Package edu.cmu.tetrad.search

Class FgesMb

java.lang.Object
edu.cmu.tetrad.search.FgesMb
public final class FgesMb extends Object

Restricts the FGES algorithm (see) to the operations needed just to find the graph over the Markov blanket of a variable X (or a graph over the Markov blankets of a list of variables X1,..,Xn), together with the target X (or, respectively, the targets X1,...,Xn). The reference is this:

Ramsey, J., Glymour, M., Sanchez-Romero, R., & Glymour, C. (2017). A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. International journal of data science and analytics, 3, 121-129.

This class is configured to respect knowledge of forbidden and required edges, including knowledge of temporal tiers.

Author:
josephramsey
See Also:

  • Constructor Details

    • FgesMb

      public FgesMb(Score score)
      Constructor. Construct a Score and pass it in here. The totalScore should return a positive value in case of conditional dependence and a negative values in case of conditional independence. See Chickering (2002), locally consistent scoring criterion.

  • Method Details

    • setFaithfulnessAssumed

      public void setFaithfulnessAssumed(boolean faithfulnessAssumed)
      Set to true if it is assumed that all path pairs with one length 1 path do not cancel.
      Parameters:
      faithfulnessAssumed - True if so.
      See Also:

    • search

      public Graph search(Node target)
      Greedy equivalence search: Start from the empty graph, add edges till model is significant. Then start deleting edges till a minimum is achieved.
      Returns:
      the resulting CPDAG.

    • search

      public Graph search(List<Node> targets)
      Searches over a specific list of target variables.
      Parameters:
      targets - The targets.
      Returns:
      The union of the edges for the Markov blanekt graph for each target.

    • getKnowledge

      public Knowledge getKnowledge()
      Returns the background knowledge.
      Returns:
      This knowledge.

    • setKnowledge

      public void setKnowledge(Knowledge knowledge)
      Sets the background knowledge.
      Parameters:
      knowledge - the knowledge object, specifying forbidden and required edges.

    • getElapsedTime

      public long getElapsedTime()
      Returns the elapsed time of the search.
      Returns:
      This time.

    • setTrueGraph

      public void setTrueGraph(Graph trueGraph)
      If the true graph is set, askterisks will be printed in log output for the true edges.
      Parameters:
      trueGraph - The true graph.

    • getScore

      public double getScore(Graph dag)
      Returns the totalScore of the given DAG, up to a constant.
      Parameters:
      dag - The dag to score.
      Returns:
      The scre of the DAG.

    • getTopGraphs

      public LinkedList<ScoredGraph> getTopGraphs()
      Returns the list of top scoring graphs.
      Returns:
      The top scoring graphs.

    • getnumCPDAGsToStore

      public int getnumCPDAGsToStore()
      Returns the number of CPDAGs to store.
      Returns:
      This number.

    • setNumCpdagsToStore

      public void setNumCpdagsToStore(int numCpdagsToStore)
      Sets the number of CPDAGs to store. This should be set to zero for fast search.
      Parameters:
      numCpdagsToStore - This number.

    • setExternalGraph

      public void setExternalGraph(Graph externalGraph)
      Sets the external graph. The search graph is initialized to this.

    • setVerbose

      public void setVerbose(boolean verbose)
      Sets whether verbose output should be produced.
      Parameters:
      verbose - True if so.

    • setOut

      public void setOut(PrintStream out)
      Sets the output stream that output (except for log output) should be sent to. By detault System.out.
      Parameters:
      out - The output print stream, by default System.out.

    • setAdjacencies

      public void setAdjacencies(Graph adjacencies)
      Sets the graph of preset adjacenies for the algorithm; edges not in this adjacencies graph will not be added.
      Parameters:
      adjacencies - This graph.

    • setCycleBound

      public void setCycleBound(int cycleBound)
      A bound on cycle length.
      Parameters:
      cycleBound - The bound, ≥ 1, or -1 for unlimited.

    • setParallelized

      public void setParallelized(boolean parallelized)
      Creates a new processors pool with the specified number of threads.
      Parameters:
      parallelized - True if parallelized,

    • getMaxDegree

      public int getMaxDegree()
      The maximum of parents any nodes can have in output CPDAG.
      Returns:
      -1 for unlimited.

    • setMaxDegree

      public void setMaxDegree(int maxDegree)
      The maximum of parents any nodes can have in output CPDAG.
      Parameters:
      maxDegree - -1 for unlimited.

    • getMinChunk

      public int getMinChunk(int n)

    • scoreDag

      public double scoreDag(Graph dag)
      Scores the given DAG, up to a constant.