LightAutoML
Hyperactive
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| LightAutoML | Hyperactive | |
|---|---|---|
| 1 | 8 | |
| 767 | 487 | |
| - | - | |
| 9.2 | 7.7 | |
| about 2 years ago | 4 months ago | |
| Python | Python | |
| Apache License 2.0 | 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.
LightAutoML
Hyperactive
- Hyperactive Version 4.5 Released
- Hyperactive: An optimization and data collection toolbox for AutoML
- Hyperactive: Optimize computationally expensive models with powerful algorithms
- Show HN: Hyperactive – A highly versatile AutoML Toolbox
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Hyperactive – Easy Neural Architecture Search for Deep Learning in Python
Check out the Neural Architecture Search Tutorial here: https://nbviewer.jupyter.org/github/SimonBlanke/hyperactive-...
Neural Architecture Search is just one of many optimization applications you can work on with Hyperactive. Check out the examples in the official github repository: https://github.com/SimonBlanke/Hyperactive/tree/master/examp...
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
Gradient-Free-Optimizers is a lightweight optimization package that serves as a backend for Hyperactive: https://github.com/SimonBlanke/Hyperactive
Hyperactive can do parallel computing with multiprocessing or joblib, or a custom wrapper-function.
What are some alternatives?
FEDOT - Automated modeling and machine learning framework FEDOT
mango - Parallel Hyperparameter Tuning in Python
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
jupyter - Jupyter metapackage for installation, docs and chat
optuna-examples - Examples for https://github.com/optuna/optuna
lazypredict - Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
Language_Identifier - Language Identification classification using XGBoost
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.