# blackbox: A Python module for parallel optimization of expensive black-box functions

## What is this?

Let's say you need to find optimal parameters of some computationally intensive system (for example, hyperparameters of a neural network). If you can construct a simple Python function, that takes a set of trial parameters, performs evaluation, and returns some scalar measure of how good chosen parameters are, then the problem becomes a mathematical optimization. However, a corresponding function is expensive (one evaluation can take hours) and is a black-box (has input-output nature).

**blackbox** is a minimalistic and easy-to-use Python module that efficiently searches for a global optimum (minimum) of an expensive black-box function. User needs to provide a function, a search region (ranges of values for each input parameter) and a number of function evaluations available. A code scales well on clusters and multicore CPUs by performing all expensive function evaluations in parallel.

A mathematical method behind the code is described in this arXiv note: https://arxiv.org/pdf/1605.00998.pdf

Feel free to cite this note if you are using method/code in your research.

## Demo

(a) - function given on `0 < x,y < 1`

.

(b) - running a procedure using 15 evaluations.

(c) - running a procedure using 30 evaluations.

## How do I represent my objective function?

It simply needs to be wrapped into a Python function. If an external application is used, it can be accessed using system call:

```
def fun(par):
# running external application for given set of parameters
os.system(...)
# calculating output
...
return output
```

`par`

is a vector of input parameters (a Python list), `output`

is a scalar measure to be minimized.

## How do I run the procedure?

No installation is needed. Just place `blackbox.py`

into your working directory. Main file should look like that:

```
import blackbox as bb
def fun(par):
return par[0]**2 + par[1]**2 # dummy 2D example
def main():
bb.search(f=fun, # given function
box=[[-10., 10.], [-10., 10.]], # range of values for each parameter (2D case)
n=20, # number of function calls on initial stage (global search)
m=20, # number of function calls on subsequent stage (local search)
batch=4, # number of calls that will be evaluated in parallel
resfile='output.csv') # text file where results will be saved
if __name__ == '__main__':
main()
```

**Important:**

- All function calls are divided into batches that are evaluated in parallel. Total number of these parallel cycles is
`(n+m)/batch`

. `n`

must be greater than the number of parameters,`m`

must be greater than 1,`batch`

should not exceed the number of CPU cores available.- An optional parameter
`executor=...`

should be specified when calling`bb.search()`

in case when code is used on a cluster with some custom parallel engine (ipyparallel, dask.distributed, pathos etc).`executor`

should be an object that has a`map`

method.

## How about results?

Iterations are sorted by function value (best solution is in the top) and saved in a text file with the following structure:

Parameter #1 | Parameter #2 | ... | Parameter #n | Function value |
---|---|---|---|---|

+1.6355e+01 | -4.7364e+03 | ... | +6.4012e+00 | +1.1937e-04 |

... | ... | ... | ... | ... |

## Author

Paul Knysh (paul.knysh@gmail.com)

I receive tons of useful feedback that helps me to improve the code. Feel free to email me if you have any questions or comments.