optuna
tuneta
| optuna | tuneta | |
|---|---|---|
| 34 | 12 | |
| 9,681 | 377 | |
| 2.2% | - | |
| 9.9 | 4.6 | |
| 7 days ago | 7 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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optuna
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Optuna – A Hyperparameter Optimization Framework
I didn’t even know WandB did hyperparameter optimization, I figured it was a neural network visualizer based on 2 minute papers. Didn’t seem like many alternatives out there to Optuna with TPE + persistence in conditional continuous & discrete spaces.
Anyway, it’s doable to make a multi objective decide_to_prune function with Optuna, here’s an example https://github.com/optuna/optuna/issues/3450#issuecomment-19...
- How to test optimal parameters
- FOSS hyperparameter optimization framework to automate hyperparameter search
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How did you make that?!
The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
- How to tune more than 2 hyperparameters in Grid Search in Python?
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Suggestion to optimize algo
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
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Best practices for training PyTorch model
Research the type of model to get an idea of what hyper parameters to use. I recommend using a hyper parameter optimization library like Optuna to get the best configuration
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
tuneta
- tuneta: NEW Other Models - star count:203.0
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Optuna: An open source hyperparameter optimization framework to automate hyperparameter search
Optuna is a great library and I do use it in tuneta for optimizing technical indicator parameters. However, certain Optuna algos suggest the same parameters in separate trials resulting in many duplicate parameters (issue) which needs to be managed external of the lib.
- tuneta: NEW Other Models - star count:130.0
What are some alternatives?
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.
pandas-ta - Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
TradingView-Machine-Learning-GUI - Embark on a trading journey with this project's cutting-edge stop loss/take profit generator, fine-tuning your TradingView strategy to perfection. Harness the power of sklearn's machine learning algorithms to unlock unparalleled strategy optimization and unleash your trading potential.
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
tiingo-python - Python client for interacting with the Tiingo Financial Data API (stock ticker and news data)
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
easyopt - zero-code hyperparameters optimization framework
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
LiuAlgoTrader - Framework for algorithmic trading
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
bulbea - :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling