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Updates to pymc.Hypergeometric docstrings #5488

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Merged
merged 6 commits into from
Mar 3, 2022
Merged

Updates to pymc.Hypergeometric docstrings #5488

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21498ef
Added bounds to the docstring of the Hypergeometric dist
cuchoi Feb 18, 2022
6250180
Merge pull request #1 from cuchoi/cuchoi-hypergeometric-docstring-update
cuchoi Feb 18, 2022
063440c
Updated pymc.Hypergeometric docstring
cuchoi Feb 18, 2022
413ed0e
Added array_like types for parameters
cuchoi Feb 19, 2022
30898b9
Added array_like types for parameters
cuchoi Feb 19, 2022
2827e57
Update pymc/distributions/discrete.py
OriolAbril Mar 3, 2022
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24 changes: 17 additions & 7 deletions pymc/distributions/discrete.py
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Original file line number Diff line number Diff line change
Expand Up @@ -971,12 +971,12 @@ class HyperGeometric(Discrete):

Parameters
----------
N : integer
Total size of the population
k : integer
Number of successful individuals in the population
n : integer
Number of samples drawn from the population
N : tensor_like of integer
Total size of the population (N > 0)
k : tensor_like of integer
Number of successful individuals in the population (0 <= k <= N)
n : tensor_like of integer
Number of samples drawn from the population (0 <= n <= N)
"""

rv_op = hypergeometric
Expand Down Expand Up @@ -1004,6 +1004,10 @@ def logp(value, good, bad, n):
value : numeric
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
good : integer, array_like or TensorVariable
Number of successful individuals in the population. Alias for parameter :math:`k`.
bad : integer, array_like or TensorVariable
Number of unsuccessful individuals in the population. Alias for :math:`N-k`.
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Should we also use "tensor_like of integer" here @OriolAbril ?

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We are in the process of removing the distribution methods from the documentation and leaving only the dist one. The Distribution class is still being refactored, see #5308 for some more details (I don't really understand what is going on very well) and those methods might be removed completely, and because they should not be used themselves already but called via pm.logp() or pm.logcdf

I think it's best to leave them be for now.


Returns
-------
Expand Down Expand Up @@ -1042,8 +1046,14 @@ def logcdf(value, good, bad, n):

Parameters
----------
value: numeric
value : numeric
Value for which log CDF is calculated.
good : integer
Number of successful individuals in the population. Alias for parameter :math:`k`.
bad : integer
Number of unsuccessful individuals in the population. Alias for :math:`N-k`.
n : integer
Number of samples drawn from the population (0 <= n <= N)

Returns
-------
Expand Down