key_list.append(('cancer', f'p{p}'))
num_test = 5
avg_diff = dict()
variance = dict()
time_cost = dict()
ans = dict()
name = 'EPBP'
res = np.zeros((len(key_list), num_test))
for j in range(num_test):
bp = EPBP(g, n=10, proposal_approximation='simple')
start_time = time.process_time()
bp.run(10, log_enable=True)
time_cost[name] = (time.process_time() -
start_time) / num_test + time_cost.get(name, 0)
print(name, f'time {time.process_time() - start_time}')
for i, key in enumerate(key_list):
res[i, j] = bp.probability(0.8, 1, rvs_table[key])
for i, key in enumerate(key_list):
ans[key] = np.average(res[i, :])
variance[name] = np.average(np.var(res, axis=1))
print(name, 'var', variance[name])
# save ans
ans_array = list()
for key in key_list:
ans_array.append((f'{key[0]},{key[1]}', ans[key]))
np.save('Data/smoker_ans', np.array(ans_array))
crv_marg_params = sampling_utils.fit_scalar_gm_from_samples(
cont_samples[:, crv_idx], K=num_gm_components_for_crv)
marg_logpdf = utils.get_scalar_gm_log_prob(
None, *crv_marg_params, get_fun=True)
margs[i] = marg_logpdf
# save baseline
baseline_mmap = mmap
baseline_margs = margs
marg_kls = np.zeros_like(mmap)
cpu_time = wall_time = 0 # don't care
elif algo_name == 'EPBP':
bp = EPBP(g, n=20, proposal_approximation='simple')
start_time = time.process_time()
start_wall_time = time.time()
bp.run(10, log_enable=False)
cpu_time = time.process_time() - start_time
wall_time = time.time() - start_wall_time
for i, rv in enumerate(query_rvs):
mmap[i] = bp.map(rv)
# marg_logpdf = lambda x: bp.belief(x, rv, log_belief=True) # probly slightly faster if not plotting
marg_logpdf = utils.curry_epbp_belief(bp, rv, log_belief=True)
margs[i] = marg_logpdf
assert rv.domain_type[
0] == 'c', 'only looking at kl for cnode queries for now'
# lb, ub = -np.inf, np.inf
lb, ub = -np.inf, np.inf
marg_kl = kl_continuous_logpdf(log_p=baseline_margs[i],
log_q=margs[i],
a=lb,
if rv.value is None: # only test non-evidence nodes
print(rv, key, osi.map(obs_rvs=[], query_rv=rv))
import matplotlib.pyplot as plt
for key in record:
plt.plot(record[key], label=key)
plt.legend(loc='best')
save_name = __file__.split('.py')[0]
plt.savefig('%s.png' % save_name)
# EPBP inference
from EPBPLogVersion import EPBP
bp = EPBP(g, n=50, proposal_approximation='simple')
bp.run(30, log_enable=True)
for key, rv in rvs_table.items():
if rv.value is None: # only test non-evidence nodes
print(key, bp.map(rv))
else:
K = 3
lr = 1e-2
its = 1
import tensorflow as tf
for T in [10, 20, 50, 100]:
print('grad check, T =', T)
utils.set_seed(seed)
osi = OneShot(g=g, K=K, T=T, seed=seed)