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Refactor sample_posterior_predictive_w for V4 #5800

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Refactor sample_posterior_predictive_w for V4 #5800

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6 changes: 2 additions & 4 deletions pymc/sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2032,8 +2032,6 @@ def sample_posterior_predictive_w(
weighted models (default), or a dictionary with variable names as keys, and samples as
numpy arrays.
"""
raise NotImplementedError(f"sample_posterior_predictive_w has not yet been ported to PyMC 4.0.")

if isinstance(traces[0], InferenceData):
n_samples = [
trace.posterior.sizes["chain"] * trace.posterior.sizes["draw"] for trace in traces
Expand Down Expand Up @@ -2140,13 +2138,13 @@ def sample_posterior_predictive_w(
# TODO sample_posterior_predictive_w is currently only work for model with
# one observed.
# XXX: This needs to be refactored
# ppc[var.name].append(draw_values([var], point=param, size=size[idx])[0])
raise NotImplementedError()
ppcl[var.name].append(draw([var])[0])
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This won't be conditioned on the posterior samples.

You will have to create a custom function similarly to what sample_posterior_predictive does. See recent changes in #5759

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@ricardoV94 ricardoV94 May 26, 2022 •
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It might also be useful to compare what changed between V3 and V4 in sample_posterior_predictive because some of the things in sample_posterior_predictive_w might no longer be needed / make sense


except KeyboardInterrupt:
pass
else:
ppcd = {k: np.asarray(v) for k, v in ppcl.items()}
return ppcd
if not return_inferencedata:
return ppcd
ikwargs: Dict[str, Any] = dict(model=models)
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3 changes: 0 additions & 3 deletions pymc/tests/test_sampling.py
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Expand Up @@ -935,9 +935,6 @@ def test_deterministics_out_of_idata(self, multitrace):
assert np.all(np.abs(ppc.posterior_predictive.c + 4) <= 0.1)


@pytest.mark.xfail(
reason="sample_posterior_predictive_w not refactored for v4", raises=NotImplementedError
)
class TestSamplePPCW(SeededTest):
def test_sample_posterior_predictive_w(self):
data0 = np.random.normal(0, 1, size=50)
Expand Down