Add ModelSamplerEstimator for PyMC Marketing model benchmarking

[cetagostini]

Description

Introduces the ModelSamplerEstimator utility to estimate computational time of a PyMC models using JAX/NumPyro, including logp/gradient evaluation time and NUTS step counts. The output should be the minimum sample time from their models. Doing so, users could compare the minimum amount of time that their given model will take.

Adds corresponding tests to validate estimator output and schema for simple and multidimensional models.

Example:

est = ModelSamplerEstimator(
    tune=1000, draws=1000, chains=4, sequential_chains=1, seed=1
)
df = est.run(model)

Important

Only working with JAX at the moment.

Related Issue

• Closes #
• Related to #

Checklist

• Checked that the pre-commit linting/style checks pass. Feel free to comment pre-commit.ci autofix to auto-fix.
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks) using numpydoc format.
• If you are a pro: each commit corresponds to a relevant logical change

---

📚 Documentation preview 📚: https://pymc-marketing--1943.org.readthedocs.build/en/1943/

[cetagostini]

I have a few PRs, I'll add examples in the notebook on another PR :)

[codecov]

Codecov Report

❌ Patch coverage is 96.72131% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 92.05%. Comparing base (6b30c10) to head (a53d5c6).

Files with missing lines	Patch %	Lines
pymc_marketing/pytensor_utils.py	96.72%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1943      +/-   ##
==========================================
+ Coverage   92.01%   92.05%   +0.03%     
==========================================
  Files          67       67              
  Lines        7932     7993      +61     
==========================================
+ Hits         7299     7358      +59     
- Misses        633      635       +2     

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[cetagostini]

@juanitorduz or @williambdean ready for review!