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.
Examples
This section contains real-world examples of multiverse analyses conducted using this library.
- Preventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse [Code] [Paper]
- Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness [Code] [Paper]
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]'.
1 maintainer
Metadata
1 maintainer
Release files for multiversum 0.7.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| multiversum-0.7.2.tar.gz | 38.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| multiversum-0.7.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 68.4 kB
Release files / multiversum-0.7.2.tar.gz
| Download URL | multiversum-0.7.2.tar.gz |
|---|---|
| Size | 38.6 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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| Size | 29.8 kB |
| Tags | Python 3 |
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| Uploaded via |
twine/6.1.0 CPython/3.13.7
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