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🪜 Bayesian Hierarchical Models at Scale

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# Bayesian Hierarchical Modelling at Scale

Accompanying source code to the blog post [Finally! Bayesian Hierarchical Modelling at Scale](https://florianwilhelm.info/2020/10/bayesian_hierarchical_modelling_at_scale/).

> **_NOTE:_**  The code is GPL-2 licensed. If you require a more permissive licence, e.g. for
> commercial reasons, contact me to obtain a licence for your business.

## Installation

In order to set up the necessary environment:

1. create an environment `bhm-at-scale` with the help of [conda],
   ```
   conda env create -f environment.yaml # or `environment.lock.yaml` for exact reproduction
   ```
2. activate the new environment with
   ```
   conda activate bhm-at-scale
   ```
3. install `bhm-at-scale` with:
   ```
   python setup.py install # or `develop`
   ```

Then take a look into the `notebooks` folders.

## Dependency Management & Reproducibility

1. Always keep your abstract (unpinned) dependencies updated in `environment.yaml` and eventually
   in `setup.cfg` if you want to ship and install your package via `pip` later on.
2. Create concrete dependencies as `environment.lock.yaml` for the exact reproduction of your
   environment with:
   ```
   conda env export -n bhm-at-scale -f environment.lock.yaml
   ```
   For multi-OS development, consider using `--no-builds` during the export.
3. Update your current environment with respect to a new `environment.lock.yaml` using:
   ```
   conda env update -f environment.lock.yaml --prune
   ```
## Project Organization

```
├── AUTHORS.rst             <- List of developers and maintainers.
├── CHANGELOG.rst           <- Changelog to keep track of new features and fixes.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yaml        <- The conda environment file for reproducibility.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- Use `python setup.py develop` to install for development or
|                              or create a distribution with `python setup.py bdist_wheel`.
├── src
│   └── bhm_at_scale        <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `py.test`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.
```

## Note

This project has been set up using PyScaffold 3.2.3 and the [dsproject extension] 0.4.
For details and usage information on PyScaffold see https://pyscaffold.org/.

[conda]: https://docs.conda.io/
[pre-commit]: https://pre-commit.com/
[Jupyter]: https://jupyter.org/
[nbstripout]: https://github.com/kynan/nbstripout
[Google style]: http://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings
[dsproject extension]: https://github.com/pyscaffold/pyscaffoldext-dsproject

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