def test_service_birnbaum_importance_with_no_scaling(
full_example_system: SystemGraph, full_example_data_1: Dict):
b_imps = services.get_birnbaum_importances(full_example_system,
full_example_data_1, "data",
{"SCALE_METRICS": "NONE"})
assert len(b_imps) != 0
assert approx(sum(b_imps.values()) < 1)
def test_service_birnbaum_importance_default_scaling(
full_example_system: SystemGraph, full_example_data_1: Dict):
b_imps = services.get_birnbaum_importances(full_example_system,
full_example_data_1, "data")
assert len(b_imps) != 0
assert approx(max(b_imps.values()) == 1)
assert approx(min(b_imps.values()) == 0)
async def node_importance_sensitivity(sg_id: str,
data_source: str,
rq: RequestBody = Body(...),
node_id: Optional[str] = None):
sg = services.get_system_graph(sg_id, repo)
result = services.get_birnbaum_importances(sg, rq.data, data_source)
if node_id is None:
return dict(name="node_importance_sensitivity",
payload=result,
node_id=node_id,
data_source=data_source)
else:
return dict(name="node_importance_sensitivity",
payload={node_id: result[node_id]},
node_id=node_id,
data_source=data_source)
def test_get_birnbaum_importance(minimal: SystemGraph):
data = {
"nodes": {
"x1": {
"risk": 0.5
},
"x2": {
"risk": 0.5
},
"x3": {
"risk": 0.5
}
}
}
b_imps = services.get_birnbaum_importances(minimal, data, data_src="data")
assert len(b_imps) != 0
def main():
args = parse_args()
repo = FakeRepository()
with open(args.input) as infile:
sg = load_system_graph_json_str(infile.read())
print("*** System Graph ***")
pprint(dump_system_graph_json(sg))
print("\n\n*** Birnbaum Structural Importances ***")
pprint(get_birnbaum_structural_importances(sg, repo))
print("\n\n*** Birnbaum Importances ***")
pprint(get_birnbaum_importances(sg, repo))
print("\n\n*** Risk ***")
pprint(get_risk(sg, repo))