Package edu.cmu.tetrad.search.score

Class GraphScore

java.lang.Object
edu.cmu.tetrad.search.score.GraphScore
All Implemented Interfaces:
Score
public class GraphScore extends Object implements Score
Implements a pscudo-"score" that implmenets implements Chickering and Meek's (2002) locally consistent score criterion. This is not a true score; rather, a -1 is returned in case mseparation holds and a 1 in case mseparation does not hold. This is only meant to be used in the context of FGES, and allows the search to follow its path prescribed by the locally consistent scoring criterion. For a reference to the latter, pleasee this article:

Chickering (2002) "Optimal structure identification with greedy search" Journal of Machine Learning Research.

For further discussion of using m-separation in the GES search, see:

Nandy, P., Hauser, A., & Maathuis, M. H. (2018). High-dimensional consistency in score-based and hybrid structure learning. The Annals of Statistics, 46(6A), 3151-3183.

For more discussion please see:

Shen, X., Zhu, S., Zhang, J., Hu, S., & Chen, Z. (2022, August). Reframed GES with a neural conditional dependence measure. In Uncertainty in Artificial Intelligence (pp. 1782-1791). PMLR.

Version:
$Id: $Id
Author:
josephramsey
See Also:

  • Constructor Details

    • GraphScore

      public GraphScore(Graph dag)
      Constructs a GraphScore from a DAG.
      Parameters:
      dag - A directed acyclic graph.

    • GraphScore

      public GraphScore(IndependenceFacts facts)
      Constructs a GraphScore from a list of independence facts.
      Parameters:
      facts - A list known independence facts; a lookup will be donw from these facts.
      See Also:

  • Method Details

    • localScore

      public double localScore(int y, int[] z)
      Calculates the sample likelihood and BIC score for y given its z in a simple SEM model.
      Specified by:
      localScore in interface Score
      Parameters:
      y - a int
      z - an array of objects
      Returns:
      this score.

    • localScoreDiff

      public double localScoreDiff(int x, int y, int[] z)
      Returns the score difference of the graph.

      Returns a "score difference", which amounts to a conditional local scoring criterion results. Only difference methods is implemented, since the other methods don't make sense here.

      Specified by:
      localScoreDiff in interface Score
      Parameters:
      x - A node.
      y - TAhe node.
      z - A set of nodes.
      Returns:
      The score difference.

    • localScoreDiff

      public double localScoreDiff(int x, int y)
      Returns the local score difference of the graph.

      The "unconditional difference." Only difference methods is implemented, since the other methods don't make sense here.

      Specified by:
      localScoreDiff in interface Score
      Parameters:
      x - A node.
      y - The node.
      Returns:
      The local score difference.

    • localScore

      public double localScore(int i, int parent)

      localScore.

      Specified by:
      localScore in interface Score
      Parameters:
      i - a int
      parent - a int
      Returns:
      a double
      Throws:
      UnsupportedOperationException - Since the method doesn't make sense here.

    • localScore

      public double localScore(int i)
      Returns the local score of the gien node in the graph.
      Specified by:
      localScore in interface Score
      Parameters:
      i - A node.
      Returns:
      The local score.

    • isEffectEdge

      public boolean isEffectEdge(double bump)
      Returns true iff the edge between x and y is an effect edge.

      Returns a judgment for FGES whether a score with the bump is for an effect edge.

      Specified by:
      isEffectEdge in interface Score
      Parameters:
      bump - The bump.
      Returns:
      True iff the edge between x and y is an effect edge.
      See Also:

    • getVariables

      public List<Node> getVariables()
      The variables of the score.

      Returns the list of variables.

      Specified by:
      getVariables in interface Score
      Returns:
      This list.

    • getMaxDegree

      public int getMaxDegree()
      Returns the max degree, by default 1000.

      Returns the maximum degree, which is set to 1000.

      Specified by:
      getMaxDegree in interface Score
      Returns:
      The max degree.

    • getSampleSize

      public int getSampleSize()

      getSampleSize.

      Specified by:
      getSampleSize in interface Score
      Returns:
      a int
      Throws:
      UnsupportedOperationException - Since the method doesn't make sense here.

    • getDag

      public Graph getDag()
      Returns a copy of the DAG being searched over.
      Returns:
      This DAG.