def test_gof(continuous_dist):
Dist = continuous_dist.pyro_dist
if Dist in [dist.LKJ, dist.LKJCholesky]:
pytest.xfail(reason="incorrect submanifold scaling")
num_samples = 50000
for i in range(continuous_dist.get_num_test_data()):
d = Dist(**continuous_dist.get_dist_params(i))
samples = d.sample(torch.Size([num_samples]))
with xfail_if_not_implemented():
probs = d.log_prob(samples).exp()
dim = None
if "ProjectedNormal" in Dist.__name__:
dim = samples.size(-1) - 1
# Test each batch independently.
probs = probs.reshape(num_samples, -1)
samples = samples.reshape(probs.shape + d.event_shape)
if "Dirichlet" in Dist.__name__:
# The Dirichlet density is over all but one of the probs.
samples = samples[..., :-1]
for b in range(probs.size(-1)):
gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)
assert gof > TEST_FAILURE_RATE
def test_von_mises_3d_gof(scale):
concentration = torch.randn(3)
concentration = concentration * (scale / concentration.norm(2))
d = VonMises3D(concentration, validate_args=True)
with xfail_if_not_implemented():
samples = d.sample(torch.Size([2000]))
probs = d.log_prob(samples).exp()
gof = auto_goodness_of_fit(samples, probs, dim=2)
assert gof > TEST_FAILURE_RATE
def test_von_mises_gof(loc, concentration):
d = VonMises(loc, concentration)
samples = d.sample(torch.Size([100000]))
probs = d.log_prob(samples).exp()
gof = auto_goodness_of_fit(samples, probs, dim=1)
assert gof > TEST_FAILURE_RATE