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Sample ppc does not shuffle by default #3212

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Merged
merged 2 commits into from
Oct 7, 2018
+6 −5
Merged

Sample ppc does not shuffle by default #3212

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cece696
Sample ppc does not shuffle by default
ColCarroll Sep 25, 2018
9828145
Cycle through ppc samples
ColCarroll Sep 27, 2018
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3 changes: 2 additions & 1 deletion RELEASE-NOTES.md
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Original file line number Diff line number Diff line change
@@ -6,8 +6,9 @@

- Track the model log-likelihood as a sampler stat for NUTS and HMC samplers
(accessible as `trace.get_sampler_stats('model_logp')`) (#3134)
- Add Incomplete Beta function `incomplete_beta(a, b, value)`
- Add Incomplete Beta function `incomplete_beta(a, b, value)`
- Add log CDF functions to continuous distributions: `Beta`, `Cauchy`, `ExGaussian`, `Exponential`, `Flat`, `Gumbel`, `HalfCauchy`, `HalfFlat`, `HalfNormal`, `Laplace`, `Logistic`, `Lognormal`, `Normal`, `Pareto`, `StudentT`, `Triangular`, `Uniform`, `Wald`, `Weibull`.
- Behavior of `sample_posterior_predictive` is now to produce posterior predictive samples, in order, from all values of the `trace`. Previously, by default it would produce 1 chain worth of samples, using a random selection from the `trace` (#3212)

### Maintenance

8 changes: 4 additions & 4 deletions pymc3/sampling.py
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Original file line number Diff line number Diff line change
@@ -1098,7 +1098,7 @@ def sample_posterior_predictive(trace, samples=None, model=None, vars=None, size
nchain = 1

if samples is None:
samples = len(trace)
samples = sum(len(v) for v in trace._straces.values())

model = modelcontext(model)

@@ -1108,7 +1108,7 @@ def sample_posterior_predictive(trace, samples=None, model=None, vars=None, size
if random_seed is not None:
np.random.seed(random_seed)

indices = np.random.randint(0, nchain * len_trace, samples)
indices = np.arange(samples)

if progressbar:
indices = tqdm(indices, total=samples)
@@ -1126,9 +1126,9 @@ def sample_posterior_predictive(trace, samples=None, model=None, vars=None, size
for slc, idx in enumerate(indices):
if nchain > 1:
chain_idx, point_idx = np.divmod(idx, len_trace)
param = trace._straces[chain_idx].point(point_idx)
param = trace._straces[chain_idx % nchain].point(point_idx)
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Fancy ;-)

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I'll add a PR to numpy for my new divmodmod

else:
param = trace[idx]
param = trace[idx % len_trace]

values = draw_values(vars, point=param, size=size)
for k, v in zip(vars, values):