https://github.com/paulknysh/blackbox
A Python module for parallel optimization of expensive black-box functions
https://github.com/paulknysh/blackbox
black-box expensive optimization parallel python
Last synced: 7 months ago
JSON representation
A Python module for parallel optimization of expensive black-box functions
- Host: GitHub
- URL: https://github.com/paulknysh/blackbox
- Owner: paulknysh
- License: mit
- Created: 2016-02-19T22:50:50.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-05-23T01:48:16.000Z (about 1 year ago)
- Last Synced: 2024-11-11T06:21:24.867Z (8 months ago)
- Topics: black-box, expensive, optimization, parallel, python
- Language: Python
- Homepage:
- Size: 240 KB
- Stars: 438
- Watchers: 20
- Forks: 60
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# blackbox: A Python module for parallel optimization of expensive black-box functions
## What is this?
A minimalistic and easy-to-use Python module that efficiently searches for a global minimum of an expensive black-box function (e.g. optimal hyperparameters of simulation, neural network or anything that takes significant time to run). User needs to provide a function, a search domain (ranges of each input parameter) and a total number of function calls available. A code scales well on multicore CPUs and clusters: all function calls are divided into batches and each batch is evaluated in parallel.
A mathematical method behind the code is described in this arXiv note (there were few updates to the method recently): https://arxiv.org/pdf/1605.00998.pdf
Don't forget to cite this note if you are using method/code.
## Demo
(a) - demo function (unknown to a method).
(b) - running a procedure using 15 evaluations.
(c) - running a procedure using 30 evaluations.
## Installation
Have `poetry` installed (https://python-poetry.org/docs/#installation). Then run:
`poetry install`
## Objective function
Simply needs to be wrapped into a Python function.
```python
def fun(par):
...
return output
```
`par` is a vector of input parameters (a Python list), `output` is a scalar value to be minimized.
## Running the procedure
```python
import blackbox as bb
def fun(x):
return (x[0] - 1) ** 2 + (x[1] - 1) ** 2
if __name__ == '__main__':
result = bb.minimize(f=fun, # given function
domain=[[-5, 5], [-5, 5]], # ranges of each parameter
budget=20, # total number of function calls available
batch=4 # number of calls that will be evaluated in parallel
)
# best result (x and function value)
print(result["best_x"])
print(result["best_f"])
# the entire history of evaluations
# print(result["all_xs"])
# print(result["all_fs"])
```
**Important:**
* All function calls are divided into batches and each batch is evaluated in parallel. Total number of batches is `budget/batch`. The value of `batch` should correspond to the number of available computational units.
* An optional parameter `executor = ...` should be specified within `bb.minimize()` in case when custom parallel engine is used (ipyparallel, dask.distributed, pathos etc). `executor` should be an object that has a `map` method.
## Results
`bb.minimize()` returns a dictionary with the following keys:
- `"best_x"` - best iteration
- `"best_f"` - corresponding function value
- `"all_xs"` - all iterations
- `"all_fs"` - corresponding function values
## Author
Paul Knysh (paul.knysh at gmail dot com)
