A collection of commands for using COCO from Jupyter or IPython.
The main data structures used in COCO are DataSet, which
corresponds to data of one algorithm on one problem, and
DataSetList, which is for collections of DataSet
instances. Both classes are implemented in cocopp.pproc.
Examples:
Start by importing
cocopp:>>> import cocopp >>> cocopp.genericsettings.verbose = False # ensure to make below doctests work >>> def print_(*args, **kwargs): pass >>> cocopp.archives.bbob._print = print_ # avoid download notification
Load a data set, assign to variable
ds:>>> path = cocopp.archives.bbob.get(4) >>> print('ESC'); dsl = cocopp.load(path) # a dataset list # doctest:+ELLIPSIS ESC... >>> ds = dsl[0]Get some information on a
DataSetListinstance:>>> print(dsl) # doctest:+ELLIPSIS [DataSet(BIPOP-CMA-ES_hansen on f1 2-D), DataSet(BIPOP-CMA-ES... >>> dsl.info() 144 data set(s) Algorithm(s): BIPOP-CMA-ES_hansen 24 Functions with IDs 1-24 Dimension(s): 2, 3, 5, 10, 20, 40 Max evals: [1625595, 2349823, 3114271, 5884514, 12102699, 36849608]
Get some information on a
DataSetinstance:>>> print(ds) DataSet(BIPOP-CMA-ES_hansen on f1 2-D) >>> ds.info() Algorithm: BIPOP-CMA-ES_hansen Function ID: 1 Dimension DIM = 2 Number of trials: 15 Final target Df: 1e-08 min / max number of evals per trial: 224 / 333 evals/DIM: best 15% 50% 85% max | ERT/DIM nsucc ---Df---|-----------------------------------------|---------------- 1.0e+03 | 0 0 0 0 0 | 0.5 15 1.0e+01 | 0 0 2 8 10 | 2.9 15 1.0e-01 | 8 13 22 38 52 | 24.2 15 1.0e-03 | 34 48 56 74 77 | 58.2 15 1.0e-05 | 64 70 89 100 102 | 86.1 15 1.0e-08 | 112 116 128 150 166 | 130.9 15
| Class | |
The all property is an OrderedDict with all (dimension, funcId)- |
| Function | info |
Display more info on an instance of DatasetList. |
| Function | load |
[currently broken when further used within cocopp, see load2] Create a DataSetList instance from a file or folder. |
| Function | load2 |
[WIP] return a dict of dict of DataSetLists with dimension and pathname as keys. |
| Function | systeminfo |
Display information on the system. |
| Function | true |
return number of actually conducted trials for DataSet ds |
| Function | _pickle |
Pickle a DataSetList. |
[currently broken when further used within cocopp, see load2] Create a DataSetList instance from a file or folder.
Input argument filename can be a single :file:`info` file name, a single pickle filename or a folder name. In the latter case, the folder is browsed recursively for :file:`info` or :file:`pickle` files.
Details: due to newly implemented side effects when data are read in,
the returned data set list may not work anymore when used with plotting
functions of the cocopp module, see also load2.
[WIP] return a dict of dict of DataSetLists with dimension and pathname as keys.
args is a string or a list of strings passed to
cocopp.official_archives.all.get_extended to determine the desired
data sets which can also come from a local folder or a zip-file.
Examples:
>>> import cocopp >>> def load2(s): ... print(s) # predictable output ... return cocopp.load2(s) >>> def pprld(dsl): ... print('_') # predictable output ... with cocopp.toolsdivers.InfolderGoneWithTheWind(): ... cocopp.compall.pprldmany.main(dsl) # writes pprldmany_default.* >>> ddsl = load2('bbob/2009/B*') # doctest:+ELLIPSIS bbob/200... >>> assert sorted(ddsl) == [2, 3, 5, 10, 20, 40], ddsl >>> assert all([len(ddsl[i]) == 3 for i in ddsl]), ddsl # 3 algorithms >>> pprld(ddsl[2]) # doctest:+ELLIPSIS _...
>> ddsl31 = load('bbob/2009/*') # 31 data sets, takes ~3 minutes at first ever loading
>> assert sorted(ddsl31) == [2, 3, 5, 10, 20, 40], ddsl
>> assert len(ddsl31[3]) == 31, ddsl