Fix bug with compute_p for partially observed OrderedLogistic and OrderedProbit variables
#7349
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This fixes a bug when auto-imputation takes place, in which case the logic to retrieve `p` wasn't valid. This will also show `p` as an input of the underlying Categorical in the graphviz.
This fixes a bug when auto-imputation takes place, in which case the logic to retrieve `p` wasn't valid. This will also show `p` as an input of the underlying Categorical in the graphviz.
ricardoV94
commented
Jun 5, 2024
| if compute_p: | ||
| pm.Deterministic(f"{name}_probs", out_rv.owner.inputs[3], dims=kwargs.get("dims")) | ||
| p = pm.Deterministic(f"{name}_probs", p, dims=kwargs.get("dims")) |
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The changes have the nice side-effect that p will now be an arrow going into the underlying Categorical
AlexAndorra
approved these changes
Jun 5, 2024
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This fixes a bug when auto-imputation takes place, in which case the logic to retrieve
pwasn't valid. This will also showpas an input of the underlying Categorical in the graphviz.Description
This PR fixes a bug when setting
compute_p=Truefor partially observed OrderedLogistic/Probit. The logic of retrievingpfrom the third input does not work in this case, because when there is imputation a different kind of variable is returned (the joined vector of imputed and observed variables)Checklist
Type of change
📚 Documentation preview 📚: https://pymc--7349.org.readthedocs.build/en/7349/