arrow.getX();
Node y = arrow.getY();
clearArrow(x, y);
if (!validDelete(arrow.getHOrT(), arrow.getNaYX(), graph)) {
continue;
}
List<Node> h = arrow.getHOrT();
double bump = arrow.getBump();
delete(x, y, h, graph, bump);
score += bump;
rebuildPattern(graph);
storeGraph(graph);
initializeArrowsBackward(
graph); // Rebuilds Arrows from scratch each time. Fast enough for backwards.
}
}
// Expensive
// Concurrent.
private void initializeArrowsForward(final List<Node> nodes) {
final List<Node> emptyList = new ArrayList<Node>(0);
final Set<Node> emptySet = new HashSet<Node>(0);
List<Callable<Boolean>> callables = new ArrayList<Callable<Boolean>>();
for (int t = 0; t < NTHREADS; t++) {
final int _t = t;
Callable<Boolean> worker =
new Callable<Boolean>() {
@Override
public Boolean call() throws Exception {
int chunk = nodes.size() / NTHREADS + 1;
for (int j = _t * chunk; j < Math.min((_t + 1) * chunk, nodes.size()); j++) {
if (log && verbose) {
if ((j + 1) % 10 == 0) out.println("Initializing arrows forward: " + (j + 1));
}
for (int i = j + 1; i < nodes.size(); i++) {
Node x = nodes.get(i);
Node y = nodes.get(j);
if (!knowledgeEmpty()) {
if (getKnowledge().isForbidden(x.getName(), y.getName())) {
continue;
}
if (!validSetByKnowledge(y, emptyList)) {
continue;
}
}
double bump = scoreGraphChange(y, Collections.singleton(x), emptySet);
if (bump > 0.0) {
Arrow arrow = new Arrow(bump, x, y, emptyList, emptyList);
sortedArrows.add(arrow);
addLookupArrow(x, y, arrow);
Arrow arrow2 = new Arrow(bump, y, x, emptyList, emptyList);
sortedArrows.add(arrow2);
addLookupArrow(y, x, arrow);
}
}
}
return true;
}
};
callables.add(worker);
}
pool.invokeAll(callables);
}
private boolean knowledgeEmpty() {
// if (!checkedKnowledgeEmpty) {
// knowledgeEmpty = knowledge.isEmpty();
// checkedKnowledgeEmpty = true;
// }
//
// return knowledgeEmpty;
// return knowledge.isEmpty();
return false;
}
private void initializeArrowsBackward(Graph graph) {
sortedArrows.clear();
lookupArrows.clear();
for (Edge edge : graph.getEdges()) {
Node x = edge.getNode1();
Node y = edge.getNode2();
if (!knowledgeEmpty()) {
if (!getKnowledge().noEdgeRequired(x.getName(), y.getName())) {
continue;
}
}
if (Edges.isDirectedEdge(edge)) {
calculateArrowsBackward(x, y, graph);
} else {
calculateArrowsBackward(x, y, graph);
calculateArrowsBackward(y, x, graph);
}
}
}
private void reevaluateForward(
final Graph graph, final List<Node> nodes, final Node x, final Node y) {
List<Callable<Boolean>> callables = new ArrayList<Callable<Boolean>>();
for (int t = 0; t < NTHREADS; t++) {
final int _t = t;
Callable<Boolean> worker =
new Callable<Boolean>() {
@Override
public Boolean call() {
int chunk = nodes.size() / NTHREADS + 1;
for (int _w = _t * chunk; _w < Math.min((_t + 1) * chunk, nodes.size()); _w++) {
final Node w = nodes.get(_w);
if (w == x) continue;
if (w == y) continue;
if (!graph.isAdjacentTo(w, x)) {
calculateArrowsForward(w, x, graph);
if (graph.isAdjacentTo(w, y)) {
calculateArrowsForward(x, w, graph);
}
}
if (!graph.isAdjacentTo(w, y)) {
calculateArrowsForward(w, y, graph);
if (graph.isAdjacentTo(w, x)) {
calculateArrowsForward(y, w, graph);
}
}
}
return true;
}
};
callables.add(worker);
}
pool.invokeAll(callables);
}
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);
}
}
}
// 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);
}
}
}
/** True iff log output should be produced. */
public boolean isLog() {
return log;
}
public void setLog(boolean log) {
this.log = log;
}
public Graph getInitialGraph() {
return initialGraph;
}
// 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 void setVerbose(boolean verbose) {
this.verbose = verbose;
}
public void setOut(PrintStream out) {
this.out = out;
}
public PrintStream getOut() {
return out;
}
// Concurrent OK.
