Inference in Markov Networks¶
Inference is the process that consists in computing new probabilistc information from a Markov network and some evidence. aGrUM/pyAgrum mainly focus and the computation of (joint) posterior for some variables of the Markov networks given soft or hard evidence that are the form of likelihoods on some variables. Inference is a hard task (NP-complete). For now, aGrUM/pyAgrum implements only one exact inference for Markov Network.
Shafer Shenoy Inference in Markov Network¶
- class pyAgrum.ShaferShenoyMNInference(MN, use_binary_join_tree=True)¶
Class used for Shafer-Shenoy inferences for Markov network.
- ShaferShenoyInference(bn) -> ShaferShenoyInference
- Parameters:
mn (pyAgrum.MarkovNet) – a Markov network
- Parameters
MN (
IMarkovNet
) –use_binary_join_tree (
bool
) –
- H(*args)¶
- Parameters
X (int) – a node Id
nodeName (str) – a node name
- Returns
the computed Shanon’s entropy of a node given the observation
- Return type
float
- I(X, Y)¶
- Parameters
X (int or str) – a node Id or a node name
Y (int or str) –
another node Id or node name
Returns
------- –
float – the Mutual Information of X and Y given the observation
- Return type
float
- MN()¶
- VI(X, Y)¶
- Parameters
X (int or str) – a node Id or a node name
Y (int or str) –
another node Id or node name
Returns
------- –
float – variation of information between X and Y
- Return type
float
- addAllTargets()¶
Add all the nodes as targets.
- Return type
None
- addEvidence(*args)¶
Adds a new evidence on a node (might be soft or hard).
- Parameters
id (int) – a node Id
nodeName (int) – a node name
val – (int) a node value
val – (str) the label of the node value
vals (list) – a list of values
- Raises
pyAgrum.InvalidArgument – If the node already has an evidence
pyAgrum.InvalidArgument – If val is not a value for the node
pyAgrum.InvalidArgument – If the size of vals is different from the domain side of the node
pyAgrum.FatalError – If vals is a vector of 0s
pyAgrum.UndefinedElement – If the node does not belong to the Bayesian network
- Return type
None
- addJointTarget(targets)¶
Add a list of nodes as a new joint target. As a collateral effect, every node is added as a marginal target.
- Parameters
list – a list of names of nodes
targets (
object
) –
- Raises
pyAgrum.UndefinedElement – If some node(s) do not belong to the Bayesian network
- Return type
None
- addTarget(*args)¶
Add a marginal target to the list of targets.
- Parameters
target (int) – a node Id
nodeName (str) – a node name
- Raises
pyAgrum.UndefinedElement – If target is not a NodeId in the Bayes net
- Return type
None
- chgEvidence(*args)¶
Change the value of an already existing evidence on a node (might be soft or hard).
- Parameters
id (int) – a node Id
nodeName (int) – a node name
val (intstr) – a node value or the label of the node value
vals (List[float]) – a list of values
- Raises
pyAgrum.InvalidArgument – If the node does not already have an evidence
pyAgrum.InvalidArgument – If val is not a value for the node
pyAgrum.InvalidArgument – If the size of vals is different from the domain side of the node
pyAgrum.FatalError – If vals is a vector of 0s
pyAgrum.UndefinedElement – If the node does not belong to the Bayesian network
- Return type
None
- eraseAllEvidence()¶
Removes all the evidence entered into the network.
- Return type
None
- eraseAllJointTargets()¶
Clear all previously defined joint targets.
- Return type
None
- eraseAllMarginalTargets()¶
Clear all the previously defined marginal targets.
- Return type
None
- eraseAllTargets()¶
Clear all previously defined targets (marginal and joint targets).
As a result, no posterior can be computed (since we can only compute the posteriors of the marginal or joint targets that have been added by the user).
- Return type
None
- eraseEvidence(*args)¶
Remove the evidence, if any, corresponding to the node Id or name.
- Parameters
id (int) – a node Id
nodeName (int) – a node name
- Raises
pyAgrum.IndexError – If the node does not belong to the Bayesian network
- Return type
None
- eraseJointTarget(targets)¶
Remove, if existing, the joint target.
- Parameters
list – a list of names or Ids of nodes
targets (
object
) –
- Raises
pyAgrum.IndexError – If one of the node does not belong to the Bayesian network
pyAgrum.UndefinedElement – If node Id is not in the Bayesian network
- Return type
None
- eraseTarget(*args)¶
Remove, if existing, the marginal target.
- Parameters
target (int) – a node Id
nodeName (int) – a node name
- Raises
pyAgrum.IndexError – If one of the node does not belong to the Bayesian network
pyAgrum.UndefinedElement – If node Id is not in the Bayesian network
- Return type
None
- evidenceImpact(target, evs)¶
Create a pyAgrum.Potential for P(target|evs) (for all instanciation of target and evs)
- Parameters
target (set) – a set of targets ids or names.
evs (set) – a set of nodes ids or names.
Warning
if some evs are d-separated, they are not included in the Potential.
- Returns
a Potential for P(targets|evs)
- Return type
- evidenceJointImpact(*args)¶
Create a pyAgrum.Potential for P(joint targets|evs) (for all instanciation of targets and evs)
- Parameters
targets (List[intstr]) – a list of node Ids or node names
evs (Set[intstr]) – a set of nodes ids or names.
- Returns
a Potential for P(target|evs)
- Return type
- Raises
pyAgrum.Exception – If some evidene entered into the Bayes net are incompatible (their joint proba = 0)
- evidenceProbability()¶
- Returns
the probability of evidence
- Return type
float
- getNumberOfThreads()¶
returns the number of threads used by LazyPropagation during inferences.
