numpyro-oop 0.0.1

A convenient object-oriented wrapper for working with numpyro models.

An object-oriented interface to numpyro

This package provides a wrapper for working with numpyro models. It aims to remain model-agnostic, but package up a lot of the model fitting code to reduce repetition.

It is intended to make life a bit easier for people who are already familiar with Numpyro and Bayesian modelling. It is not intended to fulfil the same high-level wrapper role as packages such as brms. The user is still required to write the model.

Getting started

pip install numpyro-oop

The basic idea is that the user defines a new class that inherits from BaseNumpyroModel, and defines (minimally) the model to be fit by overwriting the model method:

from numpyro-oop import BaseNumpyroModel

class DemoModel(BaseNumpyroModel):
    def model(self, data=None):
        ...

m1 = DemoModel(data=df, seed=42)

Then all other sampling and prediction steps are handled by numpyro-oop, or related libraries (e.g. arviz):

m1.sample()  # sample from the model
preds = m1.predict()  # generate model predictions for the dataset given at initialization, or pass a new dataset
m1.generate_arviz_data()  # generate an Arviz InferenceData object stored in self.arviz_data

A more complete demo can be found in /scripts/demo_1.ipynb.

Roadmap after initial release

• include doctest, improved examples
• demo and tests for multiple group variables
• export docs to some static page (readthedocs or similar); detail info on class methods and attributes
• CI test setup
• Contributor guidelines
• Fix type hints via linter checks

Development notes

• Update dependencies with make update-deps
• Update and (re)install the environment with make update-and-install