Undirected Graphical Model

class pyagrum.MarkovRandomField(*args)

MarkovRandomField represents a Markov random field.

MarkovRandomField(name=’’) -> MarkovRandomField : Parameters: : - name (str) – the name of the Bayes Net

MarkovRandomField(source) -> MarkovRandomField : Parameters: : - source (pyagrum.MarkovRandomField) – the Markov random field to copy

add(*args)

Add a variable to the pyagrum.MarkovRandomField.

• Parameters:
  • variable (pyagrum.DiscreteVariable) – the variable added
  • name (str) – the variable name
  • nbrmod (int) – the number of modalities for the new variable
  • id (int) – the variable forced id in the pyagrum.MarkovRandomField
• Returns: the id of the new node
• Return type: int
• Raises:
  • pyagrum.DuplicateLabel – If variable.name() is already used in this pyagrum.MarkovRandomField.
  • pyagrum.OperationNotAllowed – If nbrmod is less than 2
  • pyagrum.DuplicateElement – If id is already used.

addFactor(*args)

Add a factor from a list or a set of id or str. If the argument is a set, the order is the order of the IDs of the variables

• Parameters: seq (sequence (**list or set ) of int or string) – The sequence (ordered or not) of node id or names
• Return type: Tensor

addStructureListener(whenNodeAdded=None, whenNodeDeleted=None, whenEdgeAdded=None, whenedgeDeleted=None)

Add the listeners in parameters to the list of existing ones.

• Parameters:
  • whenNodeAdded (lambda expression) – a function for when a node is added
  • whenNodeDeleted (lambda expression) – a function for when a node is removed
  • whenEdgeAdded (lambda expression) – a function for when an edge is added
  • whenEdgeDeleted (lambda expression) – a function for when an edge is removed

addVariables(listFastVariables, default_nbr_mod=2)

Add a list of variable in the form of ‘fast’ syntax.

• Parameters:
  • listFastVariables (List [**str ]) – the list of variables in ‘fast’ syntax.
  • default_nbr_mod (int) – the number of modalities for the variable if not specified following fast syntax. Note that default_nbr_mod=1 is mandatory to create variables with only one modality (for utility for instance).
• Returns: the list of created ids.
• Return type: List[int]

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

beginTopologyTransformation()

• Return type: None

changeVariableLabel(*args)

change the label of the variable associated to nodeId to the new value.

• Parameters:
  • var (Union [**int ,**str ]) – a variable’s id (int) or name
  • old_label (str) – the old label
  • new_label (str) – the new label
• Raises: pyagrum.NotFound – if id/name is not a variable or if old_label does not exist.
• Return type: None

changeVariableName(*args)

Changes a variable’s name in the pyagrum.MarkovRandomField.

This will change the “pyagrum.DiscreteVariable” names in the pyagrum.MarkovRandomField.

• Parameters:
  • car (Union [**int ,**str ]) – a variable’s id (int) or name
  • new_name (str) – the new name of the variable
• Raises:
  • pyagrum.DuplicateLabel – If new_name is already used in this MarkovRandomField.
  • pyagrum.NotFound – If no variable matches id.
• Return type: None

clear()

Clear the whole MarkovRandomField

• Return type: None

completeInstantiation()

Give an instantiation over all the variables of the model

• Returns: a complete Instantiation for the model
• Return type: pyagrum.Instantiation

connectedComponents()

connected components from a graph/graphical models

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

The firstly visited node for each component is called a ‘root’ and is used as a key for the component. This root has been arbitrarily chosen during the algorithm.

• Returns: dict of connected components (as set of nodeIds (int)) with a nodeId (root) of each component as key.
• Return type: dict(int,Set[int])

dim()

• Return type: int

edges()

• Return type: object

empty()

Check if there are some variables in the model.

• Returns: True if there is no variable in the model.
• Return type: bool

endTopologyTransformation()

Terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions. End Multiple Change for all CPTs.

• Return type: pyagrum.MarkovRandomField

erase(*args)

Remove a variable from the pyagrum.MarkovRandomField.

Removes the corresponding variable from the pyagrum.MarkovRandomField and from all of it’s children “pyagrum.Tensor”.

If no variable matches the given id, then nothing is done.

• Parameters: var (Union [**int ,**str ,pyagrum.DiscreteVariable ]) – a variable’s id (int) or name of variable or a reference of this variable to remove.
• Return type: None

eraseFactor(*args)

• Return type: None

exists(*args)

Check if a node with this name or id exists

• Parameters: norid (str |**int) – name or id of the searched node
• Returns: True if there is a node with such a name or id
• Return type: bool

existsEdge(*args)

• Return type: bool

factor(*args)

Returns the factor of a set of variables (if existing).

