Blackbox Optimization
Various blackbox optimization algorithms with a common interface along with useful helpers like parallel optimization loops, analysis and visualization scripts.
Random search is provided as an example optimizer along with tests for the interface.
New optimizers can require blackboxopt as a dependency, which is just the light-weight
interface definition.
If you want all optimizer implementations that come with this package, install
blackboxopt[all]
Alternatively, you can get individual optimizers with e.g. blackboxopt[bohb]
This software is a research prototype. The software is not ready for production use. It has neither been developed nor tested for a specific use case. However, the license conditions of the applicable Open Source licenses allow you to adapt the software to your needs. Before using it in a safety relevant setting, make sure that the software fulfills your requirements and adjust it according to any applicable safety standards (e.g. ISO 26262).
Documentation
Visit boschresearch.github.io/blackboxopt
Development
Install uv
Install the blackboxopt package from source by running the following from the root
directory of this repository
uv sync
(Optional) Install pre-commit hooks to check code standards before committing changes:
uv run pre-commit install
Test
Make sure to install all extras before running tests
uv sync --extra all
uv run pytest tests/
For HTML test coverage reports run
uv run pytest --cov=. --cov-report=html tests/
Building Documentation
Make sure to install all necessary dependencies:
uv sync --extra all
The documentation can be built from the repository root as follows:
uv run mkdocs build --clean --no-directory-urls
For serving it locally while working on the documentation run:
uv run mkdocs serve
Architectural Decision Records
Create evaluation result from specification
In the context of initializing an evaluation result from a specification, facing the concern that having a constructor with a specification argument while the specification attributes end up as toplevel attributes and not summarized under a specification attribute we decided for unpacking the evaluation specification like a dictionary into the result constructor to prevent the said cognitive dissonance, accepting that the unpacking operator can feel unintuitive and that users might tend to matching the attributes explictly to the init arguments.
Report multiple evaluations
In the context of many optimizers just sequentally reporting the individual evaluations
when multiple evaluations are reported at once and thus not leveraging any batch
reporting benefits, facing the concern that representing that common behaviour in the
optimizer base class requires the definition of an abstract report single and an
abstract report multi method for which the report single does not need to be implemented
if the report multi is, we decided to refactor the arising redundancy into a function
call_functions_with_evaluations_and_collect_errors, accepting that this increases the
cognitive load when reading the code.
License
blackboxopt is open-sourced under the Apache-2.0 license. See the LICENSE
file for details.
For a list of other open source components included in blackboxopt, see the file
3rd-party-licenses.txt.
1 maintainer
Metadata
1 maintainer
Release files for blackboxopt 5.3.8
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| File | Size | Uploaded | |
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| blackboxopt-5.3.8.tar.gz | 317.6 kB | Details |
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|---|---|---|---|---|
| blackboxopt-5.3.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 389.7 kB
Release files / blackboxopt-5.3.8.tar.gz
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| Size | 317.6 kB |
| Tags | Source |
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| Tags | Python 3 |
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