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Fix MarginalModel with Data containers #304

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Feb 10, 2024
Merged

Fix MarginalModel with Data containers #304

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10 changes: 6 additions & 4 deletions pymc_experimental/model/marginal_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,8 +212,10 @@ def logp(self, vars=None, **kwargs):
return m._logp(vars=vars, **kwargs)

def clone(self):
m = MarginalModel()
vars = self.basic_RVs + self.potentials + self.deterministics + self.marginalized_rvs
m = MarginalModel(coords=self.coords)
model_vars = self.basic_RVs + self.potentials + self.deterministics + self.marginalized_rvs
data_vars = [var for name, var in self.named_vars.items() if var not in model_vars]
vars = model_vars + data_vars
cloned_vars = clone_replace(vars)
vars_to_clone = {var: cloned_var for var, cloned_var in zip(vars, cloned_vars)}
m.vars_to_clone = vars_to_clone
Expand Down Expand Up @@ -598,7 +600,7 @@ def replace_finite_discrete_marginal_subgraph(fgraph, rv_to_marginalize, all_rvs
# can ultimately be generated that is proportional to the support domain and not
# to the variables dimensions
# We don't need to worry about this if the RV is scalar.
if np.prod(constant_fold(tuple(rv_to_marginalize.shape))) > 1:
if np.prod(constant_fold(tuple(rv_to_marginalize.shape), raise_not_constant=False)) != 1:
if not is_elemwise_subgraph(rv_to_marginalize, dependent_rvs_input_rvs, dependent_rvs):
raise NotImplementedError(
"The subgraph between a marginalized RV and its dependents includes non Elemwise operations. "
Expand Down Expand Up @@ -682,7 +684,7 @@ def finite_discrete_marginal_rv_logp(op, values, *inputs, **kwargs):
# batched dimensions of the marginalized RV

# PyMC does not allow RVs in the logp graph, even if we are just using the shape
marginalized_rv_shape = constant_fold(tuple(marginalized_rv.shape))
marginalized_rv_shape = constant_fold(tuple(marginalized_rv.shape), raise_not_constant=False)
marginalized_rv_domain = get_domain_of_finite_discrete_rv(marginalized_rv)
marginalized_rv_domain_tensor = pt.moveaxis(
pt.full(
Expand Down
26 changes: 26 additions & 0 deletions pymc_experimental/tests/model/test_marginal_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -598,3 +598,29 @@ def test_is_conditional_dependent_static_shape():
x2 = pt.matrix("x2", shape=(9, 5))
y2 = pt.random.normal(size=pt.shape(x2))
assert not is_conditional_dependent(y2, x2, [x2, y2])


def test_data_container():
"""Test that MarginalModel can handle Data containers."""
with MarginalModel(coords_mutable={"obs": [0]}) as marginal_m:
x = pm.MutableData("x", 2.5)
idx = pm.Bernoulli("idx", p=0.7, dims="obs")
y = pm.Normal("y", idx * x, dims="obs")

marginal_m.marginalize([idx])

logp_fn = marginal_m.compile_logp()

with pm.Model(coords_mutable={"obs": [0]}) as m_ref:
x = pm.MutableData("x", 2.5)
y = pm.NormalMixture("y", w=[0.3, 0.7], mu=[0, x], dims="obs")

ref_logp_fn = m_ref.compile_logp()

for i, x_val in enumerate((-1.5, 2.5, 3.5), start=1):
for m in (marginal_m, m_ref):
m.set_dim("obs", new_length=i, coord_values=tuple(range(i)))
pm.set_data({"x": x_val}, model=m)

ip = marginal_m.initial_point()
np.testing.assert_allclose(logp_fn(ip), ref_logp_fn(ip))
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