Model objects

[swo]

Right now, you need a separate script to fit each model (e.g. fit_baseline_model.py). I would want all of that functionality inside the package, so that each "model" follows a common pattern for doing MCMC and then producing the samples.

My approach here was:

• Define an object (which I have no better name for than a "model"), e.g., Baseline
• This object has a factory for the function that you feed into the MCMC. E.g.,

  my_model = linmod.models.Baseline(divisions, counts, N=None, num_lineages=None)
  my_mcmc_model = my_model.make_mcmc_model()
  mcmc = MCMC(NUTS(my_mcmc_model), etc.)

• This object has a method for doing the postprocessing:

  samples = my_model.postprocess_mcmc(mcmc)

The ideas here are:

• For a given "model" (e.g., baseline), you put the numpyro.sample "model" and the postprocessing into one place, in the package.
• You can then access models programmatically. Eg given the string "Baseline", you can find the Baseline object with linmod.models.__dict__["Baseline"]. This means you can have a single fit_model.py that takes "Baseline" as a command line argument. This is a good thing, because then you can have a yaml config that reads in a list of models like "Baseline" and then can run that way.
• You minimize the passing around of things like the number of chains, etc.

Something I didn't understand was the use of the word "samples," which seems to used both for the thing that comes out of the model (which is what I would have thought) but also metadata.csv. I might have made some mistake there.

[afmagee42]

Superseded by #25