https://github.com/hansbug/hbayes
A Python implementation of global optimization with gaussian processes.
https://github.com/hansbug/hbayes
bayesian-optimization
Last synced: about 1 year ago
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A Python implementation of global optimization with gaussian processes.
- Host: GitHub
- URL: https://github.com/hansbug/hbayes
- Owner: HansBug
- License: mit
- Created: 2022-05-29T14:01:42.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-05-31T16:49:14.000Z (about 4 years ago)
- Last Synced: 2025-03-27T05:23:55.170Z (about 1 year ago)
- Topics: bayesian-optimization
- Language: Python
- Homepage: https://hansbug.github.io/hbayes/
- Size: 31.4 MB
- Stars: 2
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Bayesian Optimization
[](https://pypi.org/project/hbayes/)
[](https://pypi.org/project/hbayes/)
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[](https://github.com/HansBug/hbayes/actions?query=workflow%3A%22Badge+Creation%22)
[](https://github.com/HansBug/hbayes/actions?query=workflow%3A%22Package+Release%22)
[](https://codecov.io/gh/HansBug/BayesianOptimization)
[](https://github.com/HansBug/hbayes/stargazers)
[](https://github.com/HansBug/hbayes/network)
[](https://github.com/HansBug/hbayes/issues)
[](https://github.com/HansBug/hbayes/pulls)
[](https://github.com/HansBug/hbayes/graphs/contributors)
[](https://github.com/HansBug/hbayes/blob/master/LICENSE)
An extended implementation of Bayesian Optimization.
This is a forked project based on [fmfn/BayesianOptimization v1.2.0](https://github.com/fmfn/BayesianOptimization). Most of the usage and features from the original repository will be kept for a long time.
## Installation
You can simply install it with `pip` command line from the official PyPI site.
```shell
pip install hbayes
```
For more information about installation, you can refer to [Installation](https://hansbug.github.io/hbayes/main/tutorials/installation/index.html).
## Documentation
The detailed documentation are hosted on [https://hansbug.github.io/hbayes/main/index.html](https://hansbug.github.io/hbayes/main/index.html).
Only english version is provided now, the chinese documentation is still under development.
## Quick Start
A painless example
```python
from hbayes import BayesianOptimization
def black_box_function(x, y):
"""Function with unknown internals we wish to maximize.
This is just serving as an example, for all intents and
purposes think of the internals of this function, i.e.: the process
which generates its output values, as unknown.
"""
return -x ** 2 - (y - 1) ** 2 + 1
# Bounded region of parameter space
pbounds = {'x': (2, 4), 'y': (-3, 3)}
optimizer = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
random_state=1,
verbose=2,
)
optimizer.maximize(
init_points=10,
n_iter=25,
)
print(optimizer.max)
```
The output should be
```
| iter | target | x | y |
-------------------------------------------------
| 1 | -7.135 | 2.834 | 1.322 |
| 2 | -7.78 | 2.0 | -1.186 |
| 3 | -16.13 | 2.294 | -2.446 |
| 4 | -8.341 | 2.373 | -0.9266 |
| 5 | -7.392 | 2.794 | 0.2329 |
| 6 | -7.069 | 2.838 | 1.111 |
| 7 | -6.412 | 2.409 | 2.269 |
| 8 | -3.223 | 2.055 | 1.023 |
| 9 | -7.455 | 2.835 | 0.3521 |
| 10 | -12.11 | 2.281 | -1.811 |
| 11 | -7.0 | 2.0 | 3.0 |
| 12 | -19.0 | 4.0 | 3.0 |
| 13 | -3.383 | 2.0 | 0.3812 |
| 14 | -3.43 | 2.0 | 1.656 |
| 15 | -3.035 | 2.0 | 0.8129 |
| 16 | -17.03 | 4.0 | -0.4244 |
| 17 | -3.012 | 2.0 | 1.109 |
| 18 | -3.0 | 2.0 | 0.9813 |
| 19 | -3.0 | 2.0 | 0.9911 |
| 20 | -3.0 | 2.0 | 0.994 |
| 21 | -3.0 | 2.0 | 0.9957 |
| 22 | -3.0 | 2.0 | 0.9971 |
| 23 | -3.0 | 2.0 | 0.9994 |
| 24 | -3.0 | 2.0 | 1.004 |
| 25 | -3.0 | 2.0 | 0.978 |
| 26 | -3.001 | 2.0 | 1.024 |
| 27 | -3.001 | 2.0 | 0.9735 |
| 28 | -3.001 | 2.0 | 1.024 |
| 29 | -3.001 | 2.0 | 0.9729 |
| 30 | -3.001 | 2.0 | 1.024 |
| 31 | -3.0 | 2.0 | 1.021 |
| 32 | -3.001 | 2.0 | 0.9709 |
| 33 | -3.001 | 2.0 | 0.9749 |
| 34 | -3.001 | 2.0 | 1.023 |
| 35 | -3.001 | 2.0 | 0.9755 |
=================================================
{'target': -3.00000039014846, 'params': {'x': 2.0, 'y': 0.9993753813483197}}
```
For more tutorial of usages and practices, take a look at [Best Practice](https://hansbug.github.io/hbayes/main/best_practice/advanced-tour.html) in documentation.
# Contributing
We appreciate all contributions to improve `hbayes`, both logic and system designs. Please refer to CONTRIBUTING.md for more guides.
# License
`hbayes` released under the MIT license.