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Made sample_shape same across all contexts in draw_values #4305

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merged 6 commits into from
Dec 11, 2020
Merged

Made sample_shape same across all contexts in draw_values #4305

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aa33cd1
Made sample_shape same across all contexts, thereby resolves #3758
Sayam753 Dec 6, 2020
ec8e671
Pass the failing test
Sayam753 Dec 6, 2020
7a3583a
Worked on suggestions
Sayam753 Dec 10, 2020
34e1317
Used to_tuple for size
Sayam753 Dec 10, 2020
da81fd8
Given a mention in release notes
Sayam753 Dec 11, 2020
a749d75
Update RELEASE-NOTES.md
twiecki Dec 11, 2020
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1 change: 1 addition & 0 deletions pymc3/distributions/distribution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -690,6 +690,7 @@ def draw_values(params, point=None, size=None):
"""
# The following check intercepts and redirects calls to
# draw_values in the context of sample_posterior_predictive
size = to_tuple(size)
ppc_sampler = vectorized_ppc.get(None)
if ppc_sampler is not None:
# this is being done inside new, vectorized sample_posterior_predictive
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4 changes: 2 additions & 2 deletions pymc3/distributions/simulator.py
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Expand Up @@ -115,10 +115,10 @@ def random(self, point=None, size=None):
array
"""
params = draw_values([*self.params], point=point, size=size)
if size is None:
if not size:
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Suggested change
if not size:
if len(size) != 0:

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Oh, I am checking for size being empty tuple here. So, do you mean len(size) == 0?

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Yes.

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But if there is a call to this random method directly, then size will be None. I prefer to leave it as is, since it handles both cases (being None or empty tuple).

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@twiecki twiecki Dec 10, 2020 •
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Relying on the truthiness of an object can lead to really subtle and difficult to debug bugs. In that case I would be explicit, even if it doesn't look as clean:
if (size is None) or (len(size) == 0):

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can lead to really subtle and difficult to debug bugs

Indeed

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Yes. I like this idea. Now, I have opted to use to_tuple to better handle corner cases.

return self.function(*params)
else:
return np.array([self.function(*params) for _ in range(size)])
return np.array([self.function(*params) for _ in range(size[0])])
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I handled size this way because now it is passed as a tuple. Ping @aloctavodia to review as this is related to SMC stuff.
Also, test suite passes. :)


def _str_repr(self, name=None, dist=None, formatting="plain"):
if dist is None:
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4 changes: 4 additions & 0 deletions pymc3/tests/test_distributions_random.py
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Expand Up @@ -1664,6 +1664,10 @@ def test_issue_3758(self):
for var in "abcd":
assert not np.isnan(np.std(samples[var]))

for var in "bcd":
std = np.std(samples[var] - samples["a"])
np.testing.assert_allclose(std, 1, rtol=1e-2)

def test_issue_3829(self):
with pm.Model() as model:
x = pm.MvNormal("x", mu=np.zeros(5), cov=np.eye(5), shape=(2, 5))
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