Handle NaNs in the logp in SMC

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Bayesian Modeling and Probabilistic Programming in Python

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Handle NaNs in the logp in SMC #7293

Open astoeriko opened 5 months ago

astoeriko commented 5 months ago

Description

The changes deal with the case that the logp is NaN in the SMC sampler. With the changes, samples with a NaN-logp will be simply discarded during the resampling step by assigning them a logp of -inf.

I know that this is not an ideal solution to the problem, but rather a pragmatic workaround. Maybe it would be wise to add a warning that there were NaN values?

Related Issue

[x] Closes #7292
[ ] Related to #

Checklist

[ ] Checked that the pre-commit linting/style checks pass
[ ] Included tests that prove the fix is effective or that the new feature works
[ ] Added necessary documentation (docstrings and/or example notebooks)
[ ] If you are a pro: each commit corresponds to a relevant logical change

Type of change

[ ] New feature / enhancement
[x] Bug fix
[ ] Documentation
[ ] Maintenance
[ ] Other (please specify):

📚 Documentation preview 📚: https://pymc--7293.org.readthedocs.build/en/7293/

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aseyboldt commented 5 months ago

Thank you for the PR!

It would be nice if we can come up with a test that this is in fact enough. Maybe we can arbitrarily introduce some nan values into a model, and sample it? So for instance

with pm.Model():
    x = pm.Normal("x")
    pm.Normal("y", mu=x, sigma=0.1, observed=1)
    # Return nan in 50% of the prior draws
    pm.Potential("make_nan", pt.where(pt.geq(x, 0), 0, np.nan))

astoeriko commented 5 months ago

Testing this with a simple example would be great! I was wondering if there is a way to artificially introduce NaN values in a model. So thanks for providing an example, this will help me verify with a simpler model if the problems I am seeing when sampling with SMC are indeed related to not handling NaN values. I am still not very clear about what the test should test in the end. That we do not end up with a single sample per chain?

aseyboldt commented 5 months ago

I think checking that all samples are positive and the posterior variance is reasonable should be enough. The true posterior standard deviation should be $\sqrt{(1 + 1/100)^{-1}} \approx 0.1$, so maybe we just check that it's between 0.05 and 0.2 or so? We could also be more thorough and do a ks test against the true posterior, but I think for our purposes here that shouldn't be necessary.

aloctavodia commented 2 months ago

Hi @astoeriko, are you still interested in this PR?