multiversum 0.7.1

A helper package to conduct multiverse analyses in Python

multiversum

multiversum is a package designed to make it easy to conduct multiverse analyses in Python. The package is intended to seemlessly integrate into a normal analysis or ML workflow and can also be added to an existing pipeline.

Features

• Simple 🧩: Built with the goal of being as simple as possible to integrate into existing workflows.
• Parallel 👯: Different universes are automatically evaluated in parallel.
• Notebooks 📓: Analyses can be written as Jupyter notebooks or Python scripts.
• Play/Pause ⏯️: Interrupt and then continue a multiverse analysis where you left it.

Installation

Install this library using pip:

pip install multiversum

Usage

The package always works with two different files: The multiverse.toml (or json / py) ✨️, specifying the different dimensions (and their options) and the universe.ipynb (or py) ⭐️ containing the actual analysis code. The universe file is then evaluated (in parallel) using different dimension-combinations, by running python -m multiversum.

An example using a machine learning workflow in scikit-learn can be found here.

Getting Started

To quickly get started with running your own multiverse analysis, you can use the multiversum starter template.

Development

To contribute to this library, first checkout the code. Then create a new virtual environment:

cd multiversum
python -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

python -m pip install -e '.[test]'

To run the tests:

python -m pytest

Formatting

Ruff is used for formatting and linting. Formatting can be automatically checked / applied wherever possible via ruff check . --fix && ruff format.

Docs

The documentation is generated using mkdocs and can be locally run using mkdocs serve after installing its dependencies using python -m pip install -e '.[docs]'.