private static class Arrow implements Comparable {
private double bump;
private Node x;
private Node y;
private List<Node> hOrT;
private List<Node> naYX;
public Arrow(double bump, Node x, Node y, List<Node> hOrT, List<Node> naYX) {
this.bump = bump;
this.x = x;
this.y = y;
this.hOrT = hOrT;
this.naYX = naYX;
}
public double getBump() {
return bump;
}
public Node getX() {
return x;
}
public Node getY() {
return y;
}
public List<Node> getHOrT() {
return hOrT;
}
public List<Node> getNaYX() {
return naYX;
}
// Sorting is by bump, high to low.
public int compareTo(Object o) {
Arrow arrow = (Arrow) o;
return Double.compare(arrow.getBump(), getBump());
}
public String toString() {
return "Arrow<"
+ x
+ "->"
+ y
+ " bump = "
+ bump
+ " t/h = "
+ hOrT
+ " naYX = "
+ naYX
+ ">";
}
}
/** Get all nodes that are connected to Y by an undirected edge and not adjacent to X. */
private static List<Node> getTNeighbors(Node x, Node y, Graph graph) {
List<Edge> yEdges = graph.getEdges(y);
List<Node> tNeighbors = new ArrayList<Node>();
for (Edge edge : yEdges) {
if (!Edges.isUndirectedEdge(edge)) {
continue;
}
Node z = edge.getDistalNode(y);
if (graph.isAdjacentTo(z, x)) {
continue;
}
tNeighbors.add(z);
}
return tNeighbors;
}
/** Evaluate the Insert(X, Y, T) operator (Definition 12 from Chickering, 2002). */
private double insertEval(Node x, Node y, List<Node> t, List<Node> naYX, Graph graph) {
Set<Node> set1 = new HashSet<Node>(naYX);
set1.addAll(t);
List<Node> paY = graph.getParents(y);
set1.addAll(paY);
Set<Node> set2 = new HashSet<Node>(set1);
set1.add(x);
return scoreGraphChange(y, set1, set2);
}
/** Evaluate the Delete(X, Y, T) operator (Definition 12 from Chickering, 2002). */
// Can be done concurrently.
private double deleteEval(Node x, Node y, List<Node> h, List<Node> naYX, Graph graph) {
List<Node> paY = graph.getParents(y);
Set<Node> paYMinuxX = new HashSet<Node>(paY);
paYMinuxX.remove(x);
Set<Node> set1 = new HashSet<Node>(naYX);
set1.removeAll(h);
set1.addAll(paYMinuxX);
Set<Node> set2 = new HashSet<Node>(naYX);
set2.removeAll(h);
set2.addAll(paY);
return scoreGraphChange(y, set1, set2);
}
/*
* Do an actual insertion
* (Definition 12 from Chickering, 2002).
**/
// serial.
private void insert(Node x, Node y, List<Node> t, Graph graph, double bump) {
if (graph.isAdjacentTo(x, y)) {
return; // The initial graph may already have put this edge in the graph.
