def test_model_selection_exact():
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=30, dim=20, r=3,
amplitude=1, noise=0, jitter=0, prng=prng)
labels = (signal > 0).astype(int)
P1 = os.multivariate_stat(data, labels=labels)
P1.init_hidden_variables()
P1.evaluate(nsimu=100, burnin=10, verbose=verbose)
L1 = P1.compute_log_region_likelihood()
Prior1 = P1.compute_log_prior()
#v, m_mean, m_var = P1.v.copy(), P1.m_mean.copy(), P1.m_var.copy()
Post1 = P1.compute_log_posterior(nsimu=1e2, burnin=1e2, verbose=verbose)
M1 = L1 + Prior1[:-1] - Post1[:-1]
yield assert_almost_equal(M1.mean(),
P1.compute_marginal_likelihood().mean(), 0)
P0 = os.multivariate_stat(data, labels=labels)
P0.network *= 0
P0.init_hidden_variables()
P0.evaluate(nsimu=100, burnin=100, verbose=verbose)
L0 = P0.compute_log_region_likelihood()
Prior0 = P0.compute_log_prior()
Post0 = P0.compute_log_posterior(nsimu=1e2, burnin=1e2,
verbose=verbose)
M0 = L0 + Prior0[:-1] - Post0[:-1]
yield assert_almost_equal(M0.mean(),
P0.compute_marginal_likelihood().mean(), 0)
yield assert_true(M1[1] > M0[1])
yield assert_true(M1[0] < M0[0])
def test_evaluate_exact():
# without mfx nor spatial relaxation
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=20,
dim=np.array([20,20,20]), r=3, amplitude=5, noise=0,
jitter=0, prng=prng)
p = len(signal)
XYZvol *= 0
XYZvol[list(XYZ)] = np.arange(p)
P = os.multivariate_stat(data)
P.init_hidden_variables()
P.evaluate(nsimu=100, burnin=100, J=[XYZvol[5, 5, 5]],
compute_post_mean=True, verbose=verbose)
P.log_likelihood_values = P.compute_log_region_likelihood()
# Verify code consistency
Q = os.multivariate_stat(data, vardata*0, XYZ, std=0, sigma=5)
Q.init_hidden_variables()
Q.evaluate(nsimu=100, burnin=100, J = [XYZvol[5,5,5]],
compute_post_mean=True, update_spatial=False,
verbose=verbose)
Q.log_likelihood_values = Q.compute_log_region_likelihood()
yield assert_almost_equal(P.mean_m.mean(),
Q.mean_m.mean(),
int(np.log10(P.nsimu))-1)
yield assert_almost_equal(Q.log_likelihood_values.sum(),
P.log_likelihood_values.sum(), 0)
def test_model_selection_mfx_spatial_rand_walk():
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=20,
dim=np.array([1,20,20]),
r=3, amplitude=3, noise=1,
jitter=0.5, prng=prng)
labels = (signal > 0).astype(int)
P = os.multivariate_stat(data, vardata, XYZ, std=0.5, sigma=5, labels=labels)
P.network[:] = 0
P.init_hidden_variables()
P.evaluate(nsimu=100, burnin=100, verbose=verbose,
proposal='rand_walk', proposal_std=0.5)
L00 = P.compute_log_region_likelihood()
# Test simulated annealing procedure
P.estimate_displacements_SA(nsimu=100, c=0.99,
proposal_std=P.proposal_std, verbose=verbose)
L0 = P.compute_log_region_likelihood()
yield assert_true(L0.sum() > L00.sum())
#Prior0 = P.compute_log_prior()
#Post0 = P.compute_log_posterior(nsimu=1e2, burnin=1e2, verbose=verbose)
#M0 = L0 + Prior0[:-1] - Post0[:-1]
M0 = P.compute_marginal_likelihood(update_spatial=True)
#yield assert_almost_equal(M0.sum(), P.compute_marginal_likelihood(verbose=verbose).sum(), 0)
P.network[1] = 1
#P.init_hidden_variables(init_spatial=False)
P.init_hidden_variables(init_spatial=False)
P.evaluate(nsimu=100, burnin=100, verbose=verbose,
update_spatial=False, proposal_std=P.proposal_std)
#L1 = P.compute_log_region_likelihood()
#Prior1 = P.compute_log_prior()
#Post1 = P.compute_log_posterior(nsimu=1e2, burnin=1e2, verbose=verbose)
#M1 = L1 + Prior1[:-1] - Post1[:-1]
M1 = P.compute_marginal_likelihood(update_spatial=True)
#yield assert_almost_equal(0.1*M1.sum(), 0.1*P.compute_marginal_likelihood(verbose=verbose).sum(), 0)
yield assert_true(M1 > M0)
def test_update_labels():
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=20, dim=20, r=3,
amplitude=5, noise=1, jitter=1, prng=prng)
P = os.multivariate_stat(data, vardata, XYZ)
P.init_hidden_variables()
p = P.data.shape[1]
P.labels_prior = np.ones((1, p), float)
P.label_values = np.zeros((1, p), int)
P.labels_prior_mask = np.arange(p)
P.update_labels()
yield assert_equal(max(abs(P.labels - np.zeros(p, int))), 0)
def test_evaluate_mfx_spatial():
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=20,
dim=10, r=3, amplitude=5, noise=1, jitter=1,
prng=prng)
P = os.multivariate_stat(data, vardata, XYZ, std=1, sigma=3)
P.init_hidden_variables()
P.evaluate(nsimu=5, burnin=5,
J=[P.D.XYZ_vol[10, 10, 10]],
verbose=verbose, mode='mcmc')
# Test log_likelihood computation
v = P.v.copy()
m_var = P.m_var.copy()
m_mean = P.m_mean.copy()
L1 = P.compute_log_region_likelihood_slow(v, m_mean, m_var)
L2 = P.compute_log_region_likelihood(v, m_mean, m_var)
yield assert_almost_equal(-L1.sum(), L2.sum()*2, 2)
