Fragment of Bayesian networks
This class proposes a shallow copy of a part of Bayesian network. It can be used as a Bayesian network for inference algorithms (for instance).
- class pyAgrum.BayesNetFragment(bn)
BayesNetFragment represents a part of a Bayesian network (subset of nodes). By default, the arcs and the CPTs are the same as the BN but local CPTs can be build to express different local dependencies. All the non local CPTs are not copied. Therefore a BayesNetFragment is a light object.
- BayesNetFragment(BayesNet bn) -> BayesNetFragment
- Parameters:
bn (pyAgrum.BayesNet) – the bn refered by the fragment
- Parameters:
bn (
IBayesNet)
- addArcs(listArcs)
add a list of arcs in te model.
- Parameters:
listArcs (List[Tuple[intstr,intstr]]) – the list of arcs
- addStructureListener(whenNodeAdded=None, whenNodeDeleted=None, whenArcAdded=None, whenArcDeleted=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
whenArcAdded (lambda expression) – a function for when an arc is added
whenArcDeleted (lambda expression) – a function for when an arc 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
- ancestors(norid)
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:
Set[int]
- arcs()
- Returns:
The lisf of arcs in the IBayesNet
- Return type:
list
- check()
Check if the BayesNet is consistent (variables, CPT, …)
- Returns:
list of found issues
- Return type:
List[str]
- checkConsistency(*args)
If a variable is added to the fragment but not its parents, there is no CPT consistant for this variable. This function checks the consistency for a variable of for all.
- Parameters:
n (int, str (optional)) – the id or the name of the variable. If no argument, the function checks all the variables.
- Returns:
True if the variable(s) is consistant.
- Return type:
boolean
- Raises:
if the node is not found. –
- children(norid)
- Parameters:
id (int) – the id of the parent
norid (
object)
- Returns:
the set of all the children
- Return type:
Set
- completeInstantiation()
Give an instantiation over all the variables of the model
- Returns:
a complete Instantiation for the model
- Return type:
- 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])
- cpt(*args)
Returns the CPT of a variable.
- Parameters:
VarId (int) – A variable’s id in the pyAgrum.IBayesNet.
name (str) – A variable’s name in the pyAgrum.IBayesNet.
- Returns:
The variable’s CPT.
- Return type:
- Raises:
pyAgrum.NotFound – If no variable’s id matches varId.
- dag()
- Returns:
a constant reference to the dag of this BayesNet.
- Return type:
- descendants(norid)
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:
Set[int]
- dim()
Returns the dimension (the number of free parameters) in this BayesNet.
- Returns:
the dimension of the BayesNet
- Return type:
int
- empty()
Check if there are some variables in the model.
- Returns:
True if there is no variable in the model.
- Return type:
bool
- 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
- existsArc(*args)
Check if an arc exists
- Parameters:
tail (str|int) – the name or id of the tail of the arc
head (str|int) – the name or the id of the head of the arc
- Returns:
True if tail->head is an arc.
- Return type:
bool
- family(norid)
give the set of parents of a node and the node
- Parameters:
norid (str|int) – the node
- Returns:
the set of nodeId of the family of the node norid
- Return type:
Set[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
- idFromName(name)
Returns a variable’s id given its name in the graph.
- Parameters:
name (str) – The variable’s name from which the id is returned.
Notes
A convenient shortcut for g.variableFromName(name) is g[name].
- Returns:
The variable’s node id.
- Return type:
int
- Raises:
pyAgrum.NotFound – If name does not match a variable in the graph
- 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]
- installAscendants(*args)
Add the variable and all its ascendants in the fragment. No inconsistant node are created.
- Parameters:
n (int, str) – the id or the name of the variable.
- Raises:
if the node is not found. –
- Return type:
None
- installCPT(*args)
Install a local CPT for a node. Doing so, it changes the parents of the node in the fragment.
- Parameters:
n (int, str) – the id or the name of the variable.
pot (Potential) – the Potential to install
- Raises:
pyAgrum.NotFound – if the node is not found.
- Return type:
None
- installMarginal(*args)
Install a local marginal for a node. Doing so, it removes the parents of the node in the fragment.
- Parameters:
n (int, str) – the id or the name of the variable.
pot (Potential) – the Potential (marginal) to install
- Raises:
pyAgrum.NotFound – if the node is not found.
- Return type:
None
- installNode(*args)
Add a node to the fragment. The arcs that can be added between installed nodes are created. No specific CPT are created. Then either the parents of the node are already in the fragment and the node is consistant, or the parents are not in the fragment and the node is not consistant.
- Parameters:
n (int, str) – the id or the name of the variable.
- Raises:
pyAgrum.NotFound – if the node is not found.
- Return type:
None
- 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
- isInstalledNode(*args)
Check if a node is in the fragment
- Parameters:
n (int, str) – the id or the name of the variable.