// throw new IllegalArgumentException(x + " and " + y + " are already adjacent in
// the graph.");
}
Edge trueEdge = null;
if (trueGraph != null) {
Node _x = trueGraph.getNode(x.getName());
Node _y = trueGraph.getNode(y.getName());
trueEdge = trueGraph.getEdge(_x, _y);
}
graph.addDirectedEdge(x, y);
if (log) {
String label = trueGraph != null && trueEdge != null ? "*" : "";
TetradLogger.getInstance()
.log(
"insertedEdges",
graph.getNumEdges()
+ ". INSERT "
+ graph.getEdge(x, y)
+ " "
+ t
+ " "
+ bump
+ " "
+ label);
} else {
int numEdges = graph.getNumEdges() - 1;
if (verbose) {
if (numEdges % 50 == 0) out.println(numEdges);
}
}
if (verbose) {
String label = trueGraph != null && trueEdge != null ? "*" : "";
out.println(
graph.getNumEdges()
+ ". INSERT "
+ graph.getEdge(x, y)
+ " "
+ t
+ " "
+ bump
+ " "
+ label);
} else {
int numEdges = graph.getNumEdges() - 1;
if (verbose) {
if (numEdges % 50 == 0) out.println(numEdges);
}
}
for (Node _t : t) {
Edge oldEdge = graph.getEdge(_t, y);
if (oldEdge == null) throw new IllegalArgumentException("Not adjacent: " + _t + ", " + y);
graph.removeEdge(_t, y);
graph.addDirectedEdge(_t, y);
if (log && verbose) {
TetradLogger.getInstance()
.log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(_t, y));
out.println("--- Directing " + oldEdge + " to " + graph.getEdge(_t, y));
}
}
}
/** Do an actual deletion (Definition 13 from Chickering, 2002). */
private void delete(Node x, Node y, List<Node> subset, Graph graph, double bump) {
Edge trueEdge = null;
if (trueGraph != null) {
Node _x = trueGraph.getNode(x.getName());
Node _y = trueGraph.getNode(y.getName());
trueEdge = trueGraph.getEdge(_x, _y);
}
if (log && verbose) {
Edge oldEdge = graph.getEdge(x, y);
String label = trueGraph != null && trueEdge != null ? "*" : "";
TetradLogger.getInstance()
.log(
"deletedEdges",
(graph.getNumEdges() - 1)
+ ". DELETE "
+ oldEdge
+ " "
+ subset
+ " ("
+ bump
+ ") "
+ label);
out.println(
(graph.getNumEdges() - 1)
+ ". DELETE "
+ oldEdge
+ " "
+ subset
+ " ("
+ bump
+ ") "
+ label);
} else {
int numEdges = graph.getNumEdges() - 1;
if (numEdges % 50 == 0) out.println(numEdges);
}
graph.removeEdge(x, y);
for (Node h : subset) {
Edge oldEdge = graph.getEdge(y, h);
graph.removeEdge(y, h);
graph.addDirectedEdge(y, h);
if (log) {
TetradLogger.getInstance()
.log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(y, h));
}
if (verbose) {
out.println("--- Directing " + oldEdge + " to " + graph.getEdge(y, h));
}
if (Edges.isUndirectedEdge(graph.getEdge(x, h))) {
if (!graph.isAdjacentTo(x, h))
throw new IllegalArgumentException("Not adjacent: " + x + ", " + h);
oldEdge = graph.getEdge(x, h);
graph.removeEdge(x, h);
graph.addDirectedEdge(x, h);
if (log) {
TetradLogger.getInstance()
.log("directedEdges", "--- Directing " + oldEdge + " to " + graph.getEdge(x, h));
}
if (verbose) {
out.println("--- Directing " + oldEdge + " to " + graph.getEdge(x, h));
}
}
}
}
/*
* Test if the candidate insertion is a valid operation
* (Theorem 15 from Chickering, 2002).
