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Check for observed variables in the trace #7641

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Merged
merged 5 commits into from
Jan 20, 2025
Merged

Check for observed variables in the trace #7641

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a592208
Check for observed variables in the trace as well as the model
zaxtax Jan 12, 2025
e33e517
Bugfix
zaxtax Jan 12, 2025
e895a5c
Update tests
zaxtax Jan 19, 2025
d813da1
Add logic to handle conditional nodes for observed variables
zaxtax Jan 19, 2025
2fcf395
Remove redundant test
zaxtax Jan 20, 2025
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12 changes: 10 additions & 2 deletions pymc/sampling/forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -345,10 +345,13 @@ def draw(
return [np.stack(v) for v in drawn_values]


def observed_dependent_deterministics(model: Model):
def observed_dependent_deterministics(model: Model, extra_observeds=None):
"""Find deterministics that depend directly on observed variables."""
if extra_observeds is None:
extra_observeds = []

deterministics = model.deterministics
observed_rvs = set(model.observed_RVs)
observed_rvs = set(model.observed_RVs + extra_observeds)
blockers = model.basic_RVs
return [
deterministic
Expand Down Expand Up @@ -767,13 +770,15 @@ def sample_posterior_predictive(
if "coords" not in idata_kwargs:
idata_kwargs["coords"] = {}
idata: InferenceData | None = None
observed_data = None
stacked_dims = None
if isinstance(trace, InferenceData):
_constant_data = getattr(trace, "constant_data", None)
if _constant_data is not None:
trace_coords.update({str(k): v.data for k, v in _constant_data.coords.items()})
constant_data.update({str(k): v.data for k, v in _constant_data.items()})
idata = trace
observed_data = trace.get("observed_data", None)
trace = trace["posterior"]
if isinstance(trace, xarray.Dataset):
trace_coords.update({str(k): v.data for k, v in trace.coords.items()})
Expand Down Expand Up @@ -817,6 +822,9 @@ def sample_posterior_predictive(
vars_ = [model[x] for x in var_names]
else:
vars_ = model.observed_RVs + observed_dependent_deterministics(model)
if observed_data is not None:
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BTW, the the observed_dependent_deterministics above is not going to work if these variables are not observed in the model.

That happens with auto-imputation models, which I assume the as_model wrapper won't handle correctly either because the models are different depending on whether you pass data or not.

Just something to keep in mind

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I agree with this. You'll have to adapt observed_dependent_deterministics to also accept a list of extra variables that will depend on your observed_data

vars_ += [model[x] for x in observed_data if x in model and x not in vars_]
vars_ += observed_dependent_deterministics(model, vars_)
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@ricardoV94 ricardoV94 Jan 20, 2025 •
edited
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This is going to duplicate deterministics of an observed variable, in case there's a mix of observed model variables and implied observed variables from the idata. the + observed_dependent_determininstics) should be called only once after the if branch?


vars_to_sample = list(get_default_varnames(vars_, include_transformed=False))

Expand Down
44 changes: 44 additions & 0 deletions tests/sampling/test_forward.py
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Expand Up @@ -540,6 +540,50 @@ def test_normal_scalar_idata(self):
ppc = pm.sample_posterior_predictive(idata, return_inferencedata=False)
assert ppc["a"].shape == (nchains, ndraws)

def test_external_trace(self):
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Remove this test? The one with det is strictly more comprehensive than this?

nchains = 2
ndraws = 500
with pm.Model() as model:
mu = pm.Normal("mu", 0.0, 1.0)
a = pm.Normal("a", mu=mu, sigma=1, observed=0.0)
trace = pm.sample(
draws=ndraws,
chains=nchains,
)

# test that trace is used in ppc
with pm.Model() as model_ppc:
mu = pm.Normal("mu", 0.0, 1.0)
a = pm.Normal("a", mu=mu, sigma=1)

ppc = pm.sample_posterior_predictive(
trace=trace, model=model_ppc, return_inferencedata=False
)
assert list(ppc.keys()) == ["a"]

def test_external_trace_det(self):
nchains = 2
ndraws = 500
with pm.Model() as model:
mu = pm.Normal("mu", 0.0, 1.0)
a = pm.Normal("a", mu=mu, sigma=1, observed=0.0)
b = pm.Deterministic("b", a + 1)
trace = pm.sample(
draws=ndraws,
chains=nchains,
)

# test that trace is used in ppc
with pm.Model() as model_ppc:
mu = pm.Normal("mu", 0.0, 1.0)
a = pm.Normal("a", mu=mu, sigma=1)
c = pm.Deterministic("c", a + 1)

ppc = pm.sample_posterior_predictive(
trace=trace, model=model_ppc, return_inferencedata=False
)
assert list(ppc.keys()) == ["a", "c"]

def test_normal_vector(self):
with pm.Model() as model:
mu = pm.Normal("mu", 0.0, 1.0)
Expand Down
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