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Add QuadPotentialFullAdapt in pm.sample init #3858

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Merged
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Mar 28, 2020
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Add QuadPotentialFullAdapt in pm.sample init #3858

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1 change: 1 addition & 0 deletions RELEASE-NOTES.md
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Expand Up @@ -10,6 +10,7 @@
- Add `fast_sample_posterior_predictive`, a vectorized alternative to `sample_posterior_predictive`. This alternative is substantially faster for large models.
- `sample_posterior_predictive` can now feed on `xarray.Dataset` - e.g. from `InferenceData.posterior`. (see [#3846](https://github.com/pymc-devs/pymc3/pull/3846))
- `SamplerReport` (`MultiTrace.report`) now has properties `n_tune`, `n_draws`, `t_sampling` for increased convenience (see [#3827](https://github.com/pymc-devs/pymc3/pull/3827))
- `pm.sample` now has support for adapting dense mass matrix using `QuadPotentialFullAdapt` (see [#3596](https://github.com/pymc-devs/pymc3/pull/3596), [#3705](https://github.com/pymc-devs/pymc3/pull/3705) and [#3858](https://github.com/pymc-devs/pymc3/pull/3858))

### Maintenance
- Remove `sample_ppc` and `sample_ppc_w` that were deprecated in 3.6.
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7 changes: 7 additions & 0 deletions pymc3/sampling.py
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Expand Up @@ -275,6 +275,7 @@ def sample(
* advi_map: Initialize ADVI with MAP and use MAP as starting point.
* map: Use the MAP as starting point. This is discouraged.
* nuts: Run NUTS and estimate posterior mean and mass matrix from the trace.
* adapt_full: Adapt a dense mass matrix using the sample covariances
step: function or iterable of functions
A step function or collection of functions. If there are variables without step methods,
step methods for those variables will be assigned automatically. By default the NUTS step
Expand Down Expand Up @@ -1866,6 +1867,7 @@ def init_nuts(
* map: Use the MAP as starting point. This is discouraged.
* nuts: Run NUTS and estimate posterior mean and mass matrix from
the trace.
* adapt_full: Adapt a dense mass matrix using the sample covariances
chains: int
Number of jobs to start.
n_init: int
Expand Down Expand Up @@ -2006,6 +2008,11 @@ def init_nuts(
cov = np.atleast_1d(pm.trace_cov(init_trace))
start = list(np.random.choice(init_trace, chains))
potential = quadpotential.QuadPotentialFull(cov)
elif init == "adapt_full":
start = [model.test_point] * chains
mean = np.mean([model.dict_to_array(vals) for vals in start], axis=0)
cov = np.ones((model.ndim, model.ndim))
potential = quadpotential.QuadPotentialFullAdapt(model.ndim, mean, cov, 10)
else:
raise ValueError("Unknown initializer: {}.".format(init))

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