cg.run()
print('number of rvs in cg', len(cg.rvs))
print('number of factors in cg', len(cg.factors))
if algo_name == 'LOSI':
vi = LiftedOneShot(g=cg, K=K, T=T, seed=seed)
else:
vi = LiftedNPVI(g=cg, K=K, T=T, seed=seed)
if cond: # clean up; only needed cond_g.init_nb() for defining symbolic objective
for i, rv in enumerate(g.rvs_list):
rv.nb = g_rv_nbs[
i] # restore; undo possible mutation from cond_g.init_nb()
start_time = time.process_time()
start_wall_time = time.time()
res = vi.run(lr=lr,
its=its,
fix_mix_its=fix_mix_its,
logging_itv=logging_itv)
cpu_time = time.process_time() - start_time
wall_time = time.time() - start_wall_time
obj = res['record']['obj'][-1]
for i, rv in enumerate(query_rvs):
if cond:
m = vi.map(obs_rvs=[], query_rv=rv)
else:
m = vi.map(obs_rvs=obs_rvs, query_rv=rv)
mmap[i] = m
assert rv.domain_type[
0] == 'c', 'only looking at kl for cnode queries for now'
crv_marg_params = vi.params['w'], rv.belief_params[
'mu'], rv.belief_params['var']
for i, rv in enumerate(rvs):
mmap_res[algo, i] = bp.map(rv)
algo += 1
name = names[algo]
utils.set_log_potential_funs(
g.factors_list) # OSI assumes factors have callable .log_potential_fun
K = 2
T = 10
lr = 0.2
its = 10
fix_mix_its = int(its * 0.8)
osi = OneShot(
g, K,
T) # can be moved outside of all loops if the ground MRF doesn't change
osi.run(its, lr=lr)
for i, rv in enumerate(rvs):
mmap_res[algo, i] = osi.map(rv)
# ground truth
# first, need to convert factor.log_potential_funs to one of (LogTable, LogQuadratic, LogHybridQuadratic);
# this can be done by first converting the corresponding potentials to one of (TablePotential, QuadraticPotential,
# HybridQuadraticPotential), then calling the .to_log_potential method on the potential objects
# manual conversion here:
# factors[0].potential = HybridQuadraticPotential(
# A=-factors[0].potential.w * np.array([np.array([[1., 0], [0, 0]]), np.array([[1., 0.], [0., 0.]])]),
# b=-factors[0].potential.w * np.array([[-16., 0], [14., 0.]]),
# c=-factors[0].potential.w * np.array([64., 49.])
# )
# sampling baseline:
err_var[name] = np.average(np.average(
res**2, axis=1)) / num_test + err_var.get(name, 0)
print(name, 'diff', np.average(np.average(abs(res), axis=1)))
print(
name, 'var',
np.average(np.average(res**2, axis=1)) -
np.average(np.average(abs(res), axis=1))**2)
name = 'OSI'
res = np.zeros((len(key_list), 5))
osi = OneShot(
g, num_mixtures=1, num_quadrature_points=2
) # can be moved outside of all loops, as the ground MRF doesn't change
for j in range(5):
start_time = time.process_time()
osi.run(200, lr=2)
time_cost[name] = (time.process_time() -
start_time) / 5 / 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] = osi.map(rvs_table[key])
for i, key in enumerate(key_list):
res[i, :] -= ans[key]
avg_diff[name] = np.average(np.average(
abs(res), axis=1)) / num_test + avg_diff.get(name, 0)
err_var[name] = np.average(np.average(
res**2, axis=1)) / num_test + err_var.get(name, 0)
print(name, 'diff', np.average(np.average(abs(res), axis=1)))
print(
name, 'var',
np.average(np.average(res**2, axis=1)) -
np.average(np.average(res**2, axis=1)) -
np.average(np.average(abs(res), axis=1))**2)
name = 'OSI'
K = 5
T = 20
lr = 0.1
its = 200
fix_mix_its = int(its * 0.)
res = np.zeros((len(key_list), num_runs))
osi = OneShot(
g=g, K=K, T=T, seed=seed
) # can be moved outside of all loops if the ground MRF doesn't change
for j in range(num_runs):
start_time = time.process_time()
osi.run(lr=lr, its=its, fix_mix_its=fix_mix_its)
time_cost[name] = (time.process_time() -
start_time) / num_runs / num_tests + time_cost.get(
name, 0)
print(name, f'time {time.process_time() - start_time}')
for i, key in enumerate(key_list):
res[i, j] = osi.map(rvs_table[key])
print(res[:, j])
# print(osi.params)
print('Mu =\n', osi.params['Mu'], '\nVar =\n', osi.params['Var'])
for i, key in enumerate(key_list):
res[i, :] -= ans[key]
avg_diff[name] = np.average(np.average(
abs(res), axis=1)) / num_tests + avg_diff.get(name, 0)
err_var[name] = np.average(np.average(
res**2, axis=1)) / num_tests + err_var.get(name, 0)
rel_g.data = data
g, rvs_table = rel_g.grounded_graph()
from OneShot import OneShot
utils.set_log_potential_funs(
g.factors_list) # OSI assumes factors have callable .log_potential_fun
grad_check = False
if not grad_check:
K = 3
T = 20
lr = 1e-2
its = 500
osi = OneShot(g=g, K=K, T=T, seed=seed)
res = osi.run(lr=lr, its=its)
record = res['record']
del res['record']
print(res)
for key, rv in rvs_table.items():
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)
rel_g.atoms = (atom_friend, atom_smoke_x, atom_cancer)
rel_g.param_factors = (f1, f2)
rel_g.init_nb()
rel_g.data = data
g, rvs_table = rel_g.grounded_graph()
print(rvs_table)
from OneShot import OneShot
utils.set_log_potential_funs(g.factors_list)
K = 4
T = 8
osi = OneShot(g=g, K=K, T=T, seed=seed)
res = osi.run(lr=1e-1, its=200)
for key, rv in sorted(rvs_table.items()):
if rv.value is None: # only test non-evidence nodes
print(key, osi.map(obs_rvs=[], query_rv=rv))
import matplotlib.pyplot as plt
record = res['record']
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
