def test_process_prior(prior):
prior, parameter_dim, numpy_simulator = process_prior(prior)
batch_size = 2
theta = prior.sample((batch_size, ))
assert theta.shape == torch.Size(
(batch_size,
parameter_dim)), "Number of sampled parameters must match batch size."
assert (prior.log_prob(theta).shape[0] == batch_size
), "Number of log probs must match number of input values."
def test_process_simulator(simulator: Callable, prior: Distribution):
prior, theta_dim, prior_returns_numpy = process_prior(prior)
simulator = process_simulator(simulator, prior, prior_returns_numpy)
n_batch = 1
x = simulator(prior.sample((n_batch, )))
assert isinstance(x, Tensor), "Processed simulator must return Tensor."
assert (
x.shape[0] == n_batch
), "Processed simulator must return as many data points as parameters in batch."
def test_reinterpreted_batch_dim_prior():
"""Test whether the right warning and error are raised for reinterpreted priors."""
# Both must raise ValueError because we don't reinterpret batch dims anymore.
with pytest.raises(ValueError):
process_prior(Uniform(zeros(3), ones(3)))
with pytest.raises(ValueError):
process_prior(MultivariateNormal(zeros(2, 3), ones(3)))
# This must pass without warnings or errors.
process_prior(BoxUniform(zeros(3), ones(3)))