GPflowOpt
Hyperactive
Our great sponsors
| GPflowOpt | Hyperactive | |
|---|---|---|
| 1 | 8 | |
| 263 | 490 | |
| 0.0% | - | |
| 1.8 | 7.7 | |
| over 3 years ago | 5 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.
GPflowOpt
-
[D] Choosing best parameters from an optimization
1- Hyperparameter optimization as already suggested by u/sener87 but I think your validation does not have to be change as it tests generalization as far as I understand you right. If you have more parameter/larger search space, you may look into Bayesian optimization for this task as implemented e.g. with tensorflow, torch or numpy frameworks.
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
-
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...
-
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?
modAL - A modular active learning framework for Python
mango - Parallel Hyperparameter Tuning in Python
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
optuna-examples - Examples for https://github.com/optuna/optuna
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
anovos - Anovos - An Open Source Library for Scalable feature engineering Using Apache-Spark
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
bytehub - ByteHub: making feature stores simple