Benchmarking framework for all types of black-box optimization algorithms, postprocessing.
Project description
|
COmparing Continuous Optimisers (COCO) PostProcess |
The cocopp
Python package
implements the postprocess part of COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting, comparing not only continuous optimisers.
The package uses data generated with the COCO framework and produces output figures and tables in html
format and for inclusion into LaTeX
documents.
Documentation
The main documentation pages for the coco-postprocess
package cocopp
can be found at
- getting-started
- API documentation
- Issue tracker and bug reports
- source code page on GitHub
- COCO home page
Below are the installation instruction and some usage examples.
Installation
To install the latest release from PyPI:
pip install -U cocopp
To install the current main branch:
git clone https://github.com/numbbo/coco-postprocess.git
cd coco-postprocess
pip install .
Usage
The main interface to the cocopp
package is the main
method (an alias to cocopp.rungeneric.main
). The main
method allows for basic usage of cocopp
through a shell command-line interface. The recommended use is however from an IPython/Jupyter shell or notebook:
>>> import cocopp >>> dslist = 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
>>> dslist = cocopp.main('bfgs!')
All of them can be processed like
>>> dslist = cocopp.main('bfgs*')
Only a trailing *
is accepted and any string containing the substring is matched. The postprocessing result of
>>> dslist = 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
>>> dslist = cocopp.main('exdata/my_output bfgs!')
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