def build_linked_spn_from_scope_graph(scope_graph,
k,
root_scope=None,
feature_values=None):
"""
Turning a ScopeGraph into an SPN by puttin k sum nodes for each scope
and a combinatorial number of product nodes to wire the partition nodes
This is the algorithm used in Poon2011 and is shown (and used) as BuildSPN in Dennis2012
"""
if not root_scope:
root_scope = scope_graph.root
n_vars = len(root_scope.vars)
if not feature_values:
#
# assuming binary r.v.s
feature_values = [2 for _i in range(n_vars)]
#
# adding leaves
leaves_dict = defaultdict(list)
leaves_list = []
for var in sorted(root_scope.vars):
for var_val in range(feature_values[var]):
leaf = CategoricalIndicatorNode(var, var_val)
leaves_list.append(leaf)
leaves_dict[var].append(leaf)
input_layer = CategoricalIndicatorLayer(nodes=leaves_list,
vars=list(sorted(root_scope.vars)))
#
# in a first pass we need to assign each scope/region k sum nodes
sum_nodes_assoc = {}
for r in scope_graph.traverse_scopes(root_scope=root_scope):
num_sum_nodes = k
if r == root_scope:
num_sum_nodes = 1
added_sum_nodes = [
SumNode(var_scope=r.vars) for i in range(num_sum_nodes)
]
#
# creating a sum layer
sum_layer = SumLayer(added_sum_nodes)
sum_nodes_assoc[r] = sum_layer
#
# if this is a univariate scope, we link it to leaves corresponding to its r.v.
if r.is_atomic():
single_rv = set(r.vars).pop()
rv_leaves = leaves_dict[single_rv]
uniform_weight = 1.0 / len(rv_leaves)
for s in added_sum_nodes:
for leaf in rv_leaves:
s.add_child(leaf, uniform_weight)
#
# linking to input layer
sum_layer.add_input_layer(input_layer)
input_layer.add_output_layer(sum_layer)
layers = []
#
# looping again to add and wire product nodes
for r in scope_graph.traverse_scopes(root_scope=root_scope):
sum_layer = sum_nodes_assoc[r]
layers.append(sum_layer)
for p in r.partitions:
sum_layer_descs = [sum_nodes_assoc[r_p] for r_p in p.scopes]
sum_nodes_lists = [
list(layer.nodes()) for layer in sum_layer_descs
]
num_prod_nodes = numpy.prod([len(r_p) for r_p in sum_nodes_lists])
#
# adding product nodes
added_prod_nodes = [
ProductNode(var_scope=r.vars) for i in range(num_prod_nodes)
]
#
# adding product layer and linking
prod_layer = ProductLayer(added_prod_nodes)
sum_layer.add_input_layer(prod_layer)
prod_layer.add_output_layer(sum_layer)
for desc in sum_layer_descs:
prod_layer.add_input_layer(desc)
desc.add_output_layer(prod_layer)
layers.append(prod_layer)
#
# linking to parents
sum_nodes_parents = sum_layer.nodes()
for sum_node in sum_nodes_parents:
uniform_weight = 1.0 / (len(added_prod_nodes) *
len(r.partitions))
for prod_node in added_prod_nodes:
sum_node.add_child(prod_node, uniform_weight)
#
# linking to children
sum_nodes_to_wire = list(itertools.product(*sum_nodes_lists))
assert len(added_prod_nodes) == len(sum_nodes_to_wire)
for prod_node, sum_nodes in zip(added_prod_nodes,
sum_nodes_to_wire):
for sum_node in sum_nodes:
prod_node.add_child(sum_node)
#
# toposort
layers = topological_layer_sort(layers)
spn = LinkedSpn(layers=layers, input_layer=input_layer)
return spn
def test_compute_block_layer_depths_II():
input_layer = CategoricalIndicatorLayer([])
sum_layer_1 = SumLayer([])
prod_layer_21 = ProductLayer([])
prod_layer_22 = ProductLayer([])
sum_layer_3 = SumLayer([])
prod_layer_41 = ProductLayer([])
prod_layer_42 = ProductLayer([])
sum_layer_5 = SumLayer([])
#
# linking them
sum_layer_1.add_input_layer(input_layer)
input_layer.add_output_layer(sum_layer_1)
prod_layer_21.add_input_layer(sum_layer_1)
prod_layer_22.add_input_layer(sum_layer_1)
sum_layer_1.add_output_layer(prod_layer_21)
sum_layer_1.add_output_layer(prod_layer_22)
sum_layer_3.add_input_layer(prod_layer_21)
sum_layer_3.add_input_layer(prod_layer_22)
sum_layer_3.add_input_layer(input_layer)
prod_layer_21.add_output_layer(sum_layer_3)
prod_layer_22.add_output_layer(sum_layer_3)
input_layer.add_output_layer(sum_layer_3)
prod_layer_41.add_input_layer(sum_layer_3)
prod_layer_41.add_input_layer(sum_layer_1)
prod_layer_42.add_input_layer(sum_layer_3)
prod_layer_42.add_input_layer(input_layer)
sum_layer_3.add_output_layer(prod_layer_41)
sum_layer_3.add_output_layer(prod_layer_42)
sum_layer_1.add_output_layer(prod_layer_41)
input_layer.add_output_layer(prod_layer_42)
sum_layer_5.add_input_layer(prod_layer_41)
sum_layer_5.add_input_layer(prod_layer_42)
prod_layer_41.add_output_layer(sum_layer_5)
prod_layer_42.add_output_layer(sum_layer_5)
#
# creating an SPN with unordered layer list
spn = LinkedSpn(input_layer=input_layer,
layers=[
sum_layer_1, sum_layer_3, sum_layer_5, prod_layer_21,
prod_layer_22, prod_layer_41, prod_layer_42
])
depth_dict = compute_block_layer_depths(spn)
print(spn)
for layer, depth in depth_dict.items():
print(layer.id, depth)
