def test_competence(self, variable, has_grad, outcome):
with Model() as pmodel:
Normal('n', 0, 2, shape=(3, ))
Binomial('b', n=2, p=0.3)
assert DEMetropolisZ.competence(pmodel[variable],
has_grad=has_grad) == outcome
pass
def test_assign_step_methods():
with Model() as model:
x = Bernoulli('x', 0.5)
steps = assign_step_methods(model, [])
assert isinstance(steps, BinaryMetropolis)
with Model() as model:
x = Normal('x', 0, 1)
steps = assign_step_methods(model, [])
assert isinstance(steps, NUTS)
with Model() as model:
x = Categorical('x', np.array([0.25, 0.75]))
steps = assign_step_methods(model, [])
assert isinstance(steps, BinaryMetropolis)
with Model() as model:
x = Categorical('x', np.array([0.25, 0.70, 0.05]))
steps = assign_step_methods(model, [])
assert isinstance(steps, Metropolis)
with Model() as model:
x = Binomial('x', 10, 0.5)
steps = assign_step_methods(model, [])
assert isinstance(steps, Metropolis)
def test_competence(self, variable, has_grad, outcome):
"""Test if competence function returns expected
results for different models"""
with Model() as pmodel:
Normal("n", 0, 2, shape=(3, ))
Binomial("b", n=2, p=0.3)
assert MLDA.competence(pmodel[variable], has_grad=has_grad) == outcome
def test_multiple_samplers(self, caplog):
with Model():
prob = Beta("prob", alpha=5.0, beta=3.0)
Binomial("outcome", n=1, p=prob)
caplog.clear()
sample(3, tune=2, discard_tuned_samples=False, n_init=None, chains=1)
messages = [msg.msg for msg in caplog.records]
assert all("boolean index did not" not in msg for msg in messages)
def test_multiple_samplers(self):
with Model():
prob = Beta('prob', alpha=5, beta=3)
Binomial('outcome', n=1, p=prob)
with warnings.catch_warnings(record=True) as warns:
sample(3, tune=2, discard_tuned_samples=False, n_init=None)
messages = [warn.message.args[0] for warn in warns]
assert any("contains only 3" in msg for msg in messages)
assert all('boolean index did not' not in msg for msg in messages)
def test_multiple_samplers(self):
with Model():
prob = Beta('prob', alpha=5., beta=3.)
Binomial('outcome', n=1, p=prob)
# Catching warnings through multiprocessing doesn't work,
# so we have to use single threaded sampling.
with pytest.warns(None) as warns:
sample(3, tune=2, discard_tuned_samples=False,
n_init=None, chains=1)
messages = [warn.message.args[0] for warn in warns]
assert any("contains only 3" in msg for msg in messages)
assert all('boolean index did not' not in msg for msg in messages)
def test_binomial(self):
"""Test binomial distribution is assigned metropolis method."""
with Model() as model:
Binomial('x', 10, 0.5)
steps = assign_step_methods(model, [])
self.assertIsInstance(steps, Metropolis)