Loading PPC data into arviz in pymc3

Currently, the easiest way to get data from pymc3 into arviz is via pm.sample(return_inferencedata=True), which gives back an arviz InferenceData object. However, pm.sample_posterior_predictive() only supports dictionary returns.

It’s unclear what the right approach is, as at this point the InferenceData object is already created. I guess we either need an easy, dimension-aware way to add the PPC data later or create a new object with it.

[kyleabeauchamp]

Thanks, the az.concat idea is working, and I’m testing out the PR branch of pymc3 that fixes the keep_size issue. So I think I’m now on track again 😃.

[OriolAbril]

To add posterior predictive samples to an inference data object with pymc3, I think the best approach is:

...
    idata = pm.sample(..., return_inference_data=True)
...
    ppc = pm.sample_posterior_predictive(..., keep_size=True)
az.concat(idata, az.from_dict(posterior_predictive=ppc), inplace=True)

There is also issue #1239 to make this last line less convoluted.

As Robert pointed out, to add predictions (out of sample posterior predictive samples) the way to go should be from_pymc3_predictions as this will add the samples returned by sample_posterior_predictive to predictions groups as well as the data used to generate them to constant_data_predictions (assuming you are using pm.Data container).

I commented in the issue you linked as there seems to be a bug with keep_size is a dataset is passed so most of this won’t work for now 😕