#1035 Distribution new customer enhancements

[Ishaanjolly]

Distribution new customer enhancements

Description

I added n_samples: int = 1000 to _distribution_new_customers and modified this line within:

        if dataset.sizes["chain"] == 1 and dataset.sizes["draw"] == 1:
            # For map fit add a dummy draw dimension
            dataset = dataset.squeeze("draw").expand_dims(draw=range(n_samples))

Additionally:
I added n_samples: int = 1 to distribution_new_customer_recency_frequency and modified this:

self._distribution_new_customers(
            data=data,
            T=T,
            random_seed=random_seed,
            var_names=["recency_frequency"],
            n_samples=n_samples,
        )["recency_frequency"]

Related Issue

• Closes #
• Related to distribution_new_customer Enhancements #1035

Checklist

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

Modules affected

• MMM
• CLV

Type of change

• New feature / enhancement
• Bug fix
• Documentation
• Maintenance
• Other (please specify):

---

📚 Documentation preview 📚: https://pymc-marketing--1061.org.readthedocs.build/en/1061/

[juanitorduz]

Can we leave this change out of this PR? 🙏

[Ishaanjolly]

Just done this thanks

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 95.60%. Comparing base (1a1703c) to head (7b14ccc).
Report is 95 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1061      +/-   ##
==========================================
- Coverage   95.85%   95.60%   -0.26%     
==========================================
  Files          39       39              
  Lines        3934     3934              
==========================================
- Hits         3771     3761      -10     
- Misses        163      173      +10     

Flag	Coverage Δ
	95.60% <100.00%> (-0.26%)	⬇️

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[juanitorduz]

Thank you @Ishaanjolly !

[ColtAllen]

Sorry that I was late in seeing this, but the same changes can also be applied to ParetoNBDModel. It's also worth noting this PR is related to #878 but I'm not entirely sure if that issue is worth pursuing.

[juanitorduz]

Thanks @ColtAllen ! @Ishaanjolly would you wanna do something similar to ParetoNBDModel?

[Ishaanjolly]

Sure, I'll create a new issue for it and pursue it. Thanks @juanitorduz and @ColtAllen

@ColtAllen would also appreciate your suggestions on best way to cache log_p outputs.