Will Dean

mock MMM posterior with prior

13

## Description ## Related Issue - [x] Closes #518 Closes #86 - [ ] Related to # ## Checklist - [ ] Checked that [the pre-commit linting/style checks pass](https://docs.pymc.io/en/latest/contributing/python_style.html) -...

Image comparison in test suite

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In order to improve the tests for the clv plotting module, a comparison of image generated would confirm expected behavior. Helpful resources might be [`matplotlib.testing` module](https://matplotlib.org/stable/api/testing_api.html#module-matplotlib.testing.compare).

The current recommendation for creating a custom MMM is to use inheritance. However, it is unclear on what is required to make a custom MMM There should be some form...

mock posterior with prior for MMM tests

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Follow up from #515 and #514 Goal: - don't call fit but use a mocked posterior for testing majority of the methods - call fit and check for convergence and...

Either more explicit sections in the documentation or code that checks the general format would be helpful to users when specifying custom mod configs. Dictionaries with no contraints are very...

Pull out `_create_likelihood_distribution` method as function

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I think this would be better suited as a function outside of the MMM class. https://github.com/pymc-labs/pymc-marketing/blob/9480869b9b9e03774a081eb7e5f2da96f1bb95a4/pymc_marketing/mmm/delayed_saturated_mmm.py#L152-L248 Same thoughts on _get_distribution

idata is removed if fit is called

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Example: ```python import pandas as pd from pymc_marketing.mmm import DelayedSaturatedMMM df = pd.read_csv("datasets/mmm_example.csv", parse_dates=["date_week"]) mmm = DelayedSaturatedMMM( date_column="date_week", channel_columns=["x1", "x2"], adstock_max_lag=12, ) y = df.pop("y") mmm.sample_prior_predictive(df, extend_idata=True) # mmm.idata is...

Noticed if someone is on [PyPI](https://pypi.org/project/pymc-marketing/) then the PyMC-marketing logo doesn't show up. Would hate for someone to not see it 😄 Pretty sure it has to do with the...

closer access to Michaelis-Menten curve fit

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Fitting the Michaelis-Menten curve (and sigmoid) require the arguments in DataFrame and xarray object. This is great for the use case but makes it hard to interact with for other...

From running this: ```python import pandas as pd from pymc_marketing.mmm import DelayedSaturatedMMM df = pd.read_csv("datasets/mmm_example.csv", parse_dates=["date_week"]) mmm = DelayedSaturatedMMM( date_column="date_week", channel_columns=["x1", "x2"], adstock_max_lag=7, ) mmm.fit(df, df["y"]) ``` I'm getting this...