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[ENH] Creating a new Bayesian Regressor with PyMC as a backend #358
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
…ons have diff. behaviors wrt tensor mutability)
skpro/regression/bayesian.py
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# Priors for unknown model parameters | ||
self.intercept = pm.Normal("intercept", mu=self.intercept_mu, sigma=self.intercept_sigma) | ||
self.slopes = pm.Normal("slopes", mu=self.slopes_mu, sigma=self.slopes_sigma, shape = self._X.shape[1], dims=("pred_id")) | ||
self.noise = pm.HalfNormal("noise", sigma=self.noise_sigma) |
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would inverse gamma not be more standard here, as it is conjugate to the normal?
Strange import error - is this related to an upper bound of any of the imports implied by 3.9, e.g., |
Apparently there is a bug with Arviz 0.17 and scipy>=1.13 (source 1) (source 2). The bug is no longer present in Arviz 0.18 but this requires Python 3.10 and above. As a temporary solution, I've locked the scipy version in |
Makes sense. From a maintenance perspective, applying the version bound in the Could you add the lock instead in the |
…ressor dependencies
Makes sense! But I've tried this a couple of times and for some reason, without the It might be that other libraries are pulling in a conflicting version, but I haven't managed to find the exact cause.. Any ideas? |
Why are you trying to bound |
PS: why did you close the notebook PR? That was a nice notebook, and indeed it would be nice as separate PR. |
Ah ok! I was trying to make it work for python 3.9. I've now locked the python version and arviz version in the latest commit
Just closed it temporarily as I'm still working on adding the |
is this PR ready for merge? I would really suggest to chunk PRs in smaller, self-contained additions, so we can merge your contributions more quickly and PR do not get too large. For instance, |
Yes it is!
Sure, noted! Yes, you're right, we can add the Also, I've documented the logic behind this class in this PR, could you please take a look at this when you have time? Thank you! |
Then you should adjust the tagging/signposting:
|
PS: there is a conflict in the |
PS on the
Instead of introducing a potentially misleading |
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The code looks great!
There are some testing issues:
- the failure on 3.9 still seems there, even though we clearly set the tag. Could you check if you can understand what is going on there?
get_test_params
should be populated with at least two examples.
Reference Issues/PRs
#7
What does this implement/fix? Explain your changes.
This WIP PR implements a Bayesian Linear Regressor with PyMC as a backend
Does your contribution introduce a new dependency? If yes, which one?
Yes - it depends on PyMC family: PyMC itself, XArray and ArviZ
What should a reviewer concentrate their feedback on?
The design of the BayesianLinearRegressor. Especially:
Did you add any tests for the change?
Not yet
Any other comments?
N/A
PR checklist
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skpro
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). Common badges:code
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For new estimators
(This is not yet done)
docs/source/api_reference/taskname.rst
, follow the pattern.Examples
section.python_dependencies
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