**/
private boolean validInsert(Node x, Node y, List<Node> t, List<Node> naYX, Graph graph) {
List<Node> union = new ArrayList<Node>(t); // t and nayx are disjoint
union.addAll(naYX);
return isClique(union, graph) && !existsUnblockedSemiDirectedPath(y, x, union, graph);
}
/** Test if the candidate deletion is a valid operation (Theorem 17 from Chickering, 2002). */
private static boolean validDelete(List<Node> h, List<Node> naXY, Graph graph) {
List<Node> list = new ArrayList<Node>(naXY);
list.removeAll(h);
return isClique(list, graph);
}
// ---Background knowledge methods.
private void addRequiredEdges(Graph graph) {
if (true) return;
if (knowledgeEmpty()) return;
for (Iterator<KnowledgeEdge> it = getKnowledge().requiredEdgesIterator(); it.hasNext(); ) {
KnowledgeEdge next = it.next();
Node nodeA = graph.getNode(next.getFrom());
Node nodeB = graph.getNode(next.getTo());
if (!graph.isAncestorOf(nodeB, nodeA)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeA, nodeB);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeA, nodeB));
}
}
for (Edge edge : graph.getEdges()) {
final String A = edge.getNode1().getName();
final String B = edge.getNode2().getName();
if (knowledge.isForbidden(A, B)) {
Node nodeA = edge.getNode1();
Node nodeB = edge.getNode2();
if (nodeA != null
&& nodeB != null
&& graph.isAdjacentTo(nodeA, nodeB)
&& !graph.isChildOf(nodeA, nodeB)) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
if (!graph.isChildOf(nodeA, nodeB)
&& getKnowledge().isForbidden(nodeA.getName(), nodeB.getName())) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
} else if (knowledge.isForbidden(B, A)) {
Node nodeA = edge.getNode2();
Node nodeB = edge.getNode1();
if (nodeA != null
&& nodeB != null
&& graph.isAdjacentTo(nodeA, nodeB)
&& !graph.isChildOf(nodeA, nodeB)) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
if (!graph.isChildOf(nodeA, nodeB)
&& getKnowledge().isForbidden(nodeA.getName(), nodeB.getName())) {
if (!graph.isAncestorOf(nodeA, nodeB)) {
graph.removeEdges(nodeA, nodeB);
graph.addDirectedEdge(nodeB, nodeA);
TetradLogger.getInstance()
.log("insertedEdges", "Adding edge by knowledge: " + graph.getEdge(nodeB, nodeA));
}
}
}
}
}
private String getString(KnowledgeEdge next) {
return next.getTo();
}
/**
* Use background knowledge to decide if an insert or delete operation does not orient edges in a
* forbidden direction according to prior knowledge. If some orientation is forbidden in the
* subset, the whole subset is forbidden.
*/
private boolean validSetByKnowledge(Node y, List<Node> subset) {
for (Node node : subset) {
if (getKnowledge().isForbidden(node.getName(), y.getName())) {
return false;
}
}
return true;
}
// --Auxiliary methods.
/**
* Find all nodes that are connected to Y by an undirected edge that are adjacent to X (that is,
* by undirected or directed edge).
