def test_nb_edges_increase_monotonically_paths_via_endpoints(
get_sample_hypothgraph):
hypothgraph = get_sample_hypothgraph
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
sorted_paths_via_endpoints = list(
paths.passing_via_endpoints_sorted(hypothgraph, source, target))
# we test by iteratively creating a sub hypothesis graph, and checking that
# the two consecutively produced snapshots of the sub hypothesis graph will
# contain monotonically increasing number of edges. Moreover, at every time
# step the hypothesis graph topology is preserved
# we collect the number of produced edges
nb_edges_snapshots = []
iterative_sub_hypothgraph = nx.DiGraph()
for sorted_path in sorted_paths_via_endpoints:
iterative_sub_hypothgraph.add_path(sorted_path)
nb_edges_snapshots.append(len(iterative_sub_hypothgraph.edges()))
# check monotonicity
for previous, current in zip(nb_edges_snapshots, nb_edges_snapshots[1:]):
assert current >= previous
def get_sample_configuration():
hypothgraph = sample_graphs.sample_hypothgraph()
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
subgraph = sub_hypothgraph.generate_sub_hypothgraph(
hypothgraph, source, target)
return hypothgraph, subgraph, source, target
def test_generate_rich_endpoints(get_digraph_with_cycle):
hypothgraph = get_digraph_with_cycle
min_nb_paths = 4
# just run 100 tests
for _ in xrange(100):
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph, min_nb_paths)
all_simple_paths = list(nx.all_simple_paths(hypothgraph, source, target))
assert len(all_simple_paths) >= min_nb_paths
def test_paths_pass_through_endpoints_are_sorted(get_sample_hypothgraph):
hypothgraph = get_sample_hypothgraph
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
sorted_paths_via_endpoints = list(
paths.passing_via_endpoints_sorted(hypothgraph, source, target))
# paths are monotonically increasing
for previous, current in zip(sorted_paths_via_endpoints,
sorted_paths_via_endpoints[1:]):
assert len(current) >= len(previous)
def sample_big_and_small(ratio_endpoints_paths=0.5,
ratio_on_boundary_paths=0.5):
big = sample_graphs.sample_hypothgraph()
source, target = hypoth_conf.generate_rich_endpoints(big, min_nb_paths=5)
small = sub_hypothgraph.generate_sub_hypothgraph(
big,
source,
target,
ratio_endpoints_paths=ratio_endpoints_paths,
ratio_on_boundary_paths=ratio_on_boundary_paths,
data=True)
return big, small, source, target
def test_uniqueness_of_simple_endpoints_paths(get_sample_hypothgraph):
hypothgraph = get_sample_hypothgraph
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
endpoints_paths = list(nx.all_simple_paths(hypothgraph, source, target))
nb_paths = len(endpoints_paths)
# no path is repeated in all the paths, you cannot just use
# `set(all_paths)`, since a path is a list, an unhashable type
for path in endpoints_paths:
other_paths = [other_path for other_path in endpoints_paths
if not other_path == path]
# number of other paths should be `nb_paths - 1`
assert len(other_paths) == nb_paths-1
def test_ratio_endpoints_paths_sample_hypothgraph(get_sample_hypothgraph):
hypothgraph = get_sample_hypothgraph
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
ratio_paths = 0.7
endpoints_paths = list(nx.all_simple_paths(hypothgraph, source, target))
endpoints_ratio_paths = list(
paths.take_ratio_endpoints_paths_rand(hypothgraph, source, target, ratio=ratio_paths))
# nb of generated paths is at least the given ratio to the total amount of
# paths
nb_ratio_paths = len(endpoints_ratio_paths)
nb_total_paths = len(endpoints_paths)
assert nb_ratio_paths == math.ceil(ratio_paths * nb_total_paths)
def test_uniqueness_of_simple_on_boundary_paths(get_sample_hypothgraph):
hypothgraph = get_sample_hypothgraph
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
on_boundary_nodes = boundary.on_boundary(hypothgraph, source, target)
on_boundary_paths = list(
paths.chain_many_to_many_path_generator(hypothgraph, on_boundary_nodes))
nb_paths = len(on_boundary_paths)
# no path is repeated in all the paths, you cannot just use
# `set(all_paths)`, since a path is a list, an unhashable type
for path in on_boundary_paths:
other_paths = [other_path for other_path in on_boundary_paths
if not other_path == path]
assert len(other_paths) == nb_paths-1
def test_paths_pass_through_endpoints(get_sample_hypothgraph):
hypothgraph = get_sample_hypothgraph
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
paths_via_endpoints = list(
paths.passing_via_endpoints(hypothgraph, source, target))
for path in paths_via_endpoints:
assert source in path
assert target in path
# generator over all complement paths
all_paths = paths.chain_many_to_many_path_generator(
hypothgraph, hypothgraph.nodes_iter())
other_paths = (other_path for other_path in all_paths
if not other_path in paths_via_endpoints)
for other_path in other_paths:
assert not (source in other_path and target in other_path)
def test_ratio_on_boundary_paths_sample_hypothgraph(get_sample_hypothgraph):
hypothgraph = get_sample_hypothgraph
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
ratio_paths = 0.7
on_boundary_nodes = boundary.on_boundary(hypothgraph, source, target)
boundary_paths = list(
paths.chain_many_to_many_path_generator(hypothgraph, on_boundary_nodes))
boundary_ratio_paths = list(
paths.take_ratio_boundary_paths_rand(hypothgraph, source, target, ratio=ratio_paths))
# nb of generated paths is at least the given ratio to the total amount of
# paths
nb_ratio_paths = len(boundary_ratio_paths)
nb_total_paths = len(boundary_paths)
assert nb_ratio_paths == math.ceil(ratio_paths * nb_total_paths)
def get_sample_hypothgraph_and_configurations():
hypothgraph = sample_graphs.sample_hypothgraph()
source, target = hypoth_conf.generate_rich_endpoints(hypothgraph)
partial_nodes = list(
boundary.partial_nodes_boundary_interior(hypothgraph, source, target))
full_nodes = list(
boundary.in_boundary_interior(hypothgraph, source, target))
nothing = Hypoth_Conf(source=source, target=target, evidenced_nodes=[])
partial = Hypoth_Conf(source=source,
target=target,
evidenced_nodes=partial_nodes)
full = Hypoth_Conf(source=source,
target=target,
evidenced_nodes=full_nodes)
return {
'hypothgraph': hypothgraph,
'nothing': nothing,
'partial_within': partial,
'full_within': full
}