BayesianOptimization
Hyperactive
BayesianOptimization | Hyperactive | |
---|---|---|
5 | 8 | |
7,499 | 490 | |
1.2% | - | |
5.5 | 7.7 | |
12 days ago | 5 months ago | |
Python | Python | |
MIT License | MIT License |
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BayesianOptimization
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How best to compress a list of objective function evaluations in numerical optimization?
Yes but that’s a pretty broad label- is there a specific implementation you’re working with (for example ) that pinpoints the memory overhead you want to shrink?
- It's so fun and useful to me
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[P] Bonsai: Bayesian Optimization for Gradient Boosted Trees
Sure, I’m only aware of the Bayesian Optimization package (https://github.com/fmfn/BayesianOptimization), but if you can recommend some other GP-based methods that integrate well with Gradient boosted machines, that would be nice.
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How to optimize multiple variables to minimize the output?
I've previously used Bayesian optimisation for this kind of problem, if you're working in python this is a pretty great starting point (https://github.com/fmfn/BayesianOptimization). Black box optimisation is, to the best of my knowledge, a pretty large field and certainly a very difficult problem. You could certainly do a lot worse than BayesOpt.
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
This looks super interesting, I have previously considered using the Bayesian Optimization[0] package for some work, but the ability to switch out the underlying algorithms is appealing.
Perhaps a bit of a far out question - I would be interested in using this for optimizing real-world (ie slow, expensive, noisy) processes. A caveat with this is that the work is done in batches (eg N experiments at a time). Is there a mechanism by which I could feed in my results from previous rounds and have the algorithm suggest the next N configurations that are sufficiently uncorrelated to explore promising space without bunching on top of each-other? My immediate read is that I could use the package to pick the next optimal point, but would then have to lean on a random search for the remainder of the batch?
0: https://github.com/fmfn/BayesianOptimization
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?
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
mango - Parallel Hyperparameter Tuning in Python
nhentai-favorites-auto-pagination - This is an infinity randomly picker doujinshi from yours favorite list with auto scroll and pagination
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
ix - Simple dotfile pre-processor with a per-file configuration and no dependencies.
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
surrogate-models - A collection of surrogate models for sequence model based optimization techniques
optuna-examples - Examples for https://github.com/optuna/optuna
Bayesian-Optimization-in-FSharp - Bayesian Optimization via Gaussian Processes in F#