def __init__(self, bnet, verbose=False, is_quantum=False):
"""
Constructor. Note that the constructor of every inference engine is
designed so that one of its objects can be created just once at the
beginning and then reused to calculate probabilities under several
evidence assumptions.
Parameters
----------
bnet : BayesNet
verbose : bool
is_quantum : bool
Returns
-------
"""
InferenceEngine.__init__(self, bnet, verbose, is_quantum)
moral_graph = MoralGraph(self.bnet)
tri_graph = TriangulatedGraph(moral_graph)
self.jtree = JoinTree(tri_graph, bnet)
if verbose:
print("------------------triangulated graph:")
tri_graph.print_neighbors()
print("------------------JoinTree:")
self.jtree.describe_yourself()
def __init__(self, bnet, do_print=False, is_quantum=False):
"""
Constructor
Parameters
----------
bnet : BayesNet
do_print : bool
is_quantum : bool
Returns
-------
"""
InferenceEngine.__init__(self, bnet, do_print, is_quantum)
moral_graph = MoralGraph(self.bnet)
tri_graph = TriangulatedGraph(moral_graph)
self.jtree = JoinTree(tri_graph, bnet)
if do_print:
tri_graph.describe_yourself()
self.jtree.describe_yourself()
def __init__(self, bnet, do_print=False, is_quantum=False):
"""
Constructor
Parameters
----------
bnet : BayesNet
do_print : bool
is_quantum : bool
Returns
-------
"""
InferenceEngine.__init__(self, bnet, do_print, is_quantum)
moral_graph = MoralGraph(self.bnet)
tri_graph = TriangulatedGraph(moral_graph)
self.jtree = JoinTree(tri_graph, bnet)
if do_print:
tri_graph.describe_yourself()
self.jtree.describe_yourself()
class JoinTreeEngine(InferenceEngine):
"""
Our implementation of the Join Tree (or Junction Tree) inference
algorithm follows very closely the very detailed and clear reference:
"Belief Networks: A Procedural Guide" By Cecil Huang an Adnan Darwiche (
1996).
As far as I know, the Join Tree algorithm has only been used in the past
for CBnets, but this computer program applies it to both CBnets and
QBnets with only a few modifications. Most of the steps of the algorithm
are topological (or graph theoretic) in nature and those steps apply to
both the CBnet and QBnet cases. The main difference between CBnets and
QBnets arises whenever the norm of a potential is required and there one
simply uses the 1-norm for classical and the 2-norm for quantum.
This algorithm first creates a MoralGraph, then a TriangulatedGraph,
then a JoinTree. A list of UniPots is then computed after performing a
global propagation on the JoinTree.
Attributes
----------
jtree : JoinTree
"""
def __init__(self, bnet, verbose=False, is_quantum=False):
"""
Constructor. Note that the constructor of every inference engine is
designed so that one of its objects can be created just once at the
beginning and then reused to calculate probabilities under several
evidence assumptions.
Parameters
----------
bnet : BayesNet
verbose : bool
is_quantum : bool
Returns
-------
"""
InferenceEngine.__init__(self, bnet, verbose, is_quantum)
moral_graph = MoralGraph(self.bnet)
tri_graph = TriangulatedGraph(moral_graph)
self.jtree = JoinTree(tri_graph, bnet)
if verbose:
print("------------------triangulated graph:")
tri_graph.print_neighbors()
print("------------------JoinTree:")
self.jtree.describe_yourself()
def get_unipot_list(self, node_list):
"""
For each node in node_list, this method returns a uni-potential that
gives the probabilities for the states of that node. Obviously,
such a PD has the active states of the node as support.
Parameters
----------
node_list : list[BayesNode]
Returns
-------
list[DiscreteUniPot]
"""
self.global_propagation()
pot_list = []
for node in node_list:
cl_pot = cp.deepcopy(node.clique.potential)
# print("\nnode:", node.name)
# print("clique pot initial", cl_pot)
pot = cl_pot.get_new_marginal([node])
# print("its marginal", pot)
pot1 = DiscreteUniPot(self.is_quantum, node, pot_arr=pot.pot_arr)
pot1.normalize_self()
# print("normalized marg", pot1)
if self.is_quantum:
pot1 = pot1.get_probs_from_amps()
pot_list.append(pot1)
return pot_list
def global_propagation(self):
"""
Given the JoinTree, this method does all the calculations necessary
to give to each clique and sepset a potential suitable for
marginalization.
