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use inference data in end of sampling report #3883

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Apr 20, 2020
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use inference data in end of sampling report #3883

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9b697f6
use from_pymc3(..., log_likelihood=False) and update requirements
OriolAbril Apr 18, 2020
ecb70a6
update release notes
OriolAbril Apr 19, 2020
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2 changes: 2 additions & 0 deletions RELEASE-NOTES.md
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,8 @@
- Dropped the outdated 'nuts' initialization method for `pm.sample` (see [#3863](https://github.com/pymc-devs/pymc3/pull/3863)).
- Moved argument division out of `NegativeBinomial` `random` method. Fixes [#3864](https://github.com/pymc-devs/pymc3/issues/3864) in the style of [#3509](https://github.com/pymc-devs/pymc3/pull/3509).
- The Dirichlet distribution now raises a ValueError when it's initialized with <= 0 values (see [#3853](https://github.com/pymc-devs/pymc3/pull/3853)).
- End of sampling report now uses `arviz.InferenceData` internally and avoids storing
pointwise log likelihood (see [#3883](https://github.com/pymc-devs/pymc3/pull/3883))

## PyMC3 3.8 (November 29 2019)

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8 changes: 5 additions & 3 deletions pymc3/backends/report.py
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Expand Up @@ -87,7 +87,7 @@ def n_draws(self) -> typing.Optional[int]:
def t_sampling(self) -> typing.Optional[float]:
"""
Number of seconds that the sampling procedure took.

(Includes parallelization overhead.)
"""
return self._t_sampling
Expand All @@ -108,6 +108,7 @@ def _run_convergence_checks(self, trace, model):
return

from pymc3 import rhat, ess
from arviz import from_pymc3

valid_name = [rv.name for rv in model.free_RVs + model.deterministics]
varnames = []
Expand All @@ -119,8 +120,9 @@ def _run_convergence_checks(self, trace, model):
if rv_name in trace.varnames:
varnames.append(rv_name)

self._ess = ess = ess(trace, var_names=varnames)
self._rhat = rhat = rhat(trace, var_names=varnames)
idata = from_pymc3(trace, log_likelihood=False)
self._ess = ess = ess(idata, var_names=varnames)
self._rhat = rhat = rhat(idata, var_names=varnames)

warnings = []
rhat_max = max(val.max() for val in rhat.values())
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4 changes: 2 additions & 2 deletions requirements.txt
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@@ -1,4 +1,4 @@
arviz>=0.4.1
arviz>=0.7.0
theano>=1.0.4
numpy>=1.13.0
scipy>=0.18.1
Expand All @@ -7,4 +7,4 @@ patsy>=0.5.1
fastprogress>=0.2.0
h5py>=2.7.0
typing-extensions>=3.7.4
contextvars; python_version < '3.7'
contextvars; python_version < '3.7'