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Update pymc.TruncatedNormal docstring #5546

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Merged
merged 7 commits into from
Mar 12, 2022
Merged

Update pymc.TruncatedNormal docstring #5546

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df1850c
fixed TruncateNormal docsting
purna135 Mar 4, 2022
4f6fff4
Merge remote-tracking branch 'upstream/main' into doc_continuous_Trun…
purna135 Mar 6, 2022
4a12878
fixed TruncateNormal docsting
purna135 Mar 6, 2022
c972983
removed sd from dist
purna135 Mar 10, 2022
016b88e
Merge remote-tracking branch 'upstream/main' into doc_continuous_Trun…
purna135 Mar 10, 2022
d36d34c
undo the sd change
purna135 Mar 12, 2022
fbf1190
Merge remote-tracking branch 'upstream/main' into doc_continuous_Trun…
purna135 Mar 12, 2022
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16 changes: 8 additions & 8 deletions pymc/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -673,14 +673,14 @@ class TruncatedNormal(BoundedContinuous):

Parameters
----------
mu: float
mu : tensor_like of float, optional
Mean.
sigma: float
sigma : tensor_like of float, optional
Standard deviation (sigma > 0).
lower: float (optional)
Left bound.
upper: float (optional)
Right bound.
lower : tensor_like of float, optional
Left bound. Defaults to 0.
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I think the defaults are wrong (same for upper). I'd bet the default is no bound or infinity or somehting of the sort

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I am unable to find out the default value for mu and sigma.
could you please guide me, how to identify these dafult value so that it can help me for other methords.

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First check the description in the docstring, sometimes the defaults are incorrectly explained in the extended summary section. If they are not you should read the .dist method and follow the variables defined there and/or function calls to see what the defaults are. Here are some examples, I hope they are illustrative and you can follow the pattern for the rest.

mu

pymc/pymc/distributions/continuous.py

Line 720 in 4223b6e

mu = at.as_tensor_variable(floatX(mu))

it is defined straight away here, so it seems like it is not optional actually. @ricardoV94 is this intended? If the value in the function call were 0 instead of None then you would use the default 0 instead of optional for example, however, if some function is called to get the default, it probably means you should use optional and explain the default (or combinations of defaults depending on other parameters) in the description

lower

pymc/pymc/distributions/continuous.py

Line 724 in 4223b6e

lower = at.as_tensor_variable(floatX(lower)) if lower is not None else at.constant(-np.inf)

the default in the function call is None, and when it is None, what is actually used is - infinity. When a value is given, that value is used. So lower should be lower : tensor_like of float, default -numpy.inf (and check in the readthedocs preview that it gets rendered correctly).

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Looks like the default of mu should indeed be zero, not None

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transform as a positional argument is outdated an should be removed from the dist signature

upper : tensor_like of float, optional
Right bound. Defaults to 1.

Examples
--------
Expand Down Expand Up @@ -762,9 +762,9 @@ def logp(

Parameters
----------
value: numeric
value : tensor_like of float
Value(s) for which log-probability is calculated. If the log probabilities for multiple
values are desired the values must be provided in a numpy array or Aesara tensor
values are desired the values must be provided in a numpy array or Aesara tensor.

Returns
-------
Expand Down