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Drop nuts init method from pm.sample #3863

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Merged
merged 3 commits into from
Mar 31, 2020
+4 −16
Merged

Drop nuts init method from pm.sample #3863

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3b35141
Dropped nuts init method
AlexAndorra Mar 31, 2020
5fe3caa
Dropped nuts init method from tests
AlexAndorra Mar 31, 2020
43e6411
Refined doc string and added release note
AlexAndorra Mar 31, 2020
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12 changes: 1 addition & 11 deletions pymc3/sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -274,7 +274,6 @@ def sample(
* advi: Run ADVI to estimate posterior mean and diagonal mass matrix.
* 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,
Expand Down Expand Up @@ -1865,14 +1864,12 @@ def init_nuts(
* advi: Run ADVI to estimate posterior mean and diagonal mass matrix.
* 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
chains: int
Number of jobs to start.
n_init: int
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I think n_init is only used for advi now, right? COuld make that clear in the doc string.

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Done

Number of iterations of initializer
If 'ADVI', number of iterations, if 'nuts', number of draws.
If 'ADVI', number of iterations.
model: Model (optional if in ``with`` context)
progressbar: bool
Whether or not to display a progressbar for advi sampling.
Expand Down Expand Up @@ -2001,13 +1998,6 @@ def init_nuts(
cov = pm.find_hessian(point=start)
start = [start] * chains
potential = quadpotential.QuadPotentialFull(cov)
elif init == "nuts":
init_trace = pm.sample(
draws=n_init, step=pm.NUTS(), tune=n_init // 2, random_seed=random_seed
)
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)
Expand Down
3 changes: 1 addition & 2 deletions pymc3/tests/test_sampling.py
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Expand Up @@ -87,7 +87,7 @@ def test_sample(self):

def test_sample_init(self):
with self.model:
for init in ("advi", "advi_map", "map", "nuts"):
for init in ("advi", "advi_map", "map"):
pm.sample(
init=init,
tune=0,
Expand Down Expand Up @@ -675,7 +675,6 @@ def test_sample_posterior_predictive_w(self):
"advi+adapt_diag_grad",
"map",
"advi_map",
"nuts",
],
)
def test_exec_nuts_init(method):
Expand Down