fix default prior variable names #3591
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I think this is ready to merge. It fixes the issue in the cases I tested locally and I added a test to make sure |
aloctavodia
reviewed
Aug 16, 2019
pymc3/sampling.py
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| vars = set(model.named_vars.keys()) | ||
| vars_ = model.named_vars | ||
| prior_pred_vars = model.observed_RVs | ||
| prior_vars = get_default_varnames(model.unobserved_RVs, include_transformed=True) |
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This will exclude potentials (model.potentials)
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I did not know these existed. Should they be included in prior_vars or prior_pred_vars?
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Potentials are a way to add arbitrary terms to the computation of the logp. You can use them to add constraints to the model. I will add them to prior_vars
michaelosthege
requested changes
Aug 16, 2019
| @@ -505,12 +505,14 @@ def test_ignores_observed(self): | |||
| observed = np.random.normal(10, 1, size=200) | |||
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Fix #3588. I have also noted that
sample_prior_predictivesamples both the prior and the prior_predictive. Considering how default var names are defined now. It would be quite easy to divide the function in 2, asample_priorand asample_prior_predictivebut it would be quite a breaking change. Are you interested in dividing the function at all? Would you rather do in now or in some future PR after having added FutureWarnings here?I think the best idea will probably be to make this PR only about fixing #3588 so it can be merged before the discussion about prior and prior predictive. I can send another PR afterwards with whatever is decided.