Returns
-------
None
"""
self.jtree.set_clique_and_sepset_pots_to_one(self.is_quantum)
self.jtree.init_clique_potentials_with_bnet_info()
# Here and only here is where we introduce the
# evidence. To handle multiple evidence cases,
# can use same jointree but must do a global propagation
# for each evidence case.
# Once Clique pots are masked, the new sepset pots
# obtained by marginalizing clique pots become masked too,
# so masking sepset pots is unnecessary.
self.jtree.mask_clique_potentials()
# print("\ndeviation before global prop",
# self.get_deviation())
# pick start_clique to be one owned by a node
# with the lowest topological index number
min_nd_topo_index = min([node.topo_index for node in self.bnet.nodes])
start_clique = self.bnet.get_node_with_topo_index(
min_nd_topo_index).clique
if self.verbose:
print("start clique", start_clique.name)
self.jtree.unmark_all_nodes()
if self.verbose:
print("\nNext will pass messages towards start_clique")
# Below, from_clique=None, to_clique=start_clique, sepset=None
self.collect_evidence(None, start_clique, None)
self.jtree.unmark_all_nodes()
if self.verbose:
print("\nNext will pass messages away from start_clique")
self.distribute_evidence(start_clique)
# def get_deviation(self):
# """
# For debugging purposes only
#
# Returns
# -------
#
# """
#
# full_sep_pot = Potential(list(self.bnet.nodes), bias=1)
# for clique in self.jtree.nodes:
# for sepset in clique.sepsets:
# sepset.flag = False
# for clique in self.jtree.nodes:
# for sepset in clique.sepsets:
# if not sepset.flag:
# full_sep_pot *= sepset.potential
# sepset.flag = True
#
# full_bnet_pot = Potential(list(self.bnet.nodes), bias=1)
# for node in self.bnet.nodes:
# full_bnet_pot *= node.potential
# full_bnet_pot.mask_self()
#
# full_jtree_pot = Potential(list(self.bnet.nodes), bias=1)
# for clique in self.jtree.nodes:
# full_jtree_pot *= clique.potential
# full_jtree_pot /= full_sep_pot
#
# return Potential.distance(full_jtree_pot, full_bnet_pot)
def pass_message(self, from_clique, to_clique, sepset):
"""
Pass a message from 'from_clique' to 'to_clique' connected by 'sepset'
Parameters
----------
from_clique : Clique
to_clique : Clique
sepset : Sepset
Returns
-------
None
"""
# deepcopy for pots has been defined so that
# no deepcopy of nodes, only of pot_arr
old_sepset_pot = cp.deepcopy(sepset.potential)
sepset.potential = from_clique.potential.get_new_marginal(
sepset.potential.ord_nodes)
# if self.verbose:
# old_to_clique_pot = cp.deepcopy(to_clique.potential)
# Absorb the sepset pot ratio into the to_clique pot
to_clique.potential *= (sepset.potential / old_sepset_pot)
if self.verbose:
print("\npassing message from ", from_clique.name, " to ",
to_clique.name)
# print("deviation after message was passed =", self.get_deviation())
# print("from_clique pot", from_clique.potential)
# print("new sepset pot", sepset.potential)
# print("old sepset pot", old_sepset_pot)
# print("old to_clique pot:", old_to_clique_pot)
# print("new to_clique pot:", to_clique.potential)
def collect_evidence(self,
from_clique,
to_clique,
sepset,
clique_counter=1):
"""
Pass messages from outer cliques towards the start clique.
