Ejemplo n.º 1
0
  /////////////////////////////////////////////////////////////////////////////
  // 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;
        }
      }
    }
  }
Ejemplo n.º 2
0
  /** Constructs a new FCI search for the given independence test and background knowledge. */
  public Rfci(IndependenceTest independenceTest) {
    if (independenceTest == null || knowledge == null) {
      throw new NullPointerException();
    }

    this.independenceTest = independenceTest;
    this.variables.addAll(independenceTest.getVariables());
  }
Ejemplo n.º 3
0
 private boolean localMarkovIndep(Node x, Node y, Graph pattern, IndependenceTest test) {
   List<Node> future = pattern.getDescendants(Collections.singletonList(x));
   List<Node> boundary = pattern.getAdjacentNodes(x);
   boundary.removeAll(future);
   List<Node> closure = new ArrayList<>(boundary);
   closure.add(x);
   closure.remove(y);
   if (future.contains(y) || boundary.contains(y)) return false;
   return test.isIndependent(x, y, boundary);
 }
Ejemplo n.º 4
0
  /**
   * Constructs a new FCI search for the given independence test and background knowledge and a list
   * of variables to search over.
   */
  public Rfci(IndependenceTest independenceTest, List<Node> searchVars) {
    if (independenceTest == null || knowledge == null) {
      throw new NullPointerException();
    }

    this.independenceTest = independenceTest;
    this.variables.addAll(independenceTest.getVariables());

    Set<Node> remVars = new HashSet<Node>();
    for (Node node1 : this.variables) {
      boolean search = false;
      for (Node node2 : searchVars) {
        if (node1.getName().equals(node2.getName())) {
          search = true;
        }
      }
      if (!search) {
        remVars.add(node1);
      }
    }
    this.variables.removeAll(remVars);
  }
Ejemplo n.º 5
0
  ////////////////////////////////////////////
  // RFCI Algorithm 4.4 (Colombo et al, 2012)
  // Orient colliders
  ////////////////////////////////////////////
  private void ruleR0_RFCI(List<Node[]> rTuples) {
    List<Node[]> lTuples = new ArrayList<Node[]>();

    List<Node> nodes = graph.getNodes();

    ///////////////////////////////
    // process tuples in rTuples
    while (!rTuples.isEmpty()) {
      Node[] thisTuple = rTuples.remove(0);

      Node i = thisTuple[0];
      Node j = thisTuple[1];
      Node k = thisTuple[2];

      final List<Node> nodes1 = getSepset(i, k);

      if (nodes1 == null) continue;

      List<Node> sepSet = new ArrayList<Node>(nodes1);
      sepSet.remove(j);

      boolean independent1 = false;
      if (knowledge.noEdgeRequired(i.getName(), j.getName())) // if BK allows
      {
        try {
          independent1 = independenceTest.isIndependent(i, j, sepSet);
        } catch (Exception e) {
          independent1 = true;
        }
      }

      boolean independent2 = false;
      if (knowledge.noEdgeRequired(j.getName(), k.getName())) // if BK allows
      {
        try {
          independent2 = independenceTest.isIndependent(j, k, sepSet);
        } catch (Exception e) {
          independent2 = true;
        }
      }

      if (!independent1 && !independent2) {
        lTuples.add(thisTuple);
      } else {
        // set sepSets to minimal separating sets
        if (independent1) {
          setMinSepSet(sepSet, i, j);
          graph.removeEdge(i, j);
        }
        if (independent2) {
          setMinSepSet(sepSet, j, k);
          graph.removeEdge(j, k);
        }

        // add new unshielded tuples to rTuples
        for (Node thisNode : nodes) {
          List<Node> adjacentNodes = graph.getAdjacentNodes(thisNode);
          if (independent1) // <i, ., j>
          {
            if (adjacentNodes.contains(i) && adjacentNodes.contains(j)) {
              Node[] newTuple = {i, thisNode, j};
              rTuples.add(newTuple);
            }
          }
          if (independent2) // <j, ., k>
          {
            if (adjacentNodes.contains(j) && adjacentNodes.contains(k)) {
              Node[] newTuple = {j, thisNode, k};
              rTuples.add(newTuple);
            }
          }
        }

        // remove tuples involving either (if independent1) <i, j>
        // or (if independent2) <j, k> from rTuples
        Iterator<Node[]> iter = rTuples.iterator();
        while (iter.hasNext()) {
          Node[] curTuple = iter.next();
          if ((independent1 && (curTuple[1] == i) && ((curTuple[0] == j) || (curTuple[2] == j)))
              || (independent2 && (curTuple[1] == k) && ((curTuple[0] == j) || (curTuple[2] == j)))
              || (independent1 && (curTuple[1] == j) && ((curTuple[0] == i) || (curTuple[2] == i)))
              || (independent2
                  && (curTuple[1] == j)
                  && ((curTuple[0] == k) || (curTuple[2] == k)))) {
            iter.remove();
          }
        }

        // remove tuples involving either (if independent1) <i, j>
        // or (if independent2) <j, k> from lTuples
        iter = lTuples.iterator();
        while (iter.hasNext()) {
          Node[] curTuple = iter.next();
          if ((independent1 && (curTuple[1] == i) && ((curTuple[0] == j) || (curTuple[2] == j)))
              || (independent2 && (curTuple[1] == k) && ((curTuple[0] == j) || (curTuple[2] == j)))
              || (independent1 && (curTuple[1] == j) && ((curTuple[0] == i) || (curTuple[2] == i)))
              || (independent2
                  && (curTuple[1] == j)
                  && ((curTuple[0] == k) || (curTuple[2] == k)))) {
            iter.remove();
          }
        }
      }
    }

