def test_models_pickle():
models = (
bb,
bnb,
gp,
nich,
dd(4),
bbnc,
niw(3),
dm(5),
)
for model in models:
bstr = pickle.dumps(model)
model1 = pickle.loads(bstr)
assert_equals(model.name(), model1.name())
if model.name() == 'dd':
assert_equals(len(model.default_hyperparams()['alphas']),
len(model1.default_hyperparams()['alphas']))
elif model.name() == 'niw':
assert_equals(len(model.default_hyperparams()['mu']),
len(model1.default_hyperparams()['mu']))
elif model.name() == 'dm':
assert_equals(model.py_desc().get_np_dtype().shape,
model1.py_desc().get_np_dtype().shape)
def test_models_pickle():
models = (
bb,
bnb,
gp,
nich,
dd(4),
bbnc,
niw(3),
dm(5),
)
for model in models:
bstr = pickle.dumps(model)
model1 = pickle.loads(bstr)
assert_equals(model.name(), model1.name())
if model.name() == 'dd':
assert_equals(len(model.default_hyperparams()['alphas']),
len(model1.default_hyperparams()['alphas']))
elif model.name() == 'niw':
assert_equals(len(model.default_hyperparams()['mu']),
len(model1.default_hyperparams()['mu']))
elif model.name() == 'dm':
assert_equals(model.py_desc().get_np_dtype().shape,
model1.py_desc().get_np_dtype().shape)
def test_model_definition_pickle():
defn = model_definition(10, [bb, niw(3)])
bstr = pickle.dumps(defn)
defn1 = pickle.loads(bstr)
assert_equals(defn.n(), defn1.n())
assert_equals(len(defn.models()), len(defn1.models()))
for a, b in zip(defn.models(), defn1.models()):
assert_equals(a.name(), b.name())
def test_runner_default_kernel_grid():
models = [bb, nich, niw(3)]
def kc_fn(defn):
return list(it.chain(
runner.default_assign_kernel_config(defn),
runner.default_grid_feature_hp_kernel_config(defn)))
_test_runner_kernel_config(kc_fn, models)
def test_sample_sanity():
# just a sanity check
defn = model_definition(10, [bb, bnb, gp, nich, dd(5), niw(4)])
clusters, samplers = sample(defn)
assert_equals(len(clusters), len(samplers))
for cluster in clusters:
assert_true(len(cluster) > 0)
for v in cluster:
assert_equals(len(v), len(defn.models()))
def test_model_definition_pickle():
defn = model_definition([10, 12], [((0, 0), bb), ((1, 0), niw(3))])
bstr = pickle.dumps(defn)
defn1 = pickle.loads(bstr)
assert_list_equal(defn.domains(), defn1.domains())
assert_equals(defn.relations(), defn1.relations())
zipped_models = zip(defn.relation_models(), defn1.relation_models())
for model, model1 in zipped_models:
assert_equals(model.name(), model1.name())
def test_model_definition_copy():
defn = model_definition(10, [bb, niw(3)])
defn_shallow = copy.copy(defn)
defn_deep = copy.deepcopy(defn)
assert_is_not(defn, defn_shallow)
assert_is_not(defn, defn_deep)
assert_is_not(defn._models, defn_deep._models)
assert_equals(defn.n(), defn_shallow.n())
assert_equals(defn.n(), defn_deep.n())
def test_runner_multyvac():
defn = model_definition(10, [bb, nich, niw(3)])
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [model.initialize(defn, view, prng)
for _ in xrange(2)]
runners = [runner.runner(defn, view, latent, kc) for latent in latents]
r = parallel.runner(runners, backend='multyvac', layer='perf', core='f2')
r.run(r=prng, niters=1000)
r.run(r=prng, niters=1000)
def test_runner_multiprocessing():
defn = model_definition(10, [bb, nich, niw(3)])
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [model.initialize(defn, view, prng)
for _ in xrange(mp.cpu_count())]
runners = [runner.runner(defn, view, latent, kc) for latent in latents]
r = parallel.runner(runners)
# check it is restartable
r.run(r=prng, niters=10)
r.run(r=prng, niters=10)
def _test_multivariate_models(initialize_fn, dataview, bind, gibbs_assign, R):
# XXX: this test only checks that the operations don't crash
mu = np.ones(3)
kappa = 0.3
Q = random_orthonormal_matrix(3)
psi = np.dot(Q, np.dot(np.diag([1.0, 0.5, 0.2]), Q.T))
nu = 6
N = 10
def genrow():
return tuple([
np.random.choice([False, True]),
[np.random.uniform(-3.0, 3.0) for _ in xrange(3)]
])
X = np.array([genrow() for _ in xrange(N)],
dtype=[('', bool), ('', float, (3, ))])
view = dataview(X)
defn = model_definition(N, [bb, niw(3)])
s = initialize_fn(defn,
view,
cluster_hp={'alpha': 2.},
feature_hps=[{
'alpha': 2.,
'beta': 2.
}, {
'mu': mu,
'kappa': kappa,
'psi': psi,
'nu': nu
}],
r=R)
bound_s = bind(s, view)
for _ in xrange(10):
gibbs_assign(bound_s, R)
def _test_multivariate_models(initialize_fn,
dataview,
bind,
gibbs_assign,
R):
# XXX: this test only checks that the operations don't crash
mu = np.ones(3)
kappa = 0.3
Q = random_orthonormal_matrix(3)
psi = np.dot(Q, np.dot(np.diag([1.0, 0.5, 0.2]), Q.T))
nu = 6
N = 10
def genrow():
return tuple(
[np.random.choice([False, True]),
[np.random.uniform(-3.0, 3.0) for _ in xrange(3)]])
X = np.array([genrow()
for _ in xrange(N)], dtype=[('', bool), ('', float, (3,))])
view = dataview(X)
defn = model_definition(N, [bb, niw(3)])
s = initialize_fn(
defn,
view,
cluster_hp={'alpha': 2.},
feature_hps=[
{'alpha': 2., 'beta': 2.},
{'mu': mu, 'kappa': kappa, 'psi': psi, 'nu': nu}
],
r=R)
bound_s = bind(s, view)
for _ in xrange(10):
gibbs_assign(bound_s, R)
def test_default_parameters():
models = [bb, bnb, gp, nich, dd(5), bbnc, niw(10)]
for m in models:
typ = m.py_desc()._model_module
s = typ.Shared()
s.load(m.default_hyperparams())
def test_runner_default_kernel_config_nonconj():
models = [bbnc, nich, niw(3)]
_test_runner_kernel_config(runner.default_kernel_config, models)
def test_copy_state():
defn = model_definition(10, [bb, niw(3)])
_test_copy_state(defn, cxx_initialize, bind)
def test_copy_state():
defn = model_definition(10, [bb, niw(3)])
_test_copy_state(defn, cxx_initialize, bind)
def test_models_dtype():
assert_equals(niw(3).py_desc().get_np_dtype().shape, (3,))
assert_equals(niw(5).py_desc().get_np_dtype().shape, (5,))
def test_models_dtype():
assert_equals(niw(3).py_desc().get_np_dtype().shape, (3, ))
assert_equals(niw(5).py_desc().get_np_dtype().shape, (5, ))