• Parameters: vars (Union [**Set [**int ] ,**Set [**str ] ]) – A set of ids or names of variable the pyagrum.MarkovRandomField.
• Returns: The factor of the set of nodes.
• Return type: pyagrum.Tensor
• Raises: pyagrum.NotFound – If no variable’s id matches varId.

factors()

• Return type: list[set[int]]

family(*args)

• Return type: list[int]

static fastPrototype(*args)

Create a Markov random field with a modified dot-like syntax which specifies: : - the structure a-b-c;b-d-e;. The substring a-b-c indicates a factor with the scope (a,b,c).

• the type of the variables with different syntax (cf documentation).

Examples

>>> import pyagrum as gum
>>> bn=pyagrum.MarkovRandomField.fastPrototype('A--B[1,3]-C{yes|No}--D[2,4]--E[1,2.5,3.9]',6)

• Parameters:
  • dotlike (str) – the string containing the specification
  • domainSize (int or str) – the default domain size or the default domain for variables
• Returns: the resulting Markov random field
• Return type: pyagrum.MarkovRandomField

static fromBN(bn)

• Parameters: bn (BayesNet)
• Return type: MarkovRandomField

generateFactor(vars)

Randomly generate factor parameters for a given factor in a given structure.

• Parameters:
  • node (Union [**int ,**str ]) – a variable’s id (int) or name
  • vars (list[int])
• Return type: None

generateFactors()

Randomly generates factors parameters for a given structure.

• Return type: None

graph()

• Return type: UndiGraph

hasSameStructure(other)

idFromName(name)

• Parameters: name (str)
• Return type: int

ids(names)

List of ids for a list of names of variables in the model

• Parameters:
  • lov (List [**str ]) – List of variable names
  • names (list[str])
• Returns: The ids for the list of names of the graph variables
• Return type: List[int]

isIndependent(*args)

check if nodes X and nodes Y are independent given nodes Z

• Parameters:
  • X (str |**intList [**str |**int ]) – a list of of nodeIds or names
  • Y (str |**intList [**str |**int ]) – a list of of nodeIds or names
  • Z (str |**intList [**str |**int ]) – a list of of nodeIds or names
• Raises: InvalidArgument – if X and Y share variables
• Returns: True if X and Y are independent given Z in the model
• Return type: bool

loadUAI(*args)

Load an UAI file.

• Parameters:
  • name (str) – the name’s file
  • l (list) – list of functions to execute
• Raises:
  • pyagrum.IOError – If file not found
  • pyagrum.FatalError – If file is not valid
• Return type: str

log10DomainSize()

returns the log10 of the domain size of the model defined as the product of the domain sizes of the variables in the model.

• Returns: the log10 domain size.
• Return type: float

maxNonOneParam()

• Return type: float

maxParam()

• Return type: float

maxVarDomainSize()

• Return type: int

minNonZeroParam()

• Return type: float

minParam()

• Return type: float

minimalCondSet(*args)

• Return type: object

names()

Set of names of variables in the model

• Returns: The names of the graph variables
• Return type: Set[str]

neighbours(norid)

• Parameters: norid (object)
• Return type: object

nodeId(var)

• Parameters: var (DiscreteVariable)
• Return type: int

nodes()

• Return type: object

nodeset(names)

Set of ids for a list of names of variables in the model

• Parameters:
  • lov (List [**str ]) – List of variable names
  • names (list[str])
• Returns: The set of ids for the list of names of the graph variables
• Return type: Set[int]

properties()

• Return type: list[str]

saveUAI(name)

Save the MarkovRandomField in an UAI file.

• Parameters: name (str) – the file’s name
• Return type: None

size()

• Return type: int

sizeEdges()

• Return type: int

smallestFactorFromNode(node)

• Parameters: node (int)
• Return type: list[int]

property thisown

The membership flag

toDot()

• Return type: str

toDotAsFactorGraph()

• Return type: str

toFast(filename=None)

Export the MRF as fast syntax (in a string or in a python file)

• Parameters: filename (Optional [**str ]) – the name of the file (including the prefix), if None , use sys.stdout
• Return type: str

variable(*args)

• Return type: DiscreteVariable

variableFromName(name)

• Parameters: name (str)
• Return type: DiscreteVariable

variableNodeMap()

variables(*args)