fix(mmm): allow constant tensors in adstock sample_prior (fixes #1749)

[shivamlalakiya]

Description

Fixes #1749

Previously, sample_prior would fail if an adstock transformation (e.g., GeometricAdstock) was initialized with constant tensors instead of PyMC distributions, as pm.sample_prior_predictive requires free random variables.

This PR adds a fallback mechanism: if no free random variables are detected in the model, it evaluates the constant tensors deterministically and returns a valid InferenceData object.

Changes

• Modified sample_prior in transformers.py to handle ValueError from sample_prior_predictive.
• Added regression test tests/mmm/test_issue_1749.py to verify constant inputs work.

Related Issue

• Closes plot curve workflow with constant parameters #1749
• 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--2304.org.readthedocs.build/en/2304/

[cursor]

PR Summary

Low Risk
Small, localized change to prior-sampling that only affects transformations initialized with constant tensors; main risk is slightly different prior datasets due to newly-added pm.Deterministic nodes.

Overview
Fixes Transformation.sample_prior to work when transformation parameters are provided as constant tensors (and to include them in the returned idata.prior). It now registers non-distribution priors as pm.Deterministic variables before calling pm.sample_prior_predictive, covering both all-constant and mixed constant/distribution cases.

Adds regression tests for issue #1749 verifying sample_prior succeeds and returns expected constant values for GeometricAdstock (alpha) and for generic transformations, including with coordinate dimensions.

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[cursor]

Cursor Bugbot has reviewed your changes and found 1 potential issue.

[ricardoV94]

There's not really any prior being sampled...

[shivamlalakiya]

@ricardoV94 You're right — no actual sampling is happening here. The goal was to unblock the plot curve workflow from #1749 which needs an InferenceData to proceed. Would you prefer a different approach? For example, wrapping constants in pm.DiracDelta, emitting a warnings.warn to be explicit, or something else entirely? Happy to rework it based on your guidance.

[ricardoV94]

I was being just pedantic. It makes sense to support plot_curve, it's just that there's no prior to be sampled from so sample_prior was a bit iffy. But being pragmatic this is probably fine

[codecov]

Codecov Report

❌ Patch coverage is 0% with 7 lines in your changes missing coverage. Please review.
✅ Project coverage is 23.31%. Comparing base (941bf5f) to head (c940cf4).
⚠️ Report is 46 commits behind head on main.

Files with missing lines	Patch %	Lines
pymc_marketing/mmm/components/base.py	0.00%	7 Missing ⚠️

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Additional details and impacted files

@@             Coverage Diff             @@
##             main    #2304       +/-   ##
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- Coverage   93.05%   23.31%   -69.74%     
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  Files          78       78               
  Lines       12230    12235        +5     
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- Hits        11381     2853     -8528     
- Misses        849     9382     +8533     

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