Parameters
----------
from_clique : Clique | None
to_clique : Clique
sepset : Sepset | None
clique_counter : int
Returns
-------
None
"""
if clique_counter > len(self.jtree.nodes):
return None
else:
to_clique.visited = True
for sep in to_clique.sepsets:
# Do a DFS search of the tree, only visiting
# unvisited clique nodes
neighbor_cliq = sep.get_other_clique(to_clique)
if not neighbor_cliq.visited:
self.collect_evidence(to_clique, neighbor_cliq, sep,
clique_counter + 1)
# After we have iterated
# over all neighbors, send back a message from each
# back towards the start clique
if clique_counter > 1:
self.pass_message(to_clique, from_clique, sepset)
def distribute_evidence(self, cur_clique, clique_counter=1):
"""
Pass messages away from the start clique.
Parameters
----------
cur_clique : Clique
clique_counter : int
Returns
-------
None
"""
if clique_counter > len(self.jtree.nodes):
return None
else:
cur_clique.visited = True
for sep in cur_clique.sepsets:
# Do a DFS search of the tree, only visiting
# unvisited clique nodes
neighbor_cliq = sep.get_other_clique(cur_clique)
if not neighbor_cliq.visited:
self.pass_message(cur_clique, neighbor_cliq, sep)
self.distribute_evidence(neighbor_cliq, clique_counter + 1)
class JoinTreeEngine(InferenceEngine):
"""
Our implementation of the Join Tree (or Junction Tree) inference
algorithm follows very closely the very detailed and clear reference:
"Belief Networks: A Procedural Guide" By Cecil Huang an Adnan Darwiche (
1996).
As far as I know, the Join Tree algorithm has only been used in the past
for CBnets, but this computer program applies it to both CBnets and
QBnets with only a few modifications and no hitches. Most of the steps
of the algorithm are topological (or graph theoretic) in nature and
those steps apply to both the CBnet and QBnet cases. The main difference
between CBnets and QBnets arises whenever taking the norm of a potential
is required and there one simply uses the 1-norm for classical and the
2-norm for quantum.
This algorithm first creates a MoralGraph, then a TriangulatedGraph,
then a JoinTree. A list of UniPots is then computed after performing a
global propagation on the JoinTree.
Attributes
----------
jtree : JoinTree
bnet : BayesNet
do_print : bool
is_quantum : bool
"""
def __init__(self, bnet, do_print=False, is_quantum=False):
"""
Constructor
Parameters
----------
bnet : BayesNet
do_print : bool
is_quantum : bool
Returns
-------
"""
InferenceEngine.__init__(self, bnet, do_print, is_quantum)
moral_graph = MoralGraph(self.bnet)
tri_graph = TriangulatedGraph(moral_graph)
self.jtree = JoinTree(tri_graph, bnet)
if do_print:
tri_graph.describe_yourself()
self.jtree.describe_yourself()
def get_unipot_list(self, node_list):
"""
For each node in node_list, this method returns a uni-potential that
gives the probabilities for the states of that node. Obviously,
such a PD has the active states of the node as support.
Parameters
----------
node_list : list[BayesNode]
Returns
-------
list[DiscreteUniPot]
"""
self.global_propagation()
pot_list = []
for node in node_list:
cl_pot = cp.deepcopy(node.clique.potential)
# print("\nnode:", node.name)
# print("clique pot initial", cl_pot)
pot = cl_pot.get_new_marginal([node])
# print("its marginal", pot)
pot1 = DiscreteUniPot(self.is_quantum, node, pot_arr=pot.pot_arr)
pot1.normalize_self()
# print("normalized marg", pot1)
if self.is_quantum:
pot1 = pot1.get_probs_from_amps()
pot_list.append(pot1)
return pot_list
def global_propagation(self):
"""
Given the JoinTree, this method does all the calculations necessary
to give the cliques amd sepsets a potential suitable for
marginalization.
Returns
-------
None
"""
self.jtree.set_clique_and_sepset_pots_to_one(self.is_quantum)
self.jtree.init_clique_potentials_with_bnet_info()
