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Auto generated dimensions in posterior predictive don't match with observed dimensions #7572

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lucianopaz opened this issue Nov 13, 2024 · 1 comment · May be fixed by arviz-devs/arviz#2395
Open

Auto generated dimensions in posterior predictive don't match with observed dimensions #7572

lucianopaz opened this issue Nov 13, 2024 · 1 comment · May be fixed by arviz-devs/arviz#2395
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@lucianopaz
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lucianopaz commented Nov 13, 2024 •
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Describe the issue:

When a dimension is left unnamed, pymc will auto generate the dimension's name based on the tensor's name and the axis number. The problem is that pymc.sample_posterior_predictive returns a dimension name that is not aligned with what one gets in the observed_data group.

Reproduceable code example:

import pymc as pm

with pm.Model():
    a = pm.Normal("a")
    b = pm.Normal("b", a, observed=[-1, 2, 4])
    idata = pm.sample()
    idata.extend(pm.sample_prior_predictive())
    idata = pm.sample_posterior_predictive(idata, extend_inferencedata=True)

assert idata.observed_data.b.dims == idata.prior_predictive.b.dims[2:]  # This works!
assert idata.observed_data.b.dims == idata.posterior_predictive.b.dims[2:]  # This fails

Error message:

It's a simple `AssertionError`. The telling thing is that the posterior predictive group gets a wrong imputed name because it doesn't ignore the chain and draw dimensions


>>> print(idata.observed_data.b.dims, idata.prior_predictive.b.dims, idata.posterior_predictive.b.dims)
('b_dim_0',) ('chain', 'draw', 'b_dim_0') ('chain', 'draw', 'b_dim_2')

PyMC version information:

PyMC main

Context for the issue:

No response
@lucianopaz lucianopaz added the bug label Nov 13, 2024
@lucianopaz lucianopaz linked a pull request Nov 13, 2024 that will close this issue
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@lucianopaz
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@OriolAbril, could you have a look at this? I opened a PR over at arviz trying to fix it

@lucianopaz lucianopaz mentioned this issue Nov 14, 2024
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Align autogenerated dimension names when dims and default_dims are provided lucianopaz/arviz
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