- Returns
the number of threads used by LazyPropagation during inferences
- Return type
int
- hardEvidenceNodes()¶
- Returns
the set of nodes with hard evidence
- Return type
set
- hasEvidence(*args)¶
- Parameters
id (int) – a node Id
nodeName (str) – a node name
- Returns
True if some node(s) (or the one in parameters) have received evidence
- Return type
bool
- Raises
pyAgrum.IndexError – If the node does not belong to the Bayesian network
- hasHardEvidence(nodeName)¶
- Parameters
id (int) – a node Id
nodeName (str) – a node name
- Returns
True if node has received a hard evidence
- Return type
bool
- Raises
pyAgrum.IndexError – If the node does not belong to the Bayesian network
- hasSoftEvidence(*args)¶
- Parameters
id (int) – a node Id
nodeName (str) – a node name
- Returns
True if node has received a soft evidence
- Return type
bool
- Raises
pyAgrum.IndexError – If the node does not belong to the Bayesian network
- isGumNumberOfThreadsOverriden()¶
Indicates whether LazyPropagation currently overrides aGrUM’s default number of threads (see method setNumberOfThreads).
- Returns
A Boolean indicating whether LazyPropagation currently overrides aGrUM’s default number of threads
- Return type
bool
- isJointTarget(targets)¶
- Parameters
list – a list of nodes ids or names.
targets (
object
) –
- Returns
True if target is a joint target.
- Return type
bool
- Raises
pyAgrum.IndexError – If the node does not belong to the Bayesian network
pyAgrum.UndefinedElement – If node Id is not in the Bayesian network
- isTarget(*args)¶
- Parameters
variable (int) – a node Id
nodeName (str) – a node name
- Returns
True if variable is a (marginal) target
- Return type
bool
- Raises
pyAgrum.IndexError – If the node does not belong to the Bayesian network
pyAgrum.UndefinedElement – If node Id is not in the Bayesian network
- joinTree()¶
- Returns
the current join tree used
- Return type
- jointMutualInformation(targets)¶
- Parameters
targets (
object
) –- Return type
float
- jointPosterior(targets)¶
Compute the joint posterior of a set of nodes.
- Parameters
list – the list of nodes whose posterior joint probability is wanted
Warning
The order of the variables given by the list here or when the jointTarget is declared can not be assumed to be used by the Potential.
- Returns
a const ref to the posterior joint probability of the set of nodes.
- Return type
- Raises
pyAgrum.UndefinedElement – If an element of nodes is not in targets
- Parameters
targets (
object
) –
- jointTargets()¶
- Returns
the list of target sets
- Return type
list
- junctionTree()¶
- Returns
the current junction tree
- Return type
- makeInference()¶
Perform the heavy computations needed to compute the targets’ posteriors
In a Junction tree propagation scheme, for instance, the heavy computations are those of the messages sent in the JT. This is precisely what makeInference should compute. Later, the computations of the posteriors can be done ‘lightly’ by multiplying and projecting those messages.
- Return type
None
- nbrEvidence()¶
- Returns
the number of evidence entered into the Bayesian network
- Return type
int
- nbrHardEvidence()¶
- Returns
the number of hard evidence entered into the Bayesian network
- Return type
int
- nbrJointTargets()¶
- Returns
the number of joint targets
- Return type
int
- nbrSoftEvidence()¶
- Returns
the number of soft evidence entered into the Bayesian network
- Return type
int
- nbrTargets()¶
- Returns
the number of marginal targets
- Return type
int
- posterior(*args)¶
Computes and returns the posterior of a node.
- Parameters
var (int) – the node Id of the node for which we need a posterior probability
nodeName (str) – the node name of the node for which we need a posterior probability
- Returns
a const ref to the posterior probability of the node
- Return type
- Raises
pyAgrum.UndefinedElement – If an element of nodes is not in targets
- setEvidence(evidces)¶
Erase all the evidences and apply addEvidence(key,value) for every pairs in evidces.
- Parameters
evidces (dict) – a dict of evidences
- Raises
pyAgrum.InvalidArgument – If one value is not a value for the node
pyAgrum.InvalidArgument – If the size of a value is different from the domain side of the node
pyAgrum.FatalError – If one value is a vector of 0s
pyAgrum.UndefinedElement – If one node does not belong to the Bayesian network
- setMaxMemory(gigabytes)¶
sets an upper bound on the memory consumption admissible
- Parameters
gigabytes (float) – this upper bound in gigabytes.
- Return type
None
- setNumberOfThreads(nb)¶
If the argument nb is different from 0, this number of threads will be used during inferences, hence overriding aGrUM’s default number of threads. If, on the contrary, nb is equal to 0, the parallelized inference engine will comply with aGrUM’s default number of threads.
- Parameters
nb (int) – the number of threads to be used by ShaferShenoyMNInference
- Return type
None
- setTargets(targets)¶
Remove all the targets and add the ones in parameter.
- Parameters
targets (set) – a set of targets
- Raises
pyAgrum.UndefinedElement – If one target is not in the Bayes net
- softEvidenceNodes()¶
- Returns
the set of nodes with soft evidence
- Return type
set
- targets()¶
- Returns
the list of marginal targets
- Return type
list
- property thisown¶
The membership flag
- updateEvidence(evidces)¶
Apply chgEvidence(key,value) for every pairs in evidces (or addEvidence).
- Parameters
evidces (dict) – a dict of evidences
- Raises
pyAgrum.InvalidArgument – If one value is not a value for the node
pyAgrum.InvalidArgument – If the size of a value is different from the domain side of the node
pyAgrum.FatalError – If one value is a vector of 0s
pyAgrum.UndefinedElement – If one node does not belong to the Bayesian network