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Updated docstrings in pymc.step_methods.metropolis for BinaryGibbsMetropolis to follow numpydoc #7212

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Updated docstrings in pymc.step_methods.metropolis for BinaryGibbsMetropolis to follow numpydoc #7212

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d1fa3b9
fixed docstring of BinaryGibbsMetropolis
HangenYuu Mar 22, 2024
fc416e1
Merge branch 'pymc-devs:main' into metropolis-docstring
HangenYuu Apr 7, 2024
440d894
fixed docstring of BinaryGibbsMetropolis
HangenYuu Mar 22, 2024
54b5ed7
Fixed docstring based on Oriol's suggestions
HangenYuu Apr 7, 2024
69e1347
Merge branch 'metropolis-docstring' of github.com:HangenYuu/pymc into…
HangenYuu Apr 7, 2024
ee9be0c
Update metropolis.py
HangenYuu Apr 7, 2024
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24 changes: 12 additions & 12 deletions pymc/step_methods/metropolis.py
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Expand Up @@ -140,22 +140,22 @@ def __init__(

Parameters
----------
vars: list
vars : list
List of value variables for sampler
S: standard deviation or covariance matrix
S : standard deviation or covariance matrix
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standard deviation or covariance matrix aren't types, this is mixing type with description. I think I have never used Metropolis, so my guess about behaviour here might be wrong but here is a proposal:

S : array_like with shape (N,) or (N, N), optional

Then in proposal_dist description:

Function that returns zero-mean deviates when parameterized with
`S` (and n). If `S` is one dimensional, it defaults to a normal, with `S` as its standard deviation;
If `S` is two dimensional, it defaults to a multivariate normal with `S` as its covariance matrix.

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I have changed this one based on your suggestions. I also changed S description to match.

Some measure of variance to parameterize proposal distribution
proposal_dist: function
proposal_dist : function
Function that returns zero-mean deviates when parameterized with
S (and n). Defaults to normal.
scaling: scalar or array
scaling : scalar or array
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Suggested change
scaling : scalar or array
scaling : scalar or array_like, default 1

for inputs unless it is required they are already a numpy array it is better to use array_like instead.

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I have changed this one based on your suggestions.

Initial scale factor for proposal. Defaults to 1.
tune: bool
tune : bool
Flag for tuning. Defaults to True.
tune_interval: int
tune_interval : int
The frequency of tuning. Defaults to 100 iterations.
model: PyMC Model
model : PyMC Model
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Suggested change
model : PyMC Model
model : Model

using only Model should trigger the use of this alias: https://github.com/pymc-devs/pymc/blob/main/docs/source/conf.py#L73 and render that as a link to the pymc.Model api documentation.

note, it might not work right now because a while back some objects were undocumented. For example, the Model class might not be currently documented at pymc.Model. If that were the case ignore it; the docs for pymc.Model will be fixed at some point and everything will work again.

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I have changed this one based on your suggestions.

Optional model for sampling step. Defaults to None (taken from context).
mode: string or `Mode` instance.
mode : string or `Mode` instance.
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I don't really know what Mode is so it would be nice to have it as a link, unfortunately I can't give a suggestion unless I know where it should point to. Hopefully someone else can comment on this

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It's a pytensor thing... Not sure we have a more precise signature anywhere. The class is https://github.com/pymc-devs/pytensor/blob/a76172ee6a9753266150fc7bac4dd66906967a26/pytensor/compile/mode.py#L275

But it can also be one of a predefined set of strings :D

compilation mode passed to PyTensor functions
"""

Expand Down Expand Up @@ -443,15 +443,15 @@ class BinaryGibbsMetropolis(ArrayStep):

Parameters
----------
vars: list
vars : list
List of value variables for sampler
order: list or 'random'
order : list or 'random'
List of integers indicating the Gibbs update order
e.g., [0, 2, 1, ...]. Default is random
transit_p: float
transit_p : float
The diagonal of the transition kernel. A value > .5 gives anticorrelated proposals,
which resulting in more efficient antithetical sampling. Default is 0.8
model: PyMC Model
model : PyMC Model
Optional model for sampling step. Defaults to None (taken from context).

"""
Expand Down