Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.
Open source
24 alternativesSacred
A tool to help you configure, organize, log and reproduce experiments.
About Sacred
Sacred is a Python tool to configure, organize, log and reproduce experiments. It tracks experiment parameters, runs experiments with different settings, saves configurations for individual runs, and supports reproduction of results.
It uses config scopes to define parameters, injects parameters into functions by name, provides a command-line interface to change parameters and run variants, and uses observers to log dependencies, machine information and results to MongoDB. It also uses automatic seeding to control randomness. It installs via pip, with numpy and pymongo as optional dependencies.
Key features
- Define experiment parameters with config scopes using function locals
- Access configuration parameters in functions by automatic name injection
- Change parameters and run variants via command-line interface
- Log experiment info, dependencies, machine and results via observers
- Save run configurations to MongoDB for later access
- Control randomness with automatic seeding for reproducibility
Best for: Researchers running Python experiments who need to track parameters and reproduce runs.
Good for
- Installs directly from the Python Package Index with pip
- Saves run configurations and results to MongoDB for later access
- Uses automatic seeding to help keep results reproducible
Sacred at a glance
- Website
- github.com/IDSIA/sacred
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Sacred FAQ
How do I install Sacred?
Run pip install sacred, or clone the repository and run python setup.py install. numpy and pymongo are optional dependencies.
Where does Sacred save experiment information?
Observers log the configuration, dependencies, machine details and result, which can be saved to MongoDB.
How does Sacred help with reproducibility?
It keeps track of experiment parameters and uses automatic seeding to control randomness.
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