#984 uses init!! to implement predict. However, the implementation of include_all=false seems a bit wasteful because it first constructs a chain using all parameters (including the ones we don't want) before then subsetting the chain. It seems more sensible to, inside the loop, filter the dictionary of varname => value pairs in each iteration so that those variables don't end up in the chain to begin with.
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predictive_samples = map(iters) do (sample_idx, chain_idx) |
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# Extract values from the chain |
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values_dict = chain_sample_to_varname_dict(parameter_only_chain, sample_idx, chain_idx) |
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# Resample any variables that are not present in `values_dict` |
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_, varinfo = DynamicPPL.init!!( |
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rng, |
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model, |
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varinfo, |
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DynamicPPL.InitFromParams(values_dict, DynamicPPL.InitFromPrior()), |
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) |
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vals = DynamicPPL.values_as_in_model(model, false, varinfo) |
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varname_vals = mapreduce( |
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collect, |
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vcat, |
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map(AbstractPPL.varname_and_value_leaves, keys(vals), values(vals)), |
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) |
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return (varname_and_values=varname_vals, logp=DynamicPPL.getlogjoint(varinfo)) |
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end |
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chain_result = reduce( |
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MCMCChains.chainscat, |
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[ |
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_predictive_samples_to_chains(predictive_samples[:, chain_idx]) for |
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chain_idx in 1:size(predictive_samples, 2) |
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], |
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) |
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parameter_names = if include_all |
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MCMCChains.names(chain_result, :parameters) |
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else |
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filter( |
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k -> !(k in MCMCChains.names(parameter_only_chain, :parameters)), |
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names(chain_result, :parameters), |
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) |
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end |
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return chain_result[parameter_names] |
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end |
Not making this change in #984 to avoid complicating matters.