    ///////////////////////////////////////////////////////
    // orient colliders (similar to original FCI ruleR0)
    for (Node[] thisTuple : lTuples) {
      Node i = thisTuple[0];
      Node j = thisTuple[1];
      Node k = thisTuple[2];

      List<Node> sepset = getSepset(i, k);

      if (sepset == null) {
        continue;
      }

      if (!sepset.contains(j) && graph.isAdjacentTo(i, j) && graph.isAdjacentTo(j, k)) {

        if (!isArrowpointAllowed(i, j)) {
          continue;
        }

        if (!isArrowpointAllowed(k, j)) {
          continue;
        }

        graph.setEndpoint(i, j, Endpoint.ARROW);
        graph.setEndpoint(k, j, Endpoint.ARROW);

        printWrongColliderMessage(i, j, k, "R0_RFCI");
      }
    }
  }
Ejemplo n.º 6
0
 /**
  * Runs PC starting with a complete graph over all nodes of the given conditional independence
  * test, using the given independence test and knowledge and returns the resultant graph. The
  * returned graph will be a pattern if the independence information is consistent with the
  * hypothesis that there are no latent common causes. It may, however, contain cycles or
  * bidirected edges if this assumption is not born out, either due to the actual presence of
  * latent common causes, or due to statistical errors in conditional independence judgments.
  */
 public Graph search() {
   return search(independenceTest.getVariables());
 }
  private Void findSeeds() {
    Tetrad tetrad = null;
    List<Node> empty = new ArrayList();
    if (variables.size() < 4) {
      Set<Set<Integer>> ESeeds = new HashSet<Set<Integer>>();
    }

    Map<Node, Set<Node>> adjacencies;

    if (depth == -2) {
      adjacencies = new HashMap<Node, Set<Node>>();

      for (Node node : variables) {
        HashSet<Node> _nodes = new HashSet<Node>(variables);
        _nodes.remove(node);
        adjacencies.put(node, _nodes);
      }
    } else {
      //            System.out.println("Running PC adjacency search...");
      Graph graph = new EdgeListGraph(variables);
      Fas fas = new Fas(graph, indTest);
      fas.setVerbose(false);
      fas.setDepth(depth); // 1?
      adjacencies = fas.searchMapOnly();
      //            System.out.println("...done.");
    }

    List<Integer> allVariables = new ArrayList<Integer>();
    for (int i = 0; i < variables.size(); i++) allVariables.add(i);

    log("Finding seeds.", true);

    ChoiceGenerator gen = new ChoiceGenerator(allVariables.size(), 3);
    int[] choice;
    CHOICE:
    while ((choice = gen.next()) != null) {
      int n1 = allVariables.get(choice[0]);
      int n2 = allVariables.get(choice[1]);
      int n3 = allVariables.get(choice[2]);
      Node v1 = variables.get(choice[0]);
      Node v2 = variables.get(choice[1]);
      Node v3 = variables.get(choice[2]);

      Set<Integer> triple = triple(n1, n2, n3);

      if (!clique(triple, adjacencies)) {
        continue;
      }

      boolean EPure = true;
      boolean CPure1 = true;
      boolean CPure2 = true;
      boolean CPure3 = true;

      for (int o : allVariables) {
        if (triple.contains(o)) {
          continue;
        }

        Node v4 = variables.get(o);
        tetrad = new Tetrad(v1, v2, v3, v4);

        if (deltaTest.getPValue(tetrad) > alpha) {
          EPure = false;
          if (indTest.isDependent(v1, v4, empty)) {
            CPure1 = false;
          }
          if (indTest.isDependent(v2, v4, empty)) {
            CPure2 = false;
          }
        }
        tetrad = new Tetrad(v1, v3, v2, v4);
        if (deltaTest.getPValue(tetrad) > alpha) {
          EPure = false;
          if (indTest.isDependent(v3, v4, empty)) {
            CPure3 = false;
          }
        }

        if (!(EPure || CPure1 || CPure2 || CPure3)) {
          continue CHOICE;
        }
      }

      HashSet<Integer> _cluster = new HashSet<Integer>(triple);

      if (verbose) {
        log("++" + variablesForIndices(new ArrayList<Integer>(triple)), false);
      }

      if (EPure) {
        ESeeds.add(_cluster);
      }
      if (!EPure) {
        if (CPure1) {
          Set<Integer> _cluster1 = new HashSet<Integer>(n2, n3);
          _cluster1.addAll(CSeeds.get(n1));
          CSeeds.set(n1, _cluster1);
        }
        if (CPure2) {
          Set<Integer> _cluster2 = new HashSet<Integer>(n1, n3);
          _cluster2.addAll(CSeeds.get(n2));
          CSeeds.set(n2, _cluster2);
        }
        if (CPure3) {
          Set<Integer> _cluster3 = new HashSet<Integer>(n1, n2);
          _cluster3.addAll(CSeeds.get(n3));
          CSeeds.set(n3, _cluster3);
        }
      }
    }
    return null;
  }