- Return type:
bool
- jointProbability(i)
- Parameters:
i (pyAgrum.instantiation) – an instantiation of the variables
- Returns:
a parameter of the joint probability for the BayesNet
- Return type:
float
Warning
a variable not present in the instantiation is assumed to be instantiated to 0
- 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
- log2JointProbability(i)
- Parameters:
i (pyAgrum.instantiation) – an instantiation of the variables
- Returns:
a parameter of the log joint probability for the BayesNet
- Return type:
float
Warning
a variable not present in the instantiation is assumed to be instantiated to 0
- maxNonOneParam()
- Returns:
The biggest value (not equal to 1) in the CPTs of the BayesNet
- Return type:
float
- maxParam()
- Returns:
the biggest value in the CPTs of the BayesNet
- Return type:
float
- maxVarDomainSize()
- Returns:
the biggest domain size among the variables of the BayesNet
- Return type:
int
- memoryFootprint()
get the size (in byte) of the (main footprint) of the BayesNet
- Returns:
the size in byte of the representation (of the parameters) of the BayesNet
- Return type:
int
- minNonZeroParam()
- Returns:
the smallest value (not equal to 0) in the CPTs of the IBayesNet
- Return type:
float
- minParam()
- Returns:
the smallest value in the CPTs of the IBayesNet
- Return type:
float
- minimalCondSet(*args)
Returns, given one or many targets and a list of variables, the minimal set of those needed to calculate the target/targets.
- Parameters:
target (int) – The id of the target
targets (List[int]) – The ids of the targets
list (List[int]) – The list of available variables
- Returns:
The minimal set of variables
- Return type:
Set[int]
- moralGraph()
Returns the moral graph of the BayesNet, formed by adding edges between all pairs of nodes that have a common child, and then making all edges in the graph undirected.
- Returns:
The moral graph
- Return type:
- moralizedAncestralGraph(nodes)
build a UndiGraph by moralizing the Ancestral Graph of a list of nodes
- Parameters:
nodes (str|intList[str|int]) – the list of of nodeIds or names
Warning
pyAgrum.UndiGraph only knows NodeId. Hence the moralized ancestral graph does not include the names of the variables.graph
- Returns:
the moralized ancestral graph of the nodes
- Return type:
- names()
Set of names of variables in the model
- Returns:
The names of the graph variables
- Return type:
Set[str]
- nodeId(var)
- Parameters:
var (pyAgrum.DiscreteVariable) – a variable
- Returns:
the id of the variable
- Return type:
int
- Raises:
pyAgrum.IndexError – If the graph does not contain the variable
- nodes()
- Returns:
the set of ids
- Return type:
Set[int]
- 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]
- parents(norid)
- Parameters:
id – The id of the child node
norid (
object)
- Returns:
the set of the parents ids.
- Return type:
Set
- properties()
- Return type:
List[str]
- property(name)
Returns the value associated to this property.
Properties are a way to keep some (name,value) together with de model.
- Parameters:
name (str) – the name of the property
- Raises:
NotFound – if no name property is found
- Returns:
The value associated to this name
- Return type:
str
- propertyWithDefault(name, byDefault)
Returns the value associated to this property or the default value if there is no such property.
Properties are a way to keep some information (name,value) together with de model.
- Parameters:
name (str) – the name of the property
byDefault (str) – the value by default if no property has been found.
- Returns:
The value associated to this name or the value by default.
- Return type:
str
- setProperty(name, value)
Create or change the couple (name,value) in the properties.
Properties are a way to keep some information (name,value) together with de model.
- Parameters:
name (str) – the name of the property
value (str) – the value of the property.
- Return type:
None
- size()
- Returns:
the number of nodes in the graph
- Return type:
int
- sizeArcs()
- Returns:
the number of arcs in the graph
- Return type:
int
- toBN()
Create a BayesNet from a fragment.
- Raises:
pyAgrum.OperationNotAllowed – if the fragment is not consistent.
- Return type:
- toDot()
- Returns:
a friendly display of the graph in DOT format
- Return type:
str
- topologicalOrder()
- Returns:
the list of the nodes Ids in a topological order
- Return type:
List
- Raises:
pyAgrum.InvalidDirectedCycle – If this graph contains cycles
- uninstallCPT(*args)
Remove a local CPT. The fragment can become inconsistant.
- Parameters:
n (int, str) – the id or the name of the variable.
- Raises:
pyAgrum.NotFound – if the node is not found.
- Return type:
None
- uninstallNode(*args)
Remove a node from the fragment. The fragment can become inconsistant.
- Parameters:
n (int, str) – the id or the name of the variable.
- Raises:
pyAgrum.NotFound – if the node is not found.
- Return type:
None
- variable(*args)
- Parameters:
id (int) – a variable’s id
name (str) – a variable’s name
- Returns:
the variable
- Return type:
- Raises:
pyAgrum.IndexError – If the graph does not contain the variable
- variableFromName(name)
- Parameters:
name (str) – a variable’s name
- Returns:
the variable
- Return type:
- Raises:
pyAgrum.IndexError – If the graph does not contain the variable
- variableNodeMap()
- Returns:
the variable node map
- Return type:
pyAgrum.variableNodeMap
- whenArcAdded(src, _from, to)
- Parameters:
src (
object)_from (
int)to (
int)
- Return type:
None
- whenArcDeleted(src, _from, to)
- Parameters:
src (
object)_from (
int)to (
int)
- Return type:
None
- whenNodeAdded(src, id)
- Parameters:
src (
object)id (
int)
- Return type:
None
- whenNodeDeleted(src, id)
- Parameters:
src (
object)id (
int)
- Return type:
None