FEDOT
Sklearn-genetic-opt
FEDOT | Sklearn-genetic-opt | |
---|---|---|
4 | 6 | |
606 | 273 | |
1.7% | - | |
8.4 | 4.6 | |
about 19 hours ago | 1 day ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
FEDOT
-
Winning a Flood-Forecasting Hackathon with Hydrology and AutoML
Hi to everyone! I am a developer of the FEDOT framework, and our team and I (NSS_lab team) recently won a hackathon EmergencyDataHack (rus). There was a recent post on TowardsDataSciense based on our competition things: Winning a Flood-Forecasting Hackathon with Hydrology and AutoML.
-
[P] FEDOT - AutoML framework for composite pipelines
Hi! I want to discuss the academic project FEDOT (https://github.com/nccr-itmo/FEDOT) that is devoted to the evolutionary AutoML for composite pipelines. I am one of the developers of this framework and hope to obtain some feedback on this solution and share our experience.
-
The experience of the AutoML application in hackathons
ITMO University's Natural Systems Simulation Lab is integrating hackathons into education and research. One of its first results is an application of the AutoML framework called FEDOT that allows obtaining the result that impressed the expert board and brought the lab’s team a victory at a river flood forecasting hackathon organized by the Ministry of Emergency Situations. The final model is hybrid and combined several data-driven models and equation-based domain-specific models.
-
FEDOT framework for evolutionary design of composite pipelines
Hi all! I am a member of the academic AI/ML research team. I want to share the information about the development of the open-source AutoML tool for the design of composite ML pipelines using an evolutionary approach. It called FEDOT and available in the https://github.com/nccr-itmo/FEDOT
Sklearn-genetic-opt
- GitHub - rodrigo-arenas/Sklearn-genetic-opt: Hyperparameters tuning and feature selection, using evolutionary algorithms.
- New Python AutoML Package
-
Looking for contributors AutoML project in Python
The project is open for collaborators of different levels of expertise, there are some issues about new features, enchacements on docs, etc. Repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt
- I've been working on an machine learning hyperparameters tuning open source project
-
Looking for open source contributors: AutoML
Here is the repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt
-
Introducing Sklearn-genetic-opt: Hyperparameters tuning using evolutionary algorithms [project]
If you want to know more the details or contribute, you can check the Github repository
What are some alternatives?
LightAutoML - LAMA - automatic model creation framework
genetic-algorithm-in-python - A genetic algorithm written in Python for educational purposes.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
auto-drive - A machine learning AI in Python trained to play my car game.
evalml - EvalML is an AutoML library written in python.
de-torch - Minimal PyTorch Library for Differential Evolution
sklearn-deap - Use evolutionary algorithms instead of gridsearch in scikit-learn
tune - An abstraction layer for parameter tuning
arja - Multi-Objective GP for Automated Repair of Java
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.