# Here and only here is where we introduce the
# evidence. To handle multiple evidence cases,
# can use same jointree but must do a global propagation
# for each evidence case.
# Once Clique pots are masked, the new sepset pots
# obtained by marginalizing clique pots become masked too,
# so masking sepset pots is unnecessary.
self.jtree.mask_clique_potentials()
# print("\ndeviation before global prop",
# self.get_deviation())
# pick start_clique to be one owned by a node
# with the lowest topological index number
min_nd_topo_index = min([node.topo_index for node in self.bnet.nodes])
start_clique = self.bnet.get_node_with_topo_index(
min_nd_topo_index).clique
if self.do_print:
print("start clique", start_clique.name)
self.jtree.unmark_all_nodes()
if self.do_print:
print("\nNext will pass messages towards start_clique")
# Below, from_clique=None, to_clique=start_clique, sepset=None
self.collect_evidence(None, start_clique, None)
self.jtree.unmark_all_nodes()
if self.do_print:
print("\nNext will pass messages away from start_clique")
self.distribute_evidence(start_clique)
# def get_deviation(self):
# """
# For debugging purposes only
#
# Returns
# -------
#
# """
#
# full_sep_pot = Potential(list(self.bnet.nodes), bias=1)
# for clique in self.jtree.nodes:
# for sepset in clique.sepsets:
# sepset.flag = False
# for clique in self.jtree.nodes:
# for sepset in clique.sepsets:
# if not sepset.flag:
# full_sep_pot *= sepset.potential
# sepset.flag = True
#
# full_bnet_pot = Potential(list(self.bnet.nodes), bias=1)
# for node in self.bnet.nodes:
# full_bnet_pot *= node.potential
# full_bnet_pot.mask_self()
#
# full_jtree_pot = Potential(list(self.bnet.nodes), bias=1)
# for clique in self.jtree.nodes:
# full_jtree_pot *= clique.potential
# full_jtree_pot /= full_sep_pot
#
# return Potential.distance(full_jtree_pot, full_bnet_pot)
def pass_message(self, from_clique, to_clique, sepset):
"""
Pass a message from 'from_clique' to 'to_clique' connected by 'sepset'
Parameters
----------
from_clique : Clique
to_clique : Clique
sepset : Sepset
Returns
-------
None
"""
# deepcopy for pots has been defined so that
# no deepcopy of nodes, only of pot_arr
old_sepset_pot = cp.deepcopy(sepset.potential)
sepset.potential = from_clique.potential.get_new_marginal(
sepset.potential.ord_nodes)
# if self.do_print:
# old_to_clique_pot = cp.deepcopy(to_clique.potential)
# Absorb the sepset pot ratio into the to_clique pot
to_clique.potential *= (sepset.potential/old_sepset_pot)
if self.do_print:
print("\npassing message from ",
from_clique.name, " to ", to_clique.name)
# print("deviation after message was passed =", self.get_deviation())
# print("from_clique pot", from_clique.potential)
# print("new sepset pot", sepset.potential)
# print("old sepset pot", old_sepset_pot)
# print("old to_clique pot:", old_to_clique_pot)
# print("new to_clique pot:", to_clique.potential)
def collect_evidence(self, from_clique, to_clique,
sepset, clique_counter=1):
"""
Pass messages from outern cliques towards the start clique.
Parameters
----------
from_clique : Clique | None
to_clique : Clique
sepset : Sepset | None
clique_counter : int
Returns
-------
None
"""
if clique_counter > len(self.jtree.nodes):
return None
else:
to_clique.visited = True
for sep in to_clique.sepsets:
# Do a DFS search of the tree, only visiting
# unvisited clique nodes
neighbor_cliq = sep.get_other_clique(to_clique)
if not neighbor_cliq.visited:
self.collect_evidence(
to_clique, neighbor_cliq, sep, clique_counter + 1)
# After we have iterated
# over all neighbors, send back a message from each
# back towards the start clique
if clique_counter > 1:
self.pass_message(
to_clique, from_clique, sepset)
def distribute_evidence(self, cur_clique, clique_counter=1):
"""
Pass messages away from the start clique.
Parameters
----------
cur_clique : Clique
clique_counter : int
Returns
-------
None
"""
if clique_counter > len(self.jtree.nodes):
return None
else:
cur_clique.visited = 1
for sep in cur_clique.sepsets:
# Perform DFS passing messages as we go from
# one clique node to the next
neighbor_cliq = sep.get_other_clique(cur_clique)
if not neighbor_cliq.visited:
self.pass_message(cur_clique, neighbor_cliq, sep)
self.distribute_evidence(neighbor_cliq, clique_counter + 1)