Welcome to the SimbaML Documentation!#
Date: Oct 10, 2023 Version: 1.0.0rc14+1.gdba84d0
SimbaML is an all-in-one framework for integrating prior knowledge of ODE models into the ML process by synthetic data augmentation. It allows for the convenient generation of realistic synthetic data by sparsifying and adding noise. Furthermore, our framework provides customizable pipelines for various ML experiments, such as identifying needs for data collection and transfer learning.
Useful links: Installation | Source Repository | Issue Tracker | Mailing List
Installation#
SimbaML requires Python 3.10 or newer and can be installed via pip:
pip install simba_ml
You can check if the installation was successful by importing the package and checking the version:
import simba_ml
simba_ml.__version__
For more detailed installation instructions and requirements, see Installation.
For an example usage of SimbaML, see Quickstart.
Reference#
When using SimbaML in a scientific publication, please include the following references to relevant papers.
@inproceedings{DBLP:conf/iclr/KleisslDHZIWRB23,
author = {Maximilian Kleissl and
Lukas Drews and
Benedict B. Heyder and
Julian Zabbarov and
Pascal Iversen and
Simon Witzke and
Bernhard Y. Renard and
Katharina Baum},
editor = {Krystal Maughan and
Rosanne Liu and
Thomas F. Burns},
title = {SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented
Data},
booktitle = {The First Tiny Papers Track at {ICLR} 2023, Tiny Papers @ {ICLR} 2023,
Kigali, Rwanda, May 5, 2023},
publisher = {OpenReview.net},
year = {2023},
url = {https://openreview.net/pdf?id=1wtUadpmVzu},
timestamp = {Wed, 19 Jul 2023 17:21:16 +0200},
biburl = {https://dblp.org/rec/conf/iclr/KleisslDHZIWRB23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}