def test_invalid_tune(self, tune_setting):
with Model() as pmodel:
Normal('n', 0, 2, shape=(3, ))
with pytest.raises(ValueError):
DEMetropolisZ(tune=tune_setting)
pass
def test_normal(self):
"""Test normal distribution is assigned NUTS method"""
with Model() as model:
Normal("x", 0, 1)
steps = assign_step_methods(model, [])
assert isinstance(steps, NUTS)
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, [])
assert isinstance(steps, Metropolis)
def test_discrete_uniform_negative():
model = Model()
with model:
x = DiscreteUniform("x", lower=-10, upper=0)
assert model.test_point["x"] == -5
def test_categorical_mode():
model = Model()
with model:
x = Categorical("x", p=np.eye(4), shape=4)
assert np.allclose(model.test_point["x"], np.arange(4))
def test_categorical(self):
"""Test categorical distribution is assigned binary gibbs metropolis method"""
with Model() as model:
Categorical('x', np.array([0.25, 0.75]))
steps = assign_step_methods(model, [])
self.assertIsInstance(steps, BinaryGibbsMetropolis)
def test_default_discrete_uniform():
with Model():
x = DiscreteUniform("x", lower=1, upper=2)
assert x.init_value == 1
def test_default_a():
with Model():
x = DistTest("x", 1, 2, defaults=["a"])
assert x.tag.test_value == 1
def test_default_b():
with Model():
x = DistTest("x", np.nan, 2, defaults=["a", "b"])
assert x.tag.test_value == 2
def test_default_testval():
with Model():
x = DistTest("x", 1, 2, testval=5, defaults=[])
assert x.tag.test_value == 5
def test_default_testval_nan():
with Model():
x = DistTest("x", 1, 2, testval=np.nan, defaults=["a"])
np.testing.assert_almost_equal(x.tag.test_value, np.nan)
def test_default_empty_fail():
with Model(), pytest.raises(AttributeError):
DistTest("x", 1, 2, defaults=[])
def test_default_nan_fail():
with Model(), pytest.raises(AttributeError):
DistTest("x", np.nan, 2, defaults=["a"])
def test_bad_init(self):
with Model():
HalfNormal('a', sd=1, testval=-1, transform=None)
with pytest.raises(ValueError) as error:
sample(init=None)
error.match('Bad initial')
def test_bad_init_parallel(self):
with Model():
HalfNormal("a", sigma=1, testval=-1, transform=None)
with pytest.raises(ParallelSamplingError) as error:
sample(init=None, cores=2, random_seed=1)
error.match("Bad initial")
def test_default_c():
with Model():
y = DistTest("y", 7, 8, testval=94)
x = DistTest("x", y, 2, defaults=["a", "b"])
assert x.tag.test_value == 94
def test_bernoulli(self):
"""Test bernoulli distribution is assigned binary gibbs metropolis method"""
with Model() as model:
Bernoulli('x', 0.5)
steps = assign_step_methods(model, [])
assert isinstance(steps, BinaryGibbsMetropolis)
def test_constant_step(self):
with Model():
x = Normal('x', 0, 1)
start = {'x': -1}
tr = sample(10, step=Constant([x]), start=start)
assert_almost_equal(tr['x'], start['x'], decimal=10)
