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proper handling of chain_idx in sample() #4495

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Merged
merged 7 commits into from
Mar 3, 2021
Merged

proper handling of chain_idx in sample() #4495

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18d4ff3
don't add chain offset in loop already taking care of this
Spaak Mar 1, 2021
bc637df
don't default to chain 0 when computing sampler stats; use provided c…
Spaak Mar 1, 2021
6a56983
adding test script for chain_idx in sample()
Spaak Mar 1, 2021
b2ef671
marking test as xfail for now
Spaak Mar 1, 2021
65e758c
take care of chain indices in sample_posterior_predictive
Spaak Mar 2, 2021
0469c67
update test to include sample_posterior_predictive
Spaak Mar 2, 2021
93b76f9
use reproducable order of ppc samples wrt multiple chains
Spaak Mar 3, 2021
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15 changes: 11 additions & 4 deletions pymc3/sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -599,7 +599,7 @@ def sample(
# count the number of tune/draw iterations that happened
# ideally via the "tune" statistic, but not all samplers record it!
if "tune" in trace.stat_names:
stat = trace.get_sampler_stats("tune", chains=0)
stat = trace.get_sampler_stats("tune", chains=chain_idx)
# when CompoundStep is used, the stat is 2 dimensional!
if len(stat.shape) == 2:
stat = stat[:, 0]
Expand Down Expand Up @@ -1452,9 +1452,9 @@ def _mp_sample(
# dict does not contain all parameters
update_start_vals(start[idx - chain], model.test_point, model)
if step.generates_stats and strace.supports_sampler_stats:
strace.setup(draws + tune, idx + chain, step.stats_dtypes)
strace.setup(draws + tune, idx, step.stats_dtypes)
else:
strace.setup(draws + tune, idx + chain)
strace.setup(draws + tune, idx)
traces.append(strace)

sampler = ps.ParallelSampler(
Expand Down Expand Up @@ -1715,12 +1715,19 @@ def sample_posterior_predictive(

ppc_trace_t = _DefaultTrace(samples)
try:
if hasattr(_trace, "_straces"):
# trace dict is unordered, but we want to return ppc samples in
# a predictable ordering, so sort the chain indices
chain_idx_mapping = sorted(_trace._straces.keys())
for idx in indices:
if nchain > 1:
# the trace object will either be a MultiTrace (and have _straces)...
if hasattr(_trace, "_straces"):
chain_idx, point_idx = np.divmod(idx, len_trace)
param = cast(MultiTrace, _trace)._straces[chain_idx % nchain].point(point_idx)
chain_idx = chain_idx % nchain
# chain indices might not always start at 0, convert to proper index
chain_idx = chain_idx_mapping[chain_idx]
param = cast(MultiTrace, _trace)._straces[chain_idx].point(point_idx)
# ... or a PointList
else:
param = cast(PointList, _trace)[idx % (len_trace * nchain)]
Expand Down
15 changes: 15 additions & 0 deletions pymc3/tests/test_sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -321,6 +321,21 @@ def test_partial_trace_sample():
trace = pm.sample(trace=[a])


@pytest.mark.xfail
def test_chain_idx():
# see https://github.com/pymc-devs/pymc3/issues/4469
with pm.Model():
mu = pm.Normal("mu")
x = pm.Normal("x", mu=mu, sigma=1, observed=np.asarray(3))
# note draws-tune must be >100 AND we need an observed RV for this to properly
# trigger convergence checks, which is one particular case in which this failed
# before
trace = pm.sample(draws=150, tune=10, chain_idx=1)

ppc = pm.sample_posterior_predictive(trace)
ppc = pm.sample_posterior_predictive(trace, keep_size=True)


@pytest.mark.parametrize(
"n_points, tune, expected_length, expected_n_traces",
[
Expand Down