def convert_graphs_to_text(graphs):
# For each graph convert it into a paragraph
paragraphs = {}
for key, graph in graphs.items():
# Implement graph to text
summary = ""
if not is_directed_acyclic_graph(graph):
print("Graph is not a DAG for ", key)
#print(graph.edges)
#for e in graph.edges:
# print(e)
#print(len(list(nx.simple_cycles(graph))), " cycles for ", key)
max_len = 10000000
smallest_cycle = []
for c in nx.simple_cycles(graph):
if len(c) < max_len:
max_len = len(c)
smallest_cycle = c
for v in smallest_cycle:
print(v)
#continue
topo_sorted_vertices = nx.topological_sort(graph)
i = 0
for vertex in topo_sorted_vertices:
i += 1
tokens = vertex.split('#')
summary += tokens[0]
if i != graph.number_of_nodes():
# Prevent an extra '.' from appearing at the end
summary += '.'
paragraphs[key] = summary
return paragraphs
def __init__(self,
g,
X,
causes,
effects,
variable_types=None,
expectation=False,
density=True):
if not is_directed_acyclic_graph(g):
raise Exception("Directed, Acyclic graph required.")
self.g = g
self.causes = causes
self.effects = effects
self.find_predecessors()
self.admissable_set = self.predecessors
if self.assumptions_satisfied():
self.effect = CausalEffect(X,
causes,
effects,
admissable_set=list(self.predecessors),
variable_types=variable_types,
expectation=expectation,
density=density)
else:
raise Exception("Adjustment assumptions not satisfied")
def assumptions_satisfied(self, g, causes, effects, predecessors):
if not is_directed_acyclic_graph(g):
return False
if not len(set(effects).intersection(
set(causes).union(predecessors))) == 0:
return False
return True
def prog_16(fname):
graphs = {}
f = open(fname)
n = map(int, f.readline().strip().split())
n = n[0]
f.readline()
for i in xrange(n):
graph = nx.DiGraph()
line = f.readline()
while line:
edges = map(int, line.strip().split())
graph.add_edge(edges[0],edges[1])
line = f.readline()
# print line
line = line.strip()
# print 'xxxxx ',line
graphs[i]=graph
f.close()
print graphs
for k,g in graphs.iteritems():
if is_directed_acyclic_graph(g):
print 1,
else:
print -1,
def __init__(self, g, X, causes, effects, variable_types=None, expectation=False, density=True):
if not is_directed_acyclic_graph(g):
raise Exception("Directed, Acyclic graph required.")
self.g = g
self.causes = causes
self.effects = effects
self.find_predecessors()
self.admissable_set = self.predecessors
if self.assumptions_satisfied():
self.effect = CausalEffect(X, causes, effects, admissable_set=list(self.predecessors),
variable_types=variable_types, expectation=expectation,
density=density)
else:
raise Exception("Adjustment assumptions not satisfied")
def deterministic_topological_ordering(nodes, links, start_node):
"""
Topological sort that is deterministic because it sorts (alphabetically)
candidates to check
"""
graph = DiGraph()
graph.add_nodes_from(nodes)
for link in links:
graph.add_edge(*link)
if not is_directed_acyclic_graph(graph):
raise NetworkXUnfeasible
task_names = sorted(graph.successors(start_node))
task_set = set(task_names)
graph.remove_node(start_node)
result = [start_node]
while task_names:
for name in task_names:
if graph.in_degree(name) == 0:
result.append(name)
# it is OK to modify task_names because we break out
# of loop below
task_names.remove(name)
new_successors = [
t for t in graph.successors(name) if t not in task_set
]
task_names.extend(new_successors)
task_names.sort()
task_set.update(set(new_successors))
graph.remove_node(name)
break
return result
def deterministic_topological_ordering(nodes, links, start_node):
"""
Topological sort that is deterministic because it sorts (alphabetically)
candidates to check
"""
graph = DiGraph()
graph.add_nodes_from(nodes)
for link in links:
graph.add_edge(*link)
if not is_directed_acyclic_graph(graph):
raise NetworkXUnfeasible
task_names = sorted(graph.successors(start_node))
task_set = set(task_names)
graph.remove_node(start_node)
result = [start_node]
while task_names:
for name in task_names:
if graph.in_degree(name) == 0:
result.append(name)
# it is OK to modify task_names because we break out
# of loop below
task_names.remove(name)
new_successors = [t for t in graph.successors(name)
if t not in task_set]
task_names.extend(new_successors)
task_names.sort()
task_set.update(set(new_successors))
graph.remove_node(name)
break
return result
def assumptions_satisfied(self, g, causes, effects, predecessors):
if not is_directed_acyclic_graph(g):
return False
if not len(set(effects).intersection(set(causes).union(predecessors))) == 0:
return False
return True
