def test_simple_nonconj():
rng = irm.RNG()
irm_model = irmio.create_model_from_data(data_simple_nonconj, rng=rng)
irmio.set_model_latent(irm_model, latent_simple_nonconj, rng=rng)
a = irm_model.domains['d1'].get_assignments()
axes = irm_model.relations['R1'].get_axes()
axes_objs = [(irm_model.domains[dn], irm_model.domains[dn].get_relation_pos('R1'))
for dn in axes]
comps = model.get_components_in_relation(axes_objs,
irm_model.relations['R1'])
g0 = a[0]
g1 = a[2]
g2 = a[4]
assert_approx_equal(comps[g0, g0]['p'], 0.0)
assert_approx_equal(comps[g0, g1]['p'], 0.01)
assert_approx_equal(comps[g0, g2]['p'], 0.02)
assert_approx_equal(comps[g1, g0]['p'], 0.1)
assert_approx_equal(comps[g1, g1]['p'], 0.11)
assert_approx_equal(comps[g1, g2]['p'], 0.12)
assert_approx_equal(comps[g2, g0]['p'], 0.2)
assert_approx_equal(comps[g2, g1]['p'], 0.21)
assert_approx_equal(comps[g2, g2]['p'], 0.22)
def test_simple_nonconj():
rng = irm.RNG()
irm_model = irmio.create_model_from_data(data_simple_nonconj, rng=rng)
irmio.set_model_latent(irm_model, latent_simple_nonconj, rng=rng)
a = irm_model.domains["d1"].get_assignments()
axes = irm_model.relations["R1"].get_axes()
axes_objs = [(irm_model.domains[dn], irm_model.domains[dn].get_relation_pos("R1")) for dn in axes]
comps = model.get_components_in_relation(axes_objs, irm_model.relations["R1"])
g0 = a[0]
g1 = a[2]
g2 = a[4]
assert_approx_equal(comps[g0, g0]["p"], 0.0)
assert_approx_equal(comps[g0, g1]["p"], 0.01)
assert_approx_equal(comps[g0, g2]["p"], 0.02)
assert_approx_equal(comps[g1, g0]["p"], 0.1)
assert_approx_equal(comps[g1, g1]["p"], 0.11)
assert_approx_equal(comps[g1, g2]["p"], 0.12)
assert_approx_equal(comps[g2, g0]["p"], 0.2)
assert_approx_equal(comps[g2, g1]["p"], 0.21)
assert_approx_equal(comps[g2, g2]["p"], 0.22)
def test_slice_nonconj():
T1_N = 10
T2_N = 20
np.random.seed(0)
rng = irm.RNG()
data = np.random.rand(T1_N, T2_N) > 0.5
data.shape = T1_N, T2_N
m = models.BetaBernoulliNonConj()
r = irm.Relation([('T1', T1_N), ('T2', T2_N)],
data,m)
hps = m.create_hps()
hps['alpha'] = 1.0
hps['beta'] = 1.0
r.set_hps(hps)
tf_1 = model.DomainInterface(T1_N, {'r': ('T1', r)})
tf_1.set_hps({'alpha' : 1.0})
tf_2 = model.DomainInterface(T2_N, {'r' : ('T2', r)})
tf_2.set_hps({'alpha' : 1.0})
T1_GRPN = 4
t1_assign = np.arange(T1_N) % T1_GRPN
t1_grps = {}
for i, gi in enumerate(t1_assign):
if gi not in t1_grps:
g = tf_1.create_group(rng)
t1_grps[gi] = g
tf_1.add_entity_to_group(t1_grps[gi], i)
T2_GRPN = 4
t2_assign = np.arange(T2_N) % T2_GRPN
t2_grps = {}
for i, gi in enumerate(t2_assign):
if gi not in t2_grps:
g = tf_2.create_group(rng)
t2_grps[gi] = g
tf_2.add_entity_to_group(t2_grps[gi], i)
t1_assign_g = tf_1.get_assignments()
t2_assign_g = tf_2.get_assignments()
# build list of coords / heads/tails
coord_data = {}
for t1_g in np.unique(t1_assign_g):
for t2_g in np.unique(t2_assign_g):
t1_entities = np.argwhere(t1_assign_g == t1_g).flatten()
t2_entities = np.argwhere(t2_assign_g == t2_g).flatten()
dps = []
for e1 in t1_entities:
for e2 in t2_entities:
dps.append(data[e1, e2])
heads = np.sum(np.array(dps)==1)
tails = np.sum(np.array(dps)==0)
# coords = ((tf_1.get_relation_groupid(0, t1_g),
# tf_2.get_relation_groupid(0, t2_g)))
coord_data[(t1_g, t2_g)] = (heads, tails)
# get all the components from this relation
# now the histograms
for alpha, beta in [(1.0, 1.0), (10.0, 1.0),
(1.0, 10.0),(0.1, 5.0)]:
coords_hist = {k : [] for k in coord_data}
print "alpha=", alpha, "beta=", beta, "="*50
hps['alpha'] = alpha
hps['beta'] = beta
r.set_hps(hps)
ITERS = 100000
for i in range(ITERS):
r.apply_comp_kernel("slice_sample", rng, {'width' : 0.4})
component_data = model.get_components_in_relation([(tf_1, 0),
(tf_2, 0)],
r)
for c in coord_data:
coords_hist[c].append(component_data[c]['p'])
for c in coords_hist:
heads, tails = coord_data[c]
empirical_p = np.mean(coords_hist[c])
true_map_p = float(heads + alpha) / (heads +tails + alpha + beta)
print empirical_p - true_map_p
np.testing.assert_approx_equal(empirical_p, true_map_p, 2)
