def train_behavioural_bn(bn: OpenBooleanNetwork) -> (OpenBooleanNetwork, VNSOutput):
spawn_points = GLOBALS.generate_spawn_points()
terminal_nodes = get_terminal_nodes(bn)
to_never_flip = set(map(hash, bn.input_nodes + terminal_nodes))
train_scores_cmp = GLOBALS.eval_cmp_function
train_eval_fn = lambda bn, ct: pt_evaluation_for_train(bn, ct, spawn_points)
train_scramble_strategy = lambda bn, nf, e: bn_scramble_strategy(bn, nf, e.union(to_never_flip))
train_tidy_strategy = edit_boolean_network
pvns = VNS(
sol_evaluator=train_eval_fn,
sols_comparator=train_scores_cmp,
sol_scrambler=train_scramble_strategy,
sol_tidier=train_tidy_strategy
)
params = VNSParameters(
target_score=flat((GLOBALS.sd_target_score, float('+inf')), to=tuple),
min_flips=GLOBALS.sd_min_flips,
max_flips=sum(2**n.arity for n in bn.nodes if hash(n.label) not in to_never_flip),
max_iters=GLOBALS.sd_max_iters,
max_stalls=GLOBALS.sd_max_stalls,
max_stagnation=GLOBALS.sd_max_stagnation
)
return pvns(bn, params)
def test_bncontrollers(bns: dict):
'''
Test each BN in the collection on the same set of points.
Return the collected evaluation data for each test.
'''
spawn_points = GLOBALS.generate_spawn_points()
data = dict()
for k in bns:
logger.info(f"Boolean Network {k}")
test_params = generate_test_params(spawn_points)
data[k] = test_bncontroller(bns[k], test_params)
return data