def test_io_score_t1t2():
rng = irm.RNG()
for D1_N, D2_N in [(10, 20), (20, 30), (200, 300)]:
for model_name in ["BetaBernoulliNonConj",
"LogisticDistance",
"LinearDistance"]:
d = {'domains' : {'d1' : {'N' : D1_N},
'd2' : {'N' : D2_N}},
'relations' : {'R1' : {'relation' : ('d1', 'd2'),
'model' : model_name}}}
l = {}
new_latent, new_data = data.synth.prior_generate(l, d)
irm_model = irmio.create_model_from_data(new_data, rng=rng)
irmio.set_model_latent(irm_model, new_latent, rng=rng)
s1 = irm_model.total_score()
extracted_latent = irmio.get_latent(irm_model)
irm_model2 = irmio.create_model_from_data(new_data, rng=rng)
irmio.set_model_latent(irm_model, extracted_latent, rng=rng)
s2 = irm_model.total_score()
np.testing.assert_approx_equal(s1, s2, 5)
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_io_score_t1t2():
rng = irm.RNG()
for D1_N, D2_N in [(10, 20), (20, 30), (200, 300)]:
for model_name in ["BetaBernoulliNonConj", "LogisticDistance", "LinearDistance"]:
d = {
"domains": {"d1": {"N": D1_N}, "d2": {"N": D2_N}},
"relations": {"R1": {"relation": ("d1", "d2"), "model": model_name}},
}
l = {}
new_latent, new_data = data.synth.prior_generate(l, d)
irm_model = irmio.create_model_from_data(new_data, rng=rng)
irmio.set_model_latent(irm_model, new_latent, rng=rng)
s1 = irm_model.total_score()
extracted_latent = irmio.get_latent(irm_model)
irm_model2 = irmio.create_model_from_data(new_data, rng=rng)
irmio.set_model_latent(irm_model, extracted_latent, rng=rng)
s2 = irm_model.total_score()
np.testing.assert_approx_equal(s1, s2, 5)
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_inout():
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)
latent = irmio.get_latent(irm_model)
irmio.latent_equality(latent_simple_nonconj, latent, data_simple_nonconj)
def test_simple_nonconj_inout():
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)
latent = irmio.get_latent(irm_model)
irmio.latent_equality(latent_simple_nonconj, latent, data_simple_nonconj)
data_filename = a[1][1]
outfilename = latent_filename[:-(len("latent"))] + 'scores'
if 'conj' in latent_filename:
yield (latent_filename, data_filename), outfilename
@follows(t1_t2_datasets)
@follows(t1_t1_datasets)
@files(score_params)
def score((latent_filename, data_filename), outfilename):
latent = pickle.load(open(latent_filename, 'r'))
data = pickle.load(open(data_filename, 'r'))
rng = irm.RNG()
irm_model = irmio.create_model_from_data(data, rng=rng)
irmio.set_model_latent(irm_model, latent, rng)
# now we go through and score every possible latent
domain_names = sorted(data['domains'].keys())
domain_sizes = [data['domains'][dn]['N'] for dn in domain_names]
# create the dict
candidate_partitions = list(putil.enumerate_possible_latents(domain_sizes))
CANDIDATE_N = len(candidate_partitions)
scores = {}
for cpi, cp in enumerate(candidate_partitions):
t1 = time.time()
for di, av in enumerate(cp):
domain_name = domain_names[di]
latent['domains'][domain_name]['assignment'] = av
irmio.set_model_latent(irm_model, latent, rng)
outfilename = latent_filename[:-(len("latent"))] + 'scores'
if 'conj' in latent_filename:
yield (latent_filename, data_filename), outfilename
@follows(t1_t2_datasets)
@follows(t1_t1_datasets)
@files(score_params)
def score((latent_filename, data_filename), outfilename):
latent = pickle.load(open(latent_filename, 'r'))
data = pickle.load(open(data_filename, 'r'))
rng = irm.RNG()
irm_model = irmio.create_model_from_data(data, rng=rng)
irmio.set_model_latent(irm_model, latent, rng)
# now we go through and score every possible latent
domain_names = sorted(data['domains'].keys())
domain_sizes = [data['domains'][dn]['N'] for dn in domain_names]
# create the dict
candidate_partitions = list(putil.enumerate_possible_latents(domain_sizes))
CANDIDATE_N = len(candidate_partitions)
scores = {}
for cpi, cp in enumerate(candidate_partitions):
t1 = time.time()
for di, av in enumerate(cp):
domain_name = domain_names[di]
latent['domains'][domain_name]['assignment'] = av
irmio.set_model_latent(irm_model, latent, rng)
