def cross_entropy(bn1: BayesNet, bn2: BayesNet, nsamples: int = None) -> float: cross_ent = .0 if nsamples is None: bn1_vars = bn1.nodes.keys() for sample in all_dicts(bn1_vars): cross_ent -= np.exp(bn1.sample_log_prob(sample)) * bn2.sample_log_prob(sample) else: for _ in range(nsamples): cross_ent -= bn2.sample_log_prob(bn1.sample()) cross_ent /= nsamples return cross_ent
def main():
args = get_args()
table_bn = BayesNet(bn_file=args.file_name)
mle_bn = MLEBayesNet(bn_file=args.file_name)
parametric_bn = ParametricBayesNet(bn_file=args.file_name)
print("Initial params MLE bn:")
print(mle_bn.pretty_print())
print("Initial params parametric bn:")
print(parametric_bn.pretty_print())
print("========== Frequentist MLE ==========")
samples = read_samples(args.samples_file_name)
mle_bn.learn_cpds(samples)
print("Reference BN")
print(table_bn.pretty_print())
print("MLE BayesNet after learning CPDs")
print(mle_bn.pretty_print())
print("========== Parametric MLE ==========")
# ref_cent = cross_entropy(table_bn, table_bn)
# cent = cross_entropy(table_bn, parametric_bn, nsamples=100)
# print("Step %6d | CE: %6.3f / %6.3f" % (0, cent, ref_cent))
for step in range(1, 1000):
sample = table_bn.sample()
parametric_bn.learn(sample, learning_rate=args.lr)
if step % 500 == 0:
print("step: ", step)
# cent = cross_entropy(table_bn, parametric_bn, nsamples=200)
# print("Step %6d | CE: %6.3f / %6.3f" % (step, cent, ref_cent))
# print(f"Step {step:6d} | CE: {cent:6.3f} / {ref_cent:6.3f}")
print("Reference BN")
print(table_bn.pretty_print())
print("Parametric BayesNet after learning CPDs")
print(parametric_bn.pretty_print())