Model API cleanup #6309
Model API cleanup #6309
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Codecov Report
Additional details and impacted files@@ Coverage Diff @@
## main #6309 +/- ##
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+ Coverage 91.87% 94.18% +2.31%
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Files 111 111
Lines 23917 23908 -9
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+ Hits 21973 22518 +545
+ Misses 1944 1390 -554
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This also disables prior_predictive sampling of transformed variables
This property was initially added just to handle deterministics created by automatic imputation, in order to ensure the combined tensor of missing and observed components showed up in prior and posterior predictive sampling. At the same time, it allowed hiding the deterministic during mcmc sampling, saving memory use for large datasets. This last benefit is lost for the sake of simplicity. If a user is concerned, they can manually split the observed and missing components of a dataset when defining their model.
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| @@ -343,7 +362,7 @@ def sample_prior_predictive( | |||
| var_names : Iterable[str] | |||
| A list of names of variables for which to compute the prior predictive | |||
| samples. Defaults to both observed and unobserved RVs. Transformed values | |||
| are not included unless explicitly defined in var_names. | |||
| are not allowed. | |||
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Any reason why transformed (unconstrained) values are not allowed here? I need to get prior samples in the transformed space, what should I do? Thanks!
(MCMC sampling by removing observations from the likelihood is not a good option. I guess I could also use sample_prior_predictive to get prior parameter samples and then transform back to the unconstrained space but it seems not easy to do it with Transforms in an efficient vectorized way, i.e., it seems I have to do a for-loop.)
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A helper to go from unconstrained back to constrained draws is needed elsewhere (specially since we don't save them in InferenceData after sampling), so we can add that and will also cover your use case.
I think there is an open issue for that.
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Thanks for the quick reply! Transforming efficiently between unconstrained and constrained draws would be super useful for our use case. What would be needed for its implementation? In the meantime, what could be a good workaround? (I need to transform a lot of prior samples from constrained to unconstrained. Or alternatively, unconstrained prior samples are even more useful to me than constrained samples. So it would be better if I could get them directly.) Can I modify back sample_prior_predictive to include the transformed variables like sd_log__?
For the InferenceData, pm.sample(..., idata_kwargs={"include_transformed": True}) seems to include the transformed samples.
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Hi, please see my comment above. Any reason why transformed (unconstrained) values are not allowed in |
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No we shouldn't change I'll open a PR soon to add the functionality. |
Trying to standardize mapping names a bit more to facilitate model transformations such as in pymc-devs/pymc-extras#91
Closes #6305
Closes #5076
Major / Breaking Changes
prior_predictiveis no longer allowedBugfixes / New features
Docs / Maintenance
Modelvariables