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Benchmarking framework for all types of black-box optimization algorithms, postprocessing.
COmparing Continuous Optimisers (COCO) Post-Processing |
The (cocopp
) package uses data generated with the COCO framework (comparing not only continuous optimisers) and produces output figures and tables in html
format and for inclusion into LaTeX
documents. The main documentation page can be found at getting-started and in the API documentation, but see also here.
To install the latest release from PyPI:
pip install cocopp
To install the current main branch:
git clone https://github.com/numbbo/coco-postprocess.git
cd coco-postprocess
pip install .
The main method of the cocopp
package is main
(currently aliased to cocopp.rungeneric.main
). The main
method also allows basic use of the post-processing through a shell command-line interface. The recommended use is however from an IPython/Jupyter shell or notebook:
>>> import cocopp >>> cocopp.main('exdata/my_output another_folder yet_another_or_not')
postprocesses data from one or several folders, for example data generated with the help from the cocoex
module. Each folder should contain data of a full experiment with a single algorithm. (Within the folder the data can be distributed over subfolders). Results can be explored from the ppdata/index.html file, unless a a different output folder is specified with the -o option. Comparative data from over 200 full experiments are archived online and can be listed, filtered, and retrieved from archives which are attributes of cocopp.archives
and processed alone or together with local data. For example
>>> cocopp.archives.bbob('bfgs') ['2009/BFGS_...
lists all data sets run on the bbob
testbed containing 'bfgs' in their name. The first in the list can be postprocessed by
>>> cocopp.main('bfgs!')
All of them can be processed like
>>> cocopp.main('bfgs*')
Only a trailing *
is accepted and any string containing the substring is matched. The postprocessing result of
>>> cocopp.main('bbob/2009/*')
can be browsed at https://numbbo.github.io/ppdata-archive/bbob/2009. To display algorithms in the background, the cocopp.genericsettings.background
variable needs to be set:
>>> cocopp.genericsettings.background = {None: cocopp.archives.bbob.get_all('bfgs')}
where None
invokes the default color (grey) and line style (solid) cocopp.genericsettings.background_default_style
. Now we could compare our own data with the first 'bfgs'-matching archived algorithm where all other archived BFGS data are shown in the background with the command
>>> cocopp.main('exdata/my_output bfgs!')
FAQs
Benchmarking framework for all types of black-box optimization algorithms, postprocessing.
We found that cocopp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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