Explanation and analysis

This tools aimed to provide some different views on the Bayesian network in order to explore its qualitative and/or quantitave behaviours.

class pyagrum.JunctionTreeGenerator

JunctionTreeGenerator is use to generate junction tree or binary junction tree from Bayesian networks.

JunctionTreeGenerator() -> JunctionTreeGenerator : default constructor

binaryJoinTree(*args)

Computes the binary joint tree for its parameters. If the first parameter is a graph, the heurisitcs assume that all the node have the same domain size (2). If given, the heuristic takes into account the partial order for its elimination order.

• Parameters:
  • g (pyagrum.UndiGraph) – a undirected graph
  • dag (pyagrum.DAG) – a dag
  • bn (pyagrum.BayesNet) – a BayesianNetwork
  • partial_order (list of list of int) – a partial order among the nodeIDs
• Returns: the current binary joint tree
• Return type: pyagrum.CliqueGraph

eliminationOrder(*args)

Computes the elimination for its parameters. If the first parameter is a graph, the heurisitcs assume that all the node have the same domain size (2). If given, the heuristic takes into account the partial order for its elimination order.

• Parameters:
  • g (pyagrum.UndiGraph) – a undirected graph
  • dag (pyagrum.DAG) – a dag
  • bn (pyagrum.BayesNet) – a BayesianNetwork
  • partial_order (list of list of int) – a partial order among the nodeIDs
• Returns: the current elimination order.
• Return type: list[int]

junctionTree(*args)

Computes the junction tree for its parameters. If the first parameter is a graph, the heurisitcs assume that all the node have the same domain size (2). If given, the heuristic takes into account the partial order for its elimination order.

• Parameters:
  • g (pyagrum.UndiGraph) – a undirected graph
  • dag (pyagrum.DAG) – a dag
  • bn (pyagrum.BayesNet) – a BayesianNetwork
  • partial_order (list of list of int) – a partial order among the nodeIDs
• Returns: the current junction tree.
• Return type: pyagrum.CliqueGraph

class pyagrum.EssentialGraph(*args)

Class building the essential graph from a BN.

Essential graph is a mixed graph (Chain Graph) that represents the class of markov equivalent Bayesian networks (with the same independency model).

EssentialGraph(m) -> EssentialGraph : Parameters: : - m (pyagrum.DAGmodel) – a DAGmodel

adjacencyMatrix()

adjacency matrix from a graph/graphical models

Compute the adjacency matrix of a pyAgrum’s graph or graphical models (more generally an object that has nodes, children/parents or neighbours methods)

• Returns: adjacency matrix (as numpy.ndarray) with nodeId as key.
• Return type: numpy.ndarray

ancestors(*args)

give the set of nodeid of ancestors of a node

• Parameters: norid (str *|*int) – the name or the id of the node
• Returns: the set of ids of the ancestors of node norid.
• Return type: list[int]

arcs()

• Returns: The lisf of arcs in the EssentialGraph
• Return type: set[tuple[int, int]]

children(id)

• Parameters: id (int) – the id of the parent
• Returns: the set of all the children
• Return type: list[int]

connectedComponents()

Return the connected components of the essential graph.

Each node is mapped to the id of its component root (an arbitrarily chosen node from the same component).

• Returns: mapping node id → component root id
• Return type: dict[int, int]

connectedComponentsCount()

number of connected components

• Returns: the number of connected components in the graph.
• Return type: int

connectedComponentsList()

connected components as a dict of sets

• Returns: dict of connected components (as sets of nodeIds) keyed by an arbitrary root nodeId per component.
• Return type: dict(int, set[int])

descendants(*args)

give the set of nodeid of descendants of a node

• Parameters: norid (str *|*int) – the name or the id of the node
• Returns: the set of ids of the descendants of node norid.
• Return type: list[int]

edges()

• Returns: the list of the edges
• Return type: set[tuple[int, int]]

idFromName(name)

• Parameters: name (str) – the name of the variable in the model
• Returns: the nodeId from the name of the variable in the model
• Return type: int

nameFromId(node)

• Parameters: node (int) – the nodeId of the variable in the model
• Returns: the name of the variable in the model from the nodeId
• Return type: str

neighbours(id)

• Parameters: id (int) – the id of the checked node
• Returns: The set of edges adjacent to the given node
• Return type: list[int]

nodes()

Return the set of node ids in the essential graph.

• Returns: the set of all node ids
• Return type: set[int]

parents(id)

• Parameters: id (int) – The id of the child node
• Returns: the set of the parents ids.
• Return type: list[int]

pdag()

• Returns: the PDAG (Partially Directed Graph)
• Return type: PDAG

size()

• Returns: the number of nodes in the graph
• Return type: int

sizeArcs()

• Returns: the number of arcs in the graph
• Return type: int

sizeEdges()

• Returns: the number of edges in the graph
• Return type: int

sizeNodes()

• Returns: the number of nodes in the graph
• Return type: int

skeleton()

Return the skeleton of the essential graph (the underlying undirected graph without arc orientations).

• Returns: the skeleton of the essential graph
• Return type: UndiGraph

toDot()

• Returns: a friendly display of the graph in DOT format
• Return type: str

class pyagrum.MarkovBlanket(*args)

Class building the Markov blanket of a node in a graph.

MarkovBlanket(m,n) -> MarkovBlanket : Parameters: : - m (pyagrum.DAGmodel) – a DAGmodel - n (int) – a node id

MarkovBlanket(m,name) -> MarkovBlanket : Parameters: : - m (pyagrum.DAGmodel) – a DAGmodel - name (str) – a node name

adjacencyMatrix()

adjacency matrix from a graph/graphical models

Compute the adjacency matrix of a pyAgrum’s graph or graphical models (more generally an object that has nodes, children/parents or neighbours methods)

• Returns: adjacency matrix (as numpy.ndarray) with nodeId as key.
• Return type: numpy.ndarray

ancestors(*args)

give the set of nodeid of ancestors of a node

• Parameters: norid (str *|*int) – the name or the id of the node
• Returns: the set of ids of the ancestors of node norid.
• Return type: list[int]

arcs()

• Returns: the list of the arcs
• Return type: set[tuple[int, int]]

children(id)

• Parameters: id (int) – the id of the parent
• Returns: the set of all the children
• Return type: list[int]

connectedComponentsCount()

number of connected components

• Returns: the number of connected components in the graph.
• Return type: int

connectedComponentsList()

connected components as a dict of sets

• Returns: dict of connected components (as sets of nodeIds) keyed by an arbitrary root nodeId per component.
• Return type: dict(int, set[int])

dag()

• Returns: a copy of the DAG
• Return type: DAG

descendants(*args)

give the set of nodeid of descendants of a node

• Parameters: norid (str *|*int) – the name or the id of the node
• Returns: the set of ids of the descendants of node norid.
• Return type: list[int]

hasSameStructure(other)

• Parameters: pyagrum.DAGmodel – a direct acyclic model
• Returns: True if all the named node are the same and all the named arcs are the same
• Return type: bool

nodes()

• Returns: the set of ids
• Return type: set[int]

parents(id)

• Parameters: id (int) – The id of the child node
• Returns: the set of the parents ids.
• Return type: list[int]

size()

• Returns: the number of nodes in the graph
• Return type: int

sizeArcs()

• Returns: the number of arcs in the graph
• Return type: int

sizeNodes()

• Returns: the number of nodes in the graph
• Return type: int

toDot()

• Returns: a friendly display of the graph in DOT format
• Return type: str