def get_trivial_example():
subgraph = nx.DiGraph()
subgraph.add_edge(0, 3)
big_graph = nx.complete_graph(4, create_using=nx.DiGraph())
source, target = 0, 3
big_hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(big_graph)
sub_hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(subgraph)
# big, small, source, target
return big_hypothgraph, sub_hypothgraph, source, target
def get_digraph_with_cycle():
digraph = nx.complete_graph(5, create_using=nx.DiGraph())
digraph.add_path(xrange(4, 10))
hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(digraph)
return hypothgraph
def get_path_graph_and_configurations():
path_graph = nx.path_graph(10, nx.DiGraph())
hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(path_graph)
# hypothesis configurations (source, target, evidenced_nodes), we fix
# source and target to some nodes
source = 0
target = 4
# nothing evidenced, partially within boundary, full within boundary,
# nothing inside, some outside
nothing = Hypoth_Conf(source=source, target=target, evidenced_nodes=[])
partial_within = Hypoth_Conf(source=source,
target=target,
evidenced_nodes=[0, 2])
full_within = Hypoth_Conf(source=source,
target=target,
evidenced_nodes=[0, 1, 2, 3, 4])
return {
'hypothgraph': hypothgraph,
'nothing': nothing,
'partial_within': partial_within,
'full_within': full_within,
}
def generate_sub_hypothgraph(hypothgraph, source, target,
ratio_endpoints_paths=0.5,
ratio_on_boundary_paths=0.5,
data=False):
sub_hypothgraph = nx.DiGraph()
sub_hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(sub_hypothgraph)
# adding endpoints paths
endpoints_paths = paths.take_ratio_endpoints_paths_rand(
hypothgraph, source, target, ratio=ratio_endpoints_paths)
for endpoints_path in endpoints_paths:
sub_hypothgraph.add_path(endpoints_path)
# adding on boundary paths
on_boundary_paths = paths.take_ratio_boundary_paths_rand(
hypothgraph, source, target, ratio=ratio_on_boundary_paths)
for on_boundary_path in on_boundary_paths:
sub_hypothgraph.add_path(on_boundary_path)
# add data if required
sub_hypothgraph = copy_data_to_subgraph(hypothgraph, sub_hypothgraph)
return sub_hypothgraph
def get_richer_example():
big_graph = nx.complete_graph(10, create_using=nx.DiGraph())
subgraph = big_graph.subgraph(xrange(4))
# add some paths to a far node in the big graph
subgraph.add_edge(2, 6)
subgraph.add_edge(3, 6)
subgraph.add_edge(3, 5)
subgraph.add_edge(5, 6)
# convert to hypothesis graphs and return
big_hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(big_graph)
sub_hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(subgraph)
# big, small, source, target
return big_hypothgraph, sub_hypothgraph, 2, 6
def sample_hypothgraph(digraph=None):
if digraph:
digraph = digraph.copy()
else:
digraph = sample_digraph()
hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(digraph)
return hypothgraph
def test_convert_to_hypothgraph(get_complete_graph):
digraph = get_complete_graph
# assert there are no computed meta data like `evidence_weight` etc.
for node, node_data in digraph.nodes_iter(data=True):
assert not 'evidence_weight' in node_data
assert not 'importance_weight' in node_data
hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(digraph)
# every node should have computed btwin_centrality
for node, node_data in hypothgraph.nodes_iter(data=True):
assert 'evidence_weight' in node_data
assert 'importance_weight' in node_data
def get_complex_graph_and_configurations():
digraph = nx.path_graph(11, create_using=nx.DiGraph())
digraph.add_cycle([2, 3, 4])
digraph.add_cycle([6, 7, 8])
hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(digraph)
source, target = 2, 8
nothing = Hypoth_Conf(source=source, target=target, evidenced_nodes=[])
partial = Hypoth_Conf(source=source, target=target, evidenced_nodes=[3, 6, 7])
full = Hypoth_Conf(source=source, target=target, evidenced_nodes=range(2,9))
return {
'hypothgraph': hypothgraph,
'nothing': nothing,
'partial_within': partial,
'full_within': full
}
def get_trivial_example():
digraph = nx.complete_graph(6, create_using=nx.DiGraph())
hypothgraph = convert_to_hypothgraph.convert_to_hypothgraph(digraph)
return hypothgraph
