def test_evidenced_outside_on_path_graph(get_complex_graph_and_configurations):
test_data = get_complex_graph_and_configurations
hypothgraph = test_data['hypothgraph']
partial_within = test_data['partial_within']
source = partial_within.source
target = partial_within.target
before_confidence = compute_confidence.confidence(hypothgraph, partial_within)
before_max_confidence = compute_confidence.max_confidence(hypothgraph, source, target)
before_norm_confidence = compute_confidence.normalized_confidence(hypothgraph, partial_within)
# Evidence nodes which will not contribute to the causation confidence
on_boundary_nodes = boundary.on_boundary(hypothgraph, source, target)
outside_evidence_nodes = partial_within.evidenced_nodes + list(on_boundary_nodes)
# create new hypothesis configuration
new_conf = Hypoth_Conf(source=source,
target=target,
evidenced_nodes=outside_evidence_nodes)
after_confidence = compute_confidence.confidence(hypothgraph, new_conf)
after_max_confidence = compute_confidence.max_confidence(hypothgraph, source, target)
after_norm_confidence = compute_confidence.normalized_confidence(hypothgraph, new_conf)
assert before_confidence == after_confidence
assert before_max_confidence == after_max_confidence
assert before_norm_confidence == after_norm_confidence
def take_ratio_boundary_paths_rand(hypothgraph, source, target, ratio=0.5):
on_boundary_nodes = boundary.on_boundary(hypothgraph, source, target)
boundary_paths = chain_many_to_many_path_generator(hypothgraph,
on_boundary_nodes)
return take_ratio_paths_rand(hypothgraph, boundary_paths, ratio=ratio)
def test_nodes_on_boundary(get_digraph_with_cycle):
hypothgraph, source, target = get_digraph_with_cycle
nodes_on_boundary = boundary.on_boundary(hypothgraph, source, target)
nodes_in_boundary_interior = list(boundary.in_boundary_interior(hypothgraph, source, target))
for node_on_boundary in nodes_on_boundary:
assert not node_on_boundary in nodes_in_boundary_interior
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_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)