[ENH] interfacing Poisson regressor from sklearn

[nilesh05apr]

Reference Issues/PRs

#7

What does this implement/fix? Explain your changes.

Added interface for Poisson Regressor

Does your contribution introduce a new dependency? If yes, which one?

None

Did you add any tests for the change?

None

PR checklist

For all contributions

• I've added myself to the list of contributors with any new badges I've earned :-)
  How to: add yourself to the all-contributors file in the skpro root directory (not the CONTRIBUTORS.md). Common badges: code - fixing a bug, or adding code logic. doc - writing or improving documentation or docstrings. bug - reporting or diagnosing a bug (get this plus code if you also fixed the bug in the PR).maintenance - CI, test framework, release.
  See here for full badge reference
• The PR title starts with either [ENH], [MNT], [DOC], or [BUG]. [BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving code, [DOC] - writing or improving documentation or docstrings.

For new estimators

• I've added the estimator to the API reference - in docs/source/api_reference/taskname.rst, follow the pattern.
• I've added one or more illustrative usage examples to the docstring, in a pydocstyle compliant Examples section.
• If the estimator relies on a soft dependency, I've set the python_dependencies tag and ensured
  dependency isolation, see the estimator dependencies guide.

[nilesh05apr]

@fkiraly can you please approve changes? I will keep adding more linear models.

[fkiraly]

Thanks, nice idea!

However, I think more is needed to add the poisson regressor.

The current SklearnProbaReg assumes a normal distribution, and predict having an return_std argument. This is not true for the sklearn PoissonRegressor:

• the assumed return distribution is the Poisson distribution
• predict does not have a return_std, it simply returns the rate parameter.

To make this work, you would have to:

• add a tabular Poisson distribution to the distributions module
• use that to encode the output of predict (the rate parameter)

[nilesh05apr]

@fkiraly thanks for the review, I will implement the changes soon

[fkiraly]

Abandoned - wrapped it up so it can be merged