*/
private static List<Node> getNaYX(Node x, Node y, Graph graph) {
List<Edge> yEdges = graph.getEdges(y);
List<Node> nayx = new ArrayList<Node>();
for (Edge edge : yEdges) {
if (!Edges.isUndirectedEdge(edge)) {
continue;
}
Node z = edge.getDistalNode(y);
if (!graph.isAdjacentTo(z, x)) {
continue;
}
nayx.add(z);
}
return nayx;
}
/** Returns true iif the given set forms a clique in the given graph. */
private static boolean isClique(List<Node> nodes, Graph graph) {
for (int i = 0; i < nodes.size() - 1; i++) {
for (int j = i + 1; j < nodes.size(); j++) {
if (!graph.isAdjacentTo(nodes.get(i), nodes.get(j))) {
return false;
}
}
}
return true;
}
private boolean existsUnblockedSemiDirectedPath(Node from, Node to, List<Node> cond, Graph G) {
Queue<Node> Q = new LinkedList<Node>();
Set<Node> V = new HashSet<Node>();
Q.offer(from);
V.add(from);
while (!Q.isEmpty()) {
Node t = Q.remove();
if (t == to) return true;
for (Node u : G.getAdjacentNodes(t)) {
Edge edge = G.getEdge(t, u);
Node c = Edges.traverseSemiDirected(t, edge);
if (c == null) continue;
if (cond.contains(c)) continue;
if (c == to) return true;
if (!V.contains(c)) {
V.add(c);
Q.offer(c);
}
}
}
return false;
}
/**
* 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;
}
/**
* Fully direct a graph with background knowledge. I am not sure how to adapt Chickering's
* suggested algorithm above (dagToPdag) to incorporate background knowledge, so I am also
* implementing this algorithm based on Meek's 1995 UAI paper. Notice it is the same implemented
* in PcSearch. *IMPORTANT!* *It assumes all colliders are oriented, as well as arrows dictated by
* time order.*
*/
private void meekOrient(Graph graph, IKnowledge knowledge) {
MeekRules rules = new MeekRules();
rules.setOrientInPlace(false);
rules.setKnowledge(knowledge);
rules.orientImplied(graph);
}
private void setDataSet(DataSet dataSet) {
List<String> _varNames = dataSet.getVariableNames();
this.variables = dataSet.getVariables();
this.dataSet = dataSet;
this.discrete = dataSet.isDiscrete();
if (!isDiscrete()) {
this.covariances = new CovarianceMatrix(dataSet);
}
this.sampleSize = dataSet.getNumRows();
}
private void setCovMatrix(ICovarianceMatrix covarianceMatrix) {
this.covariances = covarianceMatrix;
this.variables = covarianceMatrix.getVariables();
this.sampleSize = covarianceMatrix.getSampleSize();
}
private void buildIndexing(Graph graph) {
this.hashIndices = new HashMap<Node, Integer>();
for (Node node : graph.getNodes()) {
this.hashIndices.put(node, variables.indexOf(node));
}
}
private void clearArrow(Node x, Node y) {
final OrderedPair<Node> pair = new OrderedPair<Node>(x, y);
final Set<Arrow> lookupArrows = this.lookupArrows.get(pair);
if (lookupArrows != null) {
sortedArrows.removeAll(lookupArrows);
}
this.lookupArrows.remove(pair);
}
private void addLookupArrow(Node i, Node j, Arrow arrow) {
OrderedPair<Node> pair = new OrderedPair<Node>(i, j);
Set<Arrow> arrows = lookupArrows.get(pair);
if (arrows == null) {
arrows = new HashSet<Arrow>();
lookupArrows.put(pair, arrows);
}
arrows.add(arrow);
}
// ===========================SCORING METHODS===================//
public double scoreDag(Graph graph) {
Graph dag = new EdgeListGraphSingleConnections(graph);
buildIndexing(graph);
double score = 0.0;
for (Node y : dag.getNodes()) {
Set<Node> parents = new HashSet<Node>(dag.getParents(y));
int nextIndex = -1;
for (int i = 0; i < getVariables().size(); i++) {
nextIndex = hashIndices.get(variables.get(i));
}
int parentIndices[] = new int[parents.size()];
Iterator<Node> pi = parents.iterator();
int count = 0;
while (pi.hasNext()) {
Node nextParent = pi.next();
parentIndices[count++] = hashIndices.get(nextParent);
}
if (this.isDiscrete()) {
score += localDiscreteScore(nextIndex, parentIndices);
} else {
score += localSemScore(nextIndex, parentIndices);
}
}
return score;
}
private double scoreGraphChange(Node y, Set<Node> parents1, Set<Node> parents2) {
int yIndex = hashIndices.get(y);
double score1, score2;
int[] parentIndices1 = new int[parents1.size()];
int count = -1;
for (Node parent : parents1) {
parentIndices1[++count] = hashIndices.get(parent);
}
if (isDiscrete()) {
score1 = localDiscreteScore(yIndex, parentIndices1);
} else {
score1 = localSemScore(yIndex, parentIndices1);
}
int[] parentIndices2 = new int[parents2.size()];
int count2 = -1;
for (Node parent : parents2) {
parentIndices2[++count2] = hashIndices.get(parent);
}
if (isDiscrete()) {
score2 = localDiscreteScore(yIndex, parentIndices2);
} else {
score2 = localSemScore(yIndex, parentIndices2);
}
return score1 - score2;
}
/** Compute the local BDeu score of (i, parents(i)). See (Chickering, 2002). */
private double localDiscreteScore(int i, int parents[]) {
return getDiscreteScore().localScore(i, parents);
}
/**
* Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model.
