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good first issueGood for newcomers . Doesn't require extensive knowledge of the repo and packageGood for newcomers . Doesn't require extensive knowledge of the repo and packagemaintenanceno releasenotesresearch-doneresearch_completeresearch_needed
Description
Following merge of pymc-devs/pymc#7887, the pymc.testing module can be used for this runner
pymc-marketing/scripts/run_notebooks/injected.py
Lines 8 to 36 in 9c429ed
| def mock_sample(*args, **kwargs): | |
| random_seed = kwargs.get("random_seed", None) | |
| model = kwargs.get("model", None) | |
| samples = 10 | |
| idata = pm.sample_prior_predictive( | |
| model=model, | |
| random_seed=random_seed, | |
| draws=samples, | |
| ) | |
| idata.add_groups(posterior=idata.prior) | |
| # Create mock sample stats with diverging data | |
| if "sample_stats" not in idata: | |
| n_chains = 1 | |
| n_draws = samples | |
| sample_stats = xr.Dataset( | |
| { | |
| "diverging": xr.DataArray( | |
| np.zeros((n_chains, n_draws), dtype=int), | |
| dims=("chain", "draw"), | |
| ) | |
| } | |
| ) | |
| idata.add_groups(sample_stats=sample_stats) | |
| del idata.prior | |
| if "prior_predictive" in idata: | |
| del idata.prior_predictive | |
| return idata |
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good first issueGood for newcomers . Doesn't require extensive knowledge of the repo and packageGood for newcomers . Doesn't require extensive knowledge of the repo and packagemaintenanceno releasenotesresearch-doneresearch_completeresearch_needed