def test_logpt_basic():
"""Make sure we can compute a log-likelihood for a hierarchical model with transforms."""
with Model() as m:
a = Uniform("a", 0.0, 1.0)
c = Normal("c")
b_l = c * a + 2.0
b = Uniform("b", b_l, b_l + 1.0)
a_value_var = m.rvs_to_values[a]
assert a_value_var.tag.transform
b_value_var = m.rvs_to_values[b]
assert b_value_var.tag.transform
c_value_var = m.rvs_to_values[c]
b_logp = logpt(b, b_value_var, sum=False)
res_ancestors = list(walk_model((b_logp,), walk_past_rvs=True))
res_rv_ancestors = [
v for v in res_ancestors if v.owner and isinstance(v.owner.op, RandomVariable)
]
# There shouldn't be any `RandomVariable`s in the resulting graph
assert len(res_rv_ancestors) == 0
assert b_value_var in res_ancestors
assert c_value_var in res_ancestors
assert a_value_var in res_ancestors
def test_model_unchanged_logprob_access():
# Issue #5007
with Model() as model:
a = Normal("a")
c = Uniform("c", lower=a - 1, upper=1)
original_inputs = set(aesara.graph.graph_inputs([c]))
# Extract model.logpt
model.logpt
new_inputs = set(aesara.graph.graph_inputs([c]))
assert original_inputs == new_inputs
def test_interval_missing_observations():
with Model() as model:
obs1 = ma.masked_values([1, 2, -1, 4, -1], value=-1)
obs2 = ma.masked_values([-1, -1, 6, -1, 8], value=-1)
rng = aesara.shared(np.random.RandomState(2323), borrow=True)
with pytest.warns(ImputationWarning):
theta1 = Uniform("theta1", 0, 5, observed=obs1, rng=rng)
with pytest.warns(ImputationWarning):
theta2 = Normal("theta2", mu=theta1, observed=obs2, rng=rng)
assert "theta1_observed_interval__" in model.named_vars
assert "theta1_missing_interval__" in model.named_vars
assert isinstance(
model.rvs_to_values[model.named_vars["theta1_observed"]].tag.transform, interval
)
prior_trace = sample_prior_predictive(return_inferencedata=False)
# Make sure the observed + missing combined deterministics have the
# same shape as the original observations vectors
assert prior_trace["theta1"].shape[-1] == obs1.shape[0]
assert prior_trace["theta2"].shape[-1] == obs2.shape[0]
# Make sure that the observed values are newly generated samples
assert np.all(np.var(prior_trace["theta1_observed"], 0) > 0.0)
assert np.all(np.var(prior_trace["theta2_observed"], 0) > 0.0)
# Make sure the missing parts of the combined deterministic matches the
# sampled missing and observed variable values
assert np.mean(prior_trace["theta1"][:, obs1.mask] - prior_trace["theta1_missing"]) == 0.0
assert np.mean(prior_trace["theta1"][:, ~obs1.mask] - prior_trace["theta1_observed"]) == 0.0
assert np.mean(prior_trace["theta2"][:, obs2.mask] - prior_trace["theta2_missing"]) == 0.0
assert np.mean(prior_trace["theta2"][:, ~obs2.mask] - prior_trace["theta2_observed"]) == 0.0
assert {"theta1", "theta2"} <= set(prior_trace.keys())
trace = sample(
chains=1, draws=50, compute_convergence_checks=False, return_inferencedata=False
)
assert np.all(0 < trace["theta1_missing"].mean(0))
assert np.all(0 < trace["theta2_missing"].mean(0))
assert "theta1" not in trace.varnames
assert "theta2" not in trace.varnames
# Make sure that the observed values are newly generated samples and that
# the observed and deterministic matche
pp_trace = sample_posterior_predictive(trace, return_inferencedata=False)
assert np.all(np.var(pp_trace["theta1"], 0) > 0.0)
assert np.all(np.var(pp_trace["theta2"], 0) > 0.0)
assert np.mean(pp_trace["theta1"][:, ~obs1.mask] - pp_trace["theta1_observed"]) == 0.0
assert np.mean(pp_trace["theta2"][:, ~obs2.mask] - pp_trace["theta2_observed"]) == 0.0
def test_get_scaling():
assert _get_scaling(None, (2, 3), 2).eval() == 1
# ndim >=1 & ndim<1
assert _get_scaling(45, (2, 3), 1).eval() == 22.5
assert _get_scaling(45, (2, 3), 0).eval() == 45
# list or tuple tests
# total_size contains other than Ellipsis, None and Int
with pytest.raises(TypeError, match="Unrecognized `total_size` type"):
_get_scaling([2, 4, 5, 9, 11.5], (2, 3), 2)
# check with Ellipsis
with pytest.raises(ValueError,
match="Double Ellipsis in `total_size` is restricted"):
_get_scaling([1, 2, 5, Ellipsis, Ellipsis], (2, 3), 2)
with pytest.raises(
ValueError,
match=
"Length of `total_size` is too big, number of scalings is bigger that ndim",
):
_get_scaling([1, 2, 5, Ellipsis], (2, 3), 2)
assert _get_scaling([Ellipsis], (2, 3), 2).eval() == 1
assert _get_scaling([4, 5, 9, Ellipsis, 32, 12], (2, 3, 2),
5).eval() == 960
assert _get_scaling([4, 5, 9, Ellipsis], (2, 3, 2), 5).eval() == 15
# total_size with no Ellipsis (end = [ ])
with pytest.raises(
ValueError,
match=
"Length of `total_size` is too big, number of scalings is bigger that ndim",
):
_get_scaling([1, 2, 5], (2, 3), 2)
assert _get_scaling([], (2, 3), 2).eval() == 1
assert _get_scaling((), (2, 3), 2).eval() == 1
# total_size invalid type
with pytest.raises(
TypeError,
match=
"Unrecognized `total_size` type, expected int or list of ints, got {1, 2, 5}",
):
_get_scaling({1, 2, 5}, (2, 3), 2)
# test with rvar from model graph
with Model() as m2:
rv_var = Uniform("a", 0.0, 1.0)
total_size = []
assert _get_scaling(total_size, shape=rv_var.shape,
ndim=rv_var.ndim).eval() == 1.0