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 relative_confidence_spectrum(big, small, source, target):
evidenced_nodes = []
iterative_hypoth_conf = Hypoth_Conf(source, target, evidenced_nodes)
dict_confidences = {}
# shortcut names for confidence functions
func_conf = compute_confidence.confidence
func_conf_norm = compute_confidence.normalized_confidence
# compute max confidences in the small and in the big graphs
max_small_confidence = compute_confidence.max_confidence(
small, source, target)
max_big_confidence = compute_confidence.max_confidence(big, source, target)
# all nodes in the boundary interior of the SUBGRAPH
nodes_boundary_interior = boundary.in_boundary_interior(
small, source, target)
# initial confidence values
dict_confidences['sub_confidence_spectrum'] = [
func_conf(small, iterative_hypoth_conf)
]
dict_confidences['big_confidence_spectrum'] = [
func_conf(big, iterative_hypoth_conf)
]
dict_confidences['sub_confidence_normalized_spectrum'] = [
func_conf_norm(small, iterative_hypoth_conf)
]
dict_confidences['big_confidence_normalized_spectrum'] = [
func_conf_norm(big, iterative_hypoth_conf)
]
# add confidence values
for boundary_interior_node in nodes_boundary_interior:
evidenced_nodes.append(boundary_interior_node)
iterative_hypoth_conf = Hypoth_Conf(source, target, evidenced_nodes)
dict_confidences['sub_confidence_spectrum'].append(
func_conf(small, iterative_hypoth_conf))
dict_confidences['big_confidence_spectrum'].append(
func_conf(big, iterative_hypoth_conf))
dict_confidences['sub_confidence_normalized_spectrum'].append(
func_conf_norm(small, iterative_hypoth_conf))
dict_confidences['big_confidence_normalized_spectrum'].append(
func_conf_norm(big, iterative_hypoth_conf))
return dict_confidences
def test_nothing_evidenced_on_path_graph(get_complex_graph_and_configurations):
test_data = get_complex_graph_and_configurations
hypothgraph = test_data['hypothgraph']
nothing = test_data['nothing']
# confidence with nothing evidenced should be 0
confidence = compute_confidence.confidence(hypothgraph, nothing)
assert confidence == 0
# max confidence for the same boundary should be bigger than confidence
max_confidence = compute_confidence.max_confidence(hypothgraph, nothing.source, nothing.target)
assert max_confidence > confidence
# normalized confidence should also be zero since we have nothing evidenced
normalized_confidence = compute_confidence.normalized_confidence(hypothgraph, nothing)
assert normalized_confidence == 0
def test_partial_evidenced_within_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']
# confidence with nothing evidenced should be 0
confidence = compute_confidence.confidence(hypothgraph, partial_within)
assert confidence > 0
# max confidence for the same boundary should be bigger than confidence
max_confidence = compute_confidence.max_confidence(
hypothgraph, partial_within.source, partial_within.target)
assert max_confidence > confidence
# normalized confidence should be in (0..1)
normalized_confidence = compute_confidence.normalized_confidence(
hypothgraph, partial_within)
assert normalized_confidence > 0 and normalized_confidence < 1
def test_full_evidenced_within_on_path_graph(get_complex_graph_and_configurations):
test_data = get_complex_graph_and_configurations
hypothgraph = test_data['hypothgraph']
full_within = test_data['full_within']
# confidence with nothing evidenced should be 0
confidence = compute_confidence.confidence(hypothgraph, full_within)
assert confidence > 0
# max confidence for the same boundary should be equal to confidence
max_confidence = compute_confidence.max_confidence(
hypothgraph, full_within.source, full_within.target)
assert max_confidence == confidence
# normalized confidence should be 1
normalized_confidence = compute_confidence.normalized_confidence(
hypothgraph, full_within)
assert normalized_confidence == 1