def test_score_errors_non_broadcastable_data_shape(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
shape = d.shape()
non_broadcastable_shape = (shape[0] + 1,) + shape[1:]
test_data_non_broadcastable = torch.ones(non_broadcastable_shape)
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_non_broadcastable)
def test_score_errors_non_broadcastable_data_shape(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
shape = d.shape()
non_broadcastable_shape = (shape[0] + 1,) + shape[1:]
test_data_non_broadcastable = torch.ones(non_broadcastable_shape)
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_non_broadcastable)
def test_batch_log_prob(dist):
if dist.scipy_arg_fn is None:
pytest.skip('{}.log_prob_sum has no scipy equivalent'.format(dist.pyro_dist.__name__))
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data = dist.get_test_data(idx)
log_prob_sum_pyro = d.log_prob(test_data).sum().item()
log_prob_sum_np = np.sum(dist.get_scipy_batch_logpdf(-1))
assert_equal(log_prob_sum_pyro, log_prob_sum_np)
def test_batch_log_prob(dist):
if dist.scipy_arg_fn is None:
pytest.skip('{}.log_prob_sum has no scipy equivalent'.format(dist.pyro_dist.__name__))
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data = dist.get_test_data(idx)
log_prob_sum_pyro = d.log_prob(test_data).sum().item()
log_prob_sum_np = np.sum(dist.get_scipy_batch_logpdf(-1))
assert_equal(log_prob_sum_pyro, log_prob_sum_np)
def test_score_errors_non_broadcastable_data_shape(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
if dist.get_test_distribution_name() == "LKJCholesky":
pytest.skip("https://github.com/pytorch/pytorch/issues/52724")
shape = d.shape()
non_broadcastable_shape = (shape[0] + 1, ) + shape[1:]
test_data_non_broadcastable = torch.ones(non_broadcastable_shape)
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_non_broadcastable)
def test_score_errors_event_dim_mismatch(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data_wrong_dims = torch.ones(d.shape() + (1,))
if len(d.event_shape) > 0:
if dist.get_test_distribution_name() == 'MultivariateNormal':
pytest.skip('MultivariateNormal does not do shape validation in log_prob.')
elif dist.get_test_distribution_name() == 'LowRankMultivariateNormal':
pytest.skip('LowRankMultivariateNormal does not do shape validation in log_prob.')
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_wrong_dims)
def test_score_errors_event_dim_mismatch(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data_wrong_dims = torch.ones(d.shape() + (1,))
if len(d.event_shape) > 0:
if dist.get_test_distribution_name() == 'MultivariateNormal':
pytest.skip('MultivariateNormal does not do shape validation in log_prob.')
if dist.get_test_distribution_name() == 'LowRankMultivariateNormal':
pytest.skip('LowRankMultivariateNormal does not do shape validation in log_prob.')
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_wrong_dims)