*/
private double localSemScore(int i, int[] parents) {
try {
ICovarianceMatrix cov = getCovMatrix();
double varianceY = cov.getValue(i, i);
double residualVariance = varianceY;
int n = sampleSize();
int p = parents.length;
int k = (p * (p + 1)) / 2 + p;
// int k = (p + 1) * (p + 1);
// int k = p + 1;
TetradMatrix covxx = cov.getSelection(parents, parents);
TetradMatrix covxxInv = covxx.inverse();
TetradVector covxy = cov.getSelection(parents, new int[] {i}).getColumn(0);
TetradVector b = covxxInv.times(covxy);
residualVariance -= covxy.dotProduct(b);
if (residualVariance <= 0 && verbose) {
out.println(
"Nonpositive residual varianceY: resVar / varianceY = "
+ (residualVariance / varianceY));
return Double.NaN;
}
double c = getPenaltyDiscount();
// return -n * log(residualVariance) - 2 * k; //AIC
return -n * Math.log(residualVariance) - c * k * Math.log(n);
// return -n * log(residualVariance) - c * k * (log(n) - log(2 * PI));
} catch (Exception e) {
e.printStackTrace();
throw new RuntimeException(e);
// throwMinimalLinearDependentSet(parents, cov);
}
}
// private void throwMinimalLinearDependentSet(int[] parents, TetradMatrix cov) {
// List<Node> _parents = new ArrayList<Node>();
// for (int p : parents) _parents.add(variables.get(p));
//
// DepthChoiceGenerator gen = new DepthChoiceGenerator(_parents.size(), _parents.size());
// int[] choice;
//
// while ((choice = gen.next()) != null) {
// int[] sel = new int[choice.length];
// List<Node> _sel = new ArrayList<Node>();
// for (int m = 0; m < choice.length; m++) {
// sel[m] = parents[m];
// _sel.add(variables.get(sel[m]));
// }
//
// TetradMatrix m = cov.getSelection(sel, sel);
//
// try {
// m.inverse();
// } catch (Exception e2) {
// throw new RuntimeException("Linear dependence among variables: " + _sel);
// }
// }
// }
private int sampleSize() {
return this.sampleSize;
}
private List<Node> getVariables() {
return variables;
}
private ICovarianceMatrix getCovMatrix() {
return covariances;
}
private DataSet dataSet() {
return dataSet;
}
private boolean isDiscrete() {
return discrete;
}
private void fireGraphChange(Graph graph) {
for (PropertyChangeListener l : getListeners()) {
l.propertyChange(new PropertyChangeEvent(this, "graph", null, graph));
}
}
private List<PropertyChangeListener> getListeners() {
if (listeners == null) {
listeners = new ArrayList<PropertyChangeListener>();
}
return listeners;
}
private void storeGraph(Graph graph) {
if (numPatternsToStore < 1) return;
if (topGraphs.isEmpty() || score > topGraphs.first().getScore()) {
Graph graphCopy = new EdgeListGraphSingleConnections(graph);
topGraphs.add(new ScoredGraph(graphCopy, score));
if (topGraphs.size() > getNumPatternsToStore()) {
topGraphs.remove(topGraphs.first());
}
}
}